Completed (GI-N2K)
"},{"code":"AM1-2","description":"Analytical capabilities of a GIS make use of spatial and non-spatial (attribute) data to answer questions and solve problems that are of spatial relevance. We now make a distinction between analysis (or analytical operations) and analytical models (often referred to as “modelling”). And by analysis we actually mean only a subset of what is usually implied by the term: we do not specifically deal with advanced statistical analysis (such as cluster detection or geostatistics).\r\n\r\nAnalysis of spatial data can be defined as computing new information to provide new insights from existing spatial data. Consider an example from the domain of road construction. In mountainous areas, this is a complex engineering task with many cost factors, including the number of tunnels and bridges to be constructed, the total length of the tarmac, and the volume of rock and soil to be moved. GISs can help to compute such costs on the basis of an up-to-date digital elevation model and a soil map. The exact nature of the analysis will depend on the application requirements, but computations and analytical functions can operate on both spatial and non-spatial data.","hasChildren":true,"name":"Analytical approaches","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM1","description":"Geospatial data analysis has foundations in many different disciplines. As a result, there are many different schools of thought or analytical approaches including spatial analysis, spatial modeling, geostatistics, spatial econometrics, spatial statistics, qualitative analysis, map algebra, and network analysis. This unit compares and contrasts these approaches.","hasChildren":true,"hasParent":true,"name":"Foundations of analytical methods","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM10-1","description":"Difficulties in dealing with large spatial databases, especially those arising from spatial heterogeneity and data quality issues.","hasChildren":true,"name":"Problems of large spatial databases","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM10-2","description":"Data mining knows a variety of approaches, such as cluster analysis, analytical reasoning, association, prediction, etc.","hasChildren":true,"name":"Data mining approaches","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM10-3","description":"Knowledge discovery involves the identification of useful patterns in spatial databases using techniques of data mining, trend analysis, etc.","hasChildren":true,"name":"Knowledge discovery","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM10","description":"Algorithms have been developed to scan and search through extremely large data sets in order to find patterns within the data. These data mining and knowledge discovery techniques have been expanded to the spatial case. Legal and ethical concerns associated with such practices are considered in Knowledge Areas GS GIS and T and Society and OI Organizational and Institutional Aspects.","hasChildren":true,"hasParent":true,"name":"Data mining","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM11-1","description":"A network is a connected set of lines representing some geographic phenomenon, typically to do with transportation. The “goods” transported can be almost anything: people, cars and other vehicles along a road network, commercial goods along a logistic network, phone calls along a telephone network, or water pollution along a stream/river network.\r\n\r\nDirect vs. Non-directed Networks\r\nA fundamental characteristic of any network is whether the network lines are considered to be directed or not. Directed networks associate with each line a direction of transportation; undirected networks do not. In the latter, the “goods” can be transported along a line in both directions. We discuss here vector network analysis, and assume that the network is a set of connected line features that intersect only at the lines’ nodes, not at internal vertices. (But we do mention under- and overpasses.)\r\n\r\nPlanar vs. Non-Planar Networks\r\nFor many applications of network analysis, a planar network, i.e. one that can be embedded in a two-dimensional plane, will do the job. Many networks are naturally planar, such as stream/river networks. A large-scale traffic network, on the other hand, is not planar: motorways have multi-level crossings and are constructed with underpasses and overpasses. Planar networks are easier to deal with computationally, as they have simpler topological rules. Not all GISs accommodate non-planar networks, or they can only do so using “tricks”. These tricks may involve the splitting of overpassing lines at the intersection vertex and the creation of four lines from the two original lines. Without further attention, the network will then allow one to make a turn onto another line at this new intersection node, which in reality would be impossible. In some GISs we can allocate a cost for turning at a node—see our discussion on turning costs below—and that cost, in the case of the overpass trick, can be made infinite to ensure it is prohibited. But, as mentioned, this is a work around to fit a non-planar situation into a data layer that presumes planarity. The above is a good illustration of geometry not fully determining the network’s behaviour. Additional application-specific rules are usually required to define what can and cannot happen in the network. Most GISs provide rule-based tools that allow the definition of these extra application rules.","hasChildren":true,"name":"Networks defined","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM11-2","description":"Identifying and listing all elements does not describe a system in full. There may be many different ways in which elements may be connected or related to each other. The interactions, relationships between elements are essential to describe a system.\r\n\r\nRelationships between elements can be described by two types of flows:\r\nflows of material, and flows of information.\r\n\r\nMaterial flows connect elements between which there is an exchange of some substance. This can be some kind of material (water, food, cement, biomass, etc.), energy (light, heat, electricity, etc.), money, etc. It is something that can be measured and tracked. Also if an element is a donor of this substance the amount of substance in this element will decrease as a result of the exchange, while at the same time the amount of this substance will increase in the receptor element. There is always a mass, or energy conservation law in place. Nothing appears from nothing, and nothing can disappear to nowhere.\r\n\r\nThe second type of exchange is with an information flow. In this case element A gets information from element B. Element B at the same time may have no information about element A. Even when element A gets information about B, B does not lose anything. Information can be about the state of an element, about the quantity that it contains, about its presence or absence, etc. Information flows can be used to describe rules and policies. Information flows can modify the rates of flow between elements, they can switch certain processes and interactions on and off. But the process through which policies, interventions and norms for action are established, and could for example define the values of such information flows, are themselves the result of social interaction between relevant stakeholders from public, private or civil society.\r\n\r\nThe simplest is to acknowledge the existence of a relationship between certain elements, like this is done in a graph. In a graph a node presents an element and a link between any two nodes shows that these two elements are related. However there is no evidence of the direction of the relationship: we do not distinguish between the element x influencing element y or vice versa. This relationship can be further specified by an oriented graph that shows the direction of the relationship between elements. An element can be also connected to itself, to show that its behaviour depends on its state. We can further detail the description by identifying whether element x has a positive or negative effect on element y.\r\n\r\nWith networks, interesting questions arise that have to do with connectivity and network capacity. These relate to applications such as traffic monitoring and watershed management. With network elements—i.e. the lines that make up the network—extra values are commonly associated, such as distance, quality of the link or the carrying capacity.","hasChildren":true,"name":"Graph theoretic descriptive measures of networks","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM11-3","description":"Optimal-path finding techniques are used when a least-cost path between two nodes in a network must be found. The two nodes are called origin and destination. The aim is to find a sequence of connected lines to traverse from the origin to the destination at the lowest possible cost.\r\n\r\nIn Optimal-path finding, the cost function can be simple: for instance, it can be defined as the total length of all lines of the path. The cost function can also be more elaborate and take into account not only length of the lines but also their capacity, maximum transmission (travel) rate and other line characteristics, for instance to obtain a reasonable approximation of travel time. There can even be cases in which the nodes visited add to the cost of the path as well. These may be called turning costs, which are defined in a separate turning-cost table for each node, indicating the cost of turning at the node when entering from one line and continuing on another. This is illustrated in Figure 1 of the examples.\r\n\r\nProblems related to optimal-path finding may require ordered optimal path finding or unordered optimal-path finding. Both have as an extra requirement that a number of additional nodes need to be visited along the path. In ordered optimal-path finding, the sequence in which these extra nodes are visited matters; in unordered optimal-path finding it does not.","hasChildren":true,"name":"Least-cost shortest path","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM11-4","description":"There are phenomena that do not spread in all directions, but move or “flows” along a given, least-cost path, determined by characteristics of local terrain. The typical case arises when we want to determine drainage patterns in a catchment area: rain water “chooses” a way to leave the area. \r\n\r\nWe can illustrate the principles involved in this typical case with a simple elevation raster. For each cell in that raster, the steepest downward slope to a neighbour cell is computed and its direction is stored in a new raster. This computation determines the elevation difference between the cell and the neighbour cell and it takes into account cell distance - 1 for neighbour cells in N–S or W–E direction, 2 for cells in a NE–SW or NW–SE direction. From among its eight neighbour cells, it picks the one with the steepest path to it. The directions thus obtained in an output raster are encoded in integer values, which can be called the flow-direction raster. From this raster, the GIS can compute the accumulated flow-count raster, a raster that for each cell indicates how many cells have their water flow into that cell.\r\n\r\nCells with a high accumulated flow count represent areas of concentrated flow and may, thus, belong to a stream. By using some appropriately chosen threshold value in a map algebra expression, we may decide whether they do or not. Cells with an accumulated flow count of zero are local topographic highs and can be used to identify ridges.","hasChildren":true,"name":"Flow modeling","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM11-5","description":"The Classic Transportation Problem considers minimizing the cost of getting an object or subject from origin to destination.","hasChildren":true,"name":"The Classic Transportation Problem","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM11-6","description":"Classic network problems are examples of networking problems such as the Traveling Salesman Problem and the Chinese Postman Problem that need graph algorithms to be solved.","hasChildren":true,"name":"Other classic network problems","selfAssesment":"GI-N2K
"},{"code":"AM11-7","description":"Accessibility is the extend in which it is difficult/easy to reach a location or object.","hasChildren":true,"name":"Accessibility modeling","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM11","description":"Network analysis encompasses a wide range of procedures, techniques, and methods that allow for the examination of phenomena that can be modeled in the form of connected sets of edges and vertices. Such sets are termed a network or a graph, and the mathematical basis for network analysis is known as graph theory. Graph theory contains descriptive measures and indices of networks such as connectivity, adjacency, capacity, and flow as well as methods for proving the properties of networks. Networks have long been recognized as an efficient way to model many types of geographic data, including transportation networks, river networks, and utility networks electric, cable, sewer and water, etc. to name just a few. The data structures to support network analysis are covered in [DM4-7] Network models.","hasChildren":true,"hasParent":true,"name":"Network analysis","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM12-1","description":"The modeling of problems in a formal language, working in a solution space and applying constraints.","hasChildren":true,"name":"Operations research modeling and location modeling principles","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM12-2","description":"A formal programming method to support operational research in which linear constraints are applied.","hasChildren":true,"name":"Linear programming","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM12-3","description":"A formal programming method to support operational research in which variables are constrained to integers.","hasChildren":true,"name":"Integer programming","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM12-4","description":"Location-allocation modeling involves the determination of locations by minimizing the distance between object/subjects in space, such as between customers and facilities.","hasChildren":true,"name":"Location-allocation modeling and p-median problems","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM12","description":"A wide variety of optimization techniques are now solvable within the GIS and T domain. Operations research is a branch of mathematics practiced in the allied fields of business and engineering. New models and software tools allow for the solution of transportation routing, facility location, and a host of other location-allocation modeling problems.","hasChildren":true,"hasParent":true,"name":"Optimization and location-allocation modeling","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM13-1","description":"The effects such as the loss of data quality and data integrity that are the results of data transformations.","hasChildren":true,"name":"Impacts of transformations","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM13-2","description":"A data model is an abstract model that organizes elements of data and standardizes how they relate to one another and to the properties of real-world entities. The term data model can refer to two distinct but closely related concepts. In relation to the field of geoinformation the term data model refers to the set of concepts used in defining such formalizations as entities, attributes, relations, tables which is implemented by a mathematical construct for representing geographic objects or surfaces as data. There are two most frequently used data models, which are vector and raster. For example, the vector data model represents geography as collections of points, lines and polygons and more complex structures crated from these three. The raster data model represent geography as cell matrices that store numeric values. Among these two data models we also stand out data formats in which data sets can be stored. File format is a standard of encoding geographical information into a computer file. There are the following basic file formats for encoding data:\r\nFor vectors:\r\n-\tShapefile\r\n-\tGeography Markup Language (GML)\r\n-\tXYZ Point Cloud\r\n-\tGeoJSON\r\n-\tGeoMedia\r\n-\t\r\nFor rasters:\r\n-\tGeoTIFF\r\n-\tIMG\r\n-\tJPEG2000\r\n-\tEsri grid\r\nThe GIS projects often require the conversion of the data formats. Data conversion is the process of moving data from one format to another, whether it is from one data model to another or from one data format to another. Data conversion is a complex process which is not only associated with changing the binary format of the file but also requires changing the structure of the data. For example, the GML data format always comes with an UML diagram, which is necessary to convert attributes stored in GML structure for example to a table of contest in a shapefile data format. In a well-managed GIS project it is important to store data in specific data model or data format. It is sometimes dictated by software capabilities and another times by team’s technical capabilities. With large amounts of geographic data used in the project it is more cost-effective to convert the data from one format to another than re-create it.","hasChildren":true,"name":"Data model and format conversion","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM13-3","description":"Interpolation is used to create a GIS layer out of point observations on a continuous variable. The reason for doing this could be manifold: for visualization purposes, for making a proper reference with other data, or for making a combination of different layers.","hasChildren":true,"hasParent":true,"name":"Interpolation","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM13-4","description":"Any vector data containing point, polyline, polygon can be converted into the raster dataset and vice versa. The vector data can be stored in shapefiles, databases or various others GIS file formats. The raster data are made of pixels or grid calls and can be represented by the discrete - categorical data (e.g. land cover map) or non-discrete - continuous data (e.g. satellite images, surface data). The process of conversion of vector to raster data is called rasterization. The vector to raster conversion requires the following parameters: the field value from the attribute table used to assign values to the output raster, the pixel size for the output raster, the output raster format (i.e. geotiff, img) and optionally the method of assigning values of point, polyline or polygon to the call raster, i.e. maximum length or area, cell centre. The output of the rasterised vector looks like a gridded version of the vector and it depends on the grid cell size. The process of vectorisation refers to the conversion of raster to vector dataset. The raster dataset can be converted to vector point, polyline or polygon. In order to convert raster to vector the following parameters should be provided: attribute field of the input raster dataset which will become an attribute in the output vector class, determining if the output polygon or polyline will be smoothed into simpler shapes or conform to the input raster's cell edges (stair stepping). For each raster pixel or grid cell a point will be created at the centre of the cell. The non-discrete continuous raster data have to converted to the categorical data type before converting to vector data. The conversion of vector to raster and raster to vector degrade the data to some extent causing loss of details, accuracy, and changing the original data.","hasChildren":true,"name":"Vector-to-raster and raster-to-vector conversions","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM13-5","description":"Raster resampling refers to change of spatial resolution (increasing or decreasing) of the raster dataset. The resampling process calculates the new pixel values from the original digital pixel values in the uncorrected image. There are three common methods for resampling: nearest neighbour, bilinear interpolation, and cubic convolution. The nearest neighbour resampling uses the digital value from the pixel in the original image which is nearest to the new pixel location in the corrected image. This is the fastest interpolation method, which is primarily applied for discrete (categorical) raster data as it does not change the value of the pixel, but may result in some pixel values being duplicated while others are lost. Bilinear interpolation resampling takes a weighted average of four pixels in the original image nearest to the new pixel location. The averaging process alters the original pixel values and creates entirely new digital values in the output image. It is recommended for continuous data and it cause some smoothing of the data. Cubic convolution resampling is based on calculation of a distance weighted average of a block of sixteen pixels from the original image which surround the new output pixel location. As with bilinear interpolation, this method results in completely new pixel values. However, the last two methods both produce images which have a much sharper appearance and avoid the blocky appearance of the nearest neighbour method. The disadvantage of the Cubic method is that its requires more processing time.","hasChildren":true,"name":"Raster resampling","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM13-6","description":"Users of geoinformation often need transformations from a particular 2D coordinate system to another system. This includes the transformation of polar coordinates into Cartesian map coordinates, or the change of map projection - transformation from one 2D Cartesian (x, y) system of a specific map projection into another 2D Cartesian (x′, y′) system of a defined map projection. This transformation is based on relating the two systems on the basis of a set of selected points whose coordinates are known in both systems, such as ground control points or common points such as corners of houses or road intersections. Image and scanned data are usually transformed by this method. The transformations may be conformal, affine, polynomial or of another type, depending on the geometric errors in the data set. A datum transformation involves the change of the horizontal datum which is often accompanied with a change of map projection.","hasChildren":true,"name":"Coordinate transformations","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM13","description":"GIS is a cyclical rather than a linear system, unlike computer aided drafting (CAD) and computer assisted cartographic systems. Changes in projection, grid systems, data forms, and formats take place during the modeling process for which GIS was designed. Many non-analytical manipulations are necessary to accommodate the analytical power of the GIS. The manipulations of spatial and spatio-temporal data involve two general classes of operation: 1.\tTheir transformation into formats that facilitate subsequent analysis 2. Generalization and aggregation that affect the accuracy and integrity of the data used for analysis (see [AM14]). Other knowledge areas have identified different forms of data structures, data models, projections, and other forms of geospatial data representation. These differences present both opportunities and challenges for analysis and modeling. The ability to transform one representation to another, in a manner that maintains the integrity of the information as much as possible, can enhance the analysis and visualization of geospatial data. The raster and vector data models are described in [DM3] Tesselation data models and [DM4] Vector data model, Feature based modelling, Applications. The principles of coordinate systems, datums, and projections are also considered in Knowledge Area [GD] Geospatial Data","hasChildren":true,"hasParent":true,"name":"Representation transformation","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM14-1","description":"In the practice of spatial data handling, one often comes across questions like “What is the resolution of the data?” or “At what scale is your data set?” Now that we have moved firmly into the digital age, these questions sometimes defy an easy answer. Map scale can be defined as the ratio between the distance on a printed map and the distance of the same stretch in the terrain.\r\n\r\nA 1:50,000 scale map means that 1 cm on the map represents 50,000 cm (i.e. 500 m) in the terrain. “Large-scale” means that the ratio is relatively large, so typically it means there is much detail to see, as on a 1:1000 printed map. “Small-scale”, in contrast, means a small ratio, hence less detail, as on a 1:2,500,000 printed map.\r\nDigital spatial data, as stored in a GIS, are essentially without scale: scale is a ratio notion associated with visual output, such as a map or on-screen display, not with the data that was used to produce the map or display. When digital spatial data sets have been collected with a specific map-making purpose in mind, and all maps have been designed to use one single map scale, for instance 1:25,000, we may assume that the data carries the characteristic of “a 1:25,000 digital data set.”\r\n\r\nThere is a relationship between the effectiveness of a map for a given purpose and the map’s scale. The Public Works department of a city council cannot use a 1:250,000 map for replacing broken sewer pipes, and the map of Figure 1 cannot be reproduced at scale 1:10,000.\r\n\r\nMaps that show much detail of a small area are called large-scale maps. Scale indications on maps can be given verbally, such as “one-inch-to the- mile”, or as a representative fraction like 1:200,000,000 (1 cm on the map equals 200,000,000 cm (or 2000 km) in reality), or by a graphic representation such as the scale bar. The advantage of using scale bars in digital environments is that its length also changes when the map is zoomed in, or enlarged, before printing. Sometimes it is necessary to convert maps from one scale to another, which may lead to problems of cartographic generalization.\r\n\r\nSpatial and temporal scales can not only be attached to processes, but also to observations. An example is given below, which summarizes the spatial and temporal scales of a few well-known Earth observation systems.\r\n\r\nScales of RS observations\r\nSensor Spatial scale\t Temporal scale\r\nMeteosat\t Hemisphere\t 15 minutes\r\nNOAA-AVHRR\t 3000 km\t daily\r\nLandsat TM\t 180 km\t 16 days\r\nSpot\t 60 km\t 26 days (pointable)","hasChildren":true,"name":"Scale and generalization","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM14-2","description":"Techniques that support the generalisation of map content when changing to smaller map scales. These include line simplification, object selection, etc.","hasChildren":true,"name":"Approaches to point, line, and area generalization","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM14-3","description":"Classification is a technique for purposely removing detail from an input data set in the hope of revealing important patterns (of spatial distribution). In the process, we produce an output data set, so that the input set can be left intact. This output set is produced by assigning a characteristic value to each element in the input set, which is usually a collection of spatial features that could be raster cells or points, lines or polygons. If the number of characteristic values in the output set is small in comparison to the size of the input set, we have classified the input set.\r\n\r\nThe input data set may, itself, have been the result of a classification. In such cases we refer to the output data set as a reclassification. For example, we may have a soil map that shows different soil type units and we would like to show the suitability of units for a specific crop. In this case, it is better to assign to the soil units an attribute of suitability for the crop. Since different soil types may have the same crop suitability, a classification may merge soil units of different type into the same category of crop suitability.\r\n\r\nIn classification of vector data, there are two possible results. In the first, the input features may become the output features in a new data layer, with an additional category assigned. In other words, nothing changes with respect to the spatial extents of the original features. Figure a of Examples illustrates this first type of output. A second type of output is obtained when adjacent features of the same category are merged into one bigger feature. Such a post-processing function is called spatial merging, aggregation or dissolving. An illustration of this second type is found in Figure b of Examples. Observe that this type of merging is only an option in vector data, as merging cells in an output raster on the basis of a classification makes little sense. Vector data classification can be performed on point sets, line sets or polygon sets; the optional merge phase only makes sense for lines and polygons.\r\n\r\nUser-controlled classifications require a classification table or user interaction. GIS software can also perform automatic classification, in which a user only specifies the number of classes in the output data set. The system automatically determines the class break points. The two main techniques of determining break points being used are the equal interval technique and the equal frequency technique.\r\n\r\nEqual Interval Technique\r\nThe minimum and maximum values vmin and vmax of the classification parameter are determined and the (constant) interval size for each category is calculated as (vmax - vmin) ∕ n, where n is the number of classes chosen by the user. This classification is useful in that it reveals the distribution pattern, as it determines the number of features in each category.\r\n\r\nEqual Frequency Technique\r\nThis technique is also known as quantile classification. The objective is to create categories with roughly equal numbers of features per category. The total number of features is determined first, then, based on the required number of categories, the number of features per category is calculated. The class break points are then determined by counting off the features in order of classification parameter value.","hasChildren":true,"name":"Classification and transformation of attribute measurement levels","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM14","description":"Generalization addresses the meaningful reduction of the map content during scale reduction. All geospatial data are generalized. Even the most detailed data represent only subsets of reality. Furthermore, data are further generalized for purposes of mapping, visualization, and efficient storage. A variety of generalization techniques have been developed to facilitate this process. All are scale dependent. Aggregation is one form of generalization that transforms large numbers of individual objects into summarized groups. This concept description is concerned with the nature of these procedures and their implications for professional practice. Generalization is an important part of cartography (and is therefore discussed conceptually in CV2 Data considerations), but is also a transformation common to many GIS procedures.","hasChildren":true,"hasParent":true,"name":"Generalization and aggregation","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM2-1","description":"Set theory is based on describing collections of members within sets. The Boolean membership function is binary, i.e. an element is either a member of the set (membership is true) or it is not a member of the set (membership is false). Such a membership notion is well-suited to the description of spatial features such as land parcels for which no ambiguity is involved and an individual ground truth sample can be judged to be either correct or incorrect. As Burrough and Frank (1996) note, increasingly, people are beginning to realize that the fundamental axioms of simple binary logic present limits to the way we think about the world. Not only in everyday situations, but also in formalized thought, it is necessary to be able to deal with concepts that are not necessarily true or false, but that operate somewhere in between. Since its original development by Zadeh (1965), there has been considerable discussion of fuzzy, or continuous, set theory as an approach for handling imprecise spatial data. In GIS, fuzzy set theory appears to have two particular benefits: the ability to handle logical modelling (map overlay) operations on inexact data; and the possibility of using a variety of natural language expressions to qualify uncertainty. Unlike Boolean sets, fuzzy or continuous sets have a membership function, which can assign to a member any value between 0 and 1.","hasChildren":true,"hasParent":true,"name":"Set theory","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM2-2","description":"The most common operator for defining queries in a relational database is the language SQL, which stands for Structured Query Language.\r\n\r\nA spatial DBMS provides support for geographic coordinate systems and transformations. It will also provide storage of the relationships between features, including the creation and storage of topological relationships. As a result, one is able to use functions for “spatial query” (exploring spatial relationships). To illustrate, a spatial query using SQL to find all the Thai restaurants within 2 km of a given hotel would look like:\r\n\r\nSELECT R.Name\r\nFROM Restaurants AS R,\r\nHotels as H\r\nWHERE R.Type = Thai AND\r\nH.name = Hilton AND\r\nIntersect(R.Geometry, Buffer(H.Geometry, 2))\r\n\r\nThe Intersect command creates a spatial join between restaurants and hotels. The Geometry column carries the spatial data. It is likely that in the near future all spatial data will be stored directly in spatial databases.","hasChildren":true,"name":"Structured Query Language (SQL) and attribute queries","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM2-3","description":"When exploring a spatial data set, the first thing one usually wants to do is select certain features, to (temporarily) restrict the exploration. Such selections can be made on geometric/spatial grounds or on the basis of attribute data associated with the spatial features. \r\n\r\nSelection conditions on attribute values can be combined using logical connectives such as AND, OR and NOT. Other techniques of selecting features can also usually be combined. Any set of selected features can be used as the input for a subsequent selection procedure. This means, for instance, that we can select all medical clinics first, then identify roads within 200 m of them, then select from those only the major roads, then select the nearest clinics to these remaining roads as the ones that should receive our financial support for maintenance. In this way, we are combining various techniques of selection.\r\n\r\nInteractive Spatial Selection\r\nIn interactive spatial selection, one defines the selection condition by pointing at or drawing spatial objects on the screen display, after having indicated the spatial data layer(s) from which to select features. The interactively defined objects are called the selection objects; they can be points, lines, or polygons. The GIS then selects the features in the indicated data layer(s) that overlap (i.e. intersect, meet, contain, or are contained in;) with the selection objects. These become the selected objects.\r\nInteractive spatial selection answers questions like “What is at …?”\r\n\r\nA spatial DBMS provides support for geographic coordinate systems and transformations. It will also provide storage of the relationships between features, including the creation and storage of topological relationships. As a result, one is able to use functions for “spatial query” (exploring spatial relationships). To illustrate, a spatial query using SQL to find all the Thai restaurants within 2 km of a given hotel would look like:\r\n\r\nSELECT R.Name\r\nFROM Restaurants AS R,\r\nHotels as H\r\nWHERE R.Type = Thai AND\r\nH.name = Hilton AND\r\nIntersect(R.Geometry, Buffer(H.Geometry, 2))\r\n\r\nThe Intersect command creates a spatial join between restaurants and hotels. The Geometry column carries the spatial data. It is likely that in the near future all spatial data will be stored directly in spatial databases.","hasChildren":true,"name":"Spatial queries","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM2","description":"Attribute and spatial query operations are core functionality in any GIS and they are often considered to be the most basic form of analysis.","hasChildren":true,"hasParent":true,"name":"Query operations and query languages","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM3-1","description":"In a 2D polar coordinate system points can be described with coordinates. Another way of defining a point in a plane is by using polar coordinates. This is the distance d from the origin to the point concerned and the angle α between a fixed (or zero) direction and the direction to the point. The angle α is called azimuth or bearing and is measured in a clockwise direction. It is given in angular units while the distance d is expressed in length units. \r\n\r\nDistance also plays a role in computations on networks, comprising a different set of analytical functions in GISs. Here, the network may consist of roads, public transport routes, high-voltage power lines, or other forms of transportation infrastructure. Analysis of networks may entail shortest path computations (in terms of distance or travel time) between two points in a network for routing purposes. Other forms are to find all points reachable within a given distance or duration from a start point for allocation purposes, or determination of the capacity of the network for transportation between an indicated source location and sink location.\r\n\r\nIn raster images, the distance function applied is the Pythagorean distance between the cell centres. The distance from a non-target cell to the target is the minimal distance one can find between that non-target cell and any target cell.","hasChildren":true,"name":"Distances and lengths","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM3-2","description":"In a 2D polar coordinate system points can be described with coordinates. Another way of defining a point in a plane is by using polar coordinates. This is the distance d from the origin to the point concerned and the angle α between a fixed (or zero) direction and the direction to the point. The angle α is called azimuth or bearing and is measured in a clockwise direction. It is given in angular units while the distance d is expressed in length units.\r\n\r\nBearings are always related to a fixed direction (initial bearing) or a datum line. In principle, this reference line can be chosen freely. Three different, widely used fixed directions are: True North, Grid North and Magnetic North. The corresponding bearings are true (or geodetic) bearings, grid bearings and magnetic (or compass) bearings, respectively.\r\n\r\nIn raster images, direction is determined by the orientation of the neighboring pixels.","hasChildren":true,"name":"Direction","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM3-3","description":"The representation of geographic objects is most naturally supported with vectors. After all, objects are identified by the parameters of location, shape, size and orientation, and many of these parameters can be expressed in terms of vectors. We can define features within the topological space that are easy to handle and that can be used as representations of geographic objects. These features are called simplices as they are the simplest geometric shapes of some dimension: point (0-simplex), line segment (1-simplex), triangle (2-simplex), and tetrahedron (3-simplex). When we combine various simplices into a single feature, we obtain a simplicial complex. When area objects are stored using a vector approach, the usual technique is to apply a boundary model. This means that each area feature is represented by some arc/node structure that determines a polygon as the area’s boundary. A polygon representation for an area object is another example of a finite approximation of a phenomenon that may have a curvilinear boundary in reality. In images, the shape of objects often helps us to identify them (built-up areas, roads and railroads, agricultural fields, etc.).","hasChildren":true,"name":"Shape","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM3-4","description":"When area objects are stored using a vector approach, the usual technique is to apply a boundary model. This means that each area feature is represented by some arc/node structure that determines a polygon as the area’s boundary. A polygon representation for an area object is another example of a finite approximation of a phenomenon that may have a curvilinear boundary in reality.\r\nCommon sense dictates that area features of the same kind are best stored in a single data layer, represented by mutually non-overlapping polygons. This results in an application-determined (i.e. adaptive) partition of space. If the object has a fuzzy boundary, a polygon is an even worse approximation, even though potentially it may be the only one possible. Clearly, we expect additional data to accompany the area data. Such information could be stored in database tables.\r\n\r\nA simple but naïve representation of area features would be to list for each polygon the list of lines that describes its boundary. Each line in the list would, as before, be a sequence that starts with a node and ends with one, possibly with vertices in between. As the same line makes up the boundary from the two polygons, this line would be stored twice in the above representation, namely once for each polygon. This is a form of data duplication—known as data redundancy—which is (at least in theory) unnecessary, although it remains a feature of some systems. Another disadvantage of such polygon-by-polygon representations is that if we want to identify the polygons that border the bottom left polygon, we have to do a complicated and time-consuming search analysis comparing the vertex lists of all boundary lines with that of the bottom left polygon. For just a few polygons, this is fine, but in a data set with 5000 polygons, and perhaps a total of 25,000 boundary lines, this becomes a tedious task, even with the fastest of computers.","hasChildren":true,"name":"Area","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM3-5","description":"Proximity computations are specific neighbourhood functions. They evaluate the characteristics of an area surrounding a feature’s location. A neighbourhood function “scans” the neighbourhood of the given feature(s), and performs a computation on it (them).\r\n\r\nExamples of proximity computations are: (1) Buffer zone generation (or buffering) is one of the best-known neighbourhood functions. It determines a spatial envelope (buffer) around a given feature or features. The buffer created may have a fixed width or a variable width that depends on characteristics of the area. (2) Thiessen Polygon generation.\r\n\r\nDistance decay functions describe the effect of the reduced influence when the distance between two locations increases.","hasChildren":true,"name":"Proximity and distance decay","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM3-6","description":"Adjacency is the meet relationship as a topological property of a geographic object in relation ship with another. The adjacency operator identifies those features that share boundaries and, therefore, applies only to line and polygon features.\r\nThis meet relationship is invariant under a continuous transformation and are referred to as a topological mapping.","hasChildren":true,"name":"Adjacency and connectivity","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM3","description":"For simple data exploration, GIS offers many basic geometric operations that help in extracting meaning from sets of data or for deriving new data for further analysis. Concepts on which these operations are based are addressed in Domains of geographic information and Relationships.\r\n\r\nWe can, for instance, measure angles on a map and use these for navigation in the real world, or for setting out a designed physical infrastructure. Or if, instead of a conformal projection such as UTM, we use an equivalent projection, we can determine the size of a parcel of land from the map—irrespective of where the parcel is on the map and at which elevation it is on the Earth.","hasChildren":true,"hasParent":true,"name":"Geometric measures","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM4-1","description":"The reclassifications tools are used to change or reclassify the values. Reclassification of vector data involves the attributes of features in the feature attribute table, on the other hand reclassification of raster data involves the grid cell values to produce a new raster data layer. Reclassification can be used for data simplification and measurement scale change. We can adjust the data for more appropriate analysis by grouping the values and changing them. The reclassification tool can also be used to remove specific values from analysis.\r\nThe Select by location tool lets you select features by how they relate to other features in another layer. Selected features are based on their location. You can select features that are near or overlap the features. Most frequently used methods are intersect, within a distance, within, completely within, contain… Features can be selected in the same or other layers.\r\nThe Select by attributes tool lets you select features that match the selection criteria. With providing a selection criteria, matching features are selected. We can provide a complex selection criteria.","hasChildren":true,"name":"Reclassification and selection operations","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM4-2","description":"Buffer analysis is one form of basic spatial analysis. It takes the vector representation (point, line, or polygon) of a real-world feature, and then creates a buffer zone based on a defined distance from the feature’s border. Thus, the created buffer zone is an area whose boundary always has the same distance to the input vector feature, e.g. the buffer zone for a point feature is a circle. Real-world examples for buffer zones could be protected areas along rivers or around nature conservation areas, or represent a simple proximity analysis. In the latter case, the buffer analysis is usually the first step of the analysis, followed by an overlay of the buffer zone with the target features to find those target features within the buffer zone, and thus within a certain distance of the original feature. Usually, the buffer zone extends outwards from the feature, but polygons can also have inner buffer zones. If the buffer zones from multiple features overlap, the analyst can decide to leave the individual boundaries of the buffer zones intact, or to dissolve them, i.e. merging the overlapping buffer zones into one larger buffer zone. The size of the buffer zone, i.e. the distance of its boundary from the original feature’s boundary, can be based on an uniform numerical value and associated spatial unit, but often, it is based on an attribute value (numerical or class) of the feature. Conceptually, buffering using raster representations of real-world features is similar a proximity analysis with a regular grid of square polygons: Departing from raster cells that form the area to be buffered, all raster cells that fall within the designated distance (overlay) from the buffer zone. With buffer analysis being a basic analytical operation, practically every GIS and many other analysis tools provide this functionality.","hasChildren":true,"name":"Buffers","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM4-3","description":"Overlay functions is one of the most frequently used functions in a GIS application. They combine two (or more) spatial data layers, comparing them position by position and treating areas of overlap - and of non-overlap - in distinct ways.\r\n\r\nStandard overlay operators take two input data layers and assume that they are georeferenced in the same system and that they overlap in the study area. If either of these requirements is not met, the use of an overlay operator is pointless. The principle of spatial overlay is to compare the characteristics of the same location in both data layers and to produce a result for each location in the output data layer. The specific result to produce is determined by the user. It might involve a calculation or some other logical function to be applied to every area or location. With raster data, as we shall see, these comparisons are carried out between pairs of cells, one from each input raster. With vector data, the same principle of comparing locations applies but the underlying computations rely on determining the spatial intersections of features from each input layer.\r\n\r\nVector overlay operators are useful but geometrically complicated, and this sometimes results in poor operator performance. Raster overlays do not suffer from this disadvantage, as most of them perform their computations cell by cell, and thus they are fast. GISs that support raster processing - as most do - usually have a language to express operations on rasters. These languages are generally referred to as map algebra or, sometimes, raster calculus. They allow a GIS to compute new rasters from existing ones, using a range of functions and operators. Unfortunately, not all implementations of map algebra offer the same functionality.","hasChildren":true,"name":"Overlay","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM4-4","description":"Neighbourhood functions evaluate the characteristics of an area surrounding a feature’s location. A neighbourhood function “scans” the neighbourhood of the given feature(s), and performs a computation on it (them). Examples of proximity computations are: (1) Buffer zone generation (or buffering) is one of the best-known neighbourhood functions. It determines a spatial envelope (buffer) around a given feature or features. The buffer created may have a fixed width or a variable width that depends on characteristics of the area. (2) Thiessen Polygon generation. For raster images: (3) Computation of diffusion (4) Flow computation.\r\n\r\nFor instance, our target might be a medical clinic. Its neighbourhood could be defined as:\r\n\r\nan area within a radius of 2 km distance as the crow flies; or\r\nan area within 2 km travelling distance; or\r\nall roads within 500 m travelling distance; or\r\nall other clinics within 10 minutes travelling time;\r\nall residential areas for which the clinic is the closest clinic.\r\n\r\nFinally, in the third step we indicate what it is we want to discover about the phenomena that exist or occur in the neighbourhood. This might simply be its spatial extent, but it might also be statistical information such as:\r\n\r\nhow many people live in the area;\r\nwhat is their average household income;\r\nare any high-risk industries located in the neighbourhood.\r\n\r\nThese are typical questions in an urban setting. When our interest is more in natural phenomena, different examples of locations, neighbourhoods and neighbourhood characteristics arise.\r\n\r\nThe principle in this case is to find out the characteristics of the vicinity, here called neighbourhood, of a location. After all, many suitability questions, for instance, depend not only on what is at a location but also on what is near the location. Thus, the GIS must allow us “to look around locally”. To perform neighbourhood analysis, we must:\r\n\r\n1. state which target locations are of interest to us and define their spatial extent;\r\n2. define how to determine the neighbourhood for each target; and\r\n3. define which characteristic(s) must be computed for each neighbourhood. \r\n\r\nSince raster data are the more commonly used in this case, neighbourhood characteristics often are obtained via statistical summary functions that compute values such as the average, minimum, maximum and standard deviation of the cells in the identified neighbourhood.\r\n\r\nTo select target locations, one can use the selection techniques. To obtain characteristics from an eventually-to-be identified neighbourhood, the same techniques apply. So what remains to be discussed here is the proper determination of a neighbourhood. One way of determining a neighbourhood around a target location is by making use of the geometric distance function. Geometric distance does not take into account direction, but certain phenomena can only be studied by doing so. Think of the spreading of pollution by rivers, groundwater flow or prevailing weather systems.\r\n\r\nDiffusion functions are based on the assumption that the phenomenon in question spreads in all directions, though not necessarily equally easily in each direction. Hence it uses local terrain characteristics to compute local resistances to diffusion.","hasChildren":true,"hasParent":true,"name":"Neighborhood analysis","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM4-5","description":"GISs that support raster processing - as most do - usually have a language to express operations on rasters. These languages are generally referred to as map algebra or, sometimes, raster calculus. They allow a GIS to compute new rasters from existing ones, using a range of functions and operators. Unfortunately, not all implementations of map algebra offer the same functionality. The discussion below is to a large extent based on general terminology; it attempts to illustrate the key operations using a logical, structured language. Again, the syntax often varies among different GIS software packages.\r\n\r\nWhen producing a new raster we must provide a name for it, and define how it is to be computed. This is done in an assignment statement of the following format:\r\n\r\nOutput raster name := Map algebra expression.\r\n\r\nThe expression on the right is evaluated by the GIS, and the raster in which it results is then stored under the name on the left. The expression may contain references to existing rasters, operators and functions; the format is made clear in each case. The raster names and constants that are used in the expression are called its operands. When the expression is evaluated, the GIS will perform the calculation on a pixel-by-pixel basis, starting from the first pixel in the first row and continuing through to the last pixel in the last row. In map algebra, there is a wide range of operators and functions available.\r\n\r\nArithmetic operators\r\nVarious arithmetic operators are supported. The standard ones are multiplication (×), division (/), subtraction (-) and addition (+). Obviously, these arithmetic operators should only be used on appropriate data values, and, for instance, not on classification values. Other arithmetic operators may include modulo division (MOD) and integer division (DIV). Modulo division returns the remainder of division: for instance, 10 MOD 3 will return 1 as 10 - 3 × 3 = 1. Similarly, 10 DIV 3 will return 3.\r\n\r\nOther operators are goniometric: sine (sin), cosine (cos), tangent (tan); and their inverse functions asin, acos, and atan, which return radian angles as real values. The assignment\r\n\r\nC1 := A + 10\r\n\r\nwill add a constant factor of 10 to all cell values of raster A and store the result as output raster C1. The assignment\r\n\r\nC2 := A + B\r\n\r\nwill add the values of A and B cell by cell, and store the result as raster C2. Finally, the assignment\r\n\r\nC3 := (A - B) ∕ (A + B) × 100\r\n\r\nwill create output raster C3, as the result of the subtraction (cell by cell, as usual) of B cell values from A cell values, divided by their sum. The result is multiplied by 100. This expression, when carried out on AVHRR channel 1 (red) and AVHRR channel 2 (near infrared) of NOAA satellite imagery, is known as the NDVI (Normalized Difference Vegetation Index). It has proven to be a good indicator of the presence of green vegetation.\r\n\r\nComparison and logical operators\r\n\r\nMap algebra also allows the comparison of rasters cell by cell. To this end, we may use the standard comparison operators (<, ⇐, =, >=, > and <>).\r\n\r\nA simple raster comparison assignment is\r\n\r\nC := A <> B.\r\n\r\nIt will store truth values—either true or false—in the output raster C. A cell value in C will be true if the cell’s value in A differs from that cell’s value in B. It will be false if they are the same. Logical connectives are also supported in many raster calculi. We have already seen the connectives of AND , OR and NOT in raster overlay operators. Another connective that is commonly offered in map algebra is exclusive OR (XOR). The expression a XOR b is true only if either a or b is true, but not both.\r\n\r\nConditional expressions\r\nThe comparison and logical operators produce rasters with the truth values true and false. In practice, we often need a conditional expression together with them that allows us to test whether a condition is fulfilled. The general format is:\r\n\r\nOutput raster := CON(condition, then expression, else expression).\r\n\r\nHere, condition stands for the condition tested, then the expression is evaluated if condition holds, and else the expression is evaluated if it does not hold. This means that an expression such as CON(A = “forest”, 10, 0) will evaluate to 10 for each cell in the output raster where the same cell in A is classified as forest. For each cell where this is not true, the else expression is evaluated, resulting in 0.\r\n\r\nOverlays using a decision table\r\nConditional expressions are powerful tools in cases where multiple criteria must be taken into account. A small example may illustrate this. Consider a suitability study in which a land use classification and a geological classification must be used. Domain expertise dictates that some combinations of land use and geology result in suitable areas, whereas other combinations do not. In our example, forests on alluvial terrain and grassland on shale are considered suitable combinations, while any others are not.\r\n\r\nWe could produce an output raster with a map algebra expression, such as\r\n\r\nSuitability := CON((Landuse = “Forest” AND Geology = “Alluvial”)\r\nOR (Landuse = “Grass” AND Geology = “Shale”),\r\n“Suitable”, “Unsuitable”)\r\n\r\nand consider ourselves lucky that there are only two “suitable” cases. In practice, many more cases must usually be covered and, then, writing up a complex CON expression is not an easy task.\r\n\r\nTo this end, some GISs accommodate setting up a separate decision table that will guide the raster overlay process. This extra table carries domain expertise and dictates which combinations of input raster-cell values will produce which output raster-cell value. This gives us a raster overlay operator using a decision table. The GIS will have supporting functions to generate the additional table from the input rasters and to enter appropriate values in the table.","hasChildren":true,"name":"Map algebra","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM4","description":"This small set of analytical operations is so commonly applied to a broad range of problems that their inclusion in software products is often used to determine if that product is a true GIS. Concepts on which these operations are based are addressed in Domains of geographic information and Relationships.","hasChildren":true,"hasParent":true,"name":"Basic analytical operations","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM5-1","description":"Point pattern analysis refers to the detection of patterns in a group of objects or subjects located in space. This may support the analysis of clusters in accidents, crime, etc.","hasChildren":true,"name":"Point pattern analysis","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM5-2","description":"The probability density function is a method with which the probability density can be estimated for points in a raster space.","hasChildren":true,"name":"Kernels and density estimation","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM5-3","description":"Spatial cluster analysis is the grouping of similar spatial objects into classes (clusters) in such a way that the objects within the cluster are highly similar compared to the objects outside of the cluster. Spatial clustering forms an important part of spatial data mining (Han et al., 2001; Miller et al., 2009). A wealth of spatial clustering tools are currently available with immense application potential. \r\n\r\nIn earth observation studies, spatial cluster techniques are often applied to identify zones with similar land covers by using earth observation data as input. An example of such a technique is the K-means classifier (Han et al., 2001; Miller et al., 2009). This unsupervised classification technique makes several clusters (e.g. land use classes) of which each pixel is assigned to the cluster with the nearest mean (Han et al., 2001). The amount of clusters can be freely defined by the user just as the input metrics to perform the classification. A drawback of the K-means classifier is the need to specify the amount of output clusters. Density Based Spatial Clustering (DBSC) overcomes this issue since it automatically defines the optimal amount of clusters (Miller et al., 2009). In this type of clustering technique, dense regions of objects (proximate objects) are clustered and separated from regions with low density (noise) (Han et al., 2001; Liu et al., 2012). Finally, another frequently applied spatial clustering technique is the hierarchical agglomerative clustering. This technique makes use of a dendrogram to decompose the data into clusters. The agglomerative approach is a bottom-up approach in which all objects are first grouped in a distinct cluster and while moving upward in the tree, pairs of clusters are merged based on some metrics (e.g. spatial proximity) (Han et al., 2001). \r\n\r\nSpatial cluster techniques have many advantages when dealing with big datasets which is often the case when working with earth observation data. Its simplicity to use and the fast increase of cloud computing power makes from it powerful techniques to extract spatial patterns out of the data. It allows to translate raw earth observation data into a more user-friendly data product by showing the spatial patterns of the data.","hasChildren":true,"name":"Spatial cluster analysis","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM5-4","description":"Spatial interaction models describe the flow of people and goods in a geographical space, in which parameters such as friction and distance play a role.","hasChildren":true,"name":"Spatial interaction","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM5-5","description":"Multidimensional attributes can be analyzed through multidimensional scaling and principle component analysis.","hasChildren":true,"hasParent":true,"name":"Analyzing multidimensional attributes","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM5-7","description":"Multi-criteria evaluation is an important aspect of decision support operations, which appear in process models. Process models in the Earth sciences describe the evolution of geo(bio)physical surface properties in time, independently from remote sensing observations. Examples of such process models on various time scales are, for instance, numerical weather prediction models (NWPs), vegetation growth models, hydrological models, oceanographic models and climate models.\r\n\r\nObservation models and process models can supplement each other to enhance the quality of the interpretation of remote sensing data and to fill gaps in time that occur when observations are not possible owing to clouds or some other cause. Interactions are possible between observation models and process models with EO data and existing geographic information (GIS and ground measurements, supplemented with decision-support systems (DSSs)).\r\n\r\nThe process model provides information to the decision-support system, which supports management actions aimed at controlling/mitigating the process, based on an multi-criteria evaluation. A good example of this is a water management system, in which one might decide to allocate water for irrigation if the observed vegetation appears to suffer from drought stress.","hasChildren":true,"name":"Multi-criteria evaluation","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM5-8","description":"Process models in the Earth sciences describe the evolution of geo(bio)physical surface properties in time, independently from remote sensing observations. Examples of such process models on various time scales are, for instance, numerical weather prediction models (NWPs), vegetation growth models, hydrological models, oceanographic models and climate models.\r\nProcess models in the geosciences usually rely on regular observations at many locations spread over a large area. Traditionally, these observations were mostly made in the field with a variety of instruments. Remote sensing techniques have tremendously increased the capability of spatial sampling and the consistency of the surface parameters measured. RS instruments are mostly sensitive to many physical properties of the surface, some of these may not belong to the set of properties that the user is interested in. Exceptions to this are the mapping of sea-surface temperature, laser altimetry and gravimetry, which are measurements of direct geophysical interest. In the majority of cases, however, there are only indirect relationships between what is observed with the instrument and the physical object properties of interest. In these cases, the use of observation models becomes an attractive option, since these models describe the relationships between all object properties relevant for the observation and the observed remote sensing data.","hasChildren":true,"name":"Spatial process models","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM5","description":"Building on the basic geometric measures and analytical operations found in most GIS products, a broad range of additional analytical methods form the fundamental GIS toolkit.","hasChildren":true,"hasParent":true,"name":"Basic analytical methods","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM6-2","description":"In rasters we use interpolation to determine the value of a pixel, based on its surrounding pixels. The main raster-based interpolation methods are nearest neighbour, bilinear, and bicubic interpolation. To determine the value of the centre pixel (bold), in nearest neighbour interpolation the value of the nearest original pixel is assigned, i.e. the value of the black pixel in this example. Note that the respective pixel centres, marked by small crosses, are always used for this process. In bilinear interpolation, a linear weighted average is calculated for the four nearest pixels in the original image. In bicubic interpolation a cubic weighted average of the values of 16 surrounding pixels (the black and all grey pixels) is calculated. Note that some software uses the terms “bilinear convolution” and “cubic convolution” instead of the terms introduced above.","hasChildren":true,"name":"Interpolation of surfaces","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM6-3","description":"Continuous fields have a number of characteristics not shared by discrete fields. Since the field changes continuously, we can talk of slope angle, slope aspect and concavity/convexity of the slope.\r\n\r\nThese notions are not applicable to discrete fields. The discussions in this subsection use terrain elevation as the prototype example of a continuous field, but all aspects discussed are equally applicable to other types of continuous fields. Nonetheless, we regularly refer to the continuous field representation as a DEM, to conform with the most common situation.","hasChildren":true,"name":"Surface features","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM6-4","description":"A viewshed is the area that can be “seen” (i.e. it is in the direct line-of-sight) from a specified target location. (Inter) visibility analysis can determine the area visible from a scenic lookout or the area that can be reached by a radar antenna, as well as assess how effectively a road or quarry will be hidden from view.","hasChildren":true,"name":"Intervisibility","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM6-5","description":"Firction surfaces contain information on how difficult/easy it is for a phenomenon to move from one location on the surface to another.","hasChildren":true,"name":"Friction surfaces","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM6","description":"There is a wide range of phenomena that can be studied using a set of techniques and tools that are designed to help understand the characteristics of continuous surface data. Applications of these techniques using terrain data include overland transport, flow, and siting tasks, but similar analyses can be conducted using non-tangible surfaces such as those of temperature, pressure and population density.\r\n\r\nThere are numerous examples that require more advanced computations on continuous field representations, such as:\r\n\r\nSlope angle calculation - the calculation of the slope steepness, expressed as an angle in degrees or percentages, for any or all locations.\r\n\r\nCalculating slope aspect - the calculation of the aspect (or orientation) of the slope in degrees (between 0 and 360∘), for any or all locations.\r\n\r\nSlope convexity/concavity calculation - defined as the change of the slope (negative when the slope is concave and positive when the slope is convex)—can be calculated as the second derivative of the field.\r\n\r\nSlope length calculation - with the use of neighbourhood operations, it is possible to calculate for each cell the nearest distance to a watershed boundary (the upslope length) and to the nearest stream (the downslope length). This information is useful for hydrological modelling.\r\n\r\nHillshading is used to portray relief difference and terrain morphology of hilly and mountainous areas. The application of a special filter to a DEM produces hillshading. The colour tones in a hillshading raster represent the amount of reflected light at each location, depending on its orientation relative to the illumination source. This illumination source is usually chosen to be to the northwest at an angle of 45∘ above the horizon.\r\n\r\nThree-dimensional map display - with GIS software, three-dimensional views of a DEM can be constructed in which the location of the viewer, the angle under which he or she is looking, the zoom angle, and the amplification factor of relief exaggeration can be specified. Three-dimensional views can be constructed using only a predefined mesh, covering the surface, or using other rasters (e.g. a hillshading raster) or images (e.g. satellite images) that are draped over the DEM.\r\n\r\nDetermination of change in elevation through time - the cut-and-fill volume of soil to be removed or to be brought in to make a site ready for construction can be computed by overlaying the DEM of the site before the work begins with the DEM of the expected modified topography. It is also possible to determine landslide effects by comparing DEMs of before and after a landslide event.\r\n\r\nAutomatic catchment delineation - catchment boundaries or drainage lines can be automatically generated from a good quality DEM with the use of neighbourhood functions. The system will determine the lowest point in the DEM, which is considered to be the outlet of the catchment. From there, it will repeatedly search for the neighbouring pixels with the highest altitude. This process is repeated until the highest location (i.e. the cell with the highest value) is found; the path followed determines the catchment boundary. For delineating the drainage network, the process is reversed. Then the system will work from the watershed downwards, each time looking for the lowest neighbouring cells, which determines the direction of water flow (Flow Computation).\r\n\r\nDynamic modelling - apart from the applications mentioned above, DEMs are increasingly used in GIS-based dynamic modelling, such as the computation of surface run-off and erosion, groundwater flow, the delineation of areas affected by pollution, the computation of areas that will be covered by processes such as flows of debris and lava. An example is (Diffusion).\r\n\r\nVisibility analysis - a viewshed is the area that can be “seen” (i.e. it is in the direct line-of-sight) from a specified target location. Visibility analysis can determine the area visible from a scenic lookout or the area that can be reached by a radar antenna, as well as assess how effectively a road or quarry will be hidden from view.","hasChildren":true,"hasParent":true,"name":"Analysis of surfaces","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM7-1","description":"Statistical analysis techniques based on visual interpretation through histograms, scatterplots, etc.","hasChildren":true,"name":"Graphical methods","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM7-2","description":"Environmental variables have become increasing available with the advent of GIS. These are mostly continuous in space and time. Collecting denser environmental data in discrete space and time domains are rather cost effective and time consuming. However, when the data at each spatial or time index are considered as outcomes of a random variable, stochastic processes become enviable useful to build models and predict the outcomes at locations where data were never collected. The meaningful assumptions include stationarity of the mean and the covariance to ascertain an expression for spatial dependency/autocorrelation. With a stationary process (i.e. constant mean), simple and ordinary kriging is used. Other variants like kriging with external drift, universal kriging and regression kriging also alleviate the challenge of non-stationary mean. These methods are also applicable when temporal indexes rather than spatial indexes are of interest.","hasChildren":true,"name":"Stochastic processes","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM7-3","description":"Spatial weight matrix is the popular numerical quantification of spatial dependency or spatial neighborhoods. The weight matrix should summarize information about the spatial connectivity structure of the spatial entities/features; either polygons, points, or lines. This is required for the computation of spatial dependency indices such the Moran’s index, and for spatial regression models such as the conditional autoregressive (CAR), spatial lag, and spatial error models. The connectivity information can be defined based on adjacency/contiguity or distance between pairs of spatial entities. There are other forms; they could be based on population densities between observation pairs. The simplest spatial weigh matrix is the binary adjacency spatial weight matrix with elements w_ij, such that w_ij=1 if spatial units i and j are neighbors, otherwise w_ij=0. A popular alternative is the inverse distance weight matrix with elements w_ij=1⁄d^α , where d is the distance between pairs of spatial units and α is any positive number greater than zero. By convention, w_ii=0 since spatial unit cannot have a spillover within itself.","hasChildren":true,"name":"The spatial weights matrix","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM7-4","description":"Spatial autocorrelation evaluates how things which are closer in space tend to have similar attributes. This is a common phenomenon in environmental variables which are continuous in space. For instance, temperature, soil moisture content, air quality and rainfall are all continuous in space. This idea is based on Tobler’s law of geography: “everything is related to everything but near things are more related”. Global measures of spatial association estimates the overall index of spatial autocorrelation, also called spatial clustering. Thus, it measures whether clustering is apparent throughout the study region but do not identify the location of clusters. Common global measures include the Moran’s Index and Geary’s C. These have increasing applications in domains like environmental science, agriculture, epidemiology, climate studies etc.","hasChildren":true,"name":"Global measures of spatial association","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM7-5","description":"Unlike global measures of spatial association, local measure of spatial association identifies the locations of clusters. Typical measures include the local indicator for spatial autocorrelation (LISA) or the local Moran’s index whose summation is proportional to the global Moran’s index. The spatial scan statistics has also been the commonly used method to detect local clusters.","hasChildren":true,"name":"Local measures of spatial association","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM7-6","description":"An outlier is an unexpected value that differs significantly from other observations. Definition of an outlier is not absolute and the concept itself is precisely defined only by selection of appropriate criteria in concrete statistical observations. When considering outliers, it is important to determine whether the value of the outlier is incorrect data or it is otherwise outstanding, but correct data. If we consider outliers in the case when they base on sample surveys, another assessment is necessary. Namely, the assessment of whether an outlier is representative or not. \r\nThe box plot is a useful graphical display for examining the outliers. Using median, lower and upper quartiles, extreme values are identified in the tails of the distribution. The value beyond inner fence on either side is considered a mild outlier. The value beyond an outer fence is considered an extreme outlier. Histograms also emphasize the existence of outliers. The histogram depends on how we design the classes, so we can get different histograms for the same data. Graphical and quantitative checks are obligatory if the histogram shows possible outliers. Outliers can also be examined by calculating the correlation between two datasets (Pearson correlation coefficient, Spearman rank correlation coefficient…). Scatter plots reveals a basic linear relationship with a pattern. An outliner is defined as a data point that deviates from other values. Outliers can also be examined by local outlier factor, which is based on a concept of a local density. Points with substantially lower density than their neighbours are considered as outliers.","hasChildren":true,"name":"Outliers","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM7-7","description":"Bayesian method of modelling stems from the Bayes theorem and derived using conditional probabilities. Its advantage lies in its ability to include prior knowledge of unknown parameters to ascertain their uncertainties. Thus, the prior parameters are updated by the data likelihood to obtain the posteriors. The challenge of Bayesian modelling has been the integration of the denominator which always resulted into improper integrals. This actually prolonged its wide applications. With the advent of high performance computers, solution to such integrals are easily solved using Markov chain Monte Carlo simulations. The advent robust approximation methods through integrated nested Laplace approximations (INLA) has even made parameter estimation faster; thus making Bayesian methods interesting and better. Unlike frequentist approaches, Bayesian methods can present estimates of parameters as densities from which their uncertainties and credible intervals can be estimated. They have now found wide applications in divers areas like environmental modelling, climate modeling, agriculture, epidemiology and many other domains that requires modeling.","hasChildren":true,"name":"Bayesian methods","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM7","description":"Traditional statistical methods are used to describe the central tendency, dispersion, and other characteristics of data but are not always suited to use with spatial data for which specialized techniques are often required. The field of spatial statistical analysis forms the backbone for the testing of hypotheses about the nature of spatial pattern, dependency, and heterogeneity. The techniques are widely used in both exploratory and confirmatory spatial analysis in many different fields.","hasChildren":true,"hasParent":true,"name":"Spatial statistics","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM8-1","description":"Sampling is needed to limit the observations for statistical analysis. In raster image analysis, various sampling schemes have been proposed for selecting pixels to test. Choices to be made relate to the design of the sampling strategy, the number of samples required, and the area of the samples. Recommended sampling strategies in the context of land cover data are simple random sampling or stratified random sampling. The number of samples may be related to two factors in accuracy assessment: (1) the number of samples that must be taken in order to reject a data set as being inaccurate; or (2) the number of samples required to determine the true accuracy, within some error bounds, of a data set. Sampling theory is used to determine the number of samples required. The number of samples must be traded-off against the area covered by a sample unit. A sample unit can be a point but it could also be an area of some size; it can be a single raster element but may also include surrounding raster elements. Among other considerations, the “optimal” sample-area size depends on the heterogeneity of the class.","hasChildren":true,"hasParent":true,"name":"Spatial sampling for statistical analysis","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM8-3","description":"A variogram is a tool used to describe the spatial continuity of data points. Different kinds of variograms are used, such as experimental variogram and semi-variogram.","hasChildren":true,"name":"Variogram modeling","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM8-4","description":"Predicting an observation in the presence of spatially dependent observations is termed Kriging, named after the first practitioner of these procedures, the South African mining engineer Daan Krige, who did much of his early empirical work in the Witwatersrand gold mines.","hasChildren":true,"name":"Principles of kriging","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM8-5","description":"With a stationary stochastic process (i.e. constant mean), simple and ordinary kriging is used for interpolation. Other variants like kriging with external drift, universal kriging and regression kriging also alleviate the challenge of non-stationary mean. Other variants are \r\nco-kriging log-normal kriging, disjunctive kriging, indicator kriging, factorial kriging and universal kriging.","hasChildren":true,"name":"Kriging variants","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM8","description":"Geostatistics are a variety of techniques used to analyze continuous data e.g., rainfall, elevation, air pollution. The fundamental structure of geostatistics is based on the concept of semi-variograms and their use for spatial prediction kriging. Sampling methods are also discussed in Unit GD9 Field data collection. \r\nGeostatistics is a subdiscipline of spatial statistics developed to estimate the value of a continuous spatial process at unknown locations by using the information of the value of these process at known locations. Furthermore, it aims to quantify the uncertainty related to the prediction (Calder et al., 2009; Emmanouil, 2019). In order to do such predictions, geostatistics entails some statistical methods which use as starting point the assumption of a random component that can define the spatiotemporal variability. These methods are developed to infer the parameters that can describe the spatiotemporal patterns of the input variables (e.g. soil moisture) so that finally these variables at unsampled locations can be estimated (interpolated) (Emmanouil, 2019). Geostatistical methods are strongly related with classic interpolation methods but differ by its use of random variables that allow to given an uncertainty indication associated with the prediction of variables in space and time. \r\n\r\nIn environmental research geostatistical techniques are often applied to infer (interpolate) variables at such unobserved locations by using information from known locations. One of such geostatistical techniques is Kriging, which is a geostatistical method that predicts variables by using spatial interpolation. This spatial interpolation is done by establishing a semivariogram that defines the spatial relationship between the variables of interest in function of the distance. Because of this, the Kriging technique can also give an indication on the variance or accuracy of the prediction (Calder et al., 2009); Van der Meer, 2012). On the other hand, cokriging is another important geostatistical technique and differs from Kriging by using the cross-correlation between variables to generate local estimates (Van der Meer, 2012). In earth observation studies, cokriging can be applied to better predict sparsely based data on the ground (e.g. biomass) by using the cross-correlation of this variable with a more continuously sampled satellite metric like NDVI. Furthermore, these techniques can also be used to enhance satellite image information, filling missing pixels or even downscale the information to a higher resolution (Van der Meer, 2012).","hasChildren":true,"hasParent":true,"name":"Geostatistics","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM9-1","description":"Spatial econometrics uses spatial stochastic models to determine autocorrelation between interacting agents. The techniques involved are regression, the use of a spatial weights matrix, least squares, etc.","hasChildren":true,"name":"Principles of spatial econometrics","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM9-2","description":"A spatial autoregressive (SAR) model describes the prediction of the behaviour of a random process.","hasChildren":true,"name":"Spatial autoregressive models","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM9-3","description":"In producing optimal images for interpretation, spatial filtering is applied. Filtering is usually carried out for a single band. Filters - algorithms - can be used to enhance images by, for example, reducing noise (“smoothing an image”) or sharpening a blurred image. Filter operations are also used to extract features from images, e.g. edges and lines, and to automatically recognize patterns and detect objects. There are two broad categories of filters: linear and non-linear filters.\r\n\r\nLinear filters calculate the new value of a pixel as a linear combination of the given values of the pixel and those of neighbouring pixels. A simple example of the use of a linear smoothing filter is when the average of the pixel values in a 3×3 pixel neighbourhood is computed and that average is used as the new value of the central pixel in the neighbourhood.","hasChildren":true,"name":"Spatial filtering","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM9-4","description":"Geographically Weighted Regression (GWR) makes use of local subsets of observations to perform estimates.","hasChildren":true,"name":"Spatial expansion and Geographically Weighted Regression GWR","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM9","description":"Many problems of the social sciences can be expressed in terms of spatial regression analysis. The development of spatial autoregressive models and the estimation of their parameters is the focus for the field of spatial econometrics.","hasChildren":true,"hasParent":true,"name":"Spatial regression and econometrics","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF","description":"The GIScience perspective is grounded in spatial thinking. The aim of this knowledge area is to recognize, identify, and appreciate the explicit spatial, spatio-temporal and semantic components of the geographic environment at an ontological and epistemological level in preparation for modeling the environment with geographic data and analysis. To do this, one must understand the nature of space and time as a context for geographic phenomena.This knowledge area covers the ways in which views of the geographic environment depend on philosophical viewpoints, physics, human cognition, society, and the task at hand. This knowledge area also requires an understanding of the fundamental principles in the discipline of geography, the \"language\" of spatial tasks. On a more advanced level, this area incorporates mathematical and graphical models that formalize these concepts, such as set theory, algebra, and semantic nets. Because of its wide range of foundational principles, this knowledge area forms a basis for the other knowledge areas. Wise design and use of geospatial technologies requires an understanding of the nature of geographic information, the social and philosophical context of geographic information, and the principles of geography. This knowledge area is especially closely tied to Knowledge Areas Data Modeling (DM) and Design Aspects (DA), as generic data models and application designs need to be grounded in sound conceptual models. The foundations of geographic information have developed over several decades. Philosophical and scientific views on the nature of space and time have evolved since the ancient Greeks. Early papers during the Quantitative Revolution, such as Berry (1964), began to formalize the structure of information used in geographic inquiry.The fundamental data structures and algorithms comprising the GIS software developed in the 1960`s and 1970`s were based on implicit \"common-sense\" conceptual models of geographic information. During the 1980`s, several researchers questioned these underlying assumptions. Some were refuted, other confirmed, and many extended. However, the most rapid pace of development in this area was during the 1990`s with the rise of GIScience as a distinct discipline, and the many cooperative initiatives it comprised.The new millennium has seen some of these foundational principles incorporated into commercial software, thus making theoretical knowledge even more important for practitioners. It is expected that the concepts in this knowledge area will be learned gradually. An introductory course may cover only a few topics in a cursory manner, an intermediate course on data modeling or data analysis may consider several theoretical topics of practical application, and a number of graduate courses could cover each topic in a research-oriented environment. Discussion of this knowledge area includes several terms that can have multiple meanings. For the purposes of this document, two in particular require definition: Geographic: Almost any subject or discourse involving earthly phenomena, studied from a spatial perspective at a medium scale (sub-astronomical and super-architectural). Phenomenon: Any subject of geographic discourse that is perceived to be external to the individual, including entities, events, processes, social constructs, and the like.","hasChildren":true,"hasParent":true,"name":"Conceptual Foundations","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF1-1","description":"Metaphysics involve the meaning things and concepts. Ontologies provide a way to share the semantics of concepts in some area of interest and is all about common the understanding of essential concepts, e.g., what is meant by a geometric object and its attributes.","hasChildren":true,"name":"Metaphysics and ontology","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF1-1b","description":"Brief history of GIScience as related to the history of GISystems; Definitions of GIS&T; Sub-domains of GIS&T (i.e., Geographic Information Science, Geospatial Technology, and Applications of GIS&T)","hasChildren":true,"name":"What is Geographic Information Science and Technology","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF1-2","description":"The branch of philosophy concerned with knowledge.","hasChildren":true,"name":"Epistemology","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF1-2b","description":"GIS&T draws upon insights and methods from key allied fields: Geography, Cartography, Computer and information science, Engineering, Mathematics and Statistics, Philosophy, Cognitive Science, Linguistics","hasChildren":true,"name":"Contributions to GIS and T by key allied fields","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF1-3","description":"The questions and methodologies in major philosophical movements relating to the nature of space, time, geographic phenomena and human interaction with it.","hasChildren":true,"name":"Philosophical perspectives","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF1","description":"Many branches of philosophy are relevant to an understanding of geographic information, especially metaphysics and epistemology. Philosophical theories are deeply engaged in the study of knowledge, space, time, geographic phenomena and human interaction with them. These theories influence the development of geographic ontologies and the structuring, analysis, and interpretation of geographic information. It is, therefore, crucial for professionals to understand these principles in order to bridge (rather than eliminate) the differences and work together. Philosophical perspectives on GIS practice are covered in Unit GS7 Critical GIS.","hasChildren":true,"hasParent":true,"name":"Philosophical foundations","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF1b","description":"Unit CF1 introduces the broad domain refered to as Geographic Information Science & Technology (GIS&T) and its sub-domains (i.e., Geographic Information Science, Geospatial Technology, and Applications of GIS&T). It outlines the history of Geographic Information Science as related to the history of GISystems, as well as the contributions to this multidisciplinary domain by key allied fields, such as geography, cartography, computer and information science, engineering, mathematics, philosophy, cognitive science, and linguistics.","hasChildren":true,"hasParent":true,"name":"Introduction to Geographic Information Science and Technology","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF2-1","description":"The study on how humans perceive spatial information.","hasChildren":true,"name":"Perception and cognition of geographic phenomena","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF2-1b","description":"Metaphysics and Ontology - Formal ontology - Ontological distinctions (e.g., continuants vs. occurrents, universals vs. particulars) - The problem of universals and relevant theories (realism, nominalism, conceptualism) - Ontologies of the geographic domain - Philosophical theories relating to the nature of space, time, geographic phenomena and human interaction with them","hasChildren":true,"name":"Philosophy of being","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF2-2","description":"The ways in which conceptual views of in the human mind make it into formal descriptions of information and into artefacts in databases and GIS.","hasChildren":true,"hasParent":true,"name":"From concepts to data","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF2-2b","description":"Epistemology; Theories on what constitutes knowledge; The notions of model and representation in science; The influences of epistemology on GIS practices","hasChildren":true,"name":"Philosophy of knowledge","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF2-3","description":"Principles of geography to explain the spatial occurrences of spatial entities in Geographic Information Systems.","hasChildren":true,"name":"Geography as a foundation for GIS","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF2-4","description":"Space and place are concepts that are not the same. Including concepts like landscape, it is not always obvious how to portray them unambiguously in GIS.","hasChildren":true,"name":"Place and landscape","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF2-6","description":"The ways in which the elements of culture (e.g., language, religion, education, traditions) may influence the understanding and use of geographic information.","hasChildren":true,"name":"Cultural influences","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF2-7","description":"The influences of political ideologies (e.g., Marxism, Capitalism, conservative liberal) on the understanding of geographic information.","hasChildren":true,"name":"Political influences","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF2","description":"Geographic information is observed, comprehended, organized, used in human processes, with both personal and social influences. Therefore, sound models of geographic information should be grounded on a sound understanding of human perception, cognition, memory, and behavior, as well as human institutions.","hasChildren":true,"hasParent":true,"name":"Cognitive and social foundations","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF3-1","description":"A GIS operates under the assumption that the spatial phenomena involved occur in a two- or three-dimensional Euclidean space. Euclidean space can be informally defined as a model of space in which locations are represented by coordinates—(x, y) in 2D and (x, y, z) in 3D space—and distance and direction can defined with geometric formulas. In 2D, this is known as the Euclidean plane. To represent relevant aspects of real-world phenomena inside a GIS, we first need to define what it is we are referring to. We might define a geographic phenomenon as a manifestation of an entity or process of interest that:\r\n\r\nitem can be named or described;\r\nitem can be georeferenced; and\r\nitem can be assigned a time (interval) at which it is/was present.\r\n\r\nRelevance of phenomena for the use of a GIS depends entirely on the objectives of the study at hand. For instance, in water management, relevant objects can be river basins, agro-ecological units, measurements of actual evapotranspiration, meteorological data, ground\\-water levels, irrigation levels, water budgets and measurements of total water use. All of these can be named or described, georeferenced and provided with a time interval at which each exists. In multipurpose cadastral administration, the objects of study are different: houses, land parcels, streets of various types, land use forms, sewage canals and other forms of urban infrastructure may all play a role. Again, these can be named or described, georeferenced and assigned a time interval of existence.\r\n\r\nNot all relevant information about phenomena has the form of a triplet (description, georeference, time interval). If the georeference is missing, then the object is not positioned in space: an example of this would be a legal document in a cadastral system. It is obviously somewhere, but its position in space is not considered relevant. If the time interval is missing, we might have a phenomenon of interest that exists permanently, i.e.\\ the time interval is infinite. If the description is missing, then we have something that exists in space and time, yet cannot be described. Obviously this last issue limits the usefulness of the information.\r\n\r\nTypes of geographic phenomena\r\nThe definition of geographic phenomena attempted above is necessarily abstract and is, therefore, perhaps somewhat difficult to grasp. The main reason is that geographic phenomena come in different “flavours”. Before categorizing such flavours, there are two further observations to be made.\r\n\r\nFirst, to represent a phenomenon in a GIS requires us to state what it is and where it is. We must provide a description—or at least a name—on the one hand, and a georeference on the other hand. We will ignore temporal issues for the moment and come back to these in Temporal dimension and Spatial-temporal data model, the reason being that current GISs do not provide much automatic support for time-dependent data. This topic must, therefore, be considered as an example of advanced GIS use. Second, some phenomena are manifest throughout a study area, while others only occur in specific localities. The first type of phenomena we call geographic fields; the second type we call objects.","hasChildren":true,"name":"Space","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF3-1b","description":"- Theories of human perception, cognition, and memory and their ability to model spatial knowledge acquisition (e.g., Marr on vision, Piaget on cognitive development) - Types of mental representations (i.e., analogue, propositional, procedural) - The role of metaphors and image schemata in our understanding of geographic phenomena and geographic tasks - From concepts to data (i.e., data, information, knowledge, and wisdom; transformation of a conceptual model of information for a particular task into a data model; limitations of various information stores (the mind, computers) and means (maps, graphics, and text) for representing geographic information) - Difference between real phenomena, conceptual models, and GIS data representations thereof connections with cartography and maps","hasChildren":true,"name":"Cognitive foundations","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF3-2b","description":"- Semantics - Meaning (e.g., the nature of meaning, modes of meaning) - Geospatial semantics - The role of natural language in the conceptualization of geographic phenomena","hasChildren":true,"name":"Linguistic foundations","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF3-3b","description":"- The ways in which the elements of culture (e.g., language, religion, education, traditions) may influence the understanding and use of geographic information - The influences of social theories and political ideologies and actions on human perceptions of space and place - The constraints that political forces place on geospatial applications in public and private sectors","hasChildren":true,"name":"Social foundations","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF3-4b","description":"- Common-sense views and laymen knowledge of geographic phenomena that contrast with established theories and technologies of geographic information - The impact of geospatial technologies and the geoweb (e.g., digital globes) that allow non-geospatial professionals to create, distribute, and map geographic information - The design, procedures, and results of GIS projects to non-GIS audiences (clients, managers, general public) - Difference between applications that can make use of common-sense principles of geography and those that should not","hasChildren":true,"name":"Common-sense geographies","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF3","description":"Geographic information is observed, comprehended, organized, used in human processes, with both personal and social influences. Therefore, sound models of geographic information should be grounded on a sound understanding of human perception, cognition, memory, and behavior, as well as human institutions.","hasChildren":true,"hasParent":true,"name":"Cognitive, linguistic and social foundations","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF4-2b","description":"As time is the central concept of the temporal dimension, a brief examination of the nature of time may clarify our thinking when we work with this dimension:\r\n\r\nDiscrete and continuous time: Time can be measured along a discrete or continuous scale. Discrete time is composed of discrete elements (seconds, minutes, hours, days, months, or years). For continuous time, no such discrete elements exist: for any two moments in time there is always another moment in between. We can also structure time by events (moments) or periods (intervals). When we represent intervals by a start and an end event, we can derive temporal relationships between events and periods, such as “before”, “overlap”, and “after”.\r\n\r\nValid time and transaction time: Valid time (or world time) is the time when an event really happened, or a string of events took place. Transaction time (or database time) is the time when the event was stored in the database or GIS. Note that the time at which we store something in a database is typically (much) later than when the related event took place.\r\n\r\nLinear, branching and cyclic time: Time can be considered to be linear, extending from the past to the present (‘now’), and into the future. This view gives a single time line. For some types of temporal analysis, branching time - in which different time lines from a certain point in time onwards are possible - and cyclic time - in which repeating cycles such as seasons or days of the week are recognized - make more sense and can be useful.\r\n\r\nTime granularity: When measuring time, we speak of granularity as the precision of a time value in a GIS or database (e.g. year, month, day, second). Different applications may obviously require different granularity. In cadastral applications, time granularity might well be a day, as the law requires deeds to be date-marked; in geological mapping applications, time granularity is more likely to be in the order of thousands or millions of years.\r\n\r\nAbsolute and relative time: Time can be represented as absolute or relative. Absolute time marks a point on the time line where events happen (e.g. “6 July 1999 at 11:15 p.m.”). Relative time is indicated relative to other points in time (e.g. “yesterday”, “last year”, “tomorrow”, which are all relative to “now”, or “two weeks later”, which is relative to some other arbitrary point in time.).","hasChildren":true,"name":"Time","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF4-3b","description":"The way we represent relevant components of the real world in our models determines the kinds of questions we can or cannot answer. Besides representing an object or field in 2D or 3D space, the temporal dimension is of a continuous nature. Therefore, in order to represent it in a GIS we have to discretize the time dimension.\r\n\r\nSpatio-temporal data models are ways of organizing representations of space and time in a GIS. Several representation techniques have been proposed in the literature. Perhaps the most common of these is the “snapshot state”, which represents a single moment in time of an ongoing natural or man-made process. We may store a series of these “snapshot states” to represent “change”, but we must be aware that this is by no means a comprehensive representation of that process. \r\n\r\nIn spatio-temporal analysis we consider changes of spatial and thematic attributes over time. We can keep the spatial domain fixed and look only at the attribute changes over time for a given location in space. We might be interested how land cover has changed for a given location or how land use has changed for a given land parcel over time, provided its boundary has not changed. On the other hand, we can keep the attribute domain fixed and consider the spatial changes over time for a given thematic attribute. In this case, we might want to identify locations that were covered by forest over a given period of time.\r\n\r\nFinally, we can assume both the spatial and attribute domains are variable and consider how fields or objects have changed over time. This may lead to notions of object motion - a subject receiving increasing attention in the literature. Applications of moving object research include traffic control, mobile telephony, wildlife tracking, vector-borne disease control and weather forecasting. In these types of applications, the problem of object identity becomes apparent. When does a change or movement cause an object to disappear and become something new? With wildlife this is quite obvious; with weather systems less so. But this should no longer be a surprise: we have already seen that some geographic phenomena can be nicely described as objects, while others are better represented as fields.\r\n\r\nMapping time means mapping change. This may be change in a feature’s geometry, in its attributes, or both. Examples of changing geometry are the evolving coastline of the Netherlands, the location of Europe’s national boundaries, or the position of weather fronts. Changes in the ownership of a land parcel, in land use or in road traffic intensity are other examples of changing attributes. Urban growth is a combination of both: urban boundaries expand with growth and simultaneously land use shifts from rural to urban. If maps are to represent events like these, they should be suggestive of such change.\r\n\r\nThree temporal cartographic techniques can be distinguished:\r\n\r\nSingle Static Map\r\n\r\nSpecific graphic variables and symbols are used to indicate change or represent an event. We can apply the visual variable “value” to represent for example the age of built-up areas.\r\n\r\nSeries of Static Maps\r\n\r\nA single map in the series represents a “snapshot” in time. Together, the maps depict a process of change. Change is perceived by the succession of individual maps depicting the situation in successive snapshots. It could be said that the temporal sequence is represented by a spatial sequence that the user has to follow to perceive the temporal variation. The number of images should be limited since it is difficult for the human eye to follow long series of maps.\r\n\r\nAnimated Maps\r\n\r\nChange is perceived to evolve in a single image by displaying several snapshots one after the other, just like a video clip of successive frames. The difference from the series of maps is that the variation can be deduced from real “change” seen taking place in the image itself, not from a spatial sequence. For the user of a cartographic animation, it is important to have tools available that allow for interaction while viewing the animation. Seeing an animation play will often leave users with many questions about what they have seen. And just replaying the animation is not sufficient to answer questions like “What was the position of the northern coastline during the 15th century?” Most of the general software packages for viewing animations already offer facilities such as “pause” (to look at a particular frame) and ‘(fast-)forward’ and ‘(fast-)backward’, or step-by-step display. More options have to be added, such as the possibility to go directly to a certain frame based on a task command like: “Go to 1850”.","hasChildren":true,"name":"Relationships between space and time","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF4-4b","description":"GIS data structures are used to implement the conceptual views of spatial data (vector and raster models). The power of a GIS is dependent on the richness of information contained in the spatial data structures. Vector models are based on points, lines and areas. Raster models are based on grids. Each cell has a value that is used to represent some characteristic of that location. \r\nLayers are used to display geographic datasets in various digital map environment. A layer stores the path to a source dataset and other layer properties, including symbology. You can use multiple layers on one map and specify its properties. Shapefiles represent spatial character of the object in terms of shape, size and spatial arrangement. Shapefile usually comprise three separate and distinct types of files (main files, index files and database tables). Data base files store additional attributed that can be joined to a shapefiles’ feature. Attribute data types supplement geographic spatial feature with additional information. Spatial data includes information of location and attribute data includes information about other characteristics (what, where and why). A legend is a visual presentation of the symbols that are used on the map with some additional explanations. It includes a sample of each symbol and a short description of the meaning.","hasChildren":true,"name":"Categories","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF4-5","description":"An entity obtained by abstracting the real world, having a physical nature (certain composition of material), being given a descriptive name, and observable; e.g. “house”. An object is a self-contained part of a scene having certain discriminating properties.\r\n\r\nThe primitives of vector data sets are the point, (poly)line and polygon. Related geometric measurements are location, length, distance and area size. Some of these are geometric properties of a feature in isolation (location, length, area size); others (distance) require two features to be identified.\r\n\r\nIn a GIS, features are represented together with their attributes—geometric and non-geometric—and relationships. The geometry of features is represented with primitives of the respective dimension: a windmill probably as a point; an agricultural field as a polygon. The primitives follow either the vector or the raster approach.\r\n\r\nVector data types describe an object through its boundary, thus dividing the space into parts that are occupied by the respective objects. The raster approach subdivides space into (regular) cells, mostly as a square tessellation of two or three dimensions. These cells are called pixels in 2D and voxels in 3D. The data indicate for every cell which real-world feature is covered, provided the cell represents a discrete field. In the case of a continuous field, the cell holds a representative value for that field. The Table below lists advantages and disadvantages of raster and vector representations.\r\n\r\nThe storage of a raster is, in principle, straightforward. It is stored in a file as a long list of values, one for each cell, preceded by a small list of extra data (the “file header”), which specifies how to interpret the long list. The order of the cell values in the list can, but need not necessarily, be left to right, top to bottom. This simple encoding scheme is known as row ordering. The header of the raster will typically specify how many rows and columns the raster has, which encoding scheme was used, and what sort of values are stored for each cell.\r\n\r\nData can be of a qualitative or quantitative nature. Qualitative data is also called nominal data, which exists as discrete, named values without a natural order amongst the values. Examples are different languages (e.g. English, Swahili, Dutch), different soil types (e.g. sand, clay, peat) or different land use categories (e.g. arable land, pasture). In the map, qualitative data are classified according to disciplinary insights, such as a soil classification system represented as basic geographic units: homogeneous areas associated with a single soil type, recognizable by the soil classification.\r\n\r\nQuantitative data can be measured, either along an interval or ratio scale. For data measured on an interval scale, the exact distance between values is known, but there is no absolute zero on the scale. Temperature is an example: 40 ◦C is not twice as hot as 20 ◦C, and 0 ◦C is not an absolute zero.\r\n\r\nQuantitative data with a ratio scale do have a known absolute zero. An example is income: someone earning $100 earns twice as much as someone with an income of $50. In order to generate maps, quantitative data are often classified into categories according to some mathematical method.\r\n\r\nIn between qualitative and quantitative data, one can distinguish ordinal data. These data are measured along a relative scale and are as such based on hierarchy. For instance, one knows that a particular value is “more” than another value, such as “warm” versus “cool”. Another example is a hierarchy of road types: “highway”, “main road”, “secondary road” and “track”. The different types of data are summarized in Table.","hasChildren":true,"hasParent":true,"name":"Properties","selfAssesment":"GI-N2K
"},{"code":"CF4b","description":"Geographic phenomena, geographic information, and geographic tasks are described in terms of space, time, and properties. Different theories exist as to the nature and formal representation of these aspects, including space-like dimensions, sets, and phenomenology. Information in each of these three aspects is measured and reported with respect to one of several frames of reference or domains, including both absolute and relative approaches. Early frameworks such as those of Berry (1964) and Sinton (1978) were influential in setting forth the importance of space, time, and theme in GIS&T. Besides, space, time, and properties, categories are also fundamental in the conceptualization and representation of spatial entities, phenomena, processes, and events. Distinctive features of geographic information such as scale and detail, spatial patterns, spatial integration, and regions are also critical for a complete description of its nature and representation. This unit is closely tied to the creation of data models in Knowledge Area 5: Data Modeling, Storage, and Exploitation.","hasChildren":true,"hasParent":true,"name":"Fundamentals of Geographic Information","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF5-1b","description":"Discrete entities can be found as fields or objects.\r\n\r\nDiscrete fields divide the study space in mutually exclusive, bounded parts, with all locations in one part having the same field value. Discrete fields are intermediate between continuous fields and geographic objects: discrete fields and objects both use “bounded” features.\r\n\r\nDiscrete fields divide the study space in mutually exclusive, bounded parts, with all locations in one part having the same field value. Typical examples are land classifications, for instance, using either geological classes, soil type, land use type, crop type or natural vegetation type. \r\n\r\nDiscrete fields are intermediate between continuous fields and geographic objects: discrete fields and objects both use “bounded” features. A discrete field, however, assigns a value to every location in the study area, which is not typically the case for geographic objects. These two types of fields differ in the type of cell values. A discrete field such as land use type will store cell values of the type “integer” and is therefore also called an integer raster. Discrete fields can be easily converted to polygons since it is relatively easy to draw a boundary line around a group of cells with the same value. A continuous raster is also called a “floating point” raster.\r\n\r\nGeographic objects.\r\n\r\nWhen a geographic phenomenon is not present everywhere in the study area, but somehow “sparsely” populates it, we look at it as a collection of geographic objects. Such objects are usually easily distinguished and named, and their position in space is determined by a combination of one or more of the following parameters:\r\n\r\nlocation (where is it?)\r\nshape (what form does it have?)\r\nsize (how big is it?)\r\norientation (in which direction is it facing?).\r\n\r\nHow we want to use the information determines which of these four parameters is required to represent the object. For instance, for geographic objects such as petrol stations all that matters in an in-car navigation system is where they are. Thus, in this particular context, location alone is enough, and shape, size and orientation are irrelevant. For roads, however, some notion of location (where does the road begin and end?), shape (how many lanes does it have?), size (how far can one travel on it?) and orientation (in which direction can one travel on it?) seem to be relevant components of information in the same system.","hasChildren":true,"name":"Discrete entities","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF5-2b","description":"A geographic field is a geographic phenomenon that has a value “everywhere” in the study area. We can therefore think of a field as a mathematical function f that associates a specific value with any position in the study area. Hence if (x, y) is a position in the study area, then f(x, y) expresses the value of f at location (x, y). Fields can be discrete or continuous.\r\n\r\nIn a continuous field, the underlying function is assumed to be “mathematically smooth”, meaning that the field values along any path through the study area do not change abruptly, but only gradually. Good examples of continuous fields are air temperature, barometric pressure, soil salinity and elevation. A continuous field can even be differentiable, meaning that we can determine a measure of change in the field value per unit of distance anywhere and in any direction. For example, if the field is elevation, this measure would be slope, i.e. the change of elevation per metre distance; if the field is soil salinity, it would be salinity gradient, i.e. the change of salinity per metre distance.\r\n\r\nDiscrete fields divide the study space in mutually exclusive, bounded parts, with all locations in one part having the same field value. Discrete fields are intermediate between continuous fields and geographic objects: discrete fields and objects both use “bounded” features.\r\n\r\nDiscrete fields divide the study space in mutually exclusive, bounded parts, with all locations in one part having the same field value. Discrete fields are intermediate between continuous fields and geographic objects: discrete fields and objects both use “bounded” features.\r\n\r\nDiscrete fields divide the study space in mutually exclusive, bounded parts, with all locations in one part having the same field value. Typical examples are land classifications, for instance, using either geological classes, soil type, land use type, crop type or natural vegetation type. \r\n\r\nDiscrete fields are intermediate between continuous fields and geographic objects: discrete fields and objects both use “bounded” features. A discrete field, however, assigns a value to every location in the study area, which is not typically the case for geographic objects. These two types of fields differ in the type of cell values. A discrete field such as land use type will store cell values of the type “integer” and is therefore also called an integer raster. Discrete fields can be easily converted to polygons since it is relatively easy to draw a boundary line around a group of cells with the same value. A continuous raster is also called a “floating point” raster.\r\n\r\nA field-based model consists of a finite collection of geographic fields: we may be interested in, for example, elevation, barometric pressure, mean annual rainfall and maximum daily evapotranspiration, and would therefore use four different fields to model the relevant phenomena within our study area.","hasChildren":true,"name":"Fields","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF5-3b","description":"We can structure time by events (moments) or periods (intervals). When we represent intervals by a start and an end event, we can derive temporal relationships between events and periods, such as “before”, “overlap”, and “after”.\r\nValid time (or world time) is the time when an event really happened, or a string of events took place. Transaction time (or database time) is the time when the event was stored in the database or GIS. Note that the time at which we store something in a database is typically (much) later than when the related event took place.\r\n\r\nProcess models in the Earth sciences describe the evolution of geo(bio)physical surface properties in time, independently from remote sensing observations. Examples of such process models on various time scales are, for instance, numerical weather prediction models (NWPs), vegetation growth models, hydrological models, oceanographic models and climate models.\r\n\r\nProcesses on the planet Earth are complex phenomena that are taking place in space and in time, i.e. in four dimensions.\r\n\r\nIn many of these processes, differences in one dimension (e.g. height above the geoid) can be disregarded, so that two spatial dimensions and the dimension time remain. Despite this simplification, the physical description of the phenomena remains a difficult task. To better understand the processes it often helps if the same geographic region is viewed repeatedly and, if possible, also from different directions and in different wavelength regions. Integration of data from a variety of sources can be a means to retrieving information about processes that would otherwise remain undetected.","hasChildren":true,"name":"Events and processes","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF5-4b","description":"Models that integrate the concepts of space, time, and attribute in geographic information.","hasChildren":true,"name":"Integrated models","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF5-6","description":"Geographic phenomena can be studied as single entities and in relationship with each other and then reveal patters and clusters. How the entities are distributed is subject to statistical and visualisation studies.","hasChildren":true,"name":"Spatial distribution","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF5-7","description":"We can use the topological properties of interiors and boundaries to define relationships between spatial features. Since the properties of interiors and boundaries do not change under topological mapping, we can investigate their possible relations between spatial features. We can define the interior of a region, R, as the largest set of points of R for which we can construct a disc-like environment around it (no matter how small) that also falls completely inside R. The boundary of R is the set of those points belonging to R that do not belong to the interior of R, i.e. one cannot construct a disc-like environment around such points that still belongs to R completely.\r\n\r\nLet us consider a spatial region A. It has a boundary and an interior, both seen as (infinite) sets of points, which are denoted by boundary(A) and interior(A), respectively. We consider all possible combinations of intersections (∩) between the boundary and the interior of A with those of another region, B, and test whether they are the empty set (∅) or not. From these intersection patterns, we can derive eight (mutually exclusive) spatial relationships between two regions. If, for instance, the interiors of A and B do not intersect, but their boundaries do, yet the boundary of one does not intersect the interior of the other, we say that A and B meet.","hasChildren":true,"name":"Region","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF5-8","description":"Integration of data from a variety of sources can be a means to retrieving information about processes that would otherwise remain undetected.\r\n\r\nAlthough data integration can be very useful, there are also some requirements that have to be fulfilled for it to be effective:\r\n\r\n• geospatial data have to be accurately co-registered in a common grid;\r\n• time gaps between the various data layers have to be known and accounted for;\r\n• systematic effects due to the atmosphere, the viewing angle, the Sun angle, etc., must be corrected for or taken into account.\r\n\r\nData can be integrated in an almost infinite number of ways. Results from data integration can, again, be combined with other geospatial data to produce yet other new information, and so on.\r\n\r\nData integration also comprises the incorporation of non-spatial information or point data from field measurements. These data have to be associated with precise moments in time and with precise geographic locations, or with some time interval and fuzzy-defined regions. Thus, here the important issue of the representativeness of this information for the associated time interval and geographic area comes into play.\r\n\r\nIn general, data integration forces us to consider the uncertainties or inaccuracies of the various data sources available. In some cases, meta-data may contain information about this. When integrating data for some purpose, one has to apply weights to each of them, so that the final result is a balanced compromise in which inaccurate data receive less weight than those with a high degree of certainty.","hasChildren":true,"name":"Spatial integration","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF5b","description":"The concepts below form the basic elements of common human conceptions of geographic phenomena. Concepts from many units in this knowledge area have been synthesized to create general conceptual models of geographic information. Attempts to resolve the object-field debate have led to attempts to create comprehensive models that bridge these views. Consideration of this unit should also include formal models of these elements in mathematics and other fields. Knowledge Area DM Data Modeling discusses the representation of these elements in digital models.","hasChildren":true,"hasParent":true,"name":"Elements of geographic information","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF6-1","description":"Mereology is the study of parts and wholes. In GI this involves how objects are modeled as composites of other objects.","hasChildren":true,"name":"Mereology: structural relationships","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF6-2","description":"Lineage describes the history of a data set. During the processing of data, the derived information inherits artifacts from the dataset(s) of origin. In the case of published maps, some lineage information may be provided as part of its meta-data, in the form of a note on the data sources and procedures used in the compilation of the data. Examples include the date and scale of aerial photography, and the date of field verification. Especially for digital data sets, however, lineage may be defined more formally as:\r\n\r\n“that part of the data quality statement that contains information that describes the source of observations or materials, data acquisition and compilation methods, conversions, transformations, analyses and derivations that the data has been subjected to, and the assumptions and criteria applied at any stage of its life (Clarke and Clark, 1995).”\r\n\r\nAll of these aspects affect other aspects of quality, for example positional accuracy. Clearly, if no lineage information is available, it is not possible to adequately evaluate the quality of a data set in terms of “fitness for use”.","hasChildren":true,"name":"Genealogical relationships: lineage, inheritance","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF6-3","description":"We can use the topological properties of interiors and boundaries to define relationships between spatial features. Since the properties of interiors and boundaries do not change under topological mapping, we can investigate their possible relations between spatial features. We can define the interior of a region, R, as the largest set of points of R for which we can construct a disc-like environment around it (no matter how small) that also falls completely inside R. The boundary of R is the set of those points belonging to R that do not belong to the interior of R, i.e. one cannot construct a disc-like environment around such points that still belongs to R completely.\r\n\r\nLet us consider a spatial region A. It has a boundary and an interior, both seen as (infinite) sets of points, which are denoted by boundary(A) and interior(A), respectively. We consider all possible combinations of intersections (∩) between the boundary and the interior of A with those of another region, B, and test whether they are the empty set (∅) or not. From these intersection patterns, we can derive eight (mutually exclusive) spatial relationships between two regions. If, for instance, the interiors of A and B do not intersect, but their boundaries do, yet the boundary of one does not intersect the interior of the other, we say that A and B meet. In mathematics, we can therefore define the “meets relationship” using set theory. The eight spatial relationships are disjoint, meets, equals, inside, covered by, contains, covers and overlaps.","hasChildren":true,"hasParent":true,"name":"Topological relationships","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF6-4","description":"Relationships between spatial features that define their relative position. Spatial autocorrelation is a fundamental principle based on Tobler’s first law of geography, which states that locations that are closer together are more likely to have similar values than locations that are farther apart.","hasChildren":true,"name":"Metrical relationships: distance and direction","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF6","description":"Like geography, geographic information not only models phenomena but the relationships between them. This can include relationships between entities, between attributes, between locations. In addition, one of the strengths of geography (and GIS) is its ability to use a spatial perspective to relate disparate subjects, such as climate and economy. Methods for analyzing relationships are discussed in Unit AM4 Modeling relationships and patterns.","hasChildren":true,"hasParent":true,"name":"Relationships","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF7-1","description":"Vagueness arises from lack of criteria for the applicability of certain linguistic terms. It arises from the lack knowledge about the meanings of terms.","hasChildren":true,"name":"Vagueness","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF7-2","description":"-Uncertainty-related terms, such as error, accuracy, uncertainty, precision, stochastic, probabilistic, deterministic, and random -Difference between uncertainty and vagueness -Dependence of uncertainty on scale and application -Expressions of uncertainty in language -The causes of uncertainty in geospatial data -Stochastic error models for natural phenomena -How the concepts of geographic objects and fields affect the conceptualization of uncertainty -Mathematical models of uncertainty: Probability and statistics","hasChildren":true,"name":"Error-based uncertainty","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF7","description":"Human models (mental, digital, visual, etc.) of the geographic environment are necessarily imperfect. While the mathematical principle of homomorphism (often operationalized as fitness for use) allows for imperfect data to be useful as long as they yield results adequate for the use for which they are intended, imperfections are frequently problematic. Although terminology still varies, two types of imperfection are generally accepted: vagueness (a.k.a. fuzziness, imprecision, and indeterminacy), which is generally caused by human simplification of a complex, dynamic, ambiguous, subjective world; and uncertainty (or ambiguity), generally the result of imperfect measurement processes (as discussed in Knowledge Area GD Geospatial Data). Both of these can be manifested in all forms of geographic information, including space, time, attribute, categories, and even existence. Imperfection is also dealt with in Units GD6 Data quality (in the context of measurement), GC8 Uncertainty and GC9 Fuzzy sets (for the handling and propagation of imperfections), and CV4 Graphic representation techniques (in the context of visualization).","hasChildren":true,"hasParent":true,"name":"Imperfections in geographic information","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV","description":"Geo-data visualisation necessarily includes cartography as the origin of \"mapping\" our world. Cartography methods have drastically changed over the few years since the increasing role and sophistication of digital technology applied to geo-information visualisation. It is first worth differentiating between the underlying geo-data that describes real world phenomena and the bits of information that describe the visual presentation of geo-data . Likewise, there are processing tools to collect and handle geo-data, and processing tools especially designed to create and manage geo-data visualisations. \r\nWhile cartography methods have traditionally produced printed maps (i.e. hard copy) with static scale, orientation, projection, legends (content based) and tied to a period or instant of time. Nowadays geo-data visualisations are interactive by design, meaning that the results are map-based responsive interfaces, highly customisable through dynamic objects to zoom in and out, pan and tilt, change projections and graphic expressions on the fly, as well as dynamically browse the map over time. \r\nIf the production methods have changed, also the type of authors. Map making in its widest sense is not only a privilege of a few experts but has been democratised in such a way that. everybody is able to make maps using open data and open source apps and tools for geo-data visualisation. Therefore,the new roles of open data and new forms of geo-data like geo-social media make usability, intended and ethical considerations key aspects of geo-data visualization design, production and sharing. \r\nUnder the concept of cartography and visualisation it is included a list of concepts that together comprise the science and technology of visual representation of geographic data.","hasChildren":true,"hasParent":true,"name":"Cartography and Visualization","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV1-1","description":"The evolution of cartographic representation in the previous centuries followed the most important technological and scientific developments of the time. It was driven by commercial and/or military needs and influenced by the special characteristics of the areas and/or environments to be mapped. Recent developments are the rise of open data worldwide and widely available internet technology allowing end users to get remote geo-data published elsewhere. In recent years, data and its digital presentation have become central elements of cartography, whereas paper maps have become peripheral.","hasChildren":true,"hasParent":true,"name":"History and evolution of cartography","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV1-4","description":"Art in cartography means much more than designing aesthetically pleasing maps, whether on paper or digital. Exploring the interaction at large between art and cartography involves rethinking the way we approach spatial expressions and how cultural, social and political dimensions are reflected in maps. This can be clearly observed in historical maps - in between art and science - ranging from beautiful geographical representations created in the Middle Ages to convey religious messages to the creation of modern maps showing the power of modern empires and nations. This particular relationship between art and maps entails: “developing an inclusive approach of artistic mapping expressions; facilitating and encouraging interaction between cartographers who work with the Art aspects of cartography and artists who produce cartographic artifacts; and developing conceptual elements about the relationships between art and cartography.” Besides ancient paper maps, a sum of factors led digital maps and geospatial visualization, a matter of interest to artists and designers. Thanks to powerful computing systems and with the advancements reached in computer graphics or image processing, or the rise of information visualisation, new forms of representing and visualising geodata have also appeared. Creation of digital maps are still a two-way relationship since artists have explored maps as a medium for expressing their art, and cartographers have approached art to provide more than just the representation of locations and geographic features with the intention to make maps more attractive to their audiences.","hasChildren":true,"name":"Art and geodata visualisation","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV1-5","description":"Historical maps are geographical representations made with the intention to represent spatial facts over time. Historical maps are generally considered valuable documents not just because of their historical value but also because most of them also are artistic representations by themselves. From a cartographical point of view, differentiation between historical maps and actual maps is mainly based on the advances in the history of Cartography, so once one disruptive advance in the map making process appears, maps created with previous techniques (and with some artistic or historical value) are usually considered as historical, such as ancient paper-based maps or old sea maps, for instance. Techniques such as scanning or photography can make ancient maps publicly available by converting hard-copy maps to digital ones. Once an historical map is digitised, the next step is to georeference it, which is the process of specifying and relating points of the digitalised map to actual coordinates in a geographic reference system. Because of its archival value and interest, historical maps are adequately preserved - following specific conditions - by map libraries, map societies or museums. Since digital methods and techniques have been replaced over time by new technological advances, first digitally created maps could be also considered historical, not because of its content, but of the techniques used to produce it.","hasChildren":true,"name":"Historical maps","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV1","description":"At a certain moment in time people start to create more graphical representations of their surrounding environment. New technologies offered ways to expand these representations to larger geographical extent, higher spatial resolution, finer temporal granularity and larger periods. Technologies even made it possible to include other representations of reality such as social media and data ensembles in geodata visualizations, to the extent to even blend the real world with geodata-based visualization providing an augmented – virtual reality continuum. New forms of geo-data, like geolocated sensors may challenge the way geo-data visualisations are generated, shared and, eventually, influence decision-making processes. History and trends sketch these developments and future outlook. This concept introduces the main stages and turns in development of cartography, from earliest times to the present, the most important methods in map-making and map-based visualizations.","hasChildren":true,"hasParent":true,"name":"History and trends","selfAssesment":"Completed (GI-N2K)
\r\n\r\n"},{"code":"CV2-1","description":"As mapping ( geo-data visualization) is intended to convey a certain message to a certain audience, it is essential to use data sources that allow the intended visualisation result. The data should be of the right degree of detail and its use should not cause copyright problems. The producer quality of each data set should be taken into account, as well as the fitness of the data for the intended use. Aspects: message; data quality","hasChildren":true,"name":"Data sources for mapping","selfAssesment":"
In progress (GI-N2K)
"},{"code":"CV2-2","description":"In the trajectory between raw (geo)data and their user-relevant representation, the necessary data processing includes ways of abstraction by selection, filtering, generalization, transformation and classification of geographical data. In this data processing it is essential to at one hand relate the final symbolisation to the necessities of the intended message, and at the other hand to procedures that introduce as little error as possible.","hasChildren":true,"name":"Data processing","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV2-3","description":"Map projection is fundamental to representation of spatial data and for combining different datasets. Its choice should serve the presentation type that will convey the intended message to the audience. Many mathematical principles define datum, projections, horizontal and vertical co-ordinate systems, georeferencing- introduced with the focus on visualisation issues Aspects: geodetic concepts; transformations","hasChildren":true,"name":"Mathematical base","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV2","description":"Geodata, including 3 dimensional geometry, as such can graphically be presented but most of the times the data as such doesn`t meet the presentation criteria. Especially if the dataset has to be presented in combination with other datasets. First all the geodatum, georeference and map projection are crucial but also the role of the geometry. The processing of the geometry and the related attributes may become a crucial step for an adequate presentation. Nowadays the highest precision may be used to define different graphical attributes for different zoom levels. On the other hand geodata visualisation includes also graphical datasets. Such data ensembles, the combination of geodata and graphical data, are the data sources that offer opportunities to other ways of visualisation then the traditional cartographic mapping. Facets: a.\tGeospatial location (2D) and position (3D) that data refer to b.\tDegree of detail in data origin (acquisition resolution) and in representation ('map' scale) c.\tTypes of data (e.g. imagery, field measurements, delineated objects)","hasChildren":true,"hasParent":true,"name":"Data considerations","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV3-1","description":"The combined impact of graphic design properties (balance, legibility, clarity, visual contrast, figure-ground organization, and hierarchal organization) and the map components (north arrow, scale bar, and legend) should always be carefully evaluated against the needs and the capacities of the audience.","hasChildren":true,"name":"Map design fundamentals","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV3-10","description":"Geo-gaming is a crossover between gaming elements and location, usually enabled by location based services and augmented adn/or virtual reality features. Geo-games, also known as “location-based games” or “location-aware games”, have geodata at its core, since geoinformation constitutes the central element of the game mechanics. Geo-gaming applications present unique technical challenges to meet the infrastructural and resources demands from the games and location worlds. There are mainly four different types of geo-games: exploration games (to make use of an existing spatial design); feedback games (to report about players’ experiences in a specific design); allocation games (to occupy the majority of game location); and configuration games (to occupy specific pattern of game locations). Gamers actively participate by interacting with the environment, therefore gaming scenarios are as varied as their goals, which include teaching, training, and the developing of spatial thinking skills. Geo-games offer a myriad of opportunities to developers: non-linear storytelling, physical object integration, a more visceral experience, true social interaction… which bring geo-games to another interaction level. Geo-gaming applications often rely on VGI to allow gamers adding geolocated information that may crowdsource geo-referenced data useful for other secondary purposes .","hasChildren":true,"name":"Geo-gaming","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV3-2","description":"Map symbolization entails a number of variables to produce visual, tactile, haptic, auditory, and dynamic displays. Visual variables (e.g., size, lightness, shape, hue) and graphic primitives (points, lines, areas) are commonly used in maps to represent various geographic features at all attribute measurement levels (nominal, ordinal, interval, ratio). With those a single geographic feature can be represented by various graphic primitives (e.g., land surface as a set of elevation points, as contour lines, as hypsometric layers or tints, and as a hillshaded surface). The challenge is to use effective symbols for map features to ease the interpretation of maps.","hasChildren":true,"name":"Symbols and icons","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV3-3","description":"The selection of colours to use in data representation can be influenced by various factors (e.g. the production workflow, cultural differences, involved devices and media). There are various colour models (e.g. RGB, CMYK, CIE) that describe colours in a way that they can effectively convey visual information (e.g., qualitative, sequential, diverging, spectral) according to the meaning of the underlying data. The cultural background of the consumer is also relevant when it comes to choose colours that should have real-world connotations or should express psychological concepts (e.g. harmony, concordance, balance). A final important factor is if the consumer has colour limitations","hasChildren":true,"name":"Colour","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV3-4","description":"When data representation is conveyed in words (e.g. toponyms, road codes), written text is often placed in map labels. It is important to decide on the role of the label in the context of the representation type. Algorithms for label placement are relevant, especially when label density is high. Shape and colour of the labels help to signify different types of messages. This is supported by the typographic properties (type font, size, style) of the text in the labels. Finally, it is important to use an authoritative source for the texts","hasChildren":true,"name":"Typography","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV3-5","description":"Imagery can be a source for data acquisition as well as an illustration to abstract data representations. Imagery can be made from the air (from drones to satellites) or from a terrestrial point of view (street-level imagery). Using photos from any source to illustrate stories about geographical subjects contributes as the visual aspect of telling a story. Together with maps and other narrative components, the combination embodies a storytelling medium.","hasChildren":true,"name":"Photos and imagery","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV3-6","description":"Animation is the process of making the illusion of motion and change by means of the rapid display of a sequence of static images that minimally differ from each other. In the context of maps, the temporal component is added to a map to emphasize and observe the gradual evolution of a certain monitoring phenomenon, such as changes in spatially numerical variables (for example, environment, population, mobility, land use, etc.) with respect to a static geographic area. Map animations generally consider dynamic time while space is static. Map animation helps to see patterns or trends that emerge as time passes, depicting meteorological or climate events, natural disasters, historical events and other multivariate data. It is particularly helpful to be used in educational settings.","hasChildren":true,"name":"Animation","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV3-7","description":"Sound or audios can be one of the components of a multimedia data representation. A conventional GIS usually conveys visual information, however the integration of audios in mapping could enrich GIS data to other senses. Sound can increase the amount of information that’s communicated to the user through channels other than visual to address the special needs of people with visual impairments or people who cannot use in certain circumstances their sight, such as a driver who cannot look at a map. Approaches to rendering sound information on a map fall into three broad categories: (1) to sonoficate the whole visual presentation (for simple geometric data), (2) to augment a visual system with auditory information (allowing multivariate information) and (3) to display information about the surrounding where a user is. By classifying images and creating additional audio layers that associate each pixel with a specific sound, a GIS can add a new auditory dimension to maps.","hasChildren":true,"name":"Sound","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV3-8","description":"Maps are valuable because they provide a large amount of detail in a small amount of space, and because of their capacity for telling a story. Telling stories through maps began with describing explored lands in great detail against terra incognita. Today, geographic tools, data, and multimedia on the web expand the ability to communicate stories and inform through maps to a broad audience such as journalists, decision makers and educators. Any person with a smartphone or computer can tell a story, using statics maps, or interactive web maps with text, video, audio, sketches, and photographs. Besides the technical skills to clearly communicate with a map (palette of colours, amount of information displayed…), other factors such as narrative processes, the storyboard, place, time, and characters play a crucial role. To be informative, it is important that the correct data is displayed, combining different sources of information combined to create an appealing and accurate map.","hasChildren":true,"name":"Storytelling","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV3-9","description":"Infographics are visual representations of information and data, which can contain charts, diagrams, graphs, tables, maps and lists. The aim of an infographic is to present information that can be absorbed quickly, it is easily understandable and extensively in mass communication, and thus designed with fewer assumptions about the reader's knowledge base than other types of visualizations. The role of maps in an infographic is based on the potential of maps to condense information and to support a narrative. Infographic maps - altogether with an adequate storytelling - should find a simple way to explain current complex issues, providing added value to the infographic, and being an effective and efficient way to communicate.","hasChildren":true,"name":"Infographics","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV3","description":"This concepts covers basic design principles that are used in mapping and visualization, as well as cartographic design principles specific to the display of geographic data. Both page layout design and data display are addressed.","hasChildren":true,"hasParent":true,"name":"Design principles","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV4-1","description":"A thematic map is a type of map especially designed to show a particular theme connected with a specific geographic area. These maps \"can portray physical, social, political, cultural, economic, sociological, agricultural, or any other aspects of a city, state, region, nation, or continent\". Cartographers use many methods to create thematic maps. Five techniques are especially noted: -Choropleth mapping shows statistical data aggregated over predefined regions -Proportional symbols, showing the relative value of attributes -Isarithmic or Isopleth, also known as contour maps -Dots, to show the location of a phenomenon -Dasymetric, which uses areal symbols to spatially classify volumetric data.","hasChildren":true,"name":"Thematic mapping","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV4-10","description":"Conveying uncertainty information is often done through visualization. Uncertainty is often defined, quantified, and expressed using models specific to individual application domains. In visualization however, we are limited in the number of visual channels (3D position, color, texture, opacity, etc.) available for representing the data. Thus, when moving from quantified uncertainty to visualized uncertainty, we often simplify the uncertainty to make it fit into the available visual representations. (After Potter et al., 2012). The seven challenges as formulated by MacEachren et Al. (2005) are still there to be tackled.","hasChildren":true,"name":"Visualization of uncertainty","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV4-2","description":"Relief can be represented in a two-dimensional map either through contour lines or through a raster format gridded array of elevations. Contour lines connect points of equal elevation. At regular intervals index contours are marked with elevations so a reader can more easily determine the elevation of surrounding locations. They are the preferred method for analogue topographic maps. The grid approach is used in digital mapping and known as a digital elevation model (DEM), where each raster cell represents an elevation. Scaling of the cell z value in relation to the x and y value results in terrain exaggeration, which aids visualization of topography.\r\nDEMs are used for terrain analysis and can be used to obtain derivatives such as slope and aspect. DEMs are obtained by interpolating point elevation observations, which are historically retrieved from surveyed point data (e.g. GPS locations), but more recently from LiDAR and/or Structure from Motion point clouds. TIN (triangular irregular network) analysis is commonly used for point data interpolation, in order to derive a continuous elevation surface.","hasChildren":true,"hasParent":true,"name":"Representing terrain","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV4-3","description":"Multivariate descriptive displays or plots are designed to reveal the relationship among several variables simultaneously. Bivariate and multivariate maps encode two or more data variables concurrently into a single symbolization mechanism. Their purpose is to reveal and communicate relationships between the variables that might not otherwise be apparent via a standard single-variable technique. There are basic characteristics of the relationship among variables, such as the forms of the relationships, the strength of the relationships, and the dependence of the relationships on external (usually to the pairs of variables being examined) circumstances. Therefore, these multivariate plots or maps are inherently more complex, though offer a novel means of visualizing the nuances that may exist between the mapped variables. As information-dense visual products, they can require considerable effort on behalf of the map reader, though a thoughtfully-designed map and legend can be an interesting opportunity to effectively convey a comparative dimension. Examples of multivariate plots include enhanced 2-D scatter diagrams, 3-D scatter diagrams, contour, level, and surface plots, and high-dimensional data plots","hasChildren":true,"name":"Multivariate displays","selfAssesment":"Completed (GI-N2K)
\r\n\r\n"},{"code":"CV4-4","description":"Visualization of change and movement across space and time is of increasing interest to researchers and geospatial practitioners. The visualization process of temporal data has four steps: (1) time values to be visualized, (2) point of view on time, that identifies the characteristics of the temporal values to be visualized, (3) time space: define the displayable space of the time values and (4) point of view on the visualization space, the implementation of the perceptible forms of time. The visualization of spatio-temporal data can be done in many different ways such as multi-panel plots (maps), time-series plots (graphs), space-time plots (graphs), 3D Virtual Reality (Computer generated artificial environment), animations (production of consecutive images), and tables. Spatiotemporal data comprises three important components: geographic location, temporal information and the thematic attributes describing a real-life phenomenon.","hasChildren":true,"hasParent":true,"name":"Visualization of temporal geographic data","selfAssesment":"
Completed (GI-N2K)
"},{"code":"CV4-5","description":"Dynamic and interactive displays refers to a situation where a display with a cartographical data representation changes in real time in response to user's actions","hasChildren":true,"name":"Dynamic and interactive displays","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV4-6","description":"Web mapping is the process of designing, implementing, generating and delivering maps on the World Wide Web. Dissemination via the web opens new opportunities: realtime maps, cheaper dissemination, more frequent and cheaper updates, personalized map content, distributed data sources and sharing of geographic information. Technical restrictions cause challenges like low display resolution and limited bandwidth,( in particular with mobile computing devices with small screens and using slow wireless Internet connections), copyright and security issues, reliability issues and technical complexity. Today's web maps can be interactive and integrate multiple media. So interactivity, usability and multimedia issues also play a role.","hasChildren":true,"name":"Web mapping","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV4-7","description":"Virtual reality or virtual realities (VR), also known as immersive multimedia or computer-simulated reality, is a computer technology that replicates an environment, real or imagined, and simulates a user's physical presence and environment in a way that allows the user to interact with it","hasChildren":true,"name":"Virtual and immersive environments","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV4-8","description":"An Augmented Environment can be experienced through different sets of Augmented Reality (AR) technologies, including mobile displays (tablets and smartphone screens), computer monitors, or Head-Mounted Displays (HMDs), among others. AR is a technology that layers computer-generated enhancements atop an existing reality to make it more meaningful through the ability to interact with it. AR offers the integration of digital information and imagery onto the real world in real-time. In order to broaden the vision beyond this definition, AR can be described as systems having the following features: (1) combines real and virtual; (2) interactive in real-time; and (3) registered in 3D, allowing other technologies, such as mobile technologies, monitor-based interfaces, monocular systems to overlay virtual objects on top of the real world. Currently, AR applications use the camera provided by mobile devices to produce a live view of the real world in combination with relevant, context-appropriate information such as text, videos, or pictures.\r\nThere are lots of applications and systems in the market that provide AR functionality, making it difficult to classify and name them all. Some of them are related to the real physical world and others with the abstract, virtual imagery world. Sometimes it is not easy to figure whether it is an AR, as often AR is defined as Virtual reality (VR) with transparent HMDs. In general, the concept is to mix reality with virtual reality, including information and overlay over the real world through HMDs such as they seem apparent as one environment. The virtual objects can react accordingly with the camera's movement as it is registered concerning the real world, which is also the central issue of AR.","hasChildren":true,"name":"Augmented environments","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV4-9","description":"Cartographers have recently become involved in extending geographic concepts and cartographic design approaches to the depiction of non-geographic data archives, using so-called spatialized views of information spaces. Spatializations differ from ordinary data visualisation and geovisualisation in that they may be explored as if they represented spatial information. (Fabrikant, S.I., 2003). As definitions of spatialization can be found: Spatializations are computer visualizations in which nonspatial information is depicted spatially (Montello et al., 2003). Spatialization is the transformation of high-dimensional data into lower-dimensional, geometric representations on the basis of computational methods and spatial metaphors. (Skupin 2007)","hasChildren":true,"name":"Spatialization","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV4","description":"This concept addresses mapping methods and the variations of those methods for specialized mapping and visualization instances, such as thematic mapping, dynamic and interactive mapping, Web mapping, mapping and visualization in virtual and immersive environments, using the map metaphor to display other forms of data (spatialization), and visualizing uncertainty. Analytical techniques used to derive the data employed in these graphic representations are discussed in Knowledge Area AM Analytical Methods and Unit DN2 Generalization and aggregation.","hasChildren":true,"hasParent":true,"name":"Graphic representation techniques","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV5-2","description":"Standards for map services were set by OGC and ISO, called WMS and WMTS. Producing map images on the web from a cartographic image in a GIS application is called \"publishing\". Making a web \"map\" in the broader sense of constructing data representations for Storytelling or Geo-gaming is still under development. It requires a mix of applying the map Design principles and Graphic presentation techniques, possibly in combination with software scripting.","hasChildren":true,"name":"Web map making","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV5-3","description":"Traditional \"map\" making, as opposed to the mapmaking in neogeography, focuses on reliable and reproducible products, based on expertise of high definition printing in many colours on analogue media of geodetically well-constructed images.","hasChildren":true,"name":"Traditional map making","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV5-4","description":"The aspects of reproduction of a data representation depend on the nature of the representation: is it analogue (a paper map, a mock-up) or is it digital? In the case of a paper map, its digitalisation with high fidelity is an essential step. With a source in digital form, reproduction can be a matter of the right printer. Alternatively, the source could be disseminated as a file or as a web service. If representations are dynamic and/or interactive the possibilities depend on the construction of the representation. The ease of dissemination of digital files should not result in copyright breach. Aspects: Digitalization techniques for analogue sources, Printing ( 2D, 3D), Dissemination ways, Construction of the data representation, User needs specification, Copyright issues","hasChildren":true,"name":"Map reproduction","selfAssesment":"GI-N2K
"},{"code":"CV5","description":"This concept addresses map production and reproduction, as well as computation issues that relate to those workflows.","hasChildren":true,"hasParent":true,"name":"Map production","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV6-1","description":"The potential of maps as a way to show or exert power over the population was early understood by ruling classes. A map expresses a claim by the inclusion or exclusion of map elements and how these elements are visually related and/or depicted on the map. So, the world could be modeled through the careful choice of content arranged graphically at a specific scale and in specific formats. Therefore, maps embody and project the interests of their creators. The “new cartographies” declare that maps are redefined as socially constructed arguments based upon consistent semiotic codes. Nowadays, the rise of costless, powerful and accessible tools for creating maps, put power on the side of individuals or groups of individuals with few organisation (crowdsourced data collection or VGI) capable of representing their world views. In addition, monitoring people, places or nature, for instance, should also be seen as another way to show the increasing power of maps. Surveillance mechanisms for tracking populations used by rulers, or the use of extended technologies like Google Earth by environmental organisations to track the Amazonian forest, constitute two examples of the particular use of maps to exert control over human beings or to press governments for taking specific actions, respectively.","hasChildren":true,"name":"The power of maps","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV6-2","description":"Maps today help us locate the nearest gas station or ATM on our in-car navigation system, but this use of locating what is near or surrounds a location is not new. Maps from pre-historic times provided important locational information – what was where and how to get from place to place. A map can be a relatively simple iconic device, which can be read and interpreted with only a little training. These graphic representations of the real world could be traced in sand or painted on a cave wall and shared through time. Maps even preceded written language and number systems and are found in some format in most cultures through time as a graphical language. Learning to read this language and interpret it without ambiguity is not as simple as first suggested. This complexity has increased as technology has allowed creation of 3D and 4D interactive maps which allow anyone with internet access the ability to investigate different places, topics and times and produce their own map. Today the ability to read and interpret maps is increasingly important as industry, business and government communicates within their organization and the public using maps. Becoming aware of what a “map” shows depends partly on what the senses can register of the representation as a whole. It also depends on recognition of elements in the representation that are meaningful to the observer in the sense that these elements are credible indicators of spatial features. Based on that recognition, the nature of these elements and their spatial pattern might infer thoughts about historic or ongoing processes. This interpretation will be influenced by the expertise and needs of the observer.","hasChildren":true,"name":"Map reading and interpretation","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV6-3","description":"Assessment of the usability of a data representation is about how useful it is to users. Therefore it is a test of the success of the representation design, a test of the skills of the \"map\" maker and a test for the reliability of the underlying data.","hasChildren":true,"name":"Usability analysis","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV6-6","description":"Spatial thinking is thinking that finds meaning in the shape, size, orientation, location, direction or trajectory, of objects, processes or phenomena, or the relative positions in space of multiple objects, processes or phenomena. Spatial thinking uses the properties of space as a vehicle for structuring problems, for finding answers, and for expressing solutions\" Aspects: recognizing spatiality in a collection of things; translation of the collection to a pattern of elements; recognizing structure (relations between the elements in a pattern); recognizing process (or changes over time in patterns or structures)","hasChildren":true,"name":"Spatial thinking","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV6-8","description":"Ethics is about the question if behaviour is right or wrong in a social context. In dealing with geodata, a person can do the wrong thing with respect to laws (e.g. disclose secrets, disregard privacy, copyright infringement) or to professional standards (e.g. use bad data, forget about the colour blind, downplay unpleasant details). Aspects: breach of legal standards; breach of professional standards","hasChildren":true,"name":"Map ethics Legal and privacy issues","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV6","description":"Geodata visualisation are always made with a certain purpose. The role and understanding of such graphical representation is an important field of research. Besides theories that underpin evaluation approaches and their findings the visualisation may also be confronting. The more realistic the presentation and especially when it includes human/personal related data the ethical dimension of the visualisation play a major role. Usability of visualisations has also an impact on spatial thinking as has been proved by scholars.","hasChildren":true,"hasParent":true,"name":"Usability of maps","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA","description":"Proper design of geospatial applications, models, and databases and the validation and verification of design activities are critical components of work in all areas related to GIS&T. Design failures can negate well-intentioned efforts to apply concepts and technology to solve real-world problems. While sharing a number of concerns with general systems analysis, the unique and complex spatial characteristics of geospatial information provide significant additional challenges. The focus of this knowledge area is on the design of applications and databases for a particular need. The design of general-purpose models and tools (e.g., raster and vector) is covered in Knowledge Area: Data Modeling (DM). In the context of specific implementations, design activities fall into three general classes:\r\n1. Application Design addresses the development of workflows, procedures, and customized software tools for using geospatial technologies and methods to accomplish both routinary and unique tasks that are inherently geographic.\r\n2. Analytic Model Design incorporates methods for developing mathematical models, spatial models and data processes. The design of the analytic model is often influenced by decisions that are made about data models and structures.\r\n3. Database Design concerns the optimal organization of the necessary spatial data in a computer environment in order to efficiently sustain a particular application or enterprise.","hasChildren":true,"hasParent":true,"name":"Design and Setup of Geographic Information Systems","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA1-1","description":"This concept deals with the importance of having a list of prioritized requirements as a first step to ensure a smooth and successful implementation of a GIS project.. It entails the different methodologies and approaches to ensure a GI system covers all functional and nonfunctional requirements. Requirements are not only derived from business workflows but it is advisable to gather direct input from potential users that will be translated into requirements. However, there is a need to clearly rank the importance of the requirements gathered to ensure the GI system is manageable and in line with the intended use of the GI system, in opposition with the specific interests of a particular user or ambiguous requirements. Therefore, the documentation, traceability and evaluation of requirements after the implementation are as relevant as the initial gathering of requirements to give consistency to the designed system.","hasChildren":true,"name":"Requirements gathering and analysis","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA1-2","description":"The internal process of documenting a task or a process is about “how” it is implemented and “what” is implemented. Documenting is particularly helpful if a breakdown occurs, such as when an expert working in a task leaves her job or to substitute one task in a set of interrelated processes by another. Documentation provides consistency for the taskand allows its monitoring, analysis and revision during a project. \r\nThere are different methods for documenting a task to transform tacit knowledge into explicit knowledge. Therefore, the task should be documented by describing it in video format and using visual tools that allow documentation, or the maintenance of a field diary.\r\nIn particular cases, the creation of user guides or manuals could be considered a subset of a process description particularly addressed to external users. A user manual should take into account the target users to adapt its content to them.","hasChildren":true,"name":"Methods of process description and documenting","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA1-4","description":"A workflow is a sequence of operations that altogether perform a complex, sophisticated or repetitive operation or activity. No matter the workflow type, a workflow is defined in a declarative language, either text-based or visual, and stored in a workflow document to ease sharing and maintenance. In GI systems, a workflow can be seen from distinct perspectives. One of the most well-known GI workflow types is spatial data modelling. A model is specified as a combination of processing tools that manipulate and transform the spatial data required by the model. The order in which the processing tools, inputs, and outputs are organised in a workflow will determine the results and to what extent the spatial question is addressed. However, workflows in GI systems are not only related to spatial data modelling and transformation. There are cases where certain processes in GI systems should be designed in terms of software and hardware requirements, actors needs, organisational aspects or resource usage and demand. How can people’s work contribute to define the stages of a GI architecture? How much time does a regular user spend working with spatial data? How complex is the process going to be? The definition of this sort of workflows can help, for example, in designing an optimal architecture for a GI system in a particular enterprise configuration. \r\nWhether the workflow defines specific steps to process spatial data or the stages and details to implement an enterprise GI system, having a clear idea over each stage's inputs and outputs helps GI systems to be organised, consistent and reliable. In summary, high-level workflows like business workflows put together systems, components and actors that are part of a process or operation. They represent an abstract view, focused often on organisational, functional and resources usage aspects. Conversely, low-level workflows refer to a series of executable activities that carry out data transformations, models or spatial data analysis. Examples are code scripts, specified as sequences of commands in a programming language, and graphical workflows through, for example, the Model Builder in GI systems which are enacted by workflow engines.However, workflows in GI systems are not only related to spatial data modelling and transformation. There are cases where certain processes in GI systems should be designed in terms of software and hardware requirements, actors needs, organisational aspects or resource usage and demand. How can people’s work contribute to define the stages of a GI architecture? How much time does a regular user spend working with spatial data? How complex is the process going to be? The definition of this sort of workflows can help for example in designing an optimal architecture in an enterprise configuration for a GI system. \r\nWhether the workflow defines specific steps to process spatial data or the stages and details to implement an enterprise GI system. Having a clear idea over each stage's inputs and outputs helps GI systems to be organised, consistent and reliable. In summary, high-level workflows like business workflows put together systems, components and actors that are part of a process or operation. They represent an abstract view, focused often on organisational, functional and resources usage aspects. Conversely, low-level workflows refer to a series of executable activities that carry out a complex task, service or model. Examples are code scripts, specified as sequences of commands in a programming language to carry out data transformations and spatial models and spatial data analysis; and graphical workflows through, for example, the Model Builder in GI systems which are enacted by workflow engines.","hasChildren":true,"name":"Workflow definition and consideration in GI systems","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA1-5","description":"Software and information technology are integral to any GI systems or projects, from the storage and handling of spatial data to its analysis, visualization and sharing. Therefore, the use of well-known software design and engineering techniques and methods to develop efficient, reliable, and easy-to-maintain software applications in the GIS realm is more important than ever. \r\nAmong the modern software design and engineering techniques, Agile software development methodologies like Scrum stands out. The common rationale of the Agile methods is to split a large software project into many functional pieces of software that help the software engineering team to translate their development efforts into quick prototypes, and eventually reach the final product. Therefore, the constant feedback and validation of the user’s requirements in short, iterative development circles (i.e sprints) are the main advantages of the Scrum methodology.","hasChildren":true,"name":"Software design and engineering","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA1-6","description":"User interface and usability of a GIS system","hasChildren":true,"name":"User interface and Usability","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA1-9","description":"Geodesign is a design and planning method along with geospatial modelling and technology, and simulations informed by geographic contexts to facilitate informed decisions and the creation of design proposals. A geo-design process is a problem-based, iterative process bounded by specific (geographic) constraints characterised by a collaborative effort.","hasChildren":true,"name":"Geodesign","selfAssesment":"Completed
"},{"code":"DA1","description":"This concept encloses a set of activities and workflows to ensure that the implementation of a GIS system in an organization or project is correctly planned and designed according to the particularites, user requirements and current conditions of the project ahead. In general system design is the process to promote successful GIS in an enterprise environment. As a GIS system has a direct influence on the information technology department (IT), the system design tells the organizacion how the current infrastructure can or must support the planned GIS. This process builds a set of specific recommendations on hardware and network needs based on the number of projects that depend on the GIS solucion, as well as the projected business needs and user requirements. \r\nGIS architects through the system design process need to take into account and identify several conditions: a) infrastructure requirements, b) the network communication capacity, c) hardware and software procurement requirements and, d) software development and data acquisition needs. \r\nHaving a well-defined and successful GIS deployment is not only a matter of what data or software the organization should acquire. The process of system design aligns identified business requirements (user needs/requirements) derived from business strategies or project aims, goals, and stakeholders (business processes) with identified business information systems infrastructure technology (network and platform) recommendations. \r\nThe process starts with identifying business needs, including the identification of users locations, required information, data, resources or products. The business needs are generally considered as project workflows that help the GIS architects to identify the expected data traffic and computing demand associated with each transaction, being a transaction the work unit used to translate business requirements into associated server and network loads.\r\nWithout carrying out a proper system design, a GIS system can lead to an implementation and deployment failure, deriving in unfulfilled expectations and high costs in terms of human resources and financial matters.","hasChildren":true,"hasParent":true,"name":"System design","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA2-1","description":"Project management includes the planning, organization, coordination, execution, monitoring, controlling and closing of the activities and resources - human and economic - for the timely achievement of clearly defined objectives forming a project. For the success of a project, a project manager will assure an efficient use of resources and a proper execution of tasks to deliver value to users and “clients” of products and services. The Project Management Body of Knowledge (PMI) defines “project management” as “the application of knowledge, skills, tools, and techniques to project activities to meet requirements”, being EO*GI projects are another type of information technology projects. PMI reflects different areas to take care of by project management. These areas are: Integration, Scope, Time, Cost, Quality, Human Resource, Communications, Risks, Procurement and Stakeholder. There are a variety of tools and techniques used in the areas identified by PMI, just to name a few Gantt chart, Program evaluation and review (PERT) analysis, AGILE project management, etc. that will help in project management.","hasChildren":true,"name":"Project management","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA2-2","description":"This concept embraces the factors that could affect a GI system / project and could constitute obstacles to success or even decide a project is not doable. In order to ensure the success of a GI system or a GIS project there are several criteria to take into account from the very beginning of the conception of the GI system or project. A feasibility study may encompass different perspectives (economic, legal, technical, operational or scheduling ) to inform whether or not a project is worth the investment. An organisation should list the foreseen costs from these five perspectives listed above and the benefits (tangible or intangible) of implementing a system/project. Existing resources already available in-house and internal strategic plan in place could be critical to decide to undertake a project or not. The table below presents a non-exhaustive list of criteria and under which perspectives they should be examined.\r\nFeasibility analysis should include a pilot study to evaluate and improve the system / project proposed.","hasChildren":true,"name":"Feasibility analysis","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA2-8","description":"This concept discusses the technical, organizational and monetary advantages and disadvantages of proprietary versus open source software. GIST industry and research are slowly but consistently moving toward the openness of software. Open software entails some clear advantages such as continuous development of new applications, building community of developers and users, starting a project even if limited funding is available, increasing the chances of a project’s sustainability, to name a few. On the other side, proprietary initiatives in GIST are keeping their roots to the ground by developing cutting-edge tools to handle challenging and critical environments in large private sectors and public administrations. Advantages of proprietary software include more stable software, a well developed documentation and personalised customer support service. Both open and proprietary geospatial software solutions can co-exist by applying the appropriate IPR licences for each type of solution. The future trend is to balance how proprietary and open source geospatial software complement each other and find synergies in increasingly complex and large projects.","hasChildren":true,"name":"Proprietary and open source software","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA2","description":"To design, build, and maintain a GIS, sufficient resources (e.g., labor, capital, and time) must be secured. Resource planning consists of the allocation and use of in-house resources (people, equipment, tools, rooms, etc.) to achieve the maximal efficiency of those resources. These resources are required for a variety of system elements, including design, software purchase, labor, hardware, and facilities. The crucial task is to determine whether the project is worth the required resources.","hasChildren":true,"hasParent":true,"name":"Resource planning","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA3-1","description":"The ecosystem of GIS software architectures has evolved substantially in recent years to include a variety of options ranging from desktop GIS, server-based and component-based architectures to Web-based, cloud-based, mobile-based approaches. Aligned with the main trend, geospatial software architectures or infrastructures are also moving from desktop architectures to more cloud based or server based options to meet ever-increasing requirements of interoperability, interdisciplinary work and computational power for processing large data sets and derived products. Cloud-based architectures also enable on the fly visualization of computed geospatial products, as complementary visualisation and mapping tools are seamlessly integrated into modern cloud-based based architectures. Usage of a particular architecture is fully dependent on the nature, size, requirements, functionalities, and available resources of a given project or task. Desktop and server based applications are particularly suited for small sized projects and startups while enterprise based applications are meant for larger sized projects. Cloud based infrastructure can be useful for varying sizes of projects in which the computational infrastructure is fully outsourced.","hasChildren":true,"name":"Major geospatial software architectures","selfAssesment":"In progress (GI-N2K)
\r\n\r\n"},{"code":"DA3-2","description":"Interoperability of GIS infrastructure or architecture ensures the consistent and uninterrupted usage of data and functionalities across platforms and systems. Components or tools residing on distinct platforms can “talk” to each other without friction. Interoperability is a central characteristic, especially important in distributed systems and architectures. It can be applied to different levels or layers of a system, i.e. infrastructure level, data level, business logic level, etc. For example, standard spatial data formats and protocols are especially relevant for handling GIS data across multiple systems and platforms, regardless of their underlying software architecture. This is particularly important in large-scale, collaborative projects involving various teams using heterogeneous GIS architectures. Most software providers, developers communities and standardisation bodies and committees are striving to make their architectures interoperable in an open manner, so proprietary standards and protocols are a potential hindrance to this initiative.","hasChildren":true,"name":"Interoperability","selfAssesment":"
Completed (GI-N2K)
"},{"code":"DA3-3","description":"This concept considers general architectural patterns like SOA, ROA, Web Services, etc.","hasChildren":true,"name":"Architectural Patterns","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA3-4","description":"- WebGIS, - technical pecularities of spatial data infrastructures - standardiced GI services for SDI: WMS, WFS, CSW, Transformation Services, SOS, WPS etc., - other map services and interfaces","hasChildren":true,"name":"WebGIS, SDI services, map services","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA3-5","description":"This concept deals with Reference Model of Open Distributed Processing (RM-ODP), its standards, viewpoints modeling and the RM-ODP framework","hasChildren":true,"name":"Reference Model of Open Distributed Processing","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA3-6","description":"Cloud computing provides an on-line computing transparent resource to the user, since a user doesn’t notice almost no difference between working on her own computer or the cloud. Owned and managed by infrastructure providers, cloud computing entails advantages (concurrent access by many users, software updates hosted in the cloud, cost-efficiency or outsourced maintenance in the cloud) and disadvantages (loose of control, network Connection Dependency or security breaches ). On the other side, grid computing is a full network of computers and data working together so functioning as a supercomputer. Grid computing presents advantages such as shorter resolution of complex problems, the ease of organizational collaboration or a better use of existing hardware.","hasChildren":true,"name":"Cloud and Grid computing","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA3-7","description":"Within this concept solutions based on Desktop GIS and GIS libraries will be compared and contrasted","hasChildren":true,"name":"Desktop GIS, GIS libraries","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA3","description":"This concept describes the major geospatial software architectures available currently and choices when designing GI applications and systems, including desktop GIS, server-based, Internet, and component-based custom applications.","hasChildren":true,"hasParent":true,"name":"Architectural design of a GIS system","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA4-1","description":"- Compare and contrast the relative merits of various textual and graphical tools for data modeling, including E-R diagrams, UML, and XML - Create conceptual, logical, and physical data models using automated software tools - Create E-R and UML diagrams of database designs","hasChildren":true,"name":"Modeling tools","selfAssesment":"GI-N2K
"},{"code":"DA4-2","description":"Within an initial phase of database design, a conceptual data model is created as a technology-independent specification of the data to be stored within a database.","hasChildren":true,"name":"Conceptual models","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA4-3","description":"A logical data model expresses the meaning context of a conceptual data model, and adds to that detail about data (base) structures, e.g. using topologically-organized records, relational tables, object-oriented classes, or extensible markup language (XML) construct tags","hasChildren":true,"name":"Logical models","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA4-4","description":"A physical data model documents how data are to be stored and accessed on storage media of computer hardware","hasChildren":true,"name":"Physical models","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA4","description":"The effective design of geospatial databases should follow the established methods and principles of database modeling and design developed in computer science. The basic method is a three-step process generally called the conceptual, logical, and physical models transforming the application from very human-oriented to machine-oriented. Several standards and software tools exist to aid the process of database design.","hasChildren":true,"hasParent":true,"name":"Database design","selfAssesment":"In progress (GI-N2K)
"},{"code":"DM","description":"This knowledge area deals with representation of formalized spatial and spatio-temporal reality through data models and the translation of these data models into data structures that are capable of being implemented within a computational environment (i.e., within a GIS or more likely within a spatial database). Data modelling is a crucial issue as it defines the content of a spatial database and usefulness of these content (data) for certain applications. Data Modelling is performed using system neutral languages like UML (or more seldom ER-diagrams). These conceptual models have to be transferred to logical models (i.e. tables of a database). Data is stored in spatial databases which are normally organized in an object relational way. For certain types of data specific databases are used, like triple stores, NoSQL DBs, Array DBs etc. For data modelling quite a number of ISO standards are available for deriving the conceptual model as well as for rules for application schemas, spatial schemas, temporal schemas, Quality principles, encoding, 3D modelling (CityGML) etc. Data models provide the means for formalizing the spatio-temporal conceptualizations. Examples of spatial data model types are discrete (object-based), continuous (location-based), dynamic, and probabilistic. Mastery of the objectives presented in this knowledge area require knowledge and skills presented in the bodies of knowledge of allied fields, including computer science (ACM/IEEE-CS Joint Task Force, 2001) and information systems (Gorgone & Gray, 2000; Gorgone & others, 2002).","hasChildren":true,"hasParent":true,"name":"Data Modeling, Storage and Exploitation","selfAssesment":"In progress (GI-N2K)
"},{"code":"DM1-1","description":"This topic includes the main basic database concepts: - Database, definition and overview - Database management system, definition and overview - Relational databases, overview - Object-oriented databases, overview - Object-relational databases - NoSQL databases, general overview - NoSQL databases, examples triple stores, array databases, others (overview)","hasChildren":true,"name":"Overview on database concepts","selfAssesment":"GI-N2K
"},{"code":"DM1-2","description":"The Relational Model is the most important database model, therefore it is explained in more detail here: - Basic concepts (tables, tuples, etc.) - Relation to relational algebra (RA), basics of RA - Constraints (key, domain, referential integrity) - Relation to entity relation (ER) model, basics of ER","hasChildren":true,"name":"The Relational Model","selfAssesment":"GI-N2K
"},{"code":"DM1-3","description":"Relational databases and database management systems are essential for GIS in consequence the important issues have to be treated here: - General aspects, basic architecture of a DB, advantages, features - DBMS concepts and functionalites (transactions, locks, multiuser access etc.) - Database design, techniques - Database administration - Normalization (1NF - 3NF) - Example of a database design","hasChildren":true,"name":"Relational Databases, Database Managements Systems and Database principles","selfAssesment":"GI-N2K
"},{"code":"DM1-4","description":"Database queries and especially spatial queries require specific data structures to be performed satisfactory Relevant is: - Motivation, examples of typical non-spatial and spatial queries - Trees, B-tree, R-tree, Q-tree - Graphs, overview and relation to databases","hasChildren":true,"name":"Data Structures and Indices for Databases","selfAssesment":"GI-N2K
"},{"code":"DM1-5","description":"Big data like imagery but also for example GML data sets need compression to be accessed / transferred in an acceptable time. Therefore some compression techniques have to be taught: - Motivation, examples of data sets which need compression - General introduction, vector - / raster data compression, compression lossless, lossy - Popular compression techniques, LZW (Lempel-Ziv-Welch) encoding, Huffman encoding - Techniques for raster data, runlength encoding, JPEG coding, wavelet etc. - Techniques for the reduction of vector data (Douglas Peuker etc.) - Data formats, overview and relation to compression techniques","hasChildren":true,"hasParent":true,"name":"Data compression techniques","selfAssesment":"GI-N2K
"},{"code":"DM1-6","description":"SQL is the \"standard\" to perform spatial and non-spatial queries in databases. That means each student in a GI related course has to be familiar with the main aspects if it: - Motivation, history, overview - Data definition language DDL - Data manipulation language DML - Data control language DCL - Spatial extensions of SQL","hasChildren":true,"name":"SQL and its usage for data handling, spatial extensions to SQL","selfAssesment":"In progress GI-N2K
"},{"code":"DM1-7","description":"UML is the standard for describing the schema related to GI models, but also user requirements, workflows etc. can be described in UML using the UML diagrams: - Motivation, background, purpose - Use case diagrams - Class diagrams - Sequence diagrams - Activity diagrams","hasChildren":true,"name":"UML introduction and class diagrams","selfAssesment":"In progress GI-N2K
"},{"code":"DM1-8","description":"XML knowledge is an important bases for understanding GML. Moreover XML tools like XSLT are important to transform XML or GML data sets into other XML based formats like SVG or others. Important issues: - Motivation, purpose - Relation to HTML - XML document structure - XML syntax, elements, attributes and namespaces - xlink, xpath and XSLT - XML DTD - XML schema","hasChildren":true,"name":"XML introduction","selfAssesment":"In progress GI-N2K
"},{"code":"DM1-9","description":"The long term storage of GI data in general is based on spatial databases. Therefore the following is essential for a GI course: - Relation between GIS and DB / \"Long transactions\"- Dual concepts - Characteristics of spatial databases - Spatial data in object relational databases - Spatial extensions of DBs, overview","hasChildren":true,"name":"Database concepts in GIS and Principles of spatial databases","selfAssesment":"GI-N2K
"},{"code":"DM1","description":"This unit includes the basics for data modelling, storage and exploitation. Data modelling is one of the most important activities in conjunction with Geographic Information / GIS as it determines how the data can be used and if the requirements from applications are fulfilled. Data modelling can be done in conjunction with the database, e.g. through ER diagrams or according to the ISO 191xx standards by using UML. The costs of data acquisition can be tremendous, therefore the data represents an enormous value. This value has to be conserved through a safe long term data storage. Therefore databases and especially relational and object relational databases are crucial. For a proper storage and query of geographic information databases are extended with specific data types and data structures. As data sets can be very large suitable compression techniques became important especially in the context of accessing and delivering geographical data, e.g. through services. XML based modeling languages for encoding also play and important role in this context","hasChildren":true,"hasParent":true,"name":"Foundations for Data Modelling Storage and Exploitation","selfAssesment":"In progress (GI-N2K)
"},{"code":"DM2-1","description":"GI standards, mainly from ISO and OGC are essential nowadays. Moreover also an overview on ICT standards from W3C or OMG are important as well as some understanding of standardization processes. In detail: - Motivation for standards, examples from daily life - Overview on GIS and relevant ICT standardization bodies and selected standards - De jure and De facto standards, obligation, reasons for the usage of standards - Standardization within ISO - Standardization within OGC, relation to ISO - Examples of ISO 191xx standards","hasChildren":true,"name":"Overview on relevant standards and standardisation bodies","selfAssesment":"GI-N2K
"},{"code":"DM2-2","description":"Conceptual data modeling is a key skill for GI people. (see relations to other topics) The following therefore is important: - Overview on the relevant standards like conceptual schema language, Rules for application schema - Examples of conceptual schemas","hasChildren":true,"name":"The principle of conceptual data modelling according to ISO","selfAssesment":"In progress GI-N2K
"},{"code":"DM2-3","description":"Geometric modelling is an important subtask of conceptual modelling and requires the following basics: - Overview of ISO 19107 - spatial schema - Overview of ISO 19125 - simple features - Examples of the usage of spatial schema and simple feature elements for feature class definitions - Relation to GML - Relation to DBs","hasChildren":true,"name":"Geometry data types according to spatial schema and the simple feature specification","selfAssesment":"GI-N2K
"},{"code":"DM2-4","description":"Also temporal aspects have to be considered within conceptual modelling. This also requires basics: - Motivation, examples - Temporal variability of features (move, change of structure or geometry) - Overview on ISO 19108 temporal schema - Examples of modeling temporal aspects","hasChildren":true,"name":"Temporal data types according to temporal schema","selfAssesment":"In Progress GI-N2K
"},{"code":"DM2-5","description":"Conceptual models of course have to be implemented, in general in a GIS (which is often proprietary), or in a database (which can be standard based) ,therefore here the implementation in a database is treated: - Repetition of conceptual and logical models - Examples of the transferring of a conceptual model to a logical (database) model","hasChildren":true,"name":"Transferring conceptual models to logical models","selfAssesment":"In progress (GI-N2K)
"},{"code":"DM2-6b","description":"Metadata is considered as very important for the usage as well for the search for Geodata Relevant basics are: - Motivation, importance of data quality as part of metadata - Metadata in an spatial data infrastructure with many There are quite a number of relevant standards for GI courses. Some are listed here, others might be considered, depending on the background of the course: - Select other standards and explain them, Important are: - ISO 19141 Schema for moving features, ISO 19142 Web Feature Service or others - 19109 - Rules for application schema - Selection of other standards is depending on the background of the course","hasChildren":true,"name":"Other standards","selfAssesment":"In progress GI-N2K
"},{"code":"DM2-7","description":"GML is the most important standard for the transfer of Geodata as it allows to transfer the schema information as well as the data. Important issues: - Motivation, Importance of a Geography Markup Language - History of GML, Overview 19136 - Geography Markup Language - Relation to spatial schema - Supported features in GML (Topology, 3D ...) - Structure of GNL, profiles, application schemas etc. - Transfer of models and of data - Examples","hasChildren":true,"name":"Introduction to GML","selfAssesment":"In progress (GI-N2K)
"},{"code":"DM2-8","description":"3D Models, especially 3D city models are becoming more and more important. CityGML is the most important standard within the GI domain to describe City models semantically and geometrically. Relevant issues: - Motivation, Usage of CityGML - Relation to GML - Coherence of semantics and geometry - Principles of modeling - Level of detail concept - CityGML vs KML - Examples","hasChildren":true,"name":"Introduction to CityGML","selfAssesment":"In progress GI-N2K
"},{"code":"DM2","description":"This unit includes the essentials of relevant standards for spatial data modelling. A number of ISO and OGC standards are available for deriving the conceptual model as well as for rules for application schemas, spatial schema provides data types for geometry models in various forms, Point, line, area, body based, temporal schema allows to consider temporal dimensions, Quality principles can be used to describe the quality of geodata, encoding standards (mainly GML) allow the standard based transfer of data and data models, CityGML allows a standard based 3D modelling, etc.","hasChildren":true,"hasParent":true,"name":"Standards for Spatial Data Modeling","selfAssesment":"In progress GI-N2K
"},{"code":"DM3-1b","description":"There are two basic concepts related to this topic: Features and Fields, or Geo-fields, as named by Goodchild at al. The concept of fields can be differently represented as explained here: - Repetition of basic concepts of Geographic Information Science - Explanation of the concept of continuous fields and the commonly used ways of representing geo-fields - Relation between fields and coverages, an important discretizations of a Geo-field - Types of Coverages","hasChildren":true,"name":"The concept of fields","selfAssesment":"In progress GI-N2K
"},{"code":"DM3-2","description":"The raster data model holds values in a regularly spaced matrix of cells arranged in rows and columns covering a two dimensional space. Rasters are commonly used to store continuous data like colors in an image and height values but they are also used for discrete (thematic) values like land use.","hasChildren":true,"name":"The raster model","selfAssesment":"In Progress (GI-N2K)
"},{"code":"DM3-2b","description":"Grids are on the one hand one important type of caverages and on the other hand Grids are used as basic structure in some applications. Important here is: - Definition of the concept of grid in GIS - Grid as an instance of coverages - Grids as a basic structure for certain applications / medium for aggregation of data - Examples of grid-based data such as Digital Terrain Models (DTM) - Grids in census / statistical data and Geo-marketing applications","hasChildren":true,"name":"Grid representations","selfAssesment":"In progress GI-N2K
"},{"code":"DM3-3","description":"Grid data models can contain millions of discrete values. This leads to very large datasets. Depending on the way values change over the grid, different methods can be used for an optimal (lossy or lossless) data compression. Type of data, computer power needed, application of the data, method of transport and storage all contribute to the choice of compression method.","hasChildren":true,"name":"Grid compression methods","selfAssesment":"In progress (GI-N2K)
"},{"code":"DM3-3b","description":"TINs and Voronoi tessellations are important types of coverages. TINs play a very important role also in Computer graphics. Important here is: - Basics from Graph theory - Definition of Triangulated Irregular Networks (TIN), purpose and applications - TINs and voronoi diagrams as a type of coverages - One important instance of a TIN: Delauney Triangulation - Definition of Voronoi Diagrams, purpose and applications - Relation between Delauney Triangulation and Voronoi Diagram, the \"Dual Graph\" - Examples from applications","hasChildren":true,"name":"TIN and Voronoi tesselations","selfAssesment":"In progress GI-N2K
"},{"code":"DM3-4","description":"While the classical grid structure uses rectangular cells, the hexagonal data model uses hexagons to represent raster data","hasChildren":true,"name":"The hexagonal model","selfAssesment":"In progress (GI-N2K)
"},{"code":"DM3-4b","description":"Linear referencing is 1 dimensional positioning. The position of an object is defined by the distance from the object to the start point along a line. Linear referencing is for example used in railway dispatching systems","hasChildren":true,"name":"Linear referencing","selfAssesment":"In progress GI-N2K
"},{"code":"DM3-5b","description":"Resolution of raster and gridded data - Georeferencing of data, direct and indirect methods (t.b.d.)","hasChildren":true,"name":"Resolution and georeferencing system","selfAssesment":"In Progress (GI-N2K)
"},{"code":"DM3-7","description":"In hierarchical data models data is organized in a tree-like structure. Data are connected with parent-child relations. Hierarchical structures are often used for spatial indexing.","hasChildren":true,"name":"Hierarchical data models","selfAssesment":"In progress (GI-N2K)
"},{"code":"DM3","description":"This unit includes relevant tessellation data models. Besides features (sometimes also called geo-objects) geo-fields play and important role. In recent literature tessellation models are classified as discretizations of fields. In traditional GI literature tessellations are defined as important data structure itself. Tessellation discretise a continuous surface into a set of non-overlapping polygons that cover the surface without gaps. Tessellation data models represent continuous surfaces with sets of data values that correspond to partitions. Important tessellation models are Grids, TINs and Voronoi diagrams.","hasChildren":true,"hasParent":true,"name":"Tessellation data models","selfAssesment":"In progress (GI-N2K)
"},{"code":"DM4-1","description":"This topic includes the basics for feature based modelling. There are a number of standards also relevant for this topic (see relations). The following items should be included: - Definition of a feature (in some literature also called object, or geoobject) and of feature classes respectively. - Aspects of the definition (ID, geometry, topology, thematic, time etc.) - Techniques for the definition of features / feature classes (mainly link, as they are described elsewhere, see relations)","hasChildren":true,"name":"Feature based modelling","selfAssesment":"GI-N2K
"},{"code":"DM4-2","description":"This topic describes the process of Geometric modelling using vector data, means the primitives like points, lines, areas, bodies, or raster data. There is a strong relation to ISO standards (see relations) as they provide basic data types for geometric modelling. Main issues: - Geometric modeling based on vector data - Geometric modeling based on raster data - Conversion between the models - examples, advantages, disadvantages of the models","hasChildren":true,"name":"Geometric modelling","selfAssesment":"In progress GI-N2K
\r\n\r\nIn progress (GI-N2K)
"},{"code":"DM4-4","description":"This topics deals with the definition of an application schema. There are other units which are important for this topic (see Relations). Issues to be included: - Methods to define and describe an application schema (requirement analysis, description of the schema etc.) - Feature attribute catalogues - Domains / data relevant for INSPIRE","hasChildren":true,"name":"Application models based on vector data","selfAssesment":"GI-N2K
"},{"code":"DM4-5","description":"This Topic deals with important application models, which should be chosen with relation to the course (geographically / related to the background of the course) INSPIRE should be treated in any case. In detail: - Overview on important application models relevant for the course, e.g. from topography or environment in the country - Repetition of the principles of Spatial data infrastructures - Overview on the INSPIRE initiative and the goals related - The INSPIRE data model - The architecture of INSPIRE and the necessary services - Domains / data relevant for INSPIRE","hasChildren":true,"name":"Examples of important application models","selfAssesment":"GI-N2K
"},{"code":"DM4-6","description":"This topic is dedicated to the challenges of model based interoperability and related issues, The principles of interoperability are included in DA3-2. In detail: - The challenges of model interoparability (semantics, different modelling of the same features in different models, syntacs) - Overview on IT concepts for schema integration / transformation - Approaches for model integration - Approaches for model transformations, e.g. related to INSPIRE, from the Humboldt project","hasChildren":true,"name":"Model based interoperability, model transformations","selfAssesment":"GI-N2K
"},{"code":"DM4-7","description":"Network models are crucial in some application domains, such as Navigation (roads etc.), but also in utility applications (facilities like pipes etc.) In this topic should be treated: - The network model in the database domain - Graph based NoSQL databases - Topology of network models - Data structures for storing network data - The Dijkstra algorithm - Overview on important applications","hasChildren":true,"name":"Network models","selfAssesment":"GI-N2K
"},{"code":"DM4","description":"This unit includes relevant issues related to vector data models, feature based modelling, applications. Besides imagery data the majority of GI data available is feature based and founded on vector geometry. Topology modeling also is very common nowadays, as many analysis like routing or neighborhood analysis require it. Spaghetti modelling becomes more and more and exception. In every country there are important feature and vector geometry based application models available e.g. in Topography / Cartography. In Europe every GI course should include some information on INSPIRE. As in different application domains different data models are used, sometimes for the same feature types, integration and transformation of models are an important issue also.","hasChildren":true,"hasParent":true,"name":"Vector data model, Feature based modelling, Applications","selfAssesment":"In progress GI-N2K
"},{"code":"DM5-1","description":"- Many geographical phenomena are not defined sharply but uncertain Uncertainty has a number of considerations: - Motivation, background, purpose - Conceptual model of uncertainty - Uncertainty of geographic phenomena (vagueness, ambiguity) - Uncertainty of measurements - Uncertainty of analysis - Uncertainty vs. data quality - Statistical models of uncertainty - Outline of Fuzzy approaches","hasChildren":true,"name":"Basics of uncertainty and its modelling","selfAssesment":"In progress GI-N2K
"},{"code":"DM5-2","description":"Space and time are 2 connected concepts, this topic is dedicated to some basics of modelling time and the temporal dimensions related to features and fields: - Motivation, background, purpose - Changes in time in Entity based and field based representations - A conceptual model of changes in time - Move of objects - Change of structure - Change of geometry - Examples from applications","hasChildren":true,"name":"Modelling time aspects","selfAssesment":"In progress GI-N2K
"},{"code":"DM5-3","description":"Traditionally many GIS used 2D or 2.5 D data models, but in the last decade 3D modeling mainly in form of city models or in the context of Building Information Models (BIM): - Basic concepts of 3D modelling, edge, area, volume models - The workflow of 3D modelling, general aspects, choose of the proper model - Methods of 3D modeling - Principles of Constructive Solid Geometry (CSG) - Principles of Boundary representation (BR) - Principles of Voxel-beased modeling - Comparison of the methods - The concept of BIM, principles and purpose - City models, principles and purpose - Examples / applications","hasChildren":true,"name":"Modelling 3D","selfAssesment":"GI-N2K
"},{"code":"DM5","description":"Traditional raster and vector data models cannot easily represent the more complex aspects of geographic information, such as temporal change, uncertainty, three-dimensional phenomena, and integrated multimedia. A variety of models have been proposed to represent these complexities, including both extensions to existing models and software, and entirely new models and software. During the 1990s, work in this area was largely experimental, but many solutions are now available to practitioners in commercial and open source software. The data models in this unit are based on concepts discussed in Knowledge Area CF Conceptual Foundations.","hasChildren":true,"hasParent":true,"name":"Modelling 3D, temporal and uncertain phenomena","selfAssesment":"In progress (GI-N2K)
"},{"code":"DN3-1","description":"Modification of spatial and attribute data while ensuring consistency within the database, implications of transactions on database integrity, scenarios for periodic changes in GIS database and monitoring the periodic changes.","hasChildren":true,"name":"Database change","selfAssesment":"In progress (GI-N2K)
"},{"code":"DN3-2","description":"Rules for modelling spatial database change, techniques for handling version control, techniques for managing long and short transactions, management of spatial databases in multi-user environment","hasChildren":true,"name":"Modeling database change","selfAssesment":"In progress (GI-N2K)
"},{"code":"DN3-3","description":"Reliability tests of change information, design and implementation. Logical consistency of updates.","hasChildren":true,"name":"Reconciling database change","selfAssesment":"In progress (GI-N2K)
"},{"code":"DN3-4","description":"Needs for versioned databases, queries for change scenarios using DB management tools, algorithms for performing dynamic queries, role of time-criticality and data security while choosing methods for change detection.","hasChildren":true,"name":"Managing versioned geospatial databases","selfAssesment":"In progress (GI-N2K)
"},{"code":"GC","description":"The term geocomputation dates back to the first international conference on the topic in 1996 held at the University of Leeds under the title “The art and science of solving complex spatial problems with computers’. The term “geocomputation” was coined to describe the use of computer-intensive methods for knowledge discovery in physical and human geography. This new area distinguishes it from the application of statistical techniques to spatial data in the focus on “creative and experimental applications” and in “developing relevant geo-tools within the overall context of a ‘scientific’ approach.” Other authors reinforced the unique character of geocomputation as “to provide better solutions to many geographical problems by developing new, computationally dependent tools for analysis and modelling”. Simply defined, the interdisciplinary area of geocomputation was, from the beginning, closely linked to the application of computer technology and the development of tools and applications to real-world spatio-temporal problems through the combination of geographic information system techniques, spatial modelling, cellular automata, and other non-conventional data clustering and analysis techniques.\r\nEven though geocomputation is still seeking to define the field conceptually), it is closely related to computational science, the use of high-computing performance, artificial intelligence, computational intelligence, grid infrastructure and parallel computing . Nevertheless, the evolution of new computing paradigms, such as edge-fog-cloud computing along with the new forms of data create new opportunities for the geocomputation community . \r\n\r\nWhile the underlying idea remains intact --a diverse and interdisciplinary area of research that uses geospatial data, methods and tools for applied scientific work--, the current approach to geocomputation differs from the founders in that it focuses more attention on open science, reproducible research practices, and in a vibrant collaborative community to develop new methods, tools and applications that are integrated into multiple application domains such as economics, sociology, geodemography, health, criminology, transportation, biology, remote sensing and cities . The theoretical roots and experimental emphasis of geocomputation makes it an excellent vehicle to creatively explore in parallel the theory and practice of the use of geospatial data in a computational way to solve real-world problems.","hasChildren":true,"hasParent":true,"name":"Geocomputation","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC1-1","description":"A complex system can be viewed as a system composed of many interacting parts, with the ability to generate a new collective behaviour through self-organisation, for example, though the spontaneous formation of temporal, spatial or functional structures. Complex systems are therefore adaptive as they evolve and may contain self-driving feedback loops. Most real-world systems such as global climate, an ecosystem, a city, the human brain, and the entire universe, are complex systems. Therefore, complex systems are much more than a sum of their parts.The general characteristics of the structure and dynamics of complex systems have been characterised, including path dependence, positive feedback loops, self-organisation, and emergence. Complex system types include nonlinear systems, chaotic systems, and complex adaptive systems. \r\nTraditional approaches focus on the individual system components and define a system as the sum of its parts. Whereas the modern approach relies on complexity theory and complex adaptive systems, to emphasise the linkages between system components in order to understand complex systems as a whole. Agent-based models, for example, have been highly recommended for studying complex adaptive spatial systems because they support the explicit representation of situation-dependent information for decision making within dynamic spatial environments.","hasChildren":true,"name":"Complex systems","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC1-2","description":"Computational science is a discipline focused on the design, implementation and use of mathematical models or simulations through the use of computers to analyse scientific problems, systems or processes. Computational science heavily relies on computational technologies such as high performance computing, artificial intelligence, computational intelligence, grid infrastructure and parallel computing. Geocomputation is closely related to computational science and, therefore, geocomputational methods are often derived from machine learning, clustering, simulation, parallel computing and high performance computing. Contrary to the methods and tools applied for spatial analysis described under the Analytical Methods Knowledge Area, geocomputation and spatial data science may involve the use of spatial methods available in standard GIS packages, but quite often require self-development, or at least customisation, involving computational technologies and coding to solve target problems. The aim of this topic is to provide an introduction to computational science with particular emphasis on its usage and relation to geocomputation. In this sense, the way computational technologies are used in computational science can be connected to the methodological and coding practices of geocomputation and spatial data science.","hasChildren":true,"name":"Computational science and technology","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC1-3","description":"While geocomputation is not daily used in GIS environments and traditional GIS projects, it is the focus of a vibrant collaborative and research community in developing new geocomputational methods, tools and applications that are integrated into multiple application domains such as economics, sociology, geodemography, health, criminology, transportation, biology, remote sensing and cities. Open science, reproducible research practices, and strong collaboration make geocomputing an excellent vehicle for creatively exploring together the theory and practice of using geospatial data in a computational way to solve real-world problems.","hasChildren":true,"name":"Spatio-temporal problems and applications","selfAssesment":"In progress (GI-N2K)
"},{"code":"GC1-4","description":"The origin of geocomputation dates back to the first international conference on the topic in 1996 and was coined to describe the use of computer-intensive methods for knowledge discovery in physical and human geography. According to Birkin (2009), Openshaw defined geocomputation as a computational paradigm that takes geographic information science to focus on analysis, modeling, and simulations applied. Openshaw’s definition emphasized the use of novel computational approaches at that time along with spatial data and analysis methods to find solutions to real-world problems. Longey's definition, as reported by Birkin (2009), focuses on the continuous development of GIS tools and techniques, in line with the modern emphasis on creative, experimental, data-driven and code-based practices to solve real-world problems. In this context, geocomputation is closely related to other widely known areas of knowledge within the geospatial community, such as GIScience, Spatial Information Science, Geoinformatics, and Geographic/Spatial Data Science. While these terms clearly overlap and boundaries are fuzzy, the term geocomputation puts the focus on creative and experimental applications and in developing relevant computationally geospatial tools for analysis and modelling within the overall context of a ‘scientific’ approach. Therefore, a common interpretation of geocomputation is to describe the application of computational models to geographic problems. Nowadays, the term spatial data science is gaining ground to convey essentially the same interpretation as geocomputation.","hasChildren":true,"name":"Origin of geocomputation","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC1","description":"Geocomputation represents an attempt to move the geospatial research agenda back to geographical analysis and modelling by providing a toolbox of methods to analyse and model a range of highly complex, often non-deterministic problems. In this context, complex systems and computational science are foundational aspects upon which geocomputation approaches and methods are built to address a variety of real-world, spatio-temporal issues. Similar to geocomputation, the term spatial data science has recently emerged to refer to the use of computational techniques to access, explore, visualize and perform spatial analysis on real-world data sets. Therefore, geocomputation and spatial data science share many commonalities (complex problems, use of spatial techniques and modeling, coding, real-world data) that make them interchangeable in many scenarios.","hasChildren":true,"hasParent":true,"name":"Geocomputation and complex systems","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC2-1","description":"Building a model that mimics a real-world system generally follows a series of stages: from conceptual models to mathematical models and, finally, simulation models. In model development, system analysis is a process whereby a real-world system is simplified by dividing it into simpler, more manageable parts. A conceptual model then captures the components, variables and interactions of a system, and provides a useful way of thinking about the trade-offs between abstraction and representativeness of real-world phenomena. However, taken in isolation, the interacting parts of a system fail to explain its dynamics behaviour. A conceptual model is then translated into a mathematical model to explain interrelations and relationships among the constituted parts of a system by means of equations, logical rules or other mathematical mechanisms. Lastly, a simulation model is the computer-based implementation of mathematical models that consist of interrelated equations and logical rules. However, this model development process typically does not happen all at once, but can occur in multiple iterations throughout these phases to adjust, improve, and incorporate feedback into the modelling process.\r\n\r\nWhen a simulation model runs on a computer, it iteratively recalculates the state of the modelled system as it changes over time in accordance with the relationships represented by the mathematical relationships that describe the system dynamic. Therefore, developing detailed and dynamic simulation models comes at the cost of generality and interpretability, but it brings us realism and the ability to represent real-world processes in specific contexts. \r\n\r\nSimulation modelling is often used for prediction, exploration, theory development, or even optimization of conditions to achieve desired outcomes, with the goal of examining how the interconnections and relationships that characterise complex social and environmental systems (e.g. ecosystems, urban systems, social systems, global climate system) produces patterns of behaviour over time. Therefore, simulation models are increasingly gaining relevance as scientific mechanisms for several reasons. First, simulation models allow researchers to study systems inaccessible to experimental and observational scientific methods, complementing more conventional approaches to discover or formalize theories about real world systems. Also, as many real-world systems are nonlinear, simulation modelling has turned into a necessary method to explore and understand better such systems. In addition, the availability of computational science methods and technology, together with a large amount of data available from different sources, have greatly driven the adoption of simulation models in a wide range of scientific disciplines.","hasChildren":true,"name":"Principles of computer simulation","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC2-3","description":"Rule-based models are based on logic programming with condition-action expressions, where the left side of the expressions consists of several conditions that return a logical result, and the right side consists of several actions. Therefore, rules in rule-based models indirectly specify a mathematical model. However, unlike equation-based models which refer to the overall or aggregate behaviour of a system, rule-based models focus on the behaviour of the individual components of a system. This is why the implementation of rule-based models is most often done by cellular automata models or agent-based models, in which the aggregate behaviour of the system arises from the interaction of the individual agents or cells over time. Many geographic patterns and dynamics are formed by systems of interacting actors/cells with heterogeneous characteristics and behaviours, in which such dynamic behaviours can be implemented as rules.","hasChildren":true,"name":"Rule-based models","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC2-4","description":"Equation-based models are a set of interrelated equations that capture the variability of a system over time (differential equations), and the execution (simulation) of the model means to evaluate such equations. Equation-based models do not aim at representing the behaviour of the individual components in a system. Rather, they focus on the overall or aggregate behaviour of a system. Therefore, equation-based models are well suited to represent physical processes and some topics within natural sciences, where the system to some degree can be described by physical laws. Hydrological modelling is a good example of models based on equations. However, other real-world systems can rarely be fully described by the laws of the natural sciences, and their behavior and interrelation must be represented by means of other types of mathematical mechanisms. The aim of this topic is to present the advantages and challenges in using equation-based simulation models, which are most naturally applied to systems centrally governed by physical laws rather than by information processing and flow.","hasChildren":true,"name":"Equation-based models","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC2-5","description":"Space-time dynamics is closely related to the concepts of change and process, which are inherent to our real-world world. Space-time dynamics is especially manifested when we move from a static to a dynamic representation of phenomena. Various processes taking place at different spatial and temporal scales interact with each other and lead to changes in the phenomena being modelled. \r\n\r\nThere are many different approaches to conceptualizing and understanding space-time dynamics in order to understand or predict phenomena in heterogeneous application domains ranging from human activities and urban sprawl to disease spread and traffic flow. An example is the time geography approach and its variants, such as the spatiotemporal prism, to model and understand human physical activities that occur in and are simultaneously constrained by space and time. These interactions produce space–time prisms that simultaneously situate individuals locally in physical space. Other techniques such as cellular automata also model human and physical activity in space and time, to simulate space–time and associated constraints to individual human activities.\r\n\r\nWhile the above examples are primarily oriented towards human activities, such as urban transport and mobility, these theoretical approaches have the potential to investigate and understand interactions between humans and the environment, recognizing the importance of individual human activities together with the geographic-environmental context applied to multiple scenarios, such as climatology, physical geography, and natural disasters. For the latter, for example, modelling and simulating human responses to floods or hurricanes can lead to more efficient and effective emergency plans.","hasChildren":true,"name":"Space-time dynamics","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC2-6","description":"Cellular automata are a widely used form of spatially explicit simulation model, where complex processes evolve over space and time through a lattice of cells, each linked to its neighboring cells. Typically, this spatial lattice is structured as a two-dimensional grid of square cells. Each cell holds a set of states that change over time according to transition rules, which depend on the state of the cell and its neighbors. That is, a cellular automata model allows the exploration of how local interactions lead to the emergence of global patterns, governed by clearly defined rules. A cellular automata model is defined by six key components: a lattice or framework, individual cells, neighboring cells, transition rules, initial conditions (states), and an update sequence (time). These models are well-suited to geographic information systems (GIS) due to their simple data structures and ability to represent spatial changes and patterns in an intuitive way. This has made cellular automata in simulating phenomena such as land use changes and the spread of diseases.","hasChildren":true,"name":"Cellular automata","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC2-7","description":"Agent-based modelling is a powerful approach for simulating the dynamics of geographical systems by breaking them down into individual components or agents, each with its own characteristics, properties, rules and behavior. Unlike traditional models that treat geographical components as homogeneous entities, agent-based modelling allows for the simulation of diverse agents, such as people, cities, or abstract representations, interacting with each other and their environment at various spatial and temporal scales. This bottom-up approach makes it possible to observe how individual decisions lead to complex system behaviors over time, providing deeper insights into urban problems like urban sprawl, congestion, and segregation, as well as to model natural and social phenomena such as animal behavior, pedestrian behavior, social insects and biological cells. Therefore, the macro-level behavior of the system arises from the interaction of individual agents and the environment over time.\r\n\r\nAgent-based modelling development stems from automata-based models, which use rule-based mechanisms to process information and evolve over time. Two prominent automata-based approaches—cellular automata and agent-based modelling —have been widely adopted in geographic modelling. Agent-based modelling's advantage lies in its ability to model heterogeneous agents and dynamic interactions, which traditional models, focused on aggregate behaviors, cannot capture as effectively. While agent-based modelling offers unique insights into geographical systems, it also poses challenges, such as the complexity of simulating realistic agent behaviors. Nonetheless, agent-based modelling continues to grow in popularity for its ability to represent dynamic spatial changes in a more detailed and realistic manner.","hasChildren":true,"name":"Agent-based modelling","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC2","description":"The concept of spatial simulation modelling can be better understood by looking at the meaning of its individual words. A model is widely defined as a simplified representation of a real-world system under study, which can be used to explore or to better understand the system it represents. Simulation models are computer-based implementations of a model to produce outputs based on certain model assumptions. Simulation , therefore, relies on the use of computers for virtual experimentation to gain insight into real-world problems by proposing alternative assumptions that arise from exploring “what if” questions about a dynamic problem of interest over the course of successive simulation experiments.\r\n\r\nSimulation modelling is often used for prediction, exploration, theory development, or even optimization of conditions to achieve desired outcomes, with the goal of examining how the interconnections and relationships that characterize these systems produce patterns of behaviour over time. Across broad areas of the environmental and social sciences, researchers use simulation models as a way to study systems inaccessible to experimental and observational scientific methods, and also as an essential complement of those more conventional approaches to discover or formalize theories about the real world. Simulation models are a relatively recent addition to the scientific toolbox, and the reasons for their widespread adoption are, on the one hand, the impossibility to study in-situ some complex social and environmental systems (e.g. ecosystems, urban systems, social systems, global climate system) and, on the other hand, the availability of High Performance Computing and large amount of data from different sources.\r\n\r\nFinally, simulation modelling is also useful for the study of spatial patterns over time. Spatial simulation models are relevant when the study of spatial elements and their relationships in a system are necessary for a fully understanding of that system. In this regard, spatial simulation modelling approaches include rule-based models, equation-based models, grid-based cellular automata models, discrete event simulation, and agent-based models.","hasChildren":true,"hasParent":true,"name":"Spatial simulation modelling","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC3-10-1","description":" ","hasChildren":true,"name":"Geometric object features","selfAssesment":" "},{"code":"GC3-10-2-1","description":" ","hasChildren":true,"name":"Object relations","selfAssesment":" "},{"code":"GC3-10-2","description":" ","hasChildren":true,"hasParent":true,"name":"Object features","selfAssesment":" "},{"code":"GC3-10-3-1","description":" ","hasChildren":true,"name":"Wavelets","selfAssesment":" "},{"code":"GC3-11-1","description":" ","hasChildren":true,"name":"Genetic artificial networks","selfAssesment":" "},{"code":"GC3-11-2-1","description":" ","hasChildren":true,"name":"Markov models","selfAssesment":" "},{"code":"GC3-11-2-2","description":" ","hasChildren":true,"name":"Kalman filters","selfAssesment":" "},{"code":"GC3-11-2","description":" ","hasChildren":true,"hasParent":true,"name":"Space-time dynamic reasoning","selfAssesment":" "},{"code":"GC3-11-3-1","description":" ","hasChildren":true,"name":"Multilayer perceptron","selfAssesment":" "},{"code":"GC3-11-3-2","description":" ","hasChildren":true,"name":"Backpropagation","selfAssesment":" "},{"code":"GC3-11-3-3","description":" ","hasChildren":true,"name":"Recurrent neural networks","selfAssesment":" "},{"code":"GC3-11-3-4","description":" ","hasChildren":true,"name":"Long short-term memory","selfAssesment":" "},{"code":"GC3-12-1","description":" ","hasChildren":true,"name":"Ensemble learning","selfAssesment":" "},{"code":"GC3-12-2","description":" ","hasChildren":true,"name":"Regression trees","selfAssesment":" "},{"code":"GC3-12","description":" ","hasChildren":true,"hasParent":true,"name":"AI algorithms","selfAssesment":" "},{"code":"GC3-13-1","description":" ","hasChildren":true,"name":"Physics aware AI","selfAssesment":" "},{"code":"GC3-13-2-1","description":" ","hasChildren":true,"name":"Theory of mind","selfAssesment":" "},{"code":"GC3-13-2-2","description":" ","hasChildren":true,"name":"Self-aware AI","selfAssesment":" "},{"code":"GC3-13-2","description":" ","hasChildren":true,"hasParent":true,"name":"Digital twin","selfAssesment":" "},{"code":"GC3-13","description":" ","hasChildren":true,"hasParent":true,"name":"Hybrid AI","selfAssesment":" "},{"code":"GC3-14-1-1","description":" ","hasChildren":true,"name":"Individual intelligence","selfAssesment":" "},{"code":"GC3-14-1-2","description":" ","hasChildren":true,"name":"Collective intelligence","selfAssesment":" "},{"code":"GC3-14-1-3","description":" ","hasChildren":true,"name":"Team learning","selfAssesment":" "},{"code":"GC3-14-1","description":" ","hasChildren":true,"hasParent":true,"name":"Cooperation levels","selfAssesment":" "},{"code":"GC3-14-2-1","description":" ","hasChildren":true,"name":"Logical agent","selfAssesment":" "},{"code":"GC3-14-2-2","description":" ","hasChildren":true,"name":"Inference","selfAssesment":" "},{"code":"GC3-14-2-3","description":" ","hasChildren":true,"name":"Probabilistic reasoning","selfAssesment":" "},{"code":"GC3-14-2-4","description":" ","hasChildren":true,"name":"Sequential decision problems","selfAssesment":" "},{"code":"GC3-14-2-5","description":" ","hasChildren":true,"name":"Supervised learning","selfAssesment":" "},{"code":"GC3-14-2-6","description":" ","hasChildren":true,"name":"Reinforcement learning","selfAssesment":" "},{"code":"GC3-14-2","description":" ","hasChildren":true,"hasParent":true,"name":"Intelligence type","selfAssesment":" "},{"code":"GC3-14","description":" ","hasChildren":true,"hasParent":true,"name":"Intelligent Software Agent","selfAssesment":" "},{"code":"GC3-3","description":"Biological neurons, or nerve cells, receive multiple input stimuli, combine and modify the inputs in some way, and then transmit the result to other neurons. Artificial neural networks are an attempt to emulate features of biological neural networks in order to address a range of difficult information processing, analysis and modelling problems. The principal class of ANNs are so-called feed-forward networks, but other types of ANN are for example recurrent neural networks. Among the feed-forward networks the most widely used approach is the multi-level perceptron (MLP) model. The application range is broad from non-linear regression to land cover change modelling. The aim of the topic is to introduce the principles of ANN and to understand and demonstrate its use in geospatial modelling.","hasChildren":true,"hasParent":true,"name":"Artificial Neural Networks","selfAssesment":"In progress (GI-N2K)
"},{"code":"GC3-7-1","description":" ","hasChildren":true,"name":"Cybernetics","selfAssesment":" "},{"code":"GC3-7-2","description":"Pattern recognition is the process of classifying input data into objects or classes based on key features. There are two classification methods in pattern recognition: supervised and unsupervised classification. The supervised classification of input data in the pattern recognition method uses supervised learning algorithms that create classifiers based on training data from different object classes. The classifier then accepts input data and assigns the appropriate object or class label. The unsupervised classification method works by finding hidden structures in unlabelled data using segmentation or clustering techniques. Common unsupervised classification methods include: K-means clustering, Gaussian mixture models, Hidden Markov models. The aim of the topic is to provide knowledge about the different methods in pattern recognition and how to choose the optimum method for a specific spatial problem.","hasChildren":true,"name":"Pattern recognition","selfAssesment":"In progress (GI-N2K)
"},{"code":"GC3-7-3-1","description":" ","hasChildren":true,"name":"Information-as-meaning","selfAssesment":" "},{"code":"GC3-7","description":" ","hasChildren":true,"hasParent":true,"name":"Signal processing","selfAssesment":" "},{"code":"GC3-8-1","description":" ","hasChildren":true,"name":"Natural language processing","selfAssesment":" "},{"code":"GC3-8-2","description":" ","hasChildren":true,"name":"Semantic web","selfAssesment":" "},{"code":"GC3-8","description":" ","hasChildren":true,"hasParent":true,"name":"Computational linguistics","selfAssesment":" "},{"code":"GC3-9-1-1","description":" ","hasChildren":true,"name":"Experimental learning","selfAssesment":" "},{"code":"GC3-9-1","description":" ","hasChildren":true,"hasParent":true,"name":"Knowledge representation","selfAssesment":" "},{"code":"GC3-9-2-1","description":" ","hasChildren":true,"name":"Semantic net","selfAssesment":" "},{"code":"GC3-9-2-2","description":" ","hasChildren":true,"name":"Inheritance","selfAssesment":" "},{"code":"GC3-9-2","description":" ","hasChildren":true,"hasParent":true,"name":"Knowledge organising system","selfAssesment":" "},{"code":"GC3-9-3","description":" ","hasChildren":true,"name":"Semantic categorisation","selfAssesment":" "},{"code":"GC3-9-4-1-1","description":" ","hasChildren":true,"name":"Membership functions","selfAssesment":" "},{"code":"GC3-9-4-1-2","description":" ","hasChildren":true,"name":"Class stability","selfAssesment":" "},{"code":"GC3-9-4-1","description":" ","hasChildren":true,"hasParent":true,"name":"Fuzzy logic","selfAssesment":" "},{"code":"GC3-9-4-2","description":" ","hasChildren":true,"name":"Boolean logic","selfAssesment":" "},{"code":"GC3-9","description":" ","hasChildren":true,"hasParent":true,"name":"Automated reasoning","selfAssesment":" "},{"code":"GC3","description":"Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans.","hasChildren":true,"hasParent":true,"name":"Artificial intelligence (AI) in EO and GI","selfAssesment":"New
"},{"code":"GC4-1","description":"The use of the term Open geocomputation doesn't intend to coin a new term; Open GIScience and Open GIS are well explored and discussed terms in the literature. Both embrace the idea of open data, open source, collaboration among peers, and the integration of these practices into GIS research projects, tools, services and applications. Open geocomputation brings the ideas of Open GIScience (and hence Open Science in general) into geocomputation, focussing on openness as a fundamental tenet to conduct research in geocomputation and for the development of new computational methods and tools. In fact, many community-led developments and tools have recently appeared in the field of geocomputation, notably based on R and Python. The widespread popularity and adoption of these computing environments for geocomputing and geospatial analysis is simply because they encompass open, transparent, and reproducible tool development.","hasChildren":true,"name":"Open Geocomputation","selfAssesment":"New
"},{"code":"GC4","description":"A distinguible feature of the current approach to geocomputation is the emphasis on openness: open science, open source, open data. All of this propelled by a vibrant collaborative community with the aim to develop open and reproducible methods, tools and applications applied to a variety of real-life, spatio-temporal application domains. Open Science is a paradigm that can be applied to any scientific discipline and area of knowledge, characterised by openness, access to large volumes of data and unprecedented levels of computing power, availability of community-driven tools, and new types of collaboration between multidisciplinary researchers. Open Science clearly goes beyond geocomputation, but at the same time, its practices and principles characterise recent geocomputation-related projects as well as its community. Therefore, the vision of Open Science taken here is contextualised to the field of geocomputation.","hasChildren":true,"hasParent":true,"name":"Open Science","selfAssesment":"new
"},{"code":"GD","description":"Geospatial data represent measurements of the locations and attributes of phenomena at or near Earth`s surface. Information is data made meaningful in the context of a question or problem. Information is rendered from data by analytical methods. Information quality and value depends to a large extent on the quality and currency of data (though historical data are valuable for many applications). Geospatial data may have spatial, temporal, and attribute (descriptive) components, as well as associated metadata. Data may be acquired from primary or secondary data sources. Examples of primary data sources include surveying, remote sensing (including aerial and satellite imaging), the global positioning system (GPS), work logs (e.g., police traffic crash reports), environmental monitoring stations, and field surveys. Secondary geospatial or geospatial-temporal data can be acquired by digitizing and scanning analog maps, as well as from other sources, such as governmental agencies. The legitimacy of geographic information science as a discrete field has been claimed in terms of the unique properties of geospatial data. In a paper in which he coined the term GIScience, Goodchild (1992) identified several such properties, including: 1. Geospatial data represent spatial locations and non-spatial attributes measured at certain times. 2. The Earth`s surface is highly complex in shape and continuous in extent. 3. Geospatial data tend to be spatially autocorrelated. It has long been said that data account for the largest portion of geospatial project costs. While this maxim remains true for many projects, practitioners and their clients now can reasonably expect certain kinds of data to be freely or cheaply available via the World Wide Web. Federal, state, regional, and local government agencies, as well as commercial geospatial data producers, operate clearinghouses that provide access to geospatial data. Although geospatial data are much more abundant now than they were ten years ago, data quality issues persist. Good data are expensive to produce and to maintain. Proprietary interests simultaneously increase the supply of geospatial data and impede data accessibility. Standards for geospatial data and metadata are useful in facilitating effective search, retrieval, evaluation, integration with existing data, and appropriate uses. National and international organizations, such as the Open Geospatial Consortium (OGC) and International Organization for Standardization (ISO), develop and promulgate such standards. INSPIRE directive (Infrastructure for Spatial Information in the European Community) regulates geospatial data management","hasChildren":true,"hasParent":true,"name":"Geospatial Data","selfAssesment":"In progress (GI-N2K)
"},{"code":"GD1-1","description":"Usable and accurate geospatial data are based upon proper model of the Earth`s surface. Shape of the Earth is complex and complicated to measure. Approximations are used to minimize complexity of the task and possible errors.","hasChildren":true,"name":"Earth geometry","selfAssesment":"GI-N2K
"},{"code":"GD1-2","description":"Geospatial referencing systems provide unique codes for every location on the surface of the Earth (or other celestial bodies). These codes are used to measure distances, areas, and volumes, to navigate, and to predict how and where phenomena on the Earths surface may move, spread, or contract. Point-based, vector coordinate systems specify locations in relation to the origins of planar or spherical grids. Tessellated referencing systems specify locations hierarchically, as sequences of numbers that represent smaller and smaller subdivisions of two- or three dimensional surfaces that approximate the Earths shape, Linear referencing systems specify locations in relation to distances along a path from a starting point. Tessellation data models, are considered in Unit DM3 Tessellation data models, and linear referencing models are considered in Unit DM4 Vector data models.","hasChildren":true,"name":"Georeferencing systems","selfAssesment":"GI-N2K
"},{"code":"GD1-3","description":"Horizontal datums determine the geometric relations between a coordinate system grid and a particular ellipsoid approximating the Earth`s surface. Vertical datums determine elevation reference surfaces, like mean sea level. A. Horizontal datums. Relation of coordinate system to particular ellipsoid, datum transformation options, Molodensky and Helmert transformation, other high accuracy transformations, ED50 and WGS84, historical development of horizontal datums, ETRS89. B. Vertical datums. Historical development of vertical datums, difference between vertical datum and geoid, relations between ellipsoidal (geodetic) heiht, geoidal height and orthometric elevation.","hasChildren":true,"name":"Datums","selfAssesment":"In progress GI-N2K
"},{"code":"GD1-4","description":"Map projections are systematic transformations of geographic coordinates of the surface of ellipsoid into locations in plane. Plane coordinates are based on map projection. As the transformation of a spherical grid into a plane grid causes inevitably distortions of the geometry, and, different projections cause different distortions, knowledgeable choice of appropriate projection for any particular use is crucial. A. Map projection poperties. Geometric properties that may be preserved or lost in projected grid, usefulness of compromise projection, Tissot indicatrix as an indicator of projection errors, visual appearance of the Earth`s graticule, distortion patterns for projection classes, distortions in raster data. B. Map projection classes. Three main classes of map projection based on developable surface, projection types by geometric properties preserved, mathematical basis of projecting longitude and latitude into x and y coordinates. UTM, ETM, projections used by EC. C. Map projection parameters. Standard line, projection case, latitutde and longitude of origin, aspects of projection. D. Georegistration. Rectification vs orthorectification, ground controle points in georegistration of aerial imagery.","hasChildren":true,"name":"Map projections","selfAssesment":"GI-N2K
"},{"code":"GD1","description":"Proper model of the Earth`s surface and ability to locate spatial phenomena accurately to it, is crucial in effective collection, management and use of data. Characterising size and shape of the Earth, using appropriate surfaces to approximate it, choosing suitable coordinate system and map projection is bases for efficient understanding of spatial data.","hasChildren":true,"hasParent":true,"name":"Geolocating Data to Earth","selfAssesment":"In progress (GI-N2K)
"},{"code":"GD10-4","description":"A stereoscopy acquisition mode collects remotely sensed data where each location on the ground (or the imaged objects) is covered multiple times (at least twice), from different perspectives. Stereopairs and stereoscopic coverage enable the extraction of 3D representations of the environment from remotely sensed imagery.","hasChildren":true,"hasParent":true,"name":"Stereoscopy and orthoimagery","selfAssesment":"GI-N2K
"},{"code":"GD10","description":"Since the 1940s aerial imagery has been the primary source of detailed geospatial data for extensive study areas. Photogrammetry is the profession concerned with producing precise measurements from aerial imagery. Aerial imaging and photogrammetry comprise a major component of the geospatial industry. The topics included in this unit do not comprise an exhaustive treatment of photogrammetry, but they are aspects of the field about which all geospatial professionals should be knowledgeable.","hasChildren":true,"hasParent":true,"name":"Aerial imaging and photogrammetry","selfAssesment":"GI-N2K
"},{"code":"GD11-2","description":"the physical environment to sense data without direct contact. It contains a carrier device (platform) and a sampling unit (sensor).","hasChildren":true,"name":"Platforms and sensors","selfAssesment":"GI-N2K
"},{"code":"GD11","description":"Satellite-based sensors enable frequent mapping and analysis of very large areas. Many sensing instruments are able to measure electromagnetic energy at multiple wavelengths, including those beyond the visible band. Satellite remote sensing is a key source for regional- and global-scale land use and land cover mapping, environmental resource management, mineral exploration, and global change research. Shipboard sensors employ acoustic energy to determine seafloor depth or to create imagery of the seafloor or water column. The topics included in this unit do not comprise an exhaustive treatment of remote sensing, but they are aspects of the field about which all geospatial professionals should be knowledgeable.","hasChildren":true,"hasParent":true,"name":"Satellite and shipboard remote sensing","selfAssesment":"GI-N2K
"},{"code":"GD12","description":"Meaning of geospatial metadata, elements of metadata, use of metadata, integration of metadata in data production, standards in geospatial data, ISO standard family 191xx, data warehouse, exchange protocol, transport protocols, spatial data infrastructure, INSPIRE, OGC, DCAT profiles for CKAN applications bridging metadata from GI and IT domains.","hasChildren":true,"name":"Metadata, standards, and infrastructures","selfAssesment":"GI-N2K in progress
"},{"code":"GD2-1","description":"Classic land survey methods and manual attribute data collection in the field","hasChildren":true,"name":"Land surveying and field data collection","selfAssesment":"GI-N2K
"},{"code":"GD2-2","description":"Aerial imagery has been the primary source of detailed geospatial data for extensive study areas. Photogrammetry is producing precise measurements from aerial imagery. Aerial imaging and photogrammetry comprise a major component of the geospatial data production. Satellite-based sensors enable frequent mapping and analysis of very large areas. Sensing instruments are able to measure electromagnetic energy at multiple wavelengths. Satellite remote sensing is a key source for regional- and global-scale land use and land cover mapping, environmental resource management, mineral exploration, and global change research. Shipboard sensors employ acoustic energy to determine seafloor depth or to create imagery of the seafloor or water column. Principles of aerial photography, oblique and vertical imagery, spatial and radiometric resolution, spectral sensitivity, principal point, distortions and displacements in aerial image, parallax, stereophotogrammetry, generation of an orthoimage from a vertical aerial phoptograph, aerotriangulation, vector data extraction from digital seteroimagery, mission planning. Use of UAV in photogrammetry. Main platforms and sensors in spatial image acquisition, active and passive sensors, LiDAR and microwave, multispectral and hypersepctral imagery, interpretation of imagery, supervised and unsupervised classification, pixel based and segmented classification, ground verification, main applications, bathymetric mapping. SENTINEL.","hasChildren":true,"hasParent":true,"name":"Remote sensing","selfAssesment":"In progress GI-N2K
"},{"code":"GD2-3","description":"Crowdsourcing is the practice of obtaining needed services, ideas, or content by soliciting contributions from a large group of people and especially from the online community rather than from traditional employees or suppliers. Crowdsourced spatial data collection is becoming more and more important. The advantages and disadvantages of crowdsourced data, opensource mapping tools, potential application of crowdsourcing, VGI, OSM or cell-phone based, aspects of crowdsourced data quality and reliabilty.","hasChildren":true,"name":"Crowdsourced data collection","selfAssesment":"GI-N2K
"},{"code":"GD2-4","description":"Digitizing as the main secondary spatial data production technique. Encoding vector points, lines, and polygons by tracing map sheets has diminished in importance, but remains a useful technique for incorporating historical geographies and local knowledge. \"Heads-up\" digitizing using digital imagery as a backdrop on-screen is a standard technique for editing and updating GIS databases. Tablet and on-screen digitizing, scanning and (semi)automatic vectorization.","hasChildren":true,"hasParent":true,"name":"Digitizing","selfAssesment":"GI-N2K
"},{"code":"GD2","description":"Spatial data collection / production involves measurement of locations in relation to the coordinate system, and collection of attributed data about the spatial phenomena. Measurements may be direct (e.g. surveying) or remote, data acquisition involves measurement of parameter values, evaluation of parameters, polls, interpretation of spatial imagery, and re-use of secondary data (e.g. old maps). Volunteered geographic information is becoming more important.","hasChildren":true,"hasParent":true,"name":"Data Collection","selfAssesment":"GI-N2K
"},{"code":"GD3","description":"It is quite common, that data including both spatial entities and their attribute data undergo changes. These changes need to be catalogued fully and explicitly, including initial conditions, new conditions, all intermediate stages and operations used. The geospatial data needs to contain an archival history of change.","hasChildren":true,"hasParent":true,"name":"Transaction management of geospatial data","selfAssesment":"In progress (GI-N2K)
"},{"code":"GD4-1","description":"Geometric accuracy, factors influencing it, geometric accuracy and topological fidelity, geometric accuracy in survey and GPS mesurements, thematic accuracy, relations between thematic accuracy, geometric accuracy and topological fidelity, misclassification matrix, commission and omission, logical consistency, relations between resolution, precision, and accuracy, spatial resolution, thematic resolution, and temporal resolution, precision, uncertainties associated with coordinate precision, primary and secondary data sources.\r\n\r\nParticular application. That standard varies from one application to another. In general, however, the key criteria are how much uncertainty is present in a data set and how much is acceptable. Judgments about fitness for use may be more difficult when data are acquired from secondary rather than primary sources. Aspects of data quality include accuracy, resolution, and precision. Concepts of data quality, error, and uncertainty are also covered in Knowledge Areas CF Conceptual Foundations (in a theoretical context) and GC Geocomputation (in the context of analysis); the focus here is on the measurement and assessment of data quality.","hasChildren":true,"hasParent":true,"name":"Data quality","selfAssesment":"GI-N2K
"},{"code":"GD4","description":"Data quality is the degree of data usability in relation to given objective and particular application. The expectations to data vary between different applications. The key criteria in data quality are the amount of uncertainty in data as compared to the acceptable level of uncertainty. Evaluation of the usability may be more complicated using data from secondary sources. Appropriate metadata is inevitable for these judgements. Aspects of data quality include geometric and thematic accuracy, (in)consistencies, resolution, precision, usability and others. Assurance of data quality may be improved by following proper standards and spatial data infrastructure regulations for data collection and management. System of basic data quality measures for geospatial domain in the EN ISO 19157:2013 standard.","hasChildren":true,"hasParent":true,"name":"Data Quality, Metadata and Data Infrastructure","selfAssesment":"In progress (GI-N2K)
"},{"code":"GD6-1","description":"Geometric accuracy is a measure indicating how close the geometric values of the data are to the real world position of the mapped feature.","hasChildren":true,"name":"Geometric accuracy","selfAssesment":"In progress (GI-N2K)
\r\n\r\nIn progress (GI-N2K)
"},{"code":"GD6-3","description":"The resolution of a data source indicates the smallest unit of detail provided by the data source.","hasChildren":true,"name":"Resolution","selfAssesment":"In progress (GI-N2K)
"},{"code":"GD6-4","description":"The precision of a measurement system, related to reproducibility and repeatability, is the degree to which repeated measurements under unchanged conditions show the same results.","hasChildren":true,"name":"Precision","selfAssesment":"GI-N2K
"},{"code":"GD6-5","description":"Primary data sources provide information collected directly for GIS use. Secondary sources are data sources that need to be processed before they are ready for GIS use.","hasChildren":true,"name":"Primary and secondary sources","selfAssesment":"In progress (GI-N2K)
"},{"code":"GD8-1","description":"Tablet digitizing is the conversion from physical map to digital data by re-drawing the features on the map fixed on a digitizing tablet","hasChildren":true,"name":"Tablet digitizing","selfAssesment":"In progress (GI-N2K)
\r\n\r\nIn progress (GI-N2K)
\r\n\r\nIn progress (GI-N2K)
"},{"code":"GN","description":"A Global Navigation Satellite System (GNSS) is a generic term denoting a satellite navigation system that provides continuous positioning over the globe. A GNSS involves a constellation of satellites orbiting Earth, continuously transmitting signals that enable users to determine their three-dimensional (3D) position with global coverage. The design and contents of GNSS concepts and techniques are often focused on their instrumental use in navigation.\r\nGNSS systems include GPS (Global Positioning System), Glonass (GLObal NAvigation Satellite System), Galileo, and Beidou. While the US GPS was historically the only fully operational GNSS for many years, the Russian Glonass was restored to full operation in December 2011, and the Chinese BeiDou and European Galileo systems are under development.\r\nOperational Principle\r\nThe core principle of positioning is solving an elemental geometric problem by finding the distances (ranges) of a user to a set of GNSS satellites with known coordinates. The satellites’ coordinates are calculated from navigation data transmitted by the satellites.\r\nThe basic observable in a GNSS is the time required for a signal (an electromagnetic wave) to travel from the satellite (transmitter) to the receiver. This travel time, when multiplied by the speed of light, provides a measure of the apparent distance, referred to as the pseudorange. To solve for the user's position, at least four satellites are needed to compute the three receiver coordinates and clock offset simultaneously. Using resulting signals and navigation data, user coordinates can be initially computed to an accuracy of several metres, although centimetre-level positioning can be achieved using more advanced techniques.\r\nGNSS Architecture\r\nA GNSS is organized into three primary segments:\r\n1. Space Segment: This segment consists of satellite constellations designed with enough satellites to ensure users can view a minimum of four satellites at any time from any point on Earth’s surface. Its main functions are to generate and transmit code and carrier phase signals, and to store and broadcast the navigation message. The transmissions are regulated by highly stable atomic clocks placed onboard the satellites.\r\n2. Control Segment (Ground Segment): This segment is responsible for the overall proper operation of the GNSS. Its duties include controlling and maintaining the status and configuration of the satellite constellation, predicting ephemeris and satellite clock evolution, maintaining the corresponding GNSS time scale using atomic clocks, and updating the navigation messages.\r\n3. User Segment: This segment comprises the GNSS receivers. The receivers' main function is to receive the GNSS signals, determine pseudoranges and other observables, and solve the navigation equations to provide the user with coordinates, velocity, and precise timing.\r\nGNSS technology, and the associated data processing and analysis, can target high-accuracy positioning goals, requiring accurate modelling of measurements down to the centimetre level or better.","hasChildren":true,"hasParent":true,"name":"GNSS","selfAssesment":" "},{"code":"GN1-1-1-1","description":"GNSS satellites are orbiting spacecrafts carrying precise atomic clocks, navigation payloads, antennas, processors, and communication links. They transmit coded signals that enable GNSS receivers to estimate range, time, velocity, and position.","hasChildren":true,"name":"GNSS Satellites","selfAssesment":" "},{"code":"GN1-1-1-2-1-1","description":"Front ends condition the received GNSS radio-frequency signal before digital processing. They provide filtering, amplification, down-conversion, automatic gain control, and sampling while preserving signal quality, dynamic range, and timing fidelity.","hasChildren":true,"name":"Front-Ends","selfAssesment":" "},{"code":"GN1-1-1-2-1-6","description":"GNSS algorithms compute the navigation solution from the raw measurements. They include acquisition, tracking, decoding, error modelling, estimation, integrity monitoring, ambiguity resolution, sensor fusion, and robustness techniques for challenging environments.","hasChildren":true,"name":"Algorithms","selfAssesment":" "},{"code":"GN1-1-1-2-1-7","description":"A GNSS signal is a radio wave modulated trasmitted by navigation satellites. It typically contains carrier frequency, spreading code, data message, timing, and modulation features used for ranging and information transfer.","hasChildren":true,"name":"GNSS Signal","selfAssesment":" "},{"code":"GN1-1-1-2-1","description":"Receiver components are the hardware (antennas, electronic components...) and software elements that collect, amplify, and demodulate and correlate weak satellite radio signals to derive navigation observables and solutions. They include antennas, radiofrequency front ends, automatic gain control, demodulators, analog-to-digital converters, correlators, processors, firmware, clocks, and interfaces (e.g. power, data).","hasChildren":true,"hasParent":true,"name":"Components","selfAssesment":" "},{"code":"GN1-1-1-2-2-1","description":"Mass-market GNSS receivers are low-cost devices used in phones, wearables, vehicles, and Internet of Things products. They prioritize integration, power efficiency, and availability, with accuracy potentially improved through multi-constellation and augmentation techniques.","hasChildren":true,"name":"Mass Market (low-cost)","selfAssesment":" "},{"code":"GN1-1-1-2-2-2","description":"Professional-grade receivers support higher accuracy and reliability than consumer devices. They customary provide multi-frequency tracking, external antennas, raw measurements, correction services, and rugged interfaces for surveying, mapping, timing, and engineering.","hasChildren":true,"name":"Professional grade","selfAssesment":" "},{"code":"GN1-1-1-2-2-3","description":"Scientific or geodetic receivers are high-performance GNSS instruments designed for precise positioning, timing, and geophysical monitoring. They are equiped with calibrated antennas and even stable clocks, providing multi-frequency observables, and rigorous metadata for long-term analysis.","hasChildren":true,"name":"Scientific (geodetic)","selfAssesment":" "},{"code":"GN1-1-1-2-2","description":"Receiver grade classifies GNSS equipment from consumer devices to professional and scientific geodetic instruments according to cost, accuracy, robustness, observables, antenna quality, processing capability, and intended use.","hasChildren":true,"hasParent":true,"name":"Grade","selfAssesment":" "},{"code":"GN1-1-1-2-3","description":"Simulators and test equipment generate and/or record and/or analyze GNSS signals under controlled conditions. They support receiver validation, vulnerability testing, scenario replay, performance benchmarking, certification, and development of algorithms and hardware.","hasChildren":true,"name":"Simulators and Test Equipment","selfAssesment":" "},{"code":"GN1-1-1-2-4","description":"Vector processing jointly tracks multiple GNSS signals using shared navigation-state estimates rather than independent scalar loops. It can improve sensitivity, dynamics tolerance, robustness, and integration with inertial or aiding sensors.","hasChildren":true,"name":"Vector Processing","selfAssesment":" "},{"code":"GN1-1-1-2","description":"GNSS receivers acquire, track, and decode satellite navigation signals to compute position, velocity, and time. Their block diagram includes antennas, radio-frequency front ends, baseband processing, navigation algorithms, and application (man-machine) interfaces.","hasChildren":true,"hasParent":true,"name":"GNSS Receivers","selfAssesment":" "},{"code":"GN1-1-1","description":"The GNSS space segment comprises the satellites that broadcast navigation signals, clocks, ephemerides, and integrity data. It determines coverage, geometry, signal availability, and the baseline performance achievable by users worldwide.","hasChildren":true,"hasParent":true,"name":"GNSS Space segment","selfAssesment":" "},{"code":"GN1-1-2","description":"The GNSS ground segment monitors satellites, estimates orbits and clocks, uploads navigation data, maintains system time, controls spacecraft health, and ensures continuity, accuracy, and integrity of transmitted navigation services.","hasChildren":true,"name":"GNSS Ground Segment","selfAssesment":" "},{"code":"GN1-1-3","description":"Chinese navigation satellite systems refer primarily to the BeiDou program and its evolution from regional to global capability. They provide positioning, timing, short-message, and augmentation services for civil and governmental users.","hasChildren":true,"name":"Chinese Navigation Satellite Systems","selfAssesment":" "},{"code":"GN1-1-4","description":"Navstar GPS is the United States global satellite navigation system. It provides worldwide positioning, navigation, and timing services through a constellation of medium-Earth-orbit satellites, control stations, and standardized civilian and military signals.","hasChildren":true,"name":"Navstar Global Positioning System","selfAssesment":" "},{"code":"GN1-1-5","description":"The BeiDou Navigation Satellite System (formerly known as Compass) is the China global satellite navigation constellation. It provides positioning, navigation, and timing services worldwide, supporting transportation, communications, disaster response, and scientific applications, comparable to GPS, GLONASS, and Galileo in accuracy and coverage, and civilian and military uses.","hasChildren":true,"name":"BeiDou Navigation Satellite System","selfAssesment":" "},{"code":"GN1-1-6","description":"GLONASS is the Russian Federation’s global navigation satellite system, providing positioning, navigation, and timing services for civil and governmental users. It operates as an independent GNSS constellation and contributes to multi-constellation navigation, improving availability, geometry, and robustness when combined with GPS, Galileo, or BeiDou.","hasChildren":true,"name":"Glonass Navigation Satellite System","selfAssesment":" "},{"code":"GN1-1-7","description":"Galileo is the European Union’s global navigation satellite system, designed to provide independent, high-accuracy positioning, navigation, and timing services. It offers open, commercial, safety-of-life, and governmental services, with strong emphasis on interoperability, integrity, and civilian control.","hasChildren":true,"name":"Galileo Navigation Satellite System","selfAssesment":" "},{"code":"GN1-1","description":"Global systems refer to worldwide satellite navigation constellations that provide continuous positioning, navigation, and timing services across the whole Earth, typically through multiple orbital planes, ground control infrastructure, standardized signals, and compatible (and interoperable) user receivers.","hasChildren":true,"hasParent":true,"name":"Global systems","selfAssesment":" "},{"code":"GN1-2","description":"Regional systems provide satellite navigation or augmentation over a specific geographic area rather than globally. They are optimized for local coverage, availability, integrity, accuracy, or service continuity within the intended region.","hasChildren":true,"name":"Regional Systems","selfAssesment":" "},{"code":"GN1-3","description":"IRNSS, also known as NavIC, is India’s regional navigation satellite system. It provides positioning, navigation, and timing services over India and surrounding areas using geostationary and inclined geosynchronous satellites.","hasChildren":true,"name":"IRNSS","selfAssesment":" "},{"code":"GN1-4","description":"The Quasi-Zenith Satellite System is Japan’s regional satellite navigation system designed to complement GNSS in Asia-Pacific. Its highly inclined orbits improve satellite visibility, especially in urban canyons and mountainous terrain.","hasChildren":true,"name":"Quasi-Zenith Satellite System","selfAssesment":" "},{"code":"GN1","description":"A Satellite Navigation System provides continuous positioning over the globe.\r\nThe system architecture is primarily defined by the Space Segment, which consists of orbiting Satellites. The satellites’ payload generates and transmits the code and carrier phase signals, along with the navigation message. Modernisation efforts continuously enhance these signals; for instance, the GPS third civil signal L2C is a robust signal designed to facilitate Mass Market (low-cost) receivers. Similarly, Glonass satellites are undergoing a GLONASS signal CDMA upgrade for civilian applications on the G3 band.\r\nThe core function relies on sophisticated algorithms to produce navigational outputs. The receiver solves navigation equations to perform Positioning Velocity Time (PVT) computation, delivering the user's coordinates, velocity, and precise timing. The geometric quality of the position solution is quantified using the Dillution of Precision (DOP) factor. Depending on the required precision, receivers are classified into different grade levels, ranging from mass-market devices to professional grade and scientific (geodetic) grade equipment used to achieve high-accuracy positioning, often striving for centimetre-level results or better.","hasChildren":true,"hasParent":true,"name":"Satellite Navigation systems","selfAssesment":" "},{"code":"GN2-1-1","description":"Standard Point Positioning determines position, velocity, and time using broadcast satellite ephemerides and code measurements using a single receiver. It requires no external corrections, but is limited by multipath, atmospheric, orbital and clock errors.","hasChildren":true,"name":"Standard Point Positioning (SPP)","selfAssesment":" "},{"code":"GN2-1-2","description":"Safety-of-life systems provide navigation services where failure may endanger people or critical operations. They focus on integrity, continuity, availability, certified procedures, and timely warnings for aviation, maritime, rail, and emergency applications.","hasChildren":true,"name":"Safety of Life Systems","selfAssesment":" "},{"code":"GN2-1-3","description":"Code-based Differential GNSS improves pseudorange positioning by applying corrections from reference stations at known locations. It reduces common satellite, clock, and atmospheric errors, enabling meter-level or better accuracy.","hasChildren":true,"name":"Differential GNSS (DGNSS): code based","selfAssesment":" "},{"code":"GN2-1-4","description":"Augmentation systems support safety-of-life navigation by broadcasting corrections and integrity information. Satellite-based, ground-based, or regional systems improve accuracy, availability, continuity, and confidence for certified operational users.","hasChildren":true,"name":"Safety of Life Systems: Augmentation Systems","selfAssesment":" "},{"code":"GN2-1","description":"Code-based positioning estimates receiver position from pseudorange measurements derived from GNSS spreading codes. It is robust and widely available, but generally less precise than carrier-phase methods because code measurements are noisier.","hasChildren":true,"hasParent":true,"name":"Code Based Positioning","selfAssesment":" "},{"code":"GN2-10","description":"Low-frequency radio navigation uses long-wavelength terrestrial signals that propagate over large distances and penetrate some obstacles. It can provide resilient timing or coarse positioning when GNSS is unavailable. An example is LORAN (LOng RAnge Navigation), using signals from 90 to 100 kHz.","hasChildren":true,"name":"Navigation with Low-Frequency Radio Signals","selfAssesment":" "},{"code":"GN2-11","description":"Inertial navigation sensors measure acceleration and angular rate using accelerometers and gyroscopes. Integrated over time, they provide relative motion, attitude, and short-term navigation independent of external radio signals.","hasChildren":true,"name":"Inertial Navigation Sensors","selfAssesment":" "},{"code":"GN2-12","description":"GNSS-INS integration combines satellite measurements with inertial navigation outputs to exploit GNSS long-term accuracy and INS short-term continuity to deliver robust navigation through outages, dynamics, and multipath.","hasChildren":true,"name":"GNSS-INS Integration","selfAssesment":" "},{"code":"GN2-2-1","description":"Precise Point Positioning estimates accurate positions using a single receiver together with precise satellite orbit, clock, and bias products. It does not require a nearby reference station but usually requires a longer convergence time that for standard positioning.","hasChildren":true,"name":"Precise Point Positioning (PPP)","selfAssesment":" "},{"code":"GN2-2-2","description":"Carrier-phase integer ambiguity resolution determines the unknown number of carrier wavelengths between the satellite and the receiver antennae. Correct ambiguity fixing is essential for reliable GNSS positioning with centimeter-level accuracy.","hasChildren":true,"name":"Carrier Phase Integer Ambiguity Resolution","selfAssesment":" "},{"code":"GN2-2-3","description":"Carrier-based Differential GNSS uses carrier-phase observations from reference and rover receivers to cancel common errors. Techniques such as Real Time Kinematics (RTK) can deliver centimeter-level positioning when ambiguities are correctly resolved.","hasChildren":true,"name":"Differential GNSS (DGNSS): carrier based","selfAssesment":" "},{"code":"GN2-2","description":"Carrier-phase positioning uses the phase of the GNSS carrier wave as a precise range observable. It enables centimeter-level accuracy, but requires ambiguity resolution, cycle-slip detection, and careful error modelling.","hasChildren":true,"hasParent":true,"name":"Carrier Phase Based Positioning","selfAssesment":" "},{"code":"GN2-3","description":"Integration of GNSS with Inertial Navigation Systems (INS) combines satellite navigation with inertial sensors to provide continuous position, velocity, and attitude. It improves robustness during outages, high dynamics, interference, or environments with degraded satellite visibility.","hasChildren":true,"name":"GNSS/INS Integration","selfAssesment":" "},{"code":"GN2-4","description":"Assisted GNSS uses external information, such as approximate time, position, ephemerides, or network data, to accelerate acquisition, improve sensitivity, reduce power consumption, and support positioning under weak-signal conditions.","hasChildren":true,"name":"Assisted GNSS","selfAssesment":" "},{"code":"GN2-5","description":"Nonlinear recursive estimation methods, such as extended, unscented, and particle filters, estimate navigation states from noisy measurements over time. They are central to GNSS, INS, and multi-sensor integration.","hasChildren":true,"name":"Nonlinear Recursive Estimation for Integrated Navigation Systems","selfAssesment":" "},{"code":"GN2-6","description":"Indoor navigation techniques provide positioning where GNSS signals are unavailable or degraded. They may use inertial sensors, Wi-Fi, Bluetooth, ultra-wideband, visual features, magnetic fields, maps, or signals of opportunity.","hasChildren":true,"name":"Overview of Indoor Navigation Techniques","selfAssesment":" "},{"code":"GN2-7","description":"Navigation with cellular signals of opportunity exploits existing mobile-network transmissions for positioning. Measurements such as timing, frequency, received power, or identifiers can support navigation when GNSS is weak or unavailable.","hasChildren":true,"name":"Navigation with Cellular Signals of Opportunity","selfAssesment":" "},{"code":"GN2-8","description":"Dedicated metropolitan beacon systems provide terrestrial radio signals designed for precise urban positioning and timing. They can improve availability and resilience in dense cities where satellite signals suffer blockage and multipath.","hasChildren":true,"name":"Position, Navigation and Timing with Dedicated Metropolitan Beacon Systems","selfAssesment":" "},{"code":"GN2-9","description":"Terrestrial digital broadcasting signals can be used as signals of opportunity for navigation. Their high power, known transmitters position, and broad coverage may support timing or ranging in GNSS-challenged environments.","hasChildren":true,"name":"Navigation with Terrestrial Digital Broadcasting Signals","selfAssesment":" "},{"code":"GN2","description":"GNSS positioning techniques are the methods used to determine a user's three-dimensional (3D) position, velocity, and time by processing signals transmitted by orbiting satellites. The fundamental principle involves measuring the apparent distance (pseudorange) to at least four satellites to solve for the three receiver coordinates and the receiver clock offset.\r\nPositioning methods are generally categorized based on the measurements they use and their resulting accuracy:\r\n1. Standard Point Positioning (SPP)\r\nSPP is a code-based positioning technique that uses pseudorange measurements (R).\r\n• Accuracy and Modeling: SPP typically achieves coordinate accuracy of several metres. To achieve this, errors such as satellite clock offsets, atmospheric delays (tropospheric and ionospheric), and instrumental delays are modeled. For single-frequency users, the broadcast Klobuchar ionospheric model is often used.\r\n• Process: The technique solves a nonlinear system by iteratively linearizing the geometric range around an approximate position and solving the resulting navigation equations (linear system y=Gx) using parameter estimation techniques such as least squares or Kalman filtering.\r\n2. Precise Point Positioning (PPP)\r\nPPP is an advanced technique that utilizes both code and carrier phase measurements (R and Φ) to target high-accuracy positioning.\r\n• Accuracy and Modeling: PPP aims for centimetre-level accuracy in static positioning or decimetre-level (or better) in kinematic positioning. This requires accurate measurement modelling of all delay terms (including Earth deformation and antenna biases).\r\n• Key Requirements: PPP typically uses the ionosphere-free combination of dual-frequency signals to eliminate most ionospheric refraction. It relies on precise satellite orbits and clocks (such as those from IGS) rather than broadcast ephemerides. The receiver estimates the carrier phase ambiguities as real numbers (floating ambiguities).\r\n3. Advanced Techniques (Differential and Fast PPP)\r\nOther techniques build upon PPP principles to improve speed and accuracy:\r\n• Differential Positioning (RTK/WARTK): These methods, such as Real-Time Kinematics (RTK) and Wide-Area Real-Time Kinematics (WARTK), rely on fixing the carrier phase ambiguities to their integer values. They often use double-differenced measurements between pairs of satellites and receivers to cancel out common errors, enabling centimetre-level positioning.\r\n• Fast Precise Point Positioning (F-PPP): This approach uses accurate external products, such as precise ionospheric corrections computed from a wide-area network, to accelerate the filter convergence time from approximately one hour to a few minutes, achieving better than 10 cm accuracy quickly.\r\nIn essence, positioning techniques range from simple, real-time code solutions (SPP) suitable for standard navigation, to complex carrier-based solutions (PPP) using precise external products for geodetic accuracy.","hasChildren":true,"hasParent":true,"name":"GNSS Positioning Techniques","selfAssesment":" "},{"code":"GS","description":"Geographic Information Science and Technology serve the society, but it is not a panacea. The history of its development is the sum of fragmented efforts, which have still not been fully integrated. Its potential benefits are often constrained and its potential impacts are not fully understood. Institutional and economic factors limit access to data, technology, and expertise by some of those who need it to make better decisions. Political, ideological, and personal issues aside, organizations invest in GIS&T when estimated benefits outweigh estimated costs. Evaluating costs and benefits is difficult, however and too often leads to nothing being done. For some individuals and groups, costs are prohibitive even though potential benefits are compelling. The legal framework provides a structure for regulating a number of key aspects of geographic information science, technology, and applications. Legal regimes determine who can claim the exclusive right to hold and use geospatial data, the conditions under which others may have access to the data, and what subsequent uses are permitted. Political struggles arise from conflicting proprietary and public interests about who benefits from geospatial information, and how the power to allocate the use of this information is, or should be, distributed among members of a society. The need to choose among conflicting interests sometimes poses ethical dilemmas for GIS&T professionals. The explosive growth of the geospatial information contributed by users through various application programming interfaces has made geospatial information is a powerful tool in the social media toola powerful media for the general public to communicate, but perhaps more importantly, geographic information have also become a tool media for constructive dialogs and interactions about social issues, recent growth of Web-based geospatial information and volunteered geographic information (VGI). Because so many public agencies and private organizations rely upon GIS&T for planning, decision making, and management, GIS&T increasingly affects and is used to direct daily life. Critical approaches to understanding the role of GIS in society equip practitioners to employ GIS&T reflectively. The critical approach specifically questions the assumptions and premises that underlie the economic, legal and political regimes and institutional structures within which GIS&T is implemented. Related concerns are considered in Knowledge Area OI: Organizational and Institutional Aspects.","hasChildren":true,"hasParent":true,"name":"GI and Society","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS1-1","description":"The most basic definition of a legal regime is a system or framework of rules governing some physical territory or discrete realm of action that is at least in principle rooted in some sort of law. Often the concept has been applied to specific areas of law.","hasChildren":true,"name":"The legal regime and legal framework","selfAssesment":"GI-N2K
"},{"code":"GS1-2","description":"Contract law is defined as a set of rules that govern the contractual agreements between merchants or persons. A contract is an agreement between different parties that state their responsibilities and duties to each other. A liability in contract law is when certain conditions are written into a contract that makes a party liable. Licensing is the process of giving or getting official permission to do something. A license is an agreement through which a licensee leases the rights to a legally protected piece of intellectual property from a licensor — the entity which owns or represents the property — for use in conjunction with a product or service.","hasChildren":true,"name":"Contract law, liability and licensing","selfAssesment":"GI-N2K: relevant but to be revised
"},{"code":"GS1-3","description":"Data privacy and security are two essential components of a successful strategy for data protection. Data security refers to the protection of data from unauthorized access, use, change, disclosure, and destruction. It encompasses network security, physical security, and file security. Data privacy involves protecting consumer data by eliminating or reducing the possibility of re-identifying an individual whose information is present in the data. This is done by either removing specific information or by transforming the data with random “noise” or generalization.","hasChildren":true,"name":"Privacy and Security","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS1-4","description":"Property is secured by laws that are clearly defined and enforced by the state. These laws define ownership and any associated benefits that come with holding the property. The term property is very expansive, though the legal protection for certain kinds of property varies between jurisdictions. Property is generally owned by individuals or a small group of people. The rights of property ownership can be extended by using patents and copyrights. Property rights give the owner or right holder the ability to do with the property what they choose. That includes holding on to it, selling or renting it out for profit, or transferring it to another party.","hasChildren":true,"name":"Ownership and property rights","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS1-5","description":"In economics, competition is a condition where different economic firms seek to obtain a share of a limited good by varying the elements of the marketing mix: price, product, promotion and place. Competition law is a law that promotes or seeks to maintain market competition by regulating anti-competitive conduct by companies. Public-private sector relationships deal with a particular subset of competition, i.e. competition between public and private organizations.","hasChildren":true,"name":"Competition and public-private sector relationships","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS1-6","description":"Open data is data that can be accessed, shared, used and reused without any barrier for any type of (re)user. According to the Open Definition, open data can be defined as data that be freely used, modified, and shared by anyone for any purpose subject, at most, to measures that preserve provenance and openness. Open data requires datasets to be either in the public domain, or distributed through an open license. The data must be provided as a whole, free of charge, and preferably downloadable via the Internet, including any additional information that might be necessary to comply with the open license’s terms. Openness requires the data to be provided in a readily machine-readable form. The format must be open as well, meaning that it does not place any restriction upon its use, and that the files in that format can be processed with open-source software tools. The Open Definition speaks broadly of open ‘works’, rather than of open data. Focusing on data tout court, one can move from the Open Government Data (OGD) principles. According to the OGD principles, which are arguably foundational in understanding the concept of open data, data must be: Complete; Primary; Timely; Accessible; Machine-processable; Non-discriminatory; Non-proprietary; and License-free. Compliance with the OGD principles needs to be demonstrable, i.e. there need to be accountability measures in place to allow the review of the adherence to the principles above. The concepts of Open Work and open data highlight how data needs to be both legally, technically and financially open, so either in the public domain or covered by an open license, and kept in a machine-readable and non-proprietary format. Open data aims at making information available to everybody, for any purpose, in a machine-readable and interoperable format, based on open standards and digestible by free/libre open source software (FLOSS). Also with respect to the financial accessibility open data is data available free of charge. Marginal costs of dissemination are accepted by some as a reasonable cost for users. However, open data is data that can be accessed and reused without any barrier for any type of reuse, and some user groups experience any price to be paid as a barrier.","hasChildren":true,"name":"Open data","selfAssesment":"Completed
"},{"code":"GS1","description":"Legal problems can arise when geospatial information is used for land management, among other activities. Geospatial professionals may be liable for harm that results from flawed data or the misuse of data. Understanding of contract law and liability standards is essential to mitigate risks associated with the provision of geospatial information products and services. Legal relations between public and private organizations and individuals govern data access. The nature of information in general, and the characteristics of geospatial information in particular, make it an unusual and difficult subject for a legal regime that seeks to establish and enforce the type of exclusive control associated with other commodities. Geospatial information is in many ways unlike the kinds of works that intellectual property rights were intended to protect. Still, organizations can, and do, assert proprietary interests in geospatial information. Perspectives on geospatial information as property vary between the public and private sectors and between different countries.","hasChildren":true,"hasParent":true,"name":"Legal aspects","selfAssesment":"In progress GI-N2K
"},{"code":"GS2-1","description":"Business models determine how organizations can create and deliver value, for example, through the provision or use of geographic data. A business model is a conceptual tool that contains\r\na set of interrelated elements that allow organizations to create and capture value and generate revenues. The development and implementation of an appropriate business model are considered to be a key to the success of the organization and a crucial source for value creation. \r\n\r\nAlthough business models determine how organizations create, deliver, and capture value, they should not be regarded as permanent and invariable structures or settings. Business models are shaped by both internal and external forces, and will only be successful if they are able to adapt to a changing environment. In the GI domain, several technological, regulatory, and societal developments have challenged the existing business models and opened up opportunities for new business models. Among these developments are the establishment of spatial data infrastructures (SDIs) worldwide, the democratization of geographic knowledge, and the move toward open source, open standards, and open data.\r\n\r\nSince the development and implementation of SDIs in different parts of the world, much attention has been paid to the need to find appropriate business models for GI, and in particular, for geographic data providers in the public sector. Traditional business models in which public data providers were selling their data to customers in the private industry and other public agencies were questioned, because they restricted the opportunity for data sharing. The concept of SDI is about moving to new business models, where partnerships between GI organizations are promoted to allow access to a much wider scope of geographic data and services. A key challenge in the development of these SDIs was the alignment of different existing business models of the actors in the GI domain. Moreover, the development and implementation of SDIs also led to the emergence of new business models, which was even more the case with the more recent move toward open geographic data.\r\n\r\nOrganizations can be active in different parts of the geo-information value chain, and can create and offer value in many different ways. As a result, many different GI business models exist. Data providers, data enablers, and data end users could be seen as three main categories of GI business models. Each of these categories consists of many different business models, as different value propositions\r\nwill exist, and value can be created and captured in several ways.","hasChildren":true,"name":"GI Business models","selfAssesment":"Completed (GI-N2K)
"},{"code":"GS2-5","description":"To provide a better insight into the process of adding value to GI, several authors have introduced and applied the information value chain approach. A value chain can be defined as the set of value-adding activities that one or more organizations perform in creating and distributing goods and services. The value chain concept originally was developed for the manufacturing sector, as a tool to evaluate the competitive advantage of firms. More recently, the value chain concept has been applied to other sectors, including information technology where the good or service, and the benefits it provides, is less tangible in nature. A value chain involves the progress of goods from raw materials to finished products through a number of stages, during each of which a new value is added to the original input by various activities. The value chain concept was extended into the information market, with the information value chain referring to the set of activities adding value to information and turning raw data into new information products or services. Especially important in this context is the role of information and communication technologies (ICT), which have an impact on all activities in the information value chain, such as information collection, processing, dissemination, and use. In the context of GI, the value chain relates to the series of value- adding activities to transform raw geographic data into new products that are used by certain end users. Although there are slightly different descriptions of the various steps of the GI value chain, in general, the essential steps in the value chain are: acquisition of raw data, the application of a data model, quality control, and integration with other sources, presentation, and distribution. In recent years, particular attention has been paid to different steps between the process of distributing data and the actual end use of an end product of GI. In addition, after the publication of the data, value can be added to the data in many different ways. Value can be added by making data from different sources easily accessible through repositories and data portals, by building and selling tailored solutions using the data to end users or by using geographic data to improve existing products and services delivered to an end user. In certain cases, this end product will be the first step of a next value chain.","hasChildren":true,"name":"Geo-information value chain","selfAssesment":"Completed
"},{"code":"GS2","description":"Most organizations insist that investments in GIS and T be justified in economic terms. Quantifying the value of information, and of information systems, however, is not a straightforward matter.","hasChildren":true,"hasParent":true,"name":"Economic aspects","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS3-1","description":"The use of geospatial information allows public sector organizations and actors to make better decisions and provide better services to their citizens. Geospatial information is increasingly being used at different administrative levels and in different policy areas.","hasChildren":true,"name":"Use of geospatial information in the public sector","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS3-2","description":"Geospatial information is increasingly being used by private companies for different purposes and the private sector plays an important role in the development and implementation of geospatial information infrastructures.","hasChildren":true,"name":"Use of geospatial information in the private sector","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS3-3","description":"Research and education institutions use geospatial information for various purposes, in support of their research and educational activities.","hasChildren":true,"name":"Use of geospatial information in research and education","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS3-4","description":"Effective monitoring of the environment and an improved understanding of the same requires valuable information and data that can be extracted through application of geospatial technologies. GIS can be used most effectively for environmental data analysis and planning. It allows better viewing and understanding physical features and the relationships that influence in a given critical environmental condition. GIS can help in effective planning and managing the environmental hazards and risks. In order to plan and monitor the environmental problems, the assessment of hazards and risks becomes the foundation for planning decisions and for mitigation activities. GIS supports activities in environmental assessment, monitoring, and mitigation and can also be used for generating environmental models. GIS can aid in hazard mitigation and future planning, air pollution & control, disaster management, forest fires management, managing natural resources, wastewater management, oil spills and its remedial actions etc.","hasChildren":true,"name":"Use of geospatial information in environmental issues","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS3","description":"Geospatial Information used in Government agencies and public authorities at local, state, and federal levels produce and use geospatial data for many activities, including provision of social services, public safety, economic development, environmental management, and national defence. Public participation in governing, empowered by geospatial technologies, offers the potential to strengthen democratic societies by involving grassroots community organizations and by engaging local knowledge. The private sector covers a broad range of areas of opportunity. With continued advancements in technology, greater awareness of its advantages as a powerful decision support tool the use of geospatial information use in the private sector needs to be discussed.","hasChildren":true,"hasParent":true,"name":"Use of geospatial information","selfAssesment":"In Progress GI-N2K
"},{"code":"GS4-1","description":"Public participation GIS (PPGIS) is a field within geographic information science that focuses on ways the public uses various forms of geospatial technologies to participate in public processes, such as mapping and decision making.","hasChildren":true,"name":"Public participation GIS","selfAssesment":"GI-N2K (revision)
"},{"code":"GS4-2b","description":"Social Media Geographic Information (SMGI) can be defined as any piece or collection of multimedia data or information with explicit (i.e. coordinates) or implicit (i.e. place names or toponyms) geographic reference collected through the social networking web or mobile applications. Social data are acknowledged as a good of major value in the digital economy, and their potential for enhancing more traditional analytics is of the utmost importance. A big part of social data however also features spatial (and temporal) references, thus their integration with more traditional Authoritative Geographic Information (AGI) may enable a further step towards the next generation of geospatial intelligence. SMGI is a sub-category of VGI and can be active or passive, depending on the type of application with which it is collected: applications purposefully created and/or used to collect SMGI in participatory initiatives","hasChildren":true,"name":"Social Media Geographic Information","selfAssesment":"Completed
"},{"code":"GS4-3b","description":"Volunteered geographic information (VGI) is a special kind of user-generated content. It refers to geographic information collected and shared voluntarily by the general public. Web.2.0 and associated advances in web mapping technologies have greatly enhanced the abilities to collect, share and interact with geographic information online, leading to VGI.","hasChildren":true,"name":"Citizens and volunteered geographic information","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS4","description":"Today, geo data has become a conventional and pervasively familiar data type seen at once to underpin and significantly re-characterize the digital world, with broad implications for both technology and society. Geospatial data are abundant, but access to data varies with the nature of the data, the user groups wishes to acquire it and for what purpose, under what conditions, and at what price geodata can be obtained. The explosive growth of geographic information contributed by users through various application programming interfaces has made geographic information a powerful media for the general public, but perhaps more importantly, geospatial information have also become media for constructive dialogs and interactions about social issues, recent growth of Web-based Geographic information and volunteered geographic information (VGI).","hasChildren":true,"hasParent":true,"name":"Geospatial citizenship","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS5-1b","description":"The advantages of geospatial technologies and resulting data present ethical dilemmas such as privacy and security concerns as well as the potential for stigma and discrimination resulting from being associated with particular locations. the use of geospatial technologies and the resulting data needs to be critically assessed through an ethical lens prior to implementation of programmes, analyses or partnerships. Using this lens requires not only explicit consideration of potential negative consequences of adoption but also clear articulation of the specific contexts and conditions under which benefits may be realized.","hasChildren":true,"name":"Ethics in the geospatial information society","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS5-2b","description":"A code of ethics is a guide of principles designed to help professionals conduct business honestly and with integrity. A code of ethics document may outline the mission and values of the business or organization, how professionals are supposed to approach problems, the ethical principles based on the organization's core values, and the standards to which the professional is held. Codes of ethics for geospatial professionals are intended to provide these principles and guidelines for GIS professionals","hasChildren":true,"name":"Codes of ethics for geospatial professionals","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS5","description":"Ethics provide frameworks that help individuals and organizations make decisions when confronted with choices that have moral implications. Most professional organizations develop codes of ethics to help their members do the right thing, preserve their good reputation in the community, and help their members develop as a community","hasChildren":true,"hasParent":true,"name":"Ethical aspects","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS6-1","description":"US GIS&T BoK: As GIS became a firmly established presence in geography and catalysed the emergence of GIScience, it became the target of a series of critiques regarding modes of knowledge production that were perceived as problematic. The first wave of critiques charged GIS with resuscitating logical positivism and its erroneous treatment of social phenomena as indistinguishable from natural/physical phenomena. The second wave of critiques objected to GIS on the basis that it was a representational technology. In the third wave of critiques, rather than objecting to GIS simply because it represented, scholars engaged with the ways in which GIS represents natural and social phenomena, pointing to the masculinist and heteronormative modes of knowledge production that are bound up in some, but not all, uses and applications of geographic information technologies. In response to these critiques, GIScience scholars and theorists positioned GIS as a critically realist technology by virtue of its commitment to the contingency of representation and its non-universal claims to knowledge production in geography. Contemporary engagements of GIS epistemologies emphasize the epistemological flexibility of geospatial technologies.","hasChildren":true,"name":"Epistemological and critical issues","selfAssesment":"In progress/to delete (GI-N2K)
"},{"code":"GS6-2","description":"Various types of critiques exist on the way geospatial information is being used and re-used.","hasChildren":true,"name":"Critical approach on the use of geospatial information","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS6-3","description":"Defending or refuting the argument that the \"digital divide\" that characterizes access use of geospatial information perpetuates inequities among developed and developing nations, among socio-economic groups,and between individuals, community organizations, and public agencies and private firms.","hasChildren":true,"name":"Critical aspects and invisible groups","selfAssesment":"In progress/to be delete (GI-N2K)
"},{"code":"GS6","description":"Many of the educational objectives used to define topics in this knowledge area, and in the Body of Knowledge as a whole, challenge educators and students to think critically about GI and Society. Since the 1990s, scholars have criticized cartography and the GIS science from a wide range of perspectives. Common among these critiques are questioned assumptions about the purported benefits of GI and Society and attention to its unexamined risks. By promoting reflective practice among current and aspiring geospatial information professionals, an understanding of the range of critical perspectives increases the likelihood that geospatial information will fulfil its potential to benefit all stakeholders. Philosophical, psychological, and social underpinnings of these critiques are considered in Knowledge Area CF: Conceptual Foundations.","hasChildren":true,"hasParent":true,"name":"Critical approach","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS7-1","description":"US GIS&T BoK: As GIS became a firmly established presence in geography and catalysed the emergence of GIScience, it became the target of a series of critiques regarding modes of knowledge production that were perceived as problematic. The first wave of critiques charged GIS with resuscitating logical positivism and its erroneous treatment of social phenomena as indistinguishable from natural/physical phenomena. The second wave of critiques objected to GIS on the basis that it was a representational technology. In the third wave of critiques, rather than objecting to GIS simply because it represented, scholars engaged with the ways in which GIS represents natural and social phenomena, pointing to the masculinist and heteronormative modes of knowledge production that are bound up in some, but not all, uses and applications of geographic information technologies. In response to these critiques, GIScience scholars and theorists positioned GIS as a critically realist technology by virtue of its commitment to the contingency of representation and its non-universal claims to knowledge production in geography. Contemporary engagements of GIS epistemologies emphasize the epistemological flexibility of geospatial technologies.","hasChildren":true,"name":"Epistemological critiques","selfAssesment":"GI-N2K
"},{"code":"GS7-3","description":"US GIS&T BoK: \r\n\r\nFeminist interactions with GIS started in the 1990s in the form of strong critiques against GIS inspired by feminist and postpositivist theories. Those critiques mainly highlighted a supposed epistemological dissonance between GIS and feminist scholarship. GIS was accused of being shaped by positivist and masculinist epistemologies, especially due to its emphasis on vision as the principal way of knowing. In addition, feminist critiques claimed that GIS was largely incompatible with positionality and reflexivity, two core concepts of feminist theory. Feminist critiques of GIS also discussed power issues embedded in GIS practices, including the predominance of men in the early days of the GIS industry and the development of GIS practices for the military and surveillance purposes.\r\n\r\nAt the beginning of the 21st century, feminist geographers reexamined those critiques and argued against an inherent epistemological incompatibility between GIS methods and feminist scholarship. They advocated for a reappropriation of GIS by feminist scholars in the form of critical feminist GIS practices. The critical GIS perspective promotes an unorthodox, reconstructed, and emancipatory set of GIS practices by critiquing dominant approaches of knowledge production, implementing GIS in critically informed progressive social research, and developing postpositivist techniques of GIS. Inspired by those debates, feminist scholars did reclaim GIS and effectively developed feminist GIS practices.","hasChildren":true,"name":"Feminist critiques","selfAssesment":"GI-N2K
"},{"code":"GS7-4","description":"In the early 1990s social critiques of GIS from human geographers began to appear. These initial critiques set off an ensuing debate between GISers, defending GIS and human geographers, who critiqued GIS. This debate materialized in academic journals including: Political Geography Quarterly, Environment and Planning A, and Progress in Human Geography. Schuurman (2000) notes that the GIS debate, while unique to the discipline of Geography, was part of a larger debate in other disciplines about the effects of technology. This presentation will be limited (unfortunately) to two aspects of this debate. It will first discuss conditions within human geography that made GIS a target of human geographers' critique. Second, this paper will discuss the particular critiques that were directed at GIS by human geographers. Though the reaction of such critiques and their effect on GIS is an important topic there is not enough time and space to address these issues. See Schuurman (2000) \"Trouble in the Heartland: GIS and its critics in the 1990s\" in Progress in Human Geography for a thoughtful look at this debate and its effects on the discipline of GIS.","hasChildren":true,"name":"Social critiques","selfAssesment":"GI-N2K
"},{"code":"IP","description":"Image processing and analysis comprises all relevant steps to reach from (raw) image data to [...] information via image interpretation and digital image classification. In traditional remote sensing workflows, this step follows the image acquisition process. There are two main components, i.e. (1) image processing, (2) analysis, which emphasizes the sequential nature of the process – while increasingly this dichotomy disappears.\r\nThe information production workflow aims at converting semantically rich, but unstructured image data into a set of classes, objects, arrangements, etc., to enable ultimately a complete image understanding and scene reconstruction. This scene reconstruction entails a mental component (“understanding”) and a technical one, by providing standardized classification results or even beyond, dedicated information products in form of digital maps and reports, tailored to the specific application domains and use cases, in order to make informed decisions. Such information products can be maps, reports, dashboards etc., overall it is the transformation from quantitative, semi-continuous digital numbers (“brightness”) to qualitative information using categories and figures, which can be stored and further used in a GIS environment. \r\nThe first part of the process entails image calibration, image correction (geometric, radiometric), data assimilation, and any type of enhancement (contrast manipulation, filtering, etc.) which aims to better condition the information extraction part. It ends where we achieve a significant milestone in the processing milestone, remarkably denoted as analysis-ready data (ARD). From there, we enter into the analysis realm, classically referred to as digital image classification, the process of assigning pixels to classes. In other words, the aggregation of pixel values according to their similarity into categorical (nominal) classes. The discrimination of these classes by and large depend on application domain, and ideally, these classes match with information classes. To address the issue of ambiguity and to overcome the so-called semantic gap in image interpretation by providing a stepping-stone in the information extraction process, the strategy of pre-classification (semi-concepts) has been introduced in the literature.\r\nToday, boundaries between pre-processing and classification increasingly vanish, through an increasing level of automation in the pre-processing and image correction steps. In addition, new ways of analysis emerge, in particular in large time series, including image data cubes. Instead of a processing chain, which suggests a linear – and potentially irreversible – cascade of manipulations, the automation of large parts of this part allows us to see the process more reversible and approachable from either side.","hasChildren":true,"hasParent":true,"name":"Image processing and analysis","selfAssesment":"Completed
"},{"code":"IP1-1-1","description":"The image spatial subset allows to extract the group of pixels / grid cells using a defined polygon e.g. area of interest – AOI or defining the new image extent. It is used to limit spatially the image extent to which, for example an image function or classification model will be applied.","hasChildren":true,"name":"Image subset","selfAssesment":"Completed
"},{"code":"IP1-1-2","description":"Layer stacking is a process for combining multiple images into a single image. The image stack is used to build a ‘new’ multiple band file from the georeferenced images of various pixel sizes, extents, projections. The image bands must be resampled and reprojected to a common spatial grid. The layer stacking is used for example to combine spectral bands from a Landsat, Sentinel-2 data and SRTM DEM into one multi-dimensional file. The process of layer stacking increases the size of the final stacked image, which may have consequences that increase the processing time of operations performed on the stacked image.","hasChildren":true,"name":"Layer stack","selfAssesment":"Completed
"},{"code":"IP1-1","description":"Data manipulation adjusts a dataset to the needs of a specific application by subsetting the spatial extent or the number of bands or by organizing bands from separate single layer files into a single multi-layer file.","hasChildren":true,"hasParent":true,"name":"Data manipulation","selfAssesment":"New
"},{"code":"IP1-2","description":"Fourier analysis - A characteristic of remotely sensed images is a parameter called spatial frequency, defined as the number of changes in brightness value per unit distance for any particular part of an image. There are low-frequency and high-frequency areas. Spatial frequency may be enhanced or subdued using Fourier Analysis (an alternative technique is spatial convolution filtering). Fourier analysis mathematically separates an image into its spatial frequency components. It is then possible interactively to emphasize certain groups (or bands) of frequencies relative to others and recombine the spatial frequencies to produce an enhanced image.\r\nThe signal received by a pulsed radar is a time sequence of pulses for which the amplitude and phase are measured. The frequency content of this time-domain signal is obtained by taking its Fourier transformation.","hasChildren":true,"name":"Fourier transformation","selfAssesment":"New
"},{"code":"IP1-3-1-1","description":"Structure from motion (SfM) describes the photogrammetric process for estimating the 3D structure of a scene, whereby correspondences between multiple images are established and used to detect motion parallax. When a camera moves over a surface while taking successive overlapping images, the distances between features on the surface will change from one image to the next. The changes depend on the distance of the feature points to the camera, and thus the surface elevation. This motion parallax can be used to generate an accurate 3D representation of the surface. \r\nThe photogrammetric problem of SfM is similar to stereo vision, but has gained popularity with the advent of inexpensive cameras which have variable internal geometries, unlike metrically stabilized cameras traditionally used in airborne mapping. Even with less accurate or even missing GPS location and orientation metadata, SfM still allows for the creation of (hyper)local DEMs as long as the imagery contains sufficient overlap. Airborne or spaceborne platforms can be used, provided that 2D frame-based cameras are used which can be represented with a pinhole mathematical model. \r\nGenerating a digital elevation model (DEM) from SfM is typically handled automatically using specialized software. Firstly, image correspondences are detected. Feature points are identified in the individual images using local contrast feature detectors. The features extracted from all the images are matched with all the available overlapping images and erroneous matches are filtered out. The process typically results in hundreds or thousands of tie-points per image, which allows for robust matching even with large a priori uncertainties in camera orientation. A bundle adjustment, solving for the 3D coordinates of the feature points, the position and orientation of the camera and its internal characteristics then results in an initial, so-called sparse 3D point cloud. \r\nNext, ground control points (GCPs) can be introduced. These are surface features (naturally present or introduced into the scene) which can be identified at the pixel level in the images by users. Measured also in the field with an accuracy smaller than the pixel size, they can be used to constrain the bundle adjustment solution to improve georeferencing and camera calibration to an accuracy similar to that of the GCP measurement or the GSD size. \r\nSince this process yields a match only for a small subset of all pixels, an additional step, called dense image matching is added. It starts from the exact position and orientations resulting from the bundle adjustment to rectify the images and overlay two or more images, to compare them row by row and in 16 different directions in a process called semi-global matching (SGM). Matching pixels are identified along these lines, and 3D intersection distances photogrammetrically inferred. By combining results from different directions, a 3D coordinate for almost every pixel is obtained with similar accuracy. Finally, DEM products with a regularly spaced grid are generated and exported based on the dense point cloud. Depending on the point classes used in the export (obtained through topographic filtering or deep-learning-based classification of the dense point cloud), the outcome will be a digital surface model (DSM) or digital terrain model (DTM).","hasChildren":true,"name":"DEM generation with 'Structure-from-Motion'","selfAssesment":"Completed
"},{"code":"IP1-3-1-2","description":"Photogrammetry is the science and technology of obtaining spatial measurements and other geometrically reliable derived products from photographs. Basic geometric principles applying both traditional analogue and modern digital procedures are related to the central projection of the image in case of typical cameras and to the dynamic projection mostly in case of push-broom sensors, popular in the satellite photogrammetry. The fundamental principle used by photogrammetry is called triangulation. By taking photographs from at least two different locations, so-called “lines of sight” can be developed from each camera to points in a block on the object. These lines of sight (called rays) are mathematically intersected to produce the 3-dimensional coordinates of the points of interest.\r\nWithin data processing the most important parts of photogrammetric workflow are: (1) image orientation, (2) model reconstruction, and (3) orthorectification. Image orientation is based mostly on aerial triangulation, however recently the computer vision algorithm, called structure from motion, became more popular in particularly in close range photogrammetry. Both orientation approaches include detection or measurement of the points between overlapping images in a block, control points measurements in a field defining orientation in reference system and check points verifying the orientation process. The satellite photogrammetry due to different projection and much bigger areas of imaging is usually related to Rational Polynomial Coefficients (RPCs) defining preliminary scene orientation during image orientation. However, to receive more accurate results also here the control points measured in a field are in use. The second part of the modern photogrammetric processing is 3D model reconstruction. In past, vectorization within the stereoscopic measurements was the most popular way of using photogrammetric data after the image orientation. The development of the informatics contributed to the development of the image matching algorithms that can provide dense image point clouds, which can be used to the 3D detailed modelling including digital elevation model production. The final step of photogrammetric processing is orthorectification, which delivers cartometric image called orthophoto mosaiced into orthophotomaps. This process comprises the influence of digital terrain model, model of camera (interior orientation) and image orientation (exterior orientation). Orthophotomap and elevation models derived from photogrammetric processing are applied as very popular data source in many GIS systems. The other photogrammetric outcomes are, for example a 3D measurement or 3D models of some real-world object or scene.","hasChildren":true,"name":"Photogrammetric principles","selfAssesment":"Completed
"},{"code":"IP1-3-1-3","description":"In satellite photogrammetry to obtain the orientation mostly of satellite scene Rational Polynomial Coefficients (RPCs) are applied. They provide a compact representation of a ground-to-image geometry, that allow for photogrammetric processing without requiring a physical camera model. Model with RPC is provided with satellite image and can be improved using measurements of indirect surveying methods used for control point measurement. The RPC model for the coordinates of the image point is calculated as ratios of the cubic polynomials in the coordinates of the world or object space or ground point. \r\nIn photogrammetry and remote sensing, rational polynomial coefficients (RPCs) describe a specific imaging geometry model for transforming image pixel coordinates to map coordinates (thereby accounting for terrain displacement errors). A sensor model describes the geometric relationship between the object space and the image space, or vice versa. It relates 3-D object coordinates to 2-D image coordinates. RPCs are part of a general sensor model that approximates the physical sensor model. The physical sensor model represents the physical imageing process, making use of information on the sensor's position and orientation (during image acquisition). The RPC model often refers to a specific case of the RFM (rational function model) that is in forward form, has third-order polynomials, and is usually solved by the terrain-independent scenario.","hasChildren":true,"name":"RPC correction","selfAssesment":"Completed
"},{"code":"IP1-3-1-4","description":"A ground control point (GCP) is a location of the surface of the Earth (e.g. a road intersection) that can be identified on the imagery and located accurately on the map (i.e. the reference dataset). Two distinct sets of coordinates are associated with the GCP: image coordinates in i rows and j columns, and map coordinates (e.g. x, y measured in degrees of latitude and longitude or as specified by the spatial reference system).","hasChildren":true,"name":"Ground Control Points (GCP)","selfAssesment":"Planned
"},{"code":"IP1-3-1","description":"Orthorectification is the process of removing sensor (scanner or camera), satellite/aircraft, and terrain-related distortions for creating a planimetrically correct image. \r\nTo obtain an accurately orthorectified image, the following information is required: (1) accurate elevation model, and (2) a camera model or rational polynomial coefficients (RPCs) that depicts the positional relationship of the collected image to the ground. Many companies deliver their images together with RPCs and existing software implementations can automatically read these files and apply the RPC transformation on the fly. An accurate elevation model is important to remove the influence of topography (e.g. hills, valley, etc.) on the raw image so that users can accurately compute distances, areas, and directions. Without performing orthorectification, the features in the image are tilted (especially the features located away from the center of the camera). Many satellite data products (e.g. Sentinel images, Landsat data products) are orthorectified using Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) data which is a freely available data product and has a spatial resolution of e.g. 1 arc-second (30 m). In the case of extremely jagged surface topography, i.e. areas of high relief, a DEM with a higher spatial resolution is required. \r\nTwo main models can be used in the orthorectification process: black-box and the physical-based model. The black-box model (called also the analytical model) is commonly implemented in different software because it relies solely on the RPC files. This model does not require access to any proprietary information of the sensor used to collect the image. \r\nThe physical-based models are more complex (and hence expected to be more accurate) because they account for various factors that might influence the quality of the acquired image: e.g. position of the satellite when collecting the images, atmospheric effects, etc. An example of a physical-based model is the so-called camera model. This model requires access to proprietary sensor information that has to be provided by the image owner.","hasChildren":true,"hasParent":true,"name":"Orthorectification","selfAssesment":"Completed
"},{"code":"IP1-3-2-1","description":"Image co-registration [aka Image-to-image registration] is the translation and rotation alignment process by which two images of like geometry and of the same geographic area are positioned coincident with respect to one another so that corresponding elements of the same ground area appear in the same place on the registered images (Jensen 2005 referencing Chen and Lee 1992).","hasChildren":true,"name":"Image co-registration","selfAssesment":"New
"},{"code":"IP1-3-2","description":"Spatial referencing (referred to as geo-referencing as well) is the process of aligning available EO or GIS data to a coordinate system so that further spatial analysis and image analysis tasks can be applied using these data as input. \r\nTo be able to perform spatial referencing, users have to generate the so called Ground Control Points (GCPs) with known coordinates. In case of images, the easiest features that could be used as GCPs are the intersections, isolated trees etc.","hasChildren":true,"hasParent":true,"name":"Spatial referencing","selfAssesment":"Planned
"},{"code":"IP1-3","description":"Geometric correction is concerned with placing the reflected, emitted, or back-scattered measurements or derivative products in their proper planimetric (map) location so they can be associated with other spatial information. It is usually necessary to preprocess the remotely sensed data and remove the geometric distortions so that individual picture elements (pixels) are in their proper planimetric (x, y) map locations. This allows remote sensing-derived information to be related to other thematic information in geographic information systems (GIS) or spatial decision support systems (SDSS). Geometrically corrected imagery can be used to extract accurate distance, polygon area, and direction (bearing) information.\r\n\r\nGeometric correction techniques are dedicated to resolving the geometric distortions caused by: (1) variations in sensor position; (2) Earth curvature; (3) rotation of Earth on its axis; (4) relief displacement. \r\n\r\nThere are two types of geometric distortions, namely systematic and random distortions. The former might be caused by Earth's rotation for example and, therefore they are predictable and systematic. The second type of distortions might be caused by terrain or variations in sensor altitude. \r\nGeometric correction includes georeferencing and orthorectification techniques.","hasChildren":true,"hasParent":true,"name":"Geometric correction","selfAssesment":"Completed
"},{"code":"IP1-4-1","description":"Contrast stretching (also referred to as contrast enhancement) expands the original input brightness values to make use of the total dynamic range or sensitivity of the output device (a computer display).","hasChildren":true,"name":"Contrast stretching","selfAssesment":"New
"},{"code":"IP1-4-2","description":"The histogram is a useful graphic representation of the information content of a remotely sensed image. Histograms for each band of imagery are often displayed and analysed in many remote sensing investigations because they provide the analyst with an appreciation of the quality of the original data (e.g. whether it is low in contrast, high in contrast or multimodal in nature. [...] Tabulating the frequency of occurrence of each brightness value within the image provides statistical information that can be displayed graphically in a histogram.","hasChildren":true,"name":"Histogram","selfAssesment":"New
"},{"code":"IP1-4","description":"Image enhancement algorithms are applied to remotely sensed data to improve the appearance of an image for human visual analysis or occasionally for subsequent machine analysis. The quality of results of image analysis are subjectively judged by humans as to whether they are useful. They include contrast enhancement.","hasChildren":true,"hasParent":true,"name":"Image enhancement","selfAssesment":"New
"},{"code":"IP1-6","description":"Principal component analysis (PCA) has proven to be of value in the analysis of multispectral and hyperspectral remotely sensed data. PCA is a technique that transforms the original correlated spectral dataset into a substantially smaller and easier set of uncorrelated variables that represents most of the information present in the original dataset. The first component accounts for the maximum proportion of the variance of the original dataset, and subsequent orthogonal components account for the maximum proportion of the remaining variance.","hasChildren":true,"name":"Principal component analysis (PCA)","selfAssesment":"New
"},{"code":"IP1-7-1-1","description":"Bottom-of-Atmosphere (BOA) reflectance is also called surface reflectance and consists of the solar radiation that is reflected from the Earth's surface.","hasChildren":true,"name":"Bottom-of-Atmosphere (BOA)","selfAssesment":"New
"},{"code":"IP1-7-1-4","description":"Top-Of-Atmosphere (TOA) radiance represents the radiance observed outside Earth’s atmosphere. It is derived from the Digital Numbers (DN) using metadata delivered with the image.","hasChildren":true,"name":"Top-Of-Atmosphere (TOA)","selfAssesment":"New
"},{"code":"IP1-7-1","description":"Atmospheric correction accounts for the attenuation caused by scattering and absorption in the atmosphere. It transforms top-of-atmosphere (TOA) reflectance to bottom-of-atmosphere (BOA) reflectance.\r\nThe decision to perform atmospheric correction depends on the need, i.e. the envisioned usage of the derived EO information product and the nature of the underlying problem. This includes requirements to the accuracy of extracted biophysical information. Additionally, the decision and choice of methods depends on the type of remote sensing data available, the amount of in-situ historical and/or concurrent atmospheric information available.\r\nAn atmospheric correction is essential when biophysical or geophysical parameters (e.g. of water or vegetation) are going to be extracted from the remote sensing data. If the data is not corrected, the subtle differences in reflectance among the contributing image bands may be lost. This is especially relevant when biophysical information shall be compared to that of images from other dates.\r\nHowever, some cases exist where it is unnecessary to perform atmospheric correction. For example, it is not necessary for producing an image classification product from a single date of remotely sensed data. If a maximum likelihood classification is applied that uses training data with the same relative scale for the pixel values, then, atmospheric correction has little effect on the classification accuracy. The same holds true for a post-classification change detection where the classifications of the two different dates were performed independently. \r\nThe process of (absolute) atmospheric correction requires a model atmosphere and in situ atmospheric measurements acquired at the time of remote sensor data acquisition as input. In situ data can be available from other sensors on-board the sensor platform.\r\n\r\nDark Object Subtraction (DOS) is one of the most popular empirical atmospheric correction techniques. This technique assumes that a black object has a reflectance value of zero. Yet, a dark object present in a satellite image will have a value different than zero because of the atmospheric scattering. This value is then subtracted from all pixels in a given spectral band.","hasChildren":true,"hasParent":true,"name":"Atmospheric correction","selfAssesment":"Completed
"},{"code":"IP1-7-2","description":"The number of spectral bands assocuates with a remote sensing system is referred to as its data dimensionality. Hyperspectral remote sensing systems such as AVIRIS ans MODIS obtain data in 224 and 36 bands, respectively. The greater the number of bands in a dataset (i.e., its dimensionality), the more pixels that must be stored and processed by the digital image processing system. Storage and processing consume valuable resources. It is necessary to reduce the dimensionality of hyperspectral data while retaining the information content inherent in the image. \r\nA method for dimensionality reduction in hyperspectral data and minimizing the noise in the imagery is the minimum noise fraction (MNF) transformation. The purpose is to minimize the noise in the imagery, i.e. to identify noise and segregate it from true information, and to colaps the useful information into a much smaller set of MNF images. The MNF transformation applies two cascaded principal components analyses.","hasChildren":true,"name":"Dimensionality reduction","selfAssesment":"New
"},{"code":"IP1-7-3","description":"Sensor calibration converts the sensor’s digital numbers (DNs) to at-sensor radiance above the atmosphere. A further radiometric adjustment accounts for the viewing angle and sun angle during acquisition to transform radiance values to top-of-atmosphere (TOA) reflectance. Therefore, the process requires sensor calibration information and telemetry data that satellite image providers deliver within the metadata.\r\nDNs are raw sensor data without physical units. The sensor calibration information for converting the DNs to radiance are the calibration gain (cal_gain) and calibration offset (cal_offset) values. The sensor calibration uses linear function f(DN) = DN * cal_gain + cal_offset that multiplies the DNs of each pixel in each spectral band with their corresponding cal_gain and adds the corresponding cal_offset. The resulting at-sensor radiance image is the basis for the radiometric adjustment that uses information about the viewing angle and sun angle during acquisition to transform at-sensor radiance to TOA reflectance. \r\nSensor calibration obtains TOA reflectance and is a minimum requirement for performing band math calculations to derive spectral indices such as the normalized vegetation difference index (NDVI). Uncalibrated image data would arrive at NDVI values that are distorted because the cal_gain and cal_offset parameters for the involved spectral bands were not considered.","hasChildren":true,"name":"Sensor calibration","selfAssesment":"Completed
"},{"code":"IP1-7-4","description":"As an optical remote sensing system is not perfect, noise can enter the data collection system at several points. Necessary corrections include the removal of shot noise (random bad pixels), correcting line or column drop-outs, accounting for line-start problems and radiometric correction of n-line striping caused by detector miscalibration.\r\nSAR data have global, random speckle noise. Speckle filters are designed to adapt to local image variations in order to smooth values, thus reducing speckle and enhancing lines and edges to maintain the sharpness of an image. A widely used way to reduce speckle is to apply spatial filters to the images. Typical approaches for speckle filtering include Laplace filtering for smoothing and sigma filters that preserve more of the signal with a lesser effect of smoothing.","hasChildren":true,"name":"Noise reduction","selfAssesment":"New
"},{"code":"IP1-7-5","description":"Topographic correction, or topographic effects correction, aims to adjust the spectral values of an image according to effects of solar illumination differences due to the irregular shape of the terrain. Topographic slope and aspect introduce radiometric distortion of the recorded signal. Further, terrain shadow dramatically affects the brightness values of the covered pixels in an image. Topographic effects of illumination and shadow are particularly relevant in mountainous regions and in regions towards the higher latitudes of the southern and northern hemisphere. The effects appear pronounced during the winter season. \r\nTogether with sensor calibration and atmospheric correction, topographic correction is part of the radiometric correction process to obtain true reflectance values from sensor radiance. This process is necessary when using EO data for obtaining geophysical measurements. It can also benefit the accuracy of image classifications by reducing the internal variability of vegetation types, since the corrected reflectance relates better to the geometrical or biological properties of the plant than to the original reflectance.\r\nMethods for the removal of topographic effects from remotely sensed images can simply be based on band ratios that do not require additional input. Alternatively, they use digital elevation models (DEMs) as an additional input and apply sophisticated modelling of the illumination conditions. The illumination model describes various aspects of the relationship between the sensor measurement, the sun illumination, the ground reflectance and the diffuse irradiance at the surface. The model incorporates the angles between the sun position, the ground position (described by slope and aspect from the DEM), and the sensor position. Among these methods are lambertian methods and non-lambertian methods such as the bidirectional reflectance distribution function (BRDF). The BRDF, which is more suitable to the non-Lambertian properties of the observed surfaces, describes how the reflectance varies in each cover considering the angles of incidence and observation. \r\nIf achieved with a high quality, the resulting topographically corrected image appears to be illuminated evenly as if all its pixels would be part of a flat surface without the presence of any terrain differences. However, the much larger benefit than the improved appearance is the availability of pixel values that are closest to the true reflectance when compared to TOA, BOA and DN values.","hasChildren":true,"name":"Topographic correction","selfAssesment":"Completed
"},{"code":"IP1-7","description":"Radiometric calibration and correction converts the sensor’s digital numbers (DNs) to radiance values and subsequently reflectance values. Additionally, the term “correction” points to the fact that radiometric measurements with satellite sensors contain error. Therefore, radiometric correction is concerned with improving the accuracy of surface spectral reflectance, emittance, or back-scattered measurements obtained using a remote sensing system. The Earth’s atmosphere, land and water are complex and can never be captured perfectly because of the limitations of remote sensing devices that lie in their spatial, spectral temporal and radiometric resolution. Therefore, error occurs in the data acquisition process and degrades the quality of remotely sensed data. The most common errors in remote sensing are radiometric and geometric. This concept is focused on the correction of remote sensing data to account for radiometric error that is to some degree systematic. Systematic errors in radiometric measurements come from the interaction of the sensed radiance with the atmosphere, the acquisition geometry in relation to the radiance source (the sun) and the Earth surface geometry (terrain).\r\nThere are several levels of radiometric calibration and correction. The first is sensor calibration that converts the DNs to top-of-atmosphere (TOA) reflectance. It converts to radiance values and further to reflectance values by accounting for the viewing angle and sun angle during acquisition. The second is atmospheric correction that converts TOA reflectance to bottom-of-atmosphere (BOA) reflectance. The third is topographic correction that converts BOA reflectance to surface reflectance. \r\nRadiometric calibration is necessary to ensure radiometric comparability of the measurements. There is a need for calibration when comparing different spectral bands within one image, e.g. for the calculation of geo-biophysical parameters with band math operations. Results from uncalibrated image data would differ from results achieved with calibrated data because the unaccounted cal_gain and cal_offset of the used spectral bands would lead to distortions. \r\nIn addition, radiometric calibration complements the geospatial comparability that is achieved with geo-referencing an image to geographic coordinates. Geo-referencing enables comparison of an image pixel to the geospatially matching pixel in another image acquired with a different sensor but with comparable resolution. Radiometric calibration enables a radiometric comparison between these two pixels’ radiance values. In case the two images are from different acquisition dates, a calculated radiometric difference would indicate change. This example shows the relevance of radiometric calibration for inter-sensor comparisons.\r\nRadiometric comparability is particularly relevant in studies that require inter-sensor comparisons, comparisons of surface features over time, or comparisons to laboratory or field reflectance data. Then the radiometric correction should cover atmospheric, solar and topographic effects. A full radiometric correction that also includes topographic correction can benefit the accuracy of image classifications by reducing the internal variability of vegetation types, since the corrected reflectance relates better to the geometrical or biological properties of the plant than to the original reflectance.","hasChildren":true,"hasParent":true,"name":"Radiometric calibration and correction","selfAssesment":"Completed
"},{"code":"IP1","description":"Image pre-processing focuses on transforming the electrical signal measured by a sensor to a processing level at which pixel values can be used for the next information extraction step. Therefore, pre-processing operations involve the removal of errors encountered while collecting remotely sensed data to get as close as possible to the true radiant energy and spatial characteristics of the study area at the time of data collection. Different sensor type (optical, radar, lidar) require different processing levels\r\nThe most common image pre-processing procedures include: \r\n(1)\tRadiometric calibration involves the transformation of Digital Numbers (DN) to physical unit: radiance/reflectance. Radiometric calibration can be done before the launch of a satellite sensor, i.e. pre-launch calibration, or after launch. In the second case, the calibration is performed on-board or by comparing ground measurements with satellite radiance. Through radiometric calibration various scene illumination procedures such as sun elevation correction or earth-sun distance correction are applied. Furthermore, image noises caused by striping or line drop as happened in case of Landsat TM7 due to failure of the Scan Line Corrector (SLC) are also corrected using specialized procedures.\r\n(2)\tAtmospheric correction accounts for two main processes: scattering and absorption. Scattering represents a disturbance of the electromagnetic waves caused by rayleight scattering (caused by very small particles such as the air molecules), mie scattering (caused by aerosol particles) and non-selective scattering (dust, smoke, rain etc.). Absorption occurs when the electromagnetic energy is absorbed by the atmospheric components. Therefore, atmospheric windows have to be removed before using the satellite images in the next processing steps. Atmospheric corrections can be carried out either using simple statistical methods or complex radiative transfer based methods\r\n(3)\tGeometric correction is required to remove the distortions caused by the Earth curvature, Earth rotation, panoramic distortion due to the field of view of the sensor and the topography of the terrain. Geometrics distortions are corrected using Ground Control Points (GCP) and a Digital Elevation Model (DEM). In case of airborne images, additional distortions caused by variations in the platform altitude or velocity might occur.","hasChildren":true,"hasParent":true,"name":"Image pre-processing","selfAssesment":"Completed
"},{"code":"IP2-1-1","description":"Data augmentation refers to a scheme of augmenting the observed data so as to make it more easy to analyze. An application from deep lerarning is to increase the number of input training sample images with augmented data. Examples of data augmentation techniques include horizontal flips, random crops, and principal component analysis.","hasChildren":true,"name":"Data augmentation","selfAssesment":"New
"},{"code":"IP2-1-2","description":"Data imputation refers to a scheme of replacing missing values by imputed values. Imputation can be done, for example with mean, median and mode. Imputation methods can efficiently predict multiple response variables simultaneously.","hasChildren":true,"name":"Data imputation","selfAssesment":"New
"},{"code":"IP2-1-3-1","description":"Gram-Schmidt is a pan-sharpening method that has been invented by Laben and Brover in 1998 and patented by Eastman Kodak. It makes use of the Gram-Schmidt orthogonalization to decorrelate the spectral bands (panchromatic, red, green, blue, etc.) and transform them into one multidimensional vector.","hasChildren":true,"name":"Gram-Schmidt pan-sharpening","selfAssesment":"New
"},{"code":"IP2-1-3-2","description":"This pan-sharpening method uses PCA to transfer detailed spatial information from panchromatic band to the available multispectral bands.","hasChildren":true,"name":"Principal Component Analysis (PCA)-based pan-sharpening","selfAssesment":"New
"},{"code":"IP2-1-3","description":"Pan-sharpening methods are used to enhance spatial resolution of images by merging a panchromatic image with high resolution with a multispectral image with low resolution.","hasChildren":true,"hasParent":true,"name":"Pan-sharpening","selfAssesment":"New
"},{"code":"IP2-1-4","description":"Spatiotemporal image fusion methods, called also spatiotemporal downscaling methods, represent an efficient solution to generate fine-scale images at a high temporal resolution for more detailed land cover mapping and monitoring applications. Spatiotemporal image fusion methods can be classified into three categories: (1) reconstruction-based , (2) unmixing based and (3) learning-based methods.","hasChildren":true,"name":"Spatio-temporal image fusion","selfAssesment":"New
"},{"code":"IP2-1","description":"Image fusion is defined as the “combination of two or more different images to form a new image by using a certain algorithm” Data fusion is a well-established research field. Image fusion methods are primarily used for improving the level of interpretability of the input data. Additionally, they can be utilized to address the problem of missing data caused by cloud or shadow contamination in satellite images time series. Image fusion can be performed at pixel-level, feature-level (e.g. land-cover classes of interest), and decision-level (e.g. purpose driven).","hasChildren":true,"hasParent":true,"name":"Data fusion","selfAssesment":"Planned
"},{"code":"IP2-2","description":"Data harmonization aims to transform different datasets in such a way that they fit together, both with respect to geometry and semantics. The goal is that a user, who is using data from different authorities, shall have a unified view, where conflicts in the datasets have been removed.","hasChildren":true,"name":"Data harmonisation","selfAssesment":"New
"},{"code":"IP2-3","description":"Data integration is the process of combining different geographic datasets including those derived from remote sensing data. The combined datasets can have different coverage, but they have to have the same geographic coordinates.","hasChildren":true,"name":"Data integration","selfAssesment":"Planned
"},{"code":"IP2","description":"Data assimilation is a strategy to foster data integration and data harmonisation in a bi-directional way between the measured and the modelled reality. In other words, it aims to combine measurements (observations) with the understanding of the spatio-temporal properties and evolution of system’s variables or properties and model information about them. Models can be calibrated and keeping them ‘on track’ by constraining them with observations. Vice versa, observations can be validated through models. Approached as a mathematical problem, data assimilation aims at minimizing cost functions or penalize a function to ensure optimality in fitting. Equations are used to describe system parameters and the relationships among them, It is noteworthy, that models encompass information from previous measurements, experiences, and theory. While the observations are influenced by (known) properties such as precisions, etc. of the measurement devices, the robustness of models rely on the consolidated knowledge. Because uncertainties reside in all components with unknown or even undeterminable errors, the approach is usually probabilistic, including Bayesian and other related techniques. Widely used in meteorological sciences, successful data assimilation has been boosted the reliability of weather forecast , while sensitivity to errors remains. \r\nIn Earth observation, data assimilation compensates for the fact that a specific site could be observed in a variety of measurements by satellites with different sensor types, at different dates, different angular geometries and viewing directions, illumination conditions (solar time), observation frequencies, etc. In particular, for monitoring processes, measurements over time need to assure to actually measure the status of the system or object and not the divergence in observation. To overcome these divergences and converge them with the actual properties of an observed object or target class such as spectral or geospatial properties, observation modelling can be considered an important contribution from geospatial theory. this also links to class modelling or geon modelling. The synergy of a vegetation growth model and a remote sensing observation model can be exploited to improve the retrieval of geo-biophysical information. For vegetation and crop type monitoring radiative transfer modelling (RTF) is being used as an example. \r\nData assimilation can also serve in bridging the gaps between non-availabilities of EO data and other observations, to provide estimates or prediction for geographical variables, testing of hypotheses or continuous observation (monitoring). A related aspect is data imputation, i.e. filling gaps in observations e.g. by other, complementary data sets (e.g. Radar imagery in the absence of VHR data in cloudy weather conditions). Recently, these sources can also be complemented by crowd mapping and citizen science. \r\nWhen interpretation of data comes into play, such as image classification, we introduce another level of uncertainty. Thus the community seeks for rigorus classifiers based on solid spectral models, acting across sensors. Semantic enrichment of satellite data is a related strategy for reaching to interpreted data in a rigorous way. \r\nSummarizing, data assimilation comprises steps to improve the level of interpretability of the input data, by enrichment (get rid of spatial/temporal gaps), by accounting for heterogeneity (through harmonization), and by integration (combination with other data that is relevant to the application). Thereby, datasets become more comparable to each other.","hasChildren":true,"hasParent":true,"name":"Data assimilation","selfAssesment":"Completed
"},{"code":"IP3-1-1-1","description":"Vegetation fraction (VF) is defined “as the percentage of vegetation occupying a pixel as viewed in vertical projection. It’s a comprehensive quantitative index in forest management and vegetation community cover conditions, and it’s also an important parameter in many remote sensing ecological models.”","hasChildren":true,"name":"Vegetation fraction","selfAssesment":"Planned
"},{"code":"IP3-1-1-2","description":"Leaf area index (LAI) is the ratio between the total area of the upper leaf surface of vegetation and the surface area of the pixel in question. LAI is a dimensionless value, typically ranging between 0 (for a pixel composed of bare soil) and values as high as 6 (for a dense forest).","hasChildren":true,"name":"LAI (Leaf Area Index)","selfAssesment":"Planned
"},{"code":"IP3-1-1-3","description":"Net primary production (NPP) is a measure of the inherent productivity of a region or ecological system—mainly the Earth’s production of organic matter, principally through the process of photosynthesis in plants.","hasChildren":true,"name":"Net primary production (NPP)","selfAssesment":"New
"},{"code":"IP3-1-1-4","description":"Water quality variables can be derived from Earth observation (EO) data to provide essential ocean variables. They include Sea-surface temperature (SST), Sea-surface salinity (SSS) and Air-Sea Fluxes. SST controls the atmospheric response to the ocean at both weather and climate time scales. The spatial patterns of SST reveal the structure of the underlying ocean dynamics, such as, ocean fronts, eddies, coastal upwelling and exchanges between the coastal shelf and open ocean. SSS observations contribute to monitoring the global water cycle (evaporation, precipitation and glacier and river runoff). Water quality variables can be derived from EO data by using ocean colour products from optical sensors and relating them to ground truth information from in situ sensor networks.","hasChildren":true,"name":"Water quality variables","selfAssesment":"New
"},{"code":"IP3-1-1","description":"Biophysical parameter retrieval is an approach in remote sensing that aims to estimate parameters which have physical meaning related to properties of living organisms. The goal is to provide quantitative results directly relating to the biophysical state, but independent of acquisition conditions and technology. Assessment of vegetation status is a key motivation for this, because through plant respiration and photosynthesis, vegetation is critical for modelling terrestrial ecosystems and energy cycles in environmental studies. \r\nImportant parameters describing canopy structure include leaf area index (LAI), green cover fraction (fCover), fraction of absorbed photosynthetically active radiation (fAPAR), plant height, biomass and leaf angle distribution. At leaf biochemical level, leaf chlorophyll/water, fuel moisture and leaf pigmentation content are used.\r\nVisual inspection can provide a first assessment of plant status. For detailed measurements of biophysical parameters, mostly destructive methods have been used. Chemical measurement techniques on leaf samples can measure pigment concentrations very accurately, but are time consuming and only use very limited samples. \r\nMuch more extensive data can be collected using earth observation imagery. These range from large scale spaceborne observations with high frequency at coarse resolution to dedicated UAV flights which can offer spectral information of individual plants. Radar and LiDAR acquisitions, which are insensitive to weather conditions, now complement optical observations. \r\nMethods to retrieve the parameters from remote sensing data fall into two main categories. Statistical models empirically match data to a biophysical variable. Univariate techniques use a single quantity derived from the data, usually a vegetation index whereas multivariate techniques link a combination of measurements at different wavelengths to one or more biophysical parameters.\r\nPhysically-based modeling is an alternative approach which uses advanced radiative transfer models to describe the transfer and interaction of radiation inside a leaf or canopy based on robust physical, chemical, and biological processes. They compute the interaction between solar radiation and plants and provide as such a better understanding between biophysical variables and reflectance characteristics. Good examples are Leaf optical models such as PROSPECT and LIBERTY which simulate leaf optical properties by absorption and scattering coefficients. Canopy reflectance models simulate canopy reflectance as a function of a complex description of plant structural and radiometric attributes to develop a quantitative understanding of remote sensing information.","hasChildren":true,"hasParent":true,"name":"Biophysical and geophysical parameters","selfAssesment":"Completed
"},{"code":"IP3-1-2-1","description":"This spectral index is calculated using the following formula: SAVI = [(NIR-Red)/(NIR+Red+L)]/(1+L), where L can be, for example, 1 in area with no vegetation or 0 in area with dense veegtaion. It is used to minimize the influence of the soil brightness from the vegetation indices that are based on red and near-infrared wavelengths.","hasChildren":true,"name":"Soil-adjusted Vegetation Index (SAVI)","selfAssesment":"New
"},{"code":"IP3-1-2-2","description":"This spectral index is calculate using the following formula NDSI = (green-SWIR)/(green+SWIR). It is the most popular index used to identify snow cover due to the fact that snow reflects visible wavelength stronger than middle-infrared wavelengths.","hasChildren":true,"name":"Normalized Difference Snow index (NDSI)","selfAssesment":"New
"},{"code":"IP3-1-2-3","description":"Leaves, when healthy and vigour show a characteristic green colour. This visual effect evident to humans is caused by the co-existence of two evolutionarily facts: the specific interaction of the chlorophyll pigment in living leaves to the visible spectrum (VIS, 400-700 nm wavelength) of light emitted by the sun and the sensitivity of our human eye to the same sub-spectrum. According to fundamental physical laws of radiation (Stefan Boltzmann law of blackbody radiation and Wien’s displacement law), the VIS sub-spectrum corresponds to the radiation maximum of the sun, a hot blackbody with a surface heat of about 6000 K. Living leaves are structured in specific layers exhibiting characteristic interaction with light. The chloroplasts located in the so-called palisade layer, make use of the blue and the red part of sunlight for photosynthesis, the unique process of transforming light to create energy (carbohydrates) from water and carbon dioxide. This leads to the specific behaviour of leaves to absorb large portions (up to 90%) of the blue and red part of the electromagnetic spectrum and reflect nearly 100% of the green light. The peak reflectance in green light makes leaves (and plants in general) appear in green colour in our visual perception. \r\nA second, by no means less characteristic, feature of leaves is the specific response to near infrared (NIR, at around 700 nm wavelength) light in the mesophyll tissue (transmittance, scattering and reflectance). Only a small fraction of NIR is being absorbed. \r\nThis combination of two specific spectral characteristics, the absorption in VIS (red colour) by chlorophyll a in palisade layers, and the reflectance of NIR in the spongy tissue, makes the spectral profiles of plants and vegetation exhibiting a very characteristic shape, the so-called red edge. This absorption edge between red and NIR light is sharper for higher intensity green reflectance and brighter green tones (such as grassland or bright deciduous forest) than for less intensive reflectance and darker tones (coniferous forest). \r\nThe red edge may shift for the same vegetation type due to plant maturity or plant stress. This effect we call the red shift. The red shift is sensitive to crop maturity (headed stage) and may indicate harvesting time. Notably, there is also a blue shift, indicating green plants’ exposure to geochemical stress, which causes the absorption spectra to shift towards shorter wavelengths. \r\nPlants usually do not appear in isolation but form a canopy with a certain degree of coverage (e.g., crown closure in forests), and a certain part of understorey or soil per area unit. The resulting canopy reflectance is therefore a spectral mix of soil and vegetation (or even different types of vegetation) and generally lower than the reflectance of a pure vegetation sample under lab conditions. \r\nTo capture most of these plant-typical spectral characteristics, the so-called normalised difference vegetation index (NDVI) was developed. NDVI is an arithmetic band combination of red and NIR bands in a normalised value range. \r\nThe NDVI is calculated as:\r\nNDVI=((NIR-R))/((NIR+R))\r\nThe (hypothetic) value range of the NDVI is [-1 | +1]. Under real-world conditions, the NDVI ranges from values of around -0.2 to 0.6 or 0.7. To discriminate principal land cover classes such as water, non-vegetation (soil, sealed, etc.) and vegetation the following thresholds in the continuous range are used: \r\n\tNDVI < ~ 0: water\r\n\t~ 0 < NDVI < ~ 0.2: non-vegetation (soil, sealed surfaces, bare rock, etc.)\r\n\t~ 0.2 < NDVI: vegetation.\r\nNotably, these class limits are just a very rough approximation (indicated by the ~ sign), due to the mixed pixels effect, canopy reflectance, the abundance of water plants and suspending particles, and the illumination effect of specific atmospheric or topographic conditions. \r\nWe can use the NDVI to generally mask out vegetation from other land cover types and, more specifically, to indicate vegetation vigour and health. It is also suitable for monitoring plant phenology as the relationship between vegetative growth and the (changing) conditions of the environmental conditions. A range of variations has been suggested, enhancing one or the other mathematical or statistical behaviour of the index, or making it even more sensitive to specific plant behaviour. A well-known example is the enhanced vegetation index (EVI).","hasChildren":true,"name":"Normalized Difference Vegetation Index (NDVI)","selfAssesment":"Completed
"},{"code":"IP3-1-2","description":"Spectral indices are calculated using a mathematical equation that is applied on two or more spectral reflectance bands of the image. The calculated spectral index is a ‘new’ image that highlights particular land surface features or properties e.g. vegetation, soil, water, better than the original input bands. The spectral indices vary from simple spectral ratioing of two bands to more complex combinations of multiple bands. Spectral indexes are developed based on the spectral properties of the object of interest. For example, spectral indices dedicated to the vegetation condition are developed based on the principle that the healthy vegetation reflects strongly in the near-infrared spectrum while absorbing strongly in the visible red. These properties are used to develop more complex spectral indexes for monitoring vegetation condition, phenology parameters, i.e. Normalised Difference Vegetation Index (NDVI), Advanced Vegetation Index (AVI). The spectral indices calculated using the short wave infrared spectral bands are more sensitive to vegetation water content and spongy mesophyll structure in the vegetation canopy thus are used to assess the vegetation decline, moisture that is particularly useful for drought monitoring (e.g. Normalized Difference Water Index (NDWI) or Normalized Difference Moisture Index – NDMI). The water-related spectral indices are widely applied in agricultural and ecological applications including surface water body characteristics, vegetation water stress, soil water content assessment and wetlands monitoring. The combination of near infrared and short wave infrared spectral bands is also used to detect burned area and to monitor the vegetation recovery (e.g. Normalised Burned Ratio – NBR). There are other spectral indices dedicated to snow cover and glacier monitoring, which are developed based on visual green and short wave infrared spectral bands. Snow reflects most of the radiation in the visible bands whiles absorbing in the short wave infrared.","hasChildren":true,"hasParent":true,"name":"Spectral indices","selfAssesment":"Completed
"},{"code":"IP3-1","description":"The term band maths denotes the arithmetic combination (addition/subtraction, multiplication/division) of two or more spectral bands in an early stage of image analysis. The resulting scalar values represent the spectral behaviour in different bands in a single value; such procedure makes particular sense, when spectral behaviour varies in those bands (like the red edge of vegetation spectra in the NIR band). \r\nThere are several reasons for applying band maths when working with multispectral imagery: (1) A single range of values rather than multiple bands is easier to comprehend and interpret; (2) Thresholds or class limits are applied more intuitively in a grey scale image; (3) Indices can be easily calculated and compared across different sensors; they are implemented as standard routines in many software environments as well as cloud processing environments (such as Google Earth Engine or the Proba-V exploitation platform)\r\nOut of the many possible, literature suggests a few arithmetic band combinations as application-specific quasi-standards. Band ratios (e.g. red band divided by NIR band) and indices (such as the normalised difference vegetation index, NDVI) belong to this group. Indices have the advantage over simple ratios in constraining the value range, e.g. [-1 | 1]. Designated to indicate specific land cover types (such as water index, snow index, soil index, etc.) such indices are widely used as a basis for operational information products. Another index is the normalised burn ratio (NBR) which relates near infrared and short-wave infrared reflectance to measure burn severity taking into consideration the increasing of SWIR reflectance in the course of a fire. \r\nPre-processing such as dark object subtraction and radiometric or even atmospheric correction is a key requirement prior to indexing. The coding in digital numbers (DN) is a function of the sensitivity and the radiometric resolution of the sensor. The actual recording depends on atmospheric conditions (additional brightness, haze, etc.). Therefore, in order to make the resulting values comparable among different types of sensors and scenes, radiometric correction is mandatory, converting DNs into radiances, i.e. true reflectance values as physical measurement units. \r\nTwo advanced examples of band maths beyond rationing are the perpendicular vegetation index (PVI) and the tasselled cap (TC) transformation. PVI is based on the assumption that vegetation pixels are generally separable from soil pixels (at least after unmixing or for pure pixels), and thus pixel values are located in a perpendicular direction from the soil line in a NIR/red feature space. The Euclidean distance from the soil line, determined by Pythagorean triangle, yields the PVI. Tasselled cap instead rests on the notion of a cap-like histogram shape when plotting pixels on a brightness vs. greenness plot, with the latter determined by linear combinations of VIS and NIR bands, along with empirically determined coefficients. TC 1 as a weighted sum corresponds to brightness, TC 2 to greenness, TC 3 to yellowness, sometimes referred to as wetness. A fourth TC called nonesuch likely corresponds to noise and atmospheric disturbance effects in the image.","hasChildren":true,"hasParent":true,"name":"Band maths","selfAssesment":"Completed
"},{"code":"IP3-10","description":"Semantic enrichment is the process of adding semantic metadata elements to improve the content-based image retrieval. These semantic metadata elements enable the explicit specification of the content of the images stored in the remote sensing databases.","hasChildren":true,"name":"Semantic enrichment","selfAssesment":"New
"},{"code":"IP3-11-1","description":"Different types of changes are investigated using remotely sensed data: (i) abrupt changes, such as the changes caused by a fire or flooding, and (ii) gradual changes such as urban growth. Besides these kinds of changes, remote sensing community differentiates between transitional changes and conditional changes. Transitional changes refer to a major change of land surface such as conversion of forest to pasture or the expansion of mangroves into the surrounding water. Conditional changes refer to the change in condition at the surface such as water stress in an agricultural field, forest degradation caused by pest. \r\nIn the past, many remote sensing studies used two images to detect different types of changes such as deforestation, land cover change or change in the health or condition of the vegetation (e.g. pest infestation). Meanwhile, satellite image time series are used to assess the change. Time series analysis allows for monitoring more subtle changes and for providing temporal patterns of change. In this way, the timing of changes and drivers of change can be easily identified. \r\nDifferent methods are being used in change detection studies. There are studies that analyze individual images available in the investigated time series to map the target class/phenomena/events at the time when images were collected and to identify the changes: e.g. mapping the mangroves extent on an year basis and measuring it to identify changes. Alternative studies search for breaks in time series for detecting changes. The breaks are used to segment the time series into before and after changes periods which are further classified using one of the existing supervised or unsupervised classification methods (K-means, fuzzy k-means, Random Forest, Support Vector Machine etc.).","hasChildren":true,"name":"Change detection","selfAssesment":"Completed
"},{"code":"IP3-11-2","description":"The (data)cube model for analysis of time series of earth observation raster data, represents the dataset as a multidimensional array with one or more spatial or temporal dimensions. Scalar values in the cube can be selected (or ‘filtered’) and processed based on dimension labels. This allows analysis algorithms to be thought of as a set of operations on the multidimensional array. Technologies that support this model allow to efficiently implement such algorithms.\r\nSome possible operations on a multidimensional cube include: filtering, ‘reducing’ all values along a dimension, ‘aggregating’ values in a dimension, or transforming all values along a dimension. Generally speaking, these operations require the selection of a subset of the data on which work is to be done. This allows implementing the operations efficiently even on very large datasets.\r\nIn comparison to file-based processing, most technologies that support cube-based time series analysis reduce implementation overhead, as the user does not need to read and write individual files, also more complex aspects like distributed computing for parallelization can be hidden in a cube based approach. So a cube based approach can also be thought of as an abstraction layer that effectively reduces the need for specific IT-related skills when analyzing earth observation timeseries.\r\nMultiple initiatives support cube based analysis. Some common features include a programming API, often using the Python programming language. Some tools are only accessible as web services, while others can also run locally (on a small dataset). This diversity is still a drawback, as users would need to familiarize themselves with different systems. Initiatives such as openEO try to address this by providing a common API.","hasChildren":true,"name":"Cube-based time series analysis","selfAssesment":"Planned
"},{"code":"IP3-11-3","description":"Dynamic Time Warping (DTW) works by comparing the similarity between two temporal sequences and finds their optimal alignment, resulting in a dissimilarity measure. In the case of remote sensing data, DTW can deal with temporal distortions, and can compare shifted evolution profiles and irregular sampling thanks to its ability to align radiometric profiles in an optimal manner","hasChildren":true,"name":"Dynamic Time Warping","selfAssesment":"Planned
"},{"code":"IP3-11","description":"Satellite image time series analysis plays an important role in different domains including vegetation dynamics monitoring, estimating crop yields, discriminating between different land cover classes, exploring human-nature interactions, monitoring land cover change, assessing environmental threats, or evaluating ecosystems-climate feedbacks or urbanization.\r\nTime series analysis requires high quality time series which are reconstructed by removing any source of contamination such as clouds, cloud shadows, or scan-line corrector (SLC) gaps of the Enhanced Thematic Mapper plus sensor (ETM+) on Landsat 7. Removed pixels are usually filled in with data predicted from a different date (temporal interpolation), nearby pixels (spatial interpolation) or from both (spatiotemporal interpolation). Different methods are available for screening and masking out clouds and shadows in satellite images including mono-temporal methods such as Function of mask (Fmask), or multitemporal mask (e.g. Tmask algorithm). Fmask is used by the United States Geological Survey (USGS) to produce a cloud mask layer of Landsat images. European Space Agency (ESA) is using Sen2cor processor to produce Level 2A Sentinel-2 data with a shadow and cloud shadow mask. All images used in the time series have to be co-registered, i.e. they align as closely as possible. \r\nTime series analysis is used to (1) investigate various surface properties such as evapotranspiration, land surface temperature, (2) map the cover of the Earth surface (e.g. land cover mapping, crop mapping etc.), (3) detect different type of changes such as abrupt changes (fire event) or gradual changes (urbanization), and (4) study the trends.\r\nTo map surface features from satellite image time series, numerous studies make use of the vegetation phenology extracted from a spectral-temporal trajectory of a given spectral vegetation index such as the normalized difference vegetation index (NDVI) or enhanced vegetation index (EVI). Several metrics can be used to characterized vegetation phenology: metrics of greenness and metrics of time. The metrics of greenness include the minimum and maximum spectral vegetation indices, their difference or amplitude, seasonally averaged greenness etc. The metrics of time include start and end of the growing season, duration or length of the growing season or the timing of maximum greenness. Changes, on the other hand, are identified either by investigating two images acquired at two different points in time or by identifying breaks in a dense (annual or multi-annual) satellite image time series.","hasChildren":true,"hasParent":true,"name":"Time series analysis","selfAssesment":"Completed
"},{"code":"IP3-12-1","description":"Remote sensing-derived products such as land-use and land-cover maps contain error. The error accumulates as the remote sensing data are collected and various types of processing take place. An error assessment is necessary to identify the type and amount of error in a remote sensing-derived product.","hasChildren":true,"name":"Error propagation","selfAssesment":"New
"},{"code":"IP3-12-2","description":"The precision of a measurement system, related to reproducibility and repeatability, is the degree to which repeated measurements under unchanged conditions show the same results.","hasChildren":true,"name":"Precision","selfAssesment":"New
"},{"code":"IP3-12","description":"Uncertainty is the result of the lack or imprecision of our knowledge about the world. A proposition is uncertain if we do not know whether it is true or not. In most circumstances we describe a proposition as uncertain when the reason we do not know whether it is true is that we do not possess complete and accurate knowledge about the state of the world.","hasChildren":true,"hasParent":true,"name":"Uncertainty","selfAssesment":"New
"},{"code":"IP3-13-1","description":"The main elements of visual interpretation are: tone, shape, size, pattern, texture, shadow, , association. Tone refers to the relative brightness or colour of objects in an image. It depends on the spectral properties of an object. Variation in tone allows to distinguish elements of different shape, texture and pattern. Shape refers to the general form, structure, or outline of individual objects. Straight and sharp edge shape represent typically the anthropogenic features i.e. urban or agriculture, the natural features like rivers, wetlands are more irregular in shape. Size of objects in an image is a function of scale and it depends on the spatial resolution of the image. The assessment of the size of the target’s object in relation to other objectives as well as an absolute size of the object are the important part of the interpretation. Pattern refers to the spatial arrangement of objects, i.e. network of street and houses in an urban area, orchards with the line of trees. Texture refers to the arrangement of frequency of tonal variation in particular areas of an image. Rough texture would have very large, coarse tonal variation (e.g. forest canopy), whereas smooth texture very little tonal version (e.g. uniform, homogenous surfaces). It depends on the size, shape and pattern of objects. Shadow depends on the scale and spatial resolution of an image. Shadow is useful to measure the height of an object, to distinguish the coniferous from broadleaf trees. In the radar imagery is useful for identifying topography and landforms. Association refers to the relationship between objects and features in proximity to the target interest.","hasChildren":true,"name":"Elements (cues) of interpretation","selfAssesment":"Completed
"},{"code":"IP3-13-2","description":"Information-as-data-interpretation considers information as the outcome of the cognitive process of vision that reconstructs a scene from an image.","hasChildren":true,"name":"Information-as-data-interpretation","selfAssesment":"New
"},{"code":"IP3-13-3","description":"An image interpretation key is simply reference material designed to permit rapid and accurate identification of objects or features represented on aerial images.","hasChildren":true,"name":"Interpretation keys","selfAssesment":"New
"},{"code":"IP3-13","description":"Interpretation is the processes of detection, identification, description and assessment of an object and pattern imaged. Visual interpretation is the ability of a human operator to identify an object through the data content in an image / photo by combining several elements of interpretation. The image characteristics used in the interpretation process are: shape, size, tone/colour, texture, shadow, neighbourhood and pattern. The importance of the image characteristics varied according to the spatial resolution of the images and the properties of the feature of interest. The interpretation can be performed on the single image or between several images acquired at different time, which result in the differentiation of the temporal changes. The principle of the image interpretation is the process of delineating (digitalizing) the outlines of the objects, features on the image. It is performed “on-screen” using a GIS software. The process of visual interpretation is time consuming and requires a skilled interpreter with knowledge of the study area. Even though, the image interpretation supports many applications in for example selection of the training and verification data sets for image classification and accuracy assessment.","hasChildren":true,"hasParent":true,"name":"Visual interpretation","selfAssesment":"Completed
"},{"code":"IP3-2-2-1","description":" ","hasChildren":true,"name":"Information-as-thing","selfAssesment":" "},{"code":"IP3-2-2","description":"Information theory answers two fundamental questions in communication theory: what is the ultimate data compression (answer: the entropy H) and what is the ultimate transmission rate of communication (answer: the channel capacity, C). For this reason, it is considered that information theory is a subset of communication theory.","hasChildren":true,"hasParent":true,"name":"Information theory","selfAssesment":"New
"},{"code":"IP3-2-3","description":"Keypoints are objects (or locations) on the ground that reveal locally invariant features in images and therefore are easily detectable by automatic algorithms. Methods for this process employ scale-invariant feature transform (SIFT) algorithms for the automatic detection of geospatial objects.","hasChildren":true,"name":"Keypoint detection","selfAssesment":"New
"},{"code":"IP3-2","description":"Image understanding is part of computer vision. Computer vision is an interdisciplinary field that deals with how computers can be made to gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to automate tasks that the human visual system can perform.","hasChildren":true,"hasParent":true,"name":"Computer vision in EO","selfAssesment":"New
"},{"code":"IP3-3-1","description":"A Digital Elevation Model (DEM) is a digital raster (or grid) representation of elevation values of land surface shapes and features, where each grid cell takes a single elevation value with reference to a certain vertical datum. A DEM can be global, regional or local in scope, and can be used to characterize the dry land surface (topography) or submerged surfaces (bathymetry). Since a DEM cannot contain information of shapes and features under overhanging structures, it is often referred to as 2.5D instead of truly 3D. \r\nA digital elevation model is an overarching term for either a digital surface model (DSM) or digital terrain model (DTM). A DSM includes elevations of surface features such as trees, buildings, bridges and artificial objects such as poles, power lines, cars etc., and thus contains always the highest elevations of any feature for any given raster cell. A DTM does not include such features but reflects the elevation of bare land surface shapes, excluding elevated or overhanging features.\r\nDEMs can be obtained using active or passive measurements. Active measurements involve the generation of electromagnetic signals towards a surface and timing the reception of the (return) signal(s). This can be achieved through laser scanning (LiDAR) using visible or infrared light pulses for bathymetric or topographic measurements respectively, radio waves (SONAR) used in bathymetric measurements, or microwaves (synthetic aperture radar, SAR) used in topographic mapping. The most widely known active remotely sensed global DEM is derived from the Shuttle Radar Topography Mission (SRTM) obtained by a SAR mounted on the space shuttle Endeavour, offering 30 m resolution with a vertical accuracy typically between 5 and 20 m, covering 80% of Earth’s surface.\r\nPassive measurements detect reflection of sun light, or energy radiated from the surfaces. Their distance to the detector can then be inferred from the measurement of angles. Historically, line scanning imagers were used, but nowadays, these are replaced by acquisitions of overlapping 2D frame images. On the images, corresponding land surface features are detected which act as tie-points. The distance between the sensor and the tie-points is calculated in a process called photogrammetry. The most widely known spaceborne passive remotely sensed global DEM is derived from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data onboard the Terra satellite. It offers similar resolution and accuracy compared to SRTM, but with 99% coverage. \r\nOnly LiDAR can generate both accurate DSMs and DTMs from the same data acquisition, by using multiple returns from a single emitted pulse. All other techniques generate DSMs, from which elevated features can be identified and filtered out in postprocessing to create DTMs, however with typically lower accuracy and more artefacts.","hasChildren":true,"hasParent":true,"name":"DEM generation","selfAssesment":"Complete
"},{"code":"IP3-3-2","description":"DSM can be produced automatically from stereo satellite scenes, from satellite sensors such as GeoEye, IKONOS, SPOT-5, Terra-ASTER etc. The DSM can also be provided from stereo digital aerial photography at various resolutions, depending on the quality and scale of the aerial photography. The quality of the automatic generated DSM is substantially improved if ground measurements from GPS are incorporated in the DSM stereoscopic model.","hasChildren":true,"name":"DSM generation","selfAssesment":"New
"},{"code":"IP3-3","description":"Stereo pairs of optical satellite images with the support of ground control points provide a basis for cross-stereo analysis for generating Digital Surface Models.","hasChildren":true,"hasParent":true,"name":"Cross-stereo analysis","selfAssesment":"New
"},{"code":"IP3-4-1-1","description":"The goal of filtering is to remove unnecessary components from images (e.g., noise), while emphasizing the necessary ones. In the context of spatial aggregation, low pass filters aim at removing sharp transitions in the image intensities (high spatial frequencies) and thereby focus the information content of the image on a coarser scale level.","hasChildren":true,"name":"Filtering","selfAssesment":"New
"},{"code":"IP3-4-1-2","description":"Gridding is the technique used to generate a uniform raster grid with one value for every cell in the raster. The values of the raster cells can represent different attributes such as mean, max or min of all Normalized Difference Vegetation Index (NDVI) values measured within a particular cell.","hasChildren":true,"name":"Gridding","selfAssesment":"New
"},{"code":"IP3-4-1","description":"Spatial aggregation produces images of coarser resolution (grouping pixels in a grid of coarser resolution and calculating mean values) or of coarser scale (by filtering with low-pass filters). Thereby it is a form of generalization that may improve classification results. Spatial aggregation can be applied after classification to get rid of the salt-and-pepper effect.","hasChildren":true,"hasParent":true,"name":"Spatial aggregation","selfAssesment":"New
"},{"code":"IP3-4-10-1-1","description":" ","hasChildren":true,"name":"Gradient boost","selfAssesment":" "},{"code":"IP3-4-10-1","description":" ","hasChildren":true,"hasParent":true,"name":"Feature engineering","selfAssesment":" "},{"code":"IP3-4-10","description":"Classification processes use features, also known as predictor variables, for discriminating between classes. A feature is an individual measurable property or characteristic of a geographic phenomenon being observed. Features in Earth observation include the individual bands of images and further properties derived from the image data. For example, the single band of a panchromatic image represents a feature that allows distinguishing between pixels of darker and lighter reflectance. Multispectral images have more bands and thereby enable the differentiation between classes by more features. This means, if two classes are different from each other in several of their properties, it becomes easier to distinguish them. The set of features used in a particular classification comprise the feature space where each feature represents one space dimension. \r\nWith an increased number of (uncorrelated) features it becomes possible to increase the number of classes that can be separated. For example land cover classifications have a large number of classes. For identifying suitable bands for optical EO satellites, the spectral signatures of all the target classes have to be analysed to identify in which bands they are separable from other classes. Classes like soil, water, and vegetation have spectral signatures that differ in particular in the blue, green, red, and infrared bands of the electromagnetic spectrum. These bands are present in virtually all multispectral sensors used for land cover classification. \r\nGeographic phenomena can be differentiated not only by their reflectance in different bands. Beyond multispectral features, the classification may include image derivatives like derived spectral indices, principal components, or filtered bands (convolution layers). Object-based image analysis also uses spatial features, i.e. distance and proximity features, planar geometric features and topological features.","hasChildren":true,"hasParent":true,"name":"Classification features and feature space","selfAssesment":"Completed
"},{"code":"IP3-4-2-1","description":"Bayes’s theorem is an extremely powerful means of using information at hand to estimate probabilities of outcomes related to the occurrence of preceding events. Bayes' Theorem uses a priori (subjective) and conditional probabilities to calculate the probability of an uncertain event occurring. A priori probabilities represent what the modeler believes, before testing, to be the probability of an event occurring. Conditional probabilities are probabilities that other events occur in conjunction with the original event.","hasChildren":true,"hasParent":true,"name":"Conditional probability","selfAssesment":"Planned
"},{"code":"IP3-4-2-2","description":"Maximum likelihood classification uses the training data for estimating means and variances of the classes, which are then used to estimate the probabilities. This method considers not only the mean, or average, values in assigning classification but also the variability of brightness values in each class.","hasChildren":true,"name":"Maximum likelihood","selfAssesment":"Planned
"},{"code":"IP3-4-3-1","description":"The Land Cover Classification System (LCCS) was developed by FAO to provide a consistent framework for the classification and mapping of land cover. Its main objectives were to overcome the rigidity of a-priori land cover classifications, which in many practical situations do not allow easy assignment into one of the pre-defined classes and are therefore not very suitable for mapping. LCCS instead opted for an approach based on two main phases. The first phase is an initial ‘Dichotomous Phase’, in which eight major land cover types are defined: (1) Cultivated and Managed Terrestrial Areas, (2) Natural and Semi-Natural Terrestrial Vegetation, (3) Cultivated Aquatic or Regularly Flooded Areas, (4) Natural and Semi-Natural Aquatic or Regularly Flooded Vegetation, (5) Artificial Surfaces and Associated Areas, (6) Bare Areas, (7) Artificial Waterbodies, Snow and Ice, and (8) Natural Waterbodies, Snow and Ice. The Dichotomous Phase is followed by a subsequent ‘Modular-Hierarchical Phase’, in which land cover classes are created by the combination of sets of pre-defined classifiers, which are different for each of the eight major land cover types. For example, common classifiers used for (semi-) natural terrestrial vegetation types are Life Form, Cover, Height, Macropattern. For aquatic or regularly flooded natural and semi-natural vegetation, water seasonality is an indispensable classifier. LCCS offers several advantages from a conceptual point of view. LCCS is a real a priori classification system in the sense that, for the classifiers considered, it covers all their possible combinations. The classification is also hierarchical and the more classifiers used, the greater the detail of the defined land cover class. The classes derived from the proposed classification system are all unique and unambiguous, due to the internal consistency and systematic description of the classes. LCCS is designed to map at a variety of scales, from small to large. From a practical viewpoint LCCS offers several advantages: (1) easy incorporation into GIS and databases, (2) allows flexible response to information available in a given area, project budget and time constraints, (3) unlinks the field data collection from the interpretation process.","hasChildren":true,"name":"Land cover classification system (LCCS)","selfAssesment":"Completed
"},{"code":"IP3-4-3","description":"Long-term monitoring of land cover and land use are particularly relevant for land ecosystem monitoring. Therefore, baseline datasets are necessary that allow assessing changes of land cover and land use where the class definitions remain consistent over time. Accordingly, classification schemes have been established that adhere to taxonomically correct definitions of classes of information organized according to logical criteria. If hard classification is to be performed (i.e. without fuzzy class boundaries), the classes in the classification system should normally be mutually exclusive, exhaustive, and hierarchical. Mutual exclusive classes have no taxonomic overlap and assign a land cover patch to a single class. An exhaustive classification scheme is able to cover the area of interest comprehensively and leaves no land cover patch unassigned. A hierarchical system allows combining sub-classes into higher-level categories.\r\nFrom a remote sensing classification perspective, it becomes clear that a classification scheme consists of information classes defined by human beings. Conversely, spectral classes are those inherent to EO data. An analyst must identify spectral classes and label them as information classes that satisfy bureaucratic (or scientific requirements). Additionally, the advantage of using established classification schemes is that their use in scientific studies and applications produces results that are comparable to other studies and suitable for sharing of data.\r\nEstablished classification schemes include: CORINE land cover (CLC), Land cover classification system (LCCS), American Planning Association land-based classification standard, United States Geological Survey land-use/land-cover classification system for remote sensor data, U.S. Department of the Interior Fish & Wildlife Service classification of wetland and deep water habitats of the United States, U.S. National Vegetation Classification system (NVCS), International Geosphere-Biosphere Program IGBP Land cover classification system.","hasChildren":true,"hasParent":true,"name":"Classification schemes (taxonomies)","selfAssesment":"Completed
"},{"code":"IP3-4-4","description":"Unsupervised methods are defined as the identification of natural groups, or structures, within existing data. Clustering requires only the number of to-be generated classes as an input parameter and assigns spectrally defined classes to an image.","hasChildren":true,"name":"Clustering (unsupervised)","selfAssesment":"New
"},{"code":"IP3-4-5-1-1","description":" ","hasChildren":true,"name":"Inference engine","selfAssesment":" "},{"code":"IP3-4-5-1","description":"A production system performs automatic transformation of remote sensing imagery into useful information (such as biophysical parameters, categorical maps etc). An example can be a preliminary pixel-based classifier that works top-down (deductive, physical model-driven, prior knowledge-based) and arrives at preliminary classes for each pixel of an image. Such a production system does not require interaction of an operator. The process makes use of a decision tree that encodes the prior knowledge for assigning pixels to a class.","hasChildren":true,"hasParent":true,"name":"Production systems","selfAssesment":"New
"},{"code":"IP3-4-5","description":"Decision trees is a data mining technique used in different disciplines including Remote Sensing.\r\nThe major advantages of decision tree methods include the ability to capture interactions between the variables used for modeling, the understandability of the produced models (trees) and their efficiency. Input data for decision trees are either a large number of examples or a large number of variables. This is important in the context of pixel-based classification in geographical information systems, where very large numbers of spatial units/points need to be classified. \r\nDecision tree consist of nodes, branches and leaves. Each node contains a test on an attribute, out of which branches are created with a grouped subset of data depending on the results of the node test. The resulting subsets will have as homogeneous values of the class as possible. This is done in a hierarchical manner dividing the training dataset until it reaches rules set at the start- the lowest number of training data within each leaf or set level of confidence.\r\nFor discrete attributes, a branch of the tree is typically created for each possible value of the attribute. For continuous attributes, a threshold is selected and two branches are created based on that threshold. This also determines whether the decision tree is called a classification or a regression tree: if we are dealing with classification (discrete target) or a regression problem (continuous target), respectively.\r\nDecision trees are derived from data only. As such, they represent the data driven or empirical approach, which is more appropriate when we have plenty of high-quality (reliable and relevant) measured data and little knowledge about the studied system, for instance what is the spectral response of each land cover class needed for classification.\r\n\r\nAn important mechanism used to improve decision tree performance is tree pruning. Pruning reduces the size of a decision tree by removing sections of the tree (subtrees) that are unreliable and do not contribute to the predictive performance of the tree.\r\nThe pruning reduces complexity of the tree and helps to achieve better predictive accuracy by the reduction of over-fitting and removal of sections of the tree that may be based on noisy or erroneous data. Depending when the pruning is done during the creation of the tree, it is called pre- or post-pruning.\r\nThe CART (Classification And Regression Trees) system is the first widely known and used system for learning decision trees. After that, notable ones are the C4.5 system for learning classification trees (or J4.8 as called within WEKA software), succeeded by C5.0.","hasChildren":true,"name":"Decision trees","selfAssesment":"Completed
"},{"code":"IP3-4-6-1","description":"Along with developing deep learning methods, Convolutional Neural Networks (CNNs) have emerged as a powerful tool by providing both remarkable performances in image processing and the ability to work in a wide variety of applications in the vision community. In the past few years, biologically inspired CNNs have emerged and proven effective in the image processing field, from social media to precision medicine and robotics. A beneficial characteristic of CNNs is data processing in multiple arrays and automatic feature extraction ability, which have received acknowledgment in the geoscience and remote sensing community.\r\nMoreover, the inherent characteristics of CNNs, such as local connectivity and weight sharing, allow this deep learning method to tackle the drawbacks of artificial feature extraction, by considering the 2-D structures and reducing network parameters using convolutional filters. CNN-based models have benefited from the recent exponential advances in imaging technologies, such as the availability of various image types (optical, RADAR, temperature and microwave radiometer, altimeter, etc.) with complex characteristics (high dimensionality, multiple scales, and nonstationary). CNNs are composed of a set of blocks that make them particularly suitable for image analysis. The multiple layers of operations, such as convolution, pooling, and nonlinear activation functions, allow for a hierarchical extraction of high-level abstract features. Accordingly, CNNs have been successfully used in image preprocessing, scene classification, pixel-based classification, image segmentation, and object detection. CNNs have been used in numerous studies, for instance: to improve image classification results to extract buildings and non-building regions automatically; to detect areas of build-up; to assess the quality of OpenStreetMap data; to detect oil spills, ships, and icebergs. Although CNNs can be considered newly introduced algorithms in geoscience and remote sensing, they are now clearly among the top performers in most of the applications.\r\nDespite this progress, the study of CNN-based approaches in the field of remote sensing and geoscience is currently at its beginning stages, and there is still much potential for new developments. In this perspective, the design of new network architectures for specific tasks, the generation of large-scale datasets for network training, the integration of conventional techniques for various remote sensing data, the advancement and analysis of existing networks concerning their architectures, optimization techniques, and the regularization strategies are still open topics, which are in close relation with each other and should be jointly considered.","hasChildren":true,"name":"Convolutional neural networks (CNNs)","selfAssesment":"Completed
"},{"code":"IP3-4-6","description":"Deep learning (DL), as a subfield of artificial intelligence (AI) and machine learning (ML), is the fastest-growing trend in data analysis and is regarded as a breakthrough. Over the past few years, there has been an ongoing shift toward using DL methods in different applications, mainly due to the increasing data accessibility and computational processing power. DL models characterized by neural networks are learning methods with multiple levels of representation that learn the semantic and discriminative features in a sequential bottom-to-up manner from the data. They are composed of several levels of non-linear modules that each modify the representation at a lower level into a higher or slightly more abstract level. As such, very complex functions can be learned without depending on human-crafted features.\r\nDL has been used in several research fields, such as speech recognition, stereo vision, medical image recognition, remote sensing, time-series analysis, biomedicine, agriculture, and geosciences. One of the limiting factors of using DL models is that they require significant amounts of training samples compared to conventional ML methods To date, several DL architectures have been introduced, of which the stacked autoencoder, convolutional neural network, generative adversarial network, deep belief network, and recurrent neural network have become mainstream. DL techniques have had significant successes in several fields, which have been widely accepted as challenges in recent decades. Moreover, by growing big data and their applications in practical productions and developed time-efficient networks or public online free or commercial cloud computing platforms, such as Google, Amazon, Microsoft, and IBM, much more attention will be paid to develop new DL networks for the practical projects.","hasChildren":true,"hasParent":true,"name":"Deep learning","selfAssesment":"Completed
"},{"code":"IP3-4-7-1","description":"The RF classifier is an ensemble classifier that uses a set of Classification and Regression Trees (CARTs) to make a prediction The trees are created by drawing a subset of training samples through replacement (a bagging approach).","hasChildren":true,"name":"Random forest (RF)","selfAssesment":"New
"},{"code":"IP3-4-7-2","description":"In machine learning, support vector machines (SVMs) are supervised non-parametric statistical learning techniques with associates learning algorithms that analysze data used for both classification and regression analysis. SVM algorithm was originally designed for binary classification. The SVM is based on the main hypothesis that the training set is linearly separable. Given a set of training examples, each marked as belonging to one or another of two categories, an SVM training algorithm builds a model that can assign each new occurrence into one of these two categories, making it a non-probabilistic binary linear classifier. The SVM model is a representation of the examples as points in space, mapped so that the algorithm can find the optimal line (hyperplane) which separates with minimum error the training set, and maximizes the distance, named the “gap”, between the objects of both classes and the hyperplane. Thus, instead of using the whole available training set to describe classes, SVM uses only those training samples that describe class boundaries (support vectors), thought it can be more efficient than other algorithm because it uses a subset of training points. New occurs are then mapped into that same space and predicted to belong to a category based on the side of the gap on which they fall. In addition to performing linear classification, SVMs can also efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces. Unfortunately, because of the technique used for separating classes SVM is less effective on noisier datasets with overlapping classes. When data are unlabelled, supervised learning is not possible, and an unsupervised learning approach is required. SVM is used for text classification tasks such as category assignment, spam detection and sentimental analysis. It is also commonly used for image recognition, performing particularly well in aspect-based recognition and colour-based recognition. SVM also plays a vital role in many areas of handwritten digit recognition, such as postal automation services.","hasChildren":true,"name":"Support vector machines (SVM)","selfAssesment":"Completed
"},{"code":"IP3-4-7","description":"Field of study that gives computers the ability to learn without being explicitly programmed","hasChildren":true,"hasParent":true,"name":"Machine learning","selfAssesment":"New
"},{"code":"IP3-4-8","description":"Image classification operator needs a set of terms to express the characteristics of an image. These characteristics are called interpretation elements and are used to define interpretation keys: tone/hue, texture, pattern, shape, size, height/elevation, location/association","hasChildren":true,"name":"Mental concepts and categories","selfAssesment":"New
"},{"code":"IP3-4-9-4","description":" ","hasChildren":true,"name":"Stratified random sampling","selfAssesment":" "},{"code":"IP3-4-9-5","description":" ","hasChildren":true,"name":"Sample augmentation","selfAssesment":" "},{"code":"IP3-4-9","description":"Sampling strategies or sampling pattern specifies the arrangement of observations used for training and/or validation purposes.\r\nTypically, the simple random sample of a geographic region is defined by first dividing the region to be studied into a network of cells. Each row and column in the network is numbered, then a random number table is used to select values that, taken two at a time, form coordinate pairs for defining the locations of observations. Because the coordinates are selected at random, the locations they define should be positioned at random. The random sample is probably the most powerful sampling strategy available as it yields data that can be subjected to analysis using inferential statistics.\r\nA stratified sampling pattern assigns observations to subregions of the image to ensure that the sampling effort is distributed in a rational manner. For example, a stratified sampling effort plan might assign specific numbers of observations to each category on the map to be evaluated. This procedure would ensure that every category would be sampled.\r\nSystematic sampling positions observations at equal intervals according to a specific strategy. Because selection of the starting point predetermines the positions of all subsequent observations, data derived from systematic samples will not meet the requirements of inferential statistics for randomly selected observations.","hasChildren":true,"hasParent":true,"name":"Sampling strategies","selfAssesment":"New
"},{"code":"IP3-4","description":"The process of image classification extracts information about semantic labels of pixels or objects (i.e. regions) from imagery. Apart of input imagery, the process requires an input set of target classes (classification scheme) for which their spectral (and other) properties have to be identified. A classification method has to be selected that transforms the image data and the classification scheme into semantic map information. In complement to the resulting sematic labelling products, a secondary outcome are instructions or rulesets with the used parameters that constitute the documentation of the classification process.\r\nThe input imagery consists of one or more images (optical and/or SAR data) of a specific geographic area, collected in multiple bands of the electromagnetic spectrum (that may have already undergone certain pre-processing steps; determined by the purpose). Additionally, the imagery may include derived spectral indices, principal components, filtered bands, or other features to support the classification process.\r\nThe classification purpose defines the information about the target classes. It includes classification schemes (taxonomies), spectral signatures for each class and, mental concepts and categories about the classes (that enable an analyst to distinguish classes by texture, spatial relationships etc.). Often, training areas are used to understand how an object of a particular class is discernible in the available imagery and separable from other classes. Both the input imagery and the chosen classification method determine which features of each class can be exploited for classification. For example, spectral signatures of the target classes (extracted from training areas with known class label) may be a suitable input for extracting information with a pixel-based classification. For shape features, objects are a pre-requirement, derived with segmentation. They are only available with object-based classification approaches.\r\nClassification methods: Various methods exist that can be categorized according to the classification logic that they follow when transforming the input information into the output semantic labelling products. These can be parametric or nonparametric, supervised or unsupervised, per-pixel or object-oriented, semi-automated or fully automatic, and hybrid approaches. Classification methods are for example bayesian techniques like conditional probability or maximum likelihood, clustering (unsupervised), decision trees, deep learning and machine learning.","hasChildren":true,"hasParent":true,"name":"Image classification","selfAssesment":"Completed
"},{"code":"IP3-5-1","description":"Edge detection is a fundamental tool used in many image processing applications to obtain information from the frames as a precursor step to feature extraction and object segmentation. This process detects outlines of an object and boundaries between objects and the background in the image. An edge-detection filter can also be used to improve the appearance of blurred image.","hasChildren":true,"name":"Edge-based segmentation","selfAssesment":"Planned
"},{"code":"IP3-5-2","description":"Histogram-based segmentation makes use of histogram to select the gray levels for grouping the pixels into regions, e.g. background and the object of interest","hasChildren":true,"name":"Histogram-based segmentation","selfAssesment":"New
"},{"code":"IP3-5-3","description":"Local variance can be calculated as the value of standard deviation in a small neighborhood (e.g. 3x 3 moving window), then computing the mean of these values over the entire image. The obtained value is an indicator of the local variability in the image.","hasChildren":true,"name":"Local variance","selfAssesment":"New
"},{"code":"IP3-5-4","description":"Mean Shift is defined as finding modes in a set of data samples, manifesting an underlying probability density function (PDF).","hasChildren":true,"name":"Mean-shift segmentation","selfAssesment":"New
"},{"code":"IP3-5-5","description":"Regionalization is an important concept in Geographic Information Science for synthesizing multi-dimensional data into homogeneous objects through spatially constrained clustering methods","hasChildren":true,"name":"Regionalisation","selfAssesment":"New
"},{"code":"IP3-5-6-1","description":"Multi-resolution segmentation is a region-growing algorithm. It relies on several parameters, which need to be tuned. These include the scale parameter (SP), which dictates the size and homogeneity of the resultant objects.","hasChildren":true,"name":"Multi-resolution segmentation","selfAssesment":"Planned
"},{"code":"IP3-5-6-2","description":"Watershed segmentation is a region-based method that has its origins in mathematical morphology. In watershed segmentation an image is regarded as a topographic landscape with ridges and valleys. The elevation values of the landscape are typically defined by the gray values of the respective pixels or their gradient magnitude. Based on such a 3D representation the watershed transform decomposes an image into catchment basins. For each local minimum, a catchment basin comprises all points whose path of steepest descent terminates at this minimum. Watersheds separate basins from each other. The watershed transform decomposes an image completely and thus assigns each pixel either to a region or a watershed.","hasChildren":true,"name":"Watershed segmentation","selfAssesment":"New
"},{"code":"IP3-5-6","description":"Region-based segmentation algorithms can be devided into region growing, merging and splitting techniques and their combinations. Region merging starts from all pixels on the pixel level and iteratively aggregates pixels into objects until some conditions of homogeneity imposed by the user are met.","hasChildren":true,"hasParent":true,"name":"Region-based segmentation","selfAssesment":"New
"},{"code":"IP3-5-7","description":"Spatial autocorrelation is the term used to describe the presence of systematic spatial variation in a variable.","hasChildren":true,"name":"Spatial autocorrelation","selfAssesment":"New
"},{"code":"IP3-5","description":"The term image segmentation denotes the process of algorithmically grouping neighbouring pixels that are similar. What sounds rather straight forward, is in fact a great computational challenge, some even call it an ill-posed problem, because there is a high degree of ambiguity in this process. \r\nThe two attributes in the general definition provided above, i.e. neighbouring and similar, evoke the principles of regionalisation as a fundamental concept in geography. Regionalisation is the bottom-up approach to congregate adjacent elements with the aim to form a larger unit. (Conversely, this could be understood in a top-down manner when subdividing a larger whole into smaller homogeneous units). This follows the general notion of hierarchical organisation according to general systems theory (GST). The organisation of a state in smaller administrative units is a good example for a hierarchical structure, the composition of the human body by organs, cells, etc. another. In image analysis such regions are commonly referred to image regions, originating from the concept of “photomorphic regions”, literally meaning regions formed on images – originally by human interpreter through manual delineation. Today, advanced pixel grouping algorithms aim to delineate homogenous regions in an image automatically. As those regions usually are assumed to match with real-world objects, it is often stated in literature that image segmentation generates image objects. Deriving some general heuristics on their properties (colour, size, shape, orientation, etc.) we can label these objects according to a given semantic scheme. The procedure of object delineation and classification using object features and relations is a fundamental principle in object-based image analysis (OBIA). \r\nDue to the effect of spatial autocorrelation (the tendency of neighbouring pixels to be similar irrespective of scale or geographical location), pixel grouping is ambiguous and by no means trivial, but not arbitrary either. Intuitively, image regions are those quasi-homogeneous areas that we perceive as landscape units on a specific scene (a lake, a forest patch, a single tree, a building, a residential area). According to hierarchy theory, we can assume that we find multiple scales within a single image even, according to the level of detail we are interested in. Whether or not a specific grouping of pixels is considered valid, e.g. because it corresponds to a real-world object, can hardly be answered unanimously, but rather needs to be judged by experts in the respective application domain. That is why often in literature we find the term ‘meaningful objects’. \r\nImage segmentation is as a sub-field of computer vision and aims to apply computer algorithms to generate image regions (a.k.a. tokens) within digital image analysis. There are several strategies for performing image segmentation, all resting on the following general principles: (1) regions do not overlap; (2) regions are (relatively) homogenous; regions are (relatively) different to neighbouring regions; regions are fairly equally sized (belong to one scale domain) but can be built in several hierarchical scales. General strategies include (1) edge-based segmentation and (2) region-based segmentation, and multi-scale segmentation as a specific case. \r\nAlso referred to spatial classification emphasizing the constraint of spatial contingency, image segmentation aggregates neighbouring pixels, but – as compared to statistical clustering techniques – does not provide a unique set of classes (either semantic or statistic) in the feature space. \r\nRecently the term semantic segmentation has emerged in the machine-learning community, which is in fact a combination of segmentation and categorisation (labelling) via deep learning methods (e.g. convolutional neural networks).","hasChildren":true,"hasParent":true,"name":"Image segmentation","selfAssesment":"Completed
"},{"code":"IP3-6-1","description":"Combined filtering uses different filters to arrive at more complex filters for specific purposes. \r\nFor example, Laplacian filters are derivative filters used to find areas of rapid change (edges) in images. Since derivative filters are very sensitive to noise, it is common to smooth the image (e.g., using a Gaussian filter) before applying the Laplacian. This two-step process is called the Laplacian of Gaussian (LoG) operation.","hasChildren":true,"hasParent":true,"name":"Combined filtering","selfAssesment":"New
"},{"code":"IP3-6-2","description":"The aim of sharpening filters is to highlight transitions in intensity (high frequency components) using different operators: directional (horizontal, vertical, diagonal) or isotropic (e.g. Laplacian Filter). Example of edge detectors include: Gaussian edge detector, Laplacian filter etc.","hasChildren":true,"name":"Edge detectors","selfAssesment":"New
"},{"code":"IP3-6-3-1","description":"The Lee-sigma filter is a conceptually simple but effective alternative to the Lee and other sophisticated adaptive filters. It is based on the sigma probability of the Gaussian distribution.","hasChildren":true,"name":"Lee-Sigma","selfAssesment":"New
"},{"code":"IP3-6-3","description":"High-pass filtering enhance information of high frequencies (local extremes, lines, edges)","hasChildren":true,"hasParent":true,"name":"High-pass filtering","selfAssesment":"New
"},{"code":"IP3-6-4-1","description":"Gaussian Filters are isotropic (same behavior in all directions).","hasChildren":true,"name":"Gauss filter","selfAssesment":"New
"},{"code":"IP3-6-4","description":"Spatial filters transform an image by taking into account the local neighborhood of a pixel. The goal of filtering is to remove unnecessary components from images (e.g., noise), while emphasizing the necessary ones. In this context, low pass filters aim at removing sharp transitions in the image intensities (high spatial frequencies).","hasChildren":true,"hasParent":true,"name":"Low-pass filtering","selfAssesment":"New
"},{"code":"IP3-6","description":"In contrast to the point operations used for radiometric modification of image data, techniques for geometric processing are characterized by operations over local neighborhoods of pixels. The result of a neighborhood operation is still a modified brightness value for the single pixel at the center of the neighborhood , however the new value is determined by the brightness of all the local neighbors rather than just the original brightness value of the central pixel alone.","hasChildren":true,"hasParent":true,"name":"Kernel analysis (convolution)","selfAssesment":"Planned
"},{"code":"IP3-7-1","description":"Class modelling provides flexibility in designing a transferable workflow from scene-specific high-level segmentation and classification to region-specific multi-scale modelling","hasChildren":true,"name":"Class modelling","selfAssesment":"Planned
"},{"code":"IP3-7-2","description":"Hierarchical representation refers to hierarchically scaled compositions of the classes to be classified.","hasChildren":true,"name":"Hierarchical representation","selfAssesment":"New
"},{"code":"IP3-7-3","description":"Per-parcel analysis relies on parcels or objects as the smallest units of image analysis. The parcels are usually obtained through image segmentation that partition the input images into homogeneous units, i.e. parcels, in a supervised or unsupervised manner.","hasChildren":true,"name":"Per-parcel analysis","selfAssesment":"New
"},{"code":"IP3-7-4-1","description":"Distance relationships describe how far an object is with respect to a reference. Proximity analysis allows the identification of the distance between a geographic feature of interest and its neighbors.","hasChildren":true,"name":"Distance and proximity features","selfAssesment":"New
"},{"code":"IP3-7-4-2","description":"The most important geometric features of geographic objects are their size and shape. Shape refers to general form or outline of individual objects and can be quantified using different metric such as shape index, compactness, asymmetry, density, elliptic fit, roundness, rectangular fit etc.","hasChildren":true,"name":"Planar geometric features","selfAssesment":"New
"},{"code":"IP3-7-4-3","description":"Topological features characterize qualitatively the position of spatial objects relative to each other. There are different models for representing topological relationships. Calculus-based method, for example, allows us to model five topological relationships of two spatial objects: touch, in, cross, overlap, disjoint.","hasChildren":true,"name":"Topological features","selfAssesment":"New
"},{"code":"IP3-7-4","description":"An object of a specific object class has a value on the range of values of a spatial or spectral feature. A set of features provides the feature space that is used for classification.","hasChildren":true,"hasParent":true,"name":"Spatial features","selfAssesment":"Planned
"},{"code":"IP3-7","description":"OBIA is an iterative method that starts with the segmentation of satellite imagery into homogeneous and contiguous image segments (also called image objects. In the next step, resulting image segments are assigned to the target classes.","hasChildren":true,"hasParent":true,"name":"Object-based image analysis (OBIA)","selfAssesment":"Planned
"},{"code":"IP3-8-1","description":"The feature space represents in various dimensions all the features that can be used for classification (e.g. image bands, band math parameters, derived texture properties). A point in that space is also called a vector with values for each feature (or dimension). Polyhedralization is a form of vector space quantization where a vector is assigned to the closest centre point of one polyhedron.","hasChildren":true,"name":"Feature space polyhedralization","selfAssesment":"New
"},{"code":"IP3-8-2","description":"Radiative transfer models describing the interaction between matter and electromagnetic radiation serve as cornerstones for optical remote sensing. The radiative transfer theory provides the most logical linkage between observations and physical processes that generate signals in optical remote sensing. Radiative transfer modelling is therefore an integral part of remote sensing, since it provides the most efficient tool for accurate retrievals of Earth properties from satellite data. Radiative transfer models are used in a number of different applications such as sensor radiometric calibration, atmospheric correction and the modelling radiation processes in vegetation canopies. \r\nVegetation radiative transfer models (RTMs) study the relationship between leaf and canopy biophysical variables and reflectance, absorbance and scattering mechanisms. The infinite variability of vegetation structure complicates the modeling of RT in vegetation canopies. Numerous models of RT in vegetation canopies were developed in the second half of the last century. Models differ by the details accounted for and by the simplifications introduced in the description of canopy structure and photon–vegetation interactions. Gradual improvement in RTMs accuracy, yet in complexity too, have diversified RTMs from simple turbid medium RTMs towards advanced Monte Carlo RTMs that allow for explicit 3D representations of complex canopy architectures. This evolution has resulted in an increase in the computational requirements to run the model, which bears implications towards practical applications. When choosing an RTM, a trade-off between invertibility and realism has to be made: simpler models are easier to invert but less realistic, while advanced models more realistic but require a large amount of variables to be configured. The two most widely used models are the leaf model PROSPECT and Scattering by Arbitrary Inclined Leaves (SAIL) canopy model. \r\nAtmosphere RTMs study the interaction of radiation with the atmosphere. The remotely-sensed signals at satellite or airborne platforms are combinations of surface and atmospheric contributions, with relative amounts varying across the two wavelength regions, depending on the condition of the atmosphere. The order of magnitude of atmosphere signals can be equal or larger than that of land or ocean surface signals that arise at the top of the atmosphere (TOA). In order to derive accurate sensor calibration and atmospheric correction, the contribution of the atmospheric constituents to the total retrieved signal must be understood and modelled. Atmospheric radiative transfer models simulate the radiative transfer interactions of light scattering, absorption and emission through the atmosphere. Some widely used atmospheric RTMs are 6SV, libRadtran, MODTRAN, and ATCOR.\r\nAdvances in radiative transfer modeling enhance our ability to detect and monitor changes in our planet through new methodologies and technical approaches to analyze and interpret measurements from air- and space-borne sensors.","hasChildren":true,"hasParent":true,"name":"Radiative transfer modelling","selfAssesment":"Completed
"},{"code":"IP3-8","description":"Historically, physical modelling and machine learning have often been treated as two different fields with very different scientific paradigms (theory-driven versus data-driven). Yet, in fact these approaches are complementary, with physical approaches in principle being directly interpretable and offering the potential of extrapolation beyond observed conditions, whereas data-driven approaches are highly flexible in adapting to data and are amenable to finding unexpected patterns (surprises).","hasChildren":true,"hasParent":true,"name":"Physical-model based analysis","selfAssesment":"New
"},{"code":"IP3-9-1","description":"Difference of Gaussians (DoG) method consists of subtracting two Gaussians, where a kernel has a standard deviation smaller than the previous one. The convolution between the subtraction of kernels and the input image results in the edge detection of this image.","hasChildren":true,"name":"Difference of Gaussian (DoG)","selfAssesment":"New
"},{"code":"IP3-9-2","description":"Scale Invariant Feature Transform (SIFT) is an image descriptor for image-based matching and it is used for a large number of purposes in computer vision related to point matching between different views of a 3-D scene and view-based object recognition. The SIFT descriptor is invariant to translations, rotations and scaling transformations in the image domain and robust to moderate perspective transformations and illumination variations. Experimentally, the SIFT descriptor has been proven to be very useful in practice for robust image matching and object recognition under real-world conditions.","hasChildren":true,"name":"Scale invariant feature transformation (SIFT)","selfAssesment":"New
"},{"code":"IP3-9","description":"Scale-space theory is a framework for multiscale image representation, which has been developed by the computer vision community with complementary motivations from physics and biologic vision. The idea is to handle the multiscale nature of real-world objects, which implies that objects may be perceived in different ways depending on the scale of observation. If one aims to develop automatic algorithms for interpreting images of unknown scenes, there is no way to know a priori what scales are relevant. Hence, the only reasonable approach is to consider representations at all scales simultaneously.","hasChildren":true,"hasParent":true,"name":"Scale space analysis","selfAssesment":"New
"},{"code":"IP3","description":"Image data, in order to be turned into information, require interpretation. Thereby image understanding is the process of scene reconstruction, the description and mental representation of the content of imaged, and potentially complex, realities. \r\nImage understanding thereby goes beyond single feature extraction. Instead, it aims at a complete description of the image content, i.e. the reconstruction of a real-world scene. In the early days of digital image processing, image understanding was mainly confined to identifying and labelling image primitives. Today, advanced mapping keys and hierarchical classification schemes to analyse EO data, include composite and complex target classes. Thereby ‘full’ scene description means reaching from signal processing to a symbolic representation of the scene content. This entails the relationships of real‐world objects in different scales and spatio-temporal aspects.\r\nDescribing a scene, visually or computer-aided or mixed, depends on a conceptual framework comprising (a) the underlying research question within (b) a specific field of application and (c) pre‐existing knowledge and experience of the operator. Obtaining insights from imagery requires general knowledge about the expected scene content and domain expertise. The field of image understanding is interlinked with image (pre-)processing, computer vision, and artificial intelligence (AI). Image processing conditions the data material and enhances the interpretation source. Computer vision including pattern recognition providing knowledge representation, expert systems. AI is mainly concerned with automation processes, be it via knowledge transfer to an automated system or machine / deep learning.\r\nIn analogy to the human mind, image understanding is the computational process of extracting information from images, i.e. locating, characterizing, and recognizing objects and other features in the depicted scene. However, image understanding is not a linear, but rather a cyclic process and takes place during the pre-processing and data assimilation steps. For example, cloud masks on EO images is an early product of image understanding, prior to many pre-processing tasks.\r\nIn a typical GEOBIA workflow, the process of image understanding can be illustrated by the following steps: Starting from the subset of a real‐world scene captured on an image first step may entail scaled representations by grouping neighbouring pixels on several hierarchical sales. The multi‐scale segmentation provides a set of nested objects with geospatial and spectral properties to be used in the classification process. \r\nWith object hypotheses in mind the object relation modelling can be realized by encoding expert knowledge into a rule system. This setp aims at categorizing the image objects by their spectral and spatial properties and their mutual relationships. Hereby, an object‐centred view is accomplished. This representation of the image content should meet the conceptual reality of the interpreter or user. Knowledge is stepwise adapted and improved through progressive interpretation and modelling. Experience grows, as knowledge will be enriched by analyzing unknown scenes and the transfer of knowledge may incorporate or stimulate new rules.","hasChildren":true,"hasParent":true,"name":"Image understanding","selfAssesment":"Completed
"},{"code":"IP4-1-1","description":"Once the user finds the required data, she/he needs to know how can they be accessed, possibly including authentication and authorisation.","hasChildren":true,"name":"Accessibility","selfAssesment":"New
"},{"code":"IP4-1-2","description":"Quality Indicators (QIs) should be ascribed to data and, in particular, to delivered information products, at each stage of the data processing chain - from collection and processing to delivery. A QI should provide sufficient information to allow all users to readily evaluate a product’s suitability for their particular application, i.e. its “fitness for purpose”.","hasChildren":true,"name":"GEO QA4EO","selfAssesment":"New
"},{"code":"IP4-1-4","description":"ISO is an independent, non-governmental international organization with a membership of 164 national standards bodies. Through its members, it brings together experts to share knowledge and develop voluntary, consensus-based, market relevant International Standards that support innovation and provide solutions to global challenges. ISO/TC 211 Geographic information/Geomatics provides Standardization in the field of digital geographic information. Note: This work aims to establish a structured set of standards for information concerning objects or phenomena that are directly or indirectly associated with a location relative to the Earth. These standards may specify, for geographic information, methods, tools and services for data management (including definition and description), acquiring, processing, analyzing, accessing, presenting and transferring such data in digital / electronic form between different users, systems and locations.","hasChildren":true,"name":"ISO standards","selfAssesment":"New
"},{"code":"IP4-1-5","description":"The OGC is the worldwide leading consortium of GIS industries promoting the interoperability of geographic information across platform, system, and country borders. The main field of current activity is the complete integration of the sources of geographic information based on the Internet.The Open GIS Consortium (OGC) plays an important role on the implementation level.","hasChildren":true,"name":"OGC standards","selfAssesment":"New
"},{"code":"IP4-1-6","description":"A fundamental pillar in (open) science is to verify the scientific results of others to advance knowledge. The lack of reproducibility in scientific studies brings challenges in understanding and recreating the results of others, a situation that may be common in data-based and algorithm-based research like in geocomputation. In general, many authors define reproducibility as the ability to compute exactly the same results of a study based on original input data and analysis workflow. In other words, “to rerun the same computational steps on the same data the original authors used”. Replicability is often seen as obtaining similar conclusions about a research question derived from an independent study or experiment. In the field of GIScience and geocomputation, in particular, a reproduction is always an exact copy or duplicate, with exactly the same features and scale, while a replication resembles the original but allows for variations in scale, for example. Hence, reproducibility is exact whereas replicability means confirming the original conclusions, although not necessarily with the same input data, methods, or results.","hasChildren":true,"name":"Replicability and reproducibility","selfAssesment":"Completed
"},{"code":"IP4-1-7","description":"The ultimate goal of FAIR is to optimise the reuse of data. To achieve this, metadata and data should be well-described so that they can be replicated and/or combined in different settings.","hasChildren":true,"name":"Reusability","selfAssesment":"New
"},{"code":"IP4-1","description":"Data quality standards are guiding principles and operational guidelines for the production and use of data. For example, QA4EO aims for the two key principles of accessibility / availability and suitability / reliability. The QA4EO guidelines provide instructions for the implementation of processes that follow these principles. Standards emerge from standardization processes within the community. They are based on the agreement of the members of the community.","hasChildren":true,"hasParent":true,"name":"Data quality standards","selfAssesment":"New
"},{"code":"IP4-2-1-1","description":"To correctly perform a classification accuracy (or error) assessment, it is necessary to systematically compare two sources of information: (1) pixels or polygons in a remote sensing-derived classification map, and (2) ground reference test information (which may in fact contain error). The relationship between these two sets of information is commonly summarized in an error matrix (sometimes referred to as contingency table or confusion matrix). Indeed, the error matrix provides the basis on which to both describe classification accuracy and characterize errors, which may help refine the classification or estimates derived from it.","hasChildren":true,"name":"Error matrix","selfAssesment":"New
"},{"code":"IP4-2-1-2","description":"F-score represents the harmonic mean between precision and recall. As F-score combines both precision and recall, it can be regarded as an overall quality measure. The range of F is from 0 to 1 with larger values representing higher accuracy.","hasChildren":true,"name":"F-score","selfAssesment":"New
"},{"code":"IP4-2-1-3","description":"Ground reference refers to the reference dataset for an accuracy assessment of a remote sensing classification. The process of obtaining ground reference is dedicated to support the production of suitable accuracy information. A sampling design (fitting to the produced image classification) determines the most appropriate distribution of sample locations (or regions). The response design consists of the evaluation protocol and the labeling protocol. The evaluation protocol initiates selecting the support region on the ground (represented by a pixel or polygon) where the ground information will be collected. Once the location and dimension of the sampling unit are defined, the labelling protocol is initiated and the sampling unit is assigned a hard or fuzzy ground reference label. This ground reference label (e.g. forest) is paired with the remote sensing-derived label (e.g., forest) for assignment in the error matrix.","hasChildren":true,"name":"Ground reference","selfAssesment":"New
"},{"code":"IP4-2-1-4","description":"Kappa is a value for measuring the overall accuracy of a classification that accounts for randomness of class assignment. Kappa analysis is a discrete multivariate technique of use in accuracy assessment. Kappa yields a statistic, ^K, which is an estimate of Kappa. It is a measure of agreement between the remote sensing-derived classification map and the reference data as is indicated by a) the major diagonal and b) the chance of agreement, which is indicated by the row and column totals in the error matrix.","hasChildren":true,"name":"Kappa statistics","selfAssesment":"New
"},{"code":"IP4-2-1-5","description":"These two quality assessment indicators are calculated as follows:\r\nPrecision = TP/(TP+FP) \r\nRecall = TP/(TP+FN),\r\nwhere TS is true positive, FP is false positive, FN is false negative","hasChildren":true,"name":"Precision & recall","selfAssesment":"New
"},{"code":"IP4-2-1-6","description":"Geometric correction procedures (image-to-map rectification, image-to-image rectification) are used to rectify remotely sensed data to a standard map projection whereby it may be used in conjunction with other spatial information in a GIS to solve problems. The rectification process normally involves selecting ground control point (GCP) image pixel coordinates (row and column) with their map coordinate counterparts (e.g. meters northing and easting in a UTM map projection). Rectification requires that polynomial equations (that translate from image coordinates to map coordinates) be fit to the GCP data using least squares criteria. Depending on the distortion in the imagery, the number of GCPs used, and the degree of topographic reliefdisplacement in the area, higher -order polynomial equations may be required to geometrically correct the data. To determine how well the six coefficients derived from the least-squares registration of the initial GCPs account for geometric distortion in the inpit image, for each GCP, the root-mean-square error (RMSE) is computed.","hasChildren":true,"name":"Root mean square error (RMSE)","selfAssesment":"In progress
\r\n\r\n"},{"code":"IP4-2-1","description":"A growing set of EO services and applications produce EO products that describe various aspects of the land, ocean and atmosphere. These products include for example image products at different processing levels, geometric measurements like in digital elevation models, semantic labelling products like land cover classifications, and EO-derived attribute products concerning air quality or other geophysical and biophysical parameters. Same as any geospatial data, EO products are not free of error and require accompanying documentation of their product quality. One term for describing different quality dimensions of an EO product is accuracy.\r\nAccuracy is a measure to estimate the uncertainty that originates from errors. An error is the deviation of a map value from a true value. The concept of error assumes well-defined phenomena where deviation results from imperfection of measurement equipment, environment effects, or imperfections of the observer. They cause gross errors and blunders, systematic errors, and random errors, for which different approaches are necessary to minimize error. Ideally, only random error remains that is probabilistic in nature and can be assessed with statistical approaches. For poorly defined phenomena, the concept of vagueness applies. For example in the case of thematic maps using fuzzy sets, the accuracy assessment requires a fuzzy approach as well. \r\nJudging error requires reference data with higher accuracy (by an order of magnitude) to which the map value can be compared. EO product quality dimensions about accuracy include thematic accuracy, spatial accuracy (both horizontal and vertical), radiometric accuracy, and accuracy of biophysical/geophysical parameter measurements. Respective equipment and approaches for reference data collection includes ground verification for thematic maps, GNSS positioning devices, field spectrometers, air quality sensors and in-situ biomass estimation. Ideally, reference data is collected in the field. In case of inaccessible areas of interest and/or if the service requirements allow it, approaches may rely on proxy reference data.\r\nThe design of the accuracy assessment procedure should be done with the EO product design to match the requirements of the EO service. For example, a thematic accuracy assessment consists of the main three components of response design, analysis, and sampling design. The response design ensures that reference data and map data are comparable at a location and specifies under which cases they agree or disagree. The analysis, usually performed with an error matrix, specifies which quality indicators will be calculated to quantify accuracy. The sampling design specifies the subset of locations at which the response design will be applied. Depending on the classification process and application case, different sampling strategies can be suitable (e.g. clustered sampling, stratified random sampling). \r\nFor other accuracy dimensions, respective accuracy assessment procedures exist, e.g. root mean squared error (RSME) for the positional accuracy assessment.\r\nAfter an accuracy assessment has been performed and the uncertainty in the EO product is understood, the challenge is to clarify how the uncertainty affects subsequent spatial analyses with the EO product. Different strategies exist that ignore error completely or that account for error by modelling uncertainty in the analysis outcomes. If uncertainty is judged low enough (or more hazardous, if users are unaware of the limited accuracy), subsequent analyses accept the EO product as true and ignore the accuracy value. If uncertainty is incorporated in subsequent analysis through uncertainty modelling, the results describe the bandwidth of outcomes, potentially supported with appropriate visualisations of uncertainty. The uncertainty modelling approach may greatly enhance the usability of the EO product, because it informs better how the error impacts the EO information and how much confidence a user should have in it.\r\nWith a new generation of EO products on the horizon and a largely increased user community, a large number of new applications is to be expected. They may also identify innovative accuracy assessment approaches. For example, the availability of EO archives with long time series of EO data led to response design protocols tailored to collect time series of reference data. The use of volunteered geographic information (VGI) as reference data has great potential, if approaches are implemented that ensure its reliability. Methods for object-based accuracy assessment are continued to be developed. Further, the increasing number of EO parameter products based on continuous variables creates the need to describe their accuracy. Finally, the focus on validation of EO products during EO service development and operation will make feedback from users available to service providers, ultimately leading to more meaningful EO products with more meaningful accuracy metrics and other quality indicators.","hasChildren":true,"hasParent":true,"name":"Accuracy assessment","selfAssesment":"
Completed
"},{"code":"IP4-2-2","description":"The implementation of a service that provides remote sensing derived information on a regular basis introduces process-related quality criteria like the timeliness of information provisioning. For the case of refugee camp mapping, timely arrival of map information may be critical to support the decisions in planning facilities for humanitarian assistance.","hasChildren":true,"name":"Timeliness","selfAssesment":"New
"},{"code":"IP4-2-3-1","description":"Completeness is a quality dimension that can apply to different data properties.The Data completeness is dealing with the completeness of an image, handling for example the effect of shadowing objects, sun flares on water surfaces or masking out by an object (e.g. propeller of a UAV). Spatial completeness is a feature on the area coverage. In photogrammetry (especially in stereophotogrammetry) its 3D version, the stereo completeness has extreme importance. In monitoring systems and applications the Temporal completenesster term features how the taken images represent a complete time series. The thematic completeness measure describes the image interpretation quality how the expected and defined classes are evaluated. This feature is important with the use of e.g. multiple classifiers.","hasChildren":true,"name":"Completeness","selfAssesment":"New
"},{"code":"IP4-2-3-2","description":"In remote sensing we can speak about spatial consistency in the Consistency cluster. It represents the quality of image interpretation/understanding: how are the different objects or classes recognized/evaluated integrally. A bridge above a water surface, like river can be detected in pixel-wised manner, but the question is how coherent they are in the output map. This phenomenon has very close to the thematic consistency, where the recognition integrity is represented in this way. The topological consistency is defined mainly for network-type surface objects, like roads or rivers, where the connection of all atomic segments are rated by this measure. Urban mapping focuses on the built environment objects, where e.g. house-parcel inclusions are described by this feature. The temporal consistency is for monitoring again, representing for example the possibility or impossibility of land cover changes in time. Having multiple data sources (even airborne or terrestrial), their integral usage can be qualified by this measure.","hasChildren":true,"name":"Consistency","selfAssesment":"New
"},{"code":"IP4-2-3-3","description":"Readability refers to the content of a map being presented clearly enough that the content can be perceived and understood by the user. This includes legibility, e.g. whether the text of a label is large enough to be read and has enough contrast to the background to be easily perceivable. Additionally, readability has a broader meaning that explains whether a product as a whole is simple enough to be understood and not too complex that essential information can be overlooked by the user.","hasChildren":true,"name":"Readability","selfAssesment":"New
"},{"code":"IP4-2-3","description":"Gathering information about the quality of an EO product or service by letting the user test it. The feedback from the user enables to verify whether specific quality criteria have been met.","hasChildren":true,"hasParent":true,"name":"User validation","selfAssesment":"New
"},{"code":"IP4-2","description":"A product in the sense of something that a user can use for a specific purpose requires a certain quality. Therefore, its accuracy needs to be judged with an accuracy assessment measure that the user understands and where he can interpret the meaning in relation to the purpose. The product has to be validated, i.e. it has to be known whether the product qualifies for use in a certain context. And in addition, the product needs to be available in time that the users can base their decision on it.","hasChildren":true,"hasParent":true,"name":"Product quality","selfAssesment":"New
"},{"code":"IP4-3-1","description":"The cloud cover percentage indicates the amount of area in the remote sensing image extent that is covered with clouds and therefore cannot provide information about the Earth surface conditions.The actual types of clouds included may depend on the product, but the CEOS definition includes cloud shadow. Next to that, from an optical remote sensing point of view, clouds can be roughly classified in: opaque/dense clouds, mainly composed of droplets that are highly reflective in the VIS region and generally located at low-medium altitudes and cirrus, consisting of a large number of thin non-spherical ice crystals that are normally translucent in the VIS region, relatively highly reflective in the SWIR spectrum, and located at high altitude.\r\n\r\nThe goal of cloud cover percentage is to provide a quality measure of usable information in a surface reflectance image. Earth observation product catalogs support it as a query parameter, to enable searching for products with a cloud cover percentage below a given threshold.\r\nThis simplifies for instance use cases that require only fully clear products (0% cloud cover), and may save download and processing resources by only handling images that have some valid pixels. For instance, by only using products with a cloud cover percentage smaller than 99.95%. The measure also gives an estimate of the number of valid observations in a given geographical area, allowing a quick assessment of whether minimal data requirements for a specific use case are met.\r\n\r\nThe measure is a percentage of actual observations in an image, so pixels where no data was recorded are not included. For derived products, cloud cover pixels are often also flagged separately from pixels where no data was recorded, but this may depend on the data provider. The definition specifically also includes cloud shadow pixels.\r\nReliable cloud cover percentages depend on good cloud and cloud shadow detection methods. Especially handling of translucent cirrus clouds is an open issue: a product that has a 100% cloud cover percentage due to cirrus clouds might still be usable for some cases, while for other cases they also render the product useless. \r\n\r\nThe used cloud detection algorithm will also affect the cloud cover percentage. A more strict algorithm will yield higher percentages compared to an algorithm that under detects clouds.\r\nDue to these limitations, cloud cover percentages in product metadata have a fairly high error margin. The user should take this into account when determining optimal cloud cover percentage thresholds for the use case.","hasChildren":true,"name":"Cloud cover percentage","selfAssesment":"Planned
"},{"code":"IP4-3-2","description":"The remote sensing lifecycle structures all possible phases of the data production process, from its beginning of the data's coming to existence (that includes the sensor design prior to data collection) over storage, processing and use to archiving and deletion.","hasChildren":true,"name":"Remote sensing lifecycle","selfAssesment":"New
"},{"code":"IP4-3-3","description":"The capability of a sensor or EO product to resolve anything is a function of its (spatial, temporal, spectral and radiometric) resolution and of the detail at which a geographic phenomenon of interest manifests itself in time and space. A geographic phenomenon can be named or described, georeferenced and provided with a time interval at which it exists. The geographic phenomenon of interest is the one of which a user needs information to help him make a decision. Therefore, the geographic phenomenon needs to be resolved with a low enough uncertainty and a high enough quality that allows the user to make a decision with confidence. \r\nFor example, let’s consider a helicopter pilot that wants to know whether a specific site is suitable for an emergency landing. The decision to perform an emergency landing may be supported with an EO-derived digital map of emergency landing sites that are flat enough (as well as large enough for the pilot’s helicopter and free of any obstacles on the surface and in the approach area). If we only focus on the flatness of the terrain, we need a digital elevation model (DEM) of high enough spatial resolution and accuracy in the Z dimension to calculate slope within acceptable levels of uncertainty. The pilot probably can tell us what degrees of slope are okay for his helicopter and tell us sites (e.g. football fields) where such a landing would succeed. However, this is only the input to an analysis of different DEMs to identify the minimum spatial resolution and accuracy in the Z dimension to model slope products and associated uncertainty to derive an emergency landing site product that fulfils the requirements. Thereby the capability of different DEMs to resolve emergency landing sites can be analysed.\r\nSpatial resolution is a measure of the smallest angular or linear separation between two objects that can be resolved by the remote sensing system. A useful heuristic rule of thumb is that in order to detect a feature, the nominal spatial resolution of the sensor should be less than one-half the size of the feature measured in its smallest dimension.\r\nOther types of resolution of an EO dataset are available that determine for various geographic phenomena under investigation whether it is possible to resolve them in the data. These are radiometric resolution, spectral resolution and temporal resolution. Radiometric resolution is defined as the sensitivity of a remote sensing detector to differences in signal strength as it records the radiant flux reflected, emitted, or back-scattered from the terrain. Spectral resolution is the number and dimension (size) of specific wavelength intervals (referred to as bands or channels) in the electromagnetic spectrum to which a remote sensing instrument is sensitive. The temporal resolution of a remote sensing system generally refers to how often the sensor records imagery of a particular area. For time-series analysis, the temporal resolution determines the time granularity for resolving processes that underlie the change that is observable between subsequent images.","hasChildren":true,"name":"Capability to resolve anything","selfAssesment":"In progress
"},{"code":"IP4-3-4","description":"The spatial coverage of a dataset (consisting of an image or a series of images) determines whether the dataset covers the area of the terrain that is of interest to the user of information derived from the dataset.","hasChildren":true,"name":"Spatial coverage","selfAssesment":"New
"},{"code":"IP4-3-5","description":"The temporal validity of a dataset (consisting of an image or a series of images) determines whether the acquisition date(s) (and period) match(es) the requirements for investigating a specific phenomenon and thereby enables the derivation of information about that phenomenon.","hasChildren":true,"name":"Temporal validity","selfAssesment":"New
"},{"code":"IP4-3","description":"Values (or a value) that enable(s) judging a dataset or product on their fitness for a specific purpose (e.g. whether a specific satellite image is suitable for mapping landslides). , A QI should provide sufficient information to allow all users to readily evaluate a product’s suitability for their particular application, i.e. its “fitness for purpose”.","hasChildren":true,"hasParent":true,"name":"Quality indicators","selfAssesment":"New
"},{"code":"IP4","description":"Data quality, in general, is the degree of data usability in relation to a specific application purpose. Assurance of data quality is of growing importance in remote sensing, due to the increasing relevance of remote sensing data in planning and operational decision of public bodies and private firms, and the huge amount of digital services (or apps) that exploit RS data. \r\nDifferent data quality dimensions exist according to the lifecycle phases of the remote sensing data: data acquisition, data storage, data pre-processing, processing and analysis and data visualization and delivery. Remote sensing data acquisition phase involves the following quality aspects: resolution, accessibility, spatial accuracy, temporal validity, accuracy and precision of the sensor calibration. Resolution is a multi-dimensional concept that includes the following dimensions: spatial resolution, temporal resolution, radiometric resolution, spectral resolution and temporal resolution. Temporal validity refers to the quality of an remote sensing data product in time, whereas spatial accuracy refers to the accuracy of the position of features relative the Earth. \r\nData storage includes the accessibility and completeness data quality dimensions. Accessibility includes both temporal and data accessibility. Temporal accessibility refers to the time delay between data acquisition and data delivery, whereas data accessibility refers to the availability of remote sensing data. Data completeness encompasses temporal completeness, i.e. completeness of a time series represented a phenomenon, thematic completeness, and spatial completeness which refers to the area coverage. Data preprocessing, processing and analysis phase includes consistency, completeness, temporal validity, resolution, radiometric and geometric accuracy, thematic and semantic accuracy. Thematic and sematic accuracy refers to the correctness of the remote sensing data product. The main quality dimensions of the data visualization and delivery include readability, completeness and temporal validity. \r\nDifferent metrics can be used to assess the quality of the remote sensing-derived information, such as the root-mean-square error (RMSE) measuring the differences between the true and measured values of the phenomenon under investigation, confusion matrix used for assessing the classification performance, producer’s accuracy, user’s accuracy or Cohen kappa. The quality of the remote sensing data per se can be assessed using Peak Signal-to-noise Ratio (PSNR) or the Universal Image Quality Index (UIQI).\r\nDifferent organizations are involved in the standardization of the image data and gridded data quality, including ISO/TC 211 ‘Geographic information/Geomatics’, Open Geospatial Consortium (OGC) or the Quality Assurance Framework for Earth Observation (QA4EO) developed by the Group on Earth Observation (GEO). These organizations are responsible for developing metadata standards that are further used by the remote sensing community to document the quality of the remote sensing data. According to the QA4EO, for example, all remote sensing data products need to be accompanied by a Quality Indicator (QI) which helps users assessing their fitness-for-use.","hasChildren":true,"hasParent":true,"name":"Image data quality","selfAssesment":"Completed
"},{"code":"IP5-1-1","description":"Array databases make use of arrays as the primary storage representation. Such an array-oriented data model and query language is useful in many scientific applications, where the raw data consists of large collections of imagery or sequence data that needs to be filtered, subsetted, and processed.","hasChildren":true,"name":"Array databases","selfAssesment":"New
"},{"code":"IP5-1-2","description":"The Open Data Cube (ODC) is a non-profit, open source project that was motivated by the need to better manage Satellite Data. This project was born out of the work done under the \"Unlocking the Landsat Archive\" and the Australian Geoscience Data Cube (AGDC) projects.","hasChildren":true,"name":"Open data cube","selfAssesment":"New
"},{"code":"IP5-1","description":"The term data cube originally was used in Online Analytical Processing (OLAP) of business and statistics data. Technically speaking, such a data cube represents a multidimensional array together with metadata describing the semantics of axes, coordinates, and cells. It is an efficient approach to the management and analysis of large datasets.","hasChildren":true,"hasParent":true,"name":"Data cubes","selfAssesment":"New
"},{"code":"IP5-2-1","description":"Content-based image retrieval helps users retrieve relevant images based on their contents.","hasChildren":true,"name":"Content-based image retrieval","selfAssesment":"New
"},{"code":"IP5-2-2","description":"Web Portals allow users to discover, understand, view, access and query information of their choice from local to global level for a variety of uses.","hasChildren":true,"name":"Web portals","selfAssesment":"New
"},{"code":"IP5-2","description":"Image archives are repositories for storing, managing and retrieving remote sensing data.","hasChildren":true,"hasParent":true,"name":"Image archives","selfAssesment":"New
"},{"code":"IP5-3-1","description":"As an initiative stipulated by the European Commission to foster the bridge between the Copernicus ground segment and the user segment, the Copernicus data and information access service (C-DIAS) is a generic name for different sets of cloud-based platforms providing centralised access to Copernicus data and information, as well as to processing tools. The name indicates, however, that the focus of such advanced user-centred infrastructure implementations is not only on data access, but also on ‘information’. What is specifically meant here is the provision of information services and information layers as defined in the Copernicus service portfolio. This allows the users to develop and host their own applications in the cloud and a single access point, rather than processing data locally. Currently there are five different DIAS’s implemented (CREODIAS, SOBLOO, MUNDI, WEKEO, ONDA), all with some specific technical assets, or a sector-specific application focus or any other unique selling position by e.g. targeting as specific user community. Currently, the DIAS, which have received co-funding from the European Commission as a kind of seed funding, are currently in the process of exploring opportunities and claiming market shares, striving to sustain in a competitive manner. Some of the features are highlighted in the following, without explicitly mentioning any of the associated DIAS: (i) data access of global data sets (satellite data mosaics or gridded data) by custom area; (ii) OGC interfaces, VM catalogue, SPAR QL search interface (combine searches like receive images over areas of high population density), open source (accessible via API) or pay-per-use; (iii) access to core service products (e.g. CLMS, CMEMS, CAMS); (iv) focus on integrated applications such as smart cities, urban energies, precision agriculture; access to third-mission VHR satellite data (e.g. Pléiades); (v) utilizing GitLab as a developer platform.","hasChildren":true,"name":"Data and information access service (DIAS)","selfAssesment":"Completed
"},{"code":"IP5-3-2","description":"The OpenGIS® Web Processing Service (WPS) Interface Standard provides rules for standardizing how inputs and outputs (requests and responses) for geospatial processing services are defined. It defines an interface that facilitates the publishing of geospatial processes and clients’ discovery of and binding to those processes.","hasChildren":true,"name":"OGC interfaces and OGC web processing service","selfAssesment":"New
"},{"code":"IP5-3","description":"Online processing allows users to implement and run image analysis operations online independent of the underlying software.","hasChildren":true,"hasParent":true,"name":"Online processing","selfAssesment":"Planned
"},{"code":"IP5","description":"In general, infrastructures such as cyberinfrastructures or Spatial Data Infrastructures (SDIs), allow information sharing across distributed infrastructures and communities. SDIs have gradually changed from a pool of authoritative data shared using standardized web services to a pool where the authoritative data co-exist with data collected by volunteers and different sensors. Many efforts were dedicated to data documentation, to improving the catalogues searching techniques by means of, for example, thesauri and to sharing these data using standardized web services such as Web Map Service, Web Feature Service or Web Coverage Service. Cloud computing technologies played an important role in the implementation of sustainable SDIs due to their ability to provide on-demand computational and storage capacities over the Internet. In this way, users can easily search, find and use data shared across different online platforms.\r\nMore specifically, infrastructures for image processing and analysis refer to the physical and organizational facilities that allow the storage, analysis and management of the available data and products. Traditionally, this infrastructure formed a digital image processing system consisting of computer hardware with special-purpose image processing software, and peripheral input-output devices (e.g. CD or DVD drives, internet access, printers/plotters). In recent years, Earth observation is undergoing a shift to online processing making use of data cubes and vast image archives, e.g. NSF EarthCube or Digital Earth Australia, the Swiss Data Cube, the EarthServer, the E-sensing platform or the Google Earth Engine. Available infrastructures aim at sharing remote sensing data and derived products following the FAIR metrics: Findable (F), Accessible (A), Interoperable (I), Reusable (R). Thus, remote sensing data have to be documented using metadata that support FAIR data principles as follows: (1) Findable: remote sensing data are findable through data documentation, i.e. metadata, that needs to include a unique identifier of the described data. Metadata can be stored in a catalog compliant to one of the available data cataloging standards such as the SpatioTemporal Asset Catalog (STAC) compliant catalog; (2) Accessible: all data have to be openly accessible and shared using interoperable formats that allow users to find, access and reuse them; (3) Interoperable: different standards, e.g. STAC specification, have to be used to document remote sensing data; (4) Reusable: metadata have to be comprehensive enough to allow users not only to assess the fitness for purpose (e.g. lineage) but also to provide them information about how to access the generated data.","hasChildren":true,"hasParent":true,"name":"Infrastructure","selfAssesment":"Completed
"},{"code":"IP6","description":"In an information value chain, one or more organizations perform a set of value-adding activities for creating and distributing information products and services. They support a user in decision-making and thereby benefit the user’s purpose. The information value chain is a tool for evaluating business management and profitability. It enables explaining the ultimate “value” of a product and the components along the value chain and consequently allows businesses to optimize their processes. \r\nThe value of EO data can be assessed by analysing the contribution of the data to a specific EO information product and its effective use in decision-making. The (share of) benefit attributable to the use of the given EO data is derived from the comparison of a decision taken using the EO product to a counterfactual situation where other types of information are used instead. Often, this compares the situation before a new EO service was available to the situation afterwards. An ex-post analysis may reveal improved performances, e.g. gains in output, or productivity and/or reduced costs as compared to those occurring in absence of EO-derived information. This benefit resides with the user of the EO product and may be traced to societal and environmental benefits through impact chains.\r\nThe process of EO information production and distribution is integrated in the value chain and can be defined as the image processing chain. It comprises the value-adding activities of the organization(s) that lead up to the availability of an EO product for decision making. The nature and flow of these activities and the collaboration between organizations and among participants within organizations can be modelled with business process model and notation (BPMN). BPMN is a flowchart diagram that uses swimlanes representing different participants. Processes are assigned to participants and are connected with arrows into flow sequences. Further elements complete the choice of symbols for modelling a consistent flow, including a start event, end events, and branching options. They allow organizing the flow in parallel or iterative processes. Higher-level processes can be (de-)composed with sub-processes. Additionally, it is possible to use pools and message flows for explicitly modelling collaboration between participants (from different organizations).\r\nIn the image processing (value) chain, the sequence of processing steps begins with the acquisition of EO data, followed by steps of pre-processing and information extraction (or whatever steps are necessary) and ends with an EO information product being available to a user that uses it to make his decision. The collaborating stakeholders along the chain include EO satellite operators, EO data providers, EO information providers, and the users at the end of the value chain. The stakeholders along the processing chain each perform a dedicated subsequence of processing steps. Thereby, the stakeholders contribute their share of value to the data they deliver to the next stakeholder in the chain, ultimately arriving a the EO information product for the user. The EO data products that they hand on along the chain are often described with processing levels that provide different states of processing of EO data. They start with raw instrument data (level 0 and 1) that are followed by data converted into geophysical quantities that are geo-referenced and calibrated (level 2). Further levels are quality controlled data that has been mapped on a uniform space-time grid (level 3) and data combined with models or other instrument data (level 4). In addition, EO data providers use the term analysis ready data (ARD) that have been processed to allow direct data analysis, i.e. user processing effort is reduced to a minimum. Further, the standard EO products contain a categorizing element that is related to the image processing value chain. This categorizing element organizes the EO products along the sequences of processing, descriptive analytics, predictive analytics, prescriptive analytics, aggregation, visualization, and distribution. Thereby, the products ultimately contribute to the actionable EO information product for the use in decision-making.","hasChildren":true,"name":"Image processing (value) chain","selfAssesment":"Completed
"},{"code":"MDS","description":"MDS is a dimensionality reduction technique. It can be divided into Metric multidimensional scaling, Generalized multidimensional scaling and Classical multidimensional scaling.\r\n\r\nGeneralized multidimensional scaling is an extension of metric multidimensional scaling, in which the target space is an arbitrary smooth non-Euclidean space. In cases where the dissimilarities are distances on a surface and the target space is another surface, GMDS allows finding the minimum-distortion embedding of one surface into another.\r\n\r\nClassical multidimensional scaling is also known as Principal Coordinates Analysis, Torgerson Scaling or Torgerson Gower scaling. It takes an input matrix giving dissimilarities between pairs of items and outputs a coordinate matrix whose configuration minimizes a loss function called strain.","hasChildren":true,"name":"Multidimensional scaling","selfAssesment":"Depricated (GI-N2K)
"},{"code":"no","description":"Models that describe the basic principles of randomness and probability in spatio-temporal data.","hasChildren":true,"name":"Mathematical models of uncertainty: Probability and statistics","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI","description":"This knowledge area considers the organizational and institutional aspects related to GIS&T. The focus of this knowledge area is on the organizations active in the GIS&T domain, and what happens within and between these organizations. The knowledge area is structured around five units. One unit considers the key organizations in the GIS&T domain, covering relevant public sector organizations at different administrative levels as well as organizations in other sectors of society. Among the organizational aspects covered in this knowledge area are all organizational issues related to the implementation, use and management of GI and GIS within organizations. While all topics related to the organizational structures, procedures and management of GI(S) are grouped into one unit, another unit focuses on issues related to the human factor of using GI and GIS, i.e. people, their skills and competencies, and the development and evaluation of these skills and competencies in the context of GIS&T training and education. The knowledge area includes also several inter-organizational and institutional aspects of GIS&T. Particular attention is paid to the concept of geospatial data sharing, which is about the creation of `spatial data` connections and relationships between different organizations in the GIS&T domain. Spatial data infrastructures are developed to promote, facilitate and coordinate the sharing of spatial data among data providers and data users, and consists of several technological and non-technological components. Many related topics are considered in the knowledge area GI and Society (WS), which also addresses several non-technological aspects related to GIS&T. In addition to this, also the knowledge areas `Design and Setup of Geographic Information Systems`, `Geospatial Data\" and Web-based GI` include several topics that are closely linked to the topics that are considered in this knowledge area. It can be argued that in order to fully master the knowledge and competencies that are presented in these knowledge areas, also basic knowledge and understanding of the organizational and institutional aspects is required.","hasChildren":true,"hasParent":true,"name":"Organizational and Institutional Aspects","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI1-1","description":"The development of an appropriate organizational model, which establishes the basic character of GIS operations, is a crucial element of the GIS management. The appropriate GIS organizational model for any organization is based on its intended role.Alternative GIS organizational models are based on differing arrangements concerning the scope of GIS, the degree of integration of GIS into business operations, the degree of centralization of GIS operation and use, and the degree of centralization of management control. Although many variations can arise from different combinations of these factors, GIS organizational models can generally be classified into three types: (1) enterprise GIS, (2) GIS data and service resource, and (3) GIS as a business tool (Somers, 1998).","hasChildren":true,"name":"Organizational models for GIS management","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI1-2","description":"Management of GIS can be done in a more centralized or more decentralized manner. In a a so-called enterprise or information-framework GIS, an organizational unit may be established to manage the GIS environment and run the core system, whereas usage is decentralized. In environments where GIS is used occasionally by various users, it may be set up as a separate service with a designated group that manages the GIS and also controls users' applications services. A second decision that needs to be made after the choice between more centralized or more decentralized management of GI and GIS is about where to place the GI management. Alternative options are in a line organization, in a support area, or at the executive level, each with their own advantages and disadvantages.","hasChildren":true,"name":"Managing GIS operations and infrastructure","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI1-3","description":"User roles describe the relationship between different users and the GIS in an organization. Each user role includes responsibilities (e.g. for modifying certain information) and privileges (e.g. for viewing specific information). Although many different roles can be defined, a basic distinction is made between users, who can only view certain information, and editors, who can edit certain information.","hasChildren":true,"name":"User roles","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI1-4","description":"A GIS management strategy should be unique for each organization, as organizations have unique environments, characteristics, goals, GIS requirements. An important step in developing an effective strategy for an organization is to establish the strategic vision for GI and GIS in the organization and define its role and scope. Other elements that should be covered in the GIS Strategy are the degree of centralized management of the GIS, the placement of GIS management and support in the organization, involvement of users in GIS planning and implementation, coordination of users, organizational changes, preparation of users, personnel issues, transitions to GIS operations, integration into business operations, user support, data access, and integration of technology changes (Somers, 1998).","hasChildren":true,"name":"Strategic planning","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI1-5","description":"Committee and team approaches are frequently employed for coordinating participants and users in multi-participant GIS projects. The aim of creating such committees and teams is to ensure that the varied interests of participants are addressed, as participants bring many different interests, application needs, data needs, priorities, organizational issues, and political interests to a common project the GIS. Common models for coordinating participants recognize that participants have three levels of interest in the GIS: policy, technical development, and usage. Different bodies can be established focusing on these different levels of interest: a technical committee focusing on the design and development of the GIS, an management committee providing policy guidance and support and a user`s group.","hasChildren":true,"name":"Coordinating GIS Participants and Users","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI1-6","description":"After the development and implementation of a GIS within an organization, the challenge is to maintain the system and revise and update it when necessary. This means the performance of the GIS in terms of efficiency and effectiveness should be measured and monitoring, and feedback from users on the system and applications, on the data as well as on new needs should be collected. Particular attention should be paid to the maintenance of data sets.","hasChildren":true,"name":"Ongoing GIS revision","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI1-7","description":"The introduction of GIS into organizational environments should be seen as a complex process of mutual adaptation (Nedovic-Budic, 1997). These technologies changes the established organisational processes and structures, while on the other hand the organisational context and culture modify the technological set-up and use. Therefore, knowledge and understanding of the relationship between technologies and organizations is necessary to increase the success of GIS implementations in organizations. Successful GIS implementation and adoption often require some degree of organizational change. However, this can be very difficult to effect because organizations are naturally resistant to it (Somers, 1998).","hasChildren":true,"name":"Organizational changes","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI1","description":"GIS and T implementation and use within an organization often involves a variety of participants, stakeholders, users and applications. Organizational structures and procedures address methods for developing, managing, and coordinating these multi-participant users. The development of the appropriate organizational model for managing the GIS is crucial. In certain cases, changes to the organizational structure in place might be required. Strategic planning and the establishment of coordination structures can be considered as valuable instruments for managing and coordinating all involved users, while also the different user roles need to be assigned.","hasChildren":true,"hasParent":true,"name":"Organizational structures, procedures and management","selfAssesment":"In Progress GI-N2K
"},{"code":"OI2-1","description":"GIS and T professionals can be hired for a wide range of different job positions, for which the precise skills, competences and qualifications needed will vary. Typical examples of GIS and T positions are GIS&T project managers, technicians, system developers and analyst. The recognition and certification of the competences people have acquired in informal and non-formal learning contexts is important to know which skills and competences individuals have and whether they meet the qualifications required for a certain job position.","hasChildren":true,"name":"GIS and T positions and qualifications","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI2-2","description":"Making sure staff members have the necessary skills and competences to perform geospatial activities is necessary for an effective implementation and operation of GI within an organizations. Several training methods can be adopted to ensure the development of skills and competencies of staff members. A distinction can be made between formal and informal training, but also between internal and external training programs. Another relevant issue is the assessment and evaluation of the skills and competences of staff members, to determine their future training and development needs.","hasChildren":true,"name":"GIS and T staff development and evaluation","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI2-3","description":"Programs and courses on GIS and T and related subjects are provided by a wide range of institutions. While in recent years also the use and integration of GI and GIS in primary and secondary education has received significant attention, GIS and T education is mainly organized by institutions of higher education, especially universities but also other higher education institutions. Analyses of the higher education GIS&T programs and courses in Europe showed that the offer of courses is very diverse, in terms of size (ECTS), educational level (EQF) and course content. Vocational training on GIS and T related topics is organized by different types of training providers, including the major GIS vendors, data and service providers, academic sector, professional organisations, but also the public sector.","hasChildren":true,"name":"GIS and T training and education","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI2-4","description":"A curriculum is a systematic description of a study program, in terms of learning goals, structure and sequence, learning, teaching and assessment strategies and content. A curriculum consists of both a set of related required and elective - courses along with all direct and indirect skills, competences and learning outcomes resulting from these courses. In the process of curriculum design typically particular attention is assigned to objectives, teaching methods and educational strategies, while also attention should be paid to the content organization aspects and the global structure of the curriculum. The process of designing GIS&T curricula presents many challenges, as the design of the curriculum should be aligned to both the institutional context and the expected outcomes of the learning and teaching process (Prager, 2011).","hasChildren":true,"name":"GIS and T curriculum and course design","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI2-5","description":"An important challenge in organizing GIS and T education and training is the choice and use of effective teaching and learning methods. These methods should follow recent technological developments and use the best technologies to help students acquire the necessary skills and competencies. Traditionally, most GIS and T programs and courses were taught in the context of a full-time, face-to-face setting, using traditional teaching methods such as lectures and lab-based computer practical sessions. In recent years, educational institutions and their teachers have been experimenting with more innovative teaching and learning methods, such as project-based and case-based learning, distance learning, integrated and inter-disciplinary lessons, collaboration with companies and other stakeholders, etc.","hasChildren":true,"name":"GIS and T teaching and learning methods","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI2","description":"This unit addresses GIS and T staff and workforce issues within an organization, particularly as they relate to ensuring that GIS and T is appropriately used and supported. The focus of this unit is on the skills and competencies of professionals in the GIS and T domain: how can these skills and competencies be described and evaluated, and how can they be developed through training and education.","hasChildren":true,"hasParent":true,"name":"GIS and T workforce themes","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI3-1","description":"Cost savings are an important driver or motivation for sharing geospatial data and information. As costs associated with collecting and maintaining geospatial data are high, sharing data means that users no longer need to duplicate data gathering and archiving, which leads to savings in terms of personnel, space/facilities, data acquisition and maintenance costs. One fundamental argument for sharing thus derives from scale economies in production. Because the cost of making data is high, there is a clear incentive to maximize the number of users of these data. Sharing allows data to be used repeatedly for many purposes, thus increasing their value without increasing their cost. Sharing data also leads to improved data quality. Moreover, in many cases, sharing data is the only way to get access to certain data sets, as the authority to collect and manage certain data lies with another public institution.","hasChildren":true,"name":"Drivers and incentives for sharing geospatial data","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI3-2","description":"Sharing of geospatial data can be hindered or inhibited by several types of barriers. These include technological barriers, such as a lack of common data definitions, formats and models or incompatibility of hardware and software. Among the non-technological barriers are organizational, political and legal issues and elements, such as misaligned organizational missions, diversity in organizational cultures, conflicting organizational priorities, lack of funding, lack of executive and legislative support; restrictive laws and regulations, copyright issues, data privacy and data ownership issues. However, it should be noticed that many of these barriers have been decreased or eliminated in recent years.","hasChildren":true,"name":"Barriers to geospatial information sharing","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI3-3","description":"The legal framework for geospatial data sharing is very wide and diverse, involving rules on data, coordination, standards, funding, etc. Moreover, these rules and regulations can take many different forms: legal acts adopted by parliament, executive orders or decisions, cooperation agreements, memoranda of understanding, bilateral arrangements etc. From a data perspective, the legal framework can be distinguished into two main types of policies: those that promote and those that hinder the availability of spatial data. Policies that promote spatial data availability can focus on different types of users (public bodies, private companies, citizens) and different types of use (public access, commercial and non-commercial reuse, reuse for performing public tasks). Among the policies that hinder the availability of spatial data are those dealing with privacy, liability, and intellectual property. The legal framework also includes legislation that applies to data or information in general, such as open data legislation, which may also be applicable to spatial data (e.g. legislation on freedom of information, copyright, etc.). Moreover, also general legislation relating to any interaction between people or any situation in everyday life (e.g. liability, contract law, competition law, etc.) will apply to spatial data sharing.","hasChildren":true,"name":"Legal framework for geospatial data sharing","selfAssesment":"Completed
"},{"code":"OI3-4","description":"Several types of legal mechanisms for sharing geospatial data can be used. A data sharing arrangements can be formalized by a contract or agreement between the data provider and the data user. A particular type of agreement are the framework agreements, which are agreements between two or more organisations concluded prior to the datasets or services being required. These framework agreement can involve one or multiple spatial data sets or services. Partnership agreements are often used to formalize the data sharing agreements among a broader group of partners. Participation in such a partnership often means participants share their data with other participants and get access to shared data. Another relevant mechanism is the use of licenses, which are mechanisms to give organizations and people the permission to use spatial data sets and services. A license is legally binding, and defines the conditions of use of the related spatial data sets and services. In order to reduce the number of licenses used and ensure the harmonization of the terms in these licenses, the use of standard licenses is promoted. Also the use of open data licenses is promoted for sharing geospatial data, and strongly increased in recent years.","hasChildren":true,"name":"Legal instruments for sharing geospatial data","selfAssesment":"Completed
"},{"code":"OI3","description":"Geospatial data sharing has become an essential element of the GI activities of organizations. Spatial data sharing can be defined as the electronic transfer of spatial data/information between two or more organizational units where there is independence between the holder of the data and the prospective user. Spatial data sharing has many advantages, but several technical and non-technical barriers must be overcome to put data sharing into practice. While the practice of spatial data sharing has substantially grown with the development of spatial data infrastructures, many consider data sharing as a crucial element for the success of these infrastructures.","hasChildren":true,"hasParent":true,"name":"Geospatial data sharing","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI3b","description":"A Spatial Data Infrastructure can be defined as the collection of technological and non-technological components to facilitate and coordinate the exchange of and sharing of spatial data. The concept infrastructure is used to promote the concept of a reliable, supporting environment, analogous to a road or telecommunications network, that facilitates the access to spatial data. Data, metadata, access networks, standards, coordination, policies, funding, people and institutional frameworks are often considered among the key components of an SDI. \r\n\r\nSpatial data infrastructures often are defined and described as a complex and dynamic phenomenon. Among the main reasons for the complex character of these infrastructures are the many components a spatial data infrastructure consists of, the diversity of involved stakeholders, and the many different objectives and ambitions of these stakeholders. Technological advancements, such as the emergence of web 2.0 technologies, and societal changes, such as the increasing use of geographic information in everyday life, are often mentioned as important drivers behind the dynamic character of spatial data infrastructures. \r\n\r\nA key characteristic of spatial data infrastructures is the involvement of a large and diverse group of actors. Governments are often considered as the central actors in the development and implementation of spatial data infrastructure, since they are the major producers and users of geographic information. Governments at different administrative levels and in different thematic domains are involved in the creation, management, use and sharing of geographic data. But also private companies, non-profit organisations, research and education institutions and even citizens can participate in the development and implementation of a spatial data infrastructure. It is increasingly being argued that the involvement and engagement of each of these stakeholders group is essential to the realization of a successful spatial data infrastructure. \r\n\r\nSDIs have been developed in many countries worldwide at local, national and international levels. Often a distinction is made between a between the first generation SDIs that have data as their key driver and are based on a product model and second generation SDIs in which user needs are the key driver and that are based on a process or development model. The latest generations of SDI strongly focus on the inclusion and engagement of non-government actors and organizations in the development and implementation of the SDI. Although SDI are by default distributed systems, involving many organisations, some SDI might be developed rather in an hierarchical way, while others are following a networked approach.","hasChildren":true,"hasParent":true,"name":"Spatial Data Infrastructures","selfAssesment":"Completed
"},{"code":"OI4-1","description":"The adoption and implementation of standards are two key phases in the standardization process, which starts with the definition of standardization requirements and the development of standards. The adoption and implementation of standards follows after the development phase. The distinction made between the adoption and implementation of standards is important: adoption entails the decision to apply standards, while the implementation relates to the integration of standards in software, in data development and in other processes. GI-Standards are one of the key components of each SDI, consist of both semantic and technical standards, and include standards related to the different architectural components of an SDI, i.e. standards related to spatial data sets and data products, web services, metadata and catalogues, encodings, etc.","hasChildren":true,"name":"Adoption and implementation of standards","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI4-2","description":"The SDI policy framework includes the set of policies, strategies, initiatives and projects aimed at increasing access, sharing, and effective use of spatial data. SDI policies can be divided into strategic and more operational policies. Strategic policies define the broader framework and formal structure within which the SDI initiative is developed. Operational policies provide more practical tools to facilitate access to and use of the SDI, and address specific topics related to the collection, management, use, access and dissemination of spatial data. These operational policies include a broad range of guidelines, directives, procedures and manuals that apply to the day-to-day business of organizations in developing, operating and using an SDI. To guarantee the success of an SDI, it is important to recognize the wider policy context in which these SDI`s are developed, and to link them to the overall policy environment in the jurisdiction in which they are implemented. These include policies on open government and open data, environmental policies, digital government or e-government policies and other.","hasChildren":true,"name":"Policies","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI4-3","description":"If is often argued that SDI implementation requires coordination, because without coordination all other SDI components would not be developed or would be developed in a very fragmented and inconsistent manner. In general terms, coordination is about bringing into alignment the activities of different stakeholders in the SDI landscape. A typical instrument to realize coordinate in the context of SDI, is the establishment of an effective SDI coordination structure. The SDI coordination structure should ensure that all stakeholders are involved in the development and implementation of the SDI, through the participation in one or more coordination bodies. Another important element is the establishment of clear roles and responsibilities for the different involved organizations, making a distinction between data users, data providers, services providers and a geo-broker.","hasChildren":true,"name":"Coordination and organizational structure","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI4-5","description":"Funding an SDI is about guaranteeing the long-term financial security of an SDI, by obtaining and formalizing financing for the implementation and maintenance of the different SDI components. An SDI funding model provides the answer to the central question of where and how to seek funding for implementing and maintaining an SDI. Within an SDI often different funding models will be combined, as the selection of the most appropriate funding model will be linked to different activities and the associated costs. Costs of an SDI include both set-up costs (one off costs) and maintenance costs (yearly), of which certain costs need to be made for each data sets or each data provider and other costs for the infrastructure in general. The most commonly used SDI funding models are centralized government funding, decentralized government funding (e.g. for each data provider), partnership funding, funding through revenues, and government funding based on donor agencies or on European projects.\r\n\r\nThe shift towards open data and the adoption of open data policies had an important impact on the funding model of many SDIs, as governments and organizations no longer could rely on revenues from selling their data and had to look for other funding models. As a result, new pricing strategies are employed, such as the provision of fee-based supplementary services, such as advice or tailor-made products based on open data. Also freemium/premium models, in which a basic version of the dataset is offered as open data (freemium) but the full dataset is available for a fee (premium), were considered as an alternative approach. In many cases, the loss of revenues was compensated by other funding models, such as increased government funding.","hasChildren":true,"name":"Funding an SDI","selfAssesment":"Completed
"},{"code":"OI4-5b","description":"SDI performance assessment is about collecting, analyzing and providing information on the performance of SDI initiatives. Assessment and evaluations of SDIs are a useful tool for those organizations and people directly involved in these initiatives, but also for researchers, citizens, journalists and other stakeholders. Decision makers and practitioners can use assessments to monitor the progress against the objectives of their SDI initiatives and to identify areas where improvement can be achieved. Assessment also allows to compare and benchmark the performance of different organizations or countries, and to learn from best practices. Finally, assessment also is relevant for accountability, since it enables governments and agencies to be held accountable for their decisions, activities and the resources they have invested. Assessment of SDIs, which deals with the collection and supply of information on the performance of SDI initiatives, should be seen as the first step in a logical consequence of collecting data, integrating this data in policy and management cycles and actually using the information. \r\n\r\nIn the past twenty years, many different SDI assessment frameworks have been developed by researchers and practitioners around the world. Examples of such frameworks are the INSPIRE State of Play Study, the Clearinghouse Suitability Index, the Organisational Maturity Matrix, the SDI Readiness Index, and the INSPIRE Monitoring and Reporting approach. Each of these frameworks focus on particular aspects and components of SDIs. In line with the categorization of open data assessment, also SDI assessments can be divided into three main categories: (1) readiness assessments, (2) implementation or data assessments, and (3) impact assessments. Readiness assessments analyse whether conditions are appropriate, and whether necessary components are in place for developing an SDI. Implementation or Data assessments evaluate whether geospatial data are available and accessible. Impact assessments explore the extent to which SDIs lead to benefits for government, citizens, business and society in general.","hasChildren":true,"name":"SDI performance measurement and assessment","selfAssesment":"Completed
"},{"code":"OI4-6","description":"For a long time, SDI development has focused on the development and implementation of different components with the aim of facilitating the access to and sharing of spatial data. An key challenge in future SDI development will be the integration of these SDI`s in a wider context. In order to optimally take advantage of the data and services provided by an SDI, integrating these data and services into the processes and workflows of public and private organizations will be crucial. The concept of spatial enablement refers to the challenge of developing SDI`s in such a way that they provide an enabling platform that serves the wider needs of society in a transparent manner. Moreover, the diffusion of SDIs, together with the efforts to build a Global Earth Observation System of Systems (GEOSS) and other developments in industry and civil society should be considered as elements in a the realization of a vision on the next-generation Digital Earth.","hasChildren":true,"name":"Next-generation SDIs","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI4-7","description":"The effective implementation of SDIs requires governance, which includes the structures, policies, actors and institutions by which the infrastructure is managed pertaining to decisions made for accessing, sharing, exchanging and using the relevant available spatial information. While SDIs themselves are considered as initiatives contributing to good governance or effective governance, a key challenge in the establishment of SDIs is the governance of the infrastructure itself. Governance of SDIs is essential for the implementation of different SDI components in a coordinated and consistent manner. The central challenge of governance is reconciling collective and individual needs and interests of different stakeholders in order to achieve common goals. This aims to reduce gaps, duplications, contradictions and missed opportunities in the production, management, sharing and use of the information that tend to occur in a multi-stakeholder environment.\r\n\r\nGovernance can be facilitated through the use of appropriate instruments which extend to various levels of government and take into account the distribution of powers and responsibilities among different actors and institutions with an interest in the infrastructure. The governance instruments should coordinate the activities and contributions of, inter alia, data producers, users, added-value services providers, and other stakeholders. More complex and inclusive models of governance are required to cope with the multi-level nature of SDI implementations of the current generation of SDIs. Effective and inclusive SDI governance structures are needed, that are both understood and accepted by all stakeholders. Governance of SDIs also requires expanding the scope of stakeholders to include the private sector, research bodies and other actors outside the public sector including citizens, to actively promote bottom-up and participatory processes, and to find the appropriate mechanisms and instruments to enable the participation of these non-government actors.","hasChildren":true,"name":"SDI governance","selfAssesment":"Completed
"},{"code":"OI5-1","description":"Within the European Commission there are several key GI players. GIS activities in the Commission started since 1981 (e.g. DG REGIO, Eurostat, ) with the CORINE project, the creation of DG ENV and the creation of the European Environment Agency (EEA). Together with the DG Joint Research Centre (JRC), DG ENV and EEA are in charge of the coordination of INSPIRE: DG Environment acts as an overall legislative and policy co-ordinator for INSPIRE, the JRC acts as the overall technical co-ordinator of INSPIRE and EEA is in charge of several tasks related to monitoring and reporting, and data and service sharing under INSPIRE. Also several other EC institutions are actively involved in GI(S) policies and activities (DIGIT, DG GROW, DG AGRI, DG MOVE and many others).","hasChildren":true,"name":"GI organization at the European Commission","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI5-2","description":"Although there may be certain differences between countries, in most countries many key organizations in the GIS&T field will be active at the central/federal/national level of government. Especially the traditional institutions for surveying and mapping play a key role in geospatial policies and activities. Several public authorities at the federal level are in charge of the production and maintenance of key reference and thematic data sets. In many countries, these national data producers were the leading actors in the development of national spatial data infrastructures.","hasChildren":true,"name":"Federal and national government organizations","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI5-3","description":"Local and sub-national governments are often considered among the major users of geographic information in governments, as they often are involved in many different policy areas, in which many problems with a locational component need to be tackled. Geographic data produced and maintained by authorities at lower administrative levels are often more detailed and thus interesting for other users, both within and outside the public sector. As a result, local and sub-national governments are often involved in the establishment of these infrastructures because of the wide range of highly detailed geographic information they produce and manage. As many geographic data are linked to the activities and services of local organizations, the involvement of these organizations in the maintenance of data ensures that these data are up-to-date.","hasChildren":true,"name":"Sub-national and local governments","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI5-4","description":"The European GIS&T landscape consists of many pan-European organizations and associations promoting the interest of and representing certain stakeholder groups. While some of these organisations are dealing with all sectors and aspects of geographic information, others have a more thematic focus (e.g. remote sensing, topography, geosciences) or represent a particular sector (e.g. research, business). In some cases, their clearly is an overlap in the mission and objectives of different organizations, and some organizations are working in the same field of interest. Some examples of pan-European organizations and associations are AGILE, EuroSDR, EUROGI, and EuroGeographics. Also at international level several membership organizations and associations exist.","hasChildren":true,"name":"Pan-European and global associations and professional organizations","selfAssesment":"GI-N2K
"},{"code":"OI5-5","description":"The geospatial industry consists of companies working with location specific information or services. Within the geospatial sector, several areas of activities can be identified: 1) measuring, collecting and storing of data about geo-objects; 2) processing, editing, modelling, analyzing and managing that data; 3) presenting, producing and distributing the data; and 4) advising, educating, researching and communicating about processes and use of geo-information products and services. The sector consists of both small-and-medium-sized enterprises but also big companies, including surveyors, census hard-copy map providers, aerial photos providers, base map data providers, satellite and remote sensing imagery providers, software developers (GIS-related products and services providers as well as satellite image programming platform providers) and several others.","hasChildren":true,"name":"The geospatial industry","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI5","description":"Several types of organizations play a key role in the execution and coordination of geospatial activities in society. Typically, a distinction is made between data providers and data users, while coordinating organizations exist to coordinate and support the geospatial activities of professionals and entities using GIS&T. Governments are often considered as the major users and producers of spatial data and spatial information. Within the public sector, spatial data are collected and used in different thematic areas and at different administrative levels (from local to global). However, the needs, interests, and capacities of organizations at each of these levels will be different, as well as their role in the development of spatial data infrastructures, and the execution of geospatial activities in general. Also the geospatial industry will exist of both data providers and data users, but also of organizations delivering products and services to support the collection and use of spatial data. Other key organization in the GI domain are professional organizations and associations, bringing together and representing the needs of organizations of a particular sector and/or geographic area.","hasChildren":true,"hasParent":true,"name":"Organizations in the GIS and T domain","selfAssesment":"In progress (GI-N2K)
"},{"code":"PP","description":"The knowledge of physical laws and principles regulating the emission of e.m. radiation and its interactions with the matter, as well the ones related to the design, setting-up and control of EO platforms and related instruments, are of paramount importance for a right interpretation of EO measurements in relation with the investigated Earth's phenomena and parameters. The most important physical fundaments regards: the theory of electromagnetic waves propagation described by the Maxwell's equations, the theory of e.m. radiation and of its interaction with the matter, the methods and instruments for e.m. radiation measurement and/or generation, the fundamentals of thermodynamics and of mechanics. As far as Earth Observation is concerned, further, specific topics have to be addressed which are related to: spectral-specific matter-radiation interactions, natural (e.g. Earth, Sun) and artificial (e.g. MW) sources of e.m. radiations, atmospheric physics and radiative transfer equations, basic physics of e.m., optical and MW, sensors and sources, theory of satellites orbits, theory of rockets, physical fundaments of interpretation of optical and MW data collected by passive and active techniques.","hasChildren":true,"hasParent":true,"name":"Physical principles","selfAssesment":"Completed
"},{"code":"PP1-1-1","description":"Electromagnetic radiation travels in wave form. All electromagnetic waves travel at the speed of 299.793 km/sec in a vacuum and very nearly the same speed in air. In quantum physics electromagnetic radiation is also described in terms of particles called photons whose energy is given by the equation E = hf where h is the Planck constant and f the frequency of corresponding wave. Electromagnetic wave propagation is fully described by the Maxwell Equations that unified in 1860s the laws of electricity and magnetism.","hasChildren":true,"name":"Electromagnetic Waves and Photons","selfAssesment":"Completed
"},{"code":"PP1-1-10","description":"The solar constant S is a quantity denoting the amount of total (i.e., covering the entire solar spectrum) solar energy reaching the top of the atmosphere. It is defined as the flux of solar energy (energy per unit time) across a surface of unit area normal to the solar beam at the mean distance between the sun and the earth. Solar insolation is defined as the flux of solar radiation per unit of horizontal area for a given locality. It depends primarily on the solar zenith angle and to some extent on the variable distance of the earth from the sun. It can be computed as a function of latitude and the time of year taking into account of the secular variations of Earth's orbit eccentricity e, the oblique angle ε, and the longitude of the perihelion relative to the vernal equinox ω. The daily insolation is the total solar energy received by a unit of area per one day. It may be calculated by integrating total insolation over the daylight hours. It is particularly important, together with information on cloud coverage, in order to plan and manage solar power systems. Yearly total insolation together with average cloud coverage are among the most important parameters to be considered for the choice of the best (i.e. the ones promising the higher energy production) location of solar power plants. Modeled daily solar insolation together with short/medium-term forecast of cloud coverage are also fundamental for the management (e.g. for planning the suspension of activities for maintenance) of solar energy production plants .","hasChildren":true,"name":"Solar constant, solar insolation, daily insolation","selfAssesment":"Completed
"},{"code":"PP1-1-11","description":"Earth's itself represents the second (after Sun) most powerfull natural source of e.m. radiation for EO. Even if very less powerfull than Sun such a source is available for EO day and nigth. Its average emittance can be approximated by that of a blackbody at about 290 K. The maximum of its emission, following the Wien's Law, falls then around 10 micron (in the Thermal InfraRed - TIR spectral range) being Earth's emission trascurable in the VIS-SWIR range.\r\nMost of Earth's thermally emitted radiation falls in the spectral range 8-14 microns where it benefits of a quite high atmospheric transmittance (TIR atmospheric spectral window) in standard atmospheric conditions. However thick clouds prevent TIR radiation to reach satellite sensors (adsorbing and/or reflecting backward the radiation leaving Earth's surface) so that ground resolution cells affected by clouds are usually identified (cloud-mask) in the image pre-processing phase and not considered for further elaboration devoted to investigate surface properties. Even if very low in intensity, Earth's emitted radiation in the Far InfraRed (FIR) and in the MicroWaves (MW) spectral ranges are also used for quite important investigation related to the Earth's Energy balance (FIR) and for meteo-climatological applications. The complete transparence of Earth's atmosphere to the MWs, even in presence of meteorological (not precipitating) clouds make this Earth's emitted signal particularly important for application (e.g. climatological) requiring temporal continuity (all weather) of observations of Earth's surface properties like Temperature, Soil wetness, etc.. However, due to the weakness of the Earth's emitted signal in the MW ranges, such products can be achievable just at quite low spatial resolution (e.g. > 10km) by passive EO MW sensors","hasChildren":true,"name":"Earth's radiation (intensity, spectrum, etc.)","selfAssesment":"Completed
"},{"code":"PP1-1-2","description":"In principle, the frequency f (and the wavelength λ=c/f) of an electromagnetic wave can take any value and the whole range of possible frequencies is called the electromagnetic spectrum. Different regions of the spectrum are conventionally given different names (with associated spectral ranges smoothly depending on specific science sector): \r\ngamma-rays\t λ< 1 pm\r\nx-rays\t1 nm >λ>1 pm\r\nUltraviolet (UV) 400 nm >λ>1 nm\r\nVisible (VIS) 700 nm >λ> 400 nm (blue: 455 – 492, green 492 – 577, yellow 577 – 597, red 622 – 700)\r\ninfrared (IR)\t1000μm >λ> 0,7 μm (Near-IR - NIR: 0,7-1,3; Short-Wave IR SWIR: 1,3-3; Medium IR - MIR: 3-6, Thermal IR - TIR: 6-20; Far IR - FIR: 20-1000)\r\nRadio waves\t λ> 1 mm (Microwaves MW\t1 m >λ> 1mm). Optical range (usually referring to the spectral range from VIS to TIR) and microwaves are the most important spectral region for remote EO systems.","hasChildren":true,"name":"Electromagnetic spectrum","selfAssesment":"Completed
"},{"code":"PP1-1-3","description":"Maxwell equations are a set of coupled partial differential equations that contains the fundamentals of electricity and magnetism. These equations provide electromagnetic waves that propagate into the space at the speed of the light. Increasing the wavelength there are gamma rays, X-rays, ultraviolet, (visible) light, infrared, microwaves and radio waves.","hasChildren":true,"name":"Maxwell Equations and EM waves' propagation","selfAssesment":"Planned
"},{"code":"PP1-1-4","description":"Planck's law is a mathematical relationship for the spectral radiance emitted by a blackbody (i.e. a body that absorbs all radiant energy falling on it) at a given temperature as a function of frequency or wavelength. From another point of view it can be used to define a black-body as a body emitting radiation following Planck's law. The model of black-body is fundamental to simplify the description of the radiation thermally emitted by a generic body at a pre-fixed temperature and wavelength as the product of its (specific) spectral emissivity and the value predicted (at the same wavelength) by the Planck's law for a black-body at the same temperature. This way the radiation thermally emitted by a generic body can be expressed just as a (specific, as modulated by the spectra emissivity) fraction of the one expected for a black-body. Wien’s displacement law is the relationship between the temperature of a blackbody and the wavelength at which it emits the most radiation. Wien found that the product of the peak wavelength and the temperature is an absolute constant. As far as the temperature T of the blackbody increase the intensity of the emitted e.m. radiation increases being, at whatever wavelength, grater than the one emitted by a blackbody at lower temperature (Planck). As far as the blackbody temperature increases its maximum emission occurs at lower and lower wavelengths. Wien's law is fundamental both in the selection of the spectral bands more appropriate for observing specific phenomena as well as for remotely retrieve temperature of far objects by the analysis of the emitted spectral radiances.","hasChildren":true,"name":"Planck law for the black body. Wien's displacement law","selfAssesment":"Completed
"},{"code":"PP1-1-5","description":"The Rayleigh–Jeans Law is an approximation of the Planck’s law for a blackbody that states that, under certain conditions, emitted radiance is directly proportional to the blackbody temperature. Such an approximation, fits quite well with measurements of radiation emitted by sources at around 300K of temperature (like, in average, for the Earth) at wavelengths higher than 1mm (microwaves).. Wien’s approximation can be used to describe the emission spectrum of a high temperature blackbody n the VIS-NIR spectral range lengths. The estimated errors is less than 2% at wavlengths less that 5microns when a blackbody at around 6000K (like the Sun photosphere) is considered. \r\nThe Rayleigh–Jeans approximation is widely used in the processing of satellite images collected by passive MW sensors. Its extension to the thermal infrared spectral range (TIR) is also used for calibrating TIR satellite images (in this case linearity can be guaranteed just by steps on different brigthness temperature intervals).","hasChildren":true,"name":"Rayleigh-Jeans approximation. Wien's approximation","selfAssesment":"Completed
"},{"code":"PP1-1-6","description":"The total radiant intensity B(T ) of a blackbody at the absolute temperature T can be derived by integrating the Planck function over the entire wavelength domain from 0 to∞. Since blackbody radiation is isotropic, the flux density emitted by a blackbody is therefore F = π B(T ) which is proportional to the fourth power of the absolute temperature T through the Stefan-Boltzmann constant σ = 5.67 × 10−8 J m−2 sec−1 deg−4.\r\nKirchoff's law establishes that for a medium at the thermodynamic equilibrium, the spectral emissivity ε(λ) at a given wavelength λ, is equal to the its spectral absorbance, A(λ) at the same wavelength λ. Hence ε(λ)=A(λ) at each fixed λ, for a blackbody ε(λ)=A(λ)=1 at whatever λ. Kirchoff's law is valid also in Local Thermodynamic Equilibrium (LTE) conditions as the ones usually occurring in (small volumes of) the Earth's atmosphere even in the most turbulent conditions.\r\nKirchoff's law has important applications also for the study of spectral signatures of mineral and rocks and, in general, of opaque - i.e. with spectral transmittance T(λ)=0 - bodies. In that case, the relation which relate the spectral reflectance R(λ), absorbance A(λ) and transmittance T(λ) of a body: R(λ)+A(λ)+T(λ) =1\r\nreduce to R(λ)+A(λ)=1 and in LTE conditions, thanks to the Kirchoff's law: \r\nR(λ)+ε(λ)=1 which allows to obtain measurements of spectral emissivity indirectly through (more simple and stable) measurements of spectral reflectance:\r\nε(λ)=1-R(λ)\r\nRocks and mineral exhibit important (diagnostic/discriminating) signatures in their spectral emissivity in the thermal infrared (TIR) region. Measuring spectral emissivity in a laboratory (particularly if samples have to be characterized for their properties in natural conditions) is a quite difficult task due to the difficulty to insolate the sample from the lab environment (and instruments themselves) all emitting approximately at the same (environmental) temperature. Kirchoff's law allows to obtain, for opaque bodies, spectral emissivities from spectral reflectances measurements which are much easy to realize in normal remote sensing labs.","hasChildren":true,"name":"Stefan–Boltzmann law. Kirchoff law","selfAssesment":"Completed
"},{"code":"PP1-1-7","description":"All bodies at a temperature T>0 K emit electromagnetic radiation at all wavelengths (thermal emission). Such emission at each wavelength is increasing with T and it is maximum for Black Bodies whose spectral emittance I(λ,T) (at each prefixed T and wavelength λ) is defined by the Planck function B(λ,T). Generic bodies are expected to thermally emit less than a black body (having the same temperature T) at whatever wavelength. Spectral emissivity ε(λ) is defined as the ratio of the spectral radiance I(λ,T) emitted by a generic body and the one emitted by a Black Body at the same temperature, i.e. ε(λ)= I(λ,T) / B(λ,T). By definition its value is less or equal (Black Body) than 1. The spectral emissivity concept allows to describe in a simple way the spectral radiance I(λ,T) thermally emitted by a body at a temperature T by I(λ,T)= ε(λ)*B(λ,T). It is possible to invert the Planck Function to obtain from the emitted radiance at a prefixed wavelength the temperature T=f(B, λ) of the emitting Black Body. If in such expression the spectral radiance I emitted by a generic body is used instead than B, the resulting temperature, Tb=f(I, λ), is named Brigthness Temperature being Tb<=T (with Tb=T in case the emitting body is a Black Body). The concept of Brigthness Temperature is substantially a different way to measure the spectral radiance of a generic body. It is usually preferred (for instance calibrating Thermal InfraRed – TIR – satellite images) because the interpretation of such a digital image is much more intuitive than when spectral radiances are used instead. In fact, as at each prefixed temperature generic bodies are less emitting than Black Bodies, wherever across a digital satellite image we consider the values of reported Tb, we can say that the actual temperature T of the corresponding emitting ground resolution cell is not less than Tb.","hasChildren":true,"name":"Concepts of Spectral Emissivity and Brightness Temperature.","selfAssesment":"Completed
"},{"code":"PP1-1-9","description":"Sun represents the most powerful natural source of e.m. radiation for EO. The main source of its radiation is the nuclear fusion of Hydrogen into Helium which occurs in central part (“Core”) of the Sun. Outside, the energy transfer is dominated by radiative process (“Radiative zone”) then by convection (”Convective zone”). Solar radiation at the Top of the Earth Atmosphere comes from the outer layer of the sun, the photosphere, whose estimated (conventional) temperature is 6000-6300 K. Its emittance can be approximated by that of a blackbody at about 6000 K but just its reflected component (SOR) is actually available (and just during daytime) for EO. The maximum of SOR falls in the visible spectral range. Its contribution in the thermal infrared range is neglectable but in the medium infrared SOR is still significant enough and, in daytime, superimposed to Earth's thermal emission. The high intensity of solar refelcted radiation (SOR) coupled with the high atmospheric transmittance in the VIS/NIR range, guarantee the highest signal-to-noise ratio for sensors operating in that spectral range. This huge amount of available signal, together with the development of advanced micro-sensor technology (started with the Charged Coupled Devices - CCD etc.), explains why the EO passive sensors with the highest spatial and/or spectral resolution presently achievable, are operating in the VIS/NIR range.\r\nachievable by operating in this spectral region.","hasChildren":true,"name":"Solar radiation at the Top of the Atmosphere. Solar spectrum","selfAssesment":"Completed
"},{"code":"PP1-1","description":"EM radiation is created when an electrically charge particle, such as an electron, is accelerated by a force causing it to move. The movement produces oscillating electric and magnetic fields which travel, as an harmonic EM wave, at right angles to each other. EM waves travel at 299,792,458 meters per second in a vacuum (the highest possible speed into the Universe, also known as the speed of light). \r\nThe electromagnetic field propagating through the space as EM waves is also referred as electromagnetic radiation. \r\nAn EM wave is characterized by a frequency (or by a wavelength) and by an amplitude (or by an energy). \r\nThe wavelength is the distance between two consecutive peaks of a wave. This distance is given in meters (m) or fractions thereof. Frequency is the number of waves that form in a given length of time. It is usually measured as the number of wave cycles per second, or Hertz (Hz). It is wave speed=frequency*wavelength so that, an EM wave traveling at the speed of light, can be equally identified by its wavelength or by its frequency. The amplitude (i.e. the maximum oscillation of the EM field) provide the intensity (i.e. the energy) of the EM wave. \r\nThe classical theory describes the EM radiation as electromagnetic waves which represent the oscillations of electric and magnetic fields. In the quantum mechanics theory EM radiation consists of photons, quanta of the electromagnetic energy, responsible for all electromagnetic interactions.\r\nAs far as Earth remote sensing is concerned EM radiation represents the most important vehicle of information.","hasChildren":true,"hasParent":true,"name":"EM radiation","selfAssesment":"Completed
"},{"code":"PP1-2-1","description":"The study of the absorbption/emission of electromagnetic radiation by atoms. Depending on the atomic number characteristic frequency or wavelength are absorbed or emitted. Since each element has a characteristic spectrum of absorbed/emitted wavelengths (spectral signature), atomic spectroscopy allows the determination of elemental compositions even of remote objects (e.g. stars, galaxies, etc.).\r\nStarting from the simple Bohr’s model it is possible to predict quite exactly the frequencies of e.m. radiation selectively absorbed/emitted by all atoms. Depending on the atomic number Z, characteristic frequencies f are absorbed or emitted by atoms corresponding to the electronic transitions from different energetic (quantized) states following the Bohr’s condition: fab=(Eb- Ea)/h, being Ei=-cost∙Z2/(ni)2 the electron energy corresponding to the state/level i (principal quantic number ni). By this way each atomic species has a characteristic spectrum of absorbed/emitted frequencies (atomic spectral signature) so that atomic spectroscopy allows the determination of elemental compositions even of remote objects. By this way the existence of Helium was discovered in the 1968 by Jansen and Lockyer in the Sun photosphere well before its discover on the Earth, and the knowledge of the chemical composition of stars and galaxies was possible well before the end of XIX century. Atomic spectroscopy provides a simple and powerful introduction (through the explanation of the more complex interactions of e.m. radiation with molecules and solid matter) to the fundamental concepts of spectral signature (which is at the base of most of the applications of aerial remote sensing of the Earth’s surface) and atmospheric windows (important for the design of optical sensors devoted to remotely sense Earth’s surface) being moreover propaedeutic to the understanding of methods for the atmospheric vertical sounding based on the concepts spectral lines broadening and related weighting functions.","hasChildren":true,"name":"Atomic spectroscopy","selfAssesment":"Completed
"},{"code":"PP1-2-10","description":"The Rayleigh roughness criterion is a widely used means to estimate the degree of roughness of a considered surface. Considering the phase difference between two rays scattered from separate points of the surface, this is proportional to the roughness ∆h (average deviation from the average surface height ) the cosine of the incident angle and, inversely, on the radiation wavelength (λ). The Rayleight criterion states that a surface can be considered as smooth (mostly reflecting) if the phase difference is less than π/2 radians.\r\nAs a consequence, in the case of normal incidence (i.e. θ=0), average roughness of the surface must be less than λ/8 to have an effectively smooth surface. For instance: i) at optical wavelengths (e.g. 0.5 micrometers), surface roughness ∆h must be less than about 60 nm to have a specular reflection. Only certain man-made surfaces (e.g. sheets of glass or metal) may meet such a condition; ii) at VHF radio wavelengths (e.g. 3 m), roughness height need only to be less than about 40 cm. Unlike the previous case, a number of natural surfaces may meet this condition.\r\nIt is worth noting that large values of the incident angle may satisfy the criterion more easily as compared with the normal incidence. This means that a moderately rough surface may be effectively smooth at glancing incidence. This condition may be easily experienced when eyes are struck by the glare of reflected sunlight from a low sun over an ordinary road surface. More strict conditions for classifying a surface as a mirror or a diffuser at an established whavelength λ are: ∆hcosθ/λ > 1/8 for a rough surface operating as a diffuser; ∆hcosθ/λ < 1/25 for a smooth surface operating as a mirror.","hasChildren":true,"name":"The Rayleigh roughness criterion","selfAssesment":"Completed
"},{"code":"PP1-2-11","description":"The Bidirectional Reflectance Distribution Function (BRDF) is defined as the quotient between the spectral radiance Ir(θr,φr) reflected by a sample in a particular direction (θr,φr) and the spectral irradiance F(θi,φi) from the source that illuminates it under a direction (θi,φi) . It depends on both the incidence and viewing angles. From this point of view it represents an absolute definition of reflectance whose value, as is known, depends on the geometry of the illumination and observations directions. This function well describes variability in surface anisotropy, its shape and magnitude is determined by the structure of the sample element and its optical attributes.\r\n\r\nThe BRDF is given by \r\n\r\nBRDF(θi,φi; θr,φr; λ)=(Ir(θr,φr))/(F(θi,φi))\r\n\r\nwhere Ir is the surface leaving spectral radiance and F is the spectral irradiance , θ and φ are zenithal and azimuthal angles respectively of the direction (view angles) of reflected radiance Ir(θr,φr) and of incident irradiance F(θi,φi), λ is the wavelength.","hasChildren":true,"name":"Bidirectional Reflectance Distribution Function (BRDF)","selfAssesment":"Completed
"},{"code":"PP1-2-12","description":"Measurements of BRDF allow to compare spectral signatures obtained in different laboratories in an optimal way. However its measure require well calibrated sources and quite expensive laboratory equipments. The concept of BRF (Bidirectional Reflectance Factor) allows a more simple, indirect, measurement of BRDF by using a reference sample (highly reflective so usually named \"white reference WR\") of known BRDF and two subsequent measurements of reflected radiance (one from the WR, one from the sample) obtained under identical illumination conditions. In these conditions results BRDF(sample)=BRF(sample)xBRDF(WR)","hasChildren":true,"name":"Bidirectional Reflectance Factor (BRF)","selfAssesment":"Planned
"},{"code":"PP1-2-2","description":"The absorption of e.m. radiation by molecules, in different physical states, can be attributed to specific (quantized) changes in their electronic and/or vibrational and/or rotational energy. Subsequent quantized molecular vibrational energy levels are equidistant so that all vibrational transitions occur, for each molecule, by the emission/absorption of radiation at a specific wavelength. Depending on the specific amount of energy required to modify the status of electrons within the atoms composing the molecules, as well as the one required to modify the molecule's vibrational and rotational energy, different wavelengths can be adsorbed. As in the case of atomic spectra which are fully determined by the electronic energy level structure depending on the atomic number, rotational and vibrational energy levels of molecules depends on specific characteristics (number, masses, distances, inertia momentum, elastic constant, etc.) of the atoms composing the molecule itself which make specific and characteristic for each molecule associated absorption spectra. In the Earth's atmosphere the effect of atomic/molecular absorption is significant at wavelength between 1nm and about 1cm. Considering the optical and microwave spectral ranges used in Earth's remote sensing from space it should be noted that:\r\na) Visible, Near Infrared and Short wave IR radiation (400-3000 nm) is adsorbed mostly for electronic transitions within atoms. In the SWIR region (after 1000nm) forbidden vibrational absorption lines can be observed (overtones and related combinations). \r\nb) e.m. radiation in the Medium and Thermal IR (up to 100.000 nm) spectral range are mostly adsorbed for operating vibrational energy transitions in H2O, CO2 and O3 molecules\r\nc) e.m. radiation in the Far IR up to the Microwave's spectral range (0,035-1 mm) is mostly adsorbed for operating rotational transitions in water vapur molecules. As, in principle, such electronic, vibrational and rotational transitions can contemporary occur (and usually occur considering the collective effect of the enormous number of molecules that can be present even in a small volume of terrestrial atmosphere) molecular spectra results in a complex composition of absorption lines (bands).","hasChildren":true,"name":"Molecular absorption spectra","selfAssesment":"Completed
"},{"code":"PP1-2-3","description":"In spectroscopy an absorbed (emitted) line is observed in correspondence to the transition from a lower (higher) to a higher (lower) energetic level within an atom (electronic transitions) or a molecule (electronic, vibrational, rotational transitions). Its characteristic frequency f is related to the amount of the energetic jump from an initial state E(1) to a final one E(2) through the Bohr's relation E(f) − E (i) = hf. As the distribution of the quantized energetic level are specific of each atom (depending on its atomic number N) and molecule (depending on their constituents atoms N, number and dispositions which determine their specific inertia momentum and vibrational properties) even the corresponding atomic and molecular spectra (i.e. the frequencies of the sequences of spectral lines/bands) are specific for each chemical atomic or molecular species. However monochromatic emission just at the frequency f is practically never observed. Always e.m. radiation emitted/adsorbed by atoms or molecules is observed also around the nominal (expected following Bohr's relation) frequency f mostly as a consequence of the following effect: a) changes of quantized energy levels associated to the process of emission/absorption itself: the consequent line broadening around the frequency f is reported as \"natural broadening\"; b) changes of quantized energy levels due to reciprocal collisions between atoms and molecules (\"pressure broadening\"); c) the change of the observed f due to the Doppler effect associated to the fact that emitting(adsorbing atoms or molecules are moving toward or far away with different (thermal) velocities (\"Doppler broadening\"). The natural broadening is practically negligible as compared to that caused by collisions and the Doppler effect. In the upper atmosphere, due to its temperature and pressure, we find a combination of collision and Doppler broadenings, whereas in the lower atmosphere, below about 20 km, collision broadening prevails because of the pressure effect. As far we move far from the central (expected) frequency f as much the contribution of Doppler effect can be neglected compared with the pressure broadening. This fact has important consequences on the possibility to retrieve vertical properties of the atmosphere (vertical sounding) like temperature and concentration of its chemical constituents, exploiting satellite based observations made \"off-line\" (i.e. at frequencies around but different from f) which relate investigated atmospheric levels as much higher as much far from f are the considered frequencies.","hasChildren":true,"hasParent":true,"name":"Line shape and (natural, pressure, Doppler) broadening","selfAssesment":"Completed
"},{"code":"PP1-2-4","description":"Voigt's line profile refers to the shape of a spectral line resulting from the \"pressure\" and Doppler broadening. Pressure broadening is much more important in atmosphere as far as pressure increases (heigths lower than 20 km) . Observing Earth's atmosphere in a spectral region sufficiently far from the central (unperturbed/monochromatic) absorption spectral line (off-line bands), Doppler broadening can be neglected in comparison with the pressure one. More and more off-line are the chosen spectral bands, more and more lower in atmosphere will be the atmospheric layers mostly contributing to the measured spectral radiances. \r\nSuch a relation is at the base of the inversion methods for atmospheric vertical sounding based on multi-spectral satellite observations.","hasChildren":true,"name":"Voigt's line profile","selfAssesment":"Completed
"},{"code":"PP1-2-5","description":"Radiation that is not absorbed or scattered in the atmosphere can reach and interact with the Earth's surface. There are three (3) forms of interaction that can take place when e.m. radiation strikes, or is incident (I) upon a surface. These are: absorption, transmission, and reflection. The total incident radiation will interact with the surface in one or more of these three ways. The proportions of each will depend on the wavelength of the incident radiation and the specific chemical/physical properties of the surface material. Absorption occurs when incident radiation is absorbed into the target, while transmission occurs when radiation passes through a target. Reflection occurs when radiation \"bounces\" off the target and is redirected. The spectral reflectance is defined by the ratio of reflected radiance to incident radiance at a prefixed wavelegth . The spectral transmittance of a medium is defined by the ratio of the transmitted radiance to the incident one at a prefixed wavelegth . The absorbance of a medium or target is defined by the ratio of the absorbed radiance to the incident one at a prefixed wavelegth . Conservation of energy require that, at a certain wavelenght: R+T+A=1. To express the circumstance that the reflection can occurre in different direction as the surface deviates from a specular one, becoming rough, the concept of surface scattering has been introduced (ref. [PP1-2-10] The Rayleigh roughness criterion).","hasChildren":true,"name":"Concepts of Transmittance, Absorbance, Reflectance, Scattering.","selfAssesment":"Completed
"},{"code":"PP1-2-6","description":"The emitting capability of a body surface is described by the spectral emissivity, ε(λ), a dimensionless value ranging between 0 and 1 and varying on the basis of the wavelength (λ) and the geometric configuration of the surface. Formally, spectral emissivity can be defined as the ratio of spectral exitance, M(λ,T), from an object at wavelength λ and temperature T, to that from a blackbody at the same wavelength and temperature, MBB(λ,T).\r\nA blackbody is an ideal radiator that totally absorbs and then reemits all energy incident upon it. By definition the spectral emissivity of a blackbody is equal to one (the maximum) at whatever wavelength and temperature. A blackbody radiates a continuous spectrum. Real materials do not behave like a blackbody. Natural matter could radiates more in selected spectral region (like in the case of atomic or molecular gases) more frequently with a continuous spectrum (like in the case of solids) always with spectral emissivity minor or equal to 1. \r\nAnother important concept is the one related to the graybody. For gray bodies, the spectral emissivity value is constant for each wavelength value, as for black bodies, but is always less than 1. Therefore, for any given wavelength the emitted energy of a graybody is a fraction of that of a blackbody. This behavior could be quite important even for limited spectral ranges. For instance the spectral emissivity of the sea in the TIR (Thermal InfraRed) spectral range 8-14 microns (TIR atmospheric window) can be assumed constant (about 0,98) with significant simplifications in the determination of SST (Sea Surface Temperature) from satellite sensors operating in that spectral region. \r\nAs said above, the emissivity of the most of the bodies present in nature varies depending on the wavelength. These objects are referred to as selective radiators or as being selectively radiant. This means that some materials may behave as black bodies at certain wavelengths (ε close to 1) and may have reduced emissivity at other wavelengths.","hasChildren":true,"name":"Concepts of Spectral Emissivity","selfAssesment":"Completed
"},{"code":"PP1-2-7","description":"Dielectric constants and refractive indices of the matter are generally complex quantities. Considering an electromagnetic wave entering a homogeneous medium of complex refractive index n=m+ik, it is possible to demonstrate that its intensity progressively decays depending on its wavelength λ and on the complex part k of the refractive index of the considered medium. Transparent medium correspond to medium having k=0 (i.e. real refractive index). \r\nFor instance, considering the amplitude of the electric field E(0) entering the medium, its value after traveling in it for a distance z will be reduced at E(z)=E(0)exp[ -ωkz/c] being ω the wave pulsation and c the light speed constant.","hasChildren":true,"name":"Complex dielectric constants and refractive indices","selfAssesment":"Completed
"},{"code":"PP1-2-8","description":"The complex part k of the refraction index n determines how far an e.m. wave of wavelength λ can survive crossing a specific medium. The attenuation length la is the distance after that the amplitude of an e.m. signal reduces its value by an amount of 1/e. For instance the amplitude of the Electric field E(z) of an e.m. wave proceeding along the z direction is decreasing as exp(-z/la) being la=λ/(2𝜋k) the attenuation length associated to that specific material (with n=m+ik) and wavelength λ. This way attenuation length in water can be of hundreds of meters in the visible range and just few microns in the microwaves. So that penetration of radiation in the matter depends on both, the specific (dielectric) properties of the matter (through k) AND the specific wavelength λ of considered e.m. signal.","hasChildren":true,"name":"EM rad. penetration in the matter: Attenuation Length","selfAssesment":"Completed
"},{"code":"PP1-2-9","description":"EM radiation impinging a rough surface is (partly) reflected back (scattering). When the the sine of the angle of incidence of the radiation is equal to the sine of the angle of reflection, sin(øi) = sin(ør), then the surface behaves like a mirror (Snell's Law). Furthermore, a surface is defined as a “perfect mirror” (Fig.1) if all the incident radiation is reflected in that direction saving its original intensity. A surface is defined as “Lambertian diffuser” or “isotropic reflector” (Fig. 2), when the radiation is reflected in all directions with the same intensity. A surface is defined as “perfect Lambertian” when all the incident radiation is reflected isotropically (i.e. not-absorbing, not-transmitting surface). A surface is defined as \"almost Lambertian\" (Fig.3) if the reflection does not occur in an exactly isotropic way but according to privileged directions. “Perfect mirrors” as well as “perfect Lambertian” surfaces describe ideal bodies, while natural bodies behave like “almost Lambertian” surfaces with a preferred reflection direction around the one established by the sines reflection law.","hasChildren":true,"name":"Scattering from rough surface: Lambertian and specular surfaces.","selfAssesment":"Completed
"},{"code":"PP1-2","description":"E.M. Radiation can be absorbed, scattered, emitted and transmitted by the matter. The results of such interactions (i.e. the fraction of incident radiation that is absorbed, scattered or transmitted) or emission process (i.e. the fraction of actually emitted radiation in comparison with the one expected from a black-body at the same temerature) strongly depend on the radiation wavelength and on specific chemical (e.g. composing atoms and molecules as well as their arrangement within solid cristals) and physical (e.g. Temperature, Dimensions and Shape, Roughness) properties of the matter. In some case, the result of Radiation - Matter interaction is strongly affected by observational conditions. For instance, over some angular distance between the directions of incidence and the one of measurement of the radiation, sun-glint can occur which completely mask any other results. A basic principle of the remote sensing put univocally in relation spectral absorbance, reflectance, transmittance and emissivity, curves achievable by multi-spectral EO measurements, with matter having specific chemical/physical properties. Theoretical models of radiation-matter interaction at the Earth's surface and through the atmosphere provide then suitable strategies for retrieving, from multi-spectral measurements of the radiation leaving the Earth, the most relevant chemical/physical properties of the matter composing its surface and atmosphere.","hasChildren":true,"hasParent":true,"name":"Radiation - Matter interaction","selfAssesment":"Completed
"},{"code":"PP1-3-1","description":"The natural objects can either emit radiation (radiance, emittance) or be \"illuminated\" by a source (irradiance). In the following a series of definitions for each of these terms is provided. \r\nThe first basic radiometric quantity is the radiance (Iλ) and it is defined as the ratio of the differential radiant energy (dE) to the product of effective area (dA) with the time interval (dt), wavelength interval (dλ) and differential solid angle (dΩ). Iλ can be also referred as monochromatic intensity and it is expressed in units of energy per area per time per wavelength and per steradian (W m−2 sr−1). \r\nThe monochromatic flux density (Fλ) or the monochromatic irradiance of radiant energy is defined by the normal component of Iλ integrated over the entire hemispheric solid angle. It is expressed in units of energy per area per time per wavelength (W m−2). For isotropic radiation (i.e., if the intensity is independent of the direction), the monochromatic flux density is then Fλ = π Iλ. \r\nThe total flux density of radiant energy (F), or irradiance, for all wavelengths (energy per area per time, i.e., W), can be obtained by integrating the monochromatic flux density over the entire electromagnetic spectrum.\r\nAll the above definitions refer to a point source of radiation. When the flux density or the irradiance is from an emitting surface (i.e., an extended widespread source), the quantity is called the emittance. When expressed in terms of wavelength, it is referred to as the monochromatic emittance. The intensity or the radiance is also called the brightness or luminance (photometric brightness). The total flux from an emitting surface is often called luminosity.","hasChildren":true,"name":"Radiometric quantities: radiance, irradiance, flux, brightness, emittance, luminosity, etc.","selfAssesment":"Completed
"},{"code":"PP1-3-2","description":"The attenuation of radiation emitted from a source decreases with the square of the distance from its center based on inverse square law. It considers that the size of the sources increases with the square of their radius, causing the same rate of attenuation in flux density.","hasChildren":true,"name":"Decay of the emittance with the square of distance from the source","selfAssesment":"Planned
"},{"code":"PP1-3-3","description":"The relative amount of electromagnetic radiation reflected (absorbed, transmitted, emitted) by the matter at different wavelengths depends on its specific chemical composition and physical properties. The plots of corresponding physical quantities (reflectance, absorbance, transmittance, emissivity) against wavelength, are termed spectral signatures of the specific matter under study. In principle the analysis of spectral signatures obtained by multispectral EO sensors could allow us to identify/discriminate different cover types.\r\nThe interpretation of spectral signatures requires to well understand the e.m. radiation-matter interaction process. In very simple term we expect that incident radiation I(λ)can be reflected, absorbed or transmitted by the matter so that for the energy conservation should be: \r\n\r\n\r\nI(λ)=I(λ,R)+I(λ,A), I(λ,T) \r\n\r\n \r\nbeing I(λ,R), I(λ,A) and I(λ,T) the reflected, absorbed and transmitted fraction of I(λ). From the previous relation descends (dividing both members for I) that:\r\n\r\n\r\n1=R(λ)+A(λ)+T(λ)\r\n\r\n\r\nbeing:\r\n\r\n\r\nR(λ)=I(λ,R)/I(λ) named Reflectance\r\nA(λ)=I(λ,A)/I(λ) named Absorbance\r\nT(λ)=I(λ,T)/I(λ) named Transmittance\r\n\r\n\r\nThey are all specific properties of the considered matter and are not independent each others.\r\nIn particular for an opaque medium with T(λ)=0 it is:\r\nR(λ)=1-A(λ)","hasChildren":true,"hasParent":true,"name":"Spectral Signatures of the matter","selfAssesment":"Completed
"},{"code":"PP1-3-4","description":"Vegetation, water and soil represent the most common cover types of Earth surface. Their reflectances in the VIS/NIR/SWIR spectral range, plotted against wavelength in the 0,4-2,5 micron, represent the most important (basic) spectral signatures for whatever application devoted to Earth surface study. Other spectral signatures (e.g. in emissivity) in the Thermal InfraRed range are particularly important to infer specific properties of Mineral and Rocks (ref. [PP1-3-5] Spectral Signature of Mineral and Rocks). In order to discriminate among such basic cover types, the (ref. [IP3-1-2-3]) NDVI (Normalized Difference Vegetation Index) is the most simple and powerful diagnostic tool in the VIS/NIR spectral range \r\nNDVI values ranging between the values -1 and +1, are higly positive for fully vegetated (up to NDVI=1) or partly vegetated (NDVI>0,3) targets, still positive (>0) for bare soils, negative for water bodies. Values around zero are expected for clouds thanks to their similarly high reflectances both in the NIR and VIR spectral bands (ref. [PP1-3-6] Spectral Signature of Clouds). \r\n\r\nVegetation. a) in the visible range most of the incomig radiation is adsorbed by the photosynthetic process, transmittance is very low. The residual reflected radiation has a small peak of reflectance around 0.5 microns which is responsible of the green colour associated to vegetation by the human vision sytem (limited to the VIS spectral range); b) in the NIR range vegetation exhibits its higher reflectance together its higher transmittance (very low absorbance) so that leaf density can be estimated thanks to the the contributes (decreasing with depth) of underlaying leaf layers; c) in the SWIR spectral range (in particular in the water bands around 1,4 and 1,9 microns) it is possible to appreciate the vegetation water content. As much it is, as more incident radiation is absorbed and less is the reflected fraction of radiation.\r\nBare Soil. Spectral reflectance is normally increasing moving from the VIS to the SWIR spectral region. Water features around 1,4 and 1,9 microns give information on soil water content (see before). Others specific features are described in [PP1-3-5] Spectral Signature of Mineral and Rocks\r\n\r\nWater. Spectral reflectance of clean deep water is quite low reaching quickly the zero value as soon as wavelengths passe microns. However it is important to note that such a very low reflectance is due to a very high transmittance in the VIS range and to a very high absorbance in the NIR/SWIR regions (ref. [PP2-2-5-2] Attenuation Lenght and Penetration Depth). This means that water is quite transparent in the VIS spectral range (so that, in case of shallow waters, measured reflected radiance can be significantly increased by the contribution of bottom of the sea). Water is completely opaque, instead, in the NIR/SWIR. In this spectral region, even in presence of shallow waters, the presence of suspended matter (that increases the measured reflectance both in the VIS and NIR/SWIR ranges) can be better discriminated (than in the VIS) from the contribute of the bottom of the sea that, in this spectral range, is zero.","hasChildren":true,"hasParent":true,"name":"Spectral Signature of Vegetation, Water, Soil","selfAssesment":"Completed
"},{"code":"PP1-3-5","description":"Spectral signatures of rocks and mineral provide information on their chemical composition and crystal properties, grain size and roughness over a wide range of wavelengths from the visible to the thermal infrared.\r\nIn the Visible and Near-InfraRed (VNIR; 0.4÷1.0 µm) region, spectral features are dominated by electronic processes in transition metals, such as Fe, Mn, Cu, Ni, Cr, etc. Therefore, iron is the most important constituent having spectral properties in the VNIR, and the iron-rich minerals are characterized by low reflectance (high absorbance) below 0.7 µm.\r\nOther minerals, which represent the major part of the Earth's surface rocks, such us Si, Al and some anion groups (e.g. silicates, carbonates, oxides) hydroxides, have less spectral features in the VNIR region, but exhibit much more evidences in the Short-Wave InfraRed (SWIR; 1÷3 µm) region. In fact, spectral features of hydroxyls and carbonates mark the SWIR region.\r\nThe hydroxyl ion is a widespread constituent occurring in rock forming minerals such as clays, micas, chlorite etc. It shows a vibrational fundamental absorption band at about 2.74÷2.77 µm and an overtone at 1.44 µm.\r\nCarbonates, which are commonly in the Earth surface rocks in the form of calcite (CaC03), magnesite (MgC03), dolomite [(Ca-Mg) C03] and siderite (FeC03), shows a typical absorbance feature around 2.3 µm, instead the water content can be instead evaluated by the depth of absorption at 1,4µm and 1,9 µm.\r\nThermal InfraRed (TIR; 1÷20 µm) region, from a geological point of view, is a particularly important spectral region for remote sensing aiming at compositional investigations of terrestrial materials. In fact, the fundamental vibration features of many rock-forming mineral groups (e.g. silicates, carbonates, oxides, phosphates, sulphates, nitrates, nitrites, hydroxyls) occur in the TIR region. Briefly:\r\na) the silicates, which are most abundant group of minerals in the Earth's crust, shows vibrational spectral features due to the presence of Si04-tetrahedron around 8 µm to 12 µm; b) the carbonates show a weak feature around 11.3 µm that can be detected; c) the sulphates display bands near 9 µm and 16 µm; d) the phosphates also have fundamental features near 9.25 µm and 10.3 µm; e) the features in oxides usually occupy the same range as that of bands in Si-O, i.e. 8 µm to 12 µm; g) the nitrates have spectral features at 7.2 µm and the nitrites at 8 µm and 11.8 µm; h) the hydroxyl ions display fundamental vibration bands at 11 µm.","hasChildren":true,"name":"Spectral Signature of Mineral and Rocks","selfAssesment":"Completed
"},{"code":"PP1-3-6","description":"The determination of spectral signatures for scenes with a high degree of spatial complexity is considered as one of the most persistent problems in atmospheric radiation, especially at the surface, where satellite observations can only be used indirectly to infer energy budget terms. In the shortwave (solar) spectral range, it is especially challenging to derive consistent albedo, absorption, and transmittance from spaceborne, aircraft, and ground-based observations for inhomogeneous cloud conditions and is closely related to the long-debated discrepancy between observed and modeled cloud absorption.\r\nThe cloud spatial structure is revealed as a spectral signature in shortwave irradiance through the physical mechanism of molecular scattering. However, the study of specific mechanisms is rather complex since the satellite instruments cannot completely describe the spatial distribution of cloud and the variability of scattering and absorption properties. For this reason, several studies deal with the problem described above, as a challenge for estimating spectrally the cloud optical properties (such as the albedo and transmittance) as well as scattering and absorption processes taking place in the cloud system with adequate resolution. Hence, the above mechanisms can be described using three dimensional (3-D) radiative transfer models. Those models receive auxiliary information from cloud imagery and radar observations. The molecular scattering (Rayleigh) was the only one directly dependent on the wavelength of the vertical radiative flux. Moreover, it was considered as a spectral perturbation of backtracked horizontal exchange of solar radiation due to the inhomogeneous distribution of cloud. The horizontal photon transport is highly correlated to its spectral dependence.\r\nConcerning the presence of cirrus or ice clouds, the effect of their phase function and the vertical distribution were evaluated on the scattering of far infrared radiation. Thus, the accurate reconstruction of the phase function of cirrus clouds potentially indicates the need for application of a radiative transfer model. This specific module necessarily includes scattering parameters, while the accuracy of its calculations needs to be verified against real measurements. \r\nFor several applications the preliminary detection of those portions of the scene affected by the presence of clouds (cloud detection) is mandatory. For studying properties of Earth's surface targets affected by the presence of clouds are flagged just to exclude them by further analyses. In some case clouds themselves are the object of interest. In both cases the identification of clouds (and their classification) is mostly done by using (combination of) specific spectral signatures. Generally speaking clouds are highly reflecting VIS/NIR radiation showing (due to their heigth) brigthness temperatures (in the TIR region) lower than underlying surfaces. Thin or semi-transparent clouds are still detectable for their higher reflectance over the sea which represents a quite dark bacground in the VIS/NIR/SWIR region. Over land (much more reflecting) such a test is not more efficient and more sophisticated tests (e.g. Brigthness Temperature Difference in the split window bands around 11 and 12 microns) are required. In presence of very cold, high reflective backgrounds (e.g. snow, glaciers, etc.) both tests on the VIS reflectance and on TIR brigthness temperature could fail. More specific tests exploiting the reflectance drop of snow in the SWIR (where clouds are still saving their higher reflectance) helps to discriminate the presence of clouds from clear sky conditions even over a snow background. In the microwaves clouds are quite transparent except when coupled with coarse particles related to rain, snow, hailstones (precipitating clouds). In that case Mie scattering dominates strongly reducing the amount of radiance collected at the sensor (lower brigthness temperature in the microwave spectral range).","hasChildren":true,"name":"Spectral Signature of Clouds","selfAssesment":"Completed
"},{"code":"PP1-3-7","description":"If the resolution is low enough that disparate materials can jointly occupy a single pixel, the resulting spectral measurement, made by the sensor, will be the composite of the individual spectra. Under the linear mixing model (LMM), each observed spectrum in each pixel of a given image is assumed to result from the linear combination of the N endmember spectra present in the pixel. The reflectance spectrum of each endmember is weighted by the fractional area coverage of it in the pixel. \r\nHowever, if the components of interest in a pixel are in an intimate association, like sand grains of different composition in a beach deposit, light typically interacts with more than one component as it is multiply scattered, and the mixing between these different components are nonlinear. Such nonlinear effects have been recognized in spectra of: particulate mineral mixtures, aerosols and atmospheric particles, vegetation and canopy. In this case a non-linear mixing model (NLMM) should be applied. To summarize: Linear mixture model assumes that endmember substances are sitting side-by-side within the pixel; Nonlinear mixture model assumes that endmember components are randomly distributed throughout the pixel, causing multiple scattering effects. \r\nIn the linear mixing case, the basic premise of mixture modelling is that within a given scene, the surface is dominated by a small number of distinct materials that have relatively constant spectral properties. These distinct substances (e.g., water, grass, mineral types), characterized by a well-defined spectral signature are called endmembers, and the fractions in which they appear in a mixed pixel are called fractional abundances. Then, finding the endmembers that can be used to ‘unmix’ other mixed pixels becomes a crucial issue. \r\nIdentify fractional abundances of distinct substances from the spectral signal of a mixed pixel is one of the application in which hyperspectral images can provide an valuable support.","hasChildren":true,"name":"Composition of spectral signatures (Linear Mixing)","selfAssesment":"Completed
"},{"code":"PP1-3-8","description":"One of the most common ways to classify remote sensing systems consists in distinguishing them into the passive systems, which detect naturally occurring radiation, and the active systems, which emit radiation and analyse what is sent back to them. The passive systems can be further subdivided into those that detect radiation emitted by the Sun (this radiation consists mostly of ultraviolet, visible and near-infrared radiation), and those that detect the thermal radiation that is emitted by all objects that are not at absolute zero (i.e. all objects). For objects at typical terrestrial temperatures, this thermal emission occurs mostly in the infrared part of the spectrum, at wavelengths of the order of 10 μm (the so called thermal infrared region), although measurable quantities of radiation also occur at longer wavelengths, as far as the microwave part of the spectrum. Active systems can, in principle, use any type of electromagnetic radiation, resulting able to obtain measurements anytime, regardless of the time of day or season. In practice, however, they are restricted by the transparency of the Earth’s atmosphere at the specific spectral range considered. In any case they can be used for examining wavelengths that are not sufficiently provided by the sun, such as microwaves, or to better control the way a target is illuminated. Active sensors may be classified according to the use that is made of the returned signal. Two main methods have been identified to this aim so far: the Ranging technique mostly concerns with the time delay between transmission and reception of the signal, while the Scattering one is mostly focused on the strength of the received signal.","hasChildren":true,"name":"Definition of active and passive remote sensing techniques","selfAssesment":"Completed
"},{"code":"PP1-3-9","description":"Light has a key role for aquatic ecosystems, both in marine and freshwater. It penetrates underwater and interacts with dissolved and particulate water constituents, the optically active constituents (OACs). They absorb and scatter the light, giving water its characteristic colour and affect the light availability underwater. The three main OACs are phytoplankton, coloured dissolved organic matter (CDOM) and suspended particulate matter (SPM) and vary in time and space. Absorption and scattering represent the inherent optical properties (IOPs) of water and depend solely on the OACs present in the water. In addition, water bodies have apparent optical properties (AOPs) that depend both on OACs and the incident light field.\r\nThe chlorophyll in the phytoplankton absorbs blue and red wavelengths and reflects green. Therefore, the oceans appear blue-green depending on the concentration of phytoplankton. CDOM is primarily tannin-stained water released from decaying detritus. High CDOM concentrations appear yellow-green to brown. CDOM absorbs ultraviolet (UV) light in the surface waters which is harmful for phytoplankton but competes with phytoplankton for light. Inorganic suspended matter (ISM) is the suspended sediment in the water. It is a component of SPM and strongly scatters longer (red) wavelengths. High ISM concentrations give water a reddish-brown colour. Pure water, however, absorbs longer wavelength red light. As natural waters vary in their composition, oceanographers introduced ocean classification schemes based on the optical properties of water. The main differentiation is between Case 1 open ocean waters and Case 2 coastal waters. In open ocean waters, the optical properties are dominated by phytoplankton and covarying material. In coastal waters, optical properties are dominated by suspended sediments and CDOM that vary independently of phytoplankton.","hasChildren":true,"name":"Optical properties of water","selfAssesment":"Completed
"},{"code":"PP1-3","description":"Measuring the signal emitted (received) by a radiation source (detector)","hasChildren":true,"hasParent":true,"name":"Sensing of EM radiation.","selfAssesment":"Planned
"},{"code":"PP1-4-1","description":"Radiative transfer equation (RTE) is the governing equation of radiation propagation in a media, which plays a central role in the analysis of radiative transfer in gases, semitransparent liquids and solids, porous materials, and particulate media, and is important in many scientific and engineering disciplines. \r\nThe RTE states that when radiation (a light-ray) propagates through matter (gas, dust, liquid), the incident radiation could be absorbed or scattered by matter, or radiation emitted from matter could append to the incident radiation. As a result, the intensity of radiation would change temporally, spatially, and directionally. The study of the propagating way of radiation in matter is the radiative transfer. In more detail, the radiation traversing a medium may be attenuated due to the density, mass scattering and absorption of material. In contrast, the radiation’s intensity can be strengthened by emissions from the material plus multiple scattering from all directions. All the above interactions are described mathematically by the general radiative transfer equation.\r\nThere are different forms of RTEs that are suitable for different applications, including the RTE under different coordinate systems, the transformed RTE having good numerical properties, the RTE for refractive media, etc.. Furthermore, several fundamental numerical methods for solving RTEs are proposed up to now focusing on the deterministic methods, such as the spherical harmonics method, discrete-ordinate method, finite volume method, and finite element method.","hasChildren":true,"name":"General equation of radiative transfer.","selfAssesment":"Completed
"},{"code":"PP1-4-10","description":"The inversion approach aims at retrievals of trace gas concentration and temperature profiles of atmospheric state, namely the modeled state vector, based on the measured radiance transmitted or reflected or scattered (SCIAMACHY spectrometer) by the Earth-Atmosphere system. Satellite instruments measure the radiance L that reaches the top of the atmosphere at given frequency v. The measured radiance is related to geophysical variables of Earth's atmosphere (e.g. temperature vertical profiles and chemical composition, aerosols, clouds, rain, etc.) and surface (e.g. temperature, spectral emissivity and reflectance, etc.) by the Radiative Transfer Equation (RTE). In RTE measured spectral radiances are assumed as the result of different contributions:\r\na) thermal emission from the different layers (at heigt z) of atmosphere at temperature T(z) modulated by the atmospheric transmittance from z to the sensor heigt. It depends on both temperature profile T(z) and trace gas concentration along the optical path;\r\nb) Surface emission. It depends mostly on Eart's surface temperature T(0) and spectral emissivity\r\nc) Surface reflection/scattering. It depends on spectral reflectance and local properties like surface rugosity \r\nOthers, more complex contributions comes from: cloud/rain, aerosols, etc.\r\nIn its simplified form, terms a) and b) dominate as far as InfraRed (IR) radiances are considered. Term a) can be neglected in those bands where atmosphere is transparent (atmospheric windows). Term b) can be negletcted in the IR spectral bands (sounding channels) where it is fully adsorbed by some specific constituent of the atmosphere. Among the IR sounding channels some ones are selected being associated to atmospheric constituents (like CO2 or oxygen) whose mixing ratio in the atmosphere is known to be constant. For radiances measured in these bands term a) in RTE depends only on T(z) (through a Fredholm equation of the first kind) that can be then retrieved by inversion methods. When T(z) are known trace gas concentrations survive as the only unknown of term a) and can be retrieved by inversion methods using radiances measured in their corresponding sounding channels. Similar inversion strategies have been suggested as far as radiances (emitted, transmitted, reflected, adsorbed) measured in different spectral ranges (from the Visible to the Microwaves) are considered.","hasChildren":true,"name":"Retrieval of atmospheric parameters by inversion of multi-spectral radiances","selfAssesment":"Completed
"},{"code":"PP1-4-2","description":"In the field of radiation scattering and absorption, the cross-section, analogous to the shape of a particle, is used to determine the amount of energy diverted from the original beam by the particle. This parameter is called mass cross section, when it is in reference to unit mass (cm2g-1).","hasChildren":true,"name":"Cross Section of Extinction (Absorption, Scattering) per Mass Unit","selfAssesment":"Planned
"},{"code":"PP1-4-3","description":"When the mass cross-section is multiplied by the density of particle, the extinction coefficient is calculated, namely the sum of absorption and scattering coefficient, whose the units are related to length. Especially, the absorption coefficient (k (cm•atm)-1) is the product of strength of absorption with the Loschmidt’s number.","hasChildren":true,"name":"Absorption Coefficient","selfAssesment":"Planned
"},{"code":"PP1-4-4","description":"The source function, Jλ, has units of radiant intensity and it is defined as the ratio of the source function coefficient to the mass extinction cross section. The Jλ determines the intensity that are acquired in a homogeneous medium.","hasChildren":true,"name":"Source Function (Coefficient)","selfAssesment":"Planned
"},{"code":"PP1-4-5","description":"If the monochromatic beam (Iλ) of radiation attenuates due to absorption, but it remains unaffected from emission contributions and multiple scattering of homogeneous Earth-Atmosphere system, it can be expressed by Beer-Bouguer-Lambert law. This law also expresses the monochromatic optical depth (τλ) and transmissivity (Τλ) of the above system.","hasChildren":true,"name":"Beer-Bouguer-Lambert law.","selfAssesment":"Planned
"},{"code":"PP1-4-6","description":"The Schwarzschild equation provides an interpretation for the infrared radiation that undergoes the absorption and emission processes simultaneously, while the scattering efficiency is considered negligible. Hence, its solution is obtained by the integrating of relationship that invokes Kirchhoff’s law and summing the two above processes along a ray path.","hasChildren":true,"name":"Schwarzshild equation and its solutions","selfAssesment":"Planned
"},{"code":"PP1-4-7","description":"The Optical Path (OP) describe the total concentration along a path of constituents extinguishing (by absorption or scattering) the electromagnetic radiation traveling through a medium at a specified whavelength λ. Its value depends then on the efficiency of absorption and scattering phenomena which occur during the travel itself. The Earth's atmosphere is usually the medium that a monochromatic beam (Iλ) of radiation travels through before reaching satellite sensors. In an homogeneously estinguishing medium (i.e. a medium with extinction coefficient for mass unit K constant along the optical path) the Optical Thickness OT is defined as OT=K x OP. It give a measure of the cumulative depletion of Iλ directed in straight-downward. As far as the Optical Thickness is large, the medium is more and more optically thick (i.e. radiation is largely absorbed). If the Optical Thickness is small it means that the medium is optically thin (i.e. radiation travels through it easily).","hasChildren":true,"name":"Concepts of Optical path and Optical thickness.","selfAssesment":"Completed
"},{"code":"PP1-4-8","description":"Radiative transfer is highly nonlinear and non-local against the cloud structure at a high spatial resolution. Hence, a Monte Carlo approach can be used for the representation of cloud structure and interactions between photons and clouds. This approach is more efficient than the method of representing clouds as horizontally homogeneous.","hasChildren":true,"name":"Radiative transfer in presence of clouds","selfAssesment":"Planned
"},{"code":"PP1-4-9","description":"The line by line radiative transfer model (LBLRTM) is an accurate and flexible model for the estimation of the spectral radiance and transmittance over the full spectral range (microwave to ultraviolet), using a first-order perturbation algorithm. It is considered as the basic tool for the creation of retrieval algorithms employed by the ground-based and satellite instruments, while the latest updates in spectroscopic factors are derived from the high-resolution transmission molecular absorption (HITRAN) database. A LBLRTMs is continuously updated and validated against highly accurate spectral measurements. Its errors are related to uncertainties in line parameters and shape. The shape is a Voigt line which is a linear combination of approximating functions for the description of all atmospheric levels. LBLRTML is combined with the continuum MT_CKD (Mlawer, Tobin, Clough, Kneizys, Davies) model which in turn includes the atmospheric constituents of water vapor, carbon dioxide (CO2), molecular oxygen (O2), molecular nitrogen (N2), and ozone (O3), and the molecular extinction process (Rayleigh scattering). A recent version of LBLRTM calculates analytically the Jacobians equations for obtaining meteorological parameters. Also, this model version retrieves the optical parameters of clouds related to scattering and emissivity. The LBLRTM is widely used in radiation and climate applications. It is capable to calculate the absorption degrees of various atmospheric constituents which are utilized afterward from climate and weather prediction models for estimating the broadband solar irradiance and the heating rates. Additionally, the complex radiative transfer models with fast computational time are initiated and trained by the LBRTM, since they are used subsequently on numerical weather prediction (NWP) assimilation systems.","hasChildren":true,"name":"Line-by-line radiative transfer models","selfAssesment":"completed
"},{"code":"PP1-4","description":"Theory of radiative transfer describes the transmission of the electromagnetic radiation through a medium. The electromagnetic radiation can be emitted, absorbed, scattered by constituents of the medium depending on the composition of the medium and the physical state of its constituents, as well as the wavelength of the radiation itself. Retrieving geophysical parameters from radiation measurements requires to know this kind of interaction which is described through the Equation of Radiative Transfer. In the field of Earth Observations from space, the considered medium is normally the Earth's atmosphere through which the e.m. radiation travel before reaching aerial multi-spectral sensors. Radiative transfer models allow to foreseen spectral radiances at whatever altitude in atmosphere (radiance at the sensor) starting from the knowledge of atmospheric vertical profiles of temperature and chemical constituents concentrations (direct problem). The possibility to retrieve atmospheric temperature profiles and chemical constituents concentrations from multi/iper spectral radiances measurements in selected bands (inverse problem) is the scope of the inversion techniques widely applied in meteorology and of a specific set of sensors devoted to the vertical sounding of the atmosphere. Clouds and scattering particles, like aerosols - requiring the inclusion of additional information on the atmospheric constituents (e.g water phases involved, dimensions and geometry of scattering particles, etc.) - make radiative transfer model more complex.","hasChildren":true,"hasParent":true,"name":"Fundamentals of Radiative Transfer","selfAssesment":"Planned
"},{"code":"PP1-5-1","description":"Light is the electromagnetic phenomenon we exploit for remote sensing. Its basic laws concerning the transmission through the interface of two different media are governed by reflection and refraction. Reflection governs the way light is backpropagated and refraction dictates how light is transmitted. Refraction is related to the real refractive index of a medium. Dispersion relates to the way the light of a given wavelength is transmitted. Since light of different wavelengths are transmitted at different angles, the phenomenon leads to the concept of dispersion. These three simple principles are at the core of the understanding technology of remote sensing.","hasChildren":true,"name":"Reflection, Refraction and Dispersion of the light","selfAssesment":"Planned
"},{"code":"PP1-5-11","description":"The theory provides the bulk of physical explanation and related laws, which govern absorption, emission and spontaneous emission from the ordinary matter. Early laws about thermal radiation and the blackbody emission, such as Rayleigh-Jeans, Wien, Planck laws are cast in a single theory and formalism through the concept of quantized energy at the level of atoms emission/absorption of light. Explain the modern concept of quantum optics and their link to the design of modern devices for the measurements and/or production of coherent light.","hasChildren":true,"name":"Einstein’s theory of radiation: photons, photoelectric effect, absorption, emission; Stimulated emission: the laser","selfAssesment":"Planned
"},{"code":"PP1-5-14","description":"Solid state modern detectors rely on non-metal junction, which can be designed and operated to yield a bandgap energy according to the spectral range (infrared, visible, UV) to be detected. The basic principles of how these devices are designed and fabricated is important to develop and design new sensors useful for the various remote sensing applications.","hasChildren":true,"name":"Electric conduction in solids: semiconductors, p-n- junction, diode and transistors","selfAssesment":"Planned
"},{"code":"PP1-5-15","description":"Modern detectors of electromagnetic radiation in the infrared, VIS, UV spectral regions are designed and fabricated based on suitable junctions or electro-optical devices. The performance of these systems needs to be assessed in terms of accuracy and precision. This is made through figures of merit such as Noise Power Spectral Density, Noise Equivalent Power. Detectors can be classified as photovoltaic or photoconductive devices, which allows to better classify the various noise sources: shot noise, 1/f noise, Johnson noise, generation-recombination noise.","hasChildren":true,"name":"Photovoltaic and photoconductive detectors: MCT, InSb, bolometer, CCD devices","selfAssesment":"Planned
"},{"code":"PP1-5-2","description":"Interference and diffraction are phenomena related to the wave nature of electromagnetic radiation. They explain how light propagates in presence of obstacles. These phenomena are largely used in the fabrications of optical systems for remote sensing: e.g. radiometers and spectrometers.","hasChildren":true,"name":"Interference and Diffraction.","selfAssesment":"Planned
"},{"code":"PP1-5-3","description":"The Michelson interferometer is the instrument that exploits and evidence the interference of light. A masterpiece of experimental physics, the Michelson interferometer is the key architecture of the modern optical interferometers, which make it possible to measure the emitted Earth spectrum with hyperspectral resolution.","hasChildren":true,"name":"Michelson Interferometer","selfAssesment":"Planned
"},{"code":"PP1-5-4","description":"The celebrated principle of constant speed of light and independence of the reference frame is important to explain the basic principles of instruments such as the Michelson interferometer. The basic physics theory to explain how electromagnetic fields propagates and the inter-relationship between electric and magnetic fields.","hasChildren":true,"name":"Special relativity; Electromagnetic fields equations and propagations","selfAssesment":"Planned
"},{"code":"PP1-5-6","description":"Helmotz’s wave equation arises in light and acoustic scattering problem and yields the general framework to investigate and analyse the scattering of time-harmonic acoustic and electromagnetic waves by a penetrable inhomogeneous medium.","hasChildren":true,"name":"Helmotz’s equations; Scattering from inhomogeneous media.","selfAssesment":"Planned
"},{"code":"PP1-5-7","description":"Geometrical optics is governed by the laws of reflection, refraction and dispersion. Its applications are relevant to many optical systems involving ray tracing, wavefront propagation, thin film calculators (which underly many optical engineering calculations).","hasChildren":true,"name":"Foundations of geometrical optics, geometrical theory of optical imaging","selfAssesment":"Planned
"},{"code":"PP1-5-8","description":"Optical interferometers are nowadays used to develop and implement Fourier Transform Spectrometers, which can measure the emission spectrum of a given source with high spectral resolution at a constant sampling. This instrumentation is now at the core of modern hyperspectral sounders from satellite and have opened the way to the sounding of the Earth atmosphere with unprecedented spatial vertical resolution.","hasChildren":true,"name":"Elements of the theory of interference and interferometers","selfAssesment":"Planned
"},{"code":"PP1-5-9","description":"Diffraction gratings and dispersive element are the basic ingredients for radiometers and grating spectrometers. They are in some cases preferred to Interferometer systems because the optical layouts can be designed and implemented with no moving part or components. Many of the today satellite instruments, including sounder and imagers, rely on diffraction and/or grating spectrometers","hasChildren":true,"name":"Elements of the theory of diffraction and grating spectrometers","selfAssesment":"Planned
"},{"code":"PP1-5","description":"This section describes the theoretical fundaments of Optics and Modern Physics of Sensors relevant to the Earth Observation.","hasChildren":true,"hasParent":true,"name":"Basics of Optics and Modern Physics of Sensors","selfAssesment":"Completed
"},{"code":"PP1-6-1","description":"The temperature and pressure profiles determine the atmospheric structure. The latter consists of four basic levels, considering the vertical variability of the temperature. These main four levels are troposphere, stratosphere, mesosphere, and thermosphere. In the troposphere (0-12km), which is the lowest layer of the atmosphere, all the meteorological processes that affect our everyday life take place. The lowest part of the troposphere is known as the boundary layer (0-3km), where all the surface-atmosphere interactions and exchanges take place. The troposphere concentrates the water vapor and 90% of atmospheric mass, while the chemical composition of all atmospheric layers consists of nitrogen, oxygen, argon and trace gases. The main parameters that characterize the atmosphere structure are pressure, density, and temperature. All the aforementioned parameters are related to the atmospheric composition and vary with altitude, latitude, longitude and season. Additionally, the stratosphere, which is the layer above the troposphere, contains almost all of the ozone abundance (~90%) of the atmosphere in a region named as ozone layer and traced between 15 and 35km. The interaction of the incoming solar radiation with ozone in this layer causes the reduction of the incoming harmful UV radiation provoking the temperature increase in the stratospheric layer. The 99.9% of total atmospheric mass is concentrated in lower atmosphere (<50km) with Nitrogen (N2, 78.08%), Oxygen (O2, 20.95%) and argon (Ar, 0.93%) being the major constituents of the atmosphere. Water vapor (H2O) is considered as a significant factor, too. Despite the fact that it depicts a very small amount of total atmospheric mass, it’s one of the most important greenhouse gases, along with carbon dioxide (CO2) and methane (CH4), absorbing the Earth’s longwave (infrared) radiation, affecting the energy balance of Earth-Atmosphere system. Furthermore, water vapor plays a decisive role in the formation of clouds and precipitation. Together with the basic chemical (atoms, molecules, ions) constituents of a \"standard\" atmosphere, aerosols of natural and anthropogenic origin have to be considered too, as far as the interaction of e.m. radiation with atmosphere is concerned.","hasChildren":true,"name":"Structure and chemical-physical composition of Earth's atmosphere","selfAssesment":"Completed
"},{"code":"PP1-6-10","description":"The water vapour is the major radiative and dynamic parameter in the atmosphere. Its concentrations vary highly in space and time, with the tropospheric water vapor being determined by the hydrological cycle processes, namely the evaporation, condensation and precipitation and by large-scale transport processes. Specific humidity decreases rapidly with pressure (following an exponential function) and with latitude. In particular, the variability of the H2O concentration shows a bimodal distribution: it’s very small in the equatorial region and poleward, relatively small in stratosphere and shows a maximum in the subtropics of both hemispheres. The concentration of H2O in the lower stratosphere is controlled by the temperature of the tropical tropopause, and by the formation and dissipation of cirrus. The water vapor can condense into water droplets when it has a particle to condense upon. The atmosphere continuously contains aerosol particles ranging in size from ∼10−3 to ∼20 μm. These aerosols are known to be produced by natural processes (volcanic dust, smoke from forest fires, particles from sea spray, windblown dust, and small particles produced by the chemical reactions of natural gases) as well as by human activity (particles directly emitted during combustion processes and particles formed from gases emitted during combustion). Some aerosols are effective condensation and ice nuclei upon which cloud particles may form. For the hygroscopic type, the size of the aerosol depends on relative humidity. Thin layers of aerosols are observed to persist for a long period of time in some altitudes of the stratosphere. \r\nClouds are global in nature and regularly cover more than 50% of the sky. There are various types of clouds. Cirrus in the tropics and stratus in the Arctic, and near the coastal areas are climatologically persistent. The microphysical composition of clouds in terms of particle size distribution and cloud thickness varies significantly with cloud type. Clouds can also generate precipitation, an event generally associated with midlatitude weather disturbances and tropical cumulus convection.","hasChildren":true,"name":"Water vapour and Cloud formation","selfAssesment":"Completed
"},{"code":"PP1-6-11","description":"The radiative equilibrium is the principle, where the radiative emission and absorption are in balance based on Kirchhoff’s and Planck’s law, resulting in the steady temperature of planet. The adiabatic lapse rate displays the decrease of vertical temperature of a parcel with rate higher than 1oC per 100 metres.","hasChildren":true,"name":"Radiative Equilibrium. Adiabatic lapse rate","selfAssesment":"Planned
"},{"code":"PP1-6-12","description":"The atoms of carbon are building blocks of living organisms and they can move among organisms as a part of carbon cycle. Their transport rate to the atmosphere as carbon dioxide is vital, because this gas trap heat in the atmosphere, increasing the Earth’s temperature and causing Greenhouse effect.","hasChildren":true,"name":"The Carbon Cycle, Greenhouse Effect","selfAssesment":"Planned
"},{"code":"PP1-6-2","description":"The atmospheric absorption can cause an excitation or falling into the energy state of a particle, while the scattering is related to absorption and re-emission of radiation at all directions without changes in its frequency. Particularly, the main contributors of the incoming solar radiation absorptions are various molecules like the nitrogen (N2), oxygen (O2), ozone (O3), water vapor (H2O). Additionally, other constituents of the atmosphere such as CO2 and CH4, and other trace gases, aerosols, and cloud droplets can also absorb significant portion of the incoming solar radiation. Generally, the absorption of solar radiation is related to the wavelength of the solar spectrum. For example, gases and specific type of aerosols (black carbon, BC) or elementary carbon (EC) absorb in the ultraviolet (UV) and visible (VIS) part of solar spectrum. On the contrary, cloud droplets which are suspended in the atmosphere mainly scatter in UV and VIS and absorb in the infrared. The absorption of the incoming solar radiation from the atmospheric constituents reduces the harmful UV radiation and it is considered as the driving of atmospheric photochemistry. Moreover, scattering in the atmosphere can be divided into two mainly categories, firstly, the Rayleigh scattering which is the scattering of radiation by gases (mainly N2 and O2) and, secondly, the Mie scattering which is the scattering by aerosol particles and cloud droplets. The main difference between Rayleigh and Mie scattering is the direction of the re-emission of the incident solar radiation. For example, in the Rayleigh scattering the light have symmetrical direction either forward or backward whereas in Mie scattering the light is mainly scattered in the forward direction, depending on the size of the particle.","hasChildren":true,"name":"Absorption and scattering of solar radiation in the Atmosphere","selfAssesment":"Completed
"},{"code":"PP1-6-3","description":"Mie scattering refers primarily to the elastic scattering of light from atomic and molecular particles whose diameter is similar or larger than the wavelength of the incident light. We can say that, when the particle has a diameter greater than about a tenth of the wavelength, we are in the field of Mie scattering.\r\nThis scattering produces a pattern like an antenna lobe, with a forward lobe sharper and more intense than the back one, the larger the particle size the greater the intensity and sharpness of the anterior lobe. Unlike Rayleigh scattering, Mie scattering is not strongly wavelength dependent. In this case the predominant component for the quantification of scattering (in addition to the particle dimension) is the direction of the incident solar radiation.\r\nMore specifically, the amount of scattering in the backward direction depends upon a wave relation tending to decrease in accordance with the growth of the particle size until it reaches a certain value for which the back scattering becomes a constant quantity. This condition is reached when the diameter of the particle is approximately equal to the wavelength of the incident radiation.\r\nIn the atmosphere the Mie scattering is commonly caused by particles (aerosols) floating in the atmosphere (due to Dust, smoke, fog, rain drop). \r\nIn nature it is possible to see the effects of Mie scattering, for example, in the evenings when there is a lot of fog and the dazzling headlights of our car do not allow us to see the road ahead. \r\nThe Mie theory provides the solution for the amount of scattering in case of a spherical medium due to an incident wave.","hasChildren":true,"hasParent":true,"name":"Mie Scattering in the Earth's Atmosphere","selfAssesment":"Completed
"},{"code":"PP1-6-4","description":"Scattering is a physical process by which a particle in the path of an electromagnetic wave continuously exstracts energy from the incident wave and reradiates that energy in all directions. In more detail, it occurs when a photon’s electromagnetic field hits a particle’s electric field in the atmosphere and is deflected into another direction. The Rayleigh scattering falls into the elastic scattering phenomena, in which the individual photon changes its direction of propagation but non its energy. The Rayleigh scattering involves air molecules (mainly N2 and O2) whose diameter (x) is much smaller (one-tenth at least) than the incident radiation wavelength (λ) (i.e., x << λ). The amount of scattered intensity (I) depends on the incident light wavelength (λ) and the refractive index (n) of air molecules. However, the refractive index can be considered relatively negligible as compared to the explicit wavelength term. In this way, the intensity scattered by air molecules in a specific direction is strongly dependent on the wavelength (λ), as expressed in the form Iλ~1/λ4. The inverse dependence of the scattered intensity on the wavelength to the fourth power allows at explaining the blue color of sky, caused by the scattering of sunlight off the atmosphere molecules. To better understand this phenomenon, it is worth considering that a large portion of solar energy is contained between the blue and red regions of the visible spectrum, where blue light (0.425 µm) has a shorter wavelength than red light (0.650 µm). Consequently, based on the above-mentioned equation, blue light scatters about 5.5 times more intensity than red light. For this reason, more blue light is scattered than red, green, and yellow, and so the sky appears blue, when viewed away from the sun’s disk. The Rayleigh scattering of unpolarized sunlight by air molecules has maxima in the forward and backward directions, whereas it shows minima in the side directions. Furthermore, the light scattered by particles is not delimited only on the incidence plane, but is visible in all the azimuthal directions. The derived scattering patterns are symmetrical in the three-dimensional space, because of the spherical symmetry assumed for air molecules.","hasChildren":true,"name":"Rayleigh Scattering in the Earth's Atmosphere","selfAssesment":"Completed
"},{"code":"PP1-6-5","description":"When we talk about “thermal infrared (or terrestrial) radiation” we commonly refer to the energy emitted from the Earth-atmosphere system. Trapping of thermal infrared radiation by atmospheric gases is typical of the atmosphere and is therefore called the “atmospheric effect”. The atmospheric effect is sometimes referred to as the “greenhouse effect” because in a similar way glass, which covers a greenhouse, transmits short-wave solar radiation, however absorbs long-wave thermal infrared radiation. Imagine a beam of radiation travelling through a small section of air. The air is made up of changing concentrations of different species, with all molecules absorbing and emitting thermal radiation at different rates. As the radiation travels through different layers of the atmosphere, the intensity of radiation will constantly be modified by both absorption and emission processes as described by the Schwarzschild's equation. In case of a sensor on board of a satellite, the net radiation measured would be that which is attenuated through each layer (as small increments of absorption and emission) from the surface to the top of the atmosphere plus the radiation emitted directly from the atmosphere. In this case, this process can be described by the radiative transfer equation (RTE). \r\nThe equation of radiative transfer simply says that as a beam of radiation travels through the atmosphere, it loses energy to absorption, gains energy by emission, and redistributes energy by scattering. Many radiative transfer codes exist which are able, i.e. on the basis of known properties of the atmosphere, to computed the effect of the atmosphere on the thermal infrared radiation providing atmospheric transmittance (absorption), atmospheric scattering and atmosphere path emission. Commonly, in satellite remote sensing, the thermal infrared region is defined as the region of the electromagnetic spectrum comprised between 8 and 14 micron. In an atmosphere free of particles (aerosols due to phenomena like fires, volcanic eruption, dust storm, etc.) the thermal infrared radiation is mainly affected by triatomic gases like water vapor, carbon dioxide and ozone.","hasChildren":true,"name":"Thermal infrared radiation transfer in the atmosphere","selfAssesment":"Completed
"},{"code":"PP1-6-6","description":"Light scattering by particles is the process by which small particles cause optical phenomena, such as rainbows, the blue color of the sky, and halos. Mie scattering defines the interaction of light with particulate matter with a dimension comparable to the wavelength of the incident radiation. It can be regarded as the radiation resulting from a large number of coherently excited elementary emitters (molecules for example) in a particle. Since the linear dimension of the particle is comparable to the wavelength of the radiation, interference effects occur. The most noticeable difference to Rayleigh scattering is, generally, the much weaker wavelength dependence and a strong dominance of the forward direction in the scattered light. The calculation of the Mie scattering cross section, which involves summing over slowly converging series, is complicated even for spherical particles, it is worse for particles of an arbitrary shape. However, the Mie theory for spherical particles is well developed and a number of numerical models exist to calculate scattering phase functions and extinction coefficients for given aerosol types and particle size distributions.","hasChildren":true,"hasParent":true,"name":"Light scattering by atmospheric particulates","selfAssesment":"Planned
"},{"code":"PP1-6-7","description":"Each time radiation passes through the atmosphere it is attenuated to some extent. We refer to this attenuation with the term 'atmosphere transmittance'. The typical atmospheric transmittance between wavelengths of 250 nm and 2500 nm, i.e. in the ultraviolet, visible, near-infrared and short-wave-infrared regions of the spectrum is dominated bywater vapour, although methane, carbon dioxide and molecular oxygen are also responsible for a few absorption lines. The behaviour in the visible region is dominated by molecular Rayleigh scattering. At the short-wavelength end of the spectrum, in the ultraviolet, absorption by ozone becomes very significant. Above 2500 nm up to the upper limit (13500 nm) of the optical electromagnetic spectrum useful for Remote Sensing, the atmosphere transmittance is mainly affected by triatomic molecules (H20, CO2 and O3). However, the atmospheric effects (transmittance) is strongly depending on the electromagntic wavelength. Remote Sensing exploits the region of relative atmospheric transparency called atmospheric windows.","hasChildren":true,"name":"Earth's (standard) Atmosphere Transmittance","selfAssesment":"Planned
"},{"code":"PP1-6-8","description":"With the term 'atmospheric windows' we refer to the regions of the electromagnetic spectrum where the interaction between the atmosphere constituents (i.e., molecules, aerosols, and cloud particles) and the electromagnetic radiation is minimized, namely the mechanisms of scattering and absorption of the radiation are less relevant than the transmission one. Therefore, the radiation collected at the sensor in these spectral regions is strictly depending on the Earth surface features, allowing to infer information about the processes/phenomena there in progress at the time of the acquisition. There are three main spectral ‘windows’ in the Earth's atmosphere. The first of these includes the visible and near-infrared (VNIR) parts of the spectrum up to the medium infrared, between wavelengths of about 0.38 μm and 3.5 μm, although it does also contain a number of opaque regions. This spectral interval includes the small portion of the electromagnetic spectrum to which human eyes are sensitive to (i.e, the visibile region between 0.4 and 0.7 μm). The second is a rather narrow region between about 8 μm and 15 μm, in which is found the bulk of the thermal infrared (TIR) radiation from objects at typical terrestrial temperatures. In this region there is only a main opaque interval, around 9.6 μm due to the presence of the ozone band. The third more or less corresponds to the microwave region, between wavelengths of a few millimeters and a few meters. Therefore, each remote sensing instrument that should be able to fully penetrate the Earth’s atmosphere has to be designed to operate in one of these three ‘window’ regions.","hasChildren":true,"name":"Atmospheric (spectral) windows for EO","selfAssesment":"Completed
"},{"code":"PP1-6-9","description":"The water cycle is a continuous purification process of water on Earth due to the movement of water species among various reservoirs. This cycle is vital for Earth’s life, ecosystems, and living organisms. The water cycle includes mainly four processes. Water is evaporated from ocean and land surfaces driven by solar heating. The resulting water vapor rises upwards into the atmosphere, transported by the winds, cools, and due to low air temperature condensates into liquid droplets and ice crystals to form clouds. The ice or/and liquid droplets collide, increase their size, and precipitate as snow or rain to Earth’s surface and oceans. The subtraction of energy (latent heat of evaporation) at low latitudes related to the evaporation processes as well as its release (latent heat of condensation) at higher latitudes related to the condensation processes is a formidable way to guarantees the heat transport from the warmer part of the Earth to the colder ones mantaining local air temperature more compatible with the human life. The starting point of the water cycle is not unique, but the oceans can be selected as the initial reservoir. Other important reservoirs are considered ice sheets, lakes, and rivers. \r\nThe hydrosphere is defined by the various water reservoirs which are characterized by different residence times – the time spends the water molecules in a reservoir. The water residence time – the rate at which the water comes out the reservoirs – varies for each reservoir extending from hundreds (Greenland Ice Sheet) or thousands of years (Antarctic Ice Sheet) to years and days for rivers and lakes, respectively. It also defines the energy transferred from the Earth to the Atmosphere which increases for short-term residence times. In long-term temporal scales, this energy is defined as the evaporation rate (E) and balances with the precipitation rate (P). This global energy balance breaks for shorter time scales depending also on the local and regional climate. For example, in regions located in the Inter-Tropical Convergence Zone (ITCZ), the energy balance in the water cycle does not exist since the precipitation rate is much higher than the evaporation rate (P>>E) due to the horizontal movement of converging trade winds.","hasChildren":true,"name":"The Water Cycle","selfAssesment":"Completed
"},{"code":"PP1-6","description":"Atmospheric Physics describe the processes affecting the physical, chemical and thermodynamic status of planetary atmospheres. In the context of EO sciences, it particularly refers to the physics of the interactions of e.m. radiation traveling across (or emitted by) the atmosphere as the main source of information collected by satellite (in general aerial) sensors.","hasChildren":true,"hasParent":true,"name":"Basics of Atmospheric Physics","selfAssesment":"Completed
"},{"code":"PP1-7-1","description":"According to the second law of thermodynamics, heat is a measure of the movement or the flow of energy from hotter substances to colder ones and it is measured in Joules. In microscale, heat is known as internal energy. Two regions in thermal contact have the same temperature when there is no net exchange of internal energy between them. Heat is the net transfer of internal energy from one region to another, while temperature, which is the degree of hotness or coldness of an object, describes the average kinetic energy of molecules within substances. The faster the particles are moving, the higher their kinetic energy. Since the motion of the particles within an object is random, they do not move at the same speed and in the same direction, some of them move faster. Therefore, those particles have more kinetic energy than the others. Thermodynamic temperature can be defined for substances at (even Local) Thermodynamic Equilibrium (i.e. in condition of density/pressure which allows an efficient equipartition of kinetic energy among molecules). Temperature is then the measure of the average kinetic energy of such a system, and is usually expressed in Celsius (°C). When, particular conditions of very low pressure/density (like in the Earth's thermosphere) cannot guarantee energy equipartition among molecules (i.e. outside thermodynamic equilibrium) the concept of Kinetic Temperature should be used instead. The Celsius temperature scale is defined by international agreement in terms of two fixed points: the temperature of the ice point, which is defined as 0° Celsius, and the steam point as 100° Celsius. The Fahrenheit (°F) temperature scale is mainly used in the United States; on this scale, water freezes at 32 degrees Fahrenheit, and the temperature of boiling water is 212 F. The Kelvin scale (K) is the base unit of temperature in the International System of Units (SI). This temperature scale is obtained by shifting the Celsius scale by −273.15°; zero Kelvin is also called absolute zero.","hasChildren":true,"name":"Temperature and heat","selfAssesment":"Completed
"},{"code":"PP1-7-10","description":"Irreversible thermodynamics investigates the regularities in transport phenomena, namely heat and mass transfer, and their relaxation. It is based on the first law of Thermodynamics, which correlate the heat flow density with pressure and viscosity, and the second law that describe the temporal variations of local entropy for local continuous mass.","hasChildren":true,"name":"The constitutive equations of irreversible fluxes","selfAssesment":"Planned
"},{"code":"PP1-7-11","description":"The Adiabatic process of homogeneous system occurs, when flow of heat is not exchanged across the boundaries of system and the system is characterized from uniform phase (solid or liquid or gases). In this case, the variations of entropy can be determined for some parts of system.","hasChildren":true,"name":"Heat equation and special adiabatic systems, special adiabats of homogeneous systems","selfAssesment":"Planned
"},{"code":"PP1-7-12","description":"The thermodynamic diagrams are used for the study of vertical structure and properties of the Atmosphere above a specific location. Especially, a static diagram represents a) an atmosphere with fixed potential temperature or b) a process curve of the change of variables of air parcel that rises adiabatically.","hasChildren":true,"name":"Thermodynamics diagram, atmosphere static","selfAssesment":"Planned
"},{"code":"PP1-7-2","description":"Kinetic theory of gases is based on a simplified molecular description of gases, from which the properties of volume, pressure and temperature can be derived. The assumptions of this theory are based on the random movements of molecules, their elastic collisions and the transfer of kinetic energy between them.","hasChildren":true,"name":"Kinetic theory of gases","selfAssesment":"Planned
"},{"code":"PP1-7-3","description":"The ideal gas law or general gas equation describes the equation of state of hypothetical ideal gas. This equation correlates the pressure and volume with its temperature, while is characterized as a combination of the empirical laws of Boyle, Charles, Avogadro and Gay-Lussac.","hasChildren":true,"name":"Ideal gas laws","selfAssesment":"Planned
"},{"code":"PP1-7-4","description":"The state functions of ideal gas are the pressure, volume, temperature, internal energy and entropy, which remain unchangeable in compared with the path. The internal energy is expressed through Joule’s law as a function of temperature of gas, while the entropy depends on the variation of volume and temperature.","hasChildren":true,"name":"State function of ideal gases","selfAssesment":"Planned
"},{"code":"PP1-7-5","description":"The phase rule for condensation is expressed as P+F=C+1. The terms of P, F and C describe the number of phases, minimum fixed variables and independent chemical species respectively. Concerning the condensed phases to distinguish the gases from liquids and solids, these are the density, molecular order, diffusion, etc.","hasChildren":true,"name":"State function of the condensed gas phase","selfAssesment":"Planned
"},{"code":"PP1-7-6","description":"When the system passes from initial to final state due changes in properties of temperature, pressure and volume, it is considered to have undergone thermodynamic process. The different types of thermodynamic processes are distinguished in the isothermal (fixed temperature), adiabatic, isochoric (stable volume), isobaric (stable pressure) and reversible process.","hasChildren":true,"name":"Thermodynamic process","selfAssesment":"Planned
"},{"code":"PP1-7-7","description":"Budget equations, namely heat, momentum and moisture budget, are interpreted through two frameworks, which are Eulerian and Lagrangian. Eulerian is utilized for the investigating of transfer of heat by the wind, while Lagrangian is concerned about the effects of ascending or descending airflows on the Earth-Atmosphere system.","hasChildren":true,"name":"Budget equations","selfAssesment":"Planned
"},{"code":"PP1-7-8","description":"The First Law of Thermodynamics supports that the energy is conserved. Thus, the thermal energy is defined as the sum of warming or internal energy (microscopic effect) and work occurring per unit mass (macroscopic effect). For its application to the Atmosphere, the thermal energy input is given from the following mathematical expression: Δq=Cp·ΔT-(ΔP/ρ), where Δq (J·kg–1) is the amount of thermal energy you add to a stationary mass m of air, Cp (J·kg–1·K–1) is the specific heat of air at constant pressure, ΔT (K) is the induced variation of temperature, so that Cp·ΔT represents the heat transferred per unit air mass, ΔP (Pa = J·m-3) is the pressure difference and ρ (kg· m-3) is the air density.\r\nThe term Cp·T is defined enthalpy h, thus, the first term on the right side of eq. of thermodynamic first low for atmospheric applications, which is the corresponding enthalpy change is: Δh=Cp·ΔT. It is a characteristic possessed by the air.\r\nExpressing the first law of thermodynamics for atmospheric applications in conceptual form we can state that, given a quantity Δq of thermal energy added to a stationary mass m of air, a part of this energy heats the air, increasing its internal energy, but, as air heats up, its volume expands by an amount ΔV and pushes against the surrounding atmosphere, which responds with an equal and opposite pressure P that we can assume constant. Therefore, a part of the thermal energy introduced does not go to heat the air, but goes into macroscopic movement.","hasChildren":true,"name":"First law of thermodynamic","selfAssesment":"Completed
"},{"code":"PP1-7-9","description":"A natural process that starts from an equilibrium state and ends in another state, causing changes in direction of entropy (ΔS) or statistical disorder of the system, is interpreted by Second Law of Thermodynamics. This law is considered as an irreversible process and it is expressed as ΔS=Heat transfer/Temperature.","hasChildren":true,"name":"Second law of thermodynamics","selfAssesment":"Planned
"},{"code":"PP1-7","description":"Thermodynamics is the science of the relationships between heat, work, temperature, radiation, energy and properties of matter. These relationships are governed by the four laws of thermodynamics which allow a quantitative description, through measurable macroscopic physical quantities, of processes that, at the level of microscopic constituents can be described by the statistical mechanics. Thermodynamics applies to a wide variety of topics relevant to EO science and technologies from atmospheric chemistry and meteorology up to sensor design and aeronautics.","hasChildren":true,"hasParent":true,"name":"Basics of Thermodynamics","selfAssesment":"Planned
"},{"code":"PP1-8-2","description":"Starting from the standard Rocket Equation - assuming a relative speed of the burned (emitted) fuel equal to 2,4 km/s and zero initial speed - it is possible to evaluate (for a single-stadium rocket) the mass percentage of payload that can be hosted on a platform depending on the final speed expected on the orbit. For instance a 28% payload is possible for a geostationary platform whose expected final speed on the orbit (radius 42.170 km) is 3,7km/s. Instead for a polar platform at about 800km this percentage reduce up to the 4% being the final sped on the orbit expected to be 7,5km/s.","hasChildren":true,"name":"Equation of the rocket and launch of a satellite: payload determination","selfAssesment":"Planned
"},{"code":"PP1-8-3","description":"The orbit of a satellite is commonly defined through its so called Keplerian parameters. These parameters represent the trajectory that the satellite will follow if no-perturbation are acting on it. A series of forces act on the satellite to perturb it away from the nominal orbit. We can classify these perturbations, or variations in the orbital elements, based on how they affect the Keplerian elements. The actual orbit of a satellite will result from a combination of these perturbations. Periodic maneouvers are needed to bring the orbit back to nominal conditions. The lifetime of a satellite is defined as the time interval that it takes to decay from its initial altitude to an altitude causing the satellite reentry down to the atmosphere. Therefore lifetime of a satellite should not be confused with the time during which the satellite will provide useful information (this operational phase, in general, is designed to last 5 - 7 years). In fact, all satellite terminating operational phases in orbits passing through the LEO region should be de-orbited or, where appropriate, manoeuvred to an orbit with suitably-reduced lifetime, that is, should be left in an orbit where drag and other perturbations will limit lifetime. The actual duration of the satellite in orbit will depend from the intensity of the perturbations which will affect its orbit. In case of satellite on GEO orbit, at the end of the operational phases they will be located on a disposal orbit, that is an orbit which do not cross the protected region. The protected region is the altitude region ranging from GEO - 200 km to GEO + 200 km and inclination region between -15 deg and +15 deg. Satellites in low Earth orbit, with perigee altitudes below 1000 km, are predominantly subject to atmospheric drag. This force very slowly tends to circularise and reduce the altitude of the orbit. The rate of 'decay' of the orbit becomes very rapid at altitudes less than 200 km, and by the time the satellite is down to 180 km it will only have a few hours to live before it makes a fiery re-entry down to the Earth.","hasChildren":true,"name":"Real orbits. Life time of a satellite, orbit’s decay.","selfAssesment":"Planned
"},{"code":"PP1-8-4","description":"The choice of a satellite orbit mostly depends on its main application. From this point of view it represents a crucial part of a satellite mission design. The most important parameters to describe a satellite orbit are the inclination angle i (of the orbit plane respect to the equatorial plane) its eccentricity e and its height H from the Earth's surface. In principle whatever eigth H can be used, provided that the speed of the satellite on its orbit allows the centrigugal force to exactely compensate the gravitational one at that heigth. Polar (i close to 90°) and Geostationary (i=0, H=35.800 km) orbits are the most common choices for EO satellites. In principle one single polar satellite can be sufficient to guarantee the global coverage of the Earth with equal quality of the images at all latitudes. All Geostationary satellites share the same circular orbit with H around 36000 km where the required speed exactely correspond to the one required to travel an entire orbit in 1 sideral day (orbital period P = 1 sideral day). This means that the satellite footprint is permanently in place over a specific Earth's location (e.g. for Meteosat 0°N, 0°E) allowing a quasi-continuous monitoring of a whole Earth's emisphere (with poor visibility of Earth's edges including Poles). Polar satellites' heigths are usually in between 700-800 km, with orbital periods around 100min (i.e. about 14,5 orbits/day) even if, lower orbits are also chosen particularly for very high spatial resolution payloads. Lower inclinations are also used (quasi-polar orbits) for specific applications. Due to the asphericity (and mass inhomogeneity) of the Earth, satellite orbit plane rotates around the Earth's polar axis with a period Pp producing (for elliptical orbits) the rotation of the orbit itself in its plane. A common choice for most EO polar satellites is to choose the orbital parameters in a way that Pp=1 year (Sun-Synchronous orbits). Due to the synchronism between Earth's revolution around the Sun and the orbit plane precession around Earth' axis, satellite passages happens at the same local solar time (similar illumination conditions) each time it flies over a specific region. This ensure repeatable sun illumination conditions facilitating image interpretation particularly for change detection or land monitoring applications. Other choices are possible when it is required to monitor with continuity high latitude regions.\r\n\r\nThis is the case of Molniya orbits which combine the continuity of observations typical of geostationary satellites with the possibility, offered by polar orbits, to overfly the highest latitudes regions. Its characteristics are: high eccentricity (e.g. e=0,74, axes 500 and 23.000 km), P=1/2 sideral day (Geo-Synchronous), inclination (i=63,4° or i=116,6°) which guarantees the satellite footprint at the apogee remaining positioned on a fixed ground point (non-rotating orbit). This way the satellite will spend more than 93% of its orbital period looking to the same emisphere even from a high latitude point of view. \r\n\r\nSo called altimetric orbits respond to the specific needs of altimetry. In this case the orbital parameters are chosen in order to guarantee, for example: a) that the ascending and descending sub-satellite tracks intersect at roughly 90 degrees on the Earth’s surface (so that orthogonal components of the surface slope can be determined with equal accuracy; b) the possibility to monitor all phases of tidal effects on ocean surface.\r\n\r\nParticularly important for several applications (multi-temporal analyses, change detection, etc.) are the Exactly repeating orbits.\r\nThey are conceived in order that the sub-satellite track will repeat itself exactly after a certain interval of time. This allows images having the same viewing geometry during the satellite’s lifetime making moreover available a particularly simple method of referring to the location of images (navigation or geo-referenciation) for example by referring to a ‘path and row’ system used for instance by the Landsat World Reference System (WRS). It is possible to arrange satellite orbits parameters in order to contemporary guarantee the sun-syncronism so that, not only satellite images collected on the same region can be easily super-imposed each-other but the same illumination and viewing geometry can be achieved. This is, for instance, the choice adopted for LANDSAT satellites whose images are typically available as a collection of scene of fixed dimension always similar each other when covering the same terrestrial area.","hasChildren":true,"name":"Satellite orbits parametrization and choice","selfAssesment":"Completed
"},{"code":"PP1-8","description":"Mechanics is the Physics branch dealing with the behaviour of physical bodies when subjected to forces or displacements. This section provides Mechanics basic elements necessary for determining the orbits of satellites and rockets. The different satellite trajectories will be illustrated with respect to their peculiarities","hasChildren":true,"hasParent":true,"name":"Basics of Mechanics","selfAssesment":"Planned
"},{"code":"PP1","description":"Optical Remote Sensing deals with those part of electromagnetic spectrum characterized by the wavelengths from the visible (0.4 micrometer) to the near infrared (NIR) up to thermal infrared (TIR, 15 micrometer). It regards the collection and interpretation of the e.m. radiation emitted, reflected, adsorbed and transmitted by the observed targets in order to derive their physical-chemical properties and related information. Such a possibility derives from the basic principle of (multi-spectral) remote sensing that is widely supported both theoretically (e.g. atomic and molecular spectroscopy) and experimentally (e.g. spectral signatures catalogues). It states that, in principle (e.g. disposing of sensors with ideal spectral capabilities) the matter-radiation interaction depends on the wavelength of the involved radiation and on specific (e.g. chemical/physical) properties of the matter that can be derived by the spectral analysis of the emerging (emitted, reflected, adsorbed or transmitted) radiation. As far as Earth Observation is concerned, specific related concepts have to be addressed like: the spectral matter-radiation interactions (spectral signature concept), natural sources (e.g. Earth, Sun) of optical e.m. radiation, theory of the Black Body, atmospheric physics and radiative transfer equations in the VIS-NIR and TIR spectral ranges, basic physics of e.m. optical sensors and image systems, physical fundaments of the interpretation of optical radiances collected by multi-hyperspectral passive techniques.","hasChildren":true,"hasParent":true,"name":"Basics of Optical Remote Sensing","selfAssesment":"Completed
"},{"code":"PP2-1-2-1","description":"A radar signal is a complex signal. It is represented by a real part, the in-phase component, and an imaginary part, the quadrature component. In-phase is usually annotated by “I”, and quadrature by “Q”. Considering single look complex data, each component is represented in a single image channel.","hasChildren":true,"name":"In-phase/Quadrature Component","selfAssesment":"Planned
"},{"code":"PP2-1-2-2","description":"A phasor represents a complex number and its phase and amplitude equivalent. Considering a complex SAR image’s pixel, the real and imaginary part can be represented by a 2D vector in Cartesian coordinates. Its corresponding phase and amplitude information corresponds to the direction and length of the vector, respectively.","hasChildren":true,"name":"Phasor","selfAssesment":"Planned
"},{"code":"PP2-1-2","description":"The signal emitted by a radar system is a microwave signal, which can be described using a complex wave representation. This implies that the signal can be entirely represented by a complex number, which characterizes both its magnitude and its phase at a certain moment of time. In the SAR context, the complex number is usually represented by a real part, the in-phase component (I), and an imaginary part, the quadrature component (Q), from which the corresponding magnitude and phase can be retrieved. In single look complex SAR data, each of these components is pictured in a single image channel. The terminology comes from electrical engineering, whereby the quadrature component is 90° out of phase with respect to the reference frequency and the in-phase component. This is necessary in order to retrieve the phase information during A/D conversion. The I component can be expressed as the signal amplitude multiplied by the cosine of the phase. The Q component corresponds to the amplitude of the signal multiplied by the sine of its phase. Using both components as input, the magnitude and phase for each signal echoes and location can be retrieved.\r\nThe relationship between I/Q terms and the magnitude and phase of the signal can be best represented using a phasor. A phasor represents a complex number and its phase and amplitude equivalent. It can be best illustrated by a 2D vector in a Cartesian coordinate system, which projections on the horizontal and vertical axes represents the real and imaginary part, respectively. The length of the vector correspond to the signal’s amplitude and its direction (angle between the horizontal axis and the vector) characterizes the phase of the signal. Using simple mathematical considerations, the relationship between I/Q and amplitude and phase can be established.\r\nEach signal echo and pixel of a complex SAR image can be represented with such a phasor and the necessary amplitude and phase information can be accordingly retrieved.","hasChildren":true,"hasParent":true,"name":"Complex wave description","selfAssesment":"Planned
"},{"code":"PP2-1-4","description":"Electromagnetic waves are polarized; the direction of the polarization corresponds to the direction of oscillation of the electromagnetic field. Typical and often used linear polarisations are: H (horizontally) and V (vertically) polarized waves of the plane of the electric field vector oscillations relative to the sensor coordinate system. The polarization state of a backscattered wave from a natural surface can be linked to the geometrical characteristics like shape, roughness and orientation and the intrinsic properties of the scatterer like moisture, salinity, density. The radar system is characterized by combination of polarization of transmitted and received pulse: HH, HV, VH or VV. Based on the polarization sent and obtained the radar systems are divided in three polarization modes. Single polarization refers to the same polarization transmitted and received; dual polarization, one polarization is sent and another received; or quad polarization, when system is able to transmit and receive all four types of polarization. When making a contact with a scatterer, the polarization of the EM-wave can change, depending on the geometrical and dielectrical properties of the scatterer. In order to get all necessary information about those changes, full polarimetric systems are required.","hasChildren":true,"hasParent":true,"name":"Polarisation","selfAssesment":"Completed
"},{"code":"PP2-1-5","description":"Property of signal or data set in which the phase of the constituents is measurable, and plays a significant role in the way in which several signals or data combine. Two waves with a phase difference that remains constant over time, are said to be coherent.","hasChildren":true,"name":"Coherent","selfAssesment":"Planned
"},{"code":"PP2-1-6","description":"In remote sensing, phase is the exact position within a periodic signal with respect to an arbitrary reference point. It is typically expressed as an angle and measured in degrees or radians, where one period corresponds to a phase of 360° or 2π, respectively. Mathematically, phase is the argument of a complex number, that is the angle between its geometric representation in the complex plane and the real axis. For this reason, complex algebra is often used in remote sensing to facilitate phase calculations. Due to its periodic nature, phase can only be measured unambiguously within one period. Consequently, phase measurements are commonly subject to 2π phase ambiguities. These ambiguities can often be resolved in a process called phase unwrapping, using a priori information about the signal, typically related to its continuity. Phase measurements are crucial for the creation of synthetic aperture radar (SAR) images, as well as for many SAR imaging techniques, including interferometric SAR (InSAR).","hasChildren":true,"name":"Phase","selfAssesment":"Completed
"},{"code":"PP2-1-7","description":"Shift in frequency caused by relative montion along the line of sight between sensor and the observed scene.","hasChildren":true,"name":"Doppler effect","selfAssesment":"Planned
"},{"code":"PP2-1-8","description":"The wave-particle dualism (duality) is a theory according to which all matter exhibits the attributes of waves and particles.","hasChildren":true,"name":"Wave-particle dualism","selfAssesment":"Planned
"},{"code":"PP2-1","description":"The microwave portion of the electromagnetic (EM) spectrum ranges from 1 millimeter to 1 meter. Imaging radars are independent of weather conditions and can operate day or night. EM-waves are polarized. Normally only the horizontal (H) or vertical (V) linear polarizations are used. The radar system is characterized by combination of polarization of transmitted and received pulse: HH, HV, VH or VV. When making a contact with a scatterer, the polarization of the EM-wave can change, depending on the geometrical and dielectrical properties of the scatterer.The data can be acquired from both the ascending (northwards) and descending (southwards) satellite passes. Water clouds can interfere with the radars operating below 2 cm in wavelength. The effects of rain can be generally ignored at wavelengths above 4 cm. For longer wavelengths (above 20 cm), an effect called Faraday rotation caused by the ionosphere, i.e., free charges (electrons) and the Earth’s magnetic field, can lead to a rotation of the polarization plane. In the presence of Faraday rotation, the data, usually fully polarimetric, should be corrected. The radar systems operate in different bands that uses different wavelengths. The most common frequences/wavelengths (frequency = Speed of Light / wavelength) for environmental applications are X (5,75-10,90 GHz), C-(4,20-5,75 GHz), S-(1,550-4,20 GHz), L-(0,390-1,550 GHz) and P-(0,255-0,390 GHz) band. The selection of SAR system for acquiring data depends on their application. Longer wavelengths are mainly devoted to communication and navigation purposes. Radars penetrate atmosphere and clouds. For example for forestry, longer wavelengths starting from C- or S-band are preferred.","hasChildren":true,"hasParent":true,"name":"Microwave portion of electromagnetic spectrum","selfAssesment":"Completed
"},{"code":"PP2-2-1","description":"Diffraction is defined as interaction of waves with any solid object, not surfaces, and is not to be confused with refraction. More precisely, diffraction describes the phenomena of interaction of waves at an obstacle, such as an aperture, or an opening, such as a hole or an occurring space between two objects. Hence, diffraction is an essential form of scattering, describing ordered scattering at discrete boundaries. The effect of diffraction can be observed through extended interference patterns or simply by the bending of waves. In the field of microwave remote sensing, diffraction has the practical implication that it limits the spatial resolution of a microwave sensor since it acts on the ability of an imaging system to resolve details. This theoretical limit of resolution is called the diffraction limit. This means, the larger the aperture of the observing system compared to its employed wavelength (dependent on the frequency), the finer the resolution of an imaging system. The diffracted field can be calculated with analytical models, such as the Fraunhofer diffraction approximation in case of far field conditions, where the object is far away and the incident waves are assumed to be plane waves, or the Fresnel diffraction approximation in case of near field conditions, where the waves are spherical.\r\nOne simple example of diffraction is the diffraction of sound, for example the possibility to hear sounds around corners.","hasChildren":true,"name":"Diffraction","selfAssesment":"Completed
"},{"code":"PP2-2-2","description":"Scattering means the redirection of incident electromagnetic energy by an object. Similar to diffraction, scattering refers to the same physical process, the coherent distortion of an incident wave. However, diffraction as well as reflection can be regarded as essentially forms of scattering. Scattering explicitly describes the “random distortion of waves by elements that are similar in size or less than the wavelength” (Woodhouse, 2005). Thereby, scattering of the incident wave at an object can occur in any directions with varying strength, with the scattering pattern varying with the incident direction. Thus, the term scattering cross section, often denoted by σ, quantifies the effectiveness of a scatterer. In the field of active microwave remote sensing, the backscattering coefficient σ0 is known “as the ratio of the statistically, averaged, scattered power density to the average incident power density” (Fung, 1994). \r\nIn passive microwave remote sensing, radiometers measure the intensity of radiation emitted by a body, called brightness temperature TB. Since TB is always less than its physical temperature T, emissivity, defined as e = TB / T, is a measure of how strongly a body radiates at a given wavelength. It varies between 0 (metal) to unity (blackbody).\r\nEmission and scattering are complementary: surfaces that are good scatterers are weak emitters, and vice versa.","hasChildren":true,"name":"Scattering and emission","selfAssesment":"Completed
"},{"code":"PP2-2-3","description":"In climate change studies the carbon cycle with its crucial component the terrestrial biosphere is of great importance due to the ability of the biosphere to store environmentally harmful carbon dioxide. Radar sensors, especially SAR, can here provide a useful tool for quantifying and monitoring the biosphere. Hence, the relationship between biomass and radar backscatter responses has been studied in detail in recent decades. Results show that the sensitivity of measured radar backscatter coefficient decreases with increasing amount or density of present biomass. In the so-called saturation region, the radar backscatter saturates at a biomass depending on the employed wavelength. While for higher frequency bands like C-band (3.95-5.8 GHz), biomass can be measured up to ~50 ton/ha, the amount of measurable biomass increases with decreasing frequency (due to the increasing wavelength), such that at L-band (1-2.6 GHz) ~ 100 ton/ha and at P-band (0.23-1 GHz) ~200 ton/ha biomass can be measured. Further, the sensitivity of radar to biomass is different for co- or cross-polarized backscatter since the level of saturation depends not only on frequency but also on vegetation (e.g., height, structure, density, moisture) and soil surface (e.g., roughness, moisture) parameters. Overall, the saturation of radar backscatter depending on biomass has to be considered when analyzing SAR data.","hasChildren":true,"name":"Backscatter saturation","selfAssesment":"Completed
"},{"code":"PP2-2-4-1","description":"The radar equation is a measure of the received echo at the sensor. It defines what proportion of the transmitted energy is returned from a target. It is a function of the range between the antenna and the target, the antenna gain and the radar cross-section of the target. Mathematical expression that describes the average received signal level, compered to the additive noise level, in terms of system parameters. Principal parameters include: transmitted power, antenna gain, noise power, and radar range.","hasChildren":true,"name":"Radar equation","selfAssesment":"In progress
"},{"code":"PP2-2-4-2","description":"Coefficient sigma or sigma nought represents the average reflectivity of a horizontal material sample, normalized with respect to a unit area on the horizontal ground plane.","hasChildren":true,"name":"Sigma nought","selfAssesment":"Planned
"},{"code":"PP2-2-4-3","description":"Gamma nought represents the average reflectivity of a horizontal material sample, normalized with respect to the incident area, orthogonal to the incident ray from the radar.","hasChildren":true,"name":"Gamma nought","selfAssesment":"Planned
"},{"code":"PP2-2-4-4","description":"Radar brightness coefficient represents the reflectivity per unit area in slant range.","hasChildren":true,"name":"Beta nought (brightness)","selfAssesment":"Planned
"},{"code":"PP2-2-4","description":"Measure of radar reflectivity. The Radar Cross Section (RCS) is expressed in terms of the physical size of an hypothetical uniformly scattering sphere that would give rise to the same level of reflection as that observed from the sample target.","hasChildren":true,"hasParent":true,"name":"Radar cross-section","selfAssesment":"Planned
"},{"code":"PP2-2-5-1","description":"A material constant is a physical or chemical property of a substance, which can be expressed in numbers. Giving a precise numerical value of a constant often requires determining the external conditions (e.g. temperature, humidity). Material constants are factors that influence the interaction of microwaves with the target objects.","hasChildren":true,"name":"Material constants","selfAssesment":"Planned
"},{"code":"PP2-2-5-2","description":"The complex part k of the refraction index n=m+ik determines how far an electromagnetic wave of wavelength λ can survive crossing a specific medium. The attenuation length la is the distance after that the amplitude of an electromagnetic signal reduces its value by an amount of 1/e. For instance the amplitude of the Electric field E(z) of an electromagnetic wave proceeding along the z direction is decreasing as exp(-z/la) being la=λ/(2𝜋k) the attenuation length associated to that specific material (k) and wavelength λ. This way attenuation length in water can be of hundreds of meters in the visible range and just few microns in the microwaves. The opposite happens over solid land surfaces where optical waves can penetrate from few microns up to few millimeters (moving from the VIS-NIR to the TIR spectral range) whereas microwaves can reach depths from hundreds to thousands (as higher are their wavelength) meters allowing the exploration of subsoil and thick coulters of ice.","hasChildren":true,"name":"Attenuation lenght and penetration depth","selfAssesment":"Completed
"},{"code":"PP2-2-5-3","description":"Soil permittivity is a measure of the water content (soil moisture) in the soil and characterized by the metric of the dielectric constant of the soil. Soil moisture influences emission, absorption and propagation of microwave electromagnetic energy. Moisture decreases the ‘emissivity’ of soil, and thereby affects microwave radiation emitted from Earth’s surface. Dry soil has a low dielectric constant and low radar reflectivity. Moist and partially frozen solis have intermediate values. The higher the soil water content, the lower the radar signal penetration into the soil. In situ measurements of soil permittivity are a prerequisite for the calibration and validation of synthetic aperture radar (SAR) soil moisture retrieval algorithms. Soil moisture is a key variable in the hydrologic cycle and is recognized as an Essential Climate Variable (ECV).","hasChildren":true,"name":"Soil permittivity","selfAssesment":"Completed
"},{"code":"PP2-2-5-4","description":"The complex relative permittivity of a plant is a function of its contained amount of water, solutes (mainly their salinity) and temperature in all plant compartments (including roots). The more water and the higher the salinity are in the plant compartments, the higher is the complex relative permittivity of the plant. The complex relative permittivity of a plant refers to the complex relative dielectric constant of the plant and can be subdivided into complex relative permittivity values for the different plant compartments (roots, stem/stalk, leaves, fruit,...). The complex relative dielectric constant or permittivity parameter has a real and an imaginary part indicating the moisture content and the conductivity (loss) of the plant medium. Models of plant permittivity consist mostly of a free-water and a bound-water part. In particular, plant water is a solute of nutrients and not all water-conducting plant cells are fully filled by water, but also with air. Hence, the estimation of one plant permittivity, especially including several plant parts can be challenging to assess, to understand and to model. To acknowledge this mixture of components, dielectric mixing models containing the single material components are normally developed and applied, representing an effective complex relative permittivity of all plant components. Concerning a vegetation canopy, electromagnetic waves interact with a more or less sparsely vegetation-filled volume unit of air. A vegetation canopy represents a dielectric mixture of vegetation inclusions (leaves, twigs, branches, stems,…) distributed in a volume of air. Dielectric mixing models of canopies take this vegetation volume fraction into account.","hasChildren":true,"name":"Plant permittivity","selfAssesment":"In progress
"},{"code":"PP2-2-5","description":"The dielectric properties of any material can be described by the complex relative dielectric constant (complex relative permittivity) and contains of the real part (moisture content) and the imaginary part (conductivity/loss tangent). For instance: Reflectivity of a smooth surface and the penetration capabilities of microwaves into the material are determined by these two quantities. The complex dielectric constant changes mainly due to variations in water content, salinity, temperature of the material as well as due to the observing wavelength and polarization of the electromagnetic wave. It relates to the interaction of weakly-charged material components, like bi-polar water molecules, with irradiation of electromagnetic waves. The interaction increases with amount and charge of the material components. The complex relative permittivity is also linked to the complex index of refraction as being its square. In order to describe the complex relative permittivity of pure and saline water the single-relaxation Debye and the double-Debye dielectric model can be used. As the movement of bi-polar material components is significantly reduced when the material is put under freezing conditions (temperatures below 0 °C), the permittivity falls to almost a constant. The real part of the relative permittivity of pure ice is almost constant, when ignoring a weak temperature dependence, and amounts to approx. 3.2. For heterogeneous (mixed) materials consisting of more than one component the equivalent dielectric constant is a function of the permittivity of the single components, their volume fractions, their distribution along space and the polarization and wavelength of the interacting electromagnetic wave.","hasChildren":true,"hasParent":true,"name":"Dielectric Properties","selfAssesment":"Planned
"},{"code":"PP2-2-6-1","description":"The standard deviation of the surface height variation (or RMS height), denoted by s (or hRMS), describes the statistical variation of a random surface with height z(x). In case of an azimuthally symmetrical surface, the single-scale RMS height of the one dimensional case for discrete profile values is given by (1), where N is the number of samples, and z ̅ the mean surface height (2). \r\nAs roughness depends not only on the soil surface properties but also the wavelength λ of the electromagnetic signal, the roughness parameters are scaled by the wave number k. Hence, the electromagnetic roughness ks for surface roughness parameter s is (2π/λ)*s (3). In order to determine if a random surface may be considered as electromagnetically smooth, one common definition is given by the Rayleigh roughness criterion, where s < λ / 8*cosθ, or ks < 0.8, at incidence angle θ = 0. This criterion has been revised for the microwave region, where the wavelength is usually of the order of the RMS height, called the Fraunhofer roughness criterion, where s < λ / 36*cosθ, or ks < 0.2, at incidence angle θ = 0. Additionally, surfaces are considered as electromagnetically rough for 1 < ks < 3.","hasChildren":true,"name":"Vertical roughness component (RMS height)","selfAssesment":"Completed
"},{"code":"PP2-2-6-2","description":"The surface correlation length, denoted by l, is defined as the displacement ξ at which the surface correlation function p(ξ)= 1/e. Thus, l can be seen as the reference length up to which two points of one soil surface can be regarded as statistically independent from each other. If we imagine a perfectly smooth soil surface, l=∞ since every point on that surface correlates with all other points and can therefore be regarded as dependent from each other.\r\nAs roughness depends not only on the soil surface properties but also the wavelength λ of the electromagnetic signal, the roughness parameters are scaled by the wave number k. Hence, the electromagnetic roughness kl for surface roughness parameter l is kl=(2π/λ)*l.\r\nExperimental results indicate a weaker influence on the radar backscatter compared to the RMS height s.","hasChildren":true,"name":"Horizontal roughness component (correlation length)","selfAssesment":"Completed
"},{"code":"PP2-2-6-3","description":"The surface correlation function p(ξ) determines the degree of correlation between two lateral separated locations of one surface. Thereby, ξ is defined as displacement between two locations, (x, y) and (x', y') on the surface and given by (1).\r\nWith increasing separation between two locations on the surface p(ξ) decreases, and at a certain distance, the surface correlation length l, the heights at the two locations are considered statistically uncorrelated.\r\nThe surface scattering of electromagnetic waves can be simulated with various models. Depending on the observed roughness scale multiple surface scattering models are valid for specific roughness conditions. For example, one of the first surface scattering models for slightly rough surfaces, the small perturbation model (SPM), deals with roughness scales that are small relative to the wavelength and hence has validity conditions for ks < 0.3, kl < 3, and m < 0.3. Since then, various surface scattering models for computing the scattering and emission behavior of natural surfaces in the microwave region have been proposed, such as the Kirchhoff scattering model (KH), the geometric optics model (GO), the physical optics model (PO), or the integral equation model (IEM), to name the most common used in literature. For simulations of EM scattering at soil surfaces, assumptions of the functional forms of p(ξ) have to be made. The two most common forms for mathematically describing the surface correlation of natural surfaces are the exponential pE(ξ) and the Gaussian pG(ξ) correlation functions, defined by (2) and (3).\r\nFor some mathematically sophisticated surface scattering models, an x-Power correlation function p(x-Power)(ξ) can be assumed (4), with x as value between 1 and 2.\r\nIn literature, rather smooth surfaces are characterized by an exponential surface correlation function, while rather rough surfaces are characterized by a Gaussian surface correlation function.","hasChildren":true,"name":"Surface correlation function","selfAssesment":"Completed
"},{"code":"PP2-2-6-4","description":"The root-mean-square (RMS) slope m of a one dimensional height profile for one random surface is given by (1), with s as the standard deviation of the surface height variation (or RMS height), and p''(0) as the second derivative of the surface correlation function p(ξ), evaluated at ξ=0. Since p(ξ) is an even function, p''(0) is a negative quantity.\r\nFor modeling of electromagntic scattering at soil surfaces, assumptions of the functional forms of p(ξ) have to be made. The most common known forms are the exponential and Gaussian correlation functions. Additionally, some models allow the assumption of a x-Power correlation function, with x as value between 1 and 2. For the varying surface correlation functions, the RMS slope m is given by (2)-(4).\r\nIn literature, for L-band, the slope m should be lower than 0.3 or 0.4 in case of single scattering and bare soil surfaces with moderate RMS heights.","hasChildren":true,"name":"Surface roughness slope","selfAssesment":"Completed
"},{"code":"PP2-2-6-5","description":"In reality, one random surface has multiple roughness scales, since the commonly used surface description based on single-scale roughness parameters does not comprise all the properties of natural surfaces relevant for describing wave scattering. Depending on the wavelength λ of the microwave sensor the dimension of the surface roughness parameters s and l correspond to specific roughness scales. \r\nIn case of multi-scale roughness, the equivalent RMS height is a composite of the individual RMS heights at different roughness scales (1).\r\nA three-scale surface, as shown in Fig. 1, for example consists of a small-scale high-spatial frequency variation (c) ‘riding’ on top of the larger scales, the medium-scale perturbation (b) and the large-scale undulation (a).\r\nAt microwave frequencies, the centimeter scale is the scale of roughness of primary importance, since λ is on the order of centimeters to a few tens of centimeters. For natural surfaces it is very difficult to measure millimeter-scale roughness.","hasChildren":true,"name":"Single-scale & multi-scale roughness","selfAssesment":"Completed
"},{"code":"PP2-2-6","description":"Surface roughness defines the geometry between the pedosphere and the atmosphere (soil-air boundary).\r\nIn the field of microwave remote sensing, surface roughness affects scattering and emission characteristics of natural surfaces. The degree of roughness of a random surface is determined by statistical parameters, measured by the units of wavelength of the observing sensor. The two fundamental surface roughness parameters are the standard deviation of the surface height variation (RMS height) s, with its related surface correlation function p(ξ), and the horizontal surface correlation length l. Additional, a third roughness parameter, the root-mean-square (RMS) slope m, is important for some surface scattering models to simulate electromagnetic wave scattering of surfaces.\r\nSurface roughness determines the variation of surface height within an imaged resolution cell. The transition from smooth to rough is qualitative, and is function of both wavelength and incident angle. With decreasing frequency the soil surface appears rather smooth to microwave sensors. This results in the fact, that while one surface appears smooth when sensed at L-band (λ ≈23 cm), the same surface appears rough when sensed at X-band (λ≈3 cm). Hence, in the field of microwave remote sensing, the ‘effective’ surface roughness parameters are scaled by the wave number k= 2π/λ. Surface roughness can be observed at single or multi-scale.","hasChildren":true,"hasParent":true,"name":"Surface roughness","selfAssesment":"Completed
"},{"code":"PP2-2-7-1","description":"The Stokes vector is a four-element vector containing real-valued polarization combinations and is an alternative form of representing a full (=quad) polarimetric dataset, besides the complex-valued scattering matrix. Stokes vectors can be measured as real quantities and are preferred over the complex-valued Jones vector formalism when a coherent (phase-preserving) measurement system is absent. Stokes vectors can be used to form the 4x4 Mueller matrix for target scattering analyses, mostly used in the field of optics. First component of the Stokes vector is the sum of the co-polar fields and represents the total energy of the wave. Second component is the difference of the co-polar fields. Thrid component is the real part of the cross-correlation of the fields and fourth component is the imaginary part of it. The different polarization states can be represented by the Stokes vector and an O(3) elliptical transformation can be used to change the polarization basis, similar to the Jones vector where the SU(2) elliptical transformation is used.","hasChildren":true,"name":"Stokes Vector","selfAssesment":"Completed
"},{"code":"PP2-2-7-2","description":"The scattering matrix is a 2x2 square matrix containing four complex-valued polarization measurements (amplitude & phase) forming one full (= quad) polarimetric set of coherent observations. An often recorded set of polarizations is the combination: HH (horizontal receive - horizontal transmit), HV (horizontal recive - vertical transmit), VH (vertical receive - horizontal transmit) & VV (vertical receive - vertical transmit). The scattering matrix is fully suficient for describing scattering from coherent targets (dominating the resolution cell), but not for incoherent tragets (mix of scattering contributions in the resolution cell). For the latter, the coherency and the covariance matrices are the more appropriate descriptions of scattering from incoherent targets.","hasChildren":true,"name":"Scattering matrix","selfAssesment":"Completed
"},{"code":"PP2-2-7-3","description":"The covariance and coherence matrix are two 4x4 square matrices, which can be built out of the scattering matrix by a lexicographic and a Pauli target scattering vector. They are an alternative representation of a full polarimetric dataset allowing the analysis of incoherent targets (more than one dominant scatterer in the resolution cell) and the phenomenon of depolarisation (transformation of incoming fully polarised wave into a partially polarised wave by creating a variety of different types of polarizations during media interaction). These matrices can be converted into each other without loss of information (by unitary transformations), but not turned back into the scattering matrix due to averaging operations during formation of coherency or covariance matrices.","hasChildren":true,"name":"Covariance/Coherency matrices","selfAssesment":"Completed
"},{"code":"PP2-2-7-4","description":"Polarimetric decomposition techniques allow signal unmixing by polarimetry in order to separate different scattering contribution within one resolution cell, e.g. from soil & vegetation or snow, ice & bedrock. They can be either applied for the scattering matrix (coherent form - one dominant scatterer in the resolution cel) or for the covariance/coherency matrix (incoherent form - more than one dominant scatterer in the resolution cell). Decomposition techniques can be model- (physics) or eigen- (mathematics)-based. The eigen-based decomposition allows to diagonalize the coherency or covariance matrix in a diagonal eigenvalue matrix and a matrix of column eigenvectors. From eigenvalues and eigenvectors the polarimetric entropy, the scattering alpha angle and the polarimetric anisotropy. The polarimetric entropy is a matric for the degree of depolarization of the scattering event. The scattering alpha angle is an intrinsic scattering mechanism indicator. The polarimetric anisotropy informs about secondary scattering mechanism in evironments with high entropy. If the anisotropy is high only one secondary scattering mechanism is present, if it is low, more than one will occur.","hasChildren":true,"name":"Polarimetric decomposition techniques","selfAssesment":"Completed
"},{"code":"PP2-2-7-5","description":"All bi- or multi-polar (non-inert) media have the tendency to orient themselves in 3D-space if an external non-ionizing electro-magnetic field is excited on them. This orientation polarization is caused by negatively and positively charged areas within the media, for instance due to charges of the different molecules and atoms building up the media, under the premise that the media is able to rotate (partly) freely and is not completely fixed. Molecules of liquid water are a prime example. Here the two positively charged hydrogen atoms are oriented in a 105-degree configuration to the negatively charged oxygen atom, forming a slightly charged bi-polar medium that orients itself under electromagnetic radiation treatment, especially at the frequency range of microwaves and millimeter-waves.","hasChildren":true,"name":"Orientation polarisation of media","selfAssesment":"In progress
"},{"code":"PP2-2-7-6","description":"Polarimetric coherences are complex-valued polarimetric correlation coefficients assessing the redundance between different polarimetric observations informing about their divergence in information. They can be formed among mutual polarimetric observations showing their degree of correlation. The polarimetric coherence consists of a magnitude, ranging between zero (no correlation) and one (identical), and a phase information, running from -180° to 180°. Typically polarimetric coherences are calculated between the co-polarimetric (HH, VV) channes, as well as the cross-polarimetric channels (HV, VH). The latter polarimetric coherence assesses the system noise inherent in the recorded polarimetric data, if a monostatic systems (transmitting and receiving sensor on the same sensing platform) is used for acquisition.","hasChildren":true,"name":"Polarimetric coherences","selfAssesment":"Completed
"},{"code":"PP2-2-7-7","description":"The polarisation ellipse and the Jones vector formalism are the geometrical (three real-valued angles) and algebraic (amplitude & phase) formalisms to describe polarisation states of an electromagnetic wave. The ellipse has an orientation, an ellipticity and absolute phase angle. The three angles are integrated in one mathematical ellipse formulation that can represent linear, elliptic and circular polarisation states. The Jones vector formalism is an algebraic formulation allowing all calculus available in linear algebra. Both representations (polarisation ellipse & Jones vector) can be converted into each other seemlessly with a simple elliptical basis (special unitary SU(2)) transformation.","hasChildren":true,"name":"Polarisation ellipse / Jones vector formalism","selfAssesment":"Completed
"},{"code":"PP2-2-7-8","description":"The concept of polarisation synthesis is based on the mathematical fact that a set of polarimetric measurements in one basis, e.g. H,V, can be converted into any other polarimetric basis, by a mathematical transformation. A basis set is a set of four polarisations. Each set is orthogonal, like LC (left-circular), RC (right-circular). The striking point is that only one set of polarimetric measurements in one basis needs to be recorded and the transformation in other polarimetric bases is done in a post processing step afterwards. There is no need to measure all bases, which is quite complicated in terms of engineering for elliptical and circular polarisation states.","hasChildren":true,"name":"Polarisation synthesis","selfAssesment":"Completed
"},{"code":"PP2-2-7","description":"Polarimetry is the technique to evalute the physical phenomenon of polarisation including the measurement, the processing and the interpretation of the polarisation state of an electromagnetic wave. Polarization states are described by the scattering elipse and the Jones Vector formalism. Especially the polarization states after interaction with the media under investigation are mostly investigated to estimate media properties and states. The mostly observed fully polarimetric observation basis is H,V up to now with the single observations: HH HV, VH, VV. The concept of polarization synthesis allows to acquire fully polarimetric observations in one basis (e.g. H,V) and transform them into any other orthgonal basis (e.g. left, right circular) by a mathematical transformation in post processing. Polarimetric States are stored in different mathematical formats: Scattering matrix, polarimetric coherences , Stokes vector, Pauli-vector, lexicographic vector, coherency and covariance matrices. These mathematical representations can be decomposed according to the contained elementary scattering mechanisms in the recorded signal. The so-called polarimetric decomposition technique allow signal unmixing for differnt scattering components (e.g. from soil & vegetation). The techniques range from mathematics-based until physics-based concepts and are developed since decades starting with Huynen in 1970.","hasChildren":true,"hasParent":true,"name":"Polarimetry","selfAssesment":"Completed
"},{"code":"PP2-2","description":"A number of interactions are possible when electromagnetic energy encounters matter, whether solid, liquid or gas. In Earth Observation there are two main interactions: atmospheric and with target. Atmospheric interaction: In radar remote sensing, atmospheric interactions are limited due to the long wavelengths compared to the size of the atmospheric particles. The fact that microwaves interact with object at least as big as the wavelength is one of the greatest advantages of microwave remote sensing, since at larger wavelengths atmospheric particles are almost transparent to the signal and microwave sensors are independent from the time of day (day or night) and weather conditions. Water clouds can interfere with the radars operating below 2 cm in wavelength. The effects of rain can be generally ignored at wavelengths above 4 cm. For longer wavelengths (above 20 cm), an effect called Faraday rotation caused by the ionosphere, i.e., free charges (electrons) and the Earth’s magnetic field, can lead to a rotation of the polarization plane. Target interaction: The radar interaction with the object is a result of both radar system parameters (frequency, polarization, acquisition geometry) and the physical properties of the object (dielectric constant, i.e., water content; geometrical properties, i.e., the roughness, shape and orientation of the scatterer). Overall, various types of interactions can be distinguished – scattering, diffraction, and reflection – all describing the same process of wave interaction but at different scales.","hasChildren":true,"hasParent":true,"name":"Interaction of microwaves with matter","selfAssesment":"Completed
"},{"code":"PP2-3-1-1","description":"The goal of an radar antenna is to direct and receive the transmitted and backscattered signal in a specific angular direction. The antenna gain describes the directional sensitivity of the antenna. It is a dimensionless quantity that is constant for a specific antenna.","hasChildren":true,"hasParent":true,"name":"Antenna gain","selfAssesment":"Planned
"},{"code":"PP2-3-1-2","description":"The antenna radiation pattern shows the direction in which the antenna transmits and receives the energy in space, as well as the strength of this radiation. It is a function of angles and consists of different lobes, in which the signal is directed and received. There are two principal representation of the antenna patterns: field and power patterns, which are a function of the electric and magnetic fields of the energy being radiated.","hasChildren":true,"name":"Antenna pattern","selfAssesment":"In progress
"},{"code":"PP2-3-1","description":"Antenna is a device that radiates electromagnetic energy and collects it during reception.","hasChildren":true,"hasParent":true,"name":"Radar antennas and antenna calibration","selfAssesment":"Planned
"},{"code":"PP2-3-10-2","description":"The radargrammetric equation follows a similar principle as the stereoscopic equation, except that it uses the radar geometry. The radargrammetric observation equation allows the retrieval of 3D information about a target, based on the determination of the sensor-object stereo model. It estimates the coordinates the intersection of the two radar rays coming from the two different sensor positions with different look angles, using the coordinates of the satellites position and satellite velocity. The radargrammetric equation can be adapted in order to retrieve 3D information in layover areas (e.g. urban areas).","hasChildren":true,"name":"Radargrammetric equation","selfAssesment":"Completed
"},{"code":"PP2-3-10","description":"Radargrammetry is the technique for extracting three-dimensional information from radar images. It applies photogrammetric principles to synthetic aperture radar (SAR) images. By viewing an object from different positions separated by a baseline, the appeared object position will vary slightly (denoted parallax). The disparities for each position on the object are related to its x-y-z coordinates. In radargrammetry, such disparities are computed for an entire image. The result is the terrain elevation from the measured parallaxes between two (or more) images, acquired at different angles. Radargrammetry requires at least two SAR images acquired from different positions, normally across-track due to the configuration of a side-looking SAR. Same-side stereo-pairs with intersection angles in the range of about 10 – 20° have been a feasible compromise between reasonable geometric disparities and the accuracy of estimated heights. In general, the disparities can be estimated with higher accuracy as the angle of intersection increases (as the stereo exaggeration factor increases). However, the same points must be recognized in all images, and it is hence required that the images are as similar as possible. This improves the image matching and it is best achieved with small intersection angles, which furthermore decreases radiometric differences. \r\nA general procedure for generating an elevation model from stereo-pairs is applicable for radargrammetry when optical stereo images are replaced with the backscatter intensity of SAR images. One image is selected as reference and the other(s) is coarsely registered to the reference, e.g., by using the attached meta-data. The same points are then located in both images using image matching. A common matching criterion is the cross correlation coefficient. Then, spatial point intersections are computed, which is the least square approach to find the intersection points of SAR range circles as defined from the matched image pixels. The computed intersections result in a point cloud that finally is interpolated to a consistent elevation raster. The entire process is extensive and computationally expensive, and normally a dedicated software is required. \r\nRadargrammetry with images acquired from opposite sides have been little investigated, and was first limited to stereoscopic viewing. Some opposite-side research was later presented with limited outcomes under certain conditions. Most applications today will not consider opposite-side radargrammetry, since the alternatives are usually better. Same-side radargrammetry performs better than opposite-side, while interferometric SAR that is based on phase differences, may be even more accurate. One advantage of radargrammetry is however, that it remains less affected by atmospheric disturbances compared to interferometric SAR, because it is using the amplitude images.","hasChildren":true,"hasParent":true,"name":"Radargrammetry (same-side and opposite-side)","selfAssesment":"Completed
"},{"code":"PP2-3-11-1","description":"Differential Synthetic Aperture Radar Interferometry (DInSAR) aims the determination of deformation of the Earth’s surface that happened between two or more complex-valued SAR acquisitions.\r\nThe phase of an interferogram issued from the complex multiplication of a SAR image with the complex conjugate of a second SAR image contains five distinct components, or layers of information: (1) Two phase components arise from the geometrical baseline (slightly different position of both sensor positions): (1a) a topographical information representing the surface relief, (1b) “flat earth” pattern coming from the orbital distance of both sensor positions.\r\n(2) Two phase components result of the temporal baseline (time between both acquisitions): (2a) a deformation component, representing a possible displacement of the Earth’s surface between both acquisitions, (2b) an atmospheric component coming from different atmospheric conditions between both acquisitions. (3) A phase component corresponding to intrinsic sensor noise \r\n\r\nBoth parameters related to the temporal baseline can be retrieved using DInSAR on repeat-pass acquisitions. DInSAR cannot be used with single-pass interferometry (e.g. both acquisitions acquired at the same time).\r\nThe deformation component of the interferometric phase corresponds to the modification of the phase of the second SAR image compared to the first due to an additional range difference between the sensor position and the Earth’s surface that is induced by the motion of the Earth’s surface towards or away from the initial sensor position.\r\nUsing DInSAR, the phase components related to the geometrical baseline can be eliminated from the interferogram using an existing DEM and orbit information, or an additional interferogram showing no deformation. After DInSAR processing, neglecting the remaining sensor noise, only the deformation and atmospheric components remain. The resulting deformation image is called differential and is characterized by color bands, or fringes, from whom the amount of the displacement can be retrieved. \r\nDInSAR can be used for mapping displacements and deformations due to earthquakes, landslides, or other geophysical processes inducing deformation of the Earth’s surface.\r\nUsing only one differential interferogram, mainly sudden and large scale changes between two acquisition can be mapped and quantified. However, the atmospheric phase component remains and may induce interpretation errors if it is not possible to eliminate it through e.g. precise weather models. Techniques of differential interferogram stacking (e.g. Persistent Scatterer Interferometry and Small-Baseline Subset) have been developed for long-term deformation monitoring which allow to filter the atmospheric phase component out.","hasChildren":true,"name":"Differential Synthetic Aperture Radar Interferometry (DInSAR)","selfAssesment":"Completed
"},{"code":"PP2-3-11-2","description":"The Permanent or Persistent Scatterer (PS) approach allows the estimation of deformation time-series related to point-wise, high coherent scatterers on the ground based on processing long sequences of SAR data.\r\nPersistent Scatterer Interferometry (PSI -sometimes also called Permanent Scatterer Interferometry) is a particular DInSAR technique. It exploits multiple SAR images acquired over a specific area in order to retrieve the deformation phase component over time. In general, a minimum number of 15 SAR acquisitions is needed for PSI processing. Due to the large number of necessary acquisitions, the deformation component of the interferometric phase observations can be estimated very precisely (in the order of a few mm/yr) and other phase contributions such as atmospheric disturbances and topographic height differences can be better estimated and removed.\r\nPSI rely on so called Persistent Scatterer that are targets showing coherent phase behavior in time. Such targets are usually found on man-made structures such as buildings or bridges, or very stable features such as rocks. PSI is a technique that is therefore mainly used over urban or semi-urban terrain. Usually, PSs are selected based on their amplitude and phase power spectrum stability over time.\r\nThe main outcomes of a PSI analysis are a deformation velocity map and the displacement time-series of the single point targets, or PSs. The velocity map represents the deformation rate of the detected PSs in Line-of-Sight of the sensor, generally in mm/yr. Usually, subsidence, e.g. target moving away from the sensor, is represented in red, stable PSs in green and uplift, e.g. PSs moving toward the sensor in blue. The displacement time-series show for each PS the amount of the deformation, usually in mm, over the whole period of observation. Different phase model can be defined in order to retrieve the best possible estimate of the deformation, considering also seasonal displacements or breakpoints in the time-series.\r\nPerforming PSI analysis in both ascending and descending directions allows the fusion of the results in order to retrieve vertical and East-West component of the deformation. North-South deformation components cannot be retrieved due to the orbit configuration of the SAR satellites.\r\nPSI finds use in a large range of thematic applications related to subsidence and long-term change monitoring, such as infrastructure monitoring, groundwater reservoir monitoring, monitoring of mining areas, landslide inventory and monitoring, as well as volcanology.","hasChildren":true,"name":"Permanent Scatterer Interferometry (PSI)","selfAssesment":"Completed
"},{"code":"PP2-3-11-3","description":"Along-track InSAR (AT-InSAR) is a special mode of interferometric SAR (InSAR) where the individual SAR images have been acquired from the same flight track. With virtually identical geometric configuration of the individual SAR images, the measured phase difference is dominated by temporal changes occurring between the acquisitions. Consequently, AT-InSAR can be used to measure the displacement and/or radial velocity of targets on the ground, with the temporal offset between the acquisitions determining the time scale of the measurements. AT-InSAR can be implemented using one or more SAR sensors, in both single-pass and repeat-pass configurations, accommodating various needs. Using at least two sensors in a single-pass configuration allows the measurement of relatively high velocities, e.g., for vehicles and ocean waves. Conversely, using at least one sensor in a repeat-pass configuration allows the measurement of low velocities or displacements, e.g., for glaciers and due to volcanoes, earthquakes, subsidence, and landslides.","hasChildren":true,"name":"Along-Track Interferometry","selfAssesment":"Completed
"},{"code":"PP2-3-11-4","description":"Across-track InSAR (XT-InSAR) is a special mode of interferometric SAR (InSAR), where the individual SAR images have been acquired from slightly different look directions. The measured phase difference contains information about the elevation of the targets on the ground, but it can also be affected by temporal changes between the individual SAR images. XT-InSAR can be implemented using one or more SAR systems in both single-pass and repeat-pass configurations. To mitigate temporal change between acquisitions, the XT-InSAR configuration is selected based on the intended application and frequency used by the system. If a single SAR sensor is used in the repeat-pass mode, temporal stability can be achieved either by a selecting a lower frequency and focussing on the larger, more stable targets (e.g., P-band, 435 MHz InSAR in forests) or by selecting a higher frequency and focussing on already stable environments (e.g., X-band, 9.65 GHz XT-InSAR in urban environments). Using two or more SAR sensors in a single-pass, tandem configuration, it is possible to measure elevation of temporally instable targets using higher frequencies, as demonstrated by the SRTM and TanDEM-X systems over vegetated areas and ocean.\r\nReferences: bamler/hartl, one on SRTM or TDM for DEM, one on BIOMASS for forestry, one on Sentinel-1 for urban areas, one on TDM on vegetation","hasChildren":true,"name":"Across-Track Interferometry","selfAssesment":"Completed
"},{"code":"PP2-3-11-5","description":"Small Baseline Subset (SBAS) is a well-known technique of differential synthetic aperture radar (SAR) interferometry for the generation of surface deformation time-series by processing large sequences of SAR data acquired over the same region on Earth. \r\nThe method requires the preliminary generation of pairs of SAR images collected by slightly different orbital positions at different acquisition times. The phase difference of the interferometric SAR data pairs is extracted. The two-dimensional phase maps contains different contributions, but principally a component due to the terrain height of the observed area. The DInSAR technique relies on the estimation of the deformation of the terrain between the two interfering SAR images (i.e., the so-called master and slave images). To achieve this task, the phase contribution related to the terrain height is simulated and subtracted to the interferometric master/slave phase difference. The obtained differential SAR interferometric phase contains a direct information on the occurred deformation. Once a sequence of interferometric SAR data pairs is selected, the SBAS technique allows generating the time-series of the deformation of the terrain. The processing steps are essentially: i) the extraction of the full phase of the DInSAR interferograms, i.e., the phase unwrapping steps of the DInSAR interferograms, ii) the inversion of the sequence of unwrapped DInSAR phases, iii) the geocoding of the deformation maps from radar coordinates to geographical coordinates.","hasChildren":true,"name":"Small Baseline Subset","selfAssesment":"Completed
"},{"code":"PP2-3-11","description":"Synthetic aperture radar (SAR) interferometry, or simply InSAR, is a remote sensing technique utilising the phase difference between two or more complex-valued SAR images. Most modern SAR systems are capable of measuring both the intensity and the phase of the reflected signal, where the latter carries information about the distance travelled by the signal. Consequently, the different of phase information of two successive SAR images over a specific area contains a distance information. \r\n\r\nThe phase difference measured between two SAR images is called the interferometric phase. The interferometric phase image is an interferogram. The interferometric phase is a function of the geometry and timing of the individual SAR acquisitions. Different geometric and temporal configurations enable different applications. \r\n\r\nIf the SAR acquisitions are made from different angles and without significant temporal change of the scene, InSAR can be used to create digital elevation models (DEMs) of the Earth, as demonstrated by the NASA/JPL Shuttle Radar Topography Mission (SRTM). This configuration is called across-track interferometry. If the individual SAR acquisitions are made at different times in the same geometric configuration, i.e. in an along-track or differential interferometric configuration, then InSAR can be used to measure radial velocity of targets and to assess displacements caused by, e.g., volcanoes and earthquakes. The variation of the temporal baseline allows determining velocities ranging from several meters per second to a few millimeters per year. While standard differential interferometry can be used to retrieve changes that happened between two SAR acquisitions, differential interferometric stacking techniques, such as Persistent Scatterer Interferometry (PSI) and Small Baseline Subset (SBAS), are used to monitor deformation over a longer period of time by stacking multiple differential interferograms and filtering out the atmospheric phase contribution in order to retrieve very accurate deformation of the ground and its infrastructures.","hasChildren":true,"hasParent":true,"name":"Principles of Synthetic Aperture Radar Interferometry (InSAR)","selfAssesment":"Completed
"},{"code":"PP2-3-12","description":"Synthetic Aperture Radar (SAR) tomography uses the principle of the azimuth synthetic aperture in the elevation direction. Instead of using different positions of the radar sensor along the flight path in order to increase the aperture length, SAR tomography uses multiple passes of the radar sensor over the same area at different elevation positions, i.e. orthogonal to the azimuth-range plane, on different orbits. Similar to the synthetic aperture in azimuth direction, a larger aperture in cross-range elevation direction allows increasing the resolution in the elevation direction. Therefore, the echoes are focused in the whole 3D space (azimuth, range and elevation), and scattering contributions can be separated at different heights, even if they are situated in the same azimuth-range cell.\r\nSAR tomography exploits therefore these multiple passes of the radar sensor at different orbit positions (orbits heights) in order to retrieve 3D information about volumetric targets, where the 2D SAR signals often overlaps due to the typical side-looking geometry. \r\nThe result of tomographic processing is a tomogram, i.e. it is a hologram of a specific area of interest, usually represented as a tomographic profile along a particular direction. Using polarimetric data, the different scattering mechanisms happening at different heights can be represented in the profile, allowing a full understanding of the volumetric information and backscattering processes.\r\nUnlike the azimuthal aperture, the tomographic aperture is achieved by repeat-pass acquisitions, the antenna having to come back over the area. An important parameter is therefore the target coherence, that may decrease by longer repeat-pass cycles. In general, a 1-4 day revisit cycle is preferred for tomographic applications.\r\nSAR tomography finds applications in the imaging and monitoring of cities and single buildings, as well as in height and biomass estimation of forest stands. The use of longer wavelength that guaranty the penetration into canopy volumes allows a better retrieval of the complete forest structure and its undergrowth.","hasChildren":true,"hasParent":true,"name":"Synthetic Aperture Radar (SAR) tomography","selfAssesment":"Planned
"},{"code":"PP2-3-13","description":"Historically imaging in the microwave frequency domain was done either using passive imaging techniques (with solely recording capacities of the sensor) or using active imaging techniques (with transmitting and recording capacities of the sensor). Both imaging modi were developed in parallel for a long time in electrical engineering of microwave sensors for space-borne missions, but are combined in more recently launched missions.\r\nWith the concept of active and passive microwave imaging, both techniques are fused to record electromagnetic waves in an active (sending & receiving) and a passive (only receiving) mode either simultaneously on one carrier platform or with negligible time lag on different platforms.\r\nThe active sensor is normally a Real Aperture Radar (RAR, scatterometer) or Synthetic Aperture Radar (SAR), while the passive sensor is a radiometer or synthetic aperture radiometer. Both acquisition modes can be operated on a single platform or on different platforms depending on monolithic or distributed platform systems. The benefit of fusing both modi is in the higher spatial resolution of the active imaging modes combined with the higher sensitivity of the passive modes for intrinsic (non-structural) media properities, like permittivity or salinity.\r\nSatellite missions with active-passive imaging capabilities are the NASA missions AQUARIUS (operation started in 2011 terminated in 2015) and SMAP (operation started in April 2015 and ceased for active sensor in July 2015). Currently (2021), no dedicated active-passive microwave satellite mission is operating in orbit.","hasChildren":true,"name":"Active-Passive microwave imaging","selfAssesment":"Completed
"},{"code":"PP2-3-2","description":"Systems measuring both amplitude and phase of the incident electromagnetic radiation.","hasChildren":true,"name":"Coherent and active systems","selfAssesment":"Planned
"},{"code":"PP2-3-3","description":"This acquisition mode records only the incoming electromagnetic radiation emitted from the Earth. Radiometer instruments conduct passive microwave imaging. The energy budget of emitted radiation (from Earth) is significantly smaller than from instrument-generated, transmitted electromagnetic waves, used in the active microwave imaging mode. Hence, the signal to noise ratio is significantly worse for passive microwave imaging forcing a longer intergration time for robust signal recording. This results in a coarse spatial resolution of radiometer images (in the order of kilometers).","hasChildren":true,"hasParent":true,"name":"Passive microwave imaging","selfAssesment":"Completed
"},{"code":"PP2-3-5","description":"There are two types of imaging radar apertures: real (usually called RAR or SLAR for side-looking airborne radar or SLR for side-looking radar) and synthetic aperture radar (SAR). The SLAR imaging system uses a long antenna mounted on a platform. The synthetic aperture is used in space remote sensing applications. RAR is a radar system where the antenna beamwidth equals to the physical length of the antenna. It operates in a side-looking configuration, left or right with reference to the flight direction. It is an active, all-weather, day/night remote sensor onboar an airborne platform. Both Real Aperture and Synthetic Aperture Radar are side-looking systems having antennas aimed to the right or left of the flight path. The length of the antenna together with wavelenght determines the resolution in the azimuth direction, i.e. it is proportional to the distance to the object and inversely proportional to the length of the radar antenna.","hasChildren":true,"name":"Real Aperture Radar (RAR)","selfAssesment":"In progress
"},{"code":"PP2-3-6","description":"In contrary to a real aperture, a synthetic aperture results from an aperture “synthesis”. Synthetic aperture were built in order to overcome the limitation of real aperture and therefore enhance the resolution in azimuth direction. It uses the subsequent positions of a real aperture sensor during its forward motion along the azimuth direction to create a synthetic longer antenna. Via the analysis of the Doppler shift induced by the different echoes of the illuminated objects in the different positions of the real aperture, the azimuth resolution can be improved.","hasChildren":true,"name":"Principles of Synthetic Aperture Radar (SAR)","selfAssesment":"In progress
"},{"code":"PP2-3-7-1","description":"In navigation, the azimuth corresponds to an angle measured from a north reference or a meridian, usually in clockwise direction. In SAR terminology, the azimuth direction corresponds to the direction in which the radar platform moves. The azimuth direction is also called along-track direction and is parallel to the flight path of the radar instrument. In a SAR image, the azimuth position of an object corresponds to its relative position in the field of view of the antenna following the radar’s line of flight. The azimuth direction is perpendicular to the range direction, which corresponds to the look direction of the radar antenna. The azimuth plays an important role in the definition of the azimuth resolution of a SAR sensor. Contrary to the range resolution, the azimuth resolution is independent of the distance between sensor and illuminated area and is constant. The azimuth resolution of a radar system corresponds to the beam width of the antenna on the ground, but can be improved using multiple successive real aperture acquisitions in order to form a longer, synthetic, aperture. This implies that an object on the ground is illuminated for a longer time and from different platform positions along the azimuth direction, inducing a Doppler frequency shift at the target. The use of specific synthetic aperture acquisition modes that steer the antenna along the azimuth direction, such as Spotlight mode, improve additionally the resolution in azimuth direction.","hasChildren":true,"name":"Azimuth direction","selfAssesment":"Completed
"},{"code":"PP2-3-7-2","description":"The range direction corresponds to the direction perpendicular to the flight direction of a radar system. It is also called across-track direction. One distinguishes between slant range, i.e. range in a radar geometry, and ground range, i.e. range projected onto the Earth's surface, and between near and far range (situated farther away from the sensor and showing shallower looking angle than in near range due to viewing geometry).","hasChildren":true,"name":"Range direction","selfAssesment":"In progress
"},{"code":"PP2-3-7-3","description":"The incidence angle is the angle between the incident radar beam on a surface and the normal to a reference surface. Generally, it is distinguished between the local incidence angle and the incidence angle to the ellipsoid. The local incidence angle considers the normal to the surface at target location, i.e. it considers the local topography. The incidence angle to the ellipsoid corresponds to the angle between the incident radar beam and the normal to the local ellipsoid, regardless of the local slope and terrain. \r\n\r\nFor a flat surface and neglecting the Earth’s curvature, the incidence angle corresponds to the angle between the incident radar beam and the vertical, and it equals the look angle of the sensor, which characterizes the angle between the nadir view and the radar beam. Considering a flat surface, the incidence angle varies continuously within a SAR scene: it increases from near to far range. Depending on the considered sensor and acquisition modes, variations of the incidence angle up to 20° can be observed between near and far range.\r\n\r\nThe incidence angle has an influence on the radar backscatter intensity. Considering a surface with diffuse reflection, increasing incidence angles lead to decreasing backscatter intensities. This effect is less pronounced for rough than for smooth surfaces. A change in incidence angle may also induce a change in the occurring backscattering mechanisms or geometric distortions of the image. For example, for high incidence angles, terrain distortion due to the side-looking geometry is reduced. Due to the high dependency of the radar backscatter from the incidence angle, the choice of the optimal configuration should happen depending on the application. For example, whereas low incidence angles are more sensitive to biomass in forestry applications, higher incidence angle are preferred for distinguishing different forest types due to their structural characteristics.","hasChildren":true,"name":"Incidence Angle","selfAssesment":"Completed
"},{"code":"PP2-3-7-4","description":"The beam sent out by the radar antenna (SLAR for side-looking airborne radar or SLR for side-looking radar) illuminates an area on the targeted object. The footprint of an antenna is traditionally defined to be the area on the surface within the field of view subtended by the beamwidth of the antenna gain pattern.","hasChildren":true,"name":"Antenna footprint","selfAssesment":"Planned
"},{"code":"PP2-3-7-5","description":"The spatial resolution of a synthetic aperture radar (SAR) system is the maximal distance between two targets, which are indistinguishable in the SAR image. SAR spatial resolution is determined individually in the two principal SAR image directions: ground range and azimuth (along-track). Ground range resolution for a SAR system is derived from slant range (across-track) resolution, by projecting it onto the ground surface using the incident angle, i.e., the angle between the line-of-sight and the ground surface normal. It is thus range-dependent, with finer resolution available in far range. Assuming adequate signal processing, slant range resolution of a SAR system is proportional to the speed of light and inversely proportional to the system bandwidth, i.e., the width of the used frequency interval. This caused by the fact that each individual frequency provides an independent measurement of the slant range, so a larger bandwidth implies more independent measurements contributing to the final slant range estimate. Similar principles apply to the azimuth direction. Assuming adequate signal processing, the SAR azimuth resolution is proportional to the along-track velocity of the SAR sensor and inversely proportional to the pulse repetition frequency (PRF) of the system. A lower interval between the consecutive pulses (higher PRF) results in better azimuth resolution due to faster sampling, but at the cost of range ambiguities occurring when echoes from one pulse are recorded after the next pulse has been transmitted.","hasChildren":true,"name":"Synthetic Aperture Radar (SAR) spatial resolution","selfAssesment":"Completed
"},{"code":"PP2-3-7","description":"The Synthetic Aperture Radar (SAR) sensor is usually mounted on an aircraft or satellite. The instrument altitude above a reference surface stays constant over time, a condition that is easier to achieve for satellite sensors that stay on the same orbit than for aircrafts that are subject to atmospheric conditions. The sensor moves on a straight flight path, which is called the azimuth direction. It corresponds to the flight direction.\r\nSAR systems acquire information in oblique view, the antenna pointing sideways down to the ground. Most satellite systems use an antenna looking to the right side of the instrument. The ground area illuminated by the radar beam is called antenna footprint. As the sensor moves along the azimuth direction (along-track), the continuous strip of the ground area represented by the successive antenna footprints is called swath. \r\nThe looking direction of the SAR antenna is called range direction. It is often perpendicular to the azimuth direction (i.e. across-track), but can also present slightly differences depending on the acquisition mode. The angle between the nadir view and the range direction is called incidence angle.\r\nThe original SAR image is displayed in what is called slant-range geometry, i.e., it is based on the actual distance from the radar to each of the respective features in the scene. In the slant range direction, each point target’s backscatter is represented as a function of the time delay between the transmission of the electromagnetic pulse and its reception back at the sensor. This range depending representation induces geometric distortions in the SAR image. One distinguishes between near and far range: targets situated in near range are closer to the nadir direction and closer to the sensor than targets situated in far range. The image representation of targets is also more compressed in near range than in far range.\r\nThe slant-range representation can be converted in ground range representation, by projecting the image features orthogonally to a ground reference, allowing a proper planimetric position of the targets relative to one another.\r\nThis acquisition geometry allows the distinct mapping of scatterers corresponding to their respective distance to the sensor. It causes also geometric distortions in the radar image, i.e., relief displacement (foreshortening and layover) and shadow.","hasChildren":true,"hasParent":true,"name":"Synthetic Aperture Radar (SAR) geometric configuration","selfAssesment":"Completed
"},{"code":"PP2-3-8-2","description":"The local incidence angle is the angle between the incident radar wave and the normal to the scattering surface at target location. In case of a flat terrain, the local incidence angle equals the incidence angle. For a terrain with local slope, the local incidence angle differs from the incidence angle (for slopes facing towards the sensor, it is smaller than the incidence angle).","hasChildren":true,"name":"Local Incidence Angle","selfAssesment":"Planned
"},{"code":"PP2-3-8-3","description":"Foreshortening is a geometric distortion occurring in the SAR image due the side-looking geometry of imaging radar sensors. It occurs principally in SAR images of mountainous areas, on slopes oriented towards the sensor. These slopes appear in the radar image as if being compressed. Due to the side looking geometry and the mapping of the SAR image based on range and time measurement, the distance in the SAR image between two points situated on a slope facing the sensor appears smaller than it is in the reality and than the same distance between two points situated in flat area. This results in a compression of the radiometric information of the slope. The resulting foreshortening area is brighter in the SAR image than its surroundings, as it compresses in a few pixels the backscatter information of the whole slope. \r\n\r\nForeshortening occurs for slopes whose inclination is smaller than the look angle of the radar antenna. Due to the variation of the look angle in the SAR image, the foreshortening is more pronounced in near range than in far range. Foreshortening is therefore greater for small incidence angles. The extreme case of foreshortening happens when the slope inclination is equal to the look angle: in this case, the whole slope is mapped in one pixel of the SAR image, which results in a very bright line. When the slope inclination becomes higher than the look angle, layover occurs.","hasChildren":true,"name":"Foreshortening","selfAssesment":"Planned
"},{"code":"PP2-3-8-4","description":"Layover is a geometric distortion occurring in the SAR image due the side-looking geometry of imaging radar sensors. It occurs principally in SAR images of mountainous areas, on steep slopes oriented towards the sensor. These slopes appear in the radar image as if being flipped over. Due to the side looking geometry and the mapping of the SAR image based on range and time measurement, the summit of a mountain is closer to the sensor that the foot of that same mountain, on the side facing the sensor. The signal from the top comes back to the sensor before the signal from the foot and is therefore mapped in nearer range than the foot of the mountain. Making an analogy to sound waves, an echo from the top of the mountain will arrive sooner at the sensor than an echo from the bottom of the mountain. Due to this “leaning over” effect, the sensor facing slope signal usually overlaps with ground signal, and a “ghost” effect appears as both signals overlap. The resulting layover area is usually very bright in the SAR image, as it superimposes backscatter signals from the slope of the mountains and the ground before it. When considering SAR images of urban areas, even up to three signals may overlap in the layover area: ground, building façade and (part of the) roof area.\r\n\r\nLayover occurs for slopes whose inclination is larger than the look angle of the radar antenna. Due to the variation of the look angle in the SAR image, layover occurs more often in near range than in far range. Layover is therefore greater for small incidence angles. It represents the extreme case of foreshortening, when the slope inclination becomes higher than the look angle.","hasChildren":true,"name":"Layover","selfAssesment":"Planned
"},{"code":"PP2-3-8-5","description":"Radar shadow is a geometric distortion occurring in the SAR image due the side-looking geometry of imaging radar sensors. It occurs principally in SAR images of mountainous areas, on steep slopes oriented away from the sensor. In optical imagery, a shadow area is an area characterized by less sun illumination whose reflection is therefore weaker. In SAR imagery, shadow areas receive no signal. It occurs for example at the backside of mountains or buildings. The areas facing away from the sensor are not illuminated by the SAR sensor, as they are “hidden” from it. Also, ground area situated behind high object with respect to the sensor position are not illuminated and are situated in the radar shadow. They receive no signal information and send no information back to the sensor. Those areas are therefore very dark in SAR images. The size of the shadow area in range direction corresponds to the time delay between the last echo from the top of the mountain and the first echo of the far edge of the shadow region, where the area is not hidden from the sensor anymore.\r\n\r\nRadar shadow occurs when the slope inclination of the slope facing away from the sensor is larger than 90° minus the antenna look angle. As for the other geometric effects, the size of a shadow area for the same object depends on its situation in the image. But, unlike as for foreshortening and layover, shadow is more pronounced in far range than in near range, i.e. large incidence angles produce more shadow.\r\n\r\nA SAR image may show a return signal in a shadow area: this is principally due to internal sensor noise and does not correspond to any target return signal.","hasChildren":true,"name":"Shadow","selfAssesment":"Planned
"},{"code":"PP2-3-8","description":"Synthetic Aperture Radar (SAR) backscatter is determined both by dieletric and geometric properties of the illuminated target. While the water content of the target plays an important role, its surface roughness determines the scattering mechanisms and the amount of incoming signal sent back to the sensor.\r\nDepending on its characteristics but also on the considered wavelength, a surface appears more or less rough. On smooth surfaces, specular reflection occurs, meaning that most of the incoming signal will be reflected away from the sensor. For rough surfaces, diffuse reflection occurs, meaning that part of the signal is scattered back to the sensor, the amount of it depending on different surface roughness parameters. \r\nDepending of the observed target and surface, single or multiple scattering mechanisms occur. A particularly important scattering mechanism is the double bounce, which occurs generally at two perpendicular surfaces (e.g. ground and building wall). Through two successive specular reflections, the whole signal comes back to the sensor.\r\nDue to the side-looking geometry of SAR systems and the range dependent image representation, specific additional effects occur and affect the backscatter intensity. Whereas a flat terrain only appears more compressed in near range and more stretched in far range, larger geometric distortions appear for terrain with more topography (e.g. mountains) or high objects (e.g. trees, buildings). This relief displacement is caused by the target’s elevation. A high elevated object is closer to the sensor than the ground below it. Due to the image formation in range direction depending on the distance between sensor and targets, its signal comes back sooner to the sensor and it is represented in the SAR image in nearer range than the ground below it. High objects in the SAR image are therefore displaced horizontally toward the radar antenna. This horizontal displacements contrast with the radial displacement observed in optical imagery due to central projection. Furthermore, such objects hide part of the ground below them, which do not receive any signal and cannot scatter information back. Three particular geometric distortions exist: foreshortening, layover and shadows.\r\nDepending on the illuminated target, different scattering mechanisms occur in combination with geometric distortions, which makes the interpretation of the SAR image challenging. A good example are buildings, where layover, shadow and single- and double-bounce occur.","hasChildren":true,"hasParent":true,"name":"Terrain reflectivity and geometric distortions","selfAssesment":"Completed
"},{"code":"PP2-3-9","description":"A typical “salt-and-pepper” noise-like physical phenomenon that is not a noise but a deterministic property of SAR imagery is the so called speckle. It appears when a resolution cell of a SAR system contains more than one scatterer. In that case, the total scattering from the resolution cell is a coherent sum of the backscatter originating from the different scatterers. In order to reduce this effect, speckle reduction methods can be applied.","hasChildren":true,"name":"Speckle Formation","selfAssesment":"Planned
"},{"code":"PP2-3","description":"Microwave remote sensing systems detect and quantify the electromagnetic radiation arriving at a detector, this radiation being either emitted (passive sensors) or scatterered back (active sensors) from the objects.\r\nThree properties of the recorded electromagnetic signal are of particular interest: its intensity, its phase and its polarization. The specific quantification of each properties allows signal interpretation, as they depend on the roughness and dielectric characteristics of the surface (intensity and polarization) as well as of the range between target and sensor (phase).\r\nThe detection of the microwaves is operated through two principal sensor elements: an antenna and a receiver. The antenna collects the incoming radiation and the receiver measures the collected electric signal.\r\nAs active microwave systems produce their own electromagnetic radiation, they are equipped with two additional elements: a pulse generator and a transmitter. Usually, transmitter and receiver are situated on the same antenna.\r\nA simple detector system only detects the intensity of the signal and amplifies it. Coherent systems measure both the amplitude and the phase of the incident electromagnetic radiation.\r\nMicrowave systems can be categorized in two different types: imaging and non-imaging sytems. Whereas for non-imaging systems each echoe (collected signal) provides a single measurement, imaging systems collect a sequence of echoes that generate a two dimensional image.","hasChildren":true,"hasParent":true,"name":"Detecting microwaves","selfAssesment":"Completed
"},{"code":"PP2","description":"Microwave remote sensing operates in the microwave portion of the electromagnetic spectrum, generally using wavelengths greater than 3 cm and up to 1 m. \r\nMicrowaves are sensitive to different physical parameters than other regions of the electromagnetic spectrum. Microwaves interactions with objects are governed by geometric (structure, size, shape) and dielectric (water content) properties, whereas other regions of the electromagnetic spectrum reacts e.g. to object temperature or “color” (amount of reflection or absorption of the Sun light by a particular object).\r\nAs a general rule, microwaves interact with object at least as big as the wavelength. Smaller objects will therefore be transparent for the signal. Due to the large wavelengths, atmospheric particles are almost transparent to the signal and microwave remote sensing can penetrate clouds. Under very dry conditions, microwaves can even penetrate up to a few meters the top soil layers, therefore providing information that is not visible in other regions of the electromagnetic spectrum. Depending on the considered wavelength, microwave can also penetrate vegetation layers to different amounts.\r\nIn microwave remote sensing, three characteristics of the electromagnetic wave play an important role: its amplitude, its phase and its polarization. Depending on the application, either one characteristic or a combination of them is used to retrieve information.\r\nThere are two main types of microwave sensors: active RADAR systems and passive radiometers. RADAR is an acronym for RAdio Detection And Raging. An active radar system sends out pulses and records the echoes scattered back by the objects (scatterers) to the sensor. The systems use the two-way travel time of the radar pulse to determine the distance (range) to the illuminated object. Its backscatter intensity is determined by the radar system and object properties and depends on the quantity of energy coming back to the sensor. Active radar systems transmit a signal and record the amount of energy that is scattered back and depends of both dielectric and geometric properties. Passive radiometers record microwave energy, which is emitted by the Earth’s surface.\r\nDepending on the type of system, microwave remote sensing can be used in multiple applications. Active sensors are principally used for diverse land cover mapping applications based on the particular backscattering mechanisms and characteristics of the objects on the Earth’s surface. Using multiple acquisitions, they are also favored for topographic, deformation and velocity mapping. Passive sensors are preferred for the determination of hydrologic variables such as soil moisture, precipitation, ice water content and sea-surface temperature.","hasChildren":true,"hasParent":true,"name":"Basics of microwave remote sensing","selfAssesment":"Completed
"},{"code":"PS","description":"Remote sensing, i.e. the process of obtaining information about an object or area from a distance, is not possible without remote sensing sensors that collect this information and the platforms on which the sensors are installed and which are used to move them. Remote sensing sensors collect data by detecting energy that is reflected or emitted from Earth. There are different types of remote sensing sensors. The interaction between the sensor and the Earth's surface has two modes: active or passive. Passive sensors use solar radiation to illuminate the Earth's surface and detect reflection from the surface or measure the emitted energy. They usually record electromagnetic waves in the visible (˜430–720 nm) and near infrared (NIR) (˜750–950 nm) through short infrared (SWIR) (˜1.500-2.500 nm) to thermal infrared (TIR) (8.000-14.000 nm) ranges. The power measured by passive sensors is a function of surface composition, physical temperature, surface roughness and other physical properties of the Earth. Active sensors provide their own energy source to illuminate objects and measure their properties. These sensors use electromagnetic waves in the visible and near infrared range (e.g.laser altimeter) and radar waves (e.g. synthetic aperture radar (SAR)). As sensor technology has advanced, the integration of passive and active sensors into one system has emerged. Alternatively, remote sensing sensors can be classified into imaging sensors, i.e. that produce an image of an area, within which smaller parts of the sensor's whole view are resolved (pixels), and non-imaging sensors, i.e. that return a signal based on the intensity of the whole field of view. In terms of their spectral characteristics, the imaging sensors include optical imaging sensors, thermal imaging sensors, and radar imaging sensors. These sensors can be on satellites, mounted on aircraft, unmanned aerial vehicle (UAV), drone or ground. The collected information can be transformed into an image or set of points (e.g. cloud points), which can be further processed and analyzed to obtain the necessary information, e.g. agricultural field development phase, level of air pollution, etc.\r\nA digital imagery of Earth observation sensors is a two-dimensional representation of objects on Earth. Current images collected from different levels of acquisition, from ground to satellite, with the help of electronic sensors are examples of digital images. There are different aspects and characteristics of remote sensing data and images, such as, for example, data formats and processing levels, data storage, data properties.","hasChildren":true,"hasParent":true,"name":"Platforms, sensors and digital imagery","selfAssesment":"Completed
"},{"code":"PS1-1","description":"Remote sensing sensors has its roots in the 19th century in the development of photography. Photography was an invention that made it possible to acquire a permanent image. The first photographic image was taken in 1826 by Joseph Nicephore Nieppce. While the first aerial photograph was taken in 1858 by Felix Tournachon, known as Nadar, from a tethered baloon over Biévre Valley in France. In 1907 Julius Neubronner developed a light miniature camera that could be fitted to a pigeon's breast. It can be said that the construction camera + pigeon was the precursor of today's unmanned aerial vehicle (UAV) or drone. Further developments focused on developing new sensors (analog vs. digital frame cameras) and how to save and store images (e.g. photographic emulsions, films). The origin of other types of remote sensing can be traced to World War II, with the development of radar, sonar, and thermal infrared detection systems. Since the 1960s, sensors were designed to operate in virtually all of the electromagnetic spectrum. Both civil and military aerial photography have long been widely used in cartography to create maps. Specialized large format cameras (looking vertically down, assuming the plane is flying horizontally) were developed. Such cameras have been specially designed to perform almost vertical sequences of bird-eye exposures during aircraft flight. Hence for a long time remote sensing consisted of aerial photography and photogrammetry using analogue mechanical or optical equipment. Everything has changed with satellites and the space race. The first real success of remote sensing satellites in serious scientific work was in meteorology, weather satellite TIROS-1, launched by NASA on April 1, 1960. \r\nToday a wide variety of remote sensing instruments are available as data source for use in different applications for land, water and atmosphere monitoring.","hasChildren":true,"name":"History of remote sensing sensors","selfAssesment":"In progress
"},{"code":"PS1-2-1-1-1","description":"Along track scanner, also known as a pushbroom scanner, is an optoelectronic device that obtains images with a multispectral imaging system. The scanners are used for passive remote sensing. It records electromagnetic energy that is reflected (e.g., blue, green, red, and infrared light) or emitted (e.g., thermal infrared radiation) from the surface of the Earth. The scanners are mounted on space- or aircrafts. \r\nA two-dimensional image is created (line by line) by exploiting the platform motion along the orbital track. The data are collected along track using a linear array of detectors arranged perpendicular to the direction of travel. The array of detectors are pushed along the flight direction to scan the successive scan lines, and hence the name pushbroom scanner. \r\nThere are no moving parts on a pushbroom sensor, hence, the scanning speed can be increased compared to across track systems. A longer dwell time over each ground resolution cell increases the signal strength (high radiometric resolution, no pixel distortion). Additionally, finer spatial and spectral resolution can be achieved as the size of the ground resolution cell is determined by the Instantaneous Field of View (IFOV) of a single detector. The systems are designed for high-resolution imaging. However, a very large number of detectors is needed for high resolution images. It is a complex optical system. In addition, the pushbroom scheme requires a wide Field of View (FOV) optics system to obtain the same swath as for a corresponding whiskbroom (across track) scanner. It has narrow swath width. \r\nThe detector arrays with such a line-scanning pushbroom system are usually of the type Charge-Coupled Device (CCD).\r\nThe MultiSpectral Instrument (MSI) on board the Sentinel-2 satellite (Copernicus mission) uses a pushbroom concept.\r\nMultispectral imaging systems building the final image (line by line) exploiting the platform motion along the orbital track. No rotating mechanical part required, usually based on a CCD matrix (high spectral resolution but just up to 1 micrometer), e.g. Sentinel-2 MultiSpectral Instrument (MSI), Sentinel-3 Ocean and Land Colour Imager (OCLI).","hasChildren":true,"name":"Along track scanners","selfAssesment":"Completed
"},{"code":"PS1-2-1-2-1","description":"The cameras, usually a charge-coupled device (CCD) or Complimentary Metal Oxide Semiconductor (CMOS), that convert light into electrons that can be measured and converted into radiometric intensity value.","hasChildren":true,"name":"Digital Frame Camera","selfAssesment":"Planned
"},{"code":"PS1-2-1-2","description":"2-D systems with the ability to observe in two dimensions simultaneously.","hasChildren":true,"hasParent":true,"name":"Area Arrays","selfAssesment":"New
"},{"code":"PS1-2-1","description":"A type of a spectrometer. It is in principle, one-dimensional systems, whisk- or pushbroom, that form an image on a line-by-line basis in the scan direction.","hasChildren":true,"hasParent":true,"name":"Line detector arrays","selfAssesment":"New
"},{"code":"PS1-2-2-1-1","description":"Thermal radiometers are radiometers with the capability of measuring the spectrum of infrared emission. As such, they are characterized by a relatively high spectral resolution (normally better than 1 cm-1 in wave number units). Modern Spectrometers on board satellites have a spectral resolution better than 0.7 cm -1 in order to properly resolve CO2 lines used for the retrieval of the atmospheric temperature profile. Based on the optical layout they are further classified in grating spectrometers and Fourier Transform Spectrometers or FTIR.","hasChildren":true,"name":"Thermal Radiometers","selfAssesment":"New
"},{"code":"PS1-2-2-1-2","description":"Passive microwave radiometers are radiometers that measures energy emitted at millimetre-to-centimetre wavelengths at 0.15 - 30 cm (frequencies of 1–200 GHz). Example of a sensor: SMOS Microwave Imaging Radiometer with Aperture Synthesis (MIRAS), which aims at measuring land soil moisture and ocean salinity.","hasChildren":true,"name":"Passive Microwave Radiometers","selfAssesment":"In progress
"},{"code":"PS1-2-2-1-3","description":"An advanced multispectral sensor that detects hundreds of very narrow spectral bands throughout the visible, near-infrared, and mid-infrared portions of the electromagnetic spectrum.","hasChildren":true,"name":"Hyperspectral Radiometers","selfAssesment":"Planned
"},{"code":"PS1-2-2-1-4","description":"A radiometer that measures the intensity of radiation in multiple wavelength bands (i.e., multispectral). Example of a sensor Moderate Resolution Imaging Spectroradiometer (MODIS)","hasChildren":true,"hasParent":true,"name":"Spectroradiometers","selfAssesment":"In progress
"},{"code":"PS1-2-2-2","description":"Provide information about vertical profiles of temperature and molecular consistuent concentrations in the atmosphere (atmospheric sounders).","hasChildren":true,"name":"Atmospheric passive sounders","selfAssesment":"New
"},{"code":"PS1-2-2","description":"Radiometers are instruments which measure radiative intensities within a particular frequency window. A radiometer is further identified by the portion of the electromagnetic radiation it covers, usually the infrared or microwave regions. Normally the spectral range extends from the longwave (14-15 micron) to the shortwave (3-4 micron). This range overlaps much of the emission spectrum of Earth. The technology is classified in broadband radiometer of spectral radiometers depending on the spectral resolution. A radiometer measures the intensity of the radiative energy, but does not differenciate between the different registered wavelengths or their respective amplitude. In other terms, it provides a single value as combined result of all wavelengths within the considered frequency window.","hasChildren":true,"hasParent":true,"name":"Radiometers","selfAssesment":"In progress
"},{"code":"PS1-2","description":"Passive remote sensing systems record electromagnetic energy that is reflected (e.g., blue, green, red, and infrared light) or emitted (e.g., thermal infrared radiation) from the surface of the Earth. Passive sensors therefore rely on an external energy source (e.g. sun illumination, Earth heat emission). Contrary to passive sensors, who detect naturally occurring radiation, active sensors emit radiation and collect and analyze the signal that is sent back by the Earth’s surface or atmosphere. Active remote sensing systems produce therefore their own electromagnetic energy. They transmit and receive the radiation that is reflected or backscattered from the illuminated target. They do not necessitate an external source of radiation (e.g. Sun or Earth). Contrary to most passive sensors that are bound to detecting either the reflected Sun radiation or emitted radiation by the Earth’s surface in ranges from the ultraviolet to the thermal infrared, active sensors can use any radiation from the electromagnetic spectrum, the only limitation being the transparency of the Earth’s atmosphere. They often use wavelengths that are not sufficiently provided by the Sun, e.g. microwaves. \r\nActive systems can be categorized either according to their imaging capability, or according to the considered emitted wavelength, or also according to the way they use the returned signal. For the last category, it is generally distinguished between ranging systems, which use as principal information the time delay between transmission and reception of the electromagnetic radiation at the sensor, and scattering systems, which consider the strength (also called magnitude or intensity), of the returned signal. Some systems also register both information.\r\nAs active sensors produce their own radiation and do not rely on e.g. Sun radiation, they are daytime independent and can also retrieve information about the Earth’s surface by night. Furthermore, depending of the considered wavelength, active sensors are weather independent. For longer wavelengths of the microwave domain, clouds are transparent, as the transmitted wavelength is larger than the water particles constituting the cloud and do not interact with them. \r\nActive sensors can control the direction of their illumination to a specific target to be investigated, but require in general more energy than passive sensors as they “actively” illuminate the Earth’s surface.","hasChildren":true,"name":"Passive vs. active sensors","selfAssesment":"Completed
"},{"code":"PS1-3-1-1","description":"Imaging RADAR (RAdio Detection And Ranging) is an active remote sensing system which bounces microwave energy from a target and records the energy that returns to the sensor. The radar antenna alternately transmits and receives pulses at particular microwave wavelengths (in the range 1 cm to 1 m, which corresponds to a frequency range of about 300 MHz to 30 GHz) and polarizations (waves polarized in a single vertical or horizontal plane).\r\nMicrowave energy pulses are emitted at regular intervals and focused by the antenna into a radar beam directed downwards and to the side. The radar beam illuminates the surface obliquely at a right angle to the motion of the platform. Objects on the ground reflect the microwave energy depending on factors such as roughness and attitude. The antenna receives this reflected (or backscattered) energy.\r\nBy measuring the time delay between the transmission of a pulse and the reception of the backscattered \"echo\" from different targets, their distance from the radar and thus their location can be determined. As the sensor platform moves forward, recording and processing of the backscattered signals builds up a two-dimensional image of the surface.\r\nUnlike aerial photographs and satellite images which are passive remote sensing systems, in active systems such as radar, the brightness or darkness of the image is dependent on the portion of the transmitted energy that is returned back to the radar from targets on the surface. Bright areas are produced by strong radar response and darker areas are from weak radar responses., while the response to radar energy by the target is primarily dependent on the three factors (1) Surface roughness of the target, (2) Radar viewing and surface geometry relationship, and (3) Moisture content and electrical properties of the target.","hasChildren":true,"hasParent":true,"name":"Imaging Radar","selfAssesment":"Completed
"},{"code":"PS1-3-2-1-1","description":"Laser profilers measure 2D range profiles and operate in different environments, like spaceborne, airborne and indoor. It is the simplest application of the LIght Detection And Ranging technique. It transmits a short pulse of energy (visible or near-infrared radiation) and detects 'echo', by measuring the time delay. Knowing the speed of propagation of the pulse (speed of light), the range from the instrument to the surface can be measured.\r\nLaser profiling uses successive reflectorless laser range measurements (1D distance measurement) on adjacent points along a path, which results in a 2D profile or cross-section of the ground. A laser profiler can be terrestrial, or ground-based, or it can be mounted on an airborne or spaceborne platform. In the case of ground-based measurements, the platform is fixed but the angle of illumination changes, allowing for the cross section of the terrain to be mapped. An airborne laser profiler can transmit a continuous stream of pulses along its flight path. As a result, if the position of the platform is known, e.g. from GPS/IMU system, a surface profile along the flight path can be reconstructed using the successively recorded vertical distances between the platform and the points on the ground. The use of an additional rotational mirror allow to scan the Earth in an additional dimension, providing 3D information of the mapped surface. This is the principle of a laser scanner.\r\nThere are two principal types of laser profiling techniques: the first one is based on analog detection and the second on photon counting. In analog detection, the signal power is converted into an output voltage providing a signal strength as function of time. The analog-to-digital conversion yields either a full waveform that allows retrieving the entire time-structure of the return signal strength- and therefore the full vertical structure of the target-, or discrete returns when the signal strength exceed a certain threshold. The full waveform information is especially useful when analyzing vegetation, as every vegetation layer (canopy, stems, branches) and the ground return pulses, allowing the determination of e.g. canopy height, ground surface topography but also a deeper analysis of the canopy structure. Photon counting techniques record the arrival of single photons. The counting of photons is combined with their time-of-flight. The accumulation of single photons at a specific range is similar to the signal strength of analog detection and allows retrieving the height and structure of specific targets.","hasChildren":true,"hasParent":true,"name":"Laser profiler","selfAssesment":"Completed
"},{"code":"PS1-3-2-1-4","description":"A radar altimeter is an active, non-imaging remote sensing device. It measures the height of the terrain along the track beneath an air- or spaceborne platform using electromagnetic radiation from the microwave region of the electromagnetic spectrum. Radar altimeters operate similar to laser profilers. Both emit a short pulse of electromagnetic radiation towards the Earth’s surface and detect the time delayed echo. By measuring the time delay and knowing the speed of propagation of the pulse, the range (distance) from the instrument to the surface can be determined. By using the forward motion of the altimeter platform and transmitting a continuous stream of pulses a profile can be built up. If the exact location of the platform as a function of time is known, a surface profile can be generated. \r\nFor a high accuracy of the range resolution, a narrow antenna beam is required, which can be achieved either by using large antennas or short radar beams. In the first case, the radar altimeter is beam-limited; in the second case it is pulse-limited. As large antennas are not practical in space, pulse-limited systems are used for space-borne platforms. Pulse-limited altimeters use frequency modulated (chirp) pulses generated by a chirp generator. The accuracy of the measurements also depends on atmospheric transmission effects, as the speed of the electromagnetic radiation traveling at the speed of light will be delayed when passing through the ionosphere and the atmosphere twice. In general, the range resolution of radar altimeters is in the order of a few centimetres. \r\nIn the beginning, radar altimeters were used for measurements of surface profiles of the ocean topography to get information about currents, ocean circulation, wind and waves. Another basic application of altimetry were measurements over ice sheets and glaciers, e.g. for mass balance determination. Further application domains are geoid measurements also revealing deep sea trenches and the precise monitoring of satellite orbits.","hasChildren":true,"name":"Radar altimeters","selfAssesment":"Completed
"},{"code":"PS1-3-2-1","description":"Laser altimeters historically were the first active sensing devices used on airborne platforms, measuring range information in form of single distances since the mid-1960s. \r\nEven though laser scanners made it possible to retrieve information in a more rapid and denser coverage since the mid-1990s, laser altimeters remain of importance in the scientific community. Especially, the mapping of ice-covered surfaces, water bodies and flat land areas is still performed using laser altimeters.\r\nLaser altimeters are either airborne or spaceborne and are often used together with microwave (radar) profiler in order to calibrate the radar instruments. Whereas airborne laser altimeters are preferred for forestry application, e.g. for analyzing vertical vegetation structure, spaceborne laser altimeters are additionally used for multiple other applications. In particular, spaceborne laser profiler are of high interest for studying surface roughness of ice sheets or for mapping desert topography. Furthermore, spaceborne laser profilers are also useful in atmospheric science for retrieving cloud structure and analyzing different aerosol layers. The requirements for airborne and spaceborne laser altimeters are different. In particular, for spaceborne altimeters, both the distance travelled by the laser pulse and the platform speed are much higher than for airborne instruments, inducing the need of larger optics and more powerful laser instruments. First spaceborne laser experiments were conducted onboard the space shuttle in the mid-1990s, first aiming atmospheric research with a near infrared laser. After successful trial, the space shuttle laser altimeter was fine-tuned and follow-up missions focused on mapping terrain relief and vegetation canopies. Later missions, such as GLAS (IceSAT), ATLAS (IceSAT-2) and GEDI (ISS), used either near-infrared or green (or both) laser light and focused on improving ground coverage while allowing smaller footprints of the laser beam on ground. The revisit cycle of spaceborne laser altimeters allow the determination of regional elevation changes, e.g. monitoring of ice–sheet thickness or vegetation height, which is highly relevant for the scientific community and climate modelers.","hasChildren":true,"name":"Laser altimeter","selfAssesment":"Completed
"},{"code":"PS1-3-2-3","description":"By a ranging camera the simultaneous capturing of range measurements for dynamical (close-range) 3D applications is given. These ranging cameras allow additionally the simultaneous capturing of single range and co-registered intensity images while still maintaining high update rates (up to 100 releases per second). Typical applications are autonomous navigation of robots, driver assistance, traffic monitoring or tracking of pedestrians for building surveillance.","hasChildren":true,"name":"Ranging camera","selfAssesment":"Completed
"},{"code":"PS1-3-2-4-1","description":"Spaceborne LS (e.g. Geoscience Laser Altimeter System - GLAS) provides global measurements of the Earth's surface with the potential on capturing additionally clouds and atmospheric aerosols. The spaceborne measurements allow to globally observe ice sheet and land elevations, approximate sea ice thickness, changes in elevation through time, vegetation coverage for biomass estimation, and height profiles of clouds and aerosols. It is a large footprint profiling system developed by NASA that operates with a footprint diameter of 70 m and measures elevation changes with decimeter accuracy. The surface characteristics are determined by comparing a parametric description of the transmitted and received waveforms. Because the laser footprint is large and illuminates multiple surfaces, the resulting return waveform is an integrated, spatially non explicit representation of the range to illuminated surfaces separated both vertically and horizontally. The geometric organization of surfaces within a single footprint can therefore not be determined.","hasChildren":true,"name":"Spaceborne Laser Scanning","selfAssesment":"Completed
"},{"code":"PS1-3-2-4-2","description":"Airborne laser scanning (ALS) systems allow a direct and illumination-independent measurement of 3d objects in a fast, remote and accurate way. Beside basic range measurements, the current commercial ALS developments allow to record the waveform of the backscattered laser pulse. Latest trends in sensor developments focus on single-photon detection. Airborne Laser Scanning (ALS) for instance is used for capturing large-scale 3D environments with almost homogeneous point density with a local point density of typically 4-100 pts/m^2. Therefore, different applications are of interest, like urban planning, change detection, forestry surveying, or power line monitoring. Further to describe the 3D scene, products like digital terrain models (DTMs), digital surface models (DSMs), or city models are provided.","hasChildren":true,"name":"Airborne Laser Scanning","selfAssesment":"Completed
"},{"code":"PS1-3-2-4-3","description":"A mobile laser scanning (MLS) system consists of a moving vehicle equipped with one or more usually side-looking laser scanners to capture information about the local 3D geometry. Mobile laser scanning systems are applied for capturing dense and accurate 3D information representing local object surfaces, but the density of the measured 3D points depends on their distance to the scanning unit, which is usually mounted on a vehicle. As a consequence, an appropriate interpretation of the captured data has to face certain challenges arising from either low or varying point density.","hasChildren":true,"name":"Mobile Laser Scanning","selfAssesment":"Completed
"},{"code":"PS1-3-2-4-4","description":"Underwater Laser Scanning is applied in deep-sea as well as in shallow water regions. The ranging distance is close range and the measurement principle relies on triangulation by laser light, comparable with structured-light-projection. More recently, companies started to develop Time-of-Flight (ToF) underwater laser scanners.","hasChildren":true,"name":"Underwater Laser Scanning","selfAssesment":"Completed
"},{"code":"PS1-3-2-4-5","description":"For Bathymetric Laser Scanning System the utilized green laser light with its potential penetration capabilities in water is essential. For water surface mapping the electromagnetic radiation of the laser penetrates into the topmost layer of the water column and can also be used for mapping the water surface and shallow water bathymetry. However high resolution mapping of water level heights is important for many applications, but capturing water is still in general challenging. Area-wide water surface heights and depths are required for many disciplines such as hydrology, hydraulic engineering, flood risk management, ecology, climate change, etc.","hasChildren":true,"name":"Bathymetric Laser Scanning","selfAssesment":"In progress
"},{"code":"PS1-3-2-4","description":"Laser scanners capture data by successively considering points on a discrete, regular (typically spherical, cylindrical or line) raster, and recording the respective geometric and radiometric information. Generally, a laser scanner illuminates a scene with modulated laser light and analyzes the backscattered signal. More specifically, laser light is emitted by the scanning device and transmitted to an object. At the object surface, the laser light is (partially) reflected and, finally, a certain amount of the laser light reaches the receiver unit of the scanning device. The measurement principle is therefore of great importance as it may be based on different signal properties such as amplitude, frequency, polarization, time, or phase. Many scanning devices are based on measuring the time t between emitting and receiving a laser pulse, i.e., the respective time-of-flight, and exploiting the measured time t in order to derive the distance r between the scanning device and the respective 3D scene point. Alternatively, a range measurement r may be derived from phase information by exploiting the phase difference Δφ between emitted and received signal. In general, laser scanners may be categorized with respect to laser type, modulation technique (continuous-wave (CW) laser, pulsed laser), measurement principle (time-of-flight, phase difference), detection technique (coherent detection, direct detection), field-of-view (line scanner, pushbroom scanner, array scanner), measurement range (far range, medium range, close range), or configuration between emitting and receiving component of the device (monostatic system, bistatic system). Furthermore, different types of laser scanners may be used for different application scenarios relying on e.g. spaceborne laser scanning, airborne laser scanning, mobile laser scanning, terrestrial laser scanning, underwater laser scanning or bathymetric laser scanning.","hasChildren":true,"hasParent":true,"name":"Laser scanner","selfAssesment":"Completed
"},{"code":"PS1-3-2","description":"The main idea of LiDAR (Light Detection and Ranging) technology is based on actively scanning the scene by involving a device which emits electromagnetic radiation in the form of modulated laser light. \r\nGenerally, such scanning devices illuminate a scene with modulated laser light and analyze the backscattered signal. More specifically, laser light is emitted by the scanning device and transmitted to an object. At the object surface, the laser light is partially reflected and, finally, a certain amount of the laser light reaches the receiver unit of the scanning device. The measurement principle is therefore of great importance as it may be based on different signal properties such as amplitude, frequency, polarization, time, or phase. \r\nMany scanning devices are based on measuring the time t between emitting and receiving a laser pulse, i.e., the respective time-of-flight, and exploiting the measured time t in order to derive the distance r between the scanning device and the respective 3D scene point. Alternatively, a range measurement r may be derived from phase information by exploiting the phase difference Δφ between emitted and received signal. According to seminal work, respective scanning devices may be categorized with respect to laser type, modulation technique, measurement principle, detection technique, or configuration between emitting and receiving component of the device. \r\nIn order to get from single 3D scene points to the geometry of object surfaces, respective scanning devices are typically mounted on a platform which, in turn, allows a sequential scanning of the scene by successively measuring distances for discrete 3D points.\r\nLiDAR technology is used for a diversity of applications such as autonomous driving, forestry, biomass estimation, precision farming, archaeology, city mapping, terrain modelling, and metrology.","hasChildren":true,"hasParent":true,"name":"LiDAR (Light Detection and Ranging)","selfAssesment":"Completed
"},{"code":"PS1-3-3-1","description":"Sonar, also called ultrasonic sensing, is one the principal sensors for mapping sea-floor, i.e. bathymetry. It transmits sound waves through water and records the amount of backscattered energy. It uses frequencies higher than normal hearing. A sonar can be either passive or active. Active sonars are also called echosounders.","hasChildren":true,"name":"Sonar","selfAssesment":"New
"},{"code":"PS1-3-3-2","description":"A seismic sensor is also called seismometer and measures the motion of the ground when it is shaken by a perturbation such as an earthquake, be it a large displacement or a microquake. The physical variable associated to the measurement of a seismometer is dynamic. It can be either the amplified ground motion, the velocity or acceleration. Current seismometers transform one of these three parameters into a voltage measurement. Usually, three seismometers are needed to retrieve the three components of the displacement. As for other sensors, there exists many types of seismic sensors, and they can be distinguished in active and passive sensors as well.","hasChildren":true,"name":"Seismic sensor","selfAssesment":"New
"},{"code":"PS1-3-3","description":"Instruments that measure vertical distribution of precipitation and other atmospheric characteristics such as temperature, humidity, and cloud composition.","hasChildren":true,"hasParent":true,"name":"Sonic sensors","selfAssesment":"New
"},{"code":"PS1-3-4-1","description":"A radar scatterometer is an active, non-imaging remote sensing device with a real aperture operating in the microwave region of the electromagnetic spectrum. The main purpose of a scatterometer is the characterization of the surface backscatter properties, when a high radiometric accuracy is of interest and the spatial resolution is of secondary importance. There are scatterometers used in laboratories, in the field installed on masts, cranes or trucks, airborne (airplanes, helicopters) and spaceborne scatterometers circling the Earth in an orbit. Spaceborne scatterometers usually achieve a global coverage with a high repetition frequency. The basic principle of the scatterometer the accurate measurement the intensity of the returned radar echo from the Earth’s surface. Because of the speckle effect in radar echoes, a large number of independent observations are averaged.\r\nScatterometry (Earth observation using scatterometers) gained the attention of scientists towards the end of the 1960s when it was realized that the sea clutter observed by Second World War radar operators on their screens was not just any noise obscuring small boats and low-flying aircraft. It was in fact the signal backscatter from small ocean surface waves, comparable in dimension to the wavelength of the radar (in the order of centimetres).\r\nThe primary application of radar scatterometers is the measurement of near-surface wind vectors (wind speed and direction) over the ocean. These wind vector data are based on indirect measurements, where the wind vector is derived from the relationship between the backscattered power, the small-scale ocean surface roughness, and the local wind vector at the ocean surface.","hasChildren":true,"name":"Radar Scatterometers","selfAssesment":"Completed
"},{"code":"PS1-3-4-2-1","description":"Differential Absorption Lidar (DIAL) is a laser remote sensing technique that is used for range and/or profile measurements of atmospheric gas concentrations and constituents.","hasChildren":true,"name":"Differential Absorption Lidar","selfAssesment":"In progress
"},{"code":"PS1-3-4-2-2","description":"Doppler Wind LiDAR or Cloud-Aerosol Lidar with Orthogonal Polarization (e.g. CALIOP) is a two-wavelength polarization-sensitive LiDAR that provides high-resolution vertical profiles of atmospheric aerosols and clouds to enable an greater understanding of our climate.","hasChildren":true,"name":"Doppler Wind LiDAR","selfAssesment":"In progress
"},{"code":"PS1-4","description":"There are different ways to classify sensors used in remote sensing. One of them is the division into imaging and non-imaging sensors. Imaging sensors typically employ optical imaging systems (from VIS to TIR). They operate primarily at window frequencies, where atmospheric absorption is low and surface features can be imaged or measured. Non-imaging sensors include microwave radiometers, microwave altimeters, magnetic sensors, gravimeters, Fourier spectrometers, laser rangefinders, and laser altimeters.","hasChildren":true,"name":"Imaging vs. nonimaging sensors","selfAssesment":"New
"},{"code":"PS1-5-1-2","description":"Across track scanners, known as whiskbroom electromechanical scanners, are multispectral imaging systems building the final image (ground cell by ground cell) by combination of the platform motion along the orbital track with a mechanical rotation of the collecting optic in the across track direction. Opto-mechanical are typically multi-spectral radiometers (no limitation on bands), whiskbroom systems are usually CDD spectrometers (high spectral resolution but just up to 1 micrometer). Examples of the sensors: Landsat Multispectral Scanner (MSS), Landsat Thematic Mapper (TM).","hasChildren":true,"name":"Across track scanners","selfAssesment":"In progress
"},{"code":"PS1-5-1","description":"Speckle-pattern based sensors operate with a spatial neighborhood codification strategies to exploit a unique pattern. The label associated to a pixel is derived from the spatial pattern distribution within its local neighborhood. Thus, labels of neighboring pixels share information and provide an interdependent coding. Representing one of the most popular devices based on structured light projection, the Microsoft Kinect exploits an RGB camera, an IR camera, and an IR projector. The IR projector projects a known structured light pattern in the form of a random but unique speckle dot pattern onto the scene. As IR camera and IR projector form a stereo pair, the pattern matching in the IR image results in a raw disparity image which, in turn, is read out as depth image.","hasChildren":true,"name":"Speckle-pattern based sensor","selfAssesment":"In progress
"},{"code":"PS1-5-2","description":"A multi-temporal (sequential) binary coding uses black and white stripes to form a sequence of projection patterns for each point on the surface of the object. Binary coding technique is very reliable and less sensitive to the surface characteristics, since only binary values exist in all pixels. Thus, each pixel may be assigned a codeword consisting of its illumination value across the projected patterns. The respective patterns may, for instance, be based on binary codes or Gray codes and phase shifting. To achieve high spatial resolution, a large number of sequential patterns need to be projected. All objects in the scene have to remain static. The entire duration of 3D image acquisition may be longer than a practical 3D application allows for. These sensors are utilized in industrial environment.","hasChildren":true,"name":"Multi-temporal pattern based sensor","selfAssesment":"In progress
"},{"code":"PS1-5-3","description":"For a multi-spectral pattern based sensor, various continuously varying color patterns to encode the spatial location information are utilized.","hasChildren":true,"name":"Multi-spectral pattern based sensor","selfAssesment":"In progress
"},{"code":"PS1-5","description":"A structured-light-projection camera emits active optical radiation in the form of a coded structured light pattern in the visible or infrared spectrum, or electromagnetic radiation in the form of modulated laser light. Via the projected pattern, particular labels are assigned to 3D scene points which, in turn, may easily be decoded in images when imaging the scene and the projected pattern with a camera. The procedure reminds to conventional stereo processing, where corresponding features must be extracted from a pair of stereo images to derive the spatial information. In contrast, such synthetically generated features allow to robustly establish feature correspondences, and the respective 3D coordinates may easily and reliably be recovered via triangulation. Generally, techniques based on the use of structured light patterns may be classified depending on the pattern codification strategy.","hasChildren":true,"hasParent":true,"name":"Structured-light-projection camera","selfAssesment":"Completed
"},{"code":"PS1-6","description":"Ground penetrating radar is a non-intrusive measurement technique that uses radio waves to probe the ground. It is used to analyze and locate targets buried in the sub-surface. It transmits low-power electromagnetic energy into the ground and receives weak signals from a low-loss dielectric or conductor material. It is principally used for archeology and geology. Typical penetration depths are between a few centimeters up to 4m.","hasChildren":true,"name":"Ground penetrating RADAR (GPR)","selfAssesment":"New
"},{"code":"PS1-7","description":"An optical spectrometer is an instrument used to detect, measure and analyze the spectral content of the incident electromagnetic field (narrow-band, VIS, NIR, SWIR and TIR). It breaks down the incoming light spectrum so the whole wavelength range is mapped and each wavelength can be analysed individually. Usually, a distinction is made between optical and mass spectrometers.\r\nOptical spectrometers depict the intensity of the incoming light in function of the wavelength. Considering all wavelengths, each object has a specific spectral signature and the analyse of their particular spectrum allows the deduction of their composition ( e.g. pigments) or health.","hasChildren":true,"hasParent":true,"name":"Optical spectrometers","selfAssesment":"In progress
"},{"code":"PS1","description":"Remote sensing sensors acquire information about objects situated on the surface of e.g. the Earth remotely, e.g. from a distance, without any physical contact. They detect and measure the changes that the object imposes on its. \r\nRemote Sensing sensors are characterized according to several different properties:\r\n\tDepending on the interaction between the sensor and the Earth’s surface, one distinguishes between active (e.g. radar) and passive (e.g. optical imagery) sensors. Some systems use both kind of sensors simultaneously.\r\n\tDepending on the mapping process of the information, it can be distinguished between imaging and non-imaging sensors. Imaging sensors produce an image of an area of interest, e.g. give a spatial information about the incoming information. Spatial relationships between objects can be identified and used for visual interpretation. Non-imaging sensors register usually single response values for a specific area, and do not record how the incoming information varies across the field of view. They can be used to characterize the interaction between the received information and illuminated target.\r\n\tDepending on the platform on which the instrument is deployed, one speaks either of ground based (e.g. terrestrial laser scanner), airborne (e.g. plane, drone), or spaceborne (e.g. satellite) sensor. For spaceborne sensors, the orbit geometry (e.g. geostationary, equatorial, sun-synchronous) and altitude (high, medium and low Earth orbit) play an important role, as it most often determines the application of the satellite in combination with the deployed sensor (weather satellites or Earth observation satellite). \r\n\tDepending on the observed portion of the electromagnetic spectrum (e.g. optical, infrared, thermal, microwave). \r\n\tDepending on the instrument (e.g. imagers, altimeters, spectrometers, radiometers). \r\n\tDepending on the instrument precision, e.g. in terms of spatial resolution very high vs. low resolution sensors; in terms of spectral resolution narrow band (hyperspectral sensors) vs. broad-band sensors (mono- and multispectral sensors); in terms of radiometric resolution very high vs. low resolution sensors. Some applications do not require very high precision instruments, e.g. sea surface temperature measurements, while other, e.g. for vegetation monitoring, require high spectral and radiometric resolution for good data interpretation and analysis. \r\nOther categorization would include the specific applications of each sensor (weather, environment, urban, land, water, mapping, photogrammetry, structure-from-motion, etc.) and if is financed and used for scientific, commercial or military goals.","hasChildren":true,"hasParent":true,"name":"Types of remote sensing sensors","selfAssesment":"Completed
"},{"code":"PS2-1","description":"This topic covers information on the first remote sensing platforms that were used to obtain aerial photos. The first-known aerial photo was obtained in 1858 by Gaspard Felix Tournachon (Nadar). Afterwards, different platforms were used to obtain the information from above. The history of the development of remote sensing platforms includes platforms such as baloons, kites, rockets, pigeons, gliders, etc. to recent low-cost femtosatellites, e.g. for solar radioation pressure measurements. Historically, the main developments of the platforms as well as sensors was associated with military operations in the XXth century. Remote sensing data was used as part of photo- or/and satellite reconnaissance, i.e. aerial photos or satellite imageries used for the military purposes, mainly to make accurate maps and based on that to prepare a military strategy.","hasChildren":true,"name":"History of Remote Sensing Platforms","selfAssesment":"In progress
"},{"code":"PS2-2-1","description":"An unmanned aircraft system (UAS) includes an unmanned aerial vehicle (UAV), an aircraft without a human pilot on board, a ground-based controller, and a system of communications between the two. The system includes a full range of size classes from very small hand-launched drones to the large high-altitude observational systems.","hasChildren":true,"name":"Unmanned Aerial Systems (UAS)","selfAssesment":"New
"},{"code":"PS2-2-2-1","description":"Mission planning depends on the selected system of acquisition (sensor and platform). A detailed planning of a mission is a fundamental prerequisite for a successful acquisition of remote sensing data. Planning of an aerial photography mission (manned or unmanned) takes into account several parameters such as time of day/sun angle, weather conditions, flightline, platform. Planning and implementation of a spaceborne Earth Observation mission involves several successive life cycle ‘phases’ of conception, development, production and testing, utilization and support, and retirement, as part of an iterative and recursive process, until the satellite (space segment) is delivered and launched into orbit, and the data are exploited in the ground segment.","hasChildren":true,"name":"Mission planning","selfAssesment":"New
"},{"code":"PS2-2-2-3-2-3-1","description":"Stripmap is an acquisition mode of Synthetic Aperture Radar (SAR) data. It is the most simple, common acquisition mode of the SAR satellite sensors. In this mode, the antenna of the radar system is pointed in a fixed direction related to the flight direction. The displacement of the illuminated footprint corresponds to the displacement of the sensor along the orbit. This results in a continuous acquisition strip parallel to the flight direction. The ground coverage and resolution varies depending on the considered sensor and technical requirements. For X-band spaceborne sensors, a spatial resolution of 3 m can be achieved with a swath width in range direction of 30 km, e.g. for TerraSAR-X. In C-band, a spatial resolution up to 5 m is achieved e.g. by Sentinel-1 with a swath width of 80 km. For L-band spaceborne sensors, the spatial resolution achievable in stripmap mode varies between 3 and 10 m, with a swath width of 50-70 km, e.g. ALOS PALSAR2. \r\nContrary to other acquisition modes, no antenna steering is needed in azimuth direction and the elevation beam is fixed in a specific range direction. This allows for an uninterrupted coverage along the flight direction.\r\nStripmap data show high resolution with sufficient coverage for regional applications and can therefore be used for e.g. detailed land cover analysis at regional scale such as the mapping of urban footprints. Furthermore, it can be used for the mapping of small island or to support emergency actions.","hasChildren":true,"name":"Stripmap","selfAssesment":"Completed
"},{"code":"PS2-2-2-3-2-3-2-1","description":"The Staring Spotlight mode is only available for a few sensors. It follows the same principle of antenna steering in azimuth direction as the standard Spotlight mode, except that the rotation center of the antenna for steering is situated at a nearer range position, within the illuminated scene. This induces that the illuminated antenna footprint stays almost the same during the whole acquisition. Contrarily to the Spotlight mode, the antenna footprint does not slide along the azimuth direction during the SAR acquisition. Additionally, the steering angle is higher for the Staring Spotlight mode than for the standard Spotlight mode, increasing therefore the length of the synthetic aperture and leading to an even higher resolution in azimuth direction.\r\nThe Staring Spotlight mode is implemented on the X-Band sensor TerraSAR-X since 2013 and achieves an azimuth resolution up to 0.25 m. Similar to the standard Sportlight mode, this happens to the detriment of the coverage. The scene size is highly dependent of the incidence angle and varies from 7.5 km to 4 km in range and from 2.5 to 2.7 km in azimuth direction. A larger coverage is obtained for smaller incidence angles.\r\nDue to their extremely high resolution, staring spotlight acquisitions are principally used for the observation and/or monitoring of small scale objects and phenomena, e.g. small landslides, or for tomographic analysis.","hasChildren":true,"name":"Staring Spotlight","selfAssesment":"New
"},{"code":"PS2-2-2-3-2-3-2","description":"Spotlight is a SAR acquisition mode that allows increasing the illumination time of a particular area of interest by steering the antenna beam in azimuth direction. In this mode, the beam elevation is fixed, but the antenna is steered in azimuth direction, increasing therefore the length of the synthetic aperture. The rotation center of the antenna for steering is situated behind the scene at far range. The antenna footprint slides slightly forward over the scene in the azimuth direction during acquisition, but slower than in Stripmap mode, due to the antenna steering. The longest illumination time in azimuth direction results in an azimuth resolution that is highly enhanced compared to e.g. the Stripmap or the ScanSAR acquisition modes. However, this improvement is done to the detriment of the coverage. As for the other acquisition modes, the ground coverage and resolution depends on the considered sensor. For TerraSAR-X, a minimum coverage of 10 km in range and 5 km in azimuth direction is achieved in the Spotlight mode, with and azimuth resolution of about 1 m. The L-Band sensor Alos 2 also allow Spotlight acquisition mode, with a coverage of 25 km in both directions and a resolution of 1 m in azimuth direction, and down to 3 m in range direction.\r\nDue to the very high resolution achieved in both directions, this acquisition mode is particularly usefull for urban area analysis as it allows for the detection of small objects. Therefore, Spotlight data are often used for the detection and recognition of man-made structures and objects, such as roads, buildings and even vehicles.","hasChildren":true,"hasParent":true,"name":"Spotlight","selfAssesment":"New
"},{"code":"PS2-2-2-3-2-3-3-1","description":"The Interferometric Wide Swath Mode is a particular acquisition mode of the C-Band satellites Sentinel-1 which implements the TOPS (Terrain Observation with Progressive Scan) method. It combines an antenna steering in elevation, as in ScanSAR mode, with a counterrotation of the antenna beam from backward to forward steering, opposite to the steering happening in Spotlight mode. The data is acquired in bursts by cyclically switching the antenna beam between multiple adjacent sub-swaths.\r\n\r\nThis opposite steering direction of the antenna along the azimuth leads to a shorter target illumination and induces a decrease of the resolution, but a cyclically continuous coverage in azimuth direction. The principal difference to the other acquisition modes is that this acquisition mode implies a shrinking of the antenna footprint virtually to a ground target instead of slicing it to retrieve the target.\r\n\r\nThe Interferometric Wide Swath Mode (IW) was originally designed to solve Signal-to-Noise heterogeneities and azimuth ambiguities appearing in the ScanSAR mode.\r\n \r\nFor Sentinel-1, the IW mode provides a coverage of 250 km in range direction with an azimuth resolution of 20 m and incidence angles ranging from 29.1° in near to 46° in far range. \r\n\r\nStandard Single Look Complex Sentinel- 1 IW products contain three sub-swaths in range direction, with nine burts in azimuth direction.\r\n\r\nThe IW mode is the standard acquisition mode of the Sentinel-1 C-Band satellites and is acquired continuously over all land surfaces. The application are very diverse, ranging from agriculture and forestry to urban deformation monitoring and ship surveillance.\r\n\r\nSimilar to the IW mode, the Extra Wide Swath Mode (EW) of Sentinel-1 uses the same TOPS technique, but covers even wider areas up to 400 km in range direction, to the detriment of the resolution which decreases to 40 m. The EW Mode principally finds application in maritime applications such as artic and sea-ice monitoring, analyses of marine winds and oil pollution monitoring.","hasChildren":true,"name":"Interferometric Wide Swath Mode","selfAssesment":"New
"},{"code":"PS2-2-2-3-2-3-3-2","description":"The Extra Wide Swath Mode is an acquisition mode of the Sentinel-1 satellites. It is primarily designed and used for wide area coastal monitoring, such as ship traffic, sea-ice monitoring and oil spill detection. It uses the TOPSAR technique with a swath width of 410km and a spatial resolution of 20 m by 40 m.","hasChildren":true,"name":"Extra Wide Swath Mode","selfAssesment":"New
"},{"code":"PS2-2-2-3-2-3-3","description":"In the ScanSAR acquisition mode, the antenna beam is successively steered to different elevation angles. This results in adjacent, slightly overlapping stripes, or sub-swaths along the range direction, parallel to the azimuth direction, each stripe having a different incidence angle at its center. During antenna steering in elevation, transmitter and receiver are off. Therefore, each stripe is illuminated for a shorter time as for the StripMap mode, leading to a degradation of the azimuth resolution. However, ScanSAR allow a larger coverage in range direction than the other imaging modes. Each sub-swath is illuminated for a shorter time than in the Stripmap case. The timing is adjusted though, such that the time-varying antenna footprint repeat cyclically. Similar to the other acquisition modes, the achievable resolution and coverage of ScanSAR products depends on the considered sensor and its properties. For X-Band, e.g. for TerraSAR-X, a total swath width of 100 km in range direction can be achieved using four adjacent sub-swaths or, using a Wide ScanSAR mode with six adjacent sub-swaths, a swath width up to 270 km can be achieved. A Wide ScanSAR scene shows incidence angles ranging from 15.6° in near to 49° in far range. The azimuth resolution varies between 18.5 m and 40 m, for ScanSAR and WideScan SAR modes respectively. For the L-Band sensor ALOS-PALSAR 2, a swath width up to 40 km can be achieved, with incidence angles ranging from 8° to 70° and an azimuth resolution of 60 m. \r\nThe ScanSAR mode is well suited for large-area monitoring, e.g. for sea ice or glacier monitoring, as well as for mapping large-scale disasters, such as oil slick, or areas devastated by forest fires. Using interferometry, topography mapping and deformation monitoring is also possible.","hasChildren":true,"hasParent":true,"name":"ScanSAR","selfAssesment":"New
"},{"code":"PS2-2-2-3-2-3-5","description":"A stereoscopy acquisition mode collects remotely sensed data where each location on the ground (or the imaged objects) is covered multiple times (at least twice), from different perspectives. Stereopairs and stereoscopic coverage enable the extraction of 3D representations of the environment from remotely sensed imagery. Most aerial photographs are taken with frame cameras along flight lines, or flight strips. [...] Successive photographs are generally taken with some degree of endlap [, i.e. overlap]. Not only does this lapping ensure total coverage along a flightline, but an endlap of at least 50 percent is essential for total stereoscopic coverage of a project area. Stereoscopic coverage consists of adjacent pairs of overlapping vertical photographs called stereopairs. Stereopairs provide two different perspectives of the ground area in their region of endlap [overlap]. When images forming a stereopair are viewed through a stereoscope, each eye psychologically occupies the vantage point from which the respective image of the stereopair was taken in flight. The result is the perception of a three-dimensional stereomodel. As an input to photogrammetry analysis procedures, stereopairs from flight strips enable the extraction of digital elevation models (DEM), orthophotos, thematic GIS data, and other derived products through the use of digital raster images and relatively sophisticated analytical techniques. With the availability of close-range UAV and terrestrial hand-held camera data, 3D reconstructions of buildings (even indoors) and other objects on the terrain surface become possible.","hasChildren":true,"name":"Stereoscopy","selfAssesment":"In progress (to be deleted, merged?)
"},{"code":"PS2-2-2","description":"Since the 1940s aerial imagery has been the primary source of detailed geospatial data for extensive study areas. Photogrammetry is the profession concerned with producing precise measurements from aerial imagery. Aerial imaging and photogrammetry represent a major component of the geospatial industry. The topics included in this unit do not comprise an exhaustive treatment of photogrammetry, but they are aspects of the field about which all geospatial professionals should be knowledgeable.","hasChildren":true,"hasParent":true,"name":"Airborne platforms and systems","selfAssesment":"New
"},{"code":"PS2-2-3-1","description":"Earth observation (EO) missions are gathering information about the physical, chemical, and biological systems of the planet via remote-sensing technologies, supplemented by Earth-surveying techniques, which encompasses the collection, analysis, and presentation of satellite data.","hasChildren":true,"name":"Earth observation missions","selfAssesment":"In progress
"},{"code":"PS2-2-3-2","description":"There are essentially three types of Earth orbits: high, medium and low Earth orbit. Satellites that orbit in a medium (mid) Earth orbit include navigation and specialty satellites, designed to monitor a particular region. Most scientific satellites, including NASA’s Earth Observing System fleet, have a low Earth orbit. On which orbit a satellite will be launched to, depends mainly on its application. The orbit types can be categorized according to their height.\r\nThe orbit height of a satellite corresponds to the distance between the Earth’s surface and the satellite. It determines its speed as it rotates around the Earth. Due to Earth’s gravity, the pull of gravity is stronger for lower orbits than for higher orbits. Therefore, a satellite situated on a lower orbit will circle the Earth faster than a satellite situated on a higher orbit.\r\n\tHigh Earth orbit: it describes orbits situated at about 36000 km above the Earth’s surface (42164 km from the Earth’s center). At this exact distance, the speed of the satellite on the orbit matches the Earth’s rotation, i.e. the satellite needs 24 hours to complete a full rotation on the orbit, when the orbit is situated exactly above the equator. Such orbits are also called geosynchronous orbits, as the satellite moves at the same speed than the Earth and seems to stay in place over a specific location. Those orbits are mainly used for weather and communication satellites\r\n\tMedium Earth orbit: it describes orbits situated at about 20200 km of the Earth’s surface, or 26560 km of the Earth’s center. At this height, a satellite rotates twice around the orbit during one Earth’s rotation. This orbit is also called semi-synchronous and this is the orbit type used by Global Navigation Satellite Systems such as GPS and GLONASS. A further important medium Earth orbit is the Molniya orbit which allows the observation of the poles, otherwise nearly impossible with equatorial geosynchronous orbits.\r\n\tLow Earth orbit: this type of orbits are used from almost all dedicated scientific Earth Observation satellites. Most of them use a particular, nearly polar orbit inclination, meaning that the satellite rotates around the Earth nearly from pole to pole (instead of around the equator as it is the case for geosynchronous satellites). This rotation takes about 99 minutes, depending of the specific orbit inclination. During one half of the orbit, the satellite views the daytime side of the Earth, i.e. the illuminated side. At the pole, satellite crosses over and views the nighttime side of Earth. Back to the daylight side, the satellite can view the area adjacent to the region flown over in the last orbit path, due to the simultaneous Earth’s rotation. In 24 hours, satellites situated on these orbits view almost all the Earth twice, for optical satellites once in daylight and once in the dark. Radar satellites seen each Earth region twice, from two different illumination directions. These specific polar-orbits are called sun-synchronous, as the local solar time stays the same each time a satellite flies over a specific region. This has the advantage of providing an almost constant angle of sunlight for each region on the Earth’s surface viewed by the satellite over time and ensure repeatable sun illumination conditions; the angle will only vary seasonally due to the Earth revolution around the sun. Due to this consistency, images of a specific region would not show much illumination changes due to shadows or sunlight and image interpretation over time such as change detection or monitoring approaches are possible. Because a sun-synchronous orbit does not pass directly over the poles, there is a data gap over both poles where no data is acquired.","hasChildren":true,"hasParent":true,"name":"Types of satellite orbits","selfAssesment":"Completed
"},{"code":"PS2-2-3-3","description":"An imaging SAR system can generally make acquisitions in different modes. Which acquisition mode to choose depends of the application but also on the desired coverage and data resolution. Even if technically all acquisitions modes can be used everywhere on the Earth’s surface, specific modes are preferred for ocean applications that are different from the ones used in land applications.\r\nThe different acquisition modes can be defined either by their geometrical or by their temporal properties.\r\nThe geometrical properties refer to the geometric configuration of the SAR antenna. Usually looking sideways down in a direction perpendicular to the flight direction (Stripmap mode), the antenna can also be steered around the nadir axis in order to look at a specific target for a longer time during pass-by (Spotlight mode). This configuration allows to rachieve higher azimuth resolution but reduces coverage. It is rather used for very local application where a precise information about specific targets is needed. Other geometric configurations steer the antenna around the flight direction (ScanSAR mode), yielding to a larger swath on the ground. The distance between near and far range is increased, as well as the range of incidence angles within one acquisition. Whereas it increases the area of the scene, it comes generally with a decrease of the spatial resolution in the azimuth direction. Depending on the sensors, the name of the acquisition modes as well as particular technical properties can differ. Sentinel-1 uses a TOPS configuration (Terrain observation with Progressive Scan), which combines the antenna steering properties of both ScanSAR and Spotlight modes. \r\nThe temporal properties refer for specific techniques to the time interval between several acquisitions of the same area. Either these acquisitions are taken simultaneously in one pass over the area of interest (single-pass), or they are taken at different times, needing several passes over the area (repeat-pass).\r\nSpecific SAR techniques such as InSAR and Tomography, while relying on those geometric and temporal properties, have additional acquisition configuration characteristics. For example, the interferometric mission TanDEM-X has three acquisition modes defined by the number of satellite emitting or receiving the signal (pursuit monostatic mode, bistatic mode, alternating bistatic mode), which allows phase referencing. Tomographic SAR uses multi-baseline observations, i.e. the antenna passes several times over an area but at different heights, allowing via different incidence angles the retrieval of structural information of specific targets.","hasChildren":true,"hasParent":true,"name":"Synthetic Aperture Radar (SAR) acquisition modes","selfAssesment":"Completed
\r\n\r\n"},{"code":"PS2-2-3-4","description":"Swath width refers to the width of the ground that the satellite collects data from on each orbit. The area imaged on the surface, is referred to as the swath. Imaging swaths for spaceborne sensors generally vary between tens and hundreds of kilometres wide.","hasChildren":true,"name":"Swath","selfAssesment":"
In progress
"},{"code":"PS2-2-3","description":"Spaceborne platforms and systems are present at a great height from the earth surface. The altitude of platforms range from few hundred kilometers to several thousand kilometers. A large area can be captured in a single scene depending on altitude of sensor. The platforms can have different characteristics.","hasChildren":true,"hasParent":true,"name":"Spaceborne platforms and systems","selfAssesment":"Planned
"},{"code":"PS2-3-1","description":"Field spectroscopy generally refers to the use of non-imaging spectrometers near the ground surface and it is usually aimed at evaluating spectral reflectance of the investigated target. For this purpose, consecutive measurements of total incident solar irradiance and of radiance or irradiance upwelling from the target are collected by an operator, or more recently by new instruments for long-term and unattended field spectroscopy measurements. The incident irradiance is usually computed by measuring the radiance upwelling from a white calibrated panel which represents the ideal Lambertian surface. Upwelling fluxes are instead usually collected holding the sensor vertically over the surface (nadir view), although spectral libraries collected observing the target from different viewing angles are also available. \r\nField spectrometry is also referred to as ‘proximal sensing’ to underline that spectra are collected with portable spectroradiometers in the vicinity of the target, in contrast to ‘remote sensing’, which is instead usually performed with satellite or airborne sensors.\r\nField spectroscopy is therefore an in-situ method for characterising the reflectance of natural or artificial surfaces and thereby provides reference data for the calibration or validation (cal/val) of airborne and satellite sensors. This method provides a means of scaling-up measurements from small areas (e.g. leaves, rocks) to composite scenes (e.g. vegetation canopies), and ultimately to pixels.\r\nField spectroscopy is used in different applications, for example, soils, rocks, vegetation and chlorophyll fluorescence, water, snow surfaces and atmosphere. Long-lasting field spectroscopy campaigns based on manual measurements are extremely resource-demanding and do not ensure repeatability of the acquisition conditions as the instrument setup is initialized each day. To overcome such limitations a few research groups have initiated automatic tower-based spectral reflectance measurements using different devices. With such setups, non-imaging spectrometers are installed in the field and are operated automatically for long periods (i.e. months to years) and different networks of hyperspectral instruments are now becoming operational (e.g. RadCal Net).\r\nField spectroscopy can be also used to predict optimum spectral bands, viewing configuration, spectral calibration and time to perform a particular remote sensing task but also to develop, refine and test models relating biophysical attributes to remotely-sensed data. In this context, ground reflectance measurements are therefore mainly used as input in simulation study for sensor design, calibration/validation data for remote sensing sensors, for spectral mixture analysis and for the development of relationships between field data and radiometric variables.\r\nSince spectroscopy is the study of matter using electromagnetic radiation, point or imaging field spectrometers are instruments which allow the measurements of reflected or emitted electromagnetic radiation. In particular, portable or hand-held spectroradiometers are small instruments that spectrally measure the radiation reflected or emitted by a target and they are useful in obtaining accurate spectral data over different surfaces. In remote sensing, they generally cover the 400-2500 nm spectral range and operate with a full width at half-maximum of about 1.5/3 nm, so that they can collect radiation in a continuous way across the spectrum. The final output is therefore the hyperspectral signature of reflectance of the surfaces versus the considered wavelength.","hasChildren":true,"name":"Field spectroscopy and portable spectroradiometers","selfAssesment":"Completed
"},{"code":"PS2-3-2","description":"A terrestrial laser scanning (TLS) system is a stationary highly accurate ranging device for geodetic surveying. More specifically, TLS systems provide dense and accurate 3D point cloud data for the local environment and they may also reliably measure distances of several tens of meters. Due to these capabilities, such TLS systems are commonly used for applications such as city modeling, indoor modeling, construction surveying, deformation analysis, scene interpretation, urban accessibility analysis, or the digitization of cultural heritage objects. When using a TLS system, each captured TLS scan is represented in the form of a 3D point cloud consisting of a large number of scanned 3D points and, optionally, additional attributes for each 3D point such as color or intensity information. However, a TLS system represents a line-of-sight instrument and hence occlusions resulting from objects in the scene may be expected as well as a significant variation in point density between close and distant object surfaces. Thus, a single scan might not be sufficient in order to obtain a dense and (almost) complete 3D acquisition of interesting parts of a scene and, consequently, multiple scans have to be acquired from different locations. As each scan refers to the local coordinate system of the TLS system, all acquired scans have to be appropriately aligned in a common coordinate system. For this purpose, the respective 3D transformations between the acquired scans have to be estimated and this process is commonly referred to as point cloud registration, point set registration, or 3D scan matching.","hasChildren":true,"name":"Terrestrial Laser Scanning","selfAssesment":"Completed
"},{"code":"PS2-3","description":"Platforms and systems that acquire data from the level of earth's surface. A wide variety of ground based platforms are used in remote sensing. The acquired data are used for detailed in-situ measurements, e.g., Leaf Area Index (LAI), and for calibration/validation campaigns.","hasChildren":true,"hasParent":true,"name":"Ground platforms and systems","selfAssesment":"New
"},{"code":"PS2","description":"Remote sensing platforms and systems can be static (ground-based platforms) or moving (e.g. airborne or spaceborne platforms, UAVs). A remote sensing platform or system carry a remote sensing sensor. It can operate in near (few centimetres) or far (36,000 kilometres) altitudes ranges.","hasChildren":true,"hasParent":true,"name":"Types of remote sensing platforms and systems","selfAssesment":"Planned
"},{"code":"PS3-1","description":"The development of remote sensing data carriers has followed the evolution of the photography, remote sensing sensors and computer platforms. The first remote sensed data was stored using the photography films (e.g. aerial photography, satellite Corona program), which was later replaced by reel tapes, cartridge, and then removable and hard discs. In the era of big and fast growth of Earth observation data, and technological advancements in digital infrastructure, the satellite data are stored using cloud platforms providing different service models: Infrastructure as a Service, Platform/Software as a Service (e.g. Copernicus DIAS, Google Earth Engine, open EO). The Cloud offers infrastructure to host, store and process the large amount of data efficiently. For example, the Copernicus Data Information Access Services (DIAS) is a comprehensive cloud-based hosting and processing system for the EO data in particularly for the Sentinels data, the Google’s Earth Engine (GEE) provides access to various satellite and offers processing power with a web-based programming interface, the Amazon Web Services (AWS) has dedicated cloud called ‘Earth on AWS’, the Microsoft’s cloud called Azure facility the use of AI tools to address environmental challenges. Public solutions, as well as private ones, react with a variety of new and innovative tools, which have been recently developed (e.g. DIAS, ODC, EarthServer, EO Browser, GEE).","hasChildren":true,"name":"History of remote sensing data carriers","selfAssesment":"Completed
"},{"code":"PS3-2-1","description":"Most remotely sensed images nowadays exist in digital form. Even domestic cameras are now usually digital instruments, and the use of photographic film is becoming rarer and rarer. Analogue images, such as photographs, are continuous, both in their spatial extent (they can be enlarged almost without limit) and radiometrically (there is a continuous range of shades of grey). The word ‘picture’ is usually used for such an image.\r\nOn the other hand, a digital image is spatially and radiometrically discrete. A remote sensing sensor detects the reflected radiation of the Earth’s surface and stores it as numbers in a raster. In accordance, each area that has been detected constitutes a cell in a raster. The grey levels increment in a stepwise fashion, and the scene is made up from an array of individual elements called ‘picture elements’, abbreviated to ‘pixels’, each of which is represented by one of the discrete grey levels. A pixel is the smallest addressable element in a raster image.\r\nThe spatial resolution of a raster image refers to the size of the ground element represented by an individual pixel. The size of an area represented in a pixel depends of the capability of the sensor to detect details. A pixel cannot be subdivided, and enlargement merely produces larger pixels, which contain no more information than the original ones. We are familiar with this effect on our television or computer screen – the picture we see consists of an array of dots of light, the density of which determines the screen resolution.\r\nThe number of distinct grey levels into which the intensity of the signal is divided and that can be represented by a pixel is called radiometric resolution of a digital image, and it depends of the number of bits per pixel (bpp). A 1 bpp image uses 1 bit for each pixel, so each pixel can be either on or off (monochrome). Each additional bit doubles the number of grey levels available, so a 2 bpp image can have 4 grey levels, a 3 bpp image can have 8 grey levels, and so forth. In colour imaging systems, a colour is typically represented by three component intensities such as red, green, and blue; usually their raster images have an 8-bit resolution (256 grey levels), a 16-bit resolution (65,536 grey levels), or a 24-bit resolution (16,777,216 grey levels).","hasChildren":true,"name":"Picture element (pixel)","selfAssesment":"Completed
"},{"code":"PS3-2-2","description":"One can think of any image as consisting of tiny, equal areas, or picture elements, arranged in regular rows and columns. The position of any picture element, or pixel, is determined on an xy coordinate system. Each pixel also has a numerical value, called a digital number (DN), that records the intensity of electromagnetic energy measured for the ground resolution cell represented by that pixel. Digital numbers range from zero to some higher number on a gray scale. The image may be described in strictly numerical terms on a three-coordinate system with x and y locating each pixel and z giving the DN, which is displayed as a gray-scale intensity value. \r\nMany types of remote sensing images are routinely recorded in digital form and then processed by computers to produce images for interpreters to study. An image recorded initially on photographic film may be converted into digital format by a process known as digitization.","hasChildren":true,"name":"Image as a matrix (digital number DN)","selfAssesment":"Completed
"},{"code":"PS3-2-3","description":"In data manipulation contexts, a data cube is a multi-dimensional array of values. A data cube can be visualized as the multidimensional extension of two-dimensional table. It can be viewed as a collection of identical 2-D tables stacked upon one another. Data cubes are used to represent data that is too complex to be described by a traditional table of columns and rows. Typically, the data cube is applied in conditions where these arrays are massively larger than the hosting computer’s main memory, for example multi-terabyte data warehouses o time series of image data.","hasChildren":true,"name":"Data cubes","selfAssesment":"In progress
"},{"code":"PS3-2-4","description":"Term Big data refers to any collection of data sets so large and complex that it becomes difficult to process using on-hand data management tools or traditional data processing applications. In the field of Earth Observation (EO) is usually refers to large time series of image data which size on disk is much greater than hosting computer’s main memory. EO Big Data offers solution that allows not only storing these data on disk but also efficiently process them.","hasChildren":true,"name":"Earth Observation Big Data","selfAssesment":"In progress
"},{"code":"PS3-2","description":"Most remote sensing data exist as digital images, and appropriate image processing allows the emphasis of certain aspect and subsequent extraction of information for specific applications.\r\nA digital image is a representation of the reality as a grid of picture elements. It can be considered as an array of numbers that can be stored and handled by a digital computer. The picture elements are pixels and each pixel has a specific value (usually in grayscale). This value is a digital number (DN), which usually represents the amount of energy recorded by the sensor at this pixel position or any other characteristic recorded by the sensor, e.g. elevation. \r\nEach row of the image grid, or matrix, corresponds to one scan line. Each pixel is characterized by its row r and column c position in the image, as well as by its value. Additional geographical information is needed in order to assign a geographic location to a pixel. The digital number are integers usually compressed in one byte (= 8 bit) representation, i.e. each pixel can take 256 values.\r\nDigital images are raster data, as opposite to vector data. Whereas vector data can be points, lines or polygones, raster data always consist of pixels. A pixel is the smallest element in which an image can be divided into. The pixel size varies depending of the instrument and of the sampling used. Large pixel may contain information about several objects of the recorded scene. However, they only have one value. These are called mixed-pixel, as e.g. several land cover classes are represented within one pixel and they cannot be distinguished from another. \r\nIn multispectral imagery, each region of the electromagnetic spectrum is recorded in an independent image (band). Therefore, at a specific array position (r,c), there exist several pixels, each with a specific value corresponding to the energy recorded for the considered band. This result in a three-dimensional matrix. The bands of a multispectral image can be displayed three at a time in the computer using for each band one of the three primary colors red, green and blue (RGB). This is called a color composite image. If the color composite represents a combination of the visible red, green and blue bands in their respective color, the combination is called natural or true color composite, as it corresponds to what the human eye sees naturally. Any other combination, for example considering bands of wavelengths that are not visible for the human eye is called a false color composite. It is often used to highlight the spectral differences and particular image features in order to extract information.","hasChildren":true,"hasParent":true,"name":"Digital image terminology","selfAssesment":"Completed
"},{"code":"PS3-3-1","description":"Band interleaved by line (BIL) is one of three primary methods for encoding image data for multiband raster images in the geospatial domain, such as images obtained from satellites. This simple uncompressed raster data encoding is easily and frequently described, requiring no formal specification. BIL is not in itself an image format, but is a scheme for storing the actual pixel values of an image in a file band by band for each line, or row, of the image. The raw data has a simple form and is easily interpreted if the image dimensions in pixels, the number of spectral bands, and the number of bits per band are known. For example, given a three-band image, all three bands of data are written for row one, all three bands of data are written for row two, and so on. The BIL encoding is a compromise format, allowing fairly easy access to both spatial and spectral information. The BIL data organization can handle any number of bands, and thus accommodates black and white, grayscale, pseudocolour, true colour, and multi-spectral image data.\r\nAdditional information is needed to interpret the image data, such as the numbers of rows, columns, and bands, and relate the image to geospatial locations. This information may be supplied in a file header (typical on the tapes originally used for satellite image data) or in files associated with a raw image data file.\r\nSpatial resolution and bit-depth are not limited by the BIL encoding per se but may be constrained in some usage contexts. There is no support for colour management in the BIL encoding. Documentation of spectral values for bands, or interpretation of false colours should be supplied in an accompanying data structure.","hasChildren":true,"name":"Band interleaved by line (BIL)","selfAssesment":"Completed
"},{"code":"PS3-3-2","description":"Band interleaved by pixel (BIP) is one of three primary methods for encoding image data for multiband raster images in the geospatial domain, such as images obtained from satellites. This simple uncompressed raster data encoding is easily and frequently described, requiring no formal specification. BIP is not in itself an image format, but is a method for encoding the actual pixel values of an image in a file. The raw data has a simple form and is easily interpreted if the image dimensions in pixels, the number of spectral bands, and the number of bits per band are known. Images stored in BIP format have the first pixel for all bands in sequential order, followed by the second pixel for all bands, followed by the third pixel for all bands, etc., interleaved up to the number of pixels. The BIP data organization can handle any number of bands, and thus accommodates black and white, grayscale, pseudocolour, true colour, and multi-spectral image data.\r\nBIP data stores pixel information for separate bands within the same file, so that the user can choose to display just one specific band in a multi-band image. Therefore, BIP encoding provides optimal processing performance for spectral analysis (as compared with BIL or BSQ raster organization) as it supports efficient extraction of individual spectra and spectral averages.\r\nAdditional information is needed to interpret the image data, such as the numbers of rows, columns, and bands, and relate the image to geospatial locations. This information may be supplied in a file header (typical on the tapes originally used for satellite image data) or in files associated with a raw image data file.\r\nSpatial resolution and bit-depth are not limited by the BIP encoding per se but may be constrained in some usage contexts. There is no support for colour management in the BIP encoding. Documentation of spectral values for bands, or interpretation of false colours should be supplied in an accompanying data structure.","hasChildren":true,"name":"Band interleaved by pixel (BIP)","selfAssesment":"Completed
"},{"code":"PS3-3-3","description":"Band sequential (BSQ) is one of three primary methods for encoding image data for multiband raster images in the geospatial domain, such as images obtained from satellites. This simple uncompressed raster data encoding is easily and frequently described, requiring no formal specification. BSQ is not in itself an image format, but is a method for encoding the actual pixel values of an image in a file. BSQ format is a very simple format, where each line of the data is followed immediately by the next line in the same spectral band. The raw data has a simple form and is easily interpreted if the image dimensions in pixels, the number of spectral bands, and the number of bits per band are known. This format is optimal for spatial (x, y) access of any part of a single spectral band. The BSQ data organization can handle any number of bands, and thus accommodates black and white, grayscale, pseudocolour, true colour, and multi-spectral image data.\r\nA single band covering the entire scene is stored as a single bitstream making this encoding method convenient when only selected bands are needed. Each image band can be conveniently written to an independent file. BSQ can therefore be a preferable format for some forms of analysis as an application does not have to read past ancillary data in an image stack. As opposed to formats where the bands are interleaved (such as a multi-band GeoTIFF), BSQ data sets support convenient extraction of relevant bands. Some BSQ datasets are distributed as separate image files for each band, with common geospatial registration and a shared set of header information.\r\nAdditional information is needed to interpret the image data, such as the numbers of rows, columns, and bands, and relate the image to geospatial locations. This information may be supplied in a file header (typical on the tapes originally used for satellite image data) or in files associated with a raw image data file.\r\nSpatial resolution and bit-depth are not limited by the BSQ encoding per se but may be constrained in some usage contexts. There is no support for colour management in the encoding. Documentation of spectral values for bands, or interpretation of false colours should be supplied in an accompanying data structure.","hasChildren":true,"name":"Band sequential (BSQ)","selfAssesment":"Completed
"},{"code":"PS3-3","description":"EO data consist of unstructured image data and structured descriptive information attached to the image, which is also called metadata. EO systems are rapidly developing and data sensors resolution are continuously improving. As a result, a vast amount of EO data is generated every day, and their volumes have been in geometric progression growth. According to the current literatures, storage and management methods of EO data are divided into four groups from the perspective of basic technologies: \r\n1. File systems: Traditionally, EO data were manually managed and organized by means of file systems that share and exchange data through storage devices. However, for large amounts of EO data this method leads to inefficiency of management, extra expenses of storage spaces, and weak data security. File systems cannot efficiently support for data retrievals, analyses, and uses in practical applications and research work nowadays. For solving these problems, parallel file system and distributed file system (see below) were presented to support data-intensive applications.\r\n2. Relational Data Base Management Systems (RDBMS): At present, storage and management manners of major EO data are to combine RDBMS and middle-wares. On one hand, traditional RDBMS functionalities are expanded to adapt to the storage and management features of EO data. Adding new data types or encapsulating complicate data types as an object in RDBMS are two general ways to expand functionalities of traditional RDBMS. The former can meet basic requirements of EO data storage and management, but is unable to directly operate spatial data and create spatial indexes. This solution is mainly taken by Database Management System (DBMS) developers, such as Spatial GeoRaster of Oracle, Spatial Extender of IBM DB2, PostGIS of PostgreSQL, and Spatial Extension of MySQL. On the other hand, geographical software expands their data management abilities by developing spatial database engine middle-wares, which is always taken by software enterprises that develop geographical information system (GIS). Spatial Database Engine (SDE) is between users and DBMS. For data storing, SDE is responsible for receiving and storing user data into RDBMS; for data retrieving, it reads data from RDBMS and show them through user interfaces. This resolution stores EO data into RDBMS and interactively manages them by user interfaces provided by SDE. SDE technology is very mature and extensively used in various application fields. As SDE is developed by software enterprises of GIS, they have good comparability with integrated software platform of GIS. \r\n3. Distributed file systems: Recently distributed file system is a new technology of solving data-intensive computing problems. Several distributed file systems have emerged such as PVFS, GPFS, ZFS, GFS, HDFS, and Lustre. \r\n4. Large-scale network storage systems: It is a type of distributed file system with data sharing and remote access functionalities. As the performance improving of hardware and rapid development of network technologies, Storage Area Network (SAN) and Network Attached Storage (NAS) are introduced to distributed file systems. Large-scale network storage systems take different storage and management strategies for EO image files and their metadata. EO image files are stored and managed by HDFS, and their metadata are stored, processed, and managed in RDBMS metadata servers. Managing EO imagery files and their metadata in different ways can improve the management efficiencies of EO data, and balance the loading of distributed file systems. Such systems have already been developed including Celerra, CLARIION, and Symmetric storage solution of EMC, IBM HPSS, MSS, and RASCHAL of National Aeronautics and Space Administration (NASA), the Microsoft earth image storage system, and the Google Earth image storage system.","hasChildren":true,"hasParent":true,"name":"Data storage","selfAssesment":"Completed
"},{"code":"PS3-4-1","description":"The spectral resolution of an Earth Observation sensor refers to the number of spectral bands this sensor can capture. Spectral bands are wavelength intervals in the electromagnetic spectrum. Sometimes, spectral bands are also called spectral channels. Spectral resolution is related to a sensor’s ability to distinguish between different Earth’s surface features based on their spectral properties. A high number of spectral bands means high spectral resolution, with many bands meaning an increasingly smaller range of wavelengths covered by a single band. The spectral resolution of an Earth observation sensor can range from a single very broad band for panchromatic black and white images over a few bands in the case of multispectral sensors (e.g. Landsat family, SPOT, Sentinel-2) to 200 or even more channels for capturing hyperspectral images. Multispectral or hyperspectral sensor imagery has a higher degree of discriminating power than a single band sensor. Another definition of the spectral resolution can be given by the spectral sensitivity of a sensor, which can be specified by the definition of the full width, half maximum (FWHM) as being the spectral interval measured at the level at which the response of the instrument reaches one-half of its maximum values.\r\nSpectral satellite sensors can only gather radiation which is able to pass the Earth’s atmosphere. The atmosphere contains gases, aerosols, ice crystals and water droplets, which absorb and scatter parts of the radiation passing through the atmosphere. Wavelength ranges which do not allow radiation to pass through on their way to the satellite sensors are called absorption bands and those getting through to the sensor are called atmospheric windows. This means that spectral sensors can only operate in these atmospheric windows and the spectral bands should be placed in the wavelength ranges of the atmospheric windows.","hasChildren":true,"name":"Spectral resolution","selfAssesment":"Completed
"},{"code":"PS3-4-2","description":"The spatial resolution of an image corresponds to the size of the minimum area that can be resolved by the sensor. \r\nDue to the different techniques of acquisition of passive and active sensors, the spatial resolution is determined for both sensor types differently. \r\nFor passive sensors, the spatial resolution depends on their instantaneous field of view (IFOV), which determines the area of the Earth’s surface that is viewed at one particular moment in time by one detector element. The size of this area is called resolution cell and characterizes the spatial resolution of the sensor. Depending on the spatial resolution, whole features of the Earth’s surface can be detected homogeneously in one or several resolution cells. For features smaller than the spatial resolution, the average reflected radiation of all features within a resolution cell is recorded, leading to so-called mixed-pixels.\r\nFor imaging active systems, the spatial resolution is dependent of both the length of the transmitted pulse in looking direction and the width of the radiation beam or the antenna width in flight direction.\r\nIn all cases, the spatial resolution indicates the level of detail observable in an image. Usually, one distinguishes between coarse (low), moderate (medium) and fine (high and very high) resolution, whereby the use of this denomination is often context-dependent. Sensors with coarse resolution can only detect large features, but they usually cover a much larger area than high-resolution sensors, which can provide detailed information on small objects such as individual buildings, trees or cars, but for much smaller areas. Coarse spatial resolution mean in general a resolution cell larger than 250 m and a scene extent of several thousands of kilometers (>1000 km). Moderate resolution sensors have a spatial resolution of 30 m to 80 m, and a coverage of approximately 200 km in a single acquisition. Sensors showing spatial resolutions from 5 m or 6 m are high-resolution sensors, with a spatial coverage up to approximately 20 km. Sensors with a resolution cell’s width of less than 1 m are considered as very-high-resolution sensors.\r\nLow resolution sensors are appropriate for the analysis of broad-scale phenomena such as ocean color or cloud patterns. Medium resolution sensors are rather used for regional analysis such as land cover change and phenological response of vegetation. High-resolution sensors are particularly useful for object detection.","hasChildren":true,"hasParent":true,"name":"Spatial resolution","selfAssesment":"Completed
"},{"code":"PS3-4-3","description":"The radiometric resolution of a sensor refers its sensitivity, which is the ability to detect small differences in signal strength as it records the radiant flux reflected, emitted, or back-scattered from the terrain.\r\nThe specification of the radiometric resolution is different in the optical domain of the electromagnetic spectrum than in the radar range.\r\nIn the optical domain, the radiometric resolution is given in bits. The maximum number of brightness levels available depends on the number of bits. The larger this number, the higher the radiometric resolution. As an example, the optical sensor Sentinel-2 has a radiometric resolution of 12 bits. This means that a pixel of an image acquired by Sentinel-2 can have 2^12 = 4096 grey levels.\r\nIn the radar domain, the radiometric resolution is usually specified as a backscatter level expressed as an logarithmic value. For instance, the radiometric resolution of Radar Scattermeters lies in the range of 0.1 to 0.3 dB, whereas the radiometric resolution of SAR sensors are in the range of 1.2 – 2.5 dB. This means that only differences in radar backscatter larger than these values can be interpreted as interpretable changes the of backscatter conditions at the Earth’s surface. Smaller measurement differences could have been caused by differences in backscatter conditions or just as well by instrument noise.","hasChildren":true,"name":"Radiometric resolution","selfAssesment":"Completed
"},{"code":"PS3-4-4","description":"The concept of temporal resolution of Earth observation data refers to the revisit time or period. This is the time, which is necessary for the sensor platform (e.g. a satellite) to complete one entire orbit cycle. During one orbit cycle, the surface of the earth is completely covered by the sensor once. Temporal resolution also means the ability of a sensor to detect changes over shorter or longer periods of time. The revisit time for Earth observation satellites is usually several days. Or to express it differently: The absolute temporal resolution of a sensor orbiting the Earth is the time required to image the exact same area at the same viewing angle a second time. \r\nThe satellite orbit itself depends on the radius of the Earth, the orbit altitude above the Earth’s surface and the gravitational acceleration at planet’s surface. The time required to complete on entire orbit cycle additionally depends on the swath width of the sensor, the overlap between adjacent swaths and the geographical location at the Earth’s surface. The repetition rate increases slightly from the equator towards the north and south, which means that the revisit time is increasing with latitude. As a result, areas located in North America or Australia, for example, are covered a little more frequently than areas in Africa or South America near the equator. \r\nBut there are satellite systems that allow the pointing of their sensor to image the same area between different satellite passes separated by periods from one to five days. Thus, the actual temporal resolution of a sensor depends on a variety of factors, including the satellite/sensor capabilities, the already mentioned swath width and overlap, and latitude.","hasChildren":true,"name":"Temporal resolution","selfAssesment":"Completed
"},{"code":"PS3-4","description":"A digital image begins as an analog signal. Through computer data processing, the image becomes digitized and is sampled multiple times. The critical characteristics of a digital image are spatial resolution, spectral resolution, radiometric resolution, contrast resolution, noise, and dose efficiency. These depends upon satellite orbit configuration and sensor design. Different sensors have different resolutions.\r\nSpectral resolution describes the ability of a sensor to define fine wavelength intervals. The narrowest spectral interval that can be resolved by an instrument. Spectral resolution (spectral capability) also refers to the number of wavebands within the EM spectrum that an optical sensor is taking measurements over.\r\nRadiometric resolution can be defined as the ability of an imaging system to record many levels of brightness. Radiometric resolution refers to the range in brightness levels that can be applied to an individual pixel within an image, determined on a grayscale. E.g., Sentinel-2 sensor MSI is a 12 bit sensor imaging with 4.096 levels.\r\nSpatial resolution of an image corresponds to the size of the minimum area that can be resolved by the sensor.\r\nTemporal resolution, also referred to as the revisit cycle, is defined as the amount of time it takes for a satellite to return to collect data from exactly the same location on the Earth. Imageing of the exact same area at the same viewing angle a second time is temporal resolution.","hasChildren":true,"hasParent":true,"name":"Properties of digital imagery","selfAssesment":"Completed
"},{"code":"PS3-5-1","description":"A header file is usually a separate file associated with an image file. The header file can be either a plain ASCII-file or a binary file. It contains information about the image file it is associated with. These information can comprise the number of pixels per row (x-direction in a two-dimensional image), also called number of columns, the number of lines or rows (y-direction in a two dimensional image), the number of bands (corresponding to the z-direction), pixel spacing and spatial resolution, geographic reference information, the byte order (e.g. big-endian or little-endian), spectral information for each band, calibration constants and many more. The purpose of a header file is to provide basic information about the properties of the image data either to the user or to a software and enabling a software to correctly load and display the image content. In this way, information contained in a header file can also be called metadata, which is data about the data. The structure and the information contained in a header file of remote sensing imagery can be found in the so-called product information documents. There is also digital imagery used in remote sensing containing the information found in header files not in a separate file but as part of the digital image data itself. In this case this is called header information or a file header, which is usually found at the beginning of the image file. In some cases, image files may contain several header sections, e.g. the ESA Envisat ASAR SAR data imagery contains a Main Product Header and a Specific Product Header section. Header information as part of the image file itself may be stored in ASCII or in binary format, or in a mixed binary format, as it was used for the ESA Envisat SAR data.","hasChildren":true,"name":"Header file","selfAssesment":"Completed
"},{"code":"PS3-5","description":"The image data stored in a binary data format (BIL, BIP, BSQ) is accompanied by description files that contain a set of entries describing the image data, including acquisition time, image size, statistics, map projection, pixel digital numbers, product type, etc. This general image or product information is stored in a form of header embedded in the image file or provided in the separate file (.hdr) or metadata in XML. There are numerous image file formats, the more common are TIFF (GeoTIFF), bitmap (.bmp), JPEG (.jpg, .jpeg, JPEG2000), HDF, Raw (.raw), Extensible N-Dimensional Data Format (NDF).","hasChildren":true,"hasParent":true,"name":"Image description files","selfAssesment":"In progress
"},{"code":"PS3-6","description":"The concept of data formats refers to the way, in which the digital data are organized and stored. The data format for a remote sensing mission is usually chosen based on a number of considerations, including requirements of the sensing system, mission objective, the design and technology of data processing, archiving, and distribution systems, as well as community data standard.\r\nEarth observation data usually come as raster data. The raster data refers to a data model, which holds digital numbers or values in a regularly spaced matrix of cells arranged in rows and columns covering a two-dimensional space. A digital Earth observation image may contain several layers of this two-dimensional space, e.g. one layer for a specific spectral band in the optical or microwave region of the electromagnetic spectrum. The cells in such a layer are also called pixels, which stands for picture element. \r\nEarth observation data in an image are stored on a storage medium in one of three formats: Band-Interleaved-by-Sample (BIS), Band Sequential (BSQ), or Band-Interleaved-by-Line (BIL). These formats are determined by different ordering of the data dimensions. Other data formats used in remote sensing, which in this case refer to the file format are GeoTIFF, NetCDF, and HDF.\r\nExact details on the data format of an Earth observation data set is usually provided by the originator of the data, e.g. space administrations such as NASA or ESA or private companies.","hasChildren":true,"name":"Data formats","selfAssesment":"Completed
"},{"code":"PS3-7-1-1","description":"Depending on the sensor and the provider, remotely sensed imagery is made avalilable to the user at different processing levels. For Sentinel-2, the lowest product level made available to the user is Level-1B. THe Level-1B product provides radiometrically corrected imagery in Top-Of-Atmosphere (TOA) radiance values and in sensor geometry. Radiometric corrections applied to the Level-1B are: dark signal, pixels response non uniformity, crosstalk correction, defective pixels interpolation, high spatial resolution bands restoration (deconvolution puls denoising), binning (spatial filtering) for 60m bands.","hasChildren":true,"name":"Radiometrically corrected","selfAssesment":"New
"},{"code":"PS3-7-1-2","description":"Geometrically corrected products are of a higher processing level than radiometrically corrected products. For Sentinel-2, the geometrically corrected product is the Level-1C product. The Level-1C product results from using a Digital Elevation Model (DEM) to project the image in cartographic coordinates. Per-pixel radiometric measurements are provided in Top Of Atmosphere (TOA) reflectances with all parameters to transform them into radiances. Level-1C products are resampled with a constant Ground Sampling Distance (GSD) of 10, 20 and 60 m depending on the native resolution of the different spectral bands. Level-1C products will additionally include Land/Water, Cloud Masks and ECMWF data (total column of ozone, total column of water vapour and mean sea level pressure). (Sentinel-2 User Handbook, p.44)","hasChildren":true,"name":"Geometrically corrected products","selfAssesment":"New
"},{"code":"PS3-7-1","description":"The definition of processing levels for optical data depends on the considered sensor. Most common satellite optical imagery are available in three distinct processing levels, from level 0 to level 2. The most used processing levels are level 1 and level 2, depending on the user and the application. \r\nIn Level 0, the raw data are processed in a way that they are ready to be archived. Processing operations generally includes telemetry analysis, error detections and granule concatenation. Furthermore, relevant parameters such as acquisition date and geographical reference are annotated in the form of metadata, this information being necessary for processing higher levels. Additionally, a quicklook of the image is generated. No correction is performed at this level.\r\nLevel 1 is often divided in several sublevels. Generally, both radiometric correction and geometric refinement are performed at this level. The radiometric processing includes several radiometric corrections such as dark signal correction or spectral band binning. The radiometric correction allows the determination of physical variables (e.g. reflectance) from the digital numbers. The geometric processing includes tiles association and resampling grid computation, in order to link for each image band its native image geometry to the target geometry. The result of this processing steps is usually a geocoded, Top of Atmosphere product.\r\nLevel 2 data usually consist of atmospherically corrected Level 1 data, i.e. Bottom-of-Atmosphere data. These surface reflectance products may be accompanied by additional outputs, such as scene classification, water vapor or surface temperature maps.\r\nFor specific applications and sensors, Level 3 application ready data are available. These are derivated products such as burned area, dynamic surface water content and snow cover maps.\r\nDepending on the considered sensor and level, the name of the sublevels can differ: Sentinel 2 defines Level-1B as radiometrically corrected data. Level 1C are radiometrically and geometrically corrected data, i.e Top-Of-Atmosphere (TOA) orthoimage products. Landsat sensors distinguish between Terrain precision correction (L1TP), systematic Terrain Correction (L1GT) and Geometric systematic Correction (L1GS) depending on the quality of the reference data for geometric correction. These are usually separated into Tier 1 and Tier 2 datasets.","hasChildren":true,"hasParent":true,"name":"Processing levels of optical data","selfAssesment":"Completed
"},{"code":"PS3-7-2-1","description":"SLC is an abbreviation and stands for Single Look Complex. SLC data are one so called radar product. Like all radar products they have been derived from SAR raw data, often called Level 0 products, downloaded from the SAR satellite by the satellite operators. They apply a software called a processor to transform SAR raw data into formats that can be used by users for different applications. SLC data are often referred to as Level 1 products and are the first SAR product derived from the raw data to be made available to users.\r\nAs the name suggests, SLC data only contain one single look, which means that the azimuth compression has been carried out using the full azimuth bandwidth of the SAR sensor leading to the highest spatial resolution in azimuth direction. But as a consequence, SLC data suffers from maximum speckle. \r\nThe word “complex” in SLC means that the data are stored as complex numbers with a real and an imaginary part. In this way, SLC data contain both – phase stored in the real part and amplitude information stored in the imaginary part of the complex number for one resolution cell.\r\nSLC data are given in slant-range geometry and appears to be distorted. The is due to the fact that the spacing between pixels in the slant range direction is directly proportional to the signal travel time or time interval between backscattered and received radar pulses. And this time interval in again is directly proportional to the slant range distance between the sensor and the imaged objects at the Earth’s surface and not to the horizontal ground distance between the nadir and the imaged object. Therefore, SLC images appear distorted, which means that they look compressed in near range (close to the nadir) and getting ever more expanded in towards the far range.\r\nSLC data are the basis for further SAR products generated and are required for interferometric analysis methods, which rely on phase and amplitude information.","hasChildren":true,"name":"Single Look Complex (SLC)","selfAssesment":"Completed
"},{"code":"PS3-7-2-2","description":"From the Single Look Complex (SLC) product the Multi-look Detected/Multi-looke (MLD/MLI) can be generated. It is produced by multi-looking, i.e., averaging, over range and/or azimuth resolution cells.","hasChildren":true,"name":"Multi-looked Detected (MLD)","selfAssesment":"New
"},{"code":"PS3-7-2-3","description":"Precision Images (PRI) are the Multi-look Detected/Multi-looked Intensity (MLD/MLI) images that have been resampled into square pixels, rotated to account for the view direction of the instrument and warped by some predefined operation that the projected image pixels are georeferenced onto a specified geographical coordinate system.","hasChildren":true,"name":"Precision Images (PRI)","selfAssesment":"New
"},{"code":"PS3-7-2-4","description":"Ground Range Detected (GRD) radar imagery is a Level-1 product that has been derived from Level 0 (raw data) SLC SAR data by a Processing Facility via the application of a processing software. GRD products usually consist of focused SAR data that has been detected, multi-looked and projected to ground range using an Earth ellipsoid model.\r\nFocused SAR data are generated in a raw data processing step. During focusing, the two-dimensional signal energy of a point target that is spread in range and azimuth direction is aggregated and put into a single image pixel in the output data set.\r\nDetected means that the complex numbers representing phase and amplitude values in the original data set have been converted to real numbers by taking their absolute square (or complex conjugate). In the resulting image data, the phase information is not present any longer and only amplitude information remains as the pixel value.\r\nThe SAR imagery in GRD radar data is given in ground range geometry, which differs from the slant geometry of the SLC data. In ground range geometry, the spacing between the image objects at the Earth’s surface is in direct proportion to their real distance along a hypothetical flat ground surface. Here, image coordinates are oriented along ground range and flight direction. This means that they do not show the distorted appearance of an SLC image.","hasChildren":true,"name":"Groud Range Detected (GRD)","selfAssesment":"Completed
"},{"code":"PS3-7-2","description":"For SAR data, usually three processing levels are distinguished, ranging from level 0 (less processed) to level 2 (higher processed).\r\nLevel 0 products consist of compressed and unfocussed raw data and are the basis for the processing of higher level products. Level 0 data are principally used for research in the topic of signal processing. As for optical data, level 0 product are annotated with several metadata, such as calibration and orbit information, and acquisition time and date.\r\nLevel 1 data can be separated in two distinct product types, depending if the full complex information is used (amplitude and phase) or only the amplitude information. The product denomination depends on the sensor type; for Sentinel 1 the names Single Look Complex (SLC) and Ground range detected (GRD) are used, respectively. Both products can be generated from the Level 0 data. Level 1 data are the products that are used by most scientific users. The processing toward Level-1 data includes Doppler centroid estimation and data focusing. The Level 1 SLC product consists of the real and imaginary part of focused complex SAR data in slant range geometry, from which the phase and amplitude information can be retrieved. This is available for all acquired polarisations. Additional orbit information for georeferencing is provided with the data. The Level 1 GRD data consist of focused and multi-looked SAR data that have been projected to ground range geometry. GRD data only contain amplitude information, therefore the phase information is lost. The multi-looking step is particular for GRD data and allows both speckle reduction and square pixel resolution. As for the SLC data, the GRD data are annotated with orbit information for georeferencing. The Level-1 products are not calibrated, they include however information about calibration constants, which are sensor dependent. Further processing is needed in order to obtain calibrated radar cross section information from the original data intensity values.\r\nLevel 2 products describe geolocated derivated geophysical products such as ocean wind field or surface radial velocity. Such products are for example available for download on the Sentinel-1 Copernicus Hub. Further Level- 2 data are for example differential interferograms or change maps, which can be processed on different online platforms (e.g. Hyp3) and provide information about surface deformation or more generally changes between several acquisitions.\r\nThe denomination of the product types on the different levels may differ from sensor to sensor, but the processing steps stay almost the same, depending additionally on the considered acquisition modes. For example, GRD products are also called for other sensors Multi-Looked Detected (MLD) products.","hasChildren":true,"hasParent":true,"name":"Synthetic Aperture Radar (SAR) data","selfAssesment":"Completed
"},{"code":"PS3-7-7","description":"Data that have been processed to allow direct data analysis. User processing effort is reduced to a minimum.","hasChildren":true,"name":"Analysis Ready Data (ARD)","selfAssesment":"New
"},{"code":"PS3-7","description":"Earth Observation data are usually made available in different processing levels. The processing level is a mean of describing how much the raw data have been processed toward an informational geophysical product. The degrees of data processing usually follow a numerical hierarchy and typically range from Level 0 (less processed) up to Level 4 (highly processed). They characterize the type of data processing that has been performed between the raw data and the current product.\r\nA first effort for providing standard definitions of different processing levels has been made in the 1980s by the Committee on Data Management and Computation (CODMAC) of the National Research Council (NRC). CODMAC identified eight levels of processing, applicable for all space science data. Starting with the raw data at level 1, the degree of processing and complexity of the data increased at each new level. Level 2 describes edited data, corrected for obvious instrumentation errors and tagged with acquisition time and location; Level 3 stays for calibrated data where values are proportional to a specific physical unit. Level 4 represents resampled data, Level 5 derived data, where specific geophysical information has been retrieved and mapped based on the original data. Level 6 represents all ancillary data (i.e. instrument data) that are necessary for the previous steps of calibration and resampling. Level 7 describes so called correlative data: not directly belonging to the original data, those data represent all other science data that where necessary for the interpretation of the original spaceborne dataset. Finally, Level 8 are user description, i.e. documentation of the data.\r\nConcerning spaceborne image data, both optical and radar, an adaptation of these original levels has been made from NASA and NOAA that is used for the main current spaceborne missions, including the Copernicus program. Whereas specific adaptations may arise for specific sensors and sensor types, there are five principal processing levels. Level 0 represents the raw data that have just been edited for the correction of artifacts. Level 1 data are Level 0 data with additional annotations regarding time and geolocation information, radiometric and geometric calibration coefficients (for example Top of Atmosphere data for optical imagery). Level 2 data are already radiometrically and geometrically calibrated and represent physical variables (for example Bottom of Atmosphere data for optical imagery). Level 3 data correspond to derived variables and information (e.g. land cover) with completeness and consistency information, e.g. quality flags. Level 4 represent higher level data resulting from modelling or more complex analysis of the data with additional ancillary information.\r\nFor many applications and users, so called analysis ready data (ARD data) are required. These usually correspond to Level 2 data that have already been pre-processed in order to retrieve the physical information and can be further analyzed for the specific thematic application.","hasChildren":true,"hasParent":true,"name":"Processing levels","selfAssesment":"Completed
"},{"code":"PS3","description":"Remotely collected data is available from multiple sources and data collection techniques. Data can be obtained from different levels of data acquisition: ground, air or space, as well as using different sensors and wavelengths. Remote sensing data provides the necessary information to help monitor the Earth's surface.","hasChildren":true,"hasParent":true,"name":"Remote sensing data and imagery","selfAssesment":"Planned
"},{"code":"PS4","description":"The listed databases provide information on past, operational and future remote sensing platforms and sensors. Use the following links to get more information on the sensors and missions.","hasChildren":true,"name":"Databases of satellite and airborne sensors and missions","selfAssesment":"Completed
"},{"code":"SA","description":"A satellite system is the complete set of elements needed to provide a space-based service or product to users on Earth. It includes the space segment, the ground segment, and the user segment, plus all the interfaces, operations, and logistics that interconnect them. From a systems-engineering perspective, a satellite system starts from the mission requirements (what information or service is needed, including performance, coverage, latency, reliability, cost, etc.) and translates them into an integrated architecture (system requirements) across these three segments.\r\nKey generic elements of a satellite system:\r\n•\tMission objectives: e.g. broadband communication, earth observation and exploration, measuring sea surface temperature, providing accurate positioning, broadcasting TV, etc.\r\n•\tPerformance requirements: accuracy, resolution, timeliness, availability, continuity, integrity, data rate, etc.\r\n•\tArchitecture: number of satellites and orbits, ground stations, communication links, data processing chain, user equipment, orchestration software.\r\n•\tOperations: planning, monitoring, control, maintenance, calibration/validation, and end-of-life disposal.\r\n•\tService and products: what is ultimately delivered to the users (images, geophysical variables, timing and positioning, voice/data connectivity, etc.).\r\nThe specificities of this general concept adapted to each of the three families are as follows:\r\nEO satellite systems\r\n•\tObjective: measure geophysical variables (e.g. land cover, soil moisture, atmospheric composition, sea state) from space.\r\n•\tSpace segment: satellites carry remote sensors (optical, SAR, TIR, radiometers, GNSS-R, etc.) in orbits optimized for coverage and illumination (typically LEO, often Sun-synchronous, but also GEO).\r\n•\tGround segment: receiving stations on the Earth’s surface offering high-capacity data downlink, processing chains (from L0 to L2 and beyond), archives, and dissemination services.\r\n•\tUser segment: scientists, agencies, companies, or general public that access images and derived products via portals, APIs, etc., with specialized devices and software for, visualization, analysis, and assimilation into models.\r\n•\tSystem focus: radiometric and geometric accuracy, calibration/validation, revisit time, spatial/temporal resolution, uncertainty quantification and traceability.\r\nSatellite navigation systems\r\n•\tObjective: provide Position, Navigation and Timing (PNT) services globally.\r\n•\tSpace segment: constellations of medium-Earth orbit (MEO) satellites (GPS, Galileo, GLONASS, BeiDou), each broadcasting precise time-tagged signals on multiple frequencies. Sometimes these constellations are augmented by GEO or geo-synchronous satellites. There are plans to deploy LEO PNT constellations to augment these satellites systems.\r\n•\tGround segment: worldwide monitoring networks and control centers estimating satellite orbits and clock states, uploading navigation messages, and performing integrity checks.\r\n•\tUser segment: multitude of receivers in phones, cars, aircraft, ships, timing receivers in power grids and telecom networks, etc.\r\n•\tSystem focus: global coverage, high availability, high integrity, precise timekeeping, robust geometry (DOP), mitigation of ionospheric/tropospheric errors and multipath. Future requirements: indoor penetration\r\nSatellite communication systems\r\n•\tObjective: to relay information (voice, data, video, IoT messages) between locations on Earth (and sometimes between satellites).\r\n•\tSpace segment: GEO, MEO, and LEO satellites carrying communication payloads including transponders, antennas phased arrays, on-board processors, inter-satellite links, etc.\r\n•\tGround segment: gateways, teleports, network operation centers, and integration into terrestrial networks.\r\n•\tUser segment: terminals of many kinds: TV dishes, VSATs, satellite phones, maritime/aviation terminals, IoT nodes, single low-power IoT receivers.\r\n•\tSystem focus: capacity (data rate), coverage, availability, quality of service, spectrum efficiency, latency, and cost per bit.\r\nSatellite Systems are the umbrella that ties together the three named segments to turn orbital infrastructure into useful services for EO, navigation, or communications.","hasChildren":true,"hasParent":true,"name":"Satellite Systems","selfAssesment":" "},{"code":"SA1-1-1-1","description":"Earth observation payloads measure properties of the atmosphere, oceans, land, cryosphere, or human activity. They may use the optical/infrared or the radio parts of the spectrum and can be active (transmit a signal and reeived the associated echo), or passive (do not transmit any signal, measure electromagnetic radiation or other signals of opportunity).","hasChildren":true,"name":"Earth Observation","selfAssesment":" "},{"code":"SA1-1-1-2","description":"Communication payloads relay, process, or regenerate signals between users and gateways. They include antennas, transponders, amplifiers, filters, frequency converters, analog-to-digital converters, digital processors, and antenna beam-forming equipment.","hasChildren":true,"name":"Communications","selfAssesment":" "},{"code":"SA1-1-1-3","description":"Navigation payloads generate and transmit precise ranging and timing signals for positioning, navigation, and time transfer. They require stable clocks, signal-generation chains, navigation data, and calibrated antennas.","hasChildren":true,"name":"Navigation","selfAssesment":" "},{"code":"SA1-1-1","description":"Payload types describe the mission-specific equipment carried by a satellite. Examples include Earth observation sensors, communication transponders, navigation payloads, scientific instruments, technology demonstrators, and hosted payloads, i.e. from third-party different from the satellite owner.","hasChildren":true,"hasParent":true,"name":"Payload Types","selfAssesment":" "},{"code":"SA1-1-2-1-1","description":"Antenna types include patches, horns, reflectors, arrays, helices, dipoles, and deployable structures. Selection depends on frequency, gain, beam shape, polarization, size, mass, power, and mission geometry.","hasChildren":true,"name":"Antenna types","selfAssesment":" "},{"code":"SA1-1-2-1-5-1","description":"Antenna beamwidth is the angular width of the main radiation lobe, commonly measured between half-power points. It controls angular resolution (field of view), coverage (antenna footprint), pointing tolerance (the narrower the beams the more stringent the pointing requirements), interference susceptibility, and link-budget performance.","hasChildren":true,"name":"Antenna beamwidth","selfAssesment":" "},{"code":"SA1-1-2-1-5-2","description":"The antenna footprint is the area on the surface of the Earth or in space illuminated by an antenna beam. It depends on altitude, pointing, beamwidth, pattern shape, and propagation geometry.","hasChildren":true,"name":"Antenna footprint","selfAssesment":" "},{"code":"SA1-1-2-1-5-3","description":"Antenna losses reduce the useful radiated or received power. They may arise from conductors, dielectrics, feed networks, polarization mismatch, pointing errors, surface imperfections, or deployment distortions.","hasChildren":true,"name":"Antenna losses","selfAssesment":" "},{"code":"SA1-1-2-1-5-4","description":"Ohmic efficiency describes the fraction of antenna input power actually radiated rather than dissipated as heat in conductive or dielectric materials. It affects gain, noise temperature, thermal design, and link budget margin.","hasChildren":true,"name":"Antenna ohmic efficiency","selfAssesment":" "},{"code":"SA1-1-2-1-5-5","description":"Illumination efficiency measures how effectively a feed illuminates an aperture or reflector. Non-uniform illumination, taper, phase errors, or blockage reduce aperture gain and modify sidelobe performance.","hasChildren":true,"name":"Antenna illumination efficiency","selfAssesment":" "},{"code":"SA1-1-2-1-5-6","description":"Spillover efficiency quantifies how much feed energy illuminates the intended reflector or aperture rather than missing it. Spillover reduces gain and may increase noise by viewing the warm Earth or other spacecraft structures.","hasChildren":true,"name":"Antenna spill over efficiency","selfAssesment":" "},{"code":"SA1-1-2-1-5","description":"[SA1-1-2-1-5] Antenna Parameters","hasChildren":true,"hasParent":true,"name":"Antenna Parameters","selfAssesment":" "},{"code":"SA1-1-2-1","description":"Space antennas radiate or receive electromagnetic waves from the spacecraft. Their design determines gain, beam coverage, polarization, pointing, efficiency, interference rejection, and compatibility with mission frequencies and volumen/mass constraints.","hasChildren":true,"hasParent":true,"name":"Antenna - space","selfAssesment":" "},{"code":"SA1-1-2-2-1","description":"Amplifier parameters describe how it behaves in a system. The main parameters that characterize its performance are gain, noise figure, bandwidth, input/output matching, linearity (1dB compression point), third harmonic intercept point (IP3), power efficiency, stability, and output power capability.","hasChildren":true,"name":"Amplifier Parameters","selfAssesment":" "},{"code":"SA1-1-2-2-2","description":"A low-noise amplifier is typically the first active stage in many receivers. It boosts weak signals while adding minimal noise, strongly influencing receiver sensitivity, system noise temperature, and link performance.","hasChildren":true,"name":"Low Noise Amplifier (LNA)","selfAssesment":" "},{"code":"SA1-1-2-2-3","description":"A low-noise block downconverter combines low-noise amplification, filtering, and frequency conversion near the antenna. It delivers an intermediate-frequency signal to downstream equipment while preserving sensitivity and reducing cable losses.","hasChildren":true,"name":"Low-Noise Block Downconverter (LNB)","selfAssesment":" "},{"code":"SA1-1-2-2-4","description":"A high-power amplifier raises signals to the power needed for the radio link. Its efficiency, linearity, thermal management, and reliability are critical for satellite communication payload performance.","hasChildren":true,"name":"High Power Amplifier (HPA)","selfAssesment":" "},{"code":"SA1-1-2-2-5","description":"Multiport amplifiers distribute amplification across several input and output ports, often using hybrid networks. They support flexible power sharing, redundancy, beam allocation, and efficient operation in multibeam payloads.","hasChildren":true,"name":"Multiport-Amplifiers","selfAssesment":" "},{"code":"SA1-1-2-2-6","description":"Bidirectional amplifiers support signal amplification in both transmit and receive directions or in reversible links. They require careful isolation, switching, gain control, and linearity management to avoid instability.","hasChildren":true,"name":"Bidirectional amplifiers","selfAssesment":" "},{"code":"SA1-1-2-2","description":"An amplifier increases signal power or voltage in a payload or receiver chain.","hasChildren":true,"hasParent":true,"name":"Amplifier","selfAssesment":" "},{"code":"SA1-1-2-3-1","description":"A low-pass filter allows to pass signals below a specified cutoff frequency and attenuates higher-frequency components. It is used for anti-aliasing before sampling, noise reduction, spectral shaping, and removal of spurious signals.","hasChildren":true,"name":"Low-pass filter","selfAssesment":" "},{"code":"SA1-1-2-3-2","description":"A high-pass filter allows to pass signals above a specified cutoff frequency and attenuates lower-frequency components. It helps remove Direct Current offsets, low-frequency noise (e.g. 1/f noise), interference, and undesired spectral content.","hasChildren":true,"name":"High-pass filter","selfAssesment":" "},{"code":"SA1-1-2-3-3","description":"A band-pass filter allows to pass a defined frequency band while rejecting frequencies below and above it. It is essential for channel selection, interference mitigation, receiver protection, and spectral compliance.","hasChildren":true,"name":"Band-pass filter","selfAssesment":" "},{"code":"SA1-1-2-3-4","description":"A band-reject or stop-band filter attenuates a selected frequency range while allows to pass frequencies outside it. It is commonly used to suppress narrowband interference, spurious emissions, or unwanted channels.","hasChildren":true,"name":"Band-reject (stop-band) filter","selfAssesment":" "},{"code":"SA1-1-2-3-5","description":"A duplexer separates transmit and receive paths that share the same antenna, usually by frequency filtering. It must provide low insertion loss, high isolation, power handling, and good impedance matching.","hasChildren":true,"name":"Duplexer","selfAssesment":" "},{"code":"SA1-1-2-3-6","description":"A multiplexer combines or separates multiple frequency channels on a shared signal path. In satellite payloads it enables multichannel operation while controlling isolation, losses, bandwidth, and spectral allocation.","hasChildren":true,"name":"Multiplexer","selfAssesment":" "},{"code":"SA1-1-2-3-7","description":"Filter parameters define frequency-domain and implementation performance. Typical parameters include passband, stopband, bandwidth, insertion loss, return losses, rejection, selectivity, group delay, ripple, power handling, and stability.","hasChildren":true,"name":"Parameters","selfAssesment":" "},{"code":"SA1-1-2-3-8-1","description":"Bandwidth is the frequency range over which a component, channel, or system meets specified performance. It affects the data rate, noise power, spectral occupancy, resolution, and regulatory compatibility.","hasChildren":true,"name":"Bandwidth","selfAssesment":" "},{"code":"SA1-1-2-3-8-2","description":"Insertion loss is the reduction in signal power caused by inserting a component into a signal path. It affects link margin, receiver noise, transmitter efficiency, and thermal dissipation.","hasChildren":true,"name":"Insertion losses","selfAssesment":" "},{"code":"SA1-1-2-3-8-3","description":"Return loss measures how well a port is impedance matched by quantifying reflected power. Higher return loss indicates better matching, reduced standing waves, improved power transfer, and lower distortion risk.","hasChildren":true,"name":"Return losses","selfAssesment":" "},{"code":"SA1-1-2-3-8","description":"An output multiplexer combines high-power amplified channels into a common antenna feed while maintaining channel separation. It must handle high power, minimize losses, control distortion, and ensure spectral compliance.","hasChildren":true,"hasParent":true,"name":"Output multiplexer (OMUX)","selfAssesment":" "},{"code":"SA1-1-2-3","description":"A filter selects the desired frequency components while attenuating unwanted signals. In satellite payloads it controls bandwidth, rejects interference, separates channels, protects receivers, and shapes transmitted spectra.","hasChildren":true,"hasParent":true,"name":"Filter","selfAssesment":" "},{"code":"SA1-1-2-4-1","description":"An up-converter shifts a signal from baseband or intermediate frequency to a higher frequency for radio transmission. It must control image products, phase noise, linearity, and spectral purity.","hasChildren":true,"name":"Up-converter","selfAssesment":" "},{"code":"SA1-1-2-4-2","description":"A down-converter shifts a received radio-frequency signal to an intermediate or baseband frequency for processing. It combines mixing, filtering, and gain while preserving signal quality and timing.","hasChildren":true,"name":"Down-converter","selfAssesment":" "},{"code":"SA1-1-2-4","description":"A mixer translates signals between frequency bands by multiplying them with a local oscillator. It enables up-conversion, down-conversion, channelization, and intermediate-frequency processing in Earth observation, scientific, communication and navigation payloads.","hasChildren":true,"hasParent":true,"name":"Mixer","selfAssesment":" "},{"code":"SA1-1-2-5-1","description":"The number of bits defines converter amplitude resolution and quantization noise. More bits increase dynamic range (lowest detectable signal range) and precision, but usually require higher power, data rate, complexity, and calibration effort.","hasChildren":true,"name":"Number of bits","selfAssesment":" "},{"code":"SA1-1-2-5-2","description":"Sampling frequency is the rate at which an analog signal is converted to digital samples. It must satisfy bandwidth and aliasing requirements and strongly influences processing load and data volume.Typically the sampling frequency must be at least twice the bandwidth (Nyquist criterion), but bandpass sampling allows to sample a bandpass signal with a lower sampling frequency as long as one of the replicas falls near to baseband.","hasChildren":true,"name":"Sampling frequency","selfAssesment":" "},{"code":"SA1-1-2-5","description":"Analog-to-digital converters sample analog signals into digital words, while digital-to-analog converters reconstruct analog waveforms from digital data. The system performance is driven by: resolution (number of bits), sampling rate, jitter, noise (quantified as the effective number of bits), and linearity.","hasChildren":true,"hasParent":true,"name":"Analog-to-Digital Converter (ADC) / Digital-to-Analog Converter (DAC)","selfAssesment":" "},{"code":"SA1-1-2-6","description":"A Field Programmable Gate Array (FPGA) is a reconfigurable digital integrated circuit used for high-speed, parallel processing. In space payloads it supports signal processing, interfaces, control logic, redundancy, enabling on-orbit flexibility.","hasChildren":true,"name":"FPGA","selfAssesment":" "},{"code":"SA1-1-2-7","description":"Software-defined radio or software-defined payloads implement radio functions using configurable digital processing rather than fixed hardware. They enable flexible waveforms, adaptive operations, updates, reconfiguration, and efficient reuse.","hasChildren":true,"name":"SDR (Software Defined Radio) / Software defined payload (SDF)","selfAssesment":" "},{"code":"SA1-1-2-8","description":"GNU Radio is an open-source toolkit for building software-defined radio signal-processing flows. It supports prototyping, education, testing, and experimentation with real or simulated communication and navigation signals.","hasChildren":true,"name":"GNU Radio","selfAssesment":" "},{"code":"SA1-1-2","description":"Payload building blocks are reusable functional elements that implement mission objectives and can be used in different systems. They include antennas, amplifiers, filters, converters, processors, clocks, digitizers, and radio-frequency or optical interfaces.","hasChildren":true,"hasParent":true,"name":"Payload building blocks","selfAssesment":" "},{"code":"SA1-1-3","description":"Space atomic clocks provide highly stable timing references onboard navigation, science, or communication satellites. Their frequency stability and accuracy support precise ranging, synchronization, time transfer, and autonomous operations.","hasChildren":true,"name":"Atomic Clocks - space","selfAssesment":" "},{"code":"SA1-1-4","description":"An onboard processor executes spacecraft or payload computing tasks in orbit. It handles data processing, control, compression, storage, autonomy, fault management, and interfaces under radiation and power constraints.","hasChildren":true,"name":"On board processor","selfAssesment":" "},{"code":"SA1-1-5","description":"Telemetry, Tracking and Control supports spacecraft monitoring, orbit tracking, and command delivery. TT&C links enable health assessment, configuration changes, anomaly recovery, ranging, and safe mission operations.","hasChildren":true,"name":"Telemetry, Tracking and Control (TT&C)","selfAssesment":" "},{"code":"SA1-1","description":"The satellite or spacecraft bus provides the platform support required by the payloads. It includes structure, power, thermal control, attitude and orbit control, onboard computing, propulsion, communications, and housekeeping subsystems.","hasChildren":true,"hasParent":true,"name":"Satellite / Spacecraft bus","selfAssesment":" "},{"code":"SA1-2-1","description":"A geostationary orbit is a circular equatorial orbit at about 35,786 km altitude with a period matching Earth’s rotation. Satellites appear fixed over a given longitude, at zero latitude, enabling continuous regional coverage up to 81 degrees of latitude.","hasChildren":true,"name":"Geostationary Orbit (GEO)","selfAssesment":" "},{"code":"SA1-2-10-1","description":"Keplerian elements describe ideal two-body orbital motion using semimajor axis, eccentricity, inclination, right ascension of ascending node, argument of periapis, and true anomaly. They form a standard orbit representation.","hasChildren":true,"name":"Keplerian elements (Two-Body Problem)","selfAssesment":" "},{"code":"SA1-2-10-2","description":"Perturbed motion accounts for deviations from ideal Keplerian orbits due to gravity harmonics, acceleration of third bodies, atmospheric drag, solar radiation pressure, tides, maneuvers, and other environmental forces.","hasChildren":true,"name":"Perturbed motion","selfAssesment":" "},{"code":"SA1-2-10","description":"Orbital elements are six parameters that define an object’s position and velocity within its orbit. They support orbit propagation, tracking, mission design, coverage analysis, collision assessment, and ground-station scheduling.","hasChildren":true,"hasParent":true,"name":"Orbital elements","selfAssesment":" "},{"code":"SA1-2-2","description":"A very high throughput satellite provides extremely large communication capacity through frequency reuse, spot beams, advanced payload processing, and high-capacity gateways. It supports broadband, mobility, enterprise, and trunking services.","hasChildren":true,"name":"Very High Throughput Satellite (VHTS)","selfAssesment":" "},{"code":"SA1-2-3","description":"Low Earth orbit satellites operate at relatively low altitudes, typically from few hundred to about two thousand kilometers. They offer low latency and strong signals but require constellations of several satellites for continuous coverage.","hasChildren":true,"name":"Low earth Orbit (LEO)","selfAssesment":" "},{"code":"SA1-2-4","description":"Medium Earth Orbit lies between Low Earth Orbit and Geostationary Earth Orbit and is used mainly by global navigation satellite systems. It balances coverage, signal geometry, orbital period, latency, and constellation size for global services.","hasChildren":true,"name":"Medium Earth Orbit (MEO)","selfAssesment":" "},{"code":"SA1-2-5","description":"Very low Earth orbit satellites will operate at unusually low altitudes where atmospheric drag is significant. They will improve resolution or link budgets, but require aerodynamic design, propulsion, and frequent orbit maintenance. Note: at the time of writting this definition VLEO are in design phase, but are not operational.","hasChildren":true,"name":"VLEO (Very low earth orbiting satellite)","selfAssesment":" "},{"code":"SA1-2-6","description":"High-altitude platforms are aircraft, balloons, or airships operating in the stratosphere to provide persistent communication, observation, or navigation services. They bridge terrestrial infrastructure and satellites.","hasChildren":true,"name":"High altitude platform (HAP)","selfAssesment":" "},{"code":"SA1-2-7","description":"Multiorbit architectures combine LEO, MEO, GEO, or other platforms to exploit complementary strengths. They can improve coverage, latency, capacity, resilience, routing flexibility, and service continuity.","hasChildren":true,"name":"Multiorbit (LEO-MEO-GEO)","selfAssesment":" "},{"code":"SA1-2-8","description":"A quasi-zenith orbit keeps a satellite high in the sky over a target region for long periods. It improves availability and geometry in urban, mountainous, or high-latitude environments.","hasChildren":true,"name":"Quasi-Zenit Orbit (QZO)","selfAssesment":" "},{"code":"SA1-2-9","description":"An inclined geosynchronous orbit has Earth-synchronous period but nonzero inclination, producing a ground-track analemma (sort of 8-shaped ground-track). It can support regional coverage, navigation augmentation, or communication without maintaining strict geostationary position.","hasChildren":true,"name":"Inclined Geo-Synchronuos Orbit (IGSO)","selfAssesment":" "},{"code":"SA1-2","description":"Orbits describe the paths followed by satellites or platforms around Earth or another celestial body. Their altitude, inclination, period, eccentricity, and perturbations determine coverage, revisit time, latency, and mission performance.","hasChildren":true,"hasParent":true,"name":"Orbits","selfAssesment":" "},{"code":"SA1-3-1","description":"Satellite swarms are groups of spacecraft that cooperate with distributed or collective behavior. They may provide formation sensing, adaptive coverage, resilience, collaborative processing, or coordinated scientific measurements.","hasChildren":true,"name":"Swarms","selfAssesment":" "},{"code":"SA1-3-2","description":"A constellation is a planned set of satellites operated together to deliver a service. Its architecture determines coverage, latency, revisit time, capacity, redundancy, and required ground infrastructure.","hasChildren":true,"name":"Constellation","selfAssesment":" "},{"code":"SA1-3-3","description":"Federated satellite systems share resources, data, or services across independently operated spacecraft or missions. They can increase interoperability, resilience, utilization, and responsiveness through cooperative architectures and protocols.","hasChildren":true,"name":"Federated Satellite Systems","selfAssesment":" "},{"code":"SA1-3-4","description":"Clusters are groups of satellites operating in proximity or coordinated orbital configurations. They support formation flying, distributed apertures, cross-calibration, cooperative sensing, or local service enhancement.","hasChildren":true,"name":"Clusters","selfAssesment":" "},{"code":"SA1-3","description":"Constellations are coordinated groups of satellites designed to provide coverage, capacity, revisit, redundancy, or geometry objectives. Their design considers orbital planes, phasing, altitude, inclination, and service requirements.","hasChildren":true,"hasParent":true,"name":"Constellations","selfAssesment":" "},{"code":"SA1-4","description":"Launchers are vehicles that deliver the spacecraft from ground to space or to a transfer orbit. Their capability, fairing size, environment, reliability, schedule, and cost constrain satellite design and mission objectives","hasChildren":true,"name":"Launchers","selfAssesment":" "},{"code":"SA1","description":"The space segment is the part of the system that is physically in space: the satellites or platforms, payloads, inter-satellite links, etc. It encompasses:\r\n•\tSatellites / platforms: the bus (platform) plus the payload.\r\n•\tBus subsystems:\r\no\tStructure and mechanisms\r\no\tPower (solar arrays, batteries, power conditioning)\r\no\tAttitude and Orbit Control System (AOCS) – sensors and actuators to point and stabilize the satellite and control its orbit\r\no\tThermal control – passive and active thermal regulation\r\no\tOn-board data handling – computers, data storage, internal networks\r\no\tTelemetry, Tracking & Command (TT&C) – housekeeping communications\r\no\tPropulsion – for orbit insertion, station-keeping, collision avoidance, de-orbit\r\n•\tPayload: the mission-specific instruments or communication equipment.\r\n•\tConstellation architecture: number of satellites, orbital planes, orbital parameters, inter-satellite links, etc.\r\nSpecifically, per system type:\r\nEO – Space segment\r\n•\tOrbits: Mostly LEO, often Sun-synchronous, with altitudes ~500–800 km to balance resolution and coverage. Some missions use non-SSO (e.g. inclined orbits) or highly elliptical orbits for special coverage. Some others are GEO (e.g. many meteorological satellites, such as MeteoSat, GOESS, Himawari…)\r\n•\tPayloads: Optical imagers, multispectral/hyperspectral sensors, SAR, TIR radiometers, microwave radiometers, altimeters, GNSS-R, etc.\r\n•\tKey features:\r\no\tHigh pointing stability and knowledge to ensure geometric accuracy.\r\no\tAgile platforms for off-nadir pointing, target tracking, or stereo imaging.\r\no\tOn-board mass memory and high-rate downlinks.\r\no\tCalibration hardware (on-board lamps, blackbodies, lunar or sky views, etc.).\r\n•\tConstellations: single satellites vs. multi-sat constellations to reduce revisit time; sometimes formation flying for interferometry or tomography.\r\nNAV – Space segment\r\n•\tOrbits: Typically MEO (height ~20,000 km), near-circular, with multiple orbital planes to ensure at least 4–8 satellites are always visible anywhere on Earth.\r\n•\tPayloads: Very stable atomic clocks, navigation signal generators, RF chains and antennas broadcasting on standard frequency bands (e.g. L1, L2, L5 bands and one at S-band, as well).\r\n•\tKey features:\r\no\tVery high reliability and redundancy (multiple clocks, redundant payload units).\r\no\tCarefully controlled orbit and attitude to maintain predictable geometry.\r\no\tGlobal coverage with high availability; satellites designed for long lifetimes (10–15+ years).\r\n•\tConstellations: global constellations (GPS, Galileo, Glonass, Beidou, etc.), plus regional (NavIC, QZSS) or augmentation systems (SBAS, GBAS, etc.).\r\nCOM – Space segment\r\n•\tOrbits:\r\no\tGEO (height ~35,786 km): apparently in a fixed position in the sky, which is ideal for broad coverage broadcast and fixed services.\r\no\tMEO: e.g. some broadband or legacy systems.\r\no\tLEO: large constellations for low latency broadband and IoT, in future PNT\r\n•\tPayloads: transponders (bent-pipe or regenerative), high-throughput multi-beam payloads, regenerative processors, analog or digital signal routing, beam-forming networks, small to large (deployable) single beam or multibeam antennas, sometimes often laser communication terminals for inter-sat links.\r\n•\tKey features:\r\no\tVery high RF power and large antennas, especially in GEO.\r\no\tSophisticated on-board processing for routing, switching, beam hopping, etc.\r\no\tFlexible resource allocation (digital payloads) to adapt to traffic patterns.\r\n•\tConstellations: from a few GEO satellites to hundreds or thousands of LEO satellites.","hasChildren":true,"hasParent":true,"name":"Space segment","selfAssesment":" "},{"code":"SA2-1","description":"A Network Control Center manages communication network resources, routing, capacity allocation, service quality, and operations. In satellite systems it coordinates gateways, payload configuration, traffic, monitoring, and fault response.","hasChildren":true,"name":"Network Control Center (NCC)","selfAssesment":" "},{"code":"SA2-2-1","description":"Mission planning defines how spacecraft and ground resources are scheduled to meet mission objectives. It balances payload operations, contacts, power, thermal limits, data volumes, priorities, and constraints.","hasChildren":true,"name":"Mission Planning","selfAssesment":" "},{"code":"SA2-2-2-1-1","description":"Ground atomic clocks provide highly stable frequency and time references for control centers, timing laboratories, and navigation systems. They support orbit determination, clock products, synchronization, calibration, and traceability.","hasChildren":true,"name":"Atomic clocks - ground","selfAssesment":" "},{"code":"SA2-2-2-1","description":"A time scale is a continuous reference for keeping and measuring time. In space systems it links onboard clocks, ground timing facilities, navigation messages, UTC realizations, and user synchronization.","hasChildren":true,"hasParent":true,"name":"Time scale","selfAssesment":" "},{"code":"SA2-2-2-2","description":"Distributing time and frequency information transfers precise timing references to users, stations, or subsystems. Methods include radio links, fiber, GNSS, network protocols, calibration chains, and disciplined oscillators.","hasChildren":true,"name":"Distributing Time and Frequency Information","selfAssesment":" "},{"code":"SA2-2-2","description":"A timing facility generates, maintains, compares, and distributes precise time references for a satellite system. It supports synchronization of ground stations, spacecraft clocks, navigation messages, and service traceability.","hasChildren":true,"hasParent":true,"name":"Timing Facility","selfAssesment":" "},{"code":"SA2-2-3-1","description":"Laser ranging measures satellite distance using the round-trip time of short laser pulses reflected into laser retroreflectors. It provides precise orbit validation, geodesy, calibration, and independent tracking information.","hasChildren":true,"name":"Laser Ranging","selfAssesment":" "},{"code":"SA2-2-3-2","description":"Orbit and clock product generation estimates precise satellite trajectories and clock corrections from tracking networks. These products support precise positioning, timing, integrity monitoring, geodesy, and mission performance assessment.","hasChildren":true,"name":"Orbit and Clock Product Generation","selfAssesment":" "},{"code":"SA2-2-3","description":"Orbit determination and control estimates spacecraft trajectories and applies maneuvers to satisfy mission requirements. It uses tracking data, force models, estimation filters, propulsion planning, and includes collision avoidance.","hasChildren":true,"hasParent":true,"name":"Orbit determination and control","selfAssesment":" "},{"code":"SA2-2","description":"A control center operates and supervises a satellite mission or system. It performs commanding, monitoring, scheduling, anomaly response, orbit control, payload configuration, and coordination with ground assets.","hasChildren":true,"hasParent":true,"name":"Control Center","selfAssesment":" "},{"code":"SA2-3","description":"A tracking station observes spacecraft signals to determine range, Doppler shift, telemetry, or pointing angles. It supports orbit determination, contact scheduling, health monitoring, time transfer, and command verification.","hasChildren":true,"name":"Tracking Station","selfAssesment":" "},{"code":"SA2-4","description":"Control stations are ground facilities that command, monitor, and manage spacecraft or constellation assets. They support mission execution, anomaly response, orbit maintenance, timing, and payload configuration.","hasChildren":true,"name":"Control Stations","selfAssesment":" "},{"code":"SA2-5-1","description":"A Very Small Aperture Terminal is a compact satellite ground terminal for data, voice, or internet connectivity. VSAT networks support enterprise, remote, maritime, emergency, and consumer communications.","hasChildren":true,"name":"Very Small Aperture Terminal (VSAT)","selfAssesment":" "},{"code":"SA2-5-2","description":"Ground antennas transmit and/or receive satellite signals from Earth stations, gateways, user terminals, or tracking facilities. Their gain, pointing, polarization, frequency range, and environmental robustness determine the link performance.","hasChildren":true,"name":"Antenna - ground","selfAssesment":" "},{"code":"SA2-5-3","description":"Receivers in the ground segment acquire and process satellite downlink signals. They perform filtering, amplification, frequency conversion, demodulation, decoding, synchronization, monitoring, and data delivery to users or networks.","hasChildren":true,"name":"Receivers","selfAssesment":" "},{"code":"SA2-5-4","description":"A feeder link connects a satellite with a gateway or ground station, carrying aggregated traffic, control information, or payload data. It is separate from user links and often uses high-capacity frequencies.","hasChildren":true,"name":"Feeder link","selfAssesment":" "},{"code":"SA2-5","description":"A teleport is a ground facility providing satellite communication gateway services. It includes antennas, radio-frequency equipment, baseband systems, networking, monitoring, power, redundancy, and operations for uplink and downlink traffic.","hasChildren":true,"hasParent":true,"name":"Teleport","selfAssesment":" "},{"code":"SA2-6-1-1","description":"TT&C in the uplink antenna context refers to command and control transmissions used to operate spacecraft. These links must be reliable, secure, well pointed, and available throughout all mission phases.","hasChildren":true,"name":"TT&C","selfAssesment":" "},{"code":"SA2-6-1","description":"An uplink antenna radiates signals from a ground station or terminal toward a satellite. It must provide adequate gain, polarization, pointing accuracy, power handling, and regulatory spectral control.","hasChildren":true,"hasParent":true,"name":"Uplink antenna","selfAssesment":" "},{"code":"SA2-6","description":"Uplink stations transmit commands, navigation data, payload configuration, or communication traffic from Earth to spacecraft. They require accurate pointing, power control, spectral compliance, security, and operational monitoring.","hasChildren":true,"hasParent":true,"name":"Uplink Stations","selfAssesment":" "},{"code":"SA2","description":"The ground segment includes all the terrestrial infrastructure that supports the space segment and delivers services to the user segment. It is the backbone that makes operations, data acquisition, processing, distribution, and system management possible.\r\nIt generic components are:\r\n•\tMission control / operations centers: monitor satellite health, send commands, plan maneuvers and observations.\r\n•\tTT&C stations: antennas and equipment for uplink of commands and downlink of telemetry.\r\n•\tData reception stations: high-rate downlink stations for payload data (often separate from TT&C).\r\n•\tData processing and archiving facilities: convert raw telemetry into usable products or services.\r\n•\tNetworks and distribution infrastructure: connect ground sites and deliver products/services to users.\r\n•\tSupport infrastructure: calibration/validation networks, testbeds, simulators, etc.\r\nSpecifically by type of system:\r\nEO – Ground segment\r\n•\tAcquisition:\r\no\tNetwork of typically S-, X-, Ka-band ground stations to receive raw instrument data.\r\no\tSometimes ground stations are shared (confederated among different partners) to maximize contact opportunities.\r\n•\tProcessing:\r\no\tL0 processing (packet decoding, sorting…).\r\no\tL1 (radiometrically and geometrically calibrated data, e.g. TOA radiance, backscatter).\r\no\tL2 (retrieval of geophysical variables like soil moisture, SST, NDVI, LAI, etc.).\r\no\tHigher-level products (L3/L4) with temporal and spatial compositing, data assimilation, etc.\r\n•\tArchiving and dissemination:\r\no\tLong-term archives, cloud platforms, data cubes, catalogues with standardized metadata.\r\no\tAccess via web portals, APIs, FTP, etc.\r\n•\tMission planning:\r\no\tAcquisition planning (what and when to be imaged, in which mode of acquisition…).\r\no\tResource allocation (on-board memory, downlink windows, power).\r\n•\tCal/Val infrastructure:\r\no\tGround networks of in situ measurements, reference sites, campaigns to validate EO products.\r\nNAV – Ground segment\r\nOften called the ground control segment:\r\n•\tMonitoring stations: distributed globally, continuously tracking all satellites, measuring the signals and pseudo-ranges.\r\n•\tControl centers:\r\no\tEstimate precise satellite orbits and clock offsets from monitoring data.\r\no\tGenerate and upload navigation messages (ephemeris, clock corrections, almanacs).\r\no\tMonitor system performance, availability, integrity.\r\n•\tUplink stations: send updated navigation data and system parameters to the satellites.\r\n•\tTimekeeping infrastructure: tie the system time scale to international standards (UTC), maintain ensemble of atomic clocks on the ground.\r\n•\tAugmentation ground segments: SBAS reference stations, processing centers, uplink facilities for broadcasting corrections.\r\nThe NAV ground segment is critical for ensuring accuracy, integrity, and continuity of the PNT services.\r\nCOM – Ground segment\r\nFor satellite communications, the ground segment is often called the ground network / teleport infrastructure, and it consists of:\r\n•\tGateways / teleports:\r\no\tLarge antennas and RF chains connecting satellites to terrestrial networks.\r\no\tProvide uplinks/downlinks for user traffic (internet backhaul, TV feeds, etc.).\r\n•\tNetwork Operation Centers (NOCs):\r\no\tManage traffic, allocate resources, monitor QoS, configure beams and transponders.\r\n•\tCore network integration:\r\no\tConnect with internet backbones, mobile network cores, enterprise networks.\r\no\tSupport roaming and handover between satellite and terrestrial networks.\r\n•\tControl and TT&C facilities: often integrated or co-located with teleports.\r\n•\tService platforms:\r\no\tBilling, authentication, traffic shaping, multicast/broadcast management.\r\nIn short, the ground segment is the middle layer that makes the space assets useful and manageable, translating raw satellite capabilities into data and services.","hasChildren":true,"hasParent":true,"name":"Ground segment","selfAssesment":" "},{"code":"SA3-1-1-1","description":"Galileo Open Service provides free positioning, navigation, and timing signals for mass-market and professional users. It supports multi-frequency operation and interoperability with other global navigation satellite systems.","hasChildren":true,"name":"Open Service","selfAssesment":" "},{"code":"SA3-1-1-2","description":"Open Service Navigation Message Authentication adds cryptographic authentication to Galileo open signals. It helps users verify that navigation data are genuine, improving resilience against spoofing and manipulation.","hasChildren":true,"name":"Open Service Navigantion Message Authentication (OSNMA)","selfAssesment":" "},{"code":"SA3-1-1-3","description":"Galileo High Accuracy Service provides precise corrections enabling improved positioning accuracy for capable receivers. It supports professional applications requiring better performance than standard open-service navigation service.","hasChildren":true,"name":"High Accuracy Service (HAS)","selfAssesment":" "},{"code":"SA3-1-1-4","description":"Search and Rescue service supports detection and localization of distress beacons through satellite relays and ground infrastructure. It reduces response time, confirmation capability, and coverage for emergency situations.","hasChildren":true,"name":"Search And Rescue (SAR)","selfAssesment":" "},{"code":"SA3-1-1-5","description":"Public Regulated Service is an encrypted Galileo service for authorized governmental users. It is designed for continuity, robustness, controlled access, and improved resilience in security-sensitive applications.","hasChildren":true,"name":"Public Regulated Services (PRS) i.e. Governamental","selfAssesment":" "},{"code":"SA3-1-1","description":"Galileo services are the user services provided by the European GNSS. They include open positioning, high accuracy, authentication, search and rescue support, and regulated services for authorized governmental users.","hasChildren":true,"hasParent":true,"name":"Galileo Services","selfAssesment":" "},{"code":"SA3-1","description":"Services are the functional offerings delivered to users by satellite systems, such as positioning, timing, communication, observation, search and rescue, broadcasting, augmentation, secure governmental access…","hasChildren":true,"hasParent":true,"name":"Services","selfAssesment":" "},{"code":"SA3-2-1-1-1-1","description":"On-Off Keying is a simple digital modulation where carrier presence or absence represents binary symbols. It is easy to implement, but less power- and bandwidth-efficient than many advanced schemes.","hasChildren":true,"name":"On-Off Keying (OOK)","selfAssesment":" "},{"code":"SA3-2-1-1-1-2","description":"Audio Frequency Shift Keying represents digital data by switching between frequencies in the audio part of the spectrum (~kHz). It is simple and robust for narrowband links, telemetry, amateur satellite communications, and low-data-rate applications.","hasChildren":true,"name":"Audio Frequency Shift Keying (AFSK)","selfAssesment":" "},{"code":"SA3-2-1-1-1-3","description":"Gaussian Minimum Shift Keying is a constant-envelope digital modulation using Gaussian filtering before minimum-shift keying. It offers good spectral containment and power-amplifier efficiency for mobile and satellite links.","hasChildren":true,"name":"Gaussian Minimum Shift Keying (GMSK)","selfAssesment":" "},{"code":"SA3-2-1-1-1-4","description":"Binary Phase Shift Keying transmits one bit per symbol by switching carrier phase between two states. It is robust, simple, and common in navigation, telemetry, and satellite communication links.","hasChildren":true,"name":"Binary Phase Shift Keying (BPSK)","selfAssesment":" "},{"code":"SA3-2-1-1-1-5","description":"Quadrature Phase Shift Keying transmits two bits per symbol using four carrier phase states (in both the in-phase and quadrature components). It doubles spectral efficiency relative to BPSK while retaining moderate implementation complexity and robustness.","hasChildren":true,"name":"Quadrature Phase Shift Keying (QPSK)","selfAssesment":" "},{"code":"SA3-2-1-1-1","description":"Digital modulation represents information using discrete signal states such as amplitude, phase, or frequency levels. It enables efficient coding, synchronization, error correction, encryption, and flexible satellite communication systems.","hasChildren":true,"hasParent":true,"name":"Digital Modulation","selfAssesment":" "},{"code":"SA3-2-1-1-10","description":"Broadcast is a one-to-many communication mode in which the same signal or content is transmitted to many receivers simultaneously. Satellite broadcasting supports wide-area television, radio, data distribution, and alerts.","hasChildren":true,"name":"Broadcast","selfAssesment":" "},{"code":"SA3-2-1-1-11","description":"Unicast is a one-to-one communication mode where data are addressed to a specific receiver or terminal. Satellite unicast supports individualized internet, enterprise, mobility, and command services.","hasChildren":true,"name":"Unicast","selfAssesment":" "},{"code":"SA3-2-1-1-12","description":"Multicast is a one-to-many communication mode targeting a defined group of receivers. Satellite multicast efficiently distributes common content, software, alerts, or data streams without duplicating transmissions.","hasChildren":true,"name":"Multicast","selfAssesment":" "},{"code":"SA3-2-1-1-2-1","description":"Amplitude modulation varies the carrier amplitude in proportion to the information signal. It is simple and compatible with envelope detection, but it is sensitive to noise, fading, and amplifier nonlinearities.","hasChildren":true,"name":"Amplitude modulation (AM)","selfAssesment":" "},{"code":"SA3-2-1-1-2-2","description":"Frequency modulation varies the carrier frequency according to the information signal. It offers improved noise immunity as compared to amplitude modulation and it is widely-used for audio, telemetry, and legacy communication links.","hasChildren":true,"name":"Frequency modulation (FM)","selfAssesment":" "},{"code":"SA3-2-1-1-2-3","description":"Phase modulation varies the instantaneous phase of a carrier according to the information signal. It is related to frequency modulation and it is in the base of many analog and digital communication techniques.","hasChildren":true,"name":"Phase modulation (PM)","selfAssesment":" "},{"code":"SA3-2-1-1-2","description":"Analog modulation varies a continuous carrier parameter according to an analog information signal. Classical forms include amplitude, frequency, and phase modulation, historically important for voice, telemetry, and broadcasting.","hasChildren":true,"hasParent":true,"name":"Analog Modulation","selfAssesment":" "},{"code":"SA3-2-1-1-3","description":"Spread spectrum distributes signal energy over a bandwidth wider than the information rate. It improves resistance to interference, interception, and multipath, and enables code-based multiple access and ranging.","hasChildren":true,"name":"Spread Spectrum","selfAssesment":" "},{"code":"SA3-2-1-1-4","description":"Frequency Division Multiple Access assigns different frequency bands to different users or channels. It enables simultaneous transmissions but requires guard bands, filtering, frequency planning, and management of intermodulation.","hasChildren":true,"name":"Frequency Division Multiple Access (FDMA)","selfAssesment":" "},{"code":"SA3-2-1-1-5","description":"Time Division Multiple Access assigns users different time slots on a shared frequency channel. It requires synchronization and scheduling, enabling efficient sharing of satellite resources among many terminals.","hasChildren":true,"name":"Time Division Multiple Access (TDMA)","selfAssesment":" "},{"code":"SA3-2-1-1-6","description":"Code Division Multiple Access separates users by distinct spreading codes transmitted over the same frequency band. It supports simultaneous access, interference averaging, ranging, and flexible capacity allocation. The number of users is not limited by the number of time or frequency slots, and SNR gracefully degrades as the number of users is increased.","hasChildren":true,"name":"Code Division Multiple Access (CDMA)","selfAssesment":" "},{"code":"SA3-2-1-1-7","description":"Orthogonal Frequency Division Multiplexing divides data across many orthogonal subcarriers. It handles frequency-selective channels efficiently, supports adaptive loading, and is widely-used in broadband communication systems.","hasChildren":true,"name":"Orthogonal Frequency Division Multiplexing (OFDM)","selfAssesment":" "},{"code":"SA3-2-1-1-8","description":"Adaptive Coding and Modulation changes modulation order and coding rate according to link conditions. It improves throughput and availability by trading data rate against robustness during fading or rain attenuation.","hasChildren":true,"name":"Adaptive Coding and Modulation (ACM)","selfAssesment":" "},{"code":"SA3-2-1-1-9","description":"Digital Video Broadcasting-Satellite defines standards for satellite delivery of digital television, radio, and data services. It specifies modulation, coding, framing, and transport mechanisms for efficient broadcast links.","hasChildren":true,"name":"Digital Video Broadcasting - Satellite (DVB-S)","selfAssesment":" "},{"code":"SA3-2-1-1","description":"Modulation and demodulation are the processes of encoding information onto a carrier and recovering it at the receiver. They affect spectral occupancy, noise tolerance, data rate, and hardware complexity.","hasChildren":true,"hasParent":true,"name":"Modulation/demodulation","selfAssesment":" "},{"code":"SA3-2-1-2","description":"Frequency reuse increases capacity by using the same frequencies in separated beams, polarizations, regions, or times. It requires interference management, antenna discrimination, planning, and sometimes precoding.","hasChildren":true,"name":"Frequency Reuse","selfAssesment":" "},{"code":"SA3-2-1-3","description":"Channel capacity is the theoretical absolute maximum reliable data rate of a communication channel, constrained by bandwidth and signal-to-noise ratio. The Shannon limit guides coding, modulation, and link design.","hasChildren":true,"name":"Channel Capacity (Shannon limit)","selfAssesment":" "},{"code":"SA3-2-1-4","description":"Satellite capacity is the amount of traffic a satellite system can deliver. It depends on bandwidth, power, beams, frequency reuse, gateway resources, coding, modulation, and traffic management.","hasChildren":true,"name":"Satellite Capacity","selfAssesment":" "},{"code":"SA3-2-1-5","description":"Distortion is any unwanted alteration of signal amplitude, phase, frequency, or waveform shape. It can arise from nonlinear amplifiers, filters, converters, propagation, saturation, or imperfect compensation.","hasChildren":true,"name":"Distorsion","selfAssesment":" "},{"code":"SA3-2-1-6","description":"Coding adds structured redundancy or transformations to information before transmission to support error detection, error correction, compression, security, synchronization, and efficient use of satellite link resources.","hasChildren":true,"name":"Coding","selfAssesment":" "},{"code":"SA3-2-1-7","description":"Pre-coding shapes transmitted signals before propagation to manage interference, beam coupling, or channel effects. It is important in multibeam satellites, Multiple Input Multiple Output systems, and high-throughput architectures.","hasChildren":true,"name":"Pre-coding","selfAssesment":" "},{"code":"SA3-2-1","description":"Modulations and techniques describe how information is impressed on a carrier signal and how shared resources are accessed. They determine bandwidth efficiency, robustness, synchronization, receiver complexity, and link performance.","hasChildren":true,"hasParent":true,"name":"Modulations and Techniques","selfAssesment":" "},{"code":"SA3-2-2-1","description":"Amateur bands are radio-frequency allocations available to licensed amateur operators and educational missions. In satellites they support telemetry, command, experiments, outreach, and low-cost communication payloads.","hasChildren":true,"name":"Amateur bands","selfAssesment":" "},{"code":"SA3-2-2-2","description":"Commercial bands are spectrum allocations used for revenue-generating satellite services such as broadband, broadcasting, mobility, and enterprise connectivity. They require licensing, coordination, interference management, and regulatory compliance according to the International Telecommunications Union (ITU).","hasChildren":true,"name":"Commercial bands","selfAssesment":" "},{"code":"SA3-2-2","description":"Frequency bands are allocated ranges of radio spectrum used for satellite links and services. Their propagation, bandwidth, antenna size, rain attenuation, interference environment, and regulations affect system design.","hasChildren":true,"hasParent":true,"name":"Frequency bands","selfAssesment":" "},{"code":"SA3-2-3-1","description":"Frequency allocation, planning, and interoperability assign spectrum to services and ensure that systems can coexist. They consider interference, standards, orbital slots, service requirements, and cross-system compatibility.","hasChildren":true,"name":"Frequency Allocation/Planning/Interoperability","selfAssesment":" "},{"code":"SA3-2-3-2","description":"Frequency coordination is the process of negotiating and documenting compatible spectrum use among satellite and terrestrial systems. It prevents harmful interference and supports regulatory filings and operational coexistence.","hasChildren":true,"name":"Frequency Coordination","selfAssesment":" "},{"code":"SA3-2-3-3","description":"Spectrum management organizes radio-frequency use through allocation, licensing, monitoring, enforcement, and planning. It balances technical efficiency, service demand, interference protection, and national or international policy objectives.","hasChildren":true,"name":"Spectrum Management","selfAssesment":" "},{"code":"SA3-2-3-4","description":"The International Telecommunication Union coordinates global radio spectrum and satellite orbit use, develops recommendations, maintains radio regulations, and supports international agreements for interoperable telecommunications services.","hasChildren":true,"name":"International Telecommunications Union (ITU)","selfAssesment":" "},{"code":"SA3-2-3-5","description":"5G is the fifth-generation mobile communication standard family, enabling enhanced broadband, low-latency services, massive machine communications, and integration with non-terrestrial networks including satellite components.","hasChildren":true,"name":"5G (Fifth generation)","selfAssesment":" "},{"code":"SA3-2-3-6","description":"3GPP develops global technical specifications for cellular systems, including LTE, 5G, and non-terrestrial networks. Its standards define architectures, protocols, radio interfaces, and interoperability requirements.","hasChildren":true,"name":"3GPP","selfAssesment":" "},{"code":"SA3-2-3","description":"Regulation defines the legal and technical framework governing satellite spectrum, orbits, services, equipment, and operations. It ensures efficient use, interference protection, safety, and international coordination.","hasChildren":true,"hasParent":true,"name":"Regulation","selfAssesment":" "},{"code":"SA3-2-4-15-1","description":"Radio-frequency interference is unwanted electromagnetic energy within a receiver’s operating band. It can raise noise, distort measurements, block acquisition, saturate electronics, or reduce communication and navigation performance.","hasChildren":true,"name":"Radio frequency Interference (RFI)","selfAssesment":" "},{"code":"SA3-2-4-15-2","description":"Electromagnetic interference is unwanted electromagnetic coupling that disrupts equipment operation. It may be radiated or conducted and can affect receivers, processors, power systems, sensors, and payload electronics.","hasChildren":true,"name":"Electromagnetic Interference (EMI)","selfAssesment":" "},{"code":"SA3-2-4-15-3","description":"Inter-symbol interference occurs when channel dispersion or filtering causes symbols to overlap in time. It increases detection errors and requires equalization, pulse shaping, guard intervals, or robust modulation.","hasChildren":true,"name":"Inter-symbol Interference (ISI)","selfAssesment":" "},{"code":"SA3-2-4-15-4","description":"Jamming is intentional radio interference designed to deny or degrade communication, navigation, or sensing services. Countermeasures include filtering, antenna nulling, spread spectrum, monitoring, authentication, and other resilience planning techniques.","hasChildren":true,"name":"Jamming","selfAssesment":" "},{"code":"SA3-2-4-15-5","description":"Spoofing is the deliberate transmission of counterfeit or manipulated signals to mislead receivers. In navigation it can produce false position, velocity, or time and requires detection and authentication.","hasChildren":true,"name":"Spoofing","selfAssesment":" "},{"code":"SA3-2-4-15-6","description":"Multipath occurs when signals reach a receiver through multiple propagation paths after reflection, diffraction, or scattering. It can cause fading, ranging errors, distortion, or even useful measurements such as in GNSS-Reflectometry","hasChildren":true,"name":"Multipath","selfAssesment":" "},{"code":"SA3-2-4-15-7","description":"Ionospheric effects arise from signal propagation through charged upper-atmosphere layers. They introduce delay, scintillation, dispersion, and phase changes that must be monitored, modelled, corrected, or mitigated.","hasChildren":true,"name":"Ionospheric Effects, Monitoring, and Mitigation Techniques","selfAssesment":" "},{"code":"SA3-2-4-15","description":"Interference occurs when unwanted signals degrade reception of a desired signal. It may be co-channel, adjacent-channel, impulsive, intentional, unintentional, natural, or caused by system nonlinearities.","hasChildren":true,"hasParent":true,"name":"Interference","selfAssesment":" "},{"code":"SA3-2-4","description":"Radio-wave propagation describes how electromagnetic waves travel through space, atmosphere, terrain, buildings, and media. It determines path loss, fading, delay, polarization changes, attenuation, and interference behavior.","hasChildren":true,"hasParent":true,"name":"Radio-wave Propagation","selfAssesment":" "},{"code":"SA3-2-5-1-1","description":"Resilience is the ability of a satellite system or service to withstand, adapt to, and recover from failures, interference, cyberattacks, outages, or environmental disturbances while maintaining essential functions.","hasChildren":true,"name":"Resilience","selfAssesment":" "},{"code":"SA3-2-5-1","description":"Security protects satellite signals, systems, data, and services against unauthorized access, manipulation, disruption, or disclosure. It includes encryption, authentication, access control, monitoring, redundancy, and operational procedures.","hasChildren":true,"hasParent":true,"name":"Security","selfAssesment":" "},{"code":"SA3-2-5","description":"Messages are structured data carried by satellite signals or communication links. They may contain navigation data, timing, ephemerides, commands, telemetry, service information, authentication, user traffic, or alerts.","hasChildren":true,"hasParent":true,"name":"Messages","selfAssesment":" "},{"code":"SA3-2-6","description":"Codes and modulations define the spreading sequences, symbols, and waveform structures used to transmit information and support ranging. They influence interoperability, spectrum use, acquisition, tracking, robustness, and receiver complexity.","hasChildren":true,"name":"Codes and modulations","selfAssesment":" "},{"code":"SA3-2","description":"Signals are electromagnetic waveforms used to carry information or enable measurements. In satellite systems they define frequency, modulation, coding, power, polarization, timing, and compatibility with receivers.","hasChildren":true,"hasParent":true,"name":"Signals","selfAssesment":" "},{"code":"SA3-3-1","description":"Antenna hardware includes radiating elements, feeds, reflectors, arrays, radomes, deployment mechanisms, mounts, and RF interfaces. It determines gain, polarization, beamwidth, pointing accuracy requirements, and environmental survivability.","hasChildren":true,"name":"Antenna hardware","selfAssesment":" "},{"code":"SA3-3-2","description":"Receiver hardware includes antennas, front ends, oscillators, converters, processors, memory, interfaces, and power supplies. It determines sensitivity, dynamic range, timing stability, data throughput, and environmental robustness.","hasChildren":true,"name":"Receiver hardware","selfAssesment":" "},{"code":"SA3-3","description":"Hardware includes the physical electronic, mechanical, radio-frequency, optical, thermal, and structural components that implement a satellite or user sensing, communications, or navigation system. Its design determines performance, reliability, manufacturability, and cost.","hasChildren":true,"hasParent":true,"name":"Hardware","selfAssesment":" "},{"code":"SA3-4-1-1","description":"Agriculture applications use satellite services for precision farming, machinery guidance, field mapping, irrigation, yield monitoring, livestock management, and environmental compliance, among other uses, improving productivity and resource efficiency.","hasChildren":true,"name":"Agriculture","selfAssesment":" "},{"code":"SA3-4-1-10","description":"Insurance and finance applications use satellite data and positioning for risk assessment, assets verification, events monitoring, support claims, model risk exposure, improve financial decision making, among other uses.","hasChildren":true,"name":"Insurance and Finance","selfAssesment":" "},{"code":"SA3-4-1-11","description":"Maritime and inland waters applications use satellite navigation and communication for vessel tracking, routing, safety, port operations, fisheries monitoring, inland navigation, environmental surveillance, and emergency response, among other uses.","hasChildren":true,"name":"Maritime and Inland Waters","selfAssesment":" "},{"code":"SA3-4-1-12","description":"Rail applications use satellite positioning, timing, and communication to support train localization, signalling, asset monitoring, maintenance, safety supervision, passenger information, and future automated railway operations, among other uses.","hasChildren":true,"name":"Rail","selfAssesment":" "},{"code":"SA3-4-1-13","description":"Road and automotive applications use satellite navigation, timing, and connectivity for routing, tolling, fleet management, emergency calls, autonomous driving, traffic management, insurance, and advanced driver assistance, among other uses.","hasChildren":true,"name":"Road and Automotive","selfAssesment":" "},{"code":"SA3-4-1-14","description":"Space applications use satellite navigation, communication, and timing for spacecraft operations, formation flying, orbit determination, inter-satellite links, space situational awareness, and lunar or deep-space mission support, among other uses.","hasChildren":true,"name":"Space","selfAssesment":" "},{"code":"SA3-4-1-15","description":"Urban development and cultural heritage applications use satellite data for city planning, infrastructure monitoring, asset mapping, deformation analysis, heritage documentation, risk assessment, and sustainable urban management, among other uses.","hasChildren":true,"name":"Urban Development and Cultural Heritage","selfAssesment":" "},{"code":"SA3-4-1-2","description":"Aviation and drone applications use satellite positioning, timing, and communications for navigation, surveillance, approach procedures, unmanned traffic management, fleet operations, geofencing, and safety-critical services, among other uses.","hasChildren":true,"name":"Aviation and Drones","selfAssesment":" "},{"code":"SA3-4-1-3","description":"Climate, environment, and biodiversity applications use satellite observations and positioning to monitor ecosystems, emissions, hazards, water, land cover, species habitats, and for environmental change for policy and management, among other uses.","hasChildren":true,"name":"Climate, Environment, and Biodiversity","selfAssesment":" "},{"code":"SA3-4-1-4","description":"Consumer solutions, tourism, and health applications use satellite positioning and connectivity in smartphones, wearables, travel services, fitness, personal safety, telemedicine, location-based services, and assistive technologies, among other uses.","hasChildren":true,"name":"Consumer Solutions, Tourism and Health","selfAssesment":" "},{"code":"SA3-4-1-5","description":"Emergency management and humanitarian aid applications use satellite navigation, communication, and observation to support e.g. disaster preparedness, response coordination, search and rescue, damage assessment, logistics, and situational awareness, among other uses.","hasChildren":true,"name":"Emergency Management and Humanitarian Aid","selfAssesment":" "},{"code":"SA3-4-1-6","description":"Energy and raw materials applications use satellite services for exploration, infrastructure monitoring, grid synchronization, offshore operations, renewable-energy assessment, mining logistics, safety, and environmental compliance, among other uses.","hasChildren":true,"name":"Energy and Raw Materials","selfAssesment":" "},{"code":"SA3-4-1-7","description":"Fisheries and aquaculture applications use satellite positioning, communication, and observation for vessel monitoring, sustainable catch management, farm monitoring, environmental conditions, safety, traceability, and regulatory compliance, among other uses.","hasChildren":true,"name":"Fisheries and Aquaculture","selfAssesment":" "},{"code":"SA3-4-1-8","description":"Forestry applications use satellite observations and positioning for forest inventory, deforestation monitoring, fire risk assessment, carbon accounting, biodiversity protection, illegal logging detection, and sustainable management, among other uses.","hasChildren":true,"name":"Forestry","selfAssesment":" "},{"code":"SA3-4-1-9","description":"Infrastructure applications use satellite navigation, communication, and observation for asset mapping, construction monitoring, deformation detection, timing synchronization, maintenance planning, risk assessment, and resilience management, among other uses .","hasChildren":true,"name":"Infrastructure","selfAssesment":" "},{"code":"SA3-4-1","description":"EUSPA market segments classify downstream uses of EU space services across economic sectors. They help analyze user needs, adoption, benefits, service requirements, and commercial opportunities for satellite technologies.","hasChildren":true,"hasParent":true,"name":"EUSPA Market Segments","selfAssesment":" "},{"code":"SA3-4","description":"Applications are practical uses of satellite navigation, communication, or observation services. They translate system capabilities into value for transport, environment, safety, industry, science, infrastructure, and consumer markets.","hasChildren":true,"hasParent":true,"name":"Applications","selfAssesment":" "},{"code":"SA3","description":"The user segment comprises all the equipment and entities that consume the service or data provided by the satellite system. It includes:\r\n•\tUser terminals / receivers / antennas\r\n•\tUser-side software and processing\r\n•\tInterfaces to applications and end-users\r\nThis segment is extremely different in scale and complexity across the different EO, NAV, and COM services.\r\nEO – User segment\r\nFor EO, the concept of “user segment” often includes:\r\n•\tProfessional users: agencies, research institutions, companies that:\r\no\tDownload data from data hubs, cloud platforms, or direct broadcast.\r\no\tRun processing chains (e.g. atmospheric correction, bio-geophysical parameter retrieval, data assimilation).\r\no\tIntegrate EO data into decision support systems (e.g. agriculture, forestry, hydrology, disaster management…).\r\n•\tUser equipment:\r\no\tStandard computers/servers, sometimes HPC or cloud resources.\r\no\tSpecialized software (e.g. SNAP, QGIS, ENVI or other commercial or custom software packages).\r\no\tIn some cases, local receiving stations with dedicated antennas and receivers to downlink data directly from satellites.\r\n•\tUsage patterns: often data-centric and offline/near-real-time, with strong emphasis on data quality, traceability, and metadata for later reprocessing.\r\nNAV – User segment\r\nFor satellite navigation, the user segment is massive, diverse and disperse, ranging from consumer to high-end.\r\n•\tReceivers:\r\no\tEmbedded GNSS chips in smartphones, cars, wearables.\r\no\tProfessional receivers for surveying, geodesy, agriculture, precision timing.\r\no\tAviation and maritime certified receivers with integrity monitoring.\r\n•\tAntennas: usually small patch or helix antennas; for high-precision or harsh environments, more sophisticated multi-frequency, choke-ring, or array antennas.\r\n•\tUser processing:\r\no\tSingle-point positioning (SPP) using broadcast ephemeris.\r\no\tHigh-accuracy techniques like RTK, PPP, PPP-RTK, often with corrections from reference networks.\r\no\tIntegration with inertial sensors, odometers, etc. for robustness.\r\n•\tApplications: navigation apps, fleet management, autonomous driving, UAV operations, scientific geodesy, timing for telecom and power grids.\r\nIn general, the NAV user segment is defined as any device or system that uses satellite signals to obtain position, velocity, and time.\r\nCOM – User segment\r\nFor communications, the user segment is essentially the entire set of satellite terminals and the people or systems using them.\r\n•\tUser terminals:\r\no\tFixed terminals (VSATs) for enterprise networks and backhaul.\r\no\tConsumer terminals (e.g. small dishes for TV, LEO broadband user terminals).\r\no\tMobile terminals for maritime, aeronautical, land mobility (sat phones, BGAN, in-flight connectivity, connected ships, trains, vehicles).\r\no\tD2D (direct to device) applications compatible with standard mobile phones\r\no\tIoT/M2M terminals – very low data rate devices with low-gain antennas and low power.\r\n•\tKey parameters: antenna size and gain, transmit power, required data rate, link availability, mobility support.\r\n•\tIntegration: terminals connect to local networks (Wi-Fi, Ethernet, cellular) so that users see a “normal” connection; the satellite link is largely hidden.\r\nIn short, the User segment is where the service is finally “consumed”: EO users transform data into information, NAV users get PNT, and COM users get connectivity.","hasChildren":true,"hasParent":true,"name":"User Segment","selfAssesment":" "},{"code":"SC","description":"Satellite Communications (SatCom) are systems and technologies that use satellites to relay information between points on Earth (and increasingly between satellites as well). The satellite acts as a repeater or node in a communication network, receiving signals on an uplink, processing or frequency-translating them, and transmitting them on a downlink—often to a different location or to many locations simultaneously.\r\nBasic principles\r\n•\tLinks:\r\no\tUplink: Earth → satellite.\r\no\tDownlink: satellite → Earth.\r\no\tInter-satellite link (ISL): satellite ↔ satellite (RF or optical).\r\n•\tFrequency bands: typically in the microwave and millimeter-wave range (L, S, C, X, Ku, Ka, Q/V bands), and increasingly optical (laser links).\r\n•\tPropagation: signals travel through the atmosphere (affected by rain, clouds, ionosphere) and vacuum; link budgets must account for free-space loss, atmospheric attenuation, antenna gains, noise, and interference.\r\nOrbits and architectures\r\n•\tGEO (Geostationary Earth Orbit):\r\no\tSatellite appears fixed in the sky for a given ground location.\r\no\tIdeal for broadcast (TV, radio), fixed services, and internet (e.g. HughesNet and Viasat).\r\no\tHigh latency ( 2·height/c~250 ms).\r\n•\tMEO:\r\no\tIntermediate altitude; used by some broadband and legacy systems.\r\no\tLower latency than GEO, fewer satellites than LEO.\r\n•\tLEO:\r\no\tLow altitude, satellites move quickly across the sky.\r\no\tRequires constellations of many satellites (thousands) and continuous handover (e.g. Starlink and in a future Kuiper)\r\no\tLow latency and good link budgets, good for broadband and also for IoT.\r\nArchitectures can be bent-pipe (transparent transponders) or regenerative:\r\n•\tBent-pipe: satellite simply shifts frequency and amplifies; all routing and processing done on the ground (simpler payload).\r\n•\tRegenerative / processing satellites: demodulate, decode, switch/route, re-encode, and re-transmit; enables flexible resource allocation, onboard switching, and more advanced services.\r\nMultiple access and waveform aspects\r\nTo share limited spectrum and satellite resources among many users, SatCom systems use:\r\n•\tMultiple access schemes: TDMA, FDMA, CDMA, OFDMA, NOMA, etc.\r\n•\tAdvanced modulation and coding: QPSK, APSK, QAM, LDPC / Turbo / Polar codes, adaptive coding and modulation (ACM) to match link conditions.\r\n•\tBeamforming: fixed or electronically steered spot beams to reuse frequencies and increase capacity (high-throughput satellites, HTS).\r\nApplications\r\n•\tBroadcasting:\r\no\tDirect-to-home (DTH) TV, radio, large-scale content distribution.\r\n•\tFixed satellite services (FSS):\r\no\tCorporate networks, government networks, backhaul for remote sites, trunk links for islands, etc.\r\n•\tMobile satellite services (MSS):\r\no\tSatellite phones, aero and maritime connectivity, land mobile terminals.\r\no\tD2D direct to device (compatible with standard cellphones)\r\n•\tBroadband access:\r\no\tConsumer and enterprise internet in underserved or remote regions.\r\n•\tEmergency and disaster communications:\r\no\tRapid deployment where terrestrial networks are damaged or absent.\r\n•\tIoT / M2M:\r\no\tLow-data-rate connectivity for sensors, tracking devices, environmental monitoring, etc.\r\nAdvantages and challenges\r\nAdvantages:\r\n•\tWide coverage: a single satellite (especially GEO or MEO) can cover huge areas, including oceans and remote regions.\r\n•\tBroadcast capability: inherently suited for point-to-multipoint services.\r\n•\tInfrastructure independence: no need to deploy extensive terrestrial infrastructure in the coverage area.\r\n•\tResilience: can provide backup when terrestrial networks fail.\r\n•\tBroadband capabilities: especially for LEOs due to favorable link budget\r\nChallenges:\r\n•\tLatency: especially in GEO, affecting interactive applications and some protocols.\r\n•\tDoppler effect: depending on orbits and relative speeds \r\n•\tCapacity and spectrum scarcity: need efficient spectrum reuse and advanced techniques to increase capacity. That can be partly overcome by using optical links, specially between satellites and in future for feeder links\r\n•\tPropagation impairments: rain fade in Ku/Ka/Q/V bands, scintillation, RF interference.\r\n•\tCost and complexity: satellite manufacturing/launch, regulatory coordination, gateway deployments.\r\n•\tOrchestration: complex and autonomous orchestration software (AI based) required for efficient operation\r\nFrom the [SA] Satellite Systems point of view, Satellite Communications is a specific class of satellite systems where:\r\n•\tThe space segment is optimized for RF/optical relay (antennas, transponders, processors, ISLs).\r\n•\tThe ground segment is essentially a telecommunication network infrastructure.\r\n•\tThe user segment are all the terminals that provide connectivity to end-users or machines.\r\nNote: EO and NAV also make use of communication links (e.g. EO data downlinks, NAV signal broadcasting), but in [SC] Satellite Communications the communication itself is the primary mission, not just a support function.","hasChildren":true,"hasParent":true,"name":"Satellite Communication","selfAssesment":" "},{"code":"SC1-1","description":"A transmitter generates and radiates communication signals towards a receiver. It includes modulation, frequency conversion, amplification, filtering, antenna interfacing, power control, and spectral-compliance functions.","hasChildren":true,"name":"Transmitter","selfAssesment":" "},{"code":"SC1-2","description":"A receiver captures, conditions, demodulates, and decodes incoming satellite signals. Link availability and data quality are determined by its sensitivity, selectivity, synchronization, dynamic range, and signal processing.","hasChildren":true,"name":"Receiver","selfAssesment":" "},{"code":"SC1-3","description":"A transponder receives an uplink signal, shifts frequency, amplifies or processes it, and retransmits it on the downlink. It is a core element of many satellite communication payloads.","hasChildren":true,"name":"Transponder","selfAssesment":" "},{"code":"SC1-4","description":"A transceiver combines transmitter and receiver functions in one unit or subsystem. It enables two-way communication while sharing interfaces, frequency references, control logic, antennas, or packaging resources.","hasChildren":true,"name":"Transceiver","selfAssesment":" "},{"code":"SC1-5-1","description":"A transparent repeater forwards received signals after amplification, filtering, and frequency conversion without decoding the information content. It preserves waveform flexibility, but passes uplink impairments to the downlink.","hasChildren":true,"name":"Transparent repeater","selfAssesment":" "},{"code":"SC1-5-2","description":"A regenerative repeater demodulates, decodes, processes, and re-encodes received signals before retransmission. It can improve link quality, routing, security, and resource management, but increases payload complexity.","hasChildren":true,"name":"Regenerative repeater","selfAssesment":" "},{"code":"SC1-5","description":"A repeater receives a signal and retransmits it to extend coverage or connectivity. Satellite repeaters may be transparent, simply relaying signals, or regenerative, decoding and rebuilding them.","hasChildren":true,"hasParent":true,"name":"Repeater","selfAssesment":" "},{"code":"SC1-6","description":"An inter-satellite link connects spacecrafts directly using radio-frequency or optical signals. It enables data relay, routing, synchronization, constellation management, reduced latency, and coverage beyond ground-station visibility.","hasChildren":true,"name":"Inter-Satellite Link (RF or Optical)","selfAssesment":" "},{"code":"SC1-7","description":"An uplink is the communication path from a ground terminal, gateway, or user to a satellite. Its performance depends on power, antenna gain, propagation losses, pointing, modulation, and interference.","hasChildren":true,"name":"Uplink","selfAssesment":" "},{"code":"SC1-8","description":"A downlink is the communication path from a satellite to a ground terminal, gateway, or user. It carries payload data, broadcast content, telemetry, navigation information, or communication traffic.","hasChildren":true,"name":"Downlink","selfAssesment":" "},{"code":"SC1","description":"Satellite communication payload subsystems perform signal transmission, reception, processing, amplification, frequency conversion, routing, and antenna interfacing to enable reliable relay of information between users, gateways, and networks.","hasChildren":true,"hasParent":true,"name":"Subsystems (payload)","selfAssesment":" "},{"code":"SC2-1","description":"Full-duplex communication allows simultaneous transmission and reception, often using separate frequencies, polarization, or cancellation techniques. It supports interactive services with low delay, but requires isolation and careful design.","hasChildren":true,"name":"Full-duplex","selfAssesment":" "},{"code":"SC2-2","description":"Half-duplex communication supports transmission and reception in both directions, but not simultaneously. It reduces hardware and spectrum complexity, though scheduling and turnaround time limit interactive performance.","hasChildren":true,"name":"Half-duplex","selfAssesment":" "},{"code":"SC2","description":"Communication mode describes how a link operates in terms of simultaneity, direction, access, or service style. Modes influence terminal design, spectrum use, protocols, latency, and operational procedures.","hasChildren":true,"hasParent":true,"name":"Communications mode","selfAssesment":" "},{"code":"SC3-1","description":"High-speed satellite communication provides large data rates for broadband, backhaul, imaging downlinks, gateways, and mobility services. It requires wide bandwidth, efficient modulation, high link margins, and capable terminals.","hasChildren":true,"name":"High speed","selfAssesment":" "},{"code":"SC3-2-1","description":"Internet of Thing and Machine to Machine satellite communications connect sensors, machines, and remote assets that exchange small data packets. They support monitoring, tracking, automation, logistics, agriculture, utilities, and environmental services.","hasChildren":true,"name":"IoT/M2M communications","selfAssesment":" "},{"code":"SC3-2","description":"Low-speed satellite communication supports small data volumes, telemetry, messaging, tracking, and remote sensors. It prioritizes coverage, power efficiency, reliability, low terminal cost, and long battery life.","hasChildren":true,"hasParent":true,"name":"Low speed","selfAssesment":" "},{"code":"SC3","description":"Communications speed describes the data rate or throughput delivered by a satellite link (in bps or bits per second). It depends on bandwidth, modulation, coding, signal quality, access scheme, protocols, and network congestion.","hasChildren":true,"hasParent":true,"name":"Communications speed","selfAssesment":" "},{"code":"SC4-1","description":"An up-link carries signals from Earth-based equipment to a satellite. It may transport user traffic, gateway feeder data, commands, software updates, timing, or payload-control information.","hasChildren":true,"name":"Up-link","selfAssesment":" "},{"code":"SC4-2","description":"A down-link carries signals from a satellite to Earth-based equipment. It may deliver communication traffic, broadcast content, telemetry, Earth-observation data, navigation messages, or scientific measurements.","hasChildren":true,"name":"Down-link","selfAssesment":" "},{"code":"SC4-3","description":"A cross-link is a direct communication path between satellites or space platforms. It supports routing, relay, synchronization, autonomous constellation operation, data sharing, and reduced dependence on ground stations.","hasChildren":true,"name":"Cross-link","selfAssesment":" "},{"code":"SC4","description":"Communication direction indicates the orientation of information flow between terminals, satellites, and networks. It includes uplinks, downlinks, cross-links, and sometimes bidirectional paths used for complete services.","hasChildren":true,"hasParent":true,"name":"Communications direction","selfAssesment":" "},{"code":"SC5-1","description":"A non-terrestrial network integrates satellites or aerial platforms into cellular or internet architectures. It extends coverage, resilience, and mobility beyond terrestrial infrastructure using standardized interfaces and protocols.","hasChildren":true,"name":"Non-terrestrial Network (NTN)","selfAssesment":" "},{"code":"SC5-2","description":"Security and cybersecurity protect satellite networks from unauthorized access, interception, interference, malware, supply-chain compromise, and service disruption. Both require encryption, authentication, monitoring, segmentation, and operational resilience.","hasChildren":true,"name":"Security / cybersecurity","selfAssesment":" "},{"code":"SC5-3","description":"Quantum Key Distribution (QKD) uses quantum states to establish cryptographic keys with detectable eavesdropping. Satellite QKD can extend secure key exchange over long distances beyond fiber limitations.","hasChildren":true,"name":"Quantum Key Distribution (QKD)","selfAssesment":" "},{"code":"SC5-4","description":"Vertical networks are satellite-enabled communication solutions tailored to specific industries such as maritime, aviation, energy, agriculture, or emergency services. They combine connectivity, applications, security, and service-level requirements.","hasChildren":true,"name":"Vertical Networks","selfAssesment":" "},{"code":"SC5","description":"Satellite networks interconnect space, ground, and user segments to provide communication services. They include routing, access control, resource management, gateways, terminals, protocols, security, and service orchestration.","hasChildren":true,"hasParent":true,"name":"Networks","selfAssesment":" "},{"code":"SC6-1","description":"Autonomous driving applications may use satellite communications for connectivity, map updates, fleet coordination, remote supervision, positioning assistance, and resilience in areas lacking terrestrial coverage.","hasChildren":true,"name":"Autonomous driving","selfAssesment":" "},{"code":"SC6-10","description":"Inflight connectivity provides internet, voice, entertainment, operational data, and passenger services to aircraft through satellite links. It requires aeronautical terminals, beam tracking, regulatory approval, and high service availability.","hasChildren":true,"name":"Inflight connectivity/communication (IFC)","selfAssesment":" "},{"code":"SC6-11","description":"Cybersecurity applications use satellite communications to support secure connectivity, resilient backup links, monitoring, incident response, and protected data exchange for critical infrastructure, government, and enterprise users.","hasChildren":true,"name":"Cybersecurity","selfAssesment":" "},{"code":"SC6-2","description":"Internet of Things applications use satellite links to connect distributed sensors and devices in remote or mobile environments. They support tracking, monitoring, automation, alerts, and low-power data collection.","hasChildren":true,"name":"IoT applications","selfAssesment":" "},{"code":"SC6-3","description":"Air traffic applications use satellite communications for aircraft connectivity, surveillance, air-traffic management, command links, safety services, and coverage in oceanic or remote regions.","hasChildren":true,"name":"Air traffic","selfAssesment":" "},{"code":"SC6-4","description":"TV-radio broadcasting uses satellites to distribute television, radio, and multimedia content over wide areas. It supports direct-to-home, contribution links, content distribution, emergency alerts, and regional coverage.","hasChildren":true,"name":"TV-Radio broadcasting","selfAssesment":" "},{"code":"SC6-5","description":"Maritime traffic applications use satellite communications for vessel connectivity, safety, tracking, route optimization, crew welfare, regulatory reporting, cargo monitoring, and operations beyond coastal networks.","hasChildren":true,"name":"Maritime traffic","selfAssesment":" "},{"code":"SC6-6","description":"Mobile Satellite Services provide communication to mobile users or platforms such as ships, aircraft, vehicles, and handheld terminals. They emphasize coverage, mobility management, reliability, and service continuity.","hasChildren":true,"name":"Mobile Satellite Services (MSS)","selfAssesment":" "},{"code":"SC6-7","description":"Fixed Satellite Services provide communication links between fixed Earth stations or terminals. They support broadcasting, backhaul, enterprise networks, trunking, feeder links, and broadband in remote areas.","hasChildren":true,"name":"Fixed Satellite Services (FSS)","selfAssesment":" "},{"code":"SC6-8","description":"Broadcast Satellite Services deliver content directly or indirectly to the public, typically television, radio, and data. They require coordinated spectrum, coverage planning, and receiver-compatible standards.","hasChildren":true,"name":"Broadcast Satellite Services (BSS)","selfAssesment":" "},{"code":"SC6-9","description":"Satcom on-the-move provides satellite connectivity to moving vehicles, ships, aircraft, or trains. It requires tracking antennas, mobility management, handovers, robust waveforms, and dynamic link adaptation.","hasChildren":true,"name":"Satcom On-The-Move (SOTM)","selfAssesment":" "},{"code":"SC6","description":"SatCom-specific applications are use cases that depend on satellite communications for coverage, mobility, resilience, broadcasting, or remote connectivity where terrestrial networks are unavailable, insufficient, or economically impractical.","hasChildren":true,"hasParent":true,"name":"SatCom-specific Applications","selfAssesment":" "},{"code":"SC7-1","description":"The International Telecommunication Union develops global telecommunications regulations and recommendations, coordinates electromagnetic spectrum and satellite orbit use, and provides frameworks for interference management and international service interoperability.","hasChildren":true,"name":"International Telecommunications Union (ITU)","selfAssesment":" "},{"code":"SC7-2","description":"Digital Video Broadcasting-Satellite standards specify digital satellite broadcasting waveforms, framing, coding, and modulation. They have influenced European Telecommunications Standards Institute (ETSI) standards and support efficient delivery of television, radio, and data services.","hasChildren":true,"name":"DVB-S (some adopted by ETSI - see below)","selfAssesment":" "},{"code":"SC7-3","description":"The Institute of Electrical and Electronics Engineers develops technical standards, publications, and professional guidance relevant to communications, networking, signal processing, timing, and electronics used in satellite systems.","hasChildren":true,"name":"Institute of Electrical and Electronics Engineers (IEEE)","selfAssesment":" "},{"code":"SC7-4","description":"The European Telecommunications Standards Institute develops European and global telecommunications standards. In satellite communications it addresses interoperability, terminal requirements, non-terrestrial networks, broadcasting, emergency services, and regulatory support.","hasChildren":true,"name":"European Telecommunications Standards Institute (ETSI)","selfAssesment":" "},{"code":"SC7-5","description":"The Internet Engineering Task Force develops open internet standards, especially protocols used for routing, transport, security, naming, and applications. Its work is relevant to satellite IP networking.","hasChildren":true,"name":"Internet Engineering Task Force (IETF)","selfAssesment":" "},{"code":"SC7-6","description":"The Consultative Committee for Space Data Systems develops standards for space mission data, communications, navigation, operations, and interoperability. Its recommendations support reliable exchange between spacecraft and ground systems.","hasChildren":true,"name":"Consultative Committee for Space Data Systems (CCSDS)","selfAssesment":" "},{"code":"SC7-7","description":"Military standards define technical and procedural requirements for defense systems, including communications, interfaces, environmental qualification, security, and interoperability. They support robustness, procurement consistency, and mission assurance.","hasChildren":true,"name":"Military Standards (MIL-STD)","selfAssesment":" "},{"code":"SC7","description":"Standards and recommendations define common technical rules, interfaces, procedures, and performance criteria. They enable interoperability, safety, spectrum compatibility, procurement consistency, certification, and global market adoption.","hasChildren":true,"hasParent":true,"name":"Standards/recommendations","selfAssesment":" "},{"code":"SD","description":"Based on Waldo Tobler`s first law of geography( Tobler, 1970), this property is set on the principle that \"everything is related, but that which is closer is more closely related\".","hasChildren":true,"name":"Spatial dependency","selfAssesment":"In progress (GI-N2K)
"},{"code":"SH","description":"This principle, as set forth by Anselin, determines that \"expectations vary along the earth`s surface\" which means that any spatial analysis is dependent explicitly on the borders of study fields, i.e. the tracing of (spatial) analysis units.","hasChildren":true,"name":"Spatial heterogeneity","selfAssesment":"In progress (GI-N2K)
"},{"code":"TA","description":"This area of knowledge deals with the use of EO / GI techniques and data in different themes and areas of application. It includes the user community of EO services and applications, societal and environmental challenges, EO services and applications, and standard EO products that are made available to users.","hasChildren":true,"hasParent":true,"name":"Thematic and application domains","selfAssesment":"Planned
"},{"code":"TA11-1-1","description":"The EO/GI users in agriculture are active in Agricultural commodities/Trading, agricultural production / Horticulture, Agricultural services, Agriculture machinery, Agriculture and Rural Development Policy, Agro chemicals / Plants & Fertilizers, Animal production / Livestock. The EO/GI users also include agriculture and rural policy makers. \r\nThey benefit from EO information, for example, by managment support for their crop production through forecasting crop yield, assess risks of damage/loss because of storms, disease or other stress factors, and water monitoring. Use in agriculture: knowledge and information products to forge a viable strategy for farming operations. Understand the health of his crop, extent of infestation or stress damage, or potential yield and soil conditions","hasChildren":true,"name":"Users in agriculture","selfAssesment":"New
"},{"code":"TA11-1-2","description":"The users in fishing are active in Fish stock management, Fishing fleets, Fishery distribution logistics, Aquaculture / fish farms, Coastal management agencies. In addition, the users include Fisheries authorities / policy makers. \r\nThe marine environment in particular is relevant to fishing. Fishing fleets move to the fishing grounds to catch fish. Finding them is challenging. However, fish shoals can be directly visible from above. Navigating to the fishing grounds can be risky: Coastline and shallows may pose a risk to ships. Additionally, skippers may have to deal with challenging weather conditions at sea. Environmental threats to the fishing grounds are oil slicks and other types of pollution. A problem from an economical perspective and for adhering to catch quota is illegal fishing. Noumerous opportunities exist to support fishing with EO information.","hasChildren":true,"name":"Users in fishing","selfAssesment":"New
"},{"code":"TA11-1-3","description":"The users in forestry are active in Forest management, Forest Services, Commodities, Logging industry, Wood, paper and pulp industry, Forest policy, Forest machinery. They also include Forest Policy makers.\r\nUse in forestry: Understand depletion due to natural causes (fires and infestations) or human activity (clear-cutting, burning, land conversion), and monitoring of health and growth for effective commercial exploitation and conservation.\r\nForests are a resource that is harvested all over the Globe for different purposes like construction or heating. Additionally, the forests represent an ecosystem that provides various ecosystem services. Proper management is a key to a healthy forestry industry that has to be aligned well with global environmental management activities. There is a need to avoid deforestation and forest degradation, keep the environmental impact of forestry within bounds, be aware of changes in the carbon balance. Economically relevant is especially a good understading of forest types, forest damage due to storms or insects, as well as wildfires. A threat to the environment results from illegal forest activities.","hasChildren":true,"name":"Users in forestry","selfAssesment":"New
"},{"code":"TA11-1","description":"Users in managed living resources refer to human activities exploiting natural organic resources. Knowledge and information products to forge a viable strategy for the user’s operations such as the assessment of the status of the resource due natural or human activity for effective commercial exploitation and conservation. This includes agriculture, fishing and forestry occupations for our society.","hasChildren":true,"hasParent":true,"name":"Users in managed living resources","selfAssesment":"New
"},{"code":"TA11-2-1","description":"The users in alternative energy consist of Solar energy providers, Wind energy providers, Tidal energy providers, Hydroelectric energy providers, Energy and Carbon traders, Local and regional planners, and National policy makers. Energy providers need information about the state of the environment to make the most use out of natural resources. Planners and policy makers have to weigh up whether and which type of alternative energy is justifiable and sensible for a specific region.\r\nEO data can be used to build maps that show resource information. For solar energy, those maps contain information about solar radiation, but also shadowing effects. Forecast products for irradiance are available to be able to plan the energy production for the coming days. Tidal waves can be depicted by sea surface heights. As tidal currents are periodical, they can be predicted well by the initial state of sea surface heights. In addition, also the speed of tidal waves can be determined by EO measurements. In the wind energy sector EO data is analysed to plan and monitor wind farms. Maps can show areas, where winds are suitable for wind energy production. After the construction of a wind farm, wind strength and direction during operation can be monitored. Finally, for hydroelectric power stations EO is used to monitor water reservoirs. As well hydrometeorological data is used to forecast water-related events and to monitor drought or floods.","hasChildren":true,"name":"Users in alternative energy","selfAssesment":"Completed
"},{"code":"TA11-2-2","description":"The EO/GI user community in oil & gas consists of offshore exploration and production, on-shore exploration and production, drilling and support services, oil and gas commodities trading, and energy planners. Due to their activities both on-shore and offshore their need for EO-derived information about the land, the ocean and the atmosphere. They need EO-derived information about geological features (for exploration), for asset infrastructure monitoring, construction and buildings. Safe offshore operations (ocean&atmosphere: forecast and monitoring current movement and drift, monitor sea-ice and icebergs, detect and monitor hurricanes and typhoons; land: map and assess flooding, detect wildfires . A large set of information needs results from their need to adhere to environmental regulations. They have to assess and monitor their environmental impact, ocean quality and productivity, land ecosystems and biodiversity, groundwater and run-off \r\nMany problems faced by oil, gas, including the selection and development of exploration areas, detection and mapping of illegal mining activities, or monitoring dams, pipelines and terrain movements, can be efficiently addressed by extracting information from geospatial imagery. Remote Sensing based applications reduce the need for field work, minimize environmental impacts, and ultimately safe costs, to help achieve results faster during exploration, extraction, and remediation/reclamation stages.","hasChildren":true,"name":"Users in oil & gas","selfAssesment":"New
"},{"code":"TA11-2-3","description":"The EO/GI community in minerals and mining consists of mining and quarrying companies, exploration and survey specialists, commodities traders, exploration and extraction equipment suppliers, drilling, excavation and support services, and regional planners / policy makers.\r\nTypical spatial questions for the users in minerals and mining are concerned with prospecting, e.g. \"Where can we find the minerals that are worth exploitation?\", and operation of mining sites: \"How much material has already been excavated in the mine and how much material was deposited in dedicated dump areas?\". Additionally relevant are arising risks through mining activities, e.g. \"How do the mining activities affect settlements in the vicinity?\" or \"How do the mining activities affect the environment?\". Concequently, the EO/GI users in minerals and mining benefit from EO information through mapping geological features, monitor mineral extraction, measure land use statistics, assessing environmental impact of human activities, detect and monitor ground movement, and monitor land pollution.","hasChildren":true,"name":"Users in minerals & mining","selfAssesment":"New
"},{"code":"TA11-2","description":"Users in energy and mineral resources deal with the harvesting of energy from renewable resources and extractive industries including oil and gas and raw materials. EO information helps them in exploring locations where to build new mines or power plants, in identifying risks from infrastructure and in managing the environmental impact of their operations.\r\nUses that apply to the extractive industries: study of landforms, structures, and the subsurface, to understand physical processes creating and modifying the earth's crust. EO/GI should play a key role to transform data into information and knowledge about the potencial feasibility and viability of renewable resources, in particular solar and wind at the natural and urban ecosystems, and in particular to support Sustainable Development Goals SDG 7 Affordable and Clean Energy and SDG 11 Sustainable Cities and Communities.","hasChildren":true,"hasParent":true,"name":"Users in energy and mineral resources","selfAssesment":"New
"},{"code":"TA11-3-1","description":"EO/GI users in construction include construction companies, civil engineering consultancies, architect and design companies, planning authorities, and national land agencies. \r\nThey benefit from EO through monitor building development, assess environmental impact of human activities, map and assess flooding, detect land movement, subsidence, heave, and monitor land-use statistics","hasChildren":true,"name":"Users in construction","selfAssesment":"New
"},{"code":"TA11-3-2","description":"Utilities (water, electricity, waste): Power station operators, Water plants operators, Survey companies, Hydroelectric suppliers, Regulatory Bodies, Distribution companies, Landfill and waste, Regional planners / policy makers.\r\nThe benefit from EO information that monitor pollution in rivers and lakes, assess changes in the carbon balance, assess environmental impact of human activities, monitor land pollution, assess changes to urban and rural areas, assess and monitor water quality, assess ground water and run-off.","hasChildren":true,"name":"Users in utilities & supplies","selfAssesment":"New
"},{"code":"TA11-3-3","description":"Users of EO/GI in communications and connectivity are mostly mobile telecommunications providers and fixed telecommunication providers. Theire business is to connect people via telephone and internet. The assets for their services include the infrastructure of communication networks physically installed in the ground, the cellphone towers distributed over the land surface, particularly in higly populated areas, as well as other installations (e.g. company buildings) and equipment (communication satellites).\r\nSpecific spatial questions of these users are concerned with the reception quality that the network can provide in an area. The network coverage would neet to react to changes of the built environment. New settlement infrastructure may cause a new population distribution and subsequently the need to network adaptations to cover new areas or cover some areas with higher band widths because more people are living there. Additionaly, the coverage of cellphone antennas depends on the arrangement of environmental obstacles that degrade or block the radio signal. Any place where the built environment or the vegetation changes can change the reception quality within the covered area of an existing cellphone tower. \r\nThe benefit of EO information for the user group of communications and connectivity comes from monitoring building development, assessing changes to urban and rural areas, and mapping line of sight visibility (terrain height, land cover).","hasChildren":true,"name":"Users in communications & connectivity","selfAssesment":"New
"},{"code":"TA11-3-4","description":"EO/GI users in transport and logistics include road transport operators, haulage, road infrastructure operators, tolls, airport operators, rail operators, airlines and airline services, and transport engineers.","hasChildren":true,"name":"Users in transport & logistics","selfAssesment":"New
"},{"code":"TA11-3-5","description":"EO/GI users in marine include ports & harbors administration, bulk cargo carriers, cruise liners operators, ferry operators, naval operations, and rescue and safety at sea.","hasChildren":true,"name":"Users in marine","selfAssesment":"New
"},{"code":"TA11-3-6","description":"From a conceptual point of view travelling is crossing the space from one location to another. Tourism mostly requires a travel to the desired destination and typically also includes moving inside a specific area. Therefore both tourism and travel are highly dependent on spatial phenomena which are often captured using EO.All kinds of travelling are highly dependent on weather conditions which can be observed with meteorological satellites. Also the current traffic conditions like congestion, road condition and natural hazards can be discovered with EO.\r\n\r\nThe types of tourism which are outside of buildings require sufficient weather forecast. Especially outdoor tourism at the coast or in mountain areas have a need for specific information about the current and the near future conditions of the natural environment. Examples are avalanche reports and forecasts for wind or wave heights of water bodies. Local tour organizers can utilise this information in order to better plan offers for tourists and also ensure overall safety during their stay.\r\n\r\nTourism and travelling are import economic factors. Consequently both the public and the private sector are interested in ensuring safe and convenient travel conditions and furthermore in creating an attractive environment for travellers and touristic visitors. This includes recognising environmental pollution, since this discourages tourist from visiting an area. Not only incoming, but also outgoing tourism is an important factor in local economies. Travel agencies, for example, are highly dependent on retrieving accurate information about foreign regions which are typically obtained with earth observation technology.\r\n\r\nOf course tourism and travelling itself also can be observed from space, this is especially true for mass tourism and areas where traffic has increased a lot during the last time. Typical effects are the increase of settlement area and the additionally used space for roads, parking lots, airports and harbors. These changes to the earth surface can be quantified with the help of land cover change detection.In many cases local administrations and decion makers want to mitigate the negative consequences of mass tourism, the insights of the mentioned EO measurements provide a useful foundation for sustainable planning.","hasChildren":true,"name":"Users in travel & tourism","selfAssesment":"Completed
"},{"code":"TA11-3","description":"Users in transport and infrastructure apply to all manufacturing and physical supply in land but also marine domains including transport & logistics, utilities, construction, communication & connectivity, and tourism.","hasChildren":true,"hasParent":true,"name":"Users in infrastructure & transport","selfAssesment":"New
"},{"code":"TA11-4-1","description":"EO/GI users in insurance and real estate include primary insurance companies, re-insurance sector, insurance brokers, insurance service suppliers, commercial banks, major projects, and international financial institutions. \r\nProduction processes (including primary production like farming), property and real estate are often insured against certain risks, e.g. from natural hazards. \r\nUsers benefit from EO information through applications that monitor building development, assess crop damage due to storms (including to forecast and map large waves), assess damage from earthquakes, detect and monitor wildfires, map and assess flooding, detect land movement, subsidence, heave, forecast and assess landslides.","hasChildren":true,"name":"Users in insurance & real estate","selfAssesment":"New
"},{"code":"TA11-4-2","description":"EO/GI users in retail and geo-marketing include Retail centres and Advertising and Marketing agencies. They use EO/GI data in the field of Navigation and LBS, Shopping chains or Logistics.","hasChildren":true,"name":"Users in retail & geo-marketing","selfAssesment":"New
"},{"code":"TA11-4-3","description":"Users in news and media are Television companies, Broadcasting providers, News and Information agencies, Web service providers, and Entertainment software providers. They benefit from monitoring, forecasting and assessing of natural risks/disasters.","hasChildren":true,"name":"Users in news & media","selfAssesment":"New
"},{"code":"TA11-4-4","description":"Users in ICT include fixed and mobile telecommunications providers. They can make use of EO/GI data by monitoring building development and changes to urban areas.","hasChildren":true,"name":"Users in ICT, knowledge and digital interfaces","selfAssesment":"New
"},{"code":"TA11-4","description":"Users in financial and digital services cover a broad area of activity that touches on many other market sectors such insurance & real estate, retail, news & media and digital interfaces. The categories included are identifiable as a “service” (tertiary sector: attention, advice, access, experience, and affective labour) and not part of the physical supply of goods.","hasChildren":true,"hasParent":true,"name":"Users in financial & digital services","selfAssesment":"New
"},{"code":"TA11-5-1","description":"The users in smart cities are multiverse and include large number of profiles. This include urban planners, architects, spatial planning offices, urban policy makers, transportation/environment/health departments but also citizen themselves.\r\nThe users benefit from additional information and knowledge extracted from EO data. This information and knowledge can help them to better tackle with challenges arising from climate change and urbanization. As each urban area is unique, EO can provide relevant information by detecting, evaluating and measuring these localities.\r\nThis EO based information can be extracted on one occasion or continuously, benefiting from revisiting satellites. EO can support investigation of archive data to extract trends or by investigating current state to set a baseline. This baseline is then further used to monitor the changes or to assess the impact of different decisions and actions. In most cases, this information is further used in various GIS analyses or modelling procedures.\r\nThe topics where EO can contribute are as follows: urban land cover, urban heat islands, air/water/soil quality, tree/vegetation health, detection of invasive vegetation species, damage detection on buildings or infrastructure, development of infrastructure and many more.\r\nAs listed, EO can support various domains that can be fitted under Nature-based solutions (NBS). NBS have been gaining attention as multifunctional solutions that may help cities to address challenges arising from climate change and urbanization.\r\nThe concept of Nature-based Solutions (NBS) has evolved as an umbrella concept embracing concepts such as green/blue/nature infrastructure, ecosystem approach, ecosystem services or natural systems agriculture, natural solutions, ecosystem-based approaches, and ecological engineering. NBS can include solutions such as water purification, reduction of flood risk, or deliberate efforts to decrease temperature and improve air quality.","hasChildren":true,"name":"Users in smart cities","selfAssesment":"Completed
"},{"code":"TA11-5-2","description":"The users in local and regional planning include spatial planning departments of municipalities, spatial planning offices, and spatial planning policy makers. Land use management in densely populated areas involves negotiation of conflicting land-use demands for settlement, production system (including agriculture and forestry) and infrastructure. The users benefit from EO information to manage the use of land and its impacts.","hasChildren":true,"name":"Users in local & regional planning","selfAssesment":"New
"},{"code":"TA11-5","description":"Users in urban development and users involved in the development of rural settlements perform tasks on local and regional scale (to the scale of nations). These users benefit from EO information to manage the use of land & its impacts. Users such as urban planners, architects, spatial planning offices, urban policy makers in public/private sectors in smart cities or generic urban local/regional planning belong to this category. EO/GI becomes a key data and information to support Sustainable Development Goals - SDG 11 Sustainable Cities and Communities in particular to set up at geospatial and temporal basis the evolution of urban environmental and socioeconomical factors for a better distribution and equality of resources, benefits and impacts (environmental urban justice maps)","hasChildren":true,"hasParent":true,"name":"Users in urban development","selfAssesment":"New
"},{"code":"TA11-6-1","description":"Users in defense, security and military are border control organisations, police and rescue forces, military services, and intelligence services. Use of EO/GI data can be made in the field of detecting and monitoring high risk areas (natural and humanitarian), monitoring border incursions, or monitoring maritime movements.","hasChildren":true,"name":"Users in defense, security & military","selfAssesment":"New
"},{"code":"TA11-6-2","description":"EO/GI users in emergency services are coast guards, ambulance services, fire services, police services, civil protection organisations, and rescue services. They benefit from monitoring, detecting and assessing natural risks/disasters.","hasChildren":true,"name":"Users in emergency & social protection","selfAssesment":"New
"},{"code":"TA11-6-3","description":"The EO/GI users in humanitarian operations correspond to humanitarian aid organisations, humanitarian support organisations and overall humanitarian response such as border control organisations, police and rescue forces, coast guards, civil protection, military services, and intelligence services. They can use EO services to detect and monitor high risk areas produced naturally or by humans, monitor border incursions or maritime movements. They provide support to local populations that have experienced a crisis, e.g. they fled from a conflict or are affected by a natural disaster. The organisations therefore support the population's needs for sustenance. Consequently, any related risks are relevant as well. The users benefit from the EO capability to identify and monitor people in need, i.e. to assess pressures on populations and migration, and to monitor humanitarian movement and camps. They additionally benefit from EO through mapping disaster areas for situation awareness and detecting sensitive risk areas. Some examples of users at European level are DG RELEX, DG ECHO, DG ENV/ MIC. At UN, the users include OCHA, UNHCR, UNDPKO, UNDP, UNOPS, UNITAR, UNICEF, UNESCO, WFP. Further, international users include IFRC, WHO, WB, and donor organizations. At the national level, the users include Civil Protection Agencies, Ministries of Internal Affairs / Civil Protection Department, Development and Aid agencies.","hasChildren":true,"name":"Users in humanitarian operations","selfAssesment":"New
"},{"code":"TA11-6","description":"Users in defence and security work in the field of military, emergency and social protection and define, collect, analyse information to provide intelligence & safety. Some examples are activities under humanitarian response such as border control organisations, police and rescue forces, coast guards, civil protection, military services, and intelligence services which can use EO services to detect and monitor high risk areas produced naturally or by humans, monitor border incursions or maritime movements.","hasChildren":true,"hasParent":true,"name":"Users in defense & security","selfAssesment":"New
"},{"code":"TA11-7-1","description":"EO/GI users in environmental ecosystems & pollution include scientists, consultants, planners and policy makers with interest in environmental issues.","hasChildren":true,"name":"Users in environmental ecosystems & pollution","selfAssesment":"New
"},{"code":"TA11-7-2","description":"Users in health care health-related services include services on site-specific field conditions as well as import phenological timing events, which helps to make predictions for monitoring air quality, forecasting epidemics and diseases, as well as forecasting sunlight exposure.","hasChildren":true,"name":"Users in health care","selfAssesment":"New
"},{"code":"TA11-7-3","description":"EO/GI users in meteo and climate; use of satellite-based observations in addressing key climate science questions for user-centric climate change risk assessment applications or climate-related issues","hasChildren":true,"name":"Users in meteo & climate","selfAssesment":"New
"},{"code":"TA11-7","description":"Users in the public administrations or private organizations using EO to assist environmental or climate change impact policy making decisions i.e, assisting in developing monitoring to evaluate and deliver policy goals, provide assessment of ecosystems, rapid response to major environmental risk events, or those associated health security & care events. These users are largely related with international treaties and hence a strong international collaboration. EO/GI becomes a key data and information to support Sustainable Development Goals (SDG) in particular in terms of environmental, climate and health towards SDG 11, SDG 13 Climate Action; SDG 14 Life Below Water; or SDG 15 Life on Land.","hasChildren":true,"hasParent":true,"name":"Users in environmental, climate & health","selfAssesment":"New
"},{"code":"TA11-8-1","description":"EO/GI users of customer solutions; easier for society to use and engage with EO services through mobile devices, social media platforms, apps. Enormous potential to use citizen-driven observations in combination with EO data","hasChildren":true,"name":"Users of consumer solutions","selfAssesment":"New
"},{"code":"TA11-8-2","description":"EO/GI users in leisure; basic public understanding on EO Services","hasChildren":true,"name":"Users in leisure","selfAssesment":"New
"},{"code":"TA11-8-3","description":"The community of users in education includes instructors (1) who are teaching or conducting research in some aspect of GIScience, such as coding, remote sensing, field methods, geodetic control, web mapping, spatial analysis, or related topics, or (2) who are using GIS as a teaching tool in a discipline, such as business, biology, economics, or health sciences. By extension, this community includes students and supportive deans and other educational administrators. The benefits that these users gain from EO information includes a set of best practices vetted by experts in the field that they can use to teach modern GIS workflows more effectively. \r\nThe goals of this user community are focused on a deeper and a broader implementation of geotechnology, methods, and spatial data throughout the educational system—primary, secondary, university, and lifelong learning (libraries, museums, and other informal settings). Deeper implementation implies embracing GIS as a platform, including its field data gathering tools and citizen science workflows, spatial analysis, building web maps and apps, communicating with multimedia maps derived from web GIS, systems configuration work, and the coding that is behind modern GIS infrastructure. Broader implementation implies the use of GIS in a multitude of disciplines at all levels of education, formal and informal; occurring wherever changes over space and time are being examined. \r\nAt all levels of education the challenge of sufficient bandwidth and the use of a professional systems-based tool such as GIS, along with devices capable of running web GIS tools, are barriers in many areas throughout the world. However, educational and societal forces represent a stronger challenge than technological ones. These educational and societal challenges that this user community faces include the lack of educational content standards at the primary and secondary level that support the use of geotechnologies in education, and at the university level, a lack of awareness of and access to modern SaaS GIS tools and open data portals. \r\nThe risks that the community faces in not facing the challenge of the use of GIS in the education sector is a lack of geographic and spatial literacy among students and faculty. This will translate to research that does not consider spatiotemporal implications of 21st Century challenges, a workforce ill-equipped to deal with them, and consequently an increasingly unstable and dysfunctional world. To build a workforce that can meet global challenges in energy, biodiversity, climate, natural resources, natural hazards, human health, economic inequality, and others, a deep and wide implementation of GIS technology and methods must take place throughout the educational system. The actions that society can take to face that challenge is to provide professional development opportunities for faculty, curricular resources, assessment instruments, relevant spatial data and open data portals, examples of best practices, and a network for educators and researchers in which to interact. EO can provide all of these elements in partnership with educational institutions, government, nonprofits, and industry to meet this challenge. In so doing, an increasingly sustainable, healthier, resilient world can be achieved from the community to the global level.","hasChildren":true,"name":"Users in education, training & research","selfAssesment":"Completed
"},{"code":"TA11-8","description":"Citizens and society in general use and engage with EO services through mobile devices, social media platforms, apps. We do also categorize in this section the users in education, research and training providing knowledge and learning outcomes.\r\nActive and engaged citizens are one of the main driving forces of EO/GI. Nowadays, there is a growing amount of location-based contents generated by connected “produsers”, mainly equipped with smartphones. The exponential growth of ambient geographic information through social networks became the basic feature of a spatially enabled society, in which it behaves as a vessel where millions of people share their current thoughts, observations and opinions, showing to provide more reliable and trustworthy information than traditional methods like questionnaires and other sources.\r\nA spatially enabled citizen is explained through his ability to express, formalize, equip (technologically and cognitively), and (un)consciously activate an efficiently use of his spatial skills. Harvesting this ambient geospatial information provides a unique opportunity to gain valuable insight on information flow and social networking within a society, support a greater mapping, understand the human landscape and its evolution over time. With these insights, city planners can make use of the gathered affective data to detect positive or negative trends developing in the city, managing to take early countermeasures.\r\nNevertheless, assembling and analyzing EO/GI provide us with unparalleled insight on a broad variety of cultural, societal, and human factors, particularly as they relate to human and social dynamics, for example: 1) mapping the manner in which ideas and information propagate in a society, information that can be used to identify appropriate strategies for information dissemination during a crisis situation. 2) Mapping people’s opinions and reaction on specific topics and current events, thus improving our ability to collect precise cultural, political, economic and health data, and to do so at near real-time rates. 3) Identifying emerging socio-cultural hotspots.","hasChildren":true,"hasParent":true,"name":"Users among citizens & society","selfAssesment":"New
"},{"code":"TA11","description":"The EO/GI user community pools sub-communities (stakeholders) that share common needs for EO/GI information. From an economic perspective, market sectors represent user communities. Users of a community have a common interest in specific aspects of societal or economical benefits to be realized by the implementation of EO services. A user-led community is active at specific locations/regions or in specific environments on the Earth. Their activities are associated with particular features and objects of the environment and related processes that can be detected and monitored with EO satellites. EO information therefore is relevant to the user community's management of their assets, the risks to their assets, and the impact that their activities may have on other aspects of the environment. User objectives (use cases) with EO information include: Enforce regulations; Develop strategies and policies; Manage assets; Plan and design project implementations; Analyse and understand impact / consequences.\r\nUser communities can profit from EO services and applications in the field of managed living resources, energy and mineral resources, infrastructure and transport, financial and digital services, urban development, defense and security, environmental, climate and health, or citizens and society. EO/GI becomes a key data and information to support Sustainable Development Goals -SDG in particular in terms of users in managed livimgs resources towards SDG 2 Zero Hunger; SDG 8 Decent Work and Economic Growth; SDG 9 Industry, Innovation and Infrastructure; SDG 14 Life Below Water; or SDG 15 Life on Land","hasChildren":true,"hasParent":true,"name":"User community of EO services and applications","selfAssesment":"Completed
"},{"code":"TA12-1","description":"Climate change observations show the warming of the climate system. The changes since the 1950s are unprecedented over decades to millennia.The atmosphere and ocean have warmed, the amounts of snow and ice have diminished, and sea level has risen. The anthropogenic emissions of greenhouse gases are the highest in history. Recent climate changes have had widespread impacts on human and natural systems. There is an urgant need for climate action through mitigation and adaptation. Mitigation actions prevent or reduce the emission of greenhuse gases into the atmoshpere with the objective to make the impacts of climate change less severe. Adapting to climate change increases our resilience to impacts like extreme weather events (e.g. hazards like floods and droughts) that get more frequent and intense in many regions. Current climate change will get worse in the future even if the reduction of emissions is effective with negative effects on ecosystems, economy, human health and well-being. There is extensive need for actions to adapt to the impacts of climate change.","hasChildren":true,"name":"EO for climate change mitigation & adaptation","selfAssesment":"New
"},{"code":"TA12-10","description":"\"Sustainable urban development is a goal of the global society. It summarizes a specific set of problems that cities face all over the world. Cities want to provide a high quality of life to their residents. However, this goal is threatened by urban growth at the cost of urban green infrastructure’s accessibility by citizens etc. Communities that address this: C40 (association of the largest cities of the globe), CitiesIPCC, related SDGs of the UN, etc. Skills: Explain how the monitoring of urban areas contributes to sustainable urban development through its capability to provide regularly updated information about the benefit of urban green infrastructures and their ecosystem services to the quality of life in a city\r\n\"","hasChildren":true,"name":"EO for sustainable urban development","selfAssesment":"New
"},{"code":"TA12-2","description":"Biodiversity describes the variety of ecosystems (natural capital), species and genes in the world or in a particular habitat. Ecosystem services sustain our economies and societies and are essential to human wellbeing.","hasChildren":true,"name":"EO for biodiversity & ecosystems","selfAssesment":"New
"},{"code":"TA12-3","description":"Worldwide countries follow a digital agenda for the economy and initiatives to foster new skills among the workforce to cope with transformation processes with massive impact on the labour market.","hasChildren":true,"name":"EO for digital agenda & new skills","selfAssesment":"New
"},{"code":"TA12-4","description":"Energy transition is a thematic area whose EO experts are proficient in relevant EO data and its processing methods and infrastructure to derive information for energy transition [and its regulatory context, etc.]. The expertise of each expert may be very specialized. In sum, the experts have: The relevant domain knowledge (knowledge about type of monitored entities and their properties, e.g. reflectance properties of sea ice and related EO sensors for detecting them), and The relevant workflow knowledge and processing skills for extracting and providing targeted information for energy transition. [may share strategic objectives… such as „gaining thorough understanding of Energy transition“, „foster usage of EO information for energy transition“]","hasChildren":true,"name":"EO for energy transition","selfAssesment":"New
"},{"code":"TA12-5","description":"Agricultural activity is sustained by good environmental conditions that allow farmers to harness natural resources, create their produce and earn a living. This fosters a sustainable rural economy while food produced by agriculture sustains society as a whole.","hasChildren":true,"name":"EO for sustainable agriculture & food production","selfAssesment":"New
"},{"code":"TA12-6","description":"This societal challenge aims to provide efficient, safe and environmentally friendly mobility solutions.","hasChildren":true,"name":"EO for infrastructure & transport","selfAssesment":"New
"},{"code":"TA12-7","description":"In recent decades, society has fought communicable diseases with success through treatment and prevention. The Covid-19 pandemic shows that communicable diseases are still a threat to the health of citizens. Spread can gappen very quickly from one country to another. Challenges lie in the (re-)emergence of infectious diseases, antimicobial resistance and vaccine hesitancy. Policies of states focus on surveillance, rapid detection and rapid response.","hasChildren":true,"name":"EO for health surveillance","selfAssesment":"New
"},{"code":"TA12-8","description":"There is a rising geostrategic competition and power pilitics challenging rule-based multilateralism. Further, there are armed confilct, civil wars and instability in the EU's broader neighbourhood. \r\nFurther, natural disasters pose a threat to society, where the Sendai Framework of disaster risk reduction focuses on.","hasChildren":true,"name":"EO for emergency, security & defense","selfAssesment":"New
"},{"code":"TA12-9","description":"Water is an essential resource for food production. Growing crops requires significant quantities of water. Without sufficient, good quality and easily accessible water, agri-food production is under threat.","hasChildren":true,"name":"EO for water sustainability","selfAssesment":"New
"},{"code":"TA12","description":"EO provides timely, continuous and independent data for monitoring indicators of the progress of the society in various societal challenges.\r\nEO monitoring supports activities that address societal & environmental challenges. This happens indirectly along a chain: e.g. a regularly provided EO information product derived from EO data of a satellite is integrated as a parameter in a climate model / Earth system model. This climate model enables the development of regulations (and their enforcement through constant monitoring) to implement climate change mitigation measures. Thereby, the chain is characterized by seveal connected nodes: from societal challenges to use cases of users to EO applications to EO products to specific satellites and their sensors.\r\n[Communities that promote collaboration among diverse stakeholders from academia, industry, public administration as well as local residents] \r\nScientific agendas address societal challenges and the EO/GI community can contribute to them. Consortia usually include experts from academia (researchers, developers, scientists), EO companies, and members from the user community such as public authorities.","hasChildren":true,"hasParent":true,"name":"EO for societal and environmental challenges","selfAssesment":"New
"},{"code":"TA13-1-1","description":"Monitor the atmosphere includes monitoring of the atmosphere composition and air quality, as well as forecasting of sunlight exposure. Timely, continuous, and independent data on the atmosphere is useful in various domains like health, agriculture, renewable energies, urban planning, climate sciences and biology.\r\nThe atmosphere composition includes greenhouse gases (GHG) like carbon dioxide, methane, NO2 and SO2. They are part of the Earth system and have a strong impact on the climate. To monitor changes in atmosphere composition enables modelling climate change and understanding the impact of human-induced emissions of GHG relative to natural sources. EO-derived products include inventory of emission data as an input to atmospheric chemistry transport models and forecast models. Inventories are based on a combination of existing data sets and new information, describing emissions from fossil fuel use, ships, volcanoes, and vegetation. This ensures good consistency between the emissions of greenhouse gases, reactive gases, and aerosol particles and their precursors.\r\nAir quality describes the composition of the atmosphere from gases and particles near the Earth's surface. Local emissions from different sources (e.g. energy production, industrial production, traffic) cause changes to the atmospheric composition that are highly variable in space and time. The quality of the air we breathe can significantly impact our health and the environment. Therefore, it is highly relevant to monitor air quality and emissions. EO satellites are capable of monitoring aerosols, tropospheric O3, tropospheric NO2, CO, HCHO, SO2, and particulate matter (of the sizes PM 2.5 and PM 10). Products like air quality assessment reports, daily ozone forecasts, and UV-index forecast maps are produced that are applied in specific use cases, particularly related to health.\r\nThe amount of solar radiation that arrives at a location on the Earth surface depends on the atmosphere composition and varies over the day and the seasons. Information on solar radiation is useful in various domains. Applications of sunlight and ozone data are for example real-time UV radiation forecasting and risk assessment, skin health services, climate change studies, assessment of ozone protection policies effectiveness, plant growth and disease control, evaporation and irrigation models, power generation, solar heating systems planning and monitoring.","hasChildren":true,"name":"Monitor the atmosphere","selfAssesment":"Completed
"},{"code":"TA13-1-2","description":"Monitoring the climate includes monitoring climate forcing and the carbon balance and assessing climate change risks.\r\nClimate forcing describes the imbalance of the Earth’s energy budget due to natural or human-induced sources. This imbalance results in a change in the globally-averaged temperature. Amongst the contributors of positive climate forcing, that leads to an increase in the globally-averaged temperature, the increase of carbon dioxide in the atmospheric composition is considered to be the most important factor. Changes in the carbon dioxide concentration indicate that the exchanges between carbon sources and sinks are not balanced. It can be shown that human-induced emissions of carbon dioxide are responsible for the increase of the carbon dioxide since the industrialisation.\r\nWith EO, we can monitor changes in greenhouse gases (GHG), aeorosols, albedo, and solar radiation. The dynamic nature of the climate makes it necessary to apply equally dynamic EO monitoring that allows to deliver key information on historical, seasonal forecast and projection periods for climate-related indicators.\r\nRelevant EO products include estimates of the climate forcing of aerosol, ozone and greenhouse gases. The dynamic nature of the climate makes it necessary to apply equally dynamic EO monitoring that allows to deliver key information on historical, seasonal forecast and projection periods for climate-related indicators. \r\nThe products are particularly relevant to the European energy sector in terms of electricity demand and the production of power from wind, solar and hydro sources. \r\nMoreover, water management uses EO-derived information about climate change to mitigate effects of changing precipitation patterns to adapt their strategies, and to prepare for climate variability and change in the water sector, e.g. because of changes in river discharge, droughts and floods.\r\nFinally, insurance uses climate change information for assessing the weather risks to insured assets that change with the climate-related increase in extreme weather conditions. This includes products like up-to-date catalogue of wind storms and their associated impacts on the ground.","hasChildren":true,"name":"Monitor the climate","selfAssesment":"Completed
"},{"code":"TA13-1-3","description":"The weather is the state of the atmosphere measurable by its temperature, humidity, precipitation, and other atmospheric variables. To forecast the weather is a major branch in the field of meteorology. In comparison to climate, weather can only be predicted for a short period of time (minutes to month), because it describes the state of the atmosphere for specific days at specific locations. For a reliable weather forecast, a good numerical prediction model with precise initial conditions is needed. Models are sensitive to changes in the initial condition, that is why at the moment weather predictions are only accurate for few days. However, both models and the determination of initial conditions are steadily improved. EO makes a significant contribution to improving the initial conditions by providing global information several times a day. As the quality of the EO products improves, the weather forecast also improves. \r\nSince decades, satellites are used to monitor and forecast weather. Therefore, it is one of the most established sectors of satellite data applications. There are geostationary and polar-orbiting weather satellites that measure all kinds of meteorologically relevant variables, e.g. cloud coverage, wind speed [...] via passive or active imagery. However, not only satellites are used to collect information, but also other remote sensing techniques that can be airborne or ground-based such as Lidar.\r\nWeather forecasts are used by citizens for decisions in everyday life, in agriculture for crop cultivation decisions and in the stock markets. Other domains of applications are hydrometeorology, aviation, maritime navigation, and the military and nuclear sectors.","hasChildren":true,"name":"Forecast the weather","selfAssesment":"Completed
"},{"code":"TA13-1","description":"Monitor the atmosphere and climate includes all change-focused services/applications which assess, monitor, forecast and provide timely, continuous and independent data (e.g. temperature, humidity, emissions, greenhouse gases, solar UV radiation, aorosols,...). It closely monitors each of the Earth's different subsystems and, besides being the basis for weather forecasts, helps to better understand and evaluate the impact of the climate change.","hasChildren":true,"hasParent":true,"name":"Monitor the atmosphere and climate","selfAssesment":"New
"},{"code":"TA13-2-1","description":"Monitor critical information about offensive and defensive systems. This deserves a category in its own right since the nature of observations is quite different from many others.","hasChildren":true,"name":"Monitor critical assets","selfAssesment":"New
"},{"code":"TA13-2-2","description":"Monitoring health can be delivered indirectly by monitoring environmental changes that can cause endemic and chronic diseases. Typically monitored environmental factors are temperature, humidity, stagnant water, NDVI, land cover, or soil type.","hasChildren":true,"name":"Monitor health","selfAssesment":"New
"},{"code":"TA13-2-3","description":"Monitoring food security includes the monitoring of food availability by environmental conditions (land cover, NDVI,...), as well as the monitoring of migration patterns. Risks that can lead to food insecurity are hazards or conflicts.","hasChildren":true,"name":"Food security monitoring","selfAssesment":"New
"},{"code":"TA13-2-4","description":"Monitoring borders includes monitoring the land and marine border incursions, monitoring transport routes, assessing pressures on poplulations, and monitoring humanitarian movement.","hasChildren":true,"name":"Monitor borders","selfAssesment":"New
"},{"code":"TA13-2","description":"Monitor security and safety describes the collection and analysis of information to provide intelligence services & safety. The task is to give early warnings in case of emergencies, to monitor infrasturcture, transport routes (land and water) and borders, to surveil security and sovereignty.","hasChildren":true,"hasParent":true,"name":"Monitor security & safety","selfAssesment":"New
"},{"code":"TA13-3-1","description":"EO is capable to repeatedly map flood extent directly after flooding, including further aspects (flood plain, extend mapping, frequency, rainfall, flash floods, vulnerability, inundation, risk-based mapping & management; flood spread and depth followed by automated insurance payouts). Modelling (hydrological modelling and monitoring focused on seasonal dynamics of water availability) based on EO data (digital elevation models) supports flood risk assessment.","hasChildren":true,"name":"Map and assess flooding","selfAssesment":"New
"},{"code":"TA13-3-2","description":"For the outbreak of forest fires, satellite remote sensing can be continuously track and monitor, in a timely manner to grasp the development of forest fires. Beyond, weather monitoring enables to forecast weather conditions where fires are likely, allowing authorities to prepare.","hasChildren":true,"name":"Detect and monitor wildfires","selfAssesment":"New
"},{"code":"TA13-3-3","description":"Damages from earthquakes to infrastrcture can be detected directly, e.g. by mapping collapsed buildings in optical data to derive rapid response products. Use of SAR interferograms enables to identify geotectonic shifts. Modelling enables to identify hotspot areas.","hasChildren":true,"name":"Assess damage from earthquakes","selfAssesment":"New
"},{"code":"TA13-3-4","description":"Landslides are a natural hazard posing a threat to human life, property, infrastructure, and natural environment. Every year, slope instabilities have a significant impact on societies and economies. Consequently, landslide documentation is used for risk assessments, policy making and enforcing of construction regulations. Landslide monitoring is used to ensure safety of infrastructure operation. Rapid mapping of landslides and associated damages is done for response actions, e.g. of civil protection organizations. As ground surveys are very costly and time-consuming, satellite remote sensing is increasingly used to assess damage resulting from landslides.\r\nLandslides lead to local terrain changes after a downslope movement of material under the effect of gravity. They vary by type of movement (e.g. falling, toppling, gliding and flowing), by size (from small rocks to entire mountain slopes) and velocity (from a couple of millimetres per year up to free-fall speed). Landslides can be triggered both by natural causes (like earthquakes or heavy rainfall events) and human causes, e.g. mining activities that lead to slope failures. Landslides can initiate other natural hazards, e.g. when a landslide blocks a river a lake can be formed which poses a risk for an outburst flood. \r\nLandslides are diverse in appearance, and therefore are challenging to detect. EO-based assessment methods aim for detecting changes to the land surface and surface displacements. \r\nEO satellites and airborne remote sensing use optical sensors for detecting landslides in post-event images and land cover changes caused by landslides, primarily indicated by the removal of vegetation and the exposure of bare soil, by comparing pre-event and post-event images. Typical resolutions of optical EO data for mapping rapid landslides are between 0.4 m and 30 m, depending on the size of landslides caused by the triggering event. Optical data from unmanned aerial vehicles are used in cases where single landslides or concise regions have to be covered. Additionally, synthetic aperture radar (SAR) sensors allow the detection of subtle changes in ground deformation caused by landslides. Therefore, time-series of radar images are used. Further, airborne laser scanning enables the generation of digital elevation models (DEMs) that allow identification of landslide surface structures and, in case of repeated coverage, detection of elevation changes. DEM generation for analysing landslides is also possible with photogrammetry on stereographic optical data and radargrammetry on SAR images.\r\nThe diversity of appearances of landslides leads to challenges for (semi-)automatic image processing and makes visual interpretation of EO data by a landslide expert a commonly used method for landslide mapping. However, visual interpretation is subjective and experts’ results can be very diverse. Additionally, it is a slow and time-consuming process. Semi-automated classification based on optical and DEM data using object-based image analysis (OBIA) can achieve detailed interpretations of landslides while reducing the analysis time. Interferometic SAR (InSAR) techniques, such as persistant scatterer interferometry (PSI) or Small Baseline Subset (SBAS), are primarily used to identify and monitor slow-moving landslides and for quantifying movement rates. Integrated analysis of optical, DEM and SAR data allow to fully exploit the potential of EO data from different sensors for landslide mapping and assessment.","hasChildren":true,"name":"Forecast and assess landslides","selfAssesment":"Completed
"},{"code":"TA13-3-5","description":"In context of volcanic activities and volcanos, EO methods are capable to provide information about various aspects, including ground motion (seismic), volcanic eruptions (pre-eruptive, sin-eruptive, atmospheric ash, dispersion), Rapid damage estimation (prevention), earthquake damage extent (loss adjuster dispatch). classification of land cover types","hasChildren":true,"name":"Assess and monitor volcanic activities","selfAssesment":"New
"},{"code":"TA13-3-6","description":"Multi-hazard assessment both focuses on regions prone to several geohazards and on the interrelationships between hazards, i.e. what happens if two disasters strike at the same time or what happens when one disaster is causing a cascade of disasters with a strongly amplified impact (e.g. a landslide causing a dammed river causing an outburstflood with a magnitude beyond the design of protective measures; or an earthquake in a coastal region that is followed by a tsunami). EO can provide imformation on the single disasters and, through integration and comprehensive impact assessment, enables multi-hazard assessment.","hasChildren":true,"name":"Multi-hazard assessment","selfAssesment":"New
"},{"code":"TA13-3","description":"Assess disasters and geohazards by EO includes alert & early warning, emergency mapping, and risk & recovery mapping. It relates to observations, controlling, assessments that are linked to natural and human made risks. Typical disasters that can be assessed by EO are in particular floods, droughts, forest fires, landslides, tsunamis, earthquakes, cyclonic storms and volcanic eruptions. Since with EO it is possible to quickly analyse the risk or damage it is used to effectively plan emergency response actions.\r\nThere are several measures to minimize or prevent the damage caused by disasters. Some of them have to be carried out in anticipation of a disaster, others after the occurrence of an event. The different phases that are needed to reduce or avoid the impact and to assure rapid response and recovery are described in the disaster management cycle. Depending on the cycle phase, EO has to meet different requirements. The Mitigation and Preparedness phase are passed through in anticipation of a disaster event. Thus, requirements to EO products may focus on high completeness of mapping or high accuracy of mapping. In contrast, Response and Recovery phase include rapid mapping, thus EO capabilities must meet near real-time delivery requirements. \r\nAs well, the nature of the disaster determines which EO products are used. Optical sensors are used throughout the different types; however, landslides are mostly assessed by radar sensors and thermal sensors are additionally used for forest fires.","hasChildren":true,"hasParent":true,"name":"Assess disasters & geohazards","selfAssesment":"New
"},{"code":"TA13-4-1","description":"To monitor crops and agriculture with EO-based methods is relevant for various applications, including to assess environmental impact of farming, assess crop damage due to storms, to detect ollegal or undesired crops, to monitor water use on crops and horticulture, and to monitor land degradation neutrality. EO mapping of crops happens on all scales with both optical and SAR sensors. Relevant EO products include degradation, agri-environment, ecosystem, damage estimation, warning-service, food-security, impact, crop health (disease and stress), leaf area index, crop acreage and yield harvest (inventories / statistics), crop types (extent, growth, health, stress), land surface temperature, illicit crops, estimates, cultivation patterns, soil water index, surface soil moisture, run-off, land cover (land cover change), land productivity (net primary productivity, NPP), carbon stocks (soil organic carbon, SOC).","hasChildren":true,"name":"Monitor crops","selfAssesment":"New
"},{"code":"TA13-4-2","description":"Monitor the forest focuses on regular and periodic measurement of certain parameters of forests (physical, chemical, and biological) to determine baselines to detect and observe changes over time. Typical applications include to assess deforestation and forest degradation, assess forest damage due to storms or insects, to monitor forest resources, detect illegal forest activities, assess the environmental impact of forerstry, and to monitor the forest carbon content. Moderate resolution sensors have been used to map forests at large scales. Modern very high resolution optical sensors provide enough spatial and spectral detail to map individual trees. Further sensors for forest monitoring include SAR and LIDAR. Integration of optical sensors, LIDAR and in-situ measurements seems an accurate method to achieve third dimension forest mapping.","hasChildren":true,"name":"Monitor the forest","selfAssesment":"New
"},{"code":"TA13-4-3","description":"EO provides the opportunity to monitor bodies of water, i.e. inland waters, and to assess ground water and run-off. For lakes, this includes products about water quality, pollution, turbidity, suspended sediment concentrations (quantitative, qualitative), waterbody (temperature, extent, volume, quantity), algal blooms, alkaline water, evaporation, surface temperature. For ground water and run-off, the products focus on water run-off (water quantity), hydrological network and catchment areas (water catchment), run-off season, groundwater. Various scales are addressed, from local catchments to the global water cycle. For inland water quality, sensors are optical medium resolution (300 meters) for achieving a (strongly cloud-cover dependent) update frequency of 10-20 times per year and high resolution (5 meters) for update frequency of 3-5 times per year.","hasChildren":true,"name":"Monitor bodies of water","selfAssesment":"New
"},{"code":"TA13-4-4","description":"Monitoring of snow and ice focuses on glaciers and their retreat due to climate change (extent, mass balance), the seasonal snow cover (its extent, depth, temperature and snow water equivalent), and the ice on rivers and lakes (inland ice, thickness, freezing period, melting period, ice extent). Glacial monitoring in the mountainous regions around the globe, and of the Greenland and Antarctic ice shields uses optical EO data of high and very high resolution and SAR data. Satellite based daily snow covered area products can reliably be provided down to a spatial resolution of 500 meters. Global products are possible with weekly updates. Applications include, among others, climate change impact monitoring, relevant for modelling runoff patterns in catchments for etimating hydroelectric power generation potential.","hasChildren":true,"name":"Monitor snow and ice","selfAssesment":"New
"},{"code":"TA13-4-5","description":"EO is used to monitor land ecosystems and biodiversity, environmental impact of human activities, land pollution and vegetation encroachment. A tool for this is land cover mapping and mapping of land cover change about a wide set of categories, lincuding basic forest types, major agricultural surface types, conservation areas, settlements, infrastructure, primary roads, bare soil, water bodies, rivers, wetlands following standard classification schemes according to CORINE or FAO LCCS. Main source are optical EO data and associated pixel-based and object-based image classification methods. For discriminating vegetation classes, they often making use of various vegetation indices and biophysical parameters.","hasChildren":true,"name":"Monitor land ecosystems","selfAssesment":"New
"},{"code":"TA13-4-6","description":"EO technologies (both optical and SAR) are capable to categorize bio-physical coverage of land to produce land cover maps like CORINE Land Cover (CLC). The EO method is objective and allows for frequent updates. EO-derived land cover is an excellent basis for mapping land use, the socioeconomic use that is made of land. Land use products are used in a wide range of applications (e.g. agriculture, forestry, spatial planning, determining and implementing environmental policy, land accounting). In a humanitarian context, land use mapping is applied to map refugee camps, population and pressures on population that cause migration.","hasChildren":true,"name":"Monitor land use","selfAssesment":"New
"},{"code":"TA13-4-7","description":"EO is capable to monitor topography with various types of land surface elevation data (both digital terrain models and digital surface models) and also focus on land surface changes and ground deformation / movement due to e.g. soil erosion or permafrost thawing, frost heaving. This includes also the mapping of stable zones where such changes do not happen. The main ways of creating a digital elevation model (DEM) from EO data are deriving it from interferometric synthetic aperture radar (InSAR), from stereoscopic pairs of optical images acquired from different viewing angles, and deriving them via laser scanning.","hasChildren":true,"name":"Monitor topography","selfAssesment":"New
"},{"code":"TA13-4-8","description":"EO is able to extract information about subsurface geology, including near surface features, lithology features, and linear disturbance features (faults & discontinuities). Concerning monitoring of mineral extraction EO supports by mapping ground surface, illegal activities, mine waste (erosion, land subsistence, biodiversity/habitat loss, destruction & disturbance of ecosystems). Disturbance of ecosystems may happen by carbon seeps from reservoirs or pipelines. Their detection can also be done with EO data.","hasChildren":true,"name":"Extract information about subsurface geology","selfAssesment":"New
"},{"code":"TA13-4","description":"Services that monitor land cover all services/applications that are focused on monitoring, assessing, managing, planning and improving land areas, its ecosystems (land, soil and inland water monitoring/quality/availability & usage assessments) and evolution of the land surface (use, cover, seasonal and annual changes and monitors variables) even if it involves human intervention (environmental challenges, impact evaluation or suitability analysis).\r\nMonitoring is possible by deriving information from variables measured by EO in different domains, like vegetation, energy, water, and cryosphere. For vegetation, those variables are for example land cover, NDVI, burnt area, or surface soil moisture. In the energy domain, land surface temperature and surface albedo are known variables, for water it is water surface temperature or water quality. Finally, for the cryosphere lake ice and snow cover extent, and snow water equivalent are variables that are used for land monitoring services.","hasChildren":true,"hasParent":true,"name":"Monitor land","selfAssesment":"Completed
"},{"code":"TA13-5-1","description":"The full range of EO satellite sensors are capable of monitoring particular aspects of urban areas. The most relevant include SAR satellites such as TerraSAR-X that distinguish between urban fabric and other land cover. Further, optical satellites in the resolution range HR and VHR are used to map imperviousness and soil sealing. Beyond such land cover classifications with low granularity, HR and VHR data are used for producing detailed land use and land cover classifications that distinguish different settlement densities or, in combination with additional data, different land use such as transport, residential etc. as defined in Classification schemes specialized on urban areas. Airborne laser scanning (and stereographic analysis) maps building and vegetation heights. InSAR methods allow to measure land subsidence that is highly relevant e.g. in coastal cities close to or below the sea surface elevation. Night-time optical data maps lights. Thermal sensors allow mapping the heat that is radiated from cities. Typical applications include monitoring urban growth/sprawl, transport networks, urban heat islands, and generating city maps and 3D city models for urban planning that are relevant to users in smart cities and in local/regional planning.","hasChildren":true,"name":"Monitor urban areas","selfAssesment":"Completed
"},{"code":"TA13-5-2","description":"EO is capable of monitoring infrastrcture in general, i.e. buildings (and their construction) and transport networks (roads, rails). Additionally, infrastructure for renewable energy harvesting (solar and wind farms, hydroelectric powerplants) and identification of suitable sites (through mapping solar radiation, wind roses, speed and direction, hydrological network mapping). A basis is land surface mapping for deriving digital elevation models (DEMs) that is required for modelling renewable energy potential and for spatial planning and landscape visibility analysis (visual impact assessments for planned infrastructure). Further, EO is capable of assessing damage from industrial accidents. A wide range of EO technologies is used here, infrastrcture can be directly detected and mapped with optical and SAR sensors, where the resolution depends on the targeted assets. DEMs can be generated from SAR and stereographic optical data. Wind energy related parameters can be derived from satellites focused on atmosphere and weather monitoring. Further, there are various GI methods in use, too (in particular focused on spatial planning and impact assessment).","hasChildren":true,"name":"Monitor infrastructure","selfAssesment":"New
"},{"code":"TA13-5","description":"Monitoring the built environment provides information about urban structures, transport networks and particular infrastructure, e.g. dedicated to energy provision. It covers all urban and infrastructure related service/applications on site development information, planning support or suitability analysis. As well, it includes pressure and threats analysis on the urban areas.","hasChildren":true,"hasParent":true,"name":"Monitor the built environment","selfAssesment":"New
"},{"code":"TA13-6-1","description":"Oceanic waters cover approximately 70% of the Earth´s surface and play a key role in regulating Earth temperature and climate, support important marine ecosystems and provide food and transport. Ocean waters occupy large areas and involve highly dynamic processes with different temporal and spatial scales. In-situ measurements taken by ships and buoys can provide accurate information but only at specific locations, being limited to understand large-scale processes. To characterise the heterogeneity and dynamics of ocean waters, it would be required to perform exhaustive field campaigns with associated high costs and infrastructure challenges. EO is an efficient tool to monitor ocean waters and to complement ocean in-situ monitoring programmes as it can provide cost-effective information over vast areas at continuous temporal and spatial scales. \r\nSince the first EO satellite specifically designed to study the oceans (SeaSat) has been launch in the 1970s, many sensors and platforms have been developed. This variety of sensors have provided measurements of a broad range of ocean physical and biological variables to the present day. For example, satellite observations in the visible and near-infrared bands have provided information about ocean colour that can be used to estimate chlorophyll-a concentration for monitoring water quality, productivity and algal blooms. Thermal infrared (TIR) sensors have provided data of Sea Surface Temperature (SST) that is of importance for the study of currents and ocean warming. Microwave radiometers have registered sea surface salinity (SSS), critical to determine the global water balance, understanding ocean currents and estimating evaporation rates. EO can also provide information about physical ocean features such as surface elevation and ocean currents, sea surface winds, ocean waves, vessels and pollutants such as oil spills. \r\nThe versatility of EO data have been proved in a broad range of applications, including the monitoring of water quality, climate change effects, hurricane tracking and prediction, monitor maritime traffic and pollution, harmful algal blooms and fisheries management. In recent years, the Copernicus programme has launched a series of satellite missions for water and land monitoring that guarantee the provision of long-term observations giving continuity to previous satellite missions. Within the Copernicus programme, especially the Sentinel-3 mission will have relevance for ocean observations. Currently, two satellites Sentinel-3A and Sentinel-3B, launched respectively in 2016 and 2018, are providing near-real-time data on the state of the ocean surface, including sea surface temperature, marine ecosystems, water quality and pollution monitoring. New hyperspectral missions such as the Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) developed by NASA, are currently under development. In the near future, they will complement the existing satellite missions and will register data in a high number of spectral bands. This information will be essential in diverse applications such as aquatic ecology and biochemistry. Ocean EO is still an evolving field that will need skilled professionals that exploit the data from the new and upcoming missions for the advancement of ocean knowledge and monitoring.","hasChildren":true,"name":"Monitor the marine ecosystem","selfAssesment":"Complete
"},{"code":"TA13-6-2","description":"In coastal areas, EO is capable to monitor water depth and shallow water bathymetry (charting), coastal ecosystem parameters about water temperature, water transparency, oxygen, phytoplankton abundance, bathing water indicators, detection harmful algal blooms, sediment (qualitative, quantitative), turbidity (quality, quantitative), visibility, chlorophyll-a concentration, suspended sediment may be indicative of estuarine processes, re-suspension or pollution. Further, this includes coastline monitoring with a focus on shoreline and its change as well as coastal land cover (and terrain) and its change. A widse set of EO sensors and technologies is used to monitor coastal areas. Optical satellite imagery is analyzed to detect and map suspended sediment concentrations. Etc.","hasChildren":true,"name":"Monitor coastal areas","selfAssesment":"New
"},{"code":"TA13-6-3","description":"EO is capable to monitor weather impact on ocean surface and metocean features as a basis for forecasting furture ocean conditions. This includes ocean surface topography, ocean dynamics and circulation like tides and ocean current movements and drift, ocean winds, wave and climate conditions at ocean locations (meteocean). Further, this covers the mapping of extreme waves like tsunamis and the monitoring of hurricanes and typhoons. Involved EO technologies are for example satellite altimetry that maps ocean surface with 2 cm to 3 cm accuracy, mathematical forecast models. Repeated altimetry measurements allow mapping speed and direction of ocean's currents and tides. Available EO-based RADAR systems monitor wave height and direction, wind speed and sea-surface elevation. Near-realtime processing and delivery workflows enable the use of these parameters in weather forecasting, navigation and offshore installations protection.","hasChildren":true,"name":"Monitor weather impact on ocean surface","selfAssesment":"New
"},{"code":"TA13-6-4","description":"To support an ecosystem-based approach for fisheries management, EO images with global and daily systematic coverage with high-resolution images can help in identifying potential fishing zones and to assess fish stocks. They help assessing and understanding changing abundancy and spatial distribution of exploited fish stocks. Therefore, they analyse various key environmental parameters that can be detected with satellite remote sensing. This includes sea surface temperatures (SSTs), sea surface height anomalies, and sea surface colour revealing the abundance of chlorophyll a. This relates to phytoplacton production that is directly related to total fish landings. Additionally, EO can detect harmful algal bloom. A further threat to sustainable fish stocks management are illegal fishing. Where localization of licensed fishing vessels and fleet management services are supported by EO to avoid overexplotation and enable recovery of fish stocks. EO complements identification, detection and tracking of vessels with SAR and optical remote sensing.","hasChildren":true,"name":"Monitor fisheries","selfAssesment":"New
"},{"code":"TA13-6-5","description":"For shipping, navigation, and monitoring sea-traffic and pollution, remote sensing and satellite technologies allow detecting vessels in the wider ocean. EO can detect the vessels themselves, their wake trailing behind them, sandbanks and reefs that pose a threat for safe navigation. Additionally, EO can detect pollution from the ships, e.g. when illegal waste disposal happens. Ship detection and classification is possible with the use of optical and synthetic aperture radar (SAR) imagery. The methods complement each other.","hasChildren":true,"name":"Detect and monitor ships","selfAssesment":"New
"},{"code":"TA13-6-6","description":"Information on sea ice and icebergs is important for managing operation of ships or offshore platforms in hazardous sea ice conditions. EO technologies give the possibility to study sea ice and measure its thickness, spatial distribution, motion and ridges (as well as ice berg positions). Satellite imagery provides wide area, synoptic pictures of the ice conditions. Since the scale of ice fields is quite large, mainly moderate resolutions have to be accepted, down to around 10m in scale, while ensuring comprehensive coverage. Multispectral imagery can provide more information on ice-type but in the main, SAR imagery is used due to its all-weather and day/night capability. The data collected can be more accurate than in-situ measurements due to a higher and faster coverage of a whole area. Subsequent modelling that incorporates ocean weather (wind, waves, ocean current) provides expected drifting paths. Constant monitoring is most important to identify the risk and opportunities, for instance for ship routing, and safety of oil rigs.","hasChildren":true,"name":"Monitor sea-ice and icebergs","selfAssesment":"New
"},{"code":"TA13-6","description":"Monitoring marine inlucdes monitoring of marine safety (e.g. marine operations, oil spill combat, ship routing, defence, search & rescue, ...), marine resources (e.g. fish stock management, ...), marine and coastal environment (e.g. water quality, pollution, coastal activities, ...), and climate and seasonal forecasting (e.g. ice survey, seasonal forecasting, ...).","hasChildren":true,"hasParent":true,"name":"Monitor marine","selfAssesment":"New
"},{"code":"TA13","description":"EO services and applications are organized according to thematic areas. EO is used for a wide set of services. There are many applications of EO that show how a service produces information for a particular client. EO service and applications are best described by the purpose they serve or by the need of the user. The main user needs to EO are to monitor, to map, to forecast, to assess, to detect, and to analyse. \r\nTo monitor means to watch and check a situation carefully for a period of time in order to discover something about it, i.e. keeping track of how the natural and manmade environment change (their status) over time. Typical alternative verbs are track, observe, record, follow, understand, or surveil. \r\nTo map means to represent an area of land in the form of a map, i.e. to feature and locate the way it is arranged or organized. Synonymous verbs are locate, identify, classify, trace, or record.\r\nTo forecast means to provide statements covering a range of different outcomes, to say what you expect to happen in the future; i.e. to predict future events based on specified assumptions (about information extracted from EO change and time series data), where different sets of assumptions describe scenarios. Equivalent terms are predict, plan, model, estimate, or project.\r\nTo assess means to judge or decide the amount, value, quality or importance of something, i.e. to evaluate and measure the status of and changes in natural and manmade built environments. Alternative verbs are evaluate, measure, understand, review, or quantify.\r\nTo detect allows to notice something that is partly hidden or not clear, or to discover something, especially using a special method, i.e. to identify and locate the changes in the Earth’s environment. Similar terms are locate, warn, identify, highlight, or spot.\r\nTo analyse means to study or examine something in detail, in order to discover more about it, i.e. to detail the elements of a whole and critically examine and relate these component parts separately and/or in relation to the whole. Sometimes, the terms to process, to parse, or to detail are used in exchange for to analyse.","hasChildren":true,"hasParent":true,"name":"EO services and applications","selfAssesment":"New
"},{"code":"TA14-1-1-1","description":"Ocean colour can be made visible in atmospherically corrected EO data. Specific spectral bands are necessary to derive physical and biologic parameters of the water from the EO data.","hasChildren":true,"name":"Ocean colour","selfAssesment":"New
"},{"code":"TA14-1-1","description":"Band combinations are pre-defined for (visually) analysing images for a dedicated purpose. Examples are dedicated band combinations for land us land cover classification, ocean colour, etc.","hasChildren":true,"hasParent":true,"name":"Band combinations","selfAssesment":"New
"},{"code":"TA14-1-2","description":"The spectral and refractive information from optical and SAR data enables direct and indirect derivation of biophysical and geophysical EO parameters that are properties of the sensed land surface, ocean surface and atmosphere volume.","hasChildren":true,"hasParent":true,"name":"EO parameters","selfAssesment":"New
"},{"code":"TA14-1","description":"Processing products are image products from raw data to all different processing stages. The transformation processes between the stages include operations such as atmospheric correction, cloud detection and radiometric calibration to provide data in a form suitable for subsequent analysis. Processing products consider a product as being an output of a process.They appear as \"intermediate products\" along all steps of the processing chain.","hasChildren":true,"hasParent":true,"name":"Processing-related and preparatory products","selfAssesment":"New
"},{"code":"TA14-2-1-1","description":"Point clouds represent a set of points with X, Y, Z coordinates and associated attributes. A source of acquisition is Light Detection and Ranging (LIDAR) sensor.\r\n Depending on the location of the recording device, i.e. where and on which the LIDAR systems are mounted, it can be divided into: Terrestrial Laser Scanning (TLS), Airborne Laser Scanning -ALS) and Spaceborne Laser Scanning (SLS).\r\nThe LIDAR system uses the near-infrared part of the electromagnetic spectrum (1064 nm) for active data collection, day or night, in the shade, but also in low visibility conditions (e.g. under clouds). Due to the footprint of the beam itself, when interacting with vegetation, one part will be reflected back, registering the height of the vegetation, and part of the beam will pass to another surface from which the other part of the beam will be reflected. Depending on the beam intensity and vegetation density this can happen a few times until it hits a hard surface and the rest of the beam is reflected.\r\nIn this way, precise information on the height and density of vegetation can be obtained, but also using automatic and semi-automatic data filtering techniques, it is possible to create several very high resolution products from source data: digital elevation model (DEM), digital relief model (DMR) digital canopy model (DCM) , digital surface model (DSM).\r\nDepending where the sensor is mounted, the density of collected point clouds can be from 15 points per m2 to as many as 250 points per m2 (in the case of UAV dana collection). This is also depending on the speed and altitude of the flight and the speed and power of the emitted pulse or beam. The biggest advantage of LIDAR scanning is that in most cases, a sufficient number of beams will always penetrate to the ground, allowing the creation of a very precise digital relief model which is the basis for further analysis. This is not always possible in very dense vegetation areas (rainforests).\r\nThe advantage of LIDAR point clouds lies in the fact that it truly provides a huge amount of information gathered in a short period of time, that are of exceptional precision. These point clouds have very wide application from forestry, surveying, architecture to archeology.\r\nGiven the development of technology, it is possible to obtain a similar point cloud by photogrammetry methods. However, photogrammetric cameras (eg orthophotos and infrared cameras) have one significant drawback, they cannot penetrate clouds, vegetation and water, and only DSM product can be extracted from them.","hasChildren":true,"name":"Point clouds","selfAssesment":"Completed
"},{"code":"TA14-2-1-2","description":"Elevation data in the form of a digital elevation model (DEM) is an essential component of many analyses derived from EO. DEMs are used to represent every kind of surface, including terrain surface, vegetation canopy surface, sea surface, sea-ice surface, glacier surface etc. This description focuses on DEMs for representing terrain. A digital terrain model (DTM) describes the bare ground of the terrain, a digital surface models (DSM) described heights of vegetation (e.g. trees) and of man-made structures (e.g. buildings) reaching above the terrain. DEM is often used as an umbrella term for DTM and DSM. EO-derived DEMs are usually DSMs and require removal of vegetation and buildings in order to represent the terrain (DTM). DEMs are multi-purpose products used in various applications. They are available for global scale (SRTM, WorldDEMTM), regional scale (ArcticDEM, Copernicus EU-DEM v1.1) or for national levels and local regions. Various techniques exist to generate DEMs from SAR data, stereographic optical EO (as well as airborne and drone) data and from airborne laser scanning.","hasChildren":true,"name":"Digital elevation models","selfAssesment":"Completed
"},{"code":"TA14-2-1-3","description":"By comparing elevation models of different dates, the change in elevation and volume can be identified. Thereby, they measure surface deformation, land subsidence, ice shield loss due to melting, etc.","hasChildren":true,"name":"Elevation change maps","selfAssesment":"New
"},{"code":"TA14-2-1-4","description":"Vector fields capture the movement directions of locations on a continuous surface, e.g. of the ocean, or in a 3D grid of locations, e.g. of the atmosphere. The atmosphere and the ocean are highly dynamic features. Vector fields are used to represent wind directions and current movement directions. Further vector fields derived from EO data include geoid undulation / gravity maps.","hasChildren":true,"name":"Vector fields","selfAssesment":"New
"},{"code":"TA14-2-1-5","description":"When a moving feature (i.e. object) is detected in subsequent images, its trajectory of movement can be mapped. Such products map ship movements, sea ice movements, etc.","hasChildren":true,"name":"Feature trajectories","selfAssesment":"New
"},{"code":"TA14-2-1","description":"Geometrically measured EO products origin from EO-derived distance measurements, measurements of direction, tracking of moving objects, and changes of distance measurements. The used EO methods include for example SAR interferometry and stereographic analysis of optical data.","hasChildren":true,"hasParent":true,"name":"Geometrically measured EO products","selfAssesment":"New
"},{"code":"TA14-2-2-1-1","description":"Land cover maps represent spatial information on different types (classes) of physical coverage of the Earth's surface, e.g. forests, grasslands, croplands, lakes, wetlands. An example is the European Copernicus product CORINE land cover (CLC) with 44 classes. Initiated in 1985 (reference year 1990), updates followed in 2000 and every 6 years afterwards. Apart from CLC, the European Copernicus Land products also include the High Resolution Layers. They includes for example the imperviousness product that captures the percentage of soil sealing. Land cover classification products are multi-purpose products that are relevant for various applications. They are available on national levels, regional levels and global levels. They have different scales and granularity of their associated classification scheme. The products are updated on a regular basis. Update cycles can vary depending on the resolution (i.e. likelihood for observable change of the land surface) and the capability of production processes. An additional example on a global scale is the Global Urban Footprint. The products are provided by public organisations and private EO companies and based on various EO sensors.","hasChildren":true,"name":"Land cover maps","selfAssesment":"Completed
"},{"code":"TA14-2-2-1-2","description":"Land use documents how people are using the land. Getting from physical land type (land cover) to land use requires skill in interpretation and involves integration and consultation of ancillary data. Land use maps are multi-purpose products that are relevant for many applications. The products are updated on a regular basis (e.g. 6 years for Urban Atlas).","hasChildren":true,"name":"Land use maps","selfAssesment":"New
"},{"code":"TA14-2-2-1-3","description":"Cloud masks for optical EO data distingush cloudy pixels from cloud-free pixels. They may differentiate between serveral cloud types, i.e. opaque clouds and Cirrus clouds (that are transparent). Most land monitoring applications based on optical data require cloud-free images. Therefore, cloud masks are a product that is used early on in image processing for selecting suitable imagery for analysis (e.g. by screening images of an archive by the derived cloud cover percentage of the image). Therefore, cloud masks are made available as metadata by the EO data provider. Clouds are identified with threshoulding of reflectance values of the blue band and, to adapt for cloud/snow confusion, specific short-wave infrared (SWIR) bands.","hasChildren":true,"name":"Cloud mask","selfAssesment":"New
"},{"code":"TA14-2-2-1-4","description":"Detected features are objects from one or more classes and are the result of a comprehensive (and mostly automatic or semi-automated) search of all locations in an image that decides whether such features are present and where they are located. Examples inculde man-made objects (e.g. vehicles, ships, buildings, etc.) with sharp boundaries and are independent from the background, and landscape objects, such as land-use/land-cover (LULC) parcels that have vague boundaries and are part of the background environment. Only the latter type would locate features for all locations of an image.","hasChildren":true,"name":"Detected features","selfAssesment":"New
"},{"code":"TA14-2-2-1","description":"Static EO derived thematic classification products and masks (e.g. land use land cover classifications). Additionally, static EO detected features (planes on apron of airports, dwellings) that consist of a set of point locations (or polygons) and do not end up in a comprehensive classification of all pixels of an image. Static EO derived thematic classification products and masks (e.g. land use land cover classifications). Additionally, static EO detected features (planes on apron of airports, dwellings) that consist of a set of point locations (or polygons) and do not end up in a comprehensive classification of all pixels of an image. Thematic classifications and feature detection identify a surface by a class label that represents a more or less persistent state. A good example product is the Copernicus Urban Atlas. The most recent available version is assumed to represent the \"current\" state (Certainly, an update cycle is necessary for providing a product that remains up-to-date).","hasChildren":true,"hasParent":true,"name":"Thematic classifications and feature detection","selfAssesment":"New
"},{"code":"TA14-2-2-2","description":"Event maps and thematic change (evolution) maps indicate that some process happened that changed the area at a location from one class to the other. For example, a burnt area map indicates locations where vegetation has been burnt by a fire and changed to bare ground. A typical mapping method is the use of pre- and post-event satellite images for detection of the areas affected by the process. Eventually burnt areas contain identifiable burn marks that allow direct identification in one single post-event satellite image. Nevertheless, it is the process that is central to the analysis. Similarly, the concepts aforestation and deforestation would fall under the heading \"Event maps.\" They may come from a comparison of two status maps of different dates. Some processes benefit from analysis of more than two states. Such change evolution maps can be produced with time-series analysis. On land, more examples include landslide maps, flooded area maps and other land surface dynamics (e.g. aforestation and deforestation). Further, change detection maps are available for other domains (atmosphere, marine, land, climate, etc.)","hasChildren":true,"name":"Event maps and thematic change (evolution) maps","selfAssesment":"New
"},{"code":"TA14-2-2","description":"The semantic labelling products result from methods that assign labels to objects or locations in a field. The labels correspond to the categories of a classification or, in case of masks and detected features, to a single target class. Such labels may also identify classes of change or change evolution.","hasChildren":true,"hasParent":true,"name":"Semantic labelling products","selfAssesment":"New
"},{"code":"TA14-2-3","description":"EO-derived attribute products describe the state and evolution of specific attributes of a feature or at a field location. They describe for example air quality, soil moisture or water quality & quantity.","hasChildren":true,"name":"EO-derived attribute products","selfAssesment":"New
"},{"code":"TA14-2","description":"Descriptive analytics products provide analytical results which describe the present (and past) situation as it is recorded in EO images. Therefore, it contains information that can directly be extracted from EO images or EO image time series. These products are diverse in various aspects: they capture static and dynamic information; they concern information about objects or fields; and they have qualitative (nominal scale) or quantitative (ordinal, interval, ratio scale) levels of measurement.","hasChildren":true,"hasParent":true,"name":"Descriptive analytics products","selfAssesment":"New
"},{"code":"TA14-3","description":"Providing analytical (modelling) results which predict the future situation (e.g. air pollution forecasts). [interpolation in space, i.e. not only prediction into the future, filling gaps in time series...]\r\nInformation that can be modelled based on descriptive analytics products. by extrapolating time series (forecasting/predicting), by modelling of processes (e.g. flood risk maps, landslide susceptibility)","hasChildren":true,"name":"Predictive modelling products","selfAssesment":"New
"},{"code":"TA14-4","description":"Prescriptive modelling products and services focus on providing analytical results that are a guide to action. The often result from an impact assessment. One example is the identification of construction sites leading to sales opportunities.","hasChildren":true,"name":"Prescriptive modelling products and services","selfAssesment":"New
"},{"code":"TA14-5-1","description":"A textured 3D model uses a 3D model derived from elevation data. Additionally, each separate surface of the 3D model receives its own texture derived from optical image data. Typically used for visualisation purposes.","hasChildren":true,"name":"Textured 3D models","selfAssesment":"New
"},{"code":"TA14-5-2","description":"A semantic 3D model consists of a 3D model derived from elevation data with an integrated image classification. A classified object thereby consists of a 3D surface or a grouped set of 3D surfaces. A typical example is a 3D city model in the CityGML format.","hasChildren":true,"name":"Semantic 3D models","selfAssesment":"New
"},{"code":"TA14-5","description":"Combining the satellite data with other information sources. Resulting in an integration of several descriptive analytics products and processing products, e.g. a textured 3D model or a semantic 3D model.","hasChildren":true,"hasParent":true,"name":"Aggregation and integration products","selfAssesment":"New
"},{"code":"TA14-6-1","description":"Sentinel-2 cloud-free mosaics for display, satellite maps in books etc.","hasChildren":true,"name":"Satellite maps","selfAssesment":"New
"},{"code":"TA14-6-2","description":"Layouted maps in a file (PDF, SVG, etc.) for printing or visualisation on screen, embedding in reports or as static displays on websites etc.","hasChildren":true,"name":"Layouted digital maps","selfAssesment":"New
"},{"code":"TA14-6-3","description":"Digital layouted maps in an online map viewer; 3D visualisations on the screen / 3D screen and online map viewers with 3D capabilities etc.","hasChildren":true,"name":"Web visualisations in 2D and 3D","selfAssesment":"New
"},{"code":"TA14-6-4","description":"Printed maps, 3D plots of 3D models, hologram 3D maps etc.","hasChildren":true,"name":"Analogue visualisation products","selfAssesment":"New
"},{"code":"TA14-6-5","description":"A video is a structured file of 2D grids link by the time, is a regular file of values which has been processed to sensor units (e.g. calibrated). The result can be a single date acquisition or a combination of dates. For each point, the value represents a parameter imaged by the sensor. Videos of EO data present for example time series of satellite maps and other EO products (e.g. Arctic sea ice evolution in a time-series map video over the past 30 years).","hasChildren":true,"name":"Time series map videos","selfAssesment":"New
"},{"code":"TA14-6","description":"Visualisation products are used for presentation of EO information to the user. The user's interaction with the visualisations is predominantly viewing and interpretation of the informational content and arriving at decisions in the context of the user'S objective with the EO information. In addition, users of visualisation are all involved actors during image processing. For example, an EO analyst may use visualisations of EO data and preliminary EO products for getting a better understanding of the contained information and adapt his processing workflow to arrive ad improved results. Typical visualisation products include satellite maps, layouted digital maps, web visualisations in 2D and 3D, and analogue visualisation products.","hasChildren":true,"hasParent":true,"name":"EO visualisation products","selfAssesment":"New
"},{"code":"TA14-7","description":"Users need access to EO products if they shall be able to benefit from them. Additionally, providers of value added products act as users of EO products earlier in the information processing value chain. Concequently, various distribution services provide access from raw data to processed information and processing infrastructure. Provision of access to raw data or processed information happens via direct download (FTP), via application programming interfaces (API) or web services (e.g. Hubs). Further, access to processing infractructure happens via web services.","hasChildren":true,"name":"Distribution services","selfAssesment":"New
"},{"code":"TA14","description":"Products in relation to EO appear along the entire image processing value chain as inputs and outputs of processing steps. Ultimately, at the end of that chain, the output EO products represent information that supports actions. The standard EO products are categorized by the type of problems they help to solve or the type of question they help answering.","hasChildren":true,"hasParent":true,"name":"Standard EO products","selfAssesment":"New
"},{"code":"WB","description":"This knowledge area is about Web Based Geographic Information management aspects and therefore it was given the name \"Web Based GI\" or \"WBG\" in short. It is implied by this name that the differentiating factor for this KA is the \"Web\". One must then be able to answer the questions like \"What functions do we delegate to the Web?\" or \"how WBGI is different from the traditional GI?\" Sticking to the functions of a GIS, which are inserting (adding), storing, manipulating, analysing and presenting the data, there is not a single system for effecting all these tasks anymore but the Web itself. For instance, there is no single database and its known-to-its users-definition, anymore but many different stores and many different definitions. Similarly, many different manipulation, analysis and presentation options compared with the options offered by a single or limited number of systems of traditional GI. In general, Web provides the means of leveraging distributed \"resources\" like data, information, or software. It is a \"collaboration medium\". A collaboration that enables rapid production or decision making. A collaboration that certainly introduces new dimensions to traditional GI handling. This is the justification of proposing this KA in addition to the KAs of the original BoK. For the mentioned collaboration to happen, data or any other type of a resource have to accessible on the Web. This means that it should have a Web \"address\" and a \"definition\" that is understandable either by \"human\" or \"machine\". \"Machine understandable definitions\" refers to the dimension of \"semantics\" and \"ontologies\" which are also included under this KA. When one talks about publishing resources then \"catalogue services\" and more importantly \"discovery\" dimension comes into the scene. On the other hand, \"Linked Data (LOD)\" and \"Open Data\", highly popular recent trends and two of the above mentioned dimensions of Web GI have also been covered under this KA. Like the other dimensions of Web GI, both LD and OD aspects must be known to GI communities with differing degrees of expertise. The concepts of \"interoperability\" and \"Spatial Data Infrastructure (SDI)\", hot topics of GI communities for many years, have been thought to be dealt with under this KA as well with the justification that \"Web GI\" is a much broader concept than SDI, This is by the fact that SDI refers to a much narrower content and context of \"collaboration\" then Web GI. Therefore, Geospatial data interoperability and some of the related concepts which were classified under KA, \"Geospatial data in the original BoK were moved under KA11 with the updated context. Another issue is the coverage of Spatial Analysis (SA), data manipulation aspects of GI by KA11. The SA aspects are covered by other KAs like \"Geocomputation\" and \"Analytical methods\". If the analysis operations, in an undertaking, would be handled by web services this is already covered by \"data processing\" web services, application development unit and Web services composition under that unit. The important thing is to have the knowledge about a specific analysis operation; Employing it as a web service would require no more knowledge than using any other web service. SA is covered by KA11 in as much as it should have been.","hasChildren":true,"hasParent":true,"name":"Web-based GI","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB1-1","description":"The basic principles on which web services build. The concept of Service Oriented Architecture and the importance of APIs","hasChildren":true,"name":"Fundamentals of web services","selfAssesment":"In progress/to be revised (GI-N2K)
"},{"code":"WB1-2","description":"This concept will cover web services based on the Simple Object Access Protocol (SOAP)","hasChildren":true,"name":"SOAP web services","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB1-3","description":"This concept will cover web services based on the representational state transfer (REST) protocol","hasChildren":true,"name":"REST web services","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB1-4","description":"The Open Geospatial Consortium (OGC) defines standards and best practices for web services in the geospatial domain. OGC standards are developed using a consensus model allowing all stakeholder to participate in the process. As a result the OGC web services are widely implemented.","hasChildren":true,"name":"OGC web services","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB1","description":"In the most simplistic way a Web service may be defined as \"a Web accesable program code which performs a task of either processing or serving some data. Although there are many other definitions in the related literature, the one in W3C (2004) seems to be quite complete and refering to also lately popular REST style Web services. It states that \" We can identify two major classes of Web services: REST-compliant Web services, in which the primary purpose of the service is to manipulate XML representations of Web resources using a uniform set of \"stateless\" operations; and arbitrary Web services, in which the service may expose an arbitrary set of operations.","hasChildren":true,"hasParent":true,"name":"Web services","selfAssesment":"In progress GI-N2K
"},{"code":"WB2-1","description":"To be able to discover and assess available data or services, these resources have to be documented. This concept describes the standardized languages used for these descriptions","hasChildren":true,"name":"Languages for the definition of non-spatial data and services","selfAssesment":"GI-N2K
"},{"code":"WB2-2","description":"Different standardized ways to define geospatial data exist. GML, GeoJSON, WKT and GeoSPARQL are examples. What are common points and differences","hasChildren":true,"name":"Definition of geospatial data","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB2-3","description":"Defining a common language is a crucial step for sharing or combining data. Vocabularies, taxonomies, ontologies are are tools to reach this goal.","hasChildren":true,"name":"Ontologies development reuse and patterns","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB2","description":"A \"resource\" could be \"anything\" including data and services, identifiable over the Web. A resource should be defined in a language to be discoverable on the Web. Over the years, two major bodies W3C for non-spatial and OGC concerning spatial data have developed many specifications for defining data and services. On the W3C side, Resource Description Framework (RDF) has gained a great momentum in recent years in relation to the recent popularity of Linked Data as well. In the OGC front, the acceptance of GML was a major step concerning the long time effort of geospatial communities for having a standard for the definition of both geospatial features and geometry.","hasChildren":true,"hasParent":true,"name":"Resource Definition","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB3-1","description":"Metadata is information about the data to be published. It helps the user to discover the data, allows the user to evaluate the fitness for use and it explains how and under which conditions the data can be retrieved and used. Metadata are a core component of data infrastructures and as such, standardization is a requirement for the correct exchange and interpretation of the metadata.","hasChildren":true,"name":"Metadata and standards","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB3-2","description":"A resource can be added manually to a catalogue service by creating or uploading its metadata, but metadata can also be added by automated crawling of other catalogues.","hasChildren":true,"name":"Manual and automated forms of publishing","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB3-3","description":"Catalogue services allow to publish and search resources through their metadata","hasChildren":true,"name":"Catalogue services","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB3-4","description":"Open data is data that is free to use, re-use and share without limitations on who uses it or for what purpose. Publishing open data is making the data discoverable and accessible in a convenient way (technical openness).","hasChildren":true,"name":"Publishing open data","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB3-5","description":"Adding semantic information to the data allows computers to understand the structure and meaning of data. This allows automatic searching, processing and integrating data with other semantic sources.","hasChildren":true,"name":"Publishing via a semantic definition of data","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB3-6","description":"Linked (open) data provides structured data which is interlinked in a machine readable way. This allows to discover, access and combine data in an automatic way. This concept discusses the steps needed to make existing data available in a linked open way.","hasChildren":true,"name":"Publishing linked open data","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB3","description":"\"Publishing\" means making a resource available for the use of others. A \"resource\" could be \"anything\" including data and services, identifiable over the Web. Publishing may be done on the basis of either the \"characteristics\" of the data or the data itself. When only some \"characteristics\" of a resource is published then some of the contents would naturally be left out. The \"characteristics\" include metadata and some keywords. This kind of publishing may be named as \"limited contents\" publishing or \"publishing by metadata\". One of the issues become then what characteristics to use to define the data. Or what what metadata definition to use. Another aspect of publish is \"manual entry\" and \"automated collection\". In the former publisher enters metadata while in the latter some harvesting mechanism collects metadata in an automated fashion. On the contrary, there is \"unlimited contents publishing\" where there is no limitation on the published contents. Open data publishing is in this class. In additon, some \"additional semantics\" may be subject of this type publishing through new relationships in the ontologies of publishing, which have not been explicit in the exisiting data model but are inherent in the data. And this last type is covered under the topic, \"Publishing via a semantic definition of data.\"","hasChildren":true,"hasParent":true,"name":"Resource Publishing","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB4-1","description":"Syntactic discovery is the discovery of resources based on the structure of the resources","hasChildren":true,"name":"Syntactic discovery","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB4-2","description":"Semantic discovery is the discovery of resources based on the meaning of the data.","hasChildren":true,"name":"Semantic discovery","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB4-3","description":"Linked (open) data provides structured data which is interlinked in a machine readable way. This allows to discover, access and combine data in an automatic way.","hasChildren":true,"name":"Discovery over linked open data","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB4","description":"Resource discovery means the discovery of resources including data and services needed for an application. Syntactic discovery refers to the discovery on the basis of syntactic comparison operations. It is classified as \"keyword-based\" and \"full-text-based\" discovery. Semantic discovery on the other hand, refers to the discovery of resources on he basis of some semantic definition. Therefore, semantic discovery requires that a resource be published by a semantic definition as defined in the topic WB3-5.","hasChildren":true,"hasParent":true,"name":"Resource Discovery","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB5-1","description":"The workflow to integrate geospatial data in an application often relies on a combination of different OGC web services. Searching and finding the data and the corresponding services, binding to these services to view, filtering and or downloading the data are different steps in this process","hasChildren":true,"name":"Integrating data from OGC web services","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB5-2","description":"The alignment of data structures and vocabularies/ontologies used are important steps towards the data harmonisation needed for a combined use of datasets","hasChildren":true,"name":"Schema matching and ontology alignment","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB5-3","description":"A data mashup is a combination of data from different sources to produce new applications of new datasets","hasChildren":true,"name":"Data mash ups","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB5","description":"The term \"application development\" refers to the collection of activities or the \"workflow\" through which the user reaches her final goal. Being one of these activities, \"data integration\" means the transformation of data from one representation to another which might be of either the client`s one or some other representation. An example for data integration might be the case where the data is transfered from an OGC WFS and integrated into a client GIS.","hasChildren":true,"hasParent":true,"name":"Application development via Data Integration","selfAssesment":"In Progress GI-N2K
"},{"code":"WB6-1","description":"Manual Web Service Composition is manually (by human) combining the activities of discovery, composition and invocation to fulfil a certain task.","hasChildren":true,"name":"Manual Web Services Composition","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB6-2","description":"Providing standardized descriptions of the specifics of available webservices creates an environment where the composition of services to create a web application can be automated.","hasChildren":true,"name":"Semi automated and Full-automated WSC","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB6","description":"Web Services Composition can be defined as bringing together a number of web services in a certain workflow to achieve a certain task that cannot be achieved by any of the composed services alone. In general, it involves first the discovery of the suitable services over the Web, and compose them in a certain workflow order and finally run the composed service which is the invocation stage. WSC has been a highly active research topic since the emergence of Web services in 2000s. \"Manual\" WSC is the form that the activities of discovery, composition and invocation are all done manually (by human). In the \"Semi-automated\" way, the discovery is done by the machine. In the \"full-automated\" approach all the above activities are done by the machine. There are no tools at the moment that achieve full automated composition. Web API composition is like WSC, the only difference is the fact that instead of web services there are Web APIs in WAPIC. There is no doubt that One would run into the very same problems of WSC concerning full automated composition. In other words, WAPIC would in no way be easier than WSC. Nevertheless, as far as semi automated form can be achived, WAPIC is valuable because the number of Web APIs increase drastically from day to day. The site \"programmableWeb\" lists 14 957 APIs at the moment. It is not easy to search for all those APIs manually for the discovery of suitable APIs for a given task.","hasChildren":true,"hasParent":true,"name":"Application development via Web services composition","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB7-1","description":"Hypertext markup scripting and styling are the base for each web page or application. Styling defines the look and feel while scripting is used to implement the behavior of the web application","hasChildren":true,"name":"Hypertext markup scripting and styling","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB7-2","description":"Web map APIs allow developers to integrate resources made available by web services in their application or web sites.","hasChildren":true,"name":"Web Map APIs and Libraries","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB7-3","description":"A web application framework provides the generic and reusable building blocks needed to create web applications. Geoportal frameworks provide the functionality to build geospatial portals.","hasChildren":true,"name":"Web application Frameworks and Geoportal frameworks","selfAssesment":"In Progress (GI-N2K)
"},{"code":"WB7","description":"Characteristic examples are included under this topic. The APIs, for instance other than the ones included under this unit, and libraries could have been included as well. However, since the important thing is to highlight the functionality then there is no need to include them all. By the inclusion of topic \"WB7-3\"under this unit, the aim was to cover one of the very \"hot\" topics of Web2.0 for both the main concepts about Web application frameworks and also how they are related to portal frameworks and geoportals. By the topic \"WB7-1 Building blocks\" the core components of Web application development are covered. On top of this core, there comes a great variety of \"Web application frameworks for both enabling rapid web application development and ensuring scalable, high-performance applications. Finally, there are \"Web APIs and Libraries\" certainly deserving being a separate topic for their current popularity. They also mean rapid application development for developers by code reuse and versatility for \"end users\" in creating their \"end products\".","hasChildren":true,"hasParent":true,"name":"Web Application development elements","selfAssesment":"In progress (GI-N2K)
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Retrieved from: https://earsc-portal.eu/display/EOwiki/Assess+and+Monitor+Volcanic+Activity","url":"https://earsc-portal.eu/display/EOwiki/Assess+and+Monitor+Volcanic+Activity"},{"concepts":[1132],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Assess and monitor water bodies. Retrieved from https://earsc-portal.eu/display/EOwiki/Assess+and+monitor+water+bodies","url":"https://earsc-portal.eu/display/EOwiki/Assess+and+monitor+water+bodies"},{"concepts":[1115],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Assess changes in the carbon balance. Retrieved from https://earsc-portal.eu/display/EOwiki/Assess+changes+in+the+carbon+balance","url":"https://earsc-portal.eu/display/EOwiki/Assess+changes+in+the+carbon+balance"},{"concepts":[1130],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Assess crop damage due to storms. Retrieved from https://earsc-portal.eu/display/EOwiki/Assess+crop+damage+due+to+storms","url":"https://earsc-portal.eu/display/EOwiki/Assess+crop+damage+due+to+storms"},{"concepts":[1125],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Assess damage from earthquakes. Retrieved from https://earsc-portal.eu/display/EOwiki/Assess+damage+from+earthquakes","url":"https://earsc-portal.eu/display/EOwiki/Assess+damage+from+earthquakes"},{"concepts":[1131],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Assess Deforestation or Forest Degradation. Retrieved from https://earsc-portal.eu/display/EOwiki/Assess+Deforestation+or+Forest+Degradation","url":"https://earsc-portal.eu/display/EOwiki/Assess+Deforestation+or+Forest+Degradation"},{"concepts":[1130],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Assess Environmental impact of farming. Retrieved from https://earsc-portal.eu/display/EOwiki/Assess+Environmental+impact+of+farming","url":"https://earsc-portal.eu/display/EOwiki/Assess+Environmental+impact+of+farming"},{"concepts":[1131],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Assess environmental impact of forestry. Retrieved from https://earsc-portal.eu/display/EOwiki/Assess+environmental+impact+of+forestry","url":"https://earsc-portal.eu/display/EOwiki/Assess+environmental+impact+of+forestry"},{"concepts":[1134],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Assess environmental impact of human activities . Retrieved from https://earsc-portal.eu/display/EOwiki/Assess+environmental+impact+of+human+activities","url":"https://earsc-portal.eu/display/EOwiki/Assess+environmental+impact+of+human+activities"},{"concepts":[1131],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Assess forest damage due to storms or insects. Retrieved from https://earsc-portal.eu/display/EOwiki/Assess+forest+damage+due+to+storms+or+insects","url":"https://earsc-portal.eu/display/EOwiki/Assess+forest+damage+due+to+storms+or+insects"},{"concepts":[1132],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Assess ground water and run-off. Retrieved from https://earsc-portal.eu/display/EOwiki/Assess+ground+water+and+run-off","url":"https://earsc-portal.eu/display/EOwiki/Assess+ground+water+and+run-off"},{"concepts":[1135],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Assess land value, ownership, type use. Retrieved from https://earsc-portal.eu/display/EOwiki/Assess+land+value%2C+ownership%2C+type%2C+use","url":"https://earsc-portal.eu/display/EOwiki/Assess+land+value%2C+ownership%2C+type%2C+use"},{"concepts":[1135],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Assess pressures on populations and migration. Retrieved from https://earsc-portal.eu/display/EOwiki/Assess+pressures+on+populations+and+migration","url":"https://earsc-portal.eu/display/EOwiki/Assess+pressures+on+populations+and+migration"},{"concepts":[1136],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Baseline mapping. Retrieved from https://earsc-portal.eu/display/EOwiki/Baseline+mapping","url":"https://earsc-portal.eu/display/EOwiki/Baseline+mapping"},{"concepts":[1136],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Detect and monitor ground movement. Retrieved from https://earsc-portal.eu/display/EOwiki/Detect+and+monitor+ground+movement","url":"https://earsc-portal.eu/display/EOwiki/Detect+and+monitor+ground+movement"},{"concepts":[1144],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Detect and monitor hurricanes and typhoons. Retrieved from https://earsc-portal.eu/display/EOwiki/Detect+and+monitor+hurricanes+and+typhoons","url":"https://earsc-portal.eu/display/EOwiki/Detect+and+monitor+hurricanes+and+typhoons"},{"concepts":[1147],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Detect and monitor ice-risk at sea. Retrieved from https://earsc-portal.eu/display/EOwiki/Detect+and+monitor+ice-risk+at+sea","url":"https://earsc-portal.eu/display/EOwiki/Detect+and+monitor+ice-risk+at+sea"},{"concepts":[1145],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Detect and monitor illegal fishing. Retrieved from https://earsc-portal.eu/display/EOwiki/Detect+and+monitor+illegal+fishing","url":"https://earsc-portal.eu/display/EOwiki/Detect+and+monitor+illegal+fishing"},{"concepts":[1142],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Detect and monitor oil slicks. Retrieved from https://earsc-portal.eu/display/EOwiki/Detect+and+monitor+oil+slicks","url":"https://earsc-portal.eu/display/EOwiki/Detect+and+monitor+oil+slicks"},{"concepts":[1129,1124],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Detect and monitor wildfires. Retrieved from https://earsc-portal.eu/display/EOwiki/Detect+and+monitor+wildfires","url":"https://earsc-portal.eu/display/EOwiki/Detect+and+monitor+wildfires"},{"concepts":[1133],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Detect changes in glaciers. Retrieved from https://earsc-portal.eu/display/EOwiki/Detect+changes+in+glaciers","url":"https://earsc-portal.eu/display/EOwiki/Detect+changes+in+glaciers"},{"concepts":[1131],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Detect illegal forest activities. Retrieved from https://earsc-portal.eu/display/EOwiki/Detect+illegal+forest+activities","url":"https://earsc-portal.eu/display/EOwiki/Detect+illegal+forest+activities"},{"concepts":[1135],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Detect illegal mining activities . Retrieved from https://earsc-portal.eu/display/EOwiki/Detect+illegal+mining+activities","url":"https://earsc-portal.eu/display/EOwiki/Detect+illegal+mining+activities"},{"concepts":[1130],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Detect illegal or undesired crops. Retrieved from https://earsc-portal.eu/display/EOwiki/Detect+illegal+or+undesired+crops","url":"https://earsc-portal.eu/display/EOwiki/Detect+illegal+or+undesired+crops"},{"concepts":[1146],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Detect ships in critical areas. Retrieved from https://earsc-portal.eu/display/EOwiki/Detect+ships+in+critical+areas","url":"https://earsc-portal.eu/display/EOwiki/Detect+ships+in+critical+areas"},{"concepts":[1066,1102,1070,1067,1068,1069,1074,1072,1073,1081,1075,1076,1077,1078,1079,1080,1086,1082,1083,1084,1085,1089,1087,1088,1093,1090,1091,1100,1094,1097,1095,1096,1101,1098,1099,1149,1122,1092,1119,1120,1121],"description":" ","name":"European Association of Remote Sensing Companies. (2020). EO Services (Markets). Retrieved from https://earsc-portal.eu/pages/viewpage.action?pageId=78221916","url":"https://earsc-portal.eu/pages/viewpage.action?pageId=78221916"},{"concepts":[1144],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Forecast and map large waves. Retrieved from https://earsc-portal.eu/display/EOwiki/Forecast+and+map+large+waves","url":"https://earsc-portal.eu/display/EOwiki/Forecast+and+map+large+waves"},{"concepts":[1071,1144],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Forecast and monitor current movement and drift. Retrieved from https://earsc-portal.eu/display/EOwiki/Forecast+and+monitor+current+movement+and+drift","url":"https://earsc-portal.eu/display/EOwiki/Forecast+and+monitor+current+movement+and+drift"},{"concepts":[1144],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Forecast and monitor ocean winds and waves. Retrieved from https://earsc-portal.eu/display/EOwiki/Forecast+and+monitor+ocean+winds+and+waves","url":"https://earsc-portal.eu/display/EOwiki/Forecast+and+monitor+ocean+winds+and+waves"},{"concepts":[1130],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Forecast crop yields. Retrieved from https://earsc-portal.eu/display/EOwiki/Forecast+crop+yields","url":"https://earsc-portal.eu/display/EOwiki/Forecast+crop+yields"},{"concepts":[1116],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Forecast weather. Retrieved from https://earsc-portal.eu/display/EOwiki/Forecast+weather","url":"https://earsc-portal.eu/display/EOwiki/Forecast+weather"},{"concepts":[1114],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Forecasting sunlight exposure. Retrieved from https://earsc-portal.eu/display/EOwiki/Forecasting+sunlight+exposure","url":"https://earsc-portal.eu/display/EOwiki/Forecasting+sunlight+exposure"},{"concepts":[1137],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Identify hydrocarbon seeps in soil. Retrieved from https://earsc-portal.eu/display/EOwiki/Identify+hydrocarbon+seeps+in+soil","url":"https://earsc-portal.eu/display/EOwiki/Identify+hydrocarbon+seeps+in+soil"},{"concepts":[1129,1123],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Map and assess flooding. Retrieved from https://earsc-portal.eu/display/EOwiki/Map+and+assess+flooding","url":"https://earsc-portal.eu/display/EOwiki/Map+and+assess+flooding"},{"concepts":[1071],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Map and monitor hydroelectric energy. Retrieved from https://earsc-portal.eu/display/EOwiki/Map+and+monitor+hydroelectric+energy","url":"https://earsc-portal.eu/display/EOwiki/Map+and+monitor+hydroelectric+energy"},{"concepts":[1071],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Map and monitor solar energy (solar farms). Retrieved from https://earsc-portal.eu/pages/viewpage.action?pageId=78221967","url":"https://earsc-portal.eu/pages/viewpage.action?pageId=78221967"},{"concepts":[1071],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Map and monitor wind energy (wind farms). Retrieved from https://earsc-portal.eu/pages/viewpage.action?pageId=78221973","url":"https://earsc-portal.eu/pages/viewpage.action?pageId=78221973"},{"concepts":[1145],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Map fish shoals. Retrieved from https://earsc-portal.eu/display/EOwiki/Map+fish+shoals","url":"https://earsc-portal.eu/display/EOwiki/Map+fish+shoals"},{"concepts":[1137],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Map geological features. Retrieved from https://earsc-portal.eu/display/EOwiki/Map+geological+features","url":"https://earsc-portal.eu/display/EOwiki/Map+geological+features"},{"concepts":[1137],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Map seismic survey operations. Retrieved from https://earsc-portal.eu/display/EOwiki/Map+seismic+survey+operations","url":"https://earsc-portal.eu/display/EOwiki/Map+seismic+survey+operations"},{"concepts":[1143],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Map water depth or charting. Retrieved from https://earsc-portal.eu/display/EOwiki/Map+water+depth+or+charting","url":"https://earsc-portal.eu/display/EOwiki/Map+water+depth+or+charting"},{"concepts":[1136],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Measure & detect land surface change. Retrieved from https://earsc-portal.eu/display/EOwiki/Measure+detect+land+surface+change","url":"https://earsc-portal.eu/display/EOwiki/Measure+detect+land+surface+change"},{"concepts":[1135],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Measure land use statistics. Retrieved from https://earsc-portal.eu/display/EOwiki/Measure+land+use+statistics","url":"https://earsc-portal.eu/display/EOwiki/Measure+land+use+statistics"},{"concepts":[1114],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor air quality & emissions. Retrieved from https://earsc-portal.eu/pages/viewpage.action?pageId=78221935","url":"https://earsc-portal.eu/pages/viewpage.action?pageId=78221935"},{"concepts":[1143],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor coastal ecosystem. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+coastal+ecosystem","url":"https://earsc-portal.eu/display/EOwiki/Monitor+coastal+ecosystem"},{"concepts":[1141,1140],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor construction and buildings. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+construction+and+buildings","url":"https://earsc-portal.eu/display/EOwiki/Monitor+construction+and+buildings"},{"concepts":[1131],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor forest carbon content. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+forest+carbon+content","url":"https://earsc-portal.eu/display/EOwiki/Monitor+forest+carbon+content"},{"concepts":[1131],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor forest resources. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+forest+resources","url":"https://earsc-portal.eu/display/EOwiki/Monitor+forest+resources"},{"concepts":[1135],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor humanitarian movement and camps. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+humanitarian+movement+and+camps","url":"https://earsc-portal.eu/display/EOwiki/Monitor+humanitarian+movement+and+camps"},{"concepts":[1133],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor ice on rivers and lakes. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+ice+on+rivers+and+lakes","url":"https://earsc-portal.eu/display/EOwiki/Monitor+ice+on+rivers+and+lakes"},{"concepts":[1134],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor land cover and detect change. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+land+cover+and+detect+change","url":"https://earsc-portal.eu/display/EOwiki/Monitor+land+cover+and+detect+change"},{"concepts":[1134],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor land ecosystems and biodiversity. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+land+ecosystems+and+biodiversity","url":"https://earsc-portal.eu/display/EOwiki/Monitor+land+ecosystems+and+biodiversity"},{"concepts":[1134],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor land pollution. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+land+pollution","url":"https://earsc-portal.eu/display/EOwiki/Monitor+land+pollution"},{"concepts":[1142],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor marine habitats. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+marine+habitats","url":"https://earsc-portal.eu/display/EOwiki/Monitor+marine+habitats"},{"concepts":[1137],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor mineral extraction. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+mineral+extraction","url":"https://earsc-portal.eu/display/EOwiki/Monitor+mineral+extraction"},{"concepts":[1143],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor ocean level and surface. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+ocean+level+and+surface","url":"https://earsc-portal.eu/display/EOwiki/Monitor+ocean+level+and+surface"},{"concepts":[1142],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor ocean quality and productivity. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+ocean+quality+and+productivity","url":"https://earsc-portal.eu/display/EOwiki/Monitor+ocean+quality+and+productivity"},{"concepts":[1142],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor oil rigs and flares. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+oil+rigs+and+flares","url":"https://earsc-portal.eu/display/EOwiki/Monitor+oil+rigs+and+flares"},{"concepts":[1142],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor pollution at sea. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+pollution+at+sea","url":"https://earsc-portal.eu/display/EOwiki/Monitor+pollution+at+sea"},{"concepts":[1118],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor sensitive risk areas. Retrieved from: https://earsc-portal.eu/display/EOwiki/Monitor+sensitive+risk+areas","url":"https://earsc-portal.eu/display/EOwiki/Monitor+sensitive+risk+areas"},{"concepts":[1146],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor ships movements. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+ships+movements","url":"https://earsc-portal.eu/display/EOwiki/Monitor+ships+movements"},{"concepts":[1133],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor snow cover. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+snow+cover","url":"https://earsc-portal.eu/display/EOwiki/Monitor+snow+cover"},{"concepts":[1143],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor the coast line. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+the+coast+line","url":"https://earsc-portal.eu/display/EOwiki/Monitor+the+coast+line"},{"concepts":[1141,1139],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor urban areas. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+urban+areas","url":"https://earsc-portal.eu/display/EOwiki/Monitor+urban+areas"},{"concepts":[1135],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor vegetation encroachment. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+vegetation+encroachment","url":"https://earsc-portal.eu/display/EOwiki/Monitor+vegetation+encroachment"},{"concepts":[1130],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor water use on crops and horticulture. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+water+use+on+crops+and+horticulture","url":"https://earsc-portal.eu/display/EOwiki/Monitor+water+use+on+crops+and+horticulture"},{"concepts":[1114],"description":" ","name":"European Association of Remote Sensing Companies. (n.d.). Product sheet: Air Quality CO2. Retrieved from https://earsc-portal.eu/display/EO4RawMaterials/Product+Sheet%3A+Air+Quality+CO2","url":"https://earsc-portal.eu/display/EO4RawMaterials/Product+Sheet%3A+Air+Quality+CO2"},{"concepts":[1147],"description":" ","name":"European Centre for Medium-Range Weather Forecasts, & Copernicus Programme. (2020). Global Shipping Project - Copernicus. Retrieved from https://climate.copernicus.eu/index.php/global-shipping-project","url":"https://climate.copernicus.eu/index.php/global-shipping-project"},{"concepts":[1114],"description":" ","name":"European Comission. (2015). An Operational Anthropogenic CO₂ Emissions Monitoring & Verification Support Capacity.","url":"https://www.copernicus.eu/sites/default/files/2019-09/CO2_Blue_report_2015.pdf"},{"concepts":[1114],"description":" ","name":"European Comission. (2017). An Operational Anthropogenic CO₂ Emissions Monitoring & Verification Support Capacity.","url":"https://www.copernicus.eu/sites/default/files/2019-09/CO2_Red_Report_2017.pdf"},{"concepts":[1114],"description":" ","name":"European Comission. (2019). An Operational Anthropogenic CO₂ Emissions Monitoring & Verification Support Capacity.","url":"https://www.copernicus.eu/sites/default/files/2019-09/CO2_Green_Report_2019.pdf"},{"concepts":[1145],"description":" ","name":"European Comission. (n.d.). Managing fisheries. Retrieved from: https://ec.europa.eu/fisheries/cfp/fishing_rules_en","url":"https://ec.europa.eu/fisheries/cfp/fishing_rules_en"},{"concepts":[1066,1113],"description":" ","name":"European Commision. (n.d.). Societal Challenges. Retrieved from: https://ec.europa.eu/programmes/horizon2020/en/h2020-section/societal-challenges","url":"https://ec.europa.eu/programmes/horizon2020/en/h2020-section/societal-challenges"},{"concepts":[1134],"description":" ","name":"European Commission Joint Research Centre. (2020). Vegetation - Copernicus landm monitoring service. Retrieved from https://land.copernicus.eu/global/themes/Vegetation","url":"https://land.copernicus.eu/global/themes/Vegetation"},{"concepts":[1106],"description":" ","name":"European Commission. (2020). Digital skills and jobs - Shaping Europe's digital future. Retrived from https://ec.europa.eu/digital-single-market/en/policies/digital-skills","url":"https://ec.europa.eu/digital-single-market/en/policies/digital-skills"},{"concepts":[1106],"description":" ","name":"European Commission. (2020). Employment, Social Affairs & Inclusion. Retrived from https://ec.europa.eu/social/main.jsp?catId=1223","url":"https://ec.europa.eu/social/main.jsp?catId=1223"},{"concepts":[438],"description":" ","name":"European Commission. (2020). INSPIRE Knowledge base - Infrastructure for spatial information in Europe - Data Harmonisation. Retrieved from https://inspire.ec.europa.eu/training/data-harmonisation","url":"https://inspire.ec.europa.eu/training/data-harmonisation"},{"concepts":[1110],"description":" ","name":"European Commission. (2020). Overview - Public health. Retrieved from https://ec.europa.eu/health/communicable_diseases/overview_en","url":"https://ec.europa.eu/health/communicable_diseases/overview_en"},{"concepts":[1112],"description":" ","name":"European Commission. (2020). Sustainability of the water resource. Retrieved from https://ec.europa.eu/info/news/sustainability-at-the-water-source_en","url":"https://ec.europa.eu/info/news/sustainability-at-the-water-source_en"},{"concepts":[1108],"description":" ","name":"European Commission. (2020). Sustainable agriculture in the CAP. Retrieved from https://ec.europa.eu/info/food-farming-fisheries/sustainability/sustainable-cap_en","url":"https://ec.europa.eu/info/food-farming-fisheries/sustainability/sustainable-cap_en"},{"concepts":[1109],"description":" ","name":"European Commission. (2020). Transport. Retrieved from https://ec.europa.eu/info/policies/transport_en","url":"https://ec.europa.eu/info/policies/transport_en"},{"concepts":[1148],"description":" ","name":"European Environment Agency. (2016). Monitoring of marine waters. Retrieved from: https://www.eea.europa.eu/publications/92-9167-001-4/page024.html","url":"https://www.eea.europa.eu/publications/92-9167-001-4/page024.html"},{"concepts":[1103],"description":" ","name":"European Environmental Agency, (2019). Climate Change Adaption. Retrieved from: https://www.eea.europa.eu/themes/climate-change-adaptation/intro.","url":"https://www.eea.europa.eu/themes/climate-change-adaptation/intro"},{"concepts":[1103],"description":" ","name":"European Environmental Agency, (2019). Climate Change Mitigation. Retrieved from: https://www.eea.europa.eu/themes/climate/intro.","url":"https://www.eea.europa.eu/themes/climate/intro"},{"concepts":[1105],"description":" ","name":"European Environmental Agency. (2008). Biodiversity - Ecosystems. Retrieved from https://www.eea.europa.eu/themes/biodiversity/intro","url":"https://www.eea.europa.eu/themes/biodiversity/intro"},{"concepts":[1111],"description":" ","name":"European External Action Service. (2020). Security, Defence and Crisis Response. Retrieved from https://eeas.europa.eu/topics/security-defence-crisis-response_en","url":"https://eeas.europa.eu/topics/security-defence-crisis-response_en"},{"concepts":[1142],"description":" ","name":"European Space Agency (2012) Sentinel 3: ESA’s Global Land and Ocean Mission for GMES Operational Services (ESA SP-1322/3, October 2012).","url":"https://sentinel.esa.int/documents/247904/351187/S3_SP-1322_3.pdf"},{"concepts":[1156],"description":" ","name":"European Space Agency. (2011). Slight surface changes detected from space. Retrieved from: http://www.esa.int/Applications/Observing_the_Earth/Envisat/Slight_surface_changes_detected_from_space","url":"http://www.esa.int/Applications/Observing_the_Earth/Envisat/Slight_surface_changes_detected_from_space"},{"concepts":[1162],"description":" ","name":"European Space Agency. (2020). Level-1C Cloud Masks - Sentinel-2 MSI Technical Guide - Sentinel Online. 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methods"},{"concepts":[73],"name":"Compare and contrast co-kriging log-normal kriging, disjunctive kriging, indicator kriging, factorial kriging and universal kriging"},{"concepts":[19],"name":"Compare and contrast covering, dispersion, and p-median models"},{"concepts":[38],"name":"Compare and contrast different shape indices, include examples of applications to which each could be applied"},{"concepts":[108],"name":"Compare and contrast differing epistemological and metaphysical viewpoints on the reality of geographic entities"},{"concepts":[378],"name":"Compare and contrast geographic information technologies that are privacy-invasive, privacy-enhancing, and privacy-sympathetic"},{"concepts":[66],"name":"Compare and contrast global and local statistics and their uses"},{"concepts":[78],"name":"Compare and contrast GWR with universal kriging using moving neighborhoods"},{"concepts":[37],"name":"Compare and contrast how direction is determined and stated in raster and vector data"},{"concepts":[57],"name":"Compare and contrast interpolation by inverse distance weighting, bi-cubic spline fitting and kriging"},{"concepts":[103],"name":"Compare and contrast models of a given spatial process using continuous and discrete perspectives of time"},{"concepts":[379],"name":"Compare and contrast National, European policy regarding rights to geospatial data with similar policies in other countries"},{"concepts":[2],"name":"Compare and contrast spatial statistical analysis, spatial data analysis, and spatial modeling"},{"concepts":[2],"name":"Compare and contrast spatial statistics and map algebra as two very different kinds of data analysis"},{"concepts":[81],"name":"Compare and contrast the ability of different theories to explain various situations"},{"concepts":[83],"name":"Compare and contrast the ability of various theories to explain different situations"},{"concepts":[104],"name":"Compare and contrast the characteristics of spatial and temporal dimensions"},{"concepts":[45],"name":"Compare and contrast the concept of overlay as it is implemented in raster and vector domains"},{"concepts":[110],"name":"Compare and contrast the concepts of continuants (entities) and occurrents (events)"},{"concepts":[16],"name":"Compare and contrast the concepts of discrete location problems and continuous location problems"},{"concepts":[110],"name":"Compare and contrast the concepts of event and process"},{"concepts":[379],"name":"Compare and contrast the consequences of different national policies about rights to geospatial data in terms of the real costs of spatial data, their coverage, accuracy, uncertainty, reliability, validity, and maintenance"},{"concepts":[396],"name":"Compare and contrast the ethical guidelines promoted by the GIS Certification Institute (GISCI) and the American Society for Photogrammetry and Remote Sensing (ASPRS)"},{"concepts":[587],"name":"Compare and contrast the impact effect of time for developing consensus-based standards with immediate operational needs"},{"concepts":[21],"name":"Compare and contrast the impacts of different conversion approaches, including the effect on spatial components"},{"concepts":[85],"name":"Compare and contrast the kinds of questions various philosophies ask, the methodologies they use, the answers they offer, and their applicability to different phenomena"},{"concepts":[121],"name":"Compare and contrast the meanings of related terms such as vague, fuzzy, imprecise, indefinite, indiscrete, unclear, and ambiguous"},{"concepts":[2],"name":"Compare and contrast the methods of analyzing aggregate data as opposed to methods of analyzing a set of individual observations"},{"concepts":[597],"name":"Compare and contrast the missions, histories, constituencies, and activities of professional organizations including Association of American Geographers (AAG), America Society for Photogrammetry and Remote Sensing (ASPRS) ..."},{"concepts":[113],"name":"Compare and contrast the opportunities and pitfalls of using regions to aggregate geographic information (e.g., census data)"},{"concepts":[5],"name":"Compare and contrast the primary types of data mining: summarization/characterization, clustering/categorization, feature extraction, and rule/relationships extraction"},{"concepts":[156,157],"name":"Compare and contrast the quality of product evaluation that can be made from process proofs and color laser prints"},{"concepts":[211],"name":"Compare and contrast the raster with other types of regular tessellations for geographic data analysis"},{"concepts":[211],"name":"Compare and contrast the raster with other types of regular tessellations for geographic data storage"},{"concepts":[88,96],"name":"Compare and contrast the symbolic and connectionist theories of human cognition and memory and their ability to model various cases"},{"concepts":[54],"name":"Compare and contrast the terms multi-criteria evaluation, weighted linear combination, and site suitability analysis"},{"concepts":[106],"name":"Compare and contrast the theory that properties are fundamental (and objects are human simplifications of patterns thereof) with the theory that objects are fundamental (and properties are attributes thereof)"},{"concepts":[88],"name":"Compare and contrast theories of spatial knowledge acquisition (e.g., Marr on vision, Piaget on childhood, Golledge on wayfinding)"},{"concepts":[577],"name":"Compare and contrast training methods utilized in a non-profit to those employed in a local government agency"},{"concepts":[707],"name":"Compare and discuss attenuation length and penetration depth of the optical and radar signal"},{"concepts":[801,803,806,802],"name":"Compare and discuss different SAR acquisition modes"},{"concepts":[590],"name":"Compare and explain different models for funding an SDI"},{"concepts":[383],"name":"Compare and explain the main business models in the GI domain"},{"concepts":[72],"name":"Compare block-kriging with areal interpolation using proportional area weighting and dasymetric mapping"},{"concepts":[311],"name":"Compare common sensors by spatial resolution, spectral sensitivity, ground coverage, and temporal resolution [e.g., AVHRR, MODIS (intermediate resolution ~500 m, high temporal) Landsat, commercial high resolution (Ikonos and Quickbird); ..."},{"concepts":[240],"name":"Compare commonalities and patterns of geocomputation to other related terms"},{"concepts":[14],"name":"Compare current accessibility models with early models of market potential"},{"concepts":[485],"name":"Compare different deep learning approaches in EO image classification"},{"concepts":[248],"name":"Compare different design choices in developing spatial simulation models"},{"concepts":[1213],"name":"compare different development components and their advantages and disadvantages"},{"concepts":[533],"name":"Compare different error metrics that are based on the error matrix"},{"concepts":[164],"name":"Compare different evaluation methods for cartography and visualization products (e.g., qualitative versus quantitative, formative versus summative studies)."},{"concepts":[591],"name":"Compare different frameworks for assessing Spatial Data Infrastructures"},{"concepts":[1189],"name":"Compare different Geospatial object and geometry definitions included under this topic"},{"concepts":[245],"name":"Compare different options of combining space-time dynamics approaches in spatial modelling"},{"concepts":[440],"name":"Compare different strategies of data assimilation"},{"concepts":[177],"name":"Compare geospatial software architecture through cost-analysis framework"},{"concepts":[1133],"name":"Compare glacier extents using EO data"},{"concepts":[1117,1114,1115],"name":"Compare human-induced emissions to natural sources"},{"concepts":[1201],"name":"Compare Linked geospatial data to SDI approaches"},{"concepts":[69],"name":"Compare methods of spatial statistical analysis for the testing of hypotheses."},{"concepts":[20],"name":"Compare models and software tools that allow for optimization"},{"concepts":[1126],"name":"Compare one optical EO method with a SAR method for landslide mapping and explain their differences"},{"concepts":[518],"name":"Compare pixel-based image classification methods with object-based techniques"},{"concepts":[814],"name":"Compare reflectance measurements from the field to reflectance values in radiometrically pre-processed EO data"},{"concepts":[120],"name":"Compare relationships between entities, between attributes and between locations."},{"concepts":[504],"name":"Compare results of the Laplacian of Gaussian filter to the original input image"},{"concepts":[391],"name":"Compare the advantages and disadvantages of group participation and individual participation"},{"concepts":[48],"name":"Compare the basic analytical operations of different GISs."},{"concepts":[322],"name":"Compare the concepts of geometric accuracy and topological fidelity"},{"concepts":[302],"name":"Compare the different cultures of Open Science"},{"concepts":[64],"name":"Compare the different types of spatial weight matrices"},{"concepts":[1142],"name":"Compare the main satellite sensors used in marine ecosystem monitoring"},{"concepts":[322],"name":"Compare the National Map Accuracy Standard with the ASPRS Coordinate Standard"},{"concepts":[137],"name":"Compare the relative merits of having map labels placed dynamically versus having them saved as annotation data"},{"concepts":[24],"name":"Compare the result of conversion vector/raster or raster/vector and examine the impact of conversion on the quality of the dataset"},{"concepts":[166],"name":"Compile the needs of individual users and tasks into enterprise-wide needs"},{"concepts":[565],"name":"Compute descriptive statistics and geostatistics of geographic data"},{"concepts":[49],"name":"Compute measures of overall dispersion and clustering of point datasets using nearest neighbor distance statistics"},{"concepts":[65],"name":"Compute measures of overall dispersion and clustering of point datasets using nearest neighbor distance statistics"},{"concepts":[65],"name":"Compute Morans I and Gearys c for patterns of attribute data measured on interval ratio scales"},{"concepts":[9],"name":"Compute the alpha, beta, and gamma indices of network connectivity"},{"concepts":[9],"name":"Compute the Detour Index and the measure of network density for a given network"},{"concepts":[9],"name":"Compute the estimated number of fundamental cycles in a graph"},{"concepts":[66],"name":"Compute the Gi and Gi* statistics"},{"concepts":[65],"name":"Compute the K function"},{"concepts":[613],"name":"Compute the maximum average roughness of a mirror for incident radiation in the microwaves spectral range"},{"concepts":[613],"name":"Compute the maximum average roughness of a mirror for incident radiation in the visible spectral range"},{"concepts":[37],"name":"Compute the mean of directional data"},{"concepts":[613],"name":"Compute the minimum average roughness of a surface operating as a diffuser of incident radiation in the visible spectral range"},{"concepts":[10],"name":"Compute the optimum path between two points through a network with Dijkstras algorithm"},{"concepts":[53],"name":"Conduct a simple hierarchical cluster analysis to classify area objects into statistically similar regions"},{"concepts":[76],"name":"Conduct a spatial econometric analysis to test for spatial dependence in the residuals from least-squares models and spatial autoregressive models"},{"concepts":[72],"name":"Conduct a spatial interpolation process using kriging from data description to final error map"},{"concepts":[159],"name":"Construct a new map from an existing one with a biased view"},{"concepts":[34],"name":"Construct a query statement to search for a specific spatial or temporal relationship"},{"concepts":[71],"name":"Construct a semi-variogram and illustrate with a semi-variogram 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placement"},{"concepts":[22],"name":"Convert a dataset from the native format of one GIS product to another"},{"concepts":[127],"name":"Convert historical maps in digital format"},{"concepts":[421],"name":"Convert multispectral image into its principal components"},{"concepts":[24],"name":"Convert vector data to raster format and back using GIS software"},{"concepts":[24],"name":"Convert vector data to raster format and back using the GIS software"},{"concepts":[128],"name":"Correlate map making methods with technological or societal factors across History"},{"concepts":[174],"name":"Create a budget of expected labor costs, including salaries, benefits, training, and other expenses"},{"concepts":[188],"name":"Create a complete design document ready for implementation"},{"concepts":[153],"name":"Create a concept map that represents the contents and topology of a physical or social process"},{"concepts":[506],"name":"Create a convolution filter that integrates the standard deviation of the entire scene in its weights"},{"concepts":[554],"name":"Create a data cube using the data model of the Open data cube initiative"},{"concepts":[8],"name":"Create a data set with network attributes and topology"},{"concepts":[186],"name":"Create a diagram of a conceptual data model for a geospatial application or enterprise database"},{"concepts":[21,130],"name":"Create a flowchart showing the sequence of transformations on a data set (e.g., geometric and radiometric correction and mosaicking of remotely sensed data)"},{"concepts":[147],"name":"Create a map that displays related variables using different mapping methods (e.g., choropleth and proportional symbol, choropleth and cartogram)"},{"concepts":[147],"name":"Create a map that displays related variables using the same mapping method (e.g., bivariate choropleth map, bivariate dot map)"},{"concepts":[146],"name":"Create a map that represents both slope and aspect on the same map using the Moellering-Kimerling coloring 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in-situ GPS measurements"},{"concepts":[148],"name":"Create a temporal sequence representing a dynamic geospatial process"},{"concepts":[167],"name":"Create a user manual to help users understand a process or task"},{"concepts":[560],"name":"Create a web interface and related system architecture that enables image processing by using OGC interfaces"},{"concepts":[226],"name":"Create an adjacency table from a sample network"},{"concepts":[135],"name":"Create an aesthetic map icon library"},{"concepts":[226],"name":"Create an incidence matrix from a sample network"},{"concepts":[439],"name":"Create an integrated population distribution map from census data and EO-based land use classification"},{"concepts":[33],"name":"Create an SQL query to retrieve elements from a GIS"},{"concepts":[185],"name":"Create conceptual, logical, and physical data models using automated software tools"},{"concepts":[50],"name":"Create density maps from point datasets using kernels and density estimation 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tools"},{"concepts":[144],"name":"Create maps using each of the following methods: choropleth, dasymetric, proportioned symbol, graduated symbol, isoline, dot, cartogram, and flow map"},{"concepts":[1180],"name":"Create new EO products out of raw data or other products"},{"concepts":[105],"name":"Create or use GIS data structures to represent categories, including attribute columns, layers themes, shapes, legends, etc."},{"concepts":[174],"name":"Create proposals and presentations to secure funding"},{"concepts":[70],"name":"Create spatial samples under a variety of requirements, such as coverage, randomness, transects"},{"concepts":[159],"name":"Create two versions of the same map addressed to different targets"},{"concepts":[188],"name":"Create UML diagrams of physical models based on logical model diagrams and software requirements"},{"concepts":[144],"name":"Create well-designed legends using the appropriate conventions for the following methods: choropleth, dasymetric, proportioned symbol, graduated symbol, isoline, dot, cartogram, and flow map"},{"concepts":[143],"name":"Critique the graphic design of several maps in terms of balance, legibility, clarity, visual contrast, figure-ground organization, and hierarchal organization"},{"concepts":[149],"name":"Critique the interactive elements of an online map"},{"concepts":[150],"name":"Critique the user interface for existing Internet mapping services"},{"concepts":[229],"name":"Deal with time aspects in modelling data"},{"concepts":[228],"name":"Deal with uncertainty aspects in modelling data"},{"concepts":[1173,1172],"name":"Decide on urban planning measures on the basis of a semantic 3D model"},{"concepts":[31],"name":"Decide which generalisation technique (aggregation, selection, etc.) is best for a specific situation of reducing map scale."},{"concepts":[141],"name":"Decide which graphical representation better reflects the messages embedded in your story"},{"concepts":[66],"name":"Decompose Morans I and Gearys c into local measures of spatial association"},{"concepts":[186],"name":"Deconstruct an application use case into its conceptual elements"},{"concepts":[404],"name":"Defend or refute the contention that critical studies have an identifiable influence on the development of the information society in general and GIScience in particular"},{"concepts":[403],"name":"Defend or refute the contention that the masculinist culture of computer work in general, and GIS work in particular, perpetuates gender inequality in GIS and T education and training and occupational segregation in the GIS and T workforce"},{"concepts":[28],"name":"Defend or refute the statement \"GIS data are scaleless\""},{"concepts":[85],"name":"Defend or refute the statement, All data are theory-laden"},{"concepts":[109],"name":"Define a field in terms of properties, space, and time"},{"concepts":[166],"name":"Define a methodology for gathering of requirements"},{"concepts":[233],"name":"Define a set of rules for modeling changes in spatial databases"},{"concepts":[223],"name":"Define and describe an application schema"},{"concepts":[393],"name":"Define and discuss enabling technologies: geotag, georeferencing, GPS and more"},{"concepts":[238],"name":"Define and discuss opportunities and limitations of computational science"},{"concepts":[393],"name":"Define and discuss volunteered geographic information"},{"concepts":[393],"name":"Define and discussing impact of Crowdsourcing on Geospatial Society"},{"concepts":[1190],"name":"Define and exemplify the reuse of ontologies - Define and identify the role of ontology patterns"},{"concepts":[1186],"name":"Define and practice the usage, in a given use case, of StyledLayerDescriptor (SLD) and Symbology Encoding (SE). Practice their usage in a given use case"},{"concepts":[391],"name":"Define and understand citizenship, democracy, maturity, and negotiation related to geo information use and participation in society /community development (at local, regional, national level)"},{"concepts":[33],"name":"Define basic terms of query processing e.g., SQL, primary and foreign keys, table join"},{"concepts":[211],"name":"Define basic terms used in the raster data model (e.g., cell, row, column, value)"},{"concepts":[179,1185],"name":"Define characteristics of REST Web services and Resource oriented Architecture (ROA)"},{"concepts":[85],"name":"Define common philosophical theories that have influenced geography and science, such as logical positivism, Marxism, phenomenology, feminism, and critical theory"},{"concepts":[83],"name":"Define common theories on what constitutes knowledge, including positivism, reflectance-correspondence, pragmatism, social constructivism, and memetics"},{"concepts":[81],"name":"Define common theories on what is real, such as realism, idealism, relativism, and experiential realism"},{"concepts":[8],"name":"Define different interpretations of cost in various routing applications"},{"concepts":[37],"name":"Define direction and its measurement in different angular measures"},{"concepts":[186],"name":"Define entities and relationships in conceptual data model"},{"concepts":[60],"name":"Define friction surface"},{"concepts":[1189],"name":"Define GeoJSON definition of Geospatial objects and describe the structure of a GeoJSON document and identify advantages and disadvantages of representing the same geospatial data in GML and in GeoJSON"},{"concepts":[59],"name":"Define intervisibility"},{"concepts":[1196],"name":"Define Mapping between legacy definition and the semantic definition of publish"},{"concepts":[1192],"name":"Define metadata and identify metadata standards like ISO 19115 and 19119 describe their metadata schema generally"},{"concepts":[1189],"name":"Define OGC Simple Features Access Schema. Well-Known Text (WKT) and Well-Known Binary (WKB) representations of Geometry"},{"concepts":[68],"name":"Define prior and posterior distributions and Markov-Chain Monte Carlo"},{"concepts":[1188],"name":"Define Resource Description Framework (RDF), its RDF graphs, RDF Schema (RDF-S)and a data set in RDF"},{"concepts":[1188],"name":"Define Semantic Web and identify the role of the languages included under this topic for Semantic Web"},{"concepts":[179,1183],"name":"Define Service Oriented Architecture (SOA) and identify main elements of it"},{"concepts":[119],"name":"Define spatial autocorrelation in the context of geographic proximity"},{"concepts":[1189],"name":"Define spatial extensions that GeoSPARQL brings over SPARQL. Identify the difference between qualitative spatial reasoning and quantitative spatial computations"},{"concepts":[106],"name":"Define Stevens four levels of measurement (nominal, ordinal, interval, ratio)"},{"concepts":[222],"name":"Define terms related to topology (e.g., adjacency, connectivity, overlap, intersect, logical consistency)"},{"concepts":[187],"name":"Define the cardinality of relationships"},{"concepts":[179,180,1183],"name":"Define the characteristics of web services and present some examples"},{"concepts":[1188],"name":"Define the components of a Web Services Description Language (WSDL) document"},{"concepts":[8],"name":"Define the following terms pertaining to a network: Loops, multiple edges, the degree of a vertex, walk, trail, path, cycle, fundamental cycle"},{"concepts":[226],"name":"Define the following terms pertaining to a network: Loops, multiple edges, the degree of a vertex, walk, trail, path, cycle, fundamental cycle"},{"concepts":[90],"name":"Define the following terms: data, information, knowledge, and wisdom"},{"concepts":[97],"name":"Define the four basic dimensions or shapes used to describe spatial objects (i.e., points, lines, regions, volumes)"},{"concepts":[93],"name":"Define the notions of cultural landscape and physical landscape"},{"concepts":[119],"name":"Define the principle of friction of distance and geographic models that are based on it (e.g., gravity models, spatial interaction models)"},{"concepts":[92],"name":"Define the properties that make a phenomenon geographic"},{"concepts":[625],"name":"Define the radiometric spectral quantities brightness, emittance, luminosity"},{"concepts":[625],"name":"Define the radiometric spectral quantities radiance, irradiance, flux"},{"concepts":[2],"name":"Define the terms spatial analysis, spatial modeling, geostatistics, spatial econometrics, spatial statistics, qualitative analysis, map algebra, and network analysis"},{"concepts":[122],"name":"Define uncertainty-related terms, such as error, accuracy, uncertainty, precision, stochastic, probabilistic, deterministic, and random"},{"concepts":[569],"name":"Define user roles for an existing or planned GIS"},{"concepts":[118],"name":"Define various terms used to describe topological relationships, such as disjoint, overlap, within, and intersect"},{"concepts":[1207],"name":"Define Web API composition (WAPIC) concept for RESTful WSs and identify main issues"},{"concepts":[1186],"name":"Define Web Coverage Service (WCS). Describe GetCapabilities, GetCoverageInfo, and GetCoverage operations in detail. Practice its usage in a given use case"},{"concepts":[1186],"name":"Define Web Feature Service (WFS). Describe GetCapabilities, DescribeFeaturetype, and GetFeature, and GetFeatureInfo operations in detail. Practice its usage in a given use case"},{"concepts":[1186],"name":"Define Web Map Service (WMS). Describe GetCapabilities, GetMap, and GetFeatureInfo operations in detail. Practice its usage in a given use case"},{"concepts":[1186],"name":"Define Web Map Tile Service (WMTS). Describe GetCapabilities, GetTile, and GetFeatureInfo operations in detail. Practice its usage in a given use case"},{"concepts":[1186],"name":"Define Web Processing Service (WPS). Describe GetCapabilities, DescribeProcess, and Execute operations in detail. Practice its usage in a given use case"},{"concepts":[1207],"name":"Define web services composition (WSC) concept and identify main issues"},{"concepts":[1183],"name":"Define Web services transport over the Web"},{"concepts":[1190],"name":"Define what an ontology is. Identify differences among ontologies, Thesauri, and taxonomies"},{"concepts":[214],"name":"Delineate a set of break lines that improve the accuracy of a TIN"},{"concepts":[113],"name":"Delineate regions using properties, spatial relationships, and geospatial technologies"},{"concepts":[176],"name":"Deliver a resources plan consistent with organisation’s concrete actions"},{"concepts":[662],"name":"Demonstrate basic knowledge of the atmospheric absorption and scattering mechanisms."},{"concepts":[602,658],"name":"Demonstrate basic knowledge of the interaction between the solar radiation and atmospheric constituents"},{"concepts":[1193],"name":"Demonstrate harvesting and crawling mechanisms for automated metadata collection"},{"concepts":[226],"name":"Demonstrate how a network is a connected set of edges and vertices"},{"concepts":[222],"name":"Demonstrate how a topological structure can be represented in a relational database structure"},{"concepts":[41],"name":"Demonstrate how adjacency and connectivity can be recorded in matrices"},{"concepts":[226],"name":"Demonstrate how attributes of networks can be used to represent cost, time, distance, or many other measures"},{"concepts":[235],"name":"Demonstrate how both the time criticality and the data security might determine whether one performs change detection on-line or off-line in a given scenario"},{"concepts":[11],"name":"Demonstrate how capacity is assigned to edges in a network using the appropriate data structure"},{"concepts":[5],"name":"Demonstrate how cluster analysis can be used as a data mining tool"},{"concepts":[10],"name":"Demonstrate how K-shortest path algorithms can be implemented to find many efficient alternate paths across the network"},{"concepts":[9],"name":"Demonstrate how networks can be measured using the number of elements in a network, the distances along network edges, and the level of connectivity of the network"},{"concepts":[71],"name":"Demonstrate how semi-variograms react to spatial nonstationarity"},{"concepts":[77],"name":"Demonstrate how spatial autocorrelation can be removed by resampling"},{"concepts":[75],"name":"Demonstrate how spatially lagged, trend surface, or dummy spatial variables can be used to create the spatial component variables missing in a standard regression analysis"},{"concepts":[148],"name":"Demonstrate how the adding time-series data reveals (or not) patterns not evident in a cross-sectional data"},{"concepts":[39],"name":"Demonstrate how the area of a region calculated from a raster data set will vary by resolution and orientation"},{"concepts":[12],"name":"Demonstrate how the Classic Transportation Problem can be structured as a linear program"},{"concepts":[45],"name":"Demonstrate how the geometric operations of intersection and overlay can be implemented in GIS"},{"concepts":[76],"name":"Demonstrate how the parameters of spatial auto-regressive models can be estimated using univariate and bivariate optimization algorithms for maximizing the likelihood function"},{"concepts":[75],"name":"Demonstrate how the spatial weights matrix is fundamental in spatial econometrics models"},{"concepts":[226],"name":"Demonstrate how the star (or forward star) data structure, which is often employed when digitally storing network information, violates relational normal form, but allows for much faster search and retrieval in network databases"},{"concepts":[1199],"name":"Demonstrate how to discover over a catalogue service; and the discovery procedure in OGC CS-W"},{"concepts":[127],"name":"Demonstrate how to georeference an historical map"},{"concepts":[1088],"name":"Demonstrate impacts of land use change"},{"concepts":[1101],"name":"Demonstrate multidisciplinarity, combining GISciences, Social Sciences, Smart Cities, Computational Sciences and Social Media"},{"concepts":[1193],"name":"Demonstrate publishing in some popular SDI (NSDI) portals like INSPIRE and GOS geoportals"},{"concepts":[33],"name":"Demonstrate the basic syntactic structure of SQL"},{"concepts":[51],"name":"Demonstrate the extension of spatial clustering to deal with clustering in space-time using the Know and Mantel tests"},{"concepts":[232],"name":"Demonstrate the importance of a clean, relatively error-free database (together with an appropriate geodetic framework) with the use of GIS software"},{"concepts":[612],"name":"Demonstrate the relationships among measured multi-spectral radiation and specific chemical (e.g. composition) and physical (e.g. temperature, pressure, etc.) properties of the observed matter."},{"concepts":[34],"name":"Demonstrate the syntactic structure of spatial and temporal operators in SQL"},{"concepts":[1203],"name":"Demonstrate the usage of popular ETL tools in an NSDI scenario"},{"concepts":[214],"name":"Demonstrate the use of the TIN model for different statistical surfaces (e.g., terrain elevation, population density, disease incidence) in a GIS software application"},{"concepts":[75],"name":"Demonstrate why spatial autocorrelation among regression residuals can be an indication that spatial variables have been omitted from the models"},{"concepts":[45],"name":"Demonstrate why the georegistration of datasets is critical to the success of any map overlay operation"},{"concepts":[172],"name":"Demonstrate why the system design is important in any GIS implementation"},{"concepts":[608],"name":"Derive the Stefan-Boltzman Law from the Planck's one"},{"concepts":[85],"name":"Describe a brief history of major philosophical movements relating to the nature of space, time, geographic phenomena and human interaction with it"},{"concepts":[149],"name":"Describe a mapping goal in which the use of each of the following would be appropriate: brushing, linking, multiple displays"},{"concepts":[46,47],"name":"Describe a real modeling situation in which map algebra would be used e.g., site selection, climate classification, least-cost path"},{"concepts":[326],"name":"Describe a scenario in which data 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impossible to adequately represent in GIS"},{"concepts":[462],"name":"Describe the elements of image interpretation"},{"concepts":[402],"name":"Describe the extent to which contemporary GIS and T supports diverse ways of understanding the world"},{"concepts":[52],"name":"Describe the formulation of the classic gravity model, the unconstrained spatial interaction model, the production constrained spatial interaction model, the attraction constrained spatial interaction model, and the doubly constrained spatial..."},{"concepts":[679],"name":"Describe the fundamental thermodynamic processes (isothermal, adiabatic, isochoric, isobaric)"},{"concepts":[117],"name":"Describe the genealogy (as identity-based change or temporal relationships) of particular geographic phenomena"},{"concepts":[75],"name":"Describe the general types of spatial econometric model"},{"concepts":[647],"name":"Describe the impact of Einstein’s theory of radiation on the design of modern devices for the measurements 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usability of (geospatial) products"},{"concepts":[591],"name":"Design an SDI assessment framework and methodology for assessing and evaluating an SDI"},{"concepts":[571],"name":"Design and implement an effective GIS coordination strategy"},{"concepts":[572],"name":"Design and implement approaches and methods for assessing the performance of GIS"},{"concepts":[572],"name":"Design and implement approaches and methods for collecting users feedback on GIS"},{"concepts":[1126],"name":"Design and test an EO-based workflow for landslide mapping"},{"concepts":[186],"name":"Design application-specific conceptual models"},{"concepts":[111],"name":"Design data models for specific applications based on these comprehensive general models"},{"concepts":[165],"name":"Design databases for spatial data management"},{"concepts":[579],"name":"Design effective teaching and learning methods for GIS&T education"},{"concepts":[578],"name":"Design GIS&T curricula and courses"},{"concepts":[135],"name":"Design icons suitable for mapping different elements"},{"concepts":[133],"name":"Design maps that are appropriate for users with vision limitations"},{"concepts":[193],"name":"Design relational databases"},{"concepts":[582],"name":"Design solutions to different types of barriers to geospatial data sharing"},{"concepts":[165],"name":"Design workflows, procedures, and customized software tools for using geospatial technologies and methods"},{"concepts":[1149],"name":"designing the description of a service for the need of a particular user of EO information"},{"concepts":[1181],"name":"Detect and monitor oil slicks"},{"concepts":[1075,1082,1136],"name":"Detect land movement, subsidence, heave"},{"concepts":[526],"name":"Determine all necessary steps to make EO-derived products of a resarch project accessible"},{"concepts":[166],"name":"Determine how to integrate or combine the proposed workflow with current applications running"},{"concepts":[381],"name":"Determine if a dataset can be considered as open data"},{"concepts":[1158],"name":"Determine object movement by comparing subsequent images"},{"concepts":[1087],"name":"Determine requirements and quality criteria for an EO information product that serves spatial planners in monitoring soil sealing"},{"concepts":[28],"name":"Determine the mathematical relationships among scale, scope, and resolution"},{"concepts":[318],"name":"Determine the most appropriate data collection method for collecting particular data"},{"concepts":[106],"name":"Determine the proper uses of attributes based on their domains"},{"concepts":[209],"name":"Determine the standards that are essential for geospatial data modelling"},{"concepts":[117],"name":"Determine whether it is important to represent the genealogy of entities for a particular application"},{"concepts":[111],"name":"Determine whether phenomena or applications exist that are not adequately represented in an existing comprehensive model"},{"concepts":[54],"name":"Determine which method to 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generation of the final modeled outputs"},{"concepts":[166],"name":"Develop use cases for potential applications using established techniques with potential users, such as questionnaires, interviews, focus groups, the Delphi method, and/or joint application development"},{"concepts":[1182],"name":"Develop Web-GIS solutions to replace each of the functions of a traditional GIS"},{"concepts":[565],"name":"Devise simple ways to represent probability information in GIS"},{"concepts":[377],"name":"Differentiate \"contracts for service\" from \"contracts of service\""},{"concepts":[146],"name":"Differentiate 3D representations from 2.5 D representations"},{"concepts":[212],"name":"Differentiate among a lattice, a tessellation, and a grid"},{"concepts":[23],"name":"Differentiate among common interpolation techniques (e.g., nearest neighbor, bilinear, bicubic)"},{"concepts":[377],"name":"Differentiate among contract liability, tort liability, and statutory 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anisotropic processes"},{"concepts":[50],"name":"Differentiate between kernel density estimation and spatial interpolation"},{"concepts":[188],"name":"Differentiate between logical and physical models, in terms of the level of detail, constraints, and range of information included"},{"concepts":[213],"name":"Differentiate between lossy and lossless compression methods"},{"concepts":[46,47],"name":"Differentiate between map algebra and matrix algebra using real examples"},{"concepts":[103],"name":"Differentiate between mathematical and phenomenological theories of the nature of time"},{"concepts":[70],"name":"Differentiate between model-based and design-based sampling schemes"},{"concepts":[26],"name":"Differentiate between polynomial coordinate transformations (including linear) and rubbersheeting"},{"concepts":[1185],"name":"Differentiate between SOAP and REST Web services. - Identify design issues of REST Web services"},{"concepts":[93],"name":"Differentiate between space and place"},{"concepts":[121],"name":"Differentiate between the following concepts: vagueness and ambiguity, well defined and poorly defined objects and fields or discord and non-specificity"},{"concepts":[52],"name":"Differentiate between the gravity model and spatial interaction models"},{"concepts":[57],"name":"Differentiate between trend surface analysis and deterministic spatial interpolation"},{"concepts":[1190],"name":"Differentiate between upper, domain, and application level ontologies"},{"concepts":[311],"name":"Differentiate push-broom and cross-track scanning technologies"},{"concepts":[414],"name":"Differentiate rectification and orthorectification"},{"concepts":[480],"name":"Differentiate supervised classification from unsupervised classification"},{"concepts":[122],"name":"Differentiate uncertainty in geospatial situations from vagueness"},{"concepts":[138],"name":"Differentiate uses for different types of imagery related to earth"},{"concepts":[109],"name":"Differentiate various sources of fields, such as substance properties (e.g., temperature), artificial constructs (e.g., population density), and fields of potential or influence (e.g., gravity)"},{"concepts":[327],"name":"Digitize and georegister a specified vector feature set to a given geometric accuracy and topological fidelity thresholds using a given map sheet, digitizing tablet, and data entry software"},{"concepts":[399],"name":"Discuss about \"mapping whose reality?\" Pros and cons of geoinformation sharing in social media, i.e. big data, \"digital shadow\" etc."},{"concepts":[387],"name":"Discuss about open data and data sharing and public/private sector"},{"concepts":[381],"name":"Discuss about open data impact on society and citizenship"},{"concepts":[151],"name":"Discuss about the advantages of different immersive display systems"},{"concepts":[159],"name":"Discuss about the degree of subjectivity and/or objectivity of a map"},{"concepts":[125],"name":"Discuss about the History of Cartography in different cultures"},{"concepts":[126],"name":"Discuss about the relationship between art and cartography"},{"concepts":[824,825,826],"name":"Discuss advantages and disadvantages of different methods of storing remote sensing data"},{"concepts":[839,840,841,842],"name":"Discuss advantages and disadvantages of different SAR data formats"},{"concepts":[770],"name":"Discuss advantages and disadvantages of passive and active sensors"},{"concepts":[732],"name":"Discuss advantages of SAR techniques over traditional measuring techniques"},{"concepts":[465],"name":"Discuss algorithms that use the detection of keypoints to identify objects in images"},{"concepts":[773],"name":"Discuss an example of using a radar altimeter"},{"concepts":[831],"name":"Discuss and compare different temporal resolutions of remote sending data"},{"concepts":[726],"name":"Discuss and compare different types of interactions of microwaves with matter"},{"concepts":[838],"name":"Discuss and compare different types of processing levels of optical data"},{"concepts":[843],"name":"Discuss and compare different types of processing levels of SAR data"},{"concepts":[381],"name":"Discuss and define open data and impact on GIS&T"},{"concepts":[563],"name":"Discuss and define the process of the Information value chain"},{"concepts":[451],"name":"Discuss cloud masks as early steps towards semantic enrichment for EO images"},{"concepts":[104],"name":"Discuss common prepositions and adjectives (in any particular language) that signify either spatial or temporal relations but are used for both kinds, such as after or longer"},{"concepts":[245],"name":"Discuss concepts of space-time dynamics for spatial modeling"},{"concepts":[1183],"name":"Discuss consensus based interoperability and its relation to geospatial data interchange. Define what a Web Service (WS) is and present characteristic scenarios. Data serving and Data Processing WSs"},{"concepts":[398],"name":"Discuss critiques of GIS as \"deterministic\" technology in relation to debates about the Quantitative quantitative revolution in the discipline of geography."},{"concepts":[402],"name":"Discuss critiques of GIS as deterministic technology in relation to debates about the Quantitative Revolution in the discipline of geography"},{"concepts":[577],"name":"Discuss different formats (tutorials, in house, online, instructor lead) for training and how they can be used by organizations"},{"concepts":[545],"name":"Discuss different methods for assessing the quality of a specific EO product"},{"concepts":[781],"name":"Discuss different types of laser scanners"},{"concepts":[810,686],"name":"Discuss different types of satellite orbits"},{"concepts":[248],"name":"Discuss different ways of simulating space and visualizing model behaviour"},{"concepts":[699],"name":"Discuss electromagnetic interactions and scattering mechanisms"},{"concepts":[816],"name":"Discuss examples of ground-based platforms and their use"},{"concepts":[809],"name":"Discuss examples of the objectives of Earth observation missions"},{"concepts":[309],"name":"Discuss future prospects for automated feature extraction from aerial imagery"},{"concepts":[575],"name":"Discuss how a code of ethics might be applied within an organization"},{"concepts":[136],"name":"Discuss how cultural differences with respect to color associations impact map design"},{"concepts":[548,828],"name":"Discuss how different spectral resolution of EO sensors influences their potential for vegetation mapping"},{"concepts":[512],"name":"Discuss how hierarchical representation is exploited for object-based image analysis"},{"concepts":[763],"name":"Discuss how line detectors array sensors work"},{"concepts":[500],"name":"Discuss how low-pass filtering of an image impacts the resulting regions derived with watershed segmentation"},{"concepts":[159],"name":"Discuss how maps express relations of power"},{"concepts":[323],"name":"Discuss how measures of spatial autocorrelation may be used to evaluate thematic accuracy"},{"concepts":[830,548],"name":"Discuss how radiometric resolution influences the granularity of a land cover classification"},{"concepts":[827,835],"name":"Discuss how remote sensing data is organized and stored"},{"concepts":[746],"name":"Discuss how the angle of SAR signal incidence affects SAR acquisition"},{"concepts":[70],"name":"Discuss how the choice of sampling strategy impacts the accuracy assesment for a classification result"},{"concepts":[492],"name":"Discuss how the choice of sampling strategy impacts the accuracy assesment for a classification result"},{"concepts":[70],"name":"Discuss how the choice of sampling strategy impacts the classification result"},{"concepts":[492],"name":"Discuss how the choice of sampling strategy impacts the classification result"},{"concepts":[836],"name":"Discuss how the radiometrically corrected data are processed"},{"concepts":[509],"name":"Discuss how the size of the neighborhood impacts the smoothing effect of a low-pass filter"},{"concepts":[388],"name":"Discuss how to approach the widening audience/participants for geospatial products and service, increasing geo-awareness and geo-enablement"},{"concepts":[143],"name":"Discuss how to create an intellectual and visual hierarchy on maps"},{"concepts":[694],"name":"Discuss how to use phase information in remote sensing"},{"concepts":[31],"name":"Discuss implications of data loss in the case of generalisation of spatial data."},{"concepts":[432],"name":"Discuss imputation methods for filling in missing data"},{"concepts":[602],"name":"Discuss in which way annual solar insolation and average cloud coverage parameters affect the choice of a solar power plant location"},{"concepts":[602],"name":"Discuss in which way modeled daily solar insolation and cloud coverage forecast could affect solar power plant day-by-day management"},{"concepts":[389],"name":"Discuss legal aspects of access to environmental data, global change/warming or sustainable development (regional, national, global) in conjunction to society."},{"concepts":[732],"name":"Discuss limitations of interferometric measurement"},{"concepts":[501],"name":"Discuss limitations of the different region-based segementation methods"},{"concepts":[823],"name":"Discuss main characteristics of digital imagery"},{"concepts":[381],"name":"Discuss of arguments for and against open data"},{"concepts":[380],"name":"Discuss of opportunities for exchange of geospatial data between public and private sector to enable more efficient analysis"},{"concepts":[243],"name":"Discuss options of combining rule-based models with other individual modelling approaches"},{"concepts":[721],"name":"Discuss orientational polarisation of media"},{"concepts":[398],"name":"Discuss over the argument that the use of Geospatial geospatial Information privileges certain views of the world over others."},{"concepts":[387],"name":"Discuss over the changing role of the private sector in the use of geospatial information"},{"concepts":[388],"name":"Discuss over the paradigm shifts and current trends in GIS&T education and pedagogical approaches for GIS teaching and learning in detail"},{"concepts":[399],"name":"Discuss over the various implications of surveillance technology"},{"concepts":[720],"name":"Discuss polarimetric decomporition techniques"},{"concepts":[393],"name":"Discuss positive and negative aspects of the term \"humans as sensors\""},{"concepts":[729],"name":"Discuss radar antennas"},{"concepts":[715],"name":"Discuss scale of roughness of microwaves"},{"concepts":[2],"name":"Discuss situations when it is desirable to adopt a spatial approach to the analysis of data"},{"concepts":[226],"name":"Discuss some of the difficulties of applying the standard process-pattern concept to lines and networks"},{"concepts":[502],"name":"Discuss spatial autocorrelation and homogeneity of image objects"},{"concepts":[174],"name":"Discuss the advantages and disadvantages of outsourcing elements of a GIS project / GI system"},{"concepts":[97],"name":"Discuss the advantages and disadvantages of the use of cartesian metric space as a basis for GIS and related technologies"},{"concepts":[324],"name":"Discuss the advantages and potential problems associated with the use of Minimum Mapping Unit (MMU) as a measure of the level of detail in land use, land cover, and soils maps"},{"concepts":[771],"name":"Discuss the application possibilities of imaging radar"},{"concepts":[787],"name":"Discuss the applications for which Differential Absorption LiDAR can be used"},{"concepts":[788],"name":"Discuss the applications for which Wind Doppler LiDAR is used"},{"concepts":[64],"name":"Discuss the appropriateness of different types of spatial weights matrices for various problems"},{"concepts":[78],"name":"Discuss the appropriateness of GWR under various conditions"},{"concepts":[532],"name":"Discuss the available data quality standards for EO"},{"concepts":[662],"name":"Discuss the basic principles of solar radiation."},{"concepts":[508],"name":"Discuss the benefits of using a gauss filter instead of a mean filter for smoothing an image"},{"concepts":[112],"name":"Discuss the causal relationship between spatial processes and spatial patterns, including the possible problems in determining causality"},{"concepts":[622],"name":"Discuss the change of attenuation length moving from visible to the microwave range and from sea water to solid land surfaces"},{"concepts":[51],"name":"Discuss the characteristics of the various cluster detection techniques"},{"concepts":[25],"name":"Discuss the consequences of increasing and decreasing resolution"},{"concepts":[111],"name":"Discuss the contributions of early attempts to integrate the concepts of space, time, and attribute in geographic information, such as Berry (1964) and Sinton (1978)"},{"concepts":[97],"name":"Discuss the contributions that different perspectives on the nature of space bring to an understanding of geographic phenomenon"},{"concepts":[111],"name":"Discuss the degree to which these models can be implemented using current technologies"},{"concepts":[759],"name":"Discuss the development of remote sensing sensors"},{"concepts":[123],"name":"Discuss the difference between vagueness and uncertainty."},{"concepts":[10],"name":"Discuss the difference of implementing Dijkstras algorithm in raster and vector modes"},{"concepts":[789],"name":"Discuss the differences between imaging and non-imaging sensors"},{"concepts":[133],"name":"Discuss the differences between maps that use the same data but are for different purposes and intended audiences"},{"concepts":[133],"name":"Discuss the differences between maps that use the same data but are for different purposes and intended audiences"},{"concepts":[548],"name":"Discuss the different types of resolution of Earth observation data"},{"concepts":[92],"name":"Discuss the differing denotations and connotations of the terms spatial, geographic, and geospatial"},{"concepts":[110],"name":"Discuss the difficulty of integrating process models into GIS software based on the entity and field views, and methods used to do so"},{"concepts":[117],"name":"Discuss the effects of temporal scale on the modeling of genealogical structures"},{"concepts":[395],"name":"Discuss the ethical implications of a local government's decision to charge fees for its data"},{"concepts":[309],"name":"Discuss the extent to which vector data extraction from aerial stereoimagery has been automated"},{"concepts":[507],"name":"Discuss the frequencies that a high-pass filter preserves and subdues"},{"concepts":[593],"name":"Discuss the governance structure in place of a particular country"},{"concepts":[222],"name":"Discuss the historical roots of the Census Bureaus creation of GBF/DIME as the foundation for the development of topological data structures"},{"concepts":[798],"name":"Discuss the history of the development of remote sensing platforms"},{"concepts":[108],"name":"Discuss the human predilection to conceptualize geographic phenomena in terms of discrete entities"},{"concepts":[392],"name":"Discuss the impact of geospatial information for the development of social media (Facebook, Twitter, Wikimapia, Flickr etc.) becoming increasingly location-based"},{"concepts":[232],"name":"Discuss the implication of long transactions on database integrity"},{"concepts":[402],"name":"Discuss the implications of interoperability on ontology"},{"concepts":[398],"name":"Discuss the implications of interoperability on ontology"},{"concepts":[324],"name":"Discuss the implications of the sampling theorem (Lambda = 0.5 delta) to the concept of resolution"},{"concepts":[28],"name":"Discuss the implications of tradeoff between data detail and data volume"},{"concepts":[107],"name":"Discuss the importance of space, time, properties, and categories as fundamentals in the conceptualization and representation of spatial entities."},{"concepts":[150],"name":"Discuss the influence of the user interface on maps and visualizations on the Web"},{"concepts":[1185],"name":"Discuss the issue whether a service is really \"RESTful\" or not"},{"concepts":[380],"name":"Discuss the legal framework related to competition and public-private sector relationships in the geospatial domain"},{"concepts":[805],"name":"Discuss the main applications using the extra wide swath mode"},{"concepts":[494],"name":"Discuss the main drawback of edge-based segmentation in partitioning an image"},{"concepts":[766],"name":"Discuss the main properties of hyperspectral radiometers"},{"concepts":[765],"name":"Discuss the main properties of passive microwave radiometers"},{"concepts":[764],"name":"Discuss the main properties of thermal radiometers"},{"concepts":[758],"name":"Discuss the main types of remote sensing data"},{"concepts":[758,817],"name":"Discuss the main types of remote sensing platforms"},{"concepts":[758],"name":"Discuss the main types of remote sensing sensors"},{"concepts":[548],"name":"Discuss the minimum spatial resolution required for detecting single houses in a satellite image"},{"concepts":[597],"name":"Discuss the mission, history, constituencies, and activities of the GIS Certification Institute (GISCI)"},{"concepts":[577],"name":"Discuss the National Research Council report on Learning to Think Spatially (2005) as it relates to spatial thinking skills needed by the GIS and T workforce"},{"concepts":[831,548],"name":"Discuss the needs for high temporal resolution for analysing crop cycles in agriculture"},{"concepts":[23],"name":"Discuss the pitfalls of using secondary data that has been generated using interpolations (e.g., Level 1 USGS DEMs)"},{"concepts":[725],"name":"Discuss the polarimetry technique"},{"concepts":[29],"name":"Discuss the possible effects on topological integrity of generalizing data sets"},{"concepts":[377],"name":"Discuss the potential legal problems associated with licensing geospatial information"},{"concepts":[403],"name":"Discuss the potential role of agency (individual action) in resisting dominant practices and in using GIS and T in ways that are consistent with feminist epistemologies and politics"},{"concepts":[498],"name":"Discuss the principles of regionalisation and their use in segmentation methods"},{"concepts":[669],"name":"Discuss the processes that describe the hydrologic cycle"},{"concepts":[404],"name":"Discuss the production, maintenance, and use of geospatial data by a government agency or private firm from the perspectives of a taxpayer, a community organization, and a member of a minority group"},{"concepts":[846],"name":"Discuss the purposes of obtaining remote sensing data"},{"concepts":[700],"name":"Discuss the radiometric anomalies of radar data"},{"concepts":[55],"name":"Discuss the relationship between spatial processes and spatial patterns"},{"concepts":[125],"name":"Discuss the relationship between the history of exploration and the development of a more accurate map of the world"},{"concepts":[30],"name":"Discuss the relationship of attribute measurement levels to database query operations"},{"concepts":[392],"name":"Discuss the role and value of \"place\" and \"space\" for geo media based social networking"},{"concepts":[136],"name":"Discuss the role of gamut in choosing colors that can be reproduced on various devices and media"},{"concepts":[222],"name":"Discuss the role of graph theory in topological structures"},{"concepts":[22],"name":"Discuss the role of metadata in facilitating conversation of data models and data structures between systems"},{"concepts":[389,394],"name":"Discuss the role of public, private sector and citizens in facilitating geospatial information in environmental/sustainable issues."},{"concepts":[380],"name":"Discuss the role of the public and private sectors in producing and dissemination of geospatial information"},{"concepts":[575],"name":"Discuss the status of professional and academic certification in GIS and T"},{"concepts":[378],"name":"Discuss the status of the concept of privacy in the U.S. legal regime"},{"concepts":[142],"name":"Discuss the strengths and weaknesses of infographics as a method of displaying geographic information"},{"concepts":[658],"name":"Discuss the structure and chemical composition of the atmosphere"},{"concepts":[0],"name":"Discuss the synergy between processes in geo-information systems and earth observation systems."},{"concepts":[63],"name":"Discuss the theory leading to the assumption of intrinsic stationarity"},{"concepts":[762],"name":"Discuss the use of area array sensors in remote sensing"},{"concepts":[768],"name":"Discuss the use of atmospheric passive sounders"},{"concepts":[767],"name":"Discuss the use of data obtained by spectroradiometer"},{"concepts":[761],"name":"Discuss the use of digital frame cameras in remote sensing"},{"concepts":[692],"name":"Discuss the use of polarization for different application domains"},{"concepts":[149],"name":"Discuss the uses of the map as a user interface element in interactive presentations of geographic information"},{"concepts":[799],"name":"Discuss the ways of using data acquired by UAS in remote sensing"},{"concepts":[797],"name":"Discuss types and classes of remote sensing sensors"},{"concepts":[550],"name":"Discuss valid time ranges for images used for landslide mapping with pre- and post-event image comparison"},{"concepts":[381],"name":"Discuss various legal aspects of public and private sectors concerning owning, controlling, sharing/ disseminating open data."},{"concepts":[381],"name":"Discuss various sources of open data (science, public and private sectors)"},{"concepts":[376],"name":"Discuss ways in which the geospatial profession is regulated under the U.S. legal regime"},{"concepts":[388],"name":"Discuss ways of working with crowdsourcing in education and research"},{"concepts":[712],"name":"Discuss what horizontal roughness component (correlation legth) is"},{"concepts":[774],"name":"Discuss what information is acquired by the laser altimeters"},{"concepts":[711],"name":"Discuss what surface height variation (or RMS height) is"},{"concepts":[833],"name":"Discuss what the header file describes"},{"concepts":[769],"name":"Discuss what the main characteristics of radiometers are"},{"concepts":[772],"name":"Discuss what types of electromagnetic waves the laser profiler uses"},{"concepts":[453],"name":"Discuss why a query through time is easier realized with a data cube than by comparison of a time series stored in image files"},{"concepts":[832],"name":"Distinguish and explain the different types of properties of digital imagery"},{"concepts":[149,139],"name":"Distinguish between animated and interactive maps"},{"concepts":[89],"name":"Distinguish between continuants and occurrents in relation with spatial phenomena."},{"concepts":[154],"name":"Distinguish between different graphic representation techniques"},{"concepts":[86],"name":"Distinguish between metaphysics and epistemology."},{"concepts":[186],"name":"Distinguish between the temporary and structural relationships in a conceptual model"},{"concepts":[27],"name":"Distinguish between transformation methods for raster and vector representations."},{"concepts":[164,170],"name":"Distinguish between usability, utility, and user needs in the context of geovisualizations"},{"concepts":[167,168],"name":"Document existing and potential tasks in terms of workflow and information flow"},{"concepts":[105],"name":"Document the personal, social, and or institutional meaning of categories used in GIS applications"},{"concepts":[150],"name":"Edit the symbology, labeling, and page layout for a map originally designed for hard copy printing so that it can be seen and used on the Web"},{"concepts":[101],"name":"Effectively communicate the design, procedures, and results of GIS projects to non-GIS audiences (clients, managers, general public)"},{"concepts":[112],"name":"Employ techniques for visualizing, describing, and analyzing distributions in space, time, and attribute"},{"concepts":[1101],"name":"Enable citizen skills spatially"},{"concepts":[23],"name":"Estimate a value between two known values using linear interpolation (e.g., spot elevations, population between census years)"},{"concepts":[1142],"name":"Estimate evaporation rates"},{"concepts":[1142,444],"name":"Estimate near-surface chlorophyll-a concentration for monitoring harmful algal blooms (HABs)"},{"concepts":[129],"name":"Estimate the cost to collect needed data from primary sources (e.g., remote sensing, GPS)"},{"concepts":[36],"name":"Estimate the fractal dimension of a sinuous line"},{"concepts":[644],"name":"Estimate the meteorological and the cloud optical properties by LBRTM and validate against high accuracy spectral measurements"},{"concepts":[127],"name":"Estimate the potential value of a historical map"},{"concepts":[531],"name":"Evaluate an EO product and its metadata on its reusability for a new application context"},{"concepts":[570],"name":"Evaluate and revise an existing GIS management strategy"},{"concepts":[1101,1098,1099],"name":"Evaluate citizen-driven observations"},{"concepts":[153],"name":"Evaluate graphic techniques used to portray spatializations"},{"concepts":[25],"name":"Evaluate methods used by contemporary GIS software to resample raster data on-the-fly during display"},{"concepts":[311],"name":"Evaluate the advantages and disadvantages of acoustic remote sensing versus airborne or satellite remote sensing for seafloor mapping"},{"concepts":[311,808,813],"name":"Evaluate the advantages and disadvantages of airborne remote sensing versus satellite remote sensing"},{"concepts":[309],"name":"Evaluate the advantages and disadvantages of photogrammetric methods and LiDAR for production of terrain elevation data"},{"concepts":[110],"name":"Evaluate the assertion that events and processes are the same thing, but viewed at different temporal scales"},{"concepts":[122],"name":"Evaluate the causes of uncertainty in geospatial data"},{"concepts":[136],"name":"Evaluate the colors used in a web map to be used indoors and outdoors"},{"concepts":[528],"name":"Evaluate the conformity of an EO imagery product to ISO 19129"},{"concepts":[93],"name":"Evaluate the differences in how various parties think or feel differently about a place being modeled"},{"concepts":[217],"name":"Evaluate the ease of measuring resolution in different types of tessellations"},{"concepts":[108],"name":"Evaluate the effectiveness of GIS data models for representing the identity, existence, and lifespan of entities"},{"concepts":[109],"name":"Evaluate the field views description of objects as conceptual discretizations of continuous patterns"},{"concepts":[1138],"name":"Evaluate the impact of changes in land areas"},{"concepts":[101],"name":"Evaluate the impact of geospatial technologies (e.g., Google Earth) that allow non-geospatial professionals to create, distribute, and map geographic information"},{"concepts":[1117,1115],"name":"Evaluate the impact of the climate change"},{"concepts":[217],"name":"Evaluate the implications of changing grid cell resolution on the results of analytical applications by using GIS software"},{"concepts":[108],"name":"Evaluate the influence of scale on the conceptualization of entities"},{"concepts":[85],"name":"Evaluate the influences of ones own philosophical views and assumptions on GIS AND T practices"},{"concepts":[81],"name":"Evaluate the influences of particular worldviews (including ones own) on GIS practices"},{"concepts":[95],"name":"Evaluate the influences of political actions, especially the allocation of territory, on human perceptions of space and place"},{"concepts":[95],"name":"Evaluate the influences of political ideologies (e.g., Marxism, Capitalism, conservative liberal) on the understanding of geographic information"},{"concepts":[589],"name":"Evaluate the institutional framework of an existing SDI initiative"},{"concepts":[222],"name":"Evaluate the positive and negative impacts of this shift from integrated topological models"},{"concepts":[213],"name":"Evaluate the relative merits of grid compression methods for storage"},{"concepts":[579],"name":"Evaluate the relevance and applicability of different teaching and learning methods for GIS&T education"},{"concepts":[109],"name":"Evaluate the representation of movement as a field of location over time (e.g. :x,y,z: = f(t) )"},{"concepts":[121],"name":"Evaluate the role that system complexity, dynamic processes, and subjectivity play in the creation of vague phenomena and concepts"},{"concepts":[144],"name":"Evaluate the strengths and limitations of different thematic mapping methods"},{"concepts":[539],"name":"Evaluate the thematic accuracy of a given soils map"},{"concepts":[242],"name":"Evaluate the tradeoffs between abstraction and representativeness in simulation model development"},{"concepts":[161],"name":"Evaluate the usability of a hard-copy map"},{"concepts":[161,170],"name":"Evaluate the usability of a web map"},{"concepts":[187],"name":"Evaluate the various general data models common in GIS project"},{"concepts":[121],"name":"Evaluate vagueness in the locations, time, attributes, and other aspects of geographic phenomena"},{"concepts":[29],"name":"Evaluate various line simplification algorithms by their usefulness in different applications"},{"concepts":[243],"name":"Evaluate when rule-based models can be applied to spatiotemporal problems"},{"concepts":[238],"name":"Examine how computational technology relates to geocomputation"},{"concepts":[449],"name":"Examine how the vegetation indices relates to the vegetation dynamics and health"},{"concepts":[449],"name":"Examine how the water-related spectral indices relates to changes in the vegetation and soil water content"},{"concepts":[1195],"name":"Examine Metadata schema and vocabularies used for open data publishing"},{"concepts":[1210],"name":"Examine the Document Object Model (DOM) in HTML documents"},{"concepts":[45],"name":"Exemplify applications in which overlay is useful, such as site suitability analysis"},{"concepts":[63],"name":"Exemplify deterministic and spatial stochastic processes"},{"concepts":[103],"name":"Exemplify different temporal frames of reference: linear and cyclical, absolute and relative"},{"concepts":[568],"name":"Exemplify each component of a needs assessment for an enterprise GIS"},{"concepts":[235],"name":"Exemplify how the lack of a data librarian to manage data can have disastrous consequences on the resulting dataset"},{"concepts":[63],"name":"Exemplify non-stationarity involving first and second order effects"},{"concepts":[113],"name":"Exemplify regions found at different scales"},{"concepts":[232],"name":"Exemplify scenarios in which one would need to perform a number of periodic changes in a real GIS database"},{"concepts":[38],"name":"Exemplify situations in which the centroid of a polygon falls outside its boundary"},{"concepts":[12],"name":"Exemplify the Classic Transportation Problem"},{"concepts":[222],"name":"Exemplify the concept of planar enforcement (e.g., TIN triangles)"},{"concepts":[215],"name":"Exemplify the uses (past and potential) of the hexagonal model"},{"concepts":[631],"name":"Explain the concept of composition of spectral signatures and apply the \"linear mixing\" models in some simple case"},{"concepts":[1087],"name":"Explain a use case of EO for smart cities, e.g. how EO derived information about urban green instrastructure supports designing nature based solutions for preserving ecosystem services"},{"concepts":[735],"name":"Explain across-track interferometry technique"},{"concepts":[734],"name":"Explain along-track interferometry technique"},{"concepts":[487],"name":"Explain an application example where SVM is used for EO image classification"},{"concepts":[449],"name":"Explain an application example where the spectral indices are used for vegetation, water or snow monitoring"},{"concepts":[207],"name":"Explain and apply GML data models"},{"concepts":[694],"name":"Explain and apply phase unwrapping"},{"concepts":[203,221],"name":"Explain and apply standards relevant for geometric modelling"},{"concepts":[749],"name":"Explain and discuss elements of Synthetic Aperture Radar (SAR) geometric configuration"},{"concepts":[716],"name":"Explain and discuss surface roughness in microwave remote sensing"},{"concepts":[689],"name":"Explain and discuss the complex elements of a radar signal"},{"concepts":[822],"name":"Explain and discuss the concept of Big Data in the field of Earth Observation"},{"concepts":[818],"name":"Explain and discuss the development of remote sensing data carriers"},{"concepts":[782],"name":"Explain and discuss the LiDAR technology"},{"concepts":[803],"name":"Explain and discuss the SAR acquisition mode spotlight"},{"concepts":[802],"name":"Explain and discuss the SAR acquisition mode staring spotlight"},{"concepts":[770],"name":"Explain and discuss types of sensing mechanisms"},{"concepts":[727],"name":"Explain and discuss what antenna gain is and why it is described as the key performance of a radar antenna"},{"concepts":[754],"name":"Explain and discuss what terrain reflectivity is and how it influences radar signal"},{"concepts":[751],"name":"Explain and discuss what the foreshortening is"},{"concepts":[752],"name":"Explain and discuss what the layover is"},{"concepts":[845],"name":"Explain and discuss what the main processing levels of remote sensing data are"},{"concepts":[832],"name":"Explain and discuss what the radiometric resolution is"},{"concepts":[745],"name":"Explain and discuss what the range direction is"},{"concepts":[753],"name":"Explain and discuss what the shadow in SAR acquisition means"},{"concepts":[832,829],"name":"Explain and discuss what the spatial resolution is"},{"concepts":[832],"name":"Explain and discuss what the spectral resolution is"},{"concepts":[832],"name":"Explain and discuss what the temporal resolution is"},{"concepts":[757,697],"name":"Explain and outline the advantages of radar sensors"},{"concepts":[197],"name":"Explain and use UML diagrams"},{"concepts":[76],"name":"Explain Anselins typology of spatial autoregressive models"},{"concepts":[37],"name":"Explain any differences in the measured direction between two places when the data are presented in a GIS in different projections"},{"concepts":[200],"name":"Explain basic aspects of data modelling, storage and exploitation, such as relation models & databases, data structures, SQL, UML and other basics"},{"concepts":[377],"name":"Explain cases of liability claims associated with misuse of geospatial information, erroneous information, and loss of proprietary interests"},{"concepts":[719],"name":"Explain covariance and coherence matrix"},{"concepts":[710],"name":"Explain dielectric properties of objects and their effect on radar data acquisition"},{"concepts":[733],"name":"Explain differences between DInSAR and PSI"},{"concepts":[757],"name":"Explain differences between optical and radar remote sensing"},{"concepts":[84],"name":"Explain from which scientific fields GIS&T borrows ideas."},{"concepts":[236],"name":"Explain geocomputation, related concepts and how the two relate"},{"concepts":[6],"name":"Explain how a Bayesian framework can incorporate expert knowledge in order to retrieve all relevant datasets given an initial user query"},{"concepts":[587],"name":"Explain how a business case analysis can be used to justify the expense of implementing consensus-based standards"},{"concepts":[467],"name":"Explain how a DSM differs from a DTM"},{"concepts":[226],"name":"Explain how a graph (network) may be directed or undirected"},{"concepts":[226],"name":"Explain how a graph can be written as an adjacency matrix and how this can be used to calculate topological shortest paths in the graph"},{"concepts":[419],"name":"Explain how a histogram is derived from an EO image"},{"concepts":[552],"name":"Explain how a lack of knowledge about data quality limits the data value"},{"concepts":[10],"name":"Explain how a leading World Wide Web-based routing system works e.g., MapQuest, Yahoo Maps, Google"},{"concepts":[40],"name":"Explain how a semi-variogram describes the distance decay in dependence between data values"},{"concepts":[411],"name":"Explain how a set of overlapping images/satellite scenes can provide digital elevation models used for orthorectification and 3D modelling"},{"concepts":[1129],"name":"Explain how a specific EO technology supports the assessments of disasters and geohazards"},{"concepts":[65],"name":"Explain how a statistic that is based on combining all the spatial data and returning a single summary value or two can be useful in understanding broad spatial trends"},{"concepts":[404],"name":"Explain how a tax assessors office adoption of GIS and T may affect power relations within a community"},{"concepts":[66],"name":"Explain how a weights matrix can be used to convert any classical statistic into a local measure of spatial association"},{"concepts":[78],"name":"Explain how allowing the parameters of the model to vary with the spatial location of the sample data can be used to accommodate spatial heterogeneity"},{"concepts":[56,1],"name":"Explain how analytical methods are used to derive analytical results from geospatial data"},{"concepts":[450],"name":"Explain how band maths can be applied to derive an index that indicates a specific land cover type like 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real-world connotations (e.g., blue=water, white=snow) can be used to determine color selections on maps"},{"concepts":[43],"name":"Explain how reclassification can be used for data simplification and measurement scale change"},{"concepts":[27],"name":"Explain how Representation transformations are related to spatial data quality."},{"concepts":[324],"name":"Explain how resampling affects the resolution of image data"},{"concepts":[587],"name":"Explain how resistance to change affects the adoption of standards in an organization coordinating a GIS"},{"concepts":[58],"name":"Explain how ridgelines and streamlines can be used to improve the result of an interpolation process"},{"concepts":[1115],"name":"Explain how sea surface temperature maps are used to predict El Nino events"},{"concepts":[32],"name":"Explain how set theory relates to spatial queries"},{"concepts":[523],"name":"Explain how SIFT algorithms can be used for enhancing orthorectification"},{"concepts":[61],"name":"Explain 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sensor"},{"concepts":[792],"name":"Explain the principles of operation of the multi-temporal pattern based sensor"},{"concepts":[791],"name":"Explain the principles of operation of the speckle-pattern based sensor"},{"concepts":[794],"name":"Explain the principles of operation of the structured-light-projection camera"},{"concepts":[16],"name":"Explain the principles of operations research modeling and location modeling"},{"concepts":[776],"name":"Explain the principles of spaceborne laser scanning operation and discuss its applications"},{"concepts":[737],"name":"Explain the principles of synthetic aperture radar (SAR) interferometry"},{"concepts":[815],"name":"Explain the principles of terrestrial laser scanning operation and discuss its applications"},{"concepts":[738],"name":"Explain the principles of the SAR tomography"},{"concepts":[779],"name":"Explain the principles of underwater laser scanning operation and discuss its applications"},{"concepts":[535],"name":"Explain the procedure how to collect ground reference data for an image classification"},{"concepts":[242],"name":"Explain the process simulation model development"},{"concepts":[430],"name":"Explain the purpose of image pre-processing"},{"concepts":[844],"name":"Explain the purpose of the analysis ready data"},{"concepts":[1126],"name":"Explain the quality criteria where EO technologies differ from each other in their capabilities to detect, monitor and forecast landslides"},{"concepts":[40],"name":"Explain the rationale for using different forms of distance decay functions"},{"concepts":[64],"name":"Explain the rationale used for each type of spatial weights matrix"},{"concepts":[199],"name":"Explain the relations between GIS and databases"},{"concepts":[113],"name":"Explain the relationship between regions and categories"},{"concepts":[706],"name":"Explain the relationship between the material constant and the interaction of microwaves with the object"},{"concepts":[390],"name":"Explain the 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geocomputation"},{"concepts":[413,416],"name":"Explain the role and selection criteria for ground control points (GCPs) in the georegistration of aerial imagery"},{"concepts":[105],"name":"Explain the role of categories in common-sense conceptual models, everyday language, and analytical procedures"},{"concepts":[17],"name":"Explain the role of constraint functions using the graphical method"},{"concepts":[17],"name":"Explain the role of constraint functions using the simplex method"},{"concepts":[433],"name":"Explain the role of Gram-Schmidt vector orthogonalization in pan-sharpening"},{"concepts":[88],"name":"Explain the role of metaphors and image schema in our understanding of geographic phenomena and geographic tasks"},{"concepts":[98],"name":"Explain the role of metaphors and image schemata in our understanding of geographic phenomena and geographic tasks."},{"concepts":[17],"name":"Explain the role of objective functions in linear programming"},{"concepts":[487],"name":"Explain the sensitivity of SVM to hyper-parameters"},{"concepts":[486],"name":"Explain the sensitivity of the Random Forests classifier to the number of trees and the number of variables used to split the tree nodes"},{"concepts":[505],"name":"Explain the shape and weights for a horizontal edge detector"},{"concepts":[59],"name":"Explain the sources and impact of errors that affect intervisibility analyses"},{"concepts":[442],"name":"Explain the value of the leaf area index for vegetation mapping"},{"concepts":[187],"name":"Explain the various types of cardinality"},{"concepts":[1080],"name":"Explain to customers the information derived from EO"},{"concepts":[1064],"name":"Explain Tobler's first law of geography."},{"concepts":[1188],"name":"Explain Web Ontology Language (OWL) and how to define a data set in OWL DL"},{"concepts":[19],"name":"Explain Webers locational triangle"},{"concepts":[383],"name":"Explain what a business model is and how is used"},{"concepts":[246],"name":"Explain what a cellular automata is and what its key components are"},{"concepts":[821],"name":"Explain what a data cube is"},{"concepts":[173],"name":"Explain what a project is, and the difference between a project, programme, and product"},{"concepts":[739],"name":"Explain what active-passive microwave imaging is"},{"concepts":[247],"name":"Explain what an agent-based model is and what its key components are"},{"concepts":[746],"name":"Explain what an incident angle is"},{"concepts":[784],"name":"Explain what can be measured with a seismic sensor or seismic sensors"},{"concepts":[785],"name":"Explain what can be measured with a sonic sensor"},{"concepts":[783],"name":"Explain what can bea measeard with a sonar sensor"},{"concepts":[208],"name":"Explain what CityGML is"},{"concepts":[693],"name":"Explain what coherent means in radar remote sensing"},{"concepts":[1205],"name":"Explain what data mashups are"},{"concepts":[191],"name":"Explain what databases are"},{"concepts":[584],"name":"Explain what framework agreements are and how they can be used for sharing geospatial data"},{"concepts":[306],"name":"Explain what horizontal and vertical datums precisely determine"},{"concepts":[2],"name":"Explain what is added to spatial analysis to make it spatio-temporal analysis"},{"concepts":[1195],"name":"Explain what is meant by \"Odata\" (Open data Protocol), an OASIS standard"},{"concepts":[38],"name":"Explain what is meant by the convex hull and minimum enclosing rectangle of a set of point data"},{"concepts":[45],"name":"Explain what is meant by the term \"planar enforcement\""},{"concepts":[4],"name":"Explain what is meant by the term contaminated data, suggesting how it can arise"},{"concepts":[2],"name":"Explain what is special i.e., difficult about geospatial data analysis and why some traditional statistical analysis techniques are not suited to geographic problems"},{"concepts":[698],"name":"Explain what it is and causes diffraction"},{"concepts":[584],"name":"Explain what licenses are and how they can be used for sharing geospatial data"},{"concepts":[216],"name":"Explain what linear referencing is and how it is used"},{"concepts":[307],"name":"Explain what map projections are"},{"concepts":[757],"name":"Explain what microwave remote sensing is"},{"concepts":[381],"name":"Explain what open data and the main principles of open data are"},{"concepts":[756],"name":"Explain what properties of microwave electromagnetic spectrum are recorded"},{"concepts":[397],"name":"Explain what relevant ethical aspects are related to the access to and use of geospatial information"},{"concepts":[593],"name":"Explain what SDI governance is and why it is important in the development and implementation of SDIs"},{"concepts":[708],"name":"Explain what soil permittivity is"},{"concepts":[812],"name":"Explain what swath represents"},{"concepts":[219],"name":"Explain what tessellation data models are"},{"concepts":[707],"name":"Explain what the attenuation length and penetration 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(RDF) is and what it can be used for"},{"concepts":[696],"name":"Explain what the wave-particle dualism is"},{"concepts":[320],"name":"Explain which elements determine the quality of geospatial data"},{"concepts":[503],"name":"Explain which principles a segmentation should follow to arrive at meaningful objects that are appropriate for a specific application"},{"concepts":[202],"name":"Explain which standards are essential for conceptual data modelling"},{"concepts":[82],"name":"Explain which technologies have an impact on GI science."},{"concepts":[315],"name":"Explain which types of geospatial data are collected through satellite remote sensing"},{"concepts":[140],"name":"Explain why a layer with audio could be of interest in certain situations"},{"concepts":[691],"name":"Explain why a radar signal needs a complex waveform description"},{"concepts":[310],"name":"Explain why aerial imaging and photogrammetry are important for the geospatial domain and the geospatial industry"},{"concepts":[50],"name":"Explain why and how density estimation transforms point data into a field representation"},{"concepts":[29],"name":"Explain why areal generalization is more difficult than line simplification"},{"concepts":[57],"name":"Explain why different interpolation algorithms produce different results and suggest ways by which these can be evaluated in the context of a specific problem"},{"concepts":[36],"name":"Explain why estimating the fractal dimension of a sinuous line has important implications for the measurement of its length"},{"concepts":[113],"name":"Explain why general-purpose regions rarely exist"},{"concepts":[46,47],"name":"Explain why georegistration is a precondition to map algebra"},{"concepts":[13],"name":"Explain why heuristic solutions are generally used to address the combinatorially complex nature of these problems and the difficulty of solving them optimally"},{"concepts":[525],"name":"Explain why image understanding goes beyond feature 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quality of geospatial data"},{"concepts":[477],"name":"Explain why multimodal distributions in training samples should be avoided when using the maximum likelihood classifier"},{"concepts":[610],"name":"Explain why passive EO sensors with the highest spectral or spatial resolution operate in the VIS/NIR spectral region"},{"concepts":[450],"name":"Explain why radiometric correction is a key requirement for deriving indices with band maths"},{"concepts":[540],"name":"Explain why rapid mapping applications have high requirements in timely availability of Earth observation products"},{"concepts":[598],"name":"Explain why software products sold by U.S. companies may predominate in foreign markets, including Europe and Australia"},{"concepts":[741],"name":"Explain why spatial resolution of passive radar system is much lower than that of active systems"},{"concepts":[569],"name":"Explain why the definition of user roles is an important element in the implementation of a GIS"},{"concepts":[695],"name":"Explain why the Doppler effect is important in radar remote sensing"},{"concepts":[583],"name":"Explain why the legal framework on geospatial data sharing can be considered as diverse and complex"},{"concepts":[583],"name":"Explain why the legal framework on geospatial data sharing consists of two main types of legislation from a data perspective"},{"concepts":[45],"name":"Explain why the process \"dissolve and merge\" often follows vector overlay operations"},{"concepts":[61],"name":"Explain why the properties of spatial continuity are characteristic of spatial surfaces"},{"concepts":[38],"name":"Explain why the shape of an object might be important in analysis"},{"concepts":[304],"name":"Explain why the shape of the Earth is complex and complicated to measure"},{"concepts":[1126],"name":"Explain why the use of multiple EO sensors for mapping landslides associated with one triggering event increases the completeness of a landslide inventory"},{"concepts":[119],"name":"Explain why Toblers First Law of Geography is fundamental to many operations in GIS and whether it should be"},{"concepts":[61],"name":"Explain why zero slopes are indicative of surface specific points such as peaks, pits and passes and list the conditions necessary for each"},{"concepts":[12],"name":"Explain why, if supply equals demand, there will always be a feasible solution to the Classic Transportation Problem"},{"concepts":[50],"name":"Explain why, in some cases, an adaptive bandwidth might be employed"},{"concepts":[325],"name":"Explain, in general terms, the difference between single and double precision and impacts on error propagation"},{"concepts":[16],"name":"Explain, using the concept of combinatorial complexity, why some location problems are very hard to solve"},{"concepts":[88],"name":"Explore the contribution of linguistics to the study of spatial cognition and the role of natural language in the conceptualization of geographic phenomena"},{"concepts":[92],"name":"Explore the history of geography including (but not limited to) Greek and Roman contributions to geography (Eratosthenes, Strabo, Ptolemy), geography and cartography in the age of discovery, military geography, and geography..."},{"concepts":[76],"name":"Find a best model"},{"concepts":[147],"name":"Find a multivariate outlier using a combination of maps and graphs"},{"concepts":[38],"name":"Find centroids of polygons under different definitions of a centroid and different polygon shapes"},{"concepts":[1142],"name":"Find oil spills in EO data for Ocean surveillance"},{"concepts":[577],"name":"Find or create training resources appropriate for GIS and T workforce in a local government organization"},{"concepts":[112],"name":"Find spatial patterns in the distribution of geographic phenomena using geographic visualization and other techniques"},{"concepts":[1101,1099],"name":"Forecast and monitor ocean winds and waves"},{"concepts":[106],"name":"Formalize attribute values and domains in terms of set theory"},{"concepts":[109],"name":"Formalize the notion of field using mathematical functions and Calculus"},{"concepts":[45],"name":"Formalize the operation called map overlay using Boolean logic"},{"concepts":[407],"name":"Generate a layer stack from bands of various EO data sources"},{"concepts":[436],"name":"Generate fine-scale images at a high temporal resolution with a spatio-temporal image fusion method"},{"concepts":[455],"name":"Generate high quality time series by removing clouds and cloud shadows from the available images"},{"concepts":[224],"name":"Give and explain an example of an application models"},{"concepts":[583],"name":"Give examples of more general types of legislation that are also applicable and relevant to geospatial data sharing"},{"concepts":[1149],"name":"Having in-depth knowledge of two of the three Copernicus-relevant topics: Land monitoring, Emergency response including Humanitarian action, and Climate change"},{"concepts":[112],"name":"Hypothesize the causes of a pattern in the spatial distribution of a phenomenon"},{"concepts":[51],"name":"Identify a clustering method which does not require the number of clusters as input"},{"concepts":[30],"name":"Identify a variety of likely measurement level transformations (e.g., the classification of ratio data yields ordinal data)"},{"concepts":[1150],"name":"Identify adequate preprocessing for deriving ocean colour from EO data"},{"concepts":[247],"name":"Identify agent-based modelling principles and methodologies"},{"concepts":[398],"name":"Identify alternatives to the \"algorithmic way of thinking\" that characterizes use of geospatial Information."},{"concepts":[402],"name":"Identify alternatives to the algorithmic way of thinking that characterizes GIS"},{"concepts":[236],"name":"Identify and compare the scenarios on which geocomputation methods are relevant"},{"concepts":[71],"name":"Identify and define the parameters of a semi-variogram range, sill, nugget"},{"concepts":[504],"name":"Identify and discuss an example of a combined filtering process"},{"concepts":[586],"name":"Identify and discuss the different components of an SDI"},{"concepts":[414],"name":"Identify and explain an equation used to perform image-to-image registration"},{"concepts":[414],"name":"Identify and explain an equation used to perform image-to-map registration"},{"concepts":[420],"name":"Identify and explain methods of image enhancement"},{"concepts":[384],"name":"Identify and explain the different actors and their roles in the geo-information value chain"},{"concepts":[455],"name":"Identify anomalies by means of surface properties such as evapotranspiration (ET) or land surface temperature (LST) derived from satellite image time series"},{"concepts":[109],"name":"Identify applications and phenomena that are not adequately modeled by the field view"},{"concepts":[1093,1090,1111,1122,1092,1121],"name":"Identify border incursions or maritime movements"},{"concepts":[1210],"name":"Identify building blocks of Javascript programming language"},{"concepts":[246],"name":"Identify cellular automata principles and pattern"},{"concepts":[101],"name":"Identify common-sense views of geographic phenomena that sharply contrast with established theories and technologies of geographic information"},{"concepts":[240],"name":"Identify commonalities and patterns of geocomputation"},{"concepts":[597],"name":"Identify conferences that are related to GIS and T hosted by professional organizations"},{"concepts":[1170],"name":"Identify construction sites"},{"concepts":[543],"name":"Identify critical design decisions that make an EO-derived map readable"},{"concepts":[172],"name":"Identify data center platform tier configuration and identify platform selection for each tier"},{"concepts":[1184],"name":"Identify design issues of SOAP web services; fine grained and coarse grained services, design patterns"},{"concepts":[1212],"name":"Identify differences, advantages and disadvantages of web application framework based and portal framework based web applications from the geospatial data perspective"},{"concepts":[65],"name":"Identify different measures of spatial autocorrelation"},{"concepts":[66],"name":"Identify different measures of spatial autocorrelation"},{"concepts":[476],"name":"Identify different methods that employ conditional probability for image classification"},{"concepts":[300],"name":"Identify different options where Artificial Intelligence can be integrated in the image processing and analysis workflow"},{"concepts":[427],"name":"Identify different types of noise and associated methods for their reduction"},{"concepts":[109],"name":"Identify examples of discrete and continuous change found in spatial, temporal, and spatio-temporal fields"},{"concepts":[149,139],"name":"Identify examples of static, animated, and interactive web maps"},{"concepts":[134],"name":"Identify gaming elements which may be part of geo-games"},{"concepts":[1137],"name":"Identify geological features"},{"concepts":[1125,1137],"name":"Identify geotectonic shifts"},{"concepts":[1093,1090,1100,1113,1111,1122,1092,1118],"name":"Identify high risk areas produced naturally or by humans"},{"concepts":[437],"name":"Identify image fusion techniques to fill gaps in image time series caused by clouds and cloud shadow"},{"concepts":[1123],"name":"Identify impact of a flood"},{"concepts":[112],"name":"Identify influences of scale on the appearance of distributions"},{"concepts":[1197],"name":"Identify issues in determining the relationships to be represented when publishing Linked Data"},{"concepts":[1196],"name":"Identify issues in developing new ontologies for geospatial data"},{"concepts":[1197],"name":"Identify issues in finding proper ontologies to annotate the data"},{"concepts":[1190],"name":"Identify issues in the development of geospatial ontologies. 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Also identify the roles of thesauri and crosswalks"},{"concepts":[574],"name":"Identify the key organizational components of a GIS&T implementation"},{"concepts":[113],"name":"Identify the kinds of phenomena that are commonly found at the boundaries of regions"},{"concepts":[377],"name":"Identify the liability implications associated with contracts"},{"concepts":[1203],"name":"Identify the main components of OGC Filter encoding and compare it to SQL"},{"concepts":[1200],"name":"Identify the main concepts of reasoning and architectural components of Reasoners"},{"concepts":[574],"name":"Identify the main organizational challenges in implementing and use GIS&T"},{"concepts":[136],"name":"Identify the most appropriate color palette for a printed map for visually-impaired people"},{"concepts":[136],"name":"Identify the most appropriate color palette for an online map for visually-impaired people"},{"concepts":[483],"name":"Identify the most popular decision tree algorithms"},{"concepts":[212],"name":"Identify the national framework datasets based on a grid model"},{"concepts":[1203],"name":"Identify the need for and main issues in spatial data interchange"},{"concepts":[81],"name":"Identify the ontological assumptions underlying the work of colleagues"},{"concepts":[577],"name":"Identify the particular skills necessary for users to perform tasks in three different workforce domains (e.g., small city, medium county agency, a business, or others)"},{"concepts":[85],"name":"Identify the philosophical views and assumptions underlying the work of colleagues"},{"concepts":[174],"name":"Identify the positions necessary to design and implement a GIS project / GI unit"},{"concepts":[575],"name":"Identify the qualifications needed for a particular GIS and T position"},{"concepts":[1188],"name":"Identify the relation between OWL-S and WSDL and give an overview of Semantic Web service definition in OWL-S"},{"concepts":[57],"name":"Identify the spatial 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time"},{"concepts":[49],"name":"Identify various types of K-function analysis"},{"concepts":[1188],"name":"Identify virtues of defining a given data set in both RDF and OWL, and compare semantic richness of both definitions"},{"concepts":[1148,1146],"name":"Identify wake trailing to detect ships using EO data"},{"concepts":[1208],"name":"Identify whether Full-automated WSC has still a value in it concerning both where we stand today on the road to 'Semantic Web' and unresolved problems in the area, which are the problems of Artificial Intelligence indeed"},{"concepts":[630],"name":"Illustrate main spectral signatures of clouds and apply them in paractical cloud-detection exercise"},{"concepts":[222],"name":"Illustrate a topological relation"},{"concepts":[391],"name":"Illustrate an example of \"local knowledge\" that is unlikely to be represented in the geospatial data maintained routinely by government agencies"},{"concepts":[670],"name":"Illustrate and apply basic concepts of Atmospheric Physics to EO science and its applications"},{"concepts":[324],"name":"Illustrate and explain the distinction between resolution, precision, and accuracy"},{"concepts":[324],"name":"Illustrate and explain the distinctions between spatial resolution, thematic resolution, and temporal resolution"},{"concepts":[628],"name":"Illustrate basic features of spectral signatures of vegetation, water and bare soil"},{"concepts":[657],"name":"Illustrate basic modern physics theory understanding their implications on the development of advanced sensors for EO"},{"concepts":[619,627],"name":"Illustrate basic radiation-matter interactions and related concepts of spectral reflectance, absorbance and transmittance as specific properties of the matter"},{"concepts":[630],"name":"Illustrate e.m. radiation intercations with/within clouds."},{"concepts":[173],"name":"Illustrate each of the project management areas with an example of a technique or tool used"},{"concepts":[166],"name":"Illustrate how a business process analysis can be used to identify requirements during a GIS implementation"},{"concepts":[139],"name":"Illustrate how an animated map reveals patterns not evident without animation"},{"concepts":[643],"name":"Illustrate how cloud presence complicate radiative transfer description in Earth's atmosphere"},{"concepts":[87],"name":"Illustrate how fields, such as geography, cartography, computer and information science, engineering, mathematics, philosophy, cognitive science, and linguistics have their influence on GI science."},{"concepts":[615],"name":"Illustrate how it is possible to estimate the BRDF of a sample through measurements of BRF"},{"concepts":[613],"name":"Illustrate how the Rayleigh criterion can help to characterize surfaces' scattering properties in relation with their roughness and wavelength of the incident radiation"},{"concepts":[618],"name":"Illustrate how the Voigt's line profile is related to the 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this specific spectral region."},{"concepts":[675],"name":"Illustrate the concept of \"kinetic temperature\" in absence of thermodynamic equilibrium"},{"concepts":[638],"name":"Illustrate the concept of Absorption Coefficient"},{"concepts":[637],"name":"Illustrate the concept of Cross Section of Extinction per Mass Unit"},{"concepts":[620],"name":"Illustrate the concept of grey body"},{"concepts":[639],"name":"Illustrate the concept of Source Function"},{"concepts":[609],"name":"Illustrate the concept of spectral emissivity and brigthness temperature and compute them in some simple real case"},{"concepts":[627],"name":"Illustrate the concept of spectral signatures of the matter"},{"concepts":[650],"name":"Illustrate the concepts of Interference and Diffraction"},{"concepts":[646],"name":"Illustrate the concepts of Reflection, Refraction and Dispersion of the light"},{"concepts":[602],"name":"Illustrate the concepts of solar constant and daily solar insolation"},{"concepts":[626],"name":"Illustrate the decay of the emittance with the distance from the source"},{"concepts":[141],"name":"Illustrate the elements of the story by proper geovisualizations"},{"concepts":[125],"name":"Illustrate the evolution of Cartography in different periods of time"},{"concepts":[213],"name":"Illustrate the existing methods for compressing gridded data (e.g., run length encoding, Lempel-Ziv, wavelets)"},{"concepts":[685],"name":"Illustrate the factors limiting lifetime of satellites on their originally planned orbits"},{"concepts":[681],"name":"Illustrate the First Law of Thermodynamic"},{"concepts":[635],"name":"Illustrate the general equation of radiative transfer."},{"concepts":[661],"name":"Illustrate the Greenhouse effect associate to CO2 emission"},{"concepts":[653],"name":"Illustrate the Helmotz’s equation"},{"concepts":[215],"name":"Illustrate the hexagonal model"},{"concepts":[676],"name":"Illustrate the ideal gas law"},{"concepts":[217],"name":"Illustrate the impact of grid cell resolution on the information that can be portrayed"},{"concepts":[24],"name":"Illustrate the impact of vector/raster/vector conversions on the quality of a dataset"},{"concepts":[603],"name":"Illustrate the importance of Earth's emitted radiation for EO from space"},{"concepts":[648],"name":"Illustrate the importance of electric conduction in solids for the design and development of advanced EO sensors"},{"concepts":[686],"name":"Illustrate the importance of the choice of the satellite orbit for the design of a satellite mission devoted to specific applications"},{"concepts":[1179,1174,1175,1176,1177,1178],"name":"Illustrate the information of EO data"},{"concepts":[183],"name":"Illustrate the landscape of GIS and related libraries"},{"concepts":[668],"name":"Illustrate the main atmospherical spectral windows"},{"concepts":[632],"name":"Illustrate the main differences among passive and active remote sensing techniques"},{"concepts":[616],"name":"Illustrate the main energetic transictions that can be associated to molecular absorption of e.m. radiation"},{"concepts":[624],"name":"Illustrate the main forms of radiation-matter interaction"},{"concepts":[51],"name":"Illustrate the main use of spatial clustering in earth observation"},{"concepts":[611],"name":"Illustrate the nature of electromagnetic radiation"},{"concepts":[218],"name":"Illustrate the quadtree model"},{"concepts":[241],"name":"Illustrate the relationships between geocomputation with other terms, disciplines and areas of knowledge"},{"concepts":[680],"name":"Illustrate the role of Eulerian and Lagrangian models in budget equations definition"},{"concepts":[652],"name":"Illustrate the role of the principle of constant speed of light within the special relativity theory"},{"concepts":[645],"name":"Illustrate the scope Radiative Transfer theory"},{"concepts":[682],"name":"Illustrate the Second Law of Thermodynamic"},{"concepts":[628],"name":"Illustrate the spectral response curves for basic environmental features (e.g., vegetation, concrete, bare soil)"},{"concepts":[669],"name":"Illustrate the transferring of Energy within the Earth-Atmosphere System"},{"concepts":[151],"name":"Illustrate the use of virtual environments"},{"concepts":[674],"name":"Illustrate the utility of thermodynamic diagrams for the study of local atmospheric properties"},{"concepts":[142],"name":"Illustrate the ways in which maps could be integrated in an infography"},{"concepts":[567],"name":"Illustrate what functions a support or service center can provide to an organization using GIS and T"},{"concepts":[614],"name":"Illustrate why we refer to the BRDF as an absolute definition of spectral reflectance"},{"concepts":[140],"name":"Illustrate with examples of maps or geovisualizations that could be improved by the addition of an audio layer"},{"concepts":[126],"name":"Illustrate with examples the relationship between art and cartography at different historical moments"},{"concepts":[678],"name":"Ilustrate the state function of the condensed gas phase"},{"concepts":[218],"name":"Implement a format for encoding quadtrees in a data file"},{"concepts":[76],"name":"Implement a maximum likelihood estimation procedure for determining key spatial econometric parameters"},{"concepts":[234],"name":"Implement a test of reliability of change information"},{"concepts":[57],"name":"Implement a trend surface analysis using either the supplied function in a GIS or a regression function from any standard statistical package"},{"concepts":[1194],"name":"Implement and configure a catalogue service"},{"concepts":[17],"name":"Implement linear programs for spatial allocation problems"},{"concepts":[12],"name":"Implement the Transportation Simplex method to determine the optimal solution"},{"concepts":[322],"name":"In contrast to the National Map Accuracy Standard, explain how the spatial accuracy of a digital road centerlines data set may be evaluated and documented"},{"concepts":[1196],"name":"Indicate an architecture and tools for organizing semantically annotated data"},{"concepts":[1211],"name":"Indicate an overview of OpenStreetMap and define its general functionality, comment its usage by Web APIs"},{"concepts":[1212],"name":"Indicate generally how \"NSDI-requiring-scenarios\"would be handled by web application framework based applications"},{"concepts":[1210],"name":"Indicate main elements of HTML5"},{"concepts":[1200],"name":"Indicate some examples of semantic discovery; Semantic search engines, highlighting projects and practice concerning GI related applications in the area"},{"concepts":[398],"name":"Indicate the extent to which contemporary use of geospatial information supports diverse ways of understanding the world."},{"concepts":[568],"name":"Indicate the possible justifications that can be used to implement an enterprise GIS"},{"concepts":[245],"name":"Interpret when space-time dynamics can be used to study geographical phenomen"},{"concepts":[172],"name":"Interpret business needs and translate them to IT needs"},{"concepts":[565],"name":"Interpret descriptive statistics and geostatistics of geographic data"},{"concepts":[135,160],"name":"Interpret different symbols and icons in a map"},{"concepts":[1212],"name":"Interpret generally the functionality offered by \"portal frameworks\" land Geoportals like Geonetwork, Opengeoportal, Esri geoportal server, Degree portal, Liferay, Jboss portal"},{"concepts":[1212],"name":"Interpret generally the main components and functionality of \"Web Application Frameworks\" such as AngularJS, Ext.js, Django, Java Server Faces (JSF), and the like"},{"concepts":[1189],"name":"interpret GML data model and GML definition of geometry. GML application schemas and GML documents"},{"concepts":[237],"name":"Interpret how individual parts contained in a complex system relate to each other"},{"concepts":[1168],"name":"Interpret information from EO products or EO time series"},{"concepts":[1080],"name":"Interpret land cover change detection"},{"concepts":[1083],"name":"Interpret location based services (LBS)"},{"concepts":[1150],"name":"Interpret ocean colour for deriving chlorophyll concentration in water"},{"concepts":[5],"name":"Interpret patterns in space and time using Dorling and Openshaws Geographical Analysis Machine GAM demonstration of disease incidence diffusion"},{"concepts":[1179,1174,1175,1176,1177,1178],"name":"Interpret the content of EO data"},{"concepts":[510],"name":"Interpret the effect of a convolution from a given mask and contained weights"},{"concepts":[211],"name":"Interpret the header of a standard raster data file"},{"concepts":[125],"name":"Interpret the impact of paper-based and web maps in their context"},{"concepts":[1154],"name":"Interpret the output of an point cloud measurement"},{"concepts":[1116],"name":"Interpret the output of numerical prediction models"},{"concepts":[73],"name":"Interpret the results of universal kriging"},{"concepts":[172],"name":"Interpret user needs as an input for the design process"},{"concepts":[92],"name":"Justify a chosen position on which disciplines should have as important a role in GIS AND T as geography"},{"concepts":[176],"name":"Justify feasibility recommendations to decision-makers"},{"concepts":[108],"name":"Justify or refute the conception of fields (e.g., temperature, density) as spatially-intensive attributes of (sometimes amorphous and anonymous) entities"},{"concepts":[92],"name":"Justify or refute whether geography (as a discipline) should have a central role in GIS AND T"},{"concepts":[97],"name":"Justify the discrepancies between the nature of locations in the real world and representations thereof (e.g., towns as points)"},{"concepts":[83],"name":"Justify the epistemological frameworks with which you agree"},{"concepts":[81],"name":"Justify the metaphysical theories with which you agree"},{"concepts":[63],"name":"Justify the stochastic process approach to spatial statistical analysis"},{"concepts":[65],"name":"Justify, compute, and test the significance of the join count statistic for a pattern of objects"},{"concepts":[612],"name":"Knowledge of the basic (selective) mechanism of the absorption/emission of electromagnetic radiation by atoms."},{"concepts":[70],"name":"List and describe several spatial sampling schemes and evaluate each one for specific applications"},{"concepts":[599],"name":"List and describe the main categories of organizations in the GIS&T domain"},{"concepts":[594],"name":"List and describe the most important producers and users of geospatial data at the European Commission"},{"concepts":[386],"name":"List and describe the types of data maintained by local, state, and federal governments"},{"concepts":[566],"name":"List and explain relevant organizational and institutional aspects related to GIS&T."},{"concepts":[375],"name":"List and explain the different societal aspects that are important in dealing with geospatial information"},{"concepts":[308],"name":"List and explain the key requirements for geolocating data to earth"},{"concepts":[226],"name":"List definitions of networks that apply to specific applications or industries"},{"concepts":[475],"name":"List different types of features that can be used for multispectral image classification"},{"concepts":[41],"name":"List different ways connectivity can be determined in a raster and in a polygon dataset"},{"concepts":[39],"name":"List reasons why the area of a polygon calculated in a GIS might not be the same as the real world object it describes"},{"concepts":[13],"name":"List several classic problems to which network analysis is applied e.g., The Traveling Salesman Problem, The Chinese Postman Problem"},{"concepts":[151],"name":"List software and hardware environments supporting immersive visualization"},{"concepts":[568],"name":"List some of the topics that should be addressed in a justification for implementing an enterprise GIS (e.g., return on investment, workflow, knowledge sharing)"},{"concepts":[559],"name":"List specifics competitive DIAS solutions over other"},{"concepts":[49],"name":"List the conditions that make point pattern analysis a suitable process"},{"concepts":[174],"name":"List the costs and benefits (tangible or intangible) of implementing a GI project"},{"concepts":[173],"name":"List the key elements of a project management"},{"concepts":[61],"name":"List the likely sources of error in slope and aspect maps derived from DEMs and state the circumstances under which these can be very severe"},{"concepts":[552],"name":"List the main international organization responsible for the standardization of the image data and gridded data quality"},{"concepts":[503],"name":"List the main segmentation methods used to group similar pixels into homogeneous objects"},{"concepts":[158],"name":"List the main variables to take into account during the planning phase of a map"},{"concepts":[133],"name":"List the major factors that should be considered in preparing a map"},{"concepts":[173],"name":"List the phases of a project management life cycle"},{"concepts":[71],"name":"List the possible sources of error in a selected and fitted model of an experimental semi-variogram"},{"concepts":[118],"name":"List the possible topological relationships between entities in space (e.g., 9-intersection) and time"},{"concepts":[136],"name":"List the range of factors that should be considered in selecting colors"},{"concepts":[63],"name":"List the two basic assumptions of the purely random process"},{"concepts":[14],"name":"List ways we can define accessibility on a network"},{"concepts":[132],"name":"List which data considerations should be taken into account when starting a GIS project"},{"concepts":[19],"name":"Locate, using location-allocation software, service facilities that meet given sets of constraints"},{"concepts":[166],"name":"Manage requirements using a management tool (such as Trello, JIRA, etc.)"},{"concepts":[1088],"name":"Manage the use of land"},{"concepts":[1082],"name":"Map and assess flooding"},{"concepts":[1077],"name":"Map line of sight visibility (terrain height, land cover)"},{"concepts":[814],"name":"Measure reflectance of surfaces of vegetation types and other thematic classes in the field"},{"concepts":[231],"name":"Model complex aspects of geographic information, such as temporal change, uncertainty and three-dimensional phenomena"},{"concepts":[190],"name":"Model geospatial data"},{"concepts":[108],"name":"Model gray area phenomena, such as categorical coverages (a.k.a. discrete fields), in terms of objects"},{"concepts":[172],"name":"Model project workflows"},{"concepts":[714],"name":"Model surface roughness slope"},{"concepts":[204],"name":"Model temporal aspects"},{"concepts":[232],"name":"Modify spatial and attribute data while ensuring consistency within the database"},{"concepts":[1086,1084,1091,1103,1106],"name":"Monitor and assess natural hazards"},{"concepts":[1075,1077,1082,1085,1089,1104,1118],"name":"Monitor building development"},{"concepts":[1081,1087,1109,1141,1140],"name":"Monitor changes in infrastructure"},{"concepts":[1076,1103,1134],"name":"Monitor land pollution"},{"concepts":[1076,1103,1112,1132,1148],"name":"Monitor pollution in rivers and lakes"},{"concepts":[1079],"name":"Monitor shipping routes"},{"concepts":[1078,1109,1141,1140],"name":"Monitor transportation routes"},{"concepts":[189],"name":"Outline a database with its main functionalities"},{"concepts":[143],"name":"Outline a map layout taking into account design principles"},{"concepts":[145],"name":"Outline a map with a reliability overlay using symbols suited to reliability 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contour-type lines from point datasets using proximity polygons, spatial averages, or inverse distance weighting"},{"concepts":[59],"name":"Outline an algorithm to determine the viewshed area visible from specific locations on surfaces specified by digital elevation models (DEM)"},{"concepts":[39],"name":"Outline an algorithm to find the area of a polygon using the coordinates of its vertices"},{"concepts":[61],"name":"Outline how higher order derivatives of height can be interpreted"},{"concepts":[175],"name":"Outline key tasks involved in the application, development and marketing of proprietary GIS software"},{"concepts":[49],"name":"Outline measures of pattern based on first and second order properties such as the mean centre and standard distance, quadrat counts, nearest neighbor distance and the more modern G, F and K functions"},{"concepts":[576],"name":"Outline methods (programs or processes) that provide effective staff development opportunities for GIS and T"},{"concepts":[379],"name":"Outline the arguments for and against the notion of information as a public good"},{"concepts":[72],"name":"Outline the basic kriging equations in their matrix formulation"},{"concepts":[49],"name":"Outline the basis of classic critiques of spatial statistical analysis in the context of point pattern analysis"},{"concepts":[237],"name":"Outline the complex problems where geocomputation is relevant"},{"concepts":[40],"name":"Outline the geometry implicit in classical gravity models of distance decay"},{"concepts":[4],"name":"Outline the implications of complexity for the application of statistical ideas in geography"},{"concepts":[36],"name":"Outline the implications of differences in distance calculations on real world applications of GIS, such as routing and determining boundary lengths and service areas"},{"concepts":[138],"name":"Outline the importance of photographs or imagery either from satellites or at street level"},{"concepts":[50],"name":"Outline the likely effects on analysis results of variations in the kernel function used and the bandwidth adopted"},{"concepts":[63],"name":"Outline the logic behind the derivation of long run expected outcomes of the independent random process using quadrat counts"},{"concepts":[45],"name":"Outline the possible sources of error in overlay operations"},{"concepts":[329],"name":"Outline the process of scanning and vectorizing features depicted on a printed map sheet using a given GIS software product, emphasizing issues that require manual intervention"},{"concepts":[181],"name":"Outline the Reference Model of Open Distributed Processing framework"},{"concepts":[241],"name":"Outline the role of computational science in geocomputation"},{"concepts":[323],"name":"Outline the SDTS and ISO TC211 standards for thematic accuracy"},{"concepts":[309],"name":"Outline the sequence of tasks involved in generating an orthoimage from a vertical aerial photograph"},{"concepts":[2],"name":"Outline the sequence of tasks required to complete the analytical process for a given spatial problem"},{"concepts":[156,157],"name":"Outline the stages in lithographic offset printing"},{"concepts":[177,184],"name":"Outline the types of geospatial software architectures"},{"concepts":[1210],"name":"Outline the use Scalable Vector Graphics (SVG) for client-side graphic processing"},{"concepts":[434],"name":"Outline the workflow for pan-sharpening an image with the PCA method"},{"concepts":[51],"name":"Perform a cluster detection analysis to detect hot spots in a point pattern"},{"concepts":[32],"name":"Perform a logic set theoretic query using GIS software"},{"concepts":[480],"name":"Perform a manual unsupervised classification given a two-dimensional array of reflectance values and ranges of reflectance values associated with a given number of land cover categories"},{"concepts":[46,47],"name":"Perform a map algebra calculation using command line, form-based, and flow charting user interfaces"},{"concepts":[174],"name":"Perform a pilot study to evaluate the feasibility of an application"},{"concepts":[248],"name":"Perform a simulation experiment using available simulation software"},{"concepts":[78],"name":"Perform an analysis using the geographically weighted regression technique"},{"concepts":[1199],"name":"Perform discovery over some popular SDI (NSDI) portals like INSPIRE and GOS geoportals"},{"concepts":[53],"name":"Perform multidimensional scaling (MDS) and principal components analysis (PCA) to reduce the number of coordinates, or dimensionality, of a problem"},{"concepts":[59],"name":"Perform siting analyses using specified visibility, slope, and other surface related constraints"},{"concepts":[1187],"name":"perform the connection to existing web services to use the resources exposed by the service"},{"concepts":[530],"name":"Plan a reproducibility project independently"},{"concepts":[800],"name":"Plan an aerial imagery mission in response to a given RFP and map of a study area, taking into consideration vertical and horizontal control, atmospheric conditions, time of year, and time of day"},{"concepts":[800,809],"name":"Plan an Earth observation mission objectives and priorities in response to user expectations, taking into account type of application, type of sensor, expected accuracy"},{"concepts":[1071,1107],"name":"Plan and design alternative energy project implementations"},{"concepts":[1073],"name":"Plan and design mineral & mining project implementations"},{"concepts":[1072],"name":"Plan and design oil & gas project implementations"},{"concepts":[1102],"name":"Plan and design project implementations"},{"concepts":[1074],"name":"Plan and design project implementations in the field of energy and mineral resources"},{"concepts":[1129],"name":"Plan emergency response actions"},{"concepts":[814],"name":"Plan in-situ measurements using a field spectroradiometer"},{"concepts":[729],"name":"Plan the calibration of the radar antenna"},{"concepts":[158],"name":"Plan the creation of a map according to a given audience"},{"concepts":[40],"name":"Plot typical forms for distance decay functions"},{"concepts":[1203],"name":"Practically apply getting data from a WCS and integrate it into a client application"},{"concepts":[1203],"name":"Practically apply getting data from a WFS and integrate it into a client application"},{"concepts":[156,157],"name":"Prepare a color map for black-and-white photocopy distribution"},{"concepts":[570],"name":"Prepare a GIS Management Strategy"},{"concepts":[574],"name":"Prepare a strategy on setting up the organizational components of a GIS&T implementation"},{"concepts":[319],"name":"Prepare and implement an effective geospatial data transaction management approach"},{"concepts":[21],"name":"Prioritize a set of algorithms designed to perform transformations based on the need to maintain data integrity [e.g., converting a digital elevation model (DEM) into a TIN]"},{"concepts":[468],"name":"Produce a digital surface model from stereographic optical EO data"},{"concepts":[751,752,753],"name":"Produce a geometrically corrected SAR image"},{"concepts":[441],"name":"Produce a map of vegetation fraction from optical EO data"},{"concepts":[428],"name":"Produce a surface corrected version of image values from BOA reflectance that removes topographic effects based on an input DSM and equations representing the relationship between sun incidence angle relative to terrain surface orientation"},{"concepts":[1142],"name":"Produce EO derived marine ecosystem information to support fisheries management"},{"concepts":[1169],"name":"Produce forecasts for flood risk areas"},{"concepts":[53],"name":"Produce plots in several data dimensions using a data matrix of attributes"},{"concepts":[480],"name":"Produce pseudocode for common unsupervised classification algorithms including chain method, ISODATA method, and clustering"},{"concepts":[644],"name":"Produce the processes of spectral calculations of radiometric quantities by the line by line radiative transfer models"},{"concepts":[235],"name":"Produce viable queries for change scenarios using GIS or database management tools"},{"concepts":[522],"name":"Produce zero-crossing maps for a DoG-filtered optical EO image"},{"concepts":[128],"name":"Propose a holistic historical perspective of maps creation and use"},{"concepts":[396],"name":"Propose a resolution to a conflict between an obligation in the GIS Code of Ethics and organizations proprietary interests"},{"concepts":[378],"name":"Propose and design solutions for dealing with particular data privacy and data security issues"},{"concepts":[377],"name":"Propose strategies for managing liability risk, including disclaimers and data quality standards"},{"concepts":[144],"name":"Propose thematic mapping methods for mapping numerical data"},{"concepts":[316],"name":"Provide examples of cases in which crouwdsourcing is the most effective data collection method"},{"concepts":[401],"name":"Provide examples of different types of critiques on GI and GIS"},{"concepts":[584],"name":"Provide examples of different types of legal instruments that can be used for supporting geospatial data sharing"},{"concepts":[390],"name":"Provide examples of the use of geospatial information in different sectors"},{"concepts":[194],"name":"Provide examples of typical non-spatial and spatial queries"},{"concepts":[381],"name":"Publish a dataset as open data"},{"concepts":[30],"name":"Reclassify (group) a nominal attribute domain to fewer, broader classes"},{"concepts":[30],"name":"Reclassify a raster before converting it into a vector file"},{"concepts":[105],"name":"Recognize and manage the potential problems associated with the use of categories (e.g., the ecological fallacy)"},{"concepts":[106],"name":"Recognize attribute domains that do not fit well into Stevens four levels of measurement (nominal, ordinal, interval, ratio), such as cycles, indexes, and hierarchies"},{"concepts":[716],"name":"Recognize different types of surface roughness on a radar image"},{"concepts":[122],"name":"Recognize expressions of uncertainty in language"},{"concepts":[106],"name":"Recognize situations and phenomena in the landscape which cannot be adequately represented by formal attributes, such as aesthetics"},{"concepts":[162],"name":"Recognize spatial schemes like patterns and shapes"},{"concepts":[565],"name":"Recognize the assumptions underlying probability and geostatistics and the situations in which they are useful analytical tools"},{"concepts":[81],"name":"Recognize the commonalities of philosophical viewpoints and appreciate differences to enable work with diverse colleagues"},{"concepts":[188],"name":"Recognize the constraints and opportunities of a particular choice of software for implementing a physical model"},{"concepts":[95],"name":"Recognize the constraints that political forces place on geospatial applications in public and private sectors"},{"concepts":[118],"name":"Recognize the contributions of Topology (the branch of mathematics) to the study of geographic relationships"},{"concepts":[122],"name":"Recognize the degree to which the importance of uncertainty depends on scale and application"},{"concepts":[121],"name":"Recognize the degree to which vagueness depends on scale"},{"concepts":[94],"name":"Recognize the impact of ones social background on ones own geographic worldview and perceptions and how it influences ones use of GIS"},{"concepts":[530],"name":"Recognize the importance of reproducible research as a fundamental pillar of modern science"},{"concepts":[83],"name":"Recognize the influences of epistemology on GIS practices"},{"concepts":[109],"name":"Recognize the influences of scale on the perception and meaning of fields"},{"concepts":[382],"name":"Recognize the relevant legal issues in a particular case of geospatial data collection, use and/of sharing"},{"concepts":[103],"name":"Recognize the role that time plays in static GISystems"},{"concepts":[115],"name":"Recommend for what applications we should use a field or an object-base approach."},{"concepts":[105],"name":"Reconcile differing common-sense and official definitions of common geospatial categories of entities, attributes, space, and time"},{"concepts":[1167],"name":"Relate EO measurements with detected features"},{"concepts":[91],"name":"Relate epistemology to spatial knowledge."},{"concepts":[53],"name":"Relate plots of multidimensional attribute data to geography by equating similarity in data space with proximity in geographical space"},{"concepts":[217],"name":"Relate the concept of grid cell resolution to the more general concept of support and granularity"},{"concepts":[109],"name":"Relate the notion of field in GIS to the mathematical notions of scalar and vector fields"},{"concepts":[124],"name":"Relate the science and technology of graphical representation of geographic data"},{"concepts":[479],"name":"Relate the spatial and spectral characteristics of EO data to the types and proportions of materials found within the scene and within pixel IFOVs to relabel spectral classes as information classes of a classification scheme"},{"concepts":[135],"name":"Relate the spatial dimension and the weight of mapped features with the attributes they represent"},{"concepts":[662],"name":"Relate to the aspects of radiation transfer through the atmosphere."},{"concepts":[1197],"name":"Relate with manual and automated methods linking data"},{"concepts":[166],"name":"Report existing and potential tasks in terms of workflow and information flow"},{"concepts":[162],"name":"Represent an object or a scene from different viewpoints"},{"concepts":[116],"name":"Represent structural relationships in GIS data"},{"concepts":[25],"name":"Resample multiple raster data sets to a single resolution to enable overlay"},{"concepts":[25],"name":"Resample raster data sets (e.g., terrain, satellite imagery) to a resolution appropriate for a map of a particular scale"},{"concepts":[387],"name":"Research and develop geospatial information for the private sector"},{"concepts":[136],"name":"Select a color palette appropriate for a representation"},{"concepts":[418],"name":"Select a contrast stretch for an image"},{"concepts":[28],"name":"Select a level of data detail and accuracy appropriate for a particular application (e.g., viewshed analysis, continental land cover change)"},{"concepts":[93],"name":"Select a place or landscape with personal meaning and discuss its importance"},{"concepts":[145],"name":"Select a technique that can be used to represent the value of each of the components of data quality (positional and attribute accuracy, logical consistency, and completeness)"},{"concepts":[167],"name":"Select among the most appropriate method for documenting a certain process"},{"concepts":[1155],"name":"Select an appropriate DEM product for usage in a specific application"},{"concepts":[796],"name":"Select an optical spectrometer suitable for your application taking into account the acquired wavelength"},{"concepts":[731,730],"name":"Select and apply the radargrammetric equation"},{"concepts":[25],"name":"Select appropriate interpolation techniques to resample particular types of values in raster data (e.g., nominal using nearest neighbor)"},{"concepts":[97],"name":"Select appropriate spatial metaphors and models of phenomena to be represented in GIS"},{"concepts":[144],"name":"Select base information suited to providing a frame of reference for thematic map symbols (e.g., network of major roads and state boundaries underlying national population map)"},{"concepts":[166],"name":"Select from conflicting requirements"},{"concepts":[796,1150],"name":"Select imagery from a satellite sensor with spectral bands suitable for mapping Ocean Colour"},{"concepts":[546],"name":"Select images for time series analysis where the cumulated cloud cover percentage in the study area is low enough for the analysis"},{"concepts":[159],"name":"Select maps that illustrate the provocative, propaganda, political, and persuasive nature of maps and geospatial data"},{"concepts":[838],"name":"Select the appropriate optical data type for the application"},{"concepts":[843],"name":"Select the appropriate SAR data type for the application"},{"concepts":[62],"name":"Select the appropriate statistical methods for the analysis of given spatial datasets by first exploring them using graphic methods"},{"concepts":[1213],"name":"select the development elements best suited for your application"},{"concepts":[137],"name":"Select the most appropriate place in a map to place a label and a legend"},{"concepts":[311],"name":"Select the most appropriate remotely sensed data source for a given analytical task, study area, budget, and availability"},{"concepts":[173],"name":"Select the most appropriate techniques for a EO*GI project"},{"concepts":[176],"name":"Select the most appropriate technology to help decision-making"},{"concepts":[154],"name":"Select the most suitable graphic representation for a given set of data"},{"concepts":[154],"name":"Select the most suitable graphic representation for a targeted audience"},{"concepts":[103],"name":"Select the temporal elements of geographic phenomena that need to be represented in particular GIS applications"},{"concepts":[817],"name":"Select the type of remote sensing platform for your specific application"},{"concepts":[797,847],"name":"Select the type of remote sensing sensor appropriate for your application"},{"concepts":[1187],"name":"select the web services best fit to expose your own resources"},{"concepts":[137],"name":"Select type font, size, style and color for labels on a map by applying basic typography design principles"},{"concepts":[1200],"name":"Semantic Discovery and its main components. Identify the areas of its use for GI related applications"},{"concepts":[137],"name":"Solve a labeling problem for a dense collection of features on a map using minimal leader lines"},{"concepts":[137],"name":"Solve ambiguities in map label by selecting the most appropriate typography"},{"concepts":[1196],"name":"Solve issues in determining what ontologies to use for semantic annotation"},{"concepts":[156,157],"name":"Specify a print job for publication, including paper, ink, lpi, proof needs, press check and other contract decisions"},{"concepts":[309],"name":"Specify the technical components of an aerotriangulation system"},{"concepts":[811],"name":"State and explain different SAR acquisition modes"},{"concepts":[754],"name":"State and explain Synthetic Aperture Radar (SAR) geometric distortions"},{"concepts":[733],"name":"State application examples of PSI methods"},{"concepts":[843],"name":"State different types of processing levels of SAR data"},{"concepts":[834],"name":"State examples of image description files used in Earth Observation"},{"concepts":[34],"name":"State questions that can be solved by selecting features based on location or spatial relationships"},{"concepts":[322],"name":"State the approximate number and spacing of control points in each order of the horizontal geodetic control network"},{"concepts":[600],"name":"State the basic physical principles for EO systems design and data analysis"},{"concepts":[52],"name":"State the classic formalization of the interaction model"},{"concepts":[322],"name":"State the geometric accuracies associated with the various orders of the U.S. horizontal geodetic control network"},{"concepts":[697],"name":"State the microwave portion of the electromagnetic spectrum"},{"concepts":[604],"name":"State the names of the most important regions of the electromagnetic spectrum"},{"concepts":[604],"name":"State the names of the regions of the electromagnetic spectrum most important for Earth's remote sensing"},{"concepts":[697],"name":"State the typical used radar bands and their application"},{"concepts":[692],"name":"State types of polarisations used in remote sensing"},{"concepts":[382],"name":"Suggest and prepare solutions for addressing particular legal issues related to the production, use and sharing of geospatial data"},{"concepts":[577],"name":"Teach necessary skills for users to successfully perform tasks in an enterprise GIS"},{"concepts":[178],"name":"Test all functionalities and data standards for interoperability"},{"concepts":[205],"name":"Transfer a conceptual model to a logical (database) model"},{"concepts":[90],"name":"Transform a conceptual model of information for a particular task into a data model"},{"concepts":[416,415],"name":"Transform an EO dataset to map coordinates using a registered image of like geometry as a reference"},{"concepts":[1210],"name":"Transform HTML documents thorugh the Document Object Model (DOM)"},{"concepts":[429],"name":"Transform imagery into radiometrically/atmospherically corrected state"},{"concepts":[25],"name":"Understand and examine the common methods for raster resampling"},{"concepts":[382],"name":"Understand and explain the main legal issues related to the production, use and sharing of geospatial data and information"},{"concepts":[198],"name":"Understand and use XML"},{"concepts":[422],"name":"Understand atmospheric parameters that influence bottom of atmosphere (BOA) reflectance"},{"concepts":[241],"name":"Understand complexity in the broadest sense"},{"concepts":[68],"name":"Understand different estimation methods for Bayesian models"},{"concepts":[237],"name":"Understand how complex systems operate"},{"concepts":[431],"name":"Understand how data augmentation can improve deep learning methods for image classification"},{"concepts":[1187],"name":"understand how different web services complement each other"},{"concepts":[1142],"name":"Understand how EO data can be used to monitor the marine ecosystem"},{"concepts":[240],"name":"Understand how geocomputation relates to other similar terms"},{"concepts":[160],"name":"Understand how graphic representations can be interpreted distinctively by culturally different audiences"},{"concepts":[541],"name":"Understand how limited temporal completness affects the usefulness of a time series analysis"},{"concepts":[160],"name":"Understand how map scale is used to provide the relationship of size of object on a map and its real-world size"},{"concepts":[244],"name":"Understand how models are translated into differential equations for execution"},{"concepts":[243],"name":"Understand how models can be specified into logical rules"},{"concepts":[1116],"name":"Understand how numerical prediction models work"},{"concepts":[539],"name":"Understand how positional/geometric accuracy of a dataset affects subsequent analysis"},{"concepts":[539,538],"name":"Understand how root mean squared error (RMSE) at tie points represents local spatial accuracy and enables calculation of total RMSE that informs about the average spatial accuracy of the entire image"},{"concepts":[455],"name":"Understand how satellite image time series can be used for mapping, trend analysis and change detection"},{"concepts":[464],"name":"Understand how the entropy represents the the average level of information contained in an image pixel"},{"concepts":[154],"name":"Understand how the representation of geographic data facilitates visual communication"},{"concepts":[236],"name":"Understand how the theoretical roots and experimental emphasis on geocomputation are integrated"},{"concepts":[1159],"name":"Understand how the tracking of moving objects is implemented"},{"concepts":[165],"name":"Understand spatial data models and structures"},{"concepts":[305],"name":"Understand spatial reference systems and apply them to an EO dataset"},{"concepts":[423],"name":"Understand sun, sun angle, and sensor parameters that influence top of atmosphere (TOA) reflectance"},{"concepts":[160],"name":"Understand that features have been omitted or generalized for clarity"},{"concepts":[483],"name":"Understand the advantages and shortcomings of decision trees"},{"concepts":[237],"name":"Understand the all-encompassing concepts of complexity"},{"concepts":[65],"name":"Understand the assumption under which spatial autocorrelation may occur"},{"concepts":[66],"name":"Understand the assumption under which spatial autocorrelation may occur"},{"concepts":[381],"name":"Understand the benefits of publishing and using open data"},{"concepts":[489],"name":"Understand the challenge in matching sensory image data to a mental model of the world-scene"},{"concepts":[242],"name":"Understand the defining characteristics of simulation models, and their applicability"},{"concepts":[186],"name":"Understand the degree to which attributes need to be conceptually modeled"},{"concepts":[633],"name":"Understand the difference between Inherent Optical Properties (IOP) and Apparent Optical Properties (AOP) of water"},{"concepts":[549],"name":"Understand the difficulties in searching and selecting satellite images with sufficient spatial coverage for time series analysis"},{"concepts":[1126],"name":"Understand the diverse set of EO technologies that are capable of mapping different landslide aspects"},{"concepts":[1067,1108,1130,1120],"name":"Understand the health of the crop, extent of infestation or stress damage, or potential yield and soil conditions"},{"concepts":[1068,1145],"name":"Understand the health of the fishing grounds"},{"concepts":[1069,1131],"name":"Understand the health of the forests"},{"concepts":[1210],"name":"Understand the importance of Cascading Style Sheets (CSS) to separate content from style in HMTL documents"},{"concepts":[539],"name":"Understand the importance of using spatially independent validation samples to assess the quality of the classification results"},{"concepts":[430],"name":"Understand the main factors generating geometric distortions of the remotely sensed images"},{"concepts":[169],"name":"Understand the main software engineering methodologies"},{"concepts":[377],"name":"Understand the nature of tort law generally and nuisance law specifically"},{"concepts":[104],"name":"Understand the physical notions of velocity and acceleration which are fundamentally about movement across space through time"},{"concepts":[530],"name":"Understand the problems associated with the lack of reproducibility"},{"concepts":[542],"name":"Understand the relevance of topological consistency for linear network features derived from Earth observation data"},{"concepts":[452],"name":"Understand the role of multi-temporal satellite images for identifying not only when a change occurred but also the changing drivers"},{"concepts":[483],"name":"Understand the role of pruning for reducing overfitting when applying decision trees for various classification purposes"},{"concepts":[559],"name":"Understand the strategic meaning of DIAS in the user segment of Copernicus"},{"concepts":[462],"name":"Understand the subjectivity of the visual interpretation"},{"concepts":[1154],"name":"Understand the technology behind LiDAR as an active sensor and what makes it different from the other existing Remote Sensing approaches"},{"concepts":[483],"name":"Understand the types of decision trees and their output"},{"concepts":[63],"name":"Understand the underlying assumptions for spatial stochastics process"},{"concepts":[454],"name":"Understand the way in which Dynamic Time Warping can align shifted temporal sequences"},{"concepts":[22],"name":"Understand various formats of storing raster and vector data"},{"concepts":[227],"name":"Understand vector data models"},{"concepts":[1154],"name":"Understand what products can be extracted from point clouds"},{"concepts":[1199],"name":"Use \"Full-text-based\" discovery; open source and commercial search engines, its use in GI related applications"},{"concepts":[1173,1171],"name":"Use 3D textured models to present study area"},{"concepts":[557],"name":"Use a web portal to retrieve EO data"},{"concepts":[558],"name":"Use an image archive to retrive Earth observation data for an application"},{"concepts":[146],"name":"Use appropriate interpolation techniques to derive DEMs from point data"},{"concepts":[105],"name":"Use categorical information in analysis, cartography, and other GIS processes, avoiding common interpretation mistakes"},{"concepts":[1138],"name":"Use EO products to assess land areas, its ecosystems, and its evolution"},{"concepts":[1129],"name":"Use EO products to assess the risk of a disaster"},{"concepts":[1117,1115],"name":"Use EO products to conduct forecasts and projections"},{"concepts":[1116],"name":"Use EO products to conduct numerical simulations"},{"concepts":[1114],"name":"Use EO products to forecast sunlight exposure"},{"concepts":[1129],"name":"Use EO products to measure impact and/or recovery"},{"concepts":[1129],"name":"Use EO products to monitor disaster prone areas"},{"concepts":[1138],"name":"Use EO products to plan land areas, its ecosystems, and its evolution"},{"concepts":[1066],"name":"Use EO/GI information to plan and design projects, monitor and assess the environment, support decision-making processes, and to tackle environmental challenges"},{"concepts":[113],"name":"Use established analysis methods that are based on the concept of region (e.g., landscape ecology)"},{"concepts":[114],"name":"Use established analysis methods that are based on the concept of spatial integration (e.g., overlay)"},{"concepts":[470],"name":"Use filtering techniques to spatially aggregate an image classification"},{"concepts":[414],"name":"Use GIS software to transform a given dataset to a specified coordinate system, projection, and datum"},{"concepts":[119],"name":"Use methods that analyze metrical relationships"},{"concepts":[118],"name":"Use methods that analyze topological relationships"},{"concepts":[1201],"name":"Use Natural language based discovery over linked data"},{"concepts":[1152],"name":"Use NDVI to estimate the vegetation cover"},{"concepts":[1195],"name":"Use open data APIs that enable the usage of Open data; identify design aspects and usage scenarios"},{"concepts":[461],"name":"Use photo interpretation keys to interpret features on aerial photographs"},{"concepts":[530],"name":"Use software tools to automate the practice of reproducible research in daily work"},{"concepts":[206],"name":"Use standards such as ISO 19141 Schema for moving features, ISO 19142 Web Feature Service and ISO 19109 - Rules for application schema"},{"concepts":[586],"name":"Use the models of ‘SDI generations’ and ‘SDI components’ to describe the main elements of an existing SDI initiative"},{"concepts":[573],"name":"Use the most effective change model depending on the nature and needs of the client's organization."},{"concepts":[1188],"name":"Use Web services description for RESTful web services, Web Application Description Language (WADL) and its use"},{"concepts":[462],"name":"Using a vertical aerial image, produce a map of land use/land cover classes"},{"concepts":[195],"name":"Work with different data compression techniques"},{"concepts":[40],"name":"Write a program to create a matrix of pair-wise distances among a set of points"},{"concepts":[211],"name":"Write a program to read and write a raster data file"},{"concepts":[40],"name":"Write typical forms for distance decay functions"},{"concepts":[11],"name":"xplain how the concept of capacity represents an upper limit on the amount of flow through the network"}],"updateDate":"2026/06/27","version":"10"},"v1":{"concepts":[{"code":"GIST","description":"Geographic Information Science and Technology","name":"Geographic Information Science and Technology"},{"code":"AM","description":"This knowledge area encompasses a wide variety of operations whose objective is to derive analytical results from geospatial data. Data analysis seeks to understand both first-order (environmental) effects and second-order (interaction) effects. Approaches that are both data-driven (exploration of geospatial data) and model-driven (testing hypotheses and creating models) are included. Data driven techniques derive summary descriptions of data, evoke insights about characteristics of data, contribute to the development of research hypotheses, and lead to the derivation of analytical results. The goal of model driven analysis is to create and test geospatial process models. In general, model-driven analysis is an advanced knowledge area where previous experience with exploratory spatial data analysis would constitute a desired prerequisite. Visual tools for data analysis are covered in Knowledge Area: Cartography and Visualization (CV) and many of the fundamental principles required to ground data analysis techniques are introduced in Knowledge Area: Conceptual Foundations (CF). Image processing techniques are considered in Knowledge Area: Geospatial Data (GD). All of the methods described in this knowledge area are more or less sensitive to data error and uncertainty as covered in Unit GC8 Uncertainty and Unit GD6 Data quality. Mastery of the educational objectives outlined in this knowledge area requires knowledge and skills in mathematics, statistics, and computer programming.","name":"Analytical Methods"},{"code":"AM1-2","description":"- Compare and contrast spatial statistical analysis, spatial data analysis, and spatial modeling - Compare and contrast spatial statistics and map algebra as two very different kinds of data analysis - Compare and contrast the methods of analyzing aggregate data as opposed to methods of analyzing a set of individual observations - Define the terms spatial analysis, spatial modeling, geostatistics, spatial econometrics, spatial statistics, qualitative analysis, map algebra, and network analysis - Differentiate between geostatistics and spatial statistics - Discuss situations when it is desirable to adopt a spatial approach to the analysis of data - Explain what is added to spatial analysis to make it spatio-temporal analysis - Explain what is special (i.e., difficult) about geospatial data analysis and why some traditional statistical analysis techniques are not suited to geographic problems","name":"Analytical approaches"},{"code":"AM1","description":"Geospatial data analysis has foundations in many different disciplines. As a result, there are many different schools of thought or analytical approaches including spatial analysis, spatial modeling, geostatistics, spatial econometrics, spatial statistics, qualitative analysis, map algebra, and network analysis. This unit compares and contrasts these approaches.","name":"Foundations of analytical methods"},{"code":"AM10-1","description":"- Describe emerging geographical analysis techniques in geocomputation derived from artificial intelligence (e.g., expert systems, artificial neural networks, genetic algorithms, and software agents) - Describe difficulties in dealing with large spatial databases, especially those arising from spatial heterogeneity - Explain what is meant by the term \"contaminated data,\"suggesting how it can arise - Explain how to recognize contaminated data in large datasets - Outline the implications of complexity for the application of statistical ideas in geography - Describe sources of Big Data","name":"Problems of large spatial databases"},{"code":"AM10-2","description":"- Describe how data mining can be used for geospatial intelligence - Differentiate between data mining approaches used for spatial and non-spatial applications - Compare and contrast the primary types of data mining: summarization/characterization, clustering/categorization, feature extraction, and rule/relationships extraction - Explain how spatial statistics techniques are used in spatial data mining - Explain how the analytical reasoning techniques, visual representations, and interaction techniques that make up the domain of visual analytics have a strong spatial component - Demonstrate how cluster analysis can be used as a data mining tool - Interpret patterns in space and time using Dorling and Openshaw`s geographical analysis machine (GAM) demonstration of disease incidence diffusion - Discuss tools (e.g. scripts, plugins) to gather location based data (e.g. Twitter feeds, address data)","name":"Data mining approaches"},{"code":"AM10-3","description":"- Explain how spatial data mining techniques can be used for knowledge discovery - Explain how visual data exploration can be combined with data mining techniques as a means of discovering research hypotheses in large spatial datasets - Explain how a Bayesian framework can incorporate expert knowledge in order to retrieve all relevant datasets given an initial user query","name":"Knowledge discovery"},{"code":"AM10-4","description":"- Differentiate among machine learning, data mining, and pattern recognition - Explain the outcome of an artificial intelligence analysis (e.g., edge recognition), including a discussion of what the human did not see that the computer identified and vice versa - Explain the principles of pattern recognition - Apply a simple spatial mean filter to an image as a means of recognizing patterns - Construct an edge-recognition filter - Design a simple spatial mean filter","name":"Pattern recognition and matching"},{"code":"AM10","description":"Algorithms have been developed to scan and search through extremely large data sets in order to find patterns within the data. These data mining and knowledge discovery techniques have been expanded to the spatial case. Legal and ethical concerns associated with such practices are considered in Knowledge Areas GS GIS and T and Society and OI Organizational and Institutional Aspects.","name":"Data mining"},{"code":"AM11-1","description":"- Define the following terms pertaining to a network: Loops, multiple edges, the degree of a vertex, walk, trail, path, cycle, fundamental cycle - Define different interpretations of \"cost\" in various routing applications - Describe networks that apply to specific applications or industries - Create a data set with network attributes and topology","name":"Networks defined"},{"code":"AM11-2","description":"- Demonstrate how networks can be measured using the number of elements in a network, the distances along network edges, and the level of connectivity of the network - Explain the concept of the diameter of a network - Compute the estimated number of fundamental cycles in a graph - Compute the alpha, beta, and gamma indices of network connectivity - Compute the detour index and the measure of network density for a given network","name":"Graph theoretic descriptive measures of networks"},{"code":"AM11-3","description":"- Describe some variants of Dijkstra`s algorithm that are even more efficient - Explain how a leading World Wide Web-based routing system works (e.g., MapQuest, Yahoo Maps, Google) - Discuss the difference of implementing Dijkstra`s algorithm in raster and vector modes - Demonstrate how K-shortest path algorithms can be implemented to find many efficient alternate paths across the network - Compute the optimum path between two points through a network with Dijkstra`s algorithm","name":"Least-cost shortest path"},{"code":"AM11-4","description":"- Describe practical situations in which flow is conserved while splitting or joining at nodes of the network - Explain how the concept of capacity represents an upper limit on the amount of flow through the network - Demonstrate how capacity is assigned to edges in a network using the appropriate data structure - Apply a maximum flow algorithm to calculate the largest flow from a source to a sink, using the edges of the network, subject to capacity constraints on the arcs and the conservation of flow - Discuss the visualization of results of flow modelling","name":"Flow modeling"},{"code":"AM11-5","description":"- Describe the classic transportation problem - Explain why, if supply equals demand, there will always be a feasible solution to the classic transportation problem - Demonstrate how the classic transportation problem can be structured as a linear program - Implement the transportation simplex method to determine the optimal solution","name":"The Classic Transportation Problem"},{"code":"AM11-6","description":"- Describe several classic problems to which network analysis is applied (e.g., the traveling salesman problem, the Chinese postman problem) - Explain why heuristic solutions are generally used to address the combinatorially complex nature of these problems and the difficulty of solving them optimally","name":"Other classic network problems"},{"code":"AM11-7","description":"- Describe alternate definitions of accessibility on a network - Describe methods for measuring different kinds of accessibility on a network - Contrast accessibility modeling at the individual level versus at an aggregated level - Compare current accessibility models with early models of market potential","name":"Accessibility modeling"},{"code":"AM11","description":"Network analysis encompasses a wide range of procedures, techniques, and methods that allow for the examination of phenomena that can be modeled in the form of connected sets of edges and vertices. Such sets are termed a network or a graph, and the mathematical basis for network analysis is known as graph theory. Graph theory contains descriptive measures and indices of networks such as connectivity, adjacency, capacity, and flow as well as methods for proving the properties of networks. Networks have long been recognized as an efficient way to model many types of geographic data, including transportation networks, river networks, and utility networks electric, cable, sewer and water, etc. to name just a few. The data structures to support network analysis are covered in Unit DM4 Vector and object data models.","name":"Network analysis"},{"code":"AM12-1","description":"- Explain how optimization models can be used to generate models of alternate options for presentation to decision makers - Explain the concept of solution space - Explain the principles of operations research modeling and location modeling - Explain, using the concept of combinatorial complexity, why some location problems are very hard to solve - Compare and contrast the concepts of discrete location problems and continuous location problems","name":"Operations research modeling and location modeling principles"},{"code":"AM12-2","description":"- Describe the structure of linear programs - Explain the role of objective functions in linear programming - Explain the role of constraint functions using the graphical method - Explain the role of constraint functions using the simplex method - Implement linear programs for spatial allocation problems","name":"Linear programming"},{"code":"AM12-3","description":"- Differentiate between a linear program and an integer program - Explain why integer programs are harder to solve than linear programs","name":"Integer programming"},{"code":"AM12-4","description":"- Describe the structure of origin-destination matrices - Explain the concepts of demand and service - Explain Weber`s locational triangle - Assess the outcome of location-allocation models using other spatial analysis techniques - Compare and contrast covering, dispersion, and p-median models - Locate, using location-allocation software, service facilities that meet given sets of constraints","name":"Location-allocation modeling and p-median problems"},{"code":"AM12","description":"A wide variety of optimization techniques are now solvable within the GIS and T domain. Operations research is a branch of mathematics practiced in the allied fields of business and engineering. New models and software tools allow for the solution of transportation routing, facility location, and a host of other location-allocation modeling problems.","name":"Optimization and location-allocation modeling"},{"code":"AM13-1","description":"- Compare and contrast the impacts of different conversion approaches, including the effect on spatial components - Prioritize a set of algorithms designed to perform transformations based on the need to main- tain data integrity (e.g., converting a digital elevation model into a TIN) - Create a flowchart showing the sequence of transformations on a data set (e.g., geometric and radiometric correction and mosaicking of remotely sensed data) - Consider the importance of accurate data in a project","name":"Impacts of transformations"},{"code":"AM13-2","description":"- Identify the conceptual and practical difficulties associated with data model and format conversion - Describe a workflow for converting and implementing a data model in a GIS involving an Entity- Relationship (E-R) diagram and the Universal Modeling Language (UML) - Discuss the role of metadata in facilitating conversation of data models and data structures between systems - Convert a data set from the native format of one GIS product to another - Discuss the importance of preserving the semantics when converting a data format - Discuss the importance of a well thought data model in a GIS project","name":"Data model and format conversion"},{"code":"AM13-3","description":"- Differentiate among common interpolation techniques (e.g., nearest neighbor, bilinear, bicubic) - Explain how the elevation values in a digital elevation model (DEM) are derived by interpolation from irregular arrays of spot elevations - Discuss the pitfalls of using secondary data that has been generated using interpolations (e.g., Level 1 USGS DEMs) - Estimate a value between two known values using linear interpolation (e.g., spot elevations, population between census years) - Discuss that the choice of interpolation technique depends on what we want to model","name":"Interpolation"},{"code":"AM13-4","description":"- Explain how the vector/raster/vector conversion process of graphic images and algorithms takes place and how the results are achieved - Convert vector data to raster format and back using GIS software - Illustrate the impact of vector/raster/vector conversions on the quality of a dataset - Create estimated tessellated data sets from point samples or isolines using interpolation operations that are appropriate to the specific situation","name":"Vector-to-raster and raster-to-vector conversions"},{"code":"AM13-5","description":"- Discuss the consequences of increasing and decreasing resolution - Evaluate methods used by contemporary GIS software to resample raster data on-the-fly during display - Select appropriate interpolation techniques to resample particular types of values in raster data (e.g., nominal using nearest neighbor) - Resample multiple raster data sets to a single resolution to enable overlay - Resample raster data sets (e.g., terrain, satellite imagery) to a resolution appropriate for a map of a particular scale","name":"Raster resampling"},{"code":"AM13-6","description":"- Cite appropriate applications of several coordinate transformation techniques (e.g., affine, simi- larity, Molodenski, Helmert) - Differentiate between polynomial coordinate transformations (including linear) and rubbersheeting - Describe the impact of map projection transformation on raster and vector data - Discuss the need for different coordinate systems depending on the type of application - Data Transformation Services","name":"Coordinate transformations"},{"code":"AM13","description":"GIS is a cyclical rather than a linear system, unlike computer aided drafting (CAD) and computer assisted cartographic systems. Changes in projection, grid systems, data forms, and formats take place during the modeling process for which GIS was designed. Many non-analytical manipulations are necessary to accommodate the analytical power of the GIS. The manipulations of spatial and spatio-temporal data involve two general classes of operation: 1.\tTheir transformation into formats that facilitate subsequent analysis (see this Unit AM13), 2.\tGeneralization and aggregation that affect the accuracy and integrity of the data used for analysis (see Unit AM14) Other knowledge areas have identified different forms of data structures, data models, projections, and other forms of geospatial data representation. These differences present both opportunities and challenges for analysis and modeling. The ability to transform one representation to another, in a manner that maintains the integrity of the information as much as possible, can enhance the analysis and visualization of geospatial data. The raster and vector data models are described in Units DM3 Tesselation data models and DM4 Vector and object data models. The principles of coordinate systems, datums, and projections are also considered in Knowledge Area GD: Geospatial Data","name":"Representation transformation"},{"code":"AM14-1","description":"- Differentiate among the concepts of scale (as in map scale), support, scope, and resolution - Determine the mathematical relationships among scale, scope, and resolution, including Töpfer`s radical law - Defend or refute the statement \"GIS data are scaleless\"- Discuss the implications of tradeoff between data detail and data volume - Select a level of data detail and accuracy appropriate for a particular application (e.g., viewshed analysis, continental land cover change)","name":"Scale and generalization"},{"code":"AM14-2","description":"- Describe the basic forms of generalization used in applications in addition to cartography (e.g., selection, simplification) - Discuss the possible effects on topological integrity of generalizing data sets - Explain why areal generalization is more difficult than line simplification - Explain the logic of the Douglas-Poiker line simplification algorithm - Explain the pitfalls of using data generalized for small scale display in a large scale application - Design an experiment that allows one to evaluate the effect of traditional approaches of carto- graphic generalization on the quality of digital data sets created from analog originals - Evaluate various line simplification algorithms by their usefulness in different applications","name":"Approaches to point, line, and area generalization"},{"code":"AM14-3","description":"- Identify a variety of likely measurement level transformations (e.g., the classification of ratio data yields ordinal data) - Discuss the relationship of attribute measurement levels to database query operations - Describe the pitfalls, in terms of information loss and analytical options, of transforming attribute measurement levels - Reclassify (group) a nominal attribute domain to fewer, is subconcept of classes - Reclassify a raster before converting it into a vector file.","name":"Classification and transformation of attribute measurement levels"},{"code":"AM14","description":"All geospatial data are generalized. Even the most detailed data represent only subsets of reality. Furthermore, data are further generalized for purposes of mapping, visualization, and efficient storage. A variety of generalization techniques have been developed to facilitate this process. All are scale dependent. Aggregation is one form of generalization that transforms large numbers of individual objects into summarized groups. This unit is concerned with the nature of these procedures and their implications for professional practice. Generalization is an important part of cartography (and is therefore discussed conceptually in Unit CV2 Data considerations), but is also a transformation common to many GIS procedures.","name":"Generalization and aggregation"},{"code":"AM2-1","description":"- Describe set theory - Explain how set theory relates to spatial queries - Explain how logic theory relates to set theory - Perform a logic (set theoretic) query using GIS software","name":"Set theory"},{"code":"AM2-2","description":"- Alternative (Non-SQL) queries, such as linked data queries","name":"Structured Query Language (SQL) and attribute queries"},{"code":"AM2-2","description":"","name":""},{"code":"AM2-3","description":"- Demonstrate the syntactic structure of spatial and temporal operators in SQL - Compare and contrast attribute query and spatial query - State questions that can be solved by selecting features based on location or spatial relationships - Construct a query statement to search for a specific spatial or temporal relationship - Construct a spatial query to extract all point objects that fall within a polygon","name":"Spatial queries"},{"code":"AM2","description":"Attribute and spatial query operations are core functionality in any GIS and they are often considered to be the most basic form of analysis.","name":"Query operations and query languages"},{"code":"AM3-1","description":"- Describe several different measures of distance between two points (e.g., Euclidean, - Manhattan, network distance, spherical) - Explain how different measures of distance can be used to calculate the spatial weights matrix - Explain why estimating the fractal dimension of a sinuous line has important implications for the measurement of its length - Explain how fractal dimension can be used in practical applications of GIS - Explain the differences in the calculated distance between the same two places when data used are in different projections - Outline the implications of differences in distance calculations on real world applications of GIS, such as routing and determining boundary lengths and service areas - Estimate the fractal dimension of a sinuous line","name":"Distances and lengths"},{"code":"AM3-2","description":"- Compute the mean of directional data","name":"Direction"},{"code":"AM3-3","description":"- Identify situations in which shape affects geometric operations - Explain what is meant by the convex hull and minimum enclosing rectangle of a set of point data - Explain why the shape of an object might be important in analysis - Exemplify situations in which the centroid of a polygon falls outside its boundary - Compare and contrast different shape indices, include examples of applications to which each could be applied - Develop a method for describing the shape of a cluster of similarly valued points by using the concept of the convex hull - Develop an algorithm to determine the skeleton of polygons - Find centroids of polygons under different definitions of a centroid and different polygon shapes - Calculate several different shape indices for a polygon dataset","name":"Shape"},{"code":"AM3-4","description":"- List reasons why the area of a polygon calculated in a GIS might not be the same as the real world object it describes - Explain how variations in the calculation of area may have real world implications, such as calculating density - Demonstrate how the area of a region calculated from a raster data set will vary by resolution and orientation - Outline an algorithm to find the area of a polygon using the coordinates of its vertices","name":"Area"},{"code":"AM3-5","description":"- Describe real world applications where distance decay is an appropriate representation of the strength of spatial relationships (e.g., shopping behavior, property values) - Describe real world applications where distance decay would not be an appropriate representation of the strength of spatial relationships (e.g., distance education, commuting, telecommunications) - Explain the rationale for using different forms of distance decay functions - Explain how a semi-variogram describes the distance decay in dependence between data values - Outline the geometry implicit in classical \"gravity\" models of distance decay - Plot typical forms for distance decay functions - Write typical forms for distance decay functions - Write a program to create a matrix of pair-wise distances among a set of points","name":"Proximity and distance decay"},{"code":"AM3-6","description":"- List different ways connectivity can be determined in a raster and in a polygon dataset - Describe real world applications where adjacency and connectivity are a critical component of analysis - Explain the nine-intersection model for spatial relationships - Demonstrate how adjacency and connectivity can be recorded in matrices - Calculate various measures of adjacency in a polygon dataset - Create a matrix describing the pattern of adjacency in a set of planar enforced polygons","name":"Adjacency and connectivity"},{"code":"AM3","description":"For simple data exploration, GIS offers many basic geometric operations that help in extracting meaning from sets of data or for deriving new data for further analysis. Concepts on which these operations are based are addressed in Unit CF3 Domains of geographic information and Unit CF5 Relationships.","name":"Geometric measures"},{"code":"AM4-1","description":"- Basic reclassification - Select by attribute - Select by location","name":"Reclassification and selection operations"},{"code":"AM4-2","description":"- Compare and contrast raster and vector definitions of buffers - Explain why a buffer is a contour on a distance surface - Outline circumstances in which buffering around an object is useful in analysis","name":"Buffers"},{"code":"AM4-3","description":"- Explain why the process \"dissolve and merge\" often follows vector overlay operations - Explain what is meant by the term \"planar enforcement\" - Outline the possible sources of error in overlay operations Exemplify applications in which overlay is useful, such as site suitability analysis - Compare and contrast the concept of overlay as it is implemented in raster and vector domains - Demonstrate how the geometric operations of intersection and overlay can be implemented in GIS - Demonstrate why the georegistration of datasets is critical to the success of any map overlay operation - Formalize the operation called map overlay using Boolean logic","name":"Overlay"},{"code":"AM4-4","description":"- Describe how map algebra performs mathematical functions on raster grids - Describe a real modeling situation in which map algebra would be used (e.g., site selection, climate classification, least-cost path) - Explain the categories of map algebra operations (i.e., local, focal, zonal, and global functions) - Explain why georegistration is a precondition to map algebra - Differentiate between map algebra and matrix algebra using real examples - Perform a map algebra calculation using command line, form-based, and flow charting user interfaces","name":"Neighborhood analysis"},{"code":"AM4-5","description":"- Describe how map algebra performs mathematical functions on raster grids - Describe a real modeling situation in which map algebra would be used (e.g., site selection, climate classification, least-cost path) - Explain the categories of map algebra operations (i.e., local, focal, zonal, and global functions) - Explain why georegistration is a precondition to map algebra - Differentiate between map algebra and matrix algebra using real examples - Perform a map algebra calculation using command line, form-based, and flow charting user interfaces","name":"Map algebra"},{"code":"AM4","description":"This small set of analytical operations is so commonly applied to a broad range of problems that their inclusion in software products is often used to determine if that product is a true GIS. Concepts on which these operations are based are addressed in Unit CF3 Domains of geographic information and Unit CF5 Relationships.","name":"Basic analytical operations"},{"code":"AM5-1","description":"- List the conditions that make point pattern analysis a suitable process - Identify the various ways point patterns may be described - Identify various types of K-function analysis - Describe how Independent Random Process/Chi-Squared Result (IRP/CSR) may be used to make statistical statements about point patterns - Outline measures of pattern based on first and second order properties such as the mean center and standard distance, quadrat counts, nearest neighbor distance, and the more modern G,F, and K functions - Outline the basis of classic critiques of spatial statistical analysis in the context of point pattern analysis - Explain how distance-based methods of point pattern measurement can be derived from a distance matrix - Explain how proximity polygons (e.g., Thiessen polygons) may be used to describe point patterns - Explain how the K function provides a scale-dependent measure of dispersion - Compute measures of overall dispersion and clustering of point datasets using nearest neighbor distance statistics","name":"Point pattern analysis"},{"code":"AM5-2","description":"- Describe the relationships between kernels and classical spatial interaction approaches, such as surfaces of potential - Differentiate between kernel density estimation and spatial interpolation - Outline the likely effects on analysis results of variations in the kernel function used and the bandwidth adopted - Explain why and how density estimation transforms point data into a field representation - Explain why, in some cases, an adaptive bandwidth might be employed - Create density maps from point datasets using kernels and density estimation techniques using standard software","name":"Kernels and density estimation"},{"code":"AM5-3","description":"- Identify several cluster detection techniques and discuss their limitations - Discuss the characteristics of the various cluster detection techniques - Demonstrate the extension of spatial clustering to deal with clustering in space-time using the now and Mantel tests - Perform a cluster detection analysis to detect \"hot spots\" in a point pattern","name":"Spatial cluster analysis"},{"code":"AM5-4","description":"- State the classic formalization of the interaction model - Differentiate between the gravity model and spatial interaction models - Describe the formulation of the classic gravity model, the unconstrained spatial interaction model, the production constrained spatial interaction model, the attraction constrained spatial interaction model, and the doubly constrained spatial interaction model - Explain how dynamic, chaotic, complex, or unpredictable aspects in some phenomena make spatial interaction models more appropriate than gravity models - Explain the concept of competing destinations, describing how traditional spatial interaction model forms are modified to account for it - Create a matrix that shows spatial interaction","name":"Spatial interaction"},{"code":"AM5-5","description":"- Relate plots of multidimensional attribute data to geography by equating similarity in data space with proximity in geographical space - Assemble a data matrix of attributes - Produce plots in several data dimensions using a data matrix of attributes - Conduct a simple hierarchical cluster analysis to classify area objects into statistically similar regions - Perform multidimensional scaling (MDS) and principal components analysis (PCA) to reduce the number of coordinates, or dimensionality, of a problem","name":"Analyzing multidimensional attributes"},{"code":"AM5-6","description":"- Describe the difference between prescriptive and descriptive cartographic models - Discuss the origins of cartographic modeling with reference to the work of Ian McHarg - Develop a flowchart of a cartographic model for a site suitability problem","name":"Cartographic modeling"},{"code":"AM5-7","description":"- Describe the implementation of an ordered weighting scheme in a multiple-criteria aggregation - Compare and contrast the terms multi-criteria evaluation, weighted linear combination, and site suitability analysis - Differentiate between contributing factors and constraints in a multi-criteria application - Explain the legacy of multi-criteria evaluation in relation to cartographic modeling - Determine which method to use to combine criteria (e.g., linear, multiplication) - Create initial weights using the analytical hierarchy process (AHP) - Calibrate a linear combination model by adjusting weights using a test data set - Discuss the issue of sensitivity analysis in the context of mca","name":"Multi-criteria evaluation"},{"code":"AM5-8","description":"- Discuss the relationship between spatial processes and spatial patterns - Differentiate between deterministic and stochastic spatial process models - Describe a simple process model that would generate a given set of spatial patterns","name":"Spatial process models"},{"code":"AM5","description":"Building on the basic geometric measures and analytical operations found in most GIS products, a broad range of additional analytical methods form the fundamental GIS toolkit.","name":"Basic analytical methods"},{"code":"AM6-1","description":"- List the likely sources of error in slope and aspect maps derived from digital elevation models (DEMs) and state the circumstances under which these can be very severe - Outline a number of different methods for calculating slope from a DEM - Outline how higher order derivatives of height can be interpreted - Explain how slope and aspect can be represented as the vector field given by the first derivative of height - Explain why the properties of spatial continuity are characteristic of spatial surfaces - Explain why zero slopes are indicative of surface specific points such as peaks, pits, and passes, and list the conditions necessary for each - Design an algorithm that calculates slope and aspect from a triangulated irregular network (TIN) model - Discuss the available DEM data (e.g. data derived from Earth Observation)","name":"Calculating surface derivatives"},{"code":"AM6-2","description":"- Identify the spatial concepts that are assumed in different interpolation algorithms - Describe how surfaces can be interpolated using splines - Compare and contrast interpolation by inverse distance weighting, bi-cubic spline fitting, and kriging - Differentiate between trend surface analysis and deterministic spatial interpolation - Explain why different interpolation algorithms produce different results and suggest ways by which these can be evaluated in the context of a specific problem - Design an algorithm that interpolates irregular point elevation data onto a regular grid - Outline algorithms to produce repeatable contour-type lines from point datasets using proximity polygons, spatial averages, or inverse distance weighting - Implement a trend surface analysis using either the supplied function in a GIS or a regression function from any standard statistical package","name":"Interpolation of surfaces"},{"code":"AM6-3","description":"- Describe how a network of stream channels and ridges can be estimated from a DEM - Explain how ridgelines and streamlines can be used to improve the result of an interpolation Process","name":"Surface features"},{"code":"AM6-4","description":"- Define \"intervisibility\" - Explain the sources and impact of errors that affect intervisibility analyses - Outline an algorithm to determine the viewshed (area visible) from specific locations on surfaces specified by DEMs - Perform siting analyses using specified visibility, slope, and other surface related constraints","name":"Intervisibility"},{"code":"AM6-5","description":"- Define \"friction surface\" - Explain how friction surfaces are enhanced by the use of impedance and barriers - Apply the principles of friction surfaces in the calculation of least-cost paths","name":"Friction surfaces"},{"code":"AM6","description":"There is a wide range of phenomena that can be studied using a set of techniques and tools that are designed to help understand the characteristics of continuous surface data. Applications of these techniques using terrain data include overland transport, flow, and siting tasks, but similar analyses can be conducted using non-tangible surfaces such as those of temperature, pressure and population density.","name":"Analysis of surfaces"},{"code":"AM7-1","description":"- Describe the statistical characteristics of a set of spatial data using a variety of graphs and plots (including scatterplots, histograms, boxplots, q–q plots) - Select the appropriate statistical methods for the analysis of given spatial datasets by first exploring them using graphic methods","name":"Graphical methods"},{"code":"AM7-2","description":"- List the two basic assumptions of the purely random process - Justify the stochastic process approach to spatial statistical analysis - Exemplify deterministic and spatial stochastic processes - Exemplify non-stationarity involving first and second order effects - Differentiate between isotropic and anisotropic processes - Discuss the theory leading to the assumption of intrinsic stationarity - Outline the logic behind the derivation of long run expected outcomes of the independent random process using quadrat counts","name":"Stochastic processes"},{"code":"AM7-3","description":"- Explain how different types of spatial weights matrices are defined and calculated - Explain the rationale used for each type of spatial weights matrix - Discuss the appropriateness of different types of spatial weights matrices for various problems - Construct a spatial weights matrix for lattice, point, and area patterns","name":"The spatial weights matrix"},{"code":"AM7-4","description":"- Describe the effect of the assumption of stationarity on global measures of spatial association - Explain how a statistic that is based on combining all the spatial data and returning a single summary value or two can be useful in understanding broad spatial trends - Explain how the K function provides a scale-dependent measure of dispersion - Compute Moran`s I and Geary`s c for patterns of attribute data measured on interval/ratio scales - Compute measures of overall dispersion and clustering of point datasets using nearest neighbor distance statistics - Compute the K function - Justify, compute, and test the significance of the join count statistic for a pattern of objects","name":"Global measures of spatial association"},{"code":"AM7-5","description":"- Describe the effect of non-stationarity on local indices of spatial association - Compare and contrast global and local statistics and their uses - Explain how a weights matrix can be used to convert any classical statistic into a local measure of spatial association - Explain how geographically weighted regression provides a local measure of spatial association - Decompose Moran`s I and Geary`s c into local measures of spatial association - Compute the Gi and Gi* statistics","name":"Local measures of spatial association"},{"code":"AM7-6","description":"- Explain how outliers affect the results of analyses - Explain how the following techniques can be used to examine outliers: tabulation, histograms, box plots, correlation analysis, scatter plots, local statistics","name":"Outliers"},{"code":"AM7-7","description":"- Define \"prior and posterior distributions\" and \"Markov-Chain Monte Carlo\" - Explain how the Bayesian perspective is a unified framework from which to view uncertainty - Compare and contrast Bayesian methods and classical \"frequentist\" statistical methods","name":"Bayesian methods"},{"code":"AM7","description":"Traditional statistical methods are used to describe the central tendency, dispersion, and other characteristics of data but are not always suited to use with spatial data for which specialized techniques are often required. The field of spatial statistical analysis forms the backbone for the testing of hypotheses about the nature of spatial pattern, dependency, and heterogeneity. The techniques are widely used in both exploratory and confirmatory spatial analysis in many different fields.","name":"Spatial statistics"},{"code":"AM8-1","description":"- List and describe several spatial sampling schemes and evaluate each one for specific applications - Describe sampling schemes for accurately estimating the mean of a spatial data set - Differentiate between model-based and design-based sampling schemes - Design a sampling scheme that will help detect when space-time clusters of events occur - Create spatial samples under a variety of requirements, such as coverage, randomness, and transects","name":"Spatial sampling for statistical analysis"},{"code":"AM8-2","description":"- Construct a semi-variogram and illustrate with a semi-variogram cloud","name":"Principles of semi-variogram construction"},{"code":"AM8-3","description":"- List the possible sources of error in a selected and fitted model of an experimental semi-variogram - Describe some commonly used semi-variogram models - Describe the conditions under which each of the commonly used semi-variograms models would be most appropriate - Explain the necessity of defining a semi-variogram model for geographic data - Apply the method of weighted least squares and maximum likelihood to fit semi-variogram models to dataset","name":"semi-variogram modeling"},{"code":"AM8-4","description":"- Describe the relationship between the semi-variogram and kriging - Explain why kriging is more suitable as an interpolation method in some applications than others - Explain why it is important to have a good model of the semi-variogram in kriging - Explain the concept of the kriging variance, and describe some of its shortcomings - Explain how block-kriging and its variants can be used to combine data sets with different spatial resolution (support) - Compare and contrast block-kriging with areal interpolation using proportional area weighting and dasymetric mapping - Outline the basic kriging equations in their matrix formulation - Conduct a spatial interpolation process using kriging from data description to final error map","name":"Principles of kriging"},{"code":"AM8-5","description":"- Compare and contrast co-kriging, log-normal kriging, disjunctive kriging, indicator kriging, factorial kriging, and universal kriging - Apply universal kriging to appropriate data sets - Interpret the results of universal kriging","name":"Kriging variants"},{"code":"AM8","description":"Geostatistics are a variety of techniques used to analyze continuous data e.g., rainfall, elevation, air pollution. The fundamental structure of geostatistics is based on the concept of semi-variograms and their use for spatial prediction kriging. Sampling methods are also discussed in Unit GD9 Field data collection.","name":"Geostatistics"},{"code":"AM9-1","description":"- Describe the general types of spatial econometric models - Explain how spatial dependence and spatial heterogeneity violate the Gauss-Markov assumptions of regression used in traditional econometrics - Demonstrate how spatially lagged, trend surface, or dummy spatial variables can be used to create the spatial component variables missing in a standard regression analysis - Demonstrate how the spatial weights matrix is fundamental in spatial econometrics models - Demonstrate why spatial autocorrelation among regression residuals can be an indication that spatial variables have been omitted from the models","name":"Principles of spatial econometrics"},{"code":"AM9-2","description":"- Explain Anselin`s typology of spatial autoregressive models - Conduct a spatial econometric analysis to test for spatial dependence in the residuals from leastsquares models and spatial autoregressive models - Demonstrate how the parameters of spatial auto-regressive models can be estimated using univariate and bivariate optimization algorithms for maximizing the likelihood function - Justify the choice of a particular spatial autoregressive model for a given application - Implement a maximum likelihood estimation procedure for determining key spatial econometric parameters - Apply spatial statistic software (e.g., GEODA) to create and estimate an autoregressive model","name":"Spatial autoregressive models"},{"code":"AM9-3","description":"- Identify modeling situations where spatial filtering might not be appropriate - Describe the relationship between factorial kriging and spatial filtering - Explain how spatial correlation can result as a side effect of the spatial aggregation in a given dataset - Explain how dissolving clusters of blocks with similar values may resolve the spatial correlation problem Explain how the Getis and Tiefelsdorf-Griffith spatial filtering techniques incorporate spatial component - variables into OLS regression analysis in order to remedy misspecification and the problem of spatially auto-correlated residuals - Demonstrate how spatial autocorrelation can be \"removed\" by resampling","name":"Spatial filtering"},{"code":"AM9-4","description":"- Describe the characteristics of the spatial expansion method - Discuss the appropriateness of GWR under various conditions - Explain how allowing the parameters of the model to vary with the spatial location of the sample data can be used to accommodate spatial heterogeneity - Explain the principles of geographically weighted regression - Compare and contrast GWR with universal kriging using moving neighborhoods - Perform an analysis using the geographically weighted regression technique - Analyze the number of degrees of freedom in GWR analyses and discuss any possible difficulties with the method based on your results","name":"Spatial expansion and Geographically Weighted Regression GWR"},{"code":"AM9","description":"Many problems of the social sciences can be expressed in terms of spatial regression analysis. The development of spatial autoregressive models and the estimation of their parameters is the focus for the field of spatial econometrics.","name":"Spatial regression and econometrics"},{"code":"CF","description":"The GIScience perspective is grounded in spatial thinking. The aim of this knowledge area is to recognize, identify, and appreciate the explicit spatial, spatio-temporal and semantic components of the geographic environment at an ontological and epistemological level in preparation for modeling the environment with geographic data and analysis. To do this, one must understand the nature of space and time as a context for geographic phenomena.This knowledge area covers the ways in which views of the geographic environment depend on philosophical viewpoints, physics, human cognition, society, and the task at hand. This knowledge area also requires an understanding of the fundamental principles in the discipline of geography, the \"language\" of spatial tasks. On a more advanced level, this area incorporates mathematical and graphical models that formalize these concepts, such as set theory, algebra, and semantic nets. Because of its wide range of foundational principles, this knowledge area forms a basis for the other knowledge areas. Wise design and use of geospatial technologies requires an understanding of the nature of geographic information, the social and philosophical context of geographic information, and the principles of geography. This knowledge area is especially closely tied to Knowledge Areas Data Modeling (DM) and Design Aspects (DA), as generic data models and application designs need to be grounded in sound conceptual models. The foundations of geographic information have developed over several decades. Philosophical and scientific views on the nature of space and time have evolved since the ancient Greeks. Early papers during the Quantitative Revolution, such as Berry (1964), began to formalize the structure of information used in geographic inquiry.The fundamental data structures and algorithms comprising the GIS software developed in the 1960`s and 1970`s were based on implicit \"common-sense\" conceptual models of geographic information. During the 1980`s, several researchers questioned these underlying assumptions. Some were refuted, other confirmed, and many extended. However, the most rapid pace of development in this area was during the 1990`s with the rise of GIScience as a distinct discipline, and the many cooperative initiatives it comprised.The new millennium has seen some of these foundational principles incorporated into commercial software, thus making theoretical knowledge even more important for practitioners. It is expected that the concepts in this knowledge area will be learned gradually. An introductory course may cover only a few topics in a cursory manner, an intermediate course on data modeling or data analysis may consider several theoretical topics of practical application, and a number of graduate courses could cover each topic in a research-oriented environment. Discussion of this knowledge area includes several terms that can have multiple meanings. For the purposes of this document, two in particular require definition: Geographic: Almost any subject or discourse involving earthly phenomena, studied from a spatial perspective at a medium scale (sub-astronomical and super-architectural). Phenomenon: Any subject of geographic discourse that is perceived to be external to the individual, including entities, events, processes, social constructs, and the like.","name":"Conceptual Foundations"},{"code":"CF1-1","description":" ","name":"Metaphysics and ontology"},{"code":"CF1-1b","description":"Brief history of GIScience as related to the history of GISystems; Definitions of GIS&T; Sub-domains of GIS&T (i.e., Geographic Information Science, Geospatial Technology, and Applications of GIS&T)","name":"What is Geographic Information Science and Technology"},{"code":"CF1-2","description":" ","name":"Epistemology"},{"code":"CF1-2b","description":"GIS&T draws upon insights and methods from key allied fields: Geography, Cartography, Computer and information science, Engineering, Mathematics and Statistics, Philosophy, Cognitive Science, Linguistics","name":"Contributions to GIS and T by key allied fields"},{"code":"CF1-3","description":" ","name":"Philosophical perspectives"},{"code":"CF1","description":"Many branches of philosophy are relevant to an understanding of geographic information, especially metaphysics and epistemology. Philosophical theories are deeply engaged in the study of knowledge, space, time, geographic phenomena and human interaction with them. These theories influence the development of geographic ontologies and the structuring, analysis, and interpretation of geographic information. It is, therefore, crucial for professionals to understand these principles in order to bridge (rather than eliminate) the differences and work together. Philosophical perspectives on GIS practice are covered in Unit GS7 Critical GIS.","name":"Philosophical foundations"},{"code":"CF1b","description":"Unit CF1 introduces the broad domain refered to as Geographic Information Science & Technology (GIS&T) and its sub-domains (i.e., Geographic Information Science, Geospatial Technology, and Applications of GIS&T). It outlines the history of Geographic Information Science as related to the history of GISystems, as well as the contributions to this multidisciplinary domain by key allied fields, such as geography, cartography, computer and information science, engineering, mathematics, philosophy, cognitive science, and linguistics.","name":"Introduction to Geographic Information Science and Technology"},{"code":"CF2-1","description":" ","name":"Perception and cognition of geographic phenomena"},{"code":"CF2-1b","description":"Metaphysics and Ontology - Formal ontology - Ontological distinctions (e.g., continuants vs. occurrents, universals vs. particulars) - The problem of universals and relevant theories (realism, nominalism, conceptualism) - Ontologies of the geographic domain - Philosophical theories relating to the nature of space, time, geographic phenomena and human interaction with them","name":"Philosophy of being"},{"code":"CF2-2","description":" ","name":"From concepts to data"},{"code":"CF2-2b","description":"Epistemology; Theories on what constitutes knowledge; The notions of model and representation in science; The influences of epistemology on GIS practices","name":"Philosophy of knowledge"},{"code":"CF2-3","description":" ","name":"Geography as a foundation for GIS"},{"code":"CF2-4","description":" ","name":"Place and landscape"},{"code":"CF2-6","description":" ","name":"Cultural influences"},{"code":"CF2-7","description":" ","name":"Political influences"},{"code":"CF2","description":"Geographic information is observed, comprehended, organized, used in human processes, with both personal and social influences. Therefore, sound models of geographic information should be grounded on a sound understanding of human perception, cognition, memory, and behavior, as well as human institutions.","name":"Cognitive and social foundations"},{"code":"CF3-1","description":"- Theories of space - contributions that different perspectives on the nature of space bring to an understanding of geographic entities and phenomena - Spatial frames of reference - Differing denotations and connotations of the terms spatial, geographic, and geospatial - Nature and characteristics of spatial entities - Place (i.e., difference between space and place, how the concept of place encompasses more than just location) - Landscape, various concepts and definitions - Basic primitives used to describe spatial objects (i.e., points, lines, regions, volumes) - Discrepancies between the nature of locations in the real world and representations thereof, (e.g., generalisation of towns as points) - Spatial metaphors and models of phenomena to be represented in GIS - Advantages and disadvantages of the use of Cartesian/metric space as a basis for GIS and related technologies - Methods for representing non-Cartesian models of space in GIS - Limitations of representation of place, landscape, and spatial phenomena in GIS","name":"Space"},{"code":"CF3-1b","description":"- Theories of human perception, cognition, and memory and their ability to model spatial knowledge acquisition (e.g., Marr on vision, Piaget on cognitive development) - Types of mental representations (i.e., analogue, propositional, procedural) - The role of metaphors and image schemata in our understanding of geographic phenomena and geographic tasks - From concepts to data (i.e., data, information, knowledge, and wisdom; transformation of a conceptual model of information for a particular task into a data model; limitations of various information stores (the mind, computers) and means (maps, graphics, and text) for representing geographic information) - Difference between real phenomena, conceptual models, and GIS data representations thereof – connections with cartography and maps","name":"Cognitive foundations"},{"code":"CF3-2b","description":"- Semantics - Meaning (e.g., the nature of meaning, modes of meaning) - Geospatial semantics - The role of natural language in the conceptualization of geographic phenomena","name":"Linguistic foundations"},{"code":"CF3-3b","description":"- The ways in which the elements of culture (e.g., language, religion, education, traditions) may influence the understanding and use of geographic information - The influences of social theories and political ideologies and actions on human perceptions of space and place - The constraints that political forces place on geospatial applications in public and private sectors","name":"Social foundations"},{"code":"CF3-4b","description":"- Common-sense views and laymen knowledge of geographic phenomena that contrast with established theories and technologies of geographic information - The impact of geospatial technologies and the geoweb (e.g., digital globes) that allow non-geospatial professionals to create, distribute, and map geographic information - The design, procedures, and results of GIS projects to non-GIS audiences (clients, managers, general public) - Difference between applications that can make use of common-sense principles of geography and those that should not","name":"Common-sense geographies"},{"code":"CF3","description":"Geographic information is observed, comprehended, organized, used in human processes, with both personal and social influences. Therefore, sound models of geographic information should be grounded on a sound understanding of human perception, cognition, memory, and behavior, as well as human institutions.","name":"Cognitive, linguistic and social foundations"},{"code":"CF4-2b","description":"- Mathematical and phenomenological theories of the nature of time - Different temporal frames of reference: linear and cyclical, absolute and relative - The role that time plays in static GIS - Models of a given spatial process using continuous and discrete perspectives of time - Temporal elements of geographic phenomena that need to be represented in particular GIS applications","name":"Time"},{"code":"CF4-3b","description":"- Characteristics of spatial and temporal dimensions - Types of geographic interactions in space and time - Types of movement, change, and evolution","name":"Relationships between space and time"},{"code":"CF4-4b","description":"- GIS data structures to represent categories, including attribute columns, layers/themes, shapes, and legends","name":"Categories"},{"code":"CF4-5","description":"- Difference between the theory holding that properties are fundamental (and objects are human simplifications of patterns thereof) and the theory holding that objects are fundamental (and properties are attributes thereof) - Stevens` four levels of measurement (i.e., nominal, ordinal, interval, ratio) - Attribute domains in GIS, including continuous and discrete, qualitative and quantitative, absolute and relative - proper uses of attributes based on their domains - Attribute domains that do not fit well into Stevens` four levels of measurement such as cycles, indexes, and hierarchies - Set theory to formalize attribute values and domains - Alternative forms of representations for situations in which attributes do not adequately capture meaning (such as aesthetics)","name":"Properties"},{"code":"CF4b","description":"Geographic phenomena, geographic information, and geographic tasks are described in terms of space, time, and properties. Different theories exist as to the nature and formal representation of these aspects, including space-like dimensions, sets, and phenomenology. Information in each of these three aspects is measured and reported with respect to one of several frames of reference or domains, including both absolute and relative approaches. Early frameworks such as those of Berry (1964) and Sinton (1978) were influential in setting forth the importance of space, time, and theme in GIS&T. Besides, space, time, and properties, categories are also fundamental in the conceptualization and representation of spatial entities, phenomena, processes, and events. Distinctive features of geographic information such as scale and detail, spatial patterns, spatial integration, and regions are also critical for a complete description of its nature and representation. This unit is closely tied to the creation of data models in Knowledge Area 5: Data Modeling, Storage, and Exploitation.","name":"Fundamentals of Geographic Information"},{"code":"CF5-1b","description":"-The predominance of discrete entities (in terms of space, time, and properties) in human conceptualizations of geographic phenomena -Perceptual processes (e.g., edge detection) that aid cognitive objectification -Differing epistemological and metaphysical viewpoints on the \"reality\" of geographic entities -Entity types that need to be modeled in a particular GIS application or procedure -Phenomena that are difficult or impossible to conceptualize in terms of entities -Difficulties in modeling entities with ill-defined edges -The effectiveness of GIS data models for representing the identity, existence, and lifespan of entities -The influence of scale on the conceptualization of entities","name":"Discrete entities"},{"code":"CF5-2b","description":" ","name":"Fields"},{"code":"CF5-3","description":" ","name":"Genealogical relationships: lineage, inheritance"},{"code":"CF5-3b","description":"-Continuants (entities) vs. occurrents (events) -Event vs. process -Description of particular events or processes in terms of identity, categories, attributes, and locations -Formal systems for describing continuous spatio-temporal processes -The \"actor\" role that entities and fields play in events and processes -The difficulty of integrating process models into GIS software based on the entity and field views, and methods used to do so","name":"Events and processes"},{"code":"CF5-4b","description":"-Phenomena or applications that are not adequately represented in an existing comprehensive model -Early attempts to integrate the concepts of space, time, and attribute in geographic information, such as Berry (1964) and Sinton (1978) -Major integrated models of geographic information, such as Peuquet`s triad, Mennis` pyramid, and Yuan`s three-domain -The degree to which integrated models can be implemented using current technologies","name":"Integrated models"},{"code":"CF5-6","description":" ","name":"Spatial distribution"},{"code":"CF5-7","description":" ","name":"Region"},{"code":"CF5-8","description":" ","name":"Spatial integration"},{"code":"CF5","description":"The concepts below form the basic elements of common human conceptions of geographic phenomena. Concepts from many units in this knowledge area have been synthesized to create general conceptual models of geographic information. Attempts to resolve the object-field debate have led to attempts to create comprehensive models that bridge these views. Consideration of this unit should also include formal models of these elements in mathematics and other fields. Knowledge Area DM Data Modeling discusses the representation of these elements in digital models.","name":"Elements of geographic information"},{"code":"CF5b","description":"The concepts below form the basic elements of common human conceptions of geographic phenomena. Concepts from many units in this knowledge area have been synthesized to create general conceptual models of geographic information. Attempts to resolve the object-field debate have led to attempts to create comprehensive models that bridge these views. Consideration of this unit should also include formal models of these elements in mathematics and other fields. Knowledge Area DM Data Modeling discusses the representation of these elements in digital models.","name":"Elements of geographic information"},{"code":"CF6-1","description":" ","name":"Mereology: structural relationships"},{"code":"CF6-2","description":"-How a geographic entity can be created from one or more others -Identity-based change or temporal relationships of particular geographic phenomena -When to represent the genealogy of entities for a particular application -The effects of temporal scale on the modeling of genealogical structures","name":"Genealogical relationships: lineage, inheritance"},{"code":"CF6-3","description":"-Topology (the branch of mathematics) and the study of geographic relationships -Terms used to describe topological relationships, such as disjoint, overlap, within, and intersect -Description of geographic phenomena in terms of their topological relationships in space and time to other phenomena -Possible topological relationships between entities in space (e.g., 9-intersection) and time -Methods that analyze topological relationships","name":"Topological relationships"},{"code":"CF6-4","description":"-Geographic phenomena in terms of their distances and directions (in space and time) -Spatial autocorrelation in the context of geographic proximity -Methods that analyze metrical relationships -Categories of distance (e.g. Euclidean distances, Manhattan distance, geodetic distance, spherical distance) -Tobler`s first law of geography and GIS operations -Situations in which Tobler`s first law of geography is valuable or does not apply -The principle of friction of distance and geographic models that are based on it (e.g., gravity models, spatial interaction models)","name":"Metrical relationships: distance and direction"},{"code":"CF6","description":"Like geography, geographic information not only models phenomena but the relationships between them. This can include relationships between entities, between attributes, between locations. In addition, one of the strengths of geography (and GIS) is its ability to use a spatial perspective to relate disparate subjects, such as climate and economy. Methods for analyzing relationships are discussed in Unit AM4 Modeling relationships and patterns.","name":"Relationships"},{"code":"CF7-1","description":"-Meanings of related terms such as vague, fuzzy, imprecise, indefinite, indiscrete, unclear, and ambiguous -The role that system complexity, dynamic processes, and subjectivity play in the creation of vague phenomena and concepts -Cognitive processes that tend to create vagueness -Vagueness and language -Vagueness and scale (granularity) -Vagueness in different aspects of geographic phenomena -When is vagueness acceptable and when it is not -Difference between: vagueness and ambiguity, well defined and poorly defined objects and fields -Mathematical models of vagueness (e.g., fuzzy sets, rough sets)","name":"Vagueness"},{"code":"CF7-2","description":"-Uncertainty-related terms, such as error, accuracy, uncertainty, precision, stochastic, probabilistic, deterministic, and random -Difference between uncertainty and vagueness -Dependence of uncertainty on scale and application -Expressions of uncertainty in language -The causes of uncertainty in geospatial data -Stochastic error models for natural phenomena -How the concepts of geographic objects and fields affect the conceptualization of uncertainty -Mathematical models of uncertainty: Probability and statistics","name":"Error-based uncertainty"},{"code":"CF7","description":"Human models (mental, digital, visual, etc.) of the geographic environment are necessarily imperfect. While the mathematical principle of homomorphism (often operationalized as fitness for use) allows for imperfect data to be useful as long as they yield results adequate for the use for which they are intended, imperfections are frequently problematic. Although terminology still varies, two types of imperfection are generally accepted: vagueness (a.k.a. fuzziness, imprecision, and indeterminacy), which is generally caused by human simplification of a complex, dynamic, ambiguous, subjective world; and uncertainty (or ambiguity), generally the result of imperfect measurement processes (as discussed in Knowledge Area GD Geospatial Data). Both of these can be manifested in all forms of geographic information, including space, time, attribute, categories, and even existence. Imperfection is also dealt with in Units GD6 Data quality (in the context of measurement), GC8 Uncertainty and GC9 Fuzzy sets (for the handling and propagation of imperfections), and CV4 Graphic representation techniques (in the context of visualization).","name":"Imperfections in geographic information"},{"code":"CV","description":"Cartography and visualization primarily relate to the visual display of geographic information. This knowledge area addresses the complex issues involved in effective visual thinking and communication of geospatial data and of the results of geospatial analysis. This knowledge area reflects much of the domain of cartography and visualization, although some concepts and skills in these areas can be found in other knowledge areas. For example, the process of visualization encompasses aspects of analysis as well as cartography. Specifically, visualization is currently being reformulated as visual analytics in the context of homeland security.","name":"Cartography and Visualization"},{"code":"CV1-1","description":"The evolution of cartographical representation of geography in the previous centuries followed technological and scientific developments. It was driven by commercial and/or military needs and influenced by the nature of the area to be mapped. More recent developments are the rise of open data in some countries and the widely available internet technology allowing end users to get geodata from the web. The last decades the data and their presentation have become central to cartography, whereas paper maps became peripheral. Facets: a.\tMeasuring position and direction b.\tReference systems c.\tMapmakers and their influence d.\tAnalog tools for graphic representation e. Governmental map production f.\tGIS technology g.\tInternet technology h.\tOpen Data movement","name":"History of cartography"},{"code":"CV1-3","description":"The evolution of cartographical representation of geography in the previous centuries followed technological and scientific developments. It was driven by commercial and/or military needs and influenced by the nature of the area to be mapped. More recent developments are the rise of open data in some countries and the widely available internet technology allowing end users to get geodata from the web. The last decades the data and their presentation have become central to cartography, whereas paper maps became peripheral.\r\n\r\nFacets: a.\tMeasuring position and direction b.\tReference systems c.\tMapmakers and their influence d.\tAnalog tools for graphic representation e.\tGovernmental map production f. GIS technology g.\tInternet technology h.\tOpen Data movement","name":"Paradigm and technology shifts"},{"code":"CV1-4","description":"Art in cartography means much more than designing aesthetically pleasing maps. Exploring the interaction at large between art and cartography involves rethinking the way we approach spatial expressions. This entails: developing an inclusive approach of artistic mapping expressions; facilitating and encouraging interaction between cartographers who work with the Art aspects of cartography and artists who produce cartographic artifacts; and developing conceptual elements about the relationships between art and cartography.\"Art is a diverse range of human activities in creating visual, auditory or performing artifacts – artworks, expressing the author's imaginative or technical skill, intended to be appreciated for their beauty or emotional power. In their most general form these activities include the production of works of art, the criticism of art, the study of the history of art, and the aesthetic dissemination of art","name":"Art and geo-data visualisation"},{"code":"CV1-5","description":"Historical maps can be maps that are out of date and have become obsolete by the passing of time. Historical maps can also be representations made with the intention to show a situation in the past. This is independent of the technology used to produce the map or the technology to manipulate the data that underlies the map. Facets: the old map was made in the analog past, the picture itself is the source; If possible, a high resolution scan of the map image should be georeferenced before use in a GIS; Digital representations of historic situations should come with metadata about their origins; the legibility of historical maps.","name":"Historical maps"},{"code":"CV1","description":"The history of cartography can be described as an interplay of change in: the motives for mapping, the history of exploration, printing technologies, data collection technologies, design technologies, scientific understanding of map use, visual analysis of graphic displays, application domains and creative design innovations.","name":"History and trends"},{"code":"CV2-1","description":"As mapping ( geo-data visualization) is intended to convey a certain message to a certain audience, it is essential to use data sources that allow the intended visualisation result. The data should be of the right degree of detail and its use should not cause copyright problems. The producer quality of each data set should be taken into account, as well as the fitness of the data for the intended use. Aspects: message; data quality","name":"Source materials for mapping"},{"code":"CV2-2","description":"In the trajectory between raw (geo)data and their user-relevant representation, the necessary data processing includes ways of abstraction by selection, filtering, generalization, transformation and classification of geographical data. In this data processing it is essential to at one hand relate the final symbolisation to the necessities of the intended message, and at the other hand to procedures that introduce as little error as possible.","name":"Data processing"},{"code":"CV2-3","description":"Map projection is fundamental to representation of spatial data and for combining different datasets. Its choice should serve the presentation type that will convey the intended message to the audience. Many mathematical principles define datum, projections, horizontal and vertical co-ordinate systems, georeferencing- introduced with the focus on visualisation issues Aspects: geodetic concepts; transformations","name":"Mathematical base"},{"code":"CV2","description":"Geodata, including 3 dimensional geometry, as such can graphically be presented but most of the times the data as such doesn`t meet the presentation criteria. Especially if the dataset has to be presented in combination with other datasets. First all the geodatum, georeference and map projection are crucial but also the role of the geometry. The processing of the geometry and the related attributes may become a crucial step for an adequate presentation. Nowadays the highest precision may be used to define different graphical attributes for different zoom levels. On the other hand geodata visualisation includes also graphical datasets. Such data ensembles, the combination of geodata and graphical data, are the data sources that offer opportunities to other ways of visualisation then the traditional cartographic mapping. Facets: a.\tGeospatial location (2D) and position (3D) that data refer to b.\tDegree of detail in data origin (acquisition resolution) and in representation ('map' scale) c.\tTypes of data (e.g. imagery, field measurements, delineated objects)","name":"Data considerations"},{"code":"CV3-1","description":" ","name":"Map design fundamentals"},{"code":"CV3-2","description":" ","name":"Basic concepts of symbolization"},{"code":"CV3-3","description":" ","name":"Color for cartography and visualization"},{"code":"CV3-4","description":" ","name":"Typography for cartography and visualization"},{"code":"CV3","description":"This topic covers basic design principles that are used in mapping and visualization, as well as cartographic design principles specific to the display of geographic data. Both page layout design and data display are addressed.","name":"Design principles"},{"code":"CV4-1","description":"A thematic map is a type of map especially designed to show a particular theme connected with a specific geographic area. These maps \"can portray physical, social, political, cultural, economic, sociological, agricultural, or any other aspects of a city, state, region, nation, or continent\". Cartographers use many methods to create thematic maps. Five techniques are especially noted: -Choropleth mapping shows statistical data aggregated over predefined regions -Proportional symbols, showing the relative value of attributes -Isarithmic or Isopleth, also known as contour maps -Dots, to show the location of a phenomenon -Dasymetric, which uses areal symbols to spatially classify volumetric data.","name":"Thematic mapping"},{"code":"CV4-10","description":"Conveying uncertainty information is often done through visualization. Uncertainty is often defined, quantified, and expressed using models specific to individual application domains. In visualization however, we are limited in the number of visual channels (3D position, color, texture, opacity, etc.) available for representing the data. Thus, when moving from quantified uncertainty to visualized uncertainty, we often simplify the uncertainty to make it fit into the available visual representations. (After Potter et al., 2012). The seven challenges as formulated by MacEachren et Al. (2005) are still there to be tackled.","name":"Visualization of uncertainty"},{"code":"CV4-2","description":"This topic is about representing the earth`s relief. Key terms are: Contour line, contour interval, DEM (digital elevation model), elevation, Interpolation, profile, relief, slope, terrain exaggeration, terrain skeleton, TIN (triangular irregular network) and topography. Aspects: data; representation; extent.","name":"Representing terrain"},{"code":"CV4-3","description":"Multivariate descriptive displays or plots are designed to reveal the relationship among several variables simultaneously. There are several basic characteristics of the relationship among sets of variables that are of interest. These include: - the forms of the relationships - the strength of the relationships, and - the dependence of the relationships on external (usually to the pairs of variables being examined) circumstances. Multivariate plot examples are: - enhanced 2-D scatter diagrams - 3-D scatter diagrams - contour, level, and surface plots - high-dimensional data plots","name":"Multivariate displays"},{"code":"CV4-4","description":"According to Daassi et al. (2006) the visualization process of temporal data has four steps: (1) time values to be visualized, (2) point of view on time, that identifies the characteristics of the temporal values to be visualized, (3) time space: define the displayable space of the time values and (4) point of view on the visualization space, the implementation of the perceptible forms of time. The visualization of spatio-temporal data can be done in many different ways such as multi-panel plots (maps), time-series plots (graphs), space-time plots (graphs), animations, and tables (Pebesma, 2012) Aspects: Space; Time; representation with visual means","name":"Visualization of temporal geographic data"},{"code":"CV4-5","description":" ","name":"Dynamic and interactive displays"},{"code":"CV4-6","description":" ","name":"Web mapping"},{"code":"CV4-7","description":" ","name":"Virtual and immersive environments"},{"code":"CV4-8","description":"Augmented reality (AR) is the integration of digital information with live video or the user's environment in real time. It requires three steps: 1. Recognition: Recognition of an image, an object, a face or a body 2. Tracking: Real-time localization in space of the image, object, face, or body 3. Mix: Superposition of a media (video, 3D, 2D, text, etc…) on top of this image, object, face or body","name":"Augmented environments"},{"code":"CV4-9","description":"Cartographers have recently become involved in extending geographic concepts and cartographic design approaches to the depiction of non-geographic data archives, using so-called spatialized views of information spaces. Spatialisations differ from ordinary data visualisation and geovisualisation in that they may be explored as if they represented spatial information. (Fabrikant, S.I., 2003). As definitions of spatialization can be found: Spatializations are computer visualizations in which nonspatial information is depicted spatially (Montello et al., 2003). Spatialization is the transformation of high-dimensional data into lower-dimensional, geometric representations on the basis of computational methods and spatial metaphors. (Skupin 2007)","name":"Spatialization"},{"code":"CV4","description":"This unit addresses mapping methods and the variations of those methods for specialized mapping and visualization instances, such as thematic mapping, dynamic and interactive mapping, Web mapping, mapping and visualization in virtual and immersive environments, using the map metaphor to display other forms of data (spatialization), and visualizing uncertainty. Analytical techniques used to derive the data employed in these graphic representations are discussed in Knowledge Area AM Analytical Methods and Unit DN2 Generalization and aggregation.","name":"Graphic representation techniques"},{"code":"CV5-1","description":"Geospatial data representation can make high demands on computational facilities. Examples are: - Infrastructural connections to datasets and processing models - Processing capacity: speed and volume - Access to storage capacity: speed and volume - Display facilities: size, resolution, speed - Peripheral devices like printers for large format hard copy, or VR headsets","name":"Computational demands"},{"code":"CV5-2","description":"Standards for map services were set by OGC and ISO, called WMS and WMTS. Producing map images on the web from a cartographic image in a GIS application is called \"publishing\". Making a web \"map\" in the is subconcept of sense of constructing data representations for Storytelling or Geo-gaming is still under development. It requires a mix of applying the map Design principles and Graphic presentation techniques, possibly in combination with software scripting.","name":"Web map making"},{"code":"CV5-3","description":"Traditional \"map\" making, as opposed to the mapmaking in neogeography, focuses on reliable and reproducible products, based on expertise of high definition printing in many colours on analogue media of geodetically well-constructed images.","name":"Traditional map making"},{"code":"CV5-4","description":"The aspects of reproduction of a data representation depend on the nature of the representation: is it analogue (a paper map, a mock-up) or is it digital? In the case of a paper map, its digitalisation with high fidelity is an essential step. With a source in digital form, reproduction can be a matter of the right printer. Alternatively, the source could be disseminated as a file or as a web service. If representations are dynamic and/or interactive the possibilities depend on the construction of the representation. The ease of dissemination of digital files should not result in copyright breach. Aspects: Digitalization techniques for analogue sources, Printing ( 2D, 3D), Dissemination ways, Construction of the data representation, User needs specification, Copyright issues","name":"Map reproduction"},{"code":"CV5","description":"This unit addresses map production and reproduction, as well as computation issues that relate to those workflows.","name":"Map production"},{"code":"CV6-1","description":"The name of this topic was the title of a book by Wood and Fels in 1992. Their view on maps was that they do not represent anything. Instead, they present an argument about the world through the careful choice of content arranged graphically at a specific scale. The book has been a linchpin of the \"new cartographies\" in which maps are redefined as socially constructed arguments based upon consistent semiotic codes. (Wikipedia) Like paintings, they express a point of view. Maps embody and project the interests of their creators. (WorldCat).","name":"The power of maps"},{"code":"CV6-2","description":"Becoming aware of what a \"map\" shows depends partly on what the senses can register of the representation as a whole. It also depends on recognition of elements in the representation that are meaningful to the observer in the sense that these elements are credible indicators of spatial features. Based on that recognition, the nature of these elements and their spatial pattern might infer thoughts about historic or ongoing processes. This interpretation will be influenced by the expertise and needs of the observer. Aspects: Data representation in one or more media, static or dynamic; Senses of the observer; Interpretation by the observer","name":"Map reading and interpretation"},{"code":"CV6-3","description":"Assessment of the usability of a data representation is about how useful it is to users. Therefore it is a test of the success of the representation design, a test of the skills of the \"map\" maker and a test for the reliability of the underlying data.","name":"Usability analysis"},{"code":"CV6-4","description":" ","name":"Map analysis"},{"code":"CV6-4","description":"This topic is about testing for legal and privacy issues in representations of geographical data.","name":"Map ethics Evaluation and testing"},{"code":"CV6-4bwY0pA","description":"This topic is about testing for legal and privacy issues in representations of geographical data.","name":"Map ethics Evaluation and testing"},{"code":"CV6-5","description":"Ethics is about the question if behaviour is right or wrong in a social context. In dealing with geodata, a person can do the wrong thing with respect to laws (e.g. disclose secrets, disregard privacy, copyright infringement) or to professional standards (e.g. use bad data, forget about the colour blind, downplay unpleasant details). Aspects: breach of legal standards; breach of professional standards","name":"Map ethics Legal and privacy issues"},{"code":"CV6-5bDkkOU","description":"Ethics is about the question if behaviour is right or wrong in a social context. In dealing with geodata, a person can do the wrong thing with respect to laws (e.g. disclose secrets, disregard privacy, copyright infringement) or to professional standards (e.g. use bad data, forget about the colour blind, downplay unpleasant details). Aspects: breach of legal standards; breach of professional standards","name":"Map ethics Legal and privacy issues"},{"code":"CV6-6","description":"Spatial thinking is thinking that finds meaning in the shape, size, orientation, location, direction or trajectory, of objects, processes or phenomena, or the relative positions in space of multiple objects, processes or phenomena. Spatial thinking uses the properties of space as a vehicle for structuring problems, for finding answers, and for expressing solutions\" Aspects: recognizing spatiality in a collection of things; translation of the collection to a pattern of elements; recognizing structure (relations between the elements in a pattern); recognizing process (or changes over time in patterns or structures)","name":"Spatial thinking"},{"code":"CV6","description":"Geodata visualisation are always made with a certain purpose. The role and understanding of such graphical representation is an important field of research. Besides theories that underpin evaluation approaches and their findings the visualisation may also be confronting. The more realistic the presentation and especially when it includes human/personal related data the ethical dimension of the visualisation play a major role. Usability of visualisations has also an impact on spatial thinking as has been proved by scholars.","name":"Usability"},{"code":"DA","description":"Proper design of geospatial applications, models, and databases, and the validation and verification of design activities, are critical components of work in all areas related to GIS and T. Design failures can negate well-intentioned efforts to apply concepts and technology to solve real-world problems. While sharing a number of concerns with general systems analysis, the unique and complex spatial elements of geospatial information provide significant additional challenges. The focus of this knowledge area is on the design of applications and databases for a particular need. The design of general-purpose models and tools (e.g., raster and vector) is covered in Knowledge Area DM Data Modeling. In the context of specific implementations, design activities fall into three general classes: 1. Application Design addresses the development of workflows, procedures, and customized software tools for using geospatial technologies and methods to accomplish both routine and unique tasks that are inherently geographic. 2. Analytic Model Design incorporates methods for developing effective mathematical and other models of spatial situations and processes. The design of the analytic model is often influenced by decisions that are made about data models and structures. 3. Database Design concerns the optimal organization of the necessary spatial data in a computer environment in order to efficiently sustain a particular application or enterprise. Several units in Knowledge Area GD Geospatial Data follow from Knowledge Area DA Design Aspects, especially those that discuss the collection of data in conformance with the designs discussed herein. This knowledge area is also closely related to Knowledge Area OI Organizational and Institutional Aspects, which discusses several issues relating to the management of systems in organizations after they are designed and implemented. Beyond GIS and T, this knowledge area has strong ties to information science and technology (e.g., Gorgone, G. B. and Gray, P., 2000, and Gorgone, G. B. and others, 2002), and to business management in the area of resource planning. Some of the methods of geospatial system design are identical to established methods in information system design, while others are unique.","name":"Design and Setup of Geographic Information Systems"},{"code":"DA1-1","description":" ","name":"Methods of Requirement analysis, requirements tracing, requirements description"},{"code":"DA1-2","description":"- Methods for documenting and analyzing processes - Describe how spatial data and GIS&T can be integrated into a workflow process - Evaluate how external spatial data sources can be incorporated into the business process","name":"Methods of process description and analysis"},{"code":"DA1-3","description":"- Analysis of application processes - Languages for business process descriptions - Transformation of application processes into systems","name":"Transformation of application processes into systems"},{"code":"DA1-4","description":"- Workflow definition and consideration in GI systems - workflow definition diagramms - use of workflow models to specifiy sequences of activities","name":"Workflow definition and consideration in GI systems"},{"code":"DA1-5","description":"- Software construction - Software testing - Software maintenance - Software engineering management, process and models","name":"Overview on Software engineering"},{"code":"DA1-6","description":"- human computer interactions - user interface design principles and issues - GUI development - Software construction tools: GUI Builders - usability, usability tests and evaluation","name":"User interface and Usability"},{"code":"DA1-7","description":"- Software design and construction fundamentals - construction planning - software construction tools, development environments","name":"Software design and construction"},{"code":"DA1-8","description":"- Software and data lifecycle - archiving aims and requirements","name":"Software and data lifecycle, archiving"},{"code":"DA1","description":"This unit adresses topics of system design and particularly focuses on the pecularities of GIS&T system design.","name":"System design"},{"code":"DA2-1","description":"- basic project management documents, plans, designs etc. - project management tools and techniques - initiating, planning, executing, monitoring, controlling and closing of project processes - time, cost, quality and human ressource management","name":"Introduction to project management"},{"code":"DA2-10","description":"- aims and benefits of data archiving - data archiving technical and organizational issues","name":"Archiving"},{"code":"DA2-2","description":"- List the costs and benefits (financial and intangible) of implementing geospatial technology for a particular application or an entire institution - Compare and contrast the relative merits of outsourcing the feasibility analysis and system design processes or doing them in-house - Identify major obstacles to the success of a GIS proposal - Evaluate possible solutions to the major obstacles that stand in the way of a successful GIS proposal - List some of the topics that should be addressed in such a justification of geospatial technology (e.g., ROI, workflow, knowledge sharing) - Decide whether geospatial technology should be used for a particular task - Perform a pilot study to evaluate the feasibility of an application - Justify feasibility recommendations to decision makers","name":"Feasibility analysis"},{"code":"DA2-3","description":"- Identify potential sources of data (free or commercial) needed for a particular application or enterprise - Estimate the cost to collect needed data from primary sources (e.g., remote sensing, GPS) - Judge the relative merits of obtaining free data, purchasing data, outsourcing data creation, or producing and managing data in-house for a particular application or enterprise - data costs - open data - data licensing","name":"Data availability"},{"code":"DA2-4","description":"Identify the positions necessary to design and implement a GIS - Discuss the advantages and disadvantages of outsourcing elements of the implementation of a geospatial system, such as data entry - Evaluate the labor needed in past cases to build a new geospatial enterprise - Create a budget of expected labor costs, including salaries, benefits, training, and other expenses - competence building measures - system migration issues","name":"Labor and management"},{"code":"DA2-5","description":"- Identify the hardware and space that will be needed for a GIS implementation - Hypothesize the ways in which capital needs for GIS may change in the future - Compare and contrast the relative merits of housing GISs within IT (information technology) and MIS (management information system) facilities versus keeping them separate - Collaborate effectively with various units in an institution to develop efficient hardware and space solutions","name":"Capital: facilities and equipment"},{"code":"DA2-6","description":"Identify potential sources of funding (internal and external) for a project or enterprise GIS - Analyze previous attempts at funding to identify successful and unsuccessful techniques - Create proposals and presentations to secure funding","name":"Funding"},{"code":"DA2-7","description":"- cloud hosting services - discusses the technical, organizational and monetary advantages advantages and disadvantages of hosted and inhouse solutions","name":"Hosted and inhouse solutions"},{"code":"DA2-8","description":"- discusses the technical, organizational and monetary advantages advantages and disadvantages of commercial and open source software - software licensing","name":"Commercial and open source software"},{"code":"DA2-9","description":"advantages and disadvantages of commercial of the shelf software, customizing, professional services","name":"COTS, customizing, professional services"},{"code":"DA2","description":"In order to design, build, and maintain a GIS, sufficient resources (e.g., labor, capital, and time) must be secured. These resources are needed for a variety of elements of the system, including design, software purchase, labor, hardware, and facilities. The most crucial task is to determine whether the project is worthy of the required resources. The focus here is on the initial startup costs: budgeting for ongoing management, and the design of management infrastructure, is discussed in Unit OI2 Managing the GI system, which should also be mastered to complete this process successfully. Further consideration of economic issues is found in Knowledge Area GS GIS and T and Society, Unit GS2 Economic aspects. Data sources and characteristics are covered in Knowledge Area GD Geospatial Data.","name":"Resource planning"},{"code":"DA3-1","description":"- Describe the major geospatial software architectures available currently, including desktop GIS, server-based, Internet, and component-based custom applications - Identify software options that meet functionality needs for a given task or enterprise - Evaluate software options that meet functionality needs for a given task or enterprise","name":"Major geospatial software architectures"},{"code":"DA3-2","description":"This topic addresses all architectural interoperability issues that enable GIS systems and applications to work togehter: interoperability problems and requirements, standardized interfaces and services etc.","name":"Interoperability"},{"code":"DA3-3","description":"This topic considers general architecutural patterns like SOA, ROA, Web Services, etc.","name":"Architectural Patterns"},{"code":"DA3-4","description":"- WebGIS, - technical pecularities of spatial data infrastructures - standardiced GI services for SDI: WMS, WFS, CSW, Transformation Services, SOS, WPS etc., - other map services and interfaces","name":"WebGIS, SDI services, map services"},{"code":"DA3-5","description":"- Reference Model of Open Distributed Processing - RM-ODP Standards, Viewpoints modeling and the RM-ODP framework - enterprice viewpoint - information viewpoint - computational viewpoint - engineering viewpoint - technology viewpoint","name":"RM-ODP"},{"code":"DA3-6","description":"- Compare and contrast cloud and grid computing, their advantages and disadvantages - IaaS, PaaS, SaaS - Cloud deployment models: private cloud, public cloud, hybrid cloud, etc. - security and provacy issues of cloud solutions - variation of grid computing: e.g. volunteer computing networks, utility computing etc.","name":"Cloud and Grid computing"},{"code":"DA3-7","description":"- market overwiew on currently available desktop GIS and available GIS libraries - Compare and contrast solutions based on Desktop GIS and GIS libraries respectively","name":"Desktop GIS, GIS libraries"},{"code":"DA3-8","description":"Describe non-spatial software that can be used in geospatial applications, such as databases, Web services, and programming environments","name":"Non-spatial software used in geospatial applications"},{"code":"DA3","description":"This unit describes the major geospatial software architectures available currently and coices when designing GI applications and systems, including desktop GIS, server-based, Internet, and component-based custom applications.","name":"Architectural design"},{"code":"DA4-1","description":"- Compare and contrast the relative merits of various textual and graphical tools for data modeling, including E-R diagrams, UML, and XML - Create conceptual, logical, and physical data models using automated software tools - Create E-R and UML diagrams of database designs","name":"Modeling tools"},{"code":"DA4-2","description":"- Define entities and relationships as used in conceptual data models - Describe the degree to which attributes need to be modeled in the conceptual modeling phase - Explain the objectives of the conceptual modeling phase of design - Deconstruct an application use case into conceptual components - Create a conceptual model diagram of data needed in a geospatial application or enterprise database - Design application-specific conceptual models","name":"Conceptual models"},{"code":"DA4-3","description":"- Differentiate between conceptual and logical models, in terms of the level of detail, constraints, and range of information included - Define the cardinality of relationships - Explain the various types of cardinality found in databases - Distinguish between the incidental and structural relationships found in a conceptual model - Determine which relationships need to be stored explicitly in the database - Evaluate the various general data models common in GIS&T for a given project, and select the most appropriate solutions - Create logical models based on conceptual models and general data models using UML or other tools","name":"Logical models"},{"code":"DA4-4","description":"- Differentiate between logical and physical models, in terms of the level of detail, constraints, and range of information included - Recognize the constraints and opportunities of a particular choice of software for implementing a logical model - Create physical model diagrams, using UML or other tools, based on logical model diagrams and software requirements - Create a complete design document ready for implementation","name":"Physical models"},{"code":"DA4-5","description":"Techniques for indexing spatial databases (quad trees etc.)","name":"Spatial Indexing"},{"code":"DA4","description":"The effective design of geospatial databases should follow the established methods and principles of database modeling and design developed in computer science. The basic method is a three-step process—generally called the conceptual, logical, and physical models—transforming the application from very human-oriented to machine-oriented. Several standards and software tools exist to aid the process of database design.","name":"Database design"},{"code":"DM","description":"This knowledge area deals with representation of formalized spatial and spatio-temporal reality through data models and the translation of these data models into data structures that are capable of being implemented within a computational environment (i.e., within a GIS or more likely within a spatial data base). Data modelling is a crucial issue as it defines the content of a spatial database and usefulness of these content (data) for certain applications. Data Modelling is performed using system neutral languages like UML (or more seldom ER-diagrams). These conceptual models have to be transferred to logical models (i.e. tables of a database). Data is stored in spatial data bases which are normally organized in an object relational way. For certain types of data specific data bases are used, like triple stores, NoSQL DBs, Array DBs etc. For data modelling quite a number of ISO standards are available for deriving the conceptual model as well as for rules for application schemas, spatial schemas, temporal schemas, Quality principles, encoding, 3D modelling (CityGML) etc. Data models provide the means for formalizing the spatio-temporal conceptualizations. Examples of spatial data model types are discrete (object-based), continuous (location-based), dynamic, and probabilistic. Mastery of the objectives presented in this knowledge area require knowledge and skills presented in the bodies of knowledge of allied fields, including computer science (ACM/IEEE-CS Joint Task Force, 2001) and information systems (Gorgone & Gray, 2000; Gorgone & others, 2002).","name":"Data Modeling, Storage and Exploitation"},{"code":"DM1-1","description":"This topic includes the main basic data base concepts: - Data base, definition and overview - Data base management system, definition and overview - Relational data bases, overview - Object-oriented data bases, overview - Object-relational data bases - NoSQL data bases, general overview - NoSQL data bases, examples triple stores, array databases, others (overview)","name":"Overview on data base concepts"},{"code":"DM1-10","description":"To be defined depending of the background of the course etc.","name":"Data base practice"},{"code":"DM1-2","description":"The Relational Model is the most important data base model, therefore it is explained in more detail here: - Basic concepts (tables, tuples, etc.) - Relation to relational algebra (RA), basics of RA - Constraints (key, domain, referential integrity) - Relation to entity relation (ER) model, basics of ER","name":"The Relational Model"},{"code":"DM1-3","description":"Relational data bases and data base management systems are essential for GIS in consequence the important issues have to be treated here: - General aspects, basic architecture of a DB, advantages, features - DBMS concepts and functionalites (transactions, locks, multiuser access etc.) - Data base design, techniques - Database administration - Normalization (1NF - 3NF) - Example of a data base design","name":"Relational Data Bases, Data Base Managements Systems and Data base principles"},{"code":"DM1-4","description":"Data base queries and especially spatial queries require specific data structures to be performed satisfactory Relevant is: - Motivation, examples of typical non-spatial and spatial queries - Trees, B-tree, R-tree, Q-tree - Graphs, overview and relation to DB","name":"Data Structures and Indices for Data Bases"},{"code":"DM1-5","description":"Big data like imagery but also for example GML data sets need compression to be accessed / transferred in an acceptable time. Therefore some compression techniques have to be taught: - Motivation, examples of data sets which need compression - General introduction, vector - / raster data compression, compression lossless, lossy - Popular compression techniques, LZW (Lempel-Ziv-Welch) encoding, Huffman encoding - Techniques for raster data, runlength encoding, JPEG coding, wavelet etc. - Techniques for the reduction of vector data (Douglas Peuker etc.) - Data formats, overview and relation to compression techniques","name":"Data compression techniques"},{"code":"DM1-6","description":"SQL is the \"standard\" to perform spatial and non-spatial queries in data bases. That means each student in a GI related course has to be familiar with the main aspects if it: - Motivation, history, overview - Data definition language DDL - Data manipulation language DML - Data control language DCL - Spatial extensions of SQL","name":"SQL and its usage for data handling, spatial extensions to SQL"},{"code":"DM1-7","description":"UML is the standard for describing the schema related to GI models, but also user requirements, workflows etc. can be described in UML using the UML diagrams: - Motivation, background, purpose - Use case diagrams - Class diagrams - Sequence diagrams - Activity diagrams","name":"UML introduction and class diagrams"},{"code":"DM1-8","description":"XML knowledge is an important bases for understanding GML. Moreover XML tools like XSLT are important to transform XML or GML data sets into other XML based formats like SVG or others. Important issues: - Motivation, purpose - Relation to HTML - XML document structure - XML syntax, elements, attributes and namespaces - xlink, xpath and XSLT - XML DTD - XML schema","name":"XML introduction"},{"code":"DM1-9","description":"The long term storage of GI data in general is based on spatial data bases. Therefore the following is essential for a GI course: - Relation between GIS and DB / \"Long transactions\"- Dual concepts - Characteristics of spatial data bases - Spatial data in object relational data bases - Spatial extensions of DBs, overview","name":"Data Base concepts in GIS and Principles of spatial data bases"},{"code":"DM1","description":"This unit includes the basics for data modelling, storage and exploitation. Data modelling is one of the most important activities in conjunction with Geographic Information / GIS as it determines how the data can be used and if the requirements from applications are fulfilled. Data modelling can be done in conjunction with the data base, e.g. through ER diagrams or according to the ISO 191xx standards by using UML. The costs of data acquisition can be tremendous, therefore the data represents an enormous value. This value has to be conserved through a safe long term data storage. Therefore data bases and especially relational and object relational data bases are crucial. For a proper storage and query of geographic information data bases are extended with specific data types and data structures. As data sets can be very large suitable compression techniques became important especially in the context of accessing and delivering geographical data, e.g. through services. XML based modeling languages for encoding also play and important role in this context","name":"Foundations for Data Modelling Storage and Exploitation"},{"code":"DM2-1","description":"GI standards, mainly from ISO and OGC are essential nowadays. Moreover also an overview on ICT standards from W3C or OMG are important as well as some understanding of standardization processes. In detail: - Motivation for standards, examples from daily life - Overview on GIS and relevant ICT standardization bodies and selected standards - De jure and De facto standards, obligation, reasons for the usage of standards - Standardization within ISO - Standardization within OGC, relation to ISO - Examples of ISO 191xx standards","name":"Overview on relevant standards and standardisation bodies"},{"code":"DM2-2","description":"Conceptual data modeling is a key skill for GI people. (see relations to other topics) The following therefore is important: - Overview on the relevant standards like conceptual schema language, Rules for application schema - Examples of conceptual schemas","name":"The principle of conceptual data modelling according to ISO"},{"code":"DM2-3","description":"Geometric modelling is an important subtask of conceptual modelling and requires the following basics: - Overview of ISO 19107 - spatial schema - Overview of ISO 19125 - simple features - Examples of the usage of spatial schema and simple feature elements for feature class definitions - Relation to GML - Relation to DBs","name":"Geometry data types according to spatial schema and the simple feature specification"},{"code":"DM2-4","description":"Also temporal aspects have to be considered within conceptual modelling. This also requires basics: - Motivation, examples - Temporal variability of features (move, change of structure or geometry) - Overview on ISO 19108 temporal schema - Examples of modeling temporal aspects","name":"Temporal data types according to temporal schema"},{"code":"DM2-5","description":"Conceptual models of course have to be implemented, in general in a GIS (which is often proprietary), or in a data base (which can be standard based),therefore here the implementation in a data base is treated: - Repetition of conceptual and logical models - Examples of the transferring of a conceptual model to a logical (data base) model","name":"Transferring conceptual models to logical models"},{"code":"DM2-6b","description":"Metadata is considered as very important for the usage as well for the search for Geodata Relevant basics are: - Motivation, importance of data quality as part of metadata - Metadata in an spatial data infrastructure with many There are quite a number of relevant standards for GI courses. Some are listed here, others might be considered, depending on the background of the course: - Select other standards and explain them, Important are: - ISO 19141 Schema for moving features, ISO 19142 Web Feature Service or others - 19109 - Rules for application schema - Selection of other standards is depending on the background of the course","name":"Other standards"},{"code":"DM2-7","description":"GML is the most important standard for the transfer of Geodata as it allows to transfer the schema information as well as the data. Important issues: - Motivation, Importance of a Geography Markup Language - History of GML, Overview 19136 - Geography Markup Language - Relation to spatial schema - Supported features in GML (Topology, 3D ...) - Structure of GNL, profiles, application schemas etc. - Transfer of models and of data - Examples","name":"Introduction to GML"},{"code":"DM2-8","description":"3D Models, especially 3D city models are becoming more and more important. CityGML is the most important standard within the GI domain to describe City models semantically and geometrically. Relevant issues: - Motivation, Usage of CityGML - Relation to GML - Coherence of semantics and geometry - Principles of modeling - Level of detail concept - CityGML vs KML - Examples","name":"Introduction to CityGML"},{"code":"DM2","description":"This unit includes the essentials of relevant standards for spatial data modelling. A number of ISO and OGC standards are available for deriving the conceptual model as well as for rules for application schemas, spatial schema provides data types for geometry models in various forms, Point, line, area, body based, temporal schema allows to consider temporal dimensions, Quality principles can be used to describe the quality of geodata, encoding standards (mainly GML) allow the standard based transfer of data and data models, CityGML allows a standard based 3D modelling, etc.","name":"Standards for Spatial Data Modeling"},{"code":"DM3-1","description":" ","name":"Grid representations"},{"code":"DM3-1b","description":"There are two basic concepts related to this topic: Features and Fields, or Geo-fields, as named by Goodchild at al. The concept of fields can be differently represented as explained here: - Repetition of basic concepts of Geographic Information Science - Explanation of the concept of continuous fields and the commonly used ways of representing geo-fields - Relation between fields and coverages, an important discretizations of a Geo-field - Types of Coverages","name":"The concept of fields"},{"code":"DM3-2","description":" ","name":"The raster model"},{"code":"DM3-2b","description":"Grids are on the one hand one important type of caverages and on the other hand Grids are used as basic structure in some applications. Important here is: - Definition of the concept of grid in GIS - Grid as an instance of coverages - Grids as a basic structure for certain applications / medium for aggregation of data - Examples of grid-based data such as Digital Terrain Models (DTM) - Grids in census / statistical data and Geo-marketing applications","name":"Grid representations"},{"code":"DM3-3","description":" ","name":"Grid compression methods"},{"code":"DM3-3b","description":"TINs and Voronoi tessellations are important types of coverages. TINs play a very important role also in Computer graphics. Important here is: - Basics from Graph theory - Definition of Triangulated Irregular Networks (TIN), purpose and applications - TINs and voronoi diagrams as a type of coverages - One important instance of a TIN: Delauney Triangulation - Definition of Voronoi Diagrams, purpose and applications - Relation between Delauney Triangulation and Voronoi Diagram, the \"Dual Graph\" - Examples from applications","name":"TIN and Voronoi tesselations"},{"code":"DM3-4","description":" ","name":"The hexagonal model"},{"code":"DM3-4b","description":"- Other relevant models - Linear referencing (t.b.d)","name":"Other models like linear referencing"},{"code":"DM3-5","description":" ","name":"The Triangulated Irregular Network (TIN) model"},{"code":"DM3-5b","description":"Resolution of raster and gridded data - Georeferencing of data, direct and indirect methods (t.b.d.)","name":"Resolution and georeferencing system"},{"code":"DM3-6","description":" ","name":"Resolution"},{"code":"DM3-7","description":" ","name":"Hierarchical data models"},{"code":"DM3","description":"This unit includes relevant tessellation data models. Besides features (sometimes also called geo-objects) geo-fields play and important role. In recent literature tessellation models are classified as discretizations of fields. In traditional GI literature tessellations are defined as important data structure itself. Tessellation discretise a continuous surface into a set of non-overlapping polygons that cover the surface without gaps. Tessellation data models represent continuous surfaces with sets of data values that correspond to partitions. Important tessellation models are Grids, TINs and Voronoi diagrams.","name":"Tessellation data models"},{"code":"DM4-1","description":"This topic includes the basics for feature based modelling. There are a number of standards also relevant for this topic (see relations). The following items should be included: - Definition of a feature (in some literature also called object, or geoobject) and of feature classes respectively. - Aspects of the definition (ID, geometry, topology, thematic, time etc.) - Techniques for the definition of features / feature classes (mainly link, as they are described elsewhere, see relations)","name":"Feature based modelling"},{"code":"DM4-2","description":"This topic describes the process of Geometric modelling using vector data, means the primitives like points, lines, areas, bodies, or raster data. There is a strong relation to ISO standards (see relations) as they provide basic data types for geometric modelling. Main issues: - Geometric modeling based on vector data - Geometric modeling based on raster data - Conversion between the models - examples, advantages, disadvantages of the models","name":"Geometric modelling"},{"code":"DM4-3","description":"- Examples of analysis which requires topology","name":"Topological modelling"},{"code":"DM4-4","description":"This topics deals with the definition of an application schema. There are other units which are important for this topic (see Relations). Issues to be included: - Methods to define and describe an application schema (requirement analysis, description of the schema etc.) - Feature attribute catalogues - Domains / data relevant for INSPIRE","name":"Application models based on vector data"},{"code":"DM4-5","description":"This Topic deals with important application models, which should be chosen with relation to the course (geographically / related to the background of the course) INSPIRE should be treated in any case. In detail: - Overview on important application models relevant for the course, e.g. from topography or environment in the country - Repetition of the principles of Spatial data infrastructures - Overview on the INSPIRE initiative and the goals related - The INSPIRE data model - The architecture of INSPIRE and the necessary services - Domains / data relevant for INSPIRE","name":"Examples of important application models"},{"code":"DM4-6","description":"This topic is dedicated to the challenges of model based interoperability and related issues, The principles of interoperability are included in DA3-2. In detail: - The challenges of model interoparability (semantics, different modelling of the same features in different models, syntacs) - Overview on IT concepts for schema integration / transformation - Approaches for model integration - Approaches for model transformations, e.g. related to INSPIRE, from the Humboldt project","name":"Model based interoperability, model transformations"},{"code":"DM4-7","description":"Network models are crucial in some application domains, such as Navigation (roads etc.), but also in utility applications (facilities like pipes etc.) In this topic should be treated: - The network model in the data base domain - Graph based NoSQL data bases - Topology of network models - Data structures for storing network data - The Dijkstra algorithm - Overview on important applications","name":"Network models"},{"code":"DM4","description":"This unit includes relevant issues related to vector data models, feature based modelling, applications. Besides imagery data the majority of GI data available is feature based and founded on vector geometry. Topology modeling also is very common nowadays, as many analysis like routing or neighborhood analysis require it. Spaghetti modelling becomes more and more and exception. In every country there are important feature and vector geometry based application models available e.g. in Topography / Cartography. In Europe every GI course should include some information on INSPIRE. As in different application domains different data models are used, sometimes for the same feature types, integration and transformation of models are an important issue also.","name":"Vector data model, Feature based modelling, Applications"},{"code":"DM5-1","description":"- Many geographical phenomena are not defined sharply but uncertain Uncertainty has a number of considerations: - Motivation, background, purpose - Conceptual model of uncertainty - Uncertainty of geographic phenomena (vagueness, ambiguity) - Uncertainty of measurements - Uncertainty of analysis - Uncertainty vs. data quality - Statistical models of uncertainty - Outline of Fuzzy approaches","name":"Basics of uncertainty and its modelling"},{"code":"DM5-2","description":"Space and time are 2 connected concepts, this topic is dedicated to some basics of modelling time and the temporal dimensions related to features and fields: - Motivation, background, purpose - Changes in time in Entity based and field based representations - A conceptual model of changes in time - Move of objects - Change of structure - Change of geometry - Examples from applications","name":"Modelling time aspects"},{"code":"DM5-3","description":"Traditionally many GIS used 2D or 2.5 D data models, but in the last decade 3D modeling mainly in form of city models or in the context of Building Information Models (BIM): - Basic concepts of 3D modelling, edge, area, volume models - The workflow of 3D modelling, general aspects, choose of the proper model - Methods of 3D modeling - Principles of Constructive Solid Geometry (CSG) - Principles of Boundary representation (BR) - Principles of Voxel-beased modeling - Comparison of the methods - The concept of BIM, principles and purpose - City models, principles and purpose - Examples / applications","name":"Modelling 3D"},{"code":"DM5","description":"Traditional raster and vector data models cannot easily represent the more complex aspects of geographic information, such as temporal change, uncertainty, three-dimensional phenomena, and integrated multimedia. A variety of models have been proposed to represent these complexities, including both extensions to existing models and software, and entirely new models and software. During the 1990s, work in this area was largely experimental, but many solutions are now available to practitioners in commercial and open source software. The data models in this unit are based on concepts discussed in Knowledge Area CF Conceptual Foundations.","name":"Modelling 3D, temporal and uncertain phenomena"},{"code":"DN3-1","description":"Modification of spatial and attribute data while ensuring consistency within the database, implications of transactions on database integrity, scenarios for periodic changes in GIS database and monitoring the periodic changes.","name":"Database change"},{"code":"DN3-2","description":"Rules for modelling spatial database change, techniques for handling version control, techniques for managing long and short transactions, management of spatial databases in multi-user environment","name":"Modeling database change"},{"code":"DN3-3","description":"Reliability tests of change information, design and implementation. Logical consistency of updates.","name":"Reconciling database change"},{"code":"DN3-4","description":"Needs for versioned databases, queries for change scenarios using DB management tools, algorithms for performing dynamic queries, role of time-criticality and data security while choosing methods for change detection.","name":"Managing versioned geospatial databases"},{"code":"DN3","description":"It is quite common, that data including both spatial entities and their attribute data undergo changes. These changes need to be catalogued fully and explicitly, including initial conditions, new conditions, all intermediate stages and operations used. The geospatial data needs to contain an archival history of change.","name":"Transaction management of geospatial data"},{"code":"GC","description":"At the first international conference on `GeoComputation` held at Leeds University in 1996, a new research agenda on geographical analysis and modelling was launched under the title `The art and science of solving complex spatial problems with computers`. Geocomputation in short. Geocomputation covers a wide range of theories and methods aiming at studying complex spatio-temporal problems, which are difficult to analyse and model applying traditional spatial analytical and statistical methods due to data complexity and computational demands. As a rather new research agenda, Geocomputation is still seeking to define the field conceptually, although much efforts have been done. Being closely related to computational science, Geocomputation benefits from the still increasing performance in information and communication technology allowing geoprocessing to utilise parallel computing and distributed cloud computing. However, the close connection to computational science also requires frequent discussion on adopting new related topics like Big Data and Linked Data to the Geocomputation research agenda. Geocomputation has a very strong connection to Knowledge Area AM – Analytical methods. Skills in computer programming are generally needed to effectively apply most of the methods and tools under the Geocomputation headline.","name":"Geocomputation"},{"code":"GC1-1","description":"A complex system can be viewed as a system composed of many interacting components. Most real-world systems like the global climate, an ecosystem, a city, the human brain, and the entire universe are complex systems. General features of the structure and dynamics of complex systems have been characterised, including path dependence, positive feedback, self-organisation and emergence. The types of complex systems include nonlinear systems, chaotic systems, and complex adaptive systems. The aim of this topic is to introduce the notion of complexity, and its role in Geocomputation.","name":"Complex systems"},{"code":"GC1-2","description":"The geocomputational methods are often derived from machine learning, clustering and simulation, and relies heavily on parallel computing and High Performance computing. Contrary to the methods and tools applied for spatial analysis described under the Analytical Methods Knowledge Area, the methods in Geocomputation rather often are unavailable in standard GIS packages and therefore requires self-development or at least customisation of the users. Some macro programming languages like MATLAB and R have some build in functions to support development and execution of Geocomputation methods. The aim of this topic is to provide an introduction to the Geocomputation universe with particular emphasis on the computational aspects.","name":"Geocomputational methods"},{"code":"GC1-3","description":"Geocomputation is not daily use in most GIS environments, and mostly used in (advanced) research communities. However, there is a large potential for being used more frequently within the emergence of Big Data Analytics, where standard software is unsufficient due to the vast amount of data with high degree of complexity. To extract useful results from the information requires new approaches, where machine learning and clustering represent obvious solutions. Also, the emergence of e-Governance with focus on efficiency and delicate balancing between different interest requires extended knowledge about the future and this can be provided by simulation. The aim of this topic is to give an introduction to the current an potential use of Geocomputation in solving challenging geospatial problems.","name":"Areas of application"},{"code":"GC1","description":"Geocomputation represents an attempt to move the GI research agenda back to geographical analysis and modelling by providing a toolbox of methods to analyse and model a range of highly complex, often non-deterministic problems.","name":"Theory of Geocomputation and complex systems"},{"code":"GC2-1","description":"Whether experimentally, empirically or theoretically derived, quantitative relationships among the state variables in a system can be used to build a simulation model. In a simulation model, a computer is programmed to iteratively recalculate the modelled system state as it changes over time in accordance with the relationships represented by the mathematical and other relationships that describe the system.","name":"Principles of computer simulation and numerical experiments"},{"code":"GC2-2","description":"Many typologies of models have been developed and these tend to emphasise dichotomies, such as `complicated` versus `simple`, in model design. Although such black and white dichotomies are invariably simplistic, they provide a useful way of thinking about the trade-offs that must be made when developing appropriate and manageable abstractions or representations of real-world phenomena. Thus, developing detailed, dynamic, spatial models comes at some cost in generality and interpretability, but buys us realism and the ability to represent specific processes in specific contexts. The aim of the topic is to provide basic understanding in the overall principles in choosing different approaches to model development.","name":"Model development"},{"code":"GC2-3","description":"Rule-based models are based on logic programming with condition-action expressions, where the left side of the expressions consists of several conditions that returns a logical result, and the right side consists of several actions. The implementation of rule-based models is most often done by cellular automata models or agent-based models. Many geographic patterns and dynamics are formed by systems of interacting actors with heterogeneous characteristics and behaviours. A cellular automaton (CA) is a discrete dynamic system in which space is divided into regular spatial cells, and time progresses in discrete steps. Each cell in the system has one of a finite number of states. Agent-base models are constructed to represent these actors, their environments, and their interactions with one another. The aim of this topic is to provide knowledge about rule based models and to understand their advantages and disadvantages.","name":"Rule-based models"},{"code":"GC2-4","description":"Sometimes the modelling can be described with (partial) differential equations. This is particularly the case in some topics within natural science, where the system in at least some degree can be described with the laws in physics. Hydrological modelling is a good example on equation based models. However, dealing with real world problems the full system can seldom be descried totally with the laws from natural science, and needs to be amended with other types of models. The aim of this topic is to present the use of equations based models and their advantages and challenges.","name":"Equation-based models"},{"code":"GC2","description":"Across broad areas of the environmental and social sciences, simulation models are an important way to study systems inaccessible to scientific experimental and observational methods, and also an essential complement of those more conventional approaches. Simulation models are a relatively recent addition to the scientific toolbox, and the reasons for their widespread adoption are that High Performance Computing and huge amount of data from different sources have made this approach possible. In addition, some systems of interest like ecosystems, urban systems, social systems, and the global climate system are not amenable to experiments. Finally, the nonlinear behaviour of many natural systems provides challenges building traditional mathematical models based on linearization.","name":"Spatial simulation modelling"},{"code":"GC3-1","description":"Among the recent artificial intelligence techniques are those pertaining to heuristics. A heuristic technique is an approach to problem solving, that employs a practical method, which is necessarily not optimal or perfect, but in many situations sufficient. Heuristic methods can be useful, where the optimal solution in practice is impossible. The aim of the topic is to provide insight into the principles of heuristics and the most important algorithms.","name":"Heuristics"},{"code":"GC3-2","description":"Genetic algorithms, genetic programming and evolutionary computing are terms that fall within the general sphere of `Evolutionary Computation`. Genetic algorithms (GAs) are computationally intensive global search heuristics with very wide applicability, but their implementation is often highly problem specific and there is only a relatively loose understanding as to why they often work rather well. The central idea behind GAs is to mimic the Darwinian notion that selective breeding seeks optimum individuals in a given environment. In order to do this a GA requires a way of representing a solution to a (spatial) problem as if it were an individual viewed as a chromosome or `genome` like object. The aim of the topic is to provide fundamental understanding of the principles behind genetic algorithms, and its application in solving geospatial problems.","name":"Genetic algorithm in geospatial modelling"},{"code":"GC3-3","description":"Biological neurons, or nerve cells, receive multiple input stimuli, combine and modify the inputs in some way, and then transmit the result to other neurons. Artificial neural networks are an attempt to emulate features of biological neural networks in order to address a range of difficult information processing, analysis and modelling problems. The principal class of ANNs are so-called feed-forward networks, but other types of ANN are for example recurrent neural networks. Among the feed-forward networks the most widely used approach is the multi-level perceptron (MLP) model. The application range is broad from non-linear regression to land cover change modelling. The aim of the topic is to introduce the principles of ANN and to understand and demonstrate its use in geospatial modelling.","name":"Artificial Neural Networks"},{"code":"GC3-4","description":"Pattern recognition is the process of classifying input data into objects or classes based on key features. There are two classification methods in pattern recognition: supervised and unsupervised classification. The supervised classification of input data in the pattern recognition method uses supervised learning algorithms that create classifiers based on training data from different object classes. The classifier then accepts input data and assigns the appropriate object or class label. The unsupervised classification method works by finding hidden structures in unlabelled data using segmentation or clustering techniques. Common unsupervised classification methods include: K-means clustering, Gaussian mixture models, Hidden Markov models. The aim of the topic is to provide knowledge about the different methods in pattern recognition and how to choose the optimum method for a specific spatial problem.","name":"Pattern recognition"},{"code":"GC3-5","description":"Understanding natural and human-induced structures and processes in space and time has long been the agenda of geographical research. Through theoretical and experimental studies, geographers have accumulated a wealth of knowledge about our physical and man-made world over the years. Knowledge is often discovered through critical observations of phenomena in space and time. Due to the rapidly expanding amount of data and information the problem is often not having enough data but having too much and too complex a database. The aim of the topic is to provide insight into several methods to carry out spatio-temporal knowledge discovery through spatial data mining and clustering techniques.","name":"Spatio-temporal knowledge discovery and data mining"},{"code":"GC3-6","description":"Data-intensive computing is now starting to be considered as the basis for a new, fourth paradigm for science. Two factors are encouraging this trend. First, vast amounts of data are becoming available in more and more application areas. Second, the infrastructures allowing to persistently store these data for sharing and processing are becoming a reality. The technical and scientific issues related to this context have been designated as `Big Data` challenges and have been identified as highly strategic by major research agencies. The aim of this topic is to introduce Big Data as a concept, and the needed methods to navigate through the vast amount of heterogeneous information.","name":"Big data filtering"},{"code":"GC3","description":"The amount of data in current geospatial repositories along with their high-dimensional nature requires a sophisticated set of analysis capabilities in order to extract new and unexpected patterns, trends, and relationships embedded in that data. Artificial intelligence and data mining methods constitute an alternative to explore and extract knowledge from geospatial data, which is less assumption dependent. Data Mining is a step in the knowledge discovery process that automatically detects patterns in data, and Geographic Data Mining is a special type of data mining that seeks to apply standard data mining tools modified to take into account the special features of geospatial data","name":"Artificial Intelligence and Data Mining"},{"code":"GD","description":"Geospatial data represent measurements of the locations and attributes of phenomena at or near Earth`s surface. Information is data made meaningful in the context of a question or problem. Information is rendered from data by analytical methods. Information quality and value depends to a large extent on the quality and currency of data (though historical data are valuable for many applications). Geospatial data may have spatial, temporal, and attribute (descriptive) components, as well as associated metadata. Data may be acquired from primary or secondary data sources. Examples of primary data sources include surveying, remote sensing (including aerial and satellite imaging), the global positioning system (GPS), work logs (e.g., police traffic crash reports), environmental monitoring stations, and field surveys. Secondary geospatial or geospatial-temporal data can be acquired by digitizing and scanning analog maps, as well as from other sources, such as governmental agencies. The legitimacy of geographic information science as a discrete field has been claimed in terms of the unique properties of geospatial data. In a paper in which he coined the term GIScience, Goodchild (1992) identified several such properties, including: 1. Geospatial data represent spatial locations and non-spatial attributes measured at certain times. 2. The Earth`s surface is highly complex in shape and continuous in extent. 3. Geospatial data tend to be spatially autocorrelated. It has long been said that data account for the largest portion of geospatial project costs. While this maxim remains true for many projects, practitioners and their clients now can reasonably expect certain kinds of data to be freely or cheaply available via the World Wide Web. Federal, state, regional, and local government agencies, as well as commercial geospatial data producers, operate clearinghouses that provide access to geospatial data. Although geospatial data are much more abundant now than they were ten years ago, data quality issues persist. Good data are expensive to produce and to maintain. Proprietary interests simultaneously increase the supply of geospatial data and impede data accessibility. Standards for geospatial data and metadata are useful in facilitating effective search, retrieval, evaluation, integration with existing data, and appropriate uses. National and international organizations, such as the Open Geospatial Consortium (OGC) and International Organization for Standardization (ISO), develop and promulgate such standards. INSPIRE directive (Infrastructure for Spatial Information in the European Community) regulates geospatial data management","name":"Geospatial Data"},{"code":"GD1-1","description":"Usable and accurate geospatial data are based upon proper model of the Earth`s surface. Shape of the Earth is complex and complicated to measure. Approximations are used to minimize complexity of the task and possible errors.","name":"Earth geometry"},{"code":"GD1-2","description":"Geospatial referencing systems provide unique codes for every location on the surface of the Earth (or other celestial bodies). These codes are used to measure distances, areas, and volumes, to navigate, and to predict how and where phenomena on the Earths surface may move, spread, or contract. Point-based, vector coordinate systems specify locations in relation to the origins of planar or spherical grids. Tessellated referencing systems specify locations hierarchically, as sequences of numbers that represent smaller and smaller subdivisions of two- or three dimensional surfaces that approximate the Earths shape, Linear referencing systems specify locations in relation to distances along a path from a starting point. Tessellation data models, are considered in Unit DM3 Tessellation data models, and linear referencing models are considered in Unit DM4 Vector data models.","name":"Georeferencing systems"},{"code":"GD1-3","description":"Horizontal datums determine the geometric relations between a coordinate system grid and a particular ellipsoid approximating the Earth`s surface. Vertical datums determine elevation reference surfaces, like mean sea level. A. Horizontal datums. Relation of coordinate system to particular ellipsoid, datum transformation options, Molodensky and Helmert transformation, other high accuracy transformations, ED50 and WGS84, historical development of horizontal datums, ETRS89. B. Vertical datums. Historical development of vertical datums, difference between vertical datum and geoid, relations between ellipsoidal (geodetic) heiht, geoidal height and orthometric elevation.","name":"Datums"},{"code":"GD1-4","description":"Map projections are systematic transformations of geographic coordinates of the surface of ellipsoid into locations in plane. Plane coordinates are based on map projection. As the transformation of a spherical grid into a plane grid causes inevitably distortions of the geometry, and, different projections cause different distortions, knowledgeable choice of appropriate projection for any particular use is crucial. A. Map projection poperties. Geometric properties that may be preserved or lost in projected grid, usefulness of compromise projection, Tissot indicatrix as an indicator of projection errors, visual appearance of the Earth`s graticule, distortion patterns for projection classes, distortions in raster data. B. Map projection classes. Three main classes of map projection based on developable surface, projection types by geometric properties preserved, mathematical basis of projecting longitude and latitude into x and y coordinates. UTM, ETM, projections used by EC. C. Map projection parameters. Standard line, projection case, latitutde and longitude of origin, aspects of projection. D. Georegistration. Rectification vs orthorectification, ground controle points in georegistration of aerial imagery.","name":"Map projections"},{"code":"GD1","description":"Proper model of the Earth`s surface and ability to locate spatial phenomena accurately to it, is crucial in effective collection, management and use of data. Characterising size and shape of the Earth, using appropriate surfaces to approximate it, choosing suitable coordinate system and map projection is bases for efficient understanding of spatial data.","name":"Geolocating Data to Earth"},{"code":"GD10-1","description":" ","name":"Nature of aerial image data"},{"code":"GD10-2","description":" ","name":"Platforms and sensors"},{"code":"GD10-3","description":" ","name":"Aerial image interpretation"},{"code":"GD10-4","description":" ","name":"Stereoscopy and orthoimagery"},{"code":"GD10-5","description":" ","name":"Vector data extraction"},{"code":"GD10-6","description":" ","name":"Mission planning"},{"code":"GD10","description":"Since the 1940s aerial imagery has been the primary source of detailed geospatial data for extensive study areas. Photogrammetry is the profession concerned with producing precise measurements from aerial imagery. Aerial imaging and photogrammetry comprise a major component of the geospatial industry. The topics included in this unit do not comprise an exhaustive treatment of photogrammetry, but they are aspects of the field about which all geospatial professionals should be knowledgeable.","name":"Aerial imaging and photogrammetry"},{"code":"GD11-1","description":" ","name":"Nature of multispectral image data"},{"code":"GD11-2","description":"the physical environment to sense data without direct contact. It contains a carrier device (platform) and a sampling unit (sensor).","name":"Platforms and sensors"},{"code":"GD11-3","description":" ","name":"Algorithms and processing"},{"code":"GD11-4","description":" ","name":"Ground verification and accuracy assessment"},{"code":"GD11-5","description":" ","name":"Applications and settings"},{"code":"GD11","description":"Satellite-based sensors enable frequent mapping and analysis of very large areas. Many sensing instruments are able to measure electromagnetic energy at multiple wavelengths, including those beyond the visible band. Satellite remote sensing is a key source for regional- and global-scale land use and land cover mapping, environmental resource management, mineral exploration, and global change research. Shipboard sensors employ acoustic energy to determine seafloor depth or to create imagery of the seafloor or water column. The topics included in this unit do not comprise an exhaustive treatment of remote sensing, but they are aspects of the field about which all geospatial professionals should be knowledgeable.","name":"Satellite and shipboard remote sensing"},{"code":"GD12","description":"Meaning of geospatial metadata, elements of metadata, use of metadata, integration of metadata in data production, standards in geospatial data, ISO standard family 191xx, data warehouse, exchange protocol, transport protocols, spatial data infrastructure, INSPIRE, OGC, DCAT profiles for CKAN applications – bridging metadata from GI and IT domains.","name":"Metadata, standards, and infrastructures"},{"code":"GD2-1","description":" ","name":"Land surveying and field data collection"},{"code":"GD2-2","description":"Aerial imagery has been the primary source of detailed geospatial data for extensive study areas. Photogrammetry is producing precise measurements from aerial imagery. Aerial imaging and photogrammetry comprise a major component of the geospatial data production. Satellite-based sensors enable frequent mapping and analysis of very large areas. Sensing instruments are able to measure electromagnetic energy at multiple wavelengths. Satellite remote sensing is a key source for regional- and global-scale land use and land cover mapping, environmental resource management, mineral exploration, and global change research. Shipboard sensors employ acoustic energy to determine seafloor depth or to create imagery of the seafloor or water column. Principles of aerial photography, oblique and vertical imagery, spatial and radiometric resolution, spectral sensitivity, principal point, distortions and displacements in aerial image, parallax, stereophotogrammetry, generation of an orthoimage from a vertical aerial phoptograph, aerotriangulation, vector data extraction from digital seteroimagery, mission planning. Use of UAV in photogrammetry. Main platforms and sensors in spatial image acquisition, active and passive sensors, LiDAR and microwave, multispectral and hypersepctral imagery, interpretation of imagery, supervised and unsupervised classification, pixel based and segmented classification, ground verification, main applications, bathymetric mapping. SENTINEL.","name":"Remote sensing"},{"code":"GD2-3","description":"Crowdsourcing is the practice of obtaining needed services, ideas, or content by soliciting contributions from a large group of people and especially from the online community rather than from traditional employees or suppliers. Crowdsourced spatial data collection is becoming more and more important. The advantages and disadvantages of crowdsourced data, opensource mapping tools, potential application of crowdsourcing, VGI, OSM or cell-phone based, aspects of crowdsourced data quality and reliabilty.","name":"Crowdsourced data collection"},{"code":"GD2-4","description":"Digitizing as the main secondary spatial data production technique. Encoding vector points, lines, and polygons by tracing map sheets has diminished in importance, but remains a useful technique for incorporating historical geographies and local knowledge. \"Heads-up\" digitizing using digital imagery as a backdrop on-screen is a standard technique for editing and updating GIS databases. Tablet and on-screen digitizing, scanning and (semi)automatic vectorization.","name":"Digitizing"},{"code":"GD2","description":"Spatial data collection / production involves measurement of locations in relation to the coordinate system, and collection of attributed data about the spatial phenomena. Measurements may be direct (e.g. surveying) or remote, data acquisition involves measurement of parameter values, evaluation of parameters, polls, interpretation of spatial imagery, and re-use of secondary data (e.g. old maps). Volunteered geographic information is becoming more important.","name":"Data Collection"},{"code":"GD3","description":"It is quite common, that data including both spatial entities and their attribute data undergo changes. These changes need to be catalogued fully and explicitly, including initial conditions, new conditions, all intermediate stages and operations used. The geospatial data needs to contain an archival history of change.","name":"Transaction management of geospatial data"},{"code":"GD4-1","description":"Geometric accuracy, factors influencing it, geometric accuracy and topological fidelity, geometric accuracy in survey and GPS mesurements, thematic accuracy, relations between thematic accuracy, geometric accuracy and topological fidelity, misclassification matrix, commission and omission, logical consistency, relations between resolution, precision, and accuracy, spatial resolution, thematic resolution, and temporal resolution, precision, uncertainties associated with coordinate precision, primary and secondary data sources.","name":"Data quality"},{"code":"GD4-2","description":"Meaning of geospatial metadata, elements of metadata, use of metadata, integration of metadata in data production, standards in geospatial data, ISO standard family 191xx, data warehouse, exchange protocol, transport protocols, spatial data infrastructure, INSPIRE, OGC, DCAT profiles for CKAN applications – bridging metadata from GI and IT domains.","name":"Metadata, standards, and infrastructures"},{"code":"GD4","description":"Data quality is the degree of data usability in relation to given objective and particular application. The expectations to data vary between different applications. The key criteria in data quality are the amount of uncertainty in data as compared to the acceptable level of uncertainty. Evaluation of the usability may be more complicated using data from secondary sources. Appropriate metadata is inevitable for these judgements. Aspects of data quality include geometric and thematic accuracy, (in)consistencies, resolution, precision, usability and others. Assurance of data quality may be improved by following proper standards and spatial data infrastructure – regulations for data collection and management. System of basic data quality measures for geospatial domain in the EN ISO 19157:2013 standard.","name":"Data Quality, Metadata and Data Infrastructure"},{"code":"GD5-1","description":" ","name":"Map projection properties"},{"code":"GD5-2","description":" ","name":"Map projection classes"},{"code":"GD5-3","description":" ","name":"Map projection parameters"},{"code":"GD5-4","description":" ","name":"Georegistration"},{"code":"GD5","description":"Visualization, especially Unit CV2 Data considerations, while procedures for transforming data between projections are considered in Unit DN1 Representation transformation.","name":"Map projections"},{"code":"GD6-1","description":" ","name":"Geometric accuracy"},{"code":"GD6-2","description":" ","name":"Thematic accuracy"},{"code":"GD6-3","description":" ","name":"Resolution"},{"code":"GD6-4","description":" ","name":"Precision"},{"code":"GD6-5","description":" ","name":"Primary and secondary sources"},{"code":"GD6","description":"particular application. That standard varies from one application to another. In general, however, the key criteria are how much uncertainty is present in a data set and how much is acceptable. Judgments about fitness for use may be more difficult when data are acquired from secondary rather than primary sources. Aspects of data quality include accuracy, resolution, and precision. Concepts of data quality, error, and uncertainty are also covered in Knowledge Areas CF Conceptual Foundations (in a theoretical context) and GC Geocomputation (in the context of analysis); the focus here is on the measurement and assessment of data quality.","name":"Data quality"},{"code":"","description":" ","name":" "},{"code":"","description":" ","name":""},{"code":"","description":" ","name":""},{"code":"GD8-1","description":" ","name":"Tablet digitizing"},{"code":"GD8-2","description":" ","name":"On-screen digitizing"},{"code":"GD8-3","description":" ","name":"Scanning and automated vectorization techniques"},{"code":"","description":" ","name":""},{"code":"","description":" ","name":""},{"code":"","description":" ","name":""},{"code":"","description":" ","name":""},{"code":"GS","description":"Geographic Information Science and Technology serve the society, but it is not a panacea. The history of its development is the sum of fragmented efforts, which have still not been fully integrated. Its potential benefits are often constrained and its potential impacts are not fully understood. Institutional and economic factors limit access to data, technology, and expertise by some of those who need it to make better decisions. Political, ideological, and personal issues aside, organizations invest in GIS&T when estimated benefits outweigh estimated costs. Evaluating costs and benefits is difficult, however and too often leads to nothing being done. For some individuals and groups, costs are prohibitive even though potential benefits are compelling. The legal framework provides a structure for regulating a number of key aspects of geographic information science, technology, and applications. Legal regimes determine who can claim the exclusive right to hold and use geospatial data, the conditions under which others may have access to the data, and what subsequent uses are permitted. Political struggles arise from conflicting proprietary and public interests about who benefits from geospatial information, and how the power to allocate the use of this information is, or should be, distributed among members of a society. The need to choose among conflicting interests sometimes poses ethical dilemmas for GIS&T professionals. The explosive growth of the geospatial information contributed by users through various application programming interfaces has made geospatial information is a powerful tool in the social media toola powerful media for the general public to communicate, but perhaps more importantly, geographic information have also become a tool media for constructive dialogs and interactions about social issues, recent growth of Web-based geospatial information and volunteered geographic information (VGI). Because so many public agencies and private organizations rely upon GIS&T for planning, decision making, and management, GIS&T increasingly affects and is used to direct daily life. Critical approaches to understanding the role of GIS in society equip practitioners to employ GIS&T reflectively. The critical approach specifically questions the assumptions and premises that underlie the economic, legal and political regimes and institutional structures within which GIS&T is implemented. Related concerns are considered in Knowledge Area OI: Organizational and Institutional Aspects.","name":"GI and Society"},{"code":"GS1-1","description":"Ways in which the geospatial profession is regulated under European/ National legal regime and framework. Discussion of various impact of frameworks on development of Geospatial Information (SDI, INSPIRE, PSI). Compare and contrast the relationship of the geospatial profession and the European legal regime and framework with similar relationships in other regions and countries","name":"The legal regime and legal framework"},{"code":"GS1-2","description":"Differentiating \"contracts for service\" from \"contracts of service\" - Identifying the liability implications associated with contracts - Discuss potential legal problems associated with licensing geospatial information - Describing the nature of tort law generally and nuisance law specifically - Differentiating among contract liability, tort liability, and statutory liability - Describing cases of liability claims associated with misuse of geospatial information, erroneous information, and loss of proprietary interests - Describing strategies for managing liability risk, including disclaimers and data quality standards","name":"Contract law, liability and licensing"},{"code":"GS1-3","description":"Discussion of the status of the concepts of privacy and security in the European legal regime - Explaining how data aggregation is used to protect personal privacy in data production - Explaining how conversion of land records data from analog to digital form increases risk to personal privacy - Compare and contrast geographic information technologies that are privacy-invasive, privacy -enhancing, and privacy-sympathetic - Explaining the argument that human tracking systems enable \"geoslavery\" - Security and privacy challenges for Internet, citizen-generated and linked data i.e. volunteered geographic information (VGI) - Citizen and privacy. Discussion of confidentiality issues and policies related to the utilization and dissemination of geospatial data for different scenarios including environment/public health applications","name":"Privacy and Security"},{"code":"GS1-4","description":"Discussion of legal definition of the concepts \"ownership\" and \"property rights\" - Description of organizations` and governments` incentives to treat geospatial information as property - Arguments for and against the treatment of geospatial information as a commodity - Outlines of arguments for and against the notion of information as a public good - Compare and contrast National, European policy regarding rights to geospatial data with similar policies in other countries - Explaining how geospatial information might be used in a taking of private property through a government`s claim of its right of eminent domain - Compare and contrast the consequences of different national policies about rights to geospatial data in terms of the real costs of spatial data, their coverage, accuracy, uncertainty, reliability, validity, and maintenance","name":"Ownership and property rights"},{"code":"GS1-5","description":"- Looking at rights and management of geospatial data, coverage, accuracy, reliability and validity in both public and private organisational contexts - Discussion of the role of the public and private sectors in producing and dissemination of geospatial information - Legal framework and competition and public-private sector relationships - Discussion of opportunities for exchange of geospatial data between public and private sector to enable more efficient analysis","name":"Competition and public-private sector relationships"},{"code":"GS1-6","description":"- Discussion of various legal aspects of public and private sectors concerning owning, controlling, sharing/ disseminating open data - Discussion of and define open data and impact on GIS&T - Discussion of various sources of open data (science, public and private sectors) - Discussion of arguments for and against open data - Discussion of open data impact on society and citizenship","name":"Open data"},{"code":"GS1","description":"Legal problems can arise when geospatial information is used for land management, among other activities. Geospatial professionals may be liable for harm that results from flawed data or the misuse of data. Understanding of contract law and liability standards is essential to mitigate risks associated with the provision of geospatial information products and services. Legal relations between public and private organizations and individuals govern data access. The nature of information in general, and the characteristics of geospatial information in particular, make it an unusual and difficult subject for a legal regime that seeks to establish and enforce the type of exclusive control associated with other commodities. Geospatial information is in many ways unlike the kinds of works that intellectual property rights were intended to protect. Still, organizations can, and do, assert proprietary interests in geospatial information. Perspectives on geospatial information as property vary between the public and private sectors and between different countries.","name":"Legal aspects"},{"code":"GS2-1","description":"- Discussion of the general role of information in economics - Describing the role of economics in public and private production of geospatial information - Describing the role of economics in the use of geospatial information - Describing perspectives on the nature and scope of system benefits among agency officials, organizational personnel, and citizens - Discussion of implications of unequal economic power on the kinds of organizations that use, and benefits from, GIS&T","name":"Business models and funding models"},{"code":"GS2-2","description":"- Describing recent models of the benefits of GIS&T applications - Discussion of the extent to which external costs and benefits enhance the economic case for GIS - Explain how profit considerations have shaped the evolution of GIS&T - Outlining the elements of a business case that justifies an organization`s investment in an enterprise geospatial information infrastructure - Describing the potential benefits of geospatial information in terms of efficiency, effectiveness, and equity - Explaining how cost-benefit analyses can be manipulated - Compare and contrast the evaluation of benefits at different scales (e.g., national, regional/state,local) true for an organization that is already collecting data as part of its regular operations - Outlining the categories of costs that an organization should anticipate as it plans to design and implement a GIS - Outlining sources of additional costs associated with development of an enterprise GIS","name":"Costs, benefits and risks"},{"code":"GS2-3","description":"- Distinguishing between operational, organizational, and societal activities that rely upon geospatial Information - Discussion of relations between marketing and economical factors in substainablity/environmental issues by using geospatial information - Discussion of and defining the process of the Information value chain - Identifying practical problems in defining and measuring the value of geospatial information in land or other business decisions - Describing some non-fiduciary barriers to GIS implementation - Summarizing what the literature suggests as means for overcoming some of the non-fiduciary barriers to GIS implementation - Explaining how cost-benefit analyses can be manipulated - Compare and contrast the evaluation of benefits at different scales (e.g., national, regional/state,local) - Explaining how the saying \"developing data is the largest single cost of implementing GIS\" could be true for an organization that is already collecting data as part of its regular operations","name":"Valuing and measuring aspects"},{"code":"GS2-4","description":" ","name":"Agency, organizational, and individual perspectives"},{"code":"GS2","description":"Most organizations insist that investments in GIS and T be justified in economic terms. Quantifying the value of information, and of information systems, however, is not a straightforward matter.","name":"Economic aspects"},{"code":"GS3-1","description":"- Listing and describing the types of data maintained by local, state, and federal governments - Describing how geospatial data are used and maintained for land use planning, property value assessment, maintenance of public works, and other applications - Explaining the concept of a \"spatial decision support system\"","name":"Use of geospatial information in the public sector"},{"code":"GS3-2","description":"- Discussion of open data and data sharing and public/private sector - Discussion of the changing role of the private sector in the use of geospatial information - Describing private sector impact in development of solutions in collecting, processing and managing geospatial information - GI infrastructure development and the role of the private sector - Private sector and Organizational organizational integration, training in the use of geospatial information - Private sector and Research research and development in the use of Geospatial geospatial information","name":"Use of geospatial information in the private sector"},{"code":"GS3-3","description":"- Relationship between research and education, private/public sectors, and citizens - Discussion of the paradigm shifts and current trends in GIS&T education and pedagogical approaches for GIS teaching and learning in detail - GIS&T in formal as well as informal learning environments - Professional growth of teacher and trainer in the field of GIS&T education (TPACK) and GIS and pedagogical approaches for teaching GIS. - Aspects of citizen science and public engagement in research - Discuss how to approach the widening audience/participants for geospatial products and service, increasing geo-awareness and geo-enablement - Discuss ways of working with crowd sourcing in education and research","name":"Use of geospatial information in research and education"},{"code":"GS3-4","description":"- Discussion of the role of public, private sector and the citizen in facilitating geospatial information in environmental/sustainable issues. - Discussion of legal aspects of access to environmental data, global change/warming or sustainable development (regional, national, global) in conjunction to society.","name":"Use of geospatial information in environmental issues"},{"code":"GS3","description":"Geospatial Information used in Government agencies and public authorities at local, state, and federal levels produce and use geospatial data for many activities, including provision of social services, public safety, economic development, environmental management, and national defence. Public participation in governing, empowered by geospatial technologies, offers the potential to strengthen democratic societies by involving grassroots community organizations and by engaging local knowledge. The private sector covers a broad range of areas of opportunity. With continued advancements in technology, greater awareness of its advantages as a powerful decision support tool the use of geospatial information use in the private sector needs to be discussed.","name":"Use of geospatial information"},{"code":"GS4-1","description":"- Definition of and understanding of citizenship, democracy, maturity, and negotiation related to geo information use and participation in society /community development (local, regional, national level) - Differentiating among universal/deliberative, pluralist/representative, and participatory models of citizen participation - Comparing the advantages and disadvantages of group participation and individual participation - Describing increasing participation in governmental decision-making - Describing the range of spatial scales at which community organizations operate - Describing an example of \"local knowledge\" that is unlikely to be represented in the geospatial data maintained routinely by government agencies - Explaining how community organizations represent the interests of citizens, politicians, and specialists","name":"Public participation and citizenship"},{"code":"GS4-2b","description":"- Components and characteristics of the Geoweb, digital geo media / \"new spatial media\". - Defining and discussing impacts of crowdsourcing on geospatial sSociety. - Discussing the impact of geospatial information for the development of social media (Facebook, Twitter, Wikimapia, Flickr etc.) becoming increasingly location-based. - Discussing the role and value of \"place\" and \"space\" for geo media based social networking. - Differentiating between consumption, analysis, prosumption and production of geoinformation within digital geo media.","name":"GI and social media"},{"code":"GS4-3b","description":"- Defining and discussing volunteered geographic information. - Defining and discussing enabling technologies: geotag, georeferencing, GPS and more. - Discussion of positive and negative aspects of the term \"humans as sensors\". - Application domains and roles regarding geoinformation use in society: \"spatial information systems manager\", \"spatial analyst\", \"spatial citizen\". - Defining and discussing impact of Crowdsourcing on Geospatial Society.","name":"Citizens and volunteered geographic information"},{"code":"GS4","description":"Today, geo data has become a conventional and pervasively familiar data type seen at once to underpin and significantly re-characterize the digital world, with broad implications for both technology and society. Geospatial data are abundant, but access to data varies with the nature of the data, the user groups wishes to acquire it and for what purpose, under what conditions, and at what price geodata can be obtained. The explosive growth of geographic information contributed by users through various application programming interfaces has made geographic information a powerful media for the general public, but perhaps more importantly, geospatial information have also become media for constructive dialogs and interactions about social issues, recent growth of Web-based Geographic information and volunteered geographic information (VGI).","name":"Geospatial citizenship"},{"code":"GS5-1b","description":"- Describing a variety of philosophical frameworks upon which codes of professional ethics may be based. - Discussing the ethical implications of a local government`s decision to charge fees for its data. - Describe a scenario in which you would find it necessary to report misconduct by a colleague or friend. - Describe examples of ethical obligations of public/private sectors.","name":"Ethics in the geospatial information society"},{"code":"GS5-2b","description":"- Compare and contrast the ethical guidelines from different Associations associations. - Discussing obligations to : society, obligations to Employers employers and funders, obligations to colleagues and the profession, obligations to individuals in society. - Explaining how one or more obligations in the GIS Code of Ethics may conflict with organizations` proprietary interests. - Proposing a resolution to a conflict between an obligation in the GIS Code of Ethics and organizations` proprietary interests.","name":"Codes of ethics for geospatial professionals"},{"code":"GS5","description":"Ethics provide frameworks that help individuals and organizations make decisions when confronted with choices that have moral implications. Most professional organizations develop codes of ethics to help their members do the right thing, preserve their good reputation in the community, and help their members develop as a community","name":"Ethical aspects"},{"code":"GS6-1","description":"- Discussiion of the argument that the use of Geospatial geospatial Information information privileges certain views of the world over others. - Identifying alternatives to the \"algorithmic way of thinking\" that characterizes use of geospatial Information. - Discussing critiques of GIS as \"deterministic\" technology in relation to debates about the Quantitative quantitative revolution in the discipline of geography. - Describing the extent to which contemporary use of Geospatial geospatial information supports diverse ways of understanding the world. - Discuss the implications of interoperability on ontology.","name":"Epistemological and critical issues"},{"code":"GS6-2","description":"- Discussionof the various implications of surveillance technology. - Critical aspects of data collection and analysis. - Discussion of \" mapping who`s` reality?\" Pros and cons of geoinformation sharing in social media, i.e. big data, \"digital shadow\" etc.","name":"Critical approach on the use of geospatial information"},{"code":"GS6-3","description":"Defending or refuting the argument that the \"digital divide\" that characterizes access use of geospatial information perpetuates inequities among developed and developing nations, among socio-economic groups,and between individuals, community organizations, and public agencies and private firms.","name":"Critical aspects and invisible groups"},{"code":"GS6","description":"Many of the educational objectives used to define topics in this knowledge area, and in the Body of Knowledge as a whole, challenge educators and students to think critically about GI and Society. Since the 1990s, scholars have criticized cartography and the GIS science from a wide range of perspectives. Common among these critiques are questioned assumptions about the purported benefits of GI and Society and attention to its unexamined risks. By promoting reflective practice among current and aspiring geospatial information professionals, an understanding of the range of critical perspectives increases the likelihood that geospatial information will fulfil its potential to benefit all stakeholders. Philosophical, psychological, and social underpinnings of these critiques are considered in Knowledge Area CF: Conceptual Foundations.","name":"Critical approach"},{"code":"GS7-1","description":" ","name":"Epistemological critiques"},{"code":"GS7-2","description":" ","name":"Ethical critiques"},{"code":"GS7-3","description":" ","name":"Feminist critiques"},{"code":"GS7-4","description":" ","name":"Social critiques"},{"code":"MDS","description":"MDS is a dimensionality reduction technique.","name":"Multidimensional scaling"},{"code":"MDSClassical","description":"It is also known as Principal Coordinates Analysis, Torgerson Scaling or Torgerson–Gower scaling. It takes an input matrix giving dissimilarities between pairs of items and outputs a coordinate matrix whose configuration minimizes a loss function called strain.","name":"Classical multidimensional scaling"},{"code":"MDSGeneralized","description":"An extension of metric multidimensional scaling, in which the target space is an arbitrary smooth non-Euclidean space. In cases where the dissimilarities are distances on a surface and the target space is another surface, GMDS allows finding the minimum-distortion embedding of one surface into another.","name":"Generalized multidimensional scaling"},{"code":"MDSMetric","description":" ","name":"Metric multidimensional scaling"},{"code":"no","description":" ","name":"Mathematical models of uncertainty: Probability and statistics"},{"code":"no10","description":"Geospatial data are abundant, but access to data varies with the nature of the data, who wishes to acquire it and for what purpose, under what conditions, and at what price. Legal relations between public and private organizations and individuals govern data access. Complementary topics appear in Knowledge Area GD Geospatial Data (especially Unit GD12 Data standards and infrastructures), and Knowledge Area OI (Units 0I5 Institutional and Inter-intuitional aspects and OI6 Coordinating organizations).","name":"Dissemination of geospatial information"},{"code":"OI","description":"This knowledge area considers the organizational and institutional aspects related to GIS&T. The focus of this knowledge area is on the organizations active in the GIS&T domain, and what happens within and between these organizations. The knowledge area is structured around five units. One unit considers the key organizations in the GIS&T domain, covering relevant public sector organizations at different administrative levels as well as organizations in other sectors of society. Among the organizational aspects covered in this knowledge area are all organizational issues related to the implementation, use and management of GI and GIS within organizations. While all topics related to the organizational structures, procedures and management of GI(S) are grouped into one unit, another unit focuses on issues related to the human factor of using GI and GIS, i.e. people, their skills and competencies, and the development and evaluation of these skills and competencies in the context of GIS&T training and education. The knowledge area includes also several inter-organizational and institutional aspects of GIS&T. Particular attention is paid to the concept of geospatial data sharing, which is about the creation of `spatial data` connections and relationships between different organizations in the GIS&T domain. Spatial data infrastructures are developed to promote, facilitate and coordinate the sharing of spatial data among data providers and data users, and consists of several technological and non-technological components. Many related topics are considered in the knowledge area GI and Society (WS), which also addresses several non-technological aspects related to GIS&T. In addition to this, also the knowledge areas `Design and Setup of Geographic Information Systems`, `Geospatial Data\" and Web-based GI` include several topics that are closely linked to the topics that are considered in this knowledge area. It can be argued that in order to fully master the knowledge and competencies that are presented in these knowledge areas, also basic knowledge and understanding of the organizational and institutional aspects is required.","name":"Organizational and Institutional Aspects"},{"code":"OI1-1","description":"The development of an appropriate organizational model, which establishes the basic character of GIS operations, is a crucial element of the GIS management. The appropriate GIS organizational model for any organization is based on its intended role.Alternative GIS organizational models are based on differing arrangements concerning the scope of GIS, the degree of integration of GIS into business operations, the degree of centralization of GIS operation and use, and the degree of centralization of management control. Although many variations can arise from different combinations of these factors, GIS organizational models can generally be classified into three types: (1) enterprise GIS, (2) GIS data and service resource, and (3) GIS as a business tool (Somers, 1998).","name":"Organizational models for GIS management"},{"code":"OI1-2","description":"Management of GIS can be done in a more centralized or more decentralized manner. In a a so-called enterprise or information-framework GIS, an organizational unit may be established to manage the GIS environment and run the core system, whereas usage is decentralized. In environments where GIS is used occasionally by various users, it may be set up as a separate service with a designated group that manages the GIS and also controls users` applications services. A second decision that needs to be made after the choice between more centralized or more decentralized management of GI and GIS is about where to place the GI management. Alternative options are in a line organization, in a support area, or at the executive level, each with their own advantages and disadvantages.","name":"Managing GIS operations and infrastructure"},{"code":"OI1-3","description":"User roles describe the relationship between different users and the GIS in an organization. Each user role includes responsibilities (e.g. for modifying certain information) and privileges (e.g. for viewing specific information). Although many different roles can be defined, a basic distinction is made between users, who can only view certain information, and editors, who can edit certain information.","name":"User roles"},{"code":"OI1-4","description":"A GIS management strategy should be unique for each organization, as organizations have unique environments, characteristics, goals, GIS requirements. An important step in developing an effective strategy for an organization is to establish the strategic vision for GI and GIS in the organization and define its role and scope. Other elements that should be covered in the GIS Strategy are the degree of centralized management of the GIS, the placement of GIS management and support in the organization, involvement of users in GIS planning and implementation, coordination of users, organizational changes, preparation of users, personnel issues, transitions to GIS operations, integration into business operations, user support, data access, and integration of technology changes (Somers, 1998).","name":"Strategic planning"},{"code":"OI1-5","description":"Committee and team approaches are frequently employed for coordinating participants and users in multi-participant GIS projects. The aim of creating such committees and teams is to ensure that the varied interests of participants are addressed, as participants bring many different interests, application needs, data needs, priorities, organizational issues, and political interests to a common project—the GIS. Common models for coordinating participants recognize that participants have three levels of interest in the GIS: policy, technical development, and usage. Different bodies can be established focusing on these different levels of interest: a technical committee focusing on the design and development of the GIS, an management committee providing policy guidance and support and a user`s group.","name":"Coordinating GIS Participants and Users"},{"code":"OI1-6","description":"After the development and implementation of a GIS within an organization, the challenge is to maintain the system and revise and update it when necessary. This means the performance of the GIS in terms of efficiency and effectiveness should be measured and monitoring, and feedback from users on the system and applications, on the data as well as on new needs should be collected. Particular attention should be paid to the maintenance of data sets.","name":"Ongoing GIS revision"},{"code":"OI1-7","description":"The introduction of GIS into organizational environments should be seen as a complex process of mutual adaptation (Nedovic-Budic, 1997). These technologies changes the established organisational processes and structures, while on the other hand the organisational context and culture modify the technological set-up and use. Therefore, knowledge and understanding of the relationship between technologies and organizations is necessary to increase the success of GIS implementations in organizations. Successful GIS implementation and adoption often require some degree of organizational change. However, this can be very difficult to effect because organizations are naturally resistant to it (Somers, 1998).","name":"Organizational changes"},{"code":"OI1","description":"GIS and T implementation and use within an organization often involves a variety of participants, stakeholders, users and applications. Organizational structures and procedures address methods for developing, managing, and coordinating these multi-participant users. The development of the appropriate organizational model for managing the GIS is crucial. In certain cases, changes to the organizational structure in place might be required. Strategic planning and the establishment of coordination structures can be considered as valuable instruments for managing and coordinating all involved users, while also the different user roles need to be assigned.","name":"Organizational structures, procedures and management"},{"code":"OI2-1","description":"GIS and T professionals can be hired for a wide range of different job positions, for which the precise skills, competences and qualifications needed will vary. Typical examples of GIS and T positions are GIS&T project managers, technicians, system developers and analyst. The recognition and certification of the competences people have acquired in informal and non-formal learning contexts is important to know which skills and competences individuals have and whether they meet the qualifications required for a certain job position.","name":"GIS and T positions and qualifications"},{"code":"OI2-1","description":"Management of GIS can be done in a more centralized or more decentralized manner. In a a so-called enterprise or information-framework GIS, an organizational unit may be established to manage the GIS environment and run the core system, whereas usage is decentralized. In environments where GIS is used occasionally by various users, it may be set up as a separate service with a designated group that manages the GIS and also controls users’ applications services. A second decision that needs to be made after the choice between more centralized or more decentralized management of GI and GIS is about where to place the GI management. Alternative options are in a line organization, in a support area, or at the executive level, each with their own advantages and disadvantages.","name":"Managing GIS operations and infrastructure"},{"code":"OI2-2","description":"Making sure staff members have the necessary skills and competences to perform geospatial activities is necessary for an effective implementation and operation of GI within an organizations. Several training methods can be adopted to ensure the development of skills and competencies of staff members. A distinction can be made between formal and informal training, but also between internal and external training programs. Another relevant issue is the assessment and evaluation of the skills and competences of staff members, to determine their future training and development needs.","name":"GIS and T staff development and evaluation"},{"code":"OI2-3","description":"Programs and courses on GIS and T and related subjects are provided by a wide range of institutions. While in recent years also the use and integration of GI and GIS in primary and secondary education has received significant attention, GIS and T education is mainly organized by institutions of higher education, especially universities but also other higher education institutions. Analyses of the higher education GIS&T programs and courses in Europe showed that the offer of courses is very diverse, in terms of size (ECTS), educational level (EQF) and course content. Vocational training on GIS and T related topics is organized by different types of training providers, including the major GIS vendors, data and service providers, academic sector, professional organisations, but also the public sector.","name":"GIS and T training and education"},{"code":"OI2-4","description":"A curriculum is a systematic description of a study program, in terms of learning goals, structure and sequence, learning, teaching and assessment strategies and content. A curriculum consists of both a set of related – required and elective - courses along with all direct and indirect skills, competences and learning outcomes resulting from these courses. In the process of curriculum design typically particular attention is assigned to objectives, teaching methods and educational strategies, while also attention should be paid to the content organization aspects and the global structure of the curriculum. The process of designing GIS&T curricula presents many challenges, as the design of the curriculum should be aligned to both the institutional context and the expected outcomes of the learning and teaching process (Prager, 2011).","name":"GIS and T curriculum and course design"},{"code":"OI2-5","description":"An important challenge in organizing GIS and T education and training is the choice and use of effective teaching and learning methods. These methods should follow recent technological developments and use the best technologies to help students acquire the necessary skills and competencies. Traditionally, most GIS and T programs and courses were taught in the context of a full-time, face-to-face setting, using traditional teaching methods such as lectures and lab-based computer practical sessions. In recent years, educational institutions and their teachers have been experimenting with more innovative teaching and learning methods, such as project-based and case-based learning, distance learning, integrated and inter-disciplinary lessons, collaboration with companies and other stakeholders, etc.","name":"GIS and T teaching and learning methods"},{"code":"OI2","description":"This unit addresses GIS and T staff and workforce issues within an organization, particularly as they relate to ensuring that GIS and T is appropriately used and supported. The focus of this unit is on the skills and competencies of professionals in the GIS and T domain: how can these skills and competencies be described and evaluated, and how can they be developed through training and education.","name":"GIS and T workforce themes"},{"code":"OI3-1","description":"Cost savings are an important driver or motivation for sharing geospatial data and information. As costs associated with collecting and maintaining geospatial data are high, sharing data means that users no longer need to duplicate data gathering and archiving, which leads to savings in terms of personnel, space/facilities, data acquisition and maintenance costs. One fundamental argument for sharing thus derives from scale economies in production. Because the cost of making data is high, there is a clear incentive to maximize the number of users of these data. Sharing allows data to be used repeatedly for many purposes, thus increasing their value without increasing their cost. Sharing data also leads to improved data quality. Moreover, in many cases, sharing data is the only way to get access to certain data sets, as the authority to collect and manage certain data lies with another public institution.","name":"Drivers and incentives for sharing geospatial data"},{"code":"OI3-2","description":"Sharing of geospatial data can be hindered or inhibited by several types of barriers. These include technological barriers, such as a lack of common data definitions, formats and models or incompatibility of hardware and software. Among the non-technological barriers are organizational, political and legal issues and elements, such as misaligned organizational missions, diversity in organizational cultures, conflicting organizational priorities, lack of funding, lack of executive and legislative support; restrictive laws and regulations, copyright issues, data privacy and data ownership issues. However, it should be noticed that many of these barriers have been decreased or eliminated in recent years.","name":"Barriers to geospatial information sharing"},{"code":"OI1-9","description":" ","name":"Organizational models for coordinating GISs and/or program participants and stakeholders"},{"code":"OI3-3","description":"The legal framework for a spatial data sharing consists of two main types of information policies: those that promote and those that hinder the availability of spatial data. Policies that promote spatial data availability can focus on different types of users (public bodies, private companies, citizens) and different types of use (public access, commercial and non-commercial reuse, reuse for performing public tasks). Among the policies that hinder the availability of spatial data are those dealing with privacy, liability, and intellectual property. The legal framework also includes legislation that applies to data or information in general, which may also be applicable to spatial data (e.g. legislation on freedom of information, copyright, etc.). Moreover, also general legislation relating to any interaction between people or any situation in everyday life (e.g. liability, contract law, competition law, etc.) will apply to spatial data sharing. decreased or eliminated in recent years.","name":"Legal framework for geospatial data sharing"},{"code":"OI3-4","description":"Several types of legal mechanisms for sharing geospatial data can be used. A data sharing arrangements can be formalized by a contract or agreement between the data provider and the data user. A particular type of agreement are the framework agreements, which are agreements between two or more organisations concluded prior to the datasets or services being required. These framework agreement can involve one or multiple spatial data sets or services. Partnership agreements are often used to formalize the data sharing agreements among a is subconcept of group of partners. Participation in such a partnership often means participants share their data with other participants and get access to shared data. Another relevant mechanism is the use of licenses, which are mechanisms to give organizations and people the permission to use spatial data sets and services. A license is legally binding, and defines the conditions of use of the related spatial data sets and services. In order to reduce the number of licenses used and ensure the harmonization of the terms in these licenses, the use of standard licenses is promoted.","name":"Legal instruments for sharing geospatial data"},{"code":"OI3","description":"Geospatial data sharing has become an essential element of the GI activities of organizations. Spatial data sharing can be defined as the electronic transfer of spatial data/information between two or more organizational units where there is independence between the holder of the data and the prospective user. Spatial data sharing has many advantages, but several technical and non-technical barriers must be overcome to put data sharing into practice. While the practice of spatial data sharing has substantially grown with the development of spatial data infrastructures, many consider data sharing as a crucial element for the success of these infrastructures.","name":"Geospatial data sharing"},{"code":"OI3b","description":"A Spatial Data Infrastructure can be defined as the collection of technological and non-technological components to facilitate and coordinate the exchange of and sharing of spatial data. The concept infrastructure is used to promote the concept of a reliable, supporting environment, analogous to a road or telecommunications network, that facilitates the access to spatial data. Data, metadata, access networks, standards, coordination, policies, funding, people and institutional frameworks are often considered among the key components of an SDI. SDIs have been developed in many countries worldwide at local, national and international levels. Often a distinction is made between a between the first generation SDIs that have data as their key driver and are based on a product model and second generation SDIs in which user needs are the key driver and that are based on a process or development model.","name":"Spatial data infrastructures"},{"code":"OI4-1","description":"The adoption and implementation of standards are two key phases in the standardization process, which starts with the definition of standardization requirements and the development of standards. The adoption and implementation of standards follows after the development phase. The distinction made between the adoption and implementation of standards is important: adoption entails the decision to apply standards, while the implementation relates to the integration of standards in software, in data development and in other processes. GI-Standards are one of the key components of each SDI, consist of both semantic and technical standards, and include standards related to the different architectural components of an SDI, i.e. standards related to spatial data sets and data products, web services, metadata and catalogues, encodings, etc.","name":"Adoption and implementation of standards"},{"code":"OI4-2","description":"The SDI policy framework includes the set of policies, strategies, initiatives and projects aimed at increasing access, sharing, and effective use of spatial data. SDI policies can be divided into strategic and more operational policies. Strategic policies define the is subconcept of framework and formal structure within which the SDI initiative is developed. Operational policies provide more practical tools to facilitate access to and use of the SDI, and address specific topics related to the collection, management, use, access and dissemination of spatial data. These operational policies include a broad range of guidelines, directives, procedures and manuals that apply to the day-to-day business of organizations in developing, operating and using an SDI. To guarantee the success of an SDI, it is important to recognize the wider policy context in which these SDI`s are developed, and to link them to the overall policy environment in the jurisdiction in which they are implemented. These include policies on open government and open data, environmental policies, digital government or e-government policies and other.","name":"Policies"},{"code":"OI4-3","description":"If is often argued that SDI implementation requires coordination, because without coordination all other SDI components would not be developed or would be developed in a very fragmented and inconsistent manner. In general terms, coordination is about bringing into alignment the activities of different stakeholders in the SDI landscape. A typical instrument to realize coordinate in the context of SDI, is the establishment of an effective SDI coordination structure. The SDI coordination structure should ensure that all stakeholders are involved in the development and implementation of the SDI, through the participation in one or more coordination bodies. Another important element is the establishment of clear roles and responsibilities for the different involved organizations, making a distinction between data users, data providers, services providers and a geo-broker.","name":"Coordination and organizational structure"},{"code":"OI4-5","description":"Funding an SDI is about guaranteeing the – long-term – financial security of an SDI, by obtaining and formalizing financing for the implementation and maintenance of the different SDI components. An SDI funding model provides the answer to the central question of where and how to seek funding for implementing and maintaining an SDI. Within an SDI often different funding models will be combined, as the selection of the most appropriate funding model will be linked to different activities and the associated costs. Costs of an SDI include both set-up costs (one off costs) and maintenance costs (yearly), of which certain costs need to be made for each data sets or each data provider and other costs for the infrastructure in general. The most commonly used SDI funding models are centralized government funding, decentralized government funding (e.g. for each data provider), partnership funding, funding through revenues, and government funding based on donor agencies or on European projects.","name":"Funding an SDI"},{"code":"OI4-5b","description":" ","name":"Performance measurement and assessment"},{"code":"OI4-6","description":"For a long time, SDI development has focused on the development and implementation of different components with the aim of facilitating the access to and sharing of spatial data. An key challenge in future SDI development will be the integration of these SDI`s in a wider context. In order to optimally take advantage of the data and services provided by an SDI, integrating these data and services into the processes and workflows of – public and private – organizations will be crucial. The concept of spatial enablement refers to the challenge of developing SDI`s in such a way that they provide an enabling platform that serves the wider needs of society in a transparent manner. Moreover, the diffusion of SDIs, together with the efforts to build a Global Earth Observation System of Systems (GEOSS) and other developments in industry and civil society should be considered as elements in a the realization of a vision on the next-generation Digital Earth.","name":"Next-generation SDIs"},{"code":"OI5-1","description":"Within the European Commission there are several key GI players. GIS activities in the Commission started since 1981 (e.g. DG REGIO, Eurostat, ) with the CORINE project, the creation of DG ENV and the creation of the European Environment Agency (EEA). Together with the DG Joint Research Centre (JRC), DG ENV and EEA are in charge of the coordination of INSPIRE: DG Environment acts as an overall legislative and policy co-ordinator for INSPIRE, the JRC acts as the overall technical co-ordinator of INSPIRE and EEA is in charge of several tasks related to monitoring and reporting, and data and service sharing under INSPIRE. Also several other EC institutions are actively involved in GI(S) policies and activities (DIGIT, DG GROW, DG AGRI, DG MOVE and many others).","name":"GI organization at the European Commission"},{"code":"OI5-2","description":"Although there may be certain differences between countries, in most countries many key organizations in the GIS&T field will be active at the central/federal/national level of government. Especially the traditional institutions for surveying and mapping play a key role in geospatial policies and activities. Several public authorities at the federal level are in charge of the production and maintenance of key reference and thematic data sets. In many countries, these national data producers were the leading actors in the development of – national – spatial data infrastructures.","name":"Federal and national government organizations"},{"code":"OI5-3","description":"Local and sub-national governments are often considered among the major users of geographic information in governments, as they often are involved in many different policy areas, in which many problems with a locational component need to be tackled. Geographic data produced and maintained by authorities at lower administrative levels are often more detailed and thus interesting for other users, both within and outside the public sector. As a result, local and sub-national governments are often involved in the establishment of these infrastructures because of the wide range of highly detailed geographic information they produce and manage. As many geographic data are linked to the activities and services of local organizations, the involvement of these organizations in the maintenance of data ensures that these data are up-to-date.","name":"Sub-national and local governments"},{"code":"OI5-4","description":"The European GIS&T landscape consists of many pan-European organizations and associations promoting the interest of and representing certain stakeholder groups. While some of these organisations are dealing with all sectors and aspects of geographic information, others have a more thematic focus (e.g. remote sensing, topography, geosciences) or represent a particular sector (e.g. research, business). In some cases, their clearly is an overlap in the mission and objectives of different organizations, and some organizations are working in the same field of interest. Some examples of pan-European organizations and associations are AGILE, EuroSDR, EUROGI, and EuroGeographics. Also at international level several membership organizations and associations exist.","name":"Pan-European and global associations and professional organizations"},{"code":"OI5-5","description":"The geospatial industry consists of companies working with location specific information or services. Within the geospatial sector, several areas of activities can be identified: 1) measuring, collecting and storing of data about geo-objects; 2) processing, editing, modelling, analyzing and managing that data; 3) presenting, producing and distributing the data; and 4) advising, educating, researching and communicating about processes and use of geo-information products and services. The sector consists of both small-and-medium-sized enterprises but also big companies, including surveyors, census hard-copy map providers, aerial photos providers, base map data providers, satellite and remote sensing imagery providers, software developers (GIS-related products and services providers as well as satellite image programming platform providers) and several others.","name":"The geospatial industry"},{"code":"OI5-5b","description":" ","name":"The geospatial community"},{"code":"OI5","description":"Several types of organizations play a key role in the execution and coordination of geospatial activities in society. Typically, a distinction is made between data providers and data users, while coordinating organizations exist to coordinate and support the geospatial activities of professionals and entities using GIS&T. Governments are often considered as the major users and producers of spatial data and spatial information. Within the public sector, spatial data are collected and used in different thematic areas and at different administrative levels (from local to global). However, the needs, interests, and capacities of organizations at each of these levels will be different, as well as their role in the development of spatial data infrastructures, and the execution of geospatial activities in general. Also the geospatial industry will exist of both data providers and data users, but also of organizations delivering products and services to support the collection and use of spatial data. Other key organization in the GI domain are professional organizations and associations, bringing together and representing the needs of organizations of a particular sector and/or geographic area.","name":"Organizations in the GIS and T domain"},{"code":"OI6-1","description":" ","name":"Federal agencies and national and international organizations and programs"},{"code":"OI6-2","description":" ","name":"State and regional coordinating bodies"},{"code":"OI6-4","description":" ","name":"Publications"},{"code":"OI6","description":"A number of organizations (public, private, and non-profit) exist to coordinate, inform, and support geospatial activities of professionals, and entities using GIS and T. Informed geospatial professionals and organizations are familiar with the mission, history, constituencies, modes of operation, products, and levels of success of these organizations.","name":"Coordinating organizations (national and international)"},{"code":"SD","description":"Based on Waldo Tobler`s first law of geography( Tobler, 1970), this property is set on the principle that \"everything is related, but that which is closer is more closely related\".","name":"Spatial dependency"},{"code":"SH","description":"This principle, as set forth by Anselin, determines that \"expectations vary along the earth`s surface\" which means that any spatial analysis is dependent explicitly on the borders of study fields, i.e. the tracing of (spatial) analysis units.","name":"Spatial heterogeneity"},{"code":"WB","description":"This knowledge area is about Web Based Geographic Information management aspects and therefore it was given the name \"Web Based GI\" or \"WBG\" in short. It is implied by this name that the differentiating factor for this KA is the \"Web\". One must then be able to answer the questions like \"What functions do we delegate to the Web?\" or \"how WBGI is different from the traditional GI?\" Sticking to the functions of a GIS, which are inserting (adding), storing, manipulating, analysing and presenting the data, there is not a single system for effecting all these tasks anymore but the Web itself. For instance, there is no single database and its known-to-its users-definition, anymore but many different stores and many different definitions. Similarly, many different manipulation, analysis and presentation options compared with the options offered by a single or limited number of systems of traditional GI. In general, Web provides the means of leveraging distributed \"resources\" like data, information, or software. It is a \"collaboration medium\". A collaboration that enables rapid production or decision making. A collaboration that certainly introduces new dimensions to traditional GI handling. This is the justification of proposing this KA in addition to the KAs of the original BoK. For the mentioned collaboration to happen, data or any other type of a resource have to accessible on the Web. This means that it should have a Web \"address\" and a \"definition\" that is understandable either by \"human\" or \"machine\". \"Machine understandable definitions\" refers to the dimension of \"semantics\" and \"ontologies\" which are also included under this KA. When one talks about publishing resources then \"catalogue services\" and more importantly \"discovery\" dimension comes into the scene. On the other hand, \"Linked Data (LOD)\" and \"Open Data\", highly popular recent trends and two of the above mentioned dimensions of Web GI have also been covered under this KA. Like the other dimensions of Web GI, both LD and OD aspects must be known to GI communities with differing degrees of expertise. The concepts of \"interoperability\" and \"Spatial Data Infrastructure (SDI)\", hot topics of GI communities for many years, have been thought to be dealt with under this KA as well with the justification that \"Web GI\" is a much is subconcept of concept than SDI, This is by the fact that SDI refers to a much narrower content and context of \"collaboration\" then Web GI. Therefore, Geospatial data interoperability and some of the related concepts which were classified under KA, \"Geospatial data in the original BoK were moved under KA11 with the updated context. Another issue is the coverage of Spatial Analysis (SA), data manipulation aspects of GI by KA11. The SA aspects are covered by other KAs like \"Geocomputation\" and \"Analytical methods\". If the analysis operations, in an undertaking, would be handled by web services this is already covered by \"data processing\" web services, application development unit and Web services composition under that unit. The important thing is to have the knowledge about a specific analysis operation; Employing it as a web service would require no more knowledge than using any other web service. SA is covered by KA11 in as much as it should have been.","name":"Web-based GI"},{"code":"WB1-1","description":"Define Service Oriented Architecture (SOA) and identify main elements of it. Discuss concensus based interoperability and its relation to geospatial data interchange. Define what a Web Service (WS) is and present characteristic scenarios. Data serving and Data Processing WSs. Define their characteristics and present some examples. Define Web services transport over the Web. Describe generally the hypertext transfer protocol and its main operations like POST and GET.","name":"Fundamentals of web services"},{"code":"WB1-2","description":"- Identify design issues of SOAP web services; fine grained and coarse grained services, design patterns.","name":"SOAP web services"},{"code":"WB1-3","description":"- Define characteristics of REST Web services and Resource oriented Architecture (ROA). - Differentiate between SOAP and REST Web services. - Identify design issues of REST Web services. - Discuss the issue whether a service is really \"RESTful\" or not","name":"REST web services"},{"code":"WB1-4","description":"- Define Web Map Service (WMS). Describe GetCapabilities, GetMap, and GetFeatureInfo operations in detail. Practice its usage in a given use case. - Define Web Feature Service (WFS). Describe GetCapabilities, DescribeFeaturetype, and GetFeature, and GetFeatureInfo operations in detail. Practice its usage in a given use case. - Define Web Coverage Service (WCS). Describe GetCapabilities, GetCoverageInfo, and GetCoverage operations in detail. Practice its usage in a given use case. - Define Web Processing Service (WPS). Describe GetCapabilities, DescribeProcess, and Execute operations in detail. Practice its usage in a given use case. - Define Web Map Tile Service (WMTS). Describe GetCapabilities, GetTile, and GetFeatureInfo operations in detail. Practice its usage in a given use case. - Define and practice the usage, in a given use case, of StyledLayerDescriptor (SLD) and Symbology Encoding (SE). Practice their usage in a given use case.","name":"OGC web services"},{"code":"WB1","description":"In the most simplistic way a Web service may be defined as \"a Web accessable program code which performs a task of either processing or serving some data. Although there are many other definitions in the related literature, the one in W3C (2004) seems to be quite complete and refering to also lately popular REST style Web services. It states that \" We can identify two major classes of Web services: REST-compliant Web services, in which the primary purpose of the service is to manipulate XML representations of Web resources using a uniform set of \"stateless\" operations; and arbitrary Web services, in which the service may expose an arbitrary set of operations.","name":"Web services"},{"code":"WB2-1","description":"- Resource Description Framework (RDF), RDF graphs, RDF Schema (RDF-S). Define a data set in RDF. - Give an overview of Web Ontology Language (OWL). Describe how to define a data set in OWL DL. - Identify virtues of defining a given data set in both RDF and OWL, and compare semantic richness of both definitions. - Define Semantic Web and identify the role of the languages included under this topic for Semantic Web. - Give an overview of Semantic Web service definition in OWL-S. Identify the relation betweem OWL-S and WSDL. - Define the components of a Web Services Description Language (WSDL) document. - Web services description for RESTful web services, Web Application Description Language (WADL) and its use.","name":"Languages for the definition of non-spatial data and services"},{"code":"WB2-2","description":"- Describe OGC Simple Features Access Schema. Well-Known Text (WKT) and Well-Known Binary (WKB) representations of Geometry. - Describe GML data model and GML definition of geometry. GML application schemas and GML documents. - Define spatial extensions that GeoSPARQL brings over SPARQL. Identify the difference between qualitative spatial reasoning and quantitative spatial computations. - Describe GeoJason definition of Geospatial objects. Describe the structure of a GeoJSON document. Identify advantages and disadvantages of representing the same geospatial data by GML and by GeoJSON. - Compare different Geospatial object and geometry definitions included under this topic.","name":"Definition of geospatial data"},{"code":"WB2-3","description":"- Define what an ontology is. Identify differences among ontologies, Thesauri, and taxonomies. - Differentiate between upper, domain, and application level ontologies. - Identify issues in the development of geospatial ontologies. Criticise the role of ontology development methodologies and ontology evaluation in the development of ontologies. - Define and exemplify the reuse of ontologies - Define and identify the role of ontology patterns","name":"Ontologies development reuse and patterns"},{"code":"WB2","description":"A \"resource\" could be \"anything\" including data and services, identifiable over the Web. A resource should be defined in a language to be discoverable on the Web. Over the years, two major bodies W3C for non-spatial and OGC concerning spatial data have developed many specifications for defining data and services. On the W3C side, Resource Description Framework (RDF) has gained a great momentum in recent years in relation to the recent popularity of Linked Data as well. In the OGC front, the acceptance of GML was a major step concerning the long time effort of geospaial communities for having a standart for the definition of both geospatial feautures and geometry.","name":"Resource Definition"},{"code":"WB3-1","description":"- Define metadata and identfify metadata standards like ISO 19115 and 19119 describe their metadata schemas generally. - Differentiate between a metadata standard and a medadata profile. - Identify the aspects of selecting keywords which would characterize the data properly. - Identify the issues in mapping between different metadata standards. Also identify the roles of thesauri and crosswalks. - Describe briefly INSPIRE Metadata handling Scheme.","name":"Metadata and standards"},{"code":"WB3-2","description":"- Identify main components of manual metadata creation software tools - Describe harvesting and crawling mechanisms for automated metadata collection. - Practically apply harvesting using GeoNetwork Open Source tool. - Practice publishing in some popular SDI (NSDI) portals like INSPIRE and GOS geoportals","name":"Manual and automated forms of publishing"},{"code":"WB3-3","description":" ","name":"Catalogue services"},{"code":"WB3-4","description":"- Describe Metadata schemas and vocabularies used for open data publish. - Open data APIs that enable the usage of Open data; identify design aspects and usage scenarious. - Practice open data publishing using CKAN Open source tool - Describe what is meant by \"Odata\" (Open data Protocol), an OASIS standard. - Identify the technical aspects that open data paradigm would affect concerning Spatial Data Infrastructures including NSDIs.","name":"Publishing open data"},{"code":"WB3-5","description":"- Describe semantic annotation of data and services. - Issues in determining what ontologies to use for semantic annotation. - Identify issues in developing new ontologies for geospatial data. - Define Mapping between legacy definition and the semantic definition of publish - Describe an architecture and tools for organizing semantically annotated data","name":"Publishing via a semantic definition of data"},{"code":"WB3-6","description":"- Describe stages of publishing a relational database as Linked Data - Identify issues in finding proper ontologies to annotate the data - Identify issues in determining the relationships to be represented when publishing Linked Data - Linking the data; manual and automated methods - Practicaly apply publishing a relational database as Linked Data","name":"Publishing linked open data"},{"code":"WB3","description":"\"Publishing\" means making a resource available for the use of others. A \"resource\" could be \"anything\" including data and services, identifiable over the Web. Publishing may be done on the basis of either the \"characteristics\" of the data or the data itself. When only some \"characteristics\" of a resource is published then some of the contents would naturally be left out. The \"characteristics\" include metadata and some keywords. This kind of publishing may be named as \"limited contents\" publishing or \"publishing by metadata\". One of the issues become then what characteristics to use to define the data. Or what what metadata definition to use. Another aspect of publish is \"manual entry\" and \"automated collection\". In the former publisher enters metadata while in the latter some harvesting mechanism collects metadata in an automated fashion. On the contrary, there is \"unlimited contents publishing\" where there is no limitation on the published contents. Open data publishing is in this class. In additon, some \"additional semantics\" may be subject of this type publishing through new relationships in the ontologies of publishing, which have not been explicit in the exisiting data model but are inherent in the data. And this last type is covered under the topic, \"Publishing via a semantic definition of data.\"","name":"Resource Publishing"},{"code":"WB4-1","description":"- Identify main issues in \"keyword-based\" discovery of data and services. - Discovery over a catalogue service; Discovery procedure in OGC CS-W - Practice discovery over some popular SDI (NSDI) portals like INSPIRE and GOS geoportals. - Describe \"Full-text-based\" discovery; open source and commercial search engines, its use in GI related applications.","name":"Syntactic discovery"},{"code":"WB4-2","description":"- Describe Semantic Discovery and its main components. Identify the areas of its use for GI related applications. - Identify the main concepts of reasoning and architectural components of Reasoners - Identify main issues in Semantic discovery. - Present some examples of semantic discovery; Semantic search engines, highlighting projects and practice concerning GI related applications in the area. .","name":"Semantic discovery"},{"code":"WB4-3","description":"- Describe Querying Linked Data; SPARQL and GeoSPARQL - Describe Linked Data Browsers; Define Faceted browsers and identify what problems of linked data discovery they aim to solve. - Comment on Natural language based discovery over linked data. - Compare Linked geospatial data to SDI approaches.","name":"Discovery over linked open data"},{"code":"WB4","description":"Resource discovery means the discovery of resources including data and services needed for an application. Syntactic discovery refers to the discovery on the basis of syntactic comparison operations. It is classified as \"keyword-based\" and \"full-text-based\" discovery. Semantic discovery on the other hand, refers to the discovery of resources on he basis of some semantic definition. Therefore, semantic discovery requires that a resource be published by a semantic definition as defined in the topic WB3-5.","name":"Resource Discovery"},{"code":"WB5-1","description":"- Indentify the need for and main issues in spatial data interchange - Describe the main components of OGC Filter encoding and compare it to SQL. - Practically apply getting data from a WFS and integrate it into a client application - Practically apply getting data from a WCS and integrate it into a client application - Practice the usage of popular ETL tools in an NSDI scenario.","name":"Integrating data from OGC web services"},{"code":"WB5-2","description":" ","name":"Schema matching and ontology alignment"},{"code":"WB5-3","description":"- Create a web map mashup application with OpenLayers, Google Maps API and OpenStreetMap. In this example, OpenLayers will be used for javascript mapping functionality. Google Maps and OpenStreetMap will be baselayers for this example. Add your GeoJSON data as a layer to this application. Add your WMS service as a layer to this application.","name":"Data mash ups"},{"code":"WB5","description":"The term \"application development\" refers to the collection of activities or the \"workflow\" through which the user reaches her final goal. Being one of these activities, \"data integration\" means the transformation of data from one representation to another which might be of either the client`s one or some other representation. An example for data integration might be the case where the data is transfered from an OGC WFS and integrated into a client GIS.","name":"Application development via Data Integration"},{"code":"WB6-1","description":"- Define web services composition (WSC) concept and identify main issues. - Define Web API composition (WAPIC) concept for RESTful WSs and identify main issues. - Practice a WSC for a certain use case in Taverna workbench using OGC WPS services","name":"Manual Web Services Composition"},{"code":"WB6-2","description":"- Identify whether Full-automated WSC has still a value in it concerning both where we stand today on the road to “Semantic Web†and unresolved problems in the area, which are the problems of Artificial Intelligence indeed.","name":"Semi automated and Full-automated WSC"},{"code":"WB6","description":"Web Services Composition can be defined as bringing together a number of web services in a certain workflow to achieve a certain task that cannot be achieved by any of the composed services alone. In general, it involves first the discovery of the suitable services over the Web, and compose them in a certain workflow order and finally run the composed service which is the invocation stage. WSC has been a highly active research topic since the emergence of Web services in 2000s. \"Manual\" WSC is the form that the activities of discovery, composition and invocation are all done manually (by human). In the \"Semi-automated\" way, the discovery is done by the machine. In the \"full-automated\" approach all the above activities are done by the machine. There are no tools at the moment that achieve full automated composition. Web API composition is like WSC, the only difference is the fact that instead of web services there are Web APIs in WAPIC. There is no doubt that One would run into the very same problems of WSC concerning full automated composition. In other words, WAPIC would in no way be easier than WSC. Nevertheless, as far as semi automated form can be achived, WAPIC is valuable because the number of Web APIs increase drastically from day to day. The site \"programmableWeb\" lists 14 957 APIs at the moment. It is not easy to search for all those APIs manually for the discovery of suitable APIs for a given task.","name":"Application development via Web services composition"},{"code":"WB7-1","description":"- Describe main elements of HTML5. Identify the extensions HTML5 brings over older HTML versions. Create a sample HTML5 Web page. - Identify building blocks of Javascript programming language. Write a Javascript function which, for instance, filters out points with height values greater than 100 m. from a GeoJSON file. Add this function to an HTML5 web page. - Describe Cascading Style Sheets (CSS), identify the virtue of its role for separating the presentation style of HTML documents from the content of documents. - Describe Scalable Vector Graphics (SVG) and identify its role for client side processing. - Describe Document Object Model (DOM). Identify its role for the processing of a \"loaded\" HTML document.","name":"Hypertext markup scripting and styling"},{"code":"WB7-2","description":"- Identify main elements and functionality Google maps, describe some of its most popular API operations and how they are employed. - Identify main components and functionality of Openlayers library, describe its main functions and how they are employed. - Identify main components and functionality of Leaflet library, describe its main functions and how they are employed. - Identify main elements and functionality Mapbox, describe some of its most popular API operations and how they are employed. - Present an overview of OpenStreetMap and define its general functionality, comment its usage by Web APIs.","name":"Web Map APIs and Libraries"},{"code":"WB7-3","description":"- Describe generally the main components and functionality of \"Web Application Frameworks\" such as AngularJS, Ext.js, Django, Java Server Faces (JSF), and the like. - Describe generally the functionality offered by \"portal frameworks\" land Geoportals ike Geonetwork, Opengeoportal, Esri geoportal server, Deegree portal, Liferay, Jboss portal. - Identify differences, advantages and disadvantages of web application framework based and portal framework based web applications from the geospatial data perspective. - Describe generaly how \"NSDI-requiring-scenarious\"would be handled by web application framework based applications. - Describe generally how JSON (GeoJSON)`s \"schema-less\"structure may be transformed into an application schema.","name":"Web application Frameworks and Geoportal frameworks"},{"code":"WB7","description":"Characteristic examples are included under this topic. The APIs, for instance other than the ones included under this unit, and libraries could have been included as well. However, since the important thing is to highlight the functionality then there is no need to include them all. By the inclusion of topic \"WB7-3\"under this unit, the aim was to cover one of the very \"hot\"topics of Web2.0 for both the main concepts about Web application frameworks and also how they are related to portal frameworks and geoportals. By the topic \"WB7-1 Building blocks\"the core components of Web application development are covered. On top of this core, there comes a great variety of \"Web application frameworks for both enabling rapid web application development and ensuring scalable, high-performance applications. Finally, there are \"Web APIs and Libraries\" certainly deserving being a separate topic for their current popularity. They also mean rapid application development for developers by code reuse and versatality for \"end users\" in creating their \"end products\".","name":"Web Application development elements"},{"code":"DM3Dummy","description":"just for demonstration purposes","name":"Dummy concept"}],"creationYear":2016,"references":[{"concepts":[0],"description":" ","name":" ","url":" "}],"relations":[{"name":"is subconcept of","source":2,"target":3},{"name":"is subconcept of","source":4,"target":8},{"name":"is subconcept of","source":5,"target":8},{"name":"is subconcept of","source":6,"target":8},{"name":"is subconcept of","source":7,"target":8},{"name":"is subconcept of","source":8,"target":1},{"name":"is subconcept of","source":9,"target":16},{"name":"is subconcept of","source":10,"target":16},{"name":"is subconcept of","source":11,"target":16},{"name":"is subconcept of","source":12,"target":16},{"name":"is subconcept of","source":13,"target":16},{"name":"is subconcept 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model (e.g., cell, row, column, value)"},{"concepts":[89],"name":"Define common philosophical theories that have influenced geography and science, such as logical positivism, Marxism, phenomenology, feminism, and critical theory"},{"concepts":[87],"name":"Define common theories on what constitutes knowledge, including positivism, reflectance-correspondence, pragmatism, social constructivism, and memetics"},{"concepts":[85],"name":"Define common theories on what is real, such as realism, idealism, relativism, and experiential realism"},{"concepts":[8],"name":"Define different interpretations of cost in various routing applications"},{"concepts":[38],"name":"Define direction and its measurement in different angular measures"},{"concepts":[202],"name":"Define entities and relationships as used in conceptual data models"},{"concepts":[63],"name":"Define friction surface"},{"concepts":[62],"name":"Define intervisibility"},{"concepts":[306],"name":"Define key terms such as standard line, 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wisdom"},{"concepts":[101],"name":"Define the four basic dimensions or shapes used to describe spatial objects (i.e., points, lines, regions, volumes)"},{"concepts":[97],"name":"Define the notions of cultural landscape and physical landscape"},{"concepts":[125],"name":"Define the principle of friction of distance and geographic models that are based on it (e.g., gravity models, spatial interaction models)"},{"concepts":[96],"name":"Define the properties that make a phenomenon geographic"},{"concepts":[1],"name":"Define the terms spatial analysis, spatial modeling, geostatistics, spatial econometrics, spatial statistics, qualitative analysis, map algebra, and network analysis"},{"concepts":[128],"name":"Define uncertainty-related terms, such as error, accuracy, uncertainty, precision, stochastic, probabilistic, deterministic, and random"},{"concepts":[124],"name":"Define various terms used to describe topological relationships, such as disjoint, overlap, within, and intersect"},{"concepts":[236],"name":"Delineate a set of break lines that improve the accuracy of a TIN"},{"concepts":[118],"name":"Delineate regions using properties, spatial relationships, and geospatial technologies"},{"concepts":[247],"name":"Demonstrate how a network is a connected set of edges and vertices"},{"concepts":[243],"name":"Demonstrate how a topological structure can be represented in a relational database structure"},{"concepts":[42],"name":"Demonstrate how adjacency and connectivity can be recorded in matrices"},{"concepts":[247],"name":"Demonstrate how attributes of networks can be used to represent cost, time, distance, or many other measures"},{"concepts":[149],"name":"Demonstrate how Bertins graphic variables can be extended to include animation effects"},{"concepts":[256],"name":"Demonstrate how both the time criticality and the data security might determine whether one performs change detection on-line or off-line in a given scenario"},{"concepts":[11],"name":"Demonstrate how capacity is assigned to edges in a network using the appropriate data structure"},{"concepts":[4],"name":"Demonstrate how cluster analysis can be used as a data mining tool"},{"concepts":[161],"name":"Demonstrate how different methods of data classification for a single dataset can produce maps that will be interpreted very differently by the user"},{"concepts":[10],"name":"Demonstrate how K-shortest path algorithms can be implemented to find many efficient alternate paths across the network"},{"concepts":[9],"name":"Demonstrate how networks can be measured using the number of elements in a network, the distances along network edges, and the level of connectivity of the network"},{"concepts":[74],"name":"Demonstrate how semi-variograms react to spatial nonstationarity"},{"concepts":[81],"name":"Demonstrate how spatial autocorrelation can be removed by resampling"},{"concepts":[79],"name":"Demonstrate how spatially lagged, trend surface, or dummy spatial variables can be used to create the spatial component variables missing in a standard regression analysis"},{"concepts":[40],"name":"Demonstrate how the area of a region calculated from a raster data set will vary by resolution and orientation"},{"concepts":[12],"name":"Demonstrate how the Classic Transportation Problem can be structured as a linear program"},{"concepts":[80],"name":"Demonstrate how the parameters of spatial auto-regressive models can be estimated using univariate and bivariate optimization algorithms for maximizing the likelihood function"},{"concepts":[79],"name":"Demonstrate how the spatial weights matrix is fundamental in spatial econometrics models"},{"concepts":[247],"name":"Demonstrate how the star (or forward star) data structure, which is often employed when digitally storing network information, violates relational normal form, but allows for much faster search and retrieval in network databases"},{"concepts":[33],"name":"Demonstrate the basic syntactic structure of SQL"},{"concepts":[52],"name":"Demonstrate the extension of spatial clustering to deal with clustering in space-time using the Know and Mantel tests"},{"concepts":[253],"name":"Demonstrate the importance of a clean, relatively error-free database (together with an appropriate geodetic framework) with the use of GIS software"},{"concepts":[34],"name":"Demonstrate the syntactic structure of spatial and temporal operators in SQL"},{"concepts":[236],"name":"Demonstrate the use of the TIN model for different statistical surfaces (e.g., terrain elevation, population density, disease incidence) in a GIS software application"},{"concepts":[79],"name":"Demonstrate why spatial autocorrelation among regression residuals can be an indication that spatial variables have been omitted from the models"},{"concepts":[89],"name":"Describe a brief history of major philosophical movements relating to the nature of space, time, geographic phenomena and human interaction with it"},{"concepts":[150],"name":"Describe a mapping goal in which the use of each of the following would be appropriate: brushing, linking, multiple displays"},{"concepts":[48],"name":"Describe a real modeling situation in which map algebra would be used e.g., site selection, climate classification, least-cost path"},{"concepts":[313],"name":"Describe a scenario in which data from a secondary source may pose obstacles to effective and efficient use"},{"concepts":[348],"name":"Describe a scenario in which you would find it necessary to report misconduct by a colleague or friend"},{"concepts":[57],"name":"Describe a simple process model that would generate a given set of spatial patterns"},{"concepts":[290],"name":"Describe a situation in which filtered data are more useful than the original unfiltered data"},{"concepts":[128],"name":"Describe a stochastic error model for a natural phenomenon"},{"concepts":[146],"name":"Describe a technique that can be used to represent the value of each of the components of data quality (positional and attribute accuracy, logical consistency, and completeness)"},{"concepts":[348],"name":"Describe a variety of philosophical frameworks upon which codes of professional ethics may be based"},{"concepts":[22],"name":"Describe a workflow for converting a implementing a data model in a GIS involving an Entity-Relationship (E-R) diagram and the Universal Modeling Language (UML)"},{"concepts":[239],"name":"Describe alternatives to quadtrees for representing hierarchical tessellations (e.g., hextrees, r-trees, pyramids)"},{"concepts":[256],"name":"Describe an application in which it is crucial to maintain previous versions of the database"},{"concepts":[290],"name":"Describe an application of hyperspectral image data"},{"concepts":[288],"name":"Describe an application that requires integration of remotely sensed data with GIS and/or GPS data"},{"concepts":[142],"name":"Describe color decisions made for various production workflows"},{"concepts":[151],"name":"Describe considerations for using maps on the Web as a method for downloading data"},{"concepts":[140],"name":"Describe differences in design needed for a map that is to be viewed on the Internet versus as a 5x7 foot poster, including a discussion of the effect of viewing distance, lighting, and media type"},{"concepts":[383],"name":"Describe different organizational models for coordinating GIS and T participants and stakeholders"},{"concepts":[108],"name":"Describe different types of movement and change"},{"concepts":[3],"name":"Describe difficulties in dealing with large spatial databases, especially those arising from spatial heterogeneity"},{"concepts":[3],"name":"Describe emerging geographical analysis techniques in geocomputation derived from artificial intelligence e.g., expert systems, artificial neural networks, genetic algorithms, and software agents"},{"concepts":[256],"name":"Describe existing algorithms designed for performing dynamic queries"},{"concepts":[125],"name":"Describe geographic phenomena in terms of their distances and directions (in space and time) Define spatial autocorrelation in the context of geographic proximity"},{"concepts":[124],"name":"Describe geographic phenomena in terms of their topological relationships (in space and time to other phenomena"},{"concepts":[61],"name":"Describe how a network of stream channels and ridges can be estimated from a Digital Elevation Model (DEM)"},{"concepts":[149],"name":"Describe how an animated map reveals patterns not evident without animation"},{"concepts":[131],"name":"Describe how compilation, production, and distribution methods used in map making have evolved"},{"concepts":[142],"name":"Describe how cultural differences with respect to color associations impact map design"},{"concepts":[4],"name":"Describe how data mining can be used for geospatial intelligence"},{"concepts":[309],"name":"Describe how geometric accuracy should be documented in terms of the FGDC metadata standard"},{"concepts":[339],"name":"Describe how geospatial data are used and maintained for land use planning, property value assessment, maintenance of public works, and other applications"},{"concepts":[366],"name":"Describe how GI S and T can be used in the decision-making process in organizations dealing with natural resource management, business management, public management or operations management"},{"concepts":[50],"name":"Describe how Independent Random Process/Chi-Squared Result IRP/CSR may be used to make statistical statements about point patterns"},{"concepts":[48],"name":"Describe how map algebra performs mathematical functions on raster grids"},{"concepts":[161],"name":"Describe how maps such as topographic maps are produced within certain relations of power and knowledge"},{"concepts":[292],"name":"Describe how sea surface temperatures are mapped"},{"concepts":[402],"name":"Describe how state GIS Councils can be used in enterprise GIS and T implementation processes"},{"concepts":[60],"name":"Describe how surfaces can be interpolated using splines"},{"concepts":[131],"name":"Describe how symbolization methods used in map making have evolved"},{"concepts":[149],"name":"Describe how the adding time-series data reveals or does not reveal patterns not evident in a cross-sectional data"},{"concepts":[236],"name":"Describe how to generate a unique TIN solution using Delaunay triangulation"},{"concepts":[376],"name":"Describe issues that may hinder implementation and continued successful operation of a GI system if effective methods of staff development are not included in the process"},{"concepts":[164],"name":"Describe maps that can be used to find direction, distance, or position, plan routes, calculate area or volume, or describe shape"},{"concepts":[14],"name":"Describe methods for measuring different kinds of accessibility on a network"},{"concepts":[8],"name":"Describe networks that apply to specific applications or industries"},{"concepts":[38],"name":"Describe operations that can be performed on qualitative representations of direction"},{"concepts":[112],"name":"Describe particular entities in terms of space, time, and properties"},{"concepts":[115],"name":"Describe particular events or processes in terms of identity, categories, attributes, locations, etc."},{"concepts":[110],"name":"Describe particular geographic phenomena in terms of attributes"},{"concepts":[122],"name":"Describe particular geographic phenomena in terms of their place in mereonomic hierarchies (parts and composites)"},{"concepts":[337],"name":"Describe perspectives on the nature and scope of system benefits among agency officials, organizational personnel, and citizens"},{"concepts":[381],"name":"Describe political, economic, administrative, and other social forces in agencies, organizations, and citizens that inhibit or promote sharing of geospatial and other data"},{"concepts":[399],"name":"Describe possible benefits to an organization by participating in a given society that is related to GIS and T"},{"concepts":[11],"name":"Describe practical situations in which flow is conserved while splitting or joining at nodes of the network"},{"concepts":[159],"name":"Describe print quality characteristics and price differences for limited-run color map distribution"},{"concepts":[159],"name":"Describe production concerns that might be discussed with a publisher who will print a map product"},{"concepts":[42],"name":"Describe real world applications where adjacency and connectivity are a critical component of analysis"},{"concepts":[41],"name":"Describe real world applications where distance decay is an appropriate representation of the strength of spatial relationships (e.g., shopping behavior, property values)"},{"concepts":[41],"name":"Describe real world applications where distance decay would not be an appropriate representation of the strength of spatial relationships (e.g., distance education, commuting, telecommunications)"},{"concepts":[73],"name":"Describe sampling schemes for accurately estimating the mean of a spatial data set"},{"concepts":[32],"name":"Describe set theory"},{"concepts":[37],"name":"Describe several different measures of distance between two points e.g., Euclidean, Manhattan, network distance, spherical"},{"concepts":[147],"name":"Describe situations in which methods of terrain representation (e.g., shaded relief, contours, hypsometric tints, block diagrams, profiles) are well suited"},{"concepts":[147],"name":"Describe situations in which methods of terrain representation are poorly suited"},{"concepts":[75],"name":"Describe some commonly used semi-variogram models"},{"concepts":[96],"name":"Describe some insights that a spatial perspective can contribute to a given topic"},{"concepts":[10],"name":"Describe some variants of Dijkstras algorithm that are even more efficient"},{"concepts":[254],"name":"Describe techniques for handling version control in spatial databases"},{"concepts":[254],"name":"Describe techniques for managing long transactions in a multi-user environment"},{"concepts":[115],"name":"Describe the actor role that entities and fields play in events and processes"},{"concepts":[239],"name":"Describe the advantages and disadvantages of the quadtree model for geographic database representation and modeling"},{"concepts":[236],"name":"Describe the architecture of the TIN model"},{"concepts":[363],"name":"Describe the basic principles of randomness and probability"},{"concepts":[82],"name":"Describe the characteristics of the spatial expansion method"},{"concepts":[127],"name":"Describe the cognitive processes that tend to create vagueness"},{"concepts":[142],"name":"Describe the common color models used in mapping"},{"concepts":[119],"name":"Describe the common constraints on spatial integration"},{"concepts":[310],"name":"Describe the component measures and the utility of a misclassification matrix"},{"concepts":[375],"name":"Describe the components of a needs assessment for an enterprise GIS"},{"concepts":[236],"name":"Describe the conditions under which a TIN might be more practical than GRID"},{"concepts":[75],"name":"Describe the conditions under which each of the commonly used semi-variograms models would be most appropriate"},{"concepts":[131],"name":"Describe the contributions by Robinson, Jenks, Raisz, and others to US academic cartography"},{"concepts":[109],"name":"Describe the contributions of category theory to understanding the internal structure of categories"},{"concepts":[401],"name":"Describe the data programs provided by organizations such as The National Map, GeoSpatial One Stop, and National Integrated Land System"},{"concepts":[202],"name":"Describe the degree to which attributes need to be modeled in the conceptual modeling phase"},{"concepts":[145],"name":"Describe the design considerations for each of the following methods: choropleth, dasymetric, proportioned symbol, graduated symbol, isoline, dot, cartogram, and flow map"},{"concepts":[140],"name":"Describe the design needs of special purpose maps such as subdivision plans, cadastral mapping, drainage plans, nautical charts, aeronautical charts, geological maps, military maps, wire-mesh volume maps, and 3D plans of urban change"},{"concepts":[55],"name":"Describe the difference between prescriptive and descriptive cartographic models"},{"concepts":[164],"name":"Describe the differences between azimuths, bearings, and other systems for indicating directions"},{"concepts":[374],"name":"Describe the differences between licensing, certification and accreditation in relation to GIS and T positions and qualifications"},{"concepts":[92],"name":"Describe the differences between real phenomena, conceptual models, and GIS data representations thereof"},{"concepts":[310],"name":"Describe the different measurement levels on which thematic accuracy is based"},{"concepts":[112],"name":"Describe the difficulties in modeling entities with ill-defined edges"},{"concepts":[112],"name":"Describe the difficulties inherent in extending the tabletop metaphor of objects to the geographic environment"},{"concepts":[69],"name":"Describe the effect of non-stationarity on local indices of spatial association"},{"concepts":[68],"name":"Describe the effect of the assumption of stationarity on global measures of spatial association"},{"concepts":[97],"name":"Describe the elements of a sense of place or landscape that are difficult or impossible to adequately represent in GIS"},{"concepts":[283],"name":"Describe the elements of image interpretation"},{"concepts":[355],"name":"Describe the extent to which contemporary GIS and T supports diverse ways of understanding the world"},{"concepts":[53],"name":"Describe the formulation of the classic gravity model, the unconstrained spatial interaction model, the production constrained spatial interaction model, the attraction constrained spatial interaction model, and the doubly constrained spatial..."},{"concepts":[114],"name":"Describe the genealogy (as identity-based change or temporal relationships) of particular geographic phenomena"},{"concepts":[79],"name":"Describe the general types of spatial econometric model"},{"concepts":[26],"name":"Describe the impact of map projection transformation on raster and vector data"},{"concepts":[309],"name":"Describe the impact of the concept of dilution of precision on the uncertainty of GPS positioning"},{"concepts":[56],"name":"Describe the implementation of an ordered weighting scheme in a multiple-criteria aggregation"},{"concepts":[348],"name":"Describe the individuals or groups to which GIS and T professionals have ethical obligations"},{"concepts":[243],"name":"Describe the integrity constraints of integrated topological models (e.g., POLYVRT)"},{"concepts":[403],"name":"Describe the leading academic journals serving the GIS and T community"},{"concepts":[94],"name":"Describe the limitations of various information stores for representing geographic information, including the mind, computers, graphics, text, etc."},{"concepts":[281],"name":"Describe the location and geometric characteristics of the principal point of an aerial image"},{"concepts":[112],"name":"Describe the perceptual processes (e.g., edge detection) that aid cognitive objectification"},{"concepts":[81],"name":"Describe the relationship between factorial kriging and spatial filtering"},{"concepts":[76],"name":"Describe the relationship between the semi-variogram and kriging"},{"concepts":[51],"name":"Describe the relationships between kernels and classical spatial interaction approaches, such as surfaces of potential"},{"concepts":[74],"name":"Describe the relationships between semi-variograms and correlograms, and Morans indices of spatial association"},{"concepts":[143],"name":"Describe the role of labels in assisting readers in understanding feature locations (e.g., label to the right of point, label follows line indicating its position, area label assists understanding extent of feature and feature type)"},{"concepts":[383],"name":"Describe the roles and relationships of GIS and T support staff"},{"concepts":[349],"name":"Describe the sanctions imposed by ASPRS and GISCI on individuals whose professional actions violate the codes of ethics"},{"concepts":[290],"name":"Describe the sequence of tasks involved in the geometric correction of the Advanced Very High Resolution Radiometer (AVHRR) Global Land Dataset"},{"concepts":[285],"name":"Describe the source data, instrumentation, and workflow involved in extracting vector data (features and elevations) from analog and digital stereoimagery"},{"concepts":[383],"name":"Describe the stages of two different models of implementing a GIS within an organization"},{"concepts":[65],"name":"Describe the statistical characteristics of a set of spatial data using a variety of graphs and plots including scatterplots, histograms, boxplots, qq plots"},{"concepts":[17],"name":"Describe the structure of linear programs"},{"concepts":[19],"name":"Describe the structure of origin-destination matrices"},{"concepts":[398],"name":"Describe the U.S. geospatial industry including vendors, software, hardware and data"},{"concepts":[271],"name":"Describe the use of based on temporal relationships of objects and space (Crime or disease analyses are examples)"},{"concepts":[358],"name":"Describe the use of GIS from a political ecology point of view (e.g., consider the use of GIS for resource identification, conservation, and allocation by an NGO in Sub-Saharan Africa)"},{"concepts":[150],"name":"Describe the uses of the map as a user interface element in interactive presentations of geographic information"},{"concepts":[304],"name":"Describe the visual appearance of the Earths graticule"},{"concepts":[119],"name":"Describe the ways in which a spatial perspective enables the synthesis of different subjects (e.g., climate and economy)"},{"concepts":[98],"name":"Describe the ways in which the elements of culture (e.g., language, religion, education, traditions) may influence the understanding and use of geographic information"},{"concepts":[398],"name":"Describe three applications of geospatial technology for different workforce domains (e.g., first responders, forestry, water resource management, facilities management)"},{"concepts":[114],"name":"Describe ways in which a geographic entity can be created from one or more others"},{"concepts":[148],"name":"Design a map series to show the change in a geographic pattern over time"},{"concepts":[73],"name":"Design a sampling scheme that will help detect when space-time clusters of events occur"},{"concepts":[6],"name":"Design a simple spatial mean filter"},{"concepts":[148],"name":"Design a single map symbol that can be used to symbolize a set of related variables"},{"concepts":[255],"name":"Design a test of reliability of change information (e.g., the logical consistency of updates to the TIGER database)"},{"concepts":[59],"name":"Design an algorithm that calculates slope and aspect from a Triangulated Irregular Network (TIN) model"},{"concepts":[60],"name":"Design an algorithm which interpolates irregular point elevation data onto a regular grid"},{"concepts":[202],"name":"Design application-specific conceptual models"},{"concepts":[116],"name":"Design data models for specific applications based on these comprehensive general models"},{"concepts":[140],"name":"Design maps that are appropriate for users with vision limitations"},{"concepts":[148],"name":"Detect a multivariate outlier using a combination of maps and graphs"},{"concepts":[164],"name":"Determine feature counts of point, line, and area features on maps"},{"concepts":[402],"name":"Determine if your state has a Geospatial Information Office (GIO) and discuss the mission, history, constituencies and activities of a GIO"},{"concepts":[142],"name":"Determine the CMYK (cyan, magenta, yellow, and black) primary amounts in a selection of colors"},{"concepts":[110],"name":"Determine the proper uses of attributes based on their domains"},{"concepts":[114],"name":"Determine whether it is important to represent the genealogy of entities for a particular application"},{"concepts":[116],"name":"Determine whether phenomena or applications exist that are not adequately represented in an existing comprehensive model"},{"concepts":[56],"name":"Determine which method to use to combine criteria e.g., linear, multiplication"},{"concepts":[203],"name":"Determine which relationships need to be stored explicitly in the database"},{"concepts":[403],"name":"Develop a bibliography of scholarly and professional articles and/or books that are relevant to a particular GIS and T project"},{"concepts":[55],"name":"Develop a flowchart of a cartographic model for a site suitability problem"},{"concepts":[39],"name":"Develop a method for describing the shape of a cluster of similarly valued points by using the concept of the convex hull"},{"concepts":[150],"name":"Develop a useful interactive interface and legend for an animated map"},{"concepts":[110],"name":"Develop alternative forms of representations for situations in which attributes do not adequately capture meaning"},{"concepts":[39],"name":"Develop an algorithm to determine the skeleton of polygons"},{"concepts":[146],"name":"Develop graphic techniques that clearly show different forms of inexactness (e.g., existence uncertainty, boundary location uncertainty, attribute ambiguity, transitional boundary) of a given feature (e.g., a culture region)"},{"concepts":[101],"name":"Develop methods for representing non-cartesian models of space in GIS"},{"concepts":[363],"name":"Devise simple ways to represent probability information in GIS"},{"concepts":[147],"name":"Differentiate 3D representations from 2.5 D representations"},{"concepts":[231],"name":"Differentiate among a lattice, a tessellation, and a grid"},{"concepts":[23],"name":"Differentiate among common interpolation techniques (e.g., nearest neighbor, bilinear, bicubic)"},{"concepts":[118],"name":"Differentiate among different types of regions, including functional, cultural, physical, administrative, and others"},{"concepts":[117],"name":"Differentiate among distributions in space, time, and attribute"},{"concepts":[97],"name":"Differentiate among elements of the meaning of a place that can or cannot be easily represented using geospatial technologies"},{"concepts":[6],"name":"Differentiate among machine learning, data mining and pattern recognition"},{"concepts":[311],"name":"Differentiate among the spatial, spectral, radiometric, and temporal resolution of a remote sensing instrument"},{"concepts":[366],"name":"Differentiate an enterprise system from a department-centered GI system"},{"concepts":[127],"name":"Differentiate applications in which vagueness is an acceptable trait from those in which it is unacceptable"},{"concepts":[105],"name":"Differentiate applications that can make use of common-sense principles of geography from those that should not"},{"concepts":[18],"name":"Differentiate between a linear program and an integer program"},{"concepts":[101],"name":"Differentiate between absolute and relative descriptions of location"},{"concepts":[289],"name":"Differentiate between active and passive sensors, citing examples of each"},{"concepts":[101],"name":"Differentiate between common-sense, Cartesian metric, relational, relativistic, phenomenological, social constructivist, and other theories of the nature of space"},{"concepts":[203],"name":"Differentiate between conceptual and logical models, in terms of the level of detail, constraints, and range of information included"},{"concepts":[56],"name":"Differentiate between contributing factors and constraints in a multi-criteria application"},{"concepts":[4],"name":"Differentiate between data mining approaches used for spatial and non-spatial applications"},{"concepts":[57],"name":"Differentiate between deterministic and stochastic spatial process models"},{"concepts":[1],"name":"Differentiate between geostatistics, and spatial statistics"},{"concepts":[66],"name":"Differentiate between isotropic and anisotropic processes"},{"concepts":[51],"name":"Differentiate between kernel density estimation and spatial interpolation"},{"concepts":[204],"name":"Differentiate between logical and physical models, in terms of the level of detail, constraints, and range of information included"},{"concepts":[232],"name":"Differentiate between lossy and lossless compression methods"},{"concepts":[48],"name":"Differentiate between map algebra and matrix algebra using real examples"},{"concepts":[107],"name":"Differentiate between mathematical and phenomenological theories of the nature of time"},{"concepts":[73],"name":"Differentiate between model-based and design-based sampling schemes"},{"concepts":[26],"name":"Differentiate between polynomial coordinate transformations (including linear) and rubbersheeting"},{"concepts":[97],"name":"Differentiate between space and place"},{"concepts":[127],"name":"Differentiate between the following concepts: vagueness and ambiguity, well defined and poorly defined objects and fields or discord and non-specificity"},{"concepts":[53],"name":"Differentiate between the gravity model and spatial interaction models"},{"concepts":[60],"name":"Differentiate between trend surface analysis and deterministic spatial interpolation"},{"concepts":[281],"name":"Differentiate oblique and vertical aerial imagery"},{"concepts":[289],"name":"Differentiate push-broom and cross-track scanning technologies"},{"concepts":[307],"name":"Differentiate rectification and orthorectification"},{"concepts":[290],"name":"Differentiate supervised classification from unsupervised classification"},{"concepts":[148],"name":"Differentiate the interpretation of a series of three maps and a single multivariate map, each representing the same three related variables"},{"concepts":[128],"name":"Differentiate uncertainty in geospatial situations from vagueness"},{"concepts":[113],"name":"Differentiate various sources of fields, such as substance properties (e.g., temperature), artificial constructs (e.g., population density), and fields of potential or influence (e.g., gravity)"},{"concepts":[318],"name":"Digitize and georegister a specified vector feature set to a given geometric accuracy and topological fidelity thresholds using a given map sheet, digitizing tablet, and data entry software"},{"concepts":[108],"name":"Discuss common prepositions and adjectives (in any particular language) that signify either spatial or temporal relations but are used for both kinds, such as after or longer"},{"concepts":[355],"name":"Discuss critiques of GIS as deterministic technology in relation to debates about the Quantitative Revolution in the discipline of geography"},{"concepts":[377],"name":"Discuss different formats (tutorials, in house, online, instructor lead) for training and how they can be used by organizations"},{"concepts":[285],"name":"Discuss future prospects for automated feature extraction from aerial imagery"},{"concepts":[374],"name":"Discuss how a code of ethics might be applied within an organization"},{"concepts":[402],"name":"Discuss how informal and formal regional bodies (e.g., Metro GIS) can help support GIS and T in an organization"},{"concepts":[310],"name":"Discuss how measures of spatial autocorrelation may be used to evaluate thematic accuracy"},{"concepts":[161],"name":"Discuss how the choices used in the design of a road map will influence the experience visitors may have of the area"},{"concepts":[140],"name":"Discuss how to create an intellectual and visual hierarchy on maps"},{"concepts":[337],"name":"Discuss implications of unequal economic power on the kinds of organizations that use, and benefit from, GIS and T"},{"concepts":[1],"name":"Discuss situations when it is desirable to adopt a spatial approach to the analysis of data"},{"concepts":[247],"name":"Discuss some of the difficulties of applying the standard process-pattern concept to lines and networks"},{"concepts":[185],"name":"Discuss the advantages and disadvantages of outsourcing elements of the implementation of a geospatial system, such as data entry"},{"concepts":[101],"name":"Discuss the advantages and disadvantages of the use of cartesian metric space as a basis for GIS and related technologies"},{"concepts":[311],"name":"Discuss the advantages and potential problems associated with the use of Minimum Mapping Unit (MMU) as a measure of the level of detail in land use, land cover, and soils maps"},{"concepts":[67],"name":"Discuss the appropriateness of different types of spatial weights matrices for various problems"},{"concepts":[82],"name":"Discuss the appropriateness of GWR under various conditions"},{"concepts":[117],"name":"Discuss the causal relationship between spatial processes and spatial patterns, including the possible problems in determining causality"},{"concepts":[52],"name":"Discuss the characteristics of the various cluster detection techniques"},{"concepts":[25],"name":"Discuss the consequences of increasing and decreasing resolution"},{"concepts":[116],"name":"Discuss the contributions of early attempts to integrate the concepts of space, time, and attribute in geographic information, such as Berry (1964) and Sinton (1978)"},{"concepts":[101],"name":"Discuss the contributions that different perspectives on the nature of space bring to an understanding of geographic phenomenon"},{"concepts":[116],"name":"Discuss the degree to which these models can be implemented using current technologies"},{"concepts":[10],"name":"Discuss the difference of implementing Dijkstras algorithm in raster and vector modes"},{"concepts":[140],"name":"Discuss the differences between maps that use the same data but are for different purposes and intended audiences"},{"concepts":[140],"name":"Discuss the differences between maps that use the same data but are for different purposes and intended audiences"},{"concepts":[96],"name":"Discuss the differing denotations and connotations of the terms spatial, geographic, and geospatial"},{"concepts":[115],"name":"Discuss the difficulty of integrating process models into GIS software based on the entity and field views, and methods used to do so"},{"concepts":[114],"name":"Discuss the effects of temporal scale on the modeling of genealogical structures"},{"concepts":[348],"name":"Discuss the ethical implications of a local government's decision to charge fees for its data"},{"concepts":[356],"name":"Discuss the ethical implications of the use of GIS and T as a surveillance technology"},{"concepts":[285],"name":"Discuss the extent to which vector data extraction from aerial stereoimagery has been automated"},{"concepts":[243],"name":"Discuss the historical roots of the Census Bureaus creation of GBF/DIME as the foundation for the development of topological data structures"},{"concepts":[112],"name":"Discuss the human predilection to conceptualize geographic phenomena in terms of discrete entities"},{"concepts":[253],"name":"Discuss the implication of long transactions on database integrity"},{"concepts":[355],"name":"Discuss the implications of interoperability on ontology"},{"concepts":[311],"name":"Discuss the implications of the sampling theorem (Lambda = 0.5 delta) to the concept of resolution"},{"concepts":[131],"name":"Discuss the influence of some cartographers of the 16th and 17th centuries (Mercator, Ortelius, Jansson, Homann and others)"},{"concepts":[151],"name":"Discuss the influence of the user interface on maps and visualizations on the Web"},{"concepts":[401],"name":"Discuss the mission, history constituencies and activities of international organizations such as Association of Geographic Information Laboratories for Europe (AGILE) and the European GIS Education Seminar (EUGISES)"},{"concepts":[401],"name":"Discuss the mission, history, constituencies, and activities of GeoSpatial One Stop"},{"concepts":[401],"name":"Discuss the mission, history, constituencies, and activities of governmental entities such as the Bureau of Land Management (BLM), United States Geological Survey (USGS) and the Environmental Protection Agency as they related to support..."},{"concepts":[402],"name":"Discuss the mission, history, constituencies, and activities of National States Geographic Information Council (NSGIC)"},{"concepts":[401],"name":"Discuss the mission, history, constituencies, and activities of the Federal Geographic Data Committee (FGDC)"},{"concepts":[397],"name":"Discuss the mission, history, constituencies, and activities of the GIS Certification Institute (GISCI)"},{"concepts":[401],"name":"Discuss the mission, history, constituencies, and activities of the Nation Integrated Land System (NILS)"},{"concepts":[401],"name":"Discuss the mission, history, constituencies, and activities of the National Academies of Science Mapping Science Committee"},{"concepts":[401],"name":"Discuss the mission, history, constituencies, and activities of the Open Geospatial Consortium (OGC), Inc."},{"concepts":[401],"name":"Discuss the mission, history, constituencies, and activities of the USGS and its National Map vision"},{"concepts":[401],"name":"Discuss the mission, history, constituencies, and activities of University Consortium of Geographic Science (UCGIS) and the National Center for Geographic Information and Analysis (NCGIA)"},{"concepts":[377],"name":"Discuss the National Research Council report on Learning to Think Spatially (2005) as it relates to spatial thinking skills needed by the GIS and T workforce"},{"concepts":[152],"name":"Discuss the nature and use of virtual environments such as Google Earth"},{"concepts":[55],"name":"Discuss the origins of cartographic modeling with reference to the work of Ian McHarg"},{"concepts":[131],"name":"Discuss the perspectives of Brian Harley and others on the political motivation for the development of certain kinds of maps"},{"concepts":[23],"name":"Discuss the pitfalls of using secondary data that has been generated using interpolations (e.g., Level 1 USGS DEMs)"},{"concepts":[401],"name":"Discuss the political, cultural, economic, and geographic characteristics of various countries that influence their adoption and use of GIS and T"},{"concepts":[357],"name":"Discuss the potential role of agency (individual action) in resisting dominant practices and in using GIS and T in ways that are consistent with feminist epistemologies and politics"},{"concepts":[358],"name":"Discuss the production, maintenance, and use of geospatial data by a government agency or private firm from the perspectives of a taxpayer, a community organization, and a member of a minority group"},{"concepts":[57],"name":"Discuss the relationship between spatial processes and spatial patterns"},{"concepts":[131],"name":"Discuss the relationship between the history of exploration and the development of a more accurate map of the world"},{"concepts":[142],"name":"Discuss the role of gamut in choosing colors that can be reproduced on various devices and media"},{"concepts":[243],"name":"Discuss the role of graph theory in topological structures"},{"concepts":[22],"name":"Discuss the role of metadata in facilitating conversation of data models and data structures between systems"},{"concepts":[374],"name":"Discuss the status of professional and academic certification in GIS and T"},{"concepts":[329],"name":"Discuss the status of the concept of privacy in the U.S. legal regime"},{"concepts":[131],"name":"Discuss the Swiss influence on map design and production, highlighting Imhofs contributions"},{"concepts":[66],"name":"Discuss the theory leading to the assumption of intrinsic stationarity"},{"concepts":[399],"name":"Discuss the value or effect of participation in societies, conferences, and informal communities to entities managing enterprise GIS"},{"concepts":[327],"name":"Discuss ways in which the geospatial profession is regulated under the U.S. legal regime"},{"concepts":[304],"name":"Discuss what a Tissot indicatrix represents and how it can be used to assess projection-induced error"},{"concepts":[203],"name":"Distinguish between the incidental and structural relationships found in a conceptual model"},{"concepts":[109],"name":"Document the personal, social, and or institutional meaning of categories used in GIS applications"},{"concepts":[288],"name":"Draw and explain a diagram that depicts the bands in the electromagnetic spectrum at which Earths atmosphere is sufficiently transparent to allow high-altitude remote sensing"},{"concepts":[288],"name":"Draw and explain a diagram that depicts the key bands of the electromagnetic spectrum in relation to the magnitude of electromagnetic energy emitted and/or reflected by the Sun and Earth across the spectrum"},{"concepts":[151],"name":"Edit the symbology, labeling, and page layout for a map originally designed for hard copy printing so that it can be seen and used on the Web"},{"concepts":[105],"name":"Effectively communicate the design, procedures, and results of GIS projects to non-GIS audiences (clients, managers, general public)"},{"concepts":[117],"name":"Employ techniques for visualizing, describing, and analyzing distributions in space, time, and attribute"},{"concepts":[23],"name":"Estimate a value between two known values using linear interpolation (e.g., spot elevations, population between census years)"},{"concepts":[142],"name":"Estimate RGB (red, green, blue) primary amounts in a selection of colors"},{"concepts":[184],"name":"Estimate the cost to collect needed data from primary sources (e.g., remote sensing, GPS)"},{"concepts":[37],"name":"Estimate the fractal dimension of a sinuous line"},{"concepts":[154],"name":"Evaluate graphic techniques used to portray spatializations"},{"concepts":[25],"name":"Evaluate methods used by contemporary GIS software to resample raster data on-the-fly during display"},{"concepts":[183],"name":"Evaluate possible solutions to the major obstacles that stand in the way of a successful GIS proposal"},{"concepts":[289],"name":"Evaluate the advantages and disadvantages of acoustic remote sensing versus airborne or satellite remote sensing for seafloor mapping"},{"concepts":[289],"name":"Evaluate the advantages and disadvantages of airborne remote sensing versus satellite remote sensing"},{"concepts":[284],"name":"Evaluate the advantages and disadvantages of photogrammetric methods and LiDAR for production of terrain elevation data"},{"concepts":[115],"name":"Evaluate the assertion that events and processes are the same thing, but viewed at different temporal scales"},{"concepts":[128],"name":"Evaluate the causes of uncertainty in geospatial data"},{"concepts":[97],"name":"Evaluate the differences in how various parties think or feel differently about a place being modeled"},{"concepts":[238],"name":"Evaluate the ease of measuring resolution in different types of tessellations"},{"concepts":[112],"name":"Evaluate the effectiveness of GIS data models for representing the identity, existence, and lifespan of entities"},{"concepts":[152],"name":"Evaluate the extent to which a GeoWall or CAVE does or does not enhance understanding of spatial data"},{"concepts":[113],"name":"Evaluate the field views description of objects as conceptual discretizations of continuous patterns"},{"concepts":[105],"name":"Evaluate the impact of geospatial technologies (e.g., Google Earth) that allow non-geospatial professionals to create, distribute, and map geographic information"},{"concepts":[238],"name":"Evaluate the implications of changing grid cell resolution on the results of analytical applications by using GIS software"},{"concepts":[112],"name":"Evaluate the influence of scale on the conceptualization of entities"},{"concepts":[89],"name":"Evaluate the influences of ones own philosophical views and assumptions on GIS AND T practices"},{"concepts":[85],"name":"Evaluate the influences of particular worldviews (including ones own) on GIS practices"},{"concepts":[99],"name":"Evaluate the influences of political actions, especially the allocation of territory, on human perceptions of space and place"},{"concepts":[99],"name":"Evaluate the influences of political ideologies (e.g., Marxism, Capitalism, conservative liberal) on the understanding of geographic information"},{"concepts":[185],"name":"Evaluate the labor needed in past cases to build a new geospatial enterprise"},{"concepts":[243],"name":"Evaluate the positive and negative impacts of this shift from integrated topological models"},{"concepts":[232],"name":"Evaluate the relative merits of grid compression methods for storage"},{"concepts":[113],"name":"Evaluate the representation of movement as a field of location over time (e.g. :x,y,z: = f(t) )"},{"concepts":[127],"name":"Evaluate the role that system complexity, dynamic processes, and subjectivity play in the creation of vague phenomena and concepts"},{"concepts":[145],"name":"Evaluate the strengths and limitations of each of the following methods: choropleth, dasymetric, proportioned symbol, graduated symbol, isoline, dot, cartogram, and flow map"},{"concepts":[291],"name":"Evaluate the thematic accuracy of a given soils map"},{"concepts":[203],"name":"Evaluate the various general data models common in GIS and T for a given project, and select the most appropriate solutions"},{"concepts":[127],"name":"Evaluate vagueness in the locations, time, attributes, and other aspects of geographic phenomena"},{"concepts":[142],"name":"Exemplify colors for different forms of harmony, concordance, and balance"},{"concepts":[66],"name":"Exemplify deterministic and spatial stochastic processes"},{"concepts":[107],"name":"Exemplify different temporal frames of reference: linear and cyclical, absolute and relative"},{"concepts":[375],"name":"Exemplify each component of a needs assessment for an enterprise GIS"},{"concepts":[256],"name":"Exemplify how the lack of a data librarian to manage data can have disastrous consequences on the resulting dataset"},{"concepts":[383],"name":"Exemplify how to make GIS and T relevant to top managemen"},{"concepts":[161],"name":"Exemplify maps that illustrate the provocative, propaganda, political, and persuasive nature of maps and geospatial data"},{"concepts":[66],"name":"Exemplify non-stationarity involving first and second order effects"},{"concepts":[118],"name":"Exemplify regions found at different scales"},{"concepts":[253],"name":"Exemplify scenarios in which one would need to perform a number of periodic changes in a real GIS database"},{"concepts":[39],"name":"Exemplify situations in which the centroid of a polygon falls outside its boundary"},{"concepts":[12],"name":"Exemplify the Classic Transportation Problem"},{"concepts":[243],"name":"Exemplify the concept of planar enforcement (e.g., TIN triangles)"},{"concepts":[234],"name":"Exemplify the uses (past and potential) of the hexagonal model"},{"concepts":[80],"name":"Explain Anselins typology of spatial autoregressive models"},{"concepts":[38],"name":"Explain any differences in the measured direction between two places when the data are presented in a GIS in different projections"},{"concepts":[5],"name":"Explain how a Bayesian framework can incorporate expert knowledge in order to retrieve all relevant datasets given an initial user query"},{"concepts":[388],"name":"Explain how a business case analysis can be used to justify the expense of implementing consensus-based standards"},{"concepts":[247],"name":"Explain how a graph (network) may be directed or undirected"},{"concepts":[247],"name":"Explain how a graph can be written as an adjacency matrix and how this can be used to calculate topological shortest paths in the graph"},{"concepts":[10],"name":"Explain how a leading World Wide Web-based routing system works e.g., MapQuest, Yahoo Maps, Google"},{"concepts":[41],"name":"Explain how a semi-variogram describes the distance decay in dependence between data values"},{"concepts":[68],"name":"Explain how a statistic that is based on combining all the spatial data and returning a single summary value or two can be useful in understanding broad spatial trends"},{"concepts":[358],"name":"Explain how a tax assessors office adoption of GIS and T may affect power relations within a community"},{"concepts":[69],"name":"Explain how a weights matrix can be used to convert any classical statistic into a local measure of spatial association"},{"concepts":[82],"name":"Explain how allowing the parameters of the model to vary with the spatial location of the sample data can be used to accommodate spatial heterogeneity"},{"concepts":[131],"name":"Explain how Bertin has influenced trends in cartographic symbolization"},{"concepts":[76],"name":"Explain how block-kriging and its variants can be used to combine data sets with different spatial resolution support"},{"concepts":[387],"name":"Explain how clearing houses, metadata, and standards can help facilitate spatial data sharing"},{"concepts":[329],"name":"Explain how conversion of land records data from analog to digital form increases risk to personal privacy"},{"concepts":[329],"name":"Explain how data aggregation is used to protect personal privacy in data produced by the U.S. Census Bureau"},{"concepts":[37],"name":"Explain how different measures of distance can be used to calculate the spatial weights matrix"},{"concepts":[67],"name":"Explain how different types of spatial weights matrices are defined and calculated"},{"concepts":[81],"name":"Explain how dissolving clusters of blocks with similar values may resolve the spatial correlation problem"},{"concepts":[50],"name":"Explain how distance-based methods of point pattern measurement can be derived from a distance matrix"},{"concepts":[53],"name":"Explain how dynamic, chaotic, complex or unpredictable aspects in some phenomena make spatial interaction models more appropriate than gravity models"},{"concepts":[37],"name":"Explain how fractal dimension can be used in practical applications of GIS"},{"concepts":[63],"name":"Explain how friction surfaces are enhanced by the use of impedance and barriers"},{"concepts":[69],"name":"Explain how geographically weighted regression provides a local measure of spatial association"},{"concepts":[309],"name":"Explain how geometric accuracies associated with the various orders of the U.S. horizontal geodetic control network are assured"},{"concepts":[339],"name":"Explain how geospatial information might be used in a taking of private property through a governments claim of its right of eminent domain"},{"concepts":[366],"name":"Explain how GIS and T can be an integrating technology"},{"concepts":[231],"name":"Explain how grid representations embody the field-based view"},{"concepts":[150],"name":"Explain how interactivity influences map use in animated displays"},{"concepts":[161],"name":"Explain how legal issues impact the design and content of such special purpose maps as subdivision plans, nautical charts and cadastral maps"},{"concepts":[32],"name":"Explain how logic theory relates to set theory"},{"concepts":[164],"name":"Explain how maps can be used in determining an optimal route or facility selection"},{"concepts":[164],"name":"Explain how maps can be used in terrain analysis (e.g., elevation determination, surface profiles, slope, viewsheds, and gradient)"},{"concepts":[147],"name":"Explain how maps that show the landscape in profile can be used to represent terrain"},{"concepts":[349],"name":"Explain how one or more obligations in the GIS Code of Ethics may conflict with organizations proprietary interests"},{"concepts":[253],"name":"Explain how one would establish the criteria for monitoring the periodic changes in a real GIS database"},{"concepts":[16],"name":"Explain how optimization models can be used to generate models of alternate options for presentation to decision makers"},{"concepts":[70],"name":"Explain how outliers affect the results of analyses"},{"concepts":[387],"name":"Explain how privacy and commoditization of data impact influences decisions regarding spatial data infrastructures"},{"concepts":[50],"name":"Explain how proximity polygons e.g., Thiessen polygons may be used to describe point patterns"},{"concepts":[239],"name":"Explain how quadtrees and other hierarchical tessellations can be used to index large volumes of raster or vector data"},{"concepts":[142],"name":"Explain how real-world connotations (e.g., blue=water, white=snow) can be used to determine color selections on maps"},{"concepts":[311],"name":"Explain how resampling affects the resolution of image data"},{"concepts":[388],"name":"Explain how resistance to change affects the adoption of standards in an organization coordinating a GIS"},{"concepts":[61],"name":"Explain how ridgelines and streamlines can be used to improve the result of an interpolation process"},{"concepts":[292],"name":"Explain how sea surface temperature maps are used to predict El Nino events"},{"concepts":[32],"name":"Explain how set theory relates to spatial queries"},{"concepts":[59],"name":"Explain how slope and aspect can be represented as the vector field given by the first derivative of height"},{"concepts":[81],"name":"Explain how spatial correlation can result as a side effect of the spatial aggregation in a given dataset"},{"concepts":[5],"name":"Explain how spatial data mining techniques can be used for knowledge discovery"},{"concepts":[79],"name":"Explain how spatial dependence and spatial heterogeneity violate the Gauss-Markov assumptions of regression used in traditional econometrics"},{"concepts":[154],"name":"Explain how spatial metaphors can be used to illustrate the relationship among ideas"},{"concepts":[4],"name":"Explain how spatial statistics techniques are used in spatial data mining"},{"concepts":[154],"name":"Explain how spatialization is a core component of visual analytics"},{"concepts":[131],"name":"Explain how technological changes have affected cartographic design and production"},{"concepts":[231],"name":"Explain how terrain elevation can be represented by a regular tessellation and by an irregular tessellation"},{"concepts":[143],"name":"Explain how text properties can be used as visual variables to graphically represent the type and attributes of geographic features"},{"concepts":[4],"name":"Explain how the analytical reasoning techniques, visual representations, and interaction techniques that make up the domain of visual analytics have a strong spatial component"},{"concepts":[71],"name":"Explain how the Bayesian perspective is a unified framework from which to view uncertainty"},{"concepts":[97],"name":"Explain how the concept of place is more than just location"},{"concepts":[306],"name":"Explain how the concepts of the tangent and secant cases relate to the idea of a standard line"},{"concepts":[23],"name":"Explain how the elevation values in a digital elevation model (DEM) are derived by interpolation from irregular arrays of spot elevations"},{"concepts":[128],"name":"Explain how the familiar concepts of geographic objects and fields affect the conceptualization of uncertainty"},{"concepts":[70],"name":"Explain how the following techniques can be used to examine outliers: tabulation, histograms, box plots, correlation analysis, scatter plots, local statistics"},{"concepts":[81],"name":"Explain how the Getis and Tiefelsdorf Griffith spatial filtering techniques incorporate spatial component variables into OLS regression analysis in order to remedy misspecification and the problem of spatially auto-correlated residuals"},{"concepts":[50],"name":"Explain how the K function provides a scale-dependent measure of dispersion"},{"concepts":[68],"name":"Explain how the K function provides a scale-dependent measure of dispersion"},{"concepts":[230],"name":"Explain how the raster data model instantiates a grid representation"},{"concepts":[164],"name":"Explain how the types of distortion indicated by projection metadata on a map will affect map measurements"},{"concepts":[24],"name":"Explain how the vector/raster/vector conversion process of graphic images and algorithms takes place and how the results are achieved"},{"concepts":[152],"name":"Explain how the virtual and immersive environments become increasingly more complex as we move from the relatively non-immersive VRML desktop environment to a stereoscopic display (e.g., a GeoWall) to a more fully immersive CAVE"},{"concepts":[290],"name":"Explain how to enhance contrast of reflectance values clustered within a narrow band of wavelengths"},{"concepts":[143],"name":"Explain how to label features with indeterminate boundaries (canyons, oceans, etc.)"},{"concepts":[3],"name":"Explain how to recognize contaminated data in large datasets"},{"concepts":[291],"name":"Explain how U.S. Geological Survey scientists and contractors assess the accuracy of the National Land Cover Dataset"},{"concepts":[40],"name":"Explain how variations in the calculation of area may have real world implications, such as calculating density"},{"concepts":[152],"name":"Explain how various data formats and software and hardware environments support immersive visualization"},{"concepts":[5],"name":"Explain how visual data exploration can be combined with data mining techniques as a means of discovering research hypotheses in large spatial datasets"},{"concepts":[232],"name":"Explain the advantage of wavelet compression"},{"concepts":[243],"name":"Explain the advantages and disadvantages of topological data models"},{"concepts":[357],"name":"Explain the argument that GIS and remote sensing foster a disembodied way of knowing the world"},{"concepts":[358],"name":"Explain the argument that GIS is socially constructed"},{"concepts":[355],"name":"Explain the argument that GIS privileges certain views of the world over others"},{"concepts":[329],"name":"Explain the argument that human tracking systems enable geoslavery"},{"concepts":[358],"name":"Explain the argument that, throughout history, maps have been used to depict social relations"},{"concepts":[33],"name":"Explain the basic logic of SQL syntax"},{"concepts":[48],"name":"Explain the categories of map algebra operations i.e., local, focal, zonal, and global functions"},{"concepts":[305],"name":"Explain the concept developable surface and reference globe as conceptual ways of projecting the Earths surface"},{"concepts":[304],"name":"Explain the concept of a compromise projection and for which purposes it is useful"},{"concepts":[339],"name":"Explain the concept of a spatial decision support system"},{"concepts":[53],"name":"Explain the concept of competing destinations, describing how traditional spatial interaction model forms are modified to account for it"},{"concepts":[288],"name":"Explain the concept of data fusion in relation to remote sensing applications in GIS and T"},{"concepts":[309],"name":"Explain the concept of dilution of precision"},{"concepts":[312],"name":"Explain the concept of error propagation"},{"concepts":[16],"name":"Explain the concept of solution space"},{"concepts":[9],"name":"Explain the concept of the diameter of a network"},{"concepts":[76],"name":"Explain the concept of the kriging variance, and describe some of its shortcomings"},{"concepts":[19],"name":"Explain the concepts of demand and service"},{"concepts":[288],"name":"Explain the concepts of spatial resolution, radiometric resolution, and spectral sensitivity"},{"concepts":[122],"name":"Explain the contributions of formal mathematical methods such as Graph Theory to the study and application of geographic structures"},{"concepts":[164],"name":"Explain the differences between true north, magnetic north, and grid north directional references"},{"concepts":[37],"name":"Explain the differences in the calculated distance between the same two places when data used are in different projections"},{"concepts":[313],"name":"Explain the distinction between primary and secondary data sources in terms of census data, cartographic data, and remotely sensed data"},{"concepts":[310],"name":"Explain the distinction between thematic accuracy, geometric accuracy, and topological fidelity"},{"concepts":[122],"name":"Explain the effects of spatial or temporal scale on the perception of structure"},{"concepts":[309],"name":"Explain the factors that influence the geometric accuracy of data produced with Global Positioning System (GPS) receivers"},{"concepts":[309],"name":"Explain the formula for calculating root mean square error"},{"concepts":[402],"name":"Explain the functions, mission, history, constituencies, and activities of your state GIS Council and related formal and informal bodies"},{"concepts":[109],"name":"Explain the human tendency to simplify the world using categories"},{"concepts":[131],"name":"Explain the impact of advances in visualization methods in the evolution of cartography"},{"concepts":[304],"name":"Explain the kind of distortion that occurs when raster data are projected"},{"concepts":[56],"name":"Explain the legacy of multi-criteria evaluation in relation to cartographic modeling"},{"concepts":[234],"name":"Explain the limitations of the grid model compared to the hexagonal model"},{"concepts":[305],"name":"Explain the mathematical basis by which latitude and longitude locations are projected into x and y coordinate space"},{"concepts":[122],"name":"Explain the modeling of structural relationships in standard GIS data models, such as stored topology"},{"concepts":[118],"name":"Explain the nature of the Modifiable Areal Unit Problem (MAUP)"},{"concepts":[75],"name":"Explain the necessity of defining a semi-variogram model for geographic data"},{"concepts":[42],"name":"Explain the nine-intersection model for spatial relationships"},{"concepts":[87],"name":"Explain the notions of model and representation in science"},{"concepts":[202],"name":"Explain the objectives of the conceptual modeling phase of design"},{"concepts":[6],"name":"Explain the outcome of an artificial intelligence analysis e.g., edge recognition, including a discussion of what the human did not see that the computer identified and vice versa"},{"concepts":[281],"name":"Explain the phenomenon that is recorded in an aerial image"},{"concepts":[309],"name":"Explain the principle of differential correction in relation to the global positioning system"},{"concepts":[289],"name":"Explain the principle of multibeam bathymetric mapping"},{"concepts":[82],"name":"Explain the principles of geographically weighted regression"},{"concepts":[16],"name":"Explain the principles of operations research modeling and location modeling"},{"concepts":[6],"name":"Explain the principles of pattern recognition"},{"concepts":[304],"name":"Explain the rationale for the selection of the geometric property that is preserved in map projections used as the basis of the UTM and SPC systems"},{"concepts":[41],"name":"Explain the rationale for using different forms of distance decay functions"},{"concepts":[67],"name":"Explain the rationale used for each type of spatial weights matrix"},{"concepts":[148],"name":"Explain the relationship among several variables in a parallel coordinate plot"},{"concepts":[118],"name":"Explain the relationship between regions and categories"},{"concepts":[284],"name":"Explain the relevance of the concept parallax in stereoscopic aerial imagery"},{"concepts":[307],"name":"Explain the role and selection criteria for ground control points (GCPs) in the georegistration of aerial imagery"},{"concepts":[109],"name":"Explain the role of categories in common-sense conceptual models, everyday language, and analytical procedures"},{"concepts":[17],"name":"Explain the role of constraint functions using the graphical method"},{"concepts":[17],"name":"Explain the role of constraint functions using the simplex method"},{"concepts":[92],"name":"Explain the role of metaphors and image schema in our understanding of geographic phenomena and geographic tasks"},{"concepts":[17],"name":"Explain the role of objective functions in linear programming"},{"concepts":[281],"name":"Explain the significance of bit depth in aerial imaging"},{"concepts":[62],"name":"Explain the sources and impact of errors that affect intervisibility analyses"},{"concepts":[203],"name":"Explain the various types of cardinality found in databases"},{"concepts":[19],"name":"Explain Webers locational triangle"},{"concepts":[1],"name":"Explain what is added to spatial analysis to make it spatio-temporal analysis"},{"concepts":[39],"name":"Explain what is meant by the convex hull and minimum enclosing rectangle of a set of point data"},{"concepts":[3],"name":"Explain what is meant by the term contaminated data, suggesting how it can arise"},{"concepts":[1],"name":"Explain what is special i.e., difficult about geospatial data analysis and why some traditional statistical analysis techniques are not suited to geographic problems"},{"concepts":[51],"name":"Explain why and how density estimation transforms point data into a field representation"},{"concepts":[145],"name":"Explain why choropleth maps should (almost) never be used for mapping count data and suggest alternative methods for mapping count data"},{"concepts":[60],"name":"Explain why different interpolation algorithms produce different results and suggest ways by which these can be evaluated in the context of a specific problem"},{"concepts":[37],"name":"Explain why estimating the fractal dimension of a sinuous line has important implications for the measurement of its length"},{"concepts":[118],"name":"Explain why general-purpose regions rarely exist"},{"concepts":[48],"name":"Explain why georegistration is a precondition to map algebra"},{"concepts":[13],"name":"Explain why heuristic solutions are generally used to address the combinatorially complex nature of these problems and the difficulty of solving them optimally"},{"concepts":[18],"name":"Explain why integer programs are harder to solve than linear programs"},{"concepts":[243],"name":"Explain why integrated topological models have lost favor in commercial GIS software"},{"concepts":[374],"name":"Explain why it has been difficult for many agencies and organizations to define positions and roles for GIS and T professionals"},{"concepts":[76],"name":"Explain why it is important to have a good model of the semi-variogram in kriging"},{"concepts":[76],"name":"Explain why kriging is more suitable as an interpolation method in some applications than others"},{"concepts":[254],"name":"Explain why logging and rollback techniques are adequate for managing short transactions"},{"concepts":[398],"name":"Explain why software products sold by U.S. companies may predominate in foreign markets, including Europe and Australia"},{"concepts":[59],"name":"Explain why the properties of spatial continuity are characteristic of spatial surfaces"},{"concepts":[39],"name":"Explain why the shape of an object might be important in analysis"},{"concepts":[125],"name":"Explain why Toblers First Law of Geography is fundamental to many operations in GIS and whether it should be"},{"concepts":[59],"name":"Explain why zero slopes are indicative of surface specific points such as peaks, pits and passes and list the conditions necessary for each"},{"concepts":[12],"name":"Explain why, if supply equals demand, there will always be a feasible solution to the Classic Transportation Problem"},{"concepts":[51],"name":"Explain why, in some cases, an adaptive bandwidth might be employed"},{"concepts":[312],"name":"Explain, in general terms, the difference between single and double precision and impacts on error propagation"},{"concepts":[16],"name":"Explain, using the concept of combinatorial complexity, why some location problems are very hard to solve"},{"concepts":[92],"name":"Explore the contribution of linguistics to the study of spatial cognition and the role of natural language in the conceptualization of geographic phenomena"},{"concepts":[96],"name":"Explore the history of geography including (but not limited to) Greek and Roman contributions to geography (Eratosthenes, Strabo, Ptolemy), geography and cartography in the age of discovery, military geography, and geography..."},{"concepts":[80],"name":"Find a best model"},{"concepts":[39],"name":"Find centroids of polygons under different definitions of a centroid and different polygon shapes"},{"concepts":[377],"name":"Find or create training resources appropriate for GIS and T workforce in a local government organization"},{"concepts":[117],"name":"Find spatial patterns in the distribution of geographic phenomena using geographic visualization and other techniques"},{"concepts":[110],"name":"Formalize attribute values and domains in terms of set theory"},{"concepts":[113],"name":"Formalize the notion of field using mathematical functions and Calculus"},{"concepts":[117],"name":"Hypothesize the causes of a pattern in the spatial distribution of a phenomenon"},{"concepts":[186],"name":"Hypothesize the ways in which capital needs for GIS may change in the future"},{"concepts":[355],"name":"Identify alternatives to the algorithmic way of thinking that characterizes GIS"},{"concepts":[304],"name":"Identify and define the four geometric properties of the globe that may be preserved or lost in projected coordinates"},{"concepts":[74],"name":"Identify and define the parameters of a semi-variogram range, sill, nugget"},{"concepts":[307],"name":"Identify and explain an equation used to perform image-to-image registration"},{"concepts":[307],"name":"Identify and explain an equation used to perform image-to-map registration"},{"concepts":[113],"name":"Identify applications and phenomena that are not adequately modeled by the field view"},{"concepts":[105],"name":"Identify common-sense views of geographic phenomena that sharply contrast with established theories and technologies of geographic information"},{"concepts":[399],"name":"Identify conferences that are related to GIS and T"},{"concepts":[397],"name":"Identify conferences that are related to GIS and T hosted by professional organizations"},{"concepts":[113],"name":"Identify examples of discrete and continuous change found in spatial, temporal, and spatio-temporal fields"},{"concepts":[117],"name":"Identify influences of scale on the appearance of distributions"},{"concepts":[183],"name":"Identify major obstacles to the success of a GIS proposal"},{"concepts":[81],"name":"Identify modeling situations where spatial filtering might not be appropriate"},{"concepts":[401],"name":"Identify National Science Foundation (NSF) programs that support GIS and T research and education"},{"concepts":[388],"name":"Identify organizations that focus on developing standards related to GIS and T"},{"concepts":[122],"name":"Identify phenomena that are best understood as networks"},{"concepts":[112],"name":"Identify phenomena that are difficult or impossible to conceptualize in terms of entities"},{"concepts":[184],"name":"Identify potential sources of data (free or commercial) needed for a particular application or enterprise"},{"concepts":[187],"name":"Identify potential sources of funding (internal and external) for a project or enterprise GIS"},{"concepts":[52],"name":"Identify several cluster detection techniques and discuss their limitations"},{"concepts":[39],"name":"Identify situations in which shape affects geometric operations"},{"concepts":[125],"name":"Identify situations in which Toblers First Law of Geography does not apply"},{"concepts":[125],"name":"Identify situations in which Toblers First Law of Geography is valuable"},{"concepts":[109],"name":"Identify specific examples of categories of entities (i.e., common nouns), properties (i.e., adjectives), space (i.e., regions), and time (i.e., eras)"},{"concepts":[388],"name":"Identify standards that are used in GIS and T"},{"concepts":[22],"name":"Identify the conceptual and practical difficulties associated with data model and format conversion"},{"concepts":[87],"name":"Identify the epistemological assumptions underlying the work of colleagues"},{"concepts":[186],"name":"Identify the hardware and space that will be needed for a GIS implementation"},{"concepts":[127],"name":"Identify the hedges used in language to convey vagueness"},{"concepts":[118],"name":"Identify the kinds of phenomena that are commonly found at the boundaries of regions"},{"concepts":[231],"name":"Identify the national framework datasets based on a grid model"},{"concepts":[85],"name":"Identify the ontological assumptions underlying the work of colleagues"},{"concepts":[306],"name":"Identify the parameters that allow one to focus a projection on an area of interest"},{"concepts":[377],"name":"Identify the particular skills necessary for users to perform tasks in three different workforce domains (e.g., small city, medium county agency, a business, or others)"},{"concepts":[89],"name":"Identify the philosophical views and assumptions underlying the work of colleagues"},{"concepts":[185],"name":"Identify the positions necessary to design and implement a GIS"},{"concepts":[306],"name":"Identify the possible aspects of a projection and describe the graticules appearance in each aspect"},{"concepts":[374],"name":"Identify the qualifications needed for a particular GIS and T position"},{"concepts":[60],"name":"Identify the spatial concepts that are assumed in different interpolation algorithms"},{"concepts":[374],"name":"Identify the standard occupational codes that are relevant to GIS and T"},{"concepts":[112],"name":"Identify the types of features that need to be modeled in a particular GIS application or procedure"},{"concepts":[50],"name":"Identify the various ways point patterns may be described"},{"concepts":[108],"name":"Identify various types of geographic interactions in space and time"},{"concepts":[50],"name":"Identify various types of K-function analysis"},{"concepts":[243],"name":"Illustrate a topological relation"},{"concepts":[311],"name":"Illustrate and explain the distinction between resolution, precision, and accuracy"},{"concepts":[311],"name":"Illustrate and explain the distinctions between spatial resolution, thematic resolution, and temporal resolution"},{"concepts":[304],"name":"Illustrate distortion patterns associated with a given projection class"},{"concepts":[116],"name":"Illustrate major integrated models of geographic information, such as Peuquets Triad, Mennis Pyramid, and Yuans Three-Domain"},{"concepts":[377],"name":"Illustrate methods that are effective in providing opportunities for education and training when implementing a GIS in a small city"},{"concepts":[232],"name":"Illustrate the existing methods for compressing gridded data (e.g., run length encoding, Lempel-Ziv, wavelets)"},{"concepts":[305],"name":"Illustrate the graticule configurations for other projection classes, such as polyconic, pseudocylindrical, etc."},{"concepts":[234],"name":"Illustrate the hexagonal model"},{"concepts":[238],"name":"Illustrate the impact of grid cell resolution on the information that can be portrayed"},{"concepts":[24],"name":"Illustrate the impact of vector/raster/vector conversions on the quality of a dataset"},{"concepts":[239],"name":"Illustrate the quadtree model"},{"concepts":[288],"name":"Illustrate the spectral response curves for basic environmental features (e.g., vegetation, concrete, bare soil)"},{"concepts":[366],"name":"Illustrate what functions a support or service center can provide to an organization using GIS and T"},{"concepts":[239],"name":"Implement a format for encoding quadtrees in a data file"},{"concepts":[306],"name":"Implement a given map projection formula in a software program that reads geographic coordinates as input and produces projected (x, y) coordinates as output"},{"concepts":[80],"name":"Implement a maximum likelihood estimation procedure for determining key spatial econometric parameters"},{"concepts":[255],"name":"Implement a test of reliability of change information"},{"concepts":[60],"name":"Implement a trend surface analysis using either the supplied function in a GIS or a regression function from any standard statistical package"},{"concepts":[17],"name":"Implement linear programs for spatial allocation problems"},{"concepts":[12],"name":"Implement the Transportation Simplex method to determine the optimal solution"},{"concepts":[309],"name":"In contrast to the National Map Accuracy Standard, explain how the spatial accuracy of a digital road centerlines data set may be evaluated and documented"},{"concepts":[375],"name":"Indicate the possible justifications that can be used to implement an enterprise GIS"},{"concepts":[304],"name":"Interpret a given a projected graticule, continent outlines, and indicatrixes at each graticule intersection in terms of geometric properties preserved and distorted"},{"concepts":[363],"name":"Interpret descriptive statistics and geostatistics of geographic data"},{"concepts":[4],"name":"Interpret patterns in space and time using Dorling and Openshaws Geographical Analysis Machine GAM demonstration of disease incidence diffusion"},{"concepts":[230],"name":"Interpret the header of a standard raster data file"},{"concepts":[77],"name":"Interpret the results of universal kriging"},{"concepts":[184],"name":"Judge the relative merits of obtaining free data, purchasing data, outsourcing data creation, or producing and managing data in-house for a particular application or enterprise"},{"concepts":[96],"name":"Justify a chosen position on which disciplines should have as important a role in GIS AND T as geography"},{"concepts":[183],"name":"Justify feasibility recommendations to decision-makers"},{"concepts":[112],"name":"Justify or refute the conception of fields (e.g., temperature, density) as spatially-intensive attributes of (sometimes amorphous and anonymous) entities"},{"concepts":[96],"name":"Justify or refute whether geography (as a discipline) should have a central role in GIS AND T"},{"concepts":[101],"name":"Justify the discrepancies between the nature of locations in the real world and representations thereof (e.g., towns as points)"},{"concepts":[87],"name":"Justify the epistemological frameworks with which you agree"},{"concepts":[85],"name":"Justify the metaphysical theories with which you agree"},{"concepts":[66],"name":"Justify the stochastic process approach to spatial statistical analysis"},{"concepts":[68],"name":"Justify, compute, and test the significance of the join count statistic for a pattern of objects"},{"concepts":[73],"name":"List and describe several spatial sampling schemes and evaluate each one for specific applications"},{"concepts":[339],"name":"List and describe the types of data maintained by local, state, and federal governments"},{"concepts":[247],"name":"List definitions of networks that apply to specific applications or industries"},{"concepts":[42],"name":"List different ways connectivity can be determined in a raster and in a polygon dataset"},{"concepts":[40],"name":"List reasons why the area of a polygon calculated in a GIS might not be the same as the real world object it describes"},{"concepts":[13],"name":"List several classic problems to which network analysis is applied e.g., The Traveling Salesman Problem, The Chinese Postman Problem"},{"concepts":[375],"name":"List some of the topics that should be addressed in a justification for implementing an enterprise GIS (e.g., return on investment, workflow, knowledge sharing)"},{"concepts":[183],"name":"List some of the topics that should be addressed in such a justification of geospatial technology (e.g., ROI, workflow, knowledge sharing)"},{"concepts":[50],"name":"List the conditions that make point pattern analysis a suitable process"},{"concepts":[183],"name":"List the costs and benefits (financial and intangible) of implementing geospatial technology for a particular application or an entire institution"},{"concepts":[59],"name":"List the likely sources of error in slope and aspect maps derived from DEMs and state the circumstances under which these can be very severe"},{"concepts":[140],"name":"List the major factors that should be considered in preparing a map"},{"concepts":[75],"name":"List the possible sources of error in a selected and fitted model of an experimental semi-variogram"},{"concepts":[124],"name":"List the possible topological relationships between entities in space (e.g., 9-intersection) and time"},{"concepts":[142],"name":"List the range of factors that should be considered in selecting colors"},{"concepts":[66],"name":"List the two basic assumptions of the purely random process"},{"concepts":[14],"name":"List ways we can define accessibility on a network"},{"concepts":[19],"name":"Locate, using location-allocation software, service facilities that meet given sets of constraints"},{"concepts":[164],"name":"Measure point-feature movement and point-feature diffusion on maps"},{"concepts":[112],"name":"Model gray area phenomena, such as categorical coverages (a.k.a. discrete fields), in terms of objects"},{"concepts":[253],"name":"Modify spatial and attribute data while ensuring consistency within the database"},{"concepts":[143],"name":"Name the authorities used to confirm the spelling of geographic names for a specific mapping project"},{"concepts":[59],"name":"Outline a number of different methods for calculating slope from a Digital Elevation Model (DEM)"},{"concepts":[292],"name":"Outline a plausible workflow for habitat mapping, such as the benthic habitat mapping in the main Hawaiian Islands as part of the NOAA Biogeography program"},{"concepts":[292],"name":"Outline a plausible workflow used by MDA Federal (formerly EarthSat) to create the high-resolution GEOCOVER global imagery and GEOCOVER-LC global land cover datasets"},{"concepts":[319],"name":"Outline a workflow that can be used to train a new employee to update a county road centerlines database using digital aerial imagery and standard GIS editing tools"},{"concepts":[60],"name":"Outline algorithms to produce repeatable contour-type lines from point datasets using proximity polygons, spatial averages, or inverse distance weighting"},{"concepts":[62],"name":"Outline an algorithm to determine the viewshed area visible from specific locations on surfaces specified by digital elevation models (DEM)"},{"concepts":[40],"name":"Outline an algorithm to find the area of a polygon using the coordinates of its vertices"},{"concepts":[59],"name":"Outline how higher order derivatives of height can be interpreted"},{"concepts":[50],"name":"Outline measures of pattern based on first and second order properties such as the mean centre and standard distance, quadrat counts, nearest neighbor distance and the more modern G, F and K functions"},{"concepts":[376],"name":"Outline methods (programs or processes) that provide effective staff development opportunities for GIS and T"},{"concepts":[76],"name":"Outline the basic kriging equations in their matrix formulation"},{"concepts":[50],"name":"Outline the basis of classic critiques of spatial statistical analysis in the context of point pattern analysis"},{"concepts":[131],"name":"Outline the development of some of the major map projections (e.g., Mercator, Gnomonic, Robinson)"},{"concepts":[41],"name":"Outline the geometry implicit in classical gravity models of distance decay"},{"concepts":[3],"name":"Outline the implications of complexity for the application of statistical ideas in geography"},{"concepts":[37],"name":"Outline the implications of differences in distance calculations on real world applications of GIS, such as routing and determining boundary lengths and service areas"},{"concepts":[51],"name":"Outline the likely effects on analysis results of variations in the kernel function used and the bandwidth adopted"},{"concepts":[66],"name":"Outline the logic behind the derivation of long run expected outcomes of the independent random process using quadrat counts"},{"concepts":[401],"name":"Outline the principle concepts and goals of the digital earth vision articulated in 1998 by Vice President Al Gore"},{"concepts":[320],"name":"Outline the process of scanning and vectorizing features depicted on a printed map sheet using a given GIS software product, emphasizing issues that require manual intervention"},{"concepts":[310],"name":"Outline the SDTS and ISO TC211 standards for thematic accuracy"},{"concepts":[284],"name":"Outline the sequence of tasks involved in generating an orthoimage from a vertical aerial photograph"},{"concepts":[1],"name":"Outline the sequence of tasks required to complete the analytical process for a given spatial problem"},{"concepts":[159],"name":"Outline the stages in lithographic offset printing"},{"concepts":[52],"name":"Perform a cluster detection analysis to detect hot spots in a point pattern"},{"concepts":[32],"name":"Perform a logic set theoretic query using GIS software"},{"concepts":[290],"name":"Perform a manual unsupervised classification given a two-dimensional array of reflectance values and ranges of reflectance values associated with a given number of land cover categories"},{"concepts":[48],"name":"Perform a map algebra calculation using command line, form-based, and flow charting user interfaces"},{"concepts":[183],"name":"Perform a pilot study to evaluate the feasibility of an application"},{"concepts":[82],"name":"Perform an analysis using the geographically weighted regression technique"},{"concepts":[54],"name":"Perform multidimensional scaling (MDS) and principal components analysis (PCA) to reduce the number of coordinates, or dimensionality, of a problem"},{"concepts":[62],"name":"Perform siting analyses using specified visibility, slope, and other surface related constraints"},{"concepts":[286],"name":"Plan an aerial imagery mission in response to a given RFP and map of a study area, taking into consideration vertical and horizontal control, atmospheric conditions, time of year, and time of day"},{"concepts":[164],"name":"Plan an orienteering tour of a specific length that traverses slopes of an appropriate steepness and crosses streams in places that can be forded based on a topographic map"},{"concepts":[142],"name":"Plan color proofing suited for checking a map publication job"},{"concepts":[41],"name":"Plot typical forms for distance decay functions"},{"concepts":[143],"name":"Position labels on a map to name point, line, and area features"},{"concepts":[159],"name":"Prepare a color map for black-and-white photocopy distribution"},{"concepts":[140],"name":"Prepare different map layouts using the same map components (main map area, inset maps, titles, legends, scale bars, north arrows, grids and graticule) to produce maps with very distinctive purposes"},{"concepts":[140],"name":"Prepare different maps using the same data for different purposes and intended audiences (e.g., expert and novice hikers)"},{"concepts":[21],"name":"Prioritize a set of algorithms designed to perform transformations based on the need to maintain data integrity [e.g., converting a digital elevation model (DEM) into a TIN]"},{"concepts":[54],"name":"Produce plots in several data dimensions using a data matrix of attributes"},{"concepts":[290],"name":"Produce pseudocode for common unsupervised classification algorithms including chain method, ISODATA method, and clustering"},{"concepts":[256],"name":"Produce viable queries for change scenarios using GIS or database management tools"},{"concepts":[349],"name":"Propose a resolution to a conflict between an obligation in the GIS Code of Ethics and organizations proprietary interests"},{"concepts":[109],"name":"Recognize and manage the potential problems associated with the use of categories (e.g., the ecological fallacy)"},{"concepts":[110],"name":"Recognize attribute domains that do not fit well into Stevens four levels of measurement (nominal, ordinal, interval, ratio), such as cycles, indexes, and hierarchies"},{"concepts":[304],"name":"Recognize distortion patterns on a map based upon the graticule arrangement"},{"concepts":[128],"name":"Recognize expressions of uncertainty in language"},{"concepts":[110],"name":"Recognize situations and phenomena in the landscape which cannot be adequately represented by formal attributes, such as aesthetics"},{"concepts":[363],"name":"Recognize the assumptions underlying probability and geostatistics and the situations in which they are useful analytical tools"},{"concepts":[85],"name":"Recognize the commonalities of philosophical viewpoints and appreciate differences to enable work with diverse colleagues"},{"concepts":[204],"name":"Recognize the constraints and opportunities of a particular choice of software for implementing a logical model"},{"concepts":[99],"name":"Recognize the constraints that political forces place on geospatial applications in public and private sectors"},{"concepts":[124],"name":"Recognize the contributions of Topology (the branch of mathematics) to the study of geographic relationships"},{"concepts":[128],"name":"Recognize the degree to which the importance of uncertainty depends on scale and application"},{"concepts":[127],"name":"Recognize the degree to which vagueness depends on scale"},{"concepts":[281],"name":"Recognize the distortions and implications of relief displacement and radial distortion in an aerial image"},{"concepts":[98],"name":"Recognize the impact of ones social background on ones own geographic worldview and perceptions and how it influences ones use of GIS"},{"concepts":[87],"name":"Recognize the influences of epistemology on GIS practices"},{"concepts":[113],"name":"Recognize the influences of scale on the perception and meaning of fields"},{"concepts":[107],"name":"Recognize the role that time plays in static GISystems"},{"concepts":[304],"name":"Recommend the map projection property that would be useful for various mapping applications, including parcel mapping, route mapping, etc., and justify your recommendations"},{"concepts":[109],"name":"Reconcile differing common-sense and official definitions of common geospatial categories of entities, attributes, space, and time"},{"concepts":[54],"name":"Relate plots of multidimensional attribute data to geography by equating similarity in data space with proximity in geographical space"},{"concepts":[238],"name":"Relate the concept of grid cell resolution to the more general concept of support and granularity"},{"concepts":[113],"name":"Relate the notion of field in GIS to the mathematical notions of scalar and vector fields"},{"concepts":[122],"name":"Represent structural relationships in GIS data"},{"concepts":[25],"name":"Resample multiple raster data sets to a single resolution to enable overlay"},{"concepts":[25],"name":"Resample raster data sets (e.g., terrain, satellite imagery) to a resolution appropriate for a map of a particular scale"},{"concepts":[142],"name":"Select a color scheme (e.g., qualitative, sequential, diverging, spectral) that is appropriate for a given map purpose and variable"},{"concepts":[97],"name":"Select a place or landscape with personal meaning and discuss its importance"},{"concepts":[403],"name":"Select and describe the leading trade journals serving the GIS and T community"},{"concepts":[25],"name":"Select appropriate interpolation techniques to resample particular types of values in raster data (e.g., nominal using nearest neighbor)"},{"concepts":[101],"name":"Select appropriate spatial metaphors and models of phenomena to be represented in GIS"},{"concepts":[403],"name":"Select association and for-profit journals that are useful to entities managing enterprise GI systems"},{"concepts":[145],"name":"Select base information suited to providing a frame of reference for thematic map symbols (e.g., network of major roads and state boundaries underlying national population map)"},{"concepts":[142],"name":"Select colors appropriate for map readers with color limitations"},{"concepts":[65],"name":"Select the appropriate statistical methods for the analysis of given spatial datasets by first exploring them using graphic methods"},{"concepts":[289],"name":"Select the most appropriate remotely sensed data source for a given analytical task, study area, budget, and availability"},{"concepts":[107],"name":"Select the temporal elements of geographic phenomena that need to be represented in particular GIS applications"},{"concepts":[143],"name":"Set type font, size, style and color for labels on a map by applying basic typography design principles"},{"concepts":[146],"name":"Sketch a map with a reliability overlay using symbols suited to reliability representations"},{"concepts":[143],"name":"Solve a labeling problem for a dense collection of features on a map using minimal leader lines"},{"concepts":[159],"name":"Specify a print job for publication, including paper, ink, lpi, proof needs, press check and other contract decisions"},{"concepts":[142],"name":"Specify a set of colors in device-independent Commision Internationale de LEclairage (CIE) specifications"},{"concepts":[284],"name":"Specify the technical components of an aerotriangulation system"},{"concepts":[34],"name":"State questions that can be solved by selecting features based on location or spatial relationships"},{"concepts":[309],"name":"State the approximate number and spacing of control points in each order of the horizontal geodetic control network"},{"concepts":[53],"name":"State the classic formalization of the interaction model"},{"concepts":[309],"name":"State the geometric accuracies associated with the various orders of the U.S. horizontal geodetic control network"},{"concepts":[377],"name":"Teach necessary skills for users to successfully perform tasks in an enterprise GIS"},{"concepts":[94],"name":"Transform a conceptual model of information for a particular task into a data model"},{"concepts":[108],"name":"Understand the physical notions of velocity and acceleration which are fundamentally about movement across space through time"},{"concepts":[109],"name":"Use categorical information in analysis, cartography, and other GIS processes, avoiding common interpretation mistakes"},{"concepts":[118],"name":"Use established analysis methods that are based on the concept of region (e.g., landscape ecology)"},{"concepts":[119],"name":"Use established analysis methods that are based on the concept of spatial integration (e.g., overlay)"},{"concepts":[306],"name":"Use GIS software to produce a graticule that matches a target graticule"},{"concepts":[307],"name":"Use GIS software to transform a given dataset to a specified coordinate system, projection, and datum"},{"concepts":[125],"name":"Use methods that analyze metrical relationships"},{"concepts":[124],"name":"Use methods that analyze topological relationships"},{"concepts":[283],"name":"Use photo interpretation keys to interpret features on aerial photographs"},{"concepts":[283],"name":"Using a vertical aerial image, produce a map of land use/land cover classes"},{"concepts":[41],"name":"Write a program to create a matrix of pair-wise distances among a set of points"},{"concepts":[230],"name":"Write a program to read and write a raster data file"},{"concepts":[41],"name":"Write typical forms for distance decay functions"},{"concepts":[11],"name":"xplain how the concept of capacity represents an upper limit on the amount of flow through the network"}]},"v2":{"concepts":[{"code":"GIST","description":"Geographic Information Science and Technology","name":"Geographic Information Science and Technology"},{"code":"AM","description":"This knowledge area encompasses a wide variety of operations whose objective is to derive analytical results from geospatial data. Data analysis seeks to understand both first-order (environmental) effects and second-order (interaction) effects. Approaches that are both data-driven (exploration of geospatial data) and model-driven (testing hypotheses and creating models) are included. Data driven techniques derive summary descriptions of data, evoke insights about characteristics of data, contribute to the development of research hypotheses, and lead to the derivation of analytical results. The goal of model driven analysis is to create and test geospatial process models. In general, model-driven analysis is an advanced knowledge area where previous experience with exploratory spatial data analysis would constitute a desired prerequisite. Visual tools for data analysis are covered in Knowledge Area: Cartography and Visualization (CV) and many of the fundamental principles required to ground data analysis techniques are introduced in Knowledge Area: Conceptual Foundations (CF). Image processing techniques are considered in Knowledge Area: Geospatial Data (GD). All of the methods described in this knowledge area are more or less sensitive to data error and uncertainty as covered in Unit GC8 Uncertainty and Unit GD6 Data quality. Mastery of the educational objectives outlined in this knowledge area requires knowledge and skills in mathematics, statistics, and computer programming.","name":"Analytical Methods"},{"code":"AM1-2","description":"- Compare and contrast spatial statistical analysis, spatial data analysis, and spatial modeling - Compare and contrast spatial statistics and map algebra as two very different kinds of data analysis - Compare and contrast the methods of analyzing aggregate data as opposed to methods of analyzing a set of individual observations - Define the terms spatial analysis, spatial modeling, geostatistics, spatial econometrics, spatial statistics, qualitative analysis, map algebra, and network analysis - Differentiate between geostatistics and spatial statistics - Discuss situations when it is desirable to adopt a spatial approach to the analysis of data - Explain what is added to spatial analysis to make it spatio-temporal analysis - Explain what is special (i.e., difficult) about geospatial data analysis and why some traditional statistical analysis techniques are not suited to geographic problems","name":"Analytical approaches"},{"code":"AM1","description":"Geospatial data analysis has foundations in many different disciplines. As a result, there are many different schools of thought or analytical approaches including spatial analysis, spatial modeling, geostatistics, spatial econometrics, spatial statistics, qualitative analysis, map algebra, and network analysis. This unit compares and contrasts these approaches.","name":"Foundations of analytical methods"},{"code":"AM10-1","description":"- Describe emerging geographical analysis techniques in geocomputation derived from artificial intelligence (e.g., expert systems, artificial neural networks, genetic algorithms, and software agents) - Describe difficulties in dealing with large spatial databases, especially those arising from spatial heterogeneity - Explain what is meant by the term \"contaminated data,\"suggesting how it can arise - Explain how to recognize contaminated data in large datasets - Outline the implications of complexity for the application of statistical ideas in geography - Describe sources of Big Data","name":"Problems of large spatial databases"},{"code":"AM10-2","description":"- Describe how data mining can be used for geospatial intelligence - Differentiate between data mining approaches used for spatial and non-spatial applications - Compare and contrast the primary types of data mining: summarization/characterization, clustering/categorization, feature extraction, and rule/relationships extraction - Explain how spatial statistics techniques are used in spatial data mining - Explain how the analytical reasoning techniques, visual representations, and interaction techniques that make up the domain of visual analytics have a strong spatial component - Demonstrate how cluster analysis can be used as a data mining tool - Interpret patterns in space and time using Dorling and Openshaw`s geographical analysis machine (GAM) demonstration of disease incidence diffusion - Discuss tools (e.g. scripts, plugins) to gather location based data (e.g. Twitter feeds, address data)","name":"Data mining approaches"},{"code":"AM10-3","description":"- Explain how spatial data mining techniques can be used for knowledge discovery - Explain how visual data exploration can be combined with data mining techniques as a means of discovering research hypotheses in large spatial datasets - Explain how a Bayesian framework can incorporate expert knowledge in order to retrieve all relevant datasets given an initial user query","name":"Knowledge discovery"},{"code":"AM10-4","description":"- Differentiate among machine learning, data mining, and pattern recognition - Explain the outcome of an artificial intelligence analysis (e.g., edge recognition), including a discussion of what the human did not see that the computer identified and vice versa - Explain the principles of pattern recognition - Apply a simple spatial mean filter to an image as a means of recognizing patterns - Construct an edge-recognition filter - Design a simple spatial mean filter","name":"Pattern recognition and matching"},{"code":"AM10","description":"Algorithms have been developed to scan and search through extremely large data sets in order to find patterns within the data. These data mining and knowledge discovery techniques have been expanded to the spatial case. Legal and ethical concerns associated with such practices are considered in Knowledge Areas GS GIS and T and Society and OI Organizational and Institutional Aspects.","name":"Data mining"},{"code":"AM11-1","description":"- Define the following terms pertaining to a network: Loops, multiple edges, the degree of a vertex, walk, trail, path, cycle, fundamental cycle - Define different interpretations of \"cost\" in various routing applications - Describe networks that apply to specific applications or industries - Create a data set with network attributes and topology","name":"Networks defined"},{"code":"AM11-2","description":"- Demonstrate how networks can be measured using the number of elements in a network, the distances along network edges, and the level of connectivity of the network - Explain the concept of the diameter of a network - Compute the estimated number of fundamental cycles in a graph - Compute the alpha, beta, and gamma indices of network connectivity - Compute the detour index and the measure of network density for a given network","name":"Graph theoretic descriptive measures of networks"},{"code":"AM11-3","description":"- Describe some variants of Dijkstra`s algorithm that are even more efficient - Explain how a leading World Wide Web-based routing system works (e.g., MapQuest, Yahoo Maps, Google) - Discuss the difference of implementing Dijkstra`s algorithm in raster and vector modes - Demonstrate how K-shortest path algorithms can be implemented to find many efficient alternate paths across the network - Compute the optimum path between two points through a network with Dijkstra`s algorithm","name":"Least-cost shortest path"},{"code":"AM11-4","description":"- Describe practical situations in which flow is conserved while splitting or joining at nodes of the network - Explain how the concept of capacity represents an upper limit on the amount of flow through the network - Demonstrate how capacity is assigned to edges in a network using the appropriate data structure - Apply a maximum flow algorithm to calculate the largest flow from a source to a sink, using the edges of the network, subject to capacity constraints on the arcs and the conservation of flow - Discuss the visualization of results of flow modelling","name":"Flow modeling"},{"code":"AM11-5","description":"- Describe the classic transportation problem - Explain why, if supply equals demand, there will always be a feasible solution to the classic transportation problem - Demonstrate how the classic transportation problem can be structured as a linear program - Implement the transportation simplex method to determine the optimal solution","name":"The Classic Transportation Problem"},{"code":"AM11-6","description":"- Describe several classic problems to which network analysis is applied (e.g., the traveling salesman problem, the Chinese postman problem) - Explain why heuristic solutions are generally used to address the combinatorially complex nature of these problems and the difficulty of solving them optimally","name":"Other classic network problems"},{"code":"AM11-7","description":"- Describe alternate definitions of accessibility on a network - Describe methods for measuring different kinds of accessibility on a network - Contrast accessibility modeling at the individual level versus at an aggregated level - Compare current accessibility models with early models of market potential","name":"Accessibility modeling"},{"code":"AM11","description":"Network analysis encompasses a wide range of procedures, techniques, and methods that allow for the examination of phenomena that can be modeled in the form of connected sets of edges and vertices. Such sets are termed a network or a graph, and the mathematical basis for network analysis is known as graph theory. Graph theory contains descriptive measures and indices of networks such as connectivity, adjacency, capacity, and flow as well as methods for proving the properties of networks. Networks have long been recognized as an efficient way to model many types of geographic data, including transportation networks, river networks, and utility networks electric, cable, sewer and water, etc. to name just a few. The data structures to support network analysis are covered in Unit DM4 Vector and object data models.","name":"Network analysis"},{"code":"AM12-1","description":"- Explain how optimization models can be used to generate models of alternate options for presentation to decision makers - Explain the concept of solution space - Explain the principles of operations research modeling and location modeling - Explain, using the concept of combinatorial complexity, why some location problems are very hard to solve - Compare and contrast the concepts of discrete location problems and continuous location problems","name":"Operations research modeling and location modeling principles"},{"code":"AM12-2","description":"- Describe the structure of linear programs - Explain the role of objective functions in linear programming - Explain the role of constraint functions using the graphical method - Explain the role of constraint functions using the simplex method - Implement linear programs for spatial allocation problems","name":"Linear programming"},{"code":"AM12-3","description":"- Differentiate between a linear program and an integer program - Explain why integer programs are harder to solve than linear programs","name":"Integer programming"},{"code":"AM12-4","description":"- Describe the structure of origin-destination matrices - Explain the concepts of demand and service - Explain Weber`s locational triangle - Assess the outcome of location-allocation models using other spatial analysis techniques - Compare and contrast covering, dispersion, and p-median models - Locate, using location-allocation software, service facilities that meet given sets of constraints","name":"Location-allocation modeling and p-median problems"},{"code":"AM12","description":"A wide variety of optimization techniques are now solvable within the GIS and T domain. Operations research is a branch of mathematics practiced in the allied fields of business and engineering. New models and software tools allow for the solution of transportation routing, facility location, and a host of other location-allocation modeling problems.","name":"Optimization and location-allocation modeling"},{"code":"AM13-1","description":"- Compare and contrast the impacts of different conversion approaches, including the effect on spatial components - Prioritize a set of algorithms designed to perform transformations based on the need to main- tain data integrity (e.g., converting a digital elevation model into a TIN) - Create a flowchart showing the sequence of transformations on a data set (e.g., geometric and radiometric correction and mosaicking of remotely sensed data) - Consider the importance of accurate data in a project","name":"Impacts of transformations"},{"code":"AM13-2","description":"- Identify the conceptual and practical difficulties associated with data model and format conversion - Describe a workflow for converting and implementing a data model in a GIS involving an Entity- Relationship (E-R) diagram and the Universal Modeling Language (UML) - Discuss the role of metadata in facilitating conversation of data models and data structures between systems - Convert a data set from the native format of one GIS product to another - Discuss the importance of preserving the semantics when converting a data format - Discuss the importance of a well thought data model in a GIS project","name":"Data model and format conversion"},{"code":"AM13-3","description":"- Differentiate among common interpolation techniques (e.g., nearest neighbor, bilinear, bicubic) - Explain how the elevation values in a digital elevation model (DEM) are derived by interpolation from irregular arrays of spot elevations - Discuss the pitfalls of using secondary data that has been generated using interpolations (e.g., Level 1 USGS DEMs) - Estimate a value between two known values using linear interpolation (e.g., spot elevations, population between census years) - Discuss that the choice of interpolation technique depends on what we want to model","name":"Interpolation"},{"code":"AM13-4","description":"- Explain how the vector/raster/vector conversion process of graphic images and algorithms takes place and how the results are achieved - Convert vector data to raster format and back using GIS software - Illustrate the impact of vector/raster/vector conversions on the quality of a dataset - Create estimated tessellated data sets from point samples or isolines using interpolation operations that are appropriate to the specific situation","name":"Vector-to-raster and raster-to-vector conversions"},{"code":"AM13-5","description":"- Discuss the consequences of increasing and decreasing resolution - Evaluate methods used by contemporary GIS software to resample raster data on-the-fly during display - Select appropriate interpolation techniques to resample particular types of values in raster data (e.g., nominal using nearest neighbor) - Resample multiple raster data sets to a single resolution to enable overlay - Resample raster data sets (e.g., terrain, satellite imagery) to a resolution appropriate for a map of a particular scale","name":"Raster resampling"},{"code":"AM13-6","description":"- Cite appropriate applications of several coordinate transformation techniques (e.g., affine, simi- larity, Molodenski, Helmert) - Differentiate between polynomial coordinate transformations (including linear) and rubbersheeting - Describe the impact of map projection transformation on raster and vector data - Discuss the need for different coordinate systems depending on the type of application - Data Transformation Services","name":"Coordinate transformations"},{"code":"AM13","description":"GIS is a cyclical rather than a linear system, unlike computer aided drafting (CAD) and computer assisted cartographic systems. Changes in projection, grid systems, data forms, and formats take place during the modeling process for which GIS was designed. Many non-analytical manipulations are necessary to accommodate the analytical power of the GIS. The manipulations of spatial and spatio-temporal data involve two general classes of operation: 1.\tTheir transformation into formats that facilitate subsequent analysis (see this Unit AM13), 2.\tGeneralization and aggregation that affect the accuracy and integrity of the data used for analysis (see Unit AM14) Other knowledge areas have identified different forms of data structures, data models, projections, and other forms of geospatial data representation. These differences present both opportunities and challenges for analysis and modeling. The ability to transform one representation to another, in a manner that maintains the integrity of the information as much as possible, can enhance the analysis and visualization of geospatial data. The raster and vector data models are described in Units DM3 Tesselation data models and DM4 Vector and object data models. The principles of coordinate systems, datums, and projections are also considered in Knowledge Area GD: Geospatial Data","name":"Representation transformation"},{"code":"AM14-1","description":"- Differentiate among the concepts of scale (as in map scale), support, scope, and resolution - Determine the mathematical relationships among scale, scope, and resolution, including Töpfer`s radical law - Defend or refute the statement \"GIS data are scaleless\"- Discuss the implications of tradeoff between data detail and data volume - Select a level of data detail and accuracy appropriate for a particular application (e.g., viewshed analysis, continental land cover change)","name":"Scale and generalization"},{"code":"AM14-2","description":"- Describe the basic forms of generalization used in applications in addition to cartography (e.g., selection, simplification) - Discuss the possible effects on topological integrity of generalizing data sets - Explain why areal generalization is more difficult than line simplification - Explain the logic of the Douglas-Poiker line simplification algorithm - Explain the pitfalls of using data generalized for small scale display in a large scale application - Design an experiment that allows one to evaluate the effect of traditional approaches of carto- graphic generalization on the quality of digital data sets created from analog originals - Evaluate various line simplification algorithms by their usefulness in different applications","name":"Approaches to point, line, and area generalization"},{"code":"AM14-3","description":"- Identify a variety of likely measurement level transformations (e.g., the classification of ratio data yields ordinal data) - Discuss the relationship of attribute measurement levels to database query operations - Describe the pitfalls, in terms of information loss and analytical options, of transforming attribute measurement levels - Reclassify (group) a nominal attribute domain to fewer, broader classes - Reclassify a raster before converting it into a vector file.","name":"Classification and transformation of attribute measurement levels"},{"code":"AM14","description":"All geospatial data are generalized. Even the most detailed data represent only subsets of reality. Furthermore, data are further generalized for purposes of mapping, visualization, and efficient storage. A variety of generalization techniques have been developed to facilitate this process. All are scale dependent. Aggregation is one form of generalization that transforms large numbers of individual objects into summarized groups. This unit is concerned with the nature of these procedures and their implications for professional practice. Generalization is an important part of cartography (and is therefore discussed conceptually in Unit CV2 Data considerations), but is also a transformation common to many GIS procedures.","name":"Generalization and aggregation"},{"code":"AM2-1","description":"- Describe set theory - Explain how set theory relates to spatial queries - Explain how logic theory relates to set theory - Perform a logic (set theoretic) query using GIS software","name":"Set theory"},{"code":"AM2-2","description":"- Alternative (Non-SQL) queries, such as linked data queries","name":"Structured Query Language (SQL) and attribute queries"},{"code":"","description":"","name":""},{"code":"AM2-3","description":"- Demonstrate the syntactic structure of spatial and temporal operators in SQL - Compare and contrast attribute query and spatial query - State questions that can be solved by selecting features based on location or spatial relationships - Construct a query statement to search for a specific spatial or temporal relationship - Construct a spatial query to extract all point objects that fall within a polygon","name":"Spatial queries"},{"code":"AM2","description":"Attribute and spatial query operations are core functionality in any GIS and they are often considered to be the most basic form of analysis.","name":"Query operations and query languages"},{"code":"AM3-1","description":"- Describe several different measures of distance between two points (e.g., Euclidean, - Manhattan, network distance, spherical) - Explain how different measures of distance can be used to calculate the spatial weights matrix - Explain why estimating the fractal dimension of a sinuous line has important implications for the measurement of its length - Explain how fractal dimension can be used in practical applications of GIS - Explain the differences in the calculated distance between the same two places when data used are in different projections - Outline the implications of differences in distance calculations on real world applications of GIS, such as routing and determining boundary lengths and service areas - Estimate the fractal dimension of a sinuous line","name":"Distances and lengths"},{"code":"AM3-2","description":"- Compute the mean of directional data","name":"Direction"},{"code":"AM3-3","description":"- Identify situations in which shape affects geometric operations - Explain what is meant by the convex hull and minimum enclosing rectangle of a set of point data - Explain why the shape of an object might be important in analysis - Exemplify situations in which the centroid of a polygon falls outside its boundary - Compare and contrast different shape indices, include examples of applications to which each could be applied - Develop a method for describing the shape of a cluster of similarly valued points by using the concept of the convex hull - Develop an algorithm to determine the skeleton of polygons - Find centroids of polygons under different definitions of a centroid and different polygon shapes - Calculate several different shape indices for a polygon dataset","name":"Shape"},{"code":"AM3-4","description":"- List reasons why the area of a polygon calculated in a GIS might not be the same as the real world object it describes - Explain how variations in the calculation of area may have real world implications, such as calculating density - Demonstrate how the area of a region calculated from a raster data set will vary by resolution and orientation - Outline an algorithm to find the area of a polygon using the coordinates of its vertices","name":"Area"},{"code":"AM3-5","description":"- Describe real world applications where distance decay is an appropriate representation of the strength of spatial relationships (e.g., shopping behavior, property values) - Describe real world applications where distance decay would not be an appropriate representation of the strength of spatial relationships (e.g., distance education, commuting, telecommunications) - Explain the rationale for using different forms of distance decay functions - Explain how a semi-variogram describes the distance decay in dependence between data values - Outline the geometry implicit in classical \"gravity\" models of distance decay - Plot typical forms for distance decay functions - Write typical forms for distance decay functions - Write a program to create a matrix of pair-wise distances among a set of points","name":"Proximity and distance decay"},{"code":"AM3-6","description":"- List different ways connectivity can be determined in a raster and in a polygon dataset - Describe real world applications where adjacency and connectivity are a critical component of analysis - Explain the nine-intersection model for spatial relationships - Demonstrate how adjacency and connectivity can be recorded in matrices - Calculate various measures of adjacency in a polygon dataset - Create a matrix describing the pattern of adjacency in a set of planar enforced polygons","name":"Adjacency and connectivity"},{"code":"AM3","description":"For simple data exploration, GIS offers many basic geometric operations that help in extracting meaning from sets of data or for deriving new data for further analysis. Concepts on which these operations are based are addressed in Unit CF3 Domains of geographic information and Unit CF5 Relationships.","name":"Geometric measures"},{"code":"AM4-1","description":"- Basic reclassification - Select by attribute - Select by location","name":"Reclassification and selection operations"},{"code":"AM4-2","description":"Buffer analysis is one form of basic spatial analysis. It takes the vector representation (point, line, or polygon) of a real-world feature, and then creates a buffer zone based on a defined distance from the feature’s border. Thus, the created buffer zone is an area whose boundary always has the same distance to the input vector feature, e.g. the buffer zone for a point feature is a circle. Real-world examples for buffer zones could be protected areas along rivers or around nature conservation areas, or represent a simple proximity analysis. In the latter case, the buffer analysis is usually the first step of the analysis, followed by an overlay of the buffer zone with the target features to find those target features within the buffer zone, and thus within a certain distance of the original feature. Usually, the buffer zone extends outwards from the feature, but polygons can also have inner buffer zones. If the buffer zones from multiple features overlap, the analyst can decide to leave the individual boundaries of the buffer zones intact, or to dissolve them, i.e. merging the overlapping buffer zones into one larger buffer zone. The size of the buffer zone, i.e. the distance of its boundary from the original feature’s boundary, can be based on an uniform numerical value and associated spatial unit, but often, it is based on an attribute value (numerical or class) of the feature. Conceptually, buffering using raster representations of real-world features is similar a proximity analysis with a regular grid of square polygons: Departing from raster cells that form the area to be buffered, all raster cells that fall within the designated distance (overlay) from the buffer zone. With buffer analysis being a basic analytical operation, practically every GIS and many other analysis tools provide this functionality.","name":"Buffers"},{"code":"AM4-3","description":"- Explain why the process \"dissolve and merge\" often follows vector overlay operations - Explain what is meant by the term \"planar enforcement\" - Outline the possible sources of error in overlay operations Exemplify applications in which overlay is useful, such as site suitability analysis - Compare and contrast the concept of overlay as it is implemented in raster and vector domains - Demonstrate how the geometric operations of intersection and overlay can be implemented in GIS - Demonstrate why the georegistration of datasets is critical to the success of any map overlay operation - Formalize the operation called map overlay using Boolean logic","name":"Overlay"},{"code":"AM4-4","description":" ","name":"Neighborhood analysis"},{"code":"AM4-5","description":"- Describe how map algebra performs mathematical functions on raster grids - Describe a real modeling situation in which map algebra would be used (e.g., site selection, climate classification, least-cost path) - Explain the categories of map algebra operations (i.e., local, focal, zonal, and global functions) - Explain why georegistration is a precondition to map algebra - Differentiate between map algebra and matrix algebra using real examples - Perform a map algebra calculation using command line, form-based, and flow charting user interfaces","name":"Map algebra"},{"code":"AM4","description":"This small set of analytical operations is so commonly applied to a broad range of problems that their inclusion in software products is often used to determine if that product is a true GIS. Concepts on which these operations are based are addressed in Unit CF3 Domains of geographic information and Unit CF5 Relationships.","name":"Basic analytical operations"},{"code":"AM5-1","description":"- List the conditions that make point pattern analysis a suitable process - Identify the various ways point patterns may be described - Identify various types of K-function analysis - Describe how Independent Random Process/Chi-Squared Result (IRP/CSR) may be used to make statistical statements about point patterns - Outline measures of pattern based on first and second order properties such as the mean center and standard distance, quadrat counts, nearest neighbor distance, and the more modern G,F, and K functions - Outline the basis of classic critiques of spatial statistical analysis in the context of point pattern analysis - Explain how distance-based methods of point pattern measurement can be derived from a distance matrix - Explain how proximity polygons (e.g., Thiessen polygons) may be used to describe point patterns - Explain how the K function provides a scale-dependent measure of dispersion - Compute measures of overall dispersion and clustering of point datasets using nearest neighbor distance statistics","name":"Point pattern analysis"},{"code":"AM5-2","description":"- Describe the relationships between kernels and classical spatial interaction approaches, such as surfaces of potential - Differentiate between kernel density estimation and spatial interpolation - Outline the likely effects on analysis results of variations in the kernel function used and the bandwidth adopted - Explain why and how density estimation transforms point data into a field representation - Explain why, in some cases, an adaptive bandwidth might be employed - Create density maps from point datasets using kernels and density estimation techniques using standard software","name":"Kernels and density estimation"},{"code":"AM5-3","description":"- Identify several cluster detection techniques and discuss their limitations - Discuss the characteristics of the various cluster detection techniques - Demonstrate the extension of spatial clustering to deal with clustering in space-time using the now and Mantel tests - Perform a cluster detection analysis to detect \"hot spots\" in a point pattern","name":"Spatial cluster analysis"},{"code":"AM5-4","description":"- State the classic formalization of the interaction model - Differentiate between the gravity model and spatial interaction models - Describe the formulation of the classic gravity model, the unconstrained spatial interaction model, the production constrained spatial interaction model, the attraction constrained spatial interaction model, and the doubly constrained spatial interaction model - Explain how dynamic, chaotic, complex, or unpredictable aspects in some phenomena make spatial interaction models more appropriate than gravity models - Explain the concept of competing destinations, describing how traditional spatial interaction model forms are modified to account for it - Create a matrix that shows spatial interaction","name":"Spatial interaction"},{"code":"AM5-5","description":"- Relate plots of multidimensional attribute data to geography by equating similarity in data space with proximity in geographical space - Assemble a data matrix of attributes - Produce plots in several data dimensions using a data matrix of attributes - Conduct a simple hierarchical cluster analysis to classify area objects into statistically similar regions - Perform multidimensional scaling (MDS) and principal components analysis (PCA) to reduce the number of coordinates, or dimensionality, of a problem","name":"Analyzing multidimensional attributes"},{"code":"AM5-6","description":"- Describe the difference between prescriptive and descriptive cartographic models - Discuss the origins of cartographic modeling with reference to the work of Ian McHarg - Develop a flowchart of a cartographic model for a site suitability problem","name":"Cartographic modeling"},{"code":"AM5-7","description":"- Describe the implementation of an ordered weighting scheme in a multiple-criteria aggregation - Compare and contrast the terms multi-criteria evaluation, weighted linear combination, and site suitability analysis - Differentiate between contributing factors and constraints in a multi-criteria application - Explain the legacy of multi-criteria evaluation in relation to cartographic modeling - Determine which method to use to combine criteria (e.g., linear, multiplication) - Create initial weights using the analytical hierarchy process (AHP) - Calibrate a linear combination model by adjusting weights using a test data set - Discuss the issue of sensitivity analysis in the context of mca","name":"Multi-criteria evaluation"},{"code":"AM5-8","description":"- Discuss the relationship between spatial processes and spatial patterns - Differentiate between deterministic and stochastic spatial process models - Describe a simple process model that would generate a given set of spatial patterns","name":"Spatial process models"},{"code":"AM5","description":"Building on the basic geometric measures and analytical operations found in most GIS products, a broad range of additional analytical methods form the fundamental GIS toolkit.","name":"Basic analytical methods"},{"code":"AM6-1","description":"- List the likely sources of error in slope and aspect maps derived from digital elevation models (DEMs) and state the circumstances under which these can be very severe - Outline a number of different methods for calculating slope from a DEM - Outline how higher order derivatives of height can be interpreted - Explain how slope and aspect can be represented as the vector field given by the first derivative of height - Explain why the properties of spatial continuity are characteristic of spatial surfaces - Explain why zero slopes are indicative of surface specific points such as peaks, pits, and passes, and list the conditions necessary for each - Design an algorithm that calculates slope and aspect from a triangulated irregular network (TIN) model - Discuss the available DEM data (e.g. data derived from Earth Observation)","name":"Calculating surface derivatives"},{"code":"AM6-2","description":"- Identify the spatial concepts that are assumed in different interpolation algorithms - Describe how surfaces can be interpolated using splines - Compare and contrast interpolation by inverse distance weighting, bi-cubic spline fitting, and kriging - Differentiate between trend surface analysis and deterministic spatial interpolation - Explain why different interpolation algorithms produce different results and suggest ways by which these can be evaluated in the context of a specific problem - Design an algorithm that interpolates irregular point elevation data onto a regular grid - Outline algorithms to produce repeatable contour-type lines from point datasets using proximity polygons, spatial averages, or inverse distance weighting - Implement a trend surface analysis using either the supplied function in a GIS or a regression function from any standard statistical package","name":"Interpolation of surfaces"},{"code":"AM6-3","description":"- Describe how a network of stream channels and ridges can be estimated from a DEM - Explain how ridgelines and streamlines can be used to improve the result of an interpolation Process","name":"Surface features"},{"code":"AM6-4","description":"- Define \"intervisibility\" - Explain the sources and impact of errors that affect intervisibility analyses - Outline an algorithm to determine the viewshed (area visible) from specific locations on surfaces specified by DEMs - Perform siting analyses using specified visibility, slope, and other surface related constraints","name":"Intervisibility"},{"code":"AM6-5","description":"- Define \"friction surface\" - Explain how friction surfaces are enhanced by the use of impedance and barriers - Apply the principles of friction surfaces in the calculation of least-cost paths","name":"Friction surfaces"},{"code":"AM6","description":"There is a wide range of phenomena that can be studied using a set of techniques and tools that are designed to help understand the characteristics of continuous surface data. Applications of these techniques using terrain data include overland transport, flow, and siting tasks, but similar analyses can be conducted using non-tangible surfaces such as those of temperature, pressure and population density.","name":"Analysis of surfaces"},{"code":"AM7-1","description":"- Describe the statistical characteristics of a set of spatial data using a variety of graphs and plots (including scatterplots, histograms, boxplots, q q plots) - Select the appropriate statistical methods for the analysis of given spatial datasets by first exploring them using graphic methods","name":"Graphical methods"},{"code":"AM7-2","description":"- List the two basic assumptions of the purely random process - Justify the stochastic process approach to spatial statistical analysis - Exemplify deterministic and spatial stochastic processes - Exemplify non-stationarity involving first and second order effects - Differentiate between isotropic and anisotropic processes - Discuss the theory leading to the assumption of intrinsic stationarity - Outline the logic behind the derivation of long run expected outcomes of the independent random process using quadrat counts","name":"Stochastic processes"},{"code":"AM7-3","description":"- Explain how different types of spatial weights matrices are defined and calculated - Explain the rationale used for each type of spatial weights matrix - Discuss the appropriateness of different types of spatial weights matrices for various problems - Construct a spatial weights matrix for lattice, point, and area patterns","name":"The spatial weights matrix"},{"code":"AM7-4","description":"- Describe the effect of the assumption of stationarity on global measures of spatial association - Explain how a statistic that is based on combining all the spatial data and returning a single summary value or two can be useful in understanding broad spatial trends - Explain how the K function provides a scale-dependent measure of dispersion - Compute Moran`s I and Geary`s c for patterns of attribute data measured on interval/ratio scales - Compute measures of overall dispersion and clustering of point datasets using nearest neighbor distance statistics - Compute the K function - Justify, compute, and test the significance of the join count statistic for a pattern of objects","name":"Global measures of spatial association"},{"code":"AM7-5","description":"- Describe the effect of non-stationarity on local indices of spatial association - Compare and contrast global and local statistics and their uses - Explain how a weights matrix can be used to convert any classical statistic into a local measure of spatial association - Explain how geographically weighted regression provides a local measure of spatial association - Decompose Moran`s I and Geary`s c into local measures of spatial association - Compute the Gi and Gi* statistics","name":"Local measures of spatial association"},{"code":"AM7-6","description":"- Explain how outliers affect the results of analyses - Explain how the following techniques can be used to examine outliers: tabulation, histograms, box plots, correlation analysis, scatter plots, local statistics","name":"Outliers"},{"code":"AM7-7","description":"- Define \"prior and posterior distributions\" and \"Markov-Chain Monte Carlo\" - Explain how the Bayesian perspective is a unified framework from which to view uncertainty - Compare and contrast Bayesian methods and classical \"frequentist\" statistical methods","name":"Bayesian methods"},{"code":"AM7","description":"Traditional statistical methods are used to describe the central tendency, dispersion, and other characteristics of data but are not always suited to use with spatial data for which specialized techniques are often required. The field of spatial statistical analysis forms the backbone for the testing of hypotheses about the nature of spatial pattern, dependency, and heterogeneity. The techniques are widely used in both exploratory and confirmatory spatial analysis in many different fields.","name":"Spatial statistics"},{"code":"AM8-1","description":"- List and describe several spatial sampling schemes and evaluate each one for specific applications - Describe sampling schemes for accurately estimating the mean of a spatial data set - Differentiate between model-based and design-based sampling schemes - Design a sampling scheme that will help detect when space-time clusters of events occur - Create spatial samples under a variety of requirements, such as coverage, randomness, and transects","name":"Spatial sampling for statistical analysis"},{"code":"AM8-2","description":"- Construct a semi-variogram and illustrate with a semi-variogram cloud","name":"Principles of semi-variogram construction"},{"code":"AM8-3","description":"- List the possible sources of error in a selected and fitted model of an experimental semi-variogram - Describe some commonly used semi-variogram models - Describe the conditions under which each of the commonly used semi-variograms models would be most appropriate - Explain the necessity of defining a semi-variogram model for geographic data - Apply the method of weighted least squares and maximum likelihood to fit semi-variogram models to dataset","name":"semi-variogram modeling"},{"code":"AM8-4","description":"- Describe the relationship between the semi-variogram and kriging - Explain why kriging is more suitable as an interpolation method in some applications than others - Explain why it is important to have a good model of the semi-variogram in kriging - Explain the concept of the kriging variance, and describe some of its shortcomings - Explain how block-kriging and its variants can be used to combine data sets with different spatial resolution (support) - Compare and contrast block-kriging with areal interpolation using proportional area weighting and dasymetric mapping - Outline the basic kriging equations in their matrix formulation - Conduct a spatial interpolation process using kriging from data description to final error map","name":"Principles of kriging"},{"code":"AM8-5","description":"- Compare and contrast co-kriging, log-normal kriging, disjunctive kriging, indicator kriging, factorial kriging, and universal kriging - Apply universal kriging to appropriate data sets - Interpret the results of universal kriging","name":"Kriging variants"},{"code":"AM8","description":"Geostatistics are a variety of techniques used to analyze continuous data e.g., rainfall, elevation, air pollution. The fundamental structure of geostatistics is based on the concept of semi-variograms and their use for spatial prediction kriging. Sampling methods are also discussed in Unit GD9 Field data collection.","name":"Geostatistics"},{"code":"AM9-1","description":"- Describe the general types of spatial econometric models - Explain how spatial dependence and spatial heterogeneity violate the Gauss-Markov assumptions of regression used in traditional econometrics - Demonstrate how spatially lagged, trend surface, or dummy spatial variables can be used to create the spatial component variables missing in a standard regression analysis - Demonstrate how the spatial weights matrix is fundamental in spatial econometrics models - Demonstrate why spatial autocorrelation among regression residuals can be an indication that spatial variables have been omitted from the models","name":"Principles of spatial econometrics"},{"code":"AM9-2","description":"- Explain Anselin`s typology of spatial autoregressive models - Conduct a spatial econometric analysis to test for spatial dependence in the residuals from leastsquares models and spatial autoregressive models - Demonstrate how the parameters of spatial auto-regressive models can be estimated using univariate and bivariate optimization algorithms for maximizing the likelihood function - Justify the choice of a particular spatial autoregressive model for a given application - Implement a maximum likelihood estimation procedure for determining key spatial econometric parameters - Apply spatial statistic software (e.g., GEODA) to create and estimate an autoregressive model","name":"Spatial autoregressive models"},{"code":"AM9-3","description":"- Identify modeling situations where spatial filtering might not be appropriate - Describe the relationship between factorial kriging and spatial filtering - Explain how spatial correlation can result as a side effect of the spatial aggregation in a given dataset - Explain how dissolving clusters of blocks with similar values may resolve the spatial correlation problem Explain how the Getis and Tiefelsdorf-Griffith spatial filtering techniques incorporate spatial component - variables into OLS regression analysis in order to remedy misspecification and the problem of spatially auto-correlated residuals - Demonstrate how spatial autocorrelation can be \"removed\" by resampling","name":"Spatial filtering"},{"code":"AM9-4","description":"- Describe the characteristics of the spatial expansion method - Discuss the appropriateness of GWR under various conditions - Explain how allowing the parameters of the model to vary with the spatial location of the sample data can be used to accommodate spatial heterogeneity - Explain the principles of geographically weighted regression - Compare and contrast GWR with universal kriging using moving neighborhoods - Perform an analysis using the geographically weighted regression technique - Analyze the number of degrees of freedom in GWR analyses and discuss any possible difficulties with the method based on your results","name":"Spatial expansion and Geographically Weighted Regression GWR"},{"code":"AM9","description":"Many problems of the social sciences can be expressed in terms of spatial regression analysis. The development of spatial autoregressive models and the estimation of their parameters is the focus for the field of spatial econometrics.","name":"Spatial regression and econometrics"},{"code":"CF","description":"The GIScience perspective is grounded in spatial thinking. The aim of this knowledge area is to recognize, identify, and appreciate the explicit spatial, spatio-temporal and semantic components of the geographic environment at an ontological and epistemological level in preparation for modeling the environment with geographic data and analysis. To do this, one must understand the nature of space and time as a context for geographic phenomena.This knowledge area covers the ways in which views of the geographic environment depend on philosophical viewpoints, physics, human cognition, society, and the task at hand. This knowledge area also requires an understanding of the fundamental principles in the discipline of geography, the \"language\" of spatial tasks. On a more advanced level, this area incorporates mathematical and graphical models that formalize these concepts, such as set theory, algebra, and semantic nets. Because of its wide range of foundational principles, this knowledge area forms a basis for the other knowledge areas. Wise design and use of geospatial technologies requires an understanding of the nature of geographic information, the social and philosophical context of geographic information, and the principles of geography. This knowledge area is especially closely tied to Knowledge Areas Data Modeling (DM) and Design Aspects (DA), as generic data models and application designs need to be grounded in sound conceptual models. The foundations of geographic information have developed over several decades. Philosophical and scientific views on the nature of space and time have evolved since the ancient Greeks. Early papers during the Quantitative Revolution, such as Berry (1964), began to formalize the structure of information used in geographic inquiry.The fundamental data structures and algorithms comprising the GIS software developed in the 1960`s and 1970`s were based on implicit \"common-sense\" conceptual models of geographic information. During the 1980`s, several researchers questioned these underlying assumptions. Some were refuted, other confirmed, and many extended. However, the most rapid pace of development in this area was during the 1990`s with the rise of GIScience as a distinct discipline, and the many cooperative initiatives it comprised.The new millennium has seen some of these foundational principles incorporated into commercial software, thus making theoretical knowledge even more important for practitioners. It is expected that the concepts in this knowledge area will be learned gradually. An introductory course may cover only a few topics in a cursory manner, an intermediate course on data modeling or data analysis may consider several theoretical topics of practical application, and a number of graduate courses could cover each topic in a research-oriented environment. Discussion of this knowledge area includes several terms that can have multiple meanings. For the purposes of this document, two in particular require definition: Geographic: Almost any subject or discourse involving earthly phenomena, studied from a spatial perspective at a medium scale (sub-astronomical and super-architectural). Phenomenon: Any subject of geographic discourse that is perceived to be external to the individual, including entities, events, processes, social constructs, and the like.","name":"Conceptual Foundations"},{"code":"CF1-1","description":" ","name":"Metaphysics and ontology"},{"code":"CF1-1b","description":"Brief history of GIScience as related to the history of GISystems; Definitions of GIS&T; Sub-domains of GIS&T (i.e., Geographic Information Science, Geospatial Technology, and Applications of GIS&T)","name":"What is Geographic Information Science and Technology"},{"code":"CF1-2","description":" ","name":"Epistemology"},{"code":"CF1-2b","description":"GIS&T draws upon insights and methods from key allied fields: Geography, Cartography, Computer and information science, Engineering, Mathematics and Statistics, Philosophy, Cognitive Science, Linguistics","name":"Contributions to GIS and T by key allied fields"},{"code":"CF1-3","description":" ","name":"Philosophical perspectives"},{"code":"CF1","description":"Many branches of philosophy are relevant to an understanding of geographic information, especially metaphysics and epistemology. Philosophical theories are deeply engaged in the study of knowledge, space, time, geographic phenomena and human interaction with them. These theories influence the development of geographic ontologies and the structuring, analysis, and interpretation of geographic information. It is, therefore, crucial for professionals to understand these principles in order to bridge (rather than eliminate) the differences and work together. Philosophical perspectives on GIS practice are covered in Unit GS7 Critical GIS.","name":"Philosophical foundations"},{"code":"CF1b","description":"Unit CF1 introduces the broad domain refered to as Geographic Information Science & Technology (GIS&T) and its sub-domains (i.e., Geographic Information Science, Geospatial Technology, and Applications of GIS&T). It outlines the history of Geographic Information Science as related to the history of GISystems, as well as the contributions to this multidisciplinary domain by key allied fields, such as geography, cartography, computer and information science, engineering, mathematics, philosophy, cognitive science, and linguistics.","name":"Introduction to Geographic Information Science and Technology"},{"code":"CF2-1","description":" ","name":"Perception and cognition of geographic phenomena"},{"code":"CF2-1b","description":"Metaphysics and Ontology - Formal ontology - Ontological distinctions (e.g., continuants vs. occurrents, universals vs. particulars) - The problem of universals and relevant theories (realism, nominalism, conceptualism) - Ontologies of the geographic domain - Philosophical theories relating to the nature of space, time, geographic phenomena and human interaction with them","name":"Philosophy of being"},{"code":"CF2-2","description":" ","name":"From concepts to data"},{"code":"CF2-2b","description":"Epistemology; Theories on what constitutes knowledge; The notions of model and representation in science; The influences of epistemology on GIS practices","name":"Philosophy of knowledge"},{"code":"CF2-3","description":" ","name":"Geography as a foundation for GIS"},{"code":"CF2-4","description":" ","name":"Place and landscape"},{"code":"CF2-6","description":" ","name":"Cultural influences"},{"code":"CF2-7","description":" ","name":"Political influences"},{"code":"CF2","description":"Geographic information is observed, comprehended, organized, used in human processes, with both personal and social influences. Therefore, sound models of geographic information should be grounded on a sound understanding of human perception, cognition, memory, and behavior, as well as human institutions.","name":"Cognitive and social foundations"},{"code":"CF3-1","description":"- Theories of space - contributions that different perspectives on the nature of space bring to an understanding of geographic entities and phenomena - Spatial frames of reference - Differing denotations and connotations of the terms spatial, geographic, and geospatial - Nature and characteristics of spatial entities - Place (i.e., difference between space and place, how the concept of place encompasses more than just location) - Landscape, various concepts and definitions - Basic primitives used to describe spatial objects (i.e., points, lines, regions, volumes) - Discrepancies between the nature of locations in the real world and representations thereof, (e.g., generalisation of towns as points) - Spatial metaphors and models of phenomena to be represented in GIS - Advantages and disadvantages of the use of Cartesian/metric space as a basis for GIS and related technologies - Methods for representing non-Cartesian models of space in GIS - Limitations of representation of place, landscape, and spatial phenomena in GIS","name":"Space"},{"code":"CF3-1b","description":"- Theories of human perception, cognition, and memory and their ability to model spatial knowledge acquisition (e.g., Marr on vision, Piaget on cognitive development) - Types of mental representations (i.e., analogue, propositional, procedural) - The role of metaphors and image schemata in our understanding of geographic phenomena and geographic tasks - From concepts to data (i.e., data, information, knowledge, and wisdom; transformation of a conceptual model of information for a particular task into a data model; limitations of various information stores (the mind, computers) and means (maps, graphics, and text) for representing geographic information) - Difference between real phenomena, conceptual models, and GIS data representations thereof connections with cartography and maps","name":"Cognitive foundations"},{"code":"CF3-2b","description":"- Semantics - Meaning (e.g., the nature of meaning, modes of meaning) - Geospatial semantics - The role of natural language in the conceptualization of geographic phenomena","name":"Linguistic foundations"},{"code":"CF3-3b","description":"- The ways in which the elements of culture (e.g., language, religion, education, traditions) may influence the understanding and use of geographic information - The influences of social theories and political ideologies and actions on human perceptions of space and place - The constraints that political forces place on geospatial applications in public and private sectors","name":"Social foundations"},{"code":"CF3-4b","description":"- Common-sense views and laymen knowledge of geographic phenomena that contrast with established theories and technologies of geographic information - The impact of geospatial technologies and the geoweb (e.g., digital globes) that allow non-geospatial professionals to create, distribute, and map geographic information - The design, procedures, and results of GIS projects to non-GIS audiences (clients, managers, general public) - Difference between applications that can make use of common-sense principles of geography and those that should not","name":"Common-sense geographies"},{"code":"CF3","description":"Geographic information is observed, comprehended, organized, used in human processes, with both personal and social influences. Therefore, sound models of geographic information should be grounded on a sound understanding of human perception, cognition, memory, and behavior, as well as human institutions.","name":"Cognitive, linguistic and social foundations"},{"code":"CF4-2b","description":"- Mathematical and phenomenological theories of the nature of time - Different temporal frames of reference: linear and cyclical, absolute and relative - The role that time plays in static GIS - Models of a given spatial process using continuous and discrete perspectives of time - Temporal elements of geographic phenomena that need to be represented in particular GIS applications","name":"Time"},{"code":"CF4-3b","description":"- Characteristics of spatial and temporal dimensions - Types of geographic interactions in space and time - Types of movement, change, and evolution","name":"Relationships between space and time"},{"code":"CF4-4b","description":"- GIS data structures to represent categories, including attribute columns, layers/themes, shapes, and legends","name":"Categories"},{"code":"CF4-5","description":"- Difference between the theory holding that properties are fundamental (and objects are human simplifications of patterns thereof) and the theory holding that objects are fundamental (and properties are attributes thereof) - Stevens` four levels of measurement (i.e., nominal, ordinal, interval, ratio) - Attribute domains in GIS, including continuous and discrete, qualitative and quantitative, absolute and relative - proper uses of attributes based on their domains - Attribute domains that do not fit well into Stevens` four levels of measurement such as cycles, indexes, and hierarchies - Set theory to formalize attribute values and domains - Alternative forms of representations for situations in which attributes do not adequately capture meaning (such as aesthetics)","name":"Properties"},{"code":"CF4b","description":"Geographic phenomena, geographic information, and geographic tasks are described in terms of space, time, and properties. Different theories exist as to the nature and formal representation of these aspects, including space-like dimensions, sets, and phenomenology. Information in each of these three aspects is measured and reported with respect to one of several frames of reference or domains, including both absolute and relative approaches. Early frameworks such as those of Berry (1964) and Sinton (1978) were influential in setting forth the importance of space, time, and theme in GIS&T. Besides, space, time, and properties, categories are also fundamental in the conceptualization and representation of spatial entities, phenomena, processes, and events. Distinctive features of geographic information such as scale and detail, spatial patterns, spatial integration, and regions are also critical for a complete description of its nature and representation. This unit is closely tied to the creation of data models in Knowledge Area 5: Data Modeling, Storage, and Exploitation.","name":"Fundamentals of Geographic Information"},{"code":"CF5-1b","description":"-The predominance of discrete entities (in terms of space, time, and properties) in human conceptualizations of geographic phenomena -Perceptual processes (e.g., edge detection) that aid cognitive objectification -Differing epistemological and metaphysical viewpoints on the \"reality\" of geographic entities -Entity types that need to be modeled in a particular GIS application or procedure -Phenomena that are difficult or impossible to conceptualize in terms of entities -Difficulties in modeling entities with ill-defined edges -The effectiveness of GIS data models for representing the identity, existence, and lifespan of entities -The influence of scale on the conceptualization of entities","name":"Discrete entities"},{"code":"CF5-2b","description":" ","name":"Fields"},{"code":"CF5-3","description":" ","name":"Genealogical relationships: lineage, inheritance"},{"code":"CF5-3b","description":"-Continuants (entities) vs. occurrents (events) -Event vs. process -Description of particular events or processes in terms of identity, categories, attributes, and locations -Formal systems for describing continuous spatio-temporal processes -The \"actor\" role that entities and fields play in events and processes -The difficulty of integrating process models into GIS software based on the entity and field views, and methods used to do so","name":"Events and processes"},{"code":"CF5-4b","description":"-Phenomena or applications that are not adequately represented in an existing comprehensive model -Early attempts to integrate the concepts of space, time, and attribute in geographic information, such as Berry (1964) and Sinton (1978) -Major integrated models of geographic information, such as Peuquet`s triad, Mennis` pyramid, and Yuan`s three-domain -The degree to which integrated models can be implemented using current technologies","name":"Integrated models"},{"code":"CF5-6","description":" ","name":"Spatial distribution"},{"code":"CF5-7","description":" ","name":"Region"},{"code":"CF5-8","description":" ","name":"Spatial integration"},{"code":"CF5","description":"The concepts below form the basic elements of common human conceptions of geographic phenomena. Concepts from many units in this knowledge area have been synthesized to create general conceptual models of geographic information. Attempts to resolve the object-field debate have led to attempts to create comprehensive models that bridge these views. Consideration of this unit should also include formal models of these elements in mathematics and other fields. Knowledge Area DM Data Modeling discusses the representation of these elements in digital models.","name":"Elements of geographic information"},{"code":"CF5b","description":"The concepts below form the basic elements of common human conceptions of geographic phenomena. Concepts from many units in this knowledge area have been synthesized to create general conceptual models of geographic information. Attempts to resolve the object-field debate have led to attempts to create comprehensive models that bridge these views. Consideration of this unit should also include formal models of these elements in mathematics and other fields. Knowledge Area DM Data Modeling discusses the representation of these elements in digital models.","name":"Elements of geographic information"},{"code":"CF6-1","description":" ","name":"Mereology: structural relationships"},{"code":"CF6-2","description":"-How a geographic entity can be created from one or more others -Identity-based change or temporal relationships of particular geographic phenomena -When to represent the genealogy of entities for a particular application -The effects of temporal scale on the modeling of genealogical structures","name":"Genealogical relationships: lineage, inheritance"},{"code":"CF6-3","description":"-Topology (the branch of mathematics) and the study of geographic relationships -Terms used to describe topological relationships, such as disjoint, overlap, within, and intersect -Description of geographic phenomena in terms of their topological relationships in space and time to other phenomena -Possible topological relationships between entities in space (e.g., 9-intersection) and time -Methods that analyze topological relationships","name":"Topological relationships"},{"code":"CF6-4","description":"-Geographic phenomena in terms of their distances and directions (in space and time) -Spatial autocorrelation in the context of geographic proximity -Methods that analyze metrical relationships -Categories of distance (e.g. Euclidean distances, Manhattan distance, geodetic distance, spherical distance) -Tobler`s first law of geography and GIS operations -Situations in which Tobler`s first law of geography is valuable or does not apply -The principle of friction of distance and geographic models that are based on it (e.g., gravity models, spatial interaction models)","name":"Metrical relationships: distance and direction"},{"code":"CF6","description":"Like geography, geographic information not only models phenomena but the relationships between them. This can include relationships between entities, between attributes, between locations. In addition, one of the strengths of geography (and GIS) is its ability to use a spatial perspective to relate disparate subjects, such as climate and economy. Methods for analyzing relationships are discussed in Unit AM4 Modeling relationships and patterns.","name":"Relationships"},{"code":"CF7-1","description":"-Meanings of related terms such as vague, fuzzy, imprecise, indefinite, indiscrete, unclear, and ambiguous -The role that system complexity, dynamic processes, and subjectivity play in the creation of vague phenomena and concepts -Cognitive processes that tend to create vagueness -Vagueness and language -Vagueness and scale (granularity) -Vagueness in different aspects of geographic phenomena -When is vagueness acceptable and when it is not -Difference between: vagueness and ambiguity, well defined and poorly defined objects and fields -Mathematical models of vagueness (e.g., fuzzy sets, rough sets)","name":"Vagueness"},{"code":"CF7-2","description":"-Uncertainty-related terms, such as error, accuracy, uncertainty, precision, stochastic, probabilistic, deterministic, and random -Difference between uncertainty and vagueness -Dependence of uncertainty on scale and application -Expressions of uncertainty in language -The causes of uncertainty in geospatial data -Stochastic error models for natural phenomena -How the concepts of geographic objects and fields affect the conceptualization of uncertainty -Mathematical models of uncertainty: Probability and statistics","name":"Error-based uncertainty"},{"code":"CF7","description":"Human models (mental, digital, visual, etc.) of the geographic environment are necessarily imperfect. While the mathematical principle of homomorphism (often operationalized as fitness for use) allows for imperfect data to be useful as long as they yield results adequate for the use for which they are intended, imperfections are frequently problematic. Although terminology still varies, two types of imperfection are generally accepted: vagueness (a.k.a. fuzziness, imprecision, and indeterminacy), which is generally caused by human simplification of a complex, dynamic, ambiguous, subjective world; and uncertainty (or ambiguity), generally the result of imperfect measurement processes (as discussed in Knowledge Area GD Geospatial Data). Both of these can be manifested in all forms of geographic information, including space, time, attribute, categories, and even existence. Imperfection is also dealt with in Units GD6 Data quality (in the context of measurement), GC8 Uncertainty and GC9 Fuzzy sets (for the handling and propagation of imperfections), and CV4 Graphic representation techniques (in the context of visualization).","name":"Imperfections in geographic information"},{"code":"CV","description":"Cartography and visualization primarily relate to the visual display of geographic information. This knowledge area addresses the complex issues involved in effective visual thinking and communication of geospatial data and of the results of geospatial analysis. This knowledge area reflects much of the domain of cartography and visualization, although some concepts and skills in these areas can be found in other knowledge areas. For example, the process of visualization encompasses aspects of analysis as well as cartography. Specifically, visualization is currently being reformulated as visual analytics in the context of homeland security.","name":"Cartography and Visualization"},{"code":"CV1-1","description":"The evolution of cartographical representation of geography in the previous centuries followed technological and scientific developments. It was driven by commercial and/or military needs and influenced by the nature of the area to be mapped. More recent developments are the rise of open data in some countries and the widely available internet technology allowing end users to get geodata from the web. The last decades the data and their presentation have become central to cartography, whereas paper maps became peripheral. Facets: a.\tMeasuring position and direction b.\tReference systems c.\tMapmakers and their influence d.\tAnalog tools for graphic representation e. Governmental map production f.\tGIS technology g.\tInternet technology h.\tOpen Data movement","name":"History of cartography"},{"code":"CV1-3","description":"The evolution of cartographical representation of geography in the previous centuries followed technological and scientific developments. It was driven by commercial and/or military needs and influenced by the nature of the area to be mapped. More recent developments are the rise of open data in some countries and the widely available internet technology allowing end users to get geodata from the web. The last decades the data and their presentation have become central to cartography, whereas paper maps became peripheral.\r\n\r\nFacets: a.\tMeasuring position and direction b.\tReference systems c.\tMapmakers and their influence d.\tAnalog tools for graphic representation e.\tGovernmental map production f. GIS technology g.\tInternet technology h.\tOpen Data movement","name":"Paradigm and technology shifts"},{"code":"CV1-4","description":"Art in cartography means much more than designing aesthetically pleasing maps. Exploring the interaction at large between art and cartography involves rethinking the way we approach spatial expressions. This entails: developing an inclusive approach of artistic mapping expressions; facilitating and encouraging interaction between cartographers who work with the Art aspects of cartography and artists who produce cartographic artifacts; and developing conceptual elements about the relationships between art and cartography.\"Art is a diverse range of human activities in creating visual, auditory or performing artifacts artworks, expressing the author's imaginative or technical skill, intended to be appreciated for their beauty or emotional power. In their most general form these activities include the production of works of art, the criticism of art, the study of the history of art, and the aesthetic dissemination of art","name":"Art and geo-data visualisation"},{"code":"CV1-5","description":"Historical maps can be maps that are out of date and have become obsolete by the passing of time. Historical maps can also be representations made with the intention to show a situation in the past. This is independent of the technology used to produce the map or the technology to manipulate the data that underlies the map. Facets: the old map was made in the analog past, the picture itself is the source; If possible, a high resolution scan of the map image should be georeferenced before use in a GIS; Digital representations of historic situations should come with metadata about their origins; the legibility of historical maps.","name":"Historical maps"},{"code":"CV1","description":"The history of cartography can be described as an interplay of change in: the motives for mapping, the history of exploration, printing technologies, data collection technologies, design technologies, scientific understanding of map use, visual analysis of graphic displays, application domains and creative design innovations.","name":"History and trends"},{"code":"CV2-1","description":"As mapping ( geo-data visualization) is intended to convey a certain message to a certain audience, it is essential to use data sources that allow the intended visualisation result. The data should be of the right degree of detail and its use should not cause copyright problems. The producer quality of each data set should be taken into account, as well as the fitness of the data for the intended use. Aspects: message; data quality","name":"Data sources for mapping"},{"code":"CV2-2","description":"In the trajectory between raw (geo)data and their user-relevant representation, the necessary data processing includes ways of abstraction by selection, filtering, generalization, transformation and classification of geographical data. In this data processing it is essential to at one hand relate the final symbolisation to the necessities of the intended message, and at the other hand to procedures that introduce as little error as possible.","name":"Data processing"},{"code":"CV2-3","description":"Map projection is fundamental to representation of spatial data and for combining different datasets. Its choice should serve the presentation type that will convey the intended message to the audience. Many mathematical principles define datum, projections, horizontal and vertical co-ordinate systems, georeferencing- introduced with the focus on visualisation issues Aspects: geodetic concepts; transformations","name":"Mathematical base"},{"code":"CV2","description":"Geodata, including 3 dimensional geometry, as such can graphically be presented but most of the times the data as such doesn`t meet the presentation criteria. Especially if the dataset has to be presented in combination with other datasets. First all the geodatum, georeference and map projection are crucial but also the role of the geometry. The processing of the geometry and the related attributes may become a crucial step for an adequate presentation. Nowadays the highest precision may be used to define different graphical attributes for different zoom levels. On the other hand geodata visualisation includes also graphical datasets. Such data ensembles, the combination of geodata and graphical data, are the data sources that offer opportunities to other ways of visualisation then the traditional cartographic mapping. Facets: a.\tGeospatial location (2D) and position (3D) that data refer to b.\tDegree of detail in data origin (acquisition resolution) and in representation ('map' scale) c.\tTypes of data (e.g. imagery, field measurements, delineated objects)","name":"Data considerations"},{"code":"CV3-1","description":" ","name":"Map design fundamentals"},{"code":"CV3-10","description":"Geo-gaming is a crossover between computer gaming and geocaching, enabled by mobile location based information. Gamers participate by roaming about the environment. This offers myriad opportunities: non-linear storytelling, physical object integration, a more visceral experience, true social interaction. It also presents technical challenges to meet the unique infrastructural demands of geo-gaming. Such games, also called “mobile location-based games”, are also used in teaching","name":"Geo-gaming"},{"code":"CV3-2","description":"The symbolization of map data has a number of variables. They can be used to produce visual, tactile, haptic, auditory, and dynamic displays. Visual variables (e.g., size, lightness, shape, hue) and graphic primitives (points, lines, areas) are commonly used in maps to represent various geographic features at all attribute measurement levels (nominal, ordinal, interval, ratio). With those a single geographic feature can be represented by various graphic primitives (e.g., land surface as a set of elevation points, as contour lines, as hypsometric layers or tints, and as a hillshaded surface). The challenge is to use effective symbols for map features. Aspects: visual variables and graphic primitives; user capacity to distinguish between map symbols; use of variables in different display types ( visual, haptic, tactile, auditory, dynamic)","name":"Symbols and icons"},{"code":"CV3-3","description":"The selection of colours to use in data representation can be influenced by various factors (e.g. the production workflow, cultural differences, involved devices and media). There are various colour models (e.g. RGB, CMYK, CIE) that describe colours in a way that they can be ordered (e.g., qualitative, sequential, diverging, spectral) and reproduced. The cultural background of the consumer is relevant when it comes to choice of colours that should have real-world connotations or should express psychological concepts (e.g. harmony, concordance, balance). A final important factor is if the consumer has colour limitations Aspects: type of product; cultural background; colour models; limitation of consumer/user","name":"Colour"},{"code":"CV3-4","description":"For information in data representation that should be conveyed in words (e.g. toponyms, road codes) written text can be used, placed in labels. It is important to decide on the role of the label in the context of the representation type. The placing of the labels is relevant, especially when label density is high. Shape and colour of the labels help to signify different types of messages. This is supported by the typographic properties (type font, size, style) of the text in the labels. Finally, it is important to use an authoritative source for the texts. Aspects: type of data representation; role of labels; place of labels; shape and colour of labels; fonts; source of tex","name":"Typography"},{"code":"CV3-5","description":"Imagery can be a source for data acquisition as well as an illustration to abstract data representations. Imagery can be made from the air (from drones to satellites) or from a terrestrial point of view. The knowledge field describing the data acquisition process based on photos is called Remote Sensing. Using photos from any source to illustrate stories about geographical subjects contributes is the visual aspect of telling a story. Together with maps and other narrative components, the combination embodies a storytelling medium. Aspects: photos for data collection; photo as part of geo data ensemble; photo as representation of place; photo as support of representation, illustration of specific time and place","name":"Photos"},{"code":"CV3-6","description":"Animation is the process of making the illusion of motion and change by means of the rapid display of a sequence of static images that minimally differ from each other.","name":"Animation"},{"code":"CV3-7","description":"Sound can be one of the components of a multimedia data representation. Wikipedia: “Multimedia is content that uses a combination of different content forms such as text, audio, images, animation, video and interactive content. Multimedia contrasts with media that use only rudimentary computer displays such as text-only or traditional forms of printed or hand-produced material","name":"Sound"},{"code":"CV3-8","description":"Storytelling is the conveying of events in words, sound and/or images (Wikipedia). Maps are valuable because they provide a large amount of detail in a small amount of space, and because of their capacity for telling a story. Telling stories through maps began with describing explored lands in great detail against terra incognita. Today, geographic tools, data, and multimedia on the web expand the ability and audience for storytelling through maps. Any person with a smartphone or computer can use maps to tell a story, using live web maps with text, video, audio, sketches, and photographs. (After: Kerski, 2015).\r\nAspects: Data; Applied multimedia range; techniques; designing / storyboard; effectiveness/usability","name":"Storytelling"},{"code":"CV3-9","description":"Infographics are visual representations of information and data. The aim of an infographic is to present information that can be absorbed quickly and is easily understandable. Infographics can consist of Charts, Diagrams, Graphs, Tables, Maps and Lists (Davey 2014). Infographics have evolved in recent years to be for mass communication, and thus are designed with fewer assumptions about the readers knowledge base than other types of visualizations (Wikipedia Aspects: input data; representation format; canvas type; audience","name":"Info-graphics"},{"code":"CV3","description":"This topic covers basic design principles that are used in mapping and visualization, as well as cartographic design principles specific to the display of geographic data. Both page layout design and data display are addressed.","name":"Design principles"},{"code":"CV4-1","description":"A thematic map is a type of map especially designed to show a particular theme connected with a specific geographic area. These maps \"can portray physical, social, political, cultural, economic, sociological, agricultural, or any other aspects of a city, state, region, nation, or continent\". Cartographers use many methods to create thematic maps. Five techniques are especially noted: -Choropleth mapping shows statistical data aggregated over predefined regions -Proportional symbols, showing the relative value of attributes -Isarithmic or Isopleth, also known as contour maps -Dots, to show the location of a phenomenon -Dasymetric, which uses areal symbols to spatially classify volumetric data.","name":"Thematic mapping"},{"code":"CV4-10","description":"Conveying uncertainty information is often done through visualization. Uncertainty is often defined, quantified, and expressed using models specific to individual application domains. In visualization however, we are limited in the number of visual channels (3D position, color, texture, opacity, etc.) available for representing the data. Thus, when moving from quantified uncertainty to visualized uncertainty, we often simplify the uncertainty to make it fit into the available visual representations. (After Potter et al., 2012). The seven challenges as formulated by MacEachren et Al. (2005) are still there to be tackled.","name":"Visualization of uncertainty"},{"code":"CV4-2","description":"This topic is about representing the earth`s relief. Key terms are: Contour line, contour interval, DEM (digital elevation model), elevation, Interpolation, profile, relief, slope, terrain exaggeration, terrain skeleton, TIN (triangular irregular network) and topography. Aspects: data; representation; extent.","name":"Representing terrain"},{"code":"CV4-3","description":"Multivariate descriptive displays or plots are designed to reveal the relationship among several variables simultaneously. There are several basic characteristics of the relationship among sets of variables that are of interest. These include: - the forms of the relationships - the strength of the relationships, and - the dependence of the relationships on external (usually to the pairs of variables being examined) circumstances. Multivariate plot examples are: - enhanced 2-D scatter diagrams - 3-D scatter diagrams - contour, level, and surface plots - high-dimensional data plots","name":"Multivariate displays"},{"code":"CV4-4","description":"According to Daassi et al. (2006) the visualization process of temporal data has four steps: (1) time values to be visualized, (2) point of view on time, that identifies the characteristics of the temporal values to be visualized, (3) time space: define the displayable space of the time values and (4) point of view on the visualization space, the implementation of the perceptible forms of time. The visualization of spatio-temporal data can be done in many different ways such as multi-panel plots (maps), time-series plots (graphs), space-time plots (graphs), animations, and tables (Pebesma, 2012) Aspects: Space; Time; representation with visual means","name":"Visualization of temporal geographic data"},{"code":"CV4-5","description":" ","name":"Dynamic and interactive displays"},{"code":"CV4-6","description":" ","name":"Web mapping"},{"code":"CV4-7","description":" ","name":"Virtual and immersive environments"},{"code":"CV4-8","description":"Augmented reality (AR) is the integration of digital information with live video or the user's environment in real time. It requires three steps: 1. Recognition: Recognition of an image, an object, a face or a body 2. Tracking: Real-time localization in space of the image, object, face, or body 3. Mix: Superposition of a media (video, 3D, 2D, text, etc) on top of this image, object, face or body","name":"Augmented environments"},{"code":"CV4-9","description":"Cartographers have recently become involved in extending geographic concepts and cartographic design approaches to the depiction of non-geographic data archives, using so-called spatialized views of information spaces. Spatialisations differ from ordinary data visualisation and geovisualisation in that they may be explored as if they represented spatial information. (Fabrikant, S.I., 2003). As definitions of spatialization can be found: Spatializations are computer visualizations in which nonspatial information is depicted spatially (Montello et al., 2003). Spatialization is the transformation of high-dimensional data into lower-dimensional, geometric representations on the basis of computational methods and spatial metaphors. (Skupin 2007)","name":"Spatialisation"},{"code":"CV4","description":"This unit addresses mapping methods and the variations of those methods for specialized mapping and visualization instances, such as thematic mapping, dynamic and interactive mapping, Web mapping, mapping and visualization in virtual and immersive environments, using the map metaphor to display other forms of data (spatialization), and visualizing uncertainty. Analytical techniques used to derive the data employed in these graphic representations are discussed in Knowledge Area AM Analytical Methods and Unit DN2 Generalization and aggregation.","name":"Graphic representation techniques"},{"code":"CV5-1","description":"Geospatial data representation can make high demands on computational facilities. Examples are: - Infrastructural connections to datasets and processing models - Processing capacity: speed and volume - Access to storage capacity: speed and volume - Display facilities: size, resolution, speed - Peripheral devices like printers for large format hard copy, or VR headsets","name":"Computational demands"},{"code":"CV5-2","description":"Standards for map services were set by OGC and ISO, called WMS and WMTS. Producing map images on the web from a cartographic image in a GIS application is called \"publishing\". Making a web \"map\" in the broader sense of constructing data representations for Storytelling or Geo-gaming is still under development. It requires a mix of applying the map Design principles and Graphic presentation techniques, possibly in combination with software scripting.","name":"Web map making"},{"code":"CV5-3","description":"Traditional \"map\" making, as opposed to the mapmaking in neogeography, focuses on reliable and reproducible products, based on expertise of high definition printing in many colours on analogue media of geodetically well-constructed images.","name":"Traditional map making"},{"code":"CV5-4","description":"The aspects of reproduction of a data representation depend on the nature of the representation: is it analogue (a paper map, a mock-up) or is it digital? In the case of a paper map, its digitalisation with high fidelity is an essential step. With a source in digital form, reproduction can be a matter of the right printer. Alternatively, the source could be disseminated as a file or as a web service. If representations are dynamic and/or interactive the possibilities depend on the construction of the representation. The ease of dissemination of digital files should not result in copyright breach. Aspects: Digitalization techniques for analogue sources, Printing ( 2D, 3D), Dissemination ways, Construction of the data representation, User needs specification, Copyright issues","name":"Map reproduction"},{"code":"CV5","description":"This unit addresses map production and reproduction, as well as computation issues that relate to those workflows.","name":"Map production"},{"code":"CV6-1","description":"The name of this topic was the title of a book by Wood and Fels in 1992. Their view on maps was that they do not represent anything. Instead, they present an argument about the world through the careful choice of content arranged graphically at a specific scale. The book has been a linchpin of the \"new cartographies\" in which maps are redefined as socially constructed arguments based upon consistent semiotic codes. (Wikipedia) Like paintings, they express a point of view. Maps embody and project the interests of their creators. (WorldCat).","name":"The power of maps"},{"code":"CV6-2","description":"Becoming aware of what a \"map\" shows depends partly on what the senses can register of the representation as a whole. It also depends on recognition of elements in the representation that are meaningful to the observer in the sense that these elements are credible indicators of spatial features. Based on that recognition, the nature of these elements and their spatial pattern might infer thoughts about historic or ongoing processes. This interpretation will be influenced by the expertise and needs of the observer. Aspects: Data representation in one or more media, static or dynamic; Senses of the observer; Interpretation by the observer","name":"Map reading and interpretation"},{"code":"CV6-3","description":"Assessment of the usability of a data representation is about how useful it is to users. Therefore it is a test of the success of the representation design, a test of the skills of the \"map\" maker and a test for the reliability of the underlying data.","name":"Usability analysis"},{"code":"CV6-4","description":" ","name":"Map analysis"},{"code":"CV6-6","description":"Spatial thinking is thinking that finds meaning in the shape, size, orientation, location, direction or trajectory, of objects, processes or phenomena, or the relative positions in space of multiple objects, processes or phenomena. Spatial thinking uses the properties of space as a vehicle for structuring problems, for finding answers, and for expressing solutions\" Aspects: recognizing spatiality in a collection of things; translation of the collection to a pattern of elements; recognizing structure (relations between the elements in a pattern); recognizing process (or changes over time in patterns or structures)","name":"Spatial thinking"},{"code":"CV6-7","description":"This topic is about testing for legal and privacy issues in representations of geographical data.","name":"Map ethics Evaluation and testing"},{"code":"CV6-8","description":"Ethics is about the question if behaviour is right or wrong in a social context. In dealing with geodata, a person can do the wrong thing with respect to laws (e.g. disclose secrets, disregard privacy, copyright infringement) or to professional standards (e.g. use bad data, forget about the colour blind, downplay unpleasant details). Aspects: breach of legal standards; breach of professional standards","name":"Map ethics Legal and privacy issues"},{"code":"CV6","description":"Geodata visualisation are always made with a certain purpose. The role and understanding of such graphical representation is an important field of research. Besides theories that underpin evaluation approaches and their findings the visualisation may also be confronting. The more realistic the presentation and especially when it includes human/personal related data the ethical dimension of the visualisation play a major role. Usability of visualisations has also an impact on spatial thinking as has been proved by scholars.","name":"Usability"},{"code":"DA","description":"Proper design of geospatial applications, models, and databases, and the validation and verification of design activities, are critical components of work in all areas related to GIS and T. Design failures can negate well-intentioned efforts to apply concepts and technology to solve real-world problems. While sharing a number of concerns with general systems analysis, the unique and complex spatial elements of geospatial information provide significant additional challenges. The focus of this knowledge area is on the design of applications and databases for a particular need. The design of general-purpose models and tools (e.g., raster and vector) is covered in Knowledge Area DM Data Modeling. In the context of specific implementations, design activities fall into three general classes: 1. Application Design addresses the development of workflows, procedures, and customized software tools for using geospatial technologies and methods to accomplish both routine and unique tasks that are inherently geographic. 2. Analytic Model Design incorporates methods for developing effective mathematical and other models of spatial situations and processes. The design of the analytic model is often influenced by decisions that are made about data models and structures. 3. Database Design concerns the optimal organization of the necessary spatial data in a computer environment in order to efficiently sustain a particular application or enterprise. Several units in Knowledge Area GD Geospatial Data follow from Knowledge Area DA Design Aspects, especially those that discuss the collection of data in conformance with the designs discussed herein. This knowledge area is also closely related to Knowledge Area OI Organizational and Institutional Aspects, which discusses several issues relating to the management of systems in organizations after they are designed and implemented. Beyond GIS and T, this knowledge area has strong ties to information science and technology (e.g., Gorgone, G. B. and Gray, P., 2000, and Gorgone, G. B. and others, 2002), and to business management in the area of resource planning. Some of the methods of geospatial system design are identical to established methods in information system design, while others are unique.","name":"Design and Setup of Geographic Information Systems"},{"code":"DA1-1","description":" ","name":"Methods of Requirement analysis, requirements tracing, requirements description"},{"code":"DA1-2","description":"- Methods for documenting and analyzing processes - Describe how spatial data and GIS&T can be integrated into a workflow process - Evaluate how external spatial data sources can be incorporated into the business process","name":"Methods of process description and analysis"},{"code":"DA1-3","description":"- Analysis of application processes - Languages for business process descriptions - Transformation of application processes into systems","name":"Transformation of application processes into systems"},{"code":"DA1-4","description":"- Workflow definition and consideration in GI systems - workflow definition diagramms - use of workflow models to specifiy sequences of activities","name":"Workflow definition and consideration in GI systems"},{"code":"DA1-5","description":"- Software construction - Software testing - Software maintenance - Software engineering management, process and models","name":"Overview on Software engineering"},{"code":"DA1-6","description":"- human computer interactions - user interface design principles and issues - GUI development - Software construction tools: GUI Builders - usability, usability tests and evaluation","name":"User interface and Usability"},{"code":"DA1-7","description":"- Software design and construction fundamentals - construction planning - software construction tools, development environments","name":"Software design and construction"},{"code":"DA1-8","description":"- Software and data lifecycle - archiving aims and requirements","name":"Software and data lifecycle, archiving"},{"code":"DA1","description":"This unit adresses topics of system design and particularly focuses on the pecularities of GIS&T system design.","name":"System design"},{"code":"DA2-1","description":"- basic project management documents, plans, designs etc. - project management tools and techniques - initiating, planning, executing, monitoring, controlling and closing of project processes - time, cost, quality and human ressource management","name":"Introduction to project management"},{"code":"DA2-10","description":"- aims and benefits of data archiving - data archiving technical and organizational issues","name":"Archiving"},{"code":"DA2-2","description":"- List the costs and benefits (financial and intangible) of implementing geospatial technology for a particular application or an entire institution - Compare and contrast the relative merits of outsourcing the feasibility analysis and system design processes or doing them in-house - Identify major obstacles to the success of a GIS proposal - Evaluate possible solutions to the major obstacles that stand in the way of a successful GIS proposal - List some of the topics that should be addressed in such a justification of geospatial technology (e.g., ROI, workflow, knowledge sharing) - Decide whether geospatial technology should be used for a particular task - Perform a pilot study to evaluate the feasibility of an application - Justify feasibility recommendations to decision makers","name":"Feasibility analysis"},{"code":"DA2-3","description":"- Identify potential sources of data (free or commercial) needed for a particular application or enterprise - Estimate the cost to collect needed data from primary sources (e.g., remote sensing, GPS) - Judge the relative merits of obtaining free data, purchasing data, outsourcing data creation, or producing and managing data in-house for a particular application or enterprise - data costs - open data - data licensing","name":"Data availability"},{"code":"DA2-4","description":"Identify the positions necessary to design and implement a GIS - Discuss the advantages and disadvantages of outsourcing elements of the implementation of a geospatial system, such as data entry - Evaluate the labor needed in past cases to build a new geospatial enterprise - Create a budget of expected labor costs, including salaries, benefits, training, and other expenses - competence building measures - system migration issues","name":"Labor and management"},{"code":"DA2-5","description":"- Identify the hardware and space that will be needed for a GIS implementation - Hypothesize the ways in which capital needs for GIS may change in the future - Compare and contrast the relative merits of housing GISs within IT (information technology) and MIS (management information system) facilities versus keeping them separate - Collaborate effectively with various units in an institution to develop efficient hardware and space solutions","name":"Capital: facilities and equipment"},{"code":"DA2-6","description":"Identify potential sources of funding (internal and external) for a project or enterprise GIS - Analyze previous attempts at funding to identify successful and unsuccessful techniques - Create proposals and presentations to secure funding","name":"Funding"},{"code":"DA2-7","description":"- cloud hosting services - discusses the technical, organizational and monetary advantages advantages and disadvantages of hosted and inhouse solutions","name":"Hosted and inhouse solutions"},{"code":"DA2-8","description":"- discusses the technical, organizational and monetary advantages advantages and disadvantages of commercial and open source software - software licensing","name":"Commercial and open source software"},{"code":"DA2-9","description":"advantages and disadvantages of commercial of the shelf software, customizing, professional services","name":"COTS, customizing, professional services"},{"code":"DA2","description":"In order to design, build, and maintain a GIS, sufficient resources (e.g., labor, capital, and time) must be secured. These resources are needed for a variety of elements of the system, including design, software purchase, labor, hardware, and facilities. The most crucial task is to determine whether the project is worthy of the required resources. The focus here is on the initial startup costs: budgeting for ongoing management, and the design of management infrastructure, is discussed in Unit OI2 Managing the GI system, which should also be mastered to complete this process successfully. Further consideration of economic issues is found in Knowledge Area GS GIS and T and Society, Unit GS2 Economic aspects. Data sources and characteristics are covered in Knowledge Area GD Geospatial Data.","name":"Resource planning"},{"code":"DA3-1","description":"- Describe the major geospatial software architectures available currently, including desktop GIS, server-based, Internet, and component-based custom applications - Identify software options that meet functionality needs for a given task or enterprise - Evaluate software options that meet functionality needs for a given task or enterprise","name":"Major geospatial software architectures"},{"code":"DA3-2","description":"This topic addresses all architectural interoperability issues that enable GIS systems and applications to work togehter: interoperability problems and requirements, standardized interfaces and services etc.","name":"Interoperability"},{"code":"DA3-3","description":"This topic considers general architecutural patterns like SOA, ROA, Web Services, etc.","name":"Architectural Patterns"},{"code":"DA3-4","description":"- WebGIS, - technical pecularities of spatial data infrastructures - standardiced GI services for SDI: WMS, WFS, CSW, Transformation Services, SOS, WPS etc., - other map services and interfaces","name":"WebGIS, SDI services, map services"},{"code":"DA3-5","description":"- Reference Model of Open Distributed Processing - RM-ODP Standards, Viewpoints modeling and the RM-ODP framework - enterprice viewpoint - information viewpoint - computational viewpoint - engineering viewpoint - technology viewpoint","name":"RM-ODP"},{"code":"DA3-6","description":"- Compare and contrast cloud and grid computing, their advantages and disadvantages - IaaS, PaaS, SaaS - Cloud deployment models: private cloud, public cloud, hybrid cloud, etc. - security and provacy issues of cloud solutions - variation of grid computing: e.g. volunteer computing networks, utility computing etc.","name":"Cloud and Grid computing"},{"code":"DA3-7","description":"- market overwiew on currently available desktop GIS and available GIS libraries - Compare and contrast solutions based on Desktop GIS and GIS libraries respectively","name":"Desktop GIS, GIS libraries"},{"code":"DA3-8","description":"Describe non-spatial software that can be used in geospatial applications, such as databases, Web services, and programming environments","name":"Non-spatial software used in geospatial applications"},{"code":"DA3","description":"This unit describes the major geospatial software architectures available currently and coices when designing GI applications and systems, including desktop GIS, server-based, Internet, and component-based custom applications.","name":"Architectural design"},{"code":"DA4-1","description":"- Compare and contrast the relative merits of various textual and graphical tools for data modeling, including E-R diagrams, UML, and XML - Create conceptual, logical, and physical data models using automated software tools - Create E-R and UML diagrams of database designs","name":"Modeling tools"},{"code":"DA4-2","description":"- Define entities and relationships as used in conceptual data models - Describe the degree to which attributes need to be modeled in the conceptual modeling phase - Explain the objectives of the conceptual modeling phase of design - Deconstruct an application use case into conceptual components - Create a conceptual model diagram of data needed in a geospatial application or enterprise database - Design application-specific conceptual models","name":"Conceptual models"},{"code":"DA4-3","description":"- Differentiate between conceptual and logical models, in terms of the level of detail, constraints, and range of information included - Define the cardinality of relationships - Explain the various types of cardinality found in databases - Distinguish between the incidental and structural relationships found in a conceptual model - Determine which relationships need to be stored explicitly in the database - Evaluate the various general data models common in GIS&T for a given project, and select the most appropriate solutions - Create logical models based on conceptual models and general data models using UML or other tools","name":"Logical models"},{"code":"DA4-4","description":"- Differentiate between logical and physical models, in terms of the level of detail, constraints, and range of information included - Recognize the constraints and opportunities of a particular choice of software for implementing a logical model - Create physical model diagrams, using UML or other tools, based on logical model diagrams and software requirements - Create a complete design document ready for implementation","name":"Physical models"},{"code":"DA4-5","description":"Techniques for indexing spatial databases (quad trees etc.)","name":"Spatial Indexing"},{"code":"DA4","description":"The effective design of geospatial databases should follow the established methods and principles of database modeling and design developed in computer science. The basic method is a three-step process generally called the conceptual, logical, and physical models transforming the application from very human-oriented to machine-oriented. Several standards and software tools exist to aid the process of database design.","name":"Database design"},{"code":"DM","description":"This knowledge area deals with representation of formalized spatial and spatio-temporal reality through data models and the translation of these data models into data structures that are capable of being implemented within a computational environment (i.e., within a GIS or more likely within a spatial data base). Data modelling is a crucial issue as it defines the content of a spatial database and usefulness of these content (data) for certain applications. Data Modelling is performed using system neutral languages like UML (or more seldom ER-diagrams). These conceptual models have to be transferred to logical models (i.e. tables of a database). Data is stored in spatial data bases which are normally organized in an object relational way. For certain types of data specific data bases are used, like triple stores, NoSQL DBs, Array DBs etc. For data modelling quite a number of ISO standards are available for deriving the conceptual model as well as for rules for application schemas, spatial schemas, temporal schemas, Quality principles, encoding, 3D modelling (CityGML) etc. Data models provide the means for formalizing the spatio-temporal conceptualizations. Examples of spatial data model types are discrete (object-based), continuous (location-based), dynamic, and probabilistic. Mastery of the objectives presented in this knowledge area require knowledge and skills presented in the bodies of knowledge of allied fields, including computer science (ACM/IEEE-CS Joint Task Force, 2001) and information systems (Gorgone & Gray, 2000; Gorgone & others, 2002).","name":"Data Modeling, Storage and Exploitation"},{"code":"DM1-1","description":"This topic includes the main basic data base concepts: - Data base, definition and overview - Data base management system, definition and overview - Relational data bases, overview - Object-oriented data bases, overview - Object-relational data bases - NoSQL data bases, general overview - NoSQL data bases, examples triple stores, array databases, others (overview)","name":"Overview on data base concepts"},{"code":"DM1-10","description":"To be defined depending of the background of the course etc.","name":"Data base practice"},{"code":"DM1-2","description":"The Relational Model is the most important data base model, therefore it is explained in more detail here: - Basic concepts (tables, tuples, etc.) - Relation to relational algebra (RA), basics of RA - Constraints (key, domain, referential integrity) - Relation to entity relation (ER) model, basics of ER","name":"The Relational Model"},{"code":"DM1-3","description":"Relational data bases and data base management systems are essential for GIS in consequence the important issues have to be treated here: - General aspects, basic architecture of a DB, advantages, features - DBMS concepts and functionalites (transactions, locks, multiuser access etc.) - Data base design, techniques - Database administration - Normalization (1NF - 3NF) - Example of a data base design","name":"Relational Data Bases, Data Base Managements Systems and Data base principles"},{"code":"DM1-4","description":"Data base queries and especially spatial queries require specific data structures to be performed satisfactory Relevant is: - Motivation, examples of typical non-spatial and spatial queries - Trees, B-tree, R-tree, Q-tree - Graphs, overview and relation to DB","name":"Data Structures and Indices for Data Bases"},{"code":"DM1-5","description":"Big data like imagery but also for example GML data sets need compression to be accessed / transferred in an acceptable time. Therefore some compression techniques have to be taught: - Motivation, examples of data sets which need compression - General introduction, vector - / raster data compression, compression lossless, lossy - Popular compression techniques, LZW (Lempel-Ziv-Welch) encoding, Huffman encoding - Techniques for raster data, runlength encoding, JPEG coding, wavelet etc. - Techniques for the reduction of vector data (Douglas Peuker etc.) - Data formats, overview and relation to compression techniques","name":"Data compression techniques"},{"code":"DM1-6","description":"SQL is the \"standard\" to perform spatial and non-spatial queries in data bases. That means each student in a GI related course has to be familiar with the main aspects if it: - Motivation, history, overview - Data definition language DDL - Data manipulation language DML - Data control language DCL - Spatial extensions of SQL","name":"SQL and its usage for data handling, spatial extensions to SQL"},{"code":"DM1-7","description":"UML is the standard for describing the schema related to GI models, but also user requirements, workflows etc. can be described in UML using the UML diagrams: - Motivation, background, purpose - Use case diagrams - Class diagrams - Sequence diagrams - Activity diagrams","name":"UML introduction and class diagrams"},{"code":"DM1-8","description":"XML knowledge is an important bases for understanding GML. Moreover XML tools like XSLT are important to transform XML or GML data sets into other XML based formats like SVG or others. Important issues: - Motivation, purpose - Relation to HTML - XML document structure - XML syntax, elements, attributes and namespaces - xlink, xpath and XSLT - XML DTD - XML schema","name":"XML introduction"},{"code":"DM1-9","description":"The long term storage of GI data in general is based on spatial data bases. Therefore the following is essential for a GI course: - Relation between GIS and DB / \"Long transactions\"- Dual concepts - Characteristics of spatial data bases - Spatial data in object relational data bases - Spatial extensions of DBs, overview","name":"Data Base concepts in GIS and Principles of spatial data bases"},{"code":"DM1","description":"This unit includes the basics for data modelling, storage and exploitation. Data modelling is one of the most important activities in conjunction with Geographic Information / GIS as it determines how the data can be used and if the requirements from applications are fulfilled. Data modelling can be done in conjunction with the data base, e.g. through ER diagrams or according to the ISO 191xx standards by using UML. The costs of data acquisition can be tremendous, therefore the data represents an enormous value. This value has to be conserved through a safe long term data storage. Therefore data bases and especially relational and object relational data bases are crucial. For a proper storage and query of geographic information data bases are extended with specific data types and data structures. As data sets can be very large suitable compression techniques became important especially in the context of accessing and delivering geographical data, e.g. through services. XML based modeling languages for encoding also play and important role in this context","name":"Foundations for Data Modelling Storage and Exploitation"},{"code":"DM2-1","description":"GI standards, mainly from ISO and OGC are essential nowadays. Moreover also an overview on ICT standards from W3C or OMG are important as well as some understanding of standardization processes. In detail: - Motivation for standards, examples from daily life - Overview on GIS and relevant ICT standardization bodies and selected standards - De jure and De facto standards, obligation, reasons for the usage of standards - Standardization within ISO - Standardization within OGC, relation to ISO - Examples of ISO 191xx standards","name":"Overview on relevant standards and standardisation bodies"},{"code":"DM2-2","description":"Conceptual data modeling is a key skill for GI people. (see relations to other topics) The following therefore is important: - Overview on the relevant standards like conceptual schema language, Rules for application schema - Examples of conceptual schemas","name":"The principle of conceptual data modelling according to ISO"},{"code":"DM2-3","description":"Geometric modelling is an important subtask of conceptual modelling and requires the following basics: - Overview of ISO 19107 - spatial schema - Overview of ISO 19125 - simple features - Examples of the usage of spatial schema and simple feature elements for feature class definitions - Relation to GML - Relation to DBs","name":"Geometry data types according to spatial schema and the simple feature specification"},{"code":"DM2-4","description":"Also temporal aspects have to be considered within conceptual modelling. This also requires basics: - Motivation, examples - Temporal variability of features (move, change of structure or geometry) - Overview on ISO 19108 temporal schema - Examples of modeling temporal aspects","name":"Temporal data types according to temporal schema"},{"code":"DM2-5","description":"Conceptual models of course have to be implemented, in general in a GIS (which is often proprietary), or in a data base (which can be standard based),therefore here the implementation in a data base is treated: - Repetition of conceptual and logical models - Examples of the transferring of a conceptual model to a logical (data base) model","name":"Transferring conceptual models to logical models"},{"code":"DM2-6b","description":"Metadata is considered as very important for the usage as well for the search for Geodata Relevant basics are: - Motivation, importance of data quality as part of metadata - Metadata in an spatial data infrastructure with many There are quite a number of relevant standards for GI courses. Some are listed here, others might be considered, depending on the background of the course: - Select other standards and explain them, Important are: - ISO 19141 Schema for moving features, ISO 19142 Web Feature Service or others - 19109 - Rules for application schema - Selection of other standards is depending on the background of the course","name":"Other standards"},{"code":"DM2-7","description":"GML is the most important standard for the transfer of Geodata as it allows to transfer the schema information as well as the data. Important issues: - Motivation, Importance of a Geography Markup Language - History of GML, Overview 19136 - Geography Markup Language - Relation to spatial schema - Supported features in GML (Topology, 3D ...) - Structure of GNL, profiles, application schemas etc. - Transfer of models and of data - Examples","name":"Introduction to GML"},{"code":"DM2-8","description":"3D Models, especially 3D city models are becoming more and more important. CityGML is the most important standard within the GI domain to describe City models semantically and geometrically. Relevant issues: - Motivation, Usage of CityGML - Relation to GML - Coherence of semantics and geometry - Principles of modeling - Level of detail concept - CityGML vs KML - Examples","name":"Introduction to CityGML"},{"code":"DM2","description":"This unit includes the essentials of relevant standards for spatial data modelling. A number of ISO and OGC standards are available for deriving the conceptual model as well as for rules for application schemas, spatial schema provides data types for geometry models in various forms, Point, line, area, body based, temporal schema allows to consider temporal dimensions, Quality principles can be used to describe the quality of geodata, encoding standards (mainly GML) allow the standard based transfer of data and data models, CityGML allows a standard based 3D modelling, etc.","name":"Standards for Spatial Data Modeling"},{"code":"DM3-1","description":" ","name":"Grid representations"},{"code":"DM3-1b","description":"There are two basic concepts related to this topic: Features and Fields, or Geo-fields, as named by Goodchild at al. The concept of fields can be differently represented as explained here: - Repetition of basic concepts of Geographic Information Science - Explanation of the concept of continuous fields and the commonly used ways of representing geo-fields - Relation between fields and coverages, an important discretizations of a Geo-field - Types of Coverages","name":"The concept of fields"},{"code":"DM3-2","description":" ","name":"The raster model"},{"code":"DM3-2b","description":"Grids are on the one hand one important type of caverages and on the other hand Grids are used as basic structure in some applications. Important here is: - Definition of the concept of grid in GIS - Grid as an instance of coverages - Grids as a basic structure for certain applications / medium for aggregation of data - Examples of grid-based data such as Digital Terrain Models (DTM) - Grids in census / statistical data and Geo-marketing applications","name":"Grid representations"},{"code":"DM3-3","description":" ","name":"Grid compression methods"},{"code":"DM3-3b","description":"TINs and Voronoi tessellations are important types of coverages. TINs play a very important role also in Computer graphics. Important here is: - Basics from Graph theory - Definition of Triangulated Irregular Networks (TIN), purpose and applications - TINs and voronoi diagrams as a type of coverages - One important instance of a TIN: Delauney Triangulation - Definition of Voronoi Diagrams, purpose and applications - Relation between Delauney Triangulation and Voronoi Diagram, the \"Dual Graph\" - Examples from applications","name":"TIN and Voronoi tesselations"},{"code":"DM3-4","description":" ","name":"The hexagonal model"},{"code":"DM3-4b","description":"- Other relevant models - Linear referencing (t.b.d)","name":"Other models like linear referencing"},{"code":"DM3-5","description":" ","name":"The Triangulated Irregular Network (TIN) model"},{"code":"DM3-5b","description":"Resolution of raster and gridded data - Georeferencing of data, direct and indirect methods (t.b.d.)","name":"Resolution and georeferencing system"},{"code":"DM3-6","description":" ","name":"Resolution"},{"code":"DM3-7","description":" ","name":"Hierarchical data models"},{"code":"DM3","description":"This unit includes relevant tessellation data models. Besides features (sometimes also called geo-objects) geo-fields play and important role. In recent literature tessellation models are classified as discretizations of fields. In traditional GI literature tessellations are defined as important data structure itself. Tessellation discretise a continuous surface into a set of non-overlapping polygons that cover the surface without gaps. Tessellation data models represent continuous surfaces with sets of data values that correspond to partitions. Important tessellation models are Grids, TINs and Voronoi diagrams.","name":"Tessellation data models"},{"code":"DM4-1","description":"This topic includes the basics for feature based modelling. There are a number of standards also relevant for this topic (see relations). The following items should be included: - Definition of a feature (in some literature also called object, or geoobject) and of feature classes respectively. - Aspects of the definition (ID, geometry, topology, thematic, time etc.) - Techniques for the definition of features / feature classes (mainly link, as they are described elsewhere, see relations)","name":"Feature based modelling"},{"code":"DM4-2","description":"This topic describes the process of Geometric modelling using vector data, means the primitives like points, lines, areas, bodies, or raster data. There is a strong relation to ISO standards (see relations) as they provide basic data types for geometric modelling. Main issues: - Geometric modeling based on vector data - Geometric modeling based on raster data - Conversion between the models - examples, advantages, disadvantages of the models","name":"Geometric modelling"},{"code":"DM4-3","description":"- Examples of analysis which requires topology","name":"Topological modelling"},{"code":"DM4-4","description":"This topics deals with the definition of an application schema. There are other units which are important for this topic (see Relations). Issues to be included: - Methods to define and describe an application schema (requirement analysis, description of the schema etc.) - Feature attribute catalogues - Domains / data relevant for INSPIRE","name":"Application models based on vector data"},{"code":"DM4-5","description":"This Topic deals with important application models, which should be chosen with relation to the course (geographically / related to the background of the course) INSPIRE should be treated in any case. In detail: - Overview on important application models relevant for the course, e.g. from topography or environment in the country - Repetition of the principles of Spatial data infrastructures - Overview on the INSPIRE initiative and the goals related - The INSPIRE data model - The architecture of INSPIRE and the necessary services - Domains / data relevant for INSPIRE","name":"Examples of important application models"},{"code":"DM4-6","description":"This topic is dedicated to the challenges of model based interoperability and related issues, The principles of interoperability are included in DA3-2. In detail: - The challenges of model interoparability (semantics, different modelling of the same features in different models, syntacs) - Overview on IT concepts for schema integration / transformation - Approaches for model integration - Approaches for model transformations, e.g. related to INSPIRE, from the Humboldt project","name":"Model based interoperability, model transformations"},{"code":"DM4-7","description":"Network models are crucial in some application domains, such as Navigation (roads etc.), but also in utility applications (facilities like pipes etc.) In this topic should be treated: - The network model in the data base domain - Graph based NoSQL data bases - Topology of network models - Data structures for storing network data - The Dijkstra algorithm - Overview on important applications","name":"Network models"},{"code":"DM4","description":"This unit includes relevant issues related to vector data models, feature based modelling, applications. Besides imagery data the majority of GI data available is feature based and founded on vector geometry. Topology modeling also is very common nowadays, as many analysis like routing or neighborhood analysis require it. Spaghetti modelling becomes more and more and exception. In every country there are important feature and vector geometry based application models available e.g. in Topography / Cartography. In Europe every GI course should include some information on INSPIRE. As in different application domains different data models are used, sometimes for the same feature types, integration and transformation of models are an important issue also.","name":"Vector data model, Feature based modelling, Applications"},{"code":"DM5-1","description":"- Many geographical phenomena are not defined sharply but uncertain Uncertainty has a number of considerations: - Motivation, background, purpose - Conceptual model of uncertainty - Uncertainty of geographic phenomena (vagueness, ambiguity) - Uncertainty of measurements - Uncertainty of analysis - Uncertainty vs. data quality - Statistical models of uncertainty - Outline of Fuzzy approaches","name":"Basics of uncertainty and its modelling"},{"code":"DM5-2","description":"Space and time are 2 connected concepts, this topic is dedicated to some basics of modelling time and the temporal dimensions related to features and fields: - Motivation, background, purpose - Changes in time in Entity based and field based representations - A conceptual model of changes in time - Move of objects - Change of structure - Change of geometry - Examples from applications","name":"Modelling time aspects"},{"code":"DM5-3","description":"Traditionally many GIS used 2D or 2.5 D data models, but in the last decade 3D modeling mainly in form of city models or in the context of Building Information Models (BIM): - Basic concepts of 3D modelling, edge, area, volume models - The workflow of 3D modelling, general aspects, choose of the proper model - Methods of 3D modeling - Principles of Constructive Solid Geometry (CSG) - Principles of Boundary representation (BR) - Principles of Voxel-beased modeling - Comparison of the methods - The concept of BIM, principles and purpose - City models, principles and purpose - Examples / applications","name":"Modelling 3D"},{"code":"DM5","description":"Traditional raster and vector data models cannot easily represent the more complex aspects of geographic information, such as temporal change, uncertainty, three-dimensional phenomena, and integrated multimedia. A variety of models have been proposed to represent these complexities, including both extensions to existing models and software, and entirely new models and software. During the 1990s, work in this area was largely experimental, but many solutions are now available to practitioners in commercial and open source software. The data models in this unit are based on concepts discussed in Knowledge Area CF Conceptual Foundations.","name":"Modelling 3D, temporal and uncertain phenomena"},{"code":"DN3-1","description":"Modification of spatial and attribute data while ensuring consistency within the database, implications of transactions on database integrity, scenarios for periodic changes in GIS database and monitoring the periodic changes.","name":"Database change"},{"code":"DN3-2","description":"Rules for modelling spatial database change, techniques for handling version control, techniques for managing long and short transactions, management of spatial databases in multi-user environment","name":"Modeling database change"},{"code":"DN3-3","description":"Reliability tests of change information, design and implementation. Logical consistency of updates.","name":"Reconciling database change"},{"code":"DN3-4","description":"Needs for versioned databases, queries for change scenarios using DB management tools, algorithms for performing dynamic queries, role of time-criticality and data security while choosing methods for change detection.","name":"Managing versioned geospatial databases"},{"code":"DN3","description":"It is quite common, that data including both spatial entities and their attribute data undergo changes. These changes need to be catalogued fully and explicitly, including initial conditions, new conditions, all intermediate stages and operations used. The geospatial data needs to contain an archival history of change.","name":"Transaction management of geospatial data"},{"code":"GC","description":"At the first international conference on `GeoComputation` held at Leeds University in 1996, a new research agenda on geographical analysis and modelling was launched under the title `The art and science of solving complex spatial problems with computers`. Geocomputation in short. Geocomputation covers a wide range of theories and methods aiming at studying complex spatio-temporal problems, which are difficult to analyse and model applying traditional spatial analytical and statistical methods due to data complexity and computational demands. As a rather new research agenda, Geocomputation is still seeking to define the field conceptually, although much efforts have been done. Being closely related to computational science, Geocomputation benefits from the still increasing performance in information and communication technology allowing geoprocessing to utilise parallel computing and distributed cloud computing. However, the close connection to computational science also requires frequent discussion on adopting new related topics like Big Data and Linked Data to the Geocomputation research agenda. Geocomputation has a very strong connection to Knowledge Area AM Analytical methods. Skills in computer programming are generally needed to effectively apply most of the methods and tools under the Geocomputation headline.","name":"Geocomputation"},{"code":"GC1-1","description":"A complex system can be viewed as a system composed of many interacting components. Most real-world systems like the global climate, an ecosystem, a city, the human brain, and the entire universe are complex systems. General features of the structure and dynamics of complex systems have been characterised, including path dependence, positive feedback, self-organisation and emergence. The types of complex systems include nonlinear systems, chaotic systems, and complex adaptive systems. The aim of this topic is to introduce the notion of complexity, and its role in Geocomputation.","name":"Complex systems"},{"code":"GC1-2","description":"The geocomputational methods are often derived from machine learning, clustering and simulation, and relies heavily on parallel computing and High Performance computing. Contrary to the methods and tools applied for spatial analysis described under the Analytical Methods Knowledge Area, the methods in Geocomputation rather often are unavailable in standard GIS packages and therefore requires self-development or at least customisation of the users. Some macro programming languages like MATLAB and R have some build in functions to support development and execution of Geocomputation methods. The aim of this topic is to provide an introduction to the Geocomputation universe with particular emphasis on the computational aspects.","name":"Geocomputational methods"},{"code":"GC1-3","description":"Geocomputation is not daily use in most GIS environments, and mostly used in (advanced) research communities. However, there is a large potential for being used more frequently within the emergence of Big Data Analytics, where standard software is unsufficient due to the vast amount of data with high degree of complexity. To extract useful results from the information requires new approaches, where machine learning and clustering represent obvious solutions. Also, the emergence of e-Governance with focus on efficiency and delicate balancing between different interest requires extended knowledge about the future and this can be provided by simulation. The aim of this topic is to give an introduction to the current an potential use of Geocomputation in solving challenging geospatial problems.","name":"Areas of application"},{"code":"GC1","description":"Geocomputation represents an attempt to move the GI research agenda back to geographical analysis and modelling by providing a toolbox of methods to analyse and model a range of highly complex, often non-deterministic problems.","name":"Theory of Geocomputation and complex systems"},{"code":"GC2-1","description":"Whether experimentally, empirically or theoretically derived, quantitative relationships among the state variables in a system can be used to build a simulation model. In a simulation model, a computer is programmed to iteratively recalculate the modelled system state as it changes over time in accordance with the relationships represented by the mathematical and other relationships that describe the system.","name":"Principles of computer simulation and numerical experiments"},{"code":"GC2-2","description":"Many typologies of models have been developed and these tend to emphasise dichotomies, such as `complicated` versus `simple`, in model design. Although such black and white dichotomies are invariably simplistic, they provide a useful way of thinking about the trade-offs that must be made when developing appropriate and manageable abstractions or representations of real-world phenomena. Thus, developing detailed, dynamic, spatial models comes at some cost in generality and interpretability, but buys us realism and the ability to represent specific processes in specific contexts. The aim of the topic is to provide basic understanding in the overall principles in choosing different approaches to model development.","name":"Model development"},{"code":"GC2-3","description":"Rule-based models are based on logic programming with condition-action expressions, where the left side of the expressions consists of several conditions that returns a logical result, and the right side consists of several actions. The implementation of rule-based models is most often done by cellular automata models or agent-based models. Many geographic patterns and dynamics are formed by systems of interacting actors with heterogeneous characteristics and behaviours. A cellular automaton (CA) is a discrete dynamic system in which space is divided into regular spatial cells, and time progresses in discrete steps. Each cell in the system has one of a finite number of states. Agent-base models are constructed to represent these actors, their environments, and their interactions with one another. The aim of this topic is to provide knowledge about rule based models and to understand their advantages and disadvantages.","name":"Rule-based models"},{"code":"GC2-4","description":"Sometimes the modelling can be described with (partial) differential equations. This is particularly the case in some topics within natural science, where the system in at least some degree can be described with the laws in physics. Hydrological modelling is a good example on equation based models. However, dealing with real world problems the full system can seldom be descried totally with the laws from natural science, and needs to be amended with other types of models. The aim of this topic is to present the use of equations based models and their advantages and challenges.","name":"Equation-based models"},{"code":"GC2","description":"Across broad areas of the environmental and social sciences, simulation models are an important way to study systems inaccessible to scientific experimental and observational methods, and also an essential complement of those more conventional approaches. Simulation models are a relatively recent addition to the scientific toolbox, and the reasons for their widespread adoption are that High Performance Computing and huge amount of data from different sources have made this approach possible. In addition, some systems of interest like ecosystems, urban systems, social systems, and the global climate system are not amenable to experiments. Finally, the nonlinear behaviour of many natural systems provides challenges building traditional mathematical models based on linearization.","name":"Spatial simulation modelling"},{"code":"GC3-1","description":"Among the recent artificial intelligence techniques are those pertaining to heuristics. A heuristic technique is an approach to problem solving, that employs a practical method, which is necessarily not optimal or perfect, but in many situations sufficient. Heuristic methods can be useful, where the optimal solution in practice is impossible. The aim of the topic is to provide insight into the principles of heuristics and the most important algorithms.","name":"Heuristics"},{"code":"GC3-2","description":"Genetic algorithms, genetic programming and evolutionary computing are terms that fall within the general sphere of `Evolutionary Computation`. Genetic algorithms (GAs) are computationally intensive global search heuristics with very wide applicability, but their implementation is often highly problem specific and there is only a relatively loose understanding as to why they often work rather well. The central idea behind GAs is to mimic the Darwinian notion that selective breeding seeks optimum individuals in a given environment. In order to do this a GA requires a way of representing a solution to a (spatial) problem as if it were an individual viewed as a chromosome or `genome` like object. The aim of the topic is to provide fundamental understanding of the principles behind genetic algorithms, and its application in solving geospatial problems.","name":"Genetic algorithm in geospatial modelling"},{"code":"GC3-3","description":"Biological neurons, or nerve cells, receive multiple input stimuli, combine and modify the inputs in some way, and then transmit the result to other neurons. Artificial neural networks are an attempt to emulate features of biological neural networks in order to address a range of difficult information processing, analysis and modelling problems. The principal class of ANNs are so-called feed-forward networks, but other types of ANN are for example recurrent neural networks. Among the feed-forward networks the most widely used approach is the multi-level perceptron (MLP) model. The application range is broad from non-linear regression to land cover change modelling. The aim of the topic is to introduce the principles of ANN and to understand and demonstrate its use in geospatial modelling.","name":"Artificial Neural Networks"},{"code":"GC3-4","description":"Pattern recognition is the process of classifying input data into objects or classes based on key features. There are two classification methods in pattern recognition: supervised and unsupervised classification. The supervised classification of input data in the pattern recognition method uses supervised learning algorithms that create classifiers based on training data from different object classes. The classifier then accepts input data and assigns the appropriate object or class label. The unsupervised classification method works by finding hidden structures in unlabelled data using segmentation or clustering techniques. Common unsupervised classification methods include: K-means clustering, Gaussian mixture models, Hidden Markov models. The aim of the topic is to provide knowledge about the different methods in pattern recognition and how to choose the optimum method for a specific spatial problem.","name":"Pattern recognition"},{"code":"GC3-5","description":"Understanding natural and human-induced structures and processes in space and time has long been the agenda of geographical research. Through theoretical and experimental studies, geographers have accumulated a wealth of knowledge about our physical and man-made world over the years. Knowledge is often discovered through critical observations of phenomena in space and time. Due to the rapidly expanding amount of data and information the problem is often not having enough data but having too much and too complex a database. The aim of the topic is to provide insight into several methods to carry out spatio-temporal knowledge discovery through spatial data mining and clustering techniques.","name":"Spatio-temporal knowledge discovery and data mining"},{"code":"GC3-6","description":"Data-intensive computing is now starting to be considered as the basis for a new, fourth paradigm for science. Two factors are encouraging this trend. First, vast amounts of data are becoming available in more and more application areas. Second, the infrastructures allowing to persistently store these data for sharing and processing are becoming a reality. The technical and scientific issues related to this context have been designated as `Big Data` challenges and have been identified as highly strategic by major research agencies. The aim of this topic is to introduce Big Data as a concept, and the needed methods to navigate through the vast amount of heterogeneous information.","name":"Big data filtering"},{"code":"GC3","description":"The amount of data in current geospatial repositories along with their high-dimensional nature requires a sophisticated set of analysis capabilities in order to extract new and unexpected patterns, trends, and relationships embedded in that data. Artificial intelligence and data mining methods constitute an alternative to explore and extract knowledge from geospatial data, which is less assumption dependent. Data Mining is a step in the knowledge discovery process that automatically detects patterns in data, and Geographic Data Mining is a special type of data mining that seeks to apply standard data mining tools modified to take into account the special features of geospatial data","name":"Artificial Intelligence and Data Mining"},{"code":"GD","description":"Geospatial data represent measurements of the locations and attributes of phenomena at or near Earth`s surface. Information is data made meaningful in the context of a question or problem. Information is rendered from data by analytical methods. Information quality and value depends to a large extent on the quality and currency of data (though historical data are valuable for many applications). Geospatial data may have spatial, temporal, and attribute (descriptive) components, as well as associated metadata. Data may be acquired from primary or secondary data sources. Examples of primary data sources include surveying, remote sensing (including aerial and satellite imaging), the global positioning system (GPS), work logs (e.g., police traffic crash reports), environmental monitoring stations, and field surveys. Secondary geospatial or geospatial-temporal data can be acquired by digitizing and scanning analog maps, as well as from other sources, such as governmental agencies. The legitimacy of geographic information science as a discrete field has been claimed in terms of the unique properties of geospatial data. In a paper in which he coined the term GIScience, Goodchild (1992) identified several such properties, including: 1. Geospatial data represent spatial locations and non-spatial attributes measured at certain times. 2. The Earth`s surface is highly complex in shape and continuous in extent. 3. Geospatial data tend to be spatially autocorrelated. It has long been said that data account for the largest portion of geospatial project costs. While this maxim remains true for many projects, practitioners and their clients now can reasonably expect certain kinds of data to be freely or cheaply available via the World Wide Web. Federal, state, regional, and local government agencies, as well as commercial geospatial data producers, operate clearinghouses that provide access to geospatial data. Although geospatial data are much more abundant now than they were ten years ago, data quality issues persist. Good data are expensive to produce and to maintain. Proprietary interests simultaneously increase the supply of geospatial data and impede data accessibility. Standards for geospatial data and metadata are useful in facilitating effective search, retrieval, evaluation, integration with existing data, and appropriate uses. National and international organizations, such as the Open Geospatial Consortium (OGC) and International Organization for Standardization (ISO), develop and promulgate such standards. INSPIRE directive (Infrastructure for Spatial Information in the European Community) regulates geospatial data management","name":"Geospatial Data"},{"code":"GD1-1","description":"Usable and accurate geospatial data are based upon proper model of the Earth`s surface. Shape of the Earth is complex and complicated to measure. Approximations are used to minimize complexity of the task and possible errors.","name":"Earth geometry"},{"code":"GD1-2","description":"Geospatial referencing systems provide unique codes for every location on the surface of the Earth (or other celestial bodies). These codes are used to measure distances, areas, and volumes, to navigate, and to predict how and where phenomena on the Earths surface may move, spread, or contract. Point-based, vector coordinate systems specify locations in relation to the origins of planar or spherical grids. Tessellated referencing systems specify locations hierarchically, as sequences of numbers that represent smaller and smaller subdivisions of two- or three dimensional surfaces that approximate the Earths shape, Linear referencing systems specify locations in relation to distances along a path from a starting point. Tessellation data models, are considered in Unit DM3 Tessellation data models, and linear referencing models are considered in Unit DM4 Vector data models.","name":"Georeferencing systems"},{"code":"GD1-3","description":"Horizontal datums determine the geometric relations between a coordinate system grid and a particular ellipsoid approximating the Earth`s surface. Vertical datums determine elevation reference surfaces, like mean sea level. A. Horizontal datums. Relation of coordinate system to particular ellipsoid, datum transformation options, Molodensky and Helmert transformation, other high accuracy transformations, ED50 and WGS84, historical development of horizontal datums, ETRS89. B. Vertical datums. Historical development of vertical datums, difference between vertical datum and geoid, relations between ellipsoidal (geodetic) heiht, geoidal height and orthometric elevation.","name":"Datums"},{"code":"GD1-4","description":"Map projections are systematic transformations of geographic coordinates of the surface of ellipsoid into locations in plane. Plane coordinates are based on map projection. As the transformation of a spherical grid into a plane grid causes inevitably distortions of the geometry, and, different projections cause different distortions, knowledgeable choice of appropriate projection for any particular use is crucial. A. Map projection poperties. Geometric properties that may be preserved or lost in projected grid, usefulness of compromise projection, Tissot indicatrix as an indicator of projection errors, visual appearance of the Earth`s graticule, distortion patterns for projection classes, distortions in raster data. B. Map projection classes. Three main classes of map projection based on developable surface, projection types by geometric properties preserved, mathematical basis of projecting longitude and latitude into x and y coordinates. UTM, ETM, projections used by EC. C. Map projection parameters. Standard line, projection case, latitutde and longitude of origin, aspects of projection. D. Georegistration. Rectification vs orthorectification, ground controle points in georegistration of aerial imagery.","name":"Map projections"},{"code":"GD1","description":"Proper model of the Earth`s surface and ability to locate spatial phenomena accurately to it, is crucial in effective collection, management and use of data. Characterising size and shape of the Earth, using appropriate surfaces to approximate it, choosing suitable coordinate system and map projection is bases for efficient understanding of spatial data.","name":"Geolocating Data to Earth"},{"code":"GD10-1","description":" ","name":"Nature of aerial image data"},{"code":"GD10-2","description":" ","name":"Platforms and sensors"},{"code":"GD10-3","description":" ","name":"Aerial image interpretation"},{"code":"GD10-4","description":" ","name":"Stereoscopy and orthoimagery"},{"code":"GD10-5","description":" ","name":"Vector data extraction"},{"code":"GD10-6","description":" ","name":"Mission planning"},{"code":"GD10","description":"Since the 1940s aerial imagery has been the primary source of detailed geospatial data for extensive study areas. Photogrammetry is the profession concerned with producing precise measurements from aerial imagery. Aerial imaging and photogrammetry comprise a major component of the geospatial industry. The topics included in this unit do not comprise an exhaustive treatment of photogrammetry, but they are aspects of the field about which all geospatial professionals should be knowledgeable.","name":"Aerial imaging and photogrammetry"},{"code":"GD11-1","description":" ","name":"Nature of multispectral image data"},{"code":"GD11-2","description":"the physical environment to sense data without direct contact. It contains a carrier device (platform) and a sampling unit (sensor).","name":"Platforms and sensors"},{"code":"GD11-3","description":" ","name":"Algorithms and processing"},{"code":"GD11-4","description":" ","name":"Ground verification and accuracy assessment"},{"code":"GD11-5","description":" ","name":"Applications and settings"},{"code":"GD11","description":"Satellite-based sensors enable frequent mapping and analysis of very large areas. Many sensing instruments are able to measure electromagnetic energy at multiple wavelengths, including those beyond the visible band. Satellite remote sensing is a key source for regional- and global-scale land use and land cover mapping, environmental resource management, mineral exploration, and global change research. Shipboard sensors employ acoustic energy to determine seafloor depth or to create imagery of the seafloor or water column. The topics included in this unit do not comprise an exhaustive treatment of remote sensing, but they are aspects of the field about which all geospatial professionals should be knowledgeable.","name":"Satellite and shipboard remote sensing"},{"code":"GD12","description":"Meaning of geospatial metadata, elements of metadata, use of metadata, integration of metadata in data production, standards in geospatial data, ISO standard family 191xx, data warehouse, exchange protocol, transport protocols, spatial data infrastructure, INSPIRE, OGC, DCAT profiles for CKAN applications bridging metadata from GI and IT domains.","name":"Metadata, standards, and infrastructures"},{"code":"GD2-1","description":" ","name":"Land surveying and field data collection"},{"code":"GD2-2","description":"Aerial imagery has been the primary source of detailed geospatial data for extensive study areas. Photogrammetry is producing precise measurements from aerial imagery. Aerial imaging and photogrammetry comprise a major component of the geospatial data production. Satellite-based sensors enable frequent mapping and analysis of very large areas. Sensing instruments are able to measure electromagnetic energy at multiple wavelengths. Satellite remote sensing is a key source for regional- and global-scale land use and land cover mapping, environmental resource management, mineral exploration, and global change research. Shipboard sensors employ acoustic energy to determine seafloor depth or to create imagery of the seafloor or water column. Principles of aerial photography, oblique and vertical imagery, spatial and radiometric resolution, spectral sensitivity, principal point, distortions and displacements in aerial image, parallax, stereophotogrammetry, generation of an orthoimage from a vertical aerial phoptograph, aerotriangulation, vector data extraction from digital seteroimagery, mission planning. Use of UAV in photogrammetry. Main platforms and sensors in spatial image acquisition, active and passive sensors, LiDAR and microwave, multispectral and hypersepctral imagery, interpretation of imagery, supervised and unsupervised classification, pixel based and segmented classification, ground verification, main applications, bathymetric mapping. SENTINEL.","name":"Remote sensing"},{"code":"GD2-3","description":"Crowdsourcing is the practice of obtaining needed services, ideas, or content by soliciting contributions from a large group of people and especially from the online community rather than from traditional employees or suppliers. Crowdsourced spatial data collection is becoming more and more important. The advantages and disadvantages of crowdsourced data, opensource mapping tools, potential application of crowdsourcing, VGI, OSM or cell-phone based, aspects of crowdsourced data quality and reliabilty.","name":"Crowdsourced data collection"},{"code":"GD2-4","description":"Digitizing as the main secondary spatial data production technique. Encoding vector points, lines, and polygons by tracing map sheets has diminished in importance, but remains a useful technique for incorporating historical geographies and local knowledge. \"Heads-up\" digitizing using digital imagery as a backdrop on-screen is a standard technique for editing and updating GIS databases. Tablet and on-screen digitizing, scanning and (semi)automatic vectorization.","name":"Digitizing"},{"code":"GD2","description":"Spatial data collection / production involves measurement of locations in relation to the coordinate system, and collection of attributed data about the spatial phenomena. Measurements may be direct (e.g. surveying) or remote, data acquisition involves measurement of parameter values, evaluation of parameters, polls, interpretation of spatial imagery, and re-use of secondary data (e.g. old maps). Volunteered geographic information is becoming more important.","name":"Data Collection"},{"code":"GD3","description":"It is quite common, that data including both spatial entities and their attribute data undergo changes. These changes need to be catalogued fully and explicitly, including initial conditions, new conditions, all intermediate stages and operations used. The geospatial data needs to contain an archival history of change.","name":"Transaction management of geospatial data"},{"code":"GD4-1","description":"Geometric accuracy, factors influencing it, geometric accuracy and topological fidelity, geometric accuracy in survey and GPS mesurements, thematic accuracy, relations between thematic accuracy, geometric accuracy and topological fidelity, misclassification matrix, commission and omission, logical consistency, relations between resolution, precision, and accuracy, spatial resolution, thematic resolution, and temporal resolution, precision, uncertainties associated with coordinate precision, primary and secondary data sources.","name":"Data quality"},{"code":"GD4-2","description":"Meaning of geospatial metadata, elements of metadata, use of metadata, integration of metadata in data production, standards in geospatial data, ISO standard family 191xx, data warehouse, exchange protocol, transport protocols, spatial data infrastructure, INSPIRE, OGC, DCAT profiles for CKAN applications bridging metadata from GI and IT domains.","name":"Metadata, standards, and infrastructures"},{"code":"GD4","description":"Data quality is the degree of data usability in relation to given objective and particular application. The expectations to data vary between different applications. The key criteria in data quality are the amount of uncertainty in data as compared to the acceptable level of uncertainty. Evaluation of the usability may be more complicated using data from secondary sources. Appropriate metadata is inevitable for these judgements. Aspects of data quality include geometric and thematic accuracy, (in)consistencies, resolution, precision, usability and others. Assurance of data quality may be improved by following proper standards and spatial data infrastructure regulations for data collection and management. System of basic data quality measures for geospatial domain in the EN ISO 19157:2013 standard.","name":"Data Quality, Metadata and Data Infrastructure"},{"code":"GD5-1","description":" ","name":"Map projection properties"},{"code":"GD5-2","description":" ","name":"Map projection classes"},{"code":"GD5-3","description":" ","name":"Map projection parameters"},{"code":"GD5-4","description":" ","name":"Georegistration"},{"code":"GD5","description":"Visualization, especially Unit CV2 Data considerations, while procedures for transforming data between projections are considered in Unit DN1 Representation transformation.","name":"Map projections"},{"code":"GD6-1","description":" ","name":"Geometric accuracy"},{"code":"GD6-2","description":" ","name":"Thematic accuracy"},{"code":"GD6-3","description":" ","name":"Resolution"},{"code":"GD6-4","description":" ","name":"Precision"},{"code":"GD6-5","description":" ","name":"Primary and secondary sources"},{"code":"GD6","description":"particular application. That standard varies from one application to another. In general, however, the key criteria are how much uncertainty is present in a data set and how much is acceptable. Judgments about fitness for use may be more difficult when data are acquired from secondary rather than primary sources. Aspects of data quality include accuracy, resolution, and precision. Concepts of data quality, error, and uncertainty are also covered in Knowledge Areas CF Conceptual Foundations (in a theoretical context) and GC Geocomputation (in the context of analysis); the focus here is on the measurement and assessment of data quality.","name":"Data quality"},{"code":"","description":" ","name":""},{"code":"","description":" ","name":""},{"code":"","description":" ","name":""},{"code":"GD8-1","description":" ","name":"Tablet digitizing"},{"code":"GD8-2","description":" ","name":"On-screen digitizing"},{"code":"GD8-3","description":" ","name":"Scanning and automated vectorization techniques"},{"code":"","description":" ","name":""},{"code":"","description":" ","name":""},{"code":"","description":" ","name":""},{"code":"","description":" ","name":""},{"code":"","description":"","name":""},{"code":"GS","description":"Geographic Information Science and Technology serve the society, but it is not a panacea. The history of its development is the sum of fragmented efforts, which have still not been fully integrated. Its potential benefits are often constrained and its potential impacts are not fully understood. Institutional and economic factors limit access to data, technology, and expertise by some of those who need it to make better decisions. Political, ideological, and personal issues aside, organizations invest in GIS&T when estimated benefits outweigh estimated costs. Evaluating costs and benefits is difficult, however and too often leads to nothing being done. For some individuals and groups, costs are prohibitive even though potential benefits are compelling. The legal framework provides a structure for regulating a number of key aspects of geographic information science, technology, and applications. Legal regimes determine who can claim the exclusive right to hold and use geospatial data, the conditions under which others may have access to the data, and what subsequent uses are permitted. Political struggles arise from conflicting proprietary and public interests about who benefits from geospatial information, and how the power to allocate the use of this information is, or should be, distributed among members of a society. The need to choose among conflicting interests sometimes poses ethical dilemmas for GIS&T professionals. The explosive growth of the geospatial information contributed by users through various application programming interfaces has made geospatial information is a powerful tool in the social media toola powerful media for the general public to communicate, but perhaps more importantly, geographic information have also become a tool media for constructive dialogs and interactions about social issues, recent growth of Web-based geospatial information and volunteered geographic information (VGI). Because so many public agencies and private organizations rely upon GIS&T for planning, decision making, and management, GIS&T increasingly affects and is used to direct daily life. Critical approaches to understanding the role of GIS in society equip practitioners to employ GIS&T reflectively. The critical approach specifically questions the assumptions and premises that underlie the economic, legal and political regimes and institutional structures within which GIS&T is implemented. Related concerns are considered in Knowledge Area OI: Organizational and Institutional Aspects.","name":"GI and Society"},{"code":"GS1-1","description":"Ways in which the geospatial profession is regulated under European/ National legal regime and framework. Discussion of various impact of frameworks on development of Geospatial Information (SDI, INSPIRE, PSI). Compare and contrast the relationship of the geospatial profession and the European legal regime and framework with similar relationships in other regions and countries","name":"The legal regime and legal framework"},{"code":"GS1-2","description":"Differentiating \"contracts for service\" from \"contracts of service\" - Identifying the liability implications associated with contracts - Discuss potential legal problems associated with licensing geospatial information - Describing the nature of tort law generally and nuisance law specifically - Differentiating among contract liability, tort liability, and statutory liability - Describing cases of liability claims associated with misuse of geospatial information, erroneous information, and loss of proprietary interests - Describing strategies for managing liability risk, including disclaimers and data quality standards","name":"Contract law, liability and licensing"},{"code":"GS1-3","description":"Discussion of the status of the concepts of privacy and security in the European legal regime - Explaining how data aggregation is used to protect personal privacy in data production - Explaining how conversion of land records data from analog to digital form increases risk to personal privacy - Compare and contrast geographic information technologies that are privacy-invasive, privacy -enhancing, and privacy-sympathetic - Explaining the argument that human tracking systems enable \"geoslavery\" - Security and privacy challenges for Internet, citizen-generated and linked data i.e. volunteered geographic information (VGI) - Citizen and privacy. Discussion of confidentiality issues and policies related to the utilization and dissemination of geospatial data for different scenarios including environment/public health applications","name":"Privacy and Security"},{"code":"GS1-4","description":"Discussion of legal definition of the concepts \"ownership\" and \"property rights\" - Description of organizations` and governments` incentives to treat geospatial information as property - Arguments for and against the treatment of geospatial information as a commodity - Outlines of arguments for and against the notion of information as a public good - Compare and contrast National, European policy regarding rights to geospatial data with similar policies in other countries - Explaining how geospatial information might be used in a taking of private property through a government`s claim of its right of eminent domain - Compare and contrast the consequences of different national policies about rights to geospatial data in terms of the real costs of spatial data, their coverage, accuracy, uncertainty, reliability, validity, and maintenance","name":"Ownership and property rights"},{"code":"GS1-5","description":"- Looking at rights and management of geospatial data, coverage, accuracy, reliability and validity in both public and private organisational contexts - Discussion of the role of the public and private sectors in producing and dissemination of geospatial information - Legal framework and competition and public-private sector relationships - Discussion of opportunities for exchange of geospatial data between public and private sector to enable more efficient analysis","name":"Competition and public-private sector relationships"},{"code":"GS1-6","description":"- Discussion of various legal aspects of public and private sectors concerning owning, controlling, sharing/ disseminating open data - Discussion of and define open data and impact on GIS&T - Discussion of various sources of open data (science, public and private sectors) - Discussion of arguments for and against open data - Discussion of open data impact on society and citizenship","name":"Open data"},{"code":"GS1","description":"Legal problems can arise when geospatial information is used for land management, among other activities. Geospatial professionals may be liable for harm that results from flawed data or the misuse of data. Understanding of contract law and liability standards is essential to mitigate risks associated with the provision of geospatial information products and services. Legal relations between public and private organizations and individuals govern data access. The nature of information in general, and the characteristics of geospatial information in particular, make it an unusual and difficult subject for a legal regime that seeks to establish and enforce the type of exclusive control associated with other commodities. Geospatial information is in many ways unlike the kinds of works that intellectual property rights were intended to protect. Still, organizations can, and do, assert proprietary interests in geospatial information. Perspectives on geospatial information as property vary between the public and private sectors and between different countries.","name":"Legal aspects"},{"code":"GS2-1","description":"- Discussion of the general role of information in economics - Describing the role of economics in public and private production of geospatial information - Describing the role of economics in the use of geospatial information - Describing perspectives on the nature and scope of system benefits among agency officials, organizational personnel, and citizens - Discussion of implications of unequal economic power on the kinds of organizations that use, and benefits from, GIS&T","name":"Business models and funding models"},{"code":"GS2-2","description":"- Describing recent models of the benefits of GIS&T applications - Discussion of the extent to which external costs and benefits enhance the economic case for GIS - Explain how profit considerations have shaped the evolution of GIS&T - Outlining the elements of a business case that justifies an organization`s investment in an enterprise geospatial information infrastructure - Describing the potential benefits of geospatial information in terms of efficiency, effectiveness, and equity - Explaining how cost-benefit analyses can be manipulated - Compare and contrast the evaluation of benefits at different scales (e.g., national, regional/state,local) true for an organization that is already collecting data as part of its regular operations - Outlining the categories of costs that an organization should anticipate as it plans to design and implement a GIS - Outlining sources of additional costs associated with development of an enterprise GIS","name":"Costs, benefits and risks"},{"code":"GS2-3","description":"- Distinguishing between operational, organizational, and societal activities that rely upon geospatial Information - Discussion of relations between marketing and economical factors in substainablity/environmental issues by using geospatial information - Discussion of and defining the process of the Information value chain - Identifying practical problems in defining and measuring the value of geospatial information in land or other business decisions - Describing some non-fiduciary barriers to GIS implementation - Summarizing what the literature suggests as means for overcoming some of the non-fiduciary barriers to GIS implementation - Explaining how cost-benefit analyses can be manipulated - Compare and contrast the evaluation of benefits at different scales (e.g., national, regional/state,local) - Explaining how the saying \"developing data is the largest single cost of implementing GIS\" could be true for an organization that is already collecting data as part of its regular operations","name":"Valuing and measuring aspects"},{"code":"GS2-4","description":" ","name":"Agency, organizational, and individual perspectives"},{"code":"GS2","description":"Most organizations insist that investments in GIS and T be justified in economic terms. Quantifying the value of information, and of information systems, however, is not a straightforward matter.","name":"Economic aspects"},{"code":"GS3-1","description":"- Listing and describing the types of data maintained by local, state, and federal governments - Describing how geospatial data are used and maintained for land use planning, property value assessment, maintenance of public works, and other applications - Explaining the concept of a \"spatial decision support system\"","name":"Use of geospatial information in the public sector"},{"code":"GS3-2","description":"- Discussion of open data and data sharing and public/private sector - Discussion of the changing role of the private sector in the use of geospatial information - Describing private sector impact in development of solutions in collecting, processing and managing geospatial information - GI infrastructure development and the role of the private sector - Private sector and Organizational organizational integration, training in the use of geospatial information - Private sector and Research research and development in the use of Geospatial geospatial information","name":"Use of geospatial information in the private sector"},{"code":"GS3-3","description":"- Relationship between research and education, private/public sectors, and citizens - Discussion of the paradigm shifts and current trends in GIS&T education and pedagogical approaches for GIS teaching and learning in detail - GIS&T in formal as well as informal learning environments - Professional growth of teacher and trainer in the field of GIS&T education (TPACK) and GIS and pedagogical approaches for teaching GIS. - Aspects of citizen science and public engagement in research - Discuss how to approach the widening audience/participants for geospatial products and service, increasing geo-awareness and geo-enablement - Discuss ways of working with crowd sourcing in education and research","name":"Use of geospatial information in research and education"},{"code":"GS3-4","description":"- Discussion of the role of public, private sector and the citizen in facilitating geospatial information in environmental/sustainable issues. - Discussion of legal aspects of access to environmental data, global change/warming or sustainable development (regional, national, global) in conjunction to society.","name":"Use of geospatial information in environmental issues"},{"code":"GS3","description":"Geospatial Information used in Government agencies and public authorities at local, state, and federal levels produce and use geospatial data for many activities, including provision of social services, public safety, economic development, environmental management, and national defence. Public participation in governing, empowered by geospatial technologies, offers the potential to strengthen democratic societies by involving grassroots community organizations and by engaging local knowledge. The private sector covers a broad range of areas of opportunity. With continued advancements in technology, greater awareness of its advantages as a powerful decision support tool the use of geospatial information use in the private sector needs to be discussed.","name":"Use of geospatial information"},{"code":"GS4-1","description":"- Definition of and understanding of citizenship, democracy, maturity, and negotiation related to geo information use and participation in society /community development (local, regional, national level) - Differentiating among universal/deliberative, pluralist/representative, and participatory models of citizen participation - Comparing the advantages and disadvantages of group participation and individual participation - Describing increasing participation in governmental decision-making - Describing the range of spatial scales at which community organizations operate - Describing an example of \"local knowledge\" that is unlikely to be represented in the geospatial data maintained routinely by government agencies - Explaining how community organizations represent the interests of citizens, politicians, and specialists","name":"Public participation and citizenship"},{"code":"GS4-2b","description":"- Components and characteristics of the Geoweb, digital geo media / \"new spatial media\". - Defining and discussing impacts of crowdsourcing on geospatial sSociety. - Discussing the impact of geospatial information for the development of social media (Facebook, Twitter, Wikimapia, Flickr etc.) becoming increasingly location-based. - Discussing the role and value of \"place\" and \"space\" for geo media based social networking. - Differentiating between consumption, analysis, prosumption and production of geoinformation within digital geo media.","name":"GI and social media"},{"code":"GS4-3b","description":"- Defining and discussing volunteered geographic information. - Defining and discussing enabling technologies: geotag, georeferencing, GPS and more. - Discussion of positive and negative aspects of the term \"humans as sensors\". - Application domains and roles regarding geoinformation use in society: \"spatial information systems manager\", \"spatial analyst\", \"spatial citizen\". - Defining and discussing impact of Crowdsourcing on Geospatial Society.","name":"Citizens and volunteered geographic information"},{"code":"GS4","description":"Today, geo data has become a conventional and pervasively familiar data type seen at once to underpin and significantly re-characterize the digital world, with broad implications for both technology and society. Geospatial data are abundant, but access to data varies with the nature of the data, the user groups wishes to acquire it and for what purpose, under what conditions, and at what price geodata can be obtained. The explosive growth of geographic information contributed by users through various application programming interfaces has made geographic information a powerful media for the general public, but perhaps more importantly, geospatial information have also become media for constructive dialogs and interactions about social issues, recent growth of Web-based Geographic information and volunteered geographic information (VGI).","name":"Geospatial citizenship"},{"code":"GS5-1b","description":"- Describing a variety of philosophical frameworks upon which codes of professional ethics may be based. - Discussing the ethical implications of a local government`s decision to charge fees for its data. - Describe a scenario in which you would find it necessary to report misconduct by a colleague or friend. - Describe examples of ethical obligations of public/private sectors.","name":"Ethics in the geospatial information society"},{"code":"GS5-2b","description":"- Compare and contrast the ethical guidelines from different Associations associations. - Discussing obligations to : society, obligations to Employers employers and funders, obligations to colleagues and the profession, obligations to individuals in society. - Explaining how one or more obligations in the GIS Code of Ethics may conflict with organizations` proprietary interests. - Proposing a resolution to a conflict between an obligation in the GIS Code of Ethics and organizations` proprietary interests.","name":"Codes of ethics for geospatial professionals"},{"code":"GS5","description":"Ethics provide frameworks that help individuals and organizations make decisions when confronted with choices that have moral implications. Most professional organizations develop codes of ethics to help their members do the right thing, preserve their good reputation in the community, and help their members develop as a community","name":"Ethical aspects"},{"code":"GS6-1","description":"- Discussiion of the argument that the use of Geospatial geospatial Information information privileges certain views of the world over others. - Identifying alternatives to the \"algorithmic way of thinking\" that characterizes use of geospatial Information. - Discussing critiques of GIS as \"deterministic\" technology in relation to debates about the Quantitative quantitative revolution in the discipline of geography. - Describing the extent to which contemporary use of Geospatial geospatial information supports diverse ways of understanding the world. - Discuss the implications of interoperability on ontology.","name":"Epistemological and critical issues"},{"code":"GS6-2","description":"- Discussionof the various implications of surveillance technology. - Critical aspects of data collection and analysis. - Discussion of \" mapping who`s` reality?\" Pros and cons of geoinformation sharing in social media, i.e. big data, \"digital shadow\" etc.","name":"Critical approach on the use of geospatial information"},{"code":"GS6-3","description":"Defending or refuting the argument that the \"digital divide\" that characterizes access use of geospatial information perpetuates inequities among developed and developing nations, among socio-economic groups,and between individuals, community organizations, and public agencies and private firms.","name":"Critical aspects and invisible groups"},{"code":"GS6","description":"Many of the educational objectives used to define topics in this knowledge area, and in the Body of Knowledge as a whole, challenge educators and students to think critically about GI and Society. Since the 1990s, scholars have criticized cartography and the GIS science from a wide range of perspectives. Common among these critiques are questioned assumptions about the purported benefits of GI and Society and attention to its unexamined risks. By promoting reflective practice among current and aspiring geospatial information professionals, an understanding of the range of critical perspectives increases the likelihood that geospatial information will fulfil its potential to benefit all stakeholders. Philosophical, psychological, and social underpinnings of these critiques are considered in Knowledge Area CF: Conceptual Foundations.","name":"Critical approach"},{"code":"GS7-1","description":" ","name":"Epistemological critiques"},{"code":"GS7-2","description":" ","name":"Ethical critiques"},{"code":"GS7-3","description":" ","name":"Feminist critiques"},{"code":"GS7-4","description":" ","name":"Social critiques"},{"code":"IP","description":" ","name":"Image processing and analysis"},{"code":"IP1-1-1","description":"Use spatial subsetting to limit applying a function to a spatial subset of the image. Use spectral subsetting to limit applying a function to selected bands of the image.","name":"Image subset"},{"code":"IP1-1-2","description":"Use Layer Stacking to build a new multi-band file from georeferenced images of various pixel sizes, extents, and projections. The input bands will be resampled and reprojected to a common spatial grid. A common use of Layer Stacking is combining different band groups from a Landsat-8 or Sentinel-2 data into one file.","name":"Layer stack"},{"code":"IP1-1","description":"Data manipulation adjusts a dataset to the needs of a specific application by subsetting the spatial extent or the number of bands or by organizing bands from separate single layer files into a single multi-layer file. ","name":"Data manipulation"},{"code":"IP1-2","description":"Fourier analysis - A characteristic of remotely sensed images is a parameter called spatial frequency, defined as the number of changes in brightness value per unit distance for any particular part of an image. There are low-frequency and high-frequency areas. Spatial frequency may be enhanced or subdued using Fourie Analysis (an alternative technique is spatial convolution filtering). Furier analysis mathematically separates an image into its spatial frequency components. It is then possible interactively to emphasize certain groups (or bands) of frequencies relative to others and recombine the spatial frequencies to produce an enhanced image.","name":"Fourier transformation"},{"code":"IP1-3-1-1","description":"‘Structure-from-Motion’ (SfM) is a revolutionary, low-cost, user-friendly photogrammetric technique for obtaining high-resolution datasets at a range of scales. Traditional softcopy photogrammetric methods require the 3-D location and pose of the camera(s), or the 3-D location of ground control points to be known to facilitate scene triangulation and reconstruction. In contrast, the SfM method solves the camera pose and scene geometry simultaneously and automatically, using a highly redundant bundle adjustment based on matching features in multiple overlapping, offset images.","name":"DEM generation with 'Structure-from-Motion'"},{"code":"IP1-3-1-2","description":"Photogrammetry is the science and technology of obtaining spatial measurements and other geometrically reliable derived products from photographs. Basic geometric principles apply to both traditional analog and [modern] digital procedures. The aerial photogrammertic techniques and procedures are principles that hold for space-based operations. Today, photogrammetric procedures are used extensively to produce a range of GIS data products such as precise raster image backgrops for vector data [i.e. ortho imagery] and digital elevation models. The most basic aspects of the braod subject of photogrammetry include: (1) Determining the scale of a vertical photograph and estimating horizontal ground distances from measurements made on a very... [Scale as the relation between image coordinates and ground coordinates that affects distance measurements] , (2) Using area measurements made on a vertical photograph to determine the equivalent areas in a ground coordinate system , (3) Quantifying the effects of relief displacement on vertical aerial images , (4) Determination of object heights from relief displacement measurements , (5) Determination of object heights and terrain elevation by measurement of image parallax , (6) Use of ground control points , etc.","name":"Photogrammetric principles"},{"code":"IP1-3-1-3","description":"In photogrammetry and remote sensing, rational polynomial coefficients (RPCs) describe a specific imaging geometry model for transforming image pixel coordinates to map coordinates (thereby accounting for terrain displacement errors). A sensor model describes the geometric relationship between the object space and the image space, or vice versa. It relates 3-D object coordinates to 2-D image coordinates. RPCs are part of a general sensor model that approximates the physical sensor model. The physical sensor model represents the physical imageing process, making use of information on the sensor's position and orientation (during image acquisition). The RPC model often refers to a specific case of the RFM (rational function model) that is in forward form, has third-order polynomials, and is usually solved by the terrain-independent scenario.","name":"RPC correction"},{"code":"IP1-3-1-4","description":"A ground control point (GCP) is a location of the surface of the Earth (e.g. a road intersection) that can be identified on the imagery and located accurately on the map (i.e. the reference dataset). Two distinct sets of coordinates are associated with the GCP: image coordinates in i rows and j columns, and map coordinates (e.g. x, y measured in degrees of latitude and longitude or as specified by the spatial reference system).","name":"Ground Control Points (GCP)"},{"code":"IP1-3-1","description":"Orthorectification (aka Image-to-map rectification) is the process of removing the effects of image perspective (tilt) and relief (terrain) effects for the purpose of creating a planimetrically correct image. The resultant orthorectified image has a constant scale wherein features are represented in their 'true' positions. This allows for the accurate direct measurement of distances, angles, and areas.","name":"Orthorectification"},{"code":"IP1-3-2-1","description":"Image co-registration [aka Image-to-image registration] is the translation and rotation alignment process by which two images of like geometry and of the same geographic area are positioned coincident with respect to one another so that corresponding elements of the same ground area appear in the same place on the registered images (Jensen 2005 referencing Chen and Lee 1992).","name":"Image co-registration"},{"code":"IP1-3-2-2","description":"See GD1 Geolocating Data to Earth , GD1-1 Earch Geometry , GD1-2 Georeferencing systems , GD1-3 Datums , GD1-4 Map projections","name":"Spatial reference systems"},{"code":"IP1-3-2","description":" ","name":"Spatial referencing"},{"code":"IP1-3","description":"Geometric correction is concerned with placing the reflected, emitted, or back-scattered measurements or derivative products in their proper planimetric (map) location so they can be associated with other spatial information. It is usually necessary to to preprocess the remotely sensed data and remove the geometric distortion so that individual picture elements (pixels) are in their proper planimetric (x, y) map locations. This allows remote sensing-derived information to be related to other thematic information in geographic information systems (GIS) or spatial decision support systems (SDSS). Geometrically corrected imagery can be used to extract accurate distance, polygon area, and direction (bearing) information.","name":"Geometric correction"},{"code":"IP1-4-1","description":"Contrast stretching (also referred to as contrast enhancement) expands the original input brightness values to make use of the total dynamic range or sensitivity of the output device (a computer display).","name":"contrast stretching"},{"code":"IP1-4-2","description":"The histogram is a useful graphic representation of the information content of a remotely sensed image. Histograms for each band of imagery are often displayed and analysed in many remote sensing investigations becvause they provide the analyst with an appreciation of the quality of the original data (e.g. whether it is low in contrast, high in contras or multimodal in nature. [...] Tabulating the frequency of occurrence of each brightness value within the image provides statistical information that can be displayed graphically in a histogram.","name":"histogram"},{"code":"IP1-4","description":"Image enhancement algorithms are applied to remotely sensed data to improve the appearance of an image for human visual analysis or occasionally for subsequent machine analysis. The quality of results of image analysis are subjectively judged by humans as to whether they are useful. They include contrast enhancement.","name":"Image enhancement"},{"code":"IP1-5","description":"The presence of differential relief displacement in overlapping radar images acquired from different flight lines produces image parallax. This is analogous to to the parallax present in aerial photographs or electro-optical scanner data. Jus as photogrammetry can be used to measure surface topography and feature heights in optical images, radagrammetry can be used to make similar measurements in radar images. Use of two SAR images for single interferometry or time series SAR images for differential InSAR (DInSAR) for mapping surace displacement. Advanced InSAR techniques include persistent scatterer interferometry PSI and small baseline subset SBAS.","name":"Interferometry"},{"code":"IP1-6","description":"Principal components analysis has proven to be of value in the analysis of multispectral and hyperspectral remotely sensed data (Zhao and Maclean, 2000 , Mitternicht and Zinck, 2003). Principal components analysis is a technique that transforms the original remotely sensed dataset into a substantially smaller and easier to interpret set of uncorrelated variables that represent most of the information present in the original dataset. Principal components are derived from the data sucvh that the first principal component accounts for the maximum proportin of the variance of the original dataset, and subsequent orthogonal components account for the maximum proportion of the remaining variance.","name":"Principal component analysis"},{"code":"IP1-7-1-1","description":" ","name":"Bottom-of-atmosphere"},{"code":"IP1-7-1-2","description":" ","name":"Dark object subtraction"},{"code":"IP1-7-1-3","description":" ","name":"Surface correction"},{"code":"IP1-7-1-4","description":" ","name":"Top-of-atmosphere"},{"code":"IP1-7-1","description":"Even when the remote sensing system is functioning properly, radiometric error may be introduced into the remote sensing data. The two most important sources of environmental attenuation are 1) atmosphere attenuation caused by scattering and absorption in the atmosphere ans 2) topographic attenuation. Atmospheric correction is necessary if biophysical parameters are going to be extracted from water (e.g. chlorophyll a, suspended sediment, temperature) or vegetation (e.g., biomass, leaf area index, chlorophyll, percent canopy closure). There are several ways to atmospherically correct remotely sensed data, including straight forward approaches and others that are founded on physical principles and requiring a significant amount of information to function properly (Cracknell and Hayes, 1993). Two major types are absolute atmospheric correction and relative atmospheric correction.","name":"Atmospheric correction"},{"code":"IP1-7-2-1","description":" ","name":"Minimum noise fraction (MNF)"},{"code":"IP1-7-2","description":"The number of spectral bands assocuates with a remote sensing system is referred to as its data dimensionality. Hyperspectral remote sensing systems such as AVIRIS ans MODIS obtain data in 224 and 36 bands, respectively. The greater the number of bands in a dataset (i.e., its dimensionality), the more pixels that must be stored and processed by the digital image processing system. Storage and processing consume valuable resources. It is necessary to reduce the dimensionality of hyperspectral data while retaining the information content inherent in the image. On method to reduce dimensionality of hyperspectral data and minimizing the noise in the imagery is the minimum noise fraction (MNF) transformation (Green et al., 1988).","name":"Dimensionality reduction"},{"code":"IP1-7-3-1","description":"TOA reflectance calibration: the CN of each pixel image and each spectral band are converted in TOA reflectance (ρ). This conversion applies an equation that uses the satellite image’s metadata values referring to parameters of the sun’s emitted energy, the sun distance, the sun angle and the absolute calibration of the measuring instrument.","name":"Converting DN to TOA reflectance"},{"code":"IP1-7-3-2","description":" ","name":"Lidar point clouds"},{"code":"IP1-7-3-3","description":" ","name":"Radar amplitude"},{"code":"IP1-7-3-4","description":" ","name":"Radar phase"},{"code":"IP1-7-3","description":" ","name":"Radiometric calibration"},{"code":"IP1-7-4","description":" ","name":"Speckle filtering"},{"code":"IP1-7-5","description":"Topographic slope and aspect may also introduce radiometric distortion of the recorded signal (Gibson and Power, 2000). In some locations, the area of interest might even be in complete shadow, dramatically affecting the brightness values of the pixels involved. For this reason, research has been dericted towards the removal of topographic effects, especially in mountainous regions [...]. The goal of a slope-aspect correction is to remove topographically induced illumination variation so that two objects having the same reflectance properties show the same brightness value in the image despite their different orientation to the Sun's position.","name":"Topographic correction"},{"code":"IP1-7","description":"Radiometric correction is concerned with improving the accuracy of surface spectral reflectance, emittance, or back-scattered measurements obtained using a remote sensing system.","name":"Radiometric correction"},{"code":"IP1","description":"Pre-processing operations are performed on remotely sensed data prior to information extraction. Remove error encountered in remotely sensed data (most common are radiometric and geometric error) to get as close as possible to the true radiant energy and spatial characteristics of the study area at the time of data collection. Image preprocessing includes any steps that facilitate information extraction (image display and enhancement).","name":"Image pre-processing"},{"code":"IP2-1-1","description":"Data augmentation refers to a scheme of augmenting the observed data so as to make it more easy to analyze. Examples of data augmentation techniques include horizontal flips, random crops, Principal Component Analysis","name":"Data augmentation"},{"code":"IP2-1-2","description":"Data imputation refers to a scheme of replacing missing values by imputed values. Imputation can be, for example done with mean, median and mode","name":"Data imputation"},{"code":"IP2-1-3-1","description":" ","name":"Gram-Schmid"},{"code":"IP2-1-3-2","description":" ","name":"PCA-based"},{"code":"IP2-1-3","description":"Pan-sharpening methods are used to enhance spatial resolution of images.","name":"Pan-sharpening"},{"code":"IP2-1-4","description":"Spatiotemporal image fusion methods, called also spatiotemporal downscaling methods, represent an efficient solution to generate fine-scale images at a high temporal resolution for more detailed land cover mapping and monitoring applications. Spatiotemporal image fusion methods can be classified into three categories: (1) reconstruction-based , (2) unmixing based and (3) learning-based methods.","name":"Spatio-temporal image fusion"},{"code":"IP2-1","description":"Image fusion is defined as the “combination of two or more different images to form a new image by using a certain algorithm” Data fusion is a well-established research field. Image fusion methods are primarily used for improving the level of interpretability of the input data. Additionally, they can be utilized to address the problem of missing data caused by cloud or shadow contamination in satellite images time series. Image fusion can be performed at pixel-level, feature-level (e.g. land-cover classes of interest), and decision-level (e.g. purpose driven).","name":"Data fusion"},{"code":"IP2-2","description":" ","name":"Data harmonisation"},{"code":"IP2-3","description":" ","name":"Data integration"},{"code":"IP2","description":" ","name":"Data assimilation"},{"code":"IP3-1-1-1","description":"Vegetation fraction (VF) is defined “as the percentage of vegetation occupying a pixel as viewed in vertical projection. It’s a comprehensive quantitative index in forest management and vegetation community cover conditions, and it’s also an important parameter in many remote sensing ecological models.”","name":"Vegetation fraction"},{"code":"IP3-1-1-2","description":"Leaf area index (LAI) is the ratio between the total area of the upper leaf surface of vegetation and the surface area of the pixel in question. LAI is a dimensionless value, typically ranging between 0 (for a pixel composed of bare soil) and values as high as 6 (for a dense forest).","name":"LAI (Leaf Area Index)"},{"code":"IP3-1-1-3","description":"Net primary production (NPP) is a measure of the inherent productivity of a region or ecological system—mainly the Earth’s production of organic matter, principally through the process of photosynthesis in plants.","name":"Net primary production (NPP)"},{"code":"IP3-1-1","description":"An approach that uses multispectral remote sensing to estimate basic biophysical properties of the Earth’s vegetation cover.","name":"Biophysical parameters"},{"code":"IP3-1-2-1","description":"This spectral index is calculated using the following formula: SAVI = [(NIR-Red)/(NIR+Red+L)]/(1+L), where L can be, for example, 1 in area with no vegetation or 0 in area with dense veegtaion. It is used to minimize the influence of the soil brightness from the vegetation indices that are based on red and near-infrared wavelengths.","name":"Soil-adjusted Vegetation Index (SAVI)"},{"code":"IP3-1-2-2","description":"This spectral index is calculate using the following formula NDSI = (green-SWIR)/(green+SWIR). It is the most popular index used to identify snow cover due to the fact that snow reflects visible wavelength stronger than middle-infrared wavelengths.","name":"Normalized Difference Snow index (NDSI)"},{"code":"IP3-1-2-3","description":"This spectral index is calculated using the following formula: NDVI = (NIR-Red)/(NIR+Red). It gives a quantitative estimation of vegetation growth and biomass. It has values between -1 and 1. Higher values of NDVI indicates, for example, healthy and dense vegetation.","name":"Normalized Difference Vegetation Index (NDVI)"},{"code":"IP3-1-2","description":"Spectral indices are calculated based on digital brightness values.","name":"Spectral indices"},{"code":"IP3-1","description":" ","name":"Band maths"},{"code":"IP3-10","description":" ","name":"Semantic enrichment"},{"code":"IP3-11-1","description":"Change detection methods are used to assess changes in the type and condition of surface features. One can perform bitemporal change detection or Multitemporal change detection. Comparing images subsequent to classifying each is called post classification change detection.","name":"Change detection"},{"code":"IP3-11-2","description":" ","name":"Cube-based"},{"code":"IP3-11-3","description":"Dynamic Time Warping (DTW) works by comparing the similarity between two temporal sequences and finds their optimal alignment, resulting in a dissimilarity measure. In the case of remote sensing data, DTW can deal with temporal distortions, and can compare shifted evolution profiles and irregular sampling thanks to its ability to align radiometric profiles in an optimal manner","name":"Dynamic Time Warping"},{"code":"IP3-11","description":"Time series of remote sensing data can be based on series of raw digital numbers (DN) reflectance values or on variables commonly derived from the original data prior to analysis. Analysis of this data allow us to identify trends, identify anomalies or to identify changes.","name":"Time series analysis"},{"code":"IP3-12-1","description":"Remote sensing-derived products such as land-use and land-cover maps contain error. The error accumulates as the remote sensing data are collected and various types of processing take place. An error assessment is necessary to identify the type and amount of error in a remote sensing-derived product.","name":"Error propagation"},{"code":"IP3-12-2","description":"The precision of a measurement system, related to reproducibility and repeatability, is the degree to which repeated measurements under unchanged conditions show the same results.","name":"Precision"},{"code":"IP3-4-8","description":" ","name":"Concepts and categories"},{"code":"IP3-12-3-1","description":"Fuzzy logic is concerned with the formal principles of approximate reasoning. When uncertainty is a matter of vagueness, it can be handled with fuzzy set theory.","name":"Fuzzy logic"},{"code":"IP3-12-3","description":"Vagueness arises from lack of criteria for the applicability of certain linguistic terms. It arises from the lack knowledge about the meanings of terms.","name":"Vagueness"},{"code":"IP3-12","description":"Uncertainty is the result of the lack or imprecision of our knowledge about the world. A proposition is uncertain if we do not know whether it is true or not. In most circumstances we describe a proposition as uncertain when the reason we do not know whether it is true is that we do not possess complete and accurate knowledge about the state of the world.","name":"Uncertainty"},{"code":"IP3-13-1","description":"The main elements of visual interpretation are: shape, size, pattern, shadow, tone, location site, association.","name":"Elements (cues) of interpretation"},{"code":"IP3-13-2","description":" ","name":"Information-as-data-interpretation"},{"code":"IP3-13-3","description":"An image interpretation key is simply reference material designed to permit rapid and accurate identification of objects or features represented on aerial images.","name":"Interpretation keys"},{"code":"IP3-13","description":"Interpretation is the processes of detection, identification, description and assessment of significant of an object and pattern imaged. Visual interpretation is the ability of human to identify an object through the data content in an image/photo by combining several elements of interpretation.","name":"Visual interpretation"},{"code":"IP3-2-1","description":"Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans.","name":"Artificial intelligence (AI) [part of"},{"code":"IP3-2-2","description":"Information theory answers two fundamental questions in communication theory: what is the ultimate data compression (answer: the entropy H) and what is the ultimate transmission rate of communication (answer: the channel capacity, C). For this reason, it is considered that information theory is a subsetof communication theory","name":"Information theory"},{"code":"IP3-2-3","description":" ","name":"Keypoint detection"},{"code":"IP3-2","description":"Computer vision is an interdisciplinary field that deals with how computers can be made to gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to automate tasks that the human visual system can perform.","name":"Computer vision [part of"},{"code":"IP3-3-1","description":" ","name":"DEM generation"},{"code":"IP3-3-2","description":"DSM can be produced automatically from stereo satellite scenes, from satellite sensors including IKONOS, SPOT-5 and Terra-ASTER. The DSM can also be provided from stereo digital aerial photography at various resolutions, depending on the quality and scale of the aerial photography.","name":"DSM generation"},{"code":"IP3-3","description":" ","name":"Cross-stereo analysis"},{"code":"IP3-4-1-1","description":"The goal of filtering is to remove unnecessary components from images (e.g., noise), while emphasizing the necessary ones. In this context, low pass filters aim at removing sharp transitions in the image intensities (high spatial frequencies).","name":"Filtering"},{"code":"IP3-4-1-2","description":" ","name":"Gridding"},{"code":"IP3-4-1","description":" ","name":"Aggregation"},{"code":"IP3-4-2-1","description":" ","name":"Conditional propability"},{"code":"IP3-4-2-2","description":"Maximum likelihood classification uses the training data as a means of estimating means and variances of the classes, which are then used to estimate the probabilities. This method considers not only the mean, or average, values in assigning classification but also the variability of brightness values in each class.","name":"Maximum likelihood"},{"code":"IP3-4-2","description":"Bayes’s theorem is an extremely powerful means of using information at hand to estimate probabilities of outcomes related to the occurrence of preceding events","name":"Bayesian techniques"},{"code":"IP3-4-3-1","description":"The Land Cover Classification System (LCCS) has been designed with two main phases: an initial dichotomous phase, in which eight major land cover types are defined. This initial phase is followed by a subsequent so-called Modular-Hierarchical Phase, in which land cover classes are created by the combination of sets of pre-defined classifiers. These classifiers are tailored to each of the eight major land cover types.","name":"Land cover classification system (LCCS)"},{"code":"IP3-4-3","description":"A taxonomy is a hierarchical framework, or schema, for the organization of organisms, inanimate objects, events and/or concepts.","name":"Classification schemes (taxonomies)"},{"code":"IP3-4-4-1","description":"MacQueen, J., 1967, Some methods for classification and analysis of multivariate observations, Fifth Berkeley Symposium on Mathematics, Statistics and Probability, University of California Press (1967), pp. 281-297","name":"k-Means clustering"},{"code":"IP3-4-4","description":"Unsupervised methods are defined as the identification of natural groups, or structures, within existing data","name":"Clustering (unsupervised)"},{"code":"IP3-4-5-1","description":" ","name":"Production system"},{"code":"IP3-4-5","description":"see doc more details","name":"Decision trees"},{"code":"IP3-4-6-1","description":"Convolutional Neural Networks (CNNs) are among the most popular deep learning methods.","name":"Convolutional neural networks (CNN)"},{"code":"IP3-4-6","description":"Deep learning approaches have classically been divided into spatial learning (for example, convolutional neural networks for object classification) and sequence learning (for example, speech recognition)","name":"Deep learning"},{"code":"IP3-4-7-1","description":"The RF classifier is an ensemble classifier that uses a set of Classification and Regression Trees (CARTs) to make a prediction The trees are created by drawing a subset of training samples through replacement (a bagging approach).","name":"Random forest (RF)"},{"code":"IP3-4-7-2","description":"Support vector machines (SVMs) is a supervised non-parametric statistical learning technique, therefore there is no assumption made on the underlying data distribution. The method aims to find a hyperplane that separates the dataset into a discrete predefined number of classes in a fashion consistent with the training examples.","name":"Support vector machines (SVM)"},{"code":"IP3-4-7","description":"Field of study that gives computers the ability to learn without being explicitly programmed","name":"Machine learning"},{"code":"IP3-4-8","description":" ","name":"Mental concepts and categories"},{"code":"IP3-4-9-1","description":"Typically, the simple random sample of a geographic region is defined by first dividing the region to be studied into a network of cells. Each row and column in the network is numbered, then a random number table is used to select values that, taken two at a time, form coordinate pairs for defining the locations of observations. Because the coordinates are selected at random, the locations they define should be positioned at random. The random sample is probably the most powerful sampling strategy available as it yields data that can be subjected to analysis using inferential statistics.","name":"Random sampling"},{"code":"IP3-4-9-2","description":"A stratified sampling pattern assigns observations to subregions of the image to ensure that the sampling effort is distributed in a rational manner. For example, a stratified sampling effort plan might assign specific numbers of observations to each category on the map to be evaluated. This procedure would ensure that every category would be sampled.","name":"Stratified sampling"},{"code":"IP3-4-9-3","description":"Systematic sampling positions observations at equal intervals according to a specific strategy. Because selection of the starting point predetermines the positions of all subsequent observations, data derived from systematic samples will not meet the requirements of inferential statistics for randomly selected observations.","name":"Systematic sampling"},{"code":"IP3-4-9","description":"Sampling strategies or sampling pattern specifies the arrangement of observations used for training and/or validation purposes.","name":"Sampling strategies"},{"code":"IP3-4","description":" ","name":"Image classification"},{"code":"IP3-5-1","description":"Edge detection is a fundamental tool used in many image processing applications to obtain information from the frames as a precursor step to feature extraction and object segmentation. This process detects outlines of an object and boundaries between objects and the background in the image. An edge-detection filter can also be used to improve the appearance of blurred image.","name":"Edge-based segmentation"},{"code":"IP3-5-2","description":"Histogram-based segmentation makes use of histogram to select the gray levels for grouping the pixels into regions, e.g. background and the object of interest","name":"Histogram-based segmentation"},{"code":"IP3-5-3","description":"Local variance can be calculated as the value of standard deviation in a small neighborhood (e.g. 3x 3 moving window), then computing the mean of these values over the entire image. The obtained value is an indicator of the local variability in the image.","name":"Local variance"},{"code":"IP3-5-4","description":"Mean Shift is defined as finding modes in a set of data samples, manifesting an underlying probability density function (PDF).","name":"Mean-shift segmentation"},{"code":"IP3-5-5","description":"Regionalization is an important concept in Geographic Information Science for synthesizing multi-dimensional data into homogeneous objects through spatially constrained clustering methods","name":"Regionalisation"},{"code":"IP3-5-6-1","description":"Multi-resolution segmentation is a region-growing algorithm. It relies on several parameters, which need to be tuned. These include the scale parameter (SP), which dictates the size and homogeneity of the resultant objects.","name":"Multi-resolution segmentation"},{"code":"IP3-5-6-2","description":"Watershed segmentation is a region-based method that has its origins in mathematical morphology. In watershed segmentation an image is regarded as a topographic landscape with ridges and valleys. The elevation values of the landscape are typically defined by the gray values of the respective pixels or their gradient magnitude. Based on such a 3D representation the watershed transform decomposes an image into catchment basins. For each local minimum, a catchment basin comprises all points whose path of steepest descent terminates at this minimum. Watersheds separate basins from each other. The watershed transform decomposes an image completely and thus assigns each pixel either to a region or a watershed.","name":"Watershed segmentation"},{"code":"IP3-5-6","description":"Region-based segmentation starts from the pixel level and iteratively aggregates pixels into objects until some conditions of homogeneity imposed by the user are met.","name":"Region-based segmentation"},{"code":"IP3-5-7","description":"Spatial autocorrelation is the term used to describe the presence of systematic spatial variation in a variable.","name":"Spatial autocorrelation"},{"code":"IP3-5","description":"Image segmentation is the division of an image into connected regions or categories which correspond to different objects or parts of objects.","name":"Image segmentation"},{"code":"IP3-6-1-1","description":"Laplacian filters are derivative filters used to find areas of rapid change (edges) in images. Since derivative filters are very sensitive to noise, it is common to smooth the image (e.g., using a Gaussian filter) before applying the Laplacian. This two-step process is called the Laplacian of Gaussian (LoG) operation.","name":"Laplace of Gauss"},{"code":"IP3-6-1","description":" ","name":"Combined filtering"},{"code":"IP3-6-2","description":"The aim of sharpening filters is to highlight transitions in intensity (high frequency components) using different operators: directional (horizontal, vertical, diagonal) or isotropic (e.g. Laplacian Filter). Example of edge detectors include: Gaussian edge detector, Laplacian filter etc.","name":"Edge detectors"},{"code":"IP3-6-3-1","description":"The Lee-sigma filter is a conceptually simple but effective alternative to the Lee and other sophisticated adaptive filters. It is based on the sigma probability of the Gaussian distribution.","name":"Lee-Sigma"},{"code":"IP3-6-3","description":"High-pass filtering enhance information of high frequencies (local extremes, lines, edges)","name":"High-pass filtering"},{"code":"IP3-6-4-1","description":"Gaussian Filters are isotropic (same behavior in all directions).","name":"Gauss filter"},{"code":"IP3-6-4","description":"Spatial filters transform an image by taking into account the local neighborhood of a pixel. The goal of filtering is to remove unnecessary components from images (e.g., noise), while emphasizing the necessary ones. In this context, low pass filters aim at removing sharp transitions in the image intensities (high spatial frequencies).","name":"Low-pass filtering"},{"code":"IP3-6","description":"In contrast to the point operations used for radiometric modification of image data, techniques for geometric processing are characterized by operations over local neighborhoods of pixels. The result of a neighborhood operation is still a modified brightness value for the single pixel at the center of the neighborhood , however the new value is determined by the brightness of all the local neighbors rather than just the original brightness value of the central pixel alone.","name":"Neighbourhood analysis (convolution)"},{"code":"IP3-7-1","description":"Class modelling provides flexibility in designing a transferable workflow from scene-specific high-level segmentation and classification to region-specific multi-scale modelling","name":"Class modelling"},{"code":"IP3-7-2","description":"Hierarchical representation refers to hierarchically scaled compositions of the classes to be classified.","name":"Hierarchical representation"},{"code":"IP3-7-3","description":" ","name":"Per-parcel analysis"},{"code":"IP3-7-4-1","description":" ","name":"Distance and proximity"},{"code":"IP3-7-4-2","description":" ","name":"Planar geometric features"},{"code":"IP3-7-4-3","description":" ","name":"Topological features"},{"code":"IP3-7-4","description":" ","name":"Spatial features"},{"code":"IP3-7","description":"OBIA is an iterative method that starts with the segmentation of satellite imagery into homogeneous and contiguous image segments (also called image objects. In the next step, resulting image segments are assigned to the target classes.","name":"Object-based image analysis (OBIA)"},{"code":"IP3-8-1","description":" ","name":"Feature space polyhedralization"},{"code":"IP3-8-2","description":"Atmospheric radiative transfer models simulate the radiative transfer interactions of light scattering and absorption through the atmosphere. These models are typically used for the atmospheric correction of airborne/satellite data and allow retrieving atmospheric composition.","name":"Radiative transfer modelling"},{"code":"IP3-8","description":"Historically, physical modelling and machine learning have often been treated as two different fields with very different scientific paradigms (theory-driven versus data-driven). Yet, in fact these approaches are complementary, with physical approaches in principle being directly interpretable and offering the potential of extrapolation beyond observed conditions, whereas data-driven approaches are highly flexible in adapting to data and are amenable to finding unexpected patterns (surprises).","name":"Physical-model based analysis"},{"code":"IP3-9-1","description":"Difference of Gaussians (DoG) method consists of subtracting two Gaussians, where a kernel has a standard deviation smaller than the previous one. The convolution between the subtraction of kernels and the input image results in the edge detection of this image.","name":"Difference of Gaussian (DoG)"},{"code":"IP3-9-2","description":"Scale Invariant Feature Transform (SIFT) is an image descriptor for image-based matching and it is used for a large number of purposes in computer vision related to point matching between different views of a 3-D scene and view-based object recognition. The SIFT descriptor is invariant to translations, rotations and scaling transformations in the image domain and robust to moderate perspective transformations and illumination variations. Experimentally, the SIFT descriptor has been proven to be very useful in practice for robust image matching and object recognition under real-world conditions. (Source: http://www.scholarpedia.org/article/Scale_Invariant_Feature_Transform).","name":"Scale invariant feature transformation (SIFT)"},{"code":"IP3-9","description":"Scale-space theory is a framework for multiscale image representation, which has been developed by the computer vision community with complementary motivations from physics and biologic vision. The idea is to handle the multiscale nature of real-world objects, which implies that objects may be perceived in different ways depending on the scale of observation. If one aims to develop automatic algorithms for interpreting images of unknown scenes, there is no way to know a priori what scales are relevant. Hence, the only reasonable approach is to consider representations at all scales simultaneously.","name":"Scale space analysis"},{"code":"IP3","description":" ","name":"Image understanding"},{"code":"IP4-1-1","description":"Once the user finds the required data, she/he needs to know how can they be accessed, possibly including authentication and authorisation.","name":"Accessibility"},{"code":"IP4-1-2","description":"Quality Indicators (QIs) should be ascribed to data and, in particular, to delivered information products, at each stage of the data processing chain - from collection and processing to delivery. A QI should provide sufficient information to allow all users to readily evaluate a product’s suitability for their particular application, i.e. its “fitness for purpose”.","name":"GEO QA4EO"},{"code":"IP4-1-3","description":"The data usually need to be integrated with other data. In addition, the data need to interoperate with applications or workflows for analysis, storage, and processing.","name":"Interoperability"},{"code":"IP4-1-4-1","description":"ISO 19115-2:2009 extends the existing geographic metadata standard by defining the schema required for describing imagery and gridded data. It provides information about the properties of the measuring equipment used to acquire the data, the geometry of the measuring process employed by the equipment, and the production process used to digitize the raw data. This extension deals with metadata needed to describe the derivation of geographic information from raw data, including the properties of the measuring system, and the numerical methods and computational procedures used in the derivation.","name":"ISO 19115 Metadata (part 2)"},{"code":"IP4-1-4-2","description":"ISO/TS 19129:2009 defines the framework for imagery, gridded and coverage data. This framework defines a content model for the content type imagery and for other specific content types that can be represented as coverage data. These content models are represented as a set of generic UML patterns for application schemas.","name":"ISO 19129 Imagery, gridded and coverage data framework"},{"code":"IP4-1-4-3","description":"ISO 19157:2013 establishes the principles for describing the quality of geographic data. It defines components for describing data quality , specifies components and content structure of a register for data quality measures , describes general procedures for evaluating the quality of geographic data , establishes principles for reporting data quality.","name":"ISO 19157 Data quality"},{"code":"IP4-1-4","description":"ISO is an independent, non-governmental international organization with a membership of 164 national standards bodies. Through its members, it brings together experts to share knowledge and develop voluntary, consensus-based, market relevant International Standards that support innovation and provide solutions to global challenges. ISO/TC 211 Geographic information/Geomatics provides Standardization in the field of digital geographic information. Note: This work aims to establish a structured set of standards for information concerning objects or phenomena that are directly or indirectly associated with a location relative to the Earth. These standards may specify, for geographic information, methods, tools and services for data management (including definition and description), acquiring, processing, analyzing, accessing, presenting and transferring such data in digital / electronic form between different users, systems and locations.","name":"ISO standards"},{"code":"IP4-1-5","description":"The OGC is the worldwide leading consortium of GIS industries promoting the interoperability of geographic information across platform, system, and country borders. The main field of current activity is the complete integration of the sources of geographic information based on the Internet.The Open GIS Consortium (OGC) plays an important role on the implementation level.","name":"OGC standards"},{"code":"IP4-1-6","description":"Repeatability requires the data documentation and the workflow of data production to be comprehensible. Then others can repeat the process and assess the reliability of resulting data. Repeatability relates to the FAIR principles \"F2: Data are described with rich metadata\" and \"F1.3: (Meta)data meet domain-related community standards\". Open EO (https://openeo.org/) is an initiative forstering repeatability of EO workflows in the context of Big Earth Data.","name":"Repeatability"},{"code":"IP4-1-7","description":"The ultimate goal of FAIR is to optimise the reuse of data. To achieve this, metadata and data should be well-described so that they can be replicated and/or combined in different settings.","name":"Reusability"},{"code":"IP4-1","description":"Data quality standards are guiding principles and operational guidelines for the production and use of data. For example, QA4EO aims for the two key principles of accessibility / availability and suitability / reliability. The QA4EO guidelines provide instructions for the implementation of processes that follow these principles. Standards emerge from standardization processes within the community. They are based on the agreement of the members of the community.","name":"Data quality standards"},{"code":"IP4-2-1-1","description":"To correctly perform a classification accuracy (or error) assessment, it is necessary to systematically compare two sources of information: (1) pixels or polygons in a remote sensing-derived classification map, and (2) ground reference test information (which may in fact contain error). The relationship between these two sets of information is commonly summarized in an error matrix (sometimes referred to as contingency table or confusion matrix). Indeed, the error matrix provides the basis on which to both describe classification accuracy and characterize errors, which may help refine the classification or estimates derived from it.","name":"Error matrix"},{"code":"IP4-2-1-2","description":" ","name":"F stats"},{"code":"IP4-2-1-3","description":"Ground reference refers to the reference dataset for an accuracy assessment of a remote sensing classification. The process of obtaining ground reference is dedicated to support the production of suitable accuracy information. A sampling design (fitting to the produced image classification) determines the most appropriate distribution of sample locations (or regions). The response design consists of the evaluation protocol and the labeling protocol. The evaluation protocol initiates selecting the support region on the ground (represented by a pixel or polygon) where the ground information will be collected. Once the location and dimension of the sampling unit are defined, the labelling protocol is initiated and the sampling unit is assigned a hard or fuzzy ground reference label. This ground reference label (e.g. forest) is paired with the remote sensing-derived label (e.g., forest) for assignment in the error matrix.","name":"Ground reference"},{"code":"IP4-2-1-4","description":"Kappa is a value for measuring the overall accuracy of a classification that accounts for randomness of class assignment. Kappa analysis is a discrete multivariate technique of use in accuracy assessment. Kappa yields a statistic, ^K, which is an estimate of Kappa. It is a measure of agreement between the remote sensing-derived classification map and the reference data as is indicated by a) the major diagonal and b) the chance of agreement, which is indicated by the row and column totals in the error matrix.","name":"Kappa statistics"},{"code":"IP4-2-1-5","description":" ","name":"Precision & recall"},{"code":"IP4-2-1-6","description":"Geometric correction procedures (image-to-map rectification, image-to-image rectification) are used to rectify remotely sensed data to a standard map projection whereby it may be used in conjunction with other spatial information in a GIS to solve problems. The rectification process normally involves selecting ground control point (GCP) image pixel coordinates (row and column) with their map coordinate counterparts (e.g. meters northing and easting in a UTM map projection). Rectification requires that polynomial equations (that translate from image coordinates to map coordinates) be fit to the GCP data using least squares criteria. Depending on the distortion in the imagery, the number of GCPs used, and the degree of topographic reliefdisplacement in the area, higher -order polynomial equations may be required to geometrically correct the data. To determine how well the six coefficients derived from the least-squares registration of the initial GCPs account for geometric distortion in the inpit image, for each GCP, the root-mean-square error (RMSE) is computed.","name":"Root mean square error (RMSE)"},{"code":"IP4-2-1","description":"Thematic information contains error. Scientists who create remote sensing-derived thematic information should recognize the sources of error, minimize it as much as possible, and inform the user how much confidence he or she should have in the thematic information. Remote sensing-derived maps should normally be subjected to a thorough accuracy assessment before being used in scientific investigations and policy decisions.","name":"Accuracy assessment"},{"code":"IP4-2-2","description":"The implementation of a service that provides remote sensing derived information on a regular basis introduces process-related quality criteria like the timeliness of information provisioning. For the case of refugee camp mapping, timely arrival of map information may be critical to support the decisions in planning facilities for humanitarian assistance.","name":"Timeliness"},{"code":"IP4-2-3-1","description":"Completeness is a quality dimension that can apply to different data properties.The Data completeness is dealing with the completeness of an image, handling for example the effect of shadowing objects, sun flares on water surfaces or masking out by an object (e.g. propeller of a UAV). Spatial completeness is a feature on the area coverage. In photogrammetry (especially in stereophotogrammetry) its 3D version, the stereo completeness has extreme importance. In monitoring systems and applications the Temporal completenesster term features how the taken images represent a complete time series. The thematic completeness measure describes the image interpretation quality how the expected and defined classes are evaluated. This feature is important with the use of e.g. multiple classifiers.","name":"Completeness"},{"code":"IP4-2-3-2","description":"In remote sensing we can speak about spatial consistency in the Consistency cluster. It represents the quality of image interpretation/understanding: how are the different objects or classes recognized/evaluated integrally. A bridge above a water surface, like river can be detected in pixel-wised manner, but the question is how coherent they are in the output map. This phenomenon has very close to the thematic consistency, where the recognition integrity is represented in this way. The topological consistency is defined mainly for network-type surface objects, like roads or rivers, where the connection of all atomic segments are rated by this measure. Urban mapping focuses on the built environment objects, where e.g. house-parcel inclusions are described by this feature. The temporal consistency is for monitoring again, representing for example the possibility or impossibility of land cover changes in time. Having multiple data sources (even airborne or terrestrial), their integral usage can be qualified by this measure.","name":"Consistency"},{"code":"IP4-2-3-3","description":" ","name":"Readability"},{"code":"IP4-2-3","description":" ","name":"User validation"},{"code":"IP4-2","description":" ","name":"Product quality"},{"code":"IP4-3-1","description":"The cloud cover percentage indicates the amount of area in the remote sensing image extent that is covered with clouds and therefore cannot provide information about the Earth surface conditions.","name":"Cloud cover percentage"},{"code":"IP4-3-2","description":"The remote sensing lifecycle structures all possible phases of the data production process, from its beginning of the data's coming to existence (that includes the sensor design prior to data collection) over storage, processing and use to archiving and deletion.","name":"Remote sensing lifecycle"},{"code":"IP4-3-3-1","description":"The minimum spatial resolution in which features related to the phenomenon under investigation become apparent against the background and allow to detect information about the phenomenon. Jensen: Spatial resolution is a measure of the smallest angular or linear separation between two objects that can be resolved by the remote sensing system. [...] A useful heuristic rule of thumb is that in order to detect a feature, the nominal spatial resolution of the sensor should be less than one-half the size of the feature measured in its smallest dimension.","name":"Minimum Spatial resolution"},{"code":"IP4-3-3-2","description":"Radiometric resolution is defined as the sensitivity of a remote sensing detector to differences in signal strength as it records the radiant flux reflected, emitted, or back-scattered from the terrain.","name":"Radiometric resolution"},{"code":"IP4-3-3-3","description":"Spectral resolution is the number and dimension (size) of specific wavelength intervals (referred to as bands or channels) in the electromagnetic spectrum to which a remote sensing instrument is sensitive.","name":"Spectral resolution"},{"code":"IP4-3-3-4","description":"The temporal resolution of a remote sensing system generally refers to how often the sensor records imagery of a particular area.","name":"Temporal resolution"},{"code":"IP4-3-3","description":"Resolution as a quality indicator determines whether it is possible to detect information about a phenomenon under investigation with that dataset. (Alternative description: For determining a suitable resolution of data for the information need of a specific application, the target resolution is the threshold above which RS data enables the detection of information about a phenomenon under investigation)","name":"Resolution"},{"code":"IP4-3-4","description":"The spatial coverage of a dataset (consisting of an image or a series of images) determines whether the dataset covers the area of the terrain that is of interest to the user of information derived from the dataset.","name":"Spatial coverage"},{"code":"IP4-3-5","description":"The temporal validity of a dataset (consisting of an image or a series of images) determines whether the acquisition date(s) (and period) match(es) the requirements for investigating a specific phenomenon and thereby enables the derivation of information about that phenomenon.","name":"Temporal validity"},{"code":"IP4-3","description":"Values (or a value) that enable(s) judging a dataset or product on their fitness for a specific purpose (e.g. whether a specific satellite image is suitable for mapping landslides). , A QI should provide sufficient information to allow all users to readily evaluate a product’s suitability for their particular application, i.e. its “fitness for purpose”.","name":"Quality indicators"},{"code":"IP4","description":" ","name":"Data and product quality"},{"code":"IP5-1-1","description":"Array databases make use of arrays as the primary storage representation. Such an array-oriented data model and query language is useful in many scientific applications, where the raw data consists of large collections of imagery or sequence data that needs to be filtered, subsetted, and processed.","name":"Array databases"},{"code":"IP5-1-2","description":"The Open Data Cube (ODC) is a non-profit, open source project that was motivated by the need to better manage Satellite Data. This project was born out of the work done under the \"Unlocking the Landsat Archive\" and the Australian Geoscience Data Cube (AGDC) projects.","name":"Open data cube"},{"code":"IP5-1","description":"The term data cube originally was used in Online Analytical Processing (OLAP) of business and statistics data. Technically speaking, such a data cube represents a multidimensional array together with metadata describing the semantics of axes, coordinates, and cells. It is an efficient approach to the management and analysis of large datasets.","name":"Data cubes"},{"code":"IP5-2-1","description":"Content-based image retrieval helps users retrieve relevant images based on their contents.","name":"Content-based image retrieval"},{"code":"IP5-2-2","description":"Web Portals allow users to discover, understand, view, access and query information of their choice from local to global level for a variety of uses.","name":"Web portals"},{"code":"IP5-2","description":" ","name":"Image archives"},{"code":"IP5-3-1","description":"To facilitate and standardize access to data, the European Commission has funded the deployment of several cloud-based platforms providing centralized access to Copernicus data and information, as well as to processing tools. These platforms are known as the DIAS, or Data and Information Access Services.","name":"Data and information access service (DIAS)"},{"code":"IP5-3-2","description":"The OpenGIS® Web Processing Service (WPS) Interface Standard provides rules for standardizing how inputs and outputs (requests and responses) for geospatial processing services are defined. It defines an interface that facilitates the publishing of geospatial processes and clients’ discovery of and binding to those processes.","name":"OGC interfacesOGC Web Processing Service"},{"code":"IP5-3","description":" ","name":"Online processing"},{"code":"IP5","description":" ","name":"Infrastructure"},{"code":"MDS","description":"MDS is a dimensionality reduction technique.","name":"Multidimensional scaling"},{"code":"MDSClassical","description":"It is also known as Principal Coordinates Analysis, Torgerson Scaling or Torgerson Gower scaling. It takes an input matrix giving dissimilarities between pairs of items and outputs a coordinate matrix whose configuration minimizes a loss function called strain.","name":"Classical multidimensional scaling"},{"code":"MDSGeneralized","description":"An extension of metric multidimensional scaling, in which the target space is an arbitrary smooth non-Euclidean space. In cases where the dissimilarities are distances on a surface and the target space is another surface, GMDS allows finding the minimum-distortion embedding of one surface into another.","name":"Generalized multidimensional scaling"},{"code":"MDSMetric","description":" ","name":"Metric multidimensional scaling"},{"code":"no","description":" ","name":"Mathematical models of uncertainty: Probability and statistics"},{"code":"no10","description":"Geospatial data are abundant, but access to data varies with the nature of the data, who wishes to acquire it and for what purpose, under what conditions, and at what price. Legal relations between public and private organizations and individuals govern data access. Complementary topics appear in Knowledge Area GD Geospatial Data (especially Unit GD12 Data standards and infrastructures), and Knowledge Area OI (Units 0I5 Institutional and Inter-intuitional aspects and OI6 Coordinating organizations).","name":"Dissemination of geospatial information"},{"code":"OI","description":"This knowledge area considers the organizational and institutional aspects related to GIS&T. The focus of this knowledge area is on the organizations active in the GIS&T domain, and what happens within and between these organizations. The knowledge area is structured around five units. One unit considers the key organizations in the GIS&T domain, covering relevant public sector organizations at different administrative levels as well as organizations in other sectors of society. Among the organizational aspects covered in this knowledge area are all organizational issues related to the implementation, use and management of GI and GIS within organizations. While all topics related to the organizational structures, procedures and management of GI(S) are grouped into one unit, another unit focuses on issues related to the human factor of using GI and GIS, i.e. people, their skills and competencies, and the development and evaluation of these skills and competencies in the context of GIS&T training and education. The knowledge area includes also several inter-organizational and institutional aspects of GIS&T. Particular attention is paid to the concept of geospatial data sharing, which is about the creation of `spatial data` connections and relationships between different organizations in the GIS&T domain. Spatial data infrastructures are developed to promote, facilitate and coordinate the sharing of spatial data among data providers and data users, and consists of several technological and non-technological components. Many related topics are considered in the knowledge area GI and Society (WS), which also addresses several non-technological aspects related to GIS&T. In addition to this, also the knowledge areas `Design and Setup of Geographic Information Systems`, `Geospatial Data\" and Web-based GI` include several topics that are closely linked to the topics that are considered in this knowledge area. It can be argued that in order to fully master the knowledge and competencies that are presented in these knowledge areas, also basic knowledge and understanding of the organizational and institutional aspects is required.","name":"Organizational and Institutional Aspects"},{"code":"OI1-1","description":"The development of an appropriate organizational model, which establishes the basic character of GIS operations, is a crucial element of the GIS management. The appropriate GIS organizational model for any organization is based on its intended role.Alternative GIS organizational models are based on differing arrangements concerning the scope of GIS, the degree of integration of GIS into business operations, the degree of centralization of GIS operation and use, and the degree of centralization of management control. Although many variations can arise from different combinations of these factors, GIS organizational models can generally be classified into three types: (1) enterprise GIS, (2) GIS data and service resource, and (3) GIS as a business tool (Somers, 1998).","name":"Organizational models for GIS management"},{"code":"OI1-2","description":"Management of GIS can be done in a more centralized or more decentralized manner. In a a so-called enterprise or information-framework GIS, an organizational unit may be established to manage the GIS environment and run the core system, whereas usage is decentralized. In environments where GIS is used occasionally by various users, it may be set up as a separate service with a designated group that manages the GIS and also controls users` applications services. A second decision that needs to be made after the choice between more centralized or more decentralized management of GI and GIS is about where to place the GI management. Alternative options are in a line organization, in a support area, or at the executive level, each with their own advantages and disadvantages.","name":"Managing GIS operations and infrastructure"},{"code":"OI1-3","description":"User roles describe the relationship between different users and the GIS in an organization. Each user role includes responsibilities (e.g. for modifying certain information) and privileges (e.g. for viewing specific information). Although many different roles can be defined, a basic distinction is made between users, who can only view certain information, and editors, who can edit certain information.","name":"User roles"},{"code":"OI1-4","description":"A GIS management strategy should be unique for each organization, as organizations have unique environments, characteristics, goals, GIS requirements. An important step in developing an effective strategy for an organization is to establish the strategic vision for GI and GIS in the organization and define its role and scope. Other elements that should be covered in the GIS Strategy are the degree of centralized management of the GIS, the placement of GIS management and support in the organization, involvement of users in GIS planning and implementation, coordination of users, organizational changes, preparation of users, personnel issues, transitions to GIS operations, integration into business operations, user support, data access, and integration of technology changes (Somers, 1998).","name":"Strategic planning"},{"code":"OI1-5","description":"Committee and team approaches are frequently employed for coordinating participants and users in multi-participant GIS projects. The aim of creating such committees and teams is to ensure that the varied interests of participants are addressed, as participants bring many different interests, application needs, data needs, priorities, organizational issues, and political interests to a common project the GIS. Common models for coordinating participants recognize that participants have three levels of interest in the GIS: policy, technical development, and usage. Different bodies can be established focusing on these different levels of interest: a technical committee focusing on the design and development of the GIS, an management committee providing policy guidance and support and a user`s group.","name":"Coordinating GIS Participants and Users"},{"code":"OI1-6","description":"After the development and implementation of a GIS within an organization, the challenge is to maintain the system and revise and update it when necessary. This means the performance of the GIS in terms of efficiency and effectiveness should be measured and monitoring, and feedback from users on the system and applications, on the data as well as on new needs should be collected. Particular attention should be paid to the maintenance of data sets.","name":"Ongoing GIS revision"},{"code":"OI1-7","description":"The introduction of GIS into organizational environments should be seen as a complex process of mutual adaptation (Nedovic-Budic, 1997). These technologies changes the established organisational processes and structures, while on the other hand the organisational context and culture modify the technological set-up and use. Therefore, knowledge and understanding of the relationship between technologies and organizations is necessary to increase the success of GIS implementations in organizations. Successful GIS implementation and adoption often require some degree of organizational change. However, this can be very difficult to effect because organizations are naturally resistant to it (Somers, 1998).","name":"Organizational changes"},{"code":"OI1","description":"GIS and T implementation and use within an organization often involves a variety of participants, stakeholders, users and applications. Organizational structures and procedures address methods for developing, managing, and coordinating these multi-participant users. The development of the appropriate organizational model for managing the GIS is crucial. In certain cases, changes to the organizational structure in place might be required. Strategic planning and the establishment of coordination structures can be considered as valuable instruments for managing and coordinating all involved users, while also the different user roles need to be assigned.","name":"Organizational structures, procedures and management"},{"code":"OI2-1","description":"GIS and T professionals can be hired for a wide range of different job positions, for which the precise skills, competences and qualifications needed will vary. Typical examples of GIS and T positions are GIS&T project managers, technicians, system developers and analyst. The recognition and certification of the competences people have acquired in informal and non-formal learning contexts is important to know which skills and competences individuals have and whether they meet the qualifications required for a certain job position.","name":"GIS and T positions and qualifications"},{"code":"","description":"","name":""},{"code":"OI2-2","description":"Making sure staff members have the necessary skills and competences to perform geospatial activities is necessary for an effective implementation and operation of GI within an organizations. Several training methods can be adopted to ensure the development of skills and competencies of staff members. A distinction can be made between formal and informal training, but also between internal and external training programs. Another relevant issue is the assessment and evaluation of the skills and competences of staff members, to determine their future training and development needs.","name":"GIS and T staff development and evaluation"},{"code":"OI2-3","description":"Programs and courses on GIS and T and related subjects are provided by a wide range of institutions. While in recent years also the use and integration of GI and GIS in primary and secondary education has received significant attention, GIS and T education is mainly organized by institutions of higher education, especially universities but also other higher education institutions. Analyses of the higher education GIS&T programs and courses in Europe showed that the offer of courses is very diverse, in terms of size (ECTS), educational level (EQF) and course content. Vocational training on GIS and T related topics is organized by different types of training providers, including the major GIS vendors, data and service providers, academic sector, professional organisations, but also the public sector.","name":"GIS and T training and education"},{"code":"OI2-4","description":"A curriculum is a systematic description of a study program, in terms of learning goals, structure and sequence, learning, teaching and assessment strategies and content. A curriculum consists of both a set of related required and elective - courses along with all direct and indirect skills, competences and learning outcomes resulting from these courses. In the process of curriculum design typically particular attention is assigned to objectives, teaching methods and educational strategies, while also attention should be paid to the content organization aspects and the global structure of the curriculum. The process of designing GIS&T curricula presents many challenges, as the design of the curriculum should be aligned to both the institutional context and the expected outcomes of the learning and teaching process (Prager, 2011).","name":"GIS and T curriculum and course design"},{"code":"OI2-5","description":"An important challenge in organizing GIS and T education and training is the choice and use of effective teaching and learning methods. These methods should follow recent technological developments and use the best technologies to help students acquire the necessary skills and competencies. Traditionally, most GIS and T programs and courses were taught in the context of a full-time, face-to-face setting, using traditional teaching methods such as lectures and lab-based computer practical sessions. In recent years, educational institutions and their teachers have been experimenting with more innovative teaching and learning methods, such as project-based and case-based learning, distance learning, integrated and inter-disciplinary lessons, collaboration with companies and other stakeholders, etc.","name":"GIS and T teaching and learning methods"},{"code":"OI2","description":"This unit addresses GIS and T staff and workforce issues within an organization, particularly as they relate to ensuring that GIS and T is appropriately used and supported. The focus of this unit is on the skills and competencies of professionals in the GIS and T domain: how can these skills and competencies be described and evaluated, and how can they be developed through training and education.","name":"GIS and T workforce themes"},{"code":"OI3-1","description":"Cost savings are an important driver or motivation for sharing geospatial data and information. As costs associated with collecting and maintaining geospatial data are high, sharing data means that users no longer need to duplicate data gathering and archiving, which leads to savings in terms of personnel, space/facilities, data acquisition and maintenance costs. One fundamental argument for sharing thus derives from scale economies in production. Because the cost of making data is high, there is a clear incentive to maximize the number of users of these data. Sharing allows data to be used repeatedly for many purposes, thus increasing their value without increasing their cost. Sharing data also leads to improved data quality. Moreover, in many cases, sharing data is the only way to get access to certain data sets, as the authority to collect and manage certain data lies with another public institution.","name":"Drivers and incentives for sharing geospatial data"},{"code":"OI3-2","description":"Sharing of geospatial data can be hindered or inhibited by several types of barriers. These include technological barriers, such as a lack of common data definitions, formats and models or incompatibility of hardware and software. Among the non-technological barriers are organizational, political and legal issues and elements, such as misaligned organizational missions, diversity in organizational cultures, conflicting organizational priorities, lack of funding, lack of executive and legislative support; restrictive laws and regulations, copyright issues, data privacy and data ownership issues. However, it should be noticed that many of these barriers have been decreased or eliminated in recent years.","name":"Barriers to geospatial information sharing"},{"code":"OI1-9","description":" ","name":"Organizational models for coordinating GISs and/or program participants and stakeholders"},{"code":"OI3-3","description":"The legal framework for a spatial data sharing consists of two main types of information policies: those that promote and those that hinder the availability of spatial data. Policies that promote spatial data availability can focus on different types of users (public bodies, private companies, citizens) and different types of use (public access, commercial and non-commercial reuse, reuse for performing public tasks). Among the policies that hinder the availability of spatial data are those dealing with privacy, liability, and intellectual property. The legal framework also includes legislation that applies to data or information in general, which may also be applicable to spatial data (e.g. legislation on freedom of information, copyright, etc.). Moreover, also general legislation relating to any interaction between people or any situation in everyday life (e.g. liability, contract law, competition law, etc.) will apply to spatial data sharing. decreased or eliminated in recent years.","name":"Legal framework for geospatial data sharing"},{"code":"OI3-4","description":"Several types of legal mechanisms for sharing geospatial data can be used. A data sharing arrangements can be formalized by a contract or agreement between the data provider and the data user. A particular type of agreement are the framework agreements, which are agreements between two or more organisations concluded prior to the datasets or services being required. These framework agreement can involve one or multiple spatial data sets or services. Partnership agreements are often used to formalize the data sharing agreements among a broader group of partners. Participation in such a partnership often means participants share their data with other participants and get access to shared data. Another relevant mechanism is the use of licenses, which are mechanisms to give organizations and people the permission to use spatial data sets and services. A license is legally binding, and defines the conditions of use of the related spatial data sets and services. In order to reduce the number of licenses used and ensure the harmonization of the terms in these licenses, the use of standard licenses is promoted.","name":"Legal instruments for sharing geospatial data"},{"code":"OI3","description":"Geospatial data sharing has become an essential element of the GI activities of organizations. Spatial data sharing can be defined as the electronic transfer of spatial data/information between two or more organizational units where there is independence between the holder of the data and the prospective user. Spatial data sharing has many advantages, but several technical and non-technical barriers must be overcome to put data sharing into practice. While the practice of spatial data sharing has substantially grown with the development of spatial data infrastructures, many consider data sharing as a crucial element for the success of these infrastructures.","name":"Geospatial data sharing"},{"code":"OI3b","description":"A Spatial Data Infrastructure can be defined as the collection of technological and non-technological components to facilitate and coordinate the exchange of and sharing of spatial data. The concept infrastructure is used to promote the concept of a reliable, supporting environment, analogous to a road or telecommunications network, that facilitates the access to spatial data. Data, metadata, access networks, standards, coordination, policies, funding, people and institutional frameworks are often considered among the key components of an SDI. SDIs have been developed in many countries worldwide at local, national and international levels. Often a distinction is made between a between the first generation SDIs that have data as their key driver and are based on a product model and second generation SDIs in which user needs are the key driver and that are based on a process or development model.","name":"Spatial data infrastructures"},{"code":"OI4-1","description":"The adoption and implementation of standards are two key phases in the standardization process, which starts with the definition of standardization requirements and the development of standards. The adoption and implementation of standards follows after the development phase. The distinction made between the adoption and implementation of standards is important: adoption entails the decision to apply standards, while the implementation relates to the integration of standards in software, in data development and in other processes. GI-Standards are one of the key components of each SDI, consist of both semantic and technical standards, and include standards related to the different architectural components of an SDI, i.e. standards related to spatial data sets and data products, web services, metadata and catalogues, encodings, etc.","name":"Adoption and implementation of standards"},{"code":"OI4-2","description":"The SDI policy framework includes the set of policies, strategies, initiatives and projects aimed at increasing access, sharing, and effective use of spatial data. SDI policies can be divided into strategic and more operational policies. Strategic policies define the broader framework and formal structure within which the SDI initiative is developed. Operational policies provide more practical tools to facilitate access to and use of the SDI, and address specific topics related to the collection, management, use, access and dissemination of spatial data. These operational policies include a broad range of guidelines, directives, procedures and manuals that apply to the day-to-day business of organizations in developing, operating and using an SDI. To guarantee the success of an SDI, it is important to recognize the wider policy context in which these SDI`s are developed, and to link them to the overall policy environment in the jurisdiction in which they are implemented. These include policies on open government and open data, environmental policies, digital government or e-government policies and other.","name":"Policies"},{"code":"OI4-3","description":"If is often argued that SDI implementation requires coordination, because without coordination all other SDI components would not be developed or would be developed in a very fragmented and inconsistent manner. In general terms, coordination is about bringing into alignment the activities of different stakeholders in the SDI landscape. A typical instrument to realize coordinate in the context of SDI, is the establishment of an effective SDI coordination structure. The SDI coordination structure should ensure that all stakeholders are involved in the development and implementation of the SDI, through the participation in one or more coordination bodies. Another important element is the establishment of clear roles and responsibilities for the different involved organizations, making a distinction between data users, data providers, services providers and a geo-broker.","name":"Coordination and organizational structure"},{"code":"OI4-5","description":"Funding an SDI is about guaranteeing the long-term financial security of an SDI, by obtaining and formalizing financing for the implementation and maintenance of the different SDI components. An SDI funding model provides the answer to the central question of where and how to seek funding for implementing and maintaining an SDI. Within an SDI often different funding models will be combined, as the selection of the most appropriate funding model will be linked to different activities and the associated costs. Costs of an SDI include both set-up costs (one off costs) and maintenance costs (yearly), of which certain costs need to be made for each data sets or each data provider and other costs for the infrastructure in general. The most commonly used SDI funding models are centralized government funding, decentralized government funding (e.g. for each data provider), partnership funding, funding through revenues, and government funding based on donor agencies or on European projects.","name":"Funding an SDI"},{"code":"OI4-5b","description":" ","name":"Performance measurement and assessment"},{"code":"OI4-6","description":"For a long time, SDI development has focused on the development and implementation of different components with the aim of facilitating the access to and sharing of spatial data. An key challenge in future SDI development will be the integration of these SDI`s in a wider context. In order to optimally take advantage of the data and services provided by an SDI, integrating these data and services into the processes and workflows of public and private organizations will be crucial. The concept of spatial enablement refers to the challenge of developing SDI`s in such a way that they provide an enabling platform that serves the wider needs of society in a transparent manner. Moreover, the diffusion of SDIs, together with the efforts to build a Global Earth Observation System of Systems (GEOSS) and other developments in industry and civil society should be considered as elements in a the realization of a vision on the next-generation Digital Earth.","name":"Next-generation SDIs"},{"code":"OI5-1","description":"Within the European Commission there are several key GI players. GIS activities in the Commission started since 1981 (e.g. DG REGIO, Eurostat, ) with the CORINE project, the creation of DG ENV and the creation of the European Environment Agency (EEA). Together with the DG Joint Research Centre (JRC), DG ENV and EEA are in charge of the coordination of INSPIRE: DG Environment acts as an overall legislative and policy co-ordinator for INSPIRE, the JRC acts as the overall technical co-ordinator of INSPIRE and EEA is in charge of several tasks related to monitoring and reporting, and data and service sharing under INSPIRE. Also several other EC institutions are actively involved in GI(S) policies and activities (DIGIT, DG GROW, DG AGRI, DG MOVE and many others).","name":"GI organization at the European Commission"},{"code":"OI5-2","description":"Although there may be certain differences between countries, in most countries many key organizations in the GIS&T field will be active at the central/federal/national level of government. Especially the traditional institutions for surveying and mapping play a key role in geospatial policies and activities. Several public authorities at the federal level are in charge of the production and maintenance of key reference and thematic data sets. In many countries, these national data producers were the leading actors in the development of national spatial data infrastructures.","name":"Federal and national government organizations"},{"code":"OI5-3","description":"Local and sub-national governments are often considered among the major users of geographic information in governments, as they often are involved in many different policy areas, in which many problems with a locational component need to be tackled. Geographic data produced and maintained by authorities at lower administrative levels are often more detailed and thus interesting for other users, both within and outside the public sector. As a result, local and sub-national governments are often involved in the establishment of these infrastructures because of the wide range of highly detailed geographic information they produce and manage. As many geographic data are linked to the activities and services of local organizations, the involvement of these organizations in the maintenance of data ensures that these data are up-to-date.","name":"Sub-national and local governments"},{"code":"OI5-4","description":"The European GIS&T landscape consists of many pan-European organizations and associations promoting the interest of and representing certain stakeholder groups. While some of these organisations are dealing with all sectors and aspects of geographic information, others have a more thematic focus (e.g. remote sensing, topography, geosciences) or represent a particular sector (e.g. research, business). In some cases, their clearly is an overlap in the mission and objectives of different organizations, and some organizations are working in the same field of interest. Some examples of pan-European organizations and associations are AGILE, EuroSDR, EUROGI, and EuroGeographics. Also at international level several membership organizations and associations exist.","name":"Pan-European and global associations and professional organizations"},{"code":"OI5-5","description":"The geospatial industry consists of companies working with location specific information or services. Within the geospatial sector, several areas of activities can be identified: 1) measuring, collecting and storing of data about geo-objects; 2) processing, editing, modelling, analyzing and managing that data; 3) presenting, producing and distributing the data; and 4) advising, educating, researching and communicating about processes and use of geo-information products and services. The sector consists of both small-and-medium-sized enterprises but also big companies, including surveyors, census hard-copy map providers, aerial photos providers, base map data providers, satellite and remote sensing imagery providers, software developers (GIS-related products and services providers as well as satellite image programming platform providers) and several others.","name":"The geospatial industry"},{"code":"OI5-5b","description":" ","name":"The geospatial community"},{"code":"OI5","description":"Several types of organizations play a key role in the execution and coordination of geospatial activities in society. Typically, a distinction is made between data providers and data users, while coordinating organizations exist to coordinate and support the geospatial activities of professionals and entities using GIS&T. Governments are often considered as the major users and producers of spatial data and spatial information. Within the public sector, spatial data are collected and used in different thematic areas and at different administrative levels (from local to global). However, the needs, interests, and capacities of organizations at each of these levels will be different, as well as their role in the development of spatial data infrastructures, and the execution of geospatial activities in general. Also the geospatial industry will exist of both data providers and data users, but also of organizations delivering products and services to support the collection and use of spatial data. Other key organization in the GI domain are professional organizations and associations, bringing together and representing the needs of organizations of a particular sector and/or geographic area.","name":"Organizations in the GIS and T domain"},{"code":"OI6-1","description":" ","name":"Federal agencies and national and international organizations and programs"},{"code":"OI6-2","description":" ","name":"State and regional coordinating bodies"},{"code":"OI6-4","description":" ","name":"Publications"},{"code":"OI6","description":"A number of organizations (public, private, and non-profit) exist to coordinate, inform, and support geospatial activities of professionals, and entities using GIS and T. Informed geospatial professionals and organizations are familiar with the mission, history, constituencies, modes of operation, products, and levels of success of these organizations.","name":"Coordinating organizations (national and international)"},{"code":"PP","description":" ","name":"Physical principles"},{"code":"PP1-1-1","description":" ","name":"Electromagnetic Waves and Photons"},{"code":"PP1-1-10","description":" ","name":"Solar constant and solar insolation"},{"code":"PP1-1-11","description":"Earth's itself represents the second (after Sun) most powerfull natural source of e.m. radiation for EO. Its average emittance can be approximated by that of a blackbody at about 290 K. Even if very less powerfull than Sun such a source is available for EO day and nigth. The maximum of its emission falls in the thermal infrared (around 10 micron) being Earth's emission trascurable in the VIS-SWIR range.","name":"Earth's radiation (intensity, spectrum, etc.)"},{"code":"PP1-1-2","description":" ","name":"Electromagnetic spectrum"},{"code":"PP1-1-3","description":"Maxwell equations are a set of coupled partial differential equations that contains the fundamentals of electricity and magnetism. These equation provide electromagnetic waves that propagate into the space at the speed of the light. Increasing the wavelength there are gamma rays, X-rays, ultraviolet, (visible) light, infrared, microwaves and radio waves.","name":"Maxwell Equations and EM waves' propagation"},{"code":"PP1-1-4","description":"Planck's law is a mathematical relationship for the spectral radiance emitted by a blackbody (i.e. a body that absorbs all radiant energy falling on it) at a given temperature as a function of frequency or wavelength. Wien’s displacement law is the relationship between the temperature of a blackbody and the wavelength at which it emits the most radiation. Wien found that the product of the peak wavelength and the temperature is an absolute constant.","name":"Planck law for the black body. Wien's displacement law"},{"code":"PP1-1-5","description":"The Rayleigh–Jeans Law is an approximation of the Planck’s law for a blackbody through classical arguments. It states that emitted radiance is directly proportional to blackbody temperature and it fits with experimental measurements only at large wavelengths.Wien’s approximation is used to describe the spectrum of thermal radiation with thermodynamic arguments. The equation accurately describes the short wavelengths spectrum but it fails in fitting experimental data for emissions at long wavelengths.","name":"Rayleigh-Jeans approximation. Wien's approximation"},{"code":"PP1-1-6","description":" ","name":"Stefan–Boltzmann law. Kirchoff law"},{"code":"PP1-1-7","description":" ","name":"Concepts of Spectral Emissivity and Brightness Temperature."},{"code":"PP1-1-8","description":"The nuclear fusion of Hydrogen into Helium occurs in central part of the Sun (“Core”). Outside, the energy transfer is dominated by radiative process (“Raditive zone”) then by convection (”Convective zone”). Solar radiation at the Top of the Earth Atmosphere comes from the outer layer of the sun, the photosphere","name":"Solar structure."},{"code":"PP1-1-9","description":"Sun represents the most powerfull natural source of e.m. radiation for EO. Its emittance can be approximated by that of a blackbody at about 5900 K but just its reflected component (SOR) is actually available (and just during daytime) for EO. The maximum of SOR falls in the visible spectral range. Its contribution in the thermal infrared range is transcurable but in the medium infrared SOR is still significant enough and superimposed to Earth's thermal emission.","name":"Solar radiation at the Top of the Atmosphere. Solar spectrum"},{"code":"PP1-1","description":"The electromagnetic field propagates through the space radiating energy: the electromagnetic radiation. The classical theory describes this energy as electromagnetic waves which represent the oscillations of electric and magnetic fields. In the quantum mechanics theory EM radiation consists of photons, quanta of the electromagnetic force, responsible for all electromagnetic interactions","name":"EM radiation"},{"code":"PP1-2-1","description":"The study of the absorbption/emission of electromagnetic radiation by atoms. Depending on the atomic number characteristic frequency or wavelength are absorbed or emitted. Since each element has a characteristic spectrum of absorbed/emitted wavelengths (spectral signature), atomic spectroscopy allows the determination of elemental compositions even of remote objects (e.g. stars, galaxies, etc.)","name":"Atomic spectroscopy"},{"code":"PP1-2-10","description":"The Rayleigh roughness criterion is a widely used means to estimate the degree of roughness of a considered surface. Considering the phase difference between two rays scattered from separate points of the surface, this depends on the roughness height, the incident angle and, inversely, on the radiation wavelenght. The Rayleight criterion states that a surface can be considered as smooth if the phase difference is less than pigreco/2 radians.","name":"The Rayleigh roughness criterion"},{"code":"PP1-2-11","description":" ","name":"Bidirectional Reflectance Distribution Function (BRDF)"},{"code":"PP1-2-12","description":" ","name":"Bidirectional Reflectance Factor (BRF)"},{"code":"PP1-2-2","description":" ","name":"Molecular absorption spectra"},{"code":"PP1-2-3","description":" ","name":"Line shape and (natural, pressure, Doppler) broadening"},{"code":"PP1-2-4","description":" ","name":"Voigt's line profile"},{"code":"PP1-2-5","description":"Radiation that is not absorbed or scattered in the atmosphere can reach and interact with the Earth's surface. There are three (3) forms of interaction that can take place when energy strikes, or is incident (I) upon the surface. These are: absorption (A), transmission (T), and reflection (R). The total incident energy will interact with the surface in one or more of these three ways. The proportions of each will depend on the wavelength of the energy and the material and condition of the feature. Absorption (A) occurs when radiation (energy) is absorbed into the target while transmission (T) occurs when radiation passes through a target. Reflection (R) occurs when radiation \"bounces\" off the target and is redirected. The reflectance R is defined by the ratio of reflected radiant power to incident radiant power. The transmittance T of a medium is defined by the ratio of transmitted radiant power to incident radiant power. The absorptance A of a medium or target is defined by the ratio of absorbed radiant power to incident radiant power. Conservation of energy require that, at a certain wavelenght: R+T+A=1. To express the circumstance that the reflection can occurre in different direction as the surface deviates from a specular one, becoming rough the concept of surface scattering has been introduced. However, the concept of scattering concerns mainly atmopheric interaction with ELM and radar systems.","name":"Concepts of Transmittance, Absorbance, Reflectance, Scattering."},{"code":"PP1-2-6","description":"The emitting ability of a body surface is described by emissivity, ε(λ). This will vary with wavelength and viewing angle. A body is considered to be an ideal radiator when it totally absorbs and then reemits all energy incident upon it. Such a body is called black body and its emissivity is equal to one. Emissivity can be defined as the ratio of spectral exitance, M(λ,T), from an object at wavelength λ and temperature T, to that from a blackbody at the same wavelength and temperature, MBB(λ,T). The concept of graybody has also been introduced as the body having an emissivity of less than 1 and constant at all wavelengths.","name":"Concepts of Spectral Emissivity, Absorbance, Transmittance, Reflectance"},{"code":"PP1-2-7","description":" ","name":"Complex dielectric constants and refractive indices"},{"code":"PP1-2-8","description":" ","name":"EM rad. penetration in the matter: Attenuation Length"},{"code":"PP1-2-9","description":"EM radiation impinging a rough surface is (partly) reflected back (scattering). Lambertian surfaces produce a diffuse scattering (i.e. radiation is reflected similarly in all direction) and then appear equally bright from all directions, whereas specular surfaces behave like a mirror, with reflected radiation all aligned in one direction, with the reflection zenith angle equal to the incident angle of incoming radiation. Generally, the degree of \"roughness\" of a surface determines if it behaves like a Lambertian or a specular surface. ","name":"Scattering from rough surface: Lambertian and specular surfaces."},{"code":"PP1-2","description":"Radiation can be absorbed, scattered, emitted and transmitted by the matter depending on the different parts of the electromagnetic spectrum and the matter peculiarities (Atoms, molecules, particles and surfaces) and its physical state (Temperature, Concentration, Shape, Roughness). The interaction between radiation and matter depends strongly on the wavelength of radiation. ","name":"Radiation - Matter interaction"},{"code":"PP1-3-1","description":" ","name":"Radiometric quantities: radiance, irradiance, flux, brightness, emittance, luminosity,etc."},{"code":"PP1-3-2","description":" ","name":"Decay of the emittance with the square of distance from the source"},{"code":"PP1-3-3","description":"The relative amount of electromagnetic radiation reflected (absorbed, transmitted, emitted) by the matter at different wavelengths depends on its specific chemical composition and physical properties. The plots of corresponding physical quantities (reflectance, absorbance, transmittance, emissivity) against wavelength, are termed spectral signatures of the specific matter under study. In principle the analysis of spectral signatures obtained by multispectral EO sensors could allow us to identify/discriminate different cover types.","name":"Spectral Signatures of the matter"},{"code":"PP1-3-4","description":"Vegetation, water and soil represent the most common cover types of Earth surface. Their reflectances plotted against wavelength in the 0,4-2,5 micron represent the most important (basic) spectral signatures for whatever application devoted to Earth surface study. The NIR/VIS (nearInfraRed/VISible) ratio is very (quite) higher than one for vegetation (soil), negative for water bodies.","name":"Spectral Signature of Vegetation, Water, Soil"},{"code":"PP1-3-5","description":" ","name":"Spectral Signature of Mineral and Rocks"},{"code":"PP1-3-6","description":" ","name":"Spectral Signature of Clouds"},{"code":"PP1-3-7","description":"If the resolution is low enough that disparate materials can jointly occupy a single pixel, the resulting spectral measurement, made by the sensor, will be the composite of the individual spectra. Under the linear mixing model (LMM), each observed spectrum in each pixel of a given image is assumed to result from the linear combination of the N endmember spectra present in the pixel. The reflectance spectrum of each endmember is weighted by the fractional area coverage of it in the pixel.","name":"Composition of spectral signatures (Linear Mixing)"},{"code":"PP1-3-8","description":"One of the most common ways to classify remote sensing systems consists in distinguishing them into the passive systems, which detect naturally occurring radiation, and the active systems, which emit radiation and analyse what is sent back to them. The passive systems can be further subdivided into those that detect radiation emitted by the Sun (this radiation consists mostly of ultraviolet, visible and near-infrared radiation), and those that detect the thermal radiation that is emitted by all objects that are not at absolute zero (i.e. all objects). For objects at typical terrestrial temperatures, this thermal emission occurs mostly in the infrared part of the spectrum, at wavelengths of the order of 10 μm (the so called thermal infrared region), although measurable quantities of radiation also occur at longer wavelengths, as far as the microwave part of the spectrum. Active systems can, in principle, use any type of electromagnetic radiation. In practice, however, they are restricted by the transparency of the Earth’s atmosphere.","name":"Definition of active and passive remote sensing techniques"},{"code":"PP1-3","description":" ","name":"Sensing of EM radiation"},{"code":"PP1-4-1","description":"The radiation traversing a medium may be attenuated, due to the density, mass scattering and absorption of material. In contrast, the radiation’s intensity can be strengthened by emissions from the material plus multiple scattering from all directions. The above interactions follow the general radiative transfer equation.","name":"General equation of radiative transfer."},{"code":"PP1-4-10","description":"The inversion approach aims at retrievals of trace gas concentration and temperature profiles of atmospheric state, namely the modeled state vector, based on the measured radiance transmitted or reflected or scattered (SCIAMACHY spectrometer) by the Earth-Atmosphere system. The Differential Optical Absorption Spectroscopy (DOAS) is the solution of inverse approach.","name":"Retrieval of atmospheric parameters (vertical profiles of temperature and of main chemical constituents) by inversion (e.g. by Empirical Orthogonal Function Methodology) of radiances measured from satellites"},{"code":"PP1-4-2","description":"In the field of radiation scattering and absorption, the cross-section, analogous to the shape of a particle, is used to determine the amount of energy diverted from the original beam by the particle. This parameter is called mass cross section, when it is in reference to unit mass (cm2g-1).","name":"Cross Section of Extinction (Absorption, Scattering) per Mass Unit"},{"code":"PP1-4-3","description":"When the mass cross-section is multiplied by the density of particle, the extinction coefficient is calculated, namely the sum of absorption and scattering coefficient, whose the units are related to length. Especially, the absorption coefficient (k (cm•atm)-1) is the product of strength of absorption with the Loschmidt’s number.","name":"Absorption Coefficient"},{"code":"PP1-4-4","description":"The source function, Jλ, has units of radiant intensity and it is defined as the ratio of the source function coefficient to the mass extinction cross section. The Jλ determines the intensity that are acquired in a homogeneous medium.","name":"Source Function (Coefficient)"},{"code":"PP1-4-5","description":"If the monochromatic beam (Iλ) of radiation attenuates due to absorption, but it remains unaffected from emission contributions and multiple scattering of homogeneous Earth-Atmosphere system, it can be expressed by Beer-Bouguer-Lambert law. This law also expresses the monochromatic optical depth (τλ) and transmissivity (Τλ) of the above system.","name":"Beer-Bouguer-Lambert law."},{"code":"PP1-4-6","description":"The Schwarzschild equation provides an interpretation for the infrared radiation that undergoes the absorption and emission processes simultaneously, while the scattering efficiency is considered negligible. Hence, its solution is obtained by the integrating of relationship that invokes Kirchhoff’s law and summing the two above processes along a ray path. ","name":"Schwarzshild equation and its solutions"},{"code":"PP1-4-7","description":"The Atmosphere-Earth system that monochromatic beam (Iλ) of radiation travels, is called optical path. It expressed by optical path length, namely the product of geometric length and the refractive index of medium. It determines the optical thickness, namely a measure of the cumulative depletion of Iλ directed in straight-downward.","name":"Concepts of Optical path and Optical thickness."},{"code":"PP1-4-8","description":"Radiative transfer is highly nonlinear and non-local against the cloud structure at a high spatial resolution. Hence, a Monte Carlo approach is used for the representation of cloud structure and interactions between photons and clouds. This approach is more efficiency than the method of representing clouds as horizontally homogeneous.","name":"Radiative transfer in presence of clouds"},{"code":"PP1-4-9","description":"The line by line radiative transfer model is an accurate and flexible model for the estimation of radiance over the full spectral range, using a first-order perturbation algorithm. It is continually updated and validated against high accuracy spectral measurements, while its errors are uncertainties in line parameters and shape.","name":"Line-by-line radiative transfer models"},{"code":"PP1-4","description":" ","name":"Fundamentals of Radiative Transfer"},{"code":"PP1-5-1","description":" ","name":"Reflection, Refraction and Dispersion of the light"},{"code":"PP1-5-10","description":" ","name":"Scattering from inhomogeneous media."},{"code":"PP1-5-11","description":" ","name":"Einstein’s theory of radiation:"},{"code":"PP1-5-12","description":" ","name":"photons, photoelectric effect, absorption, emission"},{"code":"PP1-5-13","description":" ","name":"Stimulated emission: the laser"},{"code":"PP1-5-14","description":" ","name":"Electric conduction in solids: semiconductors, p-n- junction"},{"code":"PP1-5-15","description":" ","name":"diode and transistor, photo-electric and photovoltaic effect (and detectors)"},{"code":"PP1-5-16","description":" ","name":"MCT, InSb, bolometer, CCD detectors"},{"code":"PP1-5-17","description":" ","name":"Passive Radiometers. Radiance at the sensor and S / N Ratio"},{"code":"PP1-5-18","description":" ","name":"Passive sensors (pre-launch, in fligth) calibration"},{"code":"PP1-5-2","description":" ","name":"Interference and Diffraction."},{"code":"PP1-5-3","description":" ","name":"Michelson Interferometer"},{"code":"PP1-5-4","description":" ","name":"Special relativity"},{"code":"PP1-5-5","description":"Helmotz’s equations, principle of physical optics: foundations of geometrical optics, geometrical theory of optical imaging, elements of the theory of interference and interferometers, elements of the theory of diffraction and grating spectrometers, scattering from inhomogeneous media. Einstein’s theory of radiation: photons, photoelectric effect, absorption, emission, stimulated emission: the laser. Electric conduction in solids: semiconductors, p-n- junction, diode and transistor, photo-electric and photovoltaic effect and detectors: MCT, InSb, bolometer, CCD detectors","name":"Electromagnetic fields equations and propagations"},{"code":"PP1-5-6","description":" ","name":"Helmotz’s equations, principle of physical optics:"},{"code":"PP1-5-7","description":" ","name":"Foundations of geometrical optics, geometrical theory of optical imaging"},{"code":"PP1-5-8","description":" ","name":"Elements of the theory of interference and interferometers,"},{"code":"PP1-5-9","description":" ","name":"Elements of the theory of diffraction and grating spectrometers,"},{"code":"PP1-5","description":" ","name":"Basic of Optics and Modern Physics of Sensors"},{"code":"PP1-6-1","description":"The temperature and pressure profiles determines the atmospheric structure that consists of boundary atmospheric layer, troposphere, middle and upper atmosphere. The troposphere concentrates the water vapor and 80% of atmospheric mass, while the chemical composition of all atmopsheric layers consists of nitrogen, oxygen, argon and trace gases.","name":"Structure and chemical-physical composition of Earth's atmosphere"},{"code":"PP1-6-10","description":"The water vapour is the major radiative and dynamic parameter in atmosphere. Its concentrations vary highly in space and time, with the tropospheric water vapor being determined by the processes of hydrological cycle, namely the evaporation, condensation and precipitation. More specifically, its condensation upon dust nuclei form the clouds.","name":"Water vapour and Cloud formation"},{"code":"PP1-6-11","description":"The radiative equilibrium is the principle, where the radiative emission and absorption are in balance based on Kirchhoff’s and Planck’s law, resulting in the steady temperature of planet. The adiabatic lapse rate displays the decrease of vertical temperature of a parcel with rate higher than 1oC per 100 metres.","name":"Radiative Equilibrium. Adiabatic lapse rate"},{"code":"PP1-6-12","description":"The atoms of carbon are building blocks of living organisms and they can move among organisms as a part of carbon cycle. Their transport rate to the atmosphere as carbon dioxide is vital, because this gas trap heat in the atmosphere, increasing the Earth’s temperature and causing Greenhouse effect.","name":"The Carbon Cycle, Greenhouse Effect"},{"code":"PP1-6-2","description":"The atmospheric absorption process can cause an excitation or falling into the energy state of a particle, while the scattering is related to absorption and re-emission of radiation at all directions with changes its frequency. The main absorbers are the nitrogen, oxygen and trace gases, while the aerosols are scatterers. ","name":"Absorption and scattering of solar radiation in the Atmosphere"},{"code":"PP1-6-3","description":"Mie scattering refers primarily to the elastic scattering of light from atomic and molecular particles whose diameter is similar or larger than the wavelength of the incident light. Mie scattering is not strongly wavelength dependent . This scattering produces a pattern like an antenna lobe, with a sharper and more intense forward lobe for larger particles. In the atmosphere the Mie scattering is commonly caused by particles (aerosols) floating in the atmosphere (due to Dust, smoke, rain drop). The Mie theory provides the solution for the amount of scattering in case of a spherical medium due to an incident wave.","name":"Mie Scattering in the Earth's Atmosphere"},{"code":"PP1-6-4","description":"Rayleigh scattering refers primarily to the elastic scattering of light from atomic and molecular particles whose diameter is much smaller (one-tenth at least) than the wavelength of the incident light. The amount of scattering is strongly depending on the wavelength (λ) of the radiation (I = f(1/λ4). Then, the Rayleigh scattering explain the blue color of the sky caused by the scattering of sunlight off the molecules of the atmosphere. This because Rayleigh scattering is more effective at short wavelengths (the blue end of the visible spectrum). Therefore the light scattered down to the earth at a large angle with respect to the direction of the sun's light is predominantly in the blue end of the spectrum.","name":"Rayleigh Scattering in the Earth's Atmosphere"},{"code":"PP1-6-5","description":"When we talk about “thermal infrared (or terrestrial) radiation” we commonly referred to the energy emitted from the Earth-atmosphere system. Trapping of thermal infrared radiation by atmospheric gases is typical of the atmosphere and is therefore called the “atmospheric effect”. The atmospheric effect is sometimes referred to as the “greenhouse effect” because in a similar way glass, which covers a greenhouse, transmits short-wave solar radiation, however absorbs long-wave thermal infrared radiation. Imagine a beam of radiation travelling through a small section of air. The air is made up of changing concentrations of different species, with all molecules absorbing and emitting thermal radiation at different rates. As the radiation travels through different layers of the atmosphere, the intensity of radiation will constantly be modified by both absorption and emission processes as described by the Schwarzschild's equation. In case of a sensor on board of a satellite, the net radiation measured would be that which is attenuated through each layer (as small increments of absorption and emission) from the surface to the top of the atmosphere plus the radiation emitted directly from the surface. In this case, this process can be described by the radiative transfer equation (RTE).","name":"Thermal infrared radiation transfer in the atmosphere"},{"code":"PP1-6-6","description":"Light scattering by particles is the process by which small particles cause optical phenomena, such as rainbows, the blue color of the sky, and halos. Mie scattering defines the interaction of light with particulate matter with a dimension comparable to the wavelength of the incident radiation. It can be regarded as the radiation resulting from a large number of coherently excited elementary emitters (molecules for example) in a particle. Since the linear dimension of the particle is comparable to the wavelength of the radiation, interference effects occur. The most noticeable difference to Rayleigh scattering is, generally, the much weaker wavelength dependence and a strong dominance of the forward direction in the scattered light. The calculation of the Mie scattering cross section, which involves summing over slowly converging series, is complicated even for spherical particles, it is worse for particles of an arbitrary shape. However, the Mie theory for spherical particles is well developed and a number of numerical models exist to calculate scattering phase functions and extinction coefficients for given aerosol types and particle size distributions.","name":"Light scattering by atmospheric particulates"},{"code":"PP1-6-7","description":"Each time radiation passes through the atmosphere it is attenuated to some extent. We refer to this attenuation with the term 'atmosphere transmittance'. The typical atmospheric transmittance between wavelengths of 250 nm and 2500 nm, i.e. in the ultraviolet, visible, near-infrared and short-wave-infrared regions of the spectrum is dominated bywater vapour, although methane, carbon dioxide and molecular oxygen are also responsible for a few absorption lines. The behaviour in the visible region is dominated by molecular Rayleigh scattering. At the short-wavelength end of the spectrum, in the ultraviolet, absorption by ozone becomes very significant. Above 2500 nm up to the upper limit (13500 nm) of the optical electromagnetic spectrum useful for Remote Sensing, the atmosphere transmittance is mainly affected by triatomic molecules (H20, CO2 and O3). However, the atmospheric effects (transmittance) is strongly depending on the electromagntic wavelength. Remote Sensing exploits the region of relative atmospheric transparency called atmospheric windows.","name":"Earth's (standard) Atmosphere Transmittance"},{"code":"PP1-6-8","description":"With the term 'atmospheric windows' we refer to the regions of the electromagnetic spectrum where the interaction of the atmosphere with the electromagnetic radiation is minimized. There are three main ‘windows’ in the Earth's atmosphere. The first of these includes the visible and near-infrared (VNIR) parts of the spectrum, between wavelengths of about 0.38 μm and 3.5 μm, although it does also contain a number of opaque regions. The second is a rather narrow region between about 8 μm and 15 μm, in which is found the bulk of the thermal infrared (TIR) radiation from objects at typical terrestrial temperatures. The third more or less corresponds to the microwave region, between wavelengths of a few millimetres and a few metres. Thus we can expect that any active system designed to penetrate the Earth’s atmosphere will operate in one of these three ‘window’ regions.","name":"Atmospheric (spectral) windows for EO"},{"code":"PP1-6-9","description":"The water cycle describes four main processes of the water, which are the evaporation from the surface of the Earth, the rising of droplets into the atmosphere, the cooling and condensation of the droplets of clouds and their falling as precipitation again to surface. ","name":"The Water Cycle"},{"code":"PP1-6","description":" ","name":"Basics of Atmospheric Physics"},{"code":"PP1-7-1","description":" ","name":"Temperature and heat"},{"code":"PP1-7-10","description":" ","name":"The constitutive equations of irreversible fluxes"},{"code":"PP1-7-11","description":" ","name":"Heat equation and special adiabatic systems, special adiabats of homogeneous systems"},{"code":"PP1-7-12","description":" ","name":"Thermodynamics diagram, atmosphere static"},{"code":"PP1-7-2","description":" ","name":"Kinetic theory of gases"},{"code":"PP1-7-3","description":" ","name":"Ideal gas laws"},{"code":"PP1-7-4","description":" ","name":"State function of ideal gases"},{"code":"PP1-7-5","description":" ","name":"State function of the condensed gas phase"},{"code":"PP1-7-6","description":" ","name":"Thermodynamic process"},{"code":"PP1-7-7","description":" ","name":"Budget equations"},{"code":"PP1-7-8","description":" ","name":"First law of thermodynamic"},{"code":"PP1-7-9","description":" ","name":"Second law of thermodynamics"},{"code":"PP1-7","description":" ","name":"Basics of Thermodynamics"},{"code":"PP1-8-1","description":" ","name":"Satellite orbits: (quasi)Polar, Geostationary, Molnyia, Non-rotating, Sun-Syncronous, etc.)"},{"code":"PP1-8-2","description":" ","name":"Equation of the rocket and launch of a satellite: payload determination"},{"code":"PP1-8-3","description":"The orbit of a satellite is commonly defined through its so called Keplerian parameters. These parameters represent the trajectory that the satellite will follow if no-perturbation are acting on it. A series of forces act on the satellite to perturb it away from the nominal orbit. We can classify these perturbations, or variations in the orbital elements, based on how they affect the Keplerian elements. The actual orbit of a satellite will result from a combination of these perturbations. Periodic maneouvers are needed to bring the orbit back to nominal conditions. The lifetime of a satellite is defined as the time interval that it takes to decay from its initial altitude to an altitude causing the satellite reentry down to the atmosphere. Therefore lifetime of a satellite should not be confused with the time during which the satellite will provide useful information (this operational phase, in general, is designed to last 5 - 7 years). In fact, all satellite terminating operational phases in orbits passing through the LEO region should be de-orbited or, where appropriate, manoeuvred to an orbit with suitably-reduced lifetime, that is, should be left in an orbit where drag and other perturbations will limit lifetime. The actual duration of the satellite in orbit will depend from the intensity of the perturbations which will affect its orbit. In case of satellite on GEO orbit, at the end of the operational phases they will be located on a disposal orbit, that is an orbit which do not cross the protected region. The protected region is the altitude region ranging from GEO - 200 km to GEO + 200 km and inclination region between -15 deg and +15 deg. Satellites in low Earth orbit, with perigee altitudes below 1000 km, are predominantly subject to atmospheric drag. This force very slowly tends to circularise and reduce the altitude of the orbit. The rate of 'decay' of the orbit becomes very rapid at altitudes less than 200 km, and by the time the satellite is down to 180 km it will only have a few hours to live before it makes a fiery re-entry down to the Earth.","name":"Real orbits. Life time of a satellite, orbit’s decay."},{"code":"PP1-8","description":"Mechanics is the Physics branch dealing with the behaviour of physical bodies when subjected to forces or displacements. This section provides Mechanics basic elements necessary for determining the orbits of satellites and rockets. The different satellite trajectories will be illustrated with respect to their peculiarities","name":"Basics of Mechanics"},{"code":"PP1","description":" ","name":"Basics of Electromagnetism"},{"code":"PP2-1-1","description":"Radar is an acronym for RAdio Detection And Ranging and operates in the microwave portion of the electromagnetic spectrum. Imaging radars generally operates at wavelengths from 1 mm to 1 meter. The most commonly used frequencies are: X (5,75-10,90 GHz), C (4,20-5,75 GHz), S (1,550-4,20 GHz), L (0,390-1,550 GHz), P (0,255-0,390 GHz). Longer wavelengths are mainly devoted to communication and navigation purposes. Radars penetrate atmosphere and clouds.","name":"Radar wavelengths"},{"code":"PP2-1-2-1","description":" ","name":"In-phase/Quadrature Component"},{"code":"PP2-1-2-2","description":" ","name":"Phasor (A, phi -> I/Q complex transformation)"},{"code":"PP2-1-2","description":"A complex, using complex numbers, representation of signal by two measures magnitude and phase. In the digital SAR context, a camplex number often is represented by an equivalent pair of numbers, the in-phase (I) component and the quadrature (Q) component.","name":"Complex Wave Description"},{"code":"PP2-1-3","description":"The Fourier- transformation is a fundamental method in the signal processing procedures that changes signal from space/time domain to frequency-domain.","name":"Fourier Transform"},{"code":"PP2-1-4","description":"Orientation of the plane of the electric field relative to the Earth's surface. Typical radar polarisations are: H (horizontal) and V (vertical). The polarization state of a backscattered wave from a natural surface can be linked to the geometrical characteristics like shape, roughness and orientation and the intrinsic properties of the scatterer like moisture, salinity, density.","name":"Polarisation"},{"code":"PP2-1-5","description":"Property of signal or data set in which the phase of the constituents is measurable, and plays a significant role in the way in which several signals or data combine. Two waves with a phase difference that remains constant over time, are said to be coherent.","name":"Coherent"},{"code":"PP2-1-6","description":"In radar remote sensing, the concept of phase is usually applied to the oscillation of electromagnetic waves. It is the angle phi of a complex number.","name":"Phase"},{"code":"PP2-1-7","description":"Shift in frequency caused by relative montion along the line of sight between sensor and the observed scene.","name":"Doppler effect"},{"code":"PP2-1-8","description":"The wave-particle dualism (duality) is a theory according to which all matter exhibits the attributes of waves and particles.","name":"Wave-particle dualism"},{"code":"PP2-1","description":" ","name":"EM waves"},{"code":"PP2-2-1","description":"Interaction of waves with any solid object.","name":"Diffraction"},{"code":"PP2-2-2","description":"Scattering means the redirection of incident electomagnetic energy by an object. Scattering and Diffraction refer to the same physical process - a coherent distortion of an incident wave. Emissivity is a measure of how strongly a body radiates at a given wavelength. Emission and scattering are complemetary: surfaces that are good scatterers are weak emitters, and vice versa.","name":"Scattering and emission"},{"code":"PP2-2-3","description":" ","name":"Radiometric anomalies (e.g., saturation)"},{"code":"PP2-2-4-1","description":"Mathematical expression that describes the average received signal level, compered to the additive noise level, in terms of system parameters. Principal parameters include: transmitted power, antenna gain, noise power, and radar range.","name":"Radar equation"},{"code":"PP2-2-4-2","description":"Coefficient sigma or sigma nought represents the average reflectivity of a horizontal material sample, normalized with respect to a unit area on the horizontal ground plane.","name":"backscatter coefficient sigma"},{"code":"PP2-2-4-3","description":"Gamma nought represents the average reflectivity of a horizontal material sample, normalized with respect to the incident area, orthogonal to the incident ray from the radar.","name":"gamma nought"},{"code":"PP2-2-4-4","description":"Radar brightness coefficient represents the reflectivity per unit area in slant range.","name":"beta nought (brightness)"},{"code":"PP2-2-4","description":"Measure of radar reflectivity. The Radar Cross Section (RCS) is expressed in terms of the physical size of an hypothetical uniformly scattering sphere that would give rise to the same level of reflection as that observed from the sample target.","name":"Radar cross-section"},{"code":"PP2-2-5-1","description":" ","name":"Material constants"},{"code":"PP2-2-5-2","description":" ","name":"Attenuation Constant and Penetration Depth"},{"code":"PP2-2-5","description":"The electric properties of different materials that can be described by two quantities: relative dielectric constant (complex permittivity) and loss tangent. Reflectivity of a smooth surface and the penetration of microwaves into the material are determined by these two quantities. The complex dielectric constant changes mainly die the the change in water content.","name":"Dielectric Properties"},{"code":"PP2-2-6","description":"Variation of surface height within an imaged resolution cell. The transition from smooth to rough is qualitative, and is function of both wavelength and incident angle.","name":"Surface roughness"},{"code":"PP2-2-7-1","description":" ","name":"Stokes Vector"},{"code":"PP2-2-7-2","description":" ","name":"Scattering matrix"},{"code":"PP2-2-7-3","description":" ","name":"Covariance matrix"},{"code":"PP2-2-7-4","description":" ","name":"Polarimetric decomposition"},{"code":"PP2-2-7-5","description":" ","name":"Orientational polarization of media (e.g. water)"},{"code":"PP2-2-7","description":"Measurement of the polarisation properties of an electomagnetic wave.","name":"Polarimetry"},{"code":"PP2-2","description":" ","name":"Interaction of EM radiation with matter"},{"code":"PP2-3-1-1","description":" ","name":"Antenna gain"},{"code":"PP2-3-1-2","description":" ","name":"Antenna pattern"},{"code":"PP2-3-1-3","description":" ","name":"Aperture"},{"code":"PP2-3-1","description":"Antenna is a device that radiates electromagnetic energy and collects such energy during reception.","name":"Radar antennas and antenna calibration"},{"code":"PP2-3-10-1","description":" ","name":"Stereoscopy"},{"code":"PP2-3-10-2","description":" ","name":"Radargrammetric equation"},{"code":"PP2-3-10-3","description":" ","name":"Same-side radargrammetry"},{"code":"PP2-3-10-4","description":" ","name":"Opposite-side radargrammetry"},{"code":"PP2-3-10","description":"Radargrammetry is a methodology to extract 3D geometric information from SyntheticAperture Radar (SAR) imagery - based only on backscatter. Similarly to photogrammetry, radargrammetry forms astereo model.","name":"Radargrammetry"},{"code":"PP2-3-11-1","description":"Differential SAR interferometry is an InSAR technique that provides maps of deformation of the Earth's surface and its modifications over time","name":"Differential SAR Interferometry"},{"code":"PP2-3-11-2","description":"The PS approach allows the estimation of deformation time-series related to point-wise, high coherent scatterers on the ground based on processing long sequences of SAR data","name":"Permanent Scatterer Interferometry"},{"code":"PP2-3-11-3","description":"Along Track interferometry provides information on the motion of scatterers on the ground","name":"Along-Track Interferometry"},{"code":"PP2-3-11-4","description":"Across track interferometry is based on the combination of SAR data acquired at different orbit positions spaced across-range","name":"Across-Track Interferometry"},{"code":"PP2-3-11-5","description":"SBAS is a technique for the generation of ground deformation time-series related to distributed targets: It is based on processing only a set of small baseline interferograms in order to save the coherence even over DS objects.","name":"Small Baseline Subset"},{"code":"PP2-3-11","description":"SAR interferometry is a technique involving phase measurements from successive satellite SAR images, two or more SAR images, to generate maps of surface deformation or digital elevation, using differences in the phase of the waves returning to the sensor.","name":"Interferometry (InSAR)"},{"code":"PP2-3-12","description":" ","name":"SAR tomography"},{"code":"PP2-3-2","description":"Systems measuring both amplitude and phase of the incident electromagnetic radiation.","name":"Coherent and active systems"},{"code":"PP2-3-3-1","description":" ","name":"Explanation why spatial resolution of passive radar system is much lower than that of active systems"},{"code":"PP2-3-3","description":" ","name":"Passive imaging"},{"code":"PP2-3-4-1","description":" ","name":"Altimeters"},{"code":"PP2-3-4-2","description":" ","name":"Scatterometers"},{"code":"PP2-3-4","description":" ","name":"Active imaging"},{"code":"PP2-3-5","description":"A high-resolution real aperture radar (RAR) or an antenna using synthetic aperture having antennas aimed to the right or left of the flight path. An active, all-weather, day/night remote sensor.","name":"Side-looking Airborne Radar"},{"code":"PP2-3-6","description":"A radar system with a synthetic aperture achived through computer operations. This improves the resolution in azimuth direction in propotion to aperture size.","name":"Synthetic Aperture Radar (SAR)"},{"code":"PP2-3-7-1","description":" ","name":"Azimuth"},{"code":"PP2-3-7-2","description":" ","name":"Range (Far and near)"},{"code":"PP2-3-7-3","description":" ","name":"Incident Angle"},{"code":"PP2-3-7-4","description":" ","name":"Antenna footprint"},{"code":"PP2-3-7-5","description":" ","name":"SAR spatial resolution"},{"code":"PP2-3-7","description":"The SAR image is displayed in what is called slant-range geomtery, i.e., it is based on the actual distance from the radar to each of the respective features in the scene.","name":"SAR Geometric Configuration"},{"code":"PP2-3-8-1","description":" ","name":"Roughness"},{"code":"PP2-3-8-2","description":" ","name":"Local Incident Angle"},{"code":"PP2-3-8-3","description":" ","name":"Foreshortening"},{"code":"PP2-3-8-4","description":" ","name":"Layover"},{"code":"PP2-3-8-5","description":" ","name":"Shadow"},{"code":"PP2-3-8","description":"Refelctivity is a property of illuminated objects to reradiate a portion of the incident angle. SAR backscatter, in general, is increade by greater surface roughness. The oblique imaging geometry, and the fact that the range coordinate is determined from the slant range, introduces characteristic and radiometric distortions.","name":"Terrain Reflectivity and geometric distortions"},{"code":"PP2-3-9-1","description":" ","name":"Speckle reduction (spatial, multitemporal, frequency filtering)"},{"code":"PP2-3-9","description":"It is a characteristic type granularity or image noise caused by the interference of waves reflected from many elementary scatterers.","name":"Speckle Formation"},{"code":"PP2-3","description":"Image formation is based on the time delay and strength of the return signals, which depend primarly on the roughness and dielectric properties of the surface and its range to the satellite.","name":"Detecting Microwaves / SAR Image formation"},{"code":"PP2","description":" ","name":"Radar Principles"},{"code":"PS","description":" ","name":"Platforms, sensors and digital imagery"},{"code":"PS1-1-1-1","description":"The spectral nature of light was first scientifically documented by Sir Isaac Newton in the 17th century. He refracts white light with a prism, resolving it into its component colors: red, orange, yellow, green, blue and violet.","name":"Light and color"},{"code":"PS1-1-1-2","description":"Camera Obscura, in other words \"dark chamber\", is the natural optical phenomenon that occurs when an image of a scene is projected through a small hole as a reversed and inverted image. The camera obscura, known also as a pin-hole camera, gave rise to a photographic camera when camera obscura boxes were used to expose light-sensitive materials to the projected image.","name":"Camera obscura"},{"code":"PS1-1-1-3","description":"Any emulsion capable of recording an image due to the action of light.","name":"Light-sensitive emulsions"},{"code":"PS1-1-1-4","description":"It is a strip or sheet of transparent plastic film on one side with a light-sensitive emulsion.","name":"Photographic films"},{"code":"PS1-1-1-5-1","description":"Obtain most of the aerial photography. This cameras are used to map the planimetric location and to derive topo maps.","name":"Single-lens mapping cameras"},{"code":"PS1-1-1-5-2","description":"The cameras are used for the multispectral reconnaissance.","name":"Multiple-lens cameras"},{"code":"PS1-1-1-5","description":"A camera is an optical instrument that captures images. The images are recorded on a photographic film or a digital system. It was created by replacing the pinhole in the camera obscura with a lens. The parameters that are involved in focusing a camera are: the focal length of the camera lens, the distance between the lens and the object and the distance between the lens and the image plane.","name":"Analog frame cameras"},{"code":"PS1-1-1","description":"Remote sensing sensors has its roots in the 19th century in the development of photography. Photography was an invention that made it possible to acquire a permanent image. The first photographic image was taken in 1826 by Joseph Nicephore Nieppce.","name":"History of Photography"},{"code":"PS1-1","description":"Before the space age (conventionally dated from 1957), remote sensing, was done exclusively with photographic cameras.","name":"History of Remote Sensing Sensors"},{"code":"PS1-2-1-1-1","description":"Multispectral imaging systems building the final image (line by line) exploiting the platform motion along the orbital track. No rotating mechanical part required, usually based on a CCD matrix (high spectral resolution but just up to 1 micrometer), e.g. Sentinel-2 MultiSpectral Instrument (MSI), Sentinel-3 Ocean and Land Colour Imager (OCLI).\r\n\r\nAlong track scanner, also known as a pushbroom scanner, is an optoelectronic device that obtains images with multispectral imaging system. The scanners are used for passive remote sensing. It records electromagnetic energy that is reflected (e.g., blue, green, red, and infrared light) or emitted (e.g., thermal infrared radiation) from the surface of the Earth. The scanners are mounted on space- or aircrafts. \r\nA two-dimensional image is created (line by line) by exploiting the platform motion along the orbital track. The data are collected along track using a linear array of detectors arranged perpendicular to the direction of travel. The array of detectors are pushed along the flight direction to scan the successive scan lines, and hence the name pushbroom scanner. \r\nThere are no moving parts on a pushbroom sensor, hence, the scanning speed can be increased compared to across track systems. A longer dwell time over each ground resolution cell increases the signal strength (high radiometric resolution, no pixel distortion). Additionally, finer spatial and spectral resolution can be achieved as the size of the ground resolution cell is determined by the Instantaneous Field of View (IFOV) of a single detector. The systems are designed for high-resolution imaging. However, a very large number of detectors is needed for high resolution images. It is a complex optical system. In addition, the pushbroom scheme requires a wide Field of View (FOV) optics system to obtain the same swath as for a corresponding whiskbroom (across track) scanner. It has narrow swath width. \r\nThe detector arrays with such a line-scanning pushbroom system are usually of the type Charge-Coupled Device (CCD).\r\nThe MultiSpectral Instrument (MSI) on board of the Sentinel-2 satellite (Copernicus mission) uses a pushbroom concept.","name":"Along track scanners (Pushbroom, optoelectornic scanners)"},{"code":"PS1-2-1-1-2","description":"Multispectral imaging systems building the final image (ground cell by ground cell) by combination of the platform motion along the orbital track with a mechanical rotation of the collecting optic in the across track direction. Opto-mechanical are typically multi-spectral radiometers (no limitation on bands), whiskbroom systems are usually CDD spectrometers (high spectral resolution but just up to 1 micrometer). Examples of the sensors: Landsat Multispectral Scanner (MSS), Landsat Thematic Mapper (TM).","name":"Across track scanners (Whiskbroom, electromechanical scanners)"},{"code":"PS1-2-1-2-1","description":"The cameras, usually a charge-coupled device (CCD) or Complimentary Metal Oxide Semiconductor (CMOS), that convert light into electrons that can be measured and converted into radiometric intensity value.","name":"Digital Frame Camera"},{"code":"PS1-2-1-2","description":"2-D systems with the ability to observe in two dimensions simultaneously.","name":"Area Arrays"},{"code":"PS1-2-1","description":"In principle, one-dimensional systems, whisk- and pushbroom, that form an image on a line-by-line basis in the scan direction.","name":"Line detector arrays"},{"code":"PS1-2-2-1-1","description":"Thermal radiometers are radiometers with the capability of measuring the spectrum of infrared emission. As such, they are characterized by a relatively high spectral resolution (normally better than 1 cm-1 in wave number units). Modern Spectrometers on board satellites have a spectral resolution better than 0.7 cm -1 in order to properly resolve CO2 lines used for the retrieval of the atmospheric temperature profile. Based on the optical layout they are further classified in grating spectrometers and Fourier Transform Spectrometers or FTIR.","name":"Thermal Radiometers"},{"code":"PS1-2-2-1-2","description":"Passive microwave radiometers are radiometers that measures energy emitted at millimetre-to-centimetre wavelengths at 0.15 - 30 cm (frequencies of 1–200 GHz). Example of a sensors: SMOS Microwave Imaging Radiometer with Aperture Synthesis (MIRAS)","name":"Passive Microwave Radiometers"},{"code":"PS1-2-2-1-3","description":"An advanced multispectral sensor that detects hundreds of very narrow spectral bands throughout the visible, near-infrared, and mid-infrared portions of the electromagnetic spectrum.","name":"Hyperspectral Radiometers"},{"code":"PS1-2-2-1-4","description":"A radiometer that measures the intensity of radiation in multiple wavelength bands (i.e., multispectral). Example of a sensor Moderate Resolution Imaging Spectroradiometer (MODIS)","name":"Spectroradiometers"},{"code":"PS1-2-2-1","description":"Imaging radiometers are used to spatially map the variation of radiation. Operate primarily at window frequencies, where atmospheric absorption is low and surface features can be imaged or measured.","name":"Imaging sensors"},{"code":"PS1-2-2-2","description":"Provide information about vertical profiles of temperature and molecular consistuent concentrations in the atmosphere (atmospheric sounders).","name":"Sounders (atmospheric sounders)"},{"code":"PS1-2-2","description":"Radiometers are instruments which measure radiative intensities. A radiometer is further identified by the portion of the electromagnetic radiation it covers, usually the infrared or microwave regions. Normally the spectral range extends from the longwave (14-15 micron) to the shortwave (3-4 micron). This range overlaps much of the emission spectrum of Earth. The technology is classified in broadband radiometer of spectral radiometers depending on the spectral resolution.","name":"Radiometers"},{"code":"PS1-2","description":"Passive remote sensing systems record electromagnetic energy that is reflected (e.g., blue, green, red, and infrared light) or emitted (e.g., thermal infrared radiation) from the surface of the Earth.","name":"Passive Sensors"},{"code":"PS1-7","description":"An optical spectrometer is an instrument used to detect, measure and analyze the spectral content of the incident electromagnetic field (narrow-band, VIS, NIR, SWIR and TIR). This information is used to identify the chemical composition of the object being sensed. ","name":"Spectrometers"},{"code":"PS1-3-1-1-1","description":"PP1-1 WG4","name":"Radar antenna"},{"code":"","description":"","name":""},{"code":"","description":"","name":""},{"code":"","description":"","name":""},{"code":"","description":"","name":""},{"code":"PS1-3-1-1","description":"An imager that uses microwave wavelengths to illuminate an area on the ground. A typical radar system measures the strength and roundtrip time of the microwave signals that are emitted by a radar antenna and reflected off a target area. The sensors can be mounted on airborne, spaceborne und UVs platforms.","name":"Imaging Radar"},{"code":"PS1-3-1","description":"Active sensors that provides its own elctromagnetic energy to illuminate an area on the ground.","name":"Imagers"},{"code":"PS1-3-2-1-1","description":"It is the simplest application of the LIght Detection And Ranging technique. It transmits a short pulse of energy (visible or near-infrared radiation) and detects 'echo', by measuring the time delay and knowing the speed of propagation of the pulse, the range from the instrument to the surface can be measured.","name":"Laser profilers (LiDAR)"},{"code":"","description":"","name":""},{"code":"","description":"","name":"PARIS"},{"code":"","description":"","name":""},{"code":"","description":"","name":""},{"code":"PS1-3-2-1-2-5","description":"CH4 Atmospheric Remote Monitoring","name":"CHARM"},{"code":"","description":"","name":""},{"code":"","description":"","name":""},{"code":"PS1-3-2-1-3-1","description":"https://directory.eoportal.org/web/eoportal/satellite-missions/i/icesat-2#sensors","name":"ATLAS (Advanced Topographic Laser Altimeter System)"},{"code":"PS1-3-2-1-4","description":"The radar altimeter is similar in operation to the laser profiler, it operates at a much longer wavelength.","name":"Radar altimeters"},{"code":"PS1-3-2-1-5-1","description":"https://directory.eoportal.org/web/eoportal/satellite-missions/e/envisat","name":"RA-2 (Radar Altimeter-2)"},{"code":"PS1-3-2-1","description":" ","name":"Altimeters"},{"code":"PS1-3-2","description":"Active sensors that measure the time delay between transmission and reception of the signal.","name":"Ranging Systems"},{"code":"PS1-3-3","description":"Instruments that measure vertical distribution of precipitation and other atmospheric characteristics such as temperature, humidity, and cloud composition.","name":"Sounders"},{"code":"","description":"","name":""},{"code":"PS1-3-4-1","description":"Surface basckatter is measured as a function of the frequency, polarization, and illumination direction of the sensing signal (microwaves).","name":"Radar Scatterometers"},{"code":"PS1-3-4-2-1","description":" ","name":"Differential Absorption Lidar"},{"code":"PS1-3-4-2-2","description":" ","name":"Doppler Wind Lidar"},{"code":"PS1-3-4-2","description":"The information on an observed target is derived from the energy backscattered, reflected or reradiated by the target.","name":"Backscatter Lidar"},{"code":"PS1-3-4","description":"Scatterometers are used to measure very accuratly surface back scatter as a function of backscattered radiation.","name":"Scatterometers"},{"code":"PS1-3","description":"Active remote sensing systems produce their own electromagnetic energy, transmits and receives the radiation that is reflected or backscattered from the illuminated object.","name":"Active Sensors"},{"code":"PS1","description":"A remote sensing sensor, such as optical images or radar sensors, acquire information about an object of interest by detecting and measuring the changes that the object imposes on the surrounding electromagnetic field. The sensor is usually flown on a platform and is always equipped with other electronic devices.","name":"Remote Sensing Sensors"},{"code":"PS2-1-1","description":"History of the development of remote sensing platforms from baloons, kites, rockets, pigeons, gliders, etc. to small cubes.","name":"First remote sensing platforms"},{"code":"PS2-1-2","description":"Aerial photos used for the military purposes, mainly to make accurate maps and based on that to prepare a military strategy.","name":"Photo-reconnaissance"},{"code":"PS2-1-3","description":"Satellite images used for the military purposes, mainly to make accurate maps and based on that to prepare a military strategy.","name":"Satellite reconnaissance"},{"code":"PS2-1","description":"This topic covers information on the first remote sensing platforms that were used to obtain aerial photos. The first-known aerial photograph was obtained in 1858 by Gaspard Felix Tournachon (Nadar).","name":"History of Remote Sensing Platforms"},{"code":"PS2-2-1","description":"An unmanned aircraft system (UAS) icludes an unmanned aerial vehicle (UAV) (commonly known as a drone), an aircraft without a human pilot on board, a ground-based controller, and a system of communications between the two.","name":"Unmanned Aerial Systems"},{"code":"PS2-2-2-1","description":"Planning of an aerial photography mission taking into account time of day/sun angle, weather conditions, flightline.","name":"Mission planning"},{"code":"PS2-2-2-3-2-1","description":"Geostationary orbit is the one in which the time required for the satellite to cover one revolution is the same as that for the Earth to rotate once about its polar axis. In order to achieve this orbit period, geo-synchronous orbits are generally at very high altitude, nearly 36,000 km.","name":"Geostationary"},{"code":"PS2-2-2-3-2-3-1","description":"Antenna pointing is fixed relative to the flight line (coarse-resolution data).","name":"Stripmap"},{"code":"PS2-2-2-3-2-3-2","description":"The sensor steers its antenna beam to continuously illuminate a specific spot (high resolution data).","name":"Spotlight"},{"code":"PS2-2-2-3-2-3-3-1","description":"https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-1-sar/sar-instrument/acquisition-modes","name":"Interferometric Wide Swath Mode"},{"code":"PS2-2-2-3-2-3-3-2","description":"https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-1-sar/sar-instrument/acquisition-modes","name":"Extra Wide Swath Mode"},{"code":"PS2-2-2-3-2-3-3","description":"The sensor steers the antenna beam to illuminate a strip of terrain at any angle to the path of aircraft motion.","name":"ScanSAR"},{"code":"PS2-2-2-3-2-3-4","description":"It is possible to derive accurate topographic information from multiple SAR (synthetic aperture radar) images of a single region. In a broad sense, SAR interferometry is comparable to the use of stereo photography to determine topography of a region by observation from two different perspectives. However, SAR interferometry is applied not in the optical domain of photogrammetry, but in the realm of radar geometry, to exploit radar's ranging capability. Because SARs can accurately measure differences in slandt-range distances to the same feature from separate observation points, the technique can provide very accurate topographic information.","name":"Interferometric Synthetic Aperture Radar (InSAR)"},{"code":"PS2-2-2-3-2-3-5","description":"A stereoscopy acquisition mode collects remotely sensed data where each location on the ground (or the imaged objects) is covered multiple times (at least twice), from different perspectives. Stereopairs and stereoscopic coverage enable the extraction of 3D representations of the environment from remotely sensed imagery. Most aerial photographs are taken with frame cameras along flight lines, or flight strips. [...] Successive photographs are generally taken with some degree of endlap [, i.e. overlap]. Not only does this lapping ensure total coverage along a flightline, but an endlap of at least 50 percent is essential for total stereoscopic coverage of a project area. Stereoscopic coverage consists of adjacent pairs of overlapping vertical photographs called stereopairs. Stereopairs provide two different perspectives of the ground area in their region of endlap [overlap]. When images forming a stereopair are viewed through a stereoscope, each eye psychologically occupies the vantage point from which the respective image of the stereopair was taken in flight. The result is the perception of a three-dimensional stereomodel. As an input to photogrammetry analysis procedures, stereopairs from flight strips enable the extraction of digital elevation models (DEM), orthophotos, thematic GIS data, and other derived products through the use of digital raster images and relatively sophisticated analytical techniques. With the availability of close-range UAV and terrestrial hand-held camera data, 3D reconstructions of buildings (even indoors) and other objects on the terrain surface become possible.","name":"Stereoscopy"},{"code":"PS2-2-2-3-2-3","description":"This is an orbit with high inclination angles with an sub-orbital track that almost goes across the poles. Polar orbiting provides global (or near-global) coverage and measurement frequency can vary from 1 measurement per day to 1 per month. Their altitudes are in the range 400-1000 km, which is considerably lower than the 35,800 km of a geostationary satellite. The figure below shows the path of a typical near-polar orbit.","name":"Polar (near-polar) orbit"},{"code":"PS2-2-2-3-2-5","description":"Does not provide global coverage. The measurement frequency can vary every few hours to a few weeks.","name":"Non-Polar orbit"},{"code":"PS2-2-2-3-2-6","description":"A low Earth orbit is normally at an altitude of less than 1000 km and could be as low as 160 km above the Earth. Satellites in this circular orbit travel at a speed of around 7.8 km per second. At this speed, a satellite takes approximately 90 minutes to circle the Earth. In general, these orbits are used for remote sensing, military purposes and for human spaceflight as they offer close proximity to the Earth’s surface for imaging and the short orbital periods allow for rapid revisits. The International Space Station is in low Earth orbit.","name":"Low Earth orbit"},{"code":"PS2-2-2-3-2-7","description":"This orbit takes place at an altitude of around 1000 km and is particularly suited for constellations of satellites mainly used for telecommunications. A satellite in this orbit travels at approximately 7.3 km per second.","name":"Medium low Earth orbit"},{"code":"PS2-2-2-3-3-4","description":"A sun synchronous orbit is a near-polar orbit whose altitude is such that the satellite will always pass over a location at a given latitude at the same local solartime. In this way, the same solar illumination condition can be achieved for the images of a given location taken by the satellite.","name":"Sun-synchronous"},{"code":"PS2-2-2","description":"Since the 1940s aerial imagery has been the primary source of detailed geospatial data for extensive study areas. Photogrammetry is the profession concerned with producing precise measurements from aerial imagery. Aerial imaging and photogrammetry comprise a major component of the geospatial industry. The topics included in this unit do not comprise an exhaustive treatment of photogrammetry, but they are aspects of the field about which all geospatial professionals should be knowledgeable.","name":"Airborne platforms and systems"},{"code":"PS2-2-3-1","description":"Earth observation (EO) missions are gathering information about the physical, chemical, and biological systems of the planet via remote-sensing technologies, supplemented by Earth-surveying techniques, which encompasses the collection, analysis, and presentation of satellite data.","name":"Earth observation missions"},{"code":"PS2-2-3-2","description":"There are essentially three types of Earth orbits: high, medium and low Earth orbit. Satellites that orbit in a medium (mid) Earth orbit include navigation and specialty satellites, designed to monitor a particular region. Most scientific satellites, including NASA’s Earth Observing System fleet, have a low Earth orbit.","name":"Type of satellite orbits"},{"code":"PS2-2-3-3","description":"Modes in which the data are acquired.","name":"Acquisition modes"},{"code":"PS2-2-3-4","description":"Swath width refers to the width of the ground that the satellite collects data from on each orbit. The area imaged on the surface, is referred to as the swath. Imaging swaths for spaceborne sensors generally vary between tens and hundreds of kilometres wide.","name":"Swath"},{"code":"PS2-2-3","description":"Spaceborne platforms and systems are present at a great height from the earth surface. The altitude of platforms range from few hundred kilometers to several thousand kilometers. A large area can be captured in a single scene depending on altitude and resolution of sensor. The platforms can have different characteristics.","name":"Spaceborne platforms and systems"},{"code":"PS2-2","description":" ","name":"Moving Platforms"},{"code":"PS2-3-1","description":"Field spectroradiometers are a powerful tool for monitoring and upscaling vegetation physiology and carbon and water fluxes. Usually, full-range spectroradiometers which delivers the spectral field measurements available from any commercial field-portable spectroradiometer.","name":"Field spectroradiometers"},{"code":"PS2-3-2","description":"Terrestrial laser scanning (TLS) is a ground-based, active imaging method that rapidly acquires accurate, dense 3D point clouds of object surfaces by laser range finding.","name":"Terrestrial LiDAR"},{"code":"PS2-3","description":"remote sensing in-situ measurements, e.g., LAI, for cal/val","name":"Static/Man-portable Platforms"},{"code":"PS2","description":"Can be static or moving, it carries a remote sensing sensor, it operates in near (few centimetres) and far (36,000 kilometres) altitudes ranges.","name":"Remote Sensing Platforms and Systems"},{"code":"PS3-1","description":" ","name":"History of remote sensing data carriers"},{"code":"PS3-2-1","description":"Image pixels are normally square and represent a certain area on an image. It is important to distinguish between pixel size and spatial resolution - they are not interchangeable. If a sensor has a spatial resolution of 20 metres and an image from that sensor is displayed at full resolution, each pixel represents an area of 20m x 20m on the ground. In this case the pixel size and resolution are the same.","name":"Picture element (pixel)"},{"code":"PS3-2-2","description":"An image is an array, or a matrix, of square pixels (picture elements) arranged in columns and rows. In a (8-bit) greyscale image each picture element has an assigned intensity that ranges from 0 to 255.","name":"Image as a matrix (digital number DN)"},{"code":"PS3-2","description":"A digital image is a representation of a real image as a set of numbers that can be stored and handled by a digital computer. In order to translate the image into numbers, it is divided into small areas called pixels (picture elements).","name":"Digital image terminology"},{"code":"PS3-3-1","description":"Band interleaved by line (BIL) is one of three primary methods for encoding image data for multiband raster images in the geospatial domain, such as images obtained from satellites. BIL is not in itself an image format, but is a scheme for storing the actual pixel values of an image in a file band by band for each line, or row, of the image. For example, given a three-band image, all three bands of data are written for row one, all three bands of data are written for row two, and so on. The BIL encoding is a compromise format, allowing fairly easy access to both spatial and spectral information. The BIL data organization can handle any number of bands, and thus accommodates black and white, grayscale, pseudocolor, true color, and multi-spectral image data.","name":"Band interleaved by line (BIL)"},{"code":"PS3-3-2","description":"Band interleaved by pixel (BIP) is one of three primary methods for encoding image data for multiband raster images in the geospatial domain, such as images obtained from satellites. BIP is not in itself an image format, but is a method for encoding the actual pixel values of an image in a file. Images stored in BIP format have the first pixel for all bands in sequential order, followed by the second pixel for all bands, followed by the third pixel for all bands, etc., interleaved up to the number of pixels. The BIP data organization can handle any number of bands, and thus accommodates black and white, grayscale, pseudocolor, true color, and multi-spectral image data.","name":"Band interleaved by pixel (BIP)"},{"code":"PS3-3-3","description":"A binary raster file format for aerial photography, satellite imagery, and spectral data. The BSQ data organization can handle any number of bands, and thus accommodates black and white, grayscale, pseudocolor, true color, and multi-spectral image data. Additional information is needed to interpret the image data, such as the numbers of rows, columns, and bands, if there is a color map, and latitude and longitude to relate the image to geospatial locations.","name":"Band sequential (BSQ)"},{"code":"PS3-3","description":"Data storage consists of methods of organizing image data for multiband images.","name":"Data storage"},{"code":"PS3-4-1","description":"Spectral resolution describes the ability of a sensor to define fine wavelength intervals. The narrowest spectral interval that can be resolved by an instrument. Spectral resolution (spectral capability) also refers to the number of wavebands within the EM spectrum that an optical sensor is taking measurements over.","name":"Spectral resolution"},{"code":"PS3-4-2-1","description":"The beam sent out by the radar antenna (SAR, SLAR) illuminates an area on the targeted object. The footprint of an antenna is traditionally defined to be the area on the surface within the field of view subtended by the beamwidth of the antenna gain pattern.","name":"Footprint"},{"code":"PS3-4-2-2","description":"WG4","name":"Range resolution"},{"code":"PS3-4-2-3","description":"PP1-1 WG4","name":"Azimuth resolution"},{"code":"PS3-4-2-4","description":"The field of view (FoV) is the extent of the entire observable area by the sensor.","name":"Field of View (FoV)"},{"code":"PS3-4-2-5","description":"The instantaneous field of view (IFOV) defines the smallest area viewed by the sensor and establishes a limit for the level of spatial detail that can be represented in a digital image. It determines the area on the Earth's surface which is \"seen\" from a given altitude.","name":"Instantaneous field of view (IFOV)"},{"code":"PS3-4-2-6","description":"Satellite data with very high resolution are defined by a spatial resolution at about 1 m.","name":"Very high resolution data"},{"code":"PS3-4-2-7","description":"Satellite data with high resolution are defined by spatial resolution of about tens of meters.","name":"High resolution data"},{"code":"PS3-4-2-8","description":"Satellite data with medium resolution are defined by spatial resolution at hundrets of meters.","name":"Medium resolution data"},{"code":"PS3-4-2-9","description":"Satellite data with low resolution are defined by spatial resolution at several kilometers.","name":"Low resolution data"},{"code":"PS3-4-2","description":"Spatial resolution is the size of the smallest object that can be seen on the Earth. Spatial resolution of the sensor refers to the size of the smallest possible feature that can be detected on the scene (this is mostly the Earth's surface).","name":"Spatial resolution"},{"code":"PS3-4-3","description":"Radiometric resolution can be defined as the ability of an imaging system to record many levels of brightness. Radiometric resolution refers to the range in brightness levels that can be applied to an individual pixel within an image, determined on a grayscale. E.g., Sentinel-2 sensor MSI is a 12 bit sensor imaging with 4.096 levels.","name":"Radiometric resolution"},{"code":"PS3-4-4","description":"Temporal resolution, also referred to as the revisit cycle, is defined as the amount of time it takes for a satellite to return to collect data from exactly the same location on the Earth. Imageing of the exact same area at the same viewing angle a second time is temporal resolution.","name":"Temporal resolution"},{"code":"PS3-4","description":"A digital image begins as an analog signal. Through computer data processing, the image becomes digitized and is sampled multiple times. The critical characteristics of a digital image are spatial resolution, spectral resolution, radiometric resolution, contrast resolution, noise, and dose efficiency. These depends upon satellite orbit configuration and sensor design. Different sensors have different resolutions.","name":"Properties of digital imagery"},{"code":"PS3-5-1","description":"The header is a section of binary- or ASCII-format data normally found at the beginning of the file, containing information about the bitmap data found elsewhere in the file. The format of the header and the information stored in it varies considerably from format to format and contains fixed fields.","name":"Header file"},{"code":"PS3-5","description":" ","name":"Image description files"},{"code":"PS3-6-1","description":"A computer-generated image of an aerial photograph in which the image displacement caused by terrain relief and camera tilt has been removed.","name":"Digital Orthophoto Quadrangle DOQ"},{"code":"PS3-6-10","description":"Point Cloud format","name":".LAZ"},{"code":"PS3-6-11","description":"Formats related to the software used. (to be further discussed)","name":"Software related formats"},{"code":"PS3-6-2","description":"Hierarchical Data Format-Earth Observing Systems (HDF-EOS) is a software library designed to support NASA Earth Observing System (EOS) science data, e.g., Landsat, Terra/Aqua","name":"HDF-EOS"},{"code":"PS3-6-3","description":"Georeferenced Tagged Image File Format (GeoTIFF) is an open file format and de facto standard based on the TIFF format and is used as an interchange format for georeferenced raster imagery. For example, data acquired by the Landsat can be obtained in this format.","name":"GeoTIFF"},{"code":"PS3-6-4","description":"A highly compressed imagery in a proprietary format.","name":"MrSID"},{"code":"PS3-6-5","description":"Format of the SPOT 1-4","name":"CAP"},{"code":"PS3-6-6","description":"A format for SPOT products, introduced for the SPOT 5. The DIMAP format is a public format for describing geographic data. Although it was specially designed for image data, it can also handle vector data. SPOT products in DIMAP format now consist of two parts, one for the image and the other for a description of the image","name":"DIMAP"},{"code":"PS3-6-7","description":"Standard data format for radar data","name":"CEOS"},{"code":"PS3-6-8","description":"network Common Data Form","name":"netCDF"},{"code":"PS3-6-9","description":"Format of the Sentinel data","name":"SAFE"},{"code":"PS3-6","description":"Remote Sensing data formats in which the data are organized and stored.","name":"Data Formats"},{"code":"PS3-7-1-1-1-1","description":"Top-Of-Atmosphere (TOA) radiances in sensor geometry or in cartographic geometry.","name":"Top-Of-Atmosphere (TOA)"},{"code":"PS3-7-1-1-1-2","description":"Bottom-Of-Atmosphere (BOA) reflectances in cartographic geometry.","name":"Bottom-Of-Atmosphere (BOA)"},{"code":"PS3-7-1-1-1","description":" ","name":"Atmospheric corrected"},{"code":"PS3-7-1-1","description":"Depending on the sensor and the provider, remotely sensed imagery is made avalilable to the user at different processing levels. For Sentinel-2, the lowest product level made available to the user is Level-1B. THe Level-1B product provides radiometrically corrected imagery in Top-Of-Atmosphere (TOA) radiance values and in sensor geometry. Radiometric corrections applied to the Level-1B are: dark signal, pixels response non uniformity, crosstalk correction, defective pixels interpolation, high spatial resolution bands restoration (deconvolution puls denoising), binning (spatial filtering) for 60m bands. (Sentinel-2 User Handbook, p.44)","name":"Radiometrically corrected"},{"code":"PS3-7-1-2-1","description":" ","name":"Orthophotos"},{"code":"PS3-7-1-2","description":"Geometrically corrected products are of a higher processing level than radiometrically corrected products. For Sentinel-2, the geometrically corrected product is the Level-1C product. The Level-1C product results from using a Digital Elevation Model (DEM) to project the image in cartographic coordinates. Per-pixel radiometric measurements are provided in Top Of Atmosphere (TOA) reflectances with all parameters to transform them into radiances. Level-1C products are resampled with a constant Ground Sampling Distance (GSD) of 10, 20 and 60 m depending on the native resolution of the different spectral bands. Level-1C products will additionally include Land/Water, Cloud Masks and ECMWF data (total column of ozone, total column of water vapour and mean sea level pressure). (Sentinel-2 User Handbook, p.44)","name":"Geometrically corrected"},{"code":"PS3-7-1","description":"Proccesing levels in which optical data is made available.","name":"Optical data"},{"code":"PS3-7-2-1","description":"data with azimuth compression using the full azimuth bandwidth of the sensor, each pixel is a complex number (i.e., has a real and imaginary component) that represents the amplitude and phase","name":"Single Look Complex (SLC)"},{"code":"PS3-7-2-2","description":"multi-look intensity, not geocoded","name":"Multi-looked Detected (MLD)"},{"code":"PS3-7-2-3","description":"squre pixels, not geocoded","name":"Precision Images (PRI)"},{"code":"PS3-7-2-4","description":"ground range intensity","name":"Groud Range Detected (GRD)"},{"code":"PS3-7-2","description":"Proccesing levels in which SAR data is made available.","name":"SAR data"},{"code":"PS3-7-3","description":"Raw instrument data that may be time-referenced. The most difficult to use.","name":"Level 0&1"},{"code":"PS3-7-4","description":"Level 1 data converted into a geophysical quantity. Data geo-referenced and calibrated.","name":"Level 2"},{"code":"PS3-7-5","description":"Level 2 data that has been mapped on a uniform space-time grid, quality controlled.","name":"Level 3"},{"code":"PS3-7-6","description":"Level 3 data combined with models or other instrument data.","name":"Level 4"},{"code":"PS3-7-7","description":"Data that have been processed to allow direct data analysis. User processing effort is reduced to a minimum.","name":"Analysis Ready Data (ARD)"},{"code":"PS3-7","description":" ","name":"Processing levels"},{"code":"PS3","description":" ","name":"Remote Sensing data and imagery"},{"code":"PS4","description":"Links to the databases of the past, operational and future remote sensing platforms and sensors.","name":"Satellite and Airborne Sensors and Missions Databases"},{"code":"SD","description":"Based on Waldo Tobler`s first law of geography( Tobler, 1970), this property is set on the principle that \"everything is related, but that which is closer is more closely related\".","name":"Spatial dependency"},{"code":"SH","description":"This principle, as set forth by Anselin, determines that \"expectations vary along the earth`s surface\" which means that any spatial analysis is dependent explicitly on the borders of study fields, i.e. the tracing of (spatial) analysis units.","name":"Spatial heterogeneity"},{"code":"TA","description":" ","name":"Thematic and application domains"},{"code":"TA1-1-1-1-1","description":" ","name":"PM10"},{"code":"TA1-1-1-1-2","description":" ","name":"PM2.5"},{"code":"TA1-1-1-1-3","description":" ","name":"O3"},{"code":"TA1-1-1-1-4","description":" ","name":"SO2"},{"code":"TA1-1-1-1-5","description":" ","name":"NO2"},{"code":"TA1-1-1-1","description":"The validated annual assessment reports (AAR) describe the air quality situation that occurred two years ago and is based on CAMS air quality model results combined with validated observations provides by the European monitoring networks. Interim versions of the assessment report are available that have not been fully validated","name":"air quality assessment reports"},{"code":"TA1-1-1","description":"helping policy makers to manage and improve air quality on European, national and local level","name":"ensuring compliance to air quality regulations"},{"code":"TA1-1-2-1-1","description":"PM10, PM2.5, O3, SO2, NO2","name":"air quality evaluation components"},{"code":"TA1-1-2-1","description":"state of air quality improvement in 2020 and 2030 based on combination of daily simulations of primary pollutants and precursors (in terms of ozone and particle matter pollution) with the CHIMERE Chemistry-Transport Model at a spatial resolution of 0.25 degree over Europe","name":"air quality situation forecasts"},{"code":"TA1-1-2","description":"policy makers on European, national and local level need information about the best possibilities for improving air quality by comparing air quality forecasts of different emission scenarios","name":"developing emission reduction strategies"},{"code":"TA1-1-3-1","description":" ","name":"air quality forecast maps"},{"code":"TA1-1-3","description":"People that are sensitive to low air quality conditions (due to allergies etc.) and their medical services (pneumologists, pharmacists etc.) can benefit from easy access to air quality forecast maps and associated health recommendations to adapt their behaviour","name":"minimize exposure to low air quality conditions"},{"code":"TA1-1-4-1","description":"atmospheric particles and derived key indicators of aircraft-relevant atmospheric conditions: abrasion, clogging, and corrosion.","name":"atmospheric particle maps and forecasts"},{"code":"TA1-1-4","description":"airspace control and monitoring of aeroplanes’ exposure to harmful particles, ","name":"assessing atmospheric particles hazardous to aeroplanes"},{"code":"TA1-1","description":"User group interested in information about air quality: Policy makers that want to manage and improve air quality, people sensitive to low air quality conditions that want to minimize their exposure to harmful air quality conditions, airlines and aircraft manufacturers that want to reduce negative effects of atmospheric particles on aeroplanes ","name":"Air quality"},{"code":"TA1-2-1-1","description":"by using climatologies (historical time series) of global and direct irradiance from satellites and CAMS models that incorporate data on clouds, aerosol particles, ozone molecules and water vapour","name":"historic irradiance time series"},{"code":"TA1-2-1","description":"supporting the planning of solar energy installations, from solar farms to rooftop panels, ","name":"planning solar energy installations"},{"code":"TA1-2","description":"User group interested in the solar energy industry: policy makers and companies from the energy sector, particularly the renewable energy sub-sector, that install large solar farms or rooftop installations","name":"Solar energy"},{"code":"TA1-3-1-1","description":"available is also a historical record from 2003 to the present based on CAMS global reanalysis that combines observations with the CAMS global models describing the composition of the atmosphere","name":"daily ozone forecast map"},{"code":"TA1-3-1","description":"For ensuring successful execution of the \"Montreal Protocol on Substances that Deplete the Ozone Layer\" of 1989, an international treaty to protect the ozone layer, policy makers need information about the amounts of ozone and related chemical species in the stratosphere","name":"monitoring stratospheric ozone on a daily basis"},{"code":"TA1-3-2-1","description":"a daily measurement indicating the level of UV exposure for life on the Earth surface","name":"UV-index forecast map"},{"code":"TA1-3-2","description":"monitoring and forecasting the amount of UV radiation reaching the surface of the Earth, taking into account the effects of ozone, clouds, and aerosol particles, receiving daily alerts on ultraviolet radiation peaks to prevent skin cancer","name":"monitoring and forecasting the amount of UV radiation"},{"code":"TA1-3","description":"User group interested in information about the ozone layer and UV radiation, relates to environmental protection and health: Policy makers on the national and European level (and in other regions worldwide) dealing with ozone layer protection regulations, people in sensitive regions (e.g. Australia) that want to minimize their UV radiation exposure to prevent skin cancer","name":"Ozone layer and UV radiation"},{"code":"TA1-4-1-1","description":"as an input to atmospheric chemistry transport models and forecast models. Inventories are based on a combination of existing data sets and new information, describing emissions from fossil fuel use, ships, volcanoes and vegetation. This ensures good consistency between the emissions of greenhouse gases, reactive gases, and aerosol particles and their precursors. The CAMS emission inventories also extend existing data sets forward in time, producing timely input data for the forecast models.","name":"inventory of emission data"},{"code":"TA1-4-1-2","description":"estimating net fluxes of CO2, N2O and CH4 at Earth's surface using satellite and in-situ observations","name":"net flux estimation of greenhouse gases"},{"code":"TA1-4-1-3","description":"estimating emissions of aerosols, chemical species, and greenhouse gases from biomass burning and wildfires on a daily basis using satellite observations","name":"emission estimates for burning vegetation"},{"code":"TA1-4-1","description":"accurate information about the atmospheric composition (78% nitrogen, 21% oxygen, 0.9% argon, 0.04% carbon dioxide and a few other trace components) is required to account for human-induced and natural exchange processes between the Earth's surface and the atmosphere in air quality forecast models as well as climate models.","name":"describing the atmospheric composition"},{"code":"TA1-4","description":"Scientists that develop air quality models and climate models, ","name":"Emissions and surface Fluxes"},{"code":"TA1-5-1-1","description":"estimates of the climate forcing of aerosol, ozone and greenhouse gases.","name":"climate forcing estimates of atmospheric components"},{"code":"TA1-5-1","description":"Climate forcing, also known as Radiative Forcing, therefore determines the change in globally-averaged temperature change due to the natural or human-induced changes to the energy budget, e.g. due to human-induced changes to the atmospheric composition","name":"determining the change in globally-averaged temperature change"},{"code":"TA1-5","description":"User group of climate policy makers. Radiative forcing is a useful predictor of globally-averaged temperature change. The Intergovernmental Panel on Climate Change (IPCC) supports climate policy making by assessing scientific findings from climate researchers.","name":"Climate forcing"},{"code":"TA1","description":"Atmosphere monitoring targets air quality, solar energy, ozone layer and UV radiation, emissions and surface fluxes as well as climate forcing.","name":"Atmosphere Monitoring"},{"code":"TA2-1-1-1","description":"hydrological seasonal re-forecasts and climate change impact projections","name":"impact projections on flood frequency and water availability"},{"code":"TA2-1-1","description":"adapt their strategies in order to mitigate the effects of climate change, to prepare for climate variability and change in the water sector, e.g. because of changes in river discharge, droughts and floods","name":"mitigate effects of changing precipitation patterns"},{"code":"TA2-1","description":"water managers in the fields of, for instance, water allocation, flood management, ecological status and industrial water use","name":"Water management"},{"code":"TA2-10","description":"to facilitate climate adaptation worldwide","name":"Global users"},{"code":"TA2-2-1-1","description":"Near-real-time data will help make day-to-day farming decisions and to assess crop status.","name":"near-real-time crop status"},{"code":"TA2-2-1-2","description":"Seasonal forecasts support more systemic adaptive decisions, such as choosing crop varieties, crop–grazing balancing, commodity trading and food emergency preparedness. ","name":"seasonal crop yield forecasts"},{"code":"TA2-2-1-3","description":"Past and future climate normals, documenting climate change are required to support transformational decisions, such as breeding new crop varieties, investments in irrigation or relocation of production areas.","name":"past and future climate normals"},{"code":"TA2-2-1","description":"predict how changes in plant growth will affect crop yield, and ultimately return on investment in the agri-forestry sector","name":"planning for changes in plant growth and food production"},{"code":"TA2-2","description":"agri-forestry sector, Food security is a fundamental precondition for human well-being and the agricultural and food sector is of major economic importance. Weather and climate data are essential in managing many aspects of food security. Farmers and food security policy makers need near-real-time and seasonal forecasts for crop yields, and past and future climate normals for day-to-day decisions, seasonal planning and long-term transformational decisions. Forestry has similar needs for information about climate changes in tree growth.","name":"Agriculture and Forestry"},{"code":"TA2-3-1-1","description":"up-to-date catalogue of wind storms and their associated impacts on the ground","name":"up-to-date catalogue of wind storms"},{"code":"TA2-3-1","description":"the assessment of the weather risks associated with specific assets that change with the climate-related increase in extreme weather conditions","name":"assessing the weather risks to insured assets"},{"code":"TA2-3","description":"The insurance sector includes insurers, reinsurers and insurance industry service providers. They use climate change related EO information for developing, running and analysing risk models","name":"Insurance"},{"code":"TA2-4-1-1-1","description":"The energy-relevant climate indicators are Temperature, Precipitation, Wind (10 m and 100 m), Solar Radiation at surface, and Mean Sea Level Pressure. These are for the three streams – historical , seasonal forecasts and projections","name":"energy-relevant climate indicators"},{"code":"TA2-5-1","description":"forecast models on urban scale for intense rainfall, heat waves, extreme air pollution, city-specific climate data and impact indicators to support the infrastructure and health sectors operating in cities","name":"forecast models for intense rainfall, heat waves, extreme air pollution"},{"code":"TA2-4-1-1","description":"deliver key information on historical, seasonal forecast and projection periods for climate-related indicators relevant to the European energy sector in terms of electricity demand and the production of power from wind, solar and hydro sources.","name":"historical, seasonal forecasts and projections of climate indicators"},{"code":"TA2-5-1","description":"urban infrastructure should be designed to perform well even with extreme conditions (e.g. pluvial and fluvial flooding, heatwaves) ","name":"assessing and designing urban infrastructure appropriate for extreme weather conditions"},{"code":"TA2-4-1","description":"assessing the electricity demand and the production of power from wind, solar and hydro sources","name":"assessing the changes in supply and demand of energy"},{"code":"TA2-4","description":"The energy sector includes energy providers and policy makers that make informed choices on the future energy mix with information related to weather (wind, solar and hydro) and energy (capacity factors, demand, volatility) forecasts at a regional and national level","name":"Energy"},{"code":"TA2-5-1-1-1","description":"climate-health indicators concerning heat and cold stress, vector-borne diseases, and allergenic pollen","name":"climate-health indicators"},{"code":"TA2-5-1-1","description":"high-resolution maps of temperature and heat-wave frequency for major urban centres and forecasts of the distributions of pollen and vector-borne diseases","name":"maps and forecasts of temperature, heat-wave frequency and climate-health indicators"},{"code":"TA2-5-1","description":"developing health policies specific to urban conditions","name":"developing health policies specific to urban conditions"},{"code":"TA2-5","description":"Policy-makers of cities and related stakeholders that address issues of heat and cold stress, vector-borne diseases, and allergenic pollen","name":"Health"},{"code":"TA2-11","description":"infrastructure and health sectors operating in cities, people responsible for the management of urban infrastructure (buildings, transport systems, sewage and drainage systems), consultants and urban engineers/scientists that model urban hazards such as intense rainfall, heat waves, extreme air pollution levels, provide climate indicators specifically adapted for urban areas, as well as the surrounding regions to help build resilient cities able to mitigate the challenges that climate change pose to infrastructure","name":"Infrastructure"},{"code":"TA2-6-1-1","description":"future distribution of key ocean variables and their impacts on the aquatic ecosystem, including species distribution and possible changes in fish stocks, includes Chlorophyll-a P90 indicator","name":"future distribution of key ocean variables"},{"code":"TA2-6-1","description":"case studies include Coastal Eutrophication, Fisheries and Aquaculture, Marine Spatial Planning, and Natural Capital Accounting","name":"developing adaption and mitigation strategies for changes in species distribution and fish stocks"},{"code":"TA2-6","description":"Fisheries, aquaculture, and marine and coastal tourism are affected by climate change's impact upon the marine ecosystem and the services it provides.","name":"Coastal areas"},{"code":"TA2-7-1-1","description":"seasonal forecasting of wind, waves, ocean current, sea surface pressure, air and sea surface temperature","name":"seasonal forecasts of shipping-relevant ocean parameters"},{"code":"TA2-7-1","description":"modelling fuel consumption based on seasonal forecasting of wind, waves, ocean current, sea surface pressure, air and sea surface temperature,","name":"route planning with awareness of expected fuel consumption"},{"code":"TA2-7-2-1","description":"projected ice conditions","name":"projected ice conditions"},{"code":"TA2-7-2","description":" estimating arctic route availability based on projected ice conditions","name":"estimating arctic route availability"},{"code":"TA2-7-3-1","description":"estimating the risk of icebergs with iceberg drifting models based on histrical data and seasonal forecast of sea and air temperature, wind and currents","name":"seasonal forecasts of iceberg relevant indicators"},{"code":"TA2-7-3","description":" ","name":"route planning with awareness of iceberg risk"},{"code":"TA2-7","description":"Shipping companies need information about changing conditions on shipping routes, Winds, waves, ocean currents and other parameters greatly affect shipping routes. Accurate models based on high-quality climate data will aid the decision-making processes and support medium- and long-term planning in the shpping industry.","name":"Shipping"},{"code":"TA2-8-1-1","description":"critical pan-European climate indicators (snow conditions, Holiday Climate Index, coastal waters data, lake temperature, forest fires index), examples of indicators are: Mountain Tourism Meteorological and Snow Indicators (MTMSI) – Past conditions and long-term future projections, Holiday Climate Index (HCI) – Seasonal forecasts and long-term future projections, Fire Weather Index (FWI) – Seasonal forecasts and long-term future projections, Lake Water Surface Temperature (LWST) – Seasonal forecasts","name":"tourism-relevant climate indicators"},{"code":"TA2-8-1","description":" ","name":"facilitating ongoing and long-term adaptation of the tourism sector to a changing climate"},{"code":"TA2-8-2-1-1","description":" ","name":"Mountain Tourism Meteorological and Snow Indicators (MTMSI)"},{"code":"TA2-8-2-1","description":"This includes, for example, past and future temperature and natural and managed (including effects of grooming and snowmaking) snow season duration, as a function of altitude.","name":"past conditions and future projections of snow season duration"},{"code":"TA2-8-2","description":"mountain tourism activities, in particular operating conditions of ski resorts","name":"long-term planning for winter tourism in mountainous regions"},{"code":"TA2-8","description":"Tourism is a highly diverse user group that includes intermediaries (such as consultancy companies or environment agencies), businesses (such as tour operators or investors in tourist infrastructure and services), destination managers, tourist associations and policy makers, as well as tourists, The warming climate has the potential to significantly affect the appeal of tourist destinations. Working with experts we provide indicators able to inform personal and business decisions on seasonal and multi-decadal time-scales. ","name":"Tourism"},{"code":"TA2-9-1-1","description":"assessing the impact of temperature, rainfall, and other atmospheric, terrestrial or oceanic variables on habitat suitability, species distribution, species fitness and reproduction, and ecosystem services","name":"climate-biodiversity indicators"},{"code":"TA2-9-1","description":"biodiversity and ecosystem service assessments for both fauna and flora, in the terrestrial as well as the marine biosphere, for different climatic zones around the globe","name":"assessing biodiversity and ecoystem services"},{"code":"TA2-9","description":"nature conservation agencies, policy makers, plantation owners, scientists and private companies in the biodiversity sector use EO information in their combat against biodiversity loss by planning and monitoring ecosystem restoration and species dispersion measures","name":"Biodiversity"},{"code":"TA2","description":"The key parameters for climate change are temperature and precipitation, providing consistent and authoritative information about climate change for supporting adaptation and mitigation policies of the European Union","name":"Climate Change Monitoring"},{"code":"TA3-1-1-1","description":"to classify burnt areas after fires based on vegetation change","name":"burnt area mapping"},{"code":"TA3-1-1-2","description":"to define the growth rate or dry biomass increase of vegetation","name":"Dry Matter Productivity"},{"code":"TA3-1-1-3-1","description":"to calculate the fraction of solar radiation absorbed by live leaves","name":"Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)"},{"code":"TA3-1-1-3-2","description":"to model the fraction of ground covered by green","name":"Fraction of green Vegetation Cover (FCOVER)"},{"code":"TA3-1-1-3-3","description":"to define vegetation status, type, and health based on different indices ","name":"Vegetation indices"},{"code":"TA3-1-1-3","description":"to explore the status and variety of vegetation","name":"Vegetation Status/ Health"},{"code":"TA3-1-1-4","description":"to define different classes of physical coverage of the Earth's surface","name":"Land Cover"},{"code":"TA3-1-1","description":"to monitor the changes on continental biomass","name":"to monitor the changes on continental biomass"},{"code":"TA3-1-2-1-1","description":"to quantify the moisture condition at variuos depths in the soil","name":"Soil Water Index"},{"code":"TA3-1-2-1","description":"to quantify the soil moisture based on available indices","name":"Soil Water Indices"},{"code":"TA3-1-2-2","description":"to define the relative water content of the top few centimetres soil, describing how wet or dry the soil is in its topmost layer, expressed in percent saturation","name":"Surface Soil Moisture"},{"code":"TA3-1-2","description":"to quantify parameters such as soil water indices or the surface soil moisture","name":"to monitor the status of water contained in the soil"},{"code":"TA3-1","description":"to monitor the changes on continental biomass","name":"Vegetation"},{"code":"TA3-2-1-1-1","description":"to define as half the area of green elements of the canopy per unit horizontal ground area","name":"Leaf Area Index"},{"code":"TA3-2-1-1-2","description":"to calculate as an indicator for greenness and biomass","name":"NDVI"},{"code":"TA3-2-1-1-3","description":"to determine the difference between the current NDVi and values observed in the same period in previous years","name":"Vegetation Condition Index (VCI)"},{"code":"TA3-2-1-1-4","description":"to assess the overall vegetation condition by referencing the current value of the NDVI with the long-term statistics from the same period","name":"Vegetation Productivity Index (VPI)"},{"code":"TA3-2-1-1","description":"to determine the radiative skin temperature of the land surface ","name":"Land Surface Temperature"},{"code":"TA3-2-1-2","description":"to define the fraction of sunlight reflected by the surface of the Earth","name":"Surface Albedo"},{"code":"TA3-2-1-3","description":"to model the fraction of the sunlight reflected by the surface of the Earth in a given spectral band","name":"Top Of Canopy Reflectances"},{"code":"TA3-2-1","description":"user group interested in solar radiation","name":"to model solar radiation controls"},{"code":"TA3-2","description":"user interested in modeling the incoming solar radiation controls","name":"Energy"},{"code":"TA3-3-1-1-2","description":"to monitor the quality in lakes and reservoirs","name":"Laker Water Quality"},{"code":"TA3-3-1-1-3","description":"to detect the area covered by inland water providing minimum and maximum extent along the year","name":"Water Bodies"},{"code":"TA3-3-1-1-4","description":"to determine the water level as the height of the reflecting surface of continental water bodies","name":"Water Level"},{"code":"TA3-3-1-1","description":"to determine the temperature of the lake surface influencing the lake hydrology and biogeochemistry","name":"Lake Surface Water Temperature"},{"code":"TA3-3-1","description":"to explore the earth's water as a key element","name":"to explore the earth's water"},{"code":"TA3-3","description":"users interested in water bodies","name":"Water"},{"code":"TA3-4-1-1","description":"to classify ice for freshwater bodies into fully snow covered ice, partially snow covered ice and open water","name":"Lake Ice Extent"},{"code":"TA3-4-1-2","description":"to provide information on the snow cover extent incluenced by temerature changes and precipitation","name":"Snow Cover Extent"},{"code":"TA3-4-1-3","description":"to explore the equivalent amount of liquid water stored in the snow pack","name":"Snow Water Equivalent"},{"code":"TA3-4-1","description":"to explore the Earth's snow cover and its influence on various systems","name":"to explore the Earth's snow cover"},{"code":"TA3-4","description":"users interested in snow and ice","name":"Cryosphere"},{"code":"TA3-5-1-1","description":"to detect the change in land cover based imagery acquired on multiple time steps","name":"Land Cover Change"},{"code":"TA3-5-1","description":"to detect the change over time","name":"to detect change over time"},{"code":"TA3-5","description":"users interested in mapping and analysing specific hotspots in regard to the change in land cover over time","name":"Hot Spots Monitoring"},{"code":"TA3-6","description":"to provide in situ-measurements","name":"Groundbased Observations"},{"code":"TA3","description":"user interested in Earth's land surface","name":"Land Monitoring"},{"code":"TA4-1-1","description":" ","name":"support sustainable management of marine resources"},{"code":"TA4-1","description":"to monitor resources in marine context","name":"Marine Resources"},{"code":"TA4-2-1","description":" ","name":"support safety at sea"},{"code":"TA4-2-2","description":" ","name":"support pollution response"},{"code":"TA4-2","description":"to manage maritime safety by providing supporting services","name":"Maritime Safety"},{"code":"TA4-3-1","description":"support coastal management ","name":"support coastal management"},{"code":"TA4-3-2","description":"support environmental impact assessment","name":"support environmental impact assessment"},{"code":"TA4-3","description":"monitoring the coastal and marine environment by using monitoring indictaors such as ocean health, heat content, sea level, sea ice, climate variability, water mass and heat exchange or temperature and salinity","name":"Coastal and marine environment"},{"code":"TA4-4-1","description":"reporting on the state of the ocean","name":"reporting on the state of the ocean"},{"code":"TA4-4","description":" ","name":"weather, seasonal forecasting and climate"},{"code":"TA4","description":" ","name":"Marine Monitoring"},{"code":"TA5-1-1","description":"to manage external borders from land perspective","name":"discover border surveillance at land"},{"code":"TA5-1-2","description":"to manage external borders from sea perspective","name":"discover border surveillance at sea"},{"code":"TA5-1","description":"to manage external borders","name":"Border Surveillance"},{"code":"TA5-2-1","description":"to provide safety of navigation at sea","name":"to surveil the safety of navigation"},{"code":"TA5-2-2","description":"to monitor fishery activities","name":"to control fisheries"},{"code":"TA5-2-3","description":"to monitor marine pollution based on a number of parameters and methods","name":"to monitor marine pollution"},{"code":"TA5-2-4","description":"to provide law enforcement at sea","name":"law enforcement at sea"},{"code":"TA5-2","description":"has a strong connection to the marine monitoring domain","name":"Maritime Surveillance"},{"code":"TA5-3-1","description":"to assist in situations of crisis or emerging crisis","name":"to support peacekeeping operations"},{"code":"TA5-3-2","description":"using Earth observation methodology for conflict prevention and monitoring of conflict situations","name":"to support conflict prevention"},{"code":"TA5-3-3","description":"to provide risk assessments to supprt external action","name":"to conduct risk assessments"},{"code":"TA5-3","description":"user group to assist in crisis situations","name":"Support to EU External Action"},{"code":"TA5","description":" ","name":"Security"},{"code":"TA6-1-1","description":"First Estimate Product, Delineation products","name":"Disaster extent"},{"code":"TA6-1-2","description":"Grading products","name":"Damage Assessment"},{"code":"TA6-1-3","description":"Reference Products","name":"Reference information"},{"code":"TA6-1","description":"Rapid mapping for emergency action… for…. Volcanic Activity, Storm, Extreme temperature, Flood, Mass movement, Drought, Wildfire, Epidemic, Infestation, Industrial Accident, Transport Accident, Humanitarian","name":"Emergency Mapping"},{"code":"TA6-2-1","description":"Reference maps","name":"Risk mapping"},{"code":"TA6-2-2","description":"Pre-disaster situation maps, Post-disaster situation maps","name":"Vulnerability assessment"},{"code":"TA6-2","description":"user group interested in risk and recovery procedures in context of emergency, such as natural disaster risk","name":"Risk & Recovery mapping"},{"code":"TA6-3-1","description":"Examples: Floods/EFAS, Fires/EFFIS, Drought/DO","name":"Early warning for emergency situation"},{"code":"TA6-3","description":"Enabling preparation for emergency situation","name":"Alert & Early Warning"},{"code":"TA6","description":"user groups interested in using Earth observation methodology to prevent, monitor and support actions after emergencies, such as natural disasters","name":"Emergency"},{"code":"TA7-1","description":"EARSC, GEO, etc.","name":"EO Communities"},{"code":"TA7-2","description":"USA - UDSSR space race and beginnings of satellite-based Earth observation","name":"Globally active space agencies operating EO satellites and history of EO"},{"code":"TA7-3","description":" ","name":"Vision and mission of EO and RS"},{"code":"TA7-4","description":"generic workflow that EO information production relies on","name":"Image processing (value) chain"},{"code":"TA7-5-1","description":" ","name":"Space Strategy"},{"code":"TA7-5-2","description":"Entrusted Entities (EE) include ECMWF (https://www.ecmwf.int/en/about)","name":"Copernicus Programme"},{"code":"TA7-5","description":" ","name":"Space Policy"},{"code":"TA7","description":" ","name":"EO/GI and Society"},{"code":"WB","description":"This knowledge area is about Web Based Geographic Information management aspects and therefore it was given the name \"Web Based GI\" or \"WBG\" in short. It is implied by this name that the differentiating factor for this KA is the \"Web\". One must then be able to answer the questions like \"What functions do we delegate to the Web?\" or \"how WBGI is different from the traditional GI?\" Sticking to the functions of a GIS, which are inserting (adding), storing, manipulating, analysing and presenting the data, there is not a single system for effecting all these tasks anymore but the Web itself. For instance, there is no single database and its known-to-its users-definition, anymore but many different stores and many different definitions. Similarly, many different manipulation, analysis and presentation options compared with the options offered by a single or limited number of systems of traditional GI. In general, Web provides the means of leveraging distributed \"resources\" like data, information, or software. It is a \"collaboration medium\". A collaboration that enables rapid production or decision making. A collaboration that certainly introduces new dimensions to traditional GI handling. This is the justification of proposing this KA in addition to the KAs of the original BoK. For the mentioned collaboration to happen, data or any other type of a resource have to accessible on the Web. This means that it should have a Web \"address\" and a \"definition\" that is understandable either by \"human\" or \"machine\". \"Machine understandable definitions\" refers to the dimension of \"semantics\" and \"ontologies\" which are also included under this KA. When one talks about publishing resources then \"catalogue services\" and more importantly \"discovery\" dimension comes into the scene. On the other hand, \"Linked Data (LOD)\" and \"Open Data\", highly popular recent trends and two of the above mentioned dimensions of Web GI have also been covered under this KA. Like the other dimensions of Web GI, both LD and OD aspects must be known to GI communities with differing degrees of expertise. The concepts of \"interoperability\" and \"Spatial Data Infrastructure (SDI)\", hot topics of GI communities for many years, have been thought to be dealt with under this KA as well with the justification that \"Web GI\" is a much broader concept than SDI, This is by the fact that SDI refers to a much narrower content and context of \"collaboration\" then Web GI. Therefore, Geospatial data interoperability and some of the related concepts which were classified under KA, \"Geospatial data in the original BoK were moved under KA11 with the updated context. Another issue is the coverage of Spatial Analysis (SA), data manipulation aspects of GI by KA11. The SA aspects are covered by other KAs like \"Geocomputation\" and \"Analytical methods\". If the analysis operations, in an undertaking, would be handled by web services this is already covered by \"data processing\" web services, application development unit and Web services composition under that unit. The important thing is to have the knowledge about a specific analysis operation; Employing it as a web service would require no more knowledge than using any other web service. SA is covered by KA11 in as much as it should have been.","name":"Web-based GI"},{"code":"WB1-1","description":"Define Service Oriented Architecture (SOA) and identify main elements of it. Discuss concensus based interoperability and its relation to geospatial data interchange. Define what a Web Service (WS) is and present characteristic scenarios. Data serving and Data Processing WSs. Define their characteristics and present some examples. Define Web services transport over the Web. Describe generally the hypertext transfer protocol and its main operations like POST and GET.","name":"Fundamentals of web services"},{"code":"WB1-2","description":"- Identify design issues of SOAP web services; fine grained and coarse grained services, design patterns.","name":"SOAP web services"},{"code":"WB1-3","description":"- Define characteristics of REST Web services and Resource oriented Architecture (ROA). - Differentiate between SOAP and REST Web services. - Identify design issues of REST Web services. - Discuss the issue whether a service is really \"RESTful\" or not","name":"REST web services"},{"code":"WB1-4","description":"- Define Web Map Service (WMS). Describe GetCapabilities, GetMap, and GetFeatureInfo operations in detail. Practice its usage in a given use case. - Define Web Feature Service (WFS). Describe GetCapabilities, DescribeFeaturetype, and GetFeature, and GetFeatureInfo operations in detail. Practice its usage in a given use case. - Define Web Coverage Service (WCS). Describe GetCapabilities, GetCoverageInfo, and GetCoverage operations in detail. Practice its usage in a given use case. - Define Web Processing Service (WPS). Describe GetCapabilities, DescribeProcess, and Execute operations in detail. Practice its usage in a given use case. - Define Web Map Tile Service (WMTS). Describe GetCapabilities, GetTile, and GetFeatureInfo operations in detail. Practice its usage in a given use case. - Define and practice the usage, in a given use case, of StyledLayerDescriptor (SLD) and Symbology Encoding (SE). Practice their usage in a given use case.","name":"OGC web services"},{"code":"WB1","description":"In the most simplistic way a Web service may be defined as \"a Web accessable program code which performs a task of either processing or serving some data. Although there are many other definitions in the related literature, the one in W3C (2004) seems to be quite complete and refering to also lately popular REST style Web services. It states that \" We can identify two major classes of Web services: REST-compliant Web services, in which the primary purpose of the service is to manipulate XML representations of Web resources using a uniform set of \"stateless\" operations; and arbitrary Web services, in which the service may expose an arbitrary set of operations.","name":"Web services"},{"code":"WB2-1","description":"- Resource Description Framework (RDF), RDF graphs, RDF Schema (RDF-S). Define a data set in RDF. - Give an overview of Web Ontology Language (OWL). Describe how to define a data set in OWL DL. - Identify virtues of defining a given data set in both RDF and OWL, and compare semantic richness of both definitions. - Define Semantic Web and identify the role of the languages included under this topic for Semantic Web. - Give an overview of Semantic Web service definition in OWL-S. Identify the relation betweem OWL-S and WSDL. - Define the components of a Web Services Description Language (WSDL) document. - Web services description for RESTful web services, Web Application Description Language (WADL) and its use.","name":"Languages for the definition of non-spatial data and services"},{"code":"WB2-2","description":"- Describe OGC Simple Features Access Schema. Well-Known Text (WKT) and Well-Known Binary (WKB) representations of Geometry. - Describe GML data model and GML definition of geometry. GML application schemas and GML documents. - Define spatial extensions that GeoSPARQL brings over SPARQL. Identify the difference between qualitative spatial reasoning and quantitative spatial computations. - Describe GeoJason definition of Geospatial objects. Describe the structure of a GeoJSON document. Identify advantages and disadvantages of representing the same geospatial data by GML and by GeoJSON. - Compare different Geospatial object and geometry definitions included under this topic.","name":"Definition of geospatial data"},{"code":"WB2-3","description":"- Define what an ontology is. Identify differences among ontologies, Thesauri, and taxonomies. - Differentiate between upper, domain, and application level ontologies. - Identify issues in the development of geospatial ontologies. Criticise the role of ontology development methodologies and ontology evaluation in the development of ontologies. - Define and exemplify the reuse of ontologies - Define and identify the role of ontology patterns","name":"Ontologies development reuse and patterns"},{"code":"WB2","description":"A \"resource\" could be \"anything\" including data and services, identifiable over the Web. A resource should be defined in a language to be discoverable on the Web. Over the years, two major bodies W3C for non-spatial and OGC concerning spatial data have developed many specifications for defining data and services. On the W3C side, Resource Description Framework (RDF) has gained a great momentum in recent years in relation to the recent popularity of Linked Data as well. In the OGC front, the acceptance of GML was a major step concerning the long time effort of geospaial communities for having a standart for the definition of both geospatial feautures and geometry.","name":"Resource Definition"},{"code":"WB3-1","description":"- Define metadata and identfify metadata standards like ISO 19115 and 19119 describe their metadata schemas generally. - Differentiate between a metadata standard and a medadata profile. - Identify the aspects of selecting keywords which would characterize the data properly. - Identify the issues in mapping between different metadata standards. Also identify the roles of thesauri and crosswalks. - Describe briefly INSPIRE Metadata handling Scheme.","name":"Metadata and standards"},{"code":"WB3-2","description":"- Identify main components of manual metadata creation software tools - Describe harvesting and crawling mechanisms for automated metadata collection. - Practically apply harvesting using GeoNetwork Open Source tool. - Practice publishing in some popular SDI (NSDI) portals like INSPIRE and GOS geoportals","name":"Manual and automated forms of publishing"},{"code":"WB3-3","description":" ","name":"Catalogue services"},{"code":"WB3-4","description":"- Describe Metadata schemas and vocabularies used for open data publish. - Open data APIs that enable the usage of Open data; identify design aspects and usage scenarious. - Practice open data publishing using CKAN Open source tool - Describe what is meant by \"Odata\" (Open data Protocol), an OASIS standard. - Identify the technical aspects that open data paradigm would affect concerning Spatial Data Infrastructures including NSDIs.","name":"Publishing open data"},{"code":"WB3-5","description":"- Describe semantic annotation of data and services. - Issues in determining what ontologies to use for semantic annotation. - Identify issues in developing new ontologies for geospatial data. - Define Mapping between legacy definition and the semantic definition of publish - Describe an architecture and tools for organizing semantically annotated data","name":"Publishing via a semantic definition of data"},{"code":"WB3-6","description":"- Describe stages of publishing a relational database as Linked Data - Identify issues in finding proper ontologies to annotate the data - Identify issues in determining the relationships to be represented when publishing Linked Data - Linking the data; manual and automated methods - Practicaly apply publishing a relational database as Linked Data","name":"Publishing linked open data"},{"code":"WB3","description":"\"Publishing\" means making a resource available for the use of others. A \"resource\" could be \"anything\" including data and services, identifiable over the Web. Publishing may be done on the basis of either the \"characteristics\" of the data or the data itself. When only some \"characteristics\" of a resource is published then some of the contents would naturally be left out. The \"characteristics\" include metadata and some keywords. This kind of publishing may be named as \"limited contents\" publishing or \"publishing by metadata\". One of the issues become then what characteristics to use to define the data. Or what what metadata definition to use. Another aspect of publish is \"manual entry\" and \"automated collection\". In the former publisher enters metadata while in the latter some harvesting mechanism collects metadata in an automated fashion. On the contrary, there is \"unlimited contents publishing\" where there is no limitation on the published contents. Open data publishing is in this class. In additon, some \"additional semantics\" may be subject of this type publishing through new relationships in the ontologies of publishing, which have not been explicit in the exisiting data model but are inherent in the data. And this last type is covered under the topic, \"Publishing via a semantic definition of data.\"","name":"Resource Publishing"},{"code":"WB4-1","description":"- Identify main issues in \"keyword-based\" discovery of data and services. - Discovery over a catalogue service; Discovery procedure in OGC CS-W - Practice discovery over some popular SDI (NSDI) portals like INSPIRE and GOS geoportals. - Describe \"Full-text-based\" discovery; open source and commercial search engines, its use in GI related applications.","name":"Syntactic discovery"},{"code":"WB4-2","description":"- Describe Semantic Discovery and its main components. Identify the areas of its use for GI related applications. - Identify the main concepts of reasoning and architectural components of Reasoners - Identify main issues in Semantic discovery. - Present some examples of semantic discovery; Semantic search engines, highlighting projects and practice concerning GI related applications in the area. .","name":"Semantic discovery"},{"code":"WB4-3","description":"- Describe Querying Linked Data; SPARQL and GeoSPARQL - Describe Linked Data Browsers; Define Faceted browsers and identify what problems of linked data discovery they aim to solve. - Comment on Natural language based discovery over linked data. - Compare Linked geospatial data to SDI approaches.","name":"Discovery over linked open data"},{"code":"WB4","description":"Resource discovery means the discovery of resources including data and services needed for an application. Syntactic discovery refers to the discovery on the basis of syntactic comparison operations. It is classified as \"keyword-based\" and \"full-text-based\" discovery. Semantic discovery on the other hand, refers to the discovery of resources on he basis of some semantic definition. Therefore, semantic discovery requires that a resource be published by a semantic definition as defined in the topic WB3-5.","name":"Resource Discovery"},{"code":"WB5-1","description":"- Indentify the need for and main issues in spatial data interchange - Describe the main components of OGC Filter encoding and compare it to SQL. - Practically apply getting data from a WFS and integrate it into a client application - Practically apply getting data from a WCS and integrate it into a client application - Practice the usage of popular ETL tools in an NSDI scenario.","name":"Integrating data from OGC web services"},{"code":"WB5-2","description":" ","name":"Schema matching and ontology alignment"},{"code":"WB5-3","description":"- Create a web map mashup application with OpenLayers, Google Maps API and OpenStreetMap. In this example, OpenLayers will be used for javascript mapping functionality. Google Maps and OpenStreetMap will be baselayers for this example. Add your GeoJSON data as a layer to this application. Add your WMS service as a layer to this application.","name":"Data mash ups"},{"code":"WB5","description":"The term \"application development\" refers to the collection of activities or the \"workflow\" through which the user reaches her final goal. Being one of these activities, \"data integration\" means the transformation of data from one representation to another which might be of either the client`s one or some other representation. An example for data integration might be the case where the data is transfered from an OGC WFS and integrated into a client GIS.","name":"Application development via Data Integration"},{"code":"WB6-1","description":"- Define web services composition (WSC) concept and identify main issues. - Define Web API composition (WAPIC) concept for RESTful WSs and identify main issues. - Practice a WSC for a certain use case in Taverna workbench using OGC WPS services","name":"Manual Web Services Composition"},{"code":"WB6-2","description":"- Identify whether Full-automated WSC has still a value in it concerning both where we stand today on the road to 'Semantic Web' and unresolved problems in the area, which are the problems of Artificial Intelligence indeed.","name":"Semi automated and Full-automated WSC"},{"code":"WB6","description":"Web Services Composition can be defined as bringing together a number of web services in a certain workflow to achieve a certain task that cannot be achieved by any of the composed services alone. In general, it involves first the discovery of the suitable services over the Web, and compose them in a certain workflow order and finally run the composed service which is the invocation stage. WSC has been a highly active research topic since the emergence of Web services in 2000s. \"Manual\" WSC is the form that the activities of discovery, composition and invocation are all done manually (by human). In the \"Semi-automated\" way, the discovery is done by the machine. In the \"full-automated\" approach all the above activities are done by the machine. There are no tools at the moment that achieve full automated composition. Web API composition is like WSC, the only difference is the fact that instead of web services there are Web APIs in WAPIC. There is no doubt that One would run into the very same problems of WSC concerning full automated composition. In other words, WAPIC would in no way be easier than WSC. Nevertheless, as far as semi automated form can be achived, WAPIC is valuable because the number of Web APIs increase drastically from day to day. The site \"programmableWeb\" lists 14 957 APIs at the moment. It is not easy to search for all those APIs manually for the discovery of suitable APIs for a given task.","name":"Application development via Web services composition"},{"code":"WB7-1","description":"- Describe main elements of HTML5. Identify the extensions HTML5 brings over older HTML versions. Create a sample HTML5 Web page. - Identify building blocks of Javascript programming language. Write a Javascript function which, for instance, filters out points with height values greater than 100 m. from a GeoJSON file. Add this function to an HTML5 web page. - Describe Cascading Style Sheets (CSS), identify the virtue of its role for separating the presentation style of HTML documents from the content of documents. - Describe Scalable Vector Graphics (SVG) and identify its role for client side processing. - Describe Document Object Model (DOM). Identify its role for the processing of a \"loaded\" HTML document.","name":"Hypertext markup scripting and styling"},{"code":"WB7-2","description":"- Identify main elements and functionality Google maps, describe some of its most popular API operations and how they are employed. - Identify main components and functionality of Openlayers library, describe its main functions and how they are employed. - Identify main components and functionality of Leaflet library, describe its main functions and how they are employed. - Identify main elements and functionality Mapbox, describe some of its most popular API operations and how they are employed. - Present an overview of OpenStreetMap and define its general functionality, comment its usage by Web APIs.","name":"Web Map APIs and Libraries"},{"code":"WB7-3","description":"- Describe generally the main components and functionality of \"Web Application Frameworks\" such as AngularJS, Ext.js, Django, Java Server Faces (JSF), and the like. - Describe generally the functionality offered by \"portal frameworks\" land Geoportals ike Geonetwork, Opengeoportal, Esri geoportal server, Deegree portal, Liferay, Jboss portal. - Identify differences, advantages and disadvantages of web application framework based and portal framework based web applications from the geospatial data perspective. - Describe generaly how \"NSDI-requiring-scenarious\"would be handled by web application framework based applications. - Describe generally how JSON (GeoJSON)`s \"schema-less\"structure may be transformed into an application schema.","name":"Web application Frameworks and Geoportal frameworks"},{"code":"WB7","description":"Characteristic examples are included under this topic. The APIs, for instance other than the ones included under this unit, and libraries could have been included as well. However, since the important thing is to highlight the functionality then there is no need to include them all. By the inclusion of topic \"WB7-3\"under this unit, the aim was to cover one of the very \"hot\"topics of Web2.0 for both the main concepts about Web application frameworks and also how they are related to portal frameworks and geoportals. By the topic \"WB7-1 Building blocks\"the core components of Web application development are covered. On top of this core, there comes a great variety of \"Web application frameworks for both enabling rapid web application development and ensuring scalable, high-performance applications. Finally, there are \"Web APIs and Libraries\" certainly deserving being a separate topic for their current popularity. They also mean rapid application development for developers by code reuse and versatality for \"end users\" in creating their \"end products\".","name":"Web Application development elements"}],"creationYear":2019,"references":[{"concepts":[365],"description":" ","name":" ","url":"https://www.harrisgeospatial.com/docs/Subsetting.html"},{"concepts":[366],"description":" ","name":" ","url":"https://www.harrisgeospatial.com/docs/LayerStacking.html"},{"concepts":[373],"description":" ","name":" ","url":"https://trac.osgeo.org/ossim/wiki/orthorectification"},{"concepts":[375],"description":" ","name":" ","url":"http://gin2k.bigknowledge.net/bokwiki"},{"concepts":[390],"description":" ","name":" ","url":"https://earth.esa.int/web/sentinel/technical-guides/sentinel-2-msi/level-1c/algorithm"},{"concepts":[440],"description":" ","name":" ","url":"http://www.bioss.ac.uk/people/chris.html"},{"concepts":[473],"description":" ","name":" 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workflows"},{"concepts":[157],"name":"Describe considerations for using maps on the Web as a method for downloading data"},{"concepts":[140],"name":"Describe differences in design needed for a map that is to be viewed on the Internet versus as a 5x7 foot poster, including a discussion of the effect of viewing distance, lighting, and media type"},{"concepts":[565],"name":"Describe different organizational models for coordinating GIS and T participants and stakeholders"},{"concepts":[108],"name":"Describe different types of movement and change"},{"concepts":[3],"name":"Describe difficulties in dealing with large spatial databases, especially those arising from spatial heterogeneity"},{"concepts":[3],"name":"Describe emerging geographical analysis techniques in geocomputation derived from artificial intelligence e.g., expert systems, artificial neural networks, genetic algorithms, and software agents"},{"concepts":[260],"name":"Describe existing algorithms designed for performing dynamic queries"},{"concepts":[125],"name":"Describe geographic phenomena in terms of their distances and directions (in space and time) Define spatial autocorrelation in the context of geographic proximity"},{"concepts":[124],"name":"Describe geographic phenomena in terms of their topological relationships (in space and time to other phenomena"},{"concepts":[61],"name":"Describe how a network of stream channels and ridges can be estimated from a Digital Elevation Model (DEM)"},{"concepts":[155],"name":"Describe how an animated map reveals patterns not evident without animation"},{"concepts":[131],"name":"Describe how compilation, production, and distribution methods used in map making have evolved"},{"concepts":[143],"name":"Describe how cultural differences with respect to color associations impact map design"},{"concepts":[4],"name":"Describe how data mining can be used for geospatial intelligence"},{"concepts":[313],"name":"Describe how geometric accuracy should be documented in terms of the FGDC metadata standard"},{"concepts":[344],"name":"Describe how geospatial data are used and maintained for land use planning, property value assessment, maintenance of public works, and other applications"},{"concepts":[548],"name":"Describe how GI S and T can be used in the decision-making process in organizations dealing with natural resource management, business management, public management or operations management"},{"concepts":[50],"name":"Describe how Independent Random Process/Chi-Squared Result IRP/CSR may be used to make statistical statements about point patterns"},{"concepts":[47,48],"name":"Describe how map algebra performs mathematical functions on raster grids"},{"concepts":[167],"name":"Describe how maps such as topographic maps are produced within certain relations of power and knowledge"},{"concepts":[296],"name":"Describe how sea surface temperatures are mapped"},{"concepts":[584],"name":"Describe how state GIS Councils can be used in enterprise GIS and T implementation processes"},{"concepts":[60],"name":"Describe how surfaces can be interpolated using splines"},{"concepts":[131],"name":"Describe how symbolization methods used in map making have evolved"},{"concepts":[155],"name":"Describe how the adding time-series data reveals or does not reveal patterns not evident in a cross-sectional data"},{"concepts":[240],"name":"Describe how to generate a unique TIN solution using Delaunay triangulation"},{"concepts":[558],"name":"Describe issues that may hinder implementation and continued successful operation of a GI system if effective methods of staff development are not included in the process"},{"concepts":[170],"name":"Describe maps that can be used to find direction, distance, or position, plan routes, calculate area or volume, or describe shape"},{"concepts":[14],"name":"Describe methods for measuring different kinds of accessibility on a network"},{"concepts":[8],"name":"Describe networks that apply to specific applications or industries"},{"concepts":[38],"name":"Describe operations that can be performed on qualitative representations of direction"},{"concepts":[112],"name":"Describe particular entities in terms of space, time, and properties"},{"concepts":[115],"name":"Describe particular events or processes in terms of identity, categories, attributes, locations, etc."},{"concepts":[110],"name":"Describe particular geographic phenomena in terms of attributes"},{"concepts":[122],"name":"Describe particular geographic phenomena in terms of their place in mereonomic hierarchies (parts and composites)"},{"concepts":[342],"name":"Describe perspectives on the nature and scope of system benefits among agency officials, organizational personnel, and citizens"},{"concepts":[563],"name":"Describe political, economic, administrative, and other social forces in agencies, organizations, and citizens that inhibit or promote sharing of geospatial and other data"},{"concepts":[581],"name":"Describe possible benefits to an organization by participating in a given society that is related to GIS and T"},{"concepts":[11],"name":"Describe practical situations in which flow is conserved while splitting or joining at nodes of the network"},{"concepts":[165],"name":"Describe print quality characteristics and price differences for limited-run color map distribution"},{"concepts":[165],"name":"Describe production concerns that might be discussed with a publisher who will print a map product"},{"concepts":[42],"name":"Describe real world applications where adjacency and connectivity are a critical component of analysis"},{"concepts":[41],"name":"Describe real world applications where distance decay is an appropriate representation of the strength of spatial relationships (e.g., shopping behavior, property values)"},{"concepts":[41],"name":"Describe real world applications where distance decay would not be an appropriate representation of the strength of spatial relationships (e.g., distance education, commuting, telecommunications)"},{"concepts":[73],"name":"Describe sampling schemes for accurately estimating the mean of a spatial data set"},{"concepts":[32],"name":"Describe set theory"},{"concepts":[37],"name":"Describe several different measures of distance between two points e.g., Euclidean, Manhattan, network distance, spherical"},{"concepts":[153],"name":"Describe situations in which methods of terrain representation (e.g., shaded relief, contours, hypsometric tints, block diagrams, profiles) are well suited"},{"concepts":[153],"name":"Describe situations in which methods of terrain representation are poorly suited"},{"concepts":[75],"name":"Describe some commonly used semi-variogram models"},{"concepts":[96],"name":"Describe some insights that a spatial perspective can contribute to a given topic"},{"concepts":[10],"name":"Describe some variants of Dijkstras algorithm that are even more efficient"},{"concepts":[258],"name":"Describe techniques for handling version control in spatial databases"},{"concepts":[258],"name":"Describe techniques for managing long transactions in a multi-user environment"},{"concepts":[115],"name":"Describe the actor role that entities and fields play in events and processes"},{"concepts":[243],"name":"Describe the advantages and disadvantages of the quadtree model for geographic database representation and modeling"},{"concepts":[240],"name":"Describe the architecture of the TIN model"},{"concepts":[545],"name":"Describe the basic principles of randomness and probability"},{"concepts":[82],"name":"Describe the characteristics of the spatial expansion method"},{"concepts":[127],"name":"Describe the cognitive processes that tend to create vagueness"},{"concepts":[143],"name":"Describe the common color models used in mapping"},{"concepts":[119],"name":"Describe the common constraints on spatial integration"},{"concepts":[314],"name":"Describe the component measures and the utility of a misclassification matrix"},{"concepts":[557],"name":"Describe the components of a needs assessment for an enterprise GIS"},{"concepts":[240],"name":"Describe the conditions under which a TIN might be more practical than GRID"},{"concepts":[75],"name":"Describe the conditions under which each of the commonly used semi-variograms models would be most appropriate"},{"concepts":[131],"name":"Describe the contributions by Robinson, Jenks, Raisz, and others to US academic cartography"},{"concepts":[109],"name":"Describe the contributions of category theory to understanding the internal structure of categories"},{"concepts":[583],"name":"Describe the data programs provided by organizations such as The National Map, GeoSpatial One Stop, and National Integrated Land System"},{"concepts":[206],"name":"Describe the degree to which attributes need to be modeled in the conceptual modeling phase"},{"concepts":[151],"name":"Describe the design considerations for each of the following methods: choropleth, dasymetric, proportioned symbol, graduated symbol, isoline, dot, cartogram, and flow map"},{"concepts":[140],"name":"Describe the design needs of special purpose maps such as subdivision plans, cadastral mapping, drainage plans, nautical charts, aeronautical charts, geological maps, military maps, wire-mesh volume maps, and 3D plans of urban change"},{"concepts":[55],"name":"Describe the difference between prescriptive and descriptive cartographic models"},{"concepts":[170],"name":"Describe the differences between azimuths, bearings, and other systems for indicating directions"},{"concepts":[556],"name":"Describe the differences between licensing, certification and accreditation in relation to GIS and T positions and qualifications"},{"concepts":[92],"name":"Describe the differences between real phenomena, conceptual models, and GIS data representations thereof"},{"concepts":[314],"name":"Describe the different measurement levels on which thematic accuracy is based"},{"concepts":[112],"name":"Describe the difficulties in modeling entities with ill-defined edges"},{"concepts":[112],"name":"Describe the difficulties inherent in extending the tabletop metaphor of objects to the geographic environment"},{"concepts":[69],"name":"Describe the effect of non-stationarity on local indices of spatial association"},{"concepts":[68],"name":"Describe the effect of the assumption of stationarity on global measures of spatial association"},{"concepts":[97],"name":"Describe the elements of a sense of place or landscape that are difficult or impossible to adequately represent in GIS"},{"concepts":[287],"name":"Describe the elements of image interpretation"},{"concepts":[360],"name":"Describe the extent to which contemporary GIS and T supports diverse ways of understanding the world"},{"concepts":[53],"name":"Describe the formulation of the classic gravity model, the unconstrained spatial interaction model, the production constrained spatial interaction model, the attraction constrained spatial interaction model, and the doubly constrained spatial..."},{"concepts":[114],"name":"Describe the genealogy (as identity-based change or temporal relationships) of particular geographic phenomena"},{"concepts":[79],"name":"Describe the general types of spatial econometric model"},{"concepts":[26],"name":"Describe the impact of map projection transformation on raster and vector data"},{"concepts":[313],"name":"Describe the impact of the concept of dilution of precision on the uncertainty of GPS positioning"},{"concepts":[56],"name":"Describe the implementation of an ordered weighting scheme in a multiple-criteria aggregation"},{"concepts":[353],"name":"Describe the individuals or groups to which GIS and T professionals have ethical obligations"},{"concepts":[247],"name":"Describe the integrity constraints of integrated topological models (e.g., POLYVRT)"},{"concepts":[585],"name":"Describe the leading academic journals serving the GIS and T community"},{"concepts":[94],"name":"Describe the limitations of various information stores for representing geographic information, including the mind, computers, graphics, text, etc."},{"concepts":[285],"name":"Describe the location and geometric characteristics of the principal point of an aerial image"},{"concepts":[112],"name":"Describe the perceptual processes (e.g., edge detection) that aid cognitive objectification"},{"concepts":[81],"name":"Describe the relationship between factorial kriging and spatial filtering"},{"concepts":[76],"name":"Describe the relationship between the semi-variogram and kriging"},{"concepts":[51],"name":"Describe the relationships between kernels and classical spatial interaction approaches, such as surfaces of potential"},{"concepts":[74],"name":"Describe the relationships between semi-variograms and correlograms, and Morans indices of spatial association"},{"concepts":[144],"name":"Describe the role of labels in assisting readers in understanding feature locations (e.g., label to the right of point, label follows line indicating its position, area label assists understanding extent of feature and feature type)"},{"concepts":[565],"name":"Describe the roles and relationships of GIS and T support staff"},{"concepts":[354],"name":"Describe the sanctions imposed by ASPRS and GISCI on individuals whose professional actions violate the codes of ethics"},{"concepts":[294],"name":"Describe the sequence of tasks involved in the geometric correction of the Advanced Very High Resolution Radiometer (AVHRR) Global Land Dataset"},{"concepts":[289],"name":"Describe the source data, instrumentation, and workflow involved in extracting vector data (features and elevations) from analog and digital stereoimagery"},{"concepts":[565],"name":"Describe the stages of two different models of implementing a GIS within an organization"},{"concepts":[65],"name":"Describe the statistical characteristics of a set of spatial data using a variety of graphs and plots including scatterplots, histograms, boxplots, qq plots"},{"concepts":[17],"name":"Describe the structure of linear programs"},{"concepts":[19],"name":"Describe the structure of origin-destination matrices"},{"concepts":[580],"name":"Describe the U.S. geospatial industry including vendors, software, hardware and data"},{"concepts":[275],"name":"Describe the use of based on temporal relationships of objects and space (Crime or disease analyses are examples)"},{"concepts":[363],"name":"Describe the use of GIS from a political ecology point of view (e.g., consider the use of GIS for resource identification, conservation, and allocation by an NGO in Sub-Saharan Africa)"},{"concepts":[156],"name":"Describe the uses of the map as a user interface element in interactive presentations of geographic information"},{"concepts":[308],"name":"Describe the visual appearance of the Earths graticule"},{"concepts":[119],"name":"Describe the ways in which a spatial perspective enables the synthesis of different subjects (e.g., climate and economy)"},{"concepts":[98],"name":"Describe the ways in which the elements of culture (e.g., language, religion, education, traditions) may influence the understanding and use of geographic information"},{"concepts":[580],"name":"Describe three applications of geospatial technology for different workforce domains (e.g., first responders, forestry, water resource management, facilities management)"},{"concepts":[114],"name":"Describe ways in which a geographic entity can be created from one or more others"},{"concepts":[154],"name":"Design a map series to show the change in a geographic pattern over time"},{"concepts":[73],"name":"Design a sampling scheme that will help detect when space-time clusters of events occur"},{"concepts":[6],"name":"Design a simple spatial mean filter"},{"concepts":[154],"name":"Design a single map symbol that can be used to symbolize a set of related variables"},{"concepts":[259],"name":"Design a test of reliability of change information (e.g., the logical consistency of updates to the TIGER database)"},{"concepts":[59],"name":"Design an algorithm that calculates slope and aspect from a Triangulated Irregular Network (TIN) model"},{"concepts":[60],"name":"Design an algorithm which interpolates irregular point elevation data onto a regular grid"},{"concepts":[206],"name":"Design application-specific conceptual models"},{"concepts":[116],"name":"Design data models for specific applications based on these comprehensive general models"},{"concepts":[140],"name":"Design maps that are appropriate for users with vision limitations"},{"concepts":[154],"name":"Detect a multivariate outlier using a combination of maps and graphs"},{"concepts":[170],"name":"Determine feature counts of point, line, and area features on maps"},{"concepts":[584],"name":"Determine if your state has a Geospatial Information Office (GIO) and discuss the mission, history, constituencies and activities of a GIO"},{"concepts":[143],"name":"Determine the CMYK (cyan, magenta, yellow, and black) primary amounts in a selection of colors"},{"concepts":[110],"name":"Determine the proper uses of attributes based on their domains"},{"concepts":[114],"name":"Determine whether it is important to represent the genealogy of entities for a particular application"},{"concepts":[116],"name":"Determine whether phenomena or applications exist that are not adequately represented in an existing comprehensive model"},{"concepts":[56],"name":"Determine which method to use to combine criteria e.g., linear, multiplication"},{"concepts":[207],"name":"Determine which relationships need to be stored explicitly in the database"},{"concepts":[585],"name":"Develop a bibliography of scholarly and professional articles and/or books that are relevant to a particular GIS and T project"},{"concepts":[55],"name":"Develop a flowchart of a cartographic model for a site suitability problem"},{"concepts":[39],"name":"Develop a method for describing the shape of a cluster of similarly valued points by using the concept of the convex hull"},{"concepts":[156],"name":"Develop a useful interactive interface and legend for an animated map"},{"concepts":[110],"name":"Develop alternative forms of representations for situations in which attributes do not adequately capture meaning"},{"concepts":[39],"name":"Develop an algorithm to determine the skeleton of polygons"},{"concepts":[152],"name":"Develop graphic techniques that clearly show different forms of inexactness (e.g., existence uncertainty, boundary location uncertainty, attribute ambiguity, transitional boundary) of a given feature (e.g., a culture region)"},{"concepts":[101],"name":"Develop methods for representing non-cartesian models of space in GIS"},{"concepts":[545],"name":"Devise simple ways to represent probability information in GIS"},{"concepts":[153],"name":"Differentiate 3D representations from 2.5 D representations"},{"concepts":[235],"name":"Differentiate among a lattice, a tessellation, and a grid"},{"concepts":[23],"name":"Differentiate among common interpolation techniques (e.g., nearest neighbor, bilinear, bicubic)"},{"concepts":[118],"name":"Differentiate among different types of regions, including functional, cultural, physical, administrative, and others"},{"concepts":[117],"name":"Differentiate among distributions in space, time, and attribute"},{"concepts":[97],"name":"Differentiate among elements of the meaning of a place that can or cannot be easily represented using geospatial technologies"},{"concepts":[6],"name":"Differentiate among machine learning, data mining and pattern recognition"},{"concepts":[315],"name":"Differentiate among the spatial, spectral, radiometric, and temporal resolution of a remote sensing instrument"},{"concepts":[548],"name":"Differentiate an enterprise system from a department-centered GI system"},{"concepts":[127],"name":"Differentiate applications in which vagueness is an acceptable trait from those in which it is unacceptable"},{"concepts":[105],"name":"Differentiate applications that can make use of common-sense principles of geography from those that should not"},{"concepts":[18],"name":"Differentiate between a linear program and an integer program"},{"concepts":[101],"name":"Differentiate between absolute and relative descriptions of location"},{"concepts":[293],"name":"Differentiate between active and passive sensors, citing examples of each"},{"concepts":[101],"name":"Differentiate between common-sense, Cartesian metric, relational, relativistic, phenomenological, social constructivist, and other theories of the nature of space"},{"concepts":[207],"name":"Differentiate between conceptual and logical models, in terms of the level of detail, constraints, and range of information included"},{"concepts":[56],"name":"Differentiate between contributing factors and constraints in a multi-criteria application"},{"concepts":[4],"name":"Differentiate between data mining approaches used for spatial and non-spatial applications"},{"concepts":[57],"name":"Differentiate between deterministic and stochastic spatial process models"},{"concepts":[1],"name":"Differentiate between geostatistics, and spatial statistics"},{"concepts":[66],"name":"Differentiate between isotropic and anisotropic processes"},{"concepts":[51],"name":"Differentiate between kernel density estimation and spatial interpolation"},{"concepts":[208],"name":"Differentiate between logical and physical models, in terms of the level of detail, constraints, and range of information included"},{"concepts":[236],"name":"Differentiate between lossy and lossless compression methods"},{"concepts":[47,48],"name":"Differentiate between map algebra and matrix algebra using real examples"},{"concepts":[107],"name":"Differentiate between mathematical and phenomenological theories of the nature of time"},{"concepts":[73],"name":"Differentiate between model-based and design-based sampling schemes"},{"concepts":[26],"name":"Differentiate between polynomial coordinate transformations (including linear) and rubbersheeting"},{"concepts":[97],"name":"Differentiate between space and place"},{"concepts":[127],"name":"Differentiate between the following concepts: vagueness and ambiguity, well defined and poorly defined objects and fields or discord and non-specificity"},{"concepts":[53],"name":"Differentiate between the gravity model and spatial interaction models"},{"concepts":[60],"name":"Differentiate between trend surface analysis and deterministic spatial interpolation"},{"concepts":[285],"name":"Differentiate oblique and vertical aerial imagery"},{"concepts":[293],"name":"Differentiate push-broom and cross-track scanning technologies"},{"concepts":[311],"name":"Differentiate rectification and orthorectification"},{"concepts":[294],"name":"Differentiate supervised classification from unsupervised classification"},{"concepts":[154],"name":"Differentiate the interpretation of a series of three maps and a single multivariate map, each representing the same three related variables"},{"concepts":[128],"name":"Differentiate uncertainty in geospatial situations from vagueness"},{"concepts":[113],"name":"Differentiate various sources of fields, such as substance properties (e.g., temperature), artificial constructs (e.g., population density), and fields of potential or influence (e.g., gravity)"},{"concepts":[322],"name":"Digitize and georegister a specified vector feature set to a given geometric accuracy and topological fidelity thresholds using a given map sheet, digitizing tablet, and data entry software"},{"concepts":[108],"name":"Discuss common prepositions and adjectives (in any particular language) that signify either spatial or temporal relations but are used for both kinds, such as after or longer"},{"concepts":[360],"name":"Discuss critiques of GIS as deterministic technology in relation to debates about the Quantitative Revolution in the discipline of geography"},{"concepts":[559],"name":"Discuss different formats (tutorials, in house, online, instructor lead) for training and how they can be used by organizations"},{"concepts":[289],"name":"Discuss future prospects for automated feature extraction from aerial imagery"},{"concepts":[556],"name":"Discuss how a code of ethics might be applied within an organization"},{"concepts":[584],"name":"Discuss how informal and formal regional bodies (e.g., Metro GIS) can help support GIS and T in an organization"},{"concepts":[314],"name":"Discuss how measures of spatial autocorrelation may be used to evaluate thematic accuracy"},{"concepts":[167],"name":"Discuss how the choices used in the design of a road map will influence the experience visitors may have of the area"},{"concepts":[140],"name":"Discuss how to create an intellectual and visual hierarchy on maps"},{"concepts":[342],"name":"Discuss implications of unequal economic power on the kinds of organizations that use, and benefit from, GIS and T"},{"concepts":[1],"name":"Discuss situations when it is desirable to adopt a spatial approach to the analysis of data"},{"concepts":[251],"name":"Discuss some of the difficulties of applying the standard process-pattern concept to lines and networks"},{"concepts":[189],"name":"Discuss the advantages and disadvantages of outsourcing elements of the implementation of a geospatial system, such as data entry"},{"concepts":[101],"name":"Discuss the advantages and disadvantages of the use of cartesian metric space as a basis for GIS and related technologies"},{"concepts":[315],"name":"Discuss the advantages and potential problems associated with the use of Minimum Mapping Unit (MMU) as a measure of the level of detail in land use, land cover, and soils maps"},{"concepts":[67],"name":"Discuss the appropriateness of different types of spatial weights matrices for various problems"},{"concepts":[82],"name":"Discuss the appropriateness of GWR under various conditions"},{"concepts":[117],"name":"Discuss the causal relationship between spatial processes and spatial patterns, including the possible problems in determining causality"},{"concepts":[52],"name":"Discuss the characteristics of the various cluster detection techniques"},{"concepts":[25],"name":"Discuss the consequences of increasing and decreasing resolution"},{"concepts":[116],"name":"Discuss the contributions of early attempts to integrate the concepts of space, time, and attribute in geographic information, such as Berry (1964) and Sinton (1978)"},{"concepts":[101],"name":"Discuss the contributions that different perspectives on the nature of space bring to an understanding of geographic phenomenon"},{"concepts":[116],"name":"Discuss the degree to which these models can be implemented using current technologies"},{"concepts":[10],"name":"Discuss the difference of implementing Dijkstras algorithm in raster and vector modes"},{"concepts":[140],"name":"Discuss the differences between maps that use the same data but are for different purposes and intended audiences"},{"concepts":[140],"name":"Discuss the differences between maps that use the same data but are for different purposes and intended audiences"},{"concepts":[96],"name":"Discuss the differing denotations and connotations of the terms spatial, geographic, and geospatial"},{"concepts":[115],"name":"Discuss the difficulty of integrating process models into GIS software based on the entity and field views, and methods used to do so"},{"concepts":[114],"name":"Discuss the effects of temporal scale on the modeling of genealogical structures"},{"concepts":[353],"name":"Discuss the ethical implications of a local government's decision to charge fees for its data"},{"concepts":[361],"name":"Discuss the ethical implications of the use of GIS and T as a surveillance technology"},{"concepts":[289],"name":"Discuss the extent to which vector data extraction from aerial stereoimagery has been automated"},{"concepts":[247],"name":"Discuss the historical roots of the Census Bureaus creation of GBF/DIME as the foundation for the development of topological data structures"},{"concepts":[112],"name":"Discuss the human predilection to conceptualize geographic phenomena in terms of discrete entities"},{"concepts":[257],"name":"Discuss the implication of long transactions on database integrity"},{"concepts":[360],"name":"Discuss the implications of interoperability on ontology"},{"concepts":[315],"name":"Discuss the implications of the sampling theorem (Lambda = 0.5 delta) to the concept of resolution"},{"concepts":[131],"name":"Discuss the influence of some cartographers of the 16th and 17th centuries (Mercator, Ortelius, Jansson, Homann and others)"},{"concepts":[157],"name":"Discuss the influence of the user interface on maps and visualizations on the Web"},{"concepts":[583],"name":"Discuss the mission, history constituencies and activities of international organizations such as Association of Geographic Information Laboratories for Europe (AGILE) and the European GIS Education Seminar (EUGISES)"},{"concepts":[583],"name":"Discuss the mission, history, constituencies, and activities of GeoSpatial One Stop"},{"concepts":[583],"name":"Discuss the mission, history, constituencies, and activities of governmental entities such as the Bureau of Land Management (BLM), United States Geological Survey (USGS) and the Environmental Protection Agency as they related to support..."},{"concepts":[584],"name":"Discuss the mission, history, constituencies, and activities of National States Geographic Information Council (NSGIC)"},{"concepts":[583],"name":"Discuss the mission, history, constituencies, and activities of the Federal Geographic Data Committee (FGDC)"},{"concepts":[579],"name":"Discuss the mission, history, constituencies, and activities of the GIS Certification Institute (GISCI)"},{"concepts":[583],"name":"Discuss the mission, history, constituencies, and activities of the Nation Integrated Land System (NILS)"},{"concepts":[583],"name":"Discuss the mission, history, constituencies, and activities of the National Academies of Science Mapping Science Committee"},{"concepts":[583],"name":"Discuss the mission, history, constituencies, and activities of the Open Geospatial Consortium (OGC), Inc."},{"concepts":[583],"name":"Discuss the mission, history, constituencies, and activities of the USGS and its National Map vision"},{"concepts":[583],"name":"Discuss the mission, history, constituencies, and activities of University Consortium of Geographic Science (UCGIS) and the National Center for Geographic Information and Analysis (NCGIA)"},{"concepts":[559],"name":"Discuss the National Research Council report on Learning to Think Spatially (2005) as it relates to spatial thinking skills needed by the GIS and T workforce"},{"concepts":[158],"name":"Discuss the nature and use of virtual environments such as Google Earth"},{"concepts":[55],"name":"Discuss the origins of cartographic modeling with reference to the work of Ian McHarg"},{"concepts":[131],"name":"Discuss the perspectives of Brian Harley and others on the political motivation for the development of certain kinds of maps"},{"concepts":[23],"name":"Discuss the pitfalls of using secondary data that has been generated using interpolations (e.g., Level 1 USGS DEMs)"},{"concepts":[583],"name":"Discuss the political, cultural, economic, and geographic characteristics of various countries that influence their adoption and use of GIS and T"},{"concepts":[362],"name":"Discuss the potential role of agency (individual action) in resisting dominant practices and in using GIS and T in ways that are consistent with feminist epistemologies and politics"},{"concepts":[363],"name":"Discuss the production, maintenance, and use of geospatial data by a government agency or private firm from the perspectives of a taxpayer, a community organization, and a member of a minority group"},{"concepts":[57],"name":"Discuss the relationship between spatial processes and spatial patterns"},{"concepts":[131],"name":"Discuss the relationship between the history of exploration and the development of a more accurate map of the world"},{"concepts":[143],"name":"Discuss the role of gamut in choosing colors that can be reproduced on various devices and media"},{"concepts":[247],"name":"Discuss the role of graph theory in topological structures"},{"concepts":[22],"name":"Discuss the role of metadata in facilitating conversation of data models and data structures between systems"},{"concepts":[556],"name":"Discuss the status of professional and academic certification in GIS and T"},{"concepts":[334],"name":"Discuss the status of the concept of privacy in the U.S. legal regime"},{"concepts":[131],"name":"Discuss the Swiss influence on map design and production, highlighting Imhofs contributions"},{"concepts":[66],"name":"Discuss the theory leading to the assumption of intrinsic stationarity"},{"concepts":[581],"name":"Discuss the value or effect of participation in societies, conferences, and informal communities to entities managing enterprise GIS"},{"concepts":[332],"name":"Discuss ways in which the geospatial profession is regulated under the U.S. legal regime"},{"concepts":[308],"name":"Discuss what a Tissot indicatrix represents and how it can be used to assess projection-induced error"},{"concepts":[207],"name":"Distinguish between the incidental and structural relationships found in a conceptual model"},{"concepts":[109],"name":"Document the personal, social, and or institutional meaning of categories used in GIS applications"},{"concepts":[292],"name":"Draw and explain a diagram that depicts the bands in the electromagnetic spectrum at which Earths atmosphere is sufficiently transparent to allow high-altitude remote sensing"},{"concepts":[292],"name":"Draw and explain a diagram that depicts the key bands of the electromagnetic spectrum in relation to the magnitude of electromagnetic energy emitted and/or reflected by the Sun and Earth across the spectrum"},{"concepts":[157],"name":"Edit the symbology, labeling, and page layout for a map originally designed for hard copy printing so that it can be seen and used on the Web"},{"concepts":[105],"name":"Effectively communicate the design, procedures, and results of GIS projects to non-GIS audiences (clients, managers, general public)"},{"concepts":[117],"name":"Employ techniques for visualizing, describing, and analyzing distributions in space, time, and attribute"},{"concepts":[23],"name":"Estimate a value between two known values using linear interpolation (e.g., spot elevations, population between census years)"},{"concepts":[143],"name":"Estimate RGB (red, green, blue) primary amounts in a selection of colors"},{"concepts":[188],"name":"Estimate the cost to collect needed data from primary sources (e.g., remote sensing, GPS)"},{"concepts":[37],"name":"Estimate the fractal dimension of a sinuous line"},{"concepts":[160],"name":"Evaluate graphic techniques used to portray spatializations"},{"concepts":[25],"name":"Evaluate methods used by contemporary GIS software to resample raster data on-the-fly during display"},{"concepts":[187],"name":"Evaluate possible solutions to the major obstacles that stand in the way of a successful GIS proposal"},{"concepts":[293],"name":"Evaluate the advantages and disadvantages of acoustic remote sensing versus airborne or satellite remote sensing for seafloor mapping"},{"concepts":[293],"name":"Evaluate the advantages and disadvantages of airborne remote sensing versus satellite remote sensing"},{"concepts":[288],"name":"Evaluate the advantages and disadvantages of photogrammetric methods and LiDAR for production of terrain elevation data"},{"concepts":[115],"name":"Evaluate the assertion that events and processes are the same thing, but viewed at different temporal scales"},{"concepts":[128],"name":"Evaluate the causes of uncertainty in geospatial data"},{"concepts":[97],"name":"Evaluate the differences in how various parties think or feel differently about a place being modeled"},{"concepts":[242],"name":"Evaluate the ease of measuring resolution in different types of tessellations"},{"concepts":[112],"name":"Evaluate the effectiveness of GIS data models for representing the identity, existence, and lifespan of entities"},{"concepts":[158],"name":"Evaluate the extent to which a GeoWall or CAVE does or does not enhance understanding of spatial data"},{"concepts":[113],"name":"Evaluate the field views description of objects as conceptual discretizations of continuous patterns"},{"concepts":[105],"name":"Evaluate the impact of geospatial technologies (e.g., Google Earth) that allow non-geospatial professionals to create, distribute, and map geographic information"},{"concepts":[242],"name":"Evaluate the implications of changing grid cell resolution on the results of analytical applications by using GIS software"},{"concepts":[112],"name":"Evaluate the influence of scale on the conceptualization of entities"},{"concepts":[89],"name":"Evaluate the influences of ones own philosophical views and assumptions on GIS AND T practices"},{"concepts":[85],"name":"Evaluate the influences of particular worldviews (including ones own) on GIS practices"},{"concepts":[99],"name":"Evaluate the influences of political actions, especially the allocation of territory, on human perceptions of space and place"},{"concepts":[99],"name":"Evaluate the influences of political ideologies (e.g., Marxism, Capitalism, conservative liberal) on the understanding of geographic information"},{"concepts":[189],"name":"Evaluate the labor needed in past cases to build a new geospatial enterprise"},{"concepts":[247],"name":"Evaluate the positive and negative impacts of this shift from integrated topological models"},{"concepts":[236],"name":"Evaluate the relative merits of grid compression methods for storage"},{"concepts":[113],"name":"Evaluate the representation of movement as a field of location over time (e.g. :x,y,z: = f(t) )"},{"concepts":[127],"name":"Evaluate the role that system complexity, dynamic processes, and subjectivity play in the creation of vague phenomena and concepts"},{"concepts":[151],"name":"Evaluate the strengths and limitations of each of the following methods: choropleth, dasymetric, proportioned symbol, graduated symbol, isoline, dot, cartogram, and flow map"},{"concepts":[295],"name":"Evaluate the thematic accuracy of a given soils map"},{"concepts":[207],"name":"Evaluate the various general data models common in GIS and T for a given project, and select the most appropriate solutions"},{"concepts":[127],"name":"Evaluate vagueness in the locations, time, attributes, and other aspects of geographic phenomena"},{"concepts":[143],"name":"Exemplify colors for different forms of harmony, concordance, and balance"},{"concepts":[66],"name":"Exemplify deterministic and spatial stochastic processes"},{"concepts":[107],"name":"Exemplify different temporal frames of reference: linear and cyclical, absolute and relative"},{"concepts":[557],"name":"Exemplify each component of a needs assessment for an enterprise GIS"},{"concepts":[260],"name":"Exemplify how the lack of a data librarian to manage data can have disastrous consequences on the resulting dataset"},{"concepts":[565],"name":"Exemplify how to make GIS and T relevant to top managemen"},{"concepts":[167],"name":"Exemplify maps that illustrate the provocative, propaganda, political, and persuasive nature of maps and geospatial data"},{"concepts":[66],"name":"Exemplify non-stationarity involving first and second order effects"},{"concepts":[118],"name":"Exemplify regions found at different scales"},{"concepts":[257],"name":"Exemplify scenarios in which one would need to perform a number of periodic changes in a real GIS database"},{"concepts":[39],"name":"Exemplify situations in which the centroid of a polygon falls outside its boundary"},{"concepts":[12],"name":"Exemplify the Classic Transportation Problem"},{"concepts":[247],"name":"Exemplify the concept of planar enforcement (e.g., TIN triangles)"},{"concepts":[238],"name":"Exemplify the uses (past and potential) of the hexagonal model"},{"concepts":[80],"name":"Explain Anselins typology of spatial autoregressive models"},{"concepts":[38],"name":"Explain any differences in the measured direction between two places when the data are presented in a GIS in different projections"},{"concepts":[5],"name":"Explain how a Bayesian framework can incorporate expert knowledge in order to retrieve all relevant datasets given an initial user query"},{"concepts":[570],"name":"Explain how a business case analysis can be used to justify the expense of implementing consensus-based standards"},{"concepts":[251],"name":"Explain how a graph (network) may be directed or undirected"},{"concepts":[251],"name":"Explain how a graph can be written as an adjacency matrix and how this can be used to calculate topological shortest paths in the graph"},{"concepts":[10],"name":"Explain how a leading World Wide Web-based routing system works e.g., MapQuest, Yahoo Maps, Google"},{"concepts":[41],"name":"Explain how a semi-variogram describes the distance decay in dependence between data values"},{"concepts":[68],"name":"Explain how a statistic that is based on combining all the spatial data and returning a single summary value or two can be useful in understanding broad spatial trends"},{"concepts":[363],"name":"Explain how a tax assessors office adoption of GIS and T may affect power relations within a community"},{"concepts":[69],"name":"Explain how a weights matrix can be used to convert any classical statistic into a local measure of spatial association"},{"concepts":[82],"name":"Explain how allowing the parameters of the model to vary with the spatial location of the sample data can be used to accommodate spatial heterogeneity"},{"concepts":[131],"name":"Explain how Bertin has influenced trends in cartographic symbolization"},{"concepts":[76],"name":"Explain how block-kriging and its variants can be used to combine data sets with different spatial resolution support"},{"concepts":[569],"name":"Explain how clearing houses, metadata, and standards can help facilitate spatial data sharing"},{"concepts":[334],"name":"Explain how conversion of land records data from analog to digital form increases risk to personal privacy"},{"concepts":[334],"name":"Explain how data aggregation is used to protect personal privacy in data produced by the U.S. Census Bureau"},{"concepts":[37],"name":"Explain how different measures of distance can be used to calculate the spatial weights matrix"},{"concepts":[67],"name":"Explain how different types of spatial weights matrices are defined and calculated"},{"concepts":[81],"name":"Explain how dissolving clusters of blocks with similar values may resolve the spatial correlation problem"},{"concepts":[50],"name":"Explain how distance-based methods of point pattern measurement can be derived from a distance matrix"},{"concepts":[53],"name":"Explain how dynamic, chaotic, complex or unpredictable aspects in some phenomena make spatial interaction models more appropriate than gravity models"},{"concepts":[37],"name":"Explain how fractal dimension can be used in practical applications of GIS"},{"concepts":[63],"name":"Explain how friction surfaces are enhanced by the use of impedance and barriers"},{"concepts":[69],"name":"Explain how geographically weighted regression provides a local measure of spatial association"},{"concepts":[313],"name":"Explain how geometric accuracies associated with the various orders of the U.S. horizontal geodetic control network are assured"},{"concepts":[344],"name":"Explain how geospatial information might be used in a taking of private property through a governments claim of its right of eminent domain"},{"concepts":[548],"name":"Explain how GIS and T can be an integrating technology"},{"concepts":[235],"name":"Explain how grid representations embody the field-based view"},{"concepts":[156],"name":"Explain how interactivity influences map use in animated displays"},{"concepts":[167],"name":"Explain how legal issues impact the design and content of such special purpose maps as subdivision plans, nautical charts and cadastral maps"},{"concepts":[32],"name":"Explain how logic theory relates to set theory"},{"concepts":[170],"name":"Explain how maps can be used in determining an optimal route or facility selection"},{"concepts":[170],"name":"Explain how maps can be used in terrain analysis (e.g., elevation determination, surface profiles, slope, viewsheds, and gradient)"},{"concepts":[153],"name":"Explain how maps that show the landscape in profile can be used to represent terrain"},{"concepts":[354],"name":"Explain how one or more obligations in the GIS Code of Ethics may conflict with organizations proprietary interests"},{"concepts":[257],"name":"Explain how one would establish the criteria for monitoring the periodic changes in a real GIS database"},{"concepts":[16],"name":"Explain how optimization models can be used to generate models of alternate options for presentation to decision makers"},{"concepts":[70],"name":"Explain how outliers affect the results of analyses"},{"concepts":[569],"name":"Explain how privacy and commoditization of data impact influences decisions regarding spatial data infrastructures"},{"concepts":[50],"name":"Explain how proximity polygons e.g., Thiessen polygons may be used to describe point patterns"},{"concepts":[243],"name":"Explain how quadtrees and other hierarchical tessellations can be used to index large volumes of raster or vector data"},{"concepts":[143],"name":"Explain how real-world connotations (e.g., blue=water, white=snow) can be used to determine color selections on maps"},{"concepts":[315],"name":"Explain how resampling affects the resolution of image data"},{"concepts":[570],"name":"Explain how resistance to change affects the adoption of standards in an organization coordinating a GIS"},{"concepts":[61],"name":"Explain how ridgelines and streamlines can be used to improve the result of an interpolation process"},{"concepts":[296],"name":"Explain how sea surface temperature maps are used to predict El Nino events"},{"concepts":[32],"name":"Explain how set theory relates to spatial queries"},{"concepts":[59],"name":"Explain how slope and aspect can be represented as the vector field given by the first derivative of height"},{"concepts":[81],"name":"Explain how spatial correlation can result as a side effect of the spatial aggregation in a given dataset"},{"concepts":[5],"name":"Explain how spatial data mining techniques can be used for knowledge discovery"},{"concepts":[79],"name":"Explain how spatial dependence and spatial heterogeneity violate the Gauss-Markov assumptions of regression used in traditional econometrics"},{"concepts":[160],"name":"Explain how spatial metaphors can be used to illustrate the relationship among ideas"},{"concepts":[4],"name":"Explain how spatial statistics techniques are used in spatial data mining"},{"concepts":[160],"name":"Explain how spatialization is a core component of visual analytics"},{"concepts":[131],"name":"Explain how technological changes have affected cartographic design and production"},{"concepts":[235],"name":"Explain how terrain elevation can be represented by a regular tessellation and by an irregular tessellation"},{"concepts":[144],"name":"Explain how text properties can be used as visual variables to graphically represent the type and attributes of geographic features"},{"concepts":[4],"name":"Explain how the analytical reasoning techniques, visual representations, and interaction techniques that make up the domain of visual analytics have a strong spatial component"},{"concepts":[71],"name":"Explain how the Bayesian perspective is a unified framework from which to view uncertainty"},{"concepts":[97],"name":"Explain how the concept of place is more than just location"},{"concepts":[310],"name":"Explain how the concepts of the tangent and secant cases relate to the idea of a standard line"},{"concepts":[23],"name":"Explain how the elevation values in a digital elevation model (DEM) are derived by interpolation from irregular arrays of spot elevations"},{"concepts":[128],"name":"Explain how the familiar concepts of geographic objects and fields affect the conceptualization of uncertainty"},{"concepts":[70],"name":"Explain how the following techniques can be used to examine outliers: tabulation, histograms, box plots, correlation analysis, scatter plots, local statistics"},{"concepts":[81],"name":"Explain how the Getis and Tiefelsdorf Griffith spatial filtering techniques incorporate spatial component variables into OLS regression analysis in order to remedy misspecification and the problem of spatially auto-correlated residuals"},{"concepts":[50],"name":"Explain how the K function provides a scale-dependent measure of dispersion"},{"concepts":[68],"name":"Explain how the K function provides a scale-dependent measure of dispersion"},{"concepts":[234],"name":"Explain how the raster data model instantiates a grid representation"},{"concepts":[170],"name":"Explain how the types of distortion indicated by projection metadata on a map will affect map measurements"},{"concepts":[24],"name":"Explain how the vector/raster/vector conversion process of graphic images and algorithms takes place and how the results are achieved"},{"concepts":[158],"name":"Explain how the virtual and immersive environments become increasingly more complex as we move from the relatively non-immersive VRML desktop environment to a stereoscopic display (e.g., a GeoWall) to a more fully immersive CAVE"},{"concepts":[294],"name":"Explain how to enhance contrast of reflectance values clustered within a narrow band of wavelengths"},{"concepts":[144],"name":"Explain how to label features with indeterminate boundaries (canyons, oceans, etc.)"},{"concepts":[3],"name":"Explain how to recognize contaminated data in large datasets"},{"concepts":[295],"name":"Explain how U.S. Geological Survey scientists and contractors assess the accuracy of the National Land Cover Dataset"},{"concepts":[40],"name":"Explain how variations in the calculation of area may have real world implications, such as calculating density"},{"concepts":[158],"name":"Explain how various data formats and software and hardware environments support immersive visualization"},{"concepts":[5],"name":"Explain how visual data exploration can be combined with data mining techniques as a means of discovering research hypotheses in large spatial datasets"},{"concepts":[236],"name":"Explain the advantage of wavelet compression"},{"concepts":[763],"name":"Explain the advantages and disadvantages of the pushbroom system"},{"concepts":[247],"name":"Explain the advantages and disadvantages of topological data models"},{"concepts":[362],"name":"Explain the argument that GIS and remote sensing foster a disembodied way of knowing the world"},{"concepts":[363],"name":"Explain the argument that GIS is socially constructed"},{"concepts":[360],"name":"Explain the argument that GIS privileges certain views of the world over others"},{"concepts":[334],"name":"Explain the argument that human tracking systems enable geoslavery"},{"concepts":[363],"name":"Explain the argument that, throughout history, maps have been used to depict social relations"},{"concepts":[33],"name":"Explain the basic logic of SQL syntax"},{"concepts":[47,48],"name":"Explain the categories of map algebra operations i.e., local, focal, zonal, and global functions"},{"concepts":[309],"name":"Explain the concept developable surface and reference globe as conceptual ways of projecting the Earths surface"},{"concepts":[308],"name":"Explain the concept of a compromise projection and for which purposes it is useful"},{"concepts":[344],"name":"Explain the concept of a spatial decision support system"},{"concepts":[53],"name":"Explain the concept of competing destinations, describing how traditional spatial interaction model forms are modified to account for it"},{"concepts":[292],"name":"Explain the concept of data fusion in relation to remote sensing applications in GIS and T"},{"concepts":[313],"name":"Explain the concept of dilution of precision"},{"concepts":[316],"name":"Explain the concept of error propagation"},{"concepts":[16],"name":"Explain the concept of solution space"},{"concepts":[9],"name":"Explain the concept of the diameter of a network"},{"concepts":[76],"name":"Explain the concept of the kriging variance, and describe some of its shortcomings"},{"concepts":[19],"name":"Explain the concepts of demand and service"},{"concepts":[292],"name":"Explain the concepts of spatial resolution, radiometric resolution, and spectral sensitivity"},{"concepts":[122],"name":"Explain the contributions of formal mathematical methods such as Graph Theory to the study and application of geographic structures"},{"concepts":[170],"name":"Explain the differences between true north, magnetic north, and grid north directional references"},{"concepts":[37],"name":"Explain the differences in the calculated distance between the same two places when data used are in different projections"},{"concepts":[317],"name":"Explain the distinction between primary and secondary data sources in terms of census data, cartographic data, and remotely sensed data"},{"concepts":[314],"name":"Explain the distinction between thematic accuracy, geometric accuracy, and topological fidelity"},{"concepts":[122],"name":"Explain the effects of spatial or temporal scale on the perception of structure"},{"concepts":[313],"name":"Explain the factors that influence the geometric accuracy of data produced with Global Positioning System (GPS) receivers"},{"concepts":[313],"name":"Explain the formula for calculating root mean square error"},{"concepts":[584],"name":"Explain the functions, mission, history, constituencies, and activities of your state GIS Council and related formal and informal bodies"},{"concepts":[109],"name":"Explain the human tendency to simplify the world using categories"},{"concepts":[131],"name":"Explain the impact of advances in visualization methods in the evolution of cartography"},{"concepts":[308],"name":"Explain the kind of distortion that occurs when raster data are projected"},{"concepts":[56],"name":"Explain the legacy of multi-criteria evaluation in relation to cartographic modeling"},{"concepts":[238],"name":"Explain the limitations of the grid model compared to the hexagonal model"},{"concepts":[309],"name":"Explain the mathematical basis by which latitude and longitude locations are projected into x and y coordinate space"},{"concepts":[122],"name":"Explain the modeling of structural relationships in standard GIS data models, such as stored topology"},{"concepts":[118],"name":"Explain the nature of the Modifiable Areal Unit Problem (MAUP)"},{"concepts":[75],"name":"Explain the necessity of defining a semi-variogram model for geographic data"},{"concepts":[42],"name":"Explain the nine-intersection model for spatial relationships"},{"concepts":[87],"name":"Explain the notions of model and representation in science"},{"concepts":[206],"name":"Explain the objectives of the conceptual modeling phase of design"},{"concepts":[6],"name":"Explain the outcome of an artificial intelligence analysis e.g., edge recognition, including a discussion of what the human did not see that the computer identified and vice versa"},{"concepts":[285],"name":"Explain the phenomenon that is recorded in an aerial image"},{"concepts":[763],"name":"Explain the principle of across track scanning (pushbroom technology)"},{"concepts":[313],"name":"Explain the principle of differential correction in relation to the global positioning system"},{"concepts":[293],"name":"Explain the principle of multibeam bathymetric mapping"},{"concepts":[82],"name":"Explain the principles of geographically weighted regression"},{"concepts":[16],"name":"Explain the principles of operations research modeling and location modeling"},{"concepts":[6],"name":"Explain the principles of pattern recognition"},{"concepts":[308],"name":"Explain the rationale for the selection of the geometric property that is preserved in map projections used as the basis of the UTM and SPC systems"},{"concepts":[41],"name":"Explain the rationale for using different forms of distance decay functions"},{"concepts":[67],"name":"Explain the rationale used for each type of spatial weights matrix"},{"concepts":[154],"name":"Explain the relationship among several variables in a parallel coordinate plot"},{"concepts":[118],"name":"Explain the relationship between regions and categories"},{"concepts":[288],"name":"Explain the relevance of the concept parallax in stereoscopic aerial imagery"},{"concepts":[311],"name":"Explain the role and selection criteria for ground control points (GCPs) in the georegistration of aerial imagery"},{"concepts":[109],"name":"Explain the role of categories in common-sense conceptual models, everyday language, and analytical procedures"},{"concepts":[17],"name":"Explain the role of constraint functions using the graphical method"},{"concepts":[17],"name":"Explain the role of constraint functions using the simplex method"},{"concepts":[92],"name":"Explain the role of metaphors and image schema in our understanding of geographic phenomena and geographic tasks"},{"concepts":[17],"name":"Explain the role of objective functions in linear programming"},{"concepts":[285],"name":"Explain the significance of bit depth in aerial imaging"},{"concepts":[62],"name":"Explain the sources and impact of errors that affect intervisibility analyses"},{"concepts":[207],"name":"Explain the various types of cardinality found in databases"},{"concepts":[19],"name":"Explain Webers locational triangle"},{"concepts":[1],"name":"Explain what is added to spatial analysis to make it spatio-temporal analysis"},{"concepts":[39],"name":"Explain what is meant by the convex hull and minimum enclosing rectangle of a set of point data"},{"concepts":[3],"name":"Explain what is meant by the term contaminated data, suggesting how it can arise"},{"concepts":[1],"name":"Explain what is special i.e., difficult about geospatial data analysis and why some traditional statistical analysis techniques are not suited to geographic problems"},{"concepts":[51],"name":"Explain why and how density estimation transforms point data into a field representation"},{"concepts":[151],"name":"Explain why choropleth maps should (almost) never be used for mapping count data and suggest alternative methods for mapping count data"},{"concepts":[60],"name":"Explain why different interpolation algorithms produce different results and suggest ways by which these can be evaluated in the context of a specific problem"},{"concepts":[37],"name":"Explain why estimating the fractal dimension of a sinuous line has important implications for the measurement of its length"},{"concepts":[118],"name":"Explain why general-purpose regions rarely exist"},{"concepts":[47,48],"name":"Explain why georegistration is a precondition to map algebra"},{"concepts":[13],"name":"Explain why heuristic solutions are generally used to address the combinatorially complex nature of these problems and the difficulty of solving them optimally"},{"concepts":[18],"name":"Explain why integer programs are harder to solve than linear programs"},{"concepts":[247],"name":"Explain why integrated topological models have lost favor in commercial GIS software"},{"concepts":[556],"name":"Explain why it has been difficult for many agencies and organizations to define positions and roles for GIS and T professionals"},{"concepts":[76],"name":"Explain why it is important to have a good model of the semi-variogram in kriging"},{"concepts":[76],"name":"Explain why kriging is more suitable as an interpolation method in some applications than others"},{"concepts":[258],"name":"Explain why logging and rollback techniques are adequate for managing short transactions"},{"concepts":[580],"name":"Explain why software products sold by U.S. companies may predominate in foreign markets, including Europe and Australia"},{"concepts":[59],"name":"Explain why the properties of spatial continuity are characteristic of spatial surfaces"},{"concepts":[39],"name":"Explain why the shape of an object might be important in analysis"},{"concepts":[125],"name":"Explain why Toblers First Law of Geography is fundamental to many operations in GIS and whether it should be"},{"concepts":[59],"name":"Explain why zero slopes are indicative of surface specific points such as peaks, pits and passes and list the conditions necessary for each"},{"concepts":[12],"name":"Explain why, if supply equals demand, there will always be a feasible solution to the Classic Transportation Problem"},{"concepts":[51],"name":"Explain why, in some cases, an adaptive bandwidth might be employed"},{"concepts":[316],"name":"Explain, in general terms, the difference between single and double precision and impacts on error propagation"},{"concepts":[16],"name":"Explain, using the concept of combinatorial complexity, why some location problems are very hard to solve"},{"concepts":[92],"name":"Explore the contribution of linguistics to the study of spatial cognition and the role of natural language in the conceptualization of geographic phenomena"},{"concepts":[96],"name":"Explore the history of geography including (but not limited to) Greek and Roman contributions to geography (Eratosthenes, Strabo, Ptolemy), geography and cartography in the age of discovery, military geography, and geography..."},{"concepts":[80],"name":"Find a best model"},{"concepts":[39],"name":"Find centroids of polygons under different definitions of a centroid and different polygon shapes"},{"concepts":[559],"name":"Find or create training resources appropriate for GIS and T workforce in a local government organization"},{"concepts":[117],"name":"Find spatial patterns in the distribution of geographic phenomena using geographic visualization and other techniques"},{"concepts":[110],"name":"Formalize attribute values and domains in terms of set theory"},{"concepts":[113],"name":"Formalize the notion of field using mathematical functions and Calculus"},{"concepts":[117],"name":"Hypothesize the causes of a pattern in the spatial distribution of a phenomenon"},{"concepts":[190],"name":"Hypothesize the ways in which capital needs for GIS may change in the future"},{"concepts":[360],"name":"Identify alternatives to the algorithmic way of thinking that characterizes GIS"},{"concepts":[308],"name":"Identify and define the four geometric properties of the globe that may be preserved or lost in projected coordinates"},{"concepts":[74],"name":"Identify and define the parameters of a semi-variogram range, sill, nugget"},{"concepts":[311],"name":"Identify and explain an equation used to perform image-to-image registration"},{"concepts":[311],"name":"Identify and explain an equation used to perform image-to-map registration"},{"concepts":[113],"name":"Identify applications and phenomena that are not adequately modeled by the field view"},{"concepts":[105],"name":"Identify common-sense views of geographic phenomena that sharply contrast with established theories and technologies of geographic information"},{"concepts":[581],"name":"Identify conferences that are related to GIS and T"},{"concepts":[579],"name":"Identify conferences that are related to GIS and T hosted by professional organizations"},{"concepts":[113],"name":"Identify examples of discrete and continuous change found in spatial, temporal, and spatio-temporal fields"},{"concepts":[117],"name":"Identify influences of scale on the appearance of distributions"},{"concepts":[187],"name":"Identify major obstacles to the success of a GIS proposal"},{"concepts":[81],"name":"Identify modeling situations where spatial filtering might not be appropriate"},{"concepts":[583],"name":"Identify National Science Foundation (NSF) programs that support GIS and T research and education"},{"concepts":[570],"name":"Identify organizations that focus on developing standards related to GIS and T"},{"concepts":[122],"name":"Identify phenomena that are best understood as networks"},{"concepts":[112],"name":"Identify phenomena that are difficult or impossible to conceptualize in terms of entities"},{"concepts":[188],"name":"Identify potential sources of data (free or commercial) needed for a particular application or enterprise"},{"concepts":[191],"name":"Identify potential sources of funding (internal and external) for a project or enterprise GIS"},{"concepts":[52],"name":"Identify several cluster detection techniques and discuss their limitations"},{"concepts":[39],"name":"Identify situations in which shape affects geometric operations"},{"concepts":[125],"name":"Identify situations in which Toblers First Law of Geography does not apply"},{"concepts":[125],"name":"Identify situations in which Toblers First Law of Geography is valuable"},{"concepts":[109],"name":"Identify specific examples of categories of entities (i.e., common nouns), properties (i.e., adjectives), space (i.e., regions), and time (i.e., eras)"},{"concepts":[570],"name":"Identify standards that are used in GIS and T"},{"concepts":[22],"name":"Identify the conceptual and practical difficulties associated with data model and format conversion"},{"concepts":[87],"name":"Identify the epistemological assumptions underlying the work of colleagues"},{"concepts":[190],"name":"Identify the hardware and space that will be needed for a GIS implementation"},{"concepts":[127],"name":"Identify the hedges used in language to convey vagueness"},{"concepts":[118],"name":"Identify the kinds of phenomena that are commonly found at the boundaries of regions"},{"concepts":[235],"name":"Identify the national framework datasets based on a grid model"},{"concepts":[85],"name":"Identify the ontological assumptions underlying the work of colleagues"},{"concepts":[310],"name":"Identify the parameters that allow one to focus a projection on an area of interest"},{"concepts":[559],"name":"Identify the particular skills necessary for users to perform tasks in three different workforce domains (e.g., small city, medium county agency, a business, or others)"},{"concepts":[89],"name":"Identify the philosophical views and assumptions underlying the work of colleagues"},{"concepts":[189],"name":"Identify the positions necessary to design and implement a GIS"},{"concepts":[310],"name":"Identify the possible aspects of a projection and describe the graticules appearance in each aspect"},{"concepts":[556],"name":"Identify the qualifications needed for a particular GIS and T position"},{"concepts":[60],"name":"Identify the spatial concepts that are assumed in different interpolation algorithms"},{"concepts":[556],"name":"Identify the standard occupational codes that are relevant to GIS and T"},{"concepts":[112],"name":"Identify the types of features that need to be modeled in a particular GIS application or procedure"},{"concepts":[50],"name":"Identify the various ways point patterns may be described"},{"concepts":[108],"name":"Identify various types of geographic interactions in space and time"},{"concepts":[50],"name":"Identify various types of K-function analysis"},{"concepts":[247],"name":"Illustrate a topological relation"},{"concepts":[315],"name":"Illustrate and explain the distinction between resolution, precision, and accuracy"},{"concepts":[315],"name":"Illustrate and explain the distinctions between spatial resolution, thematic resolution, and temporal resolution"},{"concepts":[308],"name":"Illustrate distortion patterns associated with a given projection class"},{"concepts":[116],"name":"Illustrate major integrated models of geographic information, such as Peuquets Triad, Mennis Pyramid, and Yuans Three-Domain"},{"concepts":[559],"name":"Illustrate methods that are effective in providing opportunities for education and training when implementing a GIS in a small city"},{"concepts":[236],"name":"Illustrate the existing methods for compressing gridded data (e.g., run length encoding, Lempel-Ziv, wavelets)"},{"concepts":[309],"name":"Illustrate the graticule configurations for other projection classes, such as polyconic, pseudocylindrical, etc."},{"concepts":[238],"name":"Illustrate the hexagonal model"},{"concepts":[242],"name":"Illustrate the impact of grid cell resolution on the information that can be portrayed"},{"concepts":[24],"name":"Illustrate the impact of vector/raster/vector conversions on the quality of a dataset"},{"concepts":[243],"name":"Illustrate the quadtree model"},{"concepts":[292],"name":"Illustrate the spectral response curves for basic environmental features (e.g., vegetation, concrete, bare soil)"},{"concepts":[548],"name":"Illustrate what functions a support or service center can provide to an organization using GIS and T"},{"concepts":[243],"name":"Implement a format for encoding quadtrees in a data file"},{"concepts":[310],"name":"Implement a given map projection formula in a software program that reads geographic coordinates as input and produces projected (x, y) coordinates as output"},{"concepts":[80],"name":"Implement a maximum likelihood estimation procedure for determining key spatial econometric parameters"},{"concepts":[259],"name":"Implement a test of reliability of change information"},{"concepts":[60],"name":"Implement a trend surface analysis using either the supplied function in a GIS or a regression function from any standard statistical package"},{"concepts":[17],"name":"Implement linear programs for spatial allocation problems"},{"concepts":[12],"name":"Implement the Transportation Simplex method to determine the optimal solution"},{"concepts":[313],"name":"In contrast to the National Map Accuracy Standard, explain how the spatial accuracy of a digital road centerlines data set may be evaluated and documented"},{"concepts":[557],"name":"Indicate the possible justifications that can be used to implement an enterprise GIS"},{"concepts":[308],"name":"Interpret a given a projected graticule, continent outlines, and indicatrixes at each graticule intersection in terms of geometric properties preserved and distorted"},{"concepts":[545],"name":"Interpret descriptive statistics and geostatistics of geographic data"},{"concepts":[4],"name":"Interpret patterns in space and time using Dorling and Openshaws Geographical Analysis Machine GAM demonstration of disease incidence diffusion"},{"concepts":[234],"name":"Interpret the header of a standard raster data file"},{"concepts":[77],"name":"Interpret the results of universal kriging"},{"concepts":[188],"name":"Judge the relative merits of obtaining free data, purchasing data, outsourcing data creation, or producing and managing data in-house for a particular application or enterprise"},{"concepts":[96],"name":"Justify a chosen position on which disciplines should have as important a role in GIS AND T as geography"},{"concepts":[187],"name":"Justify feasibility recommendations to decision-makers"},{"concepts":[112],"name":"Justify or refute the conception of fields (e.g., temperature, density) as spatially-intensive attributes of (sometimes amorphous and anonymous) entities"},{"concepts":[96],"name":"Justify or refute whether geography (as a discipline) should have a central role in GIS AND T"},{"concepts":[101],"name":"Justify the discrepancies between the nature of locations in the real world and representations thereof (e.g., towns as points)"},{"concepts":[87],"name":"Justify the epistemological frameworks with which you agree"},{"concepts":[85],"name":"Justify the metaphysical theories with which you agree"},{"concepts":[66],"name":"Justify the stochastic process approach to spatial statistical analysis"},{"concepts":[68],"name":"Justify, compute, and test the significance of the join count statistic for a pattern of objects"},{"concepts":[73],"name":"List and describe several spatial sampling schemes and evaluate each one for specific applications"},{"concepts":[344],"name":"List and describe the types of data maintained by local, state, and federal governments"},{"concepts":[251],"name":"List definitions of networks that apply to specific applications or industries"},{"concepts":[42],"name":"List different ways connectivity can be determined in a raster and in a polygon dataset"},{"concepts":[40],"name":"List reasons why the area of a polygon calculated in a GIS might not be the same as the real world object it describes"},{"concepts":[13],"name":"List several classic problems to which network analysis is applied e.g., The Traveling Salesman Problem, The Chinese Postman Problem"},{"concepts":[557],"name":"List some of the topics that should be addressed in a justification for implementing an enterprise GIS (e.g., return on investment, workflow, knowledge sharing)"},{"concepts":[187],"name":"List some of the topics that should be addressed in such a justification of geospatial technology (e.g., ROI, workflow, knowledge sharing)"},{"concepts":[50],"name":"List the conditions that make point pattern analysis a suitable process"},{"concepts":[187],"name":"List the costs and benefits (financial and intangible) of implementing geospatial technology for a particular application or an entire institution"},{"concepts":[59],"name":"List the likely sources of error in slope and aspect maps derived from DEMs and state the circumstances under which these can be very severe"},{"concepts":[140],"name":"List the major factors that should be considered in preparing a map"},{"concepts":[75],"name":"List the possible sources of error in a selected and fitted model of an experimental semi-variogram"},{"concepts":[124],"name":"List the possible topological relationships between entities in space (e.g., 9-intersection) and time"},{"concepts":[143],"name":"List the range of factors that should be considered in selecting colors"},{"concepts":[66],"name":"List the two basic assumptions of the purely random process"},{"concepts":[14],"name":"List ways we can define accessibility on a network"},{"concepts":[19],"name":"Locate, using location-allocation software, service facilities that meet given sets of constraints"},{"concepts":[170],"name":"Measure point-feature movement and point-feature diffusion on maps"},{"concepts":[112],"name":"Model gray area phenomena, such as categorical coverages (a.k.a. discrete fields), in terms of objects"},{"concepts":[257],"name":"Modify spatial and attribute data while ensuring consistency within the database"},{"concepts":[144],"name":"Name the authorities used to confirm the spelling of geographic names for a specific mapping project"},{"concepts":[59],"name":"Outline a number of different methods for calculating slope from a Digital Elevation Model (DEM)"},{"concepts":[296],"name":"Outline a plausible workflow for habitat mapping, such as the benthic habitat mapping in the main Hawaiian Islands as part of the NOAA Biogeography program"},{"concepts":[296],"name":"Outline a plausible workflow used by MDA Federal (formerly EarthSat) to create the high-resolution GEOCOVER global imagery and GEOCOVER-LC global land cover datasets"},{"concepts":[323],"name":"Outline a workflow that can be used to train a new employee to update a county road centerlines database using digital aerial imagery and standard GIS editing tools"},{"concepts":[60],"name":"Outline algorithms to produce repeatable contour-type lines from point datasets using proximity polygons, spatial averages, or inverse distance weighting"},{"concepts":[62],"name":"Outline an algorithm to determine the viewshed area visible from specific locations on surfaces specified by digital elevation models (DEM)"},{"concepts":[40],"name":"Outline an algorithm to find the area of a polygon using the coordinates of its vertices"},{"concepts":[59],"name":"Outline how higher order derivatives of height can be interpreted"},{"concepts":[50],"name":"Outline measures of pattern based on first and second order properties such as the mean centre and standard distance, quadrat counts, nearest neighbor distance and the more modern G, F and K functions"},{"concepts":[558],"name":"Outline methods (programs or processes) that provide effective staff development opportunities for GIS and T"},{"concepts":[76],"name":"Outline the basic kriging equations in their matrix formulation"},{"concepts":[50],"name":"Outline the basis of classic critiques of spatial statistical analysis in the context of point pattern analysis"},{"concepts":[131],"name":"Outline the development of some of the major map projections (e.g., Mercator, Gnomonic, Robinson)"},{"concepts":[41],"name":"Outline the geometry implicit in classical gravity models of distance decay"},{"concepts":[3],"name":"Outline the implications of complexity for the application of statistical ideas in geography"},{"concepts":[37],"name":"Outline the implications of differences in distance calculations on real world applications of GIS, such as routing and determining boundary lengths and service areas"},{"concepts":[51],"name":"Outline the likely effects on analysis results of variations in the kernel function used and the bandwidth adopted"},{"concepts":[66],"name":"Outline the logic behind the derivation of long run expected outcomes of the independent random process using quadrat counts"},{"concepts":[583],"name":"Outline the principle concepts and goals of the digital earth vision articulated in 1998 by Vice President Al Gore"},{"concepts":[324],"name":"Outline the process of scanning and vectorizing features depicted on a printed map sheet using a given GIS software product, emphasizing issues that require manual intervention"},{"concepts":[314],"name":"Outline the SDTS and ISO TC211 standards for thematic accuracy"},{"concepts":[288],"name":"Outline the sequence of tasks involved in generating an orthoimage from a vertical aerial photograph"},{"concepts":[1],"name":"Outline the sequence of tasks required to complete the analytical process for a given spatial problem"},{"concepts":[165],"name":"Outline the stages in lithographic offset printing"},{"concepts":[52],"name":"Perform a cluster detection analysis to detect hot spots in a point pattern"},{"concepts":[32],"name":"Perform a logic set theoretic query using GIS software"},{"concepts":[294],"name":"Perform a manual unsupervised classification given a two-dimensional array of reflectance values and ranges of reflectance values associated with a given number of land cover categories"},{"concepts":[47,48],"name":"Perform a map algebra calculation using command line, form-based, and flow charting user interfaces"},{"concepts":[187],"name":"Perform a pilot study to evaluate the feasibility of an application"},{"concepts":[82],"name":"Perform an analysis using the geographically weighted regression technique"},{"concepts":[54],"name":"Perform multidimensional scaling (MDS) and principal components analysis (PCA) to reduce the number of coordinates, or dimensionality, of a problem"},{"concepts":[62],"name":"Perform siting analyses using specified visibility, slope, and other surface related constraints"},{"concepts":[290],"name":"Plan an aerial imagery mission in response to a given RFP and map of a study area, taking into consideration vertical and horizontal control, atmospheric conditions, time of year, and time of day"},{"concepts":[170],"name":"Plan an orienteering tour of a specific length that traverses slopes of an appropriate steepness and crosses streams in places that can be forded based on a topographic map"},{"concepts":[143],"name":"Plan color proofing suited for checking a map publication job"},{"concepts":[41],"name":"Plot typical forms for distance decay functions"},{"concepts":[144],"name":"Position labels on a map to name point, line, and area features"},{"concepts":[165],"name":"Prepare a color map for black-and-white photocopy distribution"},{"concepts":[140],"name":"Prepare different map layouts using the same map components (main map area, inset maps, titles, legends, scale bars, north arrows, grids and graticule) to produce maps with very distinctive purposes"},{"concepts":[140],"name":"Prepare different maps using the same data for different purposes and intended audiences (e.g., expert and novice hikers)"},{"concepts":[21],"name":"Prioritize a set of algorithms designed to perform transformations based on the need to maintain data integrity [e.g., converting a digital elevation model (DEM) into a TIN]"},{"concepts":[54],"name":"Produce plots in several data dimensions using a data matrix of attributes"},{"concepts":[294],"name":"Produce pseudocode for common unsupervised classification algorithms including chain method, ISODATA method, and clustering"},{"concepts":[260],"name":"Produce viable queries for change scenarios using GIS or database management tools"},{"concepts":[354],"name":"Propose a resolution to a conflict between an obligation in the GIS Code of Ethics and organizations proprietary interests"},{"concepts":[109],"name":"Recognize and manage the potential problems associated with the use of categories (e.g., the ecological fallacy)"},{"concepts":[110],"name":"Recognize attribute domains that do not fit well into Stevens four levels of measurement (nominal, ordinal, interval, ratio), such as cycles, indexes, and hierarchies"},{"concepts":[308],"name":"Recognize distortion patterns on a map based upon the graticule arrangement"},{"concepts":[128],"name":"Recognize expressions of uncertainty in language"},{"concepts":[110],"name":"Recognize situations and phenomena in the landscape which cannot be adequately represented by formal attributes, such as aesthetics"},{"concepts":[545],"name":"Recognize the assumptions underlying probability and geostatistics and the situations in which they are useful analytical tools"},{"concepts":[85],"name":"Recognize the commonalities of philosophical viewpoints and appreciate differences to enable work with diverse colleagues"},{"concepts":[208],"name":"Recognize the constraints and opportunities of a particular choice of software for implementing a logical model"},{"concepts":[99],"name":"Recognize the constraints that political forces place on geospatial applications in public and private sectors"},{"concepts":[124],"name":"Recognize the contributions of Topology (the branch of mathematics) to the study of geographic relationships"},{"concepts":[128],"name":"Recognize the degree to which the importance of uncertainty depends on scale and application"},{"concepts":[127],"name":"Recognize the degree to which vagueness depends on scale"},{"concepts":[285],"name":"Recognize the distortions and implications of relief displacement and radial distortion in an aerial image"},{"concepts":[98],"name":"Recognize the impact of ones social background on ones own geographic worldview and perceptions and how it influences ones use of GIS"},{"concepts":[87],"name":"Recognize the influences of epistemology on GIS practices"},{"concepts":[113],"name":"Recognize the influences of scale on the perception and meaning of fields"},{"concepts":[107],"name":"Recognize the role that time plays in static GISystems"},{"concepts":[308],"name":"Recommend the map projection property that would be useful for various mapping applications, including parcel mapping, route mapping, etc., and justify your recommendations"},{"concepts":[109],"name":"Reconcile differing common-sense and official definitions of common geospatial categories of entities, attributes, space, and time"},{"concepts":[54],"name":"Relate plots of multidimensional attribute data to geography by equating similarity in data space with proximity in geographical space"},{"concepts":[242],"name":"Relate the concept of grid cell resolution to the more general concept of support and granularity"},{"concepts":[113],"name":"Relate the notion of field in GIS to the mathematical notions of scalar and vector fields"},{"concepts":[122],"name":"Represent structural relationships in GIS data"},{"concepts":[25],"name":"Resample multiple raster data sets to a single resolution to enable overlay"},{"concepts":[25],"name":"Resample raster data sets (e.g., terrain, satellite imagery) to a resolution appropriate for a map of a particular scale"},{"concepts":[143],"name":"Select a color scheme (e.g., qualitative, sequential, diverging, spectral) that is appropriate for a given map purpose and variable"},{"concepts":[97],"name":"Select a place or landscape with personal meaning and discuss its importance"},{"concepts":[585],"name":"Select and describe the leading trade journals serving the GIS and T community"},{"concepts":[25],"name":"Select appropriate interpolation techniques to resample particular types of values in raster data (e.g., nominal using nearest neighbor)"},{"concepts":[101],"name":"Select appropriate spatial metaphors and models of phenomena to be represented in GIS"},{"concepts":[585],"name":"Select association and for-profit journals that are useful to entities managing enterprise GI systems"},{"concepts":[151],"name":"Select base information suited to providing a frame of reference for thematic map symbols (e.g., network of major roads and state boundaries underlying national population map)"},{"concepts":[143],"name":"Select colors appropriate for map readers with color limitations"},{"concepts":[65],"name":"Select the appropriate statistical methods for the analysis of given spatial datasets by first exploring them using graphic methods"},{"concepts":[293],"name":"Select the most appropriate remotely sensed data source for a given analytical task, study area, budget, and availability"},{"concepts":[107],"name":"Select the temporal elements of geographic phenomena that need to be represented in particular GIS applications"},{"concepts":[144],"name":"Set type font, size, style and color for labels on a map by applying basic typography design principles"},{"concepts":[152],"name":"Sketch a map with a reliability overlay using symbols suited to reliability representations"},{"concepts":[144],"name":"Solve a labeling problem for a dense collection of features on a map using minimal leader lines"},{"concepts":[165],"name":"Specify a print job for publication, including paper, ink, lpi, proof needs, press check and other contract decisions"},{"concepts":[143],"name":"Specify a set of colors in device-independent Commision Internationale de LEclairage (CIE) specifications"},{"concepts":[288],"name":"Specify the technical components of an aerotriangulation system"},{"concepts":[34],"name":"State questions that can be solved by selecting features based on location or spatial relationships"},{"concepts":[313],"name":"State the approximate number and spacing of control points in each order of the horizontal geodetic control network"},{"concepts":[53],"name":"State the classic formalization of the interaction model"},{"concepts":[313],"name":"State the geometric accuracies associated with the various orders of the U.S. horizontal geodetic control network"},{"concepts":[559],"name":"Teach necessary skills for users to successfully perform tasks in an enterprise GIS"},{"concepts":[94],"name":"Transform a conceptual model of information for a particular task into a data model"},{"concepts":[108],"name":"Understand the physical notions of velocity and acceleration which are fundamentally about movement across space through time"},{"concepts":[109],"name":"Use categorical information in analysis, cartography, and other GIS processes, avoiding common interpretation mistakes"},{"concepts":[118],"name":"Use established analysis methods that are based on the concept of region (e.g., landscape ecology)"},{"concepts":[119],"name":"Use established analysis methods that are based on the concept of spatial integration (e.g., overlay)"},{"concepts":[310],"name":"Use GIS software to produce a graticule that matches a target graticule"},{"concepts":[311],"name":"Use GIS software to transform a given dataset to a specified coordinate system, projection, and datum"},{"concepts":[125],"name":"Use methods that analyze metrical relationships"},{"concepts":[124],"name":"Use methods that analyze topological relationships"},{"concepts":[287],"name":"Use photo interpretation keys to interpret features on aerial photographs"},{"concepts":[287],"name":"Using a vertical aerial image, produce a map of land use/land cover classes"},{"concepts":[41],"name":"Write a program to create a matrix of pair-wise distances among a set of points"},{"concepts":[234],"name":"Write a program to read and write a raster data file"},{"concepts":[41],"name":"Write typical forms for distance decay functions"},{"concepts":[11],"name":"xplain how the concept of capacity represents an upper limit on the amount of flow through the network"}]},"v3":{"concepts":[{"code":"GIST","description":"Geographic Information Science and Technology","name":"Geographic Information Science and Technology"},{"code":"AM","description":"This knowledge area encompasses a wide variety of operations whose objective is to derive analytical results from geospatial data. Data analysis seeks to understand both first-order (environmental) effects and second-order (interaction) effects. Approaches that are both data-driven (exploration of geospatial data) and model-driven (testing hypotheses and creating models) are included. Data driven techniques derive summary descriptions of data, evoke insights about characteristics of data, contribute to the development of research hypotheses, and lead to the derivation of analytical results. The goal of model driven analysis is to create and test geospatial process models. In general, model-driven analysis is an advanced knowledge area where previous experience with exploratory spatial data analysis would constitute a desired prerequisite. Visual tools for data analysis are covered in Knowledge Area: Cartography and Visualization (CV) and many of the fundamental principles required to ground data analysis techniques are introduced in Knowledge Area: Conceptual Foundations (CF). Image processing techniques are considered in Knowledge Area: Geospatial Data (GD). All of the methods described in this knowledge area are more or less sensitive to data error and uncertainty as covered in Unit GC8 Uncertainty and Unit GD6 Data quality. Mastery of the educational objectives outlined in this knowledge area requires knowledge and skills in mathematics, statistics, and computer programming.","name":"Analytical Methods","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM1-2","description":" ","name":"Analytical approaches","selfAssesment":"GI-N2K
"},{"code":"AM1","description":"Geospatial data analysis has foundations in many different disciplines. As a result, there are many different schools of thought or analytical approaches including spatial analysis, spatial modeling, geostatistics, spatial econometrics, spatial statistics, qualitative analysis, map algebra, and network analysis. This unit compares and contrasts these approaches.","name":"Foundations of analytical methods","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM10-1","description":" ","name":"Problems of large spatial databases","selfAssesment":"GI-N2K
"},{"code":"AM10-2","description":" ","name":"Data mining approaches","selfAssesment":"GI-N2K
"},{"code":"AM10-3","description":" ","name":"Knowledge discovery","selfAssesment":"GI-N2K
"},{"code":"AM10-4","description":" ","name":"Pattern recognition and matching","selfAssesment":"GI-N2K
"},{"code":"AM10","description":"Algorithms have been developed to scan and search through extremely large data sets in order to find patterns within the data. These data mining and knowledge discovery techniques have been expanded to the spatial case. Legal and ethical concerns associated with such practices are considered in Knowledge Areas GS GIS and T and Society and OI Organizational and Institutional Aspects.","name":"Data mining","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM11-1","description":" ","name":"Networks defined","selfAssesment":"GI-N2K
"},{"code":"AM11-2","description":" ","name":"Graph theoretic descriptive measures of networks","selfAssesment":"GI-N2K
"},{"code":"AM11-3","description":" ","name":"Least-cost shortest path","selfAssesment":"GI-N2K
"},{"code":"AM11-4","description":" ","name":"Flow modeling","selfAssesment":"GI-N2K
"},{"code":"AM11-5","description":" ","name":"The Classic Transportation Problem","selfAssesment":"GI-N2K
"},{"code":"AM11-6","description":" ","name":"Other classic network problems","selfAssesment":"GI-N2K
"},{"code":"AM11-7","description":" ","name":"Accessibility modeling","selfAssesment":"GI-N2K
"},{"code":"AM11","description":"Network analysis encompasses a wide range of procedures, techniques, and methods that allow for the examination of phenomena that can be modeled in the form of connected sets of edges and vertices. Such sets are termed a network or a graph, and the mathematical basis for network analysis is known as graph theory. Graph theory contains descriptive measures and indices of networks such as connectivity, adjacency, capacity, and flow as well as methods for proving the properties of networks. Networks have long been recognized as an efficient way to model many types of geographic data, including transportation networks, river networks, and utility networks electric, cable, sewer and water, etc. to name just a few. The data structures to support network analysis are covered in Unit DM4 Vector and object data models.","name":"Network analysis","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM12-1","description":" ","name":"Operations research modeling and location modeling principles","selfAssesment":"GI-N2K
"},{"code":"AM12-2","description":" ","name":"Linear programming","selfAssesment":"GI-N2K
"},{"code":"AM12-3","description":" ","name":"Integer programming","selfAssesment":"GI-N2K
"},{"code":"AM12-4","description":" ","name":"Location-allocation modeling and p-median problems","selfAssesment":"GI-N2K
"},{"code":"AM12","description":"A wide variety of optimization techniques are now solvable within the GIS and T domain. Operations research is a branch of mathematics practiced in the allied fields of business and engineering. New models and software tools allow for the solution of transportation routing, facility location, and a host of other location-allocation modeling problems.","name":"Optimization and location-allocation modeling","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM13-1","description":" ","name":"Impacts of transformations","selfAssesment":"GI-N2K
"},{"code":"AM13-2","description":"A data model is an abstract model that organizes elements of data and standardizes how they relate to one another and to the properties of real-world entities. The term data model can refer to two distinct but closely related concepts. In relation to the field of geoinformation the term data model refers to the set of concepts used in defining such formalizations as entities, attributes, relations, tables which is implemented by a mathematical construct for representing geographic objects or surfaces as data. There are two most frequently used data models, which are vector and raster. For example, the vector data model represents geography as collections of points, lines and polygons and more complex structures crated from these three. The raster data model represent geography as cell matrices that store numeric values. Among these two data models we also stand out data formats in which data sets can be stored. File format is a standard of encoding geographical information into a computer file. There are the following basic file formats for encoding data:\r\nFor vectors:\r\n-\tShapefile\r\n-\tGeography Markup Language (GML)\r\n-\tXYZ Point Cloud\r\n-\tGeoJSON\r\n-\tGeoMedia\r\n-\t\r\nFor rasters:\r\n-\tGeoTIFF\r\n-\tIMG\r\n-\tJPEG2000\r\n-\tEsri grid\r\nThe GIS projects often require the conversion of the data formats. Data conversion is the process of moving data from one format to another, whether it is from one data model to another or from one data format to another. Data conversion is a complex process which is not only associated with changing the binary format of the file but also requires changing the structure of the data. For example, the GML data format always comes with an UML diagram, which is necessary to convert attributes stored in GML structure for example to a table of contest in a shapefile data format. In a well-managed GIS project it is important to store data in specific data model or data format. It is sometimes dictated by software capabilities and another times by team’s technical capabilities. With large amounts of geographic data used in the project it is more cost-effective to convert the data from one format to another than re-create it.","name":"Data model and format conversion","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM13-3","description":"Interpolation is used to create a GIS layer out of point observations on a continuous variable. The reason for doing this could be manifold: for visualization purposes, for making a proper reference with other data, or for making a combination of different layers.","name":"Interpolation","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM13-4","description":"Any vector data containing point, polyline, polygon can be converted into the raster dataset and vice versa. The vector data can be stored in shapefiles, databases or various others GIS file formats. The raster data are made of pixels or grid calls and can be represented by the discrete - categorical data (e.g. land cover map) or non-discrete - continuous data (e.g. satellite images, surface data). The process of conversion of vector to raster data is called rasterization. The vector to raster conversion requires the following parameters: the field value from the attribute table used to assign values to the output raster, the pixel size for the output raster, the output raster format (i.e. geotiff, img) and optionally the method of assigning values of point, polyline or polygon to the call raster, i.e. maximum length or area, cell centre. The output of the rasterised vector looks like a gridded version of the vector and it depends on the grid cell size. The process of vectorisation refers to the conversion of raster to vector dataset. The raster dataset can be converted to vector point, polyline or polygon. In order to convert raster to vector the following parameters should be provided: attribute field of the input raster dataset which will become an attribute in the output vector class, determining if the output polygon or polyline will be smoothed into simpler shapes or conform to the input raster's cell edges (stair stepping). For each raster pixel or grid cell a point will be created at the centre of the cell. The non-discrete continuous raster data have to converted to the categorical data type before converting to vector data. The conversion of vector to raster and raster to vector degrade the data to some extent causing loss of details, accuracy, and changing the original data.","name":"Vector-to-raster and raster-to-vector conversions","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM13-5","description":"Raster resampling refers to change of spatial resolution (increasing or decreasing) of the raster dataset. The resampling process calculates the new pixel values from the original digital pixel values in the uncorrected image. There are three common methods for resampling: nearest neighbour, bilinear interpolation, and cubic convolution. The nearest neighbour resampling uses the digital value from the pixel in the original image which is nearest to the new pixel location in the corrected image. This is the fastest interpolation method, which is primarily applied for discrete (categorical) raster data as it does not change the value of the pixel, but may result in some pixel values being duplicated while others are lost. Bilinear interpolation resampling takes a weighted average of four pixels in the original image nearest to the new pixel location. The averaging process alters the original pixel values and creates entirely new digital values in the output image. It is recommended for continuous data and it cause some smoothing of the data. Cubic convolution resampling is based on calculation of a distance weighted average of a block of sixteen pixels from the original image which surround the new output pixel location. As with bilinear interpolation, this method results in completely new pixel values. However, the last two methods both produce images which have a much sharper appearance and avoid the blocky appearance of the nearest neighbour method. The disadvantage of the Cubic method is that its requires more processing time.","name":"Raster resampling","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM13-6","description":"Users of geoinformation often need transformations from a particular 2D coordinate system to another system. This includes the transformation of polar coordinates into Cartesian map coordinates, or the change of map projection - transformation from one 2D Cartesian (x, y) system of a specific map projection into another 2D Cartesian (x′, y′) system of a defined map projection. This transformation is based on relating the two systems on the basis of a set of selected points whose coordinates are known in both systems, such as ground control points or common points such as corners of houses or road intersections. Image and scanned data are usually transformed by this method. The transformations may be conformal, affine, polynomial or of another type, depending on the geometric errors in the data set. A datum transformation involves the change of the horizontal datum which is often accompanied with a change of map projection.","name":"Coordinate transformations","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM13","description":"GIS is a cyclical rather than a linear system, unlike computer aided drafting (CAD) and computer assisted cartographic systems. Changes in projection, grid systems, data forms, and formats take place during the modeling process for which GIS was designed. Many non-analytical manipulations are necessary to accommodate the analytical power of the GIS. The manipulations of spatial and spatio-temporal data involve two general classes of operation: 1.\tTheir transformation into formats that facilitate subsequent analysis (see this Unit AM13), 2.\tGeneralization and aggregation that affect the accuracy and integrity of the data used for analysis (see Unit AM14) Other knowledge areas have identified different forms of data structures, data models, projections, and other forms of geospatial data representation. These differences present both opportunities and challenges for analysis and modeling. The ability to transform one representation to another, in a manner that maintains the integrity of the information as much as possible, can enhance the analysis and visualization of geospatial data. The raster and vector data models are described in Units DM3 Tesselation data models and DM4 Vector and object data models. The principles of coordinate systems, datums, and projections are also considered in Knowledge Area GD: Geospatial Data","name":"Representation transformation","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM14-1","description":"In the practice of spatial data handling, one often comes across questions like “What is the resolution of the data?” or “At what scale is your data set?” Now that we have moved firmly into the digital age, these questions sometimes defy an easy answer. Map scale can be defined as the ratio between the distance on a printed map and the distance of the same stretch in the terrain.\r\n\r\nA 1:50,000 scale map means that 1 cm on the map represents 50,000 cm (i.e. 500 m) in the terrain. “Large-scale” means that the ratio is relatively large, so typically it means there is much detail to see, as on a 1:1000 printed map. “Small-scale”, in contrast, means a small ratio, hence less detail, as on a 1:2,500,000 printed map.\r\nDigital spatial data, as stored in a GIS, are essentially without scale: scale is a ratio notion associated with visual output, such as a map or on-screen display, not with the data that was used to produce the map or display. When digital spatial data sets have been collected with a specific map-making purpose in mind, and all maps have been designed to use one single map scale, for instance 1:25,000, we may assume that the data carries the characteristic of “a 1:25,000 digital data set.”\r\n\r\nThere is a relationship between the effectiveness of a map for a given purpose and the map’s scale. The Public Works department of a city council cannot use a 1:250,000 map for replacing broken sewer pipes, and the map of Figure 1 cannot be reproduced at scale 1:10,000.\r\n\r\nMaps that show much detail of a small area are called large-scale maps. Scale indications on maps can be given verbally, such as “one-inch-to the- mile”, or as a representative fraction like 1:200,000,000 (1 cm on the map equals 200,000,000 cm (or 2000 km) in reality), or by a graphic representation such as the scale bar. The advantage of using scale bars in digital environments is that its length also changes when the map is zoomed in, or enlarged, before printing. Sometimes it is necessary to convert maps from one scale to another, which may lead to problems of cartographic generalization.\r\n\r\nSpatial and temporal scales can not only be attached to processes, but also to observations. An example is given below, which summarizes the spatial and temporal scales of a few well-known Earth observation systems.\r\n\r\nScales of RS observations\r\nSensor Spatial scale\t Temporal scale\r\nMeteosat\t Hemisphere\t 15 minutes\r\nNOAA-AVHRR\t 3000 km\t daily\r\nLandsat TM\t 180 km\t 16 days\r\nSpot\t 60 km\t 26 days (pointable)","name":"Scale and generalization","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM14-2","description":" ","name":"Approaches to point, line, and area generalization","selfAssesment":"GI-N2K
"},{"code":"AM14-3","description":" ","name":"Classification and transformation of attribute measurement levels","selfAssesment":"GI-N2K
"},{"code":"AM14","description":"All geospatial data are generalized. Even the most detailed data represent only subsets of reality. Furthermore, data are further generalized for purposes of mapping, visualization, and efficient storage. A variety of generalization techniques have been developed to facilitate this process. All are scale dependent. Aggregation is one form of generalization that transforms large numbers of individual objects into summarized groups. This unit is concerned with the nature of these procedures and their implications for professional practice. Generalization is an important part of cartography (and is therefore discussed conceptually in Unit CV2 Data considerations), but is also a transformation common to many GIS procedures.","name":"Generalization and aggregation","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM2-1","description":" ","name":"Set theory","selfAssesment":"GI-N2K
"},{"code":"AM2-2","description":"- Alternative (Non-SQL) queries, such as linked data queries","name":"Structured Query Language (SQL) and attribute queries","selfAssesment":"GI-N2K
"},{"code":"AM2-3","description":" ","name":"Spatial queries","selfAssesment":"GI-N2K
"},{"code":"AM2","description":"Attribute and spatial query operations are core functionality in any GIS and they are often considered to be the most basic form of analysis.","name":"Query operations and query languages","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM3-1","description":" ","name":"Distances and lengths","selfAssesment":"GI-N2K
"},{"code":"AM3-2","description":" ","name":"Direction","selfAssesment":" "},{"code":"AM3-3","description":" ","name":"Shape","selfAssesment":"GI-N2K
"},{"code":"AM3-4","description":" ","name":"Area","selfAssesment":"GI-N2K
"},{"code":"AM3-5","description":"- Describe real world applications where distance decay is an appropriate representation of the strength of spatial relationships (e.g., shopping behavior, property values) - Describe real world applications where distance decay would not be an appropriate representation of the strength of spatial relationships (e.g., distance education, commuting, telecommunications) - Explain the rationale for using different forms of distance decay functions - Explain how a semi-variogram describes the distance decay in dependence between data values - Outline the geometry implicit in classical \"gravity\" models of distance decay - Plot typical forms for distance decay functions - Write typical forms for distance decay functions - Write a program to create a matrix of pair-wise distances among a set of points","name":"Proximity and distance decay","selfAssesment":"GI-N2K
"},{"code":"AM3-6","description":" ","name":"Adjacency and connectivity","selfAssesment":"GI-N2K
"},{"code":"AM3","description":"For simple data exploration, GIS offers many basic geometric operations that help in extracting meaning from sets of data or for deriving new data for further analysis. Concepts on which these operations are based are addressed in Unit CF3 Domains of geographic information and Unit CF5 Relationships.","name":"Geometric measures","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM4-1","description":"The reclassifications tools are used to change or reclassify the values. Reclassification of vector data involves the attributes of features in the feature attribute table, on the other hand reclassification of raster data involves the grid cell values to produce a new raster data layer. Reclassification can be used for data simplification and measurement scale change. We can adjust the data for more appropriate analysis by grouping the values and changing them. The reclassification tool can also be used to remove specific values from analysis.\r\nThe Select by location tool lets you select features by how they relate to other features in another layer. Selected features are based on their location. You can select features that are near or overlap the features. Most frequently used methods are intersect, within a distance, within, completely within, contain… Features can be selected in the same or other layers.\r\nThe Select by attributes tool lets you select features that match the selection criteria. With providing a selection criteria, matching features are selected. We can provide a complex selection criteria.","name":"Reclassification and selection operations","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM4-2","description":"Buffer analysis is one form of basic spatial analysis. It takes the vector representation (point, line, or polygon) of a real-world feature, and then creates a buffer zone based on a defined distance from the feature’s border. Thus, the created buffer zone is an area whose boundary always has the same distance to the input vector feature, e.g. the buffer zone for a point feature is a circle. Real-world examples for buffer zones could be protected areas along rivers or around nature conservation areas, or represent a simple proximity analysis. In the latter case, the buffer analysis is usually the first step of the analysis, followed by an overlay of the buffer zone with the target features to find those target features within the buffer zone, and thus within a certain distance of the original feature. Usually, the buffer zone extends outwards from the feature, but polygons can also have inner buffer zones. If the buffer zones from multiple features overlap, the analyst can decide to leave the individual boundaries of the buffer zones intact, or to dissolve them, i.e. merging the overlapping buffer zones into one larger buffer zone. The size of the buffer zone, i.e. the distance of its boundary from the original feature’s boundary, can be based on an uniform numerical value and associated spatial unit, but often, it is based on an attribute value (numerical or class) of the feature. Conceptually, buffering using raster representations of real-world features is similar a proximity analysis with a regular grid of square polygons: Departing from raster cells that form the area to be buffered, all raster cells that fall within the designated distance (overlay) from the buffer zone. With buffer analysis being a basic analytical operation, practically every GIS and many other analysis tools provide this functionality.","name":"Buffers","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM4-3","description":" ","name":"Overlay","selfAssesment":"GI-N2K
"},{"code":"AM4-4","description":" ","name":"Neighborhood analysis","selfAssesment":"GI-N2K
"},{"code":"AM4-5","description":" ","name":"Map algebra","selfAssesment":"GI-N2K
"},{"code":"AM4","description":"This small set of analytical operations is so commonly applied to a broad range of problems that their inclusion in software products is often used to determine if that product is a true GIS. Concepts on which these operations are based are addressed in Unit CF3 Domains of geographic information and Unit CF5 Relationships.","name":"Basic analytical operations","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM5-1","description":" ","name":"Point pattern analysis","selfAssesment":"GI-N2K
"},{"code":"AM5-2","description":" ","name":"Kernels and density estimation","selfAssesment":"GI-N2K
"},{"code":"AM5-3","description":"Spatial cluster analysis is the grouping of similar spatial objects into classes (clusters) in such a way that the objects within the cluster are highly similar compared to the objects outside of the cluster. Spatial clustering forms an important part of spatial data mining (Han et al., 2001; Miller et al., 2009). A wealth of spatial clustering tools are currently available with immense application potential. \r\n\r\nIn earth observation studies, spatial cluster techniques are often applied to identify zones with similar land covers by using earth observation data as input. An example of such a technique is the K-means classifier (Han et al., 2001; Miller et al., 2009). This unsupervised classification technique makes several clusters (e.g. land use classes) of which each pixel is assigned to the cluster with the nearest mean (Han et al., 2001). The amount of clusters can be freely defined by the user just as the input metrics to perform the classification. A drawback of the K-means classifier is the need to specify the amount of output clusters. Density Based Spatial Clustering (DBSC) overcomes this issue since it automatically defines the optimal amount of clusters (Miller et al., 2009). In this type of clustering technique, dense regions of objects (proximate objects) are clustered and separated from regions with low density (noise) (Han et al., 2001; Liu et al., 2012). Finally, another frequently applied spatial clustering technique is the hierarchical agglomerative clustering. This technique makes use of a dendrogram to decompose the data into clusters. The agglomerative approach is a bottom-up approach in which all objects are first grouped in a distinct cluster and while moving upward in the tree, pairs of clusters are merged based on some metrics (e.g. spatial proximity) (Han et al., 2001). \r\n\r\nSpatial cluster techniques have many advantages when dealing with big datasets which is often the case when working with earth observation data. Its simplicity to use and the fast increase of cloud computing power makes from it powerful techniques to extract spatial patterns out of the data. It allows to translate raw earth observation data into a more user-friendly data product by showing the spatial patterns of the data.","name":"Spatial cluster analysis","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM5-4","description":" ","name":"Spatial interaction","selfAssesment":"GI-N2K
"},{"code":"AM5-5","description":" ","name":"Analyzing multidimensional attributes","selfAssesment":"GI-N2K
"},{"code":"AM5-6","description":" ","name":"Cartographic modeling","selfAssesment":"GI-N2K
"},{"code":"AM5-7","description":" ","name":"Multi-criteria evaluation","selfAssesment":"GI-N2K
"},{"code":"AM5-8","description":" ","name":"Spatial process models","selfAssesment":"GI-N2K
"},{"code":"AM5","description":"Building on the basic geometric measures and analytical operations found in most GIS products, a broad range of additional analytical methods form the fundamental GIS toolkit.","name":"Basic analytical methods","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM6-1","description":" ","name":"Calculating surface derivatives","selfAssesment":"GI-N2K
"},{"code":"AM6-2","description":" ","name":"Interpolation of surfaces","selfAssesment":"GI-N2K
"},{"code":"AM6-3","description":" ","name":"Surface features","selfAssesment":"GI-N2K
"},{"code":"AM6-4","description":" ","name":"Intervisibility","selfAssesment":"GI-N2K
"},{"code":"AM6-5","description":" ","name":"Friction surfaces","selfAssesment":" "},{"code":"AM6","description":"There is a wide range of phenomena that can be studied using a set of techniques and tools that are designed to help understand the characteristics of continuous surface data. Applications of these techniques using terrain data include overland transport, flow, and siting tasks, but similar analyses can be conducted using non-tangible surfaces such as those of temperature, pressure and population density.","name":"Analysis of surfaces","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM7-1","description":" ","name":"Graphical methods","selfAssesment":"GI-N2K
"},{"code":"AM7-2","description":"Environmental variables have become increasing available with the advent of GIS. These are mostly continuous in space and time. Collecting denser environmental data in discrete space and time domains are rather cost effective and time consuming. However, when the data at each spatial or time index are considered as outcomes of a random variable, stochastic processes become enviable useful to build models and predict the outcomes at locations where data were never collected. The meaningful assumptions include stationarity of the mean and the covariance to ascertain an expression for spatial dependency/autocorrelation. With a stationary process (i.e. constant mean), simple and ordinary kriging is used. Other variants like kriging with external drift, universal kriging and regression kriging also alleviate the challenge of non-stationary mean. These methods are also applicable when temporal indexes rather than spatial indexes are of interest.","name":"Stochastic processes","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM7-3","description":"Spatial weight matrix is the popular numerical quantification of spatial dependency or spatial neighborhoods. The weight matrix should summarize information about the spatial connectivity structure of the spatial entities/features; either polygons, points, or lines. This is required for the computation of spatial dependency indices such the Moran’s index, and for spatial regression models such as the conditional autoregressive (CAR), spatial lag, and spatial error models. The connectivity information can be defined based on adjacency/contiguity or distance between pairs of spatial entities. There are other forms; they could be based on population densities between observation pairs. The simplest spatial weigh matrix is the binary adjacency spatial weight matrix with elements w_ij, such that w_ij=1 if spatial units i and j are neighbors, otherwise w_ij=0. A popular alternative is the inverse distance weight matrix with elements w_ij=1⁄d^α , where d is the distance between pairs of spatial units and α is any positive number greater than zero. By convention, w_ii=0 since spatial unit cannot have a spillover within itself.","name":"The spatial weights matrix","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM7-4","description":"Spatial autocorrelation evaluates how things which are closer in space tend to have similar attributes. This is a common phenomenon in environmental variables which are continuous in space. For instance, temperature, soil moisture content, air quality and rainfall are all continuous in space. This idea is based on Tobler’s law of geography: “everything is related to everything but near things are more related”. Global measures of spatial association estimates the overall index of spatial autocorrelation, also called spatial clustering. Thus, it measures whether clustering is apparent throughout the study region but do not identify the location of clusters. Common global measures include the Moran’s Index and Geary’s C. These have increasing applications in domains like environmental science, agriculture, epidemiology, climate studies etc.","name":"Global measures of spatial association","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM7-5","description":"Unlike global measures of spatial association, local measure of spatial association identifies the locations of clusters. Typical measures include the local indicator for spatial autocorrelation (LISA) or the local Moran’s index whose summation is proportional to the global Moran’s index. The spatial scan statistics has also been the commonly used method to detect local clusters.","name":"Local measures of spatial association","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM7-6","description":"An outlier is an unexpected value that differs significantly from other observations. Definition of an outlier is not absolute and the concept itself is precisely defined only by selection of appropriate criteria in concrete statistical observations. When considering outliers, it is important to determine whether the value of the outlier is incorrect data or it is otherwise outstanding, but correct data. If we consider outliers in the case when they base on sample surveys, another assessment is necessary. Namely, the assessment of whether an outlier is representative or not. \r\nThe box plot is a useful graphical display for examining the outliers. Using median, lower and upper quartiles, extreme values are identified in the tails of the distribution. The value beyond inner fence on either side is considered a mild outlier. The value beyond an outer fence is considered an extreme outlier. Histograms also emphasize the existence of outliers. The histogram depends on how we design the classes, so we can get different histograms for the same data. Graphical and quantitative checks are obligatory if the histogram shows possible outliers. Outliers can also be examined by calculating the correlation between two datasets (Pearson correlation coefficient, Spearman rank correlation coefficient…). Scatter plots reveals a basic linear relationship with a pattern. An outliner is defined as a data point that deviates from other values. Outliers can also be examined by local outlier factor, which is based on a concept of a local density. Points with substantially lower density than their neighbours are considered as outliers.","name":"Outliers","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM7-7","description":"Bayesian method of modelling sterns from the Bayes theorem and derived using conditional probabilities. Its advantage lies in its ability to include prior knowledge of unknown parameters to ascertain their uncertainties. Thus, the prior parameters are updated by the data likelihood to obtain the posteriors. The challenge of Bayesian modelling has been the integration of the denominator which always resulted into improper integrals. This actually prolonged its wide applications. With the advent of high performance computers, solution to such integrals are easily solved using Markov chain Monte Carlo simulations. The advent robust approximation methods through integrated nested Laplace approximations (INLA) has even made parameter estimation faster; thus making Bayesian methods interesting and better. Unlike frequentist approaches, Bayesian methods can present estimates of parameters as densities from which their uncertainties and credible intervals can be estimated. They have now found wide applications in divers areas like environmental modelling, climate modeling, agriculture, epidemiology and many other domains that requires modeling.","name":"Bayesian methods","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM7","description":"Traditional statistical methods are used to describe the central tendency, dispersion, and other characteristics of data but are not always suited to use with spatial data for which specialized techniques are often required. The field of spatial statistical analysis forms the backbone for the testing of hypotheses about the nature of spatial pattern, dependency, and heterogeneity. The techniques are widely used in both exploratory and confirmatory spatial analysis in many different fields.","name":"Spatial statistics","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM8-1","description":" ","name":"Spatial sampling for statistical analysis","selfAssesment":"GI-N2K
"},{"code":"AM8-2","description":" ","name":"Principles of semi-variogram construction","selfAssesment":"GI-N2K
"},{"code":"AM8-3","description":" ","name":"semi-variogram modeling","selfAssesment":"GI-N2K
"},{"code":"AM8-4","description":" ","name":"Principles of kriging","selfAssesment":"GI-N2K
"},{"code":"AM8-5","description":" ","name":"Kriging variants","selfAssesment":"GI-N2K
"},{"code":"AM8","description":"Geostatistics are a variety of techniques used to analyze continuous data e.g., rainfall, elevation, air pollution. The fundamental structure of geostatistics is based on the concept of semi-variograms and their use for spatial prediction kriging. Sampling methods are also discussed in Unit GD9 Field data collection. \r\nGeostatistics is a subdiscipline of spatial statistics developed to estimate the value of a continuous spatial process at unknown locations by using the information of the value of these process at known locations. Furthermore, it aims to quantify the uncertainty related to the prediction (Calder et al., 2009; Emmanouil, 2019). In order to do such predictions, geostatistics entails some statistical methods which use as starting point the assumption of a random component that can define the spatiotemporal variability. These methods are developed to infer the parameters that can describe the spatiotemporal patterns of the input variables (e.g. soil moisture) so that finally these variables at unsampled locations can be estimated (interpolated) (Emmanouil, 2019). Geostatistical methods are strongly related with classic interpolation methods but differ by its use of random variables that allow to given an uncertainty indication associated with the prediction of variables in space and time. \r\n\r\nIn environmental research geostatistical techniques are often applied to infer (interpolate) variables at such unobserved locations by using information from known locations. One of such geostatistical techniques is Kriging, which is a geostatistical method that predicts variables by using spatial interpolation. This spatial interpolation is done by establishing a semivariogram that defines the spatial relationship between the variables of interest in function of the distance. Because of this, the Kriging technique can also give an indication on the variance or accuracy of the prediction (Calder et al., 2009); Van der Meer, 2012). On the other hand, cokriging is another important geostatistical technique and differs from Kriging by using the cross-correlation between variables to generate local estimates (Van der Meer, 2012). In earth observation studies, cokriging can be applied to better predict sparsely based data on the ground (e.g. biomass) by using the cross-correlation of this variable with a more continuously sampled satellite metric like NDVI. Furthermore, these techniques can also be used to enhance satellite image information, filling missing pixels or even downscale the information to a higher resolution (Van der Meer, 2012).","name":"Geostatistics","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM9-1","description":" ","name":"Principles of spatial econometrics","selfAssesment":"GI-N2K
"},{"code":"AM9-2","description":" ","name":"Spatial autoregressive models","selfAssesment":"GI-N2K
"},{"code":"AM9-3","description":" ","name":"Spatial filtering","selfAssesment":"GI-N2K
"},{"code":"AM9-4","description":" ","name":"Spatial expansion and Geographically Weighted Regression GWR","selfAssesment":"GI-N2K
"},{"code":"AM9","description":"Many problems of the social sciences can be expressed in terms of spatial regression analysis. The development of spatial autoregressive models and the estimation of their parameters is the focus for the field of spatial econometrics.","name":"Spatial regression and econometrics","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF","description":"The GIScience perspective is grounded in spatial thinking. The aim of this knowledge area is to recognize, identify, and appreciate the explicit spatial, spatio-temporal and semantic components of the geographic environment at an ontological and epistemological level in preparation for modeling the environment with geographic data and analysis. To do this, one must understand the nature of space and time as a context for geographic phenomena.This knowledge area covers the ways in which views of the geographic environment depend on philosophical viewpoints, physics, human cognition, society, and the task at hand. This knowledge area also requires an understanding of the fundamental principles in the discipline of geography, the \"language\" of spatial tasks. On a more advanced level, this area incorporates mathematical and graphical models that formalize these concepts, such as set theory, algebra, and semantic nets. Because of its wide range of foundational principles, this knowledge area forms a basis for the other knowledge areas. Wise design and use of geospatial technologies requires an understanding of the nature of geographic information, the social and philosophical context of geographic information, and the principles of geography. This knowledge area is especially closely tied to Knowledge Areas Data Modeling (DM) and Design Aspects (DA), as generic data models and application designs need to be grounded in sound conceptual models. The foundations of geographic information have developed over several decades. Philosophical and scientific views on the nature of space and time have evolved since the ancient Greeks. Early papers during the Quantitative Revolution, such as Berry (1964), began to formalize the structure of information used in geographic inquiry.The fundamental data structures and algorithms comprising the GIS software developed in the 1960`s and 1970`s were based on implicit \"common-sense\" conceptual models of geographic information. During the 1980`s, several researchers questioned these underlying assumptions. Some were refuted, other confirmed, and many extended. However, the most rapid pace of development in this area was during the 1990`s with the rise of GIScience as a distinct discipline, and the many cooperative initiatives it comprised.The new millennium has seen some of these foundational principles incorporated into commercial software, thus making theoretical knowledge even more important for practitioners. It is expected that the concepts in this knowledge area will be learned gradually. An introductory course may cover only a few topics in a cursory manner, an intermediate course on data modeling or data analysis may consider several theoretical topics of practical application, and a number of graduate courses could cover each topic in a research-oriented environment. Discussion of this knowledge area includes several terms that can have multiple meanings. For the purposes of this document, two in particular require definition: Geographic: Almost any subject or discourse involving earthly phenomena, studied from a spatial perspective at a medium scale (sub-astronomical and super-architectural). Phenomenon: Any subject of geographic discourse that is perceived to be external to the individual, including entities, events, processes, social constructs, and the like.","name":"Conceptual Foundations","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF1-1","description":" ","name":"Metaphysics and ontology","selfAssesment":" "},{"code":"CF1-1b","description":"Brief history of GIScience as related to the history of GISystems; Definitions of GIS&T; Sub-domains of GIS&T (i.e., Geographic Information Science, Geospatial Technology, and Applications of GIS&T)","name":"What is Geographic Information Science and Technology","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF1-2","description":" ","name":"Epistemology","selfAssesment":" "},{"code":"CF1-2b","description":"GIS&T draws upon insights and methods from key allied fields: Geography, Cartography, Computer and information science, Engineering, Mathematics and Statistics, Philosophy, Cognitive Science, Linguistics","name":"Contributions to GIS and T by key allied fields","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF1-3","description":" ","name":"Philosophical perspectives","selfAssesment":" "},{"code":"CF1","description":"Many branches of philosophy are relevant to an understanding of geographic information, especially metaphysics and epistemology. Philosophical theories are deeply engaged in the study of knowledge, space, time, geographic phenomena and human interaction with them. These theories influence the development of geographic ontologies and the structuring, analysis, and interpretation of geographic information. It is, therefore, crucial for professionals to understand these principles in order to bridge (rather than eliminate) the differences and work together. Philosophical perspectives on GIS practice are covered in Unit GS7 Critical GIS.","name":"Philosophical foundations","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF1b","description":"Unit CF1 introduces the broad domain refered to as Geographic Information Science & Technology (GIS&T) and its sub-domains (i.e., Geographic Information Science, Geospatial Technology, and Applications of GIS&T). It outlines the history of Geographic Information Science as related to the history of GISystems, as well as the contributions to this multidisciplinary domain by key allied fields, such as geography, cartography, computer and information science, engineering, mathematics, philosophy, cognitive science, and linguistics.","name":"Introduction to Geographic Information Science and Technology","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF2-1","description":" ","name":"Perception and cognition of geographic phenomena","selfAssesment":" "},{"code":"CF2-1b","description":"Metaphysics and Ontology - Formal ontology - Ontological distinctions (e.g., continuants vs. occurrents, universals vs. particulars) - The problem of universals and relevant theories (realism, nominalism, conceptualism) - Ontologies of the geographic domain - Philosophical theories relating to the nature of space, time, geographic phenomena and human interaction with them","name":"Philosophy of being","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF2-2","description":" ","name":"From concepts to data","selfAssesment":" "},{"code":"CF2-2b","description":"Epistemology; Theories on what constitutes knowledge; The notions of model and representation in science; The influences of epistemology on GIS practices","name":"Philosophy of knowledge","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF2-3","description":" ","name":"Geography as a foundation for GIS","selfAssesment":" "},{"code":"CF2-4","description":" ","name":"Place and landscape","selfAssesment":" "},{"code":"CF2-6","description":" ","name":"Cultural influences","selfAssesment":" "},{"code":"CF2-7","description":" ","name":"Political influences","selfAssesment":" "},{"code":"CF2","description":"Geographic information is observed, comprehended, organized, used in human processes, with both personal and social influences. Therefore, sound models of geographic information should be grounded on a sound understanding of human perception, cognition, memory, and behavior, as well as human institutions.","name":"Cognitive and social foundations","selfAssesment":" "},{"code":"CF3-1","description":" ","name":"Space","selfAssesment":"GI-N2K
"},{"code":"CF3-1b","description":"- Theories of human perception, cognition, and memory and their ability to model spatial knowledge acquisition (e.g., Marr on vision, Piaget on cognitive development) - Types of mental representations (i.e., analogue, propositional, procedural) - The role of metaphors and image schemata in our understanding of geographic phenomena and geographic tasks - From concepts to data (i.e., data, information, knowledge, and wisdom; transformation of a conceptual model of information for a particular task into a data model; limitations of various information stores (the mind, computers) and means (maps, graphics, and text) for representing geographic information) - Difference between real phenomena, conceptual models, and GIS data representations thereof connections with cartography and maps","name":"Cognitive foundations","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF3-2b","description":"- Semantics - Meaning (e.g., the nature of meaning, modes of meaning) - Geospatial semantics - The role of natural language in the conceptualization of geographic phenomena","name":"Linguistic foundations","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF3-3b","description":"- The ways in which the elements of culture (e.g., language, religion, education, traditions) may influence the understanding and use of geographic information - The influences of social theories and political ideologies and actions on human perceptions of space and place - The constraints that political forces place on geospatial applications in public and private sectors","name":"Social foundations","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF3-4b","description":"- Common-sense views and laymen knowledge of geographic phenomena that contrast with established theories and technologies of geographic information - The impact of geospatial technologies and the geoweb (e.g., digital globes) that allow non-geospatial professionals to create, distribute, and map geographic information - The design, procedures, and results of GIS projects to non-GIS audiences (clients, managers, general public) - Difference between applications that can make use of common-sense principles of geography and those that should not","name":"Common-sense geographies","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF3","description":"Geographic information is observed, comprehended, organized, used in human processes, with both personal and social influences. Therefore, sound models of geographic information should be grounded on a sound understanding of human perception, cognition, memory, and behavior, as well as human institutions.","name":"Cognitive, linguistic and social foundations","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF4-2b","description":" ","name":"Time","selfAssesment":"GI-N2K
"},{"code":"CF4-3b","description":" ","name":"Relationships between space and time","selfAssesment":"GI-N2K
"},{"code":"CF4-4b","description":"GIS data structures are used to implement the conceptual views of spatial data (vector and raster models). The power of a GIS is dependent on the richness of information contained in the spatial data structures. Vector models are based on points, lines and areas. Raster models are based on grids. Each cell has a value that is used to represent some characteristic of that location. \r\nLayers are used to display geographic datasets in various digital map environment. A layer stores the path to a source dataset and other layer properties, including symbology. You can use multiple layers on one map and specify its properties. Shapefiles represent spatial character of the object in terms of shape, size and spatial arrangement. Shapefile usually comprise three separate and distinct types of files (main files, index files and database tables). Data base files store additional attributed that can be joined to a shapefiles’ feature. Attribute data types supplement geographic spatial feature with additional information. Spatial data includes information of location and attribute data includes information about other characteristics (what, where and why). A legend is a visual presentation of the symbols that are used on the map with some additional explanations. It includes a sample of each symbol and a short description of the meaning.","name":"Categories","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF4-5","description":" ","name":"Properties","selfAssesment":"GI-N2K
"},{"code":"CF4b","description":"Geographic phenomena, geographic information, and geographic tasks are described in terms of space, time, and properties. Different theories exist as to the nature and formal representation of these aspects, including space-like dimensions, sets, and phenomenology. Information in each of these three aspects is measured and reported with respect to one of several frames of reference or domains, including both absolute and relative approaches. Early frameworks such as those of Berry (1964) and Sinton (1978) were influential in setting forth the importance of space, time, and theme in GIS&T. Besides, space, time, and properties, categories are also fundamental in the conceptualization and representation of spatial entities, phenomena, processes, and events. Distinctive features of geographic information such as scale and detail, spatial patterns, spatial integration, and regions are also critical for a complete description of its nature and representation. This unit is closely tied to the creation of data models in Knowledge Area 5: Data Modeling, Storage, and Exploitation.","name":"Fundamentals of Geographic Information","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF5-1b","description":" ","name":"Discrete entities","selfAssesment":"GI-N2K
"},{"code":"CF5-2b","description":" ","name":"Fields","selfAssesment":"GI-N2K
"},{"code":"CF5-3b","description":" ","name":"Events and processes","selfAssesment":"GI-N2K
"},{"code":"CF5-4b","description":" ","name":"Integrated models","selfAssesment":"GI-N2K
"},{"code":"CF5-6","description":" ","name":"Spatial distribution","selfAssesment":" "},{"code":"CF5-7","description":" ","name":"Region","selfAssesment":" "},{"code":"CF5-8","description":" ","name":"Spatial integration","selfAssesment":" "},{"code":"CF5b","description":"The concepts below form the basic elements of common human conceptions of geographic phenomena. Concepts from many units in this knowledge area have been synthesized to create general conceptual models of geographic information. Attempts to resolve the object-field debate have led to attempts to create comprehensive models that bridge these views. Consideration of this unit should also include formal models of these elements in mathematics and other fields. Knowledge Area DM Data Modeling discusses the representation of these elements in digital models.","name":"Elements of geographic information","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF6-1","description":" ","name":"Mereology: structural relationships","selfAssesment":"GI-N2K
"},{"code":"CF6-2","description":" ","name":"Genealogical relationships: lineage, inheritance","selfAssesment":"GI-N2K
"},{"code":"CF6-3","description":" ","name":"Topological relationships","selfAssesment":"GI-N2K
"},{"code":"CF6-4","description":" ","name":"Metrical relationships: distance and direction","selfAssesment":"GI-N2K
"},{"code":"CF6","description":"Like geography, geographic information not only models phenomena but the relationships between them. This can include relationships between entities, between attributes, between locations. In addition, one of the strengths of geography (and GIS) is its ability to use a spatial perspective to relate disparate subjects, such as climate and economy. Methods for analyzing relationships are discussed in Unit AM4 Modeling relationships and patterns.","name":"Relationships","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF7-1","description":"Vagueness arises from lack of criteria for the applicability of certain linguistic terms. It arises from the lack knowledge about the meanings of terms.","name":"Vagueness","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF7-2","description":"-Uncertainty-related terms, such as error, accuracy, uncertainty, precision, stochastic, probabilistic, deterministic, and random -Difference between uncertainty and vagueness -Dependence of uncertainty on scale and application -Expressions of uncertainty in language -The causes of uncertainty in geospatial data -Stochastic error models for natural phenomena -How the concepts of geographic objects and fields affect the conceptualization of uncertainty -Mathematical models of uncertainty: Probability and statistics","name":"Error-based uncertainty","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF7","description":"Human models (mental, digital, visual, etc.) of the geographic environment are necessarily imperfect. While the mathematical principle of homomorphism (often operationalized as fitness for use) allows for imperfect data to be useful as long as they yield results adequate for the use for which they are intended, imperfections are frequently problematic. Although terminology still varies, two types of imperfection are generally accepted: vagueness (a.k.a. fuzziness, imprecision, and indeterminacy), which is generally caused by human simplification of a complex, dynamic, ambiguous, subjective world; and uncertainty (or ambiguity), generally the result of imperfect measurement processes (as discussed in Knowledge Area GD Geospatial Data). Both of these can be manifested in all forms of geographic information, including space, time, attribute, categories, and even existence. Imperfection is also dealt with in Units GD6 Data quality (in the context of measurement), GC8 Uncertainty and GC9 Fuzzy sets (for the handling and propagation of imperfections), and CV4 Graphic representation techniques (in the context of visualization).","name":"Imperfections in geographic information","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV","description":"Geo-data visualisation necessarily includes cartography as the origin of \"mapping\" our world. Cartography methods have drastically changed over the few years since the increasing role and sophistication of digital technology applied to geo-information visualisation. It is first worth differentiating between the underlying geo-data that describes real world phenomena and the bits of information that describe the visual presentation of geo-data . Likewise, there are processing tools to collect and handle geo-data, and processing tools especially designed to create and manage geo-data visualisations. \r\nWhile cartography methods have traditionally produced printed maps (i.e. hard copy) with static scale, orientation, projection, legends (content based) and tied to a period or instant of time. Nowadays geo-data visualisations are interactive by design, meaning that the results are map-based responsive interfaces, highly customisable through dynamic objects to zoom in and out, pan and tilt, change projections and graphic expressions on the fly, as well as dynamically browse the map over time. \r\nIf the production methods have changed, also the type of authors. Map making in its widest sense is not only a privilege of a few experts but has been democratised in such a way that. everybody is able to make maps using open data and open source apps and tools for geo-data visualisation. Therefore,the new roles of open data and new forms of geo-data like geo-social media make usability, intended and ethical considerations key aspects of geo-data visualization design, production and sharing. \r\nUnder the concept of cartography and visualisation it is included a list of concepts that together comprise the science and technology of visual representation of geographic data.","name":"Cartography and Visualization","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV1-1","description":"The evolution of cartographic representation in the previous centuries followed the most important technological and scientific developments of the time. It was driven by commercial and/or military needs and influenced by the special characteristics of the areas and/or environments to be mapped. Recent developments are the rise of open data worldwide and widely available internet technology allowing end users to get remote geo-data published elsewhere. In recent years, data and its digital presentation have become central elements of cartography, whereas paper maps have become peripheral.","name":"History and evolution of cartography","selfAssesment":"Completed (GI-N2K)
"},{"code":"","description":"","name":"","selfAssesment":""},{"code":"CV1-4","description":"Art in cartography means much more than designing aesthetically pleasing maps, whether on paper or digital. Exploring the interaction at large between art and cartography involves rethinking the way we approach spatial expressions and how cultural, social and political dimensions are reflected in maps. This can be clearly observed in historical maps - in between art and science - ranging from beautiful geographical representations created in the Middle Ages to convey religious messages to the creation of modern maps showing the power of modern empires and nations. This particular relationship between art and maps entails: “developing an inclusive approach of artistic mapping expressions; facilitating and encouraging interaction between cartographers who work with the Art aspects of cartography and artists who produce cartographic artifacts; and developing conceptual elements about the relationships between art and cartography.” Besides ancient paper maps, a sum of factors led digital maps and geospatial visualization, a matter of interest to artists and designers. Thanks to powerful computing systems and with the advancements reached in computer graphics or image processing, or the rise of information visualisation, new forms of representing and visualising geodata have also appeared. Creation of digital maps are still a two-way relationship since artists have explored maps as a medium for expressing their art, and cartographers have approached art to provide more than just the representation of locations and geographic features with the intention to make maps more attractive to their audiences.","name":"Art and geodata visualisation","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV1-5","description":"Historical maps are geographical representations made with the intention to represent spatial facts over time. Historical maps are generally considered valuable documents not just because of their historical value but also because most of them also are artistic representations by themselves. From a cartographical point of view, differentiation between historical maps and actual maps is mainly based on the advances in the history of Cartography, so once one disruptive advance in the map making process appears, maps created with previous techniques (and with some artistic or historical value) are usually considered as historical, such as ancient paper-based maps or old sea maps, for instance. Techniques such as scanning or photography can make ancient maps publicly available by converting hard-copy maps to digital ones. Once an historical map is digitised, the next step is to georeference it, which is the process of specifying and relating points of the digitalised map to actual coordinates in a geographic reference system. Because of its archival value and interest, historical maps are adequately preserved - following specific conditions - by map libraries, map societies or museums. Since digital methods and techniques have been replaced over time by new technological advances, first digitally created maps could be also considered historical, not because of its content, but of the techniques used to produce it.","name":"Historical maps","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV1","description":"At a certain moment in time people start to create more graphical representations of their surrounding environment. New technologies offered ways to expand these representations to larger geographical extent, higher spatial resolution, finer temporal granularity and larger periods. Technologies even made it possible to include other representations of reality such as social media and data ensembles in geodata visualizations, to the extent to even blend the real world with geodata-based visualization providing an augmented – virtual reality continuum. New forms of geo-data, like geolocated sensors may challenge the way geo-data visualisations are generated, shared and, eventually, influence decision-making processes. History and trends sketch these developments and future outlook. This concept introduces the main stages and turns in development of cartography, from earliest times to the present, the most important methods in map-making and map-based visualizations.","name":"History and trends","selfAssesment":"Completed (GI-N2K)
\r\n\r\n"},{"code":"CV2-1","description":"As mapping ( geo-data visualization) is intended to convey a certain message to a certain audience, it is essential to use data sources that allow the intended visualisation result. The data should be of the right degree of detail and its use should not cause copyright problems. The producer quality of each data set should be taken into account, as well as the fitness of the data for the intended use. Aspects: message; data quality","name":"Data sources for mapping","selfAssesment":"
GI-N2K
"},{"code":"CV2-2","description":"In the trajectory between raw (geo)data and their user-relevant representation, the necessary data processing includes ways of abstraction by selection, filtering, generalization, transformation and classification of geographical data. In this data processing it is essential to at one hand relate the final symbolisation to the necessities of the intended message, and at the other hand to procedures that introduce as little error as possible.","name":"Data processing","selfAssesment":"GI-N2K
"},{"code":"CV2-3","description":"Map projection is fundamental to representation of spatial data and for combining different datasets. Its choice should serve the presentation type that will convey the intended message to the audience. Many mathematical principles define datum, projections, horizontal and vertical co-ordinate systems, georeferencing- introduced with the focus on visualisation issues Aspects: geodetic concepts; transformations","name":"Mathematical base","selfAssesment":"GI-N2K
"},{"code":"CV2","description":"Geodata, including 3 dimensional geometry, as such can graphically be presented but most of the times the data as such doesn`t meet the presentation criteria. Especially if the dataset has to be presented in combination with other datasets. First all the geodatum, georeference and map projection are crucial but also the role of the geometry. The processing of the geometry and the related attributes may become a crucial step for an adequate presentation. Nowadays the highest precision may be used to define different graphical attributes for different zoom levels. On the other hand geodata visualisation includes also graphical datasets. Such data ensembles, the combination of geodata and graphical data, are the data sources that offer opportunities to other ways of visualisation then the traditional cartographic mapping. Facets: a.\tGeospatial location (2D) and position (3D) that data refer to b.\tDegree of detail in data origin (acquisition resolution) and in representation ('map' scale) c.\tTypes of data (e.g. imagery, field measurements, delineated objects)","name":"Data considerations","selfAssesment":"GI-N2K
"},{"code":"CV3-1","description":"The combined impact of graphic design properties (balance, legibility, clarity, visual contrast, figure-ground organization, and hierarchal organization) and the map components (north arrow, scale bar, and legend) should always be carefully evaluated against the needs and the capacities of the audience.","name":"Map design fundamentals","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV3-10","description":"Geo-gaming is a crossover between computer gaming and geocaching, enabled by mobile location based information. Gamers participate by roaming about the environment. This offers myriad opportunities: non-linear storytelling, physical object integration, a more visceral experience, true social interaction. It also presents technical challenges to meet the unique infrastructural demands of geo-gaming. Such games, also called “mobile location-based games”, are also used in teaching","name":"Geo-gaming","selfAssesment":"GI-N2K
"},{"code":"CV3-2","description":"Map symbolization entails a number of variables to produce visual, tactile, haptic, auditory, and dynamic displays. Visual variables (e.g., size, lightness, shape, hue) and graphic primitives (points, lines, areas) are commonly used in maps to represent various geographic features at all attribute measurement levels (nominal, ordinal, interval, ratio). With those a single geographic feature can be represented by various graphic primitives (e.g., land surface as a set of elevation points, as contour lines, as hypsometric layers or tints, and as a hillshaded surface). The challenge is to use effective symbols for map features to ease the interpretation of maps.","name":"Symbols and icons","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV3-3","description":"The selection of colours to use in data representation can be influenced by various factors (e.g. the production workflow, cultural differences, involved devices and media). There are various colour models (e.g. RGB, CMYK, CIE) that describe colours in a way that they can effectively convey visual information (e.g., qualitative, sequential, diverging, spectral) according to the meaning of the underlying data. The cultural background of the consumer is also relevant when it comes to choose colours that should have real-world connotations or should express psychological concepts (e.g. harmony, concordance, balance). A final important factor is if the consumer has colour limitations","name":"Colour","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV3-4","description":"When data representation is conveyed in words (e.g. toponyms, road codes), written text is often placed in map labels. It is important to decide on the role of the label in the context of the representation type. Algorithms for label placement are relevant, especially when label density is high. Shape and colour of the labels help to signify different types of messages. This is supported by the typographic properties (type font, size, style) of the text in the labels. Finally, it is important to use an authoritative source for the texts","name":"Typography","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV3-5","description":"Imagery can be a source for data acquisition as well as an illustration to abstract data representations. Imagery can be made from the air (from drones to satellites) or from a terrestrial point of view. The knowledge field describing the data acquisition process based on photos is called Remote Sensing. Using photos from any source to illustrate stories about geographical subjects contributes is the visual aspect of telling a story. Together with maps and other narrative components, the combination embodies a storytelling medium. Aspects: photos for data collection; photo as part of geo data ensemble; photo as representation of place; photo as support of representation, illustration of specific time and place","name":"Photos","selfAssesment":"GI-N2K
"},{"code":"CV3-6","description":"Animation is the process of making the illusion of motion and change by means of the rapid display of a sequence of static images that minimally differ from each other. In the context of maps, the temporal component is added to a map to emphasize and observe the gradual evolution of a certain monitoring phenomenon, such as changes in spatially numerical variables (for example, environment, population, mobility, land use, etc.) with respect to a static geographic area. Map animations generally consider dynamic time while space is static. Map animation helps to see patterns or trends that emerge as time passes, depicting meteorological or climate events, natural disasters, historical events and other multivariate data. It is particularly helpful to be used in educational settings.","name":"Animation","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV3-7","description":"Sound can be one of the components of a multimedia data representation. Wikipedia: “Multimedia is content that uses a combination of different content forms such as text, audio, images, animation, video and interactive content. Multimedia contrasts with media that use only rudimentary computer displays such as text-only or traditional forms of printed or hand-produced material","name":"Sound","selfAssesment":"GI-N2K
"},{"code":"CV3-8","description":"Storytelling is the conveying of events in words, sound and/or images (Wikipedia). Maps are valuable because they provide a large amount of detail in a small amount of space, and because of their capacity for telling a story. Telling stories through maps began with describing explored lands in great detail against terra incognita. Today, geographic tools, data, and multimedia on the web expand the ability and audience for storytelling through maps. Any person with a smartphone or computer can use maps to tell a story, using live web maps with text, video, audio, sketches, and photographs. (After: Kerski, 2015).\r\nAspects: Data; Applied multimedia range; techniques; designing / storyboard; effectiveness/usability","name":"Storytelling","selfAssesment":"GI-N2K
"},{"code":"CV3-9","description":"Infographics are visual representations of information and data. The aim of an infographic is to present information that can be absorbed quickly and is easily understandable. Infographics can consist of Charts, Diagrams, Graphs, Tables, Maps and Lists (Davey 2014). Infographics have evolved in recent years to be for mass communication, and thus are designed with fewer assumptions about the readers knowledge base than other types of visualizations (Wikipedia Aspects: input data; representation format; canvas type; audience","name":"Info-graphics","selfAssesment":"GI-N2K
"},{"code":"CV3","description":"This concepts covers basic design principles that are used in mapping and visualization, as well as cartographic design principles specific to the display of geographic data. Both page layout design and data display are addressed.","name":"Design principles","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV4-1","description":"A thematic map is a type of map especially designed to show a particular theme connected with a specific geographic area. These maps \"can portray physical, social, political, cultural, economic, sociological, agricultural, or any other aspects of a city, state, region, nation, or continent\". Cartographers use many methods to create thematic maps. Five techniques are especially noted: -Choropleth mapping shows statistical data aggregated over predefined regions -Proportional symbols, showing the relative value of attributes -Isarithmic or Isopleth, also known as contour maps -Dots, to show the location of a phenomenon -Dasymetric, which uses areal symbols to spatially classify volumetric data.","name":"Thematic mapping","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV4-10","description":"Conveying uncertainty information is often done through visualization. Uncertainty is often defined, quantified, and expressed using models specific to individual application domains. In visualization however, we are limited in the number of visual channels (3D position, color, texture, opacity, etc.) available for representing the data. Thus, when moving from quantified uncertainty to visualized uncertainty, we often simplify the uncertainty to make it fit into the available visual representations. (After Potter et al., 2012). The seven challenges as formulated by MacEachren et Al. (2005) are still there to be tackled.","name":"Visualization of uncertainty","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV4-2","description":"Relief can be represented in a two-dimensional map either through contour lines or through a raster format gridded array of elevations. Contour lines connect points of equal elevation. At regular intervals index contours are marked with elevations so a reader can more easily determine the elevation of surrounding locations. They are the preferred method for analogue topographic maps. The grid approach is used in digital mapping and known as a digital elevation model (DEM), where each raster cell represents an elevation. Scaling of the cell z value in relation to the x and y value results in terrain exaggeration, which aids visualization of topography.\r\nDEMs are used for terrain analysis and can be used to obtain derivatives such as slope and aspect. DEMs are obtained by interpolating point elevation observations, which are historically retrieved from surveyed point data (e.g. GPS locations), but more recently from LiDAR and/or Structure from Motion point clouds. TIN (triangular irregular network) analysis is commonly used for point data interpolation, in order to derive a continuous elevation surface.","name":"Representing terrain","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV4-3","description":"Multivariate descriptive displays or plots are designed to reveal the relationship among several variables simultaneously. There are several basic characteristics of the relationship among sets of variables that are of interest. These include: - the forms of the relationships - the strength of the relationships, and - the dependence of the relationships on external (usually to the pairs of variables being examined) circumstances. Multivariate plot examples are: - enhanced 2-D scatter diagrams - 3-D scatter diagrams - contour, level, and surface plots - high-dimensional data plots","name":"Multivariate displays","selfAssesment":"In progress (GI-N2K)
\r\n\r\n"},{"code":"CV4-4","description":"According to Daassi et al. (2006) the visualization process of temporal data has four steps: (1) time values to be visualized, (2) point of view on time, that identifies the characteristics of the temporal values to be visualized, (3) time space: define the displayable space of the time values and (4) point of view on the visualization space, the implementation of the perceptible forms of time. The visualization of spatio-temporal data can be done in many different ways such as multi-panel plots (maps), time-series plots (graphs), space-time plots (graphs), animations, and tables (Pebesma, 2012) Aspects: Space; Time; representation with visual means","name":"Visualization of temporal geographic data","selfAssesment":"
In progress (GI-N2K)
"},{"code":"CV4-5","description":" ","name":"Dynamic and interactive displays","selfAssesment":" "},{"code":"CV4-6","description":" ","name":"Web mapping","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV4-7","description":"Virtual reality or virtual realities (VR), also known as immersive multimedia or computer-simulated reality, is a computer technology that replicates an environment, real or imagined, and simulates a user's physical presence and environment in a way that allows the user to interact with it","name":"Virtual and immersive environments","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV4-8","description":"Augmented reality (AR) is the integration of digital information with live video or the user's environment in real time.","name":"Augmented environments","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV4-9","description":"Cartographers have recently become involved in extending geographic concepts and cartographic design approaches to the depiction of non-geographic data archives, using so-called spatialized views of information spaces. Spatializations differ from ordinary data visualisation and geovisualisation in that they may be explored as if they represented spatial information. (Fabrikant, S.I., 2003). As definitions of spatialization can be found: Spatializations are computer visualizations in which nonspatial information is depicted spatially (Montello et al., 2003). Spatialization is the transformation of high-dimensional data into lower-dimensional, geometric representations on the basis of computational methods and spatial metaphors. (Skupin 2007)","name":"Spatialization","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV4","description":"This concept addresses mapping methods and the variations of those methods for specialized mapping and visualization instances, such as thematic mapping, dynamic and interactive mapping, Web mapping, mapping and visualization in virtual and immersive environments, using the map metaphor to display other forms of data (spatialization), and visualizing uncertainty. Analytical techniques used to derive the data employed in these graphic representations are discussed in Knowledge Area AM Analytical Methods and Unit DN2 Generalization and aggregation.","name":"Graphic representation techniques","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV5-1","description":"Geospatial data representation can make high demands on computational facilities. Examples are: - Infrastructural connections to datasets and processing models - Processing capacity: speed and volume - Access to storage capacity: speed and volume - Display facilities: size, resolution, speed - Peripheral devices like printers for large format hard copy, or VR headsets","name":"Computational demands","selfAssesment":"GI-N2K
"},{"code":"CV5-2","description":"Standards for map services were set by OGC and ISO, called WMS and WMTS. Producing map images on the web from a cartographic image in a GIS application is called \"publishing\". Making a web \"map\" in the broader sense of constructing data representations for Storytelling or Geo-gaming is still under development. It requires a mix of applying the map Design principles and Graphic presentation techniques, possibly in combination with software scripting.","name":"Web map making","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV5-3","description":"Traditional \"map\" making, as opposed to the mapmaking in neogeography, focuses on reliable and reproducible products, based on expertise of high definition printing in many colours on analogue media of geodetically well-constructed images.","name":"Traditional map making","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV5-4","description":"The aspects of reproduction of a data representation depend on the nature of the representation: is it analogue (a paper map, a mock-up) or is it digital? In the case of a paper map, its digitalisation with high fidelity is an essential step. With a source in digital form, reproduction can be a matter of the right printer. Alternatively, the source could be disseminated as a file or as a web service. If representations are dynamic and/or interactive the possibilities depend on the construction of the representation. The ease of dissemination of digital files should not result in copyright breach. Aspects: Digitalization techniques for analogue sources, Printing ( 2D, 3D), Dissemination ways, Construction of the data representation, User needs specification, Copyright issues","name":"Map reproduction","selfAssesment":"GI-N2K
"},{"code":"CV5","description":"This concept addresses map production and reproduction, as well as computation issues that relate to those workflows.","name":"Map production","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV6-1","description":"The potential of maps as a way to show or exert power over the population was early understood by ruling classes. A map expresses a claim by the inclusion or exclusion of map elements and how these elements are visually related and/or depicted on the map. So, the world could be modeled through the careful choice of content arranged graphically at a specific scale and in specific formats. Therefore, maps embody and project the interests of their creators. The “new cartographies” declare that maps are redefined as socially constructed arguments based upon consistent semiotic codes. Nowadays, the rise of costless, powerful and accessible tools for creating maps, put power on the side of individuals or groups of individuals with few organisation (crowdsourced data collection or VGI) capable of representing their world views. In addition, monitoring people, places or nature, for instance, should also be seen as another way to show the increasing power of maps. Surveillance mechanisms for tracking populations used by rulers, or the use of extended technologies like Google Earth by environmental organisations to track the Amazonian forest, constitute two examples of the particular use of maps to exert control over human beings or to press governments for taking specific actions, respectively.","name":"The power of maps","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV6-2","description":"Becoming aware of what a \"map\" shows depends partly on what the senses can register of the representation as a whole. It also depends on recognition of elements in the representation that are meaningful to the observer in the sense that these elements are credible indicators of spatial features. Based on that recognition, the nature of these elements and their spatial pattern might infer thoughts about historic or ongoing processes. This interpretation will be influenced by the expertise and needs of the observer. Aspects: Data representation in one or more media, static or dynamic; Senses of the observer; Interpretation by the observer","name":"Map reading and interpretation","selfAssesment":"GI-N2K
"},{"code":"CV6-3","description":"Assessment of the usability of a data representation is about how useful it is to users. Therefore it is a test of the success of the representation design, a test of the skills of the \"map\" maker and a test for the reliability of the underlying data.","name":"Usability analysis","selfAssesment":"GI-N2K
"},{"code":"CV6-6","description":"Spatial thinking is thinking that finds meaning in the shape, size, orientation, location, direction or trajectory, of objects, processes or phenomena, or the relative positions in space of multiple objects, processes or phenomena. Spatial thinking uses the properties of space as a vehicle for structuring problems, for finding answers, and for expressing solutions\" Aspects: recognizing spatiality in a collection of things; translation of the collection to a pattern of elements; recognizing structure (relations between the elements in a pattern); recognizing process (or changes over time in patterns or structures)","name":"Spatial thinking","selfAssesment":"GI-N2K
"},{"code":"","description":"","name":"","selfAssesment":""},{"code":"CV6-8","description":"Ethics is about the question if behaviour is right or wrong in a social context. In dealing with geodata, a person can do the wrong thing with respect to laws (e.g. disclose secrets, disregard privacy, copyright infringement) or to professional standards (e.g. use bad data, forget about the colour blind, downplay unpleasant details). Aspects: breach of legal standards; breach of professional standards","name":"Map ethics Legal and privacy issues","selfAssesment":" "},{"code":"CV6","description":"Geodata visualisation are always made with a certain purpose. The role and understanding of such graphical representation is an important field of research. Besides theories that underpin evaluation approaches and their findings the visualisation may also be confronting. The more realistic the presentation and especially when it includes human/personal related data the ethical dimension of the visualisation play a major role. Usability of visualisations has also an impact on spatial thinking as has been proved by scholars.","name":"Usability of maps","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA","description":"Proper design of geospatial applications, models, and databases and the validation and verification of design activities are critical components of work in all areas related to GIS&T. Design failures can negate well-intentioned efforts to apply concepts and technology to solve real-world problems. While sharing a number of concerns with general systems analysis, the unique and complex spatial characteristics of geospatial information provide significant additional challenges. The focus of this knowledge area is on the design of applications and databases for a particular need. The design of general-purpose models and tools (e.g., raster and vector) is covered in Knowledge Area: Data Modeling (DM). In the context of specific implementations, design activities fall into three general classes:\r\n1. Application Design addresses the development of workflows, procedures, and customized software tools for using geospatial technologies and methods to accomplish both routinary and unique tasks that are inherently geographic.\r\n2. Analytic Model Design incorporates methods for developing mathematical models, spatial models and data processes. The design of the analytic model is often influenced by decisions that are made about data models and structures.\r\n3. Database Design concerns the optimal organization of the necessary spatial data in a computer environment in order to efficiently sustain a particular application or enterprise.","name":"Design and Setup of Geographic Information Systems","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA1-1","description":"This concept deals with the importance of having a list of prioritized requirements as a first step to ensure a smooth and successful implementation of a GIS project.. It entails the different methodologies and approaches to ensure a GI system covers all functional and nonfunctional requirements. Requirements are not only derived from business workflows but it is advisable to gather direct input from potential users that will be translated into requirements. However, there is a need to clearly rank the importance of the requirements gathered to ensure the GI system is manageable and in line with the intended use of the GI system, in opposition with the specific interests of a particular user or ambiguous requirements. Therefore, the documentation, traceability and evaluation of requirements after the implementation are as relevant as the initial gathering of requirements to give consistency to the designed system.","name":"Requirements gathering and analysis","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA1-2","description":"The internal process of documenting a task or a process is about “how” it is implemented and “what” is implemented. Documenting is particularly helpful if a breakdown occurs, such as when an expert working in a task leaves her job or to substitute one task in a set of interrelated processes by another. Documentation provides consistency for the taskand allows its monitoring, analysis and revision during a project. \r\nThere are different methods for documenting a task to transform tacit knowledge into explicit knowledge. Therefore, the task should be documented by describing it in video format and using visual tools that allow documentation, or the maintenance of a field diary.\r\nIn particular cases, the creation of user guides or manuals could be considered a subset of a process description particularly addressed to external users. A user manual should take into account the target users to adapt its content to them.","name":"Methods of process description and documenting","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA1-3","description":"- Analysis of application processes - Languages for business process descriptions - Transformation of application processes into systems","name":"Transformation of application processes into systems","selfAssesment":"GI-N2K
"},{"code":"DA1-4","description":"- Workflow definition and consideration in GI systems - workflow definition diagramms - use of workflow models to specifiy sequences of activities","name":"Workflow definition and consideration in GI systems","selfAssesment":"GI-N2K
"},{"code":"DA1-5","description":"Software and information technology are integral to any GI systems or projects, from the storage and handling of spatial data to its analysis, visualisation and sharing . The design and creation of software play is fundamental in modern GIS projects. Therefore, the use of well-known software engineering techniques and methods to develop efficient, reliable and easy-to-maintain software applications in the GIS realm is more important than ever.","name":"Software design and engineering","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA1-6","description":"- human computer interactions - user interface design principles and issues - GUI development - Software construction tools: GUI Builders - usability, usability tests and evaluation","name":"User interface and Usability","selfAssesment":"GI-N2K
"},{"code":"","description":"","name":"","selfAssesment":""},{"code":"","description":"","name":"","selfAssesment":""},{"code":"DA1-9","description":"Geodesign is a design and planning method along with supporting technology to integrate together the creation of design proposals with impact simulations informed by geographic contexts.","name":"Geodesign","selfAssesment":"new
"},{"code":"DA1","description":"This concept encloses a set of activities and workflows to ensure that the implementation of a GIS system in an organization or project is correctly planned and designed according to the particularites, user requirements and current conditions of the project ahead. In general system design is the process to promote successful GIS in an enterprise environment. As a GIS system has a direct influence on the information technology department (IT), the system design tells the organizacion how the current infrastructure can or must support the planned GIS. This process builds a set of specific recommendations on hardware and network needs based on the number of projects that depend on the GIS solucion, as well as the projected business needs and user requirements. \r\nGIS architects through the system design process need to take into account and identify several conditions: a) infrastructure requirements, b) the network communication capacity, c) hardware and software procurement requirements and, d) software development and data acquisition needs. \r\nHaving a well-defined and successful GIS deployment is not only a matter of what data or software the organization should acquire. The process of system design aligns identified business requirements (user needs/requirements) derived from business strategies or project aims, goals, and stakeholders (business processes) with identified business information systems infrastructure technology (network and platform) recommendations. \r\nThe process starts with identifying business needs, including the identification of users locations, required information, data, resources or products. The business needs are generally considered as project workflows that help the GIS architects to identify the expected data traffic and computing demand associated with each transaction, being a transaction the work unit used to translate business requirements into associated server and network loads.\r\nWithout carrying out a proper system design, a GIS system can lead to an implementation and deployment failure, deriving in unfulfilled expectations and high costs in terms of human resources and financial matters.","name":"System design","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA2-1","description":"Project management is the planning, organization, coordination, execution, monitoring, controlling and closing of activities and resources - human and economic - for the timely achievement of clearly defined objectives forming a project.","name":"Project management","selfAssesment":"In progress (GI-N2K)
"},{"code":"","description":"","name":"","selfAssesment":""},{"code":"DA2-2","description":"This concept embraces the factors that could affect a GI system / project and could constitute obstacles to success or even decide a project is not doable. In order to ensure the success of a GI system or a GIS project there are several criteria to take into account from the very beginning of the conception of the GI system or project. A feasibility study may encompass different perspectives (economic, legal, technical, operational or scheduling ) to inform whether or not a project is worth the investment. An organisation should list the foreseen costs from these five perspectives listed above and the benefits (tangible or intangible) of implementing a system/project. Existing resources already available in-house and internal strategic plan in place could be critical to decide to undertake a project or not. The table below presents a non-exhaustive list of criteria and under which perspectives they should be examined.\r\nFeasibility analysis should include a pilot study to evaluate and improve the system / project proposed.","name":"Feasibility analysis","selfAssesment":"Completed (GI-N2K)
"},{"code":"","description":"","name":"","selfAssesment":""},{"code":"","description":"","name":"","selfAssesment":""},{"code":"","description":"","name":"","selfAssesment":""},{"code":"","description":"","name":"","selfAssesment":""},{"code":"","description":"","name":"","selfAssesment":""},{"code":"DA2-8","description":"This concept discusses the technical, organizational and monetary advantages and disadvantages of commercial versus open source software. GIS/T industry and research are slowly but consistently moving toward the openness of software and source code. Open software entails some clear advantages like continuous development of new applications, building community of developers and users, possibility to start a project even if limited funding is available, increase the sustainability chances of a project, etc. to name a few. On the other side, proprietary initiatives in GIS/T are holding their roots to the ground by developing cutting edge tools for handling challenging environments and present other advantages as well. Some of them could be: a more stable software or a dedicated support service for the client. Both open and proprietary geospatial software solutions co-exist by the application of the appropriate IPR licences to each type of solution. The future is how these commercial and open source geospatial software find synergy in handling large projects.","name":"Commercial and open source software","selfAssesment":"In progress (GI-N2K)
"},{"code":"","description":"","name":"","selfAssesment":""},{"code":"DA2","description":"To design, build, and maintain a GIS, sufficient resources (e.g., labor, capital, and time) must be secured. Resource planning consists of the allocation and use of in-house resources (people, equipment, tools, rooms, etc.) to achieve the maximal efficiency of those resources. These resources are required for a variety of system elements, including design, software purchase, labor, hardware, and facilities. The crucial task is to determine whether the project is worth the required resources.","name":"Resource planning","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA3-1","description":"The ecosystem of GIS software architectures has evolved substantially in recent years to include a variety of options ranging from desktop GIS, server-based and component-based architectures to Web-based, cloud-based, mobile-based approaches. Aligned with the main trend, geospatial software architectures or infrastructures are also moving from desktop architectures to more cloud based or server based options to meet ever-increasing requirements of interoperability, interdisciplinary work and computational power for processing large data sets and derived products. Cloud-based architectures also enable on the fly visualization of computed geospatial products, as complementary visualisation and mapping tools are seamlessly integrated into modern cloud-based based architectures. Usage of a particular architecture is fully dependent on the nature, size, requirements, functionalities, and available resources of a given project or task. Desktop and server based applications are particularly suited for small sized projects and startups while enterprise based applications are meant for larger sized projects. Cloud based infrastructure can be useful for varying sizes of projects in which the computational infrastructure is fully outsourced.","name":"Major geospatial software architectures","selfAssesment":"In progress (GI-N2K)
\r\n\r\n"},{"code":"DA3-2","description":"Interoperability of GIS infrastructure or architecture ensures the consistent and uninterrupted usage of data and functionalities across platforms and systems. Components or tools residing on distinct platforms can “talk” to each other without friction. Interoperability is a central characteristic, especially important in distributed systems and architectures. It can be applied to different levels or layers of a system, i.e. infrastructure level, data level, business logic level, etc. For example, standard spatial data formats and protocols are especially relevant for handling GIS data across multiple systems and platforms, regardless of their underlying software architecture. This is particularly important in large-scale, collaborative projects involving various teams using heterogeneous GIS architectures. Most software providers, developers communities and standardisation bodies and committees are striving to make their architectures interoperable in an open manner, so proprietary standards and protocols are a potential hindrance to this initiative.","name":"Interoperability","selfAssesment":"
In progress (GI-N2K)
"},{"code":"DA3-3","description":"Thisconcept considers general architectural patterns like SOA, ROA, Web Services, etc.","name":"Architectural Patterns","selfAssesment":"GI-N2K
"},{"code":"DA3-4","description":"- WebGIS, - technical pecularities of spatial data infrastructures - standardiced GI services for SDI: WMS, WFS, CSW, Transformation Services, SOS, WPS etc., - other map services and interfaces","name":"WebGIS, SDI services, map services","selfAssesment":"GI-N2K
"},{"code":"DA3-5","description":"this concept deasl with Reference Model of Open Distributed Processing (RM-ODP), its Standards, Viewpoints modeling and the RM-ODP framework","name":"Reference Model of Open Distributed Processing","selfAssesment":"GI-N2K
"},{"code":"DA3-6","description":"This concept deals with the advantages and disadvantages of cloud and grid computing by their comparison. Cloud deployment models: private cloud, public cloud, hybrid cloud, etc., Security and privacy issues of cloud solutions","name":"Cloud and Grid computing","selfAssesment":"GI-N2K
"},{"code":"DA3-7","description":"Within this concept solutions based on Desktop GIS and GIS libraries will be compared and contrasted","name":"Desktop GIS, GIS libraries","selfAssesment":"GI-N2K
"},{"code":"","description":"","name":"","selfAssesment":""},{"code":"DA3","description":"This concept describes the major geospatial software architectures available currently and choices when designing GI applications and systems, including desktop GIS, server-based, Internet, and component-based custom applications.","name":"Architectural design of a GIS system","selfAssesment":"GI-N2K
"},{"code":"DA4-1","description":"- Compare and contrast the relative merits of various textual and graphical tools for data modeling, including E-R diagrams, UML, and XML - Create conceptual, logical, and physical data models using automated software tools - Create E-R and UML diagrams of database designs","name":"Modeling tools","selfAssesment":"GI-N2K
"},{"code":"DA4-2","description":"Within an initial phase of database design, a conceptual data model is created as a technology-independent specification of the data to be stored within a database.","name":"Conceptual models","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA4-3","description":"A logical data model expresses the meaning context of a conceptual data model, and adds to that detail about data (base) structures, e.g. using topologically-organized records, relational tables, object-oriented classes, or extensible markup language (XML) construct tags","name":"Logical models","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA4-4","description":"A physical data model documents how data are to be stored and accessed on storage media of computer hardware","name":"Physical models","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA4","description":"The effective design of geospatial databases should follow the established methods and principles of database modeling and design developed in computer science. The basic method is a three-step process generally called the conceptual, logical, and physical models transforming the application from very human-oriented to machine-oriented. Several standards and software tools exist to aid the process of database design.","name":"Database design","selfAssesment":"GI-N2K
"},{"code":"DM","description":"This knowledge area deals with representation of formalized spatial and spatio-temporal reality through data models and the translation of these data models into data structures that are capable of being implemented within a computational environment (i.e., within a GIS or more likely within a spatial database). Data modelling is a crucial issue as it defines the content of a spatial database and usefulness of these content (data) for certain applications. Data Modelling is performed using system neutral languages like UML (or more seldom ER-diagrams). These conceptual models have to be transferred to logical models (i.e. tables of a database). Data is stored in spatial databases which are normally organized in an object relational way. For certain types of data specific databases are used, like triple stores, NoSQL DBs, Array DBs etc. For data modelling quite a number of ISO standards are available for deriving the conceptual model as well as for rules for application schemas, spatial schemas, temporal schemas, Quality principles, encoding, 3D modelling (CityGML) etc. Data models provide the means for formalizing the spatio-temporal conceptualizations. Examples of spatial data model types are discrete (object-based), continuous (location-based), dynamic, and probabilistic. Mastery of the objectives presented in this knowledge area require knowledge and skills presented in the bodies of knowledge of allied fields, including computer science (ACM/IEEE-CS Joint Task Force, 2001) and information systems (Gorgone & Gray, 2000; Gorgone & others, 2002).","name":"Data Modeling, Storage and Exploitation","selfAssesment":"GI-N2K
"},{"code":"DM1-1","description":"This topic includes the main basic database concepts: - Database, definition and overview - Database management system, definition and overview - Relational databases, overview - Object-oriented databases, overview - Object-relational databases - NoSQL databases, general overview - NoSQL databases, examples triple stores, array databases, others (overview)","name":"Overview on database concepts","selfAssesment":"GI-N2K
"},{"code":"DM1-10","description":"To be defined depending of the background of the course etc.","name":"Database practice","selfAssesment":"GI-N2K
"},{"code":"DM1-2","description":"The Relational Model is the most important database model, therefore it is explained in more detail here: - Basic concepts (tables, tuples, etc.) - Relation to relational algebra (RA), basics of RA - Constraints (key, domain, referential integrity) - Relation to entity relation (ER) model, basics of ER","name":"The Relational Model","selfAssesment":"GI-N2K
"},{"code":"DM1-3","description":"Relational databases and database management systems are essential for GIS in consequence the important issues have to be treated here: - General aspects, basic architecture of a DB, advantages, features - DBMS concepts and functionalites (transactions, locks, multiuser access etc.) - Database design, techniques - Database administration - Normalization (1NF - 3NF) - Example of a database design","name":"Relational Databases, Database Managements Systems and Database principles","selfAssesment":"GI-N2K
"},{"code":"DM1-4","description":"Database queries and especially spatial queries require specific data structures to be performed satisfactory Relevant is: - Motivation, examples of typical non-spatial and spatial queries - Trees, B-tree, R-tree, Q-tree - Graphs, overview and relation to databases","name":"Data Structures and Indices for Databases","selfAssesment":"GI-N2K
"},{"code":"DM1-5","description":"Big data like imagery but also for example GML data sets need compression to be accessed / transferred in an acceptable time. Therefore some compression techniques have to be taught: - Motivation, examples of data sets which need compression - General introduction, vector - / raster data compression, compression lossless, lossy - Popular compression techniques, LZW (Lempel-Ziv-Welch) encoding, Huffman encoding - Techniques for raster data, runlength encoding, JPEG coding, wavelet etc. - Techniques for the reduction of vector data (Douglas Peuker etc.) - Data formats, overview and relation to compression techniques","name":"Data compression techniques","selfAssesment":"GI-N2K
"},{"code":"DM1-6","description":"SQL is the \"standard\" to perform spatial and non-spatial queries in databases. That means each student in a GI related course has to be familiar with the main aspects if it: - Motivation, history, overview - Data definition language DDL - Data manipulation language DML - Data control language DCL - Spatial extensions of SQL","name":"SQL and its usage for data handling, spatial extensions to SQL","selfAssesment":"GI-N2K
"},{"code":"DM1-7","description":"UML is the standard for describing the schema related to GI models, but also user requirements, workflows etc. can be described in UML using the UML diagrams: - Motivation, background, purpose - Use case diagrams - Class diagrams - Sequence diagrams - Activity diagrams","name":"UML introduction and class diagrams","selfAssesment":"GI-N2K
"},{"code":"DM1-8","description":"XML knowledge is an important bases for understanding GML. Moreover XML tools like XSLT are important to transform XML or GML data sets into other XML based formats like SVG or others. Important issues: - Motivation, purpose - Relation to HTML - XML document structure - XML syntax, elements, attributes and namespaces - xlink, xpath and XSLT - XML DTD - XML schema","name":"XML introduction","selfAssesment":"GI-N2K
"},{"code":"DM1-9","description":"The long term storage of GI data in general is based on spatial databases. Therefore the following is essential for a GI course: - Relation between GIS and DB / \"Long transactions\"- Dual concepts - Characteristics of spatial databases - Spatial data in object relational databases - Spatial extensions of DBs, overview","name":"Database concepts in GIS and Principles of spatial databases","selfAssesment":"GI-N2K
"},{"code":"DM1","description":"This unit includes the basics for data modelling, storage and exploitation. Data modelling is one of the most important activities in conjunction with Geographic Information / GIS as it determines how the data can be used and if the requirements from applications are fulfilled. Data modelling can be done in conjunction with the database, e.g. through ER diagrams or according to the ISO 191xx standards by using UML. The costs of data acquisition can be tremendous, therefore the data represents an enormous value. This value has to be conserved through a safe long term data storage. Therefore databases and especially relational and object relational databases are crucial. For a proper storage and query of geographic information databases are extended with specific data types and data structures. As data sets can be very large suitable compression techniques became important especially in the context of accessing and delivering geographical data, e.g. through services. XML based modeling languages for encoding also play and important role in this context","name":"Foundations for Data Modelling Storage and Exploitation","selfAssesment":"GI-N2K
"},{"code":"DM2-1","description":"GI standards, mainly from ISO and OGC are essential nowadays. Moreover also an overview on ICT standards from W3C or OMG are important as well as some understanding of standardization processes. In detail: - Motivation for standards, examples from daily life - Overview on GIS and relevant ICT standardization bodies and selected standards - De jure and De facto standards, obligation, reasons for the usage of standards - Standardization within ISO - Standardization within OGC, relation to ISO - Examples of ISO 191xx standards","name":"Overview on relevant standards and standardisation bodies","selfAssesment":"GI-N2K
"},{"code":"DM2-2","description":"Conceptual data modeling is a key skill for GI people. (see relations to other topics) The following therefore is important: - Overview on the relevant standards like conceptual schema language, Rules for application schema - Examples of conceptual schemas","name":"The principle of conceptual data modelling according to ISO","selfAssesment":"GI-N2K
"},{"code":"DM2-3","description":"Geometric modelling is an important subtask of conceptual modelling and requires the following basics: - Overview of ISO 19107 - spatial schema - Overview of ISO 19125 - simple features - Examples of the usage of spatial schema and simple feature elements for feature class definitions - Relation to GML - Relation to DBs","name":"Geometry data types according to spatial schema and the simple feature specification","selfAssesment":"GI-N2K
"},{"code":"DM2-4","description":"Also temporal aspects have to be considered within conceptual modelling. This also requires basics: - Motivation, examples - Temporal variability of features (move, change of structure or geometry) - Overview on ISO 19108 temporal schema - Examples of modeling temporal aspects","name":"Temporal data types according to temporal schema","selfAssesment":"In Progress GI-N2K
"},{"code":"DM2-5","description":"Conceptual models of course have to be implemented, in general in a GIS (which is often proprietary), or in a database (which can be standard based) ,therefore here the implementation in a database is treated: - Repetition of conceptual and logical models - Examples of the transferring of a conceptual model to a logical (database) model","name":"Transferring conceptual models to logical models","selfAssesment":"GI-N2K
"},{"code":"DM2-6b","description":"Metadata is considered as very important for the usage as well for the search for Geodata Relevant basics are: - Motivation, importance of data quality as part of metadata - Metadata in an spatial data infrastructure with many There are quite a number of relevant standards for GI courses. Some are listed here, others might be considered, depending on the background of the course: - Select other standards and explain them, Important are: - ISO 19141 Schema for moving features, ISO 19142 Web Feature Service or others - 19109 - Rules for application schema - Selection of other standards is depending on the background of the course","name":"Other standards","selfAssesment":"GI-N2K
"},{"code":"DM2-7","description":"GML is the most important standard for the transfer of Geodata as it allows to transfer the schema information as well as the data. Important issues: - Motivation, Importance of a Geography Markup Language - History of GML, Overview 19136 - Geography Markup Language - Relation to spatial schema - Supported features in GML (Topology, 3D ...) - Structure of GNL, profiles, application schemas etc. - Transfer of models and of data - Examples","name":"Introduction to GML","selfAssesment":"GI-N2K
"},{"code":"DM2-8","description":"3D Models, especially 3D city models are becoming more and more important. CityGML is the most important standard within the GI domain to describe City models semantically and geometrically. Relevant issues: - Motivation, Usage of CityGML - Relation to GML - Coherence of semantics and geometry - Principles of modeling - Level of detail concept - CityGML vs KML - Examples","name":"Introduction to CityGML","selfAssesment":"GI-N2K
"},{"code":"DM2","description":"This unit includes the essentials of relevant standards for spatial data modelling. A number of ISO and OGC standards are available for deriving the conceptual model as well as for rules for application schemas, spatial schema provides data types for geometry models in various forms, Point, line, area, body based, temporal schema allows to consider temporal dimensions, Quality principles can be used to describe the quality of geodata, encoding standards (mainly GML) allow the standard based transfer of data and data models, CityGML allows a standard based 3D modelling, etc.","name":"Standards for Spatial Data Modeling","selfAssesment":"In progress GI-N2K
"},{"code":"DM3-1b","description":"There are two basic concepts related to this topic: Features and Fields, or Geo-fields, as named by Goodchild at al. The concept of fields can be differently represented as explained here: - Repetition of basic concepts of Geographic Information Science - Explanation of the concept of continuous fields and the commonly used ways of representing geo-fields - Relation between fields and coverages, an important discretizations of a Geo-field - Types of Coverages","name":"The concept of fields","selfAssesment":"GI-N2K
"},{"code":"DM3-2","description":"The raster data model holds values in a regularly spaced matrix of cells arranged in rows and columns covering a two dimensional space. Rasters are commonly used to store continuous data like colors in an image and height values but they are also used for discrete (thematic) values like land use.","name":"The raster model","selfAssesment":"In Progress (GI-N2K)
"},{"code":"DM3-2b","description":"Grids are on the one hand one important type of caverages and on the other hand Grids are used as basic structure in some applications. Important here is: - Definition of the concept of grid in GIS - Grid as an instance of coverages - Grids as a basic structure for certain applications / medium for aggregation of data - Examples of grid-based data such as Digital Terrain Models (DTM) - Grids in census / statistical data and Geo-marketing applications","name":"Grid representations","selfAssesment":"GI-N2K
"},{"code":"DM3-3","description":" ","name":"Grid compression methods","selfAssesment":"GI-N2K
"},{"code":"DM3-3b","description":"TINs and Voronoi tessellations are important types of coverages. TINs play a very important role also in Computer graphics. Important here is: - Basics from Graph theory - Definition of Triangulated Irregular Networks (TIN), purpose and applications - TINs and voronoi diagrams as a type of coverages - One important instance of a TIN: Delauney Triangulation - Definition of Voronoi Diagrams, purpose and applications - Relation between Delauney Triangulation and Voronoi Diagram, the \"Dual Graph\" - Examples from applications","name":"TIN and Voronoi tesselations","selfAssesment":"GI-N2K
"},{"code":"DM3-4","description":" ","name":"The hexagonal model","selfAssesment":"GI-N2K
"},{"code":"DM3-4b","description":"- Other relevant models - Linear referencing (t.b.d)","name":"Other models like linear referencing","selfAssesment":"GI-N2K
"},{"code":"DM3-5b","description":"Resolution of raster and gridded data - Georeferencing of data, direct and indirect methods (t.b.d.)","name":"Resolution and georeferencing system","selfAssesment":"In Progress (GI-N2K)
"},{"code":"DM3-7","description":" ","name":"Hierarchical data models","selfAssesment":"GI-N2K
"},{"code":"DM3","description":"This unit includes relevant tessellation data models. Besides features (sometimes also called geo-objects) geo-fields play and important role. In recent literature tessellation models are classified as discretizations of fields. In traditional GI literature tessellations are defined as important data structure itself. Tessellation discretise a continuous surface into a set of non-overlapping polygons that cover the surface without gaps. Tessellation data models represent continuous surfaces with sets of data values that correspond to partitions. Important tessellation models are Grids, TINs and Voronoi diagrams.","name":"Tessellation data models","selfAssesment":"GI-N2K
"},{"code":"DM4-1","description":"This topic includes the basics for feature based modelling. There are a number of standards also relevant for this topic (see relations). The following items should be included: - Definition of a feature (in some literature also called object, or geoobject) and of feature classes respectively. - Aspects of the definition (ID, geometry, topology, thematic, time etc.) - Techniques for the definition of features / feature classes (mainly link, as they are described elsewhere, see relations)","name":"Feature based modelling","selfAssesment":"GI-N2K
"},{"code":"DM4-2","description":"This topic describes the process of Geometric modelling using vector data, means the primitives like points, lines, areas, bodies, or raster data. There is a strong relation to ISO standards (see relations) as they provide basic data types for geometric modelling. Main issues: - Geometric modeling based on vector data - Geometric modeling based on raster data - Conversion between the models - examples, advantages, disadvantages of the models","name":"Geometric modelling","selfAssesment":"GI-N2K
"},{"code":"DM4-3","description":"- Examples of analysis which requires topology","name":"Topological modelling","selfAssesment":"GI-N2K
"},{"code":"DM4-4","description":"This topics deals with the definition of an application schema. There are other units which are important for this topic (see Relations). Issues to be included: - Methods to define and describe an application schema (requirement analysis, description of the schema etc.) - Feature attribute catalogues - Domains / data relevant for INSPIRE","name":"Application models based on vector data","selfAssesment":"GI-N2K
"},{"code":"DM4-5","description":"This Topic deals with important application models, which should be chosen with relation to the course (geographically / related to the background of the course) INSPIRE should be treated in any case. In detail: - Overview on important application models relevant for the course, e.g. from topography or environment in the country - Repetition of the principles of Spatial data infrastructures - Overview on the INSPIRE initiative and the goals related - The INSPIRE data model - The architecture of INSPIRE and the necessary services - Domains / data relevant for INSPIRE","name":"Examples of important application models","selfAssesment":"GI-N2K
"},{"code":"DM4-6","description":"This topic is dedicated to the challenges of model based interoperability and related issues, The principles of interoperability are included in DA3-2. In detail: - The challenges of model interoparability (semantics, different modelling of the same features in different models, syntacs) - Overview on IT concepts for schema integration / transformation - Approaches for model integration - Approaches for model transformations, e.g. related to INSPIRE, from the Humboldt project","name":"Model based interoperability, model transformations","selfAssesment":"GI-N2K
"},{"code":"DM4-7","description":"Network models are crucial in some application domains, such as Navigation (roads etc.), but also in utility applications (facilities like pipes etc.) In this topic should be treated: - The network model in the database domain - Graph based NoSQL databases - Topology of network models - Data structures for storing network data - The Dijkstra algorithm - Overview on important applications","name":"Network models","selfAssesment":"GI-N2K
"},{"code":"DM4","description":"This unit includes relevant issues related to vector data models, feature based modelling, applications. Besides imagery data the majority of GI data available is feature based and founded on vector geometry. Topology modeling also is very common nowadays, as many analysis like routing or neighborhood analysis require it. Spaghetti modelling becomes more and more and exception. In every country there are important feature and vector geometry based application models available e.g. in Topography / Cartography. In Europe every GI course should include some information on INSPIRE. As in different application domains different data models are used, sometimes for the same feature types, integration and transformation of models are an important issue also.","name":"Vector data model, Feature based modelling, Applications","selfAssesment":"GI-N2K
"},{"code":"DM5-1","description":"- Many geographical phenomena are not defined sharply but uncertain Uncertainty has a number of considerations: - Motivation, background, purpose - Conceptual model of uncertainty - Uncertainty of geographic phenomena (vagueness, ambiguity) - Uncertainty of measurements - Uncertainty of analysis - Uncertainty vs. data quality - Statistical models of uncertainty - Outline of Fuzzy approaches","name":"Basics of uncertainty and its modelling","selfAssesment":"GI-N2K
"},{"code":"DM5-2","description":"Space and time are 2 connected concepts, this topic is dedicated to some basics of modelling time and the temporal dimensions related to features and fields: - Motivation, background, purpose - Changes in time in Entity based and field based representations - A conceptual model of changes in time - Move of objects - Change of structure - Change of geometry - Examples from applications","name":"Modelling time aspects","selfAssesment":"GI-N2K
"},{"code":"DM5-3","description":"Traditionally many GIS used 2D or 2.5 D data models, but in the last decade 3D modeling mainly in form of city models or in the context of Building Information Models (BIM): - Basic concepts of 3D modelling, edge, area, volume models - The workflow of 3D modelling, general aspects, choose of the proper model - Methods of 3D modeling - Principles of Constructive Solid Geometry (CSG) - Principles of Boundary representation (BR) - Principles of Voxel-beased modeling - Comparison of the methods - The concept of BIM, principles and purpose - City models, principles and purpose - Examples / applications","name":"Modelling 3D","selfAssesment":"GI-N2K
"},{"code":"DM5","description":"Traditional raster and vector data models cannot easily represent the more complex aspects of geographic information, such as temporal change, uncertainty, three-dimensional phenomena, and integrated multimedia. A variety of models have been proposed to represent these complexities, including both extensions to existing models and software, and entirely new models and software. During the 1990s, work in this area was largely experimental, but many solutions are now available to practitioners in commercial and open source software. The data models in this unit are based on concepts discussed in Knowledge Area CF Conceptual Foundations.","name":"Modelling 3D, temporal and uncertain phenomena","selfAssesment":"GI-N2K
"},{"code":"DN3-1","description":"Modification of spatial and attribute data while ensuring consistency within the database, implications of transactions on database integrity, scenarios for periodic changes in GIS database and monitoring the periodic changes.","name":"Database change","selfAssesment":" "},{"code":"DN3-2","description":"Rules for modelling spatial database change, techniques for handling version control, techniques for managing long and short transactions, management of spatial databases in multi-user environment","name":"Modeling database change","selfAssesment":" "},{"code":"DN3-3","description":"Reliability tests of change information, design and implementation. Logical consistency of updates.","name":"Reconciling database change","selfAssesment":" "},{"code":"DN3-4","description":"Needs for versioned databases, queries for change scenarios using DB management tools, algorithms for performing dynamic queries, role of time-criticality and data security while choosing methods for change detection.","name":"Managing versioned geospatial databases","selfAssesment":" "},{"code":"GC","description":"The term geocomputation dates back to the first international conference on the topic in 1996 held at the University of Leeds under the title “The art and science of solving complex spatial problems with computers’. The term “geocomputation” was coined to describe the use of computer-intensive methods for knowledge discovery in physical and human geography. This new area distinguishes it from the application of statistical techniques to spatial data in the focus on “creative and experimental applications” and in “developing relevant geo-tools within the overall context of a ‘scientific’ approach.” Other authors reinforced the unique character of geocomputation as “to provide better solutions to many geographical problems by developing new, computationally dependent tools for analysis and modelling”. Simply defined, the interdisciplinary area of geocomputation was, from the beginning, closely linked to the application of computer technology and the development of tools and applications to real-world spatio-temporal problems through the combination of geographic information system techniques, spatial modelling, cellular automata, and other non-conventional data clustering and analysis techniques.\r\nEven though geocomputation is still seeking to define the field conceptually), it is closely related to computational science, the use of high-computing performance, artificial intelligence, computational intelligence, grid infrastructure and parallel computing . Nevertheless, the evolution of new computing paradigms, such as edge-fog-cloud computing along with the new forms of data create new opportunities for the geocomputation community . \r\n\r\nWhile the underlying idea remains intact --a diverse and interdisciplinary area of research that uses geospatial data, methods and tools for applied scientific work--, the current approach to geocomputation differs from the founders in that it focuses more attention on open science, reproducible research practices, and in a vibrant collaborative community to develop new methods, tools and applications that are integrated into multiple application domains such as economics, sociology, geodemography, health, criminology, transportation, biology, remote sensing and cities . The theoretical roots and experimental emphasis of geocomputation makes it an excellent vehicle to creatively explore in parallel the theory and practice of the use of geospatial data in a computational way to solve real-world problems.","name":"Geocomputation","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC1-1","description":"A complex system can be viewed as a system composed of many interacting parts, with the ability to generate a new collective behaviour through self-organisation, for example, though the spontaneous formation of temporal, spatial or functional structures. Complex systems are therefore adaptive as they evolve and may contain self-driving feedback loops. Most real-world systems such as global climate, an ecosystem, a city, the human brain, and the entire universe, are complex systems. Therefore, complex systems are much more than a sum of their parts.The general characteristics of the structure and dynamics of complex systems have been characterised, including path dependence, positive feedback loops, self-organisation, and emergence. Complex system types include nonlinear systems, chaotic systems, and complex adaptive systems. \r\nTraditional approaches focus on the individual system components and define a system as the sum of its parts. Whereas the modern approach relies on complexity theory and complex adaptive systems, to emphasise the linkages between system components in order to understand complex systems as a whole. Agent-based models, for example, have been highly recommended for studying complex adaptive spatial systems because they support the explicit representation of situation-dependent information for decision making within dynamic spatial environments.","name":"Complex systems","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC1-2","description":"Computational science is a discipline focused on the design, implementation and use of mathematical models or simulations through the use of computers to analyse scientific problems, systems or processes. Computational science heavily relies on computational technologies such as high performance computing, artificial intelligence, computational intelligence, grid infrastructure and parallel computing. Geocomputation is closely related to computational science and, therefore, geocomputational methods are often derived from machine learning, clustering, simulation, parallel computing and high performance computing. Contrary to the methods and tools applied for spatial analysis described under the Analytical Methods Knowledge Area, geocomputation methods may involve spatial methods available in standard GIS packages, but quite often require self-development, or at least customisation, involving computational technologies to solve target problems. The aim of this topic is to provide an introduction to computational science with particular emphasis on its usage and relation to geocomputation.","name":"Computational science and technology","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC1-3","description":"While geocomputation is not daily used in GIS environments and traditional GIS projects, it is the focus of a vibrant collaborative and research community in developing new geocomputational methods, tools and applications that are integrated into multiple application domains such as economics, sociology, geodemography, health, criminology, transportation, biology, remote sensing and cities. Open science, reproducible research practices, and strong collaboration make geocomputing an excellent vehicle for creatively exploring together the theory and practice of using geospatial data in a computational way to solve real-world problems.","name":"Spatio-temporal problems and applications","selfAssesment":"In progress (GI-N2K)
"},{"code":"GC1-4","description":"The origin of geocomputation dates back to the first international conference on the topic in 1996 and was coined to describe the use of computer-intensive methods for knowledge discovery in physical and human geography. Geocomputation is closely related to other widely known areas of knowledge within the geospatial community, such as GIScience, Spatial Information Science, GeoInformatics, and Geographic Data Science. While these terms clearly overlap and boundaries are fuzzy, the term geocomputation puts the focus on creative and experimental applications and in developing relevant computationally geospatial tools for analysis and modelling within the overall context of a ‘scientific’ approach. Therefore, a common interpretation of geocomputation is to describe the application of computational models to geographic problems.","name":"Origin of geocomputation","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC1","description":"Geocomputation represents an attempt to move the geospatial research agenda back to geographical analysis and modelling by providing a toolbox of methods to analyse and model a range of highly complex, often non-deterministic problems. In this context, complex systems and computational science are foundational aspects upon which geocomputation approaches and methods are built to address a variety of real-world, spatio-temporal issues","name":"Geocomputation and complex systems","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC2-1","description":"Building a model that mimics a real-world system generally follows a series of stages: from conceptual models to mathematical models and, finally, simulation models. In model development, system analysis is a process whereby a real-world system is simplified by dividing it into simpler, more manageable parts. A conceptual model captures the components, variables and interactions of a system, and provides a useful way of thinking about the trade-offs between abstraction and representativeness of real-world phenomena. Taken in isolation, however, the interacting parts of a system fail to explain its dynamics behavior. A conceptual model is then translated into a mathematical model to explain system dynamics and interaction. Mathematical models often take the form of equations, logical rules or other mathematical mechanisms to represent the interrelations and relationships among the constituted parts of a system. Lastly, a simulation model is the computer-based implementation of mathematical models consisting of interrelated equations and logical rules. When a simulation model runs on a computer, it iteratively recalculates the modelled system state as it changes over time in accordance with the relationships represented by the mathematical relationships that describe the system dynamic. Therefore, developing detailed and dynamic simulation models comes at the cost of generality and interpretability, but it brings us realism and the ability to represent real-world processes in specific contexts. Simulation modelling is often used for prediction, exploration, theory development, or even optimization of conditions to achieve desired outcomes, with the goal of examining how the interconnections and relationships that characterise complex social and environmental systems (e.g. ecosystems, urban systems, social systems, global climate system) produces patterns of behavior over time. Therefore, simulation models are increasingly gaining relevance as scientific mechanisms for several reasons. First, simulation models allow researchers to study systems inaccessible to experimental and observational scientific methods, complementing more conventional approaches to discover or formalize theories about real world systems. Also, aS many real-world systems are nonlinear, simulation modelling has turned into a necessary method to explore and understand better such systems. In addition, the availability of computational science methods and technology, together with a large amount of data available from different sources, have greatly driven the adoption of simulation models in a wide range of scientific disciplines.","name":"Principles of computer simulation","selfAssesment":"Completed (GI-N2K)
"},{"code":"","description":"","name":"","selfAssesment":""},{"code":"GC2-3","description":"Rule-based models are based on logic programming with condition-action expressions, where the left side of the expressions consists of several conditions that returns a logical result, and the right side consists of several actions. Rules in rule-based models indirectly specify a mathematical model. However, unlike equation-based models which refer to the overall or aggregate behaviour of a system, rule-based models focus on the behaviour of the individual components of a system. That’s why the implementation of rule-based models is most often done by cellular automata models or agent-based models, in which the aggregate behaviour of the system emerges from the interaction of the individual agents or cells over time. Many geographic patterns and dynamics are formed by systems of interacting actors/cells with heterogeneous characteristics and behaviours, in which such dynamic behaviours can be implemented as rules. The aim of this topic is to provide knowledge about rule based models and to understand their advantages and disadvantages.","name":"Rule-based models","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC2-4","description":"Equation-based models are a set of interrelated equations that capture the variability of a system over time (differential equations), and the execution (simulation) of the model means to evaluate such equations. Equation-based models do not aim at representing the behaviour of the individual components in a system. Rather, they focus on the overall or aggregate behaviour of a system. Therefore, equation-based models are well suited to represent physical processes and some topics within natural sciences, where the system to some degree can be described by physical laws. Hydrological modelling is a good example of models based on equations. However, other real-world systems can rarely be fully described by the laws of the natural sciences, and their behavior and interrelation must be represented by means of other types of mathematical mechanisms. The aim of this topic is to present the advantages and challenges in using equation-based simulation models, which are most naturally applied to systems centrally governed by physical laws rather than by information processing and flow.","name":"Equation-based models","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC2-5","description":"Space-time dynamics are closely related to the concepts of change and process, which are inherent to our dynamic world. Space-time dynamics especially manifest when we move from a static representation to a dynamic representation of phenomena. Various processes that take place at different spatial and temporal scales interact with each other and lead to complex changes to the phenomena being modeled. There exist many different approaches of conceptualizing and understanding space-time dynamics in order to understand or predict phenomena in heterogeneous application domains ranging from human activities and urban sprawl to disease spread and traffic flow.","name":"Space-time dynamics","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC2-6","description":"Cellular automata are a standard type of spatially explicit simulation model in which complex processes are modelled over space and time by means of a lattice of cells in which each cell defines its neighbouring cells. The spatial lattice composed of a two-dimensional grid of squared cells is the simple configuration of a cellular automata. Based on this regular configuration, each cell has associated a set of states that change at each iteration by the execution of transition rules, which take into account the state of each cell and those of its neighbours. As such, cellular automata consist of six defining components: a framework or lattice, cells, neighborhood, transition rules, initial conditions (states), and an update sequence (time). Cellular automata models map easily onto existing data structures widely used in geographic information systems, are easy to implement, and are able to show changes and spatial patterns in an understandable manner. All of this has contributed to their popularity in simulation modelling for applications such as measuring land use changes and monitoring disease spread","name":"Cellular automata","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC2-7","description":"Agent-based models are simulation models that decompose a complex system into small entities (agnets) with modeling properties and behavior. Contrary to modelling at an aggregate level, agent-based models are focused on the individual level, where a set of discrete agents with well-defined behaviors represents an individual, object or component of the modelled system. Therefore, the individual agent is the explicit, basic unit. The macro-level behaviour of the system emerges thereafter from the interaction of the individual agents and with the environment over time. Agent-based models are used for spatial modelling, offering possibilities to consider topological particularities of interaction and information transfer among agents and/or with the environment. In relation to spatial simulation, agent-based models have been used for example to model natural and social phenomena such as animal behaviour, pedestrian behavior, social insects and biological cells.","name":"Agent-based modelling","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC2","description":"The concept spatial simulation modelling can be better understood by looking at the meaning of its individual words. A model is widely defined as a simplified representation of a real-world system under study, which can be used to explore or to better understand the system it represents. Computer models or simulation models are computer-based implementations of a model to produce outputs based on certain model assumptions. Simulation , therefore, relies on the use of computers for virtual experimentation to gain insight into real-world problems by proposing alternative assumptions that arise from exploring “what if” questions about a dynamic problem of interest over the course of successive simulation experiments.\r\nSimulation modelling is also useful for the study of spatial patterns over time. Spatial simulation models are relevant when the study of spatial elements and their relationships in a system are necessary for a fully understanding of that system. In this sense, spatial simulation modelling approaches include rule-based models, equation-based models, grid-based cellular automata models, discrete event simulation, and agent-based models.\r\nSimulation modelling is often used for prediction, exploration, theory development, or even optimization of conditions to achieve desired outcomes, with the goal of examining how the interconnections and relationships that characterize these systems produces patterns of behavior over time. Across broad areas of the environmental and social sciences, researchers use simulation models as a way to study systems inaccessible to experimental and observational scientific methods, and also as an essential complement of those more conventional approaches to discover or formalize theories about the real world. \r\nSimulation models are a relatively recent addition to the scientific toolbox, and the reasons for their widespread adoption are, on one hand, the impossibility to study in-situ some complex social and environmental systems (e.g. ecosystems, urban systems, social systems, global climate system) and, on the other hand, the availability of High Performance Computing and large amount of data from different sources. Finally, the nonlinear behaviour of many natural systems provides challenges building traditional mathematical models based on linearization. \r\nSimulation modelling is also useful for the study of spatial patterns over time. In this sense, spatial simulation modelling approaches include rule-based models, equation-based models, grid-based cellular automata models, discrete event simulation, and agent-based models.","name":"Spatial simulation modelling","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC3-1","description":"Among the recent artificial intelligence techniques are those pertaining to heuristics. A heuristic technique is an approach to problem solving, that employs a practical method, which is necessarily not optimal or perfect, but in many situations sufficient. Heuristic methods can be useful, where the optimal solution in practice is impossible. The aim of the topic is to provide insight into the principles of heuristics and the most important algorithms.","name":"Heuristics","selfAssesment":"In progress (GI-N2K)
"},{"code":"GC3-2","description":"Genetic algorithms, genetic programming and evolutionary computing are terms that fall within the general sphere of `Evolutionary Computation`. Genetic algorithms (GAs) are computationally intensive global search heuristics with very wide applicability, but their implementation is often highly problem specific and there is only a relatively loose understanding as to why they often work rather well. The central idea behind GAs is to mimic the Darwinian notion that selective breeding seeks optimum individuals in a given environment. In order to do this a GA requires a way of representing a solution to a (spatial) problem as if it were an individual viewed as a chromosome or `genome` like object. The aim of the topic is to provide fundamental understanding of the principles behind genetic algorithms, and its application in solving geospatial problems.","name":"Genetic algorithms","selfAssesment":"In progress (GI-N2K)
"},{"code":"GC3-3","description":"Biological neurons, or nerve cells, receive multiple input stimuli, combine and modify the inputs in some way, and then transmit the result to other neurons. Artificial neural networks are an attempt to emulate features of biological neural networks in order to address a range of difficult information processing, analysis and modelling problems. The principal class of ANNs are so-called feed-forward networks, but other types of ANN are for example recurrent neural networks. Among the feed-forward networks the most widely used approach is the multi-level perceptron (MLP) model. The application range is broad from non-linear regression to land cover change modelling. The aim of the topic is to introduce the principles of ANN and to understand and demonstrate its use in geospatial modelling.","name":"Artificial Neural Networks","selfAssesment":"In progress (GI-N2K)
"},{"code":"GC3-4","description":"Pattern recognition is the process of classifying input data into objects or classes based on key features. There are two classification methods in pattern recognition: supervised and unsupervised classification. The supervised classification of input data in the pattern recognition method uses supervised learning algorithms that create classifiers based on training data from different object classes. The classifier then accepts input data and assigns the appropriate object or class label. The unsupervised classification method works by finding hidden structures in unlabelled data using segmentation or clustering techniques. Common unsupervised classification methods include: K-means clustering, Gaussian mixture models, Hidden Markov models. The aim of the topic is to provide knowledge about the different methods in pattern recognition and how to choose the optimum method for a specific spatial problem.","name":"Pattern recognition","selfAssesment":" "},{"code":"GC3-5","description":"Understanding natural and human-induced structures and processes in space and time has long been the agenda of geographical research. Through theoretical and experimental studies, geographers have accumulated a wealth of knowledge about our physical and man-made world over the years. Knowledge is often discovered through critical observations of phenomena in space and time. Due to the rapidly expanding amount of data and information the problem is often not having enough data but having too much and too complex a database. The aim of the topic is to provide insight into several methods to carry out spatio-temporal knowledge discovery through spatial data mining and clustering techniques.","name":"Spatio-temporal knowledge discovery","selfAssesment":"In progress (GI-N2K)
"},{"code":"GC3-6","description":"Data-intensive computing is now starting to be considered as the basis for a new, fourth paradigm for science. Two factors are encouraging this trend. First, vast amounts of data are becoming available in more and more application areas. Second, the infrastructures allowing to persistently store these data for sharing and processing are becoming a reality. The technical and scientific issues related to this context have been designated as `Big Data` challenges and have been identified as highly strategic by major research agencies. The aim of this topic is to introduce Big Data as a concept, and the needed methods to navigate through the vast amount of heterogeneous information.","name":"Big data filtering","selfAssesment":"In progress (GI-N2K)
"},{"code":"GC3","description":"The amount of data in current geospatial repositories along with their high-dimensional nature requires a sophisticated set of analysis capabilities in order to extract new and unexpected patterns, trends, and relationships embedded in that data. Artificial intelligence and data mining methods constitute an alternative to explore and extract knowledge from geospatial data, which is less assumption dependent. Data Mining is a step in the knowledge discovery process that automatically detects patterns in data, and Geographic Data Mining is a special type of data mining that seeks to apply standard data mining tools modified to take into account the special features of geospatial data","name":"Artificial Intelligence and Data Mining","selfAssesment":"In progress (GI-N2K)
"},{"code":"GC4-1","description":"The use of the term Open geocomputation doesn't intend to coin a new term; Open GIScience and Open GIS are well explored and discussed terms in the literature. Both embrace the idea of open data, open source, collaboration among peers, and the integration of these practices into GIS research projects, tools, services and applications. Open geocomputation brings the ideas of Open GIScience (and hence Open Science in general) into geocomputation, focussing on openness as a fundamental tenet to conduct research in geocomputation and for the development of new computational methods and tools. In fact, many community-led developments and tools have recently appeared in the field of geocomputation, notably based on R and Python. The widespread popularity and adoption of these computing environments for geocomputing and geospatial analysis is simply because they encompass open, transparent, and reproducible tool development.","name":"Open Geocomputation","selfAssesment":"New
"},{"code":"GC4","description":"A distinguible feature of the current approach to geocomputation is the emphasis on openness: open science, open source, open data. All of this propelled by a vibrant collaborative community with the aim to develop open and reproducible methods, tools and applications applied to a variety of real-life, spatio-temporal application domains. Open Science is a paradigm that can be applied to any scientific discipline and area of knowledge, characterised by openness, access to large volumes of data and unprecedented levels of computing power, availability of community-driven tools, and new types of collaboration between multidisciplinary researchers. Open Science clearly goes beyond geocomputation, but at the same time, its practices and principles characterise recent geocomputation-related projects as well as its community. Therefore, the vision of Open Science taken here is contextualised to the field of geocomputation.","name":"Open Science","selfAssesment":"new
"},{"code":"GD","description":"Geospatial data represent measurements of the locations and attributes of phenomena at or near Earth`s surface. Information is data made meaningful in the context of a question or problem. Information is rendered from data by analytical methods. Information quality and value depends to a large extent on the quality and currency of data (though historical data are valuable for many applications). Geospatial data may have spatial, temporal, and attribute (descriptive) components, as well as associated metadata. Data may be acquired from primary or secondary data sources. Examples of primary data sources include surveying, remote sensing (including aerial and satellite imaging), the global positioning system (GPS), work logs (e.g., police traffic crash reports), environmental monitoring stations, and field surveys. Secondary geospatial or geospatial-temporal data can be acquired by digitizing and scanning analog maps, as well as from other sources, such as governmental agencies. The legitimacy of geographic information science as a discrete field has been claimed in terms of the unique properties of geospatial data. In a paper in which he coined the term GIScience, Goodchild (1992) identified several such properties, including: 1. Geospatial data represent spatial locations and non-spatial attributes measured at certain times. 2. The Earth`s surface is highly complex in shape and continuous in extent. 3. Geospatial data tend to be spatially autocorrelated. It has long been said that data account for the largest portion of geospatial project costs. While this maxim remains true for many projects, practitioners and their clients now can reasonably expect certain kinds of data to be freely or cheaply available via the World Wide Web. Federal, state, regional, and local government agencies, as well as commercial geospatial data producers, operate clearinghouses that provide access to geospatial data. Although geospatial data are much more abundant now than they were ten years ago, data quality issues persist. Good data are expensive to produce and to maintain. Proprietary interests simultaneously increase the supply of geospatial data and impede data accessibility. Standards for geospatial data and metadata are useful in facilitating effective search, retrieval, evaluation, integration with existing data, and appropriate uses. National and international organizations, such as the Open Geospatial Consortium (OGC) and International Organization for Standardization (ISO), develop and promulgate such standards. INSPIRE directive (Infrastructure for Spatial Information in the European Community) regulates geospatial data management","name":"Geospatial Data","selfAssesment":"GI-N2K
"},{"code":"GD1-1","description":"Usable and accurate geospatial data are based upon proper model of the Earth`s surface. Shape of the Earth is complex and complicated to measure. Approximations are used to minimize complexity of the task and possible errors.","name":"Earth geometry","selfAssesment":"GI-N2K
"},{"code":"GD1-2","description":"Geospatial referencing systems provide unique codes for every location on the surface of the Earth (or other celestial bodies). These codes are used to measure distances, areas, and volumes, to navigate, and to predict how and where phenomena on the Earths surface may move, spread, or contract. Point-based, vector coordinate systems specify locations in relation to the origins of planar or spherical grids. Tessellated referencing systems specify locations hierarchically, as sequences of numbers that represent smaller and smaller subdivisions of two- or three dimensional surfaces that approximate the Earths shape, Linear referencing systems specify locations in relation to distances along a path from a starting point. Tessellation data models, are considered in Unit DM3 Tessellation data models, and linear referencing models are considered in Unit DM4 Vector data models.","name":"Georeferencing systems","selfAssesment":"GI-N2K
"},{"code":"GD1-3","description":"Horizontal datums determine the geometric relations between a coordinate system grid and a particular ellipsoid approximating the Earth`s surface. Vertical datums determine elevation reference surfaces, like mean sea level. A. Horizontal datums. Relation of coordinate system to particular ellipsoid, datum transformation options, Molodensky and Helmert transformation, other high accuracy transformations, ED50 and WGS84, historical development of horizontal datums, ETRS89. B. Vertical datums. Historical development of vertical datums, difference between vertical datum and geoid, relations between ellipsoidal (geodetic) heiht, geoidal height and orthometric elevation.","name":"Datums","selfAssesment":"GI-N2K
"},{"code":"GD1-4","description":"Map projections are systematic transformations of geographic coordinates of the surface of ellipsoid into locations in plane. Plane coordinates are based on map projection. As the transformation of a spherical grid into a plane grid causes inevitably distortions of the geometry, and, different projections cause different distortions, knowledgeable choice of appropriate projection for any particular use is crucial. A. Map projection poperties. Geometric properties that may be preserved or lost in projected grid, usefulness of compromise projection, Tissot indicatrix as an indicator of projection errors, visual appearance of the Earth`s graticule, distortion patterns for projection classes, distortions in raster data. B. Map projection classes. Three main classes of map projection based on developable surface, projection types by geometric properties preserved, mathematical basis of projecting longitude and latitude into x and y coordinates. UTM, ETM, projections used by EC. C. Map projection parameters. Standard line, projection case, latitutde and longitude of origin, aspects of projection. D. Georegistration. Rectification vs orthorectification, ground controle points in georegistration of aerial imagery.","name":"Map projections","selfAssesment":"GI-N2K
"},{"code":"GD1","description":"Proper model of the Earth`s surface and ability to locate spatial phenomena accurately to it, is crucial in effective collection, management and use of data. Characterising size and shape of the Earth, using appropriate surfaces to approximate it, choosing suitable coordinate system and map projection is bases for efficient understanding of spatial data.","name":"Geolocating Data to Earth","selfAssesment":"GI-N2K
"},{"code":"GD10-1","description":" ","name":"Nature of aerial image data","selfAssesment":"GI-N2K
"},{"code":"GD10-2","description":" ","name":"Platforms and sensors","selfAssesment":"GI-N2K
"},{"code":"GD10-3","description":" ","name":"Aerial image interpretation","selfAssesment":"GI-N2K
"},{"code":"GD10-4","description":"A stereoscopy acquisition mode collects remotely sensed data where each location on the ground (or the imaged objects) is covered multiple times (at least twice), from different perspectives. Stereopairs and stereoscopic coverage enable the extraction of 3D representations of the environment from remotely sensed imagery.","name":"Stereoscopy and orthoimagery","selfAssesment":"GI-N2K
"},{"code":"GD10-5","description":" ","name":"Vector data extraction","selfAssesment":"GI-N2K
"},{"code":"GD10-6","description":" ","name":"Mission planning","selfAssesment":"GI-N2K
"},{"code":"GD10","description":"Since the 1940s aerial imagery has been the primary source of detailed geospatial data for extensive study areas. Photogrammetry is the profession concerned with producing precise measurements from aerial imagery. Aerial imaging and photogrammetry comprise a major component of the geospatial industry. The topics included in this unit do not comprise an exhaustive treatment of photogrammetry, but they are aspects of the field about which all geospatial professionals should be knowledgeable.","name":"Aerial imaging and photogrammetry","selfAssesment":"GI-N2K
"},{"code":"GD11-1","description":" ","name":"Nature of multispectral image data","selfAssesment":"GI-N2K
"},{"code":"GD11-2","description":"the physical environment to sense data without direct contact. It contains a carrier device (platform) and a sampling unit (sensor).","name":"Platforms and sensors","selfAssesment":"GI-N2K
"},{"code":"GD11-3","description":" ","name":"Algorithms and processing","selfAssesment":"GI-N2K
"},{"code":"GD11-4","description":" ","name":"Ground verification and accuracy assessment","selfAssesment":"GI-N2K
"},{"code":"GD11-5","description":" ","name":"Applications and settings","selfAssesment":"GI-N2K
"},{"code":"GD11","description":"Satellite-based sensors enable frequent mapping and analysis of very large areas. Many sensing instruments are able to measure electromagnetic energy at multiple wavelengths, including those beyond the visible band. Satellite remote sensing is a key source for regional- and global-scale land use and land cover mapping, environmental resource management, mineral exploration, and global change research. Shipboard sensors employ acoustic energy to determine seafloor depth or to create imagery of the seafloor or water column. The topics included in this unit do not comprise an exhaustive treatment of remote sensing, but they are aspects of the field about which all geospatial professionals should be knowledgeable.","name":"Satellite and shipboard remote sensing","selfAssesment":"GI-N2K
"},{"code":"GD12","description":"Meaning of geospatial metadata, elements of metadata, use of metadata, integration of metadata in data production, standards in geospatial data, ISO standard family 191xx, data warehouse, exchange protocol, transport protocols, spatial data infrastructure, INSPIRE, OGC, DCAT profiles for CKAN applications bridging metadata from GI and IT domains.","name":"Metadata, standards, and infrastructures","selfAssesment":"GI-N2K
"},{"code":"GD2-1","description":"Classic land survey methods and manual attribute data collection in the field","name":"Land surveying and field data collection","selfAssesment":"GI-N2K
"},{"code":"GD2-2","description":"Aerial imagery has been the primary source of detailed geospatial data for extensive study areas. Photogrammetry is producing precise measurements from aerial imagery. Aerial imaging and photogrammetry comprise a major component of the geospatial data production. Satellite-based sensors enable frequent mapping and analysis of very large areas. Sensing instruments are able to measure electromagnetic energy at multiple wavelengths. Satellite remote sensing is a key source for regional- and global-scale land use and land cover mapping, environmental resource management, mineral exploration, and global change research. Shipboard sensors employ acoustic energy to determine seafloor depth or to create imagery of the seafloor or water column. Principles of aerial photography, oblique and vertical imagery, spatial and radiometric resolution, spectral sensitivity, principal point, distortions and displacements in aerial image, parallax, stereophotogrammetry, generation of an orthoimage from a vertical aerial phoptograph, aerotriangulation, vector data extraction from digital seteroimagery, mission planning. Use of UAV in photogrammetry. Main platforms and sensors in spatial image acquisition, active and passive sensors, LiDAR and microwave, multispectral and hypersepctral imagery, interpretation of imagery, supervised and unsupervised classification, pixel based and segmented classification, ground verification, main applications, bathymetric mapping. SENTINEL.","name":"Remote sensing","selfAssesment":"GI-N2K
"},{"code":"GD2-3","description":"Crowdsourcing is the practice of obtaining needed services, ideas, or content by soliciting contributions from a large group of people and especially from the online community rather than from traditional employees or suppliers. Crowdsourced spatial data collection is becoming more and more important. The advantages and disadvantages of crowdsourced data, opensource mapping tools, potential application of crowdsourcing, VGI, OSM or cell-phone based, aspects of crowdsourced data quality and reliabilty.","name":"Crowdsourced data collection","selfAssesment":"GI-N2K
"},{"code":"GD2-4","description":"Digitizing as the main secondary spatial data production technique. Encoding vector points, lines, and polygons by tracing map sheets has diminished in importance, but remains a useful technique for incorporating historical geographies and local knowledge. \"Heads-up\" digitizing using digital imagery as a backdrop on-screen is a standard technique for editing and updating GIS databases. Tablet and on-screen digitizing, scanning and (semi)automatic vectorization.","name":"Digitizing","selfAssesment":"GI-N2K
"},{"code":"GD2","description":"Spatial data collection / production involves measurement of locations in relation to the coordinate system, and collection of attributed data about the spatial phenomena. Measurements may be direct (e.g. surveying) or remote, data acquisition involves measurement of parameter values, evaluation of parameters, polls, interpretation of spatial imagery, and re-use of secondary data (e.g. old maps). Volunteered geographic information is becoming more important.","name":"Data Collection","selfAssesment":"GI-N2K
"},{"code":"GD3","description":"It is quite common, that data including both spatial entities and their attribute data undergo changes. These changes need to be catalogued fully and explicitly, including initial conditions, new conditions, all intermediate stages and operations used. The geospatial data needs to contain an archival history of change.","name":"Transaction management of geospatial data","selfAssesment":"GI-N2K
"},{"code":"GD4-1","description":"Geometric accuracy, factors influencing it, geometric accuracy and topological fidelity, geometric accuracy in survey and GPS mesurements, thematic accuracy, relations between thematic accuracy, geometric accuracy and topological fidelity, misclassification matrix, commission and omission, logical consistency, relations between resolution, precision, and accuracy, spatial resolution, thematic resolution, and temporal resolution, precision, uncertainties associated with coordinate precision, primary and secondary data sources.","name":"Data quality","selfAssesment":"GI-N2K
"},{"code":"GD4-2","description":"Meaning of geospatial metadata, elements of metadata, use of metadata, integration of metadata in data production, standards in geospatial data, ISO standard family 191xx, data warehouse, exchange protocol, transport protocols, spatial data infrastructure, INSPIRE, OGC, DCAT profiles for CKAN applications bridging metadata from GI and IT domains.","name":"Metadata, standards, and infrastructures","selfAssesment":"GI-N2K
"},{"code":"GD4","description":"Data quality is the degree of data usability in relation to given objective and particular application. The expectations to data vary between different applications. The key criteria in data quality are the amount of uncertainty in data as compared to the acceptable level of uncertainty. Evaluation of the usability may be more complicated using data from secondary sources. Appropriate metadata is inevitable for these judgements. Aspects of data quality include geometric and thematic accuracy, (in)consistencies, resolution, precision, usability and others. Assurance of data quality may be improved by following proper standards and spatial data infrastructure regulations for data collection and management. System of basic data quality measures for geospatial domain in the EN ISO 19157:2013 standard.","name":"Data Quality, Metadata and Data Infrastructure","selfAssesment":"GI-N2K
"},{"code":"GD5-1","description":" ","name":"Map projection properties","selfAssesment":"GI-N2K
"},{"code":"GD5-2","description":" ","name":"Map projection classes","selfAssesment":"GI-N2K
"},{"code":"GD5-3","description":" ","name":"Map projection parameters","selfAssesment":"GI-N2K
"},{"code":"GD5-4","description":" ","name":"Georegistration","selfAssesment":"GI-N2K
"},{"code":"GD5","description":"Visualization, especially Unit CV2 Data considerations, while procedures for transforming data between projections are considered in Unit DN1 Representation transformation.","name":"Map projections","selfAssesment":"GI-N2K
"},{"code":"GD6-1","description":" ","name":"Geometric accuracy","selfAssesment":"GI-N2K
"},{"code":"GD6-2","description":" ","name":"Thematic accuracy","selfAssesment":"GI-N2K
"},{"code":"GD6-3","description":" ","name":"Resolution","selfAssesment":"GI-N2K
"},{"code":"GD6-4","description":" ","name":"Precision","selfAssesment":"GI-N2K
"},{"code":"GD6-5","description":" ","name":"Primary and secondary sources","selfAssesment":"GI-N2K
"},{"code":"GD6","description":"particular application. That standard varies from one application to another. In general, however, the key criteria are how much uncertainty is present in a data set and how much is acceptable. Judgments about fitness for use may be more difficult when data are acquired from secondary rather than primary sources. Aspects of data quality include accuracy, resolution, and precision. Concepts of data quality, error, and uncertainty are also covered in Knowledge Areas CF Conceptual Foundations (in a theoretical context) and GC Geocomputation (in the context of analysis); the focus here is on the measurement and assessment of data quality.","name":"Data quality","selfAssesment":"GI-N2K
"},{"code":"","description":" ","name":" ","selfAssesment":" "},{"code":"","description":" ","name":"","selfAssesment":""},{"code":"","description":" ","name":"","selfAssesment":"GI-N2K
"},{"code":"GD8-1","description":" ","name":"Tablet digitizing","selfAssesment":"GI-N2K
"},{"code":"GD8-2","description":" ","name":"On-screen digitizing","selfAssesment":"GI-N2K
"},{"code":"GD8-3","description":" ","name":"Scanning and automated vectorization techniques","selfAssesment":"GI-N2K
"},{"code":"","description":" ","name":"","selfAssesment":""},{"code":"","description":" ","name":"","selfAssesment":""},{"code":"","description":" ","name":"","selfAssesment":""},{"code":"","description":" ","name":"","selfAssesment":""},{"code":"GS","description":"Geographic Information Science and Technology serve the society, but it is not a panacea. The history of its development is the sum of fragmented efforts, which have still not been fully integrated. Its potential benefits are often constrained and its potential impacts are not fully understood. Institutional and economic factors limit access to data, technology, and expertise by some of those who need it to make better decisions. Political, ideological, and personal issues aside, organizations invest in GIS&T when estimated benefits outweigh estimated costs. Evaluating costs and benefits is difficult, however and too often leads to nothing being done. For some individuals and groups, costs are prohibitive even though potential benefits are compelling. The legal framework provides a structure for regulating a number of key aspects of geographic information science, technology, and applications. Legal regimes determine who can claim the exclusive right to hold and use geospatial data, the conditions under which others may have access to the data, and what subsequent uses are permitted. Political struggles arise from conflicting proprietary and public interests about who benefits from geospatial information, and how the power to allocate the use of this information is, or should be, distributed among members of a society. The need to choose among conflicting interests sometimes poses ethical dilemmas for GIS&T professionals. The explosive growth of the geospatial information contributed by users through various application programming interfaces has made geospatial information is a powerful tool in the social media toola powerful media for the general public to communicate, but perhaps more importantly, geographic information have also become a tool media for constructive dialogs and interactions about social issues, recent growth of Web-based geospatial information and volunteered geographic information (VGI). Because so many public agencies and private organizations rely upon GIS&T for planning, decision making, and management, GIS&T increasingly affects and is used to direct daily life. Critical approaches to understanding the role of GIS in society equip practitioners to employ GIS&T reflectively. The critical approach specifically questions the assumptions and premises that underlie the economic, legal and political regimes and institutional structures within which GIS&T is implemented. Related concerns are considered in Knowledge Area OI: Organizational and Institutional Aspects.","name":"GI and Society","selfAssesment":"GI-N2K
"},{"code":"GS1-1","description":"The most basic definition of a legal regime is a system or framework of rules governing some physical territory or discrete realm of action that is at least in principle rooted in some sort of law. Often the concept has been applied to specific areas of law.","name":"The legal regime and legal framework","selfAssesment":"GI-N2K
"},{"code":"GS1-2","description":"Contract law is defined as a set of rules that govern the contractual agreements between merchants or persons. A contract is an agreement between different parties that state their responsibilities and duties to each other. A liability in contract law is when certain conditions are written into a contract that makes a party liable. Licensing is the process of giving or getting official permission to do something. A license is an agreement through which a licensee leases the rights to a legally protected piece of intellectual property from a licensor — the entity which owns or represents the property — for use in conjunction with a product or service.","name":"Contract law, liability and licensing","selfAssesment":"GI-N2K: relevant but to be revised
"},{"code":"GS1-3","description":"Data privacy and security are two essential components of a successful strategy for data protection. Data security refers to the protection of data from unauthorized access, use, change, disclosure, and destruction. It encompasses network security, physical security, and file security. Data privacy involves protecting consumer data by eliminating or reducing the possibility of re-identifying an individual whose information is present in the data. This is done by either removing specific information or by transforming the data with random “noise” or generalization.","name":"Privacy and Security","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS1-4","description":"Property is secured by laws that are clearly defined and enforced by the state. These laws define ownership and any associated benefits that come with holding the property. The term property is very expansive, though the legal protection for certain kinds of property varies between jurisdictions. Property is generally owned by individuals or a small group of people. The rights of property ownership can be extended by using patents and copyrights. Property rights give the owner or right holder the ability to do with the property what they choose. That includes holding on to it, selling or renting it out for profit, or transferring it to another party.","name":"Ownership and property rights","selfAssesment":"GI-N2K
"},{"code":"GS1-5","description":"In economics, competition is a condition where different economic firms seek to obtain a share of a limited good by varying the elements of the marketing mix: price, product, promotion and place. Competition law is a law that promotes or seeks to maintain market competition by regulating anti-competitive conduct by companies. Public-private sector relationships deal with a particular subset of competition, i.e. competition between public and private organizations.","name":"Competition and public-private sector relationships","selfAssesment":"GI-N2K
"},{"code":"GS1-6","description":"Open data is data that can be accessed, shared, used and reused without any barrier for any type of (re)user. According to the Open Definition, open data can be defined as data that be freely used, modified, and shared by anyone for any purpose subject, at most, to measures that preserve provenance and openness. Open data requires datasets to be either in the public domain, or distributed through an open license. The data must be provided as a whole, free of charge, and preferably downloadable via the Internet, including any additional information that might be necessary to comply with the open license’s terms. Openness requires the data to be provided in a readily machine-readable form. The format must be open as well, meaning that it does not place any restriction upon its use, and that the files in that format can be processed with open-source software tools. The Open Definition speaks broadly of open ‘works’, rather than of open data. Focusing on data tout court, one can move from the Open Government Data (OGD) principles. According to the OGD principles, which are arguably foundational in understanding the concept of open data, data must be: Complete; Primary; Timely; Accessible; Machine-processable; Non-discriminatory; Non-proprietary; and License-free. Compliance with the OGD principles needs to be demonstrable, i.e. there need to be accountability measures in place to allow the review of the adherence to the principles above. The concepts of Open Work and open data highlight how data needs to be both legally, technically and financially open, so either in the public domain or covered by an open license, and kept in a machine-readable and non-proprietary format. Open data aims at making information available to everybody, for any purpose, in a machine-readable and interoperable format, based on open standards and digestible by free/libre open source software (FLOSS). Also with respect to the financial accessibility open data is data available free of charge. Marginal costs of dissemination are accepted by some as a reasonable cost for users. However, open data is data that can be accessed and reused without any barrier for any type of reuse, and some user groups experience any price to be paid as a barrier.","name":"Open data","selfAssesment":"Completed
"},{"code":"GS1","description":"Legal problems can arise when geospatial information is used for land management, among other activities. Geospatial professionals may be liable for harm that results from flawed data or the misuse of data. Understanding of contract law and liability standards is essential to mitigate risks associated with the provision of geospatial information products and services. Legal relations between public and private organizations and individuals govern data access. The nature of information in general, and the characteristics of geospatial information in particular, make it an unusual and difficult subject for a legal regime that seeks to establish and enforce the type of exclusive control associated with other commodities. Geospatial information is in many ways unlike the kinds of works that intellectual property rights were intended to protect. Still, organizations can, and do, assert proprietary interests in geospatial information. Perspectives on geospatial information as property vary between the public and private sectors and between different countries.","name":"Legal aspects","selfAssesment":"In progress GI-N2K
"},{"code":"GS2-1","description":"Business models determine how organizations can create and deliver value, for example, through the provision or use of geographic data. A business model is a conceptual tool that contains\r\na set of interrelated elements that allow organizations to create and capture value and generate revenues. The development and implementation of an appropriate business model are considered to be a key to the success of the organization and a crucial source for value creation. \r\n\r\nAlthough business models determine how organizations create, deliver, and capture value, they should not be regarded as permanent and invariable structures or settings. Business models are shaped by both internal and external forces, and will only be successful if they are able to adapt to a changing environment. In the GI domain, several technological, regulatory, and societal developments have challenged the existing business models and opened up opportunities for new business models. Among these developments are the establishment of spatial data infrastructures (SDIs) worldwide, the democratization of geographic knowledge, and the move toward open source, open standards, and open data.\r\n\r\nSince the development and implementation of SDIs in different parts of the world, much attention has been paid to the need to find appropriate business models for GI, and in particular, for geographic data providers in the public sector. Traditional business models in which public data providers were selling their data to customers in the private industry and other public agencies were questioned, because they restricted the opportunity for data sharing. The concept of SDI is about moving to new business models, where partnerships between GI organizations are promoted to allow access to a much wider scope of geographic data and services. A key challenge in the development of these SDIs was the alignment of different existing business models of the actors in the GI domain. Moreover, the development and implementation of SDIs also led to the emergence of new business models, which was even more the case with the more recent move toward open geographic data.\r\n\r\nOrganizations can be active in different parts of the geo-information value chain, and can create and offer value in many different ways. As a result, many different GI business models exist. Data providers, data enablers, and data end users could be seen as three main categories of GI business models. Each of these categories consists of many different business models, as different value propositions\r\nwill exist, and value can be created and captured in several ways.","name":"GI Business models","selfAssesment":"Completed (GI-N2K)
"},{"code":"GS2-2","description":" ","name":"Costs, benefits and risks","selfAssesment":"GI-N2K
"},{"code":"GS2-3","description":"The value of information (VOI) contained in geospatial data is the difference between the net benefits (in present value terms) of a decision with and without the information.","name":"Valuing and measuring aspects","selfAssesment":"GI-N2K
"},{"code":"GS2-4","description":" ","name":"Agency, organizational, and individual perspectives","selfAssesment":"GI-N2K
"},{"code":"GS2-5","description":"To provide a better insight into the process of adding value to GI, several authors have introduced and applied the information value chain approach. A value chain can be defined as the set of value-adding activities that one or more organizations perform in creating and distributing goods and services. The value chain concept originally was developed for the manufacturing sector, as a tool to evaluate the competitive advantage of firms. More recently, the value chain concept has been applied to other sectors, including information technology where the good or service, and the benefits it provides, is less tangible in nature. A value chain involves the progress of goods from raw materials to finished products through a number of stages, during each of which a new value is added to the original input by various activities. The value chain concept was extended into the information market, with the information value chain referring to the set of activities adding value to information and turning raw data into new information products or services. Especially important in this context is the role of information and communication technologies (ICT), which have an impact on all activities in the information value chain, such as information collection, processing, dissemination, and use. In the context of GI, the value chain relates to the series of value- adding activities to transform raw geographic data into new products that are used by certain end users. Although there are slightly different descriptions of the various steps of the GI value chain, in general, the essential steps in the value chain are: acquisition of raw data, the application of a data model, quality control, and integration with other sources, presentation, and distribution. In recent years, particular attention has been paid to different steps between the process of distributing data and the actual end use of an end product of GI. In addition, after the publication of the data, value can be added to the data in many different ways. Value can be added by making data from different sources easily accessible through repositories and data portals, by building and selling tailored solutions using the data to end users or by using geographic data to improve existing products and services delivered to an end user. In certain cases, this end product will be the first step of a next value chain.","name":"Geo-information value chain","selfAssesment":"Completed
"},{"code":"GS2","description":"Most organizations insist that investments in GIS and T be justified in economic terms. Quantifying the value of information, and of information systems, however, is not a straightforward matter.","name":"Economic aspects","selfAssesment":"GI-N2K
"},{"code":"GS3-1","description":"The use of geospatial information allows public sector organizations and actors to make better decisions and provide better services to their citizens. Geospatial information is increasingly being used at different administrative levels and in different policy areas.","name":"Use of geospatial information in the public sector","selfAssesment":"GI-N2K
"},{"code":"GS3-2","description":"Geospatial information is increasingly being used by private companies for different purposes and the private sector plays an important role in the development and implementation of geospatial information infrastructures.","name":"Use of geospatial information in the private sector","selfAssesment":"GI-N2K
"},{"code":"GS3-3","description":"Research and education institutions use geospatial information for various purposes, in support of their research and educational activities.","name":"Use of geospatial information in research and education","selfAssesment":"GI-N2K
"},{"code":"GS3-4","description":"Effective monitoring of the environment and an improved understanding of the same requires valuable information and data that can be extracted through application of geospatial technologies. GIS can be used most effectively for environmental data analysis and planning. It allows better viewing and understanding physical features and the relationships that influence in a given critical environmental condition. GIS can help in effective planning and managing the environmental hazards and risks. In order to plan and monitor the environmental problems, the assessment of hazards and risks becomes the foundation for planning decisions and for mitigation activities. GIS supports activities in environmental assessment, monitoring, and mitigation and can also be used for generating environmental models. GIS can aid in hazard mitigation and future planning, air pollution & control, disaster management, forest fires management, managing natural resources, wastewater management, oil spills and its remedial actions etc.","name":"Use of geospatial information in environmental issues","selfAssesment":"GI-N2K
"},{"code":"GS3","description":"Geospatial Information used in Government agencies and public authorities at local, state, and federal levels produce and use geospatial data for many activities, including provision of social services, public safety, economic development, environmental management, and national defence. Public participation in governing, empowered by geospatial technologies, offers the potential to strengthen democratic societies by involving grassroots community organizations and by engaging local knowledge. The private sector covers a broad range of areas of opportunity. With continued advancements in technology, greater awareness of its advantages as a powerful decision support tool the use of geospatial information use in the private sector needs to be discussed.","name":"Use of geospatial information","selfAssesment":"In Progress GI-N2K
"},{"code":"GS4-1","description":"Public participation is about the engagement of the general public in policy-making activities. It includes activities such as sharing of government information, seeking comments on issues and policies, and using public inputs in decision making.","name":"Public participation and citizenship","selfAssesment":"GI-N2K
"},{"code":"GS4-2b","description":"Social Media Geographic Information (SMGI) can be defined as any piece or collection of multimedia data or information with explicit (i.e. coordinates) or implicit (i.e. place names or toponyms) geographic reference collected through the social networking web or mobile applications. Social data are acknowledged as a good of major value in the digital economy, and their potential for enhancing more traditional analytics is of the utmost importance. A big part of social data however also features spatial (and temporal) references, thus their integration with more traditional Authoritative Geographic Information (AGI) may enable a further step towards the next generation of geospatial intelligence. SMGI is a sub-category of VGI and can be active or passive, depending on the type of application with which it is collected: applications purposefully created and/or used to collect SMGI in participatory initiatives","name":"GI and social media","selfAssesment":"GI-N2K
"},{"code":"GS4-3b","description":"Volunteered geographic information (VGI) is a special kind of user-generated content. It refers to geographic information collected and shared voluntarily by the general public. Web.2.0 and associated advances in web mapping technologies have greatly enhanced the abilities to collect, share and interact with geographic information online, leading to VGI.","name":"Citizens and volunteered geographic information","selfAssesment":"GI-N2K
"},{"code":"GS4","description":"Today, geo data has become a conventional and pervasively familiar data type seen at once to underpin and significantly re-characterize the digital world, with broad implications for both technology and society. Geospatial data are abundant, but access to data varies with the nature of the data, the user groups wishes to acquire it and for what purpose, under what conditions, and at what price geodata can be obtained. The explosive growth of geographic information contributed by users through various application programming interfaces has made geographic information a powerful media for the general public, but perhaps more importantly, geospatial information have also become media for constructive dialogs and interactions about social issues, recent growth of Web-based Geographic information and volunteered geographic information (VGI).","name":"Geospatial citizenship","selfAssesment":"GI-N2K
"},{"code":"GS5-1b","description":"The advantages of geospatial technologies and resulting data present ethical dilemmas such as privacy and security concerns as well as the potential for stigma and discrimination resulting from being associated with particular locations. the use of geospatial technologies and the resulting data needs to be critically assessed through an ethical lens prior to implementation of programmes, analyses or partnerships. Using this lens requires not only explicit consideration of potential negative consequences of adoption but also clear articulation of the specific contexts and conditions under which benefits may be realized.","name":"Ethics in the geospatial information society","selfAssesment":"GI-N2K
"},{"code":"GS5-2b","description":"A code of ethics is a guide of principles designed to help professionals conduct business honestly and with integrity. A code of ethics document may outline the mission and values of the business or organization, how professionals are supposed to approach problems, the ethical principles based on the organization's core values, and the standards to which the professional is held. Codes of ethics for geospatial professionals are intended to provide these principles and guidelines for GIS professionals","name":"Codes of ethics for geospatial professionals","selfAssesment":"GI-N2K
"},{"code":"GS5","description":"Ethics provide frameworks that help individuals and organizations make decisions when confronted with choices that have moral implications. Most professional organizations develop codes of ethics to help their members do the right thing, preserve their good reputation in the community, and help their members develop as a community","name":"Ethical aspects","selfAssesment":"GI-N2K
"},{"code":"GS6-1","description":" ","name":"Epistemological and critical issues","selfAssesment":"GI-N2K
"},{"code":"GS6-2","description":"Various types of critiques exist on the way geospatial information is being used and re-used.","name":"Critical approach on the use of geospatial information","selfAssesment":"GI-N2K
"},{"code":"GS6-3","description":"Defending or refuting the argument that the \"digital divide\" that characterizes access use of geospatial information perpetuates inequities among developed and developing nations, among socio-economic groups,and between individuals, community organizations, and public agencies and private firms.","name":"Critical aspects and invisible groups","selfAssesment":"GI-N2K
"},{"code":"GS6","description":"Many of the educational objectives used to define topics in this knowledge area, and in the Body of Knowledge as a whole, challenge educators and students to think critically about GI and Society. Since the 1990s, scholars have criticized cartography and the GIS science from a wide range of perspectives. Common among these critiques are questioned assumptions about the purported benefits of GI and Society and attention to its unexamined risks. By promoting reflective practice among current and aspiring geospatial information professionals, an understanding of the range of critical perspectives increases the likelihood that geospatial information will fulfil its potential to benefit all stakeholders. Philosophical, psychological, and social underpinnings of these critiques are considered in Knowledge Area CF: Conceptual Foundations.","name":"Critical approach","selfAssesment":"GI-N2K
"},{"code":"GS7-1","description":"US GIS&T BoK: As GIS became a firmly established presence in geography and catalysed the emergence of GIScience, it became the target of a series of critiques regarding modes of knowledge production that were perceived as problematic. The first wave of critiques charged GIS with resuscitating logical positivism and its erroneous treatment of social phenomena as indistinguishable from natural/physical phenomena. The second wave of critiques objected to GIS on the basis that it was a representational technology. In the third wave of critiques, rather than objecting to GIS simply because it represented, scholars engaged with the ways in which GIS represents natural and social phenomena, pointing to the masculinist and heteronormative modes of knowledge production that are bound up in some, but not all, uses and applications of geographic information technologies. In response to these critiques, GIScience scholars and theorists positioned GIS as a critically realist technology by virtue of its commitment to the contingency of representation and its non-universal claims to knowledge production in geography. Contemporary engagements of GIS epistemologies emphasize the epistemological flexibility of geospatial technologies.","name":"Epistemological critiques","selfAssesment":"GI-N2K
"},{"code":"GS7-2","description":" ","name":"Ethical critiques","selfAssesment":"GI-N2K
"},{"code":"GS7-3","description":"US GIS&T BoK: \r\n\r\nFeminist interactions with GIS started in the 1990s in the form of strong critiques against GIS inspired by feminist and postpositivist theories. Those critiques mainly highlighted a supposed epistemological dissonance between GIS and feminist scholarship. GIS was accused of being shaped by positivist and masculinist epistemologies, especially due to its emphasis on vision as the principal way of knowing. In addition, feminist critiques claimed that GIS was largely incompatible with positionality and reflexivity, two core concepts of feminist theory. Feminist critiques of GIS also discussed power issues embedded in GIS practices, including the predominance of men in the early days of the GIS industry and the development of GIS practices for the military and surveillance purposes.\r\n\r\nAt the beginning of the 21st century, feminist geographers reexamined those critiques and argued against an inherent epistemological incompatibility between GIS methods and feminist scholarship. They advocated for a reappropriation of GIS by feminist scholars in the form of critical feminist GIS practices. The critical GIS perspective promotes an unorthodox, reconstructed, and emancipatory set of GIS practices by critiquing dominant approaches of knowledge production, implementing GIS in critically informed progressive social research, and developing postpositivist techniques of GIS. Inspired by those debates, feminist scholars did reclaim GIS and effectively developed feminist GIS practices.","name":"Feminist critiques","selfAssesment":"GI-N2K
"},{"code":"GS7-4","description":"In the early 1990s social critiques of GIS from human geographers began to appear. These initial critiques set off an ensuing debate between GISers, defending GIS and human geographers, who critiqued GIS. This debate materialized in academic journals including: Political Geography Quarterly, Environment and Planning A, and Progress in Human Geography. Schuurman (2000) notes that the GIS debate, while unique to the discipline of Geography, was part of a larger debate in other disciplines about the effects of technology. This presentation will be limited (unfortunately) to two aspects of this debate. It will first discuss conditions within human geography that made GIS a target of human geographers' critique. Second, this paper will discuss the particular critiques that were directed at GIS by human geographers. Though the reaction of such critiques and their effect on GIS is an important topic there is not enough time and space to address these issues. See Schuurman (2000) \"Trouble in the Heartland: GIS and its critics in the 1990s\" in Progress in Human Geography for a thoughtful look at this debate and its effects on the discipline of GIS.","name":"Social critiques","selfAssesment":"GI-N2K
"},{"code":"IP","description":"Image processing and analysis – Image processing and analysis describes the entire collection of tasks and employed methods and technologies along the information production workflow. They transform data contained in remote sensing images to information products, e.g. in form of digital maps and reports, for users in various application domains to take informed decisions.","name":"Image processing and analysis","selfAssesment":"Planned
"},{"code":"IP1-1-1","description":"The image spatial subset allows to extract the group of pixels / grid cells using a defined polygon e.g. area of interest – AOI or defining the new image extent. It is used to limit spatially the image extent to which, for example an image function or classification model will be applied.","name":"Image subset","selfAssesment":"Completed
"},{"code":"IP1-1-2","description":"Layer stacking is a process for combining multiple images into a single image. The image stack is used to build a ‘new’ multiple band file from the georeferenced images of various pixel sizes, extents, projections. The image bands must be resampled and reprojected to a common spatial grid. The layer stacking is used for example to combine spectral bands from a Landsat, Sentinel-2 data and SRTM DEM into one multi-dimensional file. The process of layer stacking increases the size of the final stacked image, which may have consequences that increase the processing time of operations performed on the stacked image.","name":"Layer stack","selfAssesment":"Completed
"},{"code":"IP1-1","description":"Data manipulation adjusts a dataset to the needs of a specific application by subsetting the spatial extent or the number of bands or by organizing bands from separate single layer files into a single multi-layer file.","name":"Data manipulation","selfAssesment":"New
"},{"code":"IP1-2","description":"Fourier analysis - A characteristic of remotely sensed images is a parameter called spatial frequency, defined as the number of changes in brightness value per unit distance for any particular part of an image. There are low-frequency and high-frequency areas. Spatial frequency may be enhanced or subdued using Fourie Analysis (an alternative technique is spatial convolution filtering). Furier analysis mathematically separates an image into its spatial frequency components. It is then possible interactively to emphasize certain groups (or bands) of frequencies relative to others and recombine the spatial frequencies to produce an enhanced image.","name":"Fourier transformation","selfAssesment":"New
"},{"code":"IP1-3-1-1","description":"Structure from motion (SfM) describes the photogrammetric process for estimating the 3D structure of a scene, whereby correspondences between multiple images are established and used to detect motion parallax. When a camera moves over a surface while taking successive overlapping images, the distances between features on the surface will change from one image to the next. The changes depend on the distance of the feature points to the camera, and thus the surface elevation. This motion parallax can be used to generate an accurate 3D representation of the surface. \r\nThe photogrammetric problem of SfM is similar to stereo vision, but has gained popularity with the advent of inexpensive cameras which have variable internal geometries, unlike metrically stabilized cameras traditionally used in airborne mapping. Even with less accurate or even missing GPS location and orientation metadata, SfM still allows for the creation of (hyper)local DEMs as long as the imagery contains sufficient overlap. Airborne or spaceborne platforms can be used, provided that 2D frame-based cameras are used which can be represented with a pinhole mathematical model. \r\nGenerating a digital elevation model (DEM) from SfM is typically handled automatically using specialized software. Firstly, image correspondences are detected. Feature points are identified in the individual images using local contrast feature detectors. The features extracted from all the images are matched with all the available overlapping images and erroneous matches are filtered out. The process typically results in hundreds or thousands of tie-points per image, which allows for robust matching even with large a priori uncertainties in camera orientation. A bundle adjustment, solving for the 3D coordinates of the feature points, the position and orientation of the camera and its internal characteristics then results in an initial, so-called sparse 3D point cloud. \r\nNext, ground control points (GCPs) can be introduced. These are surface features (naturally present or introduced into the scene) which can be identified at the pixel level in the images by users. Measured also in the field with an accuracy smaller than the pixel size, they can be used to constrain the bundle adjustment solution to improve georeferencing and camera calibration to an accuracy similar to that of the GCP measurement or the GSD size. \r\nSince this process yields a match only for a small subset of all pixels, an additional step, called dense image matching is added. It starts from the exact position and orientations resulting from the bundle adjustment to rectify the images and overlay two or more images, to compare them row by row and in 16 different directions in a process called semi-global matching (SGM). Matching pixels are identified along these lines, and 3D intersection distances photogrammetrically inferred. By combining results from different directions, a 3D coordinate for almost every pixel is obtained with similar accuracy. Finally, DEM products with a regularly spaced grid are generated and exported based on the dense point cloud. Depending on the point classes used in the export (obtained through topographic filtering or deep-learning-based classification of the dense point cloud), the outcome will be a digital surface model (DSM) or digital terrain model (DTM).","name":"DEM generation with 'Structure-from-Motion'","selfAssesment":"Completed
"},{"code":"IP1-3-1-2","description":"Photogrammetry is the science and technology of obtaining spatial measurements and other geometrically reliable derived products from photographs. Basic geometric principles applying both traditional analogue and modern digital procedures are related to the central projection of the image in case of typical cameras and to the dynamic projection mostly in case of push-broom sensors, popular in the satellite photogrammetry. The fundamental principle used by photogrammetry is called triangulation. By taking photographs from at least two different locations, so-called “lines of sight” can be developed from each camera to points in a block on the object. These lines of sight (called rays) are mathematically intersected to produce the 3-dimensional coordinates of the points of interest.\r\nWithin data processing the most important parts of photogrammetric workflow are: (1) image orientation, (2) model reconstruction, and (3) orthorectification. Image orientation is based mostly on aerial triangulation, however recently the computer vision algorithm, called structure from motion, became more popular in particularly in close range photogrammetry. Both orientation approaches include detection or measurement of the points between overlapping images in a block, control points measurements in a field defining orientation in reference system and check points verifying the orientation process. The satellite photogrammetry due to different projection and much bigger areas of imaging is usually related to Rational Polynomial Coefficients (RPCs) defining preliminary scene orientation during image orientation. However, to receive more accurate results also here the control points measured in a field are in use. The second part of the modern photogrammetric processing is 3D model reconstruction. In past, vectorization within the stereoscopic measurements was the most popular way of using photogrammetric data after the image orientation. The development of the informatics contributed to the development of the image matching algorithms that can provide dense image point clouds, which can be used to the 3D detailed modelling including digital elevation model production. The final step of photogrammetric processing is orthorectification, which delivers cartometric image called orthophoto mosaiced into orthophotomaps. This process comprises the influence of digital terrain model, model of camera (interior orientation) and image orientation (exterior orientation). Orthophotomap and elevation models derived from photogrammetric processing are applied as very popular data source in many GIS systems. The other photogrammetric outcomes are, for example a 3D measurement or 3D models of some real-world object or scene.","name":"Photogrammetric principles","selfAssesment":"Completed
"},{"code":"IP1-3-1-3","description":"In satellite photogrammetry to obtain the orientation mostly of satellite scene Rational Polynomial Coefficients (RPCs) are applied. They provide a compact representation of a ground-to-image geometry, that allow for photogrammetric processing without requiring a physical camera model. Model with RPC is provided with satellite image and can be improved using measurements of indirect surveying methods used for control point measurement. The RPC model for the coordinates of the image point is calculated as ratios of the cubic polynomials in the coordinates of the world or object space or ground point. \r\nIn photogrammetry and remote sensing, rational polynomial coefficients (RPCs) describe a specific imaging geometry model for transforming image pixel coordinates to map coordinates (thereby accounting for terrain displacement errors). A sensor model describes the geometric relationship between the object space and the image space, or vice versa. It relates 3-D object coordinates to 2-D image coordinates. RPCs are part of a general sensor model that approximates the physical sensor model. The physical sensor model represents the physical imageing process, making use of information on the sensor's position and orientation (during image acquisition). The RPC model often refers to a specific case of the RFM (rational function model) that is in forward form, has third-order polynomials, and is usually solved by the terrain-independent scenario.","name":"RPC correction","selfAssesment":"Completed
"},{"code":"IP1-3-1-4","description":"A ground control point (GCP) is a location of the surface of the Earth (e.g. a road intersection) that can be identified on the imagery and located accurately on the map (i.e. the reference dataset). Two distinct sets of coordinates are associated with the GCP: image coordinates in i rows and j columns, and map coordinates (e.g. x, y measured in degrees of latitude and longitude or as specified by the spatial reference system).","name":"Ground Control Points (GCP)","selfAssesment":"Planned
"},{"code":"IP1-3-1","description":"Orthorectification is the process of removing sensor (scanner or camera), satellite/aircraft, and terrain-related distortions for creating a planimetrically correct image. \r\nTo obtain an accurately orthorectified image, the following information is required: (1) accurate elevation model, and (2) a camera model or rational polynomial coefficients (RPCs) that depicts the positional relationship of the collected image to the ground. Many companies deliver their images together with RPCs and existing software implementations can automatically read these files and apply the RPC transformation on the fly. An accurate elevation model is important to remove the influence of topography (e.g. hills, valley, etc.) on the raw image so that users can accurately compute distances, areas, and directions. Without performing orthorectification, the features in the image are tilted (especially the features located away from the center of the camera). Many satellite data products (e.g. Sentinel images, Landsat data products) are orthorectified using Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) data which is a freely available data product and has a spatial resolution of e.g. 1 arc-second (30 m). In the case of extremely jagged surface topography, i.e. areas of high relief, a DEM with a higher spatial resolution is required. \r\nTwo main models can be used in the orthorectification process: black-box and the physical-based model. The black-box model (called also the analytical model) is commonly implemented in different software because it relies solely on the RPC files. This model does not require access to any proprietary information of the sensor used to collect the image. \r\nThe physical-based models are more complex (and hence expected to be more accurate) because they account for various factors that might influence the quality of the acquired image: e.g. position of the satellite when collecting the images, atmospheric effects, etc. An example of a physical-based model is the so-called camera model. This model requires access to proprietary sensor information that has to be provided by the image owner.","name":"Orthorectification","selfAssesment":"Completed
"},{"code":"IP1-3-2-1","description":"Image co-registration [aka Image-to-image registration] is the translation and rotation alignment process by which two images of like geometry and of the same geographic area are positioned coincident with respect to one another so that corresponding elements of the same ground area appear in the same place on the registered images (Jensen 2005 referencing Chen and Lee 1992).","name":"Image co-registration","selfAssesment":"New
"},{"code":"IP1-3-2","description":"Spatial referencing (referred to as geo-referencing as well) is the process of aligning available EO or GIS data to a coordinate system so that further spatial analysis and image analysis tasks can be applied using these data as input. \r\nTo be able to perform spatial referencing, users have to generate the so called Ground Control Points (GCPs) with known coordinates. In case of images, the easiest features that could be used as GCPs are the intersections, isolated trees etc.","name":"Spatial referencing","selfAssesment":"In progress
"},{"code":"IP1-3","description":"Geometric correction is concerned with placing the reflected, emitted, or back-scattered measurements or derivative products in their proper planimetric (map) location so they can be associated with other spatial information. It is usually necessary to preprocess the remotely sensed data and remove the geometric distortions so that individual picture elements (pixels) are in their proper planimetric (x, y) map locations. This allows remote sensing-derived information to be related to other thematic information in geographic information systems (GIS) or spatial decision support systems (SDSS). Geometrically corrected imagery can be used to extract accurate distance, polygon area, and direction (bearing) information.\r\n\r\nGeometric correction techniques are dedicated to resolving the geometric distortions caused by: (1) variations in sensor position; (2) Earth curvature; (3) rotation of Earth on its axis; (4) relief displacement. \r\n\r\nThere are two types of geometric distortions, namely systematic and random distortions. The former might be caused by Earth's rotation for example and, therefore they are predictable and systematic. The second type of distortions might be caused by terrain or variations in sensor altitude. \r\nGeometric correction includes georeferencing and orthorectification techniques.","name":"Geometric correction","selfAssesment":"Completed
"},{"code":"IP1-4-1","description":"Contrast stretching (also referred to as contrast enhancement) expands the original input brightness values to make use of the total dynamic range or sensitivity of the output device (a computer display).","name":"Contrast stretching","selfAssesment":"New
"},{"code":"IP1-4-2","description":"The histogram is a useful graphic representation of the information content of a remotely sensed image. Histograms for each band of imagery are often displayed and analysed in many remote sensing investigations becvause they provide the analyst with an appreciation of the quality of the original data (e.g. whether it is low in contrast, high in contras or multimodal in nature. [...] Tabulating the frequency of occurrence of each brightness value within the image provides statistical information that can be displayed graphically in a histogram.","name":"Histogram","selfAssesment":"New
"},{"code":"IP1-4","description":"Image enhancement algorithms are applied to remotely sensed data to improve the appearance of an image for human visual analysis or occasionally for subsequent machine analysis. The quality of results of image analysis are subjectively judged by humans as to whether they are useful. They include contrast enhancement.","name":"Image enhancement","selfAssesment":"New
"},{"code":"IP1-6","description":"Principal component analysis (PCA) has proven to be of value in the analysis of multispectral and hyperspectral remotely sensed data. PCA is a technique that transforms the original correlated spectral dataset into a substantially smaller and easier set of uncorrelated variables that represents most of the information present in the original dataset. The first component accounts for the maximum proportion of the variance of the original dataset, and subsequent orthogonal components account for the maximum proportion of the remaining variance.","name":"Principal component analysis (PCA)","selfAssesment":"New
"},{"code":"IP1-7-1-1","description":"Bottom-of-Atmosphere (BOA)","name":"Bottom-of-Atmosphere (BOA)","selfAssesment":"New
"},{"code":"IP1-7-1-4","description":"Top-Of-Atmosphere (TOA) radiance represents the radiance observed outside Earth’s atmosphere. It is derived from the Digital Numbers (DN) using metadata delivered with the image.","name":"Top-Of-Atmosphere (TOA)","selfAssesment":"New
"},{"code":"IP1-7-1","description":"Atmospheric correction accounts for the attenuation caused by scattering and absorption in the atmosphere. It transforms top-of-atmosphere (TOA) reflectance to bottom-of-atmosphere (BOA) reflectance.\r\nThe decision to perform atmospheric correction depends on the need, i.e. the envisioned usage of the derived EO information product and the nature of the underlying problem. This includes requirements to the accuracy of extracted biophysical information. Additionally, the decision and choice of methods depends on the type of remote sensing data available, the amount of in-situ historical and/or concurrent atmospheric information available.\r\nAn atmospheric correction is essential when biophysical or geophysical parameters (e.g. of water or vegetation) are going to be extracted from the remote sensing data. If the data is not corrected, the subtle differences in reflectance among the contributing image bands may be lost. This is especially relevant when biophysical information shall be compared to that of images from other dates.\r\nHowever, some cases exist where it is unnecessary to perform atmospheric correction. For example, it is not necessary for producing an image classification product from a single date of remotely sensed data. If a maximum likelihood classification is applied that uses training data with the same relative scale for the pixel values, then, atmospheric correction has little effect on the classification accuracy. The same holds true for a post-classification change detection where the classifications of the two different dates were performed independently. \r\nThe process of (absolute) atmospheric correction requires a model atmosphere and in situ atmospheric measurements acquired at the time of remote sensor data acquisition as input. In situ data can be available from other sensors on-board the sensor platform.\r\n\r\nDark Object Subtraction (DOS) is one of the most popular empirical atmospheric correction techniques. This technique assumes that a black object has a reflectance value of zero. Yet, a dark object present in a satellite image will have a value different than zero because of the atmospheric scattering. This value is then subtracted from all pixels in a given spectral band.","name":"Atmospheric correction","selfAssesment":"In progress
"},{"code":"IP1-7-2-1","description":"A method for dimensionality reduction in hyperspectral data is Minimum Noise Fraction (MNF). The purpose is to minimize the noise in the imagery, i.e. to identify noise and segregate if from true information, and to colaps the useful information into a much smaller set of MNF images. The MNF transformation applies two cascated principal components analyses.","name":"Minimum noise fraction (MNF)","selfAssesment":"New
"},{"code":"IP1-7-2","description":"The number of spectral bands assocuates with a remote sensing system is referred to as its data dimensionality. Hyperspectral remote sensing systems such as AVIRIS ans MODIS obtain data in 224 and 36 bands, respectively. The greater the number of bands in a dataset (i.e., its dimensionality), the more pixels that must be stored and processed by the digital image processing system. Storage and processing consume valuable resources. It is necessary to reduce the dimensionality of hyperspectral data while retaining the information content inherent in the image. On method to reduce dimensionality of hyperspectral data and minimizing the noise in the imagery is the minimum noise fraction (MNF) transformation (Green et al., 1988).","name":"Dimensionality reduction","selfAssesment":"New
"},{"code":"IP1-7-3","description":"Sensor calibration converts the sensor’s digital numbers (DNs) to at-sensor radiance above the atmosphere. A further radiometric adjustment accounts for the viewing angle and sun angle during acquisition to transform radiance values to top-of-atmosphere (TOA) reflectance. Therefore, the process requires sensor calibration information and telemetry data that satellite image providers deliver within the metadata.\r\nDNs are raw sensor data without physical units. The sensor calibration information for converting the DNs to radiance are the calibration gain (cal_gain) and calibration offset (cal_offset) values. The sensor calibration multiplies the DNs of each spectral band with their corresponding cal_gain and adds the corresponding cal_offset. This linear function for translation and stretching transforms DN to at-sensor radiance in each band for the entire image. The radiometric adjustment uses information about the viewing angle and sun angle during acquisition to transform at-sensor radiance to TOA reflectance. \r\nSensor calibration obtains TOA reflectance and is a minimum requirement for performing band math calculations to derive spectral indices such as the normalized vegetation difference index (NDVI). Uncalibrated image data would arrive at NDVI values that are distorted because the cal_gain and cal_offset parameters for the involved spectral bands were not considered.","name":"Sensor calibration","selfAssesment":"In progress
"},{"code":"IP1-7-4","description":"As an optical remote sensing system is not perfect, noise can enter the data collection system at several points. Necessary corrections include the removal of shot noise (random bad pixels), correcting line or column drop-outs, accounting for line-start problems and radiometric correction of n-line striping caused by detector miscalibration.\r\nSAR data have global, random speckle noise. Speckle filters are designed to adapt to local image variations in order to smooth values, thus reducing speckle and enhancing lines and edges to maintain the sharpness of an image. A widely used way to reduce speckle is to apply spatial filters to the images. Typical approaches for speckle filtering include Laplace filtering for smoothing and sigma filters that preserve more of the signal with a lesser effect of smoothing.","name":"Noise reduction","selfAssesment":"New
"},{"code":"IP1-7-5","description":"Topographic correction, or topographic effects correction, aims to adjust the spectral values of an image according to effects of solar illumination differences due to the irregular shape of the terrain. Topographic slope and aspect introduce radiometric distortion of the recorded signal. Further, terrain shadow dramatically affects the brightness values of the covered pixels in an image. Topographic effects of illumination and shadow are particularly relevant in mountainous regions and in regions towards the higher latitudes of the southern and northern hemisphere. The effects appear pronounced during the winter season. \r\nTogether with sensor calibration and atmospheric correction, topographic correction is part of the radiometric correction process to obtain true reflectance values from sensor radiance. This process is necessary when using EO data for obtaining geophysical measurements. It can also benefit the accuracy of image classifications by reducing the internal variability of vegetation types, since the corrected reflectance relates better to the geometrical or biological properties of the plant than to the original reflectance.\r\nMethods for the removal of topographic effects from remotely sensed images can simply be based on band ratios that do not require additional input. Alternatively, they use digital elevation models (DEMs) as an additional input and apply sophisticated modelling of the illumination conditions. The illumination model describes various aspects of the relationship between the sensor measurement, the sun illumination, the ground reflectance and the diffuse irradiance at the surface. The model incorporates the angles between the sun position, the ground position (described by slope and aspect from the DEM), and the sensor position. Among these methods are lambertian methods and non-lambertian methods such as the bidirectional reflectance distribution function (BRDF). The BRDF, which is more suitable to the non-Lambertian properties of the observed surfaces, describes how the reflectance varies in each cover considering the angles of incidence and observation. \r\nIf achieved with a high quality, the resulting topographically corrected image appears to be illuminated evenly as if all its pixels would be part of a flat surface without the presence of any terrain differences. However, the much larger benefit than the improved appearance is the availability of pixel values that are closest to the true reflectance when compared to TOA, BOA and DN values.","name":"Topographic correction","selfAssesment":"In progress
"},{"code":"IP1-7-6","description":"Field spectroscopy is an in-situ method for characterising the reflectance of natural surfaces and thereby provides reference data for the calibration of airborne and satellite sensors. In addition, the method provides a means of scaling-up measurements from small areas (e.g. leaves, rocks) to composite scenes (e.g. vegetation canopies), and ultimately to pixels.","name":"Field spectroscopy reference data","selfAssesment":"In progress
"},{"code":"IP1-7","description":"Radiometric calibration and correction converts the sensor’s digital numbers (DNs) to radiance values and subsequently reflectance values. Additionally, the term “correction” points to the fact that radiometric measurements with satellite sensors contain error. Therefore, radiometric correction is concerned with improving the accuracy of surface spectral reflectance, emittance, or back-scattered measurements obtained using a remote sensing system. The Earth’s atmosphere, land and water are complex and can never be captured perfectly because of the limitations of remote sensing devices that lie in their spatial, spectral temporal and radiometric resolution. Therefore, error occurs in the data acquisition process and degrades the quality of remotely sensed data. The most common errors in remote sensing are radiometric and geometric. This concept is focused on the correction of remote sensing data to account for radiometric error that is to some degree systematic. Systematic errors in radiometric measurements come from the interaction of the sensed radiance with the atmosphere, the acquisition geometry in relation to the radiance source (the sun) and the Earth surface geometry (terrain).\r\nThere are several levels of radiometric calibration and correction. The first is sensor calibration that converts the DNs to top-of-atmosphere (TOA) reflectance. It converts to radiance values and further to reflectance values by accounting for the viewing angle and sun angle during acquisition. The second is atmospheric correction that converts TOA reflectance to bottom-of-atmosphere (BOA) reflectance. The third is topographic correction that converts BOA reflectance to surface reflectance. \r\nRadiometric calibration is necessary to ensure radiometric comparability of the measurements. There is a need for calibration when comparing different spectral bands within one image, e.g. for the calculation of geo-biophysical parameters with band math operations. Results from uncalibrated image data would differ from results achieved with calibrated data because the unaccounted cal_gain and cal_offset of the used spectral bands would lead to distortions. \r\nIn addition, radiometric calibration complements the geospatial comparability that is achieved with geo-referencing an image to geographic coordinates. Geo-referencing enables comparison of an image pixel to the geospatially matching pixel in another image acquired with a different sensor but with comparable resolution. Radiometric calibration enables a radiometric comparison between these two pixels’ radiance values. In case the two images are from different acquisition dates, a calculated radiometric difference would indicate change. This example shows the relevance of radiometric calibration for inter-sensor comparisons.\r\nRadiometric comparability is particularly relevant in studies that require inter-sensor comparisons, comparisons of surface features over time, or comparisons to laboratory or field reflectance data. Then the radiometric correction should cover atmospheric, solar and topographic effects. A full radiometric correction that also includes topographic correction can benefit the accuracy of image classifications by reducing the internal variability of vegetation types, since the corrected reflectance relates better to the geometrical or biological properties of the plant than to the original reflectance.","name":"Radiometric calibration and correction","selfAssesment":"In progress
"},{"code":"IP1","description":"Pre-processing operations are performed on remotely sensed data prior to information extraction. Remove error encountered in remotely sensed data (most common are radiometric and geometric error) to get as close as possible to the true radiant energy and spatial characteristics of the study area at the time of data collection. Image preprocessing includes any steps that facilitate information extraction (image display and enhancement).\r\n\r\nPre-processing of EO data focuses on processing the electrical signal measured by a sensor to a processing level at which pixel values subsequently can be used for information extraction. This includes correction of sensor system errors, geometric and radiometric correction. EO data are an information source for multiple purposes with different needs for pre-processing. Some applications may need only basic pre-processing to be done, others need a higher level. Depending on the sensor type (optical, radar, lidar), different processing levels are established.","name":"Image pre-processing","selfAssesment":"In progress
"},{"code":"IP2-1-1","description":"Data augmentation refers to a scheme of augmenting the observed data so as to make it more easy to analyze. Examples of data augmentation techniques include horizontal flips, random crops, Principal Component Analysis","name":"Data augmentation","selfAssesment":"New
"},{"code":"IP2-1-2","description":"Data imputation refers to a scheme of replacing missing values by imputed values. Imputation can be, for example done with mean, median and mode","name":"Data imputation","selfAssesment":"New
"},{"code":"IP2-1-3-1","description":"Gram-Schmidt is a pan-sharpening method that has been invented by Laben and Brover in 1998 and patented by Eastman Kodak. It makes use of the Gram-Schmidt orthogonalization to decorrelate the spectral bands (panchromatic, red, green, blue, etc.) and transform them into one multidimensional vector.","name":"Gram-Schmidt pan-sharpening","selfAssesment":"New
"},{"code":"IP2-1-3-2","description":"This pan-sharpening method uses PCA to transfer detailed spatial information from panchromatic band to the available multispectral bands.","name":"Principal Component Analysis (PCA)-based pan-sharpening","selfAssesment":"New
"},{"code":"IP2-1-3","description":"Pan-sharpening methods are used to enhance spatial resolution of images by merging a panchromatic image with high resolution with a multispectral image with low resolution.","name":"Pan-sharpening","selfAssesment":"New
"},{"code":"IP2-1-4","description":"Spatiotemporal image fusion methods, called also spatiotemporal downscaling methods, represent an efficient solution to generate fine-scale images at a high temporal resolution for more detailed land cover mapping and monitoring applications. Spatiotemporal image fusion methods can be classified into three categories: (1) reconstruction-based , (2) unmixing based and (3) learning-based methods.","name":"Spatio-temporal image fusion","selfAssesment":"New
"},{"code":"IP2-1","description":"Image fusion is defined as the “combination of two or more different images to form a new image by using a certain algorithm” Data fusion is a well-established research field. Image fusion methods are primarily used for improving the level of interpretability of the input data. Additionally, they can be utilized to address the problem of missing data caused by cloud or shadow contamination in satellite images time series. Image fusion can be performed at pixel-level, feature-level (e.g. land-cover classes of interest), and decision-level (e.g. purpose driven).","name":"Data fusion","selfAssesment":"New
"},{"code":"IP2-2","description":"Data harmonization aims to transform different datasets in such a way that they fit together, both with respect to geometry and semantics. The goal is that a user, who is using data from different authorities, shall have a unified view, where conflicts in the datasets have been removed.","name":"Data harmonisation","selfAssesment":"New
"},{"code":"IP2-3","description":"Data integration is the process of combining different geographic datasets including those derived from remote sensing data. The combined datasets can have different coverage, but they have to have the same geographic coordinates.","name":"Data integration","selfAssesment":"New
"},{"code":"IP2","description":"Data assimilation comprises steps to improve the level of interpretability of the input data, by enrichment (get rid of spatial/temporal gaps), by accounting for heterogeneity (through harmonization), and by integration (combination with other data that is relevant to the application).","name":"Data assimilation","selfAssesment":"Planned
"},{"code":"IP3-1-1-1","description":"Vegetation fraction (VF) is defined “as the percentage of vegetation occupying a pixel as viewed in vertical projection. It’s a comprehensive quantitative index in forest management and vegetation community cover conditions, and it’s also an important parameter in many remote sensing ecological models.”","name":"Vegetation fraction","selfAssesment":"New
"},{"code":"IP3-1-1-2","description":"Leaf area index (LAI) is the ratio between the total area of the upper leaf surface of vegetation and the surface area of the pixel in question. LAI is a dimensionless value, typically ranging between 0 (for a pixel composed of bare soil) and values as high as 6 (for a dense forest).","name":"LAI (Leaf Area Index)","selfAssesment":"New
"},{"code":"IP3-1-1-3","description":"Net primary production (NPP) is a measure of the inherent productivity of a region or ecological system—mainly the Earth’s production of organic matter, principally through the process of photosynthesis in plants.","name":"Net primary production (NPP)","selfAssesment":"New
"},{"code":"IP3-1-1","description":"Biophysical parameter retrieval is an approach in remote sensing that aims to estimate parameters which have physical meaning related to properties of living organisms. The goal is to provide quantitative results directly relating to the biophysical state, but independent of acquisition conditions and technology. Assessment of vegetation status is a key motivation for this, because through plant respiration and photosynthesis, vegetation is critical for modelling terrestrial ecosystems and energy cycles in environmental studies. \r\nImportant parameters describing canopy structure include leaf area index (LAI), green cover fraction (fCover), fraction of absorbed photosynthetically active radiation (fAPAR), plant height, biomass and leaf angle distribution. At leaf biochemical level, leaf chlorophyll/water, fuel moisture and leaf pigmentation content are used.\r\nVisual inspection can provide a first assessment of plant status. For detailed measurements of biophysical parameters, mostly destructive methods have been used. Chemical measurement techniques on leaf samples can measure pigment concentrations very accurately, but are time consuming and only use very limited samples. \r\nMuch more extensive data can be collected using earth observation imagery. These range from large scale spaceborne observations with high frequency at coarse resolution to dedicated UAV flights which can offer spectral information of individual plants. Radar and LiDAR acquisitions, which are insensitive to weather conditions, now complement optical observations. \r\nMethods to retrieve the parameters from remote sensing data fall into two main categories. Statistical models empirically match data to a biophysical variable. Univariate techniques use a single quantity derived from the data, usually a vegetation index whereas multivariate techniques link a combination of measurements at different wavelengths to one or more biophysical parameters.\r\nPhysically-based modeling is an alternative approach which uses advanced radiative transfer models to describe the transfer and interaction of radiation inside a leaf or canopy based on robust physical, chemical, and biological processes. They compute the interaction between solar radiation and plants and provide as such a better understanding between biophysical variables and reflectance characteristics. Good examples are Leaf optical models such as PROSPECT and LIBERTY which simulate leaf optical properties by absorption and scattering coefficients. Canopy reflectance models simulate canopy reflectance as a function of a complex description of plant structural and radiometric attributes to develop a quantitative understanding of remote sensing information.","name":"Biophysical and geophysical parameters","selfAssesment":"Completed
"},{"code":"IP3-1-2-1","description":"This spectral index is calculated using the following formula: SAVI = [(NIR-Red)/(NIR+Red+L)]/(1+L), where L can be, for example, 1 in area with no vegetation or 0 in area with dense veegtaion. It is used to minimize the influence of the soil brightness from the vegetation indices that are based on red and near-infrared wavelengths.","name":"Soil-adjusted Vegetation Index (SAVI)","selfAssesment":"New
"},{"code":"IP3-1-2-2","description":"This spectral index is calculate using the following formula NDSI = (green-SWIR)/(green+SWIR). It is the most popular index used to identify snow cover due to the fact that snow reflects visible wavelength stronger than middle-infrared wavelengths.","name":"Normalized Difference Snow index (NDSI)","selfAssesment":"New
"},{"code":"IP3-1-2-3","description":"Leaves, when healthy and vigour show a characteristic green colour. This visual effect evident to humans is caused by the co-existence of two evolutionarily facts: the specific interaction of the chlorophyll pigment in living leaves to the visible spectrum (VIS, 400-700 nm wavelength) of light emitted by the sun and the sensitivity of our human eye to the same sub-spectrum. According to fundamental physical laws of radiation (Stefan Boltzmann law of blackbody radiation and Wien’s displacement law), the VIS sub-spectrum corresponds to the radiation maximum of the sun, a hot blackbody with a surface heat of about 6000 K. Living leaves are structured in specific layers exhibiting characteristic interaction with light. The chloroplasts located in the so-called palisade layer, make use of the blue and the red part of sunlight for photosynthesis, the unique process of transforming light to create energy (carbohydrates) from water and carbon dioxide. This leads to the specific behaviour of leaves to absorb large portions (up to 90%) of the blue and red part of the electromagnetic spectrum and reflect nearly 100% of the green light. The peak reflectance in green light makes leaves (and plants in general) appear in green colour in our visual perception. \r\nA second, by no means less characteristic, feature of leaves is the specific response to near infrared (NIR, at around 700 nm wavelength) light in the mesophyll tissue (transmittance, scattering and reflectance). Only a small fraction of NIR is being absorbed. \r\nThis combination of two specific spectral characteristics, the absorption in VIS (red colour) by chlorophyll a in palisade layers, and the reflectance of NIR in the spongy tissue, makes the spectral profiles of plants and vegetation exhibiting a very characteristic shape, the so-called red edge. This absorption edge between red and NIR light is sharper for higher intensity green reflectance and brighter green tones (such as grassland or bright deciduous forest) than for less intensive reflectance and darker tones (coniferous forest). \r\nThe red edge may shift for the same vegetation type due to plant maturity or plant stress. This effect we call the red shift. The red shift is sensitive to crop maturity (headed stage) and may indicate harvesting time. Notably, there is also a blue shift, indicating green plants’ exposure to geochemical stress, which causes the absorption spectra to shift towards shorter wavelengths. \r\nPlants usually do not appear in isolation but form a canopy with a certain degree of coverage (e.g., crown closure in forests), and a certain part of understorey or soil per area unit. The resulting canopy reflectance is therefore a spectral mix of soil and vegetation (or even different types of vegetation) and generally lower than the reflectance of a pure vegetation sample under lab conditions. \r\nTo capture most of these plant-typical spectral characteristics, the so-called normalised difference vegetation index (NDVI) was developed. NDVI is an arithmetic band combination of red and NIR bands in a normalised value range. \r\nThe NDVI is calculated as:\r\nNDVI=((NIR-R))/((NIR+R))\r\nThe (hypothetic) value range of the NDVI is [-1 | +1]. Under real-world conditions, the NDVI ranges from values of around -0.2 to 0.6 or 0.7. To discriminate principal land cover classes such as water, non-vegetation (soil, sealed, etc.) and vegetation the following thresholds in the continuous range are used: \r\n\tNDVI < ~ 0: water\r\n\t~ 0 < NDVI < ~ 0.2: non-vegetation (soil, sealed surfaces, bare rock, etc.)\r\n\t~ 0.2 < NDVI: vegetation.\r\nNotably, these class limits are just a very rough approximation (indicated by the ~ sign), due to the mixed pixels effect, canopy reflectance, the abundance of water plants and suspending particles, and the illumination effect of specific atmospheric or topographic conditions. \r\nWe can use the NDVI to generally mask out vegetation from other land cover types and, more specifically, to indicate vegetation vigour and health. It is also suitable for monitoring plant phenology as the relationship between vegetative growth and the (changing) conditions of the environmental conditions. A range of variations has been suggested, enhancing one or the other mathematical or statistical behaviour of the index, or making it even more sensitive to specific plant behaviour. A well-known example is the enhanced vegetation index (EVI).","name":"Normalized Difference Vegetation Index (NDVI)","selfAssesment":"Completed
"},{"code":"IP3-1-2","description":"Spectral indices are calculated using a mathematical equation that is applied on two or more spectral reflectance bands of the image. The calculated spectral index is a ‘new’ image that highlights particular land surface features or properties e.g. vegetation, soil, water, better than the original input bands. The spectral indices vary from simple spectral ratioing of two bands to more complex combinations of multiple bands. Spectral indexes are developed based on the spectral properties of the object of interest. For example, spectral indices dedicated to the vegetation condition are developed based on the principle that the healthy vegetation reflects strongly in the near-infrared spectrum while absorbing strongly in the visible red. These properties are used to develop more complex spectral indexes for monitoring vegetation condition, phenology parameters, i.e. Normalised Difference Vegetation Index (NDVI), Advanced Vegetation Index (AVI). The spectral indices calculated using the short wave infrared spectral bands are more sensitive to vegetation water content and spongy mesophyll structure in the vegetation canopy thus are used to assess the vegetation decline, moisture that is particularly useful for drought monitoring (e.g. Normalized Difference Water Index (NDWI) or Normalized Difference Moisture Index – NDMI). The water-related spectral indices are widely applied in agricultural and ecological applications including surface water body characteristics, vegetation water stress, soil water content assessment and wetlands monitoring. The combination of near infrared and short wave infrared spectral bands is also used to detect burned area and to monitor the vegetation recovery (e.g. Normalised Burned Ratio – NBR). There are other spectral indices dedicated to snow cover and glacier monitoring, which are developed based on visual green and short wave infrared spectral bands. Snow reflects most of the radiation in the visible bands whiles absorbing in the short wave infrared.","name":"Spectral indices","selfAssesment":"Completed
"},{"code":"IP3-1","description":"The term band maths denotes the arithmetic combination (addition/subtraction, multiplication/division) of two or more spectral bands in an early stage of image analysis. The resulting scalar values represent the spectral behaviour in different bands in a single value; such procedure makes particular sense, when spectral behaviour varies in those bands (like the red edge of vegetation spectra in the NIR band). \r\nThere are several reasons for applying band maths when working with multispectral imagery: (1) A single range of values rather than multiple bands is easier to comprehend and interpret; (2) Thresholds or class limits are applied more intuitively in a grey scale image; (3) Indices can be easily calculated and compared across different sensors; they are implemented as standard routines in many software environments as well as cloud processing environments (such as Google Earth Engine or the Proba-V exploitation platform)\r\nOut of the many possible, literature suggests a few arithmetic band combinations as application-specific quasi-standards. Band ratios (e.g. red band divided by NIR band) and indices (such as the normalised difference vegetation index, NDVI) belong to this group. Indices have the advantage over simple ratios in constraining the value range, e.g. [-1 | 1]. Designated to indicate specific land cover types (such as water index, snow index, soil index, etc.) such indices are widely used as a basis for operational information products. Another index is the normalised burn ratio (NBR) which relates near infrared and short-wave infrared reflectance to measure burn severity taking into consideration the increasing of SWIR reflectance in the course of a fire. \r\nPre-processing such as dark object subtraction and radiometric or even atmospheric correction is a key requirement prior to indexing. The coding in digital numbers (DN) is a function of the sensitivity and the radiometric resolution of the sensor. The actual recording depends on atmospheric conditions (additional brightness, haze, etc.). Therefore, in order to make the resulting values comparable among different types of sensors and scenes, radiometric correction is mandatory, converting DNs into radiances, i.e. true reflectance values as physical measurement units. \r\nTwo advanced examples of band maths beyond rationing are the perpendicular vegetation index (PVI) and the tasselled cap (TC) transformation. PVI is based on the assumption that vegetation pixels are generally separable from soil pixels (at least after unmixing or for pure pixels), and thus pixel values are located in a perpendicular direction from the soil line in a NIR/red feature space. The Euclidean distance from the soil line, determined by Pythagorean triangle, yields the PVI. Tasselled cap instead rests on the notion of a cap-like histogram shape when plotting pixels on a brightness vs. greenness plot, with the latter determined by linear combinations of VIS and NIR bands, along with empirically determined coefficients. TC 1 as a weighted sum corresponds to brightness, TC 2 to greenness, TC 3 to yellowness, sometimes referred to as wetness. A fourth TC called nonesuch likely corresponds to noise and atmospheric disturbance effects in the image.","name":"Band maths","selfAssesment":"Completed
"},{"code":"IP3-10","description":"Semantic enrichment is the process of adding semantic metadata elements to improve the content-based image retrieval. These semantic metadata elements enable the explicit specification of the content of the images stored in the remote sensing databases.","name":"Semantic enrichment","selfAssesment":"New
"},{"code":"IP3-11-1","description":"Different types of changes are investigated using remotely sensed data: (i) abrupt changes, such as the changes caused by a fire or flooding, and (ii) gradual changes such as urban growth. Besides these kinds of changes, remote sensing community differentiates between transitional changes and conditional changes. Transitional changes refer to a major change of land surface such as conversion of forest to pasture or the expansion of mangroves into the surrounding water. Conditional changes refer to the change in condition at the surface such as water stress in an agricultural field, forest degradation caused by pest. \r\nIn the past, many remote sensing studies used two images to detect different types of changes such as deforestation, land cover change or change in the health or condition of the vegetation (e.g. pest infestation). Meanwhile, satellite image time series are used to assess the change. Time series analysis allows for monitoring more subtle changes and for providing temporal patterns of change. In this way, the timing of changes and drivers of change can be easily identified. \r\nDifferent methods are being used in change detection studies. There are studies that analyze individual images available in the investigated time series to map the target class/phenomena/events at the time when images were collected and to identify the changes: e.g. mapping the mangroves extent on an year basis and measuring it to identify changes. Alternative studies search for breaks in time series for detecting changes. The breaks are used to segment the time series into before and after changes periods which are further classified using one of the existing supervised or unsupervised classification methods (K-means, fuzzy k-means, Random Forest, Support Vector Machine etc.).","name":"Change detection","selfAssesment":"Completed
"},{"code":"IP3-11-2","description":"The (data)cube model for analysis of time series of earth observation raster data, represents the dataset as a multidimensional array with one or more spatial or temporal dimensions. Scalar values in the cube can be selected (or ‘filtered’) and processed based on dimension labels. This allows analysis algorithms to be thought of as a set of operations on the multidimensional array. Technologies that support this model allow to efficiently implement such algorithms.\r\nSome possible operations on a multidimensional cube include: filtering, ‘reducing’ all values along a dimension, ‘aggregating’ values in a dimension, or transforming all values along a dimension. Generally speaking, these operations require the selection of a subset of the data on which work is to be done. This allows implementing the operations efficiently even on very large datasets.\r\nIn comparison to file-based processing, most technologies that support cube-based time series analysis reduce implementation overhead, as the user does not need to read and write individual files, also more complex aspects like distributed computing for parallelization can be hidden in a cube based approach. So a cube based approach can also be thought of as an abstraction layer that effectively reduces the need for specific IT-related skills when analyzing earth observation timeseries.\r\nMultiple initiatives support cube based analysis. Some common features include a programming API, often using the Python programming language. Some tools are only accessible as web services, while others can also run locally (on a small dataset). This diversity is still a drawback, as users would need to familiarize themselves with different systems. Initiatives such as openEO try to address this by providing a common API.","name":"Cube-based time series analysis","selfAssesment":"New
"},{"code":"IP3-11-3","description":"Dynamic Time Warping (DTW) works by comparing the similarity between two temporal sequences and finds their optimal alignment, resulting in a dissimilarity measure. In the case of remote sensing data, DTW can deal with temporal distortions, and can compare shifted evolution profiles and irregular sampling thanks to its ability to align radiometric profiles in an optimal manner","name":"Dynamic Time Warping","selfAssesment":"New
"},{"code":"IP3-11","description":"Satellite image time series analysis plays an important role in different domains including vegetation dynamics monitoring, estimating crop yields, discriminating between different land cover classes, exploring human-nature interactions, monitoring land cover change, assessing environmental threats, or evaluating ecosystems-climate feedbacks or urbanization.\r\nTime series analysis requires high quality time series which are reconstructed by removing any source of contamination such as clouds, cloud shadows, or scan-line corrector (SLC) gaps of the Enhanced Thematic Mapper plus sensor (ETM+) on Landsat 7. Removed pixels are usually filled in with data predicted from a different date (temporal interpolation), nearby pixels (spatial interpolation) or from both (spatiotemporal interpolation). Different methods are available for screening and masking out clouds and shadows in satellite images including mono-temporal methods such as Function of mask (Fmask), or multitemporal mask (e.g. Tmask algorithm). Fmask is used by the United States Geological Survey (USGS) to produce a cloud mask layer of Landsat images. European Space Agency (ESA) is using Sen2cor processor to produce Level 2A Sentinel-2 data with a shadow and cloud shadow mask. All images used in the time series have to be co-registered, i.e. they align as closely as possible. \r\nTime series analysis is used to (1) investigate various surface properties such as evapotranspiration, land surface temperature, (2) map the cover of the Earth surface (e.g. land cover mapping, crop mapping etc.), (3) detect different type of changes such as abrupt changes (fire event) or gradual changes (urbanization), and (4) study the trends.\r\nTo map surface features from satellite image time series, numerous studies make use of the vegetation phenology extracted from a spectral-temporal trajectory of a given spectral vegetation index such as the normalized difference vegetation index (NDVI) or enhanced vegetation index (EVI). Several metrics can be used to characterized vegetation phenology: metrics of greenness and metrics of time. The metrics of greenness include the minimum and maximum spectral vegetation indices, their difference or amplitude, seasonally averaged greenness etc. The metrics of time include start and end of the growing season, duration or length of the growing season or the timing of maximum greenness. Changes, on the other hand, are identified either by investigating two images acquired at two different points in time or by identifying breaks in a dense (annual or multi-annual) satellite image time series.","name":"Time series analysis","selfAssesment":"Completed
"},{"code":"IP3-12-1","description":"Remote sensing-derived products such as land-use and land-cover maps contain error. The error accumulates as the remote sensing data are collected and various types of processing take place. An error assessment is necessary to identify the type and amount of error in a remote sensing-derived product.","name":"Error propagation","selfAssesment":"New
"},{"code":"IP3-12-2","description":"The precision of a measurement system, related to reproducibility and repeatability, is the degree to which repeated measurements under unchanged conditions show the same results.","name":"Precision","selfAssesment":"New
"},{"code":"IP3-12","description":"Uncertainty is the result of the lack or imprecision of our knowledge about the world. A proposition is uncertain if we do not know whether it is true or not. In most circumstances we describe a proposition as uncertain when the reason we do not know whether it is true is that we do not possess complete and accurate knowledge about the state of the world.","name":"Uncertainty","selfAssesment":"New
"},{"code":"IP3-13-1","description":"The main elements of visual interpretation are: tone, shape, size, pattern, texture, shadow, , association. Tone refers to the relative brightness or colour of objects in an image. It depends on the spectral properties of an object. Variation in tone allows to distinguish elements of different shape, texture and pattern. Shape refers to the general form, structure, or outline of individual objects. Straight and sharp edge shape represent typically the anthropogenic features i.e. urban or agriculture, the natural features like rivers, wetlands are more irregular in shape. Size of objects in an image is a function of scale and it depends on the spatial resolution of the image. The assessment of the size of the target’s object in relation to other objectives as well as an absolute size of the object are the important part of the interpretation. Pattern refers to the spatial arrangement of objects, i.e. network of street and houses in an urban area, orchards with the line of trees. Texture refers to the arrangement of frequency of tonal variation in particular areas of an image. Rough texture would have very large, coarse tonal variation (e.g. forest canopy), whereas smooth texture very little tonal version (e.g. uniform, homogenous surfaces). It depends on the size, shape and pattern of objects. Shadow depends on the scale and spatial resolution of an image. Shadow is useful to measure the height of an object, to distinguish the coniferous from broadleaf trees. In the radar imagery is useful for identifying topography and landforms. Association refers to the relationship between objects and features in proximity to the target interest.","name":"Elements (cues) of interpretation","selfAssesment":"Completed
"},{"code":"IP3-13-2","description":"Information-as-data-interpretation considers information as the outcome of the cognitive process of vision that reconstructs a scene from an image.","name":"Information-as-data-interpretation","selfAssesment":"New
"},{"code":"IP3-13-3","description":"An image interpretation key is simply reference material designed to permit rapid and accurate identification of objects or features represented on aerial images.","name":"Interpretation keys","selfAssesment":"New
"},{"code":"IP3-13","description":"Interpretation is the processes of detection, identification, description and assessment of an object and pattern imaged. Visual interpretation is the ability of a human operator to identify an object through the data content in an image / photo by combining several elements of interpretation. The image characteristics used in the interpretation process are: shape, size, tone/colour, texture, shadow, neighbourhood and pattern. The importance of the image characteristics varied according to the spatial resolution of the images and the properties of the feature of interest. The interpretation can be performed on the single image or between several images acquired at different time, which result in the differentiation of the temporal changes. The principle of the image interpretation is the process of delineating (digitalizing) the outlines of the objects, features on the image. It is performed “on-screen” using a GIS software. The process of visual interpretation is time consuming and requires a skilled interpreter with knowledge of the study area. Even though, the image interpretation supports many applications in for example selection of the training and verification data sets for image classification and accuracy assessment.","name":"Visual interpretation","selfAssesment":"Completed
"},{"code":"IP3-2-1","description":"Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans.","name":"Artificial intelligence (AI) [part of","selfAssesment":"New
"},{"code":"IP3-2-2","description":"Information theory answers two fundamental questions in communication theory: what is the ultimate data compression (answer: the entropy H) and what is the ultimate transmission rate of communication (answer: the channel capacity, C). For this reason, it is considered that information theory is a subsetof communication theory","name":"Information theory","selfAssesment":"New
"},{"code":"IP3-2-3","description":"Keypoints are objects (or locations) on the ground that reveal locally invariant features in images and therefore are easily detectable by automatic algorithms. Methods for this process employ scale-invariant feature transform (SIFT) algorithms for the automatic detection of geospatial objects.","name":"Keypoint detection","selfAssesment":"New
"},{"code":"IP3-2","description":"Image understanding is part of computer vision. Computer vision is an interdisciplinary field that deals with how computers can be made to gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to automate tasks that the human visual system can perform.","name":"Computer vision [part of","selfAssesment":"New
"},{"code":"IP3-3-1","description":"A Digital Elevation Model (DEM) is a digital raster (or grid) representation of elevation values of land surface shapes and features, where each grid cell takes a single elevation value with reference to a certain vertical datum. A DEM can be global, regional or local in scope, and can be used to characterize the dry land surface (topography) or submerged surfaces (bathymetry). Since a DEM cannot contain information of shapes and features under overhanging structures, it is often referred to as 2.5D instead of truly 3D. \r\nA digital elevation model is an overarching term for either a digital surface model (DSM) or digital terrain model (DTM). A DSM includes elevations of surface features such as trees, buildings, bridges and artificial objects such as poles, power lines, cars etc., and thus contains always the highest elevations of any feature for any given raster cell. A DTM does not include such features but reflects the elevation of bare land surface shapes, excluding elevated or overhanging features.\r\nDEMs can be obtained using active or passive measurements. Active measurements involve the generation of electromagnetic signals towards a surface and timing the reception of the (return) signal(s). This can be achieved through laser scanning (LiDAR) using visible or infrared light pulses for bathymetric or topographic measurements respectively, radio waves (SONAR) used in bathymetric measurements, or microwaves (synthetic aperture radar, SAR) used in topographic mapping. The most widely known active remotely sensed global DEM is derived from the Shuttle Radar Topography Mission (SRTM) obtained by a SAR mounted on the space shuttle Endeavour, offering 30 m resolution with a vertical accuracy typically between 5 and 20 m, covering 80% of Earth’s surface.\r\nPassive measurements detect reflection of sun light, or energy radiated from the surfaces. Their distance to the detector can then be inferred from the measurement of angles. Historically, line scanning imagers were used, but nowadays, these are replaced by acquisitions of overlapping 2D frame images. On the images, corresponding land surface features are detected which act as tie-points. The distance between the sensor and the tie-points is calculated in a process called photogrammetry. The most widely known spaceborne passive remotely sensed global DEM is derived from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data onboard the Terra satellite. It offers similar resolution and accuracy compared to SRTM, but with 99% coverage. \r\nOnly LiDAR can generate both accurate DSMs and DTMs from the same data acquisition, by using multiple returns from a single emitted pulse. All other techniques generate DSMs, from which elevated features can be identified and filtered out in postprocessing to create DTMs, however with typically lower accuracy and more artefacts.","name":"DEM generation","selfAssesment":"Complete
"},{"code":"IP3-3-2","description":"DSM can be produced automatically from stereo satellite scenes, from satellite sensors such as GeoEye, IKONOS, SPOT-5, Terra-ASTER etc. The DSM can also be provided from stereo digital aerial photography at various resolutions, depending on the quality and scale of the aerial photography. The quality of the automatic generated DSM is substantially improved if ground measurements from GPS are incorporated in the DSM stereoscopic model.","name":"DSM generation","selfAssesment":"New
"},{"code":"IP3-3","description":"Stereo pairs of optical satellite images with the support of ground control points provide a basis for cross-stereo analysis for generating Digital Surface Models.","name":"Cross-stereo analysis","selfAssesment":"New
"},{"code":"IP3-4-1-1","description":"The goal of filtering is to remove unnecessary components from images (e.g., noise), while emphasizing the necessary ones. In this context, low pass filters aim at removing sharp transitions in the image intensities (high spatial frequencies).","name":"Filtering","selfAssesment":"New
"},{"code":"IP3-4-1-2","description":"Gridding is the technique used to generate a uniform raster grid with one value for every cell in the raster. The values of the raster cells can represent different attributes such as mean, max or min of all Normalized Difference Vegetation Index (NDVI) values measured within a particular cell.","name":"Gridding","selfAssesment":"New
"},{"code":"IP3-4-1","description":"Aggregation in image classification","name":"Aggregation","selfAssesment":"New
"},{"code":"IP3-4-2-1","description":" ","name":"Conditional probability","selfAssesment":"New
"},{"code":"IP3-4-2-2","description":"Maximum likelihood classification uses the training data for estimating means and variances of the classes, which are then used to estimate the probabilities. This method considers not only the mean, or average, values in assigning classification but also the variability of brightness values in each class.","name":"Maximum likelihood","selfAssesment":"In progress
"},{"code":"IP3-4-2","description":"Bayes’s theorem is an extremely powerful means of using information at hand to estimate probabilities of outcomes related to the occurrence of preceding events","name":"Bayesian techniques","selfAssesment":"New
"},{"code":"IP3-4-3-1","description":"The Land Cover Classification System (LCCS) was developed by FAO to provide a consistent framework for the classification and mapping of land cover. Its main objectives were to overcome the rigidity of a-priori land cover classifications, which in many practical situations do not allow easy assignment into one of the pre-defined classes and are therefore not very suitable for mapping. LCCS instead opted for an approach based on two main phases. The first phase is an initial ‘Dichotomous Phase’, in which eight major land cover types are defined: (1) Cultivated and Managed Terrestrial Areas, (2) Natural and Semi-Natural Terrestrial Vegetation, (3) Cultivated Aquatic or Regularly Flooded Areas, (4) Natural and Semi-Natural Aquatic or Regularly Flooded Vegetation, (5) Artificial Surfaces and Associated Areas, (6) Bare Areas, (7) Artificial Waterbodies, Snow and Ice, and (8) Natural Waterbodies, Snow and Ice. The Dichotomous Phase is followed by a subsequent ‘Modular-Hierarchical Phase’, in which land cover classes are created by the combination of sets of pre-defined classifiers, which are different for each of the eight major land cover types. For example, common classifiers used for (semi-) natural terrestrial vegetation types are Life Form, Cover, Height, Macropattern. For aquatic or regularly flooded natural and semi-natural vegetation, water seasonality is an indispensable classifier. LCCS offers several advantages from a conceptual point of view. LCCS is a real a priori classification system in the sense that, for the classifiers considered, it covers all their possible combinations. The classification is also hierarchical and the more classifiers used, the greater the detail of the defined land cover class. The classes derived from the proposed classification system are all unique and unambiguous, due to the internal consistency and systematic description of the classes. LCCS is designed to map at a variety of scales, from small to large. From a practical viewpoint LCCS offers several advantages: (1) easy incorporation into GIS and databases, (2) allows flexible response to information available in a given area, project budget and time constraints, (3) unlinks the field data collection from the interpretation process.","name":"Land cover classification system (LCCS)","selfAssesment":"Completed
"},{"code":"IP3-4-3","description":"Long-term monitoring of land cover and land use are particularly relevant for land ecosystem monitoring. Therefore, baseline datasets are necessary that allow assessing changes of land cover and land use where the class definitions remain consistent over time. Accordingly, classification schemes have been established that adhere to taxonomically correct definitions of classes of information organized according to logical criteria. If hard classification is to be performed (i.e. without fuzzy class boundaries), the classes in the classification system should normally be mutually exclusive, exhaustive, and hierarchical. Mutual exclusive classes have no taxonomic overlap and assign a land cover patch to a single class. An exhaustive classification scheme is able to cover the area of interest comprehensively and leaves no land cover patch unassigned. A hierarchical system allows combining sub-classes into higher-level categories.\r\nFrom a remote sensing classification perspective, it becomes clear that a classification scheme consists of information classes defined by human beings. Conversely, spectral classes are those inherent to EO data. An analyst must identify spectral classes and label them as information classes that satisfy bureaucratic (or scientific requirements). Additionally, the advantage of using established classification schemes is that their use in scientific studies and applications produces results that are comparable to other studies and suitable for sharing of data.\r\nEstablished classification schemes include: CORINE land cover (CLC), Land cover classification system (LCCS), American Planning Association land-based classification standard, United States Geological Survey land-use/land-cover classification system for remote sensor data, U.S. Department of the Interior Fish & Wildlife Service classification of wetland and deep water habitats of the United States, U.S. National Vegetation Classification system (NVCS), International Geosphere-Biosphere Program IGBP Land cover classification system.","name":"Classification schemes (taxonomies)","selfAssesment":"Completed
"},{"code":"IP3-4-4","description":"Unsupervised methods are defined as the identification of natural groups, or structures, within existing data. Clustering requires only the number of to-be generated classes as an input parameter and assigns spectrally defined classes to an image.","name":"Clustering (unsupervised)","selfAssesment":"New
"},{"code":"IP3-4-5-1","description":"Production system","name":"Production system","selfAssesment":"New
"},{"code":"IP3-4-5","description":"Decision trees is a data mining technique used in different disciplines including Remote Sensing. It uses a tree-like prediction model to identify a pattern in the input data. One of the most popular decision tree algorithms is the CART (Classification and Regression Tree) algorithm.","name":"Decision trees","selfAssesment":"New
"},{"code":"IP3-4-6-1","description":"Convolutional Neural Networks (CNNs) are among the most popular deep learning methods.","name":"Convolutional neural networks (CNN)","selfAssesment":"New
"},{"code":"IP3-4-6","description":"Deep learning approaches have classically been divided into spatial learning (for example, convolutional neural networks for object classification) and sequence learning (for example, speech recognition)","name":"Deep learning","selfAssesment":"New
"},{"code":"IP3-4-7-1","description":"The RF classifier is an ensemble classifier that uses a set of Classification and Regression Trees (CARTs) to make a prediction The trees are created by drawing a subset of training samples through replacement (a bagging approach).","name":"Random forest (RF)","selfAssesment":"New
"},{"code":"IP3-4-7-2","description":"In machine learning, support vector machines (SVMs) are supervised non-parametric statistical learning techniques with associates learning algorithms that analysze data used for both classification and regression analysis. SVM algorithm was originally designed for binary classification. The SVM is based on the main hypothesis that the training set is linearly separable. Given a set of training examples, each marked as belonging to one or another of two categories, an SVM training algorithm builds a model that can assign each new occurrence into one of these two categories, making it a non-probabilistic binary linear classifier. The SVM model is a representation of the examples as points in space, mapped so that the algorithm can find the optimal line (hyperplane) which separates with minimum error the training set, and maximizes the distance, named the “gap”, between the objects of both classes and the hyperplane. Thus, instead of using the whole available training set to describe classes, SVM uses only those training samples that describe class boundaries (support vectors), thought it can be more efficient than other algorithm because it uses a subset of training points. New occurs are then mapped into that same space and predicted to belong to a category based on the side of the gap on which they fall. In addition to performing linear classification, SVMs can also efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces. Unfortunately, because of the technique used for separating classes SVM is less effective on noisier datasets with overlapping classes. When data are unlabelled, supervised learning is not possible, and an unsupervised learning approach is required. SVM is used for text classification tasks such as category assignment, spam detection and sentimental analysis. It is also commonly used for image recognition, performing particularly well in aspect-based recognition and colour-based recognition. SVM also plays a vital role in many areas of handwritten digit recognition, such as postal automation services.","name":"Support vector machines (SVM)","selfAssesment":"Completed
"},{"code":"IP3-4-7","description":"Field of study that gives computers the ability to learn without being explicitly programmed","name":"Machine learning","selfAssesment":"New
"},{"code":"IP3-4-8","description":"Image classification operator needs a set of terms to express the characteristics of an image. These characteristics are called interpretation elements and are used to define interpretation keys: tone/hue, texture, pattern, shape, size, height/elevation, location/association","name":"Mental concepts and categories","selfAssesment":"New
"},{"code":"IP3-4-9-1","description":"Typically, the simple random sample of a geographic region is defined by first dividing the region to be studied into a network of cells. Each row and column in the network is numbered, then a random number table is used to select values that, taken two at a time, form coordinate pairs for defining the locations of observations. Because the coordinates are selected at random, the locations they define should be positioned at random. The random sample is probably the most powerful sampling strategy available as it yields data that can be subjected to analysis using inferential statistics.","name":"Random sampling","selfAssesment":"New
"},{"code":"IP3-4-9-2","description":"A stratified sampling pattern assigns observations to subregions of the image to ensure that the sampling effort is distributed in a rational manner. For example, a stratified sampling effort plan might assign specific numbers of observations to each category on the map to be evaluated. This procedure would ensure that every category would be sampled.","name":"Stratified sampling","selfAssesment":"New
"},{"code":"IP3-4-9-3","description":"Systematic sampling positions observations at equal intervals according to a specific strategy. Because selection of the starting point predetermines the positions of all subsequent observations, data derived from systematic samples will not meet the requirements of inferential statistics for randomly selected observations.","name":"Systematic sampling","selfAssesment":"New
"},{"code":"IP3-4-9","description":"Sampling strategies or sampling pattern specifies the arrangement of observations used for training and/or validation purposes.","name":"Sampling strategies","selfAssesment":"New
"},{"code":"IP3-4","description":"The process of image classification extracts information about semantic labels of pixels or objects (i.e. regions) from imagery. Apart of input imagery, the process requires an input set of target classes (classification scheme) for which their spectral (and other) properties have to be identified. A classification method has to be selected that transforms the image data and the classification scheme into semantic map information. In complement to the resulting sematic labelling products, a secondary outcome are instructions or rulesets with the used parameters that constitute the documentation of the classification process.\r\nThe input imagery consists of one or more images (optical and/or SAR data) of a specific geographic area, collected in multiple bands of the electromagnetic spectrum (that may have already undergone certain pre-processing steps; determined by the purpose). Additionally, the imagery may include derived spectral indices, principal components, filtered bands, or other features to support the classification process.\r\nThe classification purpose defines the information about the target classes. It includes classification schemes (taxonomies), spectral signatures for each class and, mental concepts and categories about the classes (that enable an analyst to distinguish classes by texture, spatial relationships etc.). Often, training areas are used to understand how an object of a particular class is discernible in the available imagery and separable from other classes. Both the input imagery and the chosen classification method determine which features of each class can be exploited for classification. For example, spectral signatures of the target classes (extracted from training areas with known class label) may be a suitable input for extracting information with a pixel-based classification. For shape features, objects are a pre-requirement, derived with segmentation. They are only available with object-based classification approaches.\r\nClassification methods: Various methods exist that can be categorized according to the classification logic that they follow when transforming the input information into the output semantic labelling products. These can be parametric or nonparametric, supervised or unsupervised, per-pixel or object-oriented, semi-automated or fully automatic, and hybrid approaches. Classification methods are for example bayesian techniques like conditional probability or maximum likelihood, clustering (unsupervised), decision trees, deep learning and machine learning.","name":"Image classification","selfAssesment":"Complete
"},{"code":"IP3-5-1","description":"Edge detection is a fundamental tool used in many image processing applications to obtain information from the frames as a precursor step to feature extraction and object segmentation. This process detects outlines of an object and boundaries between objects and the background in the image. An edge-detection filter can also be used to improve the appearance of blurred image.","name":"Edge-based segmentation","selfAssesment":"New
"},{"code":"IP3-5-2","description":"Histogram-based segmentation makes use of histogram to select the gray levels for grouping the pixels into regions, e.g. background and the object of interest","name":"Histogram-based segmentation","selfAssesment":"New
"},{"code":"IP3-5-3","description":"Local variance can be calculated as the value of standard deviation in a small neighborhood (e.g. 3x 3 moving window), then computing the mean of these values over the entire image. The obtained value is an indicator of the local variability in the image.","name":"Local variance","selfAssesment":"New
"},{"code":"IP3-5-4","description":"Mean Shift is defined as finding modes in a set of data samples, manifesting an underlying probability density function (PDF).","name":"Mean-shift segmentation","selfAssesment":"New
"},{"code":"IP3-5-5","description":"Regionalization is an important concept in Geographic Information Science for synthesizing multi-dimensional data into homogeneous objects through spatially constrained clustering methods","name":"Regionalisation","selfAssesment":"New
"},{"code":"IP3-5-6-1","description":"Multi-resolution segmentation is a region-growing algorithm. It relies on several parameters, which need to be tuned. These include the scale parameter (SP), which dictates the size and homogeneity of the resultant objects.","name":"Multi-resolution segmentation","selfAssesment":"New
"},{"code":"IP3-5-6-2","description":"Watershed segmentation is a region-based method that has its origins in mathematical morphology. In watershed segmentation an image is regarded as a topographic landscape with ridges and valleys. The elevation values of the landscape are typically defined by the gray values of the respective pixels or their gradient magnitude. Based on such a 3D representation the watershed transform decomposes an image into catchment basins. For each local minimum, a catchment basin comprises all points whose path of steepest descent terminates at this minimum. Watersheds separate basins from each other. The watershed transform decomposes an image completely and thus assigns each pixel either to a region or a watershed.","name":"Watershed segmentation","selfAssesment":"New
"},{"code":"IP3-5-6","description":"Region-based segmentation starts from the pixel level and iteratively aggregates pixels into objects until some conditions of homogeneity imposed by the user are met.","name":"Region-based segmentation","selfAssesment":"New
"},{"code":"IP3-5-7","description":"Spatial autocorrelation is the term used to describe the presence of systematic spatial variation in a variable.","name":"Spatial autocorrelation","selfAssesment":"New
"},{"code":"IP3-5","description":"The term image segmentation denotes the process of algorithmically grouping neighbouring pixels that are similar. What sounds rather straight forward, is in fact a great computational challenge, some even call it an ill-posed problem, because there is a high degree of ambiguity in this process. \r\nThe two attributes in the general definition provided above, i.e. neighbouring and similar, evoke the principles of regionalisation as a fundamental concept in geography. Regionalisation is the bottom-up approach to congregate adjacent elements with the aim to form a larger unit. (Conversely, this could be understood in a top-down manner when subdividing a larger whole into smaller homogeneous units). This follows the general notion of hierarchical organisation according to general systems theory (GST). The organisation of a state in smaller administrative units is a good example for a hierarchical structure, the composition of the human body by organs, cells, etc. another. In image analysis such regions are commonly referred to image regions, originating from the concept of “photomorphic regions”, literally meaning regions formed on images – originally by human interpreter through manual delineation. Today, advanced pixel grouping algorithms aim to delineate homogenous regions in an image automatically. As those regions usually are assumed to match with real-world objects, it is often stated in literature that image segmentation generates image objects. Deriving some general heuristics on their properties (colour, size, shape, orientation, etc.) we can label these objects according to a given semantic scheme. The procedure of object delineation and classification using object features and relations is a fundamental principle in object-based image analysis (OBIA). \r\nDue to the effect of spatial autocorrelation (the tendency of neighbouring pixels to be similar irrespective of scale or geographical location), pixel grouping is ambiguous and by no means trivial, but not arbitrary either. Intuitively, image regions are those quasi-homogeneous areas that we perceive as landscape units on a specific scene (a lake, a forest patch, a single tree, a building, a residential area). According to hierarchy theory, we can assume that we find multiple scales within a single image even, according to the level of detail we are interested in. Whether or not a specific grouping of pixels is considered valid, e.g. because it corresponds to a real-world object, can hardly be answered unanimously, but rather needs to be judged by experts in the respective application domain. That is why often in literature we find the term ‘meaningful objects’. \r\nImage segmentation is as a sub-field of computer vision and aims to apply computer algorithms to generate image regions (a.k.a. tokens) within digital image analysis. There are several strategies for performing image segmentation, all resting on the following general principles: (1) regions do not overlap; (2) regions are (relatively) homogenous; regions are (relatively) different to neighbouring regions; regions are fairly equally sized (belong to one scale domain) but can be built in several hierarchical scales. General strategies include (1) edge-based segmentation and (2) region-based segmentation, and multi-scale segmentation as a specific case. \r\nAlso referred to spatial classification emphasizing the constraint of spatial contingency, image segmentation aggregates neighbouring pixels, but – as compared to statistical clustering techniques – does not provide a unique set of classes (either semantic or statistic) in the feature space. \r\nRecently the term semantic segmentation has emerged in the machine-learning community, which is in fact a combination of segmentation and categorisation (labelling) via deep learning methods (e.g. convolutional neural networks).","name":"Image segmentation","selfAssesment":"Complete
"},{"code":"IP3-6-1","description":"Combined filtering uses different filters to arrive at more complex filters for specific purposes. \r\nFor example, Laplacian filters are derivative filters used to find areas of rapid change (edges) in images. Since derivative filters are very sensitive to noise, it is common to smooth the image (e.g., using a Gaussian filter) before applying the Laplacian. This two-step process is called the Laplacian of Gaussian (LoG) operation.","name":"Combined filtering","selfAssesment":"New
"},{"code":"IP3-6-2","description":"The aim of sharpening filters is to highlight transitions in intensity (high frequency components) using different operators: directional (horizontal, vertical, diagonal) or isotropic (e.g. Laplacian Filter). Example of edge detectors include: Gaussian edge detector, Laplacian filter etc.","name":"Edge detectors","selfAssesment":"New
"},{"code":"IP3-6-3-1","description":"The Lee-sigma filter is a conceptually simple but effective alternative to the Lee and other sophisticated adaptive filters. It is based on the sigma probability of the Gaussian distribution.","name":"Lee-Sigma","selfAssesment":"New
"},{"code":"IP3-6-3","description":"High-pass filtering enhance information of high frequencies (local extremes, lines, edges)","name":"High-pass filtering","selfAssesment":"New
"},{"code":"IP3-6-4-1","description":"Gaussian Filters are isotropic (same behavior in all directions).","name":"Gauss filter","selfAssesment":"New
"},{"code":"IP3-6-4","description":"Spatial filters transform an image by taking into account the local neighborhood of a pixel. The goal of filtering is to remove unnecessary components from images (e.g., noise), while emphasizing the necessary ones. In this context, low pass filters aim at removing sharp transitions in the image intensities (high spatial frequencies).","name":"Low-pass filtering","selfAssesment":"New
"},{"code":"IP3-6","description":"In contrast to the point operations used for radiometric modification of image data, techniques for geometric processing are characterized by operations over local neighborhoods of pixels. The result of a neighborhood operation is still a modified brightness value for the single pixel at the center of the neighborhood , however the new value is determined by the brightness of all the local neighbors rather than just the original brightness value of the central pixel alone.","name":"Neighbourhood analysis (convolution)","selfAssesment":"New
"},{"code":"IP3-7-1","description":"Class modelling provides flexibility in designing a transferable workflow from scene-specific high-level segmentation and classification to region-specific multi-scale modelling","name":"Class modelling","selfAssesment":"New
"},{"code":"IP3-7-2","description":"Hierarchical representation refers to hierarchically scaled compositions of the classes to be classified.","name":"Hierarchical representation","selfAssesment":"New
"},{"code":"IP3-7-3","description":"Per-parcel analysis relies on parcels or objects as the smallest units of image analysis. The parcels are usually obtained through image segmentation that partition the input images into homogeneous units, i.e. parcels, in a supervised or unsupervised manner.","name":"Per-parcel analysis","selfAssesment":"New
"},{"code":"IP3-7-4-1","description":"Distance relationships describe how far an object is with respect to a reference. Proximity analysis allows the identification of the distance between a geographic feature of interest and its neighbors.","name":"Distance and proximity","selfAssesment":"New
"},{"code":"IP3-7-4-2","description":"The most important geometric features of geographic objects are their size and shape. Shape refers to general form or outline of individual objects and can be quantified using different metric such as shape index, compactness, asymmetry, density, elliptic fit, roundness, rectangular fit etc.","name":"Planar geometric features","selfAssesment":"New
"},{"code":"IP3-7-4-3","description":"Topological features characterize qualitatively the position of spatial objects relative to each other. There are different models for representing topological relationships. Calculus-based method, for example, allows us to model five topological relationships of two spatial objects: touch, in, cross, overlap, disjoint.","name":"Topological features","selfAssesment":"New
"},{"code":"IP3-7-4","description":"An object of a specific object class has a value on the range of values of a spatial or spectral feature. A set of features provides the feature space that is used for classification.","name":"Spatial features","selfAssesment":"Planned
"},{"code":"IP3-7","description":"OBIA is an iterative method that starts with the segmentation of satellite imagery into homogeneous and contiguous image segments (also called image objects. In the next step, resulting image segments are assigned to the target classes.","name":"Object-based image analysis (OBIA)","selfAssesment":"New
"},{"code":"IP3-8-1","description":"The feature space represents in various dimensions all the features that can be used for classification (e.g. image bands, band math parameters, derived texture properties). A point in that space is also called a vector with values for each feature (or dimension). Polyhedralization is a form of vector space quantization where a vector is assigned to the closest centre point of one polyhedron.","name":"Feature space polyhedralization","selfAssesment":"New
"},{"code":"IP3-8-2","description":"Radiative transfer models describing the interaction between matter and electromagnetic radiation serve as cornerstones for optical remote sensing. The radiative transfer theory provides the most logical linkage between observations and physical processes that generate signals in optical remote sensing. Radiative transfer modelling is therefore an integral part of remote sensing, since it provides the most efficient tool for accurate retrievals of Earth properties from satellite data. Radiative transfer models are used in a number of different applications such as sensor radiometric calibration, atmospheric correction and the modelling radiation processes in vegetation canopies. \r\nVegetation radiative transfer models (RTMs) study the relationship between leaf and canopy biophysical variables and reflectance, absorbance and scattering mechanisms. The infinite variability of vegetation structure complicates the modeling of RT in vegetation canopies. Numerous models of RT in vegetation canopies were developed in the second half of the last century. Models differ by the details accounted for and by the simplifications introduced in the description of canopy structure and photon–vegetation interactions. Gradual improvement in RTMs accuracy, yet in complexity too, have diversified RTMs from simple turbid medium RTMs towards advanced Monte Carlo RTMs that allow for explicit 3D representations of complex canopy architectures. This evolution has resulted in an increase in the computational requirements to run the model, which bears implications towards practical applications. When choosing an RTM, a trade-off between invertibility and realism has to be made: simpler models are easier to invert but less realistic, while advanced models more realistic but require a large amount of variables to be configured. The two most widely used models are the leaf model PROSPECT and Scattering by Arbitrary Inclined Leaves (SAIL) canopy model. \r\nAtmosphere RTMs study the interaction of radiation with the atmosphere. The remotely-sensed signals at satellite or airborne platforms are combinations of surface and atmospheric contributions, with relative amounts varying across the two wavelength regions, depending on the condition of the atmosphere. The order of magnitude of atmosphere signals can be equal or larger than that of land or ocean surface signals that arise at the top of the atmosphere (TOA). In order to derive accurate sensor calibration and atmospheric correction, the contribution of the atmospheric constituents to the total retrieved signal must be understood and modelled. Atmospheric radiative transfer models simulate the radiative transfer interactions of light scattering, absorption and emission through the atmosphere. Some widely used atmospheric RTMs are 6SV, libRadtran, MODTRAN, and ATCOR.\r\nAdvances in radiative transfer modeling enhance our ability to detect and monitor changes in our planet through new methodologies and technical approaches to analyze and interpret measurements from air- and space-borne sensors.","name":"Radiative transfer modelling","selfAssesment":"Completed
"},{"code":"IP3-8","description":"Historically, physical modelling and machine learning have often been treated as two different fields with very different scientific paradigms (theory-driven versus data-driven). Yet, in fact these approaches are complementary, with physical approaches in principle being directly interpretable and offering the potential of extrapolation beyond observed conditions, whereas data-driven approaches are highly flexible in adapting to data and are amenable to finding unexpected patterns (surprises).","name":"Physical-model based analysis","selfAssesment":"New
"},{"code":"IP3-9-1","description":"Difference of Gaussians (DoG) method consists of subtracting two Gaussians, where a kernel has a standard deviation smaller than the previous one. The convolution between the subtraction of kernels and the input image results in the edge detection of this image.","name":"Difference of Gaussian (DoG)","selfAssesment":"New
"},{"code":"IP3-9-2","description":"Scale Invariant Feature Transform (SIFT) is an image descriptor for image-based matching and it is used for a large number of purposes in computer vision related to point matching between different views of a 3-D scene and view-based object recognition. The SIFT descriptor is invariant to translations, rotations and scaling transformations in the image domain and robust to moderate perspective transformations and illumination variations. Experimentally, the SIFT descriptor has been proven to be very useful in practice for robust image matching and object recognition under real-world conditions.","name":"Scale invariant feature transformation (SIFT)","selfAssesment":"New
"},{"code":"IP3-9","description":"Scale-space theory is a framework for multiscale image representation, which has been developed by the computer vision community with complementary motivations from physics and biologic vision. The idea is to handle the multiscale nature of real-world objects, which implies that objects may be perceived in different ways depending on the scale of observation. If one aims to develop automatic algorithms for interpreting images of unknown scenes, there is no way to know a priori what scales are relevant. Hence, the only reasonable approach is to consider representations at all scales simultaneously.","name":"Scale space analysis","selfAssesment":"New
"},{"code":"IP3","description":"In analogy to the human mind, image understanding is the computational process of extracting information from images, i.e. locating, characterizing, and recognizing objects and other features in the scene. In Earth observation, image understanding refers to the tasks and methods that take pre-processed and assimilated images as an input and extract information from them. For example, a human task would be visual image interpretation by delineating objects in an image scene. However, image understanding is a cyclic process and already happens during pre-processing and assimilation. For example, the cloud mask for EO images is a product of image understanding, namely of classification, that is available very early in the image processing chain prior to many pre-processing tasks.","name":"Image understanding","selfAssesment":"Planned
"},{"code":"IP4-1-1","description":"Once the user finds the required data, she/he needs to know how can they be accessed, possibly including authentication and authorisation.","name":"Accessibility","selfAssesment":"New
"},{"code":"IP4-1-2","description":"Quality Indicators (QIs) should be ascribed to data and, in particular, to delivered information products, at each stage of the data processing chain - from collection and processing to delivery. A QI should provide sufficient information to allow all users to readily evaluate a product’s suitability for their particular application, i.e. its “fitness for purpose”.","name":"GEO QA4EO","selfAssesment":"New
"},{"code":"IP4-1-4","description":"ISO is an independent, non-governmental international organization with a membership of 164 national standards bodies. Through its members, it brings together experts to share knowledge and develop voluntary, consensus-based, market relevant International Standards that support innovation and provide solutions to global challenges. ISO/TC 211 Geographic information/Geomatics provides Standardization in the field of digital geographic information. Note: This work aims to establish a structured set of standards for information concerning objects or phenomena that are directly or indirectly associated with a location relative to the Earth. These standards may specify, for geographic information, methods, tools and services for data management (including definition and description), acquiring, processing, analyzing, accessing, presenting and transferring such data in digital / electronic form between different users, systems and locations.","name":"ISO standards","selfAssesment":"New
"},{"code":"IP4-1-5","description":"The OGC is the worldwide leading consortium of GIS industries promoting the interoperability of geographic information across platform, system, and country borders. The main field of current activity is the complete integration of the sources of geographic information based on the Internet.The Open GIS Consortium (OGC) plays an important role on the implementation level.","name":"OGC standards","selfAssesment":"New
"},{"code":"IP4-1-6","description":"A fundamental pillar in (open) science is to verify the scientific results of others to advance knowledge. The lack of reproducibility in scientific studies brings challenges in understanding and recreating the results of others, a situation that may be common in data-based and algorithm-based research like in geocomputation. In general, many authors define reproducibility as the ability to compute exactly the same results of a study based on original input data and analysis workflow. In other words, “to rerun the same computational steps on the same data the original authors used”. Replicability is often seen as obtaining similar conclusions about a research question derived from an independent study or experiment. In the field of GIScience and geocomputation, in particular, a reproduction is always an exact copy or duplicate, with exactly the same features and scale, while a replication resembles the original but allows for variations in scale, for example. Hence, reproducibility is exact whereas replicability means confirming the original conclusions, although not necessarily with the same input data, methods, or results.","name":"Replicability and reproducibility","selfAssesment":"Planned
"},{"code":"IP4-1-7","description":"The ultimate goal of FAIR is to optimise the reuse of data. To achieve this, metadata and data should be well-described so that they can be replicated and/or combined in different settings.","name":"Reusability","selfAssesment":"New
"},{"code":"IP4-1","description":"Data quality standards are guiding principles and operational guidelines for the production and use of data. For example, QA4EO aims for the two key principles of accessibility / availability and suitability / reliability. The QA4EO guidelines provide instructions for the implementation of processes that follow these principles. Standards emerge from standardization processes within the community. They are based on the agreement of the members of the community.","name":"Data quality standards","selfAssesment":"New
"},{"code":"IP4-2-1-1","description":"To correctly perform a classification accuracy (or error) assessment, it is necessary to systematically compare two sources of information: (1) pixels or polygons in a remote sensing-derived classification map, and (2) ground reference test information (which may in fact contain error). The relationship between these two sets of information is commonly summarized in an error matrix (sometimes referred to as contingency table or confusion matrix). Indeed, the error matrix provides the basis on which to both describe classification accuracy and characterize errors, which may help refine the classification or estimates derived from it.","name":"Error matrix","selfAssesment":"New
"},{"code":"IP4-2-1-2","description":"F-score represents the harmonic mean between precision and recall. As F-score combines both precision and recall, it can be regarded as an overall quality measure. The range of F is from 0 to 1 with larger values representing higher accuracy.","name":"F-score","selfAssesment":"New
"},{"code":"IP4-2-1-3","description":"Ground reference refers to the reference dataset for an accuracy assessment of a remote sensing classification. The process of obtaining ground reference is dedicated to support the production of suitable accuracy information. A sampling design (fitting to the produced image classification) determines the most appropriate distribution of sample locations (or regions). The response design consists of the evaluation protocol and the labeling protocol. The evaluation protocol initiates selecting the support region on the ground (represented by a pixel or polygon) where the ground information will be collected. Once the location and dimension of the sampling unit are defined, the labelling protocol is initiated and the sampling unit is assigned a hard or fuzzy ground reference label. This ground reference label (e.g. forest) is paired with the remote sensing-derived label (e.g., forest) for assignment in the error matrix.","name":"Ground reference","selfAssesment":"New
"},{"code":"IP4-2-1-4","description":"Kappa is a value for measuring the overall accuracy of a classification that accounts for randomness of class assignment. Kappa analysis is a discrete multivariate technique of use in accuracy assessment. Kappa yields a statistic, ^K, which is an estimate of Kappa. It is a measure of agreement between the remote sensing-derived classification map and the reference data as is indicated by a) the major diagonal and b) the chance of agreement, which is indicated by the row and column totals in the error matrix.","name":"Kappa statistics","selfAssesment":"New
"},{"code":"IP4-2-1-5","description":"These two quality assessment indicators are calculated as follows:\r\nPrecision = TP/(TP+FP) \r\nRecall = TP/(TP+FN),\r\nwhere TS is true positive, FP is false positive, FN is false negative","name":"Precision & recall","selfAssesment":"New
"},{"code":"IP4-2-1-6","description":"Geometric correction procedures (image-to-map rectification, image-to-image rectification) are used to rectify remotely sensed data to a standard map projection whereby it may be used in conjunction with other spatial information in a GIS to solve problems. The rectification process normally involves selecting ground control point (GCP) image pixel coordinates (row and column) with their map coordinate counterparts (e.g. meters northing and easting in a UTM map projection). Rectification requires that polynomial equations (that translate from image coordinates to map coordinates) be fit to the GCP data using least squares criteria. Depending on the distortion in the imagery, the number of GCPs used, and the degree of topographic reliefdisplacement in the area, higher -order polynomial equations may be required to geometrically correct the data. To determine how well the six coefficients derived from the least-squares registration of the initial GCPs account for geometric distortion in the inpit image, for each GCP, the root-mean-square error (RMSE) is computed.","name":"Root mean square error (RMSE)","selfAssesment":"In progress
\r\n\r\n"},{"code":"IP4-2-1","description":"A growing set of EO services and applications produce EO products that describe various aspects of the land, ocean and atmosphere. These products include for example image products at different processing levels, geometric measurements like in digital elevation models, semantic labelling products like land cover classifications, and EO-derived attribute products concerning air quality or other geophysical and biophysical parameters. Same as any geospatial data, EO products are not free of error and require accompanying documentation of their product quality. One term for describing different quality dimensions of an EO product is accuracy.\r\nAccuracy is a measure to estimate the uncertainty that originates from errors. An error is the deviation of a map value from a true value. The concept of error assumes well-defined phenomena where deviation results from imperfection of measurement equipment, environment effects, or imperfections of the observer. They cause gross errors and blunders, systematic errors, and random errors, for which different approaches are necessary to minimize error. Ideally, only random error remains that is probabilistic in nature and can be assessed with statistical approaches. For poorly defined phenomena, the concept of vagueness applies. For example in the case of thematic maps using fuzzy sets, the accuracy assessment requires a fuzzy approach as well. \r\nJudging error requires reference data with higher accuracy (by an order of magnitude) to which the map value can be compared. EO product quality dimensions about accuracy include thematic accuracy, spatial accuracy (both horizontal and vertical), radiometric accuracy, and accuracy of biophysical/geophysical parameter measurements. Respective equipment and approaches for reference data collection includes ground verification for thematic maps, GNSS positioning devices, field spectrometers, air quality sensors and in-situ biomass estimation. Ideally, reference data is collected in the field. In case of inaccessible areas of interest and/or if the service requirements allow it, approaches may rely on proxy reference data.\r\nThe design of the accuracy assessment procedure should be done with the EO product design to match the requirements of the EO service. For example, a thematic accuracy assessment consists of the main three components of response design, analysis, and sampling design. The response design ensures that reference data and map data are comparable at a location and specifies under which cases they agree or disagree. The analysis, usually performed with an error matrix, specifies which quality indicators will be calculated to quantify accuracy. The sampling design specifies the subset of locations at which the response design will be applied. Depending on the classification process and application case, different sampling strategies can be suitable (e.g. clustered sampling, stratified random sampling). \r\nFor other accuracy dimensions, respective accuracy assessment procedures exist, e.g. root mean squared error (RSME) for the positional accuracy assessment.\r\nAfter an accuracy assessment has been performed and the uncertainty in the EO product is understood, the challenge is to clarify how the uncertainty affects subsequent spatial analyses with the EO product. Different strategies exist that ignore error completely or that account for error by modelling uncertainty in the analysis outcomes. If uncertainty is judged low enough (or more hazardous, if users are unaware of the limited accuracy), subsequent analyses accept the EO product as true and ignore the accuracy value. If uncertainty is incorporated in subsequent analysis through uncertainty modelling, the results describe the bandwidth of outcomes, potentially supported with appropriate visualisations of uncertainty. The uncertainty modelling approach may greatly enhance the usability of the EO product, because it informs better how the error impacts the EO information and how much confidence a user should have in it.\r\nWith a new generation of EO products on the horizon and a largely increased user community, a large number of new applications is to be expected. They may also identify innovative accuracy assessment approaches. For example, the availability of EO archives with long time series of EO data led to response design protocols tailored to collect time series of reference data. The use of volunteered geographic information (VGI) as reference data has great potential, if approaches are implemented that ensure its reliability. Methods for object-based accuracy assessment are continued to be developed. Further, the increasing number of EO parameter products based on continuous variables creates the need to describe their accuracy. Finally, the focus on validation of EO products during EO service development and operation will make feedback from users available to service providers, ultimately leading to more meaningful EO products with more meaningful accuracy metrics and other quality indicators.","name":"Accuracy assessment","selfAssesment":"
Completed
"},{"code":"IP4-2-2","description":"The implementation of a service that provides remote sensing derived information on a regular basis introduces process-related quality criteria like the timeliness of information provisioning. For the case of refugee camp mapping, timely arrival of map information may be critical to support the decisions in planning facilities for humanitarian assistance.","name":"Timeliness","selfAssesment":"New
"},{"code":"IP4-2-3-1","description":"Completeness is a quality dimension that can apply to different data properties.The Data completeness is dealing with the completeness of an image, handling for example the effect of shadowing objects, sun flares on water surfaces or masking out by an object (e.g. propeller of a UAV). Spatial completeness is a feature on the area coverage. In photogrammetry (especially in stereophotogrammetry) its 3D version, the stereo completeness has extreme importance. In monitoring systems and applications the Temporal completenesster term features how the taken images represent a complete time series. The thematic completeness measure describes the image interpretation quality how the expected and defined classes are evaluated. This feature is important with the use of e.g. multiple classifiers.","name":"Completeness","selfAssesment":"New
"},{"code":"IP4-2-3-2","description":"In remote sensing we can speak about spatial consistency in the Consistency cluster. It represents the quality of image interpretation/understanding: how are the different objects or classes recognized/evaluated integrally. A bridge above a water surface, like river can be detected in pixel-wised manner, but the question is how coherent they are in the output map. This phenomenon has very close to the thematic consistency, where the recognition integrity is represented in this way. The topological consistency is defined mainly for network-type surface objects, like roads or rivers, where the connection of all atomic segments are rated by this measure. Urban mapping focuses on the built environment objects, where e.g. house-parcel inclusions are described by this feature. The temporal consistency is for monitoring again, representing for example the possibility or impossibility of land cover changes in time. Having multiple data sources (even airborne or terrestrial), their integral usage can be qualified by this measure.","name":"Consistency","selfAssesment":"New
"},{"code":"IP4-2-3-3","description":"Readability refers to the content of a map being presented clearly enough that the content can be perceived and understood by the user. This includes legibility, e.g. whether the text of a label is large enough to be read and has enough contrast to the background to be easily perceivable. Additionally, readability has a broader meaning that explains whether a product as a whole is simple enough to be understood and not too complex that essential information can be overlooked by the user.","name":"Readability","selfAssesment":"New
"},{"code":"IP4-2-3","description":"Gathering information about the quality of an EO product or service by letting the user test it. The feedback from the user enables to verify whether specific quality criteria have been met.","name":"User validation","selfAssesment":"New
"},{"code":"IP4-2","description":"A product in the sense of something that a user can use for a specific purpose requires a certain quality. Therefore, its accuracy needs to be judged with an accuracy assessment measure that the user understands and where he can interpret the meaning in relation to the purpose. The product has to be validated, i.e. it has to be known whether the product qualifies for use in a certain context. And in addition, the product needs to be available in time that the users can base their decision on it.","name":"Product quality","selfAssesment":"New
"},{"code":"IP4-3-1","description":"The cloud cover percentage indicates the amount of area in the remote sensing image extent that is covered with clouds and therefore cannot provide information about the Earth surface conditions.The actual types of clouds included may depend on the product, but the CEOS definition includes cloud shadow. Next to that, from an optical remote sensing point of view, clouds can be roughly classified in: opaque/dense clouds, mainly composed of droplets that are highly reflective in the VIS region and generally located at low-medium altitudes and cirrus, consisting of a large number of thin non-spherical ice crystals that are normally translucent in the VIS region, relatively highly reflective in the SWIR spectrum, and located at high altitude.\r\n\r\nThe goal of cloud cover percentage is to provide a quality measure of usable information in a surface reflectance image. Earth observation product catalogs support it as a query parameter, to enable searching for products with a cloud cover percentage below a given threshold.\r\nThis simplifies for instance use cases that require only fully clear products (0% cloud cover), and may save download and processing resources by only handling images that have some valid pixels. For instance, by only using products with a cloud cover percentage smaller than 99.95%. The measure also gives an estimate of the number of valid observations in a given geographical area, allowing a quick assessment of whether minimal data requirements for a specific use case are met.\r\n\r\nThe measure is a percentage of actual observations in an image, so pixels where no data was recorded are not included. For derived products, cloud cover pixels are often also flagged separately from pixels where no data was recorded, but this may depend on the data provider. The definition specifically also includes cloud shadow pixels.\r\nReliable cloud cover percentages depend on good cloud and cloud shadow detection methods. Especially handling of translucent cirrus clouds is an open issue: a product that has a 100% cloud cover percentage due to cirrus clouds might still be usable for some cases, while for other cases they also render the product useless. \r\n\r\nThe used cloud detection algorithm will also affect the cloud cover percentage. A more strict algorithm will yield higher percentages compared to an algorithm that under detects clouds.\r\nDue to these limitations, cloud cover percentages in product metadata have a fairly high error margin. The user should take this into account when determining optimal cloud cover percentage thresholds for the use case.","name":"Cloud cover percentage","selfAssesment":" "},{"code":"IP4-3-2","description":"The remote sensing lifecycle structures all possible phases of the data production process, from its beginning of the data's coming to existence (that includes the sensor design prior to data collection) over storage, processing and use to archiving and deletion.","name":"Remote sensing lifecycle","selfAssesment":"New
"},{"code":"IP4-3-3-1","description":"The minimum spatial resolution in which features related to the phenomenon under investigation become apparent against the background and allow to detect information about the phenomenon. Jensen: Spatial resolution is a measure of the smallest angular or linear separation between two objects that can be resolved by the remote sensing system. [...] A useful heuristic rule of thumb is that in order to detect a feature, the nominal spatial resolution of the sensor should be less than one-half the size of the feature measured in its smallest dimension.","name":"Minimum Spatial resolution","selfAssesment":"New
"},{"code":"IP4-3-3-2","description":"Radiometric resolution is defined as the sensitivity of a remote sensing detector to differences in signal strength as it records the radiant flux reflected, emitted, or back-scattered from the terrain.","name":"Radiometric resolution","selfAssesment":"New
"},{"code":"IP4-3-3-3","description":"Spectral resolution is the number and dimension (size) of specific wavelength intervals (referred to as bands or channels) in the electromagnetic spectrum to which a remote sensing instrument is sensitive.","name":"Spectral resolution","selfAssesment":"New
"},{"code":"IP4-3-3-4","description":"The temporal resolution of a remote sensing system generally refers to how often the sensor records imagery of a particular area.","name":"Temporal resolution","selfAssesment":"New
"},{"code":"IP4-3-3","description":"Resolution as a quality indicator determines whether it is possible to detect information about a phenomenon under investigation with that dataset. (Alternative description: For determining a suitable resolution of data for the information need of a specific application, the target resolution is the threshold above which RS data enables the detection of information about a phenomenon under investigation)","name":"Resolution","selfAssesment":"New
"},{"code":"IP4-3-4","description":"The spatial coverage of a dataset (consisting of an image or a series of images) determines whether the dataset covers the area of the terrain that is of interest to the user of information derived from the dataset.","name":"Spatial coverage","selfAssesment":"New
"},{"code":"IP4-3-5","description":"The temporal validity of a dataset (consisting of an image or a series of images) determines whether the acquisition date(s) (and period) match(es) the requirements for investigating a specific phenomenon and thereby enables the derivation of information about that phenomenon.","name":"Temporal validity","selfAssesment":"New
"},{"code":"IP4-3","description":"Values (or a value) that enable(s) judging a dataset or product on their fitness for a specific purpose (e.g. whether a specific satellite image is suitable for mapping landslides). , A QI should provide sufficient information to allow all users to readily evaluate a product’s suitability for their particular application, i.e. its “fitness for purpose”.","name":"Quality indicators","selfAssesment":"New
"},{"code":"IP4","description":"Data quality is of growing importance in remote sensing, due to the growing relevance that remote sensing data have in planning and operational decision of public bodies and private firms, and the huge amount of digital services (or apps) that exploit RS data. The most important data and product quality dimensions are: accuracy, lineage, structural consistency, semantic consistency, completeness, consistency, currency, timeliness, identifiability.","name":"Data quality","selfAssesment":"Planned
"},{"code":"IP5-1-1","description":"Array databases make use of arrays as the primary storage representation. Such an array-oriented data model and query language is useful in many scientific applications, where the raw data consists of large collections of imagery or sequence data that needs to be filtered, subsetted, and processed.","name":"Array databases","selfAssesment":"New
"},{"code":"IP5-1-2","description":"The Open Data Cube (ODC) is a non-profit, open source project that was motivated by the need to better manage Satellite Data. This project was born out of the work done under the \"Unlocking the Landsat Archive\" and the Australian Geoscience Data Cube (AGDC) projects.","name":"Open data cube","selfAssesment":"New
"},{"code":"IP5-1","description":"The term data cube originally was used in Online Analytical Processing (OLAP) of business and statistics data. Technically speaking, such a data cube represents a multidimensional array together with metadata describing the semantics of axes, coordinates, and cells. It is an efficient approach to the management and analysis of large datasets.","name":"Data cubes","selfAssesment":"New
"},{"code":"IP5-2-1","description":"Content-based image retrieval helps users retrieve relevant images based on their contents.","name":"Content-based image retrieval","selfAssesment":"New
"},{"code":"IP5-2-2","description":"Web Portals allow users to discover, understand, view, access and query information of their choice from local to global level for a variety of uses.","name":"Web portals","selfAssesment":"New
"},{"code":"IP5-2","description":"Image archives are repositories for storing, managing and retrieving remote sensing data.","name":"Image archives","selfAssesment":"New
"},{"code":"IP5-3-1","description":"As an initiative stipulated by the European Commission to foster the bridge between the Copernicus ground segment and the user segment, the Copernicus data and information access service (C-DIAS) is a generic name for different sets of cloud-based platforms providing centralised access to Copernicus data and information, as well as to processing tools. The name indicates, however, that the focus of such advanced user-centred infrastructure implementations is not only on data access, but also on ‘information’. What is specifically meant here is the provision of information services and information layers as defined in the Copernicus service portfolio. This allows the users to develop and host their own applications in the cloud and a single access point, rather than processing data locally. Currently there are five different DIAS’s implemented (CREODIAS, SOBLOO, MUNDI, WEKEO, ONDA), all with some specific technical assets, or a sector-specific application focus or any other unique selling position by e.g. targeting as specific user community. Currently, the DIAS, which have received co-funding from the European Commission as a kind of seed funding, are currently in the process of exploring opportunities and claiming market shares, striving to sustain in a competitive manner. Some of the features are highlighted in the following, without explicitly mentioning any of the associated DIAS: (i) data access of global data sets (satellite data mosaics or gridded data) by custom area; (ii) OGC interfaces, VM catalogue, SPAR QL search interface (combine searches like receive images over areas of high population density), open source (accessible via API) or pay-per-use; (iii) access to core service products (e.g. CLMS, CMEMS, CAMS); (iv) focus on integrated applications such as smart cities, urban energies, precision agriculture; access to third-mission VHR satellite data (e.g. Pléiades); (v) utilizing GitLab as a developer platform.","name":"Data and information access service (DIAS)","selfAssesment":"Completed
"},{"code":"IP5-3-2","description":"The OpenGIS® Web Processing Service (WPS) Interface Standard provides rules for standardizing how inputs and outputs (requests and responses) for geospatial processing services are defined. It defines an interface that facilitates the publishing of geospatial processes and clients’ discovery of and binding to those processes.","name":"OGC interfaces and OGC web processing service","selfAssesment":"New
"},{"code":"IP5-3","description":"Online processing allows users to implement and run image analysis operations online independent of the underlying software.","name":"Online processing","selfAssesment":"New
"},{"code":"IP5","description":"Infrastructure for image processing and analysis refers to the physical and organizational facilities that allow the storage, analysis and management of the available data and products. Traditionally, this infrastructure formed a digital image procesing system consisting of computer hardware with special purpose image processing software, and peripheral input-output devices (e.g. CD or DVD drives, internet access, printers/plotters). In the recent years, Earth observation is undergoing a shift to online processing making use of data cubes and vast image archives.","name":"Infrastructure","selfAssesment":"Planned
"},{"code":"IP6","description":"The image processing (value) chain is a sequence of processing steps for EO data that are performed by a set of stakeholders to ultimately provide EO information to a user. The sequence of processing steps that begins with the acquisition of EO data, followed by steps of pre-processing and information extraction (or whatever steps are necessary) and ends with an EO information product being available to a user that uses it to make his decision. The stakeholders along the processing chain each perform a dedicated subsequence of processing steps. Thereby, the stakeholders add value to the data they deliver to the next stakeholder in the chain. A categorization of stakeholders includes EO satellite operators, EO data providers, EO information providers, and the users at the end of the value chain. \r\nThe image processing value chain is closely related to processing levels that provide different states of processing of EO data. They start with raw instrument data (level 0 and 1) that are followed by data converted into geophysical quantities that are geo-referenced and calibrated (level 2). Further levels are quality controlled data that has been mapped on a uniform space-time grid (level 3) and data combined with models or other instrument data (level 4). In addition, EO data providers use the term analysis ready data (ARD) that have been processed to allow direct data analysis, i.e. user processing effort is reduced to a minimum.\r\nFurther, the standard EO products contain a categorizing element that is related to the image processing value chain. This categorizing element organizes the EO products along the sequences of processing, descriptive analytics, predictive analytics, prescriptive analytics, aggregation, visualization, and distribution.","name":"Image processing (value) chain","selfAssesment":"In progress
"},{"code":"MDS","description":"MDS is a dimensionality reduction technique.","name":"Multidimensional scaling","selfAssesment":" "},{"code":"MDSClassical","description":"It is also known as Principal Coordinates Analysis, Torgerson Scaling or Torgerson Gower scaling. It takes an input matrix giving dissimilarities between pairs of items and outputs a coordinate matrix whose configuration minimizes a loss function called strain.","name":"Classical multidimensional scaling","selfAssesment":"GI-N2K
"},{"code":"MDSGeneralized","description":"An extension of metric multidimensional scaling, in which the target space is an arbitrary smooth non-Euclidean space. In cases where the dissimilarities are distances on a surface and the target space is another surface, GMDS allows finding the minimum-distortion embedding of one surface into another.","name":"Generalized multidimensional scaling","selfAssesment":"GI-N2K
"},{"code":"MDSMetric","description":" ","name":"Metric multidimensional scaling","selfAssesment":"GI-N2K
"},{"code":"no","description":" ","name":"Mathematical models of uncertainty: Probability and statistics","selfAssesment":" "},{"code":"no10","description":"Geospatial data are abundant, but access to data varies with the nature of the data, who wishes to acquire it and for what purpose, under what conditions, and at what price. Legal relations between public and private organizations and individuals govern data access. Complementary topics appear in Knowledge Area GD Geospatial Data (especially Unit GD12 Data standards and infrastructures), and Knowledge Area OI (Units 0I5 Institutional and Inter-intuitional aspects and OI6 Coordinating organizations).","name":"Dissemination of geospatial information","selfAssesment":" "},{"code":"OI","description":"This knowledge area considers the organizational and institutional aspects related to GIS&T. The focus of this knowledge area is on the organizations active in the GIS&T domain, and what happens within and between these organizations. The knowledge area is structured around five units. One unit considers the key organizations in the GIS&T domain, covering relevant public sector organizations at different administrative levels as well as organizations in other sectors of society. Among the organizational aspects covered in this knowledge area are all organizational issues related to the implementation, use and management of GI and GIS within organizations. While all topics related to the organizational structures, procedures and management of GI(S) are grouped into one unit, another unit focuses on issues related to the human factor of using GI and GIS, i.e. people, their skills and competencies, and the development and evaluation of these skills and competencies in the context of GIS&T training and education. The knowledge area includes also several inter-organizational and institutional aspects of GIS&T. Particular attention is paid to the concept of geospatial data sharing, which is about the creation of `spatial data` connections and relationships between different organizations in the GIS&T domain. Spatial data infrastructures are developed to promote, facilitate and coordinate the sharing of spatial data among data providers and data users, and consists of several technological and non-technological components. Many related topics are considered in the knowledge area GI and Society (WS), which also addresses several non-technological aspects related to GIS&T. In addition to this, also the knowledge areas `Design and Setup of Geographic Information Systems`, `Geospatial Data\" and Web-based GI` include several topics that are closely linked to the topics that are considered in this knowledge area. It can be argued that in order to fully master the knowledge and competencies that are presented in these knowledge areas, also basic knowledge and understanding of the organizational and institutional aspects is required.","name":"Organizational and Institutional Aspects","selfAssesment":"GI-N2K
"},{"code":"OI1-1","description":"The development of an appropriate organizational model, which establishes the basic character of GIS operations, is a crucial element of the GIS management. The appropriate GIS organizational model for any organization is based on its intended role.Alternative GIS organizational models are based on differing arrangements concerning the scope of GIS, the degree of integration of GIS into business operations, the degree of centralization of GIS operation and use, and the degree of centralization of management control. Although many variations can arise from different combinations of these factors, GIS organizational models can generally be classified into three types: (1) enterprise GIS, (2) GIS data and service resource, and (3) GIS as a business tool (Somers, 1998).","name":"Organizational models for GIS management","selfAssesment":"GI-N2K
"},{"code":"OI1-2","description":"Management of GIS can be done in a more centralized or more decentralized manner. In a a so-called enterprise or information-framework GIS, an organizational unit may be established to manage the GIS environment and run the core system, whereas usage is decentralized. In environments where GIS is used occasionally by various users, it may be set up as a separate service with a designated group that manages the GIS and also controls users' applications services. A second decision that needs to be made after the choice between more centralized or more decentralized management of GI and GIS is about where to place the GI management. Alternative options are in a line organization, in a support area, or at the executive level, each with their own advantages and disadvantages.","name":"Managing GIS operations and infrastructure","selfAssesment":"GI-N2K
"},{"code":"OI1-3","description":"User roles describe the relationship between different users and the GIS in an organization. Each user role includes responsibilities (e.g. for modifying certain information) and privileges (e.g. for viewing specific information). Although many different roles can be defined, a basic distinction is made between users, who can only view certain information, and editors, who can edit certain information.","name":"User roles","selfAssesment":"GI-N2K
"},{"code":"OI1-4","description":"A GIS management strategy should be unique for each organization, as organizations have unique environments, characteristics, goals, GIS requirements. An important step in developing an effective strategy for an organization is to establish the strategic vision for GI and GIS in the organization and define its role and scope. Other elements that should be covered in the GIS Strategy are the degree of centralized management of the GIS, the placement of GIS management and support in the organization, involvement of users in GIS planning and implementation, coordination of users, organizational changes, preparation of users, personnel issues, transitions to GIS operations, integration into business operations, user support, data access, and integration of technology changes (Somers, 1998).","name":"Strategic planning","selfAssesment":"GI-N2K
"},{"code":"OI1-5","description":"Committee and team approaches are frequently employed for coordinating participants and users in multi-participant GIS projects. The aim of creating such committees and teams is to ensure that the varied interests of participants are addressed, as participants bring many different interests, application needs, data needs, priorities, organizational issues, and political interests to a common project the GIS. Common models for coordinating participants recognize that participants have three levels of interest in the GIS: policy, technical development, and usage. Different bodies can be established focusing on these different levels of interest: a technical committee focusing on the design and development of the GIS, an management committee providing policy guidance and support and a user`s group.","name":"Coordinating GIS Participants and Users","selfAssesment":"GI-N2K
"},{"code":"OI1-6","description":"After the development and implementation of a GIS within an organization, the challenge is to maintain the system and revise and update it when necessary. This means the performance of the GIS in terms of efficiency and effectiveness should be measured and monitoring, and feedback from users on the system and applications, on the data as well as on new needs should be collected. Particular attention should be paid to the maintenance of data sets.","name":"Ongoing GIS revision","selfAssesment":"GI-N2K
"},{"code":"OI1-7","description":"The introduction of GIS into organizational environments should be seen as a complex process of mutual adaptation (Nedovic-Budic, 1997). These technologies changes the established organisational processes and structures, while on the other hand the organisational context and culture modify the technological set-up and use. Therefore, knowledge and understanding of the relationship between technologies and organizations is necessary to increase the success of GIS implementations in organizations. Successful GIS implementation and adoption often require some degree of organizational change. However, this can be very difficult to effect because organizations are naturally resistant to it (Somers, 1998).","name":"Organizational changes","selfAssesment":"GI-N2K
"},{"code":"OI1-9","description":" ","name":"Organizational models for coordinating GISs and/or program participants and stakeholders","selfAssesment":"GI-N2K
"},{"code":"OI1","description":"GIS and T implementation and use within an organization often involves a variety of participants, stakeholders, users and applications. Organizational structures and procedures address methods for developing, managing, and coordinating these multi-participant users. The development of the appropriate organizational model for managing the GIS is crucial. In certain cases, changes to the organizational structure in place might be required. Strategic planning and the establishment of coordination structures can be considered as valuable instruments for managing and coordinating all involved users, while also the different user roles need to be assigned.","name":"Organizational structures, procedures and management","selfAssesment":"In Progress GI-N2K
"},{"code":"OI2-1","description":"GIS and T professionals can be hired for a wide range of different job positions, for which the precise skills, competences and qualifications needed will vary. Typical examples of GIS and T positions are GIS&T project managers, technicians, system developers and analyst. The recognition and certification of the competences people have acquired in informal and non-formal learning contexts is important to know which skills and competences individuals have and whether they meet the qualifications required for a certain job position.","name":"GIS and T positions and qualifications","selfAssesment":"GI-N2K
"},{"code":"OI2-2","description":"Making sure staff members have the necessary skills and competences to perform geospatial activities is necessary for an effective implementation and operation of GI within an organizations. Several training methods can be adopted to ensure the development of skills and competencies of staff members. A distinction can be made between formal and informal training, but also between internal and external training programs. Another relevant issue is the assessment and evaluation of the skills and competences of staff members, to determine their future training and development needs.","name":"GIS and T staff development and evaluation","selfAssesment":"GI-N2K
"},{"code":"OI2-3","description":"Programs and courses on GIS and T and related subjects are provided by a wide range of institutions. While in recent years also the use and integration of GI and GIS in primary and secondary education has received significant attention, GIS and T education is mainly organized by institutions of higher education, especially universities but also other higher education institutions. Analyses of the higher education GIS&T programs and courses in Europe showed that the offer of courses is very diverse, in terms of size (ECTS), educational level (EQF) and course content. Vocational training on GIS and T related topics is organized by different types of training providers, including the major GIS vendors, data and service providers, academic sector, professional organisations, but also the public sector.","name":"GIS and T training and education","selfAssesment":"GI-N2K
"},{"code":"OI2-4","description":"A curriculum is a systematic description of a study program, in terms of learning goals, structure and sequence, learning, teaching and assessment strategies and content. A curriculum consists of both a set of related required and elective - courses along with all direct and indirect skills, competences and learning outcomes resulting from these courses. In the process of curriculum design typically particular attention is assigned to objectives, teaching methods and educational strategies, while also attention should be paid to the content organization aspects and the global structure of the curriculum. The process of designing GIS&T curricula presents many challenges, as the design of the curriculum should be aligned to both the institutional context and the expected outcomes of the learning and teaching process (Prager, 2011).","name":"GIS and T curriculum and course design","selfAssesment":"GI-N2K
"},{"code":"OI2-5","description":"An important challenge in organizing GIS and T education and training is the choice and use of effective teaching and learning methods. These methods should follow recent technological developments and use the best technologies to help students acquire the necessary skills and competencies. Traditionally, most GIS and T programs and courses were taught in the context of a full-time, face-to-face setting, using traditional teaching methods such as lectures and lab-based computer practical sessions. In recent years, educational institutions and their teachers have been experimenting with more innovative teaching and learning methods, such as project-based and case-based learning, distance learning, integrated and inter-disciplinary lessons, collaboration with companies and other stakeholders, etc.","name":"GIS and T teaching and learning methods","selfAssesment":"GI-N2K
"},{"code":"OI2","description":"This unit addresses GIS and T staff and workforce issues within an organization, particularly as they relate to ensuring that GIS and T is appropriately used and supported. The focus of this unit is on the skills and competencies of professionals in the GIS and T domain: how can these skills and competencies be described and evaluated, and how can they be developed through training and education.","name":"GIS and T workforce themes","selfAssesment":"GI-N2K
"},{"code":"OI3-1","description":"Cost savings are an important driver or motivation for sharing geospatial data and information. As costs associated with collecting and maintaining geospatial data are high, sharing data means that users no longer need to duplicate data gathering and archiving, which leads to savings in terms of personnel, space/facilities, data acquisition and maintenance costs. One fundamental argument for sharing thus derives from scale economies in production. Because the cost of making data is high, there is a clear incentive to maximize the number of users of these data. Sharing allows data to be used repeatedly for many purposes, thus increasing their value without increasing their cost. Sharing data also leads to improved data quality. Moreover, in many cases, sharing data is the only way to get access to certain data sets, as the authority to collect and manage certain data lies with another public institution.","name":"Drivers and incentives for sharing geospatial data","selfAssesment":"GI-N2K
"},{"code":"OI3-2","description":"Sharing of geospatial data can be hindered or inhibited by several types of barriers. These include technological barriers, such as a lack of common data definitions, formats and models or incompatibility of hardware and software. Among the non-technological barriers are organizational, political and legal issues and elements, such as misaligned organizational missions, diversity in organizational cultures, conflicting organizational priorities, lack of funding, lack of executive and legislative support; restrictive laws and regulations, copyright issues, data privacy and data ownership issues. However, it should be noticed that many of these barriers have been decreased or eliminated in recent years.","name":"Barriers to geospatial information sharing","selfAssesment":"GI-N2K
"},{"code":"OI3-3","description":"The legal framework for a spatial data sharing consists of two main types of information policies: those that promote and those that hinder the availability of spatial data. Policies that promote spatial data availability can focus on different types of users (public bodies, private companies, citizens) and different types of use (public access, commercial and non-commercial reuse, reuse for performing public tasks). Among the policies that hinder the availability of spatial data are those dealing with privacy, liability, and intellectual property. The legal framework also includes legislation that applies to data or information in general, which may also be applicable to spatial data (e.g. legislation on freedom of information, copyright, etc.). Moreover, also general legislation relating to any interaction between people or any situation in everyday life (e.g. liability, contract law, competition law, etc.) will apply to spatial data sharing. decreased or eliminated in recent years.","name":"Legal framework for geospatial data sharing","selfAssesment":"GI-N2K
"},{"code":"OI3-4","description":"Several types of legal mechanisms for sharing geospatial data can be used. A data sharing arrangements can be formalized by a contract or agreement between the data provider and the data user. A particular type of agreement are the framework agreements, which are agreements between two or more organisations concluded prior to the datasets or services being required. These framework agreement can involve one or multiple spatial data sets or services. Partnership agreements are often used to formalize the data sharing agreements among a broader group of partners. Participation in such a partnership often means participants share their data with other participants and get access to shared data. Another relevant mechanism is the use of licenses, which are mechanisms to give organizations and people the permission to use spatial data sets and services. A license is legally binding, and defines the conditions of use of the related spatial data sets and services. In order to reduce the number of licenses used and ensure the harmonization of the terms in these licenses, the use of standard licenses is promoted.","name":"Legal instruments for sharing geospatial data","selfAssesment":"GI-N2K
"},{"code":"OI3","description":"Geospatial data sharing has become an essential element of the GI activities of organizations. Spatial data sharing can be defined as the electronic transfer of spatial data/information between two or more organizational units where there is independence between the holder of the data and the prospective user. Spatial data sharing has many advantages, but several technical and non-technical barriers must be overcome to put data sharing into practice. While the practice of spatial data sharing has substantially grown with the development of spatial data infrastructures, many consider data sharing as a crucial element for the success of these infrastructures.","name":"Geospatial data sharing","selfAssesment":"GI-N2K
"},{"code":"OI3b","description":"A Spatial Data Infrastructure can be defined as the collection of technological and non-technological components to facilitate and coordinate the exchange of and sharing of spatial data. The concept infrastructure is used to promote the concept of a reliable, supporting environment, analogous to a road or telecommunications network, that facilitates the access to spatial data. Data, metadata, access networks, standards, coordination, policies, funding, people and institutional frameworks are often considered among the key components of an SDI. \r\n\r\nSpatial data infrastructures often are defined and described as a complex and dynamic phenomenon. Among the main reasons for the complex character of these infrastructures are the many components a spatial data infrastructure consists of, the diversity of involved stakeholders, and the many different objectives and ambitions of these stakeholders. Technological advancements, such as the emergence of web 2.0 technologies, and societal changes, such as the increasing use of geographic information in everyday life, are often mentioned as important drivers behind the dynamic character of spatial data infrastructures. \r\n\r\nA key characteristic of spatial data infrastructures is the involvement of a large and diverse group of actors. Governments are often considered as the central actors in the development and implementation of spatial data infrastructure, since they are the major producers and users of geographic information. Governments at different administrative levels and in different thematic domains are involved in the creation, management, use and sharing of geographic data. But also private companies, non-profit organisations, research and education institutions and even citizens can participate in the development and implementation of a spatial data infrastructure. It is increasingly being argued that the involvement and engagement of each of these stakeholders group is essential to the realization of a successful spatial data infrastructure. \r\n\r\nSDIs have been developed in many countries worldwide at local, national and international levels. Often a distinction is made between a between the first generation SDIs that have data as their key driver and are based on a product model and second generation SDIs in which user needs are the key driver and that are based on a process or development model. The latest generations of SDI strongly focus on the inclusion and engagement of non-government actors and organizations in the development and implementation of the SDI. Although SDI are by default distributed systems, involving many organisations, some SDI might be developed rather in an hierarchical way, while others are following a networked approach.","name":"Spatial Data Infrastructures","selfAssesment":"Completed
"},{"code":"OI4-1","description":"The adoption and implementation of standards are two key phases in the standardization process, which starts with the definition of standardization requirements and the development of standards. The adoption and implementation of standards follows after the development phase. The distinction made between the adoption and implementation of standards is important: adoption entails the decision to apply standards, while the implementation relates to the integration of standards in software, in data development and in other processes. GI-Standards are one of the key components of each SDI, consist of both semantic and technical standards, and include standards related to the different architectural components of an SDI, i.e. standards related to spatial data sets and data products, web services, metadata and catalogues, encodings, etc.","name":"Adoption and implementation of standards","selfAssesment":"GI-N2K
"},{"code":"OI4-2","description":"The SDI policy framework includes the set of policies, strategies, initiatives and projects aimed at increasing access, sharing, and effective use of spatial data. SDI policies can be divided into strategic and more operational policies. Strategic policies define the broader framework and formal structure within which the SDI initiative is developed. Operational policies provide more practical tools to facilitate access to and use of the SDI, and address specific topics related to the collection, management, use, access and dissemination of spatial data. These operational policies include a broad range of guidelines, directives, procedures and manuals that apply to the day-to-day business of organizations in developing, operating and using an SDI. To guarantee the success of an SDI, it is important to recognize the wider policy context in which these SDI`s are developed, and to link them to the overall policy environment in the jurisdiction in which they are implemented. These include policies on open government and open data, environmental policies, digital government or e-government policies and other.","name":"Policies","selfAssesment":"GI-N2K
"},{"code":"OI4-3","description":"If is often argued that SDI implementation requires coordination, because without coordination all other SDI components would not be developed or would be developed in a very fragmented and inconsistent manner. In general terms, coordination is about bringing into alignment the activities of different stakeholders in the SDI landscape. A typical instrument to realize coordinate in the context of SDI, is the establishment of an effective SDI coordination structure. The SDI coordination structure should ensure that all stakeholders are involved in the development and implementation of the SDI, through the participation in one or more coordination bodies. Another important element is the establishment of clear roles and responsibilities for the different involved organizations, making a distinction between data users, data providers, services providers and a geo-broker.","name":"Coordination and organizational structure","selfAssesment":"GI-N2K
"},{"code":"OI4-5","description":"Funding an SDI is about guaranteeing the long-term financial security of an SDI, by obtaining and formalizing financing for the implementation and maintenance of the different SDI components. An SDI funding model provides the answer to the central question of where and how to seek funding for implementing and maintaining an SDI. Within an SDI often different funding models will be combined, as the selection of the most appropriate funding model will be linked to different activities and the associated costs. Costs of an SDI include both set-up costs (one off costs) and maintenance costs (yearly), of which certain costs need to be made for each data sets or each data provider and other costs for the infrastructure in general. The most commonly used SDI funding models are centralized government funding, decentralized government funding (e.g. for each data provider), partnership funding, funding through revenues, and government funding based on donor agencies or on European projects.\r\n\r\nThe shift towards open data and the adoption of open data policies had an important impact on the funding model of many SDIs, as governments and organizations no longer could rely on revenues from selling their data and had to look for other funding models. As a result, new pricing strategies are employed, such as the provision of fee-based supplementary services, such as advice or tailor-made products based on open data. Also freemium/premium models, in which a basic version of the dataset is offered as open data (freemium) but the full dataset is available for a fee (premium), were considered as an alternative approach. In many cases, the loss of revenues was compensated by other funding models, such as increased government funding.","name":"Funding an SDI","selfAssesment":"Completed
"},{"code":"OI4-5b","description":"SDI performance assessment is about collecting, analyzing and providing information on the performance of SDI initiatives. Assessment and evaluations of SDIs are a useful tool for those organizations and people directly involved in these initiatives, but also for researchers, citizens, journalists and other stakeholders. Decision makers and practitioners can use assessments to monitor the progress against the objectives of their SDI initiatives and to identify areas where improvement can be achieved. Assessment also allows to compare and benchmark the performance of different organizations or countries, and to learn from best practices. Finally, assessment also is relevant for accountability, since it enables governments and agencies to be held accountable for their decisions, activities and the resources they have invested. Assessment of SDIs, which deals with the collection and supply of information on the performance of SDI initiatives, should be seen as the first step in a logical consequence of collecting data, integrating this data in policy and management cycles and actually using the information. \r\n\r\nIn the past twenty years, many different SDI assessment frameworks have been developed by researchers and practitioners around the world. Examples of such frameworks are the INSPIRE State of Play Study, the Clearinghouse Suitability Index, the Organisational Maturity Matrix, the SDI Readiness Index, and the INSPIRE Monitoring and Reporting approach. Each of these frameworks focus on particular aspects and components of SDIs. In line with the categorization of open data assessment, also SDI assessments can be divided into three main categories: (1) readiness assessments, (2) implementation or data assessments, and (3) impact assessments. Readiness assessments analyse whether conditions are appropriate, and whether necessary components are in place for developing an SDI. Implementation or Data assessments evaluate whether geospatial data are available and accessible. Impact assessments explore the extent to which SDIs lead to benefits for government, citizens, business and society in general.","name":"SDI performance measurement and assessment","selfAssesment":"Completed
"},{"code":"OI4-6","description":"For a long time, SDI development has focused on the development and implementation of different components with the aim of facilitating the access to and sharing of spatial data. An key challenge in future SDI development will be the integration of these SDI`s in a wider context. In order to optimally take advantage of the data and services provided by an SDI, integrating these data and services into the processes and workflows of public and private organizations will be crucial. The concept of spatial enablement refers to the challenge of developing SDI`s in such a way that they provide an enabling platform that serves the wider needs of society in a transparent manner. Moreover, the diffusion of SDIs, together with the efforts to build a Global Earth Observation System of Systems (GEOSS) and other developments in industry and civil society should be considered as elements in a the realization of a vision on the next-generation Digital Earth.","name":"Next-generation SDIs","selfAssesment":"GI-N2K
"},{"code":"OI4-7","description":"The effective implementation of SDIs requires governance, which includes the structures, policies, actors and institutions by which the infrastructure is managed pertaining to decisions made for accessing, sharing, exchanging and using the relevant available spatial information. While SDIs themselves are considered as initiatives contributing to good governance or effective governance, a key challenge in the establishment of SDIs is the governance of the infrastructure itself. Governance of SDIs is essential for the implementation of different SDI components in a coordinated and consistent manner. The central challenge of governance is reconciling collective and individual needs and interests of different stakeholders in order to achieve common goals. This aims to reduce gaps, duplications, contradictions and missed opportunities in the production, management, sharing and use of the information that tend to occur in a multi-stakeholder environment.\r\n\r\nGovernance can be facilitated through the use of appropriate instruments which extend to various levels of government and take into account the distribution of powers and responsibilities among different actors and institutions with an interest in the infrastructure. The governance instruments should coordinate the activities and contributions of, inter alia, data producers, users, added-value services providers, and other stakeholders. More complex and inclusive models of governance are required to cope with the multi-level nature of SDI implementations of the current generation of SDIs. Effective and inclusive SDI governance structures are needed, that are both understood and accepted by all stakeholders. Governance of SDIs also requires expanding the scope of stakeholders to include the private sector, research bodies and other actors outside the public sector including citizens, to actively promote bottom-up and participatory processes, and to find the appropriate mechanisms and instruments to enable the participation of these non-government actors.","name":"SDI governance","selfAssesment":"Completed
"},{"code":"OI5-1","description":"Within the European Commission there are several key GI players. GIS activities in the Commission started since 1981 (e.g. DG REGIO, Eurostat, ) with the CORINE project, the creation of DG ENV and the creation of the European Environment Agency (EEA). Together with the DG Joint Research Centre (JRC), DG ENV and EEA are in charge of the coordination of INSPIRE: DG Environment acts as an overall legislative and policy co-ordinator for INSPIRE, the JRC acts as the overall technical co-ordinator of INSPIRE and EEA is in charge of several tasks related to monitoring and reporting, and data and service sharing under INSPIRE. Also several other EC institutions are actively involved in GI(S) policies and activities (DIGIT, DG GROW, DG AGRI, DG MOVE and many others).","name":"GI organization at the European Commission","selfAssesment":"GI-N2K
"},{"code":"OI5-2","description":"Although there may be certain differences between countries, in most countries many key organizations in the GIS&T field will be active at the central/federal/national level of government. Especially the traditional institutions for surveying and mapping play a key role in geospatial policies and activities. Several public authorities at the federal level are in charge of the production and maintenance of key reference and thematic data sets. In many countries, these national data producers were the leading actors in the development of national spatial data infrastructures.","name":"Federal and national government organizations","selfAssesment":"GI-N2K
"},{"code":"OI5-3","description":"Local and sub-national governments are often considered among the major users of geographic information in governments, as they often are involved in many different policy areas, in which many problems with a locational component need to be tackled. Geographic data produced and maintained by authorities at lower administrative levels are often more detailed and thus interesting for other users, both within and outside the public sector. As a result, local and sub-national governments are often involved in the establishment of these infrastructures because of the wide range of highly detailed geographic information they produce and manage. As many geographic data are linked to the activities and services of local organizations, the involvement of these organizations in the maintenance of data ensures that these data are up-to-date.","name":"Sub-national and local governments","selfAssesment":"GI-N2K
"},{"code":"OI5-4","description":"The European GIS&T landscape consists of many pan-European organizations and associations promoting the interest of and representing certain stakeholder groups. While some of these organisations are dealing with all sectors and aspects of geographic information, others have a more thematic focus (e.g. remote sensing, topography, geosciences) or represent a particular sector (e.g. research, business). In some cases, their clearly is an overlap in the mission and objectives of different organizations, and some organizations are working in the same field of interest. Some examples of pan-European organizations and associations are AGILE, EuroSDR, EUROGI, and EuroGeographics. Also at international level several membership organizations and associations exist.","name":"Pan-European and global associations and professional organizations","selfAssesment":"GI-N2K
"},{"code":"OI5-5","description":"The geospatial industry consists of companies working with location specific information or services. Within the geospatial sector, several areas of activities can be identified: 1) measuring, collecting and storing of data about geo-objects; 2) processing, editing, modelling, analyzing and managing that data; 3) presenting, producing and distributing the data; and 4) advising, educating, researching and communicating about processes and use of geo-information products and services. The sector consists of both small-and-medium-sized enterprises but also big companies, including surveyors, census hard-copy map providers, aerial photos providers, base map data providers, satellite and remote sensing imagery providers, software developers (GIS-related products and services providers as well as satellite image programming platform providers) and several others.","name":"The geospatial industry","selfAssesment":"GI-N2K
"},{"code":"OI5-5b","description":" ","name":"The geospatial community","selfAssesment":"GI-N2K
"},{"code":"OI5","description":"Several types of organizations play a key role in the execution and coordination of geospatial activities in society. Typically, a distinction is made between data providers and data users, while coordinating organizations exist to coordinate and support the geospatial activities of professionals and entities using GIS&T. Governments are often considered as the major users and producers of spatial data and spatial information. Within the public sector, spatial data are collected and used in different thematic areas and at different administrative levels (from local to global). However, the needs, interests, and capacities of organizations at each of these levels will be different, as well as their role in the development of spatial data infrastructures, and the execution of geospatial activities in general. Also the geospatial industry will exist of both data providers and data users, but also of organizations delivering products and services to support the collection and use of spatial data. Other key organization in the GI domain are professional organizations and associations, bringing together and representing the needs of organizations of a particular sector and/or geographic area.","name":"Organizations in the GIS and T domain","selfAssesment":"GI-N2K
"},{"code":"OI6-1","description":" ","name":"Federal agencies and national and international organizations and programs","selfAssesment":"GI-N2K
"},{"code":"OI6-2","description":" ","name":"State and regional coordinating bodies","selfAssesment":"GI-N2K
"},{"code":"OI6-4","description":" ","name":"Publications","selfAssesment":"GI-N2K
"},{"code":"OI6","description":"A number of organizations (public, private, and non-profit) exist to coordinate, inform, and support geospatial activities of professionals, and entities using GIS and T. Informed geospatial professionals and organizations are familiar with the mission, history, constituencies, modes of operation, products, and levels of success of these organizations.","name":"Coordinating organizations (national and international)","selfAssesment":"GI-N2K
"},{"code":"PP","description":" ","name":"Physical principles","selfAssesment":" "},{"code":"PP1-1-1","description":"Electromagnetic radiation travels in wave form. All electromagnetic waves travel at the speed of 299.793 km/sec in a vacuum and very nearly the same speed in air. In quantum physics electromagnetic radiation is also described in terms of particles called photons whose energy is given by the equation E = hf where h is the Planck constant and f the frequency of corresponding wave. Electromagnetic wave propagation is fully described by the Maxwell Equations that unified in 1860s the laws of electricity and magnetism.","name":"Electromagnetic Waves and Photons","selfAssesment":"Planned
"},{"code":"PP1-1-10","description":"The solar constant S is a quantity denoting the amount of total (i.e., covering the entire solar spectrum) solar energy reaching the top of the atmosphere. It is defined as the flux of solar energy (energy per unit time) across a surface of unit area normal to the solar beam at the mean distance between the sun and the earth. Solar insolation is defined as the flux of solar radiation per unit of horizontal area for a given locality. It depends primarily on the solar zenith angle and to some extent on the variable distance of the earth from the sun. It can be computed as a function of latitude and the time of year taking into account of the secular variations of Earth's orbit eccentricity e, the oblique angle ε, and the longitude of the perihelion relative to the vernal equinox ω. The daily insolation is the total solar energy received by a unit of area per one day. It may be calculated by integrating total insolation over the daylight hours.","name":"Solar constant, solar insolation, daily insolation","selfAssesment":"In Progress
"},{"code":"PP1-1-11","description":"Earth's itself represents the second (after Sun) most powerfull natural source of e.m. radiation for EO. Its average emittance can be approximated by that of a blackbody at about 290 K. Even if very less powerfull than Sun such a source is available for EO day and nigth. The maximum of its emission falls in the thermal infrared (around 10 micron) being Earth's emission trascurable in the VIS-SWIR range.","name":"Earth's radiation (intensity, spectrum, etc.)","selfAssesment":"Planned
"},{"code":"PP1-1-2","description":"In principle, the frequency f (and the wavelength c/f) of an electromagnetic wave can take any value and the whole range of possible frequencies is called the electromagnetic spectrum. Different regions of the spectrum are conventionally given names (such as visible, radio waves, ultra-violet radiation and so on). Optical (from the visible to the infrared range) and microwaves are the most important spectral region for remote EO systems.","name":"Electromagnetic spectrum","selfAssesment":"In Progress
"},{"code":"PP1-1-3","description":"Maxwell equations are a set of coupled partial differential equations that contains the fundamentals of electricity and magnetism. These equation provide electromagnetic waves that propagate into the space at the speed of the light. Increasing the wavelength there are gamma rays, X-rays, ultraviolet, (visible) light, infrared, microwaves and radio waves.","name":"Maxwell Equations and EM waves' propagation","selfAssesment":"Planned
"},{"code":"PP1-1-4","description":"Planck's law is a mathematical relationship for the spectral radiance emitted by a blackbody (i.e. a body that absorbs all radiant energy falling on it) at a given temperature as a function of frequency or wavelength. Wien’s displacement law is the relationship between the temperature of a blackbody and the wavelength at which it emits the most radiation. Wien found that the product of the peak wavelength and the temperature is an absolute constant.","name":"Planck law for the black body. Wien's displacement law","selfAssesment":"Planned
"},{"code":"PP1-1-5","description":"The Rayleigh–Jeans Law is an approximation of the Planck’s law for a blackbody through classical arguments. It states that emitted radiance is directly proportional to blackbody temperature and it fits with experimental measurements only at large wavelengths.Wien’s approximation is used to describe the spectrum of thermal radiation with thermodynamic arguments. The equation accurately describes the short wavelengths spectrum but it fails in fitting experimental data for emissions at long wavelengths.","name":"Rayleigh-Jeans approximation. Wien's approximation","selfAssesment":"Planned
"},{"code":"PP1-1-6","description":"The total radiant intensity B(T ) of a blackbody at the absolute temperature T can be derived by integrating the Planck function over the entire wavelength domain from 0 to∞. Since blackbody radiation is isotropic, the flux density emitted by a blackbody is therefore F = π B(T ) which is proportional to the fourth power of the absolute temperature T through the Stefan-Boltzmann constant σ = 5.67 × 10−8 J m−2 sec−1 deg−4.\r\nKirchoff's law establishes that for a medium at the thermodynamic equilibrium, the emissivity ε of a given wavelength λ (defined as the ratio of its emitting intensity IE to the Planck function B), is equal to the its absorptivity, A at the same wavelength λ (defined as the ratio of its absorbed intensity IA to the Planck function B). Hence ε=A at each fixed λ, for a blackbody ε=A=1 at whatever λ. Kirchoff's law is valid also in Local Thermodynamic Equilibrium (LTE) conditions as the ones usually occurring in (small volumes of) the Earth's atmosphere even in the most turbulent conditions.","name":"Stefan–Boltzmann law. Kirchoff law","selfAssesment":"Planned
"},{"code":"PP1-1-7","description":"All bodies at a temperature T>0 K emit electromagnetic radiation at all wavelengths (thermal emission). Such emission at each wavelength is increasing with T and it is maximum for Black Bodies whose spectral emittance I(λ,T) (at each prefixed T and wavelength λ) is defined by the Planck function B(λ,T). Generic bodies are expected to thermally emit less than a black body (having the same temperature T) at whatever wavelength. Spectral emissivity ε(λ) is defined as the ratio of the spectral radiance I(λ,T) emitted by a generic body and the one emitted by a Black Body at the same temperature, i.e. ε(λ)= I(λ,T) / B(λ,T). By definition its value is less or equal (Black Body) than 1. The spectral emissivity concept allows to describe in a simple way the spectral radiance I(λ,T) thermally emitted by a body at a temperature T by I(λ,T)= ε(λ)*B(λ,T). It is possible to invert the Planck Function to obtain from the emitted radiance at a prefixed wavelength the temperature T=f(B, λ) of the emitting Black Body. If in such expression the spectral radiance I emitted by a generic body is used instead than B, the resulting temperature, Tb=f(I, λ), is named Brigthness Temperature being Tb<=T (with Tb=T in case the emitting body is a Black Body). The concept of Brigthness Temperature is substantially a different way to measure the spectral radiance of a generic body. It is usually preferred (for instance calibrating Thermal InfraRed – TIR – satellite images) because the interpretation of such a digital image is much more intuitive than when spectral radiances are used instead. In fact, as at each prefixed temperature generic bodies are less emitting than Black Bodies, wherever across a digital satellite image we consider the values of reported Tb, we can say that the actual temperature T of the corresponding emitting ground resolution cell is not less than Tb.","name":"Concepts of Spectral Emissivity and Brightness Temperature.","selfAssesment":"Completed
"},{"code":"PP1-1-8","description":"The nuclear fusion of Hydrogen into Helium occurs in central part of the Sun (“Core”). Outside, the energy transfer is dominated by radiative process (“Raditive zone”) then by convection (”Convective zone”). Solar radiation at the Top of the Earth Atmosphere comes from the outer layer of the sun, the photosphere","name":"Solar structure","selfAssesment":"Planned
"},{"code":"PP1-1-9","description":"Sun represents the most powerfull natural source of e.m. radiation for EO. Its emittance can be approximated by that of a blackbody at about 5900 K but just its reflected component (SOR) is actually available (and just during daytime) for EO. The maximum of SOR falls in the visible spectral range. Its contribution in the thermal infrared range is transcurable but in the medium infrared SOR is still significant enough and superimposed to Earth's thermal emission.","name":"Solar radiation at the Top of the Atmosphere. Solar spectrum","selfAssesment":"Planned
"},{"code":"PP1-1","description":"The electromagnetic field propagates through the space radiating energy: the electromagnetic radiation. The classical theory describes this energy as electromagnetic waves which represent the oscillations of electric and magnetic fields. In the quantum mechanics theory EM radiation consists of photons, quanta of the electromagnetic force, responsible for all electromagnetic interactions","name":"EM radiation","selfAssesment":"Planned
"},{"code":"PP1-2-1","description":"The study of the absorbption/emission of electromagnetic radiation by atoms. Depending on the atomic number characteristic frequency or wavelength are absorbed or emitted. Since each element has a characteristic spectrum of absorbed/emitted wavelengths (spectral signature), atomic spectroscopy allows the determination of elemental compositions even of remote objects (e.g. stars, galaxies, etc.).\r\nStarting from the simple Bohr’s model it is possible to predict quite exactly the frequencies of e.m. radiation selectively absorbed/emitted by all atoms. Depending on the atomic number Z, characteristic frequencies f are absorbed or emitted by atoms corresponding to the electronic transitions from different energetic (quantized) states following the Bohr’s condition: fab=(Eb- Ea)/h, being Ei=-cost∙Z2/(ni)2 the electron energy corresponding to the state/level i (principal quantic number ni). By this way each atomic species has a characteristic spectrum of absorbed/emitted frequencies (atomic spectral signature) so that atomic spectroscopy allows the determination of elemental compositions even of remote objects. By this way the existence of Helium was discovered in the 1968 by Jansen and Lockyer in the Sun photosphere well before its discover on the Earth, and the knowledge of the chemical composition of stars and galaxies was possible well before the end of XIX century. Atomic spectroscopy provides a simple and powerful introduction (through the explanation of the more complex interactions of e.m. radiation with molecules and solid matter) to the fundamental concepts of spectral signature (which is at the base of most of the applications of aerial remote sensing of the Earth’s surface) and atmospheric windows (important for the design of optical sensors devoted to remotely sense Earth’s surface) being moreover propaedeutic to the understanding of methods for the atmospheric vertical sounding based on the concepts spectral lines broadening and related weighting functions.","name":"Atomic spectroscopy","selfAssesment":"Completed
"},{"code":"PP1-2-10","description":"The Rayleigh roughness criterion is a widely used means to estimate the degree of roughness of a considered surface. Considering the phase difference between two rays scattered from separate points of the surface, this depends on the roughness height, the incident angle and, inversely, on the radiation wavelenght. The Rayleight criterion states that a surface can be considered as smooth if the phase difference is less than pigreco/2 radians.","name":"The Rayleigh roughness criterion","selfAssesment":"Planned
"},{"code":"PP1-2-11","description":"The Bidirectional Reflectance Distribution Function (BRDF) is defined as the quotient between the spectral radiance reflected by a sample and the spectral irradiance from the source that illuminates it. It depends on both the incidence and viewing angles. From this point of view it represents an absolute definition of reflectance whose value, as is known, depends on the geometry of the illumination and observations directions.","name":"Bidirectional Reflectance Distribution Function (BRDF)","selfAssesment":"Planned
"},{"code":"PP1-2-12","description":"Measurements of BRDF allow to compare spectral signatures obtained in different laboratories in an optimal way. However its measure require well calibrated sources and quite expensive laboratory equipments. The concept of BRF (Bidirectional Reflectance Factor) allows a more simple, indirect, measurement of BRDF by using a reference sample (highly reflective so usually named \"white reference WR\") of known BRDF and two subsequent measurements of reflected radiance (one from the WR, one from the sample) obtained under identical illumination conditions. In these conditions results BRDF(sample)=BRF(sample)xBRDF(WR)","name":"Bidirectional Reflectance Factor (BRF)","selfAssesment":"Planned
"},{"code":"PP1-2-2","description":"The molecular absorption spectral corresponds to the wavelengths from 190 nm up to 1000 nm and it interprets the measured absorption of radiation, when it is passing through a gas, a liquid or solid. Their absorbed energy in different states can be approximated by electronic, vibrational and rotational energy","name":"Molecular absorption spectra","selfAssesment":"Planned
"},{"code":"PP1-2-3","description":"The spectral line is a result of interactions of photon with a quantum system, while it extends over a range of frequencies. The center wavelength of its energy levels may be changed due to Broadening, namely collisions of atoms and molecules or their differences in thermal velocities.","name":"Line shape and (natural, pressure, Doppler) broadening","selfAssesment":"Planned
"},{"code":"PP1-2-4","description":"When the altitude ranges from about 20 to 50 km, spectral line shape is determined by both collisions (Pressure Broadening) and differences in thermal velocities (Doppler broadening). This shape is referred to as the Voigt profile and it satisfies the condition of normalization.","name":"Voigt's line profile","selfAssesment":"Planned
"},{"code":"","description":" ","name":" ","selfAssesment":" "},{"code":"PP1-2-6","description":"The emitting ability of a body surface is described by emissivity, ε(λ). This will vary with wavelength and viewing angle. A body is considered to be an ideal radiator when it totally absorbs and then reemits all energy incident upon it. Such a body is called black body and its emissivity is equal to one. Emissivity can be defined as the ratio of spectral exitance, M(λ,T), from an object at wavelength λ and temperature T, to that from a blackbody at the same wavelength and temperature, MBB(λ,T). The concept of graybody has also been introduced as the body having an emissivity of less than 1 and constant at all wavelengths.","name":"Concepts of Spectral Emissivity","selfAssesment":"Planned
"},{"code":"PP1-2-7","description":"\"Radiation that is not absorbed or scattered in the atmosphere can reach and interact with the Earth's surface. There are three (3) forms of interaction that can take place when energy strikes, or is incident (I) upon the surface.\r\n These are: absorption (A); transmission (T); and reflection (R). The total incident energy will interact with the surface in one or more of these three ways. The proportions of each will depend on the wavelength of the energy and the material and condition of the feature. Absorption (A) occurs when radiation (energy) is absorbed into the target while transmission (T) occurs when radiation passes through a target. Reflection (R) occurs when radiation\r\n \"\"bounces\"\" off the target and is redirected. The reflectance R is defined by the ratio of reflected radiant power to incident radiant power. The transmittance T of a medium is defined by the ratio of transmitted radiant power to incident radiant power. The absorptance A of a medium or target is defined by the ratio of absorbed radiant power to incident radiant power. Conservation of energy require that, at a certain wavelenght: R+T+A=1. To express the circumstance that the reflection can occurre in different direction as the surface deviates from a specular one, becoming rough the concept of surface scattering has been introduced. However, the concept of scattering concerns mainly atmopheric interaction with ELM and radar systems.\"","name":"Complex dielectric constants and refractive indices","selfAssesment":"Planned
"},{"code":"PP1-2-8","description":"The complex part nc of the refraction index n determines how far an e.m. wave of wavelength λ can survive crossing a specific medium. The attenuation length la is the distance after that the amplitude of an e.m. signal reduces its value by an amount of 1/e. For instance the amplitude of the Electric field E(z) of an e.m. wave proceeding along the z direction is decreasing as exp(-z/la) being la=λ/(2𝜋 nc) the attenuation length associated to that specific material (nc) and wavelength λ. This way attenuation length in water can be of hundreds of meters in the visible range and just few microns in the microwaves. So that penetration of radiation in the matter depends on both, the specific (dielectric) properties of the matter (through nc) AND the specific wavelength λ of considered e.m. signal.","name":"EM rad. penetration in the matter: Attenuation Length","selfAssesment":"Planned
"},{"code":"PP1-2-9","description":"EM radiation impinging a rough surface is (partly) reflected back (scattering). Lambertian surfaces produce a diffuse scattering (i.e. radiation is reflected similarly in all direction) and then appear equally bright from all directions, whereas specular surfaces behave like a mirror, with reflected radiation all aligned in one direction, with the reflection zenith angle equal to the incident angle of incoming radiation. Generally, the degree of \"roughness\" of a surface determines if it behaves like a Lambertian or a specular surface.","name":"Scattering from rough surface: Lambertian and specular surfaces.","selfAssesment":"Planned
"},{"code":"PP1-2","description":"Radiation can be absorbed, scattered, emitted and transmitted by the matter depending on the different parts of the electromagnetic spectrum and the matter peculiarities (Atoms, molecules, particles and surfaces) and its physical state (Temperature, Concentration, Shape, Roughness). The interaction between radiation and matter depends strongly on the wavelength of radiation.","name":"Radiation - Matter interaction","selfAssesment":" "},{"code":"PP1-3-1","description":"The first basic radiometric quantity is the radiance (Iλ) and it is defined as the ratio of the differential radiant energy to the product of effective area with the time interval, wavelength interval and differential solid angle. Based on Iλ, the monochromatic and total flux density can be calculated.","name":"Radiometric quantities: radiance, irradiance, flux, brightness, emittance, luminosity,etc.","selfAssesment":"Planned
"},{"code":"PP1-3-2","description":"The attenuation of radiation emitted from a source decreases with the square of the distance from its center based on inverse square law. It considers that the size of the sources increases with the square of their radius, causing the same rate of attenuation in flux density.","name":"Decay of the emittance with the square of distance from the source","selfAssesment":"Planned
"},{"code":"PP1-3-3","description":"The relative amount of electromagnetic radiation reflected (absorbed, transmitted, emitted) by the matter at different wavelengths depends on its specific chemical composition and physical properties. The plots of corresponding physical quantities (reflectance, absorbance, transmittance, emissivity) against wavelength, are termed spectral signatures of the specific matter under study. In principle the analysis of spectral signatures obtained by multispectral EO sensors could allow us to identify/discriminate different cover types.\r\nThe interpretation of spectral signatures requires to well understand the e.m. radiation-matter interaction process. In very simple term we expect that incident radiation I(λ)can be reflected, absorbed or transmitted by the matter so that for the energy conservation should be: \r\n\r\n\r\nI(λ)=I(λ,R)+I(λ,A), I(λ,T) \r\n\r\n \r\nbeing I(λ,R), I(λ,A) and I(λ,T) the reflected, absorbed and transmitted fraction of I(λ). From the previous relation descends (dividing both members for I) that:\r\n\r\n\r\n1=R(λ)+A(λ)+T(λ)\r\n\r\n\r\nbeing:\r\n\r\n\r\nR(λ)=I(λ,R)/I(λ) named Reflectance\r\nA(λ)=I(λ,A)/I(λ) named Absorbance\r\nT(λ)=I(λ,T)/I(λ) named Transmittance\r\n\r\n\r\nThey are all specific properties of the considered matter and are not independent each others.\r\nIn particular for an opaque medium with T(λ)=0 it is:\r\nR(λ)=1-A(λ)","name":"Spectral Signatures of the matter","selfAssesment":"Completed
"},{"code":"PP1-3-4","description":"Vegetation, water and soil represent the most common cover types of Earth surface. Their reflectances in the VIS/NIR/SWIR spectral range, plotted against wavelength in the 0,4-2,5 micron, represent the most important (basic) spectral signatures for whatever application devoted to Earth surface study. Other spectral signatures (e.g. in emissivity) in the Thermal InfraRed range are particularly important to infer specific properties of Mineral and Rocks (ref. [PP1-3-5] Spectral Signature of Mineral and Rocks). In order to discriminate among such basic cover types, the (ref. [IP3-1-2-3]) NDVI (Normalized Difference Vegetation Index) is the most simple and powerful diagnostic tool in the VIS/NIR spectral range \r\nNDVI values ranging between the values -1 and +1, are higly positive for fully vegetated (up to NDVI=1) or partly vegetated (NDVI>0,3) targets, still positive (>0) for bare soils, negative for water bodies. Values around zero are expected for clouds thanks to their similarly high reflectances both in the NIR and VIR spectral bands (ref. [PP1-3-6] Spectral Signature of Clouds). \r\n\r\nVegetation. a) in the visible range most of the incomig radiation is adsorbed by the photosynthetic process, transmittance is very low. The residual reflected radiation has a small peak of reflectance around 0.5 microns which is responsible of the green colour associated to vegetation by the human vision sytem (limited to the VIS spectral range); b) in the NIR range vegetation exhibits its higher reflectance together its higher transmittance (very low absorbance) so that leaf density can be estimated thanks to the the contributes (decreasing with depth) of underlaying leaf layers; c) in the SWIR spectral range (in particular in the water bands around 1,4 and 1,9 microns) it is possible to appreciate the vegetation water content. As much it is, as more incident radiation is absorbed and less is the reflected fraction of radiation.\r\nBare Soil. Spectral reflectance is normally increasing moving from the VIS to the SWIR spectral region. Water features around 1,4 and 1,9 microns give information on soil water content (see before). Others specific features are described in [PP1-3-5] Spectral Signature of Mineral and Rocks\r\n\r\nWater. Spectral reflectance of clean deep water is quite low reaching quickly the zero value as soon as wavelengths passe microns. However it is important to note that such a very low reflectance is due to a very high transmittance in the VIS range and to a very high absorbance in the NIR/SWIR regions (ref. [PP2-2-5-2] Attenuation Lenght and Penetration Depth). This means that water is quite transparent in the VIS spectral range (so that, in case of shallow waters, measured reflected radiance can be significantly increased by the contribution of bottom of the sea). Water is completely opaque, instead, in the NIR/SWIR. In this spectral region, even in presence of shallow waters, the presence of suspended matter (that increases the measured reflectance both in the VIS and NIR/SWIR ranges) can be better discriminated (than in the VIS) from the contribute of the bottom of the sea that, in this spectral range, is zero.","name":"Spectral Signature of Vegetation, Water, Soil","selfAssesment":"Completed
"},{"code":"PP1-3-5","description":"Spectral signatures of rocks and mineral provide information on their chemical composition and crystal properties, grain size and roughness over a wide range of wavelengths from the visible to the thermal infrared. For example, the iron-rich minerals are characterized by low reflectance (high absorbance) below 0.7 μm, carbonates show a typical absorbance feature around 2.3 μm, the water content can be instead evaluated by the depth of absorptionat 1,4 and 1,9 μm. Particualrly importantant are the spectral signatures which rocks exhibit in emissivity in the Thermal Infrared (TIR) spectral region. For instance looking at the shift of lower emission band of SiO2 from around 9 up to 10 μm as far as its concentration reduces e.g. moving from granite to peridotite.","name":"Spectral Signature of Mineral and Rocks","selfAssesment":"Planned
"},{"code":"PP1-3-6","description":"The determination of spectral signatures for scenes with a high degree of spatial complexity is considered as one of the most persistent problems in atmospheric radiation, especially at the surface, where satellite observations can only be used indirectly to infer energy budget terms. In the shortwave (solar) spectral range, it is especially challenging to derive consistent albedo, absorption, and transmittance from spaceborne, aircraft, and ground-based observations for inhomogeneous cloud conditions and is closely related to the long-debated discrepancy between observed and modeled cloud absorption.\r\nThe cloud spatial structure is revealed as a spectral signature in shortwave irradiance through the physical mechanism of molecular scattering. However, the study of specific mechanisms is rather complex since the satellite instruments cannot completely describe the spatial distribution of cloud and the variability of scattering and absorption properties. For this reason, several studies deal with the problem described above, as a challenge for estimating spectrally the cloud optical properties (such as the albedo and transmittance) as well as scattering and absorption processes taking place in the cloud system with adequate resolution. Hence, the above mechanisms can be described using three dimensional (3-D) radiative transfer models. Those models receive auxiliary information from cloud imagery and radar observations. The molecular scattering (Rayleigh) was the only one directly dependent on the wavelength of the vertical radiative flux. Moreover, it was considered as a spectral perturbation of backtracked horizontal exchange of solar radiation due to the inhomogeneous distribution of cloud. The horizontal photon transport is highly correlated to its spectral dependence.\r\nConcerning the presence of cirrus or ice clouds, the effect of their phase function and the vertical distribution were evaluated on the scattering of far infrared radiation. Thus, the accurate reconstruction of the phase function of cirrus clouds potentially indicates the need for application of a radiative transfer model. This specific module necessarily includes scattering parameters, while the accuracy of its calculations needs to be verified against real measurements. \r\nFor several applications the preliminary detection of those portions of the scene affected by the presence of clouds (cloud detection) is mandatory. For studying properties of Earth's surface targets affected by the presence of clouds are flagged just to exclude them by further analyses. In some case clouds themselves are the object of interest. In both cases the identification of clouds (and their classification) is mostly done by using (combination of) specific spectral signatures. Generally speaking clouds are highly reflecting VIS/NIR radiation showing (due to their heigth) brigthness temperatures (in the TIR region) lower than underlying surfaces. Thin or semi-transparent clouds are still detectable for their higher reflectance over the sea which represents a quite dark bacground in the VIS/NIR/SWIR region. Over land (much more reflecting) such a test is not more efficient and more sophisticated tests (e.g. Brigthness Temperature Difference in the split window bands around 11 and 12 microns) are required. In presence of very cold, high reflective backgrounds (e.g. snow, glaciers, etc.) both tests on the VIS reflectance and on TIR brigthness temperature could fail. More specific tests exploiting the reflectance drop of snow in the SWIR (where clouds are still saving their higher reflectance) helps to discriminate the presence of clouds from clear sky conditions even over a snow background. In the microwaves clouds are quite transparent except when coupled with coarse particles related to rain, snow, hailstones (precipitating clouds). In that case Mie scattering dominates strongly reducing the amount of radiance collected at the sensor (lower brigthness temperature in the microwave spectral range).","name":"Spectral Signature of Clouds","selfAssesment":"Completed
"},{"code":"PP1-3-7","description":"If the resolution is low enough that disparate materials can jointly occupy a single pixel, the resulting spectral measurement, made by the sensor, will be the composite of the individual spectra. Under the linear mixing model (LMM), each observed spectrum in each pixel of a given image is assumed to result from the linear combination of the N endmember spectra present in the pixel. The reflectance spectrum of each endmember is weighted by the fractional area coverage of it in the pixel. \r\nHowever, if the components of interest in a pixel are in an intimate association, like sand grains of different composition in a beach deposit, light typically interacts with more than one component as it is multiply scattered, and the mixing between these different components are nonlinear. Such nonlinear effects have been recognized in spectra of: particulate mineral mixtures, aerosols and atmospheric particles, vegetation and canopy. In this case a non-linear mixing model (NLMM) should be applied. To summarize: Linear mixture model assumes that endmember substances are sitting side-by-side within the pixel; Nonlinear mixture model assumes that endmember components are randomly distributed throughout the pixel, causing multiple scattering effects. \r\nIn the linear mixing case, the basic premise of mixture modelling is that within a given scene, the surface is dominated by a small number of distinct materials that have relatively constant spectral properties. These distinct substances (e.g., water, grass, mineral types), characterized by a well-defined spectral signature are called endmembers, and the fractions in which they appear in a mixed pixel are called fractional abundances. Then, finding the endmembers that can be used to ‘unmix’ other mixed pixels becomes a crucial issue. \r\nIdentify fractional abundances of distinct substances from the spectral signal of a mixed pixel is one of the application in which hyperspectral images can provide an valuable support.","name":"Composition of spectral signatures (Linear Mixing)","selfAssesment":"Completed
"},{"code":"PP1-3-8","description":"One of the most common ways to classify remote sensing systems consists in distinguishing them into the passive systems, which detect naturally occurring radiation, and the active systems, which emit radiation and analyse what is sent back to them. The passive systems can be further subdivided into those that detect radiation emitted by the Sun (this radiation consists mostly of ultraviolet, visible and near-infrared radiation), and those that detect the thermal radiation that is emitted by all objects that are not at absolute zero (i.e. all objects). For objects at typical terrestrial temperatures, this thermal emission occurs mostly in the infrared part of the spectrum, at wavelengths of the order of 10 μm (the so called thermal infrared region), although measurable quantities of radiation also occur at longer wavelengths, as far as the microwave part of the spectrum. Active systems can, in principle, use any type of electromagnetic radiation. In practice, however, they are restricted by the transparency of the Earth’s atmosphere.","name":"Definition of active and passive remote sensing techniques","selfAssesment":"Planned
"},{"code":"PP1-3","description":"Measuring the signal emitted (received) by a radiation source (detector)","name":"Sensing of EM radiation.","selfAssesment":"Planned
"},{"code":"PP1-4-1","description":"The radiation traversing a medium may be attenuated, due to the density, mass scattering and absorption of material. In contrast, the radiation’s intensity can be strengthened by emissions from the material plus multiple scattering from all directions. The above interactions follow the general radiative transfer equation.","name":"General equation of radiative transfer.","selfAssesment":"Planned
"},{"code":"PP1-4-10","description":"The inversion approach aims at retrievals of trace gas concentration and temperature profiles of atmospheric state, namely the modeled state vector, based on the measured radiance transmitted or reflected or scattered (SCIAMACHY spectrometer) by the Earth-Atmosphere system. The Differential Optical Absorption Spectroscopy (DOAS) is the solution of inverse approach.","name":"Retrieval of atmospheric parameters (vertical profiles of temperature and of main chemical constituents) by inversion of radiances measured from satellites","selfAssesment":"In Progress
"},{"code":"PP1-4-2","description":"In the field of radiation scattering and absorption, the cross-section, analogous to the shape of a particle, is used to determine the amount of energy diverted from the original beam by the particle. This parameter is called mass cross section, when it is in reference to unit mass (cm2g-1).","name":"Cross Section of Extinction (Absorption, Scattering) per Mass Unit","selfAssesment":"Planned
"},{"code":"PP1-4-3","description":"When the mass cross-section is multiplied by the density of particle, the extinction coefficient is calculated, namely the sum of absorption and scattering coefficient, whose the units are related to length. Especially, the absorption coefficient (k (cm•atm)-1) is the product of strength of absorption with the Loschmidt’s number.","name":"Absorption Coefficient","selfAssesment":"Planned
"},{"code":"PP1-4-4","description":"The source function, Jλ, has units of radiant intensity and it is defined as the ratio of the source function coefficient to the mass extinction cross section. The Jλ determines the intensity that are acquired in a homogeneous medium.","name":"Source Function (Coefficient)","selfAssesment":"Planned
"},{"code":"PP1-4-5","description":"If the monochromatic beam (Iλ) of radiation attenuates due to absorption, but it remains unaffected from emission contributions and multiple scattering of homogeneous Earth-Atmosphere system, it can be expressed by Beer-Bouguer-Lambert law. This law also expresses the monochromatic optical depth (τλ) and transmissivity (Τλ) of the above system.","name":"Beer-Bouguer-Lambert law.","selfAssesment":"Planned
"},{"code":"PP1-4-6","description":"The Schwarzschild equation provides an interpretation for the infrared radiation that undergoes the absorption and emission processes simultaneously, while the scattering efficiency is considered negligible. Hence, its solution is obtained by the integrating of relationship that invokes Kirchhoff’s law and summing the two above processes along a ray path.","name":"Schwarzshild equation and its solutions","selfAssesment":"Planned
"},{"code":"PP1-4-7","description":"The Atmosphere-Earth system that monochromatic beam (Iλ) of radiation travels, is called optical path. It expressed by optical path length, namely the product of geometric length and the refractive index of medium. It determines the optical thickness, namely a measure of the cumulative depletion of Iλ directed in straight-downward.","name":"Concepts of Optical path and Optical thickness.","selfAssesment":"Planned
"},{"code":"PP1-4-8","description":"Radiative transfer is highly nonlinear and non-local against the cloud structure at a high spatial resolution. Hence, a Monte Carlo approach is used for the representation of cloud structure and interactions between photons and clouds. This approach is more efficiency than the method of representing clouds as horizontally homogeneous.","name":"Radiative transfer in presence of clouds","selfAssesment":"Planned
"},{"code":"PP1-4-9","description":"The line by line radiative transfer model (LBLRTM) is an accurate and flexible model for the estimation of the spectral radiance and transmittance over the full spectral range (microwave to ultraviolet), using a first-order perturbation algorithm. It is considered as the basic tool for the creation of retrieval algorithms employed by the ground-based and satellite instruments, while the latest updates in spectroscopic factors are derived from the high-resolution transmission molecular absorption (HITRAN) database. A LBLRTMs is continuously updated and validated against highly accurate spectral measurements. Its errors are related to uncertainties in line parameters and shape. The shape is a Voigt line which is a linear combination of approximating functions for the description of all atmospheric levels. LBLRTML is combined with the continuum MT_CKD (Mlawer, Tobin, Clough, Kneizys, Davies) model which in turn includes the atmospheric constituents of water vapor, carbon dioxide (CO2), molecular oxygen (O2), molecular nitrogen (N2), and ozone (O3), and the molecular extinction process (Rayleigh scattering). A recent version of LBLRTM calculates analytically the Jacobians equations for obtaining meteorological parameters. Also, this model version retrieves the optical parameters of clouds related to scattering and emissivity. The LBLRTM is widely used in radiation and climate applications. It is capable to calculate the absorption degrees of various atmospheric constituents which are utilized afterward from climate and weather prediction models for estimating the broadband solar irradiance and the heating rates. Additionally, the complex radiative transfer models with fast computational time are initiated and trained by the LBRTM, since they are used subsequently on numerical weather prediction (NWP) assimilation systems.","name":"Line-by-line radiative transfer models","selfAssesment":"completed
"},{"code":"PP1-4","description":"Theory of radiative transfer describe the transmission of the electromagnetic radiation through a medium.","name":"Fundamentals of Radiative Transfer","selfAssesment":"Planned
"},{"code":"PP1-5-1","description":"Light is the electromagnetic phenomenon we exploit for remote sensing. Its basic laws concerning the transmission through the interface of two different media are governed by reflection and refraction. Reflection governs the way light is backpropagated and refraction dictates how light is transmitted. Refraction is related to the real refractive index of a medium. Dispersion relates to the way the light of a given wavelength is transmitted. Since light of different wavelengths are transmitted at different angles, the phenomenon leads to the concept of dispersion. These three simple principles are at the core of the understanding technology of remote sensing.","name":"Reflection, Refraction and Dispersion of the light","selfAssesment":"Planned
"},{"code":"PP1-5-11","description":"The theory provides the bulk of physical explanation and related laws, which govern absorption, emission and spontaneous emission from the ordinary matter. Early laws about thermal radiation and the blackbody emission, such as Rayleigh-Jeans, Wien, Planck laws are cast in a single theory and formalism through the concept of quantized energy at the level of atoms emission/absorption of light. Explain the modern concept of quantum optics and their link to the design of modern devices for the measurements and/or production of coherent light.","name":"Einstein’s theory of radiation: photons, photoelectric effect, absorption, emission; Stimulated emission: the laser","selfAssesment":"Planned
"},{"code":"PP1-5-14","description":"Solid state modern detectors rely on non-metal junction, which can be designed and operated to yield a bandgap energy according to the spectral range (infrared, visible, UV) to be detected. The basic principles of how these devices are designed and fabricated is important to develop and design new sensors useful for the various remote sensing applications.","name":"Electric conduction in solids: semiconductors, p-n- junction, diode and transistors","selfAssesment":"Planned
"},{"code":"PP1-5-15","description":"Modern detectors of electromagnetic radiation in the infrared, VIS, UV spectral regions are designed and fabricated based on suitable junctions or electro-optical devices. The performance of these systems needs to be assessed in terms of accuracy and precision. This is made through figures of merit such as Noise Power Spectral Density, Noise Equivalent Power. Detectors can be classified as photovoltaic or photoconductive devices, which allows to better classify the various noise sources: shot noise, 1/f noise, Johnson noise, generation-recombination noise.","name":"Photovoltaic and photoconductive detectors: MCT, InSb, bolometer, CCD devices","selfAssesment":"Planned
"},{"code":"PP1-5-2","description":"Interference and diffraction are phenomena related to the wave nature of electromagnetic radiation. They explain how light propagates in presence of obstacles. These phenomena are largely used in the fabrications of optical systems for remote sensing: e.g. radiometers and spectrometers.","name":"Interference and Diffraction.","selfAssesment":"Planned
"},{"code":"PP1-5-3","description":"The Michelson interferometer is the instrument that exploits and evidence the interference of light. A masterpiece of experimental physics, the Michelson interferometer is the key architecture of the modern optical interferometers, which make it possible to measure the emitted Earth spectrum with hyperspectral resolution.","name":"Michelson Interferometer","selfAssesment":"Planned
"},{"code":"PP1-5-4","description":"The celebrated principle of constant speed of light and independence of the reference frame is important to explain the basic principles of instruments such as the Michelson interferometer. The basic physics theory to explain how electromagnetic fields propagates and the inter-relationship between electric and magnetic fields.","name":"Special relativity; Electromagnetic fields equations and propagations","selfAssesment":"Planned
"},{"code":"PP1-5-6","description":"Helmotz’s wave equation arises in light and acoustic scattering problem and yields the general framework to investigate and analyse the scattering of time-harmonic acoustic and electromagnetic waves by a penetrable inhomogeneous medium.","name":"Helmotz’s equations; Scattering from inhomogeneous media.","selfAssesment":"Planned
"},{"code":"PP1-5-7","description":"Geometrical optics is governed by the laws of reflection, refraction and dispersion. Its applications to optical systems such as ray tracing, wavefront propagation, thin film calculators, which underly many optical engineering calculations.","name":"Foundations of geometrical optics, geometrical theory of optical imaging","selfAssesment":"Planned
"},{"code":"PP1-5-8","description":"Optical interferometers are nowadays used to develop and implement Fourier Transform Spectrometers, which can measure the emission spectrum of a given source with high spectral resolution at a constant sampling. This instrumentation is now at the core of modern hyperspectral sounders from satellite and have opened the way to the sounding of the Earth atmosphere with unprecedented spatial vertical resolution.","name":"Elements of the theory of interference and interferometers","selfAssesment":"Planned
"},{"code":"PP1-5-9","description":"Diffraction gratings and dispersive element are the basic ingredients for radiometers and grating spectrometers. They are in some cases preferred to Interferometer systems because the optical layouts can be designed and implemented with no moving part or components. Many of the today satellite instruments, including sounder and imagers, rely on diffraction and/or grating spectrometers","name":"Elements of the theory of diffraction and grating spectrometers","selfAssesment":"Planned
"},{"code":"PP1-5","description":"This section describes the theoretical fundaments of Optics and Modern Physics of Sensors relevant to the Earth Observation.","name":"Basics of Optics and Modern Physics of Sensors","selfAssesment":"Completed
"},{"code":"PP1-6-1","description":"The temperature and pressure profiles determine the atmospheric structure. The latter consists of four basic levels, considering the vertical variability of the temperature. These main four levels are troposphere, stratosphere, mesosphere, and thermosphere. In the troposphere (0-12km), which is the lowest layer of the atmosphere, all the meteorological processes that affect our everyday life take place. The lowest part of the troposphere is known as the boundary layer (0-3km), where all the surface-atmosphere interactions and exchanges take place. The troposphere concentrates the water vapor and 90% of atmospheric mass, while the chemical composition of all atmospheric layers consists of nitrogen, oxygen, argon and trace gases. The main parameters that characterize the atmosphere structure are pressure, density, and temperature. All the aforementioned parameters are related to the atmospheric composition and vary with altitude, latitude, longitude and season. Additionally, the stratosphere, which is the layer above the troposphere, contains almost all of the ozone abundance (~90%) of the atmosphere in a region named as ozone layer and traced between 15 and 35km. The interaction of the incoming solar radiation with ozone in this layer causes the reduction of the incoming harmful UV radiation provoking the temperature increase in the stratospheric layer. The 99.9% of total atmospheric mass is concentrated in lower atmosphere (<50km) with Nitrogen (N2, 78.08%), Oxygen (O2, 20.95%) and argon (Ar, 0.93%) being the major constituents of the atmosphere. Water vapor (H2O) is considered as a significant factor, too. Despite the fact that it depicts a very small amount of total atmospheric mass, it’s one of the most important greenhouse gases, along with carbon dioxide (CO2) and methane (CH4), absorbing the Earth’s longwave (infrared) radiation, affecting the energy balance of Earth-Atmosphere system. Furthermore, water vapor plays a decisive role in the formation of clouds and precipitation. Together with the basic chemical (atoms, molecules, ions) constituents of a \"standard\" atmosphere, aerosols of natural and anthropogenic origin have to be considered too, as far as the interaction of e.m. radiation with atmosphere is concerned.","name":"Structure and chemical-physical composition of Earth's atmosphere","selfAssesment":"Completed
"},{"code":"PP1-6-10","description":"The water vapour is the major radiative and dynamic parameter in atmosphere. Its concentrations vary highly in space and time, with the tropospheric water vapor being determined by the processes of hydrological cycle, namely the evaporation, condensation and precipitation. More specifically, its condensation upon dust nuclei form the clouds.","name":"Water vapour and Cloud formation","selfAssesment":"Planned
"},{"code":"PP1-6-11","description":"The radiative equilibrium is the principle, where the radiative emission and absorption are in balance based on Kirchhoff’s and Planck’s law, resulting in the steady temperature of planet. The adiabatic lapse rate displays the decrease of vertical temperature of a parcel with rate higher than 1oC per 100 metres.","name":"Radiative Equilibrium. Adiabatic lapse rate","selfAssesment":"Planned
"},{"code":"PP1-6-12","description":"The atoms of carbon are building blocks of living organisms and they can move among organisms as a part of carbon cycle. Their transport rate to the atmosphere as carbon dioxide is vital, because this gas trap heat in the atmosphere, increasing the Earth’s temperature and causing Greenhouse effect.","name":"The Carbon Cycle, Greenhouse Effect","selfAssesment":"Planned
"},{"code":"PP1-6-2","description":"The atmospheric absorption can cause an excitation or falling into the energy state of a particle, while the scattering is related to absorption and re-emission of radiation at all directions without changes in its frequency. Particularly, the main contributors of the incoming solar radiation absorptions are various molecules like the nitrogen (N2), oxygen (O2), ozone (O3), water vapor (H2O). Additionally, other constituents of the atmosphere such as CO2 and CH4, and other trace gases, aerosols, and cloud droplets can also absorb significant portion of the incoming solar radiation. Generally, the absorption of solar radiation is related to the wavelength of the solar spectrum. For example, gases and specific type of aerosols (black carbon, BC) or elementary carbon (EC) absorb in the ultraviolet (UV) and visible (VIS) part of solar spectrum. On the contrary, cloud droplets which are suspended in the atmosphere mainly scatter in UV and VIS and absorb in the infrared. The absorption of the incoming solar radiation from the atmospheric constituents reduces the harmful UV radiation and it is considered as the driving of atmospheric photochemistry. Moreover, scattering in the atmosphere can be divided into two mainly categories, firstly, the Rayleigh scattering which is the scattering of radiation by gases (mainly N2 and O2) and, secondly, the Mie scattering which is the scattering by aerosol particles and cloud droplets. The main difference between Rayleigh and Mie scattering is the direction of the re-emission of the incident solar radiation. For example, in the Rayleigh scattering the light have symmetrical direction either forward or backward whereas in Mie scattering the light is mainly scattered in the forward direction, depending on the size of the particle.","name":"Absorption and scattering of solar radiation in the Atmosphere","selfAssesment":"Completed
"},{"code":"PP1-6-3","description":"Mie scattering refers primarily to the elastic scattering of light from atomic and molecular particles whose diameter is similar or larger than the wavelength of the incident light. Mie scattering is not strongly wavelength dependent . This scattering produces a pattern like an antenna lobe, with a sharper and more intense forward lobe for larger particles. In the atmosphere the Mie scattering is commonly caused by particles (aerosols) floating in the atmosphere (due to Dust, smoke, rain drop). The Mie theory provides the solution for the amount of scattering in case of a spherical medium due to an incident wave.","name":"Mie Scattering in the Earth's Atmosphere","selfAssesment":"Planned
"},{"code":"PP1-6-4","description":"Rayleigh scattering refers primarily to the elastic scattering of light from atomic and molecular particles whose diameter is much smaller (one-tenth at least) than the wavelength of the incident light. The amount of scattering is strongly depending on the wavelength (λ) of the radiation (I = f(1/λ4). Then, the Rayleigh scattering explain the blue color of the sky caused by the scattering of sunlight off the molecules of the atmosphere. This because Rayleigh scattering is more effective at short wavelengths (the blue end of the visible spectrum). Therefore the light scattered down to the earth at a large angle with respect to the direction of the sun's light is predominantly in the blue end of the spectrum.","name":"Rayleigh Scattering in the Earth's Atmosphere","selfAssesment":"Planned
"},{"code":"PP1-6-5","description":"When we talk about “thermal infrared (or terrestrial) radiation” we commonly refer to the energy emitted from the Earth-atmosphere system. Trapping of thermal infrared radiation by atmospheric gases is typical of the atmosphere and is therefore called the “atmospheric effect”. The atmospheric effect is sometimes referred to as the “greenhouse effect” because in a similar way glass, which covers a greenhouse, transmits short-wave solar radiation, however absorbs long-wave thermal infrared radiation. Imagine a beam of radiation travelling through a small section of air. The air is made up of changing concentrations of different species, with all molecules absorbing and emitting thermal radiation at different rates. As the radiation travels through different layers of the atmosphere, the intensity of radiation will constantly be modified by both absorption and emission processes as described by the Schwarzschild's equation. In case of a sensor on board of a satellite, the net radiation measured would be that which is attenuated through each layer (as small increments of absorption and emission) from the surface to the top of the atmosphere plus the radiation emitted directly from the atmosphere. In this case, this process can be described by the radiative transfer equation (RTE). \r\nThe equation of radiative transfer simply says that as a beam of radiation travels through the atmosphere, it loses energy to absorption, gains energy by emission, and redistributes energy by scattering. Many radiative transfer codes exist which are able, i.e. on the basis of known properties of the atmosphere, to computed the effect of the atmosphere on the thermal infrared radiation providing atmospheric transmittance (absorption), atmospheric scattering and atmosphere path emission. Commonly, in satellite remote sensing, the thermal infrared region is defined as the region of the electromagnetic spectrum comprised between 8 and 14 micron. In an atmosphere free of particles (aerosols due to phenomena like fires, volcanic eruption, dust storm, etc.) the thermal infrared radiation is mainly affected by triatomic gases like water vapor, carbon dioxide and ozone.","name":"Thermal infrared radiation transfer in the atmosphere","selfAssesment":"Completed
"},{"code":"PP1-6-6","description":"Light scattering by particles is the process by which small particles cause optical phenomena, such as rainbows, the blue color of the sky, and halos. Mie scattering defines the interaction of light with particulate matter with a dimension comparable to the wavelength of the incident radiation. It can be regarded as the radiation resulting from a large number of coherently excited elementary emitters (molecules for example) in a particle. Since the linear dimension of the particle is comparable to the wavelength of the radiation, interference effects occur. The most noticeable difference to Rayleigh scattering is, generally, the much weaker wavelength dependence and a strong dominance of the forward direction in the scattered light. The calculation of the Mie scattering cross section, which involves summing over slowly converging series, is complicated even for spherical particles, it is worse for particles of an arbitrary shape. However, the Mie theory for spherical particles is well developed and a number of numerical models exist to calculate scattering phase functions and extinction coefficients for given aerosol types and particle size distributions.","name":"Light scattering by atmospheric particulates","selfAssesment":"Planned
"},{"code":"PP1-6-7","description":"Each time radiation passes through the atmosphere it is attenuated to some extent. We refer to this attenuation with the term 'atmosphere transmittance'. The typical atmospheric transmittance between wavelengths of 250 nm and 2500 nm, i.e. in the ultraviolet, visible, near-infrared and short-wave-infrared regions of the spectrum is dominated bywater vapour, although methane, carbon dioxide and molecular oxygen are also responsible for a few absorption lines. The behaviour in the visible region is dominated by molecular Rayleigh scattering. At the short-wavelength end of the spectrum, in the ultraviolet, absorption by ozone becomes very significant. Above 2500 nm up to the upper limit (13500 nm) of the optical electromagnetic spectrum useful for Remote Sensing, the atmosphere transmittance is mainly affected by triatomic molecules (H20, CO2 and O3). However, the atmospheric effects (transmittance) is strongly depending on the electromagntic wavelength. Remote Sensing exploits the region of relative atmospheric transparency called atmospheric windows.","name":"Earth's (standard) Atmosphere Transmittance","selfAssesment":"Planned
"},{"code":"PP1-6-8","description":"With the term 'atmospheric windows' we refer to the regions of the electromagnetic spectrum where the interaction of the atmosphere with the electromagnetic radiation is minimized. There are three main ‘windows’ in the Earth's atmosphere. The first of these includes the visible and near-infrared (VNIR) parts of the spectrum, between wavelengths of about 0.38 μm and 3.5 μm, although it does also contain a number of opaque regions. The second is a rather narrow region between about 8 μm and 15 μm, in which is found the bulk of the thermal infrared (TIR) radiation from objects at typical terrestrial temperatures. The third more or less corresponds to the microwave region, between wavelengths of a few millimetres and a few metres. Thus we can expect that any active system designed to penetrate the Earth’s atmosphere will operate in one of these three ‘window’ regions.","name":"Atmospheric (spectral) windows for EO","selfAssesment":"Planned
"},{"code":"PP1-6-9","description":"The water cycle is a continuous purification process of water on Earth due to the movement of water species among various reservoirs. This cycle is vital for Earth’s life, ecosystems, and living organisms. The water cycle includes mainly four processes. Water is evaporated from ocean and land surfaces driven by solar heating. The resulting water vapor rises upwards into the atmosphere, transported by the winds, cools, and due to low air temperature condensates into liquid droplets and ice crystals to form clouds. The ice or/and liquid droplets collide, increase their size, and precipitate as snow or rain to Earth’s surface and oceans. The subtraction of energy (latent heat of evaporation) at low latitudes related to the evaporation processes as well as its release (latent heat of condensation) at higher latitudes related to the condensation processes is a formidable way to guarantees the heat transport from the warmer part of the Earth to the colder ones mantaining local air temperature more compatible with the human life. The starting point of the water cycle is not unique, but the oceans can be selected as the initial reservoir. Other important reservoirs are considered ice sheets, lakes, and rivers. \r\nThe hydrosphere is defined by the various water reservoirs which are characterized by different residence times – the time spends the water molecules in a reservoir. The water residence time – the rate at which the water comes out the reservoirs – varies for each reservoir extending from hundreds (Greenland Ice Sheet) or thousands of years (Antarctic Ice Sheet) to years and days for rivers and lakes, respectively. It also defines the energy transferred from the Earth to the Atmosphere which increases for short-term residence times. In long-term temporal scales, this energy is defined as the evaporation rate (E) and balances with the precipitation rate (P). This global energy balance breaks for shorter time scales depending also on the local and regional climate. For example, in regions located in the Inter-Tropical Convergence Zone (ITCZ), the energy balance in the water cycle does not exist since the precipitation rate is much higher than the evaporation rate (P>>E) due to the horizontal movement of converging trade winds.","name":"The Water Cycle","selfAssesment":"Completed
"},{"code":"PP1-6","description":"Atmospheric Physics describe the processes affecting the physical, chemical and thermodynamic status of planetary atmospheres. In the context of EO sciences, it particularly refers to the physics of the interactions of e.m. radiation traveling across (or emitted by) the atmosphere as the main source of information collected by satellite (in general aerial) sensors.","name":"Basics of Atmospheric Physics","selfAssesment":"Completed
"},{"code":"PP1-7-1","description":"According to the second law of thermodynamics, heat is a measure of movement or flow of energy from hotter substances to colder ones and is measured in Joules. In microscale, heat is known as internal energy, while temperature describes the kinetic energy of molecules within substances, expressed in Celsius.","name":"Temperature and heat","selfAssesment":"Planned
"},{"code":"PP1-7-10","description":"Irreversible thermodynamics investigates the regularities in transport phenomena, namely heat and mass transfer, and their relaxation. It is based on the first law of Thermodynamics, which correlate the heat flow density with pressure and viscosity, and the second law that describe the temporal variations of local entropy for local continuous mass.","name":"The constitutive equations of irreversible fluxes","selfAssesment":"Planned
"},{"code":"PP1-7-11","description":"The Adiabatic process of homogeneous system occurs, when flow of heat is not exchanged across the boundaries of system and the system is characterized from uniform phase (solid or liquid or gases). In this case, the variations of entropy can be determined for some parts of system.","name":"Heat equation and special adiabatic systems, special adiabats of homogeneous systems","selfAssesment":"Planned
"},{"code":"PP1-7-12","description":"The thermodynamic diagrams are used for the study of vertical structure and properties of the Atmosphere above a specific location. Especially, a static diagram represents a) an atmosphere with fixed potential temperature or b) a process curve of the change of variables of air parcel that rises adiabatically.","name":"Thermodynamics diagram, atmosphere static","selfAssesment":"Planned
"},{"code":"PP1-7-2","description":"Kinetic theory of gases is based on a simplified molecular description of gases, from which the properties of volume, pressure and temperature can be derived. The assumptions of this theory are based on the random movements of molecules, their elastic collisions and the transfer of kinetic energy between them.","name":"Kinetic theory of gases","selfAssesment":"Planned
"},{"code":"PP1-7-3","description":"The ideal gas law or general gas equation describes the equation of state of hypothetical ideal gas. This equation correlates the pressure and volume with its temperature, while is characterized as a combination of the empirical laws of Boyle, Charles, Avogadro and Gay-Lussac.","name":"Ideal gas laws","selfAssesment":"Planned
"},{"code":"PP1-7-4","description":"The state functions of ideal gas are the pressure, volume, temperature, internal energy and entropy, which remain unchangeable in compared with the path. The internal energy is expressed through Joule’s law as a function of temperature of gas, while the entropy depends on the variation of volume and temperature.","name":"State function of ideal gases","selfAssesment":"Planned
"},{"code":"PP1-7-5","description":"The phase rule for condensation is expressed as P+F=C+1. The terms of P, F and C describe the number of phases, minimum fixed variables and independent chemical species respectively. Concerning the condensed phases to distinguish the gases from liquids and solids, these are the density, molecular order, diffusion, etc.","name":"State function of the condensed gas phase","selfAssesment":"Planned
"},{"code":"PP1-7-6","description":"When the system passes from initial to final state due changes in properties of temperature, pressure and volume, it is considered to have undergone thermodynamic process. The different types of thermodynamic processes are distinguished in the isothermal (fixed temperature), adiabatic, isochoric (stable volume), isobaric (stable pressure) and reversible process.","name":"Thermodynamic process","selfAssesment":"Planned
"},{"code":"PP1-7-7","description":"Budget equations, namely heat, momentum and moisture budget, are interpreted through two frameworks, which are Eulerian and Lagrangian. Eulerian is utilized for the investigating of transfer of heat by the wind, while Lagrangian is concerned about the effects of ascending or descending airflows on the Earth-Atmosphere system.","name":"Budget equations","selfAssesment":"Planned
"},{"code":"PP1-7-8","description":"The First Law of Thermodynamics supports that the energy is conserved. Thus, the thermal energy is defined as the sum of warming or internal energy (microscopic effect) and work occurring per unit mass (macroscopic effect). For its application to the Atmosphere, its mathematical expression is Δq=Cp•ΔT-(ΔP/ρ).","name":"First law of thermodynamic","selfAssesment":"Planned
"},{"code":"PP1-7-9","description":"A natural process that starts from an equilibrium state and ends in another state, causing changes in direction of entropy (ΔS) or statistical disorder of the system, is interpreted by Second Law of Thermodynamics. This law is considered as an irreversible process and it is expressed as ΔS=Heat transfer/Temperature.","name":"Second law of thermodynamics","selfAssesment":"Planned
"},{"code":"PP1-7","description":"Thermodynamics is the science of the relationships between heat, work, temperature, radiation, energy and properties of matter. These relationships are governed by the four laws of thermodynamics which allow a quantitative description, through measurable macroscopic physical quantities, of processes that, at the level of microscopic constituents can be described by the statistical mechanics. Thermodynamics applies to a wide variety of topics relevant to EO science and technologies from atmospheric chemistry and meteorology up to sensor design and aeronautics.","name":"Basics of Thermodynamics","selfAssesment":"Planned
"},{"code":"PP1-8-1","description":" ","name":"Satellite orbits: (quasi)Polar, Geostationary, Molnyia, Non-rotating, Sun-Syncronous, etc.)","selfAssesment":" "},{"code":"PP1-8-2","description":"Starting from the standard Rocket Equation - assuming a relative speed of the burned (emitted) fuel equal to 2,4 km/s and zero initial speed - it is possible to evaluate (for a single-stadium rocket) the mass percentage of payload that can be hosted on a platform depending on the final speed expected on the orbit. For instance a 28% payload is possible for a geostationary platform whose expected final speed on the orbit (radius 42.170 km) is 3,7km/s. Instead for a polar platform at about 800km this percentage reduce up to the 4% being the final sped on the orbit expected to be 7,5km/s.","name":"Equation of the rocket and launch of a satellite: payload determination","selfAssesment":"Planned
"},{"code":"PP1-8-3","description":"The orbit of a satellite is commonly defined through its so called Keplerian parameters. These parameters represent the trajectory that the satellite will follow if no-perturbation are acting on it. A series of forces act on the satellite to perturb it away from the nominal orbit. We can classify these perturbations, or variations in the orbital elements, based on how they affect the Keplerian elements. The actual orbit of a satellite will result from a combination of these perturbations. Periodic maneouvers are needed to bring the orbit back to nominal conditions. The lifetime of a satellite is defined as the time interval that it takes to decay from its initial altitude to an altitude causing the satellite reentry down to the atmosphere. Therefore lifetime of a satellite should not be confused with the time during which the satellite will provide useful information (this operational phase, in general, is designed to last 5 - 7 years). In fact, all satellite terminating operational phases in orbits passing through the LEO region should be de-orbited or, where appropriate, manoeuvred to an orbit with suitably-reduced lifetime, that is, should be left in an orbit where drag and other perturbations will limit lifetime. The actual duration of the satellite in orbit will depend from the intensity of the perturbations which will affect its orbit. In case of satellite on GEO orbit, at the end of the operational phases they will be located on a disposal orbit, that is an orbit which do not cross the protected region. The protected region is the altitude region ranging from GEO - 200 km to GEO + 200 km and inclination region between -15 deg and +15 deg. Satellites in low Earth orbit, with perigee altitudes below 1000 km, are predominantly subject to atmospheric drag. This force very slowly tends to circularise and reduce the altitude of the orbit. The rate of 'decay' of the orbit becomes very rapid at altitudes less than 200 km, and by the time the satellite is down to 180 km it will only have a few hours to live before it makes a fiery re-entry down to the Earth.","name":"Real orbits. Life time of a satellite, orbit’s decay.","selfAssesment":"Planned
"},{"code":"PP1-8-4","description":"The choice of a satellite orbit mostly depends on its main application. From this point of view it represents a crucial part of a satellite mission design. The most important parameters to describe a satellite orbit are the inclination angle i (of the orbit plane respect to the equatorial plane) its eccentricity e and its height H from the Earth's surface. In principle whatever eigth H can be used, provided that the speed of the satellite on its orbit allows the centrigugal force to exactely compensate the gravitational one at that heigth. Polar (i close to 90°) and Geostationary (i=0, H=35.800 km) orbits are the most common choices for EO satellites. In principle one single polar satellite can be sufficient to guarantee the global coverage of the Earth with equal quality of the images at all latitudes. All Geostationary satellites share the same circular orbit with H around 36000 km where the required speed exactely correspond to the one required to travel an entire orbit in 1 sideral day (orbital period P = 1 sideral day). This means that the satellite footprint is permanently in place over a specific Earth's location (e.g. for Meteosat 0°N, 0°E) allowing a quasi-continuous monitoring of a whole Earth's emisphere (with poor visibility of Earth's edges including Poles). Polar satellites' heigths are usually in between 700-800 km, with orbital periods around 100min (i.e. about 14,5 orbits/day) even if, lower orbits are also chosen particularly for very high spatial resolution payloads. Lower inclinations are also used (quasi-polar orbits) for specific applications. Due to the asphericity (and mass inhomogeneity) of the Earth, satellite orbit plane rotates around the Earth's polar axis with a period Pp producing (for elliptical orbits) the rotation of the orbit itself in its plane. A common choice for most EO polar satellites is to choose the orbital parameters in a way that Pp=1 year (Sun-Synchronous orbits). Due to the synchronism between Earth's revolution around the Sun and the orbit plane precession around Earth' axis, satellite passages happens at the same local solar time (similar illumination conditions) each time it flies over a specific region. This ensure repeatable sun illumination conditions facilitating image interpretation particularly for change detection or land monitoring applications. Other choices are possible when it is required to monitor with continuity high latitude regions.\r\n\r\nThis is the case of Molniya orbits which combine the continuity of observations typical of geostationary satellites with the possibility, offered by polar orbits, to overfly the highest latitudes regions. Its characteristics are: high eccentricity (e.g. e=0,74, axes 500 and 23.000 km), P=1/2 sideral day (Geo-Synchronous), inclination (i=63,4° or i=116,6°) which guarantees the satellite footprint at the apogee remaining positioned on a fixed ground point (non-rotating orbit). This way the satellite will spend more than 93% of its orbital period looking to the same emisphere even from a high latitude point of view. \r\n\r\nSo called altimetric orbits respond to the specific needs of altimetry. In this case the orbital parameters are chosen in order to guarantee, for example: a) that the ascending and descending sub-satellite tracks intersect at roughly 90 degrees on the Earth’s surface (so that orthogonal components of the surface slope can be determined with equal accuracy; b) the possibility to monitor all phases of tidal effects on ocean surface.\r\n\r\nParticularly important for several applications (multi-temporal analyses, change detection, etc.) are the Exactly repeating orbits.\r\nThey are conceived in order that the sub-satellite track will repeat itself exactly after a certain interval of time. This allows images having the same viewing geometry during the satellite’s lifetime making moreover available a particularly simple method of referring to the location of images (navigation or geo-referenciation) for example by referring to a ‘path and row’ system used for instance by the Landsat World Reference System (WRS). It is possible to arrange satellite orbits parameters in order to contemporary guarantee the sun-syncronism so that, not only satellite images collected on the same region can be easily super-imposed each-other but the same illumination and viewing geometry can be achieved. This is, for instance, the choice adopted for LANDSAT satellites whose images are typically available as a collection of scene of fixed dimension always similar each other when covering the same terrestrial area.","name":"Satellite orbits parametrization and choice","selfAssesment":"Completed
"},{"code":"PP1-8","description":"Mechanics is the Physics branch dealing with the behaviour of physical bodies when subjected to forces or displacements. This section provides Mechanics basic elements necessary for determining the orbits of satellites and rockets. The different satellite trajectories will be illustrated with respect to their peculiarities","name":"Basics of Mechanics","selfAssesment":"Planned
"},{"code":"PP1","description":"Physycal principles of the electromagnetic field, its radiation and propagation through the space, in the optical spectral range","name":"Basics of Optical Remote Sensing","selfAssesment":"In Progress
"},{"code":"PP2-1-2-1","description":" ","name":"In-phase/Quadrature Component","selfAssesment":" "},{"code":"PP2-1-2-2","description":" ","name":"Phasor (A, phi -> I/Q complex transformation)","selfAssesment":" "},{"code":"PP2-1-2","description":"A complex, using complex numbers, representation of signal by two measures magnitude and phase. In the digital SAR context, a camplex number often is represented by an equivalent pair of numbers, the in-phase (I) component and the quadrature (Q) component.","name":"Complex wave description","selfAssesment":"New
"},{"code":"PP2-1-3","description":"The Fourier- transformation is a fundamental method in the signal processing procedures that changes signal from space/time domain to frequency-domain.","name":"Fourier Transform","selfAssesment":"New
"},{"code":"PP2-1-4","description":"Electromagnetic waves are polarized; the direction of the polarization corresponds to the direction of oscillation of the electromagnetic field. Typical and often used linear polarisations are: H (horizontally) and V (vertically) polarized waves of the plane of the electric field vector oscillations relative to the sensor coordinate system. The polarization state of a backscattered wave from a natural surface can be linked to the geometrical characteristics like shape, roughness and orientation and the intrinsic properties of the scatterer like moisture, salinity, density. The radar system is characterized by combination of polarization of transmitted and received pulse: HH, HV, VH or VV. Based on the polarization sent and obtained the radar systems are divided in three polarization modes. Single polarization refers to the same polarization transmitted and received; dual polarization, one polarization is sent and another received; or quad polarization, when system is able to transmit and receive all four types of polarization. When making a contact with a scatterer, the polarization of the EM-wave can change, depending on the geometrical and dielectrical properties of the scatterer. In order to get all necessary information about those changes, full polarimetric systems are required.","name":"Polarisation","selfAssesment":"Completed
"},{"code":"PP2-1-5","description":"Property of signal or data set in which the phase of the constituents is measurable, and plays a significant role in the way in which several signals or data combine. Two waves with a phase difference that remains constant over time, are said to be coherent.","name":"Coherent","selfAssesment":"New
"},{"code":"PP2-1-6","description":"In remote sensing, phase is the exact position within a periodic signal with respect to an arbitrary reference point. It is typically expressed as an angle and measured in degrees or radians, where one period corresponds to a phase of 360° or 2π, respectively. Mathematically, phase is the argument of a complex number, that is the angle between its geometric representation in the complex plane and the real axis. For this reason, complex algebra is often used in remote sensing to facilitate phase calculations. Due to its periodic nature, phase can only be measured unambiguously within one period. Consequently, phase measurements are commonly subject to 2π phase ambiguities. These ambiguities can often be resolved in a process called phase unwrapping, using a priori information about the signal, typically related to its continuity. Phase measurements are crucial for the creation of synthetic aperture radar (SAR) images, as well as for many SAR imaging techniques, including interferometric SAR (InSAR).","name":"Phase","selfAssesment":"Completed
"},{"code":"PP2-1-7","description":"Shift in frequency caused by relative montion along the line of sight between sensor and the observed scene.","name":"Doppler effect","selfAssesment":"New
"},{"code":"PP2-1-8","description":"The wave-particle dualism (duality) is a theory according to which all matter exhibits the attributes of waves and particles.","name":"Wave-particle dualism","selfAssesment":"New
"},{"code":"PP2-1","description":"The radar operates in the microwave portion of the electromagnetic (EM) spectrum with a wavelength from 1 millimeter to 1 meter. Imaging radars are independent of weather conditions and can operate day or night. EM-waves are polarized. Normally only the horizontal (H) or vertical (V) linear polarizations are used. The radar system is characterized by combination of polarization of transmitted and received pulse: HH, HV, VH or VV. When making a contact with a scatterer, the polarization of the EM-wave can change, depending on the geometrical and dielectrical properties of the scatterer.The data can be acquired from both the ascending (northwards) and descending (southwards) satellite passes. Water clouds can interfere with the radars operating below 2 cm in wavelength. The effects of rain can be generally ignored at wavelengths above 4 cm. For longer wavelengths (above 20 cm), an effect called Faraday rotation caused by the ionosphere, i.e., free charges (electrons) and the Earth’s magnetic field, can lead to a rotation of the polarization plane. In the presence of Faraday rotation, the data, usually fully polarimetric, should be corrected. The radar systems operate in different bands that uses different wavelengths. The most common frequences/wavelengths (frequency = Speed of Light / wavelength) for environmental applications are X (5,75-10,90 GHz), C-(4,20-5,75 GHz), S-(1,550-4,20 GHz), L-(0,390-1,550 GHz) and P-(0,255-0,390 GHz) band. The selection of SAR system for acquiring data depends on their application. Longer wavelengths are mainly devoted to communication and navigation purposes. Radars penetrate atmosphere and clouds. For example for forestry, longer wavelengths starting from C- or S-band are preferred.","name":"Microwave portion of electromagnetic spectrum","selfAssesment":"Completed
"},{"code":"PP2-2-1","description":"Interaction of waves with any solid object.","name":"Diffraction","selfAssesment":"New
"},{"code":"PP2-2-2","description":"Scattering means the redirection of incident electomagnetic energy by an object. Scattering and Diffraction refer to the same physical process - a coherent distortion of an incident wave. Emissivity is a measure of how strongly a body radiates at a given wavelength. Emission and scattering are complemetary: surfaces that are good scatterers are weak emitters, and vice versa.","name":"Scattering and emission","selfAssesment":"New
"},{"code":"PP2-2-3","description":"Radimetric anomalies such as for example saturation","name":"Radiometric anomalies","selfAssesment":"New
"},{"code":"PP2-2-4-1","description":"Mathematical expression that describes the average received signal level, compered to the additive noise level, in terms of system parameters. Principal parameters include: transmitted power, antenna gain, noise power, and radar range.","name":"Radar equation","selfAssesment":"New
"},{"code":"PP2-2-4-2","description":"Coefficient sigma or sigma nought represents the average reflectivity of a horizontal material sample, normalized with respect to a unit area on the horizontal ground plane.","name":"Sigma nought","selfAssesment":"New
"},{"code":"PP2-2-4-3","description":"Gamma nought represents the average reflectivity of a horizontal material sample, normalized with respect to the incident area, orthogonal to the incident ray from the radar.","name":"Gamma nought","selfAssesment":"New
"},{"code":"PP2-2-4-4","description":"Radar brightness coefficient represents the reflectivity per unit area in slant range.","name":"Beta nought (brightness)","selfAssesment":"New
"},{"code":"PP2-2-4","description":"Measure of radar reflectivity. The Radar Cross Section (RCS) is expressed in terms of the physical size of an hypothetical uniformly scattering sphere that would give rise to the same level of reflection as that observed from the sample target.","name":"Radar cross-section","selfAssesment":"New
"},{"code":"PP2-2-5-1","description":" ","name":"Material constants","selfAssesment":"New
"},{"code":"PP2-2-5-2","description":"The complex part nc of the refraction index n determines how far an electromagnetic wave of wavelength λ can survive crossing a specific medium. The attenuation length la is the distance after that the amplitude of an electromagnetic signal reduces its value by an amount of 1/e. For instance the amplitude of the Electric field E(z) of an electromagnetic wave proceeding along the z direction is decreasing as exp(-z/la) being la=λ/(2𝜋 nc) the attenuation length associated to that specific material (nc) and wavelength λ. This way attenuation length in water can be of hundreds of meters in the visible range and just few microns in the microwaves. The opposite happens over solid land surfaces where optical waves can penetrate from few microns up to few millimeters (moving from the VIS-NIR to the TIR spectral range) whereas microwaves can reach depths from hundreds to towsands (as higher are their wavelength) meters allowing the exploration of subsoil and thick coulters of ice.","name":"Attenuation lenght and penetration depth","selfAssesment":"Completed
"},{"code":"PP2-2-5-3","description":"Soil permittivity is a measure of the water content (soil moisture) in the soil and characterized by the metric of the dielectric constant of the soil. Soil moisture influences emission, absorption and propagation of microwave electromagnetic energy. Moisture decreases the ‘emissivity’ of soil, and thereby affects microwave radiation emitted from Earth’s surface. Dry soil has a low dielectric constant and low radar reflectivity. Moist and partially frozen solis have intermediate values. The higher the soil water content, the lower the radar signal penetration into the soil. In situ measurements of soil permittivity are a prerequisite for the calibration and validation of synthetic aperture radar (SAR) soil moisture retrieval algorithms. Soil moisture is a key variable in the hydrologic cycle and is recognized as an Essential Climate Variable (ECV).","name":"Soil permittivity","selfAssesment":"Completed
"},{"code":"PP2-2-5-4","description":"The permittivity of a plant is a function of its contained amount of water in all plant compartments (including roots).","name":"Plant permittivity","selfAssesment":"In progress
"},{"code":"PP2-2-5","description":"The electric properties of different materials that can be described by two quantities: relative dielectric constant (complex permittivity) and loss tangent. Reflectivity of a smooth surface and the penetration of microwaves into the material are determined by these two quantities. The complex dielectric constant changes mainly die the the change in water content.","name":"Dielectric Properties","selfAssesment":"New
"},{"code":"PP2-2-6-1","description":"The standard deviation of the surface height variation (or RMS height), denoted by s (or hRMS), describes the statistical variation of a random surface with height z(x). In case of an azimuthally symmetrical surface, the single-scale RMS height of the one dimensional case for discrete profile values is given by (1), where N is the number of samples, and z ̅ the mean surface height (2). \r\nAs roughness depends not only on the soil surface properties but also the wavelength λ of the electromagnetic signal, the roughness parameters are scaled by the wave number k. Hence, the electromagnetic roughness ks for surface roughness parameter s is (2π/λ)*s (3). In order to determine if a random surface may be considered as electromagnetically smooth, one common definition is given by the Rayleigh roughness criterion, where s < λ / 8*cosθ, or ks < 0.8, at incidence angle θ = 0. This criterion has been revised for the microwave region, where the wavelength is usually of the order of the RMS height, called the Fraunhofer roughness criterion, where s < λ / 36*cosθ, or ks < 0.2, at incidence angle θ = 0. Additionally, surfaces are considered as electromagnetically rough for 1 < ks < 3.","name":"Vertical roughness component (RMS height)","selfAssesment":"Completed
"},{"code":"PP2-2-6-2","description":"The surface correlation length, denoted by l, is defined as the displacement ξ at which the surface correlation function p(ξ)= 1/e. Thus, l can be seen as the reference length up to which two points of one soil surface can be regarded as statistically independent from each other. If we imagine a perfectly smooth soil surface, l=∞ since every point on that surface correlates with all other points and can therefore be regarded as dependent from each other.\r\nAs roughness depends not only on the soil surface properties but also the wavelength λ of the electromagnetic signal, the roughness parameters are scaled by the wave number k. Hence, the electromagnetic roughness kl for surface roughness parameter l is kl=(2π/λ)*l.\r\nExperimental results indicate a weaker influence on the radar backscatter compared to the RMS height s.","name":"Horizontal roughness component (correlation length)","selfAssesment":"Completed
"},{"code":"PP2-2-6-3","description":"The surface correlation function p(ξ) determines the degree of correlation between two lateral separated locations of one surface. Thereby, ξ is defined as displacement between two locations, (x, y) and (x', y') on the surface and given by (1).\r\nWith increasing separation between two locations on the surface p(ξ) decreases, and at a certain distance, the surface correlation length l, the heights at the two locations are considered statistically uncorrelated.\r\nThe surface scattering of electromagnetic waves can be simulated with various models. Depending on the observed roughness scale multiple surface scattering models are valid for specific roughness conditions. For example, one of the first surface scattering models for slightly rough surfaces, the small perturbation model (SPM), deals with roughness scales that are small relative to the wavelength and hence has validity conditions for ks < 0.3, kl < 3, and m < 0.3. Since then, various surface scattering models for computing the scattering and emission behavior of natural surfaces in the microwave region have been proposed, such as the Kirchhoff scattering model (KH), the geometric optics model (GO), the physical optics model (PO), or the integral equation model (IEM), to name the most common used in literature. For simulations of EM scattering at soil surfaces, assumptions of the functional forms of p(ξ) have to be made. The two most common forms for mathematically describing the surface correlation of natural surfaces are the exponential pE(ξ) and the Gaussian pG(ξ) correlation functions, defined by (2) and (3).\r\nFor some mathematically sophisticated surface scattering models, an x-Power correlation function p(x-Power)(ξ) can be assumed (4), with x as value between 1 and 2.\r\nIn literature, rather smooth surfaces are characterized by an exponential surface correlation function, while rather rough surfaces are characterized by a Gaussian surface correlation function.","name":"Surface correlation function","selfAssesment":"Completed
"},{"code":"PP2-2-6-4","description":"The root-mean-square (RMS) slope m of a one dimensional height profile for one random surface is given by (1), with s as the standard deviation of the surface height variation (or RMS height), and p''(0) as the second derivative of the surface correlation function p(ξ), evaluated at ξ=0. Since p(ξ) is an even function, p''(0) is a negative quantity.\r\nFor modeling of electromagntic scattering at soil surfaces, assumptions of the functional forms of p(ξ) have to be made. The most common known forms are the exponential and Gaussian correlation functions. Additionally, some models allow the assumption of a x-Power correlation function, with x as value between 1 and 2. For the varying surface correlation functions, the RMS slope m is given by (2)-(4).\r\nIn literature, for L-band, the slope m should be lower than 0.3 or 0.4 in case of single scattering and bare soil surfaces with moderate RMS heights.","name":"Surface roughness slope","selfAssesment":"Completed
"},{"code":"PP2-2-6-5","description":"In reality, one random surface has multiple roughness scales, since the commonly used surface description based on single-scale roughness parameters does not comprise all the properties of natural surfaces relevant for describing wave scattering. Depending on the wavelength λ of the microwave sensor the dimension of the surface roughness parameters s and l correspond to specific roughness scales. \r\nIn case of multi-scale roughness, the equivalent RMS height is a composite of the individual RMS heights at different roughness scales (1).\r\nA three-scale surface, as shown in Fig. 1, for example consists of a small-scale high-spatial frequency variation (c) ‘riding’ on top of the larger scales, the medium-scale perturbation (b) and the large-scale undulation (a).\r\nAt microwave frequencies, the centimeter scale is the scale of roughness of primary importance, since λ is on the order of centimeters to a few tens of centimeters. For natural surfaces it is very difficult to measure millimeter-scale roughness.","name":"Single-scale & multi-scale roughness","selfAssesment":"Completed
"},{"code":"PP2-2-6","description":"Surface roughness defines the geometry between the pedosphere and the atmosphere (soil-air boundary).\r\nIn the field of microwave remote sensing, surface roughness affects scattering and emission characteristics of natural surfaces. The degree of roughness of a random surface is determined by statistical parameters, measured by the units of wavelength of the observing sensor. The two fundamental surface roughness parameters are the standard deviation of the surface height variation (RMS height) s, with its related surface correlation function p(ξ), and the horizontal surface correlation length l. Additional, a third roughness parameter, the root-mean-square (RMS) slope m, is important for some surface scattering models to simulate electromagnetic wave scattering of surfaces.\r\nSurface roughness determines the variation of surface height within an imaged resolution cell. The transition from smooth to rough is qualitative, and is function of both wavelength and incident angle. With decreasing frequency the soil surface appears rather smooth to microwave sensors. This results in the fact, that while one surface appears smooth when sensed at L-band (λ ≈23 cm), the same surface appears rough when sensed at X-band (λ≈3 cm). Hence, in the field of microwave remote sensing, the ‘effective’ surface roughness parameters are scaled by the wave number k= 2π/λ. Surface roughness can be observed at single or multi-scale.","name":"Surface roughness","selfAssesment":"Completed
"},{"code":"PP2-2-7-1","description":"The Stokes vector is a four-element vector containing real-valued polarization combinations and is an alternative form of representing a full (=quad) polarimetric dataset, besides the complex-valued scattering matrix. Stokes vectors can be measured as real quantities and are preferred over the complex-valued Jones vector formalism when a coherent (phase-preserving) measurement system is absent. Stokes vectors can be used to form the 4x4 Mueller matrix for target scattering analyses, mostly used in the field of optics. First component of the Stokes vector is the sum of the co-polar fields and represents the total energy of the wave. Second component is the difference of the co-polar fields. Thrid component is the real part of the cross-correlation of the fields and fourth component is the imaginary part of it. The different polarization states can be represented by the Stokes vector and an O(3) elliptical transformation can be used to change the polarization basis, similar to the Jones vector where the SU(2) elliptical transformation is used.","name":"Stokes Vector","selfAssesment":"Completed
"},{"code":"PP2-2-7-2","description":"The scattering matrix is a 2x2 square matrix containing four complex-valued polarization measurements (amplitude & phase) forming one full (= quad) polarimetric set of coherent observations. An often recorded set of polarizations is the combination: HH (horizontal receive - horizontal transmit), HV (horizontal recive - vertical transmit), VH (vertical receive - horizontal transmit) & VV (vertical receive - vertical transmit). The scattering matrix is fully suficient for describing scattering from coherent targets (dominating the resolution cell), but not for incoherent tragets (mix of scattering contributions in the resolution cell). For the latter, the coherency and the covariance matrices are the more appropriate descriptions of scattering from incoherent targets.","name":"Scattering matrix","selfAssesment":"Completed
"},{"code":"PP2-2-7-3","description":"The covariance and coherence matrix are two 4x4 square matrices, which can be built out of the scattering matrix by a lexicographic and a Pauli target scattering vector. They are an alternative representation of a full polarimetric dataset allowing the analysis of incoherent targets (more than one dominant scatterer in the resolution cell) and the phenomenon of depolarisation (transformation of incoming fully polarised wave into a partially polarised wave by creating a variety of different types of polarizations during media interaction). These matrices can be converted into each other without loss of information (by unitary transformations), but not turned back into the scattering matrix due to averaging operations during formation of coherency or covariance matrices.","name":"Covariance/Coherency matrices","selfAssesment":"Completed
"},{"code":"PP2-2-7-4","description":"Polarimetric decomposition techniques allow signal unmixing by polarimetry in order to separate different scattering contribution within one resolution cell, e.g. from soil & vegetation or snow, ice & bedrock. They can be either applied for the scattering matrix (coherent form - one dominant scatterer in the resolution cel) or for the covariance/coherency matrix (incoherent form - more than one dominant scatterer in the resolution cell). Decomposition techniques can be model- (physics) or eigen- (mathematics)-based. The eigen-based decomposition allows to diagonalize the coherency or covariance matrix in a diagonal eigenvalue matrix and a matrix of column eigenvectors. From eigenvalues and eigenvectors the polarimetric entropy, the scattering alpha angle and the polarimetric anisotropy. The polarimetric entropy is a matric for the degree of depolarization of the scattering event. The scattering alpha angle is an intrinsic scattering mechanism indicator. The polarimetric anisotropy informs about secondary scattering mechanism in evironments with high entropy. If the anisotropy is high only one secondary scattering mechanism is present, if it is low, more than one will occur.","name":"Polarimetric decomposition techniques","selfAssesment":"Completed
"},{"code":"PP2-2-7-5","description":"All bi- or multi-polar (non-inert) media have the tendency to orient themselves if an external field is excited. This orientation polarization is caused by negatively and positively charged areas within the media under the premise the media is able to rotate freely. Molecules of liquid water are a prime example.","name":"Orientational polarisation of media","selfAssesment":"In progress
"},{"code":"PP2-2-7-6","description":"Polarimetric coherences are complex-valued polarimetric correlation coefficients assessing the redundance between different polarimetric observations informing about their divergence in information. They can be formed among mutual polarimetric observations showing their degree of correlation. The polarimetric coherence consists of a magnitude, ranging between zero (no correlation) and one (identical), and a phase information, running from -180° to 180°. Typically polarimetric coherences are calculated between the co-polarimetric (HH, VV) channes, as well as the cross-polarimetric channels (HV, VH). The latter polarimetric coherence assesses the system noise inherent in the recorded polarimetric data, if a monostatic systems (transmitting and receiving sensor on the same sensing platform) is used for acquisition.","name":"Polarimetric coherences","selfAssesment":"Completed
"},{"code":"PP2-2-7-7","description":"The polarisation ellipse and the Jones vector formalism are the geometrical (three real-valued angles) and algebraic (amplitude & phase) formalisms to describe polarisation states of an electromagnetic wave. The ellipse has an orientation, an ellipticity and absolute phase angle. The three angles are integrated in one mathematical ellipse formulation that can represent linear, elliptic and circular polarisation states. The Jones vector formalism is an algebraic formulation allowing all calculus available in linear algebra. Both representations (polarisation ellipse & Jones vector) can be converted into each other seemlessly with a simple elliptical basis (special unitary SU(2)) transformation.","name":"Polarisation ellipse / Jones vector formalism","selfAssesment":"Completed
"},{"code":"PP2-2-7-8","description":"The concept of polarisation synthesis is based on the mathematical fact that a set of polarimetric measurements in one basis, e.g. H,V, can be converted into any other polarimetric basis, by a mathematical transformation. A basis set is a set of four polarisations. Each set is orthogonal, like LC (left-circular), RC (right-circular). The striking point is that only one set of polarimetric measurements in one basis needs to be recorded and the transformation in other polarimetric bases is done in a post processing step afterwards. There is no need to measure all bases, which is quite complicated in terms of engineering for elliptical and circular polarisation states.","name":"Polarisation synthesis","selfAssesment":"Completed
"},{"code":"PP2-2-7","description":"Polarimetry is the technique to evalute the physical phenomenon of polarisation including the measurement, the processing and the interpretation of the polarisation state of an electromagnetic wave. Polarization states are described by the scattering elipse and the Jones Vector formalism. Especially the polarization states after interaction with the media under investigation are mostly investigated to estimate media properties and states. The mostly observed fully polarimetric observation basis is H,V up to now with the single observations: HH HV, VH, VV. The concept of polarization synthesis allows to acquire fully polarimetric observations in one basis (e.g. H,V) and transform them into any other orthgonal basis (e.g. left, right circular) by a mathematical transformation in post processing. Polarimetric States are stored in different mathematical formats: Scattering matrix, polarimetric coherences , Stokes vector, Pauli-vector, lexicographic vector, coherency and covariance matrices. These mathematical representations can be decomposed according to the contained elementary scattering mechanisms in the recorded signal. The so-called polarimetric decomposition technique allow signal unmixing for differnt scattering components (e.g. from soil & vegetation). The techniques range from mathematics-based until physics-based concepts and are developed since decades starting with Huynen in 1970.","name":"Polarimetry","selfAssesment":"Completed
"},{"code":"PP2-2","description":" ","name":"Interaction of microwaves with matter","selfAssesment":"Planned
"},{"code":"PP2-3-1-1","description":" ","name":"Antenna gain","selfAssesment":"New
"},{"code":"PP2-3-1-2","description":" ","name":"Antenna pattern","selfAssesment":"New
"},{"code":"PP2-3-1-3","description":" ","name":"Aperture","selfAssesment":"New
"},{"code":"PP2-3-1","description":"Antenna is a device that radiates electromagnetic energy and collects such energy during reception.","name":"Radar antennas and antenna calibration","selfAssesment":"New
"},{"code":"PP2-3-10-2","description":" ","name":"Radargrammetric equation","selfAssesment":"Planned
"},{"code":"PP2-3-10","description":"Radargrammetry is the technique for extracting three-dimensional information from radar images. It applies photogrammetric principles to synthetic aperture radar (SAR) images. By viewing an object from different positions separated by a baseline, the appeared object position will vary slightly (denoted parallax). The disparities for each position on the object are related to its x-y-z coordinates. In radargrammetry, such disparities are computed for an entire image. The result is the terrain elevation from the measured parallaxes between two (or more) images, acquired at different angles. Radargrammetry requires at least two SAR images acquired from different positions, normally across-track due to the configuration of a side-looking SAR. Same-side stereo-pairs with intersection angles in the range of about 10 – 20° have been a feasible compromise between reasonable geometric disparities and the accuracy of estimated heights. In general, the disparities can be estimated with higher accuracy as the angle of intersection increases (as the stereo exaggeration factor increases). However, the same points must be recognized in all images, and it is hence required that the images are as similar as possible. This improves the image matching and it is best achieved with small intersection angles, which furthermore decreases radiometric differences. \r\nA general procedure for generating an elevation model from stereo-pairs is applicable for radargrammetry when optical stereo images are replaced with the backscatter intensity of SAR images. One image is selected as reference and the other(s) is coarsely registered to the reference, e.g., by using the attached meta-data. The same points are then located in both images using image matching. A common matching criterion is the cross correlation coefficient. Then, spatial point intersections are computed, which is the least square approach to find the intersection points of SAR range circles as defined from the matched image pixels. The computed intersections result in a point cloud that finally is interpolated to a consistent elevation raster. The entire process is extensive and computationally expensive, and normally a dedicated software is required. \r\nRadargrammetry with images acquired from opposite sides have been little investigated, and was first limited to stereoscopic viewing. Some opposite-side research was later presented with limited outcomes under certain conditions. Most applications today will not consider opposite-side radargrammetry, since the alternatives are usually better. Same-side radargrammetry performs better than opposite-side, while interferometric SAR that is based on phase differences, may be even more accurate. One advantage of radargrammetry is however, that it remains less affected by atmospheric disturbances compared to interferometric SAR, because it is using the amplitude images.","name":"Radargrammetry","selfAssesment":"Completed
"},{"code":"PP2-3-11-1","description":"Differential Synthetic Aperture Radar Interferometry (DInSAR) aims the determination of deformation of the Earth’s surface that happened between two or more complex-valued SAR acquisitions.\r\nThe phase of an interferogram issued from the complex multiplication of a SAR image with the complex conjugate of a second SAR image contains five distinct components, or layers of information:\r\n\tTwo phase components arise from the geometrical baseline (slightly different position of both sensor positions):\r\n\ta topographical information representing the surface relief, \r\n\ta “flat earth” pattern coming from the orbital distance of both sensor positions.\r\n\r\n\tTwo phase components result of the temporal baseline (time between both acquisitions):\r\n\ta deformation component, representing a possible displacement of the Earth’s surface between both acquisitions,\r\n\tan atmospheric component coming from different atmospheric conditions between both acquisitions.\r\n\r\n\tA phase component corresponding to intrinsic sensor noise \r\n\r\nBoth parameters related to the temporal baseline can be retrieved using DInSAR on repeat-pass acquisitions. DInSAR cannot be used with single-pass interferometry (e.g. both acquisitions acquired at the same time).\r\nThe deformation component of the interferometric phase corresponds to the modification of the phase of the second SAR image compared to the first due to an additional range difference between the sensor position and the Earth’s surface that is induced by the motion of the Earth’s surface towards or away from the initial sensor position.\r\nUsing DInSAR, the phase components related to the geometrical baseline can be eliminated from the interferogram using an existing DEM and orbit information, or an additional interferogram showing no deformation. After DInSAR processing, neglecting the remaining sensor noise, only the deformation and atmospheric components remain. The resulting deformation image is called differential and is characterized by color bands, or fringes, from whom the amount of the displacement can be retrieved. \r\nDInSAR can be used for mapping displacements and deformations due to earthquakes, landslides, or other geophysical processes inducing deformation of the Earth’s surface.\r\nUsing only one differential interferogram, mainly sudden and large scale changes between two acquisition can be mapped and quantified. However, the atmospheric phase component remains and may induce interpretation errors if it is not possible to eliminate it through e.g. precise weather models. Techniques of differential interferogram stacking (e.g. Persistent Scatterer Interferometry and Small-Baseline Subset) have been developed for long-term deformation monitoring which allow to filter the atmospheric phase component out.","name":"Differential Synthetic Aperture Radar Interferometry (DInSAR)","selfAssesment":"Completed
"},{"code":"PP2-3-11-2","description":"The Permanent or Persistent Scatterer (PS) approach allows the estimation of deformation time-series related to point-wise, high coherent scatterers on the ground based on processing long sequences of SAR data.\r\nPersistent Scatterer Interferometry (PSI -sometimes also called Permanent Scatterer Interferometry) is a particular DInSAR technique. It exploits multiple SAR images acquired over a specific area in order to retrieve the deformation phase component over time. In general, a minimum number of 15 SAR acquisitions is needed for PSI processing. Due to the large number of necessary acquisitions, the deformation component of the interferometric phase observations can be estimated very precisely (in the order of a few mm/yr) and other phase contributions such as atmospheric disturbances and topographic height differences can be better estimated and removed.\r\nPSI rely on so called Persistent Scatterer that are targets showing coherent phase behavior in time. Such targets are usually found on man-made structures such as buildings or bridges, or very stable features such as rocks. PSI is a technique that is therefore mainly used over urban or semi-urban terrain. Usually, PSs are selected based on their amplitude and phase power spectrum stability over time.\r\nThe main outcomes of a PSI analysis are a deformation velocity map and the displacement time-series of the single point targets, or PSs. The velocity map represents the deformation rate of the detected PSs in Line-of-Sight of the sensor, generally in mm/yr. Usually, subsidence, e.g. target moving away from the sensor, is represented in red, stable PSs in green and uplift, e.g. PSs moving toward the sensor in blue. The displacement time-series show for each PS the amount of the deformation, usually in mm, over the whole period of observation. Different phase model can be defined in order to retrieve the best possible estimate of the deformation, considering also seasonal displacements or breakpoints in the time-series.\r\nPerforming PSI analysis in both ascending and descending directions allows the fusion of the results in order to retrieve vertical and East-West component of the deformation. North-South deformation components cannot be retrieved due to the orbit configuration of the SAR satellites.\r\nPSI finds use in a large range of thematic applications related to subsidence and long-term change monitoring, such as infrastructure monitoring, groundwater reservoir monitoring, monitoring of mining areas, landslide inventory and monitoring, as well as volcanology.","name":"Permanent Scatterer Interferometry","selfAssesment":"Completed
"},{"code":"PP2-3-11-3","description":"Along-track InSAR (AT-InSAR) is a special mode of interferometric SAR (InSAR) where the individual SAR images have been acquired from the same flight track. With virtually identical geometric configuration of the individual SAR images, the measured phase difference is dominated by temporal changes occurring between the acquisitions. Consequently, AT-InSAR can be used to measure the displacement and/or radial velocity of targets on the ground, with the temporal offset between the acquisitions determining the time scale of the measurements. AT-InSAR can be implemented using one or more SAR sensors, in both single-pass and repeat-pass configurations, accommodating various needs. Using at least two sensors in a single-pass configuration allows the measurement of relatively high velocities, e.g., for vehicles and ocean waves. Conversely, using at least one sensor in a repeat-pass configuration allows the measurement of low velocities or displacements, e.g., for glaciers and due to volcanoes, earthquakes, subsidence, and landslides.","name":"Along-Track Interferometry","selfAssesment":"Completed
"},{"code":"PP2-3-11-4","description":"Across-track InSAR (XT-InSAR) is a special mode of interferometric SAR (InSAR), where the individual SAR images have been acquired from slightly different look directions. The measured phase difference contains information about the elevation of the targets on the ground, but it can also be affected by temporal changes between the individual SAR images. XT-InSAR can be implemented using one or more SAR systems in both single-pass and repeat-pass configurations. To mitigate temporal change between acquisitions, the XT-InSAR configuration is selected based on the intended application and frequency used by the system. If a single SAR sensor is used in the repeat-pass mode, temporal stability can be achieved either by a selecting a lower frequency and focussing on the larger, more stable targets (e.g., P-band, 435 MHz InSAR in forests) or by selecting a higher frequency and focussing on already stable environments (e.g., X-band, 9.65 GHz XT-InSAR in urban environments). Using two or more SAR sensors in a single-pass, tandem configuration, it is possible to measure elevation of temporally instable targets using higher frequencies, as demonstrated by the SRTM and TanDEM-X systems over vegetated areas and ocean.\r\nReferences: bamler/hartl, one on SRTM or TDM for DEM, one on BIOMASS for forestry, one on Sentinel-1 for urban areas, one on TDM on vegetation","name":"Across-Track Interferometry","selfAssesment":"Completed
"},{"code":"PP2-3-11-5","description":"Small Baseline Subset (SBAS) is a well-known technique of differential synthetic aperture radar (SAR) interferometry for the generation of surface deformation time-series by processing large sequences of SAR data acquired over the same region on Earth. \r\nThe method requires the preliminary generation of pairs of SAR images collected by slightly different orbital positions at different acquisition times. The phase difference of the interferometric SAR data pairs is extracted. The two-dimensional phase maps contains different contributions, but principally a component due to the terrain height of the observed area. The DInSAR technique relies on the estimation of the deformation of the terrain between the two interfering SAR images (i.e., the so-called master and slave images). To achieve this task, the phase contribution related to the terrain height is simulated and subtracted to the interferometric master/slave phase difference. The obtained differential SAR interferometric phase contains a direct information on the occurred deformation. Once a sequence of interferometric SAR data pairs is selected, the SBAS technique allows generating the time-series of the deformation of the terrain. The processing steps are essentially: i) the extraction of the full phase of the DInSAR interferograms, i.e., the phase unwrapping steps of the DInSAR interferograms, ii) the inversion of the sequence of unwrapped DInSAR phases, iii) the geocoding of the deformation maps from radar coordinates to geographical coordinates.","name":"Small Baseline Subset","selfAssesment":"Completed
"},{"code":"PP2-3-11","description":"Synthetic aperture radar (SAR) interferometry, or simply InSAR, is a remote sensing technique utilising the phase difference between two or more complex-valued SAR images. Most modern SAR systems are capable of measuring both the intensity and the phase of the reflected signal, where the latter carries information about the distance travelled by the signal. Consequently, the phase difference measured between two SAR images is determined by the geometry and timing of the individual SAR acquisitions. Different geometric and temporal configurations enable different applications. If the SAR acquisitions are made from different angles and without significant temporal change of the scene, InSAR can be used to create digital elevation models (DEMs) of the Earth, as demonstrated by the NASA/JPL Shuttle Radar Topography Mission (SRTM). If the individual SAR acquisitions are made at different times in the same geometric configuration, then InSAR can be used to measure radial velocity of targets and to assess displacements caused by, e.g., volcanoes and earthquakes.","name":"Principles of Interferometry","selfAssesment":"Completed
"},{"code":"PP2-3-12","description":"Multiple SAR acquisitions from different incidence angles. Allows to see e.g. 3D structure of forest. Similar to computer tomography in the medicine.","name":"Synthetic Aperture Radar (SAR) tomography","selfAssesment":"New
"},{"code":"PP2-3-13","description":"With this concept active and passive microwave imaging techniques are combined to record electromagnetic waves in an active (sending & receiving) and a passive (only receving) mode either simultaneously or with negigible time lag.\r\nThe active sensor is normally a Real Aperture Radar (RAR) or Synthetic Aperture Radar (SAR), while the passive sensor is a radiometer or synthetic aperture radiometer. Both acquisition modes can be operated on a single platform or on different platforms.\r\nSatellite missions with active-passive imaging capabilities are the NASA missions AQUARIUS and SMAP.","name":"Active-Passive microwave imaging","selfAssesment":"Completed
"},{"code":"PP2-3-2","description":"Systems measuring both amplitude and phase of the incident electromagnetic radiation.","name":"Coherent and active systems","selfAssesment":"New
"},{"code":"PP2-3-3","description":"This acquisition mode records only the incoming electromagnetic radiation emitted from the Earth. Radiometer instruments conduct passive microwave imaging. The energy budget of emitted radiation (from Earth) is significantly smaller than from instrument-generated, transmitted electromagnetic waves, used in the active microwave imaging mode. Hence, the signal to noise ratio is significantly worse for passive microwave imaging forcing a longer intergration time for robust signal recording. This results in a coarse spatial resolution of radiometer images (in the order of kilometers).","name":"Passive microwave imaging","selfAssesment":"Completed
"},{"code":"PP2-3-5","description":"A high-resolution real aperture radar (RAR) or an antenna using synthetic aperture having antennas aimed to the right or left of the flight path. An active, all-weather, day/night remote sensor.","name":"Side-looking Airborne Radar","selfAssesment":"New
"},{"code":"PP2-3-6","description":"A radar system with a synthetic aperture achived through computer operations. This improves the resolution in azimuth direction in propotion to aperture size.","name":"Synthetic Aperture Radar (SAR)","selfAssesment":"New
"},{"code":"PP2-3-7-1","description":" ","name":"Azimuth","selfAssesment":"New
"},{"code":"PP2-3-7-2","description":" ","name":"Range (far and near)","selfAssesment":"New
"},{"code":"PP2-3-7-3","description":" ","name":"Incident Angle","selfAssesment":"New
"},{"code":"PP2-3-7-4","description":"The beam sent out by the radar antenna (SAR, SLAR) illuminates an area on the targeted object. The footprint of an antenna is traditionally defined to be the area on the surface within the field of view subtended by the beamwidth of the antenna gain pattern.","name":"Antenna footprint","selfAssesment":"New
"},{"code":"PP2-3-7-5","description":"The spatial resolution of a synthetic aperture radar (SAR) system is the maximal distance between two targets, which are indistinguishable in the SAR image. SAR spatial resolution is determined individually in the two principal SAR image directions: ground range and azimuth (along-track). Ground range resolution for a SAR system is derived from slant range (across-track) resolution, by projecting it onto the ground surface using the incident angle, i.e., the angle between the line-of-sight and the ground surface normal. It is thus range-dependent, with finer resolution available in far range. Assuming adequate signal processing, slant range resolution of a SAR system is proportional to the speed of light and inversely proportional to the system bandwidth, i.e., the width of the used frequency interval. This caused by the fact that each individual frequency provides an independent measurement of the slant range, so a larger bandwidth implies more independent measurements contributing to the final slant range estimate. Similar principles apply to the azimuth direction. Assuming adequate signal processing, the SAR azimuth resolution is proportional to the along-track velocity of the SAR sensor and inversely proportional to the pulse repetition frequency (PRF) of the system. A lower interval between the consecutive pulses (higher PRF) results in better azimuth resolution due to faster sampling, but at the cost of range ambiguities occurring when echoes from one pulse are recorded after the next pulse has been transmitted.","name":"Synthetic Aperture Radar (SAR) spatial resolution","selfAssesment":"Completed
"},{"code":"PP2-3-7","description":"The Synthetic Aperture Radar (SAR) sensor is usually mounted on an aircraft or satellite. The instrument altitude above a reference surface stays constant over time, a condition that is easier to achieve for satellite sensors that stay on the same orbit than for aircrafts that are subject to atmospheric conditions. The sensor moves on a straight flight path, which is called the azimuth direction. It corresponds to the flight direction.\r\nSAR systems acquire information in oblique view, the antenna pointing sideways down to the ground. Most satellite systems use an antenna looking to the right side of the instrument. The ground area illuminated by the radar beam is called antenna footprint. As the sensor moves along the azimuth direction (along-track), the continuous strip of the ground area represented by the successive antenna footprints is called swath. \r\nThe looking direction of the SAR antenna is called range direction. It is often perpendicular to the azimuth direction (i.e. across-track), but can also present slightly differences depending on the acquisition mode. The angle between the nadir view and the range direction is called incidence angle.\r\nThe original SAR image is displayed in what is called slant-range geometry, i.e., it is based on the actual distance from the radar to each of the respective features in the scene. In the slant range direction, each point target’s backscatter is represented as a function of the time delay between the transmission of the electromagnetic pulse and its reception back at the sensor. This range depending representation induces geometric distortions in the SAR image. One distinguishes between near and far range: targets situated in near range are closer to the nadir direction and closer to the sensor than targets situated in far range. The image representation of targets is also more compressed in near range than in far range.\r\nThe slant-range representation can be converted in ground range representation, by projecting the image features orthogonally to a ground reference, allowing a proper planimetric position of the targets relative to one another.\r\nThis acquisition geometry allows the distinct mapping of scatterers corresponding to their respective distance to the sensor. It causes also geometric distortions in the radar image, i.e., relief displacement (foreshortening and layover) and shadow.","name":"Synthetic Aperture Radar (SAR) geometric configuration","selfAssesment":"Completed
"},{"code":"PP2-3-8-2","description":" ","name":"Local Incident Angle","selfAssesment":"Planned
"},{"code":"PP2-3-8-3","description":" ","name":"Foreshortening","selfAssesment":"Planned
"},{"code":"PP2-3-8-4","description":" ","name":"Layover","selfAssesment":"Planned
"},{"code":"PP2-3-8-5","description":" ","name":"Shadow","selfAssesment":"Planned
"},{"code":"PP2-3-8","description":"Synthetic Aperture Radar (SAR) backscatter is determined both by dieletric and geometric properties of the illuminated target. While the water content of the target plays an important role, its surface roughness determines the scattering mechanisms and the amount of incoming signal sent back to the sensor.\r\nDepending on its characteristics but also on the considered wavelength, a surface appears more or less rough. On smooth surfaces, specular reflection occurs, meaning that most of the incoming signal will be reflected away from the sensor. For rough surfaces, diffuse reflection occurs, meaning that part of the signal is scattered back to the sensor, the amount of it depending on different surface roughness parameters. \r\nDepending of the observed target and surface, single or multiple scattering mechanisms occur. A particularly important scattering mechanism is the double bounce, which occurs generally at two perpendicular surfaces (e.g. ground and building wall). Through two successive specular reflections, the whole signal comes back to the sensor.\r\nDue to the side-looking geometry of SAR systems and the range dependent image representation, specific additional effects occur and affect the backscatter intensity. Whereas a flat terrain only appears more compressed in near range and more stretched in far range, larger geometric distortions appear for terrain with more topography (e.g. mountains) or high objects (e.g. trees, buildings). This relief displacement is caused by the target’s elevation. A high elevated object is closer to the sensor than the ground below it. Due to the image formation in range direction depending on the distance between sensor and targets, its signal comes back sooner to the sensor and it is represented in the SAR image in nearer range than the ground below it. High objects in the SAR image are therefore displaced horizontally toward the radar antenna. This horizontal displacements contrast with the radial displacement observed in optical imagery due to central projection. Furthermore, such objects hide part of the ground below them, which do not receive any signal and cannot scatter information back. Three particular geometric distortions exist: foreshortening, layover and shadows.\r\nDepending on the illuminated target, different scattering mechanisms occur in combination with geometric distortions, which makes the interpretation of the SAR image challenging. A good example are buildings, where layover, shadow and single- and double-bounce occur.","name":"Terrain reflectivity and geometric distortions","selfAssesment":"Completed
"},{"code":"PP2-3-9","description":"It is a characteristic type granularity or image noise caused by the interference of waves reflected from many elementary scatterers.","name":"Speckle Formation","selfAssesment":"New
"},{"code":"PP2-3","description":"Microwave remote sensing systems detect and quantify the electromagnetic radiation arriving at a detector, this radiation being either emitted (passive sensors) or scatterered back (active sensors) from the objects.\r\nThree properties of the recorded electromagnetic signal are of particular interest: its intensity, its phase and its polarization. The specific quantification of each properties allows signal interpretation, as they depend on the roughness and dielectric characteristics of the surface (intensity and polarization) as well as of the range between target and sensor (phase).\r\nThe detection of the microwaves is operated through two principal sensor elements: an antenna and a receiver. The antenna collects the incoming radiation and the receiver measures the collected electric signal.\r\nAs active microwave systems produce their own electromagnetic radiation, they are equipped with two additional elements: a pulse generator and a transmitter. Usually, transmitter and receiver are situated on the same antenna.\r\nA simple detector system only detects the intensity of the signal and amplifies it. Coherent systems measure both the amplitude and the phase of the incident electromagnetic radiation.\r\nMicrowave systems can be categorized in two different types: imaging and non-imaging sytems. Whereas for non-imaging systems each echoe (collected signal) provides a single measurement, imaging systems collect a sequence of echoes that generate a two dimensional image.","name":"Detecting microwaves","selfAssesment":"Completed
"},{"code":"PP2","description":"Microwave remote sensing operates in the microwave portion of the electromagnetic spectrum, generally using wavelengths greater than 3 cm and up to 1 m. \r\nMicrowaves are sensitive to different physical parameters than other regions of the electromagnetic spectrum. Microwaves interactions with objects are governed by geometric (structure, size, shape) and dielectric (water content) properties, whereas other regions of the electromagnetic spectrum reacts e.g. to object temperature or “color” (amount of reflection or absorption of the Sun light by a particular object).\r\nAs a general rule, microwaves interact with object at least as big as the wavelength. Smaller objects will therefore be transparent for the signal. Due to the large wavelengths, atmospheric particles are almost transparent to the signal and microwave remote sensing can penetrate clouds. Under very dry conditions, microwaves can even penetrate up to a few meters the top soil layers, therefore providing information that is not visible in other regions of the electromagnetic spectrum. Depending on the considered wavelength, microwave can also penetrate vegetation layers to different amounts.\r\nIn microwave remote sensing, three characteristics of the electromagnetic wave play an important role: its amplitude, its phase and its polarization. Depending on the application, either one characteristic or a combination of them is used to retrieve information.\r\nThere are two main types of microwave sensors: active RADAR systems and passive radiometers. RADAR is an acronym for RAdio Detection And Raging. In this thesis data from an active, imaging radar were analyzed. An active radar system sends out pulses and records the echoes scattered back by the objects (scatterers) to the sensor. The systems use the two-way travel time of the radar pulse to determine the distance (range) to the illuminated object. Its backscatter intensity is determined by the radar system and object properties and depends on the quantity of energy coming back to the sensor. Active radar systems transmit a signal and record the amount of energy that is scattered back and depends of both dielectric and geometric properties. Passive radiometers record microwave energy, which is emitted by the Earth’s surface.\r\nDepending on the type of system, microwave remote sensing can be used in multiple applications. Active sensors are principally used for diverse land cover mapping applications based on the particular backscattering mechanisms and characteristics of the objects on the Earth’s surface. Using multiple acquisitions, they are also favored for topographic, deformation and velocity mapping. Passive sensors are preferred for the determination of hydrologic variables such as soil moisture, precipitation, ice water content and sea-surface temperature.","name":"Microwave remote sensing","selfAssesment":"Completed
"},{"code":"PS","description":"Remote sensing, i.e. the process of obtaining information about an object or area from a distance, is not possible without remote sensing sensors that collect this information and the platforms on which the sensors are installed and which are used to move them. Remote sensing sensors collect data by detecting energy that is reflected or emitted from Earth. There are different types of remote sensing sensors. The interaction between the sensor and the Earth's surface has two modes: active or passive. Passive sensors use solar radiation to illuminate the Earth's surface and detect reflection from the surface or measure the emitted energy. They usually record electromagnetic waves in the visible (˜430–720 nm) and near infrared (NIR) (˜750–950 nm) through short infrared (SWIR) (˜1.500-2.500 nm) to thermal infrared (TIR) (8.000-14.000 nm) ranges. The power measured by passive sensors is a function of surface composition, physical temperature, surface roughness and other physical properties of the Earth. Active sensors provide their own energy source to illuminate objects and measure their properties. These sensors use electromagnetic waves in the visible and near infrared range (e.g.laser altimeter) and radar waves (e.g. synthetic aperture radar (SAR)). As sensor technology has advanced, the integration of passive and active sensors into one system has emerged. Alternatively, remote sensing sensors can be classified into imaging sensors, i.e. that produce an image of an area, within which smaller parts of the sensor's whole view are resolved (pixels), and non-imaging sensors, i.e. that return a signal based on the intensity of the whole field of view. In terms of their spectral characteristics, the imaging sensors include optical imaging sensors, thermal imaging sensors, and radar imaging sensors. These sensors can be on satellites, mounted on aircraft, unmanned aerial vehicle (UAV), drone or ground. The collected information can be transformed into an image or set of points (e.g. cloud points), which can be further processed and analyzed to obtain the necessary information, e.g. agricultural field development phase, level of air pollution, etc.\r\nA digital imagery of Earth observation sensors is a two-dimensional representation of objects on Earth. Current images collected from different levels of acquisition, from ground to satellite, with the help of electronic sensors are examples of digital images. There are different aspects and characteristics of remote sensing data and images, such as, for example, data formats and processing levels, data storage, data properties.","name":"Platforms, sensors and digital imagery","selfAssesment":"Completed
"},{"code":"PS1-1","description":"Remote sensing sensors has its roots in the 19th century in the development of photography. Photography was an invention that made it possible to acquire a permanent image. The first photographic image was taken in 1826 by Joseph Nicephore Nieppce. While the first aerial photograph was taken in 1858 by Felix Tournachon, known as Nadar, from a tethered baloon over Biévre Valley in France. In 1907 Julius Neubronner developed a light miniature camera that could be fitted to a pigeon's breast. It can be said that the construction camera + pigeon was the precursor of today's unmanned aerial vehicle (UAV) or drone. Further developments focused on developing new sensors (analog vs. digital frame cameras) and how to save and store images (e.g. photographic emulsions, films). The origin of other types of remote sensing can be traced to World War II, with the development of radar, sonar, and thermal infrared detection systems. Since the 1960s, sensors were designed to operate in virtually all of the electromagnetic spectrum. Both civil and military aerial photography have long been widely used in cartography to create maps. Specialized large format cameras (looking vertically down, assuming the plane is flying horizontally) were developed. Such cameras have been specially designed to perform almost vertical sequences of bird-eye exposures during aircraft flight. Hence for a long time remote sensing consisted of aerial photography and photogrammetry using analogue mechanical or optical equipment. Everything has changed with satellites and the space race. The first real success of remote sensing satellites in serious scientific work was in meteorology, weather satellite TIROS-1, launched by NASA on April 1, 1960. \r\nToday a wide variety of remote sensing instruments are available as data source for use in different applications for land, water and atmosphere monitoring.","name":"History of remote sensing sensors","selfAssesment":"In progress
"},{"code":"PS1-2-1-1-1","description":"Along track scanner, also known as a pushbroom scanner, is an optoelectronic device that obtains images with a multispectral imaging system. The scanners are used for passive remote sensing. It records electromagnetic energy that is reflected (e.g., blue, green, red, and infrared light) or emitted (e.g., thermal infrared radiation) from the surface of the Earth. The scanners are mounted on space- or aircrafts. \r\nA two-dimensional image is created (line by line) by exploiting the platform motion along the orbital track. The data are collected along track using a linear array of detectors arranged perpendicular to the direction of travel. The array of detectors are pushed along the flight direction to scan the successive scan lines, and hence the name pushbroom scanner. \r\nThere are no moving parts on a pushbroom sensor, hence, the scanning speed can be increased compared to across track systems. A longer dwell time over each ground resolution cell increases the signal strength (high radiometric resolution, no pixel distortion). Additionally, finer spatial and spectral resolution can be achieved as the size of the ground resolution cell is determined by the Instantaneous Field of View (IFOV) of a single detector. The systems are designed for high-resolution imaging. However, a very large number of detectors is needed for high resolution images. It is a complex optical system. In addition, the pushbroom scheme requires a wide Field of View (FOV) optics system to obtain the same swath as for a corresponding whiskbroom (across track) scanner. It has narrow swath width. \r\nThe detector arrays with such a line-scanning pushbroom system are usually of the type Charge-Coupled Device (CCD).\r\nThe MultiSpectral Instrument (MSI) on board the Sentinel-2 satellite (Copernicus mission) uses a pushbroom concept.\r\nMultispectral imaging systems building the final image (line by line) exploiting the platform motion along the orbital track. No rotating mechanical part required, usually based on a CCD matrix (high spectral resolution but just up to 1 micrometer), e.g. Sentinel-2 MultiSpectral Instrument (MSI), Sentinel-3 Ocean and Land Colour Imager (OCLI).","name":"Along track scanners","selfAssesment":"Completed
"},{"code":"PS1-2-1-2-1","description":"The cameras, usually a charge-coupled device (CCD) or Complimentary Metal Oxide Semiconductor (CMOS), that convert light into electrons that can be measured and converted into radiometric intensity value.","name":"Digital Frame Camera","selfAssesment":" "},{"code":"PS1-2-1-2","description":"2-D systems with the ability to observe in two dimensions simultaneously.","name":"Area Arrays","selfAssesment":"New
"},{"code":"PS1-2-1","description":"In principle, one-dimensional systems, whisk- and pushbroom, that form an image on a line-by-line basis in the scan direction.","name":"Line detector arrays","selfAssesment":"New
"},{"code":"PS1-2-2-1-1","description":"Thermal radiometers are radiometers with the capability of measuring the spectrum of infrared emission. As such, they are characterized by a relatively high spectral resolution (normally better than 1 cm-1 in wave number units). Modern Spectrometers on board satellites have a spectral resolution better than 0.7 cm -1 in order to properly resolve CO2 lines used for the retrieval of the atmospheric temperature profile. Based on the optical layout they are further classified in grating spectrometers and Fourier Transform Spectrometers or FTIR.","name":"Thermal Radiometers","selfAssesment":"New
"},{"code":"PS1-2-2-1-2","description":"Passive microwave radiometers are radiometers that measures energy emitted at millimetre-to-centimetre wavelengths at 0.15 - 30 cm (frequencies of 1–200 GHz). Example of a sensor: SMOS Microwave Imaging Radiometer with Aperture Synthesis (MIRAS), which aims the measurement of land soil moisture and ocean salinity.","name":"Passive Microwave Radiometers","selfAssesment":"New
"},{"code":"PS1-2-2-1-3","description":"An advanced multispectral sensor that detects hundreds of very narrow spectral bands throughout the visible, near-infrared, and mid-infrared portions of the electromagnetic spectrum.","name":"Hyperspectral Radiometers","selfAssesment":"Planned
"},{"code":"PS1-2-2-1-4","description":"A radiometer that measures the intensity of radiation in multiple wavelength bands (i.e., multispectral). Example of a sensor Moderate Resolution Imaging Spectroradiometer (MODIS)","name":"Spectroradiometers","selfAssesment":"In progress
"},{"code":"PS1-2-2-2","description":"Provide information about vertical profiles of temperature and molecular consistuent concentrations in the atmosphere (atmospheric sounders).","name":"Atmospheric passive sounders","selfAssesment":"New
"},{"code":"PS1-2-2","description":"Radiometers are instruments which measure radiative intensities within a particular frequency window. A radiometer is further identified by the portion of the electromagnetic radiation it covers, usually the infrared or microwave regions. Normally the spectral range extends from the longwave (14-15 micron) to the shortwave (3-4 micron). This range overlaps much of the emission spectrum of Earth. The technology is classified in broadband radiometer of spectral radiometers depending on the spectral resolution. A radiometer measures the intensity of the radiative energy, but does not differenciate between the different registered wavelengths or their respective amplitude. In other terms, it provides a single value as combined result of all wavelengths within the considered frequency window.","name":"Radiometers","selfAssesment":"In progress
"},{"code":"PS1-2","description":"Passive remote sensing systems record electromagnetic energy that is reflected (e.g., blue, green, red, and infrared light) or emitted (e.g., thermal infrared radiation) from the surface of the Earth. Passive sensors therefore rely on an external energy source (e.g. sun illumination, Earth heat emission). Contrary to passive sensors, who detect naturally occurring radiation, active sensors emit radiation and collect and analyze the signal that is sent back by the Earth’s surface or atmosphere. Active remote sensing systems produce therefore their own electromagnetic energy. They transmit and receive the radiation that is reflected or backscattered from the illuminated target. They do not necessitate an external source of radiation (e.g. Sun or Earth). Contrary to most passive sensors that are bound to detecting either the reflected Sun radiation or emitted radiation by the Earth’s surface in ranges from the ultraviolet to the thermal infrared, active sensors can use any radiation from the electromagnetic spectrum, the only limitation being the transparency of the Earth’s atmosphere. They often use wavelengths that are not sufficiently provided by the Sun, e.g. microwaves. \r\nActive systems can be categorized either according to their imaging capability, or according to the considered emitted wavelength, or also according to the way they use the returned signal. For the last category, it is generally distinguished between ranging systems, which use as principal information the time delay between transmission and reception of the electromagnetic radiation at the sensor, and scattering systems, which consider the strength (also called magnitude or intensity), of the returned signal. Some systems also register both information.\r\nAs active sensors produce their own radiation and do not rely on e.g. Sun radiation, they are daytime independent and can also retrieve information about the Earth’s surface by night. Furthermore, depending of the considered wavelength, active sensors are weather independent. For longer wavelengths of the microwave domain, clouds are transparent, as the transmitted wavelength is larger than the water particles constituting the cloud and do not interact with them. \r\nActive sensors can control the direction of their illumination to a specific target to be investigated, but require in general more energy than passive sensors as they “actively” illuminate the Earth’s surface.","name":"Passive vs. active sensors","selfAssesment":"Completed
"},{"code":"","description":"","name":"","selfAssesment":" "},{"code":"","description":"","name":"","selfAssesment":" "},{"code":"","description":"","name":"","selfAssesment":" "},{"code":"","description":"","name":"","selfAssesment":" "},{"code":"PS1-3-1-1","description":"Imaging radar is an active radar system that sends out pulses and records the echoes scattered back by the objects (scatterers) to the sensor. Imaging radars are independent of weather conditions and can operate day or night. It uses microwave wavelengths, radar bands from X- to P- or VHF-band, in four polarisations to illuminate an area on the ground. Normally only the horizontal (H) or vertical (V) linear polarizations are used. The radar system is characterized by combination of polarization of transmitted and received pulse: HH, HV, VH or VV. A typical radar system measures the strength and roundtrip time of the microwave signals that are emitted by a radar antenna and reflected off a target area. An imaging radar is therefore both and imaging and a ranging system. The illuminated objects are mapped in the radar depending on their backscatter intensity and respective range to the sensor.\r\nImaging radar can be mounted on aircraft or satellite. It operates in a side-looking configuration, left or right with reference to the flight direction. This acquisition geometry allows the distinct mapping of scatterers corresponding to their respective distance to the sensor. It causes also geometric distortions in the radar image, i.e., relief displacement (foreshortening and layover) and shadow. The radar sensor operates not in the real aperture of the radar antenna, i.e., real spatial width, radar (RAR) mode but in the synthetic aperture radar (SAR) mode. Synthetic aperture is possible to set up through the forward motion of the spacecraft, which enables to “extend” the real size of the radar antenna. With a SAR, each object on the ground is sampled at several antenna positions along the flight path, i.e., as long as the antenna beam is illuminating it.\r\nImaging radar can be used for a different of land and water applications.","name":"Imaging Radar","selfAssesment":"Completed
"},{"code":"PS1-3-2-1-1","description":"Laser profilers measure 1D range profiles and operate in different environments, like spaceborne, airborne and indoor. Most of them operate top-down on flying platforms, but as well bottom-up is possible, e.g. in meteorology for cloud monitoring.\r\nIt is the simplest application of the LIght Detection And Ranging technique. It transmits a short pulse of energy (visible or near-infrared radiation) and detects 'echo', by measuring the time delay and knowing the speed of propagation of the pulse, the range from the instrument to the surface can be measured.","name":"Laser profiler","selfAssesment":"In progress
"},{"code":"","description":"","name":"","selfAssesment":" "},{"code":"","description":"","name":"","selfAssesment":" "},{"code":"","description":"","name":"","selfAssesment":" "},{"code":"","description":"","name":"","selfAssesment":" "},{"code":"","description":"","name":"","selfAssesment":" "},{"code":"","description":"","name":"","selfAssesment":" "},{"code":"PS1-3-2-1-4","description":"The radar altimeter is similar in operation to the laser profiler, it operates at a much longer wavelength.","name":"Radar altimeters","selfAssesment":"New
"},{"code":"PS1-3-2-1","description":"Laser altimeters historically were the first active sensing devices used on airborne platforms, measuring range information in form of single distances. Nowadays, they are still found on low-cost platforms like drones to determine the flight altitude. The instrument is also used aboard planet-orbiting satellites to map a planet's terrain.","name":"Laser altimeter","selfAssesment":"In progress
"},{"code":"PS1-3-2-3","description":"By a ranging camera the simultaneous capturing of range measurements in the form of a range image for an extended area of dynamical 3D applications is given. Applications are building surveillance, traffic monitoring, and driver assistance.","name":"Ranging camera","selfAssesment":"In progress
"},{"code":"PS1-3-2-4","description":"Laser scanners capture data by successively considering points on a discrete, regular (typically spherical) raster, and recording the respective geometric and radiometric information.\r\nThere are different types of laser scanners taking into account its application:\r\nSpaceborne LS (e.g. Geoscience Laser Altimeter System - GLAS) provides global measurements of the Earth's surface with the potential on capturing additionally clouds and atmospheric aerosols. The spaceborne measurements allow to globally observe ice sheet and land elevations, approximate sea ice thickness, changes in elevation through time, vegetation coverage for biomass estimation, and height profiles of clouds and aerosols.\r\nAirborne laser scanning (ALS) systems allow a direct and illumination-independent measurement from 3d objects in a fast, remote and accurate way. Beside basic range measurements, the current commercial ALS developments allow to record the waveform of the backscattered laser pulse. Latest trends in sensor developments focus on single-photon detection. Different applications are of interest, like urban planning, forestry surveying, or power line monitoring. Further to describe the 3D scene, products like digital terrain models (DTMs), digital surface models (DSMs), or city models are provided.\r\nA terrestrial laser scanning (TLS) system is a stationary highly accurate ranging device for geodetic surveying. More specifically, TLS systems provide dense and accurate 3D point cloud data for the local environment and they may also reliably measure distances of several tens of meters. Due to these capabilities, such TLS systems are commonly used for applications such as city modeling, construction surveying, scene interpretation, urban accessibility analysis, or the digitization of cultural heritage objects. When using a TLS system, each captured TLS scan is represented in the form of a 3D point cloud consisting of a large number of scanned 3D points and, optionally, additional attributes for each 3D point such as color or intensity information. However, a TLS system represents a line-of-sight instrument and hence occlusions resulting from objects in the scene may be expected as well as a significant variation in point density between close and distant object surfaces. Thus, a single scan might not be sufficient in order to obtain a dense and (almost) complete 3D acquisition of interesting parts of a scene and, consequently, multiple scans have to be acquired from different locations.\r\nA mobile laser scanning system consists of a moving vehicle equipped with one or more usually side-looking laser scanners to capture information about the local 3D geometry. \r\nUnderwater LS is applied in deep-sea as well as in shallow water regions. The ranging distance is close range and the measurement principle relies on triangulation by laser light, comparable with structured-light-projection. More recently, companies started to develop Time-of-Flight (ToF) underwater laser scanners.\r\nFor Bathymetric LS the utilized green laser light with its potential penetration capabilities in water is essential. For water surface mapping the electromagnetic radiation of the laser penetrates into the topmost layer of the water column and can also be used for mapping the water surface and shallow water bathymetry. Area-wide water surface heights and depths are required for many disciplines such as hydrology, hydraulic engineering, flood risk management, ecology, climate change, etc.","name":"Laser scanner","selfAssesment":"Completed
"},{"code":"PS1-3-2","description":"The main idea of LiDAR (Light Detection and Ranging) technology is based on actively scanning the scene by involving a device which emits electromagnetic radiation in the form of modulated laser light. \r\nGenerally, such scanning devices illuminate a scene with modulated laser light and analyze the backscattered signal. More specifically, laser light is emitted by the scanning device and transmitted to an object. At the object surface, the laser light is partially reflected and, finally, a certain amount of the laser light reaches the receiver unit of the scanning device. The measurement principle is therefore of great importance as it may be based on different signal properties such as amplitude, frequency, polarization, time, or phase. \r\nMany scanning devices are based on measuring the time t between emitting and receiving a laser pulse, i.e., the respective time-of-flight, and exploiting the measured time t in order to derive the distance r between the scanning device and the respective 3D scene point. Alternatively, a range measurement r may be derived from phase information by exploiting the phase difference Δφ between emitted and received signal. According to seminal work, respective scanning devices may be categorized with respect to laser type, modulation technique, measurement principle, detection technique, or configuration between emitting and receiving component of the device. \r\nIn order to get from single 3D scene points to the geometry of object surfaces, respective scanning devices are typically mounted on a platform which, in turn, allows a sequential scanning of the scene by successively measuring distances for discrete 3D points.\r\nLiDAR technology is used for a diversity of applications such as autonomous driving, forestry, biomass estimation, precision farming, archaeology, city mapping, terrain modelling, and metrology.","name":"LiDAR (Light Detection and Ranging)","selfAssesment":"Completed
"},{"code":"PS1-3-3-1","description":" ","name":"Sonar","selfAssesment":"New
"},{"code":"PS1-3-3-2","description":" ","name":"Seismic sensor","selfAssesment":"New
"},{"code":"PS1-3-3","description":"Instruments that measure vertical distribution of precipitation and other atmospheric characteristics such as temperature, humidity, and cloud composition.","name":"Sonic sensors","selfAssesment":"New
"},{"code":"","description":"","name":"","selfAssesment":" "},{"code":"PS1-3-4-1","description":"Surface basckatter is measured as a function of the frequency, polarization, and illumination direction of the sensing signal (microwaves).","name":"Radar Scatterometers","selfAssesment":"Planned
"},{"code":"PS1-3-4-2-1","description":"Differential Absorption Lidar (DIAL) is a laser remote sensing technique that is used for range and/or profile measurements of atmospheric gas concentrations and constituents.","name":"Differential Absorption Lidar","selfAssesment":"In progress
"},{"code":"PS1-3-4-2-2","description":"Cloud-Aerosol Lidar with Orthogonal Polarization (e.g. CALIOP) is a two-wavelength polarization-sensitive LiDAR that provides high-resolution vertical profiles of atmospheric aerosols and clouds to enable an greater understanding of our climate.","name":"Doppler Wind LiDAR","selfAssesment":"New
"},{"code":"PS1-4","description":"Imaging radiometers are used to spatially map the variation of radiation. Operate primarily at window frequencies, where atmospheric absorption is low and surface features can be imaged or measured.","name":"Imaging vs. nonimaging sensors","selfAssesment":"New
"},{"code":"PS1-5-1-2","description":"Across track scanners, known as whiskbroom electromechanical scanners, are multispectral imaging systems building the final image (ground cell by ground cell) by combination of the platform motion along the orbital track with a mechanical rotation of the collecting optic in the across track direction. Opto-mechanical are typically multi-spectral radiometers (no limitation on bands), whiskbroom systems are usually CDD spectrometers (high spectral resolution but just up to 1 micrometer). Examples of the sensors: Landsat Multispectral Scanner (MSS), Landsat Thematic Mapper (TM).","name":"Across track scanners","selfAssesment":" "},{"code":"PS1-5-1","description":"Speckle-pattern based sensors operate with a spatial neighborhood codification strategies to exploit a unique pattern. The label associated to a pixel is derived from the spatial pattern distribution within its local neighborhood. Thus, labels of neighboring pixels share information and provide an interdependent coding. Representing one of the most popular devices based on structured light projection, the Microsoft Kinect exploits an RGB camera, an IR camera, and an IR projector. The IR projector projects a known structured light pattern in the form of a random but unique speckle dot pattern onto the scene. As IR camera and IR projector form a stereo pair, the pattern matching in the IR image results in a raw disparity image which, in turn, is read out as depth image.","name":"Speckle-pattern based sensor","selfAssesment":"In progress
"},{"code":"PS1-5-2","description":"A multi-temporal (sequential) binary coding uses black and white stripes to form a sequence of projection patterns for each point on the surface of the object. Binary coding technique is very reliable and less sensitive to the surface characteristics, since only binary values exist in all pixels. Thus, each pixel may be assigned a codeword consisting of its illumination value across the projected patterns. The respective patterns may, for instance, be based on binary codes or Gray codes and phase shifting. To achieve high spatial resolution, a large number of sequential patterns need to be projected. All objects in the scene have to remain static. The entire duration of 3D image acquisition may be longer than a practical 3D application allows for. These sensors are utilized in industrial environment.","name":"Multi-temporal pattern based sensor","selfAssesment":"In progress
"},{"code":"PS1-5-3","description":"For a multi-spectral pattern based sensor, various continuously varying color patterns to encode the spatial location information are utilized.","name":"Multi-spectral pattern based sensor","selfAssesment":"New
"},{"code":"PS1-5","description":"A structured-light-projection camera emits active optical radiation in the form of a coded structured light pattern in the visible or infrared spectrum, or electromagnetic radiation in the form of modulated laser light. Via the projected pattern, particular labels are assigned to 3D scene points which, in turn, may easily be decoded in images when imaging the scene and the projected pattern with a camera. The procedure reminds to conventional stereo processing, where corresponding features must be extracted from a pair of stereo images to derive the spatial information. In contrast, such synthetically generated features allow to robustly establish feature correspondences, and the respective 3D coordinates may easily and reliably be recovered via triangulation. Generally, techniques based on the use of structured light patterns may be classified depending on the pattern codification strategy.","name":"Structured-light-projection camera","selfAssesment":"In progress
"},{"code":"PS1-6","description":" ","name":"Ground penetrating RADAR (GPR)","selfAssesment":"New
"},{"code":"PS1-7","description":"An optical spectrometer is an instrument used to detect, measure and analyze the spectral content of the incident electromagnetic field (narrow-band, VIS, NIR, SWIR and TIR). It breaks down the incoming light spectrum so the whole wavelength range is mapped and each wavelength can be analysed individually. Usually, a distinction is made between optical and mass spectrometers.\r\nOptical spectrometers depict the intensity of the incoming light in function of the wavelength. Considering all wavelengths, each object has a specific spectral signature and the analyse of their particular spectrum allows the deduction of their composition ( e.g. pigments) or health.","name":"Optical spectrometers","selfAssesment":"In progress
"},{"code":"PS1","description":"Remote sensing sensors acquire information about objects situated on the surface of e.g. the Earth remotely, e.g. from a distance, without any physical contact. They detect and measure the changes that the object imposes on its. \r\nRemote Sensing sensors are characterized according to several different properties:\r\n\tDepending on the interaction between the sensor and the Earth’s surface, one distinguishes between active (e.g. radar) and passive (e.g. optical imagery) sensors. Some systems use both kind of sensors simultaneously.\r\n\tDepending on the mapping process of the information, it can be distinguished between imaging and non-imaging sensors. Imaging sensors produce an image of an area of interest, e.g. give a spatial information about the incoming information. Spatial relationships between objects can be identified and used for visual interpretation. Non-imaging sensors register usually single response values for a specific area, and do not record how the incoming information varies across the field of view. They can be used to characterize the interaction between the received information and illuminated target.\r\n\tDepending on the platform on which the instrument is deployed, one speaks either of ground based (e.g. terrestrial laser scanner), airborne (e.g. plane, drone), or spaceborne (e.g. satellite) sensor. For spaceborne sensors, the orbit geometry (e.g. geostationary, equatorial, sun-synchronous) and altitude (high, medium and low Earth orbit) play an important role, as it most often determines the application of the satellite in combination with the deployed sensor (weather satellites or Earth observation satellite). \r\n\tDepending on the observed portion of the electromagnetic spectrum (e.g. optical, infrared, thermal, microwave). \r\n\tDepending on the instrument (e.g. imagers, altimeters, spectrometers, radiometers). \r\n\tDepending on the instrument precision, e.g. in terms of spatial resolution very high vs. low resolution sensors; in terms of spectral resolution narrow band (hyperspectral sensors) vs. broad-band sensors (mono- and multispectral sensors); in terms of radiometric resolution very high vs. low resolution sensors. Some applications do not require very high precision instruments, e.g. sea surface temperature measurements, while other, e.g. for vegetation monitoring, require high spectral and radiometric resolution for good data interpretation and analysis. \r\nOther categorization would include the specific applications of each sensor (weather, environment, urban, land, water, mapping, photogrammetry, structure-from-motion, etc.) and if is financed and used for scientific, commercial or military goals.","name":"Types of remote sensing sensors","selfAssesment":"Completed
"},{"code":"PS2-1","description":"This topic covers information on the first remote sensing platforms that were used to obtain aerial photos. The first-known aerial photo was obtained in 1858 by Gaspard Felix Tournachon (Nadar). Afterwards, different platforms were used to obtain the information from above. The history of the development of remote sensing platforms includes platforms such as baloons, kites, rockets, pigeons, gliders, etc. to recent low-cost femtosatellites, e.g. for solar radioation pressure measurements. Historically, the main developments of the platforms as well as sensors was associated with military operations in the XXth century. Remote sensing data was used as part of photo- or/and satellite reconnaissance, i.e. aerial photos or satellite imageries used for the military purposes, mainly to make accurate maps and based on that to prepare a military strategy.","name":"History of Remote Sensing Platforms","selfAssesment":"New
"},{"code":"PS2-2-1","description":"An unmanned aircraft system (UAS) includes an unmanned aerial vehicle (UAV) (commonly known as a drone), an aircraft without a human pilot on board, a ground-based controller, and a system of communications between the two.","name":"Unmanned Aerial Systems (UAS)","selfAssesment":"New
"},{"code":"PS2-2-2-1","description":"Planning of an aerial photography mission taking into account time of day/sun angle, weather conditions, flightline.","name":"Mission planning","selfAssesment":"New
"},{"code":"PS2-2-2-3-2-3-1","description":"Antenna pointing is fixed relative to the flight line (coarse-resolution data).","name":"Stripmap","selfAssesment":"New
"},{"code":"PS2-2-2-3-2-3-2-1","description":"https://ieeexplore.ieee.org/document/6587758","name":"Staring Spotlight","selfAssesment":" "},{"code":"PS2-2-2-3-2-3-2","description":"The sensor steers its antenna beam to continuously illuminate a specific spot (high resolution data).","name":"Spotlight","selfAssesment":"New
"},{"code":"PS2-2-2-3-2-3-3-1","description":"https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-1-sar/sar-instrument/acquisition-modes","name":"Interferometric Wide Swath Mode","selfAssesment":"New
"},{"code":"PS2-2-2-3-2-3-3-2","description":"https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-1-sar/sar-instrument/acquisition-modes","name":"Extra Wide Swath Mode","selfAssesment":"New
"},{"code":"PS2-2-2-3-2-3-3","description":"The sensor steers the antenna beam to illuminate a strip of terrain at any angle to the path of aircraft motion.","name":"ScanSAR","selfAssesment":"New
"},{"code":"","description":"","name":"","selfAssesment":" "},{"code":"","description":"","name":"","selfAssesment":" "},{"code":"","description":"","name":"","selfAssesment":""},{"code":"","description":" ","name":" ","selfAssesment":" "},{"code":"PS2-2-2","description":"Since the 1940s aerial imagery has been the primary source of detailed geospatial data for extensive study areas. Photogrammetry is the profession concerned with producing precise measurements from aerial imagery. Aerial imaging and photogrammetry represent a major component of the geospatial industry. The topics included in this unit do not comprise an exhaustive treatment of photogrammetry, but they are aspects of the field about which all geospatial professionals should be knowledgeable.","name":"Airborne platforms and systems","selfAssesment":"New
"},{"code":"PS2-2-3-1","description":"Earth observation (EO) missions are gathering information about the physical, chemical, and biological systems of the planet via remote-sensing technologies, supplemented by Earth-surveying techniques, which encompasses the collection, analysis, and presentation of satellite data.","name":"Earth observation missions","selfAssesment":"In progress
"},{"code":"PS2-2-3-2","description":"There are essentially three types of Earth orbits: high, medium and low Earth orbit. Satellites that orbit in a medium (mid) Earth orbit include navigation and specialty satellites, designed to monitor a particular region. Most scientific satellites, including NASA’s Earth Observing System fleet, have a low Earth orbit. On which orbit a satellite will be launched to, depends mainly on its application. The orbit types can be categorized according to their height.\r\nThe orbit height of a satellite corresponds to the distance between the Earth’s surface and the satellite. It determines its speed as it rotates around the Earth. Due to Earth’s gravity, the pull of gravity is stronger for lower orbits than for higher orbits. Therefore, a satellite situated on a lower orbit will circle the Earth faster than a satellite situated on a higher orbit.\r\n\tHigh Earth orbit: it describes orbits situated at about 36000 km above the Earth’s surface (42164 km from the Earth’s center). At this exact distance, the speed of the satellite on the orbit matches the Earth’s rotation, i.e. the satellite needs 24 hours to complete a full rotation on the orbit, when the orbit is situated exactly above the equator. Such orbits are also called geosynchronous orbits, as the satellite moves at the same speed than the Earth and seems to stay in place over a specific location. Those orbits are mainly used for weather and communication satellites\r\n\tMedium Earth orbit: it describes orbits situated at about 20200 km of the Earth’s surface, or 26560 km of the Earth’s center. At this height, a satellite rotates twice around the orbit during one Earth’s rotation. This orbit is also called semi-synchronous and this is the orbit type used by Global Navigation Satellite Systems such as GPS and GLONASS. A further important medium Earth orbit is the Molniya orbit which allows the observation of the poles, otherwise nearly impossible with equatorial geosynchronous orbits.\r\n\tLow Earth orbit: this type of orbits are used from almost all dedicated scientific Earth Observation satellites. Most of them use a particular, nearly polar orbit inclination, meaning that the satellite rotates around the Earth nearly from pole to pole (instead of around the equator as it is the case for geosynchronous satellites). This rotation takes about 99 minutes, depending of the specific orbit inclination. During one half of the orbit, the satellite views the daytime side of the Earth, i.e. the illuminated side. At the pole, satellite crosses over and views the nighttime side of Earth. Back to the daylight side, the satellite can view the area adjacent to the region flown over in the last orbit path, due to the simultaneous Earth’s rotation. In 24 hours, satellites situated on these orbits view almost all the Earth twice, for optical satellites once in daylight and once in the dark. Radar satellites seen each Earth region twice, from two different illumination directions. These specific polar-orbits are called sun-synchronous, as the local solar time stays the same each time a satellite flies over a specific region. This has the advantage of providing an almost constant angle of sunlight for each region on the Earth’s surface viewed by the satellite over time and ensure repeatable sun illumination conditions; the angle will only vary seasonally due to the Earth revolution around the sun. Due to this consistency, images of a specific region would not show much illumination changes due to shadows or sunlight and image interpretation over time such as change detection or monitoring approaches are possible. Because a sun-synchronous orbit does not pass directly over the poles, there is a data gap over both poles where no data is acquired.","name":"Types of satellite orbits","selfAssesment":"Completed
"},{"code":"PS2-2-3-3","description":"An imaging SAR system can generally make acquisitions in different modes. Which acquisition mode to choose depends of the application but also on the desired coverage and data resolution. Even if technically all acquisitions modes can be used everywhere on the Earth’s surface, specific modes are preferred for ocean applications that are different from the ones used in land applications.\r\nThe different acquisition modes can be defined either by their geometrical or by their temporal properties.\r\nThe geometrical properties refer to the geometric configuration of the SAR antenna. Usually looking sideways down in a direction perpendicular to the flight direction (Stripmap mode), the antenna can also be steered around the nadir axis in order to look at a specific target for a longer time during pass-by (Spotlight mode). This configuration allows to rachieve higher azimuth resolution but reduces coverage. It is rather used for very local application where a precise information about specific targets is needed. Other geometric configurations steer the antenna around the flight direction (ScanSAR mode), yielding to a larger swath on the ground. The distance between near and far range is increased, as well as the range of incidence angles within one acquisition. Whereas it increases the area of the scene, it comes generally with a decrease of the spatial resolution in the azimuth direction. Depending on the sensors, the name of the acquisition modes as well as particular technical properties can differ. Sentinel-1 uses a TOPS configuration (Terrain observation with Progressive Scan), which combines the antenna steering properties of both ScanSAR and Spotlight modes. \r\nThe temporal properties refer for specific techniques to the time interval between several acquisitions of the same area. Either these acquisitions are taken simultaneously in one pass over the area of interest (single-pass), or they are taken at different times, needing several passes over the area (repeat-pass).\r\nSpecific SAR techniques such as InSAR and Tomography, while relying on those geometric and temporal properties, have additional acquisition configuration characteristics. For example, the interferometric mission TanDEM-X has three acquisition modes defined by the number of satellite emitting or receiving the signal (pursuit monostatic mode, bistatic mode, alternating bistatic mode), which allows phase referencing. Tomographic SAR uses multi-baseline observations, i.e. the antenna passes several times over an area but at different heights, allowing via different incidence angles the retrieval of structural information of specific targets.","name":"Synthetic Aperture Radar (SAR) acquisition modes","selfAssesment":"Completed
\r\n\r\n"},{"code":"PS2-2-3-4","description":"Swath width refers to the width of the ground that the satellite collects data from on each orbit. The area imaged on the surface, is referred to as the swath. Imaging swaths for spaceborne sensors generally vary between tens and hundreds of kilometres wide.","name":"Swath","selfAssesment":"
New
"},{"code":"PS2-2-3","description":"Spaceborne platforms and systems are present at a great height from the earth surface. The altitude of platforms range from few hundred kilometers to several thousand kilometers. A large area can be captured in a single scene depending on altitude of sensor. The platforms can have different characteristics.","name":"Spaceborne platforms and systems","selfAssesment":"Planned
"},{"code":"PS2-3-1","description":"Field spectroradiometers are a powerful tool for monitoring and upscaling vegetation physiology and carbon and water fluxes. Usually, full-range spectroradiometers which delivers the spectral field measurements available from any commercial field-portable spectroradiometer.","name":"Field spectroradiometers","selfAssesment":"Planned
"},{"code":"PS2-3-2","description":"Terrestrial laser scanning (TLS) is a ground-based, active imaging method that rapidly acquires accurate, dense 3D point clouds of object surfaces by laser range finding.\r\nA terrestrial laser scanning (TLS) system is a stationary highly accurate ranging device for geodetic surveying. More specifically, TLS systems provide dense and accurate 3D point cloud data for the local environment and they may also reliably measure distances of several tens of meters. Due to these capabilities, such TLS systems are commonly used for applications such as city modeling, construction surveying, scene interpretation, urban accessibility analysis, or the digitization of cultural heritage objects. When using a TLS system, each captured TLS scan is represented in the form of a 3D point cloud consisting of a large number of scanned 3D points and, optionally, additional attributes for each 3D point such as color or intensity information. However, a TLS system represents a line-of-sight instrument and hence occlusions resulting from objects in the scene may be expected as well as a significant variation in point density between close and distant object surfaces. Thus, a single scan might not be sufficient in order to obtain a dense and (almost) complete 3D acquisition of interesting parts of a scene and, consequently, multiple scans have to be acquired from different locations.","name":"Terrestrial LiDAR","selfAssesment":"In progress
"},{"code":"PS2-3","description":"remote sensing in-situ measurements, e.g., LAI, for cal/val","name":"Ground platforms and systems","selfAssesment":"New
"},{"code":"PS2","description":"Can be static or moving, it carries a remote sensing sensor, it operates in near (few centimetres) and far (36,000 kilometres) altitudes ranges.","name":"Types of remote sensing platforms and systems","selfAssesment":"Planned
"},{"code":"PS3-1","description":" ","name":"History of remote sensing data carriers","selfAssesment":"New
"},{"code":"PS3-2-1","description":"The picture elements are pixels and each pixel has a specific value (usually in grayscale). Image pixels are normally square and represent a certain area on an image. It is important to distinguish between pixel size and spatial resolution - they are not interchangeable. If a sensor has a spatial resolution of 20 metres and an image from that sensor is displayed at full resolution, each pixel represents an area of 20m x 20m on the ground. In this case the pixel size and resolution are the same.","name":"Picture element (pixel)","selfAssesment":"In progress
"},{"code":"PS3-2-2","description":"An image is an array, or a matrix, of square pixels (picture elements) arranged in columns and rows. In a (8-bit) greyscale image each picture element has an assigned intensity that ranges from 0 to 255.","name":"Image as a matrix (digital number DN)","selfAssesment":"In progress
"},{"code":"PS3-2-3","description":" ","name":"Datacubes","selfAssesment":"New
"},{"code":"PS3-2-4","description":" ","name":"Earth Observation Big Data","selfAssesment":"New
"},{"code":"PS3-2","description":"Most remote sensing data exist as digital images, and appropriate image processing allows the emphasis of certain aspect and subsequent extraction of information for specific applications.\r\nA digital image is a representation of the reality as a grid of picture elements. It can be considered as an array of numbers that can be stored and handled by a digital computer. The picture elements are pixels and each pixel has a specific value (usually in grayscale). This value is a digital number (DN), which usually represents the amount of energy recorded by the sensor at this pixel position or any other characteristic recorded by the sensor, e.g. elevation. \r\nEach row of the image grid, or matrix, corresponds to one scan line. Each pixel is characterized by its row r and column c position in the image, as well as by its value. Additional geographical information is needed in order to assign a geographic location to a pixel. The digital number are integers usually compressed in one byte (= 8 bit) representation, i.e. each pixel can take 256 values.\r\nDigital images are raster data, as opposite to vector data. Whereas vector data can be points, lines or polygones, raster data always consist of pixels. A pixel is the smallest element in which an image can be divided into. The pixel size varies depending of the instrument and of the sampling used. Large pixel may contain information about several objects of the recorded scene. However, they only have one value. These are called mixed-pixel, as e.g. several land cover classes are represented within one pixel and they cannot be distinguished from another. \r\nIn multispectral imagery, each region of the electromagnetic spectrum is recorded in an independent image (band). Therefore, at a specific array position (r,c), there exist several pixels, each with a specific value corresponding to the energy recorded for the considered band. This result in a three-dimensional matrix. The bands of a multispectral image can be displayed three at a time in the computer using for each band one of the three primary colors red, green and blue (RGB). This is called a color composite image. If the color composite represents a combination of the visible red, green and blue bands in their respective color, the combination is called natural or true color composite, as it corresponds to what the human eye sees naturally. Any other combination, for example considering bands of wavelengths that are not visible for the human eye is called a false color composite. It is often used to highlight the spectral differences and particular image features in order to extract information.","name":"Digital image terminology","selfAssesment":"Completed
"},{"code":"PS3-3-1","description":"Band interleaved by line (BIL) is one of three primary methods for encoding image data for multiband raster images in the geospatial domain, such as images obtained from satellites. BIL is not in itself an image format, but is a scheme for storing the actual pixel values of an image in a file band by band for each line, or row, of the image. For example, given a three-band image, all three bands of data are written for row one, all three bands of data are written for row two, and so on. The BIL encoding is a compromise format, allowing fairly easy access to both spatial and spectral information. The BIL data organization can handle any number of bands, and thus accommodates black and white, grayscale, pseudocolor, true color, and multi-spectral image data.","name":"Band interleaved by line (BIL)","selfAssesment":"New
"},{"code":"PS3-3-2","description":"Band interleaved by pixel (BIP) is one of three primary methods for encoding image data for multiband raster images in the geospatial domain, such as images obtained from satellites. BIP is not in itself an image format, but is a method for encoding the actual pixel values of an image in a file. Images stored in BIP format have the first pixel for all bands in sequential order, followed by the second pixel for all bands, followed by the third pixel for all bands, etc., interleaved up to the number of pixels. The BIP data organization can handle any number of bands, and thus accommodates black and white, grayscale, pseudocolor, true color, and multi-spectral image data.","name":"Band interleaved by pixel (BIP)","selfAssesment":"New
"},{"code":"PS3-3-3","description":"A binary raster file format for aerial photography, satellite imagery, and spectral data. The BSQ data organization can handle any number of bands, and thus accommodates black and white, grayscale, pseudocolor, true color, and multi-spectral image data. Additional information is needed to interpret the image data, such as the numbers of rows, columns, and bands, if there is a color map, and latitude and longitude to relate the image to geospatial locations.","name":"Band sequential (BSQ)","selfAssesment":"New
"},{"code":"PS3-3","description":"Data storage consists of methods of organizing image data for multiband images.","name":"Data storage","selfAssesment":"New
"},{"code":"PS3-4-1","description":"Spectral resolution describes the ability of a sensor to define fine wavelength intervals. The narrowest spectral interval that can be resolved by an instrument. Spectral resolution (spectral capability) also refers to the number of wavebands within the EM spectrum that an optical sensor is taking measurements over.","name":"Spectral resolution","selfAssesment":" "},{"code":"PS3-4-2","description":"The spatial resolution of an image corresponds to the size of the minimum area that can be resolved by the sensor. \r\nDue to the different techniques of acquisition of passive and active sensors, the spatial resolution is determined for both sensor types differently. \r\nFor passive sensors, the spatial resolution depends on their instantaneous field of view (IFOV), which determines the area of the Earth’s surface that is viewed at one particular moment in time by one detector element. The size of this area is called resolution cell and characterizes the spatial resolution of the sensor. Depending on the spatial resolution, whole features of the Earth’s surface can be detected homogeneously in one or several resolution cells. For features smaller than the spatial resolution, the average reflected radiation of all features within a resolution cell is recorded, leading to so-called mixed-pixels.\r\nFor imaging active systems, the spatial resolution is dependent of both the length of the transmitted pulse in looking direction and the width of the radiation beam or the antenna width in flight direction.\r\nIn all cases, the spatial resolution indicates the level of detail observable in an image. Usually, one distinguishes between coarse (low), moderate (medium) and fine (high and very high) resolution, whereby the use of this denomination is often context-dependent. Sensors with coarse resolution can only detect large features, but they usually cover a much larger area than high-resolution sensors, which can provide detailed information on small objects such as individual buildings, trees or cars, but for much smaller areas. Coarse spatial resolution mean in general a resolution cell larger than 250 m and a scene extent of several thousands of kilometers (>1000 km). Moderate resolution sensors have a spatial resolution of 30 m to 80 m, and a coverage of approximately 200 km in a single acquisition. Sensors showing spatial resolutions from 5 m or 6 m are high-resolution sensors, with a spatial coverage up to approximately 20 km. Sensors with a resolution cell’s width of less than 1 m are considered as very-high-resolution sensors.\r\nLow resolution sensors are appropriate for the analysis of broad-scale phenomena such as ocean color or cloud patterns. Medium resolution sensors are rather used for regional analysis such as land cover change and phenological response of vegetation. High-resolution sensors are particularly useful for object detection.","name":"Spatial resolution","selfAssesment":"In progress
"},{"code":"PS3-4-3","description":"Radiometric resolution can be defined as the ability of an imaging system to record many levels of brightness. Radiometric resolution refers to the range in brightness levels that can be applied to an individual pixel within an image, determined on a grayscale. E.g., Sentinel-2 sensor MSI is a 12 bit sensor imaging with 4.096 levels.","name":"Radiometric resolution","selfAssesment":"Planned
"},{"code":"PS3-4-4","description":"Temporal resolution, also referred to as the revisit cycle, is defined as the amount of time it takes for a satellite to return to collect data from exactly the same location on the Earth. Imageing of the exact same area at the same viewing angle a second time is temporal resolution.","name":"Temporal resolution","selfAssesment":"New
"},{"code":"PS3-4","description":"A digital image begins as an analog signal. Through computer data processing, the image becomes digitized and is sampled multiple times. The critical characteristics of a digital image are spatial resolution, spectral resolution, radiometric resolution, contrast resolution, noise, and dose efficiency. These depends upon satellite orbit configuration and sensor design. Different sensors have different resolutions.\r\nSpectral resolution describes the ability of a sensor to define fine wavelength intervals. The narrowest spectral interval that can be resolved by an instrument. Spectral resolution (spectral capability) also refers to the number of wavebands within the EM spectrum that an optical sensor is taking measurements over.\r\nRadiometric resolution can be defined as the ability of an imaging system to record many levels of brightness. Radiometric resolution refers to the range in brightness levels that can be applied to an individual pixel within an image, determined on a grayscale. E.g., Sentinel-2 sensor MSI is a 12 bit sensor imaging with 4.096 levels.\r\nSpatial resolution of an image corresponds to the size of the minimum area that can be resolved by the sensor.\r\nTemporal resolution, also referred to as the revisit cycle, is defined as the amount of time it takes for a satellite to return to collect data from exactly the same location on the Earth. Imageing of the exact same area at the same viewing angle a second time is temporal resolution.","name":"Properties of digital imagery","selfAssesment":"Completed
"},{"code":"PS3-5-1","description":"The header is a section of binary- or ASCII-format data normally found at the beginning of the file, containing information about the bitmap data found elsewhere in the file. The format of the header and the information stored in it varies considerably from format to format and contains fixed fields.","name":"Header file","selfAssesment":" "},{"code":"PS3-5","description":" ","name":"Image description files","selfAssesment":"New
"},{"code":"PS3-6","description":"Remote Sensing data formats in which the data are organized and stored.","name":"Data Formats","selfAssesment":"Planned
"},{"code":"PS3-7-1-1","description":"Depending on the sensor and the provider, remotely sensed imagery is made avalilable to the user at different processing levels. For Sentinel-2, the lowest product level made available to the user is Level-1B. THe Level-1B product provides radiometrically corrected imagery in Top-Of-Atmosphere (TOA) radiance values and in sensor geometry. Radiometric corrections applied to the Level-1B are: dark signal, pixels response non uniformity, crosstalk correction, defective pixels interpolation, high spatial resolution bands restoration (deconvolution puls denoising), binning (spatial filtering) for 60m bands. (Sentinel-2 User Handbook, p.44)","name":"Radiometrically corrected","selfAssesment":"New
"},{"code":"PS3-7-1-2","description":"Geometrically corrected products are of a higher processing level than radiometrically corrected products. For Sentinel-2, the geometrically corrected product is the Level-1C product. The Level-1C product results from using a Digital Elevation Model (DEM) to project the image in cartographic coordinates. Per-pixel radiometric measurements are provided in Top Of Atmosphere (TOA) reflectances with all parameters to transform them into radiances. Level-1C products are resampled with a constant Ground Sampling Distance (GSD) of 10, 20 and 60 m depending on the native resolution of the different spectral bands. Level-1C products will additionally include Land/Water, Cloud Masks and ECMWF data (total column of ozone, total column of water vapour and mean sea level pressure). (Sentinel-2 User Handbook, p.44)","name":"Geometrically corrected","selfAssesment":"New
"},{"code":"PS3-7-1","description":"The definition of processing levels for optical data depends on the considered sensor. Most common satellite optical imagery are available in three distinct processing levels, from level 0 to level 2. The most used processing levels are level 1 and level 2, depending on the user and the application. \r\nIn Level 0, the raw data are processed in a way that they are ready to be archived. Processing operations generally includes telemetry analysis, error detections and granule concatenation. Furthermore, relevant parameters such as acquisition date and geographical reference are annotated in the form of metadata, this information being necessary for processing higher levels. Additionally, a quicklook of the image is generated. No correction is performed at this level.\r\nLevel 1 is often divided in several sublevels. Generally, both radiometric correction and geometric refinement are performed at this level. The radiometric processing includes several radiometric corrections such as dark signal correction or spectral band binning. The radiometric correction allows the determination of physical variables (e.g. reflectance) from the digital numbers. The geometric processing includes tiles association and resampling grid computation, in order to link for each image band its native image geometry to the target geometry. The result of this processing steps is usually a geocoded, Top of Atmosphere product.\r\nLevel 2 data usually consist of atmospherically corrected Level 1 data, i.e. Bottom-of-Atmosphere data. These surface reflectance products may be accompanied by additional outputs, such as scene classification, water vapor or surface temperature maps.\r\nFor specific applications and sensors, Level 3 application ready data are available. These are derivated products such as burned area, dynamic surface water content and snow cover maps.\r\nDepending on the considered sensor and level, the name of the sublevels can differ: Sentinel 2 defines Level-1B as radiometrically corrected data. Level 1C are radiometrically and geometrically corrected data, i.e Top-Of-Atmosphere (TOA) orthoimage products. Landsat sensors distinguish between Terrain precision correction (L1TP), systematic Terrain Correction (L1GT) and Geometric systematic Correction (L1GS) depending on the quality of the reference data for geometric correction. These are usually separated into Tier 1 and Tier 2 datasets.","name":"Optical data","selfAssesment":"Completed
"},{"code":"PS3-7-2-1","description":"data with azimuth compression using the full azimuth bandwidth of the sensor, each pixel is a complex number (i.e., has a real and imaginary component) that represents the amplitude and phase","name":"Single Look Complex (SLC)","selfAssesment":"New
"},{"code":"PS3-7-2-2","description":"multi-look intensity, not geocoded","name":"Multi-looked Detected (MLD)","selfAssesment":"New
"},{"code":"PS3-7-2-3","description":"squre pixels, not geocoded","name":"Precision Images (PRI)","selfAssesment":"New
"},{"code":"PS3-7-2-4","description":"ground range intensity","name":"Groud Range Detected (GRD)","selfAssesment":"New
"},{"code":"PS3-7-2","description":"For SAR data, usually three processing levels are distinguished, ranging from level 0 (less processed) to level 2 (higher processed).\r\nLevel 0 products consist of compressed and unfocussed raw data and are the basis for the processing of higher level products. Level 0 data are principally used for research in the topic of signal processing. As for optical data, level 0 product are annotated with several metadata, such as calibration and orbit information, and acquisition time and date.\r\nLevel 1 data can be separated in two distinct product types, depending if the full complex information is used (amplitude and phase) or only the amplitude information. The product denomination depends on the sensor type; for Sentinel 1 the names Single Look Complex (SLC) and Ground range detected (GRD) are used, respectively. Both products can be generated from the Level 0 data. Level 1 data are the products that are used by most scientific users. The processing toward Level-1 data includes Doppler centroid estimation and data focusing. The Level 1 SLC product consists of the real and imaginary part of focused complex SAR data in slant range geometry, from which the phase and amplitude information can be retrieved. This is available for all acquired polarisations. Additional orbit information for georeferencing is provided with the data. The Level 1 GRD data consist of focused and multi-looked SAR data that have been projected to ground range geometry. GRD data only contain amplitude information, therefore the phase information is lost. The multi-looking step is particular for GRD data and allows both speckle reduction and square pixel resolution. As for the SLC data, the GRD data are annotated with orbit information for georeferencing. The Level-1 products are not calibrated, they include however information about calibration constants, which are sensor dependent. Further processing is needed in order to obtain calibrated radar cross section information from the original data intensity values.\r\nLevel 2 products describe geolocated derivated geophysical products such as ocean wind field or surface radial velocity. Such products are for example available for download on the Sentinel-1 Copernicus Hub. Further Level- 2 data are for example differential interferograms or change maps, which can be processed on different online platforms (e.g. Hyp3) and provide information about surface deformation or more generally changes between several acquisitions.\r\nThe denomination of the product types on the different levels may differ from sensor to sensor, but the processing steps stay almost the same, depending additionally on the considered acquisition modes. For example, GRD products are also called for other sensors Multi-Looked Detected (MLD) products.","name":"Synthetic Aperture Radar (SAR) data","selfAssesment":"Completed
"},{"code":"PS3-7-7","description":"Data that have been processed to allow direct data analysis. User processing effort is reduced to a minimum.","name":"Analysis Ready Data (ARD)","selfAssesment":"New
"},{"code":"PS3-7","description":"Earth Observation data are usually made available in different processing levels. The processing level is a mean of describing how much the raw data have been processed toward an informational geophysical product. The degrees of data processing usually follow a numerical hierarchy and typically range from Level 0 (less processed) up to Level 4 (highly processed). They characterize the type of data processing that has been performed between the raw data and the current product.\r\nA first effort for providing standard definitions of different processing levels has been made in the 1980s by the Committee on Data Management and Computation (CODMAC) of the National Research Council (NRC). CODMAC identified eight levels of processing, applicable for all space science data. Starting with the raw data at level 1, the degree of processing and complexity of the data increased at each new level. Level 2 describes edited data, corrected for obvious instrumentation errors and tagged with acquisition time and location; Level 3 stays for calibrated data where values are proportional to a specific physical unit. Level 4 represents resampled data, Level 5 derived data, where specific geophysical information has been retrieved and mapped based on the original data. Level 6 represents all ancillary data (i.e. instrument data) that are necessary for the previous steps of calibration and resampling. Level 7 describes so called correlative data: not directly belonging to the original data, those data represent all other science data that where necessary for the interpretation of the original spaceborne dataset. Finally, Level 8 are user description, i.e. documentation of the data.\r\nConcerning spaceborne image data, both optical and radar, an adaptation of these original levels has been made from NASA and NOAA that is used for the main current spaceborne missions, including the Copernicus program. Whereas specific adaptations may arise for specific sensors and sensor types, there are five principal processing levels. Level 0 represents the raw data that have just been edited for the correction of artifacts. Level 1 data are Level 0 data with additional annotations regarding time and geolocation information, radiometric and geometric calibration coefficients (for example Top of Atmosphere data for optical imagery). Level 2 data are already radiometrically and geometrically calibrated and represent physical variables (for example Bottom of Atmosphere data for optical imagery). Level 3 data correspond to derived variables and information (e.g. land cover) with completeness and consistency information, e.g. quality flags. Level 4 represent higher level data resulting from modelling or more complex analysis of the data with additional ancillary information.\r\nFor many applications and users, so called analysis ready data (ARD data) are required. These usually correspond to Level 2 data that have already been pre-processed in order to retrieve the physical information and can be further analyzed for the specific thematic application.","name":"Processing levels","selfAssesment":"Completed
"},{"code":"PS3","description":" ","name":"Remote sensing data and imagery","selfAssesment":"Planned
"},{"code":"PS4","description":"Use the folowing links to find information on past, operational and future remote sensing platforms and sensors.","name":"Satellite and airborne sensors and missions databases","selfAssesment":"Completed
"},{"code":"SD","description":"Based on Waldo Tobler`s first law of geography( Tobler, 1970), this property is set on the principle that \"everything is related, but that which is closer is more closely related\".","name":"Spatial dependency","selfAssesment":"In progress (GI-N2K)
"},{"code":"SH","description":"This principle, as set forth by Anselin, determines that \"expectations vary along the earth`s surface\" which means that any spatial analysis is dependent explicitly on the borders of study fields, i.e. the tracing of (spatial) analysis units.","name":"Spatial heterogeneity","selfAssesment":"In progress (GI-N2K)
"},{"code":"TA","description":" ","name":"Thematic and application domains","selfAssesment":" "},{"code":"TA11-1-1","description":"The EO/GI users in agriculture are active in Agricultural commodities/Trading, agricultural production / Horticulture, Agricultural services, Agriculture machinery, Agriculture and Rural Development Policy, Agro chemicals / Plants & Fertilizers, Animal production / Livestock. The EO/GI users also include agriculture and rural policy makers. \r\nThey benefit from EO information, for example, by managment support for their crop production through forecasting crop yield, assess risks of damage/loss because of storms, disease or other stress factors, and water monitoring. Use in agriculture: knowledge and information products to forge a viable strategy for farming operations. Understand the health of his crop, extent of infestation or stress damage, or potential yield and soil conditions","name":"Users in agriculture","selfAssesment":"New
"},{"code":"TA11-1-2","description":"The users in fishing are active in Fish stock management, Fishing fleets, Fishery distribution logistics, Aquaculture / fish farms, Coastal management agencies. In addition, the users include Fisheries authorities / policy makers. \r\nThe marine environment in particular is relevant to fishing. Fishing fleets move to the fishing grounds to catch fish. Finding them is challenging. However, fish shoals can be directly visible from above. Navigating to the fishing grounds can be risky: Coastline and shallows may pose a risk to ships. Additionally, skippers may have to deal with challenging weather conditions at sea. Environmental threats to the fishing grounds are oil slicks and other types of pollution. A problem from an economical perspective and for adhering to catch quota is illegal fishing. Noumerous opportunities exist to support fishing with EO information.","name":"Users in fishing","selfAssesment":"New
"},{"code":"TA11-1-3","description":"The users in forestry are active in Forest management, Forest Services, Commodities, Logging industry, Wood, paper and pulp industry, Forest policy, Forest machinery. They also include Forest Policy makers.\r\nUse in forestry: Understand depletion due to natural causes (fires and infestations) or human activity (clear-cutting, burning, land conversion), and monitoring of health and growth for effective commercial exploitation and conservation.\r\nForests are a resource that is harvested all over the Globe for different purposes like construction or heating. Additionally, the forests represent an ecosystem that provides various ecosystem services. Proper management is a key to a healthy forestry industry that has to be aligned well with global environmental management activities. There is a need to avoid deforestation and forest degradation, keep the environmental impact of forestry within bounds, be aware of changes in the carbon balance. Economically relevant is especially a good understading of forest types, forest damage due to storms or insects, as well as wildfires. A threat to the environment results from illegal forest activities.","name":"Users in forestry","selfAssesment":"New
"},{"code":"TA11-1","description":"Users in managed living resources refer to human activities exploiting natural organic resources. Knowledge and information products to forge a viable strategy for the user’s operations such as the assessment of the status of the resource due natural or human activity for effective commercial exploitation and conservation. This includes agriculture, fishing and forestry occupations for our society.","name":"Users in managed living resources","selfAssesment":"New
"},{"code":"TA11-2-1","description":"The users in alternative energy consist of Solar energy providers, Wind energy providers, Tidal energy providers, Hydroelectric energy providers, Energy and Carbon traders, Local and regional planners, and National policy makers. Energy providers need information about the state of the environment to make the most use out of natural resources. Planners and policy makers have to weigh up whether and which type of alternative energy is justifiable and sensible for a specific region.\r\nEO data can be used to build maps that show resource information. For solar energy, those maps contain information about solar radiation, but also shadowing effects. Forecast products for irradiance are available to be able to plan the energy production for the coming days. Tidal waves can be depicted by sea surface heights. As tidal currents are periodical, they can be predicted well by the initial state of sea surface heights. In addition, also the speed of tidal waves can be determined by EO measurements. In the wind energy sector EO data is analysed to plan and monitor wind farms. Maps can show areas, where winds are suitable for wind energy production. After the construction of a wind farm, wind strength and direction during operation can be monitored. Finally, for hydroelectric power stations EO is used to monitor water reservoirs. As well hydrometeorological data is used to forecast water-related events and to monitor drought or floods.","name":"Users in alternative energy","selfAssesment":"Completed
"},{"code":"TA11-2-2","description":"The EO/GI user community in oil & gas consists of offshore exploration and production, on-shore exploration and production, drilling and support services, oil and gas commodities trading, and energy planners. Due to their activities both on-shore and offshore their need for EO-derived information about the land, the ocean and the atmosphere. They need EO-derived information about geological features (for exploration), for asset infrastructure monitoring, construction and buildings. Safe offshore operations (ocean&atmosphere: forecast and monitoring current movement and drift, monitor sea-ice and icebergs, detect and monitor hurricanes and typhoons; land: map and assess flooding, detect wildfires . A large set of information needs results from their need to adhere to environmental regulations. They have to assess and monitor their environmental impact, ocean quality and productivity, land ecosystems and biodiversity, groundwater and run-off \r\nMany problems faced by oil, gas, including the selection and development of exploration areas, detection and mapping of illegal mining activities, or monitoring dams, pipelines and terrain movements, can be efficiently addressed by extracting information from geospatial imagery. Remote Sensing based applications reduce the need for field work, minimize environmental impacts, and ultimately safe costs, to help achieve results faster during exploration, extraction, and remediation/reclamation stages.","name":"Users in oil & gas","selfAssesment":"New
"},{"code":"TA11-2-3","description":"The EO/GI community in minerals and mining consists of mining and quarrying companies, exploration and survey specialists, commodities traders, exploration and extraction equipment suppliers, drilling, excavation and support services, and regional planners / policy makers.\r\nTypical spatial questions for the users in minerals and mining are concerned with prospecting, e.g. \"Where can we find the minerals that are worth exploitation?\", and operation of mining sites: \"How much material has already been excavated in the mine and how much material was deposited in dedicated dump areas?\". Additionally relevant are arising risks through mining activities, e.g. \"How do the mining activities affect settlements in the vicinity?\" or \"How do the mining activities affect the environment?\". Concequently, the EO/GI users in minerals and mining benefit from EO information through mapping geological features, monitor mineral extraction, measure land use statistics, assessing environmental impact of human activities, detect and monitor ground movement, and monitor land pollution.","name":"Users in minerals & mining","selfAssesment":"New
"},{"code":"TA11-2","description":"Users in energy and mineral resources deal with the harvesting of energy from renewable resources and extractive industries including oil and gas and raw materials. EO information helps them in exploring locations where to build new mines or power plants, in identifying risks from infrastructure and in managing the environmental impact of their operations.\r\nUses that apply to the extractive industries: study of landforms, structures, and the subsurface, to understand physical processes creating and modifying the earth's crust.","name":"Users in energy and mineral resources","selfAssesment":"New
"},{"code":"TA11-3-1","description":"EO/GI users in construction include construction companies, civil engineering consultancies, architect and design companies, planning authorities, and national land agencies. \r\nThey benefit from EO through monitor building development, assess environmental impact of human activities, map and assess flooding, detect land movement, subsidence, heave, and monitor land-use statistics","name":"Users in construction","selfAssesment":"New
"},{"code":"TA11-3-2","description":"Utilities (water, electricity, waste): Power station operators, Water plants operators, Survey companies, Hydroelectric suppliers, Regulatory Bodies, Distribution companies, Landfill and waste, Regional planners / policy makers.\r\nThe benefit from EO information that monitor pollution in rivers and lakes, assess changes in the carbon balance, assess environmental impact of human activities, monitor land pollution, assess changes to urban and rural areas, assess and monitor water quality, assess ground water and run-off.","name":"Users in utilities & supplies","selfAssesment":"New
"},{"code":"TA11-3-3","description":"Users of EO/GI in communications and connectivity are mostly mobile telecommunications providers and fixed telecommunication providers. Theire business is to connect people via telephone and internet. The assets for their services include the infrastructure of communication networks physically installed in the ground, the cellphone towers distributed over the land surface, particularly in higly populated areas, as well as other installations (e.g. company buildings) and equipment (communication satellites).\r\nSpecific spatial questions of these users are concerned with the reception quality that the network can provide in an area. The network coverage would neet to react to changes of the built environment. New settlement infrastructure may cause a new population distribution and subsequently the need to network adaptations to cover new areas or cover some areas with higher band widths because more people are living there. Additionaly, the coverage of cellphone antennas depends on the arrangement of environmental obstacles that degrade or block the radio signal. Any place where the built environment or the vegetation changes can change the reception quality within the covered area of an existing cellphone tower. \r\nThe benefit of EO information for the user group of communications and connectivity comes from monitoring building development, assessing changes to urban and rural areas, and mapping line of sight visibility (terrain height, land cover).","name":"Users in communications & connectivity","selfAssesment":"New
"},{"code":"TA11-3-4","description":"EO/GI users in transport and logistics include road transport operators, haulage, road infrastructure operators, tolls, airport operators, rail operators, airlines and airline services, and transport engineers.","name":"Users in transport & logistics","selfAssesment":"New
"},{"code":"TA11-3-5","description":"The EO/GI users in travel and tourism include tour operators, leisure service providers, hotels, parks etc, offices of tourism, travel agencies, ski and coastal resorts, surfers & sailors. They can make use of the assessment of land use change (e.g. pollution in rivers and lakes), the mapping and assessment of natural risks or the forecast of weather conditions over the ocean (wind strength, direction, wave height, ...)\r\n\r\nFrom a conceptual point of view traveling is crossing the space from one location to another. Tourism mostly requires a travel to the desired destination and typically also includes moving inside a specific area. Therefore both tourism and travel are highly dependent on spatial phenomena which are often captured using EO.\r\n\r\nAll kinds of traveling are highly dependent on weather conditions which can be observed with meteorological satellites. Also the current traffic conditions like congestion, road condition and natural hazards can be discovered with EO.\r\n\r\nThe types of tourism which are outside of buildings require sufficient weather forecast. Especially outdoor tourism at the coast or in mountain areas have a need for specific information about the current and the near future conditions of the natural environment. Examples are avalanche reports and forecasts for wind or wave heights of water bodies.\r\n\r\nTourism and traveling are import economic factors. Consequently both the public and the private sector are interested in ensuring safe and convenient travel conditions and furthermore in creating an attractive environment for travelers and touristic visitors. This includes recognizing environmental pollution, since this discourages tourist from visiting an area.\r\n\r\nOf course tourism and traveling itself also can be observed from space, this is especially true for mass tourism and areas where traffic has increased a lot during the last time. Typical effects are the increase of settlement area and the additionally used space for roads, parking lots, airports and harbors. These changes to the earth surface can be quantified with the help of land cover change detection.","name":"Users in marine","selfAssesment":"New
"},{"code":"TA11-3-6","description":"From a conceptual point of view travelling is crossing the space from one location to another. Tourism mostly requires a travel to the desired destination and typically also includes moving inside a specific area. Therefore both tourism and travel are highly dependent on spatial phenomena which are often captured using EO.All kinds of travelling are highly dependent on weather conditions which can be observed with meteorological satellites. Also the current traffic conditions like congestion, road condition and natural hazards can be discovered with EO.\r\n\r\nThe types of tourism which are outside of buildings require sufficient weather forecast. Especially outdoor tourism at the coast or in mountain areas have a need for specific information about the current and the near future conditions of the natural environment. Examples are avalanche reports and forecasts for wind or wave heights of water bodies. Local tour organizers can utilise this information in order to better plan offers for tourists and also ensure overall safety during their stay.\r\n\r\nTourism and travelling are import economic factors. Consequently both the public and the private sector are interested in ensuring safe and convenient travel conditions and furthermore in creating an attractive environment for travellers and touristic visitors. This includes recognising environmental pollution, since this discourages tourist from visiting an area. Not only incoming, but also outgoing tourism is an important factor in local economies. Travel agencies, for example, are highly dependent on retrieving accurate information about foreign regions which are typically obtained with earth observation technology.\r\n\r\nOf course tourism and travelling itself also can be observed from space, this is especially true for mass tourism and areas where traffic has increased a lot during the last time. Typical effects are the increase of settlement area and the additionally used space for roads, parking lots, airports and harbors. These changes to the earth surface can be quantified with the help of land cover change detection.In many cases local administrations and decion makers want to mitigate the negative consequences of mass tourism, the insights of the mentioned EO measurements provide a useful foundation for sustainable planning.","name":"Users in travel & tourism","selfAssesment":"Completed
"},{"code":"TA11-3","description":"Users in transport and infrastructure apply to all manufacturing and physical supply in land but also marine domains including transport & logistics, utilities, construction, communication & connectivity, and tourism.","name":"Users in infrastructure & transport","selfAssesment":"New
"},{"code":"TA11-4-1","description":"EO/GI users in insurance and real estate include primary insurance companies, re-insurance sector, insurance brokers, insurance service suppliers, commercial banks, major projects, and international financial institutions. \r\nProduction processes (including primary production like farming), property and real estate are often insured against certain risks, e.g. from natural hazards. \r\nUsers benefit from EO information through applications that monitor building development, assess crop damage due to storms (including to forecast and map large waves), assess damage from earthquakes, detect and monitor wildfires, map and assess flooding, detect land movement, subsidence, heave, forecast and assess landslides.","name":"Users in insurance & real estate","selfAssesment":"New
"},{"code":"TA11-4-2","description":"EO/GI users in retail and geo-marketing include Retail centres and Advertising and Marketing agencies. They use EO/GI data in the field of Navigation and LBS, Shopping chains or Logistics.","name":"Users in retail & geo-marketing","selfAssesment":"New
"},{"code":"TA11-4-3","description":"Users in news and media are Television companies, Broadcasting providers, News and Information agencies, Web service providers, and Entertainment software providers. They benefit from monitoring, forecasting and assessing of natural risks/disasters.","name":"Users in news & media","selfAssesment":"New
"},{"code":"TA11-4-4","description":"Users in ICT include fixed and mobile telecommunications providers. They can make use of EO/GI data by monitoring building development and changes to urban areas.","name":"Users in ICT, knowledge and digital interfaces","selfAssesment":"New
"},{"code":"TA11-4","description":"Users in financial and digital services cover a broad area of activity that touches on many other market sectors such insurance & real estate, retail, news & media and digital interfaces. The categories included are identifiable as a “service” (tertiary sector: attention, advice, access, experience, and affective labour) and not part of the physical supply of goods.","name":"Users in financial & digital services","selfAssesment":"New
"},{"code":"TA11-5-1","description":"The users in smart cities include urban planners, architects, spatial planning offices, urban policy makers. The users benefit from EO information through map information about urban structures and related land use when managing land use, climate change adaptation, and urban green infrastructure. Typical use cases include Urban adaptation to climate change and Green infrastructure and its ecosystem services to increase quality of life of citizens (https://land.copernicus.eu/user-corner/land-use-cases)","name":"Users in smart cities","selfAssesment":"In progress
"},{"code":"TA11-5-2","description":"The users in local and regional planning include spatial planning departments of municipalities, spatial planning offices, and spatial planning policy makers. Land use management in densely populated areas involves negotiation of conflicting land-use demands for settlement, production system (including agriculture and forestry) and infrastructure. The users benefit from EO information to manage the use of land and its impacts.","name":"Users in local & regional planning","selfAssesment":"New
"},{"code":"TA11-5","description":"Users in urban development and users involved in the development of rural settlements perform tasks on local and regional scale (to the scale of nations). These users benefit from EO information to manage the use of land & its impacts. Users such as urban planners, architects, spatial planning offices, urban policy makers in public/private sectors in smart cities or generic urban local/regional planning belong to this category.","name":"Users in urban development","selfAssesment":"New
"},{"code":"TA11-6-1","description":"Users in defense, security and military are border control organisations, police and rescue forces, military services, and intelligence services. Use of EO/GI data can be made in the field of detecting and monitoring high risk areas (natural and humanitarian), monitoring border incursions, or monitoring maritime movements.","name":"Users in defense, security & military","selfAssesment":"New
"},{"code":"TA11-6-2","description":"EO/GI users in emergency services are coast guards, ambulance services, fire services, police services, civil protection organisations, and rescue services. They benefit from monitoring, detecting and assessing natural risks/disasters.","name":"Users in emergency & social protection","selfAssesment":"New
"},{"code":"TA11-6-3","description":"The EO/GI users in humanitarian operations correspond to humanitarian aid organisations, humanitarian support organisations and overall humanitarian response such as border control organisations, police and rescue forces, coast guards, civil protection, military services, and intelligence services. They can use EO services to detect and monitor high risk areas produced naturally or by humans, monitor border incursions or maritime movements. They provide support to local populations that have experienced a crisis, e.g. they fled from a conflict or are affected by a natural disaster. The organisations therefore support the population's needs for sustenance. Consequently, any related risks are relevant as well. The users benefit from the EO capability to identify and monitor people in need, i.e. to assess pressures on populations and migration, and to monitor humanitarian movement and camps. They additionally benefit from EO through mapping disaster areas for situation awareness and detecting sensitive risk areas. Some examples of users at European level are DG RELEX, DG ECHO, DG ENV/ MIC. At UN, the users include OCHA, UNHCR, UNDPKO, UNDP, UNOPS, UNITAR, UNICEF, UNESCO, WFP. Further, international users include IFRC, WHO, WB, and donor organizations. At the national level, the users include Civil Protection Agencies, Ministries of Internal Affairs / Civil Protection Department, Development and Aid agencies.","name":"Users in humanitarian operations","selfAssesment":"New
"},{"code":"TA11-6","description":"Users in defence and security work in the field of military, emergency and social protection and define, collect, analyse information to provide intelligence & safety. Some examples are activities under humanitarian response such as border control organisations, police and rescue forces, coast guards, civil protection, military services, and intelligence services which can use EO services to detect and monitor high risk areas produced naturally or by humans, monitor border incursions or maritime movements.","name":"Users in defense & security","selfAssesment":"New
"},{"code":"TA11-7-1","description":"EO/GI users in environmental ecosystems & pollution include scientists, consultants, planners and policy makers with interest in environmental issues.","name":"Users in environmental ecosystems & pollution","selfAssesment":"New
"},{"code":"TA11-7-2","description":"Users in health care health-related services include services on site-specific field conditions as well as import phenological timing events, which helps to make predictions for monitoring air quality, forecasting epidemics and diseases, as well as forecasting sunlight exposure.","name":"Users in health care","selfAssesment":"New
"},{"code":"TA11-7-3","description":"EO/GI users in meteo and climate; use of satellite-based observations in addressing key climate science questions for user-centric climate change risk assessment applications or climate-related issues","name":"Users in meteo & climate","selfAssesment":"New
"},{"code":"TA11-7","description":"Users in the public administrations or private organizations using EO to assist environmental or climate change impact policy making decisions i.e, assisting in developing monitoring to evaluate and deliver policy goals, provide assessment of ecosystems, rapid response to major environmental risk events, or those associated health security & care events. These users are largely related with international treaties and hence a strong international collaboration.","name":"Users in environmental, climate & health","selfAssesment":"New
"},{"code":"TA11-8-1","description":"EO/GI users of customer solutions; easier for society to use and engage with EO services through mobile devices, social media platforms, apps. Enormous potential to use citizen-driven observations in combination with EO data","name":"Users of consumer solutions","selfAssesment":"New
"},{"code":"TA11-8-2","description":"EO/GI users in leisure; basic public understanding on eo services","name":"Users in leisure","selfAssesment":"New
"},{"code":"TA11-8-3","description":"\"EO/GI users in education, training and research include schools and education authorities, universities, research organisations, and professional training organisations. They follow several interests. One of them is to make pupils, students and citizens aware of current societal and environmental challenges. The second of them is to train students and professionals on EO/GI methods and technologies to build up their expertise that they become the work force of the future market. And the third interest is to advance research in both societal/environmental challenges and EO/GI methods/technologies to let our society become better prepared for the future.\r\nThese users benefit from the EO/GI capability to provide information on societal and environmental challenges, i.e. from the entire bandwidth of EO/GI capabilities. Exemplary EO/GI services in this user domain are to assess changes in the carbon balance, to assess climate change risk, to map geological features, to monitor high risk areas, to assess changes to urban and rural areas.\"","name":"Users in education, training & research","selfAssesment":"New
"},{"code":"TA11-8","description":"Citizens and society in general use and engage with EO services through mobile devices, social media platforms, apps. We do also categorize in this section the users in education, research and training providing knowledge and learning outcomes.","name":"Users among citizens & society","selfAssesment":"New
"},{"code":"TA11","description":"The EO/GI user community pools sub-communities (stakeholders) that share common needs for EO/GI information. From an economic perspective, market sectors represent user communities. Users of a community have a common interest in specific aspects of societal or economical benefits to be realized by the implementation of EO services. A user-led community is active at specific locations/regions or in specific environments on the Earth. Their activities are associated with particular features and objects of the environment and related processes that can be detected and monitored with EO satellites. EO information therefore is relevant to the user community's management of their assets, the risks to their assets, and the impact that their activities may have on other aspects of the environment. User objectives (use cases) with EO information include: Enforce regulations; Develop strategies and policies; Manage assets; Plan and design project implementations; Analyse and understand impact / consequences.\r\nUser communities can profit from EO services and applications in the field of managed living resources, energy and mineral resources, infrastructure and transport, financial and digital services, urban development, defense and security, environmental, climate and health, or citizens and society.","name":"User community of EO services and applications","selfAssesment":"Completed
"},{"code":"TA12-1","description":"Climate change observations show the warming of the climate system. The changes since the 1950s are unprecedented over decades to millennia.The atmosphere and ocean have warmed, the amounts of snow and ice have diminished, and sea level has risen. The anthropogenic emissions of greenhouse gases are the highest in history. Recent climate changes have had widespread impacts on human and natural systems. There is an urgant need for climate action through mitigation and adaptation. Mitigation actions prevent or reduce the emission of greenhuse gases into the atmoshpere with the objective to make the impacts of climate change less severe. Adapting to climate change increases our resilience to impacts like extreme weather events (e.g. hazards like floods and droughts) that get more frequent and intense in many regions. Current climate change will get worse in the future even if the reduction of emissions is effective with negative effects on ecosystems, economy, human health and well-being. There is extensive need for actions to adapt to the impacts of climate change.","name":"EO for climate change mitigation & adaptation","selfAssesment":"New
"},{"code":"TA12-10","description":"\"Sustainable urban development is a goal of the global society. It summarizes a specific set of problems that cities face all over the world. Cities want to provide a high quality of life to their residents. However, this goal is threatened by urban growth at the cost of urban green infrastructure’s accessibility by citizens etc. Communities that address this: C40 (association of the largest cities of the globe), CitiesIPCC, related SDGs of the UN, etc. Skills: Explain how the monitoring of urban areas contributes to sustainable urban development through its capability to provide regularly updated information about the benefit of urban green infrastructures and their ecosystem services to the quality of life in a city\r\n\"","name":"EO for sustainable urban development","selfAssesment":"New
"},{"code":"TA12-2","description":"Biodiversity describes the variety of ecosystems (natural capital), species and genes in the world or in a particular habitat. Ecosystem services sustain our economies and societies and are essential to human wellbeing.","name":"EO for biodiversity & ecosystems","selfAssesment":"New
"},{"code":"TA12-3","description":"Worldwide countries follow a digital agenda for the economy and initiatives to foster new skills among the workforce to cope with transformation processes with massive impact on the labour market.","name":"EO for digital agenda & new skills","selfAssesment":"New
"},{"code":"TA12-4","description":"Energy transition is a thematic area whose EO experts are proficient in relevant EO data and its processing methods and infrastructure to derive information for energy transition [and its regulatory context, etc.]. The expertise of each expert may be very specialized. In sum, the experts have: The relevant domain knowledge (knowledge about type of monitored entities and their properties, e.g. reflectance properties of sea ice and related EO sensors for detecting them), and The relevant workflow knowledge and processing skills for extracting and providing targeted information for energy transition. [may share strategic objectives… such as „gaining thorough understanding of Energy transition“, „foster usage of EO information for energy transition“]","name":"EO for energy transition","selfAssesment":"New
"},{"code":"TA12-5","description":"Agricultural activity is sustained by good environmental conditions that allow farmers to harness natural resources, create their produce and earn a living. This fosters a sustainable rural economy while food produced by agriculture sustains society as a whole.","name":"EO for sustainable agriculture & food production","selfAssesment":"New
"},{"code":"TA12-6","description":"This societal challenge aims to provide efficient, safe and environmentally friendly mobility solutions.","name":"EO for infrastructure & transport","selfAssesment":"New
"},{"code":"TA12-7","description":"In recent decades, society has fought communicable diseases with success through treatment and prevention. The Covid-19 pandemic shows that communicable diseases are still a threat to the health of citizens. Spread can gappen very quickly from one country to another. Challenges lie in the (re-)emergence of infectious diseases, antimicobial resistance and vaccine hesitancy. Policies of states focus on surveillance, rapid detection and rapid response.","name":"EO for health surveillance","selfAssesment":"New
"},{"code":"TA12-8","description":"There is a rising geostrategic competition and power pilitics challenging rule-based multilateralism. Further, there are armed confilct, civil wars and instability in the EU's broader neighbourhood. \r\nFurther, natural disasters pose a threat to society, where the Sendai Framework of disaster risk reduction focuses on.","name":"EO for emergency, security & defense","selfAssesment":"New
"},{"code":"TA12-9","description":"Water is an essential resource for food production. Growing crops requires significant quantities of water. Without sufficient, good quality and easily accessible water, agri-food production is under threat.","name":"EO for water sustainability","selfAssesment":"New
"},{"code":"TA12","description":"EO provides timely, continuous and independent data for monitoring indicators of the progress of the society in various societal challenges.\r\nEO monitoring supports activities that address societal & environmental challenges. This happens indirectly along a chain: e.g. a regularly provided EO information product derived from EO data of a satellite is integrated as a parameter in a climate model / Earth system model. This climate model enables the development of regulations (and their enforcement through constant monitoring) to implement climate change mitigation measures. Thereby, the chain is characterized by seveal connected nodes: from societal challenges to use cases of users to EO applications to EO products to specific satellites and their sensors.\r\n[Communities that promote collaboration among diverse stakeholders from academia, industry, public administration as well as local residents] \r\nScientific agendas address societal challenges and the EO/GI community can contribute to them. Consortia usually include experts from academia (researchers, developers, scientists), EO companies, and members from the user community such as public authorities.","name":"EO for societal and environmental challenges","selfAssesment":"New
"},{"code":"TA13-1-1","description":"Monitor the atmosphere includes monitoring of the atmosphere composition and air quality, as well as forecasting of sunlight exposure. Timely, continuous, and independent data on the atmosphere is useful in various domains like health, agriculture, renewable energies, urban planning, climate sciences and biology.\r\nThe atmosphere composition includes greenhouse gases (GHG) like carbon dioxide, methane, NO2 and SO2. They are part of the Earth system and have a strong impact on the climate. To monitor changes in atmosphere composition enables modelling climate change and understanding the impact of human-induced emissions of GHG relative to natural sources. EO-derived products include inventory of emission data as an input to atmospheric chemistry transport models and forecast models. Inventories are based on a combination of existing data sets and new information, describing emissions from fossil fuel use, ships, volcanoes, and vegetation. This ensures good consistency between the emissions of greenhouse gases, reactive gases, and aerosol particles and their precursors.\r\nAir quality describes the composition of the atmosphere from gases and particles near the Earth's surface. Local emissions from different sources (e.g. energy production, industrial production, traffic) cause changes to the atmospheric composition that are highly variable in space and time. The quality of the air we breathe can significantly impact our health and the environment. Therefore, it is highly relevant to monitor air quality and emissions. EO satellites are capable of monitoring aerosols, tropospheric O3, tropospheric NO2, CO, HCHO, SO2, and particulate matter (of the sizes PM 2.5 and PM 10). Products like air quality assessment reports, daily ozone forecasts, and UV-index forecast maps are produced that are applied in specific use cases, particularly related to health.\r\nThe amount of solar radiation that arrives at a location on the Earth surface depends on the atmosphere composition and varies over the day and the seasons. Information on solar radiation is useful in various domains. Applications of sunlight and ozone data are for example real-time UV radiation forecasting and risk assessment, skin health services, climate change studies, assessment of ozone protection policies effectiveness, plant growth and disease control, evaporation and irrigation models, power generation, solar heating systems planning and monitoring.","name":"Monitor the atmosphere","selfAssesment":"Completed
"},{"code":"TA13-1-2","description":"Monitoring the climate includes monitoring climate forcing and the carbon balance and assessing climate change risks.\r\nClimate forcing describes the imbalance of the Earth’s energy budget due to natural or human-induced sources. This imbalance results in a change in the globally-averaged temperature. Amongst the contributors of positive climate forcing, that leads to an increase in the globally-averaged temperature, the increase of carbon dioxide in the atmospheric composition is considered to be the most important factor. Changes in the carbon dioxide concentration indicate that the exchanges between carbon sources and sinks are not balanced. It can be shown that human-induced emissions of carbon dioxide are responsible for the increase of the carbon dioxide since the industrialisation.\r\nWith EO, we can monitor changes in greenhouse gases (GHG), aeorosols, albedo, and solar radiation. The dynamic nature of the climate makes it necessary to apply equally dynamic EO monitoring that allows to deliver key information on historical, seasonal forecast and projection periods for climate-related indicators.\r\nRelevant EO products include estimates of the climate forcing of aerosol, ozone and greenhouse gases. The dynamic nature of the climate makes it necessary to apply equally dynamic EO monitoring that allows to deliver key information on historical, seasonal forecast and projection periods for climate-related indicators. \r\nThe products are particularly relevant to the European energy sector in terms of electricity demand and the production of power from wind, solar and hydro sources. \r\nMoreover, water management uses EO-derived information about climate change to mitigate effects of changing precipitation patterns to adapt their strategies, and to prepare for climate variability and change in the water sector, e.g. because of changes in river discharge, droughts and floods.\r\nFinally, insurance uses climate change information for assessing the weather risks to insured assets that change with the climate-related increase in extreme weather conditions. This includes products like up-to-date catalogue of wind storms and their associated impacts on the ground.","name":"Monitor the climate","selfAssesment":"Completed
"},{"code":"TA13-1-3","description":"The weather is the state of the atmosphere measurable by its temperature, humidity, precipitation, and other atmospheric variables. To forecast the weather is a major branch in the field of meteorology. In comparison to climate, weather can only be predicted for a short period of time (minutes to month), because it describes the state of the atmosphere for specific days at specific locations. For a reliable weather forecast, a good numerical prediction model with precise initial conditions is needed. Models are sensitive to changes in the initial condition, that is why at the moment weather predictions are only accurate for few days. However, both models and the determination of initial conditions are steadily improved. EO makes a significant contribution to improving the initial conditions by providing global information several times a day. As the quality of the EO products improves, the weather forecast also improves. \r\nSince decades, satellites are used to monitor and forecast weather. Therefore, it is one of the most established sectors of satellite data applications. There are geostationary and polar-orbiting weather satellites that measure all kinds of meteorologically relevant variables, e.g. cloud coverage, wind speed [...] via passive or active imagery. However, not only satellites are used to collect information, but also other remote sensing techniques that can be airborne or ground-based such as Lidar.\r\nWeather forecasts are used by citizens for decisions in everyday life, in agriculture for crop cultivation decisions and in the stock markets. Other domains of applications are hydrometeorology, aviation, maritime navigation, and the military and nuclear sectors.","name":"Forecast the weather","selfAssesment":"Completed
"},{"code":"TA13-1","description":"Monitor the atmosphere and climate includes all change-focused services/applications which assess, monitor, forecast and provide timely, continuous and independent data (e.g. temperature, humidity, emissions, greenhouse gases, solar UV radiation, aorosols,...). It closely monitors each of the Earth's different subsystems and, besides being the basis for weather forecasts, helps to better understand and evaluate the impact of the climate change.","name":"Monitor the atmosphere and climate","selfAssesment":"New
"},{"code":"TA13-2-1","description":"Monitor critical information about offensive and defensive systems.","name":"Monitor critical assets","selfAssesment":"New
"},{"code":"TA13-2-2","description":"Monitoring health can be delivered indirectly by monitoring environmental changes that can cause endemic and chronic diseases. Typically monitored environmental factors are temperature, humidity, stagnant water, NDVI, land cover, or soil type.","name":"Monitor health","selfAssesment":"New
"},{"code":"TA13-2-3","description":"Monitoring food security includes the monitoring of food availability by environmental conditions (land cover, NDVI,...), as well as the monitoring of migration patterns. Risks that can lead to food insecurity are hazards or conflicts.","name":"Food security monitoring","selfAssesment":"New
"},{"code":"TA13-2-4","description":"Monitoring borders includes monitoring the land and marine border incursions, monitoring transport routes, assessing pressures on poplulations, and monitoring humanitarian movement.","name":"Monitor borders","selfAssesment":"New
"},{"code":"TA13-2","description":"Monitor security and safety describes the collection and analysis of information to provide intelligence services & safety. The task is to give early warnings in case of emergencies, to monitor infrasturcture, transport routes (land and water) and borders, to surveil security and sovereignty.","name":"Monitor security & safety","selfAssesment":"New
"},{"code":"TA13-3-1","description":"EO is capable to repeatedly map flood extent directly after flooding, including further aspects (flood plain, extend mapping, frequency, rainfall, flash floods, vulnerability, inundation, risk-based mapping & management; flood spread and depth followed by automated insurance payouts). Modelling (hydrological modelling and monitoring focused on seasonal dynamics of water availability) based on EO data (digital elevation models) supports flood risk assessment.","name":"Map and assess flooding","selfAssesment":"New
"},{"code":"TA13-3-2","description":"For the outbreak of forest fires, satellite remote sensing can be continuously track and monitor, in a timely manner to grasp the development of forest fires. Beyond, weather monitoring enables to forecast weather conditions where fires are likely, allowing authorities to prepare.","name":"Detect and monitor wildfires","selfAssesment":"New
"},{"code":"TA13-3-3","description":"Damages from earthquakes to infrastrcture can be detected directly, e.g. by mapping collapsed buildings in optical data to derive rapid response products. Use of SAR interferograms enables to identify geotectonic shifts. Modelling enables to identify hotspot areas.","name":"Assess damage from earthquakes","selfAssesment":"New
"},{"code":"TA13-3-4","description":"Landslides and land subsidence are a hazard posing a threat to human life, property, infrastructures, and natural environment. Every year, slope instabilities have a significant impact on societies and economies. As ground survey is very costly and time consuming, satellite remote sensing is increasingly used to mitigate damage resulting from landslides.\r\nLandslides are a local terrain change after a downslope movement. They vary by type of movement (e.g. falling, toppling, gliding and flowing), by size (from small rocks to entire mountain slopes) and velocity (from a couple of millimetres subsidence per year up to free-fall speed). Therefore, rockfalls and debris flows count as landslides. Landslides can be triggered both by natural causes (like earthquakes or heavy rainfall events) and human causes, e.g. mining activities that lead to slope failures or land subsidence. Landslides may have cascading effects with other natural hazards, e.g. when a landslide blocks a river that forms a lake that subsequently burst through the instable landslide dam and results in an outburst flood. \r\nLandslides are diverse in appearance, often are covered by other types of land cover, and therefore are challenging to detect. Therefore, observation methods aim for detecting visual changes to the land surface and the surface displacement. \r\nEO satellites and airborne remote sensing use optical sensors for detecting spectral features of landslides in post-event images and land cover changes through landslides in comparisons of pre-event and post-event images. Typical resolutions of optical EO data for mapping rapid landslides are between 0.4 m and 30 m, depending on the size of landslides caused by the triggering event. Aerial data and optical data from unmanned aerial vehicles are used in cases where single landslides or concise regions have to be covered. Additionally, EO services use radar sensors that detect changes in surface roughness and ground deformation caused by landslides, in time-series of at least two to large numbers of radar images. Further, airborne laser scanning enables the generation of digital elevation models that allow identification of landslide surface structures and, in case of repeated coverage, detection of elevation changes. DEM generation for analysing landslides is also possible with photogrammetry on stereographic optical data and radargrammetry on SAR images.\r\nThe diversity of appearances of landslides with its challenges to (semi-)automatic image processing makes visual interpretation of EO data by a landslide expert a commonly used method of landslide mapping. However, visual interpretation is subjective and experts’ results can be very diverse. Additionally, it is a slow and time-consuming process. Semi-automated classification of optical images using object-based image analysis (OBIA) can achieve detailed interpretations of landslides that distinguish between source, transport, and accumulation areas. Additionally, semi-automated methods have shown that they can outperform visual interpretation in speed of mapping and in comprehensiveness / completeness of coverage.","name":"Forecast and assess landslides","selfAssesment":"Completed
"},{"code":"TA13-3-5","description":"In context of volcanic activities and volcanos, EO methods are capable to provide information about various aspects, including ground motion (seismic), volcanic eruptions (pre-eruptive, sin-eruptive, atmospheric ash, dispersion), Rapid damage estimation (prevention), earthquake damage extent (loss adjuster dispatch). classification of land cover types","name":"Assess and monitor volcanic activities","selfAssesment":"New
"},{"code":"TA13-3-6","description":"Multi-hazard assessment both focuses on regions prone to several geohazards and on the interrelationships between hazards, i.e. what happens if two disasters strike at the same time or what happens when one disaster is causing a cascade of disasters with a strongly amplified impact (e.g. a landslide causing a dammed river causing an outburstflood with a magnitude beyond the design of protective measures; or an earthquake in a coastal region that is followed by a tsunami). EO can provide imformation on the single disasters and, through integration and comprehensive impact assessment, enables multi-hazard assessment.","name":"Multi-hazard assessment","selfAssesment":"New
"},{"code":"TA13-3","description":"Assess disasters and geohazards by EO includes alert & early warning, emergency mapping, and risk & recovery mapping. It relates to observations, controlling, assessments that are linked to natural and human made risks. Typical disasters that can be assessed by EO are in particular floods, droughts, forest fires, landslides, tsunamis, earthquakes, cyclonic storms and volcanic eruptions. Since with EO it is possible to quickly analyse the risk or damage it is used to effectively plan emergency response actions.\r\nThere are several measures to minimize or prevent the damage caused by disasters. Some of them have to be carried out in anticipation of a disaster, others after the occurrence of an event. The different phases that are needed to reduce or avoid the impact and to assure rapid response and recovery are described in the disaster management cycle. Depending on the cycle phase, EO has to meet different requirements. The Mitigation and Preparedness phase are passed through in anticipation of a disaster event. Thus, requirements to EO products may focus on high completeness of mapping or high accuracy of mapping. In contrast, Response and Recovery phase include rapid mapping, thus EO capabilities must meet near real-time delivery requirements. \r\nAs well, the nature of the disaster determines which EO products are used. Optical sensors are used throughout the different types; however, landslides are mostly assessed by radar sensors and thermal sensors are additionally used for forest fires.","name":"Assess disasters & geohazards","selfAssesment":"New
"},{"code":"TA13-4-1","description":"To monitor crops and agriculture with EO-based methods is relevant for various applications, including to assess environmental impact of farming, assess crop damage due to storms, to detect ollegal or undesired crops, to monitor water use on crops and horticulture, and to monitor land degradation neutrality. EO mapping of crops happens on all scales with both optical and SAR sensors. Relevant EO products include degradation, agri-environment, ecosystem, damage estimation, warning-service, food-security, impact, crop health (disease and stress), leaf area index, crop acreage and yield harvest (inventories / statistics), crop types (extent, growth, health, stress), land surface temperature, illicit crops, estimates, cultivation patterns, soil water index, surface soil moisture, run-off, land cover (land cover change), land productivity (net primary productivity, NPP), carbon stocks (soil organic carbon, SOC).","name":"Monitor crops","selfAssesment":"New
"},{"code":"TA13-4-2","description":"Monitor the forest focuses on regular and periodic measurement of certain parameters of forests (physical, chemical, and biological) to determine baselines to detect and observe changes over time. Typical applications include to assess deforestation and forest degradation, assess forest damage due to storms or insects, to monitor forest resources, detect illegal forest activities, assess the environmental impact of forerstry, and to monitor the forest carbon content. Moderate resolution sensors have been used to map forests at large scales. Modern very high resolution optical sensors provide enough spatial and spectral detail to map individual trees. Further sensors for forest monitoring include SAR and LIDAR. Integration of optical sensors, LIDAR and in-situ measurements seems an accurate method to achieve third dimension forest mapping.","name":"Monitor the forest","selfAssesment":"New
"},{"code":"TA13-4-3","description":"EO provides the opportunity to monitor bodies of water, i.e. inland waters, and to assess ground water and run-off. For lakes, this includes products about water quality, pollution, turbidity, suspended sediment concentrations (quantitative, qualitative), waterbody (temperature, extent, volume, quantity), algal blooms, alkaline water, evaporation, surface temperature. For ground water and run-off, the products focus on water run-off (water quantity), hydrological network and catchment areas (water catchment), run-off season, groundwater. Various scales are addressed, from local catchments to the global water cycle. For inland water quality, sensors are optical medium resolution (300 meters) for achieving a (strongly cloud-cover dependent) update frequency of 10-20 times per year and high resolution (5 meters) for update frequency of 3-5 times per year.","name":"Monitor bodies of water","selfAssesment":"New
"},{"code":"TA13-4-4","description":"Monitoring of snow and ice focuses on glaciers and their retreat due to climate change (extent, mass balance), the seasonal snow cover (its extent, depth, temperature and snow water equivalent), and the ice on rivers and lakes (inland ice, thickness, freezing period, melting period, ice extent). Glacial monitoring in the mountainous regions around the globe, and of the Greenland and Antarctic ice shields uses optical EO data of high and very high resolution and SAR data. Satellite based daily snow covered area products can reliably be provided down to a spatial resolution of 500 meters. Global products are possible with weekly updates. Applications include, among others, climate change impact monitoring, relevant for modelling runoff patterns in catchments for etimating hydroelectric power generation potential.","name":"Monitor snow and ice","selfAssesment":"New
"},{"code":"TA13-4-5","description":"EO is used to monitor land ecosystems and biodiversity, environmental impact of human activities, land pollution and vegetation encroachment. A tool for this is land cover mapping and mapping of land cover change about a wide set of categories, lincuding basic forest types, major agricultural surface types, conservation areas, settlements, infrastructure, primary roads, bare soil, water bodies, rivers, wetlands following standard classification schemes according to CORINE or FAO LCCS. Main source are optical EO data and associated pixel-based and object-based image classification methods. For discriminating vegetation classes, they often making use of various vegetation indices and biophysical parameters.","name":"Monitor land ecosystems","selfAssesment":"New
"},{"code":"TA13-4-6","description":"EO technologies (both optical and SAR) are capable to categorize bio-physical coverage of land to produce land cover maps like CORINE Land Cover (CLC). The EO method is objective and allows for frequent updates. EO-derived land cover is an excellent basis for mapping land use, the socioeconomic use that is made of land. Land use products are used in a wide range of applications (e.g. agriculture, forestry, spatial planning, determining and implementing environmental policy, land accounting). In a humanitarian context, land use mapping is applied to map refugee camps, population and pressures on population that cause migration.","name":"Monitor land use","selfAssesment":"New
"},{"code":"TA13-4-7","description":"EO is capable to monitor topography with various types of land surface elevation data (both digital terrain models and digital surface models) and also focus on land surface changes and ground deformation / movement due to e.g. soil erosion or permafrost thawing, frost heaving. This includes also the mapping of stable zones where such changes do not happen. The main ways of creating a digital elevation model (DEM) from EO data are deriving it from interferometric synthetic aperture radar (InSAR), from stereoscopic pairs of optical images acquired from different viewing angles, and deriving them via laser scanning.","name":"Monitor topography","selfAssesment":"New
"},{"code":"TA13-4-8","description":"EO is able to extract information about subsurface geology, including near surface features, lithology features, and linear disturbance features (faults & discontinuities). Concerning monitoring of mineral extraction EO supports by mapping ground surface, illegal activities, mine waste (erosion, land subsistence, biodiversity/habitat loss, destruction & disturbance of ecosystems). Disturbance of ecosystems may happen by carbon seeps from reservoirs or pipelines. Their detection can also be done with EO data.","name":"Extract information about subsurface geology","selfAssesment":"New
"},{"code":"TA13-4","description":"Services that monitor land cover all services/applications that are focused on monitoring, assessing, managing, planning and improving land areas, its ecosystems (land, soil and inland water monitoring/quality/availability & usage assessments) and evolution of the land surface (use, cover, seasonal and annual changes and monitors variables) even if it involves human intervention (environmental challenges, impact evaluation or suitability analysis).\r\nMonitoring is possible by deriving information from variables measured by EO in different domains, like vegetation, energy, water, and cryosphere. For vegetation, those variables are for example land cover, NDVI, burnt area, or surface soil moisture. In the energy domain, land surface temperature and surface albedo are known variables, for water it is water surface temperature or water quality. Finally, for the cryosphere lake ice and snow cover extent, and snow water equivalent are variables that are used for land monitoring services.","name":"Monitor land","selfAssesment":"Completed
"},{"code":"TA13-5-1","description":"The full range of EO satellite sensors are capable of monitoring particular aspects of urban areas. The most relevant include SAR satellites such as TerraSAR-X that distinguish between urban fabric and other land cover. Further, optical satellites in the resolution range HR and VHR are used to map imperviousness and soil sealing. Beyond such land cover classifications with low granularity, HR and VHR data are used for producing detailed land use and land cover classifications that distinguish different settlement densities or, in combination with additional data, different land use such as transport, residential etc. as defined in Classification schemes specialized on urban areas. Airborne laser scanning (and stereographic analysis) maps building and vegetation heights. InSAR methods allow to measure land subsidence that is highly relevant e.g. in coastal cities close to or below the sea surface elevation. Night-time optical data maps lights. Thermal sensors allow mapping the heat that is radiated from cities. Typical applications include monitoring urban growth/sprawl, transport networks, urban heat islands, and generating city maps and 3D city models for urban planning that are relevant to users in smart cities and in local/regional planning.","name":"Monitor urban areas","selfAssesment":"Completed
"},{"code":"TA13-5-2","description":"EO is capable of monitoring infrastrcture in general, i.e. buildings (and their construction) and transport networks (roads, rails). Additionally, infrastructure for renewable energy harvesting (solar and wind farms, hydroelectric powerplants) and identification of suitable sites (through mapping solar radiation, wind roses, speed and direction, hydrological network mapping). A basis is land surface mapping for deriving digital elevation models (DEMs) that is required for modelling renewable energy potential and for spatial planning and landscape visibility analysis (visual impact assessments for planned infrastructure). Further, EO is capable of assessing damage from industrial accidents. A wide range of EO technologies is used here, infrastrcture can be directly detected and mapped with optical and SAR sensors, where the resolution depends on the targeted assets. DEMs can be generated from SAR and stereographic optical data. Wind energy related parameters can be derived from satellites focused on atmosphere and weather monitoring. Further, there are various GI methods in use, too (in particular focused on spatial planning and impact assessment).","name":"Monitor infrastructure","selfAssesment":"New
"},{"code":"TA13-5","description":"Monitoring the built environment provides information about urban structures, transport networks and particular infrastructure, e.g. dedicated to energy provision. It covers all urban and infrastructure related service/applications on site development information, planning support or suitability analysis. As well, it includes pressure and threats analysis on the urban areas.","name":"Monitor the built environment","selfAssesment":"New
"},{"code":"TA13-6-1","description":"EO is capable of monitoring ocean quality and productivity by focusing on ocean colour (that show among other thins chlorophyll and algal bloom), parameters of sea surface salinity (SSS) and sea surface temperature (SST). In addition, EO can monitor pollution at sea that that explains coastal water quality, which is relevant for aquafarms and for tourism (bathing area water quality). Further, EO satellites can detect oil slicks and spills and threats from such events. Many of these parameters and detected features are relevant for monitoring marine habitats, targeting in particular generic algal blooms, marine mammals, sea surface temperature, sediments, plumes, nutrients, dredging operation, coral reef health assessment (bleaching).","name":"Monitor the marine ecosystem","selfAssesment":"New
"},{"code":"TA13-6-2","description":"In coastal areas, EO is capable to monitor water depth and shallow water bathymetry (charting), coastal ecosystem parameters about water temperature, water transparency, oxygen, phytoplankton abundance, bathing water indicators, detection harmful algal blooms, sediment (qualitative, quantitative), turbidity (quality, quantitative), visibility, chlorophyll-a concentration, suspended sediment may be indicative of estuarine processes, re-suspension or pollution. Further, this includes coastline monitoring with a focus on shoreline and its change as well as coastal land cover (and terrain) and its change. A widse set of EO sensors and technologies is used to monitor coastal areas. Optical satellite imagery is analyzed to detect and map suspended sediment concentrations. Etc.","name":"Monitor coastal areas","selfAssesment":"New
"},{"code":"TA13-6-3","description":"EO is capable to monitor weather impact on ocean surface and metocean features as a basis for forecasting furture ocean conditions. This includes ocean surface topography, ocean dynamics and circulation like tides and ocean current movements and drift, ocean winds, wave and climate conditions at ocean locations (meteocean). Further, this covers the mapping of extreme waves like tsunamis and the monitoring of hurricanes and typhoons. Involved EO technologies are for example satellite altimetry that maps ocean surface with 2 cm to 3 cm accuracy, mathematical forecast models. Repeated altimetry measurements allow mapping speed and direction of ocean's currents and tides. Available EO-based RADAR systems monitor wave height and direction, wind speed and sea-surface elevation. Near-realtime processing and delivery workflows enable the use of these parameters in weather forecasting, navigation and offshore installations protection.","name":"Monitor weather impact on ocean surface","selfAssesment":"New
"},{"code":"TA13-6-4","description":"To support an ecosystem-based approach for fisheries management, EO images with global and daily systematic coverage with high-resolution images can help in identifying potential fishing zones and to assess fish stocks. They help assessing and understanding changing abundancy and spatial distribution of exploited fish stocks. Therefore, they analyse various key environmental parameters that can be detected with satellite remote sensing. This includes sea surface temperatures (SSTs), sea surface height anomalies, and sea surface colour revealing the abundance of chlorophyll a. This relates to phytoplacton production that is directly related to total fish landings. Additionally, EO can detect harmful algal bloom. A further threat to sustainable fish stocks management are illegal fishing. Where localization of licensed fishing vessels and fleet management services are supported by EO to avoid overexplotation and enable recovery of fish stocks. EO complements identification, detection and tracking of vessels with SAR and optical remote sensing.","name":"Monitor fisheries","selfAssesment":"New
"},{"code":"TA13-6-5","description":"For shipping, navigation, and monitoring sea-traffic and pollution, remote sensing and satellite technologies allow detecting vessels in the wider ocean. EO can detect the vessels themselves, their wake trailing behind them, sandbanks and reefs that pose a threat for safe navigation. Additionally, EO can detect pollution from the ships, e.g. when illegal waste disposal happens. Ship detection and classification is possible with the use of optical and synthetic aperture radar (SAR) imagery. The methods complement each other.","name":"Detect and monitor ships","selfAssesment":"New
"},{"code":"TA13-6-6","description":"Information on sea ice and icebergs is important for managing operation of ships or offshore platforms in hazardous sea ice conditions. EO technologies give the possibility to study sea ice and measure its thickness, spatial distribution, motion and ridges (as well as ice berg positions). Satellite imagery provides wide area, synoptic pictures of the ice conditions. Since the scale of ice fields is quite large, mainly moderate resolutions have to be accepted, down to around 10m in scale, while ensuring comprehensive coverage. Multispectral imagery can provide more information on ice-type but in the main, SAR imagery is used due to its all-weather and day/night capability. The data collected can be more accurate than in-situ measurements due to a higher and faster coverage of a whole area. Subsequent modelling that incorporates ocean weather (wind, waves, ocean current) provides expected drifting paths. Constant monitoring is most important to identify the risk and opportunities, for instance for ship routing, and safety of oil rigs.","name":"Monitor sea-ice and icebergs","selfAssesment":"New
"},{"code":"TA13-6","description":"Monitoring marine inlucdes monitoring of marine safety (e.g. marine operations, oil spill combat, ship routing, defence, search & rescue, ...), marine resources (e.g. fish stock management, ...), marine and coastal environment (e.g. water quality, pollution, coastal activities, ...), and climate and seasonal forecasting (e.g. ice survey, seasonal forecasting, ...).","name":"Monitor marine","selfAssesment":"New
"},{"code":"TA13","description":"EO services and applications are organized according to thematic areas. EO is used for a wide set of services. There are many applications of EO that show how a service produces information for a particular client. EO service and applications are best described by the purpose they serve or by the need of the user. The main user needs to EO are to monitor, to map, to forecast, to assess, to detect, and to analyse. \r\nTo monitor means to watch and check a situation carefully for a period of time in order to discover something about it, i.e. keeping track of how the natural and manmade environment change (their status) over time. Typical alternative verbs are track, observe, record, follow, understand, or surveil. \r\nTo map means to represent an area of land in the form of a map, i.e. to feature and locate the way it is arranged or organized. Synonymous verbs are locate, identify, classify, trace, or record.\r\nTo forecast means to provide statements covering a range of different outcomes, to say what you expect to happen in the future; i.e. to predict future events based on specified assumptions (about information extracted from EO change and time series data), where different sets of assumptions describe scenarios. Equivalent terms are predict, plan, model, estimate, or project.\r\nTo assess means to judge or decide the amount, value, quality or importance of something, i.e. to evaluate and measure the status of and changes in natural and manmade built environments. Alternative verbs are evaluate, measure, understand, review, or quantify.\r\nTo detect allows to notice something that is partly hidden or not clear, or to discover something, especially using a special method, i.e. to identify and locate the changes in the Earth’s environment. Similar terms are locate, warn, identify, highlight, or spot.\r\nTo analyse means to study or examine something in detail, in order to discover more about it, i.e. to detail the elements of a whole and critically examine and relate these component parts separately and/or in relation to the whole. Sometimes, the terms to process, to parse, or to detail are used in exchange for to analyse.","name":"EO services and applications","selfAssesment":"New
"},{"code":"TA14-1-1","description":"Band combinations are pre-defined for (visually) analysing images for a dedicated purpose. Examples are dedicated band combinations for land us land cover classification, ocean colour, etc.","name":"Band combinations","selfAssesment":"New
"},{"code":"TA14-1-2","description":"The spectral and refractive information from optical and SAR data enables direct and indirect derivation of biophysical and geophysical EO parameters that are properties of the sensed land surface, ocean surface and atmosphere volume.","name":"EO parameters","selfAssesment":"New
"},{"code":"TA14-1","description":"Processing products are image products from raw data to all different processing stages. The transformation processes between the stages include operations such as atmospheric correction, cloud detection and radiometric calibration to provide data in a form suitable for subsequent analysis. Processing products consider a product as being an output of a process.They appear as \"intermediate products\" along all steps of the processing chain.","name":"Processing-related and preparatory products","selfAssesment":"New
"},{"code":"TA14-2-1-1","description":"Point clouds represent a set of points with X, Y, Z coordinates and associated attributes. A source of acquisition is Light Detection and Ranging (LIDAR), an airborne surveying technique that uses laser light to measure the distance to an object on the ground.","name":"Point clouds","selfAssesment":"New
"},{"code":"TA14-2-1-2","description":"Elevation data in the form of a digital elevation model (DEM) is an essential component of many analyses derived from EO. DEMs are used to represent every kind of surface, including terrain surface, vegetation canopy surface, sea surface, sea-ice surface, glacier surface etc. This description focuses on DEMs for representing terrain. A digital terrain model (DTM) describes the bare ground of the terrain, a digital surface models (DSM) described heights of vegetation (e.g. trees) and of man-made structures (e.g. buildings) reaching above the terrain. DEM is often used as an umbrella term for DTM and DSM. EO-derived DEMs are usually DSMs and require removal of vegetation and buildings in order to represent the terrain (DTM). DEMs are multi-purpose products used in various applications. They are available for global scale (SRTM, WorldDEMTM), regional scale (ArcticDEM, Copernicus EU-DEM v1.1) or for national levels and local regions. Various techniques exist to generate DEMs from SAR data, stereographic optical EO (as well as airborne and drone) data and from airborne laser scanning.","name":"Digital elevation models","selfAssesment":"Completed
"},{"code":"TA14-2-1-3","description":"By comparing elevation models of different dates, the change in elevation and volume can be identified. Thereby, they measure surface deformation, land subsidence, ice shield loss due to melting, etc.","name":"Elevation change maps","selfAssesment":"New
"},{"code":"TA14-2-1-4","description":"Vector fields capture the movement directions of locations on a continuous surface, e.g. of the ocean, or in a 3D grid of locations, e.g. of the atmosphere. The atmosphere and the ocean are highly dynamic features. Vector fields are used to represent wind directions and current movement directions. Further vector fields derived from EO data include geoid undulation / gravity maps.","name":"Vector fields","selfAssesment":"New
"},{"code":"TA14-2-1-5","description":"When a moving feature (i.e. object) is detected in subsequent images, its trajectory of movement can be mapped. Such products map ship movements, sea ice movements, etc.","name":"Feature trajectories","selfAssesment":"New
"},{"code":"TA14-2-1","description":"Geometrically measured EO products origin from EO-derived distance measurements, measurements of direction, tracking of moving objects, and changes of distance measurements. The used EO methods include for example SAR interferometry and stereographic analysis of optical data.","name":"Geometrically measured EO products","selfAssesment":"New
"},{"code":"TA14-2-2-1-1","description":"Land cover maps represent spatial information on different types (classes) of physical coverage of the Earth's surface, e.g. forests, grasslands, croplands, lakes, wetlands. An example is the European Copernicus product CORINE land cover (CLC) with 44 classes. Initiated in 1985 (reference year 1990), updates followed in 2000 and every 6 years afterwards. Apart from CLC, the European Copernicus Land products also include the High Resolution Layers. They includes for example the imperviousness product that captures the percentage of soil sealing. Land cover classification products are multi-purpose products that are relevant for various applications. They are available on national levels, regional levels and global levels. They have different scales and granularity of their associated classification scheme. The products are updated on a regular basis. Update cycles can vary depending on the resolution (i.e. likelihood for observable change of the land surface) and the capability of production processes. An additional example on a global scale is the Global Urban Footprint. The products are provided by public organisations and private EO companies and based on various EO sensors.","name":"Land cover maps","selfAssesment":"Completed
"},{"code":"","description":" ","name":" ","selfAssesment":" "},{"code":"TA14-2-2-1-3","description":"Cloud masks for optical EO data distingush cloudy pixels from cloud-free pixels. They may differentiate between serveral cloud types, i.e. opaque clouds and Cirrus clouds (that are transparent). Most land monitoring applications based on optical data require cloud-free images. Therefore, cloud masks are a product that is used early on in image processing for selecting suitable imagery for analysis (e.g. by screening images of an archive by the derived cloud cover percentage of the image). Therefore, cloud masks are made available as metadata by the EO data provider. Clouds are identified with threshoulding of reflectance values of the blue band and, to adapt for cloud/snow confusion, specific short-wave infrared (SWIR) bands.","name":"Cloud mask","selfAssesment":"New
"},{"code":"TA14-2-2-1-4","description":"Detected features are objects from one or more classes and are the result of a comprehensive (and mostly automatic or semi-automated) search of all locations in an image that decides whether such features are present and where they are located. Examples inculde man-made objects (e.g. vehicles, ships, buildings, etc.) with sharp boundaries and are independent from the background, and landscape objects, such as land-use/land-cover (LULC) parcels that have vague boundaries and are part of the background environment. Only the latter type would locate features for all locations of an image.","name":"Detected features","selfAssesment":"New
"},{"code":"TA14-2-2-1","description":"Static EO derived thematic classification products and masks (e.g. land use land cover classifications). Additionally, static EO detected features (planes on apron of airports, dwellings) that consist of a set of point locations (or polygons) and do not end up in a comprehensive classification of all pixels of an image. Static EO derived thematic classification products and masks (e.g. land use land cover classifications). Additionally, static EO detected features (planes on apron of airports, dwellings) that consist of a set of point locations (or polygons) and do not end up in a comprehensive classification of all pixels of an image. Thematic classifications and feature detection identify a surface by a class label that represents a more or less persistent state. A good example product is the Copernicus Urban Atlas. The most recent available version is assumed to represent the \"current\" state (Certainly, an update cycle is necessary for providing a product that remains up-to-date).","name":"Thematic classifications and feature detection","selfAssesment":"New
"},{"code":"TA14-2-2-2","description":"Event maps and thematic change (evolution) maps indicate that some process happened that changed the area at a location from one class to the other. For example, a burnt area map indicates locations where vegetation has been burnt by a fire and changed to bare ground. A typical mapping method is the use of pre- and post-event satellite images for detection of the areas affected by the process. Eventually burnt areas contain identifiable burn marks that allow direct identification in one single post-event satellite image. Nevertheless, it is the process that is central to the analysis. Similarly, the concepts aforestation and deforestation would fall under the heading \"Event maps.\" They may come from a comparison of two status maps of different dates. Some processes benefit from analysis of more than two states. Such change evolution maps can be produced with time-series analysis. On land, more examples include landslide maps, flooded area maps and other land surface dynamics (e.g. aforestation and deforestation). Further, change detection maps are available for other domains (atmosphere, marine, land, climate, etc.)","name":"Event maps and thematic change (evolution) maps","selfAssesment":"New
"},{"code":"TA14-2-2","description":"The semantic labelling products result from methods that assign labels to objects or locations in a field. The labels correspond to the categories of a classification or, in case of masks and detected features, to a single target class. Such labels may also identify classes of change or change evolution.","name":"Semantic labelling products","selfAssesment":"New
"},{"code":"TA14-2-3","description":"EO-derived attribute products describe the state and evolution of specific attributes of a feature or at a field location. They describe for example air quality, soil moisture or water quality & quantity.","name":"EO-derived attribute products","selfAssesment":"New
"},{"code":"TA14-2","description":"Descriptive analytics products provide analytical results which describe the present (and past) situation as it is recorded in EO images. Therefore, it contains information that can directly be extracted from EO images or EO image time series. These products are diverse in various aspects: they capture static and dynamic information; they concern information about objects or fields; and they have qualitative (nominal scale) or quantitative (ordinal, interval, ratio scale) levels of measurement.","name":"Descriptive analytics products","selfAssesment":"New
"},{"code":"TA14-3","description":"Providing analytical (modelling) results which predict the future situation (e.g. air pollution forecasts). [interpolation in space, i.e. not only prediction into the future, filling gaps in time series...]\r\nInformation that can be modelled based on descriptive analytics products. by extrapolating time series (forecasting/predicting), by modelling of processes (e.g. flood risk maps, landslide susceptibility)","name":"Predictive modelling products","selfAssesment":"New
"},{"code":"TA14-4","description":"Prescriptive modelling products and services focus on providing analytical results that are a guide to action. The often result from an impact assessment. One example is the identification of construction sites leading to sales opportunities.","name":"Prescriptive modelling products and services","selfAssesment":"New
"},{"code":"TA14-5-1","description":"A textured 3D model uses a 3D model derived from elevation data. Additionally, each separate surface of the 3D model receives its own texture derived from optical image data. Typically used for visualisation purposes.","name":"Textured 3D models","selfAssesment":"New
"},{"code":"TA14-5-2","description":"A semantic 3D model consists of a 3D model derived from elevation data with an integrated image classification. A classified object thereby consists of a 3D surface or a grouped set of 3D surfaces. A typical example is a 3D city model in the CityGML format.","name":"Semantic 3D models","selfAssesment":"New
"},{"code":"TA14-5","description":"Combining the satellite data with other information sources. Resulting in an integration of several descriptive analytics products and processing products, e.g. a textured 3D model or a semantic 3D model.","name":"Aggregation and integration products","selfAssesment":"New
"},{"code":"TA14-6-1","description":"Sentinel-2 cloud-free mosaics for display, satellite maps in books etc.","name":"Satellite maps","selfAssesment":"New
"},{"code":"TA14-6-2","description":"Layouted maps in a file (PDF, SVG, etc.) for printing or visualisation on screen, embedding in reports or as static displays on websites etc.","name":"Layouted digital maps","selfAssesment":"New
"},{"code":"TA14-6-3","description":"Digital layouted maps in an online map viewer; 3D visualisations on the screen / 3D screen and online map viewers with 3D capabilities etc.","name":"Web visualisations in 2D and 3D","selfAssesment":"New
"},{"code":"TA14-6-4","description":"Printed maps, 3D plots of 3D models, hologram 3D maps etc.","name":"Analogue visualisation products","selfAssesment":"New
"},{"code":"TA14-6-5","description":"A video is a structured file of 2D grids link by the time, is a regular file of values which has been processed to sensor units (e.g. calibrated). The result can be a single date acquisition or a combination of dates. For each point, the value represents a parameter imaged by the sensor. Videos of EO data present for example time series of satellite maps and other EO products (e.g. Arctic sea ice evolution in a time-series map video over the past 30 years).","name":"Time series map videos","selfAssesment":"New
"},{"code":"TA14-6","description":"Visualsiation products are used for presentation of EO information to the user. The user's interaction with the visualisations is predominantly viewing and interpretation of the informational content and arriving at decisions in the context of the user'S objective with the EO information. In addition, users of visualisation are all involved actors during image processing. For example, an EO analyst may use visualisations of EO data and preliminary EO products for getting a better understanding of the contained information and adapt his processing workflow to arrive ad improved results. Typical visualisation products include satellite maps, layouted digital maps, web visualisations in 2D and 3D, and analogue visualisation products.","name":"EO visualisation products","selfAssesment":"New
"},{"code":"TA14-7","description":"Users need access to EO products if they shall be able to benefit from them. Additionally, providers of value added products act as users of EO products earlier in the information processing value chain. Concequently, various distribution services provide access from raw data to processed information and processing infrastructure. Provision of access to raw data or processed information happens via direct download (FTP), via application programming interfaces (API) or web services (e.g. Hubs). Further, access to processing infractructure happens via web services.","name":"Distribution services","selfAssesment":"New
"},{"code":"TA14","description":"Products in relation to EO appear along the entire image processing value chain as inputs and outputs of processing steps. Ultimately, at the end of that chain, the output EO products represent information that supports actions. The standard EO products are categorized by the type of problems they help to solve or the type of question they help answering.","name":"Standard EO products","selfAssesment":"New
"},{"code":"WB","description":"This knowledge area is about Web Based Geographic Information management aspects and therefore it was given the name \"Web Based GI\" or \"WBG\" in short. It is implied by this name that the differentiating factor for this KA is the \"Web\". One must then be able to answer the questions like \"What functions do we delegate to the Web?\" or \"how WBGI is different from the traditional GI?\" Sticking to the functions of a GIS, which are inserting (adding), storing, manipulating, analysing and presenting the data, there is not a single system for effecting all these tasks anymore but the Web itself. For instance, there is no single database and its known-to-its users-definition, anymore but many different stores and many different definitions. Similarly, many different manipulation, analysis and presentation options compared with the options offered by a single or limited number of systems of traditional GI. In general, Web provides the means of leveraging distributed \"resources\" like data, information, or software. It is a \"collaboration medium\". A collaboration that enables rapid production or decision making. A collaboration that certainly introduces new dimensions to traditional GI handling. This is the justification of proposing this KA in addition to the KAs of the original BoK. For the mentioned collaboration to happen, data or any other type of a resource have to accessible on the Web. This means that it should have a Web \"address\" and a \"definition\" that is understandable either by \"human\" or \"machine\". \"Machine understandable definitions\" refers to the dimension of \"semantics\" and \"ontologies\" which are also included under this KA. When one talks about publishing resources then \"catalogue services\" and more importantly \"discovery\" dimension comes into the scene. On the other hand, \"Linked Data (LOD)\" and \"Open Data\", highly popular recent trends and two of the above mentioned dimensions of Web GI have also been covered under this KA. Like the other dimensions of Web GI, both LD and OD aspects must be known to GI communities with differing degrees of expertise. The concepts of \"interoperability\" and \"Spatial Data Infrastructure (SDI)\", hot topics of GI communities for many years, have been thought to be dealt with under this KA as well with the justification that \"Web GI\" is a much broader concept than SDI, This is by the fact that SDI refers to a much narrower content and context of \"collaboration\" then Web GI. Therefore, Geospatial data interoperability and some of the related concepts which were classified under KA, \"Geospatial data in the original BoK were moved under KA11 with the updated context. Another issue is the coverage of Spatial Analysis (SA), data manipulation aspects of GI by KA11. The SA aspects are covered by other KAs like \"Geocomputation\" and \"Analytical methods\". If the analysis operations, in an undertaking, would be handled by web services this is already covered by \"data processing\" web services, application development unit and Web services composition under that unit. The important thing is to have the knowledge about a specific analysis operation; Employing it as a web service would require no more knowledge than using any other web service. SA is covered by KA11 in as much as it should have been.","name":"Web-based GI","selfAssesment":"GI-N2K
"},{"code":"WB1-1","description":"The basic principles on which web services build. The concept of Service Oriented Architecture and the importance of APIs","name":"Fundamentals of web services","selfAssesment":"GI-N2K
"},{"code":"WB1-2","description":"This concept will cover web services based on the Simple Object Access Protocol (SOAP)","name":"SOAP web services","selfAssesment":"GI-N2K
"},{"code":"WB1-3","description":"This concept will cover web services based on the representational state transfer (REST) protocol","name":"REST web services","selfAssesment":"GI-N2K
"},{"code":"WB1-4","description":"The Open Geospatial Consortium (OGC) defines standards and best practices for web services in the geospatial domain. OGC standards are developed using a consensus model allowing all stakeholder to participate in the process. As a result the OGC web services are widely implemented.","name":"OGC web services","selfAssesment":"GI-N2K
"},{"code":"WB1","description":"In the most simplistic way a Web service may be defined as \"a Web accessable program code which performs a task of either processing or serving some data. Although there are many other definitions in the related literature, the one in W3C (2004) seems to be quite complete and refering to also lately popular REST style Web services. It states that \" We can identify two major classes of Web services: REST-compliant Web services, in which the primary purpose of the service is to manipulate XML representations of Web resources using a uniform set of \"stateless\" operations; and arbitrary Web services, in which the service may expose an arbitrary set of operations.","name":"Web services","selfAssesment":"In progress GI-N2K
"},{"code":"WB2-1","description":"To be able to discover and assess available data or services, these resources have to be documented. This concept describes the standardized languages used for these descriptions","name":"Languages for the definition of non-spatial data and services","selfAssesment":"GI-N2K
"},{"code":"WB2-2","description":"Different standardized ways to define geospatial data exist. GML, GeoJSON, WKT and GeoSPARQL are examples. What are common points and differences","name":"Definition of geospatial data","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB2-3","description":"Defining a common language is a crucial step for sharing or combining data. Vocabularies, taxonomies, ontologies are are tools to reach this goal.","name":"Ontologies development reuse and patterns","selfAssesment":"GI-N2K
"},{"code":"WB2","description":"A \"resource\" could be \"anything\" including data and services, identifiable over the Web. A resource should be defined in a language to be discoverable on the Web. Over the years, two major bodies W3C for non-spatial and OGC concerning spatial data have developed many specifications for defining data and services. On the W3C side, Resource Description Framework (RDF) has gained a great momentum in recent years in relation to the recent popularity of Linked Data as well. In the OGC front, the acceptance of GML was a major step concerning the long time effort of geospatial communities for having a standard for the definition of both geospatial features and geometry.","name":"Resource Definition","selfAssesment":"GI-N2K
"},{"code":"WB3-1","description":" ","name":"Metadata and standards","selfAssesment":"GI-N2K
"},{"code":"WB3-2","description":"A resource can be added manually to a catalogue service by creating or uploading its metadata, but metadata can also be added by automated crawling of other catalogues.","name":"Manual and automated forms of publishing","selfAssesment":"GI-N2K
"},{"code":"WB3-3","description":"Catalogue services allow to publish and search resources through their metadata","name":"Catalogue services","selfAssesment":"GI-N2K
"},{"code":"WB3-4","description":" ","name":"Publishing open data","selfAssesment":"GI-N2K
"},{"code":"WB3-5","description":" ","name":"Publishing via a semantic definition of data","selfAssesment":"GI-N2K
"},{"code":"WB3-6","description":" ","name":"Publishing linked open data","selfAssesment":"GI-N2K
"},{"code":"WB3","description":"\"Publishing\" means making a resource available for the use of others. A \"resource\" could be \"anything\" including data and services, identifiable over the Web. Publishing may be done on the basis of either the \"characteristics\" of the data or the data itself. When only some \"characteristics\" of a resource is published then some of the contents would naturally be left out. The \"characteristics\" include metadata and some keywords. This kind of publishing may be named as \"limited contents\" publishing or \"publishing by metadata\". One of the issues become then what characteristics to use to define the data. Or what what metadata definition to use. Another aspect of publish is \"manual entry\" and \"automated collection\". In the former publisher enters metadata while in the latter some harvesting mechanism collects metadata in an automated fashion. On the contrary, there is \"unlimited contents publishing\" where there is no limitation on the published contents. Open data publishing is in this class. In additon, some \"additional semantics\" may be subject of this type publishing through new relationships in the ontologies of publishing, which have not been explicit in the exisiting data model but are inherent in the data. And this last type is covered under the topic, \"Publishing via a semantic definition of data.\"","name":"Resource Publishing","selfAssesment":"GI-N2K
"},{"code":"WB4-1","description":"Syntactic discovery is the discovery of resources based on the structure of the resources","name":"Syntactic discovery","selfAssesment":"GI-N2K
"},{"code":"WB4-2","description":"Semantic discovery is the discovery of resources based on the meaning of the data.","name":"Semantic discovery","selfAssesment":"GI-N2K
"},{"code":"WB4-3","description":"Linked (open) data provides structured data which is interlinked in a machine readable way. This allows to discover, access and combine data in an automatic way.","name":"Discovery over linked open data","selfAssesment":"GI-N2K
"},{"code":"WB4","description":"Resource discovery means the discovery of resources including data and services needed for an application. Syntactic discovery refers to the discovery on the basis of syntactic comparison operations. It is classified as \"keyword-based\" and \"full-text-based\" discovery. Semantic discovery on the other hand, refers to the discovery of resources on he basis of some semantic definition. Therefore, semantic discovery requires that a resource be published by a semantic definition as defined in the topic WB3-5.","name":"Resource Discovery","selfAssesment":"GI-N2K
"},{"code":"WB5-1","description":"The workflow to integrate geospatial data in an application often relies on a combination of different OGC web services. Searching and finding the data and the corresponding services, binding to these services to view, filtering and or downloading the data are different steps in this process","name":"Integrating data from OGC web services","selfAssesment":"GI-N2K
"},{"code":"WB5-2","description":"The alignment of data structures and vocabularies/ontologies used are important steps towards the data harmonisation needed for a combined use of datasets","name":"Schema matching and ontology alignment","selfAssesment":"GI-N2K
"},{"code":"WB5-3","description":"A data mashup is a combination of data from different sources to produce new applications of new datasets","name":"Data mash ups","selfAssesment":"GI-N2K
"},{"code":"WB5","description":"The term \"application development\" refers to the collection of activities or the \"workflow\" through which the user reaches her final goal. Being one of these activities, \"data integration\" means the transformation of data from one representation to another which might be of either the client`s one or some other representation. An example for data integration might be the case where the data is transfered from an OGC WFS and integrated into a client GIS.","name":"Application development via Data Integration","selfAssesment":"In Progress GI-N2K
"},{"code":"WB6-1","description":" ","name":"Manual Web Services Composition","selfAssesment":"GI-N2K
"},{"code":"WB6-2","description":" ","name":"Semi automated and Full-automated WSC","selfAssesment":"GI-N2K
"},{"code":"WB6","description":"Web Services Composition can be defined as bringing together a number of web services in a certain workflow to achieve a certain task that cannot be achieved by any of the composed services alone. In general, it involves first the discovery of the suitable services over the Web, and compose them in a certain workflow order and finally run the composed service which is the invocation stage. WSC has been a highly active research topic since the emergence of Web services in 2000s. \"Manual\" WSC is the form that the activities of discovery, composition and invocation are all done manually (by human). In the \"Semi-automated\" way, the discovery is done by the machine. In the \"full-automated\" approach all the above activities are done by the machine. There are no tools at the moment that achieve full automated composition. Web API composition is like WSC, the only difference is the fact that instead of web services there are Web APIs in WAPIC. There is no doubt that One would run into the very same problems of WSC concerning full automated composition. In other words, WAPIC would in no way be easier than WSC. Nevertheless, as far as semi automated form can be achived, WAPIC is valuable because the number of Web APIs increase drastically from day to day. The site \"programmableWeb\" lists 14 957 APIs at the moment. It is not easy to search for all those APIs manually for the discovery of suitable APIs for a given task.","name":"Application development via Web services composition","selfAssesment":"GI-N2K
"},{"code":"WB7-1","description":"Hypertext markup scripting and styling are the base for each web page or application. Styling defines the look and feel while scripting is used to implement the behavior of the web application","name":"Hypertext markup scripting and styling","selfAssesment":"GI-N2K
"},{"code":"WB7-2","description":"Web map APIs allow developers to integrate resources made available by web services in their application or web sites.","name":"Web Map APIs and Libraries","selfAssesment":"In rpogress (GI-N2K)
"},{"code":"WB7-3","description":"A web application framework provides the generic and reusable building blocks needed to create web applications. Geoportal frameworks provide the functionality to build geospatial portals.","name":"Web application Frameworks and Geoportal frameworks","selfAssesment":"In Progress (GI-N2K)
"},{"code":"WB7","description":"Characteristic examples are included under this topic. The APIs, for instance other than the ones included under this unit, and libraries could have been included as well. However, since the important thing is to highlight the functionality then there is no need to include them all. By the inclusion of topic \"WB7-3\"under this unit, the aim was to cover one of the very \"hot\"topics of Web2.0 for both the main concepts about Web application frameworks and also how they are related to portal frameworks and geoportals. By the topic \"WB7-1 Building blocks\"the core components of Web application development are covered. On top of this core, there comes a great variety of \"Web application frameworks for both enabling rapid web application development and ensuring scalable, high-performance applications. Finally, there are \"Web APIs and Libraries\" certainly deserving being a separate topic for their current popularity. They also mean rapid application development for developers by code reuse and versatality for \"end users\" in creating their \"end products\".","name":"Web Application development elements","selfAssesment":"In progress (GI-N2K)
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(2005) Dynamic visualization variables in animation to support monitoring of spatial phenomena [Doctoral dissertation] Enschede: ITC. (Netherlands Geographical Studies; 328)","url":"https://webapps.itc.utwente.nl/librarywww/papers_2005/phd/blok.pdf"},{"concepts":[256,260],"description":"ISBN: 978-1441965615","name":"Boccara, N. (2010). Modeling complex systems. Springer .","url":"http://books.google.com/books?isbn=9781441965615"},{"concepts":[695],"description":"Boerner, W. M. (2008). Introduction to synthetic aperture radar (SAR) polarimetry. Wexford Press.","name":"Boerner, W. M. (2008). Introduction to synthetic aperture radar (SAR) polarimetry. Wexford Press.","url":" "},{"concepts":[267],"description":" ","name":"Bone, C. (2018). Agent-based Modeling. In: Wilson, J. (Ed.) 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Retrieved from https://earsc-portal.eu/display/EOSTAN/Detect+illegal+or+undesired+crops","url":"https://earsc-portal.eu/display/EOSTAN/Detect+illegal+or+undesired+crops"},{"concepts":[912],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Detect ships in critical areas. Retrieved from https://earsc-portal.eu/display/EOSTAN/Detect+ships+in+critical+areas","url":"https://earsc-portal.eu/display/EOSTAN/Detect+ships+in+critical+areas"},{"concepts":[910],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Forecast and map large waves. Retrieved from https://earsc-portal.eu/display/EOSTAN/Forecast+and+map+large+waves","url":"https://earsc-portal.eu/display/EOSTAN/Forecast+and+map+large+waves"},{"concepts":[837,910],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Forecast and monitor current movement and drift. 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Retrieved from https://earsc-portal.eu/display/EOSTAN/Forecasting+sunlight+exposure","url":"https://earsc-portal.eu/display/EOSTAN/Forecasting+sunlight+exposure"},{"concepts":[903],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Identify hydrocarbon seeps in soil. Retrieved from https://earsc-portal.eu/display/EOSTAN/Product+Sheet%3A+Hydrocarbon+seep+detection","url":"https://earsc-portal.eu/display/EOSTAN/Product+Sheet%3A+Hydrocarbon+seep+detection"},{"concepts":[895,889],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Map and assess flooding. Retrieved from https://earsc-portal.eu/display/EOSTAN/Map+and+assess+flooding","url":"https://earsc-portal.eu/display/EOSTAN/Map+and+assess+flooding"},{"concepts":[837],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Map and monitor hydroelectric energy. Retrieved from https://earsc-portal.eu/display/EOSTAN/Map+and+monitor+hydroelectric+energy","url":"https://earsc-portal.eu/display/EOSTAN/Map+and+monitor+hydroelectric+energy"},{"concepts":[837],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Map and monitor solar energy (solar farms). Retrieved from https://earsc-portal.eu/pages/viewpage.action?pageId=16548947","url":"https://earsc-portal.eu/pages/viewpage.action?pageId=16548947"},{"concepts":[837],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Map and monitor wind energy (wind farms). Retrieved from https://earsc-portal.eu/pages/viewpage.action?pageId=16550140","url":"https://earsc-portal.eu/pages/viewpage.action?pageId=16550140"},{"concepts":[911],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Map fish shoals. Retrieved from https://earsc-portal.eu/display/EOSTAN/Map+fish+shoals","url":"https://earsc-portal.eu/display/EOSTAN/Map+fish+shoals"},{"concepts":[903],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Map geological features. Retrieved from https://earsc-portal.eu/display/EOSTAN/Map+geological+features","url":"https://earsc-portal.eu/display/EOSTAN/Map+geological+features"},{"concepts":[903],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Map seismic survey operations. Retrieved from https://earsc-portal.eu/display/EOSTAN/Map+seismic+survey+operations","url":"https://earsc-portal.eu/display/EOSTAN/Map+seismic+survey+operations"},{"concepts":[909],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Map water depth or charting. Retrieved from https://earsc-portal.eu/display/EOSTAN/Map+water+depth+or+charting","url":"https://earsc-portal.eu/display/EOSTAN/Map+water+depth+or+charting"},{"concepts":[902],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Measure & detect land surface change. Retrieved from https://earsc-portal.eu/display/EOSTAN/Product+Sheet%3A+Erosion+Potential","url":"https://earsc-portal.eu/display/EOSTAN/Product+Sheet%3A+Erosion+Potential"},{"concepts":[901],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Measure land use statistics. Retrieved from https://earsc-portal.eu/display/EOSTAN/Measure+land+use+statistics","url":"https://earsc-portal.eu/display/EOSTAN/Measure+land+use+statistics"},{"concepts":[880],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Monitor air quality & emissions. Retrieved from https://earsc-portal.eu/pages/viewpage.action?pageId=16549044","url":"https://earsc-portal.eu/pages/viewpage.action?pageId=16549044"},{"concepts":[909],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Monitor coastal ecosystem. Retrieved from https://earsc-portal.eu/display/EOSTAN/Monitor+coastal+ecosystem","url":"https://earsc-portal.eu/display/EOSTAN/Monitor+coastal+ecosystem"},{"concepts":[906],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Monitor construction and buildings. Retrieved from https://earsc-portal.eu/display/EOSTAN/Monitor+construction+and+buildings","url":"https://earsc-portal.eu/display/EOSTAN/Monitor+construction+and+buildings"},{"concepts":[896],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Monitor crops. Retrieved from https://earsc-portal.eu/display/EOSTAN/Forecast+crop+yields","url":"https://earsc-portal.eu/display/EOSTAN/Forecast+crop+yields"},{"concepts":[897],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Monitor forest carbon content. Retrieved from https://earsc-portal.eu/display/EOSTAN/Monitor+forest+carbon+content","url":"https://earsc-portal.eu/display/EOSTAN/Monitor+forest+carbon+content"},{"concepts":[897],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Monitor forest resources. Retrieved from https://earsc-portal.eu/display/EOSTAN/Monitor+forest+resources","url":"https://earsc-portal.eu/display/EOSTAN/Monitor+forest+resources"},{"concepts":[901],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Monitor humanitarian movement and camps. Retrieved from https://earsc-portal.eu/display/EOSTAN/Monitor+humanitarian+movement+and+camps","url":"https://earsc-portal.eu/display/EOSTAN/Monitor+humanitarian+movement+and+camps"},{"concepts":[899],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Monitor ice on rivers and lakes. Retrieved from https://earsc-portal.eu/display/EOSTAN/Monitor+ice+on+rivers+and+lakes","url":"https://earsc-portal.eu/display/EOSTAN/Monitor+ice+on+rivers+and+lakes"},{"concepts":[900],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Monitor land cover and detect change. Retrieved from https://earsc-portal.eu/display/EOSTAN/Monitor+land+cover+and+detect+change","url":"https://earsc-portal.eu/display/EOSTAN/Monitor+land+cover+and+detect+change"},{"concepts":[900],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Monitor land ecosystems and biodiversity. Retrieved from https://earsc-portal.eu/display/EOSTAN/Monitor+land+ecosystems+and+biodiversity","url":"https://earsc-portal.eu/display/EOSTAN/Monitor+land+ecosystems+and+biodiversity"},{"concepts":[900],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Monitor land pollution. Retrieved from https://earsc-portal.eu/display/EOSTAN/Monitor+land+pollution","url":"https://earsc-portal.eu/display/EOSTAN/Monitor+land+pollution"},{"concepts":[908],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Monitor marine habitats. Retrieved from https://earsc-portal.eu/display/EOSTAN/Monitor+marine+habitats","url":"https://earsc-portal.eu/display/EOSTAN/Monitor+marine+habitats"},{"concepts":[903],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Monitor mineral extraction. Retrieved from https://earsc-portal.eu/display/EOSTAN/Monitor+mineral+extraction","url":"https://earsc-portal.eu/display/EOSTAN/Monitor+mineral+extraction"},{"concepts":[909],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Monitor ocean level and surface. Retrieved from https://earsc-portal.eu/display/EOSTAN/Monitor+ocean+level+and+surface","url":"https://earsc-portal.eu/display/EOSTAN/Monitor+ocean+level+and+surface"},{"concepts":[908],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Monitor ocean quality and productivity. Retrieved from https://earsc-portal.eu/display/EOSTAN/Monitor+ocean+quality+and+productivity","url":"https://earsc-portal.eu/display/EOSTAN/Monitor+ocean+quality+and+productivity"},{"concepts":[908],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Monitor oil rigs and flares. Retrieved from https://earsc-portal.eu/display/EOSTAN/Monitor+oil+rigs+and+flares","url":"https://earsc-portal.eu/display/EOSTAN/Monitor+oil+rigs+and+flares"},{"concepts":[908],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Monitor pollution at sea. Retrieved from https://earsc-portal.eu/display/EOSTAN/Monitor+pollution+at+sea","url":"https://earsc-portal.eu/display/EOSTAN/Monitor+pollution+at+sea"},{"concepts":[912],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Monitor ships movements. Retrieved from https://earsc-portal.eu/display/EOSTAN/Monitor+ships+movements","url":"https://earsc-portal.eu/display/EOSTAN/Monitor+ships+movements"},{"concepts":[899],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Monitor snow cover. Retrieved from https://earsc-portal.eu/display/EOSTAN/Monitor+snow+cover","url":"https://earsc-portal.eu/display/EOSTAN/Monitor+snow+cover"},{"concepts":[909],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Monitor the coast line. Retrieved from https://earsc-portal.eu/display/EOSTAN/Monitor+the+coast+line","url":"https://earsc-portal.eu/display/EOSTAN/Monitor+the+coast+line"},{"concepts":[905],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Monitor urban areas. Retrieved from https://earsc-portal.eu/display/EOSTAN/Monitor+urban+areas","url":"https://earsc-portal.eu/display/EOSTAN/Monitor+urban+areas"},{"concepts":[901],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Monitor vegetation encroachment. Retrieved from https://earsc-portal.eu/display/EOSTAN/Monitor+vegetation+encroachment","url":"https://earsc-portal.eu/display/EOSTAN/Monitor+vegetation+encroachment"},{"concepts":[900],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Monitor vegetation encroachment. Retrieved from https://earsc-portal.eu/display/EOSTAN/Product+Sheet%3A+Encroachment+monitoring","url":"https://earsc-portal.eu/display/EOSTAN/Product+Sheet%3A+Encroachment+monitoring"},{"concepts":[896],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Monitor water use on crops and horticulture. Retrieved from https://earsc-portal.eu/display/EOSTAN/Monitor+water+use+on+crops+and+horticulture","url":"https://earsc-portal.eu/display/EOSTAN/Monitor+water+use+on+crops+and+horticulture"},{"concepts":[926],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Product Sheet - Land Use. Retrieved from https://earsc-portal.eu/display/EOSTAN/Product+Sheet%3A+Land+Use","url":"https://earsc-portal.eu/display/EOSTAN/Product+Sheet%3A+Land+Use"},{"concepts":[906],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Product Sheet: Asset Monitoring. Retrieved from https://earsc-portal.eu/display/EOSTAN/Product+Sheet%3A+Asset+Monitoring","url":"https://earsc-portal.eu/display/EOSTAN/Product+Sheet%3A+Asset+Monitoring"},{"concepts":[880],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Product sheet:CO2. Retrieved from https://earsc-portal.eu/display/EOSTAN/Product+Sheet%3A+CO2","url":"https://earsc-portal.eu/display/EOSTAN/Product+Sheet%3A+CO2"},{"concepts":[913],"description":" ","name":"European Centre for Medium-Range Weather Forecasts, & Copernicus Programme. (2020). Global Shipping Project - Copernicus. 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process"},{"concepts":[332],"name":"Compare and contrast the consequences of different national policies about rights to geospatial data in terms of the real costs of spatial data, their coverage, accuracy, uncertainty, reliability, validity, and maintenance"},{"concepts":[352],"name":"Compare and contrast the ethical guidelines promoted by the GIS Certification Institute (GISCI) and the American Society for Photogrammetry and Remote Sensing (ASPRS)"},{"concepts":[338],"name":"Compare and contrast the evaluation of benefits at different scales (e.g., national, regional/state,local)"},{"concepts":[337],"name":"Compare and contrast the evaluation of benefits at different scales (e.g., national, regional/state,local) true for an organization that is already collecting data as part of its regular operations"},{"concepts":[552],"name":"Compare and contrast the impact effect of time for developing consensus-based standards with immediate operational needs"},{"concepts":[22],"name":"Compare and contrast the impacts of different conversion approaches, including the effect on spatial components"},{"concepts":[89],"name":"Compare and contrast the kinds of questions various philosophies ask, the methodologies they use, the answers they offer, and their applicability to different phenomena"},{"concepts":[125],"name":"Compare and contrast the meanings of related terms such as vague, fuzzy, imprecise, indefinite, indiscrete, unclear, and ambiguous"},{"concepts":[2],"name":"Compare and contrast the methods of analyzing aggregate data as opposed to methods of analyzing a set of individual observations"},{"concepts":[562],"name":"Compare and contrast the missions, histories, constituencies, and activities of professional organizations including Association of American Geographers (AAG), America Society for Photogrammetry and Remote Sensing (ASPRS) ..."},{"concepts":[117],"name":"Compare and contrast the opportunities and pitfalls of using regions to aggregate geographic information (e.g., census data)"},{"concepts":[5],"name":"Compare and contrast the primary types of data mining: summarization/characterization, clustering/categorization, feature extraction, and rule/relationships extraction"},{"concepts":[163],"name":"Compare and contrast the quality of product evaluation that can be made from process proofs and color laser prints"},{"concepts":[230],"name":"Compare and contrast the raster with other types of regular tessellations for geographic data analysis"},{"concepts":[230],"name":"Compare and contrast the raster with other types of regular tessellations for geographic data storage"},{"concepts":[188],"name":"Compare and contrast the relative merits of housing GI systems within IT (information technology) and MIS (management information system) facilities versus keeping them separate"},{"concepts":[92],"name":"Compare and contrast the symbolic and connectionist theories of human cognition and memory and their ability to model various cases"},{"concepts":[56],"name":"Compare and contrast the terms multi-criteria evaluation, weighted linear combination, and site suitability analysis"},{"concepts":[110],"name":"Compare and contrast the theory that properties are fundamental (and objects are human simplifications of patterns thereof) with the theory that objects are fundamental (and properties are attributes thereof)"},{"concepts":[92],"name":"Compare and contrast theories of spatial knowledge acquisition (e.g., Marr on vision, Piaget on childhood, Golledge on wayfinding)"},{"concepts":[542],"name":"Compare and contrast training methods utilized in a non-profit to those employed in a local government agency"},{"concepts":[679],"name":"Compare and discuss attenuation length and penetration depth of the optical and radar signal"},{"concepts":[555],"name":"Compare and explain different models for funding an SDI"},{"concepts":[336],"name":"Compare and explain the main business models in the GI domain"},{"concepts":[76],"name":"Compare block-kriging with areal interpolation using proportional area weighting and dasymetric mapping"},{"concepts":[292],"name":"Compare common sensors by spatial resolution, spectral sensitivity, ground coverage, and temporal resolution [e.g., AVHRR, MODIS (intermediate resolution ~500 m, high temporal) Landsat, commercial high resolution (Ikonos and Quickbird); ..."},{"concepts":[285],"name":"Compare common sensors-including LiDAR, and airborne panchromatic and multispectral cameras and scanners-in terms of spatial resolution, spectral sensitivity, ground coverage, and temporal resolution"},{"concepts":[259],"name":"Compare commonalities and patterns of geocomputation to other related terms"},{"concepts":[15],"name":"Compare current accessibility models with early models of market potential"},{"concepts":[268],"name":"Compare different design choices in developing spatial simulation models"},{"concepts":[978],"name":"compare different development components and their advantages and disadvantages"},{"concepts":[556],"name":"Compare different frameworks for assessing Spatial Data Infrastructures"},{"concepts":[954],"name":"Compare different Geospatial object and geometry definitions included under this topic"},{"concepts":[265],"name":"Compare different options of combining space-time dynamics approaches in spatial modelling"},{"concepts":[194],"name":"Compare geospatial software architecture through cost-analysis framework"},{"concepts":[880,881],"name":"Compare human-induced emissions to natural sources"},{"concepts":[966],"name":"Compare Linked geospatial data to SDI approaches"},{"concepts":[892],"name":"Compare one optical EO method with a SAR method for landslide mapping and explain their differences"},{"concepts":[293],"name":"Compare pixel-based image classification methods with segmentation techniques"},{"concepts":[387],"name":"Compare reflectance measurements from the field to reflectance values in radiometrically pre-processed EO data"},{"concepts":[460],"name":"Compare results of the Laplacian of Gaussian filter to the original input image"},{"concepts":[347],"name":"Compare the advantages and disadvantages of group participation and individual participation"},{"concepts":[312],"name":"Compare the concepts of geometric accuracy and topological fidelity"},{"concepts":[277],"name":"Compare the different cultures of Open Science"},{"concepts":[67],"name":"Compare the different types of spatial weight matrices"},{"concepts":[312],"name":"Compare the National Map Accuracy Standard with the ASPRS Coordinate Standard"},{"concepts":[142],"name":"Compare the relative merits of having map labels placed dynamically versus having them saved as annotation data"},{"concepts":[25],"name":"Compare the result of conversion vector/raster or raster/vector and examine the impact of conversion on the quality of the dataset"},{"concepts":[173],"name":"Compile the needs of individual users and tasks into enterprise-wide needs"},{"concepts":[528],"name":"Compute descriptive statistics and geostatistics of geographic data"},{"concepts":[50],"name":"Compute measures of overall dispersion and clustering of point datasets using nearest neighbor distance statistics"},{"concepts":[68],"name":"Compute measures of overall dispersion and clustering of point datasets using nearest neighbor distance statistics"},{"concepts":[68],"name":"Compute Morans I and Gearys c for patterns of attribute data measured on interval ratio scales"},{"concepts":[10],"name":"Compute the alpha, beta, and gamma indices of network connectivity"},{"concepts":[10],"name":"Compute the Detour Index and the measure of network density for a given network"},{"concepts":[10],"name":"Compute the estimated number of fundamental cycles in a graph"},{"concepts":[69],"name":"Compute the Gi and Gi* statistics"},{"concepts":[68],"name":"Compute the K function"},{"concepts":[38],"name":"Compute the mean of directional data"},{"concepts":[11],"name":"Compute the optimum path between two points through a network with Dijkstras algorithm"},{"concepts":[54],"name":"Conduct a simple hierarchical cluster analysis to classify area objects into statistically similar regions"},{"concepts":[80],"name":"Conduct a spatial econometric analysis to test for spatial dependence in the residuals from least-squares models and spatial autoregressive models"},{"concepts":[76],"name":"Conduct a spatial interpolation process using kriging from data description to final error map"},{"concepts":[165],"name":"Construct a new map from an existing one with a biased view"},{"concepts":[34],"name":"Construct a query statement to search for a specific spatial or temporal relationship"},{"concepts":[74],"name":"Construct a semi-variogram and illustrate with a semi-variogram cloud"},{"concepts":[34],"name":"Construct a spatial query to extract all point objects that fall within a polygon"},{"concepts":[67],"name":"Construct a spatial weights matrix for lattice, point, and area patterns"},{"concepts":[233],"name":"Construct a TIN manually from a set of spot elevations"},{"concepts":[155],"name":"Construct a Web page that includes an interactive map"},{"concepts":[7],"name":"Construct an edge-recognition filter"},{"concepts":[690],"name":"Construct scattering matrix"},{"concepts":[109],"name":"Construct taxonomies and dictionaries (also known as formal ontologies) to communicate systems of categories"},{"concepts":[15],"name":"Contrast accessibility modeling at the individual level versus at an aggregated level"},{"concepts":[142],"name":"Contrast the strengths and limitations of methods for automatic label placement"},{"concepts":[23],"name":"Convert a data set from the native format of one GIS product to another"},{"concepts":[23],"name":"Convert a dataset from the native format of one GIS product to another"},{"concepts":[132],"name":"Convert historical maps in digital format"},{"concepts":[25],"name":"Convert vector data to raster format and back using GIS software"},{"concepts":[25],"name":"Convert vector data to raster format and back using the GIS software"},{"concepts":[133],"name":"Correlate map making methods with technological or societal factors across History"},{"concepts":[185,187],"name":"Create a budget of expected labor costs, including salaries, benefits, training, and other expenses"},{"concepts":[206],"name":"Create a complete design document ready for implementation"},{"concepts":[158],"name":"Create a concept map that represents the contents and topology of a physical or social process"},{"concepts":[9],"name":"Create a data set with network attributes and topology"},{"concepts":[204],"name":"Create a diagram of a conceptual data model for a geospatial application or enterprise database"},{"concepts":[22],"name":"Create a flowchart showing the sequence of transformations on a data set (e.g., geometric and radiometric correction and 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GPS measurements"},{"concepts":[153],"name":"Create a temporal sequence representing a dynamic geospatial process"},{"concepts":[174],"name":"Create a user manual to help users understand a process or task"},{"concepts":[245],"name":"Create an adjacency table from a sample network"},{"concepts":[140],"name":"Create an aesthetic map icon library"},{"concepts":[245],"name":"Create an incidence matrix from a sample network"},{"concepts":[34],"name":"Create an SQL query to retrieve elements from a GIS"},{"concepts":[51],"name":"Create density maps from point datasets using kernels and density estimation techniques using standard software"},{"concepts":[138],"name":"Create different map layouts using the same map components (main map area, inset maps, titles, legends, scale bars, north arrows, grids and graticule) to produce maps with very distinctive purposes"},{"concepts":[138],"name":"Create different maps using the same data for different purposes and intended audiences (e.g., expert and novice hikers)"},{"concepts":[148],"name":"Create different visual hierarchies to produce maps with different purposes"},{"concepts":[25],"name":"Create estimated tessellated data sets from point samples or isolines using interpolation operations that are appropriate to the specific situation"},{"concepts":[56],"name":"Create initial weights using the analytical hierarchy process (AHP)"},{"concepts":[205],"name":"Create logical models based on conceptual models using UML or other tools"},{"concepts":[149],"name":"Create maps using each of the following methods: choropleth, dasymetric, proportioned symbol, graduated symbol, isoline, dot, cartogram, and flow map"},{"concepts":[109],"name":"Create or use GIS data structures to represent categories, including attribute columns, layers themes, shapes, legends, etc."},{"concepts":[189],"name":"Create proposals and presentations to secure funding"},{"concepts":[185],"name":"Create proposals and presentations to secure funding"},{"concepts":[73],"name":"Create spatial samples under a variety of requirements, such as coverage, randomness, transects"},{"concepts":[165],"name":"Create two versions of the same map addressed to different targets"},{"concepts":[206],"name":"Create UML diagrams of physical models based on logical model diagrams and software requirements"},{"concepts":[149],"name":"Create well-designed legends using the appropriate conventions for the following methods: choropleth, dasymetric, proportioned symbol, graduated symbol, isoline, dot, cartogram, and flow map"},{"concepts":[148],"name":"Critique the graphic design of several maps in terms of balance, legibility, clarity, visual contrast, figure-ground organization, and hierarchal organization"},{"concepts":[154],"name":"Critique the interactive elements of an online map"},{"concepts":[155],"name":"Critique the user interface for existing Internet mapping services"},{"concepts":[69],"name":"Decompose Morans I and Gearys c into local measures of spatial association"},{"concepts":[204],"name":"Deconstruct an application use case into its conceptual elements"},{"concepts":[359],"name":"Defend or refute the argument that the digital divide that characterizes access to GIS and T perpetuates inequities among developed and developing nations, among socio-economic groups, and between individuals, community organizations, ..."},{"concepts":[359],"name":"Defend or refute the argument that the GIS and T professionals are culpable for applications that result in civilian casualties in warfare"},{"concepts":[361],"name":"Defend or refute the contention that critical studies have an identifiable influence on the development of the information society in general and GIScience in particular"},{"concepts":[360],"name":"Defend or refute the contention that the masculinist culture of computer work in general, and GIS work in particular, perpetuates gender inequality in GIS and T education and training and occupational segregation in the GIS and T workforce"},{"concepts":[29],"name":"Defend or refute the statement \"GIS data are scaleless\""},{"concepts":[89],"name":"Defend or refute the statement, All data are theory-laden"},{"concepts":[113],"name":"Define a field in terms of properties, space, and time"},{"concepts":[173],"name":"Define a methodology for gathering of requirements"},{"concepts":[252],"name":"Define a set of rules for modeling changes in spatial databases"},{"concepts":[349],"name":"Define and discuss enabling technologies: geotag, georeferencing, GPS and more"},{"concepts":[348],"name":"Define and discuss impacts of crowdsourcing on geospatial society"},{"concepts":[257],"name":"Define and discuss opportunities and limitations of computational science"},{"concepts":[349],"name":"Define and discuss volunteered geographic information"},{"concepts":[349],"name":"Define and discussing impact of Crowdsourcing on Geospatial Society"},{"concepts":[955],"name":"Define and exemplify the reuse of ontologies - Define and identify the role of ontology patterns"},{"concepts":[951],"name":"Define and practice the usage, in a given use case, of StyledLayerDescriptor (SLD) and Symbology Encoding (SE). Practice their usage in a given use case"},{"concepts":[347],"name":"Define and understand citizenship, democracy, maturity, and negotiation related to geo information use and participation in society /community development (at local, regional, national level)"},{"concepts":[34],"name":"Define basic terms of query processing e.g., SQL, primary and foreign keys, table join"},{"concepts":[230],"name":"Define basic terms used in the raster data model (e.g., cell, row, column, value)"},{"concepts":[950],"name":"Define characteristics of REST Web services and Resource oriented Architecture (ROA)"},{"concepts":[89],"name":"Define common philosophical theories that have influenced geography and science, such as logical positivism, Marxism, phenomenology, feminism, and critical theory"},{"concepts":[87],"name":"Define common theories on what constitutes knowledge, including positivism, reflectance-correspondence, pragmatism, social constructivism, and memetics"},{"concepts":[85],"name":"Define common theories on what is real, such as realism, idealism, relativism, and experiential realism"},{"concepts":[9],"name":"Define different interpretations of cost in various routing applications"},{"concepts":[38],"name":"Define direction and its measurement in different angular measures"},{"concepts":[204],"name":"Define entities and relationships in conceptual data model"},{"concepts":[63],"name":"Define friction surface"},{"concepts":[954],"name":"Define GeoJSON definition of Geospatial objects and describe the structure of a GeoJSON document and identify advantages and disadvantages of representing the same geospatial data in GML and in GeoJSON"},{"concepts":[62],"name":"Define intervisibility"},{"concepts":[309],"name":"Define key terms such as standard line, projection case, latitude and longitude of origin"},{"concepts":[961],"name":"Define Mapping between legacy definition and the semantic definition of publish"},{"concepts":[957],"name":"Define metadata and identify metadata standards like ISO 19115 and 19119 describe their metadata schema generally"},{"concepts":[954],"name":"Define OGC Simple Features Access Schema. Well-Known Text (WKT) and Well-Known Binary (WKB) representations of Geometry"},{"concepts":[71],"name":"Define prior and posterior distributions and Markov-Chain Monte Carlo"},{"concepts":[953],"name":"Define Resource Description Framework (RDF), its RDF graphs, RDF Schema (RDF-S)and a data set in RDF"},{"concepts":[953],"name":"Define Semantic Web and identify the role of the languages included under this topic for Semantic Web"},{"concepts":[948],"name":"Define Service Oriented Architecture (SOA) and identify main elements of it"},{"concepts":[123],"name":"Define spatial autocorrelation in the context of geographic proximity"},{"concepts":[954],"name":"Define spatial extensions that GeoSPARQL brings over SPARQL. Identify the difference between qualitative spatial reasoning and quantitative spatial computations"},{"concepts":[110],"name":"Define Stevens four levels of measurement (nominal, ordinal, interval, ratio)"},{"concepts":[241],"name":"Define terms related to topology (e.g., adjacency, connectivity, overlap, intersect, logical consistency)"},{"concepts":[205],"name":"Define the cardinality of relationships"},{"concepts":[948],"name":"Define the characteristics of web services and present some examples"},{"concepts":[953],"name":"Define the components of a Web Services Description Language (WSDL) document"},{"concepts":[9],"name":"Define the following terms pertaining to a network: Loops, multiple edges, the degree of a vertex, walk, trail, path, cycle, fundamental cycle"},{"concepts":[245],"name":"Define the following terms pertaining to a network: Loops, multiple edges, the degree of a vertex, walk, trail, path, cycle, fundamental cycle"},{"concepts":[94],"name":"Define the following terms: data, information, knowledge, and wisdom"},{"concepts":[101],"name":"Define the four basic dimensions or shapes used to describe spatial objects (i.e., points, lines, regions, volumes)"},{"concepts":[97],"name":"Define the notions of cultural landscape and physical landscape"},{"concepts":[123],"name":"Define the principle of friction of distance and geographic models that are based on it (e.g., gravity models, spatial interaction models)"},{"concepts":[96],"name":"Define the properties that make a phenomenon geographic"},{"concepts":[2],"name":"Define the terms spatial analysis, spatial modeling, geostatistics, spatial econometrics, spatial statistics, qualitative analysis, map algebra, and network analysis"},{"concepts":[126],"name":"Define uncertainty-related terms, such as error, accuracy, uncertainty, precision, stochastic, probabilistic, deterministic, and random"},{"concepts":[122],"name":"Define various terms used to describe topological relationships, such as disjoint, overlap, within, and intersect"},{"concepts":[972],"name":"Define Web API composition (WAPIC) concept for RESTful WSs and identify main issues"},{"concepts":[951],"name":"Define Web Coverage Service (WCS). 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Practice its usage in a given use case"},{"concepts":[972],"name":"Define web services composition (WSC) concept and identify main issues"},{"concepts":[948],"name":"Define Web services transport over the Web"},{"concepts":[955],"name":"Define what an ontology is. Identify differences among ontologies, Thesauri, and taxonomies"},{"concepts":[233],"name":"Delineate a set of break lines that improve the accuracy of a TIN"},{"concepts":[117],"name":"Delineate regions using properties, spatial relationships, and geospatial technologies"},{"concepts":[193],"name":"Deliver a resources plan consistent with organisation’s concrete actions"},{"concepts":[632],"name":"Demonstrate basic knowledge of the atmospheric absorption and scattering mechanisms."},{"concepts":[628],"name":"Demonstrate basic knowledge of the interaction between the solar radiation and atmospheric constituents"},{"concepts":[958],"name":"Demonstrate harvesting and crawling mechanisms for automated metadata collection"},{"concepts":[245],"name":"Demonstrate how a network is a connected set of edges and vertices"},{"concepts":[241],"name":"Demonstrate how a topological structure can be represented in a relational database structure"},{"concepts":[42],"name":"Demonstrate how adjacency 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critical to the success of any map overlay operation"},{"concepts":[182],"name":"Demonstrate why the system design is important in any GIS implementation"},{"concepts":[89],"name":"Describe a brief history of major philosophical movements relating to the nature of space, time, geographic phenomena and human interaction with it"},{"concepts":[154],"name":"Describe a mapping goal in which the use of each of the following would be appropriate: brushing, linking, multiple displays"},{"concepts":[47,48],"name":"Describe a real modeling situation in which map algebra would be used e.g., site selection, climate classification, least-cost path"},{"concepts":[316],"name":"Describe a scenario in which data from a secondary source may pose obstacles to effective and efficient use"},{"concepts":[351],"name":"Describe a scenario in which you would find it necessary to report misconduct by a colleague or friend"},{"concepts":[57],"name":"Describe a simple process model that would generate a given 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versions of the database"},{"concepts":[293],"name":"Describe an application of hyperspectral image data"},{"concepts":[291],"name":"Describe an application that requires integration of remotely sensed data with GIS and/or GPS data"},{"concepts":[555],"name":"Describe and explain the funding model of an existing SDI"},{"concepts":[155],"name":"Describe considerations for using maps on the Web as a method for downloading data"},{"concepts":[138],"name":"Describe differences in design needed for a map that is to be viewed on the Internet versus as a 5x7 foot poster, including a discussion of the effect of viewing distance, lighting, and media type"},{"concepts":[538],"name":"Describe different organizational models for coordinating GIS and T participants and stakeholders"},{"concepts":[108],"name":"Describe different types of movement and change"},{"concepts":[4],"name":"Describe difficulties in dealing with large spatial databases, especially those arising from spatial heterogeneity"},{"concepts":[4],"name":"Describe emerging geographical analysis techniques in geocomputation derived from artificial intelligence e.g., expert systems, artificial neural networks, genetic algorithms, and software agents"},{"concepts":[254],"name":"Describe existing algorithms designed for performing dynamic queries"},{"concepts":[948],"name":"Describe generally the hypertext transfer protocol and its main operations like POST and GET"},{"concepts":[123],"name":"Describe geographic phenomena in terms of their distances and directions (in space and time) Define spatial autocorrelation in the context of geographic proximity"},{"concepts":[122],"name":"Describe geographic phenomena in terms of their topological relationships (in space and time to other phenomena"},{"concepts":[61],"name":"Describe how a network of stream channels and ridges can be estimated from a Digital Elevation Model (DEM)"},{"concepts":[5],"name":"Describe how data mining can be used for geospatial intelligence"},{"concepts":[312],"name":"Describe how geometric accuracy should be documented in terms of the FGDC metadata standard"},{"concepts":[342],"name":"Describe how geospatial data are used and maintained for land use planning, property value assessment, maintenance of public works, and other applications"},{"concepts":[531],"name":"Describe how GI S and T can be used in the decision-making process in organizations dealing with natural resource management, business management, public management or operations management"},{"concepts":[50],"name":"Describe how Independent Random Process/Chi-Squared Result IRP/CSR may be used to make statistical statements about point patterns"},{"concepts":[47,48],"name":"Describe how map algebra performs mathematical functions on raster grids"},{"concepts":[295],"name":"Describe how sea surface temperatures are mapped"},{"concepts":[174],"name":"Describe how spatial data and GIS&T can be integrated into a workflow process"},{"concepts":[567],"name":"Describe how state GIS Councils can be used in enterprise GIS and T implementation processes"},{"concepts":[60],"name":"Describe how surfaces can be interpolated using splines"},{"concepts":[233],"name":"Describe how to generate a unique TIN solution using Delaunay triangulation"},{"concepts":[541],"name":"Describe issues that may hinder implementation and continued successful operation of a GI system if effective methods of staff development are not included in the process"},{"concepts":[966],"name":"Describe Linked Data Browsers; Define Faceted browsers and identify what problems of linked data discovery they aim to solve"},{"concepts":[15],"name":"Describe methods for measuring different kinds of accessibility on a network"},{"concepts":[9],"name":"Describe networks that apply to specific applications or industries"},{"concepts":[38],"name":"Describe operations that can be performed on qualitative representations of 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T"},{"concepts":[12],"name":"Describe practical situations in which flow is conserved while splitting or joining at nodes of the network"},{"concepts":[163],"name":"Describe print quality characteristics and price differences for limited-run color map distribution"},{"concepts":[163],"name":"Describe production concerns that might be discussed with a publisher who will print a map product"},{"concepts":[920],"name":"Describe properties of a particular DEM product"},{"concepts":[966],"name":"Describe Querying Linked Data; SPARQL and GeoSPARQL"},{"concepts":[42],"name":"Describe real world applications where adjacency and connectivity are a critical component of analysis"},{"concepts":[41],"name":"Describe real world applications where distance decay is an appropriate representation of the strength of spatial relationships (e.g., shopping behavior, property values)"},{"concepts":[41],"name":"Describe real world applications where distance decay would not be an appropriate representation of the strength of spatial relationships (e.g., distance education, commuting, telecommunications)"},{"concepts":[73],"name":"Describe sampling schemes for accurately estimating the mean of a spatial data set"},{"concepts":[33],"name":"Describe set theory"},{"concepts":[37],"name":"Describe several different measures of distance between two points e.g., Euclidean, Manhattan, network distance, spherical"},{"concepts":[151],"name":"Describe situations in which methods of terrain representation (e.g., shaded relief, contours, hypsometric tints, block diagrams, profiles) are well or poorly suited"},{"concepts":[75],"name":"Describe some commonly used semi-variogram models"},{"concepts":[96],"name":"Describe some insights that a spatial perspective can contribute to a given topic"},{"concepts":[11],"name":"Describe some variants of Dijkstras algorithm that are even more efficient"},{"concepts":[252],"name":"Describe techniques for handling version control in spatial databases"},{"concepts":[252],"name":"Describe techniques for managing long transactions in a multi-user environment"},{"concepts":[114],"name":"Describe the actor role that entities and fields play in events and processes"},{"concepts":[371],"name":"Describe the advantages and disadvantages of analytical and physical-based models for orthorectification"},{"concepts":[237],"name":"Describe the advantages and disadvantages of the quadtree model for geographic database representation and modeling"},{"concepts":[233],"name":"Describe the architecture of the TIN model"},{"concepts":[30],"name":"Describe the basic forms of generalization used in applications in addition to cartography (e.g., selection, simplification)"},{"concepts":[528],"name":"Describe the basic principles of randomness and probability"},{"concepts":[82],"name":"Describe the characteristics of the spatial expansion method"},{"concepts":[125],"name":"Describe the cognitive processes that tend to create vagueness"},{"concepts":[118],"name":"Describe the common constraints on spatial integration"},{"concepts":[313],"name":"Describe the component measures and the utility of a misclassification matrix"},{"concepts":[532],"name":"Describe the components of a needs assessment for an enterprise GIS"},{"concepts":[233],"name":"Describe the conditions under which a TIN might be more practical than GRID"},{"concepts":[75],"name":"Describe the conditions under which each of the commonly used semi-variograms models would be most appropriate"},{"concepts":[109],"name":"Describe the contributions of category theory to understanding the internal structure of categories"},{"concepts":[566],"name":"Describe the data programs provided by organizations such as The National Map, GeoSpatial One Stop, and National Integrated Land System"},{"concepts":[138],"name":"Describe the design needs of special purpose maps such as subdivision plans, cadastral mapping, drainage plans, nautical charts, aeronautical charts, geological maps, military maps, wire-mesh volume maps, and 3D plans of urban change"},{"concepts":[55],"name":"Describe the difference between prescriptive and descriptive cartographic models"},{"concepts":[540],"name":"Describe the differences between licensing, certification and accreditation in relation to GIS and T positions and qualifications"},{"concepts":[92,204],"name":"Describe the differences between real phenomena, conceptual models, and GIS data representations thereof"},{"concepts":[313],"name":"Describe the different measurement levels on which thematic accuracy is based"},{"concepts":[112],"name":"Describe the difficulties in modeling entities with ill-defined edges"},{"concepts":[112],"name":"Describe the difficulties inherent in extending the tabletop metaphor of objects to the geographic environment"},{"concepts":[69],"name":"Describe the effect of non-stationarity on local indices of spatial association"},{"concepts":[68],"name":"Describe the effect of the assumption of stationarity on global measures of spatial association"},{"concepts":[97],"name":"Describe the elements of a sense of place or landscape that are difficult or impossible to adequately represent in GIS"},{"concepts":[286],"name":"Describe the elements of image interpretation"},{"concepts":[358],"name":"Describe the extent to which contemporary GIS and T supports diverse ways of understanding the world"},{"concepts":[53],"name":"Describe the formulation of the classic gravity model, the unconstrained spatial interaction model, the production constrained spatial interaction model, the attraction constrained spatial interaction model, and the doubly constrained spatial..."},{"concepts":[121],"name":"Describe the genealogy (as identity-based change or temporal relationships) of particular geographic phenomena"},{"concepts":[79],"name":"Describe the general types of spatial econometric model"},{"concepts":[27],"name":"Describe the impact of map projection transformation on raster and vector data"},{"concepts":[312],"name":"Describe the impact of the concept of dilution of precision on the uncertainty of GPS positioning"},{"concepts":[56],"name":"Describe the implementation of an ordered weighting scheme in a multiple-criteria aggregation"},{"concepts":[374],"name":"Describe the importance of geometric correction when using Earth Observation data"},{"concepts":[351],"name":"Describe the individuals or groups to which GIS and T professionals have ethical obligations"},{"concepts":[241],"name":"Describe the integrity constraints of integrated topological models (e.g., POLYVRT)"},{"concepts":[568],"name":"Describe the leading academic journals serving the GIS and T community"},{"concepts":[94],"name":"Describe the limitations of various information stores for representing geographic information, including the mind, computers, graphics, text, etc."},{"concepts":[284,373],"name":"Describe the location and geometric characteristics of the principal point of an aerial image"},{"concepts":[474],"name":"Describe the main advantages of object-based image analysis methods"},{"concepts":[112],"name":"Describe the perceptual processes (e.g., edge detection) that aid cognitive objectification"},{"concepts":[31],"name":"Describe the pitfalls, in terms of information loss and analytical options, of transforming attribute measurement levels"},{"concepts":[81],"name":"Describe the relationship between factorial kriging and spatial filtering"},{"concepts":[76],"name":"Describe the relationship between the semi-variogram and kriging"},{"concepts":[51],"name":"Describe the relationships between kernels and classical spatial interaction approaches, such as surfaces of potential"},{"concepts":[74],"name":"Describe the relationships between semi-variograms and correlograms, and Morans indices of spatial association"},{"concepts":[538],"name":"Describe the roles and relationships of GIS and T support staff"},{"concepts":[352],"name":"Describe the sanctions imposed by ASPRS and GISCI on individuals whose professional actions violate the codes of ethics"},{"concepts":[293,373],"name":"Describe the sequence of tasks involved in the geometric correction of the Advanced Very High Resolution Radiometer (AVHRR) Global Land Dataset"},{"concepts":[288],"name":"Describe the source data, instrumentation, and workflow involved in extracting vector data (features and elevations) from analog and digital stereoimagery"},{"concepts":[538],"name":"Describe the stages of two different models of implementing a GIS within an organization"},{"concepts":[65],"name":"Describe the statistical characteristics of a set of spatial data using a variety of graphs and plots including scatterplots, histograms, boxplots, qq plots"},{"concepts":[18],"name":"Describe the structure of linear programs"},{"concepts":[20],"name":"Describe the structure of origin-destination matrices"},{"concepts":[563],"name":"Describe the U.S. geospatial industry including vendors, software, hardware and data"},{"concepts":[361],"name":"Describe the use of GIS from a political ecology point of view (e.g., consider the use of GIS for resource identification, conservation, and allocation by an NGO in Sub-Saharan Africa)"},{"concepts":[307],"name":"Describe the visual appearance of the Earths graticule"},{"concepts":[118],"name":"Describe the ways in which a spatial perspective enables the synthesis of different subjects (e.g., climate and economy)"},{"concepts":[98],"name":"Describe the ways in which the elements of culture (e.g., language, religion, education, traditions) may influence the understanding and use of geographic information"},{"concepts":[23],"name":"Describe the workflow for converting data from one data model to another"},{"concepts":[563],"name":"Describe three applications of geospatial technology for different workforce domains (e.g., first responders, forestry, water resource management, facilities management)"},{"concepts":[121],"name":"Describe ways in which a geographic entity can be created from one or more others"},{"concepts":[195],"name":"Design a test project to demonstrate interoperability"},{"concepts":[152],"name":"Design a map series to show the change in a geographic pattern over time"},{"concepts":[73],"name":"Design a sampling scheme that will help detect when space-time clusters of events occur"},{"concepts":[7],"name":"Design a simple spatial mean filter"},{"concepts":[140],"name":"Design a single map symbol that can be used to symbolize a set of related variables"},{"concepts":[151],"name":"Design a stylized terrain map from a digital elevation model (DEM)"},{"concepts":[253],"name":"Design a test of reliability of change information (e.g., the logical consistency of updates to the TIGER database)"},{"concepts":[59],"name":"Design an algorithm that calculates slope and aspect from a Triangulated Irregular Network (TIN) model"},{"concepts":[60],"name":"Design an algorithm which interpolates irregular point elevation data onto a regular grid"},{"concepts":[558],"name":"Design an effective governance structure for a national SDI"},{"concepts":[30],"name":"Design an experiment that allows one to evaluate the effect of traditional approaches of cartographic generalization on the quality of digital data sets created from analog originals"},{"concepts":[556],"name":"Design an SDI assessment framework and methodology for assessing and evaluating an SDI"},{"concepts":[892],"name":"Design and test an EO-based workflow for landslide mapping"},{"concepts":[204],"name":"Design application-specific conceptual models"},{"concepts":[115],"name":"Design data models for specific applications based on these comprehensive general models"},{"concepts":[172],"name":"Design databases for spatial data management"},{"concepts":[140],"name":"Design icons suitable for mapping different elements"},{"concepts":[138],"name":"Design maps that are appropriate for users with vision limitations"},{"concepts":[172],"name":"Design workflows, procedures, and customized software tools for using geospatial technologies and methods"},{"concepts":[915],"name":"designing the description of a service for the need of a particular user of EO information"},{"concepts":[173],"name":"Determine how to integrate or combine the proposed workflow with current applications running"},{"concepts":[334],"name":"Determine if a dataset can be considered as open data"},{"concepts":[567],"name":"Determine if your state has a Geospatial Information Office (GIO) and discuss the mission, history, constituencies and activities of a GIO"},{"concepts":[853],"name":"Determine requirements and quality criteria for an EO information product that serves spatial planners in monitoring soil sealing"},{"concepts":[29],"name":"Determine the mathematical relationships among scale, scope, and resolution"},{"concepts":[110],"name":"Determine the proper uses of attributes based on their domains"},{"concepts":[121],"name":"Determine whether it is important to represent the genealogy of entities for a particular application"},{"concepts":[115],"name":"Determine whether phenomena or applications exist that are not adequately represented in an existing comprehensive model"},{"concepts":[56],"name":"Determine which method to use to combine criteria e.g., linear, multiplication"},{"concepts":[568],"name":"Develop a bibliography of scholarly and professional articles and/or books that are relevant to a particular GIS and T project"},{"concepts":[55],"name":"Develop a flowchart of a cartographic model for a site suitability problem"},{"concepts":[39],"name":"Develop a method for describing the shape of a cluster of similarly valued points by using the concept of the convex hull"},{"concepts":[556],"name":"Develop a strategy to improve the performance of an SDI initiative"},{"concepts":[154],"name":"Develop a useful interactive interface and legend"},{"concepts":[110],"name":"Develop alternative forms of representations for situations in which attributes do not adequately capture meaning"},{"concepts":[39],"name":"Develop an algorithm to determine the skeleton of polygons"},{"concepts":[474],"name":"Develop and implement an object-based image analysis workflow for a specific application context"},{"concepts":[172],"name":"Develop effective mathematical and other models of spatial situations and processes"},{"concepts":[343],"name":"Develop GI infrastructure with a the role in the private sector"},{"concepts":[150],"name":"Develop graphic techniques that clearly show different forms of inexactness (e.g., existence uncertainty, boundary location uncertainty, attribute ambiguity, transitional boundary) of a given feature (e.g., a culture region)"},{"concepts":[101],"name":"Develop methods for representing non-cartesian models of space in GIS"},{"concepts":[868,837],"name":"Develop strategies and policies"},{"concepts":[173],"name":"Develop use cases for potential applications using established techniques with potential users, such as questionnaires, interviews, focus groups, the Delphi method, and/or joint application development"},{"concepts":[528],"name":"Devise simple ways to represent probability information in GIS"},{"concepts":[330],"name":"Differentiate \"contracts for service\" from \"contracts of service\""},{"concepts":[151],"name":"Differentiate 3D representations from 2.5 D representations"},{"concepts":[231],"name":"Differentiate among a lattice, a tessellation, and a grid"},{"concepts":[24],"name":"Differentiate among common interpolation techniques (e.g., nearest neighbor, bilinear, bicubic)"},{"concepts":[330],"name":"Differentiate among contract liability, tort liability, and statutory liability"},{"concepts":[117],"name":"Differentiate among different types of regions, including functional, cultural, physical, administrative, and others"},{"concepts":[116],"name":"Differentiate among distributions in space, time, and attribute"},{"concepts":[97],"name":"Differentiate among elements of the meaning of a place that can or cannot be easily represented using geospatial technologies"},{"concepts":[7],"name":"Differentiate among machine learning, data mining and pattern recognition"},{"concepts":[29],"name":"Differentiate among the concepts of scale (as in map scale), support, scope, and resolution"},{"concepts":[314],"name":"Differentiate among the spatial, spectral, radiometric, and temporal resolution of a remote sensing instrument"},{"concepts":[347],"name":"Differentiate among universal/deliberative, pluralist/representative, and participatory models of citizen participation"},{"concepts":[531],"name":"Differentiate an enterprise system from a department-centered GI system"},{"concepts":[125],"name":"Differentiate applications in which vagueness is an acceptable trait from those in which it is unacceptable"},{"concepts":[105],"name":"Differentiate applications that can make use of common-sense principles of geography from those that should not"},{"concepts":[19],"name":"Differentiate between a linear program and an integer program"},{"concepts":[957],"name":"Differentiate between a metadata standard and a metadata profile"},{"concepts":[101],"name":"Differentiate between absolute and relative descriptions of location"},{"concepts":[292],"name":"Differentiate between active and passive sensors, citing examples of each"},{"concepts":[101],"name":"Differentiate between common-sense, Cartesian metric, relational, relativistic, phenomenological, social constructivist, and other theories of the nature of space"},{"concepts":[204,205],"name":"Differentiate between conceptual and logical models, in terms of the level of detail, constraints, and range of information included"},{"concepts":[348],"name":"Differentiate between consumption, analysis, presumption and production of geoinformation within digital geo media"},{"concepts":[56],"name":"Differentiate between contributing factors and constraints in a multi-criteria application"},{"concepts":[191],"name":"Differentiate between copypleft and permissive licenses for a software product"},{"concepts":[5],"name":"Differentiate between data mining approaches used for spatial and non-spatial applications"},{"concepts":[57],"name":"Differentiate between deterministic and stochastic spatial process models"},{"concepts":[2],"name":"Differentiate between geostatistics, and spatial statistics"},{"concepts":[264],"name":"Differentiate between individual and aggregate models"},{"concepts":[66],"name":"Differentiate between isotropic and anisotropic processes"},{"concepts":[51],"name":"Differentiate between kernel density estimation and spatial interpolation"},{"concepts":[206],"name":"Differentiate between logical and physical models, in terms of the level of detail, constraints, and range of information included"},{"concepts":[232],"name":"Differentiate between lossy and lossless compression methods"},{"concepts":[47,48],"name":"Differentiate between map algebra and matrix algebra using real examples"},{"concepts":[107],"name":"Differentiate between mathematical and phenomenological theories of the nature of time"},{"concepts":[73],"name":"Differentiate between model-based and design-based sampling schemes"},{"concepts":[27],"name":"Differentiate between polynomial coordinate transformations (including linear) and rubbersheeting"},{"concepts":[950],"name":"Differentiate between SOAP and REST Web services. - Identify design issues of REST Web services"},{"concepts":[97],"name":"Differentiate between space and place"},{"concepts":[125],"name":"Differentiate between the following concepts: vagueness and ambiguity, well defined and poorly defined objects and fields or discord and non-specificity"},{"concepts":[53],"name":"Differentiate between the gravity model and spatial interaction models"},{"concepts":[60],"name":"Differentiate between trend surface analysis and deterministic spatial interpolation"},{"concepts":[955],"name":"Differentiate between upper, domain, and application level ontologies"},{"concepts":[284],"name":"Differentiate oblique and vertical aerial imagery"},{"concepts":[292],"name":"Differentiate push-broom and cross-track scanning technologies"},{"concepts":[310],"name":"Differentiate rectification and orthorectification"},{"concepts":[293],"name":"Differentiate supervised classification from unsupervised classification"},{"concepts":[126],"name":"Differentiate uncertainty in geospatial situations from vagueness"},{"concepts":[113],"name":"Differentiate various sources of fields, such as substance properties (e.g., temperature), artificial constructs (e.g., population density), and fields of potential or influence (e.g., gravity)"},{"concepts":[321],"name":"Digitize and georegister a specified vector feature set to a given geometric accuracy and topological fidelity thresholds using a given map sheet, digitizing tablet, and data entry software"},{"concepts":[355],"name":"Discuss about \"mapping whose reality?\" Pros and cons of geoinformation sharing in social media, i.e. big data, \"digital shadow\" etc."},{"concepts":[343],"name":"Discuss about open data and data sharing and public/private sector"},{"concepts":[334],"name":"Discuss about open data impact on society and citizenship"},{"concepts":[156],"name":"Discuss about the advantages of different immersive display systems"},{"concepts":[165],"name":"Discuss about the degree of subjectivity and/or objectivity of a map"},{"concepts":[337],"name":"Discuss about the extent to which external costs and benefits enhance the economic case for GIS"},{"concepts":[337],"name":"Discuss about the general role of information in economics"},{"concepts":[129],"name":"Discuss about the History of Cartography in different cultures"},{"concepts":[131],"name":"Discuss about the relationship between art and cartography"},{"concepts":[710],"name":"Discuss advantages and disadvantages of across- and along-track interferometry"},{"concepts":[743],"name":"Discuss advantages and disadvantages of passive and active sensors"},{"concepts":[705],"name":"Discuss advantages of SAR techniques over traditional measuring techniques"},{"concepts":[820],"name":"Discuss and compare different types of processing levels of optical data"},{"concepts":[825],"name":"Discuss and compare different types of processing levels of SAR data"},{"concepts":[334],"name":"Discuss and define open data and impact on GIS&T"},{"concepts":[108],"name":"Discuss common prepositions and adjectives (in any particular language) that signify either spatial or temporal relations but are used for both kinds, such as after or longer"},{"concepts":[265],"name":"Discuss concepts of space-time dynamics for spatial modeling"},{"concepts":[948],"name":"Discuss consensus based interoperability and its relation to geospatial data interchange. Define what a Web Service (WS) is and present characteristic scenarios. Data serving and Data Processing WSs"},{"concepts":[354],"name":"Discuss critiques of GIS as \"deterministic\" technology in relation to debates about the Quantitative quantitative revolution in the discipline of geography."},{"concepts":[358],"name":"Discuss critiques of GIS as deterministic technology in relation to debates about the Quantitative Revolution in the discipline of geography"},{"concepts":[542],"name":"Discuss different formats (tutorials, in house, online, instructor lead) for training and how they can be used by organizations"},{"concepts":[759],"name":"Discuss different types of laser scanners"},{"concepts":[792,657],"name":"Discuss different types of satellite orbits"},{"concepts":[268],"name":"Discuss different ways of simulating space and visualizing model behaviour"},{"concepts":[288],"name":"Discuss future prospects for automated feature extraction from aerial imagery"},{"concepts":[540],"name":"Discuss how a code of ethics might be applied within an organization"},{"concepts":[141],"name":"Discuss how cultural differences with respect to color associations impact map design"},{"concepts":[567],"name":"Discuss how informal and formal regional bodies (e.g., Metro GIS) can help support GIS and T in an organization"},{"concepts":[165],"name":"Discuss how maps express relations of power"},{"concepts":[313],"name":"Discuss how measures of spatial autocorrelation may be used to evaluate thematic accuracy"},{"concepts":[344],"name":"Discuss how to approach the widening audience/participants for geospatial products and service, increasing geo-awareness and geo-enablement"},{"concepts":[148],"name":"Discuss how to create an intellectual and visual hierarchy on maps"},{"concepts":[666],"name":"Discuss how to use phase information in remote sensing"},{"concepts":[339],"name":"Discuss implications of unequal economic power on the kinds of organizations that use, and benefit from, GIS and T"},{"concepts":[345],"name":"Discuss legal aspects of access to environmental data, global change/warming or sustainable development (regional, national, global) in conjunction to society."},{"concepts":[705],"name":"Discuss limitations of interferometric measurement"},{"concepts":[805],"name":"Discuss main characteristics of digital imagery"},{"concepts":[338],"name":"Discuss of and define the process of the Information value chain"},{"concepts":[334],"name":"Discuss of arguments for and against open data"},{"concepts":[337],"name":"Discuss of implications of unequal economic power on the kinds of organizations that use, and benefits from, GIS&T"},{"concepts":[333],"name":"Discuss of opportunities for exchange of geospatial data between public and private sector to enable more efficient analysis"},{"concepts":[338],"name":"Discuss of relations between marketing and economical factors in sustainable/environmental issues by using geospatial information"},{"concepts":[263],"name":"Discuss options of combining rule-based models with other individual modelling approaches"},{"concepts":[693],"name":"Discuss orientational polarisation of media"},{"concepts":[354],"name":"Discuss over the argument that the use of Geospatial geospatial Information privileges certain views of the world over others."},{"concepts":[343],"name":"Discuss over the changing role of the private sector in the use of geospatial information"},{"concepts":[344],"name":"Discuss over the paradigm shifts and current trends in GIS&T education and pedagogical approaches for GIS teaching and learning in detail"},{"concepts":[355],"name":"Discuss over the various implications of surveillance technology"},{"concepts":[692],"name":"Discuss polarimetric decomporition techniques"},{"concepts":[349],"name":"Discuss positive and negative aspects of the term \"humans as sensors\""},{"concepts":[687],"name":"Discuss scale of roughness of microwaves"},{"concepts":[2],"name":"Discuss situations when it is desirable to adopt a spatial approach to the analysis of data"},{"concepts":[245],"name":"Discuss some of the difficulties of applying the standard process-pattern concept to lines and networks"},{"concepts":[185],"name":"Discuss the advantages and disadvantages of outsourcing elements of a GIS project / GI system"},{"concepts":[187],"name":"Discuss the advantages and disadvantages of outsourcing elements of the implementation of a geospatial system, such as data entry"},{"concepts":[101],"name":"Discuss the advantages and disadvantages of the use of cartesian metric space as a basis for GIS and related technologies"},{"concepts":[314],"name":"Discuss the advantages and potential problems associated with the use of Minimum Mapping Unit (MMU) as a measure of the level of detail in land use, land cover, and soils maps"},{"concepts":[748],"name":"Discuss the application possibilities of imaging radar"},{"concepts":[67],"name":"Discuss the appropriateness of different types of spatial weights matrices for various problems"},{"concepts":[82],"name":"Discuss the appropriateness of GWR under various conditions"},{"concepts":[632],"name":"Discuss the basic principles of solar radiation."},{"concepts":[116],"name":"Discuss the causal relationship between spatial processes and spatial patterns, including the possible problems in determining causality"},{"concepts":[52],"name":"Discuss the characteristics of the various cluster detection techniques"},{"concepts":[26],"name":"Discuss the consequences of increasing and decreasing resolution"},{"concepts":[115],"name":"Discuss the contributions of early attempts to integrate the concepts of space, time, and attribute in geographic information, such as Berry (1964) and Sinton (1978)"},{"concepts":[101],"name":"Discuss the contributions that different perspectives on the nature of space bring to an understanding of geographic phenomenon"},{"concepts":[115],"name":"Discuss the degree to which these models can be implemented using current technologies"},{"concepts":[732],"name":"Discuss the development of remote sensing sensors"},{"concepts":[11],"name":"Discuss the difference of implementing Dijkstras algorithm in raster and vector modes"},{"concepts":[138],"name":"Discuss the differences between maps that use the same data but are for different purposes and intended audiences"},{"concepts":[138],"name":"Discuss the differences between maps that use the same data but are for different purposes and intended audiences"},{"concepts":[96],"name":"Discuss the differing denotations and connotations of the terms spatial, geographic, and geospatial"},{"concepts":[114],"name":"Discuss the difficulty of integrating process models into GIS software based on the entity and field views, and methods used to do so"},{"concepts":[121],"name":"Discuss the effects of temporal scale on the modeling of genealogical structures"},{"concepts":[351],"name":"Discuss the ethical implications of a local government's decision to charge fees for its data"},{"concepts":[359],"name":"Discuss the ethical implications of the use of GIS and T as a surveillance technology"},{"concepts":[288],"name":"Discuss the extent to which vector data extraction from aerial stereoimagery has been automated"},{"concepts":[558],"name":"Discuss the governance structure in place of a particular country"},{"concepts":[241],"name":"Discuss the historical roots of the Census Bureaus creation of GBF/DIME as the foundation for the development of topological data structures"},{"concepts":[112],"name":"Discuss the human predilection to conceptualize geographic phenomena in terms of discrete entities"},{"concepts":[348],"name":"Discuss the impact of geospatial information for the development of social media (Facebook, Twitter, Wikimapia, Flickr etc.) becoming increasingly location-based"},{"concepts":[251],"name":"Discuss the implication of long transactions on database integrity"},{"concepts":[358],"name":"Discuss the implications of interoperability on ontology"},{"concepts":[354],"name":"Discuss the implications of interoperability on ontology"},{"concepts":[314],"name":"Discuss the implications of the sampling theorem (Lambda = 0.5 delta) to the concept of resolution"},{"concepts":[29],"name":"Discuss the implications of tradeoff between data detail and data volume"},{"concepts":[155],"name":"Discuss the influence of the user interface on maps and visualizations on the Web"},{"concepts":[950],"name":"Discuss the issue whether a service is really \"RESTful\" or not"},{"concepts":[333],"name":"Discuss the legal framework related to competition and public-private sector relationships in the geospatial domain"},{"concepts":[731],"name":"Discuss the main types of remote sensing data"},{"concepts":[731],"name":"Discuss the main types of remote sensing platforms"},{"concepts":[731],"name":"Discuss the main types of remote sensing sensors"},{"concepts":[566],"name":"Discuss the mission, history constituencies and activities of international organizations such as Association of Geographic Information Laboratories for Europe (AGILE) and the European GIS Education Seminar (EUGISES)"},{"concepts":[566],"name":"Discuss the mission, history, constituencies, and activities of GeoSpatial One Stop"},{"concepts":[566],"name":"Discuss the mission, history, constituencies, and activities of governmental entities such as the Bureau of Land Management (BLM), United States Geological Survey (USGS) and the Environmental Protection Agency as they related to support..."},{"concepts":[567],"name":"Discuss the mission, history, constituencies, and activities of National States Geographic Information Council (NSGIC)"},{"concepts":[566],"name":"Discuss the mission, history, constituencies, and activities of the Federal Geographic Data Committee (FGDC)"},{"concepts":[562],"name":"Discuss the mission, history, constituencies, and activities of the GIS Certification Institute (GISCI)"},{"concepts":[566],"name":"Discuss the mission, history, constituencies, and activities of the Nation Integrated Land System (NILS)"},{"concepts":[566],"name":"Discuss the mission, history, constituencies, and activities of the National Academies of Science Mapping Science Committee"},{"concepts":[566],"name":"Discuss the mission, history, constituencies, and activities of the Open Geospatial Consortium (OGC), Inc."},{"concepts":[566],"name":"Discuss the mission, history, constituencies, and activities of the USGS and its National Map vision"},{"concepts":[566],"name":"Discuss the mission, history, constituencies, and activities of University Consortium of Geographic Science (UCGIS) and the National Center for Geographic Information and Analysis (NCGIA)"},{"concepts":[542],"name":"Discuss the National Research Council report on Learning to Think Spatially (2005) as it relates to spatial thinking skills needed by the GIS and T workforce"},{"concepts":[55],"name":"Discuss the origins of cartographic modeling with reference to the work of Ian McHarg"},{"concepts":[24],"name":"Discuss the pitfalls of using secondary data that has been generated using interpolations (e.g., Level 1 USGS DEMs)"},{"concepts":[697],"name":"Discuss the polarimetry technique"},{"concepts":[566],"name":"Discuss the political, cultural, economic, and geographic characteristics of various countries that influence their adoption and use of GIS and T"},{"concepts":[30],"name":"Discuss the possible effects on topological integrity of generalizing data sets"},{"concepts":[330],"name":"Discuss the potential legal problems associated with licensing geospatial information"},{"concepts":[360],"name":"Discuss the potential role of agency (individual action) in resisting dominant practices and in using GIS and T in ways that are consistent with feminist epistemologies and politics"},{"concepts":[639],"name":"Discuss the processes that describe the hydrologic cycle"},{"concepts":[361],"name":"Discuss the production, maintenance, and use of geospatial data by a government agency or private firm from the perspectives of a taxpayer, a community organization, and a member of a minority group"},{"concepts":[57],"name":"Discuss the relationship between spatial processes and spatial patterns"},{"concepts":[129],"name":"Discuss the relationship between the history of exploration and the development of a more accurate map of the world"},{"concepts":[31],"name":"Discuss the relationship of attribute measurement levels to database query operations"},{"concepts":[348],"name":"Discuss the role and value of \"place\" and \"space\" for geo media based social networking"},{"concepts":[141],"name":"Discuss the role of gamut in choosing colors that can be reproduced on various devices and media"},{"concepts":[241],"name":"Discuss the role of graph theory in topological structures"},{"concepts":[23],"name":"Discuss the role of metadata in facilitating conversation of data models and data structures between systems"},{"concepts":[345],"name":"Discuss the role of public, private sector and citizens in facilitating geospatial information in environmental/sustainable issues."},{"concepts":[333],"name":"Discuss the role of the public and private sectors in producing and dissemination of geospatial information"},{"concepts":[540],"name":"Discuss the status of professional and academic certification in GIS and T"},{"concepts":[331],"name":"Discuss the status of the concept of privacy in the U.S. legal regime"},{"concepts":[628],"name":"Discuss the structure and chemical composition of the atmosphere"},{"concepts":[66],"name":"Discuss the theory leading to the assumption of intrinsic stationarity"},{"concepts":[664],"name":"Discuss the use of polarization for different application domains"},{"concepts":[154],"name":"Discuss the uses of the map as a user interface element in interactive presentations of geographic information"},{"concepts":[564],"name":"Discuss the value or effect of participation in societies, conferences, and informal communities to entities managing enterprise GIS"},{"concepts":[776],"name":"Discuss types and classes of remote sensing sensors"},{"concepts":[334],"name":"Discuss various legal aspects of public and private sectors concerning owning, controlling, sharing/ disseminating open data."},{"concepts":[334],"name":"Discuss various sources of open data (science, public and private sectors)"},{"concepts":[329],"name":"Discuss ways in which the geospatial profession is regulated under the U.S. legal regime"},{"concepts":[344],"name":"Discuss ways of working with crowdsourcing in education and research"},{"concepts":[307],"name":"Discuss what a Tissot indicatrix represents and how it can be used to assess projection-induced error"},{"concepts":[684],"name":"Discuss what horizontal roughness component (correlation legth) is"},{"concepts":[683],"name":"Discuss what surface height variation (or RMS height) is"},{"concepts":[814],"name":"Distinguish and explain the different types of properties of digital imagery"},{"concepts":[144],"name":"Distinguish between animated and interactive maps"},{"concepts":[159],"name":"Distinguish between different graphic representation techniques"},{"concepts":[338],"name":"Distinguish between operational, organizational, and societal activities that rely upon geospatial Information"},{"concepts":[204],"name":"Distinguish between the temporary and structural relationships in a conceptual model"},{"concepts":[174],"name":"Document existing and potential tasks in terms of workflow and information flow"},{"concepts":[109],"name":"Document the personal, social, and or institutional meaning of categories used in GIS applications"},{"concepts":[291],"name":"Draw and explain a diagram that depicts the bands in the electromagnetic spectrum at which Earths atmosphere is sufficiently transparent to allow high-altitude remote sensing"},{"concepts":[291],"name":"Draw and explain a diagram that depicts the key bands of the electromagnetic spectrum in relation to the magnitude of electromagnetic energy emitted and/or reflected by the Sun and Earth across the spectrum"},{"concepts":[155],"name":"Edit the symbology, labeling, and page layout for a map originally designed for hard copy printing so that it can be seen and used on the Web"},{"concepts":[105],"name":"Effectively communicate the design, procedures, and results of GIS projects to non-GIS audiences (clients, managers, general public)"},{"concepts":[116],"name":"Employ techniques for visualizing, describing, and analyzing distributions in space, time, and attribute"},{"concepts":[24],"name":"Estimate a value between two known values using linear interpolation (e.g., spot elevations, population between census years)"},{"concepts":[186],"name":"Estimate the cost to collect needed data from primary sources (e.g., remote sensing, GPS)"},{"concepts":[37],"name":"Estimate the fractal dimension of a sinuous line"},{"concepts":[614],"name":"Estimate the meteorological and the cloud optical properties by LBRTM and validate against high accuracy spectral measurements"},{"concepts":[132],"name":"Estimate the potential value of a historical map"},{"concepts":[534],"name":"Evaluate and revise an existing GIS management strategy"},{"concepts":[158],"name":"Evaluate graphic techniques used to portray spatializations"},{"concepts":[26],"name":"Evaluate methods used by contemporary GIS software to resample raster data on-the-fly during display"},{"concepts":[292],"name":"Evaluate the advantages and disadvantages of acoustic remote sensing versus airborne or satellite remote sensing for seafloor mapping"},{"concepts":[292],"name":"Evaluate the advantages and disadvantages of airborne remote sensing versus satellite remote sensing"},{"concepts":[287],"name":"Evaluate the advantages and disadvantages of photogrammetric methods and LiDAR for production of terrain elevation data"},{"concepts":[114],"name":"Evaluate the assertion that events and processes are the same thing, but viewed at different temporal scales"},{"concepts":[126],"name":"Evaluate the causes of uncertainty in geospatial data"},{"concepts":[141],"name":"Evaluate the colors used in a web map to be used indoors and outdoors"},{"concepts":[97],"name":"Evaluate the differences in how various parties think or feel differently about a place being modeled"},{"concepts":[236],"name":"Evaluate the ease of measuring resolution in different types of tessellations"},{"concepts":[112],"name":"Evaluate the effectiveness of GIS data models for representing the identity, existence, and lifespan of entities"},{"concepts":[113],"name":"Evaluate the field views description of objects as conceptual discretizations of continuous patterns"},{"concepts":[904],"name":"Evaluate the impact of changes in land areas"},{"concepts":[105],"name":"Evaluate the impact of geospatial technologies (e.g., Google Earth) that allow non-geospatial professionals to create, distribute, and map geographic information"},{"concepts":[236],"name":"Evaluate the implications of changing grid cell resolution on the results of analytical applications by using GIS software"},{"concepts":[112],"name":"Evaluate the influence of scale on the conceptualization of entities"},{"concepts":[89],"name":"Evaluate the influences of ones own philosophical views and assumptions on GIS AND T practices"},{"concepts":[85],"name":"Evaluate the influences of particular worldviews (including ones own) on GIS practices"},{"concepts":[99],"name":"Evaluate the influences of political actions, especially the allocation of territory, on human perceptions of space and place"},{"concepts":[99],"name":"Evaluate the influences of political ideologies (e.g., Marxism, Capitalism, conservative liberal) on the understanding of geographic information"},{"concepts":[187],"name":"Evaluate the labor needed in past cases to build a new geospatial enterprise"},{"concepts":[241],"name":"Evaluate the positive and negative impacts of this shift from integrated topological models"},{"concepts":[232],"name":"Evaluate the relative merits of grid compression methods for storage"},{"concepts":[113],"name":"Evaluate the representation of movement as a field of location over time (e.g. :x,y,z: = f(t) )"},{"concepts":[125],"name":"Evaluate the role that system complexity, dynamic processes, and subjectivity play in the creation of vague phenomena and concepts"},{"concepts":[149],"name":"Evaluate the strengths and limitations of different thematic mapping methods"},{"concepts":[294],"name":"Evaluate the thematic accuracy of a given soils map"},{"concepts":[261],"name":"Evaluate the tradeoffs between abstraction and representativeness in simulation model development"},{"concepts":[205],"name":"Evaluate the various general data models common in GIS project"},{"concepts":[125],"name":"Evaluate vagueness in the locations, time, attributes, and other aspects of geographic phenomena"},{"concepts":[30],"name":"Evaluate various line simplification algorithms by their usefulness in different applications"},{"concepts":[263],"name":"Evaluate when rule-based models can be applied to spatiotemporal problems"},{"concepts":[257],"name":"Examine how computational technology relates to geocomputation"},{"concepts":[407],"name":"Examine how the vegetation indices relates to the vegetation dynamics and health"},{"concepts":[407],"name":"Examine how the water-related spectral indices relates to changes in the vegetation and soil water content"},{"concepts":[960],"name":"Examine Metadata schema and vocabularies used for open data publishing"},{"concepts":[46],"name":"Exemplify applications in which overlay is useful, such as site suitability analysis"},{"concepts":[66],"name":"Exemplify deterministic and spatial stochastic processes"},{"concepts":[107],"name":"Exemplify different temporal frames of reference: linear and cyclical, absolute and relative"},{"concepts":[532],"name":"Exemplify each component of a needs assessment for an enterprise GIS"},{"concepts":[254],"name":"Exemplify how the lack of a data librarian to manage data can have disastrous consequences on the resulting dataset"},{"concepts":[538],"name":"Exemplify how to make GIS and T relevant to top managemen"},{"concepts":[66],"name":"Exemplify non-stationarity involving first and second order effects"},{"concepts":[117],"name":"Exemplify regions found at different scales"},{"concepts":[251],"name":"Exemplify scenarios in which one would need to perform a number of periodic changes in a real GIS database"},{"concepts":[39],"name":"Exemplify situations in which the centroid of a polygon falls outside its boundary"},{"concepts":[13],"name":"Exemplify the Classic Transportation Problem"},{"concepts":[241],"name":"Exemplify the concept of planar enforcement (e.g., TIN triangles)"},{"concepts":[234],"name":"Exemplify the uses (past and potential) of the hexagonal model"},{"concepts":[602],"name":"Explain the concept of composition of spectral signatures and apply the \"linear mixing\" models in some simple case"},{"concepts":[853],"name":"Explain a use case of EO for smart cities, e.g. how EO derived information about urban green instrastructure supports designing nature based solutions for preserving ecosystem services"},{"concepts":[708],"name":"Explain across-track interferometry technique"},{"concepts":[707],"name":"Explain along-track interferometry technique"},{"concepts":[442],"name":"Explain an application example where SVM is used for EO image classification"},{"concepts":[407],"name":"Explain an application example where the spectral indices are used for vegetation, water or snow monitoring"},{"concepts":[666],"name":"Explain and apply phase unwrapping"},{"concepts":[722],"name":"Explain and discuss elements of Synthetic Aperture Radar (SAR) geometric configuration"},{"concepts":[688],"name":"Explain and discuss surface roughness in microwave remote sensing"},{"concepts":[760],"name":"Explain and discuss the LiDAR technology"},{"concepts":[743],"name":"Explain and discuss types of sensing mechanisms"},{"concepts":[727],"name":"Explain and discuss what terrain reflectivity is and how it influences radar signal"},{"concepts":[827],"name":"Explain and discuss what the main processing levels of remote sensing data are"},{"concepts":[814],"name":"Explain and discuss what the radiometric resolution is"},{"concepts":[814],"name":"Explain and discuss what the spatial resolution is"},{"concepts":[814],"name":"Explain and discuss what the spectral resolution is"},{"concepts":[814],"name":"Explain and discuss what the temporal resolution is"},{"concepts":[730,669],"name":"Explain and outline the advantages of radar sensors"},{"concepts":[80],"name":"Explain Anselins typology of spatial autoregressive models"},{"concepts":[38],"name":"Explain any differences in the measured direction between two places when the data are presented in a GIS in different projections"},{"concepts":[330],"name":"Explain cases of liability claims associated with misuse of geospatial information, erroneous information, and loss of proprietary interests"},{"concepts":[691],"name":"Explain covariance and coherence matrix"},{"concepts":[706],"name":"Explain differences between DInSAR and PSI"},{"concepts":[730],"name":"Explain differences between optical and radar remote sensing"},{"concepts":[255],"name":"Explain geocomputation, related concepts and how the two relate"},{"concepts":[6],"name":"Explain how a Bayesian framework can incorporate expert knowledge in order to retrieve all relevant datasets given an initial user query"},{"concepts":[552],"name":"Explain how a business case analysis can be used to justify the expense of implementing consensus-based standards"},{"concepts":[425],"name":"Explain how a DSM differs from a DTM"},{"concepts":[245],"name":"Explain how a graph (network) may be directed or undirected"},{"concepts":[245],"name":"Explain how a graph can be written as an adjacency matrix and how this can be used to calculate topological shortest paths in the graph"},{"concepts":[11],"name":"Explain how a leading World Wide Web-based routing system works e.g., MapQuest, Yahoo Maps, Google"},{"concepts":[41],"name":"Explain how a semi-variogram describes the distance decay in dependence between data values"},{"concepts":[368],"name":"Explain how a set of overlapping images/satellite scenes can provide digital elevation models used for orthorectification and 3D modelling"},{"concepts":[895],"name":"Explain how a specific EO technology supports the assessments of disasters and geohazards"},{"concepts":[68],"name":"Explain how a statistic that is based on combining all the spatial data and returning a single summary value or two can be useful in understanding broad spatial trends"},{"concepts":[361],"name":"Explain how a tax assessors office adoption of GIS and T may affect power relations within a community"},{"concepts":[69],"name":"Explain how a weights matrix can be used to convert any classical statistic into a local measure of spatial association"},{"concepts":[82],"name":"Explain how allowing the parameters of the model to vary with the spatial location of the sample data can be used to accommodate spatial heterogeneity"},{"concepts":[58,1],"name":"Explain how analytical methods are used to derive analytical results from geospatial data"},{"concepts":[408],"name":"Explain how band maths can be applied to derive an index that indicates a specific land cover type like vegetation"},{"concepts":[76],"name":"Explain how block-kriging and its variants can be used to combine data sets with different spatial resolution support"},{"concepts":[45],"name":"Explain how buffers can be used in GI analysis"},{"concepts":[347],"name":"Explain how community organizations represent the interests of citizens, politicians, and specialists"},{"concepts":[331],"name":"Explain how conversion of land records data from analog to digital form increases risk to personal privacy"},{"concepts":[337],"name":"Explain how cost-benefit analyses can be manipulated"},{"concepts":[338],"name":"Explain how cost-benefit analyses can be manipulated"},{"concepts":[331],"name":"Explain how data aggregation is used to protect personal privacy in data produced by the U.S. Census Bureau"},{"concepts":[37],"name":"Explain how different measures of distance can be used to calculate the spatial weights matrix"},{"concepts":[67],"name":"Explain how different types of spatial weights matrices are defined and calculated"},{"concepts":[81],"name":"Explain how dissolving clusters of blocks with similar values may resolve the spatial correlation problem"},{"concepts":[50],"name":"Explain how distance-based methods of point pattern measurement can be derived from a distance matrix"},{"concepts":[53],"name":"Explain how dynamic, chaotic, complex or unpredictable aspects in some phenomena make spatial interaction models more appropriate than gravity models"},{"concepts":[37],"name":"Explain how fractal dimension can be used in practical applications of GIS"},{"concepts":[63],"name":"Explain how friction surfaces are enhanced by the use of impedance and barriers"},{"concepts":[346],"name":"Explain how geographic information is valuable to different sectors"},{"concepts":[69],"name":"Explain how geographically weighted regression provides a local measure of spatial association"},{"concepts":[312],"name":"Explain how geometric accuracies associated with the various orders of the U.S. horizontal geodetic control network are assured"},{"concepts":[332],"name":"Explain how geospatial information might be used in a taking of private property through a government's claim of its right of eminent domain"},{"concepts":[342],"name":"Explain how geospatial information might be used in a taking of private property through a governments claim of its right of eminent domain"},{"concepts":[531],"name":"Explain how GIS and T can be an integrating technology"},{"concepts":[231],"name":"Explain how grid representations embody the field-based view"},{"concepts":[154],"name":"Explain how interactivity influences map use"},{"concepts":[977],"name":"Explain how JSON (GeoJSON)`s \"schema-less\"structure may be transformed into an application schema"},{"concepts":[33],"name":"Explain how logic theory relates to set theory"},{"concepts":[165],"name":"Explain how maps such as topographic maps are produced within certain relations of power and knowledge"},{"concepts":[151],"name":"Explain how maps that show the landscape in profile can be used to represent terrain"},{"concepts":[352],"name":"Explain how one or more obligations in the GIS Code of Ethics may conflict with organizations proprietary interests"},{"concepts":[251],"name":"Explain how one would establish the criteria for monitoring the periodic changes in a real GIS database"},{"concepts":[17],"name":"Explain how optimization models can be used to generate models of alternate options for presentation to decision makers"},{"concepts":[70],"name":"Explain how outliers affect the results of analyses"},{"concepts":[337],"name":"Explain how profit considerations have shaped the evolution of GIS&T"},{"concepts":[50],"name":"Explain how proximity polygons e.g., Thiessen polygons may be used to describe point patterns"},{"concepts":[237],"name":"Explain how quadtrees and other hierarchical tessellations can be used to index large volumes of raster or vector data"},{"concepts":[730],"name":"Explain how radar images are used for specific applications"},{"concepts":[141],"name":"Explain how real-world connotations (e.g., blue=water, white=snow) can be used to determine color selections on maps"},{"concepts":[44],"name":"Explain how reclassification can be used for data simplification and measurement scale change"},{"concepts":[314],"name":"Explain how resampling affects the resolution of image data"},{"concepts":[552],"name":"Explain how resistance to change affects the adoption of standards in an organization coordinating a GIS"},{"concepts":[61],"name":"Explain how ridgelines and streamlines can be used to improve the result of an interpolation process"},{"concepts":[295],"name":"Explain how sea surface temperature maps are used to predict El Nino events"},{"concepts":[33],"name":"Explain how set theory relates to spatial queries"},{"concepts":[59],"name":"Explain how slope and aspect can be represented as the vector field given by the first derivative of height"},{"concepts":[81],"name":"Explain how spatial correlation can result as a side effect of the spatial aggregation in a given dataset"},{"concepts":[6],"name":"Explain how spatial data mining techniques can be used for knowledge discovery"},{"concepts":[79],"name":"Explain how spatial dependence and spatial heterogeneity violate the Gauss-Markov assumptions of regression used in traditional econometrics"},{"concepts":[158],"name":"Explain how spatial metaphors can be used to illustrate the relationship among ideas"},{"concepts":[268],"name":"Explain how spatial simulation models can be used to advance scientific knowledge in different geographic scenarios (e.g. transportation, health geography, urban and regional analysis)"},{"concepts":[5],"name":"Explain how spatial statistics techniques are used in spatial data mining"},{"concepts":[158],"name":"Explain how spatialization is a core component of visual analytics"},{"concepts":[368],"name":"Explain how stereoscopic imagery allows to derive an orthorectified image for the overlapping image areas"},{"concepts":[231],"name":"Explain how terrain elevation can be represented by a regular tessellation and by an irregular tessellation"},{"concepts":[142],"name":"Explain how text properties can be used as visual variables to graphically represent the type and attributes of geographic features"},{"concepts":[5],"name":"Explain how the analytical reasoning techniques, visual representations, and interaction techniques that make up the domain of visual analytics have a strong spatial component"},{"concepts":[71],"name":"Explain how the Bayesian perspective is a unified framework from which to view uncertainty"},{"concepts":[97],"name":"Explain how the concept of place is more than just location"},{"concepts":[309],"name":"Explain how the concepts of the tangent and secant cases relate to the idea of a standard line"},{"concepts":[925],"name":"Explain how the CORINE Land Cover product quality depends on its source EO data and how this affects its usage for regional planning."},{"concepts":[367],"name":"Explain how the DEM generation with SfM works and discuss its differences to the traditional method of DEM extraction with stereographic photogrammetry"},{"concepts":[24],"name":"Explain how the elevation values in a digital elevation model (DEM) are derived by interpolation from irregular arrays of spot elevations"},{"concepts":[126],"name":"Explain how the familiar concepts of geographic objects and fields affect the conceptualization of uncertainty"},{"concepts":[70],"name":"Explain how the following techniques can be used to examine outliers: tabulation, histograms, box plots, correlation analysis, scatter plots, local statistics"},{"concepts":[81],"name":"Explain how the Getis and Tiefelsdorf Griffith spatial filtering techniques incorporate spatial component variables into OLS regression analysis in order to remedy misspecification and the problem of spatially auto-correlated residuals"},{"concepts":[50],"name":"Explain how the K function provides a scale-dependent measure of dispersion"},{"concepts":[68],"name":"Explain how the K function provides a scale-dependent measure of dispersion"},{"concepts":[729],"name":"Explain how the microwave signal is detected"},{"concepts":[405],"name":"Explain how the NDSI relates to snow properties"},{"concepts":[406],"name":"Explain how the NDVI relates to vegetation activity/health"},{"concepts":[230],"name":"Explain how the raster data model instantiates a grid representation"},{"concepts":[404],"name":"Explain how the SAVI relates to soil and vegetation properties"},{"concepts":[338],"name":"Explain how the saying \"developing data is the largest single cost of implementing GIS\" could be true for an organization that is already collecting data as part of its regular operations"},{"concepts":[680],"name":"Explain how the soil permittivity influences radar signal"},{"concepts":[926],"name":"Explain how the Urban Atlas product quality depends on its source EO data and how this affects its usage for urban planning."},{"concepts":[25],"name":"Explain how the vector/raster/vector conversion process of graphic images and algorithms takes place and how the results are achieved"},{"concepts":[156],"name":"Explain how the virtual and immersive environments become increasingly more complex as we move from the relatively non-immersive VRML desktop environment to a stereoscopic display (e.g., a GeoWall) to a more fully immersive CAVE"},{"concepts":[293],"name":"Explain how to enhance contrast of reflectance values clustered within a narrow band of wavelengths"},{"concepts":[142],"name":"Explain how to label features with indeterminate boundaries (canyons, oceans, etc.)"},{"concepts":[4],"name":"Explain how to recognize contaminated data in large datasets"},{"concepts":[294],"name":"Explain how U.S. Geological Survey scientists and contractors assess the accuracy of the National Land Cover Dataset"},{"concepts":[40],"name":"Explain how variations in the calculation of area may have real world implications, such as calculating density"},{"concepts":[6],"name":"Explain how visual data exploration can be combined with data mining techniques as a means of discovering research hypotheses in large spatial datasets"},{"concepts":[403],"name":"Explain one biophysical parameter and the EO technologies to estimate it for a specific region of interest"},{"concepts":[425],"name":"Explain one of the EO methods that allow DEM generation"},{"concepts":[332],"name":"Explain organizations’ and governments’ incentives to treat geospatial information as property and arguments for and against the treatment of geospatial information as a commodity"},{"concepts":[694],"name":"Explain polarimetric coherences"},{"concepts":[695],"name":"Explain polarisation ellipse"},{"concepts":[748],"name":"Explain principles of imaging radar"},{"concepts":[714],"name":"Explain principles of passive microwave imaging"},{"concepts":[706],"name":"Explain principles of permanent/persistent scatterer interferometry"},{"concepts":[709],"name":"Explain SBAS technique"},{"concepts":[690],"name":"Explain scattering matrix"},{"concepts":[961],"name":"Explain semantic annotation of data and services"},{"concepts":[689],"name":"Explain Stokes vector"},{"concepts":[685],"name":"Explain surface correlation function"},{"concepts":[71],"name":"Explain the advantage of Bayesian methods over frequentist methods"},{"concepts":[78],"name":"Explain the advantage of the cokriging method in earth observation studies"},{"concepts":[78],"name":"Explain the advantage of the cokriging method in earth observation studies"},{"concepts":[232],"name":"Explain the advantage of wavelet compression"},{"concepts":[733],"name":"Explain the advantages and disadvantages of the pushbroom system"},{"concepts":[241],"name":"Explain the advantages and disadvantages of topological data models"},{"concepts":[467],"name":"Explain the advantages and limitations of rule-based classification method"},{"concepts":[519],"name":"Explain the advantages of cloud-based processing over downloading and processing data locally"},{"concepts":[449],"name":"Explain the advantages of object-based classification approaches over pixel-based approaches"},{"concepts":[410],"name":"Explain the advantages of satellite image time series for change detection"},{"concepts":[905],"name":"Explain the application of EO information for monitoring urban sprawl"},{"concepts":[360],"name":"Explain the argument that GIS and remote sensing foster a disembodied way of knowing the world"},{"concepts":[361],"name":"Explain the argument that GIS is socially constructed"},{"concepts":[358],"name":"Explain the argument that GIS privileges certain views of the world over others"},{"concepts":[331],"name":"Explain the argument that human tracking systems enable geoslavery"},{"concepts":[361],"name":"Explain the argument that, throughout history, maps have been used to depict social relations"},{"concepts":[34],"name":"Explain the basic logic of SQL syntax"},{"concepts":[434],"name":"Explain the benefits of a flexible hierarchical classification system like LCCS"},{"concepts":[892],"name":"Explain the capabilities and limitations of a particular EO technology for mapping landslides"},{"concepts":[47,48],"name":"Explain the categories of map algebra operations i.e., local, focal, zonal, and global functions"},{"concepts":[141],"name":"Explain the common color models used in mapping"},{"concepts":[308],"name":"Explain the concept developable surface and reference globe as conceptual ways of projecting the Earths surface"},{"concepts":[307],"name":"Explain the concept of a compromise projection and for which purposes it is useful"},{"concepts":[342],"name":"Explain the concept of a spatial decision support system"},{"concepts":[53],"name":"Explain the concept of competing destinations, describing how traditional spatial interaction model forms are modified to account for it"},{"concepts":[291],"name":"Explain the concept of data fusion in relation to remote sensing applications in GIS and T"},{"concepts":[312],"name":"Explain the concept of dilution of precision"},{"concepts":[315],"name":"Explain the concept of error propagation"},{"concepts":[696],"name":"Explain the concept of polarisation synthesis"},{"concepts":[17],"name":"Explain the concept of solution space"},{"concepts":[10],"name":"Explain the concept of the diameter of a network"},{"concepts":[76],"name":"Explain the concept of the kriging variance, and describe some of its shortcomings"},{"concepts":[20],"name":"Explain the concepts of demand and service"},{"concepts":[291],"name":"Explain the concepts of spatial resolution, radiometric resolution, and spectral sensitivity"},{"concepts":[120],"name":"Explain the contributions of formal mathematical methods such as Graph Theory to the study and application of geographic structures"},{"concepts":[149],"name":"Explain the design considerations for different thematic maps"},{"concepts":[331],"name":"Explain the difference between data privacy and data security"},{"concepts":[69],"name":"Explain the difference between local and global measures of spatial autocorrelation"},{"concepts":[37],"name":"Explain the differences in the calculated distance between the same two places when data used are in different projections"},{"concepts":[534],"name":"Explain the different components of a GIS management strategy"},{"concepts":[66],"name":"Explain the different forms of kriging"},{"concepts":[340],"name":"Explain the different steps in the geo-information value chain"},{"concepts":[316],"name":"Explain the distinction between primary and secondary data sources in terms of census data, cartographic data, and remotely sensed data"},{"concepts":[313],"name":"Explain the distinction between thematic accuracy, geometric accuracy, and topological fidelity"},{"concepts":[120],"name":"Explain the effects of spatial or temporal scale on the perception of structure"},{"concepts":[312],"name":"Explain the factors that influence the geometric accuracy of data produced with Global Positioning System (GPS) receivers"},{"concepts":[312],"name":"Explain the formula for calculating root mean square error"},{"concepts":[567],"name":"Explain the functions, mission, history, constituencies, and activities of your state GIS Council and related formal and informal bodies"},{"concepts":[705],"name":"Explain the fundamentals of Differential SAR Interferometry"},{"concepts":[109],"name":"Explain the human tendency to simplify the world using categories"},{"concepts":[555],"name":"Explain the impact of open data policies on SDI funding models"},{"concepts":[26],"name":"Explain the impact of the applied resampling method on the quality of the output dataset"},{"concepts":[128],"name":"Explain the importance of visualisation of cartographic materials over time"},{"concepts":[307],"name":"Explain the kind of distortion that occurs when raster data are projected"},{"concepts":[759],"name":"Explain the laser scanner technology"},{"concepts":[56],"name":"Explain the legacy of multi-criteria evaluation in relation to cartographic modeling"},{"concepts":[332],"name":"Explain the legal definition of the concepts \"ownership\" and \"property rights\""},{"concepts":[234],"name":"Explain the limitations of the grid model compared to the hexagonal model"},{"concepts":[30],"name":"Explain the logic of the Douglas-Peucker line simplification algorithm"},{"concepts":[374],"name":"Explain the main causes of geometric distortions"},{"concepts":[331],"name":"Explain the main challenges in dealing with data privacy and data security issues"},{"concepts":[371],"name":"Explain the main differences between image orthorectification, geo-referencing, and co-registration"},{"concepts":[277],"name":"Explain the main features and elements of Open Science"},{"concepts":[551],"name":"Explain the main objectives of an SDI"},{"concepts":[434],"name":"Explain the main two phases of the LCCS approach to land cover classification"},{"concepts":[308],"name":"Explain the mathematical basis by which latitude and longitude locations are projected into x and y coordinate space"},{"concepts":[120],"name":"Explain the modeling of structural relationships in standard GIS data models, such as stored topology"},{"concepts":[117],"name":"Explain the nature of the Modifiable Areal Unit Problem (MAUP)"},{"concepts":[75],"name":"Explain the necessity of defining a semi-variogram model for geographic data"},{"concepts":[66],"name":"Explain the need for the stationarity assumption"},{"concepts":[42],"name":"Explain the nine-intersection model for spatial relationships"},{"concepts":[87],"name":"Explain the notions of model and representation in science"},{"concepts":[204],"name":"Explain the objectives of the design phase of a conceptual model"},{"concepts":[7],"name":"Explain the outcome of an artificial intelligence analysis e.g., edge recognition, including a discussion of what the human did not see that the computer identified and vice versa"},{"concepts":[181],"name":"Explain the phases involved in a geodesign-based project"},{"concepts":[284],"name":"Explain the phenomenon that is recorded in an aerial image"},{"concepts":[30],"name":"Explain the pitfalls of using data generalized for small scale display in a large scale application"},{"concepts":[733],"name":"Explain the principle of across track scanning (pushbroom technology)"},{"concepts":[312],"name":"Explain the principle of differential correction in relation to the global positioning system"},{"concepts":[292],"name":"Explain the principle of multibeam bathymetric mapping"},{"concepts":[82],"name":"Explain the principles of geographically weighted regression"},{"concepts":[17],"name":"Explain the principles of operations research modeling and location modeling"},{"concepts":[7],"name":"Explain the principles of pattern recognition"},{"concepts":[710],"name":"Explain the principles of synthetic aperture radar (SAR) interferometry"},{"concepts":[261],"name":"Explain the process simulation model development"},{"concepts":[892],"name":"Explain the quality criteria where EO technologies differ from each other in their capabilities to detect, monitor and forecast landslides"},{"concepts":[307],"name":"Explain the rationale for the selection of the geometric property that is preserved in map projections used as the basis of the UTM and SPC systems"},{"concepts":[41],"name":"Explain the rationale for using different forms of distance decay functions"},{"concepts":[67],"name":"Explain the rationale used for each type of spatial weights matrix"},{"concepts":[117],"name":"Explain the relationship between regions and categories"},{"concepts":[346],"name":"Explain the relevance and added value of geospatial information in particular use cases"},{"concepts":[287],"name":"Explain the relevance of the concept parallax in stereoscopic aerial imagery"},{"concepts":[551],"name":"Explain the relevant legal and organizational issues around development and implementation of Spatial Data Infrastructures (SDI)"},{"concepts":[551],"name":"Explain the relevant technological issues around development and implementation of Spatial Data Infrastructures (SDI)"},{"concepts":[194],"name":"Explain the requirements that best match each geospatial software architecture"},{"concepts":[556],"name":"Explain the results of an SDI assessment"},{"concepts":[261],"name":"Explain the role and purpose of computer simulation methods in geocomputation"},{"concepts":[310,373],"name":"Explain the role and selection criteria for ground control points (GCPs) in the georegistration of aerial imagery"},{"concepts":[109],"name":"Explain the role of categories in common-sense conceptual models, everyday language, and analytical procedures"},{"concepts":[18],"name":"Explain the role of constraint functions using the graphical method"},{"concepts":[18],"name":"Explain the role of constraint functions using the simplex method"},{"concepts":[92],"name":"Explain the role of metaphors and image schema in our understanding of geographic phenomena and geographic tasks"},{"concepts":[18],"name":"Explain the role of objective functions in linear programming"},{"concepts":[284],"name":"Explain the significance of bit depth in aerial imaging"},{"concepts":[62],"name":"Explain the sources and impact of errors that affect intervisibility analyses"},{"concepts":[205],"name":"Explain the various types of cardinality"},{"concepts":[846],"name":"Explain to customers the information derived from EO"},{"concepts":[953],"name":"Explain Web Ontology Language (OWL) and how to define a data set in OWL DL"},{"concepts":[20],"name":"Explain Webers locational triangle"},{"concepts":[336],"name":"Explain what a business model is and how is used"},{"concepts":[266],"name":"Explain what a cellular automata is and what its key components are"},{"concepts":[183],"name":"Explain what a project is, and the difference between a project, programme, and product"},{"concepts":[712],"name":"Explain what active-passive microwave imaging is"},{"concepts":[267],"name":"Explain what an agent-based model is and what its key components are"},{"concepts":[2],"name":"Explain what is added to spatial analysis to make it spatio-temporal analysis"},{"concepts":[960],"name":"Explain what is meant by \"Odata\" (Open data Protocol), an OASIS standard"},{"concepts":[39],"name":"Explain what is meant by the convex hull and minimum enclosing rectangle of a set of point data"},{"concepts":[46],"name":"Explain what is meant by the term \"planar enforcement\""},{"concepts":[4],"name":"Explain what is meant by the term contaminated data, suggesting how it can arise"},{"concepts":[2],"name":"Explain what is special i.e., difficult about geospatial data analysis and why some traditional statistical analysis techniques are not suited to geographic problems"},{"concepts":[730],"name":"Explain what microwave remote sensing is"},{"concepts":[334],"name":"Explain what open data and the main principles of open data are"},{"concepts":[729],"name":"Explain what properties of microwave electromagnetic spectrum are recorded"},{"concepts":[558],"name":"Explain what SDI governance is and why it is important in the development and implementation of SDIs"},{"concepts":[680],"name":"Explain what soil permittivity is"},{"concepts":[679],"name":"Explain what the attenuation length and penetration depth are"},{"concepts":[802],"name":"Explain what the digital number is"},{"concepts":[721],"name":"Explain what the ground range and azimuth resolution are"},{"concepts":[666],"name":"Explain what the mathematical description of the phase is"},{"concepts":[666],"name":"Explain what the phase in remote sensing means and in what units is expressed"},{"concepts":[801],"name":"Explain what the picture element is"},{"concepts":[459],"name":"Explain which principles a segmentation should follow to arrive at meaningful objects that are appropriate for a specific application"},{"concepts":[51],"name":"Explain why and how density estimation transforms point data into a field representation"},{"concepts":[30],"name":"Explain why areal generalization is more difficult than line simplification"},{"concepts":[60],"name":"Explain why different interpolation algorithms produce different results and suggest ways by which these can be evaluated in the context of a specific problem"},{"concepts":[37],"name":"Explain why estimating the fractal dimension of a sinuous line has important implications for the measurement of its length"},{"concepts":[117],"name":"Explain why general-purpose regions rarely exist"},{"concepts":[47,48],"name":"Explain why georegistration is a precondition to map algebra"},{"concepts":[14],"name":"Explain why heuristic solutions are generally used to address the combinatorially complex nature of these problems and the difficulty of solving them optimally"},{"concepts":[19],"name":"Explain why integer programs are harder to solve than linear programs"},{"concepts":[241],"name":"Explain why integrated topological models have lost favor in commercial GIS software"},{"concepts":[540],"name":"Explain why it has been difficult for many agencies and organizations to define positions and roles for GIS and T professionals"},{"concepts":[76],"name":"Explain why it is important to have a good model of the semi-variogram in kriging"},{"concepts":[76],"name":"Explain why kriging is more suitable as an interpolation method in some applications than others"},{"concepts":[252],"name":"Explain why logging and rollback techniques are adequate for managing short transactions"},{"concepts":[408],"name":"Explain why radiometric correction is a key requirement for deriving indices with band maths"},{"concepts":[563],"name":"Explain why software products sold by U.S. companies may predominate in foreign markets, including Europe and Australia"},{"concepts":[714],"name":"Explain why spatial resolution of passive radar system is much lower than that of active systems"},{"concepts":[46],"name":"Explain why the process \"dissolve and merge\" often follows vector overlay operations"},{"concepts":[59],"name":"Explain why the properties of spatial continuity are characteristic of spatial surfaces"},{"concepts":[39],"name":"Explain why the shape of an object might be important in analysis"},{"concepts":[892],"name":"Explain why the use of multiple EO sensors for mapping landslides associated with one triggering event increases the completeness of a landslide inventory"},{"concepts":[123],"name":"Explain why Toblers First Law of Geography is fundamental to many operations in GIS and whether it should be"},{"concepts":[59],"name":"Explain why zero slopes are indicative of surface specific points such as peaks, pits and passes and list the conditions necessary for each"},{"concepts":[13],"name":"Explain why, if supply equals demand, there will always be a feasible solution to the Classic Transportation Problem"},{"concepts":[51],"name":"Explain why, in some cases, an adaptive bandwidth might be employed"},{"concepts":[315],"name":"Explain, in general terms, the difference between single and double precision and impacts on error propagation"},{"concepts":[17],"name":"Explain, using the concept of combinatorial complexity, why some location problems are very hard to solve"},{"concepts":[92],"name":"Explore the contribution of linguistics to the study of spatial cognition and the role of natural language in the conceptualization of geographic phenomena"},{"concepts":[96],"name":"Explore the history of geography including (but not limited to) Greek and Roman contributions to geography (Eratosthenes, Strabo, Ptolemy), geography and cartography in the age of discovery, military geography, and geography..."},{"concepts":[80],"name":"Find a best model"},{"concepts":[152],"name":"Find a multivariate outlier using a combination of maps and graphs"},{"concepts":[39],"name":"Find centroids of polygons under different definitions of a centroid and different polygon shapes"},{"concepts":[542],"name":"Find or create training resources appropriate for GIS and T workforce in a local government organization"},{"concepts":[116],"name":"Find spatial patterns in the distribution of geographic phenomena using geographic visualization and other techniques"},{"concepts":[110],"name":"Formalize attribute values and domains in terms of set theory"},{"concepts":[113],"name":"Formalize the notion of field using mathematical functions and Calculus"},{"concepts":[46],"name":"Formalize the operation called map overlay using Boolean logic"},{"concepts":[364],"name":"Generate a layer stack from bands of various EO data sources"},{"concepts":[413],"name":"Generate high quality time series by removing clouds and cloud shadows from the available images"},{"concepts":[915],"name":"Having in-depth knowledge of two of the three Copernicus-relevant topics: Land monitoring, Emergency response including Humanitarian action, and Climate change"},{"concepts":[116],"name":"Hypothesize the causes of a pattern in the spatial distribution of a phenomenon"},{"concepts":[188],"name":"Hypothesize the ways in which capital needs for GIS may change in the future"},{"concepts":[52],"name":"Identify a clustering method which does not require the number of clusters as input"},{"concepts":[31],"name":"Identify a variety of likely measurement level transformations (e.g., the classification of ratio data yields ordinal data)"},{"concepts":[267],"name":"Identify agent-based modelling principles and methodologies"},{"concepts":[354],"name":"Identify alternatives to the \"algorithmic way of thinking\" that characterizes use of geospatial Information."},{"concepts":[358],"name":"Identify alternatives to the algorithmic way of thinking that characterizes GIS"},{"concepts":[255],"name":"Identify and compare the scenarios on which geocomputation methods are relevant"},{"concepts":[307],"name":"Identify and define the four geometric properties of the globe that may be preserved or lost in projected coordinates"},{"concepts":[74],"name":"Identify and define the parameters of a semi-variogram range, sill, nugget"},{"concepts":[551],"name":"Identify and discuss the different components of an SDI"},{"concepts":[310],"name":"Identify and explain an equation used to perform image-to-image registration"},{"concepts":[310],"name":"Identify and explain an equation used to perform image-to-map registration"},{"concepts":[340],"name":"Identify and explain the different actors and their roles in the geo-information value chain"},{"concepts":[413],"name":"Identify anomalies by means of surface properties such as evapotranspiration (ET) or land surface temperature (LST) derived from satellite image time series"},{"concepts":[113],"name":"Identify applications and phenomena that are not adequately modeled by the field view"},{"concepts":[975],"name":"Identify building blocks of Javascript programming language. Write a Javascript function which, for instance, filters out points with height values greater than 100 m. from a GeoJSON file. Add this function to an HTML5 web page"},{"concepts":[266],"name":"Identify cellular automata principles and pattern"},{"concepts":[105],"name":"Identify common-sense views of geographic phenomena that sharply contrast with established theories and technologies of geographic information"},{"concepts":[259],"name":"Identify commonalities and patterns of geocomputation"},{"concepts":[564],"name":"Identify conferences that are related to GIS and T"},{"concepts":[562],"name":"Identify conferences that are related to GIS and T hosted by professional organizations"},{"concepts":[182],"name":"Identify data center platform tier configuration and identify platform selection for each tier"},{"concepts":[949],"name":"Identify design issues of SOAP web services; fine grained and coarse grained services, design patterns"},{"concepts":[977],"name":"Identify differences, advantages and disadvantages of web application framework based and portal framework based web applications from the geospatial data perspective"},{"concepts":[68],"name":"Identify different measures of spatial autocorrelation"},{"concepts":[69],"name":"Identify different measures of spatial autocorrelation"},{"concepts":[113],"name":"Identify examples of discrete and continuous change found in spatial, temporal, and spatio-temporal fields"},{"concepts":[144],"name":"Identify examples of static, animated, and interactive web maps"},{"concepts":[116],"name":"Identify influences of scale on the appearance of distributions"},{"concepts":[962],"name":"Identify issues in determining the relationships to be represented when publishing Linked Data"},{"concepts":[961],"name":"Identify issues in developing new ontologies for geospatial data"},{"concepts":[962],"name":"Identify issues in finding proper ontologies to annotate the data"},{"concepts":[955],"name":"Identify issues in the development of geospatial ontologies. Criticise the role of ontology development methodologies and ontology evaluation in the development of ontologies"},{"concepts":[976],"name":"Identify main components and functionality of Leaflet library, describe its main functions and how they are employed"},{"concepts":[976],"name":"Identify main components and functionality of Openlayers library, describe its main functions and how they are employed"},{"concepts":[958],"name":"Identify main components of manual metadata creation software tools"},{"concepts":[976],"name":"Identify main elements and functionality Google maps, describe some of its most popular API operations and how they are employed"},{"concepts":[976],"name":"Identify main elements and functionality Mapbox, describe some of its most popular API operations and how they are employed"},{"concepts":[964],"name":"Identify main issues in \"keyword-based\" discovery of data and services"},{"concepts":[965],"name":"Identify main issues in Semantic discovery"},{"concepts":[185],"name":"Identify major obstacles to the success of a GIS proposal"},{"concepts":[81],"name":"Identify modeling situations where spatial filtering might not be appropriate"},{"concepts":[566],"name":"Identify National Science Foundation (NSF) programs that support GIS and T research and education"},{"concepts":[552],"name":"Identify organizations that focus on developing standards related to GIS and T"},{"concepts":[120],"name":"Identify phenomena that are best understood as networks"},{"concepts":[112],"name":"Identify phenomena that are difficult or impossible to conceptualize in terms of entities"},{"concepts":[473,417],"name":"Identify physical, semantic and spatial properties used to assigned objects to the target classes"},{"concepts":[182],"name":"Identify platform assignment for each workflow software component peak transaction processing load"},{"concepts":[185,186],"name":"Identify potential sources of data (free or commercial) needed for a particular application or enterprise"},{"concepts":[189],"name":"Identify potential sources of funding (internal and external) for a project or enterprise GIS"},{"concepts":[338],"name":"Identify practical problems in defining and measuring the value of geospatial information in land or other business decisions"},{"concepts":[52],"name":"Identify several cluster detection techniques and discuss their limitations"},{"concepts":[39],"name":"Identify situations in which shape affects geometric operations"},{"concepts":[123],"name":"Identify situations in which Toblers First Law of Geography does not apply"},{"concepts":[123],"name":"Identify situations in which Toblers First Law of Geography is valuable"},{"concepts":[109],"name":"Identify specific examples of categories of entities (i.e., common nouns), properties (i.e., adjectives), space (i.e., regions), and time (i.e., eras)"},{"concepts":[552],"name":"Identify standards that are used in GIS and T"},{"concepts":[957],"name":"Identify the aspects of selecting keywords which would characterize the data properly"},{"concepts":[23],"name":"Identify the conceptual and practical difficulties associated with data model and format conversion"},{"concepts":[23],"name":"Identify the conceptual and practical difficulties associated with data model and format conversion"},{"concepts":[276],"name":"Identify the defining characteristics of an open geocomputation project"},{"concepts":[971],"name":"Identify the different barriers for the integration of datasets"},{"concepts":[67],"name":"Identify the different methods for constructing spatial weigh matrix"},{"concepts":[87],"name":"Identify the epistemological assumptions underlying the work of colleagues"},{"concepts":[188],"name":"Identify the hardware and space that will be needed for a GIS implementation"},{"concepts":[125],"name":"Identify the hedges used in language to convey vagueness"},{"concepts":[957],"name":"Identify the issues in mapping between different metadata standards. Also identify the roles of thesauri and crosswalks"},{"concepts":[539],"name":"Identify the key organizational components of a GIS&T implementation"},{"concepts":[117],"name":"Identify the kinds of phenomena that are commonly found at the boundaries of regions"},{"concepts":[330],"name":"Identify the liability implications associated with contracts"},{"concepts":[968],"name":"Identify the main components of OGC Filter encoding and compare it to SQL"},{"concepts":[965],"name":"Identify the main concepts of reasoning and architectural components of Reasoners"},{"concepts":[539],"name":"Identify the main organizational challenges in implementing and use GIS&T"},{"concepts":[141],"name":"Identify the most appropriate color palette for a printed map for visually-impaired people"},{"concepts":[141],"name":"Identify the most appropriate color palette for an online map for visually-impaired people"},{"concepts":[231],"name":"Identify the national framework datasets based on a grid model"},{"concepts":[968],"name":"Identify the need for and main issues in spatial data interchange"},{"concepts":[85],"name":"Identify the ontological assumptions underlying the work of colleagues"},{"concepts":[309],"name":"Identify the parameters that allow one to focus a projection on an area of interest"},{"concepts":[542],"name":"Identify the particular skills necessary for users to perform tasks in three different workforce domains (e.g., small city, medium county agency, a business, or others)"},{"concepts":[89],"name":"Identify the philosophical views and assumptions underlying the work of colleagues"},{"concepts":[187],"name":"Identify the positions necessary to design and implement a GIS"},{"concepts":[185],"name":"Identify the positions necessary to design and implement a GIS project / GI unit"},{"concepts":[309],"name":"Identify the possible aspects of a projection and describe the graticules appearance in each aspect"},{"concepts":[540],"name":"Identify the qualifications needed for a particular GIS and T position"},{"concepts":[953],"name":"Identify the relation between OWL-S and WSDL and give an overview of Semantic Web service definition in OWL-S"},{"concepts":[60],"name":"Identify the spatial concepts that are assumed in different interpolation algorithms"},{"concepts":[540],"name":"Identify the standard occupational codes that are relevant to GIS and T"},{"concepts":[960],"name":"Identify the technical aspects that open data paradigm would affect concerning Spatial Data Infrastructures including NSDIs"},{"concepts":[112],"name":"Identify the types of features that need to be modeled in a particular GIS application or procedure"},{"concepts":[258,259],"name":"Identify the types of geography problems geocomputation solves"},{"concepts":[50],"name":"Identify the various ways point patterns may be described"},{"concepts":[191],"name":"Identify the viability of a commercial GIS application"},{"concepts":[952],"name":"identify the web services needed for a particular use case"},{"concepts":[182],"name":"Identify user locations, network connectivity, and data center server locations"},{"concepts":[108],"name":"Identify various types of geographic interactions in space and time"},{"concepts":[50],"name":"Identify various types of K-function analysis"},{"concepts":[953],"name":"Identify virtues of defining a given data set in both RDF and OWL, and compare semantic richness of both definitions"},{"concepts":[973],"name":"Identify whether Full-automated WSC has still a value in it concerning both where we stand today on the road to 'Semantic Web' and unresolved problems in the area, which are the problems of Artificial Intelligence indeed"},{"concepts":[601],"name":"Illustrate main spectral signatures of clouds and apply them in paractical cloud-detection exercise"},{"concepts":[241],"name":"Illustrate a topological relation"},{"concepts":[347],"name":"Illustrate an example of \"local knowledge\" that is unlikely to be represented in the geospatial data maintained routinely by government agencies"},{"concepts":[640],"name":"Illustrate and apply basic concepts of Atmospheric Physics to EO science and its applications"},{"concepts":[314],"name":"Illustrate and explain the distinction between resolution, precision, and accuracy"},{"concepts":[314],"name":"Illustrate and explain the distinctions between spatial resolution, thematic resolution, and temporal resolution"},{"concepts":[599],"name":"Illustrate basic features of spectral signatures of vegetation, water and bare soil"},{"concepts":[627],"name":"Illustrate basic modern physics theory understanding their implications on the development of advanced sensors for EO"},{"concepts":[590,598],"name":"Illustrate basic radiation-matter interactions and related concepts of spectral reflectance, absorbance and transmittance as specific properties of the matter"},{"concepts":[307],"name":"Illustrate distortion patterns associated with a given projection class"},{"concepts":[601],"name":"Illustrate e.m. radiation intercations with/within clouds."},{"concepts":[183],"name":"Illustrate each of the project management areas with an example of a technique or tool used"},{"concepts":[173],"name":"Illustrate how a business process analysis can be used to identify requirements during a GIS implementation"},{"concepts":[144],"name":"Illustrate how an animated map reveals patterns not evident without animation"},{"concepts":[115],"name":"Illustrate major integrated models of geographic information, such as Peuquets Triad, Mennis Pyramid, and Yuans Three-Domain"},{"concepts":[542],"name":"Illustrate methods that are effective in providing opportunities for education and training when implementing a GIS in a small city"},{"concepts":[962],"name":"Illustrate stages of publishing a relational database as Linked Data"},{"concepts":[635],"name":"Illustrate the interaction of e.m. radiation in the thermal infrared with the atmosphere understanding specifc characteristics of radiative transfer in this specific spectral region."},{"concepts":[579],"name":"Illustrate the concept of spectral emissivity and brigthness temperature and compute them in some simple real case"},{"concepts":[598],"name":"Illustrate the concept of spectral signatures of the matter"},{"concepts":[129],"name":"Illustrate the evolution of Cartography in different periods of time"},{"concepts":[232],"name":"Illustrate the existing methods for compressing gridded data (e.g., run length encoding, Lempel-Ziv, wavelets)"},{"concepts":[308],"name":"Illustrate the graticule configurations for other projection classes, such as polyconic, pseudocylindrical, etc."},{"concepts":[234],"name":"Illustrate the hexagonal model"},{"concepts":[236],"name":"Illustrate the impact of grid cell resolution on the information that can be portrayed"},{"concepts":[25],"name":"Illustrate the impact of vector/raster/vector conversions on the quality of a dataset"},{"concepts":[657],"name":"Illustrate the importance of the choice of the satellite orbit for the design of a satellite mission devoted to specific applications"},{"concepts":[52],"name":"Illustrate the main use of spatial clustering in earth observation"},{"concepts":[237],"name":"Illustrate the quadtree model"},{"concepts":[260],"name":"Illustrate the relationships between geocomputation with other terms, disciplines and areas of knowledge"},{"concepts":[291,599],"name":"Illustrate the spectral response curves for basic environmental features (e.g., vegetation, concrete, bare soil)"},{"concepts":[639],"name":"Illustrate the transferring of Energy within the Earth-Atmosphere System"},{"concepts":[156],"name":"Illustrate the use of virtual environments"},{"concepts":[531],"name":"Illustrate what functions a support or service center can provide to an organization using GIS and T"},{"concepts":[131],"name":"Illustrate with examples the relationship between art and cartography at different historical moments"},{"concepts":[237],"name":"Implement a format for encoding quadtrees in a data file"},{"concepts":[309],"name":"Implement a given map projection formula in a software program that reads geographic coordinates as input and produces projected (x, y) coordinates as output"},{"concepts":[80],"name":"Implement a maximum likelihood estimation procedure for determining key spatial econometric parameters"},{"concepts":[253],"name":"Implement a test of reliability of change information"},{"concepts":[60],"name":"Implement a trend surface analysis using either the supplied function in a GIS or a regression function from any standard statistical package"},{"concepts":[18],"name":"Implement linear programs for spatial allocation problems"},{"concepts":[13],"name":"Implement the Transportation Simplex method to determine the optimal solution"},{"concepts":[312],"name":"In contrast to the National Map Accuracy Standard, explain how the spatial accuracy of a digital road centerlines data set may be evaluated and documented"},{"concepts":[961],"name":"Indicate an architecture and tools for organizing semantically annotated data"},{"concepts":[976],"name":"Indicate an overview of OpenStreetMap and define its general functionality, comment its usage by Web APIs"},{"concepts":[975],"name":"Indicate Document Object Model (DOM) and Identify its role for the processing of a \"loaded\" HTML document"},{"concepts":[977],"name":"Indicate generally how \"NSDI-requiring-scenarios\"would be handled by web application framework based applications"},{"concepts":[975],"name":"Indicate main elements of HTML5. Identify the extensions HTML5 brings over older HTML versions. Create a sample HTML5 Web page"},{"concepts":[965],"name":"Indicate some examples of semantic discovery; Semantic search engines, highlighting projects and practice concerning GI related applications in the area"},{"concepts":[354],"name":"Indicate the extent to which contemporary use of geospatial information supports diverse ways of understanding the world."},{"concepts":[532],"name":"Indicate the possible justifications that can be used to implement an enterprise GIS"},{"concepts":[265],"name":"Interpret when space-time dynamics can be used to study geographical phenomen"},{"concepts":[307],"name":"Interpret a given a projected graticule, continent outlines, and indicatrixes at each graticule intersection in terms of geometric properties preserved and distorted"},{"concepts":[182],"name":"Interpret business needs and translate them to IT needs"},{"concepts":[528],"name":"Interpret descriptive statistics and geostatistics of geographic data"},{"concepts":[140],"name":"Interpret different symbols and icons in a map"},{"concepts":[977],"name":"Interpret generally the functionality offered by \"portal frameworks\" land Geoportals like Geonetwork, Opengeoportal, Esri geoportal server, Degree portal, Liferay, Jboss portal"},{"concepts":[977],"name":"Interpret generally the main components and functionality of \"Web Application Frameworks\" such as AngularJS, Ext.js, Django, Java Server Faces (JSF), and the like"},{"concepts":[954],"name":"interpret GML data model and GML definition of geometry. GML application schemas and GML documents"},{"concepts":[256],"name":"Interpret how individual parts contained in a complex system relate to each other"},{"concepts":[5],"name":"Interpret patterns in space and time using Dorling and Openshaws Geographical Analysis Machine GAM demonstration of disease incidence diffusion"},{"concepts":[230],"name":"Interpret the header of a standard raster data file"},{"concepts":[129],"name":"Interpret the impact of paper-based and web maps in their context"},{"concepts":[882],"name":"Interpret the output of numerical prediction models"},{"concepts":[77],"name":"Interpret the results of universal kriging"},{"concepts":[182],"name":"Interpret user needs as an input for the design process"},{"concepts":[186],"name":"Judge the relative merits of obtaining free data, purchasing data, outsourcing data creation, or producing and managing data in-house for a particular application or enterprise"},{"concepts":[96],"name":"Justify a chosen position on which disciplines should have as important a role in GIS AND T as geography"},{"concepts":[193],"name":"Justify feasibility recommendations to decision-makers"},{"concepts":[112],"name":"Justify or refute the conception of fields (e.g., temperature, density) as spatially-intensive attributes of (sometimes amorphous and anonymous) entities"},{"concepts":[96],"name":"Justify or refute whether geography (as a discipline) should have a central role in GIS AND T"},{"concepts":[101],"name":"Justify the discrepancies between the nature of locations in the real world and representations thereof (e.g., towns as points)"},{"concepts":[87],"name":"Justify the epistemological frameworks with which you agree"},{"concepts":[85],"name":"Justify the metaphysical theories with which you agree"},{"concepts":[66],"name":"Justify the stochastic process approach to spatial statistical analysis"},{"concepts":[68],"name":"Justify, compute, and test the significance of the join count statistic for a pattern of objects"},{"concepts":[583],"name":"Knowledge of the basic (selective) mechanism of the absorption/emission of electromagnetic radiation by atoms."},{"concepts":[73],"name":"List and describe several spatial sampling schemes and evaluate each one for specific applications"},{"concepts":[342],"name":"List and describe the types of data maintained by local, state, and federal governments"},{"concepts":[245],"name":"List definitions of networks that apply to specific applications or industries"},{"concepts":[42],"name":"List different ways connectivity can be determined in a raster and in a polygon dataset"},{"concepts":[40],"name":"List reasons why the area of a polygon calculated in a GIS might not be the same as the real world object it describes"},{"concepts":[14],"name":"List several classic problems to which network analysis is applied e.g., The Traveling Salesman Problem, The Chinese Postman Problem"},{"concepts":[156],"name":"List software and hardware environments supporting immersive visualization"},{"concepts":[532],"name":"List some of the topics that should be addressed in a justification for implementing an enterprise GIS (e.g., return on investment, workflow, knowledge sharing)"},{"concepts":[519],"name":"List specifics competitive DIAS solutions over other"},{"concepts":[50],"name":"List the conditions that make point pattern analysis a suitable process"},{"concepts":[185],"name":"List the costs and benefits (tangible or intangible) of implementing a GI project"},{"concepts":[183],"name":"List the key elements of a project management"},{"concepts":[59],"name":"List the likely sources of error in slope and aspect maps derived from DEMs and state the circumstances under which these can be very severe"},{"concepts":[459],"name":"List the main segmentation methods used to group similar pixels into homogeneous objects"},{"concepts":[138],"name":"List the major factors that should be considered in preparing a map"},{"concepts":[183],"name":"List the phases of a project management life cycle"},{"concepts":[75],"name":"List the possible sources of error in a selected and fitted model of an experimental semi-variogram"},{"concepts":[122],"name":"List the possible topological relationships between entities in space (e.g., 9-intersection) and time"},{"concepts":[141],"name":"List the range of factors that should be considered in selecting colors"},{"concepts":[66],"name":"List the two basic assumptions of the purely random process"},{"concepts":[15],"name":"List ways we can define accessibility on a network"},{"concepts":[20],"name":"Locate, using location-allocation software, service facilities that meet given sets of constraints"},{"concepts":[173],"name":"Manage requirements using a management tool (such as Trello, JIRA, etc.)"},{"concepts":[387],"name":"Measure reflectance of surfaces of vegetation types and other thematic classes in the field"},{"concepts":[112],"name":"Model gray area phenomena, such as categorical coverages (a.k.a. discrete fields), in terms of objects"},{"concepts":[182],"name":"Model project workflows"},{"concepts":[686],"name":"Model surface roughness slope"},{"concepts":[251],"name":"Modify spatial and attribute data while ensuring consistency within the database"},{"concepts":[148],"name":"Outline a map layout taking into account design principles"},{"concepts":[59],"name":"Outline a number of different methods for calculating slope from a Digital Elevation Model (DEM)"},{"concepts":[295],"name":"Outline a plausible workflow for habitat mapping, such as the benthic habitat mapping in the main Hawaiian Islands as part of the NOAA Biogeography program"},{"concepts":[295],"name":"Outline a plausible workflow used by MDA Federal (formerly EarthSat) to create the high-resolution GEOCOVER global imagery and GEOCOVER-LC global land cover datasets"},{"concepts":[322],"name":"Outline a workflow that can be used to train a new employee to update a county road centerlines database using digital 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methods (programs or processes) that provide effective staff development opportunities for GIS and T"},{"concepts":[337],"name":"Outline sources of additional costs associated with development of an enterprise GIS"},{"concepts":[332],"name":"Outline the arguments for and against the notion of information as a public good"},{"concepts":[76],"name":"Outline the basic kriging equations in their matrix formulation"},{"concepts":[50],"name":"Outline the basis of classic critiques of spatial statistical analysis in the context of point pattern analysis"},{"concepts":[337],"name":"Outline the categories of costs that an organization should anticipate as it plans to design and implement a GIS"},{"concepts":[256],"name":"Outline the complex problems where geocomputation is relevant"},{"concepts":[337],"name":"Outline the elements of a business case that justifies an organization's investment in an enterprise geospatial information infrastructure"},{"concepts":[41],"name":"Outline the geometry implicit in classical gravity models of distance decay"},{"concepts":[4],"name":"Outline the implications of complexity for the application of statistical ideas in geography"},{"concepts":[37],"name":"Outline the implications of differences in distance calculations on real world applications of GIS, such as routing and determining boundary lengths and service areas"},{"concepts":[51],"name":"Outline the likely effects on analysis results of variations in the kernel function used and the bandwidth adopted"},{"concepts":[66],"name":"Outline the logic behind the derivation of long run expected outcomes of the independent random process using quadrat counts"},{"concepts":[46],"name":"Outline the possible sources of error in overlay operations"},{"concepts":[566],"name":"Outline the principle concepts and goals of the digital earth vision articulated in 1998 by Vice President Al Gore"},{"concepts":[323],"name":"Outline the process of scanning and vectorizing features depicted on a printed map sheet using a given GIS software product, emphasizing issues that require manual intervention"},{"concepts":[260],"name":"Outline the role of computational science in geocomputation"},{"concepts":[313],"name":"Outline the SDTS and ISO TC211 standards for thematic accuracy"},{"concepts":[287],"name":"Outline the sequence of tasks involved in generating an orthoimage from a vertical aerial photograph"},{"concepts":[2],"name":"Outline the sequence of tasks required to complete the analytical process for a given spatial problem"},{"concepts":[163],"name":"Outline the stages in lithographic offset printing"},{"concepts":[194],"name":"Outline the types of geospatial software architectures"},{"concepts":[52],"name":"Perform a cluster detection analysis to detect hot spots in a point pattern"},{"concepts":[33],"name":"Perform a logic set theoretic query using GIS software"},{"concepts":[293],"name":"Perform a manual unsupervised classification given a two-dimensional array of reflectance values and ranges of reflectance values associated with a given number of land cover categories"},{"concepts":[47,48],"name":"Perform a map algebra calculation using command line, form-based, and flow charting user interfaces"},{"concepts":[185],"name":"Perform a pilot study to evaluate the feasibility of an application"},{"concepts":[268],"name":"Perform a simulation experiment using available simulation software"},{"concepts":[82],"name":"Perform an analysis using the geographically weighted regression technique"},{"concepts":[964],"name":"Perform discovery over some popular SDI (NSDI) portals like INSPIRE and GOS geoportals"},{"concepts":[54],"name":"Perform multidimensional scaling (MDS) and principal components analysis (PCA) to reduce the number of coordinates, or dimensionality, of a problem"},{"concepts":[62],"name":"Perform siting analyses using specified visibility, slope, and other surface related constraints"},{"concepts":[952],"name":"perform the connection to existing web services to use the resources exposed by the service"},{"concepts":[486],"name":"Plan a reproducibility project independently"},{"concepts":[289],"name":"Plan an aerial imagery mission in response to a given RFP and map of a study area, taking into consideration vertical and horizontal control, atmospheric conditions, time of year, and time of day"},{"concepts":[868,837],"name":"Plan and design project implementations"},{"concepts":[895],"name":"Plan emergency response actions"},{"concepts":[41],"name":"Plot typical forms for distance decay functions"},{"concepts":[968],"name":"Practically apply getting data from a WCS and integrate it into a client application"},{"concepts":[968],"name":"Practically apply getting data from a WFS and integrate it into a client application"},{"concepts":[163],"name":"Prepare a color map for black-and-white photocopy distribution"},{"concepts":[534],"name":"Prepare a GIS Management Strategy"},{"concepts":[539],"name":"Prepare a strategy on setting up the organizational components of a GIS&T implementation"},{"concepts":[22],"name":"Prioritize a set of algorithms designed to perform transformations based on the need to maintain data integrity [e.g., converting a digital elevation model (DEM) into a TIN]"},{"concepts":[386],"name":"Produce a surface corrected version of image values from BOA reflectance that removes topographic effects based on an input DSM and equations representing the relationship between sun incidence angle relative to terrain surface orientation"},{"concepts":[54],"name":"Produce plots in several data dimensions using a data matrix of attributes"},{"concepts":[293],"name":"Produce pseudocode for common unsupervised classification algorithms including chain method, ISODATA method, and clustering"},{"concepts":[614],"name":"Produce the processes of spectral calculations of radiometric quantities by the line by line radiative transfer models"},{"concepts":[254],"name":"Produce viable queries for change scenarios using GIS or database management tools"},{"concepts":[133],"name":"Propose a holistic historical perspective of maps creation and use"},{"concepts":[352],"name":"Propose a resolution to a conflict between an obligation in the GIS Code of Ethics and organizations proprietary interests"},{"concepts":[331],"name":"Propose and design solutions for dealing with particular data privacy and data security issues"},{"concepts":[330],"name":"Propose strategies for managing liability risk, including disclaimers and data quality standards"},{"concepts":[149],"name":"Propose thematic mapping methods for mapping numerical data"},{"concepts":[346],"name":"Provide examples of the use of geospatial information in different sectors"},{"concepts":[334],"name":"Publish a dataset as open data"},{"concepts":[31],"name":"Reclassify (group) a nominal attribute domain to fewer, broader classes"},{"concepts":[31],"name":"Reclassify a raster before converting it into a vector file"},{"concepts":[109],"name":"Recognize and manage the potential problems associated with the use of categories (e.g., the ecological fallacy)"},{"concepts":[110],"name":"Recognize attribute domains that do not fit well into Stevens four levels of measurement (nominal, ordinal, interval, ratio), such as cycles, indexes, and hierarchies"},{"concepts":[688],"name":"Recognize different types of surface roughness on a radar image"},{"concepts":[307],"name":"Recognize distortion patterns on a map based upon the graticule arrangement"},{"concepts":[126],"name":"Recognize expressions of uncertainty in language"},{"concepts":[110],"name":"Recognize situations and phenomena in the landscape which cannot be adequately represented by formal attributes, such as aesthetics"},{"concepts":[528],"name":"Recognize the assumptions underlying probability and geostatistics and the situations in which they are useful analytical tools"},{"concepts":[85],"name":"Recognize the commonalities of philosophical viewpoints and appreciate differences to enable work with diverse colleagues"},{"concepts":[206],"name":"Recognize the constraints and opportunities of a particular choice of software for implementing a physical model"},{"concepts":[99],"name":"Recognize the constraints that political forces place on geospatial applications in public and private sectors"},{"concepts":[122],"name":"Recognize the contributions of Topology (the branch of mathematics) to the study of geographic relationships"},{"concepts":[126],"name":"Recognize the degree to which the importance of uncertainty depends on scale and application"},{"concepts":[125],"name":"Recognize the degree to which vagueness depends on scale"},{"concepts":[284],"name":"Recognize the distortions and implications of relief displacement and radial distortion in an aerial image"},{"concepts":[98],"name":"Recognize the impact of ones social background on ones own geographic worldview and perceptions and how it influences ones use of GIS"},{"concepts":[486],"name":"Recognize the importance of reproducible research as a fundamental pillar of modern science"},{"concepts":[87],"name":"Recognize the influences of epistemology on GIS practices"},{"concepts":[113],"name":"Recognize the influences of scale on the perception and meaning of fields"},{"concepts":[335],"name":"Recognize the relevant legal issues in a particular case of geospatial data collection, use and/of sharing"},{"concepts":[107],"name":"Recognize the role that time plays in static GISystems"},{"concepts":[307],"name":"Recommend the map projection property that would be useful for various mapping applications, including parcel mapping, route mapping, etc., and justify your recommendations"},{"concepts":[109],"name":"Reconcile differing common-sense and official definitions of common geospatial categories of entities, attributes, space, and time"},{"concepts":[54],"name":"Relate plots of multidimensional attribute data to geography by equating similarity in data space with proximity in geographical space"},{"concepts":[236],"name":"Relate the concept of grid cell resolution to the more general concept of support and granularity"},{"concepts":[113],"name":"Relate the notion of field in GIS to the mathematical notions of scalar and vector fields"},{"concepts":[128],"name":"Relate the science and technology of graphical representation of geographic data"},{"concepts":[435],"name":"Relate the spatial and spectral characteristics of EO data to the types and proportions of materials found within the scene and within pixel IFOVs to relabel spectral classes as information classes of a classification scheme"},{"concepts":[140],"name":"Relate the spatial dimension and the weight of mapped features with the attributes they represent"},{"concepts":[632],"name":"Relate to the aspects of radiation transfer through the atmosphere."},{"concepts":[962],"name":"Relate with manual and automated methods linking data"},{"concepts":[173],"name":"Report existing and potential tasks in terms of workflow and information flow"},{"concepts":[120],"name":"Represent structural relationships in GIS data"},{"concepts":[26],"name":"Resample multiple raster data sets to a single resolution to enable overlay"},{"concepts":[26],"name":"Resample raster data sets (e.g., terrain, satellite imagery) to a resolution appropriate for a map of a particular scale"},{"concepts":[343],"name":"Research and develop geospatial information for the private sector"},{"concepts":[141],"name":"Select a color palette appropriate for a representation"},{"concepts":[29],"name":"Select a level of data detail and accuracy appropriate for a particular application (e.g., viewshed analysis, continental land cover change)"},{"concepts":[97],"name":"Select a place or landscape with personal meaning and discuss its importance"},{"concepts":[174],"name":"Select among the most appropriate method for documenting a certain process"},{"concepts":[920],"name":"Select an appropriate DEM product for usage in a specific application"},{"concepts":[568],"name":"Select and describe the leading trade journals serving the GIS and T community"},{"concepts":[26],"name":"Select appropriate interpolation techniques to resample particular types of values in raster data (e.g., nominal using nearest neighbor)"},{"concepts":[101],"name":"Select appropriate spatial metaphors and models of phenomena to be represented in GIS"},{"concepts":[568],"name":"Select association and for-profit journals that are useful to entities managing enterprise GI systems"},{"concepts":[149],"name":"Select base information suited to providing a frame of reference for thematic map symbols (e.g., network of major roads and state boundaries underlying national population map)"},{"concepts":[173],"name":"Select from conflicting requirements"},{"concepts":[165],"name":"Select maps that illustrate the provocative, propaganda, political, and persuasive nature of maps and geospatial data"},{"concepts":[820],"name":"Select the appropriate optical data type for the application"},{"concepts":[825],"name":"Select the appropriate SAR data type for the application"},{"concepts":[65],"name":"Select the appropriate statistical methods for the analysis of given spatial datasets by first exploring them using graphic methods"},{"concepts":[978],"name":"select the development elements best suited for your application"},{"concepts":[142],"name":"Select the most appropriate place in a map to place a label and a legend"},{"concepts":[292],"name":"Select the most appropriate remotely sensed data source for a given analytical task, study area, budget, and availability"},{"concepts":[183],"name":"Select the most appropriate techniques for a EO*GI project"},{"concepts":[193],"name":"Select the most appropriate technology to help decision-making"},{"concepts":[159],"name":"Select the most suitable graphic representation for a given set of data"},{"concepts":[159],"name":"Select the most suitable graphic representation for a targeted audience"},{"concepts":[107],"name":"Select the temporal elements of geographic phenomena that need to be represented in particular GIS applications"},{"concepts":[776,829],"name":"Select the type of remote sensing sensor appropriate for your application"},{"concepts":[952],"name":"select the web services best fit to expose your own resources"},{"concepts":[142],"name":"Select type font, size, style and color for labels on a map by applying basic typography design principles"},{"concepts":[965],"name":"Semantic Discovery and its main components. Identify the areas of its use for GI related applications"},{"concepts":[150],"name":"Sketch a map with a reliability overlay using symbols suited to reliability representations"},{"concepts":[142],"name":"Solve a labeling problem for a dense collection of features on a map using minimal leader lines"},{"concepts":[142],"name":"Solve ambiguities in map label by selecting the most appropriate typography"},{"concepts":[961],"name":"Solve issues in determining what ontologies to use for semantic annotation"},{"concepts":[163],"name":"Specify a print job for publication, including paper, ink, lpi, proof needs, press check and other contract decisions"},{"concepts":[287],"name":"Specify the technical components of an aerotriangulation system"},{"concepts":[793],"name":"State and explain different SAR acquisition modes"},{"concepts":[727],"name":"State and explain Synthetic Aperture Radar (SAR) geometric distortions"},{"concepts":[706],"name":"State application examples of PSI methods"},{"concepts":[825],"name":"State different types of processing levels of SAR data"},{"concepts":[34],"name":"State questions that can be solved by selecting features based on location or spatial relationships"},{"concepts":[312],"name":"State the approximate number and spacing of control points in each order of the horizontal geodetic control network"},{"concepts":[53],"name":"State the classic formalization of the interaction model"},{"concepts":[312],"name":"State the geometric accuracies associated with the various orders of the U.S. horizontal geodetic control network"},{"concepts":[669],"name":"State the microwave portion of the electromagnetic spectrum"},{"concepts":[669],"name":"State the typical used radar bands and their application"},{"concepts":[664],"name":"State types of polarisations used in remote sensing"},{"concepts":[335],"name":"Suggest and prepare solutions for addressing particular legal issues related to the production, use and sharing of geospatial data"},{"concepts":[542],"name":"Teach necessary skills for users to successfully perform tasks in an enterprise GIS"},{"concepts":[195],"name":"Test all functionalities and data standards for interoperability"},{"concepts":[94],"name":"Transform a conceptual model of information for a particular task into a data model"},{"concepts":[373,372],"name":"Transform an EO dataset to map coordinates using a registered image of like geometry as a reference"},{"concepts":[388],"name":"Transform imagery into radiometrically/atmospherically corrected state"},{"concepts":[26],"name":"Understand and examine the common methods for raster resampling"},{"concepts":[335],"name":"Understand and explain the main legal issues related to the production, use and sharing of geospatial data and information"},{"concepts":[379],"name":"Understand atmospheric parameters that influence bottom of atmosphere (BOA) reflectance"},{"concepts":[260],"name":"Understand complexity in the broadest sense"},{"concepts":[71],"name":"Understand different estimation methods for Bayesian models"},{"concepts":[256],"name":"Understand how complex systems operate"},{"concepts":[952],"name":"understand how different web services complement each other"},{"concepts":[259],"name":"Understand how geocomputation relates to other similar terms"},{"concepts":[264],"name":"Understand how models are translated into differential equations for execution"},{"concepts":[263],"name":"Understand how models can be specified into logical rules"},{"concepts":[882],"name":"Understand how numerical prediction models work"},{"concepts":[495],"name":"Understand how positional/geometric accuracy of a dataset affects subsequent analysis"},{"concepts":[495,494],"name":"Understand how root mean squared error (RMSE) at tie points represents local spatial accuracy and enables calculation of total RMSE that informs about the average spatial accuracy of the entire image"},{"concepts":[159],"name":"Understand how the representation of geographic data facilitates visual communication"},{"concepts":[255],"name":"Understand how the theoretical roots and experimental emphasis on geocomputation are integrated"},{"concepts":[172],"name":"Understand spatial data models and structures"},{"concepts":[280],"name":"Understand spatial reference systems and apply them to an EO dataset"},{"concepts":[380],"name":"Understand sun, sun angle, and sensor parameters that influence top of atmosphere (TOA) reflectance"},{"concepts":[256],"name":"Understand the all-encompassing concepts of complexity"},{"concepts":[68],"name":"Understand the assumption under which spatial autocorrelation may occur"},{"concepts":[69],"name":"Understand the assumption under which spatial autocorrelation may occur"},{"concepts":[334],"name":"Understand the benefits of publishing and using open data"},{"concepts":[261],"name":"Understand the defining characteristics of simulation models, and their applicability"},{"concepts":[204],"name":"Understand the degree to which attributes need to be conceptually modeled"},{"concepts":[892],"name":"Understand the diverse set of EO technologies that are capable of mapping different landslide aspects"},{"concepts":[837,846],"name":"Understand the information that has been derived from EO products"},{"concepts":[177],"name":"Understand the main software engineering methodologies"},{"concepts":[330],"name":"Understand the nature of tort law generally and nuisance law specifically"},{"concepts":[108],"name":"Understand the physical notions of velocity and acceleration which are fundamentally about movement across space through time"},{"concepts":[486],"name":"Understand the problems associated with the lack of reproducibility"},{"concepts":[519],"name":"Understand the strategic meaning of DIAS in the user segment of Copernicus"},{"concepts":[66],"name":"Understand the underlying assumptions for spatial stochastics process"},{"concepts":[23],"name":"Understand various formats of storing raster and vector data"},{"concepts":[964],"name":"Use \"Full-text-based\" discovery; open source and commercial search engines, its use in GI related applications"},{"concepts":[151],"name":"Use appropriate interpolation techniques to derive DEMs from point data"},{"concepts":[975],"name":"Use Cascading Style Sheets (CSS), identify the virtue of its role for separating the presentation style of HTML documents from the content of documents"},{"concepts":[109],"name":"Use categorical information in analysis, cartography, and other GIS processes, avoiding common interpretation mistakes"},{"concepts":[904],"name":"Use EO products to assess land areas, its ecosystems, and its evolution"},{"concepts":[895],"name":"Use EO products to assess the risk of a disaster"},{"concepts":[881],"name":"Use EO products to conduct forecasts and projections"},{"concepts":[882],"name":"Use EO products to conduct numerical simulations"},{"concepts":[880],"name":"Use EO products to forecast sunlight exposure"},{"concepts":[895],"name":"Use EO products to measure impact and/or recovery"},{"concepts":[895],"name":"Use EO products to monitor disaster prone areas"},{"concepts":[904],"name":"Use EO products to plan land areas, its ecosystems, and its evolution"},{"concepts":[117],"name":"Use established analysis methods that are based on the concept of region (e.g., landscape ecology)"},{"concepts":[118],"name":"Use established analysis methods that are based on the concept of spatial integration (e.g., overlay)"},{"concepts":[309],"name":"Use GIS software to produce a graticule that matches a target graticule"},{"concepts":[310],"name":"Use GIS software to transform a given dataset to a specified coordinate system, projection, and datum"},{"concepts":[123],"name":"Use methods that analyze metrical relationships"},{"concepts":[122],"name":"Use methods that analyze topological relationships"},{"concepts":[966],"name":"Use Natural language based discovery over linked data"},{"concepts":[960],"name":"Use open data APIs that enable the usage of Open data; identify design aspects and usage scenarios"},{"concepts":[286],"name":"Use photo interpretation keys to interpret features on aerial photographs"},{"concepts":[975],"name":"Use Scalable Vector Graphics (SVG) and identify its role for client side processing"},{"concepts":[486],"name":"Use software tools to automate the practice of reproducible research in daily work"},{"concepts":[551],"name":"Use the models of ‘SDI generations’ and ‘SDI components’ to describe the main elements of an existing SDI initiative"},{"concepts":[953],"name":"Use Web services description for RESTful web services, Web Application Description Language (WADL) and its use"},{"concepts":[286],"name":"Using a vertical aerial image, produce a map of land use/land cover classes"},{"concepts":[41],"name":"Write a program to create a matrix of pair-wise distances among a set of points"},{"concepts":[230],"name":"Write a program to read and write a raster data file"},{"concepts":[41],"name":"Write typical forms for distance decay functions"},{"concepts":[12],"name":"xplain how the concept of capacity represents an upper limit on the amount of flow through the network"}]},"v4":{"concepts":[{"code":"GIST","description":"Geographic Information Science and Technology","name":"Geographic Information Science and Technology"},{"code":"AM","description":"This knowledge area encompasses a wide variety of operations whose objective is to derive analytical results from geospatial data. Data analysis seeks to understand both first-order (environmental) effects and second-order (interaction) effects. Approaches that are both data-driven (exploration of geospatial data) and model-driven (testing hypotheses and creating models) are included. Data driven techniques derive summary descriptions of data, evoke insights about characteristics of data, contribute to the development of research hypotheses, and lead to the derivation of analytical results. The goal of model driven analysis is to create and test geospatial process models. In general, model-driven analysis is an advanced knowledge area where previous experience with exploratory spatial data analysis would constitute a desired prerequisite. Visual tools for data analysis are covered in Knowledge Area: Cartography and Visualization (CV) and many of the fundamental principles required to ground data analysis techniques are introduced in Knowledge Area: Conceptual Foundations (CF). Image processing techniques are considered in Knowledge Area: Geospatial Data (GD). All of the methods described in this knowledge area are more or less sensitive to data error and uncertainty as covered in Unit GC8 Uncertainty and Unit GD6 Data quality. Mastery of the educational objectives outlined in this knowledge area requires knowledge and skills in mathematics, statistics, and computer programming.","name":"Analytical Methods","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM1-2","description":"Analytical capabilities of a GIS make use of spatial and non-spatial (attribute) data to answer questions and solve problems that are of spatial relevance. We now make a distinction between analysis (or analytical operations) and analytical models (often referred to as “modelling”). And by analysis we actually mean only a subset of what is usually implied by the term: we do not specifically deal with advanced statistical analysis (such as cluster detection or geostatistics).\r\n\r\nAnalysis of spatial data can be defined as computing new information to provide new insights from existing spatial data. Consider an example from the domain of road construction. In mountainous areas, this is a complex engineering task with many cost factors, including the number of tunnels and bridges to be constructed, the total length of the tarmac, and the volume of rock and soil to be moved. GISs can help to compute such costs on the basis of an up-to-date digital elevation model and a soil map. The exact nature of the analysis will depend on the application requirements, but computations and analytical functions can operate on both spatial and non-spatial data.","name":"Analytical approaches","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM1","description":"Geospatial data analysis has foundations in many different disciplines. As a result, there are many different schools of thought or analytical approaches including spatial analysis, spatial modeling, geostatistics, spatial econometrics, spatial statistics, qualitative analysis, map algebra, and network analysis. This unit compares and contrasts these approaches.","name":"Foundations of analytical methods","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM10-1","description":"Difficulties in dealing with large spatial databases, especially those arising from spatial heterogeneity and data quality issues.","name":"Problems of large spatial databases","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM10-2","description":"Data mining knows a variety of approaches, such as cluster analysis, analytical reasoning, association, prediction, etc.","name":"Data mining approaches","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM10-3","description":"Knowledge discovery involves the identification of useful patterns in spatial databases using techniques of data mining, trend analysis, etc.","name":"Knowledge discovery","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM10","description":"Algorithms have been developed to scan and search through extremely large data sets in order to find patterns within the data. These data mining and knowledge discovery techniques have been expanded to the spatial case. Legal and ethical concerns associated with such practices are considered in Knowledge Areas GS GIS and T and Society and OI Organizational and Institutional Aspects.","name":"Data mining","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM11-1","description":"A network is a connected set of lines representing some geographic phenomenon, typically to do with transportation. The “goods” transported can be almost anything: people, cars and other vehicles along a road network, commercial goods along a logistic network, phone calls along a telephone network, or water pollution along a stream/river network.\r\n\r\nDirect vs. Non-directed Networks\r\nA fundamental characteristic of any network is whether the network lines are considered to be directed or not. Directed networks associate with each line a direction of transportation; undirected networks do not. In the latter, the “goods” can be transported along a line in both directions. We discuss here vector network analysis, and assume that the network is a set of connected line features that intersect only at the lines’ nodes, not at internal vertices. (But we do mention under- and overpasses.)\r\n\r\nPlanar vs. Non-Planar Networks\r\nFor many applications of network analysis, a planar network, i.e. one that can be embedded in a two-dimensional plane, will do the job. Many networks are naturally planar, such as stream/river networks. A large-scale traffic network, on the other hand, is not planar: motorways have multi-level crossings and are constructed with underpasses and overpasses. Planar networks are easier to deal with computationally, as they have simpler topological rules. Not all GISs accommodate non-planar networks, or they can only do so using “tricks”. These tricks may involve the splitting of overpassing lines at the intersection vertex and the creation of four lines from the two original lines. Without further attention, the network will then allow one to make a turn onto another line at this new intersection node, which in reality would be impossible. In some GISs we can allocate a cost for turning at a node—see our discussion on turning costs below—and that cost, in the case of the overpass trick, can be made infinite to ensure it is prohibited. But, as mentioned, this is a work around to fit a non-planar situation into a data layer that presumes planarity. The above is a good illustration of geometry not fully determining the network’s behaviour. Additional application-specific rules are usually required to define what can and cannot happen in the network. Most GISs provide rule-based tools that allow the definition of these extra application rules.","name":"Networks defined","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM11-2","description":"Identifying and listing all elements does not describe a system in full. There may be many different ways in which elements may be connected or related to each other. The interactions, relationships between elements are essential to describe a system.\r\n\r\nRelationships between elements can be described by two types of flows:\r\nflows of material, and flows of information.\r\n\r\nMaterial flows connect elements between which there is an exchange of some substance. This can be some kind of material (water, food, cement, biomass, etc.), energy (light, heat, electricity, etc.), money, etc. It is something that can be measured and tracked. Also if an element is a donor of this substance the amount of substance in this element will decrease as a result of the exchange, while at the same time the amount of this substance will increase in the receptor element. There is always a mass, or energy conservation law in place. Nothing appears from nothing, and nothing can disappear to nowhere.\r\n\r\nThe second type of exchange is with an information flow. In this case element A gets information from element B. Element B at the same time may have no information about element A. Even when element A gets information about B, B does not lose anything. Information can be about the state of an element, about the quantity that it contains, about its presence or absence, etc. Information flows can be used to describe rules and policies. Information flows can modify the rates of flow between elements, they can switch certain processes and interactions on and off. But the process through which policies, interventions and norms for action are established, and could for example define the values of such information flows, are themselves the result of social interaction between relevant stakeholders from public, private or civil society.\r\n\r\nThe simplest is to acknowledge the existence of a relationship between certain elements, like this is done in a graph. In a graph a node presents an element and a link between any two nodes shows that these two elements are related. However there is no evidence of the direction of the relationship: we do not distinguish between the element x influencing element y or vice versa. This relationship can be further specified by an oriented graph that shows the direction of the relationship between elements. An element can be also connected to itself, to show that its behaviour depends on its state. We can further detail the description by identifying whether element x has a positive or negative effect on element y.\r\n\r\nWith networks, interesting questions arise that have to do with connectivity and network capacity. These relate to applications such as traffic monitoring and watershed management. With network elements—i.e. the lines that make up the network—extra values are commonly associated, such as distance, quality of the link or the carrying capacity.","name":"Graph theoretic descriptive measures of networks","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM11-3","description":"Optimal-path finding techniques are used when a least-cost path between two nodes in a network must be found. The two nodes are called origin and destination. The aim is to find a sequence of connected lines to traverse from the origin to the destination at the lowest possible cost.\r\n\r\nIn Optimal-path finding, the cost function can be simple: for instance, it can be defined as the total length of all lines of the path. The cost function can also be more elaborate and take into account not only length of the lines but also their capacity, maximum transmission (travel) rate and other line characteristics, for instance to obtain a reasonable approximation of travel time. There can even be cases in which the nodes visited add to the cost of the path as well. These may be called turning costs, which are defined in a separate turning-cost table for each node, indicating the cost of turning at the node when entering from one line and continuing on another. This is illustrated in Figure 1 of the examples.\r\n\r\nProblems related to optimal-path finding may require ordered optimal path finding or unordered optimal-path finding. Both have as an extra requirement that a number of additional nodes need to be visited along the path. In ordered optimal-path finding, the sequence in which these extra nodes are visited matters; in unordered optimal-path finding it does not.","name":"Least-cost shortest path","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM11-4","description":"There are phenomena that do not spread in all directions, but move or “flows” along a given, least-cost path, determined by characteristics of local terrain. The typical case arises when we want to determine drainage patterns in a catchment area: rain water “chooses” a way to leave the area. \r\n\r\nWe can illustrate the principles involved in this typical case with a simple elevation raster. For each cell in that raster, the steepest downward slope to a neighbour cell is computed and its direction is stored in a new raster. This computation determines the elevation difference between the cell and the neighbour cell and it takes into account cell distance - 1 for neighbour cells in N–S or W–E direction, 2 for cells in a NE–SW or NW–SE direction. From among its eight neighbour cells, it picks the one with the steepest path to it. The directions thus obtained in an output raster are encoded in integer values, which can be called the flow-direction raster. From this raster, the GIS can compute the accumulated flow-count raster, a raster that for each cell indicates how many cells have their water flow into that cell.\r\n\r\nCells with a high accumulated flow count represent areas of concentrated flow and may, thus, belong to a stream. By using some appropriately chosen threshold value in a map algebra expression, we may decide whether they do or not. Cells with an accumulated flow count of zero are local topographic highs and can be used to identify ridges.","name":"Flow modeling","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM11-5","description":"The Classic Transportation Problem considers minimizing the cost of getting an object or subject from origin to destination.","name":"The Classic Transportation Problem","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM11-6","description":"Classic network problems are examples of networking problems such as the Traveling Salesman Problem and the Chinese Postman Problem that need graph algorithms to be solved.","name":"Other classic network problems","selfAssesment":"GI-N2K
"},{"code":"AM11-7","description":"Accessibility is the extend in which it is difficult/easy to reach a location or object.","name":"Accessibility modeling","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM11","description":"Network analysis encompasses a wide range of procedures, techniques, and methods that allow for the examination of phenomena that can be modeled in the form of connected sets of edges and vertices. Such sets are termed a network or a graph, and the mathematical basis for network analysis is known as graph theory. Graph theory contains descriptive measures and indices of networks such as connectivity, adjacency, capacity, and flow as well as methods for proving the properties of networks. Networks have long been recognized as an efficient way to model many types of geographic data, including transportation networks, river networks, and utility networks electric, cable, sewer and water, etc. to name just a few. The data structures to support network analysis are covered in Unit DM4 Vector and object data models.","name":"Network analysis","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM12-1","description":"The modeling of problems in a formal language, working in a solution space and applying constraints.","name":"Operations research modeling and location modeling principles","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM12-2","description":"A formal programming method to support operational research in which linear constraints are applied.","name":"Linear programming","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM12-3","description":"A formal programming method to support operational research in which variables are constrained to integers.","name":"Integer programming","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM12-4","description":"Location-allocation modeling involves the determination of locations by minimizing the distance between object/subjects in space, such as between customers and facilities.","name":"Location-allocation modeling and p-median problems","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM12","description":"A wide variety of optimization techniques are now solvable within the GIS and T domain. Operations research is a branch of mathematics practiced in the allied fields of business and engineering. New models and software tools allow for the solution of transportation routing, facility location, and a host of other location-allocation modeling problems.","name":"Optimization and location-allocation modeling","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM13-1","description":"The effects such as the loss of data quality and data integrity that are the results of data transformations.","name":"Impacts of transformations","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM13-2","description":"A data model is an abstract model that organizes elements of data and standardizes how they relate to one another and to the properties of real-world entities. The term data model can refer to two distinct but closely related concepts. In relation to the field of geoinformation the term data model refers to the set of concepts used in defining such formalizations as entities, attributes, relations, tables which is implemented by a mathematical construct for representing geographic objects or surfaces as data. There are two most frequently used data models, which are vector and raster. For example, the vector data model represents geography as collections of points, lines and polygons and more complex structures crated from these three. The raster data model represent geography as cell matrices that store numeric values. Among these two data models we also stand out data formats in which data sets can be stored. File format is a standard of encoding geographical information into a computer file. There are the following basic file formats for encoding data:\r\nFor vectors:\r\n-\tShapefile\r\n-\tGeography Markup Language (GML)\r\n-\tXYZ Point Cloud\r\n-\tGeoJSON\r\n-\tGeoMedia\r\n-\t\r\nFor rasters:\r\n-\tGeoTIFF\r\n-\tIMG\r\n-\tJPEG2000\r\n-\tEsri grid\r\nThe GIS projects often require the conversion of the data formats. Data conversion is the process of moving data from one format to another, whether it is from one data model to another or from one data format to another. Data conversion is a complex process which is not only associated with changing the binary format of the file but also requires changing the structure of the data. For example, the GML data format always comes with an UML diagram, which is necessary to convert attributes stored in GML structure for example to a table of contest in a shapefile data format. In a well-managed GIS project it is important to store data in specific data model or data format. It is sometimes dictated by software capabilities and another times by team’s technical capabilities. With large amounts of geographic data used in the project it is more cost-effective to convert the data from one format to another than re-create it.","name":"Data model and format conversion","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM13-3","description":"Interpolation is used to create a GIS layer out of point observations on a continuous variable. The reason for doing this could be manifold: for visualization purposes, for making a proper reference with other data, or for making a combination of different layers.","name":"Interpolation","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM13-4","description":"Any vector data containing point, polyline, polygon can be converted into the raster dataset and vice versa. The vector data can be stored in shapefiles, databases or various others GIS file formats. The raster data are made of pixels or grid calls and can be represented by the discrete - categorical data (e.g. land cover map) or non-discrete - continuous data (e.g. satellite images, surface data). The process of conversion of vector to raster data is called rasterization. The vector to raster conversion requires the following parameters: the field value from the attribute table used to assign values to the output raster, the pixel size for the output raster, the output raster format (i.e. geotiff, img) and optionally the method of assigning values of point, polyline or polygon to the call raster, i.e. maximum length or area, cell centre. The output of the rasterised vector looks like a gridded version of the vector and it depends on the grid cell size. The process of vectorisation refers to the conversion of raster to vector dataset. The raster dataset can be converted to vector point, polyline or polygon. In order to convert raster to vector the following parameters should be provided: attribute field of the input raster dataset which will become an attribute in the output vector class, determining if the output polygon or polyline will be smoothed into simpler shapes or conform to the input raster's cell edges (stair stepping). For each raster pixel or grid cell a point will be created at the centre of the cell. The non-discrete continuous raster data have to converted to the categorical data type before converting to vector data. The conversion of vector to raster and raster to vector degrade the data to some extent causing loss of details, accuracy, and changing the original data.","name":"Vector-to-raster and raster-to-vector conversions","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM13-5","description":"Raster resampling refers to change of spatial resolution (increasing or decreasing) of the raster dataset. The resampling process calculates the new pixel values from the original digital pixel values in the uncorrected image. There are three common methods for resampling: nearest neighbour, bilinear interpolation, and cubic convolution. The nearest neighbour resampling uses the digital value from the pixel in the original image which is nearest to the new pixel location in the corrected image. This is the fastest interpolation method, which is primarily applied for discrete (categorical) raster data as it does not change the value of the pixel, but may result in some pixel values being duplicated while others are lost. Bilinear interpolation resampling takes a weighted average of four pixels in the original image nearest to the new pixel location. The averaging process alters the original pixel values and creates entirely new digital values in the output image. It is recommended for continuous data and it cause some smoothing of the data. Cubic convolution resampling is based on calculation of a distance weighted average of a block of sixteen pixels from the original image which surround the new output pixel location. As with bilinear interpolation, this method results in completely new pixel values. However, the last two methods both produce images which have a much sharper appearance and avoid the blocky appearance of the nearest neighbour method. The disadvantage of the Cubic method is that its requires more processing time.","name":"Raster resampling","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM13-6","description":"Users of geoinformation often need transformations from a particular 2D coordinate system to another system. This includes the transformation of polar coordinates into Cartesian map coordinates, or the change of map projection - transformation from one 2D Cartesian (x, y) system of a specific map projection into another 2D Cartesian (x′, y′) system of a defined map projection. This transformation is based on relating the two systems on the basis of a set of selected points whose coordinates are known in both systems, such as ground control points or common points such as corners of houses or road intersections. Image and scanned data are usually transformed by this method. The transformations may be conformal, affine, polynomial or of another type, depending on the geometric errors in the data set. A datum transformation involves the change of the horizontal datum which is often accompanied with a change of map projection.","name":"Coordinate transformations","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM13","description":"GIS is a cyclical rather than a linear system, unlike computer aided drafting (CAD) and computer assisted cartographic systems. Changes in projection, grid systems, data forms, and formats take place during the modeling process for which GIS was designed. Many non-analytical manipulations are necessary to accommodate the analytical power of the GIS. The manipulations of spatial and spatio-temporal data involve two general classes of operation: 1.\tTheir transformation into formats that facilitate subsequent analysis (see this Unit AM13), 2.\tGeneralization and aggregation that affect the accuracy and integrity of the data used for analysis (see Unit AM14) Other knowledge areas have identified different forms of data structures, data models, projections, and other forms of geospatial data representation. These differences present both opportunities and challenges for analysis and modeling. The ability to transform one representation to another, in a manner that maintains the integrity of the information as much as possible, can enhance the analysis and visualization of geospatial data. The raster and vector data models are described in Units DM3 Tesselation data models and DM4 Vector and object data models. The principles of coordinate systems, datums, and projections are also considered in Knowledge Area GD: Geospatial Data","name":"Representation transformation","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM14-1","description":"In the practice of spatial data handling, one often comes across questions like “What is the resolution of the data?” or “At what scale is your data set?” Now that we have moved firmly into the digital age, these questions sometimes defy an easy answer. Map scale can be defined as the ratio between the distance on a printed map and the distance of the same stretch in the terrain.\r\n\r\nA 1:50,000 scale map means that 1 cm on the map represents 50,000 cm (i.e. 500 m) in the terrain. “Large-scale” means that the ratio is relatively large, so typically it means there is much detail to see, as on a 1:1000 printed map. “Small-scale”, in contrast, means a small ratio, hence less detail, as on a 1:2,500,000 printed map.\r\nDigital spatial data, as stored in a GIS, are essentially without scale: scale is a ratio notion associated with visual output, such as a map or on-screen display, not with the data that was used to produce the map or display. When digital spatial data sets have been collected with a specific map-making purpose in mind, and all maps have been designed to use one single map scale, for instance 1:25,000, we may assume that the data carries the characteristic of “a 1:25,000 digital data set.”\r\n\r\nThere is a relationship between the effectiveness of a map for a given purpose and the map’s scale. The Public Works department of a city council cannot use a 1:250,000 map for replacing broken sewer pipes, and the map of Figure 1 cannot be reproduced at scale 1:10,000.\r\n\r\nMaps that show much detail of a small area are called large-scale maps. Scale indications on maps can be given verbally, such as “one-inch-to the- mile”, or as a representative fraction like 1:200,000,000 (1 cm on the map equals 200,000,000 cm (or 2000 km) in reality), or by a graphic representation such as the scale bar. The advantage of using scale bars in digital environments is that its length also changes when the map is zoomed in, or enlarged, before printing. Sometimes it is necessary to convert maps from one scale to another, which may lead to problems of cartographic generalization.\r\n\r\nSpatial and temporal scales can not only be attached to processes, but also to observations. An example is given below, which summarizes the spatial and temporal scales of a few well-known Earth observation systems.\r\n\r\nScales of RS observations\r\nSensor Spatial scale\t Temporal scale\r\nMeteosat\t Hemisphere\t 15 minutes\r\nNOAA-AVHRR\t 3000 km\t daily\r\nLandsat TM\t 180 km\t 16 days\r\nSpot\t 60 km\t 26 days (pointable)","name":"Scale and generalization","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM14-2","description":"Techniques that support the generalisation of map content when changing to smaller map scales. These include line simplification, object selection, etc.","name":"Approaches to point, line, and area generalization","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM14-3","description":"Classification is a technique for purposely removing detail from an input data set in the hope of revealing important patterns (of spatial distribution). In the process, we produce an output data set, so that the input set can be left intact. This output set is produced by assigning a characteristic value to each element in the input set, which is usually a collection of spatial features that could be raster cells or points, lines or polygons. If the number of characteristic values in the output set is small in comparison to the size of the input set, we have classified the input set.\r\n\r\nThe input data set may, itself, have been the result of a classification. In such cases we refer to the output data set as a reclassification. For example, we may have a soil map that shows different soil type units and we would like to show the suitability of units for a specific crop. In this case, it is better to assign to the soil units an attribute of suitability for the crop. Since different soil types may have the same crop suitability, a classification may merge soil units of different type into the same category of crop suitability.\r\n\r\nIn classification of vector data, there are two possible results. In the first, the input features may become the output features in a new data layer, with an additional category assigned. In other words, nothing changes with respect to the spatial extents of the original features. Figure a of Examples illustrates this first type of output. A second type of output is obtained when adjacent features of the same category are merged into one bigger feature. Such a post-processing function is called spatial merging, aggregation or dissolving. An illustration of this second type is found in Figure b of Examples. Observe that this type of merging is only an option in vector data, as merging cells in an output raster on the basis of a classification makes little sense. Vector data classification can be performed on point sets, line sets or polygon sets; the optional merge phase only makes sense for lines and polygons.\r\n\r\nUser-controlled classifications require a classification table or user interaction. GIS software can also perform automatic classification, in which a user only specifies the number of classes in the output data set. The system automatically determines the class break points. The two main techniques of determining break points being used are the equal interval technique and the equal frequency technique.\r\n\r\nEqual Interval Technique\r\nThe minimum and maximum values vmin and vmax of the classification parameter are determined and the (constant) interval size for each category is calculated as (vmax - vmin) ∕ n, where n is the number of classes chosen by the user. This classification is useful in that it reveals the distribution pattern, as it determines the number of features in each category.\r\n\r\nEqual Frequency Technique\r\nThis technique is also known as quantile classification. The objective is to create categories with roughly equal numbers of features per category. The total number of features is determined first, then, based on the required number of categories, the number of features per category is calculated. The class break points are then determined by counting off the features in order of classification parameter value.","name":"Classification and transformation of attribute measurement levels","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM14","description":"All geospatial data are generalized. Even the most detailed data represent only subsets of reality. Furthermore, data are further generalized for purposes of mapping, visualization, and efficient storage. A variety of generalization techniques have been developed to facilitate this process. All are scale dependent. Aggregation is one form of generalization that transforms large numbers of individual objects into summarized groups. This unit is concerned with the nature of these procedures and their implications for professional practice. Generalization is an important part of cartography (and is therefore discussed conceptually in Unit CV2 Data considerations), but is also a transformation common to many GIS procedures.","name":"Generalization and aggregation","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM2-1","description":"Set theory is based on describing collections of members within sets. The Boolean membership function is binary, i.e. an element is either a member of the set (membership is true) or it is not a member of the set (membership is false). Such a membership notion is well-suited to the description of spatial features such as land parcels for which no ambiguity is involved and an individual ground truth sample can be judged to be either correct or incorrect. As Burrough and Frank (1996) note, increasingly, people are beginning to realize that the fundamental axioms of simple binary logic present limits to the way we think about the world. Not only in everyday situations, but also in formalized thought, it is necessary to be able to deal with concepts that are not necessarily true or false, but that operate somewhere in between. Since its original development by Zadeh (1965), there has been considerable discussion of fuzzy, or continuous, set theory as an approach for handling imprecise spatial data. In GIS, fuzzy set theory appears to have two particular benefits: the ability to handle logical modelling (map overlay) operations on inexact data; and the possibility of using a variety of natural language expressions to qualify uncertainty. Unlike Boolean sets, fuzzy or continuous sets have a membership function, which can assign to a member any value between 0 and 1.","name":"Set theory","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM2-2","description":"The most common operator for defining queries in a relational database is the language SQL, which stands for Structured Query Language.\r\n\r\nA spatial DBMS provides support for geographic coordinate systems and transformations. It will also provide storage of the relationships between features, including the creation and storage of topological relationships. As a result, one is able to use functions for “spatial query” (exploring spatial relationships). To illustrate, a spatial query using SQL to find all the Thai restaurants within 2 km of a given hotel would look like:\r\n\r\nSELECT R.Name\r\nFROM Restaurants AS R,\r\nHotels as H\r\nWHERE R.Type = Thai AND\r\nH.name = Hilton AND\r\nIntersect(R.Geometry, Buffer(H.Geometry, 2))\r\n\r\nThe Intersect command creates a spatial join between restaurants and hotels. The Geometry column carries the spatial data. It is likely that in the near future all spatial data will be stored directly in spatial databases.","name":"Structured Query Language (SQL) and attribute queries","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM2-3","description":"When exploring a spatial data set, the first thing one usually wants to do is select certain features, to (temporarily) restrict the exploration. Such selections can be made on geometric/spatial grounds or on the basis of attribute data associated with the spatial features. \r\n\r\nSelection conditions on attribute values can be combined using logical connectives such as AND, OR and NOT. Other techniques of selecting features can also usually be combined. Any set of selected features can be used as the input for a subsequent selection procedure. This means, for instance, that we can select all medical clinics first, then identify roads within 200 m of them, then select from those only the major roads, then select the nearest clinics to these remaining roads as the ones that should receive our financial support for maintenance. In this way, we are combining various techniques of selection.\r\n\r\nInteractive Spatial Selection\r\nIn interactive spatial selection, one defines the selection condition by pointing at or drawing spatial objects on the screen display, after having indicated the spatial data layer(s) from which to select features. The interactively defined objects are called the selection objects; they can be points, lines, or polygons. The GIS then selects the features in the indicated data layer(s) that overlap (i.e. intersect, meet, contain, or are contained in;) with the selection objects. These become the selected objects.\r\nInteractive spatial selection answers questions like “What is at …?”\r\n\r\nA spatial DBMS provides support for geographic coordinate systems and transformations. It will also provide storage of the relationships between features, including the creation and storage of topological relationships. As a result, one is able to use functions for “spatial query” (exploring spatial relationships). To illustrate, a spatial query using SQL to find all the Thai restaurants within 2 km of a given hotel would look like:\r\n\r\nSELECT R.Name\r\nFROM Restaurants AS R,\r\nHotels as H\r\nWHERE R.Type = Thai AND\r\nH.name = Hilton AND\r\nIntersect(R.Geometry, Buffer(H.Geometry, 2))\r\n\r\nThe Intersect command creates a spatial join between restaurants and hotels. The Geometry column carries the spatial data. It is likely that in the near future all spatial data will be stored directly in spatial databases.","name":"Spatial queries","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM2","description":"Attribute and spatial query operations are core functionality in any GIS and they are often considered to be the most basic form of analysis.","name":"Query operations and query languages","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM3-1","description":"In a 2D polar coordinate system points can be described with coordinates. Another way of defining a point in a plane is by using polar coordinates. This is the distance d from the origin to the point concerned and the angle α between a fixed (or zero) direction and the direction to the point. The angle α is called azimuth or bearing and is measured in a clockwise direction. It is given in angular units while the distance d is expressed in length units. \r\n\r\nDistance also plays a role in computations on networks, comprising a different set of analytical functions in GISs. Here, the network may consist of roads, public transport routes, high-voltage power lines, or other forms of transportation infrastructure. Analysis of networks may entail shortest path computations (in terms of distance or travel time) between two points in a network for routing purposes. Other forms are to find all points reachable within a given distance or duration from a start point for allocation purposes, or determination of the capacity of the network for transportation between an indicated source location and sink location.\r\n\r\nIn raster images, the distance function applied is the Pythagorean distance between the cell centres. The distance from a non-target cell to the target is the minimal distance one can find between that non-target cell and any target cell.","name":"Distances and lengths","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM3-2","description":"In a 2D polar coordinate system points can be described with coordinates. Another way of defining a point in a plane is by using polar coordinates. This is the distance d from the origin to the point concerned and the angle α between a fixed (or zero) direction and the direction to the point. The angle α is called azimuth or bearing and is measured in a clockwise direction. It is given in angular units while the distance d is expressed in length units.\r\n\r\nBearings are always related to a fixed direction (initial bearing) or a datum line. In principle, this reference line can be chosen freely. Three different, widely used fixed directions are: True North, Grid North and Magnetic North. The corresponding bearings are true (or geodetic) bearings, grid bearings and magnetic (or compass) bearings, respectively.\r\n\r\nIn raster images, direction is determined by the orientation of the neighboring pixels.","name":"Direction","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM3-3","description":"The representation of geographic objects is most naturally supported with vectors. After all, objects are identified by the parameters of location, shape, size and orientation, and many of these parameters can be expressed in terms of vectors. We can define features within the topological space that are easy to handle and that can be used as representations of geographic objects. These features are called simplices as they are the simplest geometric shapes of some dimension: point (0-simplex), line segment (1-simplex), triangle (2-simplex), and tetrahedron (3-simplex). When we combine various simplices into a single feature, we obtain a simplicial complex. When area objects are stored using a vector approach, the usual technique is to apply a boundary model. This means that each area feature is represented by some arc/node structure that determines a polygon as the area’s boundary. A polygon representation for an area object is another example of a finite approximation of a phenomenon that may have a curvilinear boundary in reality. In images, the shape of objects often helps us to identify them (built-up areas, roads and railroads, agricultural fields, etc.).","name":"Shape","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM3-4","description":"When area objects are stored using a vector approach, the usual technique is to apply a boundary model. This means that each area feature is represented by some arc/node structure that determines a polygon as the area’s boundary. A polygon representation for an area object is another example of a finite approximation of a phenomenon that may have a curvilinear boundary in reality.\r\nCommon sense dictates that area features of the same kind are best stored in a single data layer, represented by mutually non-overlapping polygons. This results in an application-determined (i.e. adaptive) partition of space. If the object has a fuzzy boundary, a polygon is an even worse approximation, even though potentially it may be the only one possible. Clearly, we expect additional data to accompany the area data. Such information could be stored in database tables.\r\n\r\nA simple but naïve representation of area features would be to list for each polygon the list of lines that describes its boundary. Each line in the list would, as before, be a sequence that starts with a node and ends with one, possibly with vertices in between. As the same line makes up the boundary from the two polygons, this line would be stored twice in the above representation, namely once for each polygon. This is a form of data duplication—known as data redundancy—which is (at least in theory) unnecessary, although it remains a feature of some systems. Another disadvantage of such polygon-by-polygon representations is that if we want to identify the polygons that border the bottom left polygon, we have to do a complicated and time-consuming search analysis comparing the vertex lists of all boundary lines with that of the bottom left polygon. For just a few polygons, this is fine, but in a data set with 5000 polygons, and perhaps a total of 25,000 boundary lines, this becomes a tedious task, even with the fastest of computers.","name":"Area","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM3-5","description":"Proximity computations are specific neighbourhood functions. They evaluate the characteristics of an area surrounding a feature’s location. A neighbourhood function “scans” the neighbourhood of the given feature(s), and performs a computation on it (them).\r\n\r\nExamples of proximity computations are: (1) Buffer zone generation (or buffering) is one of the best-known neighbourhood functions. It determines a spatial envelope (buffer) around a given feature or features. The buffer created may have a fixed width or a variable width that depends on characteristics of the area. (2) Thiessen Polygon generation.\r\n\r\nDistance decay functions describe the effect of the reduced influence when the distance between two locations increases.","name":"Proximity and distance decay","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM3-6","description":"Adjacency is the meet relationship as a topological property of a geographic object in relation ship with another. The adjacency operator identifies those features that share boundaries and, therefore, applies only to line and polygon features.\r\nThis meet relationship is invariant under a continuous transformation and are referred to as a topological mapping.","name":"Adjacency and connectivity","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM3","description":"For simple data exploration, GIS offers many basic geometric operations that help in extracting meaning from sets of data or for deriving new data for further analysis. Concepts on which these operations are based are addressed in Domains of geographic information and Relationships.\r\n\r\nWe can, for instance, measure angles on a map and use these for navigation in the real world, or for setting out a designed physical infrastructure. Or if, instead of a conformal projection such as UTM, we use an equivalent projection, we can determine the size of a parcel of land from the map—irrespective of where the parcel is on the map and at which elevation it is on the Earth.","name":"Geometric measures","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM4-1","description":"The reclassifications tools are used to change or reclassify the values. Reclassification of vector data involves the attributes of features in the feature attribute table, on the other hand reclassification of raster data involves the grid cell values to produce a new raster data layer. Reclassification can be used for data simplification and measurement scale change. We can adjust the data for more appropriate analysis by grouping the values and changing them. The reclassification tool can also be used to remove specific values from analysis.\r\nThe Select by location tool lets you select features by how they relate to other features in another layer. Selected features are based on their location. You can select features that are near or overlap the features. Most frequently used methods are intersect, within a distance, within, completely within, contain… Features can be selected in the same or other layers.\r\nThe Select by attributes tool lets you select features that match the selection criteria. With providing a selection criteria, matching features are selected. We can provide a complex selection criteria.","name":"Reclassification and selection operations","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM4-2","description":"Buffer analysis is one form of basic spatial analysis. It takes the vector representation (point, line, or polygon) of a real-world feature, and then creates a buffer zone based on a defined distance from the feature’s border. Thus, the created buffer zone is an area whose boundary always has the same distance to the input vector feature, e.g. the buffer zone for a point feature is a circle. Real-world examples for buffer zones could be protected areas along rivers or around nature conservation areas, or represent a simple proximity analysis. In the latter case, the buffer analysis is usually the first step of the analysis, followed by an overlay of the buffer zone with the target features to find those target features within the buffer zone, and thus within a certain distance of the original feature. Usually, the buffer zone extends outwards from the feature, but polygons can also have inner buffer zones. If the buffer zones from multiple features overlap, the analyst can decide to leave the individual boundaries of the buffer zones intact, or to dissolve them, i.e. merging the overlapping buffer zones into one larger buffer zone. The size of the buffer zone, i.e. the distance of its boundary from the original feature’s boundary, can be based on an uniform numerical value and associated spatial unit, but often, it is based on an attribute value (numerical or class) of the feature. Conceptually, buffering using raster representations of real-world features is similar a proximity analysis with a regular grid of square polygons: Departing from raster cells that form the area to be buffered, all raster cells that fall within the designated distance (overlay) from the buffer zone. With buffer analysis being a basic analytical operation, practically every GIS and many other analysis tools provide this functionality.","name":"Buffers","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM4-3","description":"Overlay functions is one of the most frequently used functions in a GIS application. They combine two (or more) spatial data layers, comparing them position by position and treating areas of overlap - and of non-overlap - in distinct ways.\r\n\r\nStandard overlay operators take two input data layers and assume that they are georeferenced in the same system and that they overlap in the study area. If either of these requirements is not met, the use of an overlay operator is pointless. The principle of spatial overlay is to compare the characteristics of the same location in both data layers and to produce a result for each location in the output data layer. The specific result to produce is determined by the user. It might involve a calculation or some other logical function to be applied to every area or location. With raster data, as we shall see, these comparisons are carried out between pairs of cells, one from each input raster. With vector data, the same principle of comparing locations applies but the underlying computations rely on determining the spatial intersections of features from each input layer.\r\n\r\nVector overlay operators are useful but geometrically complicated, and this sometimes results in poor operator performance. Raster overlays do not suffer from this disadvantage, as most of them perform their computations cell by cell, and thus they are fast. GISs that support raster processing - as most do - usually have a language to express operations on rasters. These languages are generally referred to as map algebra or, sometimes, raster calculus. They allow a GIS to compute new rasters from existing ones, using a range of functions and operators. Unfortunately, not all implementations of map algebra offer the same functionality.","name":"Overlay","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM4-4","description":"Neighbourhood functions evaluate the characteristics of an area surrounding a feature’s location. A neighbourhood function “scans” the neighbourhood of the given feature(s), and performs a computation on it (them). Examples of proximity computations are: (1) Buffer zone generation (or buffering) is one of the best-known neighbourhood functions. It determines a spatial envelope (buffer) around a given feature or features. The buffer created may have a fixed width or a variable width that depends on characteristics of the area. (2) Thiessen Polygon generation. For raster images: (3) Computation of diffusion (4) Flow computation.\r\n\r\nFor instance, our target might be a medical clinic. Its neighbourhood could be defined as:\r\n\r\nan area within a radius of 2 km distance as the crow flies; or\r\nan area within 2 km travelling distance; or\r\nall roads within 500 m travelling distance; or\r\nall other clinics within 10 minutes travelling time;\r\nall residential areas for which the clinic is the closest clinic.\r\n\r\nFinally, in the third step we indicate what it is we want to discover about the phenomena that exist or occur in the neighbourhood. This might simply be its spatial extent, but it might also be statistical information such as:\r\n\r\nhow many people live in the area;\r\nwhat is their average household income;\r\nare any high-risk industries located in the neighbourhood.\r\n\r\nThese are typical questions in an urban setting. When our interest is more in natural phenomena, different examples of locations, neighbourhoods and neighbourhood characteristics arise.\r\n\r\nThe principle in this case is to find out the characteristics of the vicinity, here called neighbourhood, of a location. After all, many suitability questions, for instance, depend not only on what is at a location but also on what is near the location. Thus, the GIS must allow us “to look around locally”. To perform neighbourhood analysis, we must:\r\n\r\n1. state which target locations are of interest to us and define their spatial extent;\r\n2. define how to determine the neighbourhood for each target; and\r\n3. define which characteristic(s) must be computed for each neighbourhood. \r\n\r\nSince raster data are the more commonly used in this case, neighbourhood characteristics often are obtained via statistical summary functions that compute values such as the average, minimum, maximum and standard deviation of the cells in the identified neighbourhood.\r\n\r\nTo select target locations, one can use the selection techniques. To obtain characteristics from an eventually-to-be identified neighbourhood, the same techniques apply. So what remains to be discussed here is the proper determination of a neighbourhood. One way of determining a neighbourhood around a target location is by making use of the geometric distance function. Geometric distance does not take into account direction, but certain phenomena can only be studied by doing so. Think of the spreading of pollution by rivers, groundwater flow or prevailing weather systems.\r\n\r\nDiffusion functions are based on the assumption that the phenomenon in question spreads in all directions, though not necessarily equally easily in each direction. Hence it uses local terrain characteristics to compute local resistances to diffusion.","name":"Neighborhood analysis","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM4-5","description":"GISs that support raster processing - as most do - usually have a language to express operations on rasters. These languages are generally referred to as map algebra or, sometimes, raster calculus. They allow a GIS to compute new rasters from existing ones, using a range of functions and operators. Unfortunately, not all implementations of map algebra offer the same functionality. The discussion below is to a large extent based on general terminology; it attempts to illustrate the key operations using a logical, structured language. Again, the syntax often varies among different GIS software packages.\r\n\r\nWhen producing a new raster we must provide a name for it, and define how it is to be computed. This is done in an assignment statement of the following format:\r\n\r\nOutput raster name := Map algebra expression.\r\n\r\nThe expression on the right is evaluated by the GIS, and the raster in which it results is then stored under the name on the left. The expression may contain references to existing rasters, operators and functions; the format is made clear in each case. The raster names and constants that are used in the expression are called its operands. When the expression is evaluated, the GIS will perform the calculation on a pixel-by-pixel basis, starting from the first pixel in the first row and continuing through to the last pixel in the last row. In map algebra, there is a wide range of operators and functions available.\r\n\r\nArithmetic operators\r\nVarious arithmetic operators are supported. The standard ones are multiplication (×), division (/), subtraction (-) and addition (+). Obviously, these arithmetic operators should only be used on appropriate data values, and, for instance, not on classification values. Other arithmetic operators may include modulo division (MOD) and integer division (DIV). Modulo division returns the remainder of division: for instance, 10 MOD 3 will return 1 as 10 - 3 × 3 = 1. Similarly, 10 DIV 3 will return 3.\r\n\r\nOther operators are goniometric: sine (sin), cosine (cos), tangent (tan); and their inverse functions asin, acos, and atan, which return radian angles as real values. The assignment\r\n\r\nC1 := A + 10\r\n\r\nwill add a constant factor of 10 to all cell values of raster A and store the result as output raster C1. The assignment\r\n\r\nC2 := A + B\r\n\r\nwill add the values of A and B cell by cell, and store the result as raster C2. Finally, the assignment\r\n\r\nC3 := (A - B) ∕ (A + B) × 100\r\n\r\nwill create output raster C3, as the result of the subtraction (cell by cell, as usual) of B cell values from A cell values, divided by their sum. The result is multiplied by 100. This expression, when carried out on AVHRR channel 1 (red) and AVHRR channel 2 (near infrared) of NOAA satellite imagery, is known as the NDVI (Normalized Difference Vegetation Index). It has proven to be a good indicator of the presence of green vegetation.\r\n\r\nComparison and logical operators\r\n\r\nMap algebra also allows the comparison of rasters cell by cell. To this end, we may use the standard comparison operators (<, ⇐, =, >=, > and <>).\r\n\r\nA simple raster comparison assignment is\r\n\r\nC := A <> B.\r\n\r\nIt will store truth values—either true or false—in the output raster C. A cell value in C will be true if the cell’s value in A differs from that cell’s value in B. It will be false if they are the same. Logical connectives are also supported in many raster calculi. We have already seen the connectives of AND , OR and NOT in raster overlay operators. Another connective that is commonly offered in map algebra is exclusive OR (XOR). The expression a XOR b is true only if either a or b is true, but not both.\r\n\r\nConditional expressions\r\nThe comparison and logical operators produce rasters with the truth values true and false. In practice, we often need a conditional expression together with them that allows us to test whether a condition is fulfilled. The general format is:\r\n\r\nOutput raster := CON(condition, then expression, else expression).\r\n\r\nHere, condition stands for the condition tested, then the expression is evaluated if condition holds, and else the expression is evaluated if it does not hold. This means that an expression such as CON(A = “forest”, 10, 0) will evaluate to 10 for each cell in the output raster where the same cell in A is classified as forest. For each cell where this is not true, the else expression is evaluated, resulting in 0.\r\n\r\nOverlays using a decision table\r\nConditional expressions are powerful tools in cases where multiple criteria must be taken into account. A small example may illustrate this. Consider a suitability study in which a land use classification and a geological classification must be used. Domain expertise dictates that some combinations of land use and geology result in suitable areas, whereas other combinations do not. In our example, forests on alluvial terrain and grassland on shale are considered suitable combinations, while any others are not.\r\n\r\nWe could produce an output raster with a map algebra expression, such as\r\n\r\nSuitability := CON((Landuse = “Forest” AND Geology = “Alluvial”)\r\nOR (Landuse = “Grass” AND Geology = “Shale”),\r\n“Suitable”, “Unsuitable”)\r\n\r\nand consider ourselves lucky that there are only two “suitable” cases. In practice, many more cases must usually be covered and, then, writing up a complex CON expression is not an easy task.\r\n\r\nTo this end, some GISs accommodate setting up a separate decision table that will guide the raster overlay process. This extra table carries domain expertise and dictates which combinations of input raster-cell values will produce which output raster-cell value. This gives us a raster overlay operator using a decision table. The GIS will have supporting functions to generate the additional table from the input rasters and to enter appropriate values in the table.","name":"Map algebra","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM4","description":"This small set of analytical operations is so commonly applied to a broad range of problems that their inclusion in software products is often used to determine if that product is a true GIS. Concepts on which these operations are based are addressed in Domains of geographic information and Relationships.","name":"Basic analytical operations","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM5-1","description":"Point pattern analysis refers to the detection of patterns in a group of objects or subjects located in space. This may support the analysis of clusters in accidents, crime, etc.","name":"Point pattern analysis","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM5-2","description":"The probability density function is a method with which the probability density can be estimated for points in a raster space.","name":"Kernels and density estimation","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM5-3","description":"Spatial cluster analysis is the grouping of similar spatial objects into classes (clusters) in such a way that the objects within the cluster are highly similar compared to the objects outside of the cluster. Spatial clustering forms an important part of spatial data mining (Han et al., 2001; Miller et al., 2009). A wealth of spatial clustering tools are currently available with immense application potential. \r\n\r\nIn earth observation studies, spatial cluster techniques are often applied to identify zones with similar land covers by using earth observation data as input. An example of such a technique is the K-means classifier (Han et al., 2001; Miller et al., 2009). This unsupervised classification technique makes several clusters (e.g. land use classes) of which each pixel is assigned to the cluster with the nearest mean (Han et al., 2001). The amount of clusters can be freely defined by the user just as the input metrics to perform the classification. A drawback of the K-means classifier is the need to specify the amount of output clusters. Density Based Spatial Clustering (DBSC) overcomes this issue since it automatically defines the optimal amount of clusters (Miller et al., 2009). In this type of clustering technique, dense regions of objects (proximate objects) are clustered and separated from regions with low density (noise) (Han et al., 2001; Liu et al., 2012). Finally, another frequently applied spatial clustering technique is the hierarchical agglomerative clustering. This technique makes use of a dendrogram to decompose the data into clusters. The agglomerative approach is a bottom-up approach in which all objects are first grouped in a distinct cluster and while moving upward in the tree, pairs of clusters are merged based on some metrics (e.g. spatial proximity) (Han et al., 2001). \r\n\r\nSpatial cluster techniques have many advantages when dealing with big datasets which is often the case when working with earth observation data. Its simplicity to use and the fast increase of cloud computing power makes from it powerful techniques to extract spatial patterns out of the data. It allows to translate raw earth observation data into a more user-friendly data product by showing the spatial patterns of the data.","name":"Spatial cluster analysis","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM5-4","description":"Spatial interaction models describe the flow of people and goods in a geographical space, in which parameters such as friction and distance play a role.","name":"Spatial interaction","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM5-5","description":"Multidimensional attributes can be analyzed through multidimensional scaling and principle component analysis.","name":"Analyzing multidimensional attributes","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM5-7","description":"Multi-criteria evaluation is an important aspect of decision support operations, which appear in process models. Process models in the Earth sciences describe the evolution of geo(bio)physical surface properties in time, independently from remote sensing observations. Examples of such process models on various time scales are, for instance, numerical weather prediction models (NWPs), vegetation growth models, hydrological models, oceanographic models and climate models.\r\n\r\nObservation models and process models can supplement each other to enhance the quality of the interpretation of remote sensing data and to fill gaps in time that occur when observations are not possible owing to clouds or some other cause. Interactions are possible between observation models and process models with EO data and existing geographic information (GIS and ground measurements, supplemented with decision-support systems (DSSs)).\r\n\r\nThe process model provides information to the decision-support system, which supports management actions aimed at controlling/mitigating the process, based on an multi-criteria evaluation. A good example of this is a water management system, in which one might decide to allocate water for irrigation if the observed vegetation appears to suffer from drought stress.","name":"Multi-criteria evaluation","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM5-8","description":"Process models in the Earth sciences describe the evolution of geo(bio)physical surface properties in time, independently from remote sensing observations. Examples of such process models on various time scales are, for instance, numerical weather prediction models (NWPs), vegetation growth models, hydrological models, oceanographic models and climate models.\r\nProcess models in the geosciences usually rely on regular observations at many locations spread over a large area. Traditionally, these observations were mostly made in the field with a variety of instruments. Remote sensing techniques have tremendously increased the capability of spatial sampling and the consistency of the surface parameters measured. RS instruments are mostly sensitive to many physical properties of the surface, some of these may not belong to the set of properties that the user is interested in. Exceptions to this are the mapping of sea-surface temperature, laser altimetry and gravimetry, which are measurements of direct geophysical interest. In the majority of cases, however, there are only indirect relationships between what is observed with the instrument and the physical object properties of interest. In these cases, the use of observation models becomes an attractive option, since these models describe the relationships between all object properties relevant for the observation and the observed remote sensing data.","name":"Spatial process models","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM5","description":"Building on the basic geometric measures and analytical operations found in most GIS products, a broad range of additional analytical methods form the fundamental GIS toolkit.","name":"Basic analytical methods","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM6-2","description":"In rasters we use interpolation to determine the value of a pixel, based on its surrounding pixels. The main raster-based interpolation methods are nearest neighbour, bilinear, and bicubic interpolation. To determine the value of the centre pixel (bold), in nearest neighbour interpolation the value of the nearest original pixel is assigned, i.e. the value of the black pixel in this example. Note that the respective pixel centres, marked by small crosses, are always used for this process. In bilinear interpolation, a linear weighted average is calculated for the four nearest pixels in the original image. In bicubic interpolation a cubic weighted average of the values of 16 surrounding pixels (the black and all grey pixels) is calculated. Note that some software uses the terms “bilinear convolution” and “cubic convolution” instead of the terms introduced above.","name":"Interpolation of surfaces","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM6-3","description":"Continuous fields have a number of characteristics not shared by discrete fields. Since the field changes continuously, we can talk of slope angle, slope aspect and concavity/convexity of the slope.\r\n\r\nThese notions are not applicable to discrete fields. The discussions in this subsection use terrain elevation as the prototype example of a continuous field, but all aspects discussed are equally applicable to other types of continuous fields. Nonetheless, we regularly refer to the continuous field representation as a DEM, to conform with the most common situation.","name":"Surface features","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM6-4","description":"A viewshed is the area that can be “seen” (i.e. it is in the direct line-of-sight) from a specified target location. (Inter) visibility analysis can determine the area visible from a scenic lookout or the area that can be reached by a radar antenna, as well as assess how effectively a road or quarry will be hidden from view.","name":"Intervisibility","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM6-5","description":"Firction surfaces contain information on how difficult/easy it is for a phenomenon to move from one location on the surface to another.","name":"Friction surfaces","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM6","description":"There is a wide range of phenomena that can be studied using a set of techniques and tools that are designed to help understand the characteristics of continuous surface data. Applications of these techniques using terrain data include overland transport, flow, and siting tasks, but similar analyses can be conducted using non-tangible surfaces such as those of temperature, pressure and population density.\r\n\r\nThere are numerous examples that require more advanced computations on continuous field representations, such as:\r\n\r\nSlope angle calculation - the calculation of the slope steepness, expressed as an angle in degrees or percentages, for any or all locations.\r\n\r\nCalculating slope aspect - the calculation of the aspect (or orientation) of the slope in degrees (between 0 and 360∘), for any or all locations.\r\n\r\nSlope convexity/concavity calculation - defined as the change of the slope (negative when the slope is concave and positive when the slope is convex)—can be calculated as the second derivative of the field.\r\n\r\nSlope length calculation - with the use of neighbourhood operations, it is possible to calculate for each cell the nearest distance to a watershed boundary (the upslope length) and to the nearest stream (the downslope length). This information is useful for hydrological modelling.\r\n\r\nHillshading is used to portray relief difference and terrain morphology of hilly and mountainous areas. The application of a special filter to a DEM produces hillshading. The colour tones in a hillshading raster represent the amount of reflected light at each location, depending on its orientation relative to the illumination source. This illumination source is usually chosen to be to the northwest at an angle of 45∘ above the horizon.\r\n\r\nThree-dimensional map display - with GIS software, three-dimensional views of a DEM can be constructed in which the location of the viewer, the angle under which he or she is looking, the zoom angle, and the amplification factor of relief exaggeration can be specified. Three-dimensional views can be constructed using only a predefined mesh, covering the surface, or using other rasters (e.g. a hillshading raster) or images (e.g. satellite images) that are draped over the DEM.\r\n\r\nDetermination of change in elevation through time - the cut-and-fill volume of soil to be removed or to be brought in to make a site ready for construction can be computed by overlaying the DEM of the site before the work begins with the DEM of the expected modified topography. It is also possible to determine landslide effects by comparing DEMs of before and after a landslide event.\r\n\r\nAutomatic catchment delineation - catchment boundaries or drainage lines can be automatically generated from a good quality DEM with the use of neighbourhood functions. The system will determine the lowest point in the DEM, which is considered to be the outlet of the catchment. From there, it will repeatedly search for the neighbouring pixels with the highest altitude. This process is repeated until the highest location (i.e. the cell with the highest value) is found; the path followed determines the catchment boundary. For delineating the drainage network, the process is reversed. Then the system will work from the watershed downwards, each time looking for the lowest neighbouring cells, which determines the direction of water flow (Flow Computation).\r\n\r\nDynamic modelling - apart from the applications mentioned above, DEMs are increasingly used in GIS-based dynamic modelling, such as the computation of surface run-off and erosion, groundwater flow, the delineation of areas affected by pollution, the computation of areas that will be covered by processes such as flows of debris and lava. An example is (Diffusion).\r\n\r\nVisibility analysis - a viewshed is the area that can be “seen” (i.e. it is in the direct line-of-sight) from a specified target location. Visibility analysis can determine the area visible from a scenic lookout or the area that can be reached by a radar antenna, as well as assess how effectively a road or quarry will be hidden from view.","name":"Analysis of surfaces","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM7-1","description":"Statistical analysis techniques based on visual interpretation through histograms, scatterplots, etc.","name":"Graphical methods","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM7-2","description":"Environmental variables have become increasing available with the advent of GIS. These are mostly continuous in space and time. Collecting denser environmental data in discrete space and time domains are rather cost effective and time consuming. However, when the data at each spatial or time index are considered as outcomes of a random variable, stochastic processes become enviable useful to build models and predict the outcomes at locations where data were never collected. The meaningful assumptions include stationarity of the mean and the covariance to ascertain an expression for spatial dependency/autocorrelation. With a stationary process (i.e. constant mean), simple and ordinary kriging is used. Other variants like kriging with external drift, universal kriging and regression kriging also alleviate the challenge of non-stationary mean. These methods are also applicable when temporal indexes rather than spatial indexes are of interest.","name":"Stochastic processes","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM7-3","description":"Spatial weight matrix is the popular numerical quantification of spatial dependency or spatial neighborhoods. The weight matrix should summarize information about the spatial connectivity structure of the spatial entities/features; either polygons, points, or lines. This is required for the computation of spatial dependency indices such the Moran’s index, and for spatial regression models such as the conditional autoregressive (CAR), spatial lag, and spatial error models. The connectivity information can be defined based on adjacency/contiguity or distance between pairs of spatial entities. There are other forms; they could be based on population densities between observation pairs. The simplest spatial weigh matrix is the binary adjacency spatial weight matrix with elements w_ij, such that w_ij=1 if spatial units i and j are neighbors, otherwise w_ij=0. A popular alternative is the inverse distance weight matrix with elements w_ij=1⁄d^α , where d is the distance between pairs of spatial units and α is any positive number greater than zero. By convention, w_ii=0 since spatial unit cannot have a spillover within itself.","name":"The spatial weights matrix","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM7-4","description":"Spatial autocorrelation evaluates how things which are closer in space tend to have similar attributes. This is a common phenomenon in environmental variables which are continuous in space. For instance, temperature, soil moisture content, air quality and rainfall are all continuous in space. This idea is based on Tobler’s law of geography: “everything is related to everything but near things are more related”. Global measures of spatial association estimates the overall index of spatial autocorrelation, also called spatial clustering. Thus, it measures whether clustering is apparent throughout the study region but do not identify the location of clusters. Common global measures include the Moran’s Index and Geary’s C. These have increasing applications in domains like environmental science, agriculture, epidemiology, climate studies etc.","name":"Global measures of spatial association","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM7-5","description":"Unlike global measures of spatial association, local measure of spatial association identifies the locations of clusters. Typical measures include the local indicator for spatial autocorrelation (LISA) or the local Moran’s index whose summation is proportional to the global Moran’s index. The spatial scan statistics has also been the commonly used method to detect local clusters.","name":"Local measures of spatial association","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM7-6","description":"An outlier is an unexpected value that differs significantly from other observations. Definition of an outlier is not absolute and the concept itself is precisely defined only by selection of appropriate criteria in concrete statistical observations. When considering outliers, it is important to determine whether the value of the outlier is incorrect data or it is otherwise outstanding, but correct data. If we consider outliers in the case when they base on sample surveys, another assessment is necessary. Namely, the assessment of whether an outlier is representative or not. \r\nThe box plot is a useful graphical display for examining the outliers. Using median, lower and upper quartiles, extreme values are identified in the tails of the distribution. The value beyond inner fence on either side is considered a mild outlier. The value beyond an outer fence is considered an extreme outlier. Histograms also emphasize the existence of outliers. The histogram depends on how we design the classes, so we can get different histograms for the same data. Graphical and quantitative checks are obligatory if the histogram shows possible outliers. Outliers can also be examined by calculating the correlation between two datasets (Pearson correlation coefficient, Spearman rank correlation coefficient…). Scatter plots reveals a basic linear relationship with a pattern. An outliner is defined as a data point that deviates from other values. Outliers can also be examined by local outlier factor, which is based on a concept of a local density. Points with substantially lower density than their neighbours are considered as outliers.","name":"Outliers","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM7-7","description":"Bayesian method of modelling stems from the Bayes theorem and derived using conditional probabilities. Its advantage lies in its ability to include prior knowledge of unknown parameters to ascertain their uncertainties. Thus, the prior parameters are updated by the data likelihood to obtain the posteriors. The challenge of Bayesian modelling has been the integration of the denominator which always resulted into improper integrals. This actually prolonged its wide applications. With the advent of high performance computers, solution to such integrals are easily solved using Markov chain Monte Carlo simulations. The advent robust approximation methods through integrated nested Laplace approximations (INLA) has even made parameter estimation faster; thus making Bayesian methods interesting and better. Unlike frequentist approaches, Bayesian methods can present estimates of parameters as densities from which their uncertainties and credible intervals can be estimated. They have now found wide applications in divers areas like environmental modelling, climate modeling, agriculture, epidemiology and many other domains that requires modeling.","name":"Bayesian methods","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM7","description":"Traditional statistical methods are used to describe the central tendency, dispersion, and other characteristics of data but are not always suited to use with spatial data for which specialized techniques are often required. The field of spatial statistical analysis forms the backbone for the testing of hypotheses about the nature of spatial pattern, dependency, and heterogeneity. The techniques are widely used in both exploratory and confirmatory spatial analysis in many different fields.","name":"Spatial statistics","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM8-1","description":"Sampling is needed to limit the observations for statistical analysis. In raster image analysis, various sampling schemes have been proposed for selecting pixels to test. Choices to be made relate to the design of the sampling strategy, the number of samples required, and the area of the samples. Recommended sampling strategies in the context of land cover data are simple random sampling or stratified random sampling. The number of samples may be related to two factors in accuracy assessment: (1) the number of samples that must be taken in order to reject a data set as being inaccurate; or (2) the number of samples required to determine the true accuracy, within some error bounds, of a data set. Sampling theory is used to determine the number of samples required. The number of samples must be traded-off against the area covered by a sample unit. A sample unit can be a point but it could also be an area of some size; it can be a single raster element but may also include surrounding raster elements. Among other considerations, the “optimal” sample-area size depends on the heterogeneity of the class.","name":"Spatial sampling for statistical analysis","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM8-3","description":"A variogram is a tool used to describe the spatial continuity of data points. Different kinds of variograms are used, such as experimental variogram and semi-variogram.","name":"Variogram modeling","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM8-4","description":"Predicting an observation in the presence of spatially dependent observations is termed Kriging, named after the first practitioner of these procedures, the South African mining engineer Daan Krige, who did much of his early empirical work in the Witwatersrand gold mines.","name":"Principles of kriging","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM8-5","description":"With a stationary stochastic process (i.e. constant mean), simple and ordinary kriging is used for interpolation. Other variants like kriging with external drift, universal kriging and regression kriging also alleviate the challenge of non-stationary mean. Other variants are \r\nco-kriging log-normal kriging, disjunctive kriging, indicator kriging, factorial kriging and universal kriging.","name":"Kriging variants","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM8","description":"Geostatistics are a variety of techniques used to analyze continuous data e.g., rainfall, elevation, air pollution. The fundamental structure of geostatistics is based on the concept of semi-variograms and their use for spatial prediction kriging. Sampling methods are also discussed in Unit GD9 Field data collection. \r\nGeostatistics is a subdiscipline of spatial statistics developed to estimate the value of a continuous spatial process at unknown locations by using the information of the value of these process at known locations. Furthermore, it aims to quantify the uncertainty related to the prediction (Calder et al., 2009; Emmanouil, 2019). In order to do such predictions, geostatistics entails some statistical methods which use as starting point the assumption of a random component that can define the spatiotemporal variability. These methods are developed to infer the parameters that can describe the spatiotemporal patterns of the input variables (e.g. soil moisture) so that finally these variables at unsampled locations can be estimated (interpolated) (Emmanouil, 2019). Geostatistical methods are strongly related with classic interpolation methods but differ by its use of random variables that allow to given an uncertainty indication associated with the prediction of variables in space and time. \r\n\r\nIn environmental research geostatistical techniques are often applied to infer (interpolate) variables at such unobserved locations by using information from known locations. One of such geostatistical techniques is Kriging, which is a geostatistical method that predicts variables by using spatial interpolation. This spatial interpolation is done by establishing a semivariogram that defines the spatial relationship between the variables of interest in function of the distance. Because of this, the Kriging technique can also give an indication on the variance or accuracy of the prediction (Calder et al., 2009); Van der Meer, 2012). On the other hand, cokriging is another important geostatistical technique and differs from Kriging by using the cross-correlation between variables to generate local estimates (Van der Meer, 2012). In earth observation studies, cokriging can be applied to better predict sparsely based data on the ground (e.g. biomass) by using the cross-correlation of this variable with a more continuously sampled satellite metric like NDVI. Furthermore, these techniques can also be used to enhance satellite image information, filling missing pixels or even downscale the information to a higher resolution (Van der Meer, 2012).","name":"Geostatistics","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM9-1","description":"Spatial econometrics uses spatial stochastic models to determine autocorrelation between interacting agents. The techniques involved are regression, the use of a spatial weights matrix, least squares, etc.","name":"Principles of spatial econometrics","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM9-2","description":"A spatial autoregressive (SAR) model describes the prediction of the behaviour of a random process.","name":"Spatial autoregressive models","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM9-3","description":"In producing optimal images for interpretation, spatial filtering is applied. Filtering is usually carried out for a single band. Filters - algorithms - can be used to enhance images by, for example, reducing noise (“smoothing an image”) or sharpening a blurred image. Filter operations are also used to extract features from images, e.g. edges and lines, and to automatically recognize patterns and detect objects. There are two broad categories of filters: linear and non-linear filters.\r\n\r\nLinear filters calculate the new value of a pixel as a linear combination of the given values of the pixel and those of neighbouring pixels. A simple example of the use of a linear smoothing filter is when the average of the pixel values in a 3×3 pixel neighbourhood is computed and that average is used as the new value of the central pixel in the neighbourhood.","name":"Spatial filtering","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM9-4","description":"Geographically Weighted Regression (GWR) makes use of local subsets of observations to perform estimates.","name":"Spatial expansion and Geographically Weighted Regression GWR","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM9","description":"Many problems of the social sciences can be expressed in terms of spatial regression analysis. The development of spatial autoregressive models and the estimation of their parameters is the focus for the field of spatial econometrics.","name":"Spatial regression and econometrics","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF","description":"The GIScience perspective is grounded in spatial thinking. The aim of this knowledge area is to recognize, identify, and appreciate the explicit spatial, spatio-temporal and semantic components of the geographic environment at an ontological and epistemological level in preparation for modeling the environment with geographic data and analysis. To do this, one must understand the nature of space and time as a context for geographic phenomena.This knowledge area covers the ways in which views of the geographic environment depend on philosophical viewpoints, physics, human cognition, society, and the task at hand. This knowledge area also requires an understanding of the fundamental principles in the discipline of geography, the \"language\" of spatial tasks. On a more advanced level, this area incorporates mathematical and graphical models that formalize these concepts, such as set theory, algebra, and semantic nets. Because of its wide range of foundational principles, this knowledge area forms a basis for the other knowledge areas. Wise design and use of geospatial technologies requires an understanding of the nature of geographic information, the social and philosophical context of geographic information, and the principles of geography. This knowledge area is especially closely tied to Knowledge Areas Data Modeling (DM) and Design Aspects (DA), as generic data models and application designs need to be grounded in sound conceptual models. The foundations of geographic information have developed over several decades. Philosophical and scientific views on the nature of space and time have evolved since the ancient Greeks. Early papers during the Quantitative Revolution, such as Berry (1964), began to formalize the structure of information used in geographic inquiry.The fundamental data structures and algorithms comprising the GIS software developed in the 1960`s and 1970`s were based on implicit \"common-sense\" conceptual models of geographic information. During the 1980`s, several researchers questioned these underlying assumptions. Some were refuted, other confirmed, and many extended. However, the most rapid pace of development in this area was during the 1990`s with the rise of GIScience as a distinct discipline, and the many cooperative initiatives it comprised.The new millennium has seen some of these foundational principles incorporated into commercial software, thus making theoretical knowledge even more important for practitioners. It is expected that the concepts in this knowledge area will be learned gradually. An introductory course may cover only a few topics in a cursory manner, an intermediate course on data modeling or data analysis may consider several theoretical topics of practical application, and a number of graduate courses could cover each topic in a research-oriented environment. Discussion of this knowledge area includes several terms that can have multiple meanings. For the purposes of this document, two in particular require definition: Geographic: Almost any subject or discourse involving earthly phenomena, studied from a spatial perspective at a medium scale (sub-astronomical and super-architectural). Phenomenon: Any subject of geographic discourse that is perceived to be external to the individual, including entities, events, processes, social constructs, and the like.","name":"Conceptual Foundations","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF1-1","description":"Metaphysics involve the meaning things and concepts. Ontologies provide a way to share the semantics of concepts in some area of interest and is all about common the understanding of essential concepts, e.g., what is meant by a geometric object and its attributes.","name":"Metaphysics and ontology","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF1-1b","description":"Brief history of GIScience as related to the history of GISystems; Definitions of GIS&T; Sub-domains of GIS&T (i.e., Geographic Information Science, Geospatial Technology, and Applications of GIS&T)","name":"What is Geographic Information Science and Technology","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF1-2","description":"The branch of philosophy concerned with knowledge.","name":"Epistemology","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF1-2b","description":"GIS&T draws upon insights and methods from key allied fields: Geography, Cartography, Computer and information science, Engineering, Mathematics and Statistics, Philosophy, Cognitive Science, Linguistics","name":"Contributions to GIS and T by key allied fields","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF1-3","description":"The questions and methodologies in major philosophical movements relating to the nature of space, time, geographic phenomena and human interaction with it.","name":"Philosophical perspectives","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF1","description":"Many branches of philosophy are relevant to an understanding of geographic information, especially metaphysics and epistemology. Philosophical theories are deeply engaged in the study of knowledge, space, time, geographic phenomena and human interaction with them. These theories influence the development of geographic ontologies and the structuring, analysis, and interpretation of geographic information. It is, therefore, crucial for professionals to understand these principles in order to bridge (rather than eliminate) the differences and work together. Philosophical perspectives on GIS practice are covered in Unit GS7 Critical GIS.","name":"Philosophical foundations","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF1b","description":"Unit CF1 introduces the broad domain refered to as Geographic Information Science & Technology (GIS&T) and its sub-domains (i.e., Geographic Information Science, Geospatial Technology, and Applications of GIS&T). It outlines the history of Geographic Information Science as related to the history of GISystems, as well as the contributions to this multidisciplinary domain by key allied fields, such as geography, cartography, computer and information science, engineering, mathematics, philosophy, cognitive science, and linguistics.","name":"Introduction to Geographic Information Science and Technology","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF2-1","description":"The study on how humans perceive spatial information.","name":"Perception and cognition of geographic phenomena","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF2-1b","description":"Metaphysics and Ontology - Formal ontology - Ontological distinctions (e.g., continuants vs. occurrents, universals vs. particulars) - The problem of universals and relevant theories (realism, nominalism, conceptualism) - Ontologies of the geographic domain - Philosophical theories relating to the nature of space, time, geographic phenomena and human interaction with them","name":"Philosophy of being","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF2-2","description":"The ways in which conceptual views of in the human mind make it into formal descriptions of information and into artefacts in databases and GIS.","name":"From concepts to data","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF2-2b","description":"Epistemology; Theories on what constitutes knowledge; The notions of model and representation in science; The influences of epistemology on GIS practices","name":"Philosophy of knowledge","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF2-3","description":"Principles of geography to explain the spatial occurrences of spatial entities in Geographic Information Systems.","name":"Geography as a foundation for GIS","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF2-4","description":"Space and place are concepts that are not the same. Including concepts like landscape, it is not always obvious how to portray them unambiguously in GIS.","name":"Place and landscape","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF2-6","description":"The ways in which the elements of culture (e.g., language, religion, education, traditions) may influence the understanding and use of geographic information.","name":"Cultural influences","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF2-7","description":"The influences of political ideologies (e.g., Marxism, Capitalism, conservative liberal) on the understanding of geographic information.","name":"Political influences","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF2","description":"Geographic information is observed, comprehended, organized, used in human processes, with both personal and social influences. Therefore, sound models of geographic information should be grounded on a sound understanding of human perception, cognition, memory, and behavior, as well as human institutions.","name":"Cognitive and social foundations","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF3-1","description":"A GIS operates under the assumption that the spatial phenomena involved occur in a two- or three-dimensional Euclidean space. Euclidean space can be informally defined as a model of space in which locations are represented by coordinates—(x, y) in 2D and (x, y, z) in 3D space—and distance and direction can defined with geometric formulas. In 2D, this is known as the Euclidean plane. To represent relevant aspects of real-world phenomena inside a GIS, we first need to define what it is we are referring to. We might define a geographic phenomenon as a manifestation of an entity or process of interest that:\r\n\r\nitem can be named or described;\r\nitem can be georeferenced; and\r\nitem can be assigned a time (interval) at which it is/was present.\r\n\r\nRelevance of phenomena for the use of a GIS depends entirely on the objectives of the study at hand. For instance, in water management, relevant objects can be river basins, agro-ecological units, measurements of actual evapotranspiration, meteorological data, ground\\-water levels, irrigation levels, water budgets and measurements of total water use. All of these can be named or described, georeferenced and provided with a time interval at which each exists. In multipurpose cadastral administration, the objects of study are different: houses, land parcels, streets of various types, land use forms, sewage canals and other forms of urban infrastructure may all play a role. Again, these can be named or described, georeferenced and assigned a time interval of existence.\r\n\r\nNot all relevant information about phenomena has the form of a triplet (description, georeference, time interval). If the georeference is missing, then the object is not positioned in space: an example of this would be a legal document in a cadastral system. It is obviously somewhere, but its position in space is not considered relevant. If the time interval is missing, we might have a phenomenon of interest that exists permanently, i.e.\\ the time interval is infinite. If the description is missing, then we have something that exists in space and time, yet cannot be described. Obviously this last issue limits the usefulness of the information.\r\n\r\nTypes of geographic phenomena\r\nThe definition of geographic phenomena attempted above is necessarily abstract and is, therefore, perhaps somewhat difficult to grasp. The main reason is that geographic phenomena come in different “flavours”. Before categorizing such flavours, there are two further observations to be made.\r\n\r\nFirst, to represent a phenomenon in a GIS requires us to state what it is and where it is. We must provide a description—or at least a name—on the one hand, and a georeference on the other hand. We will ignore temporal issues for the moment and come back to these in Temporal dimension and Spatial-temporal data model, the reason being that current GISs do not provide much automatic support for time-dependent data. This topic must, therefore, be considered as an example of advanced GIS use. Second, some phenomena are manifest throughout a study area, while others only occur in specific localities. The first type of phenomena we call geographic fields; the second type we call objects.","name":"Space","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF3-1b","description":"- Theories of human perception, cognition, and memory and their ability to model spatial knowledge acquisition (e.g., Marr on vision, Piaget on cognitive development) - Types of mental representations (i.e., analogue, propositional, procedural) - The role of metaphors and image schemata in our understanding of geographic phenomena and geographic tasks - From concepts to data (i.e., data, information, knowledge, and wisdom; transformation of a conceptual model of information for a particular task into a data model; limitations of various information stores (the mind, computers) and means (maps, graphics, and text) for representing geographic information) - Difference between real phenomena, conceptual models, and GIS data representations thereof connections with cartography and maps","name":"Cognitive foundations","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF3-2b","description":"- Semantics - Meaning (e.g., the nature of meaning, modes of meaning) - Geospatial semantics - The role of natural language in the conceptualization of geographic phenomena","name":"Linguistic foundations","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF3-3b","description":"- The ways in which the elements of culture (e.g., language, religion, education, traditions) may influence the understanding and use of geographic information - The influences of social theories and political ideologies and actions on human perceptions of space and place - The constraints that political forces place on geospatial applications in public and private sectors","name":"Social foundations","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF3-4b","description":"- Common-sense views and laymen knowledge of geographic phenomena that contrast with established theories and technologies of geographic information - The impact of geospatial technologies and the geoweb (e.g., digital globes) that allow non-geospatial professionals to create, distribute, and map geographic information - The design, procedures, and results of GIS projects to non-GIS audiences (clients, managers, general public) - Difference between applications that can make use of common-sense principles of geography and those that should not","name":"Common-sense geographies","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF3","description":"Geographic information is observed, comprehended, organized, used in human processes, with both personal and social influences. Therefore, sound models of geographic information should be grounded on a sound understanding of human perception, cognition, memory, and behavior, as well as human institutions.","name":"Cognitive, linguistic and social foundations","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF4-2b","description":"As time is the central concept of the temporal dimension, a brief examination of the nature of time may clarify our thinking when we work with this dimension:\r\n\r\nDiscrete and continuous time: Time can be measured along a discrete or continuous scale. Discrete time is composed of discrete elements (seconds, minutes, hours, days, months, or years). For continuous time, no such discrete elements exist: for any two moments in time there is always another moment in between. We can also structure time by events (moments) or periods (intervals). When we represent intervals by a start and an end event, we can derive temporal relationships between events and periods, such as “before”, “overlap”, and “after”.\r\n\r\nValid time and transaction time: Valid time (or world time) is the time when an event really happened, or a string of events took place. Transaction time (or database time) is the time when the event was stored in the database or GIS. Note that the time at which we store something in a database is typically (much) later than when the related event took place.\r\n\r\nLinear, branching and cyclic time: Time can be considered to be linear, extending from the past to the present (‘now’), and into the future. This view gives a single time line. For some types of temporal analysis, branching time - in which different time lines from a certain point in time onwards are possible - and cyclic time - in which repeating cycles such as seasons or days of the week are recognized - make more sense and can be useful.\r\n\r\nTime granularity: When measuring time, we speak of granularity as the precision of a time value in a GIS or database (e.g. year, month, day, second). Different applications may obviously require different granularity. In cadastral applications, time granularity might well be a day, as the law requires deeds to be date-marked; in geological mapping applications, time granularity is more likely to be in the order of thousands or millions of years.\r\n\r\nAbsolute and relative time: Time can be represented as absolute or relative. Absolute time marks a point on the time line where events happen (e.g. “6 July 1999 at 11:15 p.m.”). Relative time is indicated relative to other points in time (e.g. “yesterday”, “last year”, “tomorrow”, which are all relative to “now”, or “two weeks later”, which is relative to some other arbitrary point in time.).","name":"Time","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF4-3b","description":"The way we represent relevant components of the real world in our models determines the kinds of questions we can or cannot answer. Besides representing an object or field in 2D or 3D space, the temporal dimension is of a continuous nature. Therefore, in order to represent it in a GIS we have to discretize the time dimension.\r\n\r\nSpatio-temporal data models are ways of organizing representations of space and time in a GIS. Several representation techniques have been proposed in the literature. Perhaps the most common of these is the “snapshot state”, which represents a single moment in time of an ongoing natural or man-made process. We may store a series of these “snapshot states” to represent “change”, but we must be aware that this is by no means a comprehensive representation of that process. \r\n\r\nIn spatio-temporal analysis we consider changes of spatial and thematic attributes over time. We can keep the spatial domain fixed and look only at the attribute changes over time for a given location in space. We might be interested how land cover has changed for a given location or how land use has changed for a given land parcel over time, provided its boundary has not changed. On the other hand, we can keep the attribute domain fixed and consider the spatial changes over time for a given thematic attribute. In this case, we might want to identify locations that were covered by forest over a given period of time.\r\n\r\nFinally, we can assume both the spatial and attribute domains are variable and consider how fields or objects have changed over time. This may lead to notions of object motion - a subject receiving increasing attention in the literature. Applications of moving object research include traffic control, mobile telephony, wildlife tracking, vector-borne disease control and weather forecasting. In these types of applications, the problem of object identity becomes apparent. When does a change or movement cause an object to disappear and become something new? With wildlife this is quite obvious; with weather systems less so. But this should no longer be a surprise: we have already seen that some geographic phenomena can be nicely described as objects, while others are better represented as fields.\r\n\r\nMapping time means mapping change. This may be change in a feature’s geometry, in its attributes, or both. Examples of changing geometry are the evolving coastline of the Netherlands, the location of Europe’s national boundaries, or the position of weather fronts. Changes in the ownership of a land parcel, in land use or in road traffic intensity are other examples of changing attributes. Urban growth is a combination of both: urban boundaries expand with growth and simultaneously land use shifts from rural to urban. If maps are to represent events like these, they should be suggestive of such change.\r\n\r\nThree temporal cartographic techniques can be distinguished:\r\n\r\nSingle Static Map\r\n\r\nSpecific graphic variables and symbols are used to indicate change or represent an event. We can apply the visual variable “value” to represent for example the age of built-up areas.\r\n\r\nSeries of Static Maps\r\n\r\nA single map in the series represents a “snapshot” in time. Together, the maps depict a process of change. Change is perceived by the succession of individual maps depicting the situation in successive snapshots. It could be said that the temporal sequence is represented by a spatial sequence that the user has to follow to perceive the temporal variation. The number of images should be limited since it is difficult for the human eye to follow long series of maps.\r\n\r\nAnimated Maps\r\n\r\nChange is perceived to evolve in a single image by displaying several snapshots one after the other, just like a video clip of successive frames. The difference from the series of maps is that the variation can be deduced from real “change” seen taking place in the image itself, not from a spatial sequence. For the user of a cartographic animation, it is important to have tools available that allow for interaction while viewing the animation. Seeing an animation play will often leave users with many questions about what they have seen. And just replaying the animation is not sufficient to answer questions like “What was the position of the northern coastline during the 15th century?” Most of the general software packages for viewing animations already offer facilities such as “pause” (to look at a particular frame) and ‘(fast-)forward’ and ‘(fast-)backward’, or step-by-step display. More options have to be added, such as the possibility to go directly to a certain frame based on a task command like: “Go to 1850”.","name":"Relationships between space and time","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF4-4b","description":"GIS data structures are used to implement the conceptual views of spatial data (vector and raster models). The power of a GIS is dependent on the richness of information contained in the spatial data structures. Vector models are based on points, lines and areas. Raster models are based on grids. Each cell has a value that is used to represent some characteristic of that location. \r\nLayers are used to display geographic datasets in various digital map environment. A layer stores the path to a source dataset and other layer properties, including symbology. You can use multiple layers on one map and specify its properties. Shapefiles represent spatial character of the object in terms of shape, size and spatial arrangement. Shapefile usually comprise three separate and distinct types of files (main files, index files and database tables). Data base files store additional attributed that can be joined to a shapefiles’ feature. Attribute data types supplement geographic spatial feature with additional information. Spatial data includes information of location and attribute data includes information about other characteristics (what, where and why). A legend is a visual presentation of the symbols that are used on the map with some additional explanations. It includes a sample of each symbol and a short description of the meaning.","name":"Categories","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF4-5","description":"An entity obtained by abstracting the real world, having a physical nature (certain composition of material), being given a descriptive name, and observable; e.g. “house”. An object is a self-contained part of a scene having certain discriminating properties.\r\n\r\nThe primitives of vector data sets are the point, (poly)line and polygon. Related geometric measurements are location, length, distance and area size. Some of these are geometric properties of a feature in isolation (location, length, area size); others (distance) require two features to be identified.\r\n\r\nIn a GIS, features are represented together with their attributes—geometric and non-geometric—and relationships. The geometry of features is represented with primitives of the respective dimension: a windmill probably as a point; an agricultural field as a polygon. The primitives follow either the vector or the raster approach.\r\n\r\nVector data types describe an object through its boundary, thus dividing the space into parts that are occupied by the respective objects. The raster approach subdivides space into (regular) cells, mostly as a square tessellation of two or three dimensions. These cells are called pixels in 2D and voxels in 3D. The data indicate for every cell which real-world feature is covered, provided the cell represents a discrete field. In the case of a continuous field, the cell holds a representative value for that field. The Table below lists advantages and disadvantages of raster and vector representations.\r\n\r\nThe storage of a raster is, in principle, straightforward. It is stored in a file as a long list of values, one for each cell, preceded by a small list of extra data (the “file header”), which specifies how to interpret the long list. The order of the cell values in the list can, but need not necessarily, be left to right, top to bottom. This simple encoding scheme is known as row ordering. The header of the raster will typically specify how many rows and columns the raster has, which encoding scheme was used, and what sort of values are stored for each cell.\r\n\r\nData can be of a qualitative or quantitative nature. Qualitative data is also called nominal data, which exists as discrete, named values without a natural order amongst the values. Examples are different languages (e.g. English, Swahili, Dutch), different soil types (e.g. sand, clay, peat) or different land use categories (e.g. arable land, pasture). In the map, qualitative data are classified according to disciplinary insights, such as a soil classification system represented as basic geographic units: homogeneous areas associated with a single soil type, recognizable by the soil classification.\r\n\r\nQuantitative data can be measured, either along an interval or ratio scale. For data measured on an interval scale, the exact distance between values is known, but there is no absolute zero on the scale. Temperature is an example: 40 ◦C is not twice as hot as 20 ◦C, and 0 ◦C is not an absolute zero.\r\n\r\nQuantitative data with a ratio scale do have a known absolute zero. An example is income: someone earning $100 earns twice as much as someone with an income of $50. In order to generate maps, quantitative data are often classified into categories according to some mathematical method.\r\n\r\nIn between qualitative and quantitative data, one can distinguish ordinal data. These data are measured along a relative scale and are as such based on hierarchy. For instance, one knows that a particular value is “more” than another value, such as “warm” versus “cool”. Another example is a hierarchy of road types: “highway”, “main road”, “secondary road” and “track”. The different types of data are summarized in Table.","name":"Properties","selfAssesment":"GI-N2K
"},{"code":"CF4b","description":"Geographic phenomena, geographic information, and geographic tasks are described in terms of space, time, and properties. Different theories exist as to the nature and formal representation of these aspects, including space-like dimensions, sets, and phenomenology. Information in each of these three aspects is measured and reported with respect to one of several frames of reference or domains, including both absolute and relative approaches. Early frameworks such as those of Berry (1964) and Sinton (1978) were influential in setting forth the importance of space, time, and theme in GIS&T. Besides, space, time, and properties, categories are also fundamental in the conceptualization and representation of spatial entities, phenomena, processes, and events. Distinctive features of geographic information such as scale and detail, spatial patterns, spatial integration, and regions are also critical for a complete description of its nature and representation. This unit is closely tied to the creation of data models in Knowledge Area 5: Data Modeling, Storage, and Exploitation.","name":"Fundamentals of Geographic Information","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF5-1b","description":"Discrete entities can be found as fields or objects.\r\n\r\nDiscrete fields divide the study space in mutually exclusive, bounded parts, with all locations in one part having the same field value. Discrete fields are intermediate between continuous fields and geographic objects: discrete fields and objects both use “bounded” features.\r\n\r\nDiscrete fields divide the study space in mutually exclusive, bounded parts, with all locations in one part having the same field value. Typical examples are land classifications, for instance, using either geological classes, soil type, land use type, crop type or natural vegetation type. \r\n\r\nDiscrete fields are intermediate between continuous fields and geographic objects: discrete fields and objects both use “bounded” features. A discrete field, however, assigns a value to every location in the study area, which is not typically the case for geographic objects. These two types of fields differ in the type of cell values. A discrete field such as land use type will store cell values of the type “integer” and is therefore also called an integer raster. Discrete fields can be easily converted to polygons since it is relatively easy to draw a boundary line around a group of cells with the same value. A continuous raster is also called a “floating point” raster.\r\n\r\nGeographic objects.\r\n\r\nWhen a geographic phenomenon is not present everywhere in the study area, but somehow “sparsely” populates it, we look at it as a collection of geographic objects. Such objects are usually easily distinguished and named, and their position in space is determined by a combination of one or more of the following parameters:\r\n\r\nlocation (where is it?)\r\nshape (what form does it have?)\r\nsize (how big is it?)\r\norientation (in which direction is it facing?).\r\n\r\nHow we want to use the information determines which of these four parameters is required to represent the object. For instance, for geographic objects such as petrol stations all that matters in an in-car navigation system is where they are. Thus, in this particular context, location alone is enough, and shape, size and orientation are irrelevant. For roads, however, some notion of location (where does the road begin and end?), shape (how many lanes does it have?), size (how far can one travel on it?) and orientation (in which direction can one travel on it?) seem to be relevant components of information in the same system.","name":"Discrete entities","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF5-2b","description":"A geographic field is a geographic phenomenon that has a value “everywhere” in the study area. We can therefore think of a field as a mathematical function f that associates a specific value with any position in the study area. Hence if (x, y) is a position in the study area, then f(x, y) expresses the value of f at location (x, y). Fields can be discrete or continuous.\r\n\r\nIn a continuous field, the underlying function is assumed to be “mathematically smooth”, meaning that the field values along any path through the study area do not change abruptly, but only gradually. Good examples of continuous fields are air temperature, barometric pressure, soil salinity and elevation. A continuous field can even be differentiable, meaning that we can determine a measure of change in the field value per unit of distance anywhere and in any direction. For example, if the field is elevation, this measure would be slope, i.e. the change of elevation per metre distance; if the field is soil salinity, it would be salinity gradient, i.e. the change of salinity per metre distance.\r\n\r\nDiscrete fields divide the study space in mutually exclusive, bounded parts, with all locations in one part having the same field value. Discrete fields are intermediate between continuous fields and geographic objects: discrete fields and objects both use “bounded” features.\r\n\r\nDiscrete fields divide the study space in mutually exclusive, bounded parts, with all locations in one part having the same field value. Discrete fields are intermediate between continuous fields and geographic objects: discrete fields and objects both use “bounded” features.\r\n\r\nDiscrete fields divide the study space in mutually exclusive, bounded parts, with all locations in one part having the same field value. Typical examples are land classifications, for instance, using either geological classes, soil type, land use type, crop type or natural vegetation type. \r\n\r\nDiscrete fields are intermediate between continuous fields and geographic objects: discrete fields and objects both use “bounded” features. A discrete field, however, assigns a value to every location in the study area, which is not typically the case for geographic objects. These two types of fields differ in the type of cell values. A discrete field such as land use type will store cell values of the type “integer” and is therefore also called an integer raster. Discrete fields can be easily converted to polygons since it is relatively easy to draw a boundary line around a group of cells with the same value. A continuous raster is also called a “floating point” raster.\r\n\r\nA field-based model consists of a finite collection of geographic fields: we may be interested in, for example, elevation, barometric pressure, mean annual rainfall and maximum daily evapotranspiration, and would therefore use four different fields to model the relevant phenomena within our study area.","name":"Fields","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF5-3b","description":"We can structure time by events (moments) or periods (intervals). When we represent intervals by a start and an end event, we can derive temporal relationships between events and periods, such as “before”, “overlap”, and “after”.\r\nValid time (or world time) is the time when an event really happened, or a string of events took place. Transaction time (or database time) is the time when the event was stored in the database or GIS. Note that the time at which we store something in a database is typically (much) later than when the related event took place.\r\n\r\nProcess models in the Earth sciences describe the evolution of geo(bio)physical surface properties in time, independently from remote sensing observations. Examples of such process models on various time scales are, for instance, numerical weather prediction models (NWPs), vegetation growth models, hydrological models, oceanographic models and climate models.\r\n\r\nProcesses on the planet Earth are complex phenomena that are taking place in space and in time, i.e. in four dimensions.\r\n\r\nIn many of these processes, differences in one dimension (e.g. height above the geoid) can be disregarded, so that two spatial dimensions and the dimension time remain. Despite this simplification, the physical description of the phenomena remains a difficult task. To better understand the processes it often helps if the same geographic region is viewed repeatedly and, if possible, also from different directions and in different wavelength regions. Integration of data from a variety of sources can be a means to retrieving information about processes that would otherwise remain undetected.","name":"Events and processes","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF5-4b","description":"Models that integrate the concepts of space, time, and attribute in geographic information.","name":"Integrated models","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF5-6","description":"Geographic phenomena can be studied as single entities and in relationship with each other and then reveal patters and clusters. How the entities are distributed is subject to statistical and visualisation studies.","name":"Spatial distribution","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF5-7","description":"We can use the topological properties of interiors and boundaries to define relationships between spatial features. Since the properties of interiors and boundaries do not change under topological mapping, we can investigate their possible relations between spatial features. We can define the interior of a region, R, as the largest set of points of R for which we can construct a disc-like environment around it (no matter how small) that also falls completely inside R. The boundary of R is the set of those points belonging to R that do not belong to the interior of R, i.e. one cannot construct a disc-like environment around such points that still belongs to R completely.\r\n\r\nLet us consider a spatial region A. It has a boundary and an interior, both seen as (infinite) sets of points, which are denoted by boundary(A) and interior(A), respectively. We consider all possible combinations of intersections (∩) between the boundary and the interior of A with those of another region, B, and test whether they are the empty set (∅) or not. From these intersection patterns, we can derive eight (mutually exclusive) spatial relationships between two regions. If, for instance, the interiors of A and B do not intersect, but their boundaries do, yet the boundary of one does not intersect the interior of the other, we say that A and B meet.","name":"Region","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF5-8","description":"Integration of data from a variety of sources can be a means to retrieving information about processes that would otherwise remain undetected.\r\n\r\nAlthough data integration can be very useful, there are also some requirements that have to be fulfilled for it to be effective:\r\n\r\n• geospatial data have to be accurately co-registered in a common grid;\r\n• time gaps between the various data layers have to be known and accounted for;\r\n• systematic effects due to the atmosphere, the viewing angle, the Sun angle, etc., must be corrected for or taken into account.\r\n\r\nData can be integrated in an almost infinite number of ways. Results from data integration can, again, be combined with other geospatial data to produce yet other new information, and so on.\r\n\r\nData integration also comprises the incorporation of non-spatial information or point data from field measurements. These data have to be associated with precise moments in time and with precise geographic locations, or with some time interval and fuzzy-defined regions. Thus, here the important issue of the representativeness of this information for the associated time interval and geographic area comes into play.\r\n\r\nIn general, data integration forces us to consider the uncertainties or inaccuracies of the various data sources available. In some cases, meta-data may contain information about this. When integrating data for some purpose, one has to apply weights to each of them, so that the final result is a balanced compromise in which inaccurate data receive less weight than those with a high degree of certainty.","name":"Spatial integration","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF5b","description":"The concepts below form the basic elements of common human conceptions of geographic phenomena. Concepts from many units in this knowledge area have been synthesized to create general conceptual models of geographic information. Attempts to resolve the object-field debate have led to attempts to create comprehensive models that bridge these views. Consideration of this unit should also include formal models of these elements in mathematics and other fields. Knowledge Area DM Data Modeling discusses the representation of these elements in digital models.","name":"Elements of geographic information","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF6-1","description":"Mereology is the study of parts and wholes. In GI this involves how objects are modeled as composites of other objects.","name":"Mereology: structural relationships","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF6-2","description":"Lineage describes the history of a data set. During the processing of data, the derived information inherits artifacts from the dataset(s) of origin. In the case of published maps, some lineage information may be provided as part of its meta-data, in the form of a note on the data sources and procedures used in the compilation of the data. Examples include the date and scale of aerial photography, and the date of field verification. Especially for digital data sets, however, lineage may be defined more formally as:\r\n\r\n“that part of the data quality statement that contains information that describes the source of observations or materials, data acquisition and compilation methods, conversions, transformations, analyses and derivations that the data has been subjected to, and the assumptions and criteria applied at any stage of its life (Clarke and Clark, 1995).”\r\n\r\nAll of these aspects affect other aspects of quality, for example positional accuracy. Clearly, if no lineage information is available, it is not possible to adequately evaluate the quality of a data set in terms of “fitness for use”.","name":"Genealogical relationships: lineage, inheritance","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF6-3","description":"We can use the topological properties of interiors and boundaries to define relationships between spatial features. Since the properties of interiors and boundaries do not change under topological mapping, we can investigate their possible relations between spatial features. We can define the interior of a region, R, as the largest set of points of R for which we can construct a disc-like environment around it (no matter how small) that also falls completely inside R. The boundary of R is the set of those points belonging to R that do not belong to the interior of R, i.e. one cannot construct a disc-like environment around such points that still belongs to R completely.\r\n\r\nLet us consider a spatial region A. It has a boundary and an interior, both seen as (infinite) sets of points, which are denoted by boundary(A) and interior(A), respectively. We consider all possible combinations of intersections (∩) between the boundary and the interior of A with those of another region, B, and test whether they are the empty set (∅) or not. From these intersection patterns, we can derive eight (mutually exclusive) spatial relationships between two regions. If, for instance, the interiors of A and B do not intersect, but their boundaries do, yet the boundary of one does not intersect the interior of the other, we say that A and B meet. In mathematics, we can therefore define the “meets relationship” using set theory. The eight spatial relationships are disjoint, meets, equals, inside, covered by, contains, covers and overlaps.","name":"Topological relationships","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF6-4","description":"Relationships between spatial features that define their relative position. Spatial autocorrelation is a fundamental principle based on Tobler’s first law of geography, which states that locations that are closer together are more likely to have similar values than locations that are farther apart.","name":"Metrical relationships: distance and direction","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF6","description":"Like geography, geographic information not only models phenomena but the relationships between them. This can include relationships between entities, between attributes, between locations. In addition, one of the strengths of geography (and GIS) is its ability to use a spatial perspective to relate disparate subjects, such as climate and economy. Methods for analyzing relationships are discussed in Unit AM4 Modeling relationships and patterns.","name":"Relationships","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF7-1","description":"Vagueness arises from lack of criteria for the applicability of certain linguistic terms. It arises from the lack knowledge about the meanings of terms.","name":"Vagueness","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF7-2","description":"-Uncertainty-related terms, such as error, accuracy, uncertainty, precision, stochastic, probabilistic, deterministic, and random -Difference between uncertainty and vagueness -Dependence of uncertainty on scale and application -Expressions of uncertainty in language -The causes of uncertainty in geospatial data -Stochastic error models for natural phenomena -How the concepts of geographic objects and fields affect the conceptualization of uncertainty -Mathematical models of uncertainty: Probability and statistics","name":"Error-based uncertainty","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF7","description":"Human models (mental, digital, visual, etc.) of the geographic environment are necessarily imperfect. While the mathematical principle of homomorphism (often operationalized as fitness for use) allows for imperfect data to be useful as long as they yield results adequate for the use for which they are intended, imperfections are frequently problematic. Although terminology still varies, two types of imperfection are generally accepted: vagueness (a.k.a. fuzziness, imprecision, and indeterminacy), which is generally caused by human simplification of a complex, dynamic, ambiguous, subjective world; and uncertainty (or ambiguity), generally the result of imperfect measurement processes (as discussed in Knowledge Area GD Geospatial Data). Both of these can be manifested in all forms of geographic information, including space, time, attribute, categories, and even existence. Imperfection is also dealt with in Units GD6 Data quality (in the context of measurement), GC8 Uncertainty and GC9 Fuzzy sets (for the handling and propagation of imperfections), and CV4 Graphic representation techniques (in the context of visualization).","name":"Imperfections in geographic information","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV","description":"Geo-data visualisation necessarily includes cartography as the origin of \"mapping\" our world. Cartography methods have drastically changed over the few years since the increasing role and sophistication of digital technology applied to geo-information visualisation. It is first worth differentiating between the underlying geo-data that describes real world phenomena and the bits of information that describe the visual presentation of geo-data . Likewise, there are processing tools to collect and handle geo-data, and processing tools especially designed to create and manage geo-data visualisations. \r\nWhile cartography methods have traditionally produced printed maps (i.e. hard copy) with static scale, orientation, projection, legends (content based) and tied to a period or instant of time. Nowadays geo-data visualisations are interactive by design, meaning that the results are map-based responsive interfaces, highly customisable through dynamic objects to zoom in and out, pan and tilt, change projections and graphic expressions on the fly, as well as dynamically browse the map over time. \r\nIf the production methods have changed, also the type of authors. Map making in its widest sense is not only a privilege of a few experts but has been democratised in such a way that. everybody is able to make maps using open data and open source apps and tools for geo-data visualisation. Therefore,the new roles of open data and new forms of geo-data like geo-social media make usability, intended and ethical considerations key aspects of geo-data visualization design, production and sharing. \r\nUnder the concept of cartography and visualisation it is included a list of concepts that together comprise the science and technology of visual representation of geographic data.","name":"Cartography and Visualization","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV1-1","description":"The evolution of cartographic representation in the previous centuries followed the most important technological and scientific developments of the time. It was driven by commercial and/or military needs and influenced by the special characteristics of the areas and/or environments to be mapped. Recent developments are the rise of open data worldwide and widely available internet technology allowing end users to get remote geo-data published elsewhere. In recent years, data and its digital presentation have become central elements of cartography, whereas paper maps have become peripheral.","name":"History and evolution of cartography","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV1-4","description":"Art in cartography means much more than designing aesthetically pleasing maps, whether on paper or digital. Exploring the interaction at large between art and cartography involves rethinking the way we approach spatial expressions and how cultural, social and political dimensions are reflected in maps. This can be clearly observed in historical maps - in between art and science - ranging from beautiful geographical representations created in the Middle Ages to convey religious messages to the creation of modern maps showing the power of modern empires and nations. This particular relationship between art and maps entails: “developing an inclusive approach of artistic mapping expressions; facilitating and encouraging interaction between cartographers who work with the Art aspects of cartography and artists who produce cartographic artifacts; and developing conceptual elements about the relationships between art and cartography.” Besides ancient paper maps, a sum of factors led digital maps and geospatial visualization, a matter of interest to artists and designers. Thanks to powerful computing systems and with the advancements reached in computer graphics or image processing, or the rise of information visualisation, new forms of representing and visualising geodata have also appeared. Creation of digital maps are still a two-way relationship since artists have explored maps as a medium for expressing their art, and cartographers have approached art to provide more than just the representation of locations and geographic features with the intention to make maps more attractive to their audiences.","name":"Art and geodata visualisation","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV1-5","description":"Historical maps are geographical representations made with the intention to represent spatial facts over time. Historical maps are generally considered valuable documents not just because of their historical value but also because most of them also are artistic representations by themselves. From a cartographical point of view, differentiation between historical maps and actual maps is mainly based on the advances in the history of Cartography, so once one disruptive advance in the map making process appears, maps created with previous techniques (and with some artistic or historical value) are usually considered as historical, such as ancient paper-based maps or old sea maps, for instance. Techniques such as scanning or photography can make ancient maps publicly available by converting hard-copy maps to digital ones. Once an historical map is digitised, the next step is to georeference it, which is the process of specifying and relating points of the digitalised map to actual coordinates in a geographic reference system. Because of its archival value and interest, historical maps are adequately preserved - following specific conditions - by map libraries, map societies or museums. Since digital methods and techniques have been replaced over time by new technological advances, first digitally created maps could be also considered historical, not because of its content, but of the techniques used to produce it.","name":"Historical maps","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV1","description":"At a certain moment in time people start to create more graphical representations of their surrounding environment. New technologies offered ways to expand these representations to larger geographical extent, higher spatial resolution, finer temporal granularity and larger periods. Technologies even made it possible to include other representations of reality such as social media and data ensembles in geodata visualizations, to the extent to even blend the real world with geodata-based visualization providing an augmented – virtual reality continuum. New forms of geo-data, like geolocated sensors may challenge the way geo-data visualisations are generated, shared and, eventually, influence decision-making processes. History and trends sketch these developments and future outlook. This concept introduces the main stages and turns in development of cartography, from earliest times to the present, the most important methods in map-making and map-based visualizations.","name":"History and trends","selfAssesment":"Completed (GI-N2K)
\r\n\r\n"},{"code":"CV2-1","description":"As mapping ( geo-data visualization) is intended to convey a certain message to a certain audience, it is essential to use data sources that allow the intended visualisation result. The data should be of the right degree of detail and its use should not cause copyright problems. The producer quality of each data set should be taken into account, as well as the fitness of the data for the intended use. Aspects: message; data quality","name":"Data sources for mapping","selfAssesment":"
In progress (GI-N2K)
"},{"code":"CV2-2","description":"In the trajectory between raw (geo)data and their user-relevant representation, the necessary data processing includes ways of abstraction by selection, filtering, generalization, transformation and classification of geographical data. In this data processing it is essential to at one hand relate the final symbolisation to the necessities of the intended message, and at the other hand to procedures that introduce as little error as possible.","name":"Data processing","selfAssesment":"GI-N2K
"},{"code":"CV2-3","description":"Map projection is fundamental to representation of spatial data and for combining different datasets. Its choice should serve the presentation type that will convey the intended message to the audience. Many mathematical principles define datum, projections, horizontal and vertical co-ordinate systems, georeferencing- introduced with the focus on visualisation issues Aspects: geodetic concepts; transformations","name":"Mathematical base","selfAssesment":"GI-N2K
"},{"code":"CV2","description":"Geodata, including 3 dimensional geometry, as such can graphically be presented but most of the times the data as such doesn`t meet the presentation criteria. Especially if the dataset has to be presented in combination with other datasets. First all the geodatum, georeference and map projection are crucial but also the role of the geometry. The processing of the geometry and the related attributes may become a crucial step for an adequate presentation. Nowadays the highest precision may be used to define different graphical attributes for different zoom levels. On the other hand geodata visualisation includes also graphical datasets. Such data ensembles, the combination of geodata and graphical data, are the data sources that offer opportunities to other ways of visualisation then the traditional cartographic mapping. Facets: a.\tGeospatial location (2D) and position (3D) that data refer to b.\tDegree of detail in data origin (acquisition resolution) and in representation ('map' scale) c.\tTypes of data (e.g. imagery, field measurements, delineated objects)","name":"Data considerations","selfAssesment":"GI-N2K
"},{"code":"CV3-1","description":"The combined impact of graphic design properties (balance, legibility, clarity, visual contrast, figure-ground organization, and hierarchal organization) and the map components (north arrow, scale bar, and legend) should always be carefully evaluated against the needs and the capacities of the audience.","name":"Map design fundamentals","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV3-10","description":"Geo-gaming is a crossover between gaming elements and location, usually enabled by location based services and augmented adn/or virtual reality features. Geo-games, also known as “location-based games” or “location-aware games”, have geodata at its core, since geoinformation constitutes the central element of the game mechanics. Geo-gaming applications present unique technical challenges to meet the infrastructural and resources demands from the games and location worlds. There are mainly four different types of geo-games: exploration games (to make use of an existing spatial design); feedback games (to report about players’ experiences in a specific design); allocation games (to occupy the majority of game location); and configuration games (to occupy specific pattern of game locations). Gamers actively participate by interacting with the environment, therefore gaming scenarios are as varied as their goals, which include teaching, training, and the developing of spatial thinking skills. Geo-games offer a myriad of opportunities to developers: non-linear storytelling, physical object integration, a more visceral experience, true social interaction… which bring geo-games to another interaction level. Geo-gaming applications often rely on VGI to allow gamers adding geolocated information that may crowdsource geo-referenced data useful for other secondary purposes .","name":"Geo-gaming","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV3-2","description":"Map symbolization entails a number of variables to produce visual, tactile, haptic, auditory, and dynamic displays. Visual variables (e.g., size, lightness, shape, hue) and graphic primitives (points, lines, areas) are commonly used in maps to represent various geographic features at all attribute measurement levels (nominal, ordinal, interval, ratio). With those a single geographic feature can be represented by various graphic primitives (e.g., land surface as a set of elevation points, as contour lines, as hypsometric layers or tints, and as a hillshaded surface). The challenge is to use effective symbols for map features to ease the interpretation of maps.","name":"Symbols and icons","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV3-3","description":"The selection of colours to use in data representation can be influenced by various factors (e.g. the production workflow, cultural differences, involved devices and media). There are various colour models (e.g. RGB, CMYK, CIE) that describe colours in a way that they can effectively convey visual information (e.g., qualitative, sequential, diverging, spectral) according to the meaning of the underlying data. The cultural background of the consumer is also relevant when it comes to choose colours that should have real-world connotations or should express psychological concepts (e.g. harmony, concordance, balance). A final important factor is if the consumer has colour limitations","name":"Colour","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV3-4","description":"When data representation is conveyed in words (e.g. toponyms, road codes), written text is often placed in map labels. It is important to decide on the role of the label in the context of the representation type. Algorithms for label placement are relevant, especially when label density is high. Shape and colour of the labels help to signify different types of messages. This is supported by the typographic properties (type font, size, style) of the text in the labels. Finally, it is important to use an authoritative source for the texts","name":"Typography","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV3-5","description":"Imagery can be a source for data acquisition as well as an illustration to abstract data representations. Imagery can be made from the air (from drones to satellites) or from a terrestrial point of view. The knowledge field describing the data acquisition process based on photos is called Remote Sensing. Using photos from any source to illustrate stories about geographical subjects contributes is the visual aspect of telling a story. Together with maps and other narrative components, the combination embodies a storytelling medium. Aspects: photos for data collection; photo as part of geo data ensemble; photo as representation of place; photo as support of representation, illustration of specific time and place","name":"Photos","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV3-6","description":"Animation is the process of making the illusion of motion and change by means of the rapid display of a sequence of static images that minimally differ from each other. In the context of maps, the temporal component is added to a map to emphasize and observe the gradual evolution of a certain monitoring phenomenon, such as changes in spatially numerical variables (for example, environment, population, mobility, land use, etc.) with respect to a static geographic area. Map animations generally consider dynamic time while space is static. Map animation helps to see patterns or trends that emerge as time passes, depicting meteorological or climate events, natural disasters, historical events and other multivariate data. It is particularly helpful to be used in educational settings.","name":"Animation","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV3-7","description":"Sound can be one of the components of a multimedia data representation. Wikipedia: “Multimedia is content that uses a combination of different content forms such as text, audio, images, animation, video and interactive content. Multimedia contrasts with media that use only rudimentary computer displays such as text-only or traditional forms of printed or hand-produced material","name":"Sound","selfAssesment":"GI-N2K
"},{"code":"CV3-8","description":"Maps are valuable because they provide a large amount of detail in a small amount of space, and because of their capacity for telling a story. Telling stories through maps began with describing explored lands in great detail against terra incognita. Today, geographic tools, data, and multimedia on the web expand the ability and audience for storytelling through maps, being animated maps of particular interest. Any person with a smartphone or computer can use maps to tell a story, using live web maps completed with text, video, audio, sketches, and photographs.","name":"Storytelling","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV3-9","description":"Infographics are visual representations of information and data. The aim of an infographic is to present information that can be absorbed quickly and is easily understandable. Infographics can consist of Charts, Diagrams, Graphs, Tables, Maps and Lists. Infographics have evolved in recent years to be for mass communication, and thus are designed with fewer assumptions about the readers knowledge base than other types of visualizations","name":"Infographics","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV3","description":"This concepts covers basic design principles that are used in mapping and visualization, as well as cartographic design principles specific to the display of geographic data. Both page layout design and data display are addressed.","name":"Design principles","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV4-1","description":"A thematic map is a type of map especially designed to show a particular theme connected with a specific geographic area. These maps \"can portray physical, social, political, cultural, economic, sociological, agricultural, or any other aspects of a city, state, region, nation, or continent\". Cartographers use many methods to create thematic maps. Five techniques are especially noted: -Choropleth mapping shows statistical data aggregated over predefined regions -Proportional symbols, showing the relative value of attributes -Isarithmic or Isopleth, also known as contour maps -Dots, to show the location of a phenomenon -Dasymetric, which uses areal symbols to spatially classify volumetric data.","name":"Thematic mapping","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV4-10","description":"Conveying uncertainty information is often done through visualization. Uncertainty is often defined, quantified, and expressed using models specific to individual application domains. In visualization however, we are limited in the number of visual channels (3D position, color, texture, opacity, etc.) available for representing the data. Thus, when moving from quantified uncertainty to visualized uncertainty, we often simplify the uncertainty to make it fit into the available visual representations. (After Potter et al., 2012). The seven challenges as formulated by MacEachren et Al. (2005) are still there to be tackled.","name":"Visualization of uncertainty","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV4-2","description":"Relief can be represented in a two-dimensional map either through contour lines or through a raster format gridded array of elevations. Contour lines connect points of equal elevation. At regular intervals index contours are marked with elevations so a reader can more easily determine the elevation of surrounding locations. They are the preferred method for analogue topographic maps. The grid approach is used in digital mapping and known as a digital elevation model (DEM), where each raster cell represents an elevation. Scaling of the cell z value in relation to the x and y value results in terrain exaggeration, which aids visualization of topography.\r\nDEMs are used for terrain analysis and can be used to obtain derivatives such as slope and aspect. DEMs are obtained by interpolating point elevation observations, which are historically retrieved from surveyed point data (e.g. GPS locations), but more recently from LiDAR and/or Structure from Motion point clouds. TIN (triangular irregular network) analysis is commonly used for point data interpolation, in order to derive a continuous elevation surface.","name":"Representing terrain","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV4-3","description":"Multivariate descriptive displays or plots are designed to reveal the relationship among several variables simultaneously. Bivariate and multivariate maps encode two or more data variables concurrently into a single symbolization mechanism. Their purpose is to reveal and communicate relationships between the variables that might not otherwise be apparent via a standard single-variable technique. There are basic characteristics of the relationship among variables, such as the forms of the relationships, the strength of the relationships, and the dependence of the relationships on external (usually to the pairs of variables being examined) circumstances. Therefore, these multivariate plots or maps are inherently more complex, though offer a novel means of visualizing the nuances that may exist between the mapped variables. As information-dense visual products, they can require considerable effort on behalf of the map reader, though a thoughtfully-designed map and legend can be an interesting opportunity to effectively convey a comparative dimension. Examples of multivariate plots include enhanced 2-D scatter diagrams, 3-D scatter diagrams, contour, level, and surface plots, and high-dimensional data plots","name":"Multivariate displays","selfAssesment":"Completed (GI-N2K)
\r\n\r\n"},{"code":"CV4-4","description":"According to Daassi et al. (2006) the visualization process of temporal data has four steps: (1) time values to be visualized, (2) point of view on time, that identifies the characteristics of the temporal values to be visualized, (3) time space: define the displayable space of the time values and (4) point of view on the visualization space, the implementation of the perceptible forms of time. The visualization of spatio-temporal data can be done in many different ways such as multi-panel plots (maps), time-series plots (graphs), space-time plots (graphs), animations, and tables (Pebesma, 2012) Aspects: Space; Time; representation with visual means","name":"Visualization of temporal geographic data","selfAssesment":"
In progress (GI-N2K)
"},{"code":"CV4-5","description":"Dynamic and interactive displays refers to a situation where a display with a cartographical data representation changes in real time in response to user's actions","name":"Dynamic and interactive displays","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV4-6","description":"Web mapping is the process of designing, implementing, generating and delivering maps on the World Wide Web. Dissemination via the web opens new opportunities: realtime maps, cheaper dissemination, more frequent and cheaper updates, personalized map content, distributed data sources and sharing of geographic information. Technical restrictions cause challenges like low display resolution and limited bandwidth,( in particular with mobile computing devices with small screens and using slow wireless Internet connections), copyright and security issues, reliability issues and technical complexity. Today's web maps can be interactive and integrate multiple media. So interactivity, usability and multimedia issues also play a role.","name":"Web mapping","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV4-7","description":"Virtual reality or virtual realities (VR), also known as immersive multimedia or computer-simulated reality, is a computer technology that replicates an environment, real or imagined, and simulates a user's physical presence and environment in a way that allows the user to interact with it","name":"Virtual and immersive environments","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV4-8","description":"An Augmented Environment can be experienced through different sets of Augmented Reality (AR) technologies, including mobile displays (tablets and smartphone screens), computer monitors, or Head-Mounted Displays (HMDs), among others. AR is a technology that layers computer-generated enhancements atop an existing reality to make it more meaningful through the ability to interact with it. AR offers the integration of digital information and imagery onto the real world in real-time. In order to broaden the vision beyond this definition, AR can be described as systems having the following features: (1) combines real and virtual; (2) interactive in real-time; and (3) registered in 3D, allowing other technologies, such as mobile technologies, monitor-based interfaces, monocular systems to overlay virtual objects on top of the real world. Currently, AR applications use the camera provided by mobile devices to produce a live view of the real world in combination with relevant, context-appropriate information such as text, videos, or pictures.\r\nThere are lots of applications and systems in the market that provide AR functionality, making it difficult to classify and name them all. Some of them are related to the real physical world and others with the abstract, virtual imagery world. Sometimes it is not easy to figure whether it is an AR, as often AR is defined as Virtual reality (VR) with transparent HMDs. In general, the concept is to mix reality with virtual reality, including information and overlay over the real world through HMDs such as they seem apparent as one environment. The virtual objects can react accordingly with the camera's movement as it is registered concerning the real world, which is also the central issue of AR.","name":"Augmented environments","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV4-9","description":"Cartographers have recently become involved in extending geographic concepts and cartographic design approaches to the depiction of non-geographic data archives, using so-called spatialized views of information spaces. Spatializations differ from ordinary data visualisation and geovisualisation in that they may be explored as if they represented spatial information. (Fabrikant, S.I., 2003). As definitions of spatialization can be found: Spatializations are computer visualizations in which nonspatial information is depicted spatially (Montello et al., 2003). Spatialization is the transformation of high-dimensional data into lower-dimensional, geometric representations on the basis of computational methods and spatial metaphors. (Skupin 2007)","name":"Spatialization","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV4","description":"This concept addresses mapping methods and the variations of those methods for specialized mapping and visualization instances, such as thematic mapping, dynamic and interactive mapping, Web mapping, mapping and visualization in virtual and immersive environments, using the map metaphor to display other forms of data (spatialization), and visualizing uncertainty. Analytical techniques used to derive the data employed in these graphic representations are discussed in Knowledge Area AM Analytical Methods and Unit DN2 Generalization and aggregation.","name":"Graphic representation techniques","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV5-1","description":"Geospatial data representation can make high demands on computational facilities. Examples are: - Infrastructural connections to datasets and processing models - Processing capacity: speed and volume - Access to storage capacity: speed and volume - Display facilities: size, resolution, speed - Peripheral devices like printers for large format hard copy, or VR headsets","name":"Computational demands","selfAssesment":"GI-N2K
"},{"code":"CV5-2","description":"Standards for map services were set by OGC and ISO, called WMS and WMTS. Producing map images on the web from a cartographic image in a GIS application is called \"publishing\". Making a web \"map\" in the broader sense of constructing data representations for Storytelling or Geo-gaming is still under development. It requires a mix of applying the map Design principles and Graphic presentation techniques, possibly in combination with software scripting.","name":"Web map making","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV5-3","description":"Traditional \"map\" making, as opposed to the mapmaking in neogeography, focuses on reliable and reproducible products, based on expertise of high definition printing in many colours on analogue media of geodetically well-constructed images.","name":"Traditional map making","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV5-4","description":"The aspects of reproduction of a data representation depend on the nature of the representation: is it analogue (a paper map, a mock-up) or is it digital? In the case of a paper map, its digitalisation with high fidelity is an essential step. With a source in digital form, reproduction can be a matter of the right printer. Alternatively, the source could be disseminated as a file or as a web service. If representations are dynamic and/or interactive the possibilities depend on the construction of the representation. The ease of dissemination of digital files should not result in copyright breach. Aspects: Digitalization techniques for analogue sources, Printing ( 2D, 3D), Dissemination ways, Construction of the data representation, User needs specification, Copyright issues","name":"Map reproduction","selfAssesment":"GI-N2K
"},{"code":"CV5","description":"This concept addresses map production and reproduction, as well as computation issues that relate to those workflows.","name":"Map production","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV6-1","description":"The potential of maps as a way to show or exert power over the population was early understood by ruling classes. A map expresses a claim by the inclusion or exclusion of map elements and how these elements are visually related and/or depicted on the map. So, the world could be modeled through the careful choice of content arranged graphically at a specific scale and in specific formats. Therefore, maps embody and project the interests of their creators. The “new cartographies” declare that maps are redefined as socially constructed arguments based upon consistent semiotic codes. Nowadays, the rise of costless, powerful and accessible tools for creating maps, put power on the side of individuals or groups of individuals with few organisation (crowdsourced data collection or VGI) capable of representing their world views. In addition, monitoring people, places or nature, for instance, should also be seen as another way to show the increasing power of maps. Surveillance mechanisms for tracking populations used by rulers, or the use of extended technologies like Google Earth by environmental organisations to track the Amazonian forest, constitute two examples of the particular use of maps to exert control over human beings or to press governments for taking specific actions, respectively.","name":"The power of maps","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV6-2","description":"Becoming aware of what a \"map\" shows depends partly on what the senses can register of the representation as a whole. It also depends on recognition of elements in the representation that are meaningful to the observer in the sense that these elements are credible indicators of spatial features. Based on that recognition, the nature of these elements and their spatial pattern might infer thoughts about historic or ongoing processes. This interpretation will be influenced by the expertise and needs of the observer.","name":"Map reading and interpretation","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV6-3","description":"Assessment of the usability of a data representation is about how useful it is to users. Therefore it is a test of the success of the representation design, a test of the skills of the \"map\" maker and a test for the reliability of the underlying data.","name":"Usability analysis","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV6-6","description":"Spatial thinking is thinking that finds meaning in the shape, size, orientation, location, direction or trajectory, of objects, processes or phenomena, or the relative positions in space of multiple objects, processes or phenomena. Spatial thinking uses the properties of space as a vehicle for structuring problems, for finding answers, and for expressing solutions\" Aspects: recognizing spatiality in a collection of things; translation of the collection to a pattern of elements; recognizing structure (relations between the elements in a pattern); recognizing process (or changes over time in patterns or structures)","name":"Spatial thinking","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV6-8","description":"Ethics is about the question if behaviour is right or wrong in a social context. In dealing with geodata, a person can do the wrong thing with respect to laws (e.g. disclose secrets, disregard privacy, copyright infringement) or to professional standards (e.g. use bad data, forget about the colour blind, downplay unpleasant details). Aspects: breach of legal standards; breach of professional standards","name":"Map ethics Legal and privacy issues","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV6","description":"Geodata visualisation are always made with a certain purpose. The role and understanding of such graphical representation is an important field of research. Besides theories that underpin evaluation approaches and their findings the visualisation may also be confronting. The more realistic the presentation and especially when it includes human/personal related data the ethical dimension of the visualisation play a major role. Usability of visualisations has also an impact on spatial thinking as has been proved by scholars.","name":"Usability of maps","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA","description":"Proper design of geospatial applications, models, and databases and the validation and verification of design activities are critical components of work in all areas related to GIS&T. Design failures can negate well-intentioned efforts to apply concepts and technology to solve real-world problems. While sharing a number of concerns with general systems analysis, the unique and complex spatial characteristics of geospatial information provide significant additional challenges. The focus of this knowledge area is on the design of applications and databases for a particular need. The design of general-purpose models and tools (e.g., raster and vector) is covered in Knowledge Area: Data Modeling (DM). In the context of specific implementations, design activities fall into three general classes:\r\n1. Application Design addresses the development of workflows, procedures, and customized software tools for using geospatial technologies and methods to accomplish both routinary and unique tasks that are inherently geographic.\r\n2. Analytic Model Design incorporates methods for developing mathematical models, spatial models and data processes. The design of the analytic model is often influenced by decisions that are made about data models and structures.\r\n3. Database Design concerns the optimal organization of the necessary spatial data in a computer environment in order to efficiently sustain a particular application or enterprise.","name":"Design and Setup of Geographic Information Systems","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA1-1","description":"This concept deals with the importance of having a list of prioritized requirements as a first step to ensure a smooth and successful implementation of a GIS project.. It entails the different methodologies and approaches to ensure a GI system covers all functional and nonfunctional requirements. Requirements are not only derived from business workflows but it is advisable to gather direct input from potential users that will be translated into requirements. However, there is a need to clearly rank the importance of the requirements gathered to ensure the GI system is manageable and in line with the intended use of the GI system, in opposition with the specific interests of a particular user or ambiguous requirements. Therefore, the documentation, traceability and evaluation of requirements after the implementation are as relevant as the initial gathering of requirements to give consistency to the designed system.","name":"Requirements gathering and analysis","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA1-2","description":"The internal process of documenting a task or a process is about “how” it is implemented and “what” is implemented. Documenting is particularly helpful if a breakdown occurs, such as when an expert working in a task leaves her job or to substitute one task in a set of interrelated processes by another. Documentation provides consistency for the taskand allows its monitoring, analysis and revision during a project. \r\nThere are different methods for documenting a task to transform tacit knowledge into explicit knowledge. Therefore, the task should be documented by describing it in video format and using visual tools that allow documentation, or the maintenance of a field diary.\r\nIn particular cases, the creation of user guides or manuals could be considered a subset of a process description particularly addressed to external users. A user manual should take into account the target users to adapt its content to them.","name":"Methods of process description and documenting","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA1-3","description":"- Analysis of application processes - Languages for business process descriptions - Transformation of application processes into systems","name":"Transformation of application processes into systems","selfAssesment":"GI-N2K
"},{"code":"DA1-4","description":"A workflow is a sequence of operations that splits real work in several steps. A workflow is often described in a document that, by the use of particular elements, serves as a visual representation of actual work.","name":"Workflow definition and consideration in GI systems","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA1-5","description":"Software and information technology are integral to any GI systems or projects, from the storage and handling of spatial data to its analysis, visualisation and sharing . The design and creation of software play is fundamental in modern GIS projects. Therefore, the use of well-known software engineering techniques and methods to develop efficient, reliable and easy-to-maintain software applications in the GIS realm is more important than ever.","name":"Software design and engineering","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA1-6","description":"User interface and usability of a GIS system","name":"User interface and Usability","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA1-9","description":"Geodesign is a design and planning method along with geospatial modelling and technology, and simulations informed by geographic contexts to facilitate informed decisions and the creation of design proposals. A geo-design process is a problem-based, iterative process bounded by specific (geographic) constraints characterised by a collaborative effort.","name":"Geodesign","selfAssesment":"Completed
"},{"code":"DA1","description":"This concept encloses a set of activities and workflows to ensure that the implementation of a GIS system in an organization or project is correctly planned and designed according to the particularites, user requirements and current conditions of the project ahead. In general system design is the process to promote successful GIS in an enterprise environment. As a GIS system has a direct influence on the information technology department (IT), the system design tells the organizacion how the current infrastructure can or must support the planned GIS. This process builds a set of specific recommendations on hardware and network needs based on the number of projects that depend on the GIS solucion, as well as the projected business needs and user requirements. \r\nGIS architects through the system design process need to take into account and identify several conditions: a) infrastructure requirements, b) the network communication capacity, c) hardware and software procurement requirements and, d) software development and data acquisition needs. \r\nHaving a well-defined and successful GIS deployment is not only a matter of what data or software the organization should acquire. The process of system design aligns identified business requirements (user needs/requirements) derived from business strategies or project aims, goals, and stakeholders (business processes) with identified business information systems infrastructure technology (network and platform) recommendations. \r\nThe process starts with identifying business needs, including the identification of users locations, required information, data, resources or products. The business needs are generally considered as project workflows that help the GIS architects to identify the expected data traffic and computing demand associated with each transaction, being a transaction the work unit used to translate business requirements into associated server and network loads.\r\nWithout carrying out a proper system design, a GIS system can lead to an implementation and deployment failure, deriving in unfulfilled expectations and high costs in terms of human resources and financial matters.","name":"System design","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA2-1","description":"Project management is the planning, organization, coordination, execution, monitoring, controlling and closing of activities and resources - human and economic - for the timely achievement of clearly defined objectives forming a project.","name":"Project management","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA2-2","description":"This concept embraces the factors that could affect a GI system / project and could constitute obstacles to success or even decide a project is not doable. In order to ensure the success of a GI system or a GIS project there are several criteria to take into account from the very beginning of the conception of the GI system or project. A feasibility study may encompass different perspectives (economic, legal, technical, operational or scheduling ) to inform whether or not a project is worth the investment. An organisation should list the foreseen costs from these five perspectives listed above and the benefits (tangible or intangible) of implementing a system/project. Existing resources already available in-house and internal strategic plan in place could be critical to decide to undertake a project or not. The table below presents a non-exhaustive list of criteria and under which perspectives they should be examined.\r\nFeasibility analysis should include a pilot study to evaluate and improve the system / project proposed.","name":"Feasibility analysis","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA2-8","description":"This concept discusses the technical, organizational and monetary advantages and disadvantages of commercial versus open source software. GIS/T industry and research are slowly but consistently moving toward the openness of software and source code. Open software entails some clear advantages like continuous development of new applications, building community of developers and users, possibility to start a project even if limited funding is available, increase the sustainability chances of a project, etc. to name a few. On the other side, proprietary initiatives in GIS/T are holding their roots to the ground by developing cutting edge tools for handling challenging environments and present other advantages as well. Some of them could be: a more stable software or a dedicated support service for the client. Both open and proprietary geospatial software solutions co-exist by the application of the appropriate IPR licences to each type of solution. The future is how these commercial and open source geospatial software find synergy in handling large projects.","name":"Commercial and open source software","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA2","description":"To design, build, and maintain a GIS, sufficient resources (e.g., labor, capital, and time) must be secured. Resource planning consists of the allocation and use of in-house resources (people, equipment, tools, rooms, etc.) to achieve the maximal efficiency of those resources. These resources are required for a variety of system elements, including design, software purchase, labor, hardware, and facilities. The crucial task is to determine whether the project is worth the required resources.","name":"Resource planning","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA3-1","description":"The ecosystem of GIS software architectures has evolved substantially in recent years to include a variety of options ranging from desktop GIS, server-based and component-based architectures to Web-based, cloud-based, mobile-based approaches. Aligned with the main trend, geospatial software architectures or infrastructures are also moving from desktop architectures to more cloud based or server based options to meet ever-increasing requirements of interoperability, interdisciplinary work and computational power for processing large data sets and derived products. Cloud-based architectures also enable on the fly visualization of computed geospatial products, as complementary visualisation and mapping tools are seamlessly integrated into modern cloud-based based architectures. Usage of a particular architecture is fully dependent on the nature, size, requirements, functionalities, and available resources of a given project or task. Desktop and server based applications are particularly suited for small sized projects and startups while enterprise based applications are meant for larger sized projects. Cloud based infrastructure can be useful for varying sizes of projects in which the computational infrastructure is fully outsourced.","name":"Major geospatial software architectures","selfAssesment":"In progress (GI-N2K)
\r\n\r\n"},{"code":"DA3-2","description":"Interoperability of GIS infrastructure or architecture ensures the consistent and uninterrupted usage of data and functionalities across platforms and systems. Components or tools residing on distinct platforms can “talk” to each other without friction. Interoperability is a central characteristic, especially important in distributed systems and architectures. It can be applied to different levels or layers of a system, i.e. infrastructure level, data level, business logic level, etc. For example, standard spatial data formats and protocols are especially relevant for handling GIS data across multiple systems and platforms, regardless of their underlying software architecture. This is particularly important in large-scale, collaborative projects involving various teams using heterogeneous GIS architectures. Most software providers, developers communities and standardisation bodies and committees are striving to make their architectures interoperable in an open manner, so proprietary standards and protocols are a potential hindrance to this initiative.","name":"Interoperability","selfAssesment":"
Completed (GI-N2K)
"},{"code":"DA3-3","description":"Thisconcept considers general architectural patterns like SOA, ROA, Web Services, etc.","name":"Architectural Patterns","selfAssesment":"GI-N2K
"},{"code":"DA3-4","description":"- WebGIS, - technical pecularities of spatial data infrastructures - standardiced GI services for SDI: WMS, WFS, CSW, Transformation Services, SOS, WPS etc., - other map services and interfaces","name":"WebGIS, SDI services, map services","selfAssesment":"GI-N2K
"},{"code":"DA3-5","description":"This concept deals with Reference Model of Open Distributed Processing (RM-ODP), its standards, viewpoints modeling and the RM-ODP framework","name":"Reference Model of Open Distributed Processing","selfAssesment":"GI-N2K
"},{"code":"DA3-6","description":"Cloud computing provides an on-line computing transparent resource to the user, since a user doesn’t notice almost no difference between working on her own computer or the cloud. Owned and managed by infrastructure providers, cloud computing entails advantages (concurrent access by many users, software updates hosted in the cloud, cost-efficiency or outsourced maintenance in the cloud) and disadvantages (loose of control, network Connection Dependency or security breaches ). On the other side, grid computing is a full network of computers and data working together so functioning as a supercomputer. Grid computing presents advantages such as shorter resolution of complex problems, the ease of organizational collaboration or a better use of existing hardware.","name":"Cloud and Grid computing","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA3-7","description":"Within this concept solutions based on Desktop GIS and GIS libraries will be compared and contrasted","name":"Desktop GIS, GIS libraries","selfAssesment":"GI-N2K
"},{"code":"DA3","description":"This concept describes the major geospatial software architectures available currently and choices when designing GI applications and systems, including desktop GIS, server-based, Internet, and component-based custom applications.","name":"Architectural design of a GIS system","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA4-1","description":"- Compare and contrast the relative merits of various textual and graphical tools for data modeling, including E-R diagrams, UML, and XML - Create conceptual, logical, and physical data models using automated software tools - Create E-R and UML diagrams of database designs","name":"Modeling tools","selfAssesment":"GI-N2K
"},{"code":"DA4-2","description":"Within an initial phase of database design, a conceptual data model is created as a technology-independent specification of the data to be stored within a database.","name":"Conceptual models","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA4-3","description":"A logical data model expresses the meaning context of a conceptual data model, and adds to that detail about data (base) structures, e.g. using topologically-organized records, relational tables, object-oriented classes, or extensible markup language (XML) construct tags","name":"Logical models","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA4-4","description":"A physical data model documents how data are to be stored and accessed on storage media of computer hardware","name":"Physical models","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA4","description":"The effective design of geospatial databases should follow the established methods and principles of database modeling and design developed in computer science. The basic method is a three-step process generally called the conceptual, logical, and physical models transforming the application from very human-oriented to machine-oriented. Several standards and software tools exist to aid the process of database design.","name":"Database design","selfAssesment":"GI-N2K
"},{"code":"DM","description":"This knowledge area deals with representation of formalized spatial and spatio-temporal reality through data models and the translation of these data models into data structures that are capable of being implemented within a computational environment (i.e., within a GIS or more likely within a spatial database). Data modelling is a crucial issue as it defines the content of a spatial database and usefulness of these content (data) for certain applications. Data Modelling is performed using system neutral languages like UML (or more seldom ER-diagrams). These conceptual models have to be transferred to logical models (i.e. tables of a database). Data is stored in spatial databases which are normally organized in an object relational way. For certain types of data specific databases are used, like triple stores, NoSQL DBs, Array DBs etc. For data modelling quite a number of ISO standards are available for deriving the conceptual model as well as for rules for application schemas, spatial schemas, temporal schemas, Quality principles, encoding, 3D modelling (CityGML) etc. Data models provide the means for formalizing the spatio-temporal conceptualizations. Examples of spatial data model types are discrete (object-based), continuous (location-based), dynamic, and probabilistic. Mastery of the objectives presented in this knowledge area require knowledge and skills presented in the bodies of knowledge of allied fields, including computer science (ACM/IEEE-CS Joint Task Force, 2001) and information systems (Gorgone & Gray, 2000; Gorgone & others, 2002).","name":"Data Modeling, Storage and Exploitation","selfAssesment":"In progress (GI-N2K)
"},{"code":"DM1-1","description":"This topic includes the main basic database concepts: - Database, definition and overview - Database management system, definition and overview - Relational databases, overview - Object-oriented databases, overview - Object-relational databases - NoSQL databases, general overview - NoSQL databases, examples triple stores, array databases, others (overview)","name":"Overview on database concepts","selfAssesment":"GI-N2K
"},{"code":"DM1-2","description":"The Relational Model is the most important database model, therefore it is explained in more detail here: - Basic concepts (tables, tuples, etc.) - Relation to relational algebra (RA), basics of RA - Constraints (key, domain, referential integrity) - Relation to entity relation (ER) model, basics of ER","name":"The Relational Model","selfAssesment":"GI-N2K
"},{"code":"DM1-3","description":"Relational databases and database management systems are essential for GIS in consequence the important issues have to be treated here: - General aspects, basic architecture of a DB, advantages, features - DBMS concepts and functionalites (transactions, locks, multiuser access etc.) - Database design, techniques - Database administration - Normalization (1NF - 3NF) - Example of a database design","name":"Relational Databases, Database Managements Systems and Database principles","selfAssesment":"GI-N2K
"},{"code":"DM1-4","description":"Database queries and especially spatial queries require specific data structures to be performed satisfactory Relevant is: - Motivation, examples of typical non-spatial and spatial queries - Trees, B-tree, R-tree, Q-tree - Graphs, overview and relation to databases","name":"Data Structures and Indices for Databases","selfAssesment":"GI-N2K
"},{"code":"DM1-5","description":"Big data like imagery but also for example GML data sets need compression to be accessed / transferred in an acceptable time. Therefore some compression techniques have to be taught: - Motivation, examples of data sets which need compression - General introduction, vector - / raster data compression, compression lossless, lossy - Popular compression techniques, LZW (Lempel-Ziv-Welch) encoding, Huffman encoding - Techniques for raster data, runlength encoding, JPEG coding, wavelet etc. - Techniques for the reduction of vector data (Douglas Peuker etc.) - Data formats, overview and relation to compression techniques","name":"Data compression techniques","selfAssesment":"GI-N2K
"},{"code":"DM1-6","description":"SQL is the \"standard\" to perform spatial and non-spatial queries in databases. That means each student in a GI related course has to be familiar with the main aspects if it: - Motivation, history, overview - Data definition language DDL - Data manipulation language DML - Data control language DCL - Spatial extensions of SQL","name":"SQL and its usage for data handling, spatial extensions to SQL","selfAssesment":"GI-N2K
"},{"code":"DM1-7","description":"UML is the standard for describing the schema related to GI models, but also user requirements, workflows etc. can be described in UML using the UML diagrams: - Motivation, background, purpose - Use case diagrams - Class diagrams - Sequence diagrams - Activity diagrams","name":"UML introduction and class diagrams","selfAssesment":"GI-N2K
"},{"code":"DM1-8","description":"XML knowledge is an important bases for understanding GML. Moreover XML tools like XSLT are important to transform XML or GML data sets into other XML based formats like SVG or others. Important issues: - Motivation, purpose - Relation to HTML - XML document structure - XML syntax, elements, attributes and namespaces - xlink, xpath and XSLT - XML DTD - XML schema","name":"XML introduction","selfAssesment":"GI-N2K
"},{"code":"DM1-9","description":"The long term storage of GI data in general is based on spatial databases. Therefore the following is essential for a GI course: - Relation between GIS and DB / \"Long transactions\"- Dual concepts - Characteristics of spatial databases - Spatial data in object relational databases - Spatial extensions of DBs, overview","name":"Database concepts in GIS and Principles of spatial databases","selfAssesment":"GI-N2K
"},{"code":"DM1","description":"This unit includes the basics for data modelling, storage and exploitation. Data modelling is one of the most important activities in conjunction with Geographic Information / GIS as it determines how the data can be used and if the requirements from applications are fulfilled. Data modelling can be done in conjunction with the database, e.g. through ER diagrams or according to the ISO 191xx standards by using UML. The costs of data acquisition can be tremendous, therefore the data represents an enormous value. This value has to be conserved through a safe long term data storage. Therefore databases and especially relational and object relational databases are crucial. For a proper storage and query of geographic information databases are extended with specific data types and data structures. As data sets can be very large suitable compression techniques became important especially in the context of accessing and delivering geographical data, e.g. through services. XML based modeling languages for encoding also play and important role in this context","name":"Foundations for Data Modelling Storage and Exploitation","selfAssesment":"In progress (GI-N2K)
"},{"code":"DM2-1","description":"GI standards, mainly from ISO and OGC are essential nowadays. Moreover also an overview on ICT standards from W3C or OMG are important as well as some understanding of standardization processes. In detail: - Motivation for standards, examples from daily life - Overview on GIS and relevant ICT standardization bodies and selected standards - De jure and De facto standards, obligation, reasons for the usage of standards - Standardization within ISO - Standardization within OGC, relation to ISO - Examples of ISO 191xx standards","name":"Overview on relevant standards and standardisation bodies","selfAssesment":"GI-N2K
"},{"code":"DM2-2","description":"Conceptual data modeling is a key skill for GI people. (see relations to other topics) The following therefore is important: - Overview on the relevant standards like conceptual schema language, Rules for application schema - Examples of conceptual schemas","name":"The principle of conceptual data modelling according to ISO","selfAssesment":"In progress GI-N2K
"},{"code":"DM2-3","description":"Geometric modelling is an important subtask of conceptual modelling and requires the following basics: - Overview of ISO 19107 - spatial schema - Overview of ISO 19125 - simple features - Examples of the usage of spatial schema and simple feature elements for feature class definitions - Relation to GML - Relation to DBs","name":"Geometry data types according to spatial schema and the simple feature specification","selfAssesment":"GI-N2K
"},{"code":"DM2-4","description":"Also temporal aspects have to be considered within conceptual modelling. This also requires basics: - Motivation, examples - Temporal variability of features (move, change of structure or geometry) - Overview on ISO 19108 temporal schema - Examples of modeling temporal aspects","name":"Temporal data types according to temporal schema","selfAssesment":"In Progress GI-N2K
"},{"code":"DM2-5","description":"Conceptual models of course have to be implemented, in general in a GIS (which is often proprietary), or in a database (which can be standard based) ,therefore here the implementation in a database is treated: - Repetition of conceptual and logical models - Examples of the transferring of a conceptual model to a logical (database) model","name":"Transferring conceptual models to logical models","selfAssesment":"In progress (GI-N2K)
"},{"code":"DM2-6b","description":"Metadata is considered as very important for the usage as well for the search for Geodata Relevant basics are: - Motivation, importance of data quality as part of metadata - Metadata in an spatial data infrastructure with many There are quite a number of relevant standards for GI courses. Some are listed here, others might be considered, depending on the background of the course: - Select other standards and explain them, Important are: - ISO 19141 Schema for moving features, ISO 19142 Web Feature Service or others - 19109 - Rules for application schema - Selection of other standards is depending on the background of the course","name":"Other standards","selfAssesment":"In progress GI-N2K
"},{"code":"DM2-7","description":"GML is the most important standard for the transfer of Geodata as it allows to transfer the schema information as well as the data. Important issues: - Motivation, Importance of a Geography Markup Language - History of GML, Overview 19136 - Geography Markup Language - Relation to spatial schema - Supported features in GML (Topology, 3D ...) - Structure of GNL, profiles, application schemas etc. - Transfer of models and of data - Examples","name":"Introduction to GML","selfAssesment":"In progress (GI-N2K)
"},{"code":"DM2-8","description":"3D Models, especially 3D city models are becoming more and more important. CityGML is the most important standard within the GI domain to describe City models semantically and geometrically. Relevant issues: - Motivation, Usage of CityGML - Relation to GML - Coherence of semantics and geometry - Principles of modeling - Level of detail concept - CityGML vs KML - Examples","name":"Introduction to CityGML","selfAssesment":"GI-N2K
"},{"code":"DM2","description":"This unit includes the essentials of relevant standards for spatial data modelling. A number of ISO and OGC standards are available for deriving the conceptual model as well as for rules for application schemas, spatial schema provides data types for geometry models in various forms, Point, line, area, body based, temporal schema allows to consider temporal dimensions, Quality principles can be used to describe the quality of geodata, encoding standards (mainly GML) allow the standard based transfer of data and data models, CityGML allows a standard based 3D modelling, etc.","name":"Standards for Spatial Data Modeling","selfAssesment":"In progress GI-N2K
"},{"code":"DM3-1b","description":"There are two basic concepts related to this topic: Features and Fields, or Geo-fields, as named by Goodchild at al. The concept of fields can be differently represented as explained here: - Repetition of basic concepts of Geographic Information Science - Explanation of the concept of continuous fields and the commonly used ways of representing geo-fields - Relation between fields and coverages, an important discretizations of a Geo-field - Types of Coverages","name":"The concept of fields","selfAssesment":"In progress GI-N2K
"},{"code":"DM3-2","description":"The raster data model holds values in a regularly spaced matrix of cells arranged in rows and columns covering a two dimensional space. Rasters are commonly used to store continuous data like colors in an image and height values but they are also used for discrete (thematic) values like land use.","name":"The raster model","selfAssesment":"In Progress (GI-N2K)
"},{"code":"DM3-2b","description":"Grids are on the one hand one important type of caverages and on the other hand Grids are used as basic structure in some applications. Important here is: - Definition of the concept of grid in GIS - Grid as an instance of coverages - Grids as a basic structure for certain applications / medium for aggregation of data - Examples of grid-based data such as Digital Terrain Models (DTM) - Grids in census / statistical data and Geo-marketing applications","name":"Grid representations","selfAssesment":"GI-N2K
"},{"code":"DM3-3","description":"Grid data models can contain millions of discrete values. This leads to very large datasets. Depending on the way values change over the grid, different methods can be used for an optimal (lossy or lossless) data compression. Type of data, computer power needed, application of the data, method of transport and storage all contribute to the choice of compression method.","name":"Grid compression methods","selfAssesment":"In progress (GI-N2K)
"},{"code":"DM3-3b","description":"TINs and Voronoi tessellations are important types of coverages. TINs play a very important role also in Computer graphics. Important here is: - Basics from Graph theory - Definition of Triangulated Irregular Networks (TIN), purpose and applications - TINs and voronoi diagrams as a type of coverages - One important instance of a TIN: Delauney Triangulation - Definition of Voronoi Diagrams, purpose and applications - Relation between Delauney Triangulation and Voronoi Diagram, the \"Dual Graph\" - Examples from applications","name":"TIN and Voronoi tesselations","selfAssesment":"GI-N2K
"},{"code":"DM3-4","description":"While the classical grid structure uses rectangular cells, the hexagonal data model uses hexagons to represent raster data","name":"The hexagonal model","selfAssesment":"In progress (GI-N2K)
"},{"code":"DM3-4b","description":"Linear referencing is 1 dimensional positioning. The position of an object is defined by the distance from the object to the start point along a line. Linear referencing is for example used in railway dispatching systems","name":"Other models like linear referencing","selfAssesment":"In progress GI-N2K
"},{"code":"DM3-5b","description":"Resolution of raster and gridded data - Georeferencing of data, direct and indirect methods (t.b.d.)","name":"Resolution and georeferencing system","selfAssesment":"In Progress (GI-N2K)
"},{"code":"DM3-7","description":"In hierarchical data models data is organized in a tree-like structure. Data are connected with parent-child relations. Hierarchical structures are often used for spatial indexing.","name":"Hierarchical data models","selfAssesment":"In progress (GI-N2K)
"},{"code":"DM3","description":"This unit includes relevant tessellation data models. Besides features (sometimes also called geo-objects) geo-fields play and important role. In recent literature tessellation models are classified as discretizations of fields. In traditional GI literature tessellations are defined as important data structure itself. Tessellation discretise a continuous surface into a set of non-overlapping polygons that cover the surface without gaps. Tessellation data models represent continuous surfaces with sets of data values that correspond to partitions. Important tessellation models are Grids, TINs and Voronoi diagrams.","name":"Tessellation data models","selfAssesment":"In progress (GI-N2K)
"},{"code":"DM4-1","description":"This topic includes the basics for feature based modelling. There are a number of standards also relevant for this topic (see relations). The following items should be included: - Definition of a feature (in some literature also called object, or geoobject) and of feature classes respectively. - Aspects of the definition (ID, geometry, topology, thematic, time etc.) - Techniques for the definition of features / feature classes (mainly link, as they are described elsewhere, see relations)","name":"Feature based modelling","selfAssesment":"GI-N2K
"},{"code":"DM4-2","description":"This topic describes the process of Geometric modelling using vector data, means the primitives like points, lines, areas, bodies, or raster data. There is a strong relation to ISO standards (see relations) as they provide basic data types for geometric modelling. Main issues: - Geometric modeling based on vector data - Geometric modeling based on raster data - Conversion between the models - examples, advantages, disadvantages of the models","name":"Geometric modelling","selfAssesment":"GI-N2K
"},{"code":"DM4-3","description":"In topological modelling the geospatial relations in a data model are represented by the position of geospatial objects, especially nodes, edges and surfaces.","name":"Topological modelling","selfAssesment":"In progress (GI-N2K)
"},{"code":"DM4-4","description":"This topics deals with the definition of an application schema. There are other units which are important for this topic (see Relations). Issues to be included: - Methods to define and describe an application schema (requirement analysis, description of the schema etc.) - Feature attribute catalogues - Domains / data relevant for INSPIRE","name":"Application models based on vector data","selfAssesment":"GI-N2K
"},{"code":"DM4-5","description":"This Topic deals with important application models, which should be chosen with relation to the course (geographically / related to the background of the course) INSPIRE should be treated in any case. In detail: - Overview on important application models relevant for the course, e.g. from topography or environment in the country - Repetition of the principles of Spatial data infrastructures - Overview on the INSPIRE initiative and the goals related - The INSPIRE data model - The architecture of INSPIRE and the necessary services - Domains / data relevant for INSPIRE","name":"Examples of important application models","selfAssesment":"GI-N2K
"},{"code":"DM4-6","description":"This topic is dedicated to the challenges of model based interoperability and related issues, The principles of interoperability are included in DA3-2. In detail: - The challenges of model interoparability (semantics, different modelling of the same features in different models, syntacs) - Overview on IT concepts for schema integration / transformation - Approaches for model integration - Approaches for model transformations, e.g. related to INSPIRE, from the Humboldt project","name":"Model based interoperability, model transformations","selfAssesment":"GI-N2K
"},{"code":"DM4-7","description":"Network models are crucial in some application domains, such as Navigation (roads etc.), but also in utility applications (facilities like pipes etc.) In this topic should be treated: - The network model in the database domain - Graph based NoSQL databases - Topology of network models - Data structures for storing network data - The Dijkstra algorithm - Overview on important applications","name":"Network models","selfAssesment":"GI-N2K
"},{"code":"DM4","description":"This unit includes relevant issues related to vector data models, feature based modelling, applications. Besides imagery data the majority of GI data available is feature based and founded on vector geometry. Topology modeling also is very common nowadays, as many analysis like routing or neighborhood analysis require it. Spaghetti modelling becomes more and more and exception. In every country there are important feature and vector geometry based application models available e.g. in Topography / Cartography. In Europe every GI course should include some information on INSPIRE. As in different application domains different data models are used, sometimes for the same feature types, integration and transformation of models are an important issue also.","name":"Vector data model, Feature based modelling, Applications","selfAssesment":"In progress GI-N2K
"},{"code":"DM5-1","description":"- Many geographical phenomena are not defined sharply but uncertain Uncertainty has a number of considerations: - Motivation, background, purpose - Conceptual model of uncertainty - Uncertainty of geographic phenomena (vagueness, ambiguity) - Uncertainty of measurements - Uncertainty of analysis - Uncertainty vs. data quality - Statistical models of uncertainty - Outline of Fuzzy approaches","name":"Basics of uncertainty and its modelling","selfAssesment":"In progress GI-N2K
"},{"code":"DM5-2","description":"Space and time are 2 connected concepts, this topic is dedicated to some basics of modelling time and the temporal dimensions related to features and fields: - Motivation, background, purpose - Changes in time in Entity based and field based representations - A conceptual model of changes in time - Move of objects - Change of structure - Change of geometry - Examples from applications","name":"Modelling time aspects","selfAssesment":"In progress GI-N2K
"},{"code":"DM5-3","description":"Traditionally many GIS used 2D or 2.5 D data models, but in the last decade 3D modeling mainly in form of city models or in the context of Building Information Models (BIM): - Basic concepts of 3D modelling, edge, area, volume models - The workflow of 3D modelling, general aspects, choose of the proper model - Methods of 3D modeling - Principles of Constructive Solid Geometry (CSG) - Principles of Boundary representation (BR) - Principles of Voxel-beased modeling - Comparison of the methods - The concept of BIM, principles and purpose - City models, principles and purpose - Examples / applications","name":"Modelling 3D","selfAssesment":"GI-N2K
"},{"code":"DM5","description":"Traditional raster and vector data models cannot easily represent the more complex aspects of geographic information, such as temporal change, uncertainty, three-dimensional phenomena, and integrated multimedia. A variety of models have been proposed to represent these complexities, including both extensions to existing models and software, and entirely new models and software. During the 1990s, work in this area was largely experimental, but many solutions are now available to practitioners in commercial and open source software. The data models in this unit are based on concepts discussed in Knowledge Area CF Conceptual Foundations.","name":"Modelling 3D, temporal and uncertain phenomena","selfAssesment":"In progress (GI-N2K)
"},{"code":"DN3-1","description":"Modification of spatial and attribute data while ensuring consistency within the database, implications of transactions on database integrity, scenarios for periodic changes in GIS database and monitoring the periodic changes.","name":"Database change","selfAssesment":"In progress (GI-N2K)
"},{"code":"DN3-2","description":"Rules for modelling spatial database change, techniques for handling version control, techniques for managing long and short transactions, management of spatial databases in multi-user environment","name":"Modeling database change","selfAssesment":"In progress (GI-N2K)
"},{"code":"DN3-3","description":"Reliability tests of change information, design and implementation. Logical consistency of updates.","name":"Reconciling database change","selfAssesment":"In progress (GI-N2K)
"},{"code":"DN3-4","description":"Needs for versioned databases, queries for change scenarios using DB management tools, algorithms for performing dynamic queries, role of time-criticality and data security while choosing methods for change detection.","name":"Managing versioned geospatial databases","selfAssesment":"In progress (GI-N2K)
"},{"code":"GC","description":"The term geocomputation dates back to the first international conference on the topic in 1996 held at the University of Leeds under the title “The art and science of solving complex spatial problems with computers’. The term “geocomputation” was coined to describe the use of computer-intensive methods for knowledge discovery in physical and human geography. This new area distinguishes it from the application of statistical techniques to spatial data in the focus on “creative and experimental applications” and in “developing relevant geo-tools within the overall context of a ‘scientific’ approach.” Other authors reinforced the unique character of geocomputation as “to provide better solutions to many geographical problems by developing new, computationally dependent tools for analysis and modelling”. Simply defined, the interdisciplinary area of geocomputation was, from the beginning, closely linked to the application of computer technology and the development of tools and applications to real-world spatio-temporal problems through the combination of geographic information system techniques, spatial modelling, cellular automata, and other non-conventional data clustering and analysis techniques.\r\nEven though geocomputation is still seeking to define the field conceptually), it is closely related to computational science, the use of high-computing performance, artificial intelligence, computational intelligence, grid infrastructure and parallel computing . Nevertheless, the evolution of new computing paradigms, such as edge-fog-cloud computing along with the new forms of data create new opportunities for the geocomputation community . \r\n\r\nWhile the underlying idea remains intact --a diverse and interdisciplinary area of research that uses geospatial data, methods and tools for applied scientific work--, the current approach to geocomputation differs from the founders in that it focuses more attention on open science, reproducible research practices, and in a vibrant collaborative community to develop new methods, tools and applications that are integrated into multiple application domains such as economics, sociology, geodemography, health, criminology, transportation, biology, remote sensing and cities . The theoretical roots and experimental emphasis of geocomputation makes it an excellent vehicle to creatively explore in parallel the theory and practice of the use of geospatial data in a computational way to solve real-world problems.","name":"Geocomputation","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC1-1","description":"A complex system can be viewed as a system composed of many interacting parts, with the ability to generate a new collective behaviour through self-organisation, for example, though the spontaneous formation of temporal, spatial or functional structures. Complex systems are therefore adaptive as they evolve and may contain self-driving feedback loops. Most real-world systems such as global climate, an ecosystem, a city, the human brain, and the entire universe, are complex systems. Therefore, complex systems are much more than a sum of their parts.The general characteristics of the structure and dynamics of complex systems have been characterised, including path dependence, positive feedback loops, self-organisation, and emergence. Complex system types include nonlinear systems, chaotic systems, and complex adaptive systems. \r\nTraditional approaches focus on the individual system components and define a system as the sum of its parts. Whereas the modern approach relies on complexity theory and complex adaptive systems, to emphasise the linkages between system components in order to understand complex systems as a whole. Agent-based models, for example, have been highly recommended for studying complex adaptive spatial systems because they support the explicit representation of situation-dependent information for decision making within dynamic spatial environments.","name":"Complex systems","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC1-2","description":"Computational science is a discipline focused on the design, implementation and use of mathematical models or simulations through the use of computers to analyse scientific problems, systems or processes. Computational science heavily relies on computational technologies such as high performance computing, artificial intelligence, computational intelligence, grid infrastructure and parallel computing. Geocomputation is closely related to computational science and, therefore, geocomputational methods are often derived from machine learning, clustering, simulation, parallel computing and high performance computing. Contrary to the methods and tools applied for spatial analysis described under the Analytical Methods Knowledge Area, geocomputation methods may involve spatial methods available in standard GIS packages, but quite often require self-development, or at least customisation, involving computational technologies to solve target problems. The aim of this topic is to provide an introduction to computational science with particular emphasis on its usage and relation to geocomputation.","name":"Computational science and technology","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC1-3","description":"While geocomputation is not daily used in GIS environments and traditional GIS projects, it is the focus of a vibrant collaborative and research community in developing new geocomputational methods, tools and applications that are integrated into multiple application domains such as economics, sociology, geodemography, health, criminology, transportation, biology, remote sensing and cities. Open science, reproducible research practices, and strong collaboration make geocomputing an excellent vehicle for creatively exploring together the theory and practice of using geospatial data in a computational way to solve real-world problems.","name":"Spatio-temporal problems and applications","selfAssesment":"In progress (GI-N2K)
"},{"code":"GC1-4","description":"The origin of geocomputation dates back to the first international conference on the topic in 1996 and was coined to describe the use of computer-intensive methods for knowledge discovery in physical and human geography. Geocomputation is closely related to other widely known areas of knowledge within the geospatial community, such as GIScience, Spatial Information Science, GeoInformatics, and Geographic Data Science. While these terms clearly overlap and boundaries are fuzzy, the term geocomputation puts the focus on creative and experimental applications and in developing relevant computationally geospatial tools for analysis and modelling within the overall context of a ‘scientific’ approach. Therefore, a common interpretation of geocomputation is to describe the application of computational models to geographic problems.","name":"Origin of geocomputation","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC1","description":"Geocomputation represents an attempt to move the geospatial research agenda back to geographical analysis and modelling by providing a toolbox of methods to analyse and model a range of highly complex, often non-deterministic problems. In this context, complex systems and computational science are foundational aspects upon which geocomputation approaches and methods are built to address a variety of real-world, spatio-temporal issues","name":"Geocomputation and complex systems","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC2-1","description":"Building a model that mimics a real-world system generally follows a series of stages: from conceptual models to mathematical models and, finally, simulation models. In model development, system analysis is a process whereby a real-world system is simplified by dividing it into simpler, more manageable parts. A conceptual model captures the components, variables and interactions of a system, and provides a useful way of thinking about the trade-offs between abstraction and representativeness of real-world phenomena. Taken in isolation, however, the interacting parts of a system fail to explain its dynamics behavior. A conceptual model is then translated into a mathematical model to explain system dynamics and interaction. Mathematical models often take the form of equations, logical rules or other mathematical mechanisms to represent the interrelations and relationships among the constituted parts of a system. Lastly, a simulation model is the computer-based implementation of mathematical models consisting of interrelated equations and logical rules. When a simulation model runs on a computer, it iteratively recalculates the modelled system state as it changes over time in accordance with the relationships represented by the mathematical relationships that describe the system dynamic. Therefore, developing detailed and dynamic simulation models comes at the cost of generality and interpretability, but it brings us realism and the ability to represent real-world processes in specific contexts. Simulation modelling is often used for prediction, exploration, theory development, or even optimization of conditions to achieve desired outcomes, with the goal of examining how the interconnections and relationships that characterise complex social and environmental systems (e.g. ecosystems, urban systems, social systems, global climate system) produces patterns of behavior over time. Therefore, simulation models are increasingly gaining relevance as scientific mechanisms for several reasons. First, simulation models allow researchers to study systems inaccessible to experimental and observational scientific methods, complementing more conventional approaches to discover or formalize theories about real world systems. Also, aS many real-world systems are nonlinear, simulation modelling has turned into a necessary method to explore and understand better such systems. In addition, the availability of computational science methods and technology, together with a large amount of data available from different sources, have greatly driven the adoption of simulation models in a wide range of scientific disciplines.","name":"Principles of computer simulation","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC2-3","description":"Rule-based models are based on logic programming with condition-action expressions, where the left side of the expressions consists of several conditions that returns a logical result, and the right side consists of several actions. Rules in rule-based models indirectly specify a mathematical model. However, unlike equation-based models which refer to the overall or aggregate behaviour of a system, rule-based models focus on the behaviour of the individual components of a system. That’s why the implementation of rule-based models is most often done by cellular automata models or agent-based models, in which the aggregate behaviour of the system emerges from the interaction of the individual agents or cells over time. Many geographic patterns and dynamics are formed by systems of interacting actors/cells with heterogeneous characteristics and behaviours, in which such dynamic behaviours can be implemented as rules. The aim of this topic is to provide knowledge about rule based models and to understand their advantages and disadvantages.","name":"Rule-based models","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC2-4","description":"Equation-based models are a set of interrelated equations that capture the variability of a system over time (differential equations), and the execution (simulation) of the model means to evaluate such equations. Equation-based models do not aim at representing the behaviour of the individual components in a system. Rather, they focus on the overall or aggregate behaviour of a system. Therefore, equation-based models are well suited to represent physical processes and some topics within natural sciences, where the system to some degree can be described by physical laws. Hydrological modelling is a good example of models based on equations. However, other real-world systems can rarely be fully described by the laws of the natural sciences, and their behavior and interrelation must be represented by means of other types of mathematical mechanisms. The aim of this topic is to present the advantages and challenges in using equation-based simulation models, which are most naturally applied to systems centrally governed by physical laws rather than by information processing and flow.","name":"Equation-based models","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC2-5","description":"Space-time dynamics are closely related to the concepts of change and process, which are inherent to our dynamic world. Space-time dynamics especially manifest when we move from a static representation to a dynamic representation of phenomena. Various processes that take place at different spatial and temporal scales interact with each other and lead to complex changes to the phenomena being modeled. There exist many different approaches of conceptualizing and understanding space-time dynamics in order to understand or predict phenomena in heterogeneous application domains ranging from human activities and urban sprawl to disease spread and traffic flow.","name":"Space-time dynamics","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC2-6","description":"Cellular automata are a standard type of spatially explicit simulation model in which complex processes are modelled over space and time by means of a lattice of cells in which each cell defines its neighbouring cells. The spatial lattice composed of a two-dimensional grid of squared cells is the simple configuration of a cellular automata. Based on this regular configuration, each cell has associated a set of states that change at each iteration by the execution of transition rules, which take into account the state of each cell and those of its neighbours. As such, cellular automata consist of six defining components: a framework or lattice, cells, neighborhood, transition rules, initial conditions (states), and an update sequence (time). Cellular automata models map easily onto existing data structures widely used in geographic information systems, are easy to implement, and are able to show changes and spatial patterns in an understandable manner. All of this has contributed to their popularity in simulation modelling for applications such as measuring land use changes and monitoring disease spread","name":"Cellular automata","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC2-7","description":"Agent-based models are simulation models that decompose a complex system into small entities (agnets) with modeling properties and behavior. Contrary to modelling at an aggregate level, agent-based models are focused on the individual level, where a set of discrete agents with well-defined behaviors represents an individual, object or component of the modelled system. Therefore, the individual agent is the explicit, basic unit. The macro-level behaviour of the system emerges thereafter from the interaction of the individual agents and with the environment over time. Agent-based models are used for spatial modelling, offering possibilities to consider topological particularities of interaction and information transfer among agents and/or with the environment. In relation to spatial simulation, agent-based models have been used for example to model natural and social phenomena such as animal behaviour, pedestrian behavior, social insects and biological cells.","name":"Agent-based modelling","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC2","description":"The concept spatial simulation modelling can be better understood by looking at the meaning of its individual words. A model is widely defined as a simplified representation of a real-world system under study, which can be used to explore or to better understand the system it represents. Computer models or simulation models are computer-based implementations of a model to produce outputs based on certain model assumptions. Simulation , therefore, relies on the use of computers for virtual experimentation to gain insight into real-world problems by proposing alternative assumptions that arise from exploring “what if” questions about a dynamic problem of interest over the course of successive simulation experiments.\r\nSimulation modelling is also useful for the study of spatial patterns over time. Spatial simulation models are relevant when the study of spatial elements and their relationships in a system are necessary for a fully understanding of that system. In this sense, spatial simulation modelling approaches include rule-based models, equation-based models, grid-based cellular automata models, discrete event simulation, and agent-based models.\r\nSimulation modelling is often used for prediction, exploration, theory development, or even optimization of conditions to achieve desired outcomes, with the goal of examining how the interconnections and relationships that characterize these systems produces patterns of behavior over time. Across broad areas of the environmental and social sciences, researchers use simulation models as a way to study systems inaccessible to experimental and observational scientific methods, and also as an essential complement of those more conventional approaches to discover or formalize theories about the real world. \r\nSimulation models are a relatively recent addition to the scientific toolbox, and the reasons for their widespread adoption are, on one hand, the impossibility to study in-situ some complex social and environmental systems (e.g. ecosystems, urban systems, social systems, global climate system) and, on the other hand, the availability of High Performance Computing and large amount of data from different sources. Finally, the nonlinear behaviour of many natural systems provides challenges building traditional mathematical models based on linearization. \r\nSimulation modelling is also useful for the study of spatial patterns over time. In this sense, spatial simulation modelling approaches include rule-based models, equation-based models, grid-based cellular automata models, discrete event simulation, and agent-based models.","name":"Spatial simulation modelling","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC3-1","description":"Among the recent artificial intelligence techniques are those pertaining to heuristics. A heuristic technique is an approach to problem solving, that employs a practical method, which is necessarily not optimal or perfect, but in many situations sufficient. Heuristic methods can be useful, where the optimal solution in practice is impossible. The aim of the topic is to provide insight into the principles of heuristics and the most important algorithms.","name":"Heuristics","selfAssesment":"In progress (GI-N2K)
"},{"code":"GC3-2","description":"Genetic algorithms, genetic programming and evolutionary computing are terms that fall within the general sphere of `Evolutionary Computation`. Genetic algorithms (GAs) are computationally intensive global search heuristics with very wide applicability, but their implementation is often highly problem specific and there is only a relatively loose understanding as to why they often work rather well. The central idea behind GAs is to mimic the Darwinian notion that selective breeding seeks optimum individuals in a given environment. In order to do this a GA requires a way of representing a solution to a (spatial) problem as if it were an individual viewed as a chromosome or `genome` like object. The aim of the topic is to provide fundamental understanding of the principles behind genetic algorithms, and its application in solving geospatial problems.","name":"Genetic algorithms","selfAssesment":"In progress (GI-N2K)
"},{"code":"GC3-3","description":"Biological neurons, or nerve cells, receive multiple input stimuli, combine and modify the inputs in some way, and then transmit the result to other neurons. Artificial neural networks are an attempt to emulate features of biological neural networks in order to address a range of difficult information processing, analysis and modelling problems. The principal class of ANNs are so-called feed-forward networks, but other types of ANN are for example recurrent neural networks. Among the feed-forward networks the most widely used approach is the multi-level perceptron (MLP) model. The application range is broad from non-linear regression to land cover change modelling. The aim of the topic is to introduce the principles of ANN and to understand and demonstrate its use in geospatial modelling.","name":"Artificial Neural Networks","selfAssesment":"In progress (GI-N2K)
"},{"code":"GC3-4","description":"Pattern recognition is the process of classifying input data into objects or classes based on key features. There are two classification methods in pattern recognition: supervised and unsupervised classification. The supervised classification of input data in the pattern recognition method uses supervised learning algorithms that create classifiers based on training data from different object classes. The classifier then accepts input data and assigns the appropriate object or class label. The unsupervised classification method works by finding hidden structures in unlabelled data using segmentation or clustering techniques. Common unsupervised classification methods include: K-means clustering, Gaussian mixture models, Hidden Markov models. The aim of the topic is to provide knowledge about the different methods in pattern recognition and how to choose the optimum method for a specific spatial problem.","name":"Pattern recognition","selfAssesment":"In progress (GI-N2K)
"},{"code":"GC3-5","description":"Understanding natural and human-induced structures and processes in space and time has long been the agenda of geographical research. Through theoretical and experimental studies, geographers have accumulated a wealth of knowledge about our physical and man-made world over the years. Knowledge is often discovered through critical observations of phenomena in space and time. Due to the rapidly expanding amount of data and information the problem is often not having enough data but having too much and too complex a database. The aim of the topic is to provide insight into several methods to carry out spatio-temporal knowledge discovery through spatial data mining and clustering techniques.","name":"Spatio-temporal knowledge discovery","selfAssesment":"In progress (GI-N2K)
"},{"code":"GC3-6","description":"Data-intensive computing is now starting to be considered as the basis for a new, fourth paradigm for science. Two factors are encouraging this trend. First, vast amounts of data are becoming available in more and more application areas. Second, the infrastructures allowing to persistently store these data for sharing and processing are becoming a reality. The technical and scientific issues related to this context have been designated as `Big Data` challenges and have been identified as highly strategic by major research agencies. The aim of this topic is to introduce Big Data as a concept, and the needed methods to navigate through the vast amount of heterogeneous information.","name":"Big data filtering","selfAssesment":"In progress (GI-N2K)
"},{"code":"GC3","description":"The amount of data in current geospatial repositories along with their high-dimensional nature requires a sophisticated set of analysis capabilities in order to extract new and unexpected patterns, trends, and relationships embedded in that data. Artificial intelligence and data mining methods constitute an alternative to explore and extract knowledge from geospatial data, which is less assumption dependent. Data Mining is a step in the knowledge discovery process that automatically detects patterns in data, and Geographic Data Mining is a special type of data mining that seeks to apply standard data mining tools modified to take into account the special features of geospatial data","name":"Artificial Intelligence and Data Mining","selfAssesment":"In progress (GI-N2K)
"},{"code":"GC4-1","description":"The use of the term Open geocomputation doesn't intend to coin a new term; Open GIScience and Open GIS are well explored and discussed terms in the literature. Both embrace the idea of open data, open source, collaboration among peers, and the integration of these practices into GIS research projects, tools, services and applications. Open geocomputation brings the ideas of Open GIScience (and hence Open Science in general) into geocomputation, focussing on openness as a fundamental tenet to conduct research in geocomputation and for the development of new computational methods and tools. In fact, many community-led developments and tools have recently appeared in the field of geocomputation, notably based on R and Python. The widespread popularity and adoption of these computing environments for geocomputing and geospatial analysis is simply because they encompass open, transparent, and reproducible tool development.","name":"Open Geocomputation","selfAssesment":"New
"},{"code":"GC4","description":"A distinguible feature of the current approach to geocomputation is the emphasis on openness: open science, open source, open data. All of this propelled by a vibrant collaborative community with the aim to develop open and reproducible methods, tools and applications applied to a variety of real-life, spatio-temporal application domains. Open Science is a paradigm that can be applied to any scientific discipline and area of knowledge, characterised by openness, access to large volumes of data and unprecedented levels of computing power, availability of community-driven tools, and new types of collaboration between multidisciplinary researchers. Open Science clearly goes beyond geocomputation, but at the same time, its practices and principles characterise recent geocomputation-related projects as well as its community. Therefore, the vision of Open Science taken here is contextualised to the field of geocomputation.","name":"Open Science","selfAssesment":"new
"},{"code":"GD","description":"Geospatial data represent measurements of the locations and attributes of phenomena at or near Earth`s surface. Information is data made meaningful in the context of a question or problem. Information is rendered from data by analytical methods. Information quality and value depends to a large extent on the quality and currency of data (though historical data are valuable for many applications). Geospatial data may have spatial, temporal, and attribute (descriptive) components, as well as associated metadata. Data may be acquired from primary or secondary data sources. Examples of primary data sources include surveying, remote sensing (including aerial and satellite imaging), the global positioning system (GPS), work logs (e.g., police traffic crash reports), environmental monitoring stations, and field surveys. Secondary geospatial or geospatial-temporal data can be acquired by digitizing and scanning analog maps, as well as from other sources, such as governmental agencies. The legitimacy of geographic information science as a discrete field has been claimed in terms of the unique properties of geospatial data. In a paper in which he coined the term GIScience, Goodchild (1992) identified several such properties, including: 1. Geospatial data represent spatial locations and non-spatial attributes measured at certain times. 2. The Earth`s surface is highly complex in shape and continuous in extent. 3. Geospatial data tend to be spatially autocorrelated. It has long been said that data account for the largest portion of geospatial project costs. While this maxim remains true for many projects, practitioners and their clients now can reasonably expect certain kinds of data to be freely or cheaply available via the World Wide Web. Federal, state, regional, and local government agencies, as well as commercial geospatial data producers, operate clearinghouses that provide access to geospatial data. Although geospatial data are much more abundant now than they were ten years ago, data quality issues persist. Good data are expensive to produce and to maintain. Proprietary interests simultaneously increase the supply of geospatial data and impede data accessibility. Standards for geospatial data and metadata are useful in facilitating effective search, retrieval, evaluation, integration with existing data, and appropriate uses. National and international organizations, such as the Open Geospatial Consortium (OGC) and International Organization for Standardization (ISO), develop and promulgate such standards. INSPIRE directive (Infrastructure for Spatial Information in the European Community) regulates geospatial data management","name":"Geospatial Data","selfAssesment":"I, progress (GI-N2K)
"},{"code":"GD1-1","description":"Usable and accurate geospatial data are based upon proper model of the Earth`s surface. Shape of the Earth is complex and complicated to measure. Approximations are used to minimize complexity of the task and possible errors.","name":"Earth geometry","selfAssesment":"GI-N2K
"},{"code":"GD1-2","description":"Geospatial referencing systems provide unique codes for every location on the surface of the Earth (or other celestial bodies). These codes are used to measure distances, areas, and volumes, to navigate, and to predict how and where phenomena on the Earths surface may move, spread, or contract. Point-based, vector coordinate systems specify locations in relation to the origins of planar or spherical grids. Tessellated referencing systems specify locations hierarchically, as sequences of numbers that represent smaller and smaller subdivisions of two- or three dimensional surfaces that approximate the Earths shape, Linear referencing systems specify locations in relation to distances along a path from a starting point. Tessellation data models, are considered in Unit DM3 Tessellation data models, and linear referencing models are considered in Unit DM4 Vector data models.","name":"Georeferencing systems","selfAssesment":"GI-N2K
"},{"code":"GD1-3","description":"Horizontal datums determine the geometric relations between a coordinate system grid and a particular ellipsoid approximating the Earth`s surface. Vertical datums determine elevation reference surfaces, like mean sea level. A. Horizontal datums. Relation of coordinate system to particular ellipsoid, datum transformation options, Molodensky and Helmert transformation, other high accuracy transformations, ED50 and WGS84, historical development of horizontal datums, ETRS89. B. Vertical datums. Historical development of vertical datums, difference between vertical datum and geoid, relations between ellipsoidal (geodetic) heiht, geoidal height and orthometric elevation.","name":"Datums","selfAssesment":"GI-N2K
"},{"code":"GD1-4","description":"Map projections are systematic transformations of geographic coordinates of the surface of ellipsoid into locations in plane. Plane coordinates are based on map projection. As the transformation of a spherical grid into a plane grid causes inevitably distortions of the geometry, and, different projections cause different distortions, knowledgeable choice of appropriate projection for any particular use is crucial. A. Map projection poperties. Geometric properties that may be preserved or lost in projected grid, usefulness of compromise projection, Tissot indicatrix as an indicator of projection errors, visual appearance of the Earth`s graticule, distortion patterns for projection classes, distortions in raster data. B. Map projection classes. Three main classes of map projection based on developable surface, projection types by geometric properties preserved, mathematical basis of projecting longitude and latitude into x and y coordinates. UTM, ETM, projections used by EC. C. Map projection parameters. Standard line, projection case, latitutde and longitude of origin, aspects of projection. D. Georegistration. Rectification vs orthorectification, ground controle points in georegistration of aerial imagery.","name":"Map projections","selfAssesment":"GI-N2K
"},{"code":"GD1","description":"Proper model of the Earth`s surface and ability to locate spatial phenomena accurately to it, is crucial in effective collection, management and use of data. Characterising size and shape of the Earth, using appropriate surfaces to approximate it, choosing suitable coordinate system and map projection is bases for efficient understanding of spatial data.","name":"Geolocating Data to Earth","selfAssesment":"In progress (GI-N2K)
"},{"code":"GD10-2","description":"Platforms and sensors are described in concept [PS]","name":"Platforms and sensors","selfAssesment":"Depricated GI-N2K
"},{"code":"GD10-4","description":"A stereoscopy acquisition mode collects remotely sensed data where each location on the ground (or the imaged objects) is covered multiple times (at least twice), from different perspectives. Stereopairs and stereoscopic coverage enable the extraction of 3D representations of the environment from remotely sensed imagery.","name":"Stereoscopy and orthoimagery","selfAssesment":"GI-N2K
"},{"code":"GD10","description":"Since the 1940s aerial imagery has been the primary source of detailed geospatial data for extensive study areas. Photogrammetry is the profession concerned with producing precise measurements from aerial imagery. Aerial imaging and photogrammetry comprise a major component of the geospatial industry. The topics included in this unit do not comprise an exhaustive treatment of photogrammetry, but they are aspects of the field about which all geospatial professionals should be knowledgeable.","name":"Aerial imaging and photogrammetry","selfAssesment":"GI-N2K
"},{"code":"GD11-2","description":"the physical environment to sense data without direct contact. It contains a carrier device (platform) and a sampling unit (sensor).","name":"Platforms and sensors","selfAssesment":"GI-N2K
"},{"code":"GD11","description":"Satellite-based sensors enable frequent mapping and analysis of very large areas. Many sensing instruments are able to measure electromagnetic energy at multiple wavelengths, including those beyond the visible band. Satellite remote sensing is a key source for regional- and global-scale land use and land cover mapping, environmental resource management, mineral exploration, and global change research. Shipboard sensors employ acoustic energy to determine seafloor depth or to create imagery of the seafloor or water column. The topics included in this unit do not comprise an exhaustive treatment of remote sensing, but they are aspects of the field about which all geospatial professionals should be knowledgeable.","name":"Satellite and shipboard remote sensing","selfAssesment":"GI-N2K
"},{"code":"GD12","description":"Meaning of geospatial metadata, elements of metadata, use of metadata, integration of metadata in data production, standards in geospatial data, ISO standard family 191xx, data warehouse, exchange protocol, transport protocols, spatial data infrastructure, INSPIRE, OGC, DCAT profiles for CKAN applications bridging metadata from GI and IT domains.","name":"Metadata, standards, and infrastructures","selfAssesment":"GI-N2K
"},{"code":"GD2-1","description":"Classic land survey methods and manual attribute data collection in the field","name":"Land surveying and field data collection","selfAssesment":"GI-N2K
"},{"code":"GD2-2","description":"Aerial imagery has been the primary source of detailed geospatial data for extensive study areas. Photogrammetry is producing precise measurements from aerial imagery. Aerial imaging and photogrammetry comprise a major component of the geospatial data production. Satellite-based sensors enable frequent mapping and analysis of very large areas. Sensing instruments are able to measure electromagnetic energy at multiple wavelengths. Satellite remote sensing is a key source for regional- and global-scale land use and land cover mapping, environmental resource management, mineral exploration, and global change research. Shipboard sensors employ acoustic energy to determine seafloor depth or to create imagery of the seafloor or water column. Principles of aerial photography, oblique and vertical imagery, spatial and radiometric resolution, spectral sensitivity, principal point, distortions and displacements in aerial image, parallax, stereophotogrammetry, generation of an orthoimage from a vertical aerial phoptograph, aerotriangulation, vector data extraction from digital seteroimagery, mission planning. Use of UAV in photogrammetry. Main platforms and sensors in spatial image acquisition, active and passive sensors, LiDAR and microwave, multispectral and hypersepctral imagery, interpretation of imagery, supervised and unsupervised classification, pixel based and segmented classification, ground verification, main applications, bathymetric mapping. SENTINEL.","name":"Remote sensing","selfAssesment":"GI-N2K
"},{"code":"GD2-3","description":"Crowdsourcing is the practice of obtaining needed services, ideas, or content by soliciting contributions from a large group of people and especially from the online community rather than from traditional employees or suppliers. Crowdsourced spatial data collection is becoming more and more important. The advantages and disadvantages of crowdsourced data, opensource mapping tools, potential application of crowdsourcing, VGI, OSM or cell-phone based, aspects of crowdsourced data quality and reliabilty.","name":"Crowdsourced data collection","selfAssesment":"GI-N2K
"},{"code":"GD2-4","description":"Digitizing as the main secondary spatial data production technique. Encoding vector points, lines, and polygons by tracing map sheets has diminished in importance, but remains a useful technique for incorporating historical geographies and local knowledge. \"Heads-up\" digitizing using digital imagery as a backdrop on-screen is a standard technique for editing and updating GIS databases. Tablet and on-screen digitizing, scanning and (semi)automatic vectorization.","name":"Digitizing","selfAssesment":"GI-N2K
"},{"code":"GD2","description":"Spatial data collection / production involves measurement of locations in relation to the coordinate system, and collection of attributed data about the spatial phenomena. Measurements may be direct (e.g. surveying) or remote, data acquisition involves measurement of parameter values, evaluation of parameters, polls, interpretation of spatial imagery, and re-use of secondary data (e.g. old maps). Volunteered geographic information is becoming more important.","name":"Data Collection","selfAssesment":"GI-N2K
"},{"code":"GD3","description":"It is quite common, that data including both spatial entities and their attribute data undergo changes. These changes need to be catalogued fully and explicitly, including initial conditions, new conditions, all intermediate stages and operations used. The geospatial data needs to contain an archival history of change.","name":"Transaction management of geospatial data","selfAssesment":"In progress (GI-N2K)
"},{"code":"GD4-1","description":"Geometric accuracy, factors influencing it, geometric accuracy and topological fidelity, geometric accuracy in survey and GPS mesurements, thematic accuracy, relations between thematic accuracy, geometric accuracy and topological fidelity, misclassification matrix, commission and omission, logical consistency, relations between resolution, precision, and accuracy, spatial resolution, thematic resolution, and temporal resolution, precision, uncertainties associated with coordinate precision, primary and secondary data sources.","name":"Data quality","selfAssesment":"GI-N2K
"},{"code":"GD4-2","description":"Meaning of geospatial metadata, elements of metadata, use of metadata, integration of metadata in data production, standards in geospatial data, ISO standard family 191xx, data warehouse, exchange protocol, transport protocols, spatial data infrastructure, INSPIRE, OGC, DCAT profiles for CKAN applications bridging metadata from GI and IT domains.","name":"Metadata, standards, and infrastructures","selfAssesment":"GI-N2K
"},{"code":"GD4","description":"Data quality is the degree of data usability in relation to given objective and particular application. The expectations to data vary between different applications. The key criteria in data quality are the amount of uncertainty in data as compared to the acceptable level of uncertainty. Evaluation of the usability may be more complicated using data from secondary sources. Appropriate metadata is inevitable for these judgements. Aspects of data quality include geometric and thematic accuracy, (in)consistencies, resolution, precision, usability and others. Assurance of data quality may be improved by following proper standards and spatial data infrastructure regulations for data collection and management. System of basic data quality measures for geospatial domain in the EN ISO 19157:2013 standard.","name":"Data Quality, Metadata and Data Infrastructure","selfAssesment":"In progress (GI-N2K)
"},{"code":"","description":" ","name":" ","selfAssesment":" "},{"code":"GD6-1","description":"Geometric accuracy is a measure indicating how close the geometric values of the data are to the real world position of the mapped feature.","name":"Geometric accuracy","selfAssesment":"GI-N2K
"},{"code":"GD6-2","description":"Thematic accuracy evaluates the correctness of attribute values of geospatial objects compared to the expected (real world) reference value","name":"Thematic accuracy","selfAssesment":"In progress (GI-N2K)
"},{"code":"GD6-3","description":"The resolution of a data source indicates the smallest unit of detail provided by the data source.","name":"Resolution","selfAssesment":"In progress (GI-N2K)
"},{"code":"GD6-4","description":"The precision of a measurement system, related to reproducibility and repeatability, is the degree to which repeated measurements under unchanged conditions show the same results.","name":"Precision","selfAssesment":"GI-N2K
"},{"code":"GD6-5","description":"Primary data sources provide information collected directly for GIS use. Secondary sources are data sources that need to be processed before they are ready for GIS use.","name":"Primary and secondary sources","selfAssesment":"In progress (GI-N2K)
"},{"code":"GD6","description":"particular application. That standard varies from one application to another. In general, however, the key criteria are how much uncertainty is present in a data set and how much is acceptable. Judgments about fitness for use may be more difficult when data are acquired from secondary rather than primary sources. Aspects of data quality include accuracy, resolution, and precision. Concepts of data quality, error, and uncertainty are also covered in Knowledge Areas CF Conceptual Foundations (in a theoretical context) and GC Geocomputation (in the context of analysis); the focus here is on the measurement and assessment of data quality.","name":"Data quality","selfAssesment":"GI-N2K
"},{"code":"GD8-1","description":"Tablet digitizing is the conversion from physical map to digital data by re-drawing the features on the map fixed on a digitizing tablet","name":"Tablet digitizing","selfAssesment":"GI-N2K
"},{"code":"GD8-2","description":"On-screen digitizing is the conversion from raster to vector data by manually drawing the features visible in the raster file on the screen.","name":"On-screen digitizing","selfAssesment":"GI-N2K
"},{"code":"GD8-3","description":"Scanning is the conversion of a physical object to a digital representation by moving a sensor over it. Vectorization is the technique to extract features from the grid information in vector format","name":"Scanning and automated vectorization techniques","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS","description":"Geographic Information Science and Technology serve the society, but it is not a panacea. The history of its development is the sum of fragmented efforts, which have still not been fully integrated. Its potential benefits are often constrained and its potential impacts are not fully understood. Institutional and economic factors limit access to data, technology, and expertise by some of those who need it to make better decisions. Political, ideological, and personal issues aside, organizations invest in GIS&T when estimated benefits outweigh estimated costs. Evaluating costs and benefits is difficult, however and too often leads to nothing being done. For some individuals and groups, costs are prohibitive even though potential benefits are compelling. The legal framework provides a structure for regulating a number of key aspects of geographic information science, technology, and applications. Legal regimes determine who can claim the exclusive right to hold and use geospatial data, the conditions under which others may have access to the data, and what subsequent uses are permitted. Political struggles arise from conflicting proprietary and public interests about who benefits from geospatial information, and how the power to allocate the use of this information is, or should be, distributed among members of a society. The need to choose among conflicting interests sometimes poses ethical dilemmas for GIS&T professionals. The explosive growth of the geospatial information contributed by users through various application programming interfaces has made geospatial information is a powerful tool in the social media toola powerful media for the general public to communicate, but perhaps more importantly, geographic information have also become a tool media for constructive dialogs and interactions about social issues, recent growth of Web-based geospatial information and volunteered geographic information (VGI). Because so many public agencies and private organizations rely upon GIS&T for planning, decision making, and management, GIS&T increasingly affects and is used to direct daily life. Critical approaches to understanding the role of GIS in society equip practitioners to employ GIS&T reflectively. The critical approach specifically questions the assumptions and premises that underlie the economic, legal and political regimes and institutional structures within which GIS&T is implemented. Related concerns are considered in Knowledge Area OI: Organizational and Institutional Aspects.","name":"GI and Society","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS1-1","description":"The most basic definition of a legal regime is a system or framework of rules governing some physical territory or discrete realm of action that is at least in principle rooted in some sort of law. Often the concept has been applied to specific areas of law.","name":"The legal regime and legal framework","selfAssesment":"GI-N2K
"},{"code":"GS1-2","description":"Contract law is defined as a set of rules that govern the contractual agreements between merchants or persons. A contract is an agreement between different parties that state their responsibilities and duties to each other. A liability in contract law is when certain conditions are written into a contract that makes a party liable. Licensing is the process of giving or getting official permission to do something. A license is an agreement through which a licensee leases the rights to a legally protected piece of intellectual property from a licensor — the entity which owns or represents the property — for use in conjunction with a product or service.","name":"Contract law, liability and licensing","selfAssesment":"GI-N2K: relevant but to be revised
"},{"code":"GS1-3","description":"Data privacy and security are two essential components of a successful strategy for data protection. Data security refers to the protection of data from unauthorized access, use, change, disclosure, and destruction. It encompasses network security, physical security, and file security. Data privacy involves protecting consumer data by eliminating or reducing the possibility of re-identifying an individual whose information is present in the data. This is done by either removing specific information or by transforming the data with random “noise” or generalization.","name":"Privacy and Security","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS1-4","description":"Property is secured by laws that are clearly defined and enforced by the state. These laws define ownership and any associated benefits that come with holding the property. The term property is very expansive, though the legal protection for certain kinds of property varies between jurisdictions. Property is generally owned by individuals or a small group of people. The rights of property ownership can be extended by using patents and copyrights. Property rights give the owner or right holder the ability to do with the property what they choose. That includes holding on to it, selling or renting it out for profit, or transferring it to another party.","name":"Ownership and property rights","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS1-5","description":"In economics, competition is a condition where different economic firms seek to obtain a share of a limited good by varying the elements of the marketing mix: price, product, promotion and place. Competition law is a law that promotes or seeks to maintain market competition by regulating anti-competitive conduct by companies. Public-private sector relationships deal with a particular subset of competition, i.e. competition between public and private organizations.","name":"Competition and public-private sector relationships","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS1-6","description":"Open data is data that can be accessed, shared, used and reused without any barrier for any type of (re)user. According to the Open Definition, open data can be defined as data that be freely used, modified, and shared by anyone for any purpose subject, at most, to measures that preserve provenance and openness. Open data requires datasets to be either in the public domain, or distributed through an open license. The data must be provided as a whole, free of charge, and preferably downloadable via the Internet, including any additional information that might be necessary to comply with the open license’s terms. Openness requires the data to be provided in a readily machine-readable form. The format must be open as well, meaning that it does not place any restriction upon its use, and that the files in that format can be processed with open-source software tools. The Open Definition speaks broadly of open ‘works’, rather than of open data. Focusing on data tout court, one can move from the Open Government Data (OGD) principles. According to the OGD principles, which are arguably foundational in understanding the concept of open data, data must be: Complete; Primary; Timely; Accessible; Machine-processable; Non-discriminatory; Non-proprietary; and License-free. Compliance with the OGD principles needs to be demonstrable, i.e. there need to be accountability measures in place to allow the review of the adherence to the principles above. The concepts of Open Work and open data highlight how data needs to be both legally, technically and financially open, so either in the public domain or covered by an open license, and kept in a machine-readable and non-proprietary format. Open data aims at making information available to everybody, for any purpose, in a machine-readable and interoperable format, based on open standards and digestible by free/libre open source software (FLOSS). Also with respect to the financial accessibility open data is data available free of charge. Marginal costs of dissemination are accepted by some as a reasonable cost for users. However, open data is data that can be accessed and reused without any barrier for any type of reuse, and some user groups experience any price to be paid as a barrier.","name":"Open data","selfAssesment":"Completed
"},{"code":"GS1","description":"Legal problems can arise when geospatial information is used for land management, among other activities. Geospatial professionals may be liable for harm that results from flawed data or the misuse of data. Understanding of contract law and liability standards is essential to mitigate risks associated with the provision of geospatial information products and services. Legal relations between public and private organizations and individuals govern data access. The nature of information in general, and the characteristics of geospatial information in particular, make it an unusual and difficult subject for a legal regime that seeks to establish and enforce the type of exclusive control associated with other commodities. Geospatial information is in many ways unlike the kinds of works that intellectual property rights were intended to protect. Still, organizations can, and do, assert proprietary interests in geospatial information. Perspectives on geospatial information as property vary between the public and private sectors and between different countries.","name":"Legal aspects","selfAssesment":"In progress GI-N2K
"},{"code":"GS2-1","description":"Business models determine how organizations can create and deliver value, for example, through the provision or use of geographic data. A business model is a conceptual tool that contains\r\na set of interrelated elements that allow organizations to create and capture value and generate revenues. The development and implementation of an appropriate business model are considered to be a key to the success of the organization and a crucial source for value creation. \r\n\r\nAlthough business models determine how organizations create, deliver, and capture value, they should not be regarded as permanent and invariable structures or settings. Business models are shaped by both internal and external forces, and will only be successful if they are able to adapt to a changing environment. In the GI domain, several technological, regulatory, and societal developments have challenged the existing business models and opened up opportunities for new business models. Among these developments are the establishment of spatial data infrastructures (SDIs) worldwide, the democratization of geographic knowledge, and the move toward open source, open standards, and open data.\r\n\r\nSince the development and implementation of SDIs in different parts of the world, much attention has been paid to the need to find appropriate business models for GI, and in particular, for geographic data providers in the public sector. Traditional business models in which public data providers were selling their data to customers in the private industry and other public agencies were questioned, because they restricted the opportunity for data sharing. The concept of SDI is about moving to new business models, where partnerships between GI organizations are promoted to allow access to a much wider scope of geographic data and services. A key challenge in the development of these SDIs was the alignment of different existing business models of the actors in the GI domain. Moreover, the development and implementation of SDIs also led to the emergence of new business models, which was even more the case with the more recent move toward open geographic data.\r\n\r\nOrganizations can be active in different parts of the geo-information value chain, and can create and offer value in many different ways. As a result, many different GI business models exist. Data providers, data enablers, and data end users could be seen as three main categories of GI business models. Each of these categories consists of many different business models, as different value propositions\r\nwill exist, and value can be created and captured in several ways.","name":"GI Business models","selfAssesment":"Completed (GI-N2K)
"},{"code":"GS2-5","description":"To provide a better insight into the process of adding value to GI, several authors have introduced and applied the information value chain approach. A value chain can be defined as the set of value-adding activities that one or more organizations perform in creating and distributing goods and services. The value chain concept originally was developed for the manufacturing sector, as a tool to evaluate the competitive advantage of firms. More recently, the value chain concept has been applied to other sectors, including information technology where the good or service, and the benefits it provides, is less tangible in nature. A value chain involves the progress of goods from raw materials to finished products through a number of stages, during each of which a new value is added to the original input by various activities. The value chain concept was extended into the information market, with the information value chain referring to the set of activities adding value to information and turning raw data into new information products or services. Especially important in this context is the role of information and communication technologies (ICT), which have an impact on all activities in the information value chain, such as information collection, processing, dissemination, and use. In the context of GI, the value chain relates to the series of value- adding activities to transform raw geographic data into new products that are used by certain end users. Although there are slightly different descriptions of the various steps of the GI value chain, in general, the essential steps in the value chain are: acquisition of raw data, the application of a data model, quality control, and integration with other sources, presentation, and distribution. In recent years, particular attention has been paid to different steps between the process of distributing data and the actual end use of an end product of GI. In addition, after the publication of the data, value can be added to the data in many different ways. Value can be added by making data from different sources easily accessible through repositories and data portals, by building and selling tailored solutions using the data to end users or by using geographic data to improve existing products and services delivered to an end user. In certain cases, this end product will be the first step of a next value chain.","name":"Geo-information value chain","selfAssesment":"Completed
"},{"code":"GS2","description":"Most organizations insist that investments in GIS and T be justified in economic terms. Quantifying the value of information, and of information systems, however, is not a straightforward matter.","name":"Economic aspects","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS3-1","description":"The use of geospatial information allows public sector organizations and actors to make better decisions and provide better services to their citizens. Geospatial information is increasingly being used at different administrative levels and in different policy areas.","name":"Use of geospatial information in the public sector","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS3-2","description":"Geospatial information is increasingly being used by private companies for different purposes and the private sector plays an important role in the development and implementation of geospatial information infrastructures.","name":"Use of geospatial information in the private sector","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS3-3","description":"Research and education institutions use geospatial information for various purposes, in support of their research and educational activities.","name":"Use of geospatial information in research and education","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS3-4","description":"Effective monitoring of the environment and an improved understanding of the same requires valuable information and data that can be extracted through application of geospatial technologies. GIS can be used most effectively for environmental data analysis and planning. It allows better viewing and understanding physical features and the relationships that influence in a given critical environmental condition. GIS can help in effective planning and managing the environmental hazards and risks. In order to plan and monitor the environmental problems, the assessment of hazards and risks becomes the foundation for planning decisions and for mitigation activities. GIS supports activities in environmental assessment, monitoring, and mitigation and can also be used for generating environmental models. GIS can aid in hazard mitigation and future planning, air pollution & control, disaster management, forest fires management, managing natural resources, wastewater management, oil spills and its remedial actions etc.","name":"Use of geospatial information in environmental issues","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS3","description":"Geospatial Information used in Government agencies and public authorities at local, state, and federal levels produce and use geospatial data for many activities, including provision of social services, public safety, economic development, environmental management, and national defence. Public participation in governing, empowered by geospatial technologies, offers the potential to strengthen democratic societies by involving grassroots community organizations and by engaging local knowledge. The private sector covers a broad range of areas of opportunity. With continued advancements in technology, greater awareness of its advantages as a powerful decision support tool the use of geospatial information use in the private sector needs to be discussed.","name":"Use of geospatial information","selfAssesment":"In Progress GI-N2K
"},{"code":"GS4-1","description":"Public participation GIS (PPGIS) is a field within geographic information science that focuses on ways the public uses various forms of geospatial technologies to participate in public processes, such as mapping and decision making.","name":"Public participation GIS","selfAssesment":"GI-N2K (revision)
"},{"code":"GS4-2b","description":"Social Media Geographic Information (SMGI) can be defined as any piece or collection of multimedia data or information with explicit (i.e. coordinates) or implicit (i.e. place names or toponyms) geographic reference collected through the social networking web or mobile applications. Social data are acknowledged as a good of major value in the digital economy, and their potential for enhancing more traditional analytics is of the utmost importance. A big part of social data however also features spatial (and temporal) references, thus their integration with more traditional Authoritative Geographic Information (AGI) may enable a further step towards the next generation of geospatial intelligence. SMGI is a sub-category of VGI and can be active or passive, depending on the type of application with which it is collected: applications purposefully created and/or used to collect SMGI in participatory initiatives","name":"Social Media Geographic Information","selfAssesment":"Completed
"},{"code":"GS4-3b","description":"Volunteered geographic information (VGI) is a special kind of user-generated content. It refers to geographic information collected and shared voluntarily by the general public. Web.2.0 and associated advances in web mapping technologies have greatly enhanced the abilities to collect, share and interact with geographic information online, leading to VGI.","name":"Citizens and volunteered geographic information","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS4","description":"Today, geo data has become a conventional and pervasively familiar data type seen at once to underpin and significantly re-characterize the digital world, with broad implications for both technology and society. Geospatial data are abundant, but access to data varies with the nature of the data, the user groups wishes to acquire it and for what purpose, under what conditions, and at what price geodata can be obtained. The explosive growth of geographic information contributed by users through various application programming interfaces has made geographic information a powerful media for the general public, but perhaps more importantly, geospatial information have also become media for constructive dialogs and interactions about social issues, recent growth of Web-based Geographic information and volunteered geographic information (VGI).","name":"Geospatial citizenship","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS5-1b","description":"The advantages of geospatial technologies and resulting data present ethical dilemmas such as privacy and security concerns as well as the potential for stigma and discrimination resulting from being associated with particular locations. the use of geospatial technologies and the resulting data needs to be critically assessed through an ethical lens prior to implementation of programmes, analyses or partnerships. Using this lens requires not only explicit consideration of potential negative consequences of adoption but also clear articulation of the specific contexts and conditions under which benefits may be realized.","name":"Ethics in the geospatial information society","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS5-2b","description":"A code of ethics is a guide of principles designed to help professionals conduct business honestly and with integrity. A code of ethics document may outline the mission and values of the business or organization, how professionals are supposed to approach problems, the ethical principles based on the organization's core values, and the standards to which the professional is held. Codes of ethics for geospatial professionals are intended to provide these principles and guidelines for GIS professionals","name":"Codes of ethics for geospatial professionals","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS5","description":"Ethics provide frameworks that help individuals and organizations make decisions when confronted with choices that have moral implications. Most professional organizations develop codes of ethics to help their members do the right thing, preserve their good reputation in the community, and help their members develop as a community","name":"Ethical aspects","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS6-1","description":"US GIS&T BoK: As GIS became a firmly established presence in geography and catalysed the emergence of GIScience, it became the target of a series of critiques regarding modes of knowledge production that were perceived as problematic. The first wave of critiques charged GIS with resuscitating logical positivism and its erroneous treatment of social phenomena as indistinguishable from natural/physical phenomena. The second wave of critiques objected to GIS on the basis that it was a representational technology. In the third wave of critiques, rather than objecting to GIS simply because it represented, scholars engaged with the ways in which GIS represents natural and social phenomena, pointing to the masculinist and heteronormative modes of knowledge production that are bound up in some, but not all, uses and applications of geographic information technologies. In response to these critiques, GIScience scholars and theorists positioned GIS as a critically realist technology by virtue of its commitment to the contingency of representation and its non-universal claims to knowledge production in geography. Contemporary engagements of GIS epistemologies emphasize the epistemological flexibility of geospatial technologies.","name":"Epistemological and critical issues","selfAssesment":"In progress/to delete (GI-N2K)
"},{"code":"GS6-2","description":"Various types of critiques exist on the way geospatial information is being used and re-used.","name":"Critical approach on the use of geospatial information","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS6-3","description":"Defending or refuting the argument that the \"digital divide\" that characterizes access use of geospatial information perpetuates inequities among developed and developing nations, among socio-economic groups,and between individuals, community organizations, and public agencies and private firms.","name":"Critical aspects and invisible groups","selfAssesment":"In progress/to be delete (GI-N2K)
"},{"code":"GS6","description":"Many of the educational objectives used to define topics in this knowledge area, and in the Body of Knowledge as a whole, challenge educators and students to think critically about GI and Society. Since the 1990s, scholars have criticized cartography and the GIS science from a wide range of perspectives. Common among these critiques are questioned assumptions about the purported benefits of GI and Society and attention to its unexamined risks. By promoting reflective practice among current and aspiring geospatial information professionals, an understanding of the range of critical perspectives increases the likelihood that geospatial information will fulfil its potential to benefit all stakeholders. Philosophical, psychological, and social underpinnings of these critiques are considered in Knowledge Area CF: Conceptual Foundations.","name":"Critical approach","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS7-1","description":"US GIS&T BoK: As GIS became a firmly established presence in geography and catalysed the emergence of GIScience, it became the target of a series of critiques regarding modes of knowledge production that were perceived as problematic. The first wave of critiques charged GIS with resuscitating logical positivism and its erroneous treatment of social phenomena as indistinguishable from natural/physical phenomena. The second wave of critiques objected to GIS on the basis that it was a representational technology. In the third wave of critiques, rather than objecting to GIS simply because it represented, scholars engaged with the ways in which GIS represents natural and social phenomena, pointing to the masculinist and heteronormative modes of knowledge production that are bound up in some, but not all, uses and applications of geographic information technologies. In response to these critiques, GIScience scholars and theorists positioned GIS as a critically realist technology by virtue of its commitment to the contingency of representation and its non-universal claims to knowledge production in geography. Contemporary engagements of GIS epistemologies emphasize the epistemological flexibility of geospatial technologies.","name":"Epistemological critiques","selfAssesment":"GI-N2K
"},{"code":"GS7-3","description":"US GIS&T BoK: \r\n\r\nFeminist interactions with GIS started in the 1990s in the form of strong critiques against GIS inspired by feminist and postpositivist theories. Those critiques mainly highlighted a supposed epistemological dissonance between GIS and feminist scholarship. GIS was accused of being shaped by positivist and masculinist epistemologies, especially due to its emphasis on vision as the principal way of knowing. In addition, feminist critiques claimed that GIS was largely incompatible with positionality and reflexivity, two core concepts of feminist theory. Feminist critiques of GIS also discussed power issues embedded in GIS practices, including the predominance of men in the early days of the GIS industry and the development of GIS practices for the military and surveillance purposes.\r\n\r\nAt the beginning of the 21st century, feminist geographers reexamined those critiques and argued against an inherent epistemological incompatibility between GIS methods and feminist scholarship. They advocated for a reappropriation of GIS by feminist scholars in the form of critical feminist GIS practices. The critical GIS perspective promotes an unorthodox, reconstructed, and emancipatory set of GIS practices by critiquing dominant approaches of knowledge production, implementing GIS in critically informed progressive social research, and developing postpositivist techniques of GIS. Inspired by those debates, feminist scholars did reclaim GIS and effectively developed feminist GIS practices.","name":"Feminist critiques","selfAssesment":"GI-N2K
"},{"code":"GS7-4","description":"In the early 1990s social critiques of GIS from human geographers began to appear. These initial critiques set off an ensuing debate between GISers, defending GIS and human geographers, who critiqued GIS. This debate materialized in academic journals including: Political Geography Quarterly, Environment and Planning A, and Progress in Human Geography. Schuurman (2000) notes that the GIS debate, while unique to the discipline of Geography, was part of a larger debate in other disciplines about the effects of technology. This presentation will be limited (unfortunately) to two aspects of this debate. It will first discuss conditions within human geography that made GIS a target of human geographers' critique. Second, this paper will discuss the particular critiques that were directed at GIS by human geographers. Though the reaction of such critiques and their effect on GIS is an important topic there is not enough time and space to address these issues. See Schuurman (2000) \"Trouble in the Heartland: GIS and its critics in the 1990s\" in Progress in Human Geography for a thoughtful look at this debate and its effects on the discipline of GIS.","name":"Social critiques","selfAssesment":"GI-N2K
"},{"code":"IP","description":"Image processing and analysis – Image processing and analysis describes the entire collection of tasks and employed methods and technologies along the information production workflow. They transform data contained in remote sensing images to information products, e.g. in form of digital maps and reports, for users in various application domains to take informed decisions.","name":"Image processing and analysis","selfAssesment":"Planned
"},{"code":"IP1-1-1","description":"The image spatial subset allows to extract the group of pixels / grid cells using a defined polygon e.g. area of interest – AOI or defining the new image extent. It is used to limit spatially the image extent to which, for example an image function or classification model will be applied.","name":"Image subset","selfAssesment":"Completed
"},{"code":"IP1-1-2","description":"Layer stacking is a process for combining multiple images into a single image. The image stack is used to build a ‘new’ multiple band file from the georeferenced images of various pixel sizes, extents, projections. The image bands must be resampled and reprojected to a common spatial grid. The layer stacking is used for example to combine spectral bands from a Landsat, Sentinel-2 data and SRTM DEM into one multi-dimensional file. The process of layer stacking increases the size of the final stacked image, which may have consequences that increase the processing time of operations performed on the stacked image.","name":"Layer stack","selfAssesment":"Completed
"},{"code":"IP1-1","description":"Data manipulation adjusts a dataset to the needs of a specific application by subsetting the spatial extent or the number of bands or by organizing bands from separate single layer files into a single multi-layer file.","name":"Data manipulation","selfAssesment":"New
"},{"code":"IP1-2","description":"Fourier analysis - A characteristic of remotely sensed images is a parameter called spatial frequency, defined as the number of changes in brightness value per unit distance for any particular part of an image. There are low-frequency and high-frequency areas. Spatial frequency may be enhanced or subdued using Fourier Analysis (an alternative technique is spatial convolution filtering). Fourier analysis mathematically separates an image into its spatial frequency components. It is then possible interactively to emphasize certain groups (or bands) of frequencies relative to others and recombine the spatial frequencies to produce an enhanced image.\r\nThe signal received by a pulsed radar is a time sequence of pulses for which the amplitude and phase are measured. The frequency content of this time-domain signal is obtained by taking its Fourier transformation.","name":"Fourier transformation","selfAssesment":"New
"},{"code":"IP1-3-1-1","description":"Structure from motion (SfM) describes the photogrammetric process for estimating the 3D structure of a scene, whereby correspondences between multiple images are established and used to detect motion parallax. When a camera moves over a surface while taking successive overlapping images, the distances between features on the surface will change from one image to the next. The changes depend on the distance of the feature points to the camera, and thus the surface elevation. This motion parallax can be used to generate an accurate 3D representation of the surface. \r\nThe photogrammetric problem of SfM is similar to stereo vision, but has gained popularity with the advent of inexpensive cameras which have variable internal geometries, unlike metrically stabilized cameras traditionally used in airborne mapping. Even with less accurate or even missing GPS location and orientation metadata, SfM still allows for the creation of (hyper)local DEMs as long as the imagery contains sufficient overlap. Airborne or spaceborne platforms can be used, provided that 2D frame-based cameras are used which can be represented with a pinhole mathematical model. \r\nGenerating a digital elevation model (DEM) from SfM is typically handled automatically using specialized software. Firstly, image correspondences are detected. Feature points are identified in the individual images using local contrast feature detectors. The features extracted from all the images are matched with all the available overlapping images and erroneous matches are filtered out. The process typically results in hundreds or thousands of tie-points per image, which allows for robust matching even with large a priori uncertainties in camera orientation. A bundle adjustment, solving for the 3D coordinates of the feature points, the position and orientation of the camera and its internal characteristics then results in an initial, so-called sparse 3D point cloud. \r\nNext, ground control points (GCPs) can be introduced. These are surface features (naturally present or introduced into the scene) which can be identified at the pixel level in the images by users. Measured also in the field with an accuracy smaller than the pixel size, they can be used to constrain the bundle adjustment solution to improve georeferencing and camera calibration to an accuracy similar to that of the GCP measurement or the GSD size. \r\nSince this process yields a match only for a small subset of all pixels, an additional step, called dense image matching is added. It starts from the exact position and orientations resulting from the bundle adjustment to rectify the images and overlay two or more images, to compare them row by row and in 16 different directions in a process called semi-global matching (SGM). Matching pixels are identified along these lines, and 3D intersection distances photogrammetrically inferred. By combining results from different directions, a 3D coordinate for almost every pixel is obtained with similar accuracy. Finally, DEM products with a regularly spaced grid are generated and exported based on the dense point cloud. Depending on the point classes used in the export (obtained through topographic filtering or deep-learning-based classification of the dense point cloud), the outcome will be a digital surface model (DSM) or digital terrain model (DTM).","name":"DEM generation with 'Structure-from-Motion'","selfAssesment":"Completed
"},{"code":"IP1-3-1-2","description":"Photogrammetry is the science and technology of obtaining spatial measurements and other geometrically reliable derived products from photographs. Basic geometric principles applying both traditional analogue and modern digital procedures are related to the central projection of the image in case of typical cameras and to the dynamic projection mostly in case of push-broom sensors, popular in the satellite photogrammetry. The fundamental principle used by photogrammetry is called triangulation. By taking photographs from at least two different locations, so-called “lines of sight” can be developed from each camera to points in a block on the object. These lines of sight (called rays) are mathematically intersected to produce the 3-dimensional coordinates of the points of interest.\r\nWithin data processing the most important parts of photogrammetric workflow are: (1) image orientation, (2) model reconstruction, and (3) orthorectification. Image orientation is based mostly on aerial triangulation, however recently the computer vision algorithm, called structure from motion, became more popular in particularly in close range photogrammetry. Both orientation approaches include detection or measurement of the points between overlapping images in a block, control points measurements in a field defining orientation in reference system and check points verifying the orientation process. The satellite photogrammetry due to different projection and much bigger areas of imaging is usually related to Rational Polynomial Coefficients (RPCs) defining preliminary scene orientation during image orientation. However, to receive more accurate results also here the control points measured in a field are in use. The second part of the modern photogrammetric processing is 3D model reconstruction. In past, vectorization within the stereoscopic measurements was the most popular way of using photogrammetric data after the image orientation. The development of the informatics contributed to the development of the image matching algorithms that can provide dense image point clouds, which can be used to the 3D detailed modelling including digital elevation model production. The final step of photogrammetric processing is orthorectification, which delivers cartometric image called orthophoto mosaiced into orthophotomaps. This process comprises the influence of digital terrain model, model of camera (interior orientation) and image orientation (exterior orientation). Orthophotomap and elevation models derived from photogrammetric processing are applied as very popular data source in many GIS systems. The other photogrammetric outcomes are, for example a 3D measurement or 3D models of some real-world object or scene.","name":"Photogrammetric principles","selfAssesment":"Completed
"},{"code":"IP1-3-1-3","description":"In satellite photogrammetry to obtain the orientation mostly of satellite scene Rational Polynomial Coefficients (RPCs) are applied. They provide a compact representation of a ground-to-image geometry, that allow for photogrammetric processing without requiring a physical camera model. Model with RPC is provided with satellite image and can be improved using measurements of indirect surveying methods used for control point measurement. The RPC model for the coordinates of the image point is calculated as ratios of the cubic polynomials in the coordinates of the world or object space or ground point. \r\nIn photogrammetry and remote sensing, rational polynomial coefficients (RPCs) describe a specific imaging geometry model for transforming image pixel coordinates to map coordinates (thereby accounting for terrain displacement errors). A sensor model describes the geometric relationship between the object space and the image space, or vice versa. It relates 3-D object coordinates to 2-D image coordinates. RPCs are part of a general sensor model that approximates the physical sensor model. The physical sensor model represents the physical imageing process, making use of information on the sensor's position and orientation (during image acquisition). The RPC model often refers to a specific case of the RFM (rational function model) that is in forward form, has third-order polynomials, and is usually solved by the terrain-independent scenario.","name":"RPC correction","selfAssesment":"Completed
"},{"code":"IP1-3-1-4","description":"A ground control point (GCP) is a location of the surface of the Earth (e.g. a road intersection) that can be identified on the imagery and located accurately on the map (i.e. the reference dataset). Two distinct sets of coordinates are associated with the GCP: image coordinates in i rows and j columns, and map coordinates (e.g. x, y measured in degrees of latitude and longitude or as specified by the spatial reference system).","name":"Ground Control Points (GCP)","selfAssesment":"Planned
"},{"code":"IP1-3-1","description":"Orthorectification is the process of removing sensor (scanner or camera), satellite/aircraft, and terrain-related distortions for creating a planimetrically correct image. \r\nTo obtain an accurately orthorectified image, the following information is required: (1) accurate elevation model, and (2) a camera model or rational polynomial coefficients (RPCs) that depicts the positional relationship of the collected image to the ground. Many companies deliver their images together with RPCs and existing software implementations can automatically read these files and apply the RPC transformation on the fly. An accurate elevation model is important to remove the influence of topography (e.g. hills, valley, etc.) on the raw image so that users can accurately compute distances, areas, and directions. Without performing orthorectification, the features in the image are tilted (especially the features located away from the center of the camera). Many satellite data products (e.g. Sentinel images, Landsat data products) are orthorectified using Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) data which is a freely available data product and has a spatial resolution of e.g. 1 arc-second (30 m). In the case of extremely jagged surface topography, i.e. areas of high relief, a DEM with a higher spatial resolution is required. \r\nTwo main models can be used in the orthorectification process: black-box and the physical-based model. The black-box model (called also the analytical model) is commonly implemented in different software because it relies solely on the RPC files. This model does not require access to any proprietary information of the sensor used to collect the image. \r\nThe physical-based models are more complex (and hence expected to be more accurate) because they account for various factors that might influence the quality of the acquired image: e.g. position of the satellite when collecting the images, atmospheric effects, etc. An example of a physical-based model is the so-called camera model. This model requires access to proprietary sensor information that has to be provided by the image owner.","name":"Orthorectification","selfAssesment":"Completed
"},{"code":"IP1-3-2-1","description":"Image co-registration [aka Image-to-image registration] is the translation and rotation alignment process by which two images of like geometry and of the same geographic area are positioned coincident with respect to one another so that corresponding elements of the same ground area appear in the same place on the registered images (Jensen 2005 referencing Chen and Lee 1992).","name":"Image co-registration","selfAssesment":"New
"},{"code":"IP1-3-2","description":"Spatial referencing (referred to as geo-referencing as well) is the process of aligning available EO or GIS data to a coordinate system so that further spatial analysis and image analysis tasks can be applied using these data as input. \r\nTo be able to perform spatial referencing, users have to generate the so called Ground Control Points (GCPs) with known coordinates. In case of images, the easiest features that could be used as GCPs are the intersections, isolated trees etc.","name":"Spatial referencing","selfAssesment":"Planned
"},{"code":"IP1-3","description":"Geometric correction is concerned with placing the reflected, emitted, or back-scattered measurements or derivative products in their proper planimetric (map) location so they can be associated with other spatial information. It is usually necessary to preprocess the remotely sensed data and remove the geometric distortions so that individual picture elements (pixels) are in their proper planimetric (x, y) map locations. This allows remote sensing-derived information to be related to other thematic information in geographic information systems (GIS) or spatial decision support systems (SDSS). Geometrically corrected imagery can be used to extract accurate distance, polygon area, and direction (bearing) information.\r\n\r\nGeometric correction techniques are dedicated to resolving the geometric distortions caused by: (1) variations in sensor position; (2) Earth curvature; (3) rotation of Earth on its axis; (4) relief displacement. \r\n\r\nThere are two types of geometric distortions, namely systematic and random distortions. The former might be caused by Earth's rotation for example and, therefore they are predictable and systematic. The second type of distortions might be caused by terrain or variations in sensor altitude. \r\nGeometric correction includes georeferencing and orthorectification techniques.","name":"Geometric correction","selfAssesment":"Completed
"},{"code":"IP1-4-1","description":"Contrast stretching (also referred to as contrast enhancement) expands the original input brightness values to make use of the total dynamic range or sensitivity of the output device (a computer display).","name":"Contrast stretching","selfAssesment":"New
"},{"code":"IP1-4-2","description":"The histogram is a useful graphic representation of the information content of a remotely sensed image. Histograms for each band of imagery are often displayed and analysed in many remote sensing investigations because they provide the analyst with an appreciation of the quality of the original data (e.g. whether it is low in contrast, high in contrast or multimodal in nature. [...] Tabulating the frequency of occurrence of each brightness value within the image provides statistical information that can be displayed graphically in a histogram.","name":"Histogram","selfAssesment":"New
"},{"code":"IP1-4","description":"Image enhancement algorithms are applied to remotely sensed data to improve the appearance of an image for human visual analysis or occasionally for subsequent machine analysis. The quality of results of image analysis are subjectively judged by humans as to whether they are useful. They include contrast enhancement.","name":"Image enhancement","selfAssesment":"New
"},{"code":"IP1-6","description":"Principal component analysis (PCA) has proven to be of value in the analysis of multispectral and hyperspectral remotely sensed data. PCA is a technique that transforms the original correlated spectral dataset into a substantially smaller and easier set of uncorrelated variables that represents most of the information present in the original dataset. The first component accounts for the maximum proportion of the variance of the original dataset, and subsequent orthogonal components account for the maximum proportion of the remaining variance.","name":"Principal component analysis (PCA)","selfAssesment":"New
"},{"code":"IP1-7-1-1","description":"Bottom-of-Atmosphere (BOA) reflectance is also called surface reflectance and consists of the solar radiation that is reflected from the Earth's surface.","name":"Bottom-of-Atmosphere (BOA)","selfAssesment":"New
"},{"code":"IP1-7-1-4","description":"Top-Of-Atmosphere (TOA) radiance represents the radiance observed outside Earth’s atmosphere. It is derived from the Digital Numbers (DN) using metadata delivered with the image.","name":"Top-Of-Atmosphere (TOA)","selfAssesment":"New
"},{"code":"IP1-7-1","description":"Atmospheric correction accounts for the attenuation caused by scattering and absorption in the atmosphere. It transforms top-of-atmosphere (TOA) reflectance to bottom-of-atmosphere (BOA) reflectance.\r\nThe decision to perform atmospheric correction depends on the need, i.e. the envisioned usage of the derived EO information product and the nature of the underlying problem. This includes requirements to the accuracy of extracted biophysical information. Additionally, the decision and choice of methods depends on the type of remote sensing data available, the amount of in-situ historical and/or concurrent atmospheric information available.\r\nAn atmospheric correction is essential when biophysical or geophysical parameters (e.g. of water or vegetation) are going to be extracted from the remote sensing data. If the data is not corrected, the subtle differences in reflectance among the contributing image bands may be lost. This is especially relevant when biophysical information shall be compared to that of images from other dates.\r\nHowever, some cases exist where it is unnecessary to perform atmospheric correction. For example, it is not necessary for producing an image classification product from a single date of remotely sensed data. If a maximum likelihood classification is applied that uses training data with the same relative scale for the pixel values, then, atmospheric correction has little effect on the classification accuracy. The same holds true for a post-classification change detection where the classifications of the two different dates were performed independently. \r\nThe process of (absolute) atmospheric correction requires a model atmosphere and in situ atmospheric measurements acquired at the time of remote sensor data acquisition as input. In situ data can be available from other sensors on-board the sensor platform.\r\n\r\nDark Object Subtraction (DOS) is one of the most popular empirical atmospheric correction techniques. This technique assumes that a black object has a reflectance value of zero. Yet, a dark object present in a satellite image will have a value different than zero because of the atmospheric scattering. This value is then subtracted from all pixels in a given spectral band.","name":"Atmospheric correction","selfAssesment":"In progress
"},{"code":"IP1-7-2-1","description":"A method for dimensionality reduction in hyperspectral data is Minimum Noise Fraction (MNF). The purpose is to minimize the noise in the imagery, i.e. to identify noise and segregate it from true information, and to colaps the useful information into a much smaller set of MNF images. The MNF transformation applies two cascaded principal components analyses.","name":"Minimum noise fraction (MNF)","selfAssesment":"New
"},{"code":"IP1-7-2","description":"The number of spectral bands assocuates with a remote sensing system is referred to as its data dimensionality. Hyperspectral remote sensing systems such as AVIRIS ans MODIS obtain data in 224 and 36 bands, respectively. The greater the number of bands in a dataset (i.e., its dimensionality), the more pixels that must be stored and processed by the digital image processing system. Storage and processing consume valuable resources. It is necessary to reduce the dimensionality of hyperspectral data while retaining the information content inherent in the image. On method to reduce dimensionality of hyperspectral data and minimizing the noise in the imagery is the minimum noise fraction (MNF) transformation (Green et al., 1988).","name":"Dimensionality reduction","selfAssesment":"New
"},{"code":"IP1-7-3","description":"Sensor calibration converts the sensor’s digital numbers (DNs) to at-sensor radiance above the atmosphere. A further radiometric adjustment accounts for the viewing angle and sun angle during acquisition to transform radiance values to top-of-atmosphere (TOA) reflectance. Therefore, the process requires sensor calibration information and telemetry data that satellite image providers deliver within the metadata.\r\nDNs are raw sensor data without physical units. The sensor calibration information for converting the DNs to radiance are the calibration gain (cal_gain) and calibration offset (cal_offset) values. The sensor calibration multiplies the DNs of each spectral band with their corresponding cal_gain and adds the corresponding cal_offset. This linear function for translation and stretching transforms DN to at-sensor radiance in each band for the entire image. The radiometric adjustment uses information about the viewing angle and sun angle during acquisition to transform at-sensor radiance to TOA reflectance. \r\nSensor calibration obtains TOA reflectance and is a minimum requirement for performing band math calculations to derive spectral indices such as the normalized vegetation difference index (NDVI). Uncalibrated image data would arrive at NDVI values that are distorted because the cal_gain and cal_offset parameters for the involved spectral bands were not considered.","name":"Sensor calibration","selfAssesment":"In progress
"},{"code":"IP1-7-4","description":"As an optical remote sensing system is not perfect, noise can enter the data collection system at several points. Necessary corrections include the removal of shot noise (random bad pixels), correcting line or column drop-outs, accounting for line-start problems and radiometric correction of n-line striping caused by detector miscalibration.\r\nSAR data have global, random speckle noise. Speckle filters are designed to adapt to local image variations in order to smooth values, thus reducing speckle and enhancing lines and edges to maintain the sharpness of an image. A widely used way to reduce speckle is to apply spatial filters to the images. Typical approaches for speckle filtering include Laplace filtering for smoothing and sigma filters that preserve more of the signal with a lesser effect of smoothing.","name":"Noise reduction","selfAssesment":"New
"},{"code":"IP1-7-5","description":"Topographic correction, or topographic effects correction, aims to adjust the spectral values of an image according to effects of solar illumination differences due to the irregular shape of the terrain. Topographic slope and aspect introduce radiometric distortion of the recorded signal. Further, terrain shadow dramatically affects the brightness values of the covered pixels in an image. Topographic effects of illumination and shadow are particularly relevant in mountainous regions and in regions towards the higher latitudes of the southern and northern hemisphere. The effects appear pronounced during the winter season. \r\nTogether with sensor calibration and atmospheric correction, topographic correction is part of the radiometric correction process to obtain true reflectance values from sensor radiance. This process is necessary when using EO data for obtaining geophysical measurements. It can also benefit the accuracy of image classifications by reducing the internal variability of vegetation types, since the corrected reflectance relates better to the geometrical or biological properties of the plant than to the original reflectance.\r\nMethods for the removal of topographic effects from remotely sensed images can simply be based on band ratios that do not require additional input. Alternatively, they use digital elevation models (DEMs) as an additional input and apply sophisticated modelling of the illumination conditions. The illumination model describes various aspects of the relationship between the sensor measurement, the sun illumination, the ground reflectance and the diffuse irradiance at the surface. The model incorporates the angles between the sun position, the ground position (described by slope and aspect from the DEM), and the sensor position. Among these methods are lambertian methods and non-lambertian methods such as the bidirectional reflectance distribution function (BRDF). The BRDF, which is more suitable to the non-Lambertian properties of the observed surfaces, describes how the reflectance varies in each cover considering the angles of incidence and observation. \r\nIf achieved with a high quality, the resulting topographically corrected image appears to be illuminated evenly as if all its pixels would be part of a flat surface without the presence of any terrain differences. However, the much larger benefit than the improved appearance is the availability of pixel values that are closest to the true reflectance when compared to TOA, BOA and DN values.","name":"Topographic correction","selfAssesment":"In progress
"},{"code":"IP1-7-6","description":"Field spectroscopy is an in-situ method for characterising the reflectance of natural surfaces and thereby provides reference data for the calibration of airborne and satellite sensors. In addition, the method provides a means of scaling-up measurements from small areas (e.g. leaves, rocks) to composite scenes (e.g. vegetation canopies), and ultimately to pixels.","name":"Field spectroscopy reference data","selfAssesment":"In progress
"},{"code":"IP1-7","description":"Radiometric calibration and correction converts the sensor’s digital numbers (DNs) to radiance values and subsequently reflectance values. Additionally, the term “correction” points to the fact that radiometric measurements with satellite sensors contain error. Therefore, radiometric correction is concerned with improving the accuracy of surface spectral reflectance, emittance, or back-scattered measurements obtained using a remote sensing system. The Earth’s atmosphere, land and water are complex and can never be captured perfectly because of the limitations of remote sensing devices that lie in their spatial, spectral temporal and radiometric resolution. Therefore, error occurs in the data acquisition process and degrades the quality of remotely sensed data. The most common errors in remote sensing are radiometric and geometric. This concept is focused on the correction of remote sensing data to account for radiometric error that is to some degree systematic. Systematic errors in radiometric measurements come from the interaction of the sensed radiance with the atmosphere, the acquisition geometry in relation to the radiance source (the sun) and the Earth surface geometry (terrain).\r\nThere are several levels of radiometric calibration and correction. The first is sensor calibration that converts the DNs to top-of-atmosphere (TOA) reflectance. It converts to radiance values and further to reflectance values by accounting for the viewing angle and sun angle during acquisition. The second is atmospheric correction that converts TOA reflectance to bottom-of-atmosphere (BOA) reflectance. The third is topographic correction that converts BOA reflectance to surface reflectance. \r\nRadiometric calibration is necessary to ensure radiometric comparability of the measurements. There is a need for calibration when comparing different spectral bands within one image, e.g. for the calculation of geo-biophysical parameters with band math operations. Results from uncalibrated image data would differ from results achieved with calibrated data because the unaccounted cal_gain and cal_offset of the used spectral bands would lead to distortions. \r\nIn addition, radiometric calibration complements the geospatial comparability that is achieved with geo-referencing an image to geographic coordinates. Geo-referencing enables comparison of an image pixel to the geospatially matching pixel in another image acquired with a different sensor but with comparable resolution. Radiometric calibration enables a radiometric comparison between these two pixels’ radiance values. In case the two images are from different acquisition dates, a calculated radiometric difference would indicate change. This example shows the relevance of radiometric calibration for inter-sensor comparisons.\r\nRadiometric comparability is particularly relevant in studies that require inter-sensor comparisons, comparisons of surface features over time, or comparisons to laboratory or field reflectance data. Then the radiometric correction should cover atmospheric, solar and topographic effects. A full radiometric correction that also includes topographic correction can benefit the accuracy of image classifications by reducing the internal variability of vegetation types, since the corrected reflectance relates better to the geometrical or biological properties of the plant than to the original reflectance.","name":"Radiometric calibration and correction","selfAssesment":"In progress
"},{"code":"IP1","description":"Pre-processing operations are performed on remotely sensed data prior to information extraction. Remove error encountered in remotely sensed data (most common are radiometric and geometric error) to get as close as possible to the true radiant energy and spatial characteristics of the study area at the time of data collection. Image preprocessing includes any steps that facilitate information extraction (image display and enhancement).\r\n\r\nPre-processing of EO data focuses on processing the electrical signal measured by a sensor to a processing level at which pixel values subsequently can be used for information extraction. This includes correction of sensor system errors, geometric and radiometric correction. EO data are an information source for multiple purposes with different needs for pre-processing. Some applications may need only basic pre-processing to be done, others need a higher level. Depending on the sensor type (optical, radar, lidar), different processing levels are established.","name":"Image pre-processing","selfAssesment":"Planned
"},{"code":"IP2-1-1","description":"Data augmentation refers to a scheme of augmenting the observed data so as to make it more easy to analyze. An application from deep lerarning is to increase the number of input training sample images with augmented data. Examples of data augmentation techniques include horizontal flips, random crops, and principal component analysis.","name":"Data augmentation","selfAssesment":"New
"},{"code":"IP2-1-2","description":"Data imputation refers to a scheme of replacing missing values by imputed values. Imputation can be done, for example with mean, median and mode. Imputation methods can efficiently predict multiple response variables simultaneously.","name":"Data imputation","selfAssesment":"New
"},{"code":"IP2-1-3-1","description":"Gram-Schmidt is a pan-sharpening method that has been invented by Laben and Brover in 1998 and patented by Eastman Kodak. It makes use of the Gram-Schmidt orthogonalization to decorrelate the spectral bands (panchromatic, red, green, blue, etc.) and transform them into one multidimensional vector.","name":"Gram-Schmidt pan-sharpening","selfAssesment":"New
"},{"code":"IP2-1-3-2","description":"This pan-sharpening method uses PCA to transfer detailed spatial information from panchromatic band to the available multispectral bands.","name":"Principal Component Analysis (PCA)-based pan-sharpening","selfAssesment":"New
"},{"code":"IP2-1-3","description":"Pan-sharpening methods are used to enhance spatial resolution of images by merging a panchromatic image with high resolution with a multispectral image with low resolution.","name":"Pan-sharpening","selfAssesment":"New
"},{"code":"IP2-1-4","description":"Spatiotemporal image fusion methods, called also spatiotemporal downscaling methods, represent an efficient solution to generate fine-scale images at a high temporal resolution for more detailed land cover mapping and monitoring applications. Spatiotemporal image fusion methods can be classified into three categories: (1) reconstruction-based , (2) unmixing based and (3) learning-based methods.","name":"Spatio-temporal image fusion","selfAssesment":"New
"},{"code":"IP2-1","description":"Image fusion is defined as the “combination of two or more different images to form a new image by using a certain algorithm” Data fusion is a well-established research field. Image fusion methods are primarily used for improving the level of interpretability of the input data. Additionally, they can be utilized to address the problem of missing data caused by cloud or shadow contamination in satellite images time series. Image fusion can be performed at pixel-level, feature-level (e.g. land-cover classes of interest), and decision-level (e.g. purpose driven).","name":"Data fusion","selfAssesment":"Planned
"},{"code":"IP2-2","description":"Data harmonization aims to transform different datasets in such a way that they fit together, both with respect to geometry and semantics. The goal is that a user, who is using data from different authorities, shall have a unified view, where conflicts in the datasets have been removed.","name":"Data harmonisation","selfAssesment":"New
"},{"code":"IP2-3","description":"Data integration is the process of combining different geographic datasets including those derived from remote sensing data. The combined datasets can have different coverage, but they have to have the same geographic coordinates.","name":"Data integration","selfAssesment":"Planned
"},{"code":"IP2","description":"Data assimilation comprises steps to improve the level of interpretability of the input data, by enrichment (get rid of spatial/temporal gaps), by accounting for heterogeneity (through harmonization), and by integration (combination with other data that is relevant to the application). Thereby, datasets become more comparable to each other.","name":"Data assimilation","selfAssesment":"Planned
"},{"code":"IP3-1-1-1","description":"Vegetation fraction (VF) is defined “as the percentage of vegetation occupying a pixel as viewed in vertical projection. It’s a comprehensive quantitative index in forest management and vegetation community cover conditions, and it’s also an important parameter in many remote sensing ecological models.”","name":"Vegetation fraction","selfAssesment":"Planned
"},{"code":"IP3-1-1-2","description":"Leaf area index (LAI) is the ratio between the total area of the upper leaf surface of vegetation and the surface area of the pixel in question. LAI is a dimensionless value, typically ranging between 0 (for a pixel composed of bare soil) and values as high as 6 (for a dense forest).","name":"LAI (Leaf Area Index)","selfAssesment":"Planned
"},{"code":"IP3-1-1-3","description":"Net primary production (NPP) is a measure of the inherent productivity of a region or ecological system—mainly the Earth’s production of organic matter, principally through the process of photosynthesis in plants.","name":"Net primary production (NPP)","selfAssesment":"New
"},{"code":"IP3-1-1","description":"Biophysical parameter retrieval is an approach in remote sensing that aims to estimate parameters which have physical meaning related to properties of living organisms. The goal is to provide quantitative results directly relating to the biophysical state, but independent of acquisition conditions and technology. Assessment of vegetation status is a key motivation for this, because through plant respiration and photosynthesis, vegetation is critical for modelling terrestrial ecosystems and energy cycles in environmental studies. \r\nImportant parameters describing canopy structure include leaf area index (LAI), green cover fraction (fCover), fraction of absorbed photosynthetically active radiation (fAPAR), plant height, biomass and leaf angle distribution. At leaf biochemical level, leaf chlorophyll/water, fuel moisture and leaf pigmentation content are used.\r\nVisual inspection can provide a first assessment of plant status. For detailed measurements of biophysical parameters, mostly destructive methods have been used. Chemical measurement techniques on leaf samples can measure pigment concentrations very accurately, but are time consuming and only use very limited samples. \r\nMuch more extensive data can be collected using earth observation imagery. These range from large scale spaceborne observations with high frequency at coarse resolution to dedicated UAV flights which can offer spectral information of individual plants. Radar and LiDAR acquisitions, which are insensitive to weather conditions, now complement optical observations. \r\nMethods to retrieve the parameters from remote sensing data fall into two main categories. Statistical models empirically match data to a biophysical variable. Univariate techniques use a single quantity derived from the data, usually a vegetation index whereas multivariate techniques link a combination of measurements at different wavelengths to one or more biophysical parameters.\r\nPhysically-based modeling is an alternative approach which uses advanced radiative transfer models to describe the transfer and interaction of radiation inside a leaf or canopy based on robust physical, chemical, and biological processes. They compute the interaction between solar radiation and plants and provide as such a better understanding between biophysical variables and reflectance characteristics. Good examples are Leaf optical models such as PROSPECT and LIBERTY which simulate leaf optical properties by absorption and scattering coefficients. Canopy reflectance models simulate canopy reflectance as a function of a complex description of plant structural and radiometric attributes to develop a quantitative understanding of remote sensing information.","name":"Biophysical and geophysical parameters","selfAssesment":"Completed
"},{"code":"IP3-1-2-1","description":"This spectral index is calculated using the following formula: SAVI = [(NIR-Red)/(NIR+Red+L)]/(1+L), where L can be, for example, 1 in area with no vegetation or 0 in area with dense veegtaion. It is used to minimize the influence of the soil brightness from the vegetation indices that are based on red and near-infrared wavelengths.","name":"Soil-adjusted Vegetation Index (SAVI)","selfAssesment":"New
"},{"code":"IP3-1-2-2","description":"This spectral index is calculate using the following formula NDSI = (green-SWIR)/(green+SWIR). It is the most popular index used to identify snow cover due to the fact that snow reflects visible wavelength stronger than middle-infrared wavelengths.","name":"Normalized Difference Snow index (NDSI)","selfAssesment":"New
"},{"code":"IP3-1-2-3","description":"Leaves, when healthy and vigour show a characteristic green colour. This visual effect evident to humans is caused by the co-existence of two evolutionarily facts: the specific interaction of the chlorophyll pigment in living leaves to the visible spectrum (VIS, 400-700 nm wavelength) of light emitted by the sun and the sensitivity of our human eye to the same sub-spectrum. According to fundamental physical laws of radiation (Stefan Boltzmann law of blackbody radiation and Wien’s displacement law), the VIS sub-spectrum corresponds to the radiation maximum of the sun, a hot blackbody with a surface heat of about 6000 K. Living leaves are structured in specific layers exhibiting characteristic interaction with light. The chloroplasts located in the so-called palisade layer, make use of the blue and the red part of sunlight for photosynthesis, the unique process of transforming light to create energy (carbohydrates) from water and carbon dioxide. This leads to the specific behaviour of leaves to absorb large portions (up to 90%) of the blue and red part of the electromagnetic spectrum and reflect nearly 100% of the green light. The peak reflectance in green light makes leaves (and plants in general) appear in green colour in our visual perception. \r\nA second, by no means less characteristic, feature of leaves is the specific response to near infrared (NIR, at around 700 nm wavelength) light in the mesophyll tissue (transmittance, scattering and reflectance). Only a small fraction of NIR is being absorbed. \r\nThis combination of two specific spectral characteristics, the absorption in VIS (red colour) by chlorophyll a in palisade layers, and the reflectance of NIR in the spongy tissue, makes the spectral profiles of plants and vegetation exhibiting a very characteristic shape, the so-called red edge. This absorption edge between red and NIR light is sharper for higher intensity green reflectance and brighter green tones (such as grassland or bright deciduous forest) than for less intensive reflectance and darker tones (coniferous forest). \r\nThe red edge may shift for the same vegetation type due to plant maturity or plant stress. This effect we call the red shift. The red shift is sensitive to crop maturity (headed stage) and may indicate harvesting time. Notably, there is also a blue shift, indicating green plants’ exposure to geochemical stress, which causes the absorption spectra to shift towards shorter wavelengths. \r\nPlants usually do not appear in isolation but form a canopy with a certain degree of coverage (e.g., crown closure in forests), and a certain part of understorey or soil per area unit. The resulting canopy reflectance is therefore a spectral mix of soil and vegetation (or even different types of vegetation) and generally lower than the reflectance of a pure vegetation sample under lab conditions. \r\nTo capture most of these plant-typical spectral characteristics, the so-called normalised difference vegetation index (NDVI) was developed. NDVI is an arithmetic band combination of red and NIR bands in a normalised value range. \r\nThe NDVI is calculated as:\r\nNDVI=((NIR-R))/((NIR+R))\r\nThe (hypothetic) value range of the NDVI is [-1 | +1]. Under real-world conditions, the NDVI ranges from values of around -0.2 to 0.6 or 0.7. To discriminate principal land cover classes such as water, non-vegetation (soil, sealed, etc.) and vegetation the following thresholds in the continuous range are used: \r\n\tNDVI < ~ 0: water\r\n\t~ 0 < NDVI < ~ 0.2: non-vegetation (soil, sealed surfaces, bare rock, etc.)\r\n\t~ 0.2 < NDVI: vegetation.\r\nNotably, these class limits are just a very rough approximation (indicated by the ~ sign), due to the mixed pixels effect, canopy reflectance, the abundance of water plants and suspending particles, and the illumination effect of specific atmospheric or topographic conditions. \r\nWe can use the NDVI to generally mask out vegetation from other land cover types and, more specifically, to indicate vegetation vigour and health. It is also suitable for monitoring plant phenology as the relationship between vegetative growth and the (changing) conditions of the environmental conditions. A range of variations has been suggested, enhancing one or the other mathematical or statistical behaviour of the index, or making it even more sensitive to specific plant behaviour. A well-known example is the enhanced vegetation index (EVI).","name":"Normalized Difference Vegetation Index (NDVI)","selfAssesment":"Completed
"},{"code":"IP3-1-2","description":"Spectral indices are calculated using a mathematical equation that is applied on two or more spectral reflectance bands of the image. The calculated spectral index is a ‘new’ image that highlights particular land surface features or properties e.g. vegetation, soil, water, better than the original input bands. The spectral indices vary from simple spectral ratioing of two bands to more complex combinations of multiple bands. Spectral indexes are developed based on the spectral properties of the object of interest. For example, spectral indices dedicated to the vegetation condition are developed based on the principle that the healthy vegetation reflects strongly in the near-infrared spectrum while absorbing strongly in the visible red. These properties are used to develop more complex spectral indexes for monitoring vegetation condition, phenology parameters, i.e. Normalised Difference Vegetation Index (NDVI), Advanced Vegetation Index (AVI). The spectral indices calculated using the short wave infrared spectral bands are more sensitive to vegetation water content and spongy mesophyll structure in the vegetation canopy thus are used to assess the vegetation decline, moisture that is particularly useful for drought monitoring (e.g. Normalized Difference Water Index (NDWI) or Normalized Difference Moisture Index – NDMI). The water-related spectral indices are widely applied in agricultural and ecological applications including surface water body characteristics, vegetation water stress, soil water content assessment and wetlands monitoring. The combination of near infrared and short wave infrared spectral bands is also used to detect burned area and to monitor the vegetation recovery (e.g. Normalised Burned Ratio – NBR). There are other spectral indices dedicated to snow cover and glacier monitoring, which are developed based on visual green and short wave infrared spectral bands. Snow reflects most of the radiation in the visible bands whiles absorbing in the short wave infrared.","name":"Spectral indices","selfAssesment":"Completed
"},{"code":"IP3-1","description":"The term band maths denotes the arithmetic combination (addition/subtraction, multiplication/division) of two or more spectral bands in an early stage of image analysis. The resulting scalar values represent the spectral behaviour in different bands in a single value; such procedure makes particular sense, when spectral behaviour varies in those bands (like the red edge of vegetation spectra in the NIR band). \r\nThere are several reasons for applying band maths when working with multispectral imagery: (1) A single range of values rather than multiple bands is easier to comprehend and interpret; (2) Thresholds or class limits are applied more intuitively in a grey scale image; (3) Indices can be easily calculated and compared across different sensors; they are implemented as standard routines in many software environments as well as cloud processing environments (such as Google Earth Engine or the Proba-V exploitation platform)\r\nOut of the many possible, literature suggests a few arithmetic band combinations as application-specific quasi-standards. Band ratios (e.g. red band divided by NIR band) and indices (such as the normalised difference vegetation index, NDVI) belong to this group. Indices have the advantage over simple ratios in constraining the value range, e.g. [-1 | 1]. Designated to indicate specific land cover types (such as water index, snow index, soil index, etc.) such indices are widely used as a basis for operational information products. Another index is the normalised burn ratio (NBR) which relates near infrared and short-wave infrared reflectance to measure burn severity taking into consideration the increasing of SWIR reflectance in the course of a fire. \r\nPre-processing such as dark object subtraction and radiometric or even atmospheric correction is a key requirement prior to indexing. The coding in digital numbers (DN) is a function of the sensitivity and the radiometric resolution of the sensor. The actual recording depends on atmospheric conditions (additional brightness, haze, etc.). Therefore, in order to make the resulting values comparable among different types of sensors and scenes, radiometric correction is mandatory, converting DNs into radiances, i.e. true reflectance values as physical measurement units. \r\nTwo advanced examples of band maths beyond rationing are the perpendicular vegetation index (PVI) and the tasselled cap (TC) transformation. PVI is based on the assumption that vegetation pixels are generally separable from soil pixels (at least after unmixing or for pure pixels), and thus pixel values are located in a perpendicular direction from the soil line in a NIR/red feature space. The Euclidean distance from the soil line, determined by Pythagorean triangle, yields the PVI. Tasselled cap instead rests on the notion of a cap-like histogram shape when plotting pixels on a brightness vs. greenness plot, with the latter determined by linear combinations of VIS and NIR bands, along with empirically determined coefficients. TC 1 as a weighted sum corresponds to brightness, TC 2 to greenness, TC 3 to yellowness, sometimes referred to as wetness. A fourth TC called nonesuch likely corresponds to noise and atmospheric disturbance effects in the image.","name":"Band maths","selfAssesment":"Completed
"},{"code":"IP3-10","description":"Semantic enrichment is the process of adding semantic metadata elements to improve the content-based image retrieval. These semantic metadata elements enable the explicit specification of the content of the images stored in the remote sensing databases.","name":"Semantic enrichment","selfAssesment":"New
"},{"code":"IP3-11-1","description":"Different types of changes are investigated using remotely sensed data: (i) abrupt changes, such as the changes caused by a fire or flooding, and (ii) gradual changes such as urban growth. Besides these kinds of changes, remote sensing community differentiates between transitional changes and conditional changes. Transitional changes refer to a major change of land surface such as conversion of forest to pasture or the expansion of mangroves into the surrounding water. Conditional changes refer to the change in condition at the surface such as water stress in an agricultural field, forest degradation caused by pest. \r\nIn the past, many remote sensing studies used two images to detect different types of changes such as deforestation, land cover change or change in the health or condition of the vegetation (e.g. pest infestation). Meanwhile, satellite image time series are used to assess the change. Time series analysis allows for monitoring more subtle changes and for providing temporal patterns of change. In this way, the timing of changes and drivers of change can be easily identified. \r\nDifferent methods are being used in change detection studies. There are studies that analyze individual images available in the investigated time series to map the target class/phenomena/events at the time when images were collected and to identify the changes: e.g. mapping the mangroves extent on an year basis and measuring it to identify changes. Alternative studies search for breaks in time series for detecting changes. The breaks are used to segment the time series into before and after changes periods which are further classified using one of the existing supervised or unsupervised classification methods (K-means, fuzzy k-means, Random Forest, Support Vector Machine etc.).","name":"Change detection","selfAssesment":"Completed
"},{"code":"IP3-11-2","description":"The (data)cube model for analysis of time series of earth observation raster data, represents the dataset as a multidimensional array with one or more spatial or temporal dimensions. Scalar values in the cube can be selected (or ‘filtered’) and processed based on dimension labels. This allows analysis algorithms to be thought of as a set of operations on the multidimensional array. Technologies that support this model allow to efficiently implement such algorithms.\r\nSome possible operations on a multidimensional cube include: filtering, ‘reducing’ all values along a dimension, ‘aggregating’ values in a dimension, or transforming all values along a dimension. Generally speaking, these operations require the selection of a subset of the data on which work is to be done. This allows implementing the operations efficiently even on very large datasets.\r\nIn comparison to file-based processing, most technologies that support cube-based time series analysis reduce implementation overhead, as the user does not need to read and write individual files, also more complex aspects like distributed computing for parallelization can be hidden in a cube based approach. So a cube based approach can also be thought of as an abstraction layer that effectively reduces the need for specific IT-related skills when analyzing earth observation timeseries.\r\nMultiple initiatives support cube based analysis. Some common features include a programming API, often using the Python programming language. Some tools are only accessible as web services, while others can also run locally (on a small dataset). This diversity is still a drawback, as users would need to familiarize themselves with different systems. Initiatives such as openEO try to address this by providing a common API.","name":"Cube-based time series analysis","selfAssesment":"Planned
"},{"code":"IP3-11-3","description":"Dynamic Time Warping (DTW) works by comparing the similarity between two temporal sequences and finds their optimal alignment, resulting in a dissimilarity measure. In the case of remote sensing data, DTW can deal with temporal distortions, and can compare shifted evolution profiles and irregular sampling thanks to its ability to align radiometric profiles in an optimal manner","name":"Dynamic Time Warping","selfAssesment":"Planned
"},{"code":"IP3-11","description":"Satellite image time series analysis plays an important role in different domains including vegetation dynamics monitoring, estimating crop yields, discriminating between different land cover classes, exploring human-nature interactions, monitoring land cover change, assessing environmental threats, or evaluating ecosystems-climate feedbacks or urbanization.\r\nTime series analysis requires high quality time series which are reconstructed by removing any source of contamination such as clouds, cloud shadows, or scan-line corrector (SLC) gaps of the Enhanced Thematic Mapper plus sensor (ETM+) on Landsat 7. Removed pixels are usually filled in with data predicted from a different date (temporal interpolation), nearby pixels (spatial interpolation) or from both (spatiotemporal interpolation). Different methods are available for screening and masking out clouds and shadows in satellite images including mono-temporal methods such as Function of mask (Fmask), or multitemporal mask (e.g. Tmask algorithm). Fmask is used by the United States Geological Survey (USGS) to produce a cloud mask layer of Landsat images. European Space Agency (ESA) is using Sen2cor processor to produce Level 2A Sentinel-2 data with a shadow and cloud shadow mask. All images used in the time series have to be co-registered, i.e. they align as closely as possible. \r\nTime series analysis is used to (1) investigate various surface properties such as evapotranspiration, land surface temperature, (2) map the cover of the Earth surface (e.g. land cover mapping, crop mapping etc.), (3) detect different type of changes such as abrupt changes (fire event) or gradual changes (urbanization), and (4) study the trends.\r\nTo map surface features from satellite image time series, numerous studies make use of the vegetation phenology extracted from a spectral-temporal trajectory of a given spectral vegetation index such as the normalized difference vegetation index (NDVI) or enhanced vegetation index (EVI). Several metrics can be used to characterized vegetation phenology: metrics of greenness and metrics of time. The metrics of greenness include the minimum and maximum spectral vegetation indices, their difference or amplitude, seasonally averaged greenness etc. The metrics of time include start and end of the growing season, duration or length of the growing season or the timing of maximum greenness. Changes, on the other hand, are identified either by investigating two images acquired at two different points in time or by identifying breaks in a dense (annual or multi-annual) satellite image time series.","name":"Time series analysis","selfAssesment":"Completed
"},{"code":"IP3-12-1","description":"Remote sensing-derived products such as land-use and land-cover maps contain error. The error accumulates as the remote sensing data are collected and various types of processing take place. An error assessment is necessary to identify the type and amount of error in a remote sensing-derived product.","name":"Error propagation","selfAssesment":"New
"},{"code":"IP3-12-2","description":"The precision of a measurement system, related to reproducibility and repeatability, is the degree to which repeated measurements under unchanged conditions show the same results.","name":"Precision","selfAssesment":"New
"},{"code":"IP3-12","description":"Uncertainty is the result of the lack or imprecision of our knowledge about the world. A proposition is uncertain if we do not know whether it is true or not. In most circumstances we describe a proposition as uncertain when the reason we do not know whether it is true is that we do not possess complete and accurate knowledge about the state of the world.","name":"Uncertainty","selfAssesment":"New
"},{"code":"IP3-13-1","description":"The main elements of visual interpretation are: tone, shape, size, pattern, texture, shadow, , association. Tone refers to the relative brightness or colour of objects in an image. It depends on the spectral properties of an object. Variation in tone allows to distinguish elements of different shape, texture and pattern. Shape refers to the general form, structure, or outline of individual objects. Straight and sharp edge shape represent typically the anthropogenic features i.e. urban or agriculture, the natural features like rivers, wetlands are more irregular in shape. Size of objects in an image is a function of scale and it depends on the spatial resolution of the image. The assessment of the size of the target’s object in relation to other objectives as well as an absolute size of the object are the important part of the interpretation. Pattern refers to the spatial arrangement of objects, i.e. network of street and houses in an urban area, orchards with the line of trees. Texture refers to the arrangement of frequency of tonal variation in particular areas of an image. Rough texture would have very large, coarse tonal variation (e.g. forest canopy), whereas smooth texture very little tonal version (e.g. uniform, homogenous surfaces). It depends on the size, shape and pattern of objects. Shadow depends on the scale and spatial resolution of an image. Shadow is useful to measure the height of an object, to distinguish the coniferous from broadleaf trees. In the radar imagery is useful for identifying topography and landforms. Association refers to the relationship between objects and features in proximity to the target interest.","name":"Elements (cues) of interpretation","selfAssesment":"Completed
"},{"code":"IP3-13-2","description":"Information-as-data-interpretation considers information as the outcome of the cognitive process of vision that reconstructs a scene from an image.","name":"Information-as-data-interpretation","selfAssesment":"New
"},{"code":"IP3-13-3","description":"An image interpretation key is simply reference material designed to permit rapid and accurate identification of objects or features represented on aerial images.","name":"Interpretation keys","selfAssesment":"New
"},{"code":"IP3-13","description":"Interpretation is the processes of detection, identification, description and assessment of an object and pattern imaged. Visual interpretation is the ability of a human operator to identify an object through the data content in an image / photo by combining several elements of interpretation. The image characteristics used in the interpretation process are: shape, size, tone/colour, texture, shadow, neighbourhood and pattern. The importance of the image characteristics varied according to the spatial resolution of the images and the properties of the feature of interest. The interpretation can be performed on the single image or between several images acquired at different time, which result in the differentiation of the temporal changes. The principle of the image interpretation is the process of delineating (digitalizing) the outlines of the objects, features on the image. It is performed “on-screen” using a GIS software. The process of visual interpretation is time consuming and requires a skilled interpreter with knowledge of the study area. Even though, the image interpretation supports many applications in for example selection of the training and verification data sets for image classification and accuracy assessment.","name":"Visual interpretation","selfAssesment":"Completed
"},{"code":"IP3-2-1","description":"Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans.","name":"Artificial intelligence (AI) in EO","selfAssesment":"New
"},{"code":"IP3-2-2","description":"Information theory answers two fundamental questions in communication theory: what is the ultimate data compression (answer: the entropy H) and what is the ultimate transmission rate of communication (answer: the channel capacity, C). For this reason, it is considered that information theory is a subset of communication theory.","name":"Information theory","selfAssesment":"New
"},{"code":"IP3-2-3","description":"Keypoints are objects (or locations) on the ground that reveal locally invariant features in images and therefore are easily detectable by automatic algorithms. Methods for this process employ scale-invariant feature transform (SIFT) algorithms for the automatic detection of geospatial objects.","name":"Keypoint detection","selfAssesment":"New
"},{"code":"IP3-2","description":"Image understanding is part of computer vision. Computer vision is an interdisciplinary field that deals with how computers can be made to gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to automate tasks that the human visual system can perform.","name":"Computer vision in EO","selfAssesment":"New
"},{"code":"IP3-3-1","description":"A Digital Elevation Model (DEM) is a digital raster (or grid) representation of elevation values of land surface shapes and features, where each grid cell takes a single elevation value with reference to a certain vertical datum. A DEM can be global, regional or local in scope, and can be used to characterize the dry land surface (topography) or submerged surfaces (bathymetry). Since a DEM cannot contain information of shapes and features under overhanging structures, it is often referred to as 2.5D instead of truly 3D. \r\nA digital elevation model is an overarching term for either a digital surface model (DSM) or digital terrain model (DTM). A DSM includes elevations of surface features such as trees, buildings, bridges and artificial objects such as poles, power lines, cars etc., and thus contains always the highest elevations of any feature for any given raster cell. A DTM does not include such features but reflects the elevation of bare land surface shapes, excluding elevated or overhanging features.\r\nDEMs can be obtained using active or passive measurements. Active measurements involve the generation of electromagnetic signals towards a surface and timing the reception of the (return) signal(s). This can be achieved through laser scanning (LiDAR) using visible or infrared light pulses for bathymetric or topographic measurements respectively, radio waves (SONAR) used in bathymetric measurements, or microwaves (synthetic aperture radar, SAR) used in topographic mapping. The most widely known active remotely sensed global DEM is derived from the Shuttle Radar Topography Mission (SRTM) obtained by a SAR mounted on the space shuttle Endeavour, offering 30 m resolution with a vertical accuracy typically between 5 and 20 m, covering 80% of Earth’s surface.\r\nPassive measurements detect reflection of sun light, or energy radiated from the surfaces. Their distance to the detector can then be inferred from the measurement of angles. Historically, line scanning imagers were used, but nowadays, these are replaced by acquisitions of overlapping 2D frame images. On the images, corresponding land surface features are detected which act as tie-points. The distance between the sensor and the tie-points is calculated in a process called photogrammetry. The most widely known spaceborne passive remotely sensed global DEM is derived from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data onboard the Terra satellite. It offers similar resolution and accuracy compared to SRTM, but with 99% coverage. \r\nOnly LiDAR can generate both accurate DSMs and DTMs from the same data acquisition, by using multiple returns from a single emitted pulse. All other techniques generate DSMs, from which elevated features can be identified and filtered out in postprocessing to create DTMs, however with typically lower accuracy and more artefacts.","name":"DEM generation","selfAssesment":"Complete
"},{"code":"IP3-3-2","description":"DSM can be produced automatically from stereo satellite scenes, from satellite sensors such as GeoEye, IKONOS, SPOT-5, Terra-ASTER etc. The DSM can also be provided from stereo digital aerial photography at various resolutions, depending on the quality and scale of the aerial photography. The quality of the automatic generated DSM is substantially improved if ground measurements from GPS are incorporated in the DSM stereoscopic model.","name":"DSM generation","selfAssesment":"New
"},{"code":"IP3-3","description":"Stereo pairs of optical satellite images with the support of ground control points provide a basis for cross-stereo analysis for generating Digital Surface Models.","name":"Cross-stereo analysis","selfAssesment":"New
"},{"code":"IP3-4-1-1","description":"The goal of filtering is to remove unnecessary components from images (e.g., noise), while emphasizing the necessary ones. In the context of spatial aggregation, low pass filters aim at removing sharp transitions in the image intensities (high spatial frequencies) and thereby focus the information content of the image on a coarser scale level.","name":"Filtering","selfAssesment":"New
"},{"code":"IP3-4-1-2","description":"Gridding is the technique used to generate a uniform raster grid with one value for every cell in the raster. The values of the raster cells can represent different attributes such as mean, max or min of all Normalized Difference Vegetation Index (NDVI) values measured within a particular cell.","name":"Gridding","selfAssesment":"New
"},{"code":"IP3-4-1","description":"Spatial aggregation produces images of coarser resolution (grouping pixels in a grid of coarser resolution and calculating mean values) or of coarser scale (by filtering with low-pass filters). Thereby it is a form of generalization that may improve classification results. Spatial aggregation can be applied after classification to get rid of the salt-and-pepper effect.","name":"Spatial aggregation","selfAssesment":"New
"},{"code":"IP3-4-2-1","description":"Bayes’s theorem is an extremely powerful means of using information at hand to estimate probabilities of outcomes related to the occurrence of preceding events. Bayes' Theorem uses a priori (subjective) and conditional probabilities to calculate the probability of an uncertain event occurring. A priori probabilities represent what the modeler believes, before testing, to be the probability of an event occurring. Conditional probabilities are probabilities that other events occur in conjunction with the original event.","name":"Conditional probability","selfAssesment":"Planned
"},{"code":"IP3-4-2-2","description":"Maximum likelihood classification uses the training data for estimating means and variances of the classes, which are then used to estimate the probabilities. This method considers not only the mean, or average, values in assigning classification but also the variability of brightness values in each class.","name":"Maximum likelihood","selfAssesment":"In progress
"},{"code":"IP3-4-3-1","description":"The Land Cover Classification System (LCCS) was developed by FAO to provide a consistent framework for the classification and mapping of land cover. Its main objectives were to overcome the rigidity of a-priori land cover classifications, which in many practical situations do not allow easy assignment into one of the pre-defined classes and are therefore not very suitable for mapping. LCCS instead opted for an approach based on two main phases. The first phase is an initial ‘Dichotomous Phase’, in which eight major land cover types are defined: (1) Cultivated and Managed Terrestrial Areas, (2) Natural and Semi-Natural Terrestrial Vegetation, (3) Cultivated Aquatic or Regularly Flooded Areas, (4) Natural and Semi-Natural Aquatic or Regularly Flooded Vegetation, (5) Artificial Surfaces and Associated Areas, (6) Bare Areas, (7) Artificial Waterbodies, Snow and Ice, and (8) Natural Waterbodies, Snow and Ice. The Dichotomous Phase is followed by a subsequent ‘Modular-Hierarchical Phase’, in which land cover classes are created by the combination of sets of pre-defined classifiers, which are different for each of the eight major land cover types. For example, common classifiers used for (semi-) natural terrestrial vegetation types are Life Form, Cover, Height, Macropattern. For aquatic or regularly flooded natural and semi-natural vegetation, water seasonality is an indispensable classifier. LCCS offers several advantages from a conceptual point of view. LCCS is a real a priori classification system in the sense that, for the classifiers considered, it covers all their possible combinations. The classification is also hierarchical and the more classifiers used, the greater the detail of the defined land cover class. The classes derived from the proposed classification system are all unique and unambiguous, due to the internal consistency and systematic description of the classes. LCCS is designed to map at a variety of scales, from small to large. From a practical viewpoint LCCS offers several advantages: (1) easy incorporation into GIS and databases, (2) allows flexible response to information available in a given area, project budget and time constraints, (3) unlinks the field data collection from the interpretation process.","name":"Land cover classification system (LCCS)","selfAssesment":"Completed
"},{"code":"IP3-4-3","description":"Long-term monitoring of land cover and land use are particularly relevant for land ecosystem monitoring. Therefore, baseline datasets are necessary that allow assessing changes of land cover and land use where the class definitions remain consistent over time. Accordingly, classification schemes have been established that adhere to taxonomically correct definitions of classes of information organized according to logical criteria. If hard classification is to be performed (i.e. without fuzzy class boundaries), the classes in the classification system should normally be mutually exclusive, exhaustive, and hierarchical. Mutual exclusive classes have no taxonomic overlap and assign a land cover patch to a single class. An exhaustive classification scheme is able to cover the area of interest comprehensively and leaves no land cover patch unassigned. A hierarchical system allows combining sub-classes into higher-level categories.\r\nFrom a remote sensing classification perspective, it becomes clear that a classification scheme consists of information classes defined by human beings. Conversely, spectral classes are those inherent to EO data. An analyst must identify spectral classes and label them as information classes that satisfy bureaucratic (or scientific requirements). Additionally, the advantage of using established classification schemes is that their use in scientific studies and applications produces results that are comparable to other studies and suitable for sharing of data.\r\nEstablished classification schemes include: CORINE land cover (CLC), Land cover classification system (LCCS), American Planning Association land-based classification standard, United States Geological Survey land-use/land-cover classification system for remote sensor data, U.S. Department of the Interior Fish & Wildlife Service classification of wetland and deep water habitats of the United States, U.S. National Vegetation Classification system (NVCS), International Geosphere-Biosphere Program IGBP Land cover classification system.","name":"Classification schemes (taxonomies)","selfAssesment":"Completed
"},{"code":"IP3-4-4","description":"Unsupervised methods are defined as the identification of natural groups, or structures, within existing data. Clustering requires only the number of to-be generated classes as an input parameter and assigns spectrally defined classes to an image.","name":"Clustering (unsupervised)","selfAssesment":"New
"},{"code":"IP3-4-5-1","description":"A production system performs automatic transformation of remote sensing imagery into useful information (such as biophysical parameters, categorical maps etc). An example can be a preliminary pixel-based classifier that works top-down (deductive, physical model-driven, prior knowledge-based) and arrives at preliminary classes for each pixel of an image. Such a production system does not require interaction of an operator. The process makes use of a decision tree that encodes the prior knowledge for assigning pixels to a class.","name":"Production system","selfAssesment":"New
"},{"code":"IP3-4-5","description":"Decision trees is a data mining technique used in different disciplines including Remote Sensing. It uses a tree-like prediction model to identify a pattern in the input data. One of the most popular decision tree algorithms is the CART (Classification and Regression Tree) algorithm.","name":"Decision trees","selfAssesment":"New
"},{"code":"IP3-4-6-1","description":"Convolutional Neural Networks (CNNs) are among the most popular deep learning methods.","name":"Convolutional neural networks (CNN)","selfAssesment":"New
"},{"code":"IP3-4-6","description":"Deep learning approaches have classically been divided into spatial learning (for example, convolutional neural networks for object classification) and sequence learning (for example, speech recognition)","name":"Deep learning","selfAssesment":"New
"},{"code":"IP3-4-7-1","description":"The RF classifier is an ensemble classifier that uses a set of Classification and Regression Trees (CARTs) to make a prediction The trees are created by drawing a subset of training samples through replacement (a bagging approach).","name":"Random forest (RF)","selfAssesment":"New
"},{"code":"IP3-4-7-2","description":"In machine learning, support vector machines (SVMs) are supervised non-parametric statistical learning techniques with associates learning algorithms that analysze data used for both classification and regression analysis. SVM algorithm was originally designed for binary classification. The SVM is based on the main hypothesis that the training set is linearly separable. Given a set of training examples, each marked as belonging to one or another of two categories, an SVM training algorithm builds a model that can assign each new occurrence into one of these two categories, making it a non-probabilistic binary linear classifier. The SVM model is a representation of the examples as points in space, mapped so that the algorithm can find the optimal line (hyperplane) which separates with minimum error the training set, and maximizes the distance, named the “gap”, between the objects of both classes and the hyperplane. Thus, instead of using the whole available training set to describe classes, SVM uses only those training samples that describe class boundaries (support vectors), thought it can be more efficient than other algorithm because it uses a subset of training points. New occurs are then mapped into that same space and predicted to belong to a category based on the side of the gap on which they fall. In addition to performing linear classification, SVMs can also efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces. Unfortunately, because of the technique used for separating classes SVM is less effective on noisier datasets with overlapping classes. When data are unlabelled, supervised learning is not possible, and an unsupervised learning approach is required. SVM is used for text classification tasks such as category assignment, spam detection and sentimental analysis. It is also commonly used for image recognition, performing particularly well in aspect-based recognition and colour-based recognition. SVM also plays a vital role in many areas of handwritten digit recognition, such as postal automation services.","name":"Support vector machines (SVM)","selfAssesment":"Completed
"},{"code":"IP3-4-7","description":"Field of study that gives computers the ability to learn without being explicitly programmed","name":"Machine learning","selfAssesment":"New
"},{"code":"IP3-4-8","description":"Image classification operator needs a set of terms to express the characteristics of an image. These characteristics are called interpretation elements and are used to define interpretation keys: tone/hue, texture, pattern, shape, size, height/elevation, location/association","name":"Mental concepts and categories","selfAssesment":"New
"},{"code":"IP3-4-9","description":"Sampling strategies or sampling pattern specifies the arrangement of observations used for training and/or validation purposes.\r\nTypically, the simple random sample of a geographic region is defined by first dividing the region to be studied into a network of cells. Each row and column in the network is numbered, then a random number table is used to select values that, taken two at a time, form coordinate pairs for defining the locations of observations. Because the coordinates are selected at random, the locations they define should be positioned at random. The random sample is probably the most powerful sampling strategy available as it yields data that can be subjected to analysis using inferential statistics.\r\nA stratified sampling pattern assigns observations to subregions of the image to ensure that the sampling effort is distributed in a rational manner. For example, a stratified sampling effort plan might assign specific numbers of observations to each category on the map to be evaluated. This procedure would ensure that every category would be sampled.\r\nSystematic sampling positions observations at equal intervals according to a specific strategy. Because selection of the starting point predetermines the positions of all subsequent observations, data derived from systematic samples will not meet the requirements of inferential statistics for randomly selected observations.","name":"Sampling strategies","selfAssesment":"New
"},{"code":"IP3-4","description":"The process of image classification extracts information about semantic labels of pixels or objects (i.e. regions) from imagery. Apart of input imagery, the process requires an input set of target classes (classification scheme) for which their spectral (and other) properties have to be identified. A classification method has to be selected that transforms the image data and the classification scheme into semantic map information. In complement to the resulting sematic labelling products, a secondary outcome are instructions or rulesets with the used parameters that constitute the documentation of the classification process.\r\nThe input imagery consists of one or more images (optical and/or SAR data) of a specific geographic area, collected in multiple bands of the electromagnetic spectrum (that may have already undergone certain pre-processing steps; determined by the purpose). Additionally, the imagery may include derived spectral indices, principal components, filtered bands, or other features to support the classification process.\r\nThe classification purpose defines the information about the target classes. It includes classification schemes (taxonomies), spectral signatures for each class and, mental concepts and categories about the classes (that enable an analyst to distinguish classes by texture, spatial relationships etc.). Often, training areas are used to understand how an object of a particular class is discernible in the available imagery and separable from other classes. Both the input imagery and the chosen classification method determine which features of each class can be exploited for classification. For example, spectral signatures of the target classes (extracted from training areas with known class label) may be a suitable input for extracting information with a pixel-based classification. For shape features, objects are a pre-requirement, derived with segmentation. They are only available with object-based classification approaches.\r\nClassification methods: Various methods exist that can be categorized according to the classification logic that they follow when transforming the input information into the output semantic labelling products. These can be parametric or nonparametric, supervised or unsupervised, per-pixel or object-oriented, semi-automated or fully automatic, and hybrid approaches. Classification methods are for example bayesian techniques like conditional probability or maximum likelihood, clustering (unsupervised), decision trees, deep learning and machine learning.","name":"Image classification","selfAssesment":"Completed
"},{"code":"IP3-5-1","description":"Edge detection is a fundamental tool used in many image processing applications to obtain information from the frames as a precursor step to feature extraction and object segmentation. This process detects outlines of an object and boundaries between objects and the background in the image. An edge-detection filter can also be used to improve the appearance of blurred image.","name":"Edge-based segmentation","selfAssesment":"Planned
"},{"code":"IP3-5-2","description":"Histogram-based segmentation makes use of histogram to select the gray levels for grouping the pixels into regions, e.g. background and the object of interest","name":"Histogram-based segmentation","selfAssesment":"New
"},{"code":"IP3-5-3","description":"Local variance can be calculated as the value of standard deviation in a small neighborhood (e.g. 3x 3 moving window), then computing the mean of these values over the entire image. The obtained value is an indicator of the local variability in the image.","name":"Local variance","selfAssesment":"New
"},{"code":"IP3-5-4","description":"Mean Shift is defined as finding modes in a set of data samples, manifesting an underlying probability density function (PDF).","name":"Mean-shift segmentation","selfAssesment":"New
"},{"code":"IP3-5-5","description":"Regionalization is an important concept in Geographic Information Science for synthesizing multi-dimensional data into homogeneous objects through spatially constrained clustering methods","name":"Regionalisation","selfAssesment":"New
"},{"code":"IP3-5-6-1","description":"Multi-resolution segmentation is a region-growing algorithm. It relies on several parameters, which need to be tuned. These include the scale parameter (SP), which dictates the size and homogeneity of the resultant objects.","name":"Multi-resolution segmentation","selfAssesment":"Planned
"},{"code":"IP3-5-6-2","description":"Watershed segmentation is a region-based method that has its origins in mathematical morphology. In watershed segmentation an image is regarded as a topographic landscape with ridges and valleys. The elevation values of the landscape are typically defined by the gray values of the respective pixels or their gradient magnitude. Based on such a 3D representation the watershed transform decomposes an image into catchment basins. For each local minimum, a catchment basin comprises all points whose path of steepest descent terminates at this minimum. Watersheds separate basins from each other. The watershed transform decomposes an image completely and thus assigns each pixel either to a region or a watershed.","name":"Watershed segmentation","selfAssesment":"New
"},{"code":"IP3-5-6","description":"Region-based segmentation algorithms can be devided into region growing, merging and splitting techniques and their combinations. Region merging starts from all pixels on the pixel level and iteratively aggregates pixels into objects until some conditions of homogeneity imposed by the user are met.","name":"Region-based segmentation","selfAssesment":"New
"},{"code":"IP3-5-7","description":"Spatial autocorrelation is the term used to describe the presence of systematic spatial variation in a variable.","name":"Spatial autocorrelation","selfAssesment":"New
"},{"code":"IP3-5","description":"The term image segmentation denotes the process of algorithmically grouping neighbouring pixels that are similar. What sounds rather straight forward, is in fact a great computational challenge, some even call it an ill-posed problem, because there is a high degree of ambiguity in this process. \r\nThe two attributes in the general definition provided above, i.e. neighbouring and similar, evoke the principles of regionalisation as a fundamental concept in geography. Regionalisation is the bottom-up approach to congregate adjacent elements with the aim to form a larger unit. (Conversely, this could be understood in a top-down manner when subdividing a larger whole into smaller homogeneous units). This follows the general notion of hierarchical organisation according to general systems theory (GST). The organisation of a state in smaller administrative units is a good example for a hierarchical structure, the composition of the human body by organs, cells, etc. another. In image analysis such regions are commonly referred to image regions, originating from the concept of “photomorphic regions”, literally meaning regions formed on images – originally by human interpreter through manual delineation. Today, advanced pixel grouping algorithms aim to delineate homogenous regions in an image automatically. As those regions usually are assumed to match with real-world objects, it is often stated in literature that image segmentation generates image objects. Deriving some general heuristics on their properties (colour, size, shape, orientation, etc.) we can label these objects according to a given semantic scheme. The procedure of object delineation and classification using object features and relations is a fundamental principle in object-based image analysis (OBIA). \r\nDue to the effect of spatial autocorrelation (the tendency of neighbouring pixels to be similar irrespective of scale or geographical location), pixel grouping is ambiguous and by no means trivial, but not arbitrary either. Intuitively, image regions are those quasi-homogeneous areas that we perceive as landscape units on a specific scene (a lake, a forest patch, a single tree, a building, a residential area). According to hierarchy theory, we can assume that we find multiple scales within a single image even, according to the level of detail we are interested in. Whether or not a specific grouping of pixels is considered valid, e.g. because it corresponds to a real-world object, can hardly be answered unanimously, but rather needs to be judged by experts in the respective application domain. That is why often in literature we find the term ‘meaningful objects’. \r\nImage segmentation is as a sub-field of computer vision and aims to apply computer algorithms to generate image regions (a.k.a. tokens) within digital image analysis. There are several strategies for performing image segmentation, all resting on the following general principles: (1) regions do not overlap; (2) regions are (relatively) homogenous; regions are (relatively) different to neighbouring regions; regions are fairly equally sized (belong to one scale domain) but can be built in several hierarchical scales. General strategies include (1) edge-based segmentation and (2) region-based segmentation, and multi-scale segmentation as a specific case. \r\nAlso referred to spatial classification emphasizing the constraint of spatial contingency, image segmentation aggregates neighbouring pixels, but – as compared to statistical clustering techniques – does not provide a unique set of classes (either semantic or statistic) in the feature space. \r\nRecently the term semantic segmentation has emerged in the machine-learning community, which is in fact a combination of segmentation and categorisation (labelling) via deep learning methods (e.g. convolutional neural networks).","name":"Image segmentation","selfAssesment":"Completed
"},{"code":"IP3-6-1","description":"Combined filtering uses different filters to arrive at more complex filters for specific purposes. \r\nFor example, Laplacian filters are derivative filters used to find areas of rapid change (edges) in images. Since derivative filters are very sensitive to noise, it is common to smooth the image (e.g., using a Gaussian filter) before applying the Laplacian. This two-step process is called the Laplacian of Gaussian (LoG) operation.","name":"Combined filtering","selfAssesment":"New
"},{"code":"IP3-6-2","description":"The aim of sharpening filters is to highlight transitions in intensity (high frequency components) using different operators: directional (horizontal, vertical, diagonal) or isotropic (e.g. Laplacian Filter). Example of edge detectors include: Gaussian edge detector, Laplacian filter etc.","name":"Edge detectors","selfAssesment":"New
"},{"code":"IP3-6-3-1","description":"The Lee-sigma filter is a conceptually simple but effective alternative to the Lee and other sophisticated adaptive filters. It is based on the sigma probability of the Gaussian distribution.","name":"Lee-Sigma","selfAssesment":"New
"},{"code":"IP3-6-3","description":"High-pass filtering enhance information of high frequencies (local extremes, lines, edges)","name":"High-pass filtering","selfAssesment":"New
"},{"code":"IP3-6-4-1","description":"Gaussian Filters are isotropic (same behavior in all directions).","name":"Gauss filter","selfAssesment":"New
"},{"code":"IP3-6-4","description":"Spatial filters transform an image by taking into account the local neighborhood of a pixel. The goal of filtering is to remove unnecessary components from images (e.g., noise), while emphasizing the necessary ones. In this context, low pass filters aim at removing sharp transitions in the image intensities (high spatial frequencies).","name":"Low-pass filtering","selfAssesment":"New
"},{"code":"IP3-6","description":"In contrast to the point operations used for radiometric modification of image data, techniques for geometric processing are characterized by operations over local neighborhoods of pixels. The result of a neighborhood operation is still a modified brightness value for the single pixel at the center of the neighborhood , however the new value is determined by the brightness of all the local neighbors rather than just the original brightness value of the central pixel alone.","name":"Neighbourhood analysis (convolution)","selfAssesment":"Planned
"},{"code":"IP3-7-1","description":"Class modelling provides flexibility in designing a transferable workflow from scene-specific high-level segmentation and classification to region-specific multi-scale modelling","name":"Class modelling","selfAssesment":"Planned
"},{"code":"IP3-7-2","description":"Hierarchical representation refers to hierarchically scaled compositions of the classes to be classified.","name":"Hierarchical representation","selfAssesment":"New
"},{"code":"IP3-7-3","description":"Per-parcel analysis relies on parcels or objects as the smallest units of image analysis. The parcels are usually obtained through image segmentation that partition the input images into homogeneous units, i.e. parcels, in a supervised or unsupervised manner.","name":"Per-parcel analysis","selfAssesment":"New
"},{"code":"IP3-7-4-1","description":"Distance relationships describe how far an object is with respect to a reference. Proximity analysis allows the identification of the distance between a geographic feature of interest and its neighbors.","name":"Distance and proximity features","selfAssesment":"New
"},{"code":"IP3-7-4-2","description":"The most important geometric features of geographic objects are their size and shape. Shape refers to general form or outline of individual objects and can be quantified using different metric such as shape index, compactness, asymmetry, density, elliptic fit, roundness, rectangular fit etc.","name":"Planar geometric features","selfAssesment":"New
"},{"code":"IP3-7-4-3","description":"Topological features characterize qualitatively the position of spatial objects relative to each other. There are different models for representing topological relationships. Calculus-based method, for example, allows us to model five topological relationships of two spatial objects: touch, in, cross, overlap, disjoint.","name":"Topological features","selfAssesment":"New
"},{"code":"IP3-7-4","description":"An object of a specific object class has a value on the range of values of a spatial or spectral feature. A set of features provides the feature space that is used for classification.","name":"Spatial features","selfAssesment":"Planned
"},{"code":"IP3-7","description":"OBIA is an iterative method that starts with the segmentation of satellite imagery into homogeneous and contiguous image segments (also called image objects. In the next step, resulting image segments are assigned to the target classes.","name":"Object-based image analysis (OBIA)","selfAssesment":"Planned
"},{"code":"IP3-8-1","description":"The feature space represents in various dimensions all the features that can be used for classification (e.g. image bands, band math parameters, derived texture properties). A point in that space is also called a vector with values for each feature (or dimension). Polyhedralization is a form of vector space quantization where a vector is assigned to the closest centre point of one polyhedron.","name":"Feature space polyhedralization","selfAssesment":"New
"},{"code":"IP3-8-2","description":"Radiative transfer models describing the interaction between matter and electromagnetic radiation serve as cornerstones for optical remote sensing. The radiative transfer theory provides the most logical linkage between observations and physical processes that generate signals in optical remote sensing. Radiative transfer modelling is therefore an integral part of remote sensing, since it provides the most efficient tool for accurate retrievals of Earth properties from satellite data. Radiative transfer models are used in a number of different applications such as sensor radiometric calibration, atmospheric correction and the modelling radiation processes in vegetation canopies. \r\nVegetation radiative transfer models (RTMs) study the relationship between leaf and canopy biophysical variables and reflectance, absorbance and scattering mechanisms. The infinite variability of vegetation structure complicates the modeling of RT in vegetation canopies. Numerous models of RT in vegetation canopies were developed in the second half of the last century. Models differ by the details accounted for and by the simplifications introduced in the description of canopy structure and photon–vegetation interactions. Gradual improvement in RTMs accuracy, yet in complexity too, have diversified RTMs from simple turbid medium RTMs towards advanced Monte Carlo RTMs that allow for explicit 3D representations of complex canopy architectures. This evolution has resulted in an increase in the computational requirements to run the model, which bears implications towards practical applications. When choosing an RTM, a trade-off between invertibility and realism has to be made: simpler models are easier to invert but less realistic, while advanced models more realistic but require a large amount of variables to be configured. The two most widely used models are the leaf model PROSPECT and Scattering by Arbitrary Inclined Leaves (SAIL) canopy model. \r\nAtmosphere RTMs study the interaction of radiation with the atmosphere. The remotely-sensed signals at satellite or airborne platforms are combinations of surface and atmospheric contributions, with relative amounts varying across the two wavelength regions, depending on the condition of the atmosphere. The order of magnitude of atmosphere signals can be equal or larger than that of land or ocean surface signals that arise at the top of the atmosphere (TOA). In order to derive accurate sensor calibration and atmospheric correction, the contribution of the atmospheric constituents to the total retrieved signal must be understood and modelled. Atmospheric radiative transfer models simulate the radiative transfer interactions of light scattering, absorption and emission through the atmosphere. Some widely used atmospheric RTMs are 6SV, libRadtran, MODTRAN, and ATCOR.\r\nAdvances in radiative transfer modeling enhance our ability to detect and monitor changes in our planet through new methodologies and technical approaches to analyze and interpret measurements from air- and space-borne sensors.","name":"Radiative transfer modelling","selfAssesment":"Completed
"},{"code":"IP3-8","description":"Historically, physical modelling and machine learning have often been treated as two different fields with very different scientific paradigms (theory-driven versus data-driven). Yet, in fact these approaches are complementary, with physical approaches in principle being directly interpretable and offering the potential of extrapolation beyond observed conditions, whereas data-driven approaches are highly flexible in adapting to data and are amenable to finding unexpected patterns (surprises).","name":"Physical-model based analysis","selfAssesment":"New
"},{"code":"IP3-9-1","description":"Difference of Gaussians (DoG) method consists of subtracting two Gaussians, where a kernel has a standard deviation smaller than the previous one. The convolution between the subtraction of kernels and the input image results in the edge detection of this image.","name":"Difference of Gaussian (DoG)","selfAssesment":"New
"},{"code":"IP3-9-2","description":"Scale Invariant Feature Transform (SIFT) is an image descriptor for image-based matching and it is used for a large number of purposes in computer vision related to point matching between different views of a 3-D scene and view-based object recognition. The SIFT descriptor is invariant to translations, rotations and scaling transformations in the image domain and robust to moderate perspective transformations and illumination variations. Experimentally, the SIFT descriptor has been proven to be very useful in practice for robust image matching and object recognition under real-world conditions.","name":"Scale invariant feature transformation (SIFT)","selfAssesment":"New
"},{"code":"IP3-9","description":"Scale-space theory is a framework for multiscale image representation, which has been developed by the computer vision community with complementary motivations from physics and biologic vision. The idea is to handle the multiscale nature of real-world objects, which implies that objects may be perceived in different ways depending on the scale of observation. If one aims to develop automatic algorithms for interpreting images of unknown scenes, there is no way to know a priori what scales are relevant. Hence, the only reasonable approach is to consider representations at all scales simultaneously.","name":"Scale space analysis","selfAssesment":"New
"},{"code":"IP3","description":"In analogy to the human mind, image understanding is the computational process of extracting information from images, i.e. locating, characterizing, and recognizing objects and other features in the scene. In Earth observation, image understanding refers to the tasks and methods that take pre-processed and assimilated images as an input and extract information from them. For example, a human task would be visual image interpretation by delineating objects in an image scene. However, image understanding is a cyclic process and already happens during pre-processing and assimilation. For example, the cloud mask for EO images is a product of image understanding, namely of classification, that is available very early in the image processing chain prior to many pre-processing tasks.","name":"Image understanding","selfAssesment":"Planned
"},{"code":"IP4-1-1","description":"Once the user finds the required data, she/he needs to know how can they be accessed, possibly including authentication and authorisation.","name":"Accessibility","selfAssesment":"New
"},{"code":"IP4-1-2","description":"Quality Indicators (QIs) should be ascribed to data and, in particular, to delivered information products, at each stage of the data processing chain - from collection and processing to delivery. A QI should provide sufficient information to allow all users to readily evaluate a product’s suitability for their particular application, i.e. its “fitness for purpose”.","name":"GEO QA4EO","selfAssesment":"New
"},{"code":"IP4-1-4","description":"ISO is an independent, non-governmental international organization with a membership of 164 national standards bodies. Through its members, it brings together experts to share knowledge and develop voluntary, consensus-based, market relevant International Standards that support innovation and provide solutions to global challenges. ISO/TC 211 Geographic information/Geomatics provides Standardization in the field of digital geographic information. Note: This work aims to establish a structured set of standards for information concerning objects or phenomena that are directly or indirectly associated with a location relative to the Earth. These standards may specify, for geographic information, methods, tools and services for data management (including definition and description), acquiring, processing, analyzing, accessing, presenting and transferring such data in digital / electronic form between different users, systems and locations.","name":"ISO standards","selfAssesment":"New
"},{"code":"IP4-1-5","description":"The OGC is the worldwide leading consortium of GIS industries promoting the interoperability of geographic information across platform, system, and country borders. The main field of current activity is the complete integration of the sources of geographic information based on the Internet.The Open GIS Consortium (OGC) plays an important role on the implementation level.","name":"OGC standards","selfAssesment":"New
"},{"code":"IP4-1-6","description":"A fundamental pillar in (open) science is to verify the scientific results of others to advance knowledge. The lack of reproducibility in scientific studies brings challenges in understanding and recreating the results of others, a situation that may be common in data-based and algorithm-based research like in geocomputation. In general, many authors define reproducibility as the ability to compute exactly the same results of a study based on original input data and analysis workflow. In other words, “to rerun the same computational steps on the same data the original authors used”. Replicability is often seen as obtaining similar conclusions about a research question derived from an independent study or experiment. In the field of GIScience and geocomputation, in particular, a reproduction is always an exact copy or duplicate, with exactly the same features and scale, while a replication resembles the original but allows for variations in scale, for example. Hence, reproducibility is exact whereas replicability means confirming the original conclusions, although not necessarily with the same input data, methods, or results.","name":"Replicability and reproducibility","selfAssesment":"Completed
"},{"code":"IP4-1-7","description":"The ultimate goal of FAIR is to optimise the reuse of data. To achieve this, metadata and data should be well-described so that they can be replicated and/or combined in different settings.","name":"Reusability","selfAssesment":"New
"},{"code":"IP4-1","description":"Data quality standards are guiding principles and operational guidelines for the production and use of data. For example, QA4EO aims for the two key principles of accessibility / availability and suitability / reliability. The QA4EO guidelines provide instructions for the implementation of processes that follow these principles. Standards emerge from standardization processes within the community. They are based on the agreement of the members of the community.","name":"Data quality standards","selfAssesment":"New
"},{"code":"IP4-2-1-1","description":"To correctly perform a classification accuracy (or error) assessment, it is necessary to systematically compare two sources of information: (1) pixels or polygons in a remote sensing-derived classification map, and (2) ground reference test information (which may in fact contain error). The relationship between these two sets of information is commonly summarized in an error matrix (sometimes referred to as contingency table or confusion matrix). Indeed, the error matrix provides the basis on which to both describe classification accuracy and characterize errors, which may help refine the classification or estimates derived from it.","name":"Error matrix","selfAssesment":"New
"},{"code":"IP4-2-1-2","description":"F-score represents the harmonic mean between precision and recall. As F-score combines both precision and recall, it can be regarded as an overall quality measure. The range of F is from 0 to 1 with larger values representing higher accuracy.","name":"F-score","selfAssesment":"New
"},{"code":"IP4-2-1-3","description":"Ground reference refers to the reference dataset for an accuracy assessment of a remote sensing classification. The process of obtaining ground reference is dedicated to support the production of suitable accuracy information. A sampling design (fitting to the produced image classification) determines the most appropriate distribution of sample locations (or regions). The response design consists of the evaluation protocol and the labeling protocol. The evaluation protocol initiates selecting the support region on the ground (represented by a pixel or polygon) where the ground information will be collected. Once the location and dimension of the sampling unit are defined, the labelling protocol is initiated and the sampling unit is assigned a hard or fuzzy ground reference label. This ground reference label (e.g. forest) is paired with the remote sensing-derived label (e.g., forest) for assignment in the error matrix.","name":"Ground reference","selfAssesment":"New
"},{"code":"IP4-2-1-4","description":"Kappa is a value for measuring the overall accuracy of a classification that accounts for randomness of class assignment. Kappa analysis is a discrete multivariate technique of use in accuracy assessment. Kappa yields a statistic, ^K, which is an estimate of Kappa. It is a measure of agreement between the remote sensing-derived classification map and the reference data as is indicated by a) the major diagonal and b) the chance of agreement, which is indicated by the row and column totals in the error matrix.","name":"Kappa statistics","selfAssesment":"New
"},{"code":"IP4-2-1-5","description":"These two quality assessment indicators are calculated as follows:\r\nPrecision = TP/(TP+FP) \r\nRecall = TP/(TP+FN),\r\nwhere TS is true positive, FP is false positive, FN is false negative","name":"Precision & recall","selfAssesment":"New
"},{"code":"IP4-2-1-6","description":"Geometric correction procedures (image-to-map rectification, image-to-image rectification) are used to rectify remotely sensed data to a standard map projection whereby it may be used in conjunction with other spatial information in a GIS to solve problems. The rectification process normally involves selecting ground control point (GCP) image pixel coordinates (row and column) with their map coordinate counterparts (e.g. meters northing and easting in a UTM map projection). Rectification requires that polynomial equations (that translate from image coordinates to map coordinates) be fit to the GCP data using least squares criteria. Depending on the distortion in the imagery, the number of GCPs used, and the degree of topographic reliefdisplacement in the area, higher -order polynomial equations may be required to geometrically correct the data. To determine how well the six coefficients derived from the least-squares registration of the initial GCPs account for geometric distortion in the inpit image, for each GCP, the root-mean-square error (RMSE) is computed.","name":"Root mean square error (RMSE)","selfAssesment":"In progress
\r\n\r\n"},{"code":"IP4-2-1","description":"A growing set of EO services and applications produce EO products that describe various aspects of the land, ocean and atmosphere. These products include for example image products at different processing levels, geometric measurements like in digital elevation models, semantic labelling products like land cover classifications, and EO-derived attribute products concerning air quality or other geophysical and biophysical parameters. Same as any geospatial data, EO products are not free of error and require accompanying documentation of their product quality. One term for describing different quality dimensions of an EO product is accuracy.\r\nAccuracy is a measure to estimate the uncertainty that originates from errors. An error is the deviation of a map value from a true value. The concept of error assumes well-defined phenomena where deviation results from imperfection of measurement equipment, environment effects, or imperfections of the observer. They cause gross errors and blunders, systematic errors, and random errors, for which different approaches are necessary to minimize error. Ideally, only random error remains that is probabilistic in nature and can be assessed with statistical approaches. For poorly defined phenomena, the concept of vagueness applies. For example in the case of thematic maps using fuzzy sets, the accuracy assessment requires a fuzzy approach as well. \r\nJudging error requires reference data with higher accuracy (by an order of magnitude) to which the map value can be compared. EO product quality dimensions about accuracy include thematic accuracy, spatial accuracy (both horizontal and vertical), radiometric accuracy, and accuracy of biophysical/geophysical parameter measurements. Respective equipment and approaches for reference data collection includes ground verification for thematic maps, GNSS positioning devices, field spectrometers, air quality sensors and in-situ biomass estimation. Ideally, reference data is collected in the field. In case of inaccessible areas of interest and/or if the service requirements allow it, approaches may rely on proxy reference data.\r\nThe design of the accuracy assessment procedure should be done with the EO product design to match the requirements of the EO service. For example, a thematic accuracy assessment consists of the main three components of response design, analysis, and sampling design. The response design ensures that reference data and map data are comparable at a location and specifies under which cases they agree or disagree. The analysis, usually performed with an error matrix, specifies which quality indicators will be calculated to quantify accuracy. The sampling design specifies the subset of locations at which the response design will be applied. Depending on the classification process and application case, different sampling strategies can be suitable (e.g. clustered sampling, stratified random sampling). \r\nFor other accuracy dimensions, respective accuracy assessment procedures exist, e.g. root mean squared error (RSME) for the positional accuracy assessment.\r\nAfter an accuracy assessment has been performed and the uncertainty in the EO product is understood, the challenge is to clarify how the uncertainty affects subsequent spatial analyses with the EO product. Different strategies exist that ignore error completely or that account for error by modelling uncertainty in the analysis outcomes. If uncertainty is judged low enough (or more hazardous, if users are unaware of the limited accuracy), subsequent analyses accept the EO product as true and ignore the accuracy value. If uncertainty is incorporated in subsequent analysis through uncertainty modelling, the results describe the bandwidth of outcomes, potentially supported with appropriate visualisations of uncertainty. The uncertainty modelling approach may greatly enhance the usability of the EO product, because it informs better how the error impacts the EO information and how much confidence a user should have in it.\r\nWith a new generation of EO products on the horizon and a largely increased user community, a large number of new applications is to be expected. They may also identify innovative accuracy assessment approaches. For example, the availability of EO archives with long time series of EO data led to response design protocols tailored to collect time series of reference data. The use of volunteered geographic information (VGI) as reference data has great potential, if approaches are implemented that ensure its reliability. Methods for object-based accuracy assessment are continued to be developed. Further, the increasing number of EO parameter products based on continuous variables creates the need to describe their accuracy. Finally, the focus on validation of EO products during EO service development and operation will make feedback from users available to service providers, ultimately leading to more meaningful EO products with more meaningful accuracy metrics and other quality indicators.","name":"Accuracy assessment","selfAssesment":"
Completed
"},{"code":"IP4-2-2","description":"The implementation of a service that provides remote sensing derived information on a regular basis introduces process-related quality criteria like the timeliness of information provisioning. For the case of refugee camp mapping, timely arrival of map information may be critical to support the decisions in planning facilities for humanitarian assistance.","name":"Timeliness","selfAssesment":"New
"},{"code":"IP4-2-3-1","description":"Completeness is a quality dimension that can apply to different data properties.The Data completeness is dealing with the completeness of an image, handling for example the effect of shadowing objects, sun flares on water surfaces or masking out by an object (e.g. propeller of a UAV). Spatial completeness is a feature on the area coverage. In photogrammetry (especially in stereophotogrammetry) its 3D version, the stereo completeness has extreme importance. In monitoring systems and applications the Temporal completenesster term features how the taken images represent a complete time series. The thematic completeness measure describes the image interpretation quality how the expected and defined classes are evaluated. This feature is important with the use of e.g. multiple classifiers.","name":"Completeness","selfAssesment":"New
"},{"code":"IP4-2-3-2","description":"In remote sensing we can speak about spatial consistency in the Consistency cluster. It represents the quality of image interpretation/understanding: how are the different objects or classes recognized/evaluated integrally. A bridge above a water surface, like river can be detected in pixel-wised manner, but the question is how coherent they are in the output map. This phenomenon has very close to the thematic consistency, where the recognition integrity is represented in this way. The topological consistency is defined mainly for network-type surface objects, like roads or rivers, where the connection of all atomic segments are rated by this measure. Urban mapping focuses on the built environment objects, where e.g. house-parcel inclusions are described by this feature. The temporal consistency is for monitoring again, representing for example the possibility or impossibility of land cover changes in time. Having multiple data sources (even airborne or terrestrial), their integral usage can be qualified by this measure.","name":"Consistency","selfAssesment":"New
"},{"code":"IP4-2-3-3","description":"Readability refers to the content of a map being presented clearly enough that the content can be perceived and understood by the user. This includes legibility, e.g. whether the text of a label is large enough to be read and has enough contrast to the background to be easily perceivable. Additionally, readability has a broader meaning that explains whether a product as a whole is simple enough to be understood and not too complex that essential information can be overlooked by the user.","name":"Readability","selfAssesment":"New
"},{"code":"IP4-2-3","description":"Gathering information about the quality of an EO product or service by letting the user test it. The feedback from the user enables to verify whether specific quality criteria have been met.","name":"User validation","selfAssesment":"New
"},{"code":"IP4-2","description":"A product in the sense of something that a user can use for a specific purpose requires a certain quality. Therefore, its accuracy needs to be judged with an accuracy assessment measure that the user understands and where he can interpret the meaning in relation to the purpose. The product has to be validated, i.e. it has to be known whether the product qualifies for use in a certain context. And in addition, the product needs to be available in time that the users can base their decision on it.","name":"Product quality","selfAssesment":"New
"},{"code":"IP4-3-1","description":"The cloud cover percentage indicates the amount of area in the remote sensing image extent that is covered with clouds and therefore cannot provide information about the Earth surface conditions.The actual types of clouds included may depend on the product, but the CEOS definition includes cloud shadow. Next to that, from an optical remote sensing point of view, clouds can be roughly classified in: opaque/dense clouds, mainly composed of droplets that are highly reflective in the VIS region and generally located at low-medium altitudes and cirrus, consisting of a large number of thin non-spherical ice crystals that are normally translucent in the VIS region, relatively highly reflective in the SWIR spectrum, and located at high altitude.\r\n\r\nThe goal of cloud cover percentage is to provide a quality measure of usable information in a surface reflectance image. Earth observation product catalogs support it as a query parameter, to enable searching for products with a cloud cover percentage below a given threshold.\r\nThis simplifies for instance use cases that require only fully clear products (0% cloud cover), and may save download and processing resources by only handling images that have some valid pixels. For instance, by only using products with a cloud cover percentage smaller than 99.95%. The measure also gives an estimate of the number of valid observations in a given geographical area, allowing a quick assessment of whether minimal data requirements for a specific use case are met.\r\n\r\nThe measure is a percentage of actual observations in an image, so pixels where no data was recorded are not included. For derived products, cloud cover pixels are often also flagged separately from pixels where no data was recorded, but this may depend on the data provider. The definition specifically also includes cloud shadow pixels.\r\nReliable cloud cover percentages depend on good cloud and cloud shadow detection methods. Especially handling of translucent cirrus clouds is an open issue: a product that has a 100% cloud cover percentage due to cirrus clouds might still be usable for some cases, while for other cases they also render the product useless. \r\n\r\nThe used cloud detection algorithm will also affect the cloud cover percentage. A more strict algorithm will yield higher percentages compared to an algorithm that under detects clouds.\r\nDue to these limitations, cloud cover percentages in product metadata have a fairly high error margin. The user should take this into account when determining optimal cloud cover percentage thresholds for the use case.","name":"Cloud cover percentage","selfAssesment":"Planned
"},{"code":"IP4-3-2","description":"The remote sensing lifecycle structures all possible phases of the data production process, from its beginning of the data's coming to existence (that includes the sensor design prior to data collection) over storage, processing and use to archiving and deletion.","name":"Remote sensing lifecycle","selfAssesment":"New
"},{"code":"IP4-3-3-1","description":"The minimum spatial resolution in which features related to the phenomenon under investigation become apparent against the background and allow to detect information about the phenomenon. Jensen: Spatial resolution is a measure of the smallest angular or linear separation between two objects that can be resolved by the remote sensing system. [...] A useful heuristic rule of thumb is that in order to detect a feature, the nominal spatial resolution of the sensor should be less than one-half the size of the feature measured in its smallest dimension.","name":"Minimum Spatial resolution","selfAssesment":"New
"},{"code":"IP4-3-3-2","description":"Radiometric resolution is defined as the sensitivity of a remote sensing detector to differences in signal strength as it records the radiant flux reflected, emitted, or back-scattered from the terrain.","name":"Radiometric resolution","selfAssesment":"New
"},{"code":"IP4-3-3-3","description":"Spectral resolution is the number and dimension (size) of specific wavelength intervals (referred to as bands or channels) in the electromagnetic spectrum to which a remote sensing instrument is sensitive.","name":"Spectral resolution","selfAssesment":"New
"},{"code":"IP4-3-3-4","description":"The temporal resolution of a remote sensing system generally refers to how often the sensor records imagery of a particular area.","name":"Temporal resolution","selfAssesment":"New
"},{"code":"IP4-3-3","description":"Resolution as a quality indicator determines whether it is possible to detect information about a phenomenon under investigation with that dataset. (Alternative description: For determining a suitable resolution of data for the information need of a specific application, the target resolution is the threshold above which RS data enables the detection of information about a phenomenon under investigation)","name":"Resolution","selfAssesment":"New
"},{"code":"IP4-3-4","description":"The spatial coverage of a dataset (consisting of an image or a series of images) determines whether the dataset covers the area of the terrain that is of interest to the user of information derived from the dataset.","name":"Spatial coverage","selfAssesment":"New
"},{"code":"IP4-3-5","description":"The temporal validity of a dataset (consisting of an image or a series of images) determines whether the acquisition date(s) (and period) match(es) the requirements for investigating a specific phenomenon and thereby enables the derivation of information about that phenomenon.","name":"Temporal validity","selfAssesment":"New
"},{"code":"IP4-3","description":"Values (or a value) that enable(s) judging a dataset or product on their fitness for a specific purpose (e.g. whether a specific satellite image is suitable for mapping landslides). , A QI should provide sufficient information to allow all users to readily evaluate a product’s suitability for their particular application, i.e. its “fitness for purpose”.","name":"Quality indicators","selfAssesment":"New
"},{"code":"IP4","description":"Data quality is of growing importance in remote sensing, due to the growing relevance that remote sensing data have in planning and operational decision of public bodies and private firms, and the huge amount of digital services (or apps) that exploit RS data. The most important data and product quality dimensions are: accuracy, lineage, structural consistency, semantic consistency, completeness, consistency, currency, timeliness, identifiability.","name":"Data quality","selfAssesment":"Planned
"},{"code":"IP5-1-1","description":"Array databases make use of arrays as the primary storage representation. Such an array-oriented data model and query language is useful in many scientific applications, where the raw data consists of large collections of imagery or sequence data that needs to be filtered, subsetted, and processed.","name":"Array databases","selfAssesment":"New
"},{"code":"IP5-1-2","description":"The Open Data Cube (ODC) is a non-profit, open source project that was motivated by the need to better manage Satellite Data. This project was born out of the work done under the \"Unlocking the Landsat Archive\" and the Australian Geoscience Data Cube (AGDC) projects.","name":"Open data cube","selfAssesment":"New
"},{"code":"IP5-1","description":"The term data cube originally was used in Online Analytical Processing (OLAP) of business and statistics data. Technically speaking, such a data cube represents a multidimensional array together with metadata describing the semantics of axes, coordinates, and cells. It is an efficient approach to the management and analysis of large datasets.","name":"Data cubes","selfAssesment":"New
"},{"code":"IP5-2-1","description":"Content-based image retrieval helps users retrieve relevant images based on their contents.","name":"Content-based image retrieval","selfAssesment":"New
"},{"code":"IP5-2-2","description":"Web Portals allow users to discover, understand, view, access and query information of their choice from local to global level for a variety of uses.","name":"Web portals","selfAssesment":"New
"},{"code":"IP5-2","description":"Image archives are repositories for storing, managing and retrieving remote sensing data.","name":"Image archives","selfAssesment":"New
"},{"code":"IP5-3-1","description":"As an initiative stipulated by the European Commission to foster the bridge between the Copernicus ground segment and the user segment, the Copernicus data and information access service (C-DIAS) is a generic name for different sets of cloud-based platforms providing centralised access to Copernicus data and information, as well as to processing tools. The name indicates, however, that the focus of such advanced user-centred infrastructure implementations is not only on data access, but also on ‘information’. What is specifically meant here is the provision of information services and information layers as defined in the Copernicus service portfolio. This allows the users to develop and host their own applications in the cloud and a single access point, rather than processing data locally. Currently there are five different DIAS’s implemented (CREODIAS, SOBLOO, MUNDI, WEKEO, ONDA), all with some specific technical assets, or a sector-specific application focus or any other unique selling position by e.g. targeting as specific user community. Currently, the DIAS, which have received co-funding from the European Commission as a kind of seed funding, are currently in the process of exploring opportunities and claiming market shares, striving to sustain in a competitive manner. Some of the features are highlighted in the following, without explicitly mentioning any of the associated DIAS: (i) data access of global data sets (satellite data mosaics or gridded data) by custom area; (ii) OGC interfaces, VM catalogue, SPAR QL search interface (combine searches like receive images over areas of high population density), open source (accessible via API) or pay-per-use; (iii) access to core service products (e.g. CLMS, CMEMS, CAMS); (iv) focus on integrated applications such as smart cities, urban energies, precision agriculture; access to third-mission VHR satellite data (e.g. Pléiades); (v) utilizing GitLab as a developer platform.","name":"Data and information access service (DIAS)","selfAssesment":"Completed
"},{"code":"IP5-3-2","description":"The OpenGIS® Web Processing Service (WPS) Interface Standard provides rules for standardizing how inputs and outputs (requests and responses) for geospatial processing services are defined. It defines an interface that facilitates the publishing of geospatial processes and clients’ discovery of and binding to those processes.","name":"OGC interfaces and OGC web processing service","selfAssesment":"New
"},{"code":"IP5-3","description":"Online processing allows users to implement and run image analysis operations online independent of the underlying software.","name":"Online processing","selfAssesment":"Planned
"},{"code":"IP5","description":"Infrastructure for image processing and analysis refers to the physical and organizational facilities that allow the storage, analysis and management of the available data and products. Traditionally, this infrastructure formed a digital image procesing system consisting of computer hardware with special purpose image processing software, and peripheral input-output devices (e.g. CD or DVD drives, internet access, printers/plotters). In the recent years, Earth observation is undergoing a shift to online processing making use of data cubes and vast image archives.","name":"Infrastructure","selfAssesment":"Planned
"},{"code":"IP6","description":"The image processing (value) chain is a sequence of processing steps for EO data that are performed by a set of stakeholders to ultimately provide EO information to a user. The sequence of processing steps that begins with the acquisition of EO data, followed by steps of pre-processing and information extraction (or whatever steps are necessary) and ends with an EO information product being available to a user that uses it to make his decision. The stakeholders along the processing chain each perform a dedicated subsequence of processing steps. Thereby, the stakeholders add value to the data they deliver to the next stakeholder in the chain. A categorization of stakeholders includes EO satellite operators, EO data providers, EO information providers, and the users at the end of the value chain. \r\nThe image processing value chain is closely related to processing levels that provide different states of processing of EO data. They start with raw instrument data (level 0 and 1) that are followed by data converted into geophysical quantities that are geo-referenced and calibrated (level 2). Further levels are quality controlled data that has been mapped on a uniform space-time grid (level 3) and data combined with models or other instrument data (level 4). In addition, EO data providers use the term analysis ready data (ARD) that have been processed to allow direct data analysis, i.e. user processing effort is reduced to a minimum.\r\nFurther, the standard EO products contain a categorizing element that is related to the image processing value chain. This categorizing element organizes the EO products along the sequences of processing, descriptive analytics, predictive analytics, prescriptive analytics, aggregation, visualization, and distribution.","name":"Image processing (value) chain","selfAssesment":"In progress
"},{"code":"MDS","description":"MDS is a dimensionality reduction technique. It can be divided into Metric multidimensional scaling, Generalized multidimensional scaling and Classical multidimensional scaling.\r\n\r\nGeneralized multidimensional scaling is an extension of metric multidimensional scaling, in which the target space is an arbitrary smooth non-Euclidean space. In cases where the dissimilarities are distances on a surface and the target space is another surface, GMDS allows finding the minimum-distortion embedding of one surface into another.\r\n\r\nClassical multidimensional scaling is also known as Principal Coordinates Analysis, Torgerson Scaling or Torgerson Gower scaling. It takes an input matrix giving dissimilarities between pairs of items and outputs a coordinate matrix whose configuration minimizes a loss function called strain.","name":"Multidimensional scaling","selfAssesment":"Depricated (GI-N2K)
"},{"code":"no","description":"Models that describe the basic principles of randomness and probability in spatio-temporal data.","name":"Mathematical models of uncertainty: Probability and statistics","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI","description":"This knowledge area considers the organizational and institutional aspects related to GIS&T. The focus of this knowledge area is on the organizations active in the GIS&T domain, and what happens within and between these organizations. The knowledge area is structured around five units. One unit considers the key organizations in the GIS&T domain, covering relevant public sector organizations at different administrative levels as well as organizations in other sectors of society. Among the organizational aspects covered in this knowledge area are all organizational issues related to the implementation, use and management of GI and GIS within organizations. While all topics related to the organizational structures, procedures and management of GI(S) are grouped into one unit, another unit focuses on issues related to the human factor of using GI and GIS, i.e. people, their skills and competencies, and the development and evaluation of these skills and competencies in the context of GIS&T training and education. The knowledge area includes also several inter-organizational and institutional aspects of GIS&T. Particular attention is paid to the concept of geospatial data sharing, which is about the creation of `spatial data` connections and relationships between different organizations in the GIS&T domain. Spatial data infrastructures are developed to promote, facilitate and coordinate the sharing of spatial data among data providers and data users, and consists of several technological and non-technological components. Many related topics are considered in the knowledge area GI and Society (WS), which also addresses several non-technological aspects related to GIS&T. In addition to this, also the knowledge areas `Design and Setup of Geographic Information Systems`, `Geospatial Data\" and Web-based GI` include several topics that are closely linked to the topics that are considered in this knowledge area. It can be argued that in order to fully master the knowledge and competencies that are presented in these knowledge areas, also basic knowledge and understanding of the organizational and institutional aspects is required.","name":"Organizational and Institutional Aspects","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI1-1","description":"The development of an appropriate organizational model, which establishes the basic character of GIS operations, is a crucial element of the GIS management. The appropriate GIS organizational model for any organization is based on its intended role.Alternative GIS organizational models are based on differing arrangements concerning the scope of GIS, the degree of integration of GIS into business operations, the degree of centralization of GIS operation and use, and the degree of centralization of management control. Although many variations can arise from different combinations of these factors, GIS organizational models can generally be classified into three types: (1) enterprise GIS, (2) GIS data and service resource, and (3) GIS as a business tool (Somers, 1998).","name":"Organizational models for GIS management","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI1-2","description":"Management of GIS can be done in a more centralized or more decentralized manner. In a a so-called enterprise or information-framework GIS, an organizational unit may be established to manage the GIS environment and run the core system, whereas usage is decentralized. In environments where GIS is used occasionally by various users, it may be set up as a separate service with a designated group that manages the GIS and also controls users' applications services. A second decision that needs to be made after the choice between more centralized or more decentralized management of GI and GIS is about where to place the GI management. Alternative options are in a line organization, in a support area, or at the executive level, each with their own advantages and disadvantages.","name":"Managing GIS operations and infrastructure","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI1-3","description":"User roles describe the relationship between different users and the GIS in an organization. Each user role includes responsibilities (e.g. for modifying certain information) and privileges (e.g. for viewing specific information). Although many different roles can be defined, a basic distinction is made between users, who can only view certain information, and editors, who can edit certain information.","name":"User roles","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI1-4","description":"A GIS management strategy should be unique for each organization, as organizations have unique environments, characteristics, goals, GIS requirements. An important step in developing an effective strategy for an organization is to establish the strategic vision for GI and GIS in the organization and define its role and scope. Other elements that should be covered in the GIS Strategy are the degree of centralized management of the GIS, the placement of GIS management and support in the organization, involvement of users in GIS planning and implementation, coordination of users, organizational changes, preparation of users, personnel issues, transitions to GIS operations, integration into business operations, user support, data access, and integration of technology changes (Somers, 1998).","name":"Strategic planning","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI1-5","description":"Committee and team approaches are frequently employed for coordinating participants and users in multi-participant GIS projects. The aim of creating such committees and teams is to ensure that the varied interests of participants are addressed, as participants bring many different interests, application needs, data needs, priorities, organizational issues, and political interests to a common project the GIS. Common models for coordinating participants recognize that participants have three levels of interest in the GIS: policy, technical development, and usage. Different bodies can be established focusing on these different levels of interest: a technical committee focusing on the design and development of the GIS, an management committee providing policy guidance and support and a user`s group.","name":"Coordinating GIS Participants and Users","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI1-6","description":"After the development and implementation of a GIS within an organization, the challenge is to maintain the system and revise and update it when necessary. This means the performance of the GIS in terms of efficiency and effectiveness should be measured and monitoring, and feedback from users on the system and applications, on the data as well as on new needs should be collected. Particular attention should be paid to the maintenance of data sets.","name":"Ongoing GIS revision","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI1-7","description":"The introduction of GIS into organizational environments should be seen as a complex process of mutual adaptation (Nedovic-Budic, 1997). These technologies changes the established organisational processes and structures, while on the other hand the organisational context and culture modify the technological set-up and use. Therefore, knowledge and understanding of the relationship between technologies and organizations is necessary to increase the success of GIS implementations in organizations. Successful GIS implementation and adoption often require some degree of organizational change. However, this can be very difficult to effect because organizations are naturally resistant to it (Somers, 1998).","name":"Organizational changes","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI1","description":"GIS and T implementation and use within an organization often involves a variety of participants, stakeholders, users and applications. Organizational structures and procedures address methods for developing, managing, and coordinating these multi-participant users. The development of the appropriate organizational model for managing the GIS is crucial. In certain cases, changes to the organizational structure in place might be required. Strategic planning and the establishment of coordination structures can be considered as valuable instruments for managing and coordinating all involved users, while also the different user roles need to be assigned.","name":"Organizational structures, procedures and management","selfAssesment":"In Progress GI-N2K
"},{"code":"OI2-1","description":"GIS and T professionals can be hired for a wide range of different job positions, for which the precise skills, competences and qualifications needed will vary. Typical examples of GIS and T positions are GIS&T project managers, technicians, system developers and analyst. The recognition and certification of the competences people have acquired in informal and non-formal learning contexts is important to know which skills and competences individuals have and whether they meet the qualifications required for a certain job position.","name":"GIS and T positions and qualifications","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI2-2","description":"Making sure staff members have the necessary skills and competences to perform geospatial activities is necessary for an effective implementation and operation of GI within an organizations. Several training methods can be adopted to ensure the development of skills and competencies of staff members. A distinction can be made between formal and informal training, but also between internal and external training programs. Another relevant issue is the assessment and evaluation of the skills and competences of staff members, to determine their future training and development needs.","name":"GIS and T staff development and evaluation","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI2-3","description":"Programs and courses on GIS and T and related subjects are provided by a wide range of institutions. While in recent years also the use and integration of GI and GIS in primary and secondary education has received significant attention, GIS and T education is mainly organized by institutions of higher education, especially universities but also other higher education institutions. Analyses of the higher education GIS&T programs and courses in Europe showed that the offer of courses is very diverse, in terms of size (ECTS), educational level (EQF) and course content. Vocational training on GIS and T related topics is organized by different types of training providers, including the major GIS vendors, data and service providers, academic sector, professional organisations, but also the public sector.","name":"GIS and T training and education","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI2-4","description":"A curriculum is a systematic description of a study program, in terms of learning goals, structure and sequence, learning, teaching and assessment strategies and content. A curriculum consists of both a set of related required and elective - courses along with all direct and indirect skills, competences and learning outcomes resulting from these courses. In the process of curriculum design typically particular attention is assigned to objectives, teaching methods and educational strategies, while also attention should be paid to the content organization aspects and the global structure of the curriculum. The process of designing GIS&T curricula presents many challenges, as the design of the curriculum should be aligned to both the institutional context and the expected outcomes of the learning and teaching process (Prager, 2011).","name":"GIS and T curriculum and course design","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI2-5","description":"An important challenge in organizing GIS and T education and training is the choice and use of effective teaching and learning methods. These methods should follow recent technological developments and use the best technologies to help students acquire the necessary skills and competencies. Traditionally, most GIS and T programs and courses were taught in the context of a full-time, face-to-face setting, using traditional teaching methods such as lectures and lab-based computer practical sessions. In recent years, educational institutions and their teachers have been experimenting with more innovative teaching and learning methods, such as project-based and case-based learning, distance learning, integrated and inter-disciplinary lessons, collaboration with companies and other stakeholders, etc.","name":"GIS and T teaching and learning methods","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI2","description":"This unit addresses GIS and T staff and workforce issues within an organization, particularly as they relate to ensuring that GIS and T is appropriately used and supported. The focus of this unit is on the skills and competencies of professionals in the GIS and T domain: how can these skills and competencies be described and evaluated, and how can they be developed through training and education.","name":"GIS and T workforce themes","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI3-1","description":"Cost savings are an important driver or motivation for sharing geospatial data and information. As costs associated with collecting and maintaining geospatial data are high, sharing data means that users no longer need to duplicate data gathering and archiving, which leads to savings in terms of personnel, space/facilities, data acquisition and maintenance costs. One fundamental argument for sharing thus derives from scale economies in production. Because the cost of making data is high, there is a clear incentive to maximize the number of users of these data. Sharing allows data to be used repeatedly for many purposes, thus increasing their value without increasing their cost. Sharing data also leads to improved data quality. Moreover, in many cases, sharing data is the only way to get access to certain data sets, as the authority to collect and manage certain data lies with another public institution.","name":"Drivers and incentives for sharing geospatial data","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI3-2","description":"Sharing of geospatial data can be hindered or inhibited by several types of barriers. These include technological barriers, such as a lack of common data definitions, formats and models or incompatibility of hardware and software. Among the non-technological barriers are organizational, political and legal issues and elements, such as misaligned organizational missions, diversity in organizational cultures, conflicting organizational priorities, lack of funding, lack of executive and legislative support; restrictive laws and regulations, copyright issues, data privacy and data ownership issues. However, it should be noticed that many of these barriers have been decreased or eliminated in recent years.","name":"Barriers to geospatial information sharing","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI3-3","description":"The legal framework for geospatial data sharing is very wide and diverse, involving rules on data, coordination, standards, funding, etc. Moreover, these rules and regulations can take many different forms: legal acts adopted by parliament, executive orders or decisions, cooperation agreements, memoranda of understanding, bilateral arrangements etc. From a data perspective, the legal framework can be distinguished into two main types of policies: those that promote and those that hinder the availability of spatial data. Policies that promote spatial data availability can focus on different types of users (public bodies, private companies, citizens) and different types of use (public access, commercial and non-commercial reuse, reuse for performing public tasks). Among the policies that hinder the availability of spatial data are those dealing with privacy, liability, and intellectual property. The legal framework also includes legislation that applies to data or information in general, such as open data legislation, which may also be applicable to spatial data (e.g. legislation on freedom of information, copyright, etc.). Moreover, also general legislation relating to any interaction between people or any situation in everyday life (e.g. liability, contract law, competition law, etc.) will apply to spatial data sharing.","name":"Legal framework for geospatial data sharing","selfAssesment":"Completed
"},{"code":"OI3-4","description":"Several types of legal mechanisms for sharing geospatial data can be used. A data sharing arrangements can be formalized by a contract or agreement between the data provider and the data user. A particular type of agreement are the framework agreements, which are agreements between two or more organisations concluded prior to the datasets or services being required. These framework agreement can involve one or multiple spatial data sets or services. Partnership agreements are often used to formalize the data sharing agreements among a broader group of partners. Participation in such a partnership often means participants share their data with other participants and get access to shared data. Another relevant mechanism is the use of licenses, which are mechanisms to give organizations and people the permission to use spatial data sets and services. A license is legally binding, and defines the conditions of use of the related spatial data sets and services. In order to reduce the number of licenses used and ensure the harmonization of the terms in these licenses, the use of standard licenses is promoted. Also the use of open data licenses is promoted for sharing geospatial data, and strongly increased in recent years.","name":"Legal instruments for sharing geospatial data","selfAssesment":"Completed
"},{"code":"OI3","description":"Geospatial data sharing has become an essential element of the GI activities of organizations. Spatial data sharing can be defined as the electronic transfer of spatial data/information between two or more organizational units where there is independence between the holder of the data and the prospective user. Spatial data sharing has many advantages, but several technical and non-technical barriers must be overcome to put data sharing into practice. While the practice of spatial data sharing has substantially grown with the development of spatial data infrastructures, many consider data sharing as a crucial element for the success of these infrastructures.","name":"Geospatial data sharing","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI3b","description":"A Spatial Data Infrastructure can be defined as the collection of technological and non-technological components to facilitate and coordinate the exchange of and sharing of spatial data. The concept infrastructure is used to promote the concept of a reliable, supporting environment, analogous to a road or telecommunications network, that facilitates the access to spatial data. Data, metadata, access networks, standards, coordination, policies, funding, people and institutional frameworks are often considered among the key components of an SDI. \r\n\r\nSpatial data infrastructures often are defined and described as a complex and dynamic phenomenon. Among the main reasons for the complex character of these infrastructures are the many components a spatial data infrastructure consists of, the diversity of involved stakeholders, and the many different objectives and ambitions of these stakeholders. Technological advancements, such as the emergence of web 2.0 technologies, and societal changes, such as the increasing use of geographic information in everyday life, are often mentioned as important drivers behind the dynamic character of spatial data infrastructures. \r\n\r\nA key characteristic of spatial data infrastructures is the involvement of a large and diverse group of actors. Governments are often considered as the central actors in the development and implementation of spatial data infrastructure, since they are the major producers and users of geographic information. Governments at different administrative levels and in different thematic domains are involved in the creation, management, use and sharing of geographic data. But also private companies, non-profit organisations, research and education institutions and even citizens can participate in the development and implementation of a spatial data infrastructure. It is increasingly being argued that the involvement and engagement of each of these stakeholders group is essential to the realization of a successful spatial data infrastructure. \r\n\r\nSDIs have been developed in many countries worldwide at local, national and international levels. Often a distinction is made between a between the first generation SDIs that have data as their key driver and are based on a product model and second generation SDIs in which user needs are the key driver and that are based on a process or development model. The latest generations of SDI strongly focus on the inclusion and engagement of non-government actors and organizations in the development and implementation of the SDI. Although SDI are by default distributed systems, involving many organisations, some SDI might be developed rather in an hierarchical way, while others are following a networked approach.","name":"Spatial Data Infrastructures","selfAssesment":"Completed
"},{"code":"OI4-1","description":"The adoption and implementation of standards are two key phases in the standardization process, which starts with the definition of standardization requirements and the development of standards. The adoption and implementation of standards follows after the development phase. The distinction made between the adoption and implementation of standards is important: adoption entails the decision to apply standards, while the implementation relates to the integration of standards in software, in data development and in other processes. GI-Standards are one of the key components of each SDI, consist of both semantic and technical standards, and include standards related to the different architectural components of an SDI, i.e. standards related to spatial data sets and data products, web services, metadata and catalogues, encodings, etc.","name":"Adoption and implementation of standards","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI4-2","description":"The SDI policy framework includes the set of policies, strategies, initiatives and projects aimed at increasing access, sharing, and effective use of spatial data. SDI policies can be divided into strategic and more operational policies. Strategic policies define the broader framework and formal structure within which the SDI initiative is developed. Operational policies provide more practical tools to facilitate access to and use of the SDI, and address specific topics related to the collection, management, use, access and dissemination of spatial data. These operational policies include a broad range of guidelines, directives, procedures and manuals that apply to the day-to-day business of organizations in developing, operating and using an SDI. To guarantee the success of an SDI, it is important to recognize the wider policy context in which these SDI`s are developed, and to link them to the overall policy environment in the jurisdiction in which they are implemented. These include policies on open government and open data, environmental policies, digital government or e-government policies and other.","name":"Policies","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI4-3","description":"If is often argued that SDI implementation requires coordination, because without coordination all other SDI components would not be developed or would be developed in a very fragmented and inconsistent manner. In general terms, coordination is about bringing into alignment the activities of different stakeholders in the SDI landscape. A typical instrument to realize coordinate in the context of SDI, is the establishment of an effective SDI coordination structure. The SDI coordination structure should ensure that all stakeholders are involved in the development and implementation of the SDI, through the participation in one or more coordination bodies. Another important element is the establishment of clear roles and responsibilities for the different involved organizations, making a distinction between data users, data providers, services providers and a geo-broker.","name":"Coordination and organizational structure","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI4-5","description":"Funding an SDI is about guaranteeing the long-term financial security of an SDI, by obtaining and formalizing financing for the implementation and maintenance of the different SDI components. An SDI funding model provides the answer to the central question of where and how to seek funding for implementing and maintaining an SDI. Within an SDI often different funding models will be combined, as the selection of the most appropriate funding model will be linked to different activities and the associated costs. Costs of an SDI include both set-up costs (one off costs) and maintenance costs (yearly), of which certain costs need to be made for each data sets or each data provider and other costs for the infrastructure in general. The most commonly used SDI funding models are centralized government funding, decentralized government funding (e.g. for each data provider), partnership funding, funding through revenues, and government funding based on donor agencies or on European projects.\r\n\r\nThe shift towards open data and the adoption of open data policies had an important impact on the funding model of many SDIs, as governments and organizations no longer could rely on revenues from selling their data and had to look for other funding models. As a result, new pricing strategies are employed, such as the provision of fee-based supplementary services, such as advice or tailor-made products based on open data. Also freemium/premium models, in which a basic version of the dataset is offered as open data (freemium) but the full dataset is available for a fee (premium), were considered as an alternative approach. In many cases, the loss of revenues was compensated by other funding models, such as increased government funding.","name":"Funding an SDI","selfAssesment":"Completed
"},{"code":"OI4-5b","description":"SDI performance assessment is about collecting, analyzing and providing information on the performance of SDI initiatives. Assessment and evaluations of SDIs are a useful tool for those organizations and people directly involved in these initiatives, but also for researchers, citizens, journalists and other stakeholders. Decision makers and practitioners can use assessments to monitor the progress against the objectives of their SDI initiatives and to identify areas where improvement can be achieved. Assessment also allows to compare and benchmark the performance of different organizations or countries, and to learn from best practices. Finally, assessment also is relevant for accountability, since it enables governments and agencies to be held accountable for their decisions, activities and the resources they have invested. Assessment of SDIs, which deals with the collection and supply of information on the performance of SDI initiatives, should be seen as the first step in a logical consequence of collecting data, integrating this data in policy and management cycles and actually using the information. \r\n\r\nIn the past twenty years, many different SDI assessment frameworks have been developed by researchers and practitioners around the world. Examples of such frameworks are the INSPIRE State of Play Study, the Clearinghouse Suitability Index, the Organisational Maturity Matrix, the SDI Readiness Index, and the INSPIRE Monitoring and Reporting approach. Each of these frameworks focus on particular aspects and components of SDIs. In line with the categorization of open data assessment, also SDI assessments can be divided into three main categories: (1) readiness assessments, (2) implementation or data assessments, and (3) impact assessments. Readiness assessments analyse whether conditions are appropriate, and whether necessary components are in place for developing an SDI. Implementation or Data assessments evaluate whether geospatial data are available and accessible. Impact assessments explore the extent to which SDIs lead to benefits for government, citizens, business and society in general.","name":"SDI performance measurement and assessment","selfAssesment":"Completed
"},{"code":"OI4-6","description":"For a long time, SDI development has focused on the development and implementation of different components with the aim of facilitating the access to and sharing of spatial data. An key challenge in future SDI development will be the integration of these SDI`s in a wider context. In order to optimally take advantage of the data and services provided by an SDI, integrating these data and services into the processes and workflows of public and private organizations will be crucial. The concept of spatial enablement refers to the challenge of developing SDI`s in such a way that they provide an enabling platform that serves the wider needs of society in a transparent manner. Moreover, the diffusion of SDIs, together with the efforts to build a Global Earth Observation System of Systems (GEOSS) and other developments in industry and civil society should be considered as elements in a the realization of a vision on the next-generation Digital Earth.","name":"Next-generation SDIs","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI4-7","description":"The effective implementation of SDIs requires governance, which includes the structures, policies, actors and institutions by which the infrastructure is managed pertaining to decisions made for accessing, sharing, exchanging and using the relevant available spatial information. While SDIs themselves are considered as initiatives contributing to good governance or effective governance, a key challenge in the establishment of SDIs is the governance of the infrastructure itself. Governance of SDIs is essential for the implementation of different SDI components in a coordinated and consistent manner. The central challenge of governance is reconciling collective and individual needs and interests of different stakeholders in order to achieve common goals. This aims to reduce gaps, duplications, contradictions and missed opportunities in the production, management, sharing and use of the information that tend to occur in a multi-stakeholder environment.\r\n\r\nGovernance can be facilitated through the use of appropriate instruments which extend to various levels of government and take into account the distribution of powers and responsibilities among different actors and institutions with an interest in the infrastructure. The governance instruments should coordinate the activities and contributions of, inter alia, data producers, users, added-value services providers, and other stakeholders. More complex and inclusive models of governance are required to cope with the multi-level nature of SDI implementations of the current generation of SDIs. Effective and inclusive SDI governance structures are needed, that are both understood and accepted by all stakeholders. Governance of SDIs also requires expanding the scope of stakeholders to include the private sector, research bodies and other actors outside the public sector including citizens, to actively promote bottom-up and participatory processes, and to find the appropriate mechanisms and instruments to enable the participation of these non-government actors.","name":"SDI governance","selfAssesment":"Completed
"},{"code":"OI5-1","description":"Within the European Commission there are several key GI players. GIS activities in the Commission started since 1981 (e.g. DG REGIO, Eurostat, ) with the CORINE project, the creation of DG ENV and the creation of the European Environment Agency (EEA). Together with the DG Joint Research Centre (JRC), DG ENV and EEA are in charge of the coordination of INSPIRE: DG Environment acts as an overall legislative and policy co-ordinator for INSPIRE, the JRC acts as the overall technical co-ordinator of INSPIRE and EEA is in charge of several tasks related to monitoring and reporting, and data and service sharing under INSPIRE. Also several other EC institutions are actively involved in GI(S) policies and activities (DIGIT, DG GROW, DG AGRI, DG MOVE and many others).","name":"GI organization at the European Commission","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI5-2","description":"Although there may be certain differences between countries, in most countries many key organizations in the GIS&T field will be active at the central/federal/national level of government. Especially the traditional institutions for surveying and mapping play a key role in geospatial policies and activities. Several public authorities at the federal level are in charge of the production and maintenance of key reference and thematic data sets. In many countries, these national data producers were the leading actors in the development of national spatial data infrastructures.","name":"Federal and national government organizations","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI5-3","description":"Local and sub-national governments are often considered among the major users of geographic information in governments, as they often are involved in many different policy areas, in which many problems with a locational component need to be tackled. Geographic data produced and maintained by authorities at lower administrative levels are often more detailed and thus interesting for other users, both within and outside the public sector. As a result, local and sub-national governments are often involved in the establishment of these infrastructures because of the wide range of highly detailed geographic information they produce and manage. As many geographic data are linked to the activities and services of local organizations, the involvement of these organizations in the maintenance of data ensures that these data are up-to-date.","name":"Sub-national and local governments","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI5-4","description":"The European GIS&T landscape consists of many pan-European organizations and associations promoting the interest of and representing certain stakeholder groups. While some of these organisations are dealing with all sectors and aspects of geographic information, others have a more thematic focus (e.g. remote sensing, topography, geosciences) or represent a particular sector (e.g. research, business). In some cases, their clearly is an overlap in the mission and objectives of different organizations, and some organizations are working in the same field of interest. Some examples of pan-European organizations and associations are AGILE, EuroSDR, EUROGI, and EuroGeographics. Also at international level several membership organizations and associations exist.","name":"Pan-European and global associations and professional organizations","selfAssesment":"GI-N2K
"},{"code":"OI5-5","description":"The geospatial industry consists of companies working with location specific information or services. Within the geospatial sector, several areas of activities can be identified: 1) measuring, collecting and storing of data about geo-objects; 2) processing, editing, modelling, analyzing and managing that data; 3) presenting, producing and distributing the data; and 4) advising, educating, researching and communicating about processes and use of geo-information products and services. The sector consists of both small-and-medium-sized enterprises but also big companies, including surveyors, census hard-copy map providers, aerial photos providers, base map data providers, satellite and remote sensing imagery providers, software developers (GIS-related products and services providers as well as satellite image programming platform providers) and several others.","name":"The geospatial industry","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI5","description":"Several types of organizations play a key role in the execution and coordination of geospatial activities in society. Typically, a distinction is made between data providers and data users, while coordinating organizations exist to coordinate and support the geospatial activities of professionals and entities using GIS&T. Governments are often considered as the major users and producers of spatial data and spatial information. Within the public sector, spatial data are collected and used in different thematic areas and at different administrative levels (from local to global). However, the needs, interests, and capacities of organizations at each of these levels will be different, as well as their role in the development of spatial data infrastructures, and the execution of geospatial activities in general. Also the geospatial industry will exist of both data providers and data users, but also of organizations delivering products and services to support the collection and use of spatial data. Other key organization in the GI domain are professional organizations and associations, bringing together and representing the needs of organizations of a particular sector and/or geographic area.","name":"Organizations in the GIS and T domain","selfAssesment":"In progress (GI-N2K)
"},{"code":"PP","description":"The knowledge of physical laws and principles regulating the emission of e.m. radiation and its interactions with the matter, as well the ones related to the design, setting-up and control of EO platforms and related instruments, are of paramount importance for a rigth interpretation of EO measurements in relation with the investigated Earth's phenomena and parameters.","name":"Physical principles","selfAssesment":"Completed
"},{"code":"PP1-1-1","description":"Electromagnetic radiation travels in wave form. All electromagnetic waves travel at the speed of 299.793 km/sec in a vacuum and very nearly the same speed in air. In quantum physics electromagnetic radiation is also described in terms of particles called photons whose energy is given by the equation E = hf where h is the Planck constant and f the frequency of corresponding wave. Electromagnetic wave propagation is fully described by the Maxwell Equations that unified in 1860s the laws of electricity and magnetism.","name":"Electromagnetic Waves and Photons","selfAssesment":"Planned
"},{"code":"PP1-1-10","description":"The solar constant S is a quantity denoting the amount of total (i.e., covering the entire solar spectrum) solar energy reaching the top of the atmosphere. It is defined as the flux of solar energy (energy per unit time) across a surface of unit area normal to the solar beam at the mean distance between the sun and the earth. Solar insolation is defined as the flux of solar radiation per unit of horizontal area for a given locality. It depends primarily on the solar zenith angle and to some extent on the variable distance of the earth from the sun. It can be computed as a function of latitude and the time of year taking into account of the secular variations of Earth's orbit eccentricity e, the oblique angle ε, and the longitude of the perihelion relative to the vernal equinox ω. The daily insolation is the total solar energy received by a unit of area per one day. It may be calculated by integrating total insolation over the daylight hours. It is particularly important, together with information on cloud coverage, in order to plan and manage solar power systems. Yearly total insolation together with average cloud coverage are among the most important parameters to be considered for the choice of the best (i.e. the ones promising the higher energy production) location of solar power plants. Modeled daily solar insolation together with short/medium-term forecast of cloud coverage are also fundamental for the management (e.g. for planning the suspension of activities for maintenance) of solar energy production plants .","name":"Solar constant, solar insolation, daily insolation","selfAssesment":"Completed
"},{"code":"PP1-1-11","description":"Earth's itself represents the second (after Sun) most powerfull natural source of e.m. radiation for EO. Its average emittance can be approximated by that of a blackbody at about 290 K. Even if very less powerfull than Sun such a source is available for EO day and nigth. The maximum of its emission falls in the thermal infrared (around 10 micron) being Earth's emission trascurable in the VIS-SWIR range.","name":"Earth's radiation (intensity, spectrum, etc.)","selfAssesment":"Planned
"},{"code":"PP1-1-2","description":"In principle, the frequency f (and the wavelength λ=c/f) of an electromagnetic wave can take any value and the whole range of possible frequencies is called the electromagnetic spectrum. Different regions of the spectrum are conventionally given different names (with associated spectral ranges smoothly depending on specific science sector): \r\ngamma-rays\t λ< 1 pm\r\nx-rays\t1 nm >λ>1 pm\r\nUltraviolet (UV) 400 nm >λ>1 nm\r\nVisible (VIS) 700 nm >λ> 400 nm (blue: 455 – 492, green 492 – 577, yellow 577 – 597, red 622 – 700)\r\ninfrared (IR)\t1000μm >λ> 0,7 μm (Near-IR - NIR: 0,7-1,3; Short-Wave IR SWIR: 1,3-3; Medium IR - MIR: 3-6, Thermal IR - TIR: 6-20; Far IR - FIR: 20-1000)\r\nRadio waves\t λ> 1 mm (Microwaves MW\t1 m >λ> 1mm). Optical range (usually referring to the spectral range from VIS to TIR) and microwaves are the most important spectral region for remote EO systems.","name":"Electromagnetic spectrum","selfAssesment":"Completed
"},{"code":"PP1-1-3","description":"Maxwell equations are a set of coupled partial differential equations that contains the fundamentals of electricity and magnetism. These equations provide electromagnetic waves that propagate into the space at the speed of the light. Increasing the wavelength there are gamma rays, X-rays, ultraviolet, (visible) light, infrared, microwaves and radio waves.","name":"Maxwell Equations and EM waves' propagation","selfAssesment":"Planned
"},{"code":"PP1-1-4","description":"Planck's law is a mathematical relationship for the spectral radiance emitted by a blackbody (i.e. a body that absorbs all radiant energy falling on it) at a given temperature as a function of frequency or wavelength. Wien’s displacement law is the relationship between the temperature of a blackbody and the wavelength at which it emits the most radiation. Wien found that the product of the peak wavelength and the temperature is an absolute constant. As far as the temperature T of the blackbody increase the intensity of the e.m.radiation emitted increase being, at whatever wavelength, grater than the one emitted by a blackbody at lower temperature (Planck). As far as the blackbody temperature increase its maximum emission occurs at lower and lower wavelengths. Wien's law is fundamental both in the selection of the spectral bands more appropriate for observing specific phenomena as well as for remotely retrieve temperature of far objects by the analysis of the emitted spectral radiances.","name":"Planck law for the black body. Wien's displacement law","selfAssesment":"Complete
"},{"code":"PP1-1-5","description":"The Rayleigh–Jeans Law is an approximation of the Planck’s law for a blackbody that states that emitted radiance is directly proportional to the blackbody temperature. Such an approximation, fits quite well with EO measurements at wavelengths higher than 1mm (microwaves). Wien’s approximation is used to describe the spectrum of the blackbody emission in the VIS-NIR spectral range lengths. The estimated errors are less than 2% at wavlengths less that 5microns. In both cases considered sources are the natural ones: the Earth at an average temperature of 300 K in the first case, the Sun at about 6000K in the second one.","name":"Rayleigh-Jeans approximation. Wien's approximation","selfAssesment":"Completed
"},{"code":"PP1-1-6","description":"The total radiant intensity B(T ) of a blackbody at the absolute temperature T can be derived by integrating the Planck function over the entire wavelength domain from 0 to∞. Since blackbody radiation is isotropic, the flux density emitted by a blackbody is therefore F = π B(T ) which is proportional to the fourth power of the absolute temperature T through the Stefan-Boltzmann constant σ = 5.67 × 10−8 J m−2 sec−1 deg−4.\r\nKirchoff's law establishes that for a medium at the thermodynamic equilibrium, the emissivity ε of a given wavelength λ (defined as the ratio of its emitting intensity IE to the Planck function B), is equal to the its absorptivity, A at the same wavelength λ (defined as the ratio of its absorbed intensity IA to the Planck function B). Hence ε=A at each fixed λ, for a blackbody ε=A=1 at whatever λ. Kirchoff's law is valid also in Local Thermodynamic Equilibrium (LTE) conditions as the ones usually occurring in (small volumes of) the Earth's atmosphere even in the most turbulent conditions.","name":"Stefan–Boltzmann law. Kirchoff law","selfAssesment":"Completed
"},{"code":"PP1-1-7","description":"All bodies at a temperature T>0 K emit electromagnetic radiation at all wavelengths (thermal emission). Such emission at each wavelength is increasing with T and it is maximum for Black Bodies whose spectral emittance I(λ,T) (at each prefixed T and wavelength λ) is defined by the Planck function B(λ,T). Generic bodies are expected to thermally emit less than a black body (having the same temperature T) at whatever wavelength. Spectral emissivity ε(λ) is defined as the ratio of the spectral radiance I(λ,T) emitted by a generic body and the one emitted by a Black Body at the same temperature, i.e. ε(λ)= I(λ,T) / B(λ,T). By definition its value is less or equal (Black Body) than 1. The spectral emissivity concept allows to describe in a simple way the spectral radiance I(λ,T) thermally emitted by a body at a temperature T by I(λ,T)= ε(λ)*B(λ,T). It is possible to invert the Planck Function to obtain from the emitted radiance at a prefixed wavelength the temperature T=f(B, λ) of the emitting Black Body. If in such expression the spectral radiance I emitted by a generic body is used instead than B, the resulting temperature, Tb=f(I, λ), is named Brigthness Temperature being Tb<=T (with Tb=T in case the emitting body is a Black Body). The concept of Brigthness Temperature is substantially a different way to measure the spectral radiance of a generic body. It is usually preferred (for instance calibrating Thermal InfraRed – TIR – satellite images) because the interpretation of such a digital image is much more intuitive than when spectral radiances are used instead. In fact, as at each prefixed temperature generic bodies are less emitting than Black Bodies, wherever across a digital satellite image we consider the values of reported Tb, we can say that the actual temperature T of the corresponding emitting ground resolution cell is not less than Tb.","name":"Concepts of Spectral Emissivity and Brightness Temperature.","selfAssesment":"Completed
"},{"code":"","description":" ","name":" ","selfAssesment":" "},{"code":"PP1-1-9","description":"Sun represents the most powerful natural source of e.m. radiation for EO. Its emittance can be approximated by that of a blackbody at about 6000 K but just its reflected component (SOR) is actually available (and just during daytime) for EO. The maximum of SOR falls in the visible spectral range. Its contribution in the thermal infrared range is neglectable but in the medium infrared SOR is still significant enough and superimposed to Earth's thermal emission.","name":"Solar radiation at the Top of the Atmosphere. Solar spectrum","selfAssesment":"Planned
"},{"code":"PP1-1","description":"The electromagnetic field propagates through the space radiating energy: the electromagnetic radiation. The classical theory describes this energy as electromagnetic waves which represent the oscillations of electric and magnetic fields. In the quantum mechanics theory EM radiation consists of photons, quanta of the electromagnetic force, responsible for all electromagnetic interactions","name":"EM radiation","selfAssesment":"Planned
"},{"code":"PP1-2-1","description":"The study of the absorbption/emission of electromagnetic radiation by atoms. Depending on the atomic number characteristic frequency or wavelength are absorbed or emitted. Since each element has a characteristic spectrum of absorbed/emitted wavelengths (spectral signature), atomic spectroscopy allows the determination of elemental compositions even of remote objects (e.g. stars, galaxies, etc.).\r\nStarting from the simple Bohr’s model it is possible to predict quite exactly the frequencies of e.m. radiation selectively absorbed/emitted by all atoms. Depending on the atomic number Z, characteristic frequencies f are absorbed or emitted by atoms corresponding to the electronic transitions from different energetic (quantized) states following the Bohr’s condition: fab=(Eb- Ea)/h, being Ei=-cost∙Z2/(ni)2 the electron energy corresponding to the state/level i (principal quantic number ni). By this way each atomic species has a characteristic spectrum of absorbed/emitted frequencies (atomic spectral signature) so that atomic spectroscopy allows the determination of elemental compositions even of remote objects. By this way the existence of Helium was discovered in the 1968 by Jansen and Lockyer in the Sun photosphere well before its discover on the Earth, and the knowledge of the chemical composition of stars and galaxies was possible well before the end of XIX century. Atomic spectroscopy provides a simple and powerful introduction (through the explanation of the more complex interactions of e.m. radiation with molecules and solid matter) to the fundamental concepts of spectral signature (which is at the base of most of the applications of aerial remote sensing of the Earth’s surface) and atmospheric windows (important for the design of optical sensors devoted to remotely sense Earth’s surface) being moreover propaedeutic to the understanding of methods for the atmospheric vertical sounding based on the concepts spectral lines broadening and related weighting functions.","name":"Atomic spectroscopy","selfAssesment":"Completed
"},{"code":"PP1-2-10","description":"The Rayleigh roughness criterion is a widely used means to estimate the degree of roughness of a considered surface. Considering the phase difference between two rays scattered from separate points of the surface, this depends on the roughness height, the incident angle and, inversely, on the radiation wavelenght. The Rayleight criterion states that a surface can be considered as smooth if the phase difference is less than pigreco/2 radians.","name":"The Rayleigh roughness criterion","selfAssesment":"Planned
"},{"code":"PP1-2-11","description":"The Bidirectional Reflectance Distribution Function (BRDF) is defined as the quotient between the spectral radiance reflected by a sample and the spectral irradiance from the source that illuminates it. It depends on both the incidence and viewing angles. From this point of view it represents an absolute definition of reflectance whose value, as is known, depends on the geometry of the illumination and observations directions.","name":"Bidirectional Reflectance Distribution Function (BRDF)","selfAssesment":"Planned
"},{"code":"PP1-2-12","description":"Measurements of BRDF allow to compare spectral signatures obtained in different laboratories in an optimal way. However its measure require well calibrated sources and quite expensive laboratory equipments. The concept of BRF (Bidirectional Reflectance Factor) allows a more simple, indirect, measurement of BRDF by using a reference sample (highly reflective so usually named \"white reference WR\") of known BRDF and two subsequent measurements of reflected radiance (one from the WR, one from the sample) obtained under identical illumination conditions. In these conditions results BRDF(sample)=BRF(sample)xBRDF(WR)","name":"Bidirectional Reflectance Factor (BRF)","selfAssesment":"Planned
"},{"code":"PP1-2-2","description":"The molecular absorption spectral corresponds to the wavelengths from 190 nm up to 1000 nm and it interprets the measured absorption of radiation, when it is passing through a gas, a liquid or solid. Their absorbed energy in different states can be approximated by electronic, vibrational and rotational energy","name":"Molecular absorption spectra","selfAssesment":"Planned
"},{"code":"PP1-2-3","description":"The spectral line is a result of interactions of photon with a quantum system, while it extends over a range of frequencies. The center wavelength of its energy levels may be changed due to Broadening, namely collisions of atoms and molecules or their differences in thermal velocities.","name":"Line shape and (natural, pressure, Doppler) broadening","selfAssesment":"Planned
"},{"code":"PP1-2-4","description":"When the altitude ranges from about 20 to 50 km, spectral line shape is determined by both collisions (Pressure Broadening) and differences in thermal velocities (Doppler broadening). This shape is referred to as the Voigt profile and it satisfies the condition of normalization.","name":"Voigt's line profile","selfAssesment":"Planned
"},{"code":"PP1-2-5","description":"Radiation that is not absorbed or scattered in the atmosphere can reach and interact with the Earth's surface. There are three (3) forms of interaction that can take place when e.m. radiation strikes, or is incident (I) upon a surface. These are: absorption, transmission, and reflection. The total incident radiation will interact with the surface in one or more of these three ways. The proportions of each will depend on the wavelength of the incident radiation and the specific chemical/physical properties of the surface material. Absorption occurs when incident radiation is absorbed into the target, while transmission occurs when radiation passes through a target. Reflection occurs when radiation \"bounces\" off the target and is redirected. The spectral reflectance is defined by the ratio of reflected radiance to incident radiance at a prefixed wavelegth . The spectral transmittance of a medium is defined by the ratio of the transmitted radiance to the incident one at a prefixed wavelegth . The absorbance of a medium or target is defined by the ratio of the absorbed radiance to the incident one at a prefixed wavelegth . Conservation of energy require that, at a certain wavelenght: R+T+A=1. To express the circumstance that the reflection can occurre in different direction as the surface deviates from a specular one, becoming rough, the concept of surface scattering has been introduced (ref. [PP1-2-10] The Rayleigh roughness criterion).","name":"Concepts of Transmittance, Absorbance, Reflectance, Scattering.","selfAssesment":"Completed
"},{"code":"PP1-2-6","description":"The emitting ability of a body surface is described by emissivity, ε(λ). This will vary with wavelength and viewing angle. A body is considered to be an ideal radiator when it totally absorbs and then reemits all energy incident upon it. Such a body is called black body and its emissivity is equal to one. Emissivity can be defined as the ratio of spectral exitance, M(λ,T), from an object at wavelength λ and temperature T, to that from a blackbody at the same wavelength and temperature, MBB(λ,T). The concept of graybody has also been introduced as the body having an emissivity of less than 1 and constant at all wavelengths.","name":"Concepts of Spectral Emissivity","selfAssesment":"Planned
"},{"code":"PP1-2-7","description":"\"Radiation that is not absorbed or scattered in the atmosphere can reach and interact with the Earth's surface. There are three (3) forms of interaction that can take place when energy strikes, or is incident (I) upon the surface.\r\n These are: absorption (A); transmission (T); and reflection (R). The total incident energy will interact with the surface in one or more of these three ways. The proportions of each will depend on the wavelength of the energy and the material and condition of the feature. Absorption (A) occurs when radiation (energy) is absorbed into the target while transmission (T) occurs when radiation passes through a target. Reflection (R) occurs when radiation\r\n \"\"bounces\"\" off the target and is redirected. The reflectance R is defined by the ratio of reflected radiant power to incident radiant power. The transmittance T of a medium is defined by the ratio of transmitted radiant power to incident radiant power. The absorptance A of a medium or target is defined by the ratio of absorbed radiant power to incident radiant power. Conservation of energy require that, at a certain wavelenght: R+T+A=1. To express the circumstance that the reflection can occurre in different direction as the surface deviates from a specular one, becoming rough the concept of surface scattering has been introduced. However, the concept of scattering concerns mainly atmopheric interaction with ELM and radar systems.\"","name":"Complex dielectric constants and refractive indices","selfAssesment":"Planned
"},{"code":"PP1-2-8","description":"The complex part nc of the refraction index n determines how far an e.m. wave of wavelength λ can survive crossing a specific medium. The attenuation length la is the distance after that the amplitude of an e.m. signal reduces its value by an amount of 1/e. For instance the amplitude of the Electric field E(z) of an e.m. wave proceeding along the z direction is decreasing as exp(-z/la) being la=λ/(2𝜋 nc) the attenuation length associated to that specific material (nc) and wavelength λ. This way attenuation length in water can be of hundreds of meters in the visible range and just few microns in the microwaves. So that penetration of radiation in the matter depends on both, the specific (dielectric) properties of the matter (through nc) AND the specific wavelength λ of considered e.m. signal.","name":"EM rad. penetration in the matter: Attenuation Length","selfAssesment":"Planned
"},{"code":"PP1-2-9","description":"EM radiation impinging a rough surface is (partly) reflected back (scattering). Lambertian surfaces produce a diffuse scattering (i.e. radiation is reflected similarly in all direction) and then appear equally bright from all directions, whereas specular surfaces behave like a mirror, with reflected radiation all aligned in one direction, with the reflection zenith angle equal to the incident angle of incoming radiation. Generally, the degree of \"roughness\" of a surface determines if it behaves like a Lambertian or a specular surface.","name":"Scattering from rough surface: Lambertian and specular surfaces.","selfAssesment":"Planned
"},{"code":"PP1-2","description":"Radiation can be absorbed, scattered, emitted and transmitted by the matter depending on the different parts of the electromagnetic spectrum and the matter peculiarities (Atoms, molecules, particles and surfaces) and its physical state (Temperature, Concentration, Shape, Roughness). The results of the interaction between radiation and matter depends strongly on the wavelength of radiation and on specific properties of the matter.","name":"Radiation - Matter interaction","selfAssesment":"Planned
"},{"code":"PP1-3-1","description":"The first basic radiometric quantity is the radiance (Iλ) and it is defined as the ratio of the differential radiant energy to the product of effective area with the time interval, wavelength interval and differential solid angle. Based on Iλ, the monochromatic and total flux density can be calculated.","name":"Radiometric quantities: radiance, irradiance, flux, brightness, emittance, luminosity,etc.","selfAssesment":"Planned
"},{"code":"PP1-3-2","description":"The attenuation of radiation emitted from a source decreases with the square of the distance from its center based on inverse square law. It considers that the size of the sources increases with the square of their radius, causing the same rate of attenuation in flux density.","name":"Decay of the emittance with the square of distance from the source","selfAssesment":"Planned
"},{"code":"PP1-3-3","description":"The relative amount of electromagnetic radiation reflected (absorbed, transmitted, emitted) by the matter at different wavelengths depends on its specific chemical composition and physical properties. The plots of corresponding physical quantities (reflectance, absorbance, transmittance, emissivity) against wavelength, are termed spectral signatures of the specific matter under study. In principle the analysis of spectral signatures obtained by multispectral EO sensors could allow us to identify/discriminate different cover types.\r\nThe interpretation of spectral signatures requires to well understand the e.m. radiation-matter interaction process. In very simple term we expect that incident radiation I(λ)can be reflected, absorbed or transmitted by the matter so that for the energy conservation should be: \r\n\r\n\r\nI(λ)=I(λ,R)+I(λ,A), I(λ,T) \r\n\r\n \r\nbeing I(λ,R), I(λ,A) and I(λ,T) the reflected, absorbed and transmitted fraction of I(λ). From the previous relation descends (dividing both members for I) that:\r\n\r\n\r\n1=R(λ)+A(λ)+T(λ)\r\n\r\n\r\nbeing:\r\n\r\n\r\nR(λ)=I(λ,R)/I(λ) named Reflectance\r\nA(λ)=I(λ,A)/I(λ) named Absorbance\r\nT(λ)=I(λ,T)/I(λ) named Transmittance\r\n\r\n\r\nThey are all specific properties of the considered matter and are not independent each others.\r\nIn particular for an opaque medium with T(λ)=0 it is:\r\nR(λ)=1-A(λ)","name":"Spectral Signatures of the matter","selfAssesment":"Completed
"},{"code":"PP1-3-4","description":"Vegetation, water and soil represent the most common cover types of Earth surface. Their reflectances in the VIS/NIR/SWIR spectral range, plotted against wavelength in the 0,4-2,5 micron, represent the most important (basic) spectral signatures for whatever application devoted to Earth surface study. Other spectral signatures (e.g. in emissivity) in the Thermal InfraRed range are particularly important to infer specific properties of Mineral and Rocks (ref. [PP1-3-5] Spectral Signature of Mineral and Rocks). In order to discriminate among such basic cover types, the (ref. [IP3-1-2-3]) NDVI (Normalized Difference Vegetation Index) is the most simple and powerful diagnostic tool in the VIS/NIR spectral range \r\nNDVI values ranging between the values -1 and +1, are higly positive for fully vegetated (up to NDVI=1) or partly vegetated (NDVI>0,3) targets, still positive (>0) for bare soils, negative for water bodies. Values around zero are expected for clouds thanks to their similarly high reflectances both in the NIR and VIR spectral bands (ref. [PP1-3-6] Spectral Signature of Clouds). \r\n\r\nVegetation. a) in the visible range most of the incomig radiation is adsorbed by the photosynthetic process, transmittance is very low. The residual reflected radiation has a small peak of reflectance around 0.5 microns which is responsible of the green colour associated to vegetation by the human vision sytem (limited to the VIS spectral range); b) in the NIR range vegetation exhibits its higher reflectance together its higher transmittance (very low absorbance) so that leaf density can be estimated thanks to the the contributes (decreasing with depth) of underlaying leaf layers; c) in the SWIR spectral range (in particular in the water bands around 1,4 and 1,9 microns) it is possible to appreciate the vegetation water content. As much it is, as more incident radiation is absorbed and less is the reflected fraction of radiation.\r\nBare Soil. Spectral reflectance is normally increasing moving from the VIS to the SWIR spectral region. Water features around 1,4 and 1,9 microns give information on soil water content (see before). Others specific features are described in [PP1-3-5] Spectral Signature of Mineral and Rocks\r\n\r\nWater. Spectral reflectance of clean deep water is quite low reaching quickly the zero value as soon as wavelengths passe microns. However it is important to note that such a very low reflectance is due to a very high transmittance in the VIS range and to a very high absorbance in the NIR/SWIR regions (ref. [PP2-2-5-2] Attenuation Lenght and Penetration Depth). This means that water is quite transparent in the VIS spectral range (so that, in case of shallow waters, measured reflected radiance can be significantly increased by the contribution of bottom of the sea). Water is completely opaque, instead, in the NIR/SWIR. In this spectral region, even in presence of shallow waters, the presence of suspended matter (that increases the measured reflectance both in the VIS and NIR/SWIR ranges) can be better discriminated (than in the VIS) from the contribute of the bottom of the sea that, in this spectral range, is zero.","name":"Spectral Signature of Vegetation, Water, Soil","selfAssesment":"Completed
"},{"code":"PP1-3-5","description":"Spectral signatures of rocks and mineral provide information on their chemical composition and crystal properties, grain size and roughness over a wide range of wavelengths from the visible to the thermal infrared. For example, the iron-rich minerals are characterized by low reflectance (high absorbance) below 0.7 μm, carbonates show a typical absorbance feature around 2.3 μm, the water content can be instead evaluated by the depth of absorptionat 1,4 and 1,9 μm. Particualrly importantant are the spectral signatures which rocks exhibit in emissivity in the Thermal Infrared (TIR) spectral region. For instance looking at the shift of lower emission band of SiO2 from around 9 up to 10 μm as far as its concentration reduces e.g. moving from granite to peridotite.","name":"Spectral Signature of Mineral and Rocks","selfAssesment":"Planned
"},{"code":"PP1-3-6","description":"The determination of spectral signatures for scenes with a high degree of spatial complexity is considered as one of the most persistent problems in atmospheric radiation, especially at the surface, where satellite observations can only be used indirectly to infer energy budget terms. In the shortwave (solar) spectral range, it is especially challenging to derive consistent albedo, absorption, and transmittance from spaceborne, aircraft, and ground-based observations for inhomogeneous cloud conditions and is closely related to the long-debated discrepancy between observed and modeled cloud absorption.\r\nThe cloud spatial structure is revealed as a spectral signature in shortwave irradiance through the physical mechanism of molecular scattering. However, the study of specific mechanisms is rather complex since the satellite instruments cannot completely describe the spatial distribution of cloud and the variability of scattering and absorption properties. For this reason, several studies deal with the problem described above, as a challenge for estimating spectrally the cloud optical properties (such as the albedo and transmittance) as well as scattering and absorption processes taking place in the cloud system with adequate resolution. Hence, the above mechanisms can be described using three dimensional (3-D) radiative transfer models. Those models receive auxiliary information from cloud imagery and radar observations. The molecular scattering (Rayleigh) was the only one directly dependent on the wavelength of the vertical radiative flux. Moreover, it was considered as a spectral perturbation of backtracked horizontal exchange of solar radiation due to the inhomogeneous distribution of cloud. The horizontal photon transport is highly correlated to its spectral dependence.\r\nConcerning the presence of cirrus or ice clouds, the effect of their phase function and the vertical distribution were evaluated on the scattering of far infrared radiation. Thus, the accurate reconstruction of the phase function of cirrus clouds potentially indicates the need for application of a radiative transfer model. This specific module necessarily includes scattering parameters, while the accuracy of its calculations needs to be verified against real measurements. \r\nFor several applications the preliminary detection of those portions of the scene affected by the presence of clouds (cloud detection) is mandatory. For studying properties of Earth's surface targets affected by the presence of clouds are flagged just to exclude them by further analyses. In some case clouds themselves are the object of interest. In both cases the identification of clouds (and their classification) is mostly done by using (combination of) specific spectral signatures. Generally speaking clouds are highly reflecting VIS/NIR radiation showing (due to their heigth) brigthness temperatures (in the TIR region) lower than underlying surfaces. Thin or semi-transparent clouds are still detectable for their higher reflectance over the sea which represents a quite dark bacground in the VIS/NIR/SWIR region. Over land (much more reflecting) such a test is not more efficient and more sophisticated tests (e.g. Brigthness Temperature Difference in the split window bands around 11 and 12 microns) are required. In presence of very cold, high reflective backgrounds (e.g. snow, glaciers, etc.) both tests on the VIS reflectance and on TIR brigthness temperature could fail. More specific tests exploiting the reflectance drop of snow in the SWIR (where clouds are still saving their higher reflectance) helps to discriminate the presence of clouds from clear sky conditions even over a snow background. In the microwaves clouds are quite transparent except when coupled with coarse particles related to rain, snow, hailstones (precipitating clouds). In that case Mie scattering dominates strongly reducing the amount of radiance collected at the sensor (lower brigthness temperature in the microwave spectral range).","name":"Spectral Signature of Clouds","selfAssesment":"Completed
"},{"code":"PP1-3-7","description":"If the resolution is low enough that disparate materials can jointly occupy a single pixel, the resulting spectral measurement, made by the sensor, will be the composite of the individual spectra. Under the linear mixing model (LMM), each observed spectrum in each pixel of a given image is assumed to result from the linear combination of the N endmember spectra present in the pixel. The reflectance spectrum of each endmember is weighted by the fractional area coverage of it in the pixel. \r\nHowever, if the components of interest in a pixel are in an intimate association, like sand grains of different composition in a beach deposit, light typically interacts with more than one component as it is multiply scattered, and the mixing between these different components are nonlinear. Such nonlinear effects have been recognized in spectra of: particulate mineral mixtures, aerosols and atmospheric particles, vegetation and canopy. In this case a non-linear mixing model (NLMM) should be applied. To summarize: Linear mixture model assumes that endmember substances are sitting side-by-side within the pixel; Nonlinear mixture model assumes that endmember components are randomly distributed throughout the pixel, causing multiple scattering effects. \r\nIn the linear mixing case, the basic premise of mixture modelling is that within a given scene, the surface is dominated by a small number of distinct materials that have relatively constant spectral properties. These distinct substances (e.g., water, grass, mineral types), characterized by a well-defined spectral signature are called endmembers, and the fractions in which they appear in a mixed pixel are called fractional abundances. Then, finding the endmembers that can be used to ‘unmix’ other mixed pixels becomes a crucial issue. \r\nIdentify fractional abundances of distinct substances from the spectral signal of a mixed pixel is one of the application in which hyperspectral images can provide an valuable support.","name":"Composition of spectral signatures (Linear Mixing)","selfAssesment":"Completed
"},{"code":"PP1-3-8","description":"One of the most common ways to classify remote sensing systems consists in distinguishing them into the passive systems, which detect naturally occurring radiation, and the active systems, which emit radiation and analyse what is sent back to them. The passive systems can be further subdivided into those that detect radiation emitted by the Sun (this radiation consists mostly of ultraviolet, visible and near-infrared radiation), and those that detect the thermal radiation that is emitted by all objects that are not at absolute zero (i.e. all objects). For objects at typical terrestrial temperatures, this thermal emission occurs mostly in the infrared part of the spectrum, at wavelengths of the order of 10 μm (the so called thermal infrared region), although measurable quantities of radiation also occur at longer wavelengths, as far as the microwave part of the spectrum. Active systems can, in principle, use any type of electromagnetic radiation. In practice, however, they are restricted by the transparency of the Earth’s atmosphere.","name":"Definition of active and passive remote sensing techniques","selfAssesment":"Planned
"},{"code":"PP1-3","description":"Measuring the signal emitted (received) by a radiation source (detector)","name":"Sensing of EM radiation.","selfAssesment":"Planned
"},{"code":"PP1-4-1","description":"The radiation traversing a medium may be attenuated, due to the density, mass scattering and absorption of material. In contrast, the radiation’s intensity can be strengthened by emissions from the material plus multiple scattering from all directions. The above interactions follow the general radiative transfer equation.","name":"General equation of radiative transfer.","selfAssesment":"Planned
"},{"code":"PP1-4-10","description":"The inversion approach aims at retrievals of trace gas concentration and temperature profiles of atmospheric state, namely the modeled state vector, based on the measured radiance transmitted or reflected or scattered (SCIAMACHY spectrometer) by the Earth-Atmosphere system. Satellite instruments measure the radiance L that reaches the top of the atmosphere at given frequency v. The measured radiance is related to geophysical variables of Earth's atmosphere (e.g. temperature vertical profiles and chemical composition, aerosols, clouds, rain, etc.) and surface (e.g. temperature, spectral emissivity and reflectance, etc.) by the Radiative Transfer Equation (RTE). In RTE measured spectral radiances are assumed as the result of different contributions:\r\na) thermal emission from the different layers (at heigt z) of atmosphere at temperature T(z) modulated by the atmospheric transmittance from z to the sensor heigt. It depends on both temperature profile T(z) and trace gas concentration along the optical path;\r\nb) Surface emission. It depends mostly on Eart's surface temperature T(0) and spectral emissivity\r\nc) Surface reflection/scattering. It depends on spectral reflectance and local properties like surface rugosity \r\nOthers, more complex contributions comes from: cloud/rain, aerosols, etc.\r\nIn its simplified form, terms a) and b) dominate as far as InfraRed (IR) radiances are considered. Term a) can be neglected in those bands where atmosphere is transparent (atmospheric windows). Term b) can be negletcted in the IR spectral bands (sounding channels) where it is fully adsorbed by some specific constituent of the atmosphere. Among the IR sounding channels some ones are selected being associated to atmospheric constituents (like CO2 or oxygen) whose mixing ratio in the atmosphere is known to be constant. For radiances measured in these bands term a) in RTE depends only on T(z) (through a Fredholm equation of the first kind) that can be then retrieved by inversion methods. When T(z) are known trace gas concentrations survive as the only unknown of term a) and can be retrieved by inversion methods using radiances measured in their corresponding sounding channels. Similar inversion strategies have been suggested as far as radiances (emitted, transmitted, reflected, adsorbed) measured in different spectral ranges (from the Visible to the Microwaves) are considered.","name":"Retrieval of atmospheric parameters by inversion of multi-spectral radiances","selfAssesment":"Completed
"},{"code":"PP1-4-2","description":"In the field of radiation scattering and absorption, the cross-section, analogous to the shape of a particle, is used to determine the amount of energy diverted from the original beam by the particle. This parameter is called mass cross section, when it is in reference to unit mass (cm2g-1).","name":"Cross Section of Extinction (Absorption, Scattering) per Mass Unit","selfAssesment":"Planned
"},{"code":"PP1-4-3","description":"When the mass cross-section is multiplied by the density of particle, the extinction coefficient is calculated, namely the sum of absorption and scattering coefficient, whose the units are related to length. Especially, the absorption coefficient (k (cm•atm)-1) is the product of strength of absorption with the Loschmidt’s number.","name":"Absorption Coefficient","selfAssesment":"Planned
"},{"code":"PP1-4-4","description":"The source function, Jλ, has units of radiant intensity and it is defined as the ratio of the source function coefficient to the mass extinction cross section. The Jλ determines the intensity that are acquired in a homogeneous medium.","name":"Source Function (Coefficient)","selfAssesment":"Planned
"},{"code":"PP1-4-5","description":"If the monochromatic beam (Iλ) of radiation attenuates due to absorption, but it remains unaffected from emission contributions and multiple scattering of homogeneous Earth-Atmosphere system, it can be expressed by Beer-Bouguer-Lambert law. This law also expresses the monochromatic optical depth (τλ) and transmissivity (Τλ) of the above system.","name":"Beer-Bouguer-Lambert law.","selfAssesment":"Planned
"},{"code":"PP1-4-6","description":"The Schwarzschild equation provides an interpretation for the infrared radiation that undergoes the absorption and emission processes simultaneously, while the scattering efficiency is considered negligible. Hence, its solution is obtained by the integrating of relationship that invokes Kirchhoff’s law and summing the two above processes along a ray path.","name":"Schwarzshild equation and its solutions","selfAssesment":"Planned
"},{"code":"PP1-4-7","description":"The Atmosphere-Earth system that monochromatic beam (Iλ) of radiation travels, is called optical path. It expressed by optical path length, namely the product of geometric length and the refractive index of medium. It determines the optical thickness, namely a measure of the cumulative depletion of Iλ directed in straight-downward.","name":"Concepts of Optical path and Optical thickness.","selfAssesment":"Planned
"},{"code":"PP1-4-8","description":"Radiative transfer is highly nonlinear and non-local against the cloud structure at a high spatial resolution. Hence, a Monte Carlo approach can be used for the representation of cloud structure and interactions between photons and clouds. This approach is more efficient than the method of representing clouds as horizontally homogeneous.","name":"Radiative transfer in presence of clouds","selfAssesment":"Planned
"},{"code":"PP1-4-9","description":"The line by line radiative transfer model (LBLRTM) is an accurate and flexible model for the estimation of the spectral radiance and transmittance over the full spectral range (microwave to ultraviolet), using a first-order perturbation algorithm. It is considered as the basic tool for the creation of retrieval algorithms employed by the ground-based and satellite instruments, while the latest updates in spectroscopic factors are derived from the high-resolution transmission molecular absorption (HITRAN) database. A LBLRTMs is continuously updated and validated against highly accurate spectral measurements. Its errors are related to uncertainties in line parameters and shape. The shape is a Voigt line which is a linear combination of approximating functions for the description of all atmospheric levels. LBLRTML is combined with the continuum MT_CKD (Mlawer, Tobin, Clough, Kneizys, Davies) model which in turn includes the atmospheric constituents of water vapor, carbon dioxide (CO2), molecular oxygen (O2), molecular nitrogen (N2), and ozone (O3), and the molecular extinction process (Rayleigh scattering). A recent version of LBLRTM calculates analytically the Jacobians equations for obtaining meteorological parameters. Also, this model version retrieves the optical parameters of clouds related to scattering and emissivity. The LBLRTM is widely used in radiation and climate applications. It is capable to calculate the absorption degrees of various atmospheric constituents which are utilized afterward from climate and weather prediction models for estimating the broadband solar irradiance and the heating rates. Additionally, the complex radiative transfer models with fast computational time are initiated and trained by the LBRTM, since they are used subsequently on numerical weather prediction (NWP) assimilation systems.","name":"Line-by-line radiative transfer models","selfAssesment":"completed
"},{"code":"PP1-4","description":"Theory of radiative transfer describe the transmission of the electromagnetic radiation through a medium.","name":"Fundamentals of Radiative Transfer","selfAssesment":"Planned
"},{"code":"PP1-5-1","description":"Light is the electromagnetic phenomenon we exploit for remote sensing. Its basic laws concerning the transmission through the interface of two different media are governed by reflection and refraction. Reflection governs the way light is backpropagated and refraction dictates how light is transmitted. Refraction is related to the real refractive index of a medium. Dispersion relates to the way the light of a given wavelength is transmitted. Since light of different wavelengths are transmitted at different angles, the phenomenon leads to the concept of dispersion. These three simple principles are at the core of the understanding technology of remote sensing.","name":"Reflection, Refraction and Dispersion of the light","selfAssesment":"Planned
"},{"code":"PP1-5-11","description":"The theory provides the bulk of physical explanation and related laws, which govern absorption, emission and spontaneous emission from the ordinary matter. Early laws about thermal radiation and the blackbody emission, such as Rayleigh-Jeans, Wien, Planck laws are cast in a single theory and formalism through the concept of quantized energy at the level of atoms emission/absorption of light. Explain the modern concept of quantum optics and their link to the design of modern devices for the measurements and/or production of coherent light.","name":"Einstein’s theory of radiation: photons, photoelectric effect, absorption, emission; Stimulated emission: the laser","selfAssesment":"Planned
"},{"code":"PP1-5-14","description":"Solid state modern detectors rely on non-metal junction, which can be designed and operated to yield a bandgap energy according to the spectral range (infrared, visible, UV) to be detected. The basic principles of how these devices are designed and fabricated is important to develop and design new sensors useful for the various remote sensing applications.","name":"Electric conduction in solids: semiconductors, p-n- junction, diode and transistors","selfAssesment":"Planned
"},{"code":"PP1-5-15","description":"Modern detectors of electromagnetic radiation in the infrared, VIS, UV spectral regions are designed and fabricated based on suitable junctions or electro-optical devices. The performance of these systems needs to be assessed in terms of accuracy and precision. This is made through figures of merit such as Noise Power Spectral Density, Noise Equivalent Power. Detectors can be classified as photovoltaic or photoconductive devices, which allows to better classify the various noise sources: shot noise, 1/f noise, Johnson noise, generation-recombination noise.","name":"Photovoltaic and photoconductive detectors: MCT, InSb, bolometer, CCD devices","selfAssesment":"Planned
"},{"code":"PP1-5-2","description":"Interference and diffraction are phenomena related to the wave nature of electromagnetic radiation. They explain how light propagates in presence of obstacles. These phenomena are largely used in the fabrications of optical systems for remote sensing: e.g. radiometers and spectrometers.","name":"Interference and Diffraction.","selfAssesment":"Planned
"},{"code":"PP1-5-3","description":"The Michelson interferometer is the instrument that exploits and evidence the interference of light. A masterpiece of experimental physics, the Michelson interferometer is the key architecture of the modern optical interferometers, which make it possible to measure the emitted Earth spectrum with hyperspectral resolution.","name":"Michelson Interferometer","selfAssesment":"Planned
"},{"code":"PP1-5-4","description":"The celebrated principle of constant speed of light and independence of the reference frame is important to explain the basic principles of instruments such as the Michelson interferometer. The basic physics theory to explain how electromagnetic fields propagates and the inter-relationship between electric and magnetic fields.","name":"Special relativity; Electromagnetic fields equations and propagations","selfAssesment":"Planned
"},{"code":"PP1-5-6","description":"Helmotz’s wave equation arises in light and acoustic scattering problem and yields the general framework to investigate and analyse the scattering of time-harmonic acoustic and electromagnetic waves by a penetrable inhomogeneous medium.","name":"Helmotz’s equations; Scattering from inhomogeneous media.","selfAssesment":"Planned
"},{"code":"PP1-5-7","description":"Geometrical optics is governed by the laws of reflection, refraction and dispersion. Its applications are relevant to many optical systems involving ray tracing, wavefront propagation, thin film calculators (which underly many optical engineering calculations).","name":"Foundations of geometrical optics, geometrical theory of optical imaging","selfAssesment":"Planned
"},{"code":"PP1-5-8","description":"Optical interferometers are nowadays used to develop and implement Fourier Transform Spectrometers, which can measure the emission spectrum of a given source with high spectral resolution at a constant sampling. This instrumentation is now at the core of modern hyperspectral sounders from satellite and have opened the way to the sounding of the Earth atmosphere with unprecedented spatial vertical resolution.","name":"Elements of the theory of interference and interferometers","selfAssesment":"Planned
"},{"code":"PP1-5-9","description":"Diffraction gratings and dispersive element are the basic ingredients for radiometers and grating spectrometers. They are in some cases preferred to Interferometer systems because the optical layouts can be designed and implemented with no moving part or components. Many of the today satellite instruments, including sounder and imagers, rely on diffraction and/or grating spectrometers","name":"Elements of the theory of diffraction and grating spectrometers","selfAssesment":"Planned
"},{"code":"PP1-5","description":"This section describes the theoretical fundaments of Optics and Modern Physics of Sensors relevant to the Earth Observation.","name":"Basics of Optics and Modern Physics of Sensors","selfAssesment":"Completed
"},{"code":"PP1-6-1","description":"The temperature and pressure profiles determine the atmospheric structure. The latter consists of four basic levels, considering the vertical variability of the temperature. These main four levels are troposphere, stratosphere, mesosphere, and thermosphere. In the troposphere (0-12km), which is the lowest layer of the atmosphere, all the meteorological processes that affect our everyday life take place. The lowest part of the troposphere is known as the boundary layer (0-3km), where all the surface-atmosphere interactions and exchanges take place. The troposphere concentrates the water vapor and 90% of atmospheric mass, while the chemical composition of all atmospheric layers consists of nitrogen, oxygen, argon and trace gases. The main parameters that characterize the atmosphere structure are pressure, density, and temperature. All the aforementioned parameters are related to the atmospheric composition and vary with altitude, latitude, longitude and season. Additionally, the stratosphere, which is the layer above the troposphere, contains almost all of the ozone abundance (~90%) of the atmosphere in a region named as ozone layer and traced between 15 and 35km. The interaction of the incoming solar radiation with ozone in this layer causes the reduction of the incoming harmful UV radiation provoking the temperature increase in the stratospheric layer. The 99.9% of total atmospheric mass is concentrated in lower atmosphere (<50km) with Nitrogen (N2, 78.08%), Oxygen (O2, 20.95%) and argon (Ar, 0.93%) being the major constituents of the atmosphere. Water vapor (H2O) is considered as a significant factor, too. Despite the fact that it depicts a very small amount of total atmospheric mass, it’s one of the most important greenhouse gases, along with carbon dioxide (CO2) and methane (CH4), absorbing the Earth’s longwave (infrared) radiation, affecting the energy balance of Earth-Atmosphere system. Furthermore, water vapor plays a decisive role in the formation of clouds and precipitation. Together with the basic chemical (atoms, molecules, ions) constituents of a \"standard\" atmosphere, aerosols of natural and anthropogenic origin have to be considered too, as far as the interaction of e.m. radiation with atmosphere is concerned.","name":"Structure and chemical-physical composition of Earth's atmosphere","selfAssesment":"Completed
"},{"code":"PP1-6-10","description":"The water vapour is the major radiative and dynamic parameter in atmosphere. Its concentrations vary highly in space and time, with the tropospheric water vapor being determined by the processes of hydrological cycle, namely the evaporation, condensation and precipitation. More specifically, its condensation upon dust nuclei form the clouds.","name":"Water vapour and Cloud formation","selfAssesment":"Planned
"},{"code":"PP1-6-11","description":"The radiative equilibrium is the principle, where the radiative emission and absorption are in balance based on Kirchhoff’s and Planck’s law, resulting in the steady temperature of planet. The adiabatic lapse rate displays the decrease of vertical temperature of a parcel with rate higher than 1oC per 100 metres.","name":"Radiative Equilibrium. Adiabatic lapse rate","selfAssesment":"Planned
"},{"code":"PP1-6-12","description":"The atoms of carbon are building blocks of living organisms and they can move among organisms as a part of carbon cycle. Their transport rate to the atmosphere as carbon dioxide is vital, because this gas trap heat in the atmosphere, increasing the Earth’s temperature and causing Greenhouse effect.","name":"The Carbon Cycle, Greenhouse Effect","selfAssesment":"Planned
"},{"code":"PP1-6-2","description":"The atmospheric absorption can cause an excitation or falling into the energy state of a particle, while the scattering is related to absorption and re-emission of radiation at all directions without changes in its frequency. Particularly, the main contributors of the incoming solar radiation absorptions are various molecules like the nitrogen (N2), oxygen (O2), ozone (O3), water vapor (H2O). Additionally, other constituents of the atmosphere such as CO2 and CH4, and other trace gases, aerosols, and cloud droplets can also absorb significant portion of the incoming solar radiation. Generally, the absorption of solar radiation is related to the wavelength of the solar spectrum. For example, gases and specific type of aerosols (black carbon, BC) or elementary carbon (EC) absorb in the ultraviolet (UV) and visible (VIS) part of solar spectrum. On the contrary, cloud droplets which are suspended in the atmosphere mainly scatter in UV and VIS and absorb in the infrared. The absorption of the incoming solar radiation from the atmospheric constituents reduces the harmful UV radiation and it is considered as the driving of atmospheric photochemistry. Moreover, scattering in the atmosphere can be divided into two mainly categories, firstly, the Rayleigh scattering which is the scattering of radiation by gases (mainly N2 and O2) and, secondly, the Mie scattering which is the scattering by aerosol particles and cloud droplets. The main difference between Rayleigh and Mie scattering is the direction of the re-emission of the incident solar radiation. For example, in the Rayleigh scattering the light have symmetrical direction either forward or backward whereas in Mie scattering the light is mainly scattered in the forward direction, depending on the size of the particle.","name":"Absorption and scattering of solar radiation in the Atmosphere","selfAssesment":"Completed
"},{"code":"PP1-6-3","description":"Mie scattering refers primarily to the elastic scattering of light from atomic and molecular particles whose diameter is similar or larger than the wavelength of the incident light. Mie scattering is not strongly wavelength dependent . This scattering produces a pattern like an antenna lobe, with a sharper and more intense forward lobe for larger particles. In the atmosphere the Mie scattering is commonly caused by particles (aerosols) floating in the atmosphere (due to Dust, smoke, rain drop). The Mie theory provides the solution for the amount of scattering in case of a spherical medium due to an incident wave.","name":"Mie Scattering in the Earth's Atmosphere","selfAssesment":"Planned
"},{"code":"PP1-6-4","description":"Rayleigh scattering refers primarily to the elastic scattering of light from atomic and molecular particles whose diameter is much smaller (one-tenth at least) than the wavelength of the incident light. The amount of scattering is strongly depending on the wavelength (λ) of the radiation (I = f(1/λ4). Then, the Rayleigh scattering explain the blue color of the sky caused by the scattering of sunlight off the molecules of the atmosphere. This because Rayleigh scattering is more effective at short wavelengths (the blue end of the visible spectrum). Therefore the light scattered down to the earth at a large angle with respect to the direction of the sun's light is predominantly in the blue end of the spectrum.","name":"Rayleigh Scattering in the Earth's Atmosphere","selfAssesment":"Planned
"},{"code":"PP1-6-5","description":"When we talk about “thermal infrared (or terrestrial) radiation” we commonly refer to the energy emitted from the Earth-atmosphere system. Trapping of thermal infrared radiation by atmospheric gases is typical of the atmosphere and is therefore called the “atmospheric effect”. The atmospheric effect is sometimes referred to as the “greenhouse effect” because in a similar way glass, which covers a greenhouse, transmits short-wave solar radiation, however absorbs long-wave thermal infrared radiation. Imagine a beam of radiation travelling through a small section of air. The air is made up of changing concentrations of different species, with all molecules absorbing and emitting thermal radiation at different rates. As the radiation travels through different layers of the atmosphere, the intensity of radiation will constantly be modified by both absorption and emission processes as described by the Schwarzschild's equation. In case of a sensor on board of a satellite, the net radiation measured would be that which is attenuated through each layer (as small increments of absorption and emission) from the surface to the top of the atmosphere plus the radiation emitted directly from the atmosphere. In this case, this process can be described by the radiative transfer equation (RTE). \r\nThe equation of radiative transfer simply says that as a beam of radiation travels through the atmosphere, it loses energy to absorption, gains energy by emission, and redistributes energy by scattering. Many radiative transfer codes exist which are able, i.e. on the basis of known properties of the atmosphere, to computed the effect of the atmosphere on the thermal infrared radiation providing atmospheric transmittance (absorption), atmospheric scattering and atmosphere path emission. Commonly, in satellite remote sensing, the thermal infrared region is defined as the region of the electromagnetic spectrum comprised between 8 and 14 micron. In an atmosphere free of particles (aerosols due to phenomena like fires, volcanic eruption, dust storm, etc.) the thermal infrared radiation is mainly affected by triatomic gases like water vapor, carbon dioxide and ozone.","name":"Thermal infrared radiation transfer in the atmosphere","selfAssesment":"Completed
"},{"code":"PP1-6-6","description":"Light scattering by particles is the process by which small particles cause optical phenomena, such as rainbows, the blue color of the sky, and halos. Mie scattering defines the interaction of light with particulate matter with a dimension comparable to the wavelength of the incident radiation. It can be regarded as the radiation resulting from a large number of coherently excited elementary emitters (molecules for example) in a particle. Since the linear dimension of the particle is comparable to the wavelength of the radiation, interference effects occur. The most noticeable difference to Rayleigh scattering is, generally, the much weaker wavelength dependence and a strong dominance of the forward direction in the scattered light. The calculation of the Mie scattering cross section, which involves summing over slowly converging series, is complicated even for spherical particles, it is worse for particles of an arbitrary shape. However, the Mie theory for spherical particles is well developed and a number of numerical models exist to calculate scattering phase functions and extinction coefficients for given aerosol types and particle size distributions.","name":"Light scattering by atmospheric particulates","selfAssesment":"Planned
"},{"code":"PP1-6-7","description":"Each time radiation passes through the atmosphere it is attenuated to some extent. We refer to this attenuation with the term 'atmosphere transmittance'. The typical atmospheric transmittance between wavelengths of 250 nm and 2500 nm, i.e. in the ultraviolet, visible, near-infrared and short-wave-infrared regions of the spectrum is dominated bywater vapour, although methane, carbon dioxide and molecular oxygen are also responsible for a few absorption lines. The behaviour in the visible region is dominated by molecular Rayleigh scattering. At the short-wavelength end of the spectrum, in the ultraviolet, absorption by ozone becomes very significant. Above 2500 nm up to the upper limit (13500 nm) of the optical electromagnetic spectrum useful for Remote Sensing, the atmosphere transmittance is mainly affected by triatomic molecules (H20, CO2 and O3). However, the atmospheric effects (transmittance) is strongly depending on the electromagntic wavelength. Remote Sensing exploits the region of relative atmospheric transparency called atmospheric windows.","name":"Earth's (standard) Atmosphere Transmittance","selfAssesment":"Planned
"},{"code":"PP1-6-8","description":"With the term 'atmospheric windows' we refer to the regions of the electromagnetic spectrum where the interaction of the atmosphere with the electromagnetic radiation is minimized. There are three main ‘windows’ in the Earth's atmosphere. The first of these includes the visible and near-infrared (VNIR) parts of the spectrum, between wavelengths of about 0.38 μm and 3.5 μm, although it does also contain a number of opaque regions. The second is a rather narrow region between about 8 μm and 15 μm, in which is found the bulk of the thermal infrared (TIR) radiation from objects at typical terrestrial temperatures. The third more or less corresponds to the microwave region, between wavelengths of a few millimetres and a few metres. Thus we can expect that any active system designed to penetrate the Earth’s atmosphere will operate in one of these three ‘window’ regions.","name":"Atmospheric (spectral) windows for EO","selfAssesment":"Planned
"},{"code":"PP1-6-9","description":"The water cycle is a continuous purification process of water on Earth due to the movement of water species among various reservoirs. This cycle is vital for Earth’s life, ecosystems, and living organisms. The water cycle includes mainly four processes. Water is evaporated from ocean and land surfaces driven by solar heating. The resulting water vapor rises upwards into the atmosphere, transported by the winds, cools, and due to low air temperature condensates into liquid droplets and ice crystals to form clouds. The ice or/and liquid droplets collide, increase their size, and precipitate as snow or rain to Earth’s surface and oceans. The subtraction of energy (latent heat of evaporation) at low latitudes related to the evaporation processes as well as its release (latent heat of condensation) at higher latitudes related to the condensation processes is a formidable way to guarantees the heat transport from the warmer part of the Earth to the colder ones mantaining local air temperature more compatible with the human life. The starting point of the water cycle is not unique, but the oceans can be selected as the initial reservoir. Other important reservoirs are considered ice sheets, lakes, and rivers. \r\nThe hydrosphere is defined by the various water reservoirs which are characterized by different residence times – the time spends the water molecules in a reservoir. The water residence time – the rate at which the water comes out the reservoirs – varies for each reservoir extending from hundreds (Greenland Ice Sheet) or thousands of years (Antarctic Ice Sheet) to years and days for rivers and lakes, respectively. It also defines the energy transferred from the Earth to the Atmosphere which increases for short-term residence times. In long-term temporal scales, this energy is defined as the evaporation rate (E) and balances with the precipitation rate (P). This global energy balance breaks for shorter time scales depending also on the local and regional climate. For example, in regions located in the Inter-Tropical Convergence Zone (ITCZ), the energy balance in the water cycle does not exist since the precipitation rate is much higher than the evaporation rate (P>>E) due to the horizontal movement of converging trade winds.","name":"The Water Cycle","selfAssesment":"Completed
"},{"code":"PP1-6","description":"Atmospheric Physics describe the processes affecting the physical, chemical and thermodynamic status of planetary atmospheres. In the context of EO sciences, it particularly refers to the physics of the interactions of e.m. radiation traveling across (or emitted by) the atmosphere as the main source of information collected by satellite (in general aerial) sensors.","name":"Basics of Atmospheric Physics","selfAssesment":"Completed
"},{"code":"PP1-7-1","description":"According to the second law of thermodynamics, heat is a measure of movement or flow of energy from hotter substances to colder ones and is measured in Joules. In microscale, heat is known as internal energy, while temperature describes the kinetic energy of molecules within substances, expressed in Celsius.","name":"Temperature and heat","selfAssesment":"Planned
"},{"code":"PP1-7-10","description":"Irreversible thermodynamics investigates the regularities in transport phenomena, namely heat and mass transfer, and their relaxation. It is based on the first law of Thermodynamics, which correlate the heat flow density with pressure and viscosity, and the second law that describe the temporal variations of local entropy for local continuous mass.","name":"The constitutive equations of irreversible fluxes","selfAssesment":"Planned
"},{"code":"PP1-7-11","description":"The Adiabatic process of homogeneous system occurs, when flow of heat is not exchanged across the boundaries of system and the system is characterized from uniform phase (solid or liquid or gases). In this case, the variations of entropy can be determined for some parts of system.","name":"Heat equation and special adiabatic systems, special adiabats of homogeneous systems","selfAssesment":"Planned
"},{"code":"PP1-7-12","description":"The thermodynamic diagrams are used for the study of vertical structure and properties of the Atmosphere above a specific location. Especially, a static diagram represents a) an atmosphere with fixed potential temperature or b) a process curve of the change of variables of air parcel that rises adiabatically.","name":"Thermodynamics diagram, atmosphere static","selfAssesment":"Planned
"},{"code":"PP1-7-2","description":"Kinetic theory of gases is based on a simplified molecular description of gases, from which the properties of volume, pressure and temperature can be derived. The assumptions of this theory are based on the random movements of molecules, their elastic collisions and the transfer of kinetic energy between them.","name":"Kinetic theory of gases","selfAssesment":"Planned
"},{"code":"PP1-7-3","description":"The ideal gas law or general gas equation describes the equation of state of hypothetical ideal gas. This equation correlates the pressure and volume with its temperature, while is characterized as a combination of the empirical laws of Boyle, Charles, Avogadro and Gay-Lussac.","name":"Ideal gas laws","selfAssesment":"Planned
"},{"code":"PP1-7-4","description":"The state functions of ideal gas are the pressure, volume, temperature, internal energy and entropy, which remain unchangeable in compared with the path. The internal energy is expressed through Joule’s law as a function of temperature of gas, while the entropy depends on the variation of volume and temperature.","name":"State function of ideal gases","selfAssesment":"Planned
"},{"code":"PP1-7-5","description":"The phase rule for condensation is expressed as P+F=C+1. The terms of P, F and C describe the number of phases, minimum fixed variables and independent chemical species respectively. Concerning the condensed phases to distinguish the gases from liquids and solids, these are the density, molecular order, diffusion, etc.","name":"State function of the condensed gas phase","selfAssesment":"Planned
"},{"code":"PP1-7-6","description":"When the system passes from initial to final state due changes in properties of temperature, pressure and volume, it is considered to have undergone thermodynamic process. The different types of thermodynamic processes are distinguished in the isothermal (fixed temperature), adiabatic, isochoric (stable volume), isobaric (stable pressure) and reversible process.","name":"Thermodynamic process","selfAssesment":"Planned
"},{"code":"PP1-7-7","description":"Budget equations, namely heat, momentum and moisture budget, are interpreted through two frameworks, which are Eulerian and Lagrangian. Eulerian is utilized for the investigating of transfer of heat by the wind, while Lagrangian is concerned about the effects of ascending or descending airflows on the Earth-Atmosphere system.","name":"Budget equations","selfAssesment":"Planned
"},{"code":"PP1-7-8","description":"The First Law of Thermodynamics supports that the energy is conserved. Thus, the thermal energy is defined as the sum of warming or internal energy (microscopic effect) and work occurring per unit mass (macroscopic effect). For its application to the Atmosphere, its mathematical expression is Δq=Cp•ΔT-(ΔP/ρ).","name":"First law of thermodynamic","selfAssesment":"Planned
"},{"code":"PP1-7-9","description":"A natural process that starts from an equilibrium state and ends in another state, causing changes in direction of entropy (ΔS) or statistical disorder of the system, is interpreted by Second Law of Thermodynamics. This law is considered as an irreversible process and it is expressed as ΔS=Heat transfer/Temperature.","name":"Second law of thermodynamics","selfAssesment":"Planned
"},{"code":"PP1-7","description":"Thermodynamics is the science of the relationships between heat, work, temperature, radiation, energy and properties of matter. These relationships are governed by the four laws of thermodynamics which allow a quantitative description, through measurable macroscopic physical quantities, of processes that, at the level of microscopic constituents can be described by the statistical mechanics. Thermodynamics applies to a wide variety of topics relevant to EO science and technologies from atmospheric chemistry and meteorology up to sensor design and aeronautics.","name":"Basics of Thermodynamics","selfAssesment":"Planned
"},{"code":"PP1-8-2","description":"Starting from the standard Rocket Equation - assuming a relative speed of the burned (emitted) fuel equal to 2,4 km/s and zero initial speed - it is possible to evaluate (for a single-stadium rocket) the mass percentage of payload that can be hosted on a platform depending on the final speed expected on the orbit. For instance a 28% payload is possible for a geostationary platform whose expected final speed on the orbit (radius 42.170 km) is 3,7km/s. Instead for a polar platform at about 800km this percentage reduce up to the 4% being the final sped on the orbit expected to be 7,5km/s.","name":"Equation of the rocket and launch of a satellite: payload determination","selfAssesment":"Planned
"},{"code":"PP1-8-3","description":"The orbit of a satellite is commonly defined through its so called Keplerian parameters. These parameters represent the trajectory that the satellite will follow if no-perturbation are acting on it. A series of forces act on the satellite to perturb it away from the nominal orbit. We can classify these perturbations, or variations in the orbital elements, based on how they affect the Keplerian elements. The actual orbit of a satellite will result from a combination of these perturbations. Periodic maneouvers are needed to bring the orbit back to nominal conditions. The lifetime of a satellite is defined as the time interval that it takes to decay from its initial altitude to an altitude causing the satellite reentry down to the atmosphere. Therefore lifetime of a satellite should not be confused with the time during which the satellite will provide useful information (this operational phase, in general, is designed to last 5 - 7 years). In fact, all satellite terminating operational phases in orbits passing through the LEO region should be de-orbited or, where appropriate, manoeuvred to an orbit with suitably-reduced lifetime, that is, should be left in an orbit where drag and other perturbations will limit lifetime. The actual duration of the satellite in orbit will depend from the intensity of the perturbations which will affect its orbit. In case of satellite on GEO orbit, at the end of the operational phases they will be located on a disposal orbit, that is an orbit which do not cross the protected region. The protected region is the altitude region ranging from GEO - 200 km to GEO + 200 km and inclination region between -15 deg and +15 deg. Satellites in low Earth orbit, with perigee altitudes below 1000 km, are predominantly subject to atmospheric drag. This force very slowly tends to circularise and reduce the altitude of the orbit. The rate of 'decay' of the orbit becomes very rapid at altitudes less than 200 km, and by the time the satellite is down to 180 km it will only have a few hours to live before it makes a fiery re-entry down to the Earth.","name":"Real orbits. Life time of a satellite, orbit’s decay.","selfAssesment":"Planned
"},{"code":"PP1-8-4","description":"The choice of a satellite orbit mostly depends on its main application. From this point of view it represents a crucial part of a satellite mission design. The most important parameters to describe a satellite orbit are the inclination angle i (of the orbit plane respect to the equatorial plane) its eccentricity e and its height H from the Earth's surface. In principle whatever eigth H can be used, provided that the speed of the satellite on its orbit allows the centrigugal force to exactely compensate the gravitational one at that heigth. Polar (i close to 90°) and Geostationary (i=0, H=35.800 km) orbits are the most common choices for EO satellites. In principle one single polar satellite can be sufficient to guarantee the global coverage of the Earth with equal quality of the images at all latitudes. All Geostationary satellites share the same circular orbit with H around 36000 km where the required speed exactely correspond to the one required to travel an entire orbit in 1 sideral day (orbital period P = 1 sideral day). This means that the satellite footprint is permanently in place over a specific Earth's location (e.g. for Meteosat 0°N, 0°E) allowing a quasi-continuous monitoring of a whole Earth's emisphere (with poor visibility of Earth's edges including Poles). Polar satellites' heigths are usually in between 700-800 km, with orbital periods around 100min (i.e. about 14,5 orbits/day) even if, lower orbits are also chosen particularly for very high spatial resolution payloads. Lower inclinations are also used (quasi-polar orbits) for specific applications. Due to the asphericity (and mass inhomogeneity) of the Earth, satellite orbit plane rotates around the Earth's polar axis with a period Pp producing (for elliptical orbits) the rotation of the orbit itself in its plane. A common choice for most EO polar satellites is to choose the orbital parameters in a way that Pp=1 year (Sun-Synchronous orbits). Due to the synchronism between Earth's revolution around the Sun and the orbit plane precession around Earth' axis, satellite passages happens at the same local solar time (similar illumination conditions) each time it flies over a specific region. This ensure repeatable sun illumination conditions facilitating image interpretation particularly for change detection or land monitoring applications. Other choices are possible when it is required to monitor with continuity high latitude regions.\r\n\r\nThis is the case of Molniya orbits which combine the continuity of observations typical of geostationary satellites with the possibility, offered by polar orbits, to overfly the highest latitudes regions. Its characteristics are: high eccentricity (e.g. e=0,74, axes 500 and 23.000 km), P=1/2 sideral day (Geo-Synchronous), inclination (i=63,4° or i=116,6°) which guarantees the satellite footprint at the apogee remaining positioned on a fixed ground point (non-rotating orbit). This way the satellite will spend more than 93% of its orbital period looking to the same emisphere even from a high latitude point of view. \r\n\r\nSo called altimetric orbits respond to the specific needs of altimetry. In this case the orbital parameters are chosen in order to guarantee, for example: a) that the ascending and descending sub-satellite tracks intersect at roughly 90 degrees on the Earth’s surface (so that orthogonal components of the surface slope can be determined with equal accuracy; b) the possibility to monitor all phases of tidal effects on ocean surface.\r\n\r\nParticularly important for several applications (multi-temporal analyses, change detection, etc.) are the Exactly repeating orbits.\r\nThey are conceived in order that the sub-satellite track will repeat itself exactly after a certain interval of time. This allows images having the same viewing geometry during the satellite’s lifetime making moreover available a particularly simple method of referring to the location of images (navigation or geo-referenciation) for example by referring to a ‘path and row’ system used for instance by the Landsat World Reference System (WRS). It is possible to arrange satellite orbits parameters in order to contemporary guarantee the sun-syncronism so that, not only satellite images collected on the same region can be easily super-imposed each-other but the same illumination and viewing geometry can be achieved. This is, for instance, the choice adopted for LANDSAT satellites whose images are typically available as a collection of scene of fixed dimension always similar each other when covering the same terrestrial area.","name":"Satellite orbits parametrization and choice","selfAssesment":"Completed
"},{"code":"PP1-8","description":"Mechanics is the Physics branch dealing with the behaviour of physical bodies when subjected to forces or displacements. This section provides Mechanics basic elements necessary for determining the orbits of satellites and rockets. The different satellite trajectories will be illustrated with respect to their peculiarities","name":"Basics of Mechanics","selfAssesment":"Planned
"},{"code":"PP1","description":"Physycal principles of the electromagnetic field, its radiation and propagation through the space, in the optical spectral range","name":"Basics of Optical Remote Sensing","selfAssesment":"In Progress
"},{"code":"PP2-1-2-1","description":"A radar signal is a complex signal. It is represented by a real part, the in-phase component, and an imaginary part, the quadrature component. In-phase is usually annotated by “I”, and quadrature by “Q”. Considering single look complex data, each component is represented in a single image channel.","name":"In-phase/Quadrature Component","selfAssesment":"New
"},{"code":"PP2-1-2-2","description":"A phasor represents a complex number and its phase and amplitude equivalent. Considering a complex SAR image’s pixel, the real and imaginary part can be represented by a 2D vector in Cartesian coordinates. Its corresponding phase and amplitude information corresponds to the direction and length of the vector, respectively.","name":"Phasor","selfAssesment":"New
"},{"code":"PP2-1-2","description":"A complex, using complex numbers, representation of signal by two measures magnitude and phase. In the digital SAR context, a camplex number often is represented by an equivalent pair of numbers, the in-phase (I) component and the quadrature (Q) component.","name":"Complex wave description","selfAssesment":"New
"},{"code":"PP2-1-4","description":"Electromagnetic waves are polarized; the direction of the polarization corresponds to the direction of oscillation of the electromagnetic field. Typical and often used linear polarisations are: H (horizontally) and V (vertically) polarized waves of the plane of the electric field vector oscillations relative to the sensor coordinate system. The polarization state of a backscattered wave from a natural surface can be linked to the geometrical characteristics like shape, roughness and orientation and the intrinsic properties of the scatterer like moisture, salinity, density. The radar system is characterized by combination of polarization of transmitted and received pulse: HH, HV, VH or VV. Based on the polarization sent and obtained the radar systems are divided in three polarization modes. Single polarization refers to the same polarization transmitted and received; dual polarization, one polarization is sent and another received; or quad polarization, when system is able to transmit and receive all four types of polarization. When making a contact with a scatterer, the polarization of the EM-wave can change, depending on the geometrical and dielectrical properties of the scatterer. In order to get all necessary information about those changes, full polarimetric systems are required.","name":"Polarisation","selfAssesment":"Completed
"},{"code":"PP2-1-5","description":"Property of signal or data set in which the phase of the constituents is measurable, and plays a significant role in the way in which several signals or data combine. Two waves with a phase difference that remains constant over time, are said to be coherent.","name":"Coherent","selfAssesment":"New
"},{"code":"PP2-1-6","description":"In remote sensing, phase is the exact position within a periodic signal with respect to an arbitrary reference point. It is typically expressed as an angle and measured in degrees or radians, where one period corresponds to a phase of 360° or 2π, respectively. Mathematically, phase is the argument of a complex number, that is the angle between its geometric representation in the complex plane and the real axis. For this reason, complex algebra is often used in remote sensing to facilitate phase calculations. Due to its periodic nature, phase can only be measured unambiguously within one period. Consequently, phase measurements are commonly subject to 2π phase ambiguities. These ambiguities can often be resolved in a process called phase unwrapping, using a priori information about the signal, typically related to its continuity. Phase measurements are crucial for the creation of synthetic aperture radar (SAR) images, as well as for many SAR imaging techniques, including interferometric SAR (InSAR).","name":"Phase","selfAssesment":"Completed
"},{"code":"PP2-1-7","description":"Shift in frequency caused by relative montion along the line of sight between sensor and the observed scene.","name":"Doppler effect","selfAssesment":"New
"},{"code":"PP2-1-8","description":"The wave-particle dualism (duality) is a theory according to which all matter exhibits the attributes of waves and particles.","name":"Wave-particle dualism","selfAssesment":"New
"},{"code":"PP2-1","description":"The radar operates in the microwave portion of the electromagnetic (EM) spectrum with a wavelength from 1 millimeter to 1 meter. Imaging radars are independent of weather conditions and can operate day or night. EM-waves are polarized. Normally only the horizontal (H) or vertical (V) linear polarizations are used. The radar system is characterized by combination of polarization of transmitted and received pulse: HH, HV, VH or VV. When making a contact with a scatterer, the polarization of the EM-wave can change, depending on the geometrical and dielectrical properties of the scatterer.The data can be acquired from both the ascending (northwards) and descending (southwards) satellite passes. Water clouds can interfere with the radars operating below 2 cm in wavelength. The effects of rain can be generally ignored at wavelengths above 4 cm. For longer wavelengths (above 20 cm), an effect called Faraday rotation caused by the ionosphere, i.e., free charges (electrons) and the Earth’s magnetic field, can lead to a rotation of the polarization plane. In the presence of Faraday rotation, the data, usually fully polarimetric, should be corrected. The radar systems operate in different bands that uses different wavelengths. The most common frequences/wavelengths (frequency = Speed of Light / wavelength) for environmental applications are X (5,75-10,90 GHz), C-(4,20-5,75 GHz), S-(1,550-4,20 GHz), L-(0,390-1,550 GHz) and P-(0,255-0,390 GHz) band. The selection of SAR system for acquiring data depends on their application. Longer wavelengths are mainly devoted to communication and navigation purposes. Radars penetrate atmosphere and clouds. For example for forestry, longer wavelengths starting from C- or S-band are preferred.","name":"Microwave portion of electromagnetic spectrum","selfAssesment":"Completed
"},{"code":"PP2-2-1","description":"Interaction of waves with any solid object.","name":"Diffraction","selfAssesment":"New
"},{"code":"PP2-2-2","description":"Scattering means the redirection of incident electomagnetic energy by an object. Scattering and Diffraction refer to the same physical process - a coherent distortion of an incident wave. Emissivity is a measure of how strongly a body radiates at a given wavelength. Emission and scattering are complemetary: surfaces that are good scatterers are weak emitters, and vice versa.","name":"Scattering and emission","selfAssesment":"New
"},{"code":"PP2-2-3","description":"Radimetric anomalies such as for example saturation","name":"Radiometric anomalies","selfAssesment":"New
"},{"code":"PP2-2-4-1","description":"The radar equation is a measure of the received echo at the sensor. It defines what proportion of the transmitted energy is returned from a target. It is a function of the range between the antenna and the target, the antenna gain and the radar cross-section of the target. Mathematical expression that describes the average received signal level, compered to the additive noise level, in terms of system parameters. Principal parameters include: transmitted power, antenna gain, noise power, and radar range.","name":"Radar equation","selfAssesment":"In progress
"},{"code":"PP2-2-4-2","description":"Coefficient sigma or sigma nought represents the average reflectivity of a horizontal material sample, normalized with respect to a unit area on the horizontal ground plane.","name":"Sigma nought","selfAssesment":"New
"},{"code":"PP2-2-4-3","description":"Gamma nought represents the average reflectivity of a horizontal material sample, normalized with respect to the incident area, orthogonal to the incident ray from the radar.","name":"Gamma nought","selfAssesment":"New
"},{"code":"PP2-2-4-4","description":"Radar brightness coefficient represents the reflectivity per unit area in slant range.","name":"Beta nought (brightness)","selfAssesment":"New
"},{"code":"PP2-2-4","description":"Measure of radar reflectivity. The Radar Cross Section (RCS) is expressed in terms of the physical size of an hypothetical uniformly scattering sphere that would give rise to the same level of reflection as that observed from the sample target.","name":"Radar cross-section","selfAssesment":"New
"},{"code":"PP2-2-5-1","description":"A material constant is a physical or chemical property of a substance, which can be expressed in numbers. Giving a precise numerical value of a constant often requires determining the external conditions (e.g. temperature, humidity). Material constants are factors that influence the interaction of microwaves with the target objects.","name":"Material constants","selfAssesment":"New
"},{"code":"PP2-2-5-2","description":"The complex part nc of the refraction index n determines how far an electromagnetic wave of wavelength λ can survive crossing a specific medium. The attenuation length la is the distance after that the amplitude of an electromagnetic signal reduces its value by an amount of 1/e. For instance the amplitude of the Electric field E(z) of an electromagnetic wave proceeding along the z direction is decreasing as exp(-z/la) being la=λ/(2𝜋 nc) the attenuation length associated to that specific material (nc) and wavelength λ. This way attenuation length in water can be of hundreds of meters in the visible range and just few microns in the microwaves. The opposite happens over solid land surfaces where optical waves can penetrate from few microns up to few millimeters (moving from the VIS-NIR to the TIR spectral range) whereas microwaves can reach depths from hundreds to towsands (as higher are their wavelength) meters allowing the exploration of subsoil and thick coulters of ice.","name":"Attenuation lenght and penetration depth","selfAssesment":"Completed
"},{"code":"PP2-2-5-3","description":"Soil permittivity is a measure of the water content (soil moisture) in the soil and characterized by the metric of the dielectric constant of the soil. Soil moisture influences emission, absorption and propagation of microwave electromagnetic energy. Moisture decreases the ‘emissivity’ of soil, and thereby affects microwave radiation emitted from Earth’s surface. Dry soil has a low dielectric constant and low radar reflectivity. Moist and partially frozen solis have intermediate values. The higher the soil water content, the lower the radar signal penetration into the soil. In situ measurements of soil permittivity are a prerequisite for the calibration and validation of synthetic aperture radar (SAR) soil moisture retrieval algorithms. Soil moisture is a key variable in the hydrologic cycle and is recognized as an Essential Climate Variable (ECV).","name":"Soil permittivity","selfAssesment":"Completed
"},{"code":"PP2-2-5-4","description":"The permittivity of a plant is a function of its contained amount of water in all plant compartments (including roots).","name":"Plant permittivity","selfAssesment":"In progress
"},{"code":"PP2-2-5","description":"The electric properties of different materials can be described by two quantities: relative dielectric constant (complex permittivity) and loss tangent. Reflectivity of a smooth surface and the penetration of microwaves into the material are determined by these two quantities. The complex dielectric constant changes mainly due to the change in water content.","name":"Dielectric Properties","selfAssesment":"New
"},{"code":"PP2-2-6-1","description":"The standard deviation of the surface height variation (or RMS height), denoted by s (or hRMS), describes the statistical variation of a random surface with height z(x). In case of an azimuthally symmetrical surface, the single-scale RMS height of the one dimensional case for discrete profile values is given by (1), where N is the number of samples, and z ̅ the mean surface height (2). \r\nAs roughness depends not only on the soil surface properties but also the wavelength λ of the electromagnetic signal, the roughness parameters are scaled by the wave number k. Hence, the electromagnetic roughness ks for surface roughness parameter s is (2π/λ)*s (3). In order to determine if a random surface may be considered as electromagnetically smooth, one common definition is given by the Rayleigh roughness criterion, where s < λ / 8*cosθ, or ks < 0.8, at incidence angle θ = 0. This criterion has been revised for the microwave region, where the wavelength is usually of the order of the RMS height, called the Fraunhofer roughness criterion, where s < λ / 36*cosθ, or ks < 0.2, at incidence angle θ = 0. Additionally, surfaces are considered as electromagnetically rough for 1 < ks < 3.","name":"Vertical roughness component (RMS height)","selfAssesment":"Completed
"},{"code":"PP2-2-6-2","description":"The surface correlation length, denoted by l, is defined as the displacement ξ at which the surface correlation function p(ξ)= 1/e. Thus, l can be seen as the reference length up to which two points of one soil surface can be regarded as statistically independent from each other. If we imagine a perfectly smooth soil surface, l=∞ since every point on that surface correlates with all other points and can therefore be regarded as dependent from each other.\r\nAs roughness depends not only on the soil surface properties but also the wavelength λ of the electromagnetic signal, the roughness parameters are scaled by the wave number k. Hence, the electromagnetic roughness kl for surface roughness parameter l is kl=(2π/λ)*l.\r\nExperimental results indicate a weaker influence on the radar backscatter compared to the RMS height s.","name":"Horizontal roughness component (correlation length)","selfAssesment":"Completed
"},{"code":"PP2-2-6-3","description":"The surface correlation function p(ξ) determines the degree of correlation between two lateral separated locations of one surface. Thereby, ξ is defined as displacement between two locations, (x, y) and (x', y') on the surface and given by (1).\r\nWith increasing separation between two locations on the surface p(ξ) decreases, and at a certain distance, the surface correlation length l, the heights at the two locations are considered statistically uncorrelated.\r\nThe surface scattering of electromagnetic waves can be simulated with various models. Depending on the observed roughness scale multiple surface scattering models are valid for specific roughness conditions. For example, one of the first surface scattering models for slightly rough surfaces, the small perturbation model (SPM), deals with roughness scales that are small relative to the wavelength and hence has validity conditions for ks < 0.3, kl < 3, and m < 0.3. Since then, various surface scattering models for computing the scattering and emission behavior of natural surfaces in the microwave region have been proposed, such as the Kirchhoff scattering model (KH), the geometric optics model (GO), the physical optics model (PO), or the integral equation model (IEM), to name the most common used in literature. For simulations of EM scattering at soil surfaces, assumptions of the functional forms of p(ξ) have to be made. The two most common forms for mathematically describing the surface correlation of natural surfaces are the exponential pE(ξ) and the Gaussian pG(ξ) correlation functions, defined by (2) and (3).\r\nFor some mathematically sophisticated surface scattering models, an x-Power correlation function p(x-Power)(ξ) can be assumed (4), with x as value between 1 and 2.\r\nIn literature, rather smooth surfaces are characterized by an exponential surface correlation function, while rather rough surfaces are characterized by a Gaussian surface correlation function.","name":"Surface correlation function","selfAssesment":"Completed
"},{"code":"PP2-2-6-4","description":"The root-mean-square (RMS) slope m of a one dimensional height profile for one random surface is given by (1), with s as the standard deviation of the surface height variation (or RMS height), and p''(0) as the second derivative of the surface correlation function p(ξ), evaluated at ξ=0. Since p(ξ) is an even function, p''(0) is a negative quantity.\r\nFor modeling of electromagntic scattering at soil surfaces, assumptions of the functional forms of p(ξ) have to be made. The most common known forms are the exponential and Gaussian correlation functions. Additionally, some models allow the assumption of a x-Power correlation function, with x as value between 1 and 2. For the varying surface correlation functions, the RMS slope m is given by (2)-(4).\r\nIn literature, for L-band, the slope m should be lower than 0.3 or 0.4 in case of single scattering and bare soil surfaces with moderate RMS heights.","name":"Surface roughness slope","selfAssesment":"Completed
"},{"code":"PP2-2-6-5","description":"In reality, one random surface has multiple roughness scales, since the commonly used surface description based on single-scale roughness parameters does not comprise all the properties of natural surfaces relevant for describing wave scattering. Depending on the wavelength λ of the microwave sensor the dimension of the surface roughness parameters s and l correspond to specific roughness scales. \r\nIn case of multi-scale roughness, the equivalent RMS height is a composite of the individual RMS heights at different roughness scales (1).\r\nA three-scale surface, as shown in Fig. 1, for example consists of a small-scale high-spatial frequency variation (c) ‘riding’ on top of the larger scales, the medium-scale perturbation (b) and the large-scale undulation (a).\r\nAt microwave frequencies, the centimeter scale is the scale of roughness of primary importance, since λ is on the order of centimeters to a few tens of centimeters. For natural surfaces it is very difficult to measure millimeter-scale roughness.","name":"Single-scale & multi-scale roughness","selfAssesment":"Completed
"},{"code":"PP2-2-6","description":"Surface roughness defines the geometry between the pedosphere and the atmosphere (soil-air boundary).\r\nIn the field of microwave remote sensing, surface roughness affects scattering and emission characteristics of natural surfaces. The degree of roughness of a random surface is determined by statistical parameters, measured by the units of wavelength of the observing sensor. The two fundamental surface roughness parameters are the standard deviation of the surface height variation (RMS height) s, with its related surface correlation function p(ξ), and the horizontal surface correlation length l. Additional, a third roughness parameter, the root-mean-square (RMS) slope m, is important for some surface scattering models to simulate electromagnetic wave scattering of surfaces.\r\nSurface roughness determines the variation of surface height within an imaged resolution cell. The transition from smooth to rough is qualitative, and is function of both wavelength and incident angle. With decreasing frequency the soil surface appears rather smooth to microwave sensors. This results in the fact, that while one surface appears smooth when sensed at L-band (λ ≈23 cm), the same surface appears rough when sensed at X-band (λ≈3 cm). Hence, in the field of microwave remote sensing, the ‘effective’ surface roughness parameters are scaled by the wave number k= 2π/λ. Surface roughness can be observed at single or multi-scale.","name":"Surface roughness","selfAssesment":"Completed
"},{"code":"PP2-2-7-1","description":"The Stokes vector is a four-element vector containing real-valued polarization combinations and is an alternative form of representing a full (=quad) polarimetric dataset, besides the complex-valued scattering matrix. Stokes vectors can be measured as real quantities and are preferred over the complex-valued Jones vector formalism when a coherent (phase-preserving) measurement system is absent. Stokes vectors can be used to form the 4x4 Mueller matrix for target scattering analyses, mostly used in the field of optics. First component of the Stokes vector is the sum of the co-polar fields and represents the total energy of the wave. Second component is the difference of the co-polar fields. Thrid component is the real part of the cross-correlation of the fields and fourth component is the imaginary part of it. The different polarization states can be represented by the Stokes vector and an O(3) elliptical transformation can be used to change the polarization basis, similar to the Jones vector where the SU(2) elliptical transformation is used.","name":"Stokes Vector","selfAssesment":"Completed
"},{"code":"PP2-2-7-2","description":"The scattering matrix is a 2x2 square matrix containing four complex-valued polarization measurements (amplitude & phase) forming one full (= quad) polarimetric set of coherent observations. An often recorded set of polarizations is the combination: HH (horizontal receive - horizontal transmit), HV (horizontal recive - vertical transmit), VH (vertical receive - horizontal transmit) & VV (vertical receive - vertical transmit). The scattering matrix is fully suficient for describing scattering from coherent targets (dominating the resolution cell), but not for incoherent tragets (mix of scattering contributions in the resolution cell). For the latter, the coherency and the covariance matrices are the more appropriate descriptions of scattering from incoherent targets.","name":"Scattering matrix","selfAssesment":"Completed
"},{"code":"PP2-2-7-3","description":"The covariance and coherence matrix are two 4x4 square matrices, which can be built out of the scattering matrix by a lexicographic and a Pauli target scattering vector. They are an alternative representation of a full polarimetric dataset allowing the analysis of incoherent targets (more than one dominant scatterer in the resolution cell) and the phenomenon of depolarisation (transformation of incoming fully polarised wave into a partially polarised wave by creating a variety of different types of polarizations during media interaction). These matrices can be converted into each other without loss of information (by unitary transformations), but not turned back into the scattering matrix due to averaging operations during formation of coherency or covariance matrices.","name":"Covariance/Coherency matrices","selfAssesment":"Completed
"},{"code":"PP2-2-7-4","description":"Polarimetric decomposition techniques allow signal unmixing by polarimetry in order to separate different scattering contribution within one resolution cell, e.g. from soil & vegetation or snow, ice & bedrock. They can be either applied for the scattering matrix (coherent form - one dominant scatterer in the resolution cel) or for the covariance/coherency matrix (incoherent form - more than one dominant scatterer in the resolution cell). Decomposition techniques can be model- (physics) or eigen- (mathematics)-based. The eigen-based decomposition allows to diagonalize the coherency or covariance matrix in a diagonal eigenvalue matrix and a matrix of column eigenvectors. From eigenvalues and eigenvectors the polarimetric entropy, the scattering alpha angle and the polarimetric anisotropy. The polarimetric entropy is a matric for the degree of depolarization of the scattering event. The scattering alpha angle is an intrinsic scattering mechanism indicator. The polarimetric anisotropy informs about secondary scattering mechanism in evironments with high entropy. If the anisotropy is high only one secondary scattering mechanism is present, if it is low, more than one will occur.","name":"Polarimetric decomposition techniques","selfAssesment":"Completed
"},{"code":"PP2-2-7-5","description":"All bi- or multi-polar (non-inert) media have the tendency to orient themselves if an external field is excited. This orientation polarization is caused by negatively and positively charged areas within the media under the premise the media is able to rotate freely. Molecules of liquid water are a prime example.","name":"Orientational polarisation of media","selfAssesment":"In progress
"},{"code":"PP2-2-7-6","description":"Polarimetric coherences are complex-valued polarimetric correlation coefficients assessing the redundance between different polarimetric observations informing about their divergence in information. They can be formed among mutual polarimetric observations showing their degree of correlation. The polarimetric coherence consists of a magnitude, ranging between zero (no correlation) and one (identical), and a phase information, running from -180° to 180°. Typically polarimetric coherences are calculated between the co-polarimetric (HH, VV) channes, as well as the cross-polarimetric channels (HV, VH). The latter polarimetric coherence assesses the system noise inherent in the recorded polarimetric data, if a monostatic systems (transmitting and receiving sensor on the same sensing platform) is used for acquisition.","name":"Polarimetric coherences","selfAssesment":"Completed
"},{"code":"PP2-2-7-7","description":"The polarisation ellipse and the Jones vector formalism are the geometrical (three real-valued angles) and algebraic (amplitude & phase) formalisms to describe polarisation states of an electromagnetic wave. The ellipse has an orientation, an ellipticity and absolute phase angle. The three angles are integrated in one mathematical ellipse formulation that can represent linear, elliptic and circular polarisation states. The Jones vector formalism is an algebraic formulation allowing all calculus available in linear algebra. Both representations (polarisation ellipse & Jones vector) can be converted into each other seemlessly with a simple elliptical basis (special unitary SU(2)) transformation.","name":"Polarisation ellipse / Jones vector formalism","selfAssesment":"Completed
"},{"code":"PP2-2-7-8","description":"The concept of polarisation synthesis is based on the mathematical fact that a set of polarimetric measurements in one basis, e.g. H,V, can be converted into any other polarimetric basis, by a mathematical transformation. A basis set is a set of four polarisations. Each set is orthogonal, like LC (left-circular), RC (right-circular). The striking point is that only one set of polarimetric measurements in one basis needs to be recorded and the transformation in other polarimetric bases is done in a post processing step afterwards. There is no need to measure all bases, which is quite complicated in terms of engineering for elliptical and circular polarisation states.","name":"Polarisation synthesis","selfAssesment":"Completed
"},{"code":"PP2-2-7","description":"Polarimetry is the technique to evalute the physical phenomenon of polarisation including the measurement, the processing and the interpretation of the polarisation state of an electromagnetic wave. Polarization states are described by the scattering elipse and the Jones Vector formalism. Especially the polarization states after interaction with the media under investigation are mostly investigated to estimate media properties and states. The mostly observed fully polarimetric observation basis is H,V up to now with the single observations: HH HV, VH, VV. The concept of polarization synthesis allows to acquire fully polarimetric observations in one basis (e.g. H,V) and transform them into any other orthgonal basis (e.g. left, right circular) by a mathematical transformation in post processing. Polarimetric States are stored in different mathematical formats: Scattering matrix, polarimetric coherences , Stokes vector, Pauli-vector, lexicographic vector, coherency and covariance matrices. These mathematical representations can be decomposed according to the contained elementary scattering mechanisms in the recorded signal. The so-called polarimetric decomposition technique allow signal unmixing for differnt scattering components (e.g. from soil & vegetation). The techniques range from mathematics-based until physics-based concepts and are developed since decades starting with Huynen in 1970.","name":"Polarimetry","selfAssesment":"Completed
"},{"code":"PP2-2","description":"A number of interactions are possible when electromagnetic energy encounters matter, whether solid, liquid or gas. In Earth Observation there are two main interactions: atmospheric and with target. \r\nAtmospheric interaction:\r\nIn radar remote sensing, atmospheric interactions are limited due to the long wavelengths compared to the size of the atmospheric particles. Water clouds can interfere with the radars operating below 2 cm in wavelength. The effects of rain can be generally ignored at wavelengths above 4 cm. For longer wavelengths (above 20 cm), an effect called Faraday rotation caused by the ionosphere, i.e., free charges (electrons) and the Earth’s magnetic field, can lead to a rotation of the polarization plane.\r\nTarget interaction:\r\nThe radar interaction with the object is a result of both radar system parameters (frequency, polarization, acquisition geometry) and the object physical properties (dielectric constant, i.e., water content; geometrical properties, i.e., the roughness, shape and orientation of the scatterer).","name":"Interaction of microwaves with matter","selfAssesment":"New
"},{"code":"PP2-3-1-1","description":"The goal of an radar antenna is to direct and receive the transmitted and backscattered signal in a specific angular direction. The antenna gain describes the directional sensitivity of the antenna. It is a dimensionless quantity that is constant for a specific antenna.","name":"Antenna gain","selfAssesment":"New
"},{"code":"PP2-3-1-2","description":"The antenna radiation pattern shows the direction in which the antenna transmits and receives the energy in space, as well as the strength of this radiation. It is a function of angles and consists of different lobes, in which the signal is directed and received. There are two principal representation of the antenna patterns: field and power patterns, which are a function of the electric and magnetic fields of the energy being radiated.","name":"Antenna pattern","selfAssesment":"New
"},{"code":"PP2-3-1","description":"Antenna is a device that radiates electromagnetic energy and collects such energy during reception.","name":"Radar antennas and antenna calibration","selfAssesment":"New
"},{"code":"PP2-3-10-2","description":"The radargrammetric equation follows a similar principle as the stereoscopic equation, except that it uses the radar geometry. The radargrammetric observation equation allows the retrieval of 3D information about a target, based on the determination of the sensor-object stereo model. It estimates the coordinates the intersection of the two radar rays coming from the two different sensor positions with different look angles, using the coordinates of the satellites position and satellite velocity. The radargrammetric equation can be adapted in order to retrieve 3D information in layover areas (e.g. urban areas).","name":"Radargrammetric equation","selfAssesment":"Planned
"},{"code":"PP2-3-10","description":"Radargrammetry is the technique for extracting three-dimensional information from radar images. It applies photogrammetric principles to synthetic aperture radar (SAR) images. By viewing an object from different positions separated by a baseline, the appeared object position will vary slightly (denoted parallax). The disparities for each position on the object are related to its x-y-z coordinates. In radargrammetry, such disparities are computed for an entire image. The result is the terrain elevation from the measured parallaxes between two (or more) images, acquired at different angles. Radargrammetry requires at least two SAR images acquired from different positions, normally across-track due to the configuration of a side-looking SAR. Same-side stereo-pairs with intersection angles in the range of about 10 – 20° have been a feasible compromise between reasonable geometric disparities and the accuracy of estimated heights. In general, the disparities can be estimated with higher accuracy as the angle of intersection increases (as the stereo exaggeration factor increases). However, the same points must be recognized in all images, and it is hence required that the images are as similar as possible. This improves the image matching and it is best achieved with small intersection angles, which furthermore decreases radiometric differences. \r\nA general procedure for generating an elevation model from stereo-pairs is applicable for radargrammetry when optical stereo images are replaced with the backscatter intensity of SAR images. One image is selected as reference and the other(s) is coarsely registered to the reference, e.g., by using the attached meta-data. The same points are then located in both images using image matching. A common matching criterion is the cross correlation coefficient. Then, spatial point intersections are computed, which is the least square approach to find the intersection points of SAR range circles as defined from the matched image pixels. The computed intersections result in a point cloud that finally is interpolated to a consistent elevation raster. The entire process is extensive and computationally expensive, and normally a dedicated software is required. \r\nRadargrammetry with images acquired from opposite sides have been little investigated, and was first limited to stereoscopic viewing. Some opposite-side research was later presented with limited outcomes under certain conditions. Most applications today will not consider opposite-side radargrammetry, since the alternatives are usually better. Same-side radargrammetry performs better than opposite-side, while interferometric SAR that is based on phase differences, may be even more accurate. One advantage of radargrammetry is however, that it remains less affected by atmospheric disturbances compared to interferometric SAR, because it is using the amplitude images.","name":"Radargrammetry","selfAssesment":"Completed
"},{"code":"PP2-3-11-1","description":"Differential Synthetic Aperture Radar Interferometry (DInSAR) aims the determination of deformation of the Earth’s surface that happened between two or more complex-valued SAR acquisitions.\r\nThe phase of an interferogram issued from the complex multiplication of a SAR image with the complex conjugate of a second SAR image contains five distinct components, or layers of information:\r\n\tTwo phase components arise from the geometrical baseline (slightly different position of both sensor positions):\r\n\ta topographical information representing the surface relief, \r\n\ta “flat earth” pattern coming from the orbital distance of both sensor positions.\r\n\r\n\tTwo phase components result of the temporal baseline (time between both acquisitions):\r\n\ta deformation component, representing a possible displacement of the Earth’s surface between both acquisitions,\r\n\tan atmospheric component coming from different atmospheric conditions between both acquisitions.\r\n\r\n\tA phase component corresponding to intrinsic sensor noise \r\n\r\nBoth parameters related to the temporal baseline can be retrieved using DInSAR on repeat-pass acquisitions. DInSAR cannot be used with single-pass interferometry (e.g. both acquisitions acquired at the same time).\r\nThe deformation component of the interferometric phase corresponds to the modification of the phase of the second SAR image compared to the first due to an additional range difference between the sensor position and the Earth’s surface that is induced by the motion of the Earth’s surface towards or away from the initial sensor position.\r\nUsing DInSAR, the phase components related to the geometrical baseline can be eliminated from the interferogram using an existing DEM and orbit information, or an additional interferogram showing no deformation. After DInSAR processing, neglecting the remaining sensor noise, only the deformation and atmospheric components remain. The resulting deformation image is called differential and is characterized by color bands, or fringes, from whom the amount of the displacement can be retrieved. \r\nDInSAR can be used for mapping displacements and deformations due to earthquakes, landslides, or other geophysical processes inducing deformation of the Earth’s surface.\r\nUsing only one differential interferogram, mainly sudden and large scale changes between two acquisition can be mapped and quantified. However, the atmospheric phase component remains and may induce interpretation errors if it is not possible to eliminate it through e.g. precise weather models. Techniques of differential interferogram stacking (e.g. Persistent Scatterer Interferometry and Small-Baseline Subset) have been developed for long-term deformation monitoring which allow to filter the atmospheric phase component out.","name":"Differential Synthetic Aperture Radar Interferometry (DInSAR)","selfAssesment":"Completed
"},{"code":"PP2-3-11-2","description":"The Permanent or Persistent Scatterer (PS) approach allows the estimation of deformation time-series related to point-wise, high coherent scatterers on the ground based on processing long sequences of SAR data.\r\nPersistent Scatterer Interferometry (PSI -sometimes also called Permanent Scatterer Interferometry) is a particular DInSAR technique. It exploits multiple SAR images acquired over a specific area in order to retrieve the deformation phase component over time. In general, a minimum number of 15 SAR acquisitions is needed for PSI processing. Due to the large number of necessary acquisitions, the deformation component of the interferometric phase observations can be estimated very precisely (in the order of a few mm/yr) and other phase contributions such as atmospheric disturbances and topographic height differences can be better estimated and removed.\r\nPSI rely on so called Persistent Scatterer that are targets showing coherent phase behavior in time. Such targets are usually found on man-made structures such as buildings or bridges, or very stable features such as rocks. PSI is a technique that is therefore mainly used over urban or semi-urban terrain. Usually, PSs are selected based on their amplitude and phase power spectrum stability over time.\r\nThe main outcomes of a PSI analysis are a deformation velocity map and the displacement time-series of the single point targets, or PSs. The velocity map represents the deformation rate of the detected PSs in Line-of-Sight of the sensor, generally in mm/yr. Usually, subsidence, e.g. target moving away from the sensor, is represented in red, stable PSs in green and uplift, e.g. PSs moving toward the sensor in blue. The displacement time-series show for each PS the amount of the deformation, usually in mm, over the whole period of observation. Different phase model can be defined in order to retrieve the best possible estimate of the deformation, considering also seasonal displacements or breakpoints in the time-series.\r\nPerforming PSI analysis in both ascending and descending directions allows the fusion of the results in order to retrieve vertical and East-West component of the deformation. North-South deformation components cannot be retrieved due to the orbit configuration of the SAR satellites.\r\nPSI finds use in a large range of thematic applications related to subsidence and long-term change monitoring, such as infrastructure monitoring, groundwater reservoir monitoring, monitoring of mining areas, landslide inventory and monitoring, as well as volcanology.","name":"Permanent Scatterer Interferometry","selfAssesment":"Completed
"},{"code":"PP2-3-11-3","description":"Along-track InSAR (AT-InSAR) is a special mode of interferometric SAR (InSAR) where the individual SAR images have been acquired from the same flight track. With virtually identical geometric configuration of the individual SAR images, the measured phase difference is dominated by temporal changes occurring between the acquisitions. Consequently, AT-InSAR can be used to measure the displacement and/or radial velocity of targets on the ground, with the temporal offset between the acquisitions determining the time scale of the measurements. AT-InSAR can be implemented using one or more SAR sensors, in both single-pass and repeat-pass configurations, accommodating various needs. Using at least two sensors in a single-pass configuration allows the measurement of relatively high velocities, e.g., for vehicles and ocean waves. Conversely, using at least one sensor in a repeat-pass configuration allows the measurement of low velocities or displacements, e.g., for glaciers and due to volcanoes, earthquakes, subsidence, and landslides.","name":"Along-Track Interferometry","selfAssesment":"Completed
"},{"code":"PP2-3-11-4","description":"Across-track InSAR (XT-InSAR) is a special mode of interferometric SAR (InSAR), where the individual SAR images have been acquired from slightly different look directions. The measured phase difference contains information about the elevation of the targets on the ground, but it can also be affected by temporal changes between the individual SAR images. XT-InSAR can be implemented using one or more SAR systems in both single-pass and repeat-pass configurations. To mitigate temporal change between acquisitions, the XT-InSAR configuration is selected based on the intended application and frequency used by the system. If a single SAR sensor is used in the repeat-pass mode, temporal stability can be achieved either by a selecting a lower frequency and focussing on the larger, more stable targets (e.g., P-band, 435 MHz InSAR in forests) or by selecting a higher frequency and focussing on already stable environments (e.g., X-band, 9.65 GHz XT-InSAR in urban environments). Using two or more SAR sensors in a single-pass, tandem configuration, it is possible to measure elevation of temporally instable targets using higher frequencies, as demonstrated by the SRTM and TanDEM-X systems over vegetated areas and ocean.\r\nReferences: bamler/hartl, one on SRTM or TDM for DEM, one on BIOMASS for forestry, one on Sentinel-1 for urban areas, one on TDM on vegetation","name":"Across-Track Interferometry","selfAssesment":"Completed
"},{"code":"PP2-3-11-5","description":"Small Baseline Subset (SBAS) is a well-known technique of differential synthetic aperture radar (SAR) interferometry for the generation of surface deformation time-series by processing large sequences of SAR data acquired over the same region on Earth. \r\nThe method requires the preliminary generation of pairs of SAR images collected by slightly different orbital positions at different acquisition times. The phase difference of the interferometric SAR data pairs is extracted. The two-dimensional phase maps contains different contributions, but principally a component due to the terrain height of the observed area. The DInSAR technique relies on the estimation of the deformation of the terrain between the two interfering SAR images (i.e., the so-called master and slave images). To achieve this task, the phase contribution related to the terrain height is simulated and subtracted to the interferometric master/slave phase difference. The obtained differential SAR interferometric phase contains a direct information on the occurred deformation. Once a sequence of interferometric SAR data pairs is selected, the SBAS technique allows generating the time-series of the deformation of the terrain. The processing steps are essentially: i) the extraction of the full phase of the DInSAR interferograms, i.e., the phase unwrapping steps of the DInSAR interferograms, ii) the inversion of the sequence of unwrapped DInSAR phases, iii) the geocoding of the deformation maps from radar coordinates to geographical coordinates.","name":"Small Baseline Subset","selfAssesment":"Completed
"},{"code":"PP2-3-11","description":"Synthetic aperture radar (SAR) interferometry, or simply InSAR, is a remote sensing technique utilising the phase difference between two or more complex-valued SAR images. Most modern SAR systems are capable of measuring both the intensity and the phase of the reflected signal, where the latter carries information about the distance travelled by the signal. Consequently, the phase difference measured between two SAR images is determined by the geometry and timing of the individual SAR acquisitions. Different geometric and temporal configurations enable different applications. If the SAR acquisitions are made from different angles and without significant temporal change of the scene, InSAR can be used to create digital elevation models (DEMs) of the Earth, as demonstrated by the NASA/JPL Shuttle Radar Topography Mission (SRTM). If the individual SAR acquisitions are made at different times in the same geometric configuration, then InSAR can be used to measure radial velocity of targets and to assess displacements caused by, e.g., volcanoes and earthquakes.","name":"Principles of Interferometry","selfAssesment":"Completed
"},{"code":"PP2-3-12","description":"SAR tomography uses the principle of the azimuth synthetic aperture in the elevation direction. It exploits multiple passes of the radar sensor at different orbit positions (orbits heights) in order to retrieve 3D information about volumetric targets, where the 2D SAR signals often overlaps due to the typical side-looking geometry.","name":"Synthetic Aperture Radar (SAR) tomography","selfAssesment":"New
"},{"code":"PP2-3-13","description":"With this concept active and passive microwave imaging techniques are combined to record electromagnetic waves in an active (sending & receiving) and a passive (only receving) mode either simultaneously or with negigible time lag.\r\nThe active sensor is normally a Real Aperture Radar (RAR) or Synthetic Aperture Radar (SAR), while the passive sensor is a radiometer or synthetic aperture radiometer. Both acquisition modes can be operated on a single platform or on different platforms.\r\nSatellite missions with active-passive imaging capabilities are the NASA missions AQUARIUS and SMAP.","name":"Active-Passive microwave imaging","selfAssesment":"Completed
"},{"code":"PP2-3-2","description":"Systems measuring both amplitude and phase of the incident electromagnetic radiation.","name":"Coherent and active systems","selfAssesment":"New
"},{"code":"PP2-3-3","description":"This acquisition mode records only the incoming electromagnetic radiation emitted from the Earth. Radiometer instruments conduct passive microwave imaging. The energy budget of emitted radiation (from Earth) is significantly smaller than from instrument-generated, transmitted electromagnetic waves, used in the active microwave imaging mode. Hence, the signal to noise ratio is significantly worse for passive microwave imaging forcing a longer intergration time for robust signal recording. This results in a coarse spatial resolution of radiometer images (in the order of kilometers).","name":"Passive microwave imaging","selfAssesment":"Completed
"},{"code":"PP2-3-5","description":"Side-looking airborne radar (SLAR) is a high-resolution real aperture radar (RAR) or a synthetic aperture radar (SAR) having antennas aimed to the right or left of the flight path. It operates in a side-looking configuration, left or right with reference to the flight direction. It is an active, all-weather, day/night remote sensor onboar an airborne platform.","name":"Side-looking Airborne Radar","selfAssesment":"New
"},{"code":"PP2-3-6","description":"There are two types of imaging radar apertures: real (usually called RAR or SLAR for side-looking airborne radar or SLR for side-looking radar) and synthetic aperture radar (SAR). The SLAR imaging system uses a long antenna mounted on a platform. The synthetic aperture is used in space remote sensing applications. In contrary to a real aperture, a synthetic aperture results from an aperture “synthesis”. Synthetic aperture were built in order to overcome the limitation of real aperture and therefore enhance the resolution in azimuth direction. It uses the subsequent positions of a real aperture sensor during its forward motion along the azimuth direction to create a synthetic longer antenna. Via the analysis of the Doppler shift induced by the different echoes of the illuminated objects in the different positions of the real aperture, the azimuth resolution can be improved.","name":"Synthetic Aperture Radar (SAR)","selfAssesment":"Planned
"},{"code":"PP2-3-7-1","description":"The azimuth direction corresponds to the flight direction of the Synthetic Aperture Radar (SAR) sensor. It is also called along-track direction since it follows the line-of-flight. It is perpendicular to the range direction.","name":"Azimuth direction","selfAssesment":"New
"},{"code":"PP2-3-7-2","description":"The range direction corresponds to the direction perpendicular to the flight direction. It is also called across-track direction. One distinguishes between slant and ground range, and between near and far range (situated farther away from the sensor and showing shallower looking angle than in near range due to viewing geometry).","name":"Range (far and near) direction","selfAssesment":"New
"},{"code":"PP2-3-7-3","description":"The incidence angle is the angle between the radar beam and the normal to the surface at target location. For a flat surface and neglecting the Earth’s curvature, the incidence angle equals the look angle of the sensor, which characterizes the angle between the nadir view and the radar beam.","name":"Incidence Angle","selfAssesment":"New
"},{"code":"PP2-3-7-4","description":"The beam sent out by the radar antenna (SLAR for side-looking airborne radar or SLR for side-looking radar) illuminates an area on the targeted object. The footprint of an antenna is traditionally defined to be the area on the surface within the field of view subtended by the beamwidth of the antenna gain pattern.","name":"Antenna footprint","selfAssesment":"New
"},{"code":"PP2-3-7-5","description":"The spatial resolution of a synthetic aperture radar (SAR) system is the maximal distance between two targets, which are indistinguishable in the SAR image. SAR spatial resolution is determined individually in the two principal SAR image directions: ground range and azimuth (along-track). Ground range resolution for a SAR system is derived from slant range (across-track) resolution, by projecting it onto the ground surface using the incident angle, i.e., the angle between the line-of-sight and the ground surface normal. It is thus range-dependent, with finer resolution available in far range. Assuming adequate signal processing, slant range resolution of a SAR system is proportional to the speed of light and inversely proportional to the system bandwidth, i.e., the width of the used frequency interval. This caused by the fact that each individual frequency provides an independent measurement of the slant range, so a larger bandwidth implies more independent measurements contributing to the final slant range estimate. Similar principles apply to the azimuth direction. Assuming adequate signal processing, the SAR azimuth resolution is proportional to the along-track velocity of the SAR sensor and inversely proportional to the pulse repetition frequency (PRF) of the system. A lower interval between the consecutive pulses (higher PRF) results in better azimuth resolution due to faster sampling, but at the cost of range ambiguities occurring when echoes from one pulse are recorded after the next pulse has been transmitted.","name":"Synthetic Aperture Radar (SAR) spatial resolution","selfAssesment":"Completed
"},{"code":"PP2-3-7","description":"The Synthetic Aperture Radar (SAR) sensor is usually mounted on an aircraft or satellite. The instrument altitude above a reference surface stays constant over time, a condition that is easier to achieve for satellite sensors that stay on the same orbit than for aircrafts that are subject to atmospheric conditions. The sensor moves on a straight flight path, which is called the azimuth direction. It corresponds to the flight direction.\r\nSAR systems acquire information in oblique view, the antenna pointing sideways down to the ground. Most satellite systems use an antenna looking to the right side of the instrument. The ground area illuminated by the radar beam is called antenna footprint. As the sensor moves along the azimuth direction (along-track), the continuous strip of the ground area represented by the successive antenna footprints is called swath. \r\nThe looking direction of the SAR antenna is called range direction. It is often perpendicular to the azimuth direction (i.e. across-track), but can also present slightly differences depending on the acquisition mode. The angle between the nadir view and the range direction is called incidence angle.\r\nThe original SAR image is displayed in what is called slant-range geometry, i.e., it is based on the actual distance from the radar to each of the respective features in the scene. In the slant range direction, each point target’s backscatter is represented as a function of the time delay between the transmission of the electromagnetic pulse and its reception back at the sensor. This range depending representation induces geometric distortions in the SAR image. One distinguishes between near and far range: targets situated in near range are closer to the nadir direction and closer to the sensor than targets situated in far range. The image representation of targets is also more compressed in near range than in far range.\r\nThe slant-range representation can be converted in ground range representation, by projecting the image features orthogonally to a ground reference, allowing a proper planimetric position of the targets relative to one another.\r\nThis acquisition geometry allows the distinct mapping of scatterers corresponding to their respective distance to the sensor. It causes also geometric distortions in the radar image, i.e., relief displacement (foreshortening and layover) and shadow.","name":"Synthetic Aperture Radar (SAR) geometric configuration","selfAssesment":"Completed
"},{"code":"PP2-3-8-2","description":"The local incidence angle is the angle between the incident radar wave and the normal to the scattering surface at target location. In case of a flat terrain, the local incidence angle equals the incidence angle. For a terrain with local slope, the local incidence angle differs from the incidence angle (for slopes facing towards the sensor, it is smaller than the incidence angle).","name":"Local Incidence Angle","selfAssesment":"Planned
"},{"code":"PP2-3-8-3","description":"Foreshortening is an effect occurring principally in SAR images of mountainous areas, on slopes oriented towards the sensor when the distance between two points appears smaller than it would in flat areas due to the side-looking geometry. This results in a compression of the radiometric information. The area appears brighter.","name":"Foreshortening","selfAssesment":"Planned
"},{"code":"PP2-3-8-4","description":"Layover appears on very steep slopes, when the distance from the sensor to targets in the valley is larger than to the related mountain tops. The ordering of surface elements on the radar image is the reverse. This effect produces very bright features on the image.","name":"Layover","selfAssesment":"Planned
"},{"code":"PP2-3-8-5","description":"Radar Shadow occurs on slope facing away from the sensor, if the slope angle is steeper than the sensor incidence angle. Shadow regions appear dark in the image, as no signal reaches them. Only small backscatter changes are principally due to system noise.","name":"Shadow","selfAssesment":"Planned
"},{"code":"PP2-3-8","description":"Synthetic Aperture Radar (SAR) backscatter is determined both by dieletric and geometric properties of the illuminated target. While the water content of the target plays an important role, its surface roughness determines the scattering mechanisms and the amount of incoming signal sent back to the sensor.\r\nDepending on its characteristics but also on the considered wavelength, a surface appears more or less rough. On smooth surfaces, specular reflection occurs, meaning that most of the incoming signal will be reflected away from the sensor. For rough surfaces, diffuse reflection occurs, meaning that part of the signal is scattered back to the sensor, the amount of it depending on different surface roughness parameters. \r\nDepending of the observed target and surface, single or multiple scattering mechanisms occur. A particularly important scattering mechanism is the double bounce, which occurs generally at two perpendicular surfaces (e.g. ground and building wall). Through two successive specular reflections, the whole signal comes back to the sensor.\r\nDue to the side-looking geometry of SAR systems and the range dependent image representation, specific additional effects occur and affect the backscatter intensity. Whereas a flat terrain only appears more compressed in near range and more stretched in far range, larger geometric distortions appear for terrain with more topography (e.g. mountains) or high objects (e.g. trees, buildings). This relief displacement is caused by the target’s elevation. A high elevated object is closer to the sensor than the ground below it. Due to the image formation in range direction depending on the distance between sensor and targets, its signal comes back sooner to the sensor and it is represented in the SAR image in nearer range than the ground below it. High objects in the SAR image are therefore displaced horizontally toward the radar antenna. This horizontal displacements contrast with the radial displacement observed in optical imagery due to central projection. Furthermore, such objects hide part of the ground below them, which do not receive any signal and cannot scatter information back. Three particular geometric distortions exist: foreshortening, layover and shadows.\r\nDepending on the illuminated target, different scattering mechanisms occur in combination with geometric distortions, which makes the interpretation of the SAR image challenging. A good example are buildings, where layover, shadow and single- and double-bounce occur.","name":"Terrain reflectivity and geometric distortions","selfAssesment":"Completed
"},{"code":"PP2-3-9","description":"A typical “salt-and-pepper” noise-like physical phenomenon that is not a noise but a deterministic property of SAR imagery is the so called speckle. It appears when a resolution cell of a SAR system contains more than one scatterer. In that case, the total scattering from the resolution cell is a coherent sum of the backscatter originating from the different scatterers. In order to reduce this effect, speckle reduction methods can be applied.","name":"Speckle Formation","selfAssesment":"New
"},{"code":"PP2-3","description":"Microwave remote sensing systems detect and quantify the electromagnetic radiation arriving at a detector, this radiation being either emitted (passive sensors) or scatterered back (active sensors) from the objects.\r\nThree properties of the recorded electromagnetic signal are of particular interest: its intensity, its phase and its polarization. The specific quantification of each properties allows signal interpretation, as they depend on the roughness and dielectric characteristics of the surface (intensity and polarization) as well as of the range between target and sensor (phase).\r\nThe detection of the microwaves is operated through two principal sensor elements: an antenna and a receiver. The antenna collects the incoming radiation and the receiver measures the collected electric signal.\r\nAs active microwave systems produce their own electromagnetic radiation, they are equipped with two additional elements: a pulse generator and a transmitter. Usually, transmitter and receiver are situated on the same antenna.\r\nA simple detector system only detects the intensity of the signal and amplifies it. Coherent systems measure both the amplitude and the phase of the incident electromagnetic radiation.\r\nMicrowave systems can be categorized in two different types: imaging and non-imaging sytems. Whereas for non-imaging systems each echoe (collected signal) provides a single measurement, imaging systems collect a sequence of echoes that generate a two dimensional image.","name":"Detecting microwaves","selfAssesment":"Completed
"},{"code":"PP2","description":"Microwave remote sensing operates in the microwave portion of the electromagnetic spectrum, generally using wavelengths greater than 3 cm and up to 1 m. \r\nMicrowaves are sensitive to different physical parameters than other regions of the electromagnetic spectrum. Microwaves interactions with objects are governed by geometric (structure, size, shape) and dielectric (water content) properties, whereas other regions of the electromagnetic spectrum reacts e.g. to object temperature or “color” (amount of reflection or absorption of the Sun light by a particular object).\r\nAs a general rule, microwaves interact with object at least as big as the wavelength. Smaller objects will therefore be transparent for the signal. Due to the large wavelengths, atmospheric particles are almost transparent to the signal and microwave remote sensing can penetrate clouds. Under very dry conditions, microwaves can even penetrate up to a few meters the top soil layers, therefore providing information that is not visible in other regions of the electromagnetic spectrum. Depending on the considered wavelength, microwave can also penetrate vegetation layers to different amounts.\r\nIn microwave remote sensing, three characteristics of the electromagnetic wave play an important role: its amplitude, its phase and its polarization. Depending on the application, either one characteristic or a combination of them is used to retrieve information.\r\nThere are two main types of microwave sensors: active RADAR systems and passive radiometers. RADAR is an acronym for RAdio Detection And Raging. In this thesis data from an active, imaging radar were analyzed. An active radar system sends out pulses and records the echoes scattered back by the objects (scatterers) to the sensor. The systems use the two-way travel time of the radar pulse to determine the distance (range) to the illuminated object. Its backscatter intensity is determined by the radar system and object properties and depends on the quantity of energy coming back to the sensor. Active radar systems transmit a signal and record the amount of energy that is scattered back and depends of both dielectric and geometric properties. Passive radiometers record microwave energy, which is emitted by the Earth’s surface.\r\nDepending on the type of system, microwave remote sensing can be used in multiple applications. Active sensors are principally used for diverse land cover mapping applications based on the particular backscattering mechanisms and characteristics of the objects on the Earth’s surface. Using multiple acquisitions, they are also favored for topographic, deformation and velocity mapping. Passive sensors are preferred for the determination of hydrologic variables such as soil moisture, precipitation, ice water content and sea-surface temperature.","name":"Microwave remote sensing","selfAssesment":"Completed
"},{"code":"PS","description":"Remote sensing, i.e. the process of obtaining information about an object or area from a distance, is not possible without remote sensing sensors that collect this information and the platforms on which the sensors are installed and which are used to move them. Remote sensing sensors collect data by detecting energy that is reflected or emitted from Earth. There are different types of remote sensing sensors. The interaction between the sensor and the Earth's surface has two modes: active or passive. Passive sensors use solar radiation to illuminate the Earth's surface and detect reflection from the surface or measure the emitted energy. They usually record electromagnetic waves in the visible (˜430–720 nm) and near infrared (NIR) (˜750–950 nm) through short infrared (SWIR) (˜1.500-2.500 nm) to thermal infrared (TIR) (8.000-14.000 nm) ranges. The power measured by passive sensors is a function of surface composition, physical temperature, surface roughness and other physical properties of the Earth. Active sensors provide their own energy source to illuminate objects and measure their properties. These sensors use electromagnetic waves in the visible and near infrared range (e.g.laser altimeter) and radar waves (e.g. synthetic aperture radar (SAR)). As sensor technology has advanced, the integration of passive and active sensors into one system has emerged. Alternatively, remote sensing sensors can be classified into imaging sensors, i.e. that produce an image of an area, within which smaller parts of the sensor's whole view are resolved (pixels), and non-imaging sensors, i.e. that return a signal based on the intensity of the whole field of view. In terms of their spectral characteristics, the imaging sensors include optical imaging sensors, thermal imaging sensors, and radar imaging sensors. These sensors can be on satellites, mounted on aircraft, unmanned aerial vehicle (UAV), drone or ground. The collected information can be transformed into an image or set of points (e.g. cloud points), which can be further processed and analyzed to obtain the necessary information, e.g. agricultural field development phase, level of air pollution, etc.\r\nA digital imagery of Earth observation sensors is a two-dimensional representation of objects on Earth. Current images collected from different levels of acquisition, from ground to satellite, with the help of electronic sensors are examples of digital images. There are different aspects and characteristics of remote sensing data and images, such as, for example, data formats and processing levels, data storage, data properties.","name":"Platforms, sensors and digital imagery","selfAssesment":"Completed
"},{"code":"PS1-1","description":"Remote sensing sensors has its roots in the 19th century in the development of photography. Photography was an invention that made it possible to acquire a permanent image. The first photographic image was taken in 1826 by Joseph Nicephore Nieppce. While the first aerial photograph was taken in 1858 by Felix Tournachon, known as Nadar, from a tethered baloon over Biévre Valley in France. In 1907 Julius Neubronner developed a light miniature camera that could be fitted to a pigeon's breast. It can be said that the construction camera + pigeon was the precursor of today's unmanned aerial vehicle (UAV) or drone. Further developments focused on developing new sensors (analog vs. digital frame cameras) and how to save and store images (e.g. photographic emulsions, films). The origin of other types of remote sensing can be traced to World War II, with the development of radar, sonar, and thermal infrared detection systems. Since the 1960s, sensors were designed to operate in virtually all of the electromagnetic spectrum. Both civil and military aerial photography have long been widely used in cartography to create maps. Specialized large format cameras (looking vertically down, assuming the plane is flying horizontally) were developed. Such cameras have been specially designed to perform almost vertical sequences of bird-eye exposures during aircraft flight. Hence for a long time remote sensing consisted of aerial photography and photogrammetry using analogue mechanical or optical equipment. Everything has changed with satellites and the space race. The first real success of remote sensing satellites in serious scientific work was in meteorology, weather satellite TIROS-1, launched by NASA on April 1, 1960. \r\nToday a wide variety of remote sensing instruments are available as data source for use in different applications for land, water and atmosphere monitoring.","name":"History of remote sensing sensors","selfAssesment":"In progress
"},{"code":"PS1-2-1-1-1","description":"Along track scanner, also known as a pushbroom scanner, is an optoelectronic device that obtains images with a multispectral imaging system. The scanners are used for passive remote sensing. It records electromagnetic energy that is reflected (e.g., blue, green, red, and infrared light) or emitted (e.g., thermal infrared radiation) from the surface of the Earth. The scanners are mounted on space- or aircrafts. \r\nA two-dimensional image is created (line by line) by exploiting the platform motion along the orbital track. The data are collected along track using a linear array of detectors arranged perpendicular to the direction of travel. The array of detectors are pushed along the flight direction to scan the successive scan lines, and hence the name pushbroom scanner. \r\nThere are no moving parts on a pushbroom sensor, hence, the scanning speed can be increased compared to across track systems. A longer dwell time over each ground resolution cell increases the signal strength (high radiometric resolution, no pixel distortion). Additionally, finer spatial and spectral resolution can be achieved as the size of the ground resolution cell is determined by the Instantaneous Field of View (IFOV) of a single detector. The systems are designed for high-resolution imaging. However, a very large number of detectors is needed for high resolution images. It is a complex optical system. In addition, the pushbroom scheme requires a wide Field of View (FOV) optics system to obtain the same swath as for a corresponding whiskbroom (across track) scanner. It has narrow swath width. \r\nThe detector arrays with such a line-scanning pushbroom system are usually of the type Charge-Coupled Device (CCD).\r\nThe MultiSpectral Instrument (MSI) on board the Sentinel-2 satellite (Copernicus mission) uses a pushbroom concept.\r\nMultispectral imaging systems building the final image (line by line) exploiting the platform motion along the orbital track. No rotating mechanical part required, usually based on a CCD matrix (high spectral resolution but just up to 1 micrometer), e.g. Sentinel-2 MultiSpectral Instrument (MSI), Sentinel-3 Ocean and Land Colour Imager (OCLI).","name":"Along track scanners","selfAssesment":"Completed
"},{"code":"PS1-2-1-2-1","description":"The cameras, usually a charge-coupled device (CCD) or Complimentary Metal Oxide Semiconductor (CMOS), that convert light into electrons that can be measured and converted into radiometric intensity value.","name":"Digital Frame Camera","selfAssesment":"Planned
"},{"code":"PS1-2-1-2","description":"2-D systems with the ability to observe in two dimensions simultaneously.","name":"Area Arrays","selfAssesment":"New
"},{"code":"PS1-2-1","description":"A type of a spectrometer. It is in principle, one-dimensional systems, whisk- or pushbroom, that form an image on a line-by-line basis in the scan direction.","name":"Line detector arrays","selfAssesment":"New
"},{"code":"PS1-2-2-1-1","description":"Thermal radiometers are radiometers with the capability of measuring the spectrum of infrared emission. As such, they are characterized by a relatively high spectral resolution (normally better than 1 cm-1 in wave number units). Modern Spectrometers on board satellites have a spectral resolution better than 0.7 cm -1 in order to properly resolve CO2 lines used for the retrieval of the atmospheric temperature profile. Based on the optical layout they are further classified in grating spectrometers and Fourier Transform Spectrometers or FTIR.","name":"Thermal Radiometers","selfAssesment":"New
"},{"code":"PS1-2-2-1-2","description":"Passive microwave radiometers are radiometers that measures energy emitted at millimetre-to-centimetre wavelengths at 0.15 - 30 cm (frequencies of 1–200 GHz). Example of a sensor: SMOS Microwave Imaging Radiometer with Aperture Synthesis (MIRAS), which aims the measurement of land soil moisture and ocean salinity.","name":"Passive Microwave Radiometers","selfAssesment":"In progress
"},{"code":"PS1-2-2-1-3","description":"An advanced multispectral sensor that detects hundreds of very narrow spectral bands throughout the visible, near-infrared, and mid-infrared portions of the electromagnetic spectrum.","name":"Hyperspectral Radiometers","selfAssesment":"Planned
"},{"code":"PS1-2-2-1-4","description":"A radiometer that measures the intensity of radiation in multiple wavelength bands (i.e., multispectral). Example of a sensor Moderate Resolution Imaging Spectroradiometer (MODIS)","name":"Spectroradiometers","selfAssesment":"In progress
"},{"code":"PS1-2-2-2","description":"Provide information about vertical profiles of temperature and molecular consistuent concentrations in the atmosphere (atmospheric sounders).","name":"Atmospheric passive sounders","selfAssesment":"New
"},{"code":"PS1-2-2","description":"Radiometers are instruments which measure radiative intensities within a particular frequency window. A radiometer is further identified by the portion of the electromagnetic radiation it covers, usually the infrared or microwave regions. Normally the spectral range extends from the longwave (14-15 micron) to the shortwave (3-4 micron). This range overlaps much of the emission spectrum of Earth. The technology is classified in broadband radiometer of spectral radiometers depending on the spectral resolution. A radiometer measures the intensity of the radiative energy, but does not differenciate between the different registered wavelengths or their respective amplitude. In other terms, it provides a single value as combined result of all wavelengths within the considered frequency window.","name":"Radiometers","selfAssesment":"In progress
"},{"code":"PS1-2","description":"Passive remote sensing systems record electromagnetic energy that is reflected (e.g., blue, green, red, and infrared light) or emitted (e.g., thermal infrared radiation) from the surface of the Earth. Passive sensors therefore rely on an external energy source (e.g. sun illumination, Earth heat emission). Contrary to passive sensors, who detect naturally occurring radiation, active sensors emit radiation and collect and analyze the signal that is sent back by the Earth’s surface or atmosphere. Active remote sensing systems produce therefore their own electromagnetic energy. They transmit and receive the radiation that is reflected or backscattered from the illuminated target. They do not necessitate an external source of radiation (e.g. Sun or Earth). Contrary to most passive sensors that are bound to detecting either the reflected Sun radiation or emitted radiation by the Earth’s surface in ranges from the ultraviolet to the thermal infrared, active sensors can use any radiation from the electromagnetic spectrum, the only limitation being the transparency of the Earth’s atmosphere. They often use wavelengths that are not sufficiently provided by the Sun, e.g. microwaves. \r\nActive systems can be categorized either according to their imaging capability, or according to the considered emitted wavelength, or also according to the way they use the returned signal. For the last category, it is generally distinguished between ranging systems, which use as principal information the time delay between transmission and reception of the electromagnetic radiation at the sensor, and scattering systems, which consider the strength (also called magnitude or intensity), of the returned signal. Some systems also register both information.\r\nAs active sensors produce their own radiation and do not rely on e.g. Sun radiation, they are daytime independent and can also retrieve information about the Earth’s surface by night. Furthermore, depending of the considered wavelength, active sensors are weather independent. For longer wavelengths of the microwave domain, clouds are transparent, as the transmitted wavelength is larger than the water particles constituting the cloud and do not interact with them. \r\nActive sensors can control the direction of their illumination to a specific target to be investigated, but require in general more energy than passive sensors as they “actively” illuminate the Earth’s surface.","name":"Passive vs. active sensors","selfAssesment":"Completed
"},{"code":"PS1-3-1-1","description":"Imaging radar is an active radar system that sends out pulses and records the echoes scattered back by the objects (scatterers) to the sensor. Imaging radars are independent of weather conditions and can operate day or night. It uses microwave wavelengths, radar bands from X- to P- or VHF-band, in four polarisations to illuminate an area on the ground. Normally only the horizontal (H) or vertical (V) linear polarizations are used. The radar system is characterized by combination of polarization of transmitted and received pulse: HH, HV, VH or VV. A typical radar system measures the strength and roundtrip time of the microwave signals that are emitted by a radar antenna and reflected off a target area. An imaging radar is therefore both and imaging and a ranging system. The illuminated objects are mapped in the radar depending on their backscatter intensity and respective range to the sensor.\r\nImaging radar can be mounted on aircraft or satellite. It operates in a side-looking configuration, left or right with reference to the flight direction. This acquisition geometry allows the distinct mapping of scatterers corresponding to their respective distance to the sensor. It causes also geometric distortions in the radar image, i.e., relief displacement (foreshortening and layover) and shadow. The radar sensor operates not in the real aperture of the radar antenna, i.e., real spatial width, radar (RAR) mode but in the synthetic aperture radar (SAR) mode. Synthetic aperture is possible to set up through the forward motion of the spacecraft, which enables to “extend” the real size of the radar antenna. With a SAR, each object on the ground is sampled at several antenna positions along the flight path, i.e., as long as the antenna beam is illuminating it.\r\nImaging radar can be used for a different of land and water applications.","name":"Imaging Radar","selfAssesment":"Completed
"},{"code":"PS1-3-2-1-1","description":"Laser profilers measure 1D range profiles and operate in different environments, like spaceborne, airborne and indoor. Most of them operate top-down on flying platforms, but as well bottom-up is possible, e.g. in meteorology for cloud monitoring.\r\nIt is the simplest application of the LIght Detection And Ranging technique. It transmits a short pulse of energy (visible or near-infrared radiation) and detects 'echo', by measuring the time delay and knowing the speed of propagation of the pulse, the range from the instrument to the surface can be measured.","name":"Laser profiler","selfAssesment":"In progress
"},{"code":"PS1-3-2-1-4","description":"The radar altimeter operates similarly to the laser profiler but it operates at a much longer wavelengths using microwaves. Radar altimeters measures the two-way travel time of a radar pulse between the radar antenna and the object (Earth's surface). Radar altimetry was originally designed for the open ocean domain.","name":"Radar altimeters","selfAssesment":"New
"},{"code":"PS1-3-2-1","description":"Laser altimeters historically were the first active sensing devices used on airborne platforms, measuring range information in form of single distances. Nowadays, they are still found on low-cost platforms like drones to determine the flight altitude. The instrument is also used aboard planet-orbiting satellites to map a planet's terrain.","name":"Laser altimeter","selfAssesment":"In progress
"},{"code":"PS1-3-2-3","description":"By a ranging camera the simultaneous capturing of range measurements in the form of a range image for an extended area of dynamical 3D applications is given. Applications are building surveillance, traffic monitoring, and driver assistance.","name":"Ranging camera","selfAssesment":"In progress
"},{"code":"PS1-3-2-4","description":"Laser scanners capture data by successively considering points on a discrete, regular (typically spherical) raster, and recording the respective geometric and radiometric information.\r\nThere are different types of laser scanners taking into account its application:\r\nSpaceborne LS (e.g. Geoscience Laser Altimeter System - GLAS) provides global measurements of the Earth's surface with the potential on capturing additionally clouds and atmospheric aerosols. The spaceborne measurements allow to globally observe ice sheet and land elevations, approximate sea ice thickness, changes in elevation through time, vegetation coverage for biomass estimation, and height profiles of clouds and aerosols.\r\nAirborne laser scanning (ALS) systems allow a direct and illumination-independent measurement from 3d objects in a fast, remote and accurate way. Beside basic range measurements, the current commercial ALS developments allow to record the waveform of the backscattered laser pulse. Latest trends in sensor developments focus on single-photon detection. Different applications are of interest, like urban planning, forestry surveying, or power line monitoring. Further to describe the 3D scene, products like digital terrain models (DTMs), digital surface models (DSMs), or city models are provided.\r\nA terrestrial laser scanning (TLS) system is a stationary highly accurate ranging device for geodetic surveying. More specifically, TLS systems provide dense and accurate 3D point cloud data for the local environment and they may also reliably measure distances of several tens of meters. Due to these capabilities, such TLS systems are commonly used for applications such as city modeling, construction surveying, scene interpretation, urban accessibility analysis, or the digitization of cultural heritage objects. When using a TLS system, each captured TLS scan is represented in the form of a 3D point cloud consisting of a large number of scanned 3D points and, optionally, additional attributes for each 3D point such as color or intensity information. However, a TLS system represents a line-of-sight instrument and hence occlusions resulting from objects in the scene may be expected as well as a significant variation in point density between close and distant object surfaces. Thus, a single scan might not be sufficient in order to obtain a dense and (almost) complete 3D acquisition of interesting parts of a scene and, consequently, multiple scans have to be acquired from different locations.\r\nA mobile laser scanning system consists of a moving vehicle equipped with one or more usually side-looking laser scanners to capture information about the local 3D geometry. \r\nUnderwater LS is applied in deep-sea as well as in shallow water regions. The ranging distance is close range and the measurement principle relies on triangulation by laser light, comparable with structured-light-projection. More recently, companies started to develop Time-of-Flight (ToF) underwater laser scanners.\r\nFor Bathymetric LS the utilized green laser light with its potential penetration capabilities in water is essential. For water surface mapping the electromagnetic radiation of the laser penetrates into the topmost layer of the water column and can also be used for mapping the water surface and shallow water bathymetry. Area-wide water surface heights and depths are required for many disciplines such as hydrology, hydraulic engineering, flood risk management, ecology, climate change, etc.","name":"Laser scanner","selfAssesment":"Completed
"},{"code":"PS1-3-2","description":"The main idea of LiDAR (Light Detection and Ranging) technology is based on actively scanning the scene by involving a device which emits electromagnetic radiation in the form of modulated laser light. \r\nGenerally, such scanning devices illuminate a scene with modulated laser light and analyze the backscattered signal. More specifically, laser light is emitted by the scanning device and transmitted to an object. At the object surface, the laser light is partially reflected and, finally, a certain amount of the laser light reaches the receiver unit of the scanning device. The measurement principle is therefore of great importance as it may be based on different signal properties such as amplitude, frequency, polarization, time, or phase. \r\nMany scanning devices are based on measuring the time t between emitting and receiving a laser pulse, i.e., the respective time-of-flight, and exploiting the measured time t in order to derive the distance r between the scanning device and the respective 3D scene point. Alternatively, a range measurement r may be derived from phase information by exploiting the phase difference Δφ between emitted and received signal. According to seminal work, respective scanning devices may be categorized with respect to laser type, modulation technique, measurement principle, detection technique, or configuration between emitting and receiving component of the device. \r\nIn order to get from single 3D scene points to the geometry of object surfaces, respective scanning devices are typically mounted on a platform which, in turn, allows a sequential scanning of the scene by successively measuring distances for discrete 3D points.\r\nLiDAR technology is used for a diversity of applications such as autonomous driving, forestry, biomass estimation, precision farming, archaeology, city mapping, terrain modelling, and metrology.","name":"LiDAR (Light Detection and Ranging)","selfAssesment":"Completed
"},{"code":"PS1-3-3-1","description":"Sonar, also called ultrasonic sensing, is one the principal sensors for mapping sea-floor, i.e. bathymetry. It transmits sound waves through water and records the amount of backscattered energy. It uses frequencies higher than normal hearing. A sonar can be either passive or active. Active sonars are also called echosounders.","name":"Sonar","selfAssesment":"New
"},{"code":"PS1-3-3-2","description":"A seismic sensor is also called seismometer and measures the motion of the ground when it is shaken by a perturbation such as an earthquake, be it a large displacement or a microquake. The physical variable associated to the measurement of a seismometer is dynamic. It can be either the amplified ground motion, the velocity or acceleration. Current seismometers transform one of these three parameters into a voltage measurement. Usually, three seismometers are needed to retrieve the three components of the displacement. As for other sensors, there exists many types of seismic sensors, and they can be distinguished in active and passive sensors as well.","name":"Seismic sensor","selfAssesment":"New
"},{"code":"PS1-3-3","description":"Instruments that measure vertical distribution of precipitation and other atmospheric characteristics such as temperature, humidity, and cloud composition.","name":"Sonic sensors","selfAssesment":"New
"},{"code":"PS1-3-4-1","description":"Radar scatterometer is a calibrated radar designed to measure the radar backscatter cross section of a target, generally an area on the earth’s surface. Surface backscatter is measured as a function of the frequency, polarization, and illumination direction of the sensing signal (microwaves).","name":"Radar Scatterometers","selfAssesment":"Planned
"},{"code":"PS1-3-4-2-1","description":"Differential Absorption Lidar (DIAL) is a laser remote sensing technique that is used for range and/or profile measurements of atmospheric gas concentrations and constituents.","name":"Differential Absorption Lidar","selfAssesment":"In progress
"},{"code":"PS1-3-4-2-2","description":"Doppler Wind LiDAR or Cloud-Aerosol Lidar with Orthogonal Polarization (e.g. CALIOP) is a two-wavelength polarization-sensitive LiDAR that provides high-resolution vertical profiles of atmospheric aerosols and clouds to enable an greater understanding of our climate.","name":"Doppler Wind LiDAR","selfAssesment":"In progress
"},{"code":"PS1-4","description":"There are different ways to classify sensors used in remote sensing. One of them is the division into imaging and non-imaging sensors. Imaging sensors typically employ optical imaging systems (from VIS to TIR). They operate primarily at window frequencies, where atmospheric absorption is low and surface features can be imaged or measured. Non-imaging sensors include microwave radiometers, microwave altimeters, magnetic sensors, gravimeters, Fourier spectrometers, laser rangefinders, and laser altimeters.","name":"Imaging vs. nonimaging sensors","selfAssesment":"New
"},{"code":"PS1-5-1-2","description":"Across track scanners, known as whiskbroom electromechanical scanners, are multispectral imaging systems building the final image (ground cell by ground cell) by combination of the platform motion along the orbital track with a mechanical rotation of the collecting optic in the across track direction. Opto-mechanical are typically multi-spectral radiometers (no limitation on bands), whiskbroom systems are usually CDD spectrometers (high spectral resolution but just up to 1 micrometer). Examples of the sensors: Landsat Multispectral Scanner (MSS), Landsat Thematic Mapper (TM).","name":"Across track scanners","selfAssesment":"Planned
"},{"code":"PS1-5-1","description":"Speckle-pattern based sensors operate with a spatial neighborhood codification strategies to exploit a unique pattern. The label associated to a pixel is derived from the spatial pattern distribution within its local neighborhood. Thus, labels of neighboring pixels share information and provide an interdependent coding. Representing one of the most popular devices based on structured light projection, the Microsoft Kinect exploits an RGB camera, an IR (infrared) camera, and an IR projector. The IR projector projects a known structured light pattern in the form of a random but unique speckle dot pattern onto the scene. As IR camera and IR projector form a stereo pair, the pattern matching in the IR image results in a raw disparity image which, in turn, is read out as depth image.","name":"Speckle-pattern based sensor","selfAssesment":"In progress
"},{"code":"PS1-5-2","description":"A multi-temporal (sequential) binary coding uses black and white stripes to form a sequence of projection patterns for each point on the surface of the object. Binary coding technique is very reliable and less sensitive to the surface characteristics, since only binary values exist in all pixels. Thus, each pixel may be assigned a codeword consisting of its illumination value across the projected patterns. The respective patterns may, for instance, be based on binary codes or Gray codes and phase shifting. To achieve high spatial resolution, a large number of sequential patterns need to be projected. All objects in the scene have to remain static. The entire duration of 3D image acquisition may be longer than a practical 3D application allows for. These sensors are utilized in industrial environment.","name":"Multi-temporal pattern based sensor","selfAssesment":"In progress
"},{"code":"PS1-5-3","description":"For a multi-spectral pattern based sensor, various continuously varying color patterns to encode the spatial location information are utilized.","name":"Multi-spectral pattern based sensor","selfAssesment":"New
"},{"code":"PS1-5","description":"A structured-light-projection camera emits active optical radiation in the form of a coded structured light pattern in the visible or infrared spectrum, or electromagnetic radiation in the form of modulated laser light. Via the projected pattern, particular labels are assigned to 3D scene points which, in turn, may easily be decoded in images when imaging the scene and the projected pattern with a camera. The procedure reminds to conventional stereo processing, where corresponding features must be extracted from a pair of stereo images to derive the spatial information. In contrast, such synthetically generated features allow to robustly establish feature correspondences, and the respective 3D coordinates may easily and reliably be recovered via triangulation. Generally, techniques based on the use of structured light patterns may be classified depending on the pattern codification strategy.","name":"Structured-light-projection camera","selfAssesment":"In progress
"},{"code":"PS1-6","description":"Ground penetrating radar is a non-intrusive measurement technique that uses radio waves to probe the ground. It is used to analyze and locate targets buried in the sub-surface. It transmits low-power electromagnetic energy into the ground and receives weak signals from a low-loss dielectric or conductor material. It is principally used for archeology and geology. Typical penetration depths are between a few centimeters up to 4m.","name":"Ground penetrating RADAR (GPR)","selfAssesment":"New
"},{"code":"PS1-7","description":"An optical spectrometer is an instrument used to detect, measure and analyze the spectral content of the incident electromagnetic field (narrow-band, VIS, NIR, SWIR and TIR). It breaks down the incoming light spectrum so the whole wavelength range is mapped and each wavelength can be analysed individually. Usually, a distinction is made between optical and mass spectrometers.\r\nOptical spectrometers depict the intensity of the incoming light in function of the wavelength. Considering all wavelengths, each object has a specific spectral signature and the analyse of their particular spectrum allows the deduction of their composition ( e.g. pigments) or health.","name":"Optical spectrometers","selfAssesment":"In progress
"},{"code":"PS1","description":"Remote sensing sensors acquire information about objects situated on the surface of e.g. the Earth remotely, e.g. from a distance, without any physical contact. They detect and measure the changes that the object imposes on its. \r\nRemote Sensing sensors are characterized according to several different properties:\r\n-\tDepending on the interaction between the sensor and the Earth’s surface, one distinguishes between active (e.g. radar) and passive (e.g. optical imagery) sensors. Some systems use both kind of sensors simultaneously.\r\n-\tDepending on the mapping process of the information, it can be distinguished between imaging and non-imaging sensors. Imaging sensors produce an image of an area of interest, e.g. give a spatial information about the incoming information. Spatial relationships between objects can be identified and used for visual interpretation. Non-imaging sensors register usually single response values for a specific area, and do not record how the incoming information varies across the field of view. They can be used to characterize the interaction between the received information and illuminated target.\r\n-\tDepending on the platform on which the instrument is deployed, one speaks either of ground based (e.g. terrestrial laser scanner), airborne (e.g. plane, drone), or spaceborne (e.g. satellite) sensor. For spaceborne sensors, the orbit geometry (e.g. geostationary, equatorial, sun-synchronous) and altitude (high, medium and low Earth orbit) play an important role, as it most often determines the application of the satellite in combination with the deployed sensor (weather satellites or Earth observation satellite). \r\n-\tDepending on the observed portion of the electromagnetic spectrum (e.g. optical, infrared, thermal, microwave). \r\n-\tDepending on the instrument (e.g. imagers, altimeters, spectrometers, radiometers). \r\n-\tDepending on the instrument precision, e.g. in terms of spatial resolution very high vs. low resolution sensors; in terms of spectral resolution narrow band (hyperspectral sensors) vs. broad-band sensors (mono- and multispectral sensors); in terms of radiometric resolution very high vs. low resolution sensors. Some applications do not require very high precision instruments, e.g. sea surface temperature measurements, while other, e.g. for vegetation monitoring, require high spectral and radiometric resolution for good data interpretation and analysis. \r\nOther categorization would include the specific applications of each sensor (weather, environment, urban, land, water, mapping, photogrammetry, structure-from-motion, etc.) and if is financed and used for scientific, commercial or military goals.","name":"Types of remote sensing sensors","selfAssesment":"Completed
"},{"code":"PS2-1","description":"This topic covers information on the first remote sensing platforms that were used to obtain aerial photos. The first-known aerial photo was obtained in 1858 by Gaspard Felix Tournachon (Nadar). Afterwards, different platforms were used to obtain the information from above. The history of the development of remote sensing platforms includes platforms such as baloons, kites, rockets, pigeons, gliders, etc. to recent low-cost femtosatellites, e.g. for solar radioation pressure measurements. Historically, the main developments of the platforms as well as sensors was associated with military operations in the XXth century. Remote sensing data was used as part of photo- or/and satellite reconnaissance, i.e. aerial photos or satellite imageries used for the military purposes, mainly to make accurate maps and based on that to prepare a military strategy.","name":"History of Remote Sensing Platforms","selfAssesment":"In progress
"},{"code":"PS2-2-1","description":"An unmanned aircraft system (UAS) includes an unmanned aerial vehicle (UAV), an aircraft without a human pilot on board, a ground-based controller, and a system of communications between the two. The system includes a full range of size classes from very small hand-launched drones to the large high-altitude observational systems.","name":"Unmanned Aerial Systems (UAS)","selfAssesment":"New
"},{"code":"PS2-2-2-1","description":"Mission planning depends on the selected system of acquisition (sensor and platform). A detailed planning of a mission is a fundamental prerequisite for a successful acquisition of remote sensing data. Planning of an aerial photography mission (manned or unmanned) takes into account several parameters such as time of day/sun angle, weather conditions, flightline, platform. Planning and implementation of a spaceborne Earth Observation mission involves several successive life cycle ‘phases’ of conception, development, production and testing, utilization and support, and retirement, as part of an iterative and recursive process, until the satellite (space segment) is delivered and launched into orbit, and the data are exploited in the ground segment.","name":"Mission planning","selfAssesment":"New
"},{"code":"PS2-2-2-3-2-3-1","description":"Stripmap is a acquisition mode of Synthetic Aperture Radar (SAR) data. By acquisition of data with the Stripmap mode radar antenna pointing is fixed relative to the flight line (coarse-resolution data).","name":"Stripmap","selfAssesment":"New
"},{"code":"PS2-2-2-3-2-3-2-1","description":"Staring SpotLight is a SAR acquisition mode currently available on the SAR satellite TerraSAR-X, allowing azimuth resolution up to 0.25 m. It uses beam steering in azimuth direction to increase the illumination time (i.e. the size of the aperture). The virtual rotation center is situated inside the image scene.","name":"Staring Spotlight","selfAssesment":"New
"},{"code":"PS2-2-2-3-2-3-2","description":"The SpotLight imaging modes steer its antenna beam in azimuth direction to increase the illumination time, i.e. the size of the synthetic aperture. This leads to a restriction in the image / scene size. Thus, the scene size is technically restricted to a defined size: 10 km x 10 km.","name":"Spotlight","selfAssesment":"New
"},{"code":"PS2-2-2-3-2-3-3-1","description":"Interferometric Wide Swath Mode (IW) is the main acquisition mode of the Sentinel-1 satellites over land. For this mode, regular revisit times and a consistent long-term archive are ensured It uses the Terrain Observation with Progressive Scans SAR (TOPSAR) acquisition technique and acquires data with a swath width of 250km at a spatial resolution of 5 m by 20m.","name":"Interferometric Wide Swath Mode","selfAssesment":"New
"},{"code":"PS2-2-2-3-2-3-3-2","description":"The Extra Wide Swath Mode is an acquisition mode of the Sentinel-1 satellites. It is primarily designed and used for wide area coastal monitoring, such as ship traffic, sea-ice monitoring and oil spill detection. It uses the TOPSAR technique with a swath width of 410km and a spatial resolution of 20 m by 40 m.","name":"Extra Wide Swath Mode","selfAssesment":"New
"},{"code":"PS2-2-2-3-2-3-3","description":"ScanSAR is an aquisition mode of a SAR system. The sensor steers the antenna beam to illuminate a strip of terrain at any angle to the path of aircraft motion.","name":"ScanSAR","selfAssesment":"New
"},{"code":"","description":" ","name":" ","selfAssesment":" "},{"code":"PS2-2-2","description":"Since the 1940s aerial imagery has been the primary source of detailed geospatial data for extensive study areas. Photogrammetry is the profession concerned with producing precise measurements from aerial imagery. Aerial imaging and photogrammetry represent a major component of the geospatial industry. The topics included in this unit do not comprise an exhaustive treatment of photogrammetry, but they are aspects of the field about which all geospatial professionals should be knowledgeable.","name":"Airborne platforms and systems","selfAssesment":"New
"},{"code":"PS2-2-3-1","description":"Earth observation (EO) missions are gathering information about the physical, chemical, and biological systems of the planet via remote-sensing technologies, supplemented by Earth-surveying techniques, which encompasses the collection, analysis, and presentation of satellite data.","name":"Earth observation missions","selfAssesment":"In progress
"},{"code":"PS2-2-3-2","description":"There are essentially three types of Earth orbits: high, medium and low Earth orbit. Satellites that orbit in a medium (mid) Earth orbit include navigation and specialty satellites, designed to monitor a particular region. Most scientific satellites, including NASA’s Earth Observing System fleet, have a low Earth orbit. On which orbit a satellite will be launched to, depends mainly on its application. The orbit types can be categorized according to their height.\r\nThe orbit height of a satellite corresponds to the distance between the Earth’s surface and the satellite. It determines its speed as it rotates around the Earth. Due to Earth’s gravity, the pull of gravity is stronger for lower orbits than for higher orbits. Therefore, a satellite situated on a lower orbit will circle the Earth faster than a satellite situated on a higher orbit.\r\n\tHigh Earth orbit: it describes orbits situated at about 36000 km above the Earth’s surface (42164 km from the Earth’s center). At this exact distance, the speed of the satellite on the orbit matches the Earth’s rotation, i.e. the satellite needs 24 hours to complete a full rotation on the orbit, when the orbit is situated exactly above the equator. Such orbits are also called geosynchronous orbits, as the satellite moves at the same speed than the Earth and seems to stay in place over a specific location. Those orbits are mainly used for weather and communication satellites\r\n\tMedium Earth orbit: it describes orbits situated at about 20200 km of the Earth’s surface, or 26560 km of the Earth’s center. At this height, a satellite rotates twice around the orbit during one Earth’s rotation. This orbit is also called semi-synchronous and this is the orbit type used by Global Navigation Satellite Systems such as GPS and GLONASS. A further important medium Earth orbit is the Molniya orbit which allows the observation of the poles, otherwise nearly impossible with equatorial geosynchronous orbits.\r\n\tLow Earth orbit: this type of orbits are used from almost all dedicated scientific Earth Observation satellites. Most of them use a particular, nearly polar orbit inclination, meaning that the satellite rotates around the Earth nearly from pole to pole (instead of around the equator as it is the case for geosynchronous satellites). This rotation takes about 99 minutes, depending of the specific orbit inclination. During one half of the orbit, the satellite views the daytime side of the Earth, i.e. the illuminated side. At the pole, satellite crosses over and views the nighttime side of Earth. Back to the daylight side, the satellite can view the area adjacent to the region flown over in the last orbit path, due to the simultaneous Earth’s rotation. In 24 hours, satellites situated on these orbits view almost all the Earth twice, for optical satellites once in daylight and once in the dark. Radar satellites seen each Earth region twice, from two different illumination directions. These specific polar-orbits are called sun-synchronous, as the local solar time stays the same each time a satellite flies over a specific region. This has the advantage of providing an almost constant angle of sunlight for each region on the Earth’s surface viewed by the satellite over time and ensure repeatable sun illumination conditions; the angle will only vary seasonally due to the Earth revolution around the sun. Due to this consistency, images of a specific region would not show much illumination changes due to shadows or sunlight and image interpretation over time such as change detection or monitoring approaches are possible. Because a sun-synchronous orbit does not pass directly over the poles, there is a data gap over both poles where no data is acquired.","name":"Types of satellite orbits","selfAssesment":"Completed
"},{"code":"PS2-2-3-3","description":"An imaging SAR system can generally make acquisitions in different modes. Which acquisition mode to choose depends of the application but also on the desired coverage and data resolution. Even if technically all acquisitions modes can be used everywhere on the Earth’s surface, specific modes are preferred for ocean applications that are different from the ones used in land applications.\r\nThe different acquisition modes can be defined either by their geometrical or by their temporal properties.\r\nThe geometrical properties refer to the geometric configuration of the SAR antenna. Usually looking sideways down in a direction perpendicular to the flight direction (Stripmap mode), the antenna can also be steered around the nadir axis in order to look at a specific target for a longer time during pass-by (Spotlight mode). This configuration allows to rachieve higher azimuth resolution but reduces coverage. It is rather used for very local application where a precise information about specific targets is needed. Other geometric configurations steer the antenna around the flight direction (ScanSAR mode), yielding to a larger swath on the ground. The distance between near and far range is increased, as well as the range of incidence angles within one acquisition. Whereas it increases the area of the scene, it comes generally with a decrease of the spatial resolution in the azimuth direction. Depending on the sensors, the name of the acquisition modes as well as particular technical properties can differ. Sentinel-1 uses a TOPS configuration (Terrain observation with Progressive Scan), which combines the antenna steering properties of both ScanSAR and Spotlight modes. \r\nThe temporal properties refer for specific techniques to the time interval between several acquisitions of the same area. Either these acquisitions are taken simultaneously in one pass over the area of interest (single-pass), or they are taken at different times, needing several passes over the area (repeat-pass).\r\nSpecific SAR techniques such as InSAR and Tomography, while relying on those geometric and temporal properties, have additional acquisition configuration characteristics. For example, the interferometric mission TanDEM-X has three acquisition modes defined by the number of satellite emitting or receiving the signal (pursuit monostatic mode, bistatic mode, alternating bistatic mode), which allows phase referencing. Tomographic SAR uses multi-baseline observations, i.e. the antenna passes several times over an area but at different heights, allowing via different incidence angles the retrieval of structural information of specific targets.","name":"Synthetic Aperture Radar (SAR) acquisition modes","selfAssesment":"Completed
\r\n\r\n"},{"code":"PS2-2-3-4","description":"Swath width refers to the width of the ground that the satellite collects data from on each orbit. The area imaged on the surface, is referred to as the swath. Imaging swaths for spaceborne sensors generally vary between tens and hundreds of kilometres wide.","name":"Swath","selfAssesment":"
New
"},{"code":"PS2-2-3","description":"Spaceborne platforms and systems are present at a great height from the earth surface. The altitude of platforms range from few hundred kilometers to several thousand kilometers. A large area can be captured in a single scene depending on altitude of sensor. The platforms can have different characteristics.","name":"Spaceborne platforms and systems","selfAssesment":"Planned
"},{"code":"PS2-3-1","description":"Field spectroradiometers are a powerful tool for monitoring and upscaling vegetation physiology and carbon and water fluxes. Usually, full-range spectroradiometers which delivers the spectral field measurements available from any commercial field-portable spectroradiometer.","name":"Field spectroradiometers","selfAssesment":"Planned
"},{"code":"PS2-3-2","description":"Terrestrial laser scanning (TLS) is a ground-based, active imaging method that rapidly acquires accurate, dense 3D point clouds of object surfaces by laser range finding.\r\nA terrestrial laser scanning (TLS) system is a stationary highly accurate ranging device for geodetic surveying. More specifically, TLS systems provide dense and accurate 3D point cloud data for the local environment and they may also reliably measure distances of several tens of meters. Due to these capabilities, such TLS systems are commonly used for applications such as city modeling, construction surveying, scene interpretation, urban accessibility analysis, or the digitization of cultural heritage objects. When using a TLS system, each captured TLS scan is represented in the form of a 3D point cloud consisting of a large number of scanned 3D points and, optionally, additional attributes for each 3D point such as color or intensity information. However, a TLS system represents a line-of-sight instrument and hence occlusions resulting from objects in the scene may be expected as well as a significant variation in point density between close and distant object surfaces. Thus, a single scan might not be sufficient in order to obtain a dense and (almost) complete 3D acquisition of interesting parts of a scene and, consequently, multiple scans have to be acquired from different locations.","name":"Terrestrial LiDAR","selfAssesment":"In progress
"},{"code":"PS2-3","description":"Platforms and systems that acquire data from the level of earth's surface. A wide variety of ground based platforms are used in remote sensing. The acquired data are used for detailed in-situ measurements, e.g., Leaf Area Index (LAI), and for calibration/validation campaigns.","name":"Ground platforms and systems","selfAssesment":"New
"},{"code":"PS2","description":"Remote sensing platforms and systems can be static (ground-based platforms) or moving (e.g. airborne or spaceborne platforms, UAVs). A remote sensing platform or system carry a remote sensing sensor. It can operate in near (few centimetres) or far (36,000 kilometres) altitudes ranges.","name":"Types of remote sensing platforms and systems","selfAssesment":"Planned
"},{"code":"PS3-1","description":"The development of remote sensing data carriers has followed the evolution of the photography, remote sensing sensors and computer platforms. The first remote sensed data was stored using the photography films (e.g. aerial photography, satellite Corona program), which was later replaced by reel tapes, cartridge, and then removable and hard discs. In the era of big and fast growth of Earth observation data, and technological advancements in digital infrastructure, the satellite data are stored using cloud platforms providing different service models: Infrastructure as a Service, Platform/Software as a Service (e.g. Copernicus DIAS, Google Earth Engine, open EO). The Cloud offers infrastructure to host, store and process the large amount of data efficiently. For example, the Copernicus Data Information Access Services (DIAS) is a comprehensive cloud-based hosting and processing system for the EO data in particularly for the Sentinels data, the Google’s Earth Engine (GEE) provides access to various satellite and offers processing power with a web-based programming interface, the Amazon Web Services (AWS) has dedicated cloud called ‘Earth on AWS’, the Microsoft’s cloud called Azure facility the use of AI tools to address environmental challenges. Public solutions, as well as private ones, react with a variety of new and innovative tools, which have been recently developed (e.g. DIAS, ODC, EarthServer, EO Browser, GEE).","name":"History of remote sensing data carriers","selfAssesment":"Completed
"},{"code":"PS3-2-1","description":"The picture elements are pixels and each pixel has a specific value (usually in grayscale). Image pixels are normally square and represent a certain area on an image. It is important to distinguish between pixel size and spatial resolution - they are not interchangeable. If a sensor has a spatial resolution of 20 metres and an image from that sensor is displayed at full resolution, each pixel represents an area of 20m x 20m on the ground. In this case the pixel size and resolution are the same.","name":"Picture element (pixel)","selfAssesment":"In progress
"},{"code":"PS3-2-2","description":"An image is an array, or a matrix, of square pixels (picture elements) arranged in columns and rows. In a (8-bit) greyscale image each picture element has an assigned intensity that ranges from 0 to 255.","name":"Image as a matrix (digital number DN)","selfAssesment":"In progress
"},{"code":"PS3-2-3","description":"In data manipulation contexts, a data cube is a multi-dimensional array of values. A data cube can be visualized as the multidimensional extension of two-dimensional table. It can be viewed as a collection of identical 2-D tables stacked upon one another. Data cubes are used to represent data that is too complex to be described by a traditional table of columns and rows. Typically, the data cube is applied in conditions where these arrays are massively larger than the hosting computer’s main memory, for example multi-terabyte data warehouses o time series of image data.","name":"Data cubes","selfAssesment":"New
"},{"code":"PS3-2-4","description":"Term Big data refers to any collection of data sets so large and complex that it becomes difficult to process using on-hand data management tools or traditional data processing applications. In the field of Earth Observation (EO) is usually refers to large time series of image data which size on disk is much greater than hosting computer’s main memory. EO Big Data offers solution that allows not only storing these data on disk but also efficiently process them.","name":"Earth Observation Big Data","selfAssesment":"New
"},{"code":"PS3-2","description":"Most remote sensing data exist as digital images, and appropriate image processing allows the emphasis of certain aspect and subsequent extraction of information for specific applications.\r\nA digital image is a representation of the reality as a grid of picture elements. It can be considered as an array of numbers that can be stored and handled by a digital computer. The picture elements are pixels and each pixel has a specific value (usually in grayscale). This value is a digital number (DN), which usually represents the amount of energy recorded by the sensor at this pixel position or any other characteristic recorded by the sensor, e.g. elevation. \r\nEach row of the image grid, or matrix, corresponds to one scan line. Each pixel is characterized by its row r and column c position in the image, as well as by its value. Additional geographical information is needed in order to assign a geographic location to a pixel. The digital number are integers usually compressed in one byte (= 8 bit) representation, i.e. each pixel can take 256 values.\r\nDigital images are raster data, as opposite to vector data. Whereas vector data can be points, lines or polygones, raster data always consist of pixels. A pixel is the smallest element in which an image can be divided into. The pixel size varies depending of the instrument and of the sampling used. Large pixel may contain information about several objects of the recorded scene. However, they only have one value. These are called mixed-pixel, as e.g. several land cover classes are represented within one pixel and they cannot be distinguished from another. \r\nIn multispectral imagery, each region of the electromagnetic spectrum is recorded in an independent image (band). Therefore, at a specific array position (r,c), there exist several pixels, each with a specific value corresponding to the energy recorded for the considered band. This result in a three-dimensional matrix. The bands of a multispectral image can be displayed three at a time in the computer using for each band one of the three primary colors red, green and blue (RGB). This is called a color composite image. If the color composite represents a combination of the visible red, green and blue bands in their respective color, the combination is called natural or true color composite, as it corresponds to what the human eye sees naturally. Any other combination, for example considering bands of wavelengths that are not visible for the human eye is called a false color composite. It is often used to highlight the spectral differences and particular image features in order to extract information.","name":"Digital image terminology","selfAssesment":"Completed
"},{"code":"PS3-3-1","description":"Band interleaved by line (BIL) is one of three primary methods for encoding image data for multiband raster images in the geospatial domain, such as images obtained from satellites. BIL is not in itself an image format, but is a scheme for storing the actual pixel values of an image in a file band by band for each line, or row, of the image. For example, given a three-band image, all three bands of data are written for row one, all three bands of data are written for row two, and so on. The BIL encoding is a compromise format, allowing fairly easy access to both spatial and spectral information. The BIL data organization can handle any number of bands, and thus accommodates black and white, grayscale, pseudocolor, true color, and multi-spectral image data.","name":"Band interleaved by line (BIL)","selfAssesment":"New
"},{"code":"PS3-3-2","description":"Band interleaved by pixel (BIP) is one of three primary methods for encoding image data for multiband raster images in the geospatial domain, such as images obtained from satellites. BIP is not in itself an image format, but is a method for encoding the actual pixel values of an image in a file. Images stored in BIP format have the first pixel for all bands in sequential order, followed by the second pixel for all bands, followed by the third pixel for all bands, etc., interleaved up to the number of pixels. The BIP data organization can handle any number of bands, and thus accommodates black and white, grayscale, pseudocolor, true color, and multi-spectral image data.","name":"Band interleaved by pixel (BIP)","selfAssesment":"New
"},{"code":"PS3-3-3","description":"A binary raster file format for aerial photography, satellite imagery, and spectral data. The BSQ data organization can handle any number of bands, and thus accommodates black and white, grayscale, pseudocolor, true color, and multi-spectral image data. Additional information is needed to interpret the image data, such as the numbers of rows, columns, and bands, if there is a color map, and latitude and longitude to relate the image to geospatial locations.","name":"Band sequential (BSQ)","selfAssesment":"New
"},{"code":"PS3-3","description":"In order to properly process remotely sensed data, the\tanalyst must know how\tthe data is organized and stored. Data storage consists of methods of organizing image data for multiband images.","name":"Data storage","selfAssesment":"New
"},{"code":"PS3-4-1","description":"Spectral resolution describes the ability of a sensor to define fine wavelength intervals. The narrowest spectral interval that can be resolved by an instrument. Spectral resolution (spectral capability) also refers to the number of wavebands within the EM spectrum that an optical sensor is taking measurements over.","name":"Spectral resolution","selfAssesment":"Planned
"},{"code":"PS3-4-2","description":"The spatial resolution of an image corresponds to the size of the minimum area that can be resolved by the sensor. \r\nDue to the different techniques of acquisition of passive and active sensors, the spatial resolution is determined for both sensor types differently. \r\nFor passive sensors, the spatial resolution depends on their instantaneous field of view (IFOV), which determines the area of the Earth’s surface that is viewed at one particular moment in time by one detector element. The size of this area is called resolution cell and characterizes the spatial resolution of the sensor. Depending on the spatial resolution, whole features of the Earth’s surface can be detected homogeneously in one or several resolution cells. For features smaller than the spatial resolution, the average reflected radiation of all features within a resolution cell is recorded, leading to so-called mixed-pixels.\r\nFor imaging active systems, the spatial resolution is dependent of both the length of the transmitted pulse in looking direction and the width of the radiation beam or the antenna width in flight direction.\r\nIn all cases, the spatial resolution indicates the level of detail observable in an image. Usually, one distinguishes between coarse (low), moderate (medium) and fine (high and very high) resolution, whereby the use of this denomination is often context-dependent. Sensors with coarse resolution can only detect large features, but they usually cover a much larger area than high-resolution sensors, which can provide detailed information on small objects such as individual buildings, trees or cars, but for much smaller areas. Coarse spatial resolution mean in general a resolution cell larger than 250 m and a scene extent of several thousands of kilometers (>1000 km). Moderate resolution sensors have a spatial resolution of 30 m to 80 m, and a coverage of approximately 200 km in a single acquisition. Sensors showing spatial resolutions from 5 m or 6 m are high-resolution sensors, with a spatial coverage up to approximately 20 km. Sensors with a resolution cell’s width of less than 1 m are considered as very-high-resolution sensors.\r\nLow resolution sensors are appropriate for the analysis of broad-scale phenomena such as ocean color or cloud patterns. Medium resolution sensors are rather used for regional analysis such as land cover change and phenological response of vegetation. High-resolution sensors are particularly useful for object detection.","name":"Spatial resolution","selfAssesment":"In progress
"},{"code":"PS3-4-3","description":"Radiometric resolution can be defined as the ability of an imaging system to record many levels of brightness. Radiometric resolution refers to the range in brightness levels that can be applied to an individual pixel within an image, determined on a grayscale. E.g., Sentinel-2 sensor MSI is a 12 bit sensor imaging with 4.096 levels.","name":"Radiometric resolution","selfAssesment":"Planned
"},{"code":"PS3-4-4","description":"Temporal resolution, also referred to as the revisit cycle, is defined as the amount of time it takes for a satellite to return to collect data from exactly the same location on the Earth. Imageing of the exact same area at the same viewing angle a second time is temporal resolution.","name":"Temporal resolution","selfAssesment":"New
"},{"code":"PS3-4","description":"A digital image begins as an analog signal. Through computer data processing, the image becomes digitized and is sampled multiple times. The critical characteristics of a digital image are spatial resolution, spectral resolution, radiometric resolution, contrast resolution, noise, and dose efficiency. These depends upon satellite orbit configuration and sensor design. Different sensors have different resolutions.\r\nSpectral resolution describes the ability of a sensor to define fine wavelength intervals. The narrowest spectral interval that can be resolved by an instrument. Spectral resolution (spectral capability) also refers to the number of wavebands within the EM spectrum that an optical sensor is taking measurements over.\r\nRadiometric resolution can be defined as the ability of an imaging system to record many levels of brightness. Radiometric resolution refers to the range in brightness levels that can be applied to an individual pixel within an image, determined on a grayscale. E.g., Sentinel-2 sensor MSI is a 12 bit sensor imaging with 4.096 levels.\r\nSpatial resolution of an image corresponds to the size of the minimum area that can be resolved by the sensor.\r\nTemporal resolution, also referred to as the revisit cycle, is defined as the amount of time it takes for a satellite to return to collect data from exactly the same location on the Earth. Imageing of the exact same area at the same viewing angle a second time is temporal resolution.","name":"Properties of digital imagery","selfAssesment":"Completed
"},{"code":"PS3-5-1","description":"The header is a section of binary- or ASCII-format data normally found at the beginning of the file, containing information about the bitmap data found elsewhere in the file. The format of the header and the information stored in it varies considerably from format to format and contains fixed fields.","name":"Header file","selfAssesment":"Planned
"},{"code":"PS3-5","description":"The image data stored in a binary data format (BIL, BIP, BSQ) is accompanied by description files that contain a set of entries describing the image data, including acquisition time, image size, statistics, map projection, pixel digital numbers, product type, etc. This general image or product information is stored in a form of header embedded in the image file or provided in the separate file (.hdr) or metadata in XML. There are numerous image file formats, the more common are TIFF (GeoTIFF), bitmap (.bmp), JPEG (.jpg, .jpeg, JPEG2000), HDF, Raw (.raw), Extensible N-Dimensional Data Format (NDF).","name":"Image description files","selfAssesment":"In progress
"},{"code":"PS3-6","description":"Remote Sensing data formats in which the data are organized and stored. The data format for a remote sensing mission is usually chosen based on a number of considerations, including requirements of the sensing system, mission objective, the design and technology of data processing, archiving, and distribution systems, as well as community data standard.","name":"Data Formats","selfAssesment":"Planned
"},{"code":"PS3-7-1-1","description":"Depending on the sensor and the provider, remotely sensed imagery is made avalilable to the user at different processing levels. For Sentinel-2, the lowest product level made available to the user is Level-1B. THe Level-1B product provides radiometrically corrected imagery in Top-Of-Atmosphere (TOA) radiance values and in sensor geometry. Radiometric corrections applied to the Level-1B are: dark signal, pixels response non uniformity, crosstalk correction, defective pixels interpolation, high spatial resolution bands restoration (deconvolution puls denoising), binning (spatial filtering) for 60m bands.","name":"Radiometrically corrected","selfAssesment":"New
"},{"code":"PS3-7-1-2","description":"Geometrically corrected products are of a higher processing level than radiometrically corrected products. For Sentinel-2, the geometrically corrected product is the Level-1C product. The Level-1C product results from using a Digital Elevation Model (DEM) to project the image in cartographic coordinates. Per-pixel radiometric measurements are provided in Top Of Atmosphere (TOA) reflectances with all parameters to transform them into radiances. Level-1C products are resampled with a constant Ground Sampling Distance (GSD) of 10, 20 and 60 m depending on the native resolution of the different spectral bands. Level-1C products will additionally include Land/Water, Cloud Masks and ECMWF data (total column of ozone, total column of water vapour and mean sea level pressure). (Sentinel-2 User Handbook, p.44)","name":"Geometrically corrected","selfAssesment":"New
"},{"code":"PS3-7-1","description":"The definition of processing levels for optical data depends on the considered sensor. Most common satellite optical imagery are available in three distinct processing levels, from level 0 to level 2. The most used processing levels are level 1 and level 2, depending on the user and the application. \r\nIn Level 0, the raw data are processed in a way that they are ready to be archived. Processing operations generally includes telemetry analysis, error detections and granule concatenation. Furthermore, relevant parameters such as acquisition date and geographical reference are annotated in the form of metadata, this information being necessary for processing higher levels. Additionally, a quicklook of the image is generated. No correction is performed at this level.\r\nLevel 1 is often divided in several sublevels. Generally, both radiometric correction and geometric refinement are performed at this level. The radiometric processing includes several radiometric corrections such as dark signal correction or spectral band binning. The radiometric correction allows the determination of physical variables (e.g. reflectance) from the digital numbers. The geometric processing includes tiles association and resampling grid computation, in order to link for each image band its native image geometry to the target geometry. The result of this processing steps is usually a geocoded, Top of Atmosphere product.\r\nLevel 2 data usually consist of atmospherically corrected Level 1 data, i.e. Bottom-of-Atmosphere data. These surface reflectance products may be accompanied by additional outputs, such as scene classification, water vapor or surface temperature maps.\r\nFor specific applications and sensors, Level 3 application ready data are available. These are derivated products such as burned area, dynamic surface water content and snow cover maps.\r\nDepending on the considered sensor and level, the name of the sublevels can differ: Sentinel 2 defines Level-1B as radiometrically corrected data. Level 1C are radiometrically and geometrically corrected data, i.e Top-Of-Atmosphere (TOA) orthoimage products. Landsat sensors distinguish between Terrain precision correction (L1TP), systematic Terrain Correction (L1GT) and Geometric systematic Correction (L1GS) depending on the quality of the reference data for geometric correction. These are usually separated into Tier 1 and Tier 2 datasets.","name":"Optical data","selfAssesment":"Completed
"},{"code":"PS3-7-2-1","description":"The Single Look Complex SAR format is a single look product of the focused signal. It means that the azimuth compression has been carried out using the full azimuth bandwidth and therefore contains the highest azimuth spatial resolution and at the same time, it suffers from maximum speckle. The data are in the radar geometry, i.e., in slant range coordinates, not projected onto any reference surface. Each pixel of the SLC product is a complex number. (i.e., has a real and imaginary component) that represents the amplitude and phase.","name":"Single Look Complex (SLC)","selfAssesment":"New
"},{"code":"PS3-7-2-2","description":"From the Single Look Complex (SLC) product the Multi-look Detected/Multi-looke (MLD/MLI) can be generated. It is produced by multi-looking, i.e., averaging, over range and/or azimuth resolution cells.","name":"Multi-looked Detected (MLD)","selfAssesment":"New
"},{"code":"PS3-7-2-3","description":"Precision Images (PRI) are the Multi-look Detected/Multi-looked Intensity (MLD/MLI) images that have been resampled into square pixels, rotated to account for the view direction of the instrument and warped by some predefined operation that the projected image pixels are georeferenced onto a specified geographical coordinate system.","name":"Precision Images (PRI)","selfAssesment":"New
"},{"code":"PS3-7-2-4","description":"Before performing multi-looking, the Single Look Complex (SLC) slant-range geometry is projected onto ground. This kind of product, i.e., in ground range geometry, is known as a Ground Range Detected (GRD), e.g., product of the Sentinel-1 mission.","name":"Groud Range Detected (GRD)","selfAssesment":"New
"},{"code":"PS3-7-2","description":"For SAR data, usually three processing levels are distinguished, ranging from level 0 (less processed) to level 2 (higher processed).\r\nLevel 0 products consist of compressed and unfocussed raw data and are the basis for the processing of higher level products. Level 0 data are principally used for research in the topic of signal processing. As for optical data, level 0 product are annotated with several metadata, such as calibration and orbit information, and acquisition time and date.\r\nLevel 1 data can be separated in two distinct product types, depending if the full complex information is used (amplitude and phase) or only the amplitude information. The product denomination depends on the sensor type; for Sentinel 1 the names Single Look Complex (SLC) and Ground range detected (GRD) are used, respectively. Both products can be generated from the Level 0 data. Level 1 data are the products that are used by most scientific users. The processing toward Level-1 data includes Doppler centroid estimation and data focusing. The Level 1 SLC product consists of the real and imaginary part of focused complex SAR data in slant range geometry, from which the phase and amplitude information can be retrieved. This is available for all acquired polarisations. Additional orbit information for georeferencing is provided with the data. The Level 1 GRD data consist of focused and multi-looked SAR data that have been projected to ground range geometry. GRD data only contain amplitude information, therefore the phase information is lost. The multi-looking step is particular for GRD data and allows both speckle reduction and square pixel resolution. As for the SLC data, the GRD data are annotated with orbit information for georeferencing. The Level-1 products are not calibrated, they include however information about calibration constants, which are sensor dependent. Further processing is needed in order to obtain calibrated radar cross section information from the original data intensity values.\r\nLevel 2 products describe geolocated derivated geophysical products such as ocean wind field or surface radial velocity. Such products are for example available for download on the Sentinel-1 Copernicus Hub. Further Level- 2 data are for example differential interferograms or change maps, which can be processed on different online platforms (e.g. Hyp3) and provide information about surface deformation or more generally changes between several acquisitions.\r\nThe denomination of the product types on the different levels may differ from sensor to sensor, but the processing steps stay almost the same, depending additionally on the considered acquisition modes. For example, GRD products are also called for other sensors Multi-Looked Detected (MLD) products.","name":"Synthetic Aperture Radar (SAR) data","selfAssesment":"Completed
"},{"code":"PS3-7-7","description":"Data that have been processed to allow direct data analysis. User processing effort is reduced to a minimum.","name":"Analysis Ready Data (ARD)","selfAssesment":"New
"},{"code":"PS3-7","description":"Earth Observation data are usually made available in different processing levels. The processing level is a mean of describing how much the raw data have been processed toward an informational geophysical product. The degrees of data processing usually follow a numerical hierarchy and typically range from Level 0 (less processed) up to Level 4 (highly processed). They characterize the type of data processing that has been performed between the raw data and the current product.\r\nA first effort for providing standard definitions of different processing levels has been made in the 1980s by the Committee on Data Management and Computation (CODMAC) of the National Research Council (NRC). CODMAC identified eight levels of processing, applicable for all space science data. Starting with the raw data at level 1, the degree of processing and complexity of the data increased at each new level. Level 2 describes edited data, corrected for obvious instrumentation errors and tagged with acquisition time and location; Level 3 stays for calibrated data where values are proportional to a specific physical unit. Level 4 represents resampled data, Level 5 derived data, where specific geophysical information has been retrieved and mapped based on the original data. Level 6 represents all ancillary data (i.e. instrument data) that are necessary for the previous steps of calibration and resampling. Level 7 describes so called correlative data: not directly belonging to the original data, those data represent all other science data that where necessary for the interpretation of the original spaceborne dataset. Finally, Level 8 are user description, i.e. documentation of the data.\r\nConcerning spaceborne image data, both optical and radar, an adaptation of these original levels has been made from NASA and NOAA that is used for the main current spaceborne missions, including the Copernicus program. Whereas specific adaptations may arise for specific sensors and sensor types, there are five principal processing levels. Level 0 represents the raw data that have just been edited for the correction of artifacts. Level 1 data are Level 0 data with additional annotations regarding time and geolocation information, radiometric and geometric calibration coefficients (for example Top of Atmosphere data for optical imagery). Level 2 data are already radiometrically and geometrically calibrated and represent physical variables (for example Bottom of Atmosphere data for optical imagery). Level 3 data correspond to derived variables and information (e.g. land cover) with completeness and consistency information, e.g. quality flags. Level 4 represent higher level data resulting from modelling or more complex analysis of the data with additional ancillary information.\r\nFor many applications and users, so called analysis ready data (ARD data) are required. These usually correspond to Level 2 data that have already been pre-processed in order to retrieve the physical information and can be further analyzed for the specific thematic application.","name":"Processing levels","selfAssesment":"Completed
"},{"code":"PS3","description":"Remotely collected data is available from multiple sources and data collection techniques. Data can be obtained from different levels of data acquisition: ground, air or space, as well as using different sensors and wavelengths. Remote sensing data provides the necessary information to help monitor the Earth's surface.","name":"Remote sensing data and imagery","selfAssesment":"Planned
"},{"code":"PS4","description":"The listed databases provide information on past, operational and future remote sensing platforms and sensors. Use the following links to get more information on the sensors and missions.","name":"Databases of satellite and airborne sensors and missions","selfAssesment":"Completed
"},{"code":"SD","description":"Based on Waldo Tobler`s first law of geography( Tobler, 1970), this property is set on the principle that \"everything is related, but that which is closer is more closely related\".","name":"Spatial dependency","selfAssesment":"In progress (GI-N2K)
"},{"code":"SH","description":"This principle, as set forth by Anselin, determines that \"expectations vary along the earth`s surface\" which means that any spatial analysis is dependent explicitly on the borders of study fields, i.e. the tracing of (spatial) analysis units.","name":"Spatial heterogeneity","selfAssesment":"In progress (GI-N2K)
"},{"code":"TA","description":"This area of knowledge deals with the use of EO / GI techniques and data in different themes and areas of application. It includes the user community of EO services and applications, societal and environmental challenges, EO services and applications, and standard EO products that are made available to users.","name":"Thematic and application domains","selfAssesment":"Planned
"},{"code":"TA11-1-1","description":"The EO/GI users in agriculture are active in Agricultural commodities/Trading, agricultural production / Horticulture, Agricultural services, Agriculture machinery, Agriculture and Rural Development Policy, Agro chemicals / Plants & Fertilizers, Animal production / Livestock. The EO/GI users also include agriculture and rural policy makers. \r\nThey benefit from EO information, for example, by managment support for their crop production through forecasting crop yield, assess risks of damage/loss because of storms, disease or other stress factors, and water monitoring. Use in agriculture: knowledge and information products to forge a viable strategy for farming operations. Understand the health of his crop, extent of infestation or stress damage, or potential yield and soil conditions","name":"Users in agriculture","selfAssesment":"New
"},{"code":"TA11-1-2","description":"The users in fishing are active in Fish stock management, Fishing fleets, Fishery distribution logistics, Aquaculture / fish farms, Coastal management agencies. In addition, the users include Fisheries authorities / policy makers. \r\nThe marine environment in particular is relevant to fishing. Fishing fleets move to the fishing grounds to catch fish. Finding them is challenging. However, fish shoals can be directly visible from above. Navigating to the fishing grounds can be risky: Coastline and shallows may pose a risk to ships. Additionally, skippers may have to deal with challenging weather conditions at sea. Environmental threats to the fishing grounds are oil slicks and other types of pollution. A problem from an economical perspective and for adhering to catch quota is illegal fishing. Noumerous opportunities exist to support fishing with EO information.","name":"Users in fishing","selfAssesment":"New
"},{"code":"TA11-1-3","description":"The users in forestry are active in Forest management, Forest Services, Commodities, Logging industry, Wood, paper and pulp industry, Forest policy, Forest machinery. They also include Forest Policy makers.\r\nUse in forestry: Understand depletion due to natural causes (fires and infestations) or human activity (clear-cutting, burning, land conversion), and monitoring of health and growth for effective commercial exploitation and conservation.\r\nForests are a resource that is harvested all over the Globe for different purposes like construction or heating. Additionally, the forests represent an ecosystem that provides various ecosystem services. Proper management is a key to a healthy forestry industry that has to be aligned well with global environmental management activities. There is a need to avoid deforestation and forest degradation, keep the environmental impact of forestry within bounds, be aware of changes in the carbon balance. Economically relevant is especially a good understading of forest types, forest damage due to storms or insects, as well as wildfires. A threat to the environment results from illegal forest activities.","name":"Users in forestry","selfAssesment":"New
"},{"code":"TA11-1","description":"Users in managed living resources refer to human activities exploiting natural organic resources. Knowledge and information products to forge a viable strategy for the user’s operations such as the assessment of the status of the resource due natural or human activity for effective commercial exploitation and conservation. This includes agriculture, fishing and forestry occupations for our society.","name":"Users in managed living resources","selfAssesment":"New
"},{"code":"TA11-2-1","description":"The users in alternative energy consist of Solar energy providers, Wind energy providers, Tidal energy providers, Hydroelectric energy providers, Energy and Carbon traders, Local and regional planners, and National policy makers. Energy providers need information about the state of the environment to make the most use out of natural resources. Planners and policy makers have to weigh up whether and which type of alternative energy is justifiable and sensible for a specific region.\r\nEO data can be used to build maps that show resource information. For solar energy, those maps contain information about solar radiation, but also shadowing effects. Forecast products for irradiance are available to be able to plan the energy production for the coming days. Tidal waves can be depicted by sea surface heights. As tidal currents are periodical, they can be predicted well by the initial state of sea surface heights. In addition, also the speed of tidal waves can be determined by EO measurements. In the wind energy sector EO data is analysed to plan and monitor wind farms. Maps can show areas, where winds are suitable for wind energy production. After the construction of a wind farm, wind strength and direction during operation can be monitored. Finally, for hydroelectric power stations EO is used to monitor water reservoirs. As well hydrometeorological data is used to forecast water-related events and to monitor drought or floods.","name":"Users in alternative energy","selfAssesment":"Completed
"},{"code":"TA11-2-2","description":"The EO/GI user community in oil & gas consists of offshore exploration and production, on-shore exploration and production, drilling and support services, oil and gas commodities trading, and energy planners. Due to their activities both on-shore and offshore their need for EO-derived information about the land, the ocean and the atmosphere. They need EO-derived information about geological features (for exploration), for asset infrastructure monitoring, construction and buildings. Safe offshore operations (ocean&atmosphere: forecast and monitoring current movement and drift, monitor sea-ice and icebergs, detect and monitor hurricanes and typhoons; land: map and assess flooding, detect wildfires . A large set of information needs results from their need to adhere to environmental regulations. They have to assess and monitor their environmental impact, ocean quality and productivity, land ecosystems and biodiversity, groundwater and run-off \r\nMany problems faced by oil, gas, including the selection and development of exploration areas, detection and mapping of illegal mining activities, or monitoring dams, pipelines and terrain movements, can be efficiently addressed by extracting information from geospatial imagery. Remote Sensing based applications reduce the need for field work, minimize environmental impacts, and ultimately safe costs, to help achieve results faster during exploration, extraction, and remediation/reclamation stages.","name":"Users in oil & gas","selfAssesment":"New
"},{"code":"TA11-2-3","description":"The EO/GI community in minerals and mining consists of mining and quarrying companies, exploration and survey specialists, commodities traders, exploration and extraction equipment suppliers, drilling, excavation and support services, and regional planners / policy makers.\r\nTypical spatial questions for the users in minerals and mining are concerned with prospecting, e.g. \"Where can we find the minerals that are worth exploitation?\", and operation of mining sites: \"How much material has already been excavated in the mine and how much material was deposited in dedicated dump areas?\". Additionally relevant are arising risks through mining activities, e.g. \"How do the mining activities affect settlements in the vicinity?\" or \"How do the mining activities affect the environment?\". Concequently, the EO/GI users in minerals and mining benefit from EO information through mapping geological features, monitor mineral extraction, measure land use statistics, assessing environmental impact of human activities, detect and monitor ground movement, and monitor land pollution.","name":"Users in minerals & mining","selfAssesment":"New
"},{"code":"TA11-2","description":"Users in energy and mineral resources deal with the harvesting of energy from renewable resources and extractive industries including oil and gas and raw materials. EO information helps them in exploring locations where to build new mines or power plants, in identifying risks from infrastructure and in managing the environmental impact of their operations.\r\nUses that apply to the extractive industries: study of landforms, structures, and the subsurface, to understand physical processes creating and modifying the earth's crust.","name":"Users in energy and mineral resources","selfAssesment":"New
"},{"code":"TA11-3-1","description":"EO/GI users in construction include construction companies, civil engineering consultancies, architect and design companies, planning authorities, and national land agencies. \r\nThey benefit from EO through monitor building development, assess environmental impact of human activities, map and assess flooding, detect land movement, subsidence, heave, and monitor land-use statistics","name":"Users in construction","selfAssesment":"New
"},{"code":"TA11-3-2","description":"Utilities (water, electricity, waste): Power station operators, Water plants operators, Survey companies, Hydroelectric suppliers, Regulatory Bodies, Distribution companies, Landfill and waste, Regional planners / policy makers.\r\nThe benefit from EO information that monitor pollution in rivers and lakes, assess changes in the carbon balance, assess environmental impact of human activities, monitor land pollution, assess changes to urban and rural areas, assess and monitor water quality, assess ground water and run-off.","name":"Users in utilities & supplies","selfAssesment":"New
"},{"code":"TA11-3-3","description":"Users of EO/GI in communications and connectivity are mostly mobile telecommunications providers and fixed telecommunication providers. Theire business is to connect people via telephone and internet. The assets for their services include the infrastructure of communication networks physically installed in the ground, the cellphone towers distributed over the land surface, particularly in higly populated areas, as well as other installations (e.g. company buildings) and equipment (communication satellites).\r\nSpecific spatial questions of these users are concerned with the reception quality that the network can provide in an area. The network coverage would neet to react to changes of the built environment. New settlement infrastructure may cause a new population distribution and subsequently the need to network adaptations to cover new areas or cover some areas with higher band widths because more people are living there. Additionaly, the coverage of cellphone antennas depends on the arrangement of environmental obstacles that degrade or block the radio signal. Any place where the built environment or the vegetation changes can change the reception quality within the covered area of an existing cellphone tower. \r\nThe benefit of EO information for the user group of communications and connectivity comes from monitoring building development, assessing changes to urban and rural areas, and mapping line of sight visibility (terrain height, land cover).","name":"Users in communications & connectivity","selfAssesment":"New
"},{"code":"TA11-3-4","description":"EO/GI users in transport and logistics include road transport operators, haulage, road infrastructure operators, tolls, airport operators, rail operators, airlines and airline services, and transport engineers.","name":"Users in transport & logistics","selfAssesment":"New
"},{"code":"TA11-3-5","description":"EO/GI users in marine include ports & harbors administration, bulk cargo carriers, cruise liners operators, ferry operators, naval operations, and rescue and safety at sea.","name":"Users in marine","selfAssesment":"New
"},{"code":"TA11-3-6","description":"From a conceptual point of view travelling is crossing the space from one location to another. Tourism mostly requires a travel to the desired destination and typically also includes moving inside a specific area. Therefore both tourism and travel are highly dependent on spatial phenomena which are often captured using EO.All kinds of travelling are highly dependent on weather conditions which can be observed with meteorological satellites. Also the current traffic conditions like congestion, road condition and natural hazards can be discovered with EO.\r\n\r\nThe types of tourism which are outside of buildings require sufficient weather forecast. Especially outdoor tourism at the coast or in mountain areas have a need for specific information about the current and the near future conditions of the natural environment. Examples are avalanche reports and forecasts for wind or wave heights of water bodies. Local tour organizers can utilise this information in order to better plan offers for tourists and also ensure overall safety during their stay.\r\n\r\nTourism and travelling are import economic factors. Consequently both the public and the private sector are interested in ensuring safe and convenient travel conditions and furthermore in creating an attractive environment for travellers and touristic visitors. This includes recognising environmental pollution, since this discourages tourist from visiting an area. Not only incoming, but also outgoing tourism is an important factor in local economies. Travel agencies, for example, are highly dependent on retrieving accurate information about foreign regions which are typically obtained with earth observation technology.\r\n\r\nOf course tourism and travelling itself also can be observed from space, this is especially true for mass tourism and areas where traffic has increased a lot during the last time. Typical effects are the increase of settlement area and the additionally used space for roads, parking lots, airports and harbors. These changes to the earth surface can be quantified with the help of land cover change detection.In many cases local administrations and decion makers want to mitigate the negative consequences of mass tourism, the insights of the mentioned EO measurements provide a useful foundation for sustainable planning.","name":"Users in travel & tourism","selfAssesment":"Completed
"},{"code":"TA11-3","description":"Users in transport and infrastructure apply to all manufacturing and physical supply in land but also marine domains including transport & logistics, utilities, construction, communication & connectivity, and tourism.","name":"Users in infrastructure & transport","selfAssesment":"New
"},{"code":"TA11-4-1","description":"EO/GI users in insurance and real estate include primary insurance companies, re-insurance sector, insurance brokers, insurance service suppliers, commercial banks, major projects, and international financial institutions. \r\nProduction processes (including primary production like farming), property and real estate are often insured against certain risks, e.g. from natural hazards. \r\nUsers benefit from EO information through applications that monitor building development, assess crop damage due to storms (including to forecast and map large waves), assess damage from earthquakes, detect and monitor wildfires, map and assess flooding, detect land movement, subsidence, heave, forecast and assess landslides.","name":"Users in insurance & real estate","selfAssesment":"New
"},{"code":"TA11-4-2","description":"EO/GI users in retail and geo-marketing include Retail centres and Advertising and Marketing agencies. They use EO/GI data in the field of Navigation and LBS, Shopping chains or Logistics.","name":"Users in retail & geo-marketing","selfAssesment":"New
"},{"code":"TA11-4-3","description":"Users in news and media are Television companies, Broadcasting providers, News and Information agencies, Web service providers, and Entertainment software providers. They benefit from monitoring, forecasting and assessing of natural risks/disasters.","name":"Users in news & media","selfAssesment":"New
"},{"code":"TA11-4-4","description":"Users in ICT include fixed and mobile telecommunications providers. They can make use of EO/GI data by monitoring building development and changes to urban areas.","name":"Users in ICT, knowledge and digital interfaces","selfAssesment":"New
"},{"code":"TA11-4","description":"Users in financial and digital services cover a broad area of activity that touches on many other market sectors such insurance & real estate, retail, news & media and digital interfaces. The categories included are identifiable as a “service” (tertiary sector: attention, advice, access, experience, and affective labour) and not part of the physical supply of goods.","name":"Users in financial & digital services","selfAssesment":"New
"},{"code":"TA11-5-1","description":"The users in smart cities include urban planners, architects, spatial planning offices, urban policy makers. The users benefit from EO information through map information about urban structures and related land use when managing land use, climate change adaptation, and urban green infrastructure. Typical use cases include Urban adaptation to climate change and Green infrastructure and its ecosystem services to increase quality of life of citizens (https://land.copernicus.eu/user-corner/land-use-cases)","name":"Users in smart cities","selfAssesment":"In progress
"},{"code":"TA11-5-2","description":"The users in local and regional planning include spatial planning departments of municipalities, spatial planning offices, and spatial planning policy makers. Land use management in densely populated areas involves negotiation of conflicting land-use demands for settlement, production system (including agriculture and forestry) and infrastructure. The users benefit from EO information to manage the use of land and its impacts.","name":"Users in local & regional planning","selfAssesment":"New
"},{"code":"TA11-5","description":"Users in urban development and users involved in the development of rural settlements perform tasks on local and regional scale (to the scale of nations). These users benefit from EO information to manage the use of land & its impacts. Users such as urban planners, architects, spatial planning offices, urban policy makers in public/private sectors in smart cities or generic urban local/regional planning belong to this category.","name":"Users in urban development","selfAssesment":"New
"},{"code":"TA11-6-1","description":"Users in defense, security and military are border control organisations, police and rescue forces, military services, and intelligence services. Use of EO/GI data can be made in the field of detecting and monitoring high risk areas (natural and humanitarian), monitoring border incursions, or monitoring maritime movements.","name":"Users in defense, security & military","selfAssesment":"New
"},{"code":"TA11-6-2","description":"EO/GI users in emergency services are coast guards, ambulance services, fire services, police services, civil protection organisations, and rescue services. They benefit from monitoring, detecting and assessing natural risks/disasters.","name":"Users in emergency & social protection","selfAssesment":"New
"},{"code":"TA11-6-3","description":"The EO/GI users in humanitarian operations correspond to humanitarian aid organisations, humanitarian support organisations and overall humanitarian response such as border control organisations, police and rescue forces, coast guards, civil protection, military services, and intelligence services. They can use EO services to detect and monitor high risk areas produced naturally or by humans, monitor border incursions or maritime movements. They provide support to local populations that have experienced a crisis, e.g. they fled from a conflict or are affected by a natural disaster. The organisations therefore support the population's needs for sustenance. Consequently, any related risks are relevant as well. The users benefit from the EO capability to identify and monitor people in need, i.e. to assess pressures on populations and migration, and to monitor humanitarian movement and camps. They additionally benefit from EO through mapping disaster areas for situation awareness and detecting sensitive risk areas. Some examples of users at European level are DG RELEX, DG ECHO, DG ENV/ MIC. At UN, the users include OCHA, UNHCR, UNDPKO, UNDP, UNOPS, UNITAR, UNICEF, UNESCO, WFP. Further, international users include IFRC, WHO, WB, and donor organizations. At the national level, the users include Civil Protection Agencies, Ministries of Internal Affairs / Civil Protection Department, Development and Aid agencies.","name":"Users in humanitarian operations","selfAssesment":"New
"},{"code":"TA11-6","description":"Users in defence and security work in the field of military, emergency and social protection and define, collect, analyse information to provide intelligence & safety. Some examples are activities under humanitarian response such as border control organisations, police and rescue forces, coast guards, civil protection, military services, and intelligence services which can use EO services to detect and monitor high risk areas produced naturally or by humans, monitor border incursions or maritime movements.","name":"Users in defense & security","selfAssesment":"New
"},{"code":"TA11-7-1","description":"EO/GI users in environmental ecosystems & pollution include scientists, consultants, planners and policy makers with interest in environmental issues.","name":"Users in environmental ecosystems & pollution","selfAssesment":"New
"},{"code":"TA11-7-2","description":"Users in health care health-related services include services on site-specific field conditions as well as import phenological timing events, which helps to make predictions for monitoring air quality, forecasting epidemics and diseases, as well as forecasting sunlight exposure.","name":"Users in health care","selfAssesment":"New
"},{"code":"TA11-7-3","description":"EO/GI users in meteo and climate; use of satellite-based observations in addressing key climate science questions for user-centric climate change risk assessment applications or climate-related issues","name":"Users in meteo & climate","selfAssesment":"New
"},{"code":"TA11-7","description":"Users in the public administrations or private organizations using EO to assist environmental or climate change impact policy making decisions i.e, assisting in developing monitoring to evaluate and deliver policy goals, provide assessment of ecosystems, rapid response to major environmental risk events, or those associated health security & care events. These users are largely related with international treaties and hence a strong international collaboration.","name":"Users in environmental, climate & health","selfAssesment":"New
"},{"code":"TA11-8-1","description":"EO/GI users of customer solutions; easier for society to use and engage with EO services through mobile devices, social media platforms, apps. Enormous potential to use citizen-driven observations in combination with EO data","name":"Users of consumer solutions","selfAssesment":"New
"},{"code":"TA11-8-2","description":"EO/GI users in leisure; basic public understanding on EO Services","name":"Users in leisure","selfAssesment":"New
"},{"code":"TA11-8-3","description":"The community of users in education includes instructors (1) who are teaching or conducting research in some aspect of GIScience, such as coding, remote sensing, field methods, geodetic control, web mapping, spatial analysis, or related topics, or (2) who are using GIS as a teaching tool in a discipline, such as business, biology, economics, or health sciences. By extension, this community includes students and supportive deans and other educational administrators. The benefits that these users gain from EO information includes a set of best practices vetted by experts in the field that they can use to teach modern GIS workflows more effectively. \r\nThe goals of this user community are focused on a deeper and a broader implementation of geotechnology, methods, and spatial data throughout the educational system—primary, secondary, university, and lifelong learning (libraries, museums, and other informal settings). Deeper implementation implies embracing GIS as a platform, including its field data gathering tools and citizen science workflows, spatial analysis, building web maps and apps, communicating with multimedia maps derived from web GIS, systems configuration work, and the coding that is behind modern GIS infrastructure. Broader implementation implies the use of GIS in a multitude of disciplines at all levels of education, formal and informal; occurring wherever changes over space and time are being examined. \r\nAt all levels of education the challenge of sufficient bandwidth and the use of a professional systems-based tool such as GIS, along with devices capable of running web GIS tools, are barriers in many areas throughout the world. However, educational and societal forces represent a stronger challenge than technological ones. These educational and societal challenges that this user community faces include the lack of educational content standards at the primary and secondary level that support the use of geotechnologies in education, and at the university level, a lack of awareness of and access to modern SaaS GIS tools and open data portals. \r\nThe risks that the community faces in not facing the challenge of the use of GIS in the education sector is a lack of geographic and spatial literacy among students and faculty. This will translate to research that does not consider spatiotemporal implications of 21st Century challenges, a workforce ill-equipped to deal with them, and consequently an increasingly unstable and dysfunctional world. To build a workforce that can meet global challenges in energy, biodiversity, climate, natural resources, natural hazards, human health, economic inequality, and others, a deep and wide implementation of GIS technology and methods must take place throughout the educational system. The actions that society can take to face that challenge is to provide professional development opportunities for faculty, curricular resources, assessment instruments, relevant spatial data and open data portals, examples of best practices, and a network for educators and researchers in which to interact. EO can provide all of these elements in partnership with educational institutions, government, nonprofits, and industry to meet this challenge. In so doing, an increasingly sustainable, healthier, resilient world can be achieved from the community to the global level.","name":"Users in education, training & research","selfAssesment":"Completed
"},{"code":"TA11-8","description":"Citizens and society in general use and engage with EO services through mobile devices, social media platforms, apps. We do also categorize in this section the users in education, research and training providing knowledge and learning outcomes.","name":"Users among citizens & society","selfAssesment":"New
"},{"code":"TA11","description":"The EO/GI user community pools sub-communities (stakeholders) that share common needs for EO/GI information. From an economic perspective, market sectors represent user communities. Users of a community have a common interest in specific aspects of societal or economical benefits to be realized by the implementation of EO services. A user-led community is active at specific locations/regions or in specific environments on the Earth. Their activities are associated with particular features and objects of the environment and related processes that can be detected and monitored with EO satellites. EO information therefore is relevant to the user community's management of their assets, the risks to their assets, and the impact that their activities may have on other aspects of the environment. User objectives (use cases) with EO information include: Enforce regulations; Develop strategies and policies; Manage assets; Plan and design project implementations; Analyse and understand impact / consequences.\r\nUser communities can profit from EO services and applications in the field of managed living resources, energy and mineral resources, infrastructure and transport, financial and digital services, urban development, defense and security, environmental, climate and health, or citizens and society.","name":"User community of EO services and applications","selfAssesment":"Completed
"},{"code":"TA12-1","description":"Climate change observations show the warming of the climate system. The changes since the 1950s are unprecedented over decades to millennia.The atmosphere and ocean have warmed, the amounts of snow and ice have diminished, and sea level has risen. The anthropogenic emissions of greenhouse gases are the highest in history. Recent climate changes have had widespread impacts on human and natural systems. There is an urgant need for climate action through mitigation and adaptation. Mitigation actions prevent or reduce the emission of greenhuse gases into the atmoshpere with the objective to make the impacts of climate change less severe. Adapting to climate change increases our resilience to impacts like extreme weather events (e.g. hazards like floods and droughts) that get more frequent and intense in many regions. Current climate change will get worse in the future even if the reduction of emissions is effective with negative effects on ecosystems, economy, human health and well-being. There is extensive need for actions to adapt to the impacts of climate change.","name":"EO for climate change mitigation & adaptation","selfAssesment":"New
"},{"code":"TA12-10","description":"\"Sustainable urban development is a goal of the global society. It summarizes a specific set of problems that cities face all over the world. Cities want to provide a high quality of life to their residents. However, this goal is threatened by urban growth at the cost of urban green infrastructure’s accessibility by citizens etc. Communities that address this: C40 (association of the largest cities of the globe), CitiesIPCC, related SDGs of the UN, etc. Skills: Explain how the monitoring of urban areas contributes to sustainable urban development through its capability to provide regularly updated information about the benefit of urban green infrastructures and their ecosystem services to the quality of life in a city\r\n\"","name":"EO for sustainable urban development","selfAssesment":"New
"},{"code":"TA12-2","description":"Biodiversity describes the variety of ecosystems (natural capital), species and genes in the world or in a particular habitat. Ecosystem services sustain our economies and societies and are essential to human wellbeing.","name":"EO for biodiversity & ecosystems","selfAssesment":"New
"},{"code":"TA12-3","description":"Worldwide countries follow a digital agenda for the economy and initiatives to foster new skills among the workforce to cope with transformation processes with massive impact on the labour market.","name":"EO for digital agenda & new skills","selfAssesment":"New
"},{"code":"TA12-4","description":"Energy transition is a thematic area whose EO experts are proficient in relevant EO data and its processing methods and infrastructure to derive information for energy transition [and its regulatory context, etc.]. The expertise of each expert may be very specialized. In sum, the experts have: The relevant domain knowledge (knowledge about type of monitored entities and their properties, e.g. reflectance properties of sea ice and related EO sensors for detecting them), and The relevant workflow knowledge and processing skills for extracting and providing targeted information for energy transition. [may share strategic objectives… such as „gaining thorough understanding of Energy transition“, „foster usage of EO information for energy transition“]","name":"EO for energy transition","selfAssesment":"New
"},{"code":"TA12-5","description":"Agricultural activity is sustained by good environmental conditions that allow farmers to harness natural resources, create their produce and earn a living. This fosters a sustainable rural economy while food produced by agriculture sustains society as a whole.","name":"EO for sustainable agriculture & food production","selfAssesment":"New
"},{"code":"TA12-6","description":"This societal challenge aims to provide efficient, safe and environmentally friendly mobility solutions.","name":"EO for infrastructure & transport","selfAssesment":"New
"},{"code":"TA12-7","description":"In recent decades, society has fought communicable diseases with success through treatment and prevention. The Covid-19 pandemic shows that communicable diseases are still a threat to the health of citizens. Spread can gappen very quickly from one country to another. Challenges lie in the (re-)emergence of infectious diseases, antimicobial resistance and vaccine hesitancy. Policies of states focus on surveillance, rapid detection and rapid response.","name":"EO for health surveillance","selfAssesment":"New
"},{"code":"TA12-8","description":"There is a rising geostrategic competition and power pilitics challenging rule-based multilateralism. Further, there are armed confilct, civil wars and instability in the EU's broader neighbourhood. \r\nFurther, natural disasters pose a threat to society, where the Sendai Framework of disaster risk reduction focuses on.","name":"EO for emergency, security & defense","selfAssesment":"New
"},{"code":"TA12-9","description":"Water is an essential resource for food production. Growing crops requires significant quantities of water. Without sufficient, good quality and easily accessible water, agri-food production is under threat.","name":"EO for water sustainability","selfAssesment":"New
"},{"code":"TA12","description":"EO provides timely, continuous and independent data for monitoring indicators of the progress of the society in various societal challenges.\r\nEO monitoring supports activities that address societal & environmental challenges. This happens indirectly along a chain: e.g. a regularly provided EO information product derived from EO data of a satellite is integrated as a parameter in a climate model / Earth system model. This climate model enables the development of regulations (and their enforcement through constant monitoring) to implement climate change mitigation measures. Thereby, the chain is characterized by seveal connected nodes: from societal challenges to use cases of users to EO applications to EO products to specific satellites and their sensors.\r\n[Communities that promote collaboration among diverse stakeholders from academia, industry, public administration as well as local residents] \r\nScientific agendas address societal challenges and the EO/GI community can contribute to them. Consortia usually include experts from academia (researchers, developers, scientists), EO companies, and members from the user community such as public authorities.","name":"EO for societal and environmental challenges","selfAssesment":"New
"},{"code":"TA13-1-1","description":"Monitor the atmosphere includes monitoring of the atmosphere composition and air quality, as well as forecasting of sunlight exposure. Timely, continuous, and independent data on the atmosphere is useful in various domains like health, agriculture, renewable energies, urban planning, climate sciences and biology.\r\nThe atmosphere composition includes greenhouse gases (GHG) like carbon dioxide, methane, NO2 and SO2. They are part of the Earth system and have a strong impact on the climate. To monitor changes in atmosphere composition enables modelling climate change and understanding the impact of human-induced emissions of GHG relative to natural sources. EO-derived products include inventory of emission data as an input to atmospheric chemistry transport models and forecast models. Inventories are based on a combination of existing data sets and new information, describing emissions from fossil fuel use, ships, volcanoes, and vegetation. This ensures good consistency between the emissions of greenhouse gases, reactive gases, and aerosol particles and their precursors.\r\nAir quality describes the composition of the atmosphere from gases and particles near the Earth's surface. Local emissions from different sources (e.g. energy production, industrial production, traffic) cause changes to the atmospheric composition that are highly variable in space and time. The quality of the air we breathe can significantly impact our health and the environment. Therefore, it is highly relevant to monitor air quality and emissions. EO satellites are capable of monitoring aerosols, tropospheric O3, tropospheric NO2, CO, HCHO, SO2, and particulate matter (of the sizes PM 2.5 and PM 10). Products like air quality assessment reports, daily ozone forecasts, and UV-index forecast maps are produced that are applied in specific use cases, particularly related to health.\r\nThe amount of solar radiation that arrives at a location on the Earth surface depends on the atmosphere composition and varies over the day and the seasons. Information on solar radiation is useful in various domains. Applications of sunlight and ozone data are for example real-time UV radiation forecasting and risk assessment, skin health services, climate change studies, assessment of ozone protection policies effectiveness, plant growth and disease control, evaporation and irrigation models, power generation, solar heating systems planning and monitoring.","name":"Monitor the atmosphere","selfAssesment":"Completed
"},{"code":"TA13-1-2","description":"Monitoring the climate includes monitoring climate forcing and the carbon balance and assessing climate change risks.\r\nClimate forcing describes the imbalance of the Earth’s energy budget due to natural or human-induced sources. This imbalance results in a change in the globally-averaged temperature. Amongst the contributors of positive climate forcing, that leads to an increase in the globally-averaged temperature, the increase of carbon dioxide in the atmospheric composition is considered to be the most important factor. Changes in the carbon dioxide concentration indicate that the exchanges between carbon sources and sinks are not balanced. It can be shown that human-induced emissions of carbon dioxide are responsible for the increase of the carbon dioxide since the industrialisation.\r\nWith EO, we can monitor changes in greenhouse gases (GHG), aeorosols, albedo, and solar radiation. The dynamic nature of the climate makes it necessary to apply equally dynamic EO monitoring that allows to deliver key information on historical, seasonal forecast and projection periods for climate-related indicators.\r\nRelevant EO products include estimates of the climate forcing of aerosol, ozone and greenhouse gases. The dynamic nature of the climate makes it necessary to apply equally dynamic EO monitoring that allows to deliver key information on historical, seasonal forecast and projection periods for climate-related indicators. \r\nThe products are particularly relevant to the European energy sector in terms of electricity demand and the production of power from wind, solar and hydro sources. \r\nMoreover, water management uses EO-derived information about climate change to mitigate effects of changing precipitation patterns to adapt their strategies, and to prepare for climate variability and change in the water sector, e.g. because of changes in river discharge, droughts and floods.\r\nFinally, insurance uses climate change information for assessing the weather risks to insured assets that change with the climate-related increase in extreme weather conditions. This includes products like up-to-date catalogue of wind storms and their associated impacts on the ground.","name":"Monitor the climate","selfAssesment":"Completed
"},{"code":"TA13-1-3","description":"The weather is the state of the atmosphere measurable by its temperature, humidity, precipitation, and other atmospheric variables. To forecast the weather is a major branch in the field of meteorology. In comparison to climate, weather can only be predicted for a short period of time (minutes to month), because it describes the state of the atmosphere for specific days at specific locations. For a reliable weather forecast, a good numerical prediction model with precise initial conditions is needed. Models are sensitive to changes in the initial condition, that is why at the moment weather predictions are only accurate for few days. However, both models and the determination of initial conditions are steadily improved. EO makes a significant contribution to improving the initial conditions by providing global information several times a day. As the quality of the EO products improves, the weather forecast also improves. \r\nSince decades, satellites are used to monitor and forecast weather. Therefore, it is one of the most established sectors of satellite data applications. There are geostationary and polar-orbiting weather satellites that measure all kinds of meteorologically relevant variables, e.g. cloud coverage, wind speed [...] via passive or active imagery. However, not only satellites are used to collect information, but also other remote sensing techniques that can be airborne or ground-based such as Lidar.\r\nWeather forecasts are used by citizens for decisions in everyday life, in agriculture for crop cultivation decisions and in the stock markets. Other domains of applications are hydrometeorology, aviation, maritime navigation, and the military and nuclear sectors.","name":"Forecast the weather","selfAssesment":"Completed
"},{"code":"TA13-1","description":"Monitor the atmosphere and climate includes all change-focused services/applications which assess, monitor, forecast and provide timely, continuous and independent data (e.g. temperature, humidity, emissions, greenhouse gases, solar UV radiation, aorosols,...). It closely monitors each of the Earth's different subsystems and, besides being the basis for weather forecasts, helps to better understand and evaluate the impact of the climate change.","name":"Monitor the atmosphere and climate","selfAssesment":"New
"},{"code":"TA13-2-1","description":"Monitor critical information about offensive and defensive systems. This deserves a category in its own right since the nature of observations is quite different from many others.","name":"Monitor critical assets","selfAssesment":"New
"},{"code":"TA13-2-2","description":"Monitoring health can be delivered indirectly by monitoring environmental changes that can cause endemic and chronic diseases. Typically monitored environmental factors are temperature, humidity, stagnant water, NDVI, land cover, or soil type.","name":"Monitor health","selfAssesment":"New
"},{"code":"TA13-2-3","description":"Monitoring food security includes the monitoring of food availability by environmental conditions (land cover, NDVI,...), as well as the monitoring of migration patterns. Risks that can lead to food insecurity are hazards or conflicts.","name":"Food security monitoring","selfAssesment":"New
"},{"code":"TA13-2-4","description":"Monitoring borders includes monitoring the land and marine border incursions, monitoring transport routes, assessing pressures on poplulations, and monitoring humanitarian movement.","name":"Monitor borders","selfAssesment":"New
"},{"code":"TA13-2","description":"Monitor security and safety describes the collection and analysis of information to provide intelligence services & safety. The task is to give early warnings in case of emergencies, to monitor infrasturcture, transport routes (land and water) and borders, to surveil security and sovereignty.","name":"Monitor security & safety","selfAssesment":"New
"},{"code":"TA13-3-1","description":"EO is capable to repeatedly map flood extent directly after flooding, including further aspects (flood plain, extend mapping, frequency, rainfall, flash floods, vulnerability, inundation, risk-based mapping & management; flood spread and depth followed by automated insurance payouts). Modelling (hydrological modelling and monitoring focused on seasonal dynamics of water availability) based on EO data (digital elevation models) supports flood risk assessment.","name":"Map and assess flooding","selfAssesment":"New
"},{"code":"TA13-3-2","description":"For the outbreak of forest fires, satellite remote sensing can be continuously track and monitor, in a timely manner to grasp the development of forest fires. Beyond, weather monitoring enables to forecast weather conditions where fires are likely, allowing authorities to prepare.","name":"Detect and monitor wildfires","selfAssesment":"New
"},{"code":"TA13-3-3","description":"Damages from earthquakes to infrastrcture can be detected directly, e.g. by mapping collapsed buildings in optical data to derive rapid response products. Use of SAR interferograms enables to identify geotectonic shifts. Modelling enables to identify hotspot areas.","name":"Assess damage from earthquakes","selfAssesment":"New
"},{"code":"TA13-3-4","description":"Landslides are a natural hazard posing a threat to human life, property, infrastructure, and natural environment. Every year, slope instabilities have a significant impact on societies and economies. Consequently, landslide documentation is used for risk assessments, policy making and enforcing of construction regulations. Landslide monitoring is used to ensure safety of infrastructure operation. Rapid mapping of landslides and associated damages is done for response actions, e.g. of civil protection organizations. As ground surveys are very costly and time-consuming, satellite remote sensing is increasingly used to assess damage resulting from landslides.\r\nLandslides lead to local terrain changes after a downslope movement of material under the effect of gravity. They vary by type of movement (e.g. falling, toppling, gliding and flowing), by size (from small rocks to entire mountain slopes) and velocity (from a couple of millimetres per year up to free-fall speed). Landslides can be triggered both by natural causes (like earthquakes or heavy rainfall events) and human causes, e.g. mining activities that lead to slope failures. Landslides can initiate other natural hazards, e.g. when a landslide blocks a river a lake can be formed which poses a risk for an outburst flood. \r\nLandslides are diverse in appearance, and therefore are challenging to detect. EO-based assessment methods aim for detecting changes to the land surface and surface displacements. \r\nEO satellites and airborne remote sensing use optical sensors for detecting landslides in post-event images and land cover changes caused by landslides, primarily indicated by the removal of vegetation and the exposure of bare soil, by comparing pre-event and post-event images. Typical resolutions of optical EO data for mapping rapid landslides are between 0.4 m and 30 m, depending on the size of landslides caused by the triggering event. Optical data from unmanned aerial vehicles are used in cases where single landslides or concise regions have to be covered. Additionally, synthetic aperture radar (SAR) sensors allow the detection of subtle changes in ground deformation caused by landslides. Therefore, time-series of radar images are used. Further, airborne laser scanning enables the generation of digital elevation models (DEMs) that allow identification of landslide surface structures and, in case of repeated coverage, detection of elevation changes. DEM generation for analysing landslides is also possible with photogrammetry on stereographic optical data and radargrammetry on SAR images.\r\nThe diversity of appearances of landslides leads to challenges for (semi-)automatic image processing and makes visual interpretation of EO data by a landslide expert a commonly used method for landslide mapping. However, visual interpretation is subjective and experts’ results can be very diverse. Additionally, it is a slow and time-consuming process. Semi-automated classification based on optical and DEM data using object-based image analysis (OBIA) can achieve detailed interpretations of landslides while reducing the analysis time. Interferometic SAR (InSAR) techniques, such as persistant scatterer interferometry (PSI) or Small Baseline Subset (SBAS), are primarily used to identify and monitor slow-moving landslides and for quantifying movement rates. Integrated analysis of optical, DEM and SAR data allow to fully exploit the potential of EO data from different sensors for landslide mapping and assessment.","name":"Forecast and assess landslides","selfAssesment":"Completed
"},{"code":"TA13-3-5","description":"In context of volcanic activities and volcanos, EO methods are capable to provide information about various aspects, including ground motion (seismic), volcanic eruptions (pre-eruptive, sin-eruptive, atmospheric ash, dispersion), Rapid damage estimation (prevention), earthquake damage extent (loss adjuster dispatch). classification of land cover types","name":"Assess and monitor volcanic activities","selfAssesment":"New
"},{"code":"TA13-3-6","description":"Multi-hazard assessment both focuses on regions prone to several geohazards and on the interrelationships between hazards, i.e. what happens if two disasters strike at the same time or what happens when one disaster is causing a cascade of disasters with a strongly amplified impact (e.g. a landslide causing a dammed river causing an outburstflood with a magnitude beyond the design of protective measures; or an earthquake in a coastal region that is followed by a tsunami). EO can provide imformation on the single disasters and, through integration and comprehensive impact assessment, enables multi-hazard assessment.","name":"Multi-hazard assessment","selfAssesment":"New
"},{"code":"TA13-3","description":"Assess disasters and geohazards by EO includes alert & early warning, emergency mapping, and risk & recovery mapping. It relates to observations, controlling, assessments that are linked to natural and human made risks. Typical disasters that can be assessed by EO are in particular floods, droughts, forest fires, landslides, tsunamis, earthquakes, cyclonic storms and volcanic eruptions. Since with EO it is possible to quickly analyse the risk or damage it is used to effectively plan emergency response actions.\r\nThere are several measures to minimize or prevent the damage caused by disasters. Some of them have to be carried out in anticipation of a disaster, others after the occurrence of an event. The different phases that are needed to reduce or avoid the impact and to assure rapid response and recovery are described in the disaster management cycle. Depending on the cycle phase, EO has to meet different requirements. The Mitigation and Preparedness phase are passed through in anticipation of a disaster event. Thus, requirements to EO products may focus on high completeness of mapping or high accuracy of mapping. In contrast, Response and Recovery phase include rapid mapping, thus EO capabilities must meet near real-time delivery requirements. \r\nAs well, the nature of the disaster determines which EO products are used. Optical sensors are used throughout the different types; however, landslides are mostly assessed by radar sensors and thermal sensors are additionally used for forest fires.","name":"Assess disasters & geohazards","selfAssesment":"New
"},{"code":"TA13-4-1","description":"To monitor crops and agriculture with EO-based methods is relevant for various applications, including to assess environmental impact of farming, assess crop damage due to storms, to detect ollegal or undesired crops, to monitor water use on crops and horticulture, and to monitor land degradation neutrality. EO mapping of crops happens on all scales with both optical and SAR sensors. Relevant EO products include degradation, agri-environment, ecosystem, damage estimation, warning-service, food-security, impact, crop health (disease and stress), leaf area index, crop acreage and yield harvest (inventories / statistics), crop types (extent, growth, health, stress), land surface temperature, illicit crops, estimates, cultivation patterns, soil water index, surface soil moisture, run-off, land cover (land cover change), land productivity (net primary productivity, NPP), carbon stocks (soil organic carbon, SOC).","name":"Monitor crops","selfAssesment":"New
"},{"code":"TA13-4-2","description":"Monitor the forest focuses on regular and periodic measurement of certain parameters of forests (physical, chemical, and biological) to determine baselines to detect and observe changes over time. Typical applications include to assess deforestation and forest degradation, assess forest damage due to storms or insects, to monitor forest resources, detect illegal forest activities, assess the environmental impact of forerstry, and to monitor the forest carbon content. Moderate resolution sensors have been used to map forests at large scales. Modern very high resolution optical sensors provide enough spatial and spectral detail to map individual trees. Further sensors for forest monitoring include SAR and LIDAR. Integration of optical sensors, LIDAR and in-situ measurements seems an accurate method to achieve third dimension forest mapping.","name":"Monitor the forest","selfAssesment":"New
"},{"code":"TA13-4-3","description":"EO provides the opportunity to monitor bodies of water, i.e. inland waters, and to assess ground water and run-off. For lakes, this includes products about water quality, pollution, turbidity, suspended sediment concentrations (quantitative, qualitative), waterbody (temperature, extent, volume, quantity), algal blooms, alkaline water, evaporation, surface temperature. For ground water and run-off, the products focus on water run-off (water quantity), hydrological network and catchment areas (water catchment), run-off season, groundwater. Various scales are addressed, from local catchments to the global water cycle. For inland water quality, sensors are optical medium resolution (300 meters) for achieving a (strongly cloud-cover dependent) update frequency of 10-20 times per year and high resolution (5 meters) for update frequency of 3-5 times per year.","name":"Monitor bodies of water","selfAssesment":"New
"},{"code":"TA13-4-4","description":"Monitoring of snow and ice focuses on glaciers and their retreat due to climate change (extent, mass balance), the seasonal snow cover (its extent, depth, temperature and snow water equivalent), and the ice on rivers and lakes (inland ice, thickness, freezing period, melting period, ice extent). Glacial monitoring in the mountainous regions around the globe, and of the Greenland and Antarctic ice shields uses optical EO data of high and very high resolution and SAR data. Satellite based daily snow covered area products can reliably be provided down to a spatial resolution of 500 meters. Global products are possible with weekly updates. Applications include, among others, climate change impact monitoring, relevant for modelling runoff patterns in catchments for etimating hydroelectric power generation potential.","name":"Monitor snow and ice","selfAssesment":"New
"},{"code":"TA13-4-5","description":"EO is used to monitor land ecosystems and biodiversity, environmental impact of human activities, land pollution and vegetation encroachment. A tool for this is land cover mapping and mapping of land cover change about a wide set of categories, lincuding basic forest types, major agricultural surface types, conservation areas, settlements, infrastructure, primary roads, bare soil, water bodies, rivers, wetlands following standard classification schemes according to CORINE or FAO LCCS. Main source are optical EO data and associated pixel-based and object-based image classification methods. For discriminating vegetation classes, they often making use of various vegetation indices and biophysical parameters.","name":"Monitor land ecosystems","selfAssesment":"New
"},{"code":"TA13-4-6","description":"EO technologies (both optical and SAR) are capable to categorize bio-physical coverage of land to produce land cover maps like CORINE Land Cover (CLC). The EO method is objective and allows for frequent updates. EO-derived land cover is an excellent basis for mapping land use, the socioeconomic use that is made of land. Land use products are used in a wide range of applications (e.g. agriculture, forestry, spatial planning, determining and implementing environmental policy, land accounting). In a humanitarian context, land use mapping is applied to map refugee camps, population and pressures on population that cause migration.","name":"Monitor land use","selfAssesment":"New
"},{"code":"TA13-4-7","description":"EO is capable to monitor topography with various types of land surface elevation data (both digital terrain models and digital surface models) and also focus on land surface changes and ground deformation / movement due to e.g. soil erosion or permafrost thawing, frost heaving. This includes also the mapping of stable zones where such changes do not happen. The main ways of creating a digital elevation model (DEM) from EO data are deriving it from interferometric synthetic aperture radar (InSAR), from stereoscopic pairs of optical images acquired from different viewing angles, and deriving them via laser scanning.","name":"Monitor topography","selfAssesment":"New
"},{"code":"TA13-4-8","description":"EO is able to extract information about subsurface geology, including near surface features, lithology features, and linear disturbance features (faults & discontinuities). Concerning monitoring of mineral extraction EO supports by mapping ground surface, illegal activities, mine waste (erosion, land subsistence, biodiversity/habitat loss, destruction & disturbance of ecosystems). Disturbance of ecosystems may happen by carbon seeps from reservoirs or pipelines. Their detection can also be done with EO data.","name":"Extract information about subsurface geology","selfAssesment":"New
"},{"code":"TA13-4","description":"Services that monitor land cover all services/applications that are focused on monitoring, assessing, managing, planning and improving land areas, its ecosystems (land, soil and inland water monitoring/quality/availability & usage assessments) and evolution of the land surface (use, cover, seasonal and annual changes and monitors variables) even if it involves human intervention (environmental challenges, impact evaluation or suitability analysis).\r\nMonitoring is possible by deriving information from variables measured by EO in different domains, like vegetation, energy, water, and cryosphere. For vegetation, those variables are for example land cover, NDVI, burnt area, or surface soil moisture. In the energy domain, land surface temperature and surface albedo are known variables, for water it is water surface temperature or water quality. Finally, for the cryosphere lake ice and snow cover extent, and snow water equivalent are variables that are used for land monitoring services.","name":"Monitor land","selfAssesment":"Completed
"},{"code":"TA13-5-1","description":"The full range of EO satellite sensors are capable of monitoring particular aspects of urban areas. The most relevant include SAR satellites such as TerraSAR-X that distinguish between urban fabric and other land cover. Further, optical satellites in the resolution range HR and VHR are used to map imperviousness and soil sealing. Beyond such land cover classifications with low granularity, HR and VHR data are used for producing detailed land use and land cover classifications that distinguish different settlement densities or, in combination with additional data, different land use such as transport, residential etc. as defined in Classification schemes specialized on urban areas. Airborne laser scanning (and stereographic analysis) maps building and vegetation heights. InSAR methods allow to measure land subsidence that is highly relevant e.g. in coastal cities close to or below the sea surface elevation. Night-time optical data maps lights. Thermal sensors allow mapping the heat that is radiated from cities. Typical applications include monitoring urban growth/sprawl, transport networks, urban heat islands, and generating city maps and 3D city models for urban planning that are relevant to users in smart cities and in local/regional planning.","name":"Monitor urban areas","selfAssesment":"Completed
"},{"code":"TA13-5-2","description":"EO is capable of monitoring infrastrcture in general, i.e. buildings (and their construction) and transport networks (roads, rails). Additionally, infrastructure for renewable energy harvesting (solar and wind farms, hydroelectric powerplants) and identification of suitable sites (through mapping solar radiation, wind roses, speed and direction, hydrological network mapping). A basis is land surface mapping for deriving digital elevation models (DEMs) that is required for modelling renewable energy potential and for spatial planning and landscape visibility analysis (visual impact assessments for planned infrastructure). Further, EO is capable of assessing damage from industrial accidents. A wide range of EO technologies is used here, infrastrcture can be directly detected and mapped with optical and SAR sensors, where the resolution depends on the targeted assets. DEMs can be generated from SAR and stereographic optical data. Wind energy related parameters can be derived from satellites focused on atmosphere and weather monitoring. Further, there are various GI methods in use, too (in particular focused on spatial planning and impact assessment).","name":"Monitor infrastructure","selfAssesment":"New
"},{"code":"TA13-5","description":"Monitoring the built environment provides information about urban structures, transport networks and particular infrastructure, e.g. dedicated to energy provision. It covers all urban and infrastructure related service/applications on site development information, planning support or suitability analysis. As well, it includes pressure and threats analysis on the urban areas.","name":"Monitor the built environment","selfAssesment":"New
"},{"code":"TA13-6-1","description":"EO is capable of monitoring ocean quality and productivity by focusing on ocean colour (that show among other thins chlorophyll and algal bloom), parameters of sea surface salinity (SSS) and sea surface temperature (SST). In addition, EO can monitor pollution at sea that that explains coastal water quality, which is relevant for aquafarms and for tourism (bathing area water quality). Further, EO satellites can detect oil slicks and spills and threats from such events. Many of these parameters and detected features are relevant for monitoring marine habitats, targeting in particular generic algal blooms, marine mammals, sea surface temperature, sediments, plumes, nutrients, dredging operation, coral reef health assessment (bleaching).","name":"Monitor the marine ecosystem","selfAssesment":"New
"},{"code":"TA13-6-2","description":"In coastal areas, EO is capable to monitor water depth and shallow water bathymetry (charting), coastal ecosystem parameters about water temperature, water transparency, oxygen, phytoplankton abundance, bathing water indicators, detection harmful algal blooms, sediment (qualitative, quantitative), turbidity (quality, quantitative), visibility, chlorophyll-a concentration, suspended sediment may be indicative of estuarine processes, re-suspension or pollution. Further, this includes coastline monitoring with a focus on shoreline and its change as well as coastal land cover (and terrain) and its change. A widse set of EO sensors and technologies is used to monitor coastal areas. Optical satellite imagery is analyzed to detect and map suspended sediment concentrations. Etc.","name":"Monitor coastal areas","selfAssesment":"New
"},{"code":"TA13-6-3","description":"EO is capable to monitor weather impact on ocean surface and metocean features as a basis for forecasting furture ocean conditions. This includes ocean surface topography, ocean dynamics and circulation like tides and ocean current movements and drift, ocean winds, wave and climate conditions at ocean locations (meteocean). Further, this covers the mapping of extreme waves like tsunamis and the monitoring of hurricanes and typhoons. Involved EO technologies are for example satellite altimetry that maps ocean surface with 2 cm to 3 cm accuracy, mathematical forecast models. Repeated altimetry measurements allow mapping speed and direction of ocean's currents and tides. Available EO-based RADAR systems monitor wave height and direction, wind speed and sea-surface elevation. Near-realtime processing and delivery workflows enable the use of these parameters in weather forecasting, navigation and offshore installations protection.","name":"Monitor weather impact on ocean surface","selfAssesment":"New
"},{"code":"TA13-6-4","description":"To support an ecosystem-based approach for fisheries management, EO images with global and daily systematic coverage with high-resolution images can help in identifying potential fishing zones and to assess fish stocks. They help assessing and understanding changing abundancy and spatial distribution of exploited fish stocks. Therefore, they analyse various key environmental parameters that can be detected with satellite remote sensing. This includes sea surface temperatures (SSTs), sea surface height anomalies, and sea surface colour revealing the abundance of chlorophyll a. This relates to phytoplacton production that is directly related to total fish landings. Additionally, EO can detect harmful algal bloom. A further threat to sustainable fish stocks management are illegal fishing. Where localization of licensed fishing vessels and fleet management services are supported by EO to avoid overexplotation and enable recovery of fish stocks. EO complements identification, detection and tracking of vessels with SAR and optical remote sensing.","name":"Monitor fisheries","selfAssesment":"New
"},{"code":"TA13-6-5","description":"For shipping, navigation, and monitoring sea-traffic and pollution, remote sensing and satellite technologies allow detecting vessels in the wider ocean. EO can detect the vessels themselves, their wake trailing behind them, sandbanks and reefs that pose a threat for safe navigation. Additionally, EO can detect pollution from the ships, e.g. when illegal waste disposal happens. Ship detection and classification is possible with the use of optical and synthetic aperture radar (SAR) imagery. The methods complement each other.","name":"Detect and monitor ships","selfAssesment":"New
"},{"code":"TA13-6-6","description":"Information on sea ice and icebergs is important for managing operation of ships or offshore platforms in hazardous sea ice conditions. EO technologies give the possibility to study sea ice and measure its thickness, spatial distribution, motion and ridges (as well as ice berg positions). Satellite imagery provides wide area, synoptic pictures of the ice conditions. Since the scale of ice fields is quite large, mainly moderate resolutions have to be accepted, down to around 10m in scale, while ensuring comprehensive coverage. Multispectral imagery can provide more information on ice-type but in the main, SAR imagery is used due to its all-weather and day/night capability. The data collected can be more accurate than in-situ measurements due to a higher and faster coverage of a whole area. Subsequent modelling that incorporates ocean weather (wind, waves, ocean current) provides expected drifting paths. Constant monitoring is most important to identify the risk and opportunities, for instance for ship routing, and safety of oil rigs.","name":"Monitor sea-ice and icebergs","selfAssesment":"New
"},{"code":"TA13-6","description":"Monitoring marine inlucdes monitoring of marine safety (e.g. marine operations, oil spill combat, ship routing, defence, search & rescue, ...), marine resources (e.g. fish stock management, ...), marine and coastal environment (e.g. water quality, pollution, coastal activities, ...), and climate and seasonal forecasting (e.g. ice survey, seasonal forecasting, ...).","name":"Monitor marine","selfAssesment":"New
"},{"code":"TA13","description":"EO services and applications are organized according to thematic areas. EO is used for a wide set of services. There are many applications of EO that show how a service produces information for a particular client. EO service and applications are best described by the purpose they serve or by the need of the user. The main user needs to EO are to monitor, to map, to forecast, to assess, to detect, and to analyse. \r\nTo monitor means to watch and check a situation carefully for a period of time in order to discover something about it, i.e. keeping track of how the natural and manmade environment change (their status) over time. Typical alternative verbs are track, observe, record, follow, understand, or surveil. \r\nTo map means to represent an area of land in the form of a map, i.e. to feature and locate the way it is arranged or organized. Synonymous verbs are locate, identify, classify, trace, or record.\r\nTo forecast means to provide statements covering a range of different outcomes, to say what you expect to happen in the future; i.e. to predict future events based on specified assumptions (about information extracted from EO change and time series data), where different sets of assumptions describe scenarios. Equivalent terms are predict, plan, model, estimate, or project.\r\nTo assess means to judge or decide the amount, value, quality or importance of something, i.e. to evaluate and measure the status of and changes in natural and manmade built environments. Alternative verbs are evaluate, measure, understand, review, or quantify.\r\nTo detect allows to notice something that is partly hidden or not clear, or to discover something, especially using a special method, i.e. to identify and locate the changes in the Earth’s environment. Similar terms are locate, warn, identify, highlight, or spot.\r\nTo analyse means to study or examine something in detail, in order to discover more about it, i.e. to detail the elements of a whole and critically examine and relate these component parts separately and/or in relation to the whole. Sometimes, the terms to process, to parse, or to detail are used in exchange for to analyse.","name":"EO services and applications","selfAssesment":"New
"},{"code":"TA14-1-1","description":"Band combinations are pre-defined for (visually) analysing images for a dedicated purpose. Examples are dedicated band combinations for land us land cover classification, ocean colour, etc.","name":"Band combinations","selfAssesment":"New
"},{"code":"TA14-1-2","description":"The spectral and refractive information from optical and SAR data enables direct and indirect derivation of biophysical and geophysical EO parameters that are properties of the sensed land surface, ocean surface and atmosphere volume.","name":"EO parameters","selfAssesment":"New
"},{"code":"TA14-1","description":"Processing products are image products from raw data to all different processing stages. The transformation processes between the stages include operations such as atmospheric correction, cloud detection and radiometric calibration to provide data in a form suitable for subsequent analysis. Processing products consider a product as being an output of a process.They appear as \"intermediate products\" along all steps of the processing chain.","name":"Processing-related and preparatory products","selfAssesment":"New
"},{"code":"TA14-2-1-1","description":"Point clouds represent a set of points with X, Y, Z coordinates and associated attributes. A source of acquisition is Light Detection and Ranging (LIDAR), an airborne surveying technique that uses laser light to measure the distance to an object on the ground.","name":"Point clouds","selfAssesment":"New
"},{"code":"TA14-2-1-2","description":"Elevation data in the form of a digital elevation model (DEM) is an essential component of many analyses derived from EO. DEMs are used to represent every kind of surface, including terrain surface, vegetation canopy surface, sea surface, sea-ice surface, glacier surface etc. This description focuses on DEMs for representing terrain. A digital terrain model (DTM) describes the bare ground of the terrain, a digital surface models (DSM) described heights of vegetation (e.g. trees) and of man-made structures (e.g. buildings) reaching above the terrain. DEM is often used as an umbrella term for DTM and DSM. EO-derived DEMs are usually DSMs and require removal of vegetation and buildings in order to represent the terrain (DTM). DEMs are multi-purpose products used in various applications. They are available for global scale (SRTM, WorldDEMTM), regional scale (ArcticDEM, Copernicus EU-DEM v1.1) or for national levels and local regions. Various techniques exist to generate DEMs from SAR data, stereographic optical EO (as well as airborne and drone) data and from airborne laser scanning.","name":"Digital elevation models","selfAssesment":"Completed
"},{"code":"TA14-2-1-3","description":"By comparing elevation models of different dates, the change in elevation and volume can be identified. Thereby, they measure surface deformation, land subsidence, ice shield loss due to melting, etc.","name":"Elevation change maps","selfAssesment":"New
"},{"code":"TA14-2-1-4","description":"Vector fields capture the movement directions of locations on a continuous surface, e.g. of the ocean, or in a 3D grid of locations, e.g. of the atmosphere. The atmosphere and the ocean are highly dynamic features. Vector fields are used to represent wind directions and current movement directions. Further vector fields derived from EO data include geoid undulation / gravity maps.","name":"Vector fields","selfAssesment":"New
"},{"code":"TA14-2-1-5","description":"When a moving feature (i.e. object) is detected in subsequent images, its trajectory of movement can be mapped. Such products map ship movements, sea ice movements, etc.","name":"Feature trajectories","selfAssesment":"New
"},{"code":"TA14-2-1","description":"Geometrically measured EO products origin from EO-derived distance measurements, measurements of direction, tracking of moving objects, and changes of distance measurements. The used EO methods include for example SAR interferometry and stereographic analysis of optical data.","name":"Geometrically measured EO products","selfAssesment":"New
"},{"code":"TA14-2-2-1-1","description":"Land cover maps represent spatial information on different types (classes) of physical coverage of the Earth's surface, e.g. forests, grasslands, croplands, lakes, wetlands. An example is the European Copernicus product CORINE land cover (CLC) with 44 classes. Initiated in 1985 (reference year 1990), updates followed in 2000 and every 6 years afterwards. Apart from CLC, the European Copernicus Land products also include the High Resolution Layers. They includes for example the imperviousness product that captures the percentage of soil sealing. Land cover classification products are multi-purpose products that are relevant for various applications. They are available on national levels, regional levels and global levels. They have different scales and granularity of their associated classification scheme. The products are updated on a regular basis. Update cycles can vary depending on the resolution (i.e. likelihood for observable change of the land surface) and the capability of production processes. An additional example on a global scale is the Global Urban Footprint. The products are provided by public organisations and private EO companies and based on various EO sensors.","name":"Land cover maps","selfAssesment":"Completed
"},{"code":"TA14-2-2-1-2","description":"Land use documents how people are using the land. Getting from physical land type (land cover) to land use requires skill in interpretation and involves integration and consultation of ancillary data. Land use maps are multi-purpose products that are relevant for many applications. The products are updated on a regular basis (e.g. 6 years for Urban Atlas).","name":"Land use maps","selfAssesment":"New
"},{"code":"TA14-2-2-1-3","description":"Cloud masks for optical EO data distingush cloudy pixels from cloud-free pixels. They may differentiate between serveral cloud types, i.e. opaque clouds and Cirrus clouds (that are transparent). Most land monitoring applications based on optical data require cloud-free images. Therefore, cloud masks are a product that is used early on in image processing for selecting suitable imagery for analysis (e.g. by screening images of an archive by the derived cloud cover percentage of the image). Therefore, cloud masks are made available as metadata by the EO data provider. Clouds are identified with threshoulding of reflectance values of the blue band and, to adapt for cloud/snow confusion, specific short-wave infrared (SWIR) bands.","name":"Cloud mask","selfAssesment":"New
"},{"code":"TA14-2-2-1-4","description":"Detected features are objects from one or more classes and are the result of a comprehensive (and mostly automatic or semi-automated) search of all locations in an image that decides whether such features are present and where they are located. Examples inculde man-made objects (e.g. vehicles, ships, buildings, etc.) with sharp boundaries and are independent from the background, and landscape objects, such as land-use/land-cover (LULC) parcels that have vague boundaries and are part of the background environment. Only the latter type would locate features for all locations of an image.","name":"Detected features","selfAssesment":"New
"},{"code":"TA14-2-2-1","description":"Static EO derived thematic classification products and masks (e.g. land use land cover classifications). Additionally, static EO detected features (planes on apron of airports, dwellings) that consist of a set of point locations (or polygons) and do not end up in a comprehensive classification of all pixels of an image. Static EO derived thematic classification products and masks (e.g. land use land cover classifications). Additionally, static EO detected features (planes on apron of airports, dwellings) that consist of a set of point locations (or polygons) and do not end up in a comprehensive classification of all pixels of an image. Thematic classifications and feature detection identify a surface by a class label that represents a more or less persistent state. A good example product is the Copernicus Urban Atlas. The most recent available version is assumed to represent the \"current\" state (Certainly, an update cycle is necessary for providing a product that remains up-to-date).","name":"Thematic classifications and feature detection","selfAssesment":"New
"},{"code":"TA14-2-2-2","description":"Event maps and thematic change (evolution) maps indicate that some process happened that changed the area at a location from one class to the other. For example, a burnt area map indicates locations where vegetation has been burnt by a fire and changed to bare ground. A typical mapping method is the use of pre- and post-event satellite images for detection of the areas affected by the process. Eventually burnt areas contain identifiable burn marks that allow direct identification in one single post-event satellite image. Nevertheless, it is the process that is central to the analysis. Similarly, the concepts aforestation and deforestation would fall under the heading \"Event maps.\" They may come from a comparison of two status maps of different dates. Some processes benefit from analysis of more than two states. Such change evolution maps can be produced with time-series analysis. On land, more examples include landslide maps, flooded area maps and other land surface dynamics (e.g. aforestation and deforestation). Further, change detection maps are available for other domains (atmosphere, marine, land, climate, etc.)","name":"Event maps and thematic change (evolution) maps","selfAssesment":"New
"},{"code":"TA14-2-2","description":"The semantic labelling products result from methods that assign labels to objects or locations in a field. The labels correspond to the categories of a classification or, in case of masks and detected features, to a single target class. Such labels may also identify classes of change or change evolution.","name":"Semantic labelling products","selfAssesment":"New
"},{"code":"TA14-2-3","description":"EO-derived attribute products describe the state and evolution of specific attributes of a feature or at a field location. They describe for example air quality, soil moisture or water quality & quantity.","name":"EO-derived attribute products","selfAssesment":"New
"},{"code":"TA14-2","description":"Descriptive analytics products provide analytical results which describe the present (and past) situation as it is recorded in EO images. Therefore, it contains information that can directly be extracted from EO images or EO image time series. These products are diverse in various aspects: they capture static and dynamic information; they concern information about objects or fields; and they have qualitative (nominal scale) or quantitative (ordinal, interval, ratio scale) levels of measurement.","name":"Descriptive analytics products","selfAssesment":"New
"},{"code":"TA14-3","description":"Providing analytical (modelling) results which predict the future situation (e.g. air pollution forecasts). [interpolation in space, i.e. not only prediction into the future, filling gaps in time series...]\r\nInformation that can be modelled based on descriptive analytics products. by extrapolating time series (forecasting/predicting), by modelling of processes (e.g. flood risk maps, landslide susceptibility)","name":"Predictive modelling products","selfAssesment":"New
"},{"code":"TA14-4","description":"Prescriptive modelling products and services focus on providing analytical results that are a guide to action. The often result from an impact assessment. One example is the identification of construction sites leading to sales opportunities.","name":"Prescriptive modelling products and services","selfAssesment":"New
"},{"code":"TA14-5-1","description":"A textured 3D model uses a 3D model derived from elevation data. Additionally, each separate surface of the 3D model receives its own texture derived from optical image data. Typically used for visualisation purposes.","name":"Textured 3D models","selfAssesment":"New
"},{"code":"TA14-5-2","description":"A semantic 3D model consists of a 3D model derived from elevation data with an integrated image classification. A classified object thereby consists of a 3D surface or a grouped set of 3D surfaces. A typical example is a 3D city model in the CityGML format.","name":"Semantic 3D models","selfAssesment":"New
"},{"code":"TA14-5","description":"Combining the satellite data with other information sources. Resulting in an integration of several descriptive analytics products and processing products, e.g. a textured 3D model or a semantic 3D model.","name":"Aggregation and integration products","selfAssesment":"New
"},{"code":"TA14-6-1","description":"Sentinel-2 cloud-free mosaics for display, satellite maps in books etc.","name":"Satellite maps","selfAssesment":"New
"},{"code":"TA14-6-2","description":"Layouted maps in a file (PDF, SVG, etc.) for printing or visualisation on screen, embedding in reports or as static displays on websites etc.","name":"Layouted digital maps","selfAssesment":"New
"},{"code":"TA14-6-3","description":"Digital layouted maps in an online map viewer; 3D visualisations on the screen / 3D screen and online map viewers with 3D capabilities etc.","name":"Web visualisations in 2D and 3D","selfAssesment":"New
"},{"code":"TA14-6-4","description":"Printed maps, 3D plots of 3D models, hologram 3D maps etc.","name":"Analogue visualisation products","selfAssesment":"New
"},{"code":"TA14-6-5","description":"A video is a structured file of 2D grids link by the time, is a regular file of values which has been processed to sensor units (e.g. calibrated). The result can be a single date acquisition or a combination of dates. For each point, the value represents a parameter imaged by the sensor. Videos of EO data present for example time series of satellite maps and other EO products (e.g. Arctic sea ice evolution in a time-series map video over the past 30 years).","name":"Time series map videos","selfAssesment":"New
"},{"code":"TA14-6","description":"Visualsiation products are used for presentation of EO information to the user. The user's interaction with the visualisations is predominantly viewing and interpretation of the informational content and arriving at decisions in the context of the user'S objective with the EO information. In addition, users of visualisation are all involved actors during image processing. For example, an EO analyst may use visualisations of EO data and preliminary EO products for getting a better understanding of the contained information and adapt his processing workflow to arrive ad improved results. Typical visualisation products include satellite maps, layouted digital maps, web visualisations in 2D and 3D, and analogue visualisation products.","name":"EO visualisation products","selfAssesment":"New
"},{"code":"TA14-7","description":"Users need access to EO products if they shall be able to benefit from them. Additionally, providers of value added products act as users of EO products earlier in the information processing value chain. Concequently, various distribution services provide access from raw data to processed information and processing infrastructure. Provision of access to raw data or processed information happens via direct download (FTP), via application programming interfaces (API) or web services (e.g. Hubs). Further, access to processing infractructure happens via web services.","name":"Distribution services","selfAssesment":"New
"},{"code":"TA14","description":"Products in relation to EO appear along the entire image processing value chain as inputs and outputs of processing steps. Ultimately, at the end of that chain, the output EO products represent information that supports actions. The standard EO products are categorized by the type of problems they help to solve or the type of question they help answering.","name":"Standard EO products","selfAssesment":"New
"},{"code":"WB","description":"This knowledge area is about Web Based Geographic Information management aspects and therefore it was given the name \"Web Based GI\" or \"WBG\" in short. It is implied by this name that the differentiating factor for this KA is the \"Web\". One must then be able to answer the questions like \"What functions do we delegate to the Web?\" or \"how WBGI is different from the traditional GI?\" Sticking to the functions of a GIS, which are inserting (adding), storing, manipulating, analysing and presenting the data, there is not a single system for effecting all these tasks anymore but the Web itself. For instance, there is no single database and its known-to-its users-definition, anymore but many different stores and many different definitions. Similarly, many different manipulation, analysis and presentation options compared with the options offered by a single or limited number of systems of traditional GI. In general, Web provides the means of leveraging distributed \"resources\" like data, information, or software. It is a \"collaboration medium\". A collaboration that enables rapid production or decision making. A collaboration that certainly introduces new dimensions to traditional GI handling. This is the justification of proposing this KA in addition to the KAs of the original BoK. For the mentioned collaboration to happen, data or any other type of a resource have to accessible on the Web. This means that it should have a Web \"address\" and a \"definition\" that is understandable either by \"human\" or \"machine\". \"Machine understandable definitions\" refers to the dimension of \"semantics\" and \"ontologies\" which are also included under this KA. When one talks about publishing resources then \"catalogue services\" and more importantly \"discovery\" dimension comes into the scene. On the other hand, \"Linked Data (LOD)\" and \"Open Data\", highly popular recent trends and two of the above mentioned dimensions of Web GI have also been covered under this KA. Like the other dimensions of Web GI, both LD and OD aspects must be known to GI communities with differing degrees of expertise. The concepts of \"interoperability\" and \"Spatial Data Infrastructure (SDI)\", hot topics of GI communities for many years, have been thought to be dealt with under this KA as well with the justification that \"Web GI\" is a much broader concept than SDI, This is by the fact that SDI refers to a much narrower content and context of \"collaboration\" then Web GI. Therefore, Geospatial data interoperability and some of the related concepts which were classified under KA, \"Geospatial data in the original BoK were moved under KA11 with the updated context. Another issue is the coverage of Spatial Analysis (SA), data manipulation aspects of GI by KA11. The SA aspects are covered by other KAs like \"Geocomputation\" and \"Analytical methods\". If the analysis operations, in an undertaking, would be handled by web services this is already covered by \"data processing\" web services, application development unit and Web services composition under that unit. The important thing is to have the knowledge about a specific analysis operation; Employing it as a web service would require no more knowledge than using any other web service. SA is covered by KA11 in as much as it should have been.","name":"Web-based GI","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB1-1","description":"The basic principles on which web services build. The concept of Service Oriented Architecture and the importance of APIs","name":"Fundamentals of web services","selfAssesment":"In progress/to be revised (GI-N2K)
"},{"code":"WB1-2","description":"This concept will cover web services based on the Simple Object Access Protocol (SOAP)","name":"SOAP web services","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB1-3","description":"This concept will cover web services based on the representational state transfer (REST) protocol","name":"REST web services","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB1-4","description":"The Open Geospatial Consortium (OGC) defines standards and best practices for web services in the geospatial domain. OGC standards are developed using a consensus model allowing all stakeholder to participate in the process. As a result the OGC web services are widely implemented.","name":"OGC web services","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB1","description":"In the most simplistic way a Web service may be defined as \"a Web accessable program code which performs a task of either processing or serving some data. Although there are many other definitions in the related literature, the one in W3C (2004) seems to be quite complete and refering to also lately popular REST style Web services. It states that \" We can identify two major classes of Web services: REST-compliant Web services, in which the primary purpose of the service is to manipulate XML representations of Web resources using a uniform set of \"stateless\" operations; and arbitrary Web services, in which the service may expose an arbitrary set of operations.","name":"Web services","selfAssesment":"In progress GI-N2K
"},{"code":"WB2-1","description":"To be able to discover and assess available data or services, these resources have to be documented. This concept describes the standardized languages used for these descriptions","name":"Languages for the definition of non-spatial data and services","selfAssesment":"GI-N2K
"},{"code":"WB2-2","description":"Different standardized ways to define geospatial data exist. GML, GeoJSON, WKT and GeoSPARQL are examples. What are common points and differences","name":"Definition of geospatial data","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB2-3","description":"Defining a common language is a crucial step for sharing or combining data. Vocabularies, taxonomies, ontologies are are tools to reach this goal.","name":"Ontologies development reuse and patterns","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB2","description":"A \"resource\" could be \"anything\" including data and services, identifiable over the Web. A resource should be defined in a language to be discoverable on the Web. Over the years, two major bodies W3C for non-spatial and OGC concerning spatial data have developed many specifications for defining data and services. On the W3C side, Resource Description Framework (RDF) has gained a great momentum in recent years in relation to the recent popularity of Linked Data as well. In the OGC front, the acceptance of GML was a major step concerning the long time effort of geospatial communities for having a standard for the definition of both geospatial features and geometry.","name":"Resource Definition","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB3-1","description":"Metadata is information about the data to be published. It helps the user to discover the data, allows the user to evaluate the fitness for use and it explains how and under which conditions the data can be retrieved and used. Metadata are a core component of data infrastructures and as such, standardization is a requirement for the correct exchange and interpretation of the metadata.","name":"Metadata and standards","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB3-2","description":"A resource can be added manually to a catalogue service by creating or uploading its metadata, but metadata can also be added by automated crawling of other catalogues.","name":"Manual and automated forms of publishing","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB3-3","description":"Catalogue services allow to publish and search resources through their metadata","name":"Catalogue services","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB3-4","description":"Open data is data that is free to use, re-use and share without limitations on who uses it or for what purpose. Publishing open data is making the data discoverable and accessible in a convenient way (technical openness).","name":"Publishing open data","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB3-5","description":"Adding semantic information to the data allows computers to understand the structure and meaning of data. This allows automatic searching, processing and integrating data with other semantic sources.","name":"Publishing via a semantic definition of data","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB3-6","description":"Linked (open) data provides structured data which is interlinked in a machine readable way. This allows to discover, access and combine data in an automatic way. This concept discusses the steps needed to make existing data available in a linked open way.","name":"Publishing linked open data","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB3","description":"\"Publishing\" means making a resource available for the use of others. A \"resource\" could be \"anything\" including data and services, identifiable over the Web. Publishing may be done on the basis of either the \"characteristics\" of the data or the data itself. When only some \"characteristics\" of a resource is published then some of the contents would naturally be left out. The \"characteristics\" include metadata and some keywords. This kind of publishing may be named as \"limited contents\" publishing or \"publishing by metadata\". One of the issues become then what characteristics to use to define the data. Or what what metadata definition to use. Another aspect of publish is \"manual entry\" and \"automated collection\". In the former publisher enters metadata while in the latter some harvesting mechanism collects metadata in an automated fashion. On the contrary, there is \"unlimited contents publishing\" where there is no limitation on the published contents. Open data publishing is in this class. In additon, some \"additional semantics\" may be subject of this type publishing through new relationships in the ontologies of publishing, which have not been explicit in the exisiting data model but are inherent in the data. And this last type is covered under the topic, \"Publishing via a semantic definition of data.\"","name":"Resource Publishing","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB4-1","description":"Syntactic discovery is the discovery of resources based on the structure of the resources","name":"Syntactic discovery","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB4-2","description":"Semantic discovery is the discovery of resources based on the meaning of the data.","name":"Semantic discovery","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB4-3","description":"Linked (open) data provides structured data which is interlinked in a machine readable way. This allows to discover, access and combine data in an automatic way.","name":"Discovery over linked open data","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB4","description":"Resource discovery means the discovery of resources including data and services needed for an application. Syntactic discovery refers to the discovery on the basis of syntactic comparison operations. It is classified as \"keyword-based\" and \"full-text-based\" discovery. Semantic discovery on the other hand, refers to the discovery of resources on he basis of some semantic definition. Therefore, semantic discovery requires that a resource be published by a semantic definition as defined in the topic WB3-5.","name":"Resource Discovery","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB5-1","description":"The workflow to integrate geospatial data in an application often relies on a combination of different OGC web services. Searching and finding the data and the corresponding services, binding to these services to view, filtering and or downloading the data are different steps in this process","name":"Integrating data from OGC web services","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB5-2","description":"The alignment of data structures and vocabularies/ontologies used are important steps towards the data harmonisation needed for a combined use of datasets","name":"Schema matching and ontology alignment","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB5-3","description":"A data mashup is a combination of data from different sources to produce new applications of new datasets","name":"Data mash ups","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB5","description":"The term \"application development\" refers to the collection of activities or the \"workflow\" through which the user reaches her final goal. Being one of these activities, \"data integration\" means the transformation of data from one representation to another which might be of either the client`s one or some other representation. An example for data integration might be the case where the data is transfered from an OGC WFS and integrated into a client GIS.","name":"Application development via Data Integration","selfAssesment":"In Progress GI-N2K
"},{"code":"WB6-1","description":"Manual Web Service Composition is manually (by human) combining the activities of discovery, composition and invocation to fulfil a certain task.","name":"Manual Web Services Composition","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB6-2","description":"Providing standardized descriptions of the specifics of available webservices creates an environment where the composition of services to create a web application can be automated.","name":"Semi automated and Full-automated WSC","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB6","description":"Web Services Composition can be defined as bringing together a number of web services in a certain workflow to achieve a certain task that cannot be achieved by any of the composed services alone. In general, it involves first the discovery of the suitable services over the Web, and compose them in a certain workflow order and finally run the composed service which is the invocation stage. WSC has been a highly active research topic since the emergence of Web services in 2000s. \"Manual\" WSC is the form that the activities of discovery, composition and invocation are all done manually (by human). In the \"Semi-automated\" way, the discovery is done by the machine. In the \"full-automated\" approach all the above activities are done by the machine. There are no tools at the moment that achieve full automated composition. Web API composition is like WSC, the only difference is the fact that instead of web services there are Web APIs in WAPIC. There is no doubt that One would run into the very same problems of WSC concerning full automated composition. In other words, WAPIC would in no way be easier than WSC. Nevertheless, as far as semi automated form can be achived, WAPIC is valuable because the number of Web APIs increase drastically from day to day. The site \"programmableWeb\" lists 14 957 APIs at the moment. It is not easy to search for all those APIs manually for the discovery of suitable APIs for a given task.","name":"Application development via Web services composition","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB7-1","description":"Hypertext markup scripting and styling are the base for each web page or application. Styling defines the look and feel while scripting is used to implement the behavior of the web application","name":"Hypertext markup scripting and styling","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB7-2","description":"Web map APIs allow developers to integrate resources made available by web services in their application or web sites.","name":"Web Map APIs and Libraries","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB7-3","description":"A web application framework provides the generic and reusable building blocks needed to create web applications. Geoportal frameworks provide the functionality to build geospatial portals.","name":"Web application Frameworks and Geoportal frameworks","selfAssesment":"In Progress (GI-N2K)
"},{"code":"WB7","description":"Characteristic examples are included under this topic. The APIs, for instance other than the ones included under this unit, and libraries could have been included as well. However, since the important thing is to highlight the functionality then there is no need to include them all. By the inclusion of topic \"WB7-3\"under this unit, the aim was to cover one of the very \"hot\"topics of Web2.0 for both the main concepts about Web application frameworks and also how they are related to portal frameworks and geoportals. By the topic \"WB7-1 Building blocks\"the core components of Web application development are covered. On top of this core, there comes a great variety of \"Web application frameworks for both enabling rapid web application development and ensuring scalable, high-performance applications. Finally, there are \"Web APIs and Libraries\" certainly deserving being a separate topic for their current popularity. They also mean rapid application development for developers by code reuse and versatality for \"end users\" in creating their \"end products\".","name":"Web Application development elements","selfAssesment":"In progress (GI-N2K)
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International Journal of Geographical Information Systems, 5, 161-174","url":" "},{"concepts":[166],"description":" ","name":"Eichelberger, P. (1993). Maturing GIS: Anecdotes, examples, and lessons, Geo Info Systems 3(5), 29-38.","url":" "},{"concepts":[385],"description":" ","name":"El Garouani, A., Alobeid, A., El Garouani, S., 2014. Digital surface model based on aerial image stereo pairs for 3D building. International Journal of Sustainable Built Environment 3, 119-126.","url":" "},{"concepts":[536],"description":" ","name":"Elachi, C., & Van Zyl, J. J. (2006). Introduction to the physics and techniques of remote sensing (Vol. 28). John Wiley & Sons.","url":" "},{"concepts":[94,95,96,100,291],"description":"ISBN/section: 9781420038330/chapter10","name":"Elmes, G. A., Epstein, E. F., McMaster, R. B., Niemann, B. J., Poore, B., Sheppard, E., and Tullock, D. L. (2004). GIS and society: Interrelation, integration, and transformation. In R.B. McMaster and L. Usery (Eds.), A research agenda for geographic information science, (287-312). Bacon Raton, FL: CRC Press.","url":"http://books.google.com/books?isbn=9781420038330"},{"concepts":[494],"description":" ","name":"Elsner, P. (2005). GIS teaching via distance learning experiences and lessons learned. Planet, 14(1), 28-29.","url":"https://doi.org/10.11120/plan.2005.00140028"},{"concepts":[294],"description":" ","name":"Elwood, S., & Leszczynski, A. (2011). Privacy, reconsidered: New representations, data practices, and the geoweb. Geoforum, 42(1), 6-15.","url":"https://doi.org/10.1016/j.geoforum.2010.08.003"},{"concepts":[309],"description":" ","name":"Elwood, S., Goodchild, M. F., & Sui, D. Z. (2012). Researching volunteered geographic information: Spatial data, geographic research, and new social practice. Annals of the association of American geographers, 102(3), 571-590.","url":"https://doi.org/10.1080/00045608.2011.595657"},{"concepts":[74],"description":" ","name":"Emmanouil, A.V. “1-Geostatistics: Mathematical and Statistical Basis”, Spatiotemporal Analysis of Extreme Hydrological Events, pp.1-38, 2019.","url":" "},{"concepts":[436],"description":" ","name":"Encyclopedia.com (2020) Image understanding. Retrieved from https://www.encyclopedia.com/computing/dictionaries-thesauruses-pictures-and-press-releases/image-understanding","url":"https://www.encyclopedia.com/computing/dictionaries-thesauruses-pictures-and-press-releases/image-understanding"},{"concepts":[258],"description":" ","name":"Engel, C. (2019). R for Geospatial Analysis and Mapping. In: Wilson, J.P. (Ed.) The Geographic Information Science & Technology Body of Knowledge (1st Quarter 2019 Edition)","url":"https://doi.org/10.22224/gistbok/2019.1.3."},{"concepts":[654],"description":" ","name":"Entekhabi, D., et. al. (2010). The Soil Moisture Active Passive (SMAP) Mission. Proceedings of the IEEE, 704-716, DOI: : 10.1109/JPROC.2010.2043918.","url":"https://ieeexplore.ieee.org/document/5460980"},{"concepts":[760,874],"description":" ","name":"EO geospatial products for the Oil & Gas Industry.(n.d.). EO4OG Products. Retrieved from: https://earsc-portal.eu/display/EO4/EO4OG+Products","url":"https://earsc-portal.eu/display/EO4/EO4OG+Products"},{"concepts":[343],"description":" ","name":"ESA (2020), Calibration - Sentinel-2 MSI Technical Guide - Sentinel Online. Retrieved from https://earth.esa.int/web/sentinel/technical-guides/sentinel-2-msi/calibration","url":"https://earth.esa.int/web/sentinel/technical-guides/sentinel-2-msi/calibration"},{"concepts":[746,343],"description":" ","name":"ESA (2020), Level-1C Algorithm - Sentinel-2 MSI Technical Guide - Sentinel Online. Retrieved from https://earth.esa.int/web/sentinel/technical-guides/sentinel-2-msi/level-1c/algorithm","url":"https://earth.esa.int/web/sentinel/technical-guides/sentinel-2-msi/level-1c/algorithm"},{"concepts":[711,715],"description":"ESA (2020). User Guides. Sentinel-1 SAR. Acquisition Modes. Extra Wide Swath. © ESA 2000 - 2020","name":"ESA (2020). User Guides. Sentinel-1 SAR. Acquisition Modes. Extra Wide Swath. © ESA 2000 - 2020","url":"https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/acquisition-modes/extra-wide-swath"},{"concepts":[604,659,660,661],"description":"ESA (2020). Radar and SAR Glossary. earthnet online.","name":"ESA (2020). Radar and SAR Glossary. earthnet online.","url":"https://earth.esa.int/handbooks/asar/CNTR5-2.html"},{"concepts":[668],"description":"ESA (2020). Radar Course 3. Shadow. Earth Online.","name":"ESA (2020). Radar Course 3. Shadow. 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Breaking new ground in mapping human settlements from space – The Global Urban Footprint. ISPRS Journal of Photogrammetry and Remote Sensing, 134, 30-42. doi:https://doi.org/10.1016/j.isprsjprs.2017.10.012","url":"https://doi.org/10.1016/j.isprsjprs.2017.10.012"},{"concepts":[6],"description":" ","name":"Ester M., Kriegel HP., Sander J. (1999) Knowledge Discovery in Spatial Databases. In: Förstner W., Buhmann J.M., Faber A., Faber P. (eds) Mustererkennung 1999. Informatik aktuell. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-60243-6_1","url":"https://link.springer.com/chapter/10.1007/978-3-642-60243-6_1"},{"concepts":[820],"description":" ","name":"European Association of Remote Sensing Companies (EARSC), (2015). Forecast and assess landslides. Retrieved from: https://earsc-portal.eu/display/EOSTAN/Forecast+and+assess+landslides.","url":"https://earsc-portal.eu/display/EOSTAN/Forecast+and+assess+landslides"},{"concepts":[760,796,764,761,762,763,768,766,767,775,769,770,771,772,773,774,780,776,777,778,779,783,781,782,787,784,785,794,788,791,789,790,795,792,793,843,816,786,813,814,815],"description":" ","name":"European Association of Remote Sensing Companies. (2015). EO Services (Markets). Retrieved from https://earsc-portal.eu/pages/viewpage.action?pageId=15794316","url":"https://earsc-portal.eu/pages/viewpage.action?pageId=15794316"},{"concepts":[837],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Assess and monitor coastal water quality. Retrieved from https://earsc-portal.eu/display/EOSTAN/Assess+and+monitor+coastal+water+quality","url":"https://earsc-portal.eu/display/EOSTAN/Assess+and+monitor+coastal+water+quality"},{"concepts":[821],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Assess and Monitor Volcanic Activity. Retrieved from: https://earsc-portal.eu/display/EOSTAN/Assess+and+Monitor+Volcanic+Activity","url":"https://earsc-portal.eu/display/EOSTAN/Assess+and+Monitor+Volcanic+Activity"},{"concepts":[826],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Assess and monitor water bodies. Retrieved from https://earsc-portal.eu/display/EOSTAN/Assess+and+monitor+water+bodies","url":"https://earsc-portal.eu/display/EOSTAN/Assess+and+monitor+water+bodies"},{"concepts":[809],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Assess changes in the carbon balance. Retrieved from https://earsc-portal.eu/display/EOSTAN/Assess+changes+in+the+carbon+balance","url":"https://earsc-portal.eu/display/EOSTAN/Assess+changes+in+the+carbon+balance"},{"concepts":[824],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Assess crop damage due to storms. Retrieved from https://earsc-portal.eu/display/EOSTAN/Assess+crop+damage+due+to+storms","url":"https://earsc-portal.eu/display/EOSTAN/Assess+crop+damage+due+to+storms"},{"concepts":[819],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Assess damage from earthquakes. Retrieved from https://earsc-portal.eu/display/EOSTAN/Assess+damage+from+earthquakes","url":"https://earsc-portal.eu/display/EOSTAN/Assess+damage+from+earthquakes"},{"concepts":[825],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Assess Deforestation or Forest Degradation. Retrieved from https://earsc-portal.eu/display/EOSTAN/Assess+Deforestation+or+Forest+Degradation","url":"https://earsc-portal.eu/display/EOSTAN/Assess+Deforestation+or+Forest+Degradation"},{"concepts":[824],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Assess Environmental impact of farming. Retrieved from https://earsc-portal.eu/display/EOSTAN/Assess+Environmental+impact+of+farming","url":"https://earsc-portal.eu/display/EOSTAN/Assess+Environmental+impact+of+farming"},{"concepts":[825],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Assess environmental impact of forestry. Retrieved from https://earsc-portal.eu/display/EOSTAN/Assess+environmental+impact+of+forestry","url":"https://earsc-portal.eu/display/EOSTAN/Assess+environmental+impact+of+forestry"},{"concepts":[828],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Assess environmental impact of human activities . Retrieved from https://earsc-portal.eu/display/EOSTAN/Assess+environmental+impact+of+human+activities","url":"https://earsc-portal.eu/display/EOSTAN/Assess+environmental+impact+of+human+activities"},{"concepts":[825],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Assess forest damage due to storms or insects. Retrieved from https://earsc-portal.eu/display/EOSTAN/Assess+forest+damage+due+to+storms+or+insects","url":"https://earsc-portal.eu/display/EOSTAN/Assess+forest+damage+due+to+storms+or+insects"},{"concepts":[826],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Assess ground water and run-off. Retrieved from https://earsc-portal.eu/display/EOSTAN/Assess+ground+water+and+run-off","url":"https://earsc-portal.eu/display/EOSTAN/Assess+ground+water+and+run-off"},{"concepts":[829],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Assess land value, ownership, type use. Retrieved from https://earsc-portal.eu/display/EOSTAN/Assess+land+value,+ownership,+type,+use","url":"https://earsc-portal.eu/display/EOSTAN/Assess+land+value,+ownership,+type,+use"},{"concepts":[829],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Assess pressures on populations and migration. Retrieved from https://earsc-portal.eu/display/EOSTAN/Assess+pressures+on+populations+and+migration","url":"https://earsc-portal.eu/display/EOSTAN/Assess+pressures+on+populations+and+migration"},{"concepts":[830],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Baseline mapping. Retrieved from https://earsc-portal.eu/display/EOSTAN/Product+Sheet%3A+Elevation","url":"https://earsc-portal.eu/display/EOSTAN/Product+Sheet%3A+Elevation"},{"concepts":[830],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Detect and monitor ground movement. Retrieved from https://earsc-portal.eu/display/EOSTAN/Detect+and+monitor+ground+movement","url":"https://earsc-portal.eu/display/EOSTAN/Detect+and+monitor+ground+movement"},{"concepts":[838],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Detect and monitor hurricanes and typhoons. Retrieved from https://earsc-portal.eu/display/EOSTAN/Detect+and+monitor+hurricanes+and+typhoons","url":"https://earsc-portal.eu/display/EOSTAN/Detect+and+monitor+hurricanes+and+typhoons"},{"concepts":[841],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Detect and monitor ice-risk at sea. Retrieved from https://earsc-portal.eu/display/EOSTAN/Detect+and+monitor+ice-risk+at+sea","url":"https://earsc-portal.eu/display/EOSTAN/Detect+and+monitor+ice-risk+at+sea"},{"concepts":[839],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Detect and monitor illegal fishing. Retrieved from https://earsc-portal.eu/display/EOSTAN/Detect+and+monitor+illegal+fishing","url":"https://earsc-portal.eu/display/EOSTAN/Detect+and+monitor+illegal+fishing"},{"concepts":[836],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Detect and monitor oil slicks. Retrieved from https://earsc-portal.eu/display/EOSTAN/Detect+and+monitor+oil+slicks","url":"https://earsc-portal.eu/display/EOSTAN/Detect+and+monitor+oil+slicks"},{"concepts":[823,818],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Detect and monitor wildfires. Retrieved from https://earsc-portal.eu/display/EOSTAN/Detect+and+monitor+wildfires","url":"https://earsc-portal.eu/display/EOSTAN/Detect+and+monitor+wildfires"},{"concepts":[827],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Detect changes in glaciers. Retrieved from https://earsc-portal.eu/display/EOSTAN/Detect+changes+in+glaciers","url":"https://earsc-portal.eu/display/EOSTAN/Detect+changes+in+glaciers"},{"concepts":[825],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Detect illegal forest activities. Retrieved from https://earsc-portal.eu/display/EOSTAN/Detect+illegal+forest+activities","url":"https://earsc-portal.eu/display/EOSTAN/Detect+illegal+forest+activities"},{"concepts":[829],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Detect illegal mining activities . Retrieved from https://earsc-portal.eu/display/EOSTAN/Detect+illegal+mining+activities","url":"https://earsc-portal.eu/display/EOSTAN/Detect+illegal+mining+activities"},{"concepts":[824],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Detect illegal or undesired crops. Retrieved from https://earsc-portal.eu/display/EOSTAN/Detect+illegal+or+undesired+crops","url":"https://earsc-portal.eu/display/EOSTAN/Detect+illegal+or+undesired+crops"},{"concepts":[840],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Detect ships in critical areas. Retrieved from https://earsc-portal.eu/display/EOSTAN/Detect+ships+in+critical+areas","url":"https://earsc-portal.eu/display/EOSTAN/Detect+ships+in+critical+areas"},{"concepts":[838],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Forecast and map large waves. Retrieved from https://earsc-portal.eu/display/EOSTAN/Forecast+and+map+large+waves","url":"https://earsc-portal.eu/display/EOSTAN/Forecast+and+map+large+waves"},{"concepts":[765,838],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Forecast and monitor current movement and drift. Retrieved from https://earsc-portal.eu/display/EOSTAN/Forecast+and+monitor+current+movement+and+drift","url":"https://earsc-portal.eu/display/EOSTAN/Forecast+and+monitor+current+movement+and+drift"},{"concepts":[838],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Forecast and monitor ocean winds and waves. Retrieved from https://earsc-portal.eu/display/EOSTAN/Forecast+and+monitor+ocean+winds+and+waves","url":"https://earsc-portal.eu/display/EOSTAN/Forecast+and+monitor+ocean+winds+and+waves"},{"concepts":[810],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Forecast weather. Retrieved from https://earsc-portal.eu/display/EOSTAN/Forecast+weather","url":"https://earsc-portal.eu/display/EOSTAN/Forecast+weather"},{"concepts":[808],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Forecasting sunlight exposure. Retrieved from https://earsc-portal.eu/display/EOSTAN/Forecasting+sunlight+exposure","url":"https://earsc-portal.eu/display/EOSTAN/Forecasting+sunlight+exposure"},{"concepts":[831],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Identify hydrocarbon seeps in soil. Retrieved from https://earsc-portal.eu/display/EOSTAN/Product+Sheet%3A+Hydrocarbon+seep+detection","url":"https://earsc-portal.eu/display/EOSTAN/Product+Sheet%3A+Hydrocarbon+seep+detection"},{"concepts":[823,817],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Map and assess flooding. Retrieved from https://earsc-portal.eu/display/EOSTAN/Map+and+assess+flooding","url":"https://earsc-portal.eu/display/EOSTAN/Map+and+assess+flooding"},{"concepts":[765],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Map and monitor hydroelectric energy. Retrieved from https://earsc-portal.eu/display/EOSTAN/Map+and+monitor+hydroelectric+energy","url":"https://earsc-portal.eu/display/EOSTAN/Map+and+monitor+hydroelectric+energy"},{"concepts":[765],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Map and monitor solar energy (solar farms). Retrieved from https://earsc-portal.eu/pages/viewpage.action?pageId=16548947","url":"https://earsc-portal.eu/pages/viewpage.action?pageId=16548947"},{"concepts":[765],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Map and monitor wind energy (wind farms). Retrieved from https://earsc-portal.eu/pages/viewpage.action?pageId=16550140","url":"https://earsc-portal.eu/pages/viewpage.action?pageId=16550140"},{"concepts":[839],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Map fish shoals. Retrieved from https://earsc-portal.eu/display/EOSTAN/Map+fish+shoals","url":"https://earsc-portal.eu/display/EOSTAN/Map+fish+shoals"},{"concepts":[831],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Map geological features. Retrieved from https://earsc-portal.eu/display/EOSTAN/Map+geological+features","url":"https://earsc-portal.eu/display/EOSTAN/Map+geological+features"},{"concepts":[831],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Map seismic survey operations. Retrieved from https://earsc-portal.eu/display/EOSTAN/Map+seismic+survey+operations","url":"https://earsc-portal.eu/display/EOSTAN/Map+seismic+survey+operations"},{"concepts":[837],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Map water depth or charting. Retrieved from https://earsc-portal.eu/display/EOSTAN/Map+water+depth+or+charting","url":"https://earsc-portal.eu/display/EOSTAN/Map+water+depth+or+charting"},{"concepts":[830],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Measure & detect land surface change. Retrieved from https://earsc-portal.eu/display/EOSTAN/Product+Sheet%3A+Erosion+Potential","url":"https://earsc-portal.eu/display/EOSTAN/Product+Sheet%3A+Erosion+Potential"},{"concepts":[829],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Measure land use statistics. Retrieved from https://earsc-portal.eu/display/EOSTAN/Measure+land+use+statistics","url":"https://earsc-portal.eu/display/EOSTAN/Measure+land+use+statistics"},{"concepts":[808],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Monitor air quality & emissions. Retrieved from https://earsc-portal.eu/pages/viewpage.action?pageId=16549044","url":"https://earsc-portal.eu/pages/viewpage.action?pageId=16549044"},{"concepts":[837],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Monitor coastal ecosystem. Retrieved from https://earsc-portal.eu/display/EOSTAN/Monitor+coastal+ecosystem","url":"https://earsc-portal.eu/display/EOSTAN/Monitor+coastal+ecosystem"},{"concepts":[835,834],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Monitor construction and buildings. Retrieved from https://earsc-portal.eu/display/EOSTAN/Monitor+construction+and+buildings","url":"https://earsc-portal.eu/display/EOSTAN/Monitor+construction+and+buildings"},{"concepts":[824],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Monitor crops. Retrieved from https://earsc-portal.eu/display/EOSTAN/Forecast+crop+yields","url":"https://earsc-portal.eu/display/EOSTAN/Forecast+crop+yields"},{"concepts":[825],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Monitor forest carbon content. Retrieved from https://earsc-portal.eu/display/EOSTAN/Monitor+forest+carbon+content","url":"https://earsc-portal.eu/display/EOSTAN/Monitor+forest+carbon+content"},{"concepts":[825],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Monitor forest resources. Retrieved from https://earsc-portal.eu/display/EOSTAN/Monitor+forest+resources","url":"https://earsc-portal.eu/display/EOSTAN/Monitor+forest+resources"},{"concepts":[829],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Monitor humanitarian movement and camps. Retrieved from https://earsc-portal.eu/display/EOSTAN/Monitor+humanitarian+movement+and+camps","url":"https://earsc-portal.eu/display/EOSTAN/Monitor+humanitarian+movement+and+camps"},{"concepts":[827],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Monitor ice on rivers and lakes. Retrieved from https://earsc-portal.eu/display/EOSTAN/Monitor+ice+on+rivers+and+lakes","url":"https://earsc-portal.eu/display/EOSTAN/Monitor+ice+on+rivers+and+lakes"},{"concepts":[828],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Monitor land cover and detect change. Retrieved from https://earsc-portal.eu/display/EOSTAN/Monitor+land+cover+and+detect+change","url":"https://earsc-portal.eu/display/EOSTAN/Monitor+land+cover+and+detect+change"},{"concepts":[828],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Monitor land ecosystems and biodiversity. Retrieved from https://earsc-portal.eu/display/EOSTAN/Monitor+land+ecosystems+and+biodiversity","url":"https://earsc-portal.eu/display/EOSTAN/Monitor+land+ecosystems+and+biodiversity"},{"concepts":[828],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Monitor land pollution. Retrieved from https://earsc-portal.eu/display/EOSTAN/Monitor+land+pollution","url":"https://earsc-portal.eu/display/EOSTAN/Monitor+land+pollution"},{"concepts":[836],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Monitor marine habitats. Retrieved from https://earsc-portal.eu/display/EOSTAN/Monitor+marine+habitats","url":"https://earsc-portal.eu/display/EOSTAN/Monitor+marine+habitats"},{"concepts":[831],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Monitor mineral extraction. 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Retrieved from https://earsc-portal.eu/display/EOSTAN/Monitor+water+use+on+crops+and+horticulture","url":"https://earsc-portal.eu/display/EOSTAN/Monitor+water+use+on+crops+and+horticulture"},{"concepts":[854],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Product Sheet - Land Use. Retrieved from https://earsc-portal.eu/display/EOSTAN/Product+Sheet%3A+Land+Use","url":"https://earsc-portal.eu/display/EOSTAN/Product+Sheet%3A+Land+Use"},{"concepts":[834],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Product Sheet: Asset Monitoring. Retrieved from https://earsc-portal.eu/display/EOSTAN/Product+Sheet%3A+Asset+Monitoring","url":"https://earsc-portal.eu/display/EOSTAN/Product+Sheet%3A+Asset+Monitoring"},{"concepts":[808],"description":" ","name":"European Association of Remote Sensing Companies. (2017). Product sheet:CO2. 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IEEE Geoscience and remote sensing magazine, 1(1), 6-43.","url":" "},{"concepts":[258],"description":" ","name":"Moreno-Sanchez, R. (2018). Openness. In: Wilson, J. (Ed.) The Geographic Information Science & Technology Body of Knowledge (4th Quarter 2017 Edition)","url":"http://doi.org/10.22224/gistbok/2018.1.5"},{"concepts":[196],"description":" ","name":"Morin. Open Data Structures: An Introduction. OPEL (Open Paths to Enriched Learning), 2. Edmonton [Alberta]: AU Press, 2013.","url":"https://doc.lagout.org/Others/Data%20Structures/Open%20Data%20Structures_%20An%20Introduction%20%5BMorin%202013-09-19%5D.pdf"},{"concepts":[284,285],"description":" ","name":"Morrison, J. L. (1995). Spatial data quality. Elements of spatial data quality, 202, 1-12.","url":"https://books.google.be/books?hl=nl&lr=&id=MUjgBAAAQBAJ&oi=fnd&pg=PA1&dq=precision+%22spatial+data%22+quality+clarke&ots=WVL22rZ9DX&sig=m4m3ip5XvRDW2yWnwwwnx0zWMPE"},{"concepts":[640],"description":"Mott, H. (2007). 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observation."},{"concepts":[94],"name":"Collaborate effectively with colleagues of differing social backgrounds in developing balanced GIS applications"},{"concepts":[101],"name":"Collaborate with non-GIS experts who use GIS to design applications that match common-sense understanding to an appropriate degree"},{"concepts":[846],"name":"Combine different bands to calculate NDVI"},{"concepts":[292],"name":"Compare and contrast and contrast the relationship of the geospatial profession and the U.S. legal regime with similar relationships in other countries"},{"concepts":[34],"name":"Compare and contrast attribute query and spatial query"},{"concepts":[68],"name":"Compare and contrast Bayesian methods and classical frequentist statistical methods"},{"concepts":[73],"name":"Compare and contrast co-kriging log-normal kriging, disjunctive kriging, indicator kriging, factorial kriging and universal kriging"},{"concepts":[19],"name":"Compare and contrast covering, dispersion, and p-median models"},{"concepts":[38],"name":"Compare and contrast different shape indices, include examples of applications to which each could be applied"},{"concepts":[108],"name":"Compare and contrast differing epistemological and metaphysical viewpoints on the reality of geographic entities"},{"concepts":[294],"name":"Compare and contrast geographic information technologies that are privacy-invasive, privacy-enhancing, and privacy-sympathetic"},{"concepts":[66],"name":"Compare and contrast global and local statistics and their uses"},{"concepts":[78],"name":"Compare and contrast GWR with universal kriging using moving neighborhoods"},{"concepts":[37],"name":"Compare and contrast how direction is determined and stated in raster and vector data"},{"concepts":[57],"name":"Compare and contrast interpolation by inverse distance weighting, bi-cubic spline fitting and kriging"},{"concepts":[103],"name":"Compare and contrast models of a given spatial process using continuous and discrete perspectives of time"},{"concepts":[295],"name":"Compare and contrast National, European policy regarding rights to geospatial data with similar policies in other countries"},{"concepts":[2],"name":"Compare and contrast spatial statistical analysis, spatial data analysis, and spatial modeling"},{"concepts":[2],"name":"Compare and contrast spatial statistics and map algebra as two very different kinds of data analysis"},{"concepts":[81],"name":"Compare and contrast the ability of different theories to explain various situations"},{"concepts":[83],"name":"Compare and contrast the ability of various theories to explain different situations"},{"concepts":[104],"name":"Compare and contrast the characteristics of spatial and temporal dimensions"},{"concepts":[45],"name":"Compare and contrast the concept of overlay as it is implemented in raster and vector domains"},{"concepts":[110],"name":"Compare and contrast the concepts of continuants (entities) and occurrents (events)"},{"concepts":[16],"name":"Compare and contrast the concepts of discrete location problems and continuous location problems"},{"concepts":[110],"name":"Compare and contrast the concepts of event and process"},{"concepts":[295],"name":"Compare and contrast the consequences of different national policies about rights to geospatial data in terms of the real costs of spatial data, their coverage, accuracy, uncertainty, reliability, validity, and maintenance"},{"concepts":[312],"name":"Compare and contrast the ethical guidelines promoted by the GIS Certification Institute (GISCI) and the American Society for Photogrammetry and Remote Sensing (ASPRS)"},{"concepts":[502],"name":"Compare and contrast the impact effect of time for developing consensus-based standards with immediate operational needs"},{"concepts":[21],"name":"Compare and contrast the impacts of different conversion approaches, including the effect on spatial components"},{"concepts":[85],"name":"Compare and contrast the kinds of questions various philosophies ask, the methodologies they use, the answers they offer, and their applicability to different phenomena"},{"concepts":[121],"name":"Compare and contrast the meanings of related terms such as vague, fuzzy, imprecise, indefinite, indiscrete, unclear, and ambiguous"},{"concepts":[2],"name":"Compare and contrast the methods of analyzing aggregate data as opposed to methods of analyzing a set of individual observations"},{"concepts":[512],"name":"Compare and contrast the missions, histories, constituencies, and activities of professional organizations including Association of American Geographers (AAG), America Society for Photogrammetry and Remote Sensing (ASPRS) ..."},{"concepts":[113],"name":"Compare and contrast the opportunities and pitfalls of using regions to aggregate geographic information (e.g., census data)"},{"concepts":[5],"name":"Compare and contrast the primary types of data mining: summarization/characterization, clustering/categorization, feature extraction, and rule/relationships extraction"},{"concepts":[158],"name":"Compare and contrast the quality of product evaluation that can be made from process proofs and color laser prints"},{"concepts":[213],"name":"Compare and contrast the raster with other types of regular tessellations for geographic data analysis"},{"concepts":[213],"name":"Compare and contrast the raster with other types of regular tessellations for geographic data storage"},{"concepts":[88,96],"name":"Compare and contrast the symbolic and connectionist theories of human cognition and memory and their ability to model various cases"},{"concepts":[54],"name":"Compare and contrast the terms multi-criteria evaluation, weighted linear combination, and site suitability analysis"},{"concepts":[106],"name":"Compare and contrast the theory that properties are fundamental (and objects are human simplifications of patterns thereof) with the theory that objects are fundamental (and properties are attributes thereof)"},{"concepts":[88],"name":"Compare and contrast theories of spatial knowledge acquisition (e.g., Marr on vision, Piaget on childhood, Golledge on wayfinding)"},{"concepts":[492],"name":"Compare and contrast training methods utilized in a non-profit to those employed in a local government agency"},{"concepts":[622],"name":"Compare and discuss attenuation length and penetration depth of the optical and radar signal"},{"concepts":[711,713,716,712],"name":"Compare and discuss different SAR acquisition modes"},{"concepts":[505],"name":"Compare and explain different models for funding an SDI"},{"concepts":[299],"name":"Compare and explain the main business models in the GI domain"},{"concepts":[72],"name":"Compare block-kriging with areal interpolation using proportional area weighting and dasymetric mapping"},{"concepts":[269],"name":"Compare common sensors by spatial resolution, spectral sensitivity, ground coverage, and temporal resolution [e.g., AVHRR, MODIS (intermediate resolution ~500 m, high temporal) Landsat, commercial high resolution (Ikonos and Quickbird); ..."},{"concepts":[266],"name":"Compare common sensors-including LiDAR, and airborne panchromatic and multispectral cameras and scanners-in terms of spatial resolution, spectral sensitivity, ground coverage, and temporal resolution"},{"concepts":[242],"name":"Compare commonalities and patterns of geocomputation to other related terms"},{"concepts":[14],"name":"Compare current accessibility models with early models of market potential"},{"concepts":[398],"name":"Compare different deep learning approaches in EO image classification"},{"concepts":[250],"name":"Compare different design choices in developing spatial simulation models"},{"concepts":[906],"name":"compare different development components and their advantages and disadvantages"},{"concepts":[444],"name":"Compare different error metrics that are based on the error matrix"},{"concepts":[165],"name":"Compare different evaluation methods for cartography and visualization products (e.g., qualitative versus quantitative, formative versus summative studies)."},{"concepts":[506],"name":"Compare different frameworks for assessing Spatial Data Infrastructures"},{"concepts":[882],"name":"Compare different Geospatial object and geometry definitions included under this topic"},{"concepts":[247],"name":"Compare different options of combining space-time dynamics approaches in spatial modelling"},{"concepts":[358],"name":"Compare different types of data assimilation"},{"concepts":[179],"name":"Compare geospatial software architecture through cost-analysis framework"},{"concepts":[827],"name":"Compare glacier extents using EO data"},{"concepts":[811,808,809],"name":"Compare human-induced emissions to natural sources"},{"concepts":[894],"name":"Compare Linked geospatial data to SDI approaches"},{"concepts":[69],"name":"Compare methods of spatial statistical analysis for the testing of hypotheses."},{"concepts":[20],"name":"Compare models and software tools that allow for optimization"},{"concepts":[820],"name":"Compare one optical EO method with a SAR method for landslide mapping and explain their differences"},{"concepts":[346],"name":"Compare reflectance measurements from the field to reflectance values in radiometrically pre-processed EO data"},{"concepts":[120],"name":"Compare relationships between entities, between attributes and between locations."},{"concepts":[415],"name":"Compare results of the Laplacian of Gaussian filter to the original input image"},{"concepts":[307],"name":"Compare the advantages and disadvantages of group participation and individual participation"},{"concepts":[48],"name":"Compare the basic analytical operations of different GISs."},{"concepts":[282],"name":"Compare the concepts of geometric accuracy and topological fidelity"},{"concepts":[259],"name":"Compare the different cultures of Open Science"},{"concepts":[64],"name":"Compare the different types of spatial weight matrices"},{"concepts":[282],"name":"Compare the National Map Accuracy Standard with the ASPRS Coordinate Standard"},{"concepts":[137],"name":"Compare the relative merits of having map labels placed dynamically versus having them saved as annotation data"},{"concepts":[24],"name":"Compare the result of conversion vector/raster or raster/vector and examine the impact of conversion on the quality of the dataset"},{"concepts":[167],"name":"Compile the needs of individual users and tasks into enterprise-wide needs"},{"concepts":[480],"name":"Compute descriptive statistics and geostatistics of geographic data"},{"concepts":[49],"name":"Compute measures of overall dispersion and clustering of point datasets using nearest neighbor distance statistics"},{"concepts":[65],"name":"Compute measures of overall dispersion and clustering of point datasets using nearest neighbor distance statistics"},{"concepts":[65],"name":"Compute Morans I and Gearys c for patterns of attribute data measured on interval ratio scales"},{"concepts":[9],"name":"Compute the alpha, beta, and gamma indices of network connectivity"},{"concepts":[9],"name":"Compute the Detour Index and the measure of network density for a given network"},{"concepts":[9],"name":"Compute the estimated number of fundamental cycles in a graph"},{"concepts":[66],"name":"Compute the Gi and Gi* statistics"},{"concepts":[65],"name":"Compute the K function"},{"concepts":[37],"name":"Compute the mean of directional data"},{"concepts":[10],"name":"Compute the optimum path between two points through a network with Dijkstras algorithm"},{"concepts":[53],"name":"Conduct a simple hierarchical cluster analysis to classify area objects into statistically similar regions"},{"concepts":[76],"name":"Conduct a spatial econometric analysis to test for spatial dependence in the residuals from least-squares models and spatial autoregressive models"},{"concepts":[72],"name":"Conduct a spatial interpolation process using kriging from data description to final error map"},{"concepts":[160],"name":"Construct a new map from an existing one with a biased view"},{"concepts":[34],"name":"Construct a query statement to search for a specific spatial or temporal relationship"},{"concepts":[71],"name":"Construct a semi-variogram and illustrate with a semi-variogram cloud"},{"concepts":[34],"name":"Construct a spatial query to extract all point objects that fall within a polygon"},{"concepts":[64],"name":"Construct a spatial weights matrix for lattice, point, and area patterns"},{"concepts":[216],"name":"Construct a TIN manually from a set of spot elevations"},{"concepts":[149],"name":"Construct a Web page that includes an interactive map"},{"concepts":[633],"name":"Construct scattering matrix"},{"concepts":[105],"name":"Construct taxonomies and dictionaries (also known as formal ontologies) to communicate systems of categories"},{"concepts":[14],"name":"Contrast accessibility modeling at the individual level versus at an aggregated level"},{"concepts":[184],"name":"Contrast cloud and grid computing technologies"},{"concepts":[137],"name":"Contrast the strengths and limitations of methods for automatic label placement"},{"concepts":[22],"name":"Convert a dataset from the native format of one GIS product to another"},{"concepts":[127],"name":"Convert historical maps in digital format"},{"concepts":[337],"name":"Convert multispectral image into its principal components"},{"concepts":[24],"name":"Convert vector data to raster format and back using GIS software"},{"concepts":[24],"name":"Convert vector data to raster format and back using the GIS software"},{"concepts":[128],"name":"Correlate map making methods with technological or societal factors across History"},{"concepts":[176],"name":"Create a budget of expected labor costs, including salaries, benefits, training, and other expenses"},{"concepts":[190],"name":"Create a complete design document ready for implementation"},{"concepts":[153],"name":"Create a concept map that represents the contents and topology of a physical or social process"},{"concepts":[417],"name":"Create a convolution filter that integrates the standard deviation of the entire scene in its weights"},{"concepts":[469],"name":"Create a data cube using the data model of the Open data cube initiative"},{"concepts":[8],"name":"Create a data set with network attributes and topology"},{"concepts":[188],"name":"Create a diagram of a conceptual data model for a geospatial application or enterprise database"},{"concepts":[21],"name":"Create a flowchart showing the sequence of transformations on a data set (e.g., geometric and radiometric correction and mosaicking of remotely sensed data)"},{"concepts":[147],"name":"Create a map that displays related variables using different mapping methods (e.g., choropleth and proportional symbol, choropleth and cartogram)"},{"concepts":[147],"name":"Create a map that displays related variables using the same mapping method (e.g., bivariate choropleth map, bivariate dot map)"},{"concepts":[146],"name":"Create a map that represents both slope and aspect on the same map using the Moellering-Kimerling coloring method"},{"concepts":[41],"name":"Create a matrix describing the pattern of adjacency in a set of planar enforced polygons"},{"concepts":[52],"name":"Create a matrix that shows spatial interaction"},{"concepts":[898],"name":"Create a new application by combining existing data from different sources"},{"concepts":[159],"name":"Create a project plan for a map, from planning to finalisation"},{"concepts":[438],"name":"Create a protocol for quality assessment of an EO information product that conforms to EO4GEO guidelines"},{"concepts":[153],"name":"Create a pseudo-topographic surface to portray the relationships in a collection of documents"},{"concepts":[903],"name":"Create a sample HTML5 Web page"},{"concepts":[435],"name":"Create a scale space for an image by applying multiple iterations of low-pass filtering"},{"concepts":[329,332],"name":"Create a set of ground control points tying image coordinates to map coordinates of a reference dataset using a digital reference dataset or in-situ GPS measurements"},{"concepts":[148],"name":"Create a temporal sequence representing a dynamic geospatial process"},{"concepts":[168],"name":"Create a user manual to help users understand a process or task"},{"concepts":[475],"name":"Create a web interface and related system architecture that enables image processing by using OGC interfaces"},{"concepts":[228],"name":"Create an adjacency table from a sample network"},{"concepts":[135],"name":"Create an aesthetic map icon library"},{"concepts":[228],"name":"Create an incidence matrix from a sample network"},{"concepts":[357],"name":"Create an integrated population distribution map from census data and EO-based land use classification"},{"concepts":[33],"name":"Create an SQL query to retrieve elements from a GIS"},{"concepts":[187],"name":"Create conceptual, logical, and physical data models using automated software tools"},{"concepts":[50],"name":"Create density maps from point datasets using kernels and density estimation techniques using standard software"},{"concepts":[133],"name":"Create different map layouts using the same map components (main map area, inset maps, titles, legends, scale bars, north arrows, grids and graticule) to produce maps with very distinctive purposes"},{"concepts":[133],"name":"Create different maps using the same data for different purposes and intended audiences (e.g., expert and novice hikers)"},{"concepts":[143],"name":"Create different visual hierarchies to produce maps with different purposes"},{"concepts":[24],"name":"Create estimated tessellated data sets from point samples or isolines using interpolation operations that are appropriate to the specific situation"},{"concepts":[54],"name":"Create initial weights using the analytical hierarchy process (AHP)"},{"concepts":[189],"name":"Create logical models based on conceptual models using UML or other tools"},{"concepts":[144],"name":"Create maps using each of the following methods: choropleth, dasymetric, proportioned symbol, graduated symbol, isoline, dot, cartogram, and flow map"},{"concepts":[873],"name":"Create new EO products out of raw data or other products"},{"concepts":[105],"name":"Create or use GIS data structures to represent categories, including attribute columns, layers themes, shapes, legends, etc."},{"concepts":[176],"name":"Create proposals and presentations to secure funding"},{"concepts":[70],"name":"Create spatial samples under a variety of requirements, such as coverage, randomness, transects"},{"concepts":[160],"name":"Create two versions of the same map addressed to different targets"},{"concepts":[190],"name":"Create UML diagrams of physical models based on logical model diagrams and software requirements"},{"concepts":[144],"name":"Create well-designed legends using the appropriate conventions for the following methods: choropleth, dasymetric, proportioned symbol, graduated symbol, isoline, dot, cartogram, and flow map"},{"concepts":[143],"name":"Critique the graphic design of several maps in terms of balance, legibility, clarity, visual contrast, figure-ground organization, and hierarchal organization"},{"concepts":[149],"name":"Critique the interactive elements of an online map"},{"concepts":[150],"name":"Critique the user interface for existing Internet mapping services"},{"concepts":[231],"name":"Deal with time aspects in modelling data"},{"concepts":[230],"name":"Deal with uncertainty aspects in modelling data"},{"concepts":[866,865],"name":"Decide on urban planning measures on the basis of a semantic 3D model"},{"concepts":[66],"name":"Decompose Morans I and Gearys c into local measures of spatial association"},{"concepts":[188],"name":"Deconstruct an application use case into its conceptual elements"},{"concepts":[320],"name":"Defend or refute the contention that critical studies have an identifiable influence on the development of the information society in general and GIScience in particular"},{"concepts":[319],"name":"Defend or refute the contention that the masculinist culture of computer work in general, and GIS work in particular, perpetuates gender inequality in GIS and T education and training and occupational segregation in the GIS and T workforce"},{"concepts":[28],"name":"Defend or refute the statement \"GIS data are scaleless\""},{"concepts":[85],"name":"Defend or refute the statement, All data are theory-laden"},{"concepts":[109],"name":"Define a field in terms of properties, space, and time"},{"concepts":[167],"name":"Define a methodology for gathering of requirements"},{"concepts":[235],"name":"Define a set of rules for modeling changes in spatial databases"},{"concepts":[225],"name":"Define and describe an application schema"},{"concepts":[309],"name":"Define and discuss enabling technologies: geotag, georeferencing, GPS and more"},{"concepts":[240],"name":"Define and discuss opportunities and limitations of computational science"},{"concepts":[309],"name":"Define and discuss volunteered geographic information"},{"concepts":[309],"name":"Define and discussing impact of Crowdsourcing on Geospatial Society"},{"concepts":[883],"name":"Define and exemplify the reuse of ontologies - Define and identify the role of ontology patterns"},{"concepts":[879],"name":"Define and practice the usage, in a given use case, of StyledLayerDescriptor (SLD) and Symbology Encoding (SE). Practice their usage in a given use case"},{"concepts":[307],"name":"Define and understand citizenship, democracy, maturity, and negotiation related to geo information use and participation in society /community development (at local, regional, national level)"},{"concepts":[33],"name":"Define basic terms of query processing e.g., SQL, primary and foreign keys, table join"},{"concepts":[213],"name":"Define basic terms used in the raster data model (e.g., cell, row, column, value)"},{"concepts":[878],"name":"Define characteristics of REST Web services and Resource oriented Architecture (ROA)"},{"concepts":[85],"name":"Define common philosophical theories that have influenced geography and science, such as logical positivism, Marxism, phenomenology, feminism, and critical theory"},{"concepts":[83],"name":"Define common theories on what constitutes knowledge, including positivism, reflectance-correspondence, pragmatism, social constructivism, and memetics"},{"concepts":[81],"name":"Define common theories on what is real, such as realism, idealism, relativism, and experiential realism"},{"concepts":[8],"name":"Define different interpretations of cost in various routing applications"},{"concepts":[37],"name":"Define direction and its measurement in different angular measures"},{"concepts":[188],"name":"Define entities and relationships in conceptual data model"},{"concepts":[60],"name":"Define friction surface"},{"concepts":[882],"name":"Define GeoJSON definition of Geospatial objects and describe the structure of a GeoJSON document and identify advantages and disadvantages of representing the same geospatial data in GML and in GeoJSON"},{"concepts":[59],"name":"Define intervisibility"},{"concepts":[889],"name":"Define Mapping between legacy definition and the semantic definition of publish"},{"concepts":[885],"name":"Define metadata and identify metadata standards like ISO 19115 and 19119 describe their metadata schema generally"},{"concepts":[882],"name":"Define OGC Simple Features Access Schema. Well-Known Text (WKT) and Well-Known Binary (WKB) representations of Geometry"},{"concepts":[68],"name":"Define prior and posterior distributions and Markov-Chain Monte Carlo"},{"concepts":[881],"name":"Define Resource Description Framework (RDF), its RDF graphs, RDF Schema (RDF-S)and a data set in RDF"},{"concepts":[881],"name":"Define Semantic Web and identify the role of the languages included under this topic for Semantic Web"},{"concepts":[876],"name":"Define Service Oriented Architecture (SOA) and identify main elements of it"},{"concepts":[119],"name":"Define spatial autocorrelation in the context of geographic proximity"},{"concepts":[882],"name":"Define spatial extensions that GeoSPARQL brings over SPARQL. Identify the difference between qualitative spatial reasoning and quantitative spatial computations"},{"concepts":[106],"name":"Define Stevens four levels of measurement (nominal, ordinal, interval, ratio)"},{"concepts":[224],"name":"Define terms related to topology (e.g., adjacency, connectivity, overlap, intersect, logical consistency)"},{"concepts":[189],"name":"Define the cardinality of relationships"},{"concepts":[876],"name":"Define the characteristics of web services and present some examples"},{"concepts":[881],"name":"Define the components of a Web Services Description Language (WSDL) document"},{"concepts":[228],"name":"Define the following terms pertaining to a network: Loops, multiple edges, the degree of a vertex, walk, trail, path, cycle, fundamental cycle"},{"concepts":[8],"name":"Define the following terms pertaining to a network: Loops, multiple edges, the degree of a vertex, walk, trail, path, cycle, fundamental cycle"},{"concepts":[90],"name":"Define the following terms: data, information, knowledge, and wisdom"},{"concepts":[97],"name":"Define the four basic dimensions or shapes used to describe spatial objects (i.e., points, lines, regions, volumes)"},{"concepts":[93],"name":"Define the notions of cultural landscape and physical landscape"},{"concepts":[119],"name":"Define the principle of friction of distance and geographic models that are based on it (e.g., gravity models, spatial interaction models)"},{"concepts":[92],"name":"Define the properties that make a phenomenon geographic"},{"concepts":[541],"name":"Define the radiometric spectral quantities brightness, emittance, luminosity"},{"concepts":[541],"name":"Define the radiometric spectral quantities radiance, irradiance, flux"},{"concepts":[2],"name":"Define the terms spatial analysis, spatial modeling, geostatistics, spatial econometrics, spatial statistics, qualitative analysis, map algebra, and network analysis"},{"concepts":[122],"name":"Define uncertainty-related terms, such as error, accuracy, uncertainty, precision, stochastic, probabilistic, deterministic, and random"},{"concepts":[484],"name":"Define user roles for an existing or planned GIS"},{"concepts":[118],"name":"Define various terms used to describe topological relationships, such as disjoint, overlap, within, and intersect"},{"concepts":[900],"name":"Define Web API composition (WAPIC) concept for RESTful WSs and identify main issues"},{"concepts":[879],"name":"Define Web Coverage Service (WCS). Describe GetCapabilities, GetCoverageInfo, and GetCoverage operations in detail. Practice its usage in a given use case"},{"concepts":[879],"name":"Define Web Feature Service (WFS). Describe GetCapabilities, DescribeFeaturetype, and GetFeature, and GetFeatureInfo operations in detail. Practice its usage in a given use case"},{"concepts":[879],"name":"Define Web Map Service (WMS). Describe GetCapabilities, GetMap, and GetFeatureInfo operations in detail. Practice its usage in a given use case"},{"concepts":[879],"name":"Define Web Map Tile Service (WMTS). Describe GetCapabilities, GetTile, and GetFeatureInfo operations in detail. Practice its usage in a given use case"},{"concepts":[879],"name":"Define Web Processing Service (WPS). Describe GetCapabilities, DescribeProcess, and Execute operations in detail. Practice its usage in a given use case"},{"concepts":[900],"name":"Define web services composition (WSC) concept and identify main issues"},{"concepts":[876],"name":"Define Web services transport over the Web"},{"concepts":[883],"name":"Define what an ontology is. Identify differences among ontologies, Thesauri, and taxonomies"},{"concepts":[216],"name":"Delineate a set of break lines that improve the accuracy of a TIN"},{"concepts":[113],"name":"Delineate regions using properties, spatial relationships, and geospatial technologies"},{"concepts":[178],"name":"Deliver a resources plan consistent with organisation’s concrete actions"},{"concepts":[577],"name":"Demonstrate basic knowledge of the atmospheric absorption and scattering mechanisms."},{"concepts":[517,573],"name":"Demonstrate basic knowledge of the interaction between the solar radiation and atmospheric constituents"},{"concepts":[886],"name":"Demonstrate harvesting and crawling mechanisms for automated metadata collection"},{"concepts":[228],"name":"Demonstrate how a network is a connected set of edges and vertices"},{"concepts":[224],"name":"Demonstrate how a topological structure can be represented in a relational database structure"},{"concepts":[41],"name":"Demonstrate how adjacency and connectivity can be recorded in matrices"},{"concepts":[228],"name":"Demonstrate how attributes of networks can be used to represent cost, time, distance, or many other measures"},{"concepts":[237],"name":"Demonstrate how both the time criticality and the data security might determine whether one performs change detection on-line or off-line in a given scenario"},{"concepts":[11],"name":"Demonstrate how capacity is assigned to edges in a network using the appropriate data structure"},{"concepts":[5],"name":"Demonstrate how cluster analysis can be used as a data mining tool"},{"concepts":[10],"name":"Demonstrate how K-shortest path algorithms can be implemented to find many efficient alternate paths across the network"},{"concepts":[9],"name":"Demonstrate how networks can be measured using the number of elements in a network, the distances along network edges, and the level of connectivity of the network"},{"concepts":[71],"name":"Demonstrate how semi-variograms react to spatial nonstationarity"},{"concepts":[77],"name":"Demonstrate how spatial autocorrelation can be removed by resampling"},{"concepts":[75],"name":"Demonstrate how spatially lagged, trend surface, or dummy spatial variables can be used to create the spatial component variables missing in a standard regression analysis"},{"concepts":[148],"name":"Demonstrate how the adding time-series data reveals (or not) patterns not evident in a cross-sectional data"},{"concepts":[39],"name":"Demonstrate how the area of a region calculated from a raster data set will vary by resolution and orientation"},{"concepts":[12],"name":"Demonstrate how the Classic Transportation Problem can be structured as a linear program"},{"concepts":[45],"name":"Demonstrate how the geometric operations of intersection and overlay can be implemented in GIS"},{"concepts":[76],"name":"Demonstrate how the parameters of spatial auto-regressive models can be estimated using univariate and bivariate optimization algorithms for maximizing the likelihood function"},{"concepts":[75],"name":"Demonstrate how the spatial weights matrix is fundamental in spatial econometrics models"},{"concepts":[228],"name":"Demonstrate how the star (or forward star) data structure, which is often employed when digitally storing network information, violates relational normal form, but allows for much faster search and retrieval in network databases"},{"concepts":[892],"name":"Demonstrate how to discover over a catalogue service; and the discovery procedure in OGC CS-W"},{"concepts":[127],"name":"Demonstrate how to georeference an historical map"},{"concepts":[782],"name":"Demonstrate impacts of land use change"},{"concepts":[886],"name":"Demonstrate publishing in some popular SDI (NSDI) portals like INSPIRE and GOS geoportals"},{"concepts":[33],"name":"Demonstrate the basic syntactic structure of SQL"},{"concepts":[51],"name":"Demonstrate the extension of spatial clustering to deal with clustering in space-time using the Know and Mantel tests"},{"concepts":[234],"name":"Demonstrate the importance of a clean, relatively error-free database (together with an appropriate geodetic framework) with the use of GIS software"},{"concepts":[528],"name":"Demonstrate the relationships among measured multi-spectral radiation and specific chemical (e.g. composition) and physical (e.g. temperature, pressure, etc.) properties of the observed matter."},{"concepts":[34],"name":"Demonstrate the syntactic structure of spatial and temporal operators in SQL"},{"concepts":[896],"name":"Demonstrate the usage of popular ETL tools in an NSDI scenario"},{"concepts":[216],"name":"Demonstrate the use of the TIN model for different statistical surfaces (e.g., terrain elevation, population density, disease incidence) in a GIS software application"},{"concepts":[75],"name":"Demonstrate why spatial autocorrelation among regression residuals can be an indication that spatial variables have been omitted from the models"},{"concepts":[45],"name":"Demonstrate why the georegistration of datasets is critical to the success of any map overlay operation"},{"concepts":[174],"name":"Demonstrate why the system design is important in any GIS implementation"},{"concepts":[523],"name":"Derive the Stefan-Boltzman Law from the Planck's one"},{"concepts":[85],"name":"Describe a brief history of major philosophical movements relating to the nature of space, time, geographic phenomena and human interaction with it"},{"concepts":[149],"name":"Describe a mapping goal in which the use of each of the following would be appropriate: brushing, linking, multiple displays"},{"concepts":[46,47],"name":"Describe a real modeling situation in which map algebra would be used e.g., site selection, climate classification, least-cost path"},{"concepts":[286],"name":"Describe a scenario in which data from a secondary source may pose obstacles to effective and efficient use"},{"concepts":[311],"name":"Describe a scenario in 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application"},{"concepts":[111],"name":"Determine whether phenomena or applications exist that are not adequately represented in an existing comprehensive model"},{"concepts":[54],"name":"Determine which method to use to combine criteria e.g., linear, multiplication"},{"concepts":[903],"name":"Develop a Javascript function that handles a GeoJSON file"},{"concepts":[38],"name":"Develop a method for describing the shape of a cluster of similarly valued points by using the concept of the convex hull"},{"concepts":[506],"name":"Develop a strategy to improve the performance of an SDI initiative"},{"concepts":[149],"name":"Develop a useful interactive interface and legend"},{"concepts":[106],"name":"Develop alternative forms of representations for situations in which attributes do not adequately capture meaning"},{"concepts":[38],"name":"Develop an algorithm to determine the skeleton of polygons"},{"concepts":[858],"name":"Develop an event map based on a time-series 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system from a department-centered GI system"},{"concepts":[121],"name":"Differentiate applications in which vagueness is an acceptable trait from those in which it is unacceptable"},{"concepts":[101],"name":"Differentiate applications that can make use of common-sense principles of geography from those that should not"},{"concepts":[18],"name":"Differentiate between a linear program and an integer program"},{"concepts":[885],"name":"Differentiate between a metadata standard and a metadata profile"},{"concepts":[97],"name":"Differentiate between absolute and relative descriptions of location"},{"concepts":[269],"name":"Differentiate between active and passive sensors, citing examples of each"},{"concepts":[97],"name":"Differentiate between common-sense, Cartesian metric, relational, relativistic, phenomenological, social constructivist, and other theories of the nature of space"},{"concepts":[188,189],"name":"Differentiate between conceptual and logical models, in terms of the level of detail, constraints, and range of information included"},{"concepts":[308],"name":"Differentiate between consumption, analysis, presumption and production of geoinformation within digital geo media"},{"concepts":[54],"name":"Differentiate between contributing factors and constraints in a multi-criteria application"},{"concepts":[177],"name":"Differentiate between copypleft and permissive licenses for a software product"},{"concepts":[5],"name":"Differentiate between data mining approaches used for spatial and non-spatial applications"},{"concepts":[55],"name":"Differentiate between deterministic and stochastic spatial process models"},{"concepts":[99],"name":"Differentiate between formal and natural language in GI science applications."},{"concepts":[2],"name":"Differentiate between geostatistics, and spatial statistics"},{"concepts":[246],"name":"Differentiate between individual and aggregate models"},{"concepts":[63],"name":"Differentiate between isotropic and anisotropic processes"},{"concepts":[50],"name":"Differentiate between kernel density estimation and spatial interpolation"},{"concepts":[190],"name":"Differentiate between logical and physical models, in terms of the level of detail, constraints, and range of information included"},{"concepts":[215],"name":"Differentiate between lossy and lossless compression methods"},{"concepts":[46,47],"name":"Differentiate between map algebra and matrix algebra using real examples"},{"concepts":[103],"name":"Differentiate between mathematical and phenomenological theories of the nature of time"},{"concepts":[70],"name":"Differentiate between model-based and design-based sampling schemes"},{"concepts":[26],"name":"Differentiate between polynomial coordinate transformations (including linear) and rubbersheeting"},{"concepts":[878],"name":"Differentiate between SOAP and REST Web services. - Identify design issues of REST Web services"},{"concepts":[93],"name":"Differentiate between space and place"},{"concepts":[121],"name":"Differentiate between the following concepts: vagueness and ambiguity, well defined and poorly defined objects and fields or discord and non-specificity"},{"concepts":[52],"name":"Differentiate between the gravity model and spatial interaction models"},{"concepts":[57],"name":"Differentiate between trend surface analysis and deterministic spatial interpolation"},{"concepts":[883],"name":"Differentiate between upper, domain, and application level ontologies"},{"concepts":[269],"name":"Differentiate push-broom and cross-track scanning technologies"},{"concepts":[122],"name":"Differentiate uncertainty in geospatial situations from vagueness"},{"concepts":[109],"name":"Differentiate various sources of fields, such as substance properties (e.g., temperature), artificial constructs (e.g., population density), and fields of potential or influence (e.g., gravity)"},{"concepts":[288],"name":"Digitize and georegister a specified vector feature set to a given geometric accuracy and topological fidelity thresholds using a given map sheet, digitizing tablet, and data entry software"},{"concepts":[315],"name":"Discuss about \"mapping whose reality?\" Pros and cons of geoinformation sharing in social media, i.e. big data, \"digital shadow\" etc."},{"concepts":[303],"name":"Discuss about open data and data sharing and public/private sector"},{"concepts":[297],"name":"Discuss about open data impact on society and citizenship"},{"concepts":[151],"name":"Discuss about the advantages of different immersive display systems"},{"concepts":[160],"name":"Discuss about the degree of subjectivity and/or objectivity of a map"},{"concepts":[125],"name":"Discuss about the History of Cartography in different cultures"},{"concepts":[126],"name":"Discuss about the relationship between art and cartography"},{"concepts":[652],"name":"Discuss advantages and disadvantages of across- and along-track interferometry"},{"concepts":[734,735,736],"name":"Discuss advantages and disadvantages of different methods of storing remote sensing data"},{"concepts":[749,750,751,752],"name":"Discuss advantages and disadvantages of different SAR data formats"},{"concepts":[685],"name":"Discuss advantages and disadvantages of passive and active sensors"},{"concepts":[647],"name":"Discuss advantages of SAR techniques over traditional measuring techniques"},{"concepts":[382],"name":"Discuss algorithms that use the detection of keypoints to identify objects in images"},{"concepts":[688],"name":"Discuss an example of using a radar altimeter"},{"concepts":[741],"name":"Discuss and compare different temporal resolutions of remote sending data"},{"concepts":[641],"name":"Discuss and compare different types of interactions of microwaves with matter"},{"concepts":[748],"name":"Discuss and compare different types of processing levels of optical data"},{"concepts":[753],"name":"Discuss and compare different types of processing levels of SAR data"},{"concepts":[297],"name":"Discuss and define open data and impact on GIS&T"},{"concepts":[368],"name":"Discuss cloud masks as early steps towards semantic enrichment for EO images"},{"concepts":[104],"name":"Discuss common prepositions and adjectives (in any particular language) that signify either spatial or temporal relations but are used for both kinds, such as after or longer"},{"concepts":[247],"name":"Discuss concepts of space-time dynamics for spatial modeling"},{"concepts":[876],"name":"Discuss consensus based interoperability and its relation to geospatial data interchange. Define what a Web Service (WS) is and present characteristic scenarios. Data serving and Data Processing WSs"},{"concepts":[314],"name":"Discuss critiques of GIS as \"deterministic\" technology in relation to debates about the Quantitative quantitative revolution in the discipline of geography."},{"concepts":[318],"name":"Discuss critiques of GIS as deterministic technology in relation to debates about the Quantitative Revolution in the discipline of geography"},{"concepts":[492],"name":"Discuss different formats (tutorials, in house, online, instructor lead) for training and how they can be used by organizations"},{"concepts":[456],"name":"Discuss different methods for assessing the quality of a specific EO product"},{"concepts":[691],"name":"Discuss different types of laser scanners"},{"concepts":[720,601],"name":"Discuss different types of satellite orbits"},{"concepts":[250],"name":"Discuss different ways of simulating space and visualizing model behaviour"},{"concepts":[614],"name":"Discuss electromagnetic interactions and scattering mechanisms"},{"concepts":[726],"name":"Discuss examples of ground-based platforms and their use"},{"concepts":[719],"name":"Discuss examples of the objectives of Earth observation missions"},{"concepts":[490],"name":"Discuss how a code of ethics might be applied within an organization"},{"concepts":[136],"name":"Discuss how cultural differences with respect to color associations impact map design"},{"concepts":[461,738],"name":"Discuss how different spectral resolution of EO sensors influences their potential for vegetation mapping"},{"concepts":[423],"name":"Discuss how hierarchical representation is exploited for object-based image analysis"},{"concepts":[678],"name":"Discuss how line detectors array sensors work"},{"concepts":[411],"name":"Discuss how low-pass filtering of an image impacts the resulting regions derived with watershed segmentation"},{"concepts":[160],"name":"Discuss how maps express relations of power"},{"concepts":[283],"name":"Discuss how measures of spatial autocorrelation may be used to evaluate thematic accuracy"},{"concepts":[740,460],"name":"Discuss how radiometric resolution influences the granularity of a land cover classification"},{"concepts":[737,745],"name":"Discuss how remote sensing data is organized and stored"},{"concepts":[661],"name":"Discuss how the angle of SAR signal incidence affects SAR acquisition"},{"concepts":[403],"name":"Discuss how the choice of sampling strategy impacts the accuracy assesment for a classification result"},{"concepts":[403],"name":"Discuss how the choice of sampling strategy impacts the classification result"},{"concepts":[746],"name":"Discuss how the radiometrically corrected data are processed"},{"concepts":[420],"name":"Discuss how the size of the neighborhood impacts the smoothing effect of a low-pass filter"},{"concepts":[304],"name":"Discuss how to approach the widening audience/participants for geospatial products and service, increasing geo-awareness and geo-enablement"},{"concepts":[143],"name":"Discuss how to create an intellectual and visual hierarchy on maps"},{"concepts":[609],"name":"Discuss how to use phase information in remote sensing"},{"concepts":[31],"name":"Discuss implications of data loss in the case of generalisation of spatial data."},{"concepts":[350],"name":"Discuss imputation methods for filling in missing data"},{"concepts":[517],"name":"Discuss in which way annual solar insolation and average cloud coverage parameters affect the choice of a solar power plant location"},{"concepts":[517],"name":"Discuss in which way modeled daily solar insolation and cloud coverage forecast could affect solar power plant day-by-day management"},{"concepts":[305],"name":"Discuss legal aspects of access to environmental data, global change/warming or sustainable development (regional, national, global) in conjunction to society."},{"concepts":[647],"name":"Discuss limitations of interferometric measurement"},{"concepts":[412],"name":"Discuss limitations of the different region-based segementation methods"},{"concepts":[733],"name":"Discuss main characteristics of digital imagery"},{"concepts":[297],"name":"Discuss of arguments for and against open data"},{"concepts":[296],"name":"Discuss of opportunities for exchange of geospatial data between public and private sector to enable more efficient analysis"},{"concepts":[245],"name":"Discuss options of combining rule-based models with other individual modelling approaches"},{"concepts":[636],"name":"Discuss orientational polarisation of media"},{"concepts":[314],"name":"Discuss over the argument that the use of Geospatial geospatial Information privileges certain views of the world over others."},{"concepts":[303],"name":"Discuss over the changing role of the private sector in the use of geospatial information"},{"concepts":[304],"name":"Discuss over the paradigm shifts and current trends in GIS&T education and pedagogical approaches for GIS teaching and learning in detail"},{"concepts":[315],"name":"Discuss over the various implications of surveillance technology"},{"concepts":[635],"name":"Discuss polarimetric decomporition techniques"},{"concepts":[309],"name":"Discuss positive and negative aspects of the term \"humans as sensors\""},{"concepts":[644],"name":"Discuss radar antennas"},{"concepts":[630],"name":"Discuss scale of roughness of microwaves"},{"concepts":[2],"name":"Discuss situations when it is desirable to adopt a spatial approach to the analysis of data"},{"concepts":[228],"name":"Discuss some of the difficulties of applying the standard process-pattern concept to lines and networks"},{"concepts":[413],"name":"Discuss spatial autocorrelation and homogeneity of image objects"},{"concepts":[176],"name":"Discuss the advantages and disadvantages of outsourcing elements of a GIS project / GI system"},{"concepts":[97],"name":"Discuss the advantages and disadvantages of the use of cartesian metric space as a basis for GIS and related technologies"},{"concepts":[284],"name":"Discuss the advantages and potential problems associated with the use of Minimum Mapping Unit (MMU) as a measure of the level of detail in land use, land cover, and soils maps"},{"concepts":[686],"name":"Discuss the application possibilities of imaging radar"},{"concepts":[697],"name":"Discuss the applications for which Differential Absorption LiDAR can be used"},{"concepts":[698],"name":"Discuss the applications for which Wind Doppler LiDAR is used"},{"concepts":[64],"name":"Discuss the appropriateness of different types of spatial weights matrices for various problems"},{"concepts":[78],"name":"Discuss the appropriateness of GWR under various conditions"},{"concepts":[443],"name":"Discuss the available data quality standards for EO"},{"concepts":[577],"name":"Discuss the basic principles of solar radiation."},{"concepts":[419],"name":"Discuss the benefits of using a gauss filter instead of a mean filter for smoothing an image"},{"concepts":[112],"name":"Discuss the causal relationship between spatial processes and spatial patterns, including the possible problems in determining causality"},{"concepts":[538],"name":"Discuss the change of attenuation length moving from visible to the microwave range and from sea water to solid land surfaces"},{"concepts":[51],"name":"Discuss the characteristics of the various cluster detection techniques"},{"concepts":[25],"name":"Discuss the consequences of increasing and decreasing resolution"},{"concepts":[111],"name":"Discuss the contributions of early attempts to integrate the concepts of space, time, and attribute in geographic information, such as Berry (1964) and Sinton (1978)"},{"concepts":[97],"name":"Discuss the contributions that different perspectives on the nature of space bring to an understanding of geographic phenomenon"},{"concepts":[111],"name":"Discuss the degree to which these models can be implemented using current technologies"},{"concepts":[674],"name":"Discuss the development of remote sensing sensors"},{"concepts":[123],"name":"Discuss the difference between vagueness and uncertainty."},{"concepts":[10],"name":"Discuss the difference of implementing Dijkstras algorithm in raster and vector modes"},{"concepts":[699],"name":"Discuss the differences between imaging and non-imaging sensors"},{"concepts":[133],"name":"Discuss the differences between maps that use the same data but are for different purposes and intended audiences"},{"concepts":[133],"name":"Discuss the differences between maps that use the same data but are for different purposes and intended audiences"},{"concepts":[463],"name":"Discuss the different types of resolution of Earth observation data"},{"concepts":[92],"name":"Discuss the differing denotations and connotations of the terms spatial, geographic, and geospatial"},{"concepts":[110],"name":"Discuss the difficulty of integrating process models into GIS software based on the entity and field views, and methods used to do so"},{"concepts":[117],"name":"Discuss the effects of temporal scale on the modeling of genealogical structures"},{"concepts":[311],"name":"Discuss the ethical implications of a local government's decision to charge fees for its data"},{"concepts":[418],"name":"Discuss the frequencies that a high-pass filter preserves and subdues"},{"concepts":[508],"name":"Discuss the governance structure in place of a particular country"},{"concepts":[224],"name":"Discuss the historical roots of the Census Bureaus creation of GBF/DIME as the foundation for the development of topological data structures"},{"concepts":[708],"name":"Discuss the history of the development of remote sensing platforms"},{"concepts":[108],"name":"Discuss the human predilection to conceptualize geographic phenomena in terms of discrete entities"},{"concepts":[308],"name":"Discuss the impact of geospatial information for the development of social media (Facebook, Twitter, Wikimapia, Flickr etc.) becoming increasingly location-based"},{"concepts":[234],"name":"Discuss the implication of long transactions on database integrity"},{"concepts":[314],"name":"Discuss the implications of interoperability on ontology"},{"concepts":[318],"name":"Discuss the implications of interoperability on ontology"},{"concepts":[284],"name":"Discuss the implications of the sampling theorem (Lambda = 0.5 delta) to the concept of resolution"},{"concepts":[28],"name":"Discuss the implications of tradeoff between data detail and data volume"},{"concepts":[107],"name":"Discuss the importance of space, time, properties, and categories as fundamentals in the conceptualization and representation of spatial entities."},{"concepts":[150],"name":"Discuss the influence of the user interface on maps and visualizations on the Web"},{"concepts":[878],"name":"Discuss the issue whether a service is really \"RESTful\" or not"},{"concepts":[296],"name":"Discuss the legal framework related to competition and public-private sector relationships in the geospatial domain"},{"concepts":[715],"name":"Discuss the main applications using the extra wide swath mode"},{"concepts":[405],"name":"Discuss the main drawback of edge-based segmentation in partitioning an image"},{"concepts":[681],"name":"Discuss the main properties of hyperspectral radiometers"},{"concepts":[680],"name":"Discuss the main properties of passive microwave radiometers"},{"concepts":[679],"name":"Discuss the main properties of thermal radiometers"},{"concepts":[673],"name":"Discuss the main types of remote sensing data"},{"concepts":[673,727],"name":"Discuss the main types of remote sensing platforms"},{"concepts":[673],"name":"Discuss the main types of remote sensing sensors"},{"concepts":[459],"name":"Discuss the minimum resolution required for detecting single houses in a satellite image"},{"concepts":[512],"name":"Discuss the mission, history, constituencies, and activities of the GIS Certification Institute (GISCI)"},{"concepts":[492],"name":"Discuss the National Research Council report on Learning to Think Spatially (2005) as it relates to spatial thinking skills needed by the GIS and T workforce"},{"concepts":[462],"name":"Discuss the needs for high temporal resolution for analysing crop cycles in agriculture"},{"concepts":[23],"name":"Discuss the pitfalls of using secondary data that has been generated using interpolations (e.g., Level 1 USGS DEMs)"},{"concepts":[640],"name":"Discuss the polarimetry technique"},{"concepts":[29],"name":"Discuss the possible effects on topological integrity of generalizing data sets"},{"concepts":[293],"name":"Discuss the potential legal problems associated with licensing geospatial information"},{"concepts":[319],"name":"Discuss the potential role of agency (individual action) in resisting dominant practices and in using GIS and T in ways that are consistent with feminist epistemologies and politics"},{"concepts":[409],"name":"Discuss the principles of regionalisation and their use in segmentation methods"},{"concepts":[584],"name":"Discuss the processes that describe the hydrologic cycle"},{"concepts":[320],"name":"Discuss the production, maintenance, and use of geospatial data by a government agency or private firm from the perspectives of a taxpayer, a community organization, and a member of a minority group"},{"concepts":[756],"name":"Discuss the purposes of obtaining remote sensing data"},{"concepts":[615],"name":"Discuss the radiometric anomalies of radar data"},{"concepts":[55],"name":"Discuss the relationship between spatial processes and spatial patterns"},{"concepts":[125],"name":"Discuss the relationship between the history of exploration and the development of a more accurate map of the world"},{"concepts":[30],"name":"Discuss the relationship of attribute measurement levels to database query operations"},{"concepts":[308],"name":"Discuss the role and value of \"place\" and \"space\" for geo media based social networking"},{"concepts":[136],"name":"Discuss the role of gamut in choosing colors that can be reproduced on various devices and media"},{"concepts":[224],"name":"Discuss the role of graph theory in topological structures"},{"concepts":[22],"name":"Discuss the role of metadata in facilitating conversation of data models and data structures between systems"},{"concepts":[305],"name":"Discuss the role of public, private sector and citizens in facilitating geospatial information in environmental/sustainable issues."},{"concepts":[296],"name":"Discuss the role of the public and private sectors in producing and dissemination of geospatial information"},{"concepts":[490],"name":"Discuss the status of professional and academic certification in GIS and T"},{"concepts":[294],"name":"Discuss the status of the concept of privacy in the U.S. legal regime"},{"concepts":[573],"name":"Discuss the structure and chemical composition of the atmosphere"},{"concepts":[0],"name":"Discuss the synergy between processes in geo-information systems and earth observation systems."},{"concepts":[63],"name":"Discuss the theory leading to the assumption of intrinsic stationarity"},{"concepts":[677],"name":"Discuss the use of area array sensors in remote sensing"},{"concepts":[683],"name":"Discuss the use of atmospheric passive sounders"},{"concepts":[682],"name":"Discuss the use of data obtained by spectroradiometer"},{"concepts":[676],"name":"Discuss the use of digital frame cameras in remote sensing"},{"concepts":[607],"name":"Discuss the use of polarization for different application domains"},{"concepts":[149],"name":"Discuss the uses of the map as a user interface element in interactive presentations of geographic information"},{"concepts":[709],"name":"Discuss the ways of using data acquired by UAS in remote sensing"},{"concepts":[707],"name":"Discuss types and classes of remote sensing sensors"},{"concepts":[465],"name":"Discuss valid time ranges for images used for landslide mapping with pre- and post-event image comparison"},{"concepts":[297],"name":"Discuss various legal aspects of public and private sectors concerning owning, controlling, sharing/ disseminating open data."},{"concepts":[297],"name":"Discuss various sources of open data (science, public and private sectors)"},{"concepts":[292],"name":"Discuss ways in which the geospatial profession is regulated under the U.S. legal regime"},{"concepts":[304],"name":"Discuss ways of working with crowdsourcing in education and research"},{"concepts":[627],"name":"Discuss what horizontal roughness component (correlation legth) is"},{"concepts":[689],"name":"Discuss what information is acquired by the laser altimeters"},{"concepts":[626],"name":"Discuss what surface height variation (or RMS height) is"},{"concepts":[743],"name":"Discuss what the header file describes"},{"concepts":[684],"name":"Discuss what the main characteristics of radiometers are"},{"concepts":[687],"name":"Discuss what types of electromagnetic waves the laser profiler uses"},{"concepts":[370],"name":"Discuss why a query through time is easier realized with a data cube than by comparison of a time series stored in image files"},{"concepts":[742],"name":"Distinguish and explain the different types of properties of digital imagery"},{"concepts":[149,139],"name":"Distinguish between animated and interactive maps"},{"concepts":[89],"name":"Distinguish between continuants and occurrents in relation with spatial phenomena."},{"concepts":[154],"name":"Distinguish between different graphic representation techniques"},{"concepts":[86],"name":"Distinguish between metaphysics and epistemology."},{"concepts":[188],"name":"Distinguish between the temporary and structural relationships in a conceptual model"},{"concepts":[165,172],"name":"Distinguish between usability, utility, and user needs in the context of geovisualizations"},{"concepts":[168,170],"name":"Document existing and potential tasks in terms of workflow and information flow"},{"concepts":[105],"name":"Document the personal, social, and or institutional meaning of categories used in GIS applications"},{"concepts":[150],"name":"Edit the symbology, labeling, and page layout for a map originally designed for hard copy printing so that it can be seen and used on the Web"},{"concepts":[101],"name":"Effectively communicate the design, procedures, and results of GIS projects to non-GIS audiences (clients, managers, general public)"},{"concepts":[112],"name":"Employ techniques for visualizing, describing, and analyzing distributions in space, time, and attribute"},{"concepts":[23],"name":"Estimate a value between two known values using linear interpolation (e.g., spot elevations, population between census years)"},{"concepts":[129],"name":"Estimate the cost to collect needed data from primary sources (e.g., remote sensing, GPS)"},{"concepts":[36],"name":"Estimate the fractal dimension of a sinuous line"},{"concepts":[559],"name":"Estimate the meteorological and the cloud optical properties by LBRTM and validate against high accuracy spectral measurements"},{"concepts":[127],"name":"Estimate the potential value of a historical map"},{"concepts":[442],"name":"Evaluate an EO product and its metadata on its reusability for a new application context"},{"concepts":[485],"name":"Evaluate and revise an existing GIS management strategy"},{"concepts":[795,792,793],"name":"Evaluate citizen-driven observations"},{"concepts":[153],"name":"Evaluate graphic techniques used to portray spatializations"},{"concepts":[25],"name":"Evaluate methods used by contemporary GIS software to resample raster data on-the-fly during display"},{"concepts":[269],"name":"Evaluate the advantages and disadvantages of acoustic remote sensing versus airborne or satellite remote sensing for seafloor mapping"},{"concepts":[269,718,723],"name":"Evaluate the advantages and disadvantages of airborne remote sensing versus satellite remote sensing"},{"concepts":[267],"name":"Evaluate the advantages and disadvantages of photogrammetric methods and LiDAR for production of terrain elevation data"},{"concepts":[110],"name":"Evaluate the assertion that events and processes are the same thing, but viewed at different temporal scales"},{"concepts":[122],"name":"Evaluate the causes of uncertainty in geospatial data"},{"concepts":[136],"name":"Evaluate the colors used in a web map to be used indoors and outdoors"},{"concepts":[439],"name":"Evaluate the conformity of an EO imagery product to ISO 19129"},{"concepts":[93],"name":"Evaluate the differences in how various parties think or feel differently about a place being modeled"},{"concepts":[219],"name":"Evaluate the ease of measuring resolution in different types of tessellations"},{"concepts":[108],"name":"Evaluate the effectiveness of GIS data models for representing the identity, existence, and lifespan of entities"},{"concepts":[109],"name":"Evaluate the field views description of objects as conceptual discretizations of continuous patterns"},{"concepts":[832],"name":"Evaluate the impact of changes in land areas"},{"concepts":[101],"name":"Evaluate the impact of geospatial technologies (e.g., Google Earth) that allow non-geospatial professionals to create, distribute, and map geographic information"},{"concepts":[811,809],"name":"Evaluate the impact of the climate change"},{"concepts":[219],"name":"Evaluate the implications of changing grid cell resolution on the results of analytical applications by using GIS software"},{"concepts":[108],"name":"Evaluate the influence of scale on the conceptualization of entities"},{"concepts":[85],"name":"Evaluate the influences of ones own philosophical views and assumptions on GIS AND T practices"},{"concepts":[81],"name":"Evaluate the influences of particular worldviews (including ones own) on GIS practices"},{"concepts":[95],"name":"Evaluate the influences of political actions, especially the allocation of territory, on human perceptions of space and place"},{"concepts":[95],"name":"Evaluate the influences of political ideologies (e.g., Marxism, Capitalism, conservative liberal) on the understanding of geographic information"},{"concepts":[504],"name":"Evaluate the institutional framework of an existing SDI initiative"},{"concepts":[224],"name":"Evaluate the positive and negative impacts of this shift from integrated topological models"},{"concepts":[215],"name":"Evaluate the relative merits of grid compression methods for storage"},{"concepts":[494],"name":"Evaluate the relevance and applicability of different teaching and learning methods for GIS&T education"},{"concepts":[109],"name":"Evaluate the representation of movement as a field of location over time (e.g. :x,y,z: = f(t) )"},{"concepts":[121],"name":"Evaluate the role that system complexity, dynamic processes, and subjectivity play in the creation of vague phenomena and concepts"},{"concepts":[144],"name":"Evaluate the strengths and limitations of different thematic mapping methods"},{"concepts":[244],"name":"Evaluate the tradeoffs between abstraction and representativeness in simulation model development"},{"concepts":[162],"name":"Evaluate the usability of a hard-copy map"},{"concepts":[162,172],"name":"Evaluate the usability of a web map"},{"concepts":[189],"name":"Evaluate the various general data models common in GIS project"},{"concepts":[121],"name":"Evaluate vagueness in the locations, time, attributes, and other aspects of geographic phenomena"},{"concepts":[29],"name":"Evaluate various line simplification algorithms by their usefulness in different applications"},{"concepts":[245],"name":"Evaluate when rule-based models can be applied to spatiotemporal problems"},{"concepts":[240],"name":"Examine how computational technology relates to geocomputation"},{"concepts":[366],"name":"Examine how the vegetation indices relates to the vegetation dynamics and health"},{"concepts":[366],"name":"Examine how the water-related spectral indices relates to changes in the vegetation and soil water content"},{"concepts":[888],"name":"Examine Metadata schema and vocabularies used for open data publishing"},{"concepts":[903],"name":"Examine the Document Object Model (DOM) in HTML documents"},{"concepts":[45],"name":"Exemplify applications in which overlay is useful, such as site suitability analysis"},{"concepts":[63],"name":"Exemplify deterministic and spatial stochastic processes"},{"concepts":[103],"name":"Exemplify different temporal frames of reference: linear and cyclical, absolute and relative"},{"concepts":[483],"name":"Exemplify each component of a needs assessment for an enterprise GIS"},{"concepts":[237],"name":"Exemplify how the lack of a data librarian to manage data can have disastrous consequences on the resulting dataset"},{"concepts":[63],"name":"Exemplify non-stationarity involving first and second order effects"},{"concepts":[113],"name":"Exemplify regions found at different scales"},{"concepts":[234],"name":"Exemplify scenarios in which one would need to perform a number of periodic changes in a real GIS database"},{"concepts":[38],"name":"Exemplify situations in which the centroid of a polygon falls outside its boundary"},{"concepts":[12],"name":"Exemplify the Classic Transportation Problem"},{"concepts":[224],"name":"Exemplify the concept of planar enforcement (e.g., TIN triangles)"},{"concepts":[217],"name":"Exemplify the uses (past and potential) of the hexagonal model"},{"concepts":[547],"name":"Explain the concept of composition of spectral signatures and apply the \"linear mixing\" models in some simple case"},{"concepts":[781],"name":"Explain a use case of EO for smart cities, e.g. how EO derived information about urban green instrastructure supports designing nature based solutions for preserving ecosystem services"},{"concepts":[650],"name":"Explain across-track interferometry technique"},{"concepts":[649],"name":"Explain along-track interferometry technique"},{"concepts":[400],"name":"Explain an application example where SVM is used for EO image classification"},{"concepts":[366],"name":"Explain an application example where the spectral indices are used for vegetation, water or snow monitoring"},{"concepts":[348],"name":"Explain an image pre-processing method"},{"concepts":[209],"name":"Explain and apply GML data models"},{"concepts":[609],"name":"Explain and apply phase unwrapping"},{"concepts":[205,223],"name":"Explain and apply standards relevant for geometric modelling"},{"concepts":[664],"name":"Explain and discuss elements of Synthetic Aperture Radar (SAR) geometric configuration"},{"concepts":[631],"name":"Explain and discuss surface roughness in microwave remote sensing"},{"concepts":[604],"name":"Explain and discuss the complex elements of a radar signal"},{"concepts":[732],"name":"Explain and discuss the concept of Big Data in the field of Earth Observation"},{"concepts":[728],"name":"Explain and discuss the development of remote sensing data carriers"},{"concepts":[692],"name":"Explain and discuss the LiDAR technology"},{"concepts":[713],"name":"Explain and discuss the SAR acquisition mode spotlight"},{"concepts":[712],"name":"Explain and discuss the SAR acquisition mode staring spotlight"},{"concepts":[685],"name":"Explain and discuss types of sensing mechanisms"},{"concepts":[642],"name":"Explain and discuss what antenna gain is and why it is described as the key performance of a radar antenna"},{"concepts":[669],"name":"Explain and discuss what terrain reflectivity is and how it influences radar signal"},{"concepts":[666],"name":"Explain and discuss what the foreshortening is"},{"concepts":[667],"name":"Explain and discuss what the layover is"},{"concepts":[755],"name":"Explain and discuss what the main processing levels of remote sensing data are"},{"concepts":[742],"name":"Explain and discuss what the radiometric resolution is"},{"concepts":[660],"name":"Explain and discuss what the range direction is"},{"concepts":[668],"name":"Explain and discuss what the shadow in SAR acquisition means"},{"concepts":[742,739],"name":"Explain and discuss what the spatial resolution is"},{"concepts":[742],"name":"Explain and discuss what the spectral resolution is"},{"concepts":[742],"name":"Explain and discuss what the temporal resolution is"},{"concepts":[672,612],"name":"Explain and outline the advantages of radar sensors"},{"concepts":[199],"name":"Explain and use UML diagrams"},{"concepts":[76],"name":"Explain Anselins typology of spatial autoregressive models"},{"concepts":[37],"name":"Explain any differences in the measured direction between two places when the data are presented in a GIS in different projections"},{"concepts":[202],"name":"Explain basic aspects of data modelling, storage and exploitation, such as relation models & databases, data structures, SQL, UML and other basics"},{"concepts":[293],"name":"Explain cases of liability claims associated with misuse of geospatial information, erroneous information, and loss of proprietary interests"},{"concepts":[634],"name":"Explain covariance and coherence matrix"},{"concepts":[625],"name":"Explain dielectric properties of objects and their effect on radar data acquisition"},{"concepts":[648],"name":"Explain differences between DInSAR and PSI"},{"concepts":[672],"name":"Explain differences between optical and radar remote sensing"},{"concepts":[84],"name":"Explain from which scientific fields GIS&T borrows ideas."},{"concepts":[238],"name":"Explain geocomputation, related concepts and how the two relate"},{"concepts":[6],"name":"Explain how a Bayesian framework can incorporate expert knowledge in order to retrieve all relevant datasets given an initial user query"},{"concepts":[502],"name":"Explain how a business case analysis can be used to justify the expense of implementing consensus-based standards"},{"concepts":[384],"name":"Explain how a DSM differs from a DTM"},{"concepts":[228],"name":"Explain how a graph (network) may be directed or undirected"},{"concepts":[228],"name":"Explain how a graph can be written as an adjacency matrix and how this can be used to calculate topological shortest paths in the graph"},{"concepts":[335],"name":"Explain how a histogram is derived from an EO image"},{"concepts":[467],"name":"Explain how a lack of knowledge about data quality limits the data value"},{"concepts":[10],"name":"Explain how a leading World Wide Web-based routing system works e.g., MapQuest, Yahoo Maps, Google"},{"concepts":[40],"name":"Explain how a semi-variogram describes the distance decay in dependence between data values"},{"concepts":[327],"name":"Explain how a set of overlapping images/satellite scenes can provide digital elevation models used for orthorectification and 3D modelling"},{"concepts":[823],"name":"Explain how a specific EO technology supports the assessments of disasters and geohazards"},{"concepts":[65],"name":"Explain how a statistic that is based on combining all the spatial data and returning a single summary value or two can be useful in understanding broad spatial trends"},{"concepts":[320],"name":"Explain how a tax assessors office adoption of GIS and T may affect power relations within a community"},{"concepts":[66],"name":"Explain how a weights matrix can be used to convert any classical statistic into a local measure of spatial association"},{"concepts":[78],"name":"Explain how allowing the parameters of the model to vary with the spatial location of the sample data can be used to accommodate spatial heterogeneity"},{"concepts":[56,1],"name":"Explain how analytical methods are used to derive analytical results from geospatial data"},{"concepts":[367],"name":"Explain how band maths can be applied to derive an index that indicates a specific land cover type like vegetation"},{"concepts":[72],"name":"Explain how block-kriging and its variants can be used to combine data sets with different spatial resolution support"},{"concepts":[44],"name":"Explain how buffers can be used in GI analysis"},{"concepts":[210],"name":"Explain how CityGML is related to GML"},{"concepts":[424],"name":"Explain how class modelling can make use of per-parcel analysis"},{"concepts":[307],"name":"Explain how community organizations represent the interests of citizens, politicians, and specialists"},{"concepts":[383],"name":"Explain how computer vision imitates the human visual system when interpreting EO images"},{"concepts":[294],"name":"Explain how conversion of land records data from analog to digital form increases risk to personal privacy"},{"concepts":[294],"name":"Explain how data aggregation is used to protect personal privacy in data produced by the U.S. Census Bureau"},{"concepts":[36],"name":"Explain how different measures of distance can be used to calculate the spatial weights matrix"},{"concepts":[64],"name":"Explain how different types of spatial weights matrices are defined and calculated"},{"concepts":[77],"name":"Explain how dissolving clusters of blocks with similar values may resolve the spatial correlation problem"},{"concepts":[49],"name":"Explain how distance-based methods of point pattern measurement can be derived from a distance matrix"},{"concepts":[52],"name":"Explain how dynamic, chaotic, complex or unpredictable aspects in some phenomena make spatial interaction models more appropriate than gravity models"},{"concepts":[356],"name":"Explain how EO applications targeting several countries at once can profit from data harmonisation"},{"concepts":[373],"name":"Explain how error propagates in the production workflow of an example EO product"},{"concepts":[325],"name":"Explain how fourier transformation is used to generate radar image"},{"concepts":[325],"name":"Explain how fourier transformation is used to reduce noise in optical imagery"},{"concepts":[36],"name":"Explain how fractal dimension can be used in practical applications of GIS"},{"concepts":[60],"name":"Explain how friction surfaces are enhanced by the use of impedance and barriers"},{"concepts":[306],"name":"Explain how geographic information is valuable to different sectors"},{"concepts":[66],"name":"Explain how geographically weighted regression provides a local measure of spatial association"},{"concepts":[282],"name":"Explain how geometric accuracies associated with the various orders of the U.S. horizontal geodetic control network are assured"},{"concepts":[295],"name":"Explain how geospatial information might be used in a taking of private property through a government's claim of its right of eminent domain"},{"concepts":[302],"name":"Explain how geospatial information might be used in a taking of private property through a governments claim of its right of eminent domain"},{"concepts":[482],"name":"Explain how GIS and T can be an integrating technology"},{"concepts":[15],"name":"Explain how graph theory plays a role in 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imagery"},{"concepts":[105],"name":"Explain the role of categories in common-sense conceptual models, everyday language, and analytical procedures"},{"concepts":[17],"name":"Explain the role of constraint functions using the graphical method"},{"concepts":[17],"name":"Explain the role of constraint functions using the simplex method"},{"concepts":[351],"name":"Explain the role of Gram-Schmidt vector orthogonalization in pan-sharpening"},{"concepts":[88],"name":"Explain the role of metaphors and image schema in our understanding of geographic phenomena and geographic tasks"},{"concepts":[98],"name":"Explain the role of metaphors and image schemata in our understanding of geographic phenomena and geographic tasks."},{"concepts":[17],"name":"Explain the role of objective functions in linear programming"},{"concepts":[400],"name":"Explain the sensitivity of SVM to hyper-parameters"},{"concepts":[399],"name":"Explain the sensitivity of the Random Forests classifier to the number of trees 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is"},{"concepts":[663],"name":"Explain what the ground range and azimuth resolution are"},{"concepts":[714],"name":"Explain what the interferometric wide swath mode is"},{"concepts":[643],"name":"Explain what the main representations of radar antenna pattern are"},{"concepts":[609],"name":"Explain what the mathematical description of the phase is"},{"concepts":[609],"name":"Explain what the phase in remote sensing means and in what units is expressed"},{"concepts":[605],"name":"Explain what the phasor represents"},{"concepts":[729],"name":"Explain what the picture element is"},{"concepts":[620],"name":"Explain what the radar cross-section is"},{"concepts":[616],"name":"Explain what the radar equation is"},{"concepts":[696],"name":"Explain what the radar scatterometer measures"},{"concepts":[884],"name":"Explain what the Resource Description Framework (RDF) is and what it can be used for"},{"concepts":[611],"name":"Explain what the wave-particle dualism is"},{"concepts":[278],"name":"Explain which elements determine the quality of geospatial data"},{"concepts":[414],"name":"Explain which principles a segmentation should follow to arrive at meaningful objects that are appropriate for a specific application"},{"concepts":[204],"name":"Explain which standards are essential for conceptual data modelling"},{"concepts":[82],"name":"Explain which technologies have an impact on GI science."},{"concepts":[273],"name":"Explain which types of geospatial data are collected through satellite remote sensing"},{"concepts":[606],"name":"Explain why a radar signal needs a complex waveform description"},{"concepts":[268],"name":"Explain why aerial imaging and photogrammetry are important for the geospatial domain and the geospatial industry"},{"concepts":[50],"name":"Explain why and how density estimation transforms point data into a field representation"},{"concepts":[29],"name":"Explain why areal generalization is more difficult than line 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commercial GIS software"},{"concepts":[490],"name":"Explain why it has been difficult for many agencies and organizations to define positions and roles for GIS and T professionals"},{"concepts":[72],"name":"Explain why it is important to have a good model of the semi-variogram in kriging"},{"concepts":[316],"name":"Explain why it is important to take into consideration the 'digital divide' when dealing with the use of and access to geographic data and information"},{"concepts":[72],"name":"Explain why kriging is more suitable as an interpolation method in some applications than others"},{"concepts":[235],"name":"Explain why logging and rollback techniques are adequate for managing short transactions"},{"concepts":[280],"name":"Explain why metadata are important for assessing and ensuring the quality of geospatial data"},{"concepts":[391],"name":"Explain why multimodal distributions in training samples should be avoided when using the maximum likelihood classifier"},{"concepts":[367],"name":"Explain why radiometric correction is a key requirement for deriving indices with band maths"},{"concepts":[451],"name":"Explain why rapid mapping applications have high requirements in timely availability of Earth observation products"},{"concepts":[513],"name":"Explain why software products sold by U.S. companies may predominate in foreign markets, including Europe and Australia"},{"concepts":[656],"name":"Explain why spatial resolution of passive radar system is much lower than that of active systems"},{"concepts":[484],"name":"Explain why the definition of user roles is an important element in the implementation of a GIS"},{"concepts":[610],"name":"Explain why the Doppler effect is important in radar remote sensing"},{"concepts":[498],"name":"Explain why the legal framework on geospatial data sharing can be considered as diverse and complex"},{"concepts":[498],"name":"Explain why the legal framework on geospatial data sharing consists of two main types of legislation from a data perspective"},{"concepts":[45],"name":"Explain why the process \"dissolve and merge\" often follows vector overlay operations"},{"concepts":[61],"name":"Explain why the properties of spatial continuity are characteristic of spatial surfaces"},{"concepts":[38],"name":"Explain why the shape of an object might be important in analysis"},{"concepts":[261],"name":"Explain why the shape of the Earth is complex and complicated to measure"},{"concepts":[820],"name":"Explain why the use of multiple EO sensors for mapping landslides associated with one triggering event increases the completeness of a landslide inventory"},{"concepts":[119],"name":"Explain why Toblers First Law of Geography is fundamental to many operations in GIS and whether it should be"},{"concepts":[61],"name":"Explain why zero slopes are indicative of surface specific points such as peaks, pits and passes and list the conditions necessary for each"},{"concepts":[12],"name":"Explain why, if supply equals demand, there will always be a feasible solution to the Classic Transportation Problem"},{"concepts":[50],"name":"Explain why, in some cases, an adaptive bandwidth might be employed"},{"concepts":[285],"name":"Explain, in general terms, the difference between single and double precision and impacts on error propagation"},{"concepts":[16],"name":"Explain, using the concept of combinatorial complexity, why some location problems are very hard to solve"},{"concepts":[88],"name":"Explore the contribution of linguistics to the study of spatial cognition and the role of natural language in the conceptualization of geographic phenomena"},{"concepts":[92],"name":"Explore the history of geography including (but not limited to) Greek and Roman contributions to geography (Eratosthenes, Strabo, Ptolemy), geography and cartography in the age of discovery, military geography, and geography..."},{"concepts":[76],"name":"Find a best model"},{"concepts":[147],"name":"Find a multivariate outlier using a combination of maps and graphs"},{"concepts":[38],"name":"Find centroids of polygons under different definitions of a centroid and different polygon shapes"},{"concepts":[492],"name":"Find or create training resources appropriate for GIS and T workforce in a local government organization"},{"concepts":[112],"name":"Find spatial patterns in the distribution of geographic phenomena using geographic visualization and other techniques"},{"concepts":[795,793],"name":"Forecast and monitor ocean winds and waves"},{"concepts":[106],"name":"Formalize attribute values and domains in terms of set theory"},{"concepts":[109],"name":"Formalize the notion of field using mathematical functions and Calculus"},{"concepts":[45],"name":"Formalize the operation called map overlay using Boolean logic"},{"concepts":[323],"name":"Generate a layer stack from bands of various EO data sources"},{"concepts":[354],"name":"Generate fine-scale images at a high temporal resolution with a spatio-temporal image fusion method"},{"concepts":[372],"name":"Generate high quality time series by removing clouds and cloud shadows from the available images"},{"concepts":[226],"name":"Give and explain an example of an application models"},{"concepts":[498],"name":"Give examples of more general types of legislation that are also applicable and relevant to geospatial data sharing"},{"concepts":[843],"name":"Having in-depth knowledge of two of the three Copernicus-relevant topics: Land monitoring, Emergency response including Humanitarian action, and Climate change"},{"concepts":[112],"name":"Hypothesize the causes of a pattern in the spatial distribution of a phenomenon"},{"concepts":[51],"name":"Identify a clustering method which does not require the number of clusters as input"},{"concepts":[30],"name":"Identify a variety of likely measurement level transformations (e.g., the classification of ratio data yields ordinal data)"},{"concepts":[249],"name":"Identify agent-based modelling principles and methodologies"},{"concepts":[314],"name":"Identify alternatives to the \"algorithmic way of thinking\" that characterizes use of geospatial Information."},{"concepts":[318],"name":"Identify alternatives to the algorithmic way of thinking that characterizes GIS"},{"concepts":[238],"name":"Identify and compare the scenarios on which geocomputation methods are relevant"},{"concepts":[71],"name":"Identify and define the parameters of a semi-variogram range, sill, nugget"},{"concepts":[415],"name":"Identify and discuss an example of a combined filtering process"},{"concepts":[501],"name":"Identify and discuss the different components of an SDI"},{"concepts":[336],"name":"Identify and explain methods of image enhancement"},{"concepts":[300],"name":"Identify and explain the different actors and their roles in the geo-information value chain"},{"concepts":[372],"name":"Identify anomalies by means of surface properties such as evapotranspiration (ET) or land surface temperature (LST) derived from satellite image time series"},{"concepts":[109],"name":"Identify applications and phenomena that are not adequately modeled by the field view"},{"concepts":[787,784,805,816,786,815],"name":"Identify border incursions or maritime movements"},{"concepts":[903],"name":"Identify building blocks of Javascript programming language"},{"concepts":[248],"name":"Identify cellular automata principles and pattern"},{"concepts":[101],"name":"Identify common-sense views of geographic phenomena that sharply contrast with established theories and technologies of geographic information"},{"concepts":[242],"name":"Identify commonalities and patterns of geocomputation"},{"concepts":[512],"name":"Identify conferences that are related to GIS and T hosted by professional organizations"},{"concepts":[863],"name":"Identify construction sites"},{"concepts":[454],"name":"Identify critical design decisions that make an EO-derived map readable"},{"concepts":[174],"name":"Identify data center platform tier configuration and identify platform selection for each tier"},{"concepts":[877],"name":"Identify design issues of SOAP web services; fine grained and coarse grained services, design patterns"},{"concepts":[905],"name":"Identify differences, advantages and disadvantages of web application framework based and portal framework based web applications from the geospatial data perspective"},{"concepts":[65],"name":"Identify different measures of spatial autocorrelation"},{"concepts":[66],"name":"Identify different measures of spatial autocorrelation"},{"concepts":[390],"name":"Identify different methods that employ conditional probability for image classification"},{"concepts":[380],"name":"Identify different options where Artificial Intelligence can be integrated in the image processing and analysis workflow"},{"concepts":[344],"name":"Identify different types of noise and associated methods for their reduction"},{"concepts":[109],"name":"Identify examples of discrete and continuous change found in spatial, temporal, and spatio-temporal fields"},{"concepts":[149,139],"name":"Identify examples of static, animated, and interactive web maps"},{"concepts":[831],"name":"Identify geological features"},{"concepts":[819,831],"name":"Identify geotectonic shifts"},{"concepts":[787,784,794,807,805,816,786,812],"name":"Identify high risk areas produced naturally or by humans"},{"concepts":[355],"name":"Identify image fusion techniques to fill gaps in image time series caused by clouds and cloud shadow"},{"concepts":[817],"name":"Identify impact of a flood"},{"concepts":[112],"name":"Identify influences of scale on the appearance of distributions"},{"concepts":[890],"name":"Identify issues in determining the relationships to be represented when publishing Linked Data"},{"concepts":[889],"name":"Identify issues in developing new ontologies for geospatial data"},{"concepts":[890],"name":"Identify issues in finding proper ontologies to annotate the data"},{"concepts":[883],"name":"Identify issues in the development of geospatial ontologies. Criticise the role of ontology development methodologies and ontology evaluation in the development of ontologies"},{"concepts":[904],"name":"Identify main components and functionality of Leaflet library, describe its main functions and how they are employed"},{"concepts":[904],"name":"Identify main components and functionality of Openlayers library, describe its main functions and how they are employed"},{"concepts":[886],"name":"Identify main components of manual metadata creation software tools"},{"concepts":[904],"name":"Identify main elements and functionality Google maps, describe some of its most popular API operations and how they are employed"},{"concepts":[904],"name":"Identify main elements and functionality Mapbox, describe some of its most popular API operations and how they are employed"},{"concepts":[892],"name":"Identify main issues in \"keyword-based\" discovery of data and services"},{"concepts":[893],"name":"Identify main issues in Semantic discovery"},{"concepts":[176],"name":"Identify major obstacles to the success of a GIS proposal"},{"concepts":[77],"name":"Identify modeling situations where spatial filtering might not be appropriate"},{"concepts":[502],"name":"Identify organizations that focus on developing standards related to GIS and T"},{"concepts":[116],"name":"Identify phenomena that are best understood as networks"},{"concepts":[108],"name":"Identify phenomena that are difficult or impossible to conceptualize in terms of entities"},{"concepts":[428,376],"name":"Identify physical, semantic and spatial properties used to assigned objects to the target classes"},{"concepts":[174],"name":"Identify platform assignment for each workflow software component peak transaction processing load"},{"concepts":[129,176],"name":"Identify potential sources of data (free or commercial) needed for a particular application or enterprise"},{"concepts":[791,789,804,813],"name":"Identify rapid response to events associated with health security & care"},{"concepts":[788,791,790,821,822],"name":"Identify rapid response to major environmental risk events"},{"concepts":[842,841],"name":"Identify sea-ice or icebergs using EO data"},{"concepts":[51],"name":"Identify several cluster detection techniques and discuss their limitations"},{"concepts":[38],"name":"Identify situations in which shape affects geometric operations"},{"concepts":[119],"name":"Identify situations in which Toblers First Law of Geography does not apply"},{"concepts":[119],"name":"Identify situations in which Toblers First Law of Geography is valuable"},{"concepts":[105],"name":"Identify specific examples of categories of entities (i.e., common nouns), properties (i.e., adjectives), space (i.e., regions), and time (i.e., eras)"},{"concepts":[502],"name":"Identify standards that are used in GIS and T"},{"concepts":[468],"name":"Identify steps of processing on large image collections that benefit from storing them in array databases"},{"concepts":[885],"name":"Identify the aspects of selecting keywords which would characterize the data properly"},{"concepts":[22],"name":"Identify the conceptual and practical difficulties associated with data model and format conversion"},{"concepts":[22],"name":"Identify the conceptual and practical difficulties associated with data model and format conversion"},{"concepts":[258],"name":"Identify the defining characteristics of an open geocomputation project"},{"concepts":[899],"name":"Identify the different barriers for the integration of datasets"},{"concepts":[64],"name":"Identify the different methods for constructing spatial weigh matrix"},{"concepts":[83],"name":"Identify the epistemological assumptions underlying the work of colleagues"},{"concepts":[903],"name":"Identify the extensions HTML5 brings over older HTML versions"},{"concepts":[121],"name":"Identify the hedges used in language to convey vagueness"},{"concepts":[885],"name":"Identify the issues in mapping between different metadata standards. Also identify the roles of thesauri and crosswalks"},{"concepts":[489],"name":"Identify the key organizational components of a GIS&T implementation"},{"concepts":[113],"name":"Identify the kinds of phenomena that are commonly found at the boundaries of regions"},{"concepts":[293],"name":"Identify the liability implications associated with contracts"},{"concepts":[896],"name":"Identify the main components of OGC Filter encoding and compare it to SQL"},{"concepts":[893],"name":"Identify the main concepts of reasoning and architectural components of Reasoners"},{"concepts":[489],"name":"Identify the main organizational challenges in implementing and use GIS&T"},{"concepts":[136],"name":"Identify the most appropriate color palette for a printed map for visually-impaired people"},{"concepts":[136],"name":"Identify the most appropriate color palette for an online map for visually-impaired people"},{"concepts":[214],"name":"Identify the national framework datasets based on a grid model"},{"concepts":[896],"name":"Identify the need for and main issues in spatial data interchange"},{"concepts":[81],"name":"Identify the ontological assumptions underlying the work of colleagues"},{"concepts":[492],"name":"Identify the particular skills necessary for users to perform tasks in three different workforce domains (e.g., small city, medium county agency, a business, or others)"},{"concepts":[85],"name":"Identify the philosophical views and assumptions underlying the work of colleagues"},{"concepts":[176],"name":"Identify the positions necessary to design and implement a GIS project / GI unit"},{"concepts":[490],"name":"Identify the qualifications needed for a particular GIS and T position"},{"concepts":[881],"name":"Identify the relation between OWL-S and WSDL and give an overview of Semantic Web service definition in OWL-S"},{"concepts":[57],"name":"Identify the spatial concepts that are assumed in different interpolation algorithms"},{"concepts":[490],"name":"Identify the standard occupational codes that are relevant to GIS and T"},{"concepts":[888],"name":"Identify the technical aspects that open data paradigm would affect concerning Spatial Data Infrastructures including NSDIs"},{"concepts":[108],"name":"Identify the types of features that need to be modeled in a particular GIS application or procedure"},{"concepts":[241,242],"name":"Identify the types of geography problems geocomputation solves"},{"concepts":[49],"name":"Identify the various ways point patterns may be described"},{"concepts":[177],"name":"Identify the viability of a commercial GIS application"},{"concepts":[880],"name":"identify the web services needed for a particular use case"},{"concepts":[174],"name":"Identify user locations, network connectivity, and data center server locations"},{"concepts":[104],"name":"Identify various types of geographic interactions in space and time"},{"concepts":[49],"name":"Identify various types of K-function analysis"},{"concepts":[881],"name":"Identify virtues of defining a given data set in both RDF and OWL, and compare semantic richness of both definitions"},{"concepts":[842,840],"name":"Identify wake trailing to detect ships using EO data"},{"concepts":[901],"name":"Identify whether Full-automated WSC has still a value in it concerning both where we stand today on the road to 'Semantic Web' and unresolved problems in the area, which are the problems of Artificial Intelligence indeed"},{"concepts":[546],"name":"Illustrate main spectral signatures of clouds and apply them in paractical cloud-detection exercise"},{"concepts":[224],"name":"Illustrate a topological relation"},{"concepts":[307],"name":"Illustrate an example of \"local knowledge\" that is unlikely to be represented in the geospatial data maintained routinely by government agencies"},{"concepts":[585],"name":"Illustrate and apply basic concepts of Atmospheric Physics to EO science and its applications"},{"concepts":[284],"name":"Illustrate and explain the distinction between resolution, precision, and accuracy"},{"concepts":[284],"name":"Illustrate and explain the distinctions between spatial resolution, thematic resolution, and temporal resolution"},{"concepts":[544],"name":"Illustrate basic features of spectral signatures of vegetation, water and bare soil"},{"concepts":[572],"name":"Illustrate basic modern physics theory understanding their implications on the development of advanced sensors for EO"},{"concepts":[535,543],"name":"Illustrate basic radiation-matter interactions and related concepts of spectral reflectance, absorbance and transmittance as specific properties of the matter"},{"concepts":[546],"name":"Illustrate e.m. radiation intercations with/within clouds."},{"concepts":[175],"name":"Illustrate each of the project management areas with an example of a technique or tool used"},{"concepts":[167],"name":"Illustrate how a business process analysis can be used to identify requirements during a GIS implementation"},{"concepts":[139],"name":"Illustrate how an animated map reveals patterns not evident without animation"},{"concepts":[558],"name":"Illustrate how cloud presence complicate radiative transfer description in Earth's atmosphere"},{"concepts":[87],"name":"Illustrate how fields, such as geography, cartography, computer and information science, engineering, mathematics, philosophy, cognitive science, and linguistics have their influence on GI science."},{"concepts":[531],"name":"Illustrate how it is possible to estimate the BRDF of a sample through measurements of BRF"},{"concepts":[534],"name":"Illustrate how the Voigt's line profile is related to the Doppler and pressure line broadening contributes"},{"concepts":[111],"name":"Illustrate major integrated models of geographic information, such as Peuquets Triad, Mennis Pyramid, and Yuans Three-Domain"},{"concepts":[492],"name":"Illustrate methods that are effective in providing opportunities for education and training when implementing a GIS in a small city"},{"concepts":[557],"name":"Illustrate of the concept of optical path"},{"concepts":[557],"name":"Illustrate of the concept of optical thickness"},{"concepts":[564],"name":"Illustrate possible noise sources related to photovoltaic and photoconductive detectors"},{"concepts":[556],"name":"Illustrate scope and conditions of validity of Schwarzshild equation."},{"concepts":[890],"name":"Illustrate stages of publishing a relational database as Linked Data"},{"concepts":[580],"name":"Illustrate the interaction of e.m. radiation in the thermal infrared with the atmosphere understanding specifc characteristics of radiative transfer in this specific spectral region."},{"concepts":[590],"name":"Illustrate the concept of \"kinetic temperature\" in absence of thermodynamic equilibrium"},{"concepts":[553],"name":"Illustrate the concept of Absorption Coefficient"},{"concepts":[552],"name":"Illustrate the concept of Cross Section of Extinction per Mass Unit"},{"concepts":[536],"name":"Illustrate the concept of grey body"},{"concepts":[554],"name":"Illustrate the concept of Source Function"},{"concepts":[524],"name":"Illustrate the concept of spectral emissivity and brigthness temperature and compute them in some simple real case"},{"concepts":[543],"name":"Illustrate the concept of spectral signatures of the matter"},{"concepts":[565],"name":"Illustrate the concepts of Interference and Diffraction"},{"concepts":[561],"name":"Illustrate the concepts of Reflection, Refraction and Dispersion of the light"},{"concepts":[517],"name":"Illustrate the concepts of solar constant and daily solar insolation"},{"concepts":[542],"name":"Illustrate the decay of the emittance with the distance from the source"},{"concepts":[125],"name":"Illustrate the evolution of Cartography in different periods of time"},{"concepts":[215],"name":"Illustrate the existing methods for compressing gridded data (e.g., run length encoding, Lempel-Ziv, wavelets)"},{"concepts":[600],"name":"Illustrate the factors limiting lifetime of satellites on their originally planned orbits"},{"concepts":[596],"name":"Illustrate the First Law of Thermodynamic"},{"concepts":[550],"name":"Illustrate the general equation of radiative transfer."},{"concepts":[576],"name":"Illustrate the Greenhouse effect associate to CO2 emission"},{"concepts":[568],"name":"Illustrate the Helmotz’s equation"},{"concepts":[217],"name":"Illustrate the hexagonal model"},{"concepts":[591],"name":"Illustrate the ideal gas law"},{"concepts":[219],"name":"Illustrate the impact of grid cell resolution on the information that can be portrayed"},{"concepts":[24],"name":"Illustrate the impact of vector/raster/vector conversions on the quality of a dataset"},{"concepts":[518,529],"name":"Illustrate the importance of Earth's emitted radiation for EO from space"},{"concepts":[563],"name":"Illustrate the importance of electric conduction in solids for the design and development of advanced EO sensors"},{"concepts":[601],"name":"Illustrate the importance of the choice of the satellite orbit for the design of a satellite mission devoted to specific applications"},{"concepts":[872,867,868,869,870,871],"name":"Illustrate the information of EO data"},{"concepts":[583],"name":"Illustrate the main atmospherical spectral windows"},{"concepts":[548],"name":"Illustrate the main differences among passive and active remote sensing techniques"},{"concepts":[532],"name":"Illustrate the main energetic transictions that can be associated to molecular absorption of e.m. radiation"},{"concepts":[540],"name":"Illustrate the main forms of radiation-matter interaction"},{"concepts":[51],"name":"Illustrate the main use of spatial clustering in earth observation"},{"concepts":[527],"name":"Illustrate the nature of electromagnetic radiation"},{"concepts":[220],"name":"Illustrate the quadtree model"},{"concepts":[243],"name":"Illustrate the relationships between geocomputation with other terms, disciplines and areas of knowledge"},{"concepts":[595],"name":"Illustrate the role of Eulerian and Lagrangian models in budget equations definition"},{"concepts":[567],"name":"Illustrate the role of the principle of constant speed of light within the special relativity theory"},{"concepts":[560],"name":"Illustrate the scope Radiative Transfer theory"},{"concepts":[597],"name":"Illustrate the Second Law of Thermodynamic"},{"concepts":[544],"name":"Illustrate the spectral response curves for basic environmental features (e.g., vegetation, concrete, bare soil)"},{"concepts":[584],"name":"Illustrate the transferring of Energy within the Earth-Atmosphere System"},{"concepts":[151],"name":"Illustrate the use of virtual environments"},{"concepts":[589],"name":"Illustrate the utility of thermodynamic diagrams for the study of local atmospheric properties"},{"concepts":[482],"name":"Illustrate what functions a support or service center can provide to an organization using GIS and T"},{"concepts":[530],"name":"Illustrate why we refer to the BRDF as an absolute definition of spectral reflectance"},{"concepts":[126],"name":"Illustrate with examples the relationship between art and cartography at different historical moments"},{"concepts":[593],"name":"Ilustrate the state function of the condensed gas phase"},{"concepts":[220],"name":"Implement a format for encoding quadtrees in a data file"},{"concepts":[76],"name":"Implement a maximum likelihood estimation procedure for determining key spatial econometric parameters"},{"concepts":[236],"name":"Implement a test of reliability of change information"},{"concepts":[57],"name":"Implement a trend surface analysis using either the supplied function in a GIS or a regression function from any standard statistical package"},{"concepts":[887],"name":"Implement and configure a catalogue service"},{"concepts":[17],"name":"Implement linear programs for spatial allocation problems"},{"concepts":[12],"name":"Implement the Transportation Simplex method to determine the optimal solution"},{"concepts":[282],"name":"In contrast to the National Map Accuracy Standard, explain how the spatial accuracy of a digital road centerlines data set may be evaluated and documented"},{"concepts":[889],"name":"Indicate an architecture and tools for organizing semantically annotated data"},{"concepts":[904],"name":"Indicate an overview of OpenStreetMap and define its general functionality, comment its usage by Web APIs"},{"concepts":[905],"name":"Indicate generally how \"NSDI-requiring-scenarios\"would be handled by web application framework based applications"},{"concepts":[903],"name":"Indicate main elements of HTML5"},{"concepts":[893],"name":"Indicate some examples of semantic discovery; Semantic search engines, highlighting projects and practice concerning GI related applications in the area"},{"concepts":[314],"name":"Indicate the extent to which contemporary use of geospatial information supports diverse ways of understanding the world."},{"concepts":[483],"name":"Indicate the possible justifications that can be used to implement an enterprise GIS"},{"concepts":[247],"name":"Interpret when space-time dynamics can be used to study geographical phenomen"},{"concepts":[174],"name":"Interpret business needs and translate them to IT needs"},{"concepts":[480],"name":"Interpret descriptive statistics and geostatistics of geographic data"},{"concepts":[135],"name":"Interpret different symbols and icons in a map"},{"concepts":[905],"name":"Interpret generally the functionality offered by \"portal frameworks\" land Geoportals like Geonetwork, Opengeoportal, Esri geoportal server, Degree portal, Liferay, Jboss portal"},{"concepts":[905],"name":"Interpret generally the main components and functionality of \"Web Application Frameworks\" such as AngularJS, Ext.js, Django, Java Server Faces (JSF), and the like"},{"concepts":[882],"name":"interpret GML data model and GML definition of geometry. GML application schemas and GML documents"},{"concepts":[239],"name":"Interpret how individual parts contained in a complex system relate to each other"},{"concepts":[861],"name":"Interpret information from EO products or EO time series"},{"concepts":[774],"name":"Interpret land cover change detection"},{"concepts":[777],"name":"Interpret location based services (LBS)"},{"concepts":[5],"name":"Interpret patterns in space and time using Dorling and Openshaws Geographical Analysis Machine GAM demonstration of disease incidence diffusion"},{"concepts":[872,867,868,869,870,871],"name":"Interpret the content of EO data"},{"concepts":[421],"name":"Interpret the effect of a convolution from a given mask and contained weights"},{"concepts":[213],"name":"Interpret the header of a standard raster data file"},{"concepts":[125],"name":"Interpret the impact of paper-based and web maps in their context"},{"concepts":[847],"name":"Interpret the output of an point cloud measurement"},{"concepts":[810],"name":"Interpret the output of numerical prediction models"},{"concepts":[73],"name":"Interpret the results of universal kriging"},{"concepts":[174],"name":"Interpret user needs as an input for the design process"},{"concepts":[92],"name":"Justify a chosen position on which disciplines should have as important a role in GIS AND T as geography"},{"concepts":[178],"name":"Justify feasibility recommendations to decision-makers"},{"concepts":[108],"name":"Justify or refute the conception of fields (e.g., temperature, density) as spatially-intensive attributes of (sometimes amorphous and anonymous) entities"},{"concepts":[92],"name":"Justify or refute whether geography (as a discipline) should have a central role in GIS AND T"},{"concepts":[97],"name":"Justify the discrepancies between the nature of locations in the real world and representations thereof (e.g., towns as points)"},{"concepts":[83],"name":"Justify the epistemological frameworks with which you agree"},{"concepts":[81],"name":"Justify the metaphysical theories with which you agree"},{"concepts":[63],"name":"Justify the stochastic process approach to spatial statistical analysis"},{"concepts":[65],"name":"Justify, compute, and test the significance of the join count statistic for a pattern of objects"},{"concepts":[528],"name":"Knowledge of the basic (selective) mechanism of the absorption/emission of electromagnetic radiation by atoms."},{"concepts":[70],"name":"List and describe several spatial sampling schemes and evaluate each one for specific applications"},{"concepts":[514],"name":"List and describe the main categories of organizations in the GIS&T domain"},{"concepts":[509],"name":"List and describe the most important producers and users of geospatial data at the European Commission"},{"concepts":[302],"name":"List and describe the types of data maintained by local, state, and federal governments"},{"concepts":[481],"name":"List and explain relevant organizational and institutional aspects related to GIS&T."},{"concepts":[291],"name":"List and explain the different societal aspects that are important in dealing with geospatial information"},{"concepts":[265],"name":"List and explain the key requirements for geolocating data to earth"},{"concepts":[228],"name":"List definitions of networks that apply to specific applications or industries"},{"concepts":[41],"name":"List different ways connectivity can be determined in a raster and in a polygon dataset"},{"concepts":[39],"name":"List reasons why the area of a polygon calculated in a GIS might not be the same as the real world object it describes"},{"concepts":[13],"name":"List several classic problems to which network analysis is applied e.g., The Traveling Salesman Problem, The Chinese Postman Problem"},{"concepts":[151],"name":"List software and hardware environments supporting immersive visualization"},{"concepts":[483],"name":"List some of the topics that should be addressed in a justification for 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relevance of topological consistency for linear network features derived from Earth observation data"},{"concepts":[369],"name":"Understand the role of multi-temporal satellite images for identifying not only when a change occurred but also the changing drivers"},{"concepts":[396],"name":"Understand the role of pruning for reducing overfitting when applying decision trees for various classification purposes"},{"concepts":[474],"name":"Understand the strategic meaning of DIAS in the user segment of Copernicus"},{"concepts":[379],"name":"Understand the subjectivity of the visual interpretation"},{"concepts":[847],"name":"Understand the technology behind LiDAR as an active sensor and what makes it different from the other existing Remote Sensing approaches"},{"concepts":[63],"name":"Understand the underlying assumptions for spatial stochastics process"},{"concepts":[371],"name":"Understand the way in which Dynamic Time Warping can align shifted temporal sequences"},{"concepts":[22],"name":"Understand various formats of storing raster and vector data"},{"concepts":[229],"name":"Understand vector data models"},{"concepts":[892],"name":"Use \"Full-text-based\" discovery; open source and commercial search engines, its use in GI related applications"},{"concepts":[866,864],"name":"Use 3D textured models to present study area"},{"concepts":[472],"name":"Use a web portal to retrieve EO data"},{"concepts":[473],"name":"Use an image archive to retrive Earth observation data for an application"},{"concepts":[146],"name":"Use appropriate interpolation techniques to derive DEMs from point data"},{"concepts":[105],"name":"Use categorical information in analysis, cartography, and other GIS processes, avoiding common interpretation mistakes"},{"concepts":[832],"name":"Use EO products to assess land areas, its ecosystems, and its evolution"},{"concepts":[823],"name":"Use EO products to assess the risk of a disaster"},{"concepts":[811,809],"name":"Use EO products to conduct forecasts and projections"},{"concepts":[810],"name":"Use EO products to conduct numerical simulations"},{"concepts":[808],"name":"Use EO products to forecast sunlight exposure"},{"concepts":[823],"name":"Use EO products to measure impact and/or recovery"},{"concepts":[823],"name":"Use EO products to monitor disaster prone areas"},{"concepts":[832],"name":"Use EO products to plan land areas, its ecosystems, and its evolution"},{"concepts":[760],"name":"Use EO/GI information to plan and design projects, monitor and assess the environment, support decision-making processes, and to tackle environmental challenges"},{"concepts":[113],"name":"Use established analysis methods that are based on the concept of region (e.g., landscape ecology)"},{"concepts":[114],"name":"Use established analysis methods that are based on the concept of spatial integration (e.g., overlay)"},{"concepts":[387],"name":"Use filtering techniques to spatially aggregate an image classification"},{"concepts":[119],"name":"Use methods that analyze metrical relationships"},{"concepts":[118],"name":"Use methods that analyze topological relationships"},{"concepts":[894],"name":"Use Natural language based discovery over linked data"},{"concepts":[845],"name":"Use NDVI to estimate the vegetation cover"},{"concepts":[888],"name":"Use open data APIs that enable the usage of Open data; identify design aspects and usage scenarios"},{"concepts":[441],"name":"Use software tools to automate the practice of reproducible research in daily work"},{"concepts":[208],"name":"Use standards such as ISO 19141 Schema for moving features, ISO 19142 Web Feature Service and ISO 19109 - Rules for application schema"},{"concepts":[501],"name":"Use the models of ‘SDI generations’ and ‘SDI components’ to describe the main elements of an existing SDI initiative"},{"concepts":[488],"name":"Use the most effective change model depending on the nature and needs of the client's organization."},{"concepts":[881],"name":"Use Web services description for RESTful web services, Web Application Description Language (WADL) and its use"},{"concepts":[197],"name":"Work with different data compression techniques"},{"concepts":[40],"name":"Write a program to create a matrix of pair-wise distances among a set of points"},{"concepts":[213],"name":"Write a program to read and write a raster data file"},{"concepts":[40],"name":"Write typical forms for distance decay functions"},{"concepts":[11],"name":"xplain how the concept of capacity represents an upper limit on the amount of flow through the network"}]},"v5":{"concepts":[{"code":"GIST","description":"Geographic Information Science and Technology","name":"Geographic Information Science and Technology"},{"code":"AM","description":"This knowledge area encompasses a wide variety of operations whose objective is to derive analytical results from geospatial data. Data analysis seeks to understand both first-order (environmental) effects and second-order (interaction) effects. Approaches that are both data-driven (exploration of geospatial data) and model-driven (testing hypotheses and creating models) are included. Data driven techniques derive summary descriptions of data, evoke insights about characteristics of data, contribute to the development of research hypotheses, and lead to the derivation of analytical results. The goal of model driven analysis is to create and test geospatial process models. In general, model-driven analysis is an advanced knowledge area where previous experience with exploratory spatial data analysis would constitute a desired prerequisite. Visual tools for data analysis are covered in Knowledge Area: Cartography and Visualization (CV) and many of the fundamental principles required to ground data analysis techniques are introduced in Knowledge Area: Conceptual Foundations (CF). Image processing techniques are considered in Knowledge Area: Geospatial Data (GD). All of the methods described in this knowledge area are more or less sensitive to data error and uncertainty as covered in Unit GC8 Uncertainty and Unit GD6 Data quality. Mastery of the educational objectives outlined in this knowledge area requires knowledge and skills in mathematics, statistics, and computer programming.","name":"Analytical Methods","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM1-2","description":"Analytical capabilities of a GIS make use of spatial and non-spatial (attribute) data to answer questions and solve problems that are of spatial relevance. We now make a distinction between analysis (or analytical operations) and analytical models (often referred to as “modelling”). And by analysis we actually mean only a subset of what is usually implied by the term: we do not specifically deal with advanced statistical analysis (such as cluster detection or geostatistics).\r\n\r\nAnalysis of spatial data can be defined as computing new information to provide new insights from existing spatial data. Consider an example from the domain of road construction. In mountainous areas, this is a complex engineering task with many cost factors, including the number of tunnels and bridges to be constructed, the total length of the tarmac, and the volume of rock and soil to be moved. GISs can help to compute such costs on the basis of an up-to-date digital elevation model and a soil map. The exact nature of the analysis will depend on the application requirements, but computations and analytical functions can operate on both spatial and non-spatial data.","name":"Analytical approaches","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM1","description":"Geospatial data analysis has foundations in many different disciplines. As a result, there are many different schools of thought or analytical approaches including spatial analysis, spatial modeling, geostatistics, spatial econometrics, spatial statistics, qualitative analysis, map algebra, and network analysis. This unit compares and contrasts these approaches.","name":"Foundations of analytical methods","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM10-1","description":"Difficulties in dealing with large spatial databases, especially those arising from spatial heterogeneity and data quality issues.","name":"Problems of large spatial databases","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM10-2","description":"Data mining knows a variety of approaches, such as cluster analysis, analytical reasoning, association, prediction, etc.","name":"Data mining approaches","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM10-3","description":"Knowledge discovery involves the identification of useful patterns in spatial databases using techniques of data mining, trend analysis, etc.","name":"Knowledge discovery","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM10","description":"Algorithms have been developed to scan and search through extremely large data sets in order to find patterns within the data. These data mining and knowledge discovery techniques have been expanded to the spatial case. Legal and ethical concerns associated with such practices are considered in Knowledge Areas GS GIS and T and Society and OI Organizational and Institutional Aspects.","name":"Data mining","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM11-1","description":"A network is a connected set of lines representing some geographic phenomenon, typically to do with transportation. The “goods” transported can be almost anything: people, cars and other vehicles along a road network, commercial goods along a logistic network, phone calls along a telephone network, or water pollution along a stream/river network.\r\n\r\nDirect vs. Non-directed Networks\r\nA fundamental characteristic of any network is whether the network lines are considered to be directed or not. Directed networks associate with each line a direction of transportation; undirected networks do not. In the latter, the “goods” can be transported along a line in both directions. We discuss here vector network analysis, and assume that the network is a set of connected line features that intersect only at the lines’ nodes, not at internal vertices. (But we do mention under- and overpasses.)\r\n\r\nPlanar vs. Non-Planar Networks\r\nFor many applications of network analysis, a planar network, i.e. one that can be embedded in a two-dimensional plane, will do the job. Many networks are naturally planar, such as stream/river networks. A large-scale traffic network, on the other hand, is not planar: motorways have multi-level crossings and are constructed with underpasses and overpasses. Planar networks are easier to deal with computationally, as they have simpler topological rules. Not all GISs accommodate non-planar networks, or they can only do so using “tricks”. These tricks may involve the splitting of overpassing lines at the intersection vertex and the creation of four lines from the two original lines. Without further attention, the network will then allow one to make a turn onto another line at this new intersection node, which in reality would be impossible. In some GISs we can allocate a cost for turning at a node—see our discussion on turning costs below—and that cost, in the case of the overpass trick, can be made infinite to ensure it is prohibited. But, as mentioned, this is a work around to fit a non-planar situation into a data layer that presumes planarity. The above is a good illustration of geometry not fully determining the network’s behaviour. Additional application-specific rules are usually required to define what can and cannot happen in the network. Most GISs provide rule-based tools that allow the definition of these extra application rules.","name":"Networks defined","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM11-2","description":"Identifying and listing all elements does not describe a system in full. There may be many different ways in which elements may be connected or related to each other. The interactions, relationships between elements are essential to describe a system.\r\n\r\nRelationships between elements can be described by two types of flows:\r\nflows of material, and flows of information.\r\n\r\nMaterial flows connect elements between which there is an exchange of some substance. This can be some kind of material (water, food, cement, biomass, etc.), energy (light, heat, electricity, etc.), money, etc. It is something that can be measured and tracked. Also if an element is a donor of this substance the amount of substance in this element will decrease as a result of the exchange, while at the same time the amount of this substance will increase in the receptor element. There is always a mass, or energy conservation law in place. Nothing appears from nothing, and nothing can disappear to nowhere.\r\n\r\nThe second type of exchange is with an information flow. In this case element A gets information from element B. Element B at the same time may have no information about element A. Even when element A gets information about B, B does not lose anything. Information can be about the state of an element, about the quantity that it contains, about its presence or absence, etc. Information flows can be used to describe rules and policies. Information flows can modify the rates of flow between elements, they can switch certain processes and interactions on and off. But the process through which policies, interventions and norms for action are established, and could for example define the values of such information flows, are themselves the result of social interaction between relevant stakeholders from public, private or civil society.\r\n\r\nThe simplest is to acknowledge the existence of a relationship between certain elements, like this is done in a graph. In a graph a node presents an element and a link between any two nodes shows that these two elements are related. However there is no evidence of the direction of the relationship: we do not distinguish between the element x influencing element y or vice versa. This relationship can be further specified by an oriented graph that shows the direction of the relationship between elements. An element can be also connected to itself, to show that its behaviour depends on its state. We can further detail the description by identifying whether element x has a positive or negative effect on element y.\r\n\r\nWith networks, interesting questions arise that have to do with connectivity and network capacity. These relate to applications such as traffic monitoring and watershed management. With network elements—i.e. the lines that make up the network—extra values are commonly associated, such as distance, quality of the link or the carrying capacity.","name":"Graph theoretic descriptive measures of networks","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM11-3","description":"Optimal-path finding techniques are used when a least-cost path between two nodes in a network must be found. The two nodes are called origin and destination. The aim is to find a sequence of connected lines to traverse from the origin to the destination at the lowest possible cost.\r\n\r\nIn Optimal-path finding, the cost function can be simple: for instance, it can be defined as the total length of all lines of the path. The cost function can also be more elaborate and take into account not only length of the lines but also their capacity, maximum transmission (travel) rate and other line characteristics, for instance to obtain a reasonable approximation of travel time. There can even be cases in which the nodes visited add to the cost of the path as well. These may be called turning costs, which are defined in a separate turning-cost table for each node, indicating the cost of turning at the node when entering from one line and continuing on another. This is illustrated in Figure 1 of the examples.\r\n\r\nProblems related to optimal-path finding may require ordered optimal path finding or unordered optimal-path finding. Both have as an extra requirement that a number of additional nodes need to be visited along the path. In ordered optimal-path finding, the sequence in which these extra nodes are visited matters; in unordered optimal-path finding it does not.","name":"Least-cost shortest path","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM11-4","description":"There are phenomena that do not spread in all directions, but move or “flows” along a given, least-cost path, determined by characteristics of local terrain. The typical case arises when we want to determine drainage patterns in a catchment area: rain water “chooses” a way to leave the area. \r\n\r\nWe can illustrate the principles involved in this typical case with a simple elevation raster. For each cell in that raster, the steepest downward slope to a neighbour cell is computed and its direction is stored in a new raster. This computation determines the elevation difference between the cell and the neighbour cell and it takes into account cell distance - 1 for neighbour cells in N–S or W–E direction, 2 for cells in a NE–SW or NW–SE direction. From among its eight neighbour cells, it picks the one with the steepest path to it. The directions thus obtained in an output raster are encoded in integer values, which can be called the flow-direction raster. From this raster, the GIS can compute the accumulated flow-count raster, a raster that for each cell indicates how many cells have their water flow into that cell.\r\n\r\nCells with a high accumulated flow count represent areas of concentrated flow and may, thus, belong to a stream. By using some appropriately chosen threshold value in a map algebra expression, we may decide whether they do or not. Cells with an accumulated flow count of zero are local topographic highs and can be used to identify ridges.","name":"Flow modeling","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM11-5","description":"The Classic Transportation Problem considers minimizing the cost of getting an object or subject from origin to destination.","name":"The Classic Transportation Problem","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM11-6","description":"Classic network problems are examples of networking problems such as the Traveling Salesman Problem and the Chinese Postman Problem that need graph algorithms to be solved.","name":"Other classic network problems","selfAssesment":"GI-N2K
"},{"code":"AM11-7","description":"Accessibility is the extend in which it is difficult/easy to reach a location or object.","name":"Accessibility modeling","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM11","description":"Network analysis encompasses a wide range of procedures, techniques, and methods that allow for the examination of phenomena that can be modeled in the form of connected sets of edges and vertices. Such sets are termed a network or a graph, and the mathematical basis for network analysis is known as graph theory. Graph theory contains descriptive measures and indices of networks such as connectivity, adjacency, capacity, and flow as well as methods for proving the properties of networks. Networks have long been recognized as an efficient way to model many types of geographic data, including transportation networks, river networks, and utility networks electric, cable, sewer and water, etc. to name just a few. The data structures to support network analysis are covered in Unit DM4 Vector and object data models.","name":"Network analysis","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM12-1","description":"The modeling of problems in a formal language, working in a solution space and applying constraints.","name":"Operations research modeling and location modeling principles","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM12-2","description":"A formal programming method to support operational research in which linear constraints are applied.","name":"Linear programming","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM12-3","description":"A formal programming method to support operational research in which variables are constrained to integers.","name":"Integer programming","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM12-4","description":"Location-allocation modeling involves the determination of locations by minimizing the distance between object/subjects in space, such as between customers and facilities.","name":"Location-allocation modeling and p-median problems","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM12","description":"A wide variety of optimization techniques are now solvable within the GIS and T domain. Operations research is a branch of mathematics practiced in the allied fields of business and engineering. New models and software tools allow for the solution of transportation routing, facility location, and a host of other location-allocation modeling problems.","name":"Optimization and location-allocation modeling","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM13-1","description":"The effects such as the loss of data quality and data integrity that are the results of data transformations.","name":"Impacts of transformations","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM13-2","description":"A data model is an abstract model that organizes elements of data and standardizes how they relate to one another and to the properties of real-world entities. The term data model can refer to two distinct but closely related concepts. In relation to the field of geoinformation the term data model refers to the set of concepts used in defining such formalizations as entities, attributes, relations, tables which is implemented by a mathematical construct for representing geographic objects or surfaces as data. There are two most frequently used data models, which are vector and raster. For example, the vector data model represents geography as collections of points, lines and polygons and more complex structures crated from these three. The raster data model represent geography as cell matrices that store numeric values. Among these two data models we also stand out data formats in which data sets can be stored. File format is a standard of encoding geographical information into a computer file. There are the following basic file formats for encoding data:\r\nFor vectors:\r\n-\tShapefile\r\n-\tGeography Markup Language (GML)\r\n-\tXYZ Point Cloud\r\n-\tGeoJSON\r\n-\tGeoMedia\r\n-\t\r\nFor rasters:\r\n-\tGeoTIFF\r\n-\tIMG\r\n-\tJPEG2000\r\n-\tEsri grid\r\nThe GIS projects often require the conversion of the data formats. Data conversion is the process of moving data from one format to another, whether it is from one data model to another or from one data format to another. Data conversion is a complex process which is not only associated with changing the binary format of the file but also requires changing the structure of the data. For example, the GML data format always comes with an UML diagram, which is necessary to convert attributes stored in GML structure for example to a table of contest in a shapefile data format. In a well-managed GIS project it is important to store data in specific data model or data format. It is sometimes dictated by software capabilities and another times by team’s technical capabilities. With large amounts of geographic data used in the project it is more cost-effective to convert the data from one format to another than re-create it.","name":"Data model and format conversion","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM13-3","description":"Interpolation is used to create a GIS layer out of point observations on a continuous variable. The reason for doing this could be manifold: for visualization purposes, for making a proper reference with other data, or for making a combination of different layers.","name":"Interpolation","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM13-4","description":"Any vector data containing point, polyline, polygon can be converted into the raster dataset and vice versa. The vector data can be stored in shapefiles, databases or various others GIS file formats. The raster data are made of pixels or grid calls and can be represented by the discrete - categorical data (e.g. land cover map) or non-discrete - continuous data (e.g. satellite images, surface data). The process of conversion of vector to raster data is called rasterization. The vector to raster conversion requires the following parameters: the field value from the attribute table used to assign values to the output raster, the pixel size for the output raster, the output raster format (i.e. geotiff, img) and optionally the method of assigning values of point, polyline or polygon to the call raster, i.e. maximum length or area, cell centre. The output of the rasterised vector looks like a gridded version of the vector and it depends on the grid cell size. The process of vectorisation refers to the conversion of raster to vector dataset. The raster dataset can be converted to vector point, polyline or polygon. In order to convert raster to vector the following parameters should be provided: attribute field of the input raster dataset which will become an attribute in the output vector class, determining if the output polygon or polyline will be smoothed into simpler shapes or conform to the input raster's cell edges (stair stepping). For each raster pixel or grid cell a point will be created at the centre of the cell. The non-discrete continuous raster data have to converted to the categorical data type before converting to vector data. The conversion of vector to raster and raster to vector degrade the data to some extent causing loss of details, accuracy, and changing the original data.","name":"Vector-to-raster and raster-to-vector conversions","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM13-5","description":"Raster resampling refers to change of spatial resolution (increasing or decreasing) of the raster dataset. The resampling process calculates the new pixel values from the original digital pixel values in the uncorrected image. There are three common methods for resampling: nearest neighbour, bilinear interpolation, and cubic convolution. The nearest neighbour resampling uses the digital value from the pixel in the original image which is nearest to the new pixel location in the corrected image. This is the fastest interpolation method, which is primarily applied for discrete (categorical) raster data as it does not change the value of the pixel, but may result in some pixel values being duplicated while others are lost. Bilinear interpolation resampling takes a weighted average of four pixels in the original image nearest to the new pixel location. The averaging process alters the original pixel values and creates entirely new digital values in the output image. It is recommended for continuous data and it cause some smoothing of the data. Cubic convolution resampling is based on calculation of a distance weighted average of a block of sixteen pixels from the original image which surround the new output pixel location. As with bilinear interpolation, this method results in completely new pixel values. However, the last two methods both produce images which have a much sharper appearance and avoid the blocky appearance of the nearest neighbour method. The disadvantage of the Cubic method is that its requires more processing time.","name":"Raster resampling","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM13-6","description":"Users of geoinformation often need transformations from a particular 2D coordinate system to another system. This includes the transformation of polar coordinates into Cartesian map coordinates, or the change of map projection - transformation from one 2D Cartesian (x, y) system of a specific map projection into another 2D Cartesian (x′, y′) system of a defined map projection. This transformation is based on relating the two systems on the basis of a set of selected points whose coordinates are known in both systems, such as ground control points or common points such as corners of houses or road intersections. Image and scanned data are usually transformed by this method. The transformations may be conformal, affine, polynomial or of another type, depending on the geometric errors in the data set. A datum transformation involves the change of the horizontal datum which is often accompanied with a change of map projection.","name":"Coordinate transformations","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM13","description":"GIS is a cyclical rather than a linear system, unlike computer aided drafting (CAD) and computer assisted cartographic systems. Changes in projection, grid systems, data forms, and formats take place during the modeling process for which GIS was designed. Many non-analytical manipulations are necessary to accommodate the analytical power of the GIS. The manipulations of spatial and spatio-temporal data involve two general classes of operation: 1.\tTheir transformation into formats that facilitate subsequent analysis 2. Generalization and aggregation that affect the accuracy and integrity of the data used for analysis (see [AM14]). Other knowledge areas have identified different forms of data structures, data models, projections, and other forms of geospatial data representation. These differences present both opportunities and challenges for analysis and modeling. The ability to transform one representation to another, in a manner that maintains the integrity of the information as much as possible, can enhance the analysis and visualization of geospatial data. The raster and vector data models are described in [DM3] Tesselation data models and [DM4] Vector data model, Feature based modelling, Applications. The principles of coordinate systems, datums, and projections are also considered in Knowledge Area [GD] Geospatial Data","name":"Representation transformation","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM14-1","description":"In the practice of spatial data handling, one often comes across questions like “What is the resolution of the data?” or “At what scale is your data set?” Now that we have moved firmly into the digital age, these questions sometimes defy an easy answer. Map scale can be defined as the ratio between the distance on a printed map and the distance of the same stretch in the terrain.\r\n\r\nA 1:50,000 scale map means that 1 cm on the map represents 50,000 cm (i.e. 500 m) in the terrain. “Large-scale” means that the ratio is relatively large, so typically it means there is much detail to see, as on a 1:1000 printed map. “Small-scale”, in contrast, means a small ratio, hence less detail, as on a 1:2,500,000 printed map.\r\nDigital spatial data, as stored in a GIS, are essentially without scale: scale is a ratio notion associated with visual output, such as a map or on-screen display, not with the data that was used to produce the map or display. When digital spatial data sets have been collected with a specific map-making purpose in mind, and all maps have been designed to use one single map scale, for instance 1:25,000, we may assume that the data carries the characteristic of “a 1:25,000 digital data set.”\r\n\r\nThere is a relationship between the effectiveness of a map for a given purpose and the map’s scale. The Public Works department of a city council cannot use a 1:250,000 map for replacing broken sewer pipes, and the map of Figure 1 cannot be reproduced at scale 1:10,000.\r\n\r\nMaps that show much detail of a small area are called large-scale maps. Scale indications on maps can be given verbally, such as “one-inch-to the- mile”, or as a representative fraction like 1:200,000,000 (1 cm on the map equals 200,000,000 cm (or 2000 km) in reality), or by a graphic representation such as the scale bar. The advantage of using scale bars in digital environments is that its length also changes when the map is zoomed in, or enlarged, before printing. Sometimes it is necessary to convert maps from one scale to another, which may lead to problems of cartographic generalization.\r\n\r\nSpatial and temporal scales can not only be attached to processes, but also to observations. An example is given below, which summarizes the spatial and temporal scales of a few well-known Earth observation systems.\r\n\r\nScales of RS observations\r\nSensor Spatial scale\t Temporal scale\r\nMeteosat\t Hemisphere\t 15 minutes\r\nNOAA-AVHRR\t 3000 km\t daily\r\nLandsat TM\t 180 km\t 16 days\r\nSpot\t 60 km\t 26 days (pointable)","name":"Scale and generalization","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM14-2","description":"Techniques that support the generalisation of map content when changing to smaller map scales. These include line simplification, object selection, etc.","name":"Approaches to point, line, and area generalization","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM14-3","description":"Classification is a technique for purposely removing detail from an input data set in the hope of revealing important patterns (of spatial distribution). In the process, we produce an output data set, so that the input set can be left intact. This output set is produced by assigning a characteristic value to each element in the input set, which is usually a collection of spatial features that could be raster cells or points, lines or polygons. If the number of characteristic values in the output set is small in comparison to the size of the input set, we have classified the input set.\r\n\r\nThe input data set may, itself, have been the result of a classification. In such cases we refer to the output data set as a reclassification. For example, we may have a soil map that shows different soil type units and we would like to show the suitability of units for a specific crop. In this case, it is better to assign to the soil units an attribute of suitability for the crop. Since different soil types may have the same crop suitability, a classification may merge soil units of different type into the same category of crop suitability.\r\n\r\nIn classification of vector data, there are two possible results. In the first, the input features may become the output features in a new data layer, with an additional category assigned. In other words, nothing changes with respect to the spatial extents of the original features. Figure a of Examples illustrates this first type of output. A second type of output is obtained when adjacent features of the same category are merged into one bigger feature. Such a post-processing function is called spatial merging, aggregation or dissolving. An illustration of this second type is found in Figure b of Examples. Observe that this type of merging is only an option in vector data, as merging cells in an output raster on the basis of a classification makes little sense. Vector data classification can be performed on point sets, line sets or polygon sets; the optional merge phase only makes sense for lines and polygons.\r\n\r\nUser-controlled classifications require a classification table or user interaction. GIS software can also perform automatic classification, in which a user only specifies the number of classes in the output data set. The system automatically determines the class break points. The two main techniques of determining break points being used are the equal interval technique and the equal frequency technique.\r\n\r\nEqual Interval Technique\r\nThe minimum and maximum values vmin and vmax of the classification parameter are determined and the (constant) interval size for each category is calculated as (vmax - vmin) ∕ n, where n is the number of classes chosen by the user. This classification is useful in that it reveals the distribution pattern, as it determines the number of features in each category.\r\n\r\nEqual Frequency Technique\r\nThis technique is also known as quantile classification. The objective is to create categories with roughly equal numbers of features per category. The total number of features is determined first, then, based on the required number of categories, the number of features per category is calculated. The class break points are then determined by counting off the features in order of classification parameter value.","name":"Classification and transformation of attribute measurement levels","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM14","description":"Generalization addresses the meaningful reduction of the map content during scale reduction. All geospatial data are generalized. Even the most detailed data represent only subsets of reality. Furthermore, data are further generalized for purposes of mapping, visualization, and efficient storage. A variety of generalization techniques have been developed to facilitate this process. All are scale dependent. Aggregation is one form of generalization that transforms large numbers of individual objects into summarized groups. This concept description is concerned with the nature of these procedures and their implications for professional practice. Generalization is an important part of cartography (and is therefore discussed conceptually in CV2 Data considerations), but is also a transformation common to many GIS procedures.","name":"Generalization and aggregation","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM2-1","description":"Set theory is based on describing collections of members within sets. The Boolean membership function is binary, i.e. an element is either a member of the set (membership is true) or it is not a member of the set (membership is false). Such a membership notion is well-suited to the description of spatial features such as land parcels for which no ambiguity is involved and an individual ground truth sample can be judged to be either correct or incorrect. As Burrough and Frank (1996) note, increasingly, people are beginning to realize that the fundamental axioms of simple binary logic present limits to the way we think about the world. Not only in everyday situations, but also in formalized thought, it is necessary to be able to deal with concepts that are not necessarily true or false, but that operate somewhere in between. Since its original development by Zadeh (1965), there has been considerable discussion of fuzzy, or continuous, set theory as an approach for handling imprecise spatial data. In GIS, fuzzy set theory appears to have two particular benefits: the ability to handle logical modelling (map overlay) operations on inexact data; and the possibility of using a variety of natural language expressions to qualify uncertainty. Unlike Boolean sets, fuzzy or continuous sets have a membership function, which can assign to a member any value between 0 and 1.","name":"Set theory","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM2-2","description":"The most common operator for defining queries in a relational database is the language SQL, which stands for Structured Query Language.\r\n\r\nA spatial DBMS provides support for geographic coordinate systems and transformations. It will also provide storage of the relationships between features, including the creation and storage of topological relationships. As a result, one is able to use functions for “spatial query” (exploring spatial relationships). To illustrate, a spatial query using SQL to find all the Thai restaurants within 2 km of a given hotel would look like:\r\n\r\nSELECT R.Name\r\nFROM Restaurants AS R,\r\nHotels as H\r\nWHERE R.Type = Thai AND\r\nH.name = Hilton AND\r\nIntersect(R.Geometry, Buffer(H.Geometry, 2))\r\n\r\nThe Intersect command creates a spatial join between restaurants and hotels. The Geometry column carries the spatial data. It is likely that in the near future all spatial data will be stored directly in spatial databases.","name":"Structured Query Language (SQL) and attribute queries","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM2-3","description":"When exploring a spatial data set, the first thing one usually wants to do is select certain features, to (temporarily) restrict the exploration. Such selections can be made on geometric/spatial grounds or on the basis of attribute data associated with the spatial features. \r\n\r\nSelection conditions on attribute values can be combined using logical connectives such as AND, OR and NOT. Other techniques of selecting features can also usually be combined. Any set of selected features can be used as the input for a subsequent selection procedure. This means, for instance, that we can select all medical clinics first, then identify roads within 200 m of them, then select from those only the major roads, then select the nearest clinics to these remaining roads as the ones that should receive our financial support for maintenance. In this way, we are combining various techniques of selection.\r\n\r\nInteractive Spatial Selection\r\nIn interactive spatial selection, one defines the selection condition by pointing at or drawing spatial objects on the screen display, after having indicated the spatial data layer(s) from which to select features. The interactively defined objects are called the selection objects; they can be points, lines, or polygons. The GIS then selects the features in the indicated data layer(s) that overlap (i.e. intersect, meet, contain, or are contained in;) with the selection objects. These become the selected objects.\r\nInteractive spatial selection answers questions like “What is at …?”\r\n\r\nA spatial DBMS provides support for geographic coordinate systems and transformations. It will also provide storage of the relationships between features, including the creation and storage of topological relationships. As a result, one is able to use functions for “spatial query” (exploring spatial relationships). To illustrate, a spatial query using SQL to find all the Thai restaurants within 2 km of a given hotel would look like:\r\n\r\nSELECT R.Name\r\nFROM Restaurants AS R,\r\nHotels as H\r\nWHERE R.Type = Thai AND\r\nH.name = Hilton AND\r\nIntersect(R.Geometry, Buffer(H.Geometry, 2))\r\n\r\nThe Intersect command creates a spatial join between restaurants and hotels. The Geometry column carries the spatial data. It is likely that in the near future all spatial data will be stored directly in spatial databases.","name":"Spatial queries","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM2","description":"Attribute and spatial query operations are core functionality in any GIS and they are often considered to be the most basic form of analysis.","name":"Query operations and query languages","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM3-1","description":"In a 2D polar coordinate system points can be described with coordinates. Another way of defining a point in a plane is by using polar coordinates. This is the distance d from the origin to the point concerned and the angle α between a fixed (or zero) direction and the direction to the point. The angle α is called azimuth or bearing and is measured in a clockwise direction. It is given in angular units while the distance d is expressed in length units. \r\n\r\nDistance also plays a role in computations on networks, comprising a different set of analytical functions in GISs. Here, the network may consist of roads, public transport routes, high-voltage power lines, or other forms of transportation infrastructure. Analysis of networks may entail shortest path computations (in terms of distance or travel time) between two points in a network for routing purposes. Other forms are to find all points reachable within a given distance or duration from a start point for allocation purposes, or determination of the capacity of the network for transportation between an indicated source location and sink location.\r\n\r\nIn raster images, the distance function applied is the Pythagorean distance between the cell centres. The distance from a non-target cell to the target is the minimal distance one can find between that non-target cell and any target cell.","name":"Distances and lengths","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM3-2","description":"In a 2D polar coordinate system points can be described with coordinates. Another way of defining a point in a plane is by using polar coordinates. This is the distance d from the origin to the point concerned and the angle α between a fixed (or zero) direction and the direction to the point. The angle α is called azimuth or bearing and is measured in a clockwise direction. It is given in angular units while the distance d is expressed in length units.\r\n\r\nBearings are always related to a fixed direction (initial bearing) or a datum line. In principle, this reference line can be chosen freely. Three different, widely used fixed directions are: True North, Grid North and Magnetic North. The corresponding bearings are true (or geodetic) bearings, grid bearings and magnetic (or compass) bearings, respectively.\r\n\r\nIn raster images, direction is determined by the orientation of the neighboring pixels.","name":"Direction","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM3-3","description":"The representation of geographic objects is most naturally supported with vectors. After all, objects are identified by the parameters of location, shape, size and orientation, and many of these parameters can be expressed in terms of vectors. We can define features within the topological space that are easy to handle and that can be used as representations of geographic objects. These features are called simplices as they are the simplest geometric shapes of some dimension: point (0-simplex), line segment (1-simplex), triangle (2-simplex), and tetrahedron (3-simplex). When we combine various simplices into a single feature, we obtain a simplicial complex. When area objects are stored using a vector approach, the usual technique is to apply a boundary model. This means that each area feature is represented by some arc/node structure that determines a polygon as the area’s boundary. A polygon representation for an area object is another example of a finite approximation of a phenomenon that may have a curvilinear boundary in reality. In images, the shape of objects often helps us to identify them (built-up areas, roads and railroads, agricultural fields, etc.).","name":"Shape","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM3-4","description":"When area objects are stored using a vector approach, the usual technique is to apply a boundary model. This means that each area feature is represented by some arc/node structure that determines a polygon as the area’s boundary. A polygon representation for an area object is another example of a finite approximation of a phenomenon that may have a curvilinear boundary in reality.\r\nCommon sense dictates that area features of the same kind are best stored in a single data layer, represented by mutually non-overlapping polygons. This results in an application-determined (i.e. adaptive) partition of space. If the object has a fuzzy boundary, a polygon is an even worse approximation, even though potentially it may be the only one possible. Clearly, we expect additional data to accompany the area data. Such information could be stored in database tables.\r\n\r\nA simple but naïve representation of area features would be to list for each polygon the list of lines that describes its boundary. Each line in the list would, as before, be a sequence that starts with a node and ends with one, possibly with vertices in between. As the same line makes up the boundary from the two polygons, this line would be stored twice in the above representation, namely once for each polygon. This is a form of data duplication—known as data redundancy—which is (at least in theory) unnecessary, although it remains a feature of some systems. Another disadvantage of such polygon-by-polygon representations is that if we want to identify the polygons that border the bottom left polygon, we have to do a complicated and time-consuming search analysis comparing the vertex lists of all boundary lines with that of the bottom left polygon. For just a few polygons, this is fine, but in a data set with 5000 polygons, and perhaps a total of 25,000 boundary lines, this becomes a tedious task, even with the fastest of computers.","name":"Area","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM3-5","description":"Proximity computations are specific neighbourhood functions. They evaluate the characteristics of an area surrounding a feature’s location. A neighbourhood function “scans” the neighbourhood of the given feature(s), and performs a computation on it (them).\r\n\r\nExamples of proximity computations are: (1) Buffer zone generation (or buffering) is one of the best-known neighbourhood functions. It determines a spatial envelope (buffer) around a given feature or features. The buffer created may have a fixed width or a variable width that depends on characteristics of the area. (2) Thiessen Polygon generation.\r\n\r\nDistance decay functions describe the effect of the reduced influence when the distance between two locations increases.","name":"Proximity and distance decay","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM3-6","description":"Adjacency is the meet relationship as a topological property of a geographic object in relation ship with another. The adjacency operator identifies those features that share boundaries and, therefore, applies only to line and polygon features.\r\nThis meet relationship is invariant under a continuous transformation and are referred to as a topological mapping.","name":"Adjacency and connectivity","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM3","description":"For simple data exploration, GIS offers many basic geometric operations that help in extracting meaning from sets of data or for deriving new data for further analysis. Concepts on which these operations are based are addressed in Domains of geographic information and Relationships.\r\n\r\nWe can, for instance, measure angles on a map and use these for navigation in the real world, or for setting out a designed physical infrastructure. Or if, instead of a conformal projection such as UTM, we use an equivalent projection, we can determine the size of a parcel of land from the map—irrespective of where the parcel is on the map and at which elevation it is on the Earth.","name":"Geometric measures","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM4-1","description":"The reclassifications tools are used to change or reclassify the values. Reclassification of vector data involves the attributes of features in the feature attribute table, on the other hand reclassification of raster data involves the grid cell values to produce a new raster data layer. Reclassification can be used for data simplification and measurement scale change. We can adjust the data for more appropriate analysis by grouping the values and changing them. The reclassification tool can also be used to remove specific values from analysis.\r\nThe Select by location tool lets you select features by how they relate to other features in another layer. Selected features are based on their location. You can select features that are near or overlap the features. Most frequently used methods are intersect, within a distance, within, completely within, contain… Features can be selected in the same or other layers.\r\nThe Select by attributes tool lets you select features that match the selection criteria. With providing a selection criteria, matching features are selected. We can provide a complex selection criteria.","name":"Reclassification and selection operations","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM4-2","description":"Buffer analysis is one form of basic spatial analysis. It takes the vector representation (point, line, or polygon) of a real-world feature, and then creates a buffer zone based on a defined distance from the feature’s border. Thus, the created buffer zone is an area whose boundary always has the same distance to the input vector feature, e.g. the buffer zone for a point feature is a circle. Real-world examples for buffer zones could be protected areas along rivers or around nature conservation areas, or represent a simple proximity analysis. In the latter case, the buffer analysis is usually the first step of the analysis, followed by an overlay of the buffer zone with the target features to find those target features within the buffer zone, and thus within a certain distance of the original feature. Usually, the buffer zone extends outwards from the feature, but polygons can also have inner buffer zones. If the buffer zones from multiple features overlap, the analyst can decide to leave the individual boundaries of the buffer zones intact, or to dissolve them, i.e. merging the overlapping buffer zones into one larger buffer zone. The size of the buffer zone, i.e. the distance of its boundary from the original feature’s boundary, can be based on an uniform numerical value and associated spatial unit, but often, it is based on an attribute value (numerical or class) of the feature. Conceptually, buffering using raster representations of real-world features is similar a proximity analysis with a regular grid of square polygons: Departing from raster cells that form the area to be buffered, all raster cells that fall within the designated distance (overlay) from the buffer zone. With buffer analysis being a basic analytical operation, practically every GIS and many other analysis tools provide this functionality.","name":"Buffers","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM4-3","description":"Overlay functions is one of the most frequently used functions in a GIS application. They combine two (or more) spatial data layers, comparing them position by position and treating areas of overlap - and of non-overlap - in distinct ways.\r\n\r\nStandard overlay operators take two input data layers and assume that they are georeferenced in the same system and that they overlap in the study area. If either of these requirements is not met, the use of an overlay operator is pointless. The principle of spatial overlay is to compare the characteristics of the same location in both data layers and to produce a result for each location in the output data layer. The specific result to produce is determined by the user. It might involve a calculation or some other logical function to be applied to every area or location. With raster data, as we shall see, these comparisons are carried out between pairs of cells, one from each input raster. With vector data, the same principle of comparing locations applies but the underlying computations rely on determining the spatial intersections of features from each input layer.\r\n\r\nVector overlay operators are useful but geometrically complicated, and this sometimes results in poor operator performance. Raster overlays do not suffer from this disadvantage, as most of them perform their computations cell by cell, and thus they are fast. GISs that support raster processing - as most do - usually have a language to express operations on rasters. These languages are generally referred to as map algebra or, sometimes, raster calculus. They allow a GIS to compute new rasters from existing ones, using a range of functions and operators. Unfortunately, not all implementations of map algebra offer the same functionality.","name":"Overlay","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM4-4","description":"Neighbourhood functions evaluate the characteristics of an area surrounding a feature’s location. A neighbourhood function “scans” the neighbourhood of the given feature(s), and performs a computation on it (them). Examples of proximity computations are: (1) Buffer zone generation (or buffering) is one of the best-known neighbourhood functions. It determines a spatial envelope (buffer) around a given feature or features. The buffer created may have a fixed width or a variable width that depends on characteristics of the area. (2) Thiessen Polygon generation. For raster images: (3) Computation of diffusion (4) Flow computation.\r\n\r\nFor instance, our target might be a medical clinic. Its neighbourhood could be defined as:\r\n\r\nan area within a radius of 2 km distance as the crow flies; or\r\nan area within 2 km travelling distance; or\r\nall roads within 500 m travelling distance; or\r\nall other clinics within 10 minutes travelling time;\r\nall residential areas for which the clinic is the closest clinic.\r\n\r\nFinally, in the third step we indicate what it is we want to discover about the phenomena that exist or occur in the neighbourhood. This might simply be its spatial extent, but it might also be statistical information such as:\r\n\r\nhow many people live in the area;\r\nwhat is their average household income;\r\nare any high-risk industries located in the neighbourhood.\r\n\r\nThese are typical questions in an urban setting. When our interest is more in natural phenomena, different examples of locations, neighbourhoods and neighbourhood characteristics arise.\r\n\r\nThe principle in this case is to find out the characteristics of the vicinity, here called neighbourhood, of a location. After all, many suitability questions, for instance, depend not only on what is at a location but also on what is near the location. Thus, the GIS must allow us “to look around locally”. To perform neighbourhood analysis, we must:\r\n\r\n1. state which target locations are of interest to us and define their spatial extent;\r\n2. define how to determine the neighbourhood for each target; and\r\n3. define which characteristic(s) must be computed for each neighbourhood. \r\n\r\nSince raster data are the more commonly used in this case, neighbourhood characteristics often are obtained via statistical summary functions that compute values such as the average, minimum, maximum and standard deviation of the cells in the identified neighbourhood.\r\n\r\nTo select target locations, one can use the selection techniques. To obtain characteristics from an eventually-to-be identified neighbourhood, the same techniques apply. So what remains to be discussed here is the proper determination of a neighbourhood. One way of determining a neighbourhood around a target location is by making use of the geometric distance function. Geometric distance does not take into account direction, but certain phenomena can only be studied by doing so. Think of the spreading of pollution by rivers, groundwater flow or prevailing weather systems.\r\n\r\nDiffusion functions are based on the assumption that the phenomenon in question spreads in all directions, though not necessarily equally easily in each direction. Hence it uses local terrain characteristics to compute local resistances to diffusion.","name":"Neighborhood analysis","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM4-5","description":"GISs that support raster processing - as most do - usually have a language to express operations on rasters. These languages are generally referred to as map algebra or, sometimes, raster calculus. They allow a GIS to compute new rasters from existing ones, using a range of functions and operators. Unfortunately, not all implementations of map algebra offer the same functionality. The discussion below is to a large extent based on general terminology; it attempts to illustrate the key operations using a logical, structured language. Again, the syntax often varies among different GIS software packages.\r\n\r\nWhen producing a new raster we must provide a name for it, and define how it is to be computed. This is done in an assignment statement of the following format:\r\n\r\nOutput raster name := Map algebra expression.\r\n\r\nThe expression on the right is evaluated by the GIS, and the raster in which it results is then stored under the name on the left. The expression may contain references to existing rasters, operators and functions; the format is made clear in each case. The raster names and constants that are used in the expression are called its operands. When the expression is evaluated, the GIS will perform the calculation on a pixel-by-pixel basis, starting from the first pixel in the first row and continuing through to the last pixel in the last row. In map algebra, there is a wide range of operators and functions available.\r\n\r\nArithmetic operators\r\nVarious arithmetic operators are supported. The standard ones are multiplication (×), division (/), subtraction (-) and addition (+). Obviously, these arithmetic operators should only be used on appropriate data values, and, for instance, not on classification values. Other arithmetic operators may include modulo division (MOD) and integer division (DIV). Modulo division returns the remainder of division: for instance, 10 MOD 3 will return 1 as 10 - 3 × 3 = 1. Similarly, 10 DIV 3 will return 3.\r\n\r\nOther operators are goniometric: sine (sin), cosine (cos), tangent (tan); and their inverse functions asin, acos, and atan, which return radian angles as real values. The assignment\r\n\r\nC1 := A + 10\r\n\r\nwill add a constant factor of 10 to all cell values of raster A and store the result as output raster C1. The assignment\r\n\r\nC2 := A + B\r\n\r\nwill add the values of A and B cell by cell, and store the result as raster C2. Finally, the assignment\r\n\r\nC3 := (A - B) ∕ (A + B) × 100\r\n\r\nwill create output raster C3, as the result of the subtraction (cell by cell, as usual) of B cell values from A cell values, divided by their sum. The result is multiplied by 100. This expression, when carried out on AVHRR channel 1 (red) and AVHRR channel 2 (near infrared) of NOAA satellite imagery, is known as the NDVI (Normalized Difference Vegetation Index). It has proven to be a good indicator of the presence of green vegetation.\r\n\r\nComparison and logical operators\r\n\r\nMap algebra also allows the comparison of rasters cell by cell. To this end, we may use the standard comparison operators (<, ⇐, =, >=, > and <>).\r\n\r\nA simple raster comparison assignment is\r\n\r\nC := A <> B.\r\n\r\nIt will store truth values—either true or false—in the output raster C. A cell value in C will be true if the cell’s value in A differs from that cell’s value in B. It will be false if they are the same. Logical connectives are also supported in many raster calculi. We have already seen the connectives of AND , OR and NOT in raster overlay operators. Another connective that is commonly offered in map algebra is exclusive OR (XOR). The expression a XOR b is true only if either a or b is true, but not both.\r\n\r\nConditional expressions\r\nThe comparison and logical operators produce rasters with the truth values true and false. In practice, we often need a conditional expression together with them that allows us to test whether a condition is fulfilled. The general format is:\r\n\r\nOutput raster := CON(condition, then expression, else expression).\r\n\r\nHere, condition stands for the condition tested, then the expression is evaluated if condition holds, and else the expression is evaluated if it does not hold. This means that an expression such as CON(A = “forest”, 10, 0) will evaluate to 10 for each cell in the output raster where the same cell in A is classified as forest. For each cell where this is not true, the else expression is evaluated, resulting in 0.\r\n\r\nOverlays using a decision table\r\nConditional expressions are powerful tools in cases where multiple criteria must be taken into account. A small example may illustrate this. Consider a suitability study in which a land use classification and a geological classification must be used. Domain expertise dictates that some combinations of land use and geology result in suitable areas, whereas other combinations do not. In our example, forests on alluvial terrain and grassland on shale are considered suitable combinations, while any others are not.\r\n\r\nWe could produce an output raster with a map algebra expression, such as\r\n\r\nSuitability := CON((Landuse = “Forest” AND Geology = “Alluvial”)\r\nOR (Landuse = “Grass” AND Geology = “Shale”),\r\n“Suitable”, “Unsuitable”)\r\n\r\nand consider ourselves lucky that there are only two “suitable” cases. In practice, many more cases must usually be covered and, then, writing up a complex CON expression is not an easy task.\r\n\r\nTo this end, some GISs accommodate setting up a separate decision table that will guide the raster overlay process. This extra table carries domain expertise and dictates which combinations of input raster-cell values will produce which output raster-cell value. This gives us a raster overlay operator using a decision table. The GIS will have supporting functions to generate the additional table from the input rasters and to enter appropriate values in the table.","name":"Map algebra","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM4","description":"This small set of analytical operations is so commonly applied to a broad range of problems that their inclusion in software products is often used to determine if that product is a true GIS. Concepts on which these operations are based are addressed in Domains of geographic information and Relationships.","name":"Basic analytical operations","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM5-1","description":"Point pattern analysis refers to the detection of patterns in a group of objects or subjects located in space. This may support the analysis of clusters in accidents, crime, etc.","name":"Point pattern analysis","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM5-2","description":"The probability density function is a method with which the probability density can be estimated for points in a raster space.","name":"Kernels and density estimation","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM5-3","description":"Spatial cluster analysis is the grouping of similar spatial objects into classes (clusters) in such a way that the objects within the cluster are highly similar compared to the objects outside of the cluster. Spatial clustering forms an important part of spatial data mining (Han et al., 2001; Miller et al., 2009). A wealth of spatial clustering tools are currently available with immense application potential. \r\n\r\nIn earth observation studies, spatial cluster techniques are often applied to identify zones with similar land covers by using earth observation data as input. An example of such a technique is the K-means classifier (Han et al., 2001; Miller et al., 2009). This unsupervised classification technique makes several clusters (e.g. land use classes) of which each pixel is assigned to the cluster with the nearest mean (Han et al., 2001). The amount of clusters can be freely defined by the user just as the input metrics to perform the classification. A drawback of the K-means classifier is the need to specify the amount of output clusters. Density Based Spatial Clustering (DBSC) overcomes this issue since it automatically defines the optimal amount of clusters (Miller et al., 2009). In this type of clustering technique, dense regions of objects (proximate objects) are clustered and separated from regions with low density (noise) (Han et al., 2001; Liu et al., 2012). Finally, another frequently applied spatial clustering technique is the hierarchical agglomerative clustering. This technique makes use of a dendrogram to decompose the data into clusters. The agglomerative approach is a bottom-up approach in which all objects are first grouped in a distinct cluster and while moving upward in the tree, pairs of clusters are merged based on some metrics (e.g. spatial proximity) (Han et al., 2001). \r\n\r\nSpatial cluster techniques have many advantages when dealing with big datasets which is often the case when working with earth observation data. Its simplicity to use and the fast increase of cloud computing power makes from it powerful techniques to extract spatial patterns out of the data. It allows to translate raw earth observation data into a more user-friendly data product by showing the spatial patterns of the data.","name":"Spatial cluster analysis","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM5-4","description":"Spatial interaction models describe the flow of people and goods in a geographical space, in which parameters such as friction and distance play a role.","name":"Spatial interaction","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM5-5","description":"Multidimensional attributes can be analyzed through multidimensional scaling and principle component analysis.","name":"Analyzing multidimensional attributes","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM5-7","description":"Multi-criteria evaluation is an important aspect of decision support operations, which appear in process models. Process models in the Earth sciences describe the evolution of geo(bio)physical surface properties in time, independently from remote sensing observations. Examples of such process models on various time scales are, for instance, numerical weather prediction models (NWPs), vegetation growth models, hydrological models, oceanographic models and climate models.\r\n\r\nObservation models and process models can supplement each other to enhance the quality of the interpretation of remote sensing data and to fill gaps in time that occur when observations are not possible owing to clouds or some other cause. Interactions are possible between observation models and process models with EO data and existing geographic information (GIS and ground measurements, supplemented with decision-support systems (DSSs)).\r\n\r\nThe process model provides information to the decision-support system, which supports management actions aimed at controlling/mitigating the process, based on an multi-criteria evaluation. A good example of this is a water management system, in which one might decide to allocate water for irrigation if the observed vegetation appears to suffer from drought stress.","name":"Multi-criteria evaluation","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM5-8","description":"Process models in the Earth sciences describe the evolution of geo(bio)physical surface properties in time, independently from remote sensing observations. Examples of such process models on various time scales are, for instance, numerical weather prediction models (NWPs), vegetation growth models, hydrological models, oceanographic models and climate models.\r\nProcess models in the geosciences usually rely on regular observations at many locations spread over a large area. Traditionally, these observations were mostly made in the field with a variety of instruments. Remote sensing techniques have tremendously increased the capability of spatial sampling and the consistency of the surface parameters measured. RS instruments are mostly sensitive to many physical properties of the surface, some of these may not belong to the set of properties that the user is interested in. Exceptions to this are the mapping of sea-surface temperature, laser altimetry and gravimetry, which are measurements of direct geophysical interest. In the majority of cases, however, there are only indirect relationships between what is observed with the instrument and the physical object properties of interest. In these cases, the use of observation models becomes an attractive option, since these models describe the relationships between all object properties relevant for the observation and the observed remote sensing data.","name":"Spatial process models","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM5","description":"Building on the basic geometric measures and analytical operations found in most GIS products, a broad range of additional analytical methods form the fundamental GIS toolkit.","name":"Basic analytical methods","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM6-2","description":"In rasters we use interpolation to determine the value of a pixel, based on its surrounding pixels. The main raster-based interpolation methods are nearest neighbour, bilinear, and bicubic interpolation. To determine the value of the centre pixel (bold), in nearest neighbour interpolation the value of the nearest original pixel is assigned, i.e. the value of the black pixel in this example. Note that the respective pixel centres, marked by small crosses, are always used for this process. In bilinear interpolation, a linear weighted average is calculated for the four nearest pixels in the original image. In bicubic interpolation a cubic weighted average of the values of 16 surrounding pixels (the black and all grey pixels) is calculated. Note that some software uses the terms “bilinear convolution” and “cubic convolution” instead of the terms introduced above.","name":"Interpolation of surfaces","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM6-3","description":"Continuous fields have a number of characteristics not shared by discrete fields. Since the field changes continuously, we can talk of slope angle, slope aspect and concavity/convexity of the slope.\r\n\r\nThese notions are not applicable to discrete fields. The discussions in this subsection use terrain elevation as the prototype example of a continuous field, but all aspects discussed are equally applicable to other types of continuous fields. Nonetheless, we regularly refer to the continuous field representation as a DEM, to conform with the most common situation.","name":"Surface features","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM6-4","description":"A viewshed is the area that can be “seen” (i.e. it is in the direct line-of-sight) from a specified target location. (Inter) visibility analysis can determine the area visible from a scenic lookout or the area that can be reached by a radar antenna, as well as assess how effectively a road or quarry will be hidden from view.","name":"Intervisibility","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM6-5","description":"Firction surfaces contain information on how difficult/easy it is for a phenomenon to move from one location on the surface to another.","name":"Friction surfaces","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM6","description":"There is a wide range of phenomena that can be studied using a set of techniques and tools that are designed to help understand the characteristics of continuous surface data. Applications of these techniques using terrain data include overland transport, flow, and siting tasks, but similar analyses can be conducted using non-tangible surfaces such as those of temperature, pressure and population density.\r\n\r\nThere are numerous examples that require more advanced computations on continuous field representations, such as:\r\n\r\nSlope angle calculation - the calculation of the slope steepness, expressed as an angle in degrees or percentages, for any or all locations.\r\n\r\nCalculating slope aspect - the calculation of the aspect (or orientation) of the slope in degrees (between 0 and 360∘), for any or all locations.\r\n\r\nSlope convexity/concavity calculation - defined as the change of the slope (negative when the slope is concave and positive when the slope is convex)—can be calculated as the second derivative of the field.\r\n\r\nSlope length calculation - with the use of neighbourhood operations, it is possible to calculate for each cell the nearest distance to a watershed boundary (the upslope length) and to the nearest stream (the downslope length). This information is useful for hydrological modelling.\r\n\r\nHillshading is used to portray relief difference and terrain morphology of hilly and mountainous areas. The application of a special filter to a DEM produces hillshading. The colour tones in a hillshading raster represent the amount of reflected light at each location, depending on its orientation relative to the illumination source. This illumination source is usually chosen to be to the northwest at an angle of 45∘ above the horizon.\r\n\r\nThree-dimensional map display - with GIS software, three-dimensional views of a DEM can be constructed in which the location of the viewer, the angle under which he or she is looking, the zoom angle, and the amplification factor of relief exaggeration can be specified. Three-dimensional views can be constructed using only a predefined mesh, covering the surface, or using other rasters (e.g. a hillshading raster) or images (e.g. satellite images) that are draped over the DEM.\r\n\r\nDetermination of change in elevation through time - the cut-and-fill volume of soil to be removed or to be brought in to make a site ready for construction can be computed by overlaying the DEM of the site before the work begins with the DEM of the expected modified topography. It is also possible to determine landslide effects by comparing DEMs of before and after a landslide event.\r\n\r\nAutomatic catchment delineation - catchment boundaries or drainage lines can be automatically generated from a good quality DEM with the use of neighbourhood functions. The system will determine the lowest point in the DEM, which is considered to be the outlet of the catchment. From there, it will repeatedly search for the neighbouring pixels with the highest altitude. This process is repeated until the highest location (i.e. the cell with the highest value) is found; the path followed determines the catchment boundary. For delineating the drainage network, the process is reversed. Then the system will work from the watershed downwards, each time looking for the lowest neighbouring cells, which determines the direction of water flow (Flow Computation).\r\n\r\nDynamic modelling - apart from the applications mentioned above, DEMs are increasingly used in GIS-based dynamic modelling, such as the computation of surface run-off and erosion, groundwater flow, the delineation of areas affected by pollution, the computation of areas that will be covered by processes such as flows of debris and lava. An example is (Diffusion).\r\n\r\nVisibility analysis - a viewshed is the area that can be “seen” (i.e. it is in the direct line-of-sight) from a specified target location. Visibility analysis can determine the area visible from a scenic lookout or the area that can be reached by a radar antenna, as well as assess how effectively a road or quarry will be hidden from view.","name":"Analysis of surfaces","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM7-1","description":"Statistical analysis techniques based on visual interpretation through histograms, scatterplots, etc.","name":"Graphical methods","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM7-2","description":"Environmental variables have become increasing available with the advent of GIS. These are mostly continuous in space and time. Collecting denser environmental data in discrete space and time domains are rather cost effective and time consuming. However, when the data at each spatial or time index are considered as outcomes of a random variable, stochastic processes become enviable useful to build models and predict the outcomes at locations where data were never collected. The meaningful assumptions include stationarity of the mean and the covariance to ascertain an expression for spatial dependency/autocorrelation. With a stationary process (i.e. constant mean), simple and ordinary kriging is used. Other variants like kriging with external drift, universal kriging and regression kriging also alleviate the challenge of non-stationary mean. These methods are also applicable when temporal indexes rather than spatial indexes are of interest.","name":"Stochastic processes","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM7-3","description":"Spatial weight matrix is the popular numerical quantification of spatial dependency or spatial neighborhoods. The weight matrix should summarize information about the spatial connectivity structure of the spatial entities/features; either polygons, points, or lines. This is required for the computation of spatial dependency indices such the Moran’s index, and for spatial regression models such as the conditional autoregressive (CAR), spatial lag, and spatial error models. The connectivity information can be defined based on adjacency/contiguity or distance between pairs of spatial entities. There are other forms; they could be based on population densities between observation pairs. The simplest spatial weigh matrix is the binary adjacency spatial weight matrix with elements w_ij, such that w_ij=1 if spatial units i and j are neighbors, otherwise w_ij=0. A popular alternative is the inverse distance weight matrix with elements w_ij=1⁄d^α , where d is the distance between pairs of spatial units and α is any positive number greater than zero. By convention, w_ii=0 since spatial unit cannot have a spillover within itself.","name":"The spatial weights matrix","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM7-4","description":"Spatial autocorrelation evaluates how things which are closer in space tend to have similar attributes. This is a common phenomenon in environmental variables which are continuous in space. For instance, temperature, soil moisture content, air quality and rainfall are all continuous in space. This idea is based on Tobler’s law of geography: “everything is related to everything but near things are more related”. Global measures of spatial association estimates the overall index of spatial autocorrelation, also called spatial clustering. Thus, it measures whether clustering is apparent throughout the study region but do not identify the location of clusters. Common global measures include the Moran’s Index and Geary’s C. These have increasing applications in domains like environmental science, agriculture, epidemiology, climate studies etc.","name":"Global measures of spatial association","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM7-5","description":"Unlike global measures of spatial association, local measure of spatial association identifies the locations of clusters. Typical measures include the local indicator for spatial autocorrelation (LISA) or the local Moran’s index whose summation is proportional to the global Moran’s index. The spatial scan statistics has also been the commonly used method to detect local clusters.","name":"Local measures of spatial association","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM7-6","description":"An outlier is an unexpected value that differs significantly from other observations. Definition of an outlier is not absolute and the concept itself is precisely defined only by selection of appropriate criteria in concrete statistical observations. When considering outliers, it is important to determine whether the value of the outlier is incorrect data or it is otherwise outstanding, but correct data. If we consider outliers in the case when they base on sample surveys, another assessment is necessary. Namely, the assessment of whether an outlier is representative or not. \r\nThe box plot is a useful graphical display for examining the outliers. Using median, lower and upper quartiles, extreme values are identified in the tails of the distribution. The value beyond inner fence on either side is considered a mild outlier. The value beyond an outer fence is considered an extreme outlier. Histograms also emphasize the existence of outliers. The histogram depends on how we design the classes, so we can get different histograms for the same data. Graphical and quantitative checks are obligatory if the histogram shows possible outliers. Outliers can also be examined by calculating the correlation between two datasets (Pearson correlation coefficient, Spearman rank correlation coefficient…). Scatter plots reveals a basic linear relationship with a pattern. An outliner is defined as a data point that deviates from other values. Outliers can also be examined by local outlier factor, which is based on a concept of a local density. Points with substantially lower density than their neighbours are considered as outliers.","name":"Outliers","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM7-7","description":"Bayesian method of modelling stems from the Bayes theorem and derived using conditional probabilities. Its advantage lies in its ability to include prior knowledge of unknown parameters to ascertain their uncertainties. Thus, the prior parameters are updated by the data likelihood to obtain the posteriors. The challenge of Bayesian modelling has been the integration of the denominator which always resulted into improper integrals. This actually prolonged its wide applications. With the advent of high performance computers, solution to such integrals are easily solved using Markov chain Monte Carlo simulations. The advent robust approximation methods through integrated nested Laplace approximations (INLA) has even made parameter estimation faster; thus making Bayesian methods interesting and better. Unlike frequentist approaches, Bayesian methods can present estimates of parameters as densities from which their uncertainties and credible intervals can be estimated. They have now found wide applications in divers areas like environmental modelling, climate modeling, agriculture, epidemiology and many other domains that requires modeling.","name":"Bayesian methods","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM7","description":"Traditional statistical methods are used to describe the central tendency, dispersion, and other characteristics of data but are not always suited to use with spatial data for which specialized techniques are often required. The field of spatial statistical analysis forms the backbone for the testing of hypotheses about the nature of spatial pattern, dependency, and heterogeneity. The techniques are widely used in both exploratory and confirmatory spatial analysis in many different fields.","name":"Spatial statistics","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM8-1","description":"Sampling is needed to limit the observations for statistical analysis. In raster image analysis, various sampling schemes have been proposed for selecting pixels to test. Choices to be made relate to the design of the sampling strategy, the number of samples required, and the area of the samples. Recommended sampling strategies in the context of land cover data are simple random sampling or stratified random sampling. The number of samples may be related to two factors in accuracy assessment: (1) the number of samples that must be taken in order to reject a data set as being inaccurate; or (2) the number of samples required to determine the true accuracy, within some error bounds, of a data set. Sampling theory is used to determine the number of samples required. The number of samples must be traded-off against the area covered by a sample unit. A sample unit can be a point but it could also be an area of some size; it can be a single raster element but may also include surrounding raster elements. Among other considerations, the “optimal” sample-area size depends on the heterogeneity of the class.","name":"Spatial sampling for statistical analysis","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM8-3","description":"A variogram is a tool used to describe the spatial continuity of data points. Different kinds of variograms are used, such as experimental variogram and semi-variogram.","name":"Variogram modeling","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM8-4","description":"Predicting an observation in the presence of spatially dependent observations is termed Kriging, named after the first practitioner of these procedures, the South African mining engineer Daan Krige, who did much of his early empirical work in the Witwatersrand gold mines.","name":"Principles of kriging","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM8-5","description":"With a stationary stochastic process (i.e. constant mean), simple and ordinary kriging is used for interpolation. Other variants like kriging with external drift, universal kriging and regression kriging also alleviate the challenge of non-stationary mean. Other variants are \r\nco-kriging log-normal kriging, disjunctive kriging, indicator kriging, factorial kriging and universal kriging.","name":"Kriging variants","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM8","description":"Geostatistics are a variety of techniques used to analyze continuous data e.g., rainfall, elevation, air pollution. The fundamental structure of geostatistics is based on the concept of semi-variograms and their use for spatial prediction kriging. Sampling methods are also discussed in Unit GD9 Field data collection. \r\nGeostatistics is a subdiscipline of spatial statistics developed to estimate the value of a continuous spatial process at unknown locations by using the information of the value of these process at known locations. Furthermore, it aims to quantify the uncertainty related to the prediction (Calder et al., 2009; Emmanouil, 2019). In order to do such predictions, geostatistics entails some statistical methods which use as starting point the assumption of a random component that can define the spatiotemporal variability. These methods are developed to infer the parameters that can describe the spatiotemporal patterns of the input variables (e.g. soil moisture) so that finally these variables at unsampled locations can be estimated (interpolated) (Emmanouil, 2019). Geostatistical methods are strongly related with classic interpolation methods but differ by its use of random variables that allow to given an uncertainty indication associated with the prediction of variables in space and time. \r\n\r\nIn environmental research geostatistical techniques are often applied to infer (interpolate) variables at such unobserved locations by using information from known locations. One of such geostatistical techniques is Kriging, which is a geostatistical method that predicts variables by using spatial interpolation. This spatial interpolation is done by establishing a semivariogram that defines the spatial relationship between the variables of interest in function of the distance. Because of this, the Kriging technique can also give an indication on the variance or accuracy of the prediction (Calder et al., 2009); Van der Meer, 2012). On the other hand, cokriging is another important geostatistical technique and differs from Kriging by using the cross-correlation between variables to generate local estimates (Van der Meer, 2012). In earth observation studies, cokriging can be applied to better predict sparsely based data on the ground (e.g. biomass) by using the cross-correlation of this variable with a more continuously sampled satellite metric like NDVI. Furthermore, these techniques can also be used to enhance satellite image information, filling missing pixels or even downscale the information to a higher resolution (Van der Meer, 2012).","name":"Geostatistics","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM9-1","description":"Spatial econometrics uses spatial stochastic models to determine autocorrelation between interacting agents. The techniques involved are regression, the use of a spatial weights matrix, least squares, etc.","name":"Principles of spatial econometrics","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM9-2","description":"A spatial autoregressive (SAR) model describes the prediction of the behaviour of a random process.","name":"Spatial autoregressive models","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM9-3","description":"In producing optimal images for interpretation, spatial filtering is applied. Filtering is usually carried out for a single band. Filters - algorithms - can be used to enhance images by, for example, reducing noise (“smoothing an image”) or sharpening a blurred image. Filter operations are also used to extract features from images, e.g. edges and lines, and to automatically recognize patterns and detect objects. There are two broad categories of filters: linear and non-linear filters.\r\n\r\nLinear filters calculate the new value of a pixel as a linear combination of the given values of the pixel and those of neighbouring pixels. A simple example of the use of a linear smoothing filter is when the average of the pixel values in a 3×3 pixel neighbourhood is computed and that average is used as the new value of the central pixel in the neighbourhood.","name":"Spatial filtering","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM9-4","description":"Geographically Weighted Regression (GWR) makes use of local subsets of observations to perform estimates.","name":"Spatial expansion and Geographically Weighted Regression GWR","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM9","description":"Many problems of the social sciences can be expressed in terms of spatial regression analysis. The development of spatial autoregressive models and the estimation of their parameters is the focus for the field of spatial econometrics.","name":"Spatial regression and econometrics","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF","description":"The GIScience perspective is grounded in spatial thinking. The aim of this knowledge area is to recognize, identify, and appreciate the explicit spatial, spatio-temporal and semantic components of the geographic environment at an ontological and epistemological level in preparation for modeling the environment with geographic data and analysis. To do this, one must understand the nature of space and time as a context for geographic phenomena.This knowledge area covers the ways in which views of the geographic environment depend on philosophical viewpoints, physics, human cognition, society, and the task at hand. This knowledge area also requires an understanding of the fundamental principles in the discipline of geography, the \"language\" of spatial tasks. On a more advanced level, this area incorporates mathematical and graphical models that formalize these concepts, such as set theory, algebra, and semantic nets. Because of its wide range of foundational principles, this knowledge area forms a basis for the other knowledge areas. Wise design and use of geospatial technologies requires an understanding of the nature of geographic information, the social and philosophical context of geographic information, and the principles of geography. This knowledge area is especially closely tied to Knowledge Areas Data Modeling (DM) and Design Aspects (DA), as generic data models and application designs need to be grounded in sound conceptual models. The foundations of geographic information have developed over several decades. Philosophical and scientific views on the nature of space and time have evolved since the ancient Greeks. Early papers during the Quantitative Revolution, such as Berry (1964), began to formalize the structure of information used in geographic inquiry.The fundamental data structures and algorithms comprising the GIS software developed in the 1960`s and 1970`s were based on implicit \"common-sense\" conceptual models of geographic information. During the 1980`s, several researchers questioned these underlying assumptions. Some were refuted, other confirmed, and many extended. However, the most rapid pace of development in this area was during the 1990`s with the rise of GIScience as a distinct discipline, and the many cooperative initiatives it comprised.The new millennium has seen some of these foundational principles incorporated into commercial software, thus making theoretical knowledge even more important for practitioners. It is expected that the concepts in this knowledge area will be learned gradually. An introductory course may cover only a few topics in a cursory manner, an intermediate course on data modeling or data analysis may consider several theoretical topics of practical application, and a number of graduate courses could cover each topic in a research-oriented environment. Discussion of this knowledge area includes several terms that can have multiple meanings. For the purposes of this document, two in particular require definition: Geographic: Almost any subject or discourse involving earthly phenomena, studied from a spatial perspective at a medium scale (sub-astronomical and super-architectural). Phenomenon: Any subject of geographic discourse that is perceived to be external to the individual, including entities, events, processes, social constructs, and the like.","name":"Conceptual Foundations","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF1-1","description":"Metaphysics involve the meaning things and concepts. Ontologies provide a way to share the semantics of concepts in some area of interest and is all about common the understanding of essential concepts, e.g., what is meant by a geometric object and its attributes.","name":"Metaphysics and ontology","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF1-1b","description":"Brief history of GIScience as related to the history of GISystems; Definitions of GIS&T; Sub-domains of GIS&T (i.e., Geographic Information Science, Geospatial Technology, and Applications of GIS&T)","name":"What is Geographic Information Science and Technology","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF1-2","description":"The branch of philosophy concerned with knowledge.","name":"Epistemology","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF1-2b","description":"GIS&T draws upon insights and methods from key allied fields: Geography, Cartography, Computer and information science, Engineering, Mathematics and Statistics, Philosophy, Cognitive Science, Linguistics","name":"Contributions to GIS and T by key allied fields","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF1-3","description":"The questions and methodologies in major philosophical movements relating to the nature of space, time, geographic phenomena and human interaction with it.","name":"Philosophical perspectives","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF1","description":"Many branches of philosophy are relevant to an understanding of geographic information, especially metaphysics and epistemology. Philosophical theories are deeply engaged in the study of knowledge, space, time, geographic phenomena and human interaction with them. These theories influence the development of geographic ontologies and the structuring, analysis, and interpretation of geographic information. It is, therefore, crucial for professionals to understand these principles in order to bridge (rather than eliminate) the differences and work together. Philosophical perspectives on GIS practice are covered in Unit GS7 Critical GIS.","name":"Philosophical foundations","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF1b","description":"Unit CF1 introduces the broad domain refered to as Geographic Information Science & Technology (GIS&T) and its sub-domains (i.e., Geographic Information Science, Geospatial Technology, and Applications of GIS&T). It outlines the history of Geographic Information Science as related to the history of GISystems, as well as the contributions to this multidisciplinary domain by key allied fields, such as geography, cartography, computer and information science, engineering, mathematics, philosophy, cognitive science, and linguistics.","name":"Introduction to Geographic Information Science and Technology","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF2-1","description":"The study on how humans perceive spatial information.","name":"Perception and cognition of geographic phenomena","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF2-1b","description":"Metaphysics and Ontology - Formal ontology - Ontological distinctions (e.g., continuants vs. occurrents, universals vs. particulars) - The problem of universals and relevant theories (realism, nominalism, conceptualism) - Ontologies of the geographic domain - Philosophical theories relating to the nature of space, time, geographic phenomena and human interaction with them","name":"Philosophy of being","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF2-2","description":"The ways in which conceptual views of in the human mind make it into formal descriptions of information and into artefacts in databases and GIS.","name":"From concepts to data","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF2-2b","description":"Epistemology; Theories on what constitutes knowledge; The notions of model and representation in science; The influences of epistemology on GIS practices","name":"Philosophy of knowledge","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF2-3","description":"Principles of geography to explain the spatial occurrences of spatial entities in Geographic Information Systems.","name":"Geography as a foundation for GIS","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF2-4","description":"Space and place are concepts that are not the same. Including concepts like landscape, it is not always obvious how to portray them unambiguously in GIS.","name":"Place and landscape","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF2-6","description":"The ways in which the elements of culture (e.g., language, religion, education, traditions) may influence the understanding and use of geographic information.","name":"Cultural influences","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF2-7","description":"The influences of political ideologies (e.g., Marxism, Capitalism, conservative liberal) on the understanding of geographic information.","name":"Political influences","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF2","description":"Geographic information is observed, comprehended, organized, used in human processes, with both personal and social influences. Therefore, sound models of geographic information should be grounded on a sound understanding of human perception, cognition, memory, and behavior, as well as human institutions.","name":"Cognitive and social foundations","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF3-1","description":"A GIS operates under the assumption that the spatial phenomena involved occur in a two- or three-dimensional Euclidean space. Euclidean space can be informally defined as a model of space in which locations are represented by coordinates—(x, y) in 2D and (x, y, z) in 3D space—and distance and direction can defined with geometric formulas. In 2D, this is known as the Euclidean plane. To represent relevant aspects of real-world phenomena inside a GIS, we first need to define what it is we are referring to. We might define a geographic phenomenon as a manifestation of an entity or process of interest that:\r\n\r\nitem can be named or described;\r\nitem can be georeferenced; and\r\nitem can be assigned a time (interval) at which it is/was present.\r\n\r\nRelevance of phenomena for the use of a GIS depends entirely on the objectives of the study at hand. For instance, in water management, relevant objects can be river basins, agro-ecological units, measurements of actual evapotranspiration, meteorological data, ground\\-water levels, irrigation levels, water budgets and measurements of total water use. All of these can be named or described, georeferenced and provided with a time interval at which each exists. In multipurpose cadastral administration, the objects of study are different: houses, land parcels, streets of various types, land use forms, sewage canals and other forms of urban infrastructure may all play a role. Again, these can be named or described, georeferenced and assigned a time interval of existence.\r\n\r\nNot all relevant information about phenomena has the form of a triplet (description, georeference, time interval). If the georeference is missing, then the object is not positioned in space: an example of this would be a legal document in a cadastral system. It is obviously somewhere, but its position in space is not considered relevant. If the time interval is missing, we might have a phenomenon of interest that exists permanently, i.e.\\ the time interval is infinite. If the description is missing, then we have something that exists in space and time, yet cannot be described. Obviously this last issue limits the usefulness of the information.\r\n\r\nTypes of geographic phenomena\r\nThe definition of geographic phenomena attempted above is necessarily abstract and is, therefore, perhaps somewhat difficult to grasp. The main reason is that geographic phenomena come in different “flavours”. Before categorizing such flavours, there are two further observations to be made.\r\n\r\nFirst, to represent a phenomenon in a GIS requires us to state what it is and where it is. We must provide a description—or at least a name—on the one hand, and a georeference on the other hand. We will ignore temporal issues for the moment and come back to these in Temporal dimension and Spatial-temporal data model, the reason being that current GISs do not provide much automatic support for time-dependent data. This topic must, therefore, be considered as an example of advanced GIS use. Second, some phenomena are manifest throughout a study area, while others only occur in specific localities. The first type of phenomena we call geographic fields; the second type we call objects.","name":"Space","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF3-1b","description":"- Theories of human perception, cognition, and memory and their ability to model spatial knowledge acquisition (e.g., Marr on vision, Piaget on cognitive development) - Types of mental representations (i.e., analogue, propositional, procedural) - The role of metaphors and image schemata in our understanding of geographic phenomena and geographic tasks - From concepts to data (i.e., data, information, knowledge, and wisdom; transformation of a conceptual model of information for a particular task into a data model; limitations of various information stores (the mind, computers) and means (maps, graphics, and text) for representing geographic information) - Difference between real phenomena, conceptual models, and GIS data representations thereof connections with cartography and maps","name":"Cognitive foundations","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF3-2b","description":"- Semantics - Meaning (e.g., the nature of meaning, modes of meaning) - Geospatial semantics - The role of natural language in the conceptualization of geographic phenomena","name":"Linguistic foundations","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF3-3b","description":"- The ways in which the elements of culture (e.g., language, religion, education, traditions) may influence the understanding and use of geographic information - The influences of social theories and political ideologies and actions on human perceptions of space and place - The constraints that political forces place on geospatial applications in public and private sectors","name":"Social foundations","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF3-4b","description":"- Common-sense views and laymen knowledge of geographic phenomena that contrast with established theories and technologies of geographic information - The impact of geospatial technologies and the geoweb (e.g., digital globes) that allow non-geospatial professionals to create, distribute, and map geographic information - The design, procedures, and results of GIS projects to non-GIS audiences (clients, managers, general public) - Difference between applications that can make use of common-sense principles of geography and those that should not","name":"Common-sense geographies","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF3","description":"Geographic information is observed, comprehended, organized, used in human processes, with both personal and social influences. Therefore, sound models of geographic information should be grounded on a sound understanding of human perception, cognition, memory, and behavior, as well as human institutions.","name":"Cognitive, linguistic and social foundations","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF4-2b","description":"As time is the central concept of the temporal dimension, a brief examination of the nature of time may clarify our thinking when we work with this dimension:\r\n\r\nDiscrete and continuous time: Time can be measured along a discrete or continuous scale. Discrete time is composed of discrete elements (seconds, minutes, hours, days, months, or years). For continuous time, no such discrete elements exist: for any two moments in time there is always another moment in between. We can also structure time by events (moments) or periods (intervals). When we represent intervals by a start and an end event, we can derive temporal relationships between events and periods, such as “before”, “overlap”, and “after”.\r\n\r\nValid time and transaction time: Valid time (or world time) is the time when an event really happened, or a string of events took place. Transaction time (or database time) is the time when the event was stored in the database or GIS. Note that the time at which we store something in a database is typically (much) later than when the related event took place.\r\n\r\nLinear, branching and cyclic time: Time can be considered to be linear, extending from the past to the present (‘now’), and into the future. This view gives a single time line. For some types of temporal analysis, branching time - in which different time lines from a certain point in time onwards are possible - and cyclic time - in which repeating cycles such as seasons or days of the week are recognized - make more sense and can be useful.\r\n\r\nTime granularity: When measuring time, we speak of granularity as the precision of a time value in a GIS or database (e.g. year, month, day, second). Different applications may obviously require different granularity. In cadastral applications, time granularity might well be a day, as the law requires deeds to be date-marked; in geological mapping applications, time granularity is more likely to be in the order of thousands or millions of years.\r\n\r\nAbsolute and relative time: Time can be represented as absolute or relative. Absolute time marks a point on the time line where events happen (e.g. “6 July 1999 at 11:15 p.m.”). Relative time is indicated relative to other points in time (e.g. “yesterday”, “last year”, “tomorrow”, which are all relative to “now”, or “two weeks later”, which is relative to some other arbitrary point in time.).","name":"Time","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF4-3b","description":"The way we represent relevant components of the real world in our models determines the kinds of questions we can or cannot answer. Besides representing an object or field in 2D or 3D space, the temporal dimension is of a continuous nature. Therefore, in order to represent it in a GIS we have to discretize the time dimension.\r\n\r\nSpatio-temporal data models are ways of organizing representations of space and time in a GIS. Several representation techniques have been proposed in the literature. Perhaps the most common of these is the “snapshot state”, which represents a single moment in time of an ongoing natural or man-made process. We may store a series of these “snapshot states” to represent “change”, but we must be aware that this is by no means a comprehensive representation of that process. \r\n\r\nIn spatio-temporal analysis we consider changes of spatial and thematic attributes over time. We can keep the spatial domain fixed and look only at the attribute changes over time for a given location in space. We might be interested how land cover has changed for a given location or how land use has changed for a given land parcel over time, provided its boundary has not changed. On the other hand, we can keep the attribute domain fixed and consider the spatial changes over time for a given thematic attribute. In this case, we might want to identify locations that were covered by forest over a given period of time.\r\n\r\nFinally, we can assume both the spatial and attribute domains are variable and consider how fields or objects have changed over time. This may lead to notions of object motion - a subject receiving increasing attention in the literature. Applications of moving object research include traffic control, mobile telephony, wildlife tracking, vector-borne disease control and weather forecasting. In these types of applications, the problem of object identity becomes apparent. When does a change or movement cause an object to disappear and become something new? With wildlife this is quite obvious; with weather systems less so. But this should no longer be a surprise: we have already seen that some geographic phenomena can be nicely described as objects, while others are better represented as fields.\r\n\r\nMapping time means mapping change. This may be change in a feature’s geometry, in its attributes, or both. Examples of changing geometry are the evolving coastline of the Netherlands, the location of Europe’s national boundaries, or the position of weather fronts. Changes in the ownership of a land parcel, in land use or in road traffic intensity are other examples of changing attributes. Urban growth is a combination of both: urban boundaries expand with growth and simultaneously land use shifts from rural to urban. If maps are to represent events like these, they should be suggestive of such change.\r\n\r\nThree temporal cartographic techniques can be distinguished:\r\n\r\nSingle Static Map\r\n\r\nSpecific graphic variables and symbols are used to indicate change or represent an event. We can apply the visual variable “value” to represent for example the age of built-up areas.\r\n\r\nSeries of Static Maps\r\n\r\nA single map in the series represents a “snapshot” in time. Together, the maps depict a process of change. Change is perceived by the succession of individual maps depicting the situation in successive snapshots. It could be said that the temporal sequence is represented by a spatial sequence that the user has to follow to perceive the temporal variation. The number of images should be limited since it is difficult for the human eye to follow long series of maps.\r\n\r\nAnimated Maps\r\n\r\nChange is perceived to evolve in a single image by displaying several snapshots one after the other, just like a video clip of successive frames. The difference from the series of maps is that the variation can be deduced from real “change” seen taking place in the image itself, not from a spatial sequence. For the user of a cartographic animation, it is important to have tools available that allow for interaction while viewing the animation. Seeing an animation play will often leave users with many questions about what they have seen. And just replaying the animation is not sufficient to answer questions like “What was the position of the northern coastline during the 15th century?” Most of the general software packages for viewing animations already offer facilities such as “pause” (to look at a particular frame) and ‘(fast-)forward’ and ‘(fast-)backward’, or step-by-step display. More options have to be added, such as the possibility to go directly to a certain frame based on a task command like: “Go to 1850”.","name":"Relationships between space and time","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF4-4b","description":"GIS data structures are used to implement the conceptual views of spatial data (vector and raster models). The power of a GIS is dependent on the richness of information contained in the spatial data structures. Vector models are based on points, lines and areas. Raster models are based on grids. Each cell has a value that is used to represent some characteristic of that location. \r\nLayers are used to display geographic datasets in various digital map environment. A layer stores the path to a source dataset and other layer properties, including symbology. You can use multiple layers on one map and specify its properties. Shapefiles represent spatial character of the object in terms of shape, size and spatial arrangement. Shapefile usually comprise three separate and distinct types of files (main files, index files and database tables). Data base files store additional attributed that can be joined to a shapefiles’ feature. Attribute data types supplement geographic spatial feature with additional information. Spatial data includes information of location and attribute data includes information about other characteristics (what, where and why). A legend is a visual presentation of the symbols that are used on the map with some additional explanations. It includes a sample of each symbol and a short description of the meaning.","name":"Categories","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF4-5","description":"An entity obtained by abstracting the real world, having a physical nature (certain composition of material), being given a descriptive name, and observable; e.g. “house”. An object is a self-contained part of a scene having certain discriminating properties.\r\n\r\nThe primitives of vector data sets are the point, (poly)line and polygon. Related geometric measurements are location, length, distance and area size. Some of these are geometric properties of a feature in isolation (location, length, area size); others (distance) require two features to be identified.\r\n\r\nIn a GIS, features are represented together with their attributes—geometric and non-geometric—and relationships. The geometry of features is represented with primitives of the respective dimension: a windmill probably as a point; an agricultural field as a polygon. The primitives follow either the vector or the raster approach.\r\n\r\nVector data types describe an object through its boundary, thus dividing the space into parts that are occupied by the respective objects. The raster approach subdivides space into (regular) cells, mostly as a square tessellation of two or three dimensions. These cells are called pixels in 2D and voxels in 3D. The data indicate for every cell which real-world feature is covered, provided the cell represents a discrete field. In the case of a continuous field, the cell holds a representative value for that field. The Table below lists advantages and disadvantages of raster and vector representations.\r\n\r\nThe storage of a raster is, in principle, straightforward. It is stored in a file as a long list of values, one for each cell, preceded by a small list of extra data (the “file header”), which specifies how to interpret the long list. The order of the cell values in the list can, but need not necessarily, be left to right, top to bottom. This simple encoding scheme is known as row ordering. The header of the raster will typically specify how many rows and columns the raster has, which encoding scheme was used, and what sort of values are stored for each cell.\r\n\r\nData can be of a qualitative or quantitative nature. Qualitative data is also called nominal data, which exists as discrete, named values without a natural order amongst the values. Examples are different languages (e.g. English, Swahili, Dutch), different soil types (e.g. sand, clay, peat) or different land use categories (e.g. arable land, pasture). In the map, qualitative data are classified according to disciplinary insights, such as a soil classification system represented as basic geographic units: homogeneous areas associated with a single soil type, recognizable by the soil classification.\r\n\r\nQuantitative data can be measured, either along an interval or ratio scale. For data measured on an interval scale, the exact distance between values is known, but there is no absolute zero on the scale. Temperature is an example: 40 ◦C is not twice as hot as 20 ◦C, and 0 ◦C is not an absolute zero.\r\n\r\nQuantitative data with a ratio scale do have a known absolute zero. An example is income: someone earning $100 earns twice as much as someone with an income of $50. In order to generate maps, quantitative data are often classified into categories according to some mathematical method.\r\n\r\nIn between qualitative and quantitative data, one can distinguish ordinal data. These data are measured along a relative scale and are as such based on hierarchy. For instance, one knows that a particular value is “more” than another value, such as “warm” versus “cool”. Another example is a hierarchy of road types: “highway”, “main road”, “secondary road” and “track”. The different types of data are summarized in Table.","name":"Properties","selfAssesment":"GI-N2K
"},{"code":"CF4b","description":"Geographic phenomena, geographic information, and geographic tasks are described in terms of space, time, and properties. Different theories exist as to the nature and formal representation of these aspects, including space-like dimensions, sets, and phenomenology. Information in each of these three aspects is measured and reported with respect to one of several frames of reference or domains, including both absolute and relative approaches. Early frameworks such as those of Berry (1964) and Sinton (1978) were influential in setting forth the importance of space, time, and theme in GIS&T. Besides, space, time, and properties, categories are also fundamental in the conceptualization and representation of spatial entities, phenomena, processes, and events. Distinctive features of geographic information such as scale and detail, spatial patterns, spatial integration, and regions are also critical for a complete description of its nature and representation. This unit is closely tied to the creation of data models in Knowledge Area 5: Data Modeling, Storage, and Exploitation.","name":"Fundamentals of Geographic Information","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF5-1b","description":"Discrete entities can be found as fields or objects.\r\n\r\nDiscrete fields divide the study space in mutually exclusive, bounded parts, with all locations in one part having the same field value. Discrete fields are intermediate between continuous fields and geographic objects: discrete fields and objects both use “bounded” features.\r\n\r\nDiscrete fields divide the study space in mutually exclusive, bounded parts, with all locations in one part having the same field value. Typical examples are land classifications, for instance, using either geological classes, soil type, land use type, crop type or natural vegetation type. \r\n\r\nDiscrete fields are intermediate between continuous fields and geographic objects: discrete fields and objects both use “bounded” features. A discrete field, however, assigns a value to every location in the study area, which is not typically the case for geographic objects. These two types of fields differ in the type of cell values. A discrete field such as land use type will store cell values of the type “integer” and is therefore also called an integer raster. Discrete fields can be easily converted to polygons since it is relatively easy to draw a boundary line around a group of cells with the same value. A continuous raster is also called a “floating point” raster.\r\n\r\nGeographic objects.\r\n\r\nWhen a geographic phenomenon is not present everywhere in the study area, but somehow “sparsely” populates it, we look at it as a collection of geographic objects. Such objects are usually easily distinguished and named, and their position in space is determined by a combination of one or more of the following parameters:\r\n\r\nlocation (where is it?)\r\nshape (what form does it have?)\r\nsize (how big is it?)\r\norientation (in which direction is it facing?).\r\n\r\nHow we want to use the information determines which of these four parameters is required to represent the object. For instance, for geographic objects such as petrol stations all that matters in an in-car navigation system is where they are. Thus, in this particular context, location alone is enough, and shape, size and orientation are irrelevant. For roads, however, some notion of location (where does the road begin and end?), shape (how many lanes does it have?), size (how far can one travel on it?) and orientation (in which direction can one travel on it?) seem to be relevant components of information in the same system.","name":"Discrete entities","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF5-2b","description":"A geographic field is a geographic phenomenon that has a value “everywhere” in the study area. We can therefore think of a field as a mathematical function f that associates a specific value with any position in the study area. Hence if (x, y) is a position in the study area, then f(x, y) expresses the value of f at location (x, y). Fields can be discrete or continuous.\r\n\r\nIn a continuous field, the underlying function is assumed to be “mathematically smooth”, meaning that the field values along any path through the study area do not change abruptly, but only gradually. Good examples of continuous fields are air temperature, barometric pressure, soil salinity and elevation. A continuous field can even be differentiable, meaning that we can determine a measure of change in the field value per unit of distance anywhere and in any direction. For example, if the field is elevation, this measure would be slope, i.e. the change of elevation per metre distance; if the field is soil salinity, it would be salinity gradient, i.e. the change of salinity per metre distance.\r\n\r\nDiscrete fields divide the study space in mutually exclusive, bounded parts, with all locations in one part having the same field value. Discrete fields are intermediate between continuous fields and geographic objects: discrete fields and objects both use “bounded” features.\r\n\r\nDiscrete fields divide the study space in mutually exclusive, bounded parts, with all locations in one part having the same field value. Discrete fields are intermediate between continuous fields and geographic objects: discrete fields and objects both use “bounded” features.\r\n\r\nDiscrete fields divide the study space in mutually exclusive, bounded parts, with all locations in one part having the same field value. Typical examples are land classifications, for instance, using either geological classes, soil type, land use type, crop type or natural vegetation type. \r\n\r\nDiscrete fields are intermediate between continuous fields and geographic objects: discrete fields and objects both use “bounded” features. A discrete field, however, assigns a value to every location in the study area, which is not typically the case for geographic objects. These two types of fields differ in the type of cell values. A discrete field such as land use type will store cell values of the type “integer” and is therefore also called an integer raster. Discrete fields can be easily converted to polygons since it is relatively easy to draw a boundary line around a group of cells with the same value. A continuous raster is also called a “floating point” raster.\r\n\r\nA field-based model consists of a finite collection of geographic fields: we may be interested in, for example, elevation, barometric pressure, mean annual rainfall and maximum daily evapotranspiration, and would therefore use four different fields to model the relevant phenomena within our study area.","name":"Fields","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF5-3b","description":"We can structure time by events (moments) or periods (intervals). When we represent intervals by a start and an end event, we can derive temporal relationships between events and periods, such as “before”, “overlap”, and “after”.\r\nValid time (or world time) is the time when an event really happened, or a string of events took place. Transaction time (or database time) is the time when the event was stored in the database or GIS. Note that the time at which we store something in a database is typically (much) later than when the related event took place.\r\n\r\nProcess models in the Earth sciences describe the evolution of geo(bio)physical surface properties in time, independently from remote sensing observations. Examples of such process models on various time scales are, for instance, numerical weather prediction models (NWPs), vegetation growth models, hydrological models, oceanographic models and climate models.\r\n\r\nProcesses on the planet Earth are complex phenomena that are taking place in space and in time, i.e. in four dimensions.\r\n\r\nIn many of these processes, differences in one dimension (e.g. height above the geoid) can be disregarded, so that two spatial dimensions and the dimension time remain. Despite this simplification, the physical description of the phenomena remains a difficult task. To better understand the processes it often helps if the same geographic region is viewed repeatedly and, if possible, also from different directions and in different wavelength regions. Integration of data from a variety of sources can be a means to retrieving information about processes that would otherwise remain undetected.","name":"Events and processes","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF5-4b","description":"Models that integrate the concepts of space, time, and attribute in geographic information.","name":"Integrated models","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF5-6","description":"Geographic phenomena can be studied as single entities and in relationship with each other and then reveal patters and clusters. How the entities are distributed is subject to statistical and visualisation studies.","name":"Spatial distribution","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF5-7","description":"We can use the topological properties of interiors and boundaries to define relationships between spatial features. Since the properties of interiors and boundaries do not change under topological mapping, we can investigate their possible relations between spatial features. We can define the interior of a region, R, as the largest set of points of R for which we can construct a disc-like environment around it (no matter how small) that also falls completely inside R. The boundary of R is the set of those points belonging to R that do not belong to the interior of R, i.e. one cannot construct a disc-like environment around such points that still belongs to R completely.\r\n\r\nLet us consider a spatial region A. It has a boundary and an interior, both seen as (infinite) sets of points, which are denoted by boundary(A) and interior(A), respectively. We consider all possible combinations of intersections (∩) between the boundary and the interior of A with those of another region, B, and test whether they are the empty set (∅) or not. From these intersection patterns, we can derive eight (mutually exclusive) spatial relationships between two regions. If, for instance, the interiors of A and B do not intersect, but their boundaries do, yet the boundary of one does not intersect the interior of the other, we say that A and B meet.","name":"Region","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF5-8","description":"Integration of data from a variety of sources can be a means to retrieving information about processes that would otherwise remain undetected.\r\n\r\nAlthough data integration can be very useful, there are also some requirements that have to be fulfilled for it to be effective:\r\n\r\n• geospatial data have to be accurately co-registered in a common grid;\r\n• time gaps between the various data layers have to be known and accounted for;\r\n• systematic effects due to the atmosphere, the viewing angle, the Sun angle, etc., must be corrected for or taken into account.\r\n\r\nData can be integrated in an almost infinite number of ways. Results from data integration can, again, be combined with other geospatial data to produce yet other new information, and so on.\r\n\r\nData integration also comprises the incorporation of non-spatial information or point data from field measurements. These data have to be associated with precise moments in time and with precise geographic locations, or with some time interval and fuzzy-defined regions. Thus, here the important issue of the representativeness of this information for the associated time interval and geographic area comes into play.\r\n\r\nIn general, data integration forces us to consider the uncertainties or inaccuracies of the various data sources available. In some cases, meta-data may contain information about this. When integrating data for some purpose, one has to apply weights to each of them, so that the final result is a balanced compromise in which inaccurate data receive less weight than those with a high degree of certainty.","name":"Spatial integration","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF5b","description":"The concepts below form the basic elements of common human conceptions of geographic phenomena. Concepts from many units in this knowledge area have been synthesized to create general conceptual models of geographic information. Attempts to resolve the object-field debate have led to attempts to create comprehensive models that bridge these views. Consideration of this unit should also include formal models of these elements in mathematics and other fields. Knowledge Area DM Data Modeling discusses the representation of these elements in digital models.","name":"Elements of geographic information","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF6-1","description":"Mereology is the study of parts and wholes. In GI this involves how objects are modeled as composites of other objects.","name":"Mereology: structural relationships","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF6-2","description":"Lineage describes the history of a data set. During the processing of data, the derived information inherits artifacts from the dataset(s) of origin. In the case of published maps, some lineage information may be provided as part of its meta-data, in the form of a note on the data sources and procedures used in the compilation of the data. Examples include the date and scale of aerial photography, and the date of field verification. Especially for digital data sets, however, lineage may be defined more formally as:\r\n\r\n“that part of the data quality statement that contains information that describes the source of observations or materials, data acquisition and compilation methods, conversions, transformations, analyses and derivations that the data has been subjected to, and the assumptions and criteria applied at any stage of its life (Clarke and Clark, 1995).”\r\n\r\nAll of these aspects affect other aspects of quality, for example positional accuracy. Clearly, if no lineage information is available, it is not possible to adequately evaluate the quality of a data set in terms of “fitness for use”.","name":"Genealogical relationships: lineage, inheritance","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF6-3","description":"We can use the topological properties of interiors and boundaries to define relationships between spatial features. Since the properties of interiors and boundaries do not change under topological mapping, we can investigate their possible relations between spatial features. We can define the interior of a region, R, as the largest set of points of R for which we can construct a disc-like environment around it (no matter how small) that also falls completely inside R. The boundary of R is the set of those points belonging to R that do not belong to the interior of R, i.e. one cannot construct a disc-like environment around such points that still belongs to R completely.\r\n\r\nLet us consider a spatial region A. It has a boundary and an interior, both seen as (infinite) sets of points, which are denoted by boundary(A) and interior(A), respectively. We consider all possible combinations of intersections (∩) between the boundary and the interior of A with those of another region, B, and test whether they are the empty set (∅) or not. From these intersection patterns, we can derive eight (mutually exclusive) spatial relationships between two regions. If, for instance, the interiors of A and B do not intersect, but their boundaries do, yet the boundary of one does not intersect the interior of the other, we say that A and B meet. In mathematics, we can therefore define the “meets relationship” using set theory. The eight spatial relationships are disjoint, meets, equals, inside, covered by, contains, covers and overlaps.","name":"Topological relationships","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF6-4","description":"Relationships between spatial features that define their relative position. Spatial autocorrelation is a fundamental principle based on Tobler’s first law of geography, which states that locations that are closer together are more likely to have similar values than locations that are farther apart.","name":"Metrical relationships: distance and direction","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF6","description":"Like geography, geographic information not only models phenomena but the relationships between them. This can include relationships between entities, between attributes, between locations. In addition, one of the strengths of geography (and GIS) is its ability to use a spatial perspective to relate disparate subjects, such as climate and economy. Methods for analyzing relationships are discussed in Unit AM4 Modeling relationships and patterns.","name":"Relationships","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF7-1","description":"Vagueness arises from lack of criteria for the applicability of certain linguistic terms. It arises from the lack knowledge about the meanings of terms.","name":"Vagueness","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF7-2","description":"-Uncertainty-related terms, such as error, accuracy, uncertainty, precision, stochastic, probabilistic, deterministic, and random -Difference between uncertainty and vagueness -Dependence of uncertainty on scale and application -Expressions of uncertainty in language -The causes of uncertainty in geospatial data -Stochastic error models for natural phenomena -How the concepts of geographic objects and fields affect the conceptualization of uncertainty -Mathematical models of uncertainty: Probability and statistics","name":"Error-based uncertainty","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF7","description":"Human models (mental, digital, visual, etc.) of the geographic environment are necessarily imperfect. While the mathematical principle of homomorphism (often operationalized as fitness for use) allows for imperfect data to be useful as long as they yield results adequate for the use for which they are intended, imperfections are frequently problematic. Although terminology still varies, two types of imperfection are generally accepted: vagueness (a.k.a. fuzziness, imprecision, and indeterminacy), which is generally caused by human simplification of a complex, dynamic, ambiguous, subjective world; and uncertainty (or ambiguity), generally the result of imperfect measurement processes (as discussed in Knowledge Area GD Geospatial Data). Both of these can be manifested in all forms of geographic information, including space, time, attribute, categories, and even existence. Imperfection is also dealt with in Units GD6 Data quality (in the context of measurement), GC8 Uncertainty and GC9 Fuzzy sets (for the handling and propagation of imperfections), and CV4 Graphic representation techniques (in the context of visualization).","name":"Imperfections in geographic information","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV","description":"Geo-data visualisation necessarily includes cartography as the origin of \"mapping\" our world. Cartography methods have drastically changed over the few years since the increasing role and sophistication of digital technology applied to geo-information visualisation. It is first worth differentiating between the underlying geo-data that describes real world phenomena and the bits of information that describe the visual presentation of geo-data . Likewise, there are processing tools to collect and handle geo-data, and processing tools especially designed to create and manage geo-data visualisations. \r\nWhile cartography methods have traditionally produced printed maps (i.e. hard copy) with static scale, orientation, projection, legends (content based) and tied to a period or instant of time. Nowadays geo-data visualisations are interactive by design, meaning that the results are map-based responsive interfaces, highly customisable through dynamic objects to zoom in and out, pan and tilt, change projections and graphic expressions on the fly, as well as dynamically browse the map over time. \r\nIf the production methods have changed, also the type of authors. Map making in its widest sense is not only a privilege of a few experts but has been democratised in such a way that. everybody is able to make maps using open data and open source apps and tools for geo-data visualisation. Therefore,the new roles of open data and new forms of geo-data like geo-social media make usability, intended and ethical considerations key aspects of geo-data visualization design, production and sharing. \r\nUnder the concept of cartography and visualisation it is included a list of concepts that together comprise the science and technology of visual representation of geographic data.","name":"Cartography and Visualization","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV1-1","description":"The evolution of cartographic representation in the previous centuries followed the most important technological and scientific developments of the time. It was driven by commercial and/or military needs and influenced by the special characteristics of the areas and/or environments to be mapped. Recent developments are the rise of open data worldwide and widely available internet technology allowing end users to get remote geo-data published elsewhere. In recent years, data and its digital presentation have become central elements of cartography, whereas paper maps have become peripheral.","name":"History and evolution of cartography","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV1-4","description":"Art in cartography means much more than designing aesthetically pleasing maps, whether on paper or digital. Exploring the interaction at large between art and cartography involves rethinking the way we approach spatial expressions and how cultural, social and political dimensions are reflected in maps. This can be clearly observed in historical maps - in between art and science - ranging from beautiful geographical representations created in the Middle Ages to convey religious messages to the creation of modern maps showing the power of modern empires and nations. This particular relationship between art and maps entails: “developing an inclusive approach of artistic mapping expressions; facilitating and encouraging interaction between cartographers who work with the Art aspects of cartography and artists who produce cartographic artifacts; and developing conceptual elements about the relationships between art and cartography.” Besides ancient paper maps, a sum of factors led digital maps and geospatial visualization, a matter of interest to artists and designers. Thanks to powerful computing systems and with the advancements reached in computer graphics or image processing, or the rise of information visualisation, new forms of representing and visualising geodata have also appeared. Creation of digital maps are still a two-way relationship since artists have explored maps as a medium for expressing their art, and cartographers have approached art to provide more than just the representation of locations and geographic features with the intention to make maps more attractive to their audiences.","name":"Art and geodata visualisation","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV1-5","description":"Historical maps are geographical representations made with the intention to represent spatial facts over time. Historical maps are generally considered valuable documents not just because of their historical value but also because most of them also are artistic representations by themselves. From a cartographical point of view, differentiation between historical maps and actual maps is mainly based on the advances in the history of Cartography, so once one disruptive advance in the map making process appears, maps created with previous techniques (and with some artistic or historical value) are usually considered as historical, such as ancient paper-based maps or old sea maps, for instance. Techniques such as scanning or photography can make ancient maps publicly available by converting hard-copy maps to digital ones. Once an historical map is digitised, the next step is to georeference it, which is the process of specifying and relating points of the digitalised map to actual coordinates in a geographic reference system. Because of its archival value and interest, historical maps are adequately preserved - following specific conditions - by map libraries, map societies or museums. Since digital methods and techniques have been replaced over time by new technological advances, first digitally created maps could be also considered historical, not because of its content, but of the techniques used to produce it.","name":"Historical maps","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV1","description":"At a certain moment in time people start to create more graphical representations of their surrounding environment. New technologies offered ways to expand these representations to larger geographical extent, higher spatial resolution, finer temporal granularity and larger periods. Technologies even made it possible to include other representations of reality such as social media and data ensembles in geodata visualizations, to the extent to even blend the real world with geodata-based visualization providing an augmented – virtual reality continuum. New forms of geo-data, like geolocated sensors may challenge the way geo-data visualisations are generated, shared and, eventually, influence decision-making processes. History and trends sketch these developments and future outlook. This concept introduces the main stages and turns in development of cartography, from earliest times to the present, the most important methods in map-making and map-based visualizations.","name":"History and trends","selfAssesment":"Completed (GI-N2K)
\r\n\r\n"},{"code":"CV2-1","description":"As mapping ( geo-data visualization) is intended to convey a certain message to a certain audience, it is essential to use data sources that allow the intended visualisation result. The data should be of the right degree of detail and its use should not cause copyright problems. The producer quality of each data set should be taken into account, as well as the fitness of the data for the intended use. Aspects: message; data quality","name":"Data sources for mapping","selfAssesment":"
In progress (GI-N2K)
"},{"code":"CV2-2","description":"In the trajectory between raw (geo)data and their user-relevant representation, the necessary data processing includes ways of abstraction by selection, filtering, generalization, transformation and classification of geographical data. In this data processing it is essential to at one hand relate the final symbolisation to the necessities of the intended message, and at the other hand to procedures that introduce as little error as possible.","name":"Data processing","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV2-3","description":"Map projection is fundamental to representation of spatial data and for combining different datasets. Its choice should serve the presentation type that will convey the intended message to the audience. Many mathematical principles define datum, projections, horizontal and vertical co-ordinate systems, georeferencing- introduced with the focus on visualisation issues Aspects: geodetic concepts; transformations","name":"Mathematical base","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV2","description":"Geodata, including 3 dimensional geometry, as such can graphically be presented but most of the times the data as such doesn`t meet the presentation criteria. Especially if the dataset has to be presented in combination with other datasets. First all the geodatum, georeference and map projection are crucial but also the role of the geometry. The processing of the geometry and the related attributes may become a crucial step for an adequate presentation. Nowadays the highest precision may be used to define different graphical attributes for different zoom levels. On the other hand geodata visualisation includes also graphical datasets. Such data ensembles, the combination of geodata and graphical data, are the data sources that offer opportunities to other ways of visualisation then the traditional cartographic mapping. Facets: a.\tGeospatial location (2D) and position (3D) that data refer to b.\tDegree of detail in data origin (acquisition resolution) and in representation ('map' scale) c.\tTypes of data (e.g. imagery, field measurements, delineated objects)","name":"Data considerations","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV3-1","description":"The combined impact of graphic design properties (balance, legibility, clarity, visual contrast, figure-ground organization, and hierarchal organization) and the map components (north arrow, scale bar, and legend) should always be carefully evaluated against the needs and the capacities of the audience.","name":"Map design fundamentals","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV3-10","description":"Geo-gaming is a crossover between gaming elements and location, usually enabled by location based services and augmented adn/or virtual reality features. Geo-games, also known as “location-based games” or “location-aware games”, have geodata at its core, since geoinformation constitutes the central element of the game mechanics. Geo-gaming applications present unique technical challenges to meet the infrastructural and resources demands from the games and location worlds. There are mainly four different types of geo-games: exploration games (to make use of an existing spatial design); feedback games (to report about players’ experiences in a specific design); allocation games (to occupy the majority of game location); and configuration games (to occupy specific pattern of game locations). Gamers actively participate by interacting with the environment, therefore gaming scenarios are as varied as their goals, which include teaching, training, and the developing of spatial thinking skills. Geo-games offer a myriad of opportunities to developers: non-linear storytelling, physical object integration, a more visceral experience, true social interaction… which bring geo-games to another interaction level. Geo-gaming applications often rely on VGI to allow gamers adding geolocated information that may crowdsource geo-referenced data useful for other secondary purposes .","name":"Geo-gaming","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV3-2","description":"Map symbolization entails a number of variables to produce visual, tactile, haptic, auditory, and dynamic displays. Visual variables (e.g., size, lightness, shape, hue) and graphic primitives (points, lines, areas) are commonly used in maps to represent various geographic features at all attribute measurement levels (nominal, ordinal, interval, ratio). With those a single geographic feature can be represented by various graphic primitives (e.g., land surface as a set of elevation points, as contour lines, as hypsometric layers or tints, and as a hillshaded surface). The challenge is to use effective symbols for map features to ease the interpretation of maps.","name":"Symbols and icons","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV3-3","description":"The selection of colours to use in data representation can be influenced by various factors (e.g. the production workflow, cultural differences, involved devices and media). There are various colour models (e.g. RGB, CMYK, CIE) that describe colours in a way that they can effectively convey visual information (e.g., qualitative, sequential, diverging, spectral) according to the meaning of the underlying data. The cultural background of the consumer is also relevant when it comes to choose colours that should have real-world connotations or should express psychological concepts (e.g. harmony, concordance, balance). A final important factor is if the consumer has colour limitations","name":"Colour","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV3-4","description":"When data representation is conveyed in words (e.g. toponyms, road codes), written text is often placed in map labels. It is important to decide on the role of the label in the context of the representation type. Algorithms for label placement are relevant, especially when label density is high. Shape and colour of the labels help to signify different types of messages. This is supported by the typographic properties (type font, size, style) of the text in the labels. Finally, it is important to use an authoritative source for the texts","name":"Typography","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV3-5","description":"Imagery can be a source for data acquisition as well as an illustration to abstract data representations. Imagery can be made from the air (from drones to satellites) or from a terrestrial point of view. The knowledge field describing the data acquisition process based on photos is called Remote Sensing. Using photos from any source to illustrate stories about geographical subjects contributes is the visual aspect of telling a story. Together with maps and other narrative components, the combination embodies a storytelling medium.","name":"Photos and imagery","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV3-6","description":"Animation is the process of making the illusion of motion and change by means of the rapid display of a sequence of static images that minimally differ from each other. In the context of maps, the temporal component is added to a map to emphasize and observe the gradual evolution of a certain monitoring phenomenon, such as changes in spatially numerical variables (for example, environment, population, mobility, land use, etc.) with respect to a static geographic area. Map animations generally consider dynamic time while space is static. Map animation helps to see patterns or trends that emerge as time passes, depicting meteorological or climate events, natural disasters, historical events and other multivariate data. It is particularly helpful to be used in educational settings.","name":"Animation","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV3-7","description":"Sound or audios can be one of the components of a multimedia data representation. A conventional GIS usually conveys visual information, however the integration of audios in mapping could enrich GIS data to other senses. Sound can increase the amount of information that’s communicated to the user through channels other than visual to address the special needs of people with visual impairments or people who cannot use in certain circumstances their sight, such as a driver who cannot look at a map. Approaches to rendering sound information on a map fall into three broad categories: (1) to sonoficate the whole visual presentation (for simple geometric data), (2) to augment a visual system with auditory information (allowing multivariate information) and (3) to display information about the surrounding where a user is. By classifying images and creating additional audio layers that associate each pixel with a specific sound, a GIS can add a new auditory dimension to maps.","name":"Sound","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV3-8","description":"Maps are valuable because they provide a large amount of detail in a small amount of space, and because of their capacity for telling a story. Telling stories through maps began with describing explored lands in great detail against terra incognita. Today, geographic tools, data, and multimedia on the web expand the ability to communicate stories and inform through maps to a broad audience such as journalists, decision makers and educators. Any person with a smartphone or computer can tell a story, using statics maps, or interactive web maps with text, video, audio, sketches, and photographs. Besides the technical skills to clearly communicate with a map (palette of colours, amount of information displayed…), other factors such as narrative processes, the storyboard, place, time, and characters play a crucial role. To be informative, it is important that the correct data is displayed, combining different sources of information combined to create an appealing and accurate map.","name":"Storytelling","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV3-9","description":"Infographics are visual representations of information and data, which can contain charts, diagrams, graphs, tables, maps and lists. The aim of an infographic is to present information that can be absorbed quickly, it is easily understandable and extensively in mass communication, and thus designed with fewer assumptions about the reader's knowledge base than other types of visualizations. The role of maps in an infographic is based on the potential of maps to condense information and to support a narrative. Infographic maps - altogether with an adequate storytelling - should find a simple way to explain current complex issues, providing added value to the infographic, and being an effective and efficient way to communicate.","name":"Infographics","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV3","description":"This concepts covers basic design principles that are used in mapping and visualization, as well as cartographic design principles specific to the display of geographic data. Both page layout design and data display are addressed.","name":"Design principles","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV4-1","description":"A thematic map is a type of map especially designed to show a particular theme connected with a specific geographic area. These maps \"can portray physical, social, political, cultural, economic, sociological, agricultural, or any other aspects of a city, state, region, nation, or continent\". Cartographers use many methods to create thematic maps. Five techniques are especially noted: -Choropleth mapping shows statistical data aggregated over predefined regions -Proportional symbols, showing the relative value of attributes -Isarithmic or Isopleth, also known as contour maps -Dots, to show the location of a phenomenon -Dasymetric, which uses areal symbols to spatially classify volumetric data.","name":"Thematic mapping","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV4-10","description":"Conveying uncertainty information is often done through visualization. Uncertainty is often defined, quantified, and expressed using models specific to individual application domains. In visualization however, we are limited in the number of visual channels (3D position, color, texture, opacity, etc.) available for representing the data. Thus, when moving from quantified uncertainty to visualized uncertainty, we often simplify the uncertainty to make it fit into the available visual representations. (After Potter et al., 2012). The seven challenges as formulated by MacEachren et Al. (2005) are still there to be tackled.","name":"Visualization of uncertainty","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV4-2","description":"Relief can be represented in a two-dimensional map either through contour lines or through a raster format gridded array of elevations. Contour lines connect points of equal elevation. At regular intervals index contours are marked with elevations so a reader can more easily determine the elevation of surrounding locations. They are the preferred method for analogue topographic maps. The grid approach is used in digital mapping and known as a digital elevation model (DEM), where each raster cell represents an elevation. Scaling of the cell z value in relation to the x and y value results in terrain exaggeration, which aids visualization of topography.\r\nDEMs are used for terrain analysis and can be used to obtain derivatives such as slope and aspect. DEMs are obtained by interpolating point elevation observations, which are historically retrieved from surveyed point data (e.g. GPS locations), but more recently from LiDAR and/or Structure from Motion point clouds. TIN (triangular irregular network) analysis is commonly used for point data interpolation, in order to derive a continuous elevation surface.","name":"Representing terrain","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV4-3","description":"Multivariate descriptive displays or plots are designed to reveal the relationship among several variables simultaneously. Bivariate and multivariate maps encode two or more data variables concurrently into a single symbolization mechanism. Their purpose is to reveal and communicate relationships between the variables that might not otherwise be apparent via a standard single-variable technique. There are basic characteristics of the relationship among variables, such as the forms of the relationships, the strength of the relationships, and the dependence of the relationships on external (usually to the pairs of variables being examined) circumstances. Therefore, these multivariate plots or maps are inherently more complex, though offer a novel means of visualizing the nuances that may exist between the mapped variables. As information-dense visual products, they can require considerable effort on behalf of the map reader, though a thoughtfully-designed map and legend can be an interesting opportunity to effectively convey a comparative dimension. Examples of multivariate plots include enhanced 2-D scatter diagrams, 3-D scatter diagrams, contour, level, and surface plots, and high-dimensional data plots","name":"Multivariate displays","selfAssesment":"Completed (GI-N2K)
\r\n\r\n"},{"code":"CV4-4","description":"Visualization of change and movement across space and time is of increasing interest to researchers and geospatial practitioners. The visualization process of temporal data has four steps: (1) time values to be visualized, (2) point of view on time, that identifies the characteristics of the temporal values to be visualized, (3) time space: define the displayable space of the time values and (4) point of view on the visualization space, the implementation of the perceptible forms of time. The visualization of spatio-temporal data can be done in many different ways such as multi-panel plots (maps), time-series plots (graphs), space-time plots (graphs), 3D Virtual Reality (Computer generated artificial environment), animations (production of consecutive images), and tables. Spatiotemporal data comprises three important components: geographic location, temporal information and the thematic attributes describing a real-life phenomenon.","name":"Visualization of temporal geographic data","selfAssesment":"
Completed (GI-N2K)
"},{"code":"CV4-5","description":"Dynamic and interactive displays refers to a situation where a display with a cartographical data representation changes in real time in response to user's actions","name":"Dynamic and interactive displays","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV4-6","description":"Web mapping is the process of designing, implementing, generating and delivering maps on the World Wide Web. Dissemination via the web opens new opportunities: realtime maps, cheaper dissemination, more frequent and cheaper updates, personalized map content, distributed data sources and sharing of geographic information. Technical restrictions cause challenges like low display resolution and limited bandwidth,( in particular with mobile computing devices with small screens and using slow wireless Internet connections), copyright and security issues, reliability issues and technical complexity. Today's web maps can be interactive and integrate multiple media. So interactivity, usability and multimedia issues also play a role.","name":"Web mapping","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV4-7","description":"Virtual reality or virtual realities (VR), also known as immersive multimedia or computer-simulated reality, is a computer technology that replicates an environment, real or imagined, and simulates a user's physical presence and environment in a way that allows the user to interact with it","name":"Virtual and immersive environments","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV4-8","description":"An Augmented Environment can be experienced through different sets of Augmented Reality (AR) technologies, including mobile displays (tablets and smartphone screens), computer monitors, or Head-Mounted Displays (HMDs), among others. AR is a technology that layers computer-generated enhancements atop an existing reality to make it more meaningful through the ability to interact with it. AR offers the integration of digital information and imagery onto the real world in real-time. In order to broaden the vision beyond this definition, AR can be described as systems having the following features: (1) combines real and virtual; (2) interactive in real-time; and (3) registered in 3D, allowing other technologies, such as mobile technologies, monitor-based interfaces, monocular systems to overlay virtual objects on top of the real world. Currently, AR applications use the camera provided by mobile devices to produce a live view of the real world in combination with relevant, context-appropriate information such as text, videos, or pictures.\r\nThere are lots of applications and systems in the market that provide AR functionality, making it difficult to classify and name them all. Some of them are related to the real physical world and others with the abstract, virtual imagery world. Sometimes it is not easy to figure whether it is an AR, as often AR is defined as Virtual reality (VR) with transparent HMDs. In general, the concept is to mix reality with virtual reality, including information and overlay over the real world through HMDs such as they seem apparent as one environment. The virtual objects can react accordingly with the camera's movement as it is registered concerning the real world, which is also the central issue of AR.","name":"Augmented environments","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV4-9","description":"Cartographers have recently become involved in extending geographic concepts and cartographic design approaches to the depiction of non-geographic data archives, using so-called spatialized views of information spaces. Spatializations differ from ordinary data visualisation and geovisualisation in that they may be explored as if they represented spatial information. (Fabrikant, S.I., 2003). As definitions of spatialization can be found: Spatializations are computer visualizations in which nonspatial information is depicted spatially (Montello et al., 2003). Spatialization is the transformation of high-dimensional data into lower-dimensional, geometric representations on the basis of computational methods and spatial metaphors. (Skupin 2007)","name":"Spatialization","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV4","description":"This concept addresses mapping methods and the variations of those methods for specialized mapping and visualization instances, such as thematic mapping, dynamic and interactive mapping, Web mapping, mapping and visualization in virtual and immersive environments, using the map metaphor to display other forms of data (spatialization), and visualizing uncertainty. Analytical techniques used to derive the data employed in these graphic representations are discussed in Knowledge Area AM Analytical Methods and Unit DN2 Generalization and aggregation.","name":"Graphic representation techniques","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV5-2","description":"Standards for map services were set by OGC and ISO, called WMS and WMTS. Producing map images on the web from a cartographic image in a GIS application is called \"publishing\". Making a web \"map\" in the broader sense of constructing data representations for Storytelling or Geo-gaming is still under development. It requires a mix of applying the map Design principles and Graphic presentation techniques, possibly in combination with software scripting.","name":"Web map making","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV5-3","description":"Traditional \"map\" making, as opposed to the mapmaking in neogeography, focuses on reliable and reproducible products, based on expertise of high definition printing in many colours on analogue media of geodetically well-constructed images.","name":"Traditional map making","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV5-4","description":"The aspects of reproduction of a data representation depend on the nature of the representation: is it analogue (a paper map, a mock-up) or is it digital? In the case of a paper map, its digitalisation with high fidelity is an essential step. With a source in digital form, reproduction can be a matter of the right printer. Alternatively, the source could be disseminated as a file or as a web service. If representations are dynamic and/or interactive the possibilities depend on the construction of the representation. The ease of dissemination of digital files should not result in copyright breach. Aspects: Digitalization techniques for analogue sources, Printing ( 2D, 3D), Dissemination ways, Construction of the data representation, User needs specification, Copyright issues","name":"Map reproduction","selfAssesment":"GI-N2K
"},{"code":"CV5","description":"This concept addresses map production and reproduction, as well as computation issues that relate to those workflows.","name":"Map production","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV6-1","description":"The potential of maps as a way to show or exert power over the population was early understood by ruling classes. A map expresses a claim by the inclusion or exclusion of map elements and how these elements are visually related and/or depicted on the map. So, the world could be modeled through the careful choice of content arranged graphically at a specific scale and in specific formats. Therefore, maps embody and project the interests of their creators. The “new cartographies” declare that maps are redefined as socially constructed arguments based upon consistent semiotic codes. Nowadays, the rise of costless, powerful and accessible tools for creating maps, put power on the side of individuals or groups of individuals with few organisation (crowdsourced data collection or VGI) capable of representing their world views. In addition, monitoring people, places or nature, for instance, should also be seen as another way to show the increasing power of maps. Surveillance mechanisms for tracking populations used by rulers, or the use of extended technologies like Google Earth by environmental organisations to track the Amazonian forest, constitute two examples of the particular use of maps to exert control over human beings or to press governments for taking specific actions, respectively.","name":"The power of maps","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV6-2","description":"Maps today help us locate the nearest gas station or ATM on our in-car navigation system, but this use of locating what is near or surrounds a location is not new. Maps from pre-historic times provided important locational information – what was where and how to get from place to place. A map can be a relatively simple iconic device, which can be read and interpreted with only a little training. These graphic representations of the real world could be traced in sand or painted on a cave wall and shared through time. Maps even preceded written language and number systems and are found in some format in most cultures through time as a graphical language. Learning to read this language and interpret it without ambiguity is not as simple as first suggested. This complexity has increased as technology has allowed creation of 3D and 4D interactive maps which allow anyone with internet access the ability to investigate different places, topics and times and produce their own map. Today the ability to read and interpret maps is increasingly important as industry, business and government communicates within their organization and the public using maps. Becoming aware of what a “map” shows depends partly on what the senses can register of the representation as a whole. It also depends on recognition of elements in the representation that are meaningful to the observer in the sense that these elements are credible indicators of spatial features. Based on that recognition, the nature of these elements and their spatial pattern might infer thoughts about historic or ongoing processes. This interpretation will be influenced by the expertise and needs of the observer.","name":"Map reading and interpretation","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV6-3","description":"Assessment of the usability of a data representation is about how useful it is to users. Therefore it is a test of the success of the representation design, a test of the skills of the \"map\" maker and a test for the reliability of the underlying data.","name":"Usability analysis","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV6-6","description":"Spatial thinking is thinking that finds meaning in the shape, size, orientation, location, direction or trajectory, of objects, processes or phenomena, or the relative positions in space of multiple objects, processes or phenomena. Spatial thinking uses the properties of space as a vehicle for structuring problems, for finding answers, and for expressing solutions\" Aspects: recognizing spatiality in a collection of things; translation of the collection to a pattern of elements; recognizing structure (relations between the elements in a pattern); recognizing process (or changes over time in patterns or structures)","name":"Spatial thinking","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV6-8","description":"Ethics is about the question if behaviour is right or wrong in a social context. In dealing with geodata, a person can do the wrong thing with respect to laws (e.g. disclose secrets, disregard privacy, copyright infringement) or to professional standards (e.g. use bad data, forget about the colour blind, downplay unpleasant details). Aspects: breach of legal standards; breach of professional standards","name":"Map ethics Legal and privacy issues","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV6","description":"Geodata visualisation are always made with a certain purpose. The role and understanding of such graphical representation is an important field of research. Besides theories that underpin evaluation approaches and their findings the visualisation may also be confronting. The more realistic the presentation and especially when it includes human/personal related data the ethical dimension of the visualisation play a major role. Usability of visualisations has also an impact on spatial thinking as has been proved by scholars.","name":"Usability of maps","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA","description":"Proper design of geospatial applications, models, and databases and the validation and verification of design activities are critical components of work in all areas related to GIS&T. Design failures can negate well-intentioned efforts to apply concepts and technology to solve real-world problems. While sharing a number of concerns with general systems analysis, the unique and complex spatial characteristics of geospatial information provide significant additional challenges. The focus of this knowledge area is on the design of applications and databases for a particular need. The design of general-purpose models and tools (e.g., raster and vector) is covered in Knowledge Area: Data Modeling (DM). In the context of specific implementations, design activities fall into three general classes:\r\n1. Application Design addresses the development of workflows, procedures, and customized software tools for using geospatial technologies and methods to accomplish both routinary and unique tasks that are inherently geographic.\r\n2. Analytic Model Design incorporates methods for developing mathematical models, spatial models and data processes. The design of the analytic model is often influenced by decisions that are made about data models and structures.\r\n3. Database Design concerns the optimal organization of the necessary spatial data in a computer environment in order to efficiently sustain a particular application or enterprise.","name":"Design and Setup of Geographic Information Systems","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA1-1","description":"This concept deals with the importance of having a list of prioritized requirements as a first step to ensure a smooth and successful implementation of a GIS project.. It entails the different methodologies and approaches to ensure a GI system covers all functional and nonfunctional requirements. Requirements are not only derived from business workflows but it is advisable to gather direct input from potential users that will be translated into requirements. However, there is a need to clearly rank the importance of the requirements gathered to ensure the GI system is manageable and in line with the intended use of the GI system, in opposition with the specific interests of a particular user or ambiguous requirements. Therefore, the documentation, traceability and evaluation of requirements after the implementation are as relevant as the initial gathering of requirements to give consistency to the designed system.","name":"Requirements gathering and analysis","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA1-2","description":"The internal process of documenting a task or a process is about “how” it is implemented and “what” is implemented. Documenting is particularly helpful if a breakdown occurs, such as when an expert working in a task leaves her job or to substitute one task in a set of interrelated processes by another. Documentation provides consistency for the taskand allows its monitoring, analysis and revision during a project. \r\nThere are different methods for documenting a task to transform tacit knowledge into explicit knowledge. Therefore, the task should be documented by describing it in video format and using visual tools that allow documentation, or the maintenance of a field diary.\r\nIn particular cases, the creation of user guides or manuals could be considered a subset of a process description particularly addressed to external users. A user manual should take into account the target users to adapt its content to them.","name":"Methods of process description and documenting","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA1-4","description":"A workflow is a sequence of operations that altogether perform a complex, sophisticated or repetitive operation or activity. No matter the workflow type, a workflow is defined in a declarative language, either text-based or visual, and stored in a workflow document to ease sharing and maintenance. In GI systems, a workflow can be seen from distinct perspectives. One of the most well-known GI workflow types is spatial data modelling. A model is specified as a combination of processing tools that manipulate and transform the spatial data required by the model. The order in which the processing tools, inputs, and outputs are organised in a workflow will determine the results and to what extent the spatial question is addressed. However, workflows in GI systems are not only related to spatial data modelling and transformation. There are cases where certain processes in GI systems should be designed in terms of software and hardware requirements, actors needs, organisational aspects or resource usage and demand. How can people’s work contribute to define the stages of a GI architecture? How much time does a regular user spend working with spatial data? How complex is the process going to be? The definition of this sort of workflows can help, for example, in designing an optimal architecture for a GI system in a particular enterprise configuration. \r\nWhether the workflow defines specific steps to process spatial data or the stages and details to implement an enterprise GI system, having a clear idea over each stage's inputs and outputs helps GI systems to be organised, consistent and reliable. In summary, high-level workflows like business workflows put together systems, components and actors that are part of a process or operation. They represent an abstract view, focused often on organisational, functional and resources usage aspects. Conversely, low-level workflows refer to a series of executable activities that carry out data transformations, models or spatial data analysis. Examples are code scripts, specified as sequences of commands in a programming language, and graphical workflows through, for example, the Model Builder in GI systems which are enacted by workflow engines.However, workflows in GI systems are not only related to spatial data modelling and transformation. There are cases where certain processes in GI systems should be designed in terms of software and hardware requirements, actors needs, organisational aspects or resource usage and demand. How can people’s work contribute to define the stages of a GI architecture? How much time does a regular user spend working with spatial data? How complex is the process going to be? The definition of this sort of workflows can help for example in designing an optimal architecture in an enterprise configuration for a GI system. \r\nWhether the workflow defines specific steps to process spatial data or the stages and details to implement an enterprise GI system. Having a clear idea over each stage's inputs and outputs helps GI systems to be organised, consistent and reliable. In summary, high-level workflows like business workflows put together systems, components and actors that are part of a process or operation. They represent an abstract view, focused often on organisational, functional and resources usage aspects. Conversely, low-level workflows refer to a series of executable activities that carry out a complex task, service or model. Examples are code scripts, specified as sequences of commands in a programming language to carry out data transformations and spatial models and spatial data analysis; and graphical workflows through, for example, the Model Builder in GI systems which are enacted by workflow engines.","name":"Workflow definition and consideration in GI systems","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA1-5","description":"Software and information technology are integral to any GI systems or projects, from the storage and handling of spatial data to its analysis, visualization and sharing. Therefore, the use of well-known software design and engineering techniques and methods to develop efficient, reliable, and easy-to-maintain software applications in the GIS realm is more important than ever. \r\nAmong the modern software design and engineering techniques, Agile software development methodologies like Scrum stands out. The common rationale of the Agile methods is to split a large software project into many functional pieces of software that help the software engineering team to translate their development efforts into quick prototypes, and eventually reach the final product. Therefore, the constant feedback and validation of the user’s requirements in short, iterative development circles (i.e sprints) are the main advantages of the Scrum methodology.","name":"Software design and engineering","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA1-6","description":"User interface and usability of a GIS system","name":"User interface and Usability","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA1-9","description":"Geodesign is a design and planning method along with geospatial modelling and technology, and simulations informed by geographic contexts to facilitate informed decisions and the creation of design proposals. A geo-design process is a problem-based, iterative process bounded by specific (geographic) constraints characterised by a collaborative effort.","name":"Geodesign","selfAssesment":"Completed
"},{"code":"DA1","description":"This concept encloses a set of activities and workflows to ensure that the implementation of a GIS system in an organization or project is correctly planned and designed according to the particularites, user requirements and current conditions of the project ahead. In general system design is the process to promote successful GIS in an enterprise environment. As a GIS system has a direct influence on the information technology department (IT), the system design tells the organizacion how the current infrastructure can or must support the planned GIS. This process builds a set of specific recommendations on hardware and network needs based on the number of projects that depend on the GIS solucion, as well as the projected business needs and user requirements. \r\nGIS architects through the system design process need to take into account and identify several conditions: a) infrastructure requirements, b) the network communication capacity, c) hardware and software procurement requirements and, d) software development and data acquisition needs. \r\nHaving a well-defined and successful GIS deployment is not only a matter of what data or software the organization should acquire. The process of system design aligns identified business requirements (user needs/requirements) derived from business strategies or project aims, goals, and stakeholders (business processes) with identified business information systems infrastructure technology (network and platform) recommendations. \r\nThe process starts with identifying business needs, including the identification of users locations, required information, data, resources or products. The business needs are generally considered as project workflows that help the GIS architects to identify the expected data traffic and computing demand associated with each transaction, being a transaction the work unit used to translate business requirements into associated server and network loads.\r\nWithout carrying out a proper system design, a GIS system can lead to an implementation and deployment failure, deriving in unfulfilled expectations and high costs in terms of human resources and financial matters.","name":"System design","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA2-1","description":"Project management includes the planning, organization, coordination, execution, monitoring, controlling and closing of the activities and resources - human and economic - for the timely achievement of clearly defined objectives forming a project. For the success of a project, a project manager will assure an efficient use of resources and a proper execution of tasks to deliver value to users and “clients” of products and services. The Project Management Body of Knowledge (PMI) defines “project management” as “the application of knowledge, skills, tools, and techniques to project activities to meet requirements”, being EO*GI projects are another type of information technology projects. PMI reflects different areas to take care of by project management. These areas are: Integration, Scope, Time, Cost, Quality, Human Resource, Communications, Risks, Procurement and Stakeholder. There are a variety of tools and techniques used in the areas identified by PMI, just to name a few Gantt chart, Program evaluation and review (PERT) analysis, AGILE project management, etc. that will help in project management.","name":"Project management","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA2-2","description":"This concept embraces the factors that could affect a GI system / project and could constitute obstacles to success or even decide a project is not doable. In order to ensure the success of a GI system or a GIS project there are several criteria to take into account from the very beginning of the conception of the GI system or project. A feasibility study may encompass different perspectives (economic, legal, technical, operational or scheduling ) to inform whether or not a project is worth the investment. An organisation should list the foreseen costs from these five perspectives listed above and the benefits (tangible or intangible) of implementing a system/project. Existing resources already available in-house and internal strategic plan in place could be critical to decide to undertake a project or not. The table below presents a non-exhaustive list of criteria and under which perspectives they should be examined.\r\nFeasibility analysis should include a pilot study to evaluate and improve the system / project proposed.","name":"Feasibility analysis","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA2-8","description":"This concept discusses the technical, organizational and monetary advantages and disadvantages of proprietary versus open source software. GIST industry and research are slowly but consistently moving toward the openness of software. Open software entails some clear advantages such as continuous development of new applications, building community of developers and users, starting a project even if limited funding is available, increasing the chances of a project’s sustainability, to name a few. On the other side, proprietary initiatives in GIST are keeping their roots to the ground by developing cutting-edge tools to handle challenging and critical environments in large private sectors and public administrations. Advantages of proprietary software include more stable software, a well developed documentation and personalised customer support service. Both open and proprietary geospatial software solutions can co-exist by applying the appropriate IPR licences for each type of solution. The future trend is to balance how proprietary and open source geospatial software complement each other and find synergies in increasingly complex and large projects.","name":"Proprietaty and open source software","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA2","description":"To design, build, and maintain a GIS, sufficient resources (e.g., labor, capital, and time) must be secured. Resource planning consists of the allocation and use of in-house resources (people, equipment, tools, rooms, etc.) to achieve the maximal efficiency of those resources. These resources are required for a variety of system elements, including design, software purchase, labor, hardware, and facilities. The crucial task is to determine whether the project is worth the required resources.","name":"Resource planning","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA3-1","description":"The ecosystem of GIS software architectures has evolved substantially in recent years to include a variety of options ranging from desktop GIS, server-based and component-based architectures to Web-based, cloud-based, mobile-based approaches. Aligned with the main trend, geospatial software architectures or infrastructures are also moving from desktop architectures to more cloud based or server based options to meet ever-increasing requirements of interoperability, interdisciplinary work and computational power for processing large data sets and derived products. Cloud-based architectures also enable on the fly visualization of computed geospatial products, as complementary visualisation and mapping tools are seamlessly integrated into modern cloud-based based architectures. Usage of a particular architecture is fully dependent on the nature, size, requirements, functionalities, and available resources of a given project or task. Desktop and server based applications are particularly suited for small sized projects and startups while enterprise based applications are meant for larger sized projects. Cloud based infrastructure can be useful for varying sizes of projects in which the computational infrastructure is fully outsourced.","name":"Major geospatial software architectures","selfAssesment":"In progress (GI-N2K)
\r\n\r\n"},{"code":"DA3-2","description":"Interoperability of GIS infrastructure or architecture ensures the consistent and uninterrupted usage of data and functionalities across platforms and systems. Components or tools residing on distinct platforms can “talk” to each other without friction. Interoperability is a central characteristic, especially important in distributed systems and architectures. It can be applied to different levels or layers of a system, i.e. infrastructure level, data level, business logic level, etc. For example, standard spatial data formats and protocols are especially relevant for handling GIS data across multiple systems and platforms, regardless of their underlying software architecture. This is particularly important in large-scale, collaborative projects involving various teams using heterogeneous GIS architectures. Most software providers, developers communities and standardisation bodies and committees are striving to make their architectures interoperable in an open manner, so proprietary standards and protocols are a potential hindrance to this initiative.","name":"Interoperability","selfAssesment":"
Completed (GI-N2K)
"},{"code":"DA3-3","description":"This concept considers general architectural patterns like SOA, ROA, Web Services, etc.","name":"Architectural Patterns","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA3-4","description":"- WebGIS, - technical pecularities of spatial data infrastructures - standardiced GI services for SDI: WMS, WFS, CSW, Transformation Services, SOS, WPS etc., - other map services and interfaces","name":"WebGIS, SDI services, map services","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA3-5","description":"This concept deals with Reference Model of Open Distributed Processing (RM-ODP), its standards, viewpoints modeling and the RM-ODP framework","name":"Reference Model of Open Distributed Processing","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA3-6","description":"Cloud computing provides an on-line computing transparent resource to the user, since a user doesn’t notice almost no difference between working on her own computer or the cloud. Owned and managed by infrastructure providers, cloud computing entails advantages (concurrent access by many users, software updates hosted in the cloud, cost-efficiency or outsourced maintenance in the cloud) and disadvantages (loose of control, network Connection Dependency or security breaches ). On the other side, grid computing is a full network of computers and data working together so functioning as a supercomputer. Grid computing presents advantages such as shorter resolution of complex problems, the ease of organizational collaboration or a better use of existing hardware.","name":"Cloud and Grid computing","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA3-7","description":"Within this concept solutions based on Desktop GIS and GIS libraries will be compared and contrasted","name":"Desktop GIS, GIS libraries","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA3","description":"This concept describes the major geospatial software architectures available currently and choices when designing GI applications and systems, including desktop GIS, server-based, Internet, and component-based custom applications.","name":"Architectural design of a GIS system","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA4-1","description":"- Compare and contrast the relative merits of various textual and graphical tools for data modeling, including E-R diagrams, UML, and XML - Create conceptual, logical, and physical data models using automated software tools - Create E-R and UML diagrams of database designs","name":"Modeling tools","selfAssesment":"GI-N2K
"},{"code":"DA4-2","description":"Within an initial phase of database design, a conceptual data model is created as a technology-independent specification of the data to be stored within a database.","name":"Conceptual models","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA4-3","description":"A logical data model expresses the meaning context of a conceptual data model, and adds to that detail about data (base) structures, e.g. using topologically-organized records, relational tables, object-oriented classes, or extensible markup language (XML) construct tags","name":"Logical models","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA4-4","description":"A physical data model documents how data are to be stored and accessed on storage media of computer hardware","name":"Physical models","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA4","description":"The effective design of geospatial databases should follow the established methods and principles of database modeling and design developed in computer science. The basic method is a three-step process generally called the conceptual, logical, and physical models transforming the application from very human-oriented to machine-oriented. Several standards and software tools exist to aid the process of database design.","name":"Database design","selfAssesment":"In progress (GI-N2K)
"},{"code":"DM","description":"This knowledge area deals with representation of formalized spatial and spatio-temporal reality through data models and the translation of these data models into data structures that are capable of being implemented within a computational environment (i.e., within a GIS or more likely within a spatial database). Data modelling is a crucial issue as it defines the content of a spatial database and usefulness of these content (data) for certain applications. Data Modelling is performed using system neutral languages like UML (or more seldom ER-diagrams). These conceptual models have to be transferred to logical models (i.e. tables of a database). Data is stored in spatial databases which are normally organized in an object relational way. For certain types of data specific databases are used, like triple stores, NoSQL DBs, Array DBs etc. For data modelling quite a number of ISO standards are available for deriving the conceptual model as well as for rules for application schemas, spatial schemas, temporal schemas, Quality principles, encoding, 3D modelling (CityGML) etc. Data models provide the means for formalizing the spatio-temporal conceptualizations. Examples of spatial data model types are discrete (object-based), continuous (location-based), dynamic, and probabilistic. Mastery of the objectives presented in this knowledge area require knowledge and skills presented in the bodies of knowledge of allied fields, including computer science (ACM/IEEE-CS Joint Task Force, 2001) and information systems (Gorgone & Gray, 2000; Gorgone & others, 2002).","name":"Data Modeling, Storage and Exploitation","selfAssesment":"In progress (GI-N2K)
"},{"code":"DM1-1","description":"This topic includes the main basic database concepts: - Database, definition and overview - Database management system, definition and overview - Relational databases, overview - Object-oriented databases, overview - Object-relational databases - NoSQL databases, general overview - NoSQL databases, examples triple stores, array databases, others (overview)","name":"Overview on database concepts","selfAssesment":"GI-N2K
"},{"code":"DM1-2","description":"The Relational Model is the most important database model, therefore it is explained in more detail here: - Basic concepts (tables, tuples, etc.) - Relation to relational algebra (RA), basics of RA - Constraints (key, domain, referential integrity) - Relation to entity relation (ER) model, basics of ER","name":"The Relational Model","selfAssesment":"GI-N2K
"},{"code":"DM1-3","description":"Relational databases and database management systems are essential for GIS in consequence the important issues have to be treated here: - General aspects, basic architecture of a DB, advantages, features - DBMS concepts and functionalites (transactions, locks, multiuser access etc.) - Database design, techniques - Database administration - Normalization (1NF - 3NF) - Example of a database design","name":"Relational Databases, Database Managements Systems and Database principles","selfAssesment":"GI-N2K
"},{"code":"DM1-4","description":"Database queries and especially spatial queries require specific data structures to be performed satisfactory Relevant is: - Motivation, examples of typical non-spatial and spatial queries - Trees, B-tree, R-tree, Q-tree - Graphs, overview and relation to databases","name":"Data Structures and Indices for Databases","selfAssesment":"GI-N2K
"},{"code":"DM1-5","description":"Big data like imagery but also for example GML data sets need compression to be accessed / transferred in an acceptable time. Therefore some compression techniques have to be taught: - Motivation, examples of data sets which need compression - General introduction, vector - / raster data compression, compression lossless, lossy - Popular compression techniques, LZW (Lempel-Ziv-Welch) encoding, Huffman encoding - Techniques for raster data, runlength encoding, JPEG coding, wavelet etc. - Techniques for the reduction of vector data (Douglas Peuker etc.) - Data formats, overview and relation to compression techniques","name":"Data compression techniques","selfAssesment":"GI-N2K
"},{"code":"DM1-6","description":"SQL is the \"standard\" to perform spatial and non-spatial queries in databases. That means each student in a GI related course has to be familiar with the main aspects if it: - Motivation, history, overview - Data definition language DDL - Data manipulation language DML - Data control language DCL - Spatial extensions of SQL","name":"SQL and its usage for data handling, spatial extensions to SQL","selfAssesment":"GI-N2K
"},{"code":"DM1-7","description":"UML is the standard for describing the schema related to GI models, but also user requirements, workflows etc. can be described in UML using the UML diagrams: - Motivation, background, purpose - Use case diagrams - Class diagrams - Sequence diagrams - Activity diagrams","name":"UML introduction and class diagrams","selfAssesment":"GI-N2K
"},{"code":"DM1-8","description":"XML knowledge is an important bases for understanding GML. Moreover XML tools like XSLT are important to transform XML or GML data sets into other XML based formats like SVG or others. Important issues: - Motivation, purpose - Relation to HTML - XML document structure - XML syntax, elements, attributes and namespaces - xlink, xpath and XSLT - XML DTD - XML schema","name":"XML introduction","selfAssesment":"GI-N2K
"},{"code":"DM1-9","description":"The long term storage of GI data in general is based on spatial databases. Therefore the following is essential for a GI course: - Relation between GIS and DB / \"Long transactions\"- Dual concepts - Characteristics of spatial databases - Spatial data in object relational databases - Spatial extensions of DBs, overview","name":"Database concepts in GIS and Principles of spatial databases","selfAssesment":"GI-N2K
"},{"code":"DM1","description":"This unit includes the basics for data modelling, storage and exploitation. Data modelling is one of the most important activities in conjunction with Geographic Information / GIS as it determines how the data can be used and if the requirements from applications are fulfilled. Data modelling can be done in conjunction with the database, e.g. through ER diagrams or according to the ISO 191xx standards by using UML. The costs of data acquisition can be tremendous, therefore the data represents an enormous value. This value has to be conserved through a safe long term data storage. Therefore databases and especially relational and object relational databases are crucial. For a proper storage and query of geographic information databases are extended with specific data types and data structures. As data sets can be very large suitable compression techniques became important especially in the context of accessing and delivering geographical data, e.g. through services. XML based modeling languages for encoding also play and important role in this context","name":"Foundations for Data Modelling Storage and Exploitation","selfAssesment":"In progress (GI-N2K)
"},{"code":"DM2-1","description":"GI standards, mainly from ISO and OGC are essential nowadays. Moreover also an overview on ICT standards from W3C or OMG are important as well as some understanding of standardization processes. In detail: - Motivation for standards, examples from daily life - Overview on GIS and relevant ICT standardization bodies and selected standards - De jure and De facto standards, obligation, reasons for the usage of standards - Standardization within ISO - Standardization within OGC, relation to ISO - Examples of ISO 191xx standards","name":"Overview on relevant standards and standardisation bodies","selfAssesment":"GI-N2K
"},{"code":"DM2-2","description":"Conceptual data modeling is a key skill for GI people. (see relations to other topics) The following therefore is important: - Overview on the relevant standards like conceptual schema language, Rules for application schema - Examples of conceptual schemas","name":"The principle of conceptual data modelling according to ISO","selfAssesment":"In progress GI-N2K
"},{"code":"DM2-3","description":"Geometric modelling is an important subtask of conceptual modelling and requires the following basics: - Overview of ISO 19107 - spatial schema - Overview of ISO 19125 - simple features - Examples of the usage of spatial schema and simple feature elements for feature class definitions - Relation to GML - Relation to DBs","name":"Geometry data types according to spatial schema and the simple feature specification","selfAssesment":"GI-N2K
"},{"code":"DM2-4","description":"Also temporal aspects have to be considered within conceptual modelling. This also requires basics: - Motivation, examples - Temporal variability of features (move, change of structure or geometry) - Overview on ISO 19108 temporal schema - Examples of modeling temporal aspects","name":"Temporal data types according to temporal schema","selfAssesment":"In Progress GI-N2K
"},{"code":"DM2-5","description":"Conceptual models of course have to be implemented, in general in a GIS (which is often proprietary), or in a database (which can be standard based) ,therefore here the implementation in a database is treated: - Repetition of conceptual and logical models - Examples of the transferring of a conceptual model to a logical (database) model","name":"Transferring conceptual models to logical models","selfAssesment":"In progress (GI-N2K)
"},{"code":"DM2-6b","description":"Metadata is considered as very important for the usage as well for the search for Geodata Relevant basics are: - Motivation, importance of data quality as part of metadata - Metadata in an spatial data infrastructure with many There are quite a number of relevant standards for GI courses. Some are listed here, others might be considered, depending on the background of the course: - Select other standards and explain them, Important are: - ISO 19141 Schema for moving features, ISO 19142 Web Feature Service or others - 19109 - Rules for application schema - Selection of other standards is depending on the background of the course","name":"Other standards","selfAssesment":"In progress GI-N2K
"},{"code":"DM2-7","description":"GML is the most important standard for the transfer of Geodata as it allows to transfer the schema information as well as the data. Important issues: - Motivation, Importance of a Geography Markup Language - History of GML, Overview 19136 - Geography Markup Language - Relation to spatial schema - Supported features in GML (Topology, 3D ...) - Structure of GNL, profiles, application schemas etc. - Transfer of models and of data - Examples","name":"Introduction to GML","selfAssesment":"In progress (GI-N2K)
"},{"code":"DM2-8","description":"3D Models, especially 3D city models are becoming more and more important. CityGML is the most important standard within the GI domain to describe City models semantically and geometrically. Relevant issues: - Motivation, Usage of CityGML - Relation to GML - Coherence of semantics and geometry - Principles of modeling - Level of detail concept - CityGML vs KML - Examples","name":"Introduction to CityGML","selfAssesment":"GI-N2K
"},{"code":"DM2","description":"This unit includes the essentials of relevant standards for spatial data modelling. A number of ISO and OGC standards are available for deriving the conceptual model as well as for rules for application schemas, spatial schema provides data types for geometry models in various forms, Point, line, area, body based, temporal schema allows to consider temporal dimensions, Quality principles can be used to describe the quality of geodata, encoding standards (mainly GML) allow the standard based transfer of data and data models, CityGML allows a standard based 3D modelling, etc.","name":"Standards for Spatial Data Modeling","selfAssesment":"In progress GI-N2K
"},{"code":"DM3-1b","description":"There are two basic concepts related to this topic: Features and Fields, or Geo-fields, as named by Goodchild at al. The concept of fields can be differently represented as explained here: - Repetition of basic concepts of Geographic Information Science - Explanation of the concept of continuous fields and the commonly used ways of representing geo-fields - Relation between fields and coverages, an important discretizations of a Geo-field - Types of Coverages","name":"The concept of fields","selfAssesment":"In progress GI-N2K
"},{"code":"DM3-2","description":"The raster data model holds values in a regularly spaced matrix of cells arranged in rows and columns covering a two dimensional space. Rasters are commonly used to store continuous data like colors in an image and height values but they are also used for discrete (thematic) values like land use.","name":"The raster model","selfAssesment":"In Progress (GI-N2K)
"},{"code":"DM3-2b","description":"Grids are on the one hand one important type of caverages and on the other hand Grids are used as basic structure in some applications. Important here is: - Definition of the concept of grid in GIS - Grid as an instance of coverages - Grids as a basic structure for certain applications / medium for aggregation of data - Examples of grid-based data such as Digital Terrain Models (DTM) - Grids in census / statistical data and Geo-marketing applications","name":"Grid representations","selfAssesment":"GI-N2K
"},{"code":"DM3-3","description":"Grid data models can contain millions of discrete values. This leads to very large datasets. Depending on the way values change over the grid, different methods can be used for an optimal (lossy or lossless) data compression. Type of data, computer power needed, application of the data, method of transport and storage all contribute to the choice of compression method.","name":"Grid compression methods","selfAssesment":"In progress (GI-N2K)
"},{"code":"DM3-3b","description":"TINs and Voronoi tessellations are important types of coverages. TINs play a very important role also in Computer graphics. Important here is: - Basics from Graph theory - Definition of Triangulated Irregular Networks (TIN), purpose and applications - TINs and voronoi diagrams as a type of coverages - One important instance of a TIN: Delauney Triangulation - Definition of Voronoi Diagrams, purpose and applications - Relation between Delauney Triangulation and Voronoi Diagram, the \"Dual Graph\" - Examples from applications","name":"TIN and Voronoi tesselations","selfAssesment":"GI-N2K
"},{"code":"DM3-4","description":"While the classical grid structure uses rectangular cells, the hexagonal data model uses hexagons to represent raster data","name":"The hexagonal model","selfAssesment":"In progress (GI-N2K)
"},{"code":"DM3-4b","description":"Linear referencing is 1 dimensional positioning. The position of an object is defined by the distance from the object to the start point along a line. Linear referencing is for example used in railway dispatching systems","name":"Other models like linear referencing","selfAssesment":"In progress GI-N2K
"},{"code":"DM3-5b","description":"Resolution of raster and gridded data - Georeferencing of data, direct and indirect methods (t.b.d.)","name":"Resolution and georeferencing system","selfAssesment":"In Progress (GI-N2K)
"},{"code":"DM3-7","description":"In hierarchical data models data is organized in a tree-like structure. Data are connected with parent-child relations. Hierarchical structures are often used for spatial indexing.","name":"Hierarchical data models","selfAssesment":"In progress (GI-N2K)
"},{"code":"DM3","description":"This unit includes relevant tessellation data models. Besides features (sometimes also called geo-objects) geo-fields play and important role. In recent literature tessellation models are classified as discretizations of fields. In traditional GI literature tessellations are defined as important data structure itself. Tessellation discretise a continuous surface into a set of non-overlapping polygons that cover the surface without gaps. Tessellation data models represent continuous surfaces with sets of data values that correspond to partitions. Important tessellation models are Grids, TINs and Voronoi diagrams.","name":"Tessellation data models","selfAssesment":"In progress (GI-N2K)
"},{"code":"DM4-1","description":"This topic includes the basics for feature based modelling. There are a number of standards also relevant for this topic (see relations). The following items should be included: - Definition of a feature (in some literature also called object, or geoobject) and of feature classes respectively. - Aspects of the definition (ID, geometry, topology, thematic, time etc.) - Techniques for the definition of features / feature classes (mainly link, as they are described elsewhere, see relations)","name":"Feature based modelling","selfAssesment":"GI-N2K
"},{"code":"DM4-2","description":"This topic describes the process of Geometric modelling using vector data, means the primitives like points, lines, areas, bodies, or raster data. There is a strong relation to ISO standards (see relations) as they provide basic data types for geometric modelling. Main issues: - Geometric modeling based on vector data - Geometric modeling based on raster data - Conversion between the models - examples, advantages, disadvantages of the models","name":"Geometric modelling","selfAssesment":"GI-N2K
"},{"code":"DM4-3","description":"In topological modelling the geospatial relations in a data model are represented by the position of geospatial objects, especially nodes, edges and surfaces.","name":"Topological modelling","selfAssesment":"In progress (GI-N2K)
"},{"code":"DM4-4","description":"This topics deals with the definition of an application schema. There are other units which are important for this topic (see Relations). Issues to be included: - Methods to define and describe an application schema (requirement analysis, description of the schema etc.) - Feature attribute catalogues - Domains / data relevant for INSPIRE","name":"Application models based on vector data","selfAssesment":"GI-N2K
"},{"code":"DM4-5","description":"This Topic deals with important application models, which should be chosen with relation to the course (geographically / related to the background of the course) INSPIRE should be treated in any case. In detail: - Overview on important application models relevant for the course, e.g. from topography or environment in the country - Repetition of the principles of Spatial data infrastructures - Overview on the INSPIRE initiative and the goals related - The INSPIRE data model - The architecture of INSPIRE and the necessary services - Domains / data relevant for INSPIRE","name":"Examples of important application models","selfAssesment":"GI-N2K
"},{"code":"DM4-6","description":"This topic is dedicated to the challenges of model based interoperability and related issues, The principles of interoperability are included in DA3-2. In detail: - The challenges of model interoparability (semantics, different modelling of the same features in different models, syntacs) - Overview on IT concepts for schema integration / transformation - Approaches for model integration - Approaches for model transformations, e.g. related to INSPIRE, from the Humboldt project","name":"Model based interoperability, model transformations","selfAssesment":"GI-N2K
"},{"code":"DM4-7","description":"Network models are crucial in some application domains, such as Navigation (roads etc.), but also in utility applications (facilities like pipes etc.) In this topic should be treated: - The network model in the database domain - Graph based NoSQL databases - Topology of network models - Data structures for storing network data - The Dijkstra algorithm - Overview on important applications","name":"Network models","selfAssesment":"GI-N2K
"},{"code":"DM4","description":"This unit includes relevant issues related to vector data models, feature based modelling, applications. Besides imagery data the majority of GI data available is feature based and founded on vector geometry. Topology modeling also is very common nowadays, as many analysis like routing or neighborhood analysis require it. Spaghetti modelling becomes more and more and exception. In every country there are important feature and vector geometry based application models available e.g. in Topography / Cartography. In Europe every GI course should include some information on INSPIRE. As in different application domains different data models are used, sometimes for the same feature types, integration and transformation of models are an important issue also.","name":"Vector data model, Feature based modelling, Applications","selfAssesment":"In progress GI-N2K
"},{"code":"DM5-1","description":"- Many geographical phenomena are not defined sharply but uncertain Uncertainty has a number of considerations: - Motivation, background, purpose - Conceptual model of uncertainty - Uncertainty of geographic phenomena (vagueness, ambiguity) - Uncertainty of measurements - Uncertainty of analysis - Uncertainty vs. data quality - Statistical models of uncertainty - Outline of Fuzzy approaches","name":"Basics of uncertainty and its modelling","selfAssesment":"In progress GI-N2K
"},{"code":"DM5-2","description":"Space and time are 2 connected concepts, this topic is dedicated to some basics of modelling time and the temporal dimensions related to features and fields: - Motivation, background, purpose - Changes in time in Entity based and field based representations - A conceptual model of changes in time - Move of objects - Change of structure - Change of geometry - Examples from applications","name":"Modelling time aspects","selfAssesment":"In progress GI-N2K
"},{"code":"DM5-3","description":"Traditionally many GIS used 2D or 2.5 D data models, but in the last decade 3D modeling mainly in form of city models or in the context of Building Information Models (BIM): - Basic concepts of 3D modelling, edge, area, volume models - The workflow of 3D modelling, general aspects, choose of the proper model - Methods of 3D modeling - Principles of Constructive Solid Geometry (CSG) - Principles of Boundary representation (BR) - Principles of Voxel-beased modeling - Comparison of the methods - The concept of BIM, principles and purpose - City models, principles and purpose - Examples / applications","name":"Modelling 3D","selfAssesment":"GI-N2K
"},{"code":"DM5","description":"Traditional raster and vector data models cannot easily represent the more complex aspects of geographic information, such as temporal change, uncertainty, three-dimensional phenomena, and integrated multimedia. A variety of models have been proposed to represent these complexities, including both extensions to existing models and software, and entirely new models and software. During the 1990s, work in this area was largely experimental, but many solutions are now available to practitioners in commercial and open source software. The data models in this unit are based on concepts discussed in Knowledge Area CF Conceptual Foundations.","name":"Modelling 3D, temporal and uncertain phenomena","selfAssesment":"In progress (GI-N2K)
"},{"code":"DN3-1","description":"Modification of spatial and attribute data while ensuring consistency within the database, implications of transactions on database integrity, scenarios for periodic changes in GIS database and monitoring the periodic changes.","name":"Database change","selfAssesment":"In progress (GI-N2K)
"},{"code":"DN3-2","description":"Rules for modelling spatial database change, techniques for handling version control, techniques for managing long and short transactions, management of spatial databases in multi-user environment","name":"Modeling database change","selfAssesment":"In progress (GI-N2K)
"},{"code":"DN3-3","description":"Reliability tests of change information, design and implementation. Logical consistency of updates.","name":"Reconciling database change","selfAssesment":"In progress (GI-N2K)
"},{"code":"DN3-4","description":"Needs for versioned databases, queries for change scenarios using DB management tools, algorithms for performing dynamic queries, role of time-criticality and data security while choosing methods for change detection.","name":"Managing versioned geospatial databases","selfAssesment":"In progress (GI-N2K)
"},{"code":"GC","description":"The term geocomputation dates back to the first international conference on the topic in 1996 held at the University of Leeds under the title “The art and science of solving complex spatial problems with computers’. The term “geocomputation” was coined to describe the use of computer-intensive methods for knowledge discovery in physical and human geography. This new area distinguishes it from the application of statistical techniques to spatial data in the focus on “creative and experimental applications” and in “developing relevant geo-tools within the overall context of a ‘scientific’ approach.” Other authors reinforced the unique character of geocomputation as “to provide better solutions to many geographical problems by developing new, computationally dependent tools for analysis and modelling”. Simply defined, the interdisciplinary area of geocomputation was, from the beginning, closely linked to the application of computer technology and the development of tools and applications to real-world spatio-temporal problems through the combination of geographic information system techniques, spatial modelling, cellular automata, and other non-conventional data clustering and analysis techniques.\r\nEven though geocomputation is still seeking to define the field conceptually), it is closely related to computational science, the use of high-computing performance, artificial intelligence, computational intelligence, grid infrastructure and parallel computing . Nevertheless, the evolution of new computing paradigms, such as edge-fog-cloud computing along with the new forms of data create new opportunities for the geocomputation community . \r\n\r\nWhile the underlying idea remains intact --a diverse and interdisciplinary area of research that uses geospatial data, methods and tools for applied scientific work--, the current approach to geocomputation differs from the founders in that it focuses more attention on open science, reproducible research practices, and in a vibrant collaborative community to develop new methods, tools and applications that are integrated into multiple application domains such as economics, sociology, geodemography, health, criminology, transportation, biology, remote sensing and cities . The theoretical roots and experimental emphasis of geocomputation makes it an excellent vehicle to creatively explore in parallel the theory and practice of the use of geospatial data in a computational way to solve real-world problems.","name":"Geocomputation","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC1-1","description":"A complex system can be viewed as a system composed of many interacting parts, with the ability to generate a new collective behaviour through self-organisation, for example, though the spontaneous formation of temporal, spatial or functional structures. Complex systems are therefore adaptive as they evolve and may contain self-driving feedback loops. Most real-world systems such as global climate, an ecosystem, a city, the human brain, and the entire universe, are complex systems. Therefore, complex systems are much more than a sum of their parts.The general characteristics of the structure and dynamics of complex systems have been characterised, including path dependence, positive feedback loops, self-organisation, and emergence. Complex system types include nonlinear systems, chaotic systems, and complex adaptive systems. \r\nTraditional approaches focus on the individual system components and define a system as the sum of its parts. Whereas the modern approach relies on complexity theory and complex adaptive systems, to emphasise the linkages between system components in order to understand complex systems as a whole. Agent-based models, for example, have been highly recommended for studying complex adaptive spatial systems because they support the explicit representation of situation-dependent information for decision making within dynamic spatial environments.","name":"Complex systems","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC1-2","description":"Computational science is a discipline focused on the design, implementation and use of mathematical models or simulations through the use of computers to analyse scientific problems, systems or processes. Computational science heavily relies on computational technologies such as high performance computing, artificial intelligence, computational intelligence, grid infrastructure and parallel computing. Geocomputation is closely related to computational science and, therefore, geocomputational methods are often derived from machine learning, clustering, simulation, parallel computing and high performance computing. Contrary to the methods and tools applied for spatial analysis described under the Analytical Methods Knowledge Area, geocomputation methods may involve spatial methods available in standard GIS packages, but quite often require self-development, or at least customisation, involving computational technologies to solve target problems. The aim of this topic is to provide an introduction to computational science with particular emphasis on its usage and relation to geocomputation.","name":"Computational science and technology","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC1-3","description":"While geocomputation is not daily used in GIS environments and traditional GIS projects, it is the focus of a vibrant collaborative and research community in developing new geocomputational methods, tools and applications that are integrated into multiple application domains such as economics, sociology, geodemography, health, criminology, transportation, biology, remote sensing and cities. Open science, reproducible research practices, and strong collaboration make geocomputing an excellent vehicle for creatively exploring together the theory and practice of using geospatial data in a computational way to solve real-world problems.","name":"Spatio-temporal problems and applications","selfAssesment":"In progress (GI-N2K)
"},{"code":"GC1-4","description":"The origin of geocomputation dates back to the first international conference on the topic in 1996 and was coined to describe the use of computer-intensive methods for knowledge discovery in physical and human geography. Geocomputation is closely related to other widely known areas of knowledge within the geospatial community, such as GIScience, Spatial Information Science, GeoInformatics, and Geographic Data Science. While these terms clearly overlap and boundaries are fuzzy, the term geocomputation puts the focus on creative and experimental applications and in developing relevant computationally geospatial tools for analysis and modelling within the overall context of a ‘scientific’ approach. Therefore, a common interpretation of geocomputation is to describe the application of computational models to geographic problems.","name":"Origin of geocomputation","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC1","description":"Geocomputation represents an attempt to move the geospatial research agenda back to geographical analysis and modelling by providing a toolbox of methods to analyse and model a range of highly complex, often non-deterministic problems. In this context, complex systems and computational science are foundational aspects upon which geocomputation approaches and methods are built to address a variety of real-world, spatio-temporal issues","name":"Geocomputation and complex systems","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC2-1","description":"Building a model that mimics a real-world system generally follows a series of stages: from conceptual models to mathematical models and, finally, simulation models. In model development, system analysis is a process whereby a real-world system is simplified by dividing it into simpler, more manageable parts. A conceptual model captures the components, variables and interactions of a system, and provides a useful way of thinking about the trade-offs between abstraction and representativeness of real-world phenomena. Taken in isolation, however, the interacting parts of a system fail to explain its dynamics behavior. A conceptual model is then translated into a mathematical model to explain system dynamics and interaction. Mathematical models often take the form of equations, logical rules or other mathematical mechanisms to represent the interrelations and relationships among the constituted parts of a system. Lastly, a simulation model is the computer-based implementation of mathematical models consisting of interrelated equations and logical rules. When a simulation model runs on a computer, it iteratively recalculates the modelled system state as it changes over time in accordance with the relationships represented by the mathematical relationships that describe the system dynamic. Therefore, developing detailed and dynamic simulation models comes at the cost of generality and interpretability, but it brings us realism and the ability to represent real-world processes in specific contexts. Simulation modelling is often used for prediction, exploration, theory development, or even optimization of conditions to achieve desired outcomes, with the goal of examining how the interconnections and relationships that characterise complex social and environmental systems (e.g. ecosystems, urban systems, social systems, global climate system) produces patterns of behavior over time. Therefore, simulation models are increasingly gaining relevance as scientific mechanisms for several reasons. First, simulation models allow researchers to study systems inaccessible to experimental and observational scientific methods, complementing more conventional approaches to discover or formalize theories about real world systems. Also, aS many real-world systems are nonlinear, simulation modelling has turned into a necessary method to explore and understand better such systems. In addition, the availability of computational science methods and technology, together with a large amount of data available from different sources, have greatly driven the adoption of simulation models in a wide range of scientific disciplines.","name":"Principles of computer simulation","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC2-3","description":"Rule-based models are based on logic programming with condition-action expressions, where the left side of the expressions consists of several conditions that returns a logical result, and the right side consists of several actions. Rules in rule-based models indirectly specify a mathematical model. However, unlike equation-based models which refer to the overall or aggregate behaviour of a system, rule-based models focus on the behaviour of the individual components of a system. That’s why the implementation of rule-based models is most often done by cellular automata models or agent-based models, in which the aggregate behaviour of the system emerges from the interaction of the individual agents or cells over time. Many geographic patterns and dynamics are formed by systems of interacting actors/cells with heterogeneous characteristics and behaviours, in which such dynamic behaviours can be implemented as rules. The aim of this topic is to provide knowledge about rule based models and to understand their advantages and disadvantages.","name":"Rule-based models","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC2-4","description":"Equation-based models are a set of interrelated equations that capture the variability of a system over time (differential equations), and the execution (simulation) of the model means to evaluate such equations. Equation-based models do not aim at representing the behaviour of the individual components in a system. Rather, they focus on the overall or aggregate behaviour of a system. Therefore, equation-based models are well suited to represent physical processes and some topics within natural sciences, where the system to some degree can be described by physical laws. Hydrological modelling is a good example of models based on equations. However, other real-world systems can rarely be fully described by the laws of the natural sciences, and their behavior and interrelation must be represented by means of other types of mathematical mechanisms. The aim of this topic is to present the advantages and challenges in using equation-based simulation models, which are most naturally applied to systems centrally governed by physical laws rather than by information processing and flow.","name":"Equation-based models","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC2-5","description":"Space-time dynamics are closely related to the concepts of change and process, which are inherent to our dynamic world. Space-time dynamics especially manifest when we move from a static representation to a dynamic representation of phenomena. Various processes that take place at different spatial and temporal scales interact with each other and lead to complex changes to the phenomena being modeled. There exist many different approaches of conceptualizing and understanding space-time dynamics in order to understand or predict phenomena in heterogeneous application domains ranging from human activities and urban sprawl to disease spread and traffic flow.","name":"Space-time dynamics","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC2-6","description":"Cellular automata are a standard type of spatially explicit simulation model in which complex processes are modelled over space and time by means of a lattice of cells in which each cell defines its neighbouring cells. The spatial lattice composed of a two-dimensional grid of squared cells is the simple configuration of a cellular automata. Based on this regular configuration, each cell has associated a set of states that change at each iteration by the execution of transition rules, which take into account the state of each cell and those of its neighbours. As such, cellular automata consist of six defining components: a framework or lattice, cells, neighborhood, transition rules, initial conditions (states), and an update sequence (time). Cellular automata models map easily onto existing data structures widely used in geographic information systems, are easy to implement, and are able to show changes and spatial patterns in an understandable manner. All of this has contributed to their popularity in simulation modelling for applications such as measuring land use changes and monitoring disease spread","name":"Cellular automata","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC2-7","description":"Agent-based models are simulation models that decompose a complex system into small entities (agents) with modeling properties and behavior. Contrary to modelling at an aggregate level, agent-based models are focused on the individual level, where a set of discrete agents with well-defined behaviors represents an individual, object or component of the modelled system. Therefore, the individual agent is the explicit, basic unit. The macro-level behaviour of the system emerges thereafter from the interaction of the individual agents and with the environment over time. Agent-based models are used for spatial modelling, offering possibilities to consider topological particularities of interaction and information transfer among agents and/or with the environment. In relation to spatial simulation, agent-based models have been used for example to model natural and social phenomena such as animal behaviour, pedestrian behavior, social insects and biological cells.","name":"Agent-based modelling","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC2","description":"The concept spatial simulation modelling can be better understood by looking at the meaning of its individual words. A model is widely defined as a simplified representation of a real-world system under study, which can be used to explore or to better understand the system it represents. Computer models or simulation models are computer-based implementations of a model to produce outputs based on certain model assumptions. Simulation , therefore, relies on the use of computers for virtual experimentation to gain insight into real-world problems by proposing alternative assumptions that arise from exploring “what if” questions about a dynamic problem of interest over the course of successive simulation experiments.\r\nSimulation modelling is also useful for the study of spatial patterns over time. Spatial simulation models are relevant when the study of spatial elements and their relationships in a system are necessary for a fully understanding of that system. In this sense, spatial simulation modelling approaches include rule-based models, equation-based models, grid-based cellular automata models, discrete event simulation, and agent-based models.\r\nSimulation modelling is often used for prediction, exploration, theory development, or even optimization of conditions to achieve desired outcomes, with the goal of examining how the interconnections and relationships that characterize these systems produces patterns of behavior over time. Across broad areas of the environmental and social sciences, researchers use simulation models as a way to study systems inaccessible to experimental and observational scientific methods, and also as an essential complement of those more conventional approaches to discover or formalize theories about the real world. \r\nSimulation models are a relatively recent addition to the scientific toolbox, and the reasons for their widespread adoption are, on one hand, the impossibility to study in-situ some complex social and environmental systems (e.g. ecosystems, urban systems, social systems, global climate system) and, on the other hand, the availability of High Performance Computing and large amount of data from different sources. Finally, the nonlinear behaviour of many natural systems provides challenges building traditional mathematical models based on linearization. \r\nSimulation modelling is also useful for the study of spatial patterns over time. In this sense, spatial simulation modelling approaches include rule-based models, equation-based models, grid-based cellular automata models, discrete event simulation, and agent-based models.","name":"Spatial simulation modelling","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC3-1","description":"Among the recent artificial intelligence techniques are those pertaining to heuristics. A heuristic technique is an approach to problem solving, that employs a practical method, which is necessarily not optimal or perfect, but in many situations sufficient. Heuristic methods can be useful, where the optimal solution in practice is impossible. The aim of the topic is to provide insight into the principles of heuristics and the most important algorithms.","name":"Heuristics","selfAssesment":"In progress (GI-N2K)
"},{"code":"GC3-2","description":"Genetic algorithms, genetic programming and evolutionary computing are terms that fall within the general sphere of `Evolutionary Computation`. Genetic algorithms (GAs) are computationally intensive global search heuristics with very wide applicability, but their implementation is often highly problem specific and there is only a relatively loose understanding as to why they often work rather well. The central idea behind GAs is to mimic the Darwinian notion that selective breeding seeks optimum individuals in a given environment. In order to do this a GA requires a way of representing a solution to a (spatial) problem as if it were an individual viewed as a chromosome or `genome` like object. The aim of the topic is to provide fundamental understanding of the principles behind genetic algorithms, and its application in solving geospatial problems.","name":"Genetic algorithms","selfAssesment":"In progress (GI-N2K)
"},{"code":"GC3-3","description":"Biological neurons, or nerve cells, receive multiple input stimuli, combine and modify the inputs in some way, and then transmit the result to other neurons. Artificial neural networks are an attempt to emulate features of biological neural networks in order to address a range of difficult information processing, analysis and modelling problems. The principal class of ANNs are so-called feed-forward networks, but other types of ANN are for example recurrent neural networks. Among the feed-forward networks the most widely used approach is the multi-level perceptron (MLP) model. The application range is broad from non-linear regression to land cover change modelling. The aim of the topic is to introduce the principles of ANN and to understand and demonstrate its use in geospatial modelling.","name":"Artificial Neural Networks","selfAssesment":"In progress (GI-N2K)
"},{"code":"GC3-4","description":"Pattern recognition is the process of classifying input data into objects or classes based on key features. There are two classification methods in pattern recognition: supervised and unsupervised classification. The supervised classification of input data in the pattern recognition method uses supervised learning algorithms that create classifiers based on training data from different object classes. The classifier then accepts input data and assigns the appropriate object or class label. The unsupervised classification method works by finding hidden structures in unlabelled data using segmentation or clustering techniques. Common unsupervised classification methods include: K-means clustering, Gaussian mixture models, Hidden Markov models. The aim of the topic is to provide knowledge about the different methods in pattern recognition and how to choose the optimum method for a specific spatial problem.","name":"Pattern recognition","selfAssesment":"In progress (GI-N2K)
"},{"code":"GC3-5","description":"Understanding natural and human-induced structures and processes in space and time has long been the agenda of geographical research. Through theoretical and experimental studies, geographers have accumulated a wealth of knowledge about our physical and man-made world over the years. Knowledge is often discovered through critical observations of phenomena in space and time. Due to the rapidly expanding amount of data and information the problem is often not having enough data but having too much and too complex a database. The aim of the topic is to provide insight into several methods to carry out spatio-temporal knowledge discovery through spatial data mining and clustering techniques.","name":"Spatio-temporal knowledge discovery","selfAssesment":"In progress (GI-N2K)
"},{"code":"GC3-6","description":"Data-intensive computing is now starting to be considered as the basis for a new, fourth paradigm for science. Two factors are encouraging this trend. First, vast amounts of data are becoming available in more and more application areas. Second, the infrastructures allowing to persistently store these data for sharing and processing are becoming a reality. The technical and scientific issues related to this context have been designated as `Big Data` challenges and have been identified as highly strategic by major research agencies. The aim of this topic is to introduce Big Data as a concept, and the needed methods to navigate through the vast amount of heterogeneous information.","name":"Big data filtering","selfAssesment":"In progress (GI-N2K)
"},{"code":"GC3","description":"The amount of data in current geospatial repositories along with their high-dimensional nature requires a sophisticated set of analysis capabilities in order to extract new and unexpected patterns, trends, and relationships embedded in that data. Artificial intelligence and data mining methods constitute an alternative to explore and extract knowledge from geospatial data, which is less assumption dependent. Data Mining is a step in the knowledge discovery process that automatically detects patterns in data, and Geographic Data Mining is a special type of data mining that seeks to apply standard data mining tools modified to take into account the special features of geospatial data","name":"Artificial Intelligence and Data Mining","selfAssesment":"In progress (GI-N2K)
"},{"code":"GC4-1","description":"The use of the term Open geocomputation doesn't intend to coin a new term; Open GIScience and Open GIS are well explored and discussed terms in the literature. Both embrace the idea of open data, open source, collaboration among peers, and the integration of these practices into GIS research projects, tools, services and applications. Open geocomputation brings the ideas of Open GIScience (and hence Open Science in general) into geocomputation, focussing on openness as a fundamental tenet to conduct research in geocomputation and for the development of new computational methods and tools. In fact, many community-led developments and tools have recently appeared in the field of geocomputation, notably based on R and Python. The widespread popularity and adoption of these computing environments for geocomputing and geospatial analysis is simply because they encompass open, transparent, and reproducible tool development.","name":"Open Geocomputation","selfAssesment":"New
"},{"code":"GC4","description":"A distinguible feature of the current approach to geocomputation is the emphasis on openness: open science, open source, open data. All of this propelled by a vibrant collaborative community with the aim to develop open and reproducible methods, tools and applications applied to a variety of real-life, spatio-temporal application domains. Open Science is a paradigm that can be applied to any scientific discipline and area of knowledge, characterised by openness, access to large volumes of data and unprecedented levels of computing power, availability of community-driven tools, and new types of collaboration between multidisciplinary researchers. Open Science clearly goes beyond geocomputation, but at the same time, its practices and principles characterise recent geocomputation-related projects as well as its community. Therefore, the vision of Open Science taken here is contextualised to the field of geocomputation.","name":"Open Science","selfAssesment":"new
"},{"code":"GD","description":"Geospatial data represent measurements of the locations and attributes of phenomena at or near Earth`s surface. Information is data made meaningful in the context of a question or problem. Information is rendered from data by analytical methods. Information quality and value depends to a large extent on the quality and currency of data (though historical data are valuable for many applications). Geospatial data may have spatial, temporal, and attribute (descriptive) components, as well as associated metadata. Data may be acquired from primary or secondary data sources. Examples of primary data sources include surveying, remote sensing (including aerial and satellite imaging), the global positioning system (GPS), work logs (e.g., police traffic crash reports), environmental monitoring stations, and field surveys. Secondary geospatial or geospatial-temporal data can be acquired by digitizing and scanning analog maps, as well as from other sources, such as governmental agencies. The legitimacy of geographic information science as a discrete field has been claimed in terms of the unique properties of geospatial data. In a paper in which he coined the term GIScience, Goodchild (1992) identified several such properties, including: 1. Geospatial data represent spatial locations and non-spatial attributes measured at certain times. 2. The Earth`s surface is highly complex in shape and continuous in extent. 3. Geospatial data tend to be spatially autocorrelated. It has long been said that data account for the largest portion of geospatial project costs. While this maxim remains true for many projects, practitioners and their clients now can reasonably expect certain kinds of data to be freely or cheaply available via the World Wide Web. Federal, state, regional, and local government agencies, as well as commercial geospatial data producers, operate clearinghouses that provide access to geospatial data. Although geospatial data are much more abundant now than they were ten years ago, data quality issues persist. Good data are expensive to produce and to maintain. Proprietary interests simultaneously increase the supply of geospatial data and impede data accessibility. Standards for geospatial data and metadata are useful in facilitating effective search, retrieval, evaluation, integration with existing data, and appropriate uses. National and international organizations, such as the Open Geospatial Consortium (OGC) and International Organization for Standardization (ISO), develop and promulgate such standards. INSPIRE directive (Infrastructure for Spatial Information in the European Community) regulates geospatial data management","name":"Geospatial Data","selfAssesment":"I, progress (GI-N2K)
"},{"code":"GD1-1","description":"Usable and accurate geospatial data are based upon proper model of the Earth`s surface. Shape of the Earth is complex and complicated to measure. Approximations are used to minimize complexity of the task and possible errors.","name":"Earth geometry","selfAssesment":"GI-N2K
"},{"code":"GD1-2","description":"Geospatial referencing systems provide unique codes for every location on the surface of the Earth (or other celestial bodies). These codes are used to measure distances, areas, and volumes, to navigate, and to predict how and where phenomena on the Earths surface may move, spread, or contract. Point-based, vector coordinate systems specify locations in relation to the origins of planar or spherical grids. Tessellated referencing systems specify locations hierarchically, as sequences of numbers that represent smaller and smaller subdivisions of two- or three dimensional surfaces that approximate the Earths shape, Linear referencing systems specify locations in relation to distances along a path from a starting point. Tessellation data models, are considered in Unit DM3 Tessellation data models, and linear referencing models are considered in Unit DM4 Vector data models.","name":"Georeferencing systems","selfAssesment":"GI-N2K
"},{"code":"GD1-3","description":"Horizontal datums determine the geometric relations between a coordinate system grid and a particular ellipsoid approximating the Earth`s surface. Vertical datums determine elevation reference surfaces, like mean sea level. A. Horizontal datums. Relation of coordinate system to particular ellipsoid, datum transformation options, Molodensky and Helmert transformation, other high accuracy transformations, ED50 and WGS84, historical development of horizontal datums, ETRS89. B. Vertical datums. Historical development of vertical datums, difference between vertical datum and geoid, relations between ellipsoidal (geodetic) heiht, geoidal height and orthometric elevation.","name":"Datums","selfAssesment":"GI-N2K
"},{"code":"GD1-4","description":"Map projections are systematic transformations of geographic coordinates of the surface of ellipsoid into locations in plane. Plane coordinates are based on map projection. As the transformation of a spherical grid into a plane grid causes inevitably distortions of the geometry, and, different projections cause different distortions, knowledgeable choice of appropriate projection for any particular use is crucial. A. Map projection poperties. Geometric properties that may be preserved or lost in projected grid, usefulness of compromise projection, Tissot indicatrix as an indicator of projection errors, visual appearance of the Earth`s graticule, distortion patterns for projection classes, distortions in raster data. B. Map projection classes. Three main classes of map projection based on developable surface, projection types by geometric properties preserved, mathematical basis of projecting longitude and latitude into x and y coordinates. UTM, ETM, projections used by EC. C. Map projection parameters. Standard line, projection case, latitutde and longitude of origin, aspects of projection. D. Georegistration. Rectification vs orthorectification, ground controle points in georegistration of aerial imagery.","name":"Map projections","selfAssesment":"GI-N2K
"},{"code":"GD1","description":"Proper model of the Earth`s surface and ability to locate spatial phenomena accurately to it, is crucial in effective collection, management and use of data. Characterising size and shape of the Earth, using appropriate surfaces to approximate it, choosing suitable coordinate system and map projection is bases for efficient understanding of spatial data.","name":"Geolocating Data to Earth","selfAssesment":"In progress (GI-N2K)
"},{"code":"GD10-4","description":"A stereoscopy acquisition mode collects remotely sensed data where each location on the ground (or the imaged objects) is covered multiple times (at least twice), from different perspectives. Stereopairs and stereoscopic coverage enable the extraction of 3D representations of the environment from remotely sensed imagery.","name":"Stereoscopy and orthoimagery","selfAssesment":"GI-N2K
"},{"code":"GD10","description":"Since the 1940s aerial imagery has been the primary source of detailed geospatial data for extensive study areas. Photogrammetry is the profession concerned with producing precise measurements from aerial imagery. Aerial imaging and photogrammetry comprise a major component of the geospatial industry. The topics included in this unit do not comprise an exhaustive treatment of photogrammetry, but they are aspects of the field about which all geospatial professionals should be knowledgeable.","name":"Aerial imaging and photogrammetry","selfAssesment":"GI-N2K
"},{"code":"GD11-2","description":"the physical environment to sense data without direct contact. It contains a carrier device (platform) and a sampling unit (sensor).","name":"Platforms and sensors","selfAssesment":"GI-N2K
"},{"code":"GD11","description":"Satellite-based sensors enable frequent mapping and analysis of very large areas. Many sensing instruments are able to measure electromagnetic energy at multiple wavelengths, including those beyond the visible band. Satellite remote sensing is a key source for regional- and global-scale land use and land cover mapping, environmental resource management, mineral exploration, and global change research. Shipboard sensors employ acoustic energy to determine seafloor depth or to create imagery of the seafloor or water column. The topics included in this unit do not comprise an exhaustive treatment of remote sensing, but they are aspects of the field about which all geospatial professionals should be knowledgeable.","name":"Satellite and shipboard remote sensing","selfAssesment":"GI-N2K
"},{"code":"GD12","description":"Meaning of geospatial metadata, elements of metadata, use of metadata, integration of metadata in data production, standards in geospatial data, ISO standard family 191xx, data warehouse, exchange protocol, transport protocols, spatial data infrastructure, INSPIRE, OGC, DCAT profiles for CKAN applications bridging metadata from GI and IT domains.","name":"Metadata, standards, and infrastructures","selfAssesment":"GI-N2K
"},{"code":"GD2-1","description":"Classic land survey methods and manual attribute data collection in the field","name":"Land surveying and field data collection","selfAssesment":"GI-N2K
"},{"code":"GD2-2","description":"Aerial imagery has been the primary source of detailed geospatial data for extensive study areas. Photogrammetry is producing precise measurements from aerial imagery. Aerial imaging and photogrammetry comprise a major component of the geospatial data production. Satellite-based sensors enable frequent mapping and analysis of very large areas. Sensing instruments are able to measure electromagnetic energy at multiple wavelengths. Satellite remote sensing is a key source for regional- and global-scale land use and land cover mapping, environmental resource management, mineral exploration, and global change research. Shipboard sensors employ acoustic energy to determine seafloor depth or to create imagery of the seafloor or water column. Principles of aerial photography, oblique and vertical imagery, spatial and radiometric resolution, spectral sensitivity, principal point, distortions and displacements in aerial image, parallax, stereophotogrammetry, generation of an orthoimage from a vertical aerial phoptograph, aerotriangulation, vector data extraction from digital seteroimagery, mission planning. Use of UAV in photogrammetry. Main platforms and sensors in spatial image acquisition, active and passive sensors, LiDAR and microwave, multispectral and hypersepctral imagery, interpretation of imagery, supervised and unsupervised classification, pixel based and segmented classification, ground verification, main applications, bathymetric mapping. SENTINEL.","name":"Remote sensing","selfAssesment":"GI-N2K
"},{"code":"GD2-3","description":"Crowdsourcing is the practice of obtaining needed services, ideas, or content by soliciting contributions from a large group of people and especially from the online community rather than from traditional employees or suppliers. Crowdsourced spatial data collection is becoming more and more important. The advantages and disadvantages of crowdsourced data, opensource mapping tools, potential application of crowdsourcing, VGI, OSM or cell-phone based, aspects of crowdsourced data quality and reliabilty.","name":"Crowdsourced data collection","selfAssesment":"GI-N2K
"},{"code":"GD2-4","description":"Digitizing as the main secondary spatial data production technique. Encoding vector points, lines, and polygons by tracing map sheets has diminished in importance, but remains a useful technique for incorporating historical geographies and local knowledge. \"Heads-up\" digitizing using digital imagery as a backdrop on-screen is a standard technique for editing and updating GIS databases. Tablet and on-screen digitizing, scanning and (semi)automatic vectorization.","name":"Digitizing","selfAssesment":"GI-N2K
"},{"code":"GD2","description":"Spatial data collection / production involves measurement of locations in relation to the coordinate system, and collection of attributed data about the spatial phenomena. Measurements may be direct (e.g. surveying) or remote, data acquisition involves measurement of parameter values, evaluation of parameters, polls, interpretation of spatial imagery, and re-use of secondary data (e.g. old maps). Volunteered geographic information is becoming more important.","name":"Data Collection","selfAssesment":"GI-N2K
"},{"code":"GD3","description":"It is quite common, that data including both spatial entities and their attribute data undergo changes. These changes need to be catalogued fully and explicitly, including initial conditions, new conditions, all intermediate stages and operations used. The geospatial data needs to contain an archival history of change.","name":"Transaction management of geospatial data","selfAssesment":"In progress (GI-N2K)
"},{"code":"GD4-1","description":"Geometric accuracy, factors influencing it, geometric accuracy and topological fidelity, geometric accuracy in survey and GPS mesurements, thematic accuracy, relations between thematic accuracy, geometric accuracy and topological fidelity, misclassification matrix, commission and omission, logical consistency, relations between resolution, precision, and accuracy, spatial resolution, thematic resolution, and temporal resolution, precision, uncertainties associated with coordinate precision, primary and secondary data sources.","name":"Data quality","selfAssesment":"GI-N2K
"},{"code":"GD4-2","description":"Meaning of geospatial metadata, elements of metadata, use of metadata, integration of metadata in data production, standards in geospatial data, ISO standard family 191xx, data warehouse, exchange protocol, transport protocols, spatial data infrastructure, INSPIRE, OGC, DCAT profiles for CKAN applications bridging metadata from GI and IT domains.","name":"Metadata, standards, and infrastructures","selfAssesment":"GI-N2K
"},{"code":"GD4","description":"Data quality is the degree of data usability in relation to given objective and particular application. The expectations to data vary between different applications. The key criteria in data quality are the amount of uncertainty in data as compared to the acceptable level of uncertainty. Evaluation of the usability may be more complicated using data from secondary sources. Appropriate metadata is inevitable for these judgements. Aspects of data quality include geometric and thematic accuracy, (in)consistencies, resolution, precision, usability and others. Assurance of data quality may be improved by following proper standards and spatial data infrastructure regulations for data collection and management. System of basic data quality measures for geospatial domain in the EN ISO 19157:2013 standard.","name":"Data Quality, Metadata and Data Infrastructure","selfAssesment":"In progress (GI-N2K)
"},{"code":"GD6-1","description":"Geometric accuracy is a measure indicating how close the geometric values of the data are to the real world position of the mapped feature.","name":"Geometric accuracy","selfAssesment":"GI-N2K
"},{"code":"GD6-2","description":"Thematic accuracy evaluates the correctness of attribute values of geospatial objects compared to the expected (real world) reference value","name":"Thematic accuracy","selfAssesment":"In progress (GI-N2K)
"},{"code":"GD6-3","description":"The resolution of a data source indicates the smallest unit of detail provided by the data source.","name":"Resolution","selfAssesment":"In progress (GI-N2K)
"},{"code":"GD6-4","description":"The precision of a measurement system, related to reproducibility and repeatability, is the degree to which repeated measurements under unchanged conditions show the same results.","name":"Precision","selfAssesment":"GI-N2K
"},{"code":"GD6-5","description":"Primary data sources provide information collected directly for GIS use. Secondary sources are data sources that need to be processed before they are ready for GIS use.","name":"Primary and secondary sources","selfAssesment":"In progress (GI-N2K)
"},{"code":"GD6","description":"particular application. That standard varies from one application to another. In general, however, the key criteria are how much uncertainty is present in a data set and how much is acceptable. Judgments about fitness for use may be more difficult when data are acquired from secondary rather than primary sources. Aspects of data quality include accuracy, resolution, and precision. Concepts of data quality, error, and uncertainty are also covered in Knowledge Areas CF Conceptual Foundations (in a theoretical context) and GC Geocomputation (in the context of analysis); the focus here is on the measurement and assessment of data quality.","name":"Data quality","selfAssesment":"GI-N2K
"},{"code":"GD8-1","description":"Tablet digitizing is the conversion from physical map to digital data by re-drawing the features on the map fixed on a digitizing tablet","name":"Tablet digitizing","selfAssesment":"GI-N2K
"},{"code":"GD8-2","description":"On-screen digitizing is the conversion from raster to vector data by manually drawing the features visible in the raster file on the screen.","name":"On-screen digitizing","selfAssesment":"GI-N2K
"},{"code":"GD8-3","description":"Scanning is the conversion of a physical object to a digital representation by moving a sensor over it. Vectorization is the technique to extract features from the grid information in vector format","name":"Scanning and automated vectorization techniques","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS","description":"Geographic Information Science and Technology serve the society, but it is not a panacea. The history of its development is the sum of fragmented efforts, which have still not been fully integrated. Its potential benefits are often constrained and its potential impacts are not fully understood. Institutional and economic factors limit access to data, technology, and expertise by some of those who need it to make better decisions. Political, ideological, and personal issues aside, organizations invest in GIS&T when estimated benefits outweigh estimated costs. Evaluating costs and benefits is difficult, however and too often leads to nothing being done. For some individuals and groups, costs are prohibitive even though potential benefits are compelling. The legal framework provides a structure for regulating a number of key aspects of geographic information science, technology, and applications. Legal regimes determine who can claim the exclusive right to hold and use geospatial data, the conditions under which others may have access to the data, and what subsequent uses are permitted. Political struggles arise from conflicting proprietary and public interests about who benefits from geospatial information, and how the power to allocate the use of this information is, or should be, distributed among members of a society. The need to choose among conflicting interests sometimes poses ethical dilemmas for GIS&T professionals. The explosive growth of the geospatial information contributed by users through various application programming interfaces has made geospatial information is a powerful tool in the social media toola powerful media for the general public to communicate, but perhaps more importantly, geographic information have also become a tool media for constructive dialogs and interactions about social issues, recent growth of Web-based geospatial information and volunteered geographic information (VGI). Because so many public agencies and private organizations rely upon GIS&T for planning, decision making, and management, GIS&T increasingly affects and is used to direct daily life. Critical approaches to understanding the role of GIS in society equip practitioners to employ GIS&T reflectively. The critical approach specifically questions the assumptions and premises that underlie the economic, legal and political regimes and institutional structures within which GIS&T is implemented. Related concerns are considered in Knowledge Area OI: Organizational and Institutional Aspects.","name":"GI and Society","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS1-1","description":"The most basic definition of a legal regime is a system or framework of rules governing some physical territory or discrete realm of action that is at least in principle rooted in some sort of law. Often the concept has been applied to specific areas of law.","name":"The legal regime and legal framework","selfAssesment":"GI-N2K
"},{"code":"GS1-2","description":"Contract law is defined as a set of rules that govern the contractual agreements between merchants or persons. A contract is an agreement between different parties that state their responsibilities and duties to each other. A liability in contract law is when certain conditions are written into a contract that makes a party liable. Licensing is the process of giving or getting official permission to do something. A license is an agreement through which a licensee leases the rights to a legally protected piece of intellectual property from a licensor — the entity which owns or represents the property — for use in conjunction with a product or service.","name":"Contract law, liability and licensing","selfAssesment":"GI-N2K: relevant but to be revised
"},{"code":"GS1-3","description":"Data privacy and security are two essential components of a successful strategy for data protection. Data security refers to the protection of data from unauthorized access, use, change, disclosure, and destruction. It encompasses network security, physical security, and file security. Data privacy involves protecting consumer data by eliminating or reducing the possibility of re-identifying an individual whose information is present in the data. This is done by either removing specific information or by transforming the data with random “noise” or generalization.","name":"Privacy and Security","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS1-4","description":"Property is secured by laws that are clearly defined and enforced by the state. These laws define ownership and any associated benefits that come with holding the property. The term property is very expansive, though the legal protection for certain kinds of property varies between jurisdictions. Property is generally owned by individuals or a small group of people. The rights of property ownership can be extended by using patents and copyrights. Property rights give the owner or right holder the ability to do with the property what they choose. That includes holding on to it, selling or renting it out for profit, or transferring it to another party.","name":"Ownership and property rights","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS1-5","description":"In economics, competition is a condition where different economic firms seek to obtain a share of a limited good by varying the elements of the marketing mix: price, product, promotion and place. Competition law is a law that promotes or seeks to maintain market competition by regulating anti-competitive conduct by companies. Public-private sector relationships deal with a particular subset of competition, i.e. competition between public and private organizations.","name":"Competition and public-private sector relationships","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS1-6","description":"Open data is data that can be accessed, shared, used and reused without any barrier for any type of (re)user. According to the Open Definition, open data can be defined as data that be freely used, modified, and shared by anyone for any purpose subject, at most, to measures that preserve provenance and openness. Open data requires datasets to be either in the public domain, or distributed through an open license. The data must be provided as a whole, free of charge, and preferably downloadable via the Internet, including any additional information that might be necessary to comply with the open license’s terms. Openness requires the data to be provided in a readily machine-readable form. The format must be open as well, meaning that it does not place any restriction upon its use, and that the files in that format can be processed with open-source software tools. The Open Definition speaks broadly of open ‘works’, rather than of open data. Focusing on data tout court, one can move from the Open Government Data (OGD) principles. According to the OGD principles, which are arguably foundational in understanding the concept of open data, data must be: Complete; Primary; Timely; Accessible; Machine-processable; Non-discriminatory; Non-proprietary; and License-free. Compliance with the OGD principles needs to be demonstrable, i.e. there need to be accountability measures in place to allow the review of the adherence to the principles above. The concepts of Open Work and open data highlight how data needs to be both legally, technically and financially open, so either in the public domain or covered by an open license, and kept in a machine-readable and non-proprietary format. Open data aims at making information available to everybody, for any purpose, in a machine-readable and interoperable format, based on open standards and digestible by free/libre open source software (FLOSS). Also with respect to the financial accessibility open data is data available free of charge. Marginal costs of dissemination are accepted by some as a reasonable cost for users. However, open data is data that can be accessed and reused without any barrier for any type of reuse, and some user groups experience any price to be paid as a barrier.","name":"Open data","selfAssesment":"Completed
"},{"code":"GS1","description":"Legal problems can arise when geospatial information is used for land management, among other activities. Geospatial professionals may be liable for harm that results from flawed data or the misuse of data. Understanding of contract law and liability standards is essential to mitigate risks associated with the provision of geospatial information products and services. Legal relations between public and private organizations and individuals govern data access. The nature of information in general, and the characteristics of geospatial information in particular, make it an unusual and difficult subject for a legal regime that seeks to establish and enforce the type of exclusive control associated with other commodities. Geospatial information is in many ways unlike the kinds of works that intellectual property rights were intended to protect. Still, organizations can, and do, assert proprietary interests in geospatial information. Perspectives on geospatial information as property vary between the public and private sectors and between different countries.","name":"Legal aspects","selfAssesment":"In progress GI-N2K
"},{"code":"GS2-1","description":"Business models determine how organizations can create and deliver value, for example, through the provision or use of geographic data. A business model is a conceptual tool that contains\r\na set of interrelated elements that allow organizations to create and capture value and generate revenues. The development and implementation of an appropriate business model are considered to be a key to the success of the organization and a crucial source for value creation. \r\n\r\nAlthough business models determine how organizations create, deliver, and capture value, they should not be regarded as permanent and invariable structures or settings. Business models are shaped by both internal and external forces, and will only be successful if they are able to adapt to a changing environment. In the GI domain, several technological, regulatory, and societal developments have challenged the existing business models and opened up opportunities for new business models. Among these developments are the establishment of spatial data infrastructures (SDIs) worldwide, the democratization of geographic knowledge, and the move toward open source, open standards, and open data.\r\n\r\nSince the development and implementation of SDIs in different parts of the world, much attention has been paid to the need to find appropriate business models for GI, and in particular, for geographic data providers in the public sector. Traditional business models in which public data providers were selling their data to customers in the private industry and other public agencies were questioned, because they restricted the opportunity for data sharing. The concept of SDI is about moving to new business models, where partnerships between GI organizations are promoted to allow access to a much wider scope of geographic data and services. A key challenge in the development of these SDIs was the alignment of different existing business models of the actors in the GI domain. Moreover, the development and implementation of SDIs also led to the emergence of new business models, which was even more the case with the more recent move toward open geographic data.\r\n\r\nOrganizations can be active in different parts of the geo-information value chain, and can create and offer value in many different ways. As a result, many different GI business models exist. Data providers, data enablers, and data end users could be seen as three main categories of GI business models. Each of these categories consists of many different business models, as different value propositions\r\nwill exist, and value can be created and captured in several ways.","name":"GI Business models","selfAssesment":"Completed (GI-N2K)
"},{"code":"GS2-5","description":"To provide a better insight into the process of adding value to GI, several authors have introduced and applied the information value chain approach. A value chain can be defined as the set of value-adding activities that one or more organizations perform in creating and distributing goods and services. The value chain concept originally was developed for the manufacturing sector, as a tool to evaluate the competitive advantage of firms. More recently, the value chain concept has been applied to other sectors, including information technology where the good or service, and the benefits it provides, is less tangible in nature. A value chain involves the progress of goods from raw materials to finished products through a number of stages, during each of which a new value is added to the original input by various activities. The value chain concept was extended into the information market, with the information value chain referring to the set of activities adding value to information and turning raw data into new information products or services. Especially important in this context is the role of information and communication technologies (ICT), which have an impact on all activities in the information value chain, such as information collection, processing, dissemination, and use. In the context of GI, the value chain relates to the series of value- adding activities to transform raw geographic data into new products that are used by certain end users. Although there are slightly different descriptions of the various steps of the GI value chain, in general, the essential steps in the value chain are: acquisition of raw data, the application of a data model, quality control, and integration with other sources, presentation, and distribution. In recent years, particular attention has been paid to different steps between the process of distributing data and the actual end use of an end product of GI. In addition, after the publication of the data, value can be added to the data in many different ways. Value can be added by making data from different sources easily accessible through repositories and data portals, by building and selling tailored solutions using the data to end users or by using geographic data to improve existing products and services delivered to an end user. In certain cases, this end product will be the first step of a next value chain.","name":"Geo-information value chain","selfAssesment":"Completed
"},{"code":"GS2","description":"Most organizations insist that investments in GIS and T be justified in economic terms. Quantifying the value of information, and of information systems, however, is not a straightforward matter.","name":"Economic aspects","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS3-1","description":"The use of geospatial information allows public sector organizations and actors to make better decisions and provide better services to their citizens. Geospatial information is increasingly being used at different administrative levels and in different policy areas.","name":"Use of geospatial information in the public sector","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS3-2","description":"Geospatial information is increasingly being used by private companies for different purposes and the private sector plays an important role in the development and implementation of geospatial information infrastructures.","name":"Use of geospatial information in the private sector","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS3-3","description":"Research and education institutions use geospatial information for various purposes, in support of their research and educational activities.","name":"Use of geospatial information in research and education","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS3-4","description":"Effective monitoring of the environment and an improved understanding of the same requires valuable information and data that can be extracted through application of geospatial technologies. GIS can be used most effectively for environmental data analysis and planning. It allows better viewing and understanding physical features and the relationships that influence in a given critical environmental condition. GIS can help in effective planning and managing the environmental hazards and risks. In order to plan and monitor the environmental problems, the assessment of hazards and risks becomes the foundation for planning decisions and for mitigation activities. GIS supports activities in environmental assessment, monitoring, and mitigation and can also be used for generating environmental models. GIS can aid in hazard mitigation and future planning, air pollution & control, disaster management, forest fires management, managing natural resources, wastewater management, oil spills and its remedial actions etc.","name":"Use of geospatial information in environmental issues","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS3","description":"Geospatial Information used in Government agencies and public authorities at local, state, and federal levels produce and use geospatial data for many activities, including provision of social services, public safety, economic development, environmental management, and national defence. Public participation in governing, empowered by geospatial technologies, offers the potential to strengthen democratic societies by involving grassroots community organizations and by engaging local knowledge. The private sector covers a broad range of areas of opportunity. With continued advancements in technology, greater awareness of its advantages as a powerful decision support tool the use of geospatial information use in the private sector needs to be discussed.","name":"Use of geospatial information","selfAssesment":"In Progress GI-N2K
"},{"code":"GS4-1","description":"Public participation GIS (PPGIS) is a field within geographic information science that focuses on ways the public uses various forms of geospatial technologies to participate in public processes, such as mapping and decision making.","name":"Public participation GIS","selfAssesment":"GI-N2K (revision)
"},{"code":"GS4-2b","description":"Social Media Geographic Information (SMGI) can be defined as any piece or collection of multimedia data or information with explicit (i.e. coordinates) or implicit (i.e. place names or toponyms) geographic reference collected through the social networking web or mobile applications. Social data are acknowledged as a good of major value in the digital economy, and their potential for enhancing more traditional analytics is of the utmost importance. A big part of social data however also features spatial (and temporal) references, thus their integration with more traditional Authoritative Geographic Information (AGI) may enable a further step towards the next generation of geospatial intelligence. SMGI is a sub-category of VGI and can be active or passive, depending on the type of application with which it is collected: applications purposefully created and/or used to collect SMGI in participatory initiatives","name":"Social Media Geographic Information","selfAssesment":"Completed
"},{"code":"GS4-3b","description":"Volunteered geographic information (VGI) is a special kind of user-generated content. It refers to geographic information collected and shared voluntarily by the general public. Web.2.0 and associated advances in web mapping technologies have greatly enhanced the abilities to collect, share and interact with geographic information online, leading to VGI.","name":"Citizens and volunteered geographic information","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS4","description":"Today, geo data has become a conventional and pervasively familiar data type seen at once to underpin and significantly re-characterize the digital world, with broad implications for both technology and society. Geospatial data are abundant, but access to data varies with the nature of the data, the user groups wishes to acquire it and for what purpose, under what conditions, and at what price geodata can be obtained. The explosive growth of geographic information contributed by users through various application programming interfaces has made geographic information a powerful media for the general public, but perhaps more importantly, geospatial information have also become media for constructive dialogs and interactions about social issues, recent growth of Web-based Geographic information and volunteered geographic information (VGI).","name":"Geospatial citizenship","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS5-1b","description":"The advantages of geospatial technologies and resulting data present ethical dilemmas such as privacy and security concerns as well as the potential for stigma and discrimination resulting from being associated with particular locations. the use of geospatial technologies and the resulting data needs to be critically assessed through an ethical lens prior to implementation of programmes, analyses or partnerships. Using this lens requires not only explicit consideration of potential negative consequences of adoption but also clear articulation of the specific contexts and conditions under which benefits may be realized.","name":"Ethics in the geospatial information society","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS5-2b","description":"A code of ethics is a guide of principles designed to help professionals conduct business honestly and with integrity. A code of ethics document may outline the mission and values of the business or organization, how professionals are supposed to approach problems, the ethical principles based on the organization's core values, and the standards to which the professional is held. Codes of ethics for geospatial professionals are intended to provide these principles and guidelines for GIS professionals","name":"Codes of ethics for geospatial professionals","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS5","description":"Ethics provide frameworks that help individuals and organizations make decisions when confronted with choices that have moral implications. Most professional organizations develop codes of ethics to help their members do the right thing, preserve their good reputation in the community, and help their members develop as a community","name":"Ethical aspects","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS6-1","description":"US GIS&T BoK: As GIS became a firmly established presence in geography and catalysed the emergence of GIScience, it became the target of a series of critiques regarding modes of knowledge production that were perceived as problematic. The first wave of critiques charged GIS with resuscitating logical positivism and its erroneous treatment of social phenomena as indistinguishable from natural/physical phenomena. The second wave of critiques objected to GIS on the basis that it was a representational technology. In the third wave of critiques, rather than objecting to GIS simply because it represented, scholars engaged with the ways in which GIS represents natural and social phenomena, pointing to the masculinist and heteronormative modes of knowledge production that are bound up in some, but not all, uses and applications of geographic information technologies. In response to these critiques, GIScience scholars and theorists positioned GIS as a critically realist technology by virtue of its commitment to the contingency of representation and its non-universal claims to knowledge production in geography. Contemporary engagements of GIS epistemologies emphasize the epistemological flexibility of geospatial technologies.","name":"Epistemological and critical issues","selfAssesment":"In progress/to delete (GI-N2K)
"},{"code":"GS6-2","description":"Various types of critiques exist on the way geospatial information is being used and re-used.","name":"Critical approach on the use of geospatial information","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS6-3","description":"Defending or refuting the argument that the \"digital divide\" that characterizes access use of geospatial information perpetuates inequities among developed and developing nations, among socio-economic groups,and between individuals, community organizations, and public agencies and private firms.","name":"Critical aspects and invisible groups","selfAssesment":"In progress/to be delete (GI-N2K)
"},{"code":"GS6","description":"Many of the educational objectives used to define topics in this knowledge area, and in the Body of Knowledge as a whole, challenge educators and students to think critically about GI and Society. Since the 1990s, scholars have criticized cartography and the GIS science from a wide range of perspectives. Common among these critiques are questioned assumptions about the purported benefits of GI and Society and attention to its unexamined risks. By promoting reflective practice among current and aspiring geospatial information professionals, an understanding of the range of critical perspectives increases the likelihood that geospatial information will fulfil its potential to benefit all stakeholders. Philosophical, psychological, and social underpinnings of these critiques are considered in Knowledge Area CF: Conceptual Foundations.","name":"Critical approach","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS7-1","description":"US GIS&T BoK: As GIS became a firmly established presence in geography and catalysed the emergence of GIScience, it became the target of a series of critiques regarding modes of knowledge production that were perceived as problematic. The first wave of critiques charged GIS with resuscitating logical positivism and its erroneous treatment of social phenomena as indistinguishable from natural/physical phenomena. The second wave of critiques objected to GIS on the basis that it was a representational technology. In the third wave of critiques, rather than objecting to GIS simply because it represented, scholars engaged with the ways in which GIS represents natural and social phenomena, pointing to the masculinist and heteronormative modes of knowledge production that are bound up in some, but not all, uses and applications of geographic information technologies. In response to these critiques, GIScience scholars and theorists positioned GIS as a critically realist technology by virtue of its commitment to the contingency of representation and its non-universal claims to knowledge production in geography. Contemporary engagements of GIS epistemologies emphasize the epistemological flexibility of geospatial technologies.","name":"Epistemological critiques","selfAssesment":"GI-N2K
"},{"code":"GS7-3","description":"US GIS&T BoK: \r\n\r\nFeminist interactions with GIS started in the 1990s in the form of strong critiques against GIS inspired by feminist and postpositivist theories. Those critiques mainly highlighted a supposed epistemological dissonance between GIS and feminist scholarship. GIS was accused of being shaped by positivist and masculinist epistemologies, especially due to its emphasis on vision as the principal way of knowing. In addition, feminist critiques claimed that GIS was largely incompatible with positionality and reflexivity, two core concepts of feminist theory. Feminist critiques of GIS also discussed power issues embedded in GIS practices, including the predominance of men in the early days of the GIS industry and the development of GIS practices for the military and surveillance purposes.\r\n\r\nAt the beginning of the 21st century, feminist geographers reexamined those critiques and argued against an inherent epistemological incompatibility between GIS methods and feminist scholarship. They advocated for a reappropriation of GIS by feminist scholars in the form of critical feminist GIS practices. The critical GIS perspective promotes an unorthodox, reconstructed, and emancipatory set of GIS practices by critiquing dominant approaches of knowledge production, implementing GIS in critically informed progressive social research, and developing postpositivist techniques of GIS. Inspired by those debates, feminist scholars did reclaim GIS and effectively developed feminist GIS practices.","name":"Feminist critiques","selfAssesment":"GI-N2K
"},{"code":"GS7-4","description":"In the early 1990s social critiques of GIS from human geographers began to appear. These initial critiques set off an ensuing debate between GISers, defending GIS and human geographers, who critiqued GIS. This debate materialized in academic journals including: Political Geography Quarterly, Environment and Planning A, and Progress in Human Geography. Schuurman (2000) notes that the GIS debate, while unique to the discipline of Geography, was part of a larger debate in other disciplines about the effects of technology. This presentation will be limited (unfortunately) to two aspects of this debate. It will first discuss conditions within human geography that made GIS a target of human geographers' critique. Second, this paper will discuss the particular critiques that were directed at GIS by human geographers. Though the reaction of such critiques and their effect on GIS is an important topic there is not enough time and space to address these issues. See Schuurman (2000) \"Trouble in the Heartland: GIS and its critics in the 1990s\" in Progress in Human Geography for a thoughtful look at this debate and its effects on the discipline of GIS.","name":"Social critiques","selfAssesment":"GI-N2K
"},{"code":"IP","description":"Image processing and analysis comprises all relevant steps to reach from (raw) image data to [...] information via image interpretation and digital image classification. In traditional remote sensing workflows, this step follows the image acquisition process. There are two main components, i.e. (1) image processing, (2) analysis, which emphasizes the sequential nature of the process – while increasingly this dichotomy disappears.\r\nThe information production workflow aims at converting semantically rich, but unstructured image data into a set of classes, objects, arrangements, etc., to enable ultimately a complete image understanding and scene reconstruction. This scene reconstruction entails a mental component (“understanding”) and a technical one, by providing standardized classification results or even beyond, dedicated information products in form of digital maps and reports, tailored to the specific application domains and use cases, in order to make informed decisions. Such information products can be maps, reports, dashboards etc., overall it is the transformation from quantitative, semi-continuous digital numbers (“brightness”) to qualitative information using categories and figures, which can be stored and further used in a GIS environment. \r\nThe first part of the process entails image calibration, image correction (geometric, radiometric), data assimilation, and any type of enhancement (contrast manipulation, filtering, etc.) which aims to better condition the information extraction part. It ends where we achieve a significant milestone in the processing milestone, remarkably denoted as analysis-ready data (ARD). From there, we enter into the analysis realm, classically referred to as digital image classification, the process of assigning pixels to classes. In other words, the aggregation of pixel values according to their similarity into categorical (nominal) classes. The discrimination of these classes by and large depend on application domain, and ideally, these classes match with information classes. To address the issue of ambiguity and to overcome the so-called semantic gap in image interpretation by providing a stepping-stone in the information extraction process, the strategy of pre-classification (semi-concepts) has been introduced in the literature.\r\nToday, boundaries between pre-processing and classification increasingly vanish, through an increasing level of automation in the pre-processing and image correction steps. In addition, new ways of analysis emerge, in particular in large time series, including image data cubes. Instead of a processing chain, which suggests a linear – and potentially irreversible – cascade of manipulations, the automation of large parts of this part allows us to see the process more reversible and approachable from either side.","name":"Image processing and analysis","selfAssesment":"Completed
"},{"code":"IP1-1-1","description":"The image spatial subset allows to extract the group of pixels / grid cells using a defined polygon e.g. area of interest – AOI or defining the new image extent. It is used to limit spatially the image extent to which, for example an image function or classification model will be applied.","name":"Image subset","selfAssesment":"Completed
"},{"code":"IP1-1-2","description":"Layer stacking is a process for combining multiple images into a single image. The image stack is used to build a ‘new’ multiple band file from the georeferenced images of various pixel sizes, extents, projections. The image bands must be resampled and reprojected to a common spatial grid. The layer stacking is used for example to combine spectral bands from a Landsat, Sentinel-2 data and SRTM DEM into one multi-dimensional file. The process of layer stacking increases the size of the final stacked image, which may have consequences that increase the processing time of operations performed on the stacked image.","name":"Layer stack","selfAssesment":"Completed
"},{"code":"IP1-1","description":"Data manipulation adjusts a dataset to the needs of a specific application by subsetting the spatial extent or the number of bands or by organizing bands from separate single layer files into a single multi-layer file.","name":"Data manipulation","selfAssesment":"New
"},{"code":"IP1-2","description":"Fourier analysis - A characteristic of remotely sensed images is a parameter called spatial frequency, defined as the number of changes in brightness value per unit distance for any particular part of an image. There are low-frequency and high-frequency areas. Spatial frequency may be enhanced or subdued using Fourier Analysis (an alternative technique is spatial convolution filtering). Fourier analysis mathematically separates an image into its spatial frequency components. It is then possible interactively to emphasize certain groups (or bands) of frequencies relative to others and recombine the spatial frequencies to produce an enhanced image.\r\nThe signal received by a pulsed radar is a time sequence of pulses for which the amplitude and phase are measured. The frequency content of this time-domain signal is obtained by taking its Fourier transformation.","name":"Fourier transformation","selfAssesment":"New
"},{"code":"IP1-3-1-1","description":"Structure from motion (SfM) describes the photogrammetric process for estimating the 3D structure of a scene, whereby correspondences between multiple images are established and used to detect motion parallax. When a camera moves over a surface while taking successive overlapping images, the distances between features on the surface will change from one image to the next. The changes depend on the distance of the feature points to the camera, and thus the surface elevation. This motion parallax can be used to generate an accurate 3D representation of the surface. \r\nThe photogrammetric problem of SfM is similar to stereo vision, but has gained popularity with the advent of inexpensive cameras which have variable internal geometries, unlike metrically stabilized cameras traditionally used in airborne mapping. Even with less accurate or even missing GPS location and orientation metadata, SfM still allows for the creation of (hyper)local DEMs as long as the imagery contains sufficient overlap. Airborne or spaceborne platforms can be used, provided that 2D frame-based cameras are used which can be represented with a pinhole mathematical model. \r\nGenerating a digital elevation model (DEM) from SfM is typically handled automatically using specialized software. Firstly, image correspondences are detected. Feature points are identified in the individual images using local contrast feature detectors. The features extracted from all the images are matched with all the available overlapping images and erroneous matches are filtered out. The process typically results in hundreds or thousands of tie-points per image, which allows for robust matching even with large a priori uncertainties in camera orientation. A bundle adjustment, solving for the 3D coordinates of the feature points, the position and orientation of the camera and its internal characteristics then results in an initial, so-called sparse 3D point cloud. \r\nNext, ground control points (GCPs) can be introduced. These are surface features (naturally present or introduced into the scene) which can be identified at the pixel level in the images by users. Measured also in the field with an accuracy smaller than the pixel size, they can be used to constrain the bundle adjustment solution to improve georeferencing and camera calibration to an accuracy similar to that of the GCP measurement or the GSD size. \r\nSince this process yields a match only for a small subset of all pixels, an additional step, called dense image matching is added. It starts from the exact position and orientations resulting from the bundle adjustment to rectify the images and overlay two or more images, to compare them row by row and in 16 different directions in a process called semi-global matching (SGM). Matching pixels are identified along these lines, and 3D intersection distances photogrammetrically inferred. By combining results from different directions, a 3D coordinate for almost every pixel is obtained with similar accuracy. Finally, DEM products with a regularly spaced grid are generated and exported based on the dense point cloud. Depending on the point classes used in the export (obtained through topographic filtering or deep-learning-based classification of the dense point cloud), the outcome will be a digital surface model (DSM) or digital terrain model (DTM).","name":"DEM generation with 'Structure-from-Motion'","selfAssesment":"Completed
"},{"code":"IP1-3-1-2","description":"Photogrammetry is the science and technology of obtaining spatial measurements and other geometrically reliable derived products from photographs. Basic geometric principles applying both traditional analogue and modern digital procedures are related to the central projection of the image in case of typical cameras and to the dynamic projection mostly in case of push-broom sensors, popular in the satellite photogrammetry. The fundamental principle used by photogrammetry is called triangulation. By taking photographs from at least two different locations, so-called “lines of sight” can be developed from each camera to points in a block on the object. These lines of sight (called rays) are mathematically intersected to produce the 3-dimensional coordinates of the points of interest.\r\nWithin data processing the most important parts of photogrammetric workflow are: (1) image orientation, (2) model reconstruction, and (3) orthorectification. Image orientation is based mostly on aerial triangulation, however recently the computer vision algorithm, called structure from motion, became more popular in particularly in close range photogrammetry. Both orientation approaches include detection or measurement of the points between overlapping images in a block, control points measurements in a field defining orientation in reference system and check points verifying the orientation process. The satellite photogrammetry due to different projection and much bigger areas of imaging is usually related to Rational Polynomial Coefficients (RPCs) defining preliminary scene orientation during image orientation. However, to receive more accurate results also here the control points measured in a field are in use. The second part of the modern photogrammetric processing is 3D model reconstruction. In past, vectorization within the stereoscopic measurements was the most popular way of using photogrammetric data after the image orientation. The development of the informatics contributed to the development of the image matching algorithms that can provide dense image point clouds, which can be used to the 3D detailed modelling including digital elevation model production. The final step of photogrammetric processing is orthorectification, which delivers cartometric image called orthophoto mosaiced into orthophotomaps. This process comprises the influence of digital terrain model, model of camera (interior orientation) and image orientation (exterior orientation). Orthophotomap and elevation models derived from photogrammetric processing are applied as very popular data source in many GIS systems. The other photogrammetric outcomes are, for example a 3D measurement or 3D models of some real-world object or scene.","name":"Photogrammetric principles","selfAssesment":"Completed
"},{"code":"IP1-3-1-3","description":"In satellite photogrammetry to obtain the orientation mostly of satellite scene Rational Polynomial Coefficients (RPCs) are applied. They provide a compact representation of a ground-to-image geometry, that allow for photogrammetric processing without requiring a physical camera model. Model with RPC is provided with satellite image and can be improved using measurements of indirect surveying methods used for control point measurement. The RPC model for the coordinates of the image point is calculated as ratios of the cubic polynomials in the coordinates of the world or object space or ground point. \r\nIn photogrammetry and remote sensing, rational polynomial coefficients (RPCs) describe a specific imaging geometry model for transforming image pixel coordinates to map coordinates (thereby accounting for terrain displacement errors). A sensor model describes the geometric relationship between the object space and the image space, or vice versa. It relates 3-D object coordinates to 2-D image coordinates. RPCs are part of a general sensor model that approximates the physical sensor model. The physical sensor model represents the physical imageing process, making use of information on the sensor's position and orientation (during image acquisition). The RPC model often refers to a specific case of the RFM (rational function model) that is in forward form, has third-order polynomials, and is usually solved by the terrain-independent scenario.","name":"RPC correction","selfAssesment":"Completed
"},{"code":"IP1-3-1-4","description":"A ground control point (GCP) is a location of the surface of the Earth (e.g. a road intersection) that can be identified on the imagery and located accurately on the map (i.e. the reference dataset). Two distinct sets of coordinates are associated with the GCP: image coordinates in i rows and j columns, and map coordinates (e.g. x, y measured in degrees of latitude and longitude or as specified by the spatial reference system).","name":"Ground Control Points (GCP)","selfAssesment":"Planned
"},{"code":"IP1-3-1","description":"Orthorectification is the process of removing sensor (scanner or camera), satellite/aircraft, and terrain-related distortions for creating a planimetrically correct image. \r\nTo obtain an accurately orthorectified image, the following information is required: (1) accurate elevation model, and (2) a camera model or rational polynomial coefficients (RPCs) that depicts the positional relationship of the collected image to the ground. Many companies deliver their images together with RPCs and existing software implementations can automatically read these files and apply the RPC transformation on the fly. An accurate elevation model is important to remove the influence of topography (e.g. hills, valley, etc.) on the raw image so that users can accurately compute distances, areas, and directions. Without performing orthorectification, the features in the image are tilted (especially the features located away from the center of the camera). Many satellite data products (e.g. Sentinel images, Landsat data products) are orthorectified using Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) data which is a freely available data product and has a spatial resolution of e.g. 1 arc-second (30 m). In the case of extremely jagged surface topography, i.e. areas of high relief, a DEM with a higher spatial resolution is required. \r\nTwo main models can be used in the orthorectification process: black-box and the physical-based model. The black-box model (called also the analytical model) is commonly implemented in different software because it relies solely on the RPC files. This model does not require access to any proprietary information of the sensor used to collect the image. \r\nThe physical-based models are more complex (and hence expected to be more accurate) because they account for various factors that might influence the quality of the acquired image: e.g. position of the satellite when collecting the images, atmospheric effects, etc. An example of a physical-based model is the so-called camera model. This model requires access to proprietary sensor information that has to be provided by the image owner.","name":"Orthorectification","selfAssesment":"Completed
"},{"code":"IP1-3-2-1","description":"Image co-registration [aka Image-to-image registration] is the translation and rotation alignment process by which two images of like geometry and of the same geographic area are positioned coincident with respect to one another so that corresponding elements of the same ground area appear in the same place on the registered images (Jensen 2005 referencing Chen and Lee 1992).","name":"Image co-registration","selfAssesment":"New
"},{"code":"IP1-3-2","description":"Spatial referencing (referred to as geo-referencing as well) is the process of aligning available EO or GIS data to a coordinate system so that further spatial analysis and image analysis tasks can be applied using these data as input. \r\nTo be able to perform spatial referencing, users have to generate the so called Ground Control Points (GCPs) with known coordinates. In case of images, the easiest features that could be used as GCPs are the intersections, isolated trees etc.","name":"Spatial referencing","selfAssesment":"Planned
"},{"code":"IP1-3","description":"Geometric correction is concerned with placing the reflected, emitted, or back-scattered measurements or derivative products in their proper planimetric (map) location so they can be associated with other spatial information. It is usually necessary to preprocess the remotely sensed data and remove the geometric distortions so that individual picture elements (pixels) are in their proper planimetric (x, y) map locations. This allows remote sensing-derived information to be related to other thematic information in geographic information systems (GIS) or spatial decision support systems (SDSS). Geometrically corrected imagery can be used to extract accurate distance, polygon area, and direction (bearing) information.\r\n\r\nGeometric correction techniques are dedicated to resolving the geometric distortions caused by: (1) variations in sensor position; (2) Earth curvature; (3) rotation of Earth on its axis; (4) relief displacement. \r\n\r\nThere are two types of geometric distortions, namely systematic and random distortions. The former might be caused by Earth's rotation for example and, therefore they are predictable and systematic. The second type of distortions might be caused by terrain or variations in sensor altitude. \r\nGeometric correction includes georeferencing and orthorectification techniques.","name":"Geometric correction","selfAssesment":"Completed
"},{"code":"IP1-4-1","description":"Contrast stretching (also referred to as contrast enhancement) expands the original input brightness values to make use of the total dynamic range or sensitivity of the output device (a computer display).","name":"Contrast stretching","selfAssesment":"New
"},{"code":"IP1-4-2","description":"The histogram is a useful graphic representation of the information content of a remotely sensed image. Histograms for each band of imagery are often displayed and analysed in many remote sensing investigations because they provide the analyst with an appreciation of the quality of the original data (e.g. whether it is low in contrast, high in contrast or multimodal in nature. [...] Tabulating the frequency of occurrence of each brightness value within the image provides statistical information that can be displayed graphically in a histogram.","name":"Histogram","selfAssesment":"New
"},{"code":"IP1-4","description":"Image enhancement algorithms are applied to remotely sensed data to improve the appearance of an image for human visual analysis or occasionally for subsequent machine analysis. The quality of results of image analysis are subjectively judged by humans as to whether they are useful. They include contrast enhancement.","name":"Image enhancement","selfAssesment":"New
"},{"code":"IP1-6","description":"Principal component analysis (PCA) has proven to be of value in the analysis of multispectral and hyperspectral remotely sensed data. PCA is a technique that transforms the original correlated spectral dataset into a substantially smaller and easier set of uncorrelated variables that represents most of the information present in the original dataset. The first component accounts for the maximum proportion of the variance of the original dataset, and subsequent orthogonal components account for the maximum proportion of the remaining variance.","name":"Principal component analysis (PCA)","selfAssesment":"New
"},{"code":"IP1-7-1-1","description":"Bottom-of-Atmosphere (BOA) reflectance is also called surface reflectance and consists of the solar radiation that is reflected from the Earth's surface.","name":"Bottom-of-Atmosphere (BOA)","selfAssesment":"New
"},{"code":"IP1-7-1-4","description":"Top-Of-Atmosphere (TOA) radiance represents the radiance observed outside Earth’s atmosphere. It is derived from the Digital Numbers (DN) using metadata delivered with the image.","name":"Top-Of-Atmosphere (TOA)","selfAssesment":"New
"},{"code":"IP1-7-1","description":"Atmospheric correction accounts for the attenuation caused by scattering and absorption in the atmosphere. It transforms top-of-atmosphere (TOA) reflectance to bottom-of-atmosphere (BOA) reflectance.\r\nThe decision to perform atmospheric correction depends on the need, i.e. the envisioned usage of the derived EO information product and the nature of the underlying problem. This includes requirements to the accuracy of extracted biophysical information. Additionally, the decision and choice of methods depends on the type of remote sensing data available, the amount of in-situ historical and/or concurrent atmospheric information available.\r\nAn atmospheric correction is essential when biophysical or geophysical parameters (e.g. of water or vegetation) are going to be extracted from the remote sensing data. If the data is not corrected, the subtle differences in reflectance among the contributing image bands may be lost. This is especially relevant when biophysical information shall be compared to that of images from other dates.\r\nHowever, some cases exist where it is unnecessary to perform atmospheric correction. For example, it is not necessary for producing an image classification product from a single date of remotely sensed data. If a maximum likelihood classification is applied that uses training data with the same relative scale for the pixel values, then, atmospheric correction has little effect on the classification accuracy. The same holds true for a post-classification change detection where the classifications of the two different dates were performed independently. \r\nThe process of (absolute) atmospheric correction requires a model atmosphere and in situ atmospheric measurements acquired at the time of remote sensor data acquisition as input. In situ data can be available from other sensors on-board the sensor platform.\r\n\r\nDark Object Subtraction (DOS) is one of the most popular empirical atmospheric correction techniques. This technique assumes that a black object has a reflectance value of zero. Yet, a dark object present in a satellite image will have a value different than zero because of the atmospheric scattering. This value is then subtracted from all pixels in a given spectral band.","name":"Atmospheric correction","selfAssesment":"Completed
"},{"code":"IP1-7-2-1","description":"A method for dimensionality reduction in hyperspectral data is Minimum Noise Fraction (MNF). The purpose is to minimize the noise in the imagery, i.e. to identify noise and segregate it from true information, and to colaps the useful information into a much smaller set of MNF images. The MNF transformation applies two cascaded principal components analyses.","name":"Minimum noise fraction (MNF)","selfAssesment":"New
"},{"code":"IP1-7-2","description":"The number of spectral bands assocuates with a remote sensing system is referred to as its data dimensionality. Hyperspectral remote sensing systems such as AVIRIS ans MODIS obtain data in 224 and 36 bands, respectively. The greater the number of bands in a dataset (i.e., its dimensionality), the more pixels that must be stored and processed by the digital image processing system. Storage and processing consume valuable resources. It is necessary to reduce the dimensionality of hyperspectral data while retaining the information content inherent in the image. On method to reduce dimensionality of hyperspectral data and minimizing the noise in the imagery is the minimum noise fraction (MNF) transformation (Green et al., 1988).","name":"Dimensionality reduction","selfAssesment":"New
"},{"code":"IP1-7-3","description":"Sensor calibration converts the sensor’s digital numbers (DNs) to at-sensor radiance above the atmosphere. A further radiometric adjustment accounts for the viewing angle and sun angle during acquisition to transform radiance values to top-of-atmosphere (TOA) reflectance. Therefore, the process requires sensor calibration information and telemetry data that satellite image providers deliver within the metadata.\r\nDNs are raw sensor data without physical units. The sensor calibration information for converting the DNs to radiance are the calibration gain (cal_gain) and calibration offset (cal_offset) values. The sensor calibration uses linear function f(DN) = DN * cal_gain + cal_offset that multiplies the DNs of each pixel in each spectral band with their corresponding cal_gain and adds the corresponding cal_offset. The resulting at-sensor radiance image is the basis for the radiometric adjustment that uses information about the viewing angle and sun angle during acquisition to transform at-sensor radiance to TOA reflectance. \r\nSensor calibration obtains TOA reflectance and is a minimum requirement for performing band math calculations to derive spectral indices such as the normalized vegetation difference index (NDVI). Uncalibrated image data would arrive at NDVI values that are distorted because the cal_gain and cal_offset parameters for the involved spectral bands were not considered.","name":"Sensor calibration","selfAssesment":"Completed
"},{"code":"IP1-7-4","description":"As an optical remote sensing system is not perfect, noise can enter the data collection system at several points. Necessary corrections include the removal of shot noise (random bad pixels), correcting line or column drop-outs, accounting for line-start problems and radiometric correction of n-line striping caused by detector miscalibration.\r\nSAR data have global, random speckle noise. Speckle filters are designed to adapt to local image variations in order to smooth values, thus reducing speckle and enhancing lines and edges to maintain the sharpness of an image. A widely used way to reduce speckle is to apply spatial filters to the images. Typical approaches for speckle filtering include Laplace filtering for smoothing and sigma filters that preserve more of the signal with a lesser effect of smoothing.","name":"Noise reduction","selfAssesment":"New
"},{"code":"IP1-7-5","description":"Topographic correction, or topographic effects correction, aims to adjust the spectral values of an image according to effects of solar illumination differences due to the irregular shape of the terrain. Topographic slope and aspect introduce radiometric distortion of the recorded signal. Further, terrain shadow dramatically affects the brightness values of the covered pixels in an image. Topographic effects of illumination and shadow are particularly relevant in mountainous regions and in regions towards the higher latitudes of the southern and northern hemisphere. The effects appear pronounced during the winter season. \r\nTogether with sensor calibration and atmospheric correction, topographic correction is part of the radiometric correction process to obtain true reflectance values from sensor radiance. This process is necessary when using EO data for obtaining geophysical measurements. It can also benefit the accuracy of image classifications by reducing the internal variability of vegetation types, since the corrected reflectance relates better to the geometrical or biological properties of the plant than to the original reflectance.\r\nMethods for the removal of topographic effects from remotely sensed images can simply be based on band ratios that do not require additional input. Alternatively, they use digital elevation models (DEMs) as an additional input and apply sophisticated modelling of the illumination conditions. The illumination model describes various aspects of the relationship between the sensor measurement, the sun illumination, the ground reflectance and the diffuse irradiance at the surface. The model incorporates the angles between the sun position, the ground position (described by slope and aspect from the DEM), and the sensor position. Among these methods are lambertian methods and non-lambertian methods such as the bidirectional reflectance distribution function (BRDF). The BRDF, which is more suitable to the non-Lambertian properties of the observed surfaces, describes how the reflectance varies in each cover considering the angles of incidence and observation. \r\nIf achieved with a high quality, the resulting topographically corrected image appears to be illuminated evenly as if all its pixels would be part of a flat surface without the presence of any terrain differences. However, the much larger benefit than the improved appearance is the availability of pixel values that are closest to the true reflectance when compared to TOA, BOA and DN values.","name":"Topographic correction","selfAssesment":"Completed
"},{"code":"IP1-7","description":"Radiometric calibration and correction converts the sensor’s digital numbers (DNs) to radiance values and subsequently reflectance values. Additionally, the term “correction” points to the fact that radiometric measurements with satellite sensors contain error. Therefore, radiometric correction is concerned with improving the accuracy of surface spectral reflectance, emittance, or back-scattered measurements obtained using a remote sensing system. The Earth’s atmosphere, land and water are complex and can never be captured perfectly because of the limitations of remote sensing devices that lie in their spatial, spectral temporal and radiometric resolution. Therefore, error occurs in the data acquisition process and degrades the quality of remotely sensed data. The most common errors in remote sensing are radiometric and geometric. This concept is focused on the correction of remote sensing data to account for radiometric error that is to some degree systematic. Systematic errors in radiometric measurements come from the interaction of the sensed radiance with the atmosphere, the acquisition geometry in relation to the radiance source (the sun) and the Earth surface geometry (terrain).\r\nThere are several levels of radiometric calibration and correction. The first is sensor calibration that converts the DNs to top-of-atmosphere (TOA) reflectance. It converts to radiance values and further to reflectance values by accounting for the viewing angle and sun angle during acquisition. The second is atmospheric correction that converts TOA reflectance to bottom-of-atmosphere (BOA) reflectance. The third is topographic correction that converts BOA reflectance to surface reflectance. \r\nRadiometric calibration is necessary to ensure radiometric comparability of the measurements. There is a need for calibration when comparing different spectral bands within one image, e.g. for the calculation of geo-biophysical parameters with band math operations. Results from uncalibrated image data would differ from results achieved with calibrated data because the unaccounted cal_gain and cal_offset of the used spectral bands would lead to distortions. \r\nIn addition, radiometric calibration complements the geospatial comparability that is achieved with geo-referencing an image to geographic coordinates. Geo-referencing enables comparison of an image pixel to the geospatially matching pixel in another image acquired with a different sensor but with comparable resolution. Radiometric calibration enables a radiometric comparison between these two pixels’ radiance values. In case the two images are from different acquisition dates, a calculated radiometric difference would indicate change. This example shows the relevance of radiometric calibration for inter-sensor comparisons.\r\nRadiometric comparability is particularly relevant in studies that require inter-sensor comparisons, comparisons of surface features over time, or comparisons to laboratory or field reflectance data. Then the radiometric correction should cover atmospheric, solar and topographic effects. A full radiometric correction that also includes topographic correction can benefit the accuracy of image classifications by reducing the internal variability of vegetation types, since the corrected reflectance relates better to the geometrical or biological properties of the plant than to the original reflectance.","name":"Radiometric calibration and correction","selfAssesment":"Completed
"},{"code":"IP1","description":"Image pre-processing focuses on transforming the electrical signal measured by a sensor to a processing level at which pixel values can be used for the next information extraction step. Therefore, pre-processing operations involve the removal of errors encountered while collecting remotely sensed data to get as close as possible to the true radiant energy and spatial characteristics of the study area at the time of data collection. Different sensor type (optical, radar, lidar) require different processing levels\r\nThe most common image pre-processing procedures include: \r\n(1)\tRadiometric calibration involves the transformation of Digital Numbers (DN) to physical unit: radiance/reflectance. Radiometric calibration can be done before the launch of a satellite sensor, i.e. pre-launch calibration, or after launch. In the second case, the calibration is performed on-board or by comparing ground measurements with satellite radiance. Through radiometric calibration various scene illumination procedures such as sun elevation correction or earth-sun distance correction are applied. Furthermore, image noises caused by striping or line drop as happened in case of Landsat TM7 due to failure of the Scan Line Corrector (SLC) are also corrected using specialized procedures.\r\n(2)\tAtmospheric correction accounts for two main processes: scattering and absorption. Scattering represents a disturbance of the electromagnetic waves caused by rayleight scattering (caused by very small particles such as the air molecules), mie scattering (caused by aerosol particles) and non-selective scattering (dust, smoke, rain etc.). Absorption occurs when the electromagnetic energy is absorbed by the atmospheric components. Therefore, atmospheric windows have to be removed before using the satellite images in the next processing steps. Atmospheric corrections can be carried out either using simple statistical methods or complex radiative transfer based methods\r\n(3)\tGeometric correction is required to remove the distortions caused by the Earth curvature, Earth rotation, panoramic distortion due to the field of view of the sensor and the topography of the terrain. Geometrics distortions are corrected using Ground Control Points (GCP) and a Digital Elevation Model (DEM). In case of airborne images, additional distortions caused by variations in the platform altitude or velocity might occur.","name":"Image pre-processing","selfAssesment":"Completed
"},{"code":"IP2-1-1","description":"Data augmentation refers to a scheme of augmenting the observed data so as to make it more easy to analyze. An application from deep lerarning is to increase the number of input training sample images with augmented data. Examples of data augmentation techniques include horizontal flips, random crops, and principal component analysis.","name":"Data augmentation","selfAssesment":"New
"},{"code":"IP2-1-2","description":"Data imputation refers to a scheme of replacing missing values by imputed values. Imputation can be done, for example with mean, median and mode. Imputation methods can efficiently predict multiple response variables simultaneously.","name":"Data imputation","selfAssesment":"New
"},{"code":"IP2-1-3-1","description":"Gram-Schmidt is a pan-sharpening method that has been invented by Laben and Brover in 1998 and patented by Eastman Kodak. It makes use of the Gram-Schmidt orthogonalization to decorrelate the spectral bands (panchromatic, red, green, blue, etc.) and transform them into one multidimensional vector.","name":"Gram-Schmidt pan-sharpening","selfAssesment":"New
"},{"code":"IP2-1-3-2","description":"This pan-sharpening method uses PCA to transfer detailed spatial information from panchromatic band to the available multispectral bands.","name":"Principal Component Analysis (PCA)-based pan-sharpening","selfAssesment":"New
"},{"code":"IP2-1-3","description":"Pan-sharpening methods are used to enhance spatial resolution of images by merging a panchromatic image with high resolution with a multispectral image with low resolution.","name":"Pan-sharpening","selfAssesment":"New
"},{"code":"IP2-1-4","description":"Spatiotemporal image fusion methods, called also spatiotemporal downscaling methods, represent an efficient solution to generate fine-scale images at a high temporal resolution for more detailed land cover mapping and monitoring applications. Spatiotemporal image fusion methods can be classified into three categories: (1) reconstruction-based , (2) unmixing based and (3) learning-based methods.","name":"Spatio-temporal image fusion","selfAssesment":"New
"},{"code":"IP2-1","description":"Image fusion is defined as the “combination of two or more different images to form a new image by using a certain algorithm” Data fusion is a well-established research field. Image fusion methods are primarily used for improving the level of interpretability of the input data. Additionally, they can be utilized to address the problem of missing data caused by cloud or shadow contamination in satellite images time series. Image fusion can be performed at pixel-level, feature-level (e.g. land-cover classes of interest), and decision-level (e.g. purpose driven).","name":"Data fusion","selfAssesment":"Planned
"},{"code":"IP2-2","description":"Data harmonization aims to transform different datasets in such a way that they fit together, both with respect to geometry and semantics. The goal is that a user, who is using data from different authorities, shall have a unified view, where conflicts in the datasets have been removed.","name":"Data harmonisation","selfAssesment":"New
"},{"code":"IP2-3","description":"Data integration is the process of combining different geographic datasets including those derived from remote sensing data. The combined datasets can have different coverage, but they have to have the same geographic coordinates.","name":"Data integration","selfAssesment":"Planned
"},{"code":"IP2","description":"Data assimilation is a strategy to foster data integration and data harmonisation in a bi-directional way between the measured and the modelled reality. In other words, it aims to combine measurements (observations) with the understanding of the spatio-temporal properties and evolution of system’s variables or properties and model information about them. Models can be calibrated and keeping them ‘on track’ by constraining them with observations. Vice versa, observations can be validated through models. Approached as a mathematical problem, data assimilation aims at minimizing cost functions or penalize a function to ensure optimality in fitting. Equations are used to describe system parameters and the relationships among them, It is noteworthy, that models encompass information from previous measurements, experiences, and theory. While the observations are influenced by (known) properties such as precisions, etc. of the measurement devices, the robustness of models rely on the consolidated knowledge. Because uncertainties reside in all components with unknown or even undeterminable errors, the approach is usually probabilistic, including Bayesian and other related techniques. Widely used in meteorological sciences, successful data assimilation has been boosted the reliability of weather forecast , while sensitivity to errors remains. \r\nIn Earth observation, data assimilation compensates for the fact that a specific site could be observed in a variety of measurements by satellites with different sensor types, at different dates, different angular geometries and viewing directions, illumination conditions (solar time), observation frequencies, etc. In particular, for monitoring processes, measurements over time need to assure to actually measure the status of the system or object and not the divergence in observation. To overcome these divergences and converge them with the actual properties of an observed object or target class such as spectral or geospatial properties, observation modelling can be considered an important contribution from geospatial theory. this also links to class modelling or geon modelling. The synergy of a vegetation growth model and a remote sensing observation model can be exploited to improve the retrieval of geo-biophysical information. For vegetation and crop type monitoring radiative transfer modelling (RTF) is being used as an example. \r\nData assimilation can also serve in bridging the gaps between non-availabilities of EO data and other observations, to provide estimates or prediction for geographical variables, testing of hypotheses or continuous observation (monitoring). A related aspect is data imputation, i.e. filling gaps in observations e.g. by other, complementary data sets (e.g. Radar imagery in the absence of VHR data in cloudy weather conditions). Recently, these sources can also be complemented by crowd mapping and citizen science. \r\nWhen interpretation of data comes into play, such as image classification, we introduce another level of uncertainty. Thus the community seeks for rigorus classifiers based on solid spectral models, acting across sensors. Semantic enrichment of satellite data is a related strategy for reaching to interpreted data in a rigorous way. \r\nSummarizing, data assimilation comprises steps to improve the level of interpretability of the input data, by enrichment (get rid of spatial/temporal gaps), by accounting for heterogeneity (through harmonization), and by integration (combination with other data that is relevant to the application). Thereby, datasets become more comparable to each other.","name":"Data assimilation","selfAssesment":"Completed
"},{"code":"IP3-1-1-1","description":"Vegetation fraction (VF) is defined “as the percentage of vegetation occupying a pixel as viewed in vertical projection. It’s a comprehensive quantitative index in forest management and vegetation community cover conditions, and it’s also an important parameter in many remote sensing ecological models.”","name":"Vegetation fraction","selfAssesment":"Planned
"},{"code":"IP3-1-1-2","description":"Leaf area index (LAI) is the ratio between the total area of the upper leaf surface of vegetation and the surface area of the pixel in question. LAI is a dimensionless value, typically ranging between 0 (for a pixel composed of bare soil) and values as high as 6 (for a dense forest).","name":"LAI (Leaf Area Index)","selfAssesment":"Planned
"},{"code":"IP3-1-1-3","description":"Net primary production (NPP) is a measure of the inherent productivity of a region or ecological system—mainly the Earth’s production of organic matter, principally through the process of photosynthesis in plants.","name":"Net primary production (NPP)","selfAssesment":"New
"},{"code":"IP3-1-1","description":"Biophysical parameter retrieval is an approach in remote sensing that aims to estimate parameters which have physical meaning related to properties of living organisms. The goal is to provide quantitative results directly relating to the biophysical state, but independent of acquisition conditions and technology. Assessment of vegetation status is a key motivation for this, because through plant respiration and photosynthesis, vegetation is critical for modelling terrestrial ecosystems and energy cycles in environmental studies. \r\nImportant parameters describing canopy structure include leaf area index (LAI), green cover fraction (fCover), fraction of absorbed photosynthetically active radiation (fAPAR), plant height, biomass and leaf angle distribution. At leaf biochemical level, leaf chlorophyll/water, fuel moisture and leaf pigmentation content are used.\r\nVisual inspection can provide a first assessment of plant status. For detailed measurements of biophysical parameters, mostly destructive methods have been used. Chemical measurement techniques on leaf samples can measure pigment concentrations very accurately, but are time consuming and only use very limited samples. \r\nMuch more extensive data can be collected using earth observation imagery. These range from large scale spaceborne observations with high frequency at coarse resolution to dedicated UAV flights which can offer spectral information of individual plants. Radar and LiDAR acquisitions, which are insensitive to weather conditions, now complement optical observations. \r\nMethods to retrieve the parameters from remote sensing data fall into two main categories. Statistical models empirically match data to a biophysical variable. Univariate techniques use a single quantity derived from the data, usually a vegetation index whereas multivariate techniques link a combination of measurements at different wavelengths to one or more biophysical parameters.\r\nPhysically-based modeling is an alternative approach which uses advanced radiative transfer models to describe the transfer and interaction of radiation inside a leaf or canopy based on robust physical, chemical, and biological processes. They compute the interaction between solar radiation and plants and provide as such a better understanding between biophysical variables and reflectance characteristics. Good examples are Leaf optical models such as PROSPECT and LIBERTY which simulate leaf optical properties by absorption and scattering coefficients. Canopy reflectance models simulate canopy reflectance as a function of a complex description of plant structural and radiometric attributes to develop a quantitative understanding of remote sensing information.","name":"Biophysical and geophysical parameters","selfAssesment":"Completed
"},{"code":"IP3-1-2-1","description":"This spectral index is calculated using the following formula: SAVI = [(NIR-Red)/(NIR+Red+L)]/(1+L), where L can be, for example, 1 in area with no vegetation or 0 in area with dense veegtaion. It is used to minimize the influence of the soil brightness from the vegetation indices that are based on red and near-infrared wavelengths.","name":"Soil-adjusted Vegetation Index (SAVI)","selfAssesment":"New
"},{"code":"IP3-1-2-2","description":"This spectral index is calculate using the following formula NDSI = (green-SWIR)/(green+SWIR). It is the most popular index used to identify snow cover due to the fact that snow reflects visible wavelength stronger than middle-infrared wavelengths.","name":"Normalized Difference Snow index (NDSI)","selfAssesment":"New
"},{"code":"IP3-1-2-3","description":"Leaves, when healthy and vigour show a characteristic green colour. This visual effect evident to humans is caused by the co-existence of two evolutionarily facts: the specific interaction of the chlorophyll pigment in living leaves to the visible spectrum (VIS, 400-700 nm wavelength) of light emitted by the sun and the sensitivity of our human eye to the same sub-spectrum. According to fundamental physical laws of radiation (Stefan Boltzmann law of blackbody radiation and Wien’s displacement law), the VIS sub-spectrum corresponds to the radiation maximum of the sun, a hot blackbody with a surface heat of about 6000 K. Living leaves are structured in specific layers exhibiting characteristic interaction with light. The chloroplasts located in the so-called palisade layer, make use of the blue and the red part of sunlight for photosynthesis, the unique process of transforming light to create energy (carbohydrates) from water and carbon dioxide. This leads to the specific behaviour of leaves to absorb large portions (up to 90%) of the blue and red part of the electromagnetic spectrum and reflect nearly 100% of the green light. The peak reflectance in green light makes leaves (and plants in general) appear in green colour in our visual perception. \r\nA second, by no means less characteristic, feature of leaves is the specific response to near infrared (NIR, at around 700 nm wavelength) light in the mesophyll tissue (transmittance, scattering and reflectance). Only a small fraction of NIR is being absorbed. \r\nThis combination of two specific spectral characteristics, the absorption in VIS (red colour) by chlorophyll a in palisade layers, and the reflectance of NIR in the spongy tissue, makes the spectral profiles of plants and vegetation exhibiting a very characteristic shape, the so-called red edge. This absorption edge between red and NIR light is sharper for higher intensity green reflectance and brighter green tones (such as grassland or bright deciduous forest) than for less intensive reflectance and darker tones (coniferous forest). \r\nThe red edge may shift for the same vegetation type due to plant maturity or plant stress. This effect we call the red shift. The red shift is sensitive to crop maturity (headed stage) and may indicate harvesting time. Notably, there is also a blue shift, indicating green plants’ exposure to geochemical stress, which causes the absorption spectra to shift towards shorter wavelengths. \r\nPlants usually do not appear in isolation but form a canopy with a certain degree of coverage (e.g., crown closure in forests), and a certain part of understorey or soil per area unit. The resulting canopy reflectance is therefore a spectral mix of soil and vegetation (or even different types of vegetation) and generally lower than the reflectance of a pure vegetation sample under lab conditions. \r\nTo capture most of these plant-typical spectral characteristics, the so-called normalised difference vegetation index (NDVI) was developed. NDVI is an arithmetic band combination of red and NIR bands in a normalised value range. \r\nThe NDVI is calculated as:\r\nNDVI=((NIR-R))/((NIR+R))\r\nThe (hypothetic) value range of the NDVI is [-1 | +1]. Under real-world conditions, the NDVI ranges from values of around -0.2 to 0.6 or 0.7. To discriminate principal land cover classes such as water, non-vegetation (soil, sealed, etc.) and vegetation the following thresholds in the continuous range are used: \r\n\tNDVI < ~ 0: water\r\n\t~ 0 < NDVI < ~ 0.2: non-vegetation (soil, sealed surfaces, bare rock, etc.)\r\n\t~ 0.2 < NDVI: vegetation.\r\nNotably, these class limits are just a very rough approximation (indicated by the ~ sign), due to the mixed pixels effect, canopy reflectance, the abundance of water plants and suspending particles, and the illumination effect of specific atmospheric or topographic conditions. \r\nWe can use the NDVI to generally mask out vegetation from other land cover types and, more specifically, to indicate vegetation vigour and health. It is also suitable for monitoring plant phenology as the relationship between vegetative growth and the (changing) conditions of the environmental conditions. A range of variations has been suggested, enhancing one or the other mathematical or statistical behaviour of the index, or making it even more sensitive to specific plant behaviour. A well-known example is the enhanced vegetation index (EVI).","name":"Normalized Difference Vegetation Index (NDVI)","selfAssesment":"Completed
"},{"code":"IP3-1-2","description":"Spectral indices are calculated using a mathematical equation that is applied on two or more spectral reflectance bands of the image. The calculated spectral index is a ‘new’ image that highlights particular land surface features or properties e.g. vegetation, soil, water, better than the original input bands. The spectral indices vary from simple spectral ratioing of two bands to more complex combinations of multiple bands. Spectral indexes are developed based on the spectral properties of the object of interest. For example, spectral indices dedicated to the vegetation condition are developed based on the principle that the healthy vegetation reflects strongly in the near-infrared spectrum while absorbing strongly in the visible red. These properties are used to develop more complex spectral indexes for monitoring vegetation condition, phenology parameters, i.e. Normalised Difference Vegetation Index (NDVI), Advanced Vegetation Index (AVI). The spectral indices calculated using the short wave infrared spectral bands are more sensitive to vegetation water content and spongy mesophyll structure in the vegetation canopy thus are used to assess the vegetation decline, moisture that is particularly useful for drought monitoring (e.g. Normalized Difference Water Index (NDWI) or Normalized Difference Moisture Index – NDMI). The water-related spectral indices are widely applied in agricultural and ecological applications including surface water body characteristics, vegetation water stress, soil water content assessment and wetlands monitoring. The combination of near infrared and short wave infrared spectral bands is also used to detect burned area and to monitor the vegetation recovery (e.g. Normalised Burned Ratio – NBR). There are other spectral indices dedicated to snow cover and glacier monitoring, which are developed based on visual green and short wave infrared spectral bands. Snow reflects most of the radiation in the visible bands whiles absorbing in the short wave infrared.","name":"Spectral indices","selfAssesment":"Completed
"},{"code":"IP3-1","description":"The term band maths denotes the arithmetic combination (addition/subtraction, multiplication/division) of two or more spectral bands in an early stage of image analysis. The resulting scalar values represent the spectral behaviour in different bands in a single value; such procedure makes particular sense, when spectral behaviour varies in those bands (like the red edge of vegetation spectra in the NIR band). \r\nThere are several reasons for applying band maths when working with multispectral imagery: (1) A single range of values rather than multiple bands is easier to comprehend and interpret; (2) Thresholds or class limits are applied more intuitively in a grey scale image; (3) Indices can be easily calculated and compared across different sensors; they are implemented as standard routines in many software environments as well as cloud processing environments (such as Google Earth Engine or the Proba-V exploitation platform)\r\nOut of the many possible, literature suggests a few arithmetic band combinations as application-specific quasi-standards. Band ratios (e.g. red band divided by NIR band) and indices (such as the normalised difference vegetation index, NDVI) belong to this group. Indices have the advantage over simple ratios in constraining the value range, e.g. [-1 | 1]. Designated to indicate specific land cover types (such as water index, snow index, soil index, etc.) such indices are widely used as a basis for operational information products. Another index is the normalised burn ratio (NBR) which relates near infrared and short-wave infrared reflectance to measure burn severity taking into consideration the increasing of SWIR reflectance in the course of a fire. \r\nPre-processing such as dark object subtraction and radiometric or even atmospheric correction is a key requirement prior to indexing. The coding in digital numbers (DN) is a function of the sensitivity and the radiometric resolution of the sensor. The actual recording depends on atmospheric conditions (additional brightness, haze, etc.). Therefore, in order to make the resulting values comparable among different types of sensors and scenes, radiometric correction is mandatory, converting DNs into radiances, i.e. true reflectance values as physical measurement units. \r\nTwo advanced examples of band maths beyond rationing are the perpendicular vegetation index (PVI) and the tasselled cap (TC) transformation. PVI is based on the assumption that vegetation pixels are generally separable from soil pixels (at least after unmixing or for pure pixels), and thus pixel values are located in a perpendicular direction from the soil line in a NIR/red feature space. The Euclidean distance from the soil line, determined by Pythagorean triangle, yields the PVI. Tasselled cap instead rests on the notion of a cap-like histogram shape when plotting pixels on a brightness vs. greenness plot, with the latter determined by linear combinations of VIS and NIR bands, along with empirically determined coefficients. TC 1 as a weighted sum corresponds to brightness, TC 2 to greenness, TC 3 to yellowness, sometimes referred to as wetness. A fourth TC called nonesuch likely corresponds to noise and atmospheric disturbance effects in the image.","name":"Band maths","selfAssesment":"Completed
"},{"code":"IP3-10","description":"Semantic enrichment is the process of adding semantic metadata elements to improve the content-based image retrieval. These semantic metadata elements enable the explicit specification of the content of the images stored in the remote sensing databases.","name":"Semantic enrichment","selfAssesment":"New
"},{"code":"IP3-11-1","description":"Different types of changes are investigated using remotely sensed data: (i) abrupt changes, such as the changes caused by a fire or flooding, and (ii) gradual changes such as urban growth. Besides these kinds of changes, remote sensing community differentiates between transitional changes and conditional changes. Transitional changes refer to a major change of land surface such as conversion of forest to pasture or the expansion of mangroves into the surrounding water. Conditional changes refer to the change in condition at the surface such as water stress in an agricultural field, forest degradation caused by pest. \r\nIn the past, many remote sensing studies used two images to detect different types of changes such as deforestation, land cover change or change in the health or condition of the vegetation (e.g. pest infestation). Meanwhile, satellite image time series are used to assess the change. Time series analysis allows for monitoring more subtle changes and for providing temporal patterns of change. In this way, the timing of changes and drivers of change can be easily identified. \r\nDifferent methods are being used in change detection studies. There are studies that analyze individual images available in the investigated time series to map the target class/phenomena/events at the time when images were collected and to identify the changes: e.g. mapping the mangroves extent on an year basis and measuring it to identify changes. Alternative studies search for breaks in time series for detecting changes. The breaks are used to segment the time series into before and after changes periods which are further classified using one of the existing supervised or unsupervised classification methods (K-means, fuzzy k-means, Random Forest, Support Vector Machine etc.).","name":"Change detection","selfAssesment":"Completed
"},{"code":"IP3-11-2","description":"The (data)cube model for analysis of time series of earth observation raster data, represents the dataset as a multidimensional array with one or more spatial or temporal dimensions. Scalar values in the cube can be selected (or ‘filtered’) and processed based on dimension labels. This allows analysis algorithms to be thought of as a set of operations on the multidimensional array. Technologies that support this model allow to efficiently implement such algorithms.\r\nSome possible operations on a multidimensional cube include: filtering, ‘reducing’ all values along a dimension, ‘aggregating’ values in a dimension, or transforming all values along a dimension. Generally speaking, these operations require the selection of a subset of the data on which work is to be done. This allows implementing the operations efficiently even on very large datasets.\r\nIn comparison to file-based processing, most technologies that support cube-based time series analysis reduce implementation overhead, as the user does not need to read and write individual files, also more complex aspects like distributed computing for parallelization can be hidden in a cube based approach. So a cube based approach can also be thought of as an abstraction layer that effectively reduces the need for specific IT-related skills when analyzing earth observation timeseries.\r\nMultiple initiatives support cube based analysis. Some common features include a programming API, often using the Python programming language. Some tools are only accessible as web services, while others can also run locally (on a small dataset). This diversity is still a drawback, as users would need to familiarize themselves with different systems. Initiatives such as openEO try to address this by providing a common API.","name":"Cube-based time series analysis","selfAssesment":"Planned
"},{"code":"IP3-11-3","description":"Dynamic Time Warping (DTW) works by comparing the similarity between two temporal sequences and finds their optimal alignment, resulting in a dissimilarity measure. In the case of remote sensing data, DTW can deal with temporal distortions, and can compare shifted evolution profiles and irregular sampling thanks to its ability to align radiometric profiles in an optimal manner","name":"Dynamic Time Warping","selfAssesment":"Planned
"},{"code":"IP3-11","description":"Satellite image time series analysis plays an important role in different domains including vegetation dynamics monitoring, estimating crop yields, discriminating between different land cover classes, exploring human-nature interactions, monitoring land cover change, assessing environmental threats, or evaluating ecosystems-climate feedbacks or urbanization.\r\nTime series analysis requires high quality time series which are reconstructed by removing any source of contamination such as clouds, cloud shadows, or scan-line corrector (SLC) gaps of the Enhanced Thematic Mapper plus sensor (ETM+) on Landsat 7. Removed pixels are usually filled in with data predicted from a different date (temporal interpolation), nearby pixels (spatial interpolation) or from both (spatiotemporal interpolation). Different methods are available for screening and masking out clouds and shadows in satellite images including mono-temporal methods such as Function of mask (Fmask), or multitemporal mask (e.g. Tmask algorithm). Fmask is used by the United States Geological Survey (USGS) to produce a cloud mask layer of Landsat images. European Space Agency (ESA) is using Sen2cor processor to produce Level 2A Sentinel-2 data with a shadow and cloud shadow mask. All images used in the time series have to be co-registered, i.e. they align as closely as possible. \r\nTime series analysis is used to (1) investigate various surface properties such as evapotranspiration, land surface temperature, (2) map the cover of the Earth surface (e.g. land cover mapping, crop mapping etc.), (3) detect different type of changes such as abrupt changes (fire event) or gradual changes (urbanization), and (4) study the trends.\r\nTo map surface features from satellite image time series, numerous studies make use of the vegetation phenology extracted from a spectral-temporal trajectory of a given spectral vegetation index such as the normalized difference vegetation index (NDVI) or enhanced vegetation index (EVI). Several metrics can be used to characterized vegetation phenology: metrics of greenness and metrics of time. The metrics of greenness include the minimum and maximum spectral vegetation indices, their difference or amplitude, seasonally averaged greenness etc. The metrics of time include start and end of the growing season, duration or length of the growing season or the timing of maximum greenness. Changes, on the other hand, are identified either by investigating two images acquired at two different points in time or by identifying breaks in a dense (annual or multi-annual) satellite image time series.","name":"Time series analysis","selfAssesment":"Completed
"},{"code":"IP3-12-1","description":"Remote sensing-derived products such as land-use and land-cover maps contain error. The error accumulates as the remote sensing data are collected and various types of processing take place. An error assessment is necessary to identify the type and amount of error in a remote sensing-derived product.","name":"Error propagation","selfAssesment":"New
"},{"code":"IP3-12-2","description":"The precision of a measurement system, related to reproducibility and repeatability, is the degree to which repeated measurements under unchanged conditions show the same results.","name":"Precision","selfAssesment":"New
"},{"code":"IP3-12","description":"Uncertainty is the result of the lack or imprecision of our knowledge about the world. A proposition is uncertain if we do not know whether it is true or not. In most circumstances we describe a proposition as uncertain when the reason we do not know whether it is true is that we do not possess complete and accurate knowledge about the state of the world.","name":"Uncertainty","selfAssesment":"New
"},{"code":"IP3-13-1","description":"The main elements of visual interpretation are: tone, shape, size, pattern, texture, shadow, , association. Tone refers to the relative brightness or colour of objects in an image. It depends on the spectral properties of an object. Variation in tone allows to distinguish elements of different shape, texture and pattern. Shape refers to the general form, structure, or outline of individual objects. Straight and sharp edge shape represent typically the anthropogenic features i.e. urban or agriculture, the natural features like rivers, wetlands are more irregular in shape. Size of objects in an image is a function of scale and it depends on the spatial resolution of the image. The assessment of the size of the target’s object in relation to other objectives as well as an absolute size of the object are the important part of the interpretation. Pattern refers to the spatial arrangement of objects, i.e. network of street and houses in an urban area, orchards with the line of trees. Texture refers to the arrangement of frequency of tonal variation in particular areas of an image. Rough texture would have very large, coarse tonal variation (e.g. forest canopy), whereas smooth texture very little tonal version (e.g. uniform, homogenous surfaces). It depends on the size, shape and pattern of objects. Shadow depends on the scale and spatial resolution of an image. Shadow is useful to measure the height of an object, to distinguish the coniferous from broadleaf trees. In the radar imagery is useful for identifying topography and landforms. Association refers to the relationship between objects and features in proximity to the target interest.","name":"Elements (cues) of interpretation","selfAssesment":"Completed
"},{"code":"IP3-13-2","description":"Information-as-data-interpretation considers information as the outcome of the cognitive process of vision that reconstructs a scene from an image.","name":"Information-as-data-interpretation","selfAssesment":"New
"},{"code":"IP3-13-3","description":"An image interpretation key is simply reference material designed to permit rapid and accurate identification of objects or features represented on aerial images.","name":"Interpretation keys","selfAssesment":"New
"},{"code":"IP3-13","description":"Interpretation is the processes of detection, identification, description and assessment of an object and pattern imaged. Visual interpretation is the ability of a human operator to identify an object through the data content in an image / photo by combining several elements of interpretation. The image characteristics used in the interpretation process are: shape, size, tone/colour, texture, shadow, neighbourhood and pattern. The importance of the image characteristics varied according to the spatial resolution of the images and the properties of the feature of interest. The interpretation can be performed on the single image or between several images acquired at different time, which result in the differentiation of the temporal changes. The principle of the image interpretation is the process of delineating (digitalizing) the outlines of the objects, features on the image. It is performed “on-screen” using a GIS software. The process of visual interpretation is time consuming and requires a skilled interpreter with knowledge of the study area. Even though, the image interpretation supports many applications in for example selection of the training and verification data sets for image classification and accuracy assessment.","name":"Visual interpretation","selfAssesment":"Completed
"},{"code":"IP3-2-1","description":"Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans.","name":"Artificial intelligence (AI) in EO","selfAssesment":"New
"},{"code":"IP3-2-2","description":"Information theory answers two fundamental questions in communication theory: what is the ultimate data compression (answer: the entropy H) and what is the ultimate transmission rate of communication (answer: the channel capacity, C). For this reason, it is considered that information theory is a subset of communication theory.","name":"Information theory","selfAssesment":"New
"},{"code":"IP3-2-3","description":"Keypoints are objects (or locations) on the ground that reveal locally invariant features in images and therefore are easily detectable by automatic algorithms. Methods for this process employ scale-invariant feature transform (SIFT) algorithms for the automatic detection of geospatial objects.","name":"Keypoint detection","selfAssesment":"New
"},{"code":"IP3-2","description":"Image understanding is part of computer vision. Computer vision is an interdisciplinary field that deals with how computers can be made to gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to automate tasks that the human visual system can perform.","name":"Computer vision in EO","selfAssesment":"New
"},{"code":"IP3-3-1","description":"A Digital Elevation Model (DEM) is a digital raster (or grid) representation of elevation values of land surface shapes and features, where each grid cell takes a single elevation value with reference to a certain vertical datum. A DEM can be global, regional or local in scope, and can be used to characterize the dry land surface (topography) or submerged surfaces (bathymetry). Since a DEM cannot contain information of shapes and features under overhanging structures, it is often referred to as 2.5D instead of truly 3D. \r\nA digital elevation model is an overarching term for either a digital surface model (DSM) or digital terrain model (DTM). A DSM includes elevations of surface features such as trees, buildings, bridges and artificial objects such as poles, power lines, cars etc., and thus contains always the highest elevations of any feature for any given raster cell. A DTM does not include such features but reflects the elevation of bare land surface shapes, excluding elevated or overhanging features.\r\nDEMs can be obtained using active or passive measurements. Active measurements involve the generation of electromagnetic signals towards a surface and timing the reception of the (return) signal(s). This can be achieved through laser scanning (LiDAR) using visible or infrared light pulses for bathymetric or topographic measurements respectively, radio waves (SONAR) used in bathymetric measurements, or microwaves (synthetic aperture radar, SAR) used in topographic mapping. The most widely known active remotely sensed global DEM is derived from the Shuttle Radar Topography Mission (SRTM) obtained by a SAR mounted on the space shuttle Endeavour, offering 30 m resolution with a vertical accuracy typically between 5 and 20 m, covering 80% of Earth’s surface.\r\nPassive measurements detect reflection of sun light, or energy radiated from the surfaces. Their distance to the detector can then be inferred from the measurement of angles. Historically, line scanning imagers were used, but nowadays, these are replaced by acquisitions of overlapping 2D frame images. On the images, corresponding land surface features are detected which act as tie-points. The distance between the sensor and the tie-points is calculated in a process called photogrammetry. The most widely known spaceborne passive remotely sensed global DEM is derived from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data onboard the Terra satellite. It offers similar resolution and accuracy compared to SRTM, but with 99% coverage. \r\nOnly LiDAR can generate both accurate DSMs and DTMs from the same data acquisition, by using multiple returns from a single emitted pulse. All other techniques generate DSMs, from which elevated features can be identified and filtered out in postprocessing to create DTMs, however with typically lower accuracy and more artefacts.","name":"DEM generation","selfAssesment":"Complete
"},{"code":"IP3-3-2","description":"DSM can be produced automatically from stereo satellite scenes, from satellite sensors such as GeoEye, IKONOS, SPOT-5, Terra-ASTER etc. The DSM can also be provided from stereo digital aerial photography at various resolutions, depending on the quality and scale of the aerial photography. The quality of the automatic generated DSM is substantially improved if ground measurements from GPS are incorporated in the DSM stereoscopic model.","name":"DSM generation","selfAssesment":"New
"},{"code":"IP3-3","description":"Stereo pairs of optical satellite images with the support of ground control points provide a basis for cross-stereo analysis for generating Digital Surface Models.","name":"Cross-stereo analysis","selfAssesment":"New
"},{"code":"IP3-4-1-1","description":"The goal of filtering is to remove unnecessary components from images (e.g., noise), while emphasizing the necessary ones. In the context of spatial aggregation, low pass filters aim at removing sharp transitions in the image intensities (high spatial frequencies) and thereby focus the information content of the image on a coarser scale level.","name":"Filtering","selfAssesment":"New
"},{"code":"IP3-4-1-2","description":"Gridding is the technique used to generate a uniform raster grid with one value for every cell in the raster. The values of the raster cells can represent different attributes such as mean, max or min of all Normalized Difference Vegetation Index (NDVI) values measured within a particular cell.","name":"Gridding","selfAssesment":"New
"},{"code":"IP3-4-1","description":"Spatial aggregation produces images of coarser resolution (grouping pixels in a grid of coarser resolution and calculating mean values) or of coarser scale (by filtering with low-pass filters). Thereby it is a form of generalization that may improve classification results. Spatial aggregation can be applied after classification to get rid of the salt-and-pepper effect.","name":"Spatial aggregation","selfAssesment":"New
"},{"code":"IP3-4-2-1","description":"Bayes’s theorem is an extremely powerful means of using information at hand to estimate probabilities of outcomes related to the occurrence of preceding events. Bayes' Theorem uses a priori (subjective) and conditional probabilities to calculate the probability of an uncertain event occurring. A priori probabilities represent what the modeler believes, before testing, to be the probability of an event occurring. Conditional probabilities are probabilities that other events occur in conjunction with the original event.","name":"Conditional probability","selfAssesment":"Planned
"},{"code":"IP3-4-2-2","description":"Maximum likelihood classification uses the training data for estimating means and variances of the classes, which are then used to estimate the probabilities. This method considers not only the mean, or average, values in assigning classification but also the variability of brightness values in each class.","name":"Maximum likelihood","selfAssesment":"In progress
"},{"code":"IP3-4-3-1","description":"The Land Cover Classification System (LCCS) was developed by FAO to provide a consistent framework for the classification and mapping of land cover. Its main objectives were to overcome the rigidity of a-priori land cover classifications, which in many practical situations do not allow easy assignment into one of the pre-defined classes and are therefore not very suitable for mapping. LCCS instead opted for an approach based on two main phases. The first phase is an initial ‘Dichotomous Phase’, in which eight major land cover types are defined: (1) Cultivated and Managed Terrestrial Areas, (2) Natural and Semi-Natural Terrestrial Vegetation, (3) Cultivated Aquatic or Regularly Flooded Areas, (4) Natural and Semi-Natural Aquatic or Regularly Flooded Vegetation, (5) Artificial Surfaces and Associated Areas, (6) Bare Areas, (7) Artificial Waterbodies, Snow and Ice, and (8) Natural Waterbodies, Snow and Ice. The Dichotomous Phase is followed by a subsequent ‘Modular-Hierarchical Phase’, in which land cover classes are created by the combination of sets of pre-defined classifiers, which are different for each of the eight major land cover types. For example, common classifiers used for (semi-) natural terrestrial vegetation types are Life Form, Cover, Height, Macropattern. For aquatic or regularly flooded natural and semi-natural vegetation, water seasonality is an indispensable classifier. LCCS offers several advantages from a conceptual point of view. LCCS is a real a priori classification system in the sense that, for the classifiers considered, it covers all their possible combinations. The classification is also hierarchical and the more classifiers used, the greater the detail of the defined land cover class. The classes derived from the proposed classification system are all unique and unambiguous, due to the internal consistency and systematic description of the classes. LCCS is designed to map at a variety of scales, from small to large. From a practical viewpoint LCCS offers several advantages: (1) easy incorporation into GIS and databases, (2) allows flexible response to information available in a given area, project budget and time constraints, (3) unlinks the field data collection from the interpretation process.","name":"Land cover classification system (LCCS)","selfAssesment":"Completed
"},{"code":"IP3-4-3","description":"Long-term monitoring of land cover and land use are particularly relevant for land ecosystem monitoring. Therefore, baseline datasets are necessary that allow assessing changes of land cover and land use where the class definitions remain consistent over time. Accordingly, classification schemes have been established that adhere to taxonomically correct definitions of classes of information organized according to logical criteria. If hard classification is to be performed (i.e. without fuzzy class boundaries), the classes in the classification system should normally be mutually exclusive, exhaustive, and hierarchical. Mutual exclusive classes have no taxonomic overlap and assign a land cover patch to a single class. An exhaustive classification scheme is able to cover the area of interest comprehensively and leaves no land cover patch unassigned. A hierarchical system allows combining sub-classes into higher-level categories.\r\nFrom a remote sensing classification perspective, it becomes clear that a classification scheme consists of information classes defined by human beings. Conversely, spectral classes are those inherent to EO data. An analyst must identify spectral classes and label them as information classes that satisfy bureaucratic (or scientific requirements). Additionally, the advantage of using established classification schemes is that their use in scientific studies and applications produces results that are comparable to other studies and suitable for sharing of data.\r\nEstablished classification schemes include: CORINE land cover (CLC), Land cover classification system (LCCS), American Planning Association land-based classification standard, United States Geological Survey land-use/land-cover classification system for remote sensor data, U.S. Department of the Interior Fish & Wildlife Service classification of wetland and deep water habitats of the United States, U.S. National Vegetation Classification system (NVCS), International Geosphere-Biosphere Program IGBP Land cover classification system.","name":"Classification schemes (taxonomies)","selfAssesment":"Completed
"},{"code":"IP3-4-4","description":"Unsupervised methods are defined as the identification of natural groups, or structures, within existing data. Clustering requires only the number of to-be generated classes as an input parameter and assigns spectrally defined classes to an image.","name":"Clustering (unsupervised)","selfAssesment":"New
"},{"code":"IP3-4-5-1","description":"A production system performs automatic transformation of remote sensing imagery into useful information (such as biophysical parameters, categorical maps etc). An example can be a preliminary pixel-based classifier that works top-down (deductive, physical model-driven, prior knowledge-based) and arrives at preliminary classes for each pixel of an image. Such a production system does not require interaction of an operator. The process makes use of a decision tree that encodes the prior knowledge for assigning pixels to a class.","name":"Production system","selfAssesment":"New
"},{"code":"IP3-4-5","description":"Decision trees is a data mining technique used in different disciplines including Remote Sensing. It uses a tree-like prediction model to identify a pattern in the input data. One of the most popular decision tree algorithms is the CART (Classification and Regression Tree) algorithm.","name":"Decision trees","selfAssesment":"New
"},{"code":"IP3-4-6-1","description":"Convolutional Neural Networks (CNNs) are among the most popular deep learning methods.","name":"Convolutional neural networks (CNN)","selfAssesment":"New
"},{"code":"IP3-4-6","description":"Deep learning approaches have classically been divided into spatial learning (for example, convolutional neural networks for object classification) and sequence learning (for example, speech recognition)","name":"Deep learning","selfAssesment":"New
"},{"code":"IP3-4-7-1","description":"The RF classifier is an ensemble classifier that uses a set of Classification and Regression Trees (CARTs) to make a prediction The trees are created by drawing a subset of training samples through replacement (a bagging approach).","name":"Random forest (RF)","selfAssesment":"New
"},{"code":"IP3-4-7-2","description":"In machine learning, support vector machines (SVMs) are supervised non-parametric statistical learning techniques with associates learning algorithms that analysze data used for both classification and regression analysis. SVM algorithm was originally designed for binary classification. The SVM is based on the main hypothesis that the training set is linearly separable. Given a set of training examples, each marked as belonging to one or another of two categories, an SVM training algorithm builds a model that can assign each new occurrence into one of these two categories, making it a non-probabilistic binary linear classifier. The SVM model is a representation of the examples as points in space, mapped so that the algorithm can find the optimal line (hyperplane) which separates with minimum error the training set, and maximizes the distance, named the “gap”, between the objects of both classes and the hyperplane. Thus, instead of using the whole available training set to describe classes, SVM uses only those training samples that describe class boundaries (support vectors), thought it can be more efficient than other algorithm because it uses a subset of training points. New occurs are then mapped into that same space and predicted to belong to a category based on the side of the gap on which they fall. In addition to performing linear classification, SVMs can also efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces. Unfortunately, because of the technique used for separating classes SVM is less effective on noisier datasets with overlapping classes. When data are unlabelled, supervised learning is not possible, and an unsupervised learning approach is required. SVM is used for text classification tasks such as category assignment, spam detection and sentimental analysis. It is also commonly used for image recognition, performing particularly well in aspect-based recognition and colour-based recognition. SVM also plays a vital role in many areas of handwritten digit recognition, such as postal automation services.","name":"Support vector machines (SVM)","selfAssesment":"Completed
"},{"code":"IP3-4-7","description":"Field of study that gives computers the ability to learn without being explicitly programmed","name":"Machine learning","selfAssesment":"New
"},{"code":"IP3-4-8","description":"Image classification operator needs a set of terms to express the characteristics of an image. These characteristics are called interpretation elements and are used to define interpretation keys: tone/hue, texture, pattern, shape, size, height/elevation, location/association","name":"Mental concepts and categories","selfAssesment":"New
"},{"code":"IP3-4-9","description":"Sampling strategies or sampling pattern specifies the arrangement of observations used for training and/or validation purposes.\r\nTypically, the simple random sample of a geographic region is defined by first dividing the region to be studied into a network of cells. Each row and column in the network is numbered, then a random number table is used to select values that, taken two at a time, form coordinate pairs for defining the locations of observations. Because the coordinates are selected at random, the locations they define should be positioned at random. The random sample is probably the most powerful sampling strategy available as it yields data that can be subjected to analysis using inferential statistics.\r\nA stratified sampling pattern assigns observations to subregions of the image to ensure that the sampling effort is distributed in a rational manner. For example, a stratified sampling effort plan might assign specific numbers of observations to each category on the map to be evaluated. This procedure would ensure that every category would be sampled.\r\nSystematic sampling positions observations at equal intervals according to a specific strategy. Because selection of the starting point predetermines the positions of all subsequent observations, data derived from systematic samples will not meet the requirements of inferential statistics for randomly selected observations.","name":"Sampling strategies","selfAssesment":"New
"},{"code":"IP3-4","description":"The process of image classification extracts information about semantic labels of pixels or objects (i.e. regions) from imagery. Apart of input imagery, the process requires an input set of target classes (classification scheme) for which their spectral (and other) properties have to be identified. A classification method has to be selected that transforms the image data and the classification scheme into semantic map information. In complement to the resulting sematic labelling products, a secondary outcome are instructions or rulesets with the used parameters that constitute the documentation of the classification process.\r\nThe input imagery consists of one or more images (optical and/or SAR data) of a specific geographic area, collected in multiple bands of the electromagnetic spectrum (that may have already undergone certain pre-processing steps; determined by the purpose). Additionally, the imagery may include derived spectral indices, principal components, filtered bands, or other features to support the classification process.\r\nThe classification purpose defines the information about the target classes. It includes classification schemes (taxonomies), spectral signatures for each class and, mental concepts and categories about the classes (that enable an analyst to distinguish classes by texture, spatial relationships etc.). Often, training areas are used to understand how an object of a particular class is discernible in the available imagery and separable from other classes. Both the input imagery and the chosen classification method determine which features of each class can be exploited for classification. For example, spectral signatures of the target classes (extracted from training areas with known class label) may be a suitable input for extracting information with a pixel-based classification. For shape features, objects are a pre-requirement, derived with segmentation. They are only available with object-based classification approaches.\r\nClassification methods: Various methods exist that can be categorized according to the classification logic that they follow when transforming the input information into the output semantic labelling products. These can be parametric or nonparametric, supervised or unsupervised, per-pixel or object-oriented, semi-automated or fully automatic, and hybrid approaches. Classification methods are for example bayesian techniques like conditional probability or maximum likelihood, clustering (unsupervised), decision trees, deep learning and machine learning.","name":"Image classification","selfAssesment":"Completed
"},{"code":"IP3-5-1","description":"Edge detection is a fundamental tool used in many image processing applications to obtain information from the frames as a precursor step to feature extraction and object segmentation. This process detects outlines of an object and boundaries between objects and the background in the image. An edge-detection filter can also be used to improve the appearance of blurred image.","name":"Edge-based segmentation","selfAssesment":"Planned
"},{"code":"IP3-5-2","description":"Histogram-based segmentation makes use of histogram to select the gray levels for grouping the pixels into regions, e.g. background and the object of interest","name":"Histogram-based segmentation","selfAssesment":"New
"},{"code":"IP3-5-3","description":"Local variance can be calculated as the value of standard deviation in a small neighborhood (e.g. 3x 3 moving window), then computing the mean of these values over the entire image. The obtained value is an indicator of the local variability in the image.","name":"Local variance","selfAssesment":"New
"},{"code":"IP3-5-4","description":"Mean Shift is defined as finding modes in a set of data samples, manifesting an underlying probability density function (PDF).","name":"Mean-shift segmentation","selfAssesment":"New
"},{"code":"IP3-5-5","description":"Regionalization is an important concept in Geographic Information Science for synthesizing multi-dimensional data into homogeneous objects through spatially constrained clustering methods","name":"Regionalisation","selfAssesment":"New
"},{"code":"IP3-5-6-1","description":"Multi-resolution segmentation is a region-growing algorithm. It relies on several parameters, which need to be tuned. These include the scale parameter (SP), which dictates the size and homogeneity of the resultant objects.","name":"Multi-resolution segmentation","selfAssesment":"Planned
"},{"code":"IP3-5-6-2","description":"Watershed segmentation is a region-based method that has its origins in mathematical morphology. In watershed segmentation an image is regarded as a topographic landscape with ridges and valleys. The elevation values of the landscape are typically defined by the gray values of the respective pixels or their gradient magnitude. Based on such a 3D representation the watershed transform decomposes an image into catchment basins. For each local minimum, a catchment basin comprises all points whose path of steepest descent terminates at this minimum. Watersheds separate basins from each other. The watershed transform decomposes an image completely and thus assigns each pixel either to a region or a watershed.","name":"Watershed segmentation","selfAssesment":"New
"},{"code":"IP3-5-6","description":"Region-based segmentation algorithms can be devided into region growing, merging and splitting techniques and their combinations. Region merging starts from all pixels on the pixel level and iteratively aggregates pixels into objects until some conditions of homogeneity imposed by the user are met.","name":"Region-based segmentation","selfAssesment":"New
"},{"code":"IP3-5-7","description":"Spatial autocorrelation is the term used to describe the presence of systematic spatial variation in a variable.","name":"Spatial autocorrelation","selfAssesment":"New
"},{"code":"IP3-5","description":"The term image segmentation denotes the process of algorithmically grouping neighbouring pixels that are similar. What sounds rather straight forward, is in fact a great computational challenge, some even call it an ill-posed problem, because there is a high degree of ambiguity in this process. \r\nThe two attributes in the general definition provided above, i.e. neighbouring and similar, evoke the principles of regionalisation as a fundamental concept in geography. Regionalisation is the bottom-up approach to congregate adjacent elements with the aim to form a larger unit. (Conversely, this could be understood in a top-down manner when subdividing a larger whole into smaller homogeneous units). This follows the general notion of hierarchical organisation according to general systems theory (GST). The organisation of a state in smaller administrative units is a good example for a hierarchical structure, the composition of the human body by organs, cells, etc. another. In image analysis such regions are commonly referred to image regions, originating from the concept of “photomorphic regions”, literally meaning regions formed on images – originally by human interpreter through manual delineation. Today, advanced pixel grouping algorithms aim to delineate homogenous regions in an image automatically. As those regions usually are assumed to match with real-world objects, it is often stated in literature that image segmentation generates image objects. Deriving some general heuristics on their properties (colour, size, shape, orientation, etc.) we can label these objects according to a given semantic scheme. The procedure of object delineation and classification using object features and relations is a fundamental principle in object-based image analysis (OBIA). \r\nDue to the effect of spatial autocorrelation (the tendency of neighbouring pixels to be similar irrespective of scale or geographical location), pixel grouping is ambiguous and by no means trivial, but not arbitrary either. Intuitively, image regions are those quasi-homogeneous areas that we perceive as landscape units on a specific scene (a lake, a forest patch, a single tree, a building, a residential area). According to hierarchy theory, we can assume that we find multiple scales within a single image even, according to the level of detail we are interested in. Whether or not a specific grouping of pixels is considered valid, e.g. because it corresponds to a real-world object, can hardly be answered unanimously, but rather needs to be judged by experts in the respective application domain. That is why often in literature we find the term ‘meaningful objects’. \r\nImage segmentation is as a sub-field of computer vision and aims to apply computer algorithms to generate image regions (a.k.a. tokens) within digital image analysis. There are several strategies for performing image segmentation, all resting on the following general principles: (1) regions do not overlap; (2) regions are (relatively) homogenous; regions are (relatively) different to neighbouring regions; regions are fairly equally sized (belong to one scale domain) but can be built in several hierarchical scales. General strategies include (1) edge-based segmentation and (2) region-based segmentation, and multi-scale segmentation as a specific case. \r\nAlso referred to spatial classification emphasizing the constraint of spatial contingency, image segmentation aggregates neighbouring pixels, but – as compared to statistical clustering techniques – does not provide a unique set of classes (either semantic or statistic) in the feature space. \r\nRecently the term semantic segmentation has emerged in the machine-learning community, which is in fact a combination of segmentation and categorisation (labelling) via deep learning methods (e.g. convolutional neural networks).","name":"Image segmentation","selfAssesment":"Completed
"},{"code":"IP3-6-1","description":"Combined filtering uses different filters to arrive at more complex filters for specific purposes. \r\nFor example, Laplacian filters are derivative filters used to find areas of rapid change (edges) in images. Since derivative filters are very sensitive to noise, it is common to smooth the image (e.g., using a Gaussian filter) before applying the Laplacian. This two-step process is called the Laplacian of Gaussian (LoG) operation.","name":"Combined filtering","selfAssesment":"New
"},{"code":"IP3-6-2","description":"The aim of sharpening filters is to highlight transitions in intensity (high frequency components) using different operators: directional (horizontal, vertical, diagonal) or isotropic (e.g. Laplacian Filter). Example of edge detectors include: Gaussian edge detector, Laplacian filter etc.","name":"Edge detectors","selfAssesment":"New
"},{"code":"IP3-6-3-1","description":"The Lee-sigma filter is a conceptually simple but effective alternative to the Lee and other sophisticated adaptive filters. It is based on the sigma probability of the Gaussian distribution.","name":"Lee-Sigma","selfAssesment":"New
"},{"code":"IP3-6-3","description":"High-pass filtering enhance information of high frequencies (local extremes, lines, edges)","name":"High-pass filtering","selfAssesment":"New
"},{"code":"IP3-6-4-1","description":"Gaussian Filters are isotropic (same behavior in all directions).","name":"Gauss filter","selfAssesment":"New
"},{"code":"IP3-6-4","description":"Spatial filters transform an image by taking into account the local neighborhood of a pixel. The goal of filtering is to remove unnecessary components from images (e.g., noise), while emphasizing the necessary ones. In this context, low pass filters aim at removing sharp transitions in the image intensities (high spatial frequencies).","name":"Low-pass filtering","selfAssesment":"New
"},{"code":"IP3-6","description":"In contrast to the point operations used for radiometric modification of image data, techniques for geometric processing are characterized by operations over local neighborhoods of pixels. The result of a neighborhood operation is still a modified brightness value for the single pixel at the center of the neighborhood , however the new value is determined by the brightness of all the local neighbors rather than just the original brightness value of the central pixel alone.","name":"Neighbourhood analysis (convolution)","selfAssesment":"Planned
"},{"code":"IP3-7-1","description":"Class modelling provides flexibility in designing a transferable workflow from scene-specific high-level segmentation and classification to region-specific multi-scale modelling","name":"Class modelling","selfAssesment":"Planned
"},{"code":"IP3-7-2","description":"Hierarchical representation refers to hierarchically scaled compositions of the classes to be classified.","name":"Hierarchical representation","selfAssesment":"New
"},{"code":"IP3-7-3","description":"Per-parcel analysis relies on parcels or objects as the smallest units of image analysis. The parcels are usually obtained through image segmentation that partition the input images into homogeneous units, i.e. parcels, in a supervised or unsupervised manner.","name":"Per-parcel analysis","selfAssesment":"New
"},{"code":"IP3-7-4-1","description":"Distance relationships describe how far an object is with respect to a reference. Proximity analysis allows the identification of the distance between a geographic feature of interest and its neighbors.","name":"Distance and proximity features","selfAssesment":"New
"},{"code":"IP3-7-4-2","description":"The most important geometric features of geographic objects are their size and shape. Shape refers to general form or outline of individual objects and can be quantified using different metric such as shape index, compactness, asymmetry, density, elliptic fit, roundness, rectangular fit etc.","name":"Planar geometric features","selfAssesment":"New
"},{"code":"IP3-7-4-3","description":"Topological features characterize qualitatively the position of spatial objects relative to each other. There are different models for representing topological relationships. Calculus-based method, for example, allows us to model five topological relationships of two spatial objects: touch, in, cross, overlap, disjoint.","name":"Topological features","selfAssesment":"New
"},{"code":"IP3-7-4","description":"An object of a specific object class has a value on the range of values of a spatial or spectral feature. A set of features provides the feature space that is used for classification.","name":"Spatial features","selfAssesment":"Planned
"},{"code":"IP3-7","description":"OBIA is an iterative method that starts with the segmentation of satellite imagery into homogeneous and contiguous image segments (also called image objects. In the next step, resulting image segments are assigned to the target classes.","name":"Object-based image analysis (OBIA)","selfAssesment":"Planned
"},{"code":"IP3-8-1","description":"The feature space represents in various dimensions all the features that can be used for classification (e.g. image bands, band math parameters, derived texture properties). A point in that space is also called a vector with values for each feature (or dimension). Polyhedralization is a form of vector space quantization where a vector is assigned to the closest centre point of one polyhedron.","name":"Feature space polyhedralization","selfAssesment":"New
"},{"code":"IP3-8-2","description":"Radiative transfer models describing the interaction between matter and electromagnetic radiation serve as cornerstones for optical remote sensing. The radiative transfer theory provides the most logical linkage between observations and physical processes that generate signals in optical remote sensing. Radiative transfer modelling is therefore an integral part of remote sensing, since it provides the most efficient tool for accurate retrievals of Earth properties from satellite data. Radiative transfer models are used in a number of different applications such as sensor radiometric calibration, atmospheric correction and the modelling radiation processes in vegetation canopies. \r\nVegetation radiative transfer models (RTMs) study the relationship between leaf and canopy biophysical variables and reflectance, absorbance and scattering mechanisms. The infinite variability of vegetation structure complicates the modeling of RT in vegetation canopies. Numerous models of RT in vegetation canopies were developed in the second half of the last century. Models differ by the details accounted for and by the simplifications introduced in the description of canopy structure and photon–vegetation interactions. Gradual improvement in RTMs accuracy, yet in complexity too, have diversified RTMs from simple turbid medium RTMs towards advanced Monte Carlo RTMs that allow for explicit 3D representations of complex canopy architectures. This evolution has resulted in an increase in the computational requirements to run the model, which bears implications towards practical applications. When choosing an RTM, a trade-off between invertibility and realism has to be made: simpler models are easier to invert but less realistic, while advanced models more realistic but require a large amount of variables to be configured. The two most widely used models are the leaf model PROSPECT and Scattering by Arbitrary Inclined Leaves (SAIL) canopy model. \r\nAtmosphere RTMs study the interaction of radiation with the atmosphere. The remotely-sensed signals at satellite or airborne platforms are combinations of surface and atmospheric contributions, with relative amounts varying across the two wavelength regions, depending on the condition of the atmosphere. The order of magnitude of atmosphere signals can be equal or larger than that of land or ocean surface signals that arise at the top of the atmosphere (TOA). In order to derive accurate sensor calibration and atmospheric correction, the contribution of the atmospheric constituents to the total retrieved signal must be understood and modelled. Atmospheric radiative transfer models simulate the radiative transfer interactions of light scattering, absorption and emission through the atmosphere. Some widely used atmospheric RTMs are 6SV, libRadtran, MODTRAN, and ATCOR.\r\nAdvances in radiative transfer modeling enhance our ability to detect and monitor changes in our planet through new methodologies and technical approaches to analyze and interpret measurements from air- and space-borne sensors.","name":"Radiative transfer modelling","selfAssesment":"Completed
"},{"code":"IP3-8","description":"Historically, physical modelling and machine learning have often been treated as two different fields with very different scientific paradigms (theory-driven versus data-driven). Yet, in fact these approaches are complementary, with physical approaches in principle being directly interpretable and offering the potential of extrapolation beyond observed conditions, whereas data-driven approaches are highly flexible in adapting to data and are amenable to finding unexpected patterns (surprises).","name":"Physical-model based analysis","selfAssesment":"New
"},{"code":"IP3-9-1","description":"Difference of Gaussians (DoG) method consists of subtracting two Gaussians, where a kernel has a standard deviation smaller than the previous one. The convolution between the subtraction of kernels and the input image results in the edge detection of this image.","name":"Difference of Gaussian (DoG)","selfAssesment":"New
"},{"code":"IP3-9-2","description":"Scale Invariant Feature Transform (SIFT) is an image descriptor for image-based matching and it is used for a large number of purposes in computer vision related to point matching between different views of a 3-D scene and view-based object recognition. The SIFT descriptor is invariant to translations, rotations and scaling transformations in the image domain and robust to moderate perspective transformations and illumination variations. Experimentally, the SIFT descriptor has been proven to be very useful in practice for robust image matching and object recognition under real-world conditions.","name":"Scale invariant feature transformation (SIFT)","selfAssesment":"New
"},{"code":"IP3-9","description":"Scale-space theory is a framework for multiscale image representation, which has been developed by the computer vision community with complementary motivations from physics and biologic vision. The idea is to handle the multiscale nature of real-world objects, which implies that objects may be perceived in different ways depending on the scale of observation. If one aims to develop automatic algorithms for interpreting images of unknown scenes, there is no way to know a priori what scales are relevant. Hence, the only reasonable approach is to consider representations at all scales simultaneously.","name":"Scale space analysis","selfAssesment":"New
"},{"code":"IP3","description":"Image data, in order to be turned into information, require interpretation. Thereby image understanding is the process of scene reconstruction, the description and mental representation of the content of imaged, and potentially complex, realities. \r\nImage understanding thereby goes beyond single feature extraction. Instead, it aims at a complete description of the image content, i.e. the reconstruction of a real-world scene. In the early days of digital image processing, image understanding was mainly confined to identifying and labelling image primitives. Today, advanced mapping keys and hierarchical classification schemes to analyse EO data, include composite and complex target classes. Thereby ‘full’ scene description means reaching from signal processing to a symbolic representation of the scene content. This entails the relationships of real‐world objects in different scales and spatio-temporal aspects.\r\nDescribing a scene, visually or computer-aided or mixed, depends on a conceptual framework comprising (a) the underlying research question within (b) a specific field of application and (c) pre‐existing knowledge and experience of the operator. Obtaining insights from imagery requires general knowledge about the expected scene content and domain expertise. The field of image understanding is interlinked with image (pre-)processing, computer vision, and artificial intelligence (AI). Image processing conditions the data material and enhances the interpretation source. Computer vision including pattern recognition providing knowledge representation, expert systems. AI is mainly concerned with automation processes, be it via knowledge transfer to an automated system or machine / deep learning.\r\nIn analogy to the human mind, image understanding is the computational process of extracting information from images, i.e. locating, characterizing, and recognizing objects and other features in the depicted scene. However, image understanding is not a linear, but rather a cyclic process and takes place during the pre-processing and data assimilation steps. For example, cloud masks on EO images is an early product of image understanding, prior to many pre-processing tasks.\r\nIn a typical GEOBIA workflow, the process of image understanding can be illustrated by the following steps: Starting from the subset of a real‐world scene captured on an image first step may entail scaled representations by grouping neighbouring pixels on several hierarchical sales. The multi‐scale segmentation provides a set of nested objects with geospatial and spectral properties to be used in the classification process. \r\nWith object hypotheses in mind the object relation modelling can be realized by encoding expert knowledge into a rule system. This setp aims at categorizing the image objects by their spectral and spatial properties and their mutual relationships. Hereby, an object‐centred view is accomplished. This representation of the image content should meet the conceptual reality of the interpreter or user. Knowledge is stepwise adapted and improved through progressive interpretation and modelling. Experience grows, as knowledge will be enriched by analyzing unknown scenes and the transfer of knowledge may incorporate or stimulate new rules.","name":"Image understanding","selfAssesment":"Completed
"},{"code":"IP4-1-1","description":"Once the user finds the required data, she/he needs to know how can they be accessed, possibly including authentication and authorisation.","name":"Accessibility","selfAssesment":"New
"},{"code":"IP4-1-2","description":"Quality Indicators (QIs) should be ascribed to data and, in particular, to delivered information products, at each stage of the data processing chain - from collection and processing to delivery. A QI should provide sufficient information to allow all users to readily evaluate a product’s suitability for their particular application, i.e. its “fitness for purpose”.","name":"GEO QA4EO","selfAssesment":"New
"},{"code":"IP4-1-4","description":"ISO is an independent, non-governmental international organization with a membership of 164 national standards bodies. Through its members, it brings together experts to share knowledge and develop voluntary, consensus-based, market relevant International Standards that support innovation and provide solutions to global challenges. ISO/TC 211 Geographic information/Geomatics provides Standardization in the field of digital geographic information. Note: This work aims to establish a structured set of standards for information concerning objects or phenomena that are directly or indirectly associated with a location relative to the Earth. These standards may specify, for geographic information, methods, tools and services for data management (including definition and description), acquiring, processing, analyzing, accessing, presenting and transferring such data in digital / electronic form between different users, systems and locations.","name":"ISO standards","selfAssesment":"New
"},{"code":"IP4-1-5","description":"The OGC is the worldwide leading consortium of GIS industries promoting the interoperability of geographic information across platform, system, and country borders. The main field of current activity is the complete integration of the sources of geographic information based on the Internet.The Open GIS Consortium (OGC) plays an important role on the implementation level.","name":"OGC standards","selfAssesment":"New
"},{"code":"IP4-1-6","description":"A fundamental pillar in (open) science is to verify the scientific results of others to advance knowledge. The lack of reproducibility in scientific studies brings challenges in understanding and recreating the results of others, a situation that may be common in data-based and algorithm-based research like in geocomputation. In general, many authors define reproducibility as the ability to compute exactly the same results of a study based on original input data and analysis workflow. In other words, “to rerun the same computational steps on the same data the original authors used”. Replicability is often seen as obtaining similar conclusions about a research question derived from an independent study or experiment. In the field of GIScience and geocomputation, in particular, a reproduction is always an exact copy or duplicate, with exactly the same features and scale, while a replication resembles the original but allows for variations in scale, for example. Hence, reproducibility is exact whereas replicability means confirming the original conclusions, although not necessarily with the same input data, methods, or results.","name":"Replicability and reproducibility","selfAssesment":"Completed
"},{"code":"IP4-1-7","description":"The ultimate goal of FAIR is to optimise the reuse of data. To achieve this, metadata and data should be well-described so that they can be replicated and/or combined in different settings.","name":"Reusability","selfAssesment":"New
"},{"code":"IP4-1","description":"Data quality standards are guiding principles and operational guidelines for the production and use of data. For example, QA4EO aims for the two key principles of accessibility / availability and suitability / reliability. The QA4EO guidelines provide instructions for the implementation of processes that follow these principles. Standards emerge from standardization processes within the community. They are based on the agreement of the members of the community.","name":"Data quality standards","selfAssesment":"New
"},{"code":"IP4-2-1-1","description":"To correctly perform a classification accuracy (or error) assessment, it is necessary to systematically compare two sources of information: (1) pixels or polygons in a remote sensing-derived classification map, and (2) ground reference test information (which may in fact contain error). The relationship between these two sets of information is commonly summarized in an error matrix (sometimes referred to as contingency table or confusion matrix). Indeed, the error matrix provides the basis on which to both describe classification accuracy and characterize errors, which may help refine the classification or estimates derived from it.","name":"Error matrix","selfAssesment":"New
"},{"code":"IP4-2-1-2","description":"F-score represents the harmonic mean between precision and recall. As F-score combines both precision and recall, it can be regarded as an overall quality measure. The range of F is from 0 to 1 with larger values representing higher accuracy.","name":"F-score","selfAssesment":"New
"},{"code":"IP4-2-1-3","description":"Ground reference refers to the reference dataset for an accuracy assessment of a remote sensing classification. The process of obtaining ground reference is dedicated to support the production of suitable accuracy information. A sampling design (fitting to the produced image classification) determines the most appropriate distribution of sample locations (or regions). The response design consists of the evaluation protocol and the labeling protocol. The evaluation protocol initiates selecting the support region on the ground (represented by a pixel or polygon) where the ground information will be collected. Once the location and dimension of the sampling unit are defined, the labelling protocol is initiated and the sampling unit is assigned a hard or fuzzy ground reference label. This ground reference label (e.g. forest) is paired with the remote sensing-derived label (e.g., forest) for assignment in the error matrix.","name":"Ground reference","selfAssesment":"New
"},{"code":"IP4-2-1-4","description":"Kappa is a value for measuring the overall accuracy of a classification that accounts for randomness of class assignment. Kappa analysis is a discrete multivariate technique of use in accuracy assessment. Kappa yields a statistic, ^K, which is an estimate of Kappa. It is a measure of agreement between the remote sensing-derived classification map and the reference data as is indicated by a) the major diagonal and b) the chance of agreement, which is indicated by the row and column totals in the error matrix.","name":"Kappa statistics","selfAssesment":"New
"},{"code":"IP4-2-1-5","description":"These two quality assessment indicators are calculated as follows:\r\nPrecision = TP/(TP+FP) \r\nRecall = TP/(TP+FN),\r\nwhere TS is true positive, FP is false positive, FN is false negative","name":"Precision & recall","selfAssesment":"New
"},{"code":"IP4-2-1-6","description":"Geometric correction procedures (image-to-map rectification, image-to-image rectification) are used to rectify remotely sensed data to a standard map projection whereby it may be used in conjunction with other spatial information in a GIS to solve problems. The rectification process normally involves selecting ground control point (GCP) image pixel coordinates (row and column) with their map coordinate counterparts (e.g. meters northing and easting in a UTM map projection). Rectification requires that polynomial equations (that translate from image coordinates to map coordinates) be fit to the GCP data using least squares criteria. Depending on the distortion in the imagery, the number of GCPs used, and the degree of topographic reliefdisplacement in the area, higher -order polynomial equations may be required to geometrically correct the data. To determine how well the six coefficients derived from the least-squares registration of the initial GCPs account for geometric distortion in the inpit image, for each GCP, the root-mean-square error (RMSE) is computed.","name":"Root mean square error (RMSE)","selfAssesment":"In progress
\r\n\r\n"},{"code":"IP4-2-1","description":"A growing set of EO services and applications produce EO products that describe various aspects of the land, ocean and atmosphere. These products include for example image products at different processing levels, geometric measurements like in digital elevation models, semantic labelling products like land cover classifications, and EO-derived attribute products concerning air quality or other geophysical and biophysical parameters. Same as any geospatial data, EO products are not free of error and require accompanying documentation of their product quality. One term for describing different quality dimensions of an EO product is accuracy.\r\nAccuracy is a measure to estimate the uncertainty that originates from errors. An error is the deviation of a map value from a true value. The concept of error assumes well-defined phenomena where deviation results from imperfection of measurement equipment, environment effects, or imperfections of the observer. They cause gross errors and blunders, systematic errors, and random errors, for which different approaches are necessary to minimize error. Ideally, only random error remains that is probabilistic in nature and can be assessed with statistical approaches. For poorly defined phenomena, the concept of vagueness applies. For example in the case of thematic maps using fuzzy sets, the accuracy assessment requires a fuzzy approach as well. \r\nJudging error requires reference data with higher accuracy (by an order of magnitude) to which the map value can be compared. EO product quality dimensions about accuracy include thematic accuracy, spatial accuracy (both horizontal and vertical), radiometric accuracy, and accuracy of biophysical/geophysical parameter measurements. Respective equipment and approaches for reference data collection includes ground verification for thematic maps, GNSS positioning devices, field spectrometers, air quality sensors and in-situ biomass estimation. Ideally, reference data is collected in the field. In case of inaccessible areas of interest and/or if the service requirements allow it, approaches may rely on proxy reference data.\r\nThe design of the accuracy assessment procedure should be done with the EO product design to match the requirements of the EO service. For example, a thematic accuracy assessment consists of the main three components of response design, analysis, and sampling design. The response design ensures that reference data and map data are comparable at a location and specifies under which cases they agree or disagree. The analysis, usually performed with an error matrix, specifies which quality indicators will be calculated to quantify accuracy. The sampling design specifies the subset of locations at which the response design will be applied. Depending on the classification process and application case, different sampling strategies can be suitable (e.g. clustered sampling, stratified random sampling). \r\nFor other accuracy dimensions, respective accuracy assessment procedures exist, e.g. root mean squared error (RSME) for the positional accuracy assessment.\r\nAfter an accuracy assessment has been performed and the uncertainty in the EO product is understood, the challenge is to clarify how the uncertainty affects subsequent spatial analyses with the EO product. Different strategies exist that ignore error completely or that account for error by modelling uncertainty in the analysis outcomes. If uncertainty is judged low enough (or more hazardous, if users are unaware of the limited accuracy), subsequent analyses accept the EO product as true and ignore the accuracy value. If uncertainty is incorporated in subsequent analysis through uncertainty modelling, the results describe the bandwidth of outcomes, potentially supported with appropriate visualisations of uncertainty. The uncertainty modelling approach may greatly enhance the usability of the EO product, because it informs better how the error impacts the EO information and how much confidence a user should have in it.\r\nWith a new generation of EO products on the horizon and a largely increased user community, a large number of new applications is to be expected. They may also identify innovative accuracy assessment approaches. For example, the availability of EO archives with long time series of EO data led to response design protocols tailored to collect time series of reference data. The use of volunteered geographic information (VGI) as reference data has great potential, if approaches are implemented that ensure its reliability. Methods for object-based accuracy assessment are continued to be developed. Further, the increasing number of EO parameter products based on continuous variables creates the need to describe their accuracy. Finally, the focus on validation of EO products during EO service development and operation will make feedback from users available to service providers, ultimately leading to more meaningful EO products with more meaningful accuracy metrics and other quality indicators.","name":"Accuracy assessment","selfAssesment":"
Completed
"},{"code":"IP4-2-2","description":"The implementation of a service that provides remote sensing derived information on a regular basis introduces process-related quality criteria like the timeliness of information provisioning. For the case of refugee camp mapping, timely arrival of map information may be critical to support the decisions in planning facilities for humanitarian assistance.","name":"Timeliness","selfAssesment":"New
"},{"code":"IP4-2-3-1","description":"Completeness is a quality dimension that can apply to different data properties.The Data completeness is dealing with the completeness of an image, handling for example the effect of shadowing objects, sun flares on water surfaces or masking out by an object (e.g. propeller of a UAV). Spatial completeness is a feature on the area coverage. In photogrammetry (especially in stereophotogrammetry) its 3D version, the stereo completeness has extreme importance. In monitoring systems and applications the Temporal completenesster term features how the taken images represent a complete time series. The thematic completeness measure describes the image interpretation quality how the expected and defined classes are evaluated. This feature is important with the use of e.g. multiple classifiers.","name":"Completeness","selfAssesment":"New
"},{"code":"IP4-2-3-2","description":"In remote sensing we can speak about spatial consistency in the Consistency cluster. It represents the quality of image interpretation/understanding: how are the different objects or classes recognized/evaluated integrally. A bridge above a water surface, like river can be detected in pixel-wised manner, but the question is how coherent they are in the output map. This phenomenon has very close to the thematic consistency, where the recognition integrity is represented in this way. The topological consistency is defined mainly for network-type surface objects, like roads or rivers, where the connection of all atomic segments are rated by this measure. Urban mapping focuses on the built environment objects, where e.g. house-parcel inclusions are described by this feature. The temporal consistency is for monitoring again, representing for example the possibility or impossibility of land cover changes in time. Having multiple data sources (even airborne or terrestrial), their integral usage can be qualified by this measure.","name":"Consistency","selfAssesment":"New
"},{"code":"IP4-2-3-3","description":"Readability refers to the content of a map being presented clearly enough that the content can be perceived and understood by the user. This includes legibility, e.g. whether the text of a label is large enough to be read and has enough contrast to the background to be easily perceivable. Additionally, readability has a broader meaning that explains whether a product as a whole is simple enough to be understood and not too complex that essential information can be overlooked by the user.","name":"Readability","selfAssesment":"New
"},{"code":"IP4-2-3","description":"Gathering information about the quality of an EO product or service by letting the user test it. The feedback from the user enables to verify whether specific quality criteria have been met.","name":"User validation","selfAssesment":"New
"},{"code":"IP4-2","description":"A product in the sense of something that a user can use for a specific purpose requires a certain quality. Therefore, its accuracy needs to be judged with an accuracy assessment measure that the user understands and where he can interpret the meaning in relation to the purpose. The product has to be validated, i.e. it has to be known whether the product qualifies for use in a certain context. And in addition, the product needs to be available in time that the users can base their decision on it.","name":"Product quality","selfAssesment":"New
"},{"code":"IP4-3-1","description":"The cloud cover percentage indicates the amount of area in the remote sensing image extent that is covered with clouds and therefore cannot provide information about the Earth surface conditions.The actual types of clouds included may depend on the product, but the CEOS definition includes cloud shadow. Next to that, from an optical remote sensing point of view, clouds can be roughly classified in: opaque/dense clouds, mainly composed of droplets that are highly reflective in the VIS region and generally located at low-medium altitudes and cirrus, consisting of a large number of thin non-spherical ice crystals that are normally translucent in the VIS region, relatively highly reflective in the SWIR spectrum, and located at high altitude.\r\n\r\nThe goal of cloud cover percentage is to provide a quality measure of usable information in a surface reflectance image. Earth observation product catalogs support it as a query parameter, to enable searching for products with a cloud cover percentage below a given threshold.\r\nThis simplifies for instance use cases that require only fully clear products (0% cloud cover), and may save download and processing resources by only handling images that have some valid pixels. For instance, by only using products with a cloud cover percentage smaller than 99.95%. The measure also gives an estimate of the number of valid observations in a given geographical area, allowing a quick assessment of whether minimal data requirements for a specific use case are met.\r\n\r\nThe measure is a percentage of actual observations in an image, so pixels where no data was recorded are not included. For derived products, cloud cover pixels are often also flagged separately from pixels where no data was recorded, but this may depend on the data provider. The definition specifically also includes cloud shadow pixels.\r\nReliable cloud cover percentages depend on good cloud and cloud shadow detection methods. Especially handling of translucent cirrus clouds is an open issue: a product that has a 100% cloud cover percentage due to cirrus clouds might still be usable for some cases, while for other cases they also render the product useless. \r\n\r\nThe used cloud detection algorithm will also affect the cloud cover percentage. A more strict algorithm will yield higher percentages compared to an algorithm that under detects clouds.\r\nDue to these limitations, cloud cover percentages in product metadata have a fairly high error margin. The user should take this into account when determining optimal cloud cover percentage thresholds for the use case.","name":"Cloud cover percentage","selfAssesment":"Planned
"},{"code":"IP4-3-2","description":"The remote sensing lifecycle structures all possible phases of the data production process, from its beginning of the data's coming to existence (that includes the sensor design prior to data collection) over storage, processing and use to archiving and deletion.","name":"Remote sensing lifecycle","selfAssesment":"New
"},{"code":"IP4-3-3","description":"The capability of a sensor or EO product to resolve anything is a function of its (spatial, temporal, spectral and radiometric) resolution and of the detail at which a geographic phenomenon of interest manifests itself in time and space. A geographic phenomenon can be named or described, georeferenced and provided with a time interval at which it exists. The geographic phenomenon of interest is the one of which a user needs information to help him make a decision. Therefore, the geographic phenomenon needs to be resolved with a low enough uncertainty and a high enough quality that allows the user to make a decision with confidence. \r\nFor example, let’s consider a helicopter pilot that wants to know whether a specific site is suitable for an emergency landing. The decision to perform an emergency landing may be supported with an EO-derived digital map of emergency landing sites that are flat enough (as well as large enough for the pilot’s helicopter and free of any obstacles on the surface and in the approach area). If we only focus on the flatness of the terrain, we need a digital elevation model (DEM) of high enough spatial resolution and accuracy in the Z dimension to calculate slope within acceptable levels of uncertainty. The pilot probably can tell us what degrees of slope are okay for his helicopter and tell us sites (e.g. football fields) where such a landing would succeed. However, this is only the input to an analysis of different DEMs to identify the minimum spatial resolution and accuracy in the Z dimension to model slope products and associated uncertainty to derive an emergency landing site product that fulfils the requirements. Thereby the capability of different DEMs to resolve emergency landing sites can be analysed.\r\nSpatial resolution is a measure of the smallest angular or linear separation between two objects that can be resolved by the remote sensing system. A useful heuristic rule of thumb is that in order to detect a feature, the nominal spatial resolution of the sensor should be less than one-half the size of the feature measured in its smallest dimension.\r\nOther types of resolution of an EO dataset are available that determine for various geographic phenomena under investigation whether it is possible to resolve them in the data. These are radiometric resolution, spectral resolution and temporal resolution. Radiometric resolution is defined as the sensitivity of a remote sensing detector to differences in signal strength as it records the radiant flux reflected, emitted, or back-scattered from the terrain. Spectral resolution is the number and dimension (size) of specific wavelength intervals (referred to as bands or channels) in the electromagnetic spectrum to which a remote sensing instrument is sensitive. The temporal resolution of a remote sensing system generally refers to how often the sensor records imagery of a particular area. For time-series analysis, the temporal resolution determines the time granularity for resolving processes that underlie the change that is observable between subsequent images.","name":"Capability to resolve anything","selfAssesment":"In progress
"},{"code":"IP4-3-4","description":"The spatial coverage of a dataset (consisting of an image or a series of images) determines whether the dataset covers the area of the terrain that is of interest to the user of information derived from the dataset.","name":"Spatial coverage","selfAssesment":"New
"},{"code":"IP4-3-5","description":"The temporal validity of a dataset (consisting of an image or a series of images) determines whether the acquisition date(s) (and period) match(es) the requirements for investigating a specific phenomenon and thereby enables the derivation of information about that phenomenon.","name":"Temporal validity","selfAssesment":"New
"},{"code":"IP4-3","description":"Values (or a value) that enable(s) judging a dataset or product on their fitness for a specific purpose (e.g. whether a specific satellite image is suitable for mapping landslides). , A QI should provide sufficient information to allow all users to readily evaluate a product’s suitability for their particular application, i.e. its “fitness for purpose”.","name":"Quality indicators","selfAssesment":"New
"},{"code":"IP4","description":"Data quality, in general, is the degree of data usability in relation to a specific application purpose. Assurance of data quality is of growing importance in remote sensing, due to the increasing relevance of remote sensing data in planning and operational decision of public bodies and private firms, and the huge amount of digital services (or apps) that exploit RS data. \r\nDifferent data quality dimensions exist according to the lifecycle phases of the remote sensing data: data acquisition, data storage, data pre-processing, processing and analysis and data visualization and delivery. Remote sensing data acquisition phase involves the following quality aspects: resolution, accessibility, spatial accuracy, temporal validity, accuracy and precision of the sensor calibration. Resolution is a multi-dimensional concept that includes the following dimensions: spatial resolution, temporal resolution, radiometric resolution, spectral resolution and temporal resolution. Temporal validity refers to the quality of an remote sensing data product in time, whereas spatial accuracy refers to the accuracy of the position of features relative the Earth. \r\nData storage includes the accessibility and completeness data quality dimensions. Accessibility includes both temporal and data accessibility. Temporal accessibility refers to the time delay between data acquisition and data delivery, whereas data accessibility refers to the availability of remote sensing data. Data completeness encompasses temporal completeness, i.e. completeness of a time series represented a phenomenon, thematic completeness, and spatial completeness which refers to the area coverage. Data preprocessing, processing and analysis phase includes consistency, completeness, temporal validity, resolution, radiometric and geometric accuracy, thematic and semantic accuracy. Thematic and sematic accuracy refers to the correctness of the remote sensing data product. The main quality dimensions of the data visualization and delivery include readability, completeness and temporal validity. \r\nDifferent metrics can be used to assess the quality of the remote sensing-derived information, such as the root-mean-square error (RMSE) measuring the differences between the true and measured values of the phenomenon under investigation, confusion matrix used for assessing the classification performance, producer’s accuracy, user’s accuracy or Cohen kappa. The quality of the remote sensing data per se can be assessed using Peak Signal-to-noise Ratio (PSNR) or the Universal Image Quality Index (UIQI).\r\nDifferent organizations are involved in the standardization of the image data and gridded data quality, including ISO/TC 211 ‘Geographic information/Geomatics’, Open Geospatial Consortium (OGC) or the Quality Assurance Framework for Earth Observation (QA4EO) developed by the Group on Earth Observation (GEO). These organizations are responsible for developing metadata standards that are further used by the remote sensing community to document the quality of the remote sensing data. According to the QA4EO, for example, all remote sensing data products need to be accompanied by a Quality Indicator (QI) which helps users assessing their fitness-for-use.","name":"Image data quality","selfAssesment":"Completed
"},{"code":"IP5-1-1","description":"Array databases make use of arrays as the primary storage representation. Such an array-oriented data model and query language is useful in many scientific applications, where the raw data consists of large collections of imagery or sequence data that needs to be filtered, subsetted, and processed.","name":"Array databases","selfAssesment":"New
"},{"code":"IP5-1-2","description":"The Open Data Cube (ODC) is a non-profit, open source project that was motivated by the need to better manage Satellite Data. This project was born out of the work done under the \"Unlocking the Landsat Archive\" and the Australian Geoscience Data Cube (AGDC) projects.","name":"Open data cube","selfAssesment":"New
"},{"code":"IP5-1","description":"The term data cube originally was used in Online Analytical Processing (OLAP) of business and statistics data. Technically speaking, such a data cube represents a multidimensional array together with metadata describing the semantics of axes, coordinates, and cells. It is an efficient approach to the management and analysis of large datasets.","name":"Data cubes","selfAssesment":"New
"},{"code":"IP5-2-1","description":"Content-based image retrieval helps users retrieve relevant images based on their contents.","name":"Content-based image retrieval","selfAssesment":"New
"},{"code":"IP5-2-2","description":"Web Portals allow users to discover, understand, view, access and query information of their choice from local to global level for a variety of uses.","name":"Web portals","selfAssesment":"New
"},{"code":"IP5-2","description":"Image archives are repositories for storing, managing and retrieving remote sensing data.","name":"Image archives","selfAssesment":"New
"},{"code":"IP5-3-1","description":"As an initiative stipulated by the European Commission to foster the bridge between the Copernicus ground segment and the user segment, the Copernicus data and information access service (C-DIAS) is a generic name for different sets of cloud-based platforms providing centralised access to Copernicus data and information, as well as to processing tools. The name indicates, however, that the focus of such advanced user-centred infrastructure implementations is not only on data access, but also on ‘information’. What is specifically meant here is the provision of information services and information layers as defined in the Copernicus service portfolio. This allows the users to develop and host their own applications in the cloud and a single access point, rather than processing data locally. Currently there are five different DIAS’s implemented (CREODIAS, SOBLOO, MUNDI, WEKEO, ONDA), all with some specific technical assets, or a sector-specific application focus or any other unique selling position by e.g. targeting as specific user community. Currently, the DIAS, which have received co-funding from the European Commission as a kind of seed funding, are currently in the process of exploring opportunities and claiming market shares, striving to sustain in a competitive manner. Some of the features are highlighted in the following, without explicitly mentioning any of the associated DIAS: (i) data access of global data sets (satellite data mosaics or gridded data) by custom area; (ii) OGC interfaces, VM catalogue, SPAR QL search interface (combine searches like receive images over areas of high population density), open source (accessible via API) or pay-per-use; (iii) access to core service products (e.g. CLMS, CMEMS, CAMS); (iv) focus on integrated applications such as smart cities, urban energies, precision agriculture; access to third-mission VHR satellite data (e.g. Pléiades); (v) utilizing GitLab as a developer platform.","name":"Data and information access service (DIAS)","selfAssesment":"Completed
"},{"code":"IP5-3-2","description":"The OpenGIS® Web Processing Service (WPS) Interface Standard provides rules for standardizing how inputs and outputs (requests and responses) for geospatial processing services are defined. It defines an interface that facilitates the publishing of geospatial processes and clients’ discovery of and binding to those processes.","name":"OGC interfaces and OGC web processing service","selfAssesment":"New
"},{"code":"IP5-3","description":"Online processing allows users to implement and run image analysis operations online independent of the underlying software.","name":"Online processing","selfAssesment":"Planned
"},{"code":"IP5","description":"In general, infrastructures such as cyberinfrastructures or Spatial Data Infrastructures (SDIs), allow information sharing across distributed infrastructures and communities. SDIs have gradually changed from a pool of authoritative data shared using standardized web services to a pool where the authoritative data co-exist with data collected by volunteers and different sensors. Many efforts were dedicated to data documentation, to improving the catalogues searching techniques by means of, for example, thesauri and to sharing these data using standardized web services such as Web Map Service, Web Feature Service or Web Coverage Service. Cloud computing technologies played an important role in the implementation of sustainable SDIs due to their ability to provide on-demand computational and storage capacities over the Internet. In this way, users can easily search, find and use data shared across different online platforms.\r\nMore specifically, infrastructures for image processing and analysis refer to the physical and organizational facilities that allow the storage, analysis and management of the available data and products. Traditionally, this infrastructure formed a digital image processing system consisting of computer hardware with special-purpose image processing software, and peripheral input-output devices (e.g. CD or DVD drives, internet access, printers/plotters). In recent years, Earth observation is undergoing a shift to online processing making use of data cubes and vast image archives, e.g. NSF EarthCube or Digital Earth Australia, the Swiss Data Cube, the EarthServer, the E-sensing platform or the Google Earth Engine. Available infrastructures aim at sharing remote sensing data and derived products following the FAIR metrics: Findable (F), Accessible (A), Interoperable (I), Reusable (R). Thus, remote sensing data have to be documented using metadata that support FAIR data principles as follows: (1) Findable: remote sensing data are findable through data documentation, i.e. metadata, that needs to include a unique identifier of the described data. Metadata can be stored in a catalog compliant to one of the available data cataloging standards such as the SpatioTemporal Asset Catalog (STAC) compliant catalog; (2) Accessible: all data have to be openly accessible and shared using interoperable formats that allow users to find, access and reuse them; (3) Interoperable: different standards, e.g. STAC specification, have to be used to document remote sensing data; (4) Reusable: metadata have to be comprehensive enough to allow users not only to assess the fitness for purpose (e.g. lineage) but also to provide them information about how to access the generated data.","name":"Infrastructure","selfAssesment":"Completed
"},{"code":"IP6","description":"In an information value chain, one or more organizations perform a set of value-adding activities for creating and distributing information products and services. They support a user in decision-making and thereby benefit the user’s purpose. The information value chain is a tool for evaluating business management and profitability. It enables explaining the ultimate “value” of a product and the components along the value chain and consequently allows businesses to optimize their processes. \r\nThe value of EO data can be assessed by analysing the contribution of the data to a specific EO information product and its effective use in decision-making. The (share of) benefit attributable to the use of the given EO data is derived from the comparison of a decision taken using the EO product to a counterfactual situation where other types of information are used instead. Often, this compares the situation before a new EO service was available to the situation afterwards. An ex-post analysis may reveal improved performances, e.g. gains in output, or productivity and/or reduced costs as compared to those occurring in absence of EO-derived information. This benefit resides with the user of the EO product and may be traced to societal and environmental benefits through impact chains.\r\nThe process of EO information production and distribution is integrated in the value chain and can be defined as the image processing chain. It comprises the value-adding activities of the organization(s) that lead up to the availability of an EO product for decision making. The nature and flow of these activities and the collaboration between organizations and among participants within organizations can be modelled with business process model and notation (BPMN). BPMN is a flowchart diagram that uses swimlanes representing different participants. Processes are assigned to participants and are connected with arrows into flow sequences. Further elements complete the choice of symbols for modelling a consistent flow, including a start event, end events, and branching options. They allow organizing the flow in parallel or iterative processes. Higher-level processes can be (de-)composed with sub-processes. Additionally, it is possible to use pools and message flows for explicitly modelling collaboration between participants (from different organizations).\r\nIn the image processing (value) chain, the sequence of processing steps begins with the acquisition of EO data, followed by steps of pre-processing and information extraction (or whatever steps are necessary) and ends with an EO information product being available to a user that uses it to make his decision. The collaborating stakeholders along the chain include EO satellite operators, EO data providers, EO information providers, and the users at the end of the value chain. The stakeholders along the processing chain each perform a dedicated subsequence of processing steps. Thereby, the stakeholders contribute their share of value to the data they deliver to the next stakeholder in the chain, ultimately arriving a the EO information product for the user. The EO data products that they hand on along the chain are often described with processing levels that provide different states of processing of EO data. They start with raw instrument data (level 0 and 1) that are followed by data converted into geophysical quantities that are geo-referenced and calibrated (level 2). Further levels are quality controlled data that has been mapped on a uniform space-time grid (level 3) and data combined with models or other instrument data (level 4). In addition, EO data providers use the term analysis ready data (ARD) that have been processed to allow direct data analysis, i.e. user processing effort is reduced to a minimum. Further, the standard EO products contain a categorizing element that is related to the image processing value chain. This categorizing element organizes the EO products along the sequences of processing, descriptive analytics, predictive analytics, prescriptive analytics, aggregation, visualization, and distribution. Thereby, the products ultimately contribute to the actionable EO information product for the use in decision-making.","name":"Image processing (value) chain","selfAssesment":"Completed
"},{"code":"MDS","description":"MDS is a dimensionality reduction technique. It can be divided into Metric multidimensional scaling, Generalized multidimensional scaling and Classical multidimensional scaling.\r\n\r\nGeneralized multidimensional scaling is an extension of metric multidimensional scaling, in which the target space is an arbitrary smooth non-Euclidean space. In cases where the dissimilarities are distances on a surface and the target space is another surface, GMDS allows finding the minimum-distortion embedding of one surface into another.\r\n\r\nClassical multidimensional scaling is also known as Principal Coordinates Analysis, Torgerson Scaling or Torgerson Gower scaling. It takes an input matrix giving dissimilarities between pairs of items and outputs a coordinate matrix whose configuration minimizes a loss function called strain.","name":"Multidimensional scaling","selfAssesment":"Depricated (GI-N2K)
"},{"code":"no","description":"Models that describe the basic principles of randomness and probability in spatio-temporal data.","name":"Mathematical models of uncertainty: Probability and statistics","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI","description":"This knowledge area considers the organizational and institutional aspects related to GIS&T. The focus of this knowledge area is on the organizations active in the GIS&T domain, and what happens within and between these organizations. The knowledge area is structured around five units. One unit considers the key organizations in the GIS&T domain, covering relevant public sector organizations at different administrative levels as well as organizations in other sectors of society. Among the organizational aspects covered in this knowledge area are all organizational issues related to the implementation, use and management of GI and GIS within organizations. While all topics related to the organizational structures, procedures and management of GI(S) are grouped into one unit, another unit focuses on issues related to the human factor of using GI and GIS, i.e. people, their skills and competencies, and the development and evaluation of these skills and competencies in the context of GIS&T training and education. The knowledge area includes also several inter-organizational and institutional aspects of GIS&T. Particular attention is paid to the concept of geospatial data sharing, which is about the creation of `spatial data` connections and relationships between different organizations in the GIS&T domain. Spatial data infrastructures are developed to promote, facilitate and coordinate the sharing of spatial data among data providers and data users, and consists of several technological and non-technological components. Many related topics are considered in the knowledge area GI and Society (WS), which also addresses several non-technological aspects related to GIS&T. In addition to this, also the knowledge areas `Design and Setup of Geographic Information Systems`, `Geospatial Data\" and Web-based GI` include several topics that are closely linked to the topics that are considered in this knowledge area. It can be argued that in order to fully master the knowledge and competencies that are presented in these knowledge areas, also basic knowledge and understanding of the organizational and institutional aspects is required.","name":"Organizational and Institutional Aspects","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI1-1","description":"The development of an appropriate organizational model, which establishes the basic character of GIS operations, is a crucial element of the GIS management. The appropriate GIS organizational model for any organization is based on its intended role.Alternative GIS organizational models are based on differing arrangements concerning the scope of GIS, the degree of integration of GIS into business operations, the degree of centralization of GIS operation and use, and the degree of centralization of management control. Although many variations can arise from different combinations of these factors, GIS organizational models can generally be classified into three types: (1) enterprise GIS, (2) GIS data and service resource, and (3) GIS as a business tool (Somers, 1998).","name":"Organizational models for GIS management","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI1-2","description":"Management of GIS can be done in a more centralized or more decentralized manner. In a a so-called enterprise or information-framework GIS, an organizational unit may be established to manage the GIS environment and run the core system, whereas usage is decentralized. In environments where GIS is used occasionally by various users, it may be set up as a separate service with a designated group that manages the GIS and also controls users' applications services. A second decision that needs to be made after the choice between more centralized or more decentralized management of GI and GIS is about where to place the GI management. Alternative options are in a line organization, in a support area, or at the executive level, each with their own advantages and disadvantages.","name":"Managing GIS operations and infrastructure","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI1-3","description":"User roles describe the relationship between different users and the GIS in an organization. Each user role includes responsibilities (e.g. for modifying certain information) and privileges (e.g. for viewing specific information). Although many different roles can be defined, a basic distinction is made between users, who can only view certain information, and editors, who can edit certain information.","name":"User roles","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI1-4","description":"A GIS management strategy should be unique for each organization, as organizations have unique environments, characteristics, goals, GIS requirements. An important step in developing an effective strategy for an organization is to establish the strategic vision for GI and GIS in the organization and define its role and scope. Other elements that should be covered in the GIS Strategy are the degree of centralized management of the GIS, the placement of GIS management and support in the organization, involvement of users in GIS planning and implementation, coordination of users, organizational changes, preparation of users, personnel issues, transitions to GIS operations, integration into business operations, user support, data access, and integration of technology changes (Somers, 1998).","name":"Strategic planning","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI1-5","description":"Committee and team approaches are frequently employed for coordinating participants and users in multi-participant GIS projects. The aim of creating such committees and teams is to ensure that the varied interests of participants are addressed, as participants bring many different interests, application needs, data needs, priorities, organizational issues, and political interests to a common project the GIS. Common models for coordinating participants recognize that participants have three levels of interest in the GIS: policy, technical development, and usage. Different bodies can be established focusing on these different levels of interest: a technical committee focusing on the design and development of the GIS, an management committee providing policy guidance and support and a user`s group.","name":"Coordinating GIS Participants and Users","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI1-6","description":"After the development and implementation of a GIS within an organization, the challenge is to maintain the system and revise and update it when necessary. This means the performance of the GIS in terms of efficiency and effectiveness should be measured and monitoring, and feedback from users on the system and applications, on the data as well as on new needs should be collected. Particular attention should be paid to the maintenance of data sets.","name":"Ongoing GIS revision","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI1-7","description":"The introduction of GIS into organizational environments should be seen as a complex process of mutual adaptation (Nedovic-Budic, 1997). These technologies changes the established organisational processes and structures, while on the other hand the organisational context and culture modify the technological set-up and use. Therefore, knowledge and understanding of the relationship between technologies and organizations is necessary to increase the success of GIS implementations in organizations. Successful GIS implementation and adoption often require some degree of organizational change. However, this can be very difficult to effect because organizations are naturally resistant to it (Somers, 1998).","name":"Organizational changes","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI1","description":"GIS and T implementation and use within an organization often involves a variety of participants, stakeholders, users and applications. Organizational structures and procedures address methods for developing, managing, and coordinating these multi-participant users. The development of the appropriate organizational model for managing the GIS is crucial. In certain cases, changes to the organizational structure in place might be required. Strategic planning and the establishment of coordination structures can be considered as valuable instruments for managing and coordinating all involved users, while also the different user roles need to be assigned.","name":"Organizational structures, procedures and management","selfAssesment":"In Progress GI-N2K
"},{"code":"OI2-1","description":"GIS and T professionals can be hired for a wide range of different job positions, for which the precise skills, competences and qualifications needed will vary. Typical examples of GIS and T positions are GIS&T project managers, technicians, system developers and analyst. The recognition and certification of the competences people have acquired in informal and non-formal learning contexts is important to know which skills and competences individuals have and whether they meet the qualifications required for a certain job position.","name":"GIS and T positions and qualifications","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI2-2","description":"Making sure staff members have the necessary skills and competences to perform geospatial activities is necessary for an effective implementation and operation of GI within an organizations. Several training methods can be adopted to ensure the development of skills and competencies of staff members. A distinction can be made between formal and informal training, but also between internal and external training programs. Another relevant issue is the assessment and evaluation of the skills and competences of staff members, to determine their future training and development needs.","name":"GIS and T staff development and evaluation","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI2-3","description":"Programs and courses on GIS and T and related subjects are provided by a wide range of institutions. While in recent years also the use and integration of GI and GIS in primary and secondary education has received significant attention, GIS and T education is mainly organized by institutions of higher education, especially universities but also other higher education institutions. Analyses of the higher education GIS&T programs and courses in Europe showed that the offer of courses is very diverse, in terms of size (ECTS), educational level (EQF) and course content. Vocational training on GIS and T related topics is organized by different types of training providers, including the major GIS vendors, data and service providers, academic sector, professional organisations, but also the public sector.","name":"GIS and T training and education","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI2-4","description":"A curriculum is a systematic description of a study program, in terms of learning goals, structure and sequence, learning, teaching and assessment strategies and content. A curriculum consists of both a set of related required and elective - courses along with all direct and indirect skills, competences and learning outcomes resulting from these courses. In the process of curriculum design typically particular attention is assigned to objectives, teaching methods and educational strategies, while also attention should be paid to the content organization aspects and the global structure of the curriculum. The process of designing GIS&T curricula presents many challenges, as the design of the curriculum should be aligned to both the institutional context and the expected outcomes of the learning and teaching process (Prager, 2011).","name":"GIS and T curriculum and course design","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI2-5","description":"An important challenge in organizing GIS and T education and training is the choice and use of effective teaching and learning methods. These methods should follow recent technological developments and use the best technologies to help students acquire the necessary skills and competencies. Traditionally, most GIS and T programs and courses were taught in the context of a full-time, face-to-face setting, using traditional teaching methods such as lectures and lab-based computer practical sessions. In recent years, educational institutions and their teachers have been experimenting with more innovative teaching and learning methods, such as project-based and case-based learning, distance learning, integrated and inter-disciplinary lessons, collaboration with companies and other stakeholders, etc.","name":"GIS and T teaching and learning methods","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI2","description":"This unit addresses GIS and T staff and workforce issues within an organization, particularly as they relate to ensuring that GIS and T is appropriately used and supported. The focus of this unit is on the skills and competencies of professionals in the GIS and T domain: how can these skills and competencies be described and evaluated, and how can they be developed through training and education.","name":"GIS and T workforce themes","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI3-1","description":"Cost savings are an important driver or motivation for sharing geospatial data and information. As costs associated with collecting and maintaining geospatial data are high, sharing data means that users no longer need to duplicate data gathering and archiving, which leads to savings in terms of personnel, space/facilities, data acquisition and maintenance costs. One fundamental argument for sharing thus derives from scale economies in production. Because the cost of making data is high, there is a clear incentive to maximize the number of users of these data. Sharing allows data to be used repeatedly for many purposes, thus increasing their value without increasing their cost. Sharing data also leads to improved data quality. Moreover, in many cases, sharing data is the only way to get access to certain data sets, as the authority to collect and manage certain data lies with another public institution.","name":"Drivers and incentives for sharing geospatial data","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI3-2","description":"Sharing of geospatial data can be hindered or inhibited by several types of barriers. These include technological barriers, such as a lack of common data definitions, formats and models or incompatibility of hardware and software. Among the non-technological barriers are organizational, political and legal issues and elements, such as misaligned organizational missions, diversity in organizational cultures, conflicting organizational priorities, lack of funding, lack of executive and legislative support; restrictive laws and regulations, copyright issues, data privacy and data ownership issues. However, it should be noticed that many of these barriers have been decreased or eliminated in recent years.","name":"Barriers to geospatial information sharing","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI3-3","description":"The legal framework for geospatial data sharing is very wide and diverse, involving rules on data, coordination, standards, funding, etc. Moreover, these rules and regulations can take many different forms: legal acts adopted by parliament, executive orders or decisions, cooperation agreements, memoranda of understanding, bilateral arrangements etc. From a data perspective, the legal framework can be distinguished into two main types of policies: those that promote and those that hinder the availability of spatial data. Policies that promote spatial data availability can focus on different types of users (public bodies, private companies, citizens) and different types of use (public access, commercial and non-commercial reuse, reuse for performing public tasks). Among the policies that hinder the availability of spatial data are those dealing with privacy, liability, and intellectual property. The legal framework also includes legislation that applies to data or information in general, such as open data legislation, which may also be applicable to spatial data (e.g. legislation on freedom of information, copyright, etc.). Moreover, also general legislation relating to any interaction between people or any situation in everyday life (e.g. liability, contract law, competition law, etc.) will apply to spatial data sharing.","name":"Legal framework for geospatial data sharing","selfAssesment":"Completed
"},{"code":"OI3-4","description":"Several types of legal mechanisms for sharing geospatial data can be used. A data sharing arrangements can be formalized by a contract or agreement between the data provider and the data user. A particular type of agreement are the framework agreements, which are agreements between two or more organisations concluded prior to the datasets or services being required. These framework agreement can involve one or multiple spatial data sets or services. Partnership agreements are often used to formalize the data sharing agreements among a broader group of partners. Participation in such a partnership often means participants share their data with other participants and get access to shared data. Another relevant mechanism is the use of licenses, which are mechanisms to give organizations and people the permission to use spatial data sets and services. A license is legally binding, and defines the conditions of use of the related spatial data sets and services. In order to reduce the number of licenses used and ensure the harmonization of the terms in these licenses, the use of standard licenses is promoted. Also the use of open data licenses is promoted for sharing geospatial data, and strongly increased in recent years.","name":"Legal instruments for sharing geospatial data","selfAssesment":"Completed
"},{"code":"OI3","description":"Geospatial data sharing has become an essential element of the GI activities of organizations. Spatial data sharing can be defined as the electronic transfer of spatial data/information between two or more organizational units where there is independence between the holder of the data and the prospective user. Spatial data sharing has many advantages, but several technical and non-technical barriers must be overcome to put data sharing into practice. While the practice of spatial data sharing has substantially grown with the development of spatial data infrastructures, many consider data sharing as a crucial element for the success of these infrastructures.","name":"Geospatial data sharing","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI3b","description":"A Spatial Data Infrastructure can be defined as the collection of technological and non-technological components to facilitate and coordinate the exchange of and sharing of spatial data. The concept infrastructure is used to promote the concept of a reliable, supporting environment, analogous to a road or telecommunications network, that facilitates the access to spatial data. Data, metadata, access networks, standards, coordination, policies, funding, people and institutional frameworks are often considered among the key components of an SDI. \r\n\r\nSpatial data infrastructures often are defined and described as a complex and dynamic phenomenon. Among the main reasons for the complex character of these infrastructures are the many components a spatial data infrastructure consists of, the diversity of involved stakeholders, and the many different objectives and ambitions of these stakeholders. Technological advancements, such as the emergence of web 2.0 technologies, and societal changes, such as the increasing use of geographic information in everyday life, are often mentioned as important drivers behind the dynamic character of spatial data infrastructures. \r\n\r\nA key characteristic of spatial data infrastructures is the involvement of a large and diverse group of actors. Governments are often considered as the central actors in the development and implementation of spatial data infrastructure, since they are the major producers and users of geographic information. Governments at different administrative levels and in different thematic domains are involved in the creation, management, use and sharing of geographic data. But also private companies, non-profit organisations, research and education institutions and even citizens can participate in the development and implementation of a spatial data infrastructure. It is increasingly being argued that the involvement and engagement of each of these stakeholders group is essential to the realization of a successful spatial data infrastructure. \r\n\r\nSDIs have been developed in many countries worldwide at local, national and international levels. Often a distinction is made between a between the first generation SDIs that have data as their key driver and are based on a product model and second generation SDIs in which user needs are the key driver and that are based on a process or development model. The latest generations of SDI strongly focus on the inclusion and engagement of non-government actors and organizations in the development and implementation of the SDI. Although SDI are by default distributed systems, involving many organisations, some SDI might be developed rather in an hierarchical way, while others are following a networked approach.","name":"Spatial Data Infrastructures","selfAssesment":"Completed
"},{"code":"OI4-1","description":"The adoption and implementation of standards are two key phases in the standardization process, which starts with the definition of standardization requirements and the development of standards. The adoption and implementation of standards follows after the development phase. The distinction made between the adoption and implementation of standards is important: adoption entails the decision to apply standards, while the implementation relates to the integration of standards in software, in data development and in other processes. GI-Standards are one of the key components of each SDI, consist of both semantic and technical standards, and include standards related to the different architectural components of an SDI, i.e. standards related to spatial data sets and data products, web services, metadata and catalogues, encodings, etc.","name":"Adoption and implementation of standards","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI4-2","description":"The SDI policy framework includes the set of policies, strategies, initiatives and projects aimed at increasing access, sharing, and effective use of spatial data. SDI policies can be divided into strategic and more operational policies. Strategic policies define the broader framework and formal structure within which the SDI initiative is developed. Operational policies provide more practical tools to facilitate access to and use of the SDI, and address specific topics related to the collection, management, use, access and dissemination of spatial data. These operational policies include a broad range of guidelines, directives, procedures and manuals that apply to the day-to-day business of organizations in developing, operating and using an SDI. To guarantee the success of an SDI, it is important to recognize the wider policy context in which these SDI`s are developed, and to link them to the overall policy environment in the jurisdiction in which they are implemented. These include policies on open government and open data, environmental policies, digital government or e-government policies and other.","name":"Policies","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI4-3","description":"If is often argued that SDI implementation requires coordination, because without coordination all other SDI components would not be developed or would be developed in a very fragmented and inconsistent manner. In general terms, coordination is about bringing into alignment the activities of different stakeholders in the SDI landscape. A typical instrument to realize coordinate in the context of SDI, is the establishment of an effective SDI coordination structure. The SDI coordination structure should ensure that all stakeholders are involved in the development and implementation of the SDI, through the participation in one or more coordination bodies. Another important element is the establishment of clear roles and responsibilities for the different involved organizations, making a distinction between data users, data providers, services providers and a geo-broker.","name":"Coordination and organizational structure","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI4-5","description":"Funding an SDI is about guaranteeing the long-term financial security of an SDI, by obtaining and formalizing financing for the implementation and maintenance of the different SDI components. An SDI funding model provides the answer to the central question of where and how to seek funding for implementing and maintaining an SDI. Within an SDI often different funding models will be combined, as the selection of the most appropriate funding model will be linked to different activities and the associated costs. Costs of an SDI include both set-up costs (one off costs) and maintenance costs (yearly), of which certain costs need to be made for each data sets or each data provider and other costs for the infrastructure in general. The most commonly used SDI funding models are centralized government funding, decentralized government funding (e.g. for each data provider), partnership funding, funding through revenues, and government funding based on donor agencies or on European projects.\r\n\r\nThe shift towards open data and the adoption of open data policies had an important impact on the funding model of many SDIs, as governments and organizations no longer could rely on revenues from selling their data and had to look for other funding models. As a result, new pricing strategies are employed, such as the provision of fee-based supplementary services, such as advice or tailor-made products based on open data. Also freemium/premium models, in which a basic version of the dataset is offered as open data (freemium) but the full dataset is available for a fee (premium), were considered as an alternative approach. In many cases, the loss of revenues was compensated by other funding models, such as increased government funding.","name":"Funding an SDI","selfAssesment":"Completed
"},{"code":"OI4-5b","description":"SDI performance assessment is about collecting, analyzing and providing information on the performance of SDI initiatives. Assessment and evaluations of SDIs are a useful tool for those organizations and people directly involved in these initiatives, but also for researchers, citizens, journalists and other stakeholders. Decision makers and practitioners can use assessments to monitor the progress against the objectives of their SDI initiatives and to identify areas where improvement can be achieved. Assessment also allows to compare and benchmark the performance of different organizations or countries, and to learn from best practices. Finally, assessment also is relevant for accountability, since it enables governments and agencies to be held accountable for their decisions, activities and the resources they have invested. Assessment of SDIs, which deals with the collection and supply of information on the performance of SDI initiatives, should be seen as the first step in a logical consequence of collecting data, integrating this data in policy and management cycles and actually using the information. \r\n\r\nIn the past twenty years, many different SDI assessment frameworks have been developed by researchers and practitioners around the world. Examples of such frameworks are the INSPIRE State of Play Study, the Clearinghouse Suitability Index, the Organisational Maturity Matrix, the SDI Readiness Index, and the INSPIRE Monitoring and Reporting approach. Each of these frameworks focus on particular aspects and components of SDIs. In line with the categorization of open data assessment, also SDI assessments can be divided into three main categories: (1) readiness assessments, (2) implementation or data assessments, and (3) impact assessments. Readiness assessments analyse whether conditions are appropriate, and whether necessary components are in place for developing an SDI. Implementation or Data assessments evaluate whether geospatial data are available and accessible. Impact assessments explore the extent to which SDIs lead to benefits for government, citizens, business and society in general.","name":"SDI performance measurement and assessment","selfAssesment":"Completed
"},{"code":"OI4-6","description":"For a long time, SDI development has focused on the development and implementation of different components with the aim of facilitating the access to and sharing of spatial data. An key challenge in future SDI development will be the integration of these SDI`s in a wider context. In order to optimally take advantage of the data and services provided by an SDI, integrating these data and services into the processes and workflows of public and private organizations will be crucial. The concept of spatial enablement refers to the challenge of developing SDI`s in such a way that they provide an enabling platform that serves the wider needs of society in a transparent manner. Moreover, the diffusion of SDIs, together with the efforts to build a Global Earth Observation System of Systems (GEOSS) and other developments in industry and civil society should be considered as elements in a the realization of a vision on the next-generation Digital Earth.","name":"Next-generation SDIs","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI4-7","description":"The effective implementation of SDIs requires governance, which includes the structures, policies, actors and institutions by which the infrastructure is managed pertaining to decisions made for accessing, sharing, exchanging and using the relevant available spatial information. While SDIs themselves are considered as initiatives contributing to good governance or effective governance, a key challenge in the establishment of SDIs is the governance of the infrastructure itself. Governance of SDIs is essential for the implementation of different SDI components in a coordinated and consistent manner. The central challenge of governance is reconciling collective and individual needs and interests of different stakeholders in order to achieve common goals. This aims to reduce gaps, duplications, contradictions and missed opportunities in the production, management, sharing and use of the information that tend to occur in a multi-stakeholder environment.\r\n\r\nGovernance can be facilitated through the use of appropriate instruments which extend to various levels of government and take into account the distribution of powers and responsibilities among different actors and institutions with an interest in the infrastructure. The governance instruments should coordinate the activities and contributions of, inter alia, data producers, users, added-value services providers, and other stakeholders. More complex and inclusive models of governance are required to cope with the multi-level nature of SDI implementations of the current generation of SDIs. Effective and inclusive SDI governance structures are needed, that are both understood and accepted by all stakeholders. Governance of SDIs also requires expanding the scope of stakeholders to include the private sector, research bodies and other actors outside the public sector including citizens, to actively promote bottom-up and participatory processes, and to find the appropriate mechanisms and instruments to enable the participation of these non-government actors.","name":"SDI governance","selfAssesment":"Completed
"},{"code":"OI5-1","description":"Within the European Commission there are several key GI players. GIS activities in the Commission started since 1981 (e.g. DG REGIO, Eurostat, ) with the CORINE project, the creation of DG ENV and the creation of the European Environment Agency (EEA). Together with the DG Joint Research Centre (JRC), DG ENV and EEA are in charge of the coordination of INSPIRE: DG Environment acts as an overall legislative and policy co-ordinator for INSPIRE, the JRC acts as the overall technical co-ordinator of INSPIRE and EEA is in charge of several tasks related to monitoring and reporting, and data and service sharing under INSPIRE. Also several other EC institutions are actively involved in GI(S) policies and activities (DIGIT, DG GROW, DG AGRI, DG MOVE and many others).","name":"GI organization at the European Commission","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI5-2","description":"Although there may be certain differences between countries, in most countries many key organizations in the GIS&T field will be active at the central/federal/national level of government. Especially the traditional institutions for surveying and mapping play a key role in geospatial policies and activities. Several public authorities at the federal level are in charge of the production and maintenance of key reference and thematic data sets. In many countries, these national data producers were the leading actors in the development of national spatial data infrastructures.","name":"Federal and national government organizations","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI5-3","description":"Local and sub-national governments are often considered among the major users of geographic information in governments, as they often are involved in many different policy areas, in which many problems with a locational component need to be tackled. Geographic data produced and maintained by authorities at lower administrative levels are often more detailed and thus interesting for other users, both within and outside the public sector. As a result, local and sub-national governments are often involved in the establishment of these infrastructures because of the wide range of highly detailed geographic information they produce and manage. As many geographic data are linked to the activities and services of local organizations, the involvement of these organizations in the maintenance of data ensures that these data are up-to-date.","name":"Sub-national and local governments","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI5-4","description":"The European GIS&T landscape consists of many pan-European organizations and associations promoting the interest of and representing certain stakeholder groups. While some of these organisations are dealing with all sectors and aspects of geographic information, others have a more thematic focus (e.g. remote sensing, topography, geosciences) or represent a particular sector (e.g. research, business). In some cases, their clearly is an overlap in the mission and objectives of different organizations, and some organizations are working in the same field of interest. Some examples of pan-European organizations and associations are AGILE, EuroSDR, EUROGI, and EuroGeographics. Also at international level several membership organizations and associations exist.","name":"Pan-European and global associations and professional organizations","selfAssesment":"GI-N2K
"},{"code":"OI5-5","description":"The geospatial industry consists of companies working with location specific information or services. Within the geospatial sector, several areas of activities can be identified: 1) measuring, collecting and storing of data about geo-objects; 2) processing, editing, modelling, analyzing and managing that data; 3) presenting, producing and distributing the data; and 4) advising, educating, researching and communicating about processes and use of geo-information products and services. The sector consists of both small-and-medium-sized enterprises but also big companies, including surveyors, census hard-copy map providers, aerial photos providers, base map data providers, satellite and remote sensing imagery providers, software developers (GIS-related products and services providers as well as satellite image programming platform providers) and several others.","name":"The geospatial industry","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI5","description":"Several types of organizations play a key role in the execution and coordination of geospatial activities in society. Typically, a distinction is made between data providers and data users, while coordinating organizations exist to coordinate and support the geospatial activities of professionals and entities using GIS&T. Governments are often considered as the major users and producers of spatial data and spatial information. Within the public sector, spatial data are collected and used in different thematic areas and at different administrative levels (from local to global). However, the needs, interests, and capacities of organizations at each of these levels will be different, as well as their role in the development of spatial data infrastructures, and the execution of geospatial activities in general. Also the geospatial industry will exist of both data providers and data users, but also of organizations delivering products and services to support the collection and use of spatial data. Other key organization in the GI domain are professional organizations and associations, bringing together and representing the needs of organizations of a particular sector and/or geographic area.","name":"Organizations in the GIS and T domain","selfAssesment":"In progress (GI-N2K)
"},{"code":"PP","description":"The knowledge of physical laws and principles regulating the emission of e.m. radiation and its interactions with the matter, as well the ones related to the design, setting-up and control of EO platforms and related instruments, are of paramount importance for a right interpretation of EO measurements in relation with the investigated Earth's phenomena and parameters. The most important physical fundaments regards: the theory of electromagnetic waves propagation described by the Maxwell's equations, the theory of e.m. radiation and of its interaction with the matter, the methods and instruments for e.m. radiation measurement and/or generation, the fundamentals of thermodynamics and of mechanics. As far as Earth Observation is concerned, further, specific topics have to be addressed which are related to: spectral-specific matter-radiation interactions, natural (e.g. Earth, Sun) and artificial (e.g. MW) sources of e.m. radiations, atmospheric physics and radiative transfer equations, basic physics of e.m., optical and MW, sensors and sources, theory of satellites orbits, theory of rockets, physical fundaments of interpretation of optical and MW data collected by passive and active techniques.","name":"Physical principles","selfAssesment":"Completed
"},{"code":"PP1-1-1","description":"Electromagnetic radiation travels in wave form. All electromagnetic waves travel at the speed of 299.793 km/sec in a vacuum and very nearly the same speed in air. In quantum physics electromagnetic radiation is also described in terms of particles called photons whose energy is given by the equation E = hf where h is the Planck constant and f the frequency of corresponding wave. Electromagnetic wave propagation is fully described by the Maxwell Equations that unified in 1860s the laws of electricity and magnetism.","name":"Electromagnetic Waves and Photons","selfAssesment":"Planned
"},{"code":"PP1-1-10","description":"The solar constant S is a quantity denoting the amount of total (i.e., covering the entire solar spectrum) solar energy reaching the top of the atmosphere. It is defined as the flux of solar energy (energy per unit time) across a surface of unit area normal to the solar beam at the mean distance between the sun and the earth. Solar insolation is defined as the flux of solar radiation per unit of horizontal area for a given locality. It depends primarily on the solar zenith angle and to some extent on the variable distance of the earth from the sun. It can be computed as a function of latitude and the time of year taking into account of the secular variations of Earth's orbit eccentricity e, the oblique angle ε, and the longitude of the perihelion relative to the vernal equinox ω. The daily insolation is the total solar energy received by a unit of area per one day. It may be calculated by integrating total insolation over the daylight hours. It is particularly important, together with information on cloud coverage, in order to plan and manage solar power systems. Yearly total insolation together with average cloud coverage are among the most important parameters to be considered for the choice of the best (i.e. the ones promising the higher energy production) location of solar power plants. Modeled daily solar insolation together with short/medium-term forecast of cloud coverage are also fundamental for the management (e.g. for planning the suspension of activities for maintenance) of solar energy production plants .","name":"Solar constant, solar insolation, daily insolation","selfAssesment":"Completed
"},{"code":"PP1-1-11","description":"Earth's itself represents the second (after Sun) most powerfull natural source of e.m. radiation for EO. Its average emittance can be approximated by that of a blackbody at about 290 K. Even if very less powerfull than Sun such a source is available for EO day and nigth. The maximum of its emission falls in the thermal infrared (around 10 micron) being Earth's emission trascurable in the VIS-SWIR range.","name":"Earth's radiation (intensity, spectrum, etc.)","selfAssesment":"Planned
"},{"code":"PP1-1-2","description":"In principle, the frequency f (and the wavelength λ=c/f) of an electromagnetic wave can take any value and the whole range of possible frequencies is called the electromagnetic spectrum. Different regions of the spectrum are conventionally given different names (with associated spectral ranges smoothly depending on specific science sector): \r\ngamma-rays\t λ< 1 pm\r\nx-rays\t1 nm >λ>1 pm\r\nUltraviolet (UV) 400 nm >λ>1 nm\r\nVisible (VIS) 700 nm >λ> 400 nm (blue: 455 – 492, green 492 – 577, yellow 577 – 597, red 622 – 700)\r\ninfrared (IR)\t1000μm >λ> 0,7 μm (Near-IR - NIR: 0,7-1,3; Short-Wave IR SWIR: 1,3-3; Medium IR - MIR: 3-6, Thermal IR - TIR: 6-20; Far IR - FIR: 20-1000)\r\nRadio waves\t λ> 1 mm (Microwaves MW\t1 m >λ> 1mm). Optical range (usually referring to the spectral range from VIS to TIR) and microwaves are the most important spectral region for remote EO systems.","name":"Electromagnetic spectrum","selfAssesment":"Completed
"},{"code":"PP1-1-3","description":"Maxwell equations are a set of coupled partial differential equations that contains the fundamentals of electricity and magnetism. These equations provide electromagnetic waves that propagate into the space at the speed of the light. Increasing the wavelength there are gamma rays, X-rays, ultraviolet, (visible) light, infrared, microwaves and radio waves.","name":"Maxwell Equations and EM waves' propagation","selfAssesment":"Planned
"},{"code":"PP1-1-4","description":"Planck's law is a mathematical relationship for the spectral radiance emitted by a blackbody (i.e. a body that absorbs all radiant energy falling on it) at a given temperature as a function of frequency or wavelength. From another point of view it can be used to define a black-body as a body emitting radiation following Planck's law. The model of black-body is fundamental to simplify the description of the radiation thermally emitted by a generic body at a pre-fixed temperature and wavelength as the product of its (specific) spectral emissivity and the value predicted (at the same wavelength) by the Planck's law for a black-body at the same temperature. This way the radiation thermally emitted by a generic body can be expressed just as a (specific, as modulated by the spectra emissivity) fraction of the one expected for a black-body. Wien’s displacement law is the relationship between the temperature of a blackbody and the wavelength at which it emits the most radiation. Wien found that the product of the peak wavelength and the temperature is an absolute constant. As far as the temperature T of the blackbody increase the intensity of the emitted e.m. radiation increases being, at whatever wavelength, grater than the one emitted by a blackbody at lower temperature (Planck). As far as the blackbody temperature increases its maximum emission occurs at lower and lower wavelengths. Wien's law is fundamental both in the selection of the spectral bands more appropriate for observing specific phenomena as well as for remotely retrieve temperature of far objects by the analysis of the emitted spectral radiances.","name":"Planck law for the black body. Wien's displacement law","selfAssesment":"Completed
"},{"code":"PP1-1-5","description":"The Rayleigh–Jeans Law is an approximation of the Planck’s law for a blackbody that states that emitted radiance is directly proportional to the blackbody temperature. Such an approximation, fits quite well with EO measurements at wavelengths higher than 1mm (microwaves). Wien’s approximation is used to describe the spectrum of the blackbody emission in the VIS-NIR spectral range lengths. The estimated errors are less than 2% at wavlengths less that 5microns. In both cases considered sources are the natural ones: the Earth at an average temperature of 300 K in the first case, the Sun at about 6000K in the second one.","name":"Rayleigh-Jeans approximation. Wien's approximation","selfAssesment":"Completed
"},{"code":"PP1-1-6","description":"The total radiant intensity B(T ) of a blackbody at the absolute temperature T can be derived by integrating the Planck function over the entire wavelength domain from 0 to∞. Since blackbody radiation is isotropic, the flux density emitted by a blackbody is therefore F = π B(T ) which is proportional to the fourth power of the absolute temperature T through the Stefan-Boltzmann constant σ = 5.67 × 10−8 J m−2 sec−1 deg−4.\r\nKirchoff's law establishes that for a medium at the thermodynamic equilibrium, the emissivity ε of a given wavelength λ (defined as the ratio of its emitting intensity IE to the Planck function B), is equal to the its absorptivity, A at the same wavelength λ (defined as the ratio of its absorbed intensity IA to the Planck function B). Hence ε=A at each fixed λ, for a blackbody ε=A=1 at whatever λ. Kirchoff's law is valid also in Local Thermodynamic Equilibrium (LTE) conditions as the ones usually occurring in (small volumes of) the Earth's atmosphere even in the most turbulent conditions.","name":"Stefan–Boltzmann law. Kirchoff law","selfAssesment":"Completed
"},{"code":"PP1-1-7","description":"All bodies at a temperature T>0 K emit electromagnetic radiation at all wavelengths (thermal emission). Such emission at each wavelength is increasing with T and it is maximum for Black Bodies whose spectral emittance I(λ,T) (at each prefixed T and wavelength λ) is defined by the Planck function B(λ,T). Generic bodies are expected to thermally emit less than a black body (having the same temperature T) at whatever wavelength. Spectral emissivity ε(λ) is defined as the ratio of the spectral radiance I(λ,T) emitted by a generic body and the one emitted by a Black Body at the same temperature, i.e. ε(λ)= I(λ,T) / B(λ,T). By definition its value is less or equal (Black Body) than 1. The spectral emissivity concept allows to describe in a simple way the spectral radiance I(λ,T) thermally emitted by a body at a temperature T by I(λ,T)= ε(λ)*B(λ,T). It is possible to invert the Planck Function to obtain from the emitted radiance at a prefixed wavelength the temperature T=f(B, λ) of the emitting Black Body. If in such expression the spectral radiance I emitted by a generic body is used instead than B, the resulting temperature, Tb=f(I, λ), is named Brigthness Temperature being Tb<=T (with Tb=T in case the emitting body is a Black Body). The concept of Brigthness Temperature is substantially a different way to measure the spectral radiance of a generic body. It is usually preferred (for instance calibrating Thermal InfraRed – TIR – satellite images) because the interpretation of such a digital image is much more intuitive than when spectral radiances are used instead. In fact, as at each prefixed temperature generic bodies are less emitting than Black Bodies, wherever across a digital satellite image we consider the values of reported Tb, we can say that the actual temperature T of the corresponding emitting ground resolution cell is not less than Tb.","name":"Concepts of Spectral Emissivity and Brightness Temperature.","selfAssesment":"Completed
"},{"code":"PP1-1-9","description":"Sun represents the most powerful natural source of e.m. radiation for EO. Its emittance can be approximated by that of a blackbody at about 6000 K but just its reflected component (SOR) is actually available (and just during daytime) for EO. The maximum of SOR falls in the visible spectral range. Its contribution in the thermal infrared range is neglectable but in the medium infrared SOR is still significant enough and superimposed to Earth's thermal emission.","name":"Solar radiation at the Top of the Atmosphere. Solar spectrum","selfAssesment":"Planned
"},{"code":"PP1-1","description":"EM radiation is created when an electrically charge particle, such as an electron, is accelerated by a force causing it to move. The movement produces oscillating electric and magnetic fields which travel, as an harmonic EM wave, at right angles to each other. EM waves travel at 299,792,458 meters per second in a vacuum (the highest possible speed into the Universe, also known as the speed of light). \r\nThe electromagnetic field propagating through the space as EM waves is also referred as electromagnetic radiation. \r\nAn EM wave is characterized by a frequency (or by a wavelength) and by an amplitude (or by an energy). \r\nThe wavelength is the distance between two consecutive peaks of a wave. This distance is given in meters (m) or fractions thereof. Frequency is the number of waves that form in a given length of time. It is usually measured as the number of wave cycles per second, or Hertz (Hz). It is wave speed=frequency*wavelength so that, an EM wave traveling at the speed of light, can be equally identified by its wavelength or by its frequency. The amplitude (i.e. the maximum oscillation of the EM field) provide the intensity (i.e. the energy) of the EM wave. \r\nThe classical theory describes the EM radiation as electromagnetic waves which represent the oscillations of electric and magnetic fields. In the quantum mechanics theory EM radiation consists of photons, quanta of the electromagnetic energy, responsible for all electromagnetic interactions.\r\nAs far as Earth remote sensing is concerned EM radiation represents the most important vehicle of information.","name":"EM radiation","selfAssesment":"Completed
"},{"code":"PP1-2-1","description":"The study of the absorbption/emission of electromagnetic radiation by atoms. Depending on the atomic number characteristic frequency or wavelength are absorbed or emitted. Since each element has a characteristic spectrum of absorbed/emitted wavelengths (spectral signature), atomic spectroscopy allows the determination of elemental compositions even of remote objects (e.g. stars, galaxies, etc.).\r\nStarting from the simple Bohr’s model it is possible to predict quite exactly the frequencies of e.m. radiation selectively absorbed/emitted by all atoms. Depending on the atomic number Z, characteristic frequencies f are absorbed or emitted by atoms corresponding to the electronic transitions from different energetic (quantized) states following the Bohr’s condition: fab=(Eb- Ea)/h, being Ei=-cost∙Z2/(ni)2 the electron energy corresponding to the state/level i (principal quantic number ni). By this way each atomic species has a characteristic spectrum of absorbed/emitted frequencies (atomic spectral signature) so that atomic spectroscopy allows the determination of elemental compositions even of remote objects. By this way the existence of Helium was discovered in the 1968 by Jansen and Lockyer in the Sun photosphere well before its discover on the Earth, and the knowledge of the chemical composition of stars and galaxies was possible well before the end of XIX century. Atomic spectroscopy provides a simple and powerful introduction (through the explanation of the more complex interactions of e.m. radiation with molecules and solid matter) to the fundamental concepts of spectral signature (which is at the base of most of the applications of aerial remote sensing of the Earth’s surface) and atmospheric windows (important for the design of optical sensors devoted to remotely sense Earth’s surface) being moreover propaedeutic to the understanding of methods for the atmospheric vertical sounding based on the concepts spectral lines broadening and related weighting functions.","name":"Atomic spectroscopy","selfAssesment":"Completed
"},{"code":"PP1-2-10","description":"The Rayleigh roughness criterion is a widely used means to estimate the degree of roughness of a considered surface. Considering the phase difference between two rays scattered from separate points of the surface, this depends on the roughness height, the incident angle and, inversely, on the radiation wavelength (λ). The Rayleight criterion states that a surface can be considered as smooth if the phase difference is less than π/2 radians.\r\nAs a consequence, in the case of normal incidence, irregularities must be less than λ/8 in height to have an effectively smooth surface. In particular: i) at optical wavelengths (e.g. 0.5 micrometers), roughness height must be less than about 60 nm to have a specular reflection from a surface. Only certain man-made surfaces (e.g. sheets of glass or metal) may meet such a condition; ii) at VHF radio wavelengths (e.g. 3 m), roughness height need only to be less than about 40 cm. Unlike the previous case, a number of natural surfaces may meet this condition.\r\nIt is worth noting that large values of the incident angle may satisfy the criterion more easily as compared with the normal incidence. This means that a moderately rough surface may be effectively smooth at glancing incidence. This condition may be easily experienced when eyes are struck by the glare of reflected sunlight from a low sun over an ordinary road surface.","name":"The Rayleigh roughness criterion","selfAssesment":"Completed
"},{"code":"PP1-2-11","description":"The Bidirectional Reflectance Distribution Function (BRDF) is defined as the quotient between the spectral radiance reflected by a sample and the spectral irradiance from the source that illuminates it. It depends on both the incidence and viewing angles. From this point of view it represents an absolute definition of reflectance whose value, as is known, depends on the geometry of the illumination and observations directions.","name":"Bidirectional Reflectance Distribution Function (BRDF)","selfAssesment":"Planned
"},{"code":"PP1-2-12","description":"Measurements of BRDF allow to compare spectral signatures obtained in different laboratories in an optimal way. However its measure require well calibrated sources and quite expensive laboratory equipments. The concept of BRF (Bidirectional Reflectance Factor) allows a more simple, indirect, measurement of BRDF by using a reference sample (highly reflective so usually named \"white reference WR\") of known BRDF and two subsequent measurements of reflected radiance (one from the WR, one from the sample) obtained under identical illumination conditions. In these conditions results BRDF(sample)=BRF(sample)xBRDF(WR)","name":"Bidirectional Reflectance Factor (BRF)","selfAssesment":"Planned
"},{"code":"PP1-2-2","description":"The molecular absorption spectral corresponds to the wavelengths from 190 nm up to 1000 nm and it interprets the measured absorption of radiation, when it is passing through a gas, a liquid or solid. Their absorbed energy in different states can be approximated by electronic, vibrational and rotational energy","name":"Molecular absorption spectra","selfAssesment":"Planned
"},{"code":"PP1-2-3","description":"The spectral line is a result of interactions of photon with a quantum system, while it extends over a range of frequencies. The center wavelength of its energy levels may be changed due to Broadening, namely collisions of atoms and molecules or their differences in thermal velocities.","name":"Line shape and (natural, pressure, Doppler) broadening","selfAssesment":"Planned
"},{"code":"PP1-2-4","description":"When the altitude ranges from about 20 to 50 km, spectral line shape is determined by both collisions (Pressure Broadening) and differences in thermal velocities (Doppler broadening). This shape is referred to as the Voigt profile and it satisfies the condition of normalization.","name":"Voigt's line profile","selfAssesment":"Planned
"},{"code":"PP1-2-5","description":"Radiation that is not absorbed or scattered in the atmosphere can reach and interact with the Earth's surface. There are three (3) forms of interaction that can take place when e.m. radiation strikes, or is incident (I) upon a surface. These are: absorption, transmission, and reflection. The total incident radiation will interact with the surface in one or more of these three ways. The proportions of each will depend on the wavelength of the incident radiation and the specific chemical/physical properties of the surface material. Absorption occurs when incident radiation is absorbed into the target, while transmission occurs when radiation passes through a target. Reflection occurs when radiation \"bounces\" off the target and is redirected. The spectral reflectance is defined by the ratio of reflected radiance to incident radiance at a prefixed wavelegth . The spectral transmittance of a medium is defined by the ratio of the transmitted radiance to the incident one at a prefixed wavelegth . The absorbance of a medium or target is defined by the ratio of the absorbed radiance to the incident one at a prefixed wavelegth . Conservation of energy require that, at a certain wavelenght: R+T+A=1. To express the circumstance that the reflection can occurre in different direction as the surface deviates from a specular one, becoming rough, the concept of surface scattering has been introduced (ref. [PP1-2-10] The Rayleigh roughness criterion).","name":"Concepts of Transmittance, Absorbance, Reflectance, Scattering.","selfAssesment":"Completed
"},{"code":"PP1-2-6","description":"The emitting capability of a body surface is described by the spectral emissivity, ε(λ), a dimensionless value ranging between 0 and 1 and varying on the basis of the wavelength (λ) and the geometric configuration of the surface. Formally, spectral emissivity can be defined as the ratio of spectral exitance, M(λ,T), from an object at wavelength λ and temperature T, to that from a blackbody at the same wavelength and temperature, MBB(λ,T).\r\nA blackbody is an ideal radiator that totally absorbs and then reemits all energy incident upon it. By definition the spectral emissivity of a blackbody is equal to one (the maximum) at whatever wavelength and temperature. A blackbody radiates a continuous spectrum. Real materials do not behave like a blackbody. Natural matter could radiates more in selected spectral region (like in the case of atomic or molecular gases) more frequently with a continuous spectrum (like in the case of solids) always with spectral emissivity minor or equal to 1. \r\nAnother important concept is the one related to the graybody. For gray bodies, the spectral emissivity value is constant for each wavelength value, as for black bodies, but is always less than 1. Therefore, for any given wavelength the emitted energy of a graybody is a fraction of that of a blackbody. This behavior could be quite important even for limited spectral ranges. For instance the spectral emissivity of the sea in the TIR (Thermal InfraRed) spectral range 8-14 microns (TIR atmospheric window) can be assumed constant (about 0,98) with significant simplifications in the determination of SST (Sea Surface Temperature) from satellite sensors operating in that spectral region. \r\nAs said above, the emissivity of the most of the bodies present in nature varies depending on the wavelength. These objects are referred to as selective radiators or as being selectively radiant. This means that some materials may behave as black bodies at certain wavelengths (ε close to 1) and may have reduced emissivity at other wavelengths.","name":"Concepts of Spectral Emissivity","selfAssesment":"Completed
"},{"code":"PP1-2-7","description":"\"Radiation that is not absorbed or scattered in the atmosphere can reach and interact with the Earth's surface. There are three (3) forms of interaction that can take place when energy strikes, or is incident (I) upon the surface.\r\n These are: absorption (A); transmission (T); and reflection (R). The total incident energy will interact with the surface in one or more of these three ways. The proportions of each will depend on the wavelength of the energy and the material and condition of the feature. Absorption (A) occurs when radiation (energy) is absorbed into the target while transmission (T) occurs when radiation passes through a target. Reflection (R) occurs when radiation\r\n \"\"bounces\"\" off the target and is redirected. The reflectance R is defined by the ratio of reflected radiant power to incident radiant power. The transmittance T of a medium is defined by the ratio of transmitted radiant power to incident radiant power. The absorptance A of a medium or target is defined by the ratio of absorbed radiant power to incident radiant power. Conservation of energy require that, at a certain wavelenght: R+T+A=1. To express the circumstance that the reflection can occurre in different direction as the surface deviates from a specular one, becoming rough the concept of surface scattering has been introduced. However, the concept of scattering concerns mainly atmopheric interaction with ELM and radar systems.\"","name":"Complex dielectric constants and refractive indices","selfAssesment":"Planned
"},{"code":"PP1-2-8","description":"The complex part nc of the refraction index n determines how far an e.m. wave of wavelength λ can survive crossing a specific medium. The attenuation length la is the distance after that the amplitude of an e.m. signal reduces its value by an amount of 1/e. For instance the amplitude of the Electric field E(z) of an e.m. wave proceeding along the z direction is decreasing as exp(-z/la) being la=λ/(2𝜋 nc) the attenuation length associated to that specific material (nc) and wavelength λ. This way attenuation length in water can be of hundreds of meters in the visible range and just few microns in the microwaves. So that penetration of radiation in the matter depends on both, the specific (dielectric) properties of the matter (through nc) AND the specific wavelength λ of considered e.m. signal.","name":"EM rad. penetration in the matter: Attenuation Length","selfAssesment":"Planned
"},{"code":"PP1-2-9","description":"EM radiation impinging a rough surface is (partly) reflected back (scattering). Lambertian surfaces produce a diffuse scattering (i.e. radiation is reflected similarly in all direction) and then appear equally bright from all directions, whereas specular surfaces behave like a mirror, with reflected radiation all aligned in one direction, with the reflection zenith angle equal to the incident angle of incoming radiation. Generally, the degree of \"roughness\" of a surface determines if it behaves like a Lambertian or a specular surface.","name":"Scattering from rough surface: Lambertian and specular surfaces.","selfAssesment":"Planned
"},{"code":"PP1-2","description":"Radiation can be absorbed, scattered, emitted and transmitted by the matter depending on the different parts of the electromagnetic spectrum and the matter peculiarities (Atoms, molecules, particles and surfaces) and its physical state (Temperature, Concentration, Shape, Roughness). The results of the interaction between radiation and matter depends strongly on the wavelength of radiation and on specific properties of the matter.","name":"Radiation - Matter interaction","selfAssesment":"Planned
"},{"code":"PP1-3-1","description":"The natural objects can either emit radiation (radiance, emittance) or be \"illuminated\" by a source (irradiance). In the following a series of definitions for each of these terms is provided. \r\nThe first basic radiometric quantity is the radiance (Iλ) and it is defined as the ratio of the differential radiant energy (dE) to the product of effective area (dA) with the time interval (dt), wavelength interval (dλ) and differential solid angle (dΩ). Iλ can be also referred as monochromatic intensity and it is expressed in units of energy per area per time per wavelength and per steradian (W m−2 sr−1). \r\nThe monochromatic flux density (Fλ) or the monochromatic irradiance of radiant energy is defined by the normal component of Iλ integrated over the entire hemispheric solid angle. It is expressed in units of energy per area per time per wavelength (W m−2). For isotropic radiation (i.e., if the intensity is independent of the direction), the monochromatic flux density is then Fλ = π Iλ. \r\nThe total flux density of radiant energy (F), or irradiance, for all wavelengths (energy per area per time, i.e., W), can be obtained by integrating the monochromatic flux density over the entire electromagnetic spectrum.\r\nAll the above definitions refer to a point source of radiation. When the flux density or the irradiance is from an emitting surface (i.e., an extended widespread source), the quantity is called the emittance. When expressed in terms of wavelength, it is referred to as the monochromatic emittance. The intensity or the radiance is also called the brightness or luminance (photometric brightness). The total flux from an emitting surface is often called luminosity.","name":"Radiometric quantities: radiance, irradiance, flux, brightness, emittance, luminosity, etc.","selfAssesment":"Planned
"},{"code":"PP1-3-2","description":"The attenuation of radiation emitted from a source decreases with the square of the distance from its center based on inverse square law. It considers that the size of the sources increases with the square of their radius, causing the same rate of attenuation in flux density.","name":"Decay of the emittance with the square of distance from the source","selfAssesment":"Planned
"},{"code":"PP1-3-3","description":"The relative amount of electromagnetic radiation reflected (absorbed, transmitted, emitted) by the matter at different wavelengths depends on its specific chemical composition and physical properties. The plots of corresponding physical quantities (reflectance, absorbance, transmittance, emissivity) against wavelength, are termed spectral signatures of the specific matter under study. In principle the analysis of spectral signatures obtained by multispectral EO sensors could allow us to identify/discriminate different cover types.\r\nThe interpretation of spectral signatures requires to well understand the e.m. radiation-matter interaction process. In very simple term we expect that incident radiation I(λ)can be reflected, absorbed or transmitted by the matter so that for the energy conservation should be: \r\n\r\n\r\nI(λ)=I(λ,R)+I(λ,A), I(λ,T) \r\n\r\n \r\nbeing I(λ,R), I(λ,A) and I(λ,T) the reflected, absorbed and transmitted fraction of I(λ). From the previous relation descends (dividing both members for I) that:\r\n\r\n\r\n1=R(λ)+A(λ)+T(λ)\r\n\r\n\r\nbeing:\r\n\r\n\r\nR(λ)=I(λ,R)/I(λ) named Reflectance\r\nA(λ)=I(λ,A)/I(λ) named Absorbance\r\nT(λ)=I(λ,T)/I(λ) named Transmittance\r\n\r\n\r\nThey are all specific properties of the considered matter and are not independent each others.\r\nIn particular for an opaque medium with T(λ)=0 it is:\r\nR(λ)=1-A(λ)","name":"Spectral Signatures of the matter","selfAssesment":"Completed
"},{"code":"PP1-3-4","description":"Vegetation, water and soil represent the most common cover types of Earth surface. Their reflectances in the VIS/NIR/SWIR spectral range, plotted against wavelength in the 0,4-2,5 micron, represent the most important (basic) spectral signatures for whatever application devoted to Earth surface study. Other spectral signatures (e.g. in emissivity) in the Thermal InfraRed range are particularly important to infer specific properties of Mineral and Rocks (ref. [PP1-3-5] Spectral Signature of Mineral and Rocks). In order to discriminate among such basic cover types, the (ref. [IP3-1-2-3]) NDVI (Normalized Difference Vegetation Index) is the most simple and powerful diagnostic tool in the VIS/NIR spectral range \r\nNDVI values ranging between the values -1 and +1, are higly positive for fully vegetated (up to NDVI=1) or partly vegetated (NDVI>0,3) targets, still positive (>0) for bare soils, negative for water bodies. Values around zero are expected for clouds thanks to their similarly high reflectances both in the NIR and VIR spectral bands (ref. [PP1-3-6] Spectral Signature of Clouds). \r\n\r\nVegetation. a) in the visible range most of the incomig radiation is adsorbed by the photosynthetic process, transmittance is very low. The residual reflected radiation has a small peak of reflectance around 0.5 microns which is responsible of the green colour associated to vegetation by the human vision sytem (limited to the VIS spectral range); b) in the NIR range vegetation exhibits its higher reflectance together its higher transmittance (very low absorbance) so that leaf density can be estimated thanks to the the contributes (decreasing with depth) of underlaying leaf layers; c) in the SWIR spectral range (in particular in the water bands around 1,4 and 1,9 microns) it is possible to appreciate the vegetation water content. As much it is, as more incident radiation is absorbed and less is the reflected fraction of radiation.\r\nBare Soil. Spectral reflectance is normally increasing moving from the VIS to the SWIR spectral region. Water features around 1,4 and 1,9 microns give information on soil water content (see before). Others specific features are described in [PP1-3-5] Spectral Signature of Mineral and Rocks\r\n\r\nWater. Spectral reflectance of clean deep water is quite low reaching quickly the zero value as soon as wavelengths passe microns. However it is important to note that such a very low reflectance is due to a very high transmittance in the VIS range and to a very high absorbance in the NIR/SWIR regions (ref. [PP2-2-5-2] Attenuation Lenght and Penetration Depth). This means that water is quite transparent in the VIS spectral range (so that, in case of shallow waters, measured reflected radiance can be significantly increased by the contribution of bottom of the sea). Water is completely opaque, instead, in the NIR/SWIR. In this spectral region, even in presence of shallow waters, the presence of suspended matter (that increases the measured reflectance both in the VIS and NIR/SWIR ranges) can be better discriminated (than in the VIS) from the contribute of the bottom of the sea that, in this spectral range, is zero.","name":"Spectral Signature of Vegetation, Water, Soil","selfAssesment":"Completed
"},{"code":"PP1-3-5","description":"Spectral signatures of rocks and mineral provide information on their chemical composition and crystal properties, grain size and roughness over a wide range of wavelengths from the visible to the thermal infrared.\r\nIn the Visible and Near-InfraRed (VNIR; 0.4÷1.0 µm) region, spectral features are dominated by electronic processes in transition metals, such as Fe, Mn, Cu, Ni, Cr, etc. Therefore, iron is the most important constituent having spectral properties in the VNIR, and the iron-rich minerals are characterized by low reflectance (high absorbance) below 0.7 µm.\r\nOther minerals, which represent the major part of the Earth's surface rocks, such us Si, Al and some anion groups (e.g. silicates, carbonates, oxides) hydroxides, have less spectral features in the VNIR region, but exhibit much more evidences in the Short-Wave InfraRed (SWIR; 1÷3 µm) region. In fact, spectral features of hydroxyls and carbonates mark the SWIR region.\r\nThe hydroxyl ion is a widespread constituent occurring in rock forming minerals such as clays, micas, chlorite etc. It shows a vibrational fundamental absorption band at about 2.74÷2.77 µm and an overtone at 1.44 µm.\r\nCarbonates, which are commonly in the Earth surface rocks in the form of calcite (CaC03), magnesite (MgC03), dolomite [(Ca-Mg) C03] and siderite (FeC03), shows a typical absorbance feature around 2.3 µm, instead the water content can be instead evaluated by the depth of absorption at 1,4µm and 1,9 µm.\r\nThermal InfraRed (TIR; 1÷20 µm) region, from a geological point of view, is a particularly important spectral region for remote sensing aiming at compositional investigations of terrestrial materials. In fact, the fundamental vibration features of many rock-forming mineral groups (e.g. silicates, carbonates, oxides, phosphates, sulphates, nitrates, nitrites, hydroxyls) occur in the TIR region. Briefly:\r\na) the silicates, which are most abundant group of minerals in the Earth's crust, shows vibrational spectral features due to the presence of Si04-tetrahedron around 8 µm to 12 µm; b) the carbonates show a weak feature around 11.3 µm that can be detected; c) the sulphates display bands near 9 µm and 16 µm; d) the phosphates also have fundamental features near 9.25 µm and 10.3 µm; e) the features in oxides usually occupy the same range as that of bands in Si-O, i.e. 8 µm to 12 µm; g) the nitrates have spectral features at 7.2 µm and the nitrites at 8 µm and 11.8 µm; h) the hydroxyl ions display fundamental vibration bands at 11 µm.","name":"Spectral Signature of Mineral and Rocks","selfAssesment":"Planned
"},{"code":"PP1-3-6","description":"The determination of spectral signatures for scenes with a high degree of spatial complexity is considered as one of the most persistent problems in atmospheric radiation, especially at the surface, where satellite observations can only be used indirectly to infer energy budget terms. In the shortwave (solar) spectral range, it is especially challenging to derive consistent albedo, absorption, and transmittance from spaceborne, aircraft, and ground-based observations for inhomogeneous cloud conditions and is closely related to the long-debated discrepancy between observed and modeled cloud absorption.\r\nThe cloud spatial structure is revealed as a spectral signature in shortwave irradiance through the physical mechanism of molecular scattering. However, the study of specific mechanisms is rather complex since the satellite instruments cannot completely describe the spatial distribution of cloud and the variability of scattering and absorption properties. For this reason, several studies deal with the problem described above, as a challenge for estimating spectrally the cloud optical properties (such as the albedo and transmittance) as well as scattering and absorption processes taking place in the cloud system with adequate resolution. Hence, the above mechanisms can be described using three dimensional (3-D) radiative transfer models. Those models receive auxiliary information from cloud imagery and radar observations. The molecular scattering (Rayleigh) was the only one directly dependent on the wavelength of the vertical radiative flux. Moreover, it was considered as a spectral perturbation of backtracked horizontal exchange of solar radiation due to the inhomogeneous distribution of cloud. The horizontal photon transport is highly correlated to its spectral dependence.\r\nConcerning the presence of cirrus or ice clouds, the effect of their phase function and the vertical distribution were evaluated on the scattering of far infrared radiation. Thus, the accurate reconstruction of the phase function of cirrus clouds potentially indicates the need for application of a radiative transfer model. This specific module necessarily includes scattering parameters, while the accuracy of its calculations needs to be verified against real measurements. \r\nFor several applications the preliminary detection of those portions of the scene affected by the presence of clouds (cloud detection) is mandatory. For studying properties of Earth's surface targets affected by the presence of clouds are flagged just to exclude them by further analyses. In some case clouds themselves are the object of interest. In both cases the identification of clouds (and their classification) is mostly done by using (combination of) specific spectral signatures. Generally speaking clouds are highly reflecting VIS/NIR radiation showing (due to their heigth) brigthness temperatures (in the TIR region) lower than underlying surfaces. Thin or semi-transparent clouds are still detectable for their higher reflectance over the sea which represents a quite dark bacground in the VIS/NIR/SWIR region. Over land (much more reflecting) such a test is not more efficient and more sophisticated tests (e.g. Brigthness Temperature Difference in the split window bands around 11 and 12 microns) are required. In presence of very cold, high reflective backgrounds (e.g. snow, glaciers, etc.) both tests on the VIS reflectance and on TIR brigthness temperature could fail. More specific tests exploiting the reflectance drop of snow in the SWIR (where clouds are still saving their higher reflectance) helps to discriminate the presence of clouds from clear sky conditions even over a snow background. In the microwaves clouds are quite transparent except when coupled with coarse particles related to rain, snow, hailstones (precipitating clouds). In that case Mie scattering dominates strongly reducing the amount of radiance collected at the sensor (lower brigthness temperature in the microwave spectral range).","name":"Spectral Signature of Clouds","selfAssesment":"Completed
"},{"code":"PP1-3-7","description":"If the resolution is low enough that disparate materials can jointly occupy a single pixel, the resulting spectral measurement, made by the sensor, will be the composite of the individual spectra. Under the linear mixing model (LMM), each observed spectrum in each pixel of a given image is assumed to result from the linear combination of the N endmember spectra present in the pixel. The reflectance spectrum of each endmember is weighted by the fractional area coverage of it in the pixel. \r\nHowever, if the components of interest in a pixel are in an intimate association, like sand grains of different composition in a beach deposit, light typically interacts with more than one component as it is multiply scattered, and the mixing between these different components are nonlinear. Such nonlinear effects have been recognized in spectra of: particulate mineral mixtures, aerosols and atmospheric particles, vegetation and canopy. In this case a non-linear mixing model (NLMM) should be applied. To summarize: Linear mixture model assumes that endmember substances are sitting side-by-side within the pixel; Nonlinear mixture model assumes that endmember components are randomly distributed throughout the pixel, causing multiple scattering effects. \r\nIn the linear mixing case, the basic premise of mixture modelling is that within a given scene, the surface is dominated by a small number of distinct materials that have relatively constant spectral properties. These distinct substances (e.g., water, grass, mineral types), characterized by a well-defined spectral signature are called endmembers, and the fractions in which they appear in a mixed pixel are called fractional abundances. Then, finding the endmembers that can be used to ‘unmix’ other mixed pixels becomes a crucial issue. \r\nIdentify fractional abundances of distinct substances from the spectral signal of a mixed pixel is one of the application in which hyperspectral images can provide an valuable support.","name":"Composition of spectral signatures (Linear Mixing)","selfAssesment":"Completed
"},{"code":"PP1-3-8","description":"One of the most common ways to classify remote sensing systems consists in distinguishing them into the passive systems, which detect naturally occurring radiation, and the active systems, which emit radiation and analyse what is sent back to them. The passive systems can be further subdivided into those that detect radiation emitted by the Sun (this radiation consists mostly of ultraviolet, visible and near-infrared radiation), and those that detect the thermal radiation that is emitted by all objects that are not at absolute zero (i.e. all objects). For objects at typical terrestrial temperatures, this thermal emission occurs mostly in the infrared part of the spectrum, at wavelengths of the order of 10 μm (the so called thermal infrared region), although measurable quantities of radiation also occur at longer wavelengths, as far as the microwave part of the spectrum. Active systems can, in principle, use any type of electromagnetic radiation, resulting able to obtain measurements anytime, regardless of the time of day or season. In practice, however, they are restricted by the transparency of the Earth’s atmosphere at the specific spectral range considered. In any case they can be used for examining wavelengths that are not sufficiently provided by the sun, such as microwaves, or to better control the way a target is illuminated. Active sensors may be classified according to the use that is made of the returned signal. Two main methods have been identified to this aim so far: the Ranging technique mostly concerns with the time delay between transmission and reception of the signal, while the Scattering one is mostly focused on the strength of the received signal.","name":"Definition of active and passive remote sensing techniques","selfAssesment":"Planned
"},{"code":"PP1-3","description":"Measuring the signal emitted (received) by a radiation source (detector)","name":"Sensing of EM radiation.","selfAssesment":"Planned
"},{"code":"PP1-4-1","description":"Radiative transfer equation (RTE) is the governing equation of radiation propagation in a media, which plays a central role in the analysis of radiative transfer in gases, semitransparent liquids and solids, porous materials, and particulate media, and is important in many scientific and engineering disciplines. \r\nThe RTE states that when radiation (a light-ray) propagates through matter (gas, dust, liquid), the incident radiation could be absorbed or scattered by matter, or radiation emitted from matter could append to the incident radiation. As a result, the intensity of radiation would change temporally, spatially, and directionally. The study of the propagating way of radiation in matter is the radiative transfer. In more detail, the radiation traversing a medium may be attenuated due to the density, mass scattering and absorption of material. In contrast, the radiation’s intensity can be strengthened by emissions from the material plus multiple scattering from all directions. All the above interactions are described mathematically by the general radiative transfer equation.\r\nThere are different forms of RTEs that are suitable for different applications, including the RTE under different coordinate systems, the transformed RTE having good numerical properties, the RTE for refractive media, etc.. Furthermore, several fundamental numerical methods for solving RTEs are proposed up to now focusing on the deterministic methods, such as the spherical harmonics method, discrete-ordinate method, finite volume method, and finite element method.","name":"General equation of radiative transfer.","selfAssesment":"Planned
"},{"code":"PP1-4-10","description":"The inversion approach aims at retrievals of trace gas concentration and temperature profiles of atmospheric state, namely the modeled state vector, based on the measured radiance transmitted or reflected or scattered (SCIAMACHY spectrometer) by the Earth-Atmosphere system. Satellite instruments measure the radiance L that reaches the top of the atmosphere at given frequency v. The measured radiance is related to geophysical variables of Earth's atmosphere (e.g. temperature vertical profiles and chemical composition, aerosols, clouds, rain, etc.) and surface (e.g. temperature, spectral emissivity and reflectance, etc.) by the Radiative Transfer Equation (RTE). In RTE measured spectral radiances are assumed as the result of different contributions:\r\na) thermal emission from the different layers (at heigt z) of atmosphere at temperature T(z) modulated by the atmospheric transmittance from z to the sensor heigt. It depends on both temperature profile T(z) and trace gas concentration along the optical path;\r\nb) Surface emission. It depends mostly on Eart's surface temperature T(0) and spectral emissivity\r\nc) Surface reflection/scattering. It depends on spectral reflectance and local properties like surface rugosity \r\nOthers, more complex contributions comes from: cloud/rain, aerosols, etc.\r\nIn its simplified form, terms a) and b) dominate as far as InfraRed (IR) radiances are considered. Term a) can be neglected in those bands where atmosphere is transparent (atmospheric windows). Term b) can be negletcted in the IR spectral bands (sounding channels) where it is fully adsorbed by some specific constituent of the atmosphere. Among the IR sounding channels some ones are selected being associated to atmospheric constituents (like CO2 or oxygen) whose mixing ratio in the atmosphere is known to be constant. For radiances measured in these bands term a) in RTE depends only on T(z) (through a Fredholm equation of the first kind) that can be then retrieved by inversion methods. When T(z) are known trace gas concentrations survive as the only unknown of term a) and can be retrieved by inversion methods using radiances measured in their corresponding sounding channels. Similar inversion strategies have been suggested as far as radiances (emitted, transmitted, reflected, adsorbed) measured in different spectral ranges (from the Visible to the Microwaves) are considered.","name":"Retrieval of atmospheric parameters by inversion of multi-spectral radiances","selfAssesment":"Completed
"},{"code":"PP1-4-2","description":"In the field of radiation scattering and absorption, the cross-section, analogous to the shape of a particle, is used to determine the amount of energy diverted from the original beam by the particle. This parameter is called mass cross section, when it is in reference to unit mass (cm2g-1).","name":"Cross Section of Extinction (Absorption, Scattering) per Mass Unit","selfAssesment":"Planned
"},{"code":"PP1-4-3","description":"When the mass cross-section is multiplied by the density of particle, the extinction coefficient is calculated, namely the sum of absorption and scattering coefficient, whose the units are related to length. Especially, the absorption coefficient (k (cm•atm)-1) is the product of strength of absorption with the Loschmidt’s number.","name":"Absorption Coefficient","selfAssesment":"Planned
"},{"code":"PP1-4-4","description":"The source function, Jλ, has units of radiant intensity and it is defined as the ratio of the source function coefficient to the mass extinction cross section. The Jλ determines the intensity that are acquired in a homogeneous medium.","name":"Source Function (Coefficient)","selfAssesment":"Planned
"},{"code":"PP1-4-5","description":"If the monochromatic beam (Iλ) of radiation attenuates due to absorption, but it remains unaffected from emission contributions and multiple scattering of homogeneous Earth-Atmosphere system, it can be expressed by Beer-Bouguer-Lambert law. This law also expresses the monochromatic optical depth (τλ) and transmissivity (Τλ) of the above system.","name":"Beer-Bouguer-Lambert law.","selfAssesment":"Planned
"},{"code":"PP1-4-6","description":"The Schwarzschild equation provides an interpretation for the infrared radiation that undergoes the absorption and emission processes simultaneously, while the scattering efficiency is considered negligible. Hence, its solution is obtained by the integrating of relationship that invokes Kirchhoff’s law and summing the two above processes along a ray path.","name":"Schwarzshild equation and its solutions","selfAssesment":"Planned
"},{"code":"PP1-4-7","description":"The Atmosphere-Earth system that monochromatic beam (Iλ) of radiation travels, is called optical path. It expressed by optical path length, namely the product of geometric length and the refractive index of medium. It determines the optical thickness, namely a measure of the cumulative depletion of Iλ directed in straight-downward.","name":"Concepts of Optical path and Optical thickness.","selfAssesment":"Planned
"},{"code":"PP1-4-8","description":"Radiative transfer is highly nonlinear and non-local against the cloud structure at a high spatial resolution. Hence, a Monte Carlo approach can be used for the representation of cloud structure and interactions between photons and clouds. This approach is more efficient than the method of representing clouds as horizontally homogeneous.","name":"Radiative transfer in presence of clouds","selfAssesment":"Planned
"},{"code":"PP1-4-9","description":"The line by line radiative transfer model (LBLRTM) is an accurate and flexible model for the estimation of the spectral radiance and transmittance over the full spectral range (microwave to ultraviolet), using a first-order perturbation algorithm. It is considered as the basic tool for the creation of retrieval algorithms employed by the ground-based and satellite instruments, while the latest updates in spectroscopic factors are derived from the high-resolution transmission molecular absorption (HITRAN) database. A LBLRTMs is continuously updated and validated against highly accurate spectral measurements. Its errors are related to uncertainties in line parameters and shape. The shape is a Voigt line which is a linear combination of approximating functions for the description of all atmospheric levels. LBLRTML is combined with the continuum MT_CKD (Mlawer, Tobin, Clough, Kneizys, Davies) model which in turn includes the atmospheric constituents of water vapor, carbon dioxide (CO2), molecular oxygen (O2), molecular nitrogen (N2), and ozone (O3), and the molecular extinction process (Rayleigh scattering). A recent version of LBLRTM calculates analytically the Jacobians equations for obtaining meteorological parameters. Also, this model version retrieves the optical parameters of clouds related to scattering and emissivity. The LBLRTM is widely used in radiation and climate applications. It is capable to calculate the absorption degrees of various atmospheric constituents which are utilized afterward from climate and weather prediction models for estimating the broadband solar irradiance and the heating rates. Additionally, the complex radiative transfer models with fast computational time are initiated and trained by the LBRTM, since they are used subsequently on numerical weather prediction (NWP) assimilation systems.","name":"Line-by-line radiative transfer models","selfAssesment":"completed
"},{"code":"PP1-4","description":"Theory of radiative transfer describe the transmission of the electromagnetic radiation through a medium.","name":"Fundamentals of Radiative Transfer","selfAssesment":"Planned
"},{"code":"PP1-5-1","description":"Light is the electromagnetic phenomenon we exploit for remote sensing. Its basic laws concerning the transmission through the interface of two different media are governed by reflection and refraction. Reflection governs the way light is backpropagated and refraction dictates how light is transmitted. Refraction is related to the real refractive index of a medium. Dispersion relates to the way the light of a given wavelength is transmitted. Since light of different wavelengths are transmitted at different angles, the phenomenon leads to the concept of dispersion. These three simple principles are at the core of the understanding technology of remote sensing.","name":"Reflection, Refraction and Dispersion of the light","selfAssesment":"Planned
"},{"code":"PP1-5-11","description":"The theory provides the bulk of physical explanation and related laws, which govern absorption, emission and spontaneous emission from the ordinary matter. Early laws about thermal radiation and the blackbody emission, such as Rayleigh-Jeans, Wien, Planck laws are cast in a single theory and formalism through the concept of quantized energy at the level of atoms emission/absorption of light. Explain the modern concept of quantum optics and their link to the design of modern devices for the measurements and/or production of coherent light.","name":"Einstein’s theory of radiation: photons, photoelectric effect, absorption, emission; Stimulated emission: the laser","selfAssesment":"Planned
"},{"code":"PP1-5-14","description":"Solid state modern detectors rely on non-metal junction, which can be designed and operated to yield a bandgap energy according to the spectral range (infrared, visible, UV) to be detected. The basic principles of how these devices are designed and fabricated is important to develop and design new sensors useful for the various remote sensing applications.","name":"Electric conduction in solids: semiconductors, p-n- junction, diode and transistors","selfAssesment":"Planned
"},{"code":"PP1-5-15","description":"Modern detectors of electromagnetic radiation in the infrared, VIS, UV spectral regions are designed and fabricated based on suitable junctions or electro-optical devices. The performance of these systems needs to be assessed in terms of accuracy and precision. This is made through figures of merit such as Noise Power Spectral Density, Noise Equivalent Power. Detectors can be classified as photovoltaic or photoconductive devices, which allows to better classify the various noise sources: shot noise, 1/f noise, Johnson noise, generation-recombination noise.","name":"Photovoltaic and photoconductive detectors: MCT, InSb, bolometer, CCD devices","selfAssesment":"Planned
"},{"code":"PP1-5-2","description":"Interference and diffraction are phenomena related to the wave nature of electromagnetic radiation. They explain how light propagates in presence of obstacles. These phenomena are largely used in the fabrications of optical systems for remote sensing: e.g. radiometers and spectrometers.","name":"Interference and Diffraction.","selfAssesment":"Planned
"},{"code":"PP1-5-3","description":"The Michelson interferometer is the instrument that exploits and evidence the interference of light. A masterpiece of experimental physics, the Michelson interferometer is the key architecture of the modern optical interferometers, which make it possible to measure the emitted Earth spectrum with hyperspectral resolution.","name":"Michelson Interferometer","selfAssesment":"Planned
"},{"code":"PP1-5-4","description":"The celebrated principle of constant speed of light and independence of the reference frame is important to explain the basic principles of instruments such as the Michelson interferometer. The basic physics theory to explain how electromagnetic fields propagates and the inter-relationship between electric and magnetic fields.","name":"Special relativity; Electromagnetic fields equations and propagations","selfAssesment":"Planned
"},{"code":"PP1-5-6","description":"Helmotz’s wave equation arises in light and acoustic scattering problem and yields the general framework to investigate and analyse the scattering of time-harmonic acoustic and electromagnetic waves by a penetrable inhomogeneous medium.","name":"Helmotz’s equations; Scattering from inhomogeneous media.","selfAssesment":"Planned
"},{"code":"PP1-5-7","description":"Geometrical optics is governed by the laws of reflection, refraction and dispersion. Its applications are relevant to many optical systems involving ray tracing, wavefront propagation, thin film calculators (which underly many optical engineering calculations).","name":"Foundations of geometrical optics, geometrical theory of optical imaging","selfAssesment":"Planned
"},{"code":"PP1-5-8","description":"Optical interferometers are nowadays used to develop and implement Fourier Transform Spectrometers, which can measure the emission spectrum of a given source with high spectral resolution at a constant sampling. This instrumentation is now at the core of modern hyperspectral sounders from satellite and have opened the way to the sounding of the Earth atmosphere with unprecedented spatial vertical resolution.","name":"Elements of the theory of interference and interferometers","selfAssesment":"Planned
"},{"code":"PP1-5-9","description":"Diffraction gratings and dispersive element are the basic ingredients for radiometers and grating spectrometers. They are in some cases preferred to Interferometer systems because the optical layouts can be designed and implemented with no moving part or components. Many of the today satellite instruments, including sounder and imagers, rely on diffraction and/or grating spectrometers","name":"Elements of the theory of diffraction and grating spectrometers","selfAssesment":"Planned
"},{"code":"PP1-5","description":"This section describes the theoretical fundaments of Optics and Modern Physics of Sensors relevant to the Earth Observation.","name":"Basics of Optics and Modern Physics of Sensors","selfAssesment":"Completed
"},{"code":"PP1-6-1","description":"The temperature and pressure profiles determine the atmospheric structure. The latter consists of four basic levels, considering the vertical variability of the temperature. These main four levels are troposphere, stratosphere, mesosphere, and thermosphere. In the troposphere (0-12km), which is the lowest layer of the atmosphere, all the meteorological processes that affect our everyday life take place. The lowest part of the troposphere is known as the boundary layer (0-3km), where all the surface-atmosphere interactions and exchanges take place. The troposphere concentrates the water vapor and 90% of atmospheric mass, while the chemical composition of all atmospheric layers consists of nitrogen, oxygen, argon and trace gases. The main parameters that characterize the atmosphere structure are pressure, density, and temperature. All the aforementioned parameters are related to the atmospheric composition and vary with altitude, latitude, longitude and season. Additionally, the stratosphere, which is the layer above the troposphere, contains almost all of the ozone abundance (~90%) of the atmosphere in a region named as ozone layer and traced between 15 and 35km. The interaction of the incoming solar radiation with ozone in this layer causes the reduction of the incoming harmful UV radiation provoking the temperature increase in the stratospheric layer. The 99.9% of total atmospheric mass is concentrated in lower atmosphere (<50km) with Nitrogen (N2, 78.08%), Oxygen (O2, 20.95%) and argon (Ar, 0.93%) being the major constituents of the atmosphere. Water vapor (H2O) is considered as a significant factor, too. Despite the fact that it depicts a very small amount of total atmospheric mass, it’s one of the most important greenhouse gases, along with carbon dioxide (CO2) and methane (CH4), absorbing the Earth’s longwave (infrared) radiation, affecting the energy balance of Earth-Atmosphere system. Furthermore, water vapor plays a decisive role in the formation of clouds and precipitation. Together with the basic chemical (atoms, molecules, ions) constituents of a \"standard\" atmosphere, aerosols of natural and anthropogenic origin have to be considered too, as far as the interaction of e.m. radiation with atmosphere is concerned.","name":"Structure and chemical-physical composition of Earth's atmosphere","selfAssesment":"Completed
"},{"code":"PP1-6-10","description":"The water vapour is the major radiative and dynamic parameter in the atmosphere. Its concentrations vary highly in space and time, with the tropospheric water vapor being determined by the hydrological cycle processes, namely the evaporation, condensation and precipitation and by large-scale transport processes. Specific humidity decreases rapidly with pressure (following an exponential function) and with latitude. In particular, the variability of the H2O concentration shows a bimodal distribution: it’s very small in the equatorial region and poleward, relatively small in stratosphere and shows a maximum in the subtropics of both hemispheres. The concentration of H2O in the lower stratosphere is controlled by the temperature of the tropical tropopause, and by the formation and dissipation of cirrus. The water vapor can condense into water droplets when it has a particle to condense upon. The atmosphere continuously contains aerosol particles ranging in size from ∼10−3 to ∼20 μm. These aerosols are known to be produced by natural processes (volcanic dust, smoke from forest fires, particles from sea spray, windblown dust, and small particles produced by the chemical reactions of natural gases) as well as by human activity (particles directly emitted during combustion processes and particles formed from gases emitted during combustion). Some aerosols are effective condensation and ice nuclei upon which cloud particles may form. For the hygroscopic type, the size of the aerosol depends on relative humidity. Thin layers of aerosols are observed to persist for a long period of time in some altitudes of the stratosphere. \r\nClouds are global in nature and regularly cover more than 50% of the sky. There are various types of clouds. Cirrus in the tropics and stratus in the Arctic, and near the coastal areas are climatologically persistent. The microphysical composition of clouds in terms of particle size distribution and cloud thickness varies significantly with cloud type. Clouds can also generate precipitation, an event generally associated with midlatitude weather disturbances and tropical cumulus convection.","name":"Water vapour and Cloud formation","selfAssesment":"Planned
"},{"code":"PP1-6-11","description":"The radiative equilibrium is the principle, where the radiative emission and absorption are in balance based on Kirchhoff’s and Planck’s law, resulting in the steady temperature of planet. The adiabatic lapse rate displays the decrease of vertical temperature of a parcel with rate higher than 1oC per 100 metres.","name":"Radiative Equilibrium. Adiabatic lapse rate","selfAssesment":"Planned
"},{"code":"PP1-6-12","description":"The atoms of carbon are building blocks of living organisms and they can move among organisms as a part of carbon cycle. Their transport rate to the atmosphere as carbon dioxide is vital, because this gas trap heat in the atmosphere, increasing the Earth’s temperature and causing Greenhouse effect.","name":"The Carbon Cycle, Greenhouse Effect","selfAssesment":"Planned
"},{"code":"PP1-6-2","description":"The atmospheric absorption can cause an excitation or falling into the energy state of a particle, while the scattering is related to absorption and re-emission of radiation at all directions without changes in its frequency. Particularly, the main contributors of the incoming solar radiation absorptions are various molecules like the nitrogen (N2), oxygen (O2), ozone (O3), water vapor (H2O). Additionally, other constituents of the atmosphere such as CO2 and CH4, and other trace gases, aerosols, and cloud droplets can also absorb significant portion of the incoming solar radiation. Generally, the absorption of solar radiation is related to the wavelength of the solar spectrum. For example, gases and specific type of aerosols (black carbon, BC) or elementary carbon (EC) absorb in the ultraviolet (UV) and visible (VIS) part of solar spectrum. On the contrary, cloud droplets which are suspended in the atmosphere mainly scatter in UV and VIS and absorb in the infrared. The absorption of the incoming solar radiation from the atmospheric constituents reduces the harmful UV radiation and it is considered as the driving of atmospheric photochemistry. Moreover, scattering in the atmosphere can be divided into two mainly categories, firstly, the Rayleigh scattering which is the scattering of radiation by gases (mainly N2 and O2) and, secondly, the Mie scattering which is the scattering by aerosol particles and cloud droplets. The main difference between Rayleigh and Mie scattering is the direction of the re-emission of the incident solar radiation. For example, in the Rayleigh scattering the light have symmetrical direction either forward or backward whereas in Mie scattering the light is mainly scattered in the forward direction, depending on the size of the particle.","name":"Absorption and scattering of solar radiation in the Atmosphere","selfAssesment":"Completed
"},{"code":"PP1-6-3","description":"Mie scattering refers primarily to the elastic scattering of light from atomic and molecular particles whose diameter is similar or larger than the wavelength of the incident light. We can say that, when the particle has a diameter greater than about a tenth of the wavelength, we are in the field of Mie scattering.\r\nThis scattering produces a pattern like an antenna lobe, with a forward lobe sharper and more intense than the back one, the larger the particle size the greater the intensity and sharpness of the anterior lobe. Unlike Rayleigh scattering, Mie scattering is not strongly wavelength dependent. In this case the predominant component for the quantification of scattering (in addition to the particle dimension) is the direction of the incident solar radiation.\r\nMore specifically, the amount of scattering in the backward direction depends upon a wave relation tending to decrease in accordance with the growth of the particle size until it reaches a certain value for which the back scattering becomes a constant quantity. This condition is reached when the diameter of the particle is approximately equal to the wavelength of the incident radiation.\r\nIn the atmosphere the Mie scattering is commonly caused by particles (aerosols) floating in the atmosphere (due to Dust, smoke, fog, rain drop). \r\nIn nature it is possible to see the effects of Mie scattering, for example, in the evenings when there is a lot of fog and the dazzling headlights of our car do not allow us to see the road ahead. \r\nThe Mie theory provides the solution for the amount of scattering in case of a spherical medium due to an incident wave.","name":"Mie Scattering in the Earth's Atmosphere","selfAssesment":"Planned
"},{"code":"PP1-6-4","description":"Scattering is a physical process by which a particle in the path of an electromagnetic wave continuously abstracts energy from the incident wave and reradiates that energy in all directions. In more detail, it occurs when a photon’s electromagnetic field hits a particle’s electric field in the atmosphere and is deflected into another direction. The Rayleigh scattering falls into the elastic scattering phenomena, in which the individual photon changes its direction of propagation but non its energy. The Rayleigh scattering involves air molecules (mainly N2 and O2) whose diameter (x) is much smaller (one-tenth at least) than the incident radiation wavelength (λ) (i.e., x << λ). The amount of scattered intensity (I) depends on the incident light wavelength (λ) and the refractive index (n) of air molecules. However, the refractive index can be considered relatively negligible as compared to the explicit wavelength term. In this way, the intensity scattered by air molecules in a specific direction is strongly dependent on the wavelength (λ), as expressed in the form Iλ~1/λ4. The inverse dependence of the scattered intensity on the wavelength to the fourth power allows at explaining the blue color of sky, caused by the scattering of sunlight off the atmosphere molecules. To better understand this phenomenon, it is worth considering that a large portion of solar energy is contained between the blue and red regions of the visible spectrum, where blue light (0.425 µm) has a shorter wavelength than red light (0.650 µm). Consequently, based on the above-mentioned equation, blue light scatters about 5.5 times more intensity than red light. For this reason, more blue light is scattered than red, green, and yellow, and so the sky appears blue, when viewed away from the sun’s disk. The Rayleigh scattering of unpolarized sunlight by air molecules has maxima in the forward and backward directions, whereas it shows minima in the side directions. Furthermore, the light scattered by particles is not delimited only on the incidence plane, but is visible in all the azimuthal directions. The derived scattering patterns are symmetrical in the three-dimensional space, because of the spherical symmetry assumed for air molecules.","name":"Rayleigh Scattering in the Earth's Atmosphere","selfAssesment":"Planned
"},{"code":"PP1-6-5","description":"When we talk about “thermal infrared (or terrestrial) radiation” we commonly refer to the energy emitted from the Earth-atmosphere system. Trapping of thermal infrared radiation by atmospheric gases is typical of the atmosphere and is therefore called the “atmospheric effect”. The atmospheric effect is sometimes referred to as the “greenhouse effect” because in a similar way glass, which covers a greenhouse, transmits short-wave solar radiation, however absorbs long-wave thermal infrared radiation. Imagine a beam of radiation travelling through a small section of air. The air is made up of changing concentrations of different species, with all molecules absorbing and emitting thermal radiation at different rates. As the radiation travels through different layers of the atmosphere, the intensity of radiation will constantly be modified by both absorption and emission processes as described by the Schwarzschild's equation. In case of a sensor on board of a satellite, the net radiation measured would be that which is attenuated through each layer (as small increments of absorption and emission) from the surface to the top of the atmosphere plus the radiation emitted directly from the atmosphere. In this case, this process can be described by the radiative transfer equation (RTE). \r\nThe equation of radiative transfer simply says that as a beam of radiation travels through the atmosphere, it loses energy to absorption, gains energy by emission, and redistributes energy by scattering. Many radiative transfer codes exist which are able, i.e. on the basis of known properties of the atmosphere, to computed the effect of the atmosphere on the thermal infrared radiation providing atmospheric transmittance (absorption), atmospheric scattering and atmosphere path emission. Commonly, in satellite remote sensing, the thermal infrared region is defined as the region of the electromagnetic spectrum comprised between 8 and 14 micron. In an atmosphere free of particles (aerosols due to phenomena like fires, volcanic eruption, dust storm, etc.) the thermal infrared radiation is mainly affected by triatomic gases like water vapor, carbon dioxide and ozone.","name":"Thermal infrared radiation transfer in the atmosphere","selfAssesment":"Completed
"},{"code":"PP1-6-6","description":"Light scattering by particles is the process by which small particles cause optical phenomena, such as rainbows, the blue color of the sky, and halos. Mie scattering defines the interaction of light with particulate matter with a dimension comparable to the wavelength of the incident radiation. It can be regarded as the radiation resulting from a large number of coherently excited elementary emitters (molecules for example) in a particle. Since the linear dimension of the particle is comparable to the wavelength of the radiation, interference effects occur. The most noticeable difference to Rayleigh scattering is, generally, the much weaker wavelength dependence and a strong dominance of the forward direction in the scattered light. The calculation of the Mie scattering cross section, which involves summing over slowly converging series, is complicated even for spherical particles, it is worse for particles of an arbitrary shape. However, the Mie theory for spherical particles is well developed and a number of numerical models exist to calculate scattering phase functions and extinction coefficients for given aerosol types and particle size distributions.","name":"Light scattering by atmospheric particulates","selfAssesment":"Planned
"},{"code":"PP1-6-7","description":"Each time radiation passes through the atmosphere it is attenuated to some extent. We refer to this attenuation with the term 'atmosphere transmittance'. The typical atmospheric transmittance between wavelengths of 250 nm and 2500 nm, i.e. in the ultraviolet, visible, near-infrared and short-wave-infrared regions of the spectrum is dominated bywater vapour, although methane, carbon dioxide and molecular oxygen are also responsible for a few absorption lines. The behaviour in the visible region is dominated by molecular Rayleigh scattering. At the short-wavelength end of the spectrum, in the ultraviolet, absorption by ozone becomes very significant. Above 2500 nm up to the upper limit (13500 nm) of the optical electromagnetic spectrum useful for Remote Sensing, the atmosphere transmittance is mainly affected by triatomic molecules (H20, CO2 and O3). However, the atmospheric effects (transmittance) is strongly depending on the electromagntic wavelength. Remote Sensing exploits the region of relative atmospheric transparency called atmospheric windows.","name":"Earth's (standard) Atmosphere Transmittance","selfAssesment":"Planned
"},{"code":"PP1-6-8","description":"With the term 'atmospheric windows' we refer to the regions of the electromagnetic spectrum where the interaction between the atmosphere constituents (i.e., molecules, aerosols, and cloud particles) and the electromagnetic radiation is minimized, namely the mechanisms of scattering and absorption of the radiation are less relevant than the transmission one. Therefore, the radiation collected at the sensor in these spectral regions is strictly depending on the Earth surface features, allowing to infer information about the processes/phenomena there in progress at the time of the acquisition. There are three main ‘windows’ in the Earth's atmosphere. The first of these includes the visible and near-infrared (VNIR) parts of the spectrum up to the medium infrared, between wavelengths of about 0.38 μm and 3.5 μm, although it does also contain a number of opaque regions. This spectral interval includes the small portion of the electromagnetic spectrum to which human eyes are sensitive to (i.e, the visibile region between 0.4 and 0.7 μm). The second is a rather narrow region between about 8 μm and 15 μm, in which is found the bulk of the thermal infrared (TIR) radiation from objects at typical terrestrial temperatures. In this region there is only a main opaque interval, around 9.6 μm due to the presence of the ozone band. The third more or less corresponds to the microwave region, between wavelengths of a few millimeters and a few meters. Therefore, each remote sensing instrument that should be able to penetrate the Earth’s atmosphere has to be designed to operate in one of these three ‘window’ regions.","name":"Atmospheric (spectral) windows for EO","selfAssesment":"Planned
"},{"code":"PP1-6-9","description":"The water cycle is a continuous purification process of water on Earth due to the movement of water species among various reservoirs. This cycle is vital for Earth’s life, ecosystems, and living organisms. The water cycle includes mainly four processes. Water is evaporated from ocean and land surfaces driven by solar heating. The resulting water vapor rises upwards into the atmosphere, transported by the winds, cools, and due to low air temperature condensates into liquid droplets and ice crystals to form clouds. The ice or/and liquid droplets collide, increase their size, and precipitate as snow or rain to Earth’s surface and oceans. The subtraction of energy (latent heat of evaporation) at low latitudes related to the evaporation processes as well as its release (latent heat of condensation) at higher latitudes related to the condensation processes is a formidable way to guarantees the heat transport from the warmer part of the Earth to the colder ones mantaining local air temperature more compatible with the human life. The starting point of the water cycle is not unique, but the oceans can be selected as the initial reservoir. Other important reservoirs are considered ice sheets, lakes, and rivers. \r\nThe hydrosphere is defined by the various water reservoirs which are characterized by different residence times – the time spends the water molecules in a reservoir. The water residence time – the rate at which the water comes out the reservoirs – varies for each reservoir extending from hundreds (Greenland Ice Sheet) or thousands of years (Antarctic Ice Sheet) to years and days for rivers and lakes, respectively. It also defines the energy transferred from the Earth to the Atmosphere which increases for short-term residence times. In long-term temporal scales, this energy is defined as the evaporation rate (E) and balances with the precipitation rate (P). This global energy balance breaks for shorter time scales depending also on the local and regional climate. For example, in regions located in the Inter-Tropical Convergence Zone (ITCZ), the energy balance in the water cycle does not exist since the precipitation rate is much higher than the evaporation rate (P>>E) due to the horizontal movement of converging trade winds.","name":"The Water Cycle","selfAssesment":"Completed
"},{"code":"PP1-6","description":"Atmospheric Physics describe the processes affecting the physical, chemical and thermodynamic status of planetary atmospheres. In the context of EO sciences, it particularly refers to the physics of the interactions of e.m. radiation traveling across (or emitted by) the atmosphere as the main source of information collected by satellite (in general aerial) sensors.","name":"Basics of Atmospheric Physics","selfAssesment":"Completed
"},{"code":"PP1-7-1","description":"According to the second law of thermodynamics, heat is a measure of the movement or the flow of energy from hotter substances to colder ones and it is measured in Joules. In microscale, heat is known as internal energy. Two regions in thermal contact have the same temperature when there is no net exchange of internal energy between them. Heat is the net transfer of internal energy from one region to another, while temperature, which is the degree of hotness or coldness of an object, describes the kinetic energy of molecules within substances. The faster the particles are moving, the higher the kinetic energy. Since the motion of the particles within an object is random, they do not move at the same speed and in the same direction, some of them move faster. Therefore, those particles have more kinetic energy than the others. Temperature is then the measure of the average kinetic energy of a system, and is usually expressed in Celsius (°C). The Celsius temperature scale is defined by international agreement in terms of two fixed points: the temperature of the ice point, which is defined as 0° Celsius, and the steam point as 100° Celsius. The Fahrenheit (°F) temperature scale is mainly used in the United States; on this scale, water freezes at 32 degrees Fahrenheit, and the temperature of boiling water is 212 F. The Kelvin scale (K) is the base unit of temperature in the International System of Units (SI). This temperature scale is obtained by shifting the Celsius scale by −273.15°; zero Kelvin is also called absolute zero.","name":"Temperature and heat","selfAssesment":"Planned
"},{"code":"PP1-7-10","description":"Irreversible thermodynamics investigates the regularities in transport phenomena, namely heat and mass transfer, and their relaxation. It is based on the first law of Thermodynamics, which correlate the heat flow density with pressure and viscosity, and the second law that describe the temporal variations of local entropy for local continuous mass.","name":"The constitutive equations of irreversible fluxes","selfAssesment":"Planned
"},{"code":"PP1-7-11","description":"The Adiabatic process of homogeneous system occurs, when flow of heat is not exchanged across the boundaries of system and the system is characterized from uniform phase (solid or liquid or gases). In this case, the variations of entropy can be determined for some parts of system.","name":"Heat equation and special adiabatic systems, special adiabats of homogeneous systems","selfAssesment":"Planned
"},{"code":"PP1-7-12","description":"The thermodynamic diagrams are used for the study of vertical structure and properties of the Atmosphere above a specific location. Especially, a static diagram represents a) an atmosphere with fixed potential temperature or b) a process curve of the change of variables of air parcel that rises adiabatically.","name":"Thermodynamics diagram, atmosphere static","selfAssesment":"Planned
"},{"code":"PP1-7-2","description":"Kinetic theory of gases is based on a simplified molecular description of gases, from which the properties of volume, pressure and temperature can be derived. The assumptions of this theory are based on the random movements of molecules, their elastic collisions and the transfer of kinetic energy between them.","name":"Kinetic theory of gases","selfAssesment":"Planned
"},{"code":"PP1-7-3","description":"The ideal gas law or general gas equation describes the equation of state of hypothetical ideal gas. This equation correlates the pressure and volume with its temperature, while is characterized as a combination of the empirical laws of Boyle, Charles, Avogadro and Gay-Lussac.","name":"Ideal gas laws","selfAssesment":"Planned
"},{"code":"PP1-7-4","description":"The state functions of ideal gas are the pressure, volume, temperature, internal energy and entropy, which remain unchangeable in compared with the path. The internal energy is expressed through Joule’s law as a function of temperature of gas, while the entropy depends on the variation of volume and temperature.","name":"State function of ideal gases","selfAssesment":"Planned
"},{"code":"PP1-7-5","description":"The phase rule for condensation is expressed as P+F=C+1. The terms of P, F and C describe the number of phases, minimum fixed variables and independent chemical species respectively. Concerning the condensed phases to distinguish the gases from liquids and solids, these are the density, molecular order, diffusion, etc.","name":"State function of the condensed gas phase","selfAssesment":"Planned
"},{"code":"PP1-7-6","description":"When the system passes from initial to final state due changes in properties of temperature, pressure and volume, it is considered to have undergone thermodynamic process. The different types of thermodynamic processes are distinguished in the isothermal (fixed temperature), adiabatic, isochoric (stable volume), isobaric (stable pressure) and reversible process.","name":"Thermodynamic process","selfAssesment":"Planned
"},{"code":"PP1-7-7","description":"Budget equations, namely heat, momentum and moisture budget, are interpreted through two frameworks, which are Eulerian and Lagrangian. Eulerian is utilized for the investigating of transfer of heat by the wind, while Lagrangian is concerned about the effects of ascending or descending airflows on the Earth-Atmosphere system.","name":"Budget equations","selfAssesment":"Planned
"},{"code":"PP1-7-8","description":"The First Law of Thermodynamics supports that the energy is conserved. Thus, the thermal energy is defined as the sum of warming or internal energy (microscopic effect) and work occurring per unit mass (macroscopic effect). For its application to the Atmosphere, the thermal energy input is given from the following mathematical expression: Δq=Cp·ΔT-(ΔP/ρ), where Δq (J·kg–1) is the amount of thermal energy you add to a stationary mass m of air, Cp (J·kg–1·K–1) is the specific heat of air at constant pressure, ΔT (K) is the heat transferred per unit air mass, ΔP (Pa = J·m-3) is the pressure difference and ρ (kg· m-3) is the air density.\r\nThe term Cp·T is defined enthalpy, thus, the first term on the right side of eq. of thermodynamic first low for atmospheric applications, which is the corresponding enthalpy change is: Δh=Cp·ΔT. It is a characteristic possessed by the air.\r\nExpressing the first law of thermodynamics for atmospheric applications in conceptual form we can state that, given a quantity Δq of thermal energy added to a stationary mass m of air, a part of this energy heats the air, increasing its internal energy, but, as air heats up, its volume expands by an amount ΔV and pushes against the surrounding atmosphere, which responds with an equal and opposite pressure P that we can assume constant. Therefore, a part of the thermal energy introduced does not go to heat the air, but goes into macroscopic movement.","name":"First law of thermodynamic","selfAssesment":"Planned
"},{"code":"PP1-7-9","description":"A natural process that starts from an equilibrium state and ends in another state, causing changes in direction of entropy (ΔS) or statistical disorder of the system, is interpreted by Second Law of Thermodynamics. This law is considered as an irreversible process and it is expressed as ΔS=Heat transfer/Temperature.","name":"Second law of thermodynamics","selfAssesment":"Planned
"},{"code":"PP1-7","description":"Thermodynamics is the science of the relationships between heat, work, temperature, radiation, energy and properties of matter. These relationships are governed by the four laws of thermodynamics which allow a quantitative description, through measurable macroscopic physical quantities, of processes that, at the level of microscopic constituents can be described by the statistical mechanics. Thermodynamics applies to a wide variety of topics relevant to EO science and technologies from atmospheric chemistry and meteorology up to sensor design and aeronautics.","name":"Basics of Thermodynamics","selfAssesment":"Planned
"},{"code":"PP1-8-2","description":"Starting from the standard Rocket Equation - assuming a relative speed of the burned (emitted) fuel equal to 2,4 km/s and zero initial speed - it is possible to evaluate (for a single-stadium rocket) the mass percentage of payload that can be hosted on a platform depending on the final speed expected on the orbit. For instance a 28% payload is possible for a geostationary platform whose expected final speed on the orbit (radius 42.170 km) is 3,7km/s. Instead for a polar platform at about 800km this percentage reduce up to the 4% being the final sped on the orbit expected to be 7,5km/s.","name":"Equation of the rocket and launch of a satellite: payload determination","selfAssesment":"Planned
"},{"code":"PP1-8-3","description":"The orbit of a satellite is commonly defined through its so called Keplerian parameters. These parameters represent the trajectory that the satellite will follow if no-perturbation are acting on it. A series of forces act on the satellite to perturb it away from the nominal orbit. We can classify these perturbations, or variations in the orbital elements, based on how they affect the Keplerian elements. The actual orbit of a satellite will result from a combination of these perturbations. Periodic maneouvers are needed to bring the orbit back to nominal conditions. The lifetime of a satellite is defined as the time interval that it takes to decay from its initial altitude to an altitude causing the satellite reentry down to the atmosphere. Therefore lifetime of a satellite should not be confused with the time during which the satellite will provide useful information (this operational phase, in general, is designed to last 5 - 7 years). In fact, all satellite terminating operational phases in orbits passing through the LEO region should be de-orbited or, where appropriate, manoeuvred to an orbit with suitably-reduced lifetime, that is, should be left in an orbit where drag and other perturbations will limit lifetime. The actual duration of the satellite in orbit will depend from the intensity of the perturbations which will affect its orbit. In case of satellite on GEO orbit, at the end of the operational phases they will be located on a disposal orbit, that is an orbit which do not cross the protected region. The protected region is the altitude region ranging from GEO - 200 km to GEO + 200 km and inclination region between -15 deg and +15 deg. Satellites in low Earth orbit, with perigee altitudes below 1000 km, are predominantly subject to atmospheric drag. This force very slowly tends to circularise and reduce the altitude of the orbit. The rate of 'decay' of the orbit becomes very rapid at altitudes less than 200 km, and by the time the satellite is down to 180 km it will only have a few hours to live before it makes a fiery re-entry down to the Earth.","name":"Real orbits. Life time of a satellite, orbit’s decay.","selfAssesment":"Planned
"},{"code":"PP1-8-4","description":"The choice of a satellite orbit mostly depends on its main application. From this point of view it represents a crucial part of a satellite mission design. The most important parameters to describe a satellite orbit are the inclination angle i (of the orbit plane respect to the equatorial plane) its eccentricity e and its height H from the Earth's surface. In principle whatever eigth H can be used, provided that the speed of the satellite on its orbit allows the centrigugal force to exactely compensate the gravitational one at that heigth. Polar (i close to 90°) and Geostationary (i=0, H=35.800 km) orbits are the most common choices for EO satellites. In principle one single polar satellite can be sufficient to guarantee the global coverage of the Earth with equal quality of the images at all latitudes. All Geostationary satellites share the same circular orbit with H around 36000 km where the required speed exactely correspond to the one required to travel an entire orbit in 1 sideral day (orbital period P = 1 sideral day). This means that the satellite footprint is permanently in place over a specific Earth's location (e.g. for Meteosat 0°N, 0°E) allowing a quasi-continuous monitoring of a whole Earth's emisphere (with poor visibility of Earth's edges including Poles). Polar satellites' heigths are usually in between 700-800 km, with orbital periods around 100min (i.e. about 14,5 orbits/day) even if, lower orbits are also chosen particularly for very high spatial resolution payloads. Lower inclinations are also used (quasi-polar orbits) for specific applications. Due to the asphericity (and mass inhomogeneity) of the Earth, satellite orbit plane rotates around the Earth's polar axis with a period Pp producing (for elliptical orbits) the rotation of the orbit itself in its plane. A common choice for most EO polar satellites is to choose the orbital parameters in a way that Pp=1 year (Sun-Synchronous orbits). Due to the synchronism between Earth's revolution around the Sun and the orbit plane precession around Earth' axis, satellite passages happens at the same local solar time (similar illumination conditions) each time it flies over a specific region. This ensure repeatable sun illumination conditions facilitating image interpretation particularly for change detection or land monitoring applications. Other choices are possible when it is required to monitor with continuity high latitude regions.\r\n\r\nThis is the case of Molniya orbits which combine the continuity of observations typical of geostationary satellites with the possibility, offered by polar orbits, to overfly the highest latitudes regions. Its characteristics are: high eccentricity (e.g. e=0,74, axes 500 and 23.000 km), P=1/2 sideral day (Geo-Synchronous), inclination (i=63,4° or i=116,6°) which guarantees the satellite footprint at the apogee remaining positioned on a fixed ground point (non-rotating orbit). This way the satellite will spend more than 93% of its orbital period looking to the same emisphere even from a high latitude point of view. \r\n\r\nSo called altimetric orbits respond to the specific needs of altimetry. In this case the orbital parameters are chosen in order to guarantee, for example: a) that the ascending and descending sub-satellite tracks intersect at roughly 90 degrees on the Earth’s surface (so that orthogonal components of the surface slope can be determined with equal accuracy; b) the possibility to monitor all phases of tidal effects on ocean surface.\r\n\r\nParticularly important for several applications (multi-temporal analyses, change detection, etc.) are the Exactly repeating orbits.\r\nThey are conceived in order that the sub-satellite track will repeat itself exactly after a certain interval of time. This allows images having the same viewing geometry during the satellite’s lifetime making moreover available a particularly simple method of referring to the location of images (navigation or geo-referenciation) for example by referring to a ‘path and row’ system used for instance by the Landsat World Reference System (WRS). It is possible to arrange satellite orbits parameters in order to contemporary guarantee the sun-syncronism so that, not only satellite images collected on the same region can be easily super-imposed each-other but the same illumination and viewing geometry can be achieved. This is, for instance, the choice adopted for LANDSAT satellites whose images are typically available as a collection of scene of fixed dimension always similar each other when covering the same terrestrial area.","name":"Satellite orbits parametrization and choice","selfAssesment":"Completed
"},{"code":"PP1-8","description":"Mechanics is the Physics branch dealing with the behaviour of physical bodies when subjected to forces or displacements. This section provides Mechanics basic elements necessary for determining the orbits of satellites and rockets. The different satellite trajectories will be illustrated with respect to their peculiarities","name":"Basics of Mechanics","selfAssesment":"Planned
"},{"code":"PP1","description":"Optical Remote Sensing deals with those part of electromagnetic spectrum characterized by the wavelengths from the visible (0.4 micrometer) to the near infrared (NIR) up to thermal infrared (TIR, 15 micrometer). It regards the collection and interpretation of the e.m. radiation emitted, reflected, adsorbed and transmitted by the observed targets in order to derive their physical-chemical properties and related information. Such a possibility derives from the basic principle of (multi-spectral) remote sensing that is widely supported both theoretically (e.g. atomic and molecular spectroscopy) and experimentally (e.g. spectral signatures catalogues). It states that, in principle (e.g. disposing of sensors with ideal spectral capabilities) the matter-radiation interaction depends on the wavelength of the involved radiation and on specific (e.g. chemical/physical) properties of the matter that can be derived by the spectral analysis of the emerging (emitted, reflected, adsorbed or transmitted) radiation. As far as Earth Observation is concerned, specific related concepts have to be addressed like: the spectral matter-radiation interactions (spectral signature concept), natural sources (e.g. Earth, Sun) of optical e.m. radiation, theory of the Black Body, atmospheric physics and radiative transfer equations in the VIS-NIR and TIR spectral ranges, basic physics of e.m. optical sensors and image systems, physical fundaments of the interpretation of optical radiances collected by multi-hyperspectral passive techniques.","name":"Basics of Optical Remote Sensing","selfAssesment":"Completed
"},{"code":"PP2-1-2-1","description":"A radar signal is a complex signal. It is represented by a real part, the in-phase component, and an imaginary part, the quadrature component. In-phase is usually annotated by “I”, and quadrature by “Q”. Considering single look complex data, each component is represented in a single image channel.","name":"In-phase/Quadrature Component","selfAssesment":"Planned
"},{"code":"PP2-1-2-2","description":"A phasor represents a complex number and its phase and amplitude equivalent. Considering a complex SAR image’s pixel, the real and imaginary part can be represented by a 2D vector in Cartesian coordinates. Its corresponding phase and amplitude information corresponds to the direction and length of the vector, respectively.","name":"Phasor","selfAssesment":"Planned
"},{"code":"PP2-1-2","description":"A complex, using complex numbers, representation of signal by two measures magnitude and phase. In the digital SAR context, a camplex number often is represented by an equivalent pair of numbers, the in-phase (I) component and the quadrature (Q) component.","name":"Complex wave description","selfAssesment":"Planned
"},{"code":"PP2-1-4","description":"Electromagnetic waves are polarized; the direction of the polarization corresponds to the direction of oscillation of the electromagnetic field. Typical and often used linear polarisations are: H (horizontally) and V (vertically) polarized waves of the plane of the electric field vector oscillations relative to the sensor coordinate system. The polarization state of a backscattered wave from a natural surface can be linked to the geometrical characteristics like shape, roughness and orientation and the intrinsic properties of the scatterer like moisture, salinity, density. The radar system is characterized by combination of polarization of transmitted and received pulse: HH, HV, VH or VV. Based on the polarization sent and obtained the radar systems are divided in three polarization modes. Single polarization refers to the same polarization transmitted and received; dual polarization, one polarization is sent and another received; or quad polarization, when system is able to transmit and receive all four types of polarization. When making a contact with a scatterer, the polarization of the EM-wave can change, depending on the geometrical and dielectrical properties of the scatterer. In order to get all necessary information about those changes, full polarimetric systems are required.","name":"Polarisation","selfAssesment":"Completed
"},{"code":"PP2-1-5","description":"Property of signal or data set in which the phase of the constituents is measurable, and plays a significant role in the way in which several signals or data combine. Two waves with a phase difference that remains constant over time, are said to be coherent.","name":"Coherent","selfAssesment":"Planned
"},{"code":"PP2-1-6","description":"In remote sensing, phase is the exact position within a periodic signal with respect to an arbitrary reference point. It is typically expressed as an angle and measured in degrees or radians, where one period corresponds to a phase of 360° or 2π, respectively. Mathematically, phase is the argument of a complex number, that is the angle between its geometric representation in the complex plane and the real axis. For this reason, complex algebra is often used in remote sensing to facilitate phase calculations. Due to its periodic nature, phase can only be measured unambiguously within one period. Consequently, phase measurements are commonly subject to 2π phase ambiguities. These ambiguities can often be resolved in a process called phase unwrapping, using a priori information about the signal, typically related to its continuity. Phase measurements are crucial for the creation of synthetic aperture radar (SAR) images, as well as for many SAR imaging techniques, including interferometric SAR (InSAR).","name":"Phase","selfAssesment":"Completed
"},{"code":"PP2-1-7","description":"Shift in frequency caused by relative montion along the line of sight between sensor and the observed scene.","name":"Doppler effect","selfAssesment":"Planned
"},{"code":"PP2-1-8","description":"The wave-particle dualism (duality) is a theory according to which all matter exhibits the attributes of waves and particles.","name":"Wave-particle dualism","selfAssesment":"Planned
"},{"code":"PP2-1","description":"The microwave portion of the electromagnetic (EM) spectrum ranges from 1 millimeter to 1 meter. Imaging radars are independent of weather conditions and can operate day or night. EM-waves are polarized. Normally only the horizontal (H) or vertical (V) linear polarizations are used. The radar system is characterized by combination of polarization of transmitted and received pulse: HH, HV, VH or VV. When making a contact with a scatterer, the polarization of the EM-wave can change, depending on the geometrical and dielectrical properties of the scatterer.The data can be acquired from both the ascending (northwards) and descending (southwards) satellite passes. Water clouds can interfere with the radars operating below 2 cm in wavelength. The effects of rain can be generally ignored at wavelengths above 4 cm. For longer wavelengths (above 20 cm), an effect called Faraday rotation caused by the ionosphere, i.e., free charges (electrons) and the Earth’s magnetic field, can lead to a rotation of the polarization plane. In the presence of Faraday rotation, the data, usually fully polarimetric, should be corrected. The radar systems operate in different bands that uses different wavelengths. The most common frequences/wavelengths (frequency = Speed of Light / wavelength) for environmental applications are X (5,75-10,90 GHz), C-(4,20-5,75 GHz), S-(1,550-4,20 GHz), L-(0,390-1,550 GHz) and P-(0,255-0,390 GHz) band. The selection of SAR system for acquiring data depends on their application. Longer wavelengths are mainly devoted to communication and navigation purposes. Radars penetrate atmosphere and clouds. For example for forestry, longer wavelengths starting from C- or S-band are preferred.","name":"Microwave portion of electromagnetic spectrum","selfAssesment":"Completed
"},{"code":"PP2-2-1","description":"Interaction of waves with any solid object.","name":"Diffraction","selfAssesment":"Planned
"},{"code":"PP2-2-2","description":"Scattering means the redirection of incident electomagnetic energy by an object. Scattering and Diffraction refer to the same physical process - a coherent distortion of an incident wave. Emissivity is a measure of how strongly a body radiates at a given wavelength. Emission and scattering are complemetary: surfaces that are good scatterers are weak emitters, and vice versa.","name":"Scattering and emission","selfAssesment":"Planned
"},{"code":"PP2-2-3","description":"Radimetric anomalies such as for example saturation","name":"Radiometric anomalies","selfAssesment":"Planned
"},{"code":"PP2-2-4-1","description":"The radar equation is a measure of the received echo at the sensor. It defines what proportion of the transmitted energy is returned from a target. It is a function of the range between the antenna and the target, the antenna gain and the radar cross-section of the target. Mathematical expression that describes the average received signal level, compered to the additive noise level, in terms of system parameters. Principal parameters include: transmitted power, antenna gain, noise power, and radar range.","name":"Radar equation","selfAssesment":"In progress
"},{"code":"PP2-2-4-2","description":"Coefficient sigma or sigma nought represents the average reflectivity of a horizontal material sample, normalized with respect to a unit area on the horizontal ground plane.","name":"Sigma nought","selfAssesment":"Planned
"},{"code":"PP2-2-4-3","description":"Gamma nought represents the average reflectivity of a horizontal material sample, normalized with respect to the incident area, orthogonal to the incident ray from the radar.","name":"Gamma nought","selfAssesment":"Planned
"},{"code":"PP2-2-4-4","description":"Radar brightness coefficient represents the reflectivity per unit area in slant range.","name":"Beta nought (brightness)","selfAssesment":"Planned
"},{"code":"PP2-2-4","description":"Measure of radar reflectivity. The Radar Cross Section (RCS) is expressed in terms of the physical size of an hypothetical uniformly scattering sphere that would give rise to the same level of reflection as that observed from the sample target.","name":"Radar cross-section","selfAssesment":"Planned
"},{"code":"PP2-2-5-1","description":"A material constant is a physical or chemical property of a substance, which can be expressed in numbers. Giving a precise numerical value of a constant often requires determining the external conditions (e.g. temperature, humidity). Material constants are factors that influence the interaction of microwaves with the target objects.","name":"Material constants","selfAssesment":"Planned
"},{"code":"PP2-2-5-2","description":"The complex part nc of the refraction index n determines how far an electromagnetic wave of wavelength λ can survive crossing a specific medium. The attenuation length la is the distance after that the amplitude of an electromagnetic signal reduces its value by an amount of 1/e. For instance the amplitude of the Electric field E(z) of an electromagnetic wave proceeding along the z direction is decreasing as exp(-z/la) being la=λ/(2𝜋 nc) the attenuation length associated to that specific material (nc) and wavelength λ. This way attenuation length in water can be of hundreds of meters in the visible range and just few microns in the microwaves. The opposite happens over solid land surfaces where optical waves can penetrate from few microns up to few millimeters (moving from the VIS-NIR to the TIR spectral range) whereas microwaves can reach depths from hundreds to towsands (as higher are their wavelength) meters allowing the exploration of subsoil and thick coulters of ice.","name":"Attenuation lenght and penetration depth","selfAssesment":"Completed
"},{"code":"PP2-2-5-3","description":"Soil permittivity is a measure of the water content (soil moisture) in the soil and characterized by the metric of the dielectric constant of the soil. Soil moisture influences emission, absorption and propagation of microwave electromagnetic energy. Moisture decreases the ‘emissivity’ of soil, and thereby affects microwave radiation emitted from Earth’s surface. Dry soil has a low dielectric constant and low radar reflectivity. Moist and partially frozen solis have intermediate values. The higher the soil water content, the lower the radar signal penetration into the soil. In situ measurements of soil permittivity are a prerequisite for the calibration and validation of synthetic aperture radar (SAR) soil moisture retrieval algorithms. Soil moisture is a key variable in the hydrologic cycle and is recognized as an Essential Climate Variable (ECV).","name":"Soil permittivity","selfAssesment":"Completed
"},{"code":"PP2-2-5-4","description":"The permittivity of a plant is a function of its contained amount of water in all plant compartments (including roots).","name":"Plant permittivity","selfAssesment":"In progress
"},{"code":"PP2-2-5","description":"The electric properties of different materials can be described by two quantities: relative dielectric constant (complex permittivity) and loss tangent. Reflectivity of a smooth surface and the penetration of microwaves into the material are determined by these two quantities. The complex dielectric constant changes mainly due to the change in water content.","name":"Dielectric Properties","selfAssesment":"Planned
"},{"code":"PP2-2-6-1","description":"The standard deviation of the surface height variation (or RMS height), denoted by s (or hRMS), describes the statistical variation of a random surface with height z(x). In case of an azimuthally symmetrical surface, the single-scale RMS height of the one dimensional case for discrete profile values is given by (1), where N is the number of samples, and z ̅ the mean surface height (2). \r\nAs roughness depends not only on the soil surface properties but also the wavelength λ of the electromagnetic signal, the roughness parameters are scaled by the wave number k. Hence, the electromagnetic roughness ks for surface roughness parameter s is (2π/λ)*s (3). In order to determine if a random surface may be considered as electromagnetically smooth, one common definition is given by the Rayleigh roughness criterion, where s < λ / 8*cosθ, or ks < 0.8, at incidence angle θ = 0. This criterion has been revised for the microwave region, where the wavelength is usually of the order of the RMS height, called the Fraunhofer roughness criterion, where s < λ / 36*cosθ, or ks < 0.2, at incidence angle θ = 0. Additionally, surfaces are considered as electromagnetically rough for 1 < ks < 3.","name":"Vertical roughness component (RMS height)","selfAssesment":"Completed
"},{"code":"PP2-2-6-2","description":"The surface correlation length, denoted by l, is defined as the displacement ξ at which the surface correlation function p(ξ)= 1/e. Thus, l can be seen as the reference length up to which two points of one soil surface can be regarded as statistically independent from each other. If we imagine a perfectly smooth soil surface, l=∞ since every point on that surface correlates with all other points and can therefore be regarded as dependent from each other.\r\nAs roughness depends not only on the soil surface properties but also the wavelength λ of the electromagnetic signal, the roughness parameters are scaled by the wave number k. Hence, the electromagnetic roughness kl for surface roughness parameter l is kl=(2π/λ)*l.\r\nExperimental results indicate a weaker influence on the radar backscatter compared to the RMS height s.","name":"Horizontal roughness component (correlation length)","selfAssesment":"Completed
"},{"code":"PP2-2-6-3","description":"The surface correlation function p(ξ) determines the degree of correlation between two lateral separated locations of one surface. Thereby, ξ is defined as displacement between two locations, (x, y) and (x', y') on the surface and given by (1).\r\nWith increasing separation between two locations on the surface p(ξ) decreases, and at a certain distance, the surface correlation length l, the heights at the two locations are considered statistically uncorrelated.\r\nThe surface scattering of electromagnetic waves can be simulated with various models. Depending on the observed roughness scale multiple surface scattering models are valid for specific roughness conditions. For example, one of the first surface scattering models for slightly rough surfaces, the small perturbation model (SPM), deals with roughness scales that are small relative to the wavelength and hence has validity conditions for ks < 0.3, kl < 3, and m < 0.3. Since then, various surface scattering models for computing the scattering and emission behavior of natural surfaces in the microwave region have been proposed, such as the Kirchhoff scattering model (KH), the geometric optics model (GO), the physical optics model (PO), or the integral equation model (IEM), to name the most common used in literature. For simulations of EM scattering at soil surfaces, assumptions of the functional forms of p(ξ) have to be made. The two most common forms for mathematically describing the surface correlation of natural surfaces are the exponential pE(ξ) and the Gaussian pG(ξ) correlation functions, defined by (2) and (3).\r\nFor some mathematically sophisticated surface scattering models, an x-Power correlation function p(x-Power)(ξ) can be assumed (4), with x as value between 1 and 2.\r\nIn literature, rather smooth surfaces are characterized by an exponential surface correlation function, while rather rough surfaces are characterized by a Gaussian surface correlation function.","name":"Surface correlation function","selfAssesment":"Completed
"},{"code":"PP2-2-6-4","description":"The root-mean-square (RMS) slope m of a one dimensional height profile for one random surface is given by (1), with s as the standard deviation of the surface height variation (or RMS height), and p''(0) as the second derivative of the surface correlation function p(ξ), evaluated at ξ=0. Since p(ξ) is an even function, p''(0) is a negative quantity.\r\nFor modeling of electromagntic scattering at soil surfaces, assumptions of the functional forms of p(ξ) have to be made. The most common known forms are the exponential and Gaussian correlation functions. Additionally, some models allow the assumption of a x-Power correlation function, with x as value between 1 and 2. For the varying surface correlation functions, the RMS slope m is given by (2)-(4).\r\nIn literature, for L-band, the slope m should be lower than 0.3 or 0.4 in case of single scattering and bare soil surfaces with moderate RMS heights.","name":"Surface roughness slope","selfAssesment":"Completed
"},{"code":"PP2-2-6-5","description":"In reality, one random surface has multiple roughness scales, since the commonly used surface description based on single-scale roughness parameters does not comprise all the properties of natural surfaces relevant for describing wave scattering. Depending on the wavelength λ of the microwave sensor the dimension of the surface roughness parameters s and l correspond to specific roughness scales. \r\nIn case of multi-scale roughness, the equivalent RMS height is a composite of the individual RMS heights at different roughness scales (1).\r\nA three-scale surface, as shown in Fig. 1, for example consists of a small-scale high-spatial frequency variation (c) ‘riding’ on top of the larger scales, the medium-scale perturbation (b) and the large-scale undulation (a).\r\nAt microwave frequencies, the centimeter scale is the scale of roughness of primary importance, since λ is on the order of centimeters to a few tens of centimeters. For natural surfaces it is very difficult to measure millimeter-scale roughness.","name":"Single-scale & multi-scale roughness","selfAssesment":"Completed
"},{"code":"PP2-2-6","description":"Surface roughness defines the geometry between the pedosphere and the atmosphere (soil-air boundary).\r\nIn the field of microwave remote sensing, surface roughness affects scattering and emission characteristics of natural surfaces. The degree of roughness of a random surface is determined by statistical parameters, measured by the units of wavelength of the observing sensor. The two fundamental surface roughness parameters are the standard deviation of the surface height variation (RMS height) s, with its related surface correlation function p(ξ), and the horizontal surface correlation length l. Additional, a third roughness parameter, the root-mean-square (RMS) slope m, is important for some surface scattering models to simulate electromagnetic wave scattering of surfaces.\r\nSurface roughness determines the variation of surface height within an imaged resolution cell. The transition from smooth to rough is qualitative, and is function of both wavelength and incident angle. With decreasing frequency the soil surface appears rather smooth to microwave sensors. This results in the fact, that while one surface appears smooth when sensed at L-band (λ ≈23 cm), the same surface appears rough when sensed at X-band (λ≈3 cm). Hence, in the field of microwave remote sensing, the ‘effective’ surface roughness parameters are scaled by the wave number k= 2π/λ. Surface roughness can be observed at single or multi-scale.","name":"Surface roughness","selfAssesment":"Completed
"},{"code":"PP2-2-7-1","description":"The Stokes vector is a four-element vector containing real-valued polarization combinations and is an alternative form of representing a full (=quad) polarimetric dataset, besides the complex-valued scattering matrix. Stokes vectors can be measured as real quantities and are preferred over the complex-valued Jones vector formalism when a coherent (phase-preserving) measurement system is absent. Stokes vectors can be used to form the 4x4 Mueller matrix for target scattering analyses, mostly used in the field of optics. First component of the Stokes vector is the sum of the co-polar fields and represents the total energy of the wave. Second component is the difference of the co-polar fields. Thrid component is the real part of the cross-correlation of the fields and fourth component is the imaginary part of it. The different polarization states can be represented by the Stokes vector and an O(3) elliptical transformation can be used to change the polarization basis, similar to the Jones vector where the SU(2) elliptical transformation is used.","name":"Stokes Vector","selfAssesment":"Completed
"},{"code":"PP2-2-7-2","description":"The scattering matrix is a 2x2 square matrix containing four complex-valued polarization measurements (amplitude & phase) forming one full (= quad) polarimetric set of coherent observations. An often recorded set of polarizations is the combination: HH (horizontal receive - horizontal transmit), HV (horizontal recive - vertical transmit), VH (vertical receive - horizontal transmit) & VV (vertical receive - vertical transmit). The scattering matrix is fully suficient for describing scattering from coherent targets (dominating the resolution cell), but not for incoherent tragets (mix of scattering contributions in the resolution cell). For the latter, the coherency and the covariance matrices are the more appropriate descriptions of scattering from incoherent targets.","name":"Scattering matrix","selfAssesment":"Completed
"},{"code":"PP2-2-7-3","description":"The covariance and coherence matrix are two 4x4 square matrices, which can be built out of the scattering matrix by a lexicographic and a Pauli target scattering vector. They are an alternative representation of a full polarimetric dataset allowing the analysis of incoherent targets (more than one dominant scatterer in the resolution cell) and the phenomenon of depolarisation (transformation of incoming fully polarised wave into a partially polarised wave by creating a variety of different types of polarizations during media interaction). These matrices can be converted into each other without loss of information (by unitary transformations), but not turned back into the scattering matrix due to averaging operations during formation of coherency or covariance matrices.","name":"Covariance/Coherency matrices","selfAssesment":"Completed
"},{"code":"PP2-2-7-4","description":"Polarimetric decomposition techniques allow signal unmixing by polarimetry in order to separate different scattering contribution within one resolution cell, e.g. from soil & vegetation or snow, ice & bedrock. They can be either applied for the scattering matrix (coherent form - one dominant scatterer in the resolution cel) or for the covariance/coherency matrix (incoherent form - more than one dominant scatterer in the resolution cell). Decomposition techniques can be model- (physics) or eigen- (mathematics)-based. The eigen-based decomposition allows to diagonalize the coherency or covariance matrix in a diagonal eigenvalue matrix and a matrix of column eigenvectors. From eigenvalues and eigenvectors the polarimetric entropy, the scattering alpha angle and the polarimetric anisotropy. The polarimetric entropy is a matric for the degree of depolarization of the scattering event. The scattering alpha angle is an intrinsic scattering mechanism indicator. The polarimetric anisotropy informs about secondary scattering mechanism in evironments with high entropy. If the anisotropy is high only one secondary scattering mechanism is present, if it is low, more than one will occur.","name":"Polarimetric decomposition techniques","selfAssesment":"Completed
"},{"code":"PP2-2-7-5","description":"All bi- or multi-polar (non-inert) media have the tendency to orient themselves if an external field is excited. This orientation polarization is caused by negatively and positively charged areas within the media under the premise the media is able to rotate freely. Molecules of liquid water are a prime example.","name":"Orientational polarisation of media","selfAssesment":"In progress
"},{"code":"PP2-2-7-6","description":"Polarimetric coherences are complex-valued polarimetric correlation coefficients assessing the redundance between different polarimetric observations informing about their divergence in information. They can be formed among mutual polarimetric observations showing their degree of correlation. The polarimetric coherence consists of a magnitude, ranging between zero (no correlation) and one (identical), and a phase information, running from -180° to 180°. Typically polarimetric coherences are calculated between the co-polarimetric (HH, VV) channes, as well as the cross-polarimetric channels (HV, VH). The latter polarimetric coherence assesses the system noise inherent in the recorded polarimetric data, if a monostatic systems (transmitting and receiving sensor on the same sensing platform) is used for acquisition.","name":"Polarimetric coherences","selfAssesment":"Completed
"},{"code":"PP2-2-7-7","description":"The polarisation ellipse and the Jones vector formalism are the geometrical (three real-valued angles) and algebraic (amplitude & phase) formalisms to describe polarisation states of an electromagnetic wave. The ellipse has an orientation, an ellipticity and absolute phase angle. The three angles are integrated in one mathematical ellipse formulation that can represent linear, elliptic and circular polarisation states. The Jones vector formalism is an algebraic formulation allowing all calculus available in linear algebra. Both representations (polarisation ellipse & Jones vector) can be converted into each other seemlessly with a simple elliptical basis (special unitary SU(2)) transformation.","name":"Polarisation ellipse / Jones vector formalism","selfAssesment":"Completed
"},{"code":"PP2-2-7-8","description":"The concept of polarisation synthesis is based on the mathematical fact that a set of polarimetric measurements in one basis, e.g. H,V, can be converted into any other polarimetric basis, by a mathematical transformation. A basis set is a set of four polarisations. Each set is orthogonal, like LC (left-circular), RC (right-circular). The striking point is that only one set of polarimetric measurements in one basis needs to be recorded and the transformation in other polarimetric bases is done in a post processing step afterwards. There is no need to measure all bases, which is quite complicated in terms of engineering for elliptical and circular polarisation states.","name":"Polarisation synthesis","selfAssesment":"Completed
"},{"code":"PP2-2-7","description":"Polarimetry is the technique to evalute the physical phenomenon of polarisation including the measurement, the processing and the interpretation of the polarisation state of an electromagnetic wave. Polarization states are described by the scattering elipse and the Jones Vector formalism. Especially the polarization states after interaction with the media under investigation are mostly investigated to estimate media properties and states. The mostly observed fully polarimetric observation basis is H,V up to now with the single observations: HH HV, VH, VV. The concept of polarization synthesis allows to acquire fully polarimetric observations in one basis (e.g. H,V) and transform them into any other orthgonal basis (e.g. left, right circular) by a mathematical transformation in post processing. Polarimetric States are stored in different mathematical formats: Scattering matrix, polarimetric coherences , Stokes vector, Pauli-vector, lexicographic vector, coherency and covariance matrices. These mathematical representations can be decomposed according to the contained elementary scattering mechanisms in the recorded signal. The so-called polarimetric decomposition technique allow signal unmixing for differnt scattering components (e.g. from soil & vegetation). The techniques range from mathematics-based until physics-based concepts and are developed since decades starting with Huynen in 1970.","name":"Polarimetry","selfAssesment":"Completed
"},{"code":"PP2-2","description":"A number of interactions are possible when electromagnetic energy encounters matter, whether solid, liquid or gas. In Earth Observation there are two main interactions: atmospheric and with target. \r\nAtmospheric interaction:\r\nIn radar remote sensing, atmospheric interactions are limited due to the long wavelengths compared to the size of the atmospheric particles. Water clouds can interfere with the radars operating below 2 cm in wavelength. The effects of rain can be generally ignored at wavelengths above 4 cm. For longer wavelengths (above 20 cm), an effect called Faraday rotation caused by the ionosphere, i.e., free charges (electrons) and the Earth’s magnetic field, can lead to a rotation of the polarization plane.\r\nTarget interaction:\r\nThe radar interaction with the object is a result of both radar system parameters (frequency, polarization, acquisition geometry) and the object physical properties (dielectric constant, i.e., water content; geometrical properties, i.e., the roughness, shape and orientation of the scatterer).","name":"Interaction of microwaves with matter","selfAssesment":"In progress
"},{"code":"PP2-3-1-1","description":"The goal of an radar antenna is to direct and receive the transmitted and backscattered signal in a specific angular direction. The antenna gain describes the directional sensitivity of the antenna. It is a dimensionless quantity that is constant for a specific antenna.","name":"Antenna gain","selfAssesment":"Planned
"},{"code":"PP2-3-1-2","description":"The antenna radiation pattern shows the direction in which the antenna transmits and receives the energy in space, as well as the strength of this radiation. It is a function of angles and consists of different lobes, in which the signal is directed and received. There are two principal representation of the antenna patterns: field and power patterns, which are a function of the electric and magnetic fields of the energy being radiated.","name":"Antenna pattern","selfAssesment":"In progress
"},{"code":"PP2-3-1","description":"Antenna is a device that radiates electromagnetic energy and collects it during reception.","name":"Radar antennas and antenna calibration","selfAssesment":"Planned
"},{"code":"PP2-3-10-2","description":"The radargrammetric equation follows a similar principle as the stereoscopic equation, except that it uses the radar geometry. The radargrammetric observation equation allows the retrieval of 3D information about a target, based on the determination of the sensor-object stereo model. It estimates the coordinates the intersection of the two radar rays coming from the two different sensor positions with different look angles, using the coordinates of the satellites position and satellite velocity. The radargrammetric equation can be adapted in order to retrieve 3D information in layover areas (e.g. urban areas).","name":"Radargrammetric equation","selfAssesment":"Planned
"},{"code":"PP2-3-10","description":"Radargrammetry is the technique for extracting three-dimensional information from radar images. It applies photogrammetric principles to synthetic aperture radar (SAR) images. By viewing an object from different positions separated by a baseline, the appeared object position will vary slightly (denoted parallax). The disparities for each position on the object are related to its x-y-z coordinates. In radargrammetry, such disparities are computed for an entire image. The result is the terrain elevation from the measured parallaxes between two (or more) images, acquired at different angles. Radargrammetry requires at least two SAR images acquired from different positions, normally across-track due to the configuration of a side-looking SAR. Same-side stereo-pairs with intersection angles in the range of about 10 – 20° have been a feasible compromise between reasonable geometric disparities and the accuracy of estimated heights. In general, the disparities can be estimated with higher accuracy as the angle of intersection increases (as the stereo exaggeration factor increases). However, the same points must be recognized in all images, and it is hence required that the images are as similar as possible. This improves the image matching and it is best achieved with small intersection angles, which furthermore decreases radiometric differences. \r\nA general procedure for generating an elevation model from stereo-pairs is applicable for radargrammetry when optical stereo images are replaced with the backscatter intensity of SAR images. One image is selected as reference and the other(s) is coarsely registered to the reference, e.g., by using the attached meta-data. The same points are then located in both images using image matching. A common matching criterion is the cross correlation coefficient. Then, spatial point intersections are computed, which is the least square approach to find the intersection points of SAR range circles as defined from the matched image pixels. The computed intersections result in a point cloud that finally is interpolated to a consistent elevation raster. The entire process is extensive and computationally expensive, and normally a dedicated software is required. \r\nRadargrammetry with images acquired from opposite sides have been little investigated, and was first limited to stereoscopic viewing. Some opposite-side research was later presented with limited outcomes under certain conditions. Most applications today will not consider opposite-side radargrammetry, since the alternatives are usually better. Same-side radargrammetry performs better than opposite-side, while interferometric SAR that is based on phase differences, may be even more accurate. One advantage of radargrammetry is however, that it remains less affected by atmospheric disturbances compared to interferometric SAR, because it is using the amplitude images.","name":"Radargrammetry (same-side and opposite-side)","selfAssesment":"Completed
"},{"code":"PP2-3-11-1","description":"Differential Synthetic Aperture Radar Interferometry (DInSAR) aims the determination of deformation of the Earth’s surface that happened between two or more complex-valued SAR acquisitions.\r\nThe phase of an interferogram issued from the complex multiplication of a SAR image with the complex conjugate of a second SAR image contains five distinct components, or layers of information:\r\n\tTwo phase components arise from the geometrical baseline (slightly different position of both sensor positions):\r\n\ta topographical information representing the surface relief, \r\n\ta “flat earth” pattern coming from the orbital distance of both sensor positions.\r\n\r\n\tTwo phase components result of the temporal baseline (time between both acquisitions):\r\n\ta deformation component, representing a possible displacement of the Earth’s surface between both acquisitions,\r\n\tan atmospheric component coming from different atmospheric conditions between both acquisitions.\r\n\r\n\tA phase component corresponding to intrinsic sensor noise \r\n\r\nBoth parameters related to the temporal baseline can be retrieved using DInSAR on repeat-pass acquisitions. DInSAR cannot be used with single-pass interferometry (e.g. both acquisitions acquired at the same time).\r\nThe deformation component of the interferometric phase corresponds to the modification of the phase of the second SAR image compared to the first due to an additional range difference between the sensor position and the Earth’s surface that is induced by the motion of the Earth’s surface towards or away from the initial sensor position.\r\nUsing DInSAR, the phase components related to the geometrical baseline can be eliminated from the interferogram using an existing DEM and orbit information, or an additional interferogram showing no deformation. After DInSAR processing, neglecting the remaining sensor noise, only the deformation and atmospheric components remain. The resulting deformation image is called differential and is characterized by color bands, or fringes, from whom the amount of the displacement can be retrieved. \r\nDInSAR can be used for mapping displacements and deformations due to earthquakes, landslides, or other geophysical processes inducing deformation of the Earth’s surface.\r\nUsing only one differential interferogram, mainly sudden and large scale changes between two acquisition can be mapped and quantified. However, the atmospheric phase component remains and may induce interpretation errors if it is not possible to eliminate it through e.g. precise weather models. Techniques of differential interferogram stacking (e.g. Persistent Scatterer Interferometry and Small-Baseline Subset) have been developed for long-term deformation monitoring which allow to filter the atmospheric phase component out.","name":"Differential Synthetic Aperture Radar Interferometry (DInSAR)","selfAssesment":"Completed
"},{"code":"PP2-3-11-2","description":"The Permanent or Persistent Scatterer (PS) approach allows the estimation of deformation time-series related to point-wise, high coherent scatterers on the ground based on processing long sequences of SAR data.\r\nPersistent Scatterer Interferometry (PSI -sometimes also called Permanent Scatterer Interferometry) is a particular DInSAR technique. It exploits multiple SAR images acquired over a specific area in order to retrieve the deformation phase component over time. In general, a minimum number of 15 SAR acquisitions is needed for PSI processing. Due to the large number of necessary acquisitions, the deformation component of the interferometric phase observations can be estimated very precisely (in the order of a few mm/yr) and other phase contributions such as atmospheric disturbances and topographic height differences can be better estimated and removed.\r\nPSI rely on so called Persistent Scatterer that are targets showing coherent phase behavior in time. Such targets are usually found on man-made structures such as buildings or bridges, or very stable features such as rocks. PSI is a technique that is therefore mainly used over urban or semi-urban terrain. Usually, PSs are selected based on their amplitude and phase power spectrum stability over time.\r\nThe main outcomes of a PSI analysis are a deformation velocity map and the displacement time-series of the single point targets, or PSs. The velocity map represents the deformation rate of the detected PSs in Line-of-Sight of the sensor, generally in mm/yr. Usually, subsidence, e.g. target moving away from the sensor, is represented in red, stable PSs in green and uplift, e.g. PSs moving toward the sensor in blue. The displacement time-series show for each PS the amount of the deformation, usually in mm, over the whole period of observation. Different phase model can be defined in order to retrieve the best possible estimate of the deformation, considering also seasonal displacements or breakpoints in the time-series.\r\nPerforming PSI analysis in both ascending and descending directions allows the fusion of the results in order to retrieve vertical and East-West component of the deformation. North-South deformation components cannot be retrieved due to the orbit configuration of the SAR satellites.\r\nPSI finds use in a large range of thematic applications related to subsidence and long-term change monitoring, such as infrastructure monitoring, groundwater reservoir monitoring, monitoring of mining areas, landslide inventory and monitoring, as well as volcanology.","name":"Permanent Scatterer Interferometry (PSI)","selfAssesment":"Completed
"},{"code":"PP2-3-11-3","description":"Along-track InSAR (AT-InSAR) is a special mode of interferometric SAR (InSAR) where the individual SAR images have been acquired from the same flight track. With virtually identical geometric configuration of the individual SAR images, the measured phase difference is dominated by temporal changes occurring between the acquisitions. Consequently, AT-InSAR can be used to measure the displacement and/or radial velocity of targets on the ground, with the temporal offset between the acquisitions determining the time scale of the measurements. AT-InSAR can be implemented using one or more SAR sensors, in both single-pass and repeat-pass configurations, accommodating various needs. Using at least two sensors in a single-pass configuration allows the measurement of relatively high velocities, e.g., for vehicles and ocean waves. Conversely, using at least one sensor in a repeat-pass configuration allows the measurement of low velocities or displacements, e.g., for glaciers and due to volcanoes, earthquakes, subsidence, and landslides.","name":"Along-Track Interferometry","selfAssesment":"Completed
"},{"code":"PP2-3-11-4","description":"Across-track InSAR (XT-InSAR) is a special mode of interferometric SAR (InSAR), where the individual SAR images have been acquired from slightly different look directions. The measured phase difference contains information about the elevation of the targets on the ground, but it can also be affected by temporal changes between the individual SAR images. XT-InSAR can be implemented using one or more SAR systems in both single-pass and repeat-pass configurations. To mitigate temporal change between acquisitions, the XT-InSAR configuration is selected based on the intended application and frequency used by the system. If a single SAR sensor is used in the repeat-pass mode, temporal stability can be achieved either by a selecting a lower frequency and focussing on the larger, more stable targets (e.g., P-band, 435 MHz InSAR in forests) or by selecting a higher frequency and focussing on already stable environments (e.g., X-band, 9.65 GHz XT-InSAR in urban environments). Using two or more SAR sensors in a single-pass, tandem configuration, it is possible to measure elevation of temporally instable targets using higher frequencies, as demonstrated by the SRTM and TanDEM-X systems over vegetated areas and ocean.\r\nReferences: bamler/hartl, one on SRTM or TDM for DEM, one on BIOMASS for forestry, one on Sentinel-1 for urban areas, one on TDM on vegetation","name":"Across-Track Interferometry","selfAssesment":"Completed
"},{"code":"PP2-3-11-5","description":"Small Baseline Subset (SBAS) is a well-known technique of differential synthetic aperture radar (SAR) interferometry for the generation of surface deformation time-series by processing large sequences of SAR data acquired over the same region on Earth. \r\nThe method requires the preliminary generation of pairs of SAR images collected by slightly different orbital positions at different acquisition times. The phase difference of the interferometric SAR data pairs is extracted. The two-dimensional phase maps contains different contributions, but principally a component due to the terrain height of the observed area. The DInSAR technique relies on the estimation of the deformation of the terrain between the two interfering SAR images (i.e., the so-called master and slave images). To achieve this task, the phase contribution related to the terrain height is simulated and subtracted to the interferometric master/slave phase difference. The obtained differential SAR interferometric phase contains a direct information on the occurred deformation. Once a sequence of interferometric SAR data pairs is selected, the SBAS technique allows generating the time-series of the deformation of the terrain. The processing steps are essentially: i) the extraction of the full phase of the DInSAR interferograms, i.e., the phase unwrapping steps of the DInSAR interferograms, ii) the inversion of the sequence of unwrapped DInSAR phases, iii) the geocoding of the deformation maps from radar coordinates to geographical coordinates.","name":"Small Baseline Subset","selfAssesment":"Completed
"},{"code":"PP2-3-11","description":"Synthetic aperture radar (SAR) interferometry, or simply InSAR, is a remote sensing technique utilising the phase difference between two or more complex-valued SAR images. Most modern SAR systems are capable of measuring both the intensity and the phase of the reflected signal, where the latter carries information about the distance travelled by the signal. Consequently, the phase difference measured between two SAR images is determined by the geometry and timing of the individual SAR acquisitions. Different geometric and temporal configurations enable different applications. If the SAR acquisitions are made from different angles and without significant temporal change of the scene, InSAR can be used to create digital elevation models (DEMs) of the Earth, as demonstrated by the NASA/JPL Shuttle Radar Topography Mission (SRTM). If the individual SAR acquisitions are made at different times in the same geometric configuration, then InSAR can be used to measure radial velocity of targets and to assess displacements caused by, e.g., volcanoes and earthquakes.","name":"Principles of Synthetic Aperture Radar Interferometry (InSAR)","selfAssesment":"Completed
"},{"code":"PP2-3-12","description":"Synthetic Aperture Radar (SAR) tomography uses the principle of the azimuth synthetic aperture in the elevation direction. It exploits multiple passes of the radar sensor at different orbit positions (orbits heights) in order to retrieve 3D information about volumetric targets, where the 2D SAR signals often overlaps due to the typical side-looking geometry.","name":"Synthetic Aperture Radar (SAR) tomography","selfAssesment":"Planned
"},{"code":"PP2-3-13","description":"With this concept active and passive microwave imaging techniques are combined to record electromagnetic waves in an active (sending & receiving) and a passive (only receving) mode either simultaneously or with negligible time lag.\r\nThe active sensor is normally a Real Aperture Radar (RAR) or Synthetic Aperture Radar (SAR), while the passive sensor is a radiometer or synthetic aperture radiometer. Both acquisition modes can be operated on a single platform or on different platforms.\r\nSatellite missions with active-passive imaging capabilities are the NASA missions AQUARIUS and SMAP.","name":"Active-Passive microwave imaging","selfAssesment":"In progress
"},{"code":"PP2-3-2","description":"Systems measuring both amplitude and phase of the incident electromagnetic radiation.","name":"Coherent and active systems","selfAssesment":"Planned
"},{"code":"PP2-3-3","description":"This acquisition mode records only the incoming electromagnetic radiation emitted from the Earth. Radiometer instruments conduct passive microwave imaging. The energy budget of emitted radiation (from Earth) is significantly smaller than from instrument-generated, transmitted electromagnetic waves, used in the active microwave imaging mode. Hence, the signal to noise ratio is significantly worse for passive microwave imaging forcing a longer intergration time for robust signal recording. This results in a coarse spatial resolution of radiometer images (in the order of kilometers).","name":"Passive microwave imaging","selfAssesment":"Completed
"},{"code":"PP2-3-5","description":"There are two types of imaging radar apertures: real (usually called RAR or SLAR for side-looking airborne radar or SLR for side-looking radar) and synthetic aperture radar (SAR). The SLAR imaging system uses a long antenna mounted on a platform. The synthetic aperture is used in space remote sensing applications. RAR is a radar system where the antenna beamwidth equals to the physical length of the antenna. It operates in a side-looking configuration, left or right with reference to the flight direction. It is an active, all-weather, day/night remote sensor onboar an airborne platform. Both Real Aperture and Synthetic Aperture Radar are side-looking systems having antennas aimed to the right or left of the flight path. The length of the antenna together with wavelenght determines the resolution in the azimuth direction, i.e. it is proportional to the distance to the object and inversely proportional to the length of the radar antenna.","name":"Real Aperture Radar (RAR)","selfAssesment":"In progress
"},{"code":"PP2-3-6","description":"In contrary to a real aperture, a synthetic aperture results from an aperture “synthesis”. Synthetic aperture were built in order to overcome the limitation of real aperture and therefore enhance the resolution in azimuth direction. It uses the subsequent positions of a real aperture sensor during its forward motion along the azimuth direction to create a synthetic longer antenna. Via the analysis of the Doppler shift induced by the different echoes of the illuminated objects in the different positions of the real aperture, the azimuth resolution can be improved.","name":"Principles of Synthetic Aperture Radar (SAR)","selfAssesment":"In progress
"},{"code":"PP2-3-7-1","description":"The azimuth direction corresponds to the flight direction of the radar sensor. It is also called along-track direction since it follows the line-of-flight. It is perpendicular to the range direction.","name":"Azimuth direction","selfAssesment":"Planned
"},{"code":"PP2-3-7-2","description":"The range direction corresponds to the direction perpendicular to the flight direction of a radar system. It is also called across-track direction. One distinguishes between slant range, i.e. range in a radar geometry, and ground range, i.e. range projected onto the Earth's surface, and between near and far range (situated farther away from the sensor and showing shallower looking angle than in near range due to viewing geometry).","name":"Range direction","selfAssesment":"Planned
"},{"code":"PP2-3-7-3","description":"The incidence angle is the angle between the radar beam and the normal to the surface at target location. For a flat surface and neglecting the Earth’s curvature, the incidence angle equals the look angle of the sensor, which characterizes the angle between the nadir view and the radar beam.","name":"Incidence Angle","selfAssesment":"Planned
"},{"code":"PP2-3-7-4","description":"The beam sent out by the radar antenna (SLAR for side-looking airborne radar or SLR for side-looking radar) illuminates an area on the targeted object. The footprint of an antenna is traditionally defined to be the area on the surface within the field of view subtended by the beamwidth of the antenna gain pattern.","name":"Antenna footprint","selfAssesment":"Planned
"},{"code":"PP2-3-7-5","description":"The spatial resolution of a synthetic aperture radar (SAR) system is the maximal distance between two targets, which are indistinguishable in the SAR image. SAR spatial resolution is determined individually in the two principal SAR image directions: ground range and azimuth (along-track). Ground range resolution for a SAR system is derived from slant range (across-track) resolution, by projecting it onto the ground surface using the incident angle, i.e., the angle between the line-of-sight and the ground surface normal. It is thus range-dependent, with finer resolution available in far range. Assuming adequate signal processing, slant range resolution of a SAR system is proportional to the speed of light and inversely proportional to the system bandwidth, i.e., the width of the used frequency interval. This caused by the fact that each individual frequency provides an independent measurement of the slant range, so a larger bandwidth implies more independent measurements contributing to the final slant range estimate. Similar principles apply to the azimuth direction. Assuming adequate signal processing, the SAR azimuth resolution is proportional to the along-track velocity of the SAR sensor and inversely proportional to the pulse repetition frequency (PRF) of the system. A lower interval between the consecutive pulses (higher PRF) results in better azimuth resolution due to faster sampling, but at the cost of range ambiguities occurring when echoes from one pulse are recorded after the next pulse has been transmitted.","name":"Synthetic Aperture Radar (SAR) spatial resolution","selfAssesment":"Completed
"},{"code":"PP2-3-7","description":"The Synthetic Aperture Radar (SAR) sensor is usually mounted on an aircraft or satellite. The instrument altitude above a reference surface stays constant over time, a condition that is easier to achieve for satellite sensors that stay on the same orbit than for aircrafts that are subject to atmospheric conditions. The sensor moves on a straight flight path, which is called the azimuth direction. It corresponds to the flight direction.\r\nSAR systems acquire information in oblique view, the antenna pointing sideways down to the ground. Most satellite systems use an antenna looking to the right side of the instrument. The ground area illuminated by the radar beam is called antenna footprint. As the sensor moves along the azimuth direction (along-track), the continuous strip of the ground area represented by the successive antenna footprints is called swath. \r\nThe looking direction of the SAR antenna is called range direction. It is often perpendicular to the azimuth direction (i.e. across-track), but can also present slightly differences depending on the acquisition mode. The angle between the nadir view and the range direction is called incidence angle.\r\nThe original SAR image is displayed in what is called slant-range geometry, i.e., it is based on the actual distance from the radar to each of the respective features in the scene. In the slant range direction, each point target’s backscatter is represented as a function of the time delay between the transmission of the electromagnetic pulse and its reception back at the sensor. This range depending representation induces geometric distortions in the SAR image. One distinguishes between near and far range: targets situated in near range are closer to the nadir direction and closer to the sensor than targets situated in far range. The image representation of targets is also more compressed in near range than in far range.\r\nThe slant-range representation can be converted in ground range representation, by projecting the image features orthogonally to a ground reference, allowing a proper planimetric position of the targets relative to one another.\r\nThis acquisition geometry allows the distinct mapping of scatterers corresponding to their respective distance to the sensor. It causes also geometric distortions in the radar image, i.e., relief displacement (foreshortening and layover) and shadow.","name":"Synthetic Aperture Radar (SAR) geometric configuration","selfAssesment":"Completed
"},{"code":"PP2-3-8-2","description":"The local incidence angle is the angle between the incident radar wave and the normal to the scattering surface at target location. In case of a flat terrain, the local incidence angle equals the incidence angle. For a terrain with local slope, the local incidence angle differs from the incidence angle (for slopes facing towards the sensor, it is smaller than the incidence angle).","name":"Local Incidence Angle","selfAssesment":"Planned
"},{"code":"PP2-3-8-3","description":"Foreshortening is an effect occurring principally in SAR images of mountainous areas, on slopes oriented towards the sensor when the distance between two points appears smaller than it would in flat areas due to the side-looking geometry. This results in a compression of the radiometric information. The area appears brighter.","name":"Foreshortening","selfAssesment":"Planned
"},{"code":"PP2-3-8-4","description":"Layover appears on very steep slopes, when the distance from the sensor to targets in the valley is larger than to the related mountain tops. The ordering of surface elements on the radar image is the reverse. This effect produces very bright features on the image.","name":"Layover","selfAssesment":"Planned
"},{"code":"PP2-3-8-5","description":"Radar Shadow occurs on slope facing away from the sensor, if the slope angle is steeper than the sensor incidence angle. Shadow regions appear dark in the image, as no signal reaches them. Only small backscatter changes are principally due to system noise.","name":"Shadow","selfAssesment":"Planned
"},{"code":"PP2-3-8","description":"Synthetic Aperture Radar (SAR) backscatter is determined both by dieletric and geometric properties of the illuminated target. While the water content of the target plays an important role, its surface roughness determines the scattering mechanisms and the amount of incoming signal sent back to the sensor.\r\nDepending on its characteristics but also on the considered wavelength, a surface appears more or less rough. On smooth surfaces, specular reflection occurs, meaning that most of the incoming signal will be reflected away from the sensor. For rough surfaces, diffuse reflection occurs, meaning that part of the signal is scattered back to the sensor, the amount of it depending on different surface roughness parameters. \r\nDepending of the observed target and surface, single or multiple scattering mechanisms occur. A particularly important scattering mechanism is the double bounce, which occurs generally at two perpendicular surfaces (e.g. ground and building wall). Through two successive specular reflections, the whole signal comes back to the sensor.\r\nDue to the side-looking geometry of SAR systems and the range dependent image representation, specific additional effects occur and affect the backscatter intensity. Whereas a flat terrain only appears more compressed in near range and more stretched in far range, larger geometric distortions appear for terrain with more topography (e.g. mountains) or high objects (e.g. trees, buildings). This relief displacement is caused by the target’s elevation. A high elevated object is closer to the sensor than the ground below it. Due to the image formation in range direction depending on the distance between sensor and targets, its signal comes back sooner to the sensor and it is represented in the SAR image in nearer range than the ground below it. High objects in the SAR image are therefore displaced horizontally toward the radar antenna. This horizontal displacements contrast with the radial displacement observed in optical imagery due to central projection. Furthermore, such objects hide part of the ground below them, which do not receive any signal and cannot scatter information back. Three particular geometric distortions exist: foreshortening, layover and shadows.\r\nDepending on the illuminated target, different scattering mechanisms occur in combination with geometric distortions, which makes the interpretation of the SAR image challenging. A good example are buildings, where layover, shadow and single- and double-bounce occur.","name":"Terrain reflectivity and geometric distortions","selfAssesment":"Completed
"},{"code":"PP2-3-9","description":"A typical “salt-and-pepper” noise-like physical phenomenon that is not a noise but a deterministic property of SAR imagery is the so called speckle. It appears when a resolution cell of a SAR system contains more than one scatterer. In that case, the total scattering from the resolution cell is a coherent sum of the backscatter originating from the different scatterers. In order to reduce this effect, speckle reduction methods can be applied.","name":"Speckle Formation","selfAssesment":"Planned
"},{"code":"PP2-3","description":"Microwave remote sensing systems detect and quantify the electromagnetic radiation arriving at a detector, this radiation being either emitted (passive sensors) or scatterered back (active sensors) from the objects.\r\nThree properties of the recorded electromagnetic signal are of particular interest: its intensity, its phase and its polarization. The specific quantification of each properties allows signal interpretation, as they depend on the roughness and dielectric characteristics of the surface (intensity and polarization) as well as of the range between target and sensor (phase).\r\nThe detection of the microwaves is operated through two principal sensor elements: an antenna and a receiver. The antenna collects the incoming radiation and the receiver measures the collected electric signal.\r\nAs active microwave systems produce their own electromagnetic radiation, they are equipped with two additional elements: a pulse generator and a transmitter. Usually, transmitter and receiver are situated on the same antenna.\r\nA simple detector system only detects the intensity of the signal and amplifies it. Coherent systems measure both the amplitude and the phase of the incident electromagnetic radiation.\r\nMicrowave systems can be categorized in two different types: imaging and non-imaging sytems. Whereas for non-imaging systems each echoe (collected signal) provides a single measurement, imaging systems collect a sequence of echoes that generate a two dimensional image.","name":"Detecting microwaves","selfAssesment":"Completed
"},{"code":"PP2","description":"Microwave remote sensing operates in the microwave portion of the electromagnetic spectrum, generally using wavelengths greater than 3 cm and up to 1 m. \r\nMicrowaves are sensitive to different physical parameters than other regions of the electromagnetic spectrum. Microwaves interactions with objects are governed by geometric (structure, size, shape) and dielectric (water content) properties, whereas other regions of the electromagnetic spectrum reacts e.g. to object temperature or “color” (amount of reflection or absorption of the Sun light by a particular object).\r\nAs a general rule, microwaves interact with object at least as big as the wavelength. Smaller objects will therefore be transparent for the signal. Due to the large wavelengths, atmospheric particles are almost transparent to the signal and microwave remote sensing can penetrate clouds. Under very dry conditions, microwaves can even penetrate up to a few meters the top soil layers, therefore providing information that is not visible in other regions of the electromagnetic spectrum. Depending on the considered wavelength, microwave can also penetrate vegetation layers to different amounts.\r\nIn microwave remote sensing, three characteristics of the electromagnetic wave play an important role: its amplitude, its phase and its polarization. Depending on the application, either one characteristic or a combination of them is used to retrieve information.\r\nThere are two main types of microwave sensors: active RADAR systems and passive radiometers. RADAR is an acronym for RAdio Detection And Raging. An active radar system sends out pulses and records the echoes scattered back by the objects (scatterers) to the sensor. The systems use the two-way travel time of the radar pulse to determine the distance (range) to the illuminated object. Its backscatter intensity is determined by the radar system and object properties and depends on the quantity of energy coming back to the sensor. Active radar systems transmit a signal and record the amount of energy that is scattered back and depends of both dielectric and geometric properties. Passive radiometers record microwave energy, which is emitted by the Earth’s surface.\r\nDepending on the type of system, microwave remote sensing can be used in multiple applications. Active sensors are principally used for diverse land cover mapping applications based on the particular backscattering mechanisms and characteristics of the objects on the Earth’s surface. Using multiple acquisitions, they are also favored for topographic, deformation and velocity mapping. Passive sensors are preferred for the determination of hydrologic variables such as soil moisture, precipitation, ice water content and sea-surface temperature.","name":"Basics of microwave remote sensing","selfAssesment":"Completed
"},{"code":"PS","description":"Remote sensing, i.e. the process of obtaining information about an object or area from a distance, is not possible without remote sensing sensors that collect this information and the platforms on which the sensors are installed and which are used to move them. Remote sensing sensors collect data by detecting energy that is reflected or emitted from Earth. There are different types of remote sensing sensors. The interaction between the sensor and the Earth's surface has two modes: active or passive. Passive sensors use solar radiation to illuminate the Earth's surface and detect reflection from the surface or measure the emitted energy. They usually record electromagnetic waves in the visible (˜430–720 nm) and near infrared (NIR) (˜750–950 nm) through short infrared (SWIR) (˜1.500-2.500 nm) to thermal infrared (TIR) (8.000-14.000 nm) ranges. The power measured by passive sensors is a function of surface composition, physical temperature, surface roughness and other physical properties of the Earth. Active sensors provide their own energy source to illuminate objects and measure their properties. These sensors use electromagnetic waves in the visible and near infrared range (e.g.laser altimeter) and radar waves (e.g. synthetic aperture radar (SAR)). As sensor technology has advanced, the integration of passive and active sensors into one system has emerged. Alternatively, remote sensing sensors can be classified into imaging sensors, i.e. that produce an image of an area, within which smaller parts of the sensor's whole view are resolved (pixels), and non-imaging sensors, i.e. that return a signal based on the intensity of the whole field of view. In terms of their spectral characteristics, the imaging sensors include optical imaging sensors, thermal imaging sensors, and radar imaging sensors. These sensors can be on satellites, mounted on aircraft, unmanned aerial vehicle (UAV), drone or ground. The collected information can be transformed into an image or set of points (e.g. cloud points), which can be further processed and analyzed to obtain the necessary information, e.g. agricultural field development phase, level of air pollution, etc.\r\nA digital imagery of Earth observation sensors is a two-dimensional representation of objects on Earth. Current images collected from different levels of acquisition, from ground to satellite, with the help of electronic sensors are examples of digital images. There are different aspects and characteristics of remote sensing data and images, such as, for example, data formats and processing levels, data storage, data properties.","name":"Platforms, sensors and digital imagery","selfAssesment":"Completed
"},{"code":"PS1-1","description":"Remote sensing sensors has its roots in the 19th century in the development of photography. Photography was an invention that made it possible to acquire a permanent image. The first photographic image was taken in 1826 by Joseph Nicephore Nieppce. While the first aerial photograph was taken in 1858 by Felix Tournachon, known as Nadar, from a tethered baloon over Biévre Valley in France. In 1907 Julius Neubronner developed a light miniature camera that could be fitted to a pigeon's breast. It can be said that the construction camera + pigeon was the precursor of today's unmanned aerial vehicle (UAV) or drone. Further developments focused on developing new sensors (analog vs. digital frame cameras) and how to save and store images (e.g. photographic emulsions, films). The origin of other types of remote sensing can be traced to World War II, with the development of radar, sonar, and thermal infrared detection systems. Since the 1960s, sensors were designed to operate in virtually all of the electromagnetic spectrum. Both civil and military aerial photography have long been widely used in cartography to create maps. Specialized large format cameras (looking vertically down, assuming the plane is flying horizontally) were developed. Such cameras have been specially designed to perform almost vertical sequences of bird-eye exposures during aircraft flight. Hence for a long time remote sensing consisted of aerial photography and photogrammetry using analogue mechanical or optical equipment. Everything has changed with satellites and the space race. The first real success of remote sensing satellites in serious scientific work was in meteorology, weather satellite TIROS-1, launched by NASA on April 1, 1960. \r\nToday a wide variety of remote sensing instruments are available as data source for use in different applications for land, water and atmosphere monitoring.","name":"History of remote sensing sensors","selfAssesment":"In progress
"},{"code":"PS1-2-1-1-1","description":"Along track scanner, also known as a pushbroom scanner, is an optoelectronic device that obtains images with a multispectral imaging system. The scanners are used for passive remote sensing. It records electromagnetic energy that is reflected (e.g., blue, green, red, and infrared light) or emitted (e.g., thermal infrared radiation) from the surface of the Earth. The scanners are mounted on space- or aircrafts. \r\nA two-dimensional image is created (line by line) by exploiting the platform motion along the orbital track. The data are collected along track using a linear array of detectors arranged perpendicular to the direction of travel. The array of detectors are pushed along the flight direction to scan the successive scan lines, and hence the name pushbroom scanner. \r\nThere are no moving parts on a pushbroom sensor, hence, the scanning speed can be increased compared to across track systems. A longer dwell time over each ground resolution cell increases the signal strength (high radiometric resolution, no pixel distortion). Additionally, finer spatial and spectral resolution can be achieved as the size of the ground resolution cell is determined by the Instantaneous Field of View (IFOV) of a single detector. The systems are designed for high-resolution imaging. However, a very large number of detectors is needed for high resolution images. It is a complex optical system. In addition, the pushbroom scheme requires a wide Field of View (FOV) optics system to obtain the same swath as for a corresponding whiskbroom (across track) scanner. It has narrow swath width. \r\nThe detector arrays with such a line-scanning pushbroom system are usually of the type Charge-Coupled Device (CCD).\r\nThe MultiSpectral Instrument (MSI) on board the Sentinel-2 satellite (Copernicus mission) uses a pushbroom concept.\r\nMultispectral imaging systems building the final image (line by line) exploiting the platform motion along the orbital track. No rotating mechanical part required, usually based on a CCD matrix (high spectral resolution but just up to 1 micrometer), e.g. Sentinel-2 MultiSpectral Instrument (MSI), Sentinel-3 Ocean and Land Colour Imager (OCLI).","name":"Along track scanners","selfAssesment":"Completed
"},{"code":"PS1-2-1-2-1","description":"The cameras, usually a charge-coupled device (CCD) or Complimentary Metal Oxide Semiconductor (CMOS), that convert light into electrons that can be measured and converted into radiometric intensity value.","name":"Digital Frame Camera","selfAssesment":"Planned
"},{"code":"PS1-2-1-2","description":"2-D systems with the ability to observe in two dimensions simultaneously.","name":"Area Arrays","selfAssesment":"New
"},{"code":"PS1-2-1","description":"A type of a spectrometer. It is in principle, one-dimensional systems, whisk- or pushbroom, that form an image on a line-by-line basis in the scan direction.","name":"Line detector arrays","selfAssesment":"New
"},{"code":"PS1-2-2-1-1","description":"Thermal radiometers are radiometers with the capability of measuring the spectrum of infrared emission. As such, they are characterized by a relatively high spectral resolution (normally better than 1 cm-1 in wave number units). Modern Spectrometers on board satellites have a spectral resolution better than 0.7 cm -1 in order to properly resolve CO2 lines used for the retrieval of the atmospheric temperature profile. Based on the optical layout they are further classified in grating spectrometers and Fourier Transform Spectrometers or FTIR.","name":"Thermal Radiometers","selfAssesment":"New
"},{"code":"PS1-2-2-1-2","description":"Passive microwave radiometers are radiometers that measures energy emitted at millimetre-to-centimetre wavelengths at 0.15 - 30 cm (frequencies of 1–200 GHz). Example of a sensor: SMOS Microwave Imaging Radiometer with Aperture Synthesis (MIRAS), which aims at measuring land soil moisture and ocean salinity.","name":"Passive Microwave Radiometers","selfAssesment":"In progress
"},{"code":"PS1-2-2-1-3","description":"An advanced multispectral sensor that detects hundreds of very narrow spectral bands throughout the visible, near-infrared, and mid-infrared portions of the electromagnetic spectrum.","name":"Hyperspectral Radiometers","selfAssesment":"Planned
"},{"code":"PS1-2-2-1-4","description":"A radiometer that measures the intensity of radiation in multiple wavelength bands (i.e., multispectral). Example of a sensor Moderate Resolution Imaging Spectroradiometer (MODIS)","name":"Spectroradiometers","selfAssesment":"In progress
"},{"code":"PS1-2-2-2","description":"Provide information about vertical profiles of temperature and molecular consistuent concentrations in the atmosphere (atmospheric sounders).","name":"Atmospheric passive sounders","selfAssesment":"New
"},{"code":"PS1-2-2","description":"Radiometers are instruments which measure radiative intensities within a particular frequency window. A radiometer is further identified by the portion of the electromagnetic radiation it covers, usually the infrared or microwave regions. Normally the spectral range extends from the longwave (14-15 micron) to the shortwave (3-4 micron). This range overlaps much of the emission spectrum of Earth. The technology is classified in broadband radiometer of spectral radiometers depending on the spectral resolution. A radiometer measures the intensity of the radiative energy, but does not differenciate between the different registered wavelengths or their respective amplitude. In other terms, it provides a single value as combined result of all wavelengths within the considered frequency window.","name":"Radiometers","selfAssesment":"In progress
"},{"code":"PS1-2","description":"Passive remote sensing systems record electromagnetic energy that is reflected (e.g., blue, green, red, and infrared light) or emitted (e.g., thermal infrared radiation) from the surface of the Earth. Passive sensors therefore rely on an external energy source (e.g. sun illumination, Earth heat emission). Contrary to passive sensors, who detect naturally occurring radiation, active sensors emit radiation and collect and analyze the signal that is sent back by the Earth’s surface or atmosphere. Active remote sensing systems produce therefore their own electromagnetic energy. They transmit and receive the radiation that is reflected or backscattered from the illuminated target. They do not necessitate an external source of radiation (e.g. Sun or Earth). Contrary to most passive sensors that are bound to detecting either the reflected Sun radiation or emitted radiation by the Earth’s surface in ranges from the ultraviolet to the thermal infrared, active sensors can use any radiation from the electromagnetic spectrum, the only limitation being the transparency of the Earth’s atmosphere. They often use wavelengths that are not sufficiently provided by the Sun, e.g. microwaves. \r\nActive systems can be categorized either according to their imaging capability, or according to the considered emitted wavelength, or also according to the way they use the returned signal. For the last category, it is generally distinguished between ranging systems, which use as principal information the time delay between transmission and reception of the electromagnetic radiation at the sensor, and scattering systems, which consider the strength (also called magnitude or intensity), of the returned signal. Some systems also register both information.\r\nAs active sensors produce their own radiation and do not rely on e.g. Sun radiation, they are daytime independent and can also retrieve information about the Earth’s surface by night. Furthermore, depending of the considered wavelength, active sensors are weather independent. For longer wavelengths of the microwave domain, clouds are transparent, as the transmitted wavelength is larger than the water particles constituting the cloud and do not interact with them. \r\nActive sensors can control the direction of their illumination to a specific target to be investigated, but require in general more energy than passive sensors as they “actively” illuminate the Earth’s surface.","name":"Passive vs. active sensors","selfAssesment":"Completed
"},{"code":"PS1-3-1-1","description":"Imaging radar is an active radar system that sends out pulses and records the echoes scattered back by the objects (scatterers) to the sensor. Imaging radars are independent of weather conditions and can operate day or night. It uses microwave wavelengths, radar bands from X- to P- or VHF-band, in four polarisations to illuminate an area on the ground. Normally only the horizontal (H) or vertical (V) linear polarizations are used. The radar system is characterized by combination of polarization of transmitted and received pulse: HH, HV, VH or VV. A typical radar system measures the strength and roundtrip time of the microwave signals that are emitted by a radar antenna and reflected off a target area. An imaging radar is therefore both and imaging and a ranging system. The illuminated objects are mapped in the radar depending on their backscatter intensity and respective range to the sensor.\r\nImaging radar can be mounted on aircraft or satellite. It operates in a side-looking configuration, left or right with reference to the flight direction. This acquisition geometry allows the distinct mapping of scatterers corresponding to their respective distance to the sensor. It causes also geometric distortions in the radar image, i.e., relief displacement (foreshortening and layover) and shadow. The radar sensor operates not in the real aperture of the radar antenna, i.e., real spatial width, radar (RAR) mode but in the synthetic aperture radar (SAR) mode. Synthetic aperture is possible to set up through the forward motion of the spacecraft, which enables to “extend” the real size of the radar antenna. With a SAR, each object on the ground is sampled at several antenna positions along the flight path, i.e., as long as the antenna beam is illuminating it.\r\nImaging radar can be used for a different of land and water applications.","name":"Imaging Radar","selfAssesment":"Completed
"},{"code":"PS1-3-2-1-1","description":"Laser profilers measure 1D range profiles and operate in different environments, like spaceborne, airborne and indoor. Most of them operate top-down on flying platforms, but as well bottom-up is possible, e.g. in meteorology for cloud monitoring.\r\nIt is the simplest application of the LIght Detection And Ranging technique. It transmits a short pulse of energy (visible or near-infrared radiation) and detects 'echo', by measuring the time delay and knowing the speed of propagation of the pulse, the range from the instrument to the surface can be measured.","name":"Laser profiler","selfAssesment":"In progress
"},{"code":"PS1-3-2-1-4","description":"The radar altimeter operates similarly to the laser profiler but it operates at a much longer wavelengths using microwaves. Radar altimeters measures the two-way travel time of a radar pulse between the radar antenna and the object (Earth's surface). Radar altimetry was originally designed for the open ocean domain.","name":"Radar altimeters","selfAssesment":"New
"},{"code":"PS1-3-2-1","description":"Laser altimeters historically were the first active sensing devices used on airborne platforms, measuring range information in form of single distances. Nowadays, they are still found on low-cost platforms like drones to determine the flight altitude. The instrument is also used aboard planet-orbiting satellites to map a planet's terrain.","name":"Laser altimeter","selfAssesment":"In progress
"},{"code":"PS1-3-2-3","description":"By a ranging camera the simultaneous capturing of range measurements in the form of a range image for an extended area of dynamical 3D applications is given. Applications are building surveillance, traffic monitoring, and driver assistance.","name":"Ranging camera","selfAssesment":"In progress
"},{"code":"PS1-3-2-4-1","description":"Spaceborne Laser Scanning (SLS; e.g. Geoscience Laser Altimeter System - GLAS, Global Ecosystem Dynamics Investigation - GEDI) provides mainly global, depending on the platform (GEDI mounted on International Space Station (ISS) provides measurements over the Earth’s surface between 51.6° N and 51.6° S), measurements of the Earth's surface, with the potential on capturing additionally clouds and atmospheric aerosols. The spaceborne measurements allow to globally observe ice sheet and land elevations, approximate sea ice thickness, changes in elevation through time, vegetation coverage for biomass estimation, and height profiles of clouds and aerosols.","name":"Spaceborne Laser Scanning","selfAssesment":"Planned
"},{"code":"PS1-3-2-4-2","description":"Airborne Laser Scanning (ALS) systems allow a direct and illumination-independent measurement from 3D objects in a fast, remote and accurate way. Beside basic range measurements, the current commercial ALS developments allow to record the waveform of the backscattered laser pulse. Latest trends in sensor developments focus on single-photon detection. Different applications are of interest, like urban planning, forestry surveying, or power line monitoring. Further to describe the 3D scene, products like digital terrain models (DTMs), digital surface models (DSMs), or city models are provided.","name":"Airborne Laser Scanning","selfAssesment":"Planned
"},{"code":"PS1-3-2-4-3","description":"A mobile laser scanning or LiDAR system (MLS) consists of a moving vehicle equipped with one or more usually side-looking laser scanners to capture information about the local 3D geometry.","name":"Mobile Laser Scanning","selfAssesment":"Planned
"},{"code":"PS1-3-2-4-4","description":"Underwater Laser Scanning is applied in deep-sea as well as in shallow water regions. The ranging distance is close range and the measurement principle relies on triangulation by laser light, comparable with structured-light-projection. More recently, companies started to develop Time-of-Flight (ToF) underwater laser scanners.","name":"Underwater Laser Scanning","selfAssesment":"Planned
"},{"code":"PS1-3-2-4-5","description":"For Bathymetric Laser Scanning System the utilized green laser light with its potential penetration capabilities in water is essential. For water surface mapping the electromagnetic radiation of the laser penetrates into the topmost layer of the water column and can also be used for mapping the water surface and shallow water bathymetry. Area-wide water surface heights and depths are required for many disciplines such as hydrology, hydraulic engineering, flood risk management, ecology, climate change, etc.","name":"Bathymetric Laser Scanning","selfAssesment":"Planned
"},{"code":"PS1-3-2-4","description":"Laser scanners capture data by successively considering points on a discrete, regular (typically spherical) raster, and recording the respective geometric and radiometric information.\r\nThere are different types of laser scanners depending on their application and the platform on which they are mounted: spaceborne, airborne, terrestrial, mobile, underwater, bathymetric.","name":"Laser scanner","selfAssesment":"Planned
"},{"code":"PS1-3-2","description":"The main idea of LiDAR (Light Detection and Ranging) technology is based on actively scanning the scene by involving a device which emits electromagnetic radiation in the form of modulated laser light. \r\nGenerally, such scanning devices illuminate a scene with modulated laser light and analyze the backscattered signal. More specifically, laser light is emitted by the scanning device and transmitted to an object. At the object surface, the laser light is partially reflected and, finally, a certain amount of the laser light reaches the receiver unit of the scanning device. The measurement principle is therefore of great importance as it may be based on different signal properties such as amplitude, frequency, polarization, time, or phase. \r\nMany scanning devices are based on measuring the time t between emitting and receiving a laser pulse, i.e., the respective time-of-flight, and exploiting the measured time t in order to derive the distance r between the scanning device and the respective 3D scene point. Alternatively, a range measurement r may be derived from phase information by exploiting the phase difference Δφ between emitted and received signal. According to seminal work, respective scanning devices may be categorized with respect to laser type, modulation technique, measurement principle, detection technique, or configuration between emitting and receiving component of the device. \r\nIn order to get from single 3D scene points to the geometry of object surfaces, respective scanning devices are typically mounted on a platform which, in turn, allows a sequential scanning of the scene by successively measuring distances for discrete 3D points.\r\nLiDAR technology is used for a diversity of applications such as autonomous driving, forestry, biomass estimation, precision farming, archaeology, city mapping, terrain modelling, and metrology.","name":"LiDAR (Light Detection and Ranging)","selfAssesment":"Completed
"},{"code":"PS1-3-3-1","description":"Sonar, also called ultrasonic sensing, is one the principal sensors for mapping sea-floor, i.e. bathymetry. It transmits sound waves through water and records the amount of backscattered energy. It uses frequencies higher than normal hearing. A sonar can be either passive or active. Active sonars are also called echosounders.","name":"Sonar","selfAssesment":"New
"},{"code":"PS1-3-3-2","description":"A seismic sensor is also called seismometer and measures the motion of the ground when it is shaken by a perturbation such as an earthquake, be it a large displacement or a microquake. The physical variable associated to the measurement of a seismometer is dynamic. It can be either the amplified ground motion, the velocity or acceleration. Current seismometers transform one of these three parameters into a voltage measurement. Usually, three seismometers are needed to retrieve the three components of the displacement. As for other sensors, there exists many types of seismic sensors, and they can be distinguished in active and passive sensors as well.","name":"Seismic sensor","selfAssesment":"New
"},{"code":"PS1-3-3","description":"Instruments that measure vertical distribution of precipitation and other atmospheric characteristics such as temperature, humidity, and cloud composition.","name":"Sonic sensors","selfAssesment":"New
"},{"code":"PS1-3-4-1","description":"Radar scatterometer is a calibrated radar designed to measure the radar backscatter cross section of a target, generally an area on the earth’s surface. Surface backscatter is measured as a function of the frequency, polarization, and illumination direction of the sensing signal (microwaves).","name":"Radar Scatterometers","selfAssesment":"Planned
"},{"code":"PS1-3-4-2-1","description":"Differential Absorption Lidar (DIAL) is a laser remote sensing technique that is used for range and/or profile measurements of atmospheric gas concentrations and constituents.","name":"Differential Absorption Lidar","selfAssesment":"In progress
"},{"code":"PS1-3-4-2-2","description":"Doppler Wind LiDAR or Cloud-Aerosol Lidar with Orthogonal Polarization (e.g. CALIOP) is a two-wavelength polarization-sensitive LiDAR that provides high-resolution vertical profiles of atmospheric aerosols and clouds to enable an greater understanding of our climate.","name":"Doppler Wind LiDAR","selfAssesment":"In progress
"},{"code":"PS1-4","description":"There are different ways to classify sensors used in remote sensing. One of them is the division into imaging and non-imaging sensors. Imaging sensors typically employ optical imaging systems (from VIS to TIR). They operate primarily at window frequencies, where atmospheric absorption is low and surface features can be imaged or measured. Non-imaging sensors include microwave radiometers, microwave altimeters, magnetic sensors, gravimeters, Fourier spectrometers, laser rangefinders, and laser altimeters.","name":"Imaging vs. nonimaging sensors","selfAssesment":"New
"},{"code":"PS1-5-1-2","description":"Across track scanners, known as whiskbroom electromechanical scanners, are multispectral imaging systems building the final image (ground cell by ground cell) by combination of the platform motion along the orbital track with a mechanical rotation of the collecting optic in the across track direction. Opto-mechanical are typically multi-spectral radiometers (no limitation on bands), whiskbroom systems are usually CDD spectrometers (high spectral resolution but just up to 1 micrometer). Examples of the sensors: Landsat Multispectral Scanner (MSS), Landsat Thematic Mapper (TM).","name":"Across track scanners","selfAssesment":"Planned
"},{"code":"PS1-5-1","description":"Speckle-pattern based sensors operate with a spatial neighborhood codification strategies to exploit a unique pattern. The label associated to a pixel is derived from the spatial pattern distribution within its local neighborhood. Thus, labels of neighboring pixels share information and provide an interdependent coding. Representing one of the most popular devices based on structured light projection, the Microsoft Kinect exploits an RGB camera, an IR (infrared) camera, and an IR projector. The IR projector projects a known structured light pattern in the form of a random but unique speckle dot pattern onto the scene. As IR camera and IR projector form a stereo pair, the pattern matching in the IR image results in a raw disparity image which, in turn, is read out as depth image.","name":"Speckle-pattern based sensor","selfAssesment":"In progress
"},{"code":"PS1-5-2","description":"A multi-temporal (sequential) binary coding uses black and white stripes to form a sequence of projection patterns for each point on the surface of the object. Binary coding technique is very reliable and less sensitive to the surface characteristics, since only binary values exist in all pixels. Thus, each pixel may be assigned a codeword consisting of its illumination value across the projected patterns. The respective patterns may, for instance, be based on binary codes or Gray codes and phase shifting. To achieve high spatial resolution, a large number of sequential patterns need to be projected. All objects in the scene have to remain static. The entire duration of 3D image acquisition may be longer than a practical 3D application allows for. These sensors are utilized in industrial environment.","name":"Multi-temporal pattern based sensor","selfAssesment":"In progress
"},{"code":"PS1-5-3","description":"For a multi-spectral pattern based sensor, various continuously varying color patterns to encode the spatial location information are utilized.","name":"Multi-spectral pattern based sensor","selfAssesment":"New
"},{"code":"PS1-5","description":"A structured-light-projection camera emits active optical radiation in the form of a coded structured light pattern in the visible or infrared spectrum, or electromagnetic radiation in the form of modulated laser light. Via the projected pattern, particular labels are assigned to 3D scene points which, in turn, may easily be decoded in images when imaging the scene and the projected pattern with a camera. The procedure reminds to conventional stereo processing, where corresponding features must be extracted from a pair of stereo images to derive the spatial information. In contrast, such synthetically generated features allow to robustly establish feature correspondences, and the respective 3D coordinates may easily and reliably be recovered via triangulation. Generally, techniques based on the use of structured light patterns may be classified depending on the pattern codification strategy.","name":"Structured-light-projection camera","selfAssesment":"In progress
"},{"code":"PS1-6","description":"Ground penetrating radar is a non-intrusive measurement technique that uses radio waves to probe the ground. It is used to analyze and locate targets buried in the sub-surface. It transmits low-power electromagnetic energy into the ground and receives weak signals from a low-loss dielectric or conductor material. It is principally used for archeology and geology. Typical penetration depths are between a few centimeters up to 4m.","name":"Ground penetrating RADAR (GPR)","selfAssesment":"New
"},{"code":"PS1-7","description":"An optical spectrometer is an instrument used to detect, measure and analyze the spectral content of the incident electromagnetic field (narrow-band, VIS, NIR, SWIR and TIR). It breaks down the incoming light spectrum so the whole wavelength range is mapped and each wavelength can be analysed individually. Usually, a distinction is made between optical and mass spectrometers.\r\nOptical spectrometers depict the intensity of the incoming light in function of the wavelength. Considering all wavelengths, each object has a specific spectral signature and the analyse of their particular spectrum allows the deduction of their composition ( e.g. pigments) or health.","name":"Optical spectrometers","selfAssesment":"In progress
"},{"code":"PS1","description":"Remote sensing sensors acquire information about objects situated on the surface of e.g. the Earth remotely, e.g. from a distance, without any physical contact. They detect and measure the changes that the object imposes on its. \r\nRemote Sensing sensors are characterized according to several different properties:\r\n\tDepending on the interaction between the sensor and the Earth’s surface, one distinguishes between active (e.g. radar) and passive (e.g. optical imagery) sensors. Some systems use both kind of sensors simultaneously.\r\n\tDepending on the mapping process of the information, it can be distinguished between imaging and non-imaging sensors. Imaging sensors produce an image of an area of interest, e.g. give a spatial information about the incoming information. Spatial relationships between objects can be identified and used for visual interpretation. Non-imaging sensors register usually single response values for a specific area, and do not record how the incoming information varies across the field of view. They can be used to characterize the interaction between the received information and illuminated target.\r\n\tDepending on the platform on which the instrument is deployed, one speaks either of ground based (e.g. terrestrial laser scanner), airborne (e.g. plane, drone), or spaceborne (e.g. satellite) sensor. For spaceborne sensors, the orbit geometry (e.g. geostationary, equatorial, sun-synchronous) and altitude (high, medium and low Earth orbit) play an important role, as it most often determines the application of the satellite in combination with the deployed sensor (weather satellites or Earth observation satellite). \r\n\tDepending on the observed portion of the electromagnetic spectrum (e.g. optical, infrared, thermal, microwave). \r\n\tDepending on the instrument (e.g. imagers, altimeters, spectrometers, radiometers). \r\n\tDepending on the instrument precision, e.g. in terms of spatial resolution very high vs. low resolution sensors; in terms of spectral resolution narrow band (hyperspectral sensors) vs. broad-band sensors (mono- and multispectral sensors); in terms of radiometric resolution very high vs. low resolution sensors. Some applications do not require very high precision instruments, e.g. sea surface temperature measurements, while other, e.g. for vegetation monitoring, require high spectral and radiometric resolution for good data interpretation and analysis. \r\nOther categorization would include the specific applications of each sensor (weather, environment, urban, land, water, mapping, photogrammetry, structure-from-motion, etc.) and if is financed and used for scientific, commercial or military goals.","name":"Types of remote sensing sensors","selfAssesment":"Completed
"},{"code":"PS2-1","description":"This topic covers information on the first remote sensing platforms that were used to obtain aerial photos. The first-known aerial photo was obtained in 1858 by Gaspard Felix Tournachon (Nadar). Afterwards, different platforms were used to obtain the information from above. The history of the development of remote sensing platforms includes platforms such as baloons, kites, rockets, pigeons, gliders, etc. to recent low-cost femtosatellites, e.g. for solar radioation pressure measurements. Historically, the main developments of the platforms as well as sensors was associated with military operations in the XXth century. Remote sensing data was used as part of photo- or/and satellite reconnaissance, i.e. aerial photos or satellite imageries used for the military purposes, mainly to make accurate maps and based on that to prepare a military strategy.","name":"History of Remote Sensing Platforms","selfAssesment":"In progress
"},{"code":"PS2-2-1","description":"An unmanned aircraft system (UAS) includes an unmanned aerial vehicle (UAV), an aircraft without a human pilot on board, a ground-based controller, and a system of communications between the two. The system includes a full range of size classes from very small hand-launched drones to the large high-altitude observational systems.","name":"Unmanned Aerial Systems (UAS)","selfAssesment":"New
"},{"code":"PS2-2-2-1","description":"Mission planning depends on the selected system of acquisition (sensor and platform). A detailed planning of a mission is a fundamental prerequisite for a successful acquisition of remote sensing data. Planning of an aerial photography mission (manned or unmanned) takes into account several parameters such as time of day/sun angle, weather conditions, flightline, platform. Planning and implementation of a spaceborne Earth Observation mission involves several successive life cycle ‘phases’ of conception, development, production and testing, utilization and support, and retirement, as part of an iterative and recursive process, until the satellite (space segment) is delivered and launched into orbit, and the data are exploited in the ground segment.","name":"Mission planning","selfAssesment":"New
"},{"code":"PS2-2-2-3-2-3-1","description":"Stripmap is a acquisition mode of Synthetic Aperture Radar (SAR) data. By acquisition of data with the Stripmap mode radar antenna pointing is fixed relative to the flight line (coarse-resolution data).","name":"Stripmap","selfAssesment":"New
"},{"code":"PS2-2-2-3-2-3-2-1","description":"Staring SpotLight is a SAR acquisition mode currently available on the SAR satellite TerraSAR-X, allowing azimuth resolution up to 0.25 m. It uses beam steering in azimuth direction to increase the illumination time (i.e. the size of the aperture). The virtual rotation center is situated inside the image scene.","name":"Staring Spotlight","selfAssesment":"New
"},{"code":"PS2-2-2-3-2-3-2","description":"The SpotLight imaging modes steer its antenna beam in azimuth direction to increase the illumination time, i.e. the size of the synthetic aperture. This leads to a restriction in the image / scene size. Thus, the scene size is technically restricted to a defined size: 10 km x 10 km.","name":"Spotlight","selfAssesment":"New
"},{"code":"PS2-2-2-3-2-3-3-1","description":"Interferometric Wide Swath Mode (IW) is the main acquisition mode of the Sentinel-1 satellites over land. For this mode, regular revisit times and a consistent long-term archive are ensured It uses the Terrain Observation with Progressive Scans SAR (TOPSAR) acquisition technique and acquires data with a swath width of 250km at a spatial resolution of 5 m by 20m.","name":"Interferometric Wide Swath Mode","selfAssesment":"New
"},{"code":"PS2-2-2-3-2-3-3-2","description":"The Extra Wide Swath Mode is an acquisition mode of the Sentinel-1 satellites. It is primarily designed and used for wide area coastal monitoring, such as ship traffic, sea-ice monitoring and oil spill detection. It uses the TOPSAR technique with a swath width of 410km and a spatial resolution of 20 m by 40 m.","name":"Extra Wide Swath Mode","selfAssesment":"New
"},{"code":"PS2-2-2-3-2-3-3","description":"ScanSAR is an aquisition mode of a SAR system. The sensor steers the antenna beam to illuminate a strip of terrain at any angle to the path of aircraft motion.","name":"ScanSAR","selfAssesment":"New
"},{"code":"PS2-2-2-3-2-3-5","description":"A stereoscopy acquisition mode collects remotely sensed data where each location on the ground (or the imaged objects) is covered multiple times (at least twice), from different perspectives. Stereopairs and stereoscopic coverage enable the extraction of 3D representations of the environment from remotely sensed imagery. Most aerial photographs are taken with frame cameras along flight lines, or flight strips. [...] Successive photographs are generally taken with some degree of endlap [, i.e. overlap]. Not only does this lapping ensure total coverage along a flightline, but an endlap of at least 50 percent is essential for total stereoscopic coverage of a project area. Stereoscopic coverage consists of adjacent pairs of overlapping vertical photographs called stereopairs. Stereopairs provide two different perspectives of the ground area in their region of endlap [overlap]. When images forming a stereopair are viewed through a stereoscope, each eye psychologically occupies the vantage point from which the respective image of the stereopair was taken in flight. The result is the perception of a three-dimensional stereomodel. As an input to photogrammetry analysis procedures, stereopairs from flight strips enable the extraction of digital elevation models (DEM), orthophotos, thematic GIS data, and other derived products through the use of digital raster images and relatively sophisticated analytical techniques. With the availability of close-range UAV and terrestrial hand-held camera data, 3D reconstructions of buildings (even indoors) and other objects on the terrain surface become possible.","name":"Stereoscopy","selfAssesment":"In progress (to be deleted, merged?)
"},{"code":"PS2-2-2","description":"Since the 1940s aerial imagery has been the primary source of detailed geospatial data for extensive study areas. Photogrammetry is the profession concerned with producing precise measurements from aerial imagery. Aerial imaging and photogrammetry represent a major component of the geospatial industry. The topics included in this unit do not comprise an exhaustive treatment of photogrammetry, but they are aspects of the field about which all geospatial professionals should be knowledgeable.","name":"Airborne platforms and systems","selfAssesment":"New
"},{"code":"PS2-2-3-1","description":"Earth observation (EO) missions are gathering information about the physical, chemical, and biological systems of the planet via remote-sensing technologies, supplemented by Earth-surveying techniques, which encompasses the collection, analysis, and presentation of satellite data.","name":"Earth observation missions","selfAssesment":"In progress
"},{"code":"PS2-2-3-2","description":"There are essentially three types of Earth orbits: high, medium and low Earth orbit. Satellites that orbit in a medium (mid) Earth orbit include navigation and specialty satellites, designed to monitor a particular region. Most scientific satellites, including NASA’s Earth Observing System fleet, have a low Earth orbit. On which orbit a satellite will be launched to, depends mainly on its application. The orbit types can be categorized according to their height.\r\nThe orbit height of a satellite corresponds to the distance between the Earth’s surface and the satellite. It determines its speed as it rotates around the Earth. Due to Earth’s gravity, the pull of gravity is stronger for lower orbits than for higher orbits. Therefore, a satellite situated on a lower orbit will circle the Earth faster than a satellite situated on a higher orbit.\r\n\tHigh Earth orbit: it describes orbits situated at about 36000 km above the Earth’s surface (42164 km from the Earth’s center). At this exact distance, the speed of the satellite on the orbit matches the Earth’s rotation, i.e. the satellite needs 24 hours to complete a full rotation on the orbit, when the orbit is situated exactly above the equator. Such orbits are also called geosynchronous orbits, as the satellite moves at the same speed than the Earth and seems to stay in place over a specific location. Those orbits are mainly used for weather and communication satellites\r\n\tMedium Earth orbit: it describes orbits situated at about 20200 km of the Earth’s surface, or 26560 km of the Earth’s center. At this height, a satellite rotates twice around the orbit during one Earth’s rotation. This orbit is also called semi-synchronous and this is the orbit type used by Global Navigation Satellite Systems such as GPS and GLONASS. A further important medium Earth orbit is the Molniya orbit which allows the observation of the poles, otherwise nearly impossible with equatorial geosynchronous orbits.\r\n\tLow Earth orbit: this type of orbits are used from almost all dedicated scientific Earth Observation satellites. Most of them use a particular, nearly polar orbit inclination, meaning that the satellite rotates around the Earth nearly from pole to pole (instead of around the equator as it is the case for geosynchronous satellites). This rotation takes about 99 minutes, depending of the specific orbit inclination. During one half of the orbit, the satellite views the daytime side of the Earth, i.e. the illuminated side. At the pole, satellite crosses over and views the nighttime side of Earth. Back to the daylight side, the satellite can view the area adjacent to the region flown over in the last orbit path, due to the simultaneous Earth’s rotation. In 24 hours, satellites situated on these orbits view almost all the Earth twice, for optical satellites once in daylight and once in the dark. Radar satellites seen each Earth region twice, from two different illumination directions. These specific polar-orbits are called sun-synchronous, as the local solar time stays the same each time a satellite flies over a specific region. This has the advantage of providing an almost constant angle of sunlight for each region on the Earth’s surface viewed by the satellite over time and ensure repeatable sun illumination conditions; the angle will only vary seasonally due to the Earth revolution around the sun. Due to this consistency, images of a specific region would not show much illumination changes due to shadows or sunlight and image interpretation over time such as change detection or monitoring approaches are possible. Because a sun-synchronous orbit does not pass directly over the poles, there is a data gap over both poles where no data is acquired.","name":"Types of satellite orbits","selfAssesment":"Completed
"},{"code":"PS2-2-3-3","description":"An imaging SAR system can generally make acquisitions in different modes. Which acquisition mode to choose depends of the application but also on the desired coverage and data resolution. Even if technically all acquisitions modes can be used everywhere on the Earth’s surface, specific modes are preferred for ocean applications that are different from the ones used in land applications.\r\nThe different acquisition modes can be defined either by their geometrical or by their temporal properties.\r\nThe geometrical properties refer to the geometric configuration of the SAR antenna. Usually looking sideways down in a direction perpendicular to the flight direction (Stripmap mode), the antenna can also be steered around the nadir axis in order to look at a specific target for a longer time during pass-by (Spotlight mode). This configuration allows to rachieve higher azimuth resolution but reduces coverage. It is rather used for very local application where a precise information about specific targets is needed. Other geometric configurations steer the antenna around the flight direction (ScanSAR mode), yielding to a larger swath on the ground. The distance between near and far range is increased, as well as the range of incidence angles within one acquisition. Whereas it increases the area of the scene, it comes generally with a decrease of the spatial resolution in the azimuth direction. Depending on the sensors, the name of the acquisition modes as well as particular technical properties can differ. Sentinel-1 uses a TOPS configuration (Terrain observation with Progressive Scan), which combines the antenna steering properties of both ScanSAR and Spotlight modes. \r\nThe temporal properties refer for specific techniques to the time interval between several acquisitions of the same area. Either these acquisitions are taken simultaneously in one pass over the area of interest (single-pass), or they are taken at different times, needing several passes over the area (repeat-pass).\r\nSpecific SAR techniques such as InSAR and Tomography, while relying on those geometric and temporal properties, have additional acquisition configuration characteristics. For example, the interferometric mission TanDEM-X has three acquisition modes defined by the number of satellite emitting or receiving the signal (pursuit monostatic mode, bistatic mode, alternating bistatic mode), which allows phase referencing. Tomographic SAR uses multi-baseline observations, i.e. the antenna passes several times over an area but at different heights, allowing via different incidence angles the retrieval of structural information of specific targets.","name":"Synthetic Aperture Radar (SAR) acquisition modes","selfAssesment":"Completed
\r\n\r\n"},{"code":"PS2-2-3-4","description":"Swath width refers to the width of the ground that the satellite collects data from on each orbit. The area imaged on the surface, is referred to as the swath. Imaging swaths for spaceborne sensors generally vary between tens and hundreds of kilometres wide.","name":"Swath","selfAssesment":"
New
"},{"code":"PS2-2-3","description":"Spaceborne platforms and systems are present at a great height from the earth surface. The altitude of platforms range from few hundred kilometers to several thousand kilometers. A large area can be captured in a single scene depending on altitude of sensor. The platforms can have different characteristics.","name":"Spaceborne platforms and systems","selfAssesment":"Planned
"},{"code":"PS2-3-1","description":"Field spectroscopy generally refers to the use of non-imaging spectrometers near the ground surface and it is usually aimed at evaluating spectral reflectance of the investigated target. For this purpose, consecutive measurements of total incident solar irradiance and of radiance or irradiance upwelling from the target are collected by an operator, or more recently by new instruments for long-term and unattended field spectroscopy measurements. The incident irradiance is usually computed by measuring the radiance upwelling from a white calibrated panel which represents the ideal Lambertian surface. Upwelling fluxes are instead usually collected holding the sensor vertically over the surface (nadir view), although spectral libraries collected observing the target from different viewing angles are also available. \r\nField spectrometry is also referred to as ‘proximal sensing’ to underline that spectra are collected with portable spectroradiometers in the vicinity of the target, in contrast to ‘remote sensing’, which is instead usually performed with satellite or airborne sensors.\r\nField spectroscopy is therefore an in-situ method for characterising the reflectance of natural or artificial surfaces and thereby provides reference data for the calibration or validation (cal/val) of airborne and satellite sensors. This method provides a means of scaling-up measurements from small areas (e.g. leaves, rocks) to composite scenes (e.g. vegetation canopies), and ultimately to pixels.\r\nField spectroscopy is used in different applications, for example, soils, rocks, vegetation and chlorophyll fluorescence, water, snow surfaces and atmosphere. Long-lasting field spectroscopy campaigns based on manual measurements are extremely resource-demanding and do not ensure repeatability of the acquisition conditions as the instrument setup is initialized each day. To overcome such limitations a few research groups have initiated automatic tower-based spectral reflectance measurements using different devices. With such setups, non-imaging spectrometers are installed in the field and are operated automatically for long periods (i.e. months to years) and different networks of hyperspectral instruments are now becoming operational (e.g. RadCal Net).\r\nField spectroscopy can be also used to predict optimum spectral bands, viewing configuration, spectral calibration and time to perform a particular remote sensing task but also to develop, refine and test models relating biophysical attributes to remotely-sensed data. In this context, ground reflectance measurements are therefore mainly used as input in simulation study for sensor design, calibration/validation data for remote sensing sensors, for spectral mixture analysis and for the development of relationships between field data and radiometric variables.\r\nSince spectroscopy is the study of matter using electromagnetic radiation, point or imaging field spectrometers are instruments which allow the measurements of reflected or emitted electromagnetic radiation. In particular, portable or hand-held spectroradiometers are small instruments that spectrally measure the radiation reflected or emitted by a target and they are useful in obtaining accurate spectral data over different surfaces. In remote sensing, they generally cover the 400-2500 nm spectral range and operate with a full width at half-maximum of about 1.5/3 nm, so that they can collect radiation in a continuous way across the spectrum. The final output is therefore the hyperspectral signature of reflectance of the surfaces versus the considered wavelength.","name":"Field spectroscopy and portable spectroradiometers","selfAssesment":"Completed
"},{"code":"PS2-3-2","description":"Terrestrial Laser Scanning (TLS) is a ground-based, active imaging method that rapidly acquires accurate, dense 3D point clouds of object surfaces by laser range finding.\r\nA terrestrial laser scanning (TLS) system is a stationary highly accurate ranging device for geodetic surveying. More specifically, TLS systems provide dense and accurate 3D point cloud data for the local environment and they may also reliably measure distances of several tens of meters. Due to these capabilities, such TLS systems are commonly used for applications such as city modeling, construction surveying, scene interpretation, urban accessibility analysis, or the digitization of cultural heritage objects. When using a TLS system, each captured TLS scan is represented in the form of a 3D point cloud consisting of a large number of scanned 3D points and, optionally, additional attributes for each 3D point such as color or intensity information. However, a TLS system represents a line-of-sight instrument and hence occlusions resulting from objects in the scene may be expected as well as a significant variation in point density between close and distant object surfaces. Thus, a single scan might not be sufficient in order to obtain a dense and (almost) complete 3D acquisition of interesting parts of a scene and, consequently, multiple scans have to be acquired from different locations.","name":"Terrestrial Laser Scanning","selfAssesment":"In progress
"},{"code":"PS2-3","description":"Platforms and systems that acquire data from the level of earth's surface. A wide variety of ground based platforms are used in remote sensing. The acquired data are used for detailed in-situ measurements, e.g., Leaf Area Index (LAI), and for calibration/validation campaigns.","name":"Ground platforms and systems","selfAssesment":"New
"},{"code":"PS2","description":"Remote sensing platforms and systems can be static (ground-based platforms) or moving (e.g. airborne or spaceborne platforms, UAVs). A remote sensing platform or system carry a remote sensing sensor. It can operate in near (few centimetres) or far (36,000 kilometres) altitudes ranges.","name":"Types of remote sensing platforms and systems","selfAssesment":"Planned
"},{"code":"PS3-1","description":"The development of remote sensing data carriers has followed the evolution of the photography, remote sensing sensors and computer platforms. The first remote sensed data was stored using the photography films (e.g. aerial photography, satellite Corona program), which was later replaced by reel tapes, cartridge, and then removable and hard discs. In the era of big and fast growth of Earth observation data, and technological advancements in digital infrastructure, the satellite data are stored using cloud platforms providing different service models: Infrastructure as a Service, Platform/Software as a Service (e.g. Copernicus DIAS, Google Earth Engine, open EO). The Cloud offers infrastructure to host, store and process the large amount of data efficiently. For example, the Copernicus Data Information Access Services (DIAS) is a comprehensive cloud-based hosting and processing system for the EO data in particularly for the Sentinels data, the Google’s Earth Engine (GEE) provides access to various satellite and offers processing power with a web-based programming interface, the Amazon Web Services (AWS) has dedicated cloud called ‘Earth on AWS’, the Microsoft’s cloud called Azure facility the use of AI tools to address environmental challenges. Public solutions, as well as private ones, react with a variety of new and innovative tools, which have been recently developed (e.g. DIAS, ODC, EarthServer, EO Browser, GEE).","name":"History of remote sensing data carriers","selfAssesment":"Completed
"},{"code":"PS3-2-1","description":"The picture elements are pixels and each pixel has a specific value (usually in grayscale). Image pixels are normally square and represent a certain area on an image. It is important to distinguish between pixel size and spatial resolution - they are not interchangeable. If a sensor has a spatial resolution of 20 metres and an image from that sensor is displayed at full resolution, each pixel represents an area of 20m x 20m on the ground. In this case the pixel size and resolution are the same.","name":"Picture element (pixel)","selfAssesment":"In progress
"},{"code":"PS3-2-2","description":"An image is an array, or a matrix, of square pixels (picture elements) arranged in columns and rows. In a (8-bit) greyscale image each picture element has an assigned intensity that ranges from 0 to 255.","name":"Image as a matrix (digital number DN)","selfAssesment":"In progress
"},{"code":"PS3-2-3","description":"In data manipulation contexts, a data cube is a multi-dimensional array of values. A data cube can be visualized as the multidimensional extension of two-dimensional table. It can be viewed as a collection of identical 2-D tables stacked upon one another. Data cubes are used to represent data that is too complex to be described by a traditional table of columns and rows. Typically, the data cube is applied in conditions where these arrays are massively larger than the hosting computer’s main memory, for example multi-terabyte data warehouses o time series of image data.","name":"Data cubes","selfAssesment":"New
"},{"code":"PS3-2-4","description":"Term Big data refers to any collection of data sets so large and complex that it becomes difficult to process using on-hand data management tools or traditional data processing applications. In the field of Earth Observation (EO) is usually refers to large time series of image data which size on disk is much greater than hosting computer’s main memory. EO Big Data offers solution that allows not only storing these data on disk but also efficiently process them.","name":"Earth Observation Big Data","selfAssesment":"New
"},{"code":"PS3-2","description":"Most remote sensing data exist as digital images, and appropriate image processing allows the emphasis of certain aspect and subsequent extraction of information for specific applications.\r\nA digital image is a representation of the reality as a grid of picture elements. It can be considered as an array of numbers that can be stored and handled by a digital computer. The picture elements are pixels and each pixel has a specific value (usually in grayscale). This value is a digital number (DN), which usually represents the amount of energy recorded by the sensor at this pixel position or any other characteristic recorded by the sensor, e.g. elevation. \r\nEach row of the image grid, or matrix, corresponds to one scan line. Each pixel is characterized by its row r and column c position in the image, as well as by its value. Additional geographical information is needed in order to assign a geographic location to a pixel. The digital number are integers usually compressed in one byte (= 8 bit) representation, i.e. each pixel can take 256 values.\r\nDigital images are raster data, as opposite to vector data. Whereas vector data can be points, lines or polygones, raster data always consist of pixels. A pixel is the smallest element in which an image can be divided into. The pixel size varies depending of the instrument and of the sampling used. Large pixel may contain information about several objects of the recorded scene. However, they only have one value. These are called mixed-pixel, as e.g. several land cover classes are represented within one pixel and they cannot be distinguished from another. \r\nIn multispectral imagery, each region of the electromagnetic spectrum is recorded in an independent image (band). Therefore, at a specific array position (r,c), there exist several pixels, each with a specific value corresponding to the energy recorded for the considered band. This result in a three-dimensional matrix. The bands of a multispectral image can be displayed three at a time in the computer using for each band one of the three primary colors red, green and blue (RGB). This is called a color composite image. If the color composite represents a combination of the visible red, green and blue bands in their respective color, the combination is called natural or true color composite, as it corresponds to what the human eye sees naturally. Any other combination, for example considering bands of wavelengths that are not visible for the human eye is called a false color composite. It is often used to highlight the spectral differences and particular image features in order to extract information.","name":"Digital image terminology","selfAssesment":"Completed
"},{"code":"PS3-3-1","description":"Band interleaved by line (BIL) is one of three primary methods for encoding image data for multiband raster images in the geospatial domain, such as images obtained from satellites. BIL is not in itself an image format, but is a scheme for storing the actual pixel values of an image in a file band by band for each line, or row, of the image. For example, given a three-band image, all three bands of data are written for row one, all three bands of data are written for row two, and so on. The BIL encoding is a compromise format, allowing fairly easy access to both spatial and spectral information. The BIL data organization can handle any number of bands, and thus accommodates black and white, grayscale, pseudocolor, true color, and multi-spectral image data.","name":"Band interleaved by line (BIL)","selfAssesment":"New
"},{"code":"PS3-3-2","description":"Band interleaved by pixel (BIP) is one of three primary methods for encoding image data for multiband raster images in the geospatial domain, such as images obtained from satellites. BIP is not in itself an image format, but is a method for encoding the actual pixel values of an image in a file. Images stored in BIP format have the first pixel for all bands in sequential order, followed by the second pixel for all bands, followed by the third pixel for all bands, etc., interleaved up to the number of pixels. The BIP data organization can handle any number of bands, and thus accommodates black and white, grayscale, pseudocolor, true color, and multi-spectral image data.","name":"Band interleaved by pixel (BIP)","selfAssesment":"New
"},{"code":"PS3-3-3","description":"A binary raster file format for aerial photography, satellite imagery, and spectral data. The BSQ data organization can handle any number of bands, and thus accommodates black and white, grayscale, pseudocolor, true color, and multi-spectral image data. Additional information is needed to interpret the image data, such as the numbers of rows, columns, and bands, if there is a color map, and latitude and longitude to relate the image to geospatial locations.","name":"Band sequential (BSQ)","selfAssesment":"New
"},{"code":"PS3-3","description":"In order to properly process remotely sensed data, the\tanalyst must know how\tthe data is organized and stored. Data storage consists of methods of organizing image data for multiband images.","name":"Data storage","selfAssesment":"New
"},{"code":"PS3-4-1","description":"Spectral resolution describes the ability of a sensor to define fine wavelength intervals. The narrowest spectral interval that can be resolved by an instrument. Spectral resolution (spectral capability) also refers to the number of wavebands within the EM spectrum that an optical sensor is taking measurements over.","name":"Spectral resolution","selfAssesment":"Planned
"},{"code":"PS3-4-2","description":"The spatial resolution of an image corresponds to the size of the minimum area that can be resolved by the sensor. \r\nDue to the different techniques of acquisition of passive and active sensors, the spatial resolution is determined for both sensor types differently. \r\nFor passive sensors, the spatial resolution depends on their instantaneous field of view (IFOV), which determines the area of the Earth’s surface that is viewed at one particular moment in time by one detector element. The size of this area is called resolution cell and characterizes the spatial resolution of the sensor. Depending on the spatial resolution, whole features of the Earth’s surface can be detected homogeneously in one or several resolution cells. For features smaller than the spatial resolution, the average reflected radiation of all features within a resolution cell is recorded, leading to so-called mixed-pixels.\r\nFor imaging active systems, the spatial resolution is dependent of both the length of the transmitted pulse in looking direction and the width of the radiation beam or the antenna width in flight direction.\r\nIn all cases, the spatial resolution indicates the level of detail observable in an image. Usually, one distinguishes between coarse (low), moderate (medium) and fine (high and very high) resolution, whereby the use of this denomination is often context-dependent. Sensors with coarse resolution can only detect large features, but they usually cover a much larger area than high-resolution sensors, which can provide detailed information on small objects such as individual buildings, trees or cars, but for much smaller areas. Coarse spatial resolution mean in general a resolution cell larger than 250 m and a scene extent of several thousands of kilometers (>1000 km). Moderate resolution sensors have a spatial resolution of 30 m to 80 m, and a coverage of approximately 200 km in a single acquisition. Sensors showing spatial resolutions from 5 m or 6 m are high-resolution sensors, with a spatial coverage up to approximately 20 km. Sensors with a resolution cell’s width of less than 1 m are considered as very-high-resolution sensors.\r\nLow resolution sensors are appropriate for the analysis of broad-scale phenomena such as ocean color or cloud patterns. Medium resolution sensors are rather used for regional analysis such as land cover change and phenological response of vegetation. High-resolution sensors are particularly useful for object detection.","name":"Spatial resolution","selfAssesment":"In progress
"},{"code":"PS3-4-3","description":"Radiometric resolution can be defined as the ability of an imaging system to record many levels of brightness. Radiometric resolution is defined as the sensitivity of a remote sensing detector to differences in signal strength as it records the radiant flux reflected, emitted, or back-scattered from the terrain. Radiometric resolution refers to the range in brightness levels that can be applied to an individual pixel within an image, determined on a grayscale. E.g., Sentinel-2 sensor MSI is a 12 bit sensor imaging with 4.096 levels.","name":"Radiometric resolution","selfAssesment":"Planned
"},{"code":"PS3-4-4","description":"Temporal resolution, also referred to as the revisit cycle, is defined as the amount of time it takes for a satellite to return to collect data from exactly the same location on the Earth. Imageing of the exact same area at the same viewing angle a second time is temporal resolution.","name":"Temporal resolution","selfAssesment":"New
"},{"code":"PS3-4","description":"A digital image begins as an analog signal. Through computer data processing, the image becomes digitized and is sampled multiple times. The critical characteristics of a digital image are spatial resolution, spectral resolution, radiometric resolution, contrast resolution, noise, and dose efficiency. These depends upon satellite orbit configuration and sensor design. Different sensors have different resolutions.\r\nSpectral resolution describes the ability of a sensor to define fine wavelength intervals. The narrowest spectral interval that can be resolved by an instrument. Spectral resolution (spectral capability) also refers to the number of wavebands within the EM spectrum that an optical sensor is taking measurements over.\r\nRadiometric resolution can be defined as the ability of an imaging system to record many levels of brightness. Radiometric resolution refers to the range in brightness levels that can be applied to an individual pixel within an image, determined on a grayscale. E.g., Sentinel-2 sensor MSI is a 12 bit sensor imaging with 4.096 levels.\r\nSpatial resolution of an image corresponds to the size of the minimum area that can be resolved by the sensor.\r\nTemporal resolution, also referred to as the revisit cycle, is defined as the amount of time it takes for a satellite to return to collect data from exactly the same location on the Earth. Imageing of the exact same area at the same viewing angle a second time is temporal resolution.","name":"Properties of digital imagery","selfAssesment":"Completed
"},{"code":"PS3-5-1","description":"The header is a section of binary- or ASCII-format data normally found at the beginning of the file, containing information about the bitmap data found elsewhere in the file. The format of the header and the information stored in it varies considerably from format to format and contains fixed fields.","name":"Header file","selfAssesment":"Planned
"},{"code":"PS3-5","description":"The image data stored in a binary data format (BIL, BIP, BSQ) is accompanied by description files that contain a set of entries describing the image data, including acquisition time, image size, statistics, map projection, pixel digital numbers, product type, etc. This general image or product information is stored in a form of header embedded in the image file or provided in the separate file (.hdr) or metadata in XML. There are numerous image file formats, the more common are TIFF (GeoTIFF), bitmap (.bmp), JPEG (.jpg, .jpeg, JPEG2000), HDF, Raw (.raw), Extensible N-Dimensional Data Format (NDF).","name":"Image description files","selfAssesment":"In progress
"},{"code":"PS3-6","description":"Remote Sensing data formats in which the data are organized and stored. The data format for a remote sensing mission is usually chosen based on a number of considerations, including requirements of the sensing system, mission objective, the design and technology of data processing, archiving, and distribution systems, as well as community data standard.","name":"Data formats","selfAssesment":"Planned
"},{"code":"PS3-7-1-1","description":"Depending on the sensor and the provider, remotely sensed imagery is made avalilable to the user at different processing levels. For Sentinel-2, the lowest product level made available to the user is Level-1B. THe Level-1B product provides radiometrically corrected imagery in Top-Of-Atmosphere (TOA) radiance values and in sensor geometry. Radiometric corrections applied to the Level-1B are: dark signal, pixels response non uniformity, crosstalk correction, defective pixels interpolation, high spatial resolution bands restoration (deconvolution puls denoising), binning (spatial filtering) for 60m bands.","name":"Radiometrically corrected","selfAssesment":"New
"},{"code":"PS3-7-1-2","description":"Geometrically corrected products are of a higher processing level than radiometrically corrected products. For Sentinel-2, the geometrically corrected product is the Level-1C product. The Level-1C product results from using a Digital Elevation Model (DEM) to project the image in cartographic coordinates. Per-pixel radiometric measurements are provided in Top Of Atmosphere (TOA) reflectances with all parameters to transform them into radiances. Level-1C products are resampled with a constant Ground Sampling Distance (GSD) of 10, 20 and 60 m depending on the native resolution of the different spectral bands. Level-1C products will additionally include Land/Water, Cloud Masks and ECMWF data (total column of ozone, total column of water vapour and mean sea level pressure). (Sentinel-2 User Handbook, p.44)","name":"Geometrically corrected","selfAssesment":"New
"},{"code":"PS3-7-1","description":"The definition of processing levels for optical data depends on the considered sensor. Most common satellite optical imagery are available in three distinct processing levels, from level 0 to level 2. The most used processing levels are level 1 and level 2, depending on the user and the application. \r\nIn Level 0, the raw data are processed in a way that they are ready to be archived. Processing operations generally includes telemetry analysis, error detections and granule concatenation. Furthermore, relevant parameters such as acquisition date and geographical reference are annotated in the form of metadata, this information being necessary for processing higher levels. Additionally, a quicklook of the image is generated. No correction is performed at this level.\r\nLevel 1 is often divided in several sublevels. Generally, both radiometric correction and geometric refinement are performed at this level. The radiometric processing includes several radiometric corrections such as dark signal correction or spectral band binning. The radiometric correction allows the determination of physical variables (e.g. reflectance) from the digital numbers. The geometric processing includes tiles association and resampling grid computation, in order to link for each image band its native image geometry to the target geometry. The result of this processing steps is usually a geocoded, Top of Atmosphere product.\r\nLevel 2 data usually consist of atmospherically corrected Level 1 data, i.e. Bottom-of-Atmosphere data. These surface reflectance products may be accompanied by additional outputs, such as scene classification, water vapor or surface temperature maps.\r\nFor specific applications and sensors, Level 3 application ready data are available. These are derivated products such as burned area, dynamic surface water content and snow cover maps.\r\nDepending on the considered sensor and level, the name of the sublevels can differ: Sentinel 2 defines Level-1B as radiometrically corrected data. Level 1C are radiometrically and geometrically corrected data, i.e Top-Of-Atmosphere (TOA) orthoimage products. Landsat sensors distinguish between Terrain precision correction (L1TP), systematic Terrain Correction (L1GT) and Geometric systematic Correction (L1GS) depending on the quality of the reference data for geometric correction. These are usually separated into Tier 1 and Tier 2 datasets.","name":"Processing levels of optical data","selfAssesment":"Completed
"},{"code":"PS3-7-2-1","description":"The Single Look Complex SAR format is a single look product of the focused signal. It means that the azimuth compression has been carried out using the full azimuth bandwidth and therefore contains the highest azimuth spatial resolution and at the same time, it suffers from maximum speckle. The data are in the radar geometry, i.e., in slant range coordinates, not projected onto any reference surface. Each pixel of the SLC product is a complex number. (i.e., has a real and imaginary component) that represents the amplitude and phase.","name":"Single Look Complex (SLC)","selfAssesment":"New
"},{"code":"PS3-7-2-2","description":"From the Single Look Complex (SLC) product the Multi-look Detected/Multi-looke (MLD/MLI) can be generated. It is produced by multi-looking, i.e., averaging, over range and/or azimuth resolution cells.","name":"Multi-looked Detected (MLD)","selfAssesment":"New
"},{"code":"PS3-7-2-3","description":"Precision Images (PRI) are the Multi-look Detected/Multi-looked Intensity (MLD/MLI) images that have been resampled into square pixels, rotated to account for the view direction of the instrument and warped by some predefined operation that the projected image pixels are georeferenced onto a specified geographical coordinate system.","name":"Precision Images (PRI)","selfAssesment":"New
"},{"code":"PS3-7-2-4","description":"Before performing multi-looking, the Single Look Complex (SLC) slant-range geometry is projected onto ground. This kind of product, i.e., in ground range geometry, is known as a Ground Range Detected (GRD), e.g., product of the Sentinel-1 mission.","name":"Groud Range Detected (GRD)","selfAssesment":"New
"},{"code":"PS3-7-2","description":"For SAR data, usually three processing levels are distinguished, ranging from level 0 (less processed) to level 2 (higher processed).\r\nLevel 0 products consist of compressed and unfocussed raw data and are the basis for the processing of higher level products. Level 0 data are principally used for research in the topic of signal processing. As for optical data, level 0 product are annotated with several metadata, such as calibration and orbit information, and acquisition time and date.\r\nLevel 1 data can be separated in two distinct product types, depending if the full complex information is used (amplitude and phase) or only the amplitude information. The product denomination depends on the sensor type; for Sentinel 1 the names Single Look Complex (SLC) and Ground range detected (GRD) are used, respectively. Both products can be generated from the Level 0 data. Level 1 data are the products that are used by most scientific users. The processing toward Level-1 data includes Doppler centroid estimation and data focusing. The Level 1 SLC product consists of the real and imaginary part of focused complex SAR data in slant range geometry, from which the phase and amplitude information can be retrieved. This is available for all acquired polarisations. Additional orbit information for georeferencing is provided with the data. The Level 1 GRD data consist of focused and multi-looked SAR data that have been projected to ground range geometry. GRD data only contain amplitude information, therefore the phase information is lost. The multi-looking step is particular for GRD data and allows both speckle reduction and square pixel resolution. As for the SLC data, the GRD data are annotated with orbit information for georeferencing. The Level-1 products are not calibrated, they include however information about calibration constants, which are sensor dependent. Further processing is needed in order to obtain calibrated radar cross section information from the original data intensity values.\r\nLevel 2 products describe geolocated derivated geophysical products such as ocean wind field or surface radial velocity. Such products are for example available for download on the Sentinel-1 Copernicus Hub. Further Level- 2 data are for example differential interferograms or change maps, which can be processed on different online platforms (e.g. Hyp3) and provide information about surface deformation or more generally changes between several acquisitions.\r\nThe denomination of the product types on the different levels may differ from sensor to sensor, but the processing steps stay almost the same, depending additionally on the considered acquisition modes. For example, GRD products are also called for other sensors Multi-Looked Detected (MLD) products.","name":"Synthetic Aperture Radar (SAR) data","selfAssesment":"Completed
"},{"code":"PS3-7-7","description":"Data that have been processed to allow direct data analysis. User processing effort is reduced to a minimum.","name":"Analysis Ready Data (ARD)","selfAssesment":"New
"},{"code":"PS3-7","description":"Earth Observation data are usually made available in different processing levels. The processing level is a mean of describing how much the raw data have been processed toward an informational geophysical product. The degrees of data processing usually follow a numerical hierarchy and typically range from Level 0 (less processed) up to Level 4 (highly processed). They characterize the type of data processing that has been performed between the raw data and the current product.\r\nA first effort for providing standard definitions of different processing levels has been made in the 1980s by the Committee on Data Management and Computation (CODMAC) of the National Research Council (NRC). CODMAC identified eight levels of processing, applicable for all space science data. Starting with the raw data at level 1, the degree of processing and complexity of the data increased at each new level. Level 2 describes edited data, corrected for obvious instrumentation errors and tagged with acquisition time and location; Level 3 stays for calibrated data where values are proportional to a specific physical unit. Level 4 represents resampled data, Level 5 derived data, where specific geophysical information has been retrieved and mapped based on the original data. Level 6 represents all ancillary data (i.e. instrument data) that are necessary for the previous steps of calibration and resampling. Level 7 describes so called correlative data: not directly belonging to the original data, those data represent all other science data that where necessary for the interpretation of the original spaceborne dataset. Finally, Level 8 are user description, i.e. documentation of the data.\r\nConcerning spaceborne image data, both optical and radar, an adaptation of these original levels has been made from NASA and NOAA that is used for the main current spaceborne missions, including the Copernicus program. Whereas specific adaptations may arise for specific sensors and sensor types, there are five principal processing levels. Level 0 represents the raw data that have just been edited for the correction of artifacts. Level 1 data are Level 0 data with additional annotations regarding time and geolocation information, radiometric and geometric calibration coefficients (for example Top of Atmosphere data for optical imagery). Level 2 data are already radiometrically and geometrically calibrated and represent physical variables (for example Bottom of Atmosphere data for optical imagery). Level 3 data correspond to derived variables and information (e.g. land cover) with completeness and consistency information, e.g. quality flags. Level 4 represent higher level data resulting from modelling or more complex analysis of the data with additional ancillary information.\r\nFor many applications and users, so called analysis ready data (ARD data) are required. These usually correspond to Level 2 data that have already been pre-processed in order to retrieve the physical information and can be further analyzed for the specific thematic application.","name":"Processing levels","selfAssesment":"Completed
"},{"code":"PS3","description":"Remotely collected data is available from multiple sources and data collection techniques. Data can be obtained from different levels of data acquisition: ground, air or space, as well as using different sensors and wavelengths. Remote sensing data provides the necessary information to help monitor the Earth's surface.","name":"Remote sensing data and imagery","selfAssesment":"Planned
"},{"code":"PS4","description":"The listed databases provide information on past, operational and future remote sensing platforms and sensors. Use the following links to get more information on the sensors and missions.","name":"Databases of satellite and airborne sensors and missions","selfAssesment":"Completed
"},{"code":"SD","description":"Based on Waldo Tobler`s first law of geography( Tobler, 1970), this property is set on the principle that \"everything is related, but that which is closer is more closely related\".","name":"Spatial dependency","selfAssesment":"In progress (GI-N2K)
"},{"code":"SH","description":"This principle, as set forth by Anselin, determines that \"expectations vary along the earth`s surface\" which means that any spatial analysis is dependent explicitly on the borders of study fields, i.e. the tracing of (spatial) analysis units.","name":"Spatial heterogeneity","selfAssesment":"In progress (GI-N2K)
"},{"code":"TA","description":"This area of knowledge deals with the use of EO / GI techniques and data in different themes and areas of application. It includes the user community of EO services and applications, societal and environmental challenges, EO services and applications, and standard EO products that are made available to users.","name":"Thematic and application domains","selfAssesment":"Planned
"},{"code":"TA11-1-1","description":"The EO/GI users in agriculture are active in Agricultural commodities/Trading, agricultural production / Horticulture, Agricultural services, Agriculture machinery, Agriculture and Rural Development Policy, Agro chemicals / Plants & Fertilizers, Animal production / Livestock. The EO/GI users also include agriculture and rural policy makers. \r\nThey benefit from EO information, for example, by managment support for their crop production through forecasting crop yield, assess risks of damage/loss because of storms, disease or other stress factors, and water monitoring. Use in agriculture: knowledge and information products to forge a viable strategy for farming operations. Understand the health of his crop, extent of infestation or stress damage, or potential yield and soil conditions","name":"Users in agriculture","selfAssesment":"New
"},{"code":"TA11-1-2","description":"The users in fishing are active in Fish stock management, Fishing fleets, Fishery distribution logistics, Aquaculture / fish farms, Coastal management agencies. In addition, the users include Fisheries authorities / policy makers. \r\nThe marine environment in particular is relevant to fishing. Fishing fleets move to the fishing grounds to catch fish. Finding them is challenging. However, fish shoals can be directly visible from above. Navigating to the fishing grounds can be risky: Coastline and shallows may pose a risk to ships. Additionally, skippers may have to deal with challenging weather conditions at sea. Environmental threats to the fishing grounds are oil slicks and other types of pollution. A problem from an economical perspective and for adhering to catch quota is illegal fishing. Noumerous opportunities exist to support fishing with EO information.","name":"Users in fishing","selfAssesment":"New
"},{"code":"TA11-1-3","description":"The users in forestry are active in Forest management, Forest Services, Commodities, Logging industry, Wood, paper and pulp industry, Forest policy, Forest machinery. They also include Forest Policy makers.\r\nUse in forestry: Understand depletion due to natural causes (fires and infestations) or human activity (clear-cutting, burning, land conversion), and monitoring of health and growth for effective commercial exploitation and conservation.\r\nForests are a resource that is harvested all over the Globe for different purposes like construction or heating. Additionally, the forests represent an ecosystem that provides various ecosystem services. Proper management is a key to a healthy forestry industry that has to be aligned well with global environmental management activities. There is a need to avoid deforestation and forest degradation, keep the environmental impact of forestry within bounds, be aware of changes in the carbon balance. Economically relevant is especially a good understading of forest types, forest damage due to storms or insects, as well as wildfires. A threat to the environment results from illegal forest activities.","name":"Users in forestry","selfAssesment":"New
"},{"code":"TA11-1","description":"Users in managed living resources refer to human activities exploiting natural organic resources. Knowledge and information products to forge a viable strategy for the user’s operations such as the assessment of the status of the resource due natural or human activity for effective commercial exploitation and conservation. This includes agriculture, fishing and forestry occupations for our society.","name":"Users in managed living resources","selfAssesment":"New
"},{"code":"TA11-2-1","description":"The users in alternative energy consist of Solar energy providers, Wind energy providers, Tidal energy providers, Hydroelectric energy providers, Energy and Carbon traders, Local and regional planners, and National policy makers. Energy providers need information about the state of the environment to make the most use out of natural resources. Planners and policy makers have to weigh up whether and which type of alternative energy is justifiable and sensible for a specific region.\r\nEO data can be used to build maps that show resource information. For solar energy, those maps contain information about solar radiation, but also shadowing effects. Forecast products for irradiance are available to be able to plan the energy production for the coming days. Tidal waves can be depicted by sea surface heights. As tidal currents are periodical, they can be predicted well by the initial state of sea surface heights. In addition, also the speed of tidal waves can be determined by EO measurements. In the wind energy sector EO data is analysed to plan and monitor wind farms. Maps can show areas, where winds are suitable for wind energy production. After the construction of a wind farm, wind strength and direction during operation can be monitored. Finally, for hydroelectric power stations EO is used to monitor water reservoirs. As well hydrometeorological data is used to forecast water-related events and to monitor drought or floods.","name":"Users in alternative energy","selfAssesment":"Completed
"},{"code":"TA11-2-2","description":"The EO/GI user community in oil & gas consists of offshore exploration and production, on-shore exploration and production, drilling and support services, oil and gas commodities trading, and energy planners. Due to their activities both on-shore and offshore their need for EO-derived information about the land, the ocean and the atmosphere. They need EO-derived information about geological features (for exploration), for asset infrastructure monitoring, construction and buildings. Safe offshore operations (ocean&atmosphere: forecast and monitoring current movement and drift, monitor sea-ice and icebergs, detect and monitor hurricanes and typhoons; land: map and assess flooding, detect wildfires . A large set of information needs results from their need to adhere to environmental regulations. They have to assess and monitor their environmental impact, ocean quality and productivity, land ecosystems and biodiversity, groundwater and run-off \r\nMany problems faced by oil, gas, including the selection and development of exploration areas, detection and mapping of illegal mining activities, or monitoring dams, pipelines and terrain movements, can be efficiently addressed by extracting information from geospatial imagery. Remote Sensing based applications reduce the need for field work, minimize environmental impacts, and ultimately safe costs, to help achieve results faster during exploration, extraction, and remediation/reclamation stages.","name":"Users in oil & gas","selfAssesment":"New
"},{"code":"TA11-2-3","description":"The EO/GI community in minerals and mining consists of mining and quarrying companies, exploration and survey specialists, commodities traders, exploration and extraction equipment suppliers, drilling, excavation and support services, and regional planners / policy makers.\r\nTypical spatial questions for the users in minerals and mining are concerned with prospecting, e.g. \"Where can we find the minerals that are worth exploitation?\", and operation of mining sites: \"How much material has already been excavated in the mine and how much material was deposited in dedicated dump areas?\". Additionally relevant are arising risks through mining activities, e.g. \"How do the mining activities affect settlements in the vicinity?\" or \"How do the mining activities affect the environment?\". Concequently, the EO/GI users in minerals and mining benefit from EO information through mapping geological features, monitor mineral extraction, measure land use statistics, assessing environmental impact of human activities, detect and monitor ground movement, and monitor land pollution.","name":"Users in minerals & mining","selfAssesment":"New
"},{"code":"TA11-2","description":"Users in energy and mineral resources deal with the harvesting of energy from renewable resources and extractive industries including oil and gas and raw materials. EO information helps them in exploring locations where to build new mines or power plants, in identifying risks from infrastructure and in managing the environmental impact of their operations.\r\nUses that apply to the extractive industries: study of landforms, structures, and the subsurface, to understand physical processes creating and modifying the earth's crust. EO/GI should play a key role to transform data into information and knowledge about the potencial feasibility and viability of renewable resources, in particular solar and wind at the natural and urban ecosystems, and in particular to support Sustainable Development Goals SDG 7 Affordable and Clean Energy and SDG 11 Sustainable Cities and Communities.","name":"Users in energy and mineral resources","selfAssesment":"New
"},{"code":"TA11-3-1","description":"EO/GI users in construction include construction companies, civil engineering consultancies, architect and design companies, planning authorities, and national land agencies. \r\nThey benefit from EO through monitor building development, assess environmental impact of human activities, map and assess flooding, detect land movement, subsidence, heave, and monitor land-use statistics","name":"Users in construction","selfAssesment":"New
"},{"code":"TA11-3-2","description":"Utilities (water, electricity, waste): Power station operators, Water plants operators, Survey companies, Hydroelectric suppliers, Regulatory Bodies, Distribution companies, Landfill and waste, Regional planners / policy makers.\r\nThe benefit from EO information that monitor pollution in rivers and lakes, assess changes in the carbon balance, assess environmental impact of human activities, monitor land pollution, assess changes to urban and rural areas, assess and monitor water quality, assess ground water and run-off.","name":"Users in utilities & supplies","selfAssesment":"New
"},{"code":"TA11-3-3","description":"Users of EO/GI in communications and connectivity are mostly mobile telecommunications providers and fixed telecommunication providers. Theire business is to connect people via telephone and internet. The assets for their services include the infrastructure of communication networks physically installed in the ground, the cellphone towers distributed over the land surface, particularly in higly populated areas, as well as other installations (e.g. company buildings) and equipment (communication satellites).\r\nSpecific spatial questions of these users are concerned with the reception quality that the network can provide in an area. The network coverage would neet to react to changes of the built environment. New settlement infrastructure may cause a new population distribution and subsequently the need to network adaptations to cover new areas or cover some areas with higher band widths because more people are living there. Additionaly, the coverage of cellphone antennas depends on the arrangement of environmental obstacles that degrade or block the radio signal. Any place where the built environment or the vegetation changes can change the reception quality within the covered area of an existing cellphone tower. \r\nThe benefit of EO information for the user group of communications and connectivity comes from monitoring building development, assessing changes to urban and rural areas, and mapping line of sight visibility (terrain height, land cover).","name":"Users in communications & connectivity","selfAssesment":"New
"},{"code":"TA11-3-4","description":"EO/GI users in transport and logistics include road transport operators, haulage, road infrastructure operators, tolls, airport operators, rail operators, airlines and airline services, and transport engineers.","name":"Users in transport & logistics","selfAssesment":"New
"},{"code":"TA11-3-5","description":"EO/GI users in marine include ports & harbors administration, bulk cargo carriers, cruise liners operators, ferry operators, naval operations, and rescue and safety at sea.","name":"Users in marine","selfAssesment":"New
"},{"code":"TA11-3-6","description":"From a conceptual point of view travelling is crossing the space from one location to another. Tourism mostly requires a travel to the desired destination and typically also includes moving inside a specific area. Therefore both tourism and travel are highly dependent on spatial phenomena which are often captured using EO.All kinds of travelling are highly dependent on weather conditions which can be observed with meteorological satellites. Also the current traffic conditions like congestion, road condition and natural hazards can be discovered with EO.\r\n\r\nThe types of tourism which are outside of buildings require sufficient weather forecast. Especially outdoor tourism at the coast or in mountain areas have a need for specific information about the current and the near future conditions of the natural environment. Examples are avalanche reports and forecasts for wind or wave heights of water bodies. Local tour organizers can utilise this information in order to better plan offers for tourists and also ensure overall safety during their stay.\r\n\r\nTourism and travelling are import economic factors. Consequently both the public and the private sector are interested in ensuring safe and convenient travel conditions and furthermore in creating an attractive environment for travellers and touristic visitors. This includes recognising environmental pollution, since this discourages tourist from visiting an area. Not only incoming, but also outgoing tourism is an important factor in local economies. Travel agencies, for example, are highly dependent on retrieving accurate information about foreign regions which are typically obtained with earth observation technology.\r\n\r\nOf course tourism and travelling itself also can be observed from space, this is especially true for mass tourism and areas where traffic has increased a lot during the last time. Typical effects are the increase of settlement area and the additionally used space for roads, parking lots, airports and harbors. These changes to the earth surface can be quantified with the help of land cover change detection.In many cases local administrations and decion makers want to mitigate the negative consequences of mass tourism, the insights of the mentioned EO measurements provide a useful foundation for sustainable planning.","name":"Users in travel & tourism","selfAssesment":"Completed
"},{"code":"TA11-3","description":"Users in transport and infrastructure apply to all manufacturing and physical supply in land but also marine domains including transport & logistics, utilities, construction, communication & connectivity, and tourism.","name":"Users in infrastructure & transport","selfAssesment":"New
"},{"code":"TA11-4-1","description":"EO/GI users in insurance and real estate include primary insurance companies, re-insurance sector, insurance brokers, insurance service suppliers, commercial banks, major projects, and international financial institutions. \r\nProduction processes (including primary production like farming), property and real estate are often insured against certain risks, e.g. from natural hazards. \r\nUsers benefit from EO information through applications that monitor building development, assess crop damage due to storms (including to forecast and map large waves), assess damage from earthquakes, detect and monitor wildfires, map and assess flooding, detect land movement, subsidence, heave, forecast and assess landslides.","name":"Users in insurance & real estate","selfAssesment":"New
"},{"code":"TA11-4-2","description":"EO/GI users in retail and geo-marketing include Retail centres and Advertising and Marketing agencies. They use EO/GI data in the field of Navigation and LBS, Shopping chains or Logistics.","name":"Users in retail & geo-marketing","selfAssesment":"New
"},{"code":"TA11-4-3","description":"Users in news and media are Television companies, Broadcasting providers, News and Information agencies, Web service providers, and Entertainment software providers. They benefit from monitoring, forecasting and assessing of natural risks/disasters.","name":"Users in news & media","selfAssesment":"New
"},{"code":"TA11-4-4","description":"Users in ICT include fixed and mobile telecommunications providers. They can make use of EO/GI data by monitoring building development and changes to urban areas.","name":"Users in ICT, knowledge and digital interfaces","selfAssesment":"New
"},{"code":"TA11-4","description":"Users in financial and digital services cover a broad area of activity that touches on many other market sectors such insurance & real estate, retail, news & media and digital interfaces. The categories included are identifiable as a “service” (tertiary sector: attention, advice, access, experience, and affective labour) and not part of the physical supply of goods.","name":"Users in financial & digital services","selfAssesment":"New
"},{"code":"TA11-5-1","description":"The users in smart cities include urban planners, architects, spatial planning offices, urban policy makers. The users benefit from EO information through map information about urban structures and related land use when managing land use, climate change adaptation, and urban green infrastructure. Typical use cases include Urban adaptation to climate change and Green infrastructure and its ecosystem services to increase quality of life of citizens (https://land.copernicus.eu/user-corner/land-use-cases)","name":"Users in smart cities","selfAssesment":"In progress
"},{"code":"TA11-5-2","description":"The users in local and regional planning include spatial planning departments of municipalities, spatial planning offices, and spatial planning policy makers. Land use management in densely populated areas involves negotiation of conflicting land-use demands for settlement, production system (including agriculture and forestry) and infrastructure. The users benefit from EO information to manage the use of land and its impacts.","name":"Users in local & regional planning","selfAssesment":"New
"},{"code":"TA11-5","description":"Users in urban development and users involved in the development of rural settlements perform tasks on local and regional scale (to the scale of nations). These users benefit from EO information to manage the use of land & its impacts. Users such as urban planners, architects, spatial planning offices, urban policy makers in public/private sectors in smart cities or generic urban local/regional planning belong to this category. EO/GI becomes a key data and information to support Sustainable Development Goals - SDG 11 Sustainable Cities and Communities in particular to set up at geospatial and temporal basis the evolution of urban environmental and socioeconomical factors for a better distribution and equality of resources, benefits and impacts (environmental urban justice maps)","name":"Users in urban development","selfAssesment":"New
"},{"code":"TA11-6-1","description":"Users in defense, security and military are border control organisations, police and rescue forces, military services, and intelligence services. Use of EO/GI data can be made in the field of detecting and monitoring high risk areas (natural and humanitarian), monitoring border incursions, or monitoring maritime movements.","name":"Users in defense, security & military","selfAssesment":"New
"},{"code":"TA11-6-2","description":"EO/GI users in emergency services are coast guards, ambulance services, fire services, police services, civil protection organisations, and rescue services. They benefit from monitoring, detecting and assessing natural risks/disasters.","name":"Users in emergency & social protection","selfAssesment":"New
"},{"code":"TA11-6-3","description":"The EO/GI users in humanitarian operations correspond to humanitarian aid organisations, humanitarian support organisations and overall humanitarian response such as border control organisations, police and rescue forces, coast guards, civil protection, military services, and intelligence services. They can use EO services to detect and monitor high risk areas produced naturally or by humans, monitor border incursions or maritime movements. They provide support to local populations that have experienced a crisis, e.g. they fled from a conflict or are affected by a natural disaster. The organisations therefore support the population's needs for sustenance. Consequently, any related risks are relevant as well. The users benefit from the EO capability to identify and monitor people in need, i.e. to assess pressures on populations and migration, and to monitor humanitarian movement and camps. They additionally benefit from EO through mapping disaster areas for situation awareness and detecting sensitive risk areas. Some examples of users at European level are DG RELEX, DG ECHO, DG ENV/ MIC. At UN, the users include OCHA, UNHCR, UNDPKO, UNDP, UNOPS, UNITAR, UNICEF, UNESCO, WFP. Further, international users include IFRC, WHO, WB, and donor organizations. At the national level, the users include Civil Protection Agencies, Ministries of Internal Affairs / Civil Protection Department, Development and Aid agencies.","name":"Users in humanitarian operations","selfAssesment":"New
"},{"code":"TA11-6","description":"Users in defence and security work in the field of military, emergency and social protection and define, collect, analyse information to provide intelligence & safety. Some examples are activities under humanitarian response such as border control organisations, police and rescue forces, coast guards, civil protection, military services, and intelligence services which can use EO services to detect and monitor high risk areas produced naturally or by humans, monitor border incursions or maritime movements.","name":"Users in defense & security","selfAssesment":"New
"},{"code":"TA11-7-1","description":"EO/GI users in environmental ecosystems & pollution include scientists, consultants, planners and policy makers with interest in environmental issues.","name":"Users in environmental ecosystems & pollution","selfAssesment":"New
"},{"code":"TA11-7-2","description":"Users in health care health-related services include services on site-specific field conditions as well as import phenological timing events, which helps to make predictions for monitoring air quality, forecasting epidemics and diseases, as well as forecasting sunlight exposure.","name":"Users in health care","selfAssesment":"New
"},{"code":"TA11-7-3","description":"EO/GI users in meteo and climate; use of satellite-based observations in addressing key climate science questions for user-centric climate change risk assessment applications or climate-related issues","name":"Users in meteo & climate","selfAssesment":"New
"},{"code":"TA11-7","description":"Users in the public administrations or private organizations using EO to assist environmental or climate change impact policy making decisions i.e, assisting in developing monitoring to evaluate and deliver policy goals, provide assessment of ecosystems, rapid response to major environmental risk events, or those associated health security & care events. These users are largely related with international treaties and hence a strong international collaboration. EO/GI becomes a key data and information to support Sustainable Development Goals (SDG) in particular in terms of environmental, climate and health towards SDG 11, SDG 13 Climate Action; SDG 14 Life Below Water; or SDG 15 Life on Land.","name":"Users in environmental, climate & health","selfAssesment":"New
"},{"code":"TA11-8-1","description":"EO/GI users of customer solutions; easier for society to use and engage with EO services through mobile devices, social media platforms, apps. Enormous potential to use citizen-driven observations in combination with EO data","name":"Users of consumer solutions","selfAssesment":"New
"},{"code":"TA11-8-2","description":"EO/GI users in leisure; basic public understanding on EO Services","name":"Users in leisure","selfAssesment":"New
"},{"code":"TA11-8-3","description":"The community of users in education includes instructors (1) who are teaching or conducting research in some aspect of GIScience, such as coding, remote sensing, field methods, geodetic control, web mapping, spatial analysis, or related topics, or (2) who are using GIS as a teaching tool in a discipline, such as business, biology, economics, or health sciences. By extension, this community includes students and supportive deans and other educational administrators. The benefits that these users gain from EO information includes a set of best practices vetted by experts in the field that they can use to teach modern GIS workflows more effectively. \r\nThe goals of this user community are focused on a deeper and a broader implementation of geotechnology, methods, and spatial data throughout the educational system—primary, secondary, university, and lifelong learning (libraries, museums, and other informal settings). Deeper implementation implies embracing GIS as a platform, including its field data gathering tools and citizen science workflows, spatial analysis, building web maps and apps, communicating with multimedia maps derived from web GIS, systems configuration work, and the coding that is behind modern GIS infrastructure. Broader implementation implies the use of GIS in a multitude of disciplines at all levels of education, formal and informal; occurring wherever changes over space and time are being examined. \r\nAt all levels of education the challenge of sufficient bandwidth and the use of a professional systems-based tool such as GIS, along with devices capable of running web GIS tools, are barriers in many areas throughout the world. However, educational and societal forces represent a stronger challenge than technological ones. These educational and societal challenges that this user community faces include the lack of educational content standards at the primary and secondary level that support the use of geotechnologies in education, and at the university level, a lack of awareness of and access to modern SaaS GIS tools and open data portals. \r\nThe risks that the community faces in not facing the challenge of the use of GIS in the education sector is a lack of geographic and spatial literacy among students and faculty. This will translate to research that does not consider spatiotemporal implications of 21st Century challenges, a workforce ill-equipped to deal with them, and consequently an increasingly unstable and dysfunctional world. To build a workforce that can meet global challenges in energy, biodiversity, climate, natural resources, natural hazards, human health, economic inequality, and others, a deep and wide implementation of GIS technology and methods must take place throughout the educational system. The actions that society can take to face that challenge is to provide professional development opportunities for faculty, curricular resources, assessment instruments, relevant spatial data and open data portals, examples of best practices, and a network for educators and researchers in which to interact. EO can provide all of these elements in partnership with educational institutions, government, nonprofits, and industry to meet this challenge. In so doing, an increasingly sustainable, healthier, resilient world can be achieved from the community to the global level.","name":"Users in education, training & research","selfAssesment":"Completed
"},{"code":"TA11-8","description":"Citizens and society in general use and engage with EO services through mobile devices, social media platforms, apps. We do also categorize in this section the users in education, research and training providing knowledge and learning outcomes.\r\nActive and engaged citizens are one of the main driving forces of EO/GI. Nowadays, there is a growing amount of location-based contents generated by connected “produsers”, mainly equipped with smartphones. The exponential growth of ambient geographic information through social networks became the basic feature of a spatially enabled society, in which it behaves as a vessel where millions of people share their current thoughts, observations and opinions, showing to provide more reliable and trustworthy information than traditional methods like questionnaires and other sources.\r\nA spatially enabled citizen is explained through his ability to express, formalize, equip (technologically and cognitively), and (un)consciously activate an efficiently use of his spatial skills. Harvesting this ambient geospatial information provides a unique opportunity to gain valuable insight on information flow and social networking within a society, support a greater mapping, understand the human landscape and its evolution over time. With these insights, city planners can make use of the gathered affective data to detect positive or negative trends developing in the city, managing to take early countermeasures.\r\nNevertheless, assembling and analyzing EO/GI provide us with unparalleled insight on a broad variety of cultural, societal, and human factors, particularly as they relate to human and social dynamics, for example: 1) mapping the manner in which ideas and information propagate in a society, information that can be used to identify appropriate strategies for information dissemination during a crisis situation. 2) Mapping people’s opinions and reaction on specific topics and current events, thus improving our ability to collect precise cultural, political, economic and health data, and to do so at near real-time rates. 3) Identifying emerging socio-cultural hotspots.","name":"Users among citizens & society","selfAssesment":"New
"},{"code":"TA11","description":"The EO/GI user community pools sub-communities (stakeholders) that share common needs for EO/GI information. From an economic perspective, market sectors represent user communities. Users of a community have a common interest in specific aspects of societal or economical benefits to be realized by the implementation of EO services. A user-led community is active at specific locations/regions or in specific environments on the Earth. Their activities are associated with particular features and objects of the environment and related processes that can be detected and monitored with EO satellites. EO information therefore is relevant to the user community's management of their assets, the risks to their assets, and the impact that their activities may have on other aspects of the environment. User objectives (use cases) with EO information include: Enforce regulations; Develop strategies and policies; Manage assets; Plan and design project implementations; Analyse and understand impact / consequences.\r\nUser communities can profit from EO services and applications in the field of managed living resources, energy and mineral resources, infrastructure and transport, financial and digital services, urban development, defense and security, environmental, climate and health, or citizens and society. EO/GI becomes a key data and information to support Sustainable Development Goals -SDG in particular in terms of users in managed livimgs resources towards SDG 2 Zero Hunger; SDG 8 Decent Work and Economic Growth; SDG 9 Industry, Innovation and Infrastructure; SDG 14 Life Below Water; or SDG 15 Life on Land","name":"User community of EO services and applications","selfAssesment":"Completed
"},{"code":"TA12-1","description":"Climate change observations show the warming of the climate system. The changes since the 1950s are unprecedented over decades to millennia.The atmosphere and ocean have warmed, the amounts of snow and ice have diminished, and sea level has risen. The anthropogenic emissions of greenhouse gases are the highest in history. Recent climate changes have had widespread impacts on human and natural systems. There is an urgant need for climate action through mitigation and adaptation. Mitigation actions prevent or reduce the emission of greenhuse gases into the atmoshpere with the objective to make the impacts of climate change less severe. Adapting to climate change increases our resilience to impacts like extreme weather events (e.g. hazards like floods and droughts) that get more frequent and intense in many regions. Current climate change will get worse in the future even if the reduction of emissions is effective with negative effects on ecosystems, economy, human health and well-being. There is extensive need for actions to adapt to the impacts of climate change.","name":"EO for climate change mitigation & adaptation","selfAssesment":"New
"},{"code":"TA12-10","description":"\"Sustainable urban development is a goal of the global society. It summarizes a specific set of problems that cities face all over the world. Cities want to provide a high quality of life to their residents. However, this goal is threatened by urban growth at the cost of urban green infrastructure’s accessibility by citizens etc. Communities that address this: C40 (association of the largest cities of the globe), CitiesIPCC, related SDGs of the UN, etc. Skills: Explain how the monitoring of urban areas contributes to sustainable urban development through its capability to provide regularly updated information about the benefit of urban green infrastructures and their ecosystem services to the quality of life in a city\r\n\"","name":"EO for sustainable urban development","selfAssesment":"New
"},{"code":"TA12-2","description":"Biodiversity describes the variety of ecosystems (natural capital), species and genes in the world or in a particular habitat. Ecosystem services sustain our economies and societies and are essential to human wellbeing.","name":"EO for biodiversity & ecosystems","selfAssesment":"New
"},{"code":"TA12-3","description":"Worldwide countries follow a digital agenda for the economy and initiatives to foster new skills among the workforce to cope with transformation processes with massive impact on the labour market.","name":"EO for digital agenda & new skills","selfAssesment":"New
"},{"code":"TA12-4","description":"Energy transition is a thematic area whose EO experts are proficient in relevant EO data and its processing methods and infrastructure to derive information for energy transition [and its regulatory context, etc.]. The expertise of each expert may be very specialized. In sum, the experts have: The relevant domain knowledge (knowledge about type of monitored entities and their properties, e.g. reflectance properties of sea ice and related EO sensors for detecting them), and The relevant workflow knowledge and processing skills for extracting and providing targeted information for energy transition. [may share strategic objectives… such as „gaining thorough understanding of Energy transition“, „foster usage of EO information for energy transition“]","name":"EO for energy transition","selfAssesment":"New
"},{"code":"TA12-5","description":"Agricultural activity is sustained by good environmental conditions that allow farmers to harness natural resources, create their produce and earn a living. This fosters a sustainable rural economy while food produced by agriculture sustains society as a whole.","name":"EO for sustainable agriculture & food production","selfAssesment":"New
"},{"code":"TA12-6","description":"This societal challenge aims to provide efficient, safe and environmentally friendly mobility solutions.","name":"EO for infrastructure & transport","selfAssesment":"New
"},{"code":"TA12-7","description":"In recent decades, society has fought communicable diseases with success through treatment and prevention. The Covid-19 pandemic shows that communicable diseases are still a threat to the health of citizens. Spread can gappen very quickly from one country to another. Challenges lie in the (re-)emergence of infectious diseases, antimicobial resistance and vaccine hesitancy. Policies of states focus on surveillance, rapid detection and rapid response.","name":"EO for health surveillance","selfAssesment":"New
"},{"code":"TA12-8","description":"There is a rising geostrategic competition and power pilitics challenging rule-based multilateralism. Further, there are armed confilct, civil wars and instability in the EU's broader neighbourhood. \r\nFurther, natural disasters pose a threat to society, where the Sendai Framework of disaster risk reduction focuses on.","name":"EO for emergency, security & defense","selfAssesment":"New
"},{"code":"TA12-9","description":"Water is an essential resource for food production. Growing crops requires significant quantities of water. Without sufficient, good quality and easily accessible water, agri-food production is under threat.","name":"EO for water sustainability","selfAssesment":"New
"},{"code":"TA12","description":"EO provides timely, continuous and independent data for monitoring indicators of the progress of the society in various societal challenges.\r\nEO monitoring supports activities that address societal & environmental challenges. This happens indirectly along a chain: e.g. a regularly provided EO information product derived from EO data of a satellite is integrated as a parameter in a climate model / Earth system model. This climate model enables the development of regulations (and their enforcement through constant monitoring) to implement climate change mitigation measures. Thereby, the chain is characterized by seveal connected nodes: from societal challenges to use cases of users to EO applications to EO products to specific satellites and their sensors.\r\n[Communities that promote collaboration among diverse stakeholders from academia, industry, public administration as well as local residents] \r\nScientific agendas address societal challenges and the EO/GI community can contribute to them. Consortia usually include experts from academia (researchers, developers, scientists), EO companies, and members from the user community such as public authorities.","name":"EO for societal and environmental challenges","selfAssesment":"New
"},{"code":"TA13-1-1","description":"Monitor the atmosphere includes monitoring of the atmosphere composition and air quality, as well as forecasting of sunlight exposure. Timely, continuous, and independent data on the atmosphere is useful in various domains like health, agriculture, renewable energies, urban planning, climate sciences and biology.\r\nThe atmosphere composition includes greenhouse gases (GHG) like carbon dioxide, methane, NO2 and SO2. They are part of the Earth system and have a strong impact on the climate. To monitor changes in atmosphere composition enables modelling climate change and understanding the impact of human-induced emissions of GHG relative to natural sources. EO-derived products include inventory of emission data as an input to atmospheric chemistry transport models and forecast models. Inventories are based on a combination of existing data sets and new information, describing emissions from fossil fuel use, ships, volcanoes, and vegetation. This ensures good consistency between the emissions of greenhouse gases, reactive gases, and aerosol particles and their precursors.\r\nAir quality describes the composition of the atmosphere from gases and particles near the Earth's surface. Local emissions from different sources (e.g. energy production, industrial production, traffic) cause changes to the atmospheric composition that are highly variable in space and time. The quality of the air we breathe can significantly impact our health and the environment. Therefore, it is highly relevant to monitor air quality and emissions. EO satellites are capable of monitoring aerosols, tropospheric O3, tropospheric NO2, CO, HCHO, SO2, and particulate matter (of the sizes PM 2.5 and PM 10). Products like air quality assessment reports, daily ozone forecasts, and UV-index forecast maps are produced that are applied in specific use cases, particularly related to health.\r\nThe amount of solar radiation that arrives at a location on the Earth surface depends on the atmosphere composition and varies over the day and the seasons. Information on solar radiation is useful in various domains. Applications of sunlight and ozone data are for example real-time UV radiation forecasting and risk assessment, skin health services, climate change studies, assessment of ozone protection policies effectiveness, plant growth and disease control, evaporation and irrigation models, power generation, solar heating systems planning and monitoring.","name":"Monitor the atmosphere","selfAssesment":"Completed
"},{"code":"TA13-1-2","description":"Monitoring the climate includes monitoring climate forcing and the carbon balance and assessing climate change risks.\r\nClimate forcing describes the imbalance of the Earth’s energy budget due to natural or human-induced sources. This imbalance results in a change in the globally-averaged temperature. Amongst the contributors of positive climate forcing, that leads to an increase in the globally-averaged temperature, the increase of carbon dioxide in the atmospheric composition is considered to be the most important factor. Changes in the carbon dioxide concentration indicate that the exchanges between carbon sources and sinks are not balanced. It can be shown that human-induced emissions of carbon dioxide are responsible for the increase of the carbon dioxide since the industrialisation.\r\nWith EO, we can monitor changes in greenhouse gases (GHG), aeorosols, albedo, and solar radiation. The dynamic nature of the climate makes it necessary to apply equally dynamic EO monitoring that allows to deliver key information on historical, seasonal forecast and projection periods for climate-related indicators.\r\nRelevant EO products include estimates of the climate forcing of aerosol, ozone and greenhouse gases. The dynamic nature of the climate makes it necessary to apply equally dynamic EO monitoring that allows to deliver key information on historical, seasonal forecast and projection periods for climate-related indicators. \r\nThe products are particularly relevant to the European energy sector in terms of electricity demand and the production of power from wind, solar and hydro sources. \r\nMoreover, water management uses EO-derived information about climate change to mitigate effects of changing precipitation patterns to adapt their strategies, and to prepare for climate variability and change in the water sector, e.g. because of changes in river discharge, droughts and floods.\r\nFinally, insurance uses climate change information for assessing the weather risks to insured assets that change with the climate-related increase in extreme weather conditions. This includes products like up-to-date catalogue of wind storms and their associated impacts on the ground.","name":"Monitor the climate","selfAssesment":"Completed
"},{"code":"TA13-1-3","description":"The weather is the state of the atmosphere measurable by its temperature, humidity, precipitation, and other atmospheric variables. To forecast the weather is a major branch in the field of meteorology. In comparison to climate, weather can only be predicted for a short period of time (minutes to month), because it describes the state of the atmosphere for specific days at specific locations. For a reliable weather forecast, a good numerical prediction model with precise initial conditions is needed. Models are sensitive to changes in the initial condition, that is why at the moment weather predictions are only accurate for few days. However, both models and the determination of initial conditions are steadily improved. EO makes a significant contribution to improving the initial conditions by providing global information several times a day. As the quality of the EO products improves, the weather forecast also improves. \r\nSince decades, satellites are used to monitor and forecast weather. Therefore, it is one of the most established sectors of satellite data applications. There are geostationary and polar-orbiting weather satellites that measure all kinds of meteorologically relevant variables, e.g. cloud coverage, wind speed [...] via passive or active imagery. However, not only satellites are used to collect information, but also other remote sensing techniques that can be airborne or ground-based such as Lidar.\r\nWeather forecasts are used by citizens for decisions in everyday life, in agriculture for crop cultivation decisions and in the stock markets. Other domains of applications are hydrometeorology, aviation, maritime navigation, and the military and nuclear sectors.","name":"Forecast the weather","selfAssesment":"Completed
"},{"code":"TA13-1","description":"Monitor the atmosphere and climate includes all change-focused services/applications which assess, monitor, forecast and provide timely, continuous and independent data (e.g. temperature, humidity, emissions, greenhouse gases, solar UV radiation, aorosols,...). It closely monitors each of the Earth's different subsystems and, besides being the basis for weather forecasts, helps to better understand and evaluate the impact of the climate change.","name":"Monitor the atmosphere and climate","selfAssesment":"New
"},{"code":"TA13-2-1","description":"Monitor critical information about offensive and defensive systems. This deserves a category in its own right since the nature of observations is quite different from many others.","name":"Monitor critical assets","selfAssesment":"New
"},{"code":"TA13-2-2","description":"Monitoring health can be delivered indirectly by monitoring environmental changes that can cause endemic and chronic diseases. Typically monitored environmental factors are temperature, humidity, stagnant water, NDVI, land cover, or soil type.","name":"Monitor health","selfAssesment":"New
"},{"code":"TA13-2-3","description":"Monitoring food security includes the monitoring of food availability by environmental conditions (land cover, NDVI,...), as well as the monitoring of migration patterns. Risks that can lead to food insecurity are hazards or conflicts.","name":"Food security monitoring","selfAssesment":"New
"},{"code":"TA13-2-4","description":"Monitoring borders includes monitoring the land and marine border incursions, monitoring transport routes, assessing pressures on poplulations, and monitoring humanitarian movement.","name":"Monitor borders","selfAssesment":"New
"},{"code":"TA13-2","description":"Monitor security and safety describes the collection and analysis of information to provide intelligence services & safety. The task is to give early warnings in case of emergencies, to monitor infrasturcture, transport routes (land and water) and borders, to surveil security and sovereignty.","name":"Monitor security & safety","selfAssesment":"New
"},{"code":"TA13-3-1","description":"EO is capable to repeatedly map flood extent directly after flooding, including further aspects (flood plain, extend mapping, frequency, rainfall, flash floods, vulnerability, inundation, risk-based mapping & management; flood spread and depth followed by automated insurance payouts). Modelling (hydrological modelling and monitoring focused on seasonal dynamics of water availability) based on EO data (digital elevation models) supports flood risk assessment.","name":"Map and assess flooding","selfAssesment":"New
"},{"code":"TA13-3-2","description":"For the outbreak of forest fires, satellite remote sensing can be continuously track and monitor, in a timely manner to grasp the development of forest fires. Beyond, weather monitoring enables to forecast weather conditions where fires are likely, allowing authorities to prepare.","name":"Detect and monitor wildfires","selfAssesment":"New
"},{"code":"TA13-3-3","description":"Damages from earthquakes to infrastrcture can be detected directly, e.g. by mapping collapsed buildings in optical data to derive rapid response products. Use of SAR interferograms enables to identify geotectonic shifts. Modelling enables to identify hotspot areas.","name":"Assess damage from earthquakes","selfAssesment":"New
"},{"code":"TA13-3-4","description":"Landslides are a natural hazard posing a threat to human life, property, infrastructure, and natural environment. Every year, slope instabilities have a significant impact on societies and economies. Consequently, landslide documentation is used for risk assessments, policy making and enforcing of construction regulations. Landslide monitoring is used to ensure safety of infrastructure operation. Rapid mapping of landslides and associated damages is done for response actions, e.g. of civil protection organizations. As ground surveys are very costly and time-consuming, satellite remote sensing is increasingly used to assess damage resulting from landslides.\r\nLandslides lead to local terrain changes after a downslope movement of material under the effect of gravity. They vary by type of movement (e.g. falling, toppling, gliding and flowing), by size (from small rocks to entire mountain slopes) and velocity (from a couple of millimetres per year up to free-fall speed). Landslides can be triggered both by natural causes (like earthquakes or heavy rainfall events) and human causes, e.g. mining activities that lead to slope failures. Landslides can initiate other natural hazards, e.g. when a landslide blocks a river a lake can be formed which poses a risk for an outburst flood. \r\nLandslides are diverse in appearance, and therefore are challenging to detect. EO-based assessment methods aim for detecting changes to the land surface and surface displacements. \r\nEO satellites and airborne remote sensing use optical sensors for detecting landslides in post-event images and land cover changes caused by landslides, primarily indicated by the removal of vegetation and the exposure of bare soil, by comparing pre-event and post-event images. Typical resolutions of optical EO data for mapping rapid landslides are between 0.4 m and 30 m, depending on the size of landslides caused by the triggering event. Optical data from unmanned aerial vehicles are used in cases where single landslides or concise regions have to be covered. Additionally, synthetic aperture radar (SAR) sensors allow the detection of subtle changes in ground deformation caused by landslides. Therefore, time-series of radar images are used. Further, airborne laser scanning enables the generation of digital elevation models (DEMs) that allow identification of landslide surface structures and, in case of repeated coverage, detection of elevation changes. DEM generation for analysing landslides is also possible with photogrammetry on stereographic optical data and radargrammetry on SAR images.\r\nThe diversity of appearances of landslides leads to challenges for (semi-)automatic image processing and makes visual interpretation of EO data by a landslide expert a commonly used method for landslide mapping. However, visual interpretation is subjective and experts’ results can be very diverse. Additionally, it is a slow and time-consuming process. Semi-automated classification based on optical and DEM data using object-based image analysis (OBIA) can achieve detailed interpretations of landslides while reducing the analysis time. Interferometic SAR (InSAR) techniques, such as persistant scatterer interferometry (PSI) or Small Baseline Subset (SBAS), are primarily used to identify and monitor slow-moving landslides and for quantifying movement rates. Integrated analysis of optical, DEM and SAR data allow to fully exploit the potential of EO data from different sensors for landslide mapping and assessment.","name":"Forecast and assess landslides","selfAssesment":"Completed
"},{"code":"TA13-3-5","description":"In context of volcanic activities and volcanos, EO methods are capable to provide information about various aspects, including ground motion (seismic), volcanic eruptions (pre-eruptive, sin-eruptive, atmospheric ash, dispersion), Rapid damage estimation (prevention), earthquake damage extent (loss adjuster dispatch). classification of land cover types","name":"Assess and monitor volcanic activities","selfAssesment":"New
"},{"code":"TA13-3-6","description":"Multi-hazard assessment both focuses on regions prone to several geohazards and on the interrelationships between hazards, i.e. what happens if two disasters strike at the same time or what happens when one disaster is causing a cascade of disasters with a strongly amplified impact (e.g. a landslide causing a dammed river causing an outburstflood with a magnitude beyond the design of protective measures; or an earthquake in a coastal region that is followed by a tsunami). EO can provide imformation on the single disasters and, through integration and comprehensive impact assessment, enables multi-hazard assessment.","name":"Multi-hazard assessment","selfAssesment":"New
"},{"code":"TA13-3","description":"Assess disasters and geohazards by EO includes alert & early warning, emergency mapping, and risk & recovery mapping. It relates to observations, controlling, assessments that are linked to natural and human made risks. Typical disasters that can be assessed by EO are in particular floods, droughts, forest fires, landslides, tsunamis, earthquakes, cyclonic storms and volcanic eruptions. Since with EO it is possible to quickly analyse the risk or damage it is used to effectively plan emergency response actions.\r\nThere are several measures to minimize or prevent the damage caused by disasters. Some of them have to be carried out in anticipation of a disaster, others after the occurrence of an event. The different phases that are needed to reduce or avoid the impact and to assure rapid response and recovery are described in the disaster management cycle. Depending on the cycle phase, EO has to meet different requirements. The Mitigation and Preparedness phase are passed through in anticipation of a disaster event. Thus, requirements to EO products may focus on high completeness of mapping or high accuracy of mapping. In contrast, Response and Recovery phase include rapid mapping, thus EO capabilities must meet near real-time delivery requirements. \r\nAs well, the nature of the disaster determines which EO products are used. Optical sensors are used throughout the different types; however, landslides are mostly assessed by radar sensors and thermal sensors are additionally used for forest fires.","name":"Assess disasters & geohazards","selfAssesment":"New
"},{"code":"TA13-4-1","description":"To monitor crops and agriculture with EO-based methods is relevant for various applications, including to assess environmental impact of farming, assess crop damage due to storms, to detect ollegal or undesired crops, to monitor water use on crops and horticulture, and to monitor land degradation neutrality. EO mapping of crops happens on all scales with both optical and SAR sensors. Relevant EO products include degradation, agri-environment, ecosystem, damage estimation, warning-service, food-security, impact, crop health (disease and stress), leaf area index, crop acreage and yield harvest (inventories / statistics), crop types (extent, growth, health, stress), land surface temperature, illicit crops, estimates, cultivation patterns, soil water index, surface soil moisture, run-off, land cover (land cover change), land productivity (net primary productivity, NPP), carbon stocks (soil organic carbon, SOC).","name":"Monitor crops","selfAssesment":"New
"},{"code":"TA13-4-2","description":"Monitor the forest focuses on regular and periodic measurement of certain parameters of forests (physical, chemical, and biological) to determine baselines to detect and observe changes over time. Typical applications include to assess deforestation and forest degradation, assess forest damage due to storms or insects, to monitor forest resources, detect illegal forest activities, assess the environmental impact of forerstry, and to monitor the forest carbon content. Moderate resolution sensors have been used to map forests at large scales. Modern very high resolution optical sensors provide enough spatial and spectral detail to map individual trees. Further sensors for forest monitoring include SAR and LIDAR. Integration of optical sensors, LIDAR and in-situ measurements seems an accurate method to achieve third dimension forest mapping.","name":"Monitor the forest","selfAssesment":"New
"},{"code":"TA13-4-3","description":"EO provides the opportunity to monitor bodies of water, i.e. inland waters, and to assess ground water and run-off. For lakes, this includes products about water quality, pollution, turbidity, suspended sediment concentrations (quantitative, qualitative), waterbody (temperature, extent, volume, quantity), algal blooms, alkaline water, evaporation, surface temperature. For ground water and run-off, the products focus on water run-off (water quantity), hydrological network and catchment areas (water catchment), run-off season, groundwater. Various scales are addressed, from local catchments to the global water cycle. For inland water quality, sensors are optical medium resolution (300 meters) for achieving a (strongly cloud-cover dependent) update frequency of 10-20 times per year and high resolution (5 meters) for update frequency of 3-5 times per year.","name":"Monitor bodies of water","selfAssesment":"New
"},{"code":"TA13-4-4","description":"Monitoring of snow and ice focuses on glaciers and their retreat due to climate change (extent, mass balance), the seasonal snow cover (its extent, depth, temperature and snow water equivalent), and the ice on rivers and lakes (inland ice, thickness, freezing period, melting period, ice extent). Glacial monitoring in the mountainous regions around the globe, and of the Greenland and Antarctic ice shields uses optical EO data of high and very high resolution and SAR data. Satellite based daily snow covered area products can reliably be provided down to a spatial resolution of 500 meters. Global products are possible with weekly updates. Applications include, among others, climate change impact monitoring, relevant for modelling runoff patterns in catchments for etimating hydroelectric power generation potential.","name":"Monitor snow and ice","selfAssesment":"New
"},{"code":"TA13-4-5","description":"EO is used to monitor land ecosystems and biodiversity, environmental impact of human activities, land pollution and vegetation encroachment. A tool for this is land cover mapping and mapping of land cover change about a wide set of categories, lincuding basic forest types, major agricultural surface types, conservation areas, settlements, infrastructure, primary roads, bare soil, water bodies, rivers, wetlands following standard classification schemes according to CORINE or FAO LCCS. Main source are optical EO data and associated pixel-based and object-based image classification methods. For discriminating vegetation classes, they often making use of various vegetation indices and biophysical parameters.","name":"Monitor land ecosystems","selfAssesment":"New
"},{"code":"TA13-4-6","description":"EO technologies (both optical and SAR) are capable to categorize bio-physical coverage of land to produce land cover maps like CORINE Land Cover (CLC). The EO method is objective and allows for frequent updates. EO-derived land cover is an excellent basis for mapping land use, the socioeconomic use that is made of land. Land use products are used in a wide range of applications (e.g. agriculture, forestry, spatial planning, determining and implementing environmental policy, land accounting). In a humanitarian context, land use mapping is applied to map refugee camps, population and pressures on population that cause migration.","name":"Monitor land use","selfAssesment":"New
"},{"code":"TA13-4-7","description":"EO is capable to monitor topography with various types of land surface elevation data (both digital terrain models and digital surface models) and also focus on land surface changes and ground deformation / movement due to e.g. soil erosion or permafrost thawing, frost heaving. This includes also the mapping of stable zones where such changes do not happen. The main ways of creating a digital elevation model (DEM) from EO data are deriving it from interferometric synthetic aperture radar (InSAR), from stereoscopic pairs of optical images acquired from different viewing angles, and deriving them via laser scanning.","name":"Monitor topography","selfAssesment":"New
"},{"code":"TA13-4-8","description":"EO is able to extract information about subsurface geology, including near surface features, lithology features, and linear disturbance features (faults & discontinuities). Concerning monitoring of mineral extraction EO supports by mapping ground surface, illegal activities, mine waste (erosion, land subsistence, biodiversity/habitat loss, destruction & disturbance of ecosystems). Disturbance of ecosystems may happen by carbon seeps from reservoirs or pipelines. Their detection can also be done with EO data.","name":"Extract information about subsurface geology","selfAssesment":"New
"},{"code":"TA13-4","description":"Services that monitor land cover all services/applications that are focused on monitoring, assessing, managing, planning and improving land areas, its ecosystems (land, soil and inland water monitoring/quality/availability & usage assessments) and evolution of the land surface (use, cover, seasonal and annual changes and monitors variables) even if it involves human intervention (environmental challenges, impact evaluation or suitability analysis).\r\nMonitoring is possible by deriving information from variables measured by EO in different domains, like vegetation, energy, water, and cryosphere. For vegetation, those variables are for example land cover, NDVI, burnt area, or surface soil moisture. In the energy domain, land surface temperature and surface albedo are known variables, for water it is water surface temperature or water quality. Finally, for the cryosphere lake ice and snow cover extent, and snow water equivalent are variables that are used for land monitoring services.","name":"Monitor land","selfAssesment":"Completed
"},{"code":"TA13-5-1","description":"The full range of EO satellite sensors are capable of monitoring particular aspects of urban areas. The most relevant include SAR satellites such as TerraSAR-X that distinguish between urban fabric and other land cover. Further, optical satellites in the resolution range HR and VHR are used to map imperviousness and soil sealing. Beyond such land cover classifications with low granularity, HR and VHR data are used for producing detailed land use and land cover classifications that distinguish different settlement densities or, in combination with additional data, different land use such as transport, residential etc. as defined in Classification schemes specialized on urban areas. Airborne laser scanning (and stereographic analysis) maps building and vegetation heights. InSAR methods allow to measure land subsidence that is highly relevant e.g. in coastal cities close to or below the sea surface elevation. Night-time optical data maps lights. Thermal sensors allow mapping the heat that is radiated from cities. Typical applications include monitoring urban growth/sprawl, transport networks, urban heat islands, and generating city maps and 3D city models for urban planning that are relevant to users in smart cities and in local/regional planning.","name":"Monitor urban areas","selfAssesment":"Completed
"},{"code":"TA13-5-2","description":"EO is capable of monitoring infrastrcture in general, i.e. buildings (and their construction) and transport networks (roads, rails). Additionally, infrastructure for renewable energy harvesting (solar and wind farms, hydroelectric powerplants) and identification of suitable sites (through mapping solar radiation, wind roses, speed and direction, hydrological network mapping). A basis is land surface mapping for deriving digital elevation models (DEMs) that is required for modelling renewable energy potential and for spatial planning and landscape visibility analysis (visual impact assessments for planned infrastructure). Further, EO is capable of assessing damage from industrial accidents. A wide range of EO technologies is used here, infrastrcture can be directly detected and mapped with optical and SAR sensors, where the resolution depends on the targeted assets. DEMs can be generated from SAR and stereographic optical data. Wind energy related parameters can be derived from satellites focused on atmosphere and weather monitoring. Further, there are various GI methods in use, too (in particular focused on spatial planning and impact assessment).","name":"Monitor infrastructure","selfAssesment":"New
"},{"code":"TA13-5","description":"Monitoring the built environment provides information about urban structures, transport networks and particular infrastructure, e.g. dedicated to energy provision. It covers all urban and infrastructure related service/applications on site development information, planning support or suitability analysis. As well, it includes pressure and threats analysis on the urban areas.","name":"Monitor the built environment","selfAssesment":"New
"},{"code":"TA13-6-1","description":"EO is capable of monitoring ocean quality and productivity by focusing on ocean colour (that show among other thins chlorophyll and algal bloom), parameters of sea surface salinity (SSS) and sea surface temperature (SST). In addition, EO can monitor pollution at sea that that explains coastal water quality, which is relevant for aquafarms and for tourism (bathing area water quality). Further, EO satellites can detect oil slicks and spills and threats from such events. Many of these parameters and detected features are relevant for monitoring marine habitats, targeting in particular generic algal blooms, marine mammals, sea surface temperature, sediments, plumes, nutrients, dredging operation, coral reef health assessment (bleaching).","name":"Monitor the marine ecosystem","selfAssesment":"New
"},{"code":"TA13-6-2","description":"In coastal areas, EO is capable to monitor water depth and shallow water bathymetry (charting), coastal ecosystem parameters about water temperature, water transparency, oxygen, phytoplankton abundance, bathing water indicators, detection harmful algal blooms, sediment (qualitative, quantitative), turbidity (quality, quantitative), visibility, chlorophyll-a concentration, suspended sediment may be indicative of estuarine processes, re-suspension or pollution. Further, this includes coastline monitoring with a focus on shoreline and its change as well as coastal land cover (and terrain) and its change. A widse set of EO sensors and technologies is used to monitor coastal areas. Optical satellite imagery is analyzed to detect and map suspended sediment concentrations. Etc.","name":"Monitor coastal areas","selfAssesment":"New
"},{"code":"TA13-6-3","description":"EO is capable to monitor weather impact on ocean surface and metocean features as a basis for forecasting furture ocean conditions. This includes ocean surface topography, ocean dynamics and circulation like tides and ocean current movements and drift, ocean winds, wave and climate conditions at ocean locations (meteocean). Further, this covers the mapping of extreme waves like tsunamis and the monitoring of hurricanes and typhoons. Involved EO technologies are for example satellite altimetry that maps ocean surface with 2 cm to 3 cm accuracy, mathematical forecast models. Repeated altimetry measurements allow mapping speed and direction of ocean's currents and tides. Available EO-based RADAR systems monitor wave height and direction, wind speed and sea-surface elevation. Near-realtime processing and delivery workflows enable the use of these parameters in weather forecasting, navigation and offshore installations protection.","name":"Monitor weather impact on ocean surface","selfAssesment":"New
"},{"code":"TA13-6-4","description":"To support an ecosystem-based approach for fisheries management, EO images with global and daily systematic coverage with high-resolution images can help in identifying potential fishing zones and to assess fish stocks. They help assessing and understanding changing abundancy and spatial distribution of exploited fish stocks. Therefore, they analyse various key environmental parameters that can be detected with satellite remote sensing. This includes sea surface temperatures (SSTs), sea surface height anomalies, and sea surface colour revealing the abundance of chlorophyll a. This relates to phytoplacton production that is directly related to total fish landings. Additionally, EO can detect harmful algal bloom. A further threat to sustainable fish stocks management are illegal fishing. Where localization of licensed fishing vessels and fleet management services are supported by EO to avoid overexplotation and enable recovery of fish stocks. EO complements identification, detection and tracking of vessels with SAR and optical remote sensing.","name":"Monitor fisheries","selfAssesment":"New
"},{"code":"TA13-6-5","description":"For shipping, navigation, and monitoring sea-traffic and pollution, remote sensing and satellite technologies allow detecting vessels in the wider ocean. EO can detect the vessels themselves, their wake trailing behind them, sandbanks and reefs that pose a threat for safe navigation. Additionally, EO can detect pollution from the ships, e.g. when illegal waste disposal happens. Ship detection and classification is possible with the use of optical and synthetic aperture radar (SAR) imagery. The methods complement each other.","name":"Detect and monitor ships","selfAssesment":"New
"},{"code":"TA13-6-6","description":"Information on sea ice and icebergs is important for managing operation of ships or offshore platforms in hazardous sea ice conditions. EO technologies give the possibility to study sea ice and measure its thickness, spatial distribution, motion and ridges (as well as ice berg positions). Satellite imagery provides wide area, synoptic pictures of the ice conditions. Since the scale of ice fields is quite large, mainly moderate resolutions have to be accepted, down to around 10m in scale, while ensuring comprehensive coverage. Multispectral imagery can provide more information on ice-type but in the main, SAR imagery is used due to its all-weather and day/night capability. The data collected can be more accurate than in-situ measurements due to a higher and faster coverage of a whole area. Subsequent modelling that incorporates ocean weather (wind, waves, ocean current) provides expected drifting paths. Constant monitoring is most important to identify the risk and opportunities, for instance for ship routing, and safety of oil rigs.","name":"Monitor sea-ice and icebergs","selfAssesment":"New
"},{"code":"TA13-6","description":"Monitoring marine inlucdes monitoring of marine safety (e.g. marine operations, oil spill combat, ship routing, defence, search & rescue, ...), marine resources (e.g. fish stock management, ...), marine and coastal environment (e.g. water quality, pollution, coastal activities, ...), and climate and seasonal forecasting (e.g. ice survey, seasonal forecasting, ...).","name":"Monitor marine","selfAssesment":"New
"},{"code":"TA13","description":"EO services and applications are organized according to thematic areas. EO is used for a wide set of services. There are many applications of EO that show how a service produces information for a particular client. EO service and applications are best described by the purpose they serve or by the need of the user. The main user needs to EO are to monitor, to map, to forecast, to assess, to detect, and to analyse. \r\nTo monitor means to watch and check a situation carefully for a period of time in order to discover something about it, i.e. keeping track of how the natural and manmade environment change (their status) over time. Typical alternative verbs are track, observe, record, follow, understand, or surveil. \r\nTo map means to represent an area of land in the form of a map, i.e. to feature and locate the way it is arranged or organized. Synonymous verbs are locate, identify, classify, trace, or record.\r\nTo forecast means to provide statements covering a range of different outcomes, to say what you expect to happen in the future; i.e. to predict future events based on specified assumptions (about information extracted from EO change and time series data), where different sets of assumptions describe scenarios. Equivalent terms are predict, plan, model, estimate, or project.\r\nTo assess means to judge or decide the amount, value, quality or importance of something, i.e. to evaluate and measure the status of and changes in natural and manmade built environments. Alternative verbs are evaluate, measure, understand, review, or quantify.\r\nTo detect allows to notice something that is partly hidden or not clear, or to discover something, especially using a special method, i.e. to identify and locate the changes in the Earth’s environment. Similar terms are locate, warn, identify, highlight, or spot.\r\nTo analyse means to study or examine something in detail, in order to discover more about it, i.e. to detail the elements of a whole and critically examine and relate these component parts separately and/or in relation to the whole. Sometimes, the terms to process, to parse, or to detail are used in exchange for to analyse.","name":"EO services and applications","selfAssesment":"New
"},{"code":"TA14-1-1-1","description":"Ocean colour can be made visible in atmospherically corrected EO data. Specific spectral bands are necessary to derive physical and biologic parameters of the water from the EO data.","name":"Ocean colour","selfAssesment":"New
"},{"code":"TA14-1-1","description":"Band combinations are pre-defined for (visually) analysing images for a dedicated purpose. Examples are dedicated band combinations for land us land cover classification, ocean colour, etc.","name":"Band combinations","selfAssesment":"New
"},{"code":"TA14-1-2","description":"The spectral and refractive information from optical and SAR data enables direct and indirect derivation of biophysical and geophysical EO parameters that are properties of the sensed land surface, ocean surface and atmosphere volume.","name":"EO parameters","selfAssesment":"New
"},{"code":"TA14-1","description":"Processing products are image products from raw data to all different processing stages. The transformation processes between the stages include operations such as atmospheric correction, cloud detection and radiometric calibration to provide data in a form suitable for subsequent analysis. Processing products consider a product as being an output of a process.They appear as \"intermediate products\" along all steps of the processing chain.","name":"Processing-related and preparatory products","selfAssesment":"New
"},{"code":"TA14-2-1-1","description":"Point clouds represent a set of points with X, Y, Z coordinates and associated attributes. A source of acquisition is Light Detection and Ranging (LIDAR), an airborne surveying technique that uses laser light to measure the distance to an object on the ground.","name":"Point clouds","selfAssesment":"New
"},{"code":"TA14-2-1-2","description":"Elevation data in the form of a digital elevation model (DEM) is an essential component of many analyses derived from EO. DEMs are used to represent every kind of surface, including terrain surface, vegetation canopy surface, sea surface, sea-ice surface, glacier surface etc. This description focuses on DEMs for representing terrain. A digital terrain model (DTM) describes the bare ground of the terrain, a digital surface models (DSM) described heights of vegetation (e.g. trees) and of man-made structures (e.g. buildings) reaching above the terrain. DEM is often used as an umbrella term for DTM and DSM. EO-derived DEMs are usually DSMs and require removal of vegetation and buildings in order to represent the terrain (DTM). DEMs are multi-purpose products used in various applications. They are available for global scale (SRTM, WorldDEMTM), regional scale (ArcticDEM, Copernicus EU-DEM v1.1) or for national levels and local regions. Various techniques exist to generate DEMs from SAR data, stereographic optical EO (as well as airborne and drone) data and from airborne laser scanning.","name":"Digital elevation models","selfAssesment":"Completed
"},{"code":"TA14-2-1-3","description":"By comparing elevation models of different dates, the change in elevation and volume can be identified. Thereby, they measure surface deformation, land subsidence, ice shield loss due to melting, etc.","name":"Elevation change maps","selfAssesment":"New
"},{"code":"TA14-2-1-4","description":"Vector fields capture the movement directions of locations on a continuous surface, e.g. of the ocean, or in a 3D grid of locations, e.g. of the atmosphere. The atmosphere and the ocean are highly dynamic features. Vector fields are used to represent wind directions and current movement directions. Further vector fields derived from EO data include geoid undulation / gravity maps.","name":"Vector fields","selfAssesment":"New
"},{"code":"TA14-2-1-5","description":"When a moving feature (i.e. object) is detected in subsequent images, its trajectory of movement can be mapped. Such products map ship movements, sea ice movements, etc.","name":"Feature trajectories","selfAssesment":"New
"},{"code":"TA14-2-1","description":"Geometrically measured EO products origin from EO-derived distance measurements, measurements of direction, tracking of moving objects, and changes of distance measurements. The used EO methods include for example SAR interferometry and stereographic analysis of optical data.","name":"Geometrically measured EO products","selfAssesment":"New
"},{"code":"TA14-2-2-1-1","description":"Land cover maps represent spatial information on different types (classes) of physical coverage of the Earth's surface, e.g. forests, grasslands, croplands, lakes, wetlands. An example is the European Copernicus product CORINE land cover (CLC) with 44 classes. Initiated in 1985 (reference year 1990), updates followed in 2000 and every 6 years afterwards. Apart from CLC, the European Copernicus Land products also include the High Resolution Layers. They includes for example the imperviousness product that captures the percentage of soil sealing. Land cover classification products are multi-purpose products that are relevant for various applications. They are available on national levels, regional levels and global levels. They have different scales and granularity of their associated classification scheme. The products are updated on a regular basis. Update cycles can vary depending on the resolution (i.e. likelihood for observable change of the land surface) and the capability of production processes. An additional example on a global scale is the Global Urban Footprint. The products are provided by public organisations and private EO companies and based on various EO sensors.","name":"Land cover maps","selfAssesment":"Completed
"},{"code":"TA14-2-2-1-2","description":"Land use documents how people are using the land. Getting from physical land type (land cover) to land use requires skill in interpretation and involves integration and consultation of ancillary data. Land use maps are multi-purpose products that are relevant for many applications. The products are updated on a regular basis (e.g. 6 years for Urban Atlas).","name":"Land use maps","selfAssesment":"New
"},{"code":"TA14-2-2-1-3","description":"Cloud masks for optical EO data distingush cloudy pixels from cloud-free pixels. They may differentiate between serveral cloud types, i.e. opaque clouds and Cirrus clouds (that are transparent). Most land monitoring applications based on optical data require cloud-free images. Therefore, cloud masks are a product that is used early on in image processing for selecting suitable imagery for analysis (e.g. by screening images of an archive by the derived cloud cover percentage of the image). Therefore, cloud masks are made available as metadata by the EO data provider. Clouds are identified with threshoulding of reflectance values of the blue band and, to adapt for cloud/snow confusion, specific short-wave infrared (SWIR) bands.","name":"Cloud mask","selfAssesment":"New
"},{"code":"TA14-2-2-1-4","description":"Detected features are objects from one or more classes and are the result of a comprehensive (and mostly automatic or semi-automated) search of all locations in an image that decides whether such features are present and where they are located. Examples inculde man-made objects (e.g. vehicles, ships, buildings, etc.) with sharp boundaries and are independent from the background, and landscape objects, such as land-use/land-cover (LULC) parcels that have vague boundaries and are part of the background environment. Only the latter type would locate features for all locations of an image.","name":"Detected features","selfAssesment":"New
"},{"code":"TA14-2-2-1","description":"Static EO derived thematic classification products and masks (e.g. land use land cover classifications). Additionally, static EO detected features (planes on apron of airports, dwellings) that consist of a set of point locations (or polygons) and do not end up in a comprehensive classification of all pixels of an image. Static EO derived thematic classification products and masks (e.g. land use land cover classifications). Additionally, static EO detected features (planes on apron of airports, dwellings) that consist of a set of point locations (or polygons) and do not end up in a comprehensive classification of all pixels of an image. Thematic classifications and feature detection identify a surface by a class label that represents a more or less persistent state. A good example product is the Copernicus Urban Atlas. The most recent available version is assumed to represent the \"current\" state (Certainly, an update cycle is necessary for providing a product that remains up-to-date).","name":"Thematic classifications and feature detection","selfAssesment":"New
"},{"code":"TA14-2-2-2","description":"Event maps and thematic change (evolution) maps indicate that some process happened that changed the area at a location from one class to the other. For example, a burnt area map indicates locations where vegetation has been burnt by a fire and changed to bare ground. A typical mapping method is the use of pre- and post-event satellite images for detection of the areas affected by the process. Eventually burnt areas contain identifiable burn marks that allow direct identification in one single post-event satellite image. Nevertheless, it is the process that is central to the analysis. Similarly, the concepts aforestation and deforestation would fall under the heading \"Event maps.\" They may come from a comparison of two status maps of different dates. Some processes benefit from analysis of more than two states. Such change evolution maps can be produced with time-series analysis. On land, more examples include landslide maps, flooded area maps and other land surface dynamics (e.g. aforestation and deforestation). Further, change detection maps are available for other domains (atmosphere, marine, land, climate, etc.)","name":"Event maps and thematic change (evolution) maps","selfAssesment":"New
"},{"code":"TA14-2-2","description":"The semantic labelling products result from methods that assign labels to objects or locations in a field. The labels correspond to the categories of a classification or, in case of masks and detected features, to a single target class. Such labels may also identify classes of change or change evolution.","name":"Semantic labelling products","selfAssesment":"New
"},{"code":"TA14-2-3","description":"EO-derived attribute products describe the state and evolution of specific attributes of a feature or at a field location. They describe for example air quality, soil moisture or water quality & quantity.","name":"EO-derived attribute products","selfAssesment":"New
"},{"code":"TA14-2","description":"Descriptive analytics products provide analytical results which describe the present (and past) situation as it is recorded in EO images. Therefore, it contains information that can directly be extracted from EO images or EO image time series. These products are diverse in various aspects: they capture static and dynamic information; they concern information about objects or fields; and they have qualitative (nominal scale) or quantitative (ordinal, interval, ratio scale) levels of measurement.","name":"Descriptive analytics products","selfAssesment":"New
"},{"code":"TA14-3","description":"Providing analytical (modelling) results which predict the future situation (e.g. air pollution forecasts). [interpolation in space, i.e. not only prediction into the future, filling gaps in time series...]\r\nInformation that can be modelled based on descriptive analytics products. by extrapolating time series (forecasting/predicting), by modelling of processes (e.g. flood risk maps, landslide susceptibility)","name":"Predictive modelling products","selfAssesment":"New
"},{"code":"TA14-4","description":"Prescriptive modelling products and services focus on providing analytical results that are a guide to action. The often result from an impact assessment. One example is the identification of construction sites leading to sales opportunities.","name":"Prescriptive modelling products and services","selfAssesment":"New
"},{"code":"TA14-5-1","description":"A textured 3D model uses a 3D model derived from elevation data. Additionally, each separate surface of the 3D model receives its own texture derived from optical image data. Typically used for visualisation purposes.","name":"Textured 3D models","selfAssesment":"New
"},{"code":"TA14-5-2","description":"A semantic 3D model consists of a 3D model derived from elevation data with an integrated image classification. A classified object thereby consists of a 3D surface or a grouped set of 3D surfaces. A typical example is a 3D city model in the CityGML format.","name":"Semantic 3D models","selfAssesment":"New
"},{"code":"TA14-5","description":"Combining the satellite data with other information sources. Resulting in an integration of several descriptive analytics products and processing products, e.g. a textured 3D model or a semantic 3D model.","name":"Aggregation and integration products","selfAssesment":"New
"},{"code":"TA14-6-1","description":"Sentinel-2 cloud-free mosaics for display, satellite maps in books etc.","name":"Satellite maps","selfAssesment":"New
"},{"code":"TA14-6-2","description":"Layouted maps in a file (PDF, SVG, etc.) for printing or visualisation on screen, embedding in reports or as static displays on websites etc.","name":"Layouted digital maps","selfAssesment":"New
"},{"code":"TA14-6-3","description":"Digital layouted maps in an online map viewer; 3D visualisations on the screen / 3D screen and online map viewers with 3D capabilities etc.","name":"Web visualisations in 2D and 3D","selfAssesment":"New
"},{"code":"TA14-6-4","description":"Printed maps, 3D plots of 3D models, hologram 3D maps etc.","name":"Analogue visualisation products","selfAssesment":"New
"},{"code":"TA14-6-5","description":"A video is a structured file of 2D grids link by the time, is a regular file of values which has been processed to sensor units (e.g. calibrated). The result can be a single date acquisition or a combination of dates. For each point, the value represents a parameter imaged by the sensor. Videos of EO data present for example time series of satellite maps and other EO products (e.g. Arctic sea ice evolution in a time-series map video over the past 30 years).","name":"Time series map videos","selfAssesment":"New
"},{"code":"TA14-6","description":"Visualisation products are used for presentation of EO information to the user. The user's interaction with the visualisations is predominantly viewing and interpretation of the informational content and arriving at decisions in the context of the user'S objective with the EO information. In addition, users of visualisation are all involved actors during image processing. For example, an EO analyst may use visualisations of EO data and preliminary EO products for getting a better understanding of the contained information and adapt his processing workflow to arrive ad improved results. Typical visualisation products include satellite maps, layouted digital maps, web visualisations in 2D and 3D, and analogue visualisation products.","name":"EO visualisation products","selfAssesment":"New
"},{"code":"TA14-7","description":"Users need access to EO products if they shall be able to benefit from them. Additionally, providers of value added products act as users of EO products earlier in the information processing value chain. Concequently, various distribution services provide access from raw data to processed information and processing infrastructure. Provision of access to raw data or processed information happens via direct download (FTP), via application programming interfaces (API) or web services (e.g. Hubs). Further, access to processing infractructure happens via web services.","name":"Distribution services","selfAssesment":"New
"},{"code":"TA14","description":"Products in relation to EO appear along the entire image processing value chain as inputs and outputs of processing steps. Ultimately, at the end of that chain, the output EO products represent information that supports actions. The standard EO products are categorized by the type of problems they help to solve or the type of question they help answering.","name":"Standard EO products","selfAssesment":"New
"},{"code":"WB","description":"This knowledge area is about Web Based Geographic Information management aspects and therefore it was given the name \"Web Based GI\" or \"WBG\" in short. It is implied by this name that the differentiating factor for this KA is the \"Web\". One must then be able to answer the questions like \"What functions do we delegate to the Web?\" or \"how WBGI is different from the traditional GI?\" Sticking to the functions of a GIS, which are inserting (adding), storing, manipulating, analysing and presenting the data, there is not a single system for effecting all these tasks anymore but the Web itself. For instance, there is no single database and its known-to-its users-definition, anymore but many different stores and many different definitions. Similarly, many different manipulation, analysis and presentation options compared with the options offered by a single or limited number of systems of traditional GI. In general, Web provides the means of leveraging distributed \"resources\" like data, information, or software. It is a \"collaboration medium\". A collaboration that enables rapid production or decision making. A collaboration that certainly introduces new dimensions to traditional GI handling. This is the justification of proposing this KA in addition to the KAs of the original BoK. For the mentioned collaboration to happen, data or any other type of a resource have to accessible on the Web. This means that it should have a Web \"address\" and a \"definition\" that is understandable either by \"human\" or \"machine\". \"Machine understandable definitions\" refers to the dimension of \"semantics\" and \"ontologies\" which are also included under this KA. When one talks about publishing resources then \"catalogue services\" and more importantly \"discovery\" dimension comes into the scene. On the other hand, \"Linked Data (LOD)\" and \"Open Data\", highly popular recent trends and two of the above mentioned dimensions of Web GI have also been covered under this KA. Like the other dimensions of Web GI, both LD and OD aspects must be known to GI communities with differing degrees of expertise. The concepts of \"interoperability\" and \"Spatial Data Infrastructure (SDI)\", hot topics of GI communities for many years, have been thought to be dealt with under this KA as well with the justification that \"Web GI\" is a much broader concept than SDI, This is by the fact that SDI refers to a much narrower content and context of \"collaboration\" then Web GI. Therefore, Geospatial data interoperability and some of the related concepts which were classified under KA, \"Geospatial data in the original BoK were moved under KA11 with the updated context. Another issue is the coverage of Spatial Analysis (SA), data manipulation aspects of GI by KA11. The SA aspects are covered by other KAs like \"Geocomputation\" and \"Analytical methods\". If the analysis operations, in an undertaking, would be handled by web services this is already covered by \"data processing\" web services, application development unit and Web services composition under that unit. The important thing is to have the knowledge about a specific analysis operation; Employing it as a web service would require no more knowledge than using any other web service. SA is covered by KA11 in as much as it should have been.","name":"Web-based GI","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB1-1","description":"The basic principles on which web services build. The concept of Service Oriented Architecture and the importance of APIs","name":"Fundamentals of web services","selfAssesment":"In progress/to be revised (GI-N2K)
"},{"code":"WB1-2","description":"This concept will cover web services based on the Simple Object Access Protocol (SOAP)","name":"SOAP web services","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB1-3","description":"This concept will cover web services based on the representational state transfer (REST) protocol","name":"REST web services","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB1-4","description":"The Open Geospatial Consortium (OGC) defines standards and best practices for web services in the geospatial domain. OGC standards are developed using a consensus model allowing all stakeholder to participate in the process. As a result the OGC web services are widely implemented.","name":"OGC web services","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB1","description":"In the most simplistic way a Web service may be defined as \"a Web accessable program code which performs a task of either processing or serving some data. Although there are many other definitions in the related literature, the one in W3C (2004) seems to be quite complete and refering to also lately popular REST style Web services. It states that \" We can identify two major classes of Web services: REST-compliant Web services, in which the primary purpose of the service is to manipulate XML representations of Web resources using a uniform set of \"stateless\" operations; and arbitrary Web services, in which the service may expose an arbitrary set of operations.","name":"Web services","selfAssesment":"In progress GI-N2K
"},{"code":"WB2-1","description":"To be able to discover and assess available data or services, these resources have to be documented. This concept describes the standardized languages used for these descriptions","name":"Languages for the definition of non-spatial data and services","selfAssesment":"GI-N2K
"},{"code":"WB2-2","description":"Different standardized ways to define geospatial data exist. GML, GeoJSON, WKT and GeoSPARQL are examples. What are common points and differences","name":"Definition of geospatial data","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB2-3","description":"Defining a common language is a crucial step for sharing or combining data. Vocabularies, taxonomies, ontologies are are tools to reach this goal.","name":"Ontologies development reuse and patterns","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB2","description":"A \"resource\" could be \"anything\" including data and services, identifiable over the Web. A resource should be defined in a language to be discoverable on the Web. Over the years, two major bodies W3C for non-spatial and OGC concerning spatial data have developed many specifications for defining data and services. On the W3C side, Resource Description Framework (RDF) has gained a great momentum in recent years in relation to the recent popularity of Linked Data as well. In the OGC front, the acceptance of GML was a major step concerning the long time effort of geospatial communities for having a standard for the definition of both geospatial features and geometry.","name":"Resource Definition","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB3-1","description":"Metadata is information about the data to be published. It helps the user to discover the data, allows the user to evaluate the fitness for use and it explains how and under which conditions the data can be retrieved and used. Metadata are a core component of data infrastructures and as such, standardization is a requirement for the correct exchange and interpretation of the metadata.","name":"Metadata and standards","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB3-2","description":"A resource can be added manually to a catalogue service by creating or uploading its metadata, but metadata can also be added by automated crawling of other catalogues.","name":"Manual and automated forms of publishing","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB3-3","description":"Catalogue services allow to publish and search resources through their metadata","name":"Catalogue services","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB3-4","description":"Open data is data that is free to use, re-use and share without limitations on who uses it or for what purpose. Publishing open data is making the data discoverable and accessible in a convenient way (technical openness).","name":"Publishing open data","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB3-5","description":"Adding semantic information to the data allows computers to understand the structure and meaning of data. This allows automatic searching, processing and integrating data with other semantic sources.","name":"Publishing via a semantic definition of data","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB3-6","description":"Linked (open) data provides structured data which is interlinked in a machine readable way. This allows to discover, access and combine data in an automatic way. This concept discusses the steps needed to make existing data available in a linked open way.","name":"Publishing linked open data","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB3","description":"\"Publishing\" means making a resource available for the use of others. A \"resource\" could be \"anything\" including data and services, identifiable over the Web. Publishing may be done on the basis of either the \"characteristics\" of the data or the data itself. When only some \"characteristics\" of a resource is published then some of the contents would naturally be left out. The \"characteristics\" include metadata and some keywords. This kind of publishing may be named as \"limited contents\" publishing or \"publishing by metadata\". One of the issues become then what characteristics to use to define the data. Or what what metadata definition to use. Another aspect of publish is \"manual entry\" and \"automated collection\". In the former publisher enters metadata while in the latter some harvesting mechanism collects metadata in an automated fashion. On the contrary, there is \"unlimited contents publishing\" where there is no limitation on the published contents. Open data publishing is in this class. In additon, some \"additional semantics\" may be subject of this type publishing through new relationships in the ontologies of publishing, which have not been explicit in the exisiting data model but are inherent in the data. And this last type is covered under the topic, \"Publishing via a semantic definition of data.\"","name":"Resource Publishing","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB4-1","description":"Syntactic discovery is the discovery of resources based on the structure of the resources","name":"Syntactic discovery","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB4-2","description":"Semantic discovery is the discovery of resources based on the meaning of the data.","name":"Semantic discovery","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB4-3","description":"Linked (open) data provides structured data which is interlinked in a machine readable way. This allows to discover, access and combine data in an automatic way.","name":"Discovery over linked open data","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB4","description":"Resource discovery means the discovery of resources including data and services needed for an application. Syntactic discovery refers to the discovery on the basis of syntactic comparison operations. It is classified as \"keyword-based\" and \"full-text-based\" discovery. Semantic discovery on the other hand, refers to the discovery of resources on he basis of some semantic definition. Therefore, semantic discovery requires that a resource be published by a semantic definition as defined in the topic WB3-5.","name":"Resource Discovery","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB5-1","description":"The workflow to integrate geospatial data in an application often relies on a combination of different OGC web services. Searching and finding the data and the corresponding services, binding to these services to view, filtering and or downloading the data are different steps in this process","name":"Integrating data from OGC web services","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB5-2","description":"The alignment of data structures and vocabularies/ontologies used are important steps towards the data harmonisation needed for a combined use of datasets","name":"Schema matching and ontology alignment","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB5-3","description":"A data mashup is a combination of data from different sources to produce new applications of new datasets","name":"Data mash ups","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB5","description":"The term \"application development\" refers to the collection of activities or the \"workflow\" through which the user reaches her final goal. Being one of these activities, \"data integration\" means the transformation of data from one representation to another which might be of either the client`s one or some other representation. An example for data integration might be the case where the data is transfered from an OGC WFS and integrated into a client GIS.","name":"Application development via Data Integration","selfAssesment":"In Progress GI-N2K
"},{"code":"WB6-1","description":"Manual Web Service Composition is manually (by human) combining the activities of discovery, composition and invocation to fulfil a certain task.","name":"Manual Web Services Composition","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB6-2","description":"Providing standardized descriptions of the specifics of available webservices creates an environment where the composition of services to create a web application can be automated.","name":"Semi automated and Full-automated WSC","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB6","description":"Web Services Composition can be defined as bringing together a number of web services in a certain workflow to achieve a certain task that cannot be achieved by any of the composed services alone. In general, it involves first the discovery of the suitable services over the Web, and compose them in a certain workflow order and finally run the composed service which is the invocation stage. WSC has been a highly active research topic since the emergence of Web services in 2000s. \"Manual\" WSC is the form that the activities of discovery, composition and invocation are all done manually (by human). In the \"Semi-automated\" way, the discovery is done by the machine. In the \"full-automated\" approach all the above activities are done by the machine. There are no tools at the moment that achieve full automated composition. Web API composition is like WSC, the only difference is the fact that instead of web services there are Web APIs in WAPIC. There is no doubt that One would run into the very same problems of WSC concerning full automated composition. In other words, WAPIC would in no way be easier than WSC. Nevertheless, as far as semi automated form can be achived, WAPIC is valuable because the number of Web APIs increase drastically from day to day. The site \"programmableWeb\" lists 14 957 APIs at the moment. It is not easy to search for all those APIs manually for the discovery of suitable APIs for a given task.","name":"Application development via Web services composition","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB7-1","description":"Hypertext markup scripting and styling are the base for each web page or application. Styling defines the look and feel while scripting is used to implement the behavior of the web application","name":"Hypertext markup scripting and styling","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB7-2","description":"Web map APIs allow developers to integrate resources made available by web services in their application or web sites.","name":"Web Map APIs and Libraries","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB7-3","description":"A web application framework provides the generic and reusable building blocks needed to create web applications. Geoportal frameworks provide the functionality to build geospatial portals.","name":"Web application Frameworks and Geoportal frameworks","selfAssesment":"In Progress (GI-N2K)
"},{"code":"WB7","description":"Characteristic examples are included under this topic. The APIs, for instance other than the ones included under this unit, and libraries could have been included as well. However, since the important thing is to highlight the functionality then there is no need to include them all. By the inclusion of topic \"WB7-3\"under this unit, the aim was to cover one of the very \"hot\"topics of Web2.0 for both the main concepts about Web application frameworks and also how they are related to portal frameworks and geoportals. By the topic \"WB7-1 Building blocks\"the core components of Web application development are covered. On top of this core, there comes a great variety of \"Web application frameworks for both enabling rapid web application development and ensuring scalable, high-performance applications. Finally, there are \"Web APIs and Libraries\" certainly deserving being a separate topic for their current popularity. They also mean rapid application development for developers by code reuse and versatality for \"end users\" in creating their \"end products\".","name":"Web Application development elements","selfAssesment":"In progress (GI-N2K)
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(2020) Cartography in the European Enlightment (The history of cartography.; volume 4) Chicago: University of Chicago Press.","url":"http://books.google.com/books?isbn=9780226184753"},{"concepts":[90,102],"description":"Digital Object Identifier (DOI): 10.1007/3-540-60392-1_1","name":"Egenhofer, M. and Mark, D. M. (1995). Naive geography. In A. Frank and W. Kuhn (Eds.), Lecture notes in computer science, 988, 1-15. Semmering, Austria: Springer-Verlag.","url":"http://dx.doi.org/10.1007/3-540-60392-1_1"},{"concepts":[120],"description":"Digital Object Identifier (DOI): 10.1007/3-540-54414-3_36","name":"Egenhofer, M.J. (1991) Reasoning about binary topological relations. Lecture Notes in Computer Science, 525, 144-160.","url":"http://dx.doi.org/10.1007/3-540-54414-3_36"},{"concepts":[222,422],"description":" ","name":"Egenhofer, M.J., & Franzosa, R.D. (1991). Point-set topological spatial relations. International Journal of Geographical Information Systems, 5, 161-174","url":" "},{"concepts":[165],"description":" ","name":"Eichelberger, P. (1993). Maturing GIS: Anecdotes, examples, and lessons, Geo Info Systems 3(5), 29-38.","url":" "},{"concepts":[380],"description":" ","name":"El Garouani, A., Alobeid, A., El Garouani, S., 2014. Digital surface model based on aerial image stereo pairs for 3D building. International Journal of Sustainable Built Environment 3, 119-126.","url":" "},{"concepts":[526],"description":" ","name":"Elachi, C., & Van Zyl, J. J. (2006). Introduction to the physics and techniques of remote sensing (Vol. 28). John Wiley & Sons.","url":" "},{"concepts":[94,95,96,100,287],"description":"ISBN/section: 9781420038330/chapter10","name":"Elmes, G. A., Epstein, E. F., McMaster, R. B., Niemann, B. J., Poore, B., Sheppard, E., and Tullock, D. L. (2004). GIS and society: Interrelation, integration, and transformation. In R.B. McMaster and L. Usery (Eds.), A research agenda for geographic information science, (287-312). Bacon Raton, FL: CRC Press.","url":"http://books.google.com/books?isbn=9781420038330"},{"concepts":[485],"description":" ","name":"Elsner, P. (2005). GIS teaching via distance learning experiences and lessons learned. Planet, 14(1), 28-29.","url":"https://doi.org/10.11120/plan.2005.00140028"},{"concepts":[315],"description":" ","name":"Elwood, S. (2008). Volunteered geographic information: Future research directions motivated by critical, participatory, and feminist GIS. GeoJournal, 72(3-4), 173-183.","url":"http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.464.751&rep=rep1&type=pdf"},{"concepts":[290],"description":" ","name":"Elwood, S., & Leszczynski, A. (2011). Privacy, reconsidered: New representations, data practices, and the geoweb. Geoforum, 42(1), 6-15.","url":"https://doi.org/10.1016/j.geoforum.2010.08.003"},{"concepts":[305],"description":" ","name":"Elwood, S., Goodchild, M. F., & Sui, D. 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Proceedings of the IEEE, 704-716, DOI: : 10.1109/JPROC.2010.2043918.","url":"https://ieeexplore.ieee.org/document/5460980"},{"concepts":[755,870],"description":" ","name":"EO geospatial products for the Oil & Gas Industry.(n.d.). EO4OG Products. Retrieved from: https://earsc-portal.eu/display/EO4/EO4OG+Products","url":"https://earsc-portal.eu/display/EO4/EO4OG+Products"},{"concepts":[339],"description":" ","name":"ESA (2020), Calibration - Sentinel-2 MSI Technical Guide - Sentinel Online. Retrieved from https://earth.esa.int/web/sentinel/technical-guides/sentinel-2-msi/calibration","url":"https://earth.esa.int/web/sentinel/technical-guides/sentinel-2-msi/calibration"},{"concepts":[741,339],"description":" ","name":"ESA (2020), Level-1C Algorithm - Sentinel-2 MSI Technical Guide - Sentinel Online. Retrieved from https://earth.esa.int/web/sentinel/technical-guides/sentinel-2-msi/level-1c/algorithm","url":"https://earth.esa.int/web/sentinel/technical-guides/sentinel-2-msi/level-1c/algorithm"},{"concepts":[706,710],"description":"ESA (2020). User Guides. Sentinel-1 SAR. Acquisition Modes. Extra Wide Swath. © ESA 2000 - 2020","name":"ESA (2020). User Guides. Sentinel-1 SAR. Acquisition Modes. Extra Wide Swath. © ESA 2000 - 2020","url":"https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/acquisition-modes/extra-wide-swath"},{"concepts":[594,649,650,651],"description":"ESA (2020). Radar and SAR Glossary. earthnet online.","name":"ESA (2020). Radar and SAR Glossary. earthnet online.","url":"https://earth.esa.int/handbooks/asar/CNTR5-2.html"},{"concepts":[658],"description":"ESA (2020). Radar Course 3. Shadow. Earth Online.","name":"ESA (2020). Radar Course 3. Shadow. Earth Online.","url":"https://earth.esa.int/web/guest/missions/esa-operational-eo-missions/ers/instruments/sar/applications/radar-courses/content-3/-/asset_publisher/mQ9R7ZVkKg5P/content/radar-course-3-shadow"},{"concepts":[656,657],"description":"ESA (2020). Radar Course 3. Slant range / ground range. Earth Online.","name":"ESA (2020). Radar Course 3. Slant range / ground range. Earth Online.","url":"https://earth.esa.int/web/guest/missions/esa-operational-eo-missions/ers/instruments/sar/applications/radar-courses/content-3/-/asset_publisher/mQ9R7ZVkKg5P/content/radar-course-3-slant-range-ground-range"},{"concepts":[715],"description":"ESA (2020). Types of orbits.","name":"ESA (2020). Types of orbits.","url":"https://www.esa.int/Our_Activities/Space_Transportation/Types_of_orbits"},{"concepts":[716],"description":" ","name":"ESA Sentinel 1 acquisition modes","url":"https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/acquisition-modes"},{"concepts":[602,714,743,752],"description":"Categorised from A to Z and by Space Agency, there are over 1000 in-depth articles of satellite missions from 1959 to 2025 and information on airborne sensors. The missions database can be filtered by a range of criteria using the Search Missions filter drop down menus below, enabling you to find specific missions easily.","name":"ESA, 2020. eo-Sharing Earth Observation Resources. eoPortal Directory.","url":"https://directory.eoportal.org/web/eoportal/satellite-missions"},{"concepts":[849],"description":" ","name":"Esch, T., Heldens, W., Hirner, A., Keil, M., Marconcini, M., Roth, A., Zeidler, J., Dech, S., Strano, E. (2017). Breaking new ground in mapping human settlements from space – The Global Urban Footprint. ISPRS Journal of Photogrammetry and Remote Sensing, 134, 30-42. doi:https://doi.org/10.1016/j.isprsjprs.2017.10.012","url":"https://doi.org/10.1016/j.isprsjprs.2017.10.012"},{"concepts":[6],"description":" ","name":"Ester M., Kriegel HP., Sander J. (1999) Knowledge Discovery in Spatial Databases. In: Förstner W., Buhmann J.M., Faber A., Faber P. (eds) Mustererkennung 1999. Informatik aktuell. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-60243-6_1","url":"https://link.springer.com/chapter/10.1007/978-3-642-60243-6_1"},{"concepts":[815],"description":" ","name":"European Association of Remote Sensing Companies (EARSC), (2020). Forecast and assess landslides. Retrieved from: https://earsc-portal.eu/display/EOwiki/Forecast+and+assess+landslides","url":"https://earsc-portal.eu/display/EOwiki/Forecast+and+assess+landslides"},{"concepts":[832],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Assess and monitor coastal water quality. Retrieved from https://earsc-portal.eu/display/EOwiki/Assess+and+monitor+coastal+water+quality","url":"https://earsc-portal.eu/display/EOwiki/Assess+and+monitor+coastal+water+quality"},{"concepts":[816],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Assess and Monitor Volcanic Activity. Retrieved from: https://earsc-portal.eu/display/EOwiki/Assess+and+Monitor+Volcanic+Activity","url":"https://earsc-portal.eu/display/EOwiki/Assess+and+Monitor+Volcanic+Activity"},{"concepts":[821],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Assess and monitor water bodies. Retrieved from https://earsc-portal.eu/display/EOwiki/Assess+and+monitor+water+bodies","url":"https://earsc-portal.eu/display/EOwiki/Assess+and+monitor+water+bodies"},{"concepts":[804],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Assess changes in the carbon balance. Retrieved from https://earsc-portal.eu/display/EOwiki/Assess+changes+in+the+carbon+balance","url":"https://earsc-portal.eu/display/EOwiki/Assess+changes+in+the+carbon+balance"},{"concepts":[819],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Assess crop damage due to storms. Retrieved from https://earsc-portal.eu/display/EOwiki/Assess+crop+damage+due+to+storms","url":"https://earsc-portal.eu/display/EOwiki/Assess+crop+damage+due+to+storms"},{"concepts":[814],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Assess damage from earthquakes. Retrieved from https://earsc-portal.eu/display/EOwiki/Assess+damage+from+earthquakes","url":"https://earsc-portal.eu/display/EOwiki/Assess+damage+from+earthquakes"},{"concepts":[820],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Assess Deforestation or Forest Degradation. Retrieved from https://earsc-portal.eu/display/EOwiki/Assess+Deforestation+or+Forest+Degradation","url":"https://earsc-portal.eu/display/EOwiki/Assess+Deforestation+or+Forest+Degradation"},{"concepts":[819],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Assess Environmental impact of farming. Retrieved from https://earsc-portal.eu/display/EOwiki/Assess+Environmental+impact+of+farming","url":"https://earsc-portal.eu/display/EOwiki/Assess+Environmental+impact+of+farming"},{"concepts":[820],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Assess environmental impact of forestry. Retrieved from https://earsc-portal.eu/display/EOwiki/Assess+environmental+impact+of+forestry","url":"https://earsc-portal.eu/display/EOwiki/Assess+environmental+impact+of+forestry"},{"concepts":[823],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Assess environmental impact of human activities . Retrieved from https://earsc-portal.eu/display/EOwiki/Assess+environmental+impact+of+human+activities","url":"https://earsc-portal.eu/display/EOwiki/Assess+environmental+impact+of+human+activities"},{"concepts":[820],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Assess forest damage due to storms or insects. Retrieved from https://earsc-portal.eu/display/EOwiki/Assess+forest+damage+due+to+storms+or+insects","url":"https://earsc-portal.eu/display/EOwiki/Assess+forest+damage+due+to+storms+or+insects"},{"concepts":[821],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Assess ground water and run-off. Retrieved from https://earsc-portal.eu/display/EOwiki/Assess+ground+water+and+run-off","url":"https://earsc-portal.eu/display/EOwiki/Assess+ground+water+and+run-off"},{"concepts":[824],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Assess land value, ownership, type use. Retrieved from https://earsc-portal.eu/display/EOwiki/Assess+land+value%2C+ownership%2C+type%2C+use","url":"https://earsc-portal.eu/display/EOwiki/Assess+land+value%2C+ownership%2C+type%2C+use"},{"concepts":[824],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Assess pressures on populations and migration. Retrieved from https://earsc-portal.eu/display/EOwiki/Assess+pressures+on+populations+and+migration","url":"https://earsc-portal.eu/display/EOwiki/Assess+pressures+on+populations+and+migration"},{"concepts":[825],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Baseline mapping. Retrieved from https://earsc-portal.eu/display/EOwiki/Baseline+mapping","url":"https://earsc-portal.eu/display/EOwiki/Baseline+mapping"},{"concepts":[825],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Detect and monitor ground movement. Retrieved from https://earsc-portal.eu/display/EOwiki/Detect+and+monitor+ground+movement","url":"https://earsc-portal.eu/display/EOwiki/Detect+and+monitor+ground+movement"},{"concepts":[833],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Detect and monitor hurricanes and typhoons. Retrieved from https://earsc-portal.eu/display/EOwiki/Detect+and+monitor+hurricanes+and+typhoons","url":"https://earsc-portal.eu/display/EOwiki/Detect+and+monitor+hurricanes+and+typhoons"},{"concepts":[836],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Detect and monitor ice-risk at sea. Retrieved from https://earsc-portal.eu/display/EOwiki/Detect+and+monitor+ice-risk+at+sea","url":"https://earsc-portal.eu/display/EOwiki/Detect+and+monitor+ice-risk+at+sea"},{"concepts":[834],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Detect and monitor illegal fishing. Retrieved from https://earsc-portal.eu/display/EOwiki/Detect+and+monitor+illegal+fishing","url":"https://earsc-portal.eu/display/EOwiki/Detect+and+monitor+illegal+fishing"},{"concepts":[831],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Detect and monitor oil slicks. Retrieved from https://earsc-portal.eu/display/EOwiki/Detect+and+monitor+oil+slicks","url":"https://earsc-portal.eu/display/EOwiki/Detect+and+monitor+oil+slicks"},{"concepts":[818,813],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Detect and monitor wildfires. Retrieved from https://earsc-portal.eu/display/EOwiki/Detect+and+monitor+wildfires","url":"https://earsc-portal.eu/display/EOwiki/Detect+and+monitor+wildfires"},{"concepts":[822],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Detect changes in glaciers. Retrieved from https://earsc-portal.eu/display/EOwiki/Detect+changes+in+glaciers","url":"https://earsc-portal.eu/display/EOwiki/Detect+changes+in+glaciers"},{"concepts":[820],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Detect illegal forest activities. Retrieved from https://earsc-portal.eu/display/EOwiki/Detect+illegal+forest+activities","url":"https://earsc-portal.eu/display/EOwiki/Detect+illegal+forest+activities"},{"concepts":[824],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Detect illegal mining activities . Retrieved from https://earsc-portal.eu/display/EOwiki/Detect+illegal+mining+activities","url":"https://earsc-portal.eu/display/EOwiki/Detect+illegal+mining+activities"},{"concepts":[819],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Detect illegal or undesired crops. Retrieved from https://earsc-portal.eu/display/EOwiki/Detect+illegal+or+undesired+crops","url":"https://earsc-portal.eu/display/EOwiki/Detect+illegal+or+undesired+crops"},{"concepts":[835],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Detect ships in critical areas. Retrieved from https://earsc-portal.eu/display/EOwiki/Detect+ships+in+critical+areas","url":"https://earsc-portal.eu/display/EOwiki/Detect+ships+in+critical+areas"},{"concepts":[755,791,759,756,757,758,763,761,762,770,764,765,766,767,768,769,775,771,772,773,774,778,776,777,782,779,780,789,783,786,784,785,790,787,788,838,811,781,808,809,810],"description":" ","name":"European Association of Remote Sensing Companies. (2020). EO Services (Markets). Retrieved from https://earsc-portal.eu/pages/viewpage.action?pageId=78221916","url":"https://earsc-portal.eu/pages/viewpage.action?pageId=78221916"},{"concepts":[833],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Forecast and map large waves. Retrieved from https://earsc-portal.eu/display/EOwiki/Forecast+and+map+large+waves","url":"https://earsc-portal.eu/display/EOwiki/Forecast+and+map+large+waves"},{"concepts":[760,833],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Forecast and monitor current movement and drift. Retrieved from https://earsc-portal.eu/display/EOwiki/Forecast+and+monitor+current+movement+and+drift","url":"https://earsc-portal.eu/display/EOwiki/Forecast+and+monitor+current+movement+and+drift"},{"concepts":[833],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Forecast and monitor ocean winds and waves. Retrieved from https://earsc-portal.eu/display/EOwiki/Forecast+and+monitor+ocean+winds+and+waves","url":"https://earsc-portal.eu/display/EOwiki/Forecast+and+monitor+ocean+winds+and+waves"},{"concepts":[819],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Forecast crop yields. Retrieved from https://earsc-portal.eu/display/EOwiki/Forecast+crop+yields","url":"https://earsc-portal.eu/display/EOwiki/Forecast+crop+yields"},{"concepts":[805],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Forecast weather. Retrieved from https://earsc-portal.eu/display/EOwiki/Forecast+weather","url":"https://earsc-portal.eu/display/EOwiki/Forecast+weather"},{"concepts":[803],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Forecasting sunlight exposure. Retrieved from https://earsc-portal.eu/display/EOwiki/Forecasting+sunlight+exposure","url":"https://earsc-portal.eu/display/EOwiki/Forecasting+sunlight+exposure"},{"concepts":[826],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Identify hydrocarbon seeps in soil. Retrieved from https://earsc-portal.eu/display/EOwiki/Identify+hydrocarbon+seeps+in+soil","url":"https://earsc-portal.eu/display/EOwiki/Identify+hydrocarbon+seeps+in+soil"},{"concepts":[818,812],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Map and assess flooding. Retrieved from https://earsc-portal.eu/display/EOwiki/Map+and+assess+flooding","url":"https://earsc-portal.eu/display/EOwiki/Map+and+assess+flooding"},{"concepts":[760],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Map and monitor hydroelectric energy. Retrieved from https://earsc-portal.eu/display/EOwiki/Map+and+monitor+hydroelectric+energy","url":"https://earsc-portal.eu/display/EOwiki/Map+and+monitor+hydroelectric+energy"},{"concepts":[760],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Map and monitor solar energy (solar farms). Retrieved from https://earsc-portal.eu/pages/viewpage.action?pageId=78221967","url":"https://earsc-portal.eu/pages/viewpage.action?pageId=78221967"},{"concepts":[760],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Map and monitor wind energy (wind farms). Retrieved from https://earsc-portal.eu/pages/viewpage.action?pageId=78221973","url":"https://earsc-portal.eu/pages/viewpage.action?pageId=78221973"},{"concepts":[834],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Map fish shoals. Retrieved from https://earsc-portal.eu/display/EOwiki/Map+fish+shoals","url":"https://earsc-portal.eu/display/EOwiki/Map+fish+shoals"},{"concepts":[826],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Map geological features. Retrieved from https://earsc-portal.eu/display/EOwiki/Map+geological+features","url":"https://earsc-portal.eu/display/EOwiki/Map+geological+features"},{"concepts":[826],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Map seismic survey operations. Retrieved from https://earsc-portal.eu/display/EOwiki/Map+seismic+survey+operations","url":"https://earsc-portal.eu/display/EOwiki/Map+seismic+survey+operations"},{"concepts":[832],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Map water depth or charting. Retrieved from https://earsc-portal.eu/display/EOwiki/Map+water+depth+or+charting","url":"https://earsc-portal.eu/display/EOwiki/Map+water+depth+or+charting"},{"concepts":[825],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Measure & detect land surface change. Retrieved from https://earsc-portal.eu/display/EOwiki/Measure+detect+land+surface+change","url":"https://earsc-portal.eu/display/EOwiki/Measure+detect+land+surface+change"},{"concepts":[824],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Measure land use statistics. Retrieved from https://earsc-portal.eu/display/EOwiki/Measure+land+use+statistics","url":"https://earsc-portal.eu/display/EOwiki/Measure+land+use+statistics"},{"concepts":[803],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor air quality & emissions. Retrieved from https://earsc-portal.eu/pages/viewpage.action?pageId=78221935","url":"https://earsc-portal.eu/pages/viewpage.action?pageId=78221935"},{"concepts":[832],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor coastal ecosystem. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+coastal+ecosystem","url":"https://earsc-portal.eu/display/EOwiki/Monitor+coastal+ecosystem"},{"concepts":[830,829],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor construction and buildings. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+construction+and+buildings","url":"https://earsc-portal.eu/display/EOwiki/Monitor+construction+and+buildings"},{"concepts":[820],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor forest carbon content. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+forest+carbon+content","url":"https://earsc-portal.eu/display/EOwiki/Monitor+forest+carbon+content"},{"concepts":[820],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor forest resources. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+forest+resources","url":"https://earsc-portal.eu/display/EOwiki/Monitor+forest+resources"},{"concepts":[824],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor humanitarian movement and camps. 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Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+land+ecosystems+and+biodiversity","url":"https://earsc-portal.eu/display/EOwiki/Monitor+land+ecosystems+and+biodiversity"},{"concepts":[823],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor land pollution. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+land+pollution","url":"https://earsc-portal.eu/display/EOwiki/Monitor+land+pollution"},{"concepts":[831],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor marine habitats. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+marine+habitats","url":"https://earsc-portal.eu/display/EOwiki/Monitor+marine+habitats"},{"concepts":[826],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor mineral extraction. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+mineral+extraction","url":"https://earsc-portal.eu/display/EOwiki/Monitor+mineral+extraction"},{"concepts":[832],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor ocean level and surface. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+ocean+level+and+surface","url":"https://earsc-portal.eu/display/EOwiki/Monitor+ocean+level+and+surface"},{"concepts":[831],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor ocean quality and productivity. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+ocean+quality+and+productivity","url":"https://earsc-portal.eu/display/EOwiki/Monitor+ocean+quality+and+productivity"},{"concepts":[831],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor oil rigs and flares. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+oil+rigs+and+flares","url":"https://earsc-portal.eu/display/EOwiki/Monitor+oil+rigs+and+flares"},{"concepts":[831],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor pollution at sea. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+pollution+at+sea","url":"https://earsc-portal.eu/display/EOwiki/Monitor+pollution+at+sea"},{"concepts":[807],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor sensitive risk areas. Retrieved from: https://earsc-portal.eu/display/EOwiki/Monitor+sensitive+risk+areas","url":"https://earsc-portal.eu/display/EOwiki/Monitor+sensitive+risk+areas"},{"concepts":[835],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor ships movements. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+ships+movements","url":"https://earsc-portal.eu/display/EOwiki/Monitor+ships+movements"},{"concepts":[822],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor snow cover. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+snow+cover","url":"https://earsc-portal.eu/display/EOwiki/Monitor+snow+cover"},{"concepts":[832],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor the coast line. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+the+coast+line","url":"https://earsc-portal.eu/display/EOwiki/Monitor+the+coast+line"},{"concepts":[830,828],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor urban areas. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+urban+areas","url":"https://earsc-portal.eu/display/EOwiki/Monitor+urban+areas"},{"concepts":[824],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor vegetation encroachment. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+vegetation+encroachment","url":"https://earsc-portal.eu/display/EOwiki/Monitor+vegetation+encroachment"},{"concepts":[819],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor water use on crops and horticulture. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+water+use+on+crops+and+horticulture","url":"https://earsc-portal.eu/display/EOwiki/Monitor+water+use+on+crops+and+horticulture"},{"concepts":[803],"description":" ","name":"European Association of Remote Sensing Companies. (n.d.). Product sheet: Air Quality CO2. Retrieved from https://earsc-portal.eu/display/EO4RawMaterials/Product+Sheet%3A+Air+Quality+CO2","url":"https://earsc-portal.eu/display/EO4RawMaterials/Product+Sheet%3A+Air+Quality+CO2"},{"concepts":[836],"description":" ","name":"European Centre for Medium-Range Weather Forecasts, & Copernicus Programme. (2020). Global Shipping Project - Copernicus. Retrieved from https://climate.copernicus.eu/index.php/global-shipping-project","url":"https://climate.copernicus.eu/index.php/global-shipping-project"},{"concepts":[803],"description":" ","name":"European Comission. (2015). An Operational Anthropogenic CO₂ Emissions Monitoring & Verification Support Capacity.","url":"https://www.copernicus.eu/sites/default/files/2019-09/CO2_Blue_report_2015.pdf"},{"concepts":[803],"description":" ","name":"European Comission. (2017). 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Retrieved from: https://ec.europa.eu/programmes/horizon2020/en/h2020-section/societal-challenges","url":"https://ec.europa.eu/programmes/horizon2020/en/h2020-section/societal-challenges"},{"concepts":[823],"description":" ","name":"European Commission Joint Research Centre. (2020). Vegetation - Copernicus landm monitoring service. Retrieved from https://land.copernicus.eu/global/themes/Vegetation","url":"https://land.copernicus.eu/global/themes/Vegetation"},{"concepts":[795],"description":" ","name":"European Commission. (2020). Digital skills and jobs - Shaping Europe's digital future. Retrived from https://ec.europa.eu/digital-single-market/en/policies/digital-skills","url":"https://ec.europa.eu/digital-single-market/en/policies/digital-skills"},{"concepts":[795],"description":" ","name":"European Commission. (2020). Employment, Social Affairs & Inclusion. 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Retrieved from https://ec.europa.eu/info/news/sustainability-at-the-water-source_en","url":"https://ec.europa.eu/info/news/sustainability-at-the-water-source_en"},{"concepts":[797],"description":" ","name":"European Commission. (2020). Sustainable agriculture in the CAP. Retrieved from https://ec.europa.eu/info/food-farming-fisheries/sustainability/sustainable-cap_en","url":"https://ec.europa.eu/info/food-farming-fisheries/sustainability/sustainable-cap_en"},{"concepts":[798],"description":" ","name":"European Commission. (2020). Transport. Retrieved from https://ec.europa.eu/info/policies/transport_en","url":"https://ec.europa.eu/info/policies/transport_en"},{"concepts":[837],"description":" ","name":"European Environment Agency. (2016). Monitoring of marine waters. 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Understanding risk with Earth observation. Retreived from: https://www.esa.int/Applications/Observing_the_Earth/Understanding_risk_with_Earth_observation","url":"https://www.esa.int/Applications/Observing_the_Earth/Understanding_risk_with_Earth_observation"},{"concepts":[807],"description":" ","name":"European Union. (2018). Critical Infrastructure Analysis. Retrieved from: https://sea.security.copernicus.eu/categories/critical-infrastructure-analysis/","url":"https://sea.security.copernicus.eu/categories/critical-infrastructure-analysis/"},{"concepts":[859],"description":" ","name":"European Union. (2020). Rapid mapping. Retrieved from: https://emergency.copernicus.eu/mapping/ems/rapid-mapping-portfolio","url":"https://emergency.copernicus.eu/mapping/ems/rapid-mapping-portfolio"},{"concepts":[124],"description":"ISBN number: 9781118653104","name":"Fairchild, M. D., (2005). 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and connectionist theories of human cognition and memory and their ability to model various cases"},{"concepts":[54],"name":"Compare and contrast the terms multi-criteria evaluation, weighted linear combination, and site suitability analysis"},{"concepts":[106],"name":"Compare and contrast the theory that properties are fundamental (and objects are human simplifications of patterns thereof) with the theory that objects are fundamental (and properties are attributes thereof)"},{"concepts":[88],"name":"Compare and contrast theories of spatial knowledge acquisition (e.g., Marr on vision, Piaget on childhood, Golledge on wayfinding)"},{"concepts":[483],"name":"Compare and contrast training methods utilized in a non-profit to those employed in a local government agency"},{"concepts":[612],"name":"Compare and discuss attenuation length and penetration depth of the optical and radar signal"},{"concepts":[706,708,711,707],"name":"Compare and discuss different SAR acquisition modes"},{"concepts":[496],"name":"Compare and explain different models for funding an SDI"},{"concepts":[295],"name":"Compare and explain the main business models in the GI domain"},{"concepts":[72],"name":"Compare block-kriging with areal interpolation using proportional area weighting and dasymetric mapping"},{"concepts":[266],"name":"Compare common sensors by spatial resolution, spectral sensitivity, ground coverage, and temporal resolution [e.g., AVHRR, MODIS (intermediate resolution ~500 m, high temporal) Landsat, commercial high resolution (Ikonos and Quickbird); ..."},{"concepts":[240],"name":"Compare commonalities and patterns of geocomputation to other related terms"},{"concepts":[14],"name":"Compare current accessibility models with early models of market potential"},{"concepts":[393],"name":"Compare different deep learning approaches in EO image classification"},{"concepts":[248],"name":"Compare different design choices in developing spatial simulation models"},{"concepts":[902],"name":"compare different development components and their advantages and disadvantages"},{"concepts":[439],"name":"Compare different error metrics that are based on the error matrix"},{"concepts":[164],"name":"Compare different evaluation methods for cartography and visualization products (e.g., qualitative versus quantitative, formative versus summative studies)."},{"concepts":[497],"name":"Compare different frameworks for assessing Spatial Data Infrastructures"},{"concepts":[878],"name":"Compare different Geospatial object and geometry definitions included under this topic"},{"concepts":[245],"name":"Compare different options of combining space-time dynamics approaches in spatial modelling"},{"concepts":[353],"name":"Compare different strategies of data assimilation"},{"concepts":[177],"name":"Compare geospatial software architecture through cost-analysis framework"},{"concepts":[822],"name":"Compare glacier extents using EO data"},{"concepts":[806,803,804],"name":"Compare human-induced emissions to natural sources"},{"concepts":[890],"name":"Compare Linked geospatial data to SDI approaches"},{"concepts":[69],"name":"Compare methods of spatial statistical analysis for the testing of hypotheses."},{"concepts":[20],"name":"Compare models and software tools that allow for optimization"},{"concepts":[815],"name":"Compare one optical EO method with a SAR method for landslide mapping and explain their differences"},{"concepts":[719],"name":"Compare reflectance measurements from the field to reflectance values in radiometrically pre-processed EO data"},{"concepts":[120],"name":"Compare relationships between entities, between attributes and between locations."},{"concepts":[410],"name":"Compare results of the Laplacian of Gaussian filter to the original input image"},{"concepts":[303],"name":"Compare the advantages and disadvantages of group participation and individual participation"},{"concepts":[48],"name":"Compare the basic analytical operations of different GISs."},{"concepts":[278],"name":"Compare the concepts of geometric accuracy and topological fidelity"},{"concepts":[257],"name":"Compare the different cultures of Open Science"},{"concepts":[64],"name":"Compare the different types of spatial weight matrices"},{"concepts":[278],"name":"Compare the National Map Accuracy Standard with the ASPRS Coordinate Standard"},{"concepts":[137],"name":"Compare the relative merits of having map labels placed dynamically versus having them saved as annotation data"},{"concepts":[24],"name":"Compare the result of conversion vector/raster or raster/vector and examine the impact of conversion on the quality of the dataset"},{"concepts":[166],"name":"Compile the needs of individual users and tasks into enterprise-wide needs"},{"concepts":[471],"name":"Compute descriptive statistics and geostatistics of geographic data"},{"concepts":[49],"name":"Compute measures of overall dispersion and clustering of point datasets using nearest neighbor distance statistics"},{"concepts":[65],"name":"Compute measures of overall dispersion and clustering of point datasets using nearest neighbor distance statistics"},{"concepts":[65],"name":"Compute Morans I and Gearys c for patterns of attribute data measured on interval ratio scales"},{"concepts":[9],"name":"Compute the alpha, beta, and gamma indices of network connectivity"},{"concepts":[9],"name":"Compute the Detour Index and the measure of network density for a given network"},{"concepts":[9],"name":"Compute the estimated number of fundamental cycles in a graph"},{"concepts":[66],"name":"Compute the Gi and Gi* statistics"},{"concepts":[65],"name":"Compute the K function"},{"concepts":[37],"name":"Compute the mean of directional data"},{"concepts":[10],"name":"Compute the optimum path between two points through a network with Dijkstras algorithm"},{"concepts":[53],"name":"Conduct a simple hierarchical cluster analysis to classify area objects into statistically similar regions"},{"concepts":[76],"name":"Conduct a spatial econometric analysis to test for spatial dependence in the residuals from least-squares models and spatial autoregressive models"},{"concepts":[72],"name":"Conduct a spatial interpolation process using kriging from data description to final error map"},{"concepts":[159],"name":"Construct a new map from an existing one with a biased view"},{"concepts":[34],"name":"Construct a query statement to search for a specific spatial or temporal relationship"},{"concepts":[71],"name":"Construct a semi-variogram and illustrate with a semi-variogram cloud"},{"concepts":[34],"name":"Construct a spatial query to extract all point objects that fall within a polygon"},{"concepts":[64],"name":"Construct a spatial weights matrix for lattice, point, and area patterns"},{"concepts":[214],"name":"Construct a TIN manually from a set of spot elevations"},{"concepts":[149],"name":"Construct a Web page that includes an interactive map"},{"concepts":[623],"name":"Construct scattering matrix"},{"concepts":[105],"name":"Construct taxonomies and dictionaries (also known as formal ontologies) to communicate systems of categories"},{"concepts":[14],"name":"Contrast accessibility modeling at the individual level versus at an aggregated level"},{"concepts":[182],"name":"Contrast cloud and grid computing technologies"},{"concepts":[134],"name":"Contrast gaming elements which are both part of traditional games and geo-games"},{"concepts":[137],"name":"Contrast the strengths and limitations of methods for automatic label placement"},{"concepts":[22],"name":"Convert a dataset from the native format of one GIS product to another"},{"concepts":[127],"name":"Convert historical maps in digital format"},{"concepts":[333],"name":"Convert multispectral image into its principal components"},{"concepts":[24],"name":"Convert vector data to raster format and back using GIS software"},{"concepts":[24],"name":"Convert vector data to raster format and back using the GIS software"},{"concepts":[128],"name":"Correlate map making methods with technological or societal factors across History"},{"concepts":[174],"name":"Create a budget of expected labor costs, including salaries, benefits, training, and other expenses"},{"concepts":[188],"name":"Create a complete design document ready for implementation"},{"concepts":[153],"name":"Create a concept map that represents the contents and topology of a physical or social process"},{"concepts":[412],"name":"Create a convolution filter that integrates the standard deviation of the entire scene in its weights"},{"concepts":[460],"name":"Create a data cube using the data model of the Open data cube initiative"},{"concepts":[8],"name":"Create a data set with network attributes and topology"},{"concepts":[186],"name":"Create a diagram of a conceptual data model for a geospatial application or enterprise database"},{"concepts":[21,130],"name":"Create a flowchart showing the sequence of transformations on a data set (e.g., geometric and radiometric correction and mosaicking of remotely sensed data)"},{"concepts":[147],"name":"Create a map that displays related variables using different mapping methods (e.g., choropleth and proportional symbol, choropleth and cartogram)"},{"concepts":[147],"name":"Create a map that displays related variables using the same mapping method (e.g., bivariate choropleth map, bivariate dot map)"},{"concepts":[146],"name":"Create a map that represents both slope and aspect on the same map using the Moellering-Kimerling coloring method"},{"concepts":[41],"name":"Create a matrix describing the pattern of adjacency in a set of planar enforced polygons"},{"concepts":[52],"name":"Create a matrix that shows spatial interaction"},{"concepts":[894],"name":"Create a new application by combining existing data from different sources"},{"concepts":[158],"name":"Create a project plan for a map, from planning to finalisation"},{"concepts":[433],"name":"Create a protocol for quality assessment of an EO information product that conforms to EO4GEO guidelines"},{"concepts":[153],"name":"Create a pseudo-topographic surface to portray the relationships in a collection of documents"},{"concepts":[899],"name":"Create a sample HTML5 Web page"},{"concepts":[430],"name":"Create a scale space for an image by applying multiple iterations of low-pass filtering"},{"concepts":[325,328],"name":"Create a set of ground control points tying image coordinates to map coordinates of a reference dataset using a digital reference dataset or in-situ GPS measurements"},{"concepts":[148],"name":"Create a temporal sequence representing a dynamic geospatial process"},{"concepts":[167],"name":"Create a user manual to help users understand a process or task"},{"concepts":[466],"name":"Create a web interface and related system architecture that enables image processing by using OGC interfaces"},{"concepts":[226],"name":"Create an adjacency table from a sample network"},{"concepts":[135],"name":"Create an aesthetic map icon library"},{"concepts":[226],"name":"Create an incidence matrix from a sample network"},{"concepts":[352],"name":"Create an integrated population distribution map from census data and EO-based land use classification"},{"concepts":[33],"name":"Create an SQL query to retrieve elements from a GIS"},{"concepts":[185],"name":"Create conceptual, logical, and physical data models using automated software tools"},{"concepts":[50],"name":"Create density maps from point datasets using kernels and density estimation techniques using standard software"},{"concepts":[133],"name":"Create different map layouts using the same map components (main map area, inset maps, titles, legends, scale bars, north arrows, grids and graticule) to produce maps with very distinctive purposes"},{"concepts":[133],"name":"Create different maps using the same data for different purposes and intended audiences (e.g., expert and novice hikers)"},{"concepts":[143],"name":"Create different visual hierarchies to produce maps with different purposes"},{"concepts":[24],"name":"Create estimated tessellated data sets from point samples or isolines using interpolation operations that are appropriate to the specific situation"},{"concepts":[54],"name":"Create initial weights using the analytical hierarchy process (AHP)"},{"concepts":[187],"name":"Create logical models based on conceptual models using UML or other tools"},{"concepts":[144],"name":"Create maps using each of the following methods: choropleth, dasymetric, proportioned symbol, graduated symbol, isoline, dot, cartogram, and flow map"},{"concepts":[869],"name":"Create new EO products out of raw data or other products"},{"concepts":[105],"name":"Create or use GIS data structures to represent categories, including attribute columns, layers themes, shapes, legends, etc."},{"concepts":[174],"name":"Create proposals and presentations to secure funding"},{"concepts":[70],"name":"Create spatial samples under a variety of requirements, such as coverage, randomness, transects"},{"concepts":[159],"name":"Create two versions of the same map addressed to different targets"},{"concepts":[188],"name":"Create UML diagrams of physical models based on logical model diagrams and software requirements"},{"concepts":[144],"name":"Create well-designed legends using the appropriate conventions for the following methods: choropleth, dasymetric, proportioned symbol, graduated symbol, isoline, dot, cartogram, and flow map"},{"concepts":[143],"name":"Critique the graphic design of several maps in terms of balance, legibility, clarity, visual contrast, figure-ground organization, and hierarchal organization"},{"concepts":[149],"name":"Critique the interactive elements of an online map"},{"concepts":[150],"name":"Critique the user interface for existing Internet mapping services"},{"concepts":[229],"name":"Deal with time aspects in modelling data"},{"concepts":[228],"name":"Deal with uncertainty aspects in modelling data"},{"concepts":[862,861],"name":"Decide on urban planning measures on the basis of a semantic 3D model"},{"concepts":[31],"name":"Decide which generalisation technique (aggregation, selection, etc.) is best for a specific situation of reducing map scale."},{"concepts":[141],"name":"Decide which graphical representation better reflects the messages embedded in your story"},{"concepts":[66],"name":"Decompose Morans I and Gearys c into local measures of spatial association"},{"concepts":[186],"name":"Deconstruct an application use case into its conceptual elements"},{"concepts":[316],"name":"Defend or refute the contention that critical studies have an identifiable influence on the development of the information society in general and GIScience in particular"},{"concepts":[315],"name":"Defend or refute the contention that the masculinist culture of computer work in general, and GIS work in particular, perpetuates gender inequality in GIS and T education and training and occupational segregation in the GIS and T workforce"},{"concepts":[28],"name":"Defend or refute the statement \"GIS data are scaleless\""},{"concepts":[85],"name":"Defend or refute the statement, All data are theory-laden"},{"concepts":[109],"name":"Define a field in terms of properties, space, and time"},{"concepts":[166],"name":"Define a methodology for gathering of requirements"},{"concepts":[233],"name":"Define a set of rules for modeling changes in spatial databases"},{"concepts":[223],"name":"Define and describe an application schema"},{"concepts":[305],"name":"Define and discuss enabling technologies: geotag, georeferencing, GPS and more"},{"concepts":[238],"name":"Define and discuss opportunities and limitations of computational science"},{"concepts":[305],"name":"Define and discuss volunteered geographic information"},{"concepts":[305],"name":"Define and discussing impact of Crowdsourcing on Geospatial Society"},{"concepts":[879],"name":"Define and exemplify the reuse of ontologies - Define and identify the role of ontology patterns"},{"concepts":[875],"name":"Define and practice the usage, in a given use case, of StyledLayerDescriptor (SLD) and Symbology Encoding (SE). Practice their usage in a given use case"},{"concepts":[303],"name":"Define and understand citizenship, democracy, maturity, and negotiation related to geo information use and participation in society /community development (at local, regional, national level)"},{"concepts":[33],"name":"Define basic terms of query processing e.g., SQL, primary and foreign keys, table join"},{"concepts":[211],"name":"Define basic terms used in the raster data model (e.g., cell, row, column, value)"},{"concepts":[179,874],"name":"Define characteristics of REST Web services and Resource oriented Architecture (ROA)"},{"concepts":[85],"name":"Define common philosophical theories that have influenced geography and science, such as logical positivism, Marxism, phenomenology, feminism, and critical theory"},{"concepts":[83],"name":"Define common theories on what constitutes knowledge, including positivism, reflectance-correspondence, pragmatism, social constructivism, and memetics"},{"concepts":[81],"name":"Define common theories on what is real, such as realism, idealism, relativism, and experiential realism"},{"concepts":[8],"name":"Define different interpretations of cost in various routing applications"},{"concepts":[37],"name":"Define direction and its measurement in different angular measures"},{"concepts":[186],"name":"Define entities and relationships in conceptual data model"},{"concepts":[60],"name":"Define friction surface"},{"concepts":[878],"name":"Define GeoJSON definition of Geospatial objects and describe the structure of a GeoJSON document and identify advantages and disadvantages of representing the same geospatial data in GML and in GeoJSON"},{"concepts":[59],"name":"Define intervisibility"},{"concepts":[885],"name":"Define Mapping between legacy definition and the semantic definition of publish"},{"concepts":[881],"name":"Define metadata and identify metadata standards like ISO 19115 and 19119 describe their metadata schema generally"},{"concepts":[878],"name":"Define OGC Simple Features Access Schema. Well-Known Text (WKT) and Well-Known Binary (WKB) representations of Geometry"},{"concepts":[68],"name":"Define prior and posterior distributions and Markov-Chain Monte Carlo"},{"concepts":[877],"name":"Define Resource Description Framework (RDF), its RDF graphs, RDF Schema (RDF-S)and a data set in RDF"},{"concepts":[877],"name":"Define Semantic Web and identify the role of the languages included under this topic for Semantic Web"},{"concepts":[179,872],"name":"Define Service Oriented Architecture (SOA) and identify main elements of it"},{"concepts":[119],"name":"Define spatial autocorrelation in the context of geographic proximity"},{"concepts":[878],"name":"Define spatial extensions that GeoSPARQL brings over SPARQL. Identify the difference between qualitative spatial reasoning and quantitative spatial computations"},{"concepts":[106],"name":"Define Stevens four levels of measurement (nominal, ordinal, interval, ratio)"},{"concepts":[222],"name":"Define terms related to topology (e.g., adjacency, connectivity, overlap, intersect, logical consistency)"},{"concepts":[187],"name":"Define the cardinality of relationships"},{"concepts":[179,180,872],"name":"Define the characteristics of web services and present some examples"},{"concepts":[877],"name":"Define the components of a Web Services Description Language (WSDL) document"},{"concepts":[226],"name":"Define the following terms pertaining to a network: Loops, multiple edges, the degree of a vertex, walk, trail, path, cycle, fundamental cycle"},{"concepts":[8],"name":"Define the following terms pertaining to a network: Loops, multiple edges, the degree of a vertex, walk, trail, path, cycle, fundamental cycle"},{"concepts":[90],"name":"Define the following terms: data, information, knowledge, and wisdom"},{"concepts":[97],"name":"Define the four basic dimensions or shapes used to describe spatial objects (i.e., points, lines, regions, volumes)"},{"concepts":[93],"name":"Define the notions of cultural landscape and physical landscape"},{"concepts":[119],"name":"Define the principle of friction of distance and geographic models that are based on it (e.g., gravity models, spatial interaction models)"},{"concepts":[92],"name":"Define the properties that make a phenomenon geographic"},{"concepts":[531],"name":"Define the radiometric spectral quantities brightness, emittance, luminosity"},{"concepts":[531],"name":"Define the radiometric spectral quantities radiance, irradiance, flux"},{"concepts":[2],"name":"Define the terms spatial analysis, spatial modeling, geostatistics, spatial econometrics, spatial statistics, qualitative analysis, map algebra, and network analysis"},{"concepts":[122],"name":"Define uncertainty-related terms, such as error, accuracy, uncertainty, precision, stochastic, probabilistic, deterministic, and random"},{"concepts":[475],"name":"Define user roles for an existing or planned GIS"},{"concepts":[118],"name":"Define various terms used to describe topological relationships, such as disjoint, overlap, within, and intersect"},{"concepts":[896],"name":"Define Web API composition (WAPIC) concept for RESTful WSs and identify main issues"},{"concepts":[875],"name":"Define Web Coverage Service (WCS). Describe GetCapabilities, GetCoverageInfo, and GetCoverage operations in detail. Practice its usage in a given use case"},{"concepts":[875],"name":"Define Web Feature Service (WFS). Describe GetCapabilities, DescribeFeaturetype, and GetFeature, and GetFeatureInfo operations in detail. Practice its usage in a given use case"},{"concepts":[875],"name":"Define Web Map Service (WMS). Describe GetCapabilities, GetMap, and GetFeatureInfo operations in detail. Practice its usage in a given use case"},{"concepts":[875],"name":"Define Web Map Tile Service (WMTS). Describe GetCapabilities, GetTile, and GetFeatureInfo operations in detail. Practice its usage in a given use case"},{"concepts":[875],"name":"Define Web Processing Service (WPS). Describe GetCapabilities, DescribeProcess, and Execute operations in detail. Practice its usage in a given use case"},{"concepts":[896],"name":"Define web services composition (WSC) concept and identify main issues"},{"concepts":[872],"name":"Define Web services transport over the Web"},{"concepts":[879],"name":"Define what an ontology is. Identify differences among ontologies, Thesauri, and taxonomies"},{"concepts":[214],"name":"Delineate a set of break lines that improve the accuracy of a TIN"},{"concepts":[113],"name":"Delineate regions using properties, spatial relationships, and geospatial technologies"},{"concepts":[176],"name":"Deliver a resources plan consistent with organisation’s concrete actions"},{"concepts":[567],"name":"Demonstrate basic knowledge of the atmospheric absorption and scattering mechanisms."},{"concepts":[508,563],"name":"Demonstrate basic knowledge of the interaction between the solar radiation and atmospheric constituents"},{"concepts":[882],"name":"Demonstrate harvesting and crawling mechanisms for automated metadata collection"},{"concepts":[226],"name":"Demonstrate how a network is a connected set of edges and vertices"},{"concepts":[222],"name":"Demonstrate how a topological structure can be represented in a relational database structure"},{"concepts":[41],"name":"Demonstrate how adjacency and connectivity can be recorded in matrices"},{"concepts":[226],"name":"Demonstrate how attributes of networks can be used to represent cost, time, distance, or many other measures"},{"concepts":[235],"name":"Demonstrate how both the time criticality and the data security might determine whether one performs change detection on-line or off-line in a given scenario"},{"concepts":[11],"name":"Demonstrate how capacity is assigned to edges in a network using the appropriate data structure"},{"concepts":[5],"name":"Demonstrate how cluster analysis can be used as a data mining tool"},{"concepts":[10],"name":"Demonstrate how K-shortest path algorithms can be implemented to find many efficient alternate paths across the network"},{"concepts":[9],"name":"Demonstrate how networks can be measured using the number of elements in a network, the distances along network edges, and the level of connectivity of the network"},{"concepts":[71],"name":"Demonstrate how semi-variograms react to spatial nonstationarity"},{"concepts":[77],"name":"Demonstrate how spatial autocorrelation can be removed by resampling"},{"concepts":[75],"name":"Demonstrate how spatially lagged, trend surface, or dummy spatial variables can be used to create the spatial component variables missing in a standard regression analysis"},{"concepts":[148],"name":"Demonstrate how the adding time-series data reveals (or not) patterns not evident in a cross-sectional data"},{"concepts":[39],"name":"Demonstrate how the area of a region calculated from a raster data set will vary by resolution and orientation"},{"concepts":[12],"name":"Demonstrate how the Classic Transportation Problem can be structured as a linear program"},{"concepts":[45],"name":"Demonstrate how the geometric operations of intersection and overlay can be implemented in GIS"},{"concepts":[76],"name":"Demonstrate how the parameters of spatial auto-regressive models can be estimated using univariate and bivariate optimization algorithms for maximizing the likelihood function"},{"concepts":[75],"name":"Demonstrate how the spatial weights matrix is fundamental in spatial econometrics models"},{"concepts":[226],"name":"Demonstrate how the star (or forward star) data structure, which is often employed when digitally storing network information, violates relational normal form, but allows for much faster search and retrieval in network databases"},{"concepts":[888],"name":"Demonstrate how to discover over a catalogue service; and the discovery procedure in OGC CS-W"},{"concepts":[127],"name":"Demonstrate how to georeference an historical map"},{"concepts":[777],"name":"Demonstrate impacts of land use change"},{"concepts":[790],"name":"Demonstrate multidisciplinarity, combining GISciences, Social Sciences, Smart Cities, Computational Sciences and Social Media"},{"concepts":[882],"name":"Demonstrate publishing in some popular SDI (NSDI) portals like INSPIRE and GOS geoportals"},{"concepts":[33],"name":"Demonstrate the basic syntactic structure of SQL"},{"concepts":[51],"name":"Demonstrate the extension of spatial clustering to deal with clustering in space-time using the Know and Mantel tests"},{"concepts":[232],"name":"Demonstrate the importance of a clean, relatively error-free database (together with an appropriate geodetic framework) with the use of GIS software"},{"concepts":[518],"name":"Demonstrate the relationships among measured multi-spectral radiation and specific chemical (e.g. composition) and physical (e.g. temperature, pressure, etc.) properties of the observed matter."},{"concepts":[34],"name":"Demonstrate the syntactic structure of spatial and temporal operators in SQL"},{"concepts":[892],"name":"Demonstrate the usage of popular ETL tools in an NSDI scenario"},{"concepts":[214],"name":"Demonstrate the use of the TIN model for different statistical surfaces (e.g., terrain elevation, population density, disease incidence) in a GIS software application"},{"concepts":[75],"name":"Demonstrate why spatial autocorrelation among regression residuals can be an indication that spatial variables have been omitted from the models"},{"concepts":[45],"name":"Demonstrate why the georegistration of datasets is critical to the success of any map overlay operation"},{"concepts":[172],"name":"Demonstrate why the system design is important in any GIS implementation"},{"concepts":[514],"name":"Derive the Stefan-Boltzman Law from the Planck's one"},{"concepts":[85],"name":"Describe a brief history of major philosophical movements relating to the nature of space, time, geographic phenomena and human interaction with it"},{"concepts":[149],"name":"Describe a mapping goal in which the use of each of the following would be appropriate: brushing, linking, multiple displays"},{"concepts":[46,47],"name":"Describe a real modeling situation in which map algebra would be used e.g., site selection, climate classification, least-cost path"},{"concepts":[282],"name":"Describe a scenario in which data from a secondary source may pose obstacles to effective and efficient use"},{"concepts":[307],"name":"Describe a scenario in which you would find it necessary to report misconduct by a colleague or friend"},{"concepts":[55],"name":"Describe a simple process model that would generate a given set of spatial patterns"},{"concepts":[122],"name":"Describe a stochastic error model for a natural phenomenon"},{"concepts":[307],"name":"Describe a variety of philosophical frameworks upon which codes of professional ethics may be based"},{"concepts":[22,185],"name":"Describe a workflow for converting a implementing a data model in a GIS involving an Entity-Relationship (E-R) diagram and the Universal Modeling Language (UML)"},{"concepts":[218],"name":"Describe alternatives to quadtrees for representing hierarchical tessellations (e.g., hextrees, r-trees, pyramids)"},{"concepts":[235],"name":"Describe an application in which it is crucial to maintain previous versions of the database"},{"concepts":[152],"name":"Describe an example where the use of an augmented environment could be of help"},{"concepts":[496],"name":"Describe and explain the funding model of an existing SDI"},{"concepts":[572],"name":"Describe atmospheric transmittance in the optical spectral range"},{"concepts":[150],"name":"Describe considerations for using maps on the Web as a method for downloading data"},{"concepts":[133],"name":"Describe differences in design needed for a map that is to be viewed on the Internet versus as a 5x7 foot poster, including a discussion of the effect of viewing distance, lighting, and media type"},{"concepts":[104],"name":"Describe different types of movement and change"},{"concepts":[4],"name":"Describe difficulties in dealing with large spatial databases, especially those arising from spatial heterogeneity"},{"concepts":[507],"name":"Describe Electromagnetic Waves in terms of Photons"},{"concepts":[4],"name":"Describe emerging geographical analysis techniques in geocomputation derived from artificial intelligence e.g., expert systems, artificial neural networks, genetic algorithms, and software agents"},{"concepts":[235],"name":"Describe existing algorithms designed for performing dynamic queries"},{"concepts":[872],"name":"Describe generally the hypertext transfer protocol and its main operations like POST and GET"},{"concepts":[119],"name":"Describe geographic phenomena in terms of their distances and directions (in space and time) Define spatial autocorrelation in the context of geographic proximity"},{"concepts":[118],"name":"Describe geographic phenomena in terms of their topological relationships (in space and time to other phenomena"},{"concepts":[556],"name":"Describe how a Michelson interferometer make it possible to measure the emitted Earth radiation with hyperspectral resolution."},{"concepts":[58,61],"name":"Describe how a network of stream channels and ridges can be estimated from a Digital Elevation Model (DEM)"},{"concepts":[80],"name":"Describe how conceptual foundations of GI Science have become implemented in GISs."},{"concepts":[5,7],"name":"Describe how data mining can be used for geospatial intelligence"},{"concepts":[278],"name":"Describe how geometric accuracy should be documented in terms of the FGDC metadata standard"},{"concepts":[298],"name":"Describe how geospatial data are used and maintained for land use planning, property value assessment, maintenance of public works, and other applications"},{"concepts":[473],"name":"Describe how GI S and T can be used in the decision-making process in organizations dealing with natural resource management, business management, public management or operations management"},{"concepts":[49],"name":"Describe how Independent Random Process/Chi-Squared Result IRP/CSR may be used to make statistical statements about point patterns"},{"concepts":[46,47],"name":"Describe how map algebra performs mathematical functions on raster grids"},{"concepts":[511],"name":"Describe how Maxwell's equation explain EM waves' propagation"},{"concepts":[167,168],"name":"Describe how spatial data and GIS&T can be integrated into a workflow process"},{"concepts":[57],"name":"Describe how surfaces can be interpolated using splines"},{"concepts":[527],"name":"Describe how the complex part of the refractive index affects the propagation of e.m. radiation through the matter"},{"concepts":[519],"name":"Describe how the Rayleigh criterion help to discriminate radiation mirroring (or diffusion) for selected surfaces and wavelengths"},{"concepts":[214],"name":"Describe how to generate a unique TIN solution using Delaunay triangulation"},{"concepts":[482],"name":"Describe issues that may hinder implementation and continued successful operation of a GI system if effective methods of staff development are not included in the process"},{"concepts":[890],"name":"Describe Linked Data Browsers; Define Faceted browsers and identify what 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geospatial and other data"},{"concepts":[11],"name":"Describe practical situations in which flow is conserved while splitting or joining at nodes of the network"},{"concepts":[156,157],"name":"Describe print quality characteristics and price differences for limited-run color map distribution"},{"concepts":[156,157],"name":"Describe production concerns that might be discussed with a publisher who will print a map product"},{"concepts":[844],"name":"Describe properties of a particular DEM product"},{"concepts":[890],"name":"Describe Querying Linked Data; SPARQL and GeoSPARQL"},{"concepts":[41],"name":"Describe real world applications where adjacency and connectivity are a critical component of analysis"},{"concepts":[40],"name":"Describe real world applications where distance decay is an appropriate representation of the strength of spatial relationships (e.g., shopping behavior, property values)"},{"concepts":[40],"name":"Describe real world applications where distance decay would not 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edges"},{"concepts":[108],"name":"Describe the difficulties inherent in extending the tabletop metaphor of objects to the geographic environment"},{"concepts":[66],"name":"Describe the effect of non-stationarity on local indices of spatial association"},{"concepts":[65],"name":"Describe the effect of the assumption of stationarity on global measures of spatial association"},{"concepts":[93],"name":"Describe the elements of a sense of place or landscape that are difficult or impossible to adequately represent in GIS"},{"concepts":[314],"name":"Describe the extent to which contemporary GIS and T supports diverse ways of understanding the world"},{"concepts":[52],"name":"Describe the formulation of the classic gravity model, the unconstrained spatial interaction model, the production constrained spatial interaction model, the attraction constrained spatial interaction model, and the doubly constrained spatial..."},{"concepts":[584],"name":"Describe the fundamental thermodynamic processes 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conditions Rayleigh Scattering in the Earth's Atmosphere occurs"},{"concepts":[545],"name":"Describe under which conditions the Beer-Bouguert-Lambert Law well approximates the general radiative transfer equation-"},{"concepts":[117],"name":"Describe ways in which a geographic entity can be created from one or more others"},{"concepts":[539],"name":"Describe what EM sensing means"},{"concepts":[178],"name":"Design a test project to demonstrate interoperability"},{"concepts":[134],"name":"Design a game mechanics of a geo-game"},{"concepts":[147],"name":"Design a map series to show the change in a geographic pattern over time"},{"concepts":[70],"name":"Design a sampling scheme that will help detect when space-time clusters of events occur"},{"concepts":[135],"name":"Design a single map symbol that can be used to symbolize a set of related variables"},{"concepts":[146],"name":"Design a stylized terrain map from a digital elevation model (DEM)"},{"concepts":[234],"name":"Design a test of 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groups, the Delphi method, and/or joint application development"},{"concepts":[871],"name":"Develop Web-GIS solutions to replace each of the functions of a traditional GIS"},{"concepts":[471],"name":"Devise simple ways to represent probability information in GIS"},{"concepts":[289],"name":"Differentiate \"contracts for service\" from \"contracts of service\""},{"concepts":[146],"name":"Differentiate 3D representations from 2.5 D representations"},{"concepts":[212],"name":"Differentiate among a lattice, a tessellation, and a grid"},{"concepts":[23],"name":"Differentiate among common interpolation techniques (e.g., nearest neighbor, bilinear, bicubic)"},{"concepts":[289],"name":"Differentiate among contract liability, tort liability, and statutory liability"},{"concepts":[113],"name":"Differentiate among different types of regions, including functional, cultural, physical, administrative, and others"},{"concepts":[112],"name":"Differentiate among distributions in space, time, and attribute"},{"concepts":[93],"name":"Differentiate among elements of the meaning of a place that can or cannot be easily represented using geospatial technologies"},{"concepts":[28],"name":"Differentiate among the concepts of scale (as in map scale), support, scope, and resolution"},{"concepts":[280],"name":"Differentiate among the spatial, spectral, radiometric, and temporal resolution of a remote sensing instrument"},{"concepts":[303],"name":"Differentiate among universal/deliberative, pluralist/representative, and participatory models of citizen participation"},{"concepts":[473],"name":"Differentiate an enterprise system from a department-centered GI system"},{"concepts":[121],"name":"Differentiate applications in which vagueness is an acceptable trait from those in which it is unacceptable"},{"concepts":[101],"name":"Differentiate applications that can make use of common-sense principles of geography from those that should not"},{"concepts":[18],"name":"Differentiate between a linear program and an integer program"},{"concepts":[881],"name":"Differentiate between a metadata standard and a metadata profile"},{"concepts":[97],"name":"Differentiate between absolute and relative descriptions of location"},{"concepts":[266],"name":"Differentiate between active and passive sensors, citing examples of each"},{"concepts":[179],"name":"Differentiate between and application built with a Service Oriented Architecture (SOA) or a Resource Oriented Architecture (ROA)"},{"concepts":[97],"name":"Differentiate between common-sense, Cartesian metric, relational, relativistic, phenomenological, social constructivist, and other theories of the nature of space"},{"concepts":[186,187],"name":"Differentiate between conceptual and logical models, in terms of the level of detail, constraints, and range of information included"},{"concepts":[304],"name":"Differentiate between consumption, analysis, presumption and production of geoinformation within digital geo media"},{"concepts":[54],"name":"Differentiate between contributing factors and constraints in a multi-criteria application"},{"concepts":[175],"name":"Differentiate between copyleft and permissive licenses for a software product"},{"concepts":[5],"name":"Differentiate between data mining approaches used for spatial and non-spatial applications"},{"concepts":[55],"name":"Differentiate between deterministic and stochastic spatial process models"},{"concepts":[99],"name":"Differentiate between formal and natural language in GI science applications."},{"concepts":[2],"name":"Differentiate between geostatistics, and spatial statistics"},{"concepts":[244],"name":"Differentiate between individual and aggregate models"},{"concepts":[63],"name":"Differentiate between isotropic and anisotropic processes"},{"concepts":[50],"name":"Differentiate between kernel density estimation and spatial interpolation"},{"concepts":[188],"name":"Differentiate between logical and physical models, in terms of the level of detail, constraints, and range of information included"},{"concepts":[213],"name":"Differentiate between lossy and lossless compression methods"},{"concepts":[46,47],"name":"Differentiate between map algebra and matrix algebra using real examples"},{"concepts":[103],"name":"Differentiate between mathematical and phenomenological theories of the nature of time"},{"concepts":[70],"name":"Differentiate between model-based and design-based sampling schemes"},{"concepts":[26],"name":"Differentiate between polynomial coordinate transformations (including linear) and rubbersheeting"},{"concepts":[874],"name":"Differentiate between SOAP and REST Web services. - Identify design issues of REST Web services"},{"concepts":[93],"name":"Differentiate between space and place"},{"concepts":[121],"name":"Differentiate between the following concepts: vagueness and ambiguity, well defined and poorly defined objects and fields or discord and non-specificity"},{"concepts":[52],"name":"Differentiate between the gravity model and spatial interaction models"},{"concepts":[57],"name":"Differentiate between trend surface analysis and deterministic spatial interpolation"},{"concepts":[879],"name":"Differentiate between upper, domain, and application level ontologies"},{"concepts":[266],"name":"Differentiate push-broom and cross-track scanning technologies"},{"concepts":[122],"name":"Differentiate uncertainty in geospatial situations from vagueness"},{"concepts":[138],"name":"Differentiate uses for different types of imagery related to earth"},{"concepts":[109],"name":"Differentiate various sources of fields, such as substance properties (e.g., temperature), artificial constructs (e.g., population density), and fields of potential or influence (e.g., gravity)"},{"concepts":[284],"name":"Digitize and georegister a specified vector feature set to a given geometric accuracy and topological fidelity thresholds using a given map sheet, digitizing tablet, and data entry software"},{"concepts":[311],"name":"Discuss about \"mapping whose reality?\" Pros and cons of geoinformation sharing in social media, i.e. big data, \"digital shadow\" etc."},{"concepts":[299],"name":"Discuss about open data and data sharing and public/private sector"},{"concepts":[293],"name":"Discuss about open data impact on society and citizenship"},{"concepts":[151],"name":"Discuss about the advantages of different immersive display systems"},{"concepts":[159],"name":"Discuss about the degree of subjectivity and/or objectivity of a map"},{"concepts":[125],"name":"Discuss about the History of Cartography in different cultures"},{"concepts":[126],"name":"Discuss about the relationship between art and cartography"},{"concepts":[642],"name":"Discuss advantages and disadvantages of across- and along-track interferometry"},{"concepts":[729,730,731],"name":"Discuss advantages and disadvantages of different methods of storing remote sensing data"},{"concepts":[744,745,746,747],"name":"Discuss advantages and disadvantages of different SAR data formats"},{"concepts":[675],"name":"Discuss advantages and disadvantages of passive and active sensors"},{"concepts":[637],"name":"Discuss advantages of SAR techniques over traditional measuring techniques"},{"concepts":[377],"name":"Discuss algorithms that use the detection of keypoints to identify objects in images"},{"concepts":[678],"name":"Discuss an example of using a radar altimeter"},{"concepts":[736],"name":"Discuss and compare different temporal resolutions of remote sending data"},{"concepts":[631],"name":"Discuss and compare different types of interactions of microwaves with matter"},{"concepts":[743],"name":"Discuss and compare different types of processing levels of optical data"},{"concepts":[748],"name":"Discuss and compare different types of processing levels of SAR data"},{"concepts":[293],"name":"Discuss and define open data and impact on GIS&T"},{"concepts":[363],"name":"Discuss cloud masks as early steps towards semantic enrichment for EO images"},{"concepts":[104],"name":"Discuss common prepositions and adjectives (in any particular language) that signify either spatial or temporal relations but are used for both kinds, such as after or longer"},{"concepts":[245],"name":"Discuss concepts of space-time dynamics for spatial modeling"},{"concepts":[872],"name":"Discuss consensus based interoperability and its relation to geospatial data interchange. Define what a Web Service (WS) is and present characteristic scenarios. Data serving and Data Processing WSs"},{"concepts":[310],"name":"Discuss critiques of GIS as \"deterministic\" technology in relation to debates about the Quantitative quantitative revolution in the discipline of geography."},{"concepts":[314],"name":"Discuss critiques of GIS as deterministic technology in relation to debates about the Quantitative Revolution in the discipline of geography"},{"concepts":[483],"name":"Discuss different formats (tutorials, in house, online, instructor lead) for training and how they can be used by organizations"},{"concepts":[451],"name":"Discuss different methods for assessing the quality of a specific EO product"},{"concepts":[686],"name":"Discuss different types of laser scanners"},{"concepts":[715,591],"name":"Discuss different types of satellite orbits"},{"concepts":[248],"name":"Discuss different ways of simulating space and visualizing model behaviour"},{"concepts":[604],"name":"Discuss electromagnetic interactions and scattering mechanisms"},{"concepts":[721],"name":"Discuss examples of ground-based platforms and their use"},{"concepts":[714],"name":"Discuss examples of the objectives of Earth observation missions"},{"concepts":[481],"name":"Discuss how a code of ethics might be applied within an organization"},{"concepts":[136],"name":"Discuss how cultural differences with respect to color associations impact map design"},{"concepts":[454,733],"name":"Discuss how different spectral resolution of EO sensors influences their potential for vegetation mapping"},{"concepts":[418],"name":"Discuss how hierarchical representation is exploited for object-based image analysis"},{"concepts":[668],"name":"Discuss how line detectors array sensors work"},{"concepts":[406],"name":"Discuss how low-pass filtering of an image impacts the resulting regions derived with watershed segmentation"},{"concepts":[159],"name":"Discuss how maps express relations of power"},{"concepts":[279],"name":"Discuss how measures of spatial autocorrelation may be used to evaluate thematic accuracy"},{"concepts":[735,454],"name":"Discuss how radiometric resolution influences the granularity of a land cover classification"},{"concepts":[732,740],"name":"Discuss how remote sensing data is organized and stored"},{"concepts":[651],"name":"Discuss how the angle of SAR signal incidence affects SAR acquisition"},{"concepts":[398],"name":"Discuss how the choice of sampling strategy impacts the accuracy assesment for a classification result"},{"concepts":[398],"name":"Discuss how the choice of sampling strategy impacts the classification result"},{"concepts":[741],"name":"Discuss how the radiometrically corrected data are processed"},{"concepts":[415],"name":"Discuss how the size of the neighborhood impacts the smoothing effect of a low-pass filter"},{"concepts":[300],"name":"Discuss how to approach the widening audience/participants for geospatial products and service, increasing geo-awareness and geo-enablement"},{"concepts":[143],"name":"Discuss how to create an intellectual and visual hierarchy on maps"},{"concepts":[599],"name":"Discuss how to use phase information in remote sensing"},{"concepts":[31],"name":"Discuss implications of data loss in the case of generalisation of spatial data."},{"concepts":[345],"name":"Discuss imputation methods for filling in missing data"},{"concepts":[508],"name":"Discuss in which way annual solar insolation and average cloud coverage parameters affect the choice of a solar power plant location"},{"concepts":[508],"name":"Discuss in which way modeled daily solar insolation and cloud coverage forecast could affect solar power plant day-by-day management"},{"concepts":[301],"name":"Discuss legal aspects of access to environmental data, global change/warming or sustainable development (regional, national, global) in conjunction to society."},{"concepts":[637],"name":"Discuss limitations of interferometric measurement"},{"concepts":[407],"name":"Discuss limitations of the different region-based segementation methods"},{"concepts":[728],"name":"Discuss main characteristics of digital imagery"},{"concepts":[293],"name":"Discuss of arguments for and against open data"},{"concepts":[292],"name":"Discuss of opportunities for exchange of geospatial data between public and private sector to enable more efficient analysis"},{"concepts":[243],"name":"Discuss options of combining rule-based models with other individual modelling approaches"},{"concepts":[626],"name":"Discuss orientational polarisation of media"},{"concepts":[310],"name":"Discuss over the argument that the use of Geospatial geospatial Information privileges certain views of the world over others."},{"concepts":[299],"name":"Discuss over the changing role of the private sector in the use of geospatial information"},{"concepts":[300],"name":"Discuss over the paradigm shifts and current trends in GIS&T education and pedagogical approaches for GIS teaching and learning in detail"},{"concepts":[311],"name":"Discuss over the various implications of surveillance technology"},{"concepts":[625],"name":"Discuss polarimetric decomporition techniques"},{"concepts":[305],"name":"Discuss positive and negative aspects of the term \"humans as sensors\""},{"concepts":[634],"name":"Discuss radar antennas"},{"concepts":[620],"name":"Discuss scale of roughness of microwaves"},{"concepts":[2],"name":"Discuss situations when it is desirable to adopt a spatial approach to the analysis of data"},{"concepts":[226],"name":"Discuss some of the difficulties of applying the standard process-pattern concept to lines and networks"},{"concepts":[408],"name":"Discuss spatial autocorrelation and homogeneity of image objects"},{"concepts":[174],"name":"Discuss the advantages and disadvantages of outsourcing elements of a GIS project / GI system"},{"concepts":[97],"name":"Discuss the advantages and disadvantages of the use of cartesian metric space as a basis for GIS and related technologies"},{"concepts":[280],"name":"Discuss the advantages and potential problems associated with the use of Minimum Mapping Unit (MMU) as a measure of the level of detail in land use, land cover, and soils maps"},{"concepts":[676],"name":"Discuss the application possibilities of imaging radar"},{"concepts":[692],"name":"Discuss the applications for which Differential Absorption LiDAR can be used"},{"concepts":[693],"name":"Discuss the applications for which Wind Doppler LiDAR is used"},{"concepts":[64],"name":"Discuss the appropriateness of different types of spatial weights matrices for various problems"},{"concepts":[78],"name":"Discuss the appropriateness of GWR under various conditions"},{"concepts":[438],"name":"Discuss the available data quality standards for EO"},{"concepts":[567],"name":"Discuss the basic principles of solar radiation."},{"concepts":[414],"name":"Discuss the benefits of using a gauss filter instead of a mean filter for smoothing an image"},{"concepts":[112],"name":"Discuss the causal relationship between spatial processes and spatial patterns, including the possible problems in determining causality"},{"concepts":[528],"name":"Discuss the change of attenuation length moving from visible to the microwave range and from sea water to solid land surfaces"},{"concepts":[51],"name":"Discuss the characteristics of the various cluster detection techniques"},{"concepts":[25],"name":"Discuss the consequences of increasing and decreasing resolution"},{"concepts":[111],"name":"Discuss the contributions of early attempts to integrate the concepts of space, time, and attribute in geographic information, such as Berry (1964) and Sinton (1978)"},{"concepts":[97],"name":"Discuss the contributions that different perspectives on the nature of space bring to an understanding of geographic phenomenon"},{"concepts":[111],"name":"Discuss the degree to which these models can be implemented using current technologies"},{"concepts":[664],"name":"Discuss the development of remote sensing sensors"},{"concepts":[123],"name":"Discuss the difference between vagueness and uncertainty."},{"concepts":[10],"name":"Discuss the difference of implementing Dijkstras algorithm in raster and vector modes"},{"concepts":[694],"name":"Discuss the differences between imaging and non-imaging sensors"},{"concepts":[133],"name":"Discuss the differences between maps that use the same data but are for different purposes and intended audiences"},{"concepts":[133],"name":"Discuss the differences between maps that use the same data but are for different purposes and intended audiences"},{"concepts":[454],"name":"Discuss the different types of resolution of Earth observation data"},{"concepts":[92],"name":"Discuss the differing denotations and connotations of the terms spatial, geographic, and geospatial"},{"concepts":[110],"name":"Discuss the difficulty of integrating process models into GIS software based on the entity and field views, and methods used to do so"},{"concepts":[117],"name":"Discuss the effects of temporal scale on the modeling of genealogical structures"},{"concepts":[307],"name":"Discuss the ethical implications of a local government's decision to charge fees for its data"},{"concepts":[413],"name":"Discuss the frequencies that a high-pass filter preserves and subdues"},{"concepts":[499],"name":"Discuss the governance structure in place of a particular country"},{"concepts":[222],"name":"Discuss the historical roots of the Census Bureaus creation of GBF/DIME as the foundation for the development of topological data structures"},{"concepts":[703],"name":"Discuss the history of the development of remote sensing platforms"},{"concepts":[108],"name":"Discuss the human predilection to conceptualize geographic phenomena in terms of discrete entities"},{"concepts":[304],"name":"Discuss the impact of geospatial information for the development of social media (Facebook, Twitter, Wikimapia, Flickr etc.) becoming increasingly location-based"},{"concepts":[232],"name":"Discuss the implication of long transactions on database integrity"},{"concepts":[314],"name":"Discuss the implications of interoperability on ontology"},{"concepts":[310],"name":"Discuss the implications of interoperability on ontology"},{"concepts":[280],"name":"Discuss the implications of the sampling theorem (Lambda = 0.5 delta) to the concept of resolution"},{"concepts":[28],"name":"Discuss the implications of tradeoff between data detail and data volume"},{"concepts":[107],"name":"Discuss the importance of space, time, properties, and categories as fundamentals in the conceptualization and representation of spatial entities."},{"concepts":[150],"name":"Discuss the influence of the user interface on maps and visualizations on the Web"},{"concepts":[874],"name":"Discuss the issue whether a service is really \"RESTful\" or not"},{"concepts":[292],"name":"Discuss the legal framework related to competition and public-private sector relationships in the geospatial domain"},{"concepts":[710],"name":"Discuss the main applications using the extra wide swath mode"},{"concepts":[400],"name":"Discuss the main drawback of edge-based segmentation in partitioning an image"},{"concepts":[671],"name":"Discuss the main properties of hyperspectral radiometers"},{"concepts":[670],"name":"Discuss the main properties of passive microwave radiometers"},{"concepts":[669],"name":"Discuss the main properties of thermal radiometers"},{"concepts":[663],"name":"Discuss the main types of remote sensing data"},{"concepts":[663,722],"name":"Discuss the main types of remote sensing platforms"},{"concepts":[663],"name":"Discuss the main types of remote sensing sensors"},{"concepts":[454],"name":"Discuss the minimum spatial resolution required for detecting single houses in a satellite image"},{"concepts":[503],"name":"Discuss the mission, history, constituencies, and activities of the GIS Certification Institute (GISCI)"},{"concepts":[483],"name":"Discuss the National Research Council report on Learning to Think Spatially (2005) as it relates to spatial thinking skills needed by the GIS and T workforce"},{"concepts":[736,454],"name":"Discuss the needs for high temporal resolution for analysing crop cycles in agriculture"},{"concepts":[23],"name":"Discuss the pitfalls of using secondary data that has been generated using interpolations (e.g., Level 1 USGS DEMs)"},{"concepts":[630],"name":"Discuss the polarimetry technique"},{"concepts":[29],"name":"Discuss the possible effects on topological integrity of generalizing data sets"},{"concepts":[289],"name":"Discuss the potential legal problems associated with licensing geospatial information"},{"concepts":[315],"name":"Discuss the potential role of agency (individual action) in resisting dominant practices and in using GIS and T in ways that are consistent with feminist epistemologies and politics"},{"concepts":[404],"name":"Discuss the principles of regionalisation and their use in segmentation methods"},{"concepts":[574],"name":"Discuss the processes that describe the hydrologic cycle"},{"concepts":[316],"name":"Discuss the production, maintenance, and use of geospatial data by a government agency or private firm from the perspectives of a taxpayer, a community organization, and a member of a minority group"},{"concepts":[751],"name":"Discuss the purposes of obtaining remote sensing data"},{"concepts":[605],"name":"Discuss the radiometric anomalies of radar data"},{"concepts":[55],"name":"Discuss the relationship between spatial processes and spatial patterns"},{"concepts":[125],"name":"Discuss the relationship between the history of exploration and the development of a more accurate map of the world"},{"concepts":[30],"name":"Discuss the relationship of attribute measurement levels to database query operations"},{"concepts":[304],"name":"Discuss the role and value of \"place\" and \"space\" for geo media based social networking"},{"concepts":[136],"name":"Discuss the role of gamut in choosing colors that can be reproduced on various devices and media"},{"concepts":[222],"name":"Discuss the role of graph theory in topological structures"},{"concepts":[22],"name":"Discuss the role of metadata in facilitating conversation of data models and data structures between systems"},{"concepts":[301,306],"name":"Discuss the role of public, private sector and citizens in facilitating geospatial information in environmental/sustainable issues."},{"concepts":[292],"name":"Discuss the role of the public and private sectors in producing and dissemination of geospatial information"},{"concepts":[481],"name":"Discuss the status of professional and academic certification in GIS and T"},{"concepts":[290],"name":"Discuss the status of the concept of privacy in the U.S. legal regime"},{"concepts":[142],"name":"Discuss the strengths and weaknesses of infographics as a method of displaying geographic information"},{"concepts":[563],"name":"Discuss the structure and chemical composition of the atmosphere"},{"concepts":[0],"name":"Discuss the synergy between processes in geo-information systems and earth observation systems."},{"concepts":[63],"name":"Discuss the theory leading to the assumption of intrinsic stationarity"},{"concepts":[667],"name":"Discuss the use of area array sensors in remote sensing"},{"concepts":[673],"name":"Discuss the use of atmospheric passive sounders"},{"concepts":[672],"name":"Discuss the use of data obtained by spectroradiometer"},{"concepts":[666],"name":"Discuss the use of digital frame cameras in remote sensing"},{"concepts":[597],"name":"Discuss the use of polarization for different application domains"},{"concepts":[149],"name":"Discuss the uses of the map as a user interface element in interactive presentations of geographic information"},{"concepts":[704],"name":"Discuss the ways of using data acquired by UAS in remote sensing"},{"concepts":[702],"name":"Discuss types and classes of remote sensing sensors"},{"concepts":[456],"name":"Discuss valid time ranges for images used for landslide mapping with pre- and post-event image comparison"},{"concepts":[293],"name":"Discuss various legal aspects of public and private sectors concerning owning, controlling, sharing/ disseminating open data."},{"concepts":[293],"name":"Discuss various sources of open data (science, public and private sectors)"},{"concepts":[288],"name":"Discuss ways in which the geospatial profession is regulated under the U.S. legal regime"},{"concepts":[300],"name":"Discuss ways of working with crowdsourcing in education and research"},{"concepts":[617],"name":"Discuss what horizontal roughness component (correlation legth) is"},{"concepts":[679],"name":"Discuss what information is acquired by the laser altimeters"},{"concepts":[616],"name":"Discuss what surface height variation (or RMS height) is"},{"concepts":[738],"name":"Discuss what the header file describes"},{"concepts":[674],"name":"Discuss what the main characteristics of radiometers are"},{"concepts":[677],"name":"Discuss what types of electromagnetic waves the laser profiler uses"},{"concepts":[365],"name":"Discuss why a query through time is easier realized with a data cube than by comparison of a time series stored in image files"},{"concepts":[737],"name":"Distinguish and explain the different types of properties of digital imagery"},{"concepts":[149,139],"name":"Distinguish between animated and interactive maps"},{"concepts":[89],"name":"Distinguish between continuants and occurrents in relation with spatial phenomena."},{"concepts":[154],"name":"Distinguish between different graphic representation techniques"},{"concepts":[86],"name":"Distinguish between metaphysics and epistemology."},{"concepts":[186],"name":"Distinguish between the temporary and structural relationships in a conceptual model"},{"concepts":[27],"name":"Distinguish between transformation methods for raster and vector representations."},{"concepts":[164,170],"name":"Distinguish between usability, utility, and user needs in the context of geovisualizations"},{"concepts":[167,168],"name":"Document existing and potential tasks in terms of workflow and information flow"},{"concepts":[105],"name":"Document the personal, social, and or institutional meaning of categories used in GIS applications"},{"concepts":[150],"name":"Edit the symbology, labeling, and page layout for a map originally designed for hard copy printing so that it can be seen and used on the Web"},{"concepts":[101],"name":"Effectively communicate the design, procedures, and results of GIS projects to non-GIS audiences (clients, managers, general public)"},{"concepts":[112],"name":"Employ techniques for visualizing, describing, and analyzing distributions in space, time, and attribute"},{"concepts":[790],"name":"Enable citizen skills spatially"},{"concepts":[23],"name":"Estimate a value between two known values using linear interpolation (e.g., spot elevations, population between census years)"},{"concepts":[129],"name":"Estimate the cost to collect needed data from primary sources (e.g., remote sensing, GPS)"},{"concepts":[36],"name":"Estimate the fractal dimension of a sinuous line"},{"concepts":[549],"name":"Estimate the meteorological and the cloud optical properties by LBRTM and validate against high accuracy spectral measurements"},{"concepts":[127],"name":"Estimate the potential value of a historical map"},{"concepts":[437],"name":"Evaluate an EO product and its metadata on its reusability for a new application context"},{"concepts":[476],"name":"Evaluate and revise an existing GIS management strategy"},{"concepts":[790,787,788],"name":"Evaluate citizen-driven observations"},{"concepts":[153],"name":"Evaluate graphic techniques used to portray spatializations"},{"concepts":[25],"name":"Evaluate methods used by contemporary GIS software to resample raster data on-the-fly during display"},{"concepts":[266],"name":"Evaluate the advantages and disadvantages of acoustic remote sensing versus airborne or satellite remote sensing for seafloor mapping"},{"concepts":[266,713,718],"name":"Evaluate the advantages and disadvantages of airborne remote sensing versus satellite remote sensing"},{"concepts":[264],"name":"Evaluate the advantages and disadvantages of photogrammetric methods and LiDAR for production of terrain elevation data"},{"concepts":[110],"name":"Evaluate the assertion that events and processes are the same thing, but viewed at different temporal scales"},{"concepts":[122],"name":"Evaluate the causes of uncertainty in geospatial data"},{"concepts":[136],"name":"Evaluate the colors used in a web map to be used indoors and outdoors"},{"concepts":[434],"name":"Evaluate the conformity of an EO imagery product to ISO 19129"},{"concepts":[93],"name":"Evaluate the differences in how various parties think or feel differently about a place being modeled"},{"concepts":[217],"name":"Evaluate the ease of measuring resolution in different types of tessellations"},{"concepts":[108],"name":"Evaluate the effectiveness of GIS data models for representing the identity, existence, and lifespan of entities"},{"concepts":[109],"name":"Evaluate the field views description of objects as conceptual discretizations of continuous patterns"},{"concepts":[827],"name":"Evaluate the impact of changes in land areas"},{"concepts":[101],"name":"Evaluate the impact of geospatial technologies (e.g., Google Earth) that allow non-geospatial professionals to create, distribute, and map geographic information"},{"concepts":[806,804],"name":"Evaluate the impact of the climate change"},{"concepts":[217],"name":"Evaluate the implications of changing grid cell resolution on the results of analytical applications by using GIS software"},{"concepts":[108],"name":"Evaluate the influence of scale on the conceptualization of entities"},{"concepts":[85],"name":"Evaluate the influences of ones own philosophical views and assumptions on GIS AND T practices"},{"concepts":[81],"name":"Evaluate the influences of particular worldviews (including ones own) on GIS practices"},{"concepts":[95],"name":"Evaluate the influences of political actions, especially the allocation of territory, on human perceptions of space and place"},{"concepts":[95],"name":"Evaluate the influences of political ideologies (e.g., Marxism, Capitalism, conservative liberal) on the understanding of geographic information"},{"concepts":[495],"name":"Evaluate the institutional framework of an existing SDI initiative"},{"concepts":[222],"name":"Evaluate the positive and negative impacts of this shift from integrated topological models"},{"concepts":[213],"name":"Evaluate the relative merits of grid compression methods for storage"},{"concepts":[485],"name":"Evaluate the relevance and applicability of different teaching and learning methods for GIS&T education"},{"concepts":[109],"name":"Evaluate the representation of movement as a field of location over time (e.g. :x,y,z: = f(t) )"},{"concepts":[121],"name":"Evaluate the role that system complexity, dynamic processes, and subjectivity play in the creation of vague phenomena and concepts"},{"concepts":[144],"name":"Evaluate the strengths and limitations of different thematic mapping methods"},{"concepts":[242],"name":"Evaluate the tradeoffs between abstraction and representativeness in simulation model development"},{"concepts":[161],"name":"Evaluate the usability of a hard-copy map"},{"concepts":[161,170],"name":"Evaluate the usability of a web map"},{"concepts":[187],"name":"Evaluate the various general data models common in GIS project"},{"concepts":[121],"name":"Evaluate vagueness in the locations, time, attributes, and other aspects of geographic phenomena"},{"concepts":[29],"name":"Evaluate various line simplification algorithms by their usefulness in different applications"},{"concepts":[243],"name":"Evaluate when rule-based models can be applied to spatiotemporal problems"},{"concepts":[238],"name":"Examine how computational technology relates to geocomputation"},{"concepts":[361],"name":"Examine how the vegetation indices relates to the vegetation dynamics and health"},{"concepts":[361],"name":"Examine how the water-related spectral indices relates to changes in the vegetation and soil water content"},{"concepts":[884],"name":"Examine Metadata schema and vocabularies used for open data publishing"},{"concepts":[899],"name":"Examine the Document Object Model (DOM) in HTML documents"},{"concepts":[45],"name":"Exemplify applications in which overlay is useful, such as site suitability analysis"},{"concepts":[63],"name":"Exemplify deterministic and spatial stochastic processes"},{"concepts":[103],"name":"Exemplify different temporal frames of reference: linear and cyclical, absolute and relative"},{"concepts":[474],"name":"Exemplify each component of a needs assessment for an enterprise GIS"},{"concepts":[235],"name":"Exemplify how the lack of a data librarian to manage data can have disastrous consequences on the resulting dataset"},{"concepts":[63],"name":"Exemplify non-stationarity involving first and second order effects"},{"concepts":[113],"name":"Exemplify regions found at different scales"},{"concepts":[232],"name":"Exemplify scenarios in which one would need to perform a number of periodic changes in a real GIS database"},{"concepts":[38],"name":"Exemplify situations in which the centroid of a polygon falls outside its boundary"},{"concepts":[12],"name":"Exemplify the Classic Transportation Problem"},{"concepts":[222],"name":"Exemplify the concept of planar enforcement (e.g., TIN triangles)"},{"concepts":[215],"name":"Exemplify the uses (past and potential) of the hexagonal model"},{"concepts":[537],"name":"Explain the concept of composition of spectral signatures and apply the \"linear mixing\" models in some simple case"},{"concepts":[776],"name":"Explain a use case of EO for smart cities, e.g. how EO derived information about urban green instrastructure supports designing nature based solutions for preserving ecosystem services"},{"concepts":[640],"name":"Explain across-track interferometry technique"},{"concepts":[639],"name":"Explain along-track interferometry technique"},{"concepts":[395],"name":"Explain an application example where SVM is used for EO image classification"},{"concepts":[361],"name":"Explain an application example where the spectral indices are used for vegetation, water or snow monitoring"},{"concepts":[207],"name":"Explain and apply GML data models"},{"concepts":[599],"name":"Explain and apply phase unwrapping"},{"concepts":[203,221],"name":"Explain and apply standards relevant for geometric modelling"},{"concepts":[654],"name":"Explain and discuss elements of Synthetic Aperture Radar (SAR) geometric configuration"},{"concepts":[621],"name":"Explain and discuss surface roughness in microwave remote sensing"},{"concepts":[594],"name":"Explain and discuss the complex elements of a radar signal"},{"concepts":[727],"name":"Explain and discuss the concept of Big Data in the field of Earth Observation"},{"concepts":[723],"name":"Explain and discuss the development of remote sensing data carriers"},{"concepts":[687],"name":"Explain and discuss the LiDAR technology"},{"concepts":[708],"name":"Explain and discuss the SAR acquisition mode spotlight"},{"concepts":[707],"name":"Explain and discuss the SAR acquisition mode staring spotlight"},{"concepts":[675],"name":"Explain and discuss types of sensing mechanisms"},{"concepts":[632],"name":"Explain and discuss what antenna gain is and why it is described as the key performance of a radar antenna"},{"concepts":[659],"name":"Explain and discuss what terrain reflectivity is and how it influences radar signal"},{"concepts":[656],"name":"Explain and discuss what the foreshortening is"},{"concepts":[657],"name":"Explain and discuss what the layover is"},{"concepts":[750],"name":"Explain and discuss what the main processing levels of remote sensing data are"},{"concepts":[737],"name":"Explain and discuss what the radiometric resolution is"},{"concepts":[650],"name":"Explain and discuss what the range direction is"},{"concepts":[658],"name":"Explain and discuss what the shadow in SAR acquisition means"},{"concepts":[737,734],"name":"Explain and discuss what the spatial resolution is"},{"concepts":[737],"name":"Explain and discuss what the spectral resolution is"},{"concepts":[737],"name":"Explain and discuss what the temporal resolution is"},{"concepts":[662,602],"name":"Explain and outline the advantages of radar sensors"},{"concepts":[197],"name":"Explain and use UML diagrams"},{"concepts":[76],"name":"Explain Anselins typology of spatial autoregressive models"},{"concepts":[37],"name":"Explain any differences in the measured direction between two places when the data are presented in a GIS in different projections"},{"concepts":[200],"name":"Explain basic aspects of data modelling, storage and exploitation, such as relation models & databases, data structures, SQL, UML and other basics"},{"concepts":[289],"name":"Explain cases of liability claims associated with misuse of geospatial information, erroneous information, and loss of proprietary interests"},{"concepts":[624],"name":"Explain covariance and coherence matrix"},{"concepts":[615],"name":"Explain dielectric properties of objects and their effect on radar data acquisition"},{"concepts":[638],"name":"Explain differences between DInSAR and PSI"},{"concepts":[662],"name":"Explain differences between optical and radar remote sensing"},{"concepts":[84],"name":"Explain from which scientific fields GIS&T borrows ideas."},{"concepts":[236],"name":"Explain geocomputation, related concepts and how the two relate"},{"concepts":[6],"name":"Explain how a Bayesian framework can incorporate expert knowledge in order to retrieve all relevant datasets given an initial user query"},{"concepts":[493],"name":"Explain how a business case analysis can be used to justify the expense of implementing consensus-based standards"},{"concepts":[379],"name":"Explain how a DSM differs from a DTM"},{"concepts":[226],"name":"Explain how a graph (network) may be directed or undirected"},{"concepts":[226],"name":"Explain how a graph can be written as an adjacency matrix and how this can be used to calculate topological shortest paths in the graph"},{"concepts":[331],"name":"Explain how a histogram is derived from an EO image"},{"concepts":[458],"name":"Explain how a lack of knowledge about data quality limits the data value"},{"concepts":[10],"name":"Explain how a leading World Wide Web-based routing system works e.g., MapQuest, Yahoo Maps, Google"},{"concepts":[40],"name":"Explain how a semi-variogram describes the distance decay in dependence between data values"},{"concepts":[323],"name":"Explain how a set of overlapping images/satellite scenes can provide digital elevation models used for orthorectification and 3D modelling"},{"concepts":[818],"name":"Explain how a specific EO technology supports the assessments of disasters and geohazards"},{"concepts":[65],"name":"Explain how a statistic that is based on combining all the spatial data and returning a single summary value or two can be useful in understanding broad spatial trends"},{"concepts":[316],"name":"Explain how a tax assessors office adoption of GIS and T may affect power relations within a community"},{"concepts":[66],"name":"Explain how a weights matrix can be used to convert any classical statistic into a local measure of spatial association"},{"concepts":[78],"name":"Explain how allowing the parameters of the model to vary with the spatial location of the sample data can be used to accommodate spatial heterogeneity"},{"concepts":[56,1],"name":"Explain how analytical methods are used to derive analytical results from geospatial data"},{"concepts":[362],"name":"Explain how band maths can be applied to derive an index that indicates a specific land cover type like vegetation"},{"concepts":[72],"name":"Explain how block-kriging and its variants can be used to combine data sets with different spatial resolution support"},{"concepts":[44],"name":"Explain how buffers can be used in GI analysis"},{"concepts":[208],"name":"Explain how CityGML is related to GML"},{"concepts":[419],"name":"Explain how class modelling can make use of per-parcel analysis"},{"concepts":[303],"name":"Explain how community organizations represent the interests of citizens, politicians, and specialists"},{"concepts":[378],"name":"Explain how computer vision imitates the human visual system when interpreting EO images"},{"concepts":[290],"name":"Explain how conversion of land records data from analog to digital form increases risk to personal privacy"},{"concepts":[290],"name":"Explain how data aggregation is used to protect personal privacy in data produced by the U.S. Census Bureau"},{"concepts":[36],"name":"Explain how different measures of distance can be used to calculate the spatial weights matrix"},{"concepts":[64],"name":"Explain how different types of spatial weights matrices are defined and calculated"},{"concepts":[77],"name":"Explain how dissolving clusters of blocks with similar values may resolve the spatial correlation problem"},{"concepts":[49],"name":"Explain how distance-based methods of point pattern measurement can be derived from a distance matrix"},{"concepts":[52],"name":"Explain how dynamic, chaotic, complex or unpredictable aspects in some phenomena make spatial interaction models more appropriate than gravity models"},{"concepts":[351],"name":"Explain how EO applications targeting several countries at once can profit from data harmonisation"},{"concepts":[368],"name":"Explain how error propagates in the production workflow of an example EO product"},{"concepts":[321],"name":"Explain how fourier transformation is used to generate radar image"},{"concepts":[321],"name":"Explain how fourier transformation is used to reduce noise in optical imagery"},{"concepts":[36],"name":"Explain how fractal dimension can be used in practical applications of GIS"},{"concepts":[60],"name":"Explain how friction surfaces are enhanced by the use of impedance and barriers"},{"concepts":[302],"name":"Explain how geographic information is valuable to different sectors"},{"concepts":[66],"name":"Explain how geographically weighted regression provides a local measure of spatial association"},{"concepts":[278],"name":"Explain how geometric accuracies associated with the various orders of the U.S. horizontal geodetic control network are assured"},{"concepts":[291],"name":"Explain how geospatial information might be used in a taking of private property through a government's claim of its right of eminent domain"},{"concepts":[298],"name":"Explain how geospatial information might be used in a taking of private property through a governments claim of its right of eminent domain"},{"concepts":[473],"name":"Explain how GIS and T can be an integrating technology"},{"concepts":[15],"name":"Explain how graph theory plays a role in network analysis."},{"concepts":[212],"name":"Explain how grid representations embody the field-based view"},{"concepts":[317],"name":"Explain how image processing and analysis methods are used to derive geospatial information from Earth observation imagery"},{"concepts":[149],"name":"Explain how interactivity influences map use"},{"concepts":[541],"name":"Explain how it is possible to retrieve atmospheric temperature and trace gases profiles form multi/iper spectral radiances"},{"concepts":[901],"name":"Explain how JSON (GeoJSON)`s \"schema-less\"structure may be transformed into an application schema"},{"concepts":[102],"name":"Explain how linguistics play a role in GI science."},{"concepts":[403],"name":"Explain how local density gradients are employed in mean-shift segmentation"},{"concepts":[32],"name":"Explain how logic theory relates to set theory"},{"concepts":[159],"name":"Explain how maps such as topographic maps are produced within certain relations of power and knowledge"},{"concepts":[146],"name":"Explain how maps that show the landscape in profile can be used to represent terrain"},{"concepts":[268,276],"name":"Explain how metadata, standards and data infrastructures are linked to each other"},{"concepts":[337],"name":"Explain how minimum noise fraction makes use of principal components analysis for dimensionality reduction"},{"concepts":[498],"name":"Explain how next-generation SDIs are different from current SDIs"},{"concepts":[435],"name":"Explain how OGC standards can be used for sharing spatial data (including Earth Observation data) across different communities and computing infrastructures"},{"concepts":[308],"name":"Explain how one or more obligations in the GIS Code of Ethics may conflict with organizations proprietary interests"},{"concepts":[232],"name":"Explain how one would establish the criteria for monitoring the periodic changes in a real GIS database"},{"concepts":[467],"name":"Explain how online processing can enhance the functionality of a web viewer for EO data"},{"concepts":[16],"name":"Explain how optimization models can be used to generate models of alternate options for presentation to decision makers"},{"concepts":[67],"name":"Explain how outliers affect the results of analyses"},{"concepts":[512],"name":"Explain how Planck function and Wien law can help to characterize blackbodies' emission"},{"concepts":[49],"name":"Explain how proximity polygons e.g., Thiessen polygons may be used to describe point patterns"},{"concepts":[218],"name":"Explain how quadtrees and other hierarchical tessellations can be used to index large volumes of raster or vector data"},{"concepts":[662],"name":"Explain how radar images are used for specific applications"},{"concepts":[136],"name":"Explain how real-world connotations (e.g., blue=water, white=snow) can be used to determine color selections on maps"},{"concepts":[43],"name":"Explain how reclassification can be used for data simplification and measurement scale change"},{"concepts":[27],"name":"Explain how Representation transformations are related to spatial data quality."},{"concepts":[280],"name":"Explain how resampling affects the resolution of image data"},{"concepts":[493],"name":"Explain how resistance to change affects the adoption of standards in an organization coordinating a GIS"},{"concepts":[58],"name":"Explain how ridgelines and streamlines can be used to improve the result of an interpolation process"},{"concepts":[32],"name":"Explain how set theory relates to spatial queries"},{"concepts":[429],"name":"Explain how SIFT algorithms can be used for enhancing orthorectification"},{"concepts":[61],"name":"Explain how slope and aspect can be represented as the vector field given by the first derivative of height"},{"concepts":[754],"name":"Explain how spatial analysis is dependent explicitly on the borders of study fields."},{"concepts":[77],"name":"Explain how spatial correlation can result as a side effect of the spatial aggregation in a given dataset"},{"concepts":[6],"name":"Explain how spatial data mining techniques can be used for knowledge discovery"},{"concepts":[75],"name":"Explain how spatial dependence and spatial heterogeneity violate the Gauss-Markov assumptions of regression used in traditional econometrics"},{"concepts":[153],"name":"Explain how spatial metaphors can be used to illustrate the relationship among ideas"},{"concepts":[248],"name":"Explain how spatial simulation models can be used to advance scientific knowledge in different geographic scenarios (e.g. transportation, health geography, urban and regional 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geographic data and information"},{"concepts":[72],"name":"Explain why kriging is more suitable as an interpolation method in some applications than others"},{"concepts":[233],"name":"Explain why logging and rollback techniques are adequate for managing short transactions"},{"concepts":[277],"name":"Explain why metadata are important for assessing and ensuring the quality of geospatial data"},{"concepts":[386],"name":"Explain why multimodal distributions in training samples should be avoided when using the maximum likelihood classifier"},{"concepts":[362],"name":"Explain why radiometric correction is a key requirement for deriving indices with band maths"},{"concepts":[446],"name":"Explain why rapid mapping applications have high requirements in timely availability of Earth observation products"},{"concepts":[504],"name":"Explain why software products sold by U.S. companies may predominate in foreign markets, including Europe and Australia"},{"concepts":[646],"name":"Explain why spatial resolution of passive radar system is much lower than that of active systems"},{"concepts":[475],"name":"Explain why the definition of user roles is an important element in the implementation of a GIS"},{"concepts":[600],"name":"Explain why the Doppler effect is important in radar remote sensing"},{"concepts":[489],"name":"Explain why the legal framework on geospatial data sharing can be considered as diverse and complex"},{"concepts":[489],"name":"Explain why the legal framework on geospatial data sharing consists of two main types of legislation from a data perspective"},{"concepts":[45],"name":"Explain why the process \"dissolve and merge\" often follows vector overlay operations"},{"concepts":[61],"name":"Explain why the properties of spatial continuity are characteristic of spatial surfaces"},{"concepts":[38],"name":"Explain why the shape of an object might be important in analysis"},{"concepts":[259],"name":"Explain why the shape of the Earth is complex and complicated to measure"},{"concepts":[815],"name":"Explain why the use of multiple EO sensors for mapping landslides associated with one triggering event increases the completeness of a landslide inventory"},{"concepts":[119],"name":"Explain why Toblers First Law of Geography is fundamental to many operations in GIS and whether it should be"},{"concepts":[61],"name":"Explain why zero slopes are indicative of surface specific points such as peaks, pits and passes and list the conditions necessary for each"},{"concepts":[12],"name":"Explain why, if supply equals demand, there will always be a feasible solution to the Classic Transportation Problem"},{"concepts":[50],"name":"Explain why, in some cases, an adaptive bandwidth might be employed"},{"concepts":[281],"name":"Explain, in general terms, the difference between single and double precision and impacts on error propagation"},{"concepts":[16],"name":"Explain, using the concept of combinatorial complexity, why some location problems are very hard to solve"},{"concepts":[88],"name":"Explore the contribution of linguistics to the study of spatial cognition and the role of natural language in the conceptualization of geographic phenomena"},{"concepts":[92],"name":"Explore the history of geography including (but not limited to) Greek and Roman contributions to geography (Eratosthenes, Strabo, Ptolemy), geography and cartography in the age of discovery, military geography, and geography..."},{"concepts":[76],"name":"Find a best model"},{"concepts":[147],"name":"Find a multivariate outlier using a combination of maps and graphs"},{"concepts":[38],"name":"Find centroids of polygons under different definitions of a centroid and different polygon shapes"},{"concepts":[483],"name":"Find or create training resources appropriate for GIS and T workforce in a local government organization"},{"concepts":[112],"name":"Find spatial patterns in the distribution of geographic phenomena using geographic visualization and other techniques"},{"concepts":[790,788],"name":"Forecast and monitor ocean winds and waves"},{"concepts":[106],"name":"Formalize attribute values and domains in terms of set theory"},{"concepts":[109],"name":"Formalize the notion of field using mathematical functions and Calculus"},{"concepts":[45],"name":"Formalize the operation called map overlay using Boolean logic"},{"concepts":[319],"name":"Generate a layer stack from bands of various EO data sources"},{"concepts":[349],"name":"Generate fine-scale images at a high temporal resolution with a spatio-temporal image fusion method"},{"concepts":[367],"name":"Generate high quality time series by removing clouds and cloud shadows from the available images"},{"concepts":[224],"name":"Give and explain an example of an application models"},{"concepts":[489],"name":"Give examples of more general types of legislation that are also applicable and relevant to geospatial data sharing"},{"concepts":[838],"name":"Having in-depth knowledge of two of the three Copernicus-relevant topics: Land monitoring, Emergency response including Humanitarian action, and Climate change"},{"concepts":[112],"name":"Hypothesize the causes of a pattern in the spatial distribution of a phenomenon"},{"concepts":[51],"name":"Identify a clustering method which does not require the number of clusters as input"},{"concepts":[30],"name":"Identify a variety of likely measurement level transformations (e.g., the classification of ratio data yields ordinal data)"},{"concepts":[839],"name":"Identify adequate preprocessing for deriving ocean colour from EO data"},{"concepts":[247],"name":"Identify agent-based modelling principles and methodologies"},{"concepts":[310],"name":"Identify alternatives to the \"algorithmic way of thinking\" that characterizes use of geospatial Information."},{"concepts":[314],"name":"Identify alternatives to the algorithmic way of thinking that characterizes GIS"},{"concepts":[236],"name":"Identify and compare the scenarios on which geocomputation methods are relevant"},{"concepts":[71],"name":"Identify and define the parameters of a semi-variogram range, sill, nugget"},{"concepts":[410],"name":"Identify and discuss an example of a combined filtering process"},{"concepts":[492],"name":"Identify and discuss the different components of an SDI"},{"concepts":[332],"name":"Identify and explain methods of image enhancement"},{"concepts":[296],"name":"Identify and explain the different actors and their roles in the geo-information value chain"},{"concepts":[367],"name":"Identify anomalies by means of surface properties such as evapotranspiration (ET) or land surface temperature (LST) derived from satellite image time series"},{"concepts":[109],"name":"Identify applications and phenomena that are not adequately modeled by the field view"},{"concepts":[782,779,800,811,781,810],"name":"Identify border incursions or maritime movements"},{"concepts":[899],"name":"Identify building blocks of Javascript programming language"},{"concepts":[246],"name":"Identify cellular automata principles and pattern"},{"concepts":[101],"name":"Identify common-sense views of geographic phenomena that sharply contrast with established theories and technologies of geographic information"},{"concepts":[240],"name":"Identify commonalities and patterns of geocomputation"},{"concepts":[503],"name":"Identify conferences that are related to GIS and T hosted by professional organizations"},{"concepts":[859],"name":"Identify construction sites"},{"concepts":[449],"name":"Identify critical design decisions that make an EO-derived map readable"},{"concepts":[172],"name":"Identify data center platform tier configuration and identify platform selection for each tier"},{"concepts":[873],"name":"Identify design issues of SOAP web services; fine grained and coarse grained services, design patterns"},{"concepts":[901],"name":"Identify differences, advantages and disadvantages of web application framework based and portal framework based web applications from the geospatial data perspective"},{"concepts":[65],"name":"Identify different measures of spatial autocorrelation"},{"concepts":[66],"name":"Identify different measures of spatial autocorrelation"},{"concepts":[385],"name":"Identify different methods that employ conditional probability for image classification"},{"concepts":[375],"name":"Identify different options where Artificial Intelligence can be integrated in the image processing and analysis workflow"},{"concepts":[340],"name":"Identify different types of noise and associated methods for their reduction"},{"concepts":[109],"name":"Identify examples of discrete and continuous change found in spatial, temporal, and spatio-temporal fields"},{"concepts":[149,139],"name":"Identify examples of static, animated, and interactive web maps"},{"concepts":[134],"name":"Identify gaming elements which may be part of geo-games"},{"concepts":[826],"name":"Identify geological features"},{"concepts":[814,826],"name":"Identify geotectonic shifts"},{"concepts":[782,779,789,802,800,811,781,807],"name":"Identify high risk areas produced naturally or by humans"},{"concepts":[350],"name":"Identify image fusion techniques to fill gaps in image time series caused by clouds and cloud shadow"},{"concepts":[812],"name":"Identify impact of a flood"},{"concepts":[112],"name":"Identify influences of scale on the appearance of distributions"},{"concepts":[886],"name":"Identify issues in determining the relationships to be represented when publishing Linked Data"},{"concepts":[885],"name":"Identify issues in developing new ontologies for geospatial data"},{"concepts":[886],"name":"Identify issues in finding proper ontologies to annotate the data"},{"concepts":[879],"name":"Identify issues in the development of geospatial ontologies. Criticise the role of ontology development methodologies and ontology evaluation in the development of ontologies"},{"concepts":[900],"name":"Identify main components and functionality of Leaflet library, describe its main functions and how they are employed"},{"concepts":[900],"name":"Identify main components and functionality of Openlayers library, describe its main functions and how they are employed"},{"concepts":[882],"name":"Identify main components of manual metadata creation software tools"},{"concepts":[900],"name":"Identify main elements and functionality Google maps, describe some of its most popular API operations and how they are employed"},{"concepts":[900],"name":"Identify main elements and functionality Mapbox, describe some of its most popular API operations and how they are employed"},{"concepts":[888],"name":"Identify main issues in \"keyword-based\" discovery of data and services"},{"concepts":[889],"name":"Identify main issues in Semantic discovery"},{"concepts":[174],"name":"Identify major obstacles to the success of a GIS proposal"},{"concepts":[77],"name":"Identify modeling situations where spatial filtering might not be appropriate"},{"concepts":[493],"name":"Identify organizations that focus on developing standards related to GIS and T"},{"concepts":[116],"name":"Identify phenomena that are best understood as networks"},{"concepts":[108],"name":"Identify phenomena that are difficult or impossible to conceptualize in terms of entities"},{"concepts":[423,371],"name":"Identify physical, semantic and spatial properties used to assigned objects to the target classes"},{"concepts":[172],"name":"Identify platform assignment for each workflow software component peak transaction processing load"},{"concepts":[129,174],"name":"Identify potential sources of data (free or commercial) needed for a particular application or enterprise"},{"concepts":[786,784,799,808],"name":"Identify rapid response to events associated with health security & care"},{"concepts":[783,786,785,816,817],"name":"Identify rapid response to major environmental risk events"},{"concepts":[837,836],"name":"Identify sea-ice or icebergs using EO data"},{"concepts":[51],"name":"Identify several cluster detection techniques and discuss their limitations"},{"concepts":[38],"name":"Identify situations in which shape affects geometric operations"},{"concepts":[119],"name":"Identify situations in which Toblers First Law of Geography does not apply"},{"concepts":[119],"name":"Identify situations in which Toblers First Law of Geography is valuable"},{"concepts":[105],"name":"Identify specific examples of categories of entities (i.e., common nouns), properties (i.e., adjectives), space (i.e., regions), and time (i.e., eras)"},{"concepts":[839],"name":"Identify spectral bands necessary for interpreting ocean colour"},{"concepts":[493],"name":"Identify standards that are used in GIS and T"},{"concepts":[459],"name":"Identify steps of processing on large image collections that benefit from storing them in array databases"},{"concepts":[881],"name":"Identify the aspects of selecting keywords which would characterize the data properly"},{"concepts":[22],"name":"Identify the conceptual and practical difficulties associated with data model and format conversion"},{"concepts":[22],"name":"Identify the conceptual and practical difficulties associated with data model and format conversion"},{"concepts":[256],"name":"Identify the defining characteristics of an open geocomputation project"},{"concepts":[895],"name":"Identify the different barriers for the integration of datasets"},{"concepts":[64],"name":"Identify the different methods for constructing spatial weigh matrix"},{"concepts":[83],"name":"Identify the epistemological assumptions underlying the work of colleagues"},{"concepts":[899],"name":"Identify the extensions HTML5 brings over older HTML versions"},{"concepts":[121],"name":"Identify the hedges used in language to convey vagueness"},{"concepts":[881],"name":"Identify the issues in mapping between different metadata standards. Also identify the roles of thesauri and crosswalks"},{"concepts":[480],"name":"Identify the key organizational components of a GIS&T implementation"},{"concepts":[113],"name":"Identify the kinds of phenomena that are commonly found at the boundaries of regions"},{"concepts":[289],"name":"Identify the liability implications associated with contracts"},{"concepts":[892],"name":"Identify the main components of OGC Filter encoding and compare it to SQL"},{"concepts":[889],"name":"Identify the main concepts of reasoning and architectural components of Reasoners"},{"concepts":[480],"name":"Identify the main organizational challenges in implementing and use GIS&T"},{"concepts":[136],"name":"Identify the most appropriate color palette for a printed map for visually-impaired people"},{"concepts":[136],"name":"Identify the most appropriate color palette for an online map for visually-impaired people"},{"concepts":[212],"name":"Identify the national framework datasets based on a grid model"},{"concepts":[892],"name":"Identify the need for and main issues in spatial data interchange"},{"concepts":[81],"name":"Identify the ontological assumptions underlying the work of colleagues"},{"concepts":[483],"name":"Identify the particular skills necessary for users to perform tasks in three different workforce domains (e.g., small city, medium county agency, a business, or others)"},{"concepts":[85],"name":"Identify the philosophical views and assumptions underlying the work of colleagues"},{"concepts":[174],"name":"Identify the positions necessary to design and implement a GIS project / GI unit"},{"concepts":[481],"name":"Identify the qualifications needed for a particular GIS and T position"},{"concepts":[877],"name":"Identify the relation between OWL-S and WSDL and give an overview of Semantic Web service definition in OWL-S"},{"concepts":[57],"name":"Identify the spatial concepts that are assumed in different interpolation algorithms"},{"concepts":[481],"name":"Identify the standard occupational codes that are relevant to GIS and T"},{"concepts":[884],"name":"Identify the technical aspects that open data paradigm would affect concerning Spatial Data Infrastructures including NSDIs"},{"concepts":[108],"name":"Identify the types of features that need to be modeled in a particular GIS application or procedure"},{"concepts":[239,240],"name":"Identify the types of geography problems geocomputation solves"},{"concepts":[49],"name":"Identify the various ways point patterns may be described"},{"concepts":[175],"name":"Identify the viability of a proprietary GIS application"},{"concepts":[876],"name":"identify the web services needed for a particular use case"},{"concepts":[172],"name":"Identify user locations, network connectivity, and data center server locations"},{"concepts":[104],"name":"Identify various types of geographic interactions in space and time"},{"concepts":[49],"name":"Identify various types of K-function analysis"},{"concepts":[877],"name":"Identify virtues of defining a given data set in both RDF and OWL, and compare semantic richness of both definitions"},{"concepts":[837,835],"name":"Identify wake trailing to detect ships using EO data"},{"concepts":[897],"name":"Identify whether Full-automated WSC has still a value in it concerning both where we stand today on the road to 'Semantic Web' and unresolved problems in the area, which are the problems of Artificial Intelligence indeed"},{"concepts":[536],"name":"Illustrate main spectral signatures of clouds and apply them in paractical cloud-detection exercise"},{"concepts":[222],"name":"Illustrate a topological relation"},{"concepts":[303],"name":"Illustrate an example of \"local knowledge\" that is unlikely to be represented in the geospatial data maintained routinely by government agencies"},{"concepts":[575],"name":"Illustrate and apply basic concepts of Atmospheric Physics to EO science and its applications"},{"concepts":[280],"name":"Illustrate and explain the distinction between resolution, precision, and accuracy"},{"concepts":[280],"name":"Illustrate and explain the distinctions between spatial resolution, thematic resolution, and temporal resolution"},{"concepts":[534],"name":"Illustrate basic features of spectral signatures of vegetation, water and bare soil"},{"concepts":[562],"name":"Illustrate basic modern physics theory understanding their implications on the development of advanced sensors for EO"},{"concepts":[525,533],"name":"Illustrate basic radiation-matter interactions and related concepts of spectral reflectance, absorbance and transmittance as specific properties of the matter"},{"concepts":[536],"name":"Illustrate e.m. radiation intercations with/within clouds."},{"concepts":[173],"name":"Illustrate each of the project management areas with an example of a technique or tool used"},{"concepts":[166],"name":"Illustrate how a business process analysis can be used to identify requirements during a GIS implementation"},{"concepts":[139],"name":"Illustrate how an animated map reveals patterns not evident without animation"},{"concepts":[548],"name":"Illustrate how cloud presence complicate radiative transfer description in Earth's atmosphere"},{"concepts":[87],"name":"Illustrate how fields, such as geography, cartography, computer and information science, engineering, mathematics, philosophy, cognitive science, and linguistics have their influence on GI science."},{"concepts":[521],"name":"Illustrate how it is possible to estimate the BRDF of a sample through measurements of BRF"},{"concepts":[524],"name":"Illustrate how the Voigt's line profile is related to the Doppler and pressure line broadening contributes"},{"concepts":[111],"name":"Illustrate major integrated models of geographic information, such as Peuquets Triad, Mennis Pyramid, and Yuans Three-Domain"},{"concepts":[483],"name":"Illustrate methods that are effective in providing opportunities for education and training when implementing a GIS in a small city"},{"concepts":[547],"name":"Illustrate of the concept of optical path"},{"concepts":[547],"name":"Illustrate of the concept of optical thickness"},{"concepts":[554],"name":"Illustrate possible noise sources related to photovoltaic and photoconductive detectors"},{"concepts":[546],"name":"Illustrate scope and conditions of validity of Schwarzshild equation."},{"concepts":[886],"name":"Illustrate stages of publishing a relational database as Linked Data"},{"concepts":[570],"name":"Illustrate the interaction of e.m. radiation in the thermal infrared with the atmosphere understanding specifc characteristics of radiative transfer in this specific spectral region."},{"concepts":[580],"name":"Illustrate the concept of \"kinetic temperature\" in absence of thermodynamic equilibrium"},{"concepts":[543],"name":"Illustrate the concept of Absorption Coefficient"},{"concepts":[542],"name":"Illustrate the concept of Cross Section of Extinction per Mass Unit"},{"concepts":[526],"name":"Illustrate the concept of grey body"},{"concepts":[544],"name":"Illustrate the concept of Source Function"},{"concepts":[515],"name":"Illustrate the concept of spectral emissivity and brigthness temperature and compute them in some simple real case"},{"concepts":[533],"name":"Illustrate the concept of spectral signatures of the matter"},{"concepts":[555],"name":"Illustrate the concepts of Interference and Diffraction"},{"concepts":[551],"name":"Illustrate the concepts of Reflection, Refraction and Dispersion of the light"},{"concepts":[508],"name":"Illustrate the concepts of solar constant and daily solar insolation"},{"concepts":[532],"name":"Illustrate the decay of the emittance with the distance from the source"},{"concepts":[141],"name":"Illustrate the elements of the story by proper geovisualizations"},{"concepts":[125],"name":"Illustrate the evolution of Cartography in different periods of time"},{"concepts":[213],"name":"Illustrate the existing methods for compressing gridded data (e.g., run length encoding, Lempel-Ziv, wavelets)"},{"concepts":[590],"name":"Illustrate the factors limiting lifetime of satellites on their originally planned orbits"},{"concepts":[586],"name":"Illustrate the First Law of Thermodynamic"},{"concepts":[540],"name":"Illustrate the general equation of radiative transfer."},{"concepts":[566],"name":"Illustrate the Greenhouse effect associate to CO2 emission"},{"concepts":[558],"name":"Illustrate the Helmotz’s equation"},{"concepts":[215],"name":"Illustrate the hexagonal model"},{"concepts":[581],"name":"Illustrate the ideal gas law"},{"concepts":[217],"name":"Illustrate the impact of grid cell resolution on the information that can be portrayed"},{"concepts":[24],"name":"Illustrate the impact of vector/raster/vector conversions on the quality of a dataset"},{"concepts":[509],"name":"Illustrate the importance of Earth's emitted radiation for EO from space"},{"concepts":[553],"name":"Illustrate the importance of electric conduction in solids for the design and development of advanced EO sensors"},{"concepts":[591],"name":"Illustrate the importance of the choice of the satellite orbit for the design of a satellite mission devoted to specific applications"},{"concepts":[868,863,864,865,866,867],"name":"Illustrate the information of EO data"},{"concepts":[183],"name":"Illustrate the landscape of GIS and related libraries"},{"concepts":[573],"name":"Illustrate the main atmospherical spectral windows"},{"concepts":[538],"name":"Illustrate the main differences among passive and active remote sensing techniques"},{"concepts":[522],"name":"Illustrate the main energetic transictions that can be associated to molecular absorption of e.m. radiation"},{"concepts":[530],"name":"Illustrate the main forms of radiation-matter interaction"},{"concepts":[51],"name":"Illustrate the main use of spatial clustering in earth observation"},{"concepts":[517],"name":"Illustrate the nature of electromagnetic radiation"},{"concepts":[218],"name":"Illustrate the quadtree model"},{"concepts":[241],"name":"Illustrate the relationships between geocomputation with other terms, disciplines and areas of knowledge"},{"concepts":[585],"name":"Illustrate the role of Eulerian and Lagrangian models in budget equations definition"},{"concepts":[557],"name":"Illustrate the role of the principle of constant speed of light within the special relativity theory"},{"concepts":[550],"name":"Illustrate the scope Radiative Transfer theory"},{"concepts":[587],"name":"Illustrate the Second Law of Thermodynamic"},{"concepts":[534],"name":"Illustrate the spectral response curves for basic environmental features (e.g., vegetation, concrete, bare soil)"},{"concepts":[574],"name":"Illustrate the transferring of Energy within the Earth-Atmosphere System"},{"concepts":[151],"name":"Illustrate the use of virtual environments"},{"concepts":[579],"name":"Illustrate the utility of thermodynamic diagrams for the study of local atmospheric properties"},{"concepts":[142],"name":"Illustrate the ways in which maps could be integrated in an infography"},{"concepts":[473],"name":"Illustrate what functions a support or service center can provide to an organization using GIS and T"},{"concepts":[520],"name":"Illustrate why we refer to the BRDF as an absolute definition of spectral reflectance"},{"concepts":[140],"name":"Illustrate with examples of maps or geovisualizations that could be improved by the addition of an audio layer"},{"concepts":[126],"name":"Illustrate with examples the relationship between art and cartography at different historical moments"},{"concepts":[583],"name":"Ilustrate the state function of the condensed gas phase"},{"concepts":[218],"name":"Implement a format for encoding quadtrees in a data file"},{"concepts":[76],"name":"Implement a maximum likelihood estimation procedure for determining key spatial econometric parameters"},{"concepts":[234],"name":"Implement a test of reliability of change information"},{"concepts":[57],"name":"Implement a trend surface analysis using either the supplied function in a GIS or a regression function from any standard statistical package"},{"concepts":[883],"name":"Implement and configure a catalogue service"},{"concepts":[17],"name":"Implement linear programs for spatial allocation problems"},{"concepts":[12],"name":"Implement the Transportation Simplex method to determine the optimal solution"},{"concepts":[278],"name":"In contrast to the National Map Accuracy Standard, explain how the spatial accuracy of a digital road centerlines data set may be evaluated and documented"},{"concepts":[885],"name":"Indicate an architecture and tools for organizing semantically annotated data"},{"concepts":[900],"name":"Indicate an overview of OpenStreetMap and define its general functionality, comment its usage by Web APIs"},{"concepts":[901],"name":"Indicate generally how \"NSDI-requiring-scenarios\"would be handled by web application framework based applications"},{"concepts":[899],"name":"Indicate main elements of HTML5"},{"concepts":[889],"name":"Indicate some examples of semantic discovery; Semantic search engines, highlighting projects and practice concerning GI related applications in the area"},{"concepts":[310],"name":"Indicate the extent to which contemporary use of geospatial information supports diverse ways of understanding the world."},{"concepts":[474],"name":"Indicate the possible justifications that can be used to implement an enterprise GIS"},{"concepts":[245],"name":"Interpret when space-time dynamics can be used to study geographical phenomen"},{"concepts":[172],"name":"Interpret business needs and translate them to IT needs"},{"concepts":[471],"name":"Interpret descriptive statistics and geostatistics of geographic data"},{"concepts":[135,160],"name":"Interpret different symbols and icons in a map"},{"concepts":[901],"name":"Interpret generally the functionality offered by \"portal frameworks\" land Geoportals like Geonetwork, Opengeoportal, Esri geoportal server, Degree portal, Liferay, Jboss portal"},{"concepts":[901],"name":"Interpret generally the main components and functionality of \"Web Application Frameworks\" such as AngularJS, Ext.js, Django, Java Server Faces (JSF), and the like"},{"concepts":[878],"name":"interpret GML data model and GML definition of geometry. GML application schemas and GML documents"},{"concepts":[237],"name":"Interpret how individual parts contained in a complex system relate to each other"},{"concepts":[857],"name":"Interpret information from EO products or EO time series"},{"concepts":[769],"name":"Interpret land cover change detection"},{"concepts":[772],"name":"Interpret location based services (LBS)"},{"concepts":[839],"name":"Interpret ocean colour for deriving chlorophyll concentration in water"},{"concepts":[5],"name":"Interpret patterns in space and time using Dorling and Openshaws Geographical Analysis Machine GAM demonstration of disease incidence diffusion"},{"concepts":[868,863,864,865,866,867],"name":"Interpret the content of EO data"},{"concepts":[416],"name":"Interpret the effect of a convolution from a given mask and contained weights"},{"concepts":[211],"name":"Interpret the header of a standard raster data file"},{"concepts":[125],"name":"Interpret the impact of paper-based and web maps in their context"},{"concepts":[843],"name":"Interpret the output of an point cloud measurement"},{"concepts":[805],"name":"Interpret the output of numerical prediction models"},{"concepts":[73],"name":"Interpret the results of universal kriging"},{"concepts":[172],"name":"Interpret user needs as an input for the design process"},{"concepts":[92],"name":"Justify a chosen position on which disciplines should have as important a role in GIS AND T as geography"},{"concepts":[176],"name":"Justify feasibility recommendations to decision-makers"},{"concepts":[108],"name":"Justify or refute the conception of fields (e.g., temperature, density) as spatially-intensive attributes of (sometimes amorphous and anonymous) entities"},{"concepts":[92],"name":"Justify or refute whether geography (as a discipline) should have a central role in GIS AND T"},{"concepts":[97],"name":"Justify the discrepancies between the nature of locations in the real world and representations thereof (e.g., towns as points)"},{"concepts":[83],"name":"Justify the epistemological frameworks with which you agree"},{"concepts":[81],"name":"Justify the metaphysical theories with which you agree"},{"concepts":[63],"name":"Justify the stochastic process approach to spatial statistical analysis"},{"concepts":[65],"name":"Justify, compute, and test the significance of the join count statistic for a pattern of objects"},{"concepts":[518],"name":"Knowledge of the basic (selective) mechanism of the absorption/emission of electromagnetic radiation by atoms."},{"concepts":[70],"name":"List and describe several spatial sampling schemes and evaluate each one for specific applications"},{"concepts":[505],"name":"List and describe the main categories of organizations in the GIS&T domain"},{"concepts":[500],"name":"List and describe the most important producers and users of geospatial data at the European Commission"},{"concepts":[298],"name":"List and describe the types of data maintained by local, state, and federal governments"},{"concepts":[472],"name":"List and explain relevant organizational and institutional aspects related to GIS&T."},{"concepts":[287],"name":"List and explain the different societal aspects that are important in dealing with geospatial information"},{"concepts":[263],"name":"List and explain the key requirements for geolocating data to earth"},{"concepts":[226],"name":"List definitions of networks that apply to specific applications or industries"},{"concepts":[41],"name":"List different ways connectivity can be determined in a raster and in a polygon dataset"},{"concepts":[39],"name":"List reasons why the area of a polygon calculated in a GIS might not be the same as the real world object it describes"},{"concepts":[13],"name":"List several classic problems to which network analysis is applied e.g., The Traveling Salesman Problem, The Chinese Postman Problem"},{"concepts":[151],"name":"List software and hardware environments supporting immersive visualization"},{"concepts":[474],"name":"List some of the topics that should be addressed in a justification for implementing an enterprise GIS (e.g., return on investment, workflow, knowledge sharing)"},{"concepts":[465],"name":"List specifics competitive DIAS solutions over other"},{"concepts":[49],"name":"List the conditions that make point pattern analysis a suitable process"},{"concepts":[174],"name":"List the costs and benefits (tangible or intangible) of implementing a GI project"},{"concepts":[173],"name":"List the key elements of a project management"},{"concepts":[61],"name":"List the likely sources of error in slope and aspect maps derived from DEMs and state the circumstances under which these can be very severe"},{"concepts":[458],"name":"List the main international organization responsible for the standardization of the image data and gridded data quality"},{"concepts":[409],"name":"List the main segmentation methods used to group similar pixels into homogeneous objects"},{"concepts":[158],"name":"List the main variables to take into account during the planning phase of a map"},{"concepts":[133],"name":"List the major factors that should be considered in preparing a map"},{"concepts":[173],"name":"List the phases of a project management life cycle"},{"concepts":[71],"name":"List the possible sources of error in a selected and fitted model of an experimental semi-variogram"},{"concepts":[118],"name":"List the possible topological relationships between entities in space (e.g., 9-intersection) and time"},{"concepts":[136],"name":"List the range of factors that should be considered in selecting colors"},{"concepts":[63],"name":"List the two basic assumptions of the purely random process"},{"concepts":[14],"name":"List ways we can define accessibility on a network"},{"concepts":[132],"name":"List which data considerations should be taken into account when starting a GIS project"},{"concepts":[19],"name":"Locate, using location-allocation software, service facilities that meet given sets of constraints"},{"concepts":[166],"name":"Manage requirements using a management tool (such as Trello, JIRA, etc.)"},{"concepts":[777],"name":"Manage the use of land"},{"concepts":[771],"name":"Map and assess flooding"},{"concepts":[766],"name":"Map line of sight visibility (terrain height, land cover)"},{"concepts":[719],"name":"Measure reflectance of surfaces of vegetation types and other thematic classes in the field"},{"concepts":[231],"name":"Model complex aspects of geographic information, such as temporal change, uncertainty and three-dimensional phenomena"},{"concepts":[190],"name":"Model geospatial data"},{"concepts":[108],"name":"Model gray area phenomena, such as categorical coverages (a.k.a. discrete fields), in terms of objects"},{"concepts":[172],"name":"Model project workflows"},{"concepts":[619],"name":"Model surface roughness slope"},{"concepts":[204],"name":"Model temporal aspects"},{"concepts":[232],"name":"Modify spatial and 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existing web services to use the resources exposed by the service"},{"concepts":[436],"name":"Plan a reproducibility project independently"},{"concepts":[705],"name":"Plan an aerial imagery mission in response to a given RFP and map of a study area, taking into consideration vertical and horizontal control, atmospheric conditions, time of year, and time of day"},{"concepts":[705,714],"name":"Plan an Earth observation mission objectives and priorities in response to user expectations, taking into account type of application, type of sensor, expected accuracy"},{"concepts":[760,796],"name":"Plan and design alternative energy project implementations"},{"concepts":[762],"name":"Plan and design mineral & mining project implementations"},{"concepts":[761],"name":"Plan and design oil & gas project implementations"},{"concepts":[791],"name":"Plan and design project implementations"},{"concepts":[763],"name":"Plan and design project implementations in the field of energy and mineral 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hierarchies"},{"concepts":[621],"name":"Recognize different types of surface roughness on a radar image"},{"concepts":[122],"name":"Recognize expressions of uncertainty in language"},{"concepts":[106],"name":"Recognize situations and phenomena in the landscape which cannot be adequately represented by formal attributes, such as aesthetics"},{"concepts":[162],"name":"Recognize spatial schemes like patterns and shapes"},{"concepts":[471],"name":"Recognize the assumptions underlying probability and geostatistics and the situations in which they are useful analytical tools"},{"concepts":[81],"name":"Recognize the commonalities of philosophical viewpoints and appreciate differences to enable work with diverse colleagues"},{"concepts":[188],"name":"Recognize the constraints and opportunities of a particular choice of software for implementing a physical model"},{"concepts":[95],"name":"Recognize the constraints that political forces place on geospatial applications in public and private sectors"},{"concepts":[118],"name":"Recognize the contributions of Topology (the branch of mathematics) to the study of geographic relationships"},{"concepts":[122],"name":"Recognize the degree to which the importance of uncertainty depends on scale and application"},{"concepts":[121],"name":"Recognize the degree to which vagueness depends on scale"},{"concepts":[94],"name":"Recognize the impact of ones social background on ones own geographic worldview and perceptions and how it influences ones use of GIS"},{"concepts":[436],"name":"Recognize the importance of reproducible research as a fundamental pillar of modern science"},{"concepts":[83],"name":"Recognize the influences of epistemology on GIS practices"},{"concepts":[109],"name":"Recognize the influences of scale on the perception and meaning of fields"},{"concepts":[294],"name":"Recognize the relevant legal issues in a particular case of geospatial data collection, use and/of sharing"},{"concepts":[103],"name":"Recognize the role that time plays in static GISystems"},{"concepts":[115],"name":"Recommend for what applications we should use a field or an object-base approach."},{"concepts":[105],"name":"Reconcile differing common-sense and official definitions of common geospatial categories of entities, attributes, space, and time"},{"concepts":[856],"name":"Relate EO measurements with detected features"},{"concepts":[91],"name":"Relate epistemology to spatial knowledge."},{"concepts":[53],"name":"Relate plots of multidimensional attribute data to geography by equating similarity in data space with proximity in geographical space"},{"concepts":[217],"name":"Relate the concept of grid cell resolution to the more general concept of support and granularity"},{"concepts":[109],"name":"Relate the notion of field in GIS to the mathematical notions of scalar and vector fields"},{"concepts":[124],"name":"Relate the science and technology of graphical representation of geographic data"},{"concepts":[388],"name":"Relate the spatial and spectral characteristics of EO data to the types and proportions of materials found within the scene and within pixel IFOVs to relabel spectral classes as information classes of a classification scheme"},{"concepts":[135],"name":"Relate the spatial dimension and the weight of mapped features with the attributes they represent"},{"concepts":[567],"name":"Relate to the aspects of radiation transfer through the atmosphere."},{"concepts":[886],"name":"Relate with manual and automated methods linking data"},{"concepts":[166],"name":"Report existing and potential tasks in terms of workflow and information flow"},{"concepts":[162],"name":"Represent an object or a scene from different viewpoints"},{"concepts":[116],"name":"Represent structural relationships in GIS data"},{"concepts":[25],"name":"Resample multiple raster data sets to a single resolution to enable overlay"},{"concepts":[25],"name":"Resample raster data sets (e.g., terrain, satellite imagery) to a resolution 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provocative, propaganda, political, and persuasive nature of maps and geospatial data"},{"concepts":[743],"name":"Select the appropriate optical data type for the application"},{"concepts":[748],"name":"Select the appropriate SAR data type for the application"},{"concepts":[62],"name":"Select the appropriate statistical methods for the analysis of given spatial datasets by first exploring them using graphic methods"},{"concepts":[902],"name":"select the development elements best suited for your application"},{"concepts":[137],"name":"Select the most appropriate place in a map to place a label and a legend"},{"concepts":[266],"name":"Select the most appropriate remotely sensed data source for a given analytical task, study area, budget, and availability"},{"concepts":[173],"name":"Select the most appropriate techniques for a EO*GI project"},{"concepts":[176],"name":"Select the most appropriate technology to help decision-making"},{"concepts":[154],"name":"Select the most suitable 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imagery into radiometrically/atmospherically corrected state"},{"concepts":[25],"name":"Understand and examine the common methods for raster resampling"},{"concepts":[294],"name":"Understand and explain the main legal issues related to the production, use and sharing of geospatial data and information"},{"concepts":[198],"name":"Understand and use XML"},{"concepts":[334],"name":"Understand atmospheric parameters that influence bottom of atmosphere (BOA) reflectance"},{"concepts":[241],"name":"Understand complexity in the broadest sense"},{"concepts":[68],"name":"Understand different estimation methods for Bayesian models"},{"concepts":[237],"name":"Understand how complex systems operate"},{"concepts":[344],"name":"Understand how data augmentation can improve deep learning methods for image classification"},{"concepts":[876],"name":"understand how different web services complement each other"},{"concepts":[240],"name":"Understand how geocomputation relates to other similar terms"},{"concepts":[160],"name":"Understand how graphic representations can be interpreted distinctively by culturally different audiences"},{"concepts":[447],"name":"Understand how limited temporal completness affects the usefulness of a time series analysis"},{"concepts":[160],"name":"Understand how map scale is used to provide the relationship of size of object on a map and its real-world size"},{"concepts":[244],"name":"Understand how models are translated into differential equations for execution"},{"concepts":[243],"name":"Understand how models can be specified into logical rules"},{"concepts":[805],"name":"Understand how numerical prediction models work"},{"concepts":[445],"name":"Understand how positional/geometric accuracy of a dataset affects subsequent analysis"},{"concepts":[445,444],"name":"Understand how root mean squared error (RMSE) at tie points represents local spatial accuracy and enables calculation of total RMSE that informs about the average spatial accuracy of the entire image"},{"concepts":[367],"name":"Understand how satellite image time series can be used for mapping, trend analysis and change detection"},{"concepts":[376],"name":"Understand how the entropy represents the the average level of information contained in an image pixel"},{"concepts":[154],"name":"Understand how the representation of geographic data facilitates visual communication"},{"concepts":[236],"name":"Understand how the theoretical roots and experimental emphasis on geocomputation are integrated"},{"concepts":[848],"name":"Understand how the tracking of moving objects is implemented"},{"concepts":[165],"name":"Understand spatial data models and structures"},{"concepts":[260],"name":"Understand spatial reference systems and apply them to an EO dataset"},{"concepts":[335],"name":"Understand sun, sun angle, and sensor parameters that influence top of atmosphere (TOA) reflectance"},{"concepts":[160],"name":"Understand that features have been omitted or generalized for clarity"},{"concepts":[237],"name":"Understand the all-encompassing concepts of complexity"},{"concepts":[65],"name":"Understand the assumption under which spatial autocorrelation may occur"},{"concepts":[66],"name":"Understand the assumption under which spatial autocorrelation may occur"},{"concepts":[293],"name":"Understand the benefits of publishing and using open data"},{"concepts":[397],"name":"Understand the challenge in matching sensory image data to a mental model of the world-scene"},{"concepts":[242],"name":"Understand the defining characteristics of simulation models, and their applicability"},{"concepts":[186],"name":"Understand the degree to which attributes need to be conceptually modeled"},{"concepts":[455],"name":"Understand the difficulties in searching and selecting satellite images with sufficient spatial coverage for time series analysis"},{"concepts":[815],"name":"Understand the diverse set of EO technologies that are capable of mapping different landslide aspects"},{"concepts":[756,797,819,809],"name":"Understand the health of the crop, extent of infestation or stress damage, or potential yield and soil conditions"},{"concepts":[757,834],"name":"Understand the health of the fishing grounds"},{"concepts":[758,820],"name":"Understand the health of the forests"},{"concepts":[899],"name":"Understand the importance of Cascading Style Sheets (CSS) to separate content from style in HMTL documents"},{"concepts":[445],"name":"Understand the importance of using spatially independent validation samples to assess the quality of the classification results"},{"concepts":[343],"name":"Understand the main factors generating geometric distortions of the remotely sensed images"},{"concepts":[169],"name":"Understand the main software engineering methodologies"},{"concepts":[289],"name":"Understand the nature of tort law generally and nuisance law specifically"},{"concepts":[104],"name":"Understand the physical notions of velocity and acceleration which are fundamentally about movement across space through time"},{"concepts":[436],"name":"Understand the problems associated with the lack of reproducibility"},{"concepts":[448],"name":"Understand the relevance of topological consistency for linear network features derived from Earth observation data"},{"concepts":[364],"name":"Understand the role of multi-temporal satellite images for identifying not only when a change occurred but also the changing drivers"},{"concepts":[391],"name":"Understand the role of pruning for reducing overfitting when applying decision trees for various classification purposes"},{"concepts":[465],"name":"Understand the strategic meaning of DIAS in the user segment of Copernicus"},{"concepts":[374],"name":"Understand the subjectivity of the visual interpretation"},{"concepts":[843],"name":"Understand the technology behind LiDAR as an active sensor and what makes it different from the other existing Remote Sensing approaches"},{"concepts":[63],"name":"Understand the underlying assumptions for spatial stochastics process"},{"concepts":[366],"name":"Understand the way in which Dynamic Time Warping can align shifted temporal sequences"},{"concepts":[22],"name":"Understand various formats of storing raster and vector data"},{"concepts":[227],"name":"Understand vector data models"},{"concepts":[888],"name":"Use \"Full-text-based\" discovery; open source and commercial search engines, its use in GI related applications"},{"concepts":[862,860],"name":"Use 3D textured models to present study area"},{"concepts":[463],"name":"Use a web portal to retrieve EO data"},{"concepts":[464],"name":"Use an image archive to retrive Earth observation data for an application"},{"concepts":[146],"name":"Use appropriate interpolation techniques to derive DEMs from point data"},{"concepts":[105],"name":"Use categorical information in analysis, cartography, and other GIS processes, avoiding common interpretation mistakes"},{"concepts":[827],"name":"Use EO products to assess land areas, its ecosystems, and its evolution"},{"concepts":[818],"name":"Use EO products to assess the risk of a disaster"},{"concepts":[806,804],"name":"Use EO products to conduct forecasts and projections"},{"concepts":[805],"name":"Use EO products to conduct numerical simulations"},{"concepts":[803],"name":"Use EO products to forecast sunlight exposure"},{"concepts":[818],"name":"Use EO products to measure impact and/or recovery"},{"concepts":[818],"name":"Use EO products to monitor disaster prone areas"},{"concepts":[827],"name":"Use EO products to plan land areas, its ecosystems, and its evolution"},{"concepts":[755],"name":"Use EO/GI information to plan and design projects, monitor and assess the environment, support decision-making processes, and to tackle environmental challenges"},{"concepts":[113],"name":"Use established analysis methods that are based on the concept of region (e.g., landscape ecology)"},{"concepts":[114],"name":"Use established analysis methods that are based on the concept of spatial integration (e.g., overlay)"},{"concepts":[382],"name":"Use filtering techniques to spatially aggregate an image classification"},{"concepts":[119],"name":"Use methods that analyze metrical relationships"},{"concepts":[118],"name":"Use methods that analyze topological relationships"},{"concepts":[890],"name":"Use Natural language based discovery over linked data"},{"concepts":[841],"name":"Use NDVI to estimate the vegetation cover"},{"concepts":[884],"name":"Use open data APIs that enable the usage of Open data; identify design aspects and usage scenarios"},{"concepts":[436],"name":"Use software tools to automate the practice of reproducible research in daily work"},{"concepts":[206],"name":"Use standards such as ISO 19141 Schema for moving features, ISO 19142 Web Feature Service and ISO 19109 - Rules for application schema"},{"concepts":[492],"name":"Use the models of ‘SDI generations’ and ‘SDI components’ to describe the main elements of an existing SDI initiative"},{"concepts":[479],"name":"Use the most effective change model depending on the nature and needs of the client's organization."},{"concepts":[877],"name":"Use Web services description for RESTful web services, Web Application Description Language (WADL) and its use"},{"concepts":[195],"name":"Work with different data compression techniques"},{"concepts":[40],"name":"Write a program to create a matrix of pair-wise distances among a set of points"},{"concepts":[211],"name":"Write a program to read and write a raster data file"},{"concepts":[40],"name":"Write typical forms for distance decay functions"},{"concepts":[11],"name":"xplain how the concept of capacity represents an upper limit on the amount of flow through the network"}]},"v6":{"concepts":[{"code":"GIST","description":"Geographic Information Science and Technology","name":"Geographic Information Science and Technology"},{"code":"AM","description":"This knowledge area encompasses a wide variety of operations whose objective is to derive analytical results from geospatial data. Data analysis seeks to understand both first-order (environmental) effects and second-order (interaction) effects. Approaches that are both data-driven (exploration of geospatial data) and model-driven (testing hypotheses and creating models) are included. Data driven techniques derive summary descriptions of data, evoke insights about characteristics of data, contribute to the development of research hypotheses, and lead to the derivation of analytical results. The goal of model driven analysis is to create and test geospatial process models. In general, model-driven analysis is an advanced knowledge area where previous experience with exploratory spatial data analysis would constitute a desired prerequisite. Visual tools for data analysis are covered in Knowledge Area: Cartography and Visualization (CV) and many of the fundamental principles required to ground data analysis techniques are introduced in Knowledge Area: Conceptual Foundations (CF). Image processing techniques are considered in Knowledge Area: Geospatial Data (GD). All of the methods described in this knowledge area are more or less sensitive to data error and uncertainty as covered in Unit GC8 Uncertainty and Unit GD6 Data quality. Mastery of the educational objectives outlined in this knowledge area requires knowledge and skills in mathematics, statistics, and computer programming.","name":"Analytical Methods","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM1-2","description":"Analytical capabilities of a GIS make use of spatial and non-spatial (attribute) data to answer questions and solve problems that are of spatial relevance. We now make a distinction between analysis (or analytical operations) and analytical models (often referred to as “modelling”). And by analysis we actually mean only a subset of what is usually implied by the term: we do not specifically deal with advanced statistical analysis (such as cluster detection or geostatistics).\r\n\r\nAnalysis of spatial data can be defined as computing new information to provide new insights from existing spatial data. Consider an example from the domain of road construction. In mountainous areas, this is a complex engineering task with many cost factors, including the number of tunnels and bridges to be constructed, the total length of the tarmac, and the volume of rock and soil to be moved. GISs can help to compute such costs on the basis of an up-to-date digital elevation model and a soil map. The exact nature of the analysis will depend on the application requirements, but computations and analytical functions can operate on both spatial and non-spatial data.","name":"Analytical approaches","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM1","description":"Geospatial data analysis has foundations in many different disciplines. As a result, there are many different schools of thought or analytical approaches including spatial analysis, spatial modeling, geostatistics, spatial econometrics, spatial statistics, qualitative analysis, map algebra, and network analysis. This unit compares and contrasts these approaches.","name":"Foundations of analytical methods","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM10-1","description":"Difficulties in dealing with large spatial databases, especially those arising from spatial heterogeneity and data quality issues.","name":"Problems of large spatial databases","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM10-2","description":"Data mining knows a variety of approaches, such as cluster analysis, analytical reasoning, association, prediction, etc.","name":"Data mining approaches","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM10-3","description":"Knowledge discovery involves the identification of useful patterns in spatial databases using techniques of data mining, trend analysis, etc.","name":"Knowledge discovery","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM10","description":"Algorithms have been developed to scan and search through extremely large data sets in order to find patterns within the data. These data mining and knowledge discovery techniques have been expanded to the spatial case. Legal and ethical concerns associated with such practices are considered in Knowledge Areas GS GIS and T and Society and OI Organizational and Institutional Aspects.","name":"Data mining","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM11-1","description":"A network is a connected set of lines representing some geographic phenomenon, typically to do with transportation. The “goods” transported can be almost anything: people, cars and other vehicles along a road network, commercial goods along a logistic network, phone calls along a telephone network, or water pollution along a stream/river network.\r\n\r\nDirect vs. Non-directed Networks\r\nA fundamental characteristic of any network is whether the network lines are considered to be directed or not. Directed networks associate with each line a direction of transportation; undirected networks do not. In the latter, the “goods” can be transported along a line in both directions. We discuss here vector network analysis, and assume that the network is a set of connected line features that intersect only at the lines’ nodes, not at internal vertices. (But we do mention under- and overpasses.)\r\n\r\nPlanar vs. Non-Planar Networks\r\nFor many applications of network analysis, a planar network, i.e. one that can be embedded in a two-dimensional plane, will do the job. Many networks are naturally planar, such as stream/river networks. A large-scale traffic network, on the other hand, is not planar: motorways have multi-level crossings and are constructed with underpasses and overpasses. Planar networks are easier to deal with computationally, as they have simpler topological rules. Not all GISs accommodate non-planar networks, or they can only do so using “tricks”. These tricks may involve the splitting of overpassing lines at the intersection vertex and the creation of four lines from the two original lines. Without further attention, the network will then allow one to make a turn onto another line at this new intersection node, which in reality would be impossible. In some GISs we can allocate a cost for turning at a node—see our discussion on turning costs below—and that cost, in the case of the overpass trick, can be made infinite to ensure it is prohibited. But, as mentioned, this is a work around to fit a non-planar situation into a data layer that presumes planarity. The above is a good illustration of geometry not fully determining the network’s behaviour. Additional application-specific rules are usually required to define what can and cannot happen in the network. Most GISs provide rule-based tools that allow the definition of these extra application rules.","name":"Networks defined","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM11-2","description":"Identifying and listing all elements does not describe a system in full. There may be many different ways in which elements may be connected or related to each other. The interactions, relationships between elements are essential to describe a system.\r\n\r\nRelationships between elements can be described by two types of flows:\r\nflows of material, and flows of information.\r\n\r\nMaterial flows connect elements between which there is an exchange of some substance. This can be some kind of material (water, food, cement, biomass, etc.), energy (light, heat, electricity, etc.), money, etc. It is something that can be measured and tracked. Also if an element is a donor of this substance the amount of substance in this element will decrease as a result of the exchange, while at the same time the amount of this substance will increase in the receptor element. There is always a mass, or energy conservation law in place. Nothing appears from nothing, and nothing can disappear to nowhere.\r\n\r\nThe second type of exchange is with an information flow. In this case element A gets information from element B. Element B at the same time may have no information about element A. Even when element A gets information about B, B does not lose anything. Information can be about the state of an element, about the quantity that it contains, about its presence or absence, etc. Information flows can be used to describe rules and policies. Information flows can modify the rates of flow between elements, they can switch certain processes and interactions on and off. But the process through which policies, interventions and norms for action are established, and could for example define the values of such information flows, are themselves the result of social interaction between relevant stakeholders from public, private or civil society.\r\n\r\nThe simplest is to acknowledge the existence of a relationship between certain elements, like this is done in a graph. In a graph a node presents an element and a link between any two nodes shows that these two elements are related. However there is no evidence of the direction of the relationship: we do not distinguish between the element x influencing element y or vice versa. This relationship can be further specified by an oriented graph that shows the direction of the relationship between elements. An element can be also connected to itself, to show that its behaviour depends on its state. We can further detail the description by identifying whether element x has a positive or negative effect on element y.\r\n\r\nWith networks, interesting questions arise that have to do with connectivity and network capacity. These relate to applications such as traffic monitoring and watershed management. With network elements—i.e. the lines that make up the network—extra values are commonly associated, such as distance, quality of the link or the carrying capacity.","name":"Graph theoretic descriptive measures of networks","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM11-3","description":"Optimal-path finding techniques are used when a least-cost path between two nodes in a network must be found. The two nodes are called origin and destination. The aim is to find a sequence of connected lines to traverse from the origin to the destination at the lowest possible cost.\r\n\r\nIn Optimal-path finding, the cost function can be simple: for instance, it can be defined as the total length of all lines of the path. The cost function can also be more elaborate and take into account not only length of the lines but also their capacity, maximum transmission (travel) rate and other line characteristics, for instance to obtain a reasonable approximation of travel time. There can even be cases in which the nodes visited add to the cost of the path as well. These may be called turning costs, which are defined in a separate turning-cost table for each node, indicating the cost of turning at the node when entering from one line and continuing on another. This is illustrated in Figure 1 of the examples.\r\n\r\nProblems related to optimal-path finding may require ordered optimal path finding or unordered optimal-path finding. Both have as an extra requirement that a number of additional nodes need to be visited along the path. In ordered optimal-path finding, the sequence in which these extra nodes are visited matters; in unordered optimal-path finding it does not.","name":"Least-cost shortest path","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM11-4","description":"There are phenomena that do not spread in all directions, but move or “flows” along a given, least-cost path, determined by characteristics of local terrain. The typical case arises when we want to determine drainage patterns in a catchment area: rain water “chooses” a way to leave the area. \r\n\r\nWe can illustrate the principles involved in this typical case with a simple elevation raster. For each cell in that raster, the steepest downward slope to a neighbour cell is computed and its direction is stored in a new raster. This computation determines the elevation difference between the cell and the neighbour cell and it takes into account cell distance - 1 for neighbour cells in N–S or W–E direction, 2 for cells in a NE–SW or NW–SE direction. From among its eight neighbour cells, it picks the one with the steepest path to it. The directions thus obtained in an output raster are encoded in integer values, which can be called the flow-direction raster. From this raster, the GIS can compute the accumulated flow-count raster, a raster that for each cell indicates how many cells have their water flow into that cell.\r\n\r\nCells with a high accumulated flow count represent areas of concentrated flow and may, thus, belong to a stream. By using some appropriately chosen threshold value in a map algebra expression, we may decide whether they do or not. Cells with an accumulated flow count of zero are local topographic highs and can be used to identify ridges.","name":"Flow modeling","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM11-5","description":"The Classic Transportation Problem considers minimizing the cost of getting an object or subject from origin to destination.","name":"The Classic Transportation Problem","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM11-6","description":"Classic network problems are examples of networking problems such as the Traveling Salesman Problem and the Chinese Postman Problem that need graph algorithms to be solved.","name":"Other classic network problems","selfAssesment":"GI-N2K
"},{"code":"AM11-7","description":"Accessibility is the extend in which it is difficult/easy to reach a location or object.","name":"Accessibility modeling","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM11","description":"Network analysis encompasses a wide range of procedures, techniques, and methods that allow for the examination of phenomena that can be modeled in the form of connected sets of edges and vertices. Such sets are termed a network or a graph, and the mathematical basis for network analysis is known as graph theory. Graph theory contains descriptive measures and indices of networks such as connectivity, adjacency, capacity, and flow as well as methods for proving the properties of networks. Networks have long been recognized as an efficient way to model many types of geographic data, including transportation networks, river networks, and utility networks electric, cable, sewer and water, etc. to name just a few. The data structures to support network analysis are covered in [DM4-7] Network models.","name":"Network analysis","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM12-1","description":"The modeling of problems in a formal language, working in a solution space and applying constraints.","name":"Operations research modeling and location modeling principles","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM12-2","description":"A formal programming method to support operational research in which linear constraints are applied.","name":"Linear programming","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM12-3","description":"A formal programming method to support operational research in which variables are constrained to integers.","name":"Integer programming","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM12-4","description":"Location-allocation modeling involves the determination of locations by minimizing the distance between object/subjects in space, such as between customers and facilities.","name":"Location-allocation modeling and p-median problems","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM12","description":"A wide variety of optimization techniques are now solvable within the GIS and T domain. Operations research is a branch of mathematics practiced in the allied fields of business and engineering. New models and software tools allow for the solution of transportation routing, facility location, and a host of other location-allocation modeling problems.","name":"Optimization and location-allocation modeling","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM13-1","description":"The effects such as the loss of data quality and data integrity that are the results of data transformations.","name":"Impacts of transformations","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM13-2","description":"A data model is an abstract model that organizes elements of data and standardizes how they relate to one another and to the properties of real-world entities. The term data model can refer to two distinct but closely related concepts. In relation to the field of geoinformation the term data model refers to the set of concepts used in defining such formalizations as entities, attributes, relations, tables which is implemented by a mathematical construct for representing geographic objects or surfaces as data. There are two most frequently used data models, which are vector and raster. For example, the vector data model represents geography as collections of points, lines and polygons and more complex structures crated from these three. The raster data model represent geography as cell matrices that store numeric values. Among these two data models we also stand out data formats in which data sets can be stored. File format is a standard of encoding geographical information into a computer file. There are the following basic file formats for encoding data:\r\nFor vectors:\r\n-\tShapefile\r\n-\tGeography Markup Language (GML)\r\n-\tXYZ Point Cloud\r\n-\tGeoJSON\r\n-\tGeoMedia\r\n-\t\r\nFor rasters:\r\n-\tGeoTIFF\r\n-\tIMG\r\n-\tJPEG2000\r\n-\tEsri grid\r\nThe GIS projects often require the conversion of the data formats. Data conversion is the process of moving data from one format to another, whether it is from one data model to another or from one data format to another. Data conversion is a complex process which is not only associated with changing the binary format of the file but also requires changing the structure of the data. For example, the GML data format always comes with an UML diagram, which is necessary to convert attributes stored in GML structure for example to a table of contest in a shapefile data format. In a well-managed GIS project it is important to store data in specific data model or data format. It is sometimes dictated by software capabilities and another times by team’s technical capabilities. With large amounts of geographic data used in the project it is more cost-effective to convert the data from one format to another than re-create it.","name":"Data model and format conversion","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM13-3","description":"Interpolation is used to create a GIS layer out of point observations on a continuous variable. The reason for doing this could be manifold: for visualization purposes, for making a proper reference with other data, or for making a combination of different layers.","name":"Interpolation","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM13-4","description":"Any vector data containing point, polyline, polygon can be converted into the raster dataset and vice versa. The vector data can be stored in shapefiles, databases or various others GIS file formats. The raster data are made of pixels or grid calls and can be represented by the discrete - categorical data (e.g. land cover map) or non-discrete - continuous data (e.g. satellite images, surface data). The process of conversion of vector to raster data is called rasterization. The vector to raster conversion requires the following parameters: the field value from the attribute table used to assign values to the output raster, the pixel size for the output raster, the output raster format (i.e. geotiff, img) and optionally the method of assigning values of point, polyline or polygon to the call raster, i.e. maximum length or area, cell centre. The output of the rasterised vector looks like a gridded version of the vector and it depends on the grid cell size. The process of vectorisation refers to the conversion of raster to vector dataset. The raster dataset can be converted to vector point, polyline or polygon. In order to convert raster to vector the following parameters should be provided: attribute field of the input raster dataset which will become an attribute in the output vector class, determining if the output polygon or polyline will be smoothed into simpler shapes or conform to the input raster's cell edges (stair stepping). For each raster pixel or grid cell a point will be created at the centre of the cell. The non-discrete continuous raster data have to converted to the categorical data type before converting to vector data. The conversion of vector to raster and raster to vector degrade the data to some extent causing loss of details, accuracy, and changing the original data.","name":"Vector-to-raster and raster-to-vector conversions","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM13-5","description":"Raster resampling refers to change of spatial resolution (increasing or decreasing) of the raster dataset. The resampling process calculates the new pixel values from the original digital pixel values in the uncorrected image. There are three common methods for resampling: nearest neighbour, bilinear interpolation, and cubic convolution. The nearest neighbour resampling uses the digital value from the pixel in the original image which is nearest to the new pixel location in the corrected image. This is the fastest interpolation method, which is primarily applied for discrete (categorical) raster data as it does not change the value of the pixel, but may result in some pixel values being duplicated while others are lost. Bilinear interpolation resampling takes a weighted average of four pixels in the original image nearest to the new pixel location. The averaging process alters the original pixel values and creates entirely new digital values in the output image. It is recommended for continuous data and it cause some smoothing of the data. Cubic convolution resampling is based on calculation of a distance weighted average of a block of sixteen pixels from the original image which surround the new output pixel location. As with bilinear interpolation, this method results in completely new pixel values. However, the last two methods both produce images which have a much sharper appearance and avoid the blocky appearance of the nearest neighbour method. The disadvantage of the Cubic method is that its requires more processing time.","name":"Raster resampling","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM13-6","description":"Users of geoinformation often need transformations from a particular 2D coordinate system to another system. This includes the transformation of polar coordinates into Cartesian map coordinates, or the change of map projection - transformation from one 2D Cartesian (x, y) system of a specific map projection into another 2D Cartesian (x′, y′) system of a defined map projection. This transformation is based on relating the two systems on the basis of a set of selected points whose coordinates are known in both systems, such as ground control points or common points such as corners of houses or road intersections. Image and scanned data are usually transformed by this method. The transformations may be conformal, affine, polynomial or of another type, depending on the geometric errors in the data set. A datum transformation involves the change of the horizontal datum which is often accompanied with a change of map projection.","name":"Coordinate transformations","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM13","description":"GIS is a cyclical rather than a linear system, unlike computer aided drafting (CAD) and computer assisted cartographic systems. Changes in projection, grid systems, data forms, and formats take place during the modeling process for which GIS was designed. Many non-analytical manipulations are necessary to accommodate the analytical power of the GIS. The manipulations of spatial and spatio-temporal data involve two general classes of operation: 1.\tTheir transformation into formats that facilitate subsequent analysis 2. Generalization and aggregation that affect the accuracy and integrity of the data used for analysis (see [AM14]). Other knowledge areas have identified different forms of data structures, data models, projections, and other forms of geospatial data representation. These differences present both opportunities and challenges for analysis and modeling. The ability to transform one representation to another, in a manner that maintains the integrity of the information as much as possible, can enhance the analysis and visualization of geospatial data. The raster and vector data models are described in [DM3] Tesselation data models and [DM4] Vector data model, Feature based modelling, Applications. The principles of coordinate systems, datums, and projections are also considered in Knowledge Area [GD] Geospatial Data","name":"Representation transformation","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM14-1","description":"In the practice of spatial data handling, one often comes across questions like “What is the resolution of the data?” or “At what scale is your data set?” Now that we have moved firmly into the digital age, these questions sometimes defy an easy answer. Map scale can be defined as the ratio between the distance on a printed map and the distance of the same stretch in the terrain.\r\n\r\nA 1:50,000 scale map means that 1 cm on the map represents 50,000 cm (i.e. 500 m) in the terrain. “Large-scale” means that the ratio is relatively large, so typically it means there is much detail to see, as on a 1:1000 printed map. “Small-scale”, in contrast, means a small ratio, hence less detail, as on a 1:2,500,000 printed map.\r\nDigital spatial data, as stored in a GIS, are essentially without scale: scale is a ratio notion associated with visual output, such as a map or on-screen display, not with the data that was used to produce the map or display. When digital spatial data sets have been collected with a specific map-making purpose in mind, and all maps have been designed to use one single map scale, for instance 1:25,000, we may assume that the data carries the characteristic of “a 1:25,000 digital data set.”\r\n\r\nThere is a relationship between the effectiveness of a map for a given purpose and the map’s scale. The Public Works department of a city council cannot use a 1:250,000 map for replacing broken sewer pipes, and the map of Figure 1 cannot be reproduced at scale 1:10,000.\r\n\r\nMaps that show much detail of a small area are called large-scale maps. Scale indications on maps can be given verbally, such as “one-inch-to the- mile”, or as a representative fraction like 1:200,000,000 (1 cm on the map equals 200,000,000 cm (or 2000 km) in reality), or by a graphic representation such as the scale bar. The advantage of using scale bars in digital environments is that its length also changes when the map is zoomed in, or enlarged, before printing. Sometimes it is necessary to convert maps from one scale to another, which may lead to problems of cartographic generalization.\r\n\r\nSpatial and temporal scales can not only be attached to processes, but also to observations. An example is given below, which summarizes the spatial and temporal scales of a few well-known Earth observation systems.\r\n\r\nScales of RS observations\r\nSensor Spatial scale\t Temporal scale\r\nMeteosat\t Hemisphere\t 15 minutes\r\nNOAA-AVHRR\t 3000 km\t daily\r\nLandsat TM\t 180 km\t 16 days\r\nSpot\t 60 km\t 26 days (pointable)","name":"Scale and generalization","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM14-2","description":"Techniques that support the generalisation of map content when changing to smaller map scales. These include line simplification, object selection, etc.","name":"Approaches to point, line, and area generalization","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM14-3","description":"Classification is a technique for purposely removing detail from an input data set in the hope of revealing important patterns (of spatial distribution). In the process, we produce an output data set, so that the input set can be left intact. This output set is produced by assigning a characteristic value to each element in the input set, which is usually a collection of spatial features that could be raster cells or points, lines or polygons. If the number of characteristic values in the output set is small in comparison to the size of the input set, we have classified the input set.\r\n\r\nThe input data set may, itself, have been the result of a classification. In such cases we refer to the output data set as a reclassification. For example, we may have a soil map that shows different soil type units and we would like to show the suitability of units for a specific crop. In this case, it is better to assign to the soil units an attribute of suitability for the crop. Since different soil types may have the same crop suitability, a classification may merge soil units of different type into the same category of crop suitability.\r\n\r\nIn classification of vector data, there are two possible results. In the first, the input features may become the output features in a new data layer, with an additional category assigned. In other words, nothing changes with respect to the spatial extents of the original features. Figure a of Examples illustrates this first type of output. A second type of output is obtained when adjacent features of the same category are merged into one bigger feature. Such a post-processing function is called spatial merging, aggregation or dissolving. An illustration of this second type is found in Figure b of Examples. Observe that this type of merging is only an option in vector data, as merging cells in an output raster on the basis of a classification makes little sense. Vector data classification can be performed on point sets, line sets or polygon sets; the optional merge phase only makes sense for lines and polygons.\r\n\r\nUser-controlled classifications require a classification table or user interaction. GIS software can also perform automatic classification, in which a user only specifies the number of classes in the output data set. The system automatically determines the class break points. The two main techniques of determining break points being used are the equal interval technique and the equal frequency technique.\r\n\r\nEqual Interval Technique\r\nThe minimum and maximum values vmin and vmax of the classification parameter are determined and the (constant) interval size for each category is calculated as (vmax - vmin) ∕ n, where n is the number of classes chosen by the user. This classification is useful in that it reveals the distribution pattern, as it determines the number of features in each category.\r\n\r\nEqual Frequency Technique\r\nThis technique is also known as quantile classification. The objective is to create categories with roughly equal numbers of features per category. The total number of features is determined first, then, based on the required number of categories, the number of features per category is calculated. The class break points are then determined by counting off the features in order of classification parameter value.","name":"Classification and transformation of attribute measurement levels","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM14","description":"Generalization addresses the meaningful reduction of the map content during scale reduction. All geospatial data are generalized. Even the most detailed data represent only subsets of reality. Furthermore, data are further generalized for purposes of mapping, visualization, and efficient storage. A variety of generalization techniques have been developed to facilitate this process. All are scale dependent. Aggregation is one form of generalization that transforms large numbers of individual objects into summarized groups. This concept description is concerned with the nature of these procedures and their implications for professional practice. Generalization is an important part of cartography (and is therefore discussed conceptually in CV2 Data considerations), but is also a transformation common to many GIS procedures.","name":"Generalization and aggregation","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM2-1","description":"Set theory is based on describing collections of members within sets. The Boolean membership function is binary, i.e. an element is either a member of the set (membership is true) or it is not a member of the set (membership is false). Such a membership notion is well-suited to the description of spatial features such as land parcels for which no ambiguity is involved and an individual ground truth sample can be judged to be either correct or incorrect. As Burrough and Frank (1996) note, increasingly, people are beginning to realize that the fundamental axioms of simple binary logic present limits to the way we think about the world. Not only in everyday situations, but also in formalized thought, it is necessary to be able to deal with concepts that are not necessarily true or false, but that operate somewhere in between. Since its original development by Zadeh (1965), there has been considerable discussion of fuzzy, or continuous, set theory as an approach for handling imprecise spatial data. In GIS, fuzzy set theory appears to have two particular benefits: the ability to handle logical modelling (map overlay) operations on inexact data; and the possibility of using a variety of natural language expressions to qualify uncertainty. Unlike Boolean sets, fuzzy or continuous sets have a membership function, which can assign to a member any value between 0 and 1.","name":"Set theory","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM2-2","description":"The most common operator for defining queries in a relational database is the language SQL, which stands for Structured Query Language.\r\n\r\nA spatial DBMS provides support for geographic coordinate systems and transformations. It will also provide storage of the relationships between features, including the creation and storage of topological relationships. As a result, one is able to use functions for “spatial query” (exploring spatial relationships). To illustrate, a spatial query using SQL to find all the Thai restaurants within 2 km of a given hotel would look like:\r\n\r\nSELECT R.Name\r\nFROM Restaurants AS R,\r\nHotels as H\r\nWHERE R.Type = Thai AND\r\nH.name = Hilton AND\r\nIntersect(R.Geometry, Buffer(H.Geometry, 2))\r\n\r\nThe Intersect command creates a spatial join between restaurants and hotels. The Geometry column carries the spatial data. It is likely that in the near future all spatial data will be stored directly in spatial databases.","name":"Structured Query Language (SQL) and attribute queries","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM2-3","description":"When exploring a spatial data set, the first thing one usually wants to do is select certain features, to (temporarily) restrict the exploration. Such selections can be made on geometric/spatial grounds or on the basis of attribute data associated with the spatial features. \r\n\r\nSelection conditions on attribute values can be combined using logical connectives such as AND, OR and NOT. Other techniques of selecting features can also usually be combined. Any set of selected features can be used as the input for a subsequent selection procedure. This means, for instance, that we can select all medical clinics first, then identify roads within 200 m of them, then select from those only the major roads, then select the nearest clinics to these remaining roads as the ones that should receive our financial support for maintenance. In this way, we are combining various techniques of selection.\r\n\r\nInteractive Spatial Selection\r\nIn interactive spatial selection, one defines the selection condition by pointing at or drawing spatial objects on the screen display, after having indicated the spatial data layer(s) from which to select features. The interactively defined objects are called the selection objects; they can be points, lines, or polygons. The GIS then selects the features in the indicated data layer(s) that overlap (i.e. intersect, meet, contain, or are contained in;) with the selection objects. These become the selected objects.\r\nInteractive spatial selection answers questions like “What is at …?”\r\n\r\nA spatial DBMS provides support for geographic coordinate systems and transformations. It will also provide storage of the relationships between features, including the creation and storage of topological relationships. As a result, one is able to use functions for “spatial query” (exploring spatial relationships). To illustrate, a spatial query using SQL to find all the Thai restaurants within 2 km of a given hotel would look like:\r\n\r\nSELECT R.Name\r\nFROM Restaurants AS R,\r\nHotels as H\r\nWHERE R.Type = Thai AND\r\nH.name = Hilton AND\r\nIntersect(R.Geometry, Buffer(H.Geometry, 2))\r\n\r\nThe Intersect command creates a spatial join between restaurants and hotels. The Geometry column carries the spatial data. It is likely that in the near future all spatial data will be stored directly in spatial databases.","name":"Spatial queries","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM2","description":"Attribute and spatial query operations are core functionality in any GIS and they are often considered to be the most basic form of analysis.","name":"Query operations and query languages","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM3-1","description":"In a 2D polar coordinate system points can be described with coordinates. Another way of defining a point in a plane is by using polar coordinates. This is the distance d from the origin to the point concerned and the angle α between a fixed (or zero) direction and the direction to the point. The angle α is called azimuth or bearing and is measured in a clockwise direction. It is given in angular units while the distance d is expressed in length units. \r\n\r\nDistance also plays a role in computations on networks, comprising a different set of analytical functions in GISs. Here, the network may consist of roads, public transport routes, high-voltage power lines, or other forms of transportation infrastructure. Analysis of networks may entail shortest path computations (in terms of distance or travel time) between two points in a network for routing purposes. Other forms are to find all points reachable within a given distance or duration from a start point for allocation purposes, or determination of the capacity of the network for transportation between an indicated source location and sink location.\r\n\r\nIn raster images, the distance function applied is the Pythagorean distance between the cell centres. The distance from a non-target cell to the target is the minimal distance one can find between that non-target cell and any target cell.","name":"Distances and lengths","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM3-2","description":"In a 2D polar coordinate system points can be described with coordinates. Another way of defining a point in a plane is by using polar coordinates. This is the distance d from the origin to the point concerned and the angle α between a fixed (or zero) direction and the direction to the point. The angle α is called azimuth or bearing and is measured in a clockwise direction. It is given in angular units while the distance d is expressed in length units.\r\n\r\nBearings are always related to a fixed direction (initial bearing) or a datum line. In principle, this reference line can be chosen freely. Three different, widely used fixed directions are: True North, Grid North and Magnetic North. The corresponding bearings are true (or geodetic) bearings, grid bearings and magnetic (or compass) bearings, respectively.\r\n\r\nIn raster images, direction is determined by the orientation of the neighboring pixels.","name":"Direction","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM3-3","description":"The representation of geographic objects is most naturally supported with vectors. After all, objects are identified by the parameters of location, shape, size and orientation, and many of these parameters can be expressed in terms of vectors. We can define features within the topological space that are easy to handle and that can be used as representations of geographic objects. These features are called simplices as they are the simplest geometric shapes of some dimension: point (0-simplex), line segment (1-simplex), triangle (2-simplex), and tetrahedron (3-simplex). When we combine various simplices into a single feature, we obtain a simplicial complex. When area objects are stored using a vector approach, the usual technique is to apply a boundary model. This means that each area feature is represented by some arc/node structure that determines a polygon as the area’s boundary. A polygon representation for an area object is another example of a finite approximation of a phenomenon that may have a curvilinear boundary in reality. In images, the shape of objects often helps us to identify them (built-up areas, roads and railroads, agricultural fields, etc.).","name":"Shape","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM3-4","description":"When area objects are stored using a vector approach, the usual technique is to apply a boundary model. This means that each area feature is represented by some arc/node structure that determines a polygon as the area’s boundary. A polygon representation for an area object is another example of a finite approximation of a phenomenon that may have a curvilinear boundary in reality.\r\nCommon sense dictates that area features of the same kind are best stored in a single data layer, represented by mutually non-overlapping polygons. This results in an application-determined (i.e. adaptive) partition of space. If the object has a fuzzy boundary, a polygon is an even worse approximation, even though potentially it may be the only one possible. Clearly, we expect additional data to accompany the area data. Such information could be stored in database tables.\r\n\r\nA simple but naïve representation of area features would be to list for each polygon the list of lines that describes its boundary. Each line in the list would, as before, be a sequence that starts with a node and ends with one, possibly with vertices in between. As the same line makes up the boundary from the two polygons, this line would be stored twice in the above representation, namely once for each polygon. This is a form of data duplication—known as data redundancy—which is (at least in theory) unnecessary, although it remains a feature of some systems. Another disadvantage of such polygon-by-polygon representations is that if we want to identify the polygons that border the bottom left polygon, we have to do a complicated and time-consuming search analysis comparing the vertex lists of all boundary lines with that of the bottom left polygon. For just a few polygons, this is fine, but in a data set with 5000 polygons, and perhaps a total of 25,000 boundary lines, this becomes a tedious task, even with the fastest of computers.","name":"Area","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM3-5","description":"Proximity computations are specific neighbourhood functions. They evaluate the characteristics of an area surrounding a feature’s location. A neighbourhood function “scans” the neighbourhood of the given feature(s), and performs a computation on it (them).\r\n\r\nExamples of proximity computations are: (1) Buffer zone generation (or buffering) is one of the best-known neighbourhood functions. It determines a spatial envelope (buffer) around a given feature or features. The buffer created may have a fixed width or a variable width that depends on characteristics of the area. (2) Thiessen Polygon generation.\r\n\r\nDistance decay functions describe the effect of the reduced influence when the distance between two locations increases.","name":"Proximity and distance decay","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM3-6","description":"Adjacency is the meet relationship as a topological property of a geographic object in relation ship with another. The adjacency operator identifies those features that share boundaries and, therefore, applies only to line and polygon features.\r\nThis meet relationship is invariant under a continuous transformation and are referred to as a topological mapping.","name":"Adjacency and connectivity","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM3","description":"For simple data exploration, GIS offers many basic geometric operations that help in extracting meaning from sets of data or for deriving new data for further analysis. Concepts on which these operations are based are addressed in Domains of geographic information and Relationships.\r\n\r\nWe can, for instance, measure angles on a map and use these for navigation in the real world, or for setting out a designed physical infrastructure. Or if, instead of a conformal projection such as UTM, we use an equivalent projection, we can determine the size of a parcel of land from the map—irrespective of where the parcel is on the map and at which elevation it is on the Earth.","name":"Geometric measures","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM4-1","description":"The reclassifications tools are used to change or reclassify the values. Reclassification of vector data involves the attributes of features in the feature attribute table, on the other hand reclassification of raster data involves the grid cell values to produce a new raster data layer. Reclassification can be used for data simplification and measurement scale change. We can adjust the data for more appropriate analysis by grouping the values and changing them. The reclassification tool can also be used to remove specific values from analysis.\r\nThe Select by location tool lets you select features by how they relate to other features in another layer. Selected features are based on their location. You can select features that are near or overlap the features. Most frequently used methods are intersect, within a distance, within, completely within, contain… Features can be selected in the same or other layers.\r\nThe Select by attributes tool lets you select features that match the selection criteria. With providing a selection criteria, matching features are selected. We can provide a complex selection criteria.","name":"Reclassification and selection operations","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM4-2","description":"Buffer analysis is one form of basic spatial analysis. It takes the vector representation (point, line, or polygon) of a real-world feature, and then creates a buffer zone based on a defined distance from the feature’s border. Thus, the created buffer zone is an area whose boundary always has the same distance to the input vector feature, e.g. the buffer zone for a point feature is a circle. Real-world examples for buffer zones could be protected areas along rivers or around nature conservation areas, or represent a simple proximity analysis. In the latter case, the buffer analysis is usually the first step of the analysis, followed by an overlay of the buffer zone with the target features to find those target features within the buffer zone, and thus within a certain distance of the original feature. Usually, the buffer zone extends outwards from the feature, but polygons can also have inner buffer zones. If the buffer zones from multiple features overlap, the analyst can decide to leave the individual boundaries of the buffer zones intact, or to dissolve them, i.e. merging the overlapping buffer zones into one larger buffer zone. The size of the buffer zone, i.e. the distance of its boundary from the original feature’s boundary, can be based on an uniform numerical value and associated spatial unit, but often, it is based on an attribute value (numerical or class) of the feature. Conceptually, buffering using raster representations of real-world features is similar a proximity analysis with a regular grid of square polygons: Departing from raster cells that form the area to be buffered, all raster cells that fall within the designated distance (overlay) from the buffer zone. With buffer analysis being a basic analytical operation, practically every GIS and many other analysis tools provide this functionality.","name":"Buffers","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM4-3","description":"Overlay functions is one of the most frequently used functions in a GIS application. They combine two (or more) spatial data layers, comparing them position by position and treating areas of overlap - and of non-overlap - in distinct ways.\r\n\r\nStandard overlay operators take two input data layers and assume that they are georeferenced in the same system and that they overlap in the study area. If either of these requirements is not met, the use of an overlay operator is pointless. The principle of spatial overlay is to compare the characteristics of the same location in both data layers and to produce a result for each location in the output data layer. The specific result to produce is determined by the user. It might involve a calculation or some other logical function to be applied to every area or location. With raster data, as we shall see, these comparisons are carried out between pairs of cells, one from each input raster. With vector data, the same principle of comparing locations applies but the underlying computations rely on determining the spatial intersections of features from each input layer.\r\n\r\nVector overlay operators are useful but geometrically complicated, and this sometimes results in poor operator performance. Raster overlays do not suffer from this disadvantage, as most of them perform their computations cell by cell, and thus they are fast. GISs that support raster processing - as most do - usually have a language to express operations on rasters. These languages are generally referred to as map algebra or, sometimes, raster calculus. They allow a GIS to compute new rasters from existing ones, using a range of functions and operators. Unfortunately, not all implementations of map algebra offer the same functionality.","name":"Overlay","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM4-4","description":"Neighbourhood functions evaluate the characteristics of an area surrounding a feature’s location. A neighbourhood function “scans” the neighbourhood of the given feature(s), and performs a computation on it (them). Examples of proximity computations are: (1) Buffer zone generation (or buffering) is one of the best-known neighbourhood functions. It determines a spatial envelope (buffer) around a given feature or features. The buffer created may have a fixed width or a variable width that depends on characteristics of the area. (2) Thiessen Polygon generation. For raster images: (3) Computation of diffusion (4) Flow computation.\r\n\r\nFor instance, our target might be a medical clinic. Its neighbourhood could be defined as:\r\n\r\nan area within a radius of 2 km distance as the crow flies; or\r\nan area within 2 km travelling distance; or\r\nall roads within 500 m travelling distance; or\r\nall other clinics within 10 minutes travelling time;\r\nall residential areas for which the clinic is the closest clinic.\r\n\r\nFinally, in the third step we indicate what it is we want to discover about the phenomena that exist or occur in the neighbourhood. This might simply be its spatial extent, but it might also be statistical information such as:\r\n\r\nhow many people live in the area;\r\nwhat is their average household income;\r\nare any high-risk industries located in the neighbourhood.\r\n\r\nThese are typical questions in an urban setting. When our interest is more in natural phenomena, different examples of locations, neighbourhoods and neighbourhood characteristics arise.\r\n\r\nThe principle in this case is to find out the characteristics of the vicinity, here called neighbourhood, of a location. After all, many suitability questions, for instance, depend not only on what is at a location but also on what is near the location. Thus, the GIS must allow us “to look around locally”. To perform neighbourhood analysis, we must:\r\n\r\n1. state which target locations are of interest to us and define their spatial extent;\r\n2. define how to determine the neighbourhood for each target; and\r\n3. define which characteristic(s) must be computed for each neighbourhood. \r\n\r\nSince raster data are the more commonly used in this case, neighbourhood characteristics often are obtained via statistical summary functions that compute values such as the average, minimum, maximum and standard deviation of the cells in the identified neighbourhood.\r\n\r\nTo select target locations, one can use the selection techniques. To obtain characteristics from an eventually-to-be identified neighbourhood, the same techniques apply. So what remains to be discussed here is the proper determination of a neighbourhood. One way of determining a neighbourhood around a target location is by making use of the geometric distance function. Geometric distance does not take into account direction, but certain phenomena can only be studied by doing so. Think of the spreading of pollution by rivers, groundwater flow or prevailing weather systems.\r\n\r\nDiffusion functions are based on the assumption that the phenomenon in question spreads in all directions, though not necessarily equally easily in each direction. Hence it uses local terrain characteristics to compute local resistances to diffusion.","name":"Neighborhood analysis","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM4-5","description":"GISs that support raster processing - as most do - usually have a language to express operations on rasters. These languages are generally referred to as map algebra or, sometimes, raster calculus. They allow a GIS to compute new rasters from existing ones, using a range of functions and operators. Unfortunately, not all implementations of map algebra offer the same functionality. The discussion below is to a large extent based on general terminology; it attempts to illustrate the key operations using a logical, structured language. Again, the syntax often varies among different GIS software packages.\r\n\r\nWhen producing a new raster we must provide a name for it, and define how it is to be computed. This is done in an assignment statement of the following format:\r\n\r\nOutput raster name := Map algebra expression.\r\n\r\nThe expression on the right is evaluated by the GIS, and the raster in which it results is then stored under the name on the left. The expression may contain references to existing rasters, operators and functions; the format is made clear in each case. The raster names and constants that are used in the expression are called its operands. When the expression is evaluated, the GIS will perform the calculation on a pixel-by-pixel basis, starting from the first pixel in the first row and continuing through to the last pixel in the last row. In map algebra, there is a wide range of operators and functions available.\r\n\r\nArithmetic operators\r\nVarious arithmetic operators are supported. The standard ones are multiplication (×), division (/), subtraction (-) and addition (+). Obviously, these arithmetic operators should only be used on appropriate data values, and, for instance, not on classification values. Other arithmetic operators may include modulo division (MOD) and integer division (DIV). Modulo division returns the remainder of division: for instance, 10 MOD 3 will return 1 as 10 - 3 × 3 = 1. Similarly, 10 DIV 3 will return 3.\r\n\r\nOther operators are goniometric: sine (sin), cosine (cos), tangent (tan); and their inverse functions asin, acos, and atan, which return radian angles as real values. The assignment\r\n\r\nC1 := A + 10\r\n\r\nwill add a constant factor of 10 to all cell values of raster A and store the result as output raster C1. The assignment\r\n\r\nC2 := A + B\r\n\r\nwill add the values of A and B cell by cell, and store the result as raster C2. Finally, the assignment\r\n\r\nC3 := (A - B) ∕ (A + B) × 100\r\n\r\nwill create output raster C3, as the result of the subtraction (cell by cell, as usual) of B cell values from A cell values, divided by their sum. The result is multiplied by 100. This expression, when carried out on AVHRR channel 1 (red) and AVHRR channel 2 (near infrared) of NOAA satellite imagery, is known as the NDVI (Normalized Difference Vegetation Index). It has proven to be a good indicator of the presence of green vegetation.\r\n\r\nComparison and logical operators\r\n\r\nMap algebra also allows the comparison of rasters cell by cell. To this end, we may use the standard comparison operators (<, ⇐, =, >=, > and <>).\r\n\r\nA simple raster comparison assignment is\r\n\r\nC := A <> B.\r\n\r\nIt will store truth values—either true or false—in the output raster C. A cell value in C will be true if the cell’s value in A differs from that cell’s value in B. It will be false if they are the same. Logical connectives are also supported in many raster calculi. We have already seen the connectives of AND , OR and NOT in raster overlay operators. Another connective that is commonly offered in map algebra is exclusive OR (XOR). The expression a XOR b is true only if either a or b is true, but not both.\r\n\r\nConditional expressions\r\nThe comparison and logical operators produce rasters with the truth values true and false. In practice, we often need a conditional expression together with them that allows us to test whether a condition is fulfilled. The general format is:\r\n\r\nOutput raster := CON(condition, then expression, else expression).\r\n\r\nHere, condition stands for the condition tested, then the expression is evaluated if condition holds, and else the expression is evaluated if it does not hold. This means that an expression such as CON(A = “forest”, 10, 0) will evaluate to 10 for each cell in the output raster where the same cell in A is classified as forest. For each cell where this is not true, the else expression is evaluated, resulting in 0.\r\n\r\nOverlays using a decision table\r\nConditional expressions are powerful tools in cases where multiple criteria must be taken into account. A small example may illustrate this. Consider a suitability study in which a land use classification and a geological classification must be used. Domain expertise dictates that some combinations of land use and geology result in suitable areas, whereas other combinations do not. In our example, forests on alluvial terrain and grassland on shale are considered suitable combinations, while any others are not.\r\n\r\nWe could produce an output raster with a map algebra expression, such as\r\n\r\nSuitability := CON((Landuse = “Forest” AND Geology = “Alluvial”)\r\nOR (Landuse = “Grass” AND Geology = “Shale”),\r\n“Suitable”, “Unsuitable”)\r\n\r\nand consider ourselves lucky that there are only two “suitable” cases. In practice, many more cases must usually be covered and, then, writing up a complex CON expression is not an easy task.\r\n\r\nTo this end, some GISs accommodate setting up a separate decision table that will guide the raster overlay process. This extra table carries domain expertise and dictates which combinations of input raster-cell values will produce which output raster-cell value. This gives us a raster overlay operator using a decision table. The GIS will have supporting functions to generate the additional table from the input rasters and to enter appropriate values in the table.","name":"Map algebra","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM4","description":"This small set of analytical operations is so commonly applied to a broad range of problems that their inclusion in software products is often used to determine if that product is a true GIS. Concepts on which these operations are based are addressed in Domains of geographic information and Relationships.","name":"Basic analytical operations","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM5-1","description":"Point pattern analysis refers to the detection of patterns in a group of objects or subjects located in space. This may support the analysis of clusters in accidents, crime, etc.","name":"Point pattern analysis","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM5-2","description":"The probability density function is a method with which the probability density can be estimated for points in a raster space.","name":"Kernels and density estimation","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM5-3","description":"Spatial cluster analysis is the grouping of similar spatial objects into classes (clusters) in such a way that the objects within the cluster are highly similar compared to the objects outside of the cluster. Spatial clustering forms an important part of spatial data mining (Han et al., 2001; Miller et al., 2009). A wealth of spatial clustering tools are currently available with immense application potential. \r\n\r\nIn earth observation studies, spatial cluster techniques are often applied to identify zones with similar land covers by using earth observation data as input. An example of such a technique is the K-means classifier (Han et al., 2001; Miller et al., 2009). This unsupervised classification technique makes several clusters (e.g. land use classes) of which each pixel is assigned to the cluster with the nearest mean (Han et al., 2001). The amount of clusters can be freely defined by the user just as the input metrics to perform the classification. A drawback of the K-means classifier is the need to specify the amount of output clusters. Density Based Spatial Clustering (DBSC) overcomes this issue since it automatically defines the optimal amount of clusters (Miller et al., 2009). In this type of clustering technique, dense regions of objects (proximate objects) are clustered and separated from regions with low density (noise) (Han et al., 2001; Liu et al., 2012). Finally, another frequently applied spatial clustering technique is the hierarchical agglomerative clustering. This technique makes use of a dendrogram to decompose the data into clusters. The agglomerative approach is a bottom-up approach in which all objects are first grouped in a distinct cluster and while moving upward in the tree, pairs of clusters are merged based on some metrics (e.g. spatial proximity) (Han et al., 2001). \r\n\r\nSpatial cluster techniques have many advantages when dealing with big datasets which is often the case when working with earth observation data. Its simplicity to use and the fast increase of cloud computing power makes from it powerful techniques to extract spatial patterns out of the data. It allows to translate raw earth observation data into a more user-friendly data product by showing the spatial patterns of the data.","name":"Spatial cluster analysis","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM5-4","description":"Spatial interaction models describe the flow of people and goods in a geographical space, in which parameters such as friction and distance play a role.","name":"Spatial interaction","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM5-5","description":"Multidimensional attributes can be analyzed through multidimensional scaling and principle component analysis.","name":"Analyzing multidimensional attributes","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM5-7","description":"Multi-criteria evaluation is an important aspect of decision support operations, which appear in process models. Process models in the Earth sciences describe the evolution of geo(bio)physical surface properties in time, independently from remote sensing observations. Examples of such process models on various time scales are, for instance, numerical weather prediction models (NWPs), vegetation growth models, hydrological models, oceanographic models and climate models.\r\n\r\nObservation models and process models can supplement each other to enhance the quality of the interpretation of remote sensing data and to fill gaps in time that occur when observations are not possible owing to clouds or some other cause. Interactions are possible between observation models and process models with EO data and existing geographic information (GIS and ground measurements, supplemented with decision-support systems (DSSs)).\r\n\r\nThe process model provides information to the decision-support system, which supports management actions aimed at controlling/mitigating the process, based on an multi-criteria evaluation. A good example of this is a water management system, in which one might decide to allocate water for irrigation if the observed vegetation appears to suffer from drought stress.","name":"Multi-criteria evaluation","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM5-8","description":"Process models in the Earth sciences describe the evolution of geo(bio)physical surface properties in time, independently from remote sensing observations. Examples of such process models on various time scales are, for instance, numerical weather prediction models (NWPs), vegetation growth models, hydrological models, oceanographic models and climate models.\r\nProcess models in the geosciences usually rely on regular observations at many locations spread over a large area. Traditionally, these observations were mostly made in the field with a variety of instruments. Remote sensing techniques have tremendously increased the capability of spatial sampling and the consistency of the surface parameters measured. RS instruments are mostly sensitive to many physical properties of the surface, some of these may not belong to the set of properties that the user is interested in. Exceptions to this are the mapping of sea-surface temperature, laser altimetry and gravimetry, which are measurements of direct geophysical interest. In the majority of cases, however, there are only indirect relationships between what is observed with the instrument and the physical object properties of interest. In these cases, the use of observation models becomes an attractive option, since these models describe the relationships between all object properties relevant for the observation and the observed remote sensing data.","name":"Spatial process models","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM5","description":"Building on the basic geometric measures and analytical operations found in most GIS products, a broad range of additional analytical methods form the fundamental GIS toolkit.","name":"Basic analytical methods","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM6-2","description":"In rasters we use interpolation to determine the value of a pixel, based on its surrounding pixels. The main raster-based interpolation methods are nearest neighbour, bilinear, and bicubic interpolation. To determine the value of the centre pixel (bold), in nearest neighbour interpolation the value of the nearest original pixel is assigned, i.e. the value of the black pixel in this example. Note that the respective pixel centres, marked by small crosses, are always used for this process. In bilinear interpolation, a linear weighted average is calculated for the four nearest pixels in the original image. In bicubic interpolation a cubic weighted average of the values of 16 surrounding pixels (the black and all grey pixels) is calculated. Note that some software uses the terms “bilinear convolution” and “cubic convolution” instead of the terms introduced above.","name":"Interpolation of surfaces","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM6-3","description":"Continuous fields have a number of characteristics not shared by discrete fields. Since the field changes continuously, we can talk of slope angle, slope aspect and concavity/convexity of the slope.\r\n\r\nThese notions are not applicable to discrete fields. The discussions in this subsection use terrain elevation as the prototype example of a continuous field, but all aspects discussed are equally applicable to other types of continuous fields. Nonetheless, we regularly refer to the continuous field representation as a DEM, to conform with the most common situation.","name":"Surface features","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM6-4","description":"A viewshed is the area that can be “seen” (i.e. it is in the direct line-of-sight) from a specified target location. (Inter) visibility analysis can determine the area visible from a scenic lookout or the area that can be reached by a radar antenna, as well as assess how effectively a road or quarry will be hidden from view.","name":"Intervisibility","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM6-5","description":"Firction surfaces contain information on how difficult/easy it is for a phenomenon to move from one location on the surface to another.","name":"Friction surfaces","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM6","description":"There is a wide range of phenomena that can be studied using a set of techniques and tools that are designed to help understand the characteristics of continuous surface data. Applications of these techniques using terrain data include overland transport, flow, and siting tasks, but similar analyses can be conducted using non-tangible surfaces such as those of temperature, pressure and population density.\r\n\r\nThere are numerous examples that require more advanced computations on continuous field representations, such as:\r\n\r\nSlope angle calculation - the calculation of the slope steepness, expressed as an angle in degrees or percentages, for any or all locations.\r\n\r\nCalculating slope aspect - the calculation of the aspect (or orientation) of the slope in degrees (between 0 and 360∘), for any or all locations.\r\n\r\nSlope convexity/concavity calculation - defined as the change of the slope (negative when the slope is concave and positive when the slope is convex)—can be calculated as the second derivative of the field.\r\n\r\nSlope length calculation - with the use of neighbourhood operations, it is possible to calculate for each cell the nearest distance to a watershed boundary (the upslope length) and to the nearest stream (the downslope length). This information is useful for hydrological modelling.\r\n\r\nHillshading is used to portray relief difference and terrain morphology of hilly and mountainous areas. The application of a special filter to a DEM produces hillshading. The colour tones in a hillshading raster represent the amount of reflected light at each location, depending on its orientation relative to the illumination source. This illumination source is usually chosen to be to the northwest at an angle of 45∘ above the horizon.\r\n\r\nThree-dimensional map display - with GIS software, three-dimensional views of a DEM can be constructed in which the location of the viewer, the angle under which he or she is looking, the zoom angle, and the amplification factor of relief exaggeration can be specified. Three-dimensional views can be constructed using only a predefined mesh, covering the surface, or using other rasters (e.g. a hillshading raster) or images (e.g. satellite images) that are draped over the DEM.\r\n\r\nDetermination of change in elevation through time - the cut-and-fill volume of soil to be removed or to be brought in to make a site ready for construction can be computed by overlaying the DEM of the site before the work begins with the DEM of the expected modified topography. It is also possible to determine landslide effects by comparing DEMs of before and after a landslide event.\r\n\r\nAutomatic catchment delineation - catchment boundaries or drainage lines can be automatically generated from a good quality DEM with the use of neighbourhood functions. The system will determine the lowest point in the DEM, which is considered to be the outlet of the catchment. From there, it will repeatedly search for the neighbouring pixels with the highest altitude. This process is repeated until the highest location (i.e. the cell with the highest value) is found; the path followed determines the catchment boundary. For delineating the drainage network, the process is reversed. Then the system will work from the watershed downwards, each time looking for the lowest neighbouring cells, which determines the direction of water flow (Flow Computation).\r\n\r\nDynamic modelling - apart from the applications mentioned above, DEMs are increasingly used in GIS-based dynamic modelling, such as the computation of surface run-off and erosion, groundwater flow, the delineation of areas affected by pollution, the computation of areas that will be covered by processes such as flows of debris and lava. An example is (Diffusion).\r\n\r\nVisibility analysis - a viewshed is the area that can be “seen” (i.e. it is in the direct line-of-sight) from a specified target location. Visibility analysis can determine the area visible from a scenic lookout or the area that can be reached by a radar antenna, as well as assess how effectively a road or quarry will be hidden from view.","name":"Analysis of surfaces","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM7-1","description":"Statistical analysis techniques based on visual interpretation through histograms, scatterplots, etc.","name":"Graphical methods","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM7-2","description":"Environmental variables have become increasing available with the advent of GIS. These are mostly continuous in space and time. Collecting denser environmental data in discrete space and time domains are rather cost effective and time consuming. However, when the data at each spatial or time index are considered as outcomes of a random variable, stochastic processes become enviable useful to build models and predict the outcomes at locations where data were never collected. The meaningful assumptions include stationarity of the mean and the covariance to ascertain an expression for spatial dependency/autocorrelation. With a stationary process (i.e. constant mean), simple and ordinary kriging is used. Other variants like kriging with external drift, universal kriging and regression kriging also alleviate the challenge of non-stationary mean. These methods are also applicable when temporal indexes rather than spatial indexes are of interest.","name":"Stochastic processes","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM7-3","description":"Spatial weight matrix is the popular numerical quantification of spatial dependency or spatial neighborhoods. The weight matrix should summarize information about the spatial connectivity structure of the spatial entities/features; either polygons, points, or lines. This is required for the computation of spatial dependency indices such the Moran’s index, and for spatial regression models such as the conditional autoregressive (CAR), spatial lag, and spatial error models. The connectivity information can be defined based on adjacency/contiguity or distance between pairs of spatial entities. There are other forms; they could be based on population densities between observation pairs. The simplest spatial weigh matrix is the binary adjacency spatial weight matrix with elements w_ij, such that w_ij=1 if spatial units i and j are neighbors, otherwise w_ij=0. A popular alternative is the inverse distance weight matrix with elements w_ij=1⁄d^α , where d is the distance between pairs of spatial units and α is any positive number greater than zero. By convention, w_ii=0 since spatial unit cannot have a spillover within itself.","name":"The spatial weights matrix","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM7-4","description":"Spatial autocorrelation evaluates how things which are closer in space tend to have similar attributes. This is a common phenomenon in environmental variables which are continuous in space. For instance, temperature, soil moisture content, air quality and rainfall are all continuous in space. This idea is based on Tobler’s law of geography: “everything is related to everything but near things are more related”. Global measures of spatial association estimates the overall index of spatial autocorrelation, also called spatial clustering. Thus, it measures whether clustering is apparent throughout the study region but do not identify the location of clusters. Common global measures include the Moran’s Index and Geary’s C. These have increasing applications in domains like environmental science, agriculture, epidemiology, climate studies etc.","name":"Global measures of spatial association","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM7-5","description":"Unlike global measures of spatial association, local measure of spatial association identifies the locations of clusters. Typical measures include the local indicator for spatial autocorrelation (LISA) or the local Moran’s index whose summation is proportional to the global Moran’s index. The spatial scan statistics has also been the commonly used method to detect local clusters.","name":"Local measures of spatial association","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM7-6","description":"An outlier is an unexpected value that differs significantly from other observations. Definition of an outlier is not absolute and the concept itself is precisely defined only by selection of appropriate criteria in concrete statistical observations. When considering outliers, it is important to determine whether the value of the outlier is incorrect data or it is otherwise outstanding, but correct data. If we consider outliers in the case when they base on sample surveys, another assessment is necessary. Namely, the assessment of whether an outlier is representative or not. \r\nThe box plot is a useful graphical display for examining the outliers. Using median, lower and upper quartiles, extreme values are identified in the tails of the distribution. The value beyond inner fence on either side is considered a mild outlier. The value beyond an outer fence is considered an extreme outlier. Histograms also emphasize the existence of outliers. The histogram depends on how we design the classes, so we can get different histograms for the same data. Graphical and quantitative checks are obligatory if the histogram shows possible outliers. Outliers can also be examined by calculating the correlation between two datasets (Pearson correlation coefficient, Spearman rank correlation coefficient…). Scatter plots reveals a basic linear relationship with a pattern. An outliner is defined as a data point that deviates from other values. Outliers can also be examined by local outlier factor, which is based on a concept of a local density. Points with substantially lower density than their neighbours are considered as outliers.","name":"Outliers","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM7-7","description":"Bayesian method of modelling stems from the Bayes theorem and derived using conditional probabilities. Its advantage lies in its ability to include prior knowledge of unknown parameters to ascertain their uncertainties. Thus, the prior parameters are updated by the data likelihood to obtain the posteriors. The challenge of Bayesian modelling has been the integration of the denominator which always resulted into improper integrals. This actually prolonged its wide applications. With the advent of high performance computers, solution to such integrals are easily solved using Markov chain Monte Carlo simulations. The advent robust approximation methods through integrated nested Laplace approximations (INLA) has even made parameter estimation faster; thus making Bayesian methods interesting and better. Unlike frequentist approaches, Bayesian methods can present estimates of parameters as densities from which their uncertainties and credible intervals can be estimated. They have now found wide applications in divers areas like environmental modelling, climate modeling, agriculture, epidemiology and many other domains that requires modeling.","name":"Bayesian methods","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM7","description":"Traditional statistical methods are used to describe the central tendency, dispersion, and other characteristics of data but are not always suited to use with spatial data for which specialized techniques are often required. The field of spatial statistical analysis forms the backbone for the testing of hypotheses about the nature of spatial pattern, dependency, and heterogeneity. The techniques are widely used in both exploratory and confirmatory spatial analysis in many different fields.","name":"Spatial statistics","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM8-1","description":"Sampling is needed to limit the observations for statistical analysis. In raster image analysis, various sampling schemes have been proposed for selecting pixels to test. Choices to be made relate to the design of the sampling strategy, the number of samples required, and the area of the samples. Recommended sampling strategies in the context of land cover data are simple random sampling or stratified random sampling. The number of samples may be related to two factors in accuracy assessment: (1) the number of samples that must be taken in order to reject a data set as being inaccurate; or (2) the number of samples required to determine the true accuracy, within some error bounds, of a data set. Sampling theory is used to determine the number of samples required. The number of samples must be traded-off against the area covered by a sample unit. A sample unit can be a point but it could also be an area of some size; it can be a single raster element but may also include surrounding raster elements. Among other considerations, the “optimal” sample-area size depends on the heterogeneity of the class.","name":"Spatial sampling for statistical analysis","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM8-3","description":"A variogram is a tool used to describe the spatial continuity of data points. Different kinds of variograms are used, such as experimental variogram and semi-variogram.","name":"Variogram modeling","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM8-4","description":"Predicting an observation in the presence of spatially dependent observations is termed Kriging, named after the first practitioner of these procedures, the South African mining engineer Daan Krige, who did much of his early empirical work in the Witwatersrand gold mines.","name":"Principles of kriging","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM8-5","description":"With a stationary stochastic process (i.e. constant mean), simple and ordinary kriging is used for interpolation. Other variants like kriging with external drift, universal kriging and regression kriging also alleviate the challenge of non-stationary mean. Other variants are \r\nco-kriging log-normal kriging, disjunctive kriging, indicator kriging, factorial kriging and universal kriging.","name":"Kriging variants","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM8","description":"Geostatistics are a variety of techniques used to analyze continuous data e.g., rainfall, elevation, air pollution. The fundamental structure of geostatistics is based on the concept of semi-variograms and their use for spatial prediction kriging. Sampling methods are also discussed in Unit GD9 Field data collection. \r\nGeostatistics is a subdiscipline of spatial statistics developed to estimate the value of a continuous spatial process at unknown locations by using the information of the value of these process at known locations. Furthermore, it aims to quantify the uncertainty related to the prediction (Calder et al., 2009; Emmanouil, 2019). In order to do such predictions, geostatistics entails some statistical methods which use as starting point the assumption of a random component that can define the spatiotemporal variability. These methods are developed to infer the parameters that can describe the spatiotemporal patterns of the input variables (e.g. soil moisture) so that finally these variables at unsampled locations can be estimated (interpolated) (Emmanouil, 2019). Geostatistical methods are strongly related with classic interpolation methods but differ by its use of random variables that allow to given an uncertainty indication associated with the prediction of variables in space and time. \r\n\r\nIn environmental research geostatistical techniques are often applied to infer (interpolate) variables at such unobserved locations by using information from known locations. One of such geostatistical techniques is Kriging, which is a geostatistical method that predicts variables by using spatial interpolation. This spatial interpolation is done by establishing a semivariogram that defines the spatial relationship between the variables of interest in function of the distance. Because of this, the Kriging technique can also give an indication on the variance or accuracy of the prediction (Calder et al., 2009); Van der Meer, 2012). On the other hand, cokriging is another important geostatistical technique and differs from Kriging by using the cross-correlation between variables to generate local estimates (Van der Meer, 2012). In earth observation studies, cokriging can be applied to better predict sparsely based data on the ground (e.g. biomass) by using the cross-correlation of this variable with a more continuously sampled satellite metric like NDVI. Furthermore, these techniques can also be used to enhance satellite image information, filling missing pixels or even downscale the information to a higher resolution (Van der Meer, 2012).","name":"Geostatistics","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM9-1","description":"Spatial econometrics uses spatial stochastic models to determine autocorrelation between interacting agents. The techniques involved are regression, the use of a spatial weights matrix, least squares, etc.","name":"Principles of spatial econometrics","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM9-2","description":"A spatial autoregressive (SAR) model describes the prediction of the behaviour of a random process.","name":"Spatial autoregressive models","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM9-3","description":"In producing optimal images for interpretation, spatial filtering is applied. Filtering is usually carried out for a single band. Filters - algorithms - can be used to enhance images by, for example, reducing noise (“smoothing an image”) or sharpening a blurred image. Filter operations are also used to extract features from images, e.g. edges and lines, and to automatically recognize patterns and detect objects. There are two broad categories of filters: linear and non-linear filters.\r\n\r\nLinear filters calculate the new value of a pixel as a linear combination of the given values of the pixel and those of neighbouring pixels. A simple example of the use of a linear smoothing filter is when the average of the pixel values in a 3×3 pixel neighbourhood is computed and that average is used as the new value of the central pixel in the neighbourhood.","name":"Spatial filtering","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM9-4","description":"Geographically Weighted Regression (GWR) makes use of local subsets of observations to perform estimates.","name":"Spatial expansion and Geographically Weighted Regression GWR","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM9","description":"Many problems of the social sciences can be expressed in terms of spatial regression analysis. The development of spatial autoregressive models and the estimation of their parameters is the focus for the field of spatial econometrics.","name":"Spatial regression and econometrics","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF","description":"The GIScience perspective is grounded in spatial thinking. The aim of this knowledge area is to recognize, identify, and appreciate the explicit spatial, spatio-temporal and semantic components of the geographic environment at an ontological and epistemological level in preparation for modeling the environment with geographic data and analysis. To do this, one must understand the nature of space and time as a context for geographic phenomena.This knowledge area covers the ways in which views of the geographic environment depend on philosophical viewpoints, physics, human cognition, society, and the task at hand. This knowledge area also requires an understanding of the fundamental principles in the discipline of geography, the \"language\" of spatial tasks. On a more advanced level, this area incorporates mathematical and graphical models that formalize these concepts, such as set theory, algebra, and semantic nets. Because of its wide range of foundational principles, this knowledge area forms a basis for the other knowledge areas. Wise design and use of geospatial technologies requires an understanding of the nature of geographic information, the social and philosophical context of geographic information, and the principles of geography. This knowledge area is especially closely tied to Knowledge Areas Data Modeling (DM) and Design Aspects (DA), as generic data models and application designs need to be grounded in sound conceptual models. The foundations of geographic information have developed over several decades. Philosophical and scientific views on the nature of space and time have evolved since the ancient Greeks. Early papers during the Quantitative Revolution, such as Berry (1964), began to formalize the structure of information used in geographic inquiry.The fundamental data structures and algorithms comprising the GIS software developed in the 1960`s and 1970`s were based on implicit \"common-sense\" conceptual models of geographic information. During the 1980`s, several researchers questioned these underlying assumptions. Some were refuted, other confirmed, and many extended. However, the most rapid pace of development in this area was during the 1990`s with the rise of GIScience as a distinct discipline, and the many cooperative initiatives it comprised.The new millennium has seen some of these foundational principles incorporated into commercial software, thus making theoretical knowledge even more important for practitioners. It is expected that the concepts in this knowledge area will be learned gradually. An introductory course may cover only a few topics in a cursory manner, an intermediate course on data modeling or data analysis may consider several theoretical topics of practical application, and a number of graduate courses could cover each topic in a research-oriented environment. Discussion of this knowledge area includes several terms that can have multiple meanings. For the purposes of this document, two in particular require definition: Geographic: Almost any subject or discourse involving earthly phenomena, studied from a spatial perspective at a medium scale (sub-astronomical and super-architectural). Phenomenon: Any subject of geographic discourse that is perceived to be external to the individual, including entities, events, processes, social constructs, and the like.","name":"Conceptual Foundations","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF1-1","description":"Metaphysics involve the meaning things and concepts. Ontologies provide a way to share the semantics of concepts in some area of interest and is all about common the understanding of essential concepts, e.g., what is meant by a geometric object and its attributes.","name":"Metaphysics and ontology","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF1-1b","description":"Brief history of GIScience as related to the history of GISystems; Definitions of GIS&T; Sub-domains of GIS&T (i.e., Geographic Information Science, Geospatial Technology, and Applications of GIS&T)","name":"What is Geographic Information Science and Technology","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF1-2","description":"The branch of philosophy concerned with knowledge.","name":"Epistemology","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF1-2b","description":"GIS&T draws upon insights and methods from key allied fields: Geography, Cartography, Computer and information science, Engineering, Mathematics and Statistics, Philosophy, Cognitive Science, Linguistics","name":"Contributions to GIS and T by key allied fields","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF1-3","description":"The questions and methodologies in major philosophical movements relating to the nature of space, time, geographic phenomena and human interaction with it.","name":"Philosophical perspectives","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF1","description":"Many branches of philosophy are relevant to an understanding of geographic information, especially metaphysics and epistemology. Philosophical theories are deeply engaged in the study of knowledge, space, time, geographic phenomena and human interaction with them. These theories influence the development of geographic ontologies and the structuring, analysis, and interpretation of geographic information. It is, therefore, crucial for professionals to understand these principles in order to bridge (rather than eliminate) the differences and work together. Philosophical perspectives on GIS practice are covered in Unit GS7 Critical GIS.","name":"Philosophical foundations","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF1b","description":"Unit CF1 introduces the broad domain refered to as Geographic Information Science & Technology (GIS&T) and its sub-domains (i.e., Geographic Information Science, Geospatial Technology, and Applications of GIS&T). It outlines the history of Geographic Information Science as related to the history of GISystems, as well as the contributions to this multidisciplinary domain by key allied fields, such as geography, cartography, computer and information science, engineering, mathematics, philosophy, cognitive science, and linguistics.","name":"Introduction to Geographic Information Science and Technology","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF2-1","description":"The study on how humans perceive spatial information.","name":"Perception and cognition of geographic phenomena","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF2-1b","description":"Metaphysics and Ontology - Formal ontology - Ontological distinctions (e.g., continuants vs. occurrents, universals vs. particulars) - The problem of universals and relevant theories (realism, nominalism, conceptualism) - Ontologies of the geographic domain - Philosophical theories relating to the nature of space, time, geographic phenomena and human interaction with them","name":"Philosophy of being","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF2-2","description":"The ways in which conceptual views of in the human mind make it into formal descriptions of information and into artefacts in databases and GIS.","name":"From concepts to data","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF2-2b","description":"Epistemology; Theories on what constitutes knowledge; The notions of model and representation in science; The influences of epistemology on GIS practices","name":"Philosophy of knowledge","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF2-3","description":"Principles of geography to explain the spatial occurrences of spatial entities in Geographic Information Systems.","name":"Geography as a foundation for GIS","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF2-4","description":"Space and place are concepts that are not the same. Including concepts like landscape, it is not always obvious how to portray them unambiguously in GIS.","name":"Place and landscape","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF2-6","description":"The ways in which the elements of culture (e.g., language, religion, education, traditions) may influence the understanding and use of geographic information.","name":"Cultural influences","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF2-7","description":"The influences of political ideologies (e.g., Marxism, Capitalism, conservative liberal) on the understanding of geographic information.","name":"Political influences","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF2","description":"Geographic information is observed, comprehended, organized, used in human processes, with both personal and social influences. Therefore, sound models of geographic information should be grounded on a sound understanding of human perception, cognition, memory, and behavior, as well as human institutions.","name":"Cognitive and social foundations","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF3-1","description":"A GIS operates under the assumption that the spatial phenomena involved occur in a two- or three-dimensional Euclidean space. Euclidean space can be informally defined as a model of space in which locations are represented by coordinates—(x, y) in 2D and (x, y, z) in 3D space—and distance and direction can defined with geometric formulas. In 2D, this is known as the Euclidean plane. To represent relevant aspects of real-world phenomena inside a GIS, we first need to define what it is we are referring to. We might define a geographic phenomenon as a manifestation of an entity or process of interest that:\r\n\r\nitem can be named or described;\r\nitem can be georeferenced; and\r\nitem can be assigned a time (interval) at which it is/was present.\r\n\r\nRelevance of phenomena for the use of a GIS depends entirely on the objectives of the study at hand. For instance, in water management, relevant objects can be river basins, agro-ecological units, measurements of actual evapotranspiration, meteorological data, ground\\-water levels, irrigation levels, water budgets and measurements of total water use. All of these can be named or described, georeferenced and provided with a time interval at which each exists. In multipurpose cadastral administration, the objects of study are different: houses, land parcels, streets of various types, land use forms, sewage canals and other forms of urban infrastructure may all play a role. Again, these can be named or described, georeferenced and assigned a time interval of existence.\r\n\r\nNot all relevant information about phenomena has the form of a triplet (description, georeference, time interval). If the georeference is missing, then the object is not positioned in space: an example of this would be a legal document in a cadastral system. It is obviously somewhere, but its position in space is not considered relevant. If the time interval is missing, we might have a phenomenon of interest that exists permanently, i.e.\\ the time interval is infinite. If the description is missing, then we have something that exists in space and time, yet cannot be described. Obviously this last issue limits the usefulness of the information.\r\n\r\nTypes of geographic phenomena\r\nThe definition of geographic phenomena attempted above is necessarily abstract and is, therefore, perhaps somewhat difficult to grasp. The main reason is that geographic phenomena come in different “flavours”. Before categorizing such flavours, there are two further observations to be made.\r\n\r\nFirst, to represent a phenomenon in a GIS requires us to state what it is and where it is. We must provide a description—or at least a name—on the one hand, and a georeference on the other hand. We will ignore temporal issues for the moment and come back to these in Temporal dimension and Spatial-temporal data model, the reason being that current GISs do not provide much automatic support for time-dependent data. This topic must, therefore, be considered as an example of advanced GIS use. Second, some phenomena are manifest throughout a study area, while others only occur in specific localities. The first type of phenomena we call geographic fields; the second type we call objects.","name":"Space","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF3-1b","description":"- Theories of human perception, cognition, and memory and their ability to model spatial knowledge acquisition (e.g., Marr on vision, Piaget on cognitive development) - Types of mental representations (i.e., analogue, propositional, procedural) - The role of metaphors and image schemata in our understanding of geographic phenomena and geographic tasks - From concepts to data (i.e., data, information, knowledge, and wisdom; transformation of a conceptual model of information for a particular task into a data model; limitations of various information stores (the mind, computers) and means (maps, graphics, and text) for representing geographic information) - Difference between real phenomena, conceptual models, and GIS data representations thereof connections with cartography and maps","name":"Cognitive foundations","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF3-2b","description":"- Semantics - Meaning (e.g., the nature of meaning, modes of meaning) - Geospatial semantics - The role of natural language in the conceptualization of geographic phenomena","name":"Linguistic foundations","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF3-3b","description":"- The ways in which the elements of culture (e.g., language, religion, education, traditions) may influence the understanding and use of geographic information - The influences of social theories and political ideologies and actions on human perceptions of space and place - The constraints that political forces place on geospatial applications in public and private sectors","name":"Social foundations","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF3-4b","description":"- Common-sense views and laymen knowledge of geographic phenomena that contrast with established theories and technologies of geographic information - The impact of geospatial technologies and the geoweb (e.g., digital globes) that allow non-geospatial professionals to create, distribute, and map geographic information - The design, procedures, and results of GIS projects to non-GIS audiences (clients, managers, general public) - Difference between applications that can make use of common-sense principles of geography and those that should not","name":"Common-sense geographies","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF3","description":"Geographic information is observed, comprehended, organized, used in human processes, with both personal and social influences. Therefore, sound models of geographic information should be grounded on a sound understanding of human perception, cognition, memory, and behavior, as well as human institutions.","name":"Cognitive, linguistic and social foundations","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF4-2b","description":"As time is the central concept of the temporal dimension, a brief examination of the nature of time may clarify our thinking when we work with this dimension:\r\n\r\nDiscrete and continuous time: Time can be measured along a discrete or continuous scale. Discrete time is composed of discrete elements (seconds, minutes, hours, days, months, or years). For continuous time, no such discrete elements exist: for any two moments in time there is always another moment in between. We can also structure time by events (moments) or periods (intervals). When we represent intervals by a start and an end event, we can derive temporal relationships between events and periods, such as “before”, “overlap”, and “after”.\r\n\r\nValid time and transaction time: Valid time (or world time) is the time when an event really happened, or a string of events took place. Transaction time (or database time) is the time when the event was stored in the database or GIS. Note that the time at which we store something in a database is typically (much) later than when the related event took place.\r\n\r\nLinear, branching and cyclic time: Time can be considered to be linear, extending from the past to the present (‘now’), and into the future. This view gives a single time line. For some types of temporal analysis, branching time - in which different time lines from a certain point in time onwards are possible - and cyclic time - in which repeating cycles such as seasons or days of the week are recognized - make more sense and can be useful.\r\n\r\nTime granularity: When measuring time, we speak of granularity as the precision of a time value in a GIS or database (e.g. year, month, day, second). Different applications may obviously require different granularity. In cadastral applications, time granularity might well be a day, as the law requires deeds to be date-marked; in geological mapping applications, time granularity is more likely to be in the order of thousands or millions of years.\r\n\r\nAbsolute and relative time: Time can be represented as absolute or relative. Absolute time marks a point on the time line where events happen (e.g. “6 July 1999 at 11:15 p.m.”). Relative time is indicated relative to other points in time (e.g. “yesterday”, “last year”, “tomorrow”, which are all relative to “now”, or “two weeks later”, which is relative to some other arbitrary point in time.).","name":"Time","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF4-3b","description":"The way we represent relevant components of the real world in our models determines the kinds of questions we can or cannot answer. Besides representing an object or field in 2D or 3D space, the temporal dimension is of a continuous nature. Therefore, in order to represent it in a GIS we have to discretize the time dimension.\r\n\r\nSpatio-temporal data models are ways of organizing representations of space and time in a GIS. Several representation techniques have been proposed in the literature. Perhaps the most common of these is the “snapshot state”, which represents a single moment in time of an ongoing natural or man-made process. We may store a series of these “snapshot states” to represent “change”, but we must be aware that this is by no means a comprehensive representation of that process. \r\n\r\nIn spatio-temporal analysis we consider changes of spatial and thematic attributes over time. We can keep the spatial domain fixed and look only at the attribute changes over time for a given location in space. We might be interested how land cover has changed for a given location or how land use has changed for a given land parcel over time, provided its boundary has not changed. On the other hand, we can keep the attribute domain fixed and consider the spatial changes over time for a given thematic attribute. In this case, we might want to identify locations that were covered by forest over a given period of time.\r\n\r\nFinally, we can assume both the spatial and attribute domains are variable and consider how fields or objects have changed over time. This may lead to notions of object motion - a subject receiving increasing attention in the literature. Applications of moving object research include traffic control, mobile telephony, wildlife tracking, vector-borne disease control and weather forecasting. In these types of applications, the problem of object identity becomes apparent. When does a change or movement cause an object to disappear and become something new? With wildlife this is quite obvious; with weather systems less so. But this should no longer be a surprise: we have already seen that some geographic phenomena can be nicely described as objects, while others are better represented as fields.\r\n\r\nMapping time means mapping change. This may be change in a feature’s geometry, in its attributes, or both. Examples of changing geometry are the evolving coastline of the Netherlands, the location of Europe’s national boundaries, or the position of weather fronts. Changes in the ownership of a land parcel, in land use or in road traffic intensity are other examples of changing attributes. Urban growth is a combination of both: urban boundaries expand with growth and simultaneously land use shifts from rural to urban. If maps are to represent events like these, they should be suggestive of such change.\r\n\r\nThree temporal cartographic techniques can be distinguished:\r\n\r\nSingle Static Map\r\n\r\nSpecific graphic variables and symbols are used to indicate change or represent an event. We can apply the visual variable “value” to represent for example the age of built-up areas.\r\n\r\nSeries of Static Maps\r\n\r\nA single map in the series represents a “snapshot” in time. Together, the maps depict a process of change. Change is perceived by the succession of individual maps depicting the situation in successive snapshots. It could be said that the temporal sequence is represented by a spatial sequence that the user has to follow to perceive the temporal variation. The number of images should be limited since it is difficult for the human eye to follow long series of maps.\r\n\r\nAnimated Maps\r\n\r\nChange is perceived to evolve in a single image by displaying several snapshots one after the other, just like a video clip of successive frames. The difference from the series of maps is that the variation can be deduced from real “change” seen taking place in the image itself, not from a spatial sequence. For the user of a cartographic animation, it is important to have tools available that allow for interaction while viewing the animation. Seeing an animation play will often leave users with many questions about what they have seen. And just replaying the animation is not sufficient to answer questions like “What was the position of the northern coastline during the 15th century?” Most of the general software packages for viewing animations already offer facilities such as “pause” (to look at a particular frame) and ‘(fast-)forward’ and ‘(fast-)backward’, or step-by-step display. More options have to be added, such as the possibility to go directly to a certain frame based on a task command like: “Go to 1850”.","name":"Relationships between space and time","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF4-4b","description":"GIS data structures are used to implement the conceptual views of spatial data (vector and raster models). The power of a GIS is dependent on the richness of information contained in the spatial data structures. Vector models are based on points, lines and areas. Raster models are based on grids. Each cell has a value that is used to represent some characteristic of that location. \r\nLayers are used to display geographic datasets in various digital map environment. A layer stores the path to a source dataset and other layer properties, including symbology. You can use multiple layers on one map and specify its properties. Shapefiles represent spatial character of the object in terms of shape, size and spatial arrangement. Shapefile usually comprise three separate and distinct types of files (main files, index files and database tables). Data base files store additional attributed that can be joined to a shapefiles’ feature. Attribute data types supplement geographic spatial feature with additional information. Spatial data includes information of location and attribute data includes information about other characteristics (what, where and why). A legend is a visual presentation of the symbols that are used on the map with some additional explanations. It includes a sample of each symbol and a short description of the meaning.","name":"Categories","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF4-5","description":"An entity obtained by abstracting the real world, having a physical nature (certain composition of material), being given a descriptive name, and observable; e.g. “house”. An object is a self-contained part of a scene having certain discriminating properties.\r\n\r\nThe primitives of vector data sets are the point, (poly)line and polygon. Related geometric measurements are location, length, distance and area size. Some of these are geometric properties of a feature in isolation (location, length, area size); others (distance) require two features to be identified.\r\n\r\nIn a GIS, features are represented together with their attributes—geometric and non-geometric—and relationships. The geometry of features is represented with primitives of the respective dimension: a windmill probably as a point; an agricultural field as a polygon. The primitives follow either the vector or the raster approach.\r\n\r\nVector data types describe an object through its boundary, thus dividing the space into parts that are occupied by the respective objects. The raster approach subdivides space into (regular) cells, mostly as a square tessellation of two or three dimensions. These cells are called pixels in 2D and voxels in 3D. The data indicate for every cell which real-world feature is covered, provided the cell represents a discrete field. In the case of a continuous field, the cell holds a representative value for that field. The Table below lists advantages and disadvantages of raster and vector representations.\r\n\r\nThe storage of a raster is, in principle, straightforward. It is stored in a file as a long list of values, one for each cell, preceded by a small list of extra data (the “file header”), which specifies how to interpret the long list. The order of the cell values in the list can, but need not necessarily, be left to right, top to bottom. This simple encoding scheme is known as row ordering. The header of the raster will typically specify how many rows and columns the raster has, which encoding scheme was used, and what sort of values are stored for each cell.\r\n\r\nData can be of a qualitative or quantitative nature. Qualitative data is also called nominal data, which exists as discrete, named values without a natural order amongst the values. Examples are different languages (e.g. English, Swahili, Dutch), different soil types (e.g. sand, clay, peat) or different land use categories (e.g. arable land, pasture). In the map, qualitative data are classified according to disciplinary insights, such as a soil classification system represented as basic geographic units: homogeneous areas associated with a single soil type, recognizable by the soil classification.\r\n\r\nQuantitative data can be measured, either along an interval or ratio scale. For data measured on an interval scale, the exact distance between values is known, but there is no absolute zero on the scale. Temperature is an example: 40 ◦C is not twice as hot as 20 ◦C, and 0 ◦C is not an absolute zero.\r\n\r\nQuantitative data with a ratio scale do have a known absolute zero. An example is income: someone earning $100 earns twice as much as someone with an income of $50. In order to generate maps, quantitative data are often classified into categories according to some mathematical method.\r\n\r\nIn between qualitative and quantitative data, one can distinguish ordinal data. These data are measured along a relative scale and are as such based on hierarchy. For instance, one knows that a particular value is “more” than another value, such as “warm” versus “cool”. Another example is a hierarchy of road types: “highway”, “main road”, “secondary road” and “track”. The different types of data are summarized in Table.","name":"Properties","selfAssesment":"GI-N2K
"},{"code":"CF4b","description":"Geographic phenomena, geographic information, and geographic tasks are described in terms of space, time, and properties. Different theories exist as to the nature and formal representation of these aspects, including space-like dimensions, sets, and phenomenology. Information in each of these three aspects is measured and reported with respect to one of several frames of reference or domains, including both absolute and relative approaches. Early frameworks such as those of Berry (1964) and Sinton (1978) were influential in setting forth the importance of space, time, and theme in GIS&T. Besides, space, time, and properties, categories are also fundamental in the conceptualization and representation of spatial entities, phenomena, processes, and events. Distinctive features of geographic information such as scale and detail, spatial patterns, spatial integration, and regions are also critical for a complete description of its nature and representation. This unit is closely tied to the creation of data models in Knowledge Area 5: Data Modeling, Storage, and Exploitation.","name":"Fundamentals of Geographic Information","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF5-1b","description":"Discrete entities can be found as fields or objects.\r\n\r\nDiscrete fields divide the study space in mutually exclusive, bounded parts, with all locations in one part having the same field value. Discrete fields are intermediate between continuous fields and geographic objects: discrete fields and objects both use “bounded” features.\r\n\r\nDiscrete fields divide the study space in mutually exclusive, bounded parts, with all locations in one part having the same field value. Typical examples are land classifications, for instance, using either geological classes, soil type, land use type, crop type or natural vegetation type. \r\n\r\nDiscrete fields are intermediate between continuous fields and geographic objects: discrete fields and objects both use “bounded” features. A discrete field, however, assigns a value to every location in the study area, which is not typically the case for geographic objects. These two types of fields differ in the type of cell values. A discrete field such as land use type will store cell values of the type “integer” and is therefore also called an integer raster. Discrete fields can be easily converted to polygons since it is relatively easy to draw a boundary line around a group of cells with the same value. A continuous raster is also called a “floating point” raster.\r\n\r\nGeographic objects.\r\n\r\nWhen a geographic phenomenon is not present everywhere in the study area, but somehow “sparsely” populates it, we look at it as a collection of geographic objects. Such objects are usually easily distinguished and named, and their position in space is determined by a combination of one or more of the following parameters:\r\n\r\nlocation (where is it?)\r\nshape (what form does it have?)\r\nsize (how big is it?)\r\norientation (in which direction is it facing?).\r\n\r\nHow we want to use the information determines which of these four parameters is required to represent the object. For instance, for geographic objects such as petrol stations all that matters in an in-car navigation system is where they are. Thus, in this particular context, location alone is enough, and shape, size and orientation are irrelevant. For roads, however, some notion of location (where does the road begin and end?), shape (how many lanes does it have?), size (how far can one travel on it?) and orientation (in which direction can one travel on it?) seem to be relevant components of information in the same system.","name":"Discrete entities","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF5-2b","description":"A geographic field is a geographic phenomenon that has a value “everywhere” in the study area. We can therefore think of a field as a mathematical function f that associates a specific value with any position in the study area. Hence if (x, y) is a position in the study area, then f(x, y) expresses the value of f at location (x, y). Fields can be discrete or continuous.\r\n\r\nIn a continuous field, the underlying function is assumed to be “mathematically smooth”, meaning that the field values along any path through the study area do not change abruptly, but only gradually. Good examples of continuous fields are air temperature, barometric pressure, soil salinity and elevation. A continuous field can even be differentiable, meaning that we can determine a measure of change in the field value per unit of distance anywhere and in any direction. For example, if the field is elevation, this measure would be slope, i.e. the change of elevation per metre distance; if the field is soil salinity, it would be salinity gradient, i.e. the change of salinity per metre distance.\r\n\r\nDiscrete fields divide the study space in mutually exclusive, bounded parts, with all locations in one part having the same field value. Discrete fields are intermediate between continuous fields and geographic objects: discrete fields and objects both use “bounded” features.\r\n\r\nDiscrete fields divide the study space in mutually exclusive, bounded parts, with all locations in one part having the same field value. Discrete fields are intermediate between continuous fields and geographic objects: discrete fields and objects both use “bounded” features.\r\n\r\nDiscrete fields divide the study space in mutually exclusive, bounded parts, with all locations in one part having the same field value. Typical examples are land classifications, for instance, using either geological classes, soil type, land use type, crop type or natural vegetation type. \r\n\r\nDiscrete fields are intermediate between continuous fields and geographic objects: discrete fields and objects both use “bounded” features. A discrete field, however, assigns a value to every location in the study area, which is not typically the case for geographic objects. These two types of fields differ in the type of cell values. A discrete field such as land use type will store cell values of the type “integer” and is therefore also called an integer raster. Discrete fields can be easily converted to polygons since it is relatively easy to draw a boundary line around a group of cells with the same value. A continuous raster is also called a “floating point” raster.\r\n\r\nA field-based model consists of a finite collection of geographic fields: we may be interested in, for example, elevation, barometric pressure, mean annual rainfall and maximum daily evapotranspiration, and would therefore use four different fields to model the relevant phenomena within our study area.","name":"Fields","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF5-3b","description":"We can structure time by events (moments) or periods (intervals). When we represent intervals by a start and an end event, we can derive temporal relationships between events and periods, such as “before”, “overlap”, and “after”.\r\nValid time (or world time) is the time when an event really happened, or a string of events took place. Transaction time (or database time) is the time when the event was stored in the database or GIS. Note that the time at which we store something in a database is typically (much) later than when the related event took place.\r\n\r\nProcess models in the Earth sciences describe the evolution of geo(bio)physical surface properties in time, independently from remote sensing observations. Examples of such process models on various time scales are, for instance, numerical weather prediction models (NWPs), vegetation growth models, hydrological models, oceanographic models and climate models.\r\n\r\nProcesses on the planet Earth are complex phenomena that are taking place in space and in time, i.e. in four dimensions.\r\n\r\nIn many of these processes, differences in one dimension (e.g. height above the geoid) can be disregarded, so that two spatial dimensions and the dimension time remain. Despite this simplification, the physical description of the phenomena remains a difficult task. To better understand the processes it often helps if the same geographic region is viewed repeatedly and, if possible, also from different directions and in different wavelength regions. Integration of data from a variety of sources can be a means to retrieving information about processes that would otherwise remain undetected.","name":"Events and processes","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF5-4b","description":"Models that integrate the concepts of space, time, and attribute in geographic information.","name":"Integrated models","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF5-6","description":"Geographic phenomena can be studied as single entities and in relationship with each other and then reveal patters and clusters. How the entities are distributed is subject to statistical and visualisation studies.","name":"Spatial distribution","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF5-7","description":"We can use the topological properties of interiors and boundaries to define relationships between spatial features. Since the properties of interiors and boundaries do not change under topological mapping, we can investigate their possible relations between spatial features. We can define the interior of a region, R, as the largest set of points of R for which we can construct a disc-like environment around it (no matter how small) that also falls completely inside R. The boundary of R is the set of those points belonging to R that do not belong to the interior of R, i.e. one cannot construct a disc-like environment around such points that still belongs to R completely.\r\n\r\nLet us consider a spatial region A. It has a boundary and an interior, both seen as (infinite) sets of points, which are denoted by boundary(A) and interior(A), respectively. We consider all possible combinations of intersections (∩) between the boundary and the interior of A with those of another region, B, and test whether they are the empty set (∅) or not. From these intersection patterns, we can derive eight (mutually exclusive) spatial relationships between two regions. If, for instance, the interiors of A and B do not intersect, but their boundaries do, yet the boundary of one does not intersect the interior of the other, we say that A and B meet.","name":"Region","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF5-8","description":"Integration of data from a variety of sources can be a means to retrieving information about processes that would otherwise remain undetected.\r\n\r\nAlthough data integration can be very useful, there are also some requirements that have to be fulfilled for it to be effective:\r\n\r\n• geospatial data have to be accurately co-registered in a common grid;\r\n• time gaps between the various data layers have to be known and accounted for;\r\n• systematic effects due to the atmosphere, the viewing angle, the Sun angle, etc., must be corrected for or taken into account.\r\n\r\nData can be integrated in an almost infinite number of ways. Results from data integration can, again, be combined with other geospatial data to produce yet other new information, and so on.\r\n\r\nData integration also comprises the incorporation of non-spatial information or point data from field measurements. These data have to be associated with precise moments in time and with precise geographic locations, or with some time interval and fuzzy-defined regions. Thus, here the important issue of the representativeness of this information for the associated time interval and geographic area comes into play.\r\n\r\nIn general, data integration forces us to consider the uncertainties or inaccuracies of the various data sources available. In some cases, meta-data may contain information about this. When integrating data for some purpose, one has to apply weights to each of them, so that the final result is a balanced compromise in which inaccurate data receive less weight than those with a high degree of certainty.","name":"Spatial integration","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF5b","description":"The concepts below form the basic elements of common human conceptions of geographic phenomena. Concepts from many units in this knowledge area have been synthesized to create general conceptual models of geographic information. Attempts to resolve the object-field debate have led to attempts to create comprehensive models that bridge these views. Consideration of this unit should also include formal models of these elements in mathematics and other fields. Knowledge Area DM Data Modeling discusses the representation of these elements in digital models.","name":"Elements of geographic information","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF6-1","description":"Mereology is the study of parts and wholes. In GI this involves how objects are modeled as composites of other objects.","name":"Mereology: structural relationships","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF6-2","description":"Lineage describes the history of a data set. During the processing of data, the derived information inherits artifacts from the dataset(s) of origin. In the case of published maps, some lineage information may be provided as part of its meta-data, in the form of a note on the data sources and procedures used in the compilation of the data. Examples include the date and scale of aerial photography, and the date of field verification. Especially for digital data sets, however, lineage may be defined more formally as:\r\n\r\n“that part of the data quality statement that contains information that describes the source of observations or materials, data acquisition and compilation methods, conversions, transformations, analyses and derivations that the data has been subjected to, and the assumptions and criteria applied at any stage of its life (Clarke and Clark, 1995).”\r\n\r\nAll of these aspects affect other aspects of quality, for example positional accuracy. Clearly, if no lineage information is available, it is not possible to adequately evaluate the quality of a data set in terms of “fitness for use”.","name":"Genealogical relationships: lineage, inheritance","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF6-3","description":"We can use the topological properties of interiors and boundaries to define relationships between spatial features. Since the properties of interiors and boundaries do not change under topological mapping, we can investigate their possible relations between spatial features. We can define the interior of a region, R, as the largest set of points of R for which we can construct a disc-like environment around it (no matter how small) that also falls completely inside R. The boundary of R is the set of those points belonging to R that do not belong to the interior of R, i.e. one cannot construct a disc-like environment around such points that still belongs to R completely.\r\n\r\nLet us consider a spatial region A. It has a boundary and an interior, both seen as (infinite) sets of points, which are denoted by boundary(A) and interior(A), respectively. We consider all possible combinations of intersections (∩) between the boundary and the interior of A with those of another region, B, and test whether they are the empty set (∅) or not. From these intersection patterns, we can derive eight (mutually exclusive) spatial relationships between two regions. If, for instance, the interiors of A and B do not intersect, but their boundaries do, yet the boundary of one does not intersect the interior of the other, we say that A and B meet. In mathematics, we can therefore define the “meets relationship” using set theory. The eight spatial relationships are disjoint, meets, equals, inside, covered by, contains, covers and overlaps.","name":"Topological relationships","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF6-4","description":"Relationships between spatial features that define their relative position. Spatial autocorrelation is a fundamental principle based on Tobler’s first law of geography, which states that locations that are closer together are more likely to have similar values than locations that are farther apart.","name":"Metrical relationships: distance and direction","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF6","description":"Like geography, geographic information not only models phenomena but the relationships between them. This can include relationships between entities, between attributes, between locations. In addition, one of the strengths of geography (and GIS) is its ability to use a spatial perspective to relate disparate subjects, such as climate and economy. Methods for analyzing relationships are discussed in Unit AM4 Modeling relationships and patterns.","name":"Relationships","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF7-1","description":"Vagueness arises from lack of criteria for the applicability of certain linguistic terms. It arises from the lack knowledge about the meanings of terms.","name":"Vagueness","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF7-2","description":"-Uncertainty-related terms, such as error, accuracy, uncertainty, precision, stochastic, probabilistic, deterministic, and random -Difference between uncertainty and vagueness -Dependence of uncertainty on scale and application -Expressions of uncertainty in language -The causes of uncertainty in geospatial data -Stochastic error models for natural phenomena -How the concepts of geographic objects and fields affect the conceptualization of uncertainty -Mathematical models of uncertainty: Probability and statistics","name":"Error-based uncertainty","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF7","description":"Human models (mental, digital, visual, etc.) of the geographic environment are necessarily imperfect. While the mathematical principle of homomorphism (often operationalized as fitness for use) allows for imperfect data to be useful as long as they yield results adequate for the use for which they are intended, imperfections are frequently problematic. Although terminology still varies, two types of imperfection are generally accepted: vagueness (a.k.a. fuzziness, imprecision, and indeterminacy), which is generally caused by human simplification of a complex, dynamic, ambiguous, subjective world; and uncertainty (or ambiguity), generally the result of imperfect measurement processes (as discussed in Knowledge Area GD Geospatial Data). Both of these can be manifested in all forms of geographic information, including space, time, attribute, categories, and even existence. Imperfection is also dealt with in Units GD6 Data quality (in the context of measurement), GC8 Uncertainty and GC9 Fuzzy sets (for the handling and propagation of imperfections), and CV4 Graphic representation techniques (in the context of visualization).","name":"Imperfections in geographic information","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV","description":"Geo-data visualisation necessarily includes cartography as the origin of \"mapping\" our world. Cartography methods have drastically changed over the few years since the increasing role and sophistication of digital technology applied to geo-information visualisation. It is first worth differentiating between the underlying geo-data that describes real world phenomena and the bits of information that describe the visual presentation of geo-data . Likewise, there are processing tools to collect and handle geo-data, and processing tools especially designed to create and manage geo-data visualisations. \r\nWhile cartography methods have traditionally produced printed maps (i.e. hard copy) with static scale, orientation, projection, legends (content based) and tied to a period or instant of time. Nowadays geo-data visualisations are interactive by design, meaning that the results are map-based responsive interfaces, highly customisable through dynamic objects to zoom in and out, pan and tilt, change projections and graphic expressions on the fly, as well as dynamically browse the map over time. \r\nIf the production methods have changed, also the type of authors. Map making in its widest sense is not only a privilege of a few experts but has been democratised in such a way that. everybody is able to make maps using open data and open source apps and tools for geo-data visualisation. Therefore,the new roles of open data and new forms of geo-data like geo-social media make usability, intended and ethical considerations key aspects of geo-data visualization design, production and sharing. \r\nUnder the concept of cartography and visualisation it is included a list of concepts that together comprise the science and technology of visual representation of geographic data.","name":"Cartography and Visualization","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV1-1","description":"The evolution of cartographic representation in the previous centuries followed the most important technological and scientific developments of the time. It was driven by commercial and/or military needs and influenced by the special characteristics of the areas and/or environments to be mapped. Recent developments are the rise of open data worldwide and widely available internet technology allowing end users to get remote geo-data published elsewhere. In recent years, data and its digital presentation have become central elements of cartography, whereas paper maps have become peripheral.","name":"History and evolution of cartography","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV1-4","description":"Art in cartography means much more than designing aesthetically pleasing maps, whether on paper or digital. Exploring the interaction at large between art and cartography involves rethinking the way we approach spatial expressions and how cultural, social and political dimensions are reflected in maps. This can be clearly observed in historical maps - in between art and science - ranging from beautiful geographical representations created in the Middle Ages to convey religious messages to the creation of modern maps showing the power of modern empires and nations. This particular relationship between art and maps entails: “developing an inclusive approach of artistic mapping expressions; facilitating and encouraging interaction between cartographers who work with the Art aspects of cartography and artists who produce cartographic artifacts; and developing conceptual elements about the relationships between art and cartography.” Besides ancient paper maps, a sum of factors led digital maps and geospatial visualization, a matter of interest to artists and designers. Thanks to powerful computing systems and with the advancements reached in computer graphics or image processing, or the rise of information visualisation, new forms of representing and visualising geodata have also appeared. Creation of digital maps are still a two-way relationship since artists have explored maps as a medium for expressing their art, and cartographers have approached art to provide more than just the representation of locations and geographic features with the intention to make maps more attractive to their audiences.","name":"Art and geodata visualisation","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV1-5","description":"Historical maps are geographical representations made with the intention to represent spatial facts over time. Historical maps are generally considered valuable documents not just because of their historical value but also because most of them also are artistic representations by themselves. From a cartographical point of view, differentiation between historical maps and actual maps is mainly based on the advances in the history of Cartography, so once one disruptive advance in the map making process appears, maps created with previous techniques (and with some artistic or historical value) are usually considered as historical, such as ancient paper-based maps or old sea maps, for instance. Techniques such as scanning or photography can make ancient maps publicly available by converting hard-copy maps to digital ones. Once an historical map is digitised, the next step is to georeference it, which is the process of specifying and relating points of the digitalised map to actual coordinates in a geographic reference system. Because of its archival value and interest, historical maps are adequately preserved - following specific conditions - by map libraries, map societies or museums. Since digital methods and techniques have been replaced over time by new technological advances, first digitally created maps could be also considered historical, not because of its content, but of the techniques used to produce it.","name":"Historical maps","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV1","description":"At a certain moment in time people start to create more graphical representations of their surrounding environment. New technologies offered ways to expand these representations to larger geographical extent, higher spatial resolution, finer temporal granularity and larger periods. Technologies even made it possible to include other representations of reality such as social media and data ensembles in geodata visualizations, to the extent to even blend the real world with geodata-based visualization providing an augmented – virtual reality continuum. New forms of geo-data, like geolocated sensors may challenge the way geo-data visualisations are generated, shared and, eventually, influence decision-making processes. History and trends sketch these developments and future outlook. This concept introduces the main stages and turns in development of cartography, from earliest times to the present, the most important methods in map-making and map-based visualizations.","name":"History and trends","selfAssesment":"Completed (GI-N2K)
\r\n\r\n"},{"code":"CV2-1","description":"As mapping ( geo-data visualization) is intended to convey a certain message to a certain audience, it is essential to use data sources that allow the intended visualisation result. The data should be of the right degree of detail and its use should not cause copyright problems. The producer quality of each data set should be taken into account, as well as the fitness of the data for the intended use. Aspects: message; data quality","name":"Data sources for mapping","selfAssesment":"
In progress (GI-N2K)
"},{"code":"CV2-2","description":"In the trajectory between raw (geo)data and their user-relevant representation, the necessary data processing includes ways of abstraction by selection, filtering, generalization, transformation and classification of geographical data. In this data processing it is essential to at one hand relate the final symbolisation to the necessities of the intended message, and at the other hand to procedures that introduce as little error as possible.","name":"Data processing","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV2-3","description":"Map projection is fundamental to representation of spatial data and for combining different datasets. Its choice should serve the presentation type that will convey the intended message to the audience. Many mathematical principles define datum, projections, horizontal and vertical co-ordinate systems, georeferencing- introduced with the focus on visualisation issues Aspects: geodetic concepts; transformations","name":"Mathematical base","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV2","description":"Geodata, including 3 dimensional geometry, as such can graphically be presented but most of the times the data as such doesn`t meet the presentation criteria. Especially if the dataset has to be presented in combination with other datasets. First all the geodatum, georeference and map projection are crucial but also the role of the geometry. The processing of the geometry and the related attributes may become a crucial step for an adequate presentation. Nowadays the highest precision may be used to define different graphical attributes for different zoom levels. On the other hand geodata visualisation includes also graphical datasets. Such data ensembles, the combination of geodata and graphical data, are the data sources that offer opportunities to other ways of visualisation then the traditional cartographic mapping. Facets: a.\tGeospatial location (2D) and position (3D) that data refer to b.\tDegree of detail in data origin (acquisition resolution) and in representation ('map' scale) c.\tTypes of data (e.g. imagery, field measurements, delineated objects)","name":"Data considerations","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV3-1","description":"The combined impact of graphic design properties (balance, legibility, clarity, visual contrast, figure-ground organization, and hierarchal organization) and the map components (north arrow, scale bar, and legend) should always be carefully evaluated against the needs and the capacities of the audience.","name":"Map design fundamentals","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV3-10","description":"Geo-gaming is a crossover between gaming elements and location, usually enabled by location based services and augmented adn/or virtual reality features. Geo-games, also known as “location-based games” or “location-aware games”, have geodata at its core, since geoinformation constitutes the central element of the game mechanics. Geo-gaming applications present unique technical challenges to meet the infrastructural and resources demands from the games and location worlds. There are mainly four different types of geo-games: exploration games (to make use of an existing spatial design); feedback games (to report about players’ experiences in a specific design); allocation games (to occupy the majority of game location); and configuration games (to occupy specific pattern of game locations). Gamers actively participate by interacting with the environment, therefore gaming scenarios are as varied as their goals, which include teaching, training, and the developing of spatial thinking skills. Geo-games offer a myriad of opportunities to developers: non-linear storytelling, physical object integration, a more visceral experience, true social interaction… which bring geo-games to another interaction level. Geo-gaming applications often rely on VGI to allow gamers adding geolocated information that may crowdsource geo-referenced data useful for other secondary purposes .","name":"Geo-gaming","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV3-2","description":"Map symbolization entails a number of variables to produce visual, tactile, haptic, auditory, and dynamic displays. Visual variables (e.g., size, lightness, shape, hue) and graphic primitives (points, lines, areas) are commonly used in maps to represent various geographic features at all attribute measurement levels (nominal, ordinal, interval, ratio). With those a single geographic feature can be represented by various graphic primitives (e.g., land surface as a set of elevation points, as contour lines, as hypsometric layers or tints, and as a hillshaded surface). The challenge is to use effective symbols for map features to ease the interpretation of maps.","name":"Symbols and icons","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV3-3","description":"The selection of colours to use in data representation can be influenced by various factors (e.g. the production workflow, cultural differences, involved devices and media). There are various colour models (e.g. RGB, CMYK, CIE) that describe colours in a way that they can effectively convey visual information (e.g., qualitative, sequential, diverging, spectral) according to the meaning of the underlying data. The cultural background of the consumer is also relevant when it comes to choose colours that should have real-world connotations or should express psychological concepts (e.g. harmony, concordance, balance). A final important factor is if the consumer has colour limitations","name":"Colour","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV3-4","description":"When data representation is conveyed in words (e.g. toponyms, road codes), written text is often placed in map labels. It is important to decide on the role of the label in the context of the representation type. Algorithms for label placement are relevant, especially when label density is high. Shape and colour of the labels help to signify different types of messages. This is supported by the typographic properties (type font, size, style) of the text in the labels. Finally, it is important to use an authoritative source for the texts","name":"Typography","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV3-5","description":"Imagery can be a source for data acquisition as well as an illustration to abstract data representations. Imagery can be made from the air (from drones to satellites) or from a terrestrial point of view (street-level imagery). Using photos from any source to illustrate stories about geographical subjects contributes as the visual aspect of telling a story. Together with maps and other narrative components, the combination embodies a storytelling medium.","name":"Photos and imagery","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV3-6","description":"Animation is the process of making the illusion of motion and change by means of the rapid display of a sequence of static images that minimally differ from each other. In the context of maps, the temporal component is added to a map to emphasize and observe the gradual evolution of a certain monitoring phenomenon, such as changes in spatially numerical variables (for example, environment, population, mobility, land use, etc.) with respect to a static geographic area. Map animations generally consider dynamic time while space is static. Map animation helps to see patterns or trends that emerge as time passes, depicting meteorological or climate events, natural disasters, historical events and other multivariate data. It is particularly helpful to be used in educational settings.","name":"Animation","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV3-7","description":"Sound or audios can be one of the components of a multimedia data representation. A conventional GIS usually conveys visual information, however the integration of audios in mapping could enrich GIS data to other senses. Sound can increase the amount of information that’s communicated to the user through channels other than visual to address the special needs of people with visual impairments or people who cannot use in certain circumstances their sight, such as a driver who cannot look at a map. Approaches to rendering sound information on a map fall into three broad categories: (1) to sonoficate the whole visual presentation (for simple geometric data), (2) to augment a visual system with auditory information (allowing multivariate information) and (3) to display information about the surrounding where a user is. By classifying images and creating additional audio layers that associate each pixel with a specific sound, a GIS can add a new auditory dimension to maps.","name":"Sound","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV3-8","description":"Maps are valuable because they provide a large amount of detail in a small amount of space, and because of their capacity for telling a story. Telling stories through maps began with describing explored lands in great detail against terra incognita. Today, geographic tools, data, and multimedia on the web expand the ability to communicate stories and inform through maps to a broad audience such as journalists, decision makers and educators. Any person with a smartphone or computer can tell a story, using statics maps, or interactive web maps with text, video, audio, sketches, and photographs. Besides the technical skills to clearly communicate with a map (palette of colours, amount of information displayed…), other factors such as narrative processes, the storyboard, place, time, and characters play a crucial role. To be informative, it is important that the correct data is displayed, combining different sources of information combined to create an appealing and accurate map.","name":"Storytelling","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV3-9","description":"Infographics are visual representations of information and data, which can contain charts, diagrams, graphs, tables, maps and lists. The aim of an infographic is to present information that can be absorbed quickly, it is easily understandable and extensively in mass communication, and thus designed with fewer assumptions about the reader's knowledge base than other types of visualizations. The role of maps in an infographic is based on the potential of maps to condense information and to support a narrative. Infographic maps - altogether with an adequate storytelling - should find a simple way to explain current complex issues, providing added value to the infographic, and being an effective and efficient way to communicate.","name":"Infographics","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV3","description":"This concepts covers basic design principles that are used in mapping and visualization, as well as cartographic design principles specific to the display of geographic data. Both page layout design and data display are addressed.","name":"Design principles","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV4-1","description":"A thematic map is a type of map especially designed to show a particular theme connected with a specific geographic area. These maps \"can portray physical, social, political, cultural, economic, sociological, agricultural, or any other aspects of a city, state, region, nation, or continent\". Cartographers use many methods to create thematic maps. Five techniques are especially noted: -Choropleth mapping shows statistical data aggregated over predefined regions -Proportional symbols, showing the relative value of attributes -Isarithmic or Isopleth, also known as contour maps -Dots, to show the location of a phenomenon -Dasymetric, which uses areal symbols to spatially classify volumetric data.","name":"Thematic mapping","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV4-10","description":"Conveying uncertainty information is often done through visualization. Uncertainty is often defined, quantified, and expressed using models specific to individual application domains. In visualization however, we are limited in the number of visual channels (3D position, color, texture, opacity, etc.) available for representing the data. Thus, when moving from quantified uncertainty to visualized uncertainty, we often simplify the uncertainty to make it fit into the available visual representations. (After Potter et al., 2012). The seven challenges as formulated by MacEachren et Al. (2005) are still there to be tackled.","name":"Visualization of uncertainty","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV4-2","description":"Relief can be represented in a two-dimensional map either through contour lines or through a raster format gridded array of elevations. Contour lines connect points of equal elevation. At regular intervals index contours are marked with elevations so a reader can more easily determine the elevation of surrounding locations. They are the preferred method for analogue topographic maps. The grid approach is used in digital mapping and known as a digital elevation model (DEM), where each raster cell represents an elevation. Scaling of the cell z value in relation to the x and y value results in terrain exaggeration, which aids visualization of topography.\r\nDEMs are used for terrain analysis and can be used to obtain derivatives such as slope and aspect. DEMs are obtained by interpolating point elevation observations, which are historically retrieved from surveyed point data (e.g. GPS locations), but more recently from LiDAR and/or Structure from Motion point clouds. TIN (triangular irregular network) analysis is commonly used for point data interpolation, in order to derive a continuous elevation surface.","name":"Representing terrain","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV4-3","description":"Multivariate descriptive displays or plots are designed to reveal the relationship among several variables simultaneously. Bivariate and multivariate maps encode two or more data variables concurrently into a single symbolization mechanism. Their purpose is to reveal and communicate relationships between the variables that might not otherwise be apparent via a standard single-variable technique. There are basic characteristics of the relationship among variables, such as the forms of the relationships, the strength of the relationships, and the dependence of the relationships on external (usually to the pairs of variables being examined) circumstances. Therefore, these multivariate plots or maps are inherently more complex, though offer a novel means of visualizing the nuances that may exist between the mapped variables. As information-dense visual products, they can require considerable effort on behalf of the map reader, though a thoughtfully-designed map and legend can be an interesting opportunity to effectively convey a comparative dimension. Examples of multivariate plots include enhanced 2-D scatter diagrams, 3-D scatter diagrams, contour, level, and surface plots, and high-dimensional data plots","name":"Multivariate displays","selfAssesment":"Completed (GI-N2K)
\r\n\r\n"},{"code":"CV4-4","description":"Visualization of change and movement across space and time is of increasing interest to researchers and geospatial practitioners. The visualization process of temporal data has four steps: (1) time values to be visualized, (2) point of view on time, that identifies the characteristics of the temporal values to be visualized, (3) time space: define the displayable space of the time values and (4) point of view on the visualization space, the implementation of the perceptible forms of time. The visualization of spatio-temporal data can be done in many different ways such as multi-panel plots (maps), time-series plots (graphs), space-time plots (graphs), 3D Virtual Reality (Computer generated artificial environment), animations (production of consecutive images), and tables. Spatiotemporal data comprises three important components: geographic location, temporal information and the thematic attributes describing a real-life phenomenon.","name":"Visualization of temporal geographic data","selfAssesment":"
Completed (GI-N2K)
"},{"code":"CV4-5","description":"Dynamic and interactive displays refers to a situation where a display with a cartographical data representation changes in real time in response to user's actions","name":"Dynamic and interactive displays","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV4-6","description":"Web mapping is the process of designing, implementing, generating and delivering maps on the World Wide Web. Dissemination via the web opens new opportunities: realtime maps, cheaper dissemination, more frequent and cheaper updates, personalized map content, distributed data sources and sharing of geographic information. Technical restrictions cause challenges like low display resolution and limited bandwidth,( in particular with mobile computing devices with small screens and using slow wireless Internet connections), copyright and security issues, reliability issues and technical complexity. Today's web maps can be interactive and integrate multiple media. So interactivity, usability and multimedia issues also play a role.","name":"Web mapping","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV4-7","description":"Virtual reality or virtual realities (VR), also known as immersive multimedia or computer-simulated reality, is a computer technology that replicates an environment, real or imagined, and simulates a user's physical presence and environment in a way that allows the user to interact with it","name":"Virtual and immersive environments","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV4-8","description":"An Augmented Environment can be experienced through different sets of Augmented Reality (AR) technologies, including mobile displays (tablets and smartphone screens), computer monitors, or Head-Mounted Displays (HMDs), among others. AR is a technology that layers computer-generated enhancements atop an existing reality to make it more meaningful through the ability to interact with it. AR offers the integration of digital information and imagery onto the real world in real-time. In order to broaden the vision beyond this definition, AR can be described as systems having the following features: (1) combines real and virtual; (2) interactive in real-time; and (3) registered in 3D, allowing other technologies, such as mobile technologies, monitor-based interfaces, monocular systems to overlay virtual objects on top of the real world. Currently, AR applications use the camera provided by mobile devices to produce a live view of the real world in combination with relevant, context-appropriate information such as text, videos, or pictures.\r\nThere are lots of applications and systems in the market that provide AR functionality, making it difficult to classify and name them all. Some of them are related to the real physical world and others with the abstract, virtual imagery world. Sometimes it is not easy to figure whether it is an AR, as often AR is defined as Virtual reality (VR) with transparent HMDs. In general, the concept is to mix reality with virtual reality, including information and overlay over the real world through HMDs such as they seem apparent as one environment. The virtual objects can react accordingly with the camera's movement as it is registered concerning the real world, which is also the central issue of AR.","name":"Augmented environments","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV4-9","description":"Cartographers have recently become involved in extending geographic concepts and cartographic design approaches to the depiction of non-geographic data archives, using so-called spatialized views of information spaces. Spatializations differ from ordinary data visualisation and geovisualisation in that they may be explored as if they represented spatial information. (Fabrikant, S.I., 2003). As definitions of spatialization can be found: Spatializations are computer visualizations in which nonspatial information is depicted spatially (Montello et al., 2003). Spatialization is the transformation of high-dimensional data into lower-dimensional, geometric representations on the basis of computational methods and spatial metaphors. (Skupin 2007)","name":"Spatialization","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV4","description":"This concept addresses mapping methods and the variations of those methods for specialized mapping and visualization instances, such as thematic mapping, dynamic and interactive mapping, Web mapping, mapping and visualization in virtual and immersive environments, using the map metaphor to display other forms of data (spatialization), and visualizing uncertainty. Analytical techniques used to derive the data employed in these graphic representations are discussed in Knowledge Area AM Analytical Methods and Unit DN2 Generalization and aggregation.","name":"Graphic representation techniques","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV5-2","description":"Standards for map services were set by OGC and ISO, called WMS and WMTS. Producing map images on the web from a cartographic image in a GIS application is called \"publishing\". Making a web \"map\" in the broader sense of constructing data representations for Storytelling or Geo-gaming is still under development. It requires a mix of applying the map Design principles and Graphic presentation techniques, possibly in combination with software scripting.","name":"Web map making","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV5-3","description":"Traditional \"map\" making, as opposed to the mapmaking in neogeography, focuses on reliable and reproducible products, based on expertise of high definition printing in many colours on analogue media of geodetically well-constructed images.","name":"Traditional map making","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV5-4","description":"The aspects of reproduction of a data representation depend on the nature of the representation: is it analogue (a paper map, a mock-up) or is it digital? In the case of a paper map, its digitalisation with high fidelity is an essential step. With a source in digital form, reproduction can be a matter of the right printer. Alternatively, the source could be disseminated as a file or as a web service. If representations are dynamic and/or interactive the possibilities depend on the construction of the representation. The ease of dissemination of digital files should not result in copyright breach. Aspects: Digitalization techniques for analogue sources, Printing ( 2D, 3D), Dissemination ways, Construction of the data representation, User needs specification, Copyright issues","name":"Map reproduction","selfAssesment":"GI-N2K
"},{"code":"CV5","description":"This concept addresses map production and reproduction, as well as computation issues that relate to those workflows.","name":"Map production","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV6-1","description":"The potential of maps as a way to show or exert power over the population was early understood by ruling classes. A map expresses a claim by the inclusion or exclusion of map elements and how these elements are visually related and/or depicted on the map. So, the world could be modeled through the careful choice of content arranged graphically at a specific scale and in specific formats. Therefore, maps embody and project the interests of their creators. The “new cartographies” declare that maps are redefined as socially constructed arguments based upon consistent semiotic codes. Nowadays, the rise of costless, powerful and accessible tools for creating maps, put power on the side of individuals or groups of individuals with few organisation (crowdsourced data collection or VGI) capable of representing their world views. In addition, monitoring people, places or nature, for instance, should also be seen as another way to show the increasing power of maps. Surveillance mechanisms for tracking populations used by rulers, or the use of extended technologies like Google Earth by environmental organisations to track the Amazonian forest, constitute two examples of the particular use of maps to exert control over human beings or to press governments for taking specific actions, respectively.","name":"The power of maps","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV6-2","description":"Maps today help us locate the nearest gas station or ATM on our in-car navigation system, but this use of locating what is near or surrounds a location is not new. Maps from pre-historic times provided important locational information – what was where and how to get from place to place. A map can be a relatively simple iconic device, which can be read and interpreted with only a little training. These graphic representations of the real world could be traced in sand or painted on a cave wall and shared through time. Maps even preceded written language and number systems and are found in some format in most cultures through time as a graphical language. Learning to read this language and interpret it without ambiguity is not as simple as first suggested. This complexity has increased as technology has allowed creation of 3D and 4D interactive maps which allow anyone with internet access the ability to investigate different places, topics and times and produce their own map. Today the ability to read and interpret maps is increasingly important as industry, business and government communicates within their organization and the public using maps. Becoming aware of what a “map” shows depends partly on what the senses can register of the representation as a whole. It also depends on recognition of elements in the representation that are meaningful to the observer in the sense that these elements are credible indicators of spatial features. Based on that recognition, the nature of these elements and their spatial pattern might infer thoughts about historic or ongoing processes. This interpretation will be influenced by the expertise and needs of the observer.","name":"Map reading and interpretation","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV6-3","description":"Assessment of the usability of a data representation is about how useful it is to users. Therefore it is a test of the success of the representation design, a test of the skills of the \"map\" maker and a test for the reliability of the underlying data.","name":"Usability analysis","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV6-6","description":"Spatial thinking is thinking that finds meaning in the shape, size, orientation, location, direction or trajectory, of objects, processes or phenomena, or the relative positions in space of multiple objects, processes or phenomena. Spatial thinking uses the properties of space as a vehicle for structuring problems, for finding answers, and for expressing solutions\" Aspects: recognizing spatiality in a collection of things; translation of the collection to a pattern of elements; recognizing structure (relations between the elements in a pattern); recognizing process (or changes over time in patterns or structures)","name":"Spatial thinking","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV6-8","description":"Ethics is about the question if behaviour is right or wrong in a social context. In dealing with geodata, a person can do the wrong thing with respect to laws (e.g. disclose secrets, disregard privacy, copyright infringement) or to professional standards (e.g. use bad data, forget about the colour blind, downplay unpleasant details). Aspects: breach of legal standards; breach of professional standards","name":"Map ethics Legal and privacy issues","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV6","description":"Geodata visualisation are always made with a certain purpose. The role and understanding of such graphical representation is an important field of research. Besides theories that underpin evaluation approaches and their findings the visualisation may also be confronting. The more realistic the presentation and especially when it includes human/personal related data the ethical dimension of the visualisation play a major role. Usability of visualisations has also an impact on spatial thinking as has been proved by scholars.","name":"Usability of maps","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA","description":"Proper design of geospatial applications, models, and databases and the validation and verification of design activities are critical components of work in all areas related to GIS&T. Design failures can negate well-intentioned efforts to apply concepts and technology to solve real-world problems. While sharing a number of concerns with general systems analysis, the unique and complex spatial characteristics of geospatial information provide significant additional challenges. The focus of this knowledge area is on the design of applications and databases for a particular need. The design of general-purpose models and tools (e.g., raster and vector) is covered in Knowledge Area: Data Modeling (DM). In the context of specific implementations, design activities fall into three general classes:\r\n1. Application Design addresses the development of workflows, procedures, and customized software tools for using geospatial technologies and methods to accomplish both routinary and unique tasks that are inherently geographic.\r\n2. Analytic Model Design incorporates methods for developing mathematical models, spatial models and data processes. The design of the analytic model is often influenced by decisions that are made about data models and structures.\r\n3. Database Design concerns the optimal organization of the necessary spatial data in a computer environment in order to efficiently sustain a particular application or enterprise.","name":"Design and Setup of Geographic Information Systems","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA1-1","description":"This concept deals with the importance of having a list of prioritized requirements as a first step to ensure a smooth and successful implementation of a GIS project.. It entails the different methodologies and approaches to ensure a GI system covers all functional and nonfunctional requirements. Requirements are not only derived from business workflows but it is advisable to gather direct input from potential users that will be translated into requirements. However, there is a need to clearly rank the importance of the requirements gathered to ensure the GI system is manageable and in line with the intended use of the GI system, in opposition with the specific interests of a particular user or ambiguous requirements. Therefore, the documentation, traceability and evaluation of requirements after the implementation are as relevant as the initial gathering of requirements to give consistency to the designed system.","name":"Requirements gathering and analysis","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA1-2","description":"The internal process of documenting a task or a process is about “how” it is implemented and “what” is implemented. Documenting is particularly helpful if a breakdown occurs, such as when an expert working in a task leaves her job or to substitute one task in a set of interrelated processes by another. Documentation provides consistency for the taskand allows its monitoring, analysis and revision during a project. \r\nThere are different methods for documenting a task to transform tacit knowledge into explicit knowledge. Therefore, the task should be documented by describing it in video format and using visual tools that allow documentation, or the maintenance of a field diary.\r\nIn particular cases, the creation of user guides or manuals could be considered a subset of a process description particularly addressed to external users. A user manual should take into account the target users to adapt its content to them.","name":"Methods of process description and documenting","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA1-4","description":"A workflow is a sequence of operations that altogether perform a complex, sophisticated or repetitive operation or activity. No matter the workflow type, a workflow is defined in a declarative language, either text-based or visual, and stored in a workflow document to ease sharing and maintenance. In GI systems, a workflow can be seen from distinct perspectives. One of the most well-known GI workflow types is spatial data modelling. A model is specified as a combination of processing tools that manipulate and transform the spatial data required by the model. The order in which the processing tools, inputs, and outputs are organised in a workflow will determine the results and to what extent the spatial question is addressed. However, workflows in GI systems are not only related to spatial data modelling and transformation. There are cases where certain processes in GI systems should be designed in terms of software and hardware requirements, actors needs, organisational aspects or resource usage and demand. How can people’s work contribute to define the stages of a GI architecture? How much time does a regular user spend working with spatial data? How complex is the process going to be? The definition of this sort of workflows can help, for example, in designing an optimal architecture for a GI system in a particular enterprise configuration. \r\nWhether the workflow defines specific steps to process spatial data or the stages and details to implement an enterprise GI system, having a clear idea over each stage's inputs and outputs helps GI systems to be organised, consistent and reliable. In summary, high-level workflows like business workflows put together systems, components and actors that are part of a process or operation. They represent an abstract view, focused often on organisational, functional and resources usage aspects. Conversely, low-level workflows refer to a series of executable activities that carry out data transformations, models or spatial data analysis. Examples are code scripts, specified as sequences of commands in a programming language, and graphical workflows through, for example, the Model Builder in GI systems which are enacted by workflow engines.However, workflows in GI systems are not only related to spatial data modelling and transformation. There are cases where certain processes in GI systems should be designed in terms of software and hardware requirements, actors needs, organisational aspects or resource usage and demand. How can people’s work contribute to define the stages of a GI architecture? How much time does a regular user spend working with spatial data? How complex is the process going to be? The definition of this sort of workflows can help for example in designing an optimal architecture in an enterprise configuration for a GI system. \r\nWhether the workflow defines specific steps to process spatial data or the stages and details to implement an enterprise GI system. Having a clear idea over each stage's inputs and outputs helps GI systems to be organised, consistent and reliable. In summary, high-level workflows like business workflows put together systems, components and actors that are part of a process or operation. They represent an abstract view, focused often on organisational, functional and resources usage aspects. Conversely, low-level workflows refer to a series of executable activities that carry out a complex task, service or model. Examples are code scripts, specified as sequences of commands in a programming language to carry out data transformations and spatial models and spatial data analysis; and graphical workflows through, for example, the Model Builder in GI systems which are enacted by workflow engines.","name":"Workflow definition and consideration in GI systems","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA1-5","description":"Software and information technology are integral to any GI systems or projects, from the storage and handling of spatial data to its analysis, visualization and sharing. Therefore, the use of well-known software design and engineering techniques and methods to develop efficient, reliable, and easy-to-maintain software applications in the GIS realm is more important than ever. \r\nAmong the modern software design and engineering techniques, Agile software development methodologies like Scrum stands out. The common rationale of the Agile methods is to split a large software project into many functional pieces of software that help the software engineering team to translate their development efforts into quick prototypes, and eventually reach the final product. Therefore, the constant feedback and validation of the user’s requirements in short, iterative development circles (i.e sprints) are the main advantages of the Scrum methodology.","name":"Software design and engineering","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA1-6","description":"User interface and usability of a GIS system","name":"User interface and Usability","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA1-9","description":"Geodesign is a design and planning method along with geospatial modelling and technology, and simulations informed by geographic contexts to facilitate informed decisions and the creation of design proposals. A geo-design process is a problem-based, iterative process bounded by specific (geographic) constraints characterised by a collaborative effort.","name":"Geodesign","selfAssesment":"Completed
"},{"code":"DA1","description":"This concept encloses a set of activities and workflows to ensure that the implementation of a GIS system in an organization or project is correctly planned and designed according to the particularites, user requirements and current conditions of the project ahead. In general system design is the process to promote successful GIS in an enterprise environment. As a GIS system has a direct influence on the information technology department (IT), the system design tells the organizacion how the current infrastructure can or must support the planned GIS. This process builds a set of specific recommendations on hardware and network needs based on the number of projects that depend on the GIS solucion, as well as the projected business needs and user requirements. \r\nGIS architects through the system design process need to take into account and identify several conditions: a) infrastructure requirements, b) the network communication capacity, c) hardware and software procurement requirements and, d) software development and data acquisition needs. \r\nHaving a well-defined and successful GIS deployment is not only a matter of what data or software the organization should acquire. The process of system design aligns identified business requirements (user needs/requirements) derived from business strategies or project aims, goals, and stakeholders (business processes) with identified business information systems infrastructure technology (network and platform) recommendations. \r\nThe process starts with identifying business needs, including the identification of users locations, required information, data, resources or products. The business needs are generally considered as project workflows that help the GIS architects to identify the expected data traffic and computing demand associated with each transaction, being a transaction the work unit used to translate business requirements into associated server and network loads.\r\nWithout carrying out a proper system design, a GIS system can lead to an implementation and deployment failure, deriving in unfulfilled expectations and high costs in terms of human resources and financial matters.","name":"System design","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA2-1","description":"Project management includes the planning, organization, coordination, execution, monitoring, controlling and closing of the activities and resources - human and economic - for the timely achievement of clearly defined objectives forming a project. For the success of a project, a project manager will assure an efficient use of resources and a proper execution of tasks to deliver value to users and “clients” of products and services. The Project Management Body of Knowledge (PMI) defines “project management” as “the application of knowledge, skills, tools, and techniques to project activities to meet requirements”, being EO*GI projects are another type of information technology projects. PMI reflects different areas to take care of by project management. These areas are: Integration, Scope, Time, Cost, Quality, Human Resource, Communications, Risks, Procurement and Stakeholder. There are a variety of tools and techniques used in the areas identified by PMI, just to name a few Gantt chart, Program evaluation and review (PERT) analysis, AGILE project management, etc. that will help in project management.","name":"Project management","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA2-2","description":"This concept embraces the factors that could affect a GI system / project and could constitute obstacles to success or even decide a project is not doable. In order to ensure the success of a GI system or a GIS project there are several criteria to take into account from the very beginning of the conception of the GI system or project. A feasibility study may encompass different perspectives (economic, legal, technical, operational or scheduling ) to inform whether or not a project is worth the investment. An organisation should list the foreseen costs from these five perspectives listed above and the benefits (tangible or intangible) of implementing a system/project. Existing resources already available in-house and internal strategic plan in place could be critical to decide to undertake a project or not. The table below presents a non-exhaustive list of criteria and under which perspectives they should be examined.\r\nFeasibility analysis should include a pilot study to evaluate and improve the system / project proposed.","name":"Feasibility analysis","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA2-8","description":"This concept discusses the technical, organizational and monetary advantages and disadvantages of proprietary versus open source software. GIST industry and research are slowly but consistently moving toward the openness of software. Open software entails some clear advantages such as continuous development of new applications, building community of developers and users, starting a project even if limited funding is available, increasing the chances of a project’s sustainability, to name a few. On the other side, proprietary initiatives in GIST are keeping their roots to the ground by developing cutting-edge tools to handle challenging and critical environments in large private sectors and public administrations. Advantages of proprietary software include more stable software, a well developed documentation and personalised customer support service. Both open and proprietary geospatial software solutions can co-exist by applying the appropriate IPR licences for each type of solution. The future trend is to balance how proprietary and open source geospatial software complement each other and find synergies in increasingly complex and large projects.","name":"Proprietaty and open source software","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA2","description":"To design, build, and maintain a GIS, sufficient resources (e.g., labor, capital, and time) must be secured. Resource planning consists of the allocation and use of in-house resources (people, equipment, tools, rooms, etc.) to achieve the maximal efficiency of those resources. These resources are required for a variety of system elements, including design, software purchase, labor, hardware, and facilities. The crucial task is to determine whether the project is worth the required resources.","name":"Resource planning","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA3-1","description":"The ecosystem of GIS software architectures has evolved substantially in recent years to include a variety of options ranging from desktop GIS, server-based and component-based architectures to Web-based, cloud-based, mobile-based approaches. Aligned with the main trend, geospatial software architectures or infrastructures are also moving from desktop architectures to more cloud based or server based options to meet ever-increasing requirements of interoperability, interdisciplinary work and computational power for processing large data sets and derived products. Cloud-based architectures also enable on the fly visualization of computed geospatial products, as complementary visualisation and mapping tools are seamlessly integrated into modern cloud-based based architectures. Usage of a particular architecture is fully dependent on the nature, size, requirements, functionalities, and available resources of a given project or task. Desktop and server based applications are particularly suited for small sized projects and startups while enterprise based applications are meant for larger sized projects. Cloud based infrastructure can be useful for varying sizes of projects in which the computational infrastructure is fully outsourced.","name":"Major geospatial software architectures","selfAssesment":"In progress (GI-N2K)
\r\n\r\n"},{"code":"DA3-2","description":"Interoperability of GIS infrastructure or architecture ensures the consistent and uninterrupted usage of data and functionalities across platforms and systems. Components or tools residing on distinct platforms can “talk” to each other without friction. Interoperability is a central characteristic, especially important in distributed systems and architectures. It can be applied to different levels or layers of a system, i.e. infrastructure level, data level, business logic level, etc. For example, standard spatial data formats and protocols are especially relevant for handling GIS data across multiple systems and platforms, regardless of their underlying software architecture. This is particularly important in large-scale, collaborative projects involving various teams using heterogeneous GIS architectures. Most software providers, developers communities and standardisation bodies and committees are striving to make their architectures interoperable in an open manner, so proprietary standards and protocols are a potential hindrance to this initiative.","name":"Interoperability","selfAssesment":"
Completed (GI-N2K)
"},{"code":"DA3-3","description":"This concept considers general architectural patterns like SOA, ROA, Web Services, etc.","name":"Architectural Patterns","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA3-4","description":"- WebGIS, - technical pecularities of spatial data infrastructures - standardiced GI services for SDI: WMS, WFS, CSW, Transformation Services, SOS, WPS etc., - other map services and interfaces","name":"WebGIS, SDI services, map services","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA3-5","description":"This concept deals with Reference Model of Open Distributed Processing (RM-ODP), its standards, viewpoints modeling and the RM-ODP framework","name":"Reference Model of Open Distributed Processing","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA3-6","description":"Cloud computing provides an on-line computing transparent resource to the user, since a user doesn’t notice almost no difference between working on her own computer or the cloud. Owned and managed by infrastructure providers, cloud computing entails advantages (concurrent access by many users, software updates hosted in the cloud, cost-efficiency or outsourced maintenance in the cloud) and disadvantages (loose of control, network Connection Dependency or security breaches ). On the other side, grid computing is a full network of computers and data working together so functioning as a supercomputer. Grid computing presents advantages such as shorter resolution of complex problems, the ease of organizational collaboration or a better use of existing hardware.","name":"Cloud and Grid computing","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA3-7","description":"Within this concept solutions based on Desktop GIS and GIS libraries will be compared and contrasted","name":"Desktop GIS, GIS libraries","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA3","description":"This concept describes the major geospatial software architectures available currently and choices when designing GI applications and systems, including desktop GIS, server-based, Internet, and component-based custom applications.","name":"Architectural design of a GIS system","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA4-1","description":"- Compare and contrast the relative merits of various textual and graphical tools for data modeling, including E-R diagrams, UML, and XML - Create conceptual, logical, and physical data models using automated software tools - Create E-R and UML diagrams of database designs","name":"Modeling tools","selfAssesment":"GI-N2K
"},{"code":"DA4-2","description":"Within an initial phase of database design, a conceptual data model is created as a technology-independent specification of the data to be stored within a database.","name":"Conceptual models","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA4-3","description":"A logical data model expresses the meaning context of a conceptual data model, and adds to that detail about data (base) structures, e.g. using topologically-organized records, relational tables, object-oriented classes, or extensible markup language (XML) construct tags","name":"Logical models","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA4-4","description":"A physical data model documents how data are to be stored and accessed on storage media of computer hardware","name":"Physical models","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA4","description":"The effective design of geospatial databases should follow the established methods and principles of database modeling and design developed in computer science. The basic method is a three-step process generally called the conceptual, logical, and physical models transforming the application from very human-oriented to machine-oriented. Several standards and software tools exist to aid the process of database design.","name":"Database design","selfAssesment":"In progress (GI-N2K)
"},{"code":"DM","description":"This knowledge area deals with representation of formalized spatial and spatio-temporal reality through data models and the translation of these data models into data structures that are capable of being implemented within a computational environment (i.e., within a GIS or more likely within a spatial database). Data modelling is a crucial issue as it defines the content of a spatial database and usefulness of these content (data) for certain applications. Data Modelling is performed using system neutral languages like UML (or more seldom ER-diagrams). These conceptual models have to be transferred to logical models (i.e. tables of a database). Data is stored in spatial databases which are normally organized in an object relational way. For certain types of data specific databases are used, like triple stores, NoSQL DBs, Array DBs etc. For data modelling quite a number of ISO standards are available for deriving the conceptual model as well as for rules for application schemas, spatial schemas, temporal schemas, Quality principles, encoding, 3D modelling (CityGML) etc. Data models provide the means for formalizing the spatio-temporal conceptualizations. Examples of spatial data model types are discrete (object-based), continuous (location-based), dynamic, and probabilistic. Mastery of the objectives presented in this knowledge area require knowledge and skills presented in the bodies of knowledge of allied fields, including computer science (ACM/IEEE-CS Joint Task Force, 2001) and information systems (Gorgone & Gray, 2000; Gorgone & others, 2002).","name":"Data Modeling, Storage and Exploitation","selfAssesment":"In progress (GI-N2K)
"},{"code":"DM1-1","description":"This topic includes the main basic database concepts: - Database, definition and overview - Database management system, definition and overview - Relational databases, overview - Object-oriented databases, overview - Object-relational databases - NoSQL databases, general overview - NoSQL databases, examples triple stores, array databases, others (overview)","name":"Overview on database concepts","selfAssesment":"GI-N2K
"},{"code":"DM1-2","description":"The Relational Model is the most important database model, therefore it is explained in more detail here: - Basic concepts (tables, tuples, etc.) - Relation to relational algebra (RA), basics of RA - Constraints (key, domain, referential integrity) - Relation to entity relation (ER) model, basics of ER","name":"The Relational Model","selfAssesment":"GI-N2K
"},{"code":"DM1-3","description":"Relational databases and database management systems are essential for GIS in consequence the important issues have to be treated here: - General aspects, basic architecture of a DB, advantages, features - DBMS concepts and functionalites (transactions, locks, multiuser access etc.) - Database design, techniques - Database administration - Normalization (1NF - 3NF) - Example of a database design","name":"Relational Databases, Database Managements Systems and Database principles","selfAssesment":"GI-N2K
"},{"code":"DM1-4","description":"Database queries and especially spatial queries require specific data structures to be performed satisfactory Relevant is: - Motivation, examples of typical non-spatial and spatial queries - Trees, B-tree, R-tree, Q-tree - Graphs, overview and relation to databases","name":"Data Structures and Indices for Databases","selfAssesment":"GI-N2K
"},{"code":"DM1-5","description":"Big data like imagery but also for example GML data sets need compression to be accessed / transferred in an acceptable time. Therefore some compression techniques have to be taught: - Motivation, examples of data sets which need compression - General introduction, vector - / raster data compression, compression lossless, lossy - Popular compression techniques, LZW (Lempel-Ziv-Welch) encoding, Huffman encoding - Techniques for raster data, runlength encoding, JPEG coding, wavelet etc. - Techniques for the reduction of vector data (Douglas Peuker etc.) - Data formats, overview and relation to compression techniques","name":"Data compression techniques","selfAssesment":"GI-N2K
"},{"code":"DM1-6","description":"SQL is the \"standard\" to perform spatial and non-spatial queries in databases. That means each student in a GI related course has to be familiar with the main aspects if it: - Motivation, history, overview - Data definition language DDL - Data manipulation language DML - Data control language DCL - Spatial extensions of SQL","name":"SQL and its usage for data handling, spatial extensions to SQL","selfAssesment":"GI-N2K
"},{"code":"DM1-7","description":"UML is the standard for describing the schema related to GI models, but also user requirements, workflows etc. can be described in UML using the UML diagrams: - Motivation, background, purpose - Use case diagrams - Class diagrams - Sequence diagrams - Activity diagrams","name":"UML introduction and class diagrams","selfAssesment":"GI-N2K
"},{"code":"DM1-8","description":"XML knowledge is an important bases for understanding GML. Moreover XML tools like XSLT are important to transform XML or GML data sets into other XML based formats like SVG or others. Important issues: - Motivation, purpose - Relation to HTML - XML document structure - XML syntax, elements, attributes and namespaces - xlink, xpath and XSLT - XML DTD - XML schema","name":"XML introduction","selfAssesment":"GI-N2K
"},{"code":"DM1-9","description":"The long term storage of GI data in general is based on spatial databases. Therefore the following is essential for a GI course: - Relation between GIS and DB / \"Long transactions\"- Dual concepts - Characteristics of spatial databases - Spatial data in object relational databases - Spatial extensions of DBs, overview","name":"Database concepts in GIS and Principles of spatial databases","selfAssesment":"GI-N2K
"},{"code":"DM1","description":"This unit includes the basics for data modelling, storage and exploitation. Data modelling is one of the most important activities in conjunction with Geographic Information / GIS as it determines how the data can be used and if the requirements from applications are fulfilled. Data modelling can be done in conjunction with the database, e.g. through ER diagrams or according to the ISO 191xx standards by using UML. The costs of data acquisition can be tremendous, therefore the data represents an enormous value. This value has to be conserved through a safe long term data storage. Therefore databases and especially relational and object relational databases are crucial. For a proper storage and query of geographic information databases are extended with specific data types and data structures. As data sets can be very large suitable compression techniques became important especially in the context of accessing and delivering geographical data, e.g. through services. XML based modeling languages for encoding also play and important role in this context","name":"Foundations for Data Modelling Storage and Exploitation","selfAssesment":"In progress (GI-N2K)
"},{"code":"DM2-1","description":"GI standards, mainly from ISO and OGC are essential nowadays. Moreover also an overview on ICT standards from W3C or OMG are important as well as some understanding of standardization processes. In detail: - Motivation for standards, examples from daily life - Overview on GIS and relevant ICT standardization bodies and selected standards - De jure and De facto standards, obligation, reasons for the usage of standards - Standardization within ISO - Standardization within OGC, relation to ISO - Examples of ISO 191xx standards","name":"Overview on relevant standards and standardisation bodies","selfAssesment":"GI-N2K
"},{"code":"DM2-2","description":"Conceptual data modeling is a key skill for GI people. (see relations to other topics) The following therefore is important: - Overview on the relevant standards like conceptual schema language, Rules for application schema - Examples of conceptual schemas","name":"The principle of conceptual data modelling according to ISO","selfAssesment":"In progress GI-N2K
"},{"code":"DM2-3","description":"Geometric modelling is an important subtask of conceptual modelling and requires the following basics: - Overview of ISO 19107 - spatial schema - Overview of ISO 19125 - simple features - Examples of the usage of spatial schema and simple feature elements for feature class definitions - Relation to GML - Relation to DBs","name":"Geometry data types according to spatial schema and the simple feature specification","selfAssesment":"GI-N2K
"},{"code":"DM2-4","description":"Also temporal aspects have to be considered within conceptual modelling. This also requires basics: - Motivation, examples - Temporal variability of features (move, change of structure or geometry) - Overview on ISO 19108 temporal schema - Examples of modeling temporal aspects","name":"Temporal data types according to temporal schema","selfAssesment":"In Progress GI-N2K
"},{"code":"DM2-5","description":"Conceptual models of course have to be implemented, in general in a GIS (which is often proprietary), or in a database (which can be standard based) ,therefore here the implementation in a database is treated: - Repetition of conceptual and logical models - Examples of the transferring of a conceptual model to a logical (database) model","name":"Transferring conceptual models to logical models","selfAssesment":"In progress (GI-N2K)
"},{"code":"DM2-6b","description":"Metadata is considered as very important for the usage as well for the search for Geodata Relevant basics are: - Motivation, importance of data quality as part of metadata - Metadata in an spatial data infrastructure with many There are quite a number of relevant standards for GI courses. Some are listed here, others might be considered, depending on the background of the course: - Select other standards and explain them, Important are: - ISO 19141 Schema for moving features, ISO 19142 Web Feature Service or others - 19109 - Rules for application schema - Selection of other standards is depending on the background of the course","name":"Other standards","selfAssesment":"In progress GI-N2K
"},{"code":"DM2-7","description":"GML is the most important standard for the transfer of Geodata as it allows to transfer the schema information as well as the data. Important issues: - Motivation, Importance of a Geography Markup Language - History of GML, Overview 19136 - Geography Markup Language - Relation to spatial schema - Supported features in GML (Topology, 3D ...) - Structure of GNL, profiles, application schemas etc. - Transfer of models and of data - Examples","name":"Introduction to GML","selfAssesment":"In progress (GI-N2K)
"},{"code":"DM2-8","description":"3D Models, especially 3D city models are becoming more and more important. CityGML is the most important standard within the GI domain to describe City models semantically and geometrically. Relevant issues: - Motivation, Usage of CityGML - Relation to GML - Coherence of semantics and geometry - Principles of modeling - Level of detail concept - CityGML vs KML - Examples","name":"Introduction to CityGML","selfAssesment":"GI-N2K
"},{"code":"DM2","description":"This unit includes the essentials of relevant standards for spatial data modelling. A number of ISO and OGC standards are available for deriving the conceptual model as well as for rules for application schemas, spatial schema provides data types for geometry models in various forms, Point, line, area, body based, temporal schema allows to consider temporal dimensions, Quality principles can be used to describe the quality of geodata, encoding standards (mainly GML) allow the standard based transfer of data and data models, CityGML allows a standard based 3D modelling, etc.","name":"Standards for Spatial Data Modeling","selfAssesment":"In progress GI-N2K
"},{"code":"DM3-1b","description":"There are two basic concepts related to this topic: Features and Fields, or Geo-fields, as named by Goodchild at al. The concept of fields can be differently represented as explained here: - Repetition of basic concepts of Geographic Information Science - Explanation of the concept of continuous fields and the commonly used ways of representing geo-fields - Relation between fields and coverages, an important discretizations of a Geo-field - Types of Coverages","name":"The concept of fields","selfAssesment":"In progress GI-N2K
"},{"code":"DM3-2","description":"The raster data model holds values in a regularly spaced matrix of cells arranged in rows and columns covering a two dimensional space. Rasters are commonly used to store continuous data like colors in an image and height values but they are also used for discrete (thematic) values like land use.","name":"The raster model","selfAssesment":"In Progress (GI-N2K)
"},{"code":"DM3-2b","description":"Grids are on the one hand one important type of caverages and on the other hand Grids are used as basic structure in some applications. Important here is: - Definition of the concept of grid in GIS - Grid as an instance of coverages - Grids as a basic structure for certain applications / medium for aggregation of data - Examples of grid-based data such as Digital Terrain Models (DTM) - Grids in census / statistical data and Geo-marketing applications","name":"Grid representations","selfAssesment":"GI-N2K
"},{"code":"DM3-3","description":"Grid data models can contain millions of discrete values. This leads to very large datasets. Depending on the way values change over the grid, different methods can be used for an optimal (lossy or lossless) data compression. Type of data, computer power needed, application of the data, method of transport and storage all contribute to the choice of compression method.","name":"Grid compression methods","selfAssesment":"In progress (GI-N2K)
"},{"code":"DM3-3b","description":"TINs and Voronoi tessellations are important types of coverages. TINs play a very important role also in Computer graphics. Important here is: - Basics from Graph theory - Definition of Triangulated Irregular Networks (TIN), purpose and applications - TINs and voronoi diagrams as a type of coverages - One important instance of a TIN: Delauney Triangulation - Definition of Voronoi Diagrams, purpose and applications - Relation between Delauney Triangulation and Voronoi Diagram, the \"Dual Graph\" - Examples from applications","name":"TIN and Voronoi tesselations","selfAssesment":"GI-N2K
"},{"code":"DM3-4","description":"While the classical grid structure uses rectangular cells, the hexagonal data model uses hexagons to represent raster data","name":"The hexagonal model","selfAssesment":"In progress (GI-N2K)
"},{"code":"DM3-4b","description":"Linear referencing is 1 dimensional positioning. The position of an object is defined by the distance from the object to the start point along a line. Linear referencing is for example used in railway dispatching systems","name":"Other models like linear referencing","selfAssesment":"In progress GI-N2K
"},{"code":"DM3-5b","description":"Resolution of raster and gridded data - Georeferencing of data, direct and indirect methods (t.b.d.)","name":"Resolution and georeferencing system","selfAssesment":"In Progress (GI-N2K)
"},{"code":"DM3-7","description":"In hierarchical data models data is organized in a tree-like structure. Data are connected with parent-child relations. Hierarchical structures are often used for spatial indexing.","name":"Hierarchical data models","selfAssesment":"In progress (GI-N2K)
"},{"code":"DM3","description":"This unit includes relevant tessellation data models. Besides features (sometimes also called geo-objects) geo-fields play and important role. In recent literature tessellation models are classified as discretizations of fields. In traditional GI literature tessellations are defined as important data structure itself. Tessellation discretise a continuous surface into a set of non-overlapping polygons that cover the surface without gaps. Tessellation data models represent continuous surfaces with sets of data values that correspond to partitions. Important tessellation models are Grids, TINs and Voronoi diagrams.","name":"Tessellation data models","selfAssesment":"In progress (GI-N2K)
"},{"code":"DM4-1","description":"This topic includes the basics for feature based modelling. There are a number of standards also relevant for this topic (see relations). The following items should be included: - Definition of a feature (in some literature also called object, or geoobject) and of feature classes respectively. - Aspects of the definition (ID, geometry, topology, thematic, time etc.) - Techniques for the definition of features / feature classes (mainly link, as they are described elsewhere, see relations)","name":"Feature based modelling","selfAssesment":"GI-N2K
"},{"code":"DM4-2","description":"This topic describes the process of Geometric modelling using vector data, means the primitives like points, lines, areas, bodies, or raster data. There is a strong relation to ISO standards (see relations) as they provide basic data types for geometric modelling. Main issues: - Geometric modeling based on vector data - Geometric modeling based on raster data - Conversion between the models - examples, advantages, disadvantages of the models","name":"Geometric modelling","selfAssesment":"In progress GI-N2K
\r\n\r\nIn progress (GI-N2K)
"},{"code":"DM4-4","description":"This topics deals with the definition of an application schema. There are other units which are important for this topic (see Relations). Issues to be included: - Methods to define and describe an application schema (requirement analysis, description of the schema etc.) - Feature attribute catalogues - Domains / data relevant for INSPIRE","name":"Application models based on vector data","selfAssesment":"GI-N2K
"},{"code":"DM4-5","description":"This Topic deals with important application models, which should be chosen with relation to the course (geographically / related to the background of the course) INSPIRE should be treated in any case. In detail: - Overview on important application models relevant for the course, e.g. from topography or environment in the country - Repetition of the principles of Spatial data infrastructures - Overview on the INSPIRE initiative and the goals related - The INSPIRE data model - The architecture of INSPIRE and the necessary services - Domains / data relevant for INSPIRE","name":"Examples of important application models","selfAssesment":"GI-N2K
"},{"code":"DM4-6","description":"This topic is dedicated to the challenges of model based interoperability and related issues, The principles of interoperability are included in DA3-2. In detail: - The challenges of model interoparability (semantics, different modelling of the same features in different models, syntacs) - Overview on IT concepts for schema integration / transformation - Approaches for model integration - Approaches for model transformations, e.g. related to INSPIRE, from the Humboldt project","name":"Model based interoperability, model transformations","selfAssesment":"GI-N2K
"},{"code":"DM4-7","description":"Network models are crucial in some application domains, such as Navigation (roads etc.), but also in utility applications (facilities like pipes etc.) In this topic should be treated: - The network model in the database domain - Graph based NoSQL databases - Topology of network models - Data structures for storing network data - The Dijkstra algorithm - Overview on important applications","name":"Network models","selfAssesment":"GI-N2K
"},{"code":"DM4","description":"This unit includes relevant issues related to vector data models, feature based modelling, applications. Besides imagery data the majority of GI data available is feature based and founded on vector geometry. Topology modeling also is very common nowadays, as many analysis like routing or neighborhood analysis require it. Spaghetti modelling becomes more and more and exception. In every country there are important feature and vector geometry based application models available e.g. in Topography / Cartography. In Europe every GI course should include some information on INSPIRE. As in different application domains different data models are used, sometimes for the same feature types, integration and transformation of models are an important issue also.","name":"Vector data model, Feature based modelling, Applications","selfAssesment":"In progress GI-N2K
"},{"code":"DM5-1","description":"- Many geographical phenomena are not defined sharply but uncertain Uncertainty has a number of considerations: - Motivation, background, purpose - Conceptual model of uncertainty - Uncertainty of geographic phenomena (vagueness, ambiguity) - Uncertainty of measurements - Uncertainty of analysis - Uncertainty vs. data quality - Statistical models of uncertainty - Outline of Fuzzy approaches","name":"Basics of uncertainty and its modelling","selfAssesment":"In progress GI-N2K
"},{"code":"DM5-2","description":"Space and time are 2 connected concepts, this topic is dedicated to some basics of modelling time and the temporal dimensions related to features and fields: - Motivation, background, purpose - Changes in time in Entity based and field based representations - A conceptual model of changes in time - Move of objects - Change of structure - Change of geometry - Examples from applications","name":"Modelling time aspects","selfAssesment":"In progress GI-N2K
"},{"code":"DM5-3","description":"Traditionally many GIS used 2D or 2.5 D data models, but in the last decade 3D modeling mainly in form of city models or in the context of Building Information Models (BIM): - Basic concepts of 3D modelling, edge, area, volume models - The workflow of 3D modelling, general aspects, choose of the proper model - Methods of 3D modeling - Principles of Constructive Solid Geometry (CSG) - Principles of Boundary representation (BR) - Principles of Voxel-beased modeling - Comparison of the methods - The concept of BIM, principles and purpose - City models, principles and purpose - Examples / applications","name":"Modelling 3D","selfAssesment":"GI-N2K
"},{"code":"DM5","description":"Traditional raster and vector data models cannot easily represent the more complex aspects of geographic information, such as temporal change, uncertainty, three-dimensional phenomena, and integrated multimedia. A variety of models have been proposed to represent these complexities, including both extensions to existing models and software, and entirely new models and software. During the 1990s, work in this area was largely experimental, but many solutions are now available to practitioners in commercial and open source software. The data models in this unit are based on concepts discussed in Knowledge Area CF Conceptual Foundations.","name":"Modelling 3D, temporal and uncertain phenomena","selfAssesment":"In progress (GI-N2K)
"},{"code":"DN3-1","description":"Modification of spatial and attribute data while ensuring consistency within the database, implications of transactions on database integrity, scenarios for periodic changes in GIS database and monitoring the periodic changes.","name":"Database change","selfAssesment":"In progress (GI-N2K)
"},{"code":"DN3-2","description":"Rules for modelling spatial database change, techniques for handling version control, techniques for managing long and short transactions, management of spatial databases in multi-user environment","name":"Modeling database change","selfAssesment":"In progress (GI-N2K)
"},{"code":"DN3-3","description":"Reliability tests of change information, design and implementation. Logical consistency of updates.","name":"Reconciling database change","selfAssesment":"In progress (GI-N2K)
"},{"code":"DN3-4","description":"Needs for versioned databases, queries for change scenarios using DB management tools, algorithms for performing dynamic queries, role of time-criticality and data security while choosing methods for change detection.","name":"Managing versioned geospatial databases","selfAssesment":"In progress (GI-N2K)
"},{"code":"GC","description":"The term geocomputation dates back to the first international conference on the topic in 1996 held at the University of Leeds under the title “The art and science of solving complex spatial problems with computers’. The term “geocomputation” was coined to describe the use of computer-intensive methods for knowledge discovery in physical and human geography. This new area distinguishes it from the application of statistical techniques to spatial data in the focus on “creative and experimental applications” and in “developing relevant geo-tools within the overall context of a ‘scientific’ approach.” Other authors reinforced the unique character of geocomputation as “to provide better solutions to many geographical problems by developing new, computationally dependent tools for analysis and modelling”. Simply defined, the interdisciplinary area of geocomputation was, from the beginning, closely linked to the application of computer technology and the development of tools and applications to real-world spatio-temporal problems through the combination of geographic information system techniques, spatial modelling, cellular automata, and other non-conventional data clustering and analysis techniques.\r\nEven though geocomputation is still seeking to define the field conceptually), it is closely related to computational science, the use of high-computing performance, artificial intelligence, computational intelligence, grid infrastructure and parallel computing . Nevertheless, the evolution of new computing paradigms, such as edge-fog-cloud computing along with the new forms of data create new opportunities for the geocomputation community . \r\n\r\nWhile the underlying idea remains intact --a diverse and interdisciplinary area of research that uses geospatial data, methods and tools for applied scientific work--, the current approach to geocomputation differs from the founders in that it focuses more attention on open science, reproducible research practices, and in a vibrant collaborative community to develop new methods, tools and applications that are integrated into multiple application domains such as economics, sociology, geodemography, health, criminology, transportation, biology, remote sensing and cities . The theoretical roots and experimental emphasis of geocomputation makes it an excellent vehicle to creatively explore in parallel the theory and practice of the use of geospatial data in a computational way to solve real-world problems.","name":"Geocomputation","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC1-1","description":"A complex system can be viewed as a system composed of many interacting parts, with the ability to generate a new collective behaviour through self-organisation, for example, though the spontaneous formation of temporal, spatial or functional structures. Complex systems are therefore adaptive as they evolve and may contain self-driving feedback loops. Most real-world systems such as global climate, an ecosystem, a city, the human brain, and the entire universe, are complex systems. Therefore, complex systems are much more than a sum of their parts.The general characteristics of the structure and dynamics of complex systems have been characterised, including path dependence, positive feedback loops, self-organisation, and emergence. Complex system types include nonlinear systems, chaotic systems, and complex adaptive systems. \r\nTraditional approaches focus on the individual system components and define a system as the sum of its parts. Whereas the modern approach relies on complexity theory and complex adaptive systems, to emphasise the linkages between system components in order to understand complex systems as a whole. Agent-based models, for example, have been highly recommended for studying complex adaptive spatial systems because they support the explicit representation of situation-dependent information for decision making within dynamic spatial environments.","name":"Complex systems","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC1-2","description":"Computational science is a discipline focused on the design, implementation and use of mathematical models or simulations through the use of computers to analyse scientific problems, systems or processes. Computational science heavily relies on computational technologies such as high performance computing, artificial intelligence, computational intelligence, grid infrastructure and parallel computing. Geocomputation is closely related to computational science and, therefore, geocomputational methods are often derived from machine learning, clustering, simulation, parallel computing and high performance computing. Contrary to the methods and tools applied for spatial analysis described under the Analytical Methods Knowledge Area, geocomputation methods may involve spatial methods available in standard GIS packages, but quite often require self-development, or at least customisation, involving computational technologies to solve target problems. The aim of this topic is to provide an introduction to computational science with particular emphasis on its usage and relation to geocomputation.","name":"Computational science and technology","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC1-3","description":"While geocomputation is not daily used in GIS environments and traditional GIS projects, it is the focus of a vibrant collaborative and research community in developing new geocomputational methods, tools and applications that are integrated into multiple application domains such as economics, sociology, geodemography, health, criminology, transportation, biology, remote sensing and cities. Open science, reproducible research practices, and strong collaboration make geocomputing an excellent vehicle for creatively exploring together the theory and practice of using geospatial data in a computational way to solve real-world problems.","name":"Spatio-temporal problems and applications","selfAssesment":"In progress (GI-N2K)
"},{"code":"GC1-4","description":"The origin of geocomputation dates back to the first international conference on the topic in 1996 and was coined to describe the use of computer-intensive methods for knowledge discovery in physical and human geography. Geocomputation is closely related to other widely known areas of knowledge within the geospatial community, such as GIScience, Spatial Information Science, GeoInformatics, and Geographic Data Science. While these terms clearly overlap and boundaries are fuzzy, the term geocomputation puts the focus on creative and experimental applications and in developing relevant computationally geospatial tools for analysis and modelling within the overall context of a ‘scientific’ approach. Therefore, a common interpretation of geocomputation is to describe the application of computational models to geographic problems.","name":"Origin of geocomputation","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC1","description":"Geocomputation represents an attempt to move the geospatial research agenda back to geographical analysis and modelling by providing a toolbox of methods to analyse and model a range of highly complex, often non-deterministic problems. In this context, complex systems and computational science are foundational aspects upon which geocomputation approaches and methods are built to address a variety of real-world, spatio-temporal issues","name":"Geocomputation and complex systems","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC2-1","description":"Building a model that mimics a real-world system generally follows a series of stages: from conceptual models to mathematical models and, finally, simulation models. In model development, system analysis is a process whereby a real-world system is simplified by dividing it into simpler, more manageable parts. A conceptual model captures the components, variables and interactions of a system, and provides a useful way of thinking about the trade-offs between abstraction and representativeness of real-world phenomena. Taken in isolation, however, the interacting parts of a system fail to explain its dynamics behavior. A conceptual model is then translated into a mathematical model to explain system dynamics and interaction. Mathematical models often take the form of equations, logical rules or other mathematical mechanisms to represent the interrelations and relationships among the constituted parts of a system. Lastly, a simulation model is the computer-based implementation of mathematical models consisting of interrelated equations and logical rules. When a simulation model runs on a computer, it iteratively recalculates the modelled system state as it changes over time in accordance with the relationships represented by the mathematical relationships that describe the system dynamic. Therefore, developing detailed and dynamic simulation models comes at the cost of generality and interpretability, but it brings us realism and the ability to represent real-world processes in specific contexts. Simulation modelling is often used for prediction, exploration, theory development, or even optimization of conditions to achieve desired outcomes, with the goal of examining how the interconnections and relationships that characterise complex social and environmental systems (e.g. ecosystems, urban systems, social systems, global climate system) produces patterns of behavior over time. Therefore, simulation models are increasingly gaining relevance as scientific mechanisms for several reasons. First, simulation models allow researchers to study systems inaccessible to experimental and observational scientific methods, complementing more conventional approaches to discover or formalize theories about real world systems. Also, aS many real-world systems are nonlinear, simulation modelling has turned into a necessary method to explore and understand better such systems. In addition, the availability of computational science methods and technology, together with a large amount of data available from different sources, have greatly driven the adoption of simulation models in a wide range of scientific disciplines.","name":"Principles of computer simulation","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC2-3","description":"Rule-based models are based on logic programming with condition-action expressions, where the left side of the expressions consists of several conditions that returns a logical result, and the right side consists of several actions. Rules in rule-based models indirectly specify a mathematical model. However, unlike equation-based models which refer to the overall or aggregate behaviour of a system, rule-based models focus on the behaviour of the individual components of a system. That’s why the implementation of rule-based models is most often done by cellular automata models or agent-based models, in which the aggregate behaviour of the system emerges from the interaction of the individual agents or cells over time. Many geographic patterns and dynamics are formed by systems of interacting actors/cells with heterogeneous characteristics and behaviours, in which such dynamic behaviours can be implemented as rules. The aim of this topic is to provide knowledge about rule based models and to understand their advantages and disadvantages.","name":"Rule-based models","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC2-4","description":"Equation-based models are a set of interrelated equations that capture the variability of a system over time (differential equations), and the execution (simulation) of the model means to evaluate such equations. Equation-based models do not aim at representing the behaviour of the individual components in a system. Rather, they focus on the overall or aggregate behaviour of a system. Therefore, equation-based models are well suited to represent physical processes and some topics within natural sciences, where the system to some degree can be described by physical laws. Hydrological modelling is a good example of models based on equations. However, other real-world systems can rarely be fully described by the laws of the natural sciences, and their behavior and interrelation must be represented by means of other types of mathematical mechanisms. The aim of this topic is to present the advantages and challenges in using equation-based simulation models, which are most naturally applied to systems centrally governed by physical laws rather than by information processing and flow.","name":"Equation-based models","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC2-5","description":"Space-time dynamics are closely related to the concepts of change and process, which are inherent to our dynamic world. Space-time dynamics especially manifest when we move from a static representation to a dynamic representation of phenomena. Various processes that take place at different spatial and temporal scales interact with each other and lead to complex changes to the phenomena being modeled. There exist many different approaches of conceptualizing and understanding space-time dynamics in order to understand or predict phenomena in heterogeneous application domains ranging from human activities and urban sprawl to disease spread and traffic flow.","name":"Space-time dynamics","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC2-6","description":"Cellular automata are a standard type of spatially explicit simulation model in which complex processes are modelled over space and time by means of a lattice of cells in which each cell defines its neighbouring cells. The spatial lattice composed of a two-dimensional grid of squared cells is the simple configuration of a cellular automata. Based on this regular configuration, each cell has associated a set of states that change at each iteration by the execution of transition rules, which take into account the state of each cell and those of its neighbours. As such, cellular automata consist of six defining components: a framework or lattice, cells, neighborhood, transition rules, initial conditions (states), and an update sequence (time). Cellular automata models map easily onto existing data structures widely used in geographic information systems, are easy to implement, and are able to show changes and spatial patterns in an understandable manner. All of this has contributed to their popularity in simulation modelling for applications such as measuring land use changes and monitoring disease spread","name":"Cellular automata","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC2-7","description":"Agent-based models are simulation models that decompose a complex system into small entities (agents) with modeling properties and behavior. Contrary to modelling at an aggregate level, agent-based models are focused on the individual level, where a set of discrete agents with well-defined behaviors represents an individual, object or component of the modelled system. Therefore, the individual agent is the explicit, basic unit. The macro-level behaviour of the system emerges thereafter from the interaction of the individual agents and with the environment over time. Agent-based models are used for spatial modelling, offering possibilities to consider topological particularities of interaction and information transfer among agents and/or with the environment. In relation to spatial simulation, agent-based models have been used for example to model natural and social phenomena such as animal behaviour, pedestrian behavior, social insects and biological cells.","name":"Agent-based modelling","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC2","description":"The concept spatial simulation modelling can be better understood by looking at the meaning of its individual words. A model is widely defined as a simplified representation of a real-world system under study, which can be used to explore or to better understand the system it represents. Computer models or simulation models are computer-based implementations of a model to produce outputs based on certain model assumptions. Simulation , therefore, relies on the use of computers for virtual experimentation to gain insight into real-world problems by proposing alternative assumptions that arise from exploring “what if” questions about a dynamic problem of interest over the course of successive simulation experiments.\r\nSimulation modelling is also useful for the study of spatial patterns over time. Spatial simulation models are relevant when the study of spatial elements and their relationships in a system are necessary for a fully understanding of that system. In this sense, spatial simulation modelling approaches include rule-based models, equation-based models, grid-based cellular automata models, discrete event simulation, and agent-based models.\r\nSimulation modelling is often used for prediction, exploration, theory development, or even optimization of conditions to achieve desired outcomes, with the goal of examining how the interconnections and relationships that characterize these systems produces patterns of behavior over time. Across broad areas of the environmental and social sciences, researchers use simulation models as a way to study systems inaccessible to experimental and observational scientific methods, and also as an essential complement of those more conventional approaches to discover or formalize theories about the real world. \r\nSimulation models are a relatively recent addition to the scientific toolbox, and the reasons for their widespread adoption are, on one hand, the impossibility to study in-situ some complex social and environmental systems (e.g. ecosystems, urban systems, social systems, global climate system) and, on the other hand, the availability of High Performance Computing and large amount of data from different sources. Finally, the nonlinear behaviour of many natural systems provides challenges building traditional mathematical models based on linearization. \r\nSimulation modelling is also useful for the study of spatial patterns over time. In this sense, spatial simulation modelling approaches include rule-based models, equation-based models, grid-based cellular automata models, discrete event simulation, and agent-based models.","name":"Spatial simulation modelling","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC3-1","description":"Among the recent artificial intelligence techniques are those pertaining to heuristics. A heuristic technique is an approach to problem solving, that employs a practical method, which is necessarily not optimal or perfect, but in many situations sufficient. Heuristic methods can be useful, where the optimal solution in practice is impossible. The aim of the topic is to provide insight into the principles of heuristics and the most important algorithms.","name":"Heuristics","selfAssesment":"In progress (GI-N2K)
"},{"code":"GC3-2","description":"Genetic algorithms, genetic programming and evolutionary computing are terms that fall within the general sphere of `Evolutionary Computation`. Genetic algorithms (GAs) are computationally intensive global search heuristics with very wide applicability, but their implementation is often highly problem specific and there is only a relatively loose understanding as to why they often work rather well. The central idea behind GAs is to mimic the Darwinian notion that selective breeding seeks optimum individuals in a given environment. In order to do this a GA requires a way of representing a solution to a (spatial) problem as if it were an individual viewed as a chromosome or `genome` like object. The aim of the topic is to provide fundamental understanding of the principles behind genetic algorithms, and its application in solving geospatial problems.","name":"Genetic algorithms","selfAssesment":"In progress (GI-N2K)
"},{"code":"GC3-3","description":"Biological neurons, or nerve cells, receive multiple input stimuli, combine and modify the inputs in some way, and then transmit the result to other neurons. Artificial neural networks are an attempt to emulate features of biological neural networks in order to address a range of difficult information processing, analysis and modelling problems. The principal class of ANNs are so-called feed-forward networks, but other types of ANN are for example recurrent neural networks. Among the feed-forward networks the most widely used approach is the multi-level perceptron (MLP) model. The application range is broad from non-linear regression to land cover change modelling. The aim of the topic is to introduce the principles of ANN and to understand and demonstrate its use in geospatial modelling.","name":"Artificial Neural Networks","selfAssesment":"In progress (GI-N2K)
"},{"code":"GC3-4","description":"Pattern recognition is the process of classifying input data into objects or classes based on key features. There are two classification methods in pattern recognition: supervised and unsupervised classification. The supervised classification of input data in the pattern recognition method uses supervised learning algorithms that create classifiers based on training data from different object classes. The classifier then accepts input data and assigns the appropriate object or class label. The unsupervised classification method works by finding hidden structures in unlabelled data using segmentation or clustering techniques. Common unsupervised classification methods include: K-means clustering, Gaussian mixture models, Hidden Markov models. The aim of the topic is to provide knowledge about the different methods in pattern recognition and how to choose the optimum method for a specific spatial problem.","name":"Pattern recognition","selfAssesment":"In progress (GI-N2K)
"},{"code":"GC3-5","description":"Understanding natural and human-induced structures and processes in space and time has long been the agenda of geographical research. Through theoretical and experimental studies, geographers have accumulated a wealth of knowledge about our physical and man-made world over the years. Knowledge is often discovered through critical observations of phenomena in space and time. Due to the rapidly expanding amount of data and information the problem is often not having enough data but having too much and too complex a database. The aim of the topic is to provide insight into several methods to carry out spatio-temporal knowledge discovery through spatial data mining and clustering techniques.","name":"Spatio-temporal knowledge discovery","selfAssesment":"In progress (GI-N2K)
"},{"code":"GC3-6","description":"Data-intensive computing is now starting to be considered as the basis for a new, fourth paradigm for science. Two factors are encouraging this trend. First, vast amounts of data are becoming available in more and more application areas. Second, the infrastructures allowing to persistently store these data for sharing and processing are becoming a reality. The technical and scientific issues related to this context have been designated as `Big Data` challenges and have been identified as highly strategic by major research agencies. The aim of this topic is to introduce Big Data as a concept, and the needed methods to navigate through the vast amount of heterogeneous information.","name":"Big data filtering","selfAssesment":"In progress (GI-N2K)
"},{"code":"GC3","description":"The amount of data in current geospatial repositories along with their high-dimensional nature requires a sophisticated set of analysis capabilities in order to extract new and unexpected patterns, trends, and relationships embedded in that data. Artificial intelligence and data mining methods constitute an alternative to explore and extract knowledge from geospatial data, which is less assumption dependent. Data Mining is a step in the knowledge discovery process that automatically detects patterns in data, and Geographic Data Mining is a special type of data mining that seeks to apply standard data mining tools modified to take into account the special features of geospatial data","name":"Artificial Intelligence and Data Mining","selfAssesment":"In progress (GI-N2K)
"},{"code":"GC4-1","description":"The use of the term Open geocomputation doesn't intend to coin a new term; Open GIScience and Open GIS are well explored and discussed terms in the literature. Both embrace the idea of open data, open source, collaboration among peers, and the integration of these practices into GIS research projects, tools, services and applications. Open geocomputation brings the ideas of Open GIScience (and hence Open Science in general) into geocomputation, focussing on openness as a fundamental tenet to conduct research in geocomputation and for the development of new computational methods and tools. In fact, many community-led developments and tools have recently appeared in the field of geocomputation, notably based on R and Python. The widespread popularity and adoption of these computing environments for geocomputing and geospatial analysis is simply because they encompass open, transparent, and reproducible tool development.","name":"Open Geocomputation","selfAssesment":"New
"},{"code":"GC4","description":"A distinguible feature of the current approach to geocomputation is the emphasis on openness: open science, open source, open data. All of this propelled by a vibrant collaborative community with the aim to develop open and reproducible methods, tools and applications applied to a variety of real-life, spatio-temporal application domains. Open Science is a paradigm that can be applied to any scientific discipline and area of knowledge, characterised by openness, access to large volumes of data and unprecedented levels of computing power, availability of community-driven tools, and new types of collaboration between multidisciplinary researchers. Open Science clearly goes beyond geocomputation, but at the same time, its practices and principles characterise recent geocomputation-related projects as well as its community. Therefore, the vision of Open Science taken here is contextualised to the field of geocomputation.","name":"Open Science","selfAssesment":"new
"},{"code":"GD","description":"Geospatial data represent measurements of the locations and attributes of phenomena at or near Earth`s surface. Information is data made meaningful in the context of a question or problem. Information is rendered from data by analytical methods. Information quality and value depends to a large extent on the quality and currency of data (though historical data are valuable for many applications). Geospatial data may have spatial, temporal, and attribute (descriptive) components, as well as associated metadata. Data may be acquired from primary or secondary data sources. Examples of primary data sources include surveying, remote sensing (including aerial and satellite imaging), the global positioning system (GPS), work logs (e.g., police traffic crash reports), environmental monitoring stations, and field surveys. Secondary geospatial or geospatial-temporal data can be acquired by digitizing and scanning analog maps, as well as from other sources, such as governmental agencies. The legitimacy of geographic information science as a discrete field has been claimed in terms of the unique properties of geospatial data. In a paper in which he coined the term GIScience, Goodchild (1992) identified several such properties, including: 1. Geospatial data represent spatial locations and non-spatial attributes measured at certain times. 2. The Earth`s surface is highly complex in shape and continuous in extent. 3. Geospatial data tend to be spatially autocorrelated. It has long been said that data account for the largest portion of geospatial project costs. While this maxim remains true for many projects, practitioners and their clients now can reasonably expect certain kinds of data to be freely or cheaply available via the World Wide Web. Federal, state, regional, and local government agencies, as well as commercial geospatial data producers, operate clearinghouses that provide access to geospatial data. Although geospatial data are much more abundant now than they were ten years ago, data quality issues persist. Good data are expensive to produce and to maintain. Proprietary interests simultaneously increase the supply of geospatial data and impede data accessibility. Standards for geospatial data and metadata are useful in facilitating effective search, retrieval, evaluation, integration with existing data, and appropriate uses. National and international organizations, such as the Open Geospatial Consortium (OGC) and International Organization for Standardization (ISO), develop and promulgate such standards. INSPIRE directive (Infrastructure for Spatial Information in the European Community) regulates geospatial data management","name":"Geospatial Data","selfAssesment":"I, progress (GI-N2K)
"},{"code":"GD1-1","description":"Usable and accurate geospatial data are based upon proper model of the Earth`s surface. Shape of the Earth is complex and complicated to measure. Approximations are used to minimize complexity of the task and possible errors.","name":"Earth geometry","selfAssesment":"GI-N2K
"},{"code":"GD1-2","description":"Geospatial referencing systems provide unique codes for every location on the surface of the Earth (or other celestial bodies). These codes are used to measure distances, areas, and volumes, to navigate, and to predict how and where phenomena on the Earths surface may move, spread, or contract. Point-based, vector coordinate systems specify locations in relation to the origins of planar or spherical grids. Tessellated referencing systems specify locations hierarchically, as sequences of numbers that represent smaller and smaller subdivisions of two- or three dimensional surfaces that approximate the Earths shape, Linear referencing systems specify locations in relation to distances along a path from a starting point. Tessellation data models, are considered in Unit DM3 Tessellation data models, and linear referencing models are considered in Unit DM4 Vector data models.","name":"Georeferencing systems","selfAssesment":"GI-N2K
"},{"code":"GD1-3","description":"Horizontal datums determine the geometric relations between a coordinate system grid and a particular ellipsoid approximating the Earth`s surface. Vertical datums determine elevation reference surfaces, like mean sea level. A. Horizontal datums. Relation of coordinate system to particular ellipsoid, datum transformation options, Molodensky and Helmert transformation, other high accuracy transformations, ED50 and WGS84, historical development of horizontal datums, ETRS89. B. Vertical datums. Historical development of vertical datums, difference between vertical datum and geoid, relations between ellipsoidal (geodetic) heiht, geoidal height and orthometric elevation.","name":"Datums","selfAssesment":"GI-N2K
"},{"code":"GD1-4","description":"Map projections are systematic transformations of geographic coordinates of the surface of ellipsoid into locations in plane. Plane coordinates are based on map projection. As the transformation of a spherical grid into a plane grid causes inevitably distortions of the geometry, and, different projections cause different distortions, knowledgeable choice of appropriate projection for any particular use is crucial. A. Map projection poperties. Geometric properties that may be preserved or lost in projected grid, usefulness of compromise projection, Tissot indicatrix as an indicator of projection errors, visual appearance of the Earth`s graticule, distortion patterns for projection classes, distortions in raster data. B. Map projection classes. Three main classes of map projection based on developable surface, projection types by geometric properties preserved, mathematical basis of projecting longitude and latitude into x and y coordinates. UTM, ETM, projections used by EC. C. Map projection parameters. Standard line, projection case, latitutde and longitude of origin, aspects of projection. D. Georegistration. Rectification vs orthorectification, ground controle points in georegistration of aerial imagery.","name":"Map projections","selfAssesment":"GI-N2K
"},{"code":"GD1","description":"Proper model of the Earth`s surface and ability to locate spatial phenomena accurately to it, is crucial in effective collection, management and use of data. Characterising size and shape of the Earth, using appropriate surfaces to approximate it, choosing suitable coordinate system and map projection is bases for efficient understanding of spatial data.","name":"Geolocating Data to Earth","selfAssesment":"In progress (GI-N2K)
"},{"code":"GD10-4","description":"A stereoscopy acquisition mode collects remotely sensed data where each location on the ground (or the imaged objects) is covered multiple times (at least twice), from different perspectives. Stereopairs and stereoscopic coverage enable the extraction of 3D representations of the environment from remotely sensed imagery.","name":"Stereoscopy and orthoimagery","selfAssesment":"GI-N2K
"},{"code":"GD10","description":"Since the 1940s aerial imagery has been the primary source of detailed geospatial data for extensive study areas. Photogrammetry is the profession concerned with producing precise measurements from aerial imagery. Aerial imaging and photogrammetry comprise a major component of the geospatial industry. The topics included in this unit do not comprise an exhaustive treatment of photogrammetry, but they are aspects of the field about which all geospatial professionals should be knowledgeable.","name":"Aerial imaging and photogrammetry","selfAssesment":"GI-N2K
"},{"code":"GD11-2","description":"the physical environment to sense data without direct contact. It contains a carrier device (platform) and a sampling unit (sensor).","name":"Platforms and sensors","selfAssesment":"GI-N2K
"},{"code":"GD11","description":"Satellite-based sensors enable frequent mapping and analysis of very large areas. Many sensing instruments are able to measure electromagnetic energy at multiple wavelengths, including those beyond the visible band. Satellite remote sensing is a key source for regional- and global-scale land use and land cover mapping, environmental resource management, mineral exploration, and global change research. Shipboard sensors employ acoustic energy to determine seafloor depth or to create imagery of the seafloor or water column. The topics included in this unit do not comprise an exhaustive treatment of remote sensing, but they are aspects of the field about which all geospatial professionals should be knowledgeable.","name":"Satellite and shipboard remote sensing","selfAssesment":"GI-N2K
"},{"code":"GD12","description":"Meaning of geospatial metadata, elements of metadata, use of metadata, integration of metadata in data production, standards in geospatial data, ISO standard family 191xx, data warehouse, exchange protocol, transport protocols, spatial data infrastructure, INSPIRE, OGC, DCAT profiles for CKAN applications bridging metadata from GI and IT domains.","name":"Metadata, standards, and infrastructures","selfAssesment":"GI-N2K in progress
"},{"code":"GD2-1","description":"Classic land survey methods and manual attribute data collection in the field","name":"Land surveying and field data collection","selfAssesment":"GI-N2K
"},{"code":"GD2-2","description":"Aerial imagery has been the primary source of detailed geospatial data for extensive study areas. Photogrammetry is producing precise measurements from aerial imagery. Aerial imaging and photogrammetry comprise a major component of the geospatial data production. Satellite-based sensors enable frequent mapping and analysis of very large areas. Sensing instruments are able to measure electromagnetic energy at multiple wavelengths. Satellite remote sensing is a key source for regional- and global-scale land use and land cover mapping, environmental resource management, mineral exploration, and global change research. Shipboard sensors employ acoustic energy to determine seafloor depth or to create imagery of the seafloor or water column. Principles of aerial photography, oblique and vertical imagery, spatial and radiometric resolution, spectral sensitivity, principal point, distortions and displacements in aerial image, parallax, stereophotogrammetry, generation of an orthoimage from a vertical aerial phoptograph, aerotriangulation, vector data extraction from digital seteroimagery, mission planning. Use of UAV in photogrammetry. Main platforms and sensors in spatial image acquisition, active and passive sensors, LiDAR and microwave, multispectral and hypersepctral imagery, interpretation of imagery, supervised and unsupervised classification, pixel based and segmented classification, ground verification, main applications, bathymetric mapping. SENTINEL.","name":"Remote sensing","selfAssesment":"GI-N2K
"},{"code":"GD2-3","description":"Crowdsourcing is the practice of obtaining needed services, ideas, or content by soliciting contributions from a large group of people and especially from the online community rather than from traditional employees or suppliers. Crowdsourced spatial data collection is becoming more and more important. The advantages and disadvantages of crowdsourced data, opensource mapping tools, potential application of crowdsourcing, VGI, OSM or cell-phone based, aspects of crowdsourced data quality and reliabilty.","name":"Crowdsourced data collection","selfAssesment":"GI-N2K
"},{"code":"GD2-4","description":"Digitizing as the main secondary spatial data production technique. Encoding vector points, lines, and polygons by tracing map sheets has diminished in importance, but remains a useful technique for incorporating historical geographies and local knowledge. \"Heads-up\" digitizing using digital imagery as a backdrop on-screen is a standard technique for editing and updating GIS databases. Tablet and on-screen digitizing, scanning and (semi)automatic vectorization.","name":"Digitizing","selfAssesment":"GI-N2K
"},{"code":"GD2","description":"Spatial data collection / production involves measurement of locations in relation to the coordinate system, and collection of attributed data about the spatial phenomena. Measurements may be direct (e.g. surveying) or remote, data acquisition involves measurement of parameter values, evaluation of parameters, polls, interpretation of spatial imagery, and re-use of secondary data (e.g. old maps). Volunteered geographic information is becoming more important.","name":"Data Collection","selfAssesment":"GI-N2K
"},{"code":"GD3","description":"It is quite common, that data including both spatial entities and their attribute data undergo changes. These changes need to be catalogued fully and explicitly, including initial conditions, new conditions, all intermediate stages and operations used. The geospatial data needs to contain an archival history of change.","name":"Transaction management of geospatial data","selfAssesment":"In progress (GI-N2K)
"},{"code":"GD4-1","description":"Geometric accuracy, factors influencing it, geometric accuracy and topological fidelity, geometric accuracy in survey and GPS mesurements, thematic accuracy, relations between thematic accuracy, geometric accuracy and topological fidelity, misclassification matrix, commission and omission, logical consistency, relations between resolution, precision, and accuracy, spatial resolution, thematic resolution, and temporal resolution, precision, uncertainties associated with coordinate precision, primary and secondary data sources.\r\n\r\nParticular application. That standard varies from one application to another. In general, however, the key criteria are how much uncertainty is present in a data set and how much is acceptable. Judgments about fitness for use may be more difficult when data are acquired from secondary rather than primary sources. Aspects of data quality include accuracy, resolution, and precision. Concepts of data quality, error, and uncertainty are also covered in Knowledge Areas CF Conceptual Foundations (in a theoretical context) and GC Geocomputation (in the context of analysis); the focus here is on the measurement and assessment of data quality.","name":"Data quality","selfAssesment":"GI-N2K
"},{"code":"GD4","description":"Data quality is the degree of data usability in relation to given objective and particular application. The expectations to data vary between different applications. The key criteria in data quality are the amount of uncertainty in data as compared to the acceptable level of uncertainty. Evaluation of the usability may be more complicated using data from secondary sources. Appropriate metadata is inevitable for these judgements. Aspects of data quality include geometric and thematic accuracy, (in)consistencies, resolution, precision, usability and others. Assurance of data quality may be improved by following proper standards and spatial data infrastructure regulations for data collection and management. System of basic data quality measures for geospatial domain in the EN ISO 19157:2013 standard.","name":"Data Quality, Metadata and Data Infrastructure","selfAssesment":"In progress (GI-N2K)
"},{"code":"GD6-1","description":"Geometric accuracy is a measure indicating how close the geometric values of the data are to the real world position of the mapped feature.","name":"Geometric accuracy","selfAssesment":"In progress (GI-N2K)
\r\n\r\nIn progress (GI-N2K)
"},{"code":"GD6-3","description":"The resolution of a data source indicates the smallest unit of detail provided by the data source.","name":"Resolution","selfAssesment":"In progress (GI-N2K)
"},{"code":"GD6-4","description":"The precision of a measurement system, related to reproducibility and repeatability, is the degree to which repeated measurements under unchanged conditions show the same results.","name":"Precision","selfAssesment":"GI-N2K
"},{"code":"GD6-5","description":"Primary data sources provide information collected directly for GIS use. Secondary sources are data sources that need to be processed before they are ready for GIS use.","name":"Primary and secondary sources","selfAssesment":"In progress (GI-N2K)
"},{"code":"GD8-1","description":"Tablet digitizing is the conversion from physical map to digital data by re-drawing the features on the map fixed on a digitizing tablet","name":"Tablet digitizing","selfAssesment":"In progress (GI-N2K)
\r\n\r\nIn progress (GI-N2K)
\r\n\r\nIn progress (GI-N2K)
"},{"code":"GS","description":"Geographic Information Science and Technology serve the society, but it is not a panacea. The history of its development is the sum of fragmented efforts, which have still not been fully integrated. Its potential benefits are often constrained and its potential impacts are not fully understood. Institutional and economic factors limit access to data, technology, and expertise by some of those who need it to make better decisions. Political, ideological, and personal issues aside, organizations invest in GIS&T when estimated benefits outweigh estimated costs. Evaluating costs and benefits is difficult, however and too often leads to nothing being done. For some individuals and groups, costs are prohibitive even though potential benefits are compelling. The legal framework provides a structure for regulating a number of key aspects of geographic information science, technology, and applications. Legal regimes determine who can claim the exclusive right to hold and use geospatial data, the conditions under which others may have access to the data, and what subsequent uses are permitted. Political struggles arise from conflicting proprietary and public interests about who benefits from geospatial information, and how the power to allocate the use of this information is, or should be, distributed among members of a society. The need to choose among conflicting interests sometimes poses ethical dilemmas for GIS&T professionals. The explosive growth of the geospatial information contributed by users through various application programming interfaces has made geospatial information is a powerful tool in the social media toola powerful media for the general public to communicate, but perhaps more importantly, geographic information have also become a tool media for constructive dialogs and interactions about social issues, recent growth of Web-based geospatial information and volunteered geographic information (VGI). Because so many public agencies and private organizations rely upon GIS&T for planning, decision making, and management, GIS&T increasingly affects and is used to direct daily life. Critical approaches to understanding the role of GIS in society equip practitioners to employ GIS&T reflectively. The critical approach specifically questions the assumptions and premises that underlie the economic, legal and political regimes and institutional structures within which GIS&T is implemented. Related concerns are considered in Knowledge Area OI: Organizational and Institutional Aspects.","name":"GI and Society","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS1-1","description":"The most basic definition of a legal regime is a system or framework of rules governing some physical territory or discrete realm of action that is at least in principle rooted in some sort of law. Often the concept has been applied to specific areas of law.","name":"The legal regime and legal framework","selfAssesment":"GI-N2K
"},{"code":"GS1-2","description":"Contract law is defined as a set of rules that govern the contractual agreements between merchants or persons. A contract is an agreement between different parties that state their responsibilities and duties to each other. A liability in contract law is when certain conditions are written into a contract that makes a party liable. Licensing is the process of giving or getting official permission to do something. A license is an agreement through which a licensee leases the rights to a legally protected piece of intellectual property from a licensor — the entity which owns or represents the property — for use in conjunction with a product or service.","name":"Contract law, liability and licensing","selfAssesment":"GI-N2K: relevant but to be revised
"},{"code":"GS1-3","description":"Data privacy and security are two essential components of a successful strategy for data protection. Data security refers to the protection of data from unauthorized access, use, change, disclosure, and destruction. It encompasses network security, physical security, and file security. Data privacy involves protecting consumer data by eliminating or reducing the possibility of re-identifying an individual whose information is present in the data. This is done by either removing specific information or by transforming the data with random “noise” or generalization.","name":"Privacy and Security","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS1-4","description":"Property is secured by laws that are clearly defined and enforced by the state. These laws define ownership and any associated benefits that come with holding the property. The term property is very expansive, though the legal protection for certain kinds of property varies between jurisdictions. Property is generally owned by individuals or a small group of people. The rights of property ownership can be extended by using patents and copyrights. Property rights give the owner or right holder the ability to do with the property what they choose. That includes holding on to it, selling or renting it out for profit, or transferring it to another party.","name":"Ownership and property rights","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS1-5","description":"In economics, competition is a condition where different economic firms seek to obtain a share of a limited good by varying the elements of the marketing mix: price, product, promotion and place. Competition law is a law that promotes or seeks to maintain market competition by regulating anti-competitive conduct by companies. Public-private sector relationships deal with a particular subset of competition, i.e. competition between public and private organizations.","name":"Competition and public-private sector relationships","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS1-6","description":"Open data is data that can be accessed, shared, used and reused without any barrier for any type of (re)user. According to the Open Definition, open data can be defined as data that be freely used, modified, and shared by anyone for any purpose subject, at most, to measures that preserve provenance and openness. Open data requires datasets to be either in the public domain, or distributed through an open license. The data must be provided as a whole, free of charge, and preferably downloadable via the Internet, including any additional information that might be necessary to comply with the open license’s terms. Openness requires the data to be provided in a readily machine-readable form. The format must be open as well, meaning that it does not place any restriction upon its use, and that the files in that format can be processed with open-source software tools. The Open Definition speaks broadly of open ‘works’, rather than of open data. Focusing on data tout court, one can move from the Open Government Data (OGD) principles. According to the OGD principles, which are arguably foundational in understanding the concept of open data, data must be: Complete; Primary; Timely; Accessible; Machine-processable; Non-discriminatory; Non-proprietary; and License-free. Compliance with the OGD principles needs to be demonstrable, i.e. there need to be accountability measures in place to allow the review of the adherence to the principles above. The concepts of Open Work and open data highlight how data needs to be both legally, technically and financially open, so either in the public domain or covered by an open license, and kept in a machine-readable and non-proprietary format. Open data aims at making information available to everybody, for any purpose, in a machine-readable and interoperable format, based on open standards and digestible by free/libre open source software (FLOSS). Also with respect to the financial accessibility open data is data available free of charge. Marginal costs of dissemination are accepted by some as a reasonable cost for users. However, open data is data that can be accessed and reused without any barrier for any type of reuse, and some user groups experience any price to be paid as a barrier.","name":"Open data","selfAssesment":"Completed
"},{"code":"GS1","description":"Legal problems can arise when geospatial information is used for land management, among other activities. Geospatial professionals may be liable for harm that results from flawed data or the misuse of data. Understanding of contract law and liability standards is essential to mitigate risks associated with the provision of geospatial information products and services. Legal relations between public and private organizations and individuals govern data access. The nature of information in general, and the characteristics of geospatial information in particular, make it an unusual and difficult subject for a legal regime that seeks to establish and enforce the type of exclusive control associated with other commodities. Geospatial information is in many ways unlike the kinds of works that intellectual property rights were intended to protect. Still, organizations can, and do, assert proprietary interests in geospatial information. Perspectives on geospatial information as property vary between the public and private sectors and between different countries.","name":"Legal aspects","selfAssesment":"In progress GI-N2K
"},{"code":"GS2-1","description":"Business models determine how organizations can create and deliver value, for example, through the provision or use of geographic data. A business model is a conceptual tool that contains\r\na set of interrelated elements that allow organizations to create and capture value and generate revenues. The development and implementation of an appropriate business model are considered to be a key to the success of the organization and a crucial source for value creation. \r\n\r\nAlthough business models determine how organizations create, deliver, and capture value, they should not be regarded as permanent and invariable structures or settings. Business models are shaped by both internal and external forces, and will only be successful if they are able to adapt to a changing environment. In the GI domain, several technological, regulatory, and societal developments have challenged the existing business models and opened up opportunities for new business models. Among these developments are the establishment of spatial data infrastructures (SDIs) worldwide, the democratization of geographic knowledge, and the move toward open source, open standards, and open data.\r\n\r\nSince the development and implementation of SDIs in different parts of the world, much attention has been paid to the need to find appropriate business models for GI, and in particular, for geographic data providers in the public sector. Traditional business models in which public data providers were selling their data to customers in the private industry and other public agencies were questioned, because they restricted the opportunity for data sharing. The concept of SDI is about moving to new business models, where partnerships between GI organizations are promoted to allow access to a much wider scope of geographic data and services. A key challenge in the development of these SDIs was the alignment of different existing business models of the actors in the GI domain. Moreover, the development and implementation of SDIs also led to the emergence of new business models, which was even more the case with the more recent move toward open geographic data.\r\n\r\nOrganizations can be active in different parts of the geo-information value chain, and can create and offer value in many different ways. As a result, many different GI business models exist. Data providers, data enablers, and data end users could be seen as three main categories of GI business models. Each of these categories consists of many different business models, as different value propositions\r\nwill exist, and value can be created and captured in several ways.","name":"GI Business models","selfAssesment":"Completed (GI-N2K)
"},{"code":"GS2-5","description":"To provide a better insight into the process of adding value to GI, several authors have introduced and applied the information value chain approach. A value chain can be defined as the set of value-adding activities that one or more organizations perform in creating and distributing goods and services. The value chain concept originally was developed for the manufacturing sector, as a tool to evaluate the competitive advantage of firms. More recently, the value chain concept has been applied to other sectors, including information technology where the good or service, and the benefits it provides, is less tangible in nature. A value chain involves the progress of goods from raw materials to finished products through a number of stages, during each of which a new value is added to the original input by various activities. The value chain concept was extended into the information market, with the information value chain referring to the set of activities adding value to information and turning raw data into new information products or services. Especially important in this context is the role of information and communication technologies (ICT), which have an impact on all activities in the information value chain, such as information collection, processing, dissemination, and use. In the context of GI, the value chain relates to the series of value- adding activities to transform raw geographic data into new products that are used by certain end users. Although there are slightly different descriptions of the various steps of the GI value chain, in general, the essential steps in the value chain are: acquisition of raw data, the application of a data model, quality control, and integration with other sources, presentation, and distribution. In recent years, particular attention has been paid to different steps between the process of distributing data and the actual end use of an end product of GI. In addition, after the publication of the data, value can be added to the data in many different ways. Value can be added by making data from different sources easily accessible through repositories and data portals, by building and selling tailored solutions using the data to end users or by using geographic data to improve existing products and services delivered to an end user. In certain cases, this end product will be the first step of a next value chain.","name":"Geo-information value chain","selfAssesment":"Completed
"},{"code":"GS2","description":"Most organizations insist that investments in GIS and T be justified in economic terms. Quantifying the value of information, and of information systems, however, is not a straightforward matter.","name":"Economic aspects","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS3-1","description":"The use of geospatial information allows public sector organizations and actors to make better decisions and provide better services to their citizens. Geospatial information is increasingly being used at different administrative levels and in different policy areas.","name":"Use of geospatial information in the public sector","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS3-2","description":"Geospatial information is increasingly being used by private companies for different purposes and the private sector plays an important role in the development and implementation of geospatial information infrastructures.","name":"Use of geospatial information in the private sector","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS3-3","description":"Research and education institutions use geospatial information for various purposes, in support of their research and educational activities.","name":"Use of geospatial information in research and education","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS3-4","description":"Effective monitoring of the environment and an improved understanding of the same requires valuable information and data that can be extracted through application of geospatial technologies. GIS can be used most effectively for environmental data analysis and planning. It allows better viewing and understanding physical features and the relationships that influence in a given critical environmental condition. GIS can help in effective planning and managing the environmental hazards and risks. In order to plan and monitor the environmental problems, the assessment of hazards and risks becomes the foundation for planning decisions and for mitigation activities. GIS supports activities in environmental assessment, monitoring, and mitigation and can also be used for generating environmental models. GIS can aid in hazard mitigation and future planning, air pollution & control, disaster management, forest fires management, managing natural resources, wastewater management, oil spills and its remedial actions etc.","name":"Use of geospatial information in environmental issues","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS3","description":"Geospatial Information used in Government agencies and public authorities at local, state, and federal levels produce and use geospatial data for many activities, including provision of social services, public safety, economic development, environmental management, and national defence. Public participation in governing, empowered by geospatial technologies, offers the potential to strengthen democratic societies by involving grassroots community organizations and by engaging local knowledge. The private sector covers a broad range of areas of opportunity. With continued advancements in technology, greater awareness of its advantages as a powerful decision support tool the use of geospatial information use in the private sector needs to be discussed.","name":"Use of geospatial information","selfAssesment":"In Progress GI-N2K
"},{"code":"GS4-1","description":"Public participation GIS (PPGIS) is a field within geographic information science that focuses on ways the public uses various forms of geospatial technologies to participate in public processes, such as mapping and decision making.","name":"Public participation GIS","selfAssesment":"GI-N2K (revision)
"},{"code":"GS4-2b","description":"Social Media Geographic Information (SMGI) can be defined as any piece or collection of multimedia data or information with explicit (i.e. coordinates) or implicit (i.e. place names or toponyms) geographic reference collected through the social networking web or mobile applications. Social data are acknowledged as a good of major value in the digital economy, and their potential for enhancing more traditional analytics is of the utmost importance. A big part of social data however also features spatial (and temporal) references, thus their integration with more traditional Authoritative Geographic Information (AGI) may enable a further step towards the next generation of geospatial intelligence. SMGI is a sub-category of VGI and can be active or passive, depending on the type of application with which it is collected: applications purposefully created and/or used to collect SMGI in participatory initiatives","name":"Social Media Geographic Information","selfAssesment":"Completed
"},{"code":"GS4-3b","description":"Volunteered geographic information (VGI) is a special kind of user-generated content. It refers to geographic information collected and shared voluntarily by the general public. Web.2.0 and associated advances in web mapping technologies have greatly enhanced the abilities to collect, share and interact with geographic information online, leading to VGI.","name":"Citizens and volunteered geographic information","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS4","description":"Today, geo data has become a conventional and pervasively familiar data type seen at once to underpin and significantly re-characterize the digital world, with broad implications for both technology and society. Geospatial data are abundant, but access to data varies with the nature of the data, the user groups wishes to acquire it and for what purpose, under what conditions, and at what price geodata can be obtained. The explosive growth of geographic information contributed by users through various application programming interfaces has made geographic information a powerful media for the general public, but perhaps more importantly, geospatial information have also become media for constructive dialogs and interactions about social issues, recent growth of Web-based Geographic information and volunteered geographic information (VGI).","name":"Geospatial citizenship","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS5-1b","description":"The advantages of geospatial technologies and resulting data present ethical dilemmas such as privacy and security concerns as well as the potential for stigma and discrimination resulting from being associated with particular locations. the use of geospatial technologies and the resulting data needs to be critically assessed through an ethical lens prior to implementation of programmes, analyses or partnerships. Using this lens requires not only explicit consideration of potential negative consequences of adoption but also clear articulation of the specific contexts and conditions under which benefits may be realized.","name":"Ethics in the geospatial information society","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS5-2b","description":"A code of ethics is a guide of principles designed to help professionals conduct business honestly and with integrity. A code of ethics document may outline the mission and values of the business or organization, how professionals are supposed to approach problems, the ethical principles based on the organization's core values, and the standards to which the professional is held. Codes of ethics for geospatial professionals are intended to provide these principles and guidelines for GIS professionals","name":"Codes of ethics for geospatial professionals","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS5","description":"Ethics provide frameworks that help individuals and organizations make decisions when confronted with choices that have moral implications. Most professional organizations develop codes of ethics to help their members do the right thing, preserve their good reputation in the community, and help their members develop as a community","name":"Ethical aspects","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS6-1","description":"US GIS&T BoK: As GIS became a firmly established presence in geography and catalysed the emergence of GIScience, it became the target of a series of critiques regarding modes of knowledge production that were perceived as problematic. The first wave of critiques charged GIS with resuscitating logical positivism and its erroneous treatment of social phenomena as indistinguishable from natural/physical phenomena. The second wave of critiques objected to GIS on the basis that it was a representational technology. In the third wave of critiques, rather than objecting to GIS simply because it represented, scholars engaged with the ways in which GIS represents natural and social phenomena, pointing to the masculinist and heteronormative modes of knowledge production that are bound up in some, but not all, uses and applications of geographic information technologies. In response to these critiques, GIScience scholars and theorists positioned GIS as a critically realist technology by virtue of its commitment to the contingency of representation and its non-universal claims to knowledge production in geography. Contemporary engagements of GIS epistemologies emphasize the epistemological flexibility of geospatial technologies.","name":"Epistemological and critical issues","selfAssesment":"In progress/to delete (GI-N2K)
"},{"code":"GS6-2","description":"Various types of critiques exist on the way geospatial information is being used and re-used.","name":"Critical approach on the use of geospatial information","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS6-3","description":"Defending or refuting the argument that the \"digital divide\" that characterizes access use of geospatial information perpetuates inequities among developed and developing nations, among socio-economic groups,and between individuals, community organizations, and public agencies and private firms.","name":"Critical aspects and invisible groups","selfAssesment":"In progress/to be delete (GI-N2K)
"},{"code":"GS6","description":"Many of the educational objectives used to define topics in this knowledge area, and in the Body of Knowledge as a whole, challenge educators and students to think critically about GI and Society. Since the 1990s, scholars have criticized cartography and the GIS science from a wide range of perspectives. Common among these critiques are questioned assumptions about the purported benefits of GI and Society and attention to its unexamined risks. By promoting reflective practice among current and aspiring geospatial information professionals, an understanding of the range of critical perspectives increases the likelihood that geospatial information will fulfil its potential to benefit all stakeholders. Philosophical, psychological, and social underpinnings of these critiques are considered in Knowledge Area CF: Conceptual Foundations.","name":"Critical approach","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS7-1","description":"US GIS&T BoK: As GIS became a firmly established presence in geography and catalysed the emergence of GIScience, it became the target of a series of critiques regarding modes of knowledge production that were perceived as problematic. The first wave of critiques charged GIS with resuscitating logical positivism and its erroneous treatment of social phenomena as indistinguishable from natural/physical phenomena. The second wave of critiques objected to GIS on the basis that it was a representational technology. In the third wave of critiques, rather than objecting to GIS simply because it represented, scholars engaged with the ways in which GIS represents natural and social phenomena, pointing to the masculinist and heteronormative modes of knowledge production that are bound up in some, but not all, uses and applications of geographic information technologies. In response to these critiques, GIScience scholars and theorists positioned GIS as a critically realist technology by virtue of its commitment to the contingency of representation and its non-universal claims to knowledge production in geography. Contemporary engagements of GIS epistemologies emphasize the epistemological flexibility of geospatial technologies.","name":"Epistemological critiques","selfAssesment":"GI-N2K
"},{"code":"GS7-3","description":"US GIS&T BoK: \r\n\r\nFeminist interactions with GIS started in the 1990s in the form of strong critiques against GIS inspired by feminist and postpositivist theories. Those critiques mainly highlighted a supposed epistemological dissonance between GIS and feminist scholarship. GIS was accused of being shaped by positivist and masculinist epistemologies, especially due to its emphasis on vision as the principal way of knowing. In addition, feminist critiques claimed that GIS was largely incompatible with positionality and reflexivity, two core concepts of feminist theory. Feminist critiques of GIS also discussed power issues embedded in GIS practices, including the predominance of men in the early days of the GIS industry and the development of GIS practices for the military and surveillance purposes.\r\n\r\nAt the beginning of the 21st century, feminist geographers reexamined those critiques and argued against an inherent epistemological incompatibility between GIS methods and feminist scholarship. They advocated for a reappropriation of GIS by feminist scholars in the form of critical feminist GIS practices. The critical GIS perspective promotes an unorthodox, reconstructed, and emancipatory set of GIS practices by critiquing dominant approaches of knowledge production, implementing GIS in critically informed progressive social research, and developing postpositivist techniques of GIS. Inspired by those debates, feminist scholars did reclaim GIS and effectively developed feminist GIS practices.","name":"Feminist critiques","selfAssesment":"GI-N2K
"},{"code":"GS7-4","description":"In the early 1990s social critiques of GIS from human geographers began to appear. These initial critiques set off an ensuing debate between GISers, defending GIS and human geographers, who critiqued GIS. This debate materialized in academic journals including: Political Geography Quarterly, Environment and Planning A, and Progress in Human Geography. Schuurman (2000) notes that the GIS debate, while unique to the discipline of Geography, was part of a larger debate in other disciplines about the effects of technology. This presentation will be limited (unfortunately) to two aspects of this debate. It will first discuss conditions within human geography that made GIS a target of human geographers' critique. Second, this paper will discuss the particular critiques that were directed at GIS by human geographers. Though the reaction of such critiques and their effect on GIS is an important topic there is not enough time and space to address these issues. See Schuurman (2000) \"Trouble in the Heartland: GIS and its critics in the 1990s\" in Progress in Human Geography for a thoughtful look at this debate and its effects on the discipline of GIS.","name":"Social critiques","selfAssesment":"GI-N2K
"},{"code":"IP","description":"Image processing and analysis comprises all relevant steps to reach from (raw) image data to [...] information via image interpretation and digital image classification. In traditional remote sensing workflows, this step follows the image acquisition process. There are two main components, i.e. (1) image processing, (2) analysis, which emphasizes the sequential nature of the process – while increasingly this dichotomy disappears.\r\nThe information production workflow aims at converting semantically rich, but unstructured image data into a set of classes, objects, arrangements, etc., to enable ultimately a complete image understanding and scene reconstruction. This scene reconstruction entails a mental component (“understanding”) and a technical one, by providing standardized classification results or even beyond, dedicated information products in form of digital maps and reports, tailored to the specific application domains and use cases, in order to make informed decisions. Such information products can be maps, reports, dashboards etc., overall it is the transformation from quantitative, semi-continuous digital numbers (“brightness”) to qualitative information using categories and figures, which can be stored and further used in a GIS environment. \r\nThe first part of the process entails image calibration, image correction (geometric, radiometric), data assimilation, and any type of enhancement (contrast manipulation, filtering, etc.) which aims to better condition the information extraction part. It ends where we achieve a significant milestone in the processing milestone, remarkably denoted as analysis-ready data (ARD). From there, we enter into the analysis realm, classically referred to as digital image classification, the process of assigning pixels to classes. In other words, the aggregation of pixel values according to their similarity into categorical (nominal) classes. The discrimination of these classes by and large depend on application domain, and ideally, these classes match with information classes. To address the issue of ambiguity and to overcome the so-called semantic gap in image interpretation by providing a stepping-stone in the information extraction process, the strategy of pre-classification (semi-concepts) has been introduced in the literature.\r\nToday, boundaries between pre-processing and classification increasingly vanish, through an increasing level of automation in the pre-processing and image correction steps. In addition, new ways of analysis emerge, in particular in large time series, including image data cubes. Instead of a processing chain, which suggests a linear – and potentially irreversible – cascade of manipulations, the automation of large parts of this part allows us to see the process more reversible and approachable from either side.","name":"Image processing and analysis","selfAssesment":"Completed
"},{"code":"IP1-1-1","description":"The image spatial subset allows to extract the group of pixels / grid cells using a defined polygon e.g. area of interest – AOI or defining the new image extent. It is used to limit spatially the image extent to which, for example an image function or classification model will be applied.","name":"Image subset","selfAssesment":"Completed
"},{"code":"IP1-1-2","description":"Layer stacking is a process for combining multiple images into a single image. The image stack is used to build a ‘new’ multiple band file from the georeferenced images of various pixel sizes, extents, projections. The image bands must be resampled and reprojected to a common spatial grid. The layer stacking is used for example to combine spectral bands from a Landsat, Sentinel-2 data and SRTM DEM into one multi-dimensional file. The process of layer stacking increases the size of the final stacked image, which may have consequences that increase the processing time of operations performed on the stacked image.","name":"Layer stack","selfAssesment":"Completed
"},{"code":"IP1-1","description":"Data manipulation adjusts a dataset to the needs of a specific application by subsetting the spatial extent or the number of bands or by organizing bands from separate single layer files into a single multi-layer file.","name":"Data manipulation","selfAssesment":"New
"},{"code":"IP1-2","description":"Fourier analysis - A characteristic of remotely sensed images is a parameter called spatial frequency, defined as the number of changes in brightness value per unit distance for any particular part of an image. There are low-frequency and high-frequency areas. Spatial frequency may be enhanced or subdued using Fourier Analysis (an alternative technique is spatial convolution filtering). Fourier analysis mathematically separates an image into its spatial frequency components. It is then possible interactively to emphasize certain groups (or bands) of frequencies relative to others and recombine the spatial frequencies to produce an enhanced image.\r\nThe signal received by a pulsed radar is a time sequence of pulses for which the amplitude and phase are measured. The frequency content of this time-domain signal is obtained by taking its Fourier transformation.","name":"Fourier transformation","selfAssesment":"New
"},{"code":"IP1-3-1-1","description":"Structure from motion (SfM) describes the photogrammetric process for estimating the 3D structure of a scene, whereby correspondences between multiple images are established and used to detect motion parallax. When a camera moves over a surface while taking successive overlapping images, the distances between features on the surface will change from one image to the next. The changes depend on the distance of the feature points to the camera, and thus the surface elevation. This motion parallax can be used to generate an accurate 3D representation of the surface. \r\nThe photogrammetric problem of SfM is similar to stereo vision, but has gained popularity with the advent of inexpensive cameras which have variable internal geometries, unlike metrically stabilized cameras traditionally used in airborne mapping. Even with less accurate or even missing GPS location and orientation metadata, SfM still allows for the creation of (hyper)local DEMs as long as the imagery contains sufficient overlap. Airborne or spaceborne platforms can be used, provided that 2D frame-based cameras are used which can be represented with a pinhole mathematical model. \r\nGenerating a digital elevation model (DEM) from SfM is typically handled automatically using specialized software. Firstly, image correspondences are detected. Feature points are identified in the individual images using local contrast feature detectors. The features extracted from all the images are matched with all the available overlapping images and erroneous matches are filtered out. The process typically results in hundreds or thousands of tie-points per image, which allows for robust matching even with large a priori uncertainties in camera orientation. A bundle adjustment, solving for the 3D coordinates of the feature points, the position and orientation of the camera and its internal characteristics then results in an initial, so-called sparse 3D point cloud. \r\nNext, ground control points (GCPs) can be introduced. These are surface features (naturally present or introduced into the scene) which can be identified at the pixel level in the images by users. Measured also in the field with an accuracy smaller than the pixel size, they can be used to constrain the bundle adjustment solution to improve georeferencing and camera calibration to an accuracy similar to that of the GCP measurement or the GSD size. \r\nSince this process yields a match only for a small subset of all pixels, an additional step, called dense image matching is added. It starts from the exact position and orientations resulting from the bundle adjustment to rectify the images and overlay two or more images, to compare them row by row and in 16 different directions in a process called semi-global matching (SGM). Matching pixels are identified along these lines, and 3D intersection distances photogrammetrically inferred. By combining results from different directions, a 3D coordinate for almost every pixel is obtained with similar accuracy. Finally, DEM products with a regularly spaced grid are generated and exported based on the dense point cloud. Depending on the point classes used in the export (obtained through topographic filtering or deep-learning-based classification of the dense point cloud), the outcome will be a digital surface model (DSM) or digital terrain model (DTM).","name":"DEM generation with 'Structure-from-Motion'","selfAssesment":"Completed
"},{"code":"IP1-3-1-2","description":"Photogrammetry is the science and technology of obtaining spatial measurements and other geometrically reliable derived products from photographs. Basic geometric principles applying both traditional analogue and modern digital procedures are related to the central projection of the image in case of typical cameras and to the dynamic projection mostly in case of push-broom sensors, popular in the satellite photogrammetry. The fundamental principle used by photogrammetry is called triangulation. By taking photographs from at least two different locations, so-called “lines of sight” can be developed from each camera to points in a block on the object. These lines of sight (called rays) are mathematically intersected to produce the 3-dimensional coordinates of the points of interest.\r\nWithin data processing the most important parts of photogrammetric workflow are: (1) image orientation, (2) model reconstruction, and (3) orthorectification. Image orientation is based mostly on aerial triangulation, however recently the computer vision algorithm, called structure from motion, became more popular in particularly in close range photogrammetry. Both orientation approaches include detection or measurement of the points between overlapping images in a block, control points measurements in a field defining orientation in reference system and check points verifying the orientation process. The satellite photogrammetry due to different projection and much bigger areas of imaging is usually related to Rational Polynomial Coefficients (RPCs) defining preliminary scene orientation during image orientation. However, to receive more accurate results also here the control points measured in a field are in use. The second part of the modern photogrammetric processing is 3D model reconstruction. In past, vectorization within the stereoscopic measurements was the most popular way of using photogrammetric data after the image orientation. The development of the informatics contributed to the development of the image matching algorithms that can provide dense image point clouds, which can be used to the 3D detailed modelling including digital elevation model production. The final step of photogrammetric processing is orthorectification, which delivers cartometric image called orthophoto mosaiced into orthophotomaps. This process comprises the influence of digital terrain model, model of camera (interior orientation) and image orientation (exterior orientation). Orthophotomap and elevation models derived from photogrammetric processing are applied as very popular data source in many GIS systems. The other photogrammetric outcomes are, for example a 3D measurement or 3D models of some real-world object or scene.","name":"Photogrammetric principles","selfAssesment":"Completed
"},{"code":"IP1-3-1-3","description":"In satellite photogrammetry to obtain the orientation mostly of satellite scene Rational Polynomial Coefficients (RPCs) are applied. They provide a compact representation of a ground-to-image geometry, that allow for photogrammetric processing without requiring a physical camera model. Model with RPC is provided with satellite image and can be improved using measurements of indirect surveying methods used for control point measurement. The RPC model for the coordinates of the image point is calculated as ratios of the cubic polynomials in the coordinates of the world or object space or ground point. \r\nIn photogrammetry and remote sensing, rational polynomial coefficients (RPCs) describe a specific imaging geometry model for transforming image pixel coordinates to map coordinates (thereby accounting for terrain displacement errors). A sensor model describes the geometric relationship between the object space and the image space, or vice versa. It relates 3-D object coordinates to 2-D image coordinates. RPCs are part of a general sensor model that approximates the physical sensor model. The physical sensor model represents the physical imageing process, making use of information on the sensor's position and orientation (during image acquisition). The RPC model often refers to a specific case of the RFM (rational function model) that is in forward form, has third-order polynomials, and is usually solved by the terrain-independent scenario.","name":"RPC correction","selfAssesment":"Completed
"},{"code":"IP1-3-1-4","description":"A ground control point (GCP) is a location of the surface of the Earth (e.g. a road intersection) that can be identified on the imagery and located accurately on the map (i.e. the reference dataset). Two distinct sets of coordinates are associated with the GCP: image coordinates in i rows and j columns, and map coordinates (e.g. x, y measured in degrees of latitude and longitude or as specified by the spatial reference system).","name":"Ground Control Points (GCP)","selfAssesment":"Planned
"},{"code":"IP1-3-1","description":"Orthorectification is the process of removing sensor (scanner or camera), satellite/aircraft, and terrain-related distortions for creating a planimetrically correct image. \r\nTo obtain an accurately orthorectified image, the following information is required: (1) accurate elevation model, and (2) a camera model or rational polynomial coefficients (RPCs) that depicts the positional relationship of the collected image to the ground. Many companies deliver their images together with RPCs and existing software implementations can automatically read these files and apply the RPC transformation on the fly. An accurate elevation model is important to remove the influence of topography (e.g. hills, valley, etc.) on the raw image so that users can accurately compute distances, areas, and directions. Without performing orthorectification, the features in the image are tilted (especially the features located away from the center of the camera). Many satellite data products (e.g. Sentinel images, Landsat data products) are orthorectified using Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) data which is a freely available data product and has a spatial resolution of e.g. 1 arc-second (30 m). In the case of extremely jagged surface topography, i.e. areas of high relief, a DEM with a higher spatial resolution is required. \r\nTwo main models can be used in the orthorectification process: black-box and the physical-based model. The black-box model (called also the analytical model) is commonly implemented in different software because it relies solely on the RPC files. This model does not require access to any proprietary information of the sensor used to collect the image. \r\nThe physical-based models are more complex (and hence expected to be more accurate) because they account for various factors that might influence the quality of the acquired image: e.g. position of the satellite when collecting the images, atmospheric effects, etc. An example of a physical-based model is the so-called camera model. This model requires access to proprietary sensor information that has to be provided by the image owner.","name":"Orthorectification","selfAssesment":"Completed
"},{"code":"IP1-3-2-1","description":"Image co-registration [aka Image-to-image registration] is the translation and rotation alignment process by which two images of like geometry and of the same geographic area are positioned coincident with respect to one another so that corresponding elements of the same ground area appear in the same place on the registered images (Jensen 2005 referencing Chen and Lee 1992).","name":"Image co-registration","selfAssesment":"New
"},{"code":"IP1-3-2","description":"Spatial referencing (referred to as geo-referencing as well) is the process of aligning available EO or GIS data to a coordinate system so that further spatial analysis and image analysis tasks can be applied using these data as input. \r\nTo be able to perform spatial referencing, users have to generate the so called Ground Control Points (GCPs) with known coordinates. In case of images, the easiest features that could be used as GCPs are the intersections, isolated trees etc.","name":"Spatial referencing","selfAssesment":"Planned
"},{"code":"IP1-3","description":"Geometric correction is concerned with placing the reflected, emitted, or back-scattered measurements or derivative products in their proper planimetric (map) location so they can be associated with other spatial information. It is usually necessary to preprocess the remotely sensed data and remove the geometric distortions so that individual picture elements (pixels) are in their proper planimetric (x, y) map locations. This allows remote sensing-derived information to be related to other thematic information in geographic information systems (GIS) or spatial decision support systems (SDSS). Geometrically corrected imagery can be used to extract accurate distance, polygon area, and direction (bearing) information.\r\n\r\nGeometric correction techniques are dedicated to resolving the geometric distortions caused by: (1) variations in sensor position; (2) Earth curvature; (3) rotation of Earth on its axis; (4) relief displacement. \r\n\r\nThere are two types of geometric distortions, namely systematic and random distortions. The former might be caused by Earth's rotation for example and, therefore they are predictable and systematic. The second type of distortions might be caused by terrain or variations in sensor altitude. \r\nGeometric correction includes georeferencing and orthorectification techniques.","name":"Geometric correction","selfAssesment":"Completed
"},{"code":"IP1-4-1","description":"Contrast stretching (also referred to as contrast enhancement) expands the original input brightness values to make use of the total dynamic range or sensitivity of the output device (a computer display).","name":"Contrast stretching","selfAssesment":"New
"},{"code":"IP1-4-2","description":"The histogram is a useful graphic representation of the information content of a remotely sensed image. Histograms for each band of imagery are often displayed and analysed in many remote sensing investigations because they provide the analyst with an appreciation of the quality of the original data (e.g. whether it is low in contrast, high in contrast or multimodal in nature. [...] Tabulating the frequency of occurrence of each brightness value within the image provides statistical information that can be displayed graphically in a histogram.","name":"Histogram","selfAssesment":"New
"},{"code":"IP1-4","description":"Image enhancement algorithms are applied to remotely sensed data to improve the appearance of an image for human visual analysis or occasionally for subsequent machine analysis. The quality of results of image analysis are subjectively judged by humans as to whether they are useful. They include contrast enhancement.","name":"Image enhancement","selfAssesment":"New
"},{"code":"IP1-6","description":"Principal component analysis (PCA) has proven to be of value in the analysis of multispectral and hyperspectral remotely sensed data. PCA is a technique that transforms the original correlated spectral dataset into a substantially smaller and easier set of uncorrelated variables that represents most of the information present in the original dataset. The first component accounts for the maximum proportion of the variance of the original dataset, and subsequent orthogonal components account for the maximum proportion of the remaining variance.","name":"Principal component analysis (PCA)","selfAssesment":"New
"},{"code":"IP1-7-1-1","description":"Bottom-of-Atmosphere (BOA) reflectance is also called surface reflectance and consists of the solar radiation that is reflected from the Earth's surface.","name":"Bottom-of-Atmosphere (BOA)","selfAssesment":"New
"},{"code":"IP1-7-1-4","description":"Top-Of-Atmosphere (TOA) radiance represents the radiance observed outside Earth’s atmosphere. It is derived from the Digital Numbers (DN) using metadata delivered with the image.","name":"Top-Of-Atmosphere (TOA)","selfAssesment":"New
"},{"code":"IP1-7-1","description":"Atmospheric correction accounts for the attenuation caused by scattering and absorption in the atmosphere. It transforms top-of-atmosphere (TOA) reflectance to bottom-of-atmosphere (BOA) reflectance.\r\nThe decision to perform atmospheric correction depends on the need, i.e. the envisioned usage of the derived EO information product and the nature of the underlying problem. This includes requirements to the accuracy of extracted biophysical information. Additionally, the decision and choice of methods depends on the type of remote sensing data available, the amount of in-situ historical and/or concurrent atmospheric information available.\r\nAn atmospheric correction is essential when biophysical or geophysical parameters (e.g. of water or vegetation) are going to be extracted from the remote sensing data. If the data is not corrected, the subtle differences in reflectance among the contributing image bands may be lost. This is especially relevant when biophysical information shall be compared to that of images from other dates.\r\nHowever, some cases exist where it is unnecessary to perform atmospheric correction. For example, it is not necessary for producing an image classification product from a single date of remotely sensed data. If a maximum likelihood classification is applied that uses training data with the same relative scale for the pixel values, then, atmospheric correction has little effect on the classification accuracy. The same holds true for a post-classification change detection where the classifications of the two different dates were performed independently. \r\nThe process of (absolute) atmospheric correction requires a model atmosphere and in situ atmospheric measurements acquired at the time of remote sensor data acquisition as input. In situ data can be available from other sensors on-board the sensor platform.\r\n\r\nDark Object Subtraction (DOS) is one of the most popular empirical atmospheric correction techniques. This technique assumes that a black object has a reflectance value of zero. Yet, a dark object present in a satellite image will have a value different than zero because of the atmospheric scattering. This value is then subtracted from all pixels in a given spectral band.","name":"Atmospheric correction","selfAssesment":"Completed
"},{"code":"IP1-7-2-1","description":"A method for dimensionality reduction in hyperspectral data is Minimum Noise Fraction (MNF). The purpose is to minimize the noise in the imagery, i.e. to identify noise and segregate it from true information, and to colaps the useful information into a much smaller set of MNF images. The MNF transformation applies two cascaded principal components analyses.","name":"Minimum noise fraction (MNF)","selfAssesment":"New
"},{"code":"IP1-7-2","description":"The number of spectral bands assocuates with a remote sensing system is referred to as its data dimensionality. Hyperspectral remote sensing systems such as AVIRIS ans MODIS obtain data in 224 and 36 bands, respectively. The greater the number of bands in a dataset (i.e., its dimensionality), the more pixels that must be stored and processed by the digital image processing system. Storage and processing consume valuable resources. It is necessary to reduce the dimensionality of hyperspectral data while retaining the information content inherent in the image. On method to reduce dimensionality of hyperspectral data and minimizing the noise in the imagery is the minimum noise fraction (MNF) transformation (Green et al., 1988).","name":"Dimensionality reduction","selfAssesment":"New
"},{"code":"IP1-7-3","description":"Sensor calibration converts the sensor’s digital numbers (DNs) to at-sensor radiance above the atmosphere. A further radiometric adjustment accounts for the viewing angle and sun angle during acquisition to transform radiance values to top-of-atmosphere (TOA) reflectance. Therefore, the process requires sensor calibration information and telemetry data that satellite image providers deliver within the metadata.\r\nDNs are raw sensor data without physical units. The sensor calibration information for converting the DNs to radiance are the calibration gain (cal_gain) and calibration offset (cal_offset) values. The sensor calibration uses linear function f(DN) = DN * cal_gain + cal_offset that multiplies the DNs of each pixel in each spectral band with their corresponding cal_gain and adds the corresponding cal_offset. The resulting at-sensor radiance image is the basis for the radiometric adjustment that uses information about the viewing angle and sun angle during acquisition to transform at-sensor radiance to TOA reflectance. \r\nSensor calibration obtains TOA reflectance and is a minimum requirement for performing band math calculations to derive spectral indices such as the normalized vegetation difference index (NDVI). Uncalibrated image data would arrive at NDVI values that are distorted because the cal_gain and cal_offset parameters for the involved spectral bands were not considered.","name":"Sensor calibration","selfAssesment":"Completed
"},{"code":"IP1-7-4","description":"As an optical remote sensing system is not perfect, noise can enter the data collection system at several points. Necessary corrections include the removal of shot noise (random bad pixels), correcting line or column drop-outs, accounting for line-start problems and radiometric correction of n-line striping caused by detector miscalibration.\r\nSAR data have global, random speckle noise. Speckle filters are designed to adapt to local image variations in order to smooth values, thus reducing speckle and enhancing lines and edges to maintain the sharpness of an image. A widely used way to reduce speckle is to apply spatial filters to the images. Typical approaches for speckle filtering include Laplace filtering for smoothing and sigma filters that preserve more of the signal with a lesser effect of smoothing.","name":"Noise reduction","selfAssesment":"New
"},{"code":"IP1-7-5","description":"Topographic correction, or topographic effects correction, aims to adjust the spectral values of an image according to effects of solar illumination differences due to the irregular shape of the terrain. Topographic slope and aspect introduce radiometric distortion of the recorded signal. Further, terrain shadow dramatically affects the brightness values of the covered pixels in an image. Topographic effects of illumination and shadow are particularly relevant in mountainous regions and in regions towards the higher latitudes of the southern and northern hemisphere. The effects appear pronounced during the winter season. \r\nTogether with sensor calibration and atmospheric correction, topographic correction is part of the radiometric correction process to obtain true reflectance values from sensor radiance. This process is necessary when using EO data for obtaining geophysical measurements. It can also benefit the accuracy of image classifications by reducing the internal variability of vegetation types, since the corrected reflectance relates better to the geometrical or biological properties of the plant than to the original reflectance.\r\nMethods for the removal of topographic effects from remotely sensed images can simply be based on band ratios that do not require additional input. Alternatively, they use digital elevation models (DEMs) as an additional input and apply sophisticated modelling of the illumination conditions. The illumination model describes various aspects of the relationship between the sensor measurement, the sun illumination, the ground reflectance and the diffuse irradiance at the surface. The model incorporates the angles between the sun position, the ground position (described by slope and aspect from the DEM), and the sensor position. Among these methods are lambertian methods and non-lambertian methods such as the bidirectional reflectance distribution function (BRDF). The BRDF, which is more suitable to the non-Lambertian properties of the observed surfaces, describes how the reflectance varies in each cover considering the angles of incidence and observation. \r\nIf achieved with a high quality, the resulting topographically corrected image appears to be illuminated evenly as if all its pixels would be part of a flat surface without the presence of any terrain differences. However, the much larger benefit than the improved appearance is the availability of pixel values that are closest to the true reflectance when compared to TOA, BOA and DN values.","name":"Topographic correction","selfAssesment":"Completed
"},{"code":"IP1-7","description":"Radiometric calibration and correction converts the sensor’s digital numbers (DNs) to radiance values and subsequently reflectance values. Additionally, the term “correction” points to the fact that radiometric measurements with satellite sensors contain error. Therefore, radiometric correction is concerned with improving the accuracy of surface spectral reflectance, emittance, or back-scattered measurements obtained using a remote sensing system. The Earth’s atmosphere, land and water are complex and can never be captured perfectly because of the limitations of remote sensing devices that lie in their spatial, spectral temporal and radiometric resolution. Therefore, error occurs in the data acquisition process and degrades the quality of remotely sensed data. The most common errors in remote sensing are radiometric and geometric. This concept is focused on the correction of remote sensing data to account for radiometric error that is to some degree systematic. Systematic errors in radiometric measurements come from the interaction of the sensed radiance with the atmosphere, the acquisition geometry in relation to the radiance source (the sun) and the Earth surface geometry (terrain).\r\nThere are several levels of radiometric calibration and correction. The first is sensor calibration that converts the DNs to top-of-atmosphere (TOA) reflectance. It converts to radiance values and further to reflectance values by accounting for the viewing angle and sun angle during acquisition. The second is atmospheric correction that converts TOA reflectance to bottom-of-atmosphere (BOA) reflectance. The third is topographic correction that converts BOA reflectance to surface reflectance. \r\nRadiometric calibration is necessary to ensure radiometric comparability of the measurements. There is a need for calibration when comparing different spectral bands within one image, e.g. for the calculation of geo-biophysical parameters with band math operations. Results from uncalibrated image data would differ from results achieved with calibrated data because the unaccounted cal_gain and cal_offset of the used spectral bands would lead to distortions. \r\nIn addition, radiometric calibration complements the geospatial comparability that is achieved with geo-referencing an image to geographic coordinates. Geo-referencing enables comparison of an image pixel to the geospatially matching pixel in another image acquired with a different sensor but with comparable resolution. Radiometric calibration enables a radiometric comparison between these two pixels’ radiance values. In case the two images are from different acquisition dates, a calculated radiometric difference would indicate change. This example shows the relevance of radiometric calibration for inter-sensor comparisons.\r\nRadiometric comparability is particularly relevant in studies that require inter-sensor comparisons, comparisons of surface features over time, or comparisons to laboratory or field reflectance data. Then the radiometric correction should cover atmospheric, solar and topographic effects. A full radiometric correction that also includes topographic correction can benefit the accuracy of image classifications by reducing the internal variability of vegetation types, since the corrected reflectance relates better to the geometrical or biological properties of the plant than to the original reflectance.","name":"Radiometric calibration and correction","selfAssesment":"Completed
"},{"code":"IP1","description":"Image pre-processing focuses on transforming the electrical signal measured by a sensor to a processing level at which pixel values can be used for the next information extraction step. Therefore, pre-processing operations involve the removal of errors encountered while collecting remotely sensed data to get as close as possible to the true radiant energy and spatial characteristics of the study area at the time of data collection. Different sensor type (optical, radar, lidar) require different processing levels\r\nThe most common image pre-processing procedures include: \r\n(1)\tRadiometric calibration involves the transformation of Digital Numbers (DN) to physical unit: radiance/reflectance. Radiometric calibration can be done before the launch of a satellite sensor, i.e. pre-launch calibration, or after launch. In the second case, the calibration is performed on-board or by comparing ground measurements with satellite radiance. Through radiometric calibration various scene illumination procedures such as sun elevation correction or earth-sun distance correction are applied. Furthermore, image noises caused by striping or line drop as happened in case of Landsat TM7 due to failure of the Scan Line Corrector (SLC) are also corrected using specialized procedures.\r\n(2)\tAtmospheric correction accounts for two main processes: scattering and absorption. Scattering represents a disturbance of the electromagnetic waves caused by rayleight scattering (caused by very small particles such as the air molecules), mie scattering (caused by aerosol particles) and non-selective scattering (dust, smoke, rain etc.). Absorption occurs when the electromagnetic energy is absorbed by the atmospheric components. Therefore, atmospheric windows have to be removed before using the satellite images in the next processing steps. Atmospheric corrections can be carried out either using simple statistical methods or complex radiative transfer based methods\r\n(3)\tGeometric correction is required to remove the distortions caused by the Earth curvature, Earth rotation, panoramic distortion due to the field of view of the sensor and the topography of the terrain. Geometrics distortions are corrected using Ground Control Points (GCP) and a Digital Elevation Model (DEM). In case of airborne images, additional distortions caused by variations in the platform altitude or velocity might occur.","name":"Image pre-processing","selfAssesment":"Completed
"},{"code":"IP2-1-1","description":"Data augmentation refers to a scheme of augmenting the observed data so as to make it more easy to analyze. An application from deep lerarning is to increase the number of input training sample images with augmented data. Examples of data augmentation techniques include horizontal flips, random crops, and principal component analysis.","name":"Data augmentation","selfAssesment":"New
"},{"code":"IP2-1-2","description":"Data imputation refers to a scheme of replacing missing values by imputed values. Imputation can be done, for example with mean, median and mode. Imputation methods can efficiently predict multiple response variables simultaneously.","name":"Data imputation","selfAssesment":"New
"},{"code":"IP2-1-3-1","description":"Gram-Schmidt is a pan-sharpening method that has been invented by Laben and Brover in 1998 and patented by Eastman Kodak. It makes use of the Gram-Schmidt orthogonalization to decorrelate the spectral bands (panchromatic, red, green, blue, etc.) and transform them into one multidimensional vector.","name":"Gram-Schmidt pan-sharpening","selfAssesment":"New
"},{"code":"IP2-1-3-2","description":"This pan-sharpening method uses PCA to transfer detailed spatial information from panchromatic band to the available multispectral bands.","name":"Principal Component Analysis (PCA)-based pan-sharpening","selfAssesment":"New
"},{"code":"IP2-1-3","description":"Pan-sharpening methods are used to enhance spatial resolution of images by merging a panchromatic image with high resolution with a multispectral image with low resolution.","name":"Pan-sharpening","selfAssesment":"New
"},{"code":"IP2-1-4","description":"Spatiotemporal image fusion methods, called also spatiotemporal downscaling methods, represent an efficient solution to generate fine-scale images at a high temporal resolution for more detailed land cover mapping and monitoring applications. Spatiotemporal image fusion methods can be classified into three categories: (1) reconstruction-based , (2) unmixing based and (3) learning-based methods.","name":"Spatio-temporal image fusion","selfAssesment":"New
"},{"code":"IP2-1","description":"Image fusion is defined as the “combination of two or more different images to form a new image by using a certain algorithm” Data fusion is a well-established research field. Image fusion methods are primarily used for improving the level of interpretability of the input data. Additionally, they can be utilized to address the problem of missing data caused by cloud or shadow contamination in satellite images time series. Image fusion can be performed at pixel-level, feature-level (e.g. land-cover classes of interest), and decision-level (e.g. purpose driven).","name":"Data fusion","selfAssesment":"Planned
"},{"code":"IP2-2","description":"Data harmonization aims to transform different datasets in such a way that they fit together, both with respect to geometry and semantics. The goal is that a user, who is using data from different authorities, shall have a unified view, where conflicts in the datasets have been removed.","name":"Data harmonisation","selfAssesment":"New
"},{"code":"IP2-3","description":"Data integration is the process of combining different geographic datasets including those derived from remote sensing data. The combined datasets can have different coverage, but they have to have the same geographic coordinates.","name":"Data integration","selfAssesment":"Planned
"},{"code":"IP2","description":"Data assimilation is a strategy to foster data integration and data harmonisation in a bi-directional way between the measured and the modelled reality. In other words, it aims to combine measurements (observations) with the understanding of the spatio-temporal properties and evolution of system’s variables or properties and model information about them. Models can be calibrated and keeping them ‘on track’ by constraining them with observations. Vice versa, observations can be validated through models. Approached as a mathematical problem, data assimilation aims at minimizing cost functions or penalize a function to ensure optimality in fitting. Equations are used to describe system parameters and the relationships among them, It is noteworthy, that models encompass information from previous measurements, experiences, and theory. While the observations are influenced by (known) properties such as precisions, etc. of the measurement devices, the robustness of models rely on the consolidated knowledge. Because uncertainties reside in all components with unknown or even undeterminable errors, the approach is usually probabilistic, including Bayesian and other related techniques. Widely used in meteorological sciences, successful data assimilation has been boosted the reliability of weather forecast , while sensitivity to errors remains. \r\nIn Earth observation, data assimilation compensates for the fact that a specific site could be observed in a variety of measurements by satellites with different sensor types, at different dates, different angular geometries and viewing directions, illumination conditions (solar time), observation frequencies, etc. In particular, for monitoring processes, measurements over time need to assure to actually measure the status of the system or object and not the divergence in observation. To overcome these divergences and converge them with the actual properties of an observed object or target class such as spectral or geospatial properties, observation modelling can be considered an important contribution from geospatial theory. this also links to class modelling or geon modelling. The synergy of a vegetation growth model and a remote sensing observation model can be exploited to improve the retrieval of geo-biophysical information. For vegetation and crop type monitoring radiative transfer modelling (RTF) is being used as an example. \r\nData assimilation can also serve in bridging the gaps between non-availabilities of EO data and other observations, to provide estimates or prediction for geographical variables, testing of hypotheses or continuous observation (monitoring). A related aspect is data imputation, i.e. filling gaps in observations e.g. by other, complementary data sets (e.g. Radar imagery in the absence of VHR data in cloudy weather conditions). Recently, these sources can also be complemented by crowd mapping and citizen science. \r\nWhen interpretation of data comes into play, such as image classification, we introduce another level of uncertainty. Thus the community seeks for rigorus classifiers based on solid spectral models, acting across sensors. Semantic enrichment of satellite data is a related strategy for reaching to interpreted data in a rigorous way. \r\nSummarizing, data assimilation comprises steps to improve the level of interpretability of the input data, by enrichment (get rid of spatial/temporal gaps), by accounting for heterogeneity (through harmonization), and by integration (combination with other data that is relevant to the application). Thereby, datasets become more comparable to each other.","name":"Data assimilation","selfAssesment":"Completed
"},{"code":"IP3-1-1-1","description":"Vegetation fraction (VF) is defined “as the percentage of vegetation occupying a pixel as viewed in vertical projection. It’s a comprehensive quantitative index in forest management and vegetation community cover conditions, and it’s also an important parameter in many remote sensing ecological models.”","name":"Vegetation fraction","selfAssesment":"Planned
"},{"code":"IP3-1-1-2","description":"Leaf area index (LAI) is the ratio between the total area of the upper leaf surface of vegetation and the surface area of the pixel in question. LAI is a dimensionless value, typically ranging between 0 (for a pixel composed of bare soil) and values as high as 6 (for a dense forest).","name":"LAI (Leaf Area Index)","selfAssesment":"Planned
"},{"code":"IP3-1-1-3","description":"Net primary production (NPP) is a measure of the inherent productivity of a region or ecological system—mainly the Earth’s production of organic matter, principally through the process of photosynthesis in plants.","name":"Net primary production (NPP)","selfAssesment":"New
"},{"code":"IP3-1-1-4","description":"Water quality variables can be derived from Earth observation (EO) data to provide essential ocean variables. They include Sea-surface temperature (SST), Sea-surface salinity (SSS) and Air-Sea Fluxes. SST controls the atmospheric response to the ocean at both weather and climate time scales. The spatial patterns of SST reveal the structure of the underlying ocean dynamics, such as, ocean fronts, eddies, coastal upwelling and exchanges between the coastal shelf and open ocean. SSS observations contribute to monitoring the global water cycle (evaporation, precipitation and glacier and river runoff). Water quality variables can be derived from EO data by using ocean colour products from optical sensors and relating them to ground truth information from in situ sensor networks.","name":"Water quality variables","selfAssesment":"New
"},{"code":"IP3-1-1","description":"Biophysical parameter retrieval is an approach in remote sensing that aims to estimate parameters which have physical meaning related to properties of living organisms. The goal is to provide quantitative results directly relating to the biophysical state, but independent of acquisition conditions and technology. Assessment of vegetation status is a key motivation for this, because through plant respiration and photosynthesis, vegetation is critical for modelling terrestrial ecosystems and energy cycles in environmental studies. \r\nImportant parameters describing canopy structure include leaf area index (LAI), green cover fraction (fCover), fraction of absorbed photosynthetically active radiation (fAPAR), plant height, biomass and leaf angle distribution. At leaf biochemical level, leaf chlorophyll/water, fuel moisture and leaf pigmentation content are used.\r\nVisual inspection can provide a first assessment of plant status. For detailed measurements of biophysical parameters, mostly destructive methods have been used. Chemical measurement techniques on leaf samples can measure pigment concentrations very accurately, but are time consuming and only use very limited samples. \r\nMuch more extensive data can be collected using earth observation imagery. These range from large scale spaceborne observations with high frequency at coarse resolution to dedicated UAV flights which can offer spectral information of individual plants. Radar and LiDAR acquisitions, which are insensitive to weather conditions, now complement optical observations. \r\nMethods to retrieve the parameters from remote sensing data fall into two main categories. Statistical models empirically match data to a biophysical variable. Univariate techniques use a single quantity derived from the data, usually a vegetation index whereas multivariate techniques link a combination of measurements at different wavelengths to one or more biophysical parameters.\r\nPhysically-based modeling is an alternative approach which uses advanced radiative transfer models to describe the transfer and interaction of radiation inside a leaf or canopy based on robust physical, chemical, and biological processes. They compute the interaction between solar radiation and plants and provide as such a better understanding between biophysical variables and reflectance characteristics. Good examples are Leaf optical models such as PROSPECT and LIBERTY which simulate leaf optical properties by absorption and scattering coefficients. Canopy reflectance models simulate canopy reflectance as a function of a complex description of plant structural and radiometric attributes to develop a quantitative understanding of remote sensing information.","name":"Biophysical and geophysical parameters","selfAssesment":"Completed
"},{"code":"IP3-1-2-1","description":"This spectral index is calculated using the following formula: SAVI = [(NIR-Red)/(NIR+Red+L)]/(1+L), where L can be, for example, 1 in area with no vegetation or 0 in area with dense veegtaion. It is used to minimize the influence of the soil brightness from the vegetation indices that are based on red and near-infrared wavelengths.","name":"Soil-adjusted Vegetation Index (SAVI)","selfAssesment":"New
"},{"code":"IP3-1-2-2","description":"This spectral index is calculate using the following formula NDSI = (green-SWIR)/(green+SWIR). It is the most popular index used to identify snow cover due to the fact that snow reflects visible wavelength stronger than middle-infrared wavelengths.","name":"Normalized Difference Snow index (NDSI)","selfAssesment":"New
"},{"code":"IP3-1-2-3","description":"Leaves, when healthy and vigour show a characteristic green colour. This visual effect evident to humans is caused by the co-existence of two evolutionarily facts: the specific interaction of the chlorophyll pigment in living leaves to the visible spectrum (VIS, 400-700 nm wavelength) of light emitted by the sun and the sensitivity of our human eye to the same sub-spectrum. According to fundamental physical laws of radiation (Stefan Boltzmann law of blackbody radiation and Wien’s displacement law), the VIS sub-spectrum corresponds to the radiation maximum of the sun, a hot blackbody with a surface heat of about 6000 K. Living leaves are structured in specific layers exhibiting characteristic interaction with light. The chloroplasts located in the so-called palisade layer, make use of the blue and the red part of sunlight for photosynthesis, the unique process of transforming light to create energy (carbohydrates) from water and carbon dioxide. This leads to the specific behaviour of leaves to absorb large portions (up to 90%) of the blue and red part of the electromagnetic spectrum and reflect nearly 100% of the green light. The peak reflectance in green light makes leaves (and plants in general) appear in green colour in our visual perception. \r\nA second, by no means less characteristic, feature of leaves is the specific response to near infrared (NIR, at around 700 nm wavelength) light in the mesophyll tissue (transmittance, scattering and reflectance). Only a small fraction of NIR is being absorbed. \r\nThis combination of two specific spectral characteristics, the absorption in VIS (red colour) by chlorophyll a in palisade layers, and the reflectance of NIR in the spongy tissue, makes the spectral profiles of plants and vegetation exhibiting a very characteristic shape, the so-called red edge. This absorption edge between red and NIR light is sharper for higher intensity green reflectance and brighter green tones (such as grassland or bright deciduous forest) than for less intensive reflectance and darker tones (coniferous forest). \r\nThe red edge may shift for the same vegetation type due to plant maturity or plant stress. This effect we call the red shift. The red shift is sensitive to crop maturity (headed stage) and may indicate harvesting time. Notably, there is also a blue shift, indicating green plants’ exposure to geochemical stress, which causes the absorption spectra to shift towards shorter wavelengths. \r\nPlants usually do not appear in isolation but form a canopy with a certain degree of coverage (e.g., crown closure in forests), and a certain part of understorey or soil per area unit. The resulting canopy reflectance is therefore a spectral mix of soil and vegetation (or even different types of vegetation) and generally lower than the reflectance of a pure vegetation sample under lab conditions. \r\nTo capture most of these plant-typical spectral characteristics, the so-called normalised difference vegetation index (NDVI) was developed. NDVI is an arithmetic band combination of red and NIR bands in a normalised value range. \r\nThe NDVI is calculated as:\r\nNDVI=((NIR-R))/((NIR+R))\r\nThe (hypothetic) value range of the NDVI is [-1 | +1]. Under real-world conditions, the NDVI ranges from values of around -0.2 to 0.6 or 0.7. To discriminate principal land cover classes such as water, non-vegetation (soil, sealed, etc.) and vegetation the following thresholds in the continuous range are used: \r\n\tNDVI < ~ 0: water\r\n\t~ 0 < NDVI < ~ 0.2: non-vegetation (soil, sealed surfaces, bare rock, etc.)\r\n\t~ 0.2 < NDVI: vegetation.\r\nNotably, these class limits are just a very rough approximation (indicated by the ~ sign), due to the mixed pixels effect, canopy reflectance, the abundance of water plants and suspending particles, and the illumination effect of specific atmospheric or topographic conditions. \r\nWe can use the NDVI to generally mask out vegetation from other land cover types and, more specifically, to indicate vegetation vigour and health. It is also suitable for monitoring plant phenology as the relationship between vegetative growth and the (changing) conditions of the environmental conditions. A range of variations has been suggested, enhancing one or the other mathematical or statistical behaviour of the index, or making it even more sensitive to specific plant behaviour. A well-known example is the enhanced vegetation index (EVI).","name":"Normalized Difference Vegetation Index (NDVI)","selfAssesment":"Completed
"},{"code":"IP3-1-2","description":"Spectral indices are calculated using a mathematical equation that is applied on two or more spectral reflectance bands of the image. The calculated spectral index is a ‘new’ image that highlights particular land surface features or properties e.g. vegetation, soil, water, better than the original input bands. The spectral indices vary from simple spectral ratioing of two bands to more complex combinations of multiple bands. Spectral indexes are developed based on the spectral properties of the object of interest. For example, spectral indices dedicated to the vegetation condition are developed based on the principle that the healthy vegetation reflects strongly in the near-infrared spectrum while absorbing strongly in the visible red. These properties are used to develop more complex spectral indexes for monitoring vegetation condition, phenology parameters, i.e. Normalised Difference Vegetation Index (NDVI), Advanced Vegetation Index (AVI). The spectral indices calculated using the short wave infrared spectral bands are more sensitive to vegetation water content and spongy mesophyll structure in the vegetation canopy thus are used to assess the vegetation decline, moisture that is particularly useful for drought monitoring (e.g. Normalized Difference Water Index (NDWI) or Normalized Difference Moisture Index – NDMI). The water-related spectral indices are widely applied in agricultural and ecological applications including surface water body characteristics, vegetation water stress, soil water content assessment and wetlands monitoring. The combination of near infrared and short wave infrared spectral bands is also used to detect burned area and to monitor the vegetation recovery (e.g. Normalised Burned Ratio – NBR). There are other spectral indices dedicated to snow cover and glacier monitoring, which are developed based on visual green and short wave infrared spectral bands. Snow reflects most of the radiation in the visible bands whiles absorbing in the short wave infrared.","name":"Spectral indices","selfAssesment":"Completed
"},{"code":"IP3-1","description":"The term band maths denotes the arithmetic combination (addition/subtraction, multiplication/division) of two or more spectral bands in an early stage of image analysis. The resulting scalar values represent the spectral behaviour in different bands in a single value; such procedure makes particular sense, when spectral behaviour varies in those bands (like the red edge of vegetation spectra in the NIR band). \r\nThere are several reasons for applying band maths when working with multispectral imagery: (1) A single range of values rather than multiple bands is easier to comprehend and interpret; (2) Thresholds or class limits are applied more intuitively in a grey scale image; (3) Indices can be easily calculated and compared across different sensors; they are implemented as standard routines in many software environments as well as cloud processing environments (such as Google Earth Engine or the Proba-V exploitation platform)\r\nOut of the many possible, literature suggests a few arithmetic band combinations as application-specific quasi-standards. Band ratios (e.g. red band divided by NIR band) and indices (such as the normalised difference vegetation index, NDVI) belong to this group. Indices have the advantage over simple ratios in constraining the value range, e.g. [-1 | 1]. Designated to indicate specific land cover types (such as water index, snow index, soil index, etc.) such indices are widely used as a basis for operational information products. Another index is the normalised burn ratio (NBR) which relates near infrared and short-wave infrared reflectance to measure burn severity taking into consideration the increasing of SWIR reflectance in the course of a fire. \r\nPre-processing such as dark object subtraction and radiometric or even atmospheric correction is a key requirement prior to indexing. The coding in digital numbers (DN) is a function of the sensitivity and the radiometric resolution of the sensor. The actual recording depends on atmospheric conditions (additional brightness, haze, etc.). Therefore, in order to make the resulting values comparable among different types of sensors and scenes, radiometric correction is mandatory, converting DNs into radiances, i.e. true reflectance values as physical measurement units. \r\nTwo advanced examples of band maths beyond rationing are the perpendicular vegetation index (PVI) and the tasselled cap (TC) transformation. PVI is based on the assumption that vegetation pixels are generally separable from soil pixels (at least after unmixing or for pure pixels), and thus pixel values are located in a perpendicular direction from the soil line in a NIR/red feature space. The Euclidean distance from the soil line, determined by Pythagorean triangle, yields the PVI. Tasselled cap instead rests on the notion of a cap-like histogram shape when plotting pixels on a brightness vs. greenness plot, with the latter determined by linear combinations of VIS and NIR bands, along with empirically determined coefficients. TC 1 as a weighted sum corresponds to brightness, TC 2 to greenness, TC 3 to yellowness, sometimes referred to as wetness. A fourth TC called nonesuch likely corresponds to noise and atmospheric disturbance effects in the image.","name":"Band maths","selfAssesment":"Completed
"},{"code":"IP3-10","description":"Semantic enrichment is the process of adding semantic metadata elements to improve the content-based image retrieval. These semantic metadata elements enable the explicit specification of the content of the images stored in the remote sensing databases.","name":"Semantic enrichment","selfAssesment":"New
"},{"code":"IP3-11-1","description":"Different types of changes are investigated using remotely sensed data: (i) abrupt changes, such as the changes caused by a fire or flooding, and (ii) gradual changes such as urban growth. Besides these kinds of changes, remote sensing community differentiates between transitional changes and conditional changes. Transitional changes refer to a major change of land surface such as conversion of forest to pasture or the expansion of mangroves into the surrounding water. Conditional changes refer to the change in condition at the surface such as water stress in an agricultural field, forest degradation caused by pest. \r\nIn the past, many remote sensing studies used two images to detect different types of changes such as deforestation, land cover change or change in the health or condition of the vegetation (e.g. pest infestation). Meanwhile, satellite image time series are used to assess the change. Time series analysis allows for monitoring more subtle changes and for providing temporal patterns of change. In this way, the timing of changes and drivers of change can be easily identified. \r\nDifferent methods are being used in change detection studies. There are studies that analyze individual images available in the investigated time series to map the target class/phenomena/events at the time when images were collected and to identify the changes: e.g. mapping the mangroves extent on an year basis and measuring it to identify changes. Alternative studies search for breaks in time series for detecting changes. The breaks are used to segment the time series into before and after changes periods which are further classified using one of the existing supervised or unsupervised classification methods (K-means, fuzzy k-means, Random Forest, Support Vector Machine etc.).","name":"Change detection","selfAssesment":"Completed
"},{"code":"IP3-11-2","description":"The (data)cube model for analysis of time series of earth observation raster data, represents the dataset as a multidimensional array with one or more spatial or temporal dimensions. Scalar values in the cube can be selected (or ‘filtered’) and processed based on dimension labels. This allows analysis algorithms to be thought of as a set of operations on the multidimensional array. Technologies that support this model allow to efficiently implement such algorithms.\r\nSome possible operations on a multidimensional cube include: filtering, ‘reducing’ all values along a dimension, ‘aggregating’ values in a dimension, or transforming all values along a dimension. Generally speaking, these operations require the selection of a subset of the data on which work is to be done. This allows implementing the operations efficiently even on very large datasets.\r\nIn comparison to file-based processing, most technologies that support cube-based time series analysis reduce implementation overhead, as the user does not need to read and write individual files, also more complex aspects like distributed computing for parallelization can be hidden in a cube based approach. So a cube based approach can also be thought of as an abstraction layer that effectively reduces the need for specific IT-related skills when analyzing earth observation timeseries.\r\nMultiple initiatives support cube based analysis. Some common features include a programming API, often using the Python programming language. Some tools are only accessible as web services, while others can also run locally (on a small dataset). This diversity is still a drawback, as users would need to familiarize themselves with different systems. Initiatives such as openEO try to address this by providing a common API.","name":"Cube-based time series analysis","selfAssesment":"Planned
"},{"code":"IP3-11-3","description":"Dynamic Time Warping (DTW) works by comparing the similarity between two temporal sequences and finds their optimal alignment, resulting in a dissimilarity measure. In the case of remote sensing data, DTW can deal with temporal distortions, and can compare shifted evolution profiles and irregular sampling thanks to its ability to align radiometric profiles in an optimal manner","name":"Dynamic Time Warping","selfAssesment":"Planned
"},{"code":"IP3-11","description":"Satellite image time series analysis plays an important role in different domains including vegetation dynamics monitoring, estimating crop yields, discriminating between different land cover classes, exploring human-nature interactions, monitoring land cover change, assessing environmental threats, or evaluating ecosystems-climate feedbacks or urbanization.\r\nTime series analysis requires high quality time series which are reconstructed by removing any source of contamination such as clouds, cloud shadows, or scan-line corrector (SLC) gaps of the Enhanced Thematic Mapper plus sensor (ETM+) on Landsat 7. Removed pixels are usually filled in with data predicted from a different date (temporal interpolation), nearby pixels (spatial interpolation) or from both (spatiotemporal interpolation). Different methods are available for screening and masking out clouds and shadows in satellite images including mono-temporal methods such as Function of mask (Fmask), or multitemporal mask (e.g. Tmask algorithm). Fmask is used by the United States Geological Survey (USGS) to produce a cloud mask layer of Landsat images. European Space Agency (ESA) is using Sen2cor processor to produce Level 2A Sentinel-2 data with a shadow and cloud shadow mask. All images used in the time series have to be co-registered, i.e. they align as closely as possible. \r\nTime series analysis is used to (1) investigate various surface properties such as evapotranspiration, land surface temperature, (2) map the cover of the Earth surface (e.g. land cover mapping, crop mapping etc.), (3) detect different type of changes such as abrupt changes (fire event) or gradual changes (urbanization), and (4) study the trends.\r\nTo map surface features from satellite image time series, numerous studies make use of the vegetation phenology extracted from a spectral-temporal trajectory of a given spectral vegetation index such as the normalized difference vegetation index (NDVI) or enhanced vegetation index (EVI). Several metrics can be used to characterized vegetation phenology: metrics of greenness and metrics of time. The metrics of greenness include the minimum and maximum spectral vegetation indices, their difference or amplitude, seasonally averaged greenness etc. The metrics of time include start and end of the growing season, duration or length of the growing season or the timing of maximum greenness. Changes, on the other hand, are identified either by investigating two images acquired at two different points in time or by identifying breaks in a dense (annual or multi-annual) satellite image time series.","name":"Time series analysis","selfAssesment":"Completed
"},{"code":"IP3-12-1","description":"Remote sensing-derived products such as land-use and land-cover maps contain error. The error accumulates as the remote sensing data are collected and various types of processing take place. An error assessment is necessary to identify the type and amount of error in a remote sensing-derived product.","name":"Error propagation","selfAssesment":"New
"},{"code":"IP3-12-2","description":"The precision of a measurement system, related to reproducibility and repeatability, is the degree to which repeated measurements under unchanged conditions show the same results.","name":"Precision","selfAssesment":"New
"},{"code":"IP3-12","description":"Uncertainty is the result of the lack or imprecision of our knowledge about the world. A proposition is uncertain if we do not know whether it is true or not. In most circumstances we describe a proposition as uncertain when the reason we do not know whether it is true is that we do not possess complete and accurate knowledge about the state of the world.","name":"Uncertainty","selfAssesment":"New
"},{"code":"IP3-13-1","description":"The main elements of visual interpretation are: tone, shape, size, pattern, texture, shadow, , association. Tone refers to the relative brightness or colour of objects in an image. It depends on the spectral properties of an object. Variation in tone allows to distinguish elements of different shape, texture and pattern. Shape refers to the general form, structure, or outline of individual objects. Straight and sharp edge shape represent typically the anthropogenic features i.e. urban or agriculture, the natural features like rivers, wetlands are more irregular in shape. Size of objects in an image is a function of scale and it depends on the spatial resolution of the image. The assessment of the size of the target’s object in relation to other objectives as well as an absolute size of the object are the important part of the interpretation. Pattern refers to the spatial arrangement of objects, i.e. network of street and houses in an urban area, orchards with the line of trees. Texture refers to the arrangement of frequency of tonal variation in particular areas of an image. Rough texture would have very large, coarse tonal variation (e.g. forest canopy), whereas smooth texture very little tonal version (e.g. uniform, homogenous surfaces). It depends on the size, shape and pattern of objects. Shadow depends on the scale and spatial resolution of an image. Shadow is useful to measure the height of an object, to distinguish the coniferous from broadleaf trees. In the radar imagery is useful for identifying topography and landforms. Association refers to the relationship between objects and features in proximity to the target interest.","name":"Elements (cues) of interpretation","selfAssesment":"Completed
"},{"code":"IP3-13-2","description":"Information-as-data-interpretation considers information as the outcome of the cognitive process of vision that reconstructs a scene from an image.","name":"Information-as-data-interpretation","selfAssesment":"New
"},{"code":"IP3-13-3","description":"An image interpretation key is simply reference material designed to permit rapid and accurate identification of objects or features represented on aerial images.","name":"Interpretation keys","selfAssesment":"New
"},{"code":"IP3-13","description":"Interpretation is the processes of detection, identification, description and assessment of an object and pattern imaged. Visual interpretation is the ability of a human operator to identify an object through the data content in an image / photo by combining several elements of interpretation. The image characteristics used in the interpretation process are: shape, size, tone/colour, texture, shadow, neighbourhood and pattern. The importance of the image characteristics varied according to the spatial resolution of the images and the properties of the feature of interest. The interpretation can be performed on the single image or between several images acquired at different time, which result in the differentiation of the temporal changes. The principle of the image interpretation is the process of delineating (digitalizing) the outlines of the objects, features on the image. It is performed “on-screen” using a GIS software. The process of visual interpretation is time consuming and requires a skilled interpreter with knowledge of the study area. Even though, the image interpretation supports many applications in for example selection of the training and verification data sets for image classification and accuracy assessment.","name":"Visual interpretation","selfAssesment":"Completed
"},{"code":"IP3-2-1","description":"Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans.","name":"Artificial intelligence (AI) in EO","selfAssesment":"New
"},{"code":"IP3-2-2","description":"Information theory answers two fundamental questions in communication theory: what is the ultimate data compression (answer: the entropy H) and what is the ultimate transmission rate of communication (answer: the channel capacity, C). For this reason, it is considered that information theory is a subset of communication theory.","name":"Information theory","selfAssesment":"New
"},{"code":"IP3-2-3","description":"Keypoints are objects (or locations) on the ground that reveal locally invariant features in images and therefore are easily detectable by automatic algorithms. Methods for this process employ scale-invariant feature transform (SIFT) algorithms for the automatic detection of geospatial objects.","name":"Keypoint detection","selfAssesment":"New
"},{"code":"IP3-2","description":"Image understanding is part of computer vision. Computer vision is an interdisciplinary field that deals with how computers can be made to gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to automate tasks that the human visual system can perform.","name":"Computer vision in EO","selfAssesment":"New
"},{"code":"IP3-3-1","description":"A Digital Elevation Model (DEM) is a digital raster (or grid) representation of elevation values of land surface shapes and features, where each grid cell takes a single elevation value with reference to a certain vertical datum. A DEM can be global, regional or local in scope, and can be used to characterize the dry land surface (topography) or submerged surfaces (bathymetry). Since a DEM cannot contain information of shapes and features under overhanging structures, it is often referred to as 2.5D instead of truly 3D. \r\nA digital elevation model is an overarching term for either a digital surface model (DSM) or digital terrain model (DTM). A DSM includes elevations of surface features such as trees, buildings, bridges and artificial objects such as poles, power lines, cars etc., and thus contains always the highest elevations of any feature for any given raster cell. A DTM does not include such features but reflects the elevation of bare land surface shapes, excluding elevated or overhanging features.\r\nDEMs can be obtained using active or passive measurements. Active measurements involve the generation of electromagnetic signals towards a surface and timing the reception of the (return) signal(s). This can be achieved through laser scanning (LiDAR) using visible or infrared light pulses for bathymetric or topographic measurements respectively, radio waves (SONAR) used in bathymetric measurements, or microwaves (synthetic aperture radar, SAR) used in topographic mapping. The most widely known active remotely sensed global DEM is derived from the Shuttle Radar Topography Mission (SRTM) obtained by a SAR mounted on the space shuttle Endeavour, offering 30 m resolution with a vertical accuracy typically between 5 and 20 m, covering 80% of Earth’s surface.\r\nPassive measurements detect reflection of sun light, or energy radiated from the surfaces. Their distance to the detector can then be inferred from the measurement of angles. Historically, line scanning imagers were used, but nowadays, these are replaced by acquisitions of overlapping 2D frame images. On the images, corresponding land surface features are detected which act as tie-points. The distance between the sensor and the tie-points is calculated in a process called photogrammetry. The most widely known spaceborne passive remotely sensed global DEM is derived from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data onboard the Terra satellite. It offers similar resolution and accuracy compared to SRTM, but with 99% coverage. \r\nOnly LiDAR can generate both accurate DSMs and DTMs from the same data acquisition, by using multiple returns from a single emitted pulse. All other techniques generate DSMs, from which elevated features can be identified and filtered out in postprocessing to create DTMs, however with typically lower accuracy and more artefacts.","name":"DEM generation","selfAssesment":"Complete
"},{"code":"IP3-3-2","description":"DSM can be produced automatically from stereo satellite scenes, from satellite sensors such as GeoEye, IKONOS, SPOT-5, Terra-ASTER etc. The DSM can also be provided from stereo digital aerial photography at various resolutions, depending on the quality and scale of the aerial photography. The quality of the automatic generated DSM is substantially improved if ground measurements from GPS are incorporated in the DSM stereoscopic model.","name":"DSM generation","selfAssesment":"New
"},{"code":"IP3-3","description":"Stereo pairs of optical satellite images with the support of ground control points provide a basis for cross-stereo analysis for generating Digital Surface Models.","name":"Cross-stereo analysis","selfAssesment":"New
"},{"code":"IP3-4-1-1","description":"The goal of filtering is to remove unnecessary components from images (e.g., noise), while emphasizing the necessary ones. In the context of spatial aggregation, low pass filters aim at removing sharp transitions in the image intensities (high spatial frequencies) and thereby focus the information content of the image on a coarser scale level.","name":"Filtering","selfAssesment":"New
"},{"code":"IP3-4-1-2","description":"Gridding is the technique used to generate a uniform raster grid with one value for every cell in the raster. The values of the raster cells can represent different attributes such as mean, max or min of all Normalized Difference Vegetation Index (NDVI) values measured within a particular cell.","name":"Gridding","selfAssesment":"New
"},{"code":"IP3-4-1","description":"Spatial aggregation produces images of coarser resolution (grouping pixels in a grid of coarser resolution and calculating mean values) or of coarser scale (by filtering with low-pass filters). Thereby it is a form of generalization that may improve classification results. Spatial aggregation can be applied after classification to get rid of the salt-and-pepper effect.","name":"Spatial aggregation","selfAssesment":"New
"},{"code":"IP3-4-10","description":"Classification processes use features, also known as predictor variables, for discriminating between classes. A feature is an individual measurable property or characteristic of a geographic phenomenon being observed. Features in Earth observation include the individual bands of images and further properties derived from the image data. For example, the single band of a panchromatic image represents a feature that allows distinguishing between pixels of darker and lighter reflectance. Multispectral images have more bands and thereby enable the differentiation between classes by more features. This means, if two classes are different from each other in several of their properties, it becomes easier to distinguish them. The set of features used in a particular classification comprise the feature space where each feature represents one space dimension. \r\nWith an increased number of features it becomes possible to increase the number of classes that can be separated. For example land cover classifications have a large number of classes. For identifying suitable bands for optical EO satellites, the spectral signatures of all the target classes have to be analysed to identify in which bands they are separable from other classes. Classes like soil, water, and vegetation have spectral signatures that differ in particular in the blue, green, red, and infrared bands of the electromagnetic spectrum. These bands are present in virtually all multispectral sensors used for land cover classification. \r\nAs geographic phenomena differ by more than their reflectance in different bands, further properties have been used for classification. In addition to multispectral features, the classification may include image derivatives like derived spectral indices, principal components, or filtered bands (convolution layers). Object-based image analysis also uses spatial features, i.e. distance and proximity features, planar geometric features and topological features.","name":"Classification features and feature space","selfAssesment":"In progress
"},{"code":"IP3-4-2-1","description":"Bayes’s theorem is an extremely powerful means of using information at hand to estimate probabilities of outcomes related to the occurrence of preceding events. Bayes' Theorem uses a priori (subjective) and conditional probabilities to calculate the probability of an uncertain event occurring. A priori probabilities represent what the modeler believes, before testing, to be the probability of an event occurring. Conditional probabilities are probabilities that other events occur in conjunction with the original event.","name":"Conditional probability","selfAssesment":"Planned
"},{"code":"IP3-4-2-2","description":"Maximum likelihood classification uses the training data for estimating means and variances of the classes, which are then used to estimate the probabilities. This method considers not only the mean, or average, values in assigning classification but also the variability of brightness values in each class.","name":"Maximum likelihood","selfAssesment":"In progress
"},{"code":"IP3-4-3-1","description":"The Land Cover Classification System (LCCS) was developed by FAO to provide a consistent framework for the classification and mapping of land cover. Its main objectives were to overcome the rigidity of a-priori land cover classifications, which in many practical situations do not allow easy assignment into one of the pre-defined classes and are therefore not very suitable for mapping. LCCS instead opted for an approach based on two main phases. The first phase is an initial ‘Dichotomous Phase’, in which eight major land cover types are defined: (1) Cultivated and Managed Terrestrial Areas, (2) Natural and Semi-Natural Terrestrial Vegetation, (3) Cultivated Aquatic or Regularly Flooded Areas, (4) Natural and Semi-Natural Aquatic or Regularly Flooded Vegetation, (5) Artificial Surfaces and Associated Areas, (6) Bare Areas, (7) Artificial Waterbodies, Snow and Ice, and (8) Natural Waterbodies, Snow and Ice. The Dichotomous Phase is followed by a subsequent ‘Modular-Hierarchical Phase’, in which land cover classes are created by the combination of sets of pre-defined classifiers, which are different for each of the eight major land cover types. For example, common classifiers used for (semi-) natural terrestrial vegetation types are Life Form, Cover, Height, Macropattern. For aquatic or regularly flooded natural and semi-natural vegetation, water seasonality is an indispensable classifier. LCCS offers several advantages from a conceptual point of view. LCCS is a real a priori classification system in the sense that, for the classifiers considered, it covers all their possible combinations. The classification is also hierarchical and the more classifiers used, the greater the detail of the defined land cover class. The classes derived from the proposed classification system are all unique and unambiguous, due to the internal consistency and systematic description of the classes. LCCS is designed to map at a variety of scales, from small to large. From a practical viewpoint LCCS offers several advantages: (1) easy incorporation into GIS and databases, (2) allows flexible response to information available in a given area, project budget and time constraints, (3) unlinks the field data collection from the interpretation process.","name":"Land cover classification system (LCCS)","selfAssesment":"Completed
"},{"code":"IP3-4-3","description":"Long-term monitoring of land cover and land use are particularly relevant for land ecosystem monitoring. Therefore, baseline datasets are necessary that allow assessing changes of land cover and land use where the class definitions remain consistent over time. Accordingly, classification schemes have been established that adhere to taxonomically correct definitions of classes of information organized according to logical criteria. If hard classification is to be performed (i.e. without fuzzy class boundaries), the classes in the classification system should normally be mutually exclusive, exhaustive, and hierarchical. Mutual exclusive classes have no taxonomic overlap and assign a land cover patch to a single class. An exhaustive classification scheme is able to cover the area of interest comprehensively and leaves no land cover patch unassigned. A hierarchical system allows combining sub-classes into higher-level categories.\r\nFrom a remote sensing classification perspective, it becomes clear that a classification scheme consists of information classes defined by human beings. Conversely, spectral classes are those inherent to EO data. An analyst must identify spectral classes and label them as information classes that satisfy bureaucratic (or scientific requirements). Additionally, the advantage of using established classification schemes is that their use in scientific studies and applications produces results that are comparable to other studies and suitable for sharing of data.\r\nEstablished classification schemes include: CORINE land cover (CLC), Land cover classification system (LCCS), American Planning Association land-based classification standard, United States Geological Survey land-use/land-cover classification system for remote sensor data, U.S. Department of the Interior Fish & Wildlife Service classification of wetland and deep water habitats of the United States, U.S. National Vegetation Classification system (NVCS), International Geosphere-Biosphere Program IGBP Land cover classification system.","name":"Classification schemes (taxonomies)","selfAssesment":"Completed
"},{"code":"IP3-4-4","description":"Unsupervised methods are defined as the identification of natural groups, or structures, within existing data. Clustering requires only the number of to-be generated classes as an input parameter and assigns spectrally defined classes to an image.","name":"Clustering (unsupervised)","selfAssesment":"New
"},{"code":"IP3-4-5-1","description":"A production system performs automatic transformation of remote sensing imagery into useful information (such as biophysical parameters, categorical maps etc). An example can be a preliminary pixel-based classifier that works top-down (deductive, physical model-driven, prior knowledge-based) and arrives at preliminary classes for each pixel of an image. Such a production system does not require interaction of an operator. The process makes use of a decision tree that encodes the prior knowledge for assigning pixels to a class.","name":"Production system","selfAssesment":"New
"},{"code":"IP3-4-5","description":"Decision trees is a data mining technique used in different disciplines including Remote Sensing. It uses a tree-like prediction model to identify a pattern in the input data. One of the most popular decision tree algorithms is the CART (Classification and Regression Tree) algorithm.","name":"Decision trees","selfAssesment":"New
"},{"code":"IP3-4-6-1","description":"Convolutional Neural Networks (CNNs) are among the most popular deep learning methods.","name":"Convolutional neural networks (CNN)","selfAssesment":"In progress
"},{"code":"IP3-4-6","description":"Deep learning approaches have classically been divided into spatial learning (for example, convolutional neural networks for object classification) and sequence learning (for example, speech recognition)","name":"Deep learning","selfAssesment":"In progress
"},{"code":"IP3-4-7-1","description":"The RF classifier is an ensemble classifier that uses a set of Classification and Regression Trees (CARTs) to make a prediction The trees are created by drawing a subset of training samples through replacement (a bagging approach).","name":"Random forest (RF)","selfAssesment":"New
"},{"code":"IP3-4-7-2","description":"In machine learning, support vector machines (SVMs) are supervised non-parametric statistical learning techniques with associates learning algorithms that analysze data used for both classification and regression analysis. SVM algorithm was originally designed for binary classification. The SVM is based on the main hypothesis that the training set is linearly separable. Given a set of training examples, each marked as belonging to one or another of two categories, an SVM training algorithm builds a model that can assign each new occurrence into one of these two categories, making it a non-probabilistic binary linear classifier. The SVM model is a representation of the examples as points in space, mapped so that the algorithm can find the optimal line (hyperplane) which separates with minimum error the training set, and maximizes the distance, named the “gap”, between the objects of both classes and the hyperplane. Thus, instead of using the whole available training set to describe classes, SVM uses only those training samples that describe class boundaries (support vectors), thought it can be more efficient than other algorithm because it uses a subset of training points. New occurs are then mapped into that same space and predicted to belong to a category based on the side of the gap on which they fall. In addition to performing linear classification, SVMs can also efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces. Unfortunately, because of the technique used for separating classes SVM is less effective on noisier datasets with overlapping classes. When data are unlabelled, supervised learning is not possible, and an unsupervised learning approach is required. SVM is used for text classification tasks such as category assignment, spam detection and sentimental analysis. It is also commonly used for image recognition, performing particularly well in aspect-based recognition and colour-based recognition. SVM also plays a vital role in many areas of handwritten digit recognition, such as postal automation services.","name":"Support vector machines (SVM)","selfAssesment":"Completed
"},{"code":"IP3-4-7","description":"Field of study that gives computers the ability to learn without being explicitly programmed","name":"Machine learning","selfAssesment":"New
"},{"code":"IP3-4-8","description":"Image classification operator needs a set of terms to express the characteristics of an image. These characteristics are called interpretation elements and are used to define interpretation keys: tone/hue, texture, pattern, shape, size, height/elevation, location/association","name":"Mental concepts and categories","selfAssesment":"New
"},{"code":"IP3-4-9","description":"Sampling strategies or sampling pattern specifies the arrangement of observations used for training and/or validation purposes.\r\nTypically, the simple random sample of a geographic region is defined by first dividing the region to be studied into a network of cells. Each row and column in the network is numbered, then a random number table is used to select values that, taken two at a time, form coordinate pairs for defining the locations of observations. Because the coordinates are selected at random, the locations they define should be positioned at random. The random sample is probably the most powerful sampling strategy available as it yields data that can be subjected to analysis using inferential statistics.\r\nA stratified sampling pattern assigns observations to subregions of the image to ensure that the sampling effort is distributed in a rational manner. For example, a stratified sampling effort plan might assign specific numbers of observations to each category on the map to be evaluated. This procedure would ensure that every category would be sampled.\r\nSystematic sampling positions observations at equal intervals according to a specific strategy. Because selection of the starting point predetermines the positions of all subsequent observations, data derived from systematic samples will not meet the requirements of inferential statistics for randomly selected observations.","name":"Sampling strategies","selfAssesment":"New
"},{"code":"IP3-4","description":"The process of image classification extracts information about semantic labels of pixels or objects (i.e. regions) from imagery. Apart of input imagery, the process requires an input set of target classes (classification scheme) for which their spectral (and other) properties have to be identified. A classification method has to be selected that transforms the image data and the classification scheme into semantic map information. In complement to the resulting sematic labelling products, a secondary outcome are instructions or rulesets with the used parameters that constitute the documentation of the classification process.\r\nThe input imagery consists of one or more images (optical and/or SAR data) of a specific geographic area, collected in multiple bands of the electromagnetic spectrum (that may have already undergone certain pre-processing steps; determined by the purpose). Additionally, the imagery may include derived spectral indices, principal components, filtered bands, or other features to support the classification process.\r\nThe classification purpose defines the information about the target classes. It includes classification schemes (taxonomies), spectral signatures for each class and, mental concepts and categories about the classes (that enable an analyst to distinguish classes by texture, spatial relationships etc.). Often, training areas are used to understand how an object of a particular class is discernible in the available imagery and separable from other classes. Both the input imagery and the chosen classification method determine which features of each class can be exploited for classification. For example, spectral signatures of the target classes (extracted from training areas with known class label) may be a suitable input for extracting information with a pixel-based classification. For shape features, objects are a pre-requirement, derived with segmentation. They are only available with object-based classification approaches.\r\nClassification methods: Various methods exist that can be categorized according to the classification logic that they follow when transforming the input information into the output semantic labelling products. These can be parametric or nonparametric, supervised or unsupervised, per-pixel or object-oriented, semi-automated or fully automatic, and hybrid approaches. Classification methods are for example bayesian techniques like conditional probability or maximum likelihood, clustering (unsupervised), decision trees, deep learning and machine learning.","name":"Image classification","selfAssesment":"Completed
"},{"code":"IP3-5-1","description":"Edge detection is a fundamental tool used in many image processing applications to obtain information from the frames as a precursor step to feature extraction and object segmentation. This process detects outlines of an object and boundaries between objects and the background in the image. An edge-detection filter can also be used to improve the appearance of blurred image.","name":"Edge-based segmentation","selfAssesment":"Planned
"},{"code":"IP3-5-2","description":"Histogram-based segmentation makes use of histogram to select the gray levels for grouping the pixels into regions, e.g. background and the object of interest","name":"Histogram-based segmentation","selfAssesment":"New
"},{"code":"IP3-5-3","description":"Local variance can be calculated as the value of standard deviation in a small neighborhood (e.g. 3x 3 moving window), then computing the mean of these values over the entire image. The obtained value is an indicator of the local variability in the image.","name":"Local variance","selfAssesment":"New
"},{"code":"IP3-5-4","description":"Mean Shift is defined as finding modes in a set of data samples, manifesting an underlying probability density function (PDF).","name":"Mean-shift segmentation","selfAssesment":"New
"},{"code":"IP3-5-5","description":"Regionalization is an important concept in Geographic Information Science for synthesizing multi-dimensional data into homogeneous objects through spatially constrained clustering methods","name":"Regionalisation","selfAssesment":"New
"},{"code":"IP3-5-6-1","description":"Multi-resolution segmentation is a region-growing algorithm. It relies on several parameters, which need to be tuned. These include the scale parameter (SP), which dictates the size and homogeneity of the resultant objects.","name":"Multi-resolution segmentation","selfAssesment":"Planned
"},{"code":"IP3-5-6-2","description":"Watershed segmentation is a region-based method that has its origins in mathematical morphology. In watershed segmentation an image is regarded as a topographic landscape with ridges and valleys. The elevation values of the landscape are typically defined by the gray values of the respective pixels or their gradient magnitude. Based on such a 3D representation the watershed transform decomposes an image into catchment basins. For each local minimum, a catchment basin comprises all points whose path of steepest descent terminates at this minimum. Watersheds separate basins from each other. The watershed transform decomposes an image completely and thus assigns each pixel either to a region or a watershed.","name":"Watershed segmentation","selfAssesment":"New
"},{"code":"IP3-5-6","description":"Region-based segmentation algorithms can be devided into region growing, merging and splitting techniques and their combinations. Region merging starts from all pixels on the pixel level and iteratively aggregates pixels into objects until some conditions of homogeneity imposed by the user are met.","name":"Region-based segmentation","selfAssesment":"New
"},{"code":"IP3-5-7","description":"Spatial autocorrelation is the term used to describe the presence of systematic spatial variation in a variable.","name":"Spatial autocorrelation","selfAssesment":"New
"},{"code":"IP3-5","description":"The term image segmentation denotes the process of algorithmically grouping neighbouring pixels that are similar. What sounds rather straight forward, is in fact a great computational challenge, some even call it an ill-posed problem, because there is a high degree of ambiguity in this process. \r\nThe two attributes in the general definition provided above, i.e. neighbouring and similar, evoke the principles of regionalisation as a fundamental concept in geography. Regionalisation is the bottom-up approach to congregate adjacent elements with the aim to form a larger unit. (Conversely, this could be understood in a top-down manner when subdividing a larger whole into smaller homogeneous units). This follows the general notion of hierarchical organisation according to general systems theory (GST). The organisation of a state in smaller administrative units is a good example for a hierarchical structure, the composition of the human body by organs, cells, etc. another. In image analysis such regions are commonly referred to image regions, originating from the concept of “photomorphic regions”, literally meaning regions formed on images – originally by human interpreter through manual delineation. Today, advanced pixel grouping algorithms aim to delineate homogenous regions in an image automatically. As those regions usually are assumed to match with real-world objects, it is often stated in literature that image segmentation generates image objects. Deriving some general heuristics on their properties (colour, size, shape, orientation, etc.) we can label these objects according to a given semantic scheme. The procedure of object delineation and classification using object features and relations is a fundamental principle in object-based image analysis (OBIA). \r\nDue to the effect of spatial autocorrelation (the tendency of neighbouring pixels to be similar irrespective of scale or geographical location), pixel grouping is ambiguous and by no means trivial, but not arbitrary either. Intuitively, image regions are those quasi-homogeneous areas that we perceive as landscape units on a specific scene (a lake, a forest patch, a single tree, a building, a residential area). According to hierarchy theory, we can assume that we find multiple scales within a single image even, according to the level of detail we are interested in. Whether or not a specific grouping of pixels is considered valid, e.g. because it corresponds to a real-world object, can hardly be answered unanimously, but rather needs to be judged by experts in the respective application domain. That is why often in literature we find the term ‘meaningful objects’. \r\nImage segmentation is as a sub-field of computer vision and aims to apply computer algorithms to generate image regions (a.k.a. tokens) within digital image analysis. There are several strategies for performing image segmentation, all resting on the following general principles: (1) regions do not overlap; (2) regions are (relatively) homogenous; regions are (relatively) different to neighbouring regions; regions are fairly equally sized (belong to one scale domain) but can be built in several hierarchical scales. General strategies include (1) edge-based segmentation and (2) region-based segmentation, and multi-scale segmentation as a specific case. \r\nAlso referred to spatial classification emphasizing the constraint of spatial contingency, image segmentation aggregates neighbouring pixels, but – as compared to statistical clustering techniques – does not provide a unique set of classes (either semantic or statistic) in the feature space. \r\nRecently the term semantic segmentation has emerged in the machine-learning community, which is in fact a combination of segmentation and categorisation (labelling) via deep learning methods (e.g. convolutional neural networks).","name":"Image segmentation","selfAssesment":"Completed
"},{"code":"IP3-6-1","description":"Combined filtering uses different filters to arrive at more complex filters for specific purposes. \r\nFor example, Laplacian filters are derivative filters used to find areas of rapid change (edges) in images. Since derivative filters are very sensitive to noise, it is common to smooth the image (e.g., using a Gaussian filter) before applying the Laplacian. This two-step process is called the Laplacian of Gaussian (LoG) operation.","name":"Combined filtering","selfAssesment":"New
"},{"code":"IP3-6-2","description":"The aim of sharpening filters is to highlight transitions in intensity (high frequency components) using different operators: directional (horizontal, vertical, diagonal) or isotropic (e.g. Laplacian Filter). Example of edge detectors include: Gaussian edge detector, Laplacian filter etc.","name":"Edge detectors","selfAssesment":"New
"},{"code":"IP3-6-3-1","description":"The Lee-sigma filter is a conceptually simple but effective alternative to the Lee and other sophisticated adaptive filters. It is based on the sigma probability of the Gaussian distribution.","name":"Lee-Sigma","selfAssesment":"New
"},{"code":"IP3-6-3","description":"High-pass filtering enhance information of high frequencies (local extremes, lines, edges)","name":"High-pass filtering","selfAssesment":"New
"},{"code":"IP3-6-4-1","description":"Gaussian Filters are isotropic (same behavior in all directions).","name":"Gauss filter","selfAssesment":"New
"},{"code":"IP3-6-4","description":"Spatial filters transform an image by taking into account the local neighborhood of a pixel. The goal of filtering is to remove unnecessary components from images (e.g., noise), while emphasizing the necessary ones. In this context, low pass filters aim at removing sharp transitions in the image intensities (high spatial frequencies).","name":"Low-pass filtering","selfAssesment":"New
"},{"code":"IP3-6","description":"In contrast to the point operations used for radiometric modification of image data, techniques for geometric processing are characterized by operations over local neighborhoods of pixels. The result of a neighborhood operation is still a modified brightness value for the single pixel at the center of the neighborhood , however the new value is determined by the brightness of all the local neighbors rather than just the original brightness value of the central pixel alone.","name":"Neighbourhood analysis (convolution)","selfAssesment":"Planned
"},{"code":"IP3-7-1","description":"Class modelling provides flexibility in designing a transferable workflow from scene-specific high-level segmentation and classification to region-specific multi-scale modelling","name":"Class modelling","selfAssesment":"Planned
"},{"code":"IP3-7-2","description":"Hierarchical representation refers to hierarchically scaled compositions of the classes to be classified.","name":"Hierarchical representation","selfAssesment":"New
"},{"code":"IP3-7-3","description":"Per-parcel analysis relies on parcels or objects as the smallest units of image analysis. The parcels are usually obtained through image segmentation that partition the input images into homogeneous units, i.e. parcels, in a supervised or unsupervised manner.","name":"Per-parcel analysis","selfAssesment":"New
"},{"code":"IP3-7-4-1","description":"Distance relationships describe how far an object is with respect to a reference. Proximity analysis allows the identification of the distance between a geographic feature of interest and its neighbors.","name":"Distance and proximity features","selfAssesment":"New
"},{"code":"IP3-7-4-2","description":"The most important geometric features of geographic objects are their size and shape. Shape refers to general form or outline of individual objects and can be quantified using different metric such as shape index, compactness, asymmetry, density, elliptic fit, roundness, rectangular fit etc.","name":"Planar geometric features","selfAssesment":"New
"},{"code":"IP3-7-4-3","description":"Topological features characterize qualitatively the position of spatial objects relative to each other. There are different models for representing topological relationships. Calculus-based method, for example, allows us to model five topological relationships of two spatial objects: touch, in, cross, overlap, disjoint.","name":"Topological features","selfAssesment":"New
"},{"code":"IP3-7-4","description":"An object of a specific object class has a value on the range of values of a spatial or spectral feature. A set of features provides the feature space that is used for classification.","name":"Spatial features","selfAssesment":"Planned
"},{"code":"IP3-7","description":"OBIA is an iterative method that starts with the segmentation of satellite imagery into homogeneous and contiguous image segments (also called image objects. In the next step, resulting image segments are assigned to the target classes.","name":"Object-based image analysis (OBIA)","selfAssesment":"Planned
"},{"code":"IP3-8-1","description":"The feature space represents in various dimensions all the features that can be used for classification (e.g. image bands, band math parameters, derived texture properties). A point in that space is also called a vector with values for each feature (or dimension). Polyhedralization is a form of vector space quantization where a vector is assigned to the closest centre point of one polyhedron.","name":"Feature space polyhedralization","selfAssesment":"New
"},{"code":"IP3-8-2","description":"Radiative transfer models describing the interaction between matter and electromagnetic radiation serve as cornerstones for optical remote sensing. The radiative transfer theory provides the most logical linkage between observations and physical processes that generate signals in optical remote sensing. Radiative transfer modelling is therefore an integral part of remote sensing, since it provides the most efficient tool for accurate retrievals of Earth properties from satellite data. Radiative transfer models are used in a number of different applications such as sensor radiometric calibration, atmospheric correction and the modelling radiation processes in vegetation canopies. \r\nVegetation radiative transfer models (RTMs) study the relationship between leaf and canopy biophysical variables and reflectance, absorbance and scattering mechanisms. The infinite variability of vegetation structure complicates the modeling of RT in vegetation canopies. Numerous models of RT in vegetation canopies were developed in the second half of the last century. Models differ by the details accounted for and by the simplifications introduced in the description of canopy structure and photon–vegetation interactions. Gradual improvement in RTMs accuracy, yet in complexity too, have diversified RTMs from simple turbid medium RTMs towards advanced Monte Carlo RTMs that allow for explicit 3D representations of complex canopy architectures. This evolution has resulted in an increase in the computational requirements to run the model, which bears implications towards practical applications. When choosing an RTM, a trade-off between invertibility and realism has to be made: simpler models are easier to invert but less realistic, while advanced models more realistic but require a large amount of variables to be configured. The two most widely used models are the leaf model PROSPECT and Scattering by Arbitrary Inclined Leaves (SAIL) canopy model. \r\nAtmosphere RTMs study the interaction of radiation with the atmosphere. The remotely-sensed signals at satellite or airborne platforms are combinations of surface and atmospheric contributions, with relative amounts varying across the two wavelength regions, depending on the condition of the atmosphere. The order of magnitude of atmosphere signals can be equal or larger than that of land or ocean surface signals that arise at the top of the atmosphere (TOA). In order to derive accurate sensor calibration and atmospheric correction, the contribution of the atmospheric constituents to the total retrieved signal must be understood and modelled. Atmospheric radiative transfer models simulate the radiative transfer interactions of light scattering, absorption and emission through the atmosphere. Some widely used atmospheric RTMs are 6SV, libRadtran, MODTRAN, and ATCOR.\r\nAdvances in radiative transfer modeling enhance our ability to detect and monitor changes in our planet through new methodologies and technical approaches to analyze and interpret measurements from air- and space-borne sensors.","name":"Radiative transfer modelling","selfAssesment":"Completed
"},{"code":"IP3-8","description":"Historically, physical modelling and machine learning have often been treated as two different fields with very different scientific paradigms (theory-driven versus data-driven). Yet, in fact these approaches are complementary, with physical approaches in principle being directly interpretable and offering the potential of extrapolation beyond observed conditions, whereas data-driven approaches are highly flexible in adapting to data and are amenable to finding unexpected patterns (surprises).","name":"Physical-model based analysis","selfAssesment":"New
"},{"code":"IP3-9-1","description":"Difference of Gaussians (DoG) method consists of subtracting two Gaussians, where a kernel has a standard deviation smaller than the previous one. The convolution between the subtraction of kernels and the input image results in the edge detection of this image.","name":"Difference of Gaussian (DoG)","selfAssesment":"New
"},{"code":"IP3-9-2","description":"Scale Invariant Feature Transform (SIFT) is an image descriptor for image-based matching and it is used for a large number of purposes in computer vision related to point matching between different views of a 3-D scene and view-based object recognition. The SIFT descriptor is invariant to translations, rotations and scaling transformations in the image domain and robust to moderate perspective transformations and illumination variations. Experimentally, the SIFT descriptor has been proven to be very useful in practice for robust image matching and object recognition under real-world conditions.","name":"Scale invariant feature transformation (SIFT)","selfAssesment":"New
"},{"code":"IP3-9","description":"Scale-space theory is a framework for multiscale image representation, which has been developed by the computer vision community with complementary motivations from physics and biologic vision. The idea is to handle the multiscale nature of real-world objects, which implies that objects may be perceived in different ways depending on the scale of observation. If one aims to develop automatic algorithms for interpreting images of unknown scenes, there is no way to know a priori what scales are relevant. Hence, the only reasonable approach is to consider representations at all scales simultaneously.","name":"Scale space analysis","selfAssesment":"New
"},{"code":"IP3","description":"Image data, in order to be turned into information, require interpretation. Thereby image understanding is the process of scene reconstruction, the description and mental representation of the content of imaged, and potentially complex, realities. \r\nImage understanding thereby goes beyond single feature extraction. Instead, it aims at a complete description of the image content, i.e. the reconstruction of a real-world scene. In the early days of digital image processing, image understanding was mainly confined to identifying and labelling image primitives. Today, advanced mapping keys and hierarchical classification schemes to analyse EO data, include composite and complex target classes. Thereby ‘full’ scene description means reaching from signal processing to a symbolic representation of the scene content. This entails the relationships of real‐world objects in different scales and spatio-temporal aspects.\r\nDescribing a scene, visually or computer-aided or mixed, depends on a conceptual framework comprising (a) the underlying research question within (b) a specific field of application and (c) pre‐existing knowledge and experience of the operator. Obtaining insights from imagery requires general knowledge about the expected scene content and domain expertise. The field of image understanding is interlinked with image (pre-)processing, computer vision, and artificial intelligence (AI). Image processing conditions the data material and enhances the interpretation source. Computer vision including pattern recognition providing knowledge representation, expert systems. AI is mainly concerned with automation processes, be it via knowledge transfer to an automated system or machine / deep learning.\r\nIn analogy to the human mind, image understanding is the computational process of extracting information from images, i.e. locating, characterizing, and recognizing objects and other features in the depicted scene. However, image understanding is not a linear, but rather a cyclic process and takes place during the pre-processing and data assimilation steps. For example, cloud masks on EO images is an early product of image understanding, prior to many pre-processing tasks.\r\nIn a typical GEOBIA workflow, the process of image understanding can be illustrated by the following steps: Starting from the subset of a real‐world scene captured on an image first step may entail scaled representations by grouping neighbouring pixels on several hierarchical sales. The multi‐scale segmentation provides a set of nested objects with geospatial and spectral properties to be used in the classification process. \r\nWith object hypotheses in mind the object relation modelling can be realized by encoding expert knowledge into a rule system. This setp aims at categorizing the image objects by their spectral and spatial properties and their mutual relationships. Hereby, an object‐centred view is accomplished. This representation of the image content should meet the conceptual reality of the interpreter or user. Knowledge is stepwise adapted and improved through progressive interpretation and modelling. Experience grows, as knowledge will be enriched by analyzing unknown scenes and the transfer of knowledge may incorporate or stimulate new rules.","name":"Image understanding","selfAssesment":"Completed
"},{"code":"IP4-1-1","description":"Once the user finds the required data, she/he needs to know how can they be accessed, possibly including authentication and authorisation.","name":"Accessibility","selfAssesment":"New
"},{"code":"IP4-1-2","description":"Quality Indicators (QIs) should be ascribed to data and, in particular, to delivered information products, at each stage of the data processing chain - from collection and processing to delivery. A QI should provide sufficient information to allow all users to readily evaluate a product’s suitability for their particular application, i.e. its “fitness for purpose”.","name":"GEO QA4EO","selfAssesment":"New
"},{"code":"IP4-1-4","description":"ISO is an independent, non-governmental international organization with a membership of 164 national standards bodies. Through its members, it brings together experts to share knowledge and develop voluntary, consensus-based, market relevant International Standards that support innovation and provide solutions to global challenges. ISO/TC 211 Geographic information/Geomatics provides Standardization in the field of digital geographic information. Note: This work aims to establish a structured set of standards for information concerning objects or phenomena that are directly or indirectly associated with a location relative to the Earth. These standards may specify, for geographic information, methods, tools and services for data management (including definition and description), acquiring, processing, analyzing, accessing, presenting and transferring such data in digital / electronic form between different users, systems and locations.","name":"ISO standards","selfAssesment":"New
"},{"code":"IP4-1-5","description":"The OGC is the worldwide leading consortium of GIS industries promoting the interoperability of geographic information across platform, system, and country borders. The main field of current activity is the complete integration of the sources of geographic information based on the Internet.The Open GIS Consortium (OGC) plays an important role on the implementation level.","name":"OGC standards","selfAssesment":"New
"},{"code":"IP4-1-6","description":"A fundamental pillar in (open) science is to verify the scientific results of others to advance knowledge. The lack of reproducibility in scientific studies brings challenges in understanding and recreating the results of others, a situation that may be common in data-based and algorithm-based research like in geocomputation. In general, many authors define reproducibility as the ability to compute exactly the same results of a study based on original input data and analysis workflow. In other words, “to rerun the same computational steps on the same data the original authors used”. Replicability is often seen as obtaining similar conclusions about a research question derived from an independent study or experiment. In the field of GIScience and geocomputation, in particular, a reproduction is always an exact copy or duplicate, with exactly the same features and scale, while a replication resembles the original but allows for variations in scale, for example. Hence, reproducibility is exact whereas replicability means confirming the original conclusions, although not necessarily with the same input data, methods, or results.","name":"Replicability and reproducibility","selfAssesment":"Completed
"},{"code":"IP4-1-7","description":"The ultimate goal of FAIR is to optimise the reuse of data. To achieve this, metadata and data should be well-described so that they can be replicated and/or combined in different settings.","name":"Reusability","selfAssesment":"New
"},{"code":"IP4-1","description":"Data quality standards are guiding principles and operational guidelines for the production and use of data. For example, QA4EO aims for the two key principles of accessibility / availability and suitability / reliability. The QA4EO guidelines provide instructions for the implementation of processes that follow these principles. Standards emerge from standardization processes within the community. They are based on the agreement of the members of the community.","name":"Data quality standards","selfAssesment":"New
"},{"code":"IP4-2-1-1","description":"To correctly perform a classification accuracy (or error) assessment, it is necessary to systematically compare two sources of information: (1) pixels or polygons in a remote sensing-derived classification map, and (2) ground reference test information (which may in fact contain error). The relationship between these two sets of information is commonly summarized in an error matrix (sometimes referred to as contingency table or confusion matrix). Indeed, the error matrix provides the basis on which to both describe classification accuracy and characterize errors, which may help refine the classification or estimates derived from it.","name":"Error matrix","selfAssesment":"New
"},{"code":"IP4-2-1-2","description":"F-score represents the harmonic mean between precision and recall. As F-score combines both precision and recall, it can be regarded as an overall quality measure. The range of F is from 0 to 1 with larger values representing higher accuracy.","name":"F-score","selfAssesment":"New
"},{"code":"IP4-2-1-3","description":"Ground reference refers to the reference dataset for an accuracy assessment of a remote sensing classification. The process of obtaining ground reference is dedicated to support the production of suitable accuracy information. A sampling design (fitting to the produced image classification) determines the most appropriate distribution of sample locations (or regions). The response design consists of the evaluation protocol and the labeling protocol. The evaluation protocol initiates selecting the support region on the ground (represented by a pixel or polygon) where the ground information will be collected. Once the location and dimension of the sampling unit are defined, the labelling protocol is initiated and the sampling unit is assigned a hard or fuzzy ground reference label. This ground reference label (e.g. forest) is paired with the remote sensing-derived label (e.g., forest) for assignment in the error matrix.","name":"Ground reference","selfAssesment":"New
"},{"code":"IP4-2-1-4","description":"Kappa is a value for measuring the overall accuracy of a classification that accounts for randomness of class assignment. Kappa analysis is a discrete multivariate technique of use in accuracy assessment. Kappa yields a statistic, ^K, which is an estimate of Kappa. It is a measure of agreement between the remote sensing-derived classification map and the reference data as is indicated by a) the major diagonal and b) the chance of agreement, which is indicated by the row and column totals in the error matrix.","name":"Kappa statistics","selfAssesment":"New
"},{"code":"IP4-2-1-5","description":"These two quality assessment indicators are calculated as follows:\r\nPrecision = TP/(TP+FP) \r\nRecall = TP/(TP+FN),\r\nwhere TS is true positive, FP is false positive, FN is false negative","name":"Precision & recall","selfAssesment":"New
"},{"code":"IP4-2-1-6","description":"Geometric correction procedures (image-to-map rectification, image-to-image rectification) are used to rectify remotely sensed data to a standard map projection whereby it may be used in conjunction with other spatial information in a GIS to solve problems. The rectification process normally involves selecting ground control point (GCP) image pixel coordinates (row and column) with their map coordinate counterparts (e.g. meters northing and easting in a UTM map projection). Rectification requires that polynomial equations (that translate from image coordinates to map coordinates) be fit to the GCP data using least squares criteria. Depending on the distortion in the imagery, the number of GCPs used, and the degree of topographic reliefdisplacement in the area, higher -order polynomial equations may be required to geometrically correct the data. To determine how well the six coefficients derived from the least-squares registration of the initial GCPs account for geometric distortion in the inpit image, for each GCP, the root-mean-square error (RMSE) is computed.","name":"Root mean square error (RMSE)","selfAssesment":"In progress
\r\n\r\n"},{"code":"IP4-2-1","description":"A growing set of EO services and applications produce EO products that describe various aspects of the land, ocean and atmosphere. These products include for example image products at different processing levels, geometric measurements like in digital elevation models, semantic labelling products like land cover classifications, and EO-derived attribute products concerning air quality or other geophysical and biophysical parameters. Same as any geospatial data, EO products are not free of error and require accompanying documentation of their product quality. One term for describing different quality dimensions of an EO product is accuracy.\r\nAccuracy is a measure to estimate the uncertainty that originates from errors. An error is the deviation of a map value from a true value. The concept of error assumes well-defined phenomena where deviation results from imperfection of measurement equipment, environment effects, or imperfections of the observer. They cause gross errors and blunders, systematic errors, and random errors, for which different approaches are necessary to minimize error. Ideally, only random error remains that is probabilistic in nature and can be assessed with statistical approaches. For poorly defined phenomena, the concept of vagueness applies. For example in the case of thematic maps using fuzzy sets, the accuracy assessment requires a fuzzy approach as well. \r\nJudging error requires reference data with higher accuracy (by an order of magnitude) to which the map value can be compared. EO product quality dimensions about accuracy include thematic accuracy, spatial accuracy (both horizontal and vertical), radiometric accuracy, and accuracy of biophysical/geophysical parameter measurements. Respective equipment and approaches for reference data collection includes ground verification for thematic maps, GNSS positioning devices, field spectrometers, air quality sensors and in-situ biomass estimation. Ideally, reference data is collected in the field. In case of inaccessible areas of interest and/or if the service requirements allow it, approaches may rely on proxy reference data.\r\nThe design of the accuracy assessment procedure should be done with the EO product design to match the requirements of the EO service. For example, a thematic accuracy assessment consists of the main three components of response design, analysis, and sampling design. The response design ensures that reference data and map data are comparable at a location and specifies under which cases they agree or disagree. The analysis, usually performed with an error matrix, specifies which quality indicators will be calculated to quantify accuracy. The sampling design specifies the subset of locations at which the response design will be applied. Depending on the classification process and application case, different sampling strategies can be suitable (e.g. clustered sampling, stratified random sampling). \r\nFor other accuracy dimensions, respective accuracy assessment procedures exist, e.g. root mean squared error (RSME) for the positional accuracy assessment.\r\nAfter an accuracy assessment has been performed and the uncertainty in the EO product is understood, the challenge is to clarify how the uncertainty affects subsequent spatial analyses with the EO product. Different strategies exist that ignore error completely or that account for error by modelling uncertainty in the analysis outcomes. If uncertainty is judged low enough (or more hazardous, if users are unaware of the limited accuracy), subsequent analyses accept the EO product as true and ignore the accuracy value. If uncertainty is incorporated in subsequent analysis through uncertainty modelling, the results describe the bandwidth of outcomes, potentially supported with appropriate visualisations of uncertainty. The uncertainty modelling approach may greatly enhance the usability of the EO product, because it informs better how the error impacts the EO information and how much confidence a user should have in it.\r\nWith a new generation of EO products on the horizon and a largely increased user community, a large number of new applications is to be expected. They may also identify innovative accuracy assessment approaches. For example, the availability of EO archives with long time series of EO data led to response design protocols tailored to collect time series of reference data. The use of volunteered geographic information (VGI) as reference data has great potential, if approaches are implemented that ensure its reliability. Methods for object-based accuracy assessment are continued to be developed. Further, the increasing number of EO parameter products based on continuous variables creates the need to describe their accuracy. Finally, the focus on validation of EO products during EO service development and operation will make feedback from users available to service providers, ultimately leading to more meaningful EO products with more meaningful accuracy metrics and other quality indicators.","name":"Accuracy assessment","selfAssesment":"
Completed
"},{"code":"IP4-2-2","description":"The implementation of a service that provides remote sensing derived information on a regular basis introduces process-related quality criteria like the timeliness of information provisioning. For the case of refugee camp mapping, timely arrival of map information may be critical to support the decisions in planning facilities for humanitarian assistance.","name":"Timeliness","selfAssesment":"New
"},{"code":"IP4-2-3-1","description":"Completeness is a quality dimension that can apply to different data properties.The Data completeness is dealing with the completeness of an image, handling for example the effect of shadowing objects, sun flares on water surfaces or masking out by an object (e.g. propeller of a UAV). Spatial completeness is a feature on the area coverage. In photogrammetry (especially in stereophotogrammetry) its 3D version, the stereo completeness has extreme importance. In monitoring systems and applications the Temporal completenesster term features how the taken images represent a complete time series. The thematic completeness measure describes the image interpretation quality how the expected and defined classes are evaluated. This feature is important with the use of e.g. multiple classifiers.","name":"Completeness","selfAssesment":"New
"},{"code":"IP4-2-3-2","description":"In remote sensing we can speak about spatial consistency in the Consistency cluster. It represents the quality of image interpretation/understanding: how are the different objects or classes recognized/evaluated integrally. A bridge above a water surface, like river can be detected in pixel-wised manner, but the question is how coherent they are in the output map. This phenomenon has very close to the thematic consistency, where the recognition integrity is represented in this way. The topological consistency is defined mainly for network-type surface objects, like roads or rivers, where the connection of all atomic segments are rated by this measure. Urban mapping focuses on the built environment objects, where e.g. house-parcel inclusions are described by this feature. The temporal consistency is for monitoring again, representing for example the possibility or impossibility of land cover changes in time. Having multiple data sources (even airborne or terrestrial), their integral usage can be qualified by this measure.","name":"Consistency","selfAssesment":"New
"},{"code":"IP4-2-3-3","description":"Readability refers to the content of a map being presented clearly enough that the content can be perceived and understood by the user. This includes legibility, e.g. whether the text of a label is large enough to be read and has enough contrast to the background to be easily perceivable. Additionally, readability has a broader meaning that explains whether a product as a whole is simple enough to be understood and not too complex that essential information can be overlooked by the user.","name":"Readability","selfAssesment":"New
"},{"code":"IP4-2-3","description":"Gathering information about the quality of an EO product or service by letting the user test it. The feedback from the user enables to verify whether specific quality criteria have been met.","name":"User validation","selfAssesment":"New
"},{"code":"IP4-2","description":"A product in the sense of something that a user can use for a specific purpose requires a certain quality. Therefore, its accuracy needs to be judged with an accuracy assessment measure that the user understands and where he can interpret the meaning in relation to the purpose. The product has to be validated, i.e. it has to be known whether the product qualifies for use in a certain context. And in addition, the product needs to be available in time that the users can base their decision on it.","name":"Product quality","selfAssesment":"New
"},{"code":"IP4-3-1","description":"The cloud cover percentage indicates the amount of area in the remote sensing image extent that is covered with clouds and therefore cannot provide information about the Earth surface conditions.The actual types of clouds included may depend on the product, but the CEOS definition includes cloud shadow. Next to that, from an optical remote sensing point of view, clouds can be roughly classified in: opaque/dense clouds, mainly composed of droplets that are highly reflective in the VIS region and generally located at low-medium altitudes and cirrus, consisting of a large number of thin non-spherical ice crystals that are normally translucent in the VIS region, relatively highly reflective in the SWIR spectrum, and located at high altitude.\r\n\r\nThe goal of cloud cover percentage is to provide a quality measure of usable information in a surface reflectance image. Earth observation product catalogs support it as a query parameter, to enable searching for products with a cloud cover percentage below a given threshold.\r\nThis simplifies for instance use cases that require only fully clear products (0% cloud cover), and may save download and processing resources by only handling images that have some valid pixels. For instance, by only using products with a cloud cover percentage smaller than 99.95%. The measure also gives an estimate of the number of valid observations in a given geographical area, allowing a quick assessment of whether minimal data requirements for a specific use case are met.\r\n\r\nThe measure is a percentage of actual observations in an image, so pixels where no data was recorded are not included. For derived products, cloud cover pixels are often also flagged separately from pixels where no data was recorded, but this may depend on the data provider. The definition specifically also includes cloud shadow pixels.\r\nReliable cloud cover percentages depend on good cloud and cloud shadow detection methods. Especially handling of translucent cirrus clouds is an open issue: a product that has a 100% cloud cover percentage due to cirrus clouds might still be usable for some cases, while for other cases they also render the product useless. \r\n\r\nThe used cloud detection algorithm will also affect the cloud cover percentage. A more strict algorithm will yield higher percentages compared to an algorithm that under detects clouds.\r\nDue to these limitations, cloud cover percentages in product metadata have a fairly high error margin. The user should take this into account when determining optimal cloud cover percentage thresholds for the use case.","name":"Cloud cover percentage","selfAssesment":"Planned
"},{"code":"IP4-3-2","description":"The remote sensing lifecycle structures all possible phases of the data production process, from its beginning of the data's coming to existence (that includes the sensor design prior to data collection) over storage, processing and use to archiving and deletion.","name":"Remote sensing lifecycle","selfAssesment":"New
"},{"code":"IP4-3-3","description":"The capability of a sensor or EO product to resolve anything is a function of its (spatial, temporal, spectral and radiometric) resolution and of the detail at which a geographic phenomenon of interest manifests itself in time and space. A geographic phenomenon can be named or described, georeferenced and provided with a time interval at which it exists. The geographic phenomenon of interest is the one of which a user needs information to help him make a decision. Therefore, the geographic phenomenon needs to be resolved with a low enough uncertainty and a high enough quality that allows the user to make a decision with confidence. \r\nFor example, let’s consider a helicopter pilot that wants to know whether a specific site is suitable for an emergency landing. The decision to perform an emergency landing may be supported with an EO-derived digital map of emergency landing sites that are flat enough (as well as large enough for the pilot’s helicopter and free of any obstacles on the surface and in the approach area). If we only focus on the flatness of the terrain, we need a digital elevation model (DEM) of high enough spatial resolution and accuracy in the Z dimension to calculate slope within acceptable levels of uncertainty. The pilot probably can tell us what degrees of slope are okay for his helicopter and tell us sites (e.g. football fields) where such a landing would succeed. However, this is only the input to an analysis of different DEMs to identify the minimum spatial resolution and accuracy in the Z dimension to model slope products and associated uncertainty to derive an emergency landing site product that fulfils the requirements. Thereby the capability of different DEMs to resolve emergency landing sites can be analysed.\r\nSpatial resolution is a measure of the smallest angular or linear separation between two objects that can be resolved by the remote sensing system. A useful heuristic rule of thumb is that in order to detect a feature, the nominal spatial resolution of the sensor should be less than one-half the size of the feature measured in its smallest dimension.\r\nOther types of resolution of an EO dataset are available that determine for various geographic phenomena under investigation whether it is possible to resolve them in the data. These are radiometric resolution, spectral resolution and temporal resolution. Radiometric resolution is defined as the sensitivity of a remote sensing detector to differences in signal strength as it records the radiant flux reflected, emitted, or back-scattered from the terrain. Spectral resolution is the number and dimension (size) of specific wavelength intervals (referred to as bands or channels) in the electromagnetic spectrum to which a remote sensing instrument is sensitive. The temporal resolution of a remote sensing system generally refers to how often the sensor records imagery of a particular area. For time-series analysis, the temporal resolution determines the time granularity for resolving processes that underlie the change that is observable between subsequent images.","name":"Capability to resolve anything","selfAssesment":"In progress
"},{"code":"IP4-3-4","description":"The spatial coverage of a dataset (consisting of an image or a series of images) determines whether the dataset covers the area of the terrain that is of interest to the user of information derived from the dataset.","name":"Spatial coverage","selfAssesment":"New
"},{"code":"IP4-3-5","description":"The temporal validity of a dataset (consisting of an image or a series of images) determines whether the acquisition date(s) (and period) match(es) the requirements for investigating a specific phenomenon and thereby enables the derivation of information about that phenomenon.","name":"Temporal validity","selfAssesment":"New
"},{"code":"IP4-3","description":"Values (or a value) that enable(s) judging a dataset or product on their fitness for a specific purpose (e.g. whether a specific satellite image is suitable for mapping landslides). , A QI should provide sufficient information to allow all users to readily evaluate a product’s suitability for their particular application, i.e. its “fitness for purpose”.","name":"Quality indicators","selfAssesment":"New
"},{"code":"IP4","description":"Data quality, in general, is the degree of data usability in relation to a specific application purpose. Assurance of data quality is of growing importance in remote sensing, due to the increasing relevance of remote sensing data in planning and operational decision of public bodies and private firms, and the huge amount of digital services (or apps) that exploit RS data. \r\nDifferent data quality dimensions exist according to the lifecycle phases of the remote sensing data: data acquisition, data storage, data pre-processing, processing and analysis and data visualization and delivery. Remote sensing data acquisition phase involves the following quality aspects: resolution, accessibility, spatial accuracy, temporal validity, accuracy and precision of the sensor calibration. Resolution is a multi-dimensional concept that includes the following dimensions: spatial resolution, temporal resolution, radiometric resolution, spectral resolution and temporal resolution. Temporal validity refers to the quality of an remote sensing data product in time, whereas spatial accuracy refers to the accuracy of the position of features relative the Earth. \r\nData storage includes the accessibility and completeness data quality dimensions. Accessibility includes both temporal and data accessibility. Temporal accessibility refers to the time delay between data acquisition and data delivery, whereas data accessibility refers to the availability of remote sensing data. Data completeness encompasses temporal completeness, i.e. completeness of a time series represented a phenomenon, thematic completeness, and spatial completeness which refers to the area coverage. Data preprocessing, processing and analysis phase includes consistency, completeness, temporal validity, resolution, radiometric and geometric accuracy, thematic and semantic accuracy. Thematic and sematic accuracy refers to the correctness of the remote sensing data product. The main quality dimensions of the data visualization and delivery include readability, completeness and temporal validity. \r\nDifferent metrics can be used to assess the quality of the remote sensing-derived information, such as the root-mean-square error (RMSE) measuring the differences between the true and measured values of the phenomenon under investigation, confusion matrix used for assessing the classification performance, producer’s accuracy, user’s accuracy or Cohen kappa. The quality of the remote sensing data per se can be assessed using Peak Signal-to-noise Ratio (PSNR) or the Universal Image Quality Index (UIQI).\r\nDifferent organizations are involved in the standardization of the image data and gridded data quality, including ISO/TC 211 ‘Geographic information/Geomatics’, Open Geospatial Consortium (OGC) or the Quality Assurance Framework for Earth Observation (QA4EO) developed by the Group on Earth Observation (GEO). These organizations are responsible for developing metadata standards that are further used by the remote sensing community to document the quality of the remote sensing data. According to the QA4EO, for example, all remote sensing data products need to be accompanied by a Quality Indicator (QI) which helps users assessing their fitness-for-use.","name":"Image data quality","selfAssesment":"Completed
"},{"code":"IP5-1-1","description":"Array databases make use of arrays as the primary storage representation. Such an array-oriented data model and query language is useful in many scientific applications, where the raw data consists of large collections of imagery or sequence data that needs to be filtered, subsetted, and processed.","name":"Array databases","selfAssesment":"New
"},{"code":"IP5-1-2","description":"The Open Data Cube (ODC) is a non-profit, open source project that was motivated by the need to better manage Satellite Data. This project was born out of the work done under the \"Unlocking the Landsat Archive\" and the Australian Geoscience Data Cube (AGDC) projects.","name":"Open data cube","selfAssesment":"New
"},{"code":"IP5-1","description":"The term data cube originally was used in Online Analytical Processing (OLAP) of business and statistics data. Technically speaking, such a data cube represents a multidimensional array together with metadata describing the semantics of axes, coordinates, and cells. It is an efficient approach to the management and analysis of large datasets.","name":"Data cubes","selfAssesment":"New
"},{"code":"IP5-2-1","description":"Content-based image retrieval helps users retrieve relevant images based on their contents.","name":"Content-based image retrieval","selfAssesment":"New
"},{"code":"IP5-2-2","description":"Web Portals allow users to discover, understand, view, access and query information of their choice from local to global level for a variety of uses.","name":"Web portals","selfAssesment":"New
"},{"code":"IP5-2","description":"Image archives are repositories for storing, managing and retrieving remote sensing data.","name":"Image archives","selfAssesment":"New
"},{"code":"IP5-3-1","description":"As an initiative stipulated by the European Commission to foster the bridge between the Copernicus ground segment and the user segment, the Copernicus data and information access service (C-DIAS) is a generic name for different sets of cloud-based platforms providing centralised access to Copernicus data and information, as well as to processing tools. The name indicates, however, that the focus of such advanced user-centred infrastructure implementations is not only on data access, but also on ‘information’. What is specifically meant here is the provision of information services and information layers as defined in the Copernicus service portfolio. This allows the users to develop and host their own applications in the cloud and a single access point, rather than processing data locally. Currently there are five different DIAS’s implemented (CREODIAS, SOBLOO, MUNDI, WEKEO, ONDA), all with some specific technical assets, or a sector-specific application focus or any other unique selling position by e.g. targeting as specific user community. Currently, the DIAS, which have received co-funding from the European Commission as a kind of seed funding, are currently in the process of exploring opportunities and claiming market shares, striving to sustain in a competitive manner. Some of the features are highlighted in the following, without explicitly mentioning any of the associated DIAS: (i) data access of global data sets (satellite data mosaics or gridded data) by custom area; (ii) OGC interfaces, VM catalogue, SPAR QL search interface (combine searches like receive images over areas of high population density), open source (accessible via API) or pay-per-use; (iii) access to core service products (e.g. CLMS, CMEMS, CAMS); (iv) focus on integrated applications such as smart cities, urban energies, precision agriculture; access to third-mission VHR satellite data (e.g. Pléiades); (v) utilizing GitLab as a developer platform.","name":"Data and information access service (DIAS)","selfAssesment":"Completed
"},{"code":"IP5-3-2","description":"The OpenGIS® Web Processing Service (WPS) Interface Standard provides rules for standardizing how inputs and outputs (requests and responses) for geospatial processing services are defined. It defines an interface that facilitates the publishing of geospatial processes and clients’ discovery of and binding to those processes.","name":"OGC interfaces and OGC web processing service","selfAssesment":"New
"},{"code":"IP5-3","description":"Online processing allows users to implement and run image analysis operations online independent of the underlying software.","name":"Online processing","selfAssesment":"Planned
"},{"code":"IP5","description":"In general, infrastructures such as cyberinfrastructures or Spatial Data Infrastructures (SDIs), allow information sharing across distributed infrastructures and communities. SDIs have gradually changed from a pool of authoritative data shared using standardized web services to a pool where the authoritative data co-exist with data collected by volunteers and different sensors. Many efforts were dedicated to data documentation, to improving the catalogues searching techniques by means of, for example, thesauri and to sharing these data using standardized web services such as Web Map Service, Web Feature Service or Web Coverage Service. Cloud computing technologies played an important role in the implementation of sustainable SDIs due to their ability to provide on-demand computational and storage capacities over the Internet. In this way, users can easily search, find and use data shared across different online platforms.\r\nMore specifically, infrastructures for image processing and analysis refer to the physical and organizational facilities that allow the storage, analysis and management of the available data and products. Traditionally, this infrastructure formed a digital image processing system consisting of computer hardware with special-purpose image processing software, and peripheral input-output devices (e.g. CD or DVD drives, internet access, printers/plotters). In recent years, Earth observation is undergoing a shift to online processing making use of data cubes and vast image archives, e.g. NSF EarthCube or Digital Earth Australia, the Swiss Data Cube, the EarthServer, the E-sensing platform or the Google Earth Engine. Available infrastructures aim at sharing remote sensing data and derived products following the FAIR metrics: Findable (F), Accessible (A), Interoperable (I), Reusable (R). Thus, remote sensing data have to be documented using metadata that support FAIR data principles as follows: (1) Findable: remote sensing data are findable through data documentation, i.e. metadata, that needs to include a unique identifier of the described data. Metadata can be stored in a catalog compliant to one of the available data cataloging standards such as the SpatioTemporal Asset Catalog (STAC) compliant catalog; (2) Accessible: all data have to be openly accessible and shared using interoperable formats that allow users to find, access and reuse them; (3) Interoperable: different standards, e.g. STAC specification, have to be used to document remote sensing data; (4) Reusable: metadata have to be comprehensive enough to allow users not only to assess the fitness for purpose (e.g. lineage) but also to provide them information about how to access the generated data.","name":"Infrastructure","selfAssesment":"Completed
"},{"code":"IP6","description":"In an information value chain, one or more organizations perform a set of value-adding activities for creating and distributing information products and services. They support a user in decision-making and thereby benefit the user’s purpose. The information value chain is a tool for evaluating business management and profitability. It enables explaining the ultimate “value” of a product and the components along the value chain and consequently allows businesses to optimize their processes. \r\nThe value of EO data can be assessed by analysing the contribution of the data to a specific EO information product and its effective use in decision-making. The (share of) benefit attributable to the use of the given EO data is derived from the comparison of a decision taken using the EO product to a counterfactual situation where other types of information are used instead. Often, this compares the situation before a new EO service was available to the situation afterwards. An ex-post analysis may reveal improved performances, e.g. gains in output, or productivity and/or reduced costs as compared to those occurring in absence of EO-derived information. This benefit resides with the user of the EO product and may be traced to societal and environmental benefits through impact chains.\r\nThe process of EO information production and distribution is integrated in the value chain and can be defined as the image processing chain. It comprises the value-adding activities of the organization(s) that lead up to the availability of an EO product for decision making. The nature and flow of these activities and the collaboration between organizations and among participants within organizations can be modelled with business process model and notation (BPMN). BPMN is a flowchart diagram that uses swimlanes representing different participants. Processes are assigned to participants and are connected with arrows into flow sequences. Further elements complete the choice of symbols for modelling a consistent flow, including a start event, end events, and branching options. They allow organizing the flow in parallel or iterative processes. Higher-level processes can be (de-)composed with sub-processes. Additionally, it is possible to use pools and message flows for explicitly modelling collaboration between participants (from different organizations).\r\nIn the image processing (value) chain, the sequence of processing steps begins with the acquisition of EO data, followed by steps of pre-processing and information extraction (or whatever steps are necessary) and ends with an EO information product being available to a user that uses it to make his decision. The collaborating stakeholders along the chain include EO satellite operators, EO data providers, EO information providers, and the users at the end of the value chain. The stakeholders along the processing chain each perform a dedicated subsequence of processing steps. Thereby, the stakeholders contribute their share of value to the data they deliver to the next stakeholder in the chain, ultimately arriving a the EO information product for the user. The EO data products that they hand on along the chain are often described with processing levels that provide different states of processing of EO data. They start with raw instrument data (level 0 and 1) that are followed by data converted into geophysical quantities that are geo-referenced and calibrated (level 2). Further levels are quality controlled data that has been mapped on a uniform space-time grid (level 3) and data combined with models or other instrument data (level 4). In addition, EO data providers use the term analysis ready data (ARD) that have been processed to allow direct data analysis, i.e. user processing effort is reduced to a minimum. Further, the standard EO products contain a categorizing element that is related to the image processing value chain. This categorizing element organizes the EO products along the sequences of processing, descriptive analytics, predictive analytics, prescriptive analytics, aggregation, visualization, and distribution. Thereby, the products ultimately contribute to the actionable EO information product for the use in decision-making.","name":"Image processing (value) chain","selfAssesment":"Completed
"},{"code":"MDS","description":"MDS is a dimensionality reduction technique. It can be divided into Metric multidimensional scaling, Generalized multidimensional scaling and Classical multidimensional scaling.\r\n\r\nGeneralized multidimensional scaling is an extension of metric multidimensional scaling, in which the target space is an arbitrary smooth non-Euclidean space. In cases where the dissimilarities are distances on a surface and the target space is another surface, GMDS allows finding the minimum-distortion embedding of one surface into another.\r\n\r\nClassical multidimensional scaling is also known as Principal Coordinates Analysis, Torgerson Scaling or Torgerson Gower scaling. It takes an input matrix giving dissimilarities between pairs of items and outputs a coordinate matrix whose configuration minimizes a loss function called strain.","name":"Multidimensional scaling","selfAssesment":"Depricated (GI-N2K)
"},{"code":"no","description":"Models that describe the basic principles of randomness and probability in spatio-temporal data.","name":"Mathematical models of uncertainty: Probability and statistics","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI","description":"This knowledge area considers the organizational and institutional aspects related to GIS&T. The focus of this knowledge area is on the organizations active in the GIS&T domain, and what happens within and between these organizations. The knowledge area is structured around five units. One unit considers the key organizations in the GIS&T domain, covering relevant public sector organizations at different administrative levels as well as organizations in other sectors of society. Among the organizational aspects covered in this knowledge area are all organizational issues related to the implementation, use and management of GI and GIS within organizations. While all topics related to the organizational structures, procedures and management of GI(S) are grouped into one unit, another unit focuses on issues related to the human factor of using GI and GIS, i.e. people, their skills and competencies, and the development and evaluation of these skills and competencies in the context of GIS&T training and education. The knowledge area includes also several inter-organizational and institutional aspects of GIS&T. Particular attention is paid to the concept of geospatial data sharing, which is about the creation of `spatial data` connections and relationships between different organizations in the GIS&T domain. Spatial data infrastructures are developed to promote, facilitate and coordinate the sharing of spatial data among data providers and data users, and consists of several technological and non-technological components. Many related topics are considered in the knowledge area GI and Society (WS), which also addresses several non-technological aspects related to GIS&T. In addition to this, also the knowledge areas `Design and Setup of Geographic Information Systems`, `Geospatial Data\" and Web-based GI` include several topics that are closely linked to the topics that are considered in this knowledge area. It can be argued that in order to fully master the knowledge and competencies that are presented in these knowledge areas, also basic knowledge and understanding of the organizational and institutional aspects is required.","name":"Organizational and Institutional Aspects","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI1-1","description":"The development of an appropriate organizational model, which establishes the basic character of GIS operations, is a crucial element of the GIS management. The appropriate GIS organizational model for any organization is based on its intended role.Alternative GIS organizational models are based on differing arrangements concerning the scope of GIS, the degree of integration of GIS into business operations, the degree of centralization of GIS operation and use, and the degree of centralization of management control. Although many variations can arise from different combinations of these factors, GIS organizational models can generally be classified into three types: (1) enterprise GIS, (2) GIS data and service resource, and (3) GIS as a business tool (Somers, 1998).","name":"Organizational models for GIS management","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI1-2","description":"Management of GIS can be done in a more centralized or more decentralized manner. In a a so-called enterprise or information-framework GIS, an organizational unit may be established to manage the GIS environment and run the core system, whereas usage is decentralized. In environments where GIS is used occasionally by various users, it may be set up as a separate service with a designated group that manages the GIS and also controls users' applications services. A second decision that needs to be made after the choice between more centralized or more decentralized management of GI and GIS is about where to place the GI management. Alternative options are in a line organization, in a support area, or at the executive level, each with their own advantages and disadvantages.","name":"Managing GIS operations and infrastructure","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI1-3","description":"User roles describe the relationship between different users and the GIS in an organization. Each user role includes responsibilities (e.g. for modifying certain information) and privileges (e.g. for viewing specific information). Although many different roles can be defined, a basic distinction is made between users, who can only view certain information, and editors, who can edit certain information.","name":"User roles","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI1-4","description":"A GIS management strategy should be unique for each organization, as organizations have unique environments, characteristics, goals, GIS requirements. An important step in developing an effective strategy for an organization is to establish the strategic vision for GI and GIS in the organization and define its role and scope. Other elements that should be covered in the GIS Strategy are the degree of centralized management of the GIS, the placement of GIS management and support in the organization, involvement of users in GIS planning and implementation, coordination of users, organizational changes, preparation of users, personnel issues, transitions to GIS operations, integration into business operations, user support, data access, and integration of technology changes (Somers, 1998).","name":"Strategic planning","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI1-5","description":"Committee and team approaches are frequently employed for coordinating participants and users in multi-participant GIS projects. The aim of creating such committees and teams is to ensure that the varied interests of participants are addressed, as participants bring many different interests, application needs, data needs, priorities, organizational issues, and political interests to a common project the GIS. Common models for coordinating participants recognize that participants have three levels of interest in the GIS: policy, technical development, and usage. Different bodies can be established focusing on these different levels of interest: a technical committee focusing on the design and development of the GIS, an management committee providing policy guidance and support and a user`s group.","name":"Coordinating GIS Participants and Users","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI1-6","description":"After the development and implementation of a GIS within an organization, the challenge is to maintain the system and revise and update it when necessary. This means the performance of the GIS in terms of efficiency and effectiveness should be measured and monitoring, and feedback from users on the system and applications, on the data as well as on new needs should be collected. Particular attention should be paid to the maintenance of data sets.","name":"Ongoing GIS revision","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI1-7","description":"The introduction of GIS into organizational environments should be seen as a complex process of mutual adaptation (Nedovic-Budic, 1997). These technologies changes the established organisational processes and structures, while on the other hand the organisational context and culture modify the technological set-up and use. Therefore, knowledge and understanding of the relationship between technologies and organizations is necessary to increase the success of GIS implementations in organizations. Successful GIS implementation and adoption often require some degree of organizational change. However, this can be very difficult to effect because organizations are naturally resistant to it (Somers, 1998).","name":"Organizational changes","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI1","description":"GIS and T implementation and use within an organization often involves a variety of participants, stakeholders, users and applications. Organizational structures and procedures address methods for developing, managing, and coordinating these multi-participant users. The development of the appropriate organizational model for managing the GIS is crucial. In certain cases, changes to the organizational structure in place might be required. Strategic planning and the establishment of coordination structures can be considered as valuable instruments for managing and coordinating all involved users, while also the different user roles need to be assigned.","name":"Organizational structures, procedures and management","selfAssesment":"In Progress GI-N2K
"},{"code":"OI2-1","description":"GIS and T professionals can be hired for a wide range of different job positions, for which the precise skills, competences and qualifications needed will vary. Typical examples of GIS and T positions are GIS&T project managers, technicians, system developers and analyst. The recognition and certification of the competences people have acquired in informal and non-formal learning contexts is important to know which skills and competences individuals have and whether they meet the qualifications required for a certain job position.","name":"GIS and T positions and qualifications","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI2-2","description":"Making sure staff members have the necessary skills and competences to perform geospatial activities is necessary for an effective implementation and operation of GI within an organizations. Several training methods can be adopted to ensure the development of skills and competencies of staff members. A distinction can be made between formal and informal training, but also between internal and external training programs. Another relevant issue is the assessment and evaluation of the skills and competences of staff members, to determine their future training and development needs.","name":"GIS and T staff development and evaluation","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI2-3","description":"Programs and courses on GIS and T and related subjects are provided by a wide range of institutions. While in recent years also the use and integration of GI and GIS in primary and secondary education has received significant attention, GIS and T education is mainly organized by institutions of higher education, especially universities but also other higher education institutions. Analyses of the higher education GIS&T programs and courses in Europe showed that the offer of courses is very diverse, in terms of size (ECTS), educational level (EQF) and course content. Vocational training on GIS and T related topics is organized by different types of training providers, including the major GIS vendors, data and service providers, academic sector, professional organisations, but also the public sector.","name":"GIS and T training and education","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI2-4","description":"A curriculum is a systematic description of a study program, in terms of learning goals, structure and sequence, learning, teaching and assessment strategies and content. A curriculum consists of both a set of related required and elective - courses along with all direct and indirect skills, competences and learning outcomes resulting from these courses. In the process of curriculum design typically particular attention is assigned to objectives, teaching methods and educational strategies, while also attention should be paid to the content organization aspects and the global structure of the curriculum. The process of designing GIS&T curricula presents many challenges, as the design of the curriculum should be aligned to both the institutional context and the expected outcomes of the learning and teaching process (Prager, 2011).","name":"GIS and T curriculum and course design","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI2-5","description":"An important challenge in organizing GIS and T education and training is the choice and use of effective teaching and learning methods. These methods should follow recent technological developments and use the best technologies to help students acquire the necessary skills and competencies. Traditionally, most GIS and T programs and courses were taught in the context of a full-time, face-to-face setting, using traditional teaching methods such as lectures and lab-based computer practical sessions. In recent years, educational institutions and their teachers have been experimenting with more innovative teaching and learning methods, such as project-based and case-based learning, distance learning, integrated and inter-disciplinary lessons, collaboration with companies and other stakeholders, etc.","name":"GIS and T teaching and learning methods","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI2","description":"This unit addresses GIS and T staff and workforce issues within an organization, particularly as they relate to ensuring that GIS and T is appropriately used and supported. The focus of this unit is on the skills and competencies of professionals in the GIS and T domain: how can these skills and competencies be described and evaluated, and how can they be developed through training and education.","name":"GIS and T workforce themes","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI3-1","description":"Cost savings are an important driver or motivation for sharing geospatial data and information. As costs associated with collecting and maintaining geospatial data are high, sharing data means that users no longer need to duplicate data gathering and archiving, which leads to savings in terms of personnel, space/facilities, data acquisition and maintenance costs. One fundamental argument for sharing thus derives from scale economies in production. Because the cost of making data is high, there is a clear incentive to maximize the number of users of these data. Sharing allows data to be used repeatedly for many purposes, thus increasing their value without increasing their cost. Sharing data also leads to improved data quality. Moreover, in many cases, sharing data is the only way to get access to certain data sets, as the authority to collect and manage certain data lies with another public institution.","name":"Drivers and incentives for sharing geospatial data","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI3-2","description":"Sharing of geospatial data can be hindered or inhibited by several types of barriers. These include technological barriers, such as a lack of common data definitions, formats and models or incompatibility of hardware and software. Among the non-technological barriers are organizational, political and legal issues and elements, such as misaligned organizational missions, diversity in organizational cultures, conflicting organizational priorities, lack of funding, lack of executive and legislative support; restrictive laws and regulations, copyright issues, data privacy and data ownership issues. However, it should be noticed that many of these barriers have been decreased or eliminated in recent years.","name":"Barriers to geospatial information sharing","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI3-3","description":"The legal framework for geospatial data sharing is very wide and diverse, involving rules on data, coordination, standards, funding, etc. Moreover, these rules and regulations can take many different forms: legal acts adopted by parliament, executive orders or decisions, cooperation agreements, memoranda of understanding, bilateral arrangements etc. From a data perspective, the legal framework can be distinguished into two main types of policies: those that promote and those that hinder the availability of spatial data. Policies that promote spatial data availability can focus on different types of users (public bodies, private companies, citizens) and different types of use (public access, commercial and non-commercial reuse, reuse for performing public tasks). Among the policies that hinder the availability of spatial data are those dealing with privacy, liability, and intellectual property. The legal framework also includes legislation that applies to data or information in general, such as open data legislation, which may also be applicable to spatial data (e.g. legislation on freedom of information, copyright, etc.). Moreover, also general legislation relating to any interaction between people or any situation in everyday life (e.g. liability, contract law, competition law, etc.) will apply to spatial data sharing.","name":"Legal framework for geospatial data sharing","selfAssesment":"Completed
"},{"code":"OI3-4","description":"Several types of legal mechanisms for sharing geospatial data can be used. A data sharing arrangements can be formalized by a contract or agreement between the data provider and the data user. A particular type of agreement are the framework agreements, which are agreements between two or more organisations concluded prior to the datasets or services being required. These framework agreement can involve one or multiple spatial data sets or services. Partnership agreements are often used to formalize the data sharing agreements among a broader group of partners. Participation in such a partnership often means participants share their data with other participants and get access to shared data. Another relevant mechanism is the use of licenses, which are mechanisms to give organizations and people the permission to use spatial data sets and services. A license is legally binding, and defines the conditions of use of the related spatial data sets and services. In order to reduce the number of licenses used and ensure the harmonization of the terms in these licenses, the use of standard licenses is promoted. Also the use of open data licenses is promoted for sharing geospatial data, and strongly increased in recent years.","name":"Legal instruments for sharing geospatial data","selfAssesment":"Completed
"},{"code":"OI3","description":"Geospatial data sharing has become an essential element of the GI activities of organizations. Spatial data sharing can be defined as the electronic transfer of spatial data/information between two or more organizational units where there is independence between the holder of the data and the prospective user. Spatial data sharing has many advantages, but several technical and non-technical barriers must be overcome to put data sharing into practice. While the practice of spatial data sharing has substantially grown with the development of spatial data infrastructures, many consider data sharing as a crucial element for the success of these infrastructures.","name":"Geospatial data sharing","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI3b","description":"A Spatial Data Infrastructure can be defined as the collection of technological and non-technological components to facilitate and coordinate the exchange of and sharing of spatial data. The concept infrastructure is used to promote the concept of a reliable, supporting environment, analogous to a road or telecommunications network, that facilitates the access to spatial data. Data, metadata, access networks, standards, coordination, policies, funding, people and institutional frameworks are often considered among the key components of an SDI. \r\n\r\nSpatial data infrastructures often are defined and described as a complex and dynamic phenomenon. Among the main reasons for the complex character of these infrastructures are the many components a spatial data infrastructure consists of, the diversity of involved stakeholders, and the many different objectives and ambitions of these stakeholders. Technological advancements, such as the emergence of web 2.0 technologies, and societal changes, such as the increasing use of geographic information in everyday life, are often mentioned as important drivers behind the dynamic character of spatial data infrastructures. \r\n\r\nA key characteristic of spatial data infrastructures is the involvement of a large and diverse group of actors. Governments are often considered as the central actors in the development and implementation of spatial data infrastructure, since they are the major producers and users of geographic information. Governments at different administrative levels and in different thematic domains are involved in the creation, management, use and sharing of geographic data. But also private companies, non-profit organisations, research and education institutions and even citizens can participate in the development and implementation of a spatial data infrastructure. It is increasingly being argued that the involvement and engagement of each of these stakeholders group is essential to the realization of a successful spatial data infrastructure. \r\n\r\nSDIs have been developed in many countries worldwide at local, national and international levels. Often a distinction is made between a between the first generation SDIs that have data as their key driver and are based on a product model and second generation SDIs in which user needs are the key driver and that are based on a process or development model. The latest generations of SDI strongly focus on the inclusion and engagement of non-government actors and organizations in the development and implementation of the SDI. Although SDI are by default distributed systems, involving many organisations, some SDI might be developed rather in an hierarchical way, while others are following a networked approach.","name":"Spatial Data Infrastructures","selfAssesment":"Completed
"},{"code":"OI4-1","description":"The adoption and implementation of standards are two key phases in the standardization process, which starts with the definition of standardization requirements and the development of standards. The adoption and implementation of standards follows after the development phase. The distinction made between the adoption and implementation of standards is important: adoption entails the decision to apply standards, while the implementation relates to the integration of standards in software, in data development and in other processes. GI-Standards are one of the key components of each SDI, consist of both semantic and technical standards, and include standards related to the different architectural components of an SDI, i.e. standards related to spatial data sets and data products, web services, metadata and catalogues, encodings, etc.","name":"Adoption and implementation of standards","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI4-2","description":"The SDI policy framework includes the set of policies, strategies, initiatives and projects aimed at increasing access, sharing, and effective use of spatial data. SDI policies can be divided into strategic and more operational policies. Strategic policies define the broader framework and formal structure within which the SDI initiative is developed. Operational policies provide more practical tools to facilitate access to and use of the SDI, and address specific topics related to the collection, management, use, access and dissemination of spatial data. These operational policies include a broad range of guidelines, directives, procedures and manuals that apply to the day-to-day business of organizations in developing, operating and using an SDI. To guarantee the success of an SDI, it is important to recognize the wider policy context in which these SDI`s are developed, and to link them to the overall policy environment in the jurisdiction in which they are implemented. These include policies on open government and open data, environmental policies, digital government or e-government policies and other.","name":"Policies","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI4-3","description":"If is often argued that SDI implementation requires coordination, because without coordination all other SDI components would not be developed or would be developed in a very fragmented and inconsistent manner. In general terms, coordination is about bringing into alignment the activities of different stakeholders in the SDI landscape. A typical instrument to realize coordinate in the context of SDI, is the establishment of an effective SDI coordination structure. The SDI coordination structure should ensure that all stakeholders are involved in the development and implementation of the SDI, through the participation in one or more coordination bodies. Another important element is the establishment of clear roles and responsibilities for the different involved organizations, making a distinction between data users, data providers, services providers and a geo-broker.","name":"Coordination and organizational structure","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI4-5","description":"Funding an SDI is about guaranteeing the long-term financial security of an SDI, by obtaining and formalizing financing for the implementation and maintenance of the different SDI components. An SDI funding model provides the answer to the central question of where and how to seek funding for implementing and maintaining an SDI. Within an SDI often different funding models will be combined, as the selection of the most appropriate funding model will be linked to different activities and the associated costs. Costs of an SDI include both set-up costs (one off costs) and maintenance costs (yearly), of which certain costs need to be made for each data sets or each data provider and other costs for the infrastructure in general. The most commonly used SDI funding models are centralized government funding, decentralized government funding (e.g. for each data provider), partnership funding, funding through revenues, and government funding based on donor agencies or on European projects.\r\n\r\nThe shift towards open data and the adoption of open data policies had an important impact on the funding model of many SDIs, as governments and organizations no longer could rely on revenues from selling their data and had to look for other funding models. As a result, new pricing strategies are employed, such as the provision of fee-based supplementary services, such as advice or tailor-made products based on open data. Also freemium/premium models, in which a basic version of the dataset is offered as open data (freemium) but the full dataset is available for a fee (premium), were considered as an alternative approach. In many cases, the loss of revenues was compensated by other funding models, such as increased government funding.","name":"Funding an SDI","selfAssesment":"Completed
"},{"code":"OI4-5b","description":"SDI performance assessment is about collecting, analyzing and providing information on the performance of SDI initiatives. Assessment and evaluations of SDIs are a useful tool for those organizations and people directly involved in these initiatives, but also for researchers, citizens, journalists and other stakeholders. Decision makers and practitioners can use assessments to monitor the progress against the objectives of their SDI initiatives and to identify areas where improvement can be achieved. Assessment also allows to compare and benchmark the performance of different organizations or countries, and to learn from best practices. Finally, assessment also is relevant for accountability, since it enables governments and agencies to be held accountable for their decisions, activities and the resources they have invested. Assessment of SDIs, which deals with the collection and supply of information on the performance of SDI initiatives, should be seen as the first step in a logical consequence of collecting data, integrating this data in policy and management cycles and actually using the information. \r\n\r\nIn the past twenty years, many different SDI assessment frameworks have been developed by researchers and practitioners around the world. Examples of such frameworks are the INSPIRE State of Play Study, the Clearinghouse Suitability Index, the Organisational Maturity Matrix, the SDI Readiness Index, and the INSPIRE Monitoring and Reporting approach. Each of these frameworks focus on particular aspects and components of SDIs. In line with the categorization of open data assessment, also SDI assessments can be divided into three main categories: (1) readiness assessments, (2) implementation or data assessments, and (3) impact assessments. Readiness assessments analyse whether conditions are appropriate, and whether necessary components are in place for developing an SDI. Implementation or Data assessments evaluate whether geospatial data are available and accessible. Impact assessments explore the extent to which SDIs lead to benefits for government, citizens, business and society in general.","name":"SDI performance measurement and assessment","selfAssesment":"Completed
"},{"code":"OI4-6","description":"For a long time, SDI development has focused on the development and implementation of different components with the aim of facilitating the access to and sharing of spatial data. An key challenge in future SDI development will be the integration of these SDI`s in a wider context. In order to optimally take advantage of the data and services provided by an SDI, integrating these data and services into the processes and workflows of public and private organizations will be crucial. The concept of spatial enablement refers to the challenge of developing SDI`s in such a way that they provide an enabling platform that serves the wider needs of society in a transparent manner. Moreover, the diffusion of SDIs, together with the efforts to build a Global Earth Observation System of Systems (GEOSS) and other developments in industry and civil society should be considered as elements in a the realization of a vision on the next-generation Digital Earth.","name":"Next-generation SDIs","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI4-7","description":"The effective implementation of SDIs requires governance, which includes the structures, policies, actors and institutions by which the infrastructure is managed pertaining to decisions made for accessing, sharing, exchanging and using the relevant available spatial information. While SDIs themselves are considered as initiatives contributing to good governance or effective governance, a key challenge in the establishment of SDIs is the governance of the infrastructure itself. Governance of SDIs is essential for the implementation of different SDI components in a coordinated and consistent manner. The central challenge of governance is reconciling collective and individual needs and interests of different stakeholders in order to achieve common goals. This aims to reduce gaps, duplications, contradictions and missed opportunities in the production, management, sharing and use of the information that tend to occur in a multi-stakeholder environment.\r\n\r\nGovernance can be facilitated through the use of appropriate instruments which extend to various levels of government and take into account the distribution of powers and responsibilities among different actors and institutions with an interest in the infrastructure. The governance instruments should coordinate the activities and contributions of, inter alia, data producers, users, added-value services providers, and other stakeholders. More complex and inclusive models of governance are required to cope with the multi-level nature of SDI implementations of the current generation of SDIs. Effective and inclusive SDI governance structures are needed, that are both understood and accepted by all stakeholders. Governance of SDIs also requires expanding the scope of stakeholders to include the private sector, research bodies and other actors outside the public sector including citizens, to actively promote bottom-up and participatory processes, and to find the appropriate mechanisms and instruments to enable the participation of these non-government actors.","name":"SDI governance","selfAssesment":"Completed
"},{"code":"OI5-1","description":"Within the European Commission there are several key GI players. GIS activities in the Commission started since 1981 (e.g. DG REGIO, Eurostat, ) with the CORINE project, the creation of DG ENV and the creation of the European Environment Agency (EEA). Together with the DG Joint Research Centre (JRC), DG ENV and EEA are in charge of the coordination of INSPIRE: DG Environment acts as an overall legislative and policy co-ordinator for INSPIRE, the JRC acts as the overall technical co-ordinator of INSPIRE and EEA is in charge of several tasks related to monitoring and reporting, and data and service sharing under INSPIRE. Also several other EC institutions are actively involved in GI(S) policies and activities (DIGIT, DG GROW, DG AGRI, DG MOVE and many others).","name":"GI organization at the European Commission","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI5-2","description":"Although there may be certain differences between countries, in most countries many key organizations in the GIS&T field will be active at the central/federal/national level of government. Especially the traditional institutions for surveying and mapping play a key role in geospatial policies and activities. Several public authorities at the federal level are in charge of the production and maintenance of key reference and thematic data sets. In many countries, these national data producers were the leading actors in the development of national spatial data infrastructures.","name":"Federal and national government organizations","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI5-3","description":"Local and sub-national governments are often considered among the major users of geographic information in governments, as they often are involved in many different policy areas, in which many problems with a locational component need to be tackled. Geographic data produced and maintained by authorities at lower administrative levels are often more detailed and thus interesting for other users, both within and outside the public sector. As a result, local and sub-national governments are often involved in the establishment of these infrastructures because of the wide range of highly detailed geographic information they produce and manage. As many geographic data are linked to the activities and services of local organizations, the involvement of these organizations in the maintenance of data ensures that these data are up-to-date.","name":"Sub-national and local governments","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI5-4","description":"The European GIS&T landscape consists of many pan-European organizations and associations promoting the interest of and representing certain stakeholder groups. While some of these organisations are dealing with all sectors and aspects of geographic information, others have a more thematic focus (e.g. remote sensing, topography, geosciences) or represent a particular sector (e.g. research, business). In some cases, their clearly is an overlap in the mission and objectives of different organizations, and some organizations are working in the same field of interest. Some examples of pan-European organizations and associations are AGILE, EuroSDR, EUROGI, and EuroGeographics. Also at international level several membership organizations and associations exist.","name":"Pan-European and global associations and professional organizations","selfAssesment":"GI-N2K
"},{"code":"OI5-5","description":"The geospatial industry consists of companies working with location specific information or services. Within the geospatial sector, several areas of activities can be identified: 1) measuring, collecting and storing of data about geo-objects; 2) processing, editing, modelling, analyzing and managing that data; 3) presenting, producing and distributing the data; and 4) advising, educating, researching and communicating about processes and use of geo-information products and services. The sector consists of both small-and-medium-sized enterprises but also big companies, including surveyors, census hard-copy map providers, aerial photos providers, base map data providers, satellite and remote sensing imagery providers, software developers (GIS-related products and services providers as well as satellite image programming platform providers) and several others.","name":"The geospatial industry","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI5","description":"Several types of organizations play a key role in the execution and coordination of geospatial activities in society. Typically, a distinction is made between data providers and data users, while coordinating organizations exist to coordinate and support the geospatial activities of professionals and entities using GIS&T. Governments are often considered as the major users and producers of spatial data and spatial information. Within the public sector, spatial data are collected and used in different thematic areas and at different administrative levels (from local to global). However, the needs, interests, and capacities of organizations at each of these levels will be different, as well as their role in the development of spatial data infrastructures, and the execution of geospatial activities in general. Also the geospatial industry will exist of both data providers and data users, but also of organizations delivering products and services to support the collection and use of spatial data. Other key organization in the GI domain are professional organizations and associations, bringing together and representing the needs of organizations of a particular sector and/or geographic area.","name":"Organizations in the GIS and T domain","selfAssesment":"In progress (GI-N2K)
"},{"code":"PP","description":"The knowledge of physical laws and principles regulating the emission of e.m. radiation and its interactions with the matter, as well the ones related to the design, setting-up and control of EO platforms and related instruments, are of paramount importance for a right interpretation of EO measurements in relation with the investigated Earth's phenomena and parameters. The most important physical fundaments regards: the theory of electromagnetic waves propagation described by the Maxwell's equations, the theory of e.m. radiation and of its interaction with the matter, the methods and instruments for e.m. radiation measurement and/or generation, the fundamentals of thermodynamics and of mechanics. As far as Earth Observation is concerned, further, specific topics have to be addressed which are related to: spectral-specific matter-radiation interactions, natural (e.g. Earth, Sun) and artificial (e.g. MW) sources of e.m. radiations, atmospheric physics and radiative transfer equations, basic physics of e.m., optical and MW, sensors and sources, theory of satellites orbits, theory of rockets, physical fundaments of interpretation of optical and MW data collected by passive and active techniques.","name":"Physical principles","selfAssesment":"Completed
"},{"code":"PP1-1-1","description":"Electromagnetic radiation travels in wave form. All electromagnetic waves travel at the speed of 299.793 km/sec in a vacuum and very nearly the same speed in air. In quantum physics electromagnetic radiation is also described in terms of particles called photons whose energy is given by the equation E = hf where h is the Planck constant and f the frequency of corresponding wave. Electromagnetic wave propagation is fully described by the Maxwell Equations that unified in 1860s the laws of electricity and magnetism.","name":"Electromagnetic Waves and Photons","selfAssesment":"Completed
"},{"code":"PP1-1-10","description":"The solar constant S is a quantity denoting the amount of total (i.e., covering the entire solar spectrum) solar energy reaching the top of the atmosphere. It is defined as the flux of solar energy (energy per unit time) across a surface of unit area normal to the solar beam at the mean distance between the sun and the earth. Solar insolation is defined as the flux of solar radiation per unit of horizontal area for a given locality. It depends primarily on the solar zenith angle and to some extent on the variable distance of the earth from the sun. It can be computed as a function of latitude and the time of year taking into account of the secular variations of Earth's orbit eccentricity e, the oblique angle ε, and the longitude of the perihelion relative to the vernal equinox ω. The daily insolation is the total solar energy received by a unit of area per one day. It may be calculated by integrating total insolation over the daylight hours. It is particularly important, together with information on cloud coverage, in order to plan and manage solar power systems. Yearly total insolation together with average cloud coverage are among the most important parameters to be considered for the choice of the best (i.e. the ones promising the higher energy production) location of solar power plants. Modeled daily solar insolation together with short/medium-term forecast of cloud coverage are also fundamental for the management (e.g. for planning the suspension of activities for maintenance) of solar energy production plants .","name":"Solar constant, solar insolation, daily insolation","selfAssesment":"Completed
"},{"code":"PP1-1-11","description":"Earth's itself represents the second (after Sun) most powerfull natural source of e.m. radiation for EO. Even if very less powerfull than Sun such a source is available for EO day and nigth. Its average emittance can be approximated by that of a blackbody at about 290 K. The maximum of its emission, following the Wien's Law, falls then around 10 micron (in the Thermal InfraRed - TIR spectral range) being Earth's emission trascurable in the VIS-SWIR range.\r\nMost of Earth's thermally emitted radiation falls in the spectral range 8-14 microns where it benefits of a quite high atmospheric transmittance (TIR atmospheric spectral window) in standard atmospheric conditions. However thick clouds prevent TIR radiation to reach satellite sensors (adsorbing and/or reflecting backward the radiation leaving Earth's surface) so that ground resolution cells affected by clouds are usually identified (cloud-mask) in the image pre-processing phase and not considered for further elaboration devoted to investigate surface properties. Even if very low in intensity, Earth's emitted radiation in the Far InfraRed (FIR) and in the MicroWaves (MW) spectral ranges are also used for quite important investigation related to the Earth's Energy balance (FIR) and for meteo-climatological applications. The complete transparence of Earth's atmosphere to the MWs, even in presence of meteorological (not precipitating) clouds make this Earth's emitted signal particularly important for application (e.g. climatological) requiring temporal continuity (all weather) of observations of Earth's surface properties like Temperature, Soil wetness, etc.. However, due to the weakness of the Earth's emitted signal in the MW ranges, such products can be achievable just at quite low spatial resolution (e.g. > 10km) by passive EO MW sensors","name":"Earth's radiation (intensity, spectrum, etc.)","selfAssesment":"Completed
"},{"code":"PP1-1-2","description":"In principle, the frequency f (and the wavelength λ=c/f) of an electromagnetic wave can take any value and the whole range of possible frequencies is called the electromagnetic spectrum. Different regions of the spectrum are conventionally given different names (with associated spectral ranges smoothly depending on specific science sector): \r\ngamma-rays\t λ< 1 pm\r\nx-rays\t1 nm >λ>1 pm\r\nUltraviolet (UV) 400 nm >λ>1 nm\r\nVisible (VIS) 700 nm >λ> 400 nm (blue: 455 – 492, green 492 – 577, yellow 577 – 597, red 622 – 700)\r\ninfrared (IR)\t1000μm >λ> 0,7 μm (Near-IR - NIR: 0,7-1,3; Short-Wave IR SWIR: 1,3-3; Medium IR - MIR: 3-6, Thermal IR - TIR: 6-20; Far IR - FIR: 20-1000)\r\nRadio waves\t λ> 1 mm (Microwaves MW\t1 m >λ> 1mm). Optical range (usually referring to the spectral range from VIS to TIR) and microwaves are the most important spectral region for remote EO systems.","name":"Electromagnetic spectrum","selfAssesment":"Completed
"},{"code":"PP1-1-3","description":"Maxwell equations are a set of coupled partial differential equations that contains the fundamentals of electricity and magnetism. These equations provide electromagnetic waves that propagate into the space at the speed of the light. Increasing the wavelength there are gamma rays, X-rays, ultraviolet, (visible) light, infrared, microwaves and radio waves.","name":"Maxwell Equations and EM waves' propagation","selfAssesment":"Planned
"},{"code":"PP1-1-4","description":"Planck's law is a mathematical relationship for the spectral radiance emitted by a blackbody (i.e. a body that absorbs all radiant energy falling on it) at a given temperature as a function of frequency or wavelength. From another point of view it can be used to define a black-body as a body emitting radiation following Planck's law. The model of black-body is fundamental to simplify the description of the radiation thermally emitted by a generic body at a pre-fixed temperature and wavelength as the product of its (specific) spectral emissivity and the value predicted (at the same wavelength) by the Planck's law for a black-body at the same temperature. This way the radiation thermally emitted by a generic body can be expressed just as a (specific, as modulated by the spectra emissivity) fraction of the one expected for a black-body. Wien’s displacement law is the relationship between the temperature of a blackbody and the wavelength at which it emits the most radiation. Wien found that the product of the peak wavelength and the temperature is an absolute constant. As far as the temperature T of the blackbody increase the intensity of the emitted e.m. radiation increases being, at whatever wavelength, grater than the one emitted by a blackbody at lower temperature (Planck). As far as the blackbody temperature increases its maximum emission occurs at lower and lower wavelengths. Wien's law is fundamental both in the selection of the spectral bands more appropriate for observing specific phenomena as well as for remotely retrieve temperature of far objects by the analysis of the emitted spectral radiances.","name":"Planck law for the black body. Wien's displacement law","selfAssesment":"Completed
"},{"code":"PP1-1-5","description":"The Rayleigh–Jeans Law is an approximation of the Planck’s law for a blackbody that states that, under certain conditions, emitted radiance is directly proportional to the blackbody temperature. Such an approximation, fits quite well with measurements of radiation emitted by sources at around 300K of temperature (like, in average, for the Earth) at wavelengths higher than 1mm (microwaves).. Wien’s approximation can be used to describe the emission spectrum of a high temperature blackbody n the VIS-NIR spectral range lengths. The estimated errors is less than 2% at wavlengths less that 5microns when a blackbody at around 6000K (like the Sun photosphere) is considered. \r\nThe Rayleigh–Jeans approximation is widely used in the processing of satellite images collected by passive MW sensors. Its extension to the thermal infrared spectral range (TIR) is also used for calibrating TIR satellite images (in this case linearity can be guaranteed just by steps on different brigthness temperature intervals).","name":"Rayleigh-Jeans approximation. Wien's approximation","selfAssesment":"Completed
"},{"code":"PP1-1-6","description":"The total radiant intensity B(T ) of a blackbody at the absolute temperature T can be derived by integrating the Planck function over the entire wavelength domain from 0 to∞. Since blackbody radiation is isotropic, the flux density emitted by a blackbody is therefore F = π B(T ) which is proportional to the fourth power of the absolute temperature T through the Stefan-Boltzmann constant σ = 5.67 × 10−8 J m−2 sec−1 deg−4.\r\nKirchoff's law establishes that for a medium at the thermodynamic equilibrium, the spectral emissivity ε(λ) at a given wavelength λ, is equal to the its spectral absorbance, A(λ) at the same wavelength λ. Hence ε(λ)=A(λ) at each fixed λ, for a blackbody ε(λ)=A(λ)=1 at whatever λ. Kirchoff's law is valid also in Local Thermodynamic Equilibrium (LTE) conditions as the ones usually occurring in (small volumes of) the Earth's atmosphere even in the most turbulent conditions.\r\nKirchoff's law has important applications also for the study of spectral signatures of mineral and rocks and, in general, of opaque - i.e. with spectral transmittance T(λ)=0 - bodies. In that case, the relation which relate the spectral reflectance R(λ), absorbance A(λ) and transmittance T(λ) of a body: R(λ)+A(λ)+T(λ) =1\r\nreduce to R(λ)+A(λ)=1 and in LTE conditions, thanks to the Kirchoff's law: \r\nR(λ)+ε(λ)=1 which allows to obtain measurements of spectral emissivity indirectly through (more simple and stable) measurements of spectral reflectance:\r\nε(λ)=1-R(λ)\r\nRocks and mineral exhibit important (diagnostic/discriminating) signatures in their spectral emissivity in the thermal infrared (TIR) region. Measuring spectral emissivity in a laboratory (particularly if samples have to be characterized for their properties in natural conditions) is a quite difficult task due to the difficulty to insolate the sample from the lab environment (and instruments themselves) all emitting approximately at the same (environmental) temperature. Kirchoff's law allows to obtain, for opaque bodies, spectral emissivities from spectral reflectances measurements which are much easy to realize in normal remote sensing labs.","name":"Stefan–Boltzmann law. Kirchoff law","selfAssesment":"Completed
"},{"code":"PP1-1-7","description":"All bodies at a temperature T>0 K emit electromagnetic radiation at all wavelengths (thermal emission). Such emission at each wavelength is increasing with T and it is maximum for Black Bodies whose spectral emittance I(λ,T) (at each prefixed T and wavelength λ) is defined by the Planck function B(λ,T). Generic bodies are expected to thermally emit less than a black body (having the same temperature T) at whatever wavelength. Spectral emissivity ε(λ) is defined as the ratio of the spectral radiance I(λ,T) emitted by a generic body and the one emitted by a Black Body at the same temperature, i.e. ε(λ)= I(λ,T) / B(λ,T). By definition its value is less or equal (Black Body) than 1. The spectral emissivity concept allows to describe in a simple way the spectral radiance I(λ,T) thermally emitted by a body at a temperature T by I(λ,T)= ε(λ)*B(λ,T). It is possible to invert the Planck Function to obtain from the emitted radiance at a prefixed wavelength the temperature T=f(B, λ) of the emitting Black Body. If in such expression the spectral radiance I emitted by a generic body is used instead than B, the resulting temperature, Tb=f(I, λ), is named Brigthness Temperature being Tb<=T (with Tb=T in case the emitting body is a Black Body). The concept of Brigthness Temperature is substantially a different way to measure the spectral radiance of a generic body. It is usually preferred (for instance calibrating Thermal InfraRed – TIR – satellite images) because the interpretation of such a digital image is much more intuitive than when spectral radiances are used instead. In fact, as at each prefixed temperature generic bodies are less emitting than Black Bodies, wherever across a digital satellite image we consider the values of reported Tb, we can say that the actual temperature T of the corresponding emitting ground resolution cell is not less than Tb.","name":"Concepts of Spectral Emissivity and Brightness Temperature.","selfAssesment":"Completed
"},{"code":" ","description":" ","name":" ","selfAssesment":" "},{"code":"PP1-1","description":"EM radiation is created when an electrically charge particle, such as an electron, is accelerated by a force causing it to move. The movement produces oscillating electric and magnetic fields which travel, as an harmonic EM wave, at right angles to each other. EM waves travel at 299,792,458 meters per second in a vacuum (the highest possible speed into the Universe, also known as the speed of light). \r\nThe electromagnetic field propagating through the space as EM waves is also referred as electromagnetic radiation. \r\nAn EM wave is characterized by a frequency (or by a wavelength) and by an amplitude (or by an energy). \r\nThe wavelength is the distance between two consecutive peaks of a wave. This distance is given in meters (m) or fractions thereof. Frequency is the number of waves that form in a given length of time. It is usually measured as the number of wave cycles per second, or Hertz (Hz). It is wave speed=frequency*wavelength so that, an EM wave traveling at the speed of light, can be equally identified by its wavelength or by its frequency. The amplitude (i.e. the maximum oscillation of the EM field) provide the intensity (i.e. the energy) of the EM wave. \r\nThe classical theory describes the EM radiation as electromagnetic waves which represent the oscillations of electric and magnetic fields. In the quantum mechanics theory EM radiation consists of photons, quanta of the electromagnetic energy, responsible for all electromagnetic interactions.\r\nAs far as Earth remote sensing is concerned EM radiation represents the most important vehicle of information.","name":"EM radiation","selfAssesment":"Completed
"},{"code":"PP1-2-1","description":"The study of the absorbption/emission of electromagnetic radiation by atoms. Depending on the atomic number characteristic frequency or wavelength are absorbed or emitted. Since each element has a characteristic spectrum of absorbed/emitted wavelengths (spectral signature), atomic spectroscopy allows the determination of elemental compositions even of remote objects (e.g. stars, galaxies, etc.).\r\nStarting from the simple Bohr’s model it is possible to predict quite exactly the frequencies of e.m. radiation selectively absorbed/emitted by all atoms. Depending on the atomic number Z, characteristic frequencies f are absorbed or emitted by atoms corresponding to the electronic transitions from different energetic (quantized) states following the Bohr’s condition: fab=(Eb- Ea)/h, being Ei=-cost∙Z2/(ni)2 the electron energy corresponding to the state/level i (principal quantic number ni). By this way each atomic species has a characteristic spectrum of absorbed/emitted frequencies (atomic spectral signature) so that atomic spectroscopy allows the determination of elemental compositions even of remote objects. By this way the existence of Helium was discovered in the 1968 by Jansen and Lockyer in the Sun photosphere well before its discover on the Earth, and the knowledge of the chemical composition of stars and galaxies was possible well before the end of XIX century. Atomic spectroscopy provides a simple and powerful introduction (through the explanation of the more complex interactions of e.m. radiation with molecules and solid matter) to the fundamental concepts of spectral signature (which is at the base of most of the applications of aerial remote sensing of the Earth’s surface) and atmospheric windows (important for the design of optical sensors devoted to remotely sense Earth’s surface) being moreover propaedeutic to the understanding of methods for the atmospheric vertical sounding based on the concepts spectral lines broadening and related weighting functions.","name":"Atomic spectroscopy","selfAssesment":"Completed
"},{"code":"PP1-2-10","description":"The Rayleigh roughness criterion is a widely used means to estimate the degree of roughness of a considered surface. Considering the phase difference between two rays scattered from separate points of the surface, this depends on the roughness height, the incident angle and, inversely, on the radiation wavelength (λ). The Rayleight criterion states that a surface can be considered as smooth if the phase difference is less than π/2 radians.\r\nAs a consequence, in the case of normal incidence, irregularities must be less than λ/8 in height to have an effectively smooth surface. In particular: i) at optical wavelengths (e.g. 0.5 micrometers), roughness height must be less than about 60 nm to have a specular reflection from a surface. Only certain man-made surfaces (e.g. sheets of glass or metal) may meet such a condition; ii) at VHF radio wavelengths (e.g. 3 m), roughness height need only to be less than about 40 cm. Unlike the previous case, a number of natural surfaces may meet this condition.\r\nIt is worth noting that large values of the incident angle may satisfy the criterion more easily as compared with the normal incidence. This means that a moderately rough surface may be effectively smooth at glancing incidence. This condition may be easily experienced when eyes are struck by the glare of reflected sunlight from a low sun over an ordinary road surface.","name":"The Rayleigh roughness criterion","selfAssesment":"Completed
"},{"code":"PP1-2-11","description":"The Bidirectional Reflectance Distribution Function (BRDF) is defined as the quotient between the spectral radiance reflected by a sample and the spectral irradiance from the source that illuminates it. It depends on both the incidence and viewing angles. From this point of view it represents an absolute definition of reflectance whose value, as is known, depends on the geometry of the illumination and observations directions.","name":"Bidirectional Reflectance Distribution Function (BRDF)","selfAssesment":"Planned
"},{"code":"PP1-2-12","description":"Measurements of BRDF allow to compare spectral signatures obtained in different laboratories in an optimal way. However its measure require well calibrated sources and quite expensive laboratory equipments. The concept of BRF (Bidirectional Reflectance Factor) allows a more simple, indirect, measurement of BRDF by using a reference sample (highly reflective so usually named \"white reference WR\") of known BRDF and two subsequent measurements of reflected radiance (one from the WR, one from the sample) obtained under identical illumination conditions. In these conditions results BRDF(sample)=BRF(sample)xBRDF(WR)","name":"Bidirectional Reflectance Factor (BRF)","selfAssesment":"Planned
"},{"code":"PP1-2-2","description":"The molecular absorption spectral corresponds to the wavelengths from 190 nm up to 1000 nm and it interprets the measured absorption of radiation, when it is passing through a gas, a liquid or solid. Their absorbed energy in different states can be approximated by electronic, vibrational and rotational energy","name":"Molecular absorption spectra","selfAssesment":"Planned
"},{"code":"PP1-2-3","description":"The spectral line is a result of interactions of photon with a quantum system, while it extends over a range of frequencies. The center wavelength of its energy levels may be changed due to Broadening, namely collisions of atoms and molecules or their differences in thermal velocities.","name":"Line shape and (natural, pressure, Doppler) broadening","selfAssesment":"Planned
"},{"code":"PP1-2-4","description":"When the altitude ranges from about 20 to 50 km, spectral line shape is determined by both collisions (Pressure Broadening) and differences in thermal velocities (Doppler broadening). This shape is referred to as the Voigt profile and it satisfies the condition of normalization.","name":"Voigt's line profile","selfAssesment":"Planned
"},{"code":"PP1-2-5","description":"Radiation that is not absorbed or scattered in the atmosphere can reach and interact with the Earth's surface. There are three (3) forms of interaction that can take place when e.m. radiation strikes, or is incident (I) upon a surface. These are: absorption, transmission, and reflection. The total incident radiation will interact with the surface in one or more of these three ways. The proportions of each will depend on the wavelength of the incident radiation and the specific chemical/physical properties of the surface material. Absorption occurs when incident radiation is absorbed into the target, while transmission occurs when radiation passes through a target. Reflection occurs when radiation \"bounces\" off the target and is redirected. The spectral reflectance is defined by the ratio of reflected radiance to incident radiance at a prefixed wavelegth . The spectral transmittance of a medium is defined by the ratio of the transmitted radiance to the incident one at a prefixed wavelegth . The absorbance of a medium or target is defined by the ratio of the absorbed radiance to the incident one at a prefixed wavelegth . Conservation of energy require that, at a certain wavelenght: R+T+A=1. To express the circumstance that the reflection can occurre in different direction as the surface deviates from a specular one, becoming rough, the concept of surface scattering has been introduced (ref. [PP1-2-10] The Rayleigh roughness criterion).","name":"Concepts of Transmittance, Absorbance, Reflectance, Scattering.","selfAssesment":"Completed
"},{"code":"PP1-2-6","description":"The emitting capability of a body surface is described by the spectral emissivity, ε(λ), a dimensionless value ranging between 0 and 1 and varying on the basis of the wavelength (λ) and the geometric configuration of the surface. Formally, spectral emissivity can be defined as the ratio of spectral exitance, M(λ,T), from an object at wavelength λ and temperature T, to that from a blackbody at the same wavelength and temperature, MBB(λ,T).\r\nA blackbody is an ideal radiator that totally absorbs and then reemits all energy incident upon it. By definition the spectral emissivity of a blackbody is equal to one (the maximum) at whatever wavelength and temperature. A blackbody radiates a continuous spectrum. Real materials do not behave like a blackbody. Natural matter could radiates more in selected spectral region (like in the case of atomic or molecular gases) more frequently with a continuous spectrum (like in the case of solids) always with spectral emissivity minor or equal to 1. \r\nAnother important concept is the one related to the graybody. For gray bodies, the spectral emissivity value is constant for each wavelength value, as for black bodies, but is always less than 1. Therefore, for any given wavelength the emitted energy of a graybody is a fraction of that of a blackbody. This behavior could be quite important even for limited spectral ranges. For instance the spectral emissivity of the sea in the TIR (Thermal InfraRed) spectral range 8-14 microns (TIR atmospheric window) can be assumed constant (about 0,98) with significant simplifications in the determination of SST (Sea Surface Temperature) from satellite sensors operating in that spectral region. \r\nAs said above, the emissivity of the most of the bodies present in nature varies depending on the wavelength. These objects are referred to as selective radiators or as being selectively radiant. This means that some materials may behave as black bodies at certain wavelengths (ε close to 1) and may have reduced emissivity at other wavelengths.","name":"Concepts of Spectral Emissivity","selfAssesment":"Completed
"},{"code":"PP1-2-7","description":"\"Radiation that is not absorbed or scattered in the atmosphere can reach and interact with the Earth's surface. There are three (3) forms of interaction that can take place when energy strikes, or is incident (I) upon the surface.\r\n These are: absorption (A); transmission (T); and reflection (R). The total incident energy will interact with the surface in one or more of these three ways. The proportions of each will depend on the wavelength of the energy and the material and condition of the feature. Absorption (A) occurs when radiation (energy) is absorbed into the target while transmission (T) occurs when radiation passes through a target. Reflection (R) occurs when radiation\r\n \"\"bounces\"\" off the target and is redirected. The reflectance R is defined by the ratio of reflected radiant power to incident radiant power. The transmittance T of a medium is defined by the ratio of transmitted radiant power to incident radiant power. The absorptance A of a medium or target is defined by the ratio of absorbed radiant power to incident radiant power. Conservation of energy require that, at a certain wavelenght: R+T+A=1. To express the circumstance that the reflection can occurre in different direction as the surface deviates from a specular one, becoming rough the concept of surface scattering has been introduced. However, the concept of scattering concerns mainly atmopheric interaction with ELM and radar systems.\"","name":"Complex dielectric constants and refractive indices","selfAssesment":"Planned
"},{"code":"PP1-2-8","description":"The complex part nc of the refraction index n determines how far an e.m. wave of wavelength λ can survive crossing a specific medium. The attenuation length la is the distance after that the amplitude of an e.m. signal reduces its value by an amount of 1/e. For instance the amplitude of the Electric field E(z) of an e.m. wave proceeding along the z direction is decreasing as exp(-z/la) being la=λ/(2𝜋 nc) the attenuation length associated to that specific material (nc) and wavelength λ. This way attenuation length in water can be of hundreds of meters in the visible range and just few microns in the microwaves. So that penetration of radiation in the matter depends on both, the specific (dielectric) properties of the matter (through nc) AND the specific wavelength λ of considered e.m. signal.","name":"EM rad. penetration in the matter: Attenuation Length","selfAssesment":"Planned
"},{"code":"PP1-2-9","description":"EM radiation impinging a rough surface is (partly) reflected back (scattering). Lambertian surfaces produce a diffuse scattering (i.e. radiation is reflected similarly in all direction) and then appear equally bright from all directions, whereas specular surfaces behave like a mirror, with reflected radiation all aligned in one direction, with the reflection zenith angle equal to the incident angle of incoming radiation. Generally, the degree of \"roughness\" of a surface determines if it behaves like a Lambertian or a specular surface.","name":"Scattering from rough surface: Lambertian and specular surfaces.","selfAssesment":"Planned
"},{"code":"PP1-2","description":"E.M. Radiation can be absorbed, scattered, emitted and transmitted by the matter. The results of such interactions (i.e. the fraction of incident radiation that is absorbed, scattered or transmitted) or emission process (i.e. the fraction of actually emitted radiation in comparison with the one expected from a black-body at the same temerature) strongly depend on the radiation wavelength and on specific chemical (e.g. composing atoms and molecules as well as their arrangement within solid cristals) and physical (e.g. Temperature, Dimensions and Shape, Roughness) properties of the matter. In some case, the result of Radiation - Matter interaction is strongly affected by observational conditions. For instance, over some angular distance between the directions of incidence and the one of measurement of the radiation, sun-glint can occur which completely mask any other results. A basic principle of the remote sensing put univocally in relation spectral absorbance, reflectance, transmittance and emissivity, curves achievable by multi-spectral EO measurements, with matter having specific chemical/physical properties. Theoretical models of radiation-matter interaction at the Earth's surface and through the atmosphere provide then suitable strategies for retrieving, from multi-spectral measurements of the radiation leaving the Earth, the most relevant chemical/physical properties of the matter composing its surface and atmosphere.","name":"Radiation - Matter interaction","selfAssesment":"Completed
"},{"code":"PP1-3-1","description":"The natural objects can either emit radiation (radiance, emittance) or be \"illuminated\" by a source (irradiance). In the following a series of definitions for each of these terms is provided. \r\nThe first basic radiometric quantity is the radiance (Iλ) and it is defined as the ratio of the differential radiant energy (dE) to the product of effective area (dA) with the time interval (dt), wavelength interval (dλ) and differential solid angle (dΩ). Iλ can be also referred as monochromatic intensity and it is expressed in units of energy per area per time per wavelength and per steradian (W m−2 sr−1). \r\nThe monochromatic flux density (Fλ) or the monochromatic irradiance of radiant energy is defined by the normal component of Iλ integrated over the entire hemispheric solid angle. It is expressed in units of energy per area per time per wavelength (W m−2). For isotropic radiation (i.e., if the intensity is independent of the direction), the monochromatic flux density is then Fλ = π Iλ. \r\nThe total flux density of radiant energy (F), or irradiance, for all wavelengths (energy per area per time, i.e., W), can be obtained by integrating the monochromatic flux density over the entire electromagnetic spectrum.\r\nAll the above definitions refer to a point source of radiation. When the flux density or the irradiance is from an emitting surface (i.e., an extended widespread source), the quantity is called the emittance. When expressed in terms of wavelength, it is referred to as the monochromatic emittance. The intensity or the radiance is also called the brightness or luminance (photometric brightness). The total flux from an emitting surface is often called luminosity.","name":"Radiometric quantities: radiance, irradiance, flux, brightness, emittance, luminosity, etc.","selfAssesment":"Planned
"},{"code":"PP1-3-2","description":"The attenuation of radiation emitted from a source decreases with the square of the distance from its center based on inverse square law. It considers that the size of the sources increases with the square of their radius, causing the same rate of attenuation in flux density.","name":"Decay of the emittance with the square of distance from the source","selfAssesment":"Planned
"},{"code":"PP1-3-3","description":"The relative amount of electromagnetic radiation reflected (absorbed, transmitted, emitted) by the matter at different wavelengths depends on its specific chemical composition and physical properties. The plots of corresponding physical quantities (reflectance, absorbance, transmittance, emissivity) against wavelength, are termed spectral signatures of the specific matter under study. In principle the analysis of spectral signatures obtained by multispectral EO sensors could allow us to identify/discriminate different cover types.\r\nThe interpretation of spectral signatures requires to well understand the e.m. radiation-matter interaction process. In very simple term we expect that incident radiation I(λ)can be reflected, absorbed or transmitted by the matter so that for the energy conservation should be: \r\n\r\n\r\nI(λ)=I(λ,R)+I(λ,A), I(λ,T) \r\n\r\n \r\nbeing I(λ,R), I(λ,A) and I(λ,T) the reflected, absorbed and transmitted fraction of I(λ). From the previous relation descends (dividing both members for I) that:\r\n\r\n\r\n1=R(λ)+A(λ)+T(λ)\r\n\r\n\r\nbeing:\r\n\r\n\r\nR(λ)=I(λ,R)/I(λ) named Reflectance\r\nA(λ)=I(λ,A)/I(λ) named Absorbance\r\nT(λ)=I(λ,T)/I(λ) named Transmittance\r\n\r\n\r\nThey are all specific properties of the considered matter and are not independent each others.\r\nIn particular for an opaque medium with T(λ)=0 it is:\r\nR(λ)=1-A(λ)","name":"Spectral Signatures of the matter","selfAssesment":"Completed
"},{"code":"PP1-3-4","description":"Vegetation, water and soil represent the most common cover types of Earth surface. Their reflectances in the VIS/NIR/SWIR spectral range, plotted against wavelength in the 0,4-2,5 micron, represent the most important (basic) spectral signatures for whatever application devoted to Earth surface study. Other spectral signatures (e.g. in emissivity) in the Thermal InfraRed range are particularly important to infer specific properties of Mineral and Rocks (ref. [PP1-3-5] Spectral Signature of Mineral and Rocks). In order to discriminate among such basic cover types, the (ref. [IP3-1-2-3]) NDVI (Normalized Difference Vegetation Index) is the most simple and powerful diagnostic tool in the VIS/NIR spectral range \r\nNDVI values ranging between the values -1 and +1, are higly positive for fully vegetated (up to NDVI=1) or partly vegetated (NDVI>0,3) targets, still positive (>0) for bare soils, negative for water bodies. Values around zero are expected for clouds thanks to their similarly high reflectances both in the NIR and VIR spectral bands (ref. [PP1-3-6] Spectral Signature of Clouds). \r\n\r\nVegetation. a) in the visible range most of the incomig radiation is adsorbed by the photosynthetic process, transmittance is very low. The residual reflected radiation has a small peak of reflectance around 0.5 microns which is responsible of the green colour associated to vegetation by the human vision sytem (limited to the VIS spectral range); b) in the NIR range vegetation exhibits its higher reflectance together its higher transmittance (very low absorbance) so that leaf density can be estimated thanks to the the contributes (decreasing with depth) of underlaying leaf layers; c) in the SWIR spectral range (in particular in the water bands around 1,4 and 1,9 microns) it is possible to appreciate the vegetation water content. As much it is, as more incident radiation is absorbed and less is the reflected fraction of radiation.\r\nBare Soil. Spectral reflectance is normally increasing moving from the VIS to the SWIR spectral region. Water features around 1,4 and 1,9 microns give information on soil water content (see before). Others specific features are described in [PP1-3-5] Spectral Signature of Mineral and Rocks\r\n\r\nWater. Spectral reflectance of clean deep water is quite low reaching quickly the zero value as soon as wavelengths passe microns. However it is important to note that such a very low reflectance is due to a very high transmittance in the VIS range and to a very high absorbance in the NIR/SWIR regions (ref. [PP2-2-5-2] Attenuation Lenght and Penetration Depth). This means that water is quite transparent in the VIS spectral range (so that, in case of shallow waters, measured reflected radiance can be significantly increased by the contribution of bottom of the sea). Water is completely opaque, instead, in the NIR/SWIR. In this spectral region, even in presence of shallow waters, the presence of suspended matter (that increases the measured reflectance both in the VIS and NIR/SWIR ranges) can be better discriminated (than in the VIS) from the contribute of the bottom of the sea that, in this spectral range, is zero.","name":"Spectral Signature of Vegetation, Water, Soil","selfAssesment":"Completed
"},{"code":"PP1-3-5","description":"Spectral signatures of rocks and mineral provide information on their chemical composition and crystal properties, grain size and roughness over a wide range of wavelengths from the visible to the thermal infrared.\r\nIn the Visible and Near-InfraRed (VNIR; 0.4÷1.0 µm) region, spectral features are dominated by electronic processes in transition metals, such as Fe, Mn, Cu, Ni, Cr, etc. Therefore, iron is the most important constituent having spectral properties in the VNIR, and the iron-rich minerals are characterized by low reflectance (high absorbance) below 0.7 µm.\r\nOther minerals, which represent the major part of the Earth's surface rocks, such us Si, Al and some anion groups (e.g. silicates, carbonates, oxides) hydroxides, have less spectral features in the VNIR region, but exhibit much more evidences in the Short-Wave InfraRed (SWIR; 1÷3 µm) region. In fact, spectral features of hydroxyls and carbonates mark the SWIR region.\r\nThe hydroxyl ion is a widespread constituent occurring in rock forming minerals such as clays, micas, chlorite etc. It shows a vibrational fundamental absorption band at about 2.74÷2.77 µm and an overtone at 1.44 µm.\r\nCarbonates, which are commonly in the Earth surface rocks in the form of calcite (CaC03), magnesite (MgC03), dolomite [(Ca-Mg) C03] and siderite (FeC03), shows a typical absorbance feature around 2.3 µm, instead the water content can be instead evaluated by the depth of absorption at 1,4µm and 1,9 µm.\r\nThermal InfraRed (TIR; 1÷20 µm) region, from a geological point of view, is a particularly important spectral region for remote sensing aiming at compositional investigations of terrestrial materials. In fact, the fundamental vibration features of many rock-forming mineral groups (e.g. silicates, carbonates, oxides, phosphates, sulphates, nitrates, nitrites, hydroxyls) occur in the TIR region. Briefly:\r\na) the silicates, which are most abundant group of minerals in the Earth's crust, shows vibrational spectral features due to the presence of Si04-tetrahedron around 8 µm to 12 µm; b) the carbonates show a weak feature around 11.3 µm that can be detected; c) the sulphates display bands near 9 µm and 16 µm; d) the phosphates also have fundamental features near 9.25 µm and 10.3 µm; e) the features in oxides usually occupy the same range as that of bands in Si-O, i.e. 8 µm to 12 µm; g) the nitrates have spectral features at 7.2 µm and the nitrites at 8 µm and 11.8 µm; h) the hydroxyl ions display fundamental vibration bands at 11 µm.","name":"Spectral Signature of Mineral and Rocks","selfAssesment":"Completed
"},{"code":"PP1-3-6","description":"The determination of spectral signatures for scenes with a high degree of spatial complexity is considered as one of the most persistent problems in atmospheric radiation, especially at the surface, where satellite observations can only be used indirectly to infer energy budget terms. In the shortwave (solar) spectral range, it is especially challenging to derive consistent albedo, absorption, and transmittance from spaceborne, aircraft, and ground-based observations for inhomogeneous cloud conditions and is closely related to the long-debated discrepancy between observed and modeled cloud absorption.\r\nThe cloud spatial structure is revealed as a spectral signature in shortwave irradiance through the physical mechanism of molecular scattering. However, the study of specific mechanisms is rather complex since the satellite instruments cannot completely describe the spatial distribution of cloud and the variability of scattering and absorption properties. For this reason, several studies deal with the problem described above, as a challenge for estimating spectrally the cloud optical properties (such as the albedo and transmittance) as well as scattering and absorption processes taking place in the cloud system with adequate resolution. Hence, the above mechanisms can be described using three dimensional (3-D) radiative transfer models. Those models receive auxiliary information from cloud imagery and radar observations. The molecular scattering (Rayleigh) was the only one directly dependent on the wavelength of the vertical radiative flux. Moreover, it was considered as a spectral perturbation of backtracked horizontal exchange of solar radiation due to the inhomogeneous distribution of cloud. The horizontal photon transport is highly correlated to its spectral dependence.\r\nConcerning the presence of cirrus or ice clouds, the effect of their phase function and the vertical distribution were evaluated on the scattering of far infrared radiation. Thus, the accurate reconstruction of the phase function of cirrus clouds potentially indicates the need for application of a radiative transfer model. This specific module necessarily includes scattering parameters, while the accuracy of its calculations needs to be verified against real measurements. \r\nFor several applications the preliminary detection of those portions of the scene affected by the presence of clouds (cloud detection) is mandatory. For studying properties of Earth's surface targets affected by the presence of clouds are flagged just to exclude them by further analyses. In some case clouds themselves are the object of interest. In both cases the identification of clouds (and their classification) is mostly done by using (combination of) specific spectral signatures. Generally speaking clouds are highly reflecting VIS/NIR radiation showing (due to their heigth) brigthness temperatures (in the TIR region) lower than underlying surfaces. Thin or semi-transparent clouds are still detectable for their higher reflectance over the sea which represents a quite dark bacground in the VIS/NIR/SWIR region. Over land (much more reflecting) such a test is not more efficient and more sophisticated tests (e.g. Brigthness Temperature Difference in the split window bands around 11 and 12 microns) are required. In presence of very cold, high reflective backgrounds (e.g. snow, glaciers, etc.) both tests on the VIS reflectance and on TIR brigthness temperature could fail. More specific tests exploiting the reflectance drop of snow in the SWIR (where clouds are still saving their higher reflectance) helps to discriminate the presence of clouds from clear sky conditions even over a snow background. In the microwaves clouds are quite transparent except when coupled with coarse particles related to rain, snow, hailstones (precipitating clouds). In that case Mie scattering dominates strongly reducing the amount of radiance collected at the sensor (lower brigthness temperature in the microwave spectral range).","name":"Spectral Signature of Clouds","selfAssesment":"Completed
"},{"code":"PP1-3-7","description":"If the resolution is low enough that disparate materials can jointly occupy a single pixel, the resulting spectral measurement, made by the sensor, will be the composite of the individual spectra. Under the linear mixing model (LMM), each observed spectrum in each pixel of a given image is assumed to result from the linear combination of the N endmember spectra present in the pixel. The reflectance spectrum of each endmember is weighted by the fractional area coverage of it in the pixel. \r\nHowever, if the components of interest in a pixel are in an intimate association, like sand grains of different composition in a beach deposit, light typically interacts with more than one component as it is multiply scattered, and the mixing between these different components are nonlinear. Such nonlinear effects have been recognized in spectra of: particulate mineral mixtures, aerosols and atmospheric particles, vegetation and canopy. In this case a non-linear mixing model (NLMM) should be applied. To summarize: Linear mixture model assumes that endmember substances are sitting side-by-side within the pixel; Nonlinear mixture model assumes that endmember components are randomly distributed throughout the pixel, causing multiple scattering effects. \r\nIn the linear mixing case, the basic premise of mixture modelling is that within a given scene, the surface is dominated by a small number of distinct materials that have relatively constant spectral properties. These distinct substances (e.g., water, grass, mineral types), characterized by a well-defined spectral signature are called endmembers, and the fractions in which they appear in a mixed pixel are called fractional abundances. Then, finding the endmembers that can be used to ‘unmix’ other mixed pixels becomes a crucial issue. \r\nIdentify fractional abundances of distinct substances from the spectral signal of a mixed pixel is one of the application in which hyperspectral images can provide an valuable support.","name":"Composition of spectral signatures (Linear Mixing)","selfAssesment":"Completed
"},{"code":"PP1-3-8","description":"One of the most common ways to classify remote sensing systems consists in distinguishing them into the passive systems, which detect naturally occurring radiation, and the active systems, which emit radiation and analyse what is sent back to them. The passive systems can be further subdivided into those that detect radiation emitted by the Sun (this radiation consists mostly of ultraviolet, visible and near-infrared radiation), and those that detect the thermal radiation that is emitted by all objects that are not at absolute zero (i.e. all objects). For objects at typical terrestrial temperatures, this thermal emission occurs mostly in the infrared part of the spectrum, at wavelengths of the order of 10 μm (the so called thermal infrared region), although measurable quantities of radiation also occur at longer wavelengths, as far as the microwave part of the spectrum. Active systems can, in principle, use any type of electromagnetic radiation, resulting able to obtain measurements anytime, regardless of the time of day or season. In practice, however, they are restricted by the transparency of the Earth’s atmosphere at the specific spectral range considered. In any case they can be used for examining wavelengths that are not sufficiently provided by the sun, such as microwaves, or to better control the way a target is illuminated. Active sensors may be classified according to the use that is made of the returned signal. Two main methods have been identified to this aim so far: the Ranging technique mostly concerns with the time delay between transmission and reception of the signal, while the Scattering one is mostly focused on the strength of the received signal.","name":"Definition of active and passive remote sensing techniques","selfAssesment":"Planned
"},{"code":"PP1-3-9","description":"Light has a key role for aquatic ecosystems, both in marine and freshwater. It penetrates underwater and interacts with dissolved and particulate water constituents, the optically active constituents (OACs). They absorb and scatter the light, giving water its characteristic colour and affect the light availability underwater. The three main OACs are phytoplankton, coloured dissolved organic matter (CDOM) and suspended particulate matter (SPM) and vary in time and space. Absorption and scattering represent the inherent optical properties (IOPs) of water and depend solely on the OACs present in the water. In addition, water bodies have apparent optical properties (AOPs) that depend both on OACs and the incident light field.\r\nThe chlorophyll in the phytoplankton absorbs blue and red wavelengths and reflects green. Therefore, the oceans appear blue-green depending on the concentration of phytoplankton. CDOM is primarily tannin-stained water released from decaying detritus. High CDOM concentrations appear yellow-green to brown. CDOM absorbs ultraviolet (UV) light in the surface waters which is harmful for phytoplankton but competes with phytoplankton for light. Inorganic suspended matter (ISM) is the suspended sediment in the water. It is a component of SPM and strongly scatters longer (red) wavelengths. High ISM concentrations give water a reddish-brown colour. Pure water, however, absorbs longer wavelength red light. As natural waters vary in their composition, oceanographers introduced ocean classification schemes based on the optical properties of water. The main differentiation is between Case 1 open ocean waters and Case 2 coastal waters. In open ocean waters, the optical properties are dominated by phytoplankton and covarying material. In coastal waters, optical properties are dominated by suspended sediments and CDOM that vary independently of phytoplankton.","name":"Optical properties of water","selfAssesment":"In progress
"},{"code":"PP1-3","description":"Measuring the signal emitted (received) by a radiation source (detector)","name":"Sensing of EM radiation.","selfAssesment":"Planned
"},{"code":"PP1-4-1","description":"Radiative transfer equation (RTE) is the governing equation of radiation propagation in a media, which plays a central role in the analysis of radiative transfer in gases, semitransparent liquids and solids, porous materials, and particulate media, and is important in many scientific and engineering disciplines. \r\nThe RTE states that when radiation (a light-ray) propagates through matter (gas, dust, liquid), the incident radiation could be absorbed or scattered by matter, or radiation emitted from matter could append to the incident radiation. As a result, the intensity of radiation would change temporally, spatially, and directionally. The study of the propagating way of radiation in matter is the radiative transfer. In more detail, the radiation traversing a medium may be attenuated due to the density, mass scattering and absorption of material. In contrast, the radiation’s intensity can be strengthened by emissions from the material plus multiple scattering from all directions. All the above interactions are described mathematically by the general radiative transfer equation.\r\nThere are different forms of RTEs that are suitable for different applications, including the RTE under different coordinate systems, the transformed RTE having good numerical properties, the RTE for refractive media, etc.. Furthermore, several fundamental numerical methods for solving RTEs are proposed up to now focusing on the deterministic methods, such as the spherical harmonics method, discrete-ordinate method, finite volume method, and finite element method.","name":"General equation of radiative transfer.","selfAssesment":"Completed
"},{"code":"PP1-4-10","description":"The inversion approach aims at retrievals of trace gas concentration and temperature profiles of atmospheric state, namely the modeled state vector, based on the measured radiance transmitted or reflected or scattered (SCIAMACHY spectrometer) by the Earth-Atmosphere system. Satellite instruments measure the radiance L that reaches the top of the atmosphere at given frequency v. The measured radiance is related to geophysical variables of Earth's atmosphere (e.g. temperature vertical profiles and chemical composition, aerosols, clouds, rain, etc.) and surface (e.g. temperature, spectral emissivity and reflectance, etc.) by the Radiative Transfer Equation (RTE). In RTE measured spectral radiances are assumed as the result of different contributions:\r\na) thermal emission from the different layers (at heigt z) of atmosphere at temperature T(z) modulated by the atmospheric transmittance from z to the sensor heigt. It depends on both temperature profile T(z) and trace gas concentration along the optical path;\r\nb) Surface emission. It depends mostly on Eart's surface temperature T(0) and spectral emissivity\r\nc) Surface reflection/scattering. It depends on spectral reflectance and local properties like surface rugosity \r\nOthers, more complex contributions comes from: cloud/rain, aerosols, etc.\r\nIn its simplified form, terms a) and b) dominate as far as InfraRed (IR) radiances are considered. Term a) can be neglected in those bands where atmosphere is transparent (atmospheric windows). Term b) can be negletcted in the IR spectral bands (sounding channels) where it is fully adsorbed by some specific constituent of the atmosphere. Among the IR sounding channels some ones are selected being associated to atmospheric constituents (like CO2 or oxygen) whose mixing ratio in the atmosphere is known to be constant. For radiances measured in these bands term a) in RTE depends only on T(z) (through a Fredholm equation of the first kind) that can be then retrieved by inversion methods. When T(z) are known trace gas concentrations survive as the only unknown of term a) and can be retrieved by inversion methods using radiances measured in their corresponding sounding channels. Similar inversion strategies have been suggested as far as radiances (emitted, transmitted, reflected, adsorbed) measured in different spectral ranges (from the Visible to the Microwaves) are considered.","name":"Retrieval of atmospheric parameters by inversion of multi-spectral radiances","selfAssesment":"Completed
"},{"code":"PP1-4-2","description":"In the field of radiation scattering and absorption, the cross-section, analogous to the shape of a particle, is used to determine the amount of energy diverted from the original beam by the particle. This parameter is called mass cross section, when it is in reference to unit mass (cm2g-1).","name":"Cross Section of Extinction (Absorption, Scattering) per Mass Unit","selfAssesment":"Planned
"},{"code":"PP1-4-3","description":"When the mass cross-section is multiplied by the density of particle, the extinction coefficient is calculated, namely the sum of absorption and scattering coefficient, whose the units are related to length. Especially, the absorption coefficient (k (cm•atm)-1) is the product of strength of absorption with the Loschmidt’s number.","name":"Absorption Coefficient","selfAssesment":"Planned
"},{"code":"PP1-4-4","description":"The source function, Jλ, has units of radiant intensity and it is defined as the ratio of the source function coefficient to the mass extinction cross section. The Jλ determines the intensity that are acquired in a homogeneous medium.","name":"Source Function (Coefficient)","selfAssesment":"Planned
"},{"code":"PP1-4-5","description":"If the monochromatic beam (Iλ) of radiation attenuates due to absorption, but it remains unaffected from emission contributions and multiple scattering of homogeneous Earth-Atmosphere system, it can be expressed by Beer-Bouguer-Lambert law. This law also expresses the monochromatic optical depth (τλ) and transmissivity (Τλ) of the above system.","name":"Beer-Bouguer-Lambert law.","selfAssesment":"Planned
"},{"code":"PP1-4-6","description":"The Schwarzschild equation provides an interpretation for the infrared radiation that undergoes the absorption and emission processes simultaneously, while the scattering efficiency is considered negligible. Hence, its solution is obtained by the integrating of relationship that invokes Kirchhoff’s law and summing the two above processes along a ray path.","name":"Schwarzshild equation and its solutions","selfAssesment":"Planned
"},{"code":"PP1-4-7","description":"The Atmosphere-Earth system that monochromatic beam (Iλ) of radiation travels, is called optical path. It expressed by optical path length, namely the product of geometric length and the refractive index of medium. It determines the optical thickness, namely a measure of the cumulative depletion of Iλ directed in straight-downward.","name":"Concepts of Optical path and Optical thickness.","selfAssesment":"Planned
"},{"code":"PP1-4-8","description":"Radiative transfer is highly nonlinear and non-local against the cloud structure at a high spatial resolution. Hence, a Monte Carlo approach can be used for the representation of cloud structure and interactions between photons and clouds. This approach is more efficient than the method of representing clouds as horizontally homogeneous.","name":"Radiative transfer in presence of clouds","selfAssesment":"Planned
"},{"code":"PP1-4-9","description":"The line by line radiative transfer model (LBLRTM) is an accurate and flexible model for the estimation of the spectral radiance and transmittance over the full spectral range (microwave to ultraviolet), using a first-order perturbation algorithm. It is considered as the basic tool for the creation of retrieval algorithms employed by the ground-based and satellite instruments, while the latest updates in spectroscopic factors are derived from the high-resolution transmission molecular absorption (HITRAN) database. A LBLRTMs is continuously updated and validated against highly accurate spectral measurements. Its errors are related to uncertainties in line parameters and shape. The shape is a Voigt line which is a linear combination of approximating functions for the description of all atmospheric levels. LBLRTML is combined with the continuum MT_CKD (Mlawer, Tobin, Clough, Kneizys, Davies) model which in turn includes the atmospheric constituents of water vapor, carbon dioxide (CO2), molecular oxygen (O2), molecular nitrogen (N2), and ozone (O3), and the molecular extinction process (Rayleigh scattering). A recent version of LBLRTM calculates analytically the Jacobians equations for obtaining meteorological parameters. Also, this model version retrieves the optical parameters of clouds related to scattering and emissivity. The LBLRTM is widely used in radiation and climate applications. It is capable to calculate the absorption degrees of various atmospheric constituents which are utilized afterward from climate and weather prediction models for estimating the broadband solar irradiance and the heating rates. Additionally, the complex radiative transfer models with fast computational time are initiated and trained by the LBRTM, since they are used subsequently on numerical weather prediction (NWP) assimilation systems.","name":"Line-by-line radiative transfer models","selfAssesment":"completed
"},{"code":"PP1-4","description":"Theory of radiative transfer describe the transmission of the electromagnetic radiation through a medium.","name":"Fundamentals of Radiative Transfer","selfAssesment":"Planned
"},{"code":"PP1-5-1","description":"Light is the electromagnetic phenomenon we exploit for remote sensing. Its basic laws concerning the transmission through the interface of two different media are governed by reflection and refraction. Reflection governs the way light is backpropagated and refraction dictates how light is transmitted. Refraction is related to the real refractive index of a medium. Dispersion relates to the way the light of a given wavelength is transmitted. Since light of different wavelengths are transmitted at different angles, the phenomenon leads to the concept of dispersion. These three simple principles are at the core of the understanding technology of remote sensing.","name":"Reflection, Refraction and Dispersion of the light","selfAssesment":"Planned
"},{"code":"PP1-5-11","description":"The theory provides the bulk of physical explanation and related laws, which govern absorption, emission and spontaneous emission from the ordinary matter. Early laws about thermal radiation and the blackbody emission, such as Rayleigh-Jeans, Wien, Planck laws are cast in a single theory and formalism through the concept of quantized energy at the level of atoms emission/absorption of light. Explain the modern concept of quantum optics and their link to the design of modern devices for the measurements and/or production of coherent light.","name":"Einstein’s theory of radiation: photons, photoelectric effect, absorption, emission; Stimulated emission: the laser","selfAssesment":"Planned
"},{"code":"PP1-5-14","description":"Solid state modern detectors rely on non-metal junction, which can be designed and operated to yield a bandgap energy according to the spectral range (infrared, visible, UV) to be detected. The basic principles of how these devices are designed and fabricated is important to develop and design new sensors useful for the various remote sensing applications.","name":"Electric conduction in solids: semiconductors, p-n- junction, diode and transistors","selfAssesment":"Planned
"},{"code":"PP1-5-15","description":"Modern detectors of electromagnetic radiation in the infrared, VIS, UV spectral regions are designed and fabricated based on suitable junctions or electro-optical devices. The performance of these systems needs to be assessed in terms of accuracy and precision. This is made through figures of merit such as Noise Power Spectral Density, Noise Equivalent Power. Detectors can be classified as photovoltaic or photoconductive devices, which allows to better classify the various noise sources: shot noise, 1/f noise, Johnson noise, generation-recombination noise.","name":"Photovoltaic and photoconductive detectors: MCT, InSb, bolometer, CCD devices","selfAssesment":"Planned
"},{"code":"PP1-5-2","description":"Interference and diffraction are phenomena related to the wave nature of electromagnetic radiation. They explain how light propagates in presence of obstacles. These phenomena are largely used in the fabrications of optical systems for remote sensing: e.g. radiometers and spectrometers.","name":"Interference and Diffraction.","selfAssesment":"Planned
"},{"code":"PP1-5-3","description":"The Michelson interferometer is the instrument that exploits and evidence the interference of light. A masterpiece of experimental physics, the Michelson interferometer is the key architecture of the modern optical interferometers, which make it possible to measure the emitted Earth spectrum with hyperspectral resolution.","name":"Michelson Interferometer","selfAssesment":"Planned
"},{"code":"PP1-5-4","description":"The celebrated principle of constant speed of light and independence of the reference frame is important to explain the basic principles of instruments such as the Michelson interferometer. The basic physics theory to explain how electromagnetic fields propagates and the inter-relationship between electric and magnetic fields.","name":"Special relativity; Electromagnetic fields equations and propagations","selfAssesment":"Planned
"},{"code":"PP1-5-6","description":"Helmotz’s wave equation arises in light and acoustic scattering problem and yields the general framework to investigate and analyse the scattering of time-harmonic acoustic and electromagnetic waves by a penetrable inhomogeneous medium.","name":"Helmotz’s equations; Scattering from inhomogeneous media.","selfAssesment":"Planned
"},{"code":"PP1-5-7","description":"Geometrical optics is governed by the laws of reflection, refraction and dispersion. Its applications are relevant to many optical systems involving ray tracing, wavefront propagation, thin film calculators (which underly many optical engineering calculations).","name":"Foundations of geometrical optics, geometrical theory of optical imaging","selfAssesment":"Planned
"},{"code":"PP1-5-8","description":"Optical interferometers are nowadays used to develop and implement Fourier Transform Spectrometers, which can measure the emission spectrum of a given source with high spectral resolution at a constant sampling. This instrumentation is now at the core of modern hyperspectral sounders from satellite and have opened the way to the sounding of the Earth atmosphere with unprecedented spatial vertical resolution.","name":"Elements of the theory of interference and interferometers","selfAssesment":"Planned
"},{"code":"PP1-5-9","description":"Diffraction gratings and dispersive element are the basic ingredients for radiometers and grating spectrometers. They are in some cases preferred to Interferometer systems because the optical layouts can be designed and implemented with no moving part or components. Many of the today satellite instruments, including sounder and imagers, rely on diffraction and/or grating spectrometers","name":"Elements of the theory of diffraction and grating spectrometers","selfAssesment":"Planned
"},{"code":"PP1-5","description":"This section describes the theoretical fundaments of Optics and Modern Physics of Sensors relevant to the Earth Observation.","name":"Basics of Optics and Modern Physics of Sensors","selfAssesment":"Completed
"},{"code":"PP1-6-1","description":"The temperature and pressure profiles determine the atmospheric structure. The latter consists of four basic levels, considering the vertical variability of the temperature. These main four levels are troposphere, stratosphere, mesosphere, and thermosphere. In the troposphere (0-12km), which is the lowest layer of the atmosphere, all the meteorological processes that affect our everyday life take place. The lowest part of the troposphere is known as the boundary layer (0-3km), where all the surface-atmosphere interactions and exchanges take place. The troposphere concentrates the water vapor and 90% of atmospheric mass, while the chemical composition of all atmospheric layers consists of nitrogen, oxygen, argon and trace gases. The main parameters that characterize the atmosphere structure are pressure, density, and temperature. All the aforementioned parameters are related to the atmospheric composition and vary with altitude, latitude, longitude and season. Additionally, the stratosphere, which is the layer above the troposphere, contains almost all of the ozone abundance (~90%) of the atmosphere in a region named as ozone layer and traced between 15 and 35km. The interaction of the incoming solar radiation with ozone in this layer causes the reduction of the incoming harmful UV radiation provoking the temperature increase in the stratospheric layer. The 99.9% of total atmospheric mass is concentrated in lower atmosphere (<50km) with Nitrogen (N2, 78.08%), Oxygen (O2, 20.95%) and argon (Ar, 0.93%) being the major constituents of the atmosphere. Water vapor (H2O) is considered as a significant factor, too. Despite the fact that it depicts a very small amount of total atmospheric mass, it’s one of the most important greenhouse gases, along with carbon dioxide (CO2) and methane (CH4), absorbing the Earth’s longwave (infrared) radiation, affecting the energy balance of Earth-Atmosphere system. Furthermore, water vapor plays a decisive role in the formation of clouds and precipitation. Together with the basic chemical (atoms, molecules, ions) constituents of a \"standard\" atmosphere, aerosols of natural and anthropogenic origin have to be considered too, as far as the interaction of e.m. radiation with atmosphere is concerned.","name":"Structure and chemical-physical composition of Earth's atmosphere","selfAssesment":"Completed
"},{"code":"PP1-6-10","description":"The water vapour is the major radiative and dynamic parameter in the atmosphere. Its concentrations vary highly in space and time, with the tropospheric water vapor being determined by the hydrological cycle processes, namely the evaporation, condensation and precipitation and by large-scale transport processes. Specific humidity decreases rapidly with pressure (following an exponential function) and with latitude. In particular, the variability of the H2O concentration shows a bimodal distribution: it’s very small in the equatorial region and poleward, relatively small in stratosphere and shows a maximum in the subtropics of both hemispheres. The concentration of H2O in the lower stratosphere is controlled by the temperature of the tropical tropopause, and by the formation and dissipation of cirrus. The water vapor can condense into water droplets when it has a particle to condense upon. The atmosphere continuously contains aerosol particles ranging in size from ∼10−3 to ∼20 μm. These aerosols are known to be produced by natural processes (volcanic dust, smoke from forest fires, particles from sea spray, windblown dust, and small particles produced by the chemical reactions of natural gases) as well as by human activity (particles directly emitted during combustion processes and particles formed from gases emitted during combustion). Some aerosols are effective condensation and ice nuclei upon which cloud particles may form. For the hygroscopic type, the size of the aerosol depends on relative humidity. Thin layers of aerosols are observed to persist for a long period of time in some altitudes of the stratosphere. \r\nClouds are global in nature and regularly cover more than 50% of the sky. There are various types of clouds. Cirrus in the tropics and stratus in the Arctic, and near the coastal areas are climatologically persistent. The microphysical composition of clouds in terms of particle size distribution and cloud thickness varies significantly with cloud type. Clouds can also generate precipitation, an event generally associated with midlatitude weather disturbances and tropical cumulus convection.","name":"Water vapour and Cloud formation","selfAssesment":"Completed
"},{"code":"PP1-6-11","description":"The radiative equilibrium is the principle, where the radiative emission and absorption are in balance based on Kirchhoff’s and Planck’s law, resulting in the steady temperature of planet. The adiabatic lapse rate displays the decrease of vertical temperature of a parcel with rate higher than 1oC per 100 metres.","name":"Radiative Equilibrium. Adiabatic lapse rate","selfAssesment":"Planned
"},{"code":"PP1-6-12","description":"The atoms of carbon are building blocks of living organisms and they can move among organisms as a part of carbon cycle. Their transport rate to the atmosphere as carbon dioxide is vital, because this gas trap heat in the atmosphere, increasing the Earth’s temperature and causing Greenhouse effect.","name":"The Carbon Cycle, Greenhouse Effect","selfAssesment":"Planned
"},{"code":"PP1-6-2","description":"The atmospheric absorption can cause an excitation or falling into the energy state of a particle, while the scattering is related to absorption and re-emission of radiation at all directions without changes in its frequency. Particularly, the main contributors of the incoming solar radiation absorptions are various molecules like the nitrogen (N2), oxygen (O2), ozone (O3), water vapor (H2O). Additionally, other constituents of the atmosphere such as CO2 and CH4, and other trace gases, aerosols, and cloud droplets can also absorb significant portion of the incoming solar radiation. Generally, the absorption of solar radiation is related to the wavelength of the solar spectrum. For example, gases and specific type of aerosols (black carbon, BC) or elementary carbon (EC) absorb in the ultraviolet (UV) and visible (VIS) part of solar spectrum. On the contrary, cloud droplets which are suspended in the atmosphere mainly scatter in UV and VIS and absorb in the infrared. The absorption of the incoming solar radiation from the atmospheric constituents reduces the harmful UV radiation and it is considered as the driving of atmospheric photochemistry. Moreover, scattering in the atmosphere can be divided into two mainly categories, firstly, the Rayleigh scattering which is the scattering of radiation by gases (mainly N2 and O2) and, secondly, the Mie scattering which is the scattering by aerosol particles and cloud droplets. The main difference between Rayleigh and Mie scattering is the direction of the re-emission of the incident solar radiation. For example, in the Rayleigh scattering the light have symmetrical direction either forward or backward whereas in Mie scattering the light is mainly scattered in the forward direction, depending on the size of the particle.","name":"Absorption and scattering of solar radiation in the Atmosphere","selfAssesment":"Completed
"},{"code":"PP1-6-3","description":"Mie scattering refers primarily to the elastic scattering of light from atomic and molecular particles whose diameter is similar or larger than the wavelength of the incident light. We can say that, when the particle has a diameter greater than about a tenth of the wavelength, we are in the field of Mie scattering.\r\nThis scattering produces a pattern like an antenna lobe, with a forward lobe sharper and more intense than the back one, the larger the particle size the greater the intensity and sharpness of the anterior lobe. Unlike Rayleigh scattering, Mie scattering is not strongly wavelength dependent. In this case the predominant component for the quantification of scattering (in addition to the particle dimension) is the direction of the incident solar radiation.\r\nMore specifically, the amount of scattering in the backward direction depends upon a wave relation tending to decrease in accordance with the growth of the particle size until it reaches a certain value for which the back scattering becomes a constant quantity. This condition is reached when the diameter of the particle is approximately equal to the wavelength of the incident radiation.\r\nIn the atmosphere the Mie scattering is commonly caused by particles (aerosols) floating in the atmosphere (due to Dust, smoke, fog, rain drop). \r\nIn nature it is possible to see the effects of Mie scattering, for example, in the evenings when there is a lot of fog and the dazzling headlights of our car do not allow us to see the road ahead. \r\nThe Mie theory provides the solution for the amount of scattering in case of a spherical medium due to an incident wave.","name":"Mie Scattering in the Earth's Atmosphere","selfAssesment":"Planned
"},{"code":"PP1-6-4","description":"Scattering is a physical process by which a particle in the path of an electromagnetic wave continuously exstracts energy from the incident wave and reradiates that energy in all directions. In more detail, it occurs when a photon’s electromagnetic field hits a particle’s electric field in the atmosphere and is deflected into another direction. The Rayleigh scattering falls into the elastic scattering phenomena, in which the individual photon changes its direction of propagation but non its energy. The Rayleigh scattering involves air molecules (mainly N2 and O2) whose diameter (x) is much smaller (one-tenth at least) than the incident radiation wavelength (λ) (i.e., x << λ). The amount of scattered intensity (I) depends on the incident light wavelength (λ) and the refractive index (n) of air molecules. However, the refractive index can be considered relatively negligible as compared to the explicit wavelength term. In this way, the intensity scattered by air molecules in a specific direction is strongly dependent on the wavelength (λ), as expressed in the form Iλ~1/λ4. The inverse dependence of the scattered intensity on the wavelength to the fourth power allows at explaining the blue color of sky, caused by the scattering of sunlight off the atmosphere molecules. To better understand this phenomenon, it is worth considering that a large portion of solar energy is contained between the blue and red regions of the visible spectrum, where blue light (0.425 µm) has a shorter wavelength than red light (0.650 µm). Consequently, based on the above-mentioned equation, blue light scatters about 5.5 times more intensity than red light. For this reason, more blue light is scattered than red, green, and yellow, and so the sky appears blue, when viewed away from the sun’s disk. The Rayleigh scattering of unpolarized sunlight by air molecules has maxima in the forward and backward directions, whereas it shows minima in the side directions. Furthermore, the light scattered by particles is not delimited only on the incidence plane, but is visible in all the azimuthal directions. The derived scattering patterns are symmetrical in the three-dimensional space, because of the spherical symmetry assumed for air molecules.","name":"Rayleigh Scattering in the Earth's Atmosphere","selfAssesment":"Completed
"},{"code":"PP1-6-5","description":"When we talk about “thermal infrared (or terrestrial) radiation” we commonly refer to the energy emitted from the Earth-atmosphere system. Trapping of thermal infrared radiation by atmospheric gases is typical of the atmosphere and is therefore called the “atmospheric effect”. The atmospheric effect is sometimes referred to as the “greenhouse effect” because in a similar way glass, which covers a greenhouse, transmits short-wave solar radiation, however absorbs long-wave thermal infrared radiation. Imagine a beam of radiation travelling through a small section of air. The air is made up of changing concentrations of different species, with all molecules absorbing and emitting thermal radiation at different rates. As the radiation travels through different layers of the atmosphere, the intensity of radiation will constantly be modified by both absorption and emission processes as described by the Schwarzschild's equation. In case of a sensor on board of a satellite, the net radiation measured would be that which is attenuated through each layer (as small increments of absorption and emission) from the surface to the top of the atmosphere plus the radiation emitted directly from the atmosphere. In this case, this process can be described by the radiative transfer equation (RTE). \r\nThe equation of radiative transfer simply says that as a beam of radiation travels through the atmosphere, it loses energy to absorption, gains energy by emission, and redistributes energy by scattering. Many radiative transfer codes exist which are able, i.e. on the basis of known properties of the atmosphere, to computed the effect of the atmosphere on the thermal infrared radiation providing atmospheric transmittance (absorption), atmospheric scattering and atmosphere path emission. Commonly, in satellite remote sensing, the thermal infrared region is defined as the region of the electromagnetic spectrum comprised between 8 and 14 micron. In an atmosphere free of particles (aerosols due to phenomena like fires, volcanic eruption, dust storm, etc.) the thermal infrared radiation is mainly affected by triatomic gases like water vapor, carbon dioxide and ozone.","name":"Thermal infrared radiation transfer in the atmosphere","selfAssesment":"Completed
"},{"code":"PP1-6-6","description":"Light scattering by particles is the process by which small particles cause optical phenomena, such as rainbows, the blue color of the sky, and halos. Mie scattering defines the interaction of light with particulate matter with a dimension comparable to the wavelength of the incident radiation. It can be regarded as the radiation resulting from a large number of coherently excited elementary emitters (molecules for example) in a particle. Since the linear dimension of the particle is comparable to the wavelength of the radiation, interference effects occur. The most noticeable difference to Rayleigh scattering is, generally, the much weaker wavelength dependence and a strong dominance of the forward direction in the scattered light. The calculation of the Mie scattering cross section, which involves summing over slowly converging series, is complicated even for spherical particles, it is worse for particles of an arbitrary shape. However, the Mie theory for spherical particles is well developed and a number of numerical models exist to calculate scattering phase functions and extinction coefficients for given aerosol types and particle size distributions.","name":"Light scattering by atmospheric particulates","selfAssesment":"Planned
"},{"code":"PP1-6-7","description":"Each time radiation passes through the atmosphere it is attenuated to some extent. We refer to this attenuation with the term 'atmosphere transmittance'. The typical atmospheric transmittance between wavelengths of 250 nm and 2500 nm, i.e. in the ultraviolet, visible, near-infrared and short-wave-infrared regions of the spectrum is dominated bywater vapour, although methane, carbon dioxide and molecular oxygen are also responsible for a few absorption lines. The behaviour in the visible region is dominated by molecular Rayleigh scattering. At the short-wavelength end of the spectrum, in the ultraviolet, absorption by ozone becomes very significant. Above 2500 nm up to the upper limit (13500 nm) of the optical electromagnetic spectrum useful for Remote Sensing, the atmosphere transmittance is mainly affected by triatomic molecules (H20, CO2 and O3). However, the atmospheric effects (transmittance) is strongly depending on the electromagntic wavelength. Remote Sensing exploits the region of relative atmospheric transparency called atmospheric windows.","name":"Earth's (standard) Atmosphere Transmittance","selfAssesment":"Planned
"},{"code":"PP1-6-8","description":"With the term 'atmospheric windows' we refer to the regions of the electromagnetic spectrum where the interaction between the atmosphere constituents (i.e., molecules, aerosols, and cloud particles) and the electromagnetic radiation is minimized, namely the mechanisms of scattering and absorption of the radiation are less relevant than the transmission one. Therefore, the radiation collected at the sensor in these spectral regions is strictly depending on the Earth surface features, allowing to infer information about the processes/phenomena there in progress at the time of the acquisition. There are three main spectral ‘windows’ in the Earth's atmosphere. The first of these includes the visible and near-infrared (VNIR) parts of the spectrum up to the medium infrared, between wavelengths of about 0.38 μm and 3.5 μm, although it does also contain a number of opaque regions. This spectral interval includes the small portion of the electromagnetic spectrum to which human eyes are sensitive to (i.e, the visibile region between 0.4 and 0.7 μm). The second is a rather narrow region between about 8 μm and 15 μm, in which is found the bulk of the thermal infrared (TIR) radiation from objects at typical terrestrial temperatures. In this region there is only a main opaque interval, around 9.6 μm due to the presence of the ozone band. The third more or less corresponds to the microwave region, between wavelengths of a few millimeters and a few meters. Therefore, each remote sensing instrument that should be able to fully penetrate the Earth’s atmosphere has to be designed to operate in one of these three ‘window’ regions.","name":"Atmospheric (spectral) windows for EO","selfAssesment":"Completed
"},{"code":"PP1-6-9","description":"The water cycle is a continuous purification process of water on Earth due to the movement of water species among various reservoirs. This cycle is vital for Earth’s life, ecosystems, and living organisms. The water cycle includes mainly four processes. Water is evaporated from ocean and land surfaces driven by solar heating. The resulting water vapor rises upwards into the atmosphere, transported by the winds, cools, and due to low air temperature condensates into liquid droplets and ice crystals to form clouds. The ice or/and liquid droplets collide, increase their size, and precipitate as snow or rain to Earth’s surface and oceans. The subtraction of energy (latent heat of evaporation) at low latitudes related to the evaporation processes as well as its release (latent heat of condensation) at higher latitudes related to the condensation processes is a formidable way to guarantees the heat transport from the warmer part of the Earth to the colder ones mantaining local air temperature more compatible with the human life. The starting point of the water cycle is not unique, but the oceans can be selected as the initial reservoir. Other important reservoirs are considered ice sheets, lakes, and rivers. \r\nThe hydrosphere is defined by the various water reservoirs which are characterized by different residence times – the time spends the water molecules in a reservoir. The water residence time – the rate at which the water comes out the reservoirs – varies for each reservoir extending from hundreds (Greenland Ice Sheet) or thousands of years (Antarctic Ice Sheet) to years and days for rivers and lakes, respectively. It also defines the energy transferred from the Earth to the Atmosphere which increases for short-term residence times. In long-term temporal scales, this energy is defined as the evaporation rate (E) and balances with the precipitation rate (P). This global energy balance breaks for shorter time scales depending also on the local and regional climate. For example, in regions located in the Inter-Tropical Convergence Zone (ITCZ), the energy balance in the water cycle does not exist since the precipitation rate is much higher than the evaporation rate (P>>E) due to the horizontal movement of converging trade winds.","name":"The Water Cycle","selfAssesment":"Completed
"},{"code":"PP1-6","description":"Atmospheric Physics describe the processes affecting the physical, chemical and thermodynamic status of planetary atmospheres. In the context of EO sciences, it particularly refers to the physics of the interactions of e.m. radiation traveling across (or emitted by) the atmosphere as the main source of information collected by satellite (in general aerial) sensors.","name":"Basics of Atmospheric Physics","selfAssesment":"Completed
"},{"code":"PP1-7-1","description":"According to the second law of thermodynamics, heat is a measure of the movement or the flow of energy from hotter substances to colder ones and it is measured in Joules. In microscale, heat is known as internal energy. Two regions in thermal contact have the same temperature when there is no net exchange of internal energy between them. Heat is the net transfer of internal energy from one region to another, while temperature, which is the degree of hotness or coldness of an object, describes the average kinetic energy of molecules within substances. The faster the particles are moving, the higher their kinetic energy. Since the motion of the particles within an object is random, they do not move at the same speed and in the same direction, some of them move faster. Therefore, those particles have more kinetic energy than the others. Thermodynamic temperature can be defined for substances at (even Local) Thermodynamic Equilibrium (i.e. in condition of density/pressure which allows an efficient equipartition of kinetic energy among molecules). Temperature is then the measure of the average kinetic energy of such a system, and is usually expressed in Celsius (°C). When, particular conditions of very low pressure/density (like in the Earth's thermosphere) cannot guarantee energy equipartition among molecules (i.e. outside thermodynamic equilibrium) the concept of Kinetic Temperature should be used instead. The Celsius temperature scale is defined by international agreement in terms of two fixed points: the temperature of the ice point, which is defined as 0° Celsius, and the steam point as 100° Celsius. The Fahrenheit (°F) temperature scale is mainly used in the United States; on this scale, water freezes at 32 degrees Fahrenheit, and the temperature of boiling water is 212 F. The Kelvin scale (K) is the base unit of temperature in the International System of Units (SI). This temperature scale is obtained by shifting the Celsius scale by −273.15°; zero Kelvin is also called absolute zero.","name":"Temperature and heat","selfAssesment":"Completed
"},{"code":"PP1-7-10","description":"Irreversible thermodynamics investigates the regularities in transport phenomena, namely heat and mass transfer, and their relaxation. It is based on the first law of Thermodynamics, which correlate the heat flow density with pressure and viscosity, and the second law that describe the temporal variations of local entropy for local continuous mass.","name":"The constitutive equations of irreversible fluxes","selfAssesment":"Planned
"},{"code":"PP1-7-11","description":"The Adiabatic process of homogeneous system occurs, when flow of heat is not exchanged across the boundaries of system and the system is characterized from uniform phase (solid or liquid or gases). In this case, the variations of entropy can be determined for some parts of system.","name":"Heat equation and special adiabatic systems, special adiabats of homogeneous systems","selfAssesment":"Planned
"},{"code":"PP1-7-12","description":"The thermodynamic diagrams are used for the study of vertical structure and properties of the Atmosphere above a specific location. Especially, a static diagram represents a) an atmosphere with fixed potential temperature or b) a process curve of the change of variables of air parcel that rises adiabatically.","name":"Thermodynamics diagram, atmosphere static","selfAssesment":"Planned
"},{"code":"PP1-7-2","description":"Kinetic theory of gases is based on a simplified molecular description of gases, from which the properties of volume, pressure and temperature can be derived. The assumptions of this theory are based on the random movements of molecules, their elastic collisions and the transfer of kinetic energy between them.","name":"Kinetic theory of gases","selfAssesment":"Planned
"},{"code":"PP1-7-3","description":"The ideal gas law or general gas equation describes the equation of state of hypothetical ideal gas. This equation correlates the pressure and volume with its temperature, while is characterized as a combination of the empirical laws of Boyle, Charles, Avogadro and Gay-Lussac.","name":"Ideal gas laws","selfAssesment":"Planned
"},{"code":"PP1-7-4","description":"The state functions of ideal gas are the pressure, volume, temperature, internal energy and entropy, which remain unchangeable in compared with the path. The internal energy is expressed through Joule’s law as a function of temperature of gas, while the entropy depends on the variation of volume and temperature.","name":"State function of ideal gases","selfAssesment":"Planned
"},{"code":"PP1-7-5","description":"The phase rule for condensation is expressed as P+F=C+1. The terms of P, F and C describe the number of phases, minimum fixed variables and independent chemical species respectively. Concerning the condensed phases to distinguish the gases from liquids and solids, these are the density, molecular order, diffusion, etc.","name":"State function of the condensed gas phase","selfAssesment":"Planned
"},{"code":"PP1-7-6","description":"When the system passes from initial to final state due changes in properties of temperature, pressure and volume, it is considered to have undergone thermodynamic process. The different types of thermodynamic processes are distinguished in the isothermal (fixed temperature), adiabatic, isochoric (stable volume), isobaric (stable pressure) and reversible process.","name":"Thermodynamic process","selfAssesment":"Planned
"},{"code":"PP1-7-7","description":"Budget equations, namely heat, momentum and moisture budget, are interpreted through two frameworks, which are Eulerian and Lagrangian. Eulerian is utilized for the investigating of transfer of heat by the wind, while Lagrangian is concerned about the effects of ascending or descending airflows on the Earth-Atmosphere system.","name":"Budget equations","selfAssesment":"Planned
"},{"code":"PP1-7-8","description":"The First Law of Thermodynamics supports that the energy is conserved. Thus, the thermal energy is defined as the sum of warming or internal energy (microscopic effect) and work occurring per unit mass (macroscopic effect). For its application to the Atmosphere, the thermal energy input is given from the following mathematical expression: Δq=Cp·ΔT-(ΔP/ρ), where Δq (J·kg–1) is the amount of thermal energy you add to a stationary mass m of air, Cp (J·kg–1·K–1) is the specific heat of air at constant pressure, ΔT (K) is the induced variation of temperature, so that Cp·ΔT represents the heat transferred per unit air mass, ΔP (Pa = J·m-3) is the pressure difference and ρ (kg· m-3) is the air density.\r\nThe term Cp·T is defined enthalpy h, thus, the first term on the right side of eq. of thermodynamic first low for atmospheric applications, which is the corresponding enthalpy change is: Δh=Cp·ΔT. It is a characteristic possessed by the air.\r\nExpressing the first law of thermodynamics for atmospheric applications in conceptual form we can state that, given a quantity Δq of thermal energy added to a stationary mass m of air, a part of this energy heats the air, increasing its internal energy, but, as air heats up, its volume expands by an amount ΔV and pushes against the surrounding atmosphere, which responds with an equal and opposite pressure P that we can assume constant. Therefore, a part of the thermal energy introduced does not go to heat the air, but goes into macroscopic movement.","name":"First law of thermodynamic","selfAssesment":"Completed
"},{"code":"PP1-7-9","description":"A natural process that starts from an equilibrium state and ends in another state, causing changes in direction of entropy (ΔS) or statistical disorder of the system, is interpreted by Second Law of Thermodynamics. This law is considered as an irreversible process and it is expressed as ΔS=Heat transfer/Temperature.","name":"Second law of thermodynamics","selfAssesment":"Planned
"},{"code":"PP1-7","description":"Thermodynamics is the science of the relationships between heat, work, temperature, radiation, energy and properties of matter. These relationships are governed by the four laws of thermodynamics which allow a quantitative description, through measurable macroscopic physical quantities, of processes that, at the level of microscopic constituents can be described by the statistical mechanics. Thermodynamics applies to a wide variety of topics relevant to EO science and technologies from atmospheric chemistry and meteorology up to sensor design and aeronautics.","name":"Basics of Thermodynamics","selfAssesment":"Planned
"},{"code":"PP1-8-2","description":"Starting from the standard Rocket Equation - assuming a relative speed of the burned (emitted) fuel equal to 2,4 km/s and zero initial speed - it is possible to evaluate (for a single-stadium rocket) the mass percentage of payload that can be hosted on a platform depending on the final speed expected on the orbit. For instance a 28% payload is possible for a geostationary platform whose expected final speed on the orbit (radius 42.170 km) is 3,7km/s. Instead for a polar platform at about 800km this percentage reduce up to the 4% being the final sped on the orbit expected to be 7,5km/s.","name":"Equation of the rocket and launch of a satellite: payload determination","selfAssesment":"Planned
"},{"code":"PP1-8-3","description":"The orbit of a satellite is commonly defined through its so called Keplerian parameters. These parameters represent the trajectory that the satellite will follow if no-perturbation are acting on it. A series of forces act on the satellite to perturb it away from the nominal orbit. We can classify these perturbations, or variations in the orbital elements, based on how they affect the Keplerian elements. The actual orbit of a satellite will result from a combination of these perturbations. Periodic maneouvers are needed to bring the orbit back to nominal conditions. The lifetime of a satellite is defined as the time interval that it takes to decay from its initial altitude to an altitude causing the satellite reentry down to the atmosphere. Therefore lifetime of a satellite should not be confused with the time during which the satellite will provide useful information (this operational phase, in general, is designed to last 5 - 7 years). In fact, all satellite terminating operational phases in orbits passing through the LEO region should be de-orbited or, where appropriate, manoeuvred to an orbit with suitably-reduced lifetime, that is, should be left in an orbit where drag and other perturbations will limit lifetime. The actual duration of the satellite in orbit will depend from the intensity of the perturbations which will affect its orbit. In case of satellite on GEO orbit, at the end of the operational phases they will be located on a disposal orbit, that is an orbit which do not cross the protected region. The protected region is the altitude region ranging from GEO - 200 km to GEO + 200 km and inclination region between -15 deg and +15 deg. Satellites in low Earth orbit, with perigee altitudes below 1000 km, are predominantly subject to atmospheric drag. This force very slowly tends to circularise and reduce the altitude of the orbit. The rate of 'decay' of the orbit becomes very rapid at altitudes less than 200 km, and by the time the satellite is down to 180 km it will only have a few hours to live before it makes a fiery re-entry down to the Earth.","name":"Real orbits. Life time of a satellite, orbit’s decay.","selfAssesment":"Planned
"},{"code":"PP1-8-4","description":"The choice of a satellite orbit mostly depends on its main application. From this point of view it represents a crucial part of a satellite mission design. The most important parameters to describe a satellite orbit are the inclination angle i (of the orbit plane respect to the equatorial plane) its eccentricity e and its height H from the Earth's surface. In principle whatever eigth H can be used, provided that the speed of the satellite on its orbit allows the centrigugal force to exactely compensate the gravitational one at that heigth. Polar (i close to 90°) and Geostationary (i=0, H=35.800 km) orbits are the most common choices for EO satellites. In principle one single polar satellite can be sufficient to guarantee the global coverage of the Earth with equal quality of the images at all latitudes. All Geostationary satellites share the same circular orbit with H around 36000 km where the required speed exactely correspond to the one required to travel an entire orbit in 1 sideral day (orbital period P = 1 sideral day). This means that the satellite footprint is permanently in place over a specific Earth's location (e.g. for Meteosat 0°N, 0°E) allowing a quasi-continuous monitoring of a whole Earth's emisphere (with poor visibility of Earth's edges including Poles). Polar satellites' heigths are usually in between 700-800 km, with orbital periods around 100min (i.e. about 14,5 orbits/day) even if, lower orbits are also chosen particularly for very high spatial resolution payloads. Lower inclinations are also used (quasi-polar orbits) for specific applications. Due to the asphericity (and mass inhomogeneity) of the Earth, satellite orbit plane rotates around the Earth's polar axis with a period Pp producing (for elliptical orbits) the rotation of the orbit itself in its plane. A common choice for most EO polar satellites is to choose the orbital parameters in a way that Pp=1 year (Sun-Synchronous orbits). Due to the synchronism between Earth's revolution around the Sun and the orbit plane precession around Earth' axis, satellite passages happens at the same local solar time (similar illumination conditions) each time it flies over a specific region. This ensure repeatable sun illumination conditions facilitating image interpretation particularly for change detection or land monitoring applications. Other choices are possible when it is required to monitor with continuity high latitude regions.\r\n\r\nThis is the case of Molniya orbits which combine the continuity of observations typical of geostationary satellites with the possibility, offered by polar orbits, to overfly the highest latitudes regions. Its characteristics are: high eccentricity (e.g. e=0,74, axes 500 and 23.000 km), P=1/2 sideral day (Geo-Synchronous), inclination (i=63,4° or i=116,6°) which guarantees the satellite footprint at the apogee remaining positioned on a fixed ground point (non-rotating orbit). This way the satellite will spend more than 93% of its orbital period looking to the same emisphere even from a high latitude point of view. \r\n\r\nSo called altimetric orbits respond to the specific needs of altimetry. In this case the orbital parameters are chosen in order to guarantee, for example: a) that the ascending and descending sub-satellite tracks intersect at roughly 90 degrees on the Earth’s surface (so that orthogonal components of the surface slope can be determined with equal accuracy; b) the possibility to monitor all phases of tidal effects on ocean surface.\r\n\r\nParticularly important for several applications (multi-temporal analyses, change detection, etc.) are the Exactly repeating orbits.\r\nThey are conceived in order that the sub-satellite track will repeat itself exactly after a certain interval of time. This allows images having the same viewing geometry during the satellite’s lifetime making moreover available a particularly simple method of referring to the location of images (navigation or geo-referenciation) for example by referring to a ‘path and row’ system used for instance by the Landsat World Reference System (WRS). It is possible to arrange satellite orbits parameters in order to contemporary guarantee the sun-syncronism so that, not only satellite images collected on the same region can be easily super-imposed each-other but the same illumination and viewing geometry can be achieved. This is, for instance, the choice adopted for LANDSAT satellites whose images are typically available as a collection of scene of fixed dimension always similar each other when covering the same terrestrial area.","name":"Satellite orbits parametrization and choice","selfAssesment":"Completed
"},{"code":"PP1-8","description":"Mechanics is the Physics branch dealing with the behaviour of physical bodies when subjected to forces or displacements. This section provides Mechanics basic elements necessary for determining the orbits of satellites and rockets. The different satellite trajectories will be illustrated with respect to their peculiarities","name":"Basics of Mechanics","selfAssesment":"Planned
"},{"code":"PP1","description":"Optical Remote Sensing deals with those part of electromagnetic spectrum characterized by the wavelengths from the visible (0.4 micrometer) to the near infrared (NIR) up to thermal infrared (TIR, 15 micrometer). It regards the collection and interpretation of the e.m. radiation emitted, reflected, adsorbed and transmitted by the observed targets in order to derive their physical-chemical properties and related information. Such a possibility derives from the basic principle of (multi-spectral) remote sensing that is widely supported both theoretically (e.g. atomic and molecular spectroscopy) and experimentally (e.g. spectral signatures catalogues). It states that, in principle (e.g. disposing of sensors with ideal spectral capabilities) the matter-radiation interaction depends on the wavelength of the involved radiation and on specific (e.g. chemical/physical) properties of the matter that can be derived by the spectral analysis of the emerging (emitted, reflected, adsorbed or transmitted) radiation. As far as Earth Observation is concerned, specific related concepts have to be addressed like: the spectral matter-radiation interactions (spectral signature concept), natural sources (e.g. Earth, Sun) of optical e.m. radiation, theory of the Black Body, atmospheric physics and radiative transfer equations in the VIS-NIR and TIR spectral ranges, basic physics of e.m. optical sensors and image systems, physical fundaments of the interpretation of optical radiances collected by multi-hyperspectral passive techniques.","name":"Basics of Optical Remote Sensing","selfAssesment":"Completed
"},{"code":"PP2-1-2-1","description":"A radar signal is a complex signal. It is represented by a real part, the in-phase component, and an imaginary part, the quadrature component. In-phase is usually annotated by “I”, and quadrature by “Q”. Considering single look complex data, each component is represented in a single image channel.","name":"In-phase/Quadrature Component","selfAssesment":"Planned
"},{"code":"PP2-1-2-2","description":"A phasor represents a complex number and its phase and amplitude equivalent. Considering a complex SAR image’s pixel, the real and imaginary part can be represented by a 2D vector in Cartesian coordinates. Its corresponding phase and amplitude information corresponds to the direction and length of the vector, respectively.","name":"Phasor","selfAssesment":"Planned
"},{"code":"PP2-1-2","description":"The signal emitted by a radar system is a microwave signal, which can be described using a complex wave representation. This implies that the signal can be entirely represented by a complex number, which characterizes both its magnitude and its phase at a certain moment of time. In the SAR context, the complex number is usually represented by a real part, the in-phase component (I), and an imaginary part, the quadrature component (Q), from which the corresponding magnitude and phase can be retrieved. In single look complex SAR data, each of these components is pictured in a single image channel. The terminology comes from electrical engineering, whereby the quadrature component is 90° out of phase with respect to the reference frequency and the in-phase component. This is necessary in order to retrieve the phase information during A/D conversion. The I component can be expressed as the signal amplitude multiplied by the cosine of the phase. The Q component corresponds to the amplitude of the signal multiplied by the sine of its phase. Using both components as input, the magnitude and phase for each signal echoes and location can be retrieved.\r\n\r\nThe relationship between I/Q terms and the magnitude and phase of the signal can be best represented using a phasor. A phasor represents a complex number and its phase and amplitude equivalent. It can be best illustrated by a 2D vector in a Cartesian coordinate system, which projections on the horizontal and vertical axes represents the real and imaginary part, respectively. The length of the vector correspond to the signal’s amplitude and its direction (angle between the horizontal axis and the vector) characterizes the phase of the signal. Using simple mathematical considerations, the relationship between I/Q and amplitude and phase can be established.\r\n\r\nEach signal echo and pixel of a complex SAR image can be represented with such a phasor and the necessary amplitude and phase information can be accordingly retrieved.","name":"Complex wave description","selfAssesment":"Planned
"},{"code":"PP2-1-4","description":"Electromagnetic waves are polarized; the direction of the polarization corresponds to the direction of oscillation of the electromagnetic field. Typical and often used linear polarisations are: H (horizontally) and V (vertically) polarized waves of the plane of the electric field vector oscillations relative to the sensor coordinate system. The polarization state of a backscattered wave from a natural surface can be linked to the geometrical characteristics like shape, roughness and orientation and the intrinsic properties of the scatterer like moisture, salinity, density. The radar system is characterized by combination of polarization of transmitted and received pulse: HH, HV, VH or VV. Based on the polarization sent and obtained the radar systems are divided in three polarization modes. Single polarization refers to the same polarization transmitted and received; dual polarization, one polarization is sent and another received; or quad polarization, when system is able to transmit and receive all four types of polarization. When making a contact with a scatterer, the polarization of the EM-wave can change, depending on the geometrical and dielectrical properties of the scatterer. In order to get all necessary information about those changes, full polarimetric systems are required.","name":"Polarisation","selfAssesment":"Completed
"},{"code":"PP2-1-5","description":"Property of signal or data set in which the phase of the constituents is measurable, and plays a significant role in the way in which several signals or data combine. Two waves with a phase difference that remains constant over time, are said to be coherent.","name":"Coherent","selfAssesment":"Planned
"},{"code":"PP2-1-6","description":"In remote sensing, phase is the exact position within a periodic signal with respect to an arbitrary reference point. It is typically expressed as an angle and measured in degrees or radians, where one period corresponds to a phase of 360° or 2π, respectively. Mathematically, phase is the argument of a complex number, that is the angle between its geometric representation in the complex plane and the real axis. For this reason, complex algebra is often used in remote sensing to facilitate phase calculations. Due to its periodic nature, phase can only be measured unambiguously within one period. Consequently, phase measurements are commonly subject to 2π phase ambiguities. These ambiguities can often be resolved in a process called phase unwrapping, using a priori information about the signal, typically related to its continuity. Phase measurements are crucial for the creation of synthetic aperture radar (SAR) images, as well as for many SAR imaging techniques, including interferometric SAR (InSAR).","name":"Phase","selfAssesment":"Completed
"},{"code":"PP2-1-7","description":"Shift in frequency caused by relative montion along the line of sight between sensor and the observed scene.","name":"Doppler effect","selfAssesment":"Planned
"},{"code":"PP2-1-8","description":"The wave-particle dualism (duality) is a theory according to which all matter exhibits the attributes of waves and particles.","name":"Wave-particle dualism","selfAssesment":"Planned
"},{"code":"PP2-1","description":"The microwave portion of the electromagnetic (EM) spectrum ranges from 1 millimeter to 1 meter. Imaging radars are independent of weather conditions and can operate day or night. EM-waves are polarized. Normally only the horizontal (H) or vertical (V) linear polarizations are used. The radar system is characterized by combination of polarization of transmitted and received pulse: HH, HV, VH or VV. When making a contact with a scatterer, the polarization of the EM-wave can change, depending on the geometrical and dielectrical properties of the scatterer.The data can be acquired from both the ascending (northwards) and descending (southwards) satellite passes. Water clouds can interfere with the radars operating below 2 cm in wavelength. The effects of rain can be generally ignored at wavelengths above 4 cm. For longer wavelengths (above 20 cm), an effect called Faraday rotation caused by the ionosphere, i.e., free charges (electrons) and the Earth’s magnetic field, can lead to a rotation of the polarization plane. In the presence of Faraday rotation, the data, usually fully polarimetric, should be corrected. The radar systems operate in different bands that uses different wavelengths. The most common frequences/wavelengths (frequency = Speed of Light / wavelength) for environmental applications are X (5,75-10,90 GHz), C-(4,20-5,75 GHz), S-(1,550-4,20 GHz), L-(0,390-1,550 GHz) and P-(0,255-0,390 GHz) band. The selection of SAR system for acquiring data depends on their application. Longer wavelengths are mainly devoted to communication and navigation purposes. Radars penetrate atmosphere and clouds. For example for forestry, longer wavelengths starting from C- or S-band are preferred.","name":"Microwave portion of electromagnetic spectrum","selfAssesment":"Completed
"},{"code":"PP2-2-1","description":"Diffraction is defined as interaction of waves with any solid object, not surfaces, and is not to be confused with refraction. More precisely, diffraction describes the phenomena of interaction of waves at an obstacle, such as an aperture, or an opening, such as a hole or an occurring space between two objects. Hence, diffraction is an essential form of scattering, describing ordered scattering at discrete boundaries. The effect of diffraction can be observed through extended interference patterns or simply by the bending of waves. In the field of microwave remote sensing, diffraction has the practical implication that it limits the spatial resolution of a microwave sensor since it acts on the ability of an imaging system to resolve details. This theoretical limit of resolution is called the diffraction limit. This means, the larger the aperture of the observing system compared to its employed wavelength (dependent on the frequency), the finer the resolution of an imaging system. The diffracted field can be calculated with analytical models, such as the Fraunhofer diffraction approximation in case of far field conditions, where the object is far away and the incident waves are assumed to be plane waves, or the Fresnel diffraction approximation in case of near field conditions, where the waves are spherical.\r\nOne simple example of diffraction is the diffraction of sound, for example the possibility to hear sounds around corners.","name":"Diffraction","selfAssesment":"Completed
"},{"code":"PP2-2-2","description":"Scattering means the redirection of incident electromagnetic energy by an object. Similar to diffraction, scattering refers to the same physical process, the coherent distortion of an incident wave. However, diffraction as well as reflection can be regarded as essentially forms of scattering. Scattering explicitly describes the “random distortion of waves by elements that are similar in size or less than the wavelength” (Woodhouse, 2005). Thereby, scattering of the incident wave at an object can occur in any directions with varying strength, with the scattering pattern varying with the incident direction. Thus, the term scattering cross section, often denoted by σ, quantifies the effectiveness of a scatterer. In the field of active microwave remote sensing, the backscattering coefficient σ0 is known “as the ratio of the statistically, averaged, scattered power density to the average incident power density” (Fung, 1994). \r\nIn passive microwave remote sensing, radiometers measure the intensity of radiation emitted by a body, called brightness temperature TB. Since TB is always less than its physical temperature T, emissivity, defined as e = TB / T, is a measure of how strongly a body radiates at a given wavelength. It varies between 0 (metal) to unity (blackbody).\r\nEmission and scattering are complementary: surfaces that are good scatterers are weak emitters, and vice versa.","name":"Scattering and emission","selfAssesment":"Completed
"},{"code":"PP2-2-3","description":"In climate change studies the carbon cycle with its crucial component the terrestrial biosphere is of great importance due to the ability of the biosphere to store environmentally harmful carbon dioxide. Radar sensors, especially SAR, can here provide a useful tool for quantifying and monitoring the biosphere. Hence, the relationship between biomass and radar backscatter responses has been studied in detail in recent decades. Results show that the sensitivity of measured radar backscatter coefficient decreases with increasing amount or density of present biomass. In the so-called saturation region, the radar backscatter saturates at a biomass depending on the employed wavelength. While for higher frequency bands like C-band (3.95-5.8 GHz), biomass can be measured up to ~50 ton/ha, the amount of measurable biomass increases with decreasing frequency (due to the increasing wavelength), such that at L-band (1-2.6 GHz) ~ 100 ton/ha and at P-band (0.23-1 GHz) ~200 ton/ha biomass can be measured. Further, the sensitivity of radar to biomass is different for co- or cross-polarized backscatter since the level of saturation depends not only on frequency but also on vegetation (e.g., height, structure, density, moisture) and soil surface (e.g., roughness, moisture) parameters. Overall, the saturation of radar backscatter depending on biomass has to be considered when analyzing SAR data.","name":"Backscatter saturation","selfAssesment":"Completed
"},{"code":"PP2-2-4-1","description":"The radar equation is a measure of the received echo at the sensor. It defines what proportion of the transmitted energy is returned from a target. It is a function of the range between the antenna and the target, the antenna gain and the radar cross-section of the target. Mathematical expression that describes the average received signal level, compered to the additive noise level, in terms of system parameters. Principal parameters include: transmitted power, antenna gain, noise power, and radar range.","name":"Radar equation","selfAssesment":"In progress
"},{"code":"PP2-2-4-2","description":"Coefficient sigma or sigma nought represents the average reflectivity of a horizontal material sample, normalized with respect to a unit area on the horizontal ground plane.","name":"Sigma nought","selfAssesment":"Planned
"},{"code":"PP2-2-4-3","description":"Gamma nought represents the average reflectivity of a horizontal material sample, normalized with respect to the incident area, orthogonal to the incident ray from the radar.","name":"Gamma nought","selfAssesment":"Planned
"},{"code":"PP2-2-4-4","description":"Radar brightness coefficient represents the reflectivity per unit area in slant range.","name":"Beta nought (brightness)","selfAssesment":"Planned
"},{"code":"PP2-2-4","description":"Measure of radar reflectivity. The Radar Cross Section (RCS) is expressed in terms of the physical size of an hypothetical uniformly scattering sphere that would give rise to the same level of reflection as that observed from the sample target.","name":"Radar cross-section","selfAssesment":"Planned
"},{"code":"PP2-2-5-1","description":"A material constant is a physical or chemical property of a substance, which can be expressed in numbers. Giving a precise numerical value of a constant often requires determining the external conditions (e.g. temperature, humidity). Material constants are factors that influence the interaction of microwaves with the target objects.","name":"Material constants","selfAssesment":"Planned
"},{"code":"PP2-2-5-2","description":"The complex part nc of the refraction index n determines how far an electromagnetic wave of wavelength λ can survive crossing a specific medium. The attenuation length la is the distance after that the amplitude of an electromagnetic signal reduces its value by an amount of 1/e. For instance the amplitude of the Electric field E(z) of an electromagnetic wave proceeding along the z direction is decreasing as exp(-z/la) being la=λ/(2𝜋 nc) the attenuation length associated to that specific material (nc) and wavelength λ. This way attenuation length in water can be of hundreds of meters in the visible range and just few microns in the microwaves. The opposite happens over solid land surfaces where optical waves can penetrate from few microns up to few millimeters (moving from the VIS-NIR to the TIR spectral range) whereas microwaves can reach depths from hundreds to towsands (as higher are their wavelength) meters allowing the exploration of subsoil and thick coulters of ice.","name":"Attenuation lenght and penetration depth","selfAssesment":"Completed
"},{"code":"PP2-2-5-3","description":"Soil permittivity is a measure of the water content (soil moisture) in the soil and characterized by the metric of the dielectric constant of the soil. Soil moisture influences emission, absorption and propagation of microwave electromagnetic energy. Moisture decreases the ‘emissivity’ of soil, and thereby affects microwave radiation emitted from Earth’s surface. Dry soil has a low dielectric constant and low radar reflectivity. Moist and partially frozen solis have intermediate values. The higher the soil water content, the lower the radar signal penetration into the soil. In situ measurements of soil permittivity are a prerequisite for the calibration and validation of synthetic aperture radar (SAR) soil moisture retrieval algorithms. Soil moisture is a key variable in the hydrologic cycle and is recognized as an Essential Climate Variable (ECV).","name":"Soil permittivity","selfAssesment":"Completed
"},{"code":"PP2-2-5-4","description":"The complex relative permittivity of a plant is a function of its contained amount of water, solutes (mainly their salinity) and temperature in all plant compartments (including roots). The more water and the higher the salinity are in the plant compartments, the higher is the complex relative permittivity of the plant. The complex relative permittivity of a plant refers to the complex relative dielectric constant of the plant and can be subdivided into complex relative permittivity values for the different plant compartments (roots, stem/stalk, leaves, fruit,...). The complex relative dielectric constant or permittivity parameter has a real and an imaginary part indicating the moisture content and the conductivity (loss) of the plant medium. Models of plant permittivity consist mostly of a free-water and a bound-water part. In particular, plant water is a solute of nutrients and not all water-conducting plant cells are fully filled by water, but also with air. Hence, the estimation of one plant permittivity, especially including several plant parts can be challenging to assess, to understand and to model. To acknowledge this mixture of components, dielectric mixing models containing the single material components are normally developed and applied, representing an effective complex relative permittivity of all plant components. Concerning a vegetation canopy, electromagnetic waves interact with a more or less sparsely vegetation-filled volume unit of air. A vegetation canopy represents a dielectric mixture of vegetation inclusions (leaves, twigs, branches, stems,…) distributed in a volume of air. Dielectric mixing models of canopies take this vegetation volume fraction into account.","name":"Plant permittivity","selfAssesment":"In progress
"},{"code":"PP2-2-5","description":"The dielectric properties of any material can be described by the complex relative dielectric constant (complex relative permittivity) and contains of the real part (moisture content) and the imaginary part (conductivity/loss tangent). For instance: Reflectivity of a smooth surface and the penetration capabilities of microwaves into the material are determined by these two quantities. The complex dielectric constant changes mainly due to variations in water content, salinity, temperature of the material as well as due to the observing wavelength and polarization of the electromagnetic wave. It relates to the interaction of weakly-charged material components, like bi-polar water molecules, with irradiation of electromagnetic waves. The interaction increases with amount and charge of the material components. The complex relative permittivity is also linked to the complex index of refraction as being its square. In order to describe the complex relative permittivity of pure and saline water the single-relaxation Debye and the double-Debye dielectric model can be used. As the movement of bi-polar material components is significantly reduced when the material is put under freezing conditions (temperatures below 0 °C), the permittivity falls to almost a constant. The real part of the relative permittivity of pure ice is almost constant, when ignoring a weak temperature dependence, and amounts to approx. 3.2. For heterogeneous (mixed) materials consisting of more than one component the equivalent dielectric constant is a function of the permittivity of the single components, their volume fractions, their distribution along space and the polarization and wavelength of the interacting electromagnetic wave.","name":"Dielectric Properties","selfAssesment":"Planned
"},{"code":"PP2-2-6-1","description":"The standard deviation of the surface height variation (or RMS height), denoted by s (or hRMS), describes the statistical variation of a random surface with height z(x). In case of an azimuthally symmetrical surface, the single-scale RMS height of the one dimensional case for discrete profile values is given by (1), where N is the number of samples, and z ̅ the mean surface height (2). \r\nAs roughness depends not only on the soil surface properties but also the wavelength λ of the electromagnetic signal, the roughness parameters are scaled by the wave number k. Hence, the electromagnetic roughness ks for surface roughness parameter s is (2π/λ)*s (3). In order to determine if a random surface may be considered as electromagnetically smooth, one common definition is given by the Rayleigh roughness criterion, where s < λ / 8*cosθ, or ks < 0.8, at incidence angle θ = 0. This criterion has been revised for the microwave region, where the wavelength is usually of the order of the RMS height, called the Fraunhofer roughness criterion, where s < λ / 36*cosθ, or ks < 0.2, at incidence angle θ = 0. Additionally, surfaces are considered as electromagnetically rough for 1 < ks < 3.","name":"Vertical roughness component (RMS height)","selfAssesment":"Completed
"},{"code":"PP2-2-6-2","description":"The surface correlation length, denoted by l, is defined as the displacement ξ at which the surface correlation function p(ξ)= 1/e. Thus, l can be seen as the reference length up to which two points of one soil surface can be regarded as statistically independent from each other. If we imagine a perfectly smooth soil surface, l=∞ since every point on that surface correlates with all other points and can therefore be regarded as dependent from each other.\r\nAs roughness depends not only on the soil surface properties but also the wavelength λ of the electromagnetic signal, the roughness parameters are scaled by the wave number k. Hence, the electromagnetic roughness kl for surface roughness parameter l is kl=(2π/λ)*l.\r\nExperimental results indicate a weaker influence on the radar backscatter compared to the RMS height s.","name":"Horizontal roughness component (correlation length)","selfAssesment":"Completed
"},{"code":"PP2-2-6-3","description":"The surface correlation function p(ξ) determines the degree of correlation between two lateral separated locations of one surface. Thereby, ξ is defined as displacement between two locations, (x, y) and (x', y') on the surface and given by (1).\r\nWith increasing separation between two locations on the surface p(ξ) decreases, and at a certain distance, the surface correlation length l, the heights at the two locations are considered statistically uncorrelated.\r\nThe surface scattering of electromagnetic waves can be simulated with various models. Depending on the observed roughness scale multiple surface scattering models are valid for specific roughness conditions. For example, one of the first surface scattering models for slightly rough surfaces, the small perturbation model (SPM), deals with roughness scales that are small relative to the wavelength and hence has validity conditions for ks < 0.3, kl < 3, and m < 0.3. Since then, various surface scattering models for computing the scattering and emission behavior of natural surfaces in the microwave region have been proposed, such as the Kirchhoff scattering model (KH), the geometric optics model (GO), the physical optics model (PO), or the integral equation model (IEM), to name the most common used in literature. For simulations of EM scattering at soil surfaces, assumptions of the functional forms of p(ξ) have to be made. The two most common forms for mathematically describing the surface correlation of natural surfaces are the exponential pE(ξ) and the Gaussian pG(ξ) correlation functions, defined by (2) and (3).\r\nFor some mathematically sophisticated surface scattering models, an x-Power correlation function p(x-Power)(ξ) can be assumed (4), with x as value between 1 and 2.\r\nIn literature, rather smooth surfaces are characterized by an exponential surface correlation function, while rather rough surfaces are characterized by a Gaussian surface correlation function.","name":"Surface correlation function","selfAssesment":"Completed
"},{"code":"PP2-2-6-4","description":"The root-mean-square (RMS) slope m of a one dimensional height profile for one random surface is given by (1), with s as the standard deviation of the surface height variation (or RMS height), and p''(0) as the second derivative of the surface correlation function p(ξ), evaluated at ξ=0. Since p(ξ) is an even function, p''(0) is a negative quantity.\r\nFor modeling of electromagntic scattering at soil surfaces, assumptions of the functional forms of p(ξ) have to be made. The most common known forms are the exponential and Gaussian correlation functions. Additionally, some models allow the assumption of a x-Power correlation function, with x as value between 1 and 2. For the varying surface correlation functions, the RMS slope m is given by (2)-(4).\r\nIn literature, for L-band, the slope m should be lower than 0.3 or 0.4 in case of single scattering and bare soil surfaces with moderate RMS heights.","name":"Surface roughness slope","selfAssesment":"Completed
"},{"code":"PP2-2-6-5","description":"In reality, one random surface has multiple roughness scales, since the commonly used surface description based on single-scale roughness parameters does not comprise all the properties of natural surfaces relevant for describing wave scattering. Depending on the wavelength λ of the microwave sensor the dimension of the surface roughness parameters s and l correspond to specific roughness scales. \r\nIn case of multi-scale roughness, the equivalent RMS height is a composite of the individual RMS heights at different roughness scales (1).\r\nA three-scale surface, as shown in Fig. 1, for example consists of a small-scale high-spatial frequency variation (c) ‘riding’ on top of the larger scales, the medium-scale perturbation (b) and the large-scale undulation (a).\r\nAt microwave frequencies, the centimeter scale is the scale of roughness of primary importance, since λ is on the order of centimeters to a few tens of centimeters. For natural surfaces it is very difficult to measure millimeter-scale roughness.","name":"Single-scale & multi-scale roughness","selfAssesment":"Completed
"},{"code":"PP2-2-6","description":"Surface roughness defines the geometry between the pedosphere and the atmosphere (soil-air boundary).\r\nIn the field of microwave remote sensing, surface roughness affects scattering and emission characteristics of natural surfaces. The degree of roughness of a random surface is determined by statistical parameters, measured by the units of wavelength of the observing sensor. The two fundamental surface roughness parameters are the standard deviation of the surface height variation (RMS height) s, with its related surface correlation function p(ξ), and the horizontal surface correlation length l. Additional, a third roughness parameter, the root-mean-square (RMS) slope m, is important for some surface scattering models to simulate electromagnetic wave scattering of surfaces.\r\nSurface roughness determines the variation of surface height within an imaged resolution cell. The transition from smooth to rough is qualitative, and is function of both wavelength and incident angle. With decreasing frequency the soil surface appears rather smooth to microwave sensors. This results in the fact, that while one surface appears smooth when sensed at L-band (λ ≈23 cm), the same surface appears rough when sensed at X-band (λ≈3 cm). Hence, in the field of microwave remote sensing, the ‘effective’ surface roughness parameters are scaled by the wave number k= 2π/λ. Surface roughness can be observed at single or multi-scale.","name":"Surface roughness","selfAssesment":"Completed
"},{"code":"PP2-2-7-1","description":"The Stokes vector is a four-element vector containing real-valued polarization combinations and is an alternative form of representing a full (=quad) polarimetric dataset, besides the complex-valued scattering matrix. Stokes vectors can be measured as real quantities and are preferred over the complex-valued Jones vector formalism when a coherent (phase-preserving) measurement system is absent. Stokes vectors can be used to form the 4x4 Mueller matrix for target scattering analyses, mostly used in the field of optics. First component of the Stokes vector is the sum of the co-polar fields and represents the total energy of the wave. Second component is the difference of the co-polar fields. Thrid component is the real part of the cross-correlation of the fields and fourth component is the imaginary part of it. The different polarization states can be represented by the Stokes vector and an O(3) elliptical transformation can be used to change the polarization basis, similar to the Jones vector where the SU(2) elliptical transformation is used.","name":"Stokes Vector","selfAssesment":"Completed
"},{"code":"PP2-2-7-2","description":"The scattering matrix is a 2x2 square matrix containing four complex-valued polarization measurements (amplitude & phase) forming one full (= quad) polarimetric set of coherent observations. An often recorded set of polarizations is the combination: HH (horizontal receive - horizontal transmit), HV (horizontal recive - vertical transmit), VH (vertical receive - horizontal transmit) & VV (vertical receive - vertical transmit). The scattering matrix is fully suficient for describing scattering from coherent targets (dominating the resolution cell), but not for incoherent tragets (mix of scattering contributions in the resolution cell). For the latter, the coherency and the covariance matrices are the more appropriate descriptions of scattering from incoherent targets.","name":"Scattering matrix","selfAssesment":"Completed
"},{"code":"PP2-2-7-3","description":"The covariance and coherence matrix are two 4x4 square matrices, which can be built out of the scattering matrix by a lexicographic and a Pauli target scattering vector. They are an alternative representation of a full polarimetric dataset allowing the analysis of incoherent targets (more than one dominant scatterer in the resolution cell) and the phenomenon of depolarisation (transformation of incoming fully polarised wave into a partially polarised wave by creating a variety of different types of polarizations during media interaction). These matrices can be converted into each other without loss of information (by unitary transformations), but not turned back into the scattering matrix due to averaging operations during formation of coherency or covariance matrices.","name":"Covariance/Coherency matrices","selfAssesment":"Completed
"},{"code":"PP2-2-7-4","description":"Polarimetric decomposition techniques allow signal unmixing by polarimetry in order to separate different scattering contribution within one resolution cell, e.g. from soil & vegetation or snow, ice & bedrock. They can be either applied for the scattering matrix (coherent form - one dominant scatterer in the resolution cel) or for the covariance/coherency matrix (incoherent form - more than one dominant scatterer in the resolution cell). Decomposition techniques can be model- (physics) or eigen- (mathematics)-based. The eigen-based decomposition allows to diagonalize the coherency or covariance matrix in a diagonal eigenvalue matrix and a matrix of column eigenvectors. From eigenvalues and eigenvectors the polarimetric entropy, the scattering alpha angle and the polarimetric anisotropy. The polarimetric entropy is a matric for the degree of depolarization of the scattering event. The scattering alpha angle is an intrinsic scattering mechanism indicator. The polarimetric anisotropy informs about secondary scattering mechanism in evironments with high entropy. If the anisotropy is high only one secondary scattering mechanism is present, if it is low, more than one will occur.","name":"Polarimetric decomposition techniques","selfAssesment":"Completed
"},{"code":"PP2-2-7-5","description":"All bi- or multi-polar (non-inert) media have the tendency to orient themselves in 3D-space if an external non-ionizing electro-magnetic field is excited on them. This orientation polarization is caused by negatively and positively charged areas within the media, for instance due to charges of the different molecules and atoms building up the media, under the premise that the media is able to rotate (partly) freely and is not completely fixed. Molecules of liquid water are a prime example. Here the two positively charged hydrogen atoms are oriented in a 105-degree configuration to the negatively charged oxygen atom, forming a slightly charged bi-polar medium that orients itself under electromagnetic radiation treatment, especially at the frequency range of microwaves and millimeter-waves.","name":"Orientation polarisation of media","selfAssesment":"In progress
"},{"code":"PP2-2-7-6","description":"Polarimetric coherences are complex-valued polarimetric correlation coefficients assessing the redundance between different polarimetric observations informing about their divergence in information. They can be formed among mutual polarimetric observations showing their degree of correlation. The polarimetric coherence consists of a magnitude, ranging between zero (no correlation) and one (identical), and a phase information, running from -180° to 180°. Typically polarimetric coherences are calculated between the co-polarimetric (HH, VV) channes, as well as the cross-polarimetric channels (HV, VH). The latter polarimetric coherence assesses the system noise inherent in the recorded polarimetric data, if a monostatic systems (transmitting and receiving sensor on the same sensing platform) is used for acquisition.","name":"Polarimetric coherences","selfAssesment":"Completed
"},{"code":"PP2-2-7-7","description":"The polarisation ellipse and the Jones vector formalism are the geometrical (three real-valued angles) and algebraic (amplitude & phase) formalisms to describe polarisation states of an electromagnetic wave. The ellipse has an orientation, an ellipticity and absolute phase angle. The three angles are integrated in one mathematical ellipse formulation that can represent linear, elliptic and circular polarisation states. The Jones vector formalism is an algebraic formulation allowing all calculus available in linear algebra. Both representations (polarisation ellipse & Jones vector) can be converted into each other seemlessly with a simple elliptical basis (special unitary SU(2)) transformation.","name":"Polarisation ellipse / Jones vector formalism","selfAssesment":"Completed
"},{"code":"PP2-2-7-8","description":"The concept of polarisation synthesis is based on the mathematical fact that a set of polarimetric measurements in one basis, e.g. H,V, can be converted into any other polarimetric basis, by a mathematical transformation. A basis set is a set of four polarisations. Each set is orthogonal, like LC (left-circular), RC (right-circular). The striking point is that only one set of polarimetric measurements in one basis needs to be recorded and the transformation in other polarimetric bases is done in a post processing step afterwards. There is no need to measure all bases, which is quite complicated in terms of engineering for elliptical and circular polarisation states.","name":"Polarisation synthesis","selfAssesment":"Completed
"},{"code":"PP2-2-7","description":"Polarimetry is the technique to evalute the physical phenomenon of polarisation including the measurement, the processing and the interpretation of the polarisation state of an electromagnetic wave. Polarization states are described by the scattering elipse and the Jones Vector formalism. Especially the polarization states after interaction with the media under investigation are mostly investigated to estimate media properties and states. The mostly observed fully polarimetric observation basis is H,V up to now with the single observations: HH HV, VH, VV. The concept of polarization synthesis allows to acquire fully polarimetric observations in one basis (e.g. H,V) and transform them into any other orthgonal basis (e.g. left, right circular) by a mathematical transformation in post processing. Polarimetric States are stored in different mathematical formats: Scattering matrix, polarimetric coherences , Stokes vector, Pauli-vector, lexicographic vector, coherency and covariance matrices. These mathematical representations can be decomposed according to the contained elementary scattering mechanisms in the recorded signal. The so-called polarimetric decomposition technique allow signal unmixing for differnt scattering components (e.g. from soil & vegetation). The techniques range from mathematics-based until physics-based concepts and are developed since decades starting with Huynen in 1970.","name":"Polarimetry","selfAssesment":"Completed
"},{"code":"PP2-2","description":"A number of interactions are possible when electromagnetic energy encounters matter, whether solid, liquid or gas. In Earth Observation there are two main interactions: atmospheric and with target. Atmospheric interaction: In radar remote sensing, atmospheric interactions are limited due to the long wavelengths compared to the size of the atmospheric particles. The fact that microwaves interact with object at least as big as the wavelength is one of the greatest advantages of microwave remote sensing, since at larger wavelengths atmospheric particles are almost transparent to the signal and microwave sensors are independent from the time of day (day or night) and weather conditions. Water clouds can interfere with the radars operating below 2 cm in wavelength. The effects of rain can be generally ignored at wavelengths above 4 cm. For longer wavelengths (above 20 cm), an effect called Faraday rotation caused by the ionosphere, i.e., free charges (electrons) and the Earth’s magnetic field, can lead to a rotation of the polarization plane. Target interaction: The radar interaction with the object is a result of both radar system parameters (frequency, polarization, acquisition geometry) and the physical properties of the object (dielectric constant, i.e., water content; geometrical properties, i.e., the roughness, shape and orientation of the scatterer). Overall, various types of interactions can be distinguished – scattering, diffraction, and reflection – all describing the same process of wave interaction but at different scales.","name":"Interaction of microwaves with matter","selfAssesment":"Completed
"},{"code":"PP2-3-1-1","description":"The goal of an radar antenna is to direct and receive the transmitted and backscattered signal in a specific angular direction. The antenna gain describes the directional sensitivity of the antenna. It is a dimensionless quantity that is constant for a specific antenna.","name":"Antenna gain","selfAssesment":"Planned
"},{"code":"PP2-3-1-2","description":"The antenna radiation pattern shows the direction in which the antenna transmits and receives the energy in space, as well as the strength of this radiation. It is a function of angles and consists of different lobes, in which the signal is directed and received. There are two principal representation of the antenna patterns: field and power patterns, which are a function of the electric and magnetic fields of the energy being radiated.","name":"Antenna pattern","selfAssesment":"In progress
"},{"code":"PP2-3-1","description":"Antenna is a device that radiates electromagnetic energy and collects it during reception.","name":"Radar antennas and antenna calibration","selfAssesment":"Planned
"},{"code":"PP2-3-10-2","description":"The radargrammetric equation follows a similar principle as the stereoscopic equation, except that it uses the radar geometry. The radargrammetric observation equation allows the retrieval of 3D information about a target, based on the determination of the sensor-object stereo model. It estimates the coordinates the intersection of the two radar rays coming from the two different sensor positions with different look angles, using the coordinates of the satellites position and satellite velocity. The radargrammetric equation can be adapted in order to retrieve 3D information in layover areas (e.g. urban areas).","name":"Radargrammetric equation","selfAssesment":"Planned
"},{"code":"PP2-3-10","description":"Radargrammetry is the technique for extracting three-dimensional information from radar images. It applies photogrammetric principles to synthetic aperture radar (SAR) images. By viewing an object from different positions separated by a baseline, the appeared object position will vary slightly (denoted parallax). The disparities for each position on the object are related to its x-y-z coordinates. In radargrammetry, such disparities are computed for an entire image. The result is the terrain elevation from the measured parallaxes between two (or more) images, acquired at different angles. Radargrammetry requires at least two SAR images acquired from different positions, normally across-track due to the configuration of a side-looking SAR. Same-side stereo-pairs with intersection angles in the range of about 10 – 20° have been a feasible compromise between reasonable geometric disparities and the accuracy of estimated heights. In general, the disparities can be estimated with higher accuracy as the angle of intersection increases (as the stereo exaggeration factor increases). However, the same points must be recognized in all images, and it is hence required that the images are as similar as possible. This improves the image matching and it is best achieved with small intersection angles, which furthermore decreases radiometric differences. \r\nA general procedure for generating an elevation model from stereo-pairs is applicable for radargrammetry when optical stereo images are replaced with the backscatter intensity of SAR images. One image is selected as reference and the other(s) is coarsely registered to the reference, e.g., by using the attached meta-data. The same points are then located in both images using image matching. A common matching criterion is the cross correlation coefficient. Then, spatial point intersections are computed, which is the least square approach to find the intersection points of SAR range circles as defined from the matched image pixels. The computed intersections result in a point cloud that finally is interpolated to a consistent elevation raster. The entire process is extensive and computationally expensive, and normally a dedicated software is required. \r\nRadargrammetry with images acquired from opposite sides have been little investigated, and was first limited to stereoscopic viewing. Some opposite-side research was later presented with limited outcomes under certain conditions. Most applications today will not consider opposite-side radargrammetry, since the alternatives are usually better. Same-side radargrammetry performs better than opposite-side, while interferometric SAR that is based on phase differences, may be even more accurate. One advantage of radargrammetry is however, that it remains less affected by atmospheric disturbances compared to interferometric SAR, because it is using the amplitude images.","name":"Radargrammetry (same-side and opposite-side)","selfAssesment":"Completed
"},{"code":"PP2-3-11-1","description":"Differential Synthetic Aperture Radar Interferometry (DInSAR) aims the determination of deformation of the Earth’s surface that happened between two or more complex-valued SAR acquisitions.\r\nThe phase of an interferogram issued from the complex multiplication of a SAR image with the complex conjugate of a second SAR image contains five distinct components, or layers of information: (1) Two phase components arise from the geometrical baseline (slightly different position of both sensor positions): (1a) a topographical information representing the surface relief, (1b) “flat earth” pattern coming from the orbital distance of both sensor positions.\r\n(2) Two phase components result of the temporal baseline (time between both acquisitions): (2a) a deformation component, representing a possible displacement of the Earth’s surface between both acquisitions, (2b) an atmospheric component coming from different atmospheric conditions between both acquisitions. (3) A phase component corresponding to intrinsic sensor noise \r\n\r\nBoth parameters related to the temporal baseline can be retrieved using DInSAR on repeat-pass acquisitions. DInSAR cannot be used with single-pass interferometry (e.g. both acquisitions acquired at the same time).\r\nThe deformation component of the interferometric phase corresponds to the modification of the phase of the second SAR image compared to the first due to an additional range difference between the sensor position and the Earth’s surface that is induced by the motion of the Earth’s surface towards or away from the initial sensor position.\r\nUsing DInSAR, the phase components related to the geometrical baseline can be eliminated from the interferogram using an existing DEM and orbit information, or an additional interferogram showing no deformation. After DInSAR processing, neglecting the remaining sensor noise, only the deformation and atmospheric components remain. The resulting deformation image is called differential and is characterized by color bands, or fringes, from whom the amount of the displacement can be retrieved. \r\nDInSAR can be used for mapping displacements and deformations due to earthquakes, landslides, or other geophysical processes inducing deformation of the Earth’s surface.\r\nUsing only one differential interferogram, mainly sudden and large scale changes between two acquisition can be mapped and quantified. However, the atmospheric phase component remains and may induce interpretation errors if it is not possible to eliminate it through e.g. precise weather models. Techniques of differential interferogram stacking (e.g. Persistent Scatterer Interferometry and Small-Baseline Subset) have been developed for long-term deformation monitoring which allow to filter the atmospheric phase component out.","name":"Differential Synthetic Aperture Radar Interferometry (DInSAR)","selfAssesment":"Completed
"},{"code":"PP2-3-11-2","description":"The Permanent or Persistent Scatterer (PS) approach allows the estimation of deformation time-series related to point-wise, high coherent scatterers on the ground based on processing long sequences of SAR data.\r\nPersistent Scatterer Interferometry (PSI -sometimes also called Permanent Scatterer Interferometry) is a particular DInSAR technique. It exploits multiple SAR images acquired over a specific area in order to retrieve the deformation phase component over time. In general, a minimum number of 15 SAR acquisitions is needed for PSI processing. Due to the large number of necessary acquisitions, the deformation component of the interferometric phase observations can be estimated very precisely (in the order of a few mm/yr) and other phase contributions such as atmospheric disturbances and topographic height differences can be better estimated and removed.\r\nPSI rely on so called Persistent Scatterer that are targets showing coherent phase behavior in time. Such targets are usually found on man-made structures such as buildings or bridges, or very stable features such as rocks. PSI is a technique that is therefore mainly used over urban or semi-urban terrain. Usually, PSs are selected based on their amplitude and phase power spectrum stability over time.\r\nThe main outcomes of a PSI analysis are a deformation velocity map and the displacement time-series of the single point targets, or PSs. The velocity map represents the deformation rate of the detected PSs in Line-of-Sight of the sensor, generally in mm/yr. Usually, subsidence, e.g. target moving away from the sensor, is represented in red, stable PSs in green and uplift, e.g. PSs moving toward the sensor in blue. The displacement time-series show for each PS the amount of the deformation, usually in mm, over the whole period of observation. Different phase model can be defined in order to retrieve the best possible estimate of the deformation, considering also seasonal displacements or breakpoints in the time-series.\r\nPerforming PSI analysis in both ascending and descending directions allows the fusion of the results in order to retrieve vertical and East-West component of the deformation. North-South deformation components cannot be retrieved due to the orbit configuration of the SAR satellites.\r\nPSI finds use in a large range of thematic applications related to subsidence and long-term change monitoring, such as infrastructure monitoring, groundwater reservoir monitoring, monitoring of mining areas, landslide inventory and monitoring, as well as volcanology.","name":"Permanent Scatterer Interferometry (PSI)","selfAssesment":"Completed
"},{"code":"PP2-3-11-3","description":"Along-track InSAR (AT-InSAR) is a special mode of interferometric SAR (InSAR) where the individual SAR images have been acquired from the same flight track. With virtually identical geometric configuration of the individual SAR images, the measured phase difference is dominated by temporal changes occurring between the acquisitions. Consequently, AT-InSAR can be used to measure the displacement and/or radial velocity of targets on the ground, with the temporal offset between the acquisitions determining the time scale of the measurements. AT-InSAR can be implemented using one or more SAR sensors, in both single-pass and repeat-pass configurations, accommodating various needs. Using at least two sensors in a single-pass configuration allows the measurement of relatively high velocities, e.g., for vehicles and ocean waves. Conversely, using at least one sensor in a repeat-pass configuration allows the measurement of low velocities or displacements, e.g., for glaciers and due to volcanoes, earthquakes, subsidence, and landslides.","name":"Along-Track Interferometry","selfAssesment":"Completed
"},{"code":"PP2-3-11-4","description":"Across-track InSAR (XT-InSAR) is a special mode of interferometric SAR (InSAR), where the individual SAR images have been acquired from slightly different look directions. The measured phase difference contains information about the elevation of the targets on the ground, but it can also be affected by temporal changes between the individual SAR images. XT-InSAR can be implemented using one or more SAR systems in both single-pass and repeat-pass configurations. To mitigate temporal change between acquisitions, the XT-InSAR configuration is selected based on the intended application and frequency used by the system. If a single SAR sensor is used in the repeat-pass mode, temporal stability can be achieved either by a selecting a lower frequency and focussing on the larger, more stable targets (e.g., P-band, 435 MHz InSAR in forests) or by selecting a higher frequency and focussing on already stable environments (e.g., X-band, 9.65 GHz XT-InSAR in urban environments). Using two or more SAR sensors in a single-pass, tandem configuration, it is possible to measure elevation of temporally instable targets using higher frequencies, as demonstrated by the SRTM and TanDEM-X systems over vegetated areas and ocean.\r\nReferences: bamler/hartl, one on SRTM or TDM for DEM, one on BIOMASS for forestry, one on Sentinel-1 for urban areas, one on TDM on vegetation","name":"Across-Track Interferometry","selfAssesment":"Completed
"},{"code":"PP2-3-11-5","description":"Small Baseline Subset (SBAS) is a well-known technique of differential synthetic aperture radar (SAR) interferometry for the generation of surface deformation time-series by processing large sequences of SAR data acquired over the same region on Earth. \r\nThe method requires the preliminary generation of pairs of SAR images collected by slightly different orbital positions at different acquisition times. The phase difference of the interferometric SAR data pairs is extracted. The two-dimensional phase maps contains different contributions, but principally a component due to the terrain height of the observed area. The DInSAR technique relies on the estimation of the deformation of the terrain between the two interfering SAR images (i.e., the so-called master and slave images). To achieve this task, the phase contribution related to the terrain height is simulated and subtracted to the interferometric master/slave phase difference. The obtained differential SAR interferometric phase contains a direct information on the occurred deformation. Once a sequence of interferometric SAR data pairs is selected, the SBAS technique allows generating the time-series of the deformation of the terrain. The processing steps are essentially: i) the extraction of the full phase of the DInSAR interferograms, i.e., the phase unwrapping steps of the DInSAR interferograms, ii) the inversion of the sequence of unwrapped DInSAR phases, iii) the geocoding of the deformation maps from radar coordinates to geographical coordinates.","name":"Small Baseline Subset","selfAssesment":"Completed
"},{"code":"PP2-3-11","description":"Synthetic aperture radar (SAR) interferometry, or simply InSAR, is a remote sensing technique utilising the phase difference between two or more complex-valued SAR images. Most modern SAR systems are capable of measuring both the intensity and the phase of the reflected signal, where the latter carries information about the distance travelled by the signal. Consequently, the different of phase information of two successive SAR images over a specific area contains a distance information. \r\n\r\nThe phase difference measured between two SAR images is called the interferometric phase. The interferometric phase image is an interferogram. The interferometric phase is a function of the geometry and timing of the individual SAR acquisitions. Different geometric and temporal configurations enable different applications. \r\n\r\nIf the SAR acquisitions are made from different angles and without significant temporal change of the scene, InSAR can be used to create digital elevation models (DEMs) of the Earth, as demonstrated by the NASA/JPL Shuttle Radar Topography Mission (SRTM). This configuration is called across-track interferometry. If the individual SAR acquisitions are made at different times in the same geometric configuration, i.e. in an along-track or differential interferometric configuration, then InSAR can be used to measure radial velocity of targets and to assess displacements caused by, e.g., volcanoes and earthquakes. The variation of the temporal baseline allows determining velocities ranging from several meters per second to a few millimeters per year. While standard differential interferometry can be used to retrieve changes that happened between two SAR acquisitions, differential interferometric stacking techniques, such as Persistent Scatterer Interferometry (PSI) and Small Baseline Subset (SBAS), are used to monitor deformation over a longer period of time by stacking multiple differential interferograms and filtering out the atmospheric phase contribution in order to retrieve very accurate deformation of the ground and its infrastructures.","name":"Principles of Synthetic Aperture Radar Interferometry (InSAR)","selfAssesment":"Completed
"},{"code":"PP2-3-12","description":"Synthetic Aperture Radar (SAR) tomography uses the principle of the azimuth synthetic aperture in the elevation direction. Instead of using different positions of the radar sensor along the flight path in order to increase the aperture length, SAR tomography uses multiple passes of the radar sensor over the same area at different elevation positions, i.e. orthogonal to the azimuth-range plane, on different orbits. Similar to the synthetic aperture in azimuth direction, a larger aperture in cross-range elevation direction allows increasing the resolution in the elevation direction. Therefore, the echoes are focused in the whole 3D space (azimuth, range and elevation), and scattering contributions can be separated at different heights, even if they are situated in the same azimuth-range cell.\r\nSAR tomography exploits therefore these multiple passes of the radar sensor at different orbit positions (orbits heights) in order to retrieve 3D information about volumetric targets, where the 2D SAR signals often overlaps due to the typical side-looking geometry. \r\nThe result of tomographic processing is a tomogram, i.e. it is a hologram of a specific area of interest, usually represented as a tomographic profile along a particular direction. Using polarimetric data, the different scattering mechanisms happening at different heights can be represented in the profile, allowing a full understanding of the volumetric information and backscattering processes.\r\nUnlike the azimuthal aperture, the tomographic aperture is achieved by repeat-pass acquisitions, the antenna having to come back over the area. An important parameter is therefore the target coherence, that may decrease by longer repeat-pass cycles. In general, a 1-4 day revisit cycle is preferred for tomographic applications.\r\nSAR tomography finds applications in the imaging and monitoring of cities and single buildings, as well as in height and biomass estimation of forest stands. The use of longer wavelength that guaranty the penetration into canopy volumes allows a better retrieval of the complete forest structure and its undergrowth.","name":"Synthetic Aperture Radar (SAR) tomography","selfAssesment":"Planned
"},{"code":"PP2-3-13","description":"Historically imaging in the microwave frequency domain was done either using passive imaging techniques (with solely recording capacities of the sensor) or using active imaging techniques (with transmitting and recording capacities of the sensor). Both imaging modi were developed in parallel for a long time in electrical engineering of microwave sensors for space-borne missions, but are combined in more recently launched missions.\r\nWith the concept of active and passive microwave imaging, both techniques are fused to record electromagnetic waves in an active (sending & receiving) and a passive (only receiving) mode either simultaneously on one carrier platform or with negligible time lag on different platforms.\r\nThe active sensor is normally a Real Aperture Radar (RAR, scatterometer) or Synthetic Aperture Radar (SAR), while the passive sensor is a radiometer or synthetic aperture radiometer. Both acquisition modes can be operated on a single platform or on different platforms depending on monolithic or distributed platform systems. The benefit of fusing both modi is in the higher spatial resolution of the active imaging modes combined with the higher sensitivity of the passive modes for intrinsic (non-structural) media properities, like permittivity or salinity.\r\nSatellite missions with active-passive imaging capabilities are the NASA missions AQUARIUS (operation started in 2011 terminated in 2015) and SMAP (operation started in April 2015 and ceased for active sensor in July 2015). Currently (2021), no dedicated active-passive microwave satellite mission is operating in orbit.","name":"Active-Passive microwave imaging","selfAssesment":"In progress
"},{"code":"PP2-3-2","description":"Systems measuring both amplitude and phase of the incident electromagnetic radiation.","name":"Coherent and active systems","selfAssesment":"Planned
"},{"code":"PP2-3-3","description":"This acquisition mode records only the incoming electromagnetic radiation emitted from the Earth. Radiometer instruments conduct passive microwave imaging. The energy budget of emitted radiation (from Earth) is significantly smaller than from instrument-generated, transmitted electromagnetic waves, used in the active microwave imaging mode. Hence, the signal to noise ratio is significantly worse for passive microwave imaging forcing a longer intergration time for robust signal recording. This results in a coarse spatial resolution of radiometer images (in the order of kilometers).","name":"Passive microwave imaging","selfAssesment":"Completed
"},{"code":"PP2-3-5","description":"There are two types of imaging radar apertures: real (usually called RAR or SLAR for side-looking airborne radar or SLR for side-looking radar) and synthetic aperture radar (SAR). The SLAR imaging system uses a long antenna mounted on a platform. The synthetic aperture is used in space remote sensing applications. RAR is a radar system where the antenna beamwidth equals to the physical length of the antenna. It operates in a side-looking configuration, left or right with reference to the flight direction. It is an active, all-weather, day/night remote sensor onboar an airborne platform. Both Real Aperture and Synthetic Aperture Radar are side-looking systems having antennas aimed to the right or left of the flight path. The length of the antenna together with wavelenght determines the resolution in the azimuth direction, i.e. it is proportional to the distance to the object and inversely proportional to the length of the radar antenna.","name":"Real Aperture Radar (RAR)","selfAssesment":"In progress
"},{"code":"PP2-3-6","description":"In contrary to a real aperture, a synthetic aperture results from an aperture “synthesis”. Synthetic aperture were built in order to overcome the limitation of real aperture and therefore enhance the resolution in azimuth direction. It uses the subsequent positions of a real aperture sensor during its forward motion along the azimuth direction to create a synthetic longer antenna. Via the analysis of the Doppler shift induced by the different echoes of the illuminated objects in the different positions of the real aperture, the azimuth resolution can be improved.","name":"Principles of Synthetic Aperture Radar (SAR)","selfAssesment":"In progress
"},{"code":"PP2-3-7-1","description":"In navigation, the azimuth corresponds to an angle measured from a north reference or a meridian, usually in clockwise direction. In SAR terminology, the azimuth direction corresponds to the direction in which the radar platform moves. The azimuth direction is also called along-track direction and is parallel to the flight path of the radar instrument. In a SAR image, the azimuth position of an object corresponds to its relative position in the field of view of the antenna following the radar’s line of flight. The azimuth direction is perpendicular to the range direction, which corresponds to the look direction of the radar antenna. The azimuth plays an important role in the definition of the azimuth resolution of a SAR sensor. Contrary to the range resolution, the azimuth resolution is independent of the distance between sensor and illuminated area and is constant. The azimuth resolution of a radar system corresponds to the beam width of the antenna on the ground, but can be improved using multiple successive real aperture acquisitions in order to form a longer, synthetic, aperture. This implies that an object on the ground is illuminated for a longer time and from different platform positions along the azimuth direction, inducing a Doppler frequency shift at the target. The use of specific synthetic aperture acquisition modes that steer the antenna along the azimuth direction, such as Spotlight mode, improve additionally the resolution in azimuth direction.","name":"Azimuth direction","selfAssesment":"Planned
"},{"code":"PP2-3-7-2","description":"The range direction corresponds to the direction perpendicular to the flight direction of a radar system. It is also called across-track direction. One distinguishes between slant range, i.e. range in a radar geometry, and ground range, i.e. range projected onto the Earth's surface, and between near and far range (situated farther away from the sensor and showing shallower looking angle than in near range due to viewing geometry).","name":"Range direction","selfAssesment":"Planned
"},{"code":"PP2-3-7-3","description":"The incidence angle is the angle between the incident radar beam on a surface and the normal to a reference surface. Generally, it is distinguished between the local incidence angle and the incidence angle to the ellipsoid. The local incidence angle considers the normal to the surface at target location, i.e. it considers the local topography. The incidence angle to the ellipsoid corresponds to the angle between the incident radar beam and the normal to the local ellipsoid, regardless of the local slope and terrain. \r\n\r\nFor a flat surface and neglecting the Earth’s curvature, the incidence angle corresponds to the angle between the incident radar beam and the vertical, and it equals the look angle of the sensor, which characterizes the angle between the nadir view and the radar beam. Considering a flat surface, the incidence angle varies continuously within a SAR scene: it increases from near to far range. Depending on the considered sensor and acquisition modes, variations of the incidence angle up to 20° can be observed between near and far range.\r\n\r\nThe incidence angle has an influence on the radar backscatter intensity. Considering a surface with diffuse reflection, increasing incidence angles lead to decreasing backscatter intensities. This effect is less pronounced for rough than for smooth surfaces. A change in incidence angle may also induce a change in the occurring backscattering mechanisms or geometric distortions of the image. For example, for high incidence angles, terrain distortion due to the side-looking geometry is reduced. Due to the high dependency of the radar backscatter from the incidence angle, the choice of the optimal configuration should happen depending on the application. For example, whereas low incidence angles are more sensitive to biomass in forestry applications, higher incidence angle are preferred for distinguishing different forest types due to their structural characteristics.","name":"Incidence Angle","selfAssesment":"Planned
"},{"code":"PP2-3-7-4","description":"The beam sent out by the radar antenna (SLAR for side-looking airborne radar or SLR for side-looking radar) illuminates an area on the targeted object. The footprint of an antenna is traditionally defined to be the area on the surface within the field of view subtended by the beamwidth of the antenna gain pattern.","name":"Antenna footprint","selfAssesment":"Planned
"},{"code":"PP2-3-7-5","description":"The spatial resolution of a synthetic aperture radar (SAR) system is the maximal distance between two targets, which are indistinguishable in the SAR image. SAR spatial resolution is determined individually in the two principal SAR image directions: ground range and azimuth (along-track). Ground range resolution for a SAR system is derived from slant range (across-track) resolution, by projecting it onto the ground surface using the incident angle, i.e., the angle between the line-of-sight and the ground surface normal. It is thus range-dependent, with finer resolution available in far range. Assuming adequate signal processing, slant range resolution of a SAR system is proportional to the speed of light and inversely proportional to the system bandwidth, i.e., the width of the used frequency interval. This caused by the fact that each individual frequency provides an independent measurement of the slant range, so a larger bandwidth implies more independent measurements contributing to the final slant range estimate. Similar principles apply to the azimuth direction. Assuming adequate signal processing, the SAR azimuth resolution is proportional to the along-track velocity of the SAR sensor and inversely proportional to the pulse repetition frequency (PRF) of the system. A lower interval between the consecutive pulses (higher PRF) results in better azimuth resolution due to faster sampling, but at the cost of range ambiguities occurring when echoes from one pulse are recorded after the next pulse has been transmitted.","name":"Synthetic Aperture Radar (SAR) spatial resolution","selfAssesment":"Completed
"},{"code":"PP2-3-7","description":"The Synthetic Aperture Radar (SAR) sensor is usually mounted on an aircraft or satellite. The instrument altitude above a reference surface stays constant over time, a condition that is easier to achieve for satellite sensors that stay on the same orbit than for aircrafts that are subject to atmospheric conditions. The sensor moves on a straight flight path, which is called the azimuth direction. It corresponds to the flight direction.\r\nSAR systems acquire information in oblique view, the antenna pointing sideways down to the ground. Most satellite systems use an antenna looking to the right side of the instrument. The ground area illuminated by the radar beam is called antenna footprint. As the sensor moves along the azimuth direction (along-track), the continuous strip of the ground area represented by the successive antenna footprints is called swath. \r\nThe looking direction of the SAR antenna is called range direction. It is often perpendicular to the azimuth direction (i.e. across-track), but can also present slightly differences depending on the acquisition mode. The angle between the nadir view and the range direction is called incidence angle.\r\nThe original SAR image is displayed in what is called slant-range geometry, i.e., it is based on the actual distance from the radar to each of the respective features in the scene. In the slant range direction, each point target’s backscatter is represented as a function of the time delay between the transmission of the electromagnetic pulse and its reception back at the sensor. This range depending representation induces geometric distortions in the SAR image. One distinguishes between near and far range: targets situated in near range are closer to the nadir direction and closer to the sensor than targets situated in far range. The image representation of targets is also more compressed in near range than in far range.\r\nThe slant-range representation can be converted in ground range representation, by projecting the image features orthogonally to a ground reference, allowing a proper planimetric position of the targets relative to one another.\r\nThis acquisition geometry allows the distinct mapping of scatterers corresponding to their respective distance to the sensor. It causes also geometric distortions in the radar image, i.e., relief displacement (foreshortening and layover) and shadow.","name":"Synthetic Aperture Radar (SAR) geometric configuration","selfAssesment":"Completed
"},{"code":"PP2-3-8-2","description":"The local incidence angle is the angle between the incident radar wave and the normal to the scattering surface at target location. In case of a flat terrain, the local incidence angle equals the incidence angle. For a terrain with local slope, the local incidence angle differs from the incidence angle (for slopes facing towards the sensor, it is smaller than the incidence angle).","name":"Local Incidence Angle","selfAssesment":"Planned
"},{"code":"PP2-3-8-3","description":"Foreshortening is a geometric distortion occurring in the SAR image due the side-looking geometry of imaging radar sensors. It occurs principally in SAR images of mountainous areas, on slopes oriented towards the sensor. These slopes appear in the radar image as if being compressed. Due to the side looking geometry and the mapping of the SAR image based on range and time measurement, the distance in the SAR image between two points situated on a slope facing the sensor appears smaller than it is in the reality and than the same distance between two points situated in flat area. This results in a compression of the radiometric information of the slope. The resulting foreshortening area is brighter in the SAR image than its surroundings, as it compresses in a few pixels the backscatter information of the whole slope. \r\n\r\nForeshortening occurs for slopes whose inclination is smaller than the look angle of the radar antenna. Due to the variation of the look angle in the SAR image, the foreshortening is more pronounced in near range than in far range. Foreshortening is therefore greater for small incidence angles. The extreme case of foreshortening happens when the slope inclination is equal to the look angle: in this case, the whole slope is mapped in one pixel of the SAR image, which results in a very bright line. When the slope inclination becomes higher than the look angle, layover occurs.","name":"Foreshortening","selfAssesment":"Planned
"},{"code":"PP2-3-8-4","description":"Layover is a geometric distortion occurring in the SAR image due the side-looking geometry of imaging radar sensors. It occurs principally in SAR images of mountainous areas, on steep slopes oriented towards the sensor. These slopes appear in the radar image as if being flipped over. Due to the side looking geometry and the mapping of the SAR image based on range and time measurement, the summit of a mountain is closer to the sensor that the foot of that same mountain, on the side facing the sensor. The signal from the top comes back to the sensor before the signal from the foot and is therefore mapped in nearer range than the foot of the mountain. Making an analogy to sound waves, an echo from the top of the mountain will arrive sooner at the sensor than an echo from the bottom of the mountain. Due to this “leaning over” effect, the sensor facing slope signal usually overlaps with ground signal, and a “ghost” effect appears as both signals overlap. The resulting layover area is usually very bright in the SAR image, as it superimposes backscatter signals from the slope of the mountains and the ground before it. When considering SAR images of urban areas, even up to three signals may overlap in the layover area: ground, building façade and (part of the) roof area.\r\n\r\nLayover occurs for slopes whose inclination is larger than the look angle of the radar antenna. Due to the variation of the look angle in the SAR image, layover occurs more often in near range than in far range. Layover is therefore greater for small incidence angles. It represents the extreme case of foreshortening, when the slope inclination becomes higher than the look angle.","name":"Layover","selfAssesment":"Planned
"},{"code":"PP2-3-8-5","description":"Radar shadow is a geometric distortion occurring in the SAR image due the side-looking geometry of imaging radar sensors. It occurs principally in SAR images of mountainous areas, on steep slopes oriented away from the sensor. In optical imagery, a shadow area is an area characterized by less sun illumination whose reflection is therefore weaker. In SAR imagery, shadow areas receive no signal. It occurs for example at the backside of mountains or buildings. The areas facing away from the sensor are not illuminated by the SAR sensor, as they are “hidden” from it. Also, ground area situated behind high object with respect to the sensor position are not illuminated and are situated in the radar shadow. They receive no signal information and send no information back to the sensor. Those areas are therefore very dark in SAR images. The size of the shadow area in range direction corresponds to the time delay between the last echo from the top of the mountain and the first echo of the far edge of the shadow region, where the area is not hidden from the sensor anymore.\r\n\r\nRadar shadow occurs when the slope inclination of the slope facing away from the sensor is larger than 90° minus the antenna look angle. As for the other geometric effects, the size of a shadow area for the same object depends on its situation in the image. But, unlike as for foreshortening and layover, shadow is more pronounced in far range than in near range, i.e. large incidence angles produce more shadow.\r\n\r\nA SAR image may show a return signal in a shadow area: this is principally due to internal sensor noise and does not correspond to any target return signal.","name":"Shadow","selfAssesment":"Planned
"},{"code":"PP2-3-8","description":"Synthetic Aperture Radar (SAR) backscatter is determined both by dieletric and geometric properties of the illuminated target. While the water content of the target plays an important role, its surface roughness determines the scattering mechanisms and the amount of incoming signal sent back to the sensor.\r\nDepending on its characteristics but also on the considered wavelength, a surface appears more or less rough. On smooth surfaces, specular reflection occurs, meaning that most of the incoming signal will be reflected away from the sensor. For rough surfaces, diffuse reflection occurs, meaning that part of the signal is scattered back to the sensor, the amount of it depending on different surface roughness parameters. \r\nDepending of the observed target and surface, single or multiple scattering mechanisms occur. A particularly important scattering mechanism is the double bounce, which occurs generally at two perpendicular surfaces (e.g. ground and building wall). Through two successive specular reflections, the whole signal comes back to the sensor.\r\nDue to the side-looking geometry of SAR systems and the range dependent image representation, specific additional effects occur and affect the backscatter intensity. Whereas a flat terrain only appears more compressed in near range and more stretched in far range, larger geometric distortions appear for terrain with more topography (e.g. mountains) or high objects (e.g. trees, buildings). This relief displacement is caused by the target’s elevation. A high elevated object is closer to the sensor than the ground below it. Due to the image formation in range direction depending on the distance between sensor and targets, its signal comes back sooner to the sensor and it is represented in the SAR image in nearer range than the ground below it. High objects in the SAR image are therefore displaced horizontally toward the radar antenna. This horizontal displacements contrast with the radial displacement observed in optical imagery due to central projection. Furthermore, such objects hide part of the ground below them, which do not receive any signal and cannot scatter information back. Three particular geometric distortions exist: foreshortening, layover and shadows.\r\nDepending on the illuminated target, different scattering mechanisms occur in combination with geometric distortions, which makes the interpretation of the SAR image challenging. A good example are buildings, where layover, shadow and single- and double-bounce occur.","name":"Terrain reflectivity and geometric distortions","selfAssesment":"Completed
"},{"code":"PP2-3-9","description":"A typical “salt-and-pepper” noise-like physical phenomenon that is not a noise but a deterministic property of SAR imagery is the so called speckle. It appears when a resolution cell of a SAR system contains more than one scatterer. In that case, the total scattering from the resolution cell is a coherent sum of the backscatter originating from the different scatterers. In order to reduce this effect, speckle reduction methods can be applied.","name":"Speckle Formation","selfAssesment":"Planned
"},{"code":"PP2-3","description":"Microwave remote sensing systems detect and quantify the electromagnetic radiation arriving at a detector, this radiation being either emitted (passive sensors) or scatterered back (active sensors) from the objects.\r\nThree properties of the recorded electromagnetic signal are of particular interest: its intensity, its phase and its polarization. The specific quantification of each properties allows signal interpretation, as they depend on the roughness and dielectric characteristics of the surface (intensity and polarization) as well as of the range between target and sensor (phase).\r\nThe detection of the microwaves is operated through two principal sensor elements: an antenna and a receiver. The antenna collects the incoming radiation and the receiver measures the collected electric signal.\r\nAs active microwave systems produce their own electromagnetic radiation, they are equipped with two additional elements: a pulse generator and a transmitter. Usually, transmitter and receiver are situated on the same antenna.\r\nA simple detector system only detects the intensity of the signal and amplifies it. Coherent systems measure both the amplitude and the phase of the incident electromagnetic radiation.\r\nMicrowave systems can be categorized in two different types: imaging and non-imaging sytems. Whereas for non-imaging systems each echoe (collected signal) provides a single measurement, imaging systems collect a sequence of echoes that generate a two dimensional image.","name":"Detecting microwaves","selfAssesment":"Completed
"},{"code":"PP2","description":"Microwave remote sensing operates in the microwave portion of the electromagnetic spectrum, generally using wavelengths greater than 3 cm and up to 1 m. \r\nMicrowaves are sensitive to different physical parameters than other regions of the electromagnetic spectrum. Microwaves interactions with objects are governed by geometric (structure, size, shape) and dielectric (water content) properties, whereas other regions of the electromagnetic spectrum reacts e.g. to object temperature or “color” (amount of reflection or absorption of the Sun light by a particular object).\r\nAs a general rule, microwaves interact with object at least as big as the wavelength. Smaller objects will therefore be transparent for the signal. Due to the large wavelengths, atmospheric particles are almost transparent to the signal and microwave remote sensing can penetrate clouds. Under very dry conditions, microwaves can even penetrate up to a few meters the top soil layers, therefore providing information that is not visible in other regions of the electromagnetic spectrum. Depending on the considered wavelength, microwave can also penetrate vegetation layers to different amounts.\r\nIn microwave remote sensing, three characteristics of the electromagnetic wave play an important role: its amplitude, its phase and its polarization. Depending on the application, either one characteristic or a combination of them is used to retrieve information.\r\nThere are two main types of microwave sensors: active RADAR systems and passive radiometers. RADAR is an acronym for RAdio Detection And Raging. An active radar system sends out pulses and records the echoes scattered back by the objects (scatterers) to the sensor. The systems use the two-way travel time of the radar pulse to determine the distance (range) to the illuminated object. Its backscatter intensity is determined by the radar system and object properties and depends on the quantity of energy coming back to the sensor. Active radar systems transmit a signal and record the amount of energy that is scattered back and depends of both dielectric and geometric properties. Passive radiometers record microwave energy, which is emitted by the Earth’s surface.\r\nDepending on the type of system, microwave remote sensing can be used in multiple applications. Active sensors are principally used for diverse land cover mapping applications based on the particular backscattering mechanisms and characteristics of the objects on the Earth’s surface. Using multiple acquisitions, they are also favored for topographic, deformation and velocity mapping. Passive sensors are preferred for the determination of hydrologic variables such as soil moisture, precipitation, ice water content and sea-surface temperature.","name":"Basics of microwave remote sensing","selfAssesment":"Completed
"},{"code":"PS","description":"Remote sensing, i.e. the process of obtaining information about an object or area from a distance, is not possible without remote sensing sensors that collect this information and the platforms on which the sensors are installed and which are used to move them. Remote sensing sensors collect data by detecting energy that is reflected or emitted from Earth. There are different types of remote sensing sensors. The interaction between the sensor and the Earth's surface has two modes: active or passive. Passive sensors use solar radiation to illuminate the Earth's surface and detect reflection from the surface or measure the emitted energy. They usually record electromagnetic waves in the visible (˜430–720 nm) and near infrared (NIR) (˜750–950 nm) through short infrared (SWIR) (˜1.500-2.500 nm) to thermal infrared (TIR) (8.000-14.000 nm) ranges. The power measured by passive sensors is a function of surface composition, physical temperature, surface roughness and other physical properties of the Earth. Active sensors provide their own energy source to illuminate objects and measure their properties. These sensors use electromagnetic waves in the visible and near infrared range (e.g.laser altimeter) and radar waves (e.g. synthetic aperture radar (SAR)). As sensor technology has advanced, the integration of passive and active sensors into one system has emerged. Alternatively, remote sensing sensors can be classified into imaging sensors, i.e. that produce an image of an area, within which smaller parts of the sensor's whole view are resolved (pixels), and non-imaging sensors, i.e. that return a signal based on the intensity of the whole field of view. In terms of their spectral characteristics, the imaging sensors include optical imaging sensors, thermal imaging sensors, and radar imaging sensors. These sensors can be on satellites, mounted on aircraft, unmanned aerial vehicle (UAV), drone or ground. The collected information can be transformed into an image or set of points (e.g. cloud points), which can be further processed and analyzed to obtain the necessary information, e.g. agricultural field development phase, level of air pollution, etc.\r\nA digital imagery of Earth observation sensors is a two-dimensional representation of objects on Earth. Current images collected from different levels of acquisition, from ground to satellite, with the help of electronic sensors are examples of digital images. There are different aspects and characteristics of remote sensing data and images, such as, for example, data formats and processing levels, data storage, data properties.","name":"Platforms, sensors and digital imagery","selfAssesment":"Completed
"},{"code":"PS1-1","description":"Remote sensing sensors has its roots in the 19th century in the development of photography. Photography was an invention that made it possible to acquire a permanent image. The first photographic image was taken in 1826 by Joseph Nicephore Nieppce. While the first aerial photograph was taken in 1858 by Felix Tournachon, known as Nadar, from a tethered baloon over Biévre Valley in France. In 1907 Julius Neubronner developed a light miniature camera that could be fitted to a pigeon's breast. It can be said that the construction camera + pigeon was the precursor of today's unmanned aerial vehicle (UAV) or drone. Further developments focused on developing new sensors (analog vs. digital frame cameras) and how to save and store images (e.g. photographic emulsions, films). The origin of other types of remote sensing can be traced to World War II, with the development of radar, sonar, and thermal infrared detection systems. Since the 1960s, sensors were designed to operate in virtually all of the electromagnetic spectrum. Both civil and military aerial photography have long been widely used in cartography to create maps. Specialized large format cameras (looking vertically down, assuming the plane is flying horizontally) were developed. Such cameras have been specially designed to perform almost vertical sequences of bird-eye exposures during aircraft flight. Hence for a long time remote sensing consisted of aerial photography and photogrammetry using analogue mechanical or optical equipment. Everything has changed with satellites and the space race. The first real success of remote sensing satellites in serious scientific work was in meteorology, weather satellite TIROS-1, launched by NASA on April 1, 1960. \r\nToday a wide variety of remote sensing instruments are available as data source for use in different applications for land, water and atmosphere monitoring.","name":"History of remote sensing sensors","selfAssesment":"In progress
"},{"code":"PS1-2-1-1-1","description":"Along track scanner, also known as a pushbroom scanner, is an optoelectronic device that obtains images with a multispectral imaging system. The scanners are used for passive remote sensing. It records electromagnetic energy that is reflected (e.g., blue, green, red, and infrared light) or emitted (e.g., thermal infrared radiation) from the surface of the Earth. The scanners are mounted on space- or aircrafts. \r\nA two-dimensional image is created (line by line) by exploiting the platform motion along the orbital track. The data are collected along track using a linear array of detectors arranged perpendicular to the direction of travel. The array of detectors are pushed along the flight direction to scan the successive scan lines, and hence the name pushbroom scanner. \r\nThere are no moving parts on a pushbroom sensor, hence, the scanning speed can be increased compared to across track systems. A longer dwell time over each ground resolution cell increases the signal strength (high radiometric resolution, no pixel distortion). Additionally, finer spatial and spectral resolution can be achieved as the size of the ground resolution cell is determined by the Instantaneous Field of View (IFOV) of a single detector. The systems are designed for high-resolution imaging. However, a very large number of detectors is needed for high resolution images. It is a complex optical system. In addition, the pushbroom scheme requires a wide Field of View (FOV) optics system to obtain the same swath as for a corresponding whiskbroom (across track) scanner. It has narrow swath width. \r\nThe detector arrays with such a line-scanning pushbroom system are usually of the type Charge-Coupled Device (CCD).\r\nThe MultiSpectral Instrument (MSI) on board the Sentinel-2 satellite (Copernicus mission) uses a pushbroom concept.\r\nMultispectral imaging systems building the final image (line by line) exploiting the platform motion along the orbital track. No rotating mechanical part required, usually based on a CCD matrix (high spectral resolution but just up to 1 micrometer), e.g. Sentinel-2 MultiSpectral Instrument (MSI), Sentinel-3 Ocean and Land Colour Imager (OCLI).","name":"Along track scanners","selfAssesment":"Completed
"},{"code":"PS1-2-1-2-1","description":"The cameras, usually a charge-coupled device (CCD) or Complimentary Metal Oxide Semiconductor (CMOS), that convert light into electrons that can be measured and converted into radiometric intensity value.","name":"Digital Frame Camera","selfAssesment":"Planned
"},{"code":"PS1-2-1-2","description":"2-D systems with the ability to observe in two dimensions simultaneously.","name":"Area Arrays","selfAssesment":"New
"},{"code":"PS1-2-1","description":"A type of a spectrometer. It is in principle, one-dimensional systems, whisk- or pushbroom, that form an image on a line-by-line basis in the scan direction.","name":"Line detector arrays","selfAssesment":"New
"},{"code":"PS1-2-2-1-1","description":"Thermal radiometers are radiometers with the capability of measuring the spectrum of infrared emission. As such, they are characterized by a relatively high spectral resolution (normally better than 1 cm-1 in wave number units). Modern Spectrometers on board satellites have a spectral resolution better than 0.7 cm -1 in order to properly resolve CO2 lines used for the retrieval of the atmospheric temperature profile. Based on the optical layout they are further classified in grating spectrometers and Fourier Transform Spectrometers or FTIR.","name":"Thermal Radiometers","selfAssesment":"New
"},{"code":"PS1-2-2-1-2","description":"Passive microwave radiometers are radiometers that measures energy emitted at millimetre-to-centimetre wavelengths at 0.15 - 30 cm (frequencies of 1–200 GHz). Example of a sensor: SMOS Microwave Imaging Radiometer with Aperture Synthesis (MIRAS), which aims at measuring land soil moisture and ocean salinity.","name":"Passive Microwave Radiometers","selfAssesment":"In progress
"},{"code":"PS1-2-2-1-3","description":"An advanced multispectral sensor that detects hundreds of very narrow spectral bands throughout the visible, near-infrared, and mid-infrared portions of the electromagnetic spectrum.","name":"Hyperspectral Radiometers","selfAssesment":"Planned
"},{"code":"PS1-2-2-1-4","description":"A radiometer that measures the intensity of radiation in multiple wavelength bands (i.e., multispectral). Example of a sensor Moderate Resolution Imaging Spectroradiometer (MODIS)","name":"Spectroradiometers","selfAssesment":"In progress
"},{"code":"PS1-2-2-2","description":"Provide information about vertical profiles of temperature and molecular consistuent concentrations in the atmosphere (atmospheric sounders).","name":"Atmospheric passive sounders","selfAssesment":"New
"},{"code":"PS1-2-2","description":"Radiometers are instruments which measure radiative intensities within a particular frequency window. A radiometer is further identified by the portion of the electromagnetic radiation it covers, usually the infrared or microwave regions. Normally the spectral range extends from the longwave (14-15 micron) to the shortwave (3-4 micron). This range overlaps much of the emission spectrum of Earth. The technology is classified in broadband radiometer of spectral radiometers depending on the spectral resolution. A radiometer measures the intensity of the radiative energy, but does not differenciate between the different registered wavelengths or their respective amplitude. In other terms, it provides a single value as combined result of all wavelengths within the considered frequency window.","name":"Radiometers","selfAssesment":"In progress
"},{"code":"PS1-2","description":"Passive remote sensing systems record electromagnetic energy that is reflected (e.g., blue, green, red, and infrared light) or emitted (e.g., thermal infrared radiation) from the surface of the Earth. Passive sensors therefore rely on an external energy source (e.g. sun illumination, Earth heat emission). Contrary to passive sensors, who detect naturally occurring radiation, active sensors emit radiation and collect and analyze the signal that is sent back by the Earth’s surface or atmosphere. Active remote sensing systems produce therefore their own electromagnetic energy. They transmit and receive the radiation that is reflected or backscattered from the illuminated target. They do not necessitate an external source of radiation (e.g. Sun or Earth). Contrary to most passive sensors that are bound to detecting either the reflected Sun radiation or emitted radiation by the Earth’s surface in ranges from the ultraviolet to the thermal infrared, active sensors can use any radiation from the electromagnetic spectrum, the only limitation being the transparency of the Earth’s atmosphere. They often use wavelengths that are not sufficiently provided by the Sun, e.g. microwaves. \r\nActive systems can be categorized either according to their imaging capability, or according to the considered emitted wavelength, or also according to the way they use the returned signal. For the last category, it is generally distinguished between ranging systems, which use as principal information the time delay between transmission and reception of the electromagnetic radiation at the sensor, and scattering systems, which consider the strength (also called magnitude or intensity), of the returned signal. Some systems also register both information.\r\nAs active sensors produce their own radiation and do not rely on e.g. Sun radiation, they are daytime independent and can also retrieve information about the Earth’s surface by night. Furthermore, depending of the considered wavelength, active sensors are weather independent. For longer wavelengths of the microwave domain, clouds are transparent, as the transmitted wavelength is larger than the water particles constituting the cloud and do not interact with them. \r\nActive sensors can control the direction of their illumination to a specific target to be investigated, but require in general more energy than passive sensors as they “actively” illuminate the Earth’s surface.","name":"Passive vs. active sensors","selfAssesment":"Completed
"},{"code":"PS1-3-1-1","description":"Imaging radar is an active radar system that sends out pulses and records the echoes scattered back by the objects (scatterers) to the sensor. Imaging radars are independent of weather conditions and can operate day or night. It uses microwave wavelengths, radar bands from X- to P- or VHF-band, in four polarisations to illuminate an area on the ground. Normally only the horizontal (H) or vertical (V) linear polarizations are used. The radar system is characterized by combination of polarization of transmitted and received pulse: HH, HV, VH or VV. A typical radar system measures the strength and roundtrip time of the microwave signals that are emitted by a radar antenna and reflected off a target area. An imaging radar is therefore both and imaging and a ranging system. The illuminated objects are mapped in the radar depending on their backscatter intensity and respective range to the sensor.\r\nImaging radar can be mounted on aircraft or satellite. It operates in a side-looking configuration, left or right with reference to the flight direction. This acquisition geometry allows the distinct mapping of scatterers corresponding to their respective distance to the sensor. It causes also geometric distortions in the radar image, i.e., relief displacement (foreshortening and layover) and shadow. The radar sensor operates not in the real aperture of the radar antenna, i.e., real spatial width, radar (RAR) mode but in the synthetic aperture radar (SAR) mode. Synthetic aperture is possible to set up through the forward motion of the spacecraft, which enables to “extend” the real size of the radar antenna. With a SAR, each object on the ground is sampled at several antenna positions along the flight path, i.e., as long as the antenna beam is illuminating it.\r\nImaging radar can be used for a different of land and water applications.","name":"Imaging Radar","selfAssesment":"Completed
"},{"code":"PS1-3-2-1-1","description":"Laser profilers measure 2D range profiles and operate in different environments, like spaceborne, airborne and indoor. It is the simplest application of the LIght Detection And Ranging technique. It transmits a short pulse of energy (visible or near-infrared radiation) and detects 'echo', by measuring the time delay. Knowing the speed of propagation of the pulse (speed of light), the range from the instrument to the surface can be measured.\r\nLaser profiling uses successive reflectorless laser range measurements (1D distance measurement) on adjacent points along a path, which results in a 2D profile or cross-section of the ground. A laser profiler can be terrestrial, or ground-based, or it can be mounted on an airborne or spaceborne platform. In the case of ground-based measurements, the platform is fixed but the angle of illumination changes, allowing for the cross section of the terrain to be mapped. An airborne laser profiler can transmit a continuous stream of pulses along its flight path. As a result, if the position of the platform is known, e.g. from GPS/IMU system, a surface profile along the flight path can be reconstructed using the successively recorded vertical distances between the platform and the points on the ground. The use of an additional rotational mirror allow to scan the Earth in an additional dimension, providing 3D information of the mapped surface. This is the principle of a laser scanner.\r\nThere are two principal types of laser profiling techniques: the first one is based on analog detection and the second on photon counting. In analog detection, the signal power is converted into an output voltage providing a signal strength as function of time. The analog-to-digital conversion yields either a full waveform that allows retrieving the entire time-structure of the return signal strength- and therefore the full vertical structure of the target-, or discrete returns when the signal strength exceed a certain threshold. The full waveform information is especially useful when analyzing vegetation, as every vegetation layer (canopy, stems, branches) and the ground return pulses, allowing the determination of e.g. canopy height, ground surface topography but also a deeper analysis of the canopy structure. Photon counting techniques record the arrival of single photons. The counting of photons is combined with their time-of-flight. The accumulation of single photons at a specific range is similar to the signal strength of analog detection and allows retrieving the height and structure of specific targets.\r\ne can be measured.","name":"Laser profiler","selfAssesment":"In progress
"},{"code":"PS1-3-2-1-4","description":"A radar altimeter is an active, non-imaging remote sensing device. It measures the height of the terrain along the track beneath an air- or spaceborne platform using electromagnetic radiation from the microwave region of the electromagnetic spectrum. Radar altimeters operate similar to laser profilers. Both emit a short pulse of electromagnetic radiation towards the Earth’s surface and detect the time delayed echo. By measuring the time delay and knowing the speed of propagation of the pulse, the range (distance) from the instrument to the surface can be determined. By using the forward motion of the altimeter platform and transmitting a continuous stream of pulses a profile can be built up. If the exact location of the platform as a function of time is known, a surface profile can be generated. \r\nFor a high accuracy of the range resolution, a narrow antenna beam is required, which can be achieved either by using large antennas or short radar beams. In the first case, the radar altimeter is beam-limited; in the second case it is pulse-limited. As large antennas are not practical in space, pulse-limited systems are used for space-borne platforms. Pulse-limited altimeters use frequency modulated (chirp) pulses generated by a chirp generator. The accuracy of the measurements also depends on atmospheric transmission effects, as the speed of the electromagnetic radiation traveling at the speed of light will be delayed when passing through the ionosphere and the atmosphere twice. In general, the range resolution of radar altimeters is in the order of a few centimetres. \r\nIn the beginning, radar altimeters were used for measurements of surface profiles of the ocean topography to get information about currents, ocean circulation, wind and waves. Another basic application of altimetry were measurements over ice sheets and glaciers, e.g. for mass balance determination. Further application domains are geoid measurements also revealing deep sea trenches and the precise monitoring of satellite orbits.","name":"Radar altimeters","selfAssesment":"Completed
"},{"code":"PS1-3-2-1","description":"Laser altimeters historically were the first active sensing devices used on airborne platforms, measuring range information in form of single distances since the mid-1960s. \r\nEven though laser scanners made it possible to retrieve information in a more rapid and denser coverage since the mid-1990s, laser altimeters remain of importance in the scientific community. Especially, the mapping of ice-covered surfaces, water bodies and flat land areas is still performed using laser altimeters.\r\nLaser altimeters are either airborne or spaceborne and are often used together with microwave (radar) profiler in order to calibrate the radar instruments. Whereas airborne laser altimeters are preferred for forestry application, e.g. for analyzing vertical vegetation structure, spaceborne laser altimeters are additionally used for multiple other applications. In particular, spaceborne laser profiler are of high interest for studying surface roughness of ice sheets or for mapping desert topography. Furthermore, spaceborne laser profilers are also useful in atmospheric science for retrieving cloud structure and analyzing different aerosol layers. The requirements for airborne and spaceborne laser altimeters are different. In particular, for spaceborne altimeters, both the distance travelled by the laser pulse and the platform speed are much higher than for airborne instruments, inducing the need of larger optics and more powerful laser instruments. First spaceborne laser experiments were conducted onboard the space shuttle in the mid-1990s, first aiming atmospheric research with a near infrared laser. After successful trial, the space shuttle laser altimeter was fine-tuned and follow-up missions focused on mapping terrain relief and vegetation canopies. Later missions, such as GLAS (IceSAT), ATLAS (IceSAT-2) and GEDI (ISS), used either near-infrared or green (or both) laser light and focused on improving ground coverage while allowing smaller footprints of the laser beam on ground. The revisit cycle of spaceborne laser altimeters allow the determination of regional elevation changes, e.g. monitoring of ice–sheet thickness or vegetation height, which is highly relevant for the scientific community and climate modelers.","name":"Laser altimeter","selfAssesment":"In progress
"},{"code":"PS1-3-2-3","description":"By a ranging camera the simultaneous capturing of range measurements in the form of a range image for an extended area of dynamical 3D applications is given. Applications are building surveillance, traffic monitoring, and driver assistance.","name":"Ranging camera","selfAssesment":"In progress
"},{"code":"PS1-3-2-4-1","description":"Spaceborne Laser Scanning (SLS; e.g. Geoscience Laser Altimeter System - GLAS, Global Ecosystem Dynamics Investigation - GEDI) provides mainly global, depending on the platform (GEDI mounted on International Space Station (ISS) provides measurements over the Earth’s surface between 51.6° N and 51.6° S), measurements of the Earth's surface, with the potential on capturing additionally clouds and atmospheric aerosols. The spaceborne measurements allow to globally observe ice sheet and land elevations, approximate sea ice thickness, changes in elevation through time, vegetation coverage for biomass estimation, and height profiles of clouds and aerosols.","name":"Spaceborne Laser Scanning","selfAssesment":"Planned
"},{"code":"PS1-3-2-4-2","description":"Airborne Laser Scanning (ALS) systems allow a direct and illumination-independent measurement from 3D objects in a fast, remote and accurate way. Beside basic range measurements, the current commercial ALS developments allow to record the waveform of the backscattered laser pulse. Latest trends in sensor developments focus on single-photon detection. Different applications are of interest, like urban planning, forestry surveying, or power line monitoring. Further to describe the 3D scene, products like digital terrain models (DTMs), digital surface models (DSMs), or city models are provided.","name":"Airborne Laser Scanning","selfAssesment":"Planned
"},{"code":"PS1-3-2-4-3","description":"A mobile laser scanning or LiDAR system (MLS) consists of a moving vehicle equipped with one or more usually side-looking laser scanners to capture information about the local 3D geometry.","name":"Mobile Laser Scanning","selfAssesment":"Planned
"},{"code":"PS1-3-2-4-4","description":"Underwater Laser Scanning is applied in deep-sea as well as in shallow water regions. The ranging distance is close range and the measurement principle relies on triangulation by laser light, comparable with structured-light-projection. More recently, companies started to develop Time-of-Flight (ToF) underwater laser scanners.","name":"Underwater Laser Scanning","selfAssesment":"Planned
"},{"code":"PS1-3-2-4-5","description":"For Bathymetric Laser Scanning System the utilized green laser light with its potential penetration capabilities in water is essential. For water surface mapping the electromagnetic radiation of the laser penetrates into the topmost layer of the water column and can also be used for mapping the water surface and shallow water bathymetry. Area-wide water surface heights and depths are required for many disciplines such as hydrology, hydraulic engineering, flood risk management, ecology, climate change, etc.","name":"Bathymetric Laser Scanning","selfAssesment":"Planned
"},{"code":"PS1-3-2-4","description":"Laser scanners capture data by successively considering points on a discrete, regular (typically spherical) raster, and recording the respective geometric and radiometric information.\r\nThere are different types of laser scanners depending on their application and the platform on which they are mounted: spaceborne, airborne, terrestrial, mobile, underwater, bathymetric.","name":"Laser scanner","selfAssesment":"Planned
"},{"code":"PS1-3-2","description":"The main idea of LiDAR (Light Detection and Ranging) technology is based on actively scanning the scene by involving a device which emits electromagnetic radiation in the form of modulated laser light. \r\nGenerally, such scanning devices illuminate a scene with modulated laser light and analyze the backscattered signal. More specifically, laser light is emitted by the scanning device and transmitted to an object. At the object surface, the laser light is partially reflected and, finally, a certain amount of the laser light reaches the receiver unit of the scanning device. The measurement principle is therefore of great importance as it may be based on different signal properties such as amplitude, frequency, polarization, time, or phase. \r\nMany scanning devices are based on measuring the time t between emitting and receiving a laser pulse, i.e., the respective time-of-flight, and exploiting the measured time t in order to derive the distance r between the scanning device and the respective 3D scene point. Alternatively, a range measurement r may be derived from phase information by exploiting the phase difference Δφ between emitted and received signal. According to seminal work, respective scanning devices may be categorized with respect to laser type, modulation technique, measurement principle, detection technique, or configuration between emitting and receiving component of the device. \r\nIn order to get from single 3D scene points to the geometry of object surfaces, respective scanning devices are typically mounted on a platform which, in turn, allows a sequential scanning of the scene by successively measuring distances for discrete 3D points.\r\nLiDAR technology is used for a diversity of applications such as autonomous driving, forestry, biomass estimation, precision farming, archaeology, city mapping, terrain modelling, and metrology.","name":"LiDAR (Light Detection and Ranging)","selfAssesment":"Completed
"},{"code":"PS1-3-3-1","description":"Sonar, also called ultrasonic sensing, is one the principal sensors for mapping sea-floor, i.e. bathymetry. It transmits sound waves through water and records the amount of backscattered energy. It uses frequencies higher than normal hearing. A sonar can be either passive or active. Active sonars are also called echosounders.","name":"Sonar","selfAssesment":"New
"},{"code":"PS1-3-3-2","description":"A seismic sensor is also called seismometer and measures the motion of the ground when it is shaken by a perturbation such as an earthquake, be it a large displacement or a microquake. The physical variable associated to the measurement of a seismometer is dynamic. It can be either the amplified ground motion, the velocity or acceleration. Current seismometers transform one of these three parameters into a voltage measurement. Usually, three seismometers are needed to retrieve the three components of the displacement. As for other sensors, there exists many types of seismic sensors, and they can be distinguished in active and passive sensors as well.","name":"Seismic sensor","selfAssesment":"New
"},{"code":"PS1-3-3","description":"Instruments that measure vertical distribution of precipitation and other atmospheric characteristics such as temperature, humidity, and cloud composition.","name":"Sonic sensors","selfAssesment":"New
"},{"code":"PS1-3-4-1","description":"Radar scatterometer is a calibrated radar designed to measure the radar backscatter cross section of a target, generally an area on the earth’s surface. Surface backscatter is measured as a function of the frequency, polarization, and illumination direction of the sensing signal (microwaves).","name":"Radar Scatterometers","selfAssesment":"Planned
"},{"code":"PS1-3-4-2-1","description":"Differential Absorption Lidar (DIAL) is a laser remote sensing technique that is used for range and/or profile measurements of atmospheric gas concentrations and constituents.","name":"Differential Absorption Lidar","selfAssesment":"In progress
"},{"code":"PS1-3-4-2-2","description":"Doppler Wind LiDAR or Cloud-Aerosol Lidar with Orthogonal Polarization (e.g. CALIOP) is a two-wavelength polarization-sensitive LiDAR that provides high-resolution vertical profiles of atmospheric aerosols and clouds to enable an greater understanding of our climate.","name":"Doppler Wind LiDAR","selfAssesment":"In progress
"},{"code":"PS1-4","description":"There are different ways to classify sensors used in remote sensing. One of them is the division into imaging and non-imaging sensors. Imaging sensors typically employ optical imaging systems (from VIS to TIR). They operate primarily at window frequencies, where atmospheric absorption is low and surface features can be imaged or measured. Non-imaging sensors include microwave radiometers, microwave altimeters, magnetic sensors, gravimeters, Fourier spectrometers, laser rangefinders, and laser altimeters.","name":"Imaging vs. nonimaging sensors","selfAssesment":"New
"},{"code":"PS1-5-1-2","description":"Across track scanners, known as whiskbroom electromechanical scanners, are multispectral imaging systems building the final image (ground cell by ground cell) by combination of the platform motion along the orbital track with a mechanical rotation of the collecting optic in the across track direction. Opto-mechanical are typically multi-spectral radiometers (no limitation on bands), whiskbroom systems are usually CDD spectrometers (high spectral resolution but just up to 1 micrometer). Examples of the sensors: Landsat Multispectral Scanner (MSS), Landsat Thematic Mapper (TM).","name":"Across track scanners","selfAssesment":"Planned
"},{"code":"PS1-5-1","description":"Speckle-pattern based sensors operate with a spatial neighborhood codification strategies to exploit a unique pattern. The label associated to a pixel is derived from the spatial pattern distribution within its local neighborhood. Thus, labels of neighboring pixels share information and provide an interdependent coding. Representing one of the most popular devices based on structured light projection, the Microsoft Kinect exploits an RGB camera, an IR (infrared) camera, and an IR projector. The IR projector projects a known structured light pattern in the form of a random but unique speckle dot pattern onto the scene. As IR camera and IR projector form a stereo pair, the pattern matching in the IR image results in a raw disparity image which, in turn, is read out as depth image.","name":"Speckle-pattern based sensor","selfAssesment":"In progress
"},{"code":"PS1-5-2","description":"A multi-temporal (sequential) binary coding uses black and white stripes to form a sequence of projection patterns for each point on the surface of the object. Binary coding technique is very reliable and less sensitive to the surface characteristics, since only binary values exist in all pixels. Thus, each pixel may be assigned a codeword consisting of its illumination value across the projected patterns. The respective patterns may, for instance, be based on binary codes or Gray codes and phase shifting. To achieve high spatial resolution, a large number of sequential patterns need to be projected. All objects in the scene have to remain static. The entire duration of 3D image acquisition may be longer than a practical 3D application allows for. These sensors are utilized in industrial environment.","name":"Multi-temporal pattern based sensor","selfAssesment":"In progress
"},{"code":"PS1-5-3","description":"For a multi-spectral pattern based sensor, various continuously varying color patterns to encode the spatial location information are utilized.","name":"Multi-spectral pattern based sensor","selfAssesment":"New
"},{"code":"PS1-5","description":"A structured-light-projection camera emits active optical radiation in the form of a coded structured light pattern in the visible or infrared spectrum, or electromagnetic radiation in the form of modulated laser light. Via the projected pattern, particular labels are assigned to 3D scene points which, in turn, may easily be decoded in images when imaging the scene and the projected pattern with a camera. The procedure reminds to conventional stereo processing, where corresponding features must be extracted from a pair of stereo images to derive the spatial information. In contrast, such synthetically generated features allow to robustly establish feature correspondences, and the respective 3D coordinates may easily and reliably be recovered via triangulation. Generally, techniques based on the use of structured light patterns may be classified depending on the pattern codification strategy.","name":"Structured-light-projection camera","selfAssesment":"In progress
"},{"code":"PS1-6","description":"Ground penetrating radar is a non-intrusive measurement technique that uses radio waves to probe the ground. It is used to analyze and locate targets buried in the sub-surface. It transmits low-power electromagnetic energy into the ground and receives weak signals from a low-loss dielectric or conductor material. It is principally used for archeology and geology. Typical penetration depths are between a few centimeters up to 4m.","name":"Ground penetrating RADAR (GPR)","selfAssesment":"New
"},{"code":"PS1-7","description":"An optical spectrometer is an instrument used to detect, measure and analyze the spectral content of the incident electromagnetic field (narrow-band, VIS, NIR, SWIR and TIR). It breaks down the incoming light spectrum so the whole wavelength range is mapped and each wavelength can be analysed individually. Usually, a distinction is made between optical and mass spectrometers.\r\nOptical spectrometers depict the intensity of the incoming light in function of the wavelength. Considering all wavelengths, each object has a specific spectral signature and the analyse of their particular spectrum allows the deduction of their composition ( e.g. pigments) or health.","name":"Optical spectrometers","selfAssesment":"In progress
"},{"code":"PS1","description":"Remote sensing sensors acquire information about objects situated on the surface of e.g. the Earth remotely, e.g. from a distance, without any physical contact. They detect and measure the changes that the object imposes on its. \r\nRemote Sensing sensors are characterized according to several different properties:\r\n\tDepending on the interaction between the sensor and the Earth’s surface, one distinguishes between active (e.g. radar) and passive (e.g. optical imagery) sensors. Some systems use both kind of sensors simultaneously.\r\n\tDepending on the mapping process of the information, it can be distinguished between imaging and non-imaging sensors. Imaging sensors produce an image of an area of interest, e.g. give a spatial information about the incoming information. Spatial relationships between objects can be identified and used for visual interpretation. Non-imaging sensors register usually single response values for a specific area, and do not record how the incoming information varies across the field of view. They can be used to characterize the interaction between the received information and illuminated target.\r\n\tDepending on the platform on which the instrument is deployed, one speaks either of ground based (e.g. terrestrial laser scanner), airborne (e.g. plane, drone), or spaceborne (e.g. satellite) sensor. For spaceborne sensors, the orbit geometry (e.g. geostationary, equatorial, sun-synchronous) and altitude (high, medium and low Earth orbit) play an important role, as it most often determines the application of the satellite in combination with the deployed sensor (weather satellites or Earth observation satellite). \r\n\tDepending on the observed portion of the electromagnetic spectrum (e.g. optical, infrared, thermal, microwave). \r\n\tDepending on the instrument (e.g. imagers, altimeters, spectrometers, radiometers). \r\n\tDepending on the instrument precision, e.g. in terms of spatial resolution very high vs. low resolution sensors; in terms of spectral resolution narrow band (hyperspectral sensors) vs. broad-band sensors (mono- and multispectral sensors); in terms of radiometric resolution very high vs. low resolution sensors. Some applications do not require very high precision instruments, e.g. sea surface temperature measurements, while other, e.g. for vegetation monitoring, require high spectral and radiometric resolution for good data interpretation and analysis. \r\nOther categorization would include the specific applications of each sensor (weather, environment, urban, land, water, mapping, photogrammetry, structure-from-motion, etc.) and if is financed and used for scientific, commercial or military goals.","name":"Types of remote sensing sensors","selfAssesment":"Completed
"},{"code":"PS2-1","description":"This topic covers information on the first remote sensing platforms that were used to obtain aerial photos. The first-known aerial photo was obtained in 1858 by Gaspard Felix Tournachon (Nadar). Afterwards, different platforms were used to obtain the information from above. The history of the development of remote sensing platforms includes platforms such as baloons, kites, rockets, pigeons, gliders, etc. to recent low-cost femtosatellites, e.g. for solar radioation pressure measurements. Historically, the main developments of the platforms as well as sensors was associated with military operations in the XXth century. Remote sensing data was used as part of photo- or/and satellite reconnaissance, i.e. aerial photos or satellite imageries used for the military purposes, mainly to make accurate maps and based on that to prepare a military strategy.","name":"History of Remote Sensing Platforms","selfAssesment":"In progress
"},{"code":"PS2-2-1","description":"An unmanned aircraft system (UAS) includes an unmanned aerial vehicle (UAV), an aircraft without a human pilot on board, a ground-based controller, and a system of communications between the two. The system includes a full range of size classes from very small hand-launched drones to the large high-altitude observational systems.","name":"Unmanned Aerial Systems (UAS)","selfAssesment":"New
"},{"code":"PS2-2-2-1","description":"Mission planning depends on the selected system of acquisition (sensor and platform). A detailed planning of a mission is a fundamental prerequisite for a successful acquisition of remote sensing data. Planning of an aerial photography mission (manned or unmanned) takes into account several parameters such as time of day/sun angle, weather conditions, flightline, platform. Planning and implementation of a spaceborne Earth Observation mission involves several successive life cycle ‘phases’ of conception, development, production and testing, utilization and support, and retirement, as part of an iterative and recursive process, until the satellite (space segment) is delivered and launched into orbit, and the data are exploited in the ground segment.","name":"Mission planning","selfAssesment":"New
"},{"code":"PS2-2-2-3-2-3-1","description":"Stripmap is an acquisition mode of Synthetic Aperture Radar (SAR) data. It is the most simple, common acquisition mode of the SAR satellite sensors. In this mode, the antenna of the radar system is pointed in a fixed direction related to the flight direction. The displacement of the illuminated footprint corresponds to the displacement of the sensor along the orbit. This results in a continuous acquisition strip parallel to the flight direction. The ground coverage and resolution varies depending on the considered sensor and technical requirements. For X-band spaceborne sensors, a spatial resolution of 3 m can be achieved with a swath width in range direction of 30 km, e.g. for TerraSAR-X. In C-band, a spatial resolution up to 5 m is achieved e.g. by Sentinel-1 with a swath width of 80 km. For L-band spaceborne sensors, the spatial resolution achievable in stripmap mode varies between 3 and 10 m, with a swath width of 50-70 km, e.g. ALOS PALSAR2. \r\n\r\nContrary to other acquisition modes, no antenna steering is needed in azimuth direction and the elevation beam is fixed in a specific range direction. This allows for an uninterrupted coverage along the flight direction.\r\n\r\nStripmap data show high resolution with sufficient coverage for regional applications and can therefore be used for e.g. detailed land cover analysis at regional scale such as the mapping of urban footprints. Furthermore, it can be used for the mapping of small island or to support emergency actions.","name":"Stripmap","selfAssesment":"New
"},{"code":"PS2-2-2-3-2-3-2-1","description":"The Staring Spotlight mode is only available for a few sensors. It follows the same principle of antenna steering in azimuth direction as the standard Spotlight mode, except that the rotation center of the antenna for steering is situated at a nearer range position, within the illuminated scene. This induces that the illuminated antenna footprint stays almost the same during the whole acquisition. Contrarily to the Spotlight mode, the antenna footprint does not slide along the azimuth direction during the SAR acquisition. Additionally, the steering angle is higher for the Staring Spotlight mode than for the standard Spotlight mode, increasing therefore the length of the synthetic aperture and leading to an even higher resolution in azimuth direction.\r\nThe Staring Spotlight mode is implemented on the X-Band sensor TerraSAR-X since 2013 and achieves an azimuth resolution up to 0.25 m. Similar to the standard Sportlight mode, this happens to the detriment of the coverage. The scene size is highly dependent of the incidence angle and varies from 7.5 km to 4 km in range and from 2.5 to 2.7 km in azimuth direction. A larger coverage is obtained for smaller incidence angles.\r\nDue to their extremely high resolution, staring spotlight acquisitions are principally used for the observation and/or monitoring of small scale objects and phenomena, e.g. small landslides, or for tomographic analysis.","name":"Staring Spotlight","selfAssesment":"New
"},{"code":"PS2-2-2-3-2-3-2","description":"Spotlight is a SAR acquisition mode that allows increasing the illumination time of a particular area of interest by steering the antenna beam in azimuth direction. In this mode, the beam elevation is fixed, but the antenna is steered in azimuth direction, increasing therefore the length of the synthetic aperture. The rotation center of the antenna for steering is situated behind the scene at far range. The antenna footprint slides slightly forward over the scene in the azimuth direction during acquisition, but slower than in Stripmap mode, due to the antenna steering. The longest illumination time in azimuth direction results in an azimuth resolution that is highly enhanced compared to e.g. the Stripmap or the ScanSAR acquisition modes. However, this improvement is done to the detriment of the coverage. As for the other acquisition modes, the ground coverage and resolution depends on the considered sensor. For TerraSAR-X, a minimum coverage of 10 km in range and 5 km in azimuth direction is achieved in the Spotlight mode, with and azimuth resolution of about 1 m. The L-Band sensor Alos 2 also allow Spotlight acquisition mode, with a coverage of 25 km in both directions and a resolution of 1 m in azimuth direction, and down to 3 m in range direction.\r\n\r\nDue to the very high resolution achieved in both directions, this acquisition mode is particularly usefull for urban area analysis as it allows for the detection of small objects. Therefore, Spotlight data are often used for the detection and recognition of man-made structures and objects, such as roads, buildings and even vehicles.","name":"Spotlight","selfAssesment":"New
"},{"code":"PS2-2-2-3-2-3-3-1","description":"The Interferometric Wide Swath Mode is a particular acquisition mode of the C-Band satellites Sentinel-1 which implements the TOPS (Terrain Observation with Progressive Scan) method. It combines an antenna steering in elevation, as in ScanSAR mode, with a counterrotation of the antenna beam from backward to forward steering, opposite to the steering happening in Spotlight mode. The data is acquired in bursts by cyclically switching the antenna beam between multiple adjacent sub-swaths.\r\n\r\nThis opposite steering direction of the antenna along the azimuth leads to a shorter target illumination and induces a decrease of the resolution, but a cyclically continuous coverage in azimuth direction. The principal difference to the other acquisition modes is that this acquisition mode implies a shrinking of the antenna footprint virtually to a ground target instead of slicing it to retrieve the target.\r\n\r\nThe Interferometric Wide Swath Mode (IW) was originally designed to solve Signal-to-Noise heterogeneities and azimuth ambiguities appearing in the ScanSAR mode.\r\n \r\nFor Sentinel-1, the IW mode provides a coverage of 250 km in range direction with an azimuth resolution of 20 m and incidence angles ranging from 29.1° in near to 46° in far range. \r\n\r\nStandard Single Look Complex Sentinel- 1 IW products contain three sub-swaths in range direction, with nine burts in azimuth direction.\r\n\r\nThe IW mode is the standard acquisition mode of the Sentinel-1 C-Band satellites and is acquired continuously over all land surfaces. The application are very diverse, ranging from agriculture and forestry to urban deformation monitoring and ship surveillance.\r\n\r\nSimilar to the IW mode, the Extra Wide Swath Mode (EW) of Sentinel-1 uses the same TOPS technique, but covers even wider areas up to 400 km in range direction, to the detriment of the resolution which decreases to 40 m. The EW Mode principally finds application in maritime applications such as artic and sea-ice monitoring, analyses of marine winds and oil pollution monitoring.","name":"Interferometric Wide Swath Mode","selfAssesment":"New
"},{"code":"PS2-2-2-3-2-3-3-2","description":"The Extra Wide Swath Mode is an acquisition mode of the Sentinel-1 satellites. It is primarily designed and used for wide area coastal monitoring, such as ship traffic, sea-ice monitoring and oil spill detection. It uses the TOPSAR technique with a swath width of 410km and a spatial resolution of 20 m by 40 m.","name":"Extra Wide Swath Mode","selfAssesment":"New
"},{"code":"PS2-2-2-3-2-3-3","description":"In the ScanSAR acquisition mode, the antenna beam is successively steered to different elevation angles. This results in adjacent, slightly overlapping stripes, or sub-swaths along the range direction, parallel to the azimuth direction, each stripe having a different incidence angle at its center. During antenna steering in elevation, transmitter and receiver are off. Therefore, each stripe is illuminated for a shorter time as for the StripMap mode, leading to a degradation of the azimuth resolution. However, ScanSAR allow a larger coverage in range direction than the other imaging modes. Each sub-swath is illuminated for a shorter time than in the Stripmap case. The timing is adjusted though, such that the time-varying antenna footprint repeat cyclically. Similar to the other acquisition modes, the achievable resolution and coverage of ScanSAR products depends on the considered sensor and its properties. For X-Band, e.g. for TerraSAR-X, a total swath width of 100 km in range direction can be achieved using four adjacent sub-swaths or, using a Wide ScanSAR mode with six adjacent sub-swaths, a swath width up to 270 km can be achieved. A Wide ScanSAR scene shows incidence angles ranging from 15.6° in near to 49° in far range. The azimuth resolution varies between 18.5 m and 40 m, for ScanSAR and WideScan SAR modes respectively. For the L-Band sensor ALOS-PALSAR 2, a swath width up to 40 km can be achieved, with incidence angles ranging from 8° to 70° and an azimuth resolution of 60 m. \r\n\r\nThe ScanSAR mode is well suited for large-area monitoring, e.g. for sea ice or glacier monitoring, as well as for mapping large-scale disasters, such as oil slick, or areas devastated by forest fires. Using interferometry, topography mapping and deformation monitoring is also possible.","name":"ScanSAR","selfAssesment":"New
"},{"code":"PS2-2-2-3-2-3-5","description":"A stereoscopy acquisition mode collects remotely sensed data where each location on the ground (or the imaged objects) is covered multiple times (at least twice), from different perspectives. Stereopairs and stereoscopic coverage enable the extraction of 3D representations of the environment from remotely sensed imagery. Most aerial photographs are taken with frame cameras along flight lines, or flight strips. [...] Successive photographs are generally taken with some degree of endlap [, i.e. overlap]. Not only does this lapping ensure total coverage along a flightline, but an endlap of at least 50 percent is essential for total stereoscopic coverage of a project area. Stereoscopic coverage consists of adjacent pairs of overlapping vertical photographs called stereopairs. Stereopairs provide two different perspectives of the ground area in their region of endlap [overlap]. When images forming a stereopair are viewed through a stereoscope, each eye psychologically occupies the vantage point from which the respective image of the stereopair was taken in flight. The result is the perception of a three-dimensional stereomodel. As an input to photogrammetry analysis procedures, stereopairs from flight strips enable the extraction of digital elevation models (DEM), orthophotos, thematic GIS data, and other derived products through the use of digital raster images and relatively sophisticated analytical techniques. With the availability of close-range UAV and terrestrial hand-held camera data, 3D reconstructions of buildings (even indoors) and other objects on the terrain surface become possible.","name":"Stereoscopy","selfAssesment":"In progress (to be deleted, merged?)
"},{"code":"PS2-2-2","description":"Since the 1940s aerial imagery has been the primary source of detailed geospatial data for extensive study areas. Photogrammetry is the profession concerned with producing precise measurements from aerial imagery. Aerial imaging and photogrammetry represent a major component of the geospatial industry. The topics included in this unit do not comprise an exhaustive treatment of photogrammetry, but they are aspects of the field about which all geospatial professionals should be knowledgeable.","name":"Airborne platforms and systems","selfAssesment":"New
"},{"code":"PS2-2-3-1","description":"Earth observation (EO) missions are gathering information about the physical, chemical, and biological systems of the planet via remote-sensing technologies, supplemented by Earth-surveying techniques, which encompasses the collection, analysis, and presentation of satellite data.","name":"Earth observation missions","selfAssesment":"In progress
"},{"code":"PS2-2-3-2","description":"There are essentially three types of Earth orbits: high, medium and low Earth orbit. Satellites that orbit in a medium (mid) Earth orbit include navigation and specialty satellites, designed to monitor a particular region. Most scientific satellites, including NASA’s Earth Observing System fleet, have a low Earth orbit. On which orbit a satellite will be launched to, depends mainly on its application. The orbit types can be categorized according to their height.\r\nThe orbit height of a satellite corresponds to the distance between the Earth’s surface and the satellite. It determines its speed as it rotates around the Earth. Due to Earth’s gravity, the pull of gravity is stronger for lower orbits than for higher orbits. Therefore, a satellite situated on a lower orbit will circle the Earth faster than a satellite situated on a higher orbit.\r\n\tHigh Earth orbit: it describes orbits situated at about 36000 km above the Earth’s surface (42164 km from the Earth’s center). At this exact distance, the speed of the satellite on the orbit matches the Earth’s rotation, i.e. the satellite needs 24 hours to complete a full rotation on the orbit, when the orbit is situated exactly above the equator. Such orbits are also called geosynchronous orbits, as the satellite moves at the same speed than the Earth and seems to stay in place over a specific location. Those orbits are mainly used for weather and communication satellites\r\n\tMedium Earth orbit: it describes orbits situated at about 20200 km of the Earth’s surface, or 26560 km of the Earth’s center. At this height, a satellite rotates twice around the orbit during one Earth’s rotation. This orbit is also called semi-synchronous and this is the orbit type used by Global Navigation Satellite Systems such as GPS and GLONASS. A further important medium Earth orbit is the Molniya orbit which allows the observation of the poles, otherwise nearly impossible with equatorial geosynchronous orbits.\r\n\tLow Earth orbit: this type of orbits are used from almost all dedicated scientific Earth Observation satellites. Most of them use a particular, nearly polar orbit inclination, meaning that the satellite rotates around the Earth nearly from pole to pole (instead of around the equator as it is the case for geosynchronous satellites). This rotation takes about 99 minutes, depending of the specific orbit inclination. During one half of the orbit, the satellite views the daytime side of the Earth, i.e. the illuminated side. At the pole, satellite crosses over and views the nighttime side of Earth. Back to the daylight side, the satellite can view the area adjacent to the region flown over in the last orbit path, due to the simultaneous Earth’s rotation. In 24 hours, satellites situated on these orbits view almost all the Earth twice, for optical satellites once in daylight and once in the dark. Radar satellites seen each Earth region twice, from two different illumination directions. These specific polar-orbits are called sun-synchronous, as the local solar time stays the same each time a satellite flies over a specific region. This has the advantage of providing an almost constant angle of sunlight for each region on the Earth’s surface viewed by the satellite over time and ensure repeatable sun illumination conditions; the angle will only vary seasonally due to the Earth revolution around the sun. Due to this consistency, images of a specific region would not show much illumination changes due to shadows or sunlight and image interpretation over time such as change detection or monitoring approaches are possible. Because a sun-synchronous orbit does not pass directly over the poles, there is a data gap over both poles where no data is acquired.","name":"Types of satellite orbits","selfAssesment":"Completed
"},{"code":"PS2-2-3-3","description":"An imaging SAR system can generally make acquisitions in different modes. Which acquisition mode to choose depends of the application but also on the desired coverage and data resolution. Even if technically all acquisitions modes can be used everywhere on the Earth’s surface, specific modes are preferred for ocean applications that are different from the ones used in land applications.\r\nThe different acquisition modes can be defined either by their geometrical or by their temporal properties.\r\nThe geometrical properties refer to the geometric configuration of the SAR antenna. Usually looking sideways down in a direction perpendicular to the flight direction (Stripmap mode), the antenna can also be steered around the nadir axis in order to look at a specific target for a longer time during pass-by (Spotlight mode). This configuration allows to rachieve higher azimuth resolution but reduces coverage. It is rather used for very local application where a precise information about specific targets is needed. Other geometric configurations steer the antenna around the flight direction (ScanSAR mode), yielding to a larger swath on the ground. The distance between near and far range is increased, as well as the range of incidence angles within one acquisition. Whereas it increases the area of the scene, it comes generally with a decrease of the spatial resolution in the azimuth direction. Depending on the sensors, the name of the acquisition modes as well as particular technical properties can differ. Sentinel-1 uses a TOPS configuration (Terrain observation with Progressive Scan), which combines the antenna steering properties of both ScanSAR and Spotlight modes. \r\nThe temporal properties refer for specific techniques to the time interval between several acquisitions of the same area. Either these acquisitions are taken simultaneously in one pass over the area of interest (single-pass), or they are taken at different times, needing several passes over the area (repeat-pass).\r\nSpecific SAR techniques such as InSAR and Tomography, while relying on those geometric and temporal properties, have additional acquisition configuration characteristics. For example, the interferometric mission TanDEM-X has three acquisition modes defined by the number of satellite emitting or receiving the signal (pursuit monostatic mode, bistatic mode, alternating bistatic mode), which allows phase referencing. Tomographic SAR uses multi-baseline observations, i.e. the antenna passes several times over an area but at different heights, allowing via different incidence angles the retrieval of structural information of specific targets.","name":"Synthetic Aperture Radar (SAR) acquisition modes","selfAssesment":"Completed
\r\n\r\n"},{"code":"PS2-2-3-4","description":"Swath width refers to the width of the ground that the satellite collects data from on each orbit. The area imaged on the surface, is referred to as the swath. Imaging swaths for spaceborne sensors generally vary between tens and hundreds of kilometres wide.","name":"Swath","selfAssesment":"
New
"},{"code":"PS2-2-3","description":"Spaceborne platforms and systems are present at a great height from the earth surface. The altitude of platforms range from few hundred kilometers to several thousand kilometers. A large area can be captured in a single scene depending on altitude of sensor. The platforms can have different characteristics.","name":"Spaceborne platforms and systems","selfAssesment":"Planned
"},{"code":"PS2-3-1","description":"Field spectroscopy generally refers to the use of non-imaging spectrometers near the ground surface and it is usually aimed at evaluating spectral reflectance of the investigated target. For this purpose, consecutive measurements of total incident solar irradiance and of radiance or irradiance upwelling from the target are collected by an operator, or more recently by new instruments for long-term and unattended field spectroscopy measurements. The incident irradiance is usually computed by measuring the radiance upwelling from a white calibrated panel which represents the ideal Lambertian surface. Upwelling fluxes are instead usually collected holding the sensor vertically over the surface (nadir view), although spectral libraries collected observing the target from different viewing angles are also available. \r\nField spectrometry is also referred to as ‘proximal sensing’ to underline that spectra are collected with portable spectroradiometers in the vicinity of the target, in contrast to ‘remote sensing’, which is instead usually performed with satellite or airborne sensors.\r\nField spectroscopy is therefore an in-situ method for characterising the reflectance of natural or artificial surfaces and thereby provides reference data for the calibration or validation (cal/val) of airborne and satellite sensors. This method provides a means of scaling-up measurements from small areas (e.g. leaves, rocks) to composite scenes (e.g. vegetation canopies), and ultimately to pixels.\r\nField spectroscopy is used in different applications, for example, soils, rocks, vegetation and chlorophyll fluorescence, water, snow surfaces and atmosphere. Long-lasting field spectroscopy campaigns based on manual measurements are extremely resource-demanding and do not ensure repeatability of the acquisition conditions as the instrument setup is initialized each day. To overcome such limitations a few research groups have initiated automatic tower-based spectral reflectance measurements using different devices. With such setups, non-imaging spectrometers are installed in the field and are operated automatically for long periods (i.e. months to years) and different networks of hyperspectral instruments are now becoming operational (e.g. RadCal Net).\r\nField spectroscopy can be also used to predict optimum spectral bands, viewing configuration, spectral calibration and time to perform a particular remote sensing task but also to develop, refine and test models relating biophysical attributes to remotely-sensed data. In this context, ground reflectance measurements are therefore mainly used as input in simulation study for sensor design, calibration/validation data for remote sensing sensors, for spectral mixture analysis and for the development of relationships between field data and radiometric variables.\r\nSince spectroscopy is the study of matter using electromagnetic radiation, point or imaging field spectrometers are instruments which allow the measurements of reflected or emitted electromagnetic radiation. In particular, portable or hand-held spectroradiometers are small instruments that spectrally measure the radiation reflected or emitted by a target and they are useful in obtaining accurate spectral data over different surfaces. In remote sensing, they generally cover the 400-2500 nm spectral range and operate with a full width at half-maximum of about 1.5/3 nm, so that they can collect radiation in a continuous way across the spectrum. The final output is therefore the hyperspectral signature of reflectance of the surfaces versus the considered wavelength.","name":"Field spectroscopy and portable spectroradiometers","selfAssesment":"Completed
"},{"code":"PS2-3-2","description":"Terrestrial Laser Scanning (TLS) is a ground-based, active imaging method that rapidly acquires accurate, dense 3D point clouds of object surfaces by laser range finding.\r\nA terrestrial laser scanning (TLS) system is a stationary highly accurate ranging device for geodetic surveying. More specifically, TLS systems provide dense and accurate 3D point cloud data for the local environment and they may also reliably measure distances of several tens of meters. Due to these capabilities, such TLS systems are commonly used for applications such as city modeling, construction surveying, scene interpretation, urban accessibility analysis, or the digitization of cultural heritage objects. When using a TLS system, each captured TLS scan is represented in the form of a 3D point cloud consisting of a large number of scanned 3D points and, optionally, additional attributes for each 3D point such as color or intensity information. However, a TLS system represents a line-of-sight instrument and hence occlusions resulting from objects in the scene may be expected as well as a significant variation in point density between close and distant object surfaces. Thus, a single scan might not be sufficient in order to obtain a dense and (almost) complete 3D acquisition of interesting parts of a scene and, consequently, multiple scans have to be acquired from different locations.","name":"Terrestrial Laser Scanning","selfAssesment":"In progress
"},{"code":"PS2-3","description":"Platforms and systems that acquire data from the level of earth's surface. A wide variety of ground based platforms are used in remote sensing. The acquired data are used for detailed in-situ measurements, e.g., Leaf Area Index (LAI), and for calibration/validation campaigns.","name":"Ground platforms and systems","selfAssesment":"New
"},{"code":"PS2","description":"Remote sensing platforms and systems can be static (ground-based platforms) or moving (e.g. airborne or spaceborne platforms, UAVs). A remote sensing platform or system carry a remote sensing sensor. It can operate in near (few centimetres) or far (36,000 kilometres) altitudes ranges.","name":"Types of remote sensing platforms and systems","selfAssesment":"Planned
"},{"code":"PS3-1","description":"The development of remote sensing data carriers has followed the evolution of the photography, remote sensing sensors and computer platforms. The first remote sensed data was stored using the photography films (e.g. aerial photography, satellite Corona program), which was later replaced by reel tapes, cartridge, and then removable and hard discs. In the era of big and fast growth of Earth observation data, and technological advancements in digital infrastructure, the satellite data are stored using cloud platforms providing different service models: Infrastructure as a Service, Platform/Software as a Service (e.g. Copernicus DIAS, Google Earth Engine, open EO). The Cloud offers infrastructure to host, store and process the large amount of data efficiently. For example, the Copernicus Data Information Access Services (DIAS) is a comprehensive cloud-based hosting and processing system for the EO data in particularly for the Sentinels data, the Google’s Earth Engine (GEE) provides access to various satellite and offers processing power with a web-based programming interface, the Amazon Web Services (AWS) has dedicated cloud called ‘Earth on AWS’, the Microsoft’s cloud called Azure facility the use of AI tools to address environmental challenges. Public solutions, as well as private ones, react with a variety of new and innovative tools, which have been recently developed (e.g. DIAS, ODC, EarthServer, EO Browser, GEE).","name":"History of remote sensing data carriers","selfAssesment":"Completed
"},{"code":"PS3-2-1","description":"The picture elements are pixels and each pixel has a specific value (usually in grayscale). Image pixels are normally square and represent a certain area on an image. It is important to distinguish between pixel size and spatial resolution - they are not interchangeable. If a sensor has a spatial resolution of 20 metres and an image from that sensor is displayed at full resolution, each pixel represents an area of 20m x 20m on the ground. In this case the pixel size and resolution are the same.","name":"Picture element (pixel)","selfAssesment":"In progress
"},{"code":"PS3-2-2","description":"An image is an array, or a matrix, of square pixels (picture elements) arranged in columns and rows. In a (8-bit) greyscale image each picture element has an assigned intensity that ranges from 0 to 255.","name":"Image as a matrix (digital number DN)","selfAssesment":"In progress
"},{"code":"PS3-2-3","description":"In data manipulation contexts, a data cube is a multi-dimensional array of values. A data cube can be visualized as the multidimensional extension of two-dimensional table. It can be viewed as a collection of identical 2-D tables stacked upon one another. Data cubes are used to represent data that is too complex to be described by a traditional table of columns and rows. Typically, the data cube is applied in conditions where these arrays are massively larger than the hosting computer’s main memory, for example multi-terabyte data warehouses o time series of image data.","name":"Data cubes","selfAssesment":"New
"},{"code":"PS3-2-4","description":"Term Big data refers to any collection of data sets so large and complex that it becomes difficult to process using on-hand data management tools or traditional data processing applications. In the field of Earth Observation (EO) is usually refers to large time series of image data which size on disk is much greater than hosting computer’s main memory. EO Big Data offers solution that allows not only storing these data on disk but also efficiently process them.","name":"Earth Observation Big Data","selfAssesment":"New
"},{"code":"PS3-2","description":"Most remote sensing data exist as digital images, and appropriate image processing allows the emphasis of certain aspect and subsequent extraction of information for specific applications.\r\nA digital image is a representation of the reality as a grid of picture elements. It can be considered as an array of numbers that can be stored and handled by a digital computer. The picture elements are pixels and each pixel has a specific value (usually in grayscale). This value is a digital number (DN), which usually represents the amount of energy recorded by the sensor at this pixel position or any other characteristic recorded by the sensor, e.g. elevation. \r\nEach row of the image grid, or matrix, corresponds to one scan line. Each pixel is characterized by its row r and column c position in the image, as well as by its value. Additional geographical information is needed in order to assign a geographic location to a pixel. The digital number are integers usually compressed in one byte (= 8 bit) representation, i.e. each pixel can take 256 values.\r\nDigital images are raster data, as opposite to vector data. Whereas vector data can be points, lines or polygones, raster data always consist of pixels. A pixel is the smallest element in which an image can be divided into. The pixel size varies depending of the instrument and of the sampling used. Large pixel may contain information about several objects of the recorded scene. However, they only have one value. These are called mixed-pixel, as e.g. several land cover classes are represented within one pixel and they cannot be distinguished from another. \r\nIn multispectral imagery, each region of the electromagnetic spectrum is recorded in an independent image (band). Therefore, at a specific array position (r,c), there exist several pixels, each with a specific value corresponding to the energy recorded for the considered band. This result in a three-dimensional matrix. The bands of a multispectral image can be displayed three at a time in the computer using for each band one of the three primary colors red, green and blue (RGB). This is called a color composite image. If the color composite represents a combination of the visible red, green and blue bands in their respective color, the combination is called natural or true color composite, as it corresponds to what the human eye sees naturally. Any other combination, for example considering bands of wavelengths that are not visible for the human eye is called a false color composite. It is often used to highlight the spectral differences and particular image features in order to extract information.","name":"Digital image terminology","selfAssesment":"Completed
"},{"code":"PS3-3-1","description":"Band interleaved by line (BIL) is one of three primary methods for encoding image data for multiband raster images in the geospatial domain, such as images obtained from satellites. BIL is not in itself an image format, but is a scheme for storing the actual pixel values of an image in a file band by band for each line, or row, of the image. For example, given a three-band image, all three bands of data are written for row one, all three bands of data are written for row two, and so on. The BIL encoding is a compromise format, allowing fairly easy access to both spatial and spectral information. The BIL data organization can handle any number of bands, and thus accommodates black and white, grayscale, pseudocolor, true color, and multi-spectral image data.","name":"Band interleaved by line (BIL)","selfAssesment":"New
"},{"code":"PS3-3-2","description":"Band interleaved by pixel (BIP) is one of three primary methods for encoding image data for multiband raster images in the geospatial domain, such as images obtained from satellites. BIP is not in itself an image format, but is a method for encoding the actual pixel values of an image in a file. Images stored in BIP format have the first pixel for all bands in sequential order, followed by the second pixel for all bands, followed by the third pixel for all bands, etc., interleaved up to the number of pixels. The BIP data organization can handle any number of bands, and thus accommodates black and white, grayscale, pseudocolor, true color, and multi-spectral image data.","name":"Band interleaved by pixel (BIP)","selfAssesment":"New
"},{"code":"PS3-3-3","description":"A binary raster file format for aerial photography, satellite imagery, and spectral data. The BSQ data organization can handle any number of bands, and thus accommodates black and white, grayscale, pseudocolor, true color, and multi-spectral image data. Additional information is needed to interpret the image data, such as the numbers of rows, columns, and bands, if there is a color map, and latitude and longitude to relate the image to geospatial locations.","name":"Band sequential (BSQ)","selfAssesment":"New
"},{"code":"PS3-3","description":"In order to properly process remotely sensed data, the\tanalyst must know how\tthe data is organized and stored. Data storage consists of methods of organizing image data for multiband images.","name":"Data storage","selfAssesment":"New
"},{"code":"PS3-4-1","description":"Spectral resolution describes the ability of a sensor to define fine wavelength intervals. The narrowest spectral interval that can be resolved by an instrument. Spectral resolution (spectral capability) also refers to the number of wavebands within the EM spectrum that an optical sensor is taking measurements over.","name":"Spectral resolution","selfAssesment":"Planned
"},{"code":"PS3-4-2","description":"The spatial resolution of an image corresponds to the size of the minimum area that can be resolved by the sensor. \r\nDue to the different techniques of acquisition of passive and active sensors, the spatial resolution is determined for both sensor types differently. \r\nFor passive sensors, the spatial resolution depends on their instantaneous field of view (IFOV), which determines the area of the Earth’s surface that is viewed at one particular moment in time by one detector element. The size of this area is called resolution cell and characterizes the spatial resolution of the sensor. Depending on the spatial resolution, whole features of the Earth’s surface can be detected homogeneously in one or several resolution cells. For features smaller than the spatial resolution, the average reflected radiation of all features within a resolution cell is recorded, leading to so-called mixed-pixels.\r\nFor imaging active systems, the spatial resolution is dependent of both the length of the transmitted pulse in looking direction and the width of the radiation beam or the antenna width in flight direction.\r\nIn all cases, the spatial resolution indicates the level of detail observable in an image. Usually, one distinguishes between coarse (low), moderate (medium) and fine (high and very high) resolution, whereby the use of this denomination is often context-dependent. Sensors with coarse resolution can only detect large features, but they usually cover a much larger area than high-resolution sensors, which can provide detailed information on small objects such as individual buildings, trees or cars, but for much smaller areas. Coarse spatial resolution mean in general a resolution cell larger than 250 m and a scene extent of several thousands of kilometers (>1000 km). Moderate resolution sensors have a spatial resolution of 30 m to 80 m, and a coverage of approximately 200 km in a single acquisition. Sensors showing spatial resolutions from 5 m or 6 m are high-resolution sensors, with a spatial coverage up to approximately 20 km. Sensors with a resolution cell’s width of less than 1 m are considered as very-high-resolution sensors.\r\nLow resolution sensors are appropriate for the analysis of broad-scale phenomena such as ocean color or cloud patterns. Medium resolution sensors are rather used for regional analysis such as land cover change and phenological response of vegetation. High-resolution sensors are particularly useful for object detection.","name":"Spatial resolution","selfAssesment":"In progress
"},{"code":"PS3-4-3","description":"Radiometric resolution can be defined as the ability of an imaging system to record many levels of brightness. Radiometric resolution is defined as the sensitivity of a remote sensing detector to differences in signal strength as it records the radiant flux reflected, emitted, or back-scattered from the terrain. Radiometric resolution refers to the range in brightness levels that can be applied to an individual pixel within an image, determined on a grayscale. E.g., Sentinel-2 sensor MSI is a 12 bit sensor imaging with 4.096 levels.","name":"Radiometric resolution","selfAssesment":"Planned
"},{"code":"PS3-4-4","description":"Temporal resolution, also referred to as the revisit cycle, is defined as the amount of time it takes for a satellite to return to collect data from exactly the same location on the Earth. Imageing of the exact same area at the same viewing angle a second time is temporal resolution.","name":"Temporal resolution","selfAssesment":"New
"},{"code":"PS3-4","description":"A digital image begins as an analog signal. Through computer data processing, the image becomes digitized and is sampled multiple times. The critical characteristics of a digital image are spatial resolution, spectral resolution, radiometric resolution, contrast resolution, noise, and dose efficiency. These depends upon satellite orbit configuration and sensor design. Different sensors have different resolutions.\r\nSpectral resolution describes the ability of a sensor to define fine wavelength intervals. The narrowest spectral interval that can be resolved by an instrument. Spectral resolution (spectral capability) also refers to the number of wavebands within the EM spectrum that an optical sensor is taking measurements over.\r\nRadiometric resolution can be defined as the ability of an imaging system to record many levels of brightness. Radiometric resolution refers to the range in brightness levels that can be applied to an individual pixel within an image, determined on a grayscale. E.g., Sentinel-2 sensor MSI is a 12 bit sensor imaging with 4.096 levels.\r\nSpatial resolution of an image corresponds to the size of the minimum area that can be resolved by the sensor.\r\nTemporal resolution, also referred to as the revisit cycle, is defined as the amount of time it takes for a satellite to return to collect data from exactly the same location on the Earth. Imageing of the exact same area at the same viewing angle a second time is temporal resolution.","name":"Properties of digital imagery","selfAssesment":"Completed
"},{"code":"PS3-5-1","description":"A header file is a seperate file associated with an image file. The header file can be either a plain ASCII-file or a binary file. It contains information about the image file it is associated with. These information can comprise the number of pixels per row (x-direction in a two dimensional image), also called number of columns, the number of lines or rows (y-direction in a two dimensional image), the number of bands (corresponding to the z-direction), pixel spacing and spatial resolution, geographic reference information, the byte order (e.g. big-endian or little-endian), spectral information for each band, calibration constants and many more. The purpose of a header file is to provide basic information about the properties of the image data either to the user or to a software and enabling a software to correctly load and display the image content. In this way, information contained in a header file can also be called metadata, which is data about the data. The structure and the information contained in a header file of remote sensing imagery can be found in the so-called product information documents. There is also digital imagery used in remote sensing containing the information found in header files not in a separate file but as part of the digital image data itself. In this case this is called header information or a file header, which is usually found at the beginning of the image file. In some cases , image files may contain several header sections, e.g. theESA Envisat ASAR SAR data imagery contains a Main Product Header and a Specific Product Header section. Header information as part of the image file itself may be stored in ASCII or in binary format, or in a mixed binary format, as it was used for the ESA Envisat SAR data.","name":"Header file","selfAssesment":"Completed
"},{"code":"PS3-5","description":"The image data stored in a binary data format (BIL, BIP, BSQ) is accompanied by description files that contain a set of entries describing the image data, including acquisition time, image size, statistics, map projection, pixel digital numbers, product type, etc. This general image or product information is stored in a form of header embedded in the image file or provided in the separate file (.hdr) or metadata in XML. There are numerous image file formats, the more common are TIFF (GeoTIFF), bitmap (.bmp), JPEG (.jpg, .jpeg, JPEG2000), HDF, Raw (.raw), Extensible N-Dimensional Data Format (NDF).","name":"Image description files","selfAssesment":"In progress
"},{"code":"PS3-6","description":"Remote Sensing data formats in which the data are organized and stored. The data format for a remote sensing mission is usually chosen based on a number of considerations, including requirements of the sensing system, mission objective, the design and technology of data processing, archiving, and distribution systems, as well as community data standard.","name":"Data formats","selfAssesment":"Planned
"},{"code":"PS3-7-1-1","description":"Depending on the sensor and the provider, remotely sensed imagery is made avalilable to the user at different processing levels. For Sentinel-2, the lowest product level made available to the user is Level-1B. THe Level-1B product provides radiometrically corrected imagery in Top-Of-Atmosphere (TOA) radiance values and in sensor geometry. Radiometric corrections applied to the Level-1B are: dark signal, pixels response non uniformity, crosstalk correction, defective pixels interpolation, high spatial resolution bands restoration (deconvolution puls denoising), binning (spatial filtering) for 60m bands.","name":"Radiometrically corrected","selfAssesment":"New
"},{"code":"PS3-7-1-2","description":"Geometrically corrected products are of a higher processing level than radiometrically corrected products. For Sentinel-2, the geometrically corrected product is the Level-1C product. The Level-1C product results from using a Digital Elevation Model (DEM) to project the image in cartographic coordinates. Per-pixel radiometric measurements are provided in Top Of Atmosphere (TOA) reflectances with all parameters to transform them into radiances. Level-1C products are resampled with a constant Ground Sampling Distance (GSD) of 10, 20 and 60 m depending on the native resolution of the different spectral bands. Level-1C products will additionally include Land/Water, Cloud Masks and ECMWF data (total column of ozone, total column of water vapour and mean sea level pressure). (Sentinel-2 User Handbook, p.44)","name":"Geometrically corrected","selfAssesment":"New
"},{"code":"PS3-7-1","description":"The definition of processing levels for optical data depends on the considered sensor. Most common satellite optical imagery are available in three distinct processing levels, from level 0 to level 2. The most used processing levels are level 1 and level 2, depending on the user and the application. \r\nIn Level 0, the raw data are processed in a way that they are ready to be archived. Processing operations generally includes telemetry analysis, error detections and granule concatenation. Furthermore, relevant parameters such as acquisition date and geographical reference are annotated in the form of metadata, this information being necessary for processing higher levels. Additionally, a quicklook of the image is generated. No correction is performed at this level.\r\nLevel 1 is often divided in several sublevels. Generally, both radiometric correction and geometric refinement are performed at this level. The radiometric processing includes several radiometric corrections such as dark signal correction or spectral band binning. The radiometric correction allows the determination of physical variables (e.g. reflectance) from the digital numbers. The geometric processing includes tiles association and resampling grid computation, in order to link for each image band its native image geometry to the target geometry. The result of this processing steps is usually a geocoded, Top of Atmosphere product.\r\nLevel 2 data usually consist of atmospherically corrected Level 1 data, i.e. Bottom-of-Atmosphere data. These surface reflectance products may be accompanied by additional outputs, such as scene classification, water vapor or surface temperature maps.\r\nFor specific applications and sensors, Level 3 application ready data are available. These are derivated products such as burned area, dynamic surface water content and snow cover maps.\r\nDepending on the considered sensor and level, the name of the sublevels can differ: Sentinel 2 defines Level-1B as radiometrically corrected data. Level 1C are radiometrically and geometrically corrected data, i.e Top-Of-Atmosphere (TOA) orthoimage products. Landsat sensors distinguish between Terrain precision correction (L1TP), systematic Terrain Correction (L1GT) and Geometric systematic Correction (L1GS) depending on the quality of the reference data for geometric correction. These are usually separated into Tier 1 and Tier 2 datasets.","name":"Processing levels of optical data","selfAssesment":"Completed
"},{"code":"PS3-7-2-1","description":"The Single Look Complex SAR format is a single look product of the focused signal. It means that the azimuth compression has been carried out using the full azimuth bandwidth and therefore contains the highest azimuth spatial resolution and at the same time, it suffers from maximum speckle. The data are in the radar geometry, i.e., in slant range coordinates, not projected onto any reference surface. Each pixel of the SLC product is a complex number. (i.e., has a real and imaginary component) that represents the amplitude and phase.","name":"Single Look Complex (SLC)","selfAssesment":"New
"},{"code":"PS3-7-2-2","description":"From the Single Look Complex (SLC) product the Multi-look Detected/Multi-looke (MLD/MLI) can be generated. It is produced by multi-looking, i.e., averaging, over range and/or azimuth resolution cells.","name":"Multi-looked Detected (MLD)","selfAssesment":"New
"},{"code":"PS3-7-2-3","description":"Precision Images (PRI) are the Multi-look Detected/Multi-looked Intensity (MLD/MLI) images that have been resampled into square pixels, rotated to account for the view direction of the instrument and warped by some predefined operation that the projected image pixels are georeferenced onto a specified geographical coordinate system.","name":"Precision Images (PRI)","selfAssesment":"New
"},{"code":"PS3-7-2-4","description":"Before performing multi-looking, the Single Look Complex (SLC) slant-range geometry is projected onto ground. This kind of product, i.e., in ground range geometry, is known as a Ground Range Detected (GRD), e.g., product of the Sentinel-1 mission.","name":"Groud Range Detected (GRD)","selfAssesment":"New
"},{"code":"PS3-7-2","description":"For SAR data, usually three processing levels are distinguished, ranging from level 0 (less processed) to level 2 (higher processed).\r\nLevel 0 products consist of compressed and unfocussed raw data and are the basis for the processing of higher level products. Level 0 data are principally used for research in the topic of signal processing. As for optical data, level 0 product are annotated with several metadata, such as calibration and orbit information, and acquisition time and date.\r\nLevel 1 data can be separated in two distinct product types, depending if the full complex information is used (amplitude and phase) or only the amplitude information. The product denomination depends on the sensor type; for Sentinel 1 the names Single Look Complex (SLC) and Ground range detected (GRD) are used, respectively. Both products can be generated from the Level 0 data. Level 1 data are the products that are used by most scientific users. The processing toward Level-1 data includes Doppler centroid estimation and data focusing. The Level 1 SLC product consists of the real and imaginary part of focused complex SAR data in slant range geometry, from which the phase and amplitude information can be retrieved. This is available for all acquired polarisations. Additional orbit information for georeferencing is provided with the data. The Level 1 GRD data consist of focused and multi-looked SAR data that have been projected to ground range geometry. GRD data only contain amplitude information, therefore the phase information is lost. The multi-looking step is particular for GRD data and allows both speckle reduction and square pixel resolution. As for the SLC data, the GRD data are annotated with orbit information for georeferencing. The Level-1 products are not calibrated, they include however information about calibration constants, which are sensor dependent. Further processing is needed in order to obtain calibrated radar cross section information from the original data intensity values.\r\nLevel 2 products describe geolocated derivated geophysical products such as ocean wind field or surface radial velocity. Such products are for example available for download on the Sentinel-1 Copernicus Hub. Further Level- 2 data are for example differential interferograms or change maps, which can be processed on different online platforms (e.g. Hyp3) and provide information about surface deformation or more generally changes between several acquisitions.\r\nThe denomination of the product types on the different levels may differ from sensor to sensor, but the processing steps stay almost the same, depending additionally on the considered acquisition modes. For example, GRD products are also called for other sensors Multi-Looked Detected (MLD) products.","name":"Synthetic Aperture Radar (SAR) data","selfAssesment":"Completed
"},{"code":"PS3-7-7","description":"Data that have been processed to allow direct data analysis. User processing effort is reduced to a minimum.","name":"Analysis Ready Data (ARD)","selfAssesment":"New
"},{"code":"PS3-7","description":"Earth Observation data are usually made available in different processing levels. The processing level is a mean of describing how much the raw data have been processed toward an informational geophysical product. The degrees of data processing usually follow a numerical hierarchy and typically range from Level 0 (less processed) up to Level 4 (highly processed). They characterize the type of data processing that has been performed between the raw data and the current product.\r\nA first effort for providing standard definitions of different processing levels has been made in the 1980s by the Committee on Data Management and Computation (CODMAC) of the National Research Council (NRC). CODMAC identified eight levels of processing, applicable for all space science data. Starting with the raw data at level 1, the degree of processing and complexity of the data increased at each new level. Level 2 describes edited data, corrected for obvious instrumentation errors and tagged with acquisition time and location; Level 3 stays for calibrated data where values are proportional to a specific physical unit. Level 4 represents resampled data, Level 5 derived data, where specific geophysical information has been retrieved and mapped based on the original data. Level 6 represents all ancillary data (i.e. instrument data) that are necessary for the previous steps of calibration and resampling. Level 7 describes so called correlative data: not directly belonging to the original data, those data represent all other science data that where necessary for the interpretation of the original spaceborne dataset. Finally, Level 8 are user description, i.e. documentation of the data.\r\nConcerning spaceborne image data, both optical and radar, an adaptation of these original levels has been made from NASA and NOAA that is used for the main current spaceborne missions, including the Copernicus program. Whereas specific adaptations may arise for specific sensors and sensor types, there are five principal processing levels. Level 0 represents the raw data that have just been edited for the correction of artifacts. Level 1 data are Level 0 data with additional annotations regarding time and geolocation information, radiometric and geometric calibration coefficients (for example Top of Atmosphere data for optical imagery). Level 2 data are already radiometrically and geometrically calibrated and represent physical variables (for example Bottom of Atmosphere data for optical imagery). Level 3 data correspond to derived variables and information (e.g. land cover) with completeness and consistency information, e.g. quality flags. Level 4 represent higher level data resulting from modelling or more complex analysis of the data with additional ancillary information.\r\nFor many applications and users, so called analysis ready data (ARD data) are required. These usually correspond to Level 2 data that have already been pre-processed in order to retrieve the physical information and can be further analyzed for the specific thematic application.","name":"Processing levels","selfAssesment":"Completed
"},{"code":"PS3","description":"Remotely collected data is available from multiple sources and data collection techniques. Data can be obtained from different levels of data acquisition: ground, air or space, as well as using different sensors and wavelengths. Remote sensing data provides the necessary information to help monitor the Earth's surface.","name":"Remote sensing data and imagery","selfAssesment":"Planned
"},{"code":"PS4","description":"The listed databases provide information on past, operational and future remote sensing platforms and sensors. Use the following links to get more information on the sensors and missions.","name":"Databases of satellite and airborne sensors and missions","selfAssesment":"Completed
"},{"code":"SD","description":"Based on Waldo Tobler`s first law of geography( Tobler, 1970), this property is set on the principle that \"everything is related, but that which is closer is more closely related\".","name":"Spatial dependency","selfAssesment":"In progress (GI-N2K)
"},{"code":"SH","description":"This principle, as set forth by Anselin, determines that \"expectations vary along the earth`s surface\" which means that any spatial analysis is dependent explicitly on the borders of study fields, i.e. the tracing of (spatial) analysis units.","name":"Spatial heterogeneity","selfAssesment":"In progress (GI-N2K)
"},{"code":"TA","description":"This area of knowledge deals with the use of EO / GI techniques and data in different themes and areas of application. It includes the user community of EO services and applications, societal and environmental challenges, EO services and applications, and standard EO products that are made available to users.","name":"Thematic and application domains","selfAssesment":"Planned
"},{"code":"TA11-1-1","description":"The EO/GI users in agriculture are active in Agricultural commodities/Trading, agricultural production / Horticulture, Agricultural services, Agriculture machinery, Agriculture and Rural Development Policy, Agro chemicals / Plants & Fertilizers, Animal production / Livestock. The EO/GI users also include agriculture and rural policy makers. \r\nThey benefit from EO information, for example, by managment support for their crop production through forecasting crop yield, assess risks of damage/loss because of storms, disease or other stress factors, and water monitoring. Use in agriculture: knowledge and information products to forge a viable strategy for farming operations. Understand the health of his crop, extent of infestation or stress damage, or potential yield and soil conditions","name":"Users in agriculture","selfAssesment":"New
"},{"code":"TA11-1-2","description":"The users in fishing are active in Fish stock management, Fishing fleets, Fishery distribution logistics, Aquaculture / fish farms, Coastal management agencies. In addition, the users include Fisheries authorities / policy makers. \r\nThe marine environment in particular is relevant to fishing. Fishing fleets move to the fishing grounds to catch fish. Finding them is challenging. However, fish shoals can be directly visible from above. Navigating to the fishing grounds can be risky: Coastline and shallows may pose a risk to ships. Additionally, skippers may have to deal with challenging weather conditions at sea. Environmental threats to the fishing grounds are oil slicks and other types of pollution. A problem from an economical perspective and for adhering to catch quota is illegal fishing. Noumerous opportunities exist to support fishing with EO information.","name":"Users in fishing","selfAssesment":"New
"},{"code":"TA11-1-3","description":"The users in forestry are active in Forest management, Forest Services, Commodities, Logging industry, Wood, paper and pulp industry, Forest policy, Forest machinery. They also include Forest Policy makers.\r\nUse in forestry: Understand depletion due to natural causes (fires and infestations) or human activity (clear-cutting, burning, land conversion), and monitoring of health and growth for effective commercial exploitation and conservation.\r\nForests are a resource that is harvested all over the Globe for different purposes like construction or heating. Additionally, the forests represent an ecosystem that provides various ecosystem services. Proper management is a key to a healthy forestry industry that has to be aligned well with global environmental management activities. There is a need to avoid deforestation and forest degradation, keep the environmental impact of forestry within bounds, be aware of changes in the carbon balance. Economically relevant is especially a good understading of forest types, forest damage due to storms or insects, as well as wildfires. A threat to the environment results from illegal forest activities.","name":"Users in forestry","selfAssesment":"New
"},{"code":"TA11-1","description":"Users in managed living resources refer to human activities exploiting natural organic resources. Knowledge and information products to forge a viable strategy for the user’s operations such as the assessment of the status of the resource due natural or human activity for effective commercial exploitation and conservation. This includes agriculture, fishing and forestry occupations for our society.","name":"Users in managed living resources","selfAssesment":"New
"},{"code":"TA11-2-1","description":"The users in alternative energy consist of Solar energy providers, Wind energy providers, Tidal energy providers, Hydroelectric energy providers, Energy and Carbon traders, Local and regional planners, and National policy makers. Energy providers need information about the state of the environment to make the most use out of natural resources. Planners and policy makers have to weigh up whether and which type of alternative energy is justifiable and sensible for a specific region.\r\nEO data can be used to build maps that show resource information. For solar energy, those maps contain information about solar radiation, but also shadowing effects. Forecast products for irradiance are available to be able to plan the energy production for the coming days. Tidal waves can be depicted by sea surface heights. As tidal currents are periodical, they can be predicted well by the initial state of sea surface heights. In addition, also the speed of tidal waves can be determined by EO measurements. In the wind energy sector EO data is analysed to plan and monitor wind farms. Maps can show areas, where winds are suitable for wind energy production. After the construction of a wind farm, wind strength and direction during operation can be monitored. Finally, for hydroelectric power stations EO is used to monitor water reservoirs. As well hydrometeorological data is used to forecast water-related events and to monitor drought or floods.","name":"Users in alternative energy","selfAssesment":"Completed
"},{"code":"TA11-2-2","description":"The EO/GI user community in oil & gas consists of offshore exploration and production, on-shore exploration and production, drilling and support services, oil and gas commodities trading, and energy planners. Due to their activities both on-shore and offshore their need for EO-derived information about the land, the ocean and the atmosphere. They need EO-derived information about geological features (for exploration), for asset infrastructure monitoring, construction and buildings. Safe offshore operations (ocean&atmosphere: forecast and monitoring current movement and drift, monitor sea-ice and icebergs, detect and monitor hurricanes and typhoons; land: map and assess flooding, detect wildfires . A large set of information needs results from their need to adhere to environmental regulations. They have to assess and monitor their environmental impact, ocean quality and productivity, land ecosystems and biodiversity, groundwater and run-off \r\nMany problems faced by oil, gas, including the selection and development of exploration areas, detection and mapping of illegal mining activities, or monitoring dams, pipelines and terrain movements, can be efficiently addressed by extracting information from geospatial imagery. Remote Sensing based applications reduce the need for field work, minimize environmental impacts, and ultimately safe costs, to help achieve results faster during exploration, extraction, and remediation/reclamation stages.","name":"Users in oil & gas","selfAssesment":"New
"},{"code":"TA11-2-3","description":"The EO/GI community in minerals and mining consists of mining and quarrying companies, exploration and survey specialists, commodities traders, exploration and extraction equipment suppliers, drilling, excavation and support services, and regional planners / policy makers.\r\nTypical spatial questions for the users in minerals and mining are concerned with prospecting, e.g. \"Where can we find the minerals that are worth exploitation?\", and operation of mining sites: \"How much material has already been excavated in the mine and how much material was deposited in dedicated dump areas?\". Additionally relevant are arising risks through mining activities, e.g. \"How do the mining activities affect settlements in the vicinity?\" or \"How do the mining activities affect the environment?\". Concequently, the EO/GI users in minerals and mining benefit from EO information through mapping geological features, monitor mineral extraction, measure land use statistics, assessing environmental impact of human activities, detect and monitor ground movement, and monitor land pollution.","name":"Users in minerals & mining","selfAssesment":"New
"},{"code":"TA11-2","description":"Users in energy and mineral resources deal with the harvesting of energy from renewable resources and extractive industries including oil and gas and raw materials. EO information helps them in exploring locations where to build new mines or power plants, in identifying risks from infrastructure and in managing the environmental impact of their operations.\r\nUses that apply to the extractive industries: study of landforms, structures, and the subsurface, to understand physical processes creating and modifying the earth's crust. EO/GI should play a key role to transform data into information and knowledge about the potencial feasibility and viability of renewable resources, in particular solar and wind at the natural and urban ecosystems, and in particular to support Sustainable Development Goals SDG 7 Affordable and Clean Energy and SDG 11 Sustainable Cities and Communities.","name":"Users in energy and mineral resources","selfAssesment":"New
"},{"code":"TA11-3-1","description":"EO/GI users in construction include construction companies, civil engineering consultancies, architect and design companies, planning authorities, and national land agencies. \r\nThey benefit from EO through monitor building development, assess environmental impact of human activities, map and assess flooding, detect land movement, subsidence, heave, and monitor land-use statistics","name":"Users in construction","selfAssesment":"New
"},{"code":"TA11-3-2","description":"Utilities (water, electricity, waste): Power station operators, Water plants operators, Survey companies, Hydroelectric suppliers, Regulatory Bodies, Distribution companies, Landfill and waste, Regional planners / policy makers.\r\nThe benefit from EO information that monitor pollution in rivers and lakes, assess changes in the carbon balance, assess environmental impact of human activities, monitor land pollution, assess changes to urban and rural areas, assess and monitor water quality, assess ground water and run-off.","name":"Users in utilities & supplies","selfAssesment":"New
"},{"code":"TA11-3-3","description":"Users of EO/GI in communications and connectivity are mostly mobile telecommunications providers and fixed telecommunication providers. Theire business is to connect people via telephone and internet. The assets for their services include the infrastructure of communication networks physically installed in the ground, the cellphone towers distributed over the land surface, particularly in higly populated areas, as well as other installations (e.g. company buildings) and equipment (communication satellites).\r\nSpecific spatial questions of these users are concerned with the reception quality that the network can provide in an area. The network coverage would neet to react to changes of the built environment. New settlement infrastructure may cause a new population distribution and subsequently the need to network adaptations to cover new areas or cover some areas with higher band widths because more people are living there. Additionaly, the coverage of cellphone antennas depends on the arrangement of environmental obstacles that degrade or block the radio signal. Any place where the built environment or the vegetation changes can change the reception quality within the covered area of an existing cellphone tower. \r\nThe benefit of EO information for the user group of communications and connectivity comes from monitoring building development, assessing changes to urban and rural areas, and mapping line of sight visibility (terrain height, land cover).","name":"Users in communications & connectivity","selfAssesment":"New
"},{"code":"TA11-3-4","description":"EO/GI users in transport and logistics include road transport operators, haulage, road infrastructure operators, tolls, airport operators, rail operators, airlines and airline services, and transport engineers.","name":"Users in transport & logistics","selfAssesment":"New
"},{"code":"TA11-3-5","description":"EO/GI users in marine include ports & harbors administration, bulk cargo carriers, cruise liners operators, ferry operators, naval operations, and rescue and safety at sea.","name":"Users in marine","selfAssesment":"New
"},{"code":"TA11-3-6","description":"From a conceptual point of view travelling is crossing the space from one location to another. Tourism mostly requires a travel to the desired destination and typically also includes moving inside a specific area. Therefore both tourism and travel are highly dependent on spatial phenomena which are often captured using EO.All kinds of travelling are highly dependent on weather conditions which can be observed with meteorological satellites. Also the current traffic conditions like congestion, road condition and natural hazards can be discovered with EO.\r\n\r\nThe types of tourism which are outside of buildings require sufficient weather forecast. Especially outdoor tourism at the coast or in mountain areas have a need for specific information about the current and the near future conditions of the natural environment. Examples are avalanche reports and forecasts for wind or wave heights of water bodies. Local tour organizers can utilise this information in order to better plan offers for tourists and also ensure overall safety during their stay.\r\n\r\nTourism and travelling are import economic factors. Consequently both the public and the private sector are interested in ensuring safe and convenient travel conditions and furthermore in creating an attractive environment for travellers and touristic visitors. This includes recognising environmental pollution, since this discourages tourist from visiting an area. Not only incoming, but also outgoing tourism is an important factor in local economies. Travel agencies, for example, are highly dependent on retrieving accurate information about foreign regions which are typically obtained with earth observation technology.\r\n\r\nOf course tourism and travelling itself also can be observed from space, this is especially true for mass tourism and areas where traffic has increased a lot during the last time. Typical effects are the increase of settlement area and the additionally used space for roads, parking lots, airports and harbors. These changes to the earth surface can be quantified with the help of land cover change detection.In many cases local administrations and decion makers want to mitigate the negative consequences of mass tourism, the insights of the mentioned EO measurements provide a useful foundation for sustainable planning.","name":"Users in travel & tourism","selfAssesment":"Completed
"},{"code":"TA11-3","description":"Users in transport and infrastructure apply to all manufacturing and physical supply in land but also marine domains including transport & logistics, utilities, construction, communication & connectivity, and tourism.","name":"Users in infrastructure & transport","selfAssesment":"New
"},{"code":"TA11-4-1","description":"EO/GI users in insurance and real estate include primary insurance companies, re-insurance sector, insurance brokers, insurance service suppliers, commercial banks, major projects, and international financial institutions. \r\nProduction processes (including primary production like farming), property and real estate are often insured against certain risks, e.g. from natural hazards. \r\nUsers benefit from EO information through applications that monitor building development, assess crop damage due to storms (including to forecast and map large waves), assess damage from earthquakes, detect and monitor wildfires, map and assess flooding, detect land movement, subsidence, heave, forecast and assess landslides.","name":"Users in insurance & real estate","selfAssesment":"New
"},{"code":"TA11-4-2","description":"EO/GI users in retail and geo-marketing include Retail centres and Advertising and Marketing agencies. They use EO/GI data in the field of Navigation and LBS, Shopping chains or Logistics.","name":"Users in retail & geo-marketing","selfAssesment":"New
"},{"code":"TA11-4-3","description":"Users in news and media are Television companies, Broadcasting providers, News and Information agencies, Web service providers, and Entertainment software providers. They benefit from monitoring, forecasting and assessing of natural risks/disasters.","name":"Users in news & media","selfAssesment":"New
"},{"code":"TA11-4-4","description":"Users in ICT include fixed and mobile telecommunications providers. They can make use of EO/GI data by monitoring building development and changes to urban areas.","name":"Users in ICT, knowledge and digital interfaces","selfAssesment":"New
"},{"code":"TA11-4","description":"Users in financial and digital services cover a broad area of activity that touches on many other market sectors such insurance & real estate, retail, news & media and digital interfaces. The categories included are identifiable as a “service” (tertiary sector: attention, advice, access, experience, and affective labour) and not part of the physical supply of goods.","name":"Users in financial & digital services","selfAssesment":"New
"},{"code":"TA11-5-1","description":"The users in smart cities include urban planners, architects, spatial planning offices, urban policy makers. The users benefit from EO information through map information about urban structures and related land use when managing land use, climate change adaptation, and urban green infrastructure. Typical use cases include Urban adaptation to climate change and Green infrastructure and its ecosystem services to increase quality of life of citizens (https://land.copernicus.eu/user-corner/land-use-cases)","name":"Users in smart cities","selfAssesment":"In progress
"},{"code":"TA11-5-2","description":"The users in local and regional planning include spatial planning departments of municipalities, spatial planning offices, and spatial planning policy makers. Land use management in densely populated areas involves negotiation of conflicting land-use demands for settlement, production system (including agriculture and forestry) and infrastructure. The users benefit from EO information to manage the use of land and its impacts.","name":"Users in local & regional planning","selfAssesment":"New
"},{"code":"TA11-5","description":"Users in urban development and users involved in the development of rural settlements perform tasks on local and regional scale (to the scale of nations). These users benefit from EO information to manage the use of land & its impacts. Users such as urban planners, architects, spatial planning offices, urban policy makers in public/private sectors in smart cities or generic urban local/regional planning belong to this category. EO/GI becomes a key data and information to support Sustainable Development Goals - SDG 11 Sustainable Cities and Communities in particular to set up at geospatial and temporal basis the evolution of urban environmental and socioeconomical factors for a better distribution and equality of resources, benefits and impacts (environmental urban justice maps)","name":"Users in urban development","selfAssesment":"New
"},{"code":"TA11-6-1","description":"Users in defense, security and military are border control organisations, police and rescue forces, military services, and intelligence services. Use of EO/GI data can be made in the field of detecting and monitoring high risk areas (natural and humanitarian), monitoring border incursions, or monitoring maritime movements.","name":"Users in defense, security & military","selfAssesment":"New
"},{"code":"TA11-6-2","description":"EO/GI users in emergency services are coast guards, ambulance services, fire services, police services, civil protection organisations, and rescue services. They benefit from monitoring, detecting and assessing natural risks/disasters.","name":"Users in emergency & social protection","selfAssesment":"New
"},{"code":"TA11-6-3","description":"The EO/GI users in humanitarian operations correspond to humanitarian aid organisations, humanitarian support organisations and overall humanitarian response such as border control organisations, police and rescue forces, coast guards, civil protection, military services, and intelligence services. They can use EO services to detect and monitor high risk areas produced naturally or by humans, monitor border incursions or maritime movements. They provide support to local populations that have experienced a crisis, e.g. they fled from a conflict or are affected by a natural disaster. The organisations therefore support the population's needs for sustenance. Consequently, any related risks are relevant as well. The users benefit from the EO capability to identify and monitor people in need, i.e. to assess pressures on populations and migration, and to monitor humanitarian movement and camps. They additionally benefit from EO through mapping disaster areas for situation awareness and detecting sensitive risk areas. Some examples of users at European level are DG RELEX, DG ECHO, DG ENV/ MIC. At UN, the users include OCHA, UNHCR, UNDPKO, UNDP, UNOPS, UNITAR, UNICEF, UNESCO, WFP. Further, international users include IFRC, WHO, WB, and donor organizations. At the national level, the users include Civil Protection Agencies, Ministries of Internal Affairs / Civil Protection Department, Development and Aid agencies.","name":"Users in humanitarian operations","selfAssesment":"New
"},{"code":"TA11-6","description":"Users in defence and security work in the field of military, emergency and social protection and define, collect, analyse information to provide intelligence & safety. Some examples are activities under humanitarian response such as border control organisations, police and rescue forces, coast guards, civil protection, military services, and intelligence services which can use EO services to detect and monitor high risk areas produced naturally or by humans, monitor border incursions or maritime movements.","name":"Users in defense & security","selfAssesment":"New
"},{"code":"TA11-7-1","description":"EO/GI users in environmental ecosystems & pollution include scientists, consultants, planners and policy makers with interest in environmental issues.","name":"Users in environmental ecosystems & pollution","selfAssesment":"New
"},{"code":"TA11-7-2","description":"Users in health care health-related services include services on site-specific field conditions as well as import phenological timing events, which helps to make predictions for monitoring air quality, forecasting epidemics and diseases, as well as forecasting sunlight exposure.","name":"Users in health care","selfAssesment":"New
"},{"code":"TA11-7-3","description":"EO/GI users in meteo and climate; use of satellite-based observations in addressing key climate science questions for user-centric climate change risk assessment applications or climate-related issues","name":"Users in meteo & climate","selfAssesment":"New
"},{"code":"TA11-7","description":"Users in the public administrations or private organizations using EO to assist environmental or climate change impact policy making decisions i.e, assisting in developing monitoring to evaluate and deliver policy goals, provide assessment of ecosystems, rapid response to major environmental risk events, or those associated health security & care events. These users are largely related with international treaties and hence a strong international collaboration. EO/GI becomes a key data and information to support Sustainable Development Goals (SDG) in particular in terms of environmental, climate and health towards SDG 11, SDG 13 Climate Action; SDG 14 Life Below Water; or SDG 15 Life on Land.","name":"Users in environmental, climate & health","selfAssesment":"New
"},{"code":"TA11-8-1","description":"EO/GI users of customer solutions; easier for society to use and engage with EO services through mobile devices, social media platforms, apps. Enormous potential to use citizen-driven observations in combination with EO data","name":"Users of consumer solutions","selfAssesment":"New
"},{"code":"TA11-8-2","description":"EO/GI users in leisure; basic public understanding on EO Services","name":"Users in leisure","selfAssesment":"New
"},{"code":"TA11-8-3","description":"The community of users in education includes instructors (1) who are teaching or conducting research in some aspect of GIScience, such as coding, remote sensing, field methods, geodetic control, web mapping, spatial analysis, or related topics, or (2) who are using GIS as a teaching tool in a discipline, such as business, biology, economics, or health sciences. By extension, this community includes students and supportive deans and other educational administrators. The benefits that these users gain from EO information includes a set of best practices vetted by experts in the field that they can use to teach modern GIS workflows more effectively. \r\nThe goals of this user community are focused on a deeper and a broader implementation of geotechnology, methods, and spatial data throughout the educational system—primary, secondary, university, and lifelong learning (libraries, museums, and other informal settings). Deeper implementation implies embracing GIS as a platform, including its field data gathering tools and citizen science workflows, spatial analysis, building web maps and apps, communicating with multimedia maps derived from web GIS, systems configuration work, and the coding that is behind modern GIS infrastructure. Broader implementation implies the use of GIS in a multitude of disciplines at all levels of education, formal and informal; occurring wherever changes over space and time are being examined. \r\nAt all levels of education the challenge of sufficient bandwidth and the use of a professional systems-based tool such as GIS, along with devices capable of running web GIS tools, are barriers in many areas throughout the world. However, educational and societal forces represent a stronger challenge than technological ones. These educational and societal challenges that this user community faces include the lack of educational content standards at the primary and secondary level that support the use of geotechnologies in education, and at the university level, a lack of awareness of and access to modern SaaS GIS tools and open data portals. \r\nThe risks that the community faces in not facing the challenge of the use of GIS in the education sector is a lack of geographic and spatial literacy among students and faculty. This will translate to research that does not consider spatiotemporal implications of 21st Century challenges, a workforce ill-equipped to deal with them, and consequently an increasingly unstable and dysfunctional world. To build a workforce that can meet global challenges in energy, biodiversity, climate, natural resources, natural hazards, human health, economic inequality, and others, a deep and wide implementation of GIS technology and methods must take place throughout the educational system. The actions that society can take to face that challenge is to provide professional development opportunities for faculty, curricular resources, assessment instruments, relevant spatial data and open data portals, examples of best practices, and a network for educators and researchers in which to interact. EO can provide all of these elements in partnership with educational institutions, government, nonprofits, and industry to meet this challenge. In so doing, an increasingly sustainable, healthier, resilient world can be achieved from the community to the global level.","name":"Users in education, training & research","selfAssesment":"Completed
"},{"code":"TA11-8","description":"Citizens and society in general use and engage with EO services through mobile devices, social media platforms, apps. We do also categorize in this section the users in education, research and training providing knowledge and learning outcomes.\r\nActive and engaged citizens are one of the main driving forces of EO/GI. Nowadays, there is a growing amount of location-based contents generated by connected “produsers”, mainly equipped with smartphones. The exponential growth of ambient geographic information through social networks became the basic feature of a spatially enabled society, in which it behaves as a vessel where millions of people share their current thoughts, observations and opinions, showing to provide more reliable and trustworthy information than traditional methods like questionnaires and other sources.\r\nA spatially enabled citizen is explained through his ability to express, formalize, equip (technologically and cognitively), and (un)consciously activate an efficiently use of his spatial skills. Harvesting this ambient geospatial information provides a unique opportunity to gain valuable insight on information flow and social networking within a society, support a greater mapping, understand the human landscape and its evolution over time. With these insights, city planners can make use of the gathered affective data to detect positive or negative trends developing in the city, managing to take early countermeasures.\r\nNevertheless, assembling and analyzing EO/GI provide us with unparalleled insight on a broad variety of cultural, societal, and human factors, particularly as they relate to human and social dynamics, for example: 1) mapping the manner in which ideas and information propagate in a society, information that can be used to identify appropriate strategies for information dissemination during a crisis situation. 2) Mapping people’s opinions and reaction on specific topics and current events, thus improving our ability to collect precise cultural, political, economic and health data, and to do so at near real-time rates. 3) Identifying emerging socio-cultural hotspots.","name":"Users among citizens & society","selfAssesment":"New
"},{"code":"TA11","description":"The EO/GI user community pools sub-communities (stakeholders) that share common needs for EO/GI information. From an economic perspective, market sectors represent user communities. Users of a community have a common interest in specific aspects of societal or economical benefits to be realized by the implementation of EO services. A user-led community is active at specific locations/regions or in specific environments on the Earth. Their activities are associated with particular features and objects of the environment and related processes that can be detected and monitored with EO satellites. EO information therefore is relevant to the user community's management of their assets, the risks to their assets, and the impact that their activities may have on other aspects of the environment. User objectives (use cases) with EO information include: Enforce regulations; Develop strategies and policies; Manage assets; Plan and design project implementations; Analyse and understand impact / consequences.\r\nUser communities can profit from EO services and applications in the field of managed living resources, energy and mineral resources, infrastructure and transport, financial and digital services, urban development, defense and security, environmental, climate and health, or citizens and society. EO/GI becomes a key data and information to support Sustainable Development Goals -SDG in particular in terms of users in managed livimgs resources towards SDG 2 Zero Hunger; SDG 8 Decent Work and Economic Growth; SDG 9 Industry, Innovation and Infrastructure; SDG 14 Life Below Water; or SDG 15 Life on Land","name":"User community of EO services and applications","selfAssesment":"Completed
"},{"code":"TA12-1","description":"Climate change observations show the warming of the climate system. The changes since the 1950s are unprecedented over decades to millennia.The atmosphere and ocean have warmed, the amounts of snow and ice have diminished, and sea level has risen. The anthropogenic emissions of greenhouse gases are the highest in history. Recent climate changes have had widespread impacts on human and natural systems. There is an urgant need for climate action through mitigation and adaptation. Mitigation actions prevent or reduce the emission of greenhuse gases into the atmoshpere with the objective to make the impacts of climate change less severe. Adapting to climate change increases our resilience to impacts like extreme weather events (e.g. hazards like floods and droughts) that get more frequent and intense in many regions. Current climate change will get worse in the future even if the reduction of emissions is effective with negative effects on ecosystems, economy, human health and well-being. There is extensive need for actions to adapt to the impacts of climate change.","name":"EO for climate change mitigation & adaptation","selfAssesment":"New
"},{"code":"TA12-10","description":"\"Sustainable urban development is a goal of the global society. It summarizes a specific set of problems that cities face all over the world. Cities want to provide a high quality of life to their residents. However, this goal is threatened by urban growth at the cost of urban green infrastructure’s accessibility by citizens etc. Communities that address this: C40 (association of the largest cities of the globe), CitiesIPCC, related SDGs of the UN, etc. Skills: Explain how the monitoring of urban areas contributes to sustainable urban development through its capability to provide regularly updated information about the benefit of urban green infrastructures and their ecosystem services to the quality of life in a city\r\n\"","name":"EO for sustainable urban development","selfAssesment":"New
"},{"code":"TA12-2","description":"Biodiversity describes the variety of ecosystems (natural capital), species and genes in the world or in a particular habitat. Ecosystem services sustain our economies and societies and are essential to human wellbeing.","name":"EO for biodiversity & ecosystems","selfAssesment":"New
"},{"code":"TA12-3","description":"Worldwide countries follow a digital agenda for the economy and initiatives to foster new skills among the workforce to cope with transformation processes with massive impact on the labour market.","name":"EO for digital agenda & new skills","selfAssesment":"New
"},{"code":"TA12-4","description":"Energy transition is a thematic area whose EO experts are proficient in relevant EO data and its processing methods and infrastructure to derive information for energy transition [and its regulatory context, etc.]. The expertise of each expert may be very specialized. In sum, the experts have: The relevant domain knowledge (knowledge about type of monitored entities and their properties, e.g. reflectance properties of sea ice and related EO sensors for detecting them), and The relevant workflow knowledge and processing skills for extracting and providing targeted information for energy transition. [may share strategic objectives… such as „gaining thorough understanding of Energy transition“, „foster usage of EO information for energy transition“]","name":"EO for energy transition","selfAssesment":"New
"},{"code":"TA12-5","description":"Agricultural activity is sustained by good environmental conditions that allow farmers to harness natural resources, create their produce and earn a living. This fosters a sustainable rural economy while food produced by agriculture sustains society as a whole.","name":"EO for sustainable agriculture & food production","selfAssesment":"New
"},{"code":"TA12-6","description":"This societal challenge aims to provide efficient, safe and environmentally friendly mobility solutions.","name":"EO for infrastructure & transport","selfAssesment":"New
"},{"code":"TA12-7","description":"In recent decades, society has fought communicable diseases with success through treatment and prevention. The Covid-19 pandemic shows that communicable diseases are still a threat to the health of citizens. Spread can gappen very quickly from one country to another. Challenges lie in the (re-)emergence of infectious diseases, antimicobial resistance and vaccine hesitancy. Policies of states focus on surveillance, rapid detection and rapid response.","name":"EO for health surveillance","selfAssesment":"New
"},{"code":"TA12-8","description":"There is a rising geostrategic competition and power pilitics challenging rule-based multilateralism. Further, there are armed confilct, civil wars and instability in the EU's broader neighbourhood. \r\nFurther, natural disasters pose a threat to society, where the Sendai Framework of disaster risk reduction focuses on.","name":"EO for emergency, security & defense","selfAssesment":"New
"},{"code":"TA12-9","description":"Water is an essential resource for food production. Growing crops requires significant quantities of water. Without sufficient, good quality and easily accessible water, agri-food production is under threat.","name":"EO for water sustainability","selfAssesment":"New
"},{"code":"TA12","description":"EO provides timely, continuous and independent data for monitoring indicators of the progress of the society in various societal challenges.\r\nEO monitoring supports activities that address societal & environmental challenges. This happens indirectly along a chain: e.g. a regularly provided EO information product derived from EO data of a satellite is integrated as a parameter in a climate model / Earth system model. This climate model enables the development of regulations (and their enforcement through constant monitoring) to implement climate change mitigation measures. Thereby, the chain is characterized by seveal connected nodes: from societal challenges to use cases of users to EO applications to EO products to specific satellites and their sensors.\r\n[Communities that promote collaboration among diverse stakeholders from academia, industry, public administration as well as local residents] \r\nScientific agendas address societal challenges and the EO/GI community can contribute to them. Consortia usually include experts from academia (researchers, developers, scientists), EO companies, and members from the user community such as public authorities.","name":"EO for societal and environmental challenges","selfAssesment":"New
"},{"code":"TA13-1-1","description":"Monitor the atmosphere includes monitoring of the atmosphere composition and air quality, as well as forecasting of sunlight exposure. Timely, continuous, and independent data on the atmosphere is useful in various domains like health, agriculture, renewable energies, urban planning, climate sciences and biology.\r\nThe atmosphere composition includes greenhouse gases (GHG) like carbon dioxide, methane, NO2 and SO2. They are part of the Earth system and have a strong impact on the climate. To monitor changes in atmosphere composition enables modelling climate change and understanding the impact of human-induced emissions of GHG relative to natural sources. EO-derived products include inventory of emission data as an input to atmospheric chemistry transport models and forecast models. Inventories are based on a combination of existing data sets and new information, describing emissions from fossil fuel use, ships, volcanoes, and vegetation. This ensures good consistency between the emissions of greenhouse gases, reactive gases, and aerosol particles and their precursors.\r\nAir quality describes the composition of the atmosphere from gases and particles near the Earth's surface. Local emissions from different sources (e.g. energy production, industrial production, traffic) cause changes to the atmospheric composition that are highly variable in space and time. The quality of the air we breathe can significantly impact our health and the environment. Therefore, it is highly relevant to monitor air quality and emissions. EO satellites are capable of monitoring aerosols, tropospheric O3, tropospheric NO2, CO, HCHO, SO2, and particulate matter (of the sizes PM 2.5 and PM 10). Products like air quality assessment reports, daily ozone forecasts, and UV-index forecast maps are produced that are applied in specific use cases, particularly related to health.\r\nThe amount of solar radiation that arrives at a location on the Earth surface depends on the atmosphere composition and varies over the day and the seasons. Information on solar radiation is useful in various domains. Applications of sunlight and ozone data are for example real-time UV radiation forecasting and risk assessment, skin health services, climate change studies, assessment of ozone protection policies effectiveness, plant growth and disease control, evaporation and irrigation models, power generation, solar heating systems planning and monitoring.","name":"Monitor the atmosphere","selfAssesment":"Completed
"},{"code":"TA13-1-2","description":"Monitoring the climate includes monitoring climate forcing and the carbon balance and assessing climate change risks.\r\nClimate forcing describes the imbalance of the Earth’s energy budget due to natural or human-induced sources. This imbalance results in a change in the globally-averaged temperature. Amongst the contributors of positive climate forcing, that leads to an increase in the globally-averaged temperature, the increase of carbon dioxide in the atmospheric composition is considered to be the most important factor. Changes in the carbon dioxide concentration indicate that the exchanges between carbon sources and sinks are not balanced. It can be shown that human-induced emissions of carbon dioxide are responsible for the increase of the carbon dioxide since the industrialisation.\r\nWith EO, we can monitor changes in greenhouse gases (GHG), aeorosols, albedo, and solar radiation. The dynamic nature of the climate makes it necessary to apply equally dynamic EO monitoring that allows to deliver key information on historical, seasonal forecast and projection periods for climate-related indicators.\r\nRelevant EO products include estimates of the climate forcing of aerosol, ozone and greenhouse gases. The dynamic nature of the climate makes it necessary to apply equally dynamic EO monitoring that allows to deliver key information on historical, seasonal forecast and projection periods for climate-related indicators. \r\nThe products are particularly relevant to the European energy sector in terms of electricity demand and the production of power from wind, solar and hydro sources. \r\nMoreover, water management uses EO-derived information about climate change to mitigate effects of changing precipitation patterns to adapt their strategies, and to prepare for climate variability and change in the water sector, e.g. because of changes in river discharge, droughts and floods.\r\nFinally, insurance uses climate change information for assessing the weather risks to insured assets that change with the climate-related increase in extreme weather conditions. This includes products like up-to-date catalogue of wind storms and their associated impacts on the ground.","name":"Monitor the climate","selfAssesment":"Completed
"},{"code":"TA13-1-3","description":"The weather is the state of the atmosphere measurable by its temperature, humidity, precipitation, and other atmospheric variables. To forecast the weather is a major branch in the field of meteorology. In comparison to climate, weather can only be predicted for a short period of time (minutes to month), because it describes the state of the atmosphere for specific days at specific locations. For a reliable weather forecast, a good numerical prediction model with precise initial conditions is needed. Models are sensitive to changes in the initial condition, that is why at the moment weather predictions are only accurate for few days. However, both models and the determination of initial conditions are steadily improved. EO makes a significant contribution to improving the initial conditions by providing global information several times a day. As the quality of the EO products improves, the weather forecast also improves. \r\nSince decades, satellites are used to monitor and forecast weather. Therefore, it is one of the most established sectors of satellite data applications. There are geostationary and polar-orbiting weather satellites that measure all kinds of meteorologically relevant variables, e.g. cloud coverage, wind speed [...] via passive or active imagery. However, not only satellites are used to collect information, but also other remote sensing techniques that can be airborne or ground-based such as Lidar.\r\nWeather forecasts are used by citizens for decisions in everyday life, in agriculture for crop cultivation decisions and in the stock markets. Other domains of applications are hydrometeorology, aviation, maritime navigation, and the military and nuclear sectors.","name":"Forecast the weather","selfAssesment":"Completed
"},{"code":"TA13-1","description":"Monitor the atmosphere and climate includes all change-focused services/applications which assess, monitor, forecast and provide timely, continuous and independent data (e.g. temperature, humidity, emissions, greenhouse gases, solar UV radiation, aorosols,...). It closely monitors each of the Earth's different subsystems and, besides being the basis for weather forecasts, helps to better understand and evaluate the impact of the climate change.","name":"Monitor the atmosphere and climate","selfAssesment":"New
"},{"code":"TA13-2-1","description":"Monitor critical information about offensive and defensive systems. This deserves a category in its own right since the nature of observations is quite different from many others.","name":"Monitor critical assets","selfAssesment":"New
"},{"code":"TA13-2-2","description":"Monitoring health can be delivered indirectly by monitoring environmental changes that can cause endemic and chronic diseases. Typically monitored environmental factors are temperature, humidity, stagnant water, NDVI, land cover, or soil type.","name":"Monitor health","selfAssesment":"New
"},{"code":"TA13-2-3","description":"Monitoring food security includes the monitoring of food availability by environmental conditions (land cover, NDVI,...), as well as the monitoring of migration patterns. Risks that can lead to food insecurity are hazards or conflicts.","name":"Food security monitoring","selfAssesment":"New
"},{"code":"TA13-2-4","description":"Monitoring borders includes monitoring the land and marine border incursions, monitoring transport routes, assessing pressures on poplulations, and monitoring humanitarian movement.","name":"Monitor borders","selfAssesment":"New
"},{"code":"TA13-2","description":"Monitor security and safety describes the collection and analysis of information to provide intelligence services & safety. The task is to give early warnings in case of emergencies, to monitor infrasturcture, transport routes (land and water) and borders, to surveil security and sovereignty.","name":"Monitor security & safety","selfAssesment":"New
"},{"code":"TA13-3-1","description":"EO is capable to repeatedly map flood extent directly after flooding, including further aspects (flood plain, extend mapping, frequency, rainfall, flash floods, vulnerability, inundation, risk-based mapping & management; flood spread and depth followed by automated insurance payouts). Modelling (hydrological modelling and monitoring focused on seasonal dynamics of water availability) based on EO data (digital elevation models) supports flood risk assessment.","name":"Map and assess flooding","selfAssesment":"New
"},{"code":"TA13-3-2","description":"For the outbreak of forest fires, satellite remote sensing can be continuously track and monitor, in a timely manner to grasp the development of forest fires. Beyond, weather monitoring enables to forecast weather conditions where fires are likely, allowing authorities to prepare.","name":"Detect and monitor wildfires","selfAssesment":"New
"},{"code":"TA13-3-3","description":"Damages from earthquakes to infrastrcture can be detected directly, e.g. by mapping collapsed buildings in optical data to derive rapid response products. Use of SAR interferograms enables to identify geotectonic shifts. Modelling enables to identify hotspot areas.","name":"Assess damage from earthquakes","selfAssesment":"New
"},{"code":"TA13-3-4","description":"Landslides are a natural hazard posing a threat to human life, property, infrastructure, and natural environment. Every year, slope instabilities have a significant impact on societies and economies. Consequently, landslide documentation is used for risk assessments, policy making and enforcing of construction regulations. Landslide monitoring is used to ensure safety of infrastructure operation. Rapid mapping of landslides and associated damages is done for response actions, e.g. of civil protection organizations. As ground surveys are very costly and time-consuming, satellite remote sensing is increasingly used to assess damage resulting from landslides.\r\nLandslides lead to local terrain changes after a downslope movement of material under the effect of gravity. They vary by type of movement (e.g. falling, toppling, gliding and flowing), by size (from small rocks to entire mountain slopes) and velocity (from a couple of millimetres per year up to free-fall speed). Landslides can be triggered both by natural causes (like earthquakes or heavy rainfall events) and human causes, e.g. mining activities that lead to slope failures. Landslides can initiate other natural hazards, e.g. when a landslide blocks a river a lake can be formed which poses a risk for an outburst flood. \r\nLandslides are diverse in appearance, and therefore are challenging to detect. EO-based assessment methods aim for detecting changes to the land surface and surface displacements. \r\nEO satellites and airborne remote sensing use optical sensors for detecting landslides in post-event images and land cover changes caused by landslides, primarily indicated by the removal of vegetation and the exposure of bare soil, by comparing pre-event and post-event images. Typical resolutions of optical EO data for mapping rapid landslides are between 0.4 m and 30 m, depending on the size of landslides caused by the triggering event. Optical data from unmanned aerial vehicles are used in cases where single landslides or concise regions have to be covered. Additionally, synthetic aperture radar (SAR) sensors allow the detection of subtle changes in ground deformation caused by landslides. Therefore, time-series of radar images are used. Further, airborne laser scanning enables the generation of digital elevation models (DEMs) that allow identification of landslide surface structures and, in case of repeated coverage, detection of elevation changes. DEM generation for analysing landslides is also possible with photogrammetry on stereographic optical data and radargrammetry on SAR images.\r\nThe diversity of appearances of landslides leads to challenges for (semi-)automatic image processing and makes visual interpretation of EO data by a landslide expert a commonly used method for landslide mapping. However, visual interpretation is subjective and experts’ results can be very diverse. Additionally, it is a slow and time-consuming process. Semi-automated classification based on optical and DEM data using object-based image analysis (OBIA) can achieve detailed interpretations of landslides while reducing the analysis time. Interferometic SAR (InSAR) techniques, such as persistant scatterer interferometry (PSI) or Small Baseline Subset (SBAS), are primarily used to identify and monitor slow-moving landslides and for quantifying movement rates. Integrated analysis of optical, DEM and SAR data allow to fully exploit the potential of EO data from different sensors for landslide mapping and assessment.","name":"Forecast and assess landslides","selfAssesment":"Completed
"},{"code":"TA13-3-5","description":"In context of volcanic activities and volcanos, EO methods are capable to provide information about various aspects, including ground motion (seismic), volcanic eruptions (pre-eruptive, sin-eruptive, atmospheric ash, dispersion), Rapid damage estimation (prevention), earthquake damage extent (loss adjuster dispatch). classification of land cover types","name":"Assess and monitor volcanic activities","selfAssesment":"New
"},{"code":"TA13-3-6","description":"Multi-hazard assessment both focuses on regions prone to several geohazards and on the interrelationships between hazards, i.e. what happens if two disasters strike at the same time or what happens when one disaster is causing a cascade of disasters with a strongly amplified impact (e.g. a landslide causing a dammed river causing an outburstflood with a magnitude beyond the design of protective measures; or an earthquake in a coastal region that is followed by a tsunami). EO can provide imformation on the single disasters and, through integration and comprehensive impact assessment, enables multi-hazard assessment.","name":"Multi-hazard assessment","selfAssesment":"New
"},{"code":"TA13-3","description":"Assess disasters and geohazards by EO includes alert & early warning, emergency mapping, and risk & recovery mapping. It relates to observations, controlling, assessments that are linked to natural and human made risks. Typical disasters that can be assessed by EO are in particular floods, droughts, forest fires, landslides, tsunamis, earthquakes, cyclonic storms and volcanic eruptions. Since with EO it is possible to quickly analyse the risk or damage it is used to effectively plan emergency response actions.\r\nThere are several measures to minimize or prevent the damage caused by disasters. Some of them have to be carried out in anticipation of a disaster, others after the occurrence of an event. The different phases that are needed to reduce or avoid the impact and to assure rapid response and recovery are described in the disaster management cycle. Depending on the cycle phase, EO has to meet different requirements. The Mitigation and Preparedness phase are passed through in anticipation of a disaster event. Thus, requirements to EO products may focus on high completeness of mapping or high accuracy of mapping. In contrast, Response and Recovery phase include rapid mapping, thus EO capabilities must meet near real-time delivery requirements. \r\nAs well, the nature of the disaster determines which EO products are used. Optical sensors are used throughout the different types; however, landslides are mostly assessed by radar sensors and thermal sensors are additionally used for forest fires.","name":"Assess disasters & geohazards","selfAssesment":"New
"},{"code":"TA13-4-1","description":"To monitor crops and agriculture with EO-based methods is relevant for various applications, including to assess environmental impact of farming, assess crop damage due to storms, to detect ollegal or undesired crops, to monitor water use on crops and horticulture, and to monitor land degradation neutrality. EO mapping of crops happens on all scales with both optical and SAR sensors. Relevant EO products include degradation, agri-environment, ecosystem, damage estimation, warning-service, food-security, impact, crop health (disease and stress), leaf area index, crop acreage and yield harvest (inventories / statistics), crop types (extent, growth, health, stress), land surface temperature, illicit crops, estimates, cultivation patterns, soil water index, surface soil moisture, run-off, land cover (land cover change), land productivity (net primary productivity, NPP), carbon stocks (soil organic carbon, SOC).","name":"Monitor crops","selfAssesment":"New
"},{"code":"TA13-4-2","description":"Monitor the forest focuses on regular and periodic measurement of certain parameters of forests (physical, chemical, and biological) to determine baselines to detect and observe changes over time. Typical applications include to assess deforestation and forest degradation, assess forest damage due to storms or insects, to monitor forest resources, detect illegal forest activities, assess the environmental impact of forerstry, and to monitor the forest carbon content. Moderate resolution sensors have been used to map forests at large scales. Modern very high resolution optical sensors provide enough spatial and spectral detail to map individual trees. Further sensors for forest monitoring include SAR and LIDAR. Integration of optical sensors, LIDAR and in-situ measurements seems an accurate method to achieve third dimension forest mapping.","name":"Monitor the forest","selfAssesment":"New
"},{"code":"TA13-4-3","description":"EO provides the opportunity to monitor bodies of water, i.e. inland waters, and to assess ground water and run-off. For lakes, this includes products about water quality, pollution, turbidity, suspended sediment concentrations (quantitative, qualitative), waterbody (temperature, extent, volume, quantity), algal blooms, alkaline water, evaporation, surface temperature. For ground water and run-off, the products focus on water run-off (water quantity), hydrological network and catchment areas (water catchment), run-off season, groundwater. Various scales are addressed, from local catchments to the global water cycle. For inland water quality, sensors are optical medium resolution (300 meters) for achieving a (strongly cloud-cover dependent) update frequency of 10-20 times per year and high resolution (5 meters) for update frequency of 3-5 times per year.","name":"Monitor bodies of water","selfAssesment":"New
"},{"code":"TA13-4-4","description":"Monitoring of snow and ice focuses on glaciers and their retreat due to climate change (extent, mass balance), the seasonal snow cover (its extent, depth, temperature and snow water equivalent), and the ice on rivers and lakes (inland ice, thickness, freezing period, melting period, ice extent). Glacial monitoring in the mountainous regions around the globe, and of the Greenland and Antarctic ice shields uses optical EO data of high and very high resolution and SAR data. Satellite based daily snow covered area products can reliably be provided down to a spatial resolution of 500 meters. Global products are possible with weekly updates. Applications include, among others, climate change impact monitoring, relevant for modelling runoff patterns in catchments for etimating hydroelectric power generation potential.","name":"Monitor snow and ice","selfAssesment":"New
"},{"code":"TA13-4-5","description":"EO is used to monitor land ecosystems and biodiversity, environmental impact of human activities, land pollution and vegetation encroachment. A tool for this is land cover mapping and mapping of land cover change about a wide set of categories, lincuding basic forest types, major agricultural surface types, conservation areas, settlements, infrastructure, primary roads, bare soil, water bodies, rivers, wetlands following standard classification schemes according to CORINE or FAO LCCS. Main source are optical EO data and associated pixel-based and object-based image classification methods. For discriminating vegetation classes, they often making use of various vegetation indices and biophysical parameters.","name":"Monitor land ecosystems","selfAssesment":"New
"},{"code":"TA13-4-6","description":"EO technologies (both optical and SAR) are capable to categorize bio-physical coverage of land to produce land cover maps like CORINE Land Cover (CLC). The EO method is objective and allows for frequent updates. EO-derived land cover is an excellent basis for mapping land use, the socioeconomic use that is made of land. Land use products are used in a wide range of applications (e.g. agriculture, forestry, spatial planning, determining and implementing environmental policy, land accounting). In a humanitarian context, land use mapping is applied to map refugee camps, population and pressures on population that cause migration.","name":"Monitor land use","selfAssesment":"New
"},{"code":"TA13-4-7","description":"EO is capable to monitor topography with various types of land surface elevation data (both digital terrain models and digital surface models) and also focus on land surface changes and ground deformation / movement due to e.g. soil erosion or permafrost thawing, frost heaving. This includes also the mapping of stable zones where such changes do not happen. The main ways of creating a digital elevation model (DEM) from EO data are deriving it from interferometric synthetic aperture radar (InSAR), from stereoscopic pairs of optical images acquired from different viewing angles, and deriving them via laser scanning.","name":"Monitor topography","selfAssesment":"New
"},{"code":"TA13-4-8","description":"EO is able to extract information about subsurface geology, including near surface features, lithology features, and linear disturbance features (faults & discontinuities). Concerning monitoring of mineral extraction EO supports by mapping ground surface, illegal activities, mine waste (erosion, land subsistence, biodiversity/habitat loss, destruction & disturbance of ecosystems). Disturbance of ecosystems may happen by carbon seeps from reservoirs or pipelines. Their detection can also be done with EO data.","name":"Extract information about subsurface geology","selfAssesment":"New
"},{"code":"TA13-4","description":"Services that monitor land cover all services/applications that are focused on monitoring, assessing, managing, planning and improving land areas, its ecosystems (land, soil and inland water monitoring/quality/availability & usage assessments) and evolution of the land surface (use, cover, seasonal and annual changes and monitors variables) even if it involves human intervention (environmental challenges, impact evaluation or suitability analysis).\r\nMonitoring is possible by deriving information from variables measured by EO in different domains, like vegetation, energy, water, and cryosphere. For vegetation, those variables are for example land cover, NDVI, burnt area, or surface soil moisture. In the energy domain, land surface temperature and surface albedo are known variables, for water it is water surface temperature or water quality. Finally, for the cryosphere lake ice and snow cover extent, and snow water equivalent are variables that are used for land monitoring services.","name":"Monitor land","selfAssesment":"Completed
"},{"code":"TA13-5-1","description":"The full range of EO satellite sensors are capable of monitoring particular aspects of urban areas. The most relevant include SAR satellites such as TerraSAR-X that distinguish between urban fabric and other land cover. Further, optical satellites in the resolution range HR and VHR are used to map imperviousness and soil sealing. Beyond such land cover classifications with low granularity, HR and VHR data are used for producing detailed land use and land cover classifications that distinguish different settlement densities or, in combination with additional data, different land use such as transport, residential etc. as defined in Classification schemes specialized on urban areas. Airborne laser scanning (and stereographic analysis) maps building and vegetation heights. InSAR methods allow to measure land subsidence that is highly relevant e.g. in coastal cities close to or below the sea surface elevation. Night-time optical data maps lights. Thermal sensors allow mapping the heat that is radiated from cities. Typical applications include monitoring urban growth/sprawl, transport networks, urban heat islands, and generating city maps and 3D city models for urban planning that are relevant to users in smart cities and in local/regional planning.","name":"Monitor urban areas","selfAssesment":"Completed
"},{"code":"TA13-5-2","description":"EO is capable of monitoring infrastrcture in general, i.e. buildings (and their construction) and transport networks (roads, rails). Additionally, infrastructure for renewable energy harvesting (solar and wind farms, hydroelectric powerplants) and identification of suitable sites (through mapping solar radiation, wind roses, speed and direction, hydrological network mapping). A basis is land surface mapping for deriving digital elevation models (DEMs) that is required for modelling renewable energy potential and for spatial planning and landscape visibility analysis (visual impact assessments for planned infrastructure). Further, EO is capable of assessing damage from industrial accidents. A wide range of EO technologies is used here, infrastrcture can be directly detected and mapped with optical and SAR sensors, where the resolution depends on the targeted assets. DEMs can be generated from SAR and stereographic optical data. Wind energy related parameters can be derived from satellites focused on atmosphere and weather monitoring. Further, there are various GI methods in use, too (in particular focused on spatial planning and impact assessment).","name":"Monitor infrastructure","selfAssesment":"New
"},{"code":"TA13-5","description":"Monitoring the built environment provides information about urban structures, transport networks and particular infrastructure, e.g. dedicated to energy provision. It covers all urban and infrastructure related service/applications on site development information, planning support or suitability analysis. As well, it includes pressure and threats analysis on the urban areas.","name":"Monitor the built environment","selfAssesment":"New
"},{"code":"TA13-6-1","description":"Oceanic waters cover approximately 70% of the Earth´s surface and play a key role in regulating Earth temperature and climate, support important marine ecosystems and provide food and transport. Ocean waters occupy large areas and involve highly dynamic processes with different temporal and spatial scales. In-situ measurements taken by ships and buoys can provide accurate information but only at specific locations, being limited to understand large-scale processes. To characterise the heterogeneity and dynamics of ocean waters, it would be required to perform exhaustive field campaigns with associated high costs and infrastructure challenges. EO is an efficient tool to monitor ocean waters and to complement ocean in-situ monitoring programmes as it can provide cost-effective information over vast areas at continuous temporal and spatial scales. \r\nSince the first EO satellite specifically designed to study the oceans (SeaSat) has been launch in the 1970s, many sensors and platforms have been developed. This variety of sensors have provided measurements of a broad range of ocean physical and biological variables to the present day. For example, satellite observations in the visible and near-infrared bands have provided information about ocean colour that can be used to estimate chlorophyll-a concentration for monitoring water quality, productivity and algal blooms. Thermal infrared (TIR) sensors have provided data of Sea Surface Temperature (SST) that is of importance for the study of currents and ocean warming. Microwave radiometers have registered sea surface salinity (SSS), critical to determine the global water balance, understanding ocean currents and estimating evaporation rates. EO can also provide information about physical ocean features such as surface elevation and ocean currents, sea surface winds, ocean waves, vessels and pollutants such as oil spills. \r\nThe versatility of EO data have been proved in a broad range of applications, including the monitoring of water quality, climate change effects, hurricane tracking and prediction, monitor maritime traffic and pollution, harmful algal blooms and fisheries management. In recent years, the Copernicus programme has launched a series of satellite missions for water and land monitoring that guarantee the provision of long-term observations giving continuity to previous satellite missions. Within the Copernicus programme, especially the Sentinel-3 mission will have relevance for ocean observations. Currently, two satellites Sentinel-3A and Sentinel-3B, launched respectively in 2016 and 2018, are providing near-real-time data on the state of the ocean surface, including sea surface temperature, marine ecosystems, water quality and pollution monitoring. New hyperspectral missions such as the Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) developed by NASA, are currently under development. In the near future, they will complement the existing satellite missions and will register data in a high number of spectral bands. This information will be essential in diverse applications such as aquatic ecology and biochemistry. Ocean EO is still an evolving field that will need skilled professionals that exploit the data from the new and upcoming missions for the advancement of ocean knowledge and monitoring.","name":"Monitor the marine ecosystem","selfAssesment":"Complete
"},{"code":"TA13-6-2","description":"In coastal areas, EO is capable to monitor water depth and shallow water bathymetry (charting), coastal ecosystem parameters about water temperature, water transparency, oxygen, phytoplankton abundance, bathing water indicators, detection harmful algal blooms, sediment (qualitative, quantitative), turbidity (quality, quantitative), visibility, chlorophyll-a concentration, suspended sediment may be indicative of estuarine processes, re-suspension or pollution. Further, this includes coastline monitoring with a focus on shoreline and its change as well as coastal land cover (and terrain) and its change. A widse set of EO sensors and technologies is used to monitor coastal areas. Optical satellite imagery is analyzed to detect and map suspended sediment concentrations. Etc.","name":"Monitor coastal areas","selfAssesment":"New
"},{"code":"TA13-6-3","description":"EO is capable to monitor weather impact on ocean surface and metocean features as a basis for forecasting furture ocean conditions. This includes ocean surface topography, ocean dynamics and circulation like tides and ocean current movements and drift, ocean winds, wave and climate conditions at ocean locations (meteocean). Further, this covers the mapping of extreme waves like tsunamis and the monitoring of hurricanes and typhoons. Involved EO technologies are for example satellite altimetry that maps ocean surface with 2 cm to 3 cm accuracy, mathematical forecast models. Repeated altimetry measurements allow mapping speed and direction of ocean's currents and tides. Available EO-based RADAR systems monitor wave height and direction, wind speed and sea-surface elevation. Near-realtime processing and delivery workflows enable the use of these parameters in weather forecasting, navigation and offshore installations protection.","name":"Monitor weather impact on ocean surface","selfAssesment":"New
"},{"code":"TA13-6-4","description":"To support an ecosystem-based approach for fisheries management, EO images with global and daily systematic coverage with high-resolution images can help in identifying potential fishing zones and to assess fish stocks. They help assessing and understanding changing abundancy and spatial distribution of exploited fish stocks. Therefore, they analyse various key environmental parameters that can be detected with satellite remote sensing. This includes sea surface temperatures (SSTs), sea surface height anomalies, and sea surface colour revealing the abundance of chlorophyll a. This relates to phytoplacton production that is directly related to total fish landings. Additionally, EO can detect harmful algal bloom. A further threat to sustainable fish stocks management are illegal fishing. Where localization of licensed fishing vessels and fleet management services are supported by EO to avoid overexplotation and enable recovery of fish stocks. EO complements identification, detection and tracking of vessels with SAR and optical remote sensing.","name":"Monitor fisheries","selfAssesment":"New
"},{"code":"TA13-6-5","description":"For shipping, navigation, and monitoring sea-traffic and pollution, remote sensing and satellite technologies allow detecting vessels in the wider ocean. EO can detect the vessels themselves, their wake trailing behind them, sandbanks and reefs that pose a threat for safe navigation. Additionally, EO can detect pollution from the ships, e.g. when illegal waste disposal happens. Ship detection and classification is possible with the use of optical and synthetic aperture radar (SAR) imagery. The methods complement each other.","name":"Detect and monitor ships","selfAssesment":"New
"},{"code":"TA13-6-6","description":"Information on sea ice and icebergs is important for managing operation of ships or offshore platforms in hazardous sea ice conditions. EO technologies give the possibility to study sea ice and measure its thickness, spatial distribution, motion and ridges (as well as ice berg positions). Satellite imagery provides wide area, synoptic pictures of the ice conditions. Since the scale of ice fields is quite large, mainly moderate resolutions have to be accepted, down to around 10m in scale, while ensuring comprehensive coverage. Multispectral imagery can provide more information on ice-type but in the main, SAR imagery is used due to its all-weather and day/night capability. The data collected can be more accurate than in-situ measurements due to a higher and faster coverage of a whole area. Subsequent modelling that incorporates ocean weather (wind, waves, ocean current) provides expected drifting paths. Constant monitoring is most important to identify the risk and opportunities, for instance for ship routing, and safety of oil rigs.","name":"Monitor sea-ice and icebergs","selfAssesment":"New
"},{"code":"TA13-6","description":"Monitoring marine inlucdes monitoring of marine safety (e.g. marine operations, oil spill combat, ship routing, defence, search & rescue, ...), marine resources (e.g. fish stock management, ...), marine and coastal environment (e.g. water quality, pollution, coastal activities, ...), and climate and seasonal forecasting (e.g. ice survey, seasonal forecasting, ...).","name":"Monitor marine","selfAssesment":"New
"},{"code":"TA13","description":"EO services and applications are organized according to thematic areas. EO is used for a wide set of services. There are many applications of EO that show how a service produces information for a particular client. EO service and applications are best described by the purpose they serve or by the need of the user. The main user needs to EO are to monitor, to map, to forecast, to assess, to detect, and to analyse. \r\nTo monitor means to watch and check a situation carefully for a period of time in order to discover something about it, i.e. keeping track of how the natural and manmade environment change (their status) over time. Typical alternative verbs are track, observe, record, follow, understand, or surveil. \r\nTo map means to represent an area of land in the form of a map, i.e. to feature and locate the way it is arranged or organized. Synonymous verbs are locate, identify, classify, trace, or record.\r\nTo forecast means to provide statements covering a range of different outcomes, to say what you expect to happen in the future; i.e. to predict future events based on specified assumptions (about information extracted from EO change and time series data), where different sets of assumptions describe scenarios. Equivalent terms are predict, plan, model, estimate, or project.\r\nTo assess means to judge or decide the amount, value, quality or importance of something, i.e. to evaluate and measure the status of and changes in natural and manmade built environments. Alternative verbs are evaluate, measure, understand, review, or quantify.\r\nTo detect allows to notice something that is partly hidden or not clear, or to discover something, especially using a special method, i.e. to identify and locate the changes in the Earth’s environment. Similar terms are locate, warn, identify, highlight, or spot.\r\nTo analyse means to study or examine something in detail, in order to discover more about it, i.e. to detail the elements of a whole and critically examine and relate these component parts separately and/or in relation to the whole. Sometimes, the terms to process, to parse, or to detail are used in exchange for to analyse.","name":"EO services and applications","selfAssesment":"New
"},{"code":"TA14-1-1-1","description":"Ocean colour can be made visible in atmospherically corrected EO data. Specific spectral bands are necessary to derive physical and biologic parameters of the water from the EO data.","name":"Ocean colour","selfAssesment":"New
"},{"code":"TA14-1-1","description":"Band combinations are pre-defined for (visually) analysing images for a dedicated purpose. Examples are dedicated band combinations for land us land cover classification, ocean colour, etc.","name":"Band combinations","selfAssesment":"New
"},{"code":"TA14-1-2","description":"The spectral and refractive information from optical and SAR data enables direct and indirect derivation of biophysical and geophysical EO parameters that are properties of the sensed land surface, ocean surface and atmosphere volume.","name":"EO parameters","selfAssesment":"New
"},{"code":"TA14-1","description":"Processing products are image products from raw data to all different processing stages. The transformation processes between the stages include operations such as atmospheric correction, cloud detection and radiometric calibration to provide data in a form suitable for subsequent analysis. Processing products consider a product as being an output of a process.They appear as \"intermediate products\" along all steps of the processing chain.","name":"Processing-related and preparatory products","selfAssesment":"New
"},{"code":"TA14-2-1-1","description":"Point clouds represent a set of points with X, Y, Z coordinates and associated attributes. A source of acquisition is Light Detection and Ranging (LIDAR), an airborne surveying technique that uses laser light to measure the distance to an object on the ground.","name":"Point clouds","selfAssesment":"New
"},{"code":"TA14-2-1-2","description":"Elevation data in the form of a digital elevation model (DEM) is an essential component of many analyses derived from EO. DEMs are used to represent every kind of surface, including terrain surface, vegetation canopy surface, sea surface, sea-ice surface, glacier surface etc. This description focuses on DEMs for representing terrain. A digital terrain model (DTM) describes the bare ground of the terrain, a digital surface models (DSM) described heights of vegetation (e.g. trees) and of man-made structures (e.g. buildings) reaching above the terrain. DEM is often used as an umbrella term for DTM and DSM. EO-derived DEMs are usually DSMs and require removal of vegetation and buildings in order to represent the terrain (DTM). DEMs are multi-purpose products used in various applications. They are available for global scale (SRTM, WorldDEMTM), regional scale (ArcticDEM, Copernicus EU-DEM v1.1) or for national levels and local regions. Various techniques exist to generate DEMs from SAR data, stereographic optical EO (as well as airborne and drone) data and from airborne laser scanning.","name":"Digital elevation models","selfAssesment":"Completed
"},{"code":"TA14-2-1-3","description":"By comparing elevation models of different dates, the change in elevation and volume can be identified. Thereby, they measure surface deformation, land subsidence, ice shield loss due to melting, etc.","name":"Elevation change maps","selfAssesment":"New
"},{"code":"TA14-2-1-4","description":"Vector fields capture the movement directions of locations on a continuous surface, e.g. of the ocean, or in a 3D grid of locations, e.g. of the atmosphere. The atmosphere and the ocean are highly dynamic features. Vector fields are used to represent wind directions and current movement directions. Further vector fields derived from EO data include geoid undulation / gravity maps.","name":"Vector fields","selfAssesment":"New
"},{"code":"TA14-2-1-5","description":"When a moving feature (i.e. object) is detected in subsequent images, its trajectory of movement can be mapped. Such products map ship movements, sea ice movements, etc.","name":"Feature trajectories","selfAssesment":"New
"},{"code":"TA14-2-1","description":"Geometrically measured EO products origin from EO-derived distance measurements, measurements of direction, tracking of moving objects, and changes of distance measurements. The used EO methods include for example SAR interferometry and stereographic analysis of optical data.","name":"Geometrically measured EO products","selfAssesment":"New
"},{"code":"TA14-2-2-1-1","description":"Land cover maps represent spatial information on different types (classes) of physical coverage of the Earth's surface, e.g. forests, grasslands, croplands, lakes, wetlands. An example is the European Copernicus product CORINE land cover (CLC) with 44 classes. Initiated in 1985 (reference year 1990), updates followed in 2000 and every 6 years afterwards. Apart from CLC, the European Copernicus Land products also include the High Resolution Layers. They includes for example the imperviousness product that captures the percentage of soil sealing. Land cover classification products are multi-purpose products that are relevant for various applications. They are available on national levels, regional levels and global levels. They have different scales and granularity of their associated classification scheme. The products are updated on a regular basis. Update cycles can vary depending on the resolution (i.e. likelihood for observable change of the land surface) and the capability of production processes. An additional example on a global scale is the Global Urban Footprint. The products are provided by public organisations and private EO companies and based on various EO sensors.","name":"Land cover maps","selfAssesment":"Completed
"},{"code":"TA14-2-2-1-2","description":"Land use documents how people are using the land. Getting from physical land type (land cover) to land use requires skill in interpretation and involves integration and consultation of ancillary data. Land use maps are multi-purpose products that are relevant for many applications. The products are updated on a regular basis (e.g. 6 years for Urban Atlas).","name":"Land use maps","selfAssesment":"New
"},{"code":"TA14-2-2-1-3","description":"Cloud masks for optical EO data distingush cloudy pixels from cloud-free pixels. They may differentiate between serveral cloud types, i.e. opaque clouds and Cirrus clouds (that are transparent). Most land monitoring applications based on optical data require cloud-free images. Therefore, cloud masks are a product that is used early on in image processing for selecting suitable imagery for analysis (e.g. by screening images of an archive by the derived cloud cover percentage of the image). Therefore, cloud masks are made available as metadata by the EO data provider. Clouds are identified with threshoulding of reflectance values of the blue band and, to adapt for cloud/snow confusion, specific short-wave infrared (SWIR) bands.","name":"Cloud mask","selfAssesment":"New
"},{"code":"TA14-2-2-1-4","description":"Detected features are objects from one or more classes and are the result of a comprehensive (and mostly automatic or semi-automated) search of all locations in an image that decides whether such features are present and where they are located. Examples inculde man-made objects (e.g. vehicles, ships, buildings, etc.) with sharp boundaries and are independent from the background, and landscape objects, such as land-use/land-cover (LULC) parcels that have vague boundaries and are part of the background environment. Only the latter type would locate features for all locations of an image.","name":"Detected features","selfAssesment":"New
"},{"code":"TA14-2-2-1","description":"Static EO derived thematic classification products and masks (e.g. land use land cover classifications). Additionally, static EO detected features (planes on apron of airports, dwellings) that consist of a set of point locations (or polygons) and do not end up in a comprehensive classification of all pixels of an image. Static EO derived thematic classification products and masks (e.g. land use land cover classifications). Additionally, static EO detected features (planes on apron of airports, dwellings) that consist of a set of point locations (or polygons) and do not end up in a comprehensive classification of all pixels of an image. Thematic classifications and feature detection identify a surface by a class label that represents a more or less persistent state. A good example product is the Copernicus Urban Atlas. The most recent available version is assumed to represent the \"current\" state (Certainly, an update cycle is necessary for providing a product that remains up-to-date).","name":"Thematic classifications and feature detection","selfAssesment":"New
"},{"code":"TA14-2-2-2","description":"Event maps and thematic change (evolution) maps indicate that some process happened that changed the area at a location from one class to the other. For example, a burnt area map indicates locations where vegetation has been burnt by a fire and changed to bare ground. A typical mapping method is the use of pre- and post-event satellite images for detection of the areas affected by the process. Eventually burnt areas contain identifiable burn marks that allow direct identification in one single post-event satellite image. Nevertheless, it is the process that is central to the analysis. Similarly, the concepts aforestation and deforestation would fall under the heading \"Event maps.\" They may come from a comparison of two status maps of different dates. Some processes benefit from analysis of more than two states. Such change evolution maps can be produced with time-series analysis. On land, more examples include landslide maps, flooded area maps and other land surface dynamics (e.g. aforestation and deforestation). Further, change detection maps are available for other domains (atmosphere, marine, land, climate, etc.)","name":"Event maps and thematic change (evolution) maps","selfAssesment":"New
"},{"code":"TA14-2-2","description":"The semantic labelling products result from methods that assign labels to objects or locations in a field. The labels correspond to the categories of a classification or, in case of masks and detected features, to a single target class. Such labels may also identify classes of change or change evolution.","name":"Semantic labelling products","selfAssesment":"New
"},{"code":"TA14-2-3","description":"EO-derived attribute products describe the state and evolution of specific attributes of a feature or at a field location. They describe for example air quality, soil moisture or water quality & quantity.","name":"EO-derived attribute products","selfAssesment":"New
"},{"code":"TA14-2","description":"Descriptive analytics products provide analytical results which describe the present (and past) situation as it is recorded in EO images. Therefore, it contains information that can directly be extracted from EO images or EO image time series. These products are diverse in various aspects: they capture static and dynamic information; they concern information about objects or fields; and they have qualitative (nominal scale) or quantitative (ordinal, interval, ratio scale) levels of measurement.","name":"Descriptive analytics products","selfAssesment":"New
"},{"code":"TA14-3","description":"Providing analytical (modelling) results which predict the future situation (e.g. air pollution forecasts). [interpolation in space, i.e. not only prediction into the future, filling gaps in time series...]\r\nInformation that can be modelled based on descriptive analytics products. by extrapolating time series (forecasting/predicting), by modelling of processes (e.g. flood risk maps, landslide susceptibility)","name":"Predictive modelling products","selfAssesment":"New
"},{"code":"TA14-4","description":"Prescriptive modelling products and services focus on providing analytical results that are a guide to action. The often result from an impact assessment. One example is the identification of construction sites leading to sales opportunities.","name":"Prescriptive modelling products and services","selfAssesment":"New
"},{"code":"TA14-5-1","description":"A textured 3D model uses a 3D model derived from elevation data. Additionally, each separate surface of the 3D model receives its own texture derived from optical image data. Typically used for visualisation purposes.","name":"Textured 3D models","selfAssesment":"New
"},{"code":"TA14-5-2","description":"A semantic 3D model consists of a 3D model derived from elevation data with an integrated image classification. A classified object thereby consists of a 3D surface or a grouped set of 3D surfaces. A typical example is a 3D city model in the CityGML format.","name":"Semantic 3D models","selfAssesment":"New
"},{"code":"TA14-5","description":"Combining the satellite data with other information sources. Resulting in an integration of several descriptive analytics products and processing products, e.g. a textured 3D model or a semantic 3D model.","name":"Aggregation and integration products","selfAssesment":"New
"},{"code":"TA14-6-1","description":"Sentinel-2 cloud-free mosaics for display, satellite maps in books etc.","name":"Satellite maps","selfAssesment":"New
"},{"code":"TA14-6-2","description":"Layouted maps in a file (PDF, SVG, etc.) for printing or visualisation on screen, embedding in reports or as static displays on websites etc.","name":"Layouted digital maps","selfAssesment":"New
"},{"code":"TA14-6-3","description":"Digital layouted maps in an online map viewer; 3D visualisations on the screen / 3D screen and online map viewers with 3D capabilities etc.","name":"Web visualisations in 2D and 3D","selfAssesment":"New
"},{"code":"TA14-6-4","description":"Printed maps, 3D plots of 3D models, hologram 3D maps etc.","name":"Analogue visualisation products","selfAssesment":"New
"},{"code":"TA14-6-5","description":"A video is a structured file of 2D grids link by the time, is a regular file of values which has been processed to sensor units (e.g. calibrated). The result can be a single date acquisition or a combination of dates. For each point, the value represents a parameter imaged by the sensor. Videos of EO data present for example time series of satellite maps and other EO products (e.g. Arctic sea ice evolution in a time-series map video over the past 30 years).","name":"Time series map videos","selfAssesment":"New
"},{"code":"TA14-6","description":"Visualisation products are used for presentation of EO information to the user. The user's interaction with the visualisations is predominantly viewing and interpretation of the informational content and arriving at decisions in the context of the user'S objective with the EO information. In addition, users of visualisation are all involved actors during image processing. For example, an EO analyst may use visualisations of EO data and preliminary EO products for getting a better understanding of the contained information and adapt his processing workflow to arrive ad improved results. Typical visualisation products include satellite maps, layouted digital maps, web visualisations in 2D and 3D, and analogue visualisation products.","name":"EO visualisation products","selfAssesment":"New
"},{"code":"TA14-7","description":"Users need access to EO products if they shall be able to benefit from them. Additionally, providers of value added products act as users of EO products earlier in the information processing value chain. Concequently, various distribution services provide access from raw data to processed information and processing infrastructure. Provision of access to raw data or processed information happens via direct download (FTP), via application programming interfaces (API) or web services (e.g. Hubs). Further, access to processing infractructure happens via web services.","name":"Distribution services","selfAssesment":"New
"},{"code":"TA14","description":"Products in relation to EO appear along the entire image processing value chain as inputs and outputs of processing steps. Ultimately, at the end of that chain, the output EO products represent information that supports actions. The standard EO products are categorized by the type of problems they help to solve or the type of question they help answering.","name":"Standard EO products","selfAssesment":"New
"},{"code":"WB","description":"This knowledge area is about Web Based Geographic Information management aspects and therefore it was given the name \"Web Based GI\" or \"WBG\" in short. It is implied by this name that the differentiating factor for this KA is the \"Web\". One must then be able to answer the questions like \"What functions do we delegate to the Web?\" or \"how WBGI is different from the traditional GI?\" Sticking to the functions of a GIS, which are inserting (adding), storing, manipulating, analysing and presenting the data, there is not a single system for effecting all these tasks anymore but the Web itself. For instance, there is no single database and its known-to-its users-definition, anymore but many different stores and many different definitions. Similarly, many different manipulation, analysis and presentation options compared with the options offered by a single or limited number of systems of traditional GI. In general, Web provides the means of leveraging distributed \"resources\" like data, information, or software. It is a \"collaboration medium\". A collaboration that enables rapid production or decision making. A collaboration that certainly introduces new dimensions to traditional GI handling. This is the justification of proposing this KA in addition to the KAs of the original BoK. For the mentioned collaboration to happen, data or any other type of a resource have to accessible on the Web. This means that it should have a Web \"address\" and a \"definition\" that is understandable either by \"human\" or \"machine\". \"Machine understandable definitions\" refers to the dimension of \"semantics\" and \"ontologies\" which are also included under this KA. When one talks about publishing resources then \"catalogue services\" and more importantly \"discovery\" dimension comes into the scene. On the other hand, \"Linked Data (LOD)\" and \"Open Data\", highly popular recent trends and two of the above mentioned dimensions of Web GI have also been covered under this KA. Like the other dimensions of Web GI, both LD and OD aspects must be known to GI communities with differing degrees of expertise. The concepts of \"interoperability\" and \"Spatial Data Infrastructure (SDI)\", hot topics of GI communities for many years, have been thought to be dealt with under this KA as well with the justification that \"Web GI\" is a much broader concept than SDI, This is by the fact that SDI refers to a much narrower content and context of \"collaboration\" then Web GI. Therefore, Geospatial data interoperability and some of the related concepts which were classified under KA, \"Geospatial data in the original BoK were moved under KA11 with the updated context. Another issue is the coverage of Spatial Analysis (SA), data manipulation aspects of GI by KA11. The SA aspects are covered by other KAs like \"Geocomputation\" and \"Analytical methods\". If the analysis operations, in an undertaking, would be handled by web services this is already covered by \"data processing\" web services, application development unit and Web services composition under that unit. The important thing is to have the knowledge about a specific analysis operation; Employing it as a web service would require no more knowledge than using any other web service. SA is covered by KA11 in as much as it should have been.","name":"Web-based GI","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB1-1","description":"The basic principles on which web services build. The concept of Service Oriented Architecture and the importance of APIs","name":"Fundamentals of web services","selfAssesment":"In progress/to be revised (GI-N2K)
"},{"code":"WB1-2","description":"This concept will cover web services based on the Simple Object Access Protocol (SOAP)","name":"SOAP web services","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB1-3","description":"This concept will cover web services based on the representational state transfer (REST) protocol","name":"REST web services","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB1-4","description":"The Open Geospatial Consortium (OGC) defines standards and best practices for web services in the geospatial domain. OGC standards are developed using a consensus model allowing all stakeholder to participate in the process. As a result the OGC web services are widely implemented.","name":"OGC web services","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB1","description":"In the most simplistic way a Web service may be defined as \"a Web accesable program code which performs a task of either processing or serving some data. Although there are many other definitions in the related literature, the one in W3C (2004) seems to be quite complete and refering to also lately popular REST style Web services. It states that \" We can identify two major classes of Web services: REST-compliant Web services, in which the primary purpose of the service is to manipulate XML representations of Web resources using a uniform set of \"stateless\" operations; and arbitrary Web services, in which the service may expose an arbitrary set of operations.","name":"Web services","selfAssesment":"In progress GI-N2K
"},{"code":"WB2-1","description":"To be able to discover and assess available data or services, these resources have to be documented. This concept describes the standardized languages used for these descriptions","name":"Languages for the definition of non-spatial data and services","selfAssesment":"GI-N2K
"},{"code":"WB2-2","description":"Different standardized ways to define geospatial data exist. GML, GeoJSON, WKT and GeoSPARQL are examples. What are common points and differences","name":"Definition of geospatial data","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB2-3","description":"Defining a common language is a crucial step for sharing or combining data. Vocabularies, taxonomies, ontologies are are tools to reach this goal.","name":"Ontologies development reuse and patterns","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB2","description":"A \"resource\" could be \"anything\" including data and services, identifiable over the Web. A resource should be defined in a language to be discoverable on the Web. Over the years, two major bodies W3C for non-spatial and OGC concerning spatial data have developed many specifications for defining data and services. On the W3C side, Resource Description Framework (RDF) has gained a great momentum in recent years in relation to the recent popularity of Linked Data as well. In the OGC front, the acceptance of GML was a major step concerning the long time effort of geospatial communities for having a standard for the definition of both geospatial features and geometry.","name":"Resource Definition","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB3-1","description":"Metadata is information about the data to be published. It helps the user to discover the data, allows the user to evaluate the fitness for use and it explains how and under which conditions the data can be retrieved and used. Metadata are a core component of data infrastructures and as such, standardization is a requirement for the correct exchange and interpretation of the metadata.","name":"Metadata and standards","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB3-2","description":"A resource can be added manually to a catalogue service by creating or uploading its metadata, but metadata can also be added by automated crawling of other catalogues.","name":"Manual and automated forms of publishing","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB3-3","description":"Catalogue services allow to publish and search resources through their metadata","name":"Catalogue services","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB3-4","description":"Open data is data that is free to use, re-use and share without limitations on who uses it or for what purpose. Publishing open data is making the data discoverable and accessible in a convenient way (technical openness).","name":"Publishing open data","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB3-5","description":"Adding semantic information to the data allows computers to understand the structure and meaning of data. This allows automatic searching, processing and integrating data with other semantic sources.","name":"Publishing via a semantic definition of data","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB3-6","description":"Linked (open) data provides structured data which is interlinked in a machine readable way. This allows to discover, access and combine data in an automatic way. This concept discusses the steps needed to make existing data available in a linked open way.","name":"Publishing linked open data","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB3","description":"\"Publishing\" means making a resource available for the use of others. A \"resource\" could be \"anything\" including data and services, identifiable over the Web. Publishing may be done on the basis of either the \"characteristics\" of the data or the data itself. When only some \"characteristics\" of a resource is published then some of the contents would naturally be left out. The \"characteristics\" include metadata and some keywords. This kind of publishing may be named as \"limited contents\" publishing or \"publishing by metadata\". One of the issues become then what characteristics to use to define the data. Or what what metadata definition to use. Another aspect of publish is \"manual entry\" and \"automated collection\". In the former publisher enters metadata while in the latter some harvesting mechanism collects metadata in an automated fashion. On the contrary, there is \"unlimited contents publishing\" where there is no limitation on the published contents. Open data publishing is in this class. In additon, some \"additional semantics\" may be subject of this type publishing through new relationships in the ontologies of publishing, which have not been explicit in the exisiting data model but are inherent in the data. And this last type is covered under the topic, \"Publishing via a semantic definition of data.\"","name":"Resource Publishing","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB4-1","description":"Syntactic discovery is the discovery of resources based on the structure of the resources","name":"Syntactic discovery","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB4-2","description":"Semantic discovery is the discovery of resources based on the meaning of the data.","name":"Semantic discovery","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB4-3","description":"Linked (open) data provides structured data which is interlinked in a machine readable way. This allows to discover, access and combine data in an automatic way.","name":"Discovery over linked open data","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB4","description":"Resource discovery means the discovery of resources including data and services needed for an application. Syntactic discovery refers to the discovery on the basis of syntactic comparison operations. It is classified as \"keyword-based\" and \"full-text-based\" discovery. Semantic discovery on the other hand, refers to the discovery of resources on he basis of some semantic definition. Therefore, semantic discovery requires that a resource be published by a semantic definition as defined in the topic WB3-5.","name":"Resource Discovery","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB5-1","description":"The workflow to integrate geospatial data in an application often relies on a combination of different OGC web services. Searching and finding the data and the corresponding services, binding to these services to view, filtering and or downloading the data are different steps in this process","name":"Integrating data from OGC web services","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB5-2","description":"The alignment of data structures and vocabularies/ontologies used are important steps towards the data harmonisation needed for a combined use of datasets","name":"Schema matching and ontology alignment","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB5-3","description":"A data mashup is a combination of data from different sources to produce new applications of new datasets","name":"Data mash ups","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB5","description":"The term \"application development\" refers to the collection of activities or the \"workflow\" through which the user reaches her final goal. Being one of these activities, \"data integration\" means the transformation of data from one representation to another which might be of either the client`s one or some other representation. An example for data integration might be the case where the data is transfered from an OGC WFS and integrated into a client GIS.","name":"Application development via Data Integration","selfAssesment":"In Progress GI-N2K
"},{"code":"WB6-1","description":"Manual Web Service Composition is manually (by human) combining the activities of discovery, composition and invocation to fulfil a certain task.","name":"Manual Web Services Composition","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB6-2","description":"Providing standardized descriptions of the specifics of available webservices creates an environment where the composition of services to create a web application can be automated.","name":"Semi automated and Full-automated WSC","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB6","description":"Web Services Composition can be defined as bringing together a number of web services in a certain workflow to achieve a certain task that cannot be achieved by any of the composed services alone. In general, it involves first the discovery of the suitable services over the Web, and compose them in a certain workflow order and finally run the composed service which is the invocation stage. WSC has been a highly active research topic since the emergence of Web services in 2000s. \"Manual\" WSC is the form that the activities of discovery, composition and invocation are all done manually (by human). In the \"Semi-automated\" way, the discovery is done by the machine. In the \"full-automated\" approach all the above activities are done by the machine. There are no tools at the moment that achieve full automated composition. Web API composition is like WSC, the only difference is the fact that instead of web services there are Web APIs in WAPIC. There is no doubt that One would run into the very same problems of WSC concerning full automated composition. In other words, WAPIC would in no way be easier than WSC. Nevertheless, as far as semi automated form can be achived, WAPIC is valuable because the number of Web APIs increase drastically from day to day. The site \"programmableWeb\" lists 14 957 APIs at the moment. It is not easy to search for all those APIs manually for the discovery of suitable APIs for a given task.","name":"Application development via Web services composition","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB7-1","description":"Hypertext markup scripting and styling are the base for each web page or application. Styling defines the look and feel while scripting is used to implement the behavior of the web application","name":"Hypertext markup scripting and styling","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB7-2","description":"Web map APIs allow developers to integrate resources made available by web services in their application or web sites.","name":"Web Map APIs and Libraries","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB7-3","description":"A web application framework provides the generic and reusable building blocks needed to create web applications. Geoportal frameworks provide the functionality to build geospatial portals.","name":"Web application Frameworks and Geoportal frameworks","selfAssesment":"In Progress (GI-N2K)
"},{"code":"WB7","description":"Characteristic examples are included under this topic. The APIs, for instance other than the ones included under this unit, and libraries could have been included as well. However, since the important thing is to highlight the functionality then there is no need to include them all. By the inclusion of topic \"WB7-3\"under this unit, the aim was to cover one of the very \"hot\" topics of Web2.0 for both the main concepts about Web application frameworks and also how they are related to portal frameworks and geoportals. By the topic \"WB7-1 Building blocks\" the core components of Web application development are covered. On top of this core, there comes a great variety of \"Web application frameworks for both enabling rapid web application development and ensuring scalable, high-performance applications. Finally, there are \"Web APIs and Libraries\" certainly deserving being a separate topic for their current popularity. They also mean rapid application development for developers by code reuse and versatility for \"end users\" in creating their \"end products\".","name":"Web Application development elements","selfAssesment":"In progress (GI-N2K)
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Retrieved from https://earth.esa.int/web/sentinel/technical-guides/sentinel-2-msi/level-1c/algorithm","url":"https://earth.esa.int/web/sentinel/technical-guides/sentinel-2-msi/level-1c/algorithm"},{"concepts":[707,710,711],"description":" ","name":"ESA (2020). User Guides. Sentinel-1 SAR. Acquisition Modes. Extra Wide Swath. © ESA 2000 - 2020","url":"https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/acquisition-modes/extra-wide-swath"},{"concepts":[597,595,650,651,652],"description":"ESA (2020). Radar and SAR Glossary. earthnet online.","name":"ESA (2020). Radar and SAR Glossary. earthnet online.","url":"https://earth.esa.int/handbooks/asar/CNTR5-2.html"},{"concepts":[659],"description":"ESA (2020). Radar Course 3. Shadow. Earth Online.","name":"ESA (2020). Radar Course 3. Shadow. 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Interferometric Wide Swath. © ESA 2000 - 2021","url":"https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/acquisition-modes/interferometric-wide-swath"},{"concepts":[710],"description":" ","name":"ESA (2021). User Guides. Sentinel-1 SAR. Applications. Mapping of Applications to Seninel-1 Modes. © ESA 2000 - 2021","url":"https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/applications/mapping-applications-s1-modes"},{"concepts":[710],"description":" ","name":"ESA (2021). User Guides. Sentinel-1 SAR. Products and Algorithms. Level-1 Algorithms and Products. Algorithm Overview. 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Retrieved from https://earsc-portal.eu/display/EOwiki/Assess+crop+damage+due+to+storms","url":"https://earsc-portal.eu/display/EOwiki/Assess+crop+damage+due+to+storms"},{"concepts":[815],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Assess damage from earthquakes. Retrieved from https://earsc-portal.eu/display/EOwiki/Assess+damage+from+earthquakes","url":"https://earsc-portal.eu/display/EOwiki/Assess+damage+from+earthquakes"},{"concepts":[821],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Assess Deforestation or Forest Degradation. Retrieved from https://earsc-portal.eu/display/EOwiki/Assess+Deforestation+or+Forest+Degradation","url":"https://earsc-portal.eu/display/EOwiki/Assess+Deforestation+or+Forest+Degradation"},{"concepts":[820],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Assess Environmental impact of farming. Retrieved from https://earsc-portal.eu/display/EOwiki/Assess+Environmental+impact+of+farming","url":"https://earsc-portal.eu/display/EOwiki/Assess+Environmental+impact+of+farming"},{"concepts":[821],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Assess environmental impact of forestry. Retrieved from https://earsc-portal.eu/display/EOwiki/Assess+environmental+impact+of+forestry","url":"https://earsc-portal.eu/display/EOwiki/Assess+environmental+impact+of+forestry"},{"concepts":[824],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Assess environmental impact of human activities . Retrieved from https://earsc-portal.eu/display/EOwiki/Assess+environmental+impact+of+human+activities","url":"https://earsc-portal.eu/display/EOwiki/Assess+environmental+impact+of+human+activities"},{"concepts":[821],"description":" ","name":"European Association of Remote Sensing Companies. (2020). 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Retrieved from https://earsc-portal.eu/display/EOwiki/Detect+and+monitor+hurricanes+and+typhoons","url":"https://earsc-portal.eu/display/EOwiki/Detect+and+monitor+hurricanes+and+typhoons"},{"concepts":[837],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Detect and monitor ice-risk at sea. Retrieved from https://earsc-portal.eu/display/EOwiki/Detect+and+monitor+ice-risk+at+sea","url":"https://earsc-portal.eu/display/EOwiki/Detect+and+monitor+ice-risk+at+sea"},{"concepts":[835],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Detect and monitor illegal fishing. Retrieved from https://earsc-portal.eu/display/EOwiki/Detect+and+monitor+illegal+fishing","url":"https://earsc-portal.eu/display/EOwiki/Detect+and+monitor+illegal+fishing"},{"concepts":[832],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Detect and monitor oil slicks. Retrieved from https://earsc-portal.eu/display/EOwiki/Detect+and+monitor+oil+slicks","url":"https://earsc-portal.eu/display/EOwiki/Detect+and+monitor+oil+slicks"},{"concepts":[819,814],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Detect and monitor wildfires. Retrieved from https://earsc-portal.eu/display/EOwiki/Detect+and+monitor+wildfires","url":"https://earsc-portal.eu/display/EOwiki/Detect+and+monitor+wildfires"},{"concepts":[823],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Detect changes in glaciers. Retrieved from https://earsc-portal.eu/display/EOwiki/Detect+changes+in+glaciers","url":"https://earsc-portal.eu/display/EOwiki/Detect+changes+in+glaciers"},{"concepts":[821],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Detect illegal forest activities. Retrieved from https://earsc-portal.eu/display/EOwiki/Detect+illegal+forest+activities","url":"https://earsc-portal.eu/display/EOwiki/Detect+illegal+forest+activities"},{"concepts":[825],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Detect illegal mining activities . Retrieved from https://earsc-portal.eu/display/EOwiki/Detect+illegal+mining+activities","url":"https://earsc-portal.eu/display/EOwiki/Detect+illegal+mining+activities"},{"concepts":[820],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Detect illegal or undesired crops. Retrieved from https://earsc-portal.eu/display/EOwiki/Detect+illegal+or+undesired+crops","url":"https://earsc-portal.eu/display/EOwiki/Detect+illegal+or+undesired+crops"},{"concepts":[836],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Detect ships in critical areas. Retrieved from https://earsc-portal.eu/display/EOwiki/Detect+ships+in+critical+areas","url":"https://earsc-portal.eu/display/EOwiki/Detect+ships+in+critical+areas"},{"concepts":[756,792,760,757,758,759,764,762,763,771,765,766,767,768,769,770,776,772,773,774,775,779,777,778,783,780,781,790,784,787,785,786,791,788,789,839,812,782,809,810,811],"description":" ","name":"European Association of Remote Sensing Companies. (2020). EO Services (Markets). Retrieved from https://earsc-portal.eu/pages/viewpage.action?pageId=78221916","url":"https://earsc-portal.eu/pages/viewpage.action?pageId=78221916"},{"concepts":[834],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Forecast and map large waves. Retrieved from https://earsc-portal.eu/display/EOwiki/Forecast+and+map+large+waves","url":"https://earsc-portal.eu/display/EOwiki/Forecast+and+map+large+waves"},{"concepts":[761,834],"description":" ","name":"European Association of Remote Sensing Companies. (2020). 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Retrieved from https://earsc-portal.eu/display/EOwiki/Forecast+weather","url":"https://earsc-portal.eu/display/EOwiki/Forecast+weather"},{"concepts":[804],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Forecasting sunlight exposure. Retrieved from https://earsc-portal.eu/display/EOwiki/Forecasting+sunlight+exposure","url":"https://earsc-portal.eu/display/EOwiki/Forecasting+sunlight+exposure"},{"concepts":[827],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Identify hydrocarbon seeps in soil. Retrieved from https://earsc-portal.eu/display/EOwiki/Identify+hydrocarbon+seeps+in+soil","url":"https://earsc-portal.eu/display/EOwiki/Identify+hydrocarbon+seeps+in+soil"},{"concepts":[819,813],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Map and assess flooding. Retrieved from https://earsc-portal.eu/display/EOwiki/Map+and+assess+flooding","url":"https://earsc-portal.eu/display/EOwiki/Map+and+assess+flooding"},{"concepts":[761],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Map and monitor hydroelectric energy. Retrieved from https://earsc-portal.eu/display/EOwiki/Map+and+monitor+hydroelectric+energy","url":"https://earsc-portal.eu/display/EOwiki/Map+and+monitor+hydroelectric+energy"},{"concepts":[761],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Map and monitor solar energy (solar farms). Retrieved from https://earsc-portal.eu/pages/viewpage.action?pageId=78221967","url":"https://earsc-portal.eu/pages/viewpage.action?pageId=78221967"},{"concepts":[761],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Map and monitor wind energy (wind farms). Retrieved from https://earsc-portal.eu/pages/viewpage.action?pageId=78221973","url":"https://earsc-portal.eu/pages/viewpage.action?pageId=78221973"},{"concepts":[835],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Map fish shoals. Retrieved from https://earsc-portal.eu/display/EOwiki/Map+fish+shoals","url":"https://earsc-portal.eu/display/EOwiki/Map+fish+shoals"},{"concepts":[827],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Map geological features. Retrieved from https://earsc-portal.eu/display/EOwiki/Map+geological+features","url":"https://earsc-portal.eu/display/EOwiki/Map+geological+features"},{"concepts":[827],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Map seismic survey operations. Retrieved from https://earsc-portal.eu/display/EOwiki/Map+seismic+survey+operations","url":"https://earsc-portal.eu/display/EOwiki/Map+seismic+survey+operations"},{"concepts":[833],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Map water depth or charting. Retrieved from https://earsc-portal.eu/display/EOwiki/Map+water+depth+or+charting","url":"https://earsc-portal.eu/display/EOwiki/Map+water+depth+or+charting"},{"concepts":[826],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Measure & detect land surface change. Retrieved from https://earsc-portal.eu/display/EOwiki/Measure+detect+land+surface+change","url":"https://earsc-portal.eu/display/EOwiki/Measure+detect+land+surface+change"},{"concepts":[825],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Measure land use statistics. Retrieved from https://earsc-portal.eu/display/EOwiki/Measure+land+use+statistics","url":"https://earsc-portal.eu/display/EOwiki/Measure+land+use+statistics"},{"concepts":[804],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor air quality & emissions. Retrieved from https://earsc-portal.eu/pages/viewpage.action?pageId=78221935","url":"https://earsc-portal.eu/pages/viewpage.action?pageId=78221935"},{"concepts":[833],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor coastal ecosystem. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+coastal+ecosystem","url":"https://earsc-portal.eu/display/EOwiki/Monitor+coastal+ecosystem"},{"concepts":[831,830],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor construction and buildings. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+construction+and+buildings","url":"https://earsc-portal.eu/display/EOwiki/Monitor+construction+and+buildings"},{"concepts":[821],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor forest carbon content. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+forest+carbon+content","url":"https://earsc-portal.eu/display/EOwiki/Monitor+forest+carbon+content"},{"concepts":[821],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor forest resources. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+forest+resources","url":"https://earsc-portal.eu/display/EOwiki/Monitor+forest+resources"},{"concepts":[825],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor humanitarian movement and camps. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+humanitarian+movement+and+camps","url":"https://earsc-portal.eu/display/EOwiki/Monitor+humanitarian+movement+and+camps"},{"concepts":[823],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor ice on rivers and lakes. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+ice+on+rivers+and+lakes","url":"https://earsc-portal.eu/display/EOwiki/Monitor+ice+on+rivers+and+lakes"},{"concepts":[824],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor land cover and detect change. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+land+cover+and+detect+change","url":"https://earsc-portal.eu/display/EOwiki/Monitor+land+cover+and+detect+change"},{"concepts":[824],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor land ecosystems and biodiversity. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+land+ecosystems+and+biodiversity","url":"https://earsc-portal.eu/display/EOwiki/Monitor+land+ecosystems+and+biodiversity"},{"concepts":[824],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor land pollution. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+land+pollution","url":"https://earsc-portal.eu/display/EOwiki/Monitor+land+pollution"},{"concepts":[832],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor marine habitats. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+marine+habitats","url":"https://earsc-portal.eu/display/EOwiki/Monitor+marine+habitats"},{"concepts":[827],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor mineral extraction. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+mineral+extraction","url":"https://earsc-portal.eu/display/EOwiki/Monitor+mineral+extraction"},{"concepts":[833],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor ocean level and surface. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+ocean+level+and+surface","url":"https://earsc-portal.eu/display/EOwiki/Monitor+ocean+level+and+surface"},{"concepts":[832],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor ocean quality and productivity. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+ocean+quality+and+productivity","url":"https://earsc-portal.eu/display/EOwiki/Monitor+ocean+quality+and+productivity"},{"concepts":[832],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor oil rigs and flares. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+oil+rigs+and+flares","url":"https://earsc-portal.eu/display/EOwiki/Monitor+oil+rigs+and+flares"},{"concepts":[832],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor pollution at sea. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+pollution+at+sea","url":"https://earsc-portal.eu/display/EOwiki/Monitor+pollution+at+sea"},{"concepts":[808],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor sensitive risk areas. Retrieved from: https://earsc-portal.eu/display/EOwiki/Monitor+sensitive+risk+areas","url":"https://earsc-portal.eu/display/EOwiki/Monitor+sensitive+risk+areas"},{"concepts":[836],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor ships movements. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+ships+movements","url":"https://earsc-portal.eu/display/EOwiki/Monitor+ships+movements"},{"concepts":[823],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor snow cover. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+snow+cover","url":"https://earsc-portal.eu/display/EOwiki/Monitor+snow+cover"},{"concepts":[833],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor the coast line. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+the+coast+line","url":"https://earsc-portal.eu/display/EOwiki/Monitor+the+coast+line"},{"concepts":[831,829],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor urban areas. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+urban+areas","url":"https://earsc-portal.eu/display/EOwiki/Monitor+urban+areas"},{"concepts":[825],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor vegetation encroachment. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+vegetation+encroachment","url":"https://earsc-portal.eu/display/EOwiki/Monitor+vegetation+encroachment"},{"concepts":[820],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor water use on crops and horticulture. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+water+use+on+crops+and+horticulture","url":"https://earsc-portal.eu/display/EOwiki/Monitor+water+use+on+crops+and+horticulture"},{"concepts":[804],"description":" ","name":"European Association of Remote Sensing Companies. (n.d.). Product sheet: Air Quality CO2. Retrieved from https://earsc-portal.eu/display/EO4RawMaterials/Product+Sheet%3A+Air+Quality+CO2","url":"https://earsc-portal.eu/display/EO4RawMaterials/Product+Sheet%3A+Air+Quality+CO2"},{"concepts":[837],"description":" ","name":"European Centre for Medium-Range Weather Forecasts, & Copernicus Programme. (2020). Global Shipping Project - Copernicus. Retrieved from https://climate.copernicus.eu/index.php/global-shipping-project","url":"https://climate.copernicus.eu/index.php/global-shipping-project"},{"concepts":[804],"description":" ","name":"European Comission. (2015). An Operational Anthropogenic CO₂ Emissions Monitoring & Verification Support Capacity.","url":"https://www.copernicus.eu/sites/default/files/2019-09/CO2_Blue_report_2015.pdf"},{"concepts":[804],"description":" ","name":"European Comission. (2017). 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Retrieved from: https://ec.europa.eu/programmes/horizon2020/en/h2020-section/societal-challenges","url":"https://ec.europa.eu/programmes/horizon2020/en/h2020-section/societal-challenges"},{"concepts":[824],"description":" ","name":"European Commission Joint Research Centre. (2020). Vegetation - Copernicus landm monitoring service. Retrieved from https://land.copernicus.eu/global/themes/Vegetation","url":"https://land.copernicus.eu/global/themes/Vegetation"},{"concepts":[796],"description":" ","name":"European Commission. (2020). Digital skills and jobs - Shaping Europe's digital future. Retrived from https://ec.europa.eu/digital-single-market/en/policies/digital-skills","url":"https://ec.europa.eu/digital-single-market/en/policies/digital-skills"},{"concepts":[796],"description":" ","name":"European Commission. (2020). Employment, Social Affairs & Inclusion. 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Retrieved from https://ec.europa.eu/info/news/sustainability-at-the-water-source_en","url":"https://ec.europa.eu/info/news/sustainability-at-the-water-source_en"},{"concepts":[798],"description":" ","name":"European Commission. (2020). Sustainable agriculture in the CAP. Retrieved from https://ec.europa.eu/info/food-farming-fisheries/sustainability/sustainable-cap_en","url":"https://ec.europa.eu/info/food-farming-fisheries/sustainability/sustainable-cap_en"},{"concepts":[799],"description":" ","name":"European Commission. (2020). Transport. Retrieved from https://ec.europa.eu/info/policies/transport_en","url":"https://ec.europa.eu/info/policies/transport_en"},{"concepts":[838],"description":" ","name":"European Environment Agency. (2016). Monitoring of marine waters. Retrieved from: https://www.eea.europa.eu/publications/92-9167-001-4/page024.html","url":"https://www.eea.europa.eu/publications/92-9167-001-4/page024.html"},{"concepts":[793],"description":" ","name":"European Environmental Agency, (2019). Climate Change Adaption. Retrieved from: https://www.eea.europa.eu/themes/climate-change-adaptation/intro.","url":"https://www.eea.europa.eu/themes/climate-change-adaptation/intro"},{"concepts":[793],"description":" ","name":"European Environmental Agency, (2019). Climate Change Mitigation. Retrieved from: https://www.eea.europa.eu/themes/climate/intro.","url":"https://www.eea.europa.eu/themes/climate/intro"},{"concepts":[795],"description":" ","name":"European Environmental Agency. (2008). Biodiversity - Ecosystems. Retrieved from https://www.eea.europa.eu/themes/biodiversity/intro","url":"https://www.eea.europa.eu/themes/biodiversity/intro"},{"concepts":[801],"description":" ","name":"European External Action Service. (2020). 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Retrieved from https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-2-msi/level-1c/cloud-masks","url":"https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-2-msi/level-1c/cloud-masks"},{"concepts":[859],"description":" ","name":"European Space Agency. (n.d.). Understanding risk with Earth observation. Retreived from: https://www.esa.int/Applications/Observing_the_Earth/Understanding_risk_with_Earth_observation","url":"https://www.esa.int/Applications/Observing_the_Earth/Understanding_risk_with_Earth_observation"},{"concepts":[808],"description":" ","name":"European Union. (2018). Critical Infrastructure Analysis. Retrieved from: https://sea.security.copernicus.eu/categories/critical-infrastructure-analysis/","url":"https://sea.security.copernicus.eu/categories/critical-infrastructure-analysis/"},{"concepts":[860],"description":" ","name":"European Union. (2020). Rapid mapping. Retrieved from: https://emergency.copernicus.eu/mapping/ems/rapid-mapping-portfolio","url":"https://emergency.copernicus.eu/mapping/ems/rapid-mapping-portfolio"},{"concepts":[124],"description":"ISBN number: 9781118653104","name":"Fairchild, M. D., (2005). Color appearance models, (2nd ed.), John Wiley and Sons.","url":"http://books.google.com/books?isbn=9781118653104"},{"concepts":[845],"description":" ","name":"Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S., Shimada, J., Umland, J., Werner, M., Oskin, M., Burbank, D., Alsdorf, D. (2007). The shuttle radar topography mission. Reviews of Geophysics, 45(2). doi:10.1029/2005RG000183","url":"https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2005RG000183"},{"concepts":[257],"description":"ISBN: 978-3-319-00025-1","name":"Fecher, B. and Friesike, S. (2014). Open Science: One Term, Five Schools of Thought. 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IEEE Transactions on Geoscience and Remote Sensing, vol. 39, no. 1, pp. 8-20,","url":"https://doi.org/10.1109/36.898661"},{"concepts":[638],"description":" ","name":"Ferretti, A., Monti-Guarnieri, A., Prati, C., Rocca, F., & Massonet, D. (2007). InSAR Principles-Guidelines for SAR Interferometry Processing and Interpretation, TM-19. The Netherlands: ESA Publications.","url":" "},{"concepts":[128],"description":" ","name":"Ferretti, F. (2014) Pioneers in the history of cartography: the Geneva map collection of Élisée Reclus and Charles Perron, Journal of Historical Geography 43, 85-95.","url":"https://www.doi.org/10.1016/j.jhg.2013.10.025"},{"concepts":[593],"description":" ","name":"Feynman, R. P., Leighton, R. B., & Sands, M. (2005). The Feynman Lectures on Physics, Volume 1. The Definitive Edition.","url":" "},{"concepts":[517,507,511],"description":" ","name":"Feynman, R. P., Leighton, R. B., & Sands, M. (2005). The Feynman Lectures on Physics: The Complete and Definitive Issue. 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GeoJournal, 72(3-4), 137-148.","url":"https://www.jstor.org/stable/41220564"},{"concepts":[895],"description":" ","name":"Fleming, L. E., Haines, A., Golding, B., Kessel, A., Cichowska, A., Sabel, C. E., ... & Cocksedge, N. (2014). Data mashups: potential contribution to decision support on climate change and health. International Journal of Environmental Research and Public Health, 11(2), 1725-1746.","url":"https://doi.org/10.3390/ijerph110201725"},{"concepts":[883],"description":" ","name":"Florczyk, A. J., López-Pellicer, F. J., Nogueras-Iso, J., & Zarazaga-Soria, F. J. (2012). Automatic generation of geospatial metadata for web resources. International Journal of Spatial Data Infrastructures Research, 7, 151-172.","url":"https://ijsdir.sadl.kuleuven.be/index.php/ijsdir/article/view/253"},{"concepts":[378],"description":" ","name":"Florinsky I.V. 2016. Digital Terrain Analysis in Soil Science and Geology. 2nd Edition, Elsevier, 506 pp. ISBN:978-0-12-804632-6.","url":" "},{"concepts":[244],"description":"ISBN: 978-1597264723","name":"Ford, A. (2009). Modeling the environment: an introduction to system dynamics models of environmental systems, 2nd ed. Island Press.","url":"http://books.google.com/books?isbn=9781597264723"},{"concepts":[285],"description":"ISBN number: 9780138621452","name":"Foresman, T. (1998). The History of geographic information systems. Upper Saddle River, NJ: Prentice Hall.","url":"http://books.google.com/books?isbn=9780138621452"},{"concepts":[644],"description":"Fornaro, G., Reale, D., Pauciullo, A., Zhu, X. X., & Bamler, R. (2012). SAR Tomography: An Advanced Tool for Spaceborne 4D Radar Scanning with Application to Imaging and Monitoring of Cities and Single Buildings. IEEE Geoscience and Remote Sensing Newsletter.","name":"Fornaro, G., Reale, D., Pauciullo, A., Zhu, X. X., & Bamler, R. (2012). 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New York: Wiley.","url":"http://books.google.com/books?isbn=9780470855256"},{"concepts":[185],"description":"ISBN: 978-0321193681","name":"Fowler, M. (2003). UML distilled: A brief guide to the standard object modeling language (3rd ed.). Addision-Wesley.","url":"http://books.google.com/books?isbn=9780321193681"},{"concepts":[656],"description":" ","name":"Franceschetti, G., Lanari, R. Synthetic Aperture Radar Processing, CRC Press: Boca Raton, FL, USA, 1999.","url":" "},{"concepts":[657],"description":" ","name":"Franceschetti, G., Lanari, R. Synthetic Aperture Radar Processing, CRC Press: Boca Raton, FL, USA, 1999.","url":" "},{"concepts":[658],"description":" ","name":"Franceschetti, G., Lanari, R. Synthetic Aperture Radar Processing, CRC Press: Boca Raton, FL, USA, 1999.","url":" "},{"concepts":[659],"description":" ","name":"Franceschetti, G., Lanari, R. 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(e.g., Microsoft, Google) in the geospatial industry"},{"concepts":[19],"name":"Assess the outcome of location-allocation models using other spatial analysis techniques"},{"concepts":[166],"name":"Assess the relative importance and immediacy of the requirements"},{"concepts":[42],"name":"Assess which geometric operations are suitable for raster and vector data sets."},{"concepts":[183],"name":"Assess which GIS APIs are more suitable for developing GIS applications"},{"concepts":[334],"name":"Atmospherically correct remotely sensed data to derive bottom of atmosphere (BOA) reflectance values from TOA data with the use of an appropriate radiative transfer modelling technique"},{"concepts":[83],"name":"Bridge the differences in epistemological viewpoints to enable work with diverse colleagues"},{"concepts":[166],"name":"Build a mechanism for converting the requirements into a product"},{"concepts":[137],"name":"Build a set of mapping problems that can be used to illustrate point, line, and area label conventions for placing text on maps"},{"concepts":[149,139],"name":"Build an animated map for a specified purpose"},{"concepts":[149],"name":"Build an interactive map suitable for a given audience"},{"concepts":[178],"name":"Build functionalities and services to ensure interoperability"},{"concepts":[462],"name":"Build semantic queries to retrieve selections of images from an EO image database"},{"concepts":[654],"name":"Calculate ground rage resolution"},{"concepts":[610],"name":"Calculate radar beta nought"},{"concepts":[609],"name":"Calculate radar gamma nought"},{"concepts":[608],"name":"Calculate radar sigma nought"},{"concepts":[38],"name":"Calculate several different shape indices for a polygon dataset"},{"concepts":[474],"name":"Calculate the estimated schedule required to carry out all of the implementation steps for an enterprise GIS of a given size"},{"concepts":[653],"name":"Calculate the radar antenna footprint taking into account the orbit of the radar system and bandwidth"},{"concepts":[649],"name":"Calculate the size of the syntheric aperture of a radar system taking into account the platform and sensor specifications"},{"concepts":[838,833],"name":"Calculate the water depth in coastal areas"},{"concepts":[41],"name":"Calculate various measures of adjacency in a polygon dataset"},{"concepts":[280],"name":"Calculate, in terms of ground area, the uncertainty associated with decimal coordinates specified to three, four, and five decimal places"},{"concepts":[54],"name":"Calibrate a linear combination model by adjusting weights using a test data set"},{"concepts":[363],"name":"Categorize different types of changes that can be identified from multitemporal images"},{"concepts":[106],"name":"Characterize the domains of attributes in a GIS, including continuous and discrete, qualitative and quantitative, absolute and relative"},{"concepts":[656],"name":"Check and discuss an local incidence angle of a SAR system in data metadata"},{"concepts":[652],"name":"Check incidance angle of a SAR system in data metadata"},{"concepts":[457],"name":"Choose a set of quality indicators for an EO product that are relevant for a specific application"},{"concepts":[757,798,820],"name":"Choose a viable strategy for farming operations"},{"concepts":[758],"name":"Choose a viable strategy for fishing operations"},{"concepts":[759,821],"name":"Choose a viable strategy for forest operations"},{"concepts":[760,810],"name":"Choose a viable strategy for operations in the field of managed living ressources"},{"concepts":[426],"name":"Choose and apply a method for atmospheric radiative transfer modelling like ATCOR"},{"concepts":[124],"name":"Choose from different options to create a map"},{"concepts":[316],"name":"Choose or define a new image extent to extract an image subset for further analysis"},{"concepts":[144],"name":"Choose suitable mapping methods for each attribute of a given type of feature in a GIS (e.g., roads with various attributes such as surface type, traffic flow, number of lanes, direction such as one-way, etc.)"},{"concepts":[135],"name":"Choose the best symbols for representing different attributes"},{"concepts":[399],"name":"Choose the right software tool to apply image classification to a specific satellite image"},{"concepts":[26],"name":"Cite appropriate applications of several coordinate transformation techniques (e.g., affine, similarity, Molodenski, Helmert)"},{"concepts":[175],"name":"Cite software licenses"},{"concepts":[79],"name":"Classify common models for spatial regression analysis."},{"concepts":[0],"name":"Classify the main knowledge domains of GI Science and Earth observation."},{"concepts":[94],"name":"Collaborate effectively with colleagues of differing social backgrounds in developing balanced GIS applications"},{"concepts":[101],"name":"Collaborate with non-GIS experts who use GIS to design applications that match common-sense understanding to an appropriate degree"},{"concepts":[843],"name":"Combine different bands to calculate NDVI"},{"concepts":[286],"name":"Compare and contrast and contrast the relationship of the geospatial profession and the U.S. legal regime with similar relationships in other countries"},{"concepts":[34],"name":"Compare and contrast attribute query and spatial query"},{"concepts":[68],"name":"Compare and contrast Bayesian methods and classical frequentist statistical methods"},{"concepts":[73],"name":"Compare and contrast co-kriging log-normal kriging, disjunctive kriging, indicator kriging, factorial kriging and universal kriging"},{"concepts":[19],"name":"Compare and contrast covering, dispersion, and p-median models"},{"concepts":[38],"name":"Compare and contrast different shape indices, include examples of applications to which each could be applied"},{"concepts":[108],"name":"Compare and contrast differing epistemological and metaphysical viewpoints on the reality of geographic entities"},{"concepts":[288],"name":"Compare and contrast geographic information technologies that are privacy-invasive, privacy-enhancing, and privacy-sympathetic"},{"concepts":[66],"name":"Compare and contrast global and local statistics and their uses"},{"concepts":[78],"name":"Compare and contrast GWR with universal kriging using moving neighborhoods"},{"concepts":[37],"name":"Compare and contrast how direction is determined and stated in raster and vector data"},{"concepts":[57],"name":"Compare and contrast interpolation by inverse distance weighting, bi-cubic spline fitting and kriging"},{"concepts":[103],"name":"Compare and contrast models of a given spatial process using continuous and discrete perspectives of time"},{"concepts":[289],"name":"Compare and contrast National, European policy regarding rights to geospatial data with similar policies in other countries"},{"concepts":[2],"name":"Compare and contrast spatial statistical analysis, spatial data analysis, and spatial modeling"},{"concepts":[2],"name":"Compare and contrast spatial statistics and map algebra as two very different kinds of data analysis"},{"concepts":[81],"name":"Compare and contrast the ability of different theories to explain various situations"},{"concepts":[83],"name":"Compare and contrast the ability of various theories to explain different situations"},{"concepts":[104],"name":"Compare and contrast the characteristics of spatial and temporal dimensions"},{"concepts":[45],"name":"Compare and contrast the concept of overlay as it is implemented in raster and vector domains"},{"concepts":[110],"name":"Compare and contrast the concepts of continuants (entities) and occurrents (events)"},{"concepts":[16],"name":"Compare and contrast the concepts of discrete location problems and continuous location problems"},{"concepts":[110],"name":"Compare and contrast the concepts of event and process"},{"concepts":[289],"name":"Compare and contrast the consequences of different national policies about rights to geospatial data in terms of the real costs of spatial data, their coverage, accuracy, uncertainty, reliability, validity, and maintenance"},{"concepts":[306],"name":"Compare and contrast the ethical guidelines promoted by the GIS Certification Institute (GISCI) and the American Society for Photogrammetry and Remote Sensing (ASPRS)"},{"concepts":[493],"name":"Compare and contrast the impact effect of time for developing consensus-based standards with immediate operational needs"},{"concepts":[21],"name":"Compare and contrast the impacts of different conversion approaches, including the effect on spatial components"},{"concepts":[85],"name":"Compare and contrast the kinds of questions various philosophies ask, the methodologies they use, the answers they offer, and their applicability to different phenomena"},{"concepts":[121],"name":"Compare and contrast the meanings of related terms such as vague, fuzzy, imprecise, indefinite, indiscrete, unclear, and ambiguous"},{"concepts":[2],"name":"Compare and contrast the methods of analyzing aggregate data as opposed to methods of analyzing a set of individual observations"},{"concepts":[503],"name":"Compare and contrast the missions, histories, constituencies, and activities of professional organizations including Association of American Geographers (AAG), America Society for Photogrammetry and Remote Sensing (ASPRS) ..."},{"concepts":[113],"name":"Compare and contrast the opportunities and pitfalls of using regions to aggregate geographic information (e.g., census data)"},{"concepts":[5],"name":"Compare and contrast the primary types of data mining: summarization/characterization, clustering/categorization, feature extraction, and rule/relationships extraction"},{"concepts":[156,157],"name":"Compare and contrast the quality of product evaluation that can be made from process proofs and color laser prints"},{"concepts":[211],"name":"Compare and contrast the raster with other types of regular tessellations for geographic data analysis"},{"concepts":[211],"name":"Compare and contrast the raster with other types of regular tessellations for geographic data storage"},{"concepts":[88,96],"name":"Compare and contrast the symbolic and connectionist theories of human cognition and memory and their ability to model various cases"},{"concepts":[54],"name":"Compare and contrast the terms multi-criteria evaluation, weighted linear combination, and site suitability analysis"},{"concepts":[106],"name":"Compare and contrast the theory that properties are fundamental (and objects are human simplifications of patterns thereof) with the theory that objects are fundamental (and properties are attributes thereof)"},{"concepts":[88],"name":"Compare and contrast theories of spatial knowledge acquisition (e.g., Marr on vision, Piaget on childhood, Golledge on wayfinding)"},{"concepts":[483],"name":"Compare and contrast training methods utilized in a non-profit to those employed in a local government agency"},{"concepts":[613],"name":"Compare and discuss attenuation length and penetration depth of the optical and radar signal"},{"concepts":[707,709,712,708],"name":"Compare and discuss different SAR acquisition modes"},{"concepts":[496],"name":"Compare and explain different models for funding an SDI"},{"concepts":[293],"name":"Compare and explain the main business models in the GI domain"},{"concepts":[72],"name":"Compare block-kriging with areal interpolation using proportional area weighting and dasymetric mapping"},{"concepts":[266],"name":"Compare common sensors by spatial resolution, spectral sensitivity, ground coverage, and temporal resolution [e.g., AVHRR, MODIS (intermediate resolution ~500 m, high temporal) Landsat, commercial high resolution (Ikonos and Quickbird); ..."},{"concepts":[240],"name":"Compare commonalities and patterns of geocomputation to other related terms"},{"concepts":[14],"name":"Compare current accessibility models with early models of market potential"},{"concepts":[393],"name":"Compare different deep learning approaches in EO image classification"},{"concepts":[248],"name":"Compare different design choices in developing spatial simulation models"},{"concepts":[903],"name":"compare different development components and their advantages and disadvantages"},{"concepts":[439],"name":"Compare different error metrics that are based on the error matrix"},{"concepts":[164],"name":"Compare different evaluation methods for cartography and visualization products (e.g., qualitative versus quantitative, formative versus summative studies)."},{"concepts":[497],"name":"Compare different frameworks for assessing Spatial Data Infrastructures"},{"concepts":[879],"name":"Compare different Geospatial object and geometry definitions included under this topic"},{"concepts":[245],"name":"Compare different options of combining space-time dynamics approaches in spatial modelling"},{"concepts":[351],"name":"Compare different strategies of data assimilation"},{"concepts":[177],"name":"Compare geospatial software architecture through cost-analysis framework"},{"concepts":[823],"name":"Compare glacier extents using EO data"},{"concepts":[807,804,805],"name":"Compare human-induced emissions to natural sources"},{"concepts":[891],"name":"Compare Linked geospatial data to SDI approaches"},{"concepts":[69],"name":"Compare methods of spatial statistical analysis for the testing of hypotheses."},{"concepts":[20],"name":"Compare models and software tools that allow for optimization"},{"concepts":[816],"name":"Compare one optical EO method with a SAR method for landslide mapping and explain their differences"},{"concepts":[720],"name":"Compare reflectance measurements from the field to reflectance values in radiometrically pre-processed EO data"},{"concepts":[120],"name":"Compare relationships between entities, between attributes and between locations."},{"concepts":[410],"name":"Compare results of the Laplacian of Gaussian filter to the original input image"},{"concepts":[301],"name":"Compare the advantages and disadvantages of group participation and individual participation"},{"concepts":[48],"name":"Compare the basic analytical operations of different GISs."},{"concepts":[277],"name":"Compare the concepts of geometric accuracy and topological fidelity"},{"concepts":[257],"name":"Compare the different cultures of Open Science"},{"concepts":[64],"name":"Compare the different types of spatial weight matrices"},{"concepts":[832],"name":"Compare the main satellite sensors used in marine ecosystem monitoring"},{"concepts":[277],"name":"Compare the National Map Accuracy Standard with the ASPRS Coordinate Standard"},{"concepts":[137],"name":"Compare the relative merits of having map labels placed dynamically versus having them saved as annotation data"},{"concepts":[24],"name":"Compare the result of conversion vector/raster or raster/vector and examine the impact of conversion on the quality of the dataset"},{"concepts":[166],"name":"Compile the needs of individual users and tasks into enterprise-wide needs"},{"concepts":[471],"name":"Compute descriptive statistics and geostatistics of geographic data"},{"concepts":[49],"name":"Compute measures of overall dispersion and clustering of point datasets using nearest neighbor distance statistics"},{"concepts":[65],"name":"Compute measures of overall dispersion and clustering of point datasets using nearest neighbor distance statistics"},{"concepts":[65],"name":"Compute Morans I and Gearys c for patterns of attribute data measured on interval ratio scales"},{"concepts":[9],"name":"Compute the alpha, beta, and gamma indices of network connectivity"},{"concepts":[9],"name":"Compute the Detour Index and the measure of network density for a given network"},{"concepts":[9],"name":"Compute the estimated number of fundamental cycles in a graph"},{"concepts":[66],"name":"Compute the Gi and Gi* statistics"},{"concepts":[65],"name":"Compute the K function"},{"concepts":[37],"name":"Compute the mean of directional data"},{"concepts":[10],"name":"Compute the optimum path between two points through a network with Dijkstras algorithm"},{"concepts":[53],"name":"Conduct a simple hierarchical cluster analysis to classify area objects into statistically similar regions"},{"concepts":[76],"name":"Conduct a spatial econometric analysis to test for spatial dependence in the residuals from least-squares models and spatial autoregressive models"},{"concepts":[72],"name":"Conduct a spatial interpolation process using kriging from data description to final error map"},{"concepts":[159],"name":"Construct a new map from an existing one with a biased view"},{"concepts":[34],"name":"Construct a query statement to search for a specific spatial or temporal relationship"},{"concepts":[71],"name":"Construct a semi-variogram and illustrate with a semi-variogram cloud"},{"concepts":[34],"name":"Construct a spatial query to extract all point objects that fall within a polygon"},{"concepts":[64],"name":"Construct a spatial weights matrix for lattice, point, and area patterns"},{"concepts":[214],"name":"Construct a TIN manually from a set of spot elevations"},{"concepts":[149],"name":"Construct a Web page that includes an interactive map"},{"concepts":[624],"name":"Construct scattering matrix"},{"concepts":[105],"name":"Construct taxonomies and dictionaries (also known as formal ontologies) to communicate systems of categories"},{"concepts":[14],"name":"Contrast accessibility modeling at the individual level versus at an aggregated level"},{"concepts":[182],"name":"Contrast cloud and grid computing technologies"},{"concepts":[134],"name":"Contrast gaming elements which are both part of traditional games and geo-games"},{"concepts":[137],"name":"Contrast the strengths and limitations of methods for automatic label placement"},{"concepts":[22],"name":"Convert a dataset from the native format of one GIS product to another"},{"concepts":[127],"name":"Convert historical maps in digital format"},{"concepts":[331],"name":"Convert multispectral image into its principal components"},{"concepts":[24],"name":"Convert vector data to raster format and back using GIS software"},{"concepts":[24],"name":"Convert vector data to raster format and back using the GIS software"},{"concepts":[128],"name":"Correlate map making methods with technological or societal factors across History"},{"concepts":[174],"name":"Create a budget of expected labor costs, including salaries, benefits, training, and other expenses"},{"concepts":[188],"name":"Create a complete design document ready for implementation"},{"concepts":[153],"name":"Create a concept map that represents the contents and topology of a physical or social process"},{"concepts":[412],"name":"Create a convolution filter that integrates the standard deviation of the entire scene in its weights"},{"concepts":[460],"name":"Create a data cube using the data model 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geospatial process"},{"concepts":[167],"name":"Create a user manual to help users understand a process or task"},{"concepts":[466],"name":"Create a web interface and related system architecture that enables image processing by using OGC interfaces"},{"concepts":[226],"name":"Create an adjacency table from a sample network"},{"concepts":[135],"name":"Create an aesthetic map icon library"},{"concepts":[226],"name":"Create an incidence matrix from a sample network"},{"concepts":[350],"name":"Create an integrated population distribution map from census data and EO-based land use classification"},{"concepts":[33],"name":"Create an SQL query to retrieve elements from a GIS"},{"concepts":[185],"name":"Create conceptual, logical, and physical data models using automated software tools"},{"concepts":[50],"name":"Create density maps from point datasets using kernels and density estimation techniques using standard software"},{"concepts":[133],"name":"Create different map layouts using the same map components (main map area, inset maps, titles, legends, scale bars, north arrows, grids and graticule) to produce maps with very distinctive purposes"},{"concepts":[133],"name":"Create different maps using the same data for different purposes and intended audiences (e.g., expert and novice hikers)"},{"concepts":[143],"name":"Create different visual hierarchies to produce maps with different purposes"},{"concepts":[24],"name":"Create estimated tessellated data sets from point samples or isolines using interpolation operations that are appropriate to the specific situation"},{"concepts":[54],"name":"Create initial weights using the analytical hierarchy process (AHP)"},{"concepts":[187],"name":"Create logical models based on conceptual models using UML or other tools"},{"concepts":[144],"name":"Create maps using each of the following methods: choropleth, dasymetric, proportioned symbol, graduated symbol, isoline, dot, cartogram, and flow map"},{"concepts":[870],"name":"Create new EO products out of raw data or other products"},{"concepts":[105],"name":"Create or use GIS data structures to represent categories, including attribute columns, layers themes, shapes, legends, etc."},{"concepts":[174],"name":"Create proposals and presentations to secure funding"},{"concepts":[70],"name":"Create spatial samples under a variety of requirements, such as coverage, randomness, transects"},{"concepts":[159],"name":"Create two versions of the same map addressed to different targets"},{"concepts":[188],"name":"Create UML diagrams of physical models based on logical model diagrams and software requirements"},{"concepts":[144],"name":"Create well-designed legends using the appropriate conventions for the following methods: choropleth, dasymetric, proportioned symbol, graduated symbol, isoline, dot, cartogram, and flow map"},{"concepts":[143],"name":"Critique the graphic design of several maps in terms of balance, legibility, clarity, visual contrast, figure-ground organization, and hierarchal organization"},{"concepts":[149],"name":"Critique the interactive elements of an online map"},{"concepts":[150],"name":"Critique the user interface for existing Internet mapping services"},{"concepts":[229],"name":"Deal with time aspects in modelling data"},{"concepts":[228],"name":"Deal with uncertainty aspects in modelling data"},{"concepts":[863,862],"name":"Decide on urban planning measures on the basis of a semantic 3D model"},{"concepts":[31],"name":"Decide which generalisation technique (aggregation, selection, etc.) is best for a specific situation of reducing map scale."},{"concepts":[141],"name":"Decide which graphical representation better reflects the messages embedded in your story"},{"concepts":[66],"name":"Decompose Morans I and Gearys c into local measures of spatial association"},{"concepts":[186],"name":"Deconstruct an application use case into its conceptual elements"},{"concepts":[314],"name":"Defend or refute the contention that critical studies have an identifiable influence on the development of the information society in general and GIScience in particular"},{"concepts":[313],"name":"Defend or refute the contention that the masculinist culture of computer work in general, and GIS work in particular, perpetuates gender inequality in GIS and T education and training and occupational segregation in the GIS and T workforce"},{"concepts":[28],"name":"Defend or refute the statement \"GIS data are scaleless\""},{"concepts":[85],"name":"Defend or refute the statement, All data are theory-laden"},{"concepts":[109],"name":"Define a field in terms of properties, space, and time"},{"concepts":[166],"name":"Define a methodology for gathering of requirements"},{"concepts":[233],"name":"Define a set of rules for modeling changes in spatial databases"},{"concepts":[223],"name":"Define and describe an application schema"},{"concepts":[303],"name":"Define and discuss enabling technologies: geotag, georeferencing, GPS and more"},{"concepts":[238],"name":"Define and discuss opportunities and limitations of computational science"},{"concepts":[303],"name":"Define and discuss volunteered geographic information"},{"concepts":[303],"name":"Define and discussing impact of Crowdsourcing on Geospatial Society"},{"concepts":[880],"name":"Define and exemplify the reuse of ontologies - Define and identify the role of ontology patterns"},{"concepts":[876],"name":"Define and practice the usage, in a given use case, of StyledLayerDescriptor (SLD) and Symbology Encoding (SE). Practice their usage in a given use case"},{"concepts":[301],"name":"Define and understand citizenship, democracy, maturity, and negotiation related to geo information use and participation in society /community development (at local, regional, national level)"},{"concepts":[33],"name":"Define basic terms of query processing e.g., SQL, primary and foreign keys, table join"},{"concepts":[211],"name":"Define basic terms used in the raster data model (e.g., cell, row, column, value)"},{"concepts":[179,875],"name":"Define characteristics of REST Web services and Resource oriented Architecture (ROA)"},{"concepts":[85],"name":"Define common philosophical theories that have influenced geography and science, such as logical positivism, Marxism, phenomenology, feminism, and critical theory"},{"concepts":[83],"name":"Define common theories on what constitutes knowledge, including positivism, reflectance-correspondence, pragmatism, social constructivism, and memetics"},{"concepts":[81],"name":"Define common theories on what is real, such as realism, idealism, relativism, and experiential realism"},{"concepts":[8],"name":"Define different interpretations of cost in various routing applications"},{"concepts":[37],"name":"Define direction and its measurement in different angular measures"},{"concepts":[186],"name":"Define entities and relationships in conceptual data model"},{"concepts":[60],"name":"Define friction surface"},{"concepts":[879],"name":"Define GeoJSON definition of Geospatial objects and describe the structure of a GeoJSON document and identify advantages and disadvantages of representing the same geospatial data in GML and in GeoJSON"},{"concepts":[59],"name":"Define intervisibility"},{"concepts":[886],"name":"Define Mapping between legacy definition and the semantic definition of publish"},{"concepts":[882],"name":"Define metadata and identify metadata standards like ISO 19115 and 19119 describe their metadata schema generally"},{"concepts":[879],"name":"Define OGC Simple Features Access Schema. Well-Known Text (WKT) and Well-Known Binary (WKB) representations of Geometry"},{"concepts":[68],"name":"Define prior and posterior distributions and Markov-Chain Monte Carlo"},{"concepts":[878],"name":"Define Resource Description Framework (RDF), its RDF graphs, RDF Schema (RDF-S)and a data set in RDF"},{"concepts":[878],"name":"Define Semantic Web and identify the role of the languages included under this topic for Semantic Web"},{"concepts":[179,873],"name":"Define Service Oriented Architecture (SOA) and identify main elements of it"},{"concepts":[119],"name":"Define spatial autocorrelation in the context of geographic proximity"},{"concepts":[879],"name":"Define spatial extensions that GeoSPARQL brings over SPARQL. Identify the difference between qualitative spatial reasoning and quantitative spatial computations"},{"concepts":[106],"name":"Define Stevens four levels of measurement (nominal, ordinal, interval, ratio)"},{"concepts":[222],"name":"Define terms related to topology (e.g., adjacency, connectivity, overlap, intersect, logical consistency)"},{"concepts":[187],"name":"Define the cardinality of relationships"},{"concepts":[179,180,873],"name":"Define the characteristics of web services and present some examples"},{"concepts":[878],"name":"Define the components of a Web Services Description Language (WSDL) document"},{"concepts":[226],"name":"Define the following terms pertaining to a network: Loops, multiple edges, the degree of a vertex, walk, trail, path, cycle, fundamental cycle"},{"concepts":[8],"name":"Define the following terms pertaining to a network: Loops, multiple edges, the degree of a vertex, walk, trail, path, cycle, fundamental cycle"},{"concepts":[90],"name":"Define the following terms: data, information, knowledge, and wisdom"},{"concepts":[97],"name":"Define the four basic dimensions or shapes used to describe spatial objects (i.e., points, lines, regions, volumes)"},{"concepts":[93],"name":"Define the notions of cultural landscape and physical landscape"},{"concepts":[119],"name":"Define the principle of friction of distance and geographic models that are based on it (e.g., gravity models, spatial interaction models)"},{"concepts":[92],"name":"Define the properties that make a phenomenon geographic"},{"concepts":[531],"name":"Define the radiometric spectral quantities brightness, emittance, luminosity"},{"concepts":[531],"name":"Define the radiometric spectral quantities radiance, irradiance, flux"},{"concepts":[2],"name":"Define the terms spatial analysis, spatial modeling, geostatistics, spatial econometrics, spatial statistics, qualitative analysis, map algebra, and network analysis"},{"concepts":[122],"name":"Define uncertainty-related terms, such as error, accuracy, uncertainty, precision, stochastic, probabilistic, deterministic, and random"},{"concepts":[475],"name":"Define user roles for an existing or planned GIS"},{"concepts":[118],"name":"Define various terms used to describe topological relationships, such as disjoint, overlap, within, and intersect"},{"concepts":[897],"name":"Define Web API composition (WAPIC) concept for RESTful WSs and identify main issues"},{"concepts":[876],"name":"Define Web Coverage Service (WCS). Describe GetCapabilities, GetCoverageInfo, and GetCoverage operations in detail. Practice its usage in a given use case"},{"concepts":[876],"name":"Define Web Feature Service (WFS). Describe GetCapabilities, DescribeFeaturetype, and GetFeature, and GetFeatureInfo operations in detail. Practice its usage in a given use case"},{"concepts":[876],"name":"Define Web Map Service (WMS). Describe GetCapabilities, GetMap, and GetFeatureInfo operations in detail. Practice its usage in a given use case"},{"concepts":[876],"name":"Define Web Map Tile Service (WMTS). Describe GetCapabilities, GetTile, and GetFeatureInfo operations in detail. Practice its usage in a given use case"},{"concepts":[876],"name":"Define Web Processing Service (WPS). Describe GetCapabilities, DescribeProcess, and Execute operations in detail. Practice its usage in a given use case"},{"concepts":[897],"name":"Define web services composition (WSC) concept and identify main issues"},{"concepts":[873],"name":"Define Web services transport over the Web"},{"concepts":[880],"name":"Define what an ontology is. 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constraints, and range of information included"},{"concepts":[302],"name":"Differentiate between consumption, analysis, presumption and production of geoinformation within digital geo media"},{"concepts":[54],"name":"Differentiate between contributing factors and constraints in a multi-criteria application"},{"concepts":[175],"name":"Differentiate between copyleft and permissive licenses for a software product"},{"concepts":[5],"name":"Differentiate between data mining approaches used for spatial and non-spatial applications"},{"concepts":[55],"name":"Differentiate between deterministic and stochastic spatial process models"},{"concepts":[99],"name":"Differentiate between formal and natural language in GI science applications."},{"concepts":[2],"name":"Differentiate between geostatistics, and spatial statistics"},{"concepts":[244],"name":"Differentiate between individual and aggregate models"},{"concepts":[63],"name":"Differentiate between isotropic and anisotropic processes"},{"concepts":[50],"name":"Differentiate between kernel density estimation and spatial interpolation"},{"concepts":[188],"name":"Differentiate between logical and physical models, in terms of the level of detail, constraints, and range of information included"},{"concepts":[213],"name":"Differentiate between lossy and lossless compression methods"},{"concepts":[46,47],"name":"Differentiate between map algebra and matrix algebra using real examples"},{"concepts":[103],"name":"Differentiate between mathematical and phenomenological theories of the nature of time"},{"concepts":[70],"name":"Differentiate between model-based and design-based sampling schemes"},{"concepts":[26],"name":"Differentiate between polynomial coordinate transformations (including linear) and rubbersheeting"},{"concepts":[875],"name":"Differentiate between SOAP and REST Web services. - Identify design issues of REST Web services"},{"concepts":[93],"name":"Differentiate between space and place"},{"concepts":[121],"name":"Differentiate between the following concepts: vagueness and ambiguity, well defined and poorly defined objects and fields or discord and non-specificity"},{"concepts":[52],"name":"Differentiate between the gravity model and spatial interaction models"},{"concepts":[57],"name":"Differentiate between trend surface analysis and deterministic spatial interpolation"},{"concepts":[880],"name":"Differentiate between upper, domain, and application level ontologies"},{"concepts":[266],"name":"Differentiate push-broom and cross-track scanning technologies"},{"concepts":[122],"name":"Differentiate uncertainty in geospatial situations from vagueness"},{"concepts":[138],"name":"Differentiate uses for different types of imagery related to earth"},{"concepts":[109],"name":"Differentiate various sources of fields, such as substance properties (e.g., temperature), artificial constructs (e.g., population density), and fields of potential or influence (e.g., gravity)"},{"concepts":[282],"name":"Digitize and georegister a specified vector feature set to a given geometric accuracy and topological fidelity thresholds using a given map sheet, digitizing tablet, and data entry software"},{"concepts":[309],"name":"Discuss about \"mapping whose reality?\" Pros and cons of geoinformation sharing in social media, i.e. big data, \"digital shadow\" etc."},{"concepts":[297],"name":"Discuss about open data and data sharing and public/private sector"},{"concepts":[291],"name":"Discuss about open data impact on society and citizenship"},{"concepts":[151],"name":"Discuss about the advantages of different immersive display systems"},{"concepts":[159],"name":"Discuss about the degree of subjectivity and/or objectivity of a map"},{"concepts":[125],"name":"Discuss about the History of Cartography in different cultures"},{"concepts":[126],"name":"Discuss about the relationship between art and cartography"},{"concepts":[730,731,732],"name":"Discuss advantages and disadvantages of different methods of storing remote sensing data"},{"concepts":[745,746,747,748],"name":"Discuss advantages and disadvantages of different SAR data formats"},{"concepts":[676],"name":"Discuss advantages and disadvantages of passive and active sensors"},{"concepts":[638],"name":"Discuss advantages of SAR techniques over traditional measuring techniques"},{"concepts":[376],"name":"Discuss algorithms that use the detection of keypoints to identify objects in images"},{"concepts":[679],"name":"Discuss an example of using a radar altimeter"},{"concepts":[737],"name":"Discuss and compare different temporal resolutions of remote sending data"},{"concepts":[632],"name":"Discuss and compare different types of interactions of microwaves with matter"},{"concepts":[744],"name":"Discuss and compare different types of processing levels of optical data"},{"concepts":[749],"name":"Discuss and compare different types of processing levels of SAR data"},{"concepts":[291],"name":"Discuss and define open data and impact on GIS&T"},{"concepts":[362],"name":"Discuss cloud masks as early steps towards semantic enrichment for EO images"},{"concepts":[104],"name":"Discuss common prepositions and adjectives (in any particular language) that signify either spatial or temporal relations but are used for both kinds, such as after or longer"},{"concepts":[245],"name":"Discuss concepts of space-time dynamics for spatial modeling"},{"concepts":[873],"name":"Discuss consensus based interoperability and its relation to geospatial data interchange. Define what a Web Service (WS) is and present characteristic scenarios. Data serving and Data Processing WSs"},{"concepts":[308],"name":"Discuss critiques of GIS as \"deterministic\" technology in relation to debates about the Quantitative quantitative revolution in the discipline of geography."},{"concepts":[312],"name":"Discuss critiques of GIS as deterministic technology in relation to debates about the Quantitative Revolution in the discipline of geography"},{"concepts":[483],"name":"Discuss different formats (tutorials, in house, online, instructor lead) for training and how they can be used by organizations"},{"concepts":[451],"name":"Discuss different methods for assessing the quality of a specific EO product"},{"concepts":[687],"name":"Discuss different types of laser scanners"},{"concepts":[716,592],"name":"Discuss different types of satellite orbits"},{"concepts":[248],"name":"Discuss different ways of simulating space and visualizing model behaviour"},{"concepts":[605],"name":"Discuss electromagnetic interactions and scattering mechanisms"},{"concepts":[722],"name":"Discuss examples of ground-based platforms and their use"},{"concepts":[715],"name":"Discuss examples of the objectives of Earth observation missions"},{"concepts":[481],"name":"Discuss how a code of ethics might be applied within an organization"},{"concepts":[136],"name":"Discuss how cultural differences with respect to color associations impact map design"},{"concepts":[454,734],"name":"Discuss how different spectral resolution of EO sensors influences their potential for vegetation mapping"},{"concepts":[418],"name":"Discuss how hierarchical representation is exploited for object-based image analysis"},{"concepts":[669],"name":"Discuss how line detectors array sensors work"},{"concepts":[406],"name":"Discuss how low-pass filtering of an image impacts the resulting regions derived with watershed segmentation"},{"concepts":[159],"name":"Discuss how maps express relations of power"},{"concepts":[278],"name":"Discuss how measures of spatial autocorrelation may be used to evaluate thematic accuracy"},{"concepts":[736,454],"name":"Discuss how radiometric resolution influences the granularity of a land cover classification"},{"concepts":[733,741],"name":"Discuss how remote sensing data is organized and stored"},{"concepts":[652],"name":"Discuss how the angle of SAR signal incidence affects SAR acquisition"},{"concepts":[398],"name":"Discuss how the choice of sampling strategy impacts the accuracy assesment for a classification result"},{"concepts":[398],"name":"Discuss how the choice of sampling strategy impacts the classification result"},{"concepts":[742],"name":"Discuss how the radiometrically corrected data are processed"},{"concepts":[415],"name":"Discuss how the size of the neighborhood impacts the smoothing effect of a low-pass filter"},{"concepts":[298],"name":"Discuss how to approach the widening audience/participants for geospatial products and service, increasing geo-awareness and geo-enablement"},{"concepts":[143],"name":"Discuss how to create an intellectual and visual hierarchy on maps"},{"concepts":[600],"name":"Discuss how to use phase information in remote sensing"},{"concepts":[31],"name":"Discuss implications of data loss in the case of generalisation of spatial data."},{"concepts":[343],"name":"Discuss imputation methods for filling in missing data"},{"concepts":[508],"name":"Discuss in which way annual solar insolation and average cloud coverage parameters affect the choice of a solar power plant location"},{"concepts":[508],"name":"Discuss in which way modeled daily solar insolation and cloud coverage forecast could affect solar power plant day-by-day management"},{"concepts":[299],"name":"Discuss legal aspects of access to environmental data, global change/warming or sustainable development (regional, national, global) in conjunction to society."},{"concepts":[638],"name":"Discuss limitations of interferometric measurement"},{"concepts":[407],"name":"Discuss limitations of the different region-based segementation methods"},{"concepts":[729],"name":"Discuss main characteristics of digital imagery"},{"concepts":[291],"name":"Discuss of arguments for and against open data"},{"concepts":[290],"name":"Discuss of opportunities for exchange of geospatial data between public and private sector to enable more efficient analysis"},{"concepts":[243],"name":"Discuss options of combining rule-based models with other individual modelling approaches"},{"concepts":[627],"name":"Discuss orientational polarisation of media"},{"concepts":[308],"name":"Discuss over the argument that the use of Geospatial geospatial Information privileges certain views of the world over others."},{"concepts":[297],"name":"Discuss over the changing role of the private sector in the use of geospatial information"},{"concepts":[298],"name":"Discuss over the paradigm shifts and current trends in GIS&T education and pedagogical approaches for GIS teaching and learning in detail"},{"concepts":[309],"name":"Discuss over the various implications of surveillance technology"},{"concepts":[626],"name":"Discuss polarimetric decomporition techniques"},{"concepts":[303],"name":"Discuss positive and negative aspects of the term \"humans as sensors\""},{"concepts":[635],"name":"Discuss radar antennas"},{"concepts":[621],"name":"Discuss scale of roughness of microwaves"},{"concepts":[2],"name":"Discuss situations when it is desirable to adopt a spatial approach to the analysis of data"},{"concepts":[226],"name":"Discuss some of the difficulties of applying the standard process-pattern concept to lines and networks"},{"concepts":[408],"name":"Discuss spatial autocorrelation and homogeneity of image objects"},{"concepts":[174],"name":"Discuss the advantages and disadvantages of outsourcing elements of a GIS project / GI system"},{"concepts":[97],"name":"Discuss the advantages and disadvantages of the use of cartesian metric space as a basis for GIS and related technologies"},{"concepts":[279],"name":"Discuss the advantages and potential problems associated with the use of Minimum Mapping Unit (MMU) as a measure of the level of detail in land use, land cover, and soils maps"},{"concepts":[677],"name":"Discuss the application possibilities of imaging radar"},{"concepts":[693],"name":"Discuss the applications for which Differential Absorption LiDAR can be used"},{"concepts":[694],"name":"Discuss the applications for which Wind Doppler LiDAR is used"},{"concepts":[64],"name":"Discuss the appropriateness of different types of spatial weights matrices for various problems"},{"concepts":[78],"name":"Discuss the appropriateness of GWR under various conditions"},{"concepts":[438],"name":"Discuss the available data quality standards for EO"},{"concepts":[568],"name":"Discuss the basic principles of solar radiation."},{"concepts":[414],"name":"Discuss the benefits of using a gauss filter instead of a mean filter for smoothing an image"},{"concepts":[112],"name":"Discuss the causal relationship between spatial processes and spatial patterns, including the possible problems in determining causality"},{"concepts":[528],"name":"Discuss the change of attenuation length moving from visible to the microwave range and from sea water to solid land surfaces"},{"concepts":[51],"name":"Discuss the characteristics of the various cluster detection techniques"},{"concepts":[25],"name":"Discuss the consequences of increasing and decreasing resolution"},{"concepts":[111],"name":"Discuss the contributions of early attempts to integrate the concepts of space, time, and attribute in geographic information, such as Berry (1964) and Sinton (1978)"},{"concepts":[97],"name":"Discuss the contributions that different perspectives on the nature of space bring to an understanding of geographic phenomenon"},{"concepts":[111],"name":"Discuss the degree to which these models can be implemented using current technologies"},{"concepts":[665],"name":"Discuss the development of remote sensing sensors"},{"concepts":[123],"name":"Discuss the difference between vagueness and uncertainty."},{"concepts":[10],"name":"Discuss the difference of implementing Dijkstras algorithm in raster and vector modes"},{"concepts":[695],"name":"Discuss the differences between imaging and non-imaging sensors"},{"concepts":[133],"name":"Discuss the differences between maps that use the same data but are for different purposes and intended audiences"},{"concepts":[133],"name":"Discuss the differences between maps that use the same data but are for different purposes and intended audiences"},{"concepts":[454],"name":"Discuss the different types of resolution of Earth observation data"},{"concepts":[92],"name":"Discuss the differing denotations and connotations of the terms spatial, geographic, and geospatial"},{"concepts":[110],"name":"Discuss the difficulty of integrating process models into GIS software based on the entity and field views, and methods used to do so"},{"concepts":[117],"name":"Discuss the effects of temporal scale on the modeling of genealogical structures"},{"concepts":[305],"name":"Discuss the ethical implications of a local government's decision to charge fees for its data"},{"concepts":[413],"name":"Discuss the frequencies that a high-pass filter preserves and subdues"},{"concepts":[499],"name":"Discuss the governance structure in place of a particular country"},{"concepts":[222],"name":"Discuss the historical roots of the Census Bureaus creation of GBF/DIME as the foundation for the development of topological data structures"},{"concepts":[704],"name":"Discuss the history of the development of remote sensing platforms"},{"concepts":[108],"name":"Discuss the human predilection to conceptualize geographic phenomena in terms of discrete entities"},{"concepts":[302],"name":"Discuss the impact of geospatial information for the development of social media (Facebook, Twitter, Wikimapia, Flickr etc.) becoming increasingly location-based"},{"concepts":[232],"name":"Discuss the implication of long transactions on database integrity"},{"concepts":[312],"name":"Discuss the implications of interoperability on ontology"},{"concepts":[308],"name":"Discuss the implications of interoperability on ontology"},{"concepts":[279],"name":"Discuss the implications of the sampling theorem (Lambda = 0.5 delta) to the concept of resolution"},{"concepts":[28],"name":"Discuss the implications of tradeoff between data detail and data volume"},{"concepts":[107],"name":"Discuss the importance of space, time, properties, and categories as fundamentals in the conceptualization and representation of spatial entities."},{"concepts":[150],"name":"Discuss the influence of the user interface on maps and visualizations on the Web"},{"concepts":[875],"name":"Discuss the issue whether a service is really \"RESTful\" or not"},{"concepts":[290],"name":"Discuss the legal framework related to competition and public-private sector relationships in the geospatial domain"},{"concepts":[711],"name":"Discuss the main applications using the extra wide swath mode"},{"concepts":[400],"name":"Discuss the main drawback of edge-based segmentation in partitioning an image"},{"concepts":[672],"name":"Discuss the main properties of hyperspectral radiometers"},{"concepts":[671],"name":"Discuss the main properties of passive microwave radiometers"},{"concepts":[670],"name":"Discuss the main properties of thermal radiometers"},{"concepts":[664],"name":"Discuss the main types of remote sensing data"},{"concepts":[664,723],"name":"Discuss the main types of remote sensing platforms"},{"concepts":[664],"name":"Discuss the main types of remote sensing sensors"},{"concepts":[454],"name":"Discuss the minimum spatial resolution required for detecting single houses in a satellite image"},{"concepts":[503],"name":"Discuss the mission, history, constituencies, and activities of the GIS Certification Institute (GISCI)"},{"concepts":[483],"name":"Discuss the National Research Council report on Learning to Think Spatially (2005) as it relates to spatial thinking skills needed by the GIS and T workforce"},{"concepts":[737,454],"name":"Discuss the needs for high temporal resolution for analysing crop cycles in agriculture"},{"concepts":[23],"name":"Discuss the pitfalls of using secondary data that has been generated using interpolations (e.g., Level 1 USGS DEMs)"},{"concepts":[631],"name":"Discuss the polarimetry technique"},{"concepts":[29],"name":"Discuss the possible effects on topological integrity of generalizing data sets"},{"concepts":[287],"name":"Discuss the potential legal problems associated with licensing geospatial information"},{"concepts":[313],"name":"Discuss the potential role of agency (individual action) in resisting dominant practices and in using GIS and T in ways that are consistent with feminist epistemologies and politics"},{"concepts":[404],"name":"Discuss the principles of regionalisation and their use in segmentation methods"},{"concepts":[575],"name":"Discuss the processes that describe the hydrologic cycle"},{"concepts":[314],"name":"Discuss the production, maintenance, and use of geospatial data by a government agency or private firm from the perspectives of a taxpayer, a community organization, and a member of a minority group"},{"concepts":[752],"name":"Discuss the purposes of obtaining remote sensing data"},{"concepts":[606],"name":"Discuss the radiometric anomalies of radar data"},{"concepts":[55],"name":"Discuss the relationship between spatial processes and spatial patterns"},{"concepts":[125],"name":"Discuss the relationship between the history of exploration and the development of a more accurate map of the world"},{"concepts":[30],"name":"Discuss the relationship of attribute measurement levels to database query operations"},{"concepts":[302],"name":"Discuss the role and value of \"place\" and \"space\" for geo media based social networking"},{"concepts":[136],"name":"Discuss the role of gamut in choosing colors that can be reproduced on various devices and media"},{"concepts":[222],"name":"Discuss the role of graph theory in topological structures"},{"concepts":[22],"name":"Discuss the role of metadata in facilitating conversation of data models and data structures between systems"},{"concepts":[299,304],"name":"Discuss the role of public, private sector and citizens in facilitating geospatial information in environmental/sustainable issues."},{"concepts":[290],"name":"Discuss the role of the public and private sectors in producing and dissemination of geospatial information"},{"concepts":[481],"name":"Discuss the status of professional and academic certification in GIS and T"},{"concepts":[288],"name":"Discuss the status of the concept of privacy in the U.S. legal regime"},{"concepts":[142],"name":"Discuss the strengths and weaknesses of infographics as a method of displaying geographic information"},{"concepts":[564],"name":"Discuss the structure and chemical composition of the atmosphere"},{"concepts":[0],"name":"Discuss the synergy between processes in geo-information systems and earth observation systems."},{"concepts":[63],"name":"Discuss the theory leading to the assumption of intrinsic stationarity"},{"concepts":[668],"name":"Discuss the use of area array sensors in remote sensing"},{"concepts":[674],"name":"Discuss the use of atmospheric passive sounders"},{"concepts":[673],"name":"Discuss the use of data obtained by spectroradiometer"},{"concepts":[667],"name":"Discuss the use of digital frame cameras in remote sensing"},{"concepts":[598],"name":"Discuss the use of polarization for different application domains"},{"concepts":[149],"name":"Discuss the uses of the map as a user interface element in interactive presentations of geographic information"},{"concepts":[705],"name":"Discuss the ways of using data acquired by UAS in remote sensing"},{"concepts":[703],"name":"Discuss types and classes of remote sensing sensors"},{"concepts":[456],"name":"Discuss valid time ranges for images used for landslide mapping with pre- and post-event image comparison"},{"concepts":[291],"name":"Discuss various legal aspects of public and private sectors concerning owning, controlling, sharing/ disseminating open data."},{"concepts":[291],"name":"Discuss various sources of open data (science, public and private sectors)"},{"concepts":[286],"name":"Discuss ways in which the geospatial profession is regulated under the U.S. legal regime"},{"concepts":[298],"name":"Discuss ways of working with crowdsourcing in education and research"},{"concepts":[618],"name":"Discuss what horizontal roughness component (correlation legth) is"},{"concepts":[680],"name":"Discuss what information is acquired by the laser altimeters"},{"concepts":[617],"name":"Discuss what surface height variation (or RMS height) is"},{"concepts":[739],"name":"Discuss what the header file describes"},{"concepts":[675],"name":"Discuss what the main characteristics of radiometers are"},{"concepts":[678],"name":"Discuss what types of electromagnetic waves the laser profiler uses"},{"concepts":[364],"name":"Discuss why a query through time is easier realized with a data cube than by comparison of a time series stored in image files"},{"concepts":[738],"name":"Distinguish and explain the different types of properties of digital imagery"},{"concepts":[149,139],"name":"Distinguish between animated and interactive maps"},{"concepts":[89],"name":"Distinguish between continuants and occurrents in relation with spatial phenomena."},{"concepts":[154],"name":"Distinguish between different graphic representation techniques"},{"concepts":[86],"name":"Distinguish between metaphysics and epistemology."},{"concepts":[186],"name":"Distinguish between the temporary and structural relationships in a conceptual model"},{"concepts":[27],"name":"Distinguish between transformation methods for raster and vector representations."},{"concepts":[164,170],"name":"Distinguish between usability, utility, and user needs in the context of geovisualizations"},{"concepts":[167,168],"name":"Document existing and potential tasks in terms of workflow and information flow"},{"concepts":[105],"name":"Document the personal, social, and or institutional meaning of categories used in GIS applications"},{"concepts":[150],"name":"Edit the symbology, labeling, and page layout for a map originally designed for hard copy printing so that it can be seen and used on the Web"},{"concepts":[101],"name":"Effectively communicate the design, procedures, and results of GIS projects to non-GIS audiences (clients, managers, general public)"},{"concepts":[112],"name":"Employ techniques for visualizing, describing, and analyzing distributions in space, time, and attribute"},{"concepts":[791],"name":"Enable citizen skills spatially"},{"concepts":[23],"name":"Estimate a value between two known values using linear interpolation (e.g., spot elevations, population between census years)"},{"concepts":[832],"name":"Estimate evaporation rates"},{"concepts":[832,355],"name":"Estimate near-surface chlorophyll-a concentration for monitoring harmful algal blooms (HABs)"},{"concepts":[129],"name":"Estimate the cost to collect needed data from primary sources (e.g., remote sensing, GPS)"},{"concepts":[36],"name":"Estimate the fractal dimension of a sinuous line"},{"concepts":[550],"name":"Estimate the meteorological and the cloud optical properties by LBRTM and validate against high accuracy spectral measurements"},{"concepts":[127],"name":"Estimate the potential value of a historical map"},{"concepts":[437],"name":"Evaluate an EO product and its metadata on its reusability for a new application context"},{"concepts":[476],"name":"Evaluate and revise an existing GIS management strategy"},{"concepts":[791,788,789],"name":"Evaluate citizen-driven observations"},{"concepts":[153],"name":"Evaluate graphic techniques used to portray spatializations"},{"concepts":[25],"name":"Evaluate methods used by contemporary GIS software to resample raster data on-the-fly during display"},{"concepts":[266],"name":"Evaluate the advantages and disadvantages of acoustic remote sensing versus airborne or satellite remote sensing for seafloor mapping"},{"concepts":[266,714,719],"name":"Evaluate the advantages and disadvantages of airborne remote sensing versus satellite remote sensing"},{"concepts":[264],"name":"Evaluate the advantages and disadvantages of photogrammetric methods and LiDAR for production of terrain elevation data"},{"concepts":[110],"name":"Evaluate the assertion that events and processes are the same thing, but viewed at different temporal scales"},{"concepts":[122],"name":"Evaluate the causes of uncertainty in geospatial data"},{"concepts":[136],"name":"Evaluate the colors used in a web map to be used indoors and outdoors"},{"concepts":[434],"name":"Evaluate the conformity of an EO imagery product to ISO 19129"},{"concepts":[93],"name":"Evaluate the differences in how various parties think or feel differently about a place being modeled"},{"concepts":[217],"name":"Evaluate the ease of measuring resolution in different types of tessellations"},{"concepts":[108],"name":"Evaluate the effectiveness of GIS data models for representing the identity, existence, and lifespan of entities"},{"concepts":[109],"name":"Evaluate the field views description of objects as conceptual discretizations of continuous patterns"},{"concepts":[828],"name":"Evaluate the impact of changes in land areas"},{"concepts":[101],"name":"Evaluate the impact of geospatial technologies (e.g., Google Earth) that allow non-geospatial professionals to create, distribute, and map geographic information"},{"concepts":[807,805],"name":"Evaluate the impact of the climate change"},{"concepts":[217],"name":"Evaluate the implications of changing grid cell resolution on the results of analytical applications by using GIS software"},{"concepts":[108],"name":"Evaluate the influence of scale on the conceptualization of entities"},{"concepts":[85],"name":"Evaluate the influences of ones own philosophical views and assumptions on GIS AND T practices"},{"concepts":[81],"name":"Evaluate the influences of particular worldviews (including ones own) on GIS practices"},{"concepts":[95],"name":"Evaluate the influences of political actions, especially the allocation of territory, on human perceptions of space and place"},{"concepts":[95],"name":"Evaluate the influences of political ideologies (e.g., Marxism, Capitalism, conservative liberal) on the understanding of geographic information"},{"concepts":[495],"name":"Evaluate the institutional framework of an existing SDI initiative"},{"concepts":[222],"name":"Evaluate the positive and negative impacts of this shift from integrated topological models"},{"concepts":[213],"name":"Evaluate the relative merits of grid compression methods for storage"},{"concepts":[485],"name":"Evaluate the relevance and applicability of different teaching and learning methods for GIS&T education"},{"concepts":[109],"name":"Evaluate the representation of movement as a field of location over time (e.g. :x,y,z: = f(t) )"},{"concepts":[121],"name":"Evaluate the role that system complexity, dynamic processes, and subjectivity play in the creation of vague phenomena and concepts"},{"concepts":[144],"name":"Evaluate the strengths and limitations of different thematic mapping methods"},{"concepts":[242],"name":"Evaluate the tradeoffs between abstraction and representativeness in simulation model development"},{"concepts":[161],"name":"Evaluate the usability of a hard-copy map"},{"concepts":[161,170],"name":"Evaluate the usability of a web map"},{"concepts":[187],"name":"Evaluate the various general data models common in GIS project"},{"concepts":[121],"name":"Evaluate vagueness in the locations, time, attributes, and other aspects of geographic phenomena"},{"concepts":[29],"name":"Evaluate various line simplification algorithms by their usefulness in different applications"},{"concepts":[243],"name":"Evaluate when rule-based models can be applied to spatiotemporal problems"},{"concepts":[238],"name":"Examine how computational technology relates to geocomputation"},{"concepts":[360],"name":"Examine how the vegetation indices relates to the vegetation dynamics and health"},{"concepts":[360],"name":"Examine how the water-related spectral indices relates to changes in the vegetation and soil water content"},{"concepts":[885],"name":"Examine Metadata schema and vocabularies used for open data publishing"},{"concepts":[900],"name":"Examine the Document Object Model (DOM) in HTML documents"},{"concepts":[45],"name":"Exemplify applications in which overlay is useful, such as site suitability analysis"},{"concepts":[63],"name":"Exemplify deterministic and spatial stochastic processes"},{"concepts":[103],"name":"Exemplify different temporal frames of reference: linear and cyclical, absolute and relative"},{"concepts":[474],"name":"Exemplify each component of a needs assessment for an enterprise GIS"},{"concepts":[235],"name":"Exemplify how the lack of a data librarian to manage data can have disastrous consequences on the resulting dataset"},{"concepts":[63],"name":"Exemplify non-stationarity involving first and second order effects"},{"concepts":[113],"name":"Exemplify regions found at different scales"},{"concepts":[232],"name":"Exemplify scenarios in which one would need to perform a number of periodic changes in a real GIS database"},{"concepts":[38],"name":"Exemplify situations in which the centroid of a polygon falls outside its boundary"},{"concepts":[12],"name":"Exemplify the Classic Transportation Problem"},{"concepts":[222],"name":"Exemplify the concept of planar enforcement (e.g., TIN triangles)"},{"concepts":[215],"name":"Exemplify the uses (past and potential) of the hexagonal model"},{"concepts":[537],"name":"Explain the concept of composition of spectral signatures and apply the \"linear mixing\" models in some simple case"},{"concepts":[777],"name":"Explain a use case of EO for smart cities, e.g. how EO derived information about urban green instrastructure supports designing nature based solutions for preserving ecosystem services"},{"concepts":[641],"name":"Explain across-track interferometry technique"},{"concepts":[640],"name":"Explain along-track interferometry technique"},{"concepts":[395],"name":"Explain an application example where SVM is used for EO image classification"},{"concepts":[360],"name":"Explain an application example where the spectral indices are used for vegetation, water or snow monitoring"},{"concepts":[207],"name":"Explain and apply GML data models"},{"concepts":[600],"name":"Explain and apply phase unwrapping"},{"concepts":[203,221],"name":"Explain and apply standards relevant for geometric modelling"},{"concepts":[655],"name":"Explain and discuss elements of Synthetic Aperture Radar (SAR) geometric configuration"},{"concepts":[622],"name":"Explain and discuss surface roughness in microwave remote sensing"},{"concepts":[595],"name":"Explain and discuss the complex elements of a radar signal"},{"concepts":[728],"name":"Explain and discuss the concept of Big Data in the field of Earth Observation"},{"concepts":[724],"name":"Explain and discuss the development of remote sensing data carriers"},{"concepts":[688],"name":"Explain and discuss the LiDAR technology"},{"concepts":[709],"name":"Explain and discuss the SAR acquisition mode spotlight"},{"concepts":[708],"name":"Explain and discuss the SAR acquisition mode staring spotlight"},{"concepts":[676],"name":"Explain and discuss types of sensing mechanisms"},{"concepts":[633],"name":"Explain and discuss what antenna gain is and why it is described as the key performance of a radar antenna"},{"concepts":[660],"name":"Explain and discuss what terrain reflectivity is and how it influences radar signal"},{"concepts":[657],"name":"Explain and discuss what the foreshortening is"},{"concepts":[658],"name":"Explain and discuss what the layover is"},{"concepts":[751],"name":"Explain and discuss what the main processing levels of remote sensing data are"},{"concepts":[738],"name":"Explain and discuss what the radiometric resolution is"},{"concepts":[651],"name":"Explain and discuss what the range direction is"},{"concepts":[659],"name":"Explain and discuss what the shadow in SAR acquisition means"},{"concepts":[738,735],"name":"Explain and discuss what the spatial resolution is"},{"concepts":[738],"name":"Explain and discuss what the spectral resolution is"},{"concepts":[738],"name":"Explain and discuss what the temporal resolution is"},{"concepts":[663,603],"name":"Explain and outline the advantages of radar sensors"},{"concepts":[197],"name":"Explain and use UML diagrams"},{"concepts":[76],"name":"Explain Anselins typology of spatial autoregressive models"},{"concepts":[37],"name":"Explain any differences in the measured direction between two places when the data are presented in a GIS in different projections"},{"concepts":[200],"name":"Explain basic aspects of data modelling, storage and exploitation, such as relation models & databases, data structures, SQL, UML and other basics"},{"concepts":[287],"name":"Explain cases of liability claims associated with misuse of geospatial information, erroneous information, and loss of proprietary interests"},{"concepts":[625],"name":"Explain covariance and coherence matrix"},{"concepts":[616],"name":"Explain dielectric properties of objects and their effect on radar data acquisition"},{"concepts":[639],"name":"Explain differences between DInSAR and PSI"},{"concepts":[663],"name":"Explain differences between optical and radar remote sensing"},{"concepts":[84],"name":"Explain from which scientific fields GIS&T borrows ideas."},{"concepts":[236],"name":"Explain geocomputation, related concepts and how the two relate"},{"concepts":[6],"name":"Explain how a Bayesian framework can incorporate expert knowledge in order to retrieve all relevant datasets given an initial user query"},{"concepts":[493],"name":"Explain how a business case analysis can be used to justify the expense of implementing consensus-based standards"},{"concepts":[378],"name":"Explain how a DSM differs from a DTM"},{"concepts":[226],"name":"Explain how a graph (network) may be directed or undirected"},{"concepts":[226],"name":"Explain how a graph can be written as an adjacency matrix and how this can be used to calculate topological shortest paths in the graph"},{"concepts":[329],"name":"Explain how a histogram is derived from an EO image"},{"concepts":[458],"name":"Explain how a lack of knowledge about data quality limits the data 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association"},{"concepts":[78],"name":"Explain how allowing the parameters of the model to vary with the spatial location of the sample data can be used to accommodate spatial heterogeneity"},{"concepts":[56,1],"name":"Explain how analytical methods are used to derive analytical results from geospatial data"},{"concepts":[361],"name":"Explain how band maths can be applied to derive an index that indicates a specific land cover type like vegetation"},{"concepts":[72],"name":"Explain how block-kriging and its variants can be used to combine data sets with different spatial resolution support"},{"concepts":[44],"name":"Explain how buffers can be used in GI analysis"},{"concepts":[208],"name":"Explain how CityGML is related to GML"},{"concepts":[419],"name":"Explain how class modelling can make use of per-parcel analysis"},{"concepts":[301],"name":"Explain how community organizations represent the interests of citizens, politicians, and specialists"},{"concepts":[377],"name":"Explain how computer vision imitates the human visual system when interpreting EO images"},{"concepts":[288],"name":"Explain how conversion of land records data from analog to digital form increases risk to personal privacy"},{"concepts":[288],"name":"Explain how data aggregation is used to protect personal privacy in data produced by the U.S. Census Bureau"},{"concepts":[36],"name":"Explain how different measures of distance can be used to calculate the spatial weights matrix"},{"concepts":[64],"name":"Explain how different types of spatial weights matrices are defined and calculated"},{"concepts":[77],"name":"Explain how dissolving clusters of blocks with similar values may resolve the spatial correlation problem"},{"concepts":[49],"name":"Explain how distance-based methods of point pattern measurement can be derived from a distance matrix"},{"concepts":[52],"name":"Explain how dynamic, chaotic, complex or unpredictable aspects in some phenomena make spatial interaction models more appropriate than gravity models"},{"concepts":[349],"name":"Explain how EO applications targeting several countries at once can profit from data harmonisation"},{"concepts":[367],"name":"Explain how error propagates in the production workflow of an example EO product"},{"concepts":[319],"name":"Explain how fourier transformation is used to generate radar image"},{"concepts":[319],"name":"Explain how fourier transformation is used to reduce noise in optical imagery"},{"concepts":[36],"name":"Explain how fractal dimension can be used in practical applications of GIS"},{"concepts":[60],"name":"Explain how friction surfaces are enhanced by the use of impedance and barriers"},{"concepts":[300],"name":"Explain how geographic information is valuable to different sectors"},{"concepts":[66],"name":"Explain how geographically weighted regression provides a local measure of spatial association"},{"concepts":[277],"name":"Explain how geometric accuracies associated with the various orders of the U.S. 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simulation models can be used to advance scientific knowledge in different geographic scenarios (e.g. transportation, health geography, urban and regional analysis)"},{"concepts":[5],"name":"Explain how spatial statistics techniques are used in spatial data mining"},{"concepts":[153],"name":"Explain how spatialization is a core component of visual analytics"},{"concepts":[380],"name":"Explain how stereo-imaging enables the derivation of information about elevation"},{"concepts":[321],"name":"Explain how stereoscopic imagery allows to derive an orthorectified image for the overlapping image areas"},{"concepts":[212],"name":"Explain how terrain elevation can be represented by a regular tessellation and by an irregular tessellation"},{"concepts":[137],"name":"Explain how text properties can be used as visual variables to graphically represent the type and attributes of geographic features"},{"concepts":[469],"name":"Explain how the acquisition, storing, and processing of EO images and derived products is distributed over a chain of stakeholders"},{"concepts":[5],"name":"Explain how the analytical reasoning techniques, visual representations, and interaction techniques that make up the domain of visual analytics have a strong spatial component"},{"concepts":[68],"name":"Explain how the Bayesian perspective is a unified framework from which to view uncertainty"},{"concepts":[93],"name":"Explain how the concept of place is more than just location"},{"concepts":[402],"name":"Explain how the consideration of local variance can enhance image segmentation results"},{"concepts":[850],"name":"Explain how the CORINE Land Cover product quality depends on its source EO data and how this affects its usage for regional planning."},{"concepts":[320],"name":"Explain how the DEM generation with SfM works and discuss its differences to the traditional method of DEM extraction with stereographic photogrammetry"},{"concepts":[23],"name":"Explain how the elevation values in a digital 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sensor"},{"concepts":[700],"name":"Explain the principles of operation of the structured-light-projection camera"},{"concepts":[16],"name":"Explain the principles of operations research modeling and location modeling"},{"concepts":[682],"name":"Explain the principles of spaceborne laser scanning operation and discuss its applications"},{"concepts":[643],"name":"Explain the principles of synthetic aperture radar (SAR) interferometry"},{"concepts":[721],"name":"Explain the principles of terrestrial laser scanning operation and discuss its applications"},{"concepts":[644],"name":"Explain the principles of the SAR tomography"},{"concepts":[685],"name":"Explain the principles of underwater laser scanning operation and discuss its applications"},{"concepts":[441],"name":"Explain the procedure how to collect ground reference data for an image classification"},{"concepts":[242],"name":"Explain the process simulation model development"},{"concepts":[341],"name":"Explain the purpose of image 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data"},{"concepts":[264,713],"name":"Explain the relevance of the concept parallax in stereoscopic aerial imagery"},{"concepts":[295],"name":"Explain the relevant economic aspects related to the access to and use of geographic information"},{"concepts":[492],"name":"Explain the relevant legal and organizational issues around development and implementation of Spatial Data Infrastructures (SDI)"},{"concepts":[492],"name":"Explain the relevant technological issues around development and implementation of Spatial Data Infrastructures (SDI)"},{"concepts":[177],"name":"Explain the requirements that best match each geospatial software architecture"},{"concepts":[497],"name":"Explain the results of an SDI assessment"},{"concepts":[242],"name":"Explain the role and purpose of computer simulation methods in geocomputation"},{"concepts":[326],"name":"Explain the role and selection criteria for ground control points (GCPs) in the georegistration of aerial imagery"},{"concepts":[105],"name":"Explain the role of categories in common-sense conceptual models, everyday language, and analytical procedures"},{"concepts":[17],"name":"Explain the role of constraint functions using the graphical method"},{"concepts":[17],"name":"Explain the role of constraint functions using the simplex method"},{"concepts":[344],"name":"Explain the role of Gram-Schmidt vector orthogonalization in pan-sharpening"},{"concepts":[88],"name":"Explain the role of metaphors and image schema in our understanding of geographic phenomena and geographic tasks"},{"concepts":[98],"name":"Explain the role of metaphors and image schemata in our understanding of geographic phenomena and geographic tasks."},{"concepts":[17],"name":"Explain the role of objective functions in linear programming"},{"concepts":[395],"name":"Explain the sensitivity of SVM to hyper-parameters"},{"concepts":[394],"name":"Explain the sensitivity of the Random Forests classifier to the number of trees and the number of variables used to split the tree nodes"},{"concepts":[411],"name":"Explain the shape and weights for a horizontal edge detector"},{"concepts":[59],"name":"Explain the sources and impact of errors that affect intervisibility analyses"},{"concepts":[353],"name":"Explain the value of the leaf area index for vegetation mapping"},{"concepts":[187],"name":"Explain the various types of cardinality"},{"concepts":[770],"name":"Explain to customers the information derived from EO"},{"concepts":[754],"name":"Explain Tobler's first law of geography."},{"concepts":[878],"name":"Explain Web Ontology Language (OWL) and how to define a data set in OWL DL"},{"concepts":[19],"name":"Explain Webers locational triangle"},{"concepts":[293],"name":"Explain what a business model is and how is used"},{"concepts":[246],"name":"Explain what a cellular automata is and what its key components are"},{"concepts":[727],"name":"Explain what a data cube is"},{"concepts":[173],"name":"Explain what a project is, and the difference between a project, programme, and product"},{"concepts":[645],"name":"Explain what active-passive microwave imaging is"},{"concepts":[247],"name":"Explain what an agent-based model is and what its key components are"},{"concepts":[652],"name":"Explain what an incident angle is"},{"concepts":[690],"name":"Explain what can be measured with a seismic sensor or seismic sensors"},{"concepts":[691],"name":"Explain what can be measured with a sonic sensor"},{"concepts":[689],"name":"Explain what can bea measeard with a sonar sensor"},{"concepts":[208],"name":"Explain what CityGML is"},{"concepts":[599],"name":"Explain what coherent means in radar remote sensing"},{"concepts":[895],"name":"Explain what data mashups are"},{"concepts":[191],"name":"Explain what databases are"},{"concepts":[490],"name":"Explain what framework agreements are and how they can be used for sharing geospatial data"},{"concepts":[261],"name":"Explain what horizontal and vertical datums 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used"},{"concepts":[262],"name":"Explain what map projections are"},{"concepts":[663],"name":"Explain what microwave remote sensing is"},{"concepts":[291],"name":"Explain what open data and the main principles of open data are"},{"concepts":[662],"name":"Explain what properties of microwave electromagnetic spectrum are recorded"},{"concepts":[307],"name":"Explain what relevant ethical aspects are related to the access to and use of geospatial information"},{"concepts":[499],"name":"Explain what SDI governance is and why it is important in the development and implementation of SDIs"},{"concepts":[614],"name":"Explain what soil permittivity is"},{"concepts":[718],"name":"Explain what swath represents"},{"concepts":[219],"name":"Explain what tessellation data models are"},{"concepts":[613],"name":"Explain what the attenuation length and penetration depth are"},{"concepts":[650],"name":"Explain what the azimuth direction is"},{"concepts":[726],"name":"Explain what the digital number is"},{"concepts":[654],"name":"Explain what the ground range and azimuth resolution are"},{"concepts":[710],"name":"Explain what the interferometric wide swath mode is"},{"concepts":[634],"name":"Explain what the main representations of radar antenna pattern are"},{"concepts":[600],"name":"Explain what the mathematical description of the phase is"},{"concepts":[600],"name":"Explain what the phase in remote sensing means and in what units is expressed"},{"concepts":[596],"name":"Explain what the phasor represents"},{"concepts":[725],"name":"Explain what the picture element is"},{"concepts":[611],"name":"Explain what the radar cross-section is"},{"concepts":[607],"name":"Explain what the radar equation is"},{"concepts":[692],"name":"Explain what the radar scatterometer measures"},{"concepts":[881],"name":"Explain what the Resource Description Framework (RDF) is and what it can be used for"},{"concepts":[602],"name":"Explain what the wave-particle dualism is"},{"concepts":[275],"name":"Explain which elements determine the quality of geospatial data"},{"concepts":[409],"name":"Explain which principles a segmentation should follow to arrive at meaningful objects that are appropriate for a specific application"},{"concepts":[202],"name":"Explain which standards are essential for conceptual data modelling"},{"concepts":[82],"name":"Explain which technologies have an impact on GI science."},{"concepts":[270],"name":"Explain which types of geospatial data are collected through satellite remote sensing"},{"concepts":[140],"name":"Explain why a layer with audio could be of interest in certain situations"},{"concepts":[597],"name":"Explain why a radar signal needs a complex waveform description"},{"concepts":[265],"name":"Explain why aerial imaging and photogrammetry are important for the geospatial domain and the geospatial industry"},{"concepts":[50],"name":"Explain why and how density estimation transforms point data into a field 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programs"},{"concepts":[222],"name":"Explain why integrated topological models have lost favor in commercial GIS software"},{"concepts":[481],"name":"Explain why it has been difficult for many agencies and organizations to define positions and roles for GIS and T professionals"},{"concepts":[72],"name":"Explain why it is important to have a good model of the semi-variogram in kriging"},{"concepts":[310],"name":"Explain why it is important to take into consideration the 'digital divide' when dealing with the use of and access to geographic data and information"},{"concepts":[72],"name":"Explain why kriging is more suitable as an interpolation method in some applications than others"},{"concepts":[233],"name":"Explain why logging and rollback techniques are adequate for managing short transactions"},{"concepts":[276],"name":"Explain why metadata are important for assessing and ensuring the quality of geospatial data"},{"concepts":[386],"name":"Explain why multimodal distributions in training samples should be avoided when using the maximum likelihood classifier"},{"concepts":[516],"name":"Explain why passive EO sensors with the highest spectral or spatial resolution operate in the VIS/NIR spectral region"},{"concepts":[361],"name":"Explain why radiometric correction is a key requirement for deriving indices with band maths"},{"concepts":[446],"name":"Explain why rapid mapping applications have high requirements in timely availability of Earth observation products"},{"concepts":[504],"name":"Explain why software products sold by U.S. companies may predominate in foreign markets, including Europe and Australia"},{"concepts":[647],"name":"Explain why spatial resolution of passive radar system is much lower than that of active systems"},{"concepts":[475],"name":"Explain why the definition of user roles is an important element in the implementation of a GIS"},{"concepts":[601],"name":"Explain why the Doppler effect is important in radar remote sensing"},{"concepts":[489],"name":"Explain why the legal framework on geospatial data sharing can be considered as diverse and complex"},{"concepts":[489],"name":"Explain why the legal framework on geospatial data sharing consists of two main types of legislation from a data perspective"},{"concepts":[45],"name":"Explain why the process \"dissolve and merge\" often follows vector overlay operations"},{"concepts":[61],"name":"Explain why the properties of spatial continuity are characteristic of spatial surfaces"},{"concepts":[38],"name":"Explain why the shape of an object might be important in analysis"},{"concepts":[259],"name":"Explain why the shape of the Earth is complex and complicated to measure"},{"concepts":[816],"name":"Explain why the use of multiple EO sensors for mapping landslides associated with one triggering event increases the completeness of a landslide inventory"},{"concepts":[119],"name":"Explain why Toblers First Law of Geography is fundamental to many operations in GIS and whether it should be"},{"concepts":[61],"name":"Explain why zero slopes are indicative of surface specific points such as peaks, pits and passes and list the conditions necessary for each"},{"concepts":[12],"name":"Explain why, if supply equals demand, there will always be a feasible solution to the Classic Transportation Problem"},{"concepts":[50],"name":"Explain why, in some cases, an adaptive bandwidth might be employed"},{"concepts":[280],"name":"Explain, in general terms, the difference between single and double precision and impacts on error propagation"},{"concepts":[16],"name":"Explain, using the concept of combinatorial complexity, why some location problems are very hard to solve"},{"concepts":[88],"name":"Explore the contribution of linguistics to the study of spatial cognition and the role of natural language in the conceptualization of geographic phenomena"},{"concepts":[92],"name":"Explore the history of geography including (but not limited to) Greek and Roman contributions to geography (Eratosthenes, Strabo, Ptolemy), geography and cartography in the age of discovery, military geography, and geography..."},{"concepts":[76],"name":"Find a best model"},{"concepts":[147],"name":"Find a multivariate outlier using a combination of maps and graphs"},{"concepts":[38],"name":"Find centroids of polygons under different definitions of a centroid and different polygon shapes"},{"concepts":[832],"name":"Find oil spills in EO data for Ocean surveillance"},{"concepts":[483],"name":"Find or create training resources appropriate for GIS and T workforce in a local government organization"},{"concepts":[112],"name":"Find spatial patterns in the distribution of geographic phenomena using geographic visualization and other techniques"},{"concepts":[791,789],"name":"Forecast and monitor ocean winds and waves"},{"concepts":[106],"name":"Formalize attribute values and domains in terms of set theory"},{"concepts":[109],"name":"Formalize the notion of field using mathematical functions and Calculus"},{"concepts":[45],"name":"Formalize the operation called map overlay using Boolean logic"},{"concepts":[317],"name":"Generate a layer stack from bands of various EO data sources"},{"concepts":[347],"name":"Generate fine-scale images at a high temporal resolution with a spatio-temporal image fusion method"},{"concepts":[366],"name":"Generate high quality time series by removing clouds and cloud shadows from the available images"},{"concepts":[224],"name":"Give and explain an example of an application models"},{"concepts":[489],"name":"Give examples of more general types of legislation that are also applicable and relevant to geospatial data sharing"},{"concepts":[839],"name":"Having in-depth knowledge of two of the three Copernicus-relevant topics: Land monitoring, Emergency response including Humanitarian action, and Climate change"},{"concepts":[112],"name":"Hypothesize the causes of a pattern in the spatial distribution of a phenomenon"},{"concepts":[51],"name":"Identify a clustering method which does not require the number of clusters as input"},{"concepts":[30],"name":"Identify a variety of likely measurement level transformations (e.g., the classification of ratio data yields ordinal data)"},{"concepts":[840],"name":"Identify adequate preprocessing for deriving ocean colour from EO data"},{"concepts":[247],"name":"Identify agent-based modelling principles and methodologies"},{"concepts":[308],"name":"Identify alternatives to the \"algorithmic way of thinking\" that characterizes use of geospatial Information."},{"concepts":[312],"name":"Identify alternatives to the algorithmic way of thinking that characterizes GIS"},{"concepts":[236],"name":"Identify and compare the scenarios on which geocomputation methods are relevant"},{"concepts":[71],"name":"Identify and define the parameters of a semi-variogram range, sill, nugget"},{"concepts":[410],"name":"Identify and discuss an example of a combined filtering 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geographic information"},{"concepts":[240],"name":"Identify commonalities and patterns of geocomputation"},{"concepts":[503],"name":"Identify conferences that are related to GIS and T hosted by professional organizations"},{"concepts":[860],"name":"Identify construction sites"},{"concepts":[449],"name":"Identify critical design decisions that make an EO-derived map readable"},{"concepts":[172],"name":"Identify data center platform tier configuration and identify platform selection for each tier"},{"concepts":[874],"name":"Identify design issues of SOAP web services; fine grained and coarse grained services, design patterns"},{"concepts":[902],"name":"Identify differences, advantages and disadvantages of web application framework based and portal framework based web applications from the geospatial data perspective"},{"concepts":[65],"name":"Identify different measures of spatial autocorrelation"},{"concepts":[66],"name":"Identify different measures of spatial 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gaps in image time series caused by clouds and cloud shadow"},{"concepts":[813],"name":"Identify impact of a flood"},{"concepts":[112],"name":"Identify influences of scale on the appearance of distributions"},{"concepts":[887],"name":"Identify issues in determining the relationships to be represented when publishing Linked Data"},{"concepts":[886],"name":"Identify issues in developing new ontologies for geospatial data"},{"concepts":[887],"name":"Identify issues in finding proper ontologies to annotate the data"},{"concepts":[880],"name":"Identify issues in the development of geospatial ontologies. 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the standard occupational codes that are relevant to GIS and T"},{"concepts":[885],"name":"Identify the technical aspects that open data paradigm would affect concerning Spatial Data Infrastructures including NSDIs"},{"concepts":[108],"name":"Identify the types of features that need to be modeled in a particular GIS application or procedure"},{"concepts":[239,240],"name":"Identify the types of geography problems geocomputation solves"},{"concepts":[49],"name":"Identify the various ways point patterns may be described"},{"concepts":[175],"name":"Identify the viability of a proprietary GIS application"},{"concepts":[877],"name":"identify the web services needed for a particular use case"},{"concepts":[172],"name":"Identify user locations, network connectivity, and data center server locations"},{"concepts":[104],"name":"Identify various types of geographic interactions in space and time"},{"concepts":[49],"name":"Identify various types of K-function 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applications"},{"concepts":[279],"name":"Illustrate and explain the distinction between resolution, precision, and accuracy"},{"concepts":[279],"name":"Illustrate and explain the distinctions between spatial resolution, thematic resolution, and temporal resolution"},{"concepts":[534],"name":"Illustrate basic features of spectral signatures of vegetation, water and bare soil"},{"concepts":[563],"name":"Illustrate basic modern physics theory understanding their implications on the development of advanced sensors for EO"},{"concepts":[525,533],"name":"Illustrate basic radiation-matter interactions and related concepts of spectral reflectance, absorbance and transmittance as specific properties of the matter"},{"concepts":[536],"name":"Illustrate e.m. radiation intercations with/within clouds."},{"concepts":[173],"name":"Illustrate each of the project management areas with an example of a technique or tool used"},{"concepts":[166],"name":"Illustrate how a business process analysis can be 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time"},{"concepts":[213],"name":"Illustrate the existing methods for compressing gridded data (e.g., run length encoding, Lempel-Ziv, wavelets)"},{"concepts":[591],"name":"Illustrate the factors limiting lifetime of satellites on their originally planned orbits"},{"concepts":[587],"name":"Illustrate the First Law of Thermodynamic"},{"concepts":[541],"name":"Illustrate the general equation of radiative transfer."},{"concepts":[567],"name":"Illustrate the Greenhouse effect associate to CO2 emission"},{"concepts":[559],"name":"Illustrate the Helmotz’s equation"},{"concepts":[215],"name":"Illustrate the hexagonal model"},{"concepts":[582],"name":"Illustrate the ideal gas law"},{"concepts":[217],"name":"Illustrate the impact of grid cell resolution on the information that can be portrayed"},{"concepts":[24],"name":"Illustrate the impact of vector/raster/vector conversions on the quality of a dataset"},{"concepts":[509],"name":"Illustrate the importance of Earth's emitted radiation for EO from space"},{"concepts":[554],"name":"Illustrate the importance of electric conduction in solids for the design and development of advanced EO sensors"},{"concepts":[592],"name":"Illustrate the importance of the choice of the satellite orbit for the design of a satellite mission devoted to specific applications"},{"concepts":[869,864,865,866,867,868],"name":"Illustrate the information of EO data"},{"concepts":[183],"name":"Illustrate the landscape of GIS and related libraries"},{"concepts":[574],"name":"Illustrate the main atmospherical spectral windows"},{"concepts":[538],"name":"Illustrate the main differences among passive and active remote sensing techniques"},{"concepts":[522],"name":"Illustrate the main energetic transictions that can be associated to molecular absorption of e.m. radiation"},{"concepts":[530],"name":"Illustrate the main forms of radiation-matter interaction"},{"concepts":[51],"name":"Illustrate the main use of spatial clustering in earth observation"},{"concepts":[517],"name":"Illustrate the nature of electromagnetic radiation"},{"concepts":[218],"name":"Illustrate the quadtree model"},{"concepts":[241],"name":"Illustrate the relationships between geocomputation with other terms, disciplines and areas of knowledge"},{"concepts":[586],"name":"Illustrate the role of Eulerian and Lagrangian models in budget equations definition"},{"concepts":[558],"name":"Illustrate the role of the principle of constant speed of light within the special relativity theory"},{"concepts":[551],"name":"Illustrate the scope Radiative Transfer theory"},{"concepts":[588],"name":"Illustrate the Second Law of Thermodynamic"},{"concepts":[534],"name":"Illustrate the spectral response curves for basic environmental features (e.g., vegetation, concrete, bare soil)"},{"concepts":[575],"name":"Illustrate the transferring of Energy within the Earth-Atmosphere System"},{"concepts":[151],"name":"Illustrate the use of virtual environments"},{"concepts":[580],"name":"Illustrate the utility of thermodynamic diagrams for the study of local atmospheric properties"},{"concepts":[142],"name":"Illustrate the ways in which maps could be integrated in an infography"},{"concepts":[473],"name":"Illustrate what functions a support or service center can provide to an organization using GIS and T"},{"concepts":[520],"name":"Illustrate why we refer to the BRDF as an absolute definition of spectral reflectance"},{"concepts":[140],"name":"Illustrate with examples of maps or geovisualizations that could be improved by the addition of an audio layer"},{"concepts":[126],"name":"Illustrate with examples the relationship between art and cartography at different historical moments"},{"concepts":[584],"name":"Ilustrate the state function of the condensed gas phase"},{"concepts":[218],"name":"Implement a format for encoding quadtrees in a data file"},{"concepts":[76],"name":"Implement a maximum likelihood estimation procedure for determining key spatial econometric parameters"},{"concepts":[234],"name":"Implement a test of reliability of change information"},{"concepts":[57],"name":"Implement a trend surface analysis using either the supplied function in a GIS or a regression function from any standard statistical package"},{"concepts":[884],"name":"Implement and configure a catalogue service"},{"concepts":[17],"name":"Implement linear programs for spatial allocation problems"},{"concepts":[12],"name":"Implement the Transportation Simplex method to determine the optimal solution"},{"concepts":[277],"name":"In contrast to the National Map Accuracy Standard, explain how the spatial accuracy of a digital road centerlines data set may be evaluated and documented"},{"concepts":[886],"name":"Indicate an architecture and tools for organizing semantically annotated data"},{"concepts":[901],"name":"Indicate an overview of OpenStreetMap and define its general functionality, comment its usage by Web APIs"},{"concepts":[902],"name":"Indicate generally how \"NSDI-requiring-scenarios\"would be handled by web application framework based applications"},{"concepts":[900],"name":"Indicate main elements of HTML5"},{"concepts":[890],"name":"Indicate some examples of semantic discovery; Semantic search engines, highlighting projects and practice concerning GI related applications in the area"},{"concepts":[308],"name":"Indicate the extent to which contemporary use of geospatial information supports diverse ways of understanding the world."},{"concepts":[474],"name":"Indicate the possible justifications that can be used to implement an enterprise GIS"},{"concepts":[245],"name":"Interpret when space-time dynamics can be used to study geographical phenomen"},{"concepts":[172],"name":"Interpret business needs and translate them to IT needs"},{"concepts":[471],"name":"Interpret descriptive statistics and geostatistics of geographic data"},{"concepts":[135,160],"name":"Interpret different symbols and icons in a map"},{"concepts":[902],"name":"Interpret generally the functionality offered by \"portal frameworks\" land Geoportals like Geonetwork, Opengeoportal, Esri geoportal server, Degree portal, Liferay, Jboss portal"},{"concepts":[902],"name":"Interpret generally the main components and functionality of \"Web Application Frameworks\" such as AngularJS, Ext.js, Django, Java Server Faces (JSF), and the like"},{"concepts":[879],"name":"interpret GML data model and GML definition of geometry. GML application schemas and GML documents"},{"concepts":[237],"name":"Interpret how individual parts contained in a complex system relate to each other"},{"concepts":[858],"name":"Interpret information from EO products or EO time series"},{"concepts":[770],"name":"Interpret land cover change detection"},{"concepts":[773],"name":"Interpret location based services (LBS)"},{"concepts":[840],"name":"Interpret ocean colour for deriving chlorophyll concentration in water"},{"concepts":[5],"name":"Interpret patterns in space and time using Dorling and Openshaws Geographical Analysis Machine GAM demonstration of disease incidence diffusion"},{"concepts":[869,864,865,866,867,868],"name":"Interpret the content of EO data"},{"concepts":[416],"name":"Interpret the effect of a convolution from a given mask and contained weights"},{"concepts":[211],"name":"Interpret the header of a standard raster data file"},{"concepts":[125],"name":"Interpret the impact of paper-based and web maps in their context"},{"concepts":[844],"name":"Interpret the output of an point cloud measurement"},{"concepts":[806],"name":"Interpret the output of numerical prediction models"},{"concepts":[73],"name":"Interpret the results of universal kriging"},{"concepts":[172],"name":"Interpret user needs as an input for the design process"},{"concepts":[92],"name":"Justify a chosen position on which disciplines should have as important a role in GIS AND T as geography"},{"concepts":[176],"name":"Justify feasibility recommendations to decision-makers"},{"concepts":[108],"name":"Justify or refute the conception of fields (e.g., temperature, density) as spatially-intensive attributes of (sometimes amorphous and anonymous) entities"},{"concepts":[92],"name":"Justify or refute whether geography (as a discipline) should have a central role in GIS AND T"},{"concepts":[97],"name":"Justify the discrepancies between the nature of locations in the real world and representations thereof (e.g., towns as points)"},{"concepts":[83],"name":"Justify the epistemological frameworks with which you agree"},{"concepts":[81],"name":"Justify the metaphysical theories with which you agree"},{"concepts":[63],"name":"Justify the stochastic process approach to spatial statistical analysis"},{"concepts":[65],"name":"Justify, compute, and test the significance of the join count statistic for a pattern of objects"},{"concepts":[518],"name":"Knowledge of the basic (selective) mechanism of the absorption/emission of electromagnetic radiation by atoms."},{"concepts":[70],"name":"List and describe several spatial sampling schemes and evaluate each one for specific applications"},{"concepts":[505],"name":"List and describe the main categories of organizations in the GIS&T domain"},{"concepts":[500],"name":"List and describe the most important producers and users of geospatial data at the European Commission"},{"concepts":[296],"name":"List and describe the types of data maintained by local, state, and federal governments"},{"concepts":[472],"name":"List and explain relevant organizational and institutional aspects related to GIS&T."},{"concepts":[285],"name":"List and explain the different societal aspects that are important in dealing with geospatial information"},{"concepts":[263],"name":"List and explain the key requirements for geolocating data to earth"},{"concepts":[226],"name":"List definitions of networks that apply to specific applications or industries"},{"concepts":[41],"name":"List different ways connectivity can be determined in a raster and in a polygon dataset"},{"concepts":[39],"name":"List reasons why the area of a polygon calculated in a GIS might not be the same as the real world object it describes"},{"concepts":[13],"name":"List several classic problems to which network analysis is applied e.g., The Traveling Salesman Problem, The Chinese Postman Problem"},{"concepts":[151],"name":"List software and hardware environments supporting immersive visualization"},{"concepts":[474],"name":"List some of the topics that should be addressed in a justification for implementing an enterprise GIS (e.g., return on investment, workflow, knowledge sharing)"},{"concepts":[465],"name":"List specifics competitive DIAS solutions over other"},{"concepts":[49],"name":"List the conditions that make point pattern analysis a suitable process"},{"concepts":[174],"name":"List the costs and benefits (tangible or intangible) of implementing a GI project"},{"concepts":[173],"name":"List the key elements of a project management"},{"concepts":[61],"name":"List the likely sources of error in slope and aspect maps derived from DEMs and state the circumstances under which these can be very severe"},{"concepts":[458],"name":"List the main international organization responsible for the standardization of the image data and gridded data quality"},{"concepts":[409],"name":"List the main segmentation methods used to group similar pixels into homogeneous objects"},{"concepts":[158],"name":"List the main variables to take into account during the planning phase of a map"},{"concepts":[133],"name":"List the major factors that should be considered in preparing a map"},{"concepts":[173],"name":"List the phases of a project management life cycle"},{"concepts":[71],"name":"List the possible sources of error in a selected and fitted model of an experimental semi-variogram"},{"concepts":[118],"name":"List the possible topological relationships between entities in space (e.g., 9-intersection) and time"},{"concepts":[136],"name":"List the range of factors that should be considered in selecting colors"},{"concepts":[63],"name":"List the two basic assumptions of the purely random process"},{"concepts":[14],"name":"List ways we can define accessibility on a network"},{"concepts":[132],"name":"List which data considerations should be taken into account when starting a GIS project"},{"concepts":[19],"name":"Locate, using location-allocation software, service facilities that meet given sets of constraints"},{"concepts":[166],"name":"Manage requirements using a management tool (such as Trello, JIRA, etc.)"},{"concepts":[778],"name":"Manage the use of land"},{"concepts":[772],"name":"Map and assess flooding"},{"concepts":[767],"name":"Map line of sight visibility (terrain height, land cover)"},{"concepts":[720],"name":"Measure reflectance of surfaces of vegetation types and other thematic classes in the field"},{"concepts":[231],"name":"Model complex aspects of geographic information, such as temporal change, uncertainty and three-dimensional phenomena"},{"concepts":[190],"name":"Model geospatial data"},{"concepts":[108],"name":"Model gray area phenomena, such as categorical coverages (a.k.a. discrete fields), in terms of objects"},{"concepts":[172],"name":"Model project workflows"},{"concepts":[620],"name":"Model surface roughness slope"},{"concepts":[204],"name":"Model temporal aspects"},{"concepts":[232],"name":"Modify spatial and attribute data while ensuring consistency within the database"},{"concepts":[776,774,781,793,796],"name":"Monitor and assess natural hazards"},{"concepts":[765,767,772,775,779,794,808],"name":"Monitor building development"},{"concepts":[771,777,799,831,830],"name":"Monitor changes in infrastructure"},{"concepts":[766,793,824],"name":"Monitor land pollution"},{"concepts":[766,793,802,822,838],"name":"Monitor pollution in rivers and lakes"},{"concepts":[769],"name":"Monitor shipping routes"},{"concepts":[768,799,831,830],"name":"Monitor transportation routes"},{"concepts":[189],"name":"Outline a database with its main functionalities"},{"concepts":[143],"name":"Outline a map layout taking into account design principles"},{"concepts":[145],"name":"Outline a map with a reliability overlay using symbols suited to reliability representations"},{"concepts":[140],"name":"Outline a multivariate visual display that incorporates sounds"},{"concepts":[61],"name":"Outline a number of different 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the implications of complexity for the application of statistical ideas in geography"},{"concepts":[36],"name":"Outline the implications of differences in distance calculations on real world applications of GIS, such as routing and determining boundary lengths and service areas"},{"concepts":[138],"name":"Outline the importance of photographs or imagery either from satellites or at street level"},{"concepts":[50],"name":"Outline the likely effects on analysis results of variations in the kernel function used and the bandwidth adopted"},{"concepts":[63],"name":"Outline the logic behind the derivation of long run expected outcomes of the independent random process using quadrat counts"},{"concepts":[45],"name":"Outline the possible sources of error in overlay operations"},{"concepts":[284],"name":"Outline the process of scanning and vectorizing features depicted on a printed map sheet using a given GIS software product, emphasizing issues that require manual intervention"},{"concepts":[181],"name":"Outline the Reference Model of Open Distributed Processing framework"},{"concepts":[241],"name":"Outline the role of computational science in geocomputation"},{"concepts":[278],"name":"Outline the SDTS and ISO TC211 standards for thematic accuracy"},{"concepts":[264],"name":"Outline the sequence of tasks involved in generating an orthoimage from a vertical aerial photograph"},{"concepts":[2],"name":"Outline the sequence of tasks required to complete the analytical process for a given spatial problem"},{"concepts":[156,157],"name":"Outline the stages in lithographic offset printing"},{"concepts":[177,184],"name":"Outline the types of geospatial software architectures"},{"concepts":[900],"name":"Outline the use Scalable Vector Graphics (SVG) for client-side graphic processing"},{"concepts":[345],"name":"Outline the workflow for pan-sharpening an image with the PCA method"},{"concepts":[51],"name":"Perform a cluster detection analysis to detect hot 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existing web services to use the resources exposed by the service"},{"concepts":[436],"name":"Plan a reproducibility project independently"},{"concepts":[706],"name":"Plan an aerial imagery mission in response to a given RFP and map of a study area, taking into consideration vertical and horizontal control, atmospheric conditions, time of year, and time of day"},{"concepts":[706,715],"name":"Plan an Earth observation mission objectives and priorities in response to user expectations, taking into account type of application, type of sensor, expected accuracy"},{"concepts":[761,797],"name":"Plan and design alternative energy project implementations"},{"concepts":[763],"name":"Plan and design mineral & mining project implementations"},{"concepts":[762],"name":"Plan and design oil & gas project implementations"},{"concepts":[792],"name":"Plan and design project implementations"},{"concepts":[764],"name":"Plan and design project implementations in the field of energy and mineral resources"},{"concepts":[819],"name":"Plan emergency response actions"},{"concepts":[720],"name":"Plan in-situ measurements using a field spectroradiometer"},{"concepts":[635],"name":"Plan the calibration of the radar antenna"},{"concepts":[158],"name":"Plan the creation of a map according to a given audience"},{"concepts":[40],"name":"Plot typical forms for distance decay functions"},{"concepts":[893],"name":"Practically apply getting data from a WCS and integrate it into a client application"},{"concepts":[893],"name":"Practically apply getting data from a WFS and integrate it into a client application"},{"concepts":[156,157],"name":"Prepare a color map for black-and-white photocopy distribution"},{"concepts":[476],"name":"Prepare a GIS Management Strategy"},{"concepts":[480],"name":"Prepare a strategy on setting up the organizational components of a GIS&T implementation"},{"concepts":[274],"name":"Prepare and implement an effective geospatial data transaction management approach"},{"concepts":[21],"name":"Prioritize a set of algorithms designed to perform transformations based on the need to maintain data integrity [e.g., converting a digital elevation model (DEM) into a TIN]"},{"concepts":[379],"name":"Produce a digital surface model from stereographic optical EO data"},{"concepts":[657,658,659],"name":"Produce a geometrically corrected SAR image"},{"concepts":[352],"name":"Produce a map of vegetation fraction from optical EO data"},{"concepts":[339],"name":"Produce a surface corrected version of image values from BOA reflectance that removes topographic effects based on an input DSM and equations representing the relationship between sun incidence angle relative to terrain surface orientation"},{"concepts":[832],"name":"Produce EO derived marine ecosystem information to support fisheries management"},{"concepts":[859],"name":"Produce forecasts for flood risk areas"},{"concepts":[53],"name":"Produce plots in several data dimensions using a data matrix of attributes"},{"concepts":[550],"name":"Produce the processes of spectral calculations of radiometric quantities by the line by line radiative transfer models"},{"concepts":[235],"name":"Produce viable queries for change scenarios using GIS or database management tools"},{"concepts":[428],"name":"Produce zero-crossing maps for a DoG-filtered optical EO image"},{"concepts":[128],"name":"Propose a holistic historical perspective of maps creation and use"},{"concepts":[306],"name":"Propose a resolution to a conflict between an obligation in the GIS Code of Ethics and organizations proprietary interests"},{"concepts":[288],"name":"Propose and design solutions for dealing with particular data privacy and data security issues"},{"concepts":[287],"name":"Propose strategies for managing liability risk, including disclaimers and data quality standards"},{"concepts":[144],"name":"Propose thematic mapping methods for mapping numerical data"},{"concepts":[271],"name":"Provide examples of cases in which crouwdsourcing is the most effective data collection method"},{"concepts":[311],"name":"Provide examples of different types of critiques on GI and GIS"},{"concepts":[490],"name":"Provide examples of different types of legal instruments that can be used for supporting geospatial data sharing"},{"concepts":[300],"name":"Provide examples of the use of geospatial information in different sectors"},{"concepts":[194],"name":"Provide examples of typical non-spatial and spatial queries"},{"concepts":[291],"name":"Publish a dataset as open data"},{"concepts":[30],"name":"Reclassify (group) a nominal attribute domain to fewer, broader classes"},{"concepts":[30],"name":"Reclassify a raster before converting it into a vector file"},{"concepts":[105],"name":"Recognize and manage the potential problems associated with the use of categories (e.g., the ecological fallacy)"},{"concepts":[106],"name":"Recognize attribute domains that do not fit well into Stevens four levels of measurement (nominal, ordinal, interval, ratio), such as cycles, indexes, and hierarchies"},{"concepts":[622],"name":"Recognize different types of surface roughness on a radar image"},{"concepts":[122],"name":"Recognize expressions of uncertainty in language"},{"concepts":[106],"name":"Recognize situations and phenomena in the landscape which cannot be adequately represented by formal attributes, such as aesthetics"},{"concepts":[162],"name":"Recognize spatial schemes like patterns and shapes"},{"concepts":[471],"name":"Recognize the assumptions underlying probability and geostatistics and the situations in which they are useful analytical tools"},{"concepts":[81],"name":"Recognize the commonalities of philosophical viewpoints and appreciate differences to enable work with diverse colleagues"},{"concepts":[188],"name":"Recognize the constraints and opportunities of a particular choice of software for implementing a physical model"},{"concepts":[95],"name":"Recognize the constraints that political forces place on geospatial applications in public and private sectors"},{"concepts":[118],"name":"Recognize the contributions of Topology (the branch of mathematics) to the study of geographic relationships"},{"concepts":[122],"name":"Recognize the degree to which the importance of uncertainty depends on scale and application"},{"concepts":[121],"name":"Recognize the degree to which vagueness depends on scale"},{"concepts":[94],"name":"Recognize the impact of ones social background on ones own geographic worldview and perceptions and how it influences ones use of GIS"},{"concepts":[436],"name":"Recognize the importance of reproducible research as a fundamental pillar of modern science"},{"concepts":[83],"name":"Recognize the influences of epistemology on GIS practices"},{"concepts":[109],"name":"Recognize the influences of scale on the perception and meaning of fields"},{"concepts":[292],"name":"Recognize the relevant legal issues in a particular case of geospatial data collection, use and/of sharing"},{"concepts":[103],"name":"Recognize the role that time plays in static GISystems"},{"concepts":[115],"name":"Recommend for what applications we should use a field or an object-base approach."},{"concepts":[105],"name":"Reconcile differing common-sense and official definitions of common geospatial categories of entities, attributes, space, and time"},{"concepts":[857],"name":"Relate EO measurements with detected features"},{"concepts":[91],"name":"Relate epistemology to spatial knowledge."},{"concepts":[53],"name":"Relate plots of multidimensional attribute data to geography by equating similarity in data space with proximity in geographical space"},{"concepts":[217],"name":"Relate the concept of grid cell resolution to the more general concept of support and granularity"},{"concepts":[109],"name":"Relate the notion of field in GIS to the mathematical notions of scalar and vector fields"},{"concepts":[124],"name":"Relate the science and technology of graphical representation of geographic data"},{"concepts":[388],"name":"Relate the spatial and spectral characteristics of EO data to the types and proportions of materials found within the scene and within pixel IFOVs to relabel spectral classes as information classes of a classification scheme"},{"concepts":[135],"name":"Relate the spatial dimension and the weight of mapped features with the attributes they represent"},{"concepts":[568],"name":"Relate to the aspects of radiation transfer through the atmosphere."},{"concepts":[887],"name":"Relate with manual and automated methods linking data"},{"concepts":[166],"name":"Report existing and potential tasks in terms of workflow and information flow"},{"concepts":[162],"name":"Represent an object or a scene from different viewpoints"},{"concepts":[116],"name":"Represent structural relationships in GIS data"},{"concepts":[25],"name":"Resample multiple raster data sets to a single resolution to enable overlay"},{"concepts":[25],"name":"Resample raster data sets (e.g., terrain, satellite imagery) to a resolution appropriate for a map of a particular scale"},{"concepts":[297],"name":"Research and develop geospatial information for the private sector"},{"concepts":[136],"name":"Select a color palette appropriate for a representation"},{"concepts":[328],"name":"Select a contrast stretch for an image"},{"concepts":[28],"name":"Select a level of data detail and accuracy appropriate for a particular application (e.g., viewshed analysis, continental land cover change)"},{"concepts":[93],"name":"Select a place or landscape with personal meaning and discuss its importance"},{"concepts":[145],"name":"Select a technique that can be used to represent the value of each of the components of data quality (positional and attribute accuracy, logical consistency, and completeness)"},{"concepts":[167],"name":"Select among the most appropriate method for documenting a certain process"},{"concepts":[845],"name":"Select an appropriate DEM product for usage in a specific application"},{"concepts":[702],"name":"Select an optical spectrometer suitable for your application taking into account the acquired wavelength"},{"concepts":[637,636],"name":"Select and apply the radargrammetric equation"},{"concepts":[25],"name":"Select appropriate interpolation techniques to resample particular types of values in raster data (e.g., nominal using nearest neighbor)"},{"concepts":[97],"name":"Select appropriate spatial metaphors and models of phenomena to be represented in GIS"},{"concepts":[144],"name":"Select base information suited to providing a frame of reference for thematic map symbols (e.g., network of major roads and state boundaries underlying national population map)"},{"concepts":[166],"name":"Select from conflicting requirements"},{"concepts":[702,840],"name":"Select imagery from a satellite sensor with spectral bands suitable for mapping Ocean Colour"},{"concepts":[452],"name":"Select images for time series analysis where the cumulated cloud cover percentage in the study area is low enough for the analysis"},{"concepts":[159],"name":"Select maps that illustrate the provocative, propaganda, political, and persuasive nature of maps and geospatial data"},{"concepts":[744],"name":"Select the appropriate optical data type for the application"},{"concepts":[749],"name":"Select the appropriate SAR data type for the application"},{"concepts":[62],"name":"Select the appropriate statistical methods for the analysis of given spatial datasets by first exploring them using graphic methods"},{"concepts":[903],"name":"select the development elements best suited for your application"},{"concepts":[137],"name":"Select the most appropriate place in a map to place a label and a legend"},{"concepts":[266],"name":"Select the most appropriate remotely sensed data source for a given analytical task, study area, budget, and availability"},{"concepts":[173],"name":"Select the most appropriate techniques for a EO*GI project"},{"concepts":[176],"name":"Select the most appropriate technology to help decision-making"},{"concepts":[154],"name":"Select the most suitable graphic representation for a given set of data"},{"concepts":[154],"name":"Select the most suitable graphic representation for a targeted audience"},{"concepts":[103],"name":"Select the temporal elements of geographic phenomena that need to be represented in particular GIS applications"},{"concepts":[723],"name":"Select the type of remote sensing platform for your specific application"},{"concepts":[703,753],"name":"Select the type of remote sensing sensor appropriate for your application"},{"concepts":[877],"name":"select the web services best fit to expose your own resources"},{"concepts":[137],"name":"Select type font, size, style and color for labels on a map by applying basic typography design principles"},{"concepts":[890],"name":"Semantic Discovery and its main components. Identify the areas of its use for GI related applications"},{"concepts":[137],"name":"Solve a labeling problem for a dense collection of features on a map using minimal leader lines"},{"concepts":[137],"name":"Solve ambiguities in map label by selecting the most appropriate typography"},{"concepts":[886],"name":"Solve issues in determining what ontologies to use for semantic annotation"},{"concepts":[156,157],"name":"Specify a print job for publication, including paper, ink, lpi, proof needs, press check and other contract decisions"},{"concepts":[264],"name":"Specify the technical components of an aerotriangulation system"},{"concepts":[717],"name":"State and explain different SAR acquisition modes"},{"concepts":[660],"name":"State and explain Synthetic Aperture Radar (SAR) geometric distortions"},{"concepts":[639],"name":"State application examples of PSI methods"},{"concepts":[749],"name":"State different types of processing levels of SAR data"},{"concepts":[740],"name":"State examples of image description files used in Earth Observation"},{"concepts":[34],"name":"State questions that can be solved by selecting features based on location or spatial relationships"},{"concepts":[277],"name":"State the approximate number and spacing of control points in each order of the horizontal geodetic control network"},{"concepts":[506],"name":"State the basic physical principles for EO systems design and data analysis"},{"concepts":[52],"name":"State the classic formalization of the interaction model"},{"concepts":[277],"name":"State the geometric accuracies associated with the various orders of the U.S. horizontal geodetic control network"},{"concepts":[603],"name":"State the microwave portion of the electromagnetic spectrum"},{"concepts":[510],"name":"State the names of the most important regions of the electromagnetic spectrum"},{"concepts":[510],"name":"State the names of the regions of the electromagnetic spectrum most important for Earth's remote sensing"},{"concepts":[603],"name":"State the typical used radar bands and their application"},{"concepts":[598],"name":"State types of polarisations used in remote sensing"},{"concepts":[292],"name":"Suggest and prepare solutions for addressing particular legal issues related to the production, use and sharing of geospatial data"},{"concepts":[483],"name":"Teach necessary skills for users to successfully perform tasks in an enterprise GIS"},{"concepts":[178],"name":"Test all functionalities and data standards for interoperability"},{"concepts":[205],"name":"Transfer a conceptual model to a logical (database) model"},{"concepts":[90],"name":"Transform a conceptual model of information for a particular task into a data model"},{"concepts":[326,325],"name":"Transform an EO dataset to map coordinates using a registered image of like geometry as a reference"},{"concepts":[900],"name":"Transform HTML documents thorugh the Document Object Model (DOM)"},{"concepts":[340],"name":"Transform imagery into radiometrically/atmospherically corrected state"},{"concepts":[25],"name":"Understand and examine the common methods for raster resampling"},{"concepts":[292],"name":"Understand and explain the main legal issues related to the production, use and sharing of geospatial data and information"},{"concepts":[198],"name":"Understand and use XML"},{"concepts":[332],"name":"Understand atmospheric parameters that influence bottom of atmosphere (BOA) reflectance"},{"concepts":[241],"name":"Understand complexity in the broadest sense"},{"concepts":[68],"name":"Understand different estimation methods for Bayesian models"},{"concepts":[237],"name":"Understand how complex systems operate"},{"concepts":[342],"name":"Understand how data augmentation can improve deep learning methods for image classification"},{"concepts":[877],"name":"understand how different web services complement each other"},{"concepts":[832],"name":"Understand how EO data can be used to monitor the marine ecosystem"},{"concepts":[240],"name":"Understand how geocomputation relates to other similar terms"},{"concepts":[160],"name":"Understand how graphic representations can be interpreted distinctively by culturally different audiences"},{"concepts":[447],"name":"Understand how limited temporal completness affects the usefulness of a time series analysis"},{"concepts":[160],"name":"Understand how map scale is used to provide the relationship of size of object on a map and its real-world size"},{"concepts":[244],"name":"Understand how models are translated into differential equations for execution"},{"concepts":[243],"name":"Understand how models can be specified into logical rules"},{"concepts":[806],"name":"Understand how numerical prediction models work"},{"concepts":[445],"name":"Understand how positional/geometric accuracy of a dataset affects subsequent analysis"},{"concepts":[445,444],"name":"Understand how root mean squared error (RMSE) at tie points represents local spatial accuracy and enables calculation of total RMSE that informs about the average spatial accuracy of the entire image"},{"concepts":[366],"name":"Understand how satellite image time series can be used for mapping, trend analysis and change detection"},{"concepts":[375],"name":"Understand how the entropy represents the the average level of information contained in an image pixel"},{"concepts":[154],"name":"Understand how the representation of geographic data facilitates visual communication"},{"concepts":[236],"name":"Understand how the theoretical roots and experimental emphasis on geocomputation are integrated"},{"concepts":[849],"name":"Understand how the tracking of moving objects is implemented"},{"concepts":[165],"name":"Understand spatial data models and structures"},{"concepts":[260],"name":"Understand spatial reference systems and apply them to an EO dataset"},{"concepts":[333],"name":"Understand sun, sun angle, and sensor parameters that influence top of atmosphere (TOA) reflectance"},{"concepts":[160],"name":"Understand that features have been omitted or generalized for clarity"},{"concepts":[237],"name":"Understand the all-encompassing concepts of complexity"},{"concepts":[65],"name":"Understand the assumption under which spatial autocorrelation may occur"},{"concepts":[66],"name":"Understand the assumption under which spatial autocorrelation may occur"},{"concepts":[291],"name":"Understand the benefits of publishing and using open data"},{"concepts":[397],"name":"Understand the challenge in matching sensory image data to a mental model of the world-scene"},{"concepts":[242],"name":"Understand the defining characteristics of simulation models, and their applicability"},{"concepts":[186],"name":"Understand the degree to which attributes need to be conceptually modeled"},{"concepts":[539],"name":"Understand the difference between Inherent Optical Properties (IOP) and Apparent Optical Properties (AOP) of water"},{"concepts":[455],"name":"Understand the difficulties in searching and selecting satellite images with sufficient spatial coverage for time series analysis"},{"concepts":[816],"name":"Understand the diverse set of EO technologies that are capable of mapping different landslide aspects"},{"concepts":[757,798,820,810],"name":"Understand the health of the crop, extent of infestation or stress damage, or potential yield and soil conditions"},{"concepts":[758,835],"name":"Understand the health of the fishing grounds"},{"concepts":[759,821],"name":"Understand the health of the forests"},{"concepts":[900],"name":"Understand the importance of Cascading Style Sheets (CSS) to separate content from style in HMTL documents"},{"concepts":[445],"name":"Understand the importance of using spatially independent validation samples to assess the quality of the classification results"},{"concepts":[341],"name":"Understand the main factors generating geometric distortions of the remotely sensed images"},{"concepts":[169],"name":"Understand the main software engineering methodologies"},{"concepts":[287],"name":"Understand the nature of tort law generally and nuisance law specifically"},{"concepts":[104],"name":"Understand the physical notions of velocity and acceleration which are fundamentally about movement across space through time"},{"concepts":[436],"name":"Understand the problems associated with the lack of reproducibility"},{"concepts":[448],"name":"Understand the relevance of topological consistency for linear network features derived from Earth observation data"},{"concepts":[363],"name":"Understand the role of multi-temporal satellite images for identifying not only when a change occurred but also the changing drivers"},{"concepts":[391],"name":"Understand the role of pruning for reducing overfitting when applying decision trees for various classification purposes"},{"concepts":[465],"name":"Understand the strategic meaning of DIAS in the user segment of Copernicus"},{"concepts":[373],"name":"Understand the subjectivity of the visual interpretation"},{"concepts":[844],"name":"Understand the technology behind LiDAR as an active sensor and what makes it different from the other existing Remote Sensing approaches"},{"concepts":[63],"name":"Understand the underlying assumptions for spatial stochastics process"},{"concepts":[365],"name":"Understand the way in which Dynamic Time Warping can align shifted temporal sequences"},{"concepts":[22],"name":"Understand various formats of storing raster and vector data"},{"concepts":[227],"name":"Understand vector data models"},{"concepts":[889],"name":"Use \"Full-text-based\" discovery; open source and commercial search engines, its use in GI related applications"},{"concepts":[863,861],"name":"Use 3D textured models to present study area"},{"concepts":[463],"name":"Use a web portal to retrieve EO data"},{"concepts":[464],"name":"Use an image archive to retrive Earth observation data for an application"},{"concepts":[146],"name":"Use appropriate interpolation techniques to derive DEMs from point data"},{"concepts":[105],"name":"Use categorical information in analysis, cartography, and other GIS processes, avoiding common interpretation mistakes"},{"concepts":[828],"name":"Use EO products to assess land areas, its ecosystems, and its evolution"},{"concepts":[819],"name":"Use EO products to assess the risk of a disaster"},{"concepts":[807,805],"name":"Use EO products to conduct forecasts and projections"},{"concepts":[806],"name":"Use EO products to conduct numerical simulations"},{"concepts":[804],"name":"Use EO products to forecast sunlight exposure"},{"concepts":[819],"name":"Use EO products to measure impact and/or recovery"},{"concepts":[819],"name":"Use EO products to monitor disaster prone areas"},{"concepts":[828],"name":"Use EO products to plan land areas, its ecosystems, and its evolution"},{"concepts":[756],"name":"Use EO/GI information to plan and design projects, monitor and assess the environment, support decision-making processes, and to tackle environmental challenges"},{"concepts":[113],"name":"Use established analysis methods that are based on the concept of region (e.g., landscape ecology)"},{"concepts":[114],"name":"Use established analysis methods that are based on the concept of spatial integration (e.g., overlay)"},{"concepts":[381],"name":"Use filtering techniques to spatially aggregate an image classification"},{"concepts":[119],"name":"Use methods that analyze metrical relationships"},{"concepts":[118],"name":"Use methods that analyze topological relationships"},{"concepts":[891],"name":"Use Natural language based discovery over linked data"},{"concepts":[842],"name":"Use NDVI to estimate the vegetation cover"},{"concepts":[885],"name":"Use open data APIs that enable the usage of Open data; identify design aspects and usage scenarios"},{"concepts":[436],"name":"Use software tools to automate the practice of reproducible research in daily work"},{"concepts":[206],"name":"Use standards such as ISO 19141 Schema for moving features, ISO 19142 Web Feature Service and ISO 19109 - Rules for application schema"},{"concepts":[492],"name":"Use the models of ‘SDI generations’ and ‘SDI components’ to describe the main elements of an existing SDI initiative"},{"concepts":[479],"name":"Use the most effective change model depending on the nature and needs of the client's organization."},{"concepts":[878],"name":"Use Web services description for RESTful web services, Web Application Description Language (WADL) and its use"},{"concepts":[195],"name":"Work with different data compression techniques"},{"concepts":[40],"name":"Write a program to create a matrix of pair-wise distances among a set of points"},{"concepts":[211],"name":"Write a program to read and write a raster data file"},{"concepts":[40],"name":"Write typical forms for distance decay functions"},{"concepts":[11],"name":"xplain how the concept of capacity represents an upper limit on the amount of flow through the network"}],"updateDate":"2021/10/15","version":"6"},"v7":{"concepts":[{"code":"GIST","description":"Geographic Information Science and Technology","name":"Geographic Information Science and Technology"},{"code":"AM","description":"This knowledge area encompasses a wide variety of operations whose objective is to derive analytical results from geospatial data. Data analysis seeks to understand both first-order (environmental) effects and second-order (interaction) effects. Approaches that are both data-driven (exploration of geospatial data) and model-driven (testing hypotheses and creating models) are included. Data driven techniques derive summary descriptions of data, evoke insights about characteristics of data, contribute to the development of research hypotheses, and lead to the derivation of analytical results. The goal of model driven analysis is to create and test geospatial process models. In general, model-driven analysis is an advanced knowledge area where previous experience with exploratory spatial data analysis would constitute a desired prerequisite. Visual tools for data analysis are covered in Knowledge Area: Cartography and Visualization (CV) and many of the fundamental principles required to ground data analysis techniques are introduced in Knowledge Area: Conceptual Foundations (CF). Image processing techniques are considered in Knowledge Area: Geospatial Data (GD). All of the methods described in this knowledge area are more or less sensitive to data error and uncertainty as covered in Unit GC8 Uncertainty and Unit GD6 Data quality. Mastery of the educational objectives outlined in this knowledge area requires knowledge and skills in mathematics, statistics, and computer programming.","name":"Analytical Methods","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM1-2","description":"Analytical capabilities of a GIS make use of spatial and non-spatial (attribute) data to answer questions and solve problems that are of spatial relevance. We now make a distinction between analysis (or analytical operations) and analytical models (often referred to as “modelling”). And by analysis we actually mean only a subset of what is usually implied by the term: we do not specifically deal with advanced statistical analysis (such as cluster detection or geostatistics).\r\n\r\nAnalysis of spatial data can be defined as computing new information to provide new insights from existing spatial data. Consider an example from the domain of road construction. In mountainous areas, this is a complex engineering task with many cost factors, including the number of tunnels and bridges to be constructed, the total length of the tarmac, and the volume of rock and soil to be moved. GISs can help to compute such costs on the basis of an up-to-date digital elevation model and a soil map. The exact nature of the analysis will depend on the application requirements, but computations and analytical functions can operate on both spatial and non-spatial data.","name":"Analytical approaches","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM1","description":"Geospatial data analysis has foundations in many different disciplines. As a result, there are many different schools of thought or analytical approaches including spatial analysis, spatial modeling, geostatistics, spatial econometrics, spatial statistics, qualitative analysis, map algebra, and network analysis. This unit compares and contrasts these approaches.","name":"Foundations of analytical methods","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM10-1","description":"Difficulties in dealing with large spatial databases, especially those arising from spatial heterogeneity and data quality issues.","name":"Problems of large spatial databases","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM10-2","description":"Data mining knows a variety of approaches, such as cluster analysis, analytical reasoning, association, prediction, etc.","name":"Data mining approaches","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM10-3","description":"Knowledge discovery involves the identification of useful patterns in spatial databases using techniques of data mining, trend analysis, etc.","name":"Knowledge discovery","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM10","description":"Algorithms have been developed to scan and search through extremely large data sets in order to find patterns within the data. These data mining and knowledge discovery techniques have been expanded to the spatial case. Legal and ethical concerns associated with such practices are considered in Knowledge Areas GS GIS and T and Society and OI Organizational and Institutional Aspects.","name":"Data mining","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM11-1","description":"A network is a connected set of lines representing some geographic phenomenon, typically to do with transportation. The “goods” transported can be almost anything: people, cars and other vehicles along a road network, commercial goods along a logistic network, phone calls along a telephone network, or water pollution along a stream/river network.\r\n\r\nDirect vs. Non-directed Networks\r\nA fundamental characteristic of any network is whether the network lines are considered to be directed or not. Directed networks associate with each line a direction of transportation; undirected networks do not. In the latter, the “goods” can be transported along a line in both directions. We discuss here vector network analysis, and assume that the network is a set of connected line features that intersect only at the lines’ nodes, not at internal vertices. (But we do mention under- and overpasses.)\r\n\r\nPlanar vs. Non-Planar Networks\r\nFor many applications of network analysis, a planar network, i.e. one that can be embedded in a two-dimensional plane, will do the job. Many networks are naturally planar, such as stream/river networks. A large-scale traffic network, on the other hand, is not planar: motorways have multi-level crossings and are constructed with underpasses and overpasses. Planar networks are easier to deal with computationally, as they have simpler topological rules. Not all GISs accommodate non-planar networks, or they can only do so using “tricks”. These tricks may involve the splitting of overpassing lines at the intersection vertex and the creation of four lines from the two original lines. Without further attention, the network will then allow one to make a turn onto another line at this new intersection node, which in reality would be impossible. In some GISs we can allocate a cost for turning at a node—see our discussion on turning costs below—and that cost, in the case of the overpass trick, can be made infinite to ensure it is prohibited. But, as mentioned, this is a work around to fit a non-planar situation into a data layer that presumes planarity. The above is a good illustration of geometry not fully determining the network’s behaviour. Additional application-specific rules are usually required to define what can and cannot happen in the network. Most GISs provide rule-based tools that allow the definition of these extra application rules.","name":"Networks defined","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM11-2","description":"Identifying and listing all elements does not describe a system in full. There may be many different ways in which elements may be connected or related to each other. The interactions, relationships between elements are essential to describe a system.\r\n\r\nRelationships between elements can be described by two types of flows:\r\nflows of material, and flows of information.\r\n\r\nMaterial flows connect elements between which there is an exchange of some substance. This can be some kind of material (water, food, cement, biomass, etc.), energy (light, heat, electricity, etc.), money, etc. It is something that can be measured and tracked. Also if an element is a donor of this substance the amount of substance in this element will decrease as a result of the exchange, while at the same time the amount of this substance will increase in the receptor element. There is always a mass, or energy conservation law in place. Nothing appears from nothing, and nothing can disappear to nowhere.\r\n\r\nThe second type of exchange is with an information flow. In this case element A gets information from element B. Element B at the same time may have no information about element A. Even when element A gets information about B, B does not lose anything. Information can be about the state of an element, about the quantity that it contains, about its presence or absence, etc. Information flows can be used to describe rules and policies. Information flows can modify the rates of flow between elements, they can switch certain processes and interactions on and off. But the process through which policies, interventions and norms for action are established, and could for example define the values of such information flows, are themselves the result of social interaction between relevant stakeholders from public, private or civil society.\r\n\r\nThe simplest is to acknowledge the existence of a relationship between certain elements, like this is done in a graph. In a graph a node presents an element and a link between any two nodes shows that these two elements are related. However there is no evidence of the direction of the relationship: we do not distinguish between the element x influencing element y or vice versa. This relationship can be further specified by an oriented graph that shows the direction of the relationship between elements. An element can be also connected to itself, to show that its behaviour depends on its state. We can further detail the description by identifying whether element x has a positive or negative effect on element y.\r\n\r\nWith networks, interesting questions arise that have to do with connectivity and network capacity. These relate to applications such as traffic monitoring and watershed management. With network elements—i.e. the lines that make up the network—extra values are commonly associated, such as distance, quality of the link or the carrying capacity.","name":"Graph theoretic descriptive measures of networks","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM11-3","description":"Optimal-path finding techniques are used when a least-cost path between two nodes in a network must be found. The two nodes are called origin and destination. The aim is to find a sequence of connected lines to traverse from the origin to the destination at the lowest possible cost.\r\n\r\nIn Optimal-path finding, the cost function can be simple: for instance, it can be defined as the total length of all lines of the path. The cost function can also be more elaborate and take into account not only length of the lines but also their capacity, maximum transmission (travel) rate and other line characteristics, for instance to obtain a reasonable approximation of travel time. There can even be cases in which the nodes visited add to the cost of the path as well. These may be called turning costs, which are defined in a separate turning-cost table for each node, indicating the cost of turning at the node when entering from one line and continuing on another. This is illustrated in Figure 1 of the examples.\r\n\r\nProblems related to optimal-path finding may require ordered optimal path finding or unordered optimal-path finding. Both have as an extra requirement that a number of additional nodes need to be visited along the path. In ordered optimal-path finding, the sequence in which these extra nodes are visited matters; in unordered optimal-path finding it does not.","name":"Least-cost shortest path","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM11-4","description":"There are phenomena that do not spread in all directions, but move or “flows” along a given, least-cost path, determined by characteristics of local terrain. The typical case arises when we want to determine drainage patterns in a catchment area: rain water “chooses” a way to leave the area. \r\n\r\nWe can illustrate the principles involved in this typical case with a simple elevation raster. For each cell in that raster, the steepest downward slope to a neighbour cell is computed and its direction is stored in a new raster. This computation determines the elevation difference between the cell and the neighbour cell and it takes into account cell distance - 1 for neighbour cells in N–S or W–E direction, 2 for cells in a NE–SW or NW–SE direction. From among its eight neighbour cells, it picks the one with the steepest path to it. The directions thus obtained in an output raster are encoded in integer values, which can be called the flow-direction raster. From this raster, the GIS can compute the accumulated flow-count raster, a raster that for each cell indicates how many cells have their water flow into that cell.\r\n\r\nCells with a high accumulated flow count represent areas of concentrated flow and may, thus, belong to a stream. By using some appropriately chosen threshold value in a map algebra expression, we may decide whether they do or not. Cells with an accumulated flow count of zero are local topographic highs and can be used to identify ridges.","name":"Flow modeling","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM11-5","description":"The Classic Transportation Problem considers minimizing the cost of getting an object or subject from origin to destination.","name":"The Classic Transportation Problem","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM11-6","description":"Classic network problems are examples of networking problems such as the Traveling Salesman Problem and the Chinese Postman Problem that need graph algorithms to be solved.","name":"Other classic network problems","selfAssesment":"GI-N2K
"},{"code":"AM11-7","description":"Accessibility is the extend in which it is difficult/easy to reach a location or object.","name":"Accessibility modeling","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM11","description":"Network analysis encompasses a wide range of procedures, techniques, and methods that allow for the examination of phenomena that can be modeled in the form of connected sets of edges and vertices. Such sets are termed a network or a graph, and the mathematical basis for network analysis is known as graph theory. Graph theory contains descriptive measures and indices of networks such as connectivity, adjacency, capacity, and flow as well as methods for proving the properties of networks. Networks have long been recognized as an efficient way to model many types of geographic data, including transportation networks, river networks, and utility networks electric, cable, sewer and water, etc. to name just a few. The data structures to support network analysis are covered in [DM4-7] Network models.","name":"Network analysis","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM12-1","description":"The modeling of problems in a formal language, working in a solution space and applying constraints.","name":"Operations research modeling and location modeling principles","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM12-2","description":"A formal programming method to support operational research in which linear constraints are applied.","name":"Linear programming","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM12-3","description":"A formal programming method to support operational research in which variables are constrained to integers.","name":"Integer programming","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM12-4","description":"Location-allocation modeling involves the determination of locations by minimizing the distance between object/subjects in space, such as between customers and facilities.","name":"Location-allocation modeling and p-median problems","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM12","description":"A wide variety of optimization techniques are now solvable within the GIS and T domain. Operations research is a branch of mathematics practiced in the allied fields of business and engineering. New models and software tools allow for the solution of transportation routing, facility location, and a host of other location-allocation modeling problems.","name":"Optimization and location-allocation modeling","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM13-1","description":"The effects such as the loss of data quality and data integrity that are the results of data transformations.","name":"Impacts of transformations","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM13-2","description":"A data model is an abstract model that organizes elements of data and standardizes how they relate to one another and to the properties of real-world entities. The term data model can refer to two distinct but closely related concepts. In relation to the field of geoinformation the term data model refers to the set of concepts used in defining such formalizations as entities, attributes, relations, tables which is implemented by a mathematical construct for representing geographic objects or surfaces as data. There are two most frequently used data models, which are vector and raster. For example, the vector data model represents geography as collections of points, lines and polygons and more complex structures crated from these three. The raster data model represent geography as cell matrices that store numeric values. Among these two data models we also stand out data formats in which data sets can be stored. File format is a standard of encoding geographical information into a computer file. There are the following basic file formats for encoding data:\r\nFor vectors:\r\n-\tShapefile\r\n-\tGeography Markup Language (GML)\r\n-\tXYZ Point Cloud\r\n-\tGeoJSON\r\n-\tGeoMedia\r\n-\t\r\nFor rasters:\r\n-\tGeoTIFF\r\n-\tIMG\r\n-\tJPEG2000\r\n-\tEsri grid\r\nThe GIS projects often require the conversion of the data formats. Data conversion is the process of moving data from one format to another, whether it is from one data model to another or from one data format to another. Data conversion is a complex process which is not only associated with changing the binary format of the file but also requires changing the structure of the data. For example, the GML data format always comes with an UML diagram, which is necessary to convert attributes stored in GML structure for example to a table of contest in a shapefile data format. In a well-managed GIS project it is important to store data in specific data model or data format. It is sometimes dictated by software capabilities and another times by team’s technical capabilities. With large amounts of geographic data used in the project it is more cost-effective to convert the data from one format to another than re-create it.","name":"Data model and format conversion","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM13-3","description":"Interpolation is used to create a GIS layer out of point observations on a continuous variable. The reason for doing this could be manifold: for visualization purposes, for making a proper reference with other data, or for making a combination of different layers.","name":"Interpolation","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM13-4","description":"Any vector data containing point, polyline, polygon can be converted into the raster dataset and vice versa. The vector data can be stored in shapefiles, databases or various others GIS file formats. The raster data are made of pixels or grid calls and can be represented by the discrete - categorical data (e.g. land cover map) or non-discrete - continuous data (e.g. satellite images, surface data). The process of conversion of vector to raster data is called rasterization. The vector to raster conversion requires the following parameters: the field value from the attribute table used to assign values to the output raster, the pixel size for the output raster, the output raster format (i.e. geotiff, img) and optionally the method of assigning values of point, polyline or polygon to the call raster, i.e. maximum length or area, cell centre. The output of the rasterised vector looks like a gridded version of the vector and it depends on the grid cell size. The process of vectorisation refers to the conversion of raster to vector dataset. The raster dataset can be converted to vector point, polyline or polygon. In order to convert raster to vector the following parameters should be provided: attribute field of the input raster dataset which will become an attribute in the output vector class, determining if the output polygon or polyline will be smoothed into simpler shapes or conform to the input raster's cell edges (stair stepping). For each raster pixel or grid cell a point will be created at the centre of the cell. The non-discrete continuous raster data have to converted to the categorical data type before converting to vector data. The conversion of vector to raster and raster to vector degrade the data to some extent causing loss of details, accuracy, and changing the original data.","name":"Vector-to-raster and raster-to-vector conversions","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM13-5","description":"Raster resampling refers to change of spatial resolution (increasing or decreasing) of the raster dataset. The resampling process calculates the new pixel values from the original digital pixel values in the uncorrected image. There are three common methods for resampling: nearest neighbour, bilinear interpolation, and cubic convolution. The nearest neighbour resampling uses the digital value from the pixel in the original image which is nearest to the new pixel location in the corrected image. This is the fastest interpolation method, which is primarily applied for discrete (categorical) raster data as it does not change the value of the pixel, but may result in some pixel values being duplicated while others are lost. Bilinear interpolation resampling takes a weighted average of four pixels in the original image nearest to the new pixel location. The averaging process alters the original pixel values and creates entirely new digital values in the output image. It is recommended for continuous data and it cause some smoothing of the data. Cubic convolution resampling is based on calculation of a distance weighted average of a block of sixteen pixels from the original image which surround the new output pixel location. As with bilinear interpolation, this method results in completely new pixel values. However, the last two methods both produce images which have a much sharper appearance and avoid the blocky appearance of the nearest neighbour method. The disadvantage of the Cubic method is that its requires more processing time.","name":"Raster resampling","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM13-6","description":"Users of geoinformation often need transformations from a particular 2D coordinate system to another system. This includes the transformation of polar coordinates into Cartesian map coordinates, or the change of map projection - transformation from one 2D Cartesian (x, y) system of a specific map projection into another 2D Cartesian (x′, y′) system of a defined map projection. This transformation is based on relating the two systems on the basis of a set of selected points whose coordinates are known in both systems, such as ground control points or common points such as corners of houses or road intersections. Image and scanned data are usually transformed by this method. The transformations may be conformal, affine, polynomial or of another type, depending on the geometric errors in the data set. A datum transformation involves the change of the horizontal datum which is often accompanied with a change of map projection.","name":"Coordinate transformations","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM13","description":"GIS is a cyclical rather than a linear system, unlike computer aided drafting (CAD) and computer assisted cartographic systems. Changes in projection, grid systems, data forms, and formats take place during the modeling process for which GIS was designed. Many non-analytical manipulations are necessary to accommodate the analytical power of the GIS. The manipulations of spatial and spatio-temporal data involve two general classes of operation: 1.\tTheir transformation into formats that facilitate subsequent analysis 2. Generalization and aggregation that affect the accuracy and integrity of the data used for analysis (see [AM14]). Other knowledge areas have identified different forms of data structures, data models, projections, and other forms of geospatial data representation. These differences present both opportunities and challenges for analysis and modeling. The ability to transform one representation to another, in a manner that maintains the integrity of the information as much as possible, can enhance the analysis and visualization of geospatial data. The raster and vector data models are described in [DM3] Tesselation data models and [DM4] Vector data model, Feature based modelling, Applications. The principles of coordinate systems, datums, and projections are also considered in Knowledge Area [GD] Geospatial Data","name":"Representation transformation","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM14-1","description":"In the practice of spatial data handling, one often comes across questions like “What is the resolution of the data?” or “At what scale is your data set?” Now that we have moved firmly into the digital age, these questions sometimes defy an easy answer. Map scale can be defined as the ratio between the distance on a printed map and the distance of the same stretch in the terrain.\r\n\r\nA 1:50,000 scale map means that 1 cm on the map represents 50,000 cm (i.e. 500 m) in the terrain. “Large-scale” means that the ratio is relatively large, so typically it means there is much detail to see, as on a 1:1000 printed map. “Small-scale”, in contrast, means a small ratio, hence less detail, as on a 1:2,500,000 printed map.\r\nDigital spatial data, as stored in a GIS, are essentially without scale: scale is a ratio notion associated with visual output, such as a map or on-screen display, not with the data that was used to produce the map or display. When digital spatial data sets have been collected with a specific map-making purpose in mind, and all maps have been designed to use one single map scale, for instance 1:25,000, we may assume that the data carries the characteristic of “a 1:25,000 digital data set.”\r\n\r\nThere is a relationship between the effectiveness of a map for a given purpose and the map’s scale. The Public Works department of a city council cannot use a 1:250,000 map for replacing broken sewer pipes, and the map of Figure 1 cannot be reproduced at scale 1:10,000.\r\n\r\nMaps that show much detail of a small area are called large-scale maps. Scale indications on maps can be given verbally, such as “one-inch-to the- mile”, or as a representative fraction like 1:200,000,000 (1 cm on the map equals 200,000,000 cm (or 2000 km) in reality), or by a graphic representation such as the scale bar. The advantage of using scale bars in digital environments is that its length also changes when the map is zoomed in, or enlarged, before printing. Sometimes it is necessary to convert maps from one scale to another, which may lead to problems of cartographic generalization.\r\n\r\nSpatial and temporal scales can not only be attached to processes, but also to observations. An example is given below, which summarizes the spatial and temporal scales of a few well-known Earth observation systems.\r\n\r\nScales of RS observations\r\nSensor Spatial scale\t Temporal scale\r\nMeteosat\t Hemisphere\t 15 minutes\r\nNOAA-AVHRR\t 3000 km\t daily\r\nLandsat TM\t 180 km\t 16 days\r\nSpot\t 60 km\t 26 days (pointable)","name":"Scale and generalization","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM14-2","description":"Techniques that support the generalisation of map content when changing to smaller map scales. These include line simplification, object selection, etc.","name":"Approaches to point, line, and area generalization","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM14-3","description":"Classification is a technique for purposely removing detail from an input data set in the hope of revealing important patterns (of spatial distribution). In the process, we produce an output data set, so that the input set can be left intact. This output set is produced by assigning a characteristic value to each element in the input set, which is usually a collection of spatial features that could be raster cells or points, lines or polygons. If the number of characteristic values in the output set is small in comparison to the size of the input set, we have classified the input set.\r\n\r\nThe input data set may, itself, have been the result of a classification. In such cases we refer to the output data set as a reclassification. For example, we may have a soil map that shows different soil type units and we would like to show the suitability of units for a specific crop. In this case, it is better to assign to the soil units an attribute of suitability for the crop. Since different soil types may have the same crop suitability, a classification may merge soil units of different type into the same category of crop suitability.\r\n\r\nIn classification of vector data, there are two possible results. In the first, the input features may become the output features in a new data layer, with an additional category assigned. In other words, nothing changes with respect to the spatial extents of the original features. Figure a of Examples illustrates this first type of output. A second type of output is obtained when adjacent features of the same category are merged into one bigger feature. Such a post-processing function is called spatial merging, aggregation or dissolving. An illustration of this second type is found in Figure b of Examples. Observe that this type of merging is only an option in vector data, as merging cells in an output raster on the basis of a classification makes little sense. Vector data classification can be performed on point sets, line sets or polygon sets; the optional merge phase only makes sense for lines and polygons.\r\n\r\nUser-controlled classifications require a classification table or user interaction. GIS software can also perform automatic classification, in which a user only specifies the number of classes in the output data set. The system automatically determines the class break points. The two main techniques of determining break points being used are the equal interval technique and the equal frequency technique.\r\n\r\nEqual Interval Technique\r\nThe minimum and maximum values vmin and vmax of the classification parameter are determined and the (constant) interval size for each category is calculated as (vmax - vmin) ∕ n, where n is the number of classes chosen by the user. This classification is useful in that it reveals the distribution pattern, as it determines the number of features in each category.\r\n\r\nEqual Frequency Technique\r\nThis technique is also known as quantile classification. The objective is to create categories with roughly equal numbers of features per category. The total number of features is determined first, then, based on the required number of categories, the number of features per category is calculated. The class break points are then determined by counting off the features in order of classification parameter value.","name":"Classification and transformation of attribute measurement levels","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM14","description":"Generalization addresses the meaningful reduction of the map content during scale reduction. All geospatial data are generalized. Even the most detailed data represent only subsets of reality. Furthermore, data are further generalized for purposes of mapping, visualization, and efficient storage. A variety of generalization techniques have been developed to facilitate this process. All are scale dependent. Aggregation is one form of generalization that transforms large numbers of individual objects into summarized groups. This concept description is concerned with the nature of these procedures and their implications for professional practice. Generalization is an important part of cartography (and is therefore discussed conceptually in CV2 Data considerations), but is also a transformation common to many GIS procedures.","name":"Generalization and aggregation","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM2-1","description":"Set theory is based on describing collections of members within sets. The Boolean membership function is binary, i.e. an element is either a member of the set (membership is true) or it is not a member of the set (membership is false). Such a membership notion is well-suited to the description of spatial features such as land parcels for which no ambiguity is involved and an individual ground truth sample can be judged to be either correct or incorrect. As Burrough and Frank (1996) note, increasingly, people are beginning to realize that the fundamental axioms of simple binary logic present limits to the way we think about the world. Not only in everyday situations, but also in formalized thought, it is necessary to be able to deal with concepts that are not necessarily true or false, but that operate somewhere in between. Since its original development by Zadeh (1965), there has been considerable discussion of fuzzy, or continuous, set theory as an approach for handling imprecise spatial data. In GIS, fuzzy set theory appears to have two particular benefits: the ability to handle logical modelling (map overlay) operations on inexact data; and the possibility of using a variety of natural language expressions to qualify uncertainty. Unlike Boolean sets, fuzzy or continuous sets have a membership function, which can assign to a member any value between 0 and 1.","name":"Set theory","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM2-2","description":"The most common operator for defining queries in a relational database is the language SQL, which stands for Structured Query Language.\r\n\r\nA spatial DBMS provides support for geographic coordinate systems and transformations. It will also provide storage of the relationships between features, including the creation and storage of topological relationships. As a result, one is able to use functions for “spatial query” (exploring spatial relationships). To illustrate, a spatial query using SQL to find all the Thai restaurants within 2 km of a given hotel would look like:\r\n\r\nSELECT R.Name\r\nFROM Restaurants AS R,\r\nHotels as H\r\nWHERE R.Type = Thai AND\r\nH.name = Hilton AND\r\nIntersect(R.Geometry, Buffer(H.Geometry, 2))\r\n\r\nThe Intersect command creates a spatial join between restaurants and hotels. The Geometry column carries the spatial data. It is likely that in the near future all spatial data will be stored directly in spatial databases.","name":"Structured Query Language (SQL) and attribute queries","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM2-3","description":"When exploring a spatial data set, the first thing one usually wants to do is select certain features, to (temporarily) restrict the exploration. Such selections can be made on geometric/spatial grounds or on the basis of attribute data associated with the spatial features. \r\n\r\nSelection conditions on attribute values can be combined using logical connectives such as AND, OR and NOT. Other techniques of selecting features can also usually be combined. Any set of selected features can be used as the input for a subsequent selection procedure. This means, for instance, that we can select all medical clinics first, then identify roads within 200 m of them, then select from those only the major roads, then select the nearest clinics to these remaining roads as the ones that should receive our financial support for maintenance. In this way, we are combining various techniques of selection.\r\n\r\nInteractive Spatial Selection\r\nIn interactive spatial selection, one defines the selection condition by pointing at or drawing spatial objects on the screen display, after having indicated the spatial data layer(s) from which to select features. The interactively defined objects are called the selection objects; they can be points, lines, or polygons. The GIS then selects the features in the indicated data layer(s) that overlap (i.e. intersect, meet, contain, or are contained in;) with the selection objects. These become the selected objects.\r\nInteractive spatial selection answers questions like “What is at …?”\r\n\r\nA spatial DBMS provides support for geographic coordinate systems and transformations. It will also provide storage of the relationships between features, including the creation and storage of topological relationships. As a result, one is able to use functions for “spatial query” (exploring spatial relationships). To illustrate, a spatial query using SQL to find all the Thai restaurants within 2 km of a given hotel would look like:\r\n\r\nSELECT R.Name\r\nFROM Restaurants AS R,\r\nHotels as H\r\nWHERE R.Type = Thai AND\r\nH.name = Hilton AND\r\nIntersect(R.Geometry, Buffer(H.Geometry, 2))\r\n\r\nThe Intersect command creates a spatial join between restaurants and hotels. The Geometry column carries the spatial data. It is likely that in the near future all spatial data will be stored directly in spatial databases.","name":"Spatial queries","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM2","description":"Attribute and spatial query operations are core functionality in any GIS and they are often considered to be the most basic form of analysis.","name":"Query operations and query languages","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM3-1","description":"In a 2D polar coordinate system points can be described with coordinates. Another way of defining a point in a plane is by using polar coordinates. This is the distance d from the origin to the point concerned and the angle α between a fixed (or zero) direction and the direction to the point. The angle α is called azimuth or bearing and is measured in a clockwise direction. It is given in angular units while the distance d is expressed in length units. \r\n\r\nDistance also plays a role in computations on networks, comprising a different set of analytical functions in GISs. Here, the network may consist of roads, public transport routes, high-voltage power lines, or other forms of transportation infrastructure. Analysis of networks may entail shortest path computations (in terms of distance or travel time) between two points in a network for routing purposes. Other forms are to find all points reachable within a given distance or duration from a start point for allocation purposes, or determination of the capacity of the network for transportation between an indicated source location and sink location.\r\n\r\nIn raster images, the distance function applied is the Pythagorean distance between the cell centres. The distance from a non-target cell to the target is the minimal distance one can find between that non-target cell and any target cell.","name":"Distances and lengths","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM3-2","description":"In a 2D polar coordinate system points can be described with coordinates. Another way of defining a point in a plane is by using polar coordinates. This is the distance d from the origin to the point concerned and the angle α between a fixed (or zero) direction and the direction to the point. The angle α is called azimuth or bearing and is measured in a clockwise direction. It is given in angular units while the distance d is expressed in length units.\r\n\r\nBearings are always related to a fixed direction (initial bearing) or a datum line. In principle, this reference line can be chosen freely. Three different, widely used fixed directions are: True North, Grid North and Magnetic North. The corresponding bearings are true (or geodetic) bearings, grid bearings and magnetic (or compass) bearings, respectively.\r\n\r\nIn raster images, direction is determined by the orientation of the neighboring pixels.","name":"Direction","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM3-3","description":"The representation of geographic objects is most naturally supported with vectors. After all, objects are identified by the parameters of location, shape, size and orientation, and many of these parameters can be expressed in terms of vectors. We can define features within the topological space that are easy to handle and that can be used as representations of geographic objects. These features are called simplices as they are the simplest geometric shapes of some dimension: point (0-simplex), line segment (1-simplex), triangle (2-simplex), and tetrahedron (3-simplex). When we combine various simplices into a single feature, we obtain a simplicial complex. When area objects are stored using a vector approach, the usual technique is to apply a boundary model. This means that each area feature is represented by some arc/node structure that determines a polygon as the area’s boundary. A polygon representation for an area object is another example of a finite approximation of a phenomenon that may have a curvilinear boundary in reality. In images, the shape of objects often helps us to identify them (built-up areas, roads and railroads, agricultural fields, etc.).","name":"Shape","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM3-4","description":"When area objects are stored using a vector approach, the usual technique is to apply a boundary model. This means that each area feature is represented by some arc/node structure that determines a polygon as the area’s boundary. A polygon representation for an area object is another example of a finite approximation of a phenomenon that may have a curvilinear boundary in reality.\r\nCommon sense dictates that area features of the same kind are best stored in a single data layer, represented by mutually non-overlapping polygons. This results in an application-determined (i.e. adaptive) partition of space. If the object has a fuzzy boundary, a polygon is an even worse approximation, even though potentially it may be the only one possible. Clearly, we expect additional data to accompany the area data. Such information could be stored in database tables.\r\n\r\nA simple but naïve representation of area features would be to list for each polygon the list of lines that describes its boundary. Each line in the list would, as before, be a sequence that starts with a node and ends with one, possibly with vertices in between. As the same line makes up the boundary from the two polygons, this line would be stored twice in the above representation, namely once for each polygon. This is a form of data duplication—known as data redundancy—which is (at least in theory) unnecessary, although it remains a feature of some systems. Another disadvantage of such polygon-by-polygon representations is that if we want to identify the polygons that border the bottom left polygon, we have to do a complicated and time-consuming search analysis comparing the vertex lists of all boundary lines with that of the bottom left polygon. For just a few polygons, this is fine, but in a data set with 5000 polygons, and perhaps a total of 25,000 boundary lines, this becomes a tedious task, even with the fastest of computers.","name":"Area","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM3-5","description":"Proximity computations are specific neighbourhood functions. They evaluate the characteristics of an area surrounding a feature’s location. A neighbourhood function “scans” the neighbourhood of the given feature(s), and performs a computation on it (them).\r\n\r\nExamples of proximity computations are: (1) Buffer zone generation (or buffering) is one of the best-known neighbourhood functions. It determines a spatial envelope (buffer) around a given feature or features. The buffer created may have a fixed width or a variable width that depends on characteristics of the area. (2) Thiessen Polygon generation.\r\n\r\nDistance decay functions describe the effect of the reduced influence when the distance between two locations increases.","name":"Proximity and distance decay","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM3-6","description":"Adjacency is the meet relationship as a topological property of a geographic object in relation ship with another. The adjacency operator identifies those features that share boundaries and, therefore, applies only to line and polygon features.\r\nThis meet relationship is invariant under a continuous transformation and are referred to as a topological mapping.","name":"Adjacency and connectivity","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM3","description":"For simple data exploration, GIS offers many basic geometric operations that help in extracting meaning from sets of data or for deriving new data for further analysis. Concepts on which these operations are based are addressed in Domains of geographic information and Relationships.\r\n\r\nWe can, for instance, measure angles on a map and use these for navigation in the real world, or for setting out a designed physical infrastructure. Or if, instead of a conformal projection such as UTM, we use an equivalent projection, we can determine the size of a parcel of land from the map—irrespective of where the parcel is on the map and at which elevation it is on the Earth.","name":"Geometric measures","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM4-1","description":"The reclassifications tools are used to change or reclassify the values. Reclassification of vector data involves the attributes of features in the feature attribute table, on the other hand reclassification of raster data involves the grid cell values to produce a new raster data layer. Reclassification can be used for data simplification and measurement scale change. We can adjust the data for more appropriate analysis by grouping the values and changing them. The reclassification tool can also be used to remove specific values from analysis.\r\nThe Select by location tool lets you select features by how they relate to other features in another layer. Selected features are based on their location. You can select features that are near or overlap the features. Most frequently used methods are intersect, within a distance, within, completely within, contain… Features can be selected in the same or other layers.\r\nThe Select by attributes tool lets you select features that match the selection criteria. With providing a selection criteria, matching features are selected. We can provide a complex selection criteria.","name":"Reclassification and selection operations","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM4-2","description":"Buffer analysis is one form of basic spatial analysis. It takes the vector representation (point, line, or polygon) of a real-world feature, and then creates a buffer zone based on a defined distance from the feature’s border. Thus, the created buffer zone is an area whose boundary always has the same distance to the input vector feature, e.g. the buffer zone for a point feature is a circle. Real-world examples for buffer zones could be protected areas along rivers or around nature conservation areas, or represent a simple proximity analysis. In the latter case, the buffer analysis is usually the first step of the analysis, followed by an overlay of the buffer zone with the target features to find those target features within the buffer zone, and thus within a certain distance of the original feature. Usually, the buffer zone extends outwards from the feature, but polygons can also have inner buffer zones. If the buffer zones from multiple features overlap, the analyst can decide to leave the individual boundaries of the buffer zones intact, or to dissolve them, i.e. merging the overlapping buffer zones into one larger buffer zone. The size of the buffer zone, i.e. the distance of its boundary from the original feature’s boundary, can be based on an uniform numerical value and associated spatial unit, but often, it is based on an attribute value (numerical or class) of the feature. Conceptually, buffering using raster representations of real-world features is similar a proximity analysis with a regular grid of square polygons: Departing from raster cells that form the area to be buffered, all raster cells that fall within the designated distance (overlay) from the buffer zone. With buffer analysis being a basic analytical operation, practically every GIS and many other analysis tools provide this functionality.","name":"Buffers","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM4-3","description":"Overlay functions is one of the most frequently used functions in a GIS application. They combine two (or more) spatial data layers, comparing them position by position and treating areas of overlap - and of non-overlap - in distinct ways.\r\n\r\nStandard overlay operators take two input data layers and assume that they are georeferenced in the same system and that they overlap in the study area. If either of these requirements is not met, the use of an overlay operator is pointless. The principle of spatial overlay is to compare the characteristics of the same location in both data layers and to produce a result for each location in the output data layer. The specific result to produce is determined by the user. It might involve a calculation or some other logical function to be applied to every area or location. With raster data, as we shall see, these comparisons are carried out between pairs of cells, one from each input raster. With vector data, the same principle of comparing locations applies but the underlying computations rely on determining the spatial intersections of features from each input layer.\r\n\r\nVector overlay operators are useful but geometrically complicated, and this sometimes results in poor operator performance. Raster overlays do not suffer from this disadvantage, as most of them perform their computations cell by cell, and thus they are fast. GISs that support raster processing - as most do - usually have a language to express operations on rasters. These languages are generally referred to as map algebra or, sometimes, raster calculus. They allow a GIS to compute new rasters from existing ones, using a range of functions and operators. Unfortunately, not all implementations of map algebra offer the same functionality.","name":"Overlay","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM4-4","description":"Neighbourhood functions evaluate the characteristics of an area surrounding a feature’s location. A neighbourhood function “scans” the neighbourhood of the given feature(s), and performs a computation on it (them). Examples of proximity computations are: (1) Buffer zone generation (or buffering) is one of the best-known neighbourhood functions. It determines a spatial envelope (buffer) around a given feature or features. The buffer created may have a fixed width or a variable width that depends on characteristics of the area. (2) Thiessen Polygon generation. For raster images: (3) Computation of diffusion (4) Flow computation.\r\n\r\nFor instance, our target might be a medical clinic. Its neighbourhood could be defined as:\r\n\r\nan area within a radius of 2 km distance as the crow flies; or\r\nan area within 2 km travelling distance; or\r\nall roads within 500 m travelling distance; or\r\nall other clinics within 10 minutes travelling time;\r\nall residential areas for which the clinic is the closest clinic.\r\n\r\nFinally, in the third step we indicate what it is we want to discover about the phenomena that exist or occur in the neighbourhood. This might simply be its spatial extent, but it might also be statistical information such as:\r\n\r\nhow many people live in the area;\r\nwhat is their average household income;\r\nare any high-risk industries located in the neighbourhood.\r\n\r\nThese are typical questions in an urban setting. When our interest is more in natural phenomena, different examples of locations, neighbourhoods and neighbourhood characteristics arise.\r\n\r\nThe principle in this case is to find out the characteristics of the vicinity, here called neighbourhood, of a location. After all, many suitability questions, for instance, depend not only on what is at a location but also on what is near the location. Thus, the GIS must allow us “to look around locally”. To perform neighbourhood analysis, we must:\r\n\r\n1. state which target locations are of interest to us and define their spatial extent;\r\n2. define how to determine the neighbourhood for each target; and\r\n3. define which characteristic(s) must be computed for each neighbourhood. \r\n\r\nSince raster data are the more commonly used in this case, neighbourhood characteristics often are obtained via statistical summary functions that compute values such as the average, minimum, maximum and standard deviation of the cells in the identified neighbourhood.\r\n\r\nTo select target locations, one can use the selection techniques. To obtain characteristics from an eventually-to-be identified neighbourhood, the same techniques apply. So what remains to be discussed here is the proper determination of a neighbourhood. One way of determining a neighbourhood around a target location is by making use of the geometric distance function. Geometric distance does not take into account direction, but certain phenomena can only be studied by doing so. Think of the spreading of pollution by rivers, groundwater flow or prevailing weather systems.\r\n\r\nDiffusion functions are based on the assumption that the phenomenon in question spreads in all directions, though not necessarily equally easily in each direction. Hence it uses local terrain characteristics to compute local resistances to diffusion.","name":"Neighborhood analysis","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM4-5","description":"GISs that support raster processing - as most do - usually have a language to express operations on rasters. These languages are generally referred to as map algebra or, sometimes, raster calculus. They allow a GIS to compute new rasters from existing ones, using a range of functions and operators. Unfortunately, not all implementations of map algebra offer the same functionality. The discussion below is to a large extent based on general terminology; it attempts to illustrate the key operations using a logical, structured language. Again, the syntax often varies among different GIS software packages.\r\n\r\nWhen producing a new raster we must provide a name for it, and define how it is to be computed. This is done in an assignment statement of the following format:\r\n\r\nOutput raster name := Map algebra expression.\r\n\r\nThe expression on the right is evaluated by the GIS, and the raster in which it results is then stored under the name on the left. The expression may contain references to existing rasters, operators and functions; the format is made clear in each case. The raster names and constants that are used in the expression are called its operands. When the expression is evaluated, the GIS will perform the calculation on a pixel-by-pixel basis, starting from the first pixel in the first row and continuing through to the last pixel in the last row. In map algebra, there is a wide range of operators and functions available.\r\n\r\nArithmetic operators\r\nVarious arithmetic operators are supported. The standard ones are multiplication (×), division (/), subtraction (-) and addition (+). Obviously, these arithmetic operators should only be used on appropriate data values, and, for instance, not on classification values. Other arithmetic operators may include modulo division (MOD) and integer division (DIV). Modulo division returns the remainder of division: for instance, 10 MOD 3 will return 1 as 10 - 3 × 3 = 1. Similarly, 10 DIV 3 will return 3.\r\n\r\nOther operators are goniometric: sine (sin), cosine (cos), tangent (tan); and their inverse functions asin, acos, and atan, which return radian angles as real values. The assignment\r\n\r\nC1 := A + 10\r\n\r\nwill add a constant factor of 10 to all cell values of raster A and store the result as output raster C1. The assignment\r\n\r\nC2 := A + B\r\n\r\nwill add the values of A and B cell by cell, and store the result as raster C2. Finally, the assignment\r\n\r\nC3 := (A - B) ∕ (A + B) × 100\r\n\r\nwill create output raster C3, as the result of the subtraction (cell by cell, as usual) of B cell values from A cell values, divided by their sum. The result is multiplied by 100. This expression, when carried out on AVHRR channel 1 (red) and AVHRR channel 2 (near infrared) of NOAA satellite imagery, is known as the NDVI (Normalized Difference Vegetation Index). It has proven to be a good indicator of the presence of green vegetation.\r\n\r\nComparison and logical operators\r\n\r\nMap algebra also allows the comparison of rasters cell by cell. To this end, we may use the standard comparison operators (<, ⇐, =, >=, > and <>).\r\n\r\nA simple raster comparison assignment is\r\n\r\nC := A <> B.\r\n\r\nIt will store truth values—either true or false—in the output raster C. A cell value in C will be true if the cell’s value in A differs from that cell’s value in B. It will be false if they are the same. Logical connectives are also supported in many raster calculi. We have already seen the connectives of AND , OR and NOT in raster overlay operators. Another connective that is commonly offered in map algebra is exclusive OR (XOR). The expression a XOR b is true only if either a or b is true, but not both.\r\n\r\nConditional expressions\r\nThe comparison and logical operators produce rasters with the truth values true and false. In practice, we often need a conditional expression together with them that allows us to test whether a condition is fulfilled. The general format is:\r\n\r\nOutput raster := CON(condition, then expression, else expression).\r\n\r\nHere, condition stands for the condition tested, then the expression is evaluated if condition holds, and else the expression is evaluated if it does not hold. This means that an expression such as CON(A = “forest”, 10, 0) will evaluate to 10 for each cell in the output raster where the same cell in A is classified as forest. For each cell where this is not true, the else expression is evaluated, resulting in 0.\r\n\r\nOverlays using a decision table\r\nConditional expressions are powerful tools in cases where multiple criteria must be taken into account. A small example may illustrate this. Consider a suitability study in which a land use classification and a geological classification must be used. Domain expertise dictates that some combinations of land use and geology result in suitable areas, whereas other combinations do not. In our example, forests on alluvial terrain and grassland on shale are considered suitable combinations, while any others are not.\r\n\r\nWe could produce an output raster with a map algebra expression, such as\r\n\r\nSuitability := CON((Landuse = “Forest” AND Geology = “Alluvial”)\r\nOR (Landuse = “Grass” AND Geology = “Shale”),\r\n“Suitable”, “Unsuitable”)\r\n\r\nand consider ourselves lucky that there are only two “suitable” cases. In practice, many more cases must usually be covered and, then, writing up a complex CON expression is not an easy task.\r\n\r\nTo this end, some GISs accommodate setting up a separate decision table that will guide the raster overlay process. This extra table carries domain expertise and dictates which combinations of input raster-cell values will produce which output raster-cell value. This gives us a raster overlay operator using a decision table. The GIS will have supporting functions to generate the additional table from the input rasters and to enter appropriate values in the table.","name":"Map algebra","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM4","description":"This small set of analytical operations is so commonly applied to a broad range of problems that their inclusion in software products is often used to determine if that product is a true GIS. Concepts on which these operations are based are addressed in Domains of geographic information and Relationships.","name":"Basic analytical operations","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM5-1","description":"Point pattern analysis refers to the detection of patterns in a group of objects or subjects located in space. This may support the analysis of clusters in accidents, crime, etc.","name":"Point pattern analysis","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM5-2","description":"The probability density function is a method with which the probability density can be estimated for points in a raster space.","name":"Kernels and density estimation","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM5-3","description":"Spatial cluster analysis is the grouping of similar spatial objects into classes (clusters) in such a way that the objects within the cluster are highly similar compared to the objects outside of the cluster. Spatial clustering forms an important part of spatial data mining (Han et al., 2001; Miller et al., 2009). A wealth of spatial clustering tools are currently available with immense application potential. \r\n\r\nIn earth observation studies, spatial cluster techniques are often applied to identify zones with similar land covers by using earth observation data as input. An example of such a technique is the K-means classifier (Han et al., 2001; Miller et al., 2009). This unsupervised classification technique makes several clusters (e.g. land use classes) of which each pixel is assigned to the cluster with the nearest mean (Han et al., 2001). The amount of clusters can be freely defined by the user just as the input metrics to perform the classification. A drawback of the K-means classifier is the need to specify the amount of output clusters. Density Based Spatial Clustering (DBSC) overcomes this issue since it automatically defines the optimal amount of clusters (Miller et al., 2009). In this type of clustering technique, dense regions of objects (proximate objects) are clustered and separated from regions with low density (noise) (Han et al., 2001; Liu et al., 2012). Finally, another frequently applied spatial clustering technique is the hierarchical agglomerative clustering. This technique makes use of a dendrogram to decompose the data into clusters. The agglomerative approach is a bottom-up approach in which all objects are first grouped in a distinct cluster and while moving upward in the tree, pairs of clusters are merged based on some metrics (e.g. spatial proximity) (Han et al., 2001). \r\n\r\nSpatial cluster techniques have many advantages when dealing with big datasets which is often the case when working with earth observation data. Its simplicity to use and the fast increase of cloud computing power makes from it powerful techniques to extract spatial patterns out of the data. It allows to translate raw earth observation data into a more user-friendly data product by showing the spatial patterns of the data.","name":"Spatial cluster analysis","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM5-4","description":"Spatial interaction models describe the flow of people and goods in a geographical space, in which parameters such as friction and distance play a role.","name":"Spatial interaction","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM5-5","description":"Multidimensional attributes can be analyzed through multidimensional scaling and principle component analysis.","name":"Analyzing multidimensional attributes","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM5-7","description":"Multi-criteria evaluation is an important aspect of decision support operations, which appear in process models. Process models in the Earth sciences describe the evolution of geo(bio)physical surface properties in time, independently from remote sensing observations. Examples of such process models on various time scales are, for instance, numerical weather prediction models (NWPs), vegetation growth models, hydrological models, oceanographic models and climate models.\r\n\r\nObservation models and process models can supplement each other to enhance the quality of the interpretation of remote sensing data and to fill gaps in time that occur when observations are not possible owing to clouds or some other cause. Interactions are possible between observation models and process models with EO data and existing geographic information (GIS and ground measurements, supplemented with decision-support systems (DSSs)).\r\n\r\nThe process model provides information to the decision-support system, which supports management actions aimed at controlling/mitigating the process, based on an multi-criteria evaluation. A good example of this is a water management system, in which one might decide to allocate water for irrigation if the observed vegetation appears to suffer from drought stress.","name":"Multi-criteria evaluation","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM5-8","description":"Process models in the Earth sciences describe the evolution of geo(bio)physical surface properties in time, independently from remote sensing observations. Examples of such process models on various time scales are, for instance, numerical weather prediction models (NWPs), vegetation growth models, hydrological models, oceanographic models and climate models.\r\nProcess models in the geosciences usually rely on regular observations at many locations spread over a large area. Traditionally, these observations were mostly made in the field with a variety of instruments. Remote sensing techniques have tremendously increased the capability of spatial sampling and the consistency of the surface parameters measured. RS instruments are mostly sensitive to many physical properties of the surface, some of these may not belong to the set of properties that the user is interested in. Exceptions to this are the mapping of sea-surface temperature, laser altimetry and gravimetry, which are measurements of direct geophysical interest. In the majority of cases, however, there are only indirect relationships between what is observed with the instrument and the physical object properties of interest. In these cases, the use of observation models becomes an attractive option, since these models describe the relationships between all object properties relevant for the observation and the observed remote sensing data.","name":"Spatial process models","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM5","description":"Building on the basic geometric measures and analytical operations found in most GIS products, a broad range of additional analytical methods form the fundamental GIS toolkit.","name":"Basic analytical methods","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM6-2","description":"In rasters we use interpolation to determine the value of a pixel, based on its surrounding pixels. The main raster-based interpolation methods are nearest neighbour, bilinear, and bicubic interpolation. To determine the value of the centre pixel (bold), in nearest neighbour interpolation the value of the nearest original pixel is assigned, i.e. the value of the black pixel in this example. Note that the respective pixel centres, marked by small crosses, are always used for this process. In bilinear interpolation, a linear weighted average is calculated for the four nearest pixels in the original image. In bicubic interpolation a cubic weighted average of the values of 16 surrounding pixels (the black and all grey pixels) is calculated. Note that some software uses the terms “bilinear convolution” and “cubic convolution” instead of the terms introduced above.","name":"Interpolation of surfaces","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM6-3","description":"Continuous fields have a number of characteristics not shared by discrete fields. Since the field changes continuously, we can talk of slope angle, slope aspect and concavity/convexity of the slope.\r\n\r\nThese notions are not applicable to discrete fields. The discussions in this subsection use terrain elevation as the prototype example of a continuous field, but all aspects discussed are equally applicable to other types of continuous fields. Nonetheless, we regularly refer to the continuous field representation as a DEM, to conform with the most common situation.","name":"Surface features","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM6-4","description":"A viewshed is the area that can be “seen” (i.e. it is in the direct line-of-sight) from a specified target location. (Inter) visibility analysis can determine the area visible from a scenic lookout or the area that can be reached by a radar antenna, as well as assess how effectively a road or quarry will be hidden from view.","name":"Intervisibility","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM6-5","description":"Firction surfaces contain information on how difficult/easy it is for a phenomenon to move from one location on the surface to another.","name":"Friction surfaces","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM6","description":"There is a wide range of phenomena that can be studied using a set of techniques and tools that are designed to help understand the characteristics of continuous surface data. Applications of these techniques using terrain data include overland transport, flow, and siting tasks, but similar analyses can be conducted using non-tangible surfaces such as those of temperature, pressure and population density.\r\n\r\nThere are numerous examples that require more advanced computations on continuous field representations, such as:\r\n\r\nSlope angle calculation - the calculation of the slope steepness, expressed as an angle in degrees or percentages, for any or all locations.\r\n\r\nCalculating slope aspect - the calculation of the aspect (or orientation) of the slope in degrees (between 0 and 360∘), for any or all locations.\r\n\r\nSlope convexity/concavity calculation - defined as the change of the slope (negative when the slope is concave and positive when the slope is convex)—can be calculated as the second derivative of the field.\r\n\r\nSlope length calculation - with the use of neighbourhood operations, it is possible to calculate for each cell the nearest distance to a watershed boundary (the upslope length) and to the nearest stream (the downslope length). This information is useful for hydrological modelling.\r\n\r\nHillshading is used to portray relief difference and terrain morphology of hilly and mountainous areas. The application of a special filter to a DEM produces hillshading. The colour tones in a hillshading raster represent the amount of reflected light at each location, depending on its orientation relative to the illumination source. This illumination source is usually chosen to be to the northwest at an angle of 45∘ above the horizon.\r\n\r\nThree-dimensional map display - with GIS software, three-dimensional views of a DEM can be constructed in which the location of the viewer, the angle under which he or she is looking, the zoom angle, and the amplification factor of relief exaggeration can be specified. Three-dimensional views can be constructed using only a predefined mesh, covering the surface, or using other rasters (e.g. a hillshading raster) or images (e.g. satellite images) that are draped over the DEM.\r\n\r\nDetermination of change in elevation through time - the cut-and-fill volume of soil to be removed or to be brought in to make a site ready for construction can be computed by overlaying the DEM of the site before the work begins with the DEM of the expected modified topography. It is also possible to determine landslide effects by comparing DEMs of before and after a landslide event.\r\n\r\nAutomatic catchment delineation - catchment boundaries or drainage lines can be automatically generated from a good quality DEM with the use of neighbourhood functions. The system will determine the lowest point in the DEM, which is considered to be the outlet of the catchment. From there, it will repeatedly search for the neighbouring pixels with the highest altitude. This process is repeated until the highest location (i.e. the cell with the highest value) is found; the path followed determines the catchment boundary. For delineating the drainage network, the process is reversed. Then the system will work from the watershed downwards, each time looking for the lowest neighbouring cells, which determines the direction of water flow (Flow Computation).\r\n\r\nDynamic modelling - apart from the applications mentioned above, DEMs are increasingly used in GIS-based dynamic modelling, such as the computation of surface run-off and erosion, groundwater flow, the delineation of areas affected by pollution, the computation of areas that will be covered by processes such as flows of debris and lava. An example is (Diffusion).\r\n\r\nVisibility analysis - a viewshed is the area that can be “seen” (i.e. it is in the direct line-of-sight) from a specified target location. Visibility analysis can determine the area visible from a scenic lookout or the area that can be reached by a radar antenna, as well as assess how effectively a road or quarry will be hidden from view.","name":"Analysis of surfaces","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM7-1","description":"Statistical analysis techniques based on visual interpretation through histograms, scatterplots, etc.","name":"Graphical methods","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM7-2","description":"Environmental variables have become increasing available with the advent of GIS. These are mostly continuous in space and time. Collecting denser environmental data in discrete space and time domains are rather cost effective and time consuming. However, when the data at each spatial or time index are considered as outcomes of a random variable, stochastic processes become enviable useful to build models and predict the outcomes at locations where data were never collected. The meaningful assumptions include stationarity of the mean and the covariance to ascertain an expression for spatial dependency/autocorrelation. With a stationary process (i.e. constant mean), simple and ordinary kriging is used. Other variants like kriging with external drift, universal kriging and regression kriging also alleviate the challenge of non-stationary mean. These methods are also applicable when temporal indexes rather than spatial indexes are of interest.","name":"Stochastic processes","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM7-3","description":"Spatial weight matrix is the popular numerical quantification of spatial dependency or spatial neighborhoods. The weight matrix should summarize information about the spatial connectivity structure of the spatial entities/features; either polygons, points, or lines. This is required for the computation of spatial dependency indices such the Moran’s index, and for spatial regression models such as the conditional autoregressive (CAR), spatial lag, and spatial error models. The connectivity information can be defined based on adjacency/contiguity or distance between pairs of spatial entities. There are other forms; they could be based on population densities between observation pairs. The simplest spatial weigh matrix is the binary adjacency spatial weight matrix with elements w_ij, such that w_ij=1 if spatial units i and j are neighbors, otherwise w_ij=0. A popular alternative is the inverse distance weight matrix with elements w_ij=1⁄d^α , where d is the distance between pairs of spatial units and α is any positive number greater than zero. By convention, w_ii=0 since spatial unit cannot have a spillover within itself.","name":"The spatial weights matrix","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM7-4","description":"Spatial autocorrelation evaluates how things which are closer in space tend to have similar attributes. This is a common phenomenon in environmental variables which are continuous in space. For instance, temperature, soil moisture content, air quality and rainfall are all continuous in space. This idea is based on Tobler’s law of geography: “everything is related to everything but near things are more related”. Global measures of spatial association estimates the overall index of spatial autocorrelation, also called spatial clustering. Thus, it measures whether clustering is apparent throughout the study region but do not identify the location of clusters. Common global measures include the Moran’s Index and Geary’s C. These have increasing applications in domains like environmental science, agriculture, epidemiology, climate studies etc.","name":"Global measures of spatial association","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM7-5","description":"Unlike global measures of spatial association, local measure of spatial association identifies the locations of clusters. Typical measures include the local indicator for spatial autocorrelation (LISA) or the local Moran’s index whose summation is proportional to the global Moran’s index. The spatial scan statistics has also been the commonly used method to detect local clusters.","name":"Local measures of spatial association","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM7-6","description":"An outlier is an unexpected value that differs significantly from other observations. Definition of an outlier is not absolute and the concept itself is precisely defined only by selection of appropriate criteria in concrete statistical observations. When considering outliers, it is important to determine whether the value of the outlier is incorrect data or it is otherwise outstanding, but correct data. If we consider outliers in the case when they base on sample surveys, another assessment is necessary. Namely, the assessment of whether an outlier is representative or not. \r\nThe box plot is a useful graphical display for examining the outliers. Using median, lower and upper quartiles, extreme values are identified in the tails of the distribution. The value beyond inner fence on either side is considered a mild outlier. The value beyond an outer fence is considered an extreme outlier. Histograms also emphasize the existence of outliers. The histogram depends on how we design the classes, so we can get different histograms for the same data. Graphical and quantitative checks are obligatory if the histogram shows possible outliers. Outliers can also be examined by calculating the correlation between two datasets (Pearson correlation coefficient, Spearman rank correlation coefficient…). Scatter plots reveals a basic linear relationship with a pattern. An outliner is defined as a data point that deviates from other values. Outliers can also be examined by local outlier factor, which is based on a concept of a local density. Points with substantially lower density than their neighbours are considered as outliers.","name":"Outliers","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM7-7","description":"Bayesian method of modelling stems from the Bayes theorem and derived using conditional probabilities. Its advantage lies in its ability to include prior knowledge of unknown parameters to ascertain their uncertainties. Thus, the prior parameters are updated by the data likelihood to obtain the posteriors. The challenge of Bayesian modelling has been the integration of the denominator which always resulted into improper integrals. This actually prolonged its wide applications. With the advent of high performance computers, solution to such integrals are easily solved using Markov chain Monte Carlo simulations. The advent robust approximation methods through integrated nested Laplace approximations (INLA) has even made parameter estimation faster; thus making Bayesian methods interesting and better. Unlike frequentist approaches, Bayesian methods can present estimates of parameters as densities from which their uncertainties and credible intervals can be estimated. They have now found wide applications in divers areas like environmental modelling, climate modeling, agriculture, epidemiology and many other domains that requires modeling.","name":"Bayesian methods","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM7","description":"Traditional statistical methods are used to describe the central tendency, dispersion, and other characteristics of data but are not always suited to use with spatial data for which specialized techniques are often required. The field of spatial statistical analysis forms the backbone for the testing of hypotheses about the nature of spatial pattern, dependency, and heterogeneity. The techniques are widely used in both exploratory and confirmatory spatial analysis in many different fields.","name":"Spatial statistics","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM8-1","description":"Sampling is needed to limit the observations for statistical analysis. In raster image analysis, various sampling schemes have been proposed for selecting pixels to test. Choices to be made relate to the design of the sampling strategy, the number of samples required, and the area of the samples. Recommended sampling strategies in the context of land cover data are simple random sampling or stratified random sampling. The number of samples may be related to two factors in accuracy assessment: (1) the number of samples that must be taken in order to reject a data set as being inaccurate; or (2) the number of samples required to determine the true accuracy, within some error bounds, of a data set. Sampling theory is used to determine the number of samples required. The number of samples must be traded-off against the area covered by a sample unit. A sample unit can be a point but it could also be an area of some size; it can be a single raster element but may also include surrounding raster elements. Among other considerations, the “optimal” sample-area size depends on the heterogeneity of the class.","name":"Spatial sampling for statistical analysis","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM8-3","description":"A variogram is a tool used to describe the spatial continuity of data points. Different kinds of variograms are used, such as experimental variogram and semi-variogram.","name":"Variogram modeling","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM8-4","description":"Predicting an observation in the presence of spatially dependent observations is termed Kriging, named after the first practitioner of these procedures, the South African mining engineer Daan Krige, who did much of his early empirical work in the Witwatersrand gold mines.","name":"Principles of kriging","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM8-5","description":"With a stationary stochastic process (i.e. constant mean), simple and ordinary kriging is used for interpolation. Other variants like kriging with external drift, universal kriging and regression kriging also alleviate the challenge of non-stationary mean. Other variants are \r\nco-kriging log-normal kriging, disjunctive kriging, indicator kriging, factorial kriging and universal kriging.","name":"Kriging variants","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM8","description":"Geostatistics are a variety of techniques used to analyze continuous data e.g., rainfall, elevation, air pollution. The fundamental structure of geostatistics is based on the concept of semi-variograms and their use for spatial prediction kriging. Sampling methods are also discussed in Unit GD9 Field data collection. \r\nGeostatistics is a subdiscipline of spatial statistics developed to estimate the value of a continuous spatial process at unknown locations by using the information of the value of these process at known locations. Furthermore, it aims to quantify the uncertainty related to the prediction (Calder et al., 2009; Emmanouil, 2019). In order to do such predictions, geostatistics entails some statistical methods which use as starting point the assumption of a random component that can define the spatiotemporal variability. These methods are developed to infer the parameters that can describe the spatiotemporal patterns of the input variables (e.g. soil moisture) so that finally these variables at unsampled locations can be estimated (interpolated) (Emmanouil, 2019). Geostatistical methods are strongly related with classic interpolation methods but differ by its use of random variables that allow to given an uncertainty indication associated with the prediction of variables in space and time. \r\n\r\nIn environmental research geostatistical techniques are often applied to infer (interpolate) variables at such unobserved locations by using information from known locations. One of such geostatistical techniques is Kriging, which is a geostatistical method that predicts variables by using spatial interpolation. This spatial interpolation is done by establishing a semivariogram that defines the spatial relationship between the variables of interest in function of the distance. Because of this, the Kriging technique can also give an indication on the variance or accuracy of the prediction (Calder et al., 2009); Van der Meer, 2012). On the other hand, cokriging is another important geostatistical technique and differs from Kriging by using the cross-correlation between variables to generate local estimates (Van der Meer, 2012). In earth observation studies, cokriging can be applied to better predict sparsely based data on the ground (e.g. biomass) by using the cross-correlation of this variable with a more continuously sampled satellite metric like NDVI. Furthermore, these techniques can also be used to enhance satellite image information, filling missing pixels or even downscale the information to a higher resolution (Van der Meer, 2012).","name":"Geostatistics","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM9-1","description":"Spatial econometrics uses spatial stochastic models to determine autocorrelation between interacting agents. The techniques involved are regression, the use of a spatial weights matrix, least squares, etc.","name":"Principles of spatial econometrics","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM9-2","description":"A spatial autoregressive (SAR) model describes the prediction of the behaviour of a random process.","name":"Spatial autoregressive models","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM9-3","description":"In producing optimal images for interpretation, spatial filtering is applied. Filtering is usually carried out for a single band. Filters - algorithms - can be used to enhance images by, for example, reducing noise (“smoothing an image”) or sharpening a blurred image. Filter operations are also used to extract features from images, e.g. edges and lines, and to automatically recognize patterns and detect objects. There are two broad categories of filters: linear and non-linear filters.\r\n\r\nLinear filters calculate the new value of a pixel as a linear combination of the given values of the pixel and those of neighbouring pixels. A simple example of the use of a linear smoothing filter is when the average of the pixel values in a 3×3 pixel neighbourhood is computed and that average is used as the new value of the central pixel in the neighbourhood.","name":"Spatial filtering","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM9-4","description":"Geographically Weighted Regression (GWR) makes use of local subsets of observations to perform estimates.","name":"Spatial expansion and Geographically Weighted Regression GWR","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM9","description":"Many problems of the social sciences can be expressed in terms of spatial regression analysis. The development of spatial autoregressive models and the estimation of their parameters is the focus for the field of spatial econometrics.","name":"Spatial regression and econometrics","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF","description":"The GIScience perspective is grounded in spatial thinking. The aim of this knowledge area is to recognize, identify, and appreciate the explicit spatial, spatio-temporal and semantic components of the geographic environment at an ontological and epistemological level in preparation for modeling the environment with geographic data and analysis. To do this, one must understand the nature of space and time as a context for geographic phenomena.This knowledge area covers the ways in which views of the geographic environment depend on philosophical viewpoints, physics, human cognition, society, and the task at hand. This knowledge area also requires an understanding of the fundamental principles in the discipline of geography, the \"language\" of spatial tasks. On a more advanced level, this area incorporates mathematical and graphical models that formalize these concepts, such as set theory, algebra, and semantic nets. Because of its wide range of foundational principles, this knowledge area forms a basis for the other knowledge areas. Wise design and use of geospatial technologies requires an understanding of the nature of geographic information, the social and philosophical context of geographic information, and the principles of geography. This knowledge area is especially closely tied to Knowledge Areas Data Modeling (DM) and Design Aspects (DA), as generic data models and application designs need to be grounded in sound conceptual models. The foundations of geographic information have developed over several decades. Philosophical and scientific views on the nature of space and time have evolved since the ancient Greeks. Early papers during the Quantitative Revolution, such as Berry (1964), began to formalize the structure of information used in geographic inquiry.The fundamental data structures and algorithms comprising the GIS software developed in the 1960`s and 1970`s were based on implicit \"common-sense\" conceptual models of geographic information. During the 1980`s, several researchers questioned these underlying assumptions. Some were refuted, other confirmed, and many extended. However, the most rapid pace of development in this area was during the 1990`s with the rise of GIScience as a distinct discipline, and the many cooperative initiatives it comprised.The new millennium has seen some of these foundational principles incorporated into commercial software, thus making theoretical knowledge even more important for practitioners. It is expected that the concepts in this knowledge area will be learned gradually. An introductory course may cover only a few topics in a cursory manner, an intermediate course on data modeling or data analysis may consider several theoretical topics of practical application, and a number of graduate courses could cover each topic in a research-oriented environment. Discussion of this knowledge area includes several terms that can have multiple meanings. For the purposes of this document, two in particular require definition: Geographic: Almost any subject or discourse involving earthly phenomena, studied from a spatial perspective at a medium scale (sub-astronomical and super-architectural). Phenomenon: Any subject of geographic discourse that is perceived to be external to the individual, including entities, events, processes, social constructs, and the like.","name":"Conceptual Foundations","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF1-1","description":"Metaphysics involve the meaning things and concepts. Ontologies provide a way to share the semantics of concepts in some area of interest and is all about common the understanding of essential concepts, e.g., what is meant by a geometric object and its attributes.","name":"Metaphysics and ontology","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF1-1b","description":"Brief history of GIScience as related to the history of GISystems; Definitions of GIS&T; Sub-domains of GIS&T (i.e., Geographic Information Science, Geospatial Technology, and Applications of GIS&T)","name":"What is Geographic Information Science and Technology","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF1-2","description":"The branch of philosophy concerned with knowledge.","name":"Epistemology","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF1-2b","description":"GIS&T draws upon insights and methods from key allied fields: Geography, Cartography, Computer and information science, Engineering, Mathematics and Statistics, Philosophy, Cognitive Science, Linguistics","name":"Contributions to GIS and T by key allied fields","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF1-3","description":"The questions and methodologies in major philosophical movements relating to the nature of space, time, geographic phenomena and human interaction with it.","name":"Philosophical perspectives","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF1","description":"Many branches of philosophy are relevant to an understanding of geographic information, especially metaphysics and epistemology. Philosophical theories are deeply engaged in the study of knowledge, space, time, geographic phenomena and human interaction with them. These theories influence the development of geographic ontologies and the structuring, analysis, and interpretation of geographic information. It is, therefore, crucial for professionals to understand these principles in order to bridge (rather than eliminate) the differences and work together. Philosophical perspectives on GIS practice are covered in Unit GS7 Critical GIS.","name":"Philosophical foundations","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF1b","description":"Unit CF1 introduces the broad domain refered to as Geographic Information Science & Technology (GIS&T) and its sub-domains (i.e., Geographic Information Science, Geospatial Technology, and Applications of GIS&T). It outlines the history of Geographic Information Science as related to the history of GISystems, as well as the contributions to this multidisciplinary domain by key allied fields, such as geography, cartography, computer and information science, engineering, mathematics, philosophy, cognitive science, and linguistics.","name":"Introduction to Geographic Information Science and Technology","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF2-1","description":"The study on how humans perceive spatial information.","name":"Perception and cognition of geographic phenomena","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF2-1b","description":"Metaphysics and Ontology - Formal ontology - Ontological distinctions (e.g., continuants vs. occurrents, universals vs. particulars) - The problem of universals and relevant theories (realism, nominalism, conceptualism) - Ontologies of the geographic domain - Philosophical theories relating to the nature of space, time, geographic phenomena and human interaction with them","name":"Philosophy of being","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF2-2","description":"The ways in which conceptual views of in the human mind make it into formal descriptions of information and into artefacts in databases and GIS.","name":"From concepts to data","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF2-2b","description":"Epistemology; Theories on what constitutes knowledge; The notions of model and representation in science; The influences of epistemology on GIS practices","name":"Philosophy of knowledge","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF2-3","description":"Principles of geography to explain the spatial occurrences of spatial entities in Geographic Information Systems.","name":"Geography as a foundation for GIS","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF2-4","description":"Space and place are concepts that are not the same. Including concepts like landscape, it is not always obvious how to portray them unambiguously in GIS.","name":"Place and landscape","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF2-6","description":"The ways in which the elements of culture (e.g., language, religion, education, traditions) may influence the understanding and use of geographic information.","name":"Cultural influences","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF2-7","description":"The influences of political ideologies (e.g., Marxism, Capitalism, conservative liberal) on the understanding of geographic information.","name":"Political influences","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF2","description":"Geographic information is observed, comprehended, organized, used in human processes, with both personal and social influences. Therefore, sound models of geographic information should be grounded on a sound understanding of human perception, cognition, memory, and behavior, as well as human institutions.","name":"Cognitive and social foundations","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF3-1","description":"A GIS operates under the assumption that the spatial phenomena involved occur in a two- or three-dimensional Euclidean space. Euclidean space can be informally defined as a model of space in which locations are represented by coordinates—(x, y) in 2D and (x, y, z) in 3D space—and distance and direction can defined with geometric formulas. In 2D, this is known as the Euclidean plane. To represent relevant aspects of real-world phenomena inside a GIS, we first need to define what it is we are referring to. We might define a geographic phenomenon as a manifestation of an entity or process of interest that:\r\n\r\nitem can be named or described;\r\nitem can be georeferenced; and\r\nitem can be assigned a time (interval) at which it is/was present.\r\n\r\nRelevance of phenomena for the use of a GIS depends entirely on the objectives of the study at hand. For instance, in water management, relevant objects can be river basins, agro-ecological units, measurements of actual evapotranspiration, meteorological data, ground\\-water levels, irrigation levels, water budgets and measurements of total water use. All of these can be named or described, georeferenced and provided with a time interval at which each exists. In multipurpose cadastral administration, the objects of study are different: houses, land parcels, streets of various types, land use forms, sewage canals and other forms of urban infrastructure may all play a role. Again, these can be named or described, georeferenced and assigned a time interval of existence.\r\n\r\nNot all relevant information about phenomena has the form of a triplet (description, georeference, time interval). If the georeference is missing, then the object is not positioned in space: an example of this would be a legal document in a cadastral system. It is obviously somewhere, but its position in space is not considered relevant. If the time interval is missing, we might have a phenomenon of interest that exists permanently, i.e.\\ the time interval is infinite. If the description is missing, then we have something that exists in space and time, yet cannot be described. Obviously this last issue limits the usefulness of the information.\r\n\r\nTypes of geographic phenomena\r\nThe definition of geographic phenomena attempted above is necessarily abstract and is, therefore, perhaps somewhat difficult to grasp. The main reason is that geographic phenomena come in different “flavours”. Before categorizing such flavours, there are two further observations to be made.\r\n\r\nFirst, to represent a phenomenon in a GIS requires us to state what it is and where it is. We must provide a description—or at least a name—on the one hand, and a georeference on the other hand. We will ignore temporal issues for the moment and come back to these in Temporal dimension and Spatial-temporal data model, the reason being that current GISs do not provide much automatic support for time-dependent data. This topic must, therefore, be considered as an example of advanced GIS use. Second, some phenomena are manifest throughout a study area, while others only occur in specific localities. The first type of phenomena we call geographic fields; the second type we call objects.","name":"Space","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF3-1b","description":"- Theories of human perception, cognition, and memory and their ability to model spatial knowledge acquisition (e.g., Marr on vision, Piaget on cognitive development) - Types of mental representations (i.e., analogue, propositional, procedural) - The role of metaphors and image schemata in our understanding of geographic phenomena and geographic tasks - From concepts to data (i.e., data, information, knowledge, and wisdom; transformation of a conceptual model of information for a particular task into a data model; limitations of various information stores (the mind, computers) and means (maps, graphics, and text) for representing geographic information) - Difference between real phenomena, conceptual models, and GIS data representations thereof connections with cartography and maps","name":"Cognitive foundations","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF3-2b","description":"- Semantics - Meaning (e.g., the nature of meaning, modes of meaning) - Geospatial semantics - The role of natural language in the conceptualization of geographic phenomena","name":"Linguistic foundations","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF3-3b","description":"- The ways in which the elements of culture (e.g., language, religion, education, traditions) may influence the understanding and use of geographic information - The influences of social theories and political ideologies and actions on human perceptions of space and place - The constraints that political forces place on geospatial applications in public and private sectors","name":"Social foundations","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF3-4b","description":"- Common-sense views and laymen knowledge of geographic phenomena that contrast with established theories and technologies of geographic information - The impact of geospatial technologies and the geoweb (e.g., digital globes) that allow non-geospatial professionals to create, distribute, and map geographic information - The design, procedures, and results of GIS projects to non-GIS audiences (clients, managers, general public) - Difference between applications that can make use of common-sense principles of geography and those that should not","name":"Common-sense geographies","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF3","description":"Geographic information is observed, comprehended, organized, used in human processes, with both personal and social influences. Therefore, sound models of geographic information should be grounded on a sound understanding of human perception, cognition, memory, and behavior, as well as human institutions.","name":"Cognitive, linguistic and social foundations","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF4-2b","description":"As time is the central concept of the temporal dimension, a brief examination of the nature of time may clarify our thinking when we work with this dimension:\r\n\r\nDiscrete and continuous time: Time can be measured along a discrete or continuous scale. Discrete time is composed of discrete elements (seconds, minutes, hours, days, months, or years). For continuous time, no such discrete elements exist: for any two moments in time there is always another moment in between. We can also structure time by events (moments) or periods (intervals). When we represent intervals by a start and an end event, we can derive temporal relationships between events and periods, such as “before”, “overlap”, and “after”.\r\n\r\nValid time and transaction time: Valid time (or world time) is the time when an event really happened, or a string of events took place. Transaction time (or database time) is the time when the event was stored in the database or GIS. Note that the time at which we store something in a database is typically (much) later than when the related event took place.\r\n\r\nLinear, branching and cyclic time: Time can be considered to be linear, extending from the past to the present (‘now’), and into the future. This view gives a single time line. For some types of temporal analysis, branching time - in which different time lines from a certain point in time onwards are possible - and cyclic time - in which repeating cycles such as seasons or days of the week are recognized - make more sense and can be useful.\r\n\r\nTime granularity: When measuring time, we speak of granularity as the precision of a time value in a GIS or database (e.g. year, month, day, second). Different applications may obviously require different granularity. In cadastral applications, time granularity might well be a day, as the law requires deeds to be date-marked; in geological mapping applications, time granularity is more likely to be in the order of thousands or millions of years.\r\n\r\nAbsolute and relative time: Time can be represented as absolute or relative. Absolute time marks a point on the time line where events happen (e.g. “6 July 1999 at 11:15 p.m.”). Relative time is indicated relative to other points in time (e.g. “yesterday”, “last year”, “tomorrow”, which are all relative to “now”, or “two weeks later”, which is relative to some other arbitrary point in time.).","name":"Time","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF4-3b","description":"The way we represent relevant components of the real world in our models determines the kinds of questions we can or cannot answer. Besides representing an object or field in 2D or 3D space, the temporal dimension is of a continuous nature. Therefore, in order to represent it in a GIS we have to discretize the time dimension.\r\n\r\nSpatio-temporal data models are ways of organizing representations of space and time in a GIS. Several representation techniques have been proposed in the literature. Perhaps the most common of these is the “snapshot state”, which represents a single moment in time of an ongoing natural or man-made process. We may store a series of these “snapshot states” to represent “change”, but we must be aware that this is by no means a comprehensive representation of that process. \r\n\r\nIn spatio-temporal analysis we consider changes of spatial and thematic attributes over time. We can keep the spatial domain fixed and look only at the attribute changes over time for a given location in space. We might be interested how land cover has changed for a given location or how land use has changed for a given land parcel over time, provided its boundary has not changed. On the other hand, we can keep the attribute domain fixed and consider the spatial changes over time for a given thematic attribute. In this case, we might want to identify locations that were covered by forest over a given period of time.\r\n\r\nFinally, we can assume both the spatial and attribute domains are variable and consider how fields or objects have changed over time. This may lead to notions of object motion - a subject receiving increasing attention in the literature. Applications of moving object research include traffic control, mobile telephony, wildlife tracking, vector-borne disease control and weather forecasting. In these types of applications, the problem of object identity becomes apparent. When does a change or movement cause an object to disappear and become something new? With wildlife this is quite obvious; with weather systems less so. But this should no longer be a surprise: we have already seen that some geographic phenomena can be nicely described as objects, while others are better represented as fields.\r\n\r\nMapping time means mapping change. This may be change in a feature’s geometry, in its attributes, or both. Examples of changing geometry are the evolving coastline of the Netherlands, the location of Europe’s national boundaries, or the position of weather fronts. Changes in the ownership of a land parcel, in land use or in road traffic intensity are other examples of changing attributes. Urban growth is a combination of both: urban boundaries expand with growth and simultaneously land use shifts from rural to urban. If maps are to represent events like these, they should be suggestive of such change.\r\n\r\nThree temporal cartographic techniques can be distinguished:\r\n\r\nSingle Static Map\r\n\r\nSpecific graphic variables and symbols are used to indicate change or represent an event. We can apply the visual variable “value” to represent for example the age of built-up areas.\r\n\r\nSeries of Static Maps\r\n\r\nA single map in the series represents a “snapshot” in time. Together, the maps depict a process of change. Change is perceived by the succession of individual maps depicting the situation in successive snapshots. It could be said that the temporal sequence is represented by a spatial sequence that the user has to follow to perceive the temporal variation. The number of images should be limited since it is difficult for the human eye to follow long series of maps.\r\n\r\nAnimated Maps\r\n\r\nChange is perceived to evolve in a single image by displaying several snapshots one after the other, just like a video clip of successive frames. The difference from the series of maps is that the variation can be deduced from real “change” seen taking place in the image itself, not from a spatial sequence. For the user of a cartographic animation, it is important to have tools available that allow for interaction while viewing the animation. Seeing an animation play will often leave users with many questions about what they have seen. And just replaying the animation is not sufficient to answer questions like “What was the position of the northern coastline during the 15th century?” Most of the general software packages for viewing animations already offer facilities such as “pause” (to look at a particular frame) and ‘(fast-)forward’ and ‘(fast-)backward’, or step-by-step display. More options have to be added, such as the possibility to go directly to a certain frame based on a task command like: “Go to 1850”.","name":"Relationships between space and time","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF4-4b","description":"GIS data structures are used to implement the conceptual views of spatial data (vector and raster models). The power of a GIS is dependent on the richness of information contained in the spatial data structures. Vector models are based on points, lines and areas. Raster models are based on grids. Each cell has a value that is used to represent some characteristic of that location. \r\nLayers are used to display geographic datasets in various digital map environment. A layer stores the path to a source dataset and other layer properties, including symbology. You can use multiple layers on one map and specify its properties. Shapefiles represent spatial character of the object in terms of shape, size and spatial arrangement. Shapefile usually comprise three separate and distinct types of files (main files, index files and database tables). Data base files store additional attributed that can be joined to a shapefiles’ feature. Attribute data types supplement geographic spatial feature with additional information. Spatial data includes information of location and attribute data includes information about other characteristics (what, where and why). A legend is a visual presentation of the symbols that are used on the map with some additional explanations. It includes a sample of each symbol and a short description of the meaning.","name":"Categories","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF4-5","description":"An entity obtained by abstracting the real world, having a physical nature (certain composition of material), being given a descriptive name, and observable; e.g. “house”. An object is a self-contained part of a scene having certain discriminating properties.\r\n\r\nThe primitives of vector data sets are the point, (poly)line and polygon. Related geometric measurements are location, length, distance and area size. Some of these are geometric properties of a feature in isolation (location, length, area size); others (distance) require two features to be identified.\r\n\r\nIn a GIS, features are represented together with their attributes—geometric and non-geometric—and relationships. The geometry of features is represented with primitives of the respective dimension: a windmill probably as a point; an agricultural field as a polygon. The primitives follow either the vector or the raster approach.\r\n\r\nVector data types describe an object through its boundary, thus dividing the space into parts that are occupied by the respective objects. The raster approach subdivides space into (regular) cells, mostly as a square tessellation of two or three dimensions. These cells are called pixels in 2D and voxels in 3D. The data indicate for every cell which real-world feature is covered, provided the cell represents a discrete field. In the case of a continuous field, the cell holds a representative value for that field. The Table below lists advantages and disadvantages of raster and vector representations.\r\n\r\nThe storage of a raster is, in principle, straightforward. It is stored in a file as a long list of values, one for each cell, preceded by a small list of extra data (the “file header”), which specifies how to interpret the long list. The order of the cell values in the list can, but need not necessarily, be left to right, top to bottom. This simple encoding scheme is known as row ordering. The header of the raster will typically specify how many rows and columns the raster has, which encoding scheme was used, and what sort of values are stored for each cell.\r\n\r\nData can be of a qualitative or quantitative nature. Qualitative data is also called nominal data, which exists as discrete, named values without a natural order amongst the values. Examples are different languages (e.g. English, Swahili, Dutch), different soil types (e.g. sand, clay, peat) or different land use categories (e.g. arable land, pasture). In the map, qualitative data are classified according to disciplinary insights, such as a soil classification system represented as basic geographic units: homogeneous areas associated with a single soil type, recognizable by the soil classification.\r\n\r\nQuantitative data can be measured, either along an interval or ratio scale. For data measured on an interval scale, the exact distance between values is known, but there is no absolute zero on the scale. Temperature is an example: 40 ◦C is not twice as hot as 20 ◦C, and 0 ◦C is not an absolute zero.\r\n\r\nQuantitative data with a ratio scale do have a known absolute zero. An example is income: someone earning $100 earns twice as much as someone with an income of $50. In order to generate maps, quantitative data are often classified into categories according to some mathematical method.\r\n\r\nIn between qualitative and quantitative data, one can distinguish ordinal data. These data are measured along a relative scale and are as such based on hierarchy. For instance, one knows that a particular value is “more” than another value, such as “warm” versus “cool”. Another example is a hierarchy of road types: “highway”, “main road”, “secondary road” and “track”. The different types of data are summarized in Table.","name":"Properties","selfAssesment":"GI-N2K
"},{"code":"CF4b","description":"Geographic phenomena, geographic information, and geographic tasks are described in terms of space, time, and properties. Different theories exist as to the nature and formal representation of these aspects, including space-like dimensions, sets, and phenomenology. Information in each of these three aspects is measured and reported with respect to one of several frames of reference or domains, including both absolute and relative approaches. Early frameworks such as those of Berry (1964) and Sinton (1978) were influential in setting forth the importance of space, time, and theme in GIS&T. Besides, space, time, and properties, categories are also fundamental in the conceptualization and representation of spatial entities, phenomena, processes, and events. Distinctive features of geographic information such as scale and detail, spatial patterns, spatial integration, and regions are also critical for a complete description of its nature and representation. This unit is closely tied to the creation of data models in Knowledge Area 5: Data Modeling, Storage, and Exploitation.","name":"Fundamentals of Geographic Information","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF5-1b","description":"Discrete entities can be found as fields or objects.\r\n\r\nDiscrete fields divide the study space in mutually exclusive, bounded parts, with all locations in one part having the same field value. Discrete fields are intermediate between continuous fields and geographic objects: discrete fields and objects both use “bounded” features.\r\n\r\nDiscrete fields divide the study space in mutually exclusive, bounded parts, with all locations in one part having the same field value. Typical examples are land classifications, for instance, using either geological classes, soil type, land use type, crop type or natural vegetation type. \r\n\r\nDiscrete fields are intermediate between continuous fields and geographic objects: discrete fields and objects both use “bounded” features. A discrete field, however, assigns a value to every location in the study area, which is not typically the case for geographic objects. These two types of fields differ in the type of cell values. A discrete field such as land use type will store cell values of the type “integer” and is therefore also called an integer raster. Discrete fields can be easily converted to polygons since it is relatively easy to draw a boundary line around a group of cells with the same value. A continuous raster is also called a “floating point” raster.\r\n\r\nGeographic objects.\r\n\r\nWhen a geographic phenomenon is not present everywhere in the study area, but somehow “sparsely” populates it, we look at it as a collection of geographic objects. Such objects are usually easily distinguished and named, and their position in space is determined by a combination of one or more of the following parameters:\r\n\r\nlocation (where is it?)\r\nshape (what form does it have?)\r\nsize (how big is it?)\r\norientation (in which direction is it facing?).\r\n\r\nHow we want to use the information determines which of these four parameters is required to represent the object. For instance, for geographic objects such as petrol stations all that matters in an in-car navigation system is where they are. Thus, in this particular context, location alone is enough, and shape, size and orientation are irrelevant. For roads, however, some notion of location (where does the road begin and end?), shape (how many lanes does it have?), size (how far can one travel on it?) and orientation (in which direction can one travel on it?) seem to be relevant components of information in the same system.","name":"Discrete entities","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF5-2b","description":"A geographic field is a geographic phenomenon that has a value “everywhere” in the study area. We can therefore think of a field as a mathematical function f that associates a specific value with any position in the study area. Hence if (x, y) is a position in the study area, then f(x, y) expresses the value of f at location (x, y). Fields can be discrete or continuous.\r\n\r\nIn a continuous field, the underlying function is assumed to be “mathematically smooth”, meaning that the field values along any path through the study area do not change abruptly, but only gradually. Good examples of continuous fields are air temperature, barometric pressure, soil salinity and elevation. A continuous field can even be differentiable, meaning that we can determine a measure of change in the field value per unit of distance anywhere and in any direction. For example, if the field is elevation, this measure would be slope, i.e. the change of elevation per metre distance; if the field is soil salinity, it would be salinity gradient, i.e. the change of salinity per metre distance.\r\n\r\nDiscrete fields divide the study space in mutually exclusive, bounded parts, with all locations in one part having the same field value. Discrete fields are intermediate between continuous fields and geographic objects: discrete fields and objects both use “bounded” features.\r\n\r\nDiscrete fields divide the study space in mutually exclusive, bounded parts, with all locations in one part having the same field value. Discrete fields are intermediate between continuous fields and geographic objects: discrete fields and objects both use “bounded” features.\r\n\r\nDiscrete fields divide the study space in mutually exclusive, bounded parts, with all locations in one part having the same field value. Typical examples are land classifications, for instance, using either geological classes, soil type, land use type, crop type or natural vegetation type. \r\n\r\nDiscrete fields are intermediate between continuous fields and geographic objects: discrete fields and objects both use “bounded” features. A discrete field, however, assigns a value to every location in the study area, which is not typically the case for geographic objects. These two types of fields differ in the type of cell values. A discrete field such as land use type will store cell values of the type “integer” and is therefore also called an integer raster. Discrete fields can be easily converted to polygons since it is relatively easy to draw a boundary line around a group of cells with the same value. A continuous raster is also called a “floating point” raster.\r\n\r\nA field-based model consists of a finite collection of geographic fields: we may be interested in, for example, elevation, barometric pressure, mean annual rainfall and maximum daily evapotranspiration, and would therefore use four different fields to model the relevant phenomena within our study area.","name":"Fields","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF5-3b","description":"We can structure time by events (moments) or periods (intervals). When we represent intervals by a start and an end event, we can derive temporal relationships between events and periods, such as “before”, “overlap”, and “after”.\r\nValid time (or world time) is the time when an event really happened, or a string of events took place. Transaction time (or database time) is the time when the event was stored in the database or GIS. Note that the time at which we store something in a database is typically (much) later than when the related event took place.\r\n\r\nProcess models in the Earth sciences describe the evolution of geo(bio)physical surface properties in time, independently from remote sensing observations. Examples of such process models on various time scales are, for instance, numerical weather prediction models (NWPs), vegetation growth models, hydrological models, oceanographic models and climate models.\r\n\r\nProcesses on the planet Earth are complex phenomena that are taking place in space and in time, i.e. in four dimensions.\r\n\r\nIn many of these processes, differences in one dimension (e.g. height above the geoid) can be disregarded, so that two spatial dimensions and the dimension time remain. Despite this simplification, the physical description of the phenomena remains a difficult task. To better understand the processes it often helps if the same geographic region is viewed repeatedly and, if possible, also from different directions and in different wavelength regions. Integration of data from a variety of sources can be a means to retrieving information about processes that would otherwise remain undetected.","name":"Events and processes","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF5-4b","description":"Models that integrate the concepts of space, time, and attribute in geographic information.","name":"Integrated models","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF5-6","description":"Geographic phenomena can be studied as single entities and in relationship with each other and then reveal patters and clusters. How the entities are distributed is subject to statistical and visualisation studies.","name":"Spatial distribution","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF5-7","description":"We can use the topological properties of interiors and boundaries to define relationships between spatial features. Since the properties of interiors and boundaries do not change under topological mapping, we can investigate their possible relations between spatial features. We can define the interior of a region, R, as the largest set of points of R for which we can construct a disc-like environment around it (no matter how small) that also falls completely inside R. The boundary of R is the set of those points belonging to R that do not belong to the interior of R, i.e. one cannot construct a disc-like environment around such points that still belongs to R completely.\r\n\r\nLet us consider a spatial region A. It has a boundary and an interior, both seen as (infinite) sets of points, which are denoted by boundary(A) and interior(A), respectively. We consider all possible combinations of intersections (∩) between the boundary and the interior of A with those of another region, B, and test whether they are the empty set (∅) or not. From these intersection patterns, we can derive eight (mutually exclusive) spatial relationships between two regions. If, for instance, the interiors of A and B do not intersect, but their boundaries do, yet the boundary of one does not intersect the interior of the other, we say that A and B meet.","name":"Region","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF5-8","description":"Integration of data from a variety of sources can be a means to retrieving information about processes that would otherwise remain undetected.\r\n\r\nAlthough data integration can be very useful, there are also some requirements that have to be fulfilled for it to be effective:\r\n\r\n• geospatial data have to be accurately co-registered in a common grid;\r\n• time gaps between the various data layers have to be known and accounted for;\r\n• systematic effects due to the atmosphere, the viewing angle, the Sun angle, etc., must be corrected for or taken into account.\r\n\r\nData can be integrated in an almost infinite number of ways. Results from data integration can, again, be combined with other geospatial data to produce yet other new information, and so on.\r\n\r\nData integration also comprises the incorporation of non-spatial information or point data from field measurements. These data have to be associated with precise moments in time and with precise geographic locations, or with some time interval and fuzzy-defined regions. Thus, here the important issue of the representativeness of this information for the associated time interval and geographic area comes into play.\r\n\r\nIn general, data integration forces us to consider the uncertainties or inaccuracies of the various data sources available. In some cases, meta-data may contain information about this. When integrating data for some purpose, one has to apply weights to each of them, so that the final result is a balanced compromise in which inaccurate data receive less weight than those with a high degree of certainty.","name":"Spatial integration","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF5b","description":"The concepts below form the basic elements of common human conceptions of geographic phenomena. Concepts from many units in this knowledge area have been synthesized to create general conceptual models of geographic information. Attempts to resolve the object-field debate have led to attempts to create comprehensive models that bridge these views. Consideration of this unit should also include formal models of these elements in mathematics and other fields. Knowledge Area DM Data Modeling discusses the representation of these elements in digital models.","name":"Elements of geographic information","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF6-1","description":"Mereology is the study of parts and wholes. In GI this involves how objects are modeled as composites of other objects.","name":"Mereology: structural relationships","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF6-2","description":"Lineage describes the history of a data set. During the processing of data, the derived information inherits artifacts from the dataset(s) of origin. In the case of published maps, some lineage information may be provided as part of its meta-data, in the form of a note on the data sources and procedures used in the compilation of the data. Examples include the date and scale of aerial photography, and the date of field verification. Especially for digital data sets, however, lineage may be defined more formally as:\r\n\r\n“that part of the data quality statement that contains information that describes the source of observations or materials, data acquisition and compilation methods, conversions, transformations, analyses and derivations that the data has been subjected to, and the assumptions and criteria applied at any stage of its life (Clarke and Clark, 1995).”\r\n\r\nAll of these aspects affect other aspects of quality, for example positional accuracy. Clearly, if no lineage information is available, it is not possible to adequately evaluate the quality of a data set in terms of “fitness for use”.","name":"Genealogical relationships: lineage, inheritance","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF6-3","description":"We can use the topological properties of interiors and boundaries to define relationships between spatial features. Since the properties of interiors and boundaries do not change under topological mapping, we can investigate their possible relations between spatial features. We can define the interior of a region, R, as the largest set of points of R for which we can construct a disc-like environment around it (no matter how small) that also falls completely inside R. The boundary of R is the set of those points belonging to R that do not belong to the interior of R, i.e. one cannot construct a disc-like environment around such points that still belongs to R completely.\r\n\r\nLet us consider a spatial region A. It has a boundary and an interior, both seen as (infinite) sets of points, which are denoted by boundary(A) and interior(A), respectively. We consider all possible combinations of intersections (∩) between the boundary and the interior of A with those of another region, B, and test whether they are the empty set (∅) or not. From these intersection patterns, we can derive eight (mutually exclusive) spatial relationships between two regions. If, for instance, the interiors of A and B do not intersect, but their boundaries do, yet the boundary of one does not intersect the interior of the other, we say that A and B meet. In mathematics, we can therefore define the “meets relationship” using set theory. The eight spatial relationships are disjoint, meets, equals, inside, covered by, contains, covers and overlaps.","name":"Topological relationships","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF6-4","description":"Relationships between spatial features that define their relative position. Spatial autocorrelation is a fundamental principle based on Tobler’s first law of geography, which states that locations that are closer together are more likely to have similar values than locations that are farther apart.","name":"Metrical relationships: distance and direction","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF6","description":"Like geography, geographic information not only models phenomena but the relationships between them. This can include relationships between entities, between attributes, between locations. In addition, one of the strengths of geography (and GIS) is its ability to use a spatial perspective to relate disparate subjects, such as climate and economy. Methods for analyzing relationships are discussed in Unit AM4 Modeling relationships and patterns.","name":"Relationships","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF7-1","description":"Vagueness arises from lack of criteria for the applicability of certain linguistic terms. It arises from the lack knowledge about the meanings of terms.","name":"Vagueness","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF7-2","description":"-Uncertainty-related terms, such as error, accuracy, uncertainty, precision, stochastic, probabilistic, deterministic, and random -Difference between uncertainty and vagueness -Dependence of uncertainty on scale and application -Expressions of uncertainty in language -The causes of uncertainty in geospatial data -Stochastic error models for natural phenomena -How the concepts of geographic objects and fields affect the conceptualization of uncertainty -Mathematical models of uncertainty: Probability and statistics","name":"Error-based uncertainty","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF7","description":"Human models (mental, digital, visual, etc.) of the geographic environment are necessarily imperfect. While the mathematical principle of homomorphism (often operationalized as fitness for use) allows for imperfect data to be useful as long as they yield results adequate for the use for which they are intended, imperfections are frequently problematic. Although terminology still varies, two types of imperfection are generally accepted: vagueness (a.k.a. fuzziness, imprecision, and indeterminacy), which is generally caused by human simplification of a complex, dynamic, ambiguous, subjective world; and uncertainty (or ambiguity), generally the result of imperfect measurement processes (as discussed in Knowledge Area GD Geospatial Data). Both of these can be manifested in all forms of geographic information, including space, time, attribute, categories, and even existence. Imperfection is also dealt with in Units GD6 Data quality (in the context of measurement), GC8 Uncertainty and GC9 Fuzzy sets (for the handling and propagation of imperfections), and CV4 Graphic representation techniques (in the context of visualization).","name":"Imperfections in geographic information","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV","description":"Geo-data visualisation necessarily includes cartography as the origin of \"mapping\" our world. Cartography methods have drastically changed over the few years since the increasing role and sophistication of digital technology applied to geo-information visualisation. It is first worth differentiating between the underlying geo-data that describes real world phenomena and the bits of information that describe the visual presentation of geo-data . Likewise, there are processing tools to collect and handle geo-data, and processing tools especially designed to create and manage geo-data visualisations. \r\nWhile cartography methods have traditionally produced printed maps (i.e. hard copy) with static scale, orientation, projection, legends (content based) and tied to a period or instant of time. Nowadays geo-data visualisations are interactive by design, meaning that the results are map-based responsive interfaces, highly customisable through dynamic objects to zoom in and out, pan and tilt, change projections and graphic expressions on the fly, as well as dynamically browse the map over time. \r\nIf the production methods have changed, also the type of authors. Map making in its widest sense is not only a privilege of a few experts but has been democratised in such a way that. everybody is able to make maps using open data and open source apps and tools for geo-data visualisation. Therefore,the new roles of open data and new forms of geo-data like geo-social media make usability, intended and ethical considerations key aspects of geo-data visualization design, production and sharing. \r\nUnder the concept of cartography and visualisation it is included a list of concepts that together comprise the science and technology of visual representation of geographic data.","name":"Cartography and Visualization","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV1-1","description":"The evolution of cartographic representation in the previous centuries followed the most important technological and scientific developments of the time. It was driven by commercial and/or military needs and influenced by the special characteristics of the areas and/or environments to be mapped. Recent developments are the rise of open data worldwide and widely available internet technology allowing end users to get remote geo-data published elsewhere. In recent years, data and its digital presentation have become central elements of cartography, whereas paper maps have become peripheral.","name":"History and evolution of cartography","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV1-4","description":"Art in cartography means much more than designing aesthetically pleasing maps, whether on paper or digital. Exploring the interaction at large between art and cartography involves rethinking the way we approach spatial expressions and how cultural, social and political dimensions are reflected in maps. This can be clearly observed in historical maps - in between art and science - ranging from beautiful geographical representations created in the Middle Ages to convey religious messages to the creation of modern maps showing the power of modern empires and nations. This particular relationship between art and maps entails: “developing an inclusive approach of artistic mapping expressions; facilitating and encouraging interaction between cartographers who work with the Art aspects of cartography and artists who produce cartographic artifacts; and developing conceptual elements about the relationships between art and cartography.” Besides ancient paper maps, a sum of factors led digital maps and geospatial visualization, a matter of interest to artists and designers. Thanks to powerful computing systems and with the advancements reached in computer graphics or image processing, or the rise of information visualisation, new forms of representing and visualising geodata have also appeared. Creation of digital maps are still a two-way relationship since artists have explored maps as a medium for expressing their art, and cartographers have approached art to provide more than just the representation of locations and geographic features with the intention to make maps more attractive to their audiences.","name":"Art and geodata visualisation","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV1-5","description":"Historical maps are geographical representations made with the intention to represent spatial facts over time. Historical maps are generally considered valuable documents not just because of their historical value but also because most of them also are artistic representations by themselves. From a cartographical point of view, differentiation between historical maps and actual maps is mainly based on the advances in the history of Cartography, so once one disruptive advance in the map making process appears, maps created with previous techniques (and with some artistic or historical value) are usually considered as historical, such as ancient paper-based maps or old sea maps, for instance. Techniques such as scanning or photography can make ancient maps publicly available by converting hard-copy maps to digital ones. Once an historical map is digitised, the next step is to georeference it, which is the process of specifying and relating points of the digitalised map to actual coordinates in a geographic reference system. Because of its archival value and interest, historical maps are adequately preserved - following specific conditions - by map libraries, map societies or museums. Since digital methods and techniques have been replaced over time by new technological advances, first digitally created maps could be also considered historical, not because of its content, but of the techniques used to produce it.","name":"Historical maps","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV1","description":"At a certain moment in time people start to create more graphical representations of their surrounding environment. New technologies offered ways to expand these representations to larger geographical extent, higher spatial resolution, finer temporal granularity and larger periods. Technologies even made it possible to include other representations of reality such as social media and data ensembles in geodata visualizations, to the extent to even blend the real world with geodata-based visualization providing an augmented – virtual reality continuum. New forms of geo-data, like geolocated sensors may challenge the way geo-data visualisations are generated, shared and, eventually, influence decision-making processes. History and trends sketch these developments and future outlook. This concept introduces the main stages and turns in development of cartography, from earliest times to the present, the most important methods in map-making and map-based visualizations.","name":"History and trends","selfAssesment":"Completed (GI-N2K)
\r\n\r\n"},{"code":"CV2-1","description":"As mapping ( geo-data visualization) is intended to convey a certain message to a certain audience, it is essential to use data sources that allow the intended visualisation result. The data should be of the right degree of detail and its use should not cause copyright problems. The producer quality of each data set should be taken into account, as well as the fitness of the data for the intended use. Aspects: message; data quality","name":"Data sources for mapping","selfAssesment":"
In progress (GI-N2K)
"},{"code":"CV2-2","description":"In the trajectory between raw (geo)data and their user-relevant representation, the necessary data processing includes ways of abstraction by selection, filtering, generalization, transformation and classification of geographical data. In this data processing it is essential to at one hand relate the final symbolisation to the necessities of the intended message, and at the other hand to procedures that introduce as little error as possible.","name":"Data processing","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV2-3","description":"Map projection is fundamental to representation of spatial data and for combining different datasets. Its choice should serve the presentation type that will convey the intended message to the audience. Many mathematical principles define datum, projections, horizontal and vertical co-ordinate systems, georeferencing- introduced with the focus on visualisation issues Aspects: geodetic concepts; transformations","name":"Mathematical base","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV2","description":"Geodata, including 3 dimensional geometry, as such can graphically be presented but most of the times the data as such doesn`t meet the presentation criteria. Especially if the dataset has to be presented in combination with other datasets. First all the geodatum, georeference and map projection are crucial but also the role of the geometry. The processing of the geometry and the related attributes may become a crucial step for an adequate presentation. Nowadays the highest precision may be used to define different graphical attributes for different zoom levels. On the other hand geodata visualisation includes also graphical datasets. Such data ensembles, the combination of geodata and graphical data, are the data sources that offer opportunities to other ways of visualisation then the traditional cartographic mapping. Facets: a.\tGeospatial location (2D) and position (3D) that data refer to b.\tDegree of detail in data origin (acquisition resolution) and in representation ('map' scale) c.\tTypes of data (e.g. imagery, field measurements, delineated objects)","name":"Data considerations","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV3-1","description":"The combined impact of graphic design properties (balance, legibility, clarity, visual contrast, figure-ground organization, and hierarchal organization) and the map components (north arrow, scale bar, and legend) should always be carefully evaluated against the needs and the capacities of the audience.","name":"Map design fundamentals","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV3-10","description":"Geo-gaming is a crossover between gaming elements and location, usually enabled by location based services and augmented adn/or virtual reality features. Geo-games, also known as “location-based games” or “location-aware games”, have geodata at its core, since geoinformation constitutes the central element of the game mechanics. Geo-gaming applications present unique technical challenges to meet the infrastructural and resources demands from the games and location worlds. There are mainly four different types of geo-games: exploration games (to make use of an existing spatial design); feedback games (to report about players’ experiences in a specific design); allocation games (to occupy the majority of game location); and configuration games (to occupy specific pattern of game locations). Gamers actively participate by interacting with the environment, therefore gaming scenarios are as varied as their goals, which include teaching, training, and the developing of spatial thinking skills. Geo-games offer a myriad of opportunities to developers: non-linear storytelling, physical object integration, a more visceral experience, true social interaction… which bring geo-games to another interaction level. Geo-gaming applications often rely on VGI to allow gamers adding geolocated information that may crowdsource geo-referenced data useful for other secondary purposes .","name":"Geo-gaming","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV3-2","description":"Map symbolization entails a number of variables to produce visual, tactile, haptic, auditory, and dynamic displays. Visual variables (e.g., size, lightness, shape, hue) and graphic primitives (points, lines, areas) are commonly used in maps to represent various geographic features at all attribute measurement levels (nominal, ordinal, interval, ratio). With those a single geographic feature can be represented by various graphic primitives (e.g., land surface as a set of elevation points, as contour lines, as hypsometric layers or tints, and as a hillshaded surface). The challenge is to use effective symbols for map features to ease the interpretation of maps.","name":"Symbols and icons","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV3-3","description":"The selection of colours to use in data representation can be influenced by various factors (e.g. the production workflow, cultural differences, involved devices and media). There are various colour models (e.g. RGB, CMYK, CIE) that describe colours in a way that they can effectively convey visual information (e.g., qualitative, sequential, diverging, spectral) according to the meaning of the underlying data. The cultural background of the consumer is also relevant when it comes to choose colours that should have real-world connotations or should express psychological concepts (e.g. harmony, concordance, balance). A final important factor is if the consumer has colour limitations","name":"Colour","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV3-4","description":"When data representation is conveyed in words (e.g. toponyms, road codes), written text is often placed in map labels. It is important to decide on the role of the label in the context of the representation type. Algorithms for label placement are relevant, especially when label density is high. Shape and colour of the labels help to signify different types of messages. This is supported by the typographic properties (type font, size, style) of the text in the labels. Finally, it is important to use an authoritative source for the texts","name":"Typography","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV3-5","description":"Imagery can be a source for data acquisition as well as an illustration to abstract data representations. Imagery can be made from the air (from drones to satellites) or from a terrestrial point of view (street-level imagery). Using photos from any source to illustrate stories about geographical subjects contributes as the visual aspect of telling a story. Together with maps and other narrative components, the combination embodies a storytelling medium.","name":"Photos and imagery","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV3-6","description":"Animation is the process of making the illusion of motion and change by means of the rapid display of a sequence of static images that minimally differ from each other. In the context of maps, the temporal component is added to a map to emphasize and observe the gradual evolution of a certain monitoring phenomenon, such as changes in spatially numerical variables (for example, environment, population, mobility, land use, etc.) with respect to a static geographic area. Map animations generally consider dynamic time while space is static. Map animation helps to see patterns or trends that emerge as time passes, depicting meteorological or climate events, natural disasters, historical events and other multivariate data. It is particularly helpful to be used in educational settings.","name":"Animation","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV3-7","description":"Sound or audios can be one of the components of a multimedia data representation. A conventional GIS usually conveys visual information, however the integration of audios in mapping could enrich GIS data to other senses. Sound can increase the amount of information that’s communicated to the user through channels other than visual to address the special needs of people with visual impairments or people who cannot use in certain circumstances their sight, such as a driver who cannot look at a map. Approaches to rendering sound information on a map fall into three broad categories: (1) to sonoficate the whole visual presentation (for simple geometric data), (2) to augment a visual system with auditory information (allowing multivariate information) and (3) to display information about the surrounding where a user is. By classifying images and creating additional audio layers that associate each pixel with a specific sound, a GIS can add a new auditory dimension to maps.","name":"Sound","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV3-8","description":"Maps are valuable because they provide a large amount of detail in a small amount of space, and because of their capacity for telling a story. Telling stories through maps began with describing explored lands in great detail against terra incognita. Today, geographic tools, data, and multimedia on the web expand the ability to communicate stories and inform through maps to a broad audience such as journalists, decision makers and educators. Any person with a smartphone or computer can tell a story, using statics maps, or interactive web maps with text, video, audio, sketches, and photographs. Besides the technical skills to clearly communicate with a map (palette of colours, amount of information displayed…), other factors such as narrative processes, the storyboard, place, time, and characters play a crucial role. To be informative, it is important that the correct data is displayed, combining different sources of information combined to create an appealing and accurate map.","name":"Storytelling","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV3-9","description":"Infographics are visual representations of information and data, which can contain charts, diagrams, graphs, tables, maps and lists. The aim of an infographic is to present information that can be absorbed quickly, it is easily understandable and extensively in mass communication, and thus designed with fewer assumptions about the reader's knowledge base than other types of visualizations. The role of maps in an infographic is based on the potential of maps to condense information and to support a narrative. Infographic maps - altogether with an adequate storytelling - should find a simple way to explain current complex issues, providing added value to the infographic, and being an effective and efficient way to communicate.","name":"Infographics","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV3","description":"This concepts covers basic design principles that are used in mapping and visualization, as well as cartographic design principles specific to the display of geographic data. Both page layout design and data display are addressed.","name":"Design principles","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV4-1","description":"A thematic map is a type of map especially designed to show a particular theme connected with a specific geographic area. These maps \"can portray physical, social, political, cultural, economic, sociological, agricultural, or any other aspects of a city, state, region, nation, or continent\". Cartographers use many methods to create thematic maps. Five techniques are especially noted: -Choropleth mapping shows statistical data aggregated over predefined regions -Proportional symbols, showing the relative value of attributes -Isarithmic or Isopleth, also known as contour maps -Dots, to show the location of a phenomenon -Dasymetric, which uses areal symbols to spatially classify volumetric data.","name":"Thematic mapping","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV4-10","description":"Conveying uncertainty information is often done through visualization. Uncertainty is often defined, quantified, and expressed using models specific to individual application domains. In visualization however, we are limited in the number of visual channels (3D position, color, texture, opacity, etc.) available for representing the data. Thus, when moving from quantified uncertainty to visualized uncertainty, we often simplify the uncertainty to make it fit into the available visual representations. (After Potter et al., 2012). The seven challenges as formulated by MacEachren et Al. (2005) are still there to be tackled.","name":"Visualization of uncertainty","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV4-2","description":"Relief can be represented in a two-dimensional map either through contour lines or through a raster format gridded array of elevations. Contour lines connect points of equal elevation. At regular intervals index contours are marked with elevations so a reader can more easily determine the elevation of surrounding locations. They are the preferred method for analogue topographic maps. The grid approach is used in digital mapping and known as a digital elevation model (DEM), where each raster cell represents an elevation. Scaling of the cell z value in relation to the x and y value results in terrain exaggeration, which aids visualization of topography.\r\nDEMs are used for terrain analysis and can be used to obtain derivatives such as slope and aspect. DEMs are obtained by interpolating point elevation observations, which are historically retrieved from surveyed point data (e.g. GPS locations), but more recently from LiDAR and/or Structure from Motion point clouds. TIN (triangular irregular network) analysis is commonly used for point data interpolation, in order to derive a continuous elevation surface.","name":"Representing terrain","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV4-3","description":"Multivariate descriptive displays or plots are designed to reveal the relationship among several variables simultaneously. Bivariate and multivariate maps encode two or more data variables concurrently into a single symbolization mechanism. Their purpose is to reveal and communicate relationships between the variables that might not otherwise be apparent via a standard single-variable technique. There are basic characteristics of the relationship among variables, such as the forms of the relationships, the strength of the relationships, and the dependence of the relationships on external (usually to the pairs of variables being examined) circumstances. Therefore, these multivariate plots or maps are inherently more complex, though offer a novel means of visualizing the nuances that may exist between the mapped variables. As information-dense visual products, they can require considerable effort on behalf of the map reader, though a thoughtfully-designed map and legend can be an interesting opportunity to effectively convey a comparative dimension. Examples of multivariate plots include enhanced 2-D scatter diagrams, 3-D scatter diagrams, contour, level, and surface plots, and high-dimensional data plots","name":"Multivariate displays","selfAssesment":"Completed (GI-N2K)
\r\n\r\n"},{"code":"CV4-4","description":"Visualization of change and movement across space and time is of increasing interest to researchers and geospatial practitioners. The visualization process of temporal data has four steps: (1) time values to be visualized, (2) point of view on time, that identifies the characteristics of the temporal values to be visualized, (3) time space: define the displayable space of the time values and (4) point of view on the visualization space, the implementation of the perceptible forms of time. The visualization of spatio-temporal data can be done in many different ways such as multi-panel plots (maps), time-series plots (graphs), space-time plots (graphs), 3D Virtual Reality (Computer generated artificial environment), animations (production of consecutive images), and tables. Spatiotemporal data comprises three important components: geographic location, temporal information and the thematic attributes describing a real-life phenomenon.","name":"Visualization of temporal geographic data","selfAssesment":"
Completed (GI-N2K)
"},{"code":"CV4-5","description":"Dynamic and interactive displays refers to a situation where a display with a cartographical data representation changes in real time in response to user's actions","name":"Dynamic and interactive displays","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV4-6","description":"Web mapping is the process of designing, implementing, generating and delivering maps on the World Wide Web. Dissemination via the web opens new opportunities: realtime maps, cheaper dissemination, more frequent and cheaper updates, personalized map content, distributed data sources and sharing of geographic information. Technical restrictions cause challenges like low display resolution and limited bandwidth,( in particular with mobile computing devices with small screens and using slow wireless Internet connections), copyright and security issues, reliability issues and technical complexity. Today's web maps can be interactive and integrate multiple media. So interactivity, usability and multimedia issues also play a role.","name":"Web mapping","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV4-7","description":"Virtual reality or virtual realities (VR), also known as immersive multimedia or computer-simulated reality, is a computer technology that replicates an environment, real or imagined, and simulates a user's physical presence and environment in a way that allows the user to interact with it","name":"Virtual and immersive environments","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV4-8","description":"An Augmented Environment can be experienced through different sets of Augmented Reality (AR) technologies, including mobile displays (tablets and smartphone screens), computer monitors, or Head-Mounted Displays (HMDs), among others. AR is a technology that layers computer-generated enhancements atop an existing reality to make it more meaningful through the ability to interact with it. AR offers the integration of digital information and imagery onto the real world in real-time. In order to broaden the vision beyond this definition, AR can be described as systems having the following features: (1) combines real and virtual; (2) interactive in real-time; and (3) registered in 3D, allowing other technologies, such as mobile technologies, monitor-based interfaces, monocular systems to overlay virtual objects on top of the real world. Currently, AR applications use the camera provided by mobile devices to produce a live view of the real world in combination with relevant, context-appropriate information such as text, videos, or pictures.\r\nThere are lots of applications and systems in the market that provide AR functionality, making it difficult to classify and name them all. Some of them are related to the real physical world and others with the abstract, virtual imagery world. Sometimes it is not easy to figure whether it is an AR, as often AR is defined as Virtual reality (VR) with transparent HMDs. In general, the concept is to mix reality with virtual reality, including information and overlay over the real world through HMDs such as they seem apparent as one environment. The virtual objects can react accordingly with the camera's movement as it is registered concerning the real world, which is also the central issue of AR.","name":"Augmented environments","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV4-9","description":"Cartographers have recently become involved in extending geographic concepts and cartographic design approaches to the depiction of non-geographic data archives, using so-called spatialized views of information spaces. Spatializations differ from ordinary data visualisation and geovisualisation in that they may be explored as if they represented spatial information. (Fabrikant, S.I., 2003). As definitions of spatialization can be found: Spatializations are computer visualizations in which nonspatial information is depicted spatially (Montello et al., 2003). Spatialization is the transformation of high-dimensional data into lower-dimensional, geometric representations on the basis of computational methods and spatial metaphors. (Skupin 2007)","name":"Spatialization","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV4","description":"This concept addresses mapping methods and the variations of those methods for specialized mapping and visualization instances, such as thematic mapping, dynamic and interactive mapping, Web mapping, mapping and visualization in virtual and immersive environments, using the map metaphor to display other forms of data (spatialization), and visualizing uncertainty. Analytical techniques used to derive the data employed in these graphic representations are discussed in Knowledge Area AM Analytical Methods and Unit DN2 Generalization and aggregation.","name":"Graphic representation techniques","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV5-2","description":"Standards for map services were set by OGC and ISO, called WMS and WMTS. Producing map images on the web from a cartographic image in a GIS application is called \"publishing\". Making a web \"map\" in the broader sense of constructing data representations for Storytelling or Geo-gaming is still under development. It requires a mix of applying the map Design principles and Graphic presentation techniques, possibly in combination with software scripting.","name":"Web map making","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV5-3","description":"Traditional \"map\" making, as opposed to the mapmaking in neogeography, focuses on reliable and reproducible products, based on expertise of high definition printing in many colours on analogue media of geodetically well-constructed images.","name":"Traditional map making","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV5-4","description":"The aspects of reproduction of a data representation depend on the nature of the representation: is it analogue (a paper map, a mock-up) or is it digital? In the case of a paper map, its digitalisation with high fidelity is an essential step. With a source in digital form, reproduction can be a matter of the right printer. Alternatively, the source could be disseminated as a file or as a web service. If representations are dynamic and/or interactive the possibilities depend on the construction of the representation. The ease of dissemination of digital files should not result in copyright breach. Aspects: Digitalization techniques for analogue sources, Printing ( 2D, 3D), Dissemination ways, Construction of the data representation, User needs specification, Copyright issues","name":"Map reproduction","selfAssesment":"GI-N2K
"},{"code":"CV5","description":"This concept addresses map production and reproduction, as well as computation issues that relate to those workflows.","name":"Map production","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV6-1","description":"The potential of maps as a way to show or exert power over the population was early understood by ruling classes. A map expresses a claim by the inclusion or exclusion of map elements and how these elements are visually related and/or depicted on the map. So, the world could be modeled through the careful choice of content arranged graphically at a specific scale and in specific formats. Therefore, maps embody and project the interests of their creators. The “new cartographies” declare that maps are redefined as socially constructed arguments based upon consistent semiotic codes. Nowadays, the rise of costless, powerful and accessible tools for creating maps, put power on the side of individuals or groups of individuals with few organisation (crowdsourced data collection or VGI) capable of representing their world views. In addition, monitoring people, places or nature, for instance, should also be seen as another way to show the increasing power of maps. Surveillance mechanisms for tracking populations used by rulers, or the use of extended technologies like Google Earth by environmental organisations to track the Amazonian forest, constitute two examples of the particular use of maps to exert control over human beings or to press governments for taking specific actions, respectively.","name":"The power of maps","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV6-2","description":"Maps today help us locate the nearest gas station or ATM on our in-car navigation system, but this use of locating what is near or surrounds a location is not new. Maps from pre-historic times provided important locational information – what was where and how to get from place to place. A map can be a relatively simple iconic device, which can be read and interpreted with only a little training. These graphic representations of the real world could be traced in sand or painted on a cave wall and shared through time. Maps even preceded written language and number systems and are found in some format in most cultures through time as a graphical language. Learning to read this language and interpret it without ambiguity is not as simple as first suggested. This complexity has increased as technology has allowed creation of 3D and 4D interactive maps which allow anyone with internet access the ability to investigate different places, topics and times and produce their own map. Today the ability to read and interpret maps is increasingly important as industry, business and government communicates within their organization and the public using maps. Becoming aware of what a “map” shows depends partly on what the senses can register of the representation as a whole. It also depends on recognition of elements in the representation that are meaningful to the observer in the sense that these elements are credible indicators of spatial features. Based on that recognition, the nature of these elements and their spatial pattern might infer thoughts about historic or ongoing processes. This interpretation will be influenced by the expertise and needs of the observer.","name":"Map reading and interpretation","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV6-3","description":"Assessment of the usability of a data representation is about how useful it is to users. Therefore it is a test of the success of the representation design, a test of the skills of the \"map\" maker and a test for the reliability of the underlying data.","name":"Usability analysis","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV6-6","description":"Spatial thinking is thinking that finds meaning in the shape, size, orientation, location, direction or trajectory, of objects, processes or phenomena, or the relative positions in space of multiple objects, processes or phenomena. Spatial thinking uses the properties of space as a vehicle for structuring problems, for finding answers, and for expressing solutions\" Aspects: recognizing spatiality in a collection of things; translation of the collection to a pattern of elements; recognizing structure (relations between the elements in a pattern); recognizing process (or changes over time in patterns or structures)","name":"Spatial thinking","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV6-8","description":"Ethics is about the question if behaviour is right or wrong in a social context. In dealing with geodata, a person can do the wrong thing with respect to laws (e.g. disclose secrets, disregard privacy, copyright infringement) or to professional standards (e.g. use bad data, forget about the colour blind, downplay unpleasant details). Aspects: breach of legal standards; breach of professional standards","name":"Map ethics Legal and privacy issues","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV6","description":"Geodata visualisation are always made with a certain purpose. The role and understanding of such graphical representation is an important field of research. Besides theories that underpin evaluation approaches and their findings the visualisation may also be confronting. The more realistic the presentation and especially when it includes human/personal related data the ethical dimension of the visualisation play a major role. Usability of visualisations has also an impact on spatial thinking as has been proved by scholars.","name":"Usability of maps","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA","description":"Proper design of geospatial applications, models, and databases and the validation and verification of design activities are critical components of work in all areas related to GIS&T. Design failures can negate well-intentioned efforts to apply concepts and technology to solve real-world problems. While sharing a number of concerns with general systems analysis, the unique and complex spatial characteristics of geospatial information provide significant additional challenges. The focus of this knowledge area is on the design of applications and databases for a particular need. The design of general-purpose models and tools (e.g., raster and vector) is covered in Knowledge Area: Data Modeling (DM). In the context of specific implementations, design activities fall into three general classes:\r\n1. Application Design addresses the development of workflows, procedures, and customized software tools for using geospatial technologies and methods to accomplish both routinary and unique tasks that are inherently geographic.\r\n2. Analytic Model Design incorporates methods for developing mathematical models, spatial models and data processes. The design of the analytic model is often influenced by decisions that are made about data models and structures.\r\n3. Database Design concerns the optimal organization of the necessary spatial data in a computer environment in order to efficiently sustain a particular application or enterprise.","name":"Design and Setup of Geographic Information Systems","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA1-1","description":"This concept deals with the importance of having a list of prioritized requirements as a first step to ensure a smooth and successful implementation of a GIS project.. It entails the different methodologies and approaches to ensure a GI system covers all functional and nonfunctional requirements. Requirements are not only derived from business workflows but it is advisable to gather direct input from potential users that will be translated into requirements. However, there is a need to clearly rank the importance of the requirements gathered to ensure the GI system is manageable and in line with the intended use of the GI system, in opposition with the specific interests of a particular user or ambiguous requirements. Therefore, the documentation, traceability and evaluation of requirements after the implementation are as relevant as the initial gathering of requirements to give consistency to the designed system.","name":"Requirements gathering and analysis","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA1-2","description":"The internal process of documenting a task or a process is about “how” it is implemented and “what” is implemented. Documenting is particularly helpful if a breakdown occurs, such as when an expert working in a task leaves her job or to substitute one task in a set of interrelated processes by another. Documentation provides consistency for the taskand allows its monitoring, analysis and revision during a project. \r\nThere are different methods for documenting a task to transform tacit knowledge into explicit knowledge. Therefore, the task should be documented by describing it in video format and using visual tools that allow documentation, or the maintenance of a field diary.\r\nIn particular cases, the creation of user guides or manuals could be considered a subset of a process description particularly addressed to external users. A user manual should take into account the target users to adapt its content to them.","name":"Methods of process description and documenting","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA1-4","description":"A workflow is a sequence of operations that altogether perform a complex, sophisticated or repetitive operation or activity. No matter the workflow type, a workflow is defined in a declarative language, either text-based or visual, and stored in a workflow document to ease sharing and maintenance. In GI systems, a workflow can be seen from distinct perspectives. One of the most well-known GI workflow types is spatial data modelling. A model is specified as a combination of processing tools that manipulate and transform the spatial data required by the model. The order in which the processing tools, inputs, and outputs are organised in a workflow will determine the results and to what extent the spatial question is addressed. However, workflows in GI systems are not only related to spatial data modelling and transformation. There are cases where certain processes in GI systems should be designed in terms of software and hardware requirements, actors needs, organisational aspects or resource usage and demand. How can people’s work contribute to define the stages of a GI architecture? How much time does a regular user spend working with spatial data? How complex is the process going to be? The definition of this sort of workflows can help, for example, in designing an optimal architecture for a GI system in a particular enterprise configuration. \r\nWhether the workflow defines specific steps to process spatial data or the stages and details to implement an enterprise GI system, having a clear idea over each stage's inputs and outputs helps GI systems to be organised, consistent and reliable. In summary, high-level workflows like business workflows put together systems, components and actors that are part of a process or operation. They represent an abstract view, focused often on organisational, functional and resources usage aspects. Conversely, low-level workflows refer to a series of executable activities that carry out data transformations, models or spatial data analysis. Examples are code scripts, specified as sequences of commands in a programming language, and graphical workflows through, for example, the Model Builder in GI systems which are enacted by workflow engines.However, workflows in GI systems are not only related to spatial data modelling and transformation. There are cases where certain processes in GI systems should be designed in terms of software and hardware requirements, actors needs, organisational aspects or resource usage and demand. How can people’s work contribute to define the stages of a GI architecture? How much time does a regular user spend working with spatial data? How complex is the process going to be? The definition of this sort of workflows can help for example in designing an optimal architecture in an enterprise configuration for a GI system. \r\nWhether the workflow defines specific steps to process spatial data or the stages and details to implement an enterprise GI system. Having a clear idea over each stage's inputs and outputs helps GI systems to be organised, consistent and reliable. In summary, high-level workflows like business workflows put together systems, components and actors that are part of a process or operation. They represent an abstract view, focused often on organisational, functional and resources usage aspects. Conversely, low-level workflows refer to a series of executable activities that carry out a complex task, service or model. Examples are code scripts, specified as sequences of commands in a programming language to carry out data transformations and spatial models and spatial data analysis; and graphical workflows through, for example, the Model Builder in GI systems which are enacted by workflow engines.","name":"Workflow definition and consideration in GI systems","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA1-5","description":"Software and information technology are integral to any GI systems or projects, from the storage and handling of spatial data to its analysis, visualization and sharing. Therefore, the use of well-known software design and engineering techniques and methods to develop efficient, reliable, and easy-to-maintain software applications in the GIS realm is more important than ever. \r\nAmong the modern software design and engineering techniques, Agile software development methodologies like Scrum stands out. The common rationale of the Agile methods is to split a large software project into many functional pieces of software that help the software engineering team to translate their development efforts into quick prototypes, and eventually reach the final product. Therefore, the constant feedback and validation of the user’s requirements in short, iterative development circles (i.e sprints) are the main advantages of the Scrum methodology.","name":"Software design and engineering","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA1-6","description":"User interface and usability of a GIS system","name":"User interface and Usability","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA1-9","description":"Geodesign is a design and planning method along with geospatial modelling and technology, and simulations informed by geographic contexts to facilitate informed decisions and the creation of design proposals. A geo-design process is a problem-based, iterative process bounded by specific (geographic) constraints characterised by a collaborative effort.","name":"Geodesign","selfAssesment":"Completed
"},{"code":"DA1","description":"This concept encloses a set of activities and workflows to ensure that the implementation of a GIS system in an organization or project is correctly planned and designed according to the particularites, user requirements and current conditions of the project ahead. In general system design is the process to promote successful GIS in an enterprise environment. As a GIS system has a direct influence on the information technology department (IT), the system design tells the organizacion how the current infrastructure can or must support the planned GIS. This process builds a set of specific recommendations on hardware and network needs based on the number of projects that depend on the GIS solucion, as well as the projected business needs and user requirements. \r\nGIS architects through the system design process need to take into account and identify several conditions: a) infrastructure requirements, b) the network communication capacity, c) hardware and software procurement requirements and, d) software development and data acquisition needs. \r\nHaving a well-defined and successful GIS deployment is not only a matter of what data or software the organization should acquire. The process of system design aligns identified business requirements (user needs/requirements) derived from business strategies or project aims, goals, and stakeholders (business processes) with identified business information systems infrastructure technology (network and platform) recommendations. \r\nThe process starts with identifying business needs, including the identification of users locations, required information, data, resources or products. The business needs are generally considered as project workflows that help the GIS architects to identify the expected data traffic and computing demand associated with each transaction, being a transaction the work unit used to translate business requirements into associated server and network loads.\r\nWithout carrying out a proper system design, a GIS system can lead to an implementation and deployment failure, deriving in unfulfilled expectations and high costs in terms of human resources and financial matters.","name":"System design","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA2-1","description":"Project management includes the planning, organization, coordination, execution, monitoring, controlling and closing of the activities and resources - human and economic - for the timely achievement of clearly defined objectives forming a project. For the success of a project, a project manager will assure an efficient use of resources and a proper execution of tasks to deliver value to users and “clients” of products and services. The Project Management Body of Knowledge (PMI) defines “project management” as “the application of knowledge, skills, tools, and techniques to project activities to meet requirements”, being EO*GI projects are another type of information technology projects. PMI reflects different areas to take care of by project management. These areas are: Integration, Scope, Time, Cost, Quality, Human Resource, Communications, Risks, Procurement and Stakeholder. There are a variety of tools and techniques used in the areas identified by PMI, just to name a few Gantt chart, Program evaluation and review (PERT) analysis, AGILE project management, etc. that will help in project management.","name":"Project management","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA2-2","description":"This concept embraces the factors that could affect a GI system / project and could constitute obstacles to success or even decide a project is not doable. In order to ensure the success of a GI system or a GIS project there are several criteria to take into account from the very beginning of the conception of the GI system or project. A feasibility study may encompass different perspectives (economic, legal, technical, operational or scheduling ) to inform whether or not a project is worth the investment. An organisation should list the foreseen costs from these five perspectives listed above and the benefits (tangible or intangible) of implementing a system/project. Existing resources already available in-house and internal strategic plan in place could be critical to decide to undertake a project or not. The table below presents a non-exhaustive list of criteria and under which perspectives they should be examined.\r\nFeasibility analysis should include a pilot study to evaluate and improve the system / project proposed.","name":"Feasibility analysis","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA2-8","description":"This concept discusses the technical, organizational and monetary advantages and disadvantages of proprietary versus open source software. GIST industry and research are slowly but consistently moving toward the openness of software. Open software entails some clear advantages such as continuous development of new applications, building community of developers and users, starting a project even if limited funding is available, increasing the chances of a project’s sustainability, to name a few. On the other side, proprietary initiatives in GIST are keeping their roots to the ground by developing cutting-edge tools to handle challenging and critical environments in large private sectors and public administrations. Advantages of proprietary software include more stable software, a well developed documentation and personalised customer support service. Both open and proprietary geospatial software solutions can co-exist by applying the appropriate IPR licences for each type of solution. The future trend is to balance how proprietary and open source geospatial software complement each other and find synergies in increasingly complex and large projects.","name":"Proprietary and open source software","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA2","description":"To design, build, and maintain a GIS, sufficient resources (e.g., labor, capital, and time) must be secured. Resource planning consists of the allocation and use of in-house resources (people, equipment, tools, rooms, etc.) to achieve the maximal efficiency of those resources. These resources are required for a variety of system elements, including design, software purchase, labor, hardware, and facilities. The crucial task is to determine whether the project is worth the required resources.","name":"Resource planning","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA3-1","description":"The ecosystem of GIS software architectures has evolved substantially in recent years to include a variety of options ranging from desktop GIS, server-based and component-based architectures to Web-based, cloud-based, mobile-based approaches. Aligned with the main trend, geospatial software architectures or infrastructures are also moving from desktop architectures to more cloud based or server based options to meet ever-increasing requirements of interoperability, interdisciplinary work and computational power for processing large data sets and derived products. Cloud-based architectures also enable on the fly visualization of computed geospatial products, as complementary visualisation and mapping tools are seamlessly integrated into modern cloud-based based architectures. Usage of a particular architecture is fully dependent on the nature, size, requirements, functionalities, and available resources of a given project or task. Desktop and server based applications are particularly suited for small sized projects and startups while enterprise based applications are meant for larger sized projects. Cloud based infrastructure can be useful for varying sizes of projects in which the computational infrastructure is fully outsourced.","name":"Major geospatial software architectures","selfAssesment":"In progress (GI-N2K)
\r\n\r\n"},{"code":"DA3-2","description":"Interoperability of GIS infrastructure or architecture ensures the consistent and uninterrupted usage of data and functionalities across platforms and systems. Components or tools residing on distinct platforms can “talk” to each other without friction. Interoperability is a central characteristic, especially important in distributed systems and architectures. It can be applied to different levels or layers of a system, i.e. infrastructure level, data level, business logic level, etc. For example, standard spatial data formats and protocols are especially relevant for handling GIS data across multiple systems and platforms, regardless of their underlying software architecture. This is particularly important in large-scale, collaborative projects involving various teams using heterogeneous GIS architectures. Most software providers, developers communities and standardisation bodies and committees are striving to make their architectures interoperable in an open manner, so proprietary standards and protocols are a potential hindrance to this initiative.","name":"Interoperability","selfAssesment":"
Completed (GI-N2K)
"},{"code":"DA3-3","description":"This concept considers general architectural patterns like SOA, ROA, Web Services, etc.","name":"Architectural Patterns","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA3-4","description":"- WebGIS, - technical pecularities of spatial data infrastructures - standardiced GI services for SDI: WMS, WFS, CSW, Transformation Services, SOS, WPS etc., - other map services and interfaces","name":"WebGIS, SDI services, map services","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA3-5","description":"This concept deals with Reference Model of Open Distributed Processing (RM-ODP), its standards, viewpoints modeling and the RM-ODP framework","name":"Reference Model of Open Distributed Processing","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA3-6","description":"Cloud computing provides an on-line computing transparent resource to the user, since a user doesn’t notice almost no difference between working on her own computer or the cloud. Owned and managed by infrastructure providers, cloud computing entails advantages (concurrent access by many users, software updates hosted in the cloud, cost-efficiency or outsourced maintenance in the cloud) and disadvantages (loose of control, network Connection Dependency or security breaches ). On the other side, grid computing is a full network of computers and data working together so functioning as a supercomputer. Grid computing presents advantages such as shorter resolution of complex problems, the ease of organizational collaboration or a better use of existing hardware.","name":"Cloud and Grid computing","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA3-7","description":"Within this concept solutions based on Desktop GIS and GIS libraries will be compared and contrasted","name":"Desktop GIS, GIS libraries","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA3","description":"This concept describes the major geospatial software architectures available currently and choices when designing GI applications and systems, including desktop GIS, server-based, Internet, and component-based custom applications.","name":"Architectural design of a GIS system","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA4-1","description":"- Compare and contrast the relative merits of various textual and graphical tools for data modeling, including E-R diagrams, UML, and XML - Create conceptual, logical, and physical data models using automated software tools - Create E-R and UML diagrams of database designs","name":"Modeling tools","selfAssesment":"GI-N2K
"},{"code":"DA4-2","description":"Within an initial phase of database design, a conceptual data model is created as a technology-independent specification of the data to be stored within a database.","name":"Conceptual models","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA4-3","description":"A logical data model expresses the meaning context of a conceptual data model, and adds to that detail about data (base) structures, e.g. using topologically-organized records, relational tables, object-oriented classes, or extensible markup language (XML) construct tags","name":"Logical models","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA4-4","description":"A physical data model documents how data are to be stored and accessed on storage media of computer hardware","name":"Physical models","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA4","description":"The effective design of geospatial databases should follow the established methods and principles of database modeling and design developed in computer science. The basic method is a three-step process generally called the conceptual, logical, and physical models transforming the application from very human-oriented to machine-oriented. Several standards and software tools exist to aid the process of database design.","name":"Database design","selfAssesment":"In progress (GI-N2K)
"},{"code":"DM","description":"This knowledge area deals with representation of formalized spatial and spatio-temporal reality through data models and the translation of these data models into data structures that are capable of being implemented within a computational environment (i.e., within a GIS or more likely within a spatial database). Data modelling is a crucial issue as it defines the content of a spatial database and usefulness of these content (data) for certain applications. Data Modelling is performed using system neutral languages like UML (or more seldom ER-diagrams). These conceptual models have to be transferred to logical models (i.e. tables of a database). Data is stored in spatial databases which are normally organized in an object relational way. For certain types of data specific databases are used, like triple stores, NoSQL DBs, Array DBs etc. For data modelling quite a number of ISO standards are available for deriving the conceptual model as well as for rules for application schemas, spatial schemas, temporal schemas, Quality principles, encoding, 3D modelling (CityGML) etc. Data models provide the means for formalizing the spatio-temporal conceptualizations. Examples of spatial data model types are discrete (object-based), continuous (location-based), dynamic, and probabilistic. Mastery of the objectives presented in this knowledge area require knowledge and skills presented in the bodies of knowledge of allied fields, including computer science (ACM/IEEE-CS Joint Task Force, 2001) and information systems (Gorgone & Gray, 2000; Gorgone & others, 2002).","name":"Data Modeling, Storage and Exploitation","selfAssesment":"In progress (GI-N2K)
"},{"code":"DM1-1","description":"This topic includes the main basic database concepts: - Database, definition and overview - Database management system, definition and overview - Relational databases, overview - Object-oriented databases, overview - Object-relational databases - NoSQL databases, general overview - NoSQL databases, examples triple stores, array databases, others (overview)","name":"Overview on database concepts","selfAssesment":"GI-N2K
"},{"code":"DM1-2","description":"The Relational Model is the most important database model, therefore it is explained in more detail here: - Basic concepts (tables, tuples, etc.) - Relation to relational algebra (RA), basics of RA - Constraints (key, domain, referential integrity) - Relation to entity relation (ER) model, basics of ER","name":"The Relational Model","selfAssesment":"GI-N2K
"},{"code":"DM1-3","description":"Relational databases and database management systems are essential for GIS in consequence the important issues have to be treated here: - General aspects, basic architecture of a DB, advantages, features - DBMS concepts and functionalites (transactions, locks, multiuser access etc.) - Database design, techniques - Database administration - Normalization (1NF - 3NF) - Example of a database design","name":"Relational Databases, Database Managements Systems and Database principles","selfAssesment":"GI-N2K
"},{"code":"DM1-4","description":"Database queries and especially spatial queries require specific data structures to be performed satisfactory Relevant is: - Motivation, examples of typical non-spatial and spatial queries - Trees, B-tree, R-tree, Q-tree - Graphs, overview and relation to databases","name":"Data Structures and Indices for Databases","selfAssesment":"GI-N2K
"},{"code":"DM1-5","description":"Big data like imagery but also for example GML data sets need compression to be accessed / transferred in an acceptable time. Therefore some compression techniques have to be taught: - Motivation, examples of data sets which need compression - General introduction, vector - / raster data compression, compression lossless, lossy - Popular compression techniques, LZW (Lempel-Ziv-Welch) encoding, Huffman encoding - Techniques for raster data, runlength encoding, JPEG coding, wavelet etc. - Techniques for the reduction of vector data (Douglas Peuker etc.) - Data formats, overview and relation to compression techniques","name":"Data compression techniques","selfAssesment":"GI-N2K
"},{"code":"DM1-6","description":"SQL is the \"standard\" to perform spatial and non-spatial queries in databases. That means each student in a GI related course has to be familiar with the main aspects if it: - Motivation, history, overview - Data definition language DDL - Data manipulation language DML - Data control language DCL - Spatial extensions of SQL","name":"SQL and its usage for data handling, spatial extensions to SQL","selfAssesment":"In progress GI-N2K
"},{"code":"DM1-7","description":"UML is the standard for describing the schema related to GI models, but also user requirements, workflows etc. can be described in UML using the UML diagrams: - Motivation, background, purpose - Use case diagrams - Class diagrams - Sequence diagrams - Activity diagrams","name":"UML introduction and class diagrams","selfAssesment":"In progress GI-N2K
"},{"code":"DM1-8","description":"XML knowledge is an important bases for understanding GML. Moreover XML tools like XSLT are important to transform XML or GML data sets into other XML based formats like SVG or others. Important issues: - Motivation, purpose - Relation to HTML - XML document structure - XML syntax, elements, attributes and namespaces - xlink, xpath and XSLT - XML DTD - XML schema","name":"XML introduction","selfAssesment":"In progress GI-N2K
"},{"code":"DM1-9","description":"The long term storage of GI data in general is based on spatial databases. Therefore the following is essential for a GI course: - Relation between GIS and DB / \"Long transactions\"- Dual concepts - Characteristics of spatial databases - Spatial data in object relational databases - Spatial extensions of DBs, overview","name":"Database concepts in GIS and Principles of spatial databases","selfAssesment":"GI-N2K
"},{"code":"DM1","description":"This unit includes the basics for data modelling, storage and exploitation. Data modelling is one of the most important activities in conjunction with Geographic Information / GIS as it determines how the data can be used and if the requirements from applications are fulfilled. Data modelling can be done in conjunction with the database, e.g. through ER diagrams or according to the ISO 191xx standards by using UML. The costs of data acquisition can be tremendous, therefore the data represents an enormous value. This value has to be conserved through a safe long term data storage. Therefore databases and especially relational and object relational databases are crucial. For a proper storage and query of geographic information databases are extended with specific data types and data structures. As data sets can be very large suitable compression techniques became important especially in the context of accessing and delivering geographical data, e.g. through services. XML based modeling languages for encoding also play and important role in this context","name":"Foundations for Data Modelling Storage and Exploitation","selfAssesment":"In progress (GI-N2K)
"},{"code":"DM2-1","description":"GI standards, mainly from ISO and OGC are essential nowadays. Moreover also an overview on ICT standards from W3C or OMG are important as well as some understanding of standardization processes. In detail: - Motivation for standards, examples from daily life - Overview on GIS and relevant ICT standardization bodies and selected standards - De jure and De facto standards, obligation, reasons for the usage of standards - Standardization within ISO - Standardization within OGC, relation to ISO - Examples of ISO 191xx standards","name":"Overview on relevant standards and standardisation bodies","selfAssesment":"GI-N2K
"},{"code":"DM2-2","description":"Conceptual data modeling is a key skill for GI people. (see relations to other topics) The following therefore is important: - Overview on the relevant standards like conceptual schema language, Rules for application schema - Examples of conceptual schemas","name":"The principle of conceptual data modelling according to ISO","selfAssesment":"In progress GI-N2K
"},{"code":"DM2-3","description":"Geometric modelling is an important subtask of conceptual modelling and requires the following basics: - Overview of ISO 19107 - spatial schema - Overview of ISO 19125 - simple features - Examples of the usage of spatial schema and simple feature elements for feature class definitions - Relation to GML - Relation to DBs","name":"Geometry data types according to spatial schema and the simple feature specification","selfAssesment":"GI-N2K
"},{"code":"DM2-4","description":"Also temporal aspects have to be considered within conceptual modelling. This also requires basics: - Motivation, examples - Temporal variability of features (move, change of structure or geometry) - Overview on ISO 19108 temporal schema - Examples of modeling temporal aspects","name":"Temporal data types according to temporal schema","selfAssesment":"In Progress GI-N2K
"},{"code":"DM2-5","description":"Conceptual models of course have to be implemented, in general in a GIS (which is often proprietary), or in a database (which can be standard based) ,therefore here the implementation in a database is treated: - Repetition of conceptual and logical models - Examples of the transferring of a conceptual model to a logical (database) model","name":"Transferring conceptual models to logical models","selfAssesment":"In progress (GI-N2K)
"},{"code":"DM2-6b","description":"Metadata is considered as very important for the usage as well for the search for Geodata Relevant basics are: - Motivation, importance of data quality as part of metadata - Metadata in an spatial data infrastructure with many There are quite a number of relevant standards for GI courses. Some are listed here, others might be considered, depending on the background of the course: - Select other standards and explain them, Important are: - ISO 19141 Schema for moving features, ISO 19142 Web Feature Service or others - 19109 - Rules for application schema - Selection of other standards is depending on the background of the course","name":"Other standards","selfAssesment":"In progress GI-N2K
"},{"code":"DM2-7","description":"GML is the most important standard for the transfer of Geodata as it allows to transfer the schema information as well as the data. Important issues: - Motivation, Importance of a Geography Markup Language - History of GML, Overview 19136 - Geography Markup Language - Relation to spatial schema - Supported features in GML (Topology, 3D ...) - Structure of GNL, profiles, application schemas etc. - Transfer of models and of data - Examples","name":"Introduction to GML","selfAssesment":"In progress (GI-N2K)
"},{"code":"DM2-8","description":"3D Models, especially 3D city models are becoming more and more important. CityGML is the most important standard within the GI domain to describe City models semantically and geometrically. Relevant issues: - Motivation, Usage of CityGML - Relation to GML - Coherence of semantics and geometry - Principles of modeling - Level of detail concept - CityGML vs KML - Examples","name":"Introduction to CityGML","selfAssesment":"In progress GI-N2K
"},{"code":"DM2","description":"This unit includes the essentials of relevant standards for spatial data modelling. A number of ISO and OGC standards are available for deriving the conceptual model as well as for rules for application schemas, spatial schema provides data types for geometry models in various forms, Point, line, area, body based, temporal schema allows to consider temporal dimensions, Quality principles can be used to describe the quality of geodata, encoding standards (mainly GML) allow the standard based transfer of data and data models, CityGML allows a standard based 3D modelling, etc.","name":"Standards for Spatial Data Modeling","selfAssesment":"In progress GI-N2K
"},{"code":"DM3-1b","description":"There are two basic concepts related to this topic: Features and Fields, or Geo-fields, as named by Goodchild at al. The concept of fields can be differently represented as explained here: - Repetition of basic concepts of Geographic Information Science - Explanation of the concept of continuous fields and the commonly used ways of representing geo-fields - Relation between fields and coverages, an important discretizations of a Geo-field - Types of Coverages","name":"The concept of fields","selfAssesment":"In progress GI-N2K
"},{"code":"DM3-2","description":"The raster data model holds values in a regularly spaced matrix of cells arranged in rows and columns covering a two dimensional space. Rasters are commonly used to store continuous data like colors in an image and height values but they are also used for discrete (thematic) values like land use.","name":"The raster model","selfAssesment":"In Progress (GI-N2K)
"},{"code":"DM3-2b","description":"Grids are on the one hand one important type of caverages and on the other hand Grids are used as basic structure in some applications. Important here is: - Definition of the concept of grid in GIS - Grid as an instance of coverages - Grids as a basic structure for certain applications / medium for aggregation of data - Examples of grid-based data such as Digital Terrain Models (DTM) - Grids in census / statistical data and Geo-marketing applications","name":"Grid representations","selfAssesment":"In progress GI-N2K
"},{"code":"DM3-3","description":"Grid data models can contain millions of discrete values. This leads to very large datasets. Depending on the way values change over the grid, different methods can be used for an optimal (lossy or lossless) data compression. Type of data, computer power needed, application of the data, method of transport and storage all contribute to the choice of compression method.","name":"Grid compression methods","selfAssesment":"In progress (GI-N2K)
"},{"code":"DM3-3b","description":"TINs and Voronoi tessellations are important types of coverages. TINs play a very important role also in Computer graphics. Important here is: - Basics from Graph theory - Definition of Triangulated Irregular Networks (TIN), purpose and applications - TINs and voronoi diagrams as a type of coverages - One important instance of a TIN: Delauney Triangulation - Definition of Voronoi Diagrams, purpose and applications - Relation between Delauney Triangulation and Voronoi Diagram, the \"Dual Graph\" - Examples from applications","name":"TIN and Voronoi tesselations","selfAssesment":"In progress GI-N2K
"},{"code":"DM3-4","description":"While the classical grid structure uses rectangular cells, the hexagonal data model uses hexagons to represent raster data","name":"The hexagonal model","selfAssesment":"In progress (GI-N2K)
"},{"code":"DM3-4b","description":"Linear referencing is 1 dimensional positioning. The position of an object is defined by the distance from the object to the start point along a line. Linear referencing is for example used in railway dispatching systems","name":"Linear referencing","selfAssesment":"In progress GI-N2K
"},{"code":"DM3-5b","description":"Resolution of raster and gridded data - Georeferencing of data, direct and indirect methods (t.b.d.)","name":"Resolution and georeferencing system","selfAssesment":"In Progress (GI-N2K)
"},{"code":"DM3-7","description":"In hierarchical data models data is organized in a tree-like structure. Data are connected with parent-child relations. Hierarchical structures are often used for spatial indexing.","name":"Hierarchical data models","selfAssesment":"In progress (GI-N2K)
"},{"code":"DM3","description":"This unit includes relevant tessellation data models. Besides features (sometimes also called geo-objects) geo-fields play and important role. In recent literature tessellation models are classified as discretizations of fields. In traditional GI literature tessellations are defined as important data structure itself. Tessellation discretise a continuous surface into a set of non-overlapping polygons that cover the surface without gaps. Tessellation data models represent continuous surfaces with sets of data values that correspond to partitions. Important tessellation models are Grids, TINs and Voronoi diagrams.","name":"Tessellation data models","selfAssesment":"In progress (GI-N2K)
"},{"code":"DM4-1","description":"This topic includes the basics for feature based modelling. There are a number of standards also relevant for this topic (see relations). The following items should be included: - Definition of a feature (in some literature also called object, or geoobject) and of feature classes respectively. - Aspects of the definition (ID, geometry, topology, thematic, time etc.) - Techniques for the definition of features / feature classes (mainly link, as they are described elsewhere, see relations)","name":"Feature based modelling","selfAssesment":"GI-N2K
"},{"code":"DM4-2","description":"This topic describes the process of Geometric modelling using vector data, means the primitives like points, lines, areas, bodies, or raster data. There is a strong relation to ISO standards (see relations) as they provide basic data types for geometric modelling. Main issues: - Geometric modeling based on vector data - Geometric modeling based on raster data - Conversion between the models - examples, advantages, disadvantages of the models","name":"Geometric modelling","selfAssesment":"In progress GI-N2K
\r\n\r\nIn progress (GI-N2K)
"},{"code":"DM4-4","description":"This topics deals with the definition of an application schema. There are other units which are important for this topic (see Relations). Issues to be included: - Methods to define and describe an application schema (requirement analysis, description of the schema etc.) - Feature attribute catalogues - Domains / data relevant for INSPIRE","name":"Application models based on vector data","selfAssesment":"GI-N2K
"},{"code":"DM4-5","description":"This Topic deals with important application models, which should be chosen with relation to the course (geographically / related to the background of the course) INSPIRE should be treated in any case. In detail: - Overview on important application models relevant for the course, e.g. from topography or environment in the country - Repetition of the principles of Spatial data infrastructures - Overview on the INSPIRE initiative and the goals related - The INSPIRE data model - The architecture of INSPIRE and the necessary services - Domains / data relevant for INSPIRE","name":"Examples of important application models","selfAssesment":"GI-N2K
"},{"code":"DM4-6","description":"This topic is dedicated to the challenges of model based interoperability and related issues, The principles of interoperability are included in DA3-2. In detail: - The challenges of model interoparability (semantics, different modelling of the same features in different models, syntacs) - Overview on IT concepts for schema integration / transformation - Approaches for model integration - Approaches for model transformations, e.g. related to INSPIRE, from the Humboldt project","name":"Model based interoperability, model transformations","selfAssesment":"GI-N2K
"},{"code":"DM4-7","description":"Network models are crucial in some application domains, such as Navigation (roads etc.), but also in utility applications (facilities like pipes etc.) In this topic should be treated: - The network model in the database domain - Graph based NoSQL databases - Topology of network models - Data structures for storing network data - The Dijkstra algorithm - Overview on important applications","name":"Network models","selfAssesment":"GI-N2K
"},{"code":"DM4","description":"This unit includes relevant issues related to vector data models, feature based modelling, applications. Besides imagery data the majority of GI data available is feature based and founded on vector geometry. Topology modeling also is very common nowadays, as many analysis like routing or neighborhood analysis require it. Spaghetti modelling becomes more and more and exception. In every country there are important feature and vector geometry based application models available e.g. in Topography / Cartography. In Europe every GI course should include some information on INSPIRE. As in different application domains different data models are used, sometimes for the same feature types, integration and transformation of models are an important issue also.","name":"Vector data model, Feature based modelling, Applications","selfAssesment":"In progress GI-N2K
"},{"code":"DM5-1","description":"- Many geographical phenomena are not defined sharply but uncertain Uncertainty has a number of considerations: - Motivation, background, purpose - Conceptual model of uncertainty - Uncertainty of geographic phenomena (vagueness, ambiguity) - Uncertainty of measurements - Uncertainty of analysis - Uncertainty vs. data quality - Statistical models of uncertainty - Outline of Fuzzy approaches","name":"Basics of uncertainty and its modelling","selfAssesment":"In progress GI-N2K
"},{"code":"DM5-2","description":"Space and time are 2 connected concepts, this topic is dedicated to some basics of modelling time and the temporal dimensions related to features and fields: - Motivation, background, purpose - Changes in time in Entity based and field based representations - A conceptual model of changes in time - Move of objects - Change of structure - Change of geometry - Examples from applications","name":"Modelling time aspects","selfAssesment":"In progress GI-N2K
"},{"code":"DM5-3","description":"Traditionally many GIS used 2D or 2.5 D data models, but in the last decade 3D modeling mainly in form of city models or in the context of Building Information Models (BIM): - Basic concepts of 3D modelling, edge, area, volume models - The workflow of 3D modelling, general aspects, choose of the proper model - Methods of 3D modeling - Principles of Constructive Solid Geometry (CSG) - Principles of Boundary representation (BR) - Principles of Voxel-beased modeling - Comparison of the methods - The concept of BIM, principles and purpose - City models, principles and purpose - Examples / applications","name":"Modelling 3D","selfAssesment":"GI-N2K
"},{"code":"DM5","description":"Traditional raster and vector data models cannot easily represent the more complex aspects of geographic information, such as temporal change, uncertainty, three-dimensional phenomena, and integrated multimedia. A variety of models have been proposed to represent these complexities, including both extensions to existing models and software, and entirely new models and software. During the 1990s, work in this area was largely experimental, but many solutions are now available to practitioners in commercial and open source software. The data models in this unit are based on concepts discussed in Knowledge Area CF Conceptual Foundations.","name":"Modelling 3D, temporal and uncertain phenomena","selfAssesment":"In progress (GI-N2K)
"},{"code":"DN3-1","description":"Modification of spatial and attribute data while ensuring consistency within the database, implications of transactions on database integrity, scenarios for periodic changes in GIS database and monitoring the periodic changes.","name":"Database change","selfAssesment":"In progress (GI-N2K)
"},{"code":"DN3-2","description":"Rules for modelling spatial database change, techniques for handling version control, techniques for managing long and short transactions, management of spatial databases in multi-user environment","name":"Modeling database change","selfAssesment":"In progress (GI-N2K)
"},{"code":"DN3-3","description":"Reliability tests of change information, design and implementation. Logical consistency of updates.","name":"Reconciling database change","selfAssesment":"In progress (GI-N2K)
"},{"code":"DN3-4","description":"Needs for versioned databases, queries for change scenarios using DB management tools, algorithms for performing dynamic queries, role of time-criticality and data security while choosing methods for change detection.","name":"Managing versioned geospatial databases","selfAssesment":"In progress (GI-N2K)
"},{"code":"GC","description":"The term geocomputation dates back to the first international conference on the topic in 1996 held at the University of Leeds under the title “The art and science of solving complex spatial problems with computers’. The term “geocomputation” was coined to describe the use of computer-intensive methods for knowledge discovery in physical and human geography. This new area distinguishes it from the application of statistical techniques to spatial data in the focus on “creative and experimental applications” and in “developing relevant geo-tools within the overall context of a ‘scientific’ approach.” Other authors reinforced the unique character of geocomputation as “to provide better solutions to many geographical problems by developing new, computationally dependent tools for analysis and modelling”. Simply defined, the interdisciplinary area of geocomputation was, from the beginning, closely linked to the application of computer technology and the development of tools and applications to real-world spatio-temporal problems through the combination of geographic information system techniques, spatial modelling, cellular automata, and other non-conventional data clustering and analysis techniques.\r\nEven though geocomputation is still seeking to define the field conceptually), it is closely related to computational science, the use of high-computing performance, artificial intelligence, computational intelligence, grid infrastructure and parallel computing . Nevertheless, the evolution of new computing paradigms, such as edge-fog-cloud computing along with the new forms of data create new opportunities for the geocomputation community . \r\n\r\nWhile the underlying idea remains intact --a diverse and interdisciplinary area of research that uses geospatial data, methods and tools for applied scientific work--, the current approach to geocomputation differs from the founders in that it focuses more attention on open science, reproducible research practices, and in a vibrant collaborative community to develop new methods, tools and applications that are integrated into multiple application domains such as economics, sociology, geodemography, health, criminology, transportation, biology, remote sensing and cities . The theoretical roots and experimental emphasis of geocomputation makes it an excellent vehicle to creatively explore in parallel the theory and practice of the use of geospatial data in a computational way to solve real-world problems.","name":"Geocomputation","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC1-1","description":"A complex system can be viewed as a system composed of many interacting parts, with the ability to generate a new collective behaviour through self-organisation, for example, though the spontaneous formation of temporal, spatial or functional structures. Complex systems are therefore adaptive as they evolve and may contain self-driving feedback loops. Most real-world systems such as global climate, an ecosystem, a city, the human brain, and the entire universe, are complex systems. Therefore, complex systems are much more than a sum of their parts.The general characteristics of the structure and dynamics of complex systems have been characterised, including path dependence, positive feedback loops, self-organisation, and emergence. Complex system types include nonlinear systems, chaotic systems, and complex adaptive systems. \r\nTraditional approaches focus on the individual system components and define a system as the sum of its parts. Whereas the modern approach relies on complexity theory and complex adaptive systems, to emphasise the linkages between system components in order to understand complex systems as a whole. Agent-based models, for example, have been highly recommended for studying complex adaptive spatial systems because they support the explicit representation of situation-dependent information for decision making within dynamic spatial environments.","name":"Complex systems","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC1-2","description":"Computational science is a discipline focused on the design, implementation and use of mathematical models or simulations through the use of computers to analyse scientific problems, systems or processes. Computational science heavily relies on computational technologies such as high performance computing, artificial intelligence, computational intelligence, grid infrastructure and parallel computing. Geocomputation is closely related to computational science and, therefore, geocomputational methods are often derived from machine learning, clustering, simulation, parallel computing and high performance computing. Contrary to the methods and tools applied for spatial analysis described under the Analytical Methods Knowledge Area, geocomputation methods may involve spatial methods available in standard GIS packages, but quite often require self-development, or at least customisation, involving computational technologies to solve target problems. The aim of this topic is to provide an introduction to computational science with particular emphasis on its usage and relation to geocomputation.","name":"Computational science and technology","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC1-3","description":"While geocomputation is not daily used in GIS environments and traditional GIS projects, it is the focus of a vibrant collaborative and research community in developing new geocomputational methods, tools and applications that are integrated into multiple application domains such as economics, sociology, geodemography, health, criminology, transportation, biology, remote sensing and cities. Open science, reproducible research practices, and strong collaboration make geocomputing an excellent vehicle for creatively exploring together the theory and practice of using geospatial data in a computational way to solve real-world problems.","name":"Spatio-temporal problems and applications","selfAssesment":"In progress (GI-N2K)
"},{"code":"GC1-4","description":"The origin of geocomputation dates back to the first international conference on the topic in 1996 and was coined to describe the use of computer-intensive methods for knowledge discovery in physical and human geography. Geocomputation is closely related to other widely known areas of knowledge within the geospatial community, such as GIScience, Spatial Information Science, GeoInformatics, and Geographic Data Science. While these terms clearly overlap and boundaries are fuzzy, the term geocomputation puts the focus on creative and experimental applications and in developing relevant computationally geospatial tools for analysis and modelling within the overall context of a ‘scientific’ approach. Therefore, a common interpretation of geocomputation is to describe the application of computational models to geographic problems.","name":"Origin of geocomputation","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC1","description":"Geocomputation represents an attempt to move the geospatial research agenda back to geographical analysis and modelling by providing a toolbox of methods to analyse and model a range of highly complex, often non-deterministic problems. In this context, complex systems and computational science are foundational aspects upon which geocomputation approaches and methods are built to address a variety of real-world, spatio-temporal issues","name":"Geocomputation and complex systems","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC2-1","description":"Building a model that mimics a real-world system generally follows a series of stages: from conceptual models to mathematical models and, finally, simulation models. In model development, system analysis is a process whereby a real-world system is simplified by dividing it into simpler, more manageable parts. A conceptual model captures the components, variables and interactions of a system, and provides a useful way of thinking about the trade-offs between abstraction and representativeness of real-world phenomena. Taken in isolation, however, the interacting parts of a system fail to explain its dynamics behavior. A conceptual model is then translated into a mathematical model to explain system dynamics and interaction. Mathematical models often take the form of equations, logical rules or other mathematical mechanisms to represent the interrelations and relationships among the constituted parts of a system. Lastly, a simulation model is the computer-based implementation of mathematical models consisting of interrelated equations and logical rules. When a simulation model runs on a computer, it iteratively recalculates the modelled system state as it changes over time in accordance with the relationships represented by the mathematical relationships that describe the system dynamic. Therefore, developing detailed and dynamic simulation models comes at the cost of generality and interpretability, but it brings us realism and the ability to represent real-world processes in specific contexts. Simulation modelling is often used for prediction, exploration, theory development, or even optimization of conditions to achieve desired outcomes, with the goal of examining how the interconnections and relationships that characterise complex social and environmental systems (e.g. ecosystems, urban systems, social systems, global climate system) produces patterns of behavior over time. Therefore, simulation models are increasingly gaining relevance as scientific mechanisms for several reasons. First, simulation models allow researchers to study systems inaccessible to experimental and observational scientific methods, complementing more conventional approaches to discover or formalize theories about real world systems. Also, aS many real-world systems are nonlinear, simulation modelling has turned into a necessary method to explore and understand better such systems. In addition, the availability of computational science methods and technology, together with a large amount of data available from different sources, have greatly driven the adoption of simulation models in a wide range of scientific disciplines.","name":"Principles of computer simulation","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC2-3","description":"Rule-based models are based on logic programming with condition-action expressions, where the left side of the expressions consists of several conditions that returns a logical result, and the right side consists of several actions. Rules in rule-based models indirectly specify a mathematical model. However, unlike equation-based models which refer to the overall or aggregate behaviour of a system, rule-based models focus on the behaviour of the individual components of a system. That’s why the implementation of rule-based models is most often done by cellular automata models or agent-based models, in which the aggregate behaviour of the system emerges from the interaction of the individual agents or cells over time. Many geographic patterns and dynamics are formed by systems of interacting actors/cells with heterogeneous characteristics and behaviours, in which such dynamic behaviours can be implemented as rules. The aim of this topic is to provide knowledge about rule based models and to understand their advantages and disadvantages.","name":"Rule-based models","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC2-4","description":"Equation-based models are a set of interrelated equations that capture the variability of a system over time (differential equations), and the execution (simulation) of the model means to evaluate such equations. Equation-based models do not aim at representing the behaviour of the individual components in a system. Rather, they focus on the overall or aggregate behaviour of a system. Therefore, equation-based models are well suited to represent physical processes and some topics within natural sciences, where the system to some degree can be described by physical laws. Hydrological modelling is a good example of models based on equations. However, other real-world systems can rarely be fully described by the laws of the natural sciences, and their behavior and interrelation must be represented by means of other types of mathematical mechanisms. The aim of this topic is to present the advantages and challenges in using equation-based simulation models, which are most naturally applied to systems centrally governed by physical laws rather than by information processing and flow.","name":"Equation-based models","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC2-5","description":"Space-time dynamics are closely related to the concepts of change and process, which are inherent to our dynamic world. Space-time dynamics especially manifest when we move from a static representation to a dynamic representation of phenomena. Various processes that take place at different spatial and temporal scales interact with each other and lead to complex changes to the phenomena being modeled. There exist many different approaches of conceptualizing and understanding space-time dynamics in order to understand or predict phenomena in heterogeneous application domains ranging from human activities and urban sprawl to disease spread and traffic flow.","name":"Space-time dynamics","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC2-6","description":"Cellular automata are a standard type of spatially explicit simulation model in which complex processes are modelled over space and time by means of a lattice of cells in which each cell defines its neighbouring cells. The spatial lattice composed of a two-dimensional grid of squared cells is the simple configuration of a cellular automata. Based on this regular configuration, each cell has associated a set of states that change at each iteration by the execution of transition rules, which take into account the state of each cell and those of its neighbours. As such, cellular automata consist of six defining components: a framework or lattice, cells, neighborhood, transition rules, initial conditions (states), and an update sequence (time). Cellular automata models map easily onto existing data structures widely used in geographic information systems, are easy to implement, and are able to show changes and spatial patterns in an understandable manner. All of this has contributed to their popularity in simulation modelling for applications such as measuring land use changes and monitoring disease spread","name":"Cellular automata","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC2-7","description":"Agent-based models are simulation models that decompose a complex system into small entities (agents) with modeling properties and behavior. Contrary to modelling at an aggregate level, agent-based models are focused on the individual level, where a set of discrete agents with well-defined behaviors represents an individual, object or component of the modelled system. Therefore, the individual agent is the explicit, basic unit. The macro-level behaviour of the system emerges thereafter from the interaction of the individual agents and with the environment over time. Agent-based models are used for spatial modelling, offering possibilities to consider topological particularities of interaction and information transfer among agents and/or with the environment. In relation to spatial simulation, agent-based models have been used for example to model natural and social phenomena such as animal behaviour, pedestrian behavior, social insects and biological cells.","name":"Agent-based modelling","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC2","description":"The concept spatial simulation modelling can be better understood by looking at the meaning of its individual words. A model is widely defined as a simplified representation of a real-world system under study, which can be used to explore or to better understand the system it represents. Computer models or simulation models are computer-based implementations of a model to produce outputs based on certain model assumptions. Simulation , therefore, relies on the use of computers for virtual experimentation to gain insight into real-world problems by proposing alternative assumptions that arise from exploring “what if” questions about a dynamic problem of interest over the course of successive simulation experiments.\r\nSimulation modelling is also useful for the study of spatial patterns over time. Spatial simulation models are relevant when the study of spatial elements and their relationships in a system are necessary for a fully understanding of that system. In this sense, spatial simulation modelling approaches include rule-based models, equation-based models, grid-based cellular automata models, discrete event simulation, and agent-based models.\r\nSimulation modelling is often used for prediction, exploration, theory development, or even optimization of conditions to achieve desired outcomes, with the goal of examining how the interconnections and relationships that characterize these systems produces patterns of behavior over time. Across broad areas of the environmental and social sciences, researchers use simulation models as a way to study systems inaccessible to experimental and observational scientific methods, and also as an essential complement of those more conventional approaches to discover or formalize theories about the real world. \r\nSimulation models are a relatively recent addition to the scientific toolbox, and the reasons for their widespread adoption are, on one hand, the impossibility to study in-situ some complex social and environmental systems (e.g. ecosystems, urban systems, social systems, global climate system) and, on the other hand, the availability of High Performance Computing and large amount of data from different sources. Finally, the nonlinear behaviour of many natural systems provides challenges building traditional mathematical models based on linearization. \r\nSimulation modelling is also useful for the study of spatial patterns over time. In this sense, spatial simulation modelling approaches include rule-based models, equation-based models, grid-based cellular automata models, discrete event simulation, and agent-based models.","name":"Spatial simulation modelling","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC3-10-1","description":" ","name":"Geometric object features","selfAssesment":" "},{"code":"GC3-10-2-1","description":" ","name":"Object relations","selfAssesment":" "},{"code":"GC3-10-2","description":" ","name":"Object features","selfAssesment":" "},{"code":"GC3-10-3-1","description":" ","name":"Wavelets","selfAssesment":" "},{"code":"GC3-11-1","description":" ","name":"Genetic artificial networks","selfAssesment":" "},{"code":"GC3-11-2-1","description":" ","name":"Markov models","selfAssesment":" "},{"code":"GC3-11-2-2","description":" ","name":"Kalman filters","selfAssesment":" "},{"code":"GC3-11-2","description":" ","name":"Space-time dynamic reasoning","selfAssesment":" "},{"code":"GC3-11-3-1","description":" ","name":"Multilayer perceptron","selfAssesment":" "},{"code":"GC3-11-3-2","description":" ","name":"Backpropagation","selfAssesment":" "},{"code":"GC3-11-3-2","description":" ","name":"Long short-term memory","selfAssesment":" "},{"code":"GC3-11-3-3","description":" ","name":"Recurrent neural networks","selfAssesment":" "},{"code":"GC3-12-1","description":" ","name":"Ensemble learning","selfAssesment":" "},{"code":"GC3-12-2","description":" ","name":"Regression trees","selfAssesment":" "},{"code":"GC3-12","description":" ","name":"AI algorithms","selfAssesment":" "},{"code":"GC3-13-1","description":" ","name":"Physics aware AI","selfAssesment":" "},{"code":"GC3-13-2-1","description":" ","name":"Theory of mind","selfAssesment":" "},{"code":"GC3-13-2-2","description":" ","name":"Self-aware AI","selfAssesment":" "},{"code":"GC3-13-2","description":" ","name":"Digital twin","selfAssesment":" "},{"code":"GC3-13","description":" ","name":"Hybrid AI","selfAssesment":" "},{"code":"GC3-14-1-1","description":" ","name":"Individual intelligence","selfAssesment":" "},{"code":"GC3-14-1-2","description":" ","name":"Collective intelligence","selfAssesment":" "},{"code":"GC3-14-1-3","description":" ","name":"Team learning","selfAssesment":" "},{"code":"GC3-14-1","description":" ","name":"Cooperation levels","selfAssesment":" "},{"code":"GC3-14-2-1","description":" ","name":"Logical agent","selfAssesment":" "},{"code":"GC3-14-2-2","description":" ","name":"Inference","selfAssesment":" "},{"code":"GC3-14-2-3","description":" ","name":"Probabilistic reasoning","selfAssesment":" "},{"code":"GC3-14-2-4","description":" ","name":"Sequential decision problems","selfAssesment":" "},{"code":"GC3-14-2-5","description":" ","name":"Supervised learning","selfAssesment":" "},{"code":"GC3-14-2-6","description":" ","name":"Reinforcement learning","selfAssesment":" "},{"code":"GC3-14-2","description":" ","name":"Intelligence type","selfAssesment":" "},{"code":"GC3-14","description":" ","name":"Intelligent Software Agent","selfAssesment":" "},{"code":"GC3-3","description":"Biological neurons, or nerve cells, receive multiple input stimuli, combine and modify the inputs in some way, and then transmit the result to other neurons. Artificial neural networks are an attempt to emulate features of biological neural networks in order to address a range of difficult information processing, analysis and modelling problems. The principal class of ANNs are so-called feed-forward networks, but other types of ANN are for example recurrent neural networks. Among the feed-forward networks the most widely used approach is the multi-level perceptron (MLP) model. The application range is broad from non-linear regression to land cover change modelling. The aim of the topic is to introduce the principles of ANN and to understand and demonstrate its use in geospatial modelling.","name":"Artificial Neural Networks","selfAssesment":"In progress (GI-N2K)
"},{"code":"GC3-7-1","description":" ","name":"Cybernetics","selfAssesment":" "},{"code":"GC3-7-2","description":"Pattern recognition is the process of classifying input data into objects or classes based on key features. There are two classification methods in pattern recognition: supervised and unsupervised classification. The supervised classification of input data in the pattern recognition method uses supervised learning algorithms that create classifiers based on training data from different object classes. The classifier then accepts input data and assigns the appropriate object or class label. The unsupervised classification method works by finding hidden structures in unlabelled data using segmentation or clustering techniques. Common unsupervised classification methods include: K-means clustering, Gaussian mixture models, Hidden Markov models. The aim of the topic is to provide knowledge about the different methods in pattern recognition and how to choose the optimum method for a specific spatial problem.","name":"Pattern recognition","selfAssesment":"In progress (GI-N2K)
"},{"code":"GC3-7-3-1","description":" ","name":"Information-as-meaning","selfAssesment":" "},{"code":"GC3-7","description":" ","name":"Signal processing","selfAssesment":" "},{"code":"GC3-8-1","description":" ","name":"Natural language processing","selfAssesment":" "},{"code":"GC3-8-2","description":" ","name":"Semantic web","selfAssesment":" "},{"code":"GC3-8","description":" ","name":"Computational linguistics","selfAssesment":" "},{"code":"GC3-9-1-1","description":" ","name":"Experimental learning","selfAssesment":" "},{"code":"GC3-9-1","description":" ","name":"Knowledge representation","selfAssesment":" "},{"code":"GC3-9-2-1","description":" ","name":"Semantic net","selfAssesment":" "},{"code":"GC3-9-2-2","description":" ","name":"Inheritance","selfAssesment":" "},{"code":"GC3-9-2","description":" ","name":"Knowledge organising system","selfAssesment":" "},{"code":"GC3-9-3","description":" ","name":"Semantic categorisation","selfAssesment":" "},{"code":"GC3-9-4-1-1","description":" ","name":"Membership functions","selfAssesment":" "},{"code":"GC3-9-4-1-2","description":" ","name":"Class stability","selfAssesment":" "},{"code":"GC3-9-4-1","description":" ","name":"Fuzzy logic","selfAssesment":" "},{"code":"GC3-9-4-2","description":" ","name":"Boolean logic","selfAssesment":" "},{"code":"GC3-9","description":" ","name":"Automated reasoning","selfAssesment":" "},{"code":"GC3","description":"Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans.","name":"Artificial intelligence (AI) in EO and GI","selfAssesment":"New
"},{"code":"GC4-1","description":"The use of the term Open geocomputation doesn't intend to coin a new term; Open GIScience and Open GIS are well explored and discussed terms in the literature. Both embrace the idea of open data, open source, collaboration among peers, and the integration of these practices into GIS research projects, tools, services and applications. Open geocomputation brings the ideas of Open GIScience (and hence Open Science in general) into geocomputation, focussing on openness as a fundamental tenet to conduct research in geocomputation and for the development of new computational methods and tools. In fact, many community-led developments and tools have recently appeared in the field of geocomputation, notably based on R and Python. The widespread popularity and adoption of these computing environments for geocomputing and geospatial analysis is simply because they encompass open, transparent, and reproducible tool development.","name":"Open Geocomputation","selfAssesment":"New
"},{"code":"GC4","description":"A distinguible feature of the current approach to geocomputation is the emphasis on openness: open science, open source, open data. All of this propelled by a vibrant collaborative community with the aim to develop open and reproducible methods, tools and applications applied to a variety of real-life, spatio-temporal application domains. Open Science is a paradigm that can be applied to any scientific discipline and area of knowledge, characterised by openness, access to large volumes of data and unprecedented levels of computing power, availability of community-driven tools, and new types of collaboration between multidisciplinary researchers. Open Science clearly goes beyond geocomputation, but at the same time, its practices and principles characterise recent geocomputation-related projects as well as its community. Therefore, the vision of Open Science taken here is contextualised to the field of geocomputation.","name":"Open Science","selfAssesment":"new
"},{"code":"GD","description":"Geospatial data represent measurements of the locations and attributes of phenomena at or near Earth`s surface. Information is data made meaningful in the context of a question or problem. Information is rendered from data by analytical methods. Information quality and value depends to a large extent on the quality and currency of data (though historical data are valuable for many applications). Geospatial data may have spatial, temporal, and attribute (descriptive) components, as well as associated metadata. Data may be acquired from primary or secondary data sources. Examples of primary data sources include surveying, remote sensing (including aerial and satellite imaging), the global positioning system (GPS), work logs (e.g., police traffic crash reports), environmental monitoring stations, and field surveys. Secondary geospatial or geospatial-temporal data can be acquired by digitizing and scanning analog maps, as well as from other sources, such as governmental agencies. The legitimacy of geographic information science as a discrete field has been claimed in terms of the unique properties of geospatial data. In a paper in which he coined the term GIScience, Goodchild (1992) identified several such properties, including: 1. Geospatial data represent spatial locations and non-spatial attributes measured at certain times. 2. The Earth`s surface is highly complex in shape and continuous in extent. 3. Geospatial data tend to be spatially autocorrelated. It has long been said that data account for the largest portion of geospatial project costs. While this maxim remains true for many projects, practitioners and their clients now can reasonably expect certain kinds of data to be freely or cheaply available via the World Wide Web. Federal, state, regional, and local government agencies, as well as commercial geospatial data producers, operate clearinghouses that provide access to geospatial data. Although geospatial data are much more abundant now than they were ten years ago, data quality issues persist. Good data are expensive to produce and to maintain. Proprietary interests simultaneously increase the supply of geospatial data and impede data accessibility. Standards for geospatial data and metadata are useful in facilitating effective search, retrieval, evaluation, integration with existing data, and appropriate uses. National and international organizations, such as the Open Geospatial Consortium (OGC) and International Organization for Standardization (ISO), develop and promulgate such standards. INSPIRE directive (Infrastructure for Spatial Information in the European Community) regulates geospatial data management","name":"Geospatial Data","selfAssesment":"In progress (GI-N2K)
"},{"code":"GD1-1","description":"Usable and accurate geospatial data are based upon proper model of the Earth`s surface. Shape of the Earth is complex and complicated to measure. Approximations are used to minimize complexity of the task and possible errors.","name":"Earth geometry","selfAssesment":"GI-N2K
"},{"code":"GD1-2","description":"Geospatial referencing systems provide unique codes for every location on the surface of the Earth (or other celestial bodies). These codes are used to measure distances, areas, and volumes, to navigate, and to predict how and where phenomena on the Earths surface may move, spread, or contract. Point-based, vector coordinate systems specify locations in relation to the origins of planar or spherical grids. Tessellated referencing systems specify locations hierarchically, as sequences of numbers that represent smaller and smaller subdivisions of two- or three dimensional surfaces that approximate the Earths shape, Linear referencing systems specify locations in relation to distances along a path from a starting point. Tessellation data models, are considered in Unit DM3 Tessellation data models, and linear referencing models are considered in Unit DM4 Vector data models.","name":"Georeferencing systems","selfAssesment":"GI-N2K
"},{"code":"GD1-3","description":"Horizontal datums determine the geometric relations between a coordinate system grid and a particular ellipsoid approximating the Earth`s surface. Vertical datums determine elevation reference surfaces, like mean sea level. A. Horizontal datums. Relation of coordinate system to particular ellipsoid, datum transformation options, Molodensky and Helmert transformation, other high accuracy transformations, ED50 and WGS84, historical development of horizontal datums, ETRS89. B. Vertical datums. Historical development of vertical datums, difference between vertical datum and geoid, relations between ellipsoidal (geodetic) heiht, geoidal height and orthometric elevation.","name":"Datums","selfAssesment":"In progress GI-N2K
"},{"code":"GD1-4","description":"Map projections are systematic transformations of geographic coordinates of the surface of ellipsoid into locations in plane. Plane coordinates are based on map projection. As the transformation of a spherical grid into a plane grid causes inevitably distortions of the geometry, and, different projections cause different distortions, knowledgeable choice of appropriate projection for any particular use is crucial. A. Map projection poperties. Geometric properties that may be preserved or lost in projected grid, usefulness of compromise projection, Tissot indicatrix as an indicator of projection errors, visual appearance of the Earth`s graticule, distortion patterns for projection classes, distortions in raster data. B. Map projection classes. Three main classes of map projection based on developable surface, projection types by geometric properties preserved, mathematical basis of projecting longitude and latitude into x and y coordinates. UTM, ETM, projections used by EC. C. Map projection parameters. Standard line, projection case, latitutde and longitude of origin, aspects of projection. D. Georegistration. Rectification vs orthorectification, ground controle points in georegistration of aerial imagery.","name":"Map projections","selfAssesment":"GI-N2K
"},{"code":"GD1","description":"Proper model of the Earth`s surface and ability to locate spatial phenomena accurately to it, is crucial in effective collection, management and use of data. Characterising size and shape of the Earth, using appropriate surfaces to approximate it, choosing suitable coordinate system and map projection is bases for efficient understanding of spatial data.","name":"Geolocating Data to Earth","selfAssesment":"In progress (GI-N2K)
"},{"code":"GD10-4","description":"A stereoscopy acquisition mode collects remotely sensed data where each location on the ground (or the imaged objects) is covered multiple times (at least twice), from different perspectives. Stereopairs and stereoscopic coverage enable the extraction of 3D representations of the environment from remotely sensed imagery.","name":"Stereoscopy and orthoimagery","selfAssesment":"GI-N2K
"},{"code":"GD10","description":"Since the 1940s aerial imagery has been the primary source of detailed geospatial data for extensive study areas. Photogrammetry is the profession concerned with producing precise measurements from aerial imagery. Aerial imaging and photogrammetry comprise a major component of the geospatial industry. The topics included in this unit do not comprise an exhaustive treatment of photogrammetry, but they are aspects of the field about which all geospatial professionals should be knowledgeable.","name":"Aerial imaging and photogrammetry","selfAssesment":"GI-N2K
"},{"code":"GD11-2","description":"the physical environment to sense data without direct contact. It contains a carrier device (platform) and a sampling unit (sensor).","name":"Platforms and sensors","selfAssesment":"GI-N2K
"},{"code":"GD11","description":"Satellite-based sensors enable frequent mapping and analysis of very large areas. Many sensing instruments are able to measure electromagnetic energy at multiple wavelengths, including those beyond the visible band. Satellite remote sensing is a key source for regional- and global-scale land use and land cover mapping, environmental resource management, mineral exploration, and global change research. Shipboard sensors employ acoustic energy to determine seafloor depth or to create imagery of the seafloor or water column. The topics included in this unit do not comprise an exhaustive treatment of remote sensing, but they are aspects of the field about which all geospatial professionals should be knowledgeable.","name":"Satellite and shipboard remote sensing","selfAssesment":"GI-N2K
"},{"code":"GD12","description":"Meaning of geospatial metadata, elements of metadata, use of metadata, integration of metadata in data production, standards in geospatial data, ISO standard family 191xx, data warehouse, exchange protocol, transport protocols, spatial data infrastructure, INSPIRE, OGC, DCAT profiles for CKAN applications bridging metadata from GI and IT domains.","name":"Metadata, standards, and infrastructures","selfAssesment":"GI-N2K in progress
"},{"code":"GD2-1","description":"Classic land survey methods and manual attribute data collection in the field","name":"Land surveying and field data collection","selfAssesment":"GI-N2K
"},{"code":"GD2-2","description":"Aerial imagery has been the primary source of detailed geospatial data for extensive study areas. Photogrammetry is producing precise measurements from aerial imagery. Aerial imaging and photogrammetry comprise a major component of the geospatial data production. Satellite-based sensors enable frequent mapping and analysis of very large areas. Sensing instruments are able to measure electromagnetic energy at multiple wavelengths. Satellite remote sensing is a key source for regional- and global-scale land use and land cover mapping, environmental resource management, mineral exploration, and global change research. Shipboard sensors employ acoustic energy to determine seafloor depth or to create imagery of the seafloor or water column. Principles of aerial photography, oblique and vertical imagery, spatial and radiometric resolution, spectral sensitivity, principal point, distortions and displacements in aerial image, parallax, stereophotogrammetry, generation of an orthoimage from a vertical aerial phoptograph, aerotriangulation, vector data extraction from digital seteroimagery, mission planning. Use of UAV in photogrammetry. Main platforms and sensors in spatial image acquisition, active and passive sensors, LiDAR and microwave, multispectral and hypersepctral imagery, interpretation of imagery, supervised and unsupervised classification, pixel based and segmented classification, ground verification, main applications, bathymetric mapping. SENTINEL.","name":"Remote sensing","selfAssesment":"In progress GI-N2K
"},{"code":"GD2-3","description":"Crowdsourcing is the practice of obtaining needed services, ideas, or content by soliciting contributions from a large group of people and especially from the online community rather than from traditional employees or suppliers. Crowdsourced spatial data collection is becoming more and more important. The advantages and disadvantages of crowdsourced data, opensource mapping tools, potential application of crowdsourcing, VGI, OSM or cell-phone based, aspects of crowdsourced data quality and reliabilty.","name":"Crowdsourced data collection","selfAssesment":"GI-N2K
"},{"code":"GD2-4","description":"Digitizing as the main secondary spatial data production technique. Encoding vector points, lines, and polygons by tracing map sheets has diminished in importance, but remains a useful technique for incorporating historical geographies and local knowledge. \"Heads-up\" digitizing using digital imagery as a backdrop on-screen is a standard technique for editing and updating GIS databases. Tablet and on-screen digitizing, scanning and (semi)automatic vectorization.","name":"Digitizing","selfAssesment":"GI-N2K
"},{"code":"GD2","description":"Spatial data collection / production involves measurement of locations in relation to the coordinate system, and collection of attributed data about the spatial phenomena. Measurements may be direct (e.g. surveying) or remote, data acquisition involves measurement of parameter values, evaluation of parameters, polls, interpretation of spatial imagery, and re-use of secondary data (e.g. old maps). Volunteered geographic information is becoming more important.","name":"Data Collection","selfAssesment":"GI-N2K
"},{"code":"GD3","description":"It is quite common, that data including both spatial entities and their attribute data undergo changes. These changes need to be catalogued fully and explicitly, including initial conditions, new conditions, all intermediate stages and operations used. The geospatial data needs to contain an archival history of change.","name":"Transaction management of geospatial data","selfAssesment":"In progress (GI-N2K)
"},{"code":"GD4-1","description":"Geometric accuracy, factors influencing it, geometric accuracy and topological fidelity, geometric accuracy in survey and GPS mesurements, thematic accuracy, relations between thematic accuracy, geometric accuracy and topological fidelity, misclassification matrix, commission and omission, logical consistency, relations between resolution, precision, and accuracy, spatial resolution, thematic resolution, and temporal resolution, precision, uncertainties associated with coordinate precision, primary and secondary data sources.\r\n\r\nParticular application. That standard varies from one application to another. In general, however, the key criteria are how much uncertainty is present in a data set and how much is acceptable. Judgments about fitness for use may be more difficult when data are acquired from secondary rather than primary sources. Aspects of data quality include accuracy, resolution, and precision. Concepts of data quality, error, and uncertainty are also covered in Knowledge Areas CF Conceptual Foundations (in a theoretical context) and GC Geocomputation (in the context of analysis); the focus here is on the measurement and assessment of data quality.","name":"Data quality","selfAssesment":"GI-N2K
"},{"code":"GD4","description":"Data quality is the degree of data usability in relation to given objective and particular application. The expectations to data vary between different applications. The key criteria in data quality are the amount of uncertainty in data as compared to the acceptable level of uncertainty. Evaluation of the usability may be more complicated using data from secondary sources. Appropriate metadata is inevitable for these judgements. Aspects of data quality include geometric and thematic accuracy, (in)consistencies, resolution, precision, usability and others. Assurance of data quality may be improved by following proper standards and spatial data infrastructure regulations for data collection and management. System of basic data quality measures for geospatial domain in the EN ISO 19157:2013 standard.","name":"Data Quality, Metadata and Data Infrastructure","selfAssesment":"In progress (GI-N2K)
"},{"code":"GD6-1","description":"Geometric accuracy is a measure indicating how close the geometric values of the data are to the real world position of the mapped feature.","name":"Geometric accuracy","selfAssesment":"In progress (GI-N2K)
\r\n\r\nIn progress (GI-N2K)
"},{"code":"GD6-3","description":"The resolution of a data source indicates the smallest unit of detail provided by the data source.","name":"Resolution","selfAssesment":"In progress (GI-N2K)
"},{"code":"GD6-4","description":"The precision of a measurement system, related to reproducibility and repeatability, is the degree to which repeated measurements under unchanged conditions show the same results.","name":"Precision","selfAssesment":"GI-N2K
"},{"code":"GD6-5","description":"Primary data sources provide information collected directly for GIS use. Secondary sources are data sources that need to be processed before they are ready for GIS use.","name":"Primary and secondary sources","selfAssesment":"In progress (GI-N2K)
"},{"code":"GD8-1","description":"Tablet digitizing is the conversion from physical map to digital data by re-drawing the features on the map fixed on a digitizing tablet","name":"Tablet digitizing","selfAssesment":"In progress (GI-N2K)
\r\n\r\nIn progress (GI-N2K)
\r\n\r\nIn progress (GI-N2K)
"},{"code":"GS","description":"Geographic Information Science and Technology serve the society, but it is not a panacea. The history of its development is the sum of fragmented efforts, which have still not been fully integrated. Its potential benefits are often constrained and its potential impacts are not fully understood. Institutional and economic factors limit access to data, technology, and expertise by some of those who need it to make better decisions. Political, ideological, and personal issues aside, organizations invest in GIS&T when estimated benefits outweigh estimated costs. Evaluating costs and benefits is difficult, however and too often leads to nothing being done. For some individuals and groups, costs are prohibitive even though potential benefits are compelling. The legal framework provides a structure for regulating a number of key aspects of geographic information science, technology, and applications. Legal regimes determine who can claim the exclusive right to hold and use geospatial data, the conditions under which others may have access to the data, and what subsequent uses are permitted. Political struggles arise from conflicting proprietary and public interests about who benefits from geospatial information, and how the power to allocate the use of this information is, or should be, distributed among members of a society. The need to choose among conflicting interests sometimes poses ethical dilemmas for GIS&T professionals. The explosive growth of the geospatial information contributed by users through various application programming interfaces has made geospatial information is a powerful tool in the social media toola powerful media for the general public to communicate, but perhaps more importantly, geographic information have also become a tool media for constructive dialogs and interactions about social issues, recent growth of Web-based geospatial information and volunteered geographic information (VGI). Because so many public agencies and private organizations rely upon GIS&T for planning, decision making, and management, GIS&T increasingly affects and is used to direct daily life. Critical approaches to understanding the role of GIS in society equip practitioners to employ GIS&T reflectively. The critical approach specifically questions the assumptions and premises that underlie the economic, legal and political regimes and institutional structures within which GIS&T is implemented. Related concerns are considered in Knowledge Area OI: Organizational and Institutional Aspects.","name":"GI and Society","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS1-1","description":"The most basic definition of a legal regime is a system or framework of rules governing some physical territory or discrete realm of action that is at least in principle rooted in some sort of law. Often the concept has been applied to specific areas of law.","name":"The legal regime and legal framework","selfAssesment":"GI-N2K
"},{"code":"GS1-2","description":"Contract law is defined as a set of rules that govern the contractual agreements between merchants or persons. A contract is an agreement between different parties that state their responsibilities and duties to each other. A liability in contract law is when certain conditions are written into a contract that makes a party liable. Licensing is the process of giving or getting official permission to do something. A license is an agreement through which a licensee leases the rights to a legally protected piece of intellectual property from a licensor — the entity which owns or represents the property — for use in conjunction with a product or service.","name":"Contract law, liability and licensing","selfAssesment":"GI-N2K: relevant but to be revised
"},{"code":"GS1-3","description":"Data privacy and security are two essential components of a successful strategy for data protection. Data security refers to the protection of data from unauthorized access, use, change, disclosure, and destruction. It encompasses network security, physical security, and file security. Data privacy involves protecting consumer data by eliminating or reducing the possibility of re-identifying an individual whose information is present in the data. This is done by either removing specific information or by transforming the data with random “noise” or generalization.","name":"Privacy and Security","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS1-4","description":"Property is secured by laws that are clearly defined and enforced by the state. These laws define ownership and any associated benefits that come with holding the property. The term property is very expansive, though the legal protection for certain kinds of property varies between jurisdictions. Property is generally owned by individuals or a small group of people. The rights of property ownership can be extended by using patents and copyrights. Property rights give the owner or right holder the ability to do with the property what they choose. That includes holding on to it, selling or renting it out for profit, or transferring it to another party.","name":"Ownership and property rights","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS1-5","description":"In economics, competition is a condition where different economic firms seek to obtain a share of a limited good by varying the elements of the marketing mix: price, product, promotion and place. Competition law is a law that promotes or seeks to maintain market competition by regulating anti-competitive conduct by companies. Public-private sector relationships deal with a particular subset of competition, i.e. competition between public and private organizations.","name":"Competition and public-private sector relationships","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS1-6","description":"Open data is data that can be accessed, shared, used and reused without any barrier for any type of (re)user. According to the Open Definition, open data can be defined as data that be freely used, modified, and shared by anyone for any purpose subject, at most, to measures that preserve provenance and openness. Open data requires datasets to be either in the public domain, or distributed through an open license. The data must be provided as a whole, free of charge, and preferably downloadable via the Internet, including any additional information that might be necessary to comply with the open license’s terms. Openness requires the data to be provided in a readily machine-readable form. The format must be open as well, meaning that it does not place any restriction upon its use, and that the files in that format can be processed with open-source software tools. The Open Definition speaks broadly of open ‘works’, rather than of open data. Focusing on data tout court, one can move from the Open Government Data (OGD) principles. According to the OGD principles, which are arguably foundational in understanding the concept of open data, data must be: Complete; Primary; Timely; Accessible; Machine-processable; Non-discriminatory; Non-proprietary; and License-free. Compliance with the OGD principles needs to be demonstrable, i.e. there need to be accountability measures in place to allow the review of the adherence to the principles above. The concepts of Open Work and open data highlight how data needs to be both legally, technically and financially open, so either in the public domain or covered by an open license, and kept in a machine-readable and non-proprietary format. Open data aims at making information available to everybody, for any purpose, in a machine-readable and interoperable format, based on open standards and digestible by free/libre open source software (FLOSS). Also with respect to the financial accessibility open data is data available free of charge. Marginal costs of dissemination are accepted by some as a reasonable cost for users. However, open data is data that can be accessed and reused without any barrier for any type of reuse, and some user groups experience any price to be paid as a barrier.","name":"Open data","selfAssesment":"Completed
"},{"code":"GS1","description":"Legal problems can arise when geospatial information is used for land management, among other activities. Geospatial professionals may be liable for harm that results from flawed data or the misuse of data. Understanding of contract law and liability standards is essential to mitigate risks associated with the provision of geospatial information products and services. Legal relations between public and private organizations and individuals govern data access. The nature of information in general, and the characteristics of geospatial information in particular, make it an unusual and difficult subject for a legal regime that seeks to establish and enforce the type of exclusive control associated with other commodities. Geospatial information is in many ways unlike the kinds of works that intellectual property rights were intended to protect. Still, organizations can, and do, assert proprietary interests in geospatial information. Perspectives on geospatial information as property vary between the public and private sectors and between different countries.","name":"Legal aspects","selfAssesment":"In progress GI-N2K
"},{"code":"GS2-1","description":"Business models determine how organizations can create and deliver value, for example, through the provision or use of geographic data. A business model is a conceptual tool that contains\r\na set of interrelated elements that allow organizations to create and capture value and generate revenues. The development and implementation of an appropriate business model are considered to be a key to the success of the organization and a crucial source for value creation. \r\n\r\nAlthough business models determine how organizations create, deliver, and capture value, they should not be regarded as permanent and invariable structures or settings. Business models are shaped by both internal and external forces, and will only be successful if they are able to adapt to a changing environment. In the GI domain, several technological, regulatory, and societal developments have challenged the existing business models and opened up opportunities for new business models. Among these developments are the establishment of spatial data infrastructures (SDIs) worldwide, the democratization of geographic knowledge, and the move toward open source, open standards, and open data.\r\n\r\nSince the development and implementation of SDIs in different parts of the world, much attention has been paid to the need to find appropriate business models for GI, and in particular, for geographic data providers in the public sector. Traditional business models in which public data providers were selling their data to customers in the private industry and other public agencies were questioned, because they restricted the opportunity for data sharing. The concept of SDI is about moving to new business models, where partnerships between GI organizations are promoted to allow access to a much wider scope of geographic data and services. A key challenge in the development of these SDIs was the alignment of different existing business models of the actors in the GI domain. Moreover, the development and implementation of SDIs also led to the emergence of new business models, which was even more the case with the more recent move toward open geographic data.\r\n\r\nOrganizations can be active in different parts of the geo-information value chain, and can create and offer value in many different ways. As a result, many different GI business models exist. Data providers, data enablers, and data end users could be seen as three main categories of GI business models. Each of these categories consists of many different business models, as different value propositions\r\nwill exist, and value can be created and captured in several ways.","name":"GI Business models","selfAssesment":"Completed (GI-N2K)
"},{"code":"GS2-5","description":"To provide a better insight into the process of adding value to GI, several authors have introduced and applied the information value chain approach. A value chain can be defined as the set of value-adding activities that one or more organizations perform in creating and distributing goods and services. The value chain concept originally was developed for the manufacturing sector, as a tool to evaluate the competitive advantage of firms. More recently, the value chain concept has been applied to other sectors, including information technology where the good or service, and the benefits it provides, is less tangible in nature. A value chain involves the progress of goods from raw materials to finished products through a number of stages, during each of which a new value is added to the original input by various activities. The value chain concept was extended into the information market, with the information value chain referring to the set of activities adding value to information and turning raw data into new information products or services. Especially important in this context is the role of information and communication technologies (ICT), which have an impact on all activities in the information value chain, such as information collection, processing, dissemination, and use. In the context of GI, the value chain relates to the series of value- adding activities to transform raw geographic data into new products that are used by certain end users. Although there are slightly different descriptions of the various steps of the GI value chain, in general, the essential steps in the value chain are: acquisition of raw data, the application of a data model, quality control, and integration with other sources, presentation, and distribution. In recent years, particular attention has been paid to different steps between the process of distributing data and the actual end use of an end product of GI. In addition, after the publication of the data, value can be added to the data in many different ways. Value can be added by making data from different sources easily accessible through repositories and data portals, by building and selling tailored solutions using the data to end users or by using geographic data to improve existing products and services delivered to an end user. In certain cases, this end product will be the first step of a next value chain.","name":"Geo-information value chain","selfAssesment":"Completed
"},{"code":"GS2","description":"Most organizations insist that investments in GIS and T be justified in economic terms. Quantifying the value of information, and of information systems, however, is not a straightforward matter.","name":"Economic aspects","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS3-1","description":"The use of geospatial information allows public sector organizations and actors to make better decisions and provide better services to their citizens. Geospatial information is increasingly being used at different administrative levels and in different policy areas.","name":"Use of geospatial information in the public sector","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS3-2","description":"Geospatial information is increasingly being used by private companies for different purposes and the private sector plays an important role in the development and implementation of geospatial information infrastructures.","name":"Use of geospatial information in the private sector","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS3-3","description":"Research and education institutions use geospatial information for various purposes, in support of their research and educational activities.","name":"Use of geospatial information in research and education","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS3-4","description":"Effective monitoring of the environment and an improved understanding of the same requires valuable information and data that can be extracted through application of geospatial technologies. GIS can be used most effectively for environmental data analysis and planning. It allows better viewing and understanding physical features and the relationships that influence in a given critical environmental condition. GIS can help in effective planning and managing the environmental hazards and risks. In order to plan and monitor the environmental problems, the assessment of hazards and risks becomes the foundation for planning decisions and for mitigation activities. GIS supports activities in environmental assessment, monitoring, and mitigation and can also be used for generating environmental models. GIS can aid in hazard mitigation and future planning, air pollution & control, disaster management, forest fires management, managing natural resources, wastewater management, oil spills and its remedial actions etc.","name":"Use of geospatial information in environmental issues","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS3","description":"Geospatial Information used in Government agencies and public authorities at local, state, and federal levels produce and use geospatial data for many activities, including provision of social services, public safety, economic development, environmental management, and national defence. Public participation in governing, empowered by geospatial technologies, offers the potential to strengthen democratic societies by involving grassroots community organizations and by engaging local knowledge. The private sector covers a broad range of areas of opportunity. With continued advancements in technology, greater awareness of its advantages as a powerful decision support tool the use of geospatial information use in the private sector needs to be discussed.","name":"Use of geospatial information","selfAssesment":"In Progress GI-N2K
"},{"code":"GS4-1","description":"Public participation GIS (PPGIS) is a field within geographic information science that focuses on ways the public uses various forms of geospatial technologies to participate in public processes, such as mapping and decision making.","name":"Public participation GIS","selfAssesment":"GI-N2K (revision)
"},{"code":"GS4-2b","description":"Social Media Geographic Information (SMGI) can be defined as any piece or collection of multimedia data or information with explicit (i.e. coordinates) or implicit (i.e. place names or toponyms) geographic reference collected through the social networking web or mobile applications. Social data are acknowledged as a good of major value in the digital economy, and their potential for enhancing more traditional analytics is of the utmost importance. A big part of social data however also features spatial (and temporal) references, thus their integration with more traditional Authoritative Geographic Information (AGI) may enable a further step towards the next generation of geospatial intelligence. SMGI is a sub-category of VGI and can be active or passive, depending on the type of application with which it is collected: applications purposefully created and/or used to collect SMGI in participatory initiatives","name":"Social Media Geographic Information","selfAssesment":"Completed
"},{"code":"GS4-3b","description":"Volunteered geographic information (VGI) is a special kind of user-generated content. It refers to geographic information collected and shared voluntarily by the general public. Web.2.0 and associated advances in web mapping technologies have greatly enhanced the abilities to collect, share and interact with geographic information online, leading to VGI.","name":"Citizens and volunteered geographic information","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS4","description":"Today, geo data has become a conventional and pervasively familiar data type seen at once to underpin and significantly re-characterize the digital world, with broad implications for both technology and society. Geospatial data are abundant, but access to data varies with the nature of the data, the user groups wishes to acquire it and for what purpose, under what conditions, and at what price geodata can be obtained. The explosive growth of geographic information contributed by users through various application programming interfaces has made geographic information a powerful media for the general public, but perhaps more importantly, geospatial information have also become media for constructive dialogs and interactions about social issues, recent growth of Web-based Geographic information and volunteered geographic information (VGI).","name":"Geospatial citizenship","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS5-1b","description":"The advantages of geospatial technologies and resulting data present ethical dilemmas such as privacy and security concerns as well as the potential for stigma and discrimination resulting from being associated with particular locations. the use of geospatial technologies and the resulting data needs to be critically assessed through an ethical lens prior to implementation of programmes, analyses or partnerships. Using this lens requires not only explicit consideration of potential negative consequences of adoption but also clear articulation of the specific contexts and conditions under which benefits may be realized.","name":"Ethics in the geospatial information society","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS5-2b","description":"A code of ethics is a guide of principles designed to help professionals conduct business honestly and with integrity. A code of ethics document may outline the mission and values of the business or organization, how professionals are supposed to approach problems, the ethical principles based on the organization's core values, and the standards to which the professional is held. Codes of ethics for geospatial professionals are intended to provide these principles and guidelines for GIS professionals","name":"Codes of ethics for geospatial professionals","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS5","description":"Ethics provide frameworks that help individuals and organizations make decisions when confronted with choices that have moral implications. Most professional organizations develop codes of ethics to help their members do the right thing, preserve their good reputation in the community, and help their members develop as a community","name":"Ethical aspects","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS6-1","description":"US GIS&T BoK: As GIS became a firmly established presence in geography and catalysed the emergence of GIScience, it became the target of a series of critiques regarding modes of knowledge production that were perceived as problematic. The first wave of critiques charged GIS with resuscitating logical positivism and its erroneous treatment of social phenomena as indistinguishable from natural/physical phenomena. The second wave of critiques objected to GIS on the basis that it was a representational technology. In the third wave of critiques, rather than objecting to GIS simply because it represented, scholars engaged with the ways in which GIS represents natural and social phenomena, pointing to the masculinist and heteronormative modes of knowledge production that are bound up in some, but not all, uses and applications of geographic information technologies. In response to these critiques, GIScience scholars and theorists positioned GIS as a critically realist technology by virtue of its commitment to the contingency of representation and its non-universal claims to knowledge production in geography. Contemporary engagements of GIS epistemologies emphasize the epistemological flexibility of geospatial technologies.","name":"Epistemological and critical issues","selfAssesment":"In progress/to delete (GI-N2K)
"},{"code":"GS6-2","description":"Various types of critiques exist on the way geospatial information is being used and re-used.","name":"Critical approach on the use of geospatial information","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS6-3","description":"Defending or refuting the argument that the \"digital divide\" that characterizes access use of geospatial information perpetuates inequities among developed and developing nations, among socio-economic groups,and between individuals, community organizations, and public agencies and private firms.","name":"Critical aspects and invisible groups","selfAssesment":"In progress/to be delete (GI-N2K)
"},{"code":"GS6","description":"Many of the educational objectives used to define topics in this knowledge area, and in the Body of Knowledge as a whole, challenge educators and students to think critically about GI and Society. Since the 1990s, scholars have criticized cartography and the GIS science from a wide range of perspectives. Common among these critiques are questioned assumptions about the purported benefits of GI and Society and attention to its unexamined risks. By promoting reflective practice among current and aspiring geospatial information professionals, an understanding of the range of critical perspectives increases the likelihood that geospatial information will fulfil its potential to benefit all stakeholders. Philosophical, psychological, and social underpinnings of these critiques are considered in Knowledge Area CF: Conceptual Foundations.","name":"Critical approach","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS7-1","description":"US GIS&T BoK: As GIS became a firmly established presence in geography and catalysed the emergence of GIScience, it became the target of a series of critiques regarding modes of knowledge production that were perceived as problematic. The first wave of critiques charged GIS with resuscitating logical positivism and its erroneous treatment of social phenomena as indistinguishable from natural/physical phenomena. The second wave of critiques objected to GIS on the basis that it was a representational technology. In the third wave of critiques, rather than objecting to GIS simply because it represented, scholars engaged with the ways in which GIS represents natural and social phenomena, pointing to the masculinist and heteronormative modes of knowledge production that are bound up in some, but not all, uses and applications of geographic information technologies. In response to these critiques, GIScience scholars and theorists positioned GIS as a critically realist technology by virtue of its commitment to the contingency of representation and its non-universal claims to knowledge production in geography. Contemporary engagements of GIS epistemologies emphasize the epistemological flexibility of geospatial technologies.","name":"Epistemological critiques","selfAssesment":"GI-N2K
"},{"code":"GS7-3","description":"US GIS&T BoK: \r\n\r\nFeminist interactions with GIS started in the 1990s in the form of strong critiques against GIS inspired by feminist and postpositivist theories. Those critiques mainly highlighted a supposed epistemological dissonance between GIS and feminist scholarship. GIS was accused of being shaped by positivist and masculinist epistemologies, especially due to its emphasis on vision as the principal way of knowing. In addition, feminist critiques claimed that GIS was largely incompatible with positionality and reflexivity, two core concepts of feminist theory. Feminist critiques of GIS also discussed power issues embedded in GIS practices, including the predominance of men in the early days of the GIS industry and the development of GIS practices for the military and surveillance purposes.\r\n\r\nAt the beginning of the 21st century, feminist geographers reexamined those critiques and argued against an inherent epistemological incompatibility between GIS methods and feminist scholarship. They advocated for a reappropriation of GIS by feminist scholars in the form of critical feminist GIS practices. The critical GIS perspective promotes an unorthodox, reconstructed, and emancipatory set of GIS practices by critiquing dominant approaches of knowledge production, implementing GIS in critically informed progressive social research, and developing postpositivist techniques of GIS. Inspired by those debates, feminist scholars did reclaim GIS and effectively developed feminist GIS practices.","name":"Feminist critiques","selfAssesment":"GI-N2K
"},{"code":"GS7-4","description":"In the early 1990s social critiques of GIS from human geographers began to appear. These initial critiques set off an ensuing debate between GISers, defending GIS and human geographers, who critiqued GIS. This debate materialized in academic journals including: Political Geography Quarterly, Environment and Planning A, and Progress in Human Geography. Schuurman (2000) notes that the GIS debate, while unique to the discipline of Geography, was part of a larger debate in other disciplines about the effects of technology. This presentation will be limited (unfortunately) to two aspects of this debate. It will first discuss conditions within human geography that made GIS a target of human geographers' critique. Second, this paper will discuss the particular critiques that were directed at GIS by human geographers. Though the reaction of such critiques and their effect on GIS is an important topic there is not enough time and space to address these issues. See Schuurman (2000) \"Trouble in the Heartland: GIS and its critics in the 1990s\" in Progress in Human Geography for a thoughtful look at this debate and its effects on the discipline of GIS.","name":"Social critiques","selfAssesment":"GI-N2K
"},{"code":"IP","description":"Image processing and analysis comprises all relevant steps to reach from (raw) image data to [...] information via image interpretation and digital image classification. In traditional remote sensing workflows, this step follows the image acquisition process. There are two main components, i.e. (1) image processing, (2) analysis, which emphasizes the sequential nature of the process – while increasingly this dichotomy disappears.\r\nThe information production workflow aims at converting semantically rich, but unstructured image data into a set of classes, objects, arrangements, etc., to enable ultimately a complete image understanding and scene reconstruction. This scene reconstruction entails a mental component (“understanding”) and a technical one, by providing standardized classification results or even beyond, dedicated information products in form of digital maps and reports, tailored to the specific application domains and use cases, in order to make informed decisions. Such information products can be maps, reports, dashboards etc., overall it is the transformation from quantitative, semi-continuous digital numbers (“brightness”) to qualitative information using categories and figures, which can be stored and further used in a GIS environment. \r\nThe first part of the process entails image calibration, image correction (geometric, radiometric), data assimilation, and any type of enhancement (contrast manipulation, filtering, etc.) which aims to better condition the information extraction part. It ends where we achieve a significant milestone in the processing milestone, remarkably denoted as analysis-ready data (ARD). From there, we enter into the analysis realm, classically referred to as digital image classification, the process of assigning pixels to classes. In other words, the aggregation of pixel values according to their similarity into categorical (nominal) classes. The discrimination of these classes by and large depend on application domain, and ideally, these classes match with information classes. To address the issue of ambiguity and to overcome the so-called semantic gap in image interpretation by providing a stepping-stone in the information extraction process, the strategy of pre-classification (semi-concepts) has been introduced in the literature.\r\nToday, boundaries between pre-processing and classification increasingly vanish, through an increasing level of automation in the pre-processing and image correction steps. In addition, new ways of analysis emerge, in particular in large time series, including image data cubes. Instead of a processing chain, which suggests a linear – and potentially irreversible – cascade of manipulations, the automation of large parts of this part allows us to see the process more reversible and approachable from either side.","name":"Image processing and analysis","selfAssesment":"Completed
"},{"code":"IP1-1-1","description":"The image spatial subset allows to extract the group of pixels / grid cells using a defined polygon e.g. area of interest – AOI or defining the new image extent. It is used to limit spatially the image extent to which, for example an image function or classification model will be applied.","name":"Image subset","selfAssesment":"Completed
"},{"code":"IP1-1-2","description":"Layer stacking is a process for combining multiple images into a single image. The image stack is used to build a ‘new’ multiple band file from the georeferenced images of various pixel sizes, extents, projections. The image bands must be resampled and reprojected to a common spatial grid. The layer stacking is used for example to combine spectral bands from a Landsat, Sentinel-2 data and SRTM DEM into one multi-dimensional file. The process of layer stacking increases the size of the final stacked image, which may have consequences that increase the processing time of operations performed on the stacked image.","name":"Layer stack","selfAssesment":"Completed
"},{"code":"IP1-1","description":"Data manipulation adjusts a dataset to the needs of a specific application by subsetting the spatial extent or the number of bands or by organizing bands from separate single layer files into a single multi-layer file.","name":"Data manipulation","selfAssesment":"New
"},{"code":"IP1-2","description":"Fourier analysis - A characteristic of remotely sensed images is a parameter called spatial frequency, defined as the number of changes in brightness value per unit distance for any particular part of an image. There are low-frequency and high-frequency areas. Spatial frequency may be enhanced or subdued using Fourier Analysis (an alternative technique is spatial convolution filtering). Fourier analysis mathematically separates an image into its spatial frequency components. It is then possible interactively to emphasize certain groups (or bands) of frequencies relative to others and recombine the spatial frequencies to produce an enhanced image.\r\nThe signal received by a pulsed radar is a time sequence of pulses for which the amplitude and phase are measured. The frequency content of this time-domain signal is obtained by taking its Fourier transformation.","name":"Fourier transformation","selfAssesment":"New
"},{"code":"IP1-3-1-1","description":"Structure from motion (SfM) describes the photogrammetric process for estimating the 3D structure of a scene, whereby correspondences between multiple images are established and used to detect motion parallax. When a camera moves over a surface while taking successive overlapping images, the distances between features on the surface will change from one image to the next. The changes depend on the distance of the feature points to the camera, and thus the surface elevation. This motion parallax can be used to generate an accurate 3D representation of the surface. \r\nThe photogrammetric problem of SfM is similar to stereo vision, but has gained popularity with the advent of inexpensive cameras which have variable internal geometries, unlike metrically stabilized cameras traditionally used in airborne mapping. Even with less accurate or even missing GPS location and orientation metadata, SfM still allows for the creation of (hyper)local DEMs as long as the imagery contains sufficient overlap. Airborne or spaceborne platforms can be used, provided that 2D frame-based cameras are used which can be represented with a pinhole mathematical model. \r\nGenerating a digital elevation model (DEM) from SfM is typically handled automatically using specialized software. Firstly, image correspondences are detected. Feature points are identified in the individual images using local contrast feature detectors. The features extracted from all the images are matched with all the available overlapping images and erroneous matches are filtered out. The process typically results in hundreds or thousands of tie-points per image, which allows for robust matching even with large a priori uncertainties in camera orientation. A bundle adjustment, solving for the 3D coordinates of the feature points, the position and orientation of the camera and its internal characteristics then results in an initial, so-called sparse 3D point cloud. \r\nNext, ground control points (GCPs) can be introduced. These are surface features (naturally present or introduced into the scene) which can be identified at the pixel level in the images by users. Measured also in the field with an accuracy smaller than the pixel size, they can be used to constrain the bundle adjustment solution to improve georeferencing and camera calibration to an accuracy similar to that of the GCP measurement or the GSD size. \r\nSince this process yields a match only for a small subset of all pixels, an additional step, called dense image matching is added. It starts from the exact position and orientations resulting from the bundle adjustment to rectify the images and overlay two or more images, to compare them row by row and in 16 different directions in a process called semi-global matching (SGM). Matching pixels are identified along these lines, and 3D intersection distances photogrammetrically inferred. By combining results from different directions, a 3D coordinate for almost every pixel is obtained with similar accuracy. Finally, DEM products with a regularly spaced grid are generated and exported based on the dense point cloud. Depending on the point classes used in the export (obtained through topographic filtering or deep-learning-based classification of the dense point cloud), the outcome will be a digital surface model (DSM) or digital terrain model (DTM).","name":"DEM generation with 'Structure-from-Motion'","selfAssesment":"Completed
"},{"code":"IP1-3-1-2","description":"Photogrammetry is the science and technology of obtaining spatial measurements and other geometrically reliable derived products from photographs. Basic geometric principles applying both traditional analogue and modern digital procedures are related to the central projection of the image in case of typical cameras and to the dynamic projection mostly in case of push-broom sensors, popular in the satellite photogrammetry. The fundamental principle used by photogrammetry is called triangulation. By taking photographs from at least two different locations, so-called “lines of sight” can be developed from each camera to points in a block on the object. These lines of sight (called rays) are mathematically intersected to produce the 3-dimensional coordinates of the points of interest.\r\nWithin data processing the most important parts of photogrammetric workflow are: (1) image orientation, (2) model reconstruction, and (3) orthorectification. Image orientation is based mostly on aerial triangulation, however recently the computer vision algorithm, called structure from motion, became more popular in particularly in close range photogrammetry. Both orientation approaches include detection or measurement of the points between overlapping images in a block, control points measurements in a field defining orientation in reference system and check points verifying the orientation process. The satellite photogrammetry due to different projection and much bigger areas of imaging is usually related to Rational Polynomial Coefficients (RPCs) defining preliminary scene orientation during image orientation. However, to receive more accurate results also here the control points measured in a field are in use. The second part of the modern photogrammetric processing is 3D model reconstruction. In past, vectorization within the stereoscopic measurements was the most popular way of using photogrammetric data after the image orientation. The development of the informatics contributed to the development of the image matching algorithms that can provide dense image point clouds, which can be used to the 3D detailed modelling including digital elevation model production. The final step of photogrammetric processing is orthorectification, which delivers cartometric image called orthophoto mosaiced into orthophotomaps. This process comprises the influence of digital terrain model, model of camera (interior orientation) and image orientation (exterior orientation). Orthophotomap and elevation models derived from photogrammetric processing are applied as very popular data source in many GIS systems. The other photogrammetric outcomes are, for example a 3D measurement or 3D models of some real-world object or scene.","name":"Photogrammetric principles","selfAssesment":"Completed
"},{"code":"IP1-3-1-3","description":"In satellite photogrammetry to obtain the orientation mostly of satellite scene Rational Polynomial Coefficients (RPCs) are applied. They provide a compact representation of a ground-to-image geometry, that allow for photogrammetric processing without requiring a physical camera model. Model with RPC is provided with satellite image and can be improved using measurements of indirect surveying methods used for control point measurement. The RPC model for the coordinates of the image point is calculated as ratios of the cubic polynomials in the coordinates of the world or object space or ground point. \r\nIn photogrammetry and remote sensing, rational polynomial coefficients (RPCs) describe a specific imaging geometry model for transforming image pixel coordinates to map coordinates (thereby accounting for terrain displacement errors). A sensor model describes the geometric relationship between the object space and the image space, or vice versa. It relates 3-D object coordinates to 2-D image coordinates. RPCs are part of a general sensor model that approximates the physical sensor model. The physical sensor model represents the physical imageing process, making use of information on the sensor's position and orientation (during image acquisition). The RPC model often refers to a specific case of the RFM (rational function model) that is in forward form, has third-order polynomials, and is usually solved by the terrain-independent scenario.","name":"RPC correction","selfAssesment":"Completed
"},{"code":"IP1-3-1-4","description":"A ground control point (GCP) is a location of the surface of the Earth (e.g. a road intersection) that can be identified on the imagery and located accurately on the map (i.e. the reference dataset). Two distinct sets of coordinates are associated with the GCP: image coordinates in i rows and j columns, and map coordinates (e.g. x, y measured in degrees of latitude and longitude or as specified by the spatial reference system).","name":"Ground Control Points (GCP)","selfAssesment":"Planned
"},{"code":"IP1-3-1","description":"Orthorectification is the process of removing sensor (scanner or camera), satellite/aircraft, and terrain-related distortions for creating a planimetrically correct image. \r\nTo obtain an accurately orthorectified image, the following information is required: (1) accurate elevation model, and (2) a camera model or rational polynomial coefficients (RPCs) that depicts the positional relationship of the collected image to the ground. Many companies deliver their images together with RPCs and existing software implementations can automatically read these files and apply the RPC transformation on the fly. An accurate elevation model is important to remove the influence of topography (e.g. hills, valley, etc.) on the raw image so that users can accurately compute distances, areas, and directions. Without performing orthorectification, the features in the image are tilted (especially the features located away from the center of the camera). Many satellite data products (e.g. Sentinel images, Landsat data products) are orthorectified using Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) data which is a freely available data product and has a spatial resolution of e.g. 1 arc-second (30 m). In the case of extremely jagged surface topography, i.e. areas of high relief, a DEM with a higher spatial resolution is required. \r\nTwo main models can be used in the orthorectification process: black-box and the physical-based model. The black-box model (called also the analytical model) is commonly implemented in different software because it relies solely on the RPC files. This model does not require access to any proprietary information of the sensor used to collect the image. \r\nThe physical-based models are more complex (and hence expected to be more accurate) because they account for various factors that might influence the quality of the acquired image: e.g. position of the satellite when collecting the images, atmospheric effects, etc. An example of a physical-based model is the so-called camera model. This model requires access to proprietary sensor information that has to be provided by the image owner.","name":"Orthorectification","selfAssesment":"Completed
"},{"code":"IP1-3-2-1","description":"Image co-registration [aka Image-to-image registration] is the translation and rotation alignment process by which two images of like geometry and of the same geographic area are positioned coincident with respect to one another so that corresponding elements of the same ground area appear in the same place on the registered images (Jensen 2005 referencing Chen and Lee 1992).","name":"Image co-registration","selfAssesment":"New
"},{"code":"IP1-3-2","description":"Spatial referencing (referred to as geo-referencing as well) is the process of aligning available EO or GIS data to a coordinate system so that further spatial analysis and image analysis tasks can be applied using these data as input. \r\nTo be able to perform spatial referencing, users have to generate the so called Ground Control Points (GCPs) with known coordinates. In case of images, the easiest features that could be used as GCPs are the intersections, isolated trees etc.","name":"Spatial referencing","selfAssesment":"Planned
"},{"code":"IP1-3","description":"Geometric correction is concerned with placing the reflected, emitted, or back-scattered measurements or derivative products in their proper planimetric (map) location so they can be associated with other spatial information. It is usually necessary to preprocess the remotely sensed data and remove the geometric distortions so that individual picture elements (pixels) are in their proper planimetric (x, y) map locations. This allows remote sensing-derived information to be related to other thematic information in geographic information systems (GIS) or spatial decision support systems (SDSS). Geometrically corrected imagery can be used to extract accurate distance, polygon area, and direction (bearing) information.\r\n\r\nGeometric correction techniques are dedicated to resolving the geometric distortions caused by: (1) variations in sensor position; (2) Earth curvature; (3) rotation of Earth on its axis; (4) relief displacement. \r\n\r\nThere are two types of geometric distortions, namely systematic and random distortions. The former might be caused by Earth's rotation for example and, therefore they are predictable and systematic. The second type of distortions might be caused by terrain or variations in sensor altitude. \r\nGeometric correction includes georeferencing and orthorectification techniques.","name":"Geometric correction","selfAssesment":"Completed
"},{"code":"IP1-4-1","description":"Contrast stretching (also referred to as contrast enhancement) expands the original input brightness values to make use of the total dynamic range or sensitivity of the output device (a computer display).","name":"Contrast stretching","selfAssesment":"New
"},{"code":"IP1-4-2","description":"The histogram is a useful graphic representation of the information content of a remotely sensed image. Histograms for each band of imagery are often displayed and analysed in many remote sensing investigations because they provide the analyst with an appreciation of the quality of the original data (e.g. whether it is low in contrast, high in contrast or multimodal in nature. [...] Tabulating the frequency of occurrence of each brightness value within the image provides statistical information that can be displayed graphically in a histogram.","name":"Histogram","selfAssesment":"New
"},{"code":"IP1-4","description":"Image enhancement algorithms are applied to remotely sensed data to improve the appearance of an image for human visual analysis or occasionally for subsequent machine analysis. The quality of results of image analysis are subjectively judged by humans as to whether they are useful. They include contrast enhancement.","name":"Image enhancement","selfAssesment":"New
"},{"code":"IP1-6","description":"Principal component analysis (PCA) has proven to be of value in the analysis of multispectral and hyperspectral remotely sensed data. PCA is a technique that transforms the original correlated spectral dataset into a substantially smaller and easier set of uncorrelated variables that represents most of the information present in the original dataset. The first component accounts for the maximum proportion of the variance of the original dataset, and subsequent orthogonal components account for the maximum proportion of the remaining variance.","name":"Principal component analysis (PCA)","selfAssesment":"New
"},{"code":"IP1-7-1-1","description":"Bottom-of-Atmosphere (BOA) reflectance is also called surface reflectance and consists of the solar radiation that is reflected from the Earth's surface.","name":"Bottom-of-Atmosphere (BOA)","selfAssesment":"New
"},{"code":"IP1-7-1-4","description":"Top-Of-Atmosphere (TOA) radiance represents the radiance observed outside Earth’s atmosphere. It is derived from the Digital Numbers (DN) using metadata delivered with the image.","name":"Top-Of-Atmosphere (TOA)","selfAssesment":"New
"},{"code":"IP1-7-1","description":"Atmospheric correction accounts for the attenuation caused by scattering and absorption in the atmosphere. It transforms top-of-atmosphere (TOA) reflectance to bottom-of-atmosphere (BOA) reflectance.\r\nThe decision to perform atmospheric correction depends on the need, i.e. the envisioned usage of the derived EO information product and the nature of the underlying problem. This includes requirements to the accuracy of extracted biophysical information. Additionally, the decision and choice of methods depends on the type of remote sensing data available, the amount of in-situ historical and/or concurrent atmospheric information available.\r\nAn atmospheric correction is essential when biophysical or geophysical parameters (e.g. of water or vegetation) are going to be extracted from the remote sensing data. If the data is not corrected, the subtle differences in reflectance among the contributing image bands may be lost. This is especially relevant when biophysical information shall be compared to that of images from other dates.\r\nHowever, some cases exist where it is unnecessary to perform atmospheric correction. For example, it is not necessary for producing an image classification product from a single date of remotely sensed data. If a maximum likelihood classification is applied that uses training data with the same relative scale for the pixel values, then, atmospheric correction has little effect on the classification accuracy. The same holds true for a post-classification change detection where the classifications of the two different dates were performed independently. \r\nThe process of (absolute) atmospheric correction requires a model atmosphere and in situ atmospheric measurements acquired at the time of remote sensor data acquisition as input. In situ data can be available from other sensors on-board the sensor platform.\r\n\r\nDark Object Subtraction (DOS) is one of the most popular empirical atmospheric correction techniques. This technique assumes that a black object has a reflectance value of zero. Yet, a dark object present in a satellite image will have a value different than zero because of the atmospheric scattering. This value is then subtracted from all pixels in a given spectral band.","name":"Atmospheric correction","selfAssesment":"Completed
"},{"code":"IP1-7-2","description":"The number of spectral bands assocuates with a remote sensing system is referred to as its data dimensionality. Hyperspectral remote sensing systems such as AVIRIS ans MODIS obtain data in 224 and 36 bands, respectively. The greater the number of bands in a dataset (i.e., its dimensionality), the more pixels that must be stored and processed by the digital image processing system. Storage and processing consume valuable resources. It is necessary to reduce the dimensionality of hyperspectral data while retaining the information content inherent in the image. \r\nA method for dimensionality reduction in hyperspectral data and minimizing the noise in the imagery is the minimum noise fraction (MNF) transformation. The purpose is to minimize the noise in the imagery, i.e. to identify noise and segregate it from true information, and to colaps the useful information into a much smaller set of MNF images. The MNF transformation applies two cascaded principal components analyses.","name":"Dimensionality reduction","selfAssesment":"New
"},{"code":"IP1-7-3","description":"Sensor calibration converts the sensor’s digital numbers (DNs) to at-sensor radiance above the atmosphere. A further radiometric adjustment accounts for the viewing angle and sun angle during acquisition to transform radiance values to top-of-atmosphere (TOA) reflectance. Therefore, the process requires sensor calibration information and telemetry data that satellite image providers deliver within the metadata.\r\nDNs are raw sensor data without physical units. The sensor calibration information for converting the DNs to radiance are the calibration gain (cal_gain) and calibration offset (cal_offset) values. The sensor calibration uses linear function f(DN) = DN * cal_gain + cal_offset that multiplies the DNs of each pixel in each spectral band with their corresponding cal_gain and adds the corresponding cal_offset. The resulting at-sensor radiance image is the basis for the radiometric adjustment that uses information about the viewing angle and sun angle during acquisition to transform at-sensor radiance to TOA reflectance. \r\nSensor calibration obtains TOA reflectance and is a minimum requirement for performing band math calculations to derive spectral indices such as the normalized vegetation difference index (NDVI). Uncalibrated image data would arrive at NDVI values that are distorted because the cal_gain and cal_offset parameters for the involved spectral bands were not considered.","name":"Sensor calibration","selfAssesment":"Completed
"},{"code":"IP1-7-4","description":"As an optical remote sensing system is not perfect, noise can enter the data collection system at several points. Necessary corrections include the removal of shot noise (random bad pixels), correcting line or column drop-outs, accounting for line-start problems and radiometric correction of n-line striping caused by detector miscalibration.\r\nSAR data have global, random speckle noise. Speckle filters are designed to adapt to local image variations in order to smooth values, thus reducing speckle and enhancing lines and edges to maintain the sharpness of an image. A widely used way to reduce speckle is to apply spatial filters to the images. Typical approaches for speckle filtering include Laplace filtering for smoothing and sigma filters that preserve more of the signal with a lesser effect of smoothing.","name":"Noise reduction","selfAssesment":"New
"},{"code":"IP1-7-5","description":"Topographic correction, or topographic effects correction, aims to adjust the spectral values of an image according to effects of solar illumination differences due to the irregular shape of the terrain. Topographic slope and aspect introduce radiometric distortion of the recorded signal. Further, terrain shadow dramatically affects the brightness values of the covered pixels in an image. Topographic effects of illumination and shadow are particularly relevant in mountainous regions and in regions towards the higher latitudes of the southern and northern hemisphere. The effects appear pronounced during the winter season. \r\nTogether with sensor calibration and atmospheric correction, topographic correction is part of the radiometric correction process to obtain true reflectance values from sensor radiance. This process is necessary when using EO data for obtaining geophysical measurements. It can also benefit the accuracy of image classifications by reducing the internal variability of vegetation types, since the corrected reflectance relates better to the geometrical or biological properties of the plant than to the original reflectance.\r\nMethods for the removal of topographic effects from remotely sensed images can simply be based on band ratios that do not require additional input. Alternatively, they use digital elevation models (DEMs) as an additional input and apply sophisticated modelling of the illumination conditions. The illumination model describes various aspects of the relationship between the sensor measurement, the sun illumination, the ground reflectance and the diffuse irradiance at the surface. The model incorporates the angles between the sun position, the ground position (described by slope and aspect from the DEM), and the sensor position. Among these methods are lambertian methods and non-lambertian methods such as the bidirectional reflectance distribution function (BRDF). The BRDF, which is more suitable to the non-Lambertian properties of the observed surfaces, describes how the reflectance varies in each cover considering the angles of incidence and observation. \r\nIf achieved with a high quality, the resulting topographically corrected image appears to be illuminated evenly as if all its pixels would be part of a flat surface without the presence of any terrain differences. However, the much larger benefit than the improved appearance is the availability of pixel values that are closest to the true reflectance when compared to TOA, BOA and DN values.","name":"Topographic correction","selfAssesment":"Completed
"},{"code":"IP1-7","description":"Radiometric calibration and correction converts the sensor’s digital numbers (DNs) to radiance values and subsequently reflectance values. Additionally, the term “correction” points to the fact that radiometric measurements with satellite sensors contain error. Therefore, radiometric correction is concerned with improving the accuracy of surface spectral reflectance, emittance, or back-scattered measurements obtained using a remote sensing system. The Earth’s atmosphere, land and water are complex and can never be captured perfectly because of the limitations of remote sensing devices that lie in their spatial, spectral temporal and radiometric resolution. Therefore, error occurs in the data acquisition process and degrades the quality of remotely sensed data. The most common errors in remote sensing are radiometric and geometric. This concept is focused on the correction of remote sensing data to account for radiometric error that is to some degree systematic. Systematic errors in radiometric measurements come from the interaction of the sensed radiance with the atmosphere, the acquisition geometry in relation to the radiance source (the sun) and the Earth surface geometry (terrain).\r\nThere are several levels of radiometric calibration and correction. The first is sensor calibration that converts the DNs to top-of-atmosphere (TOA) reflectance. It converts to radiance values and further to reflectance values by accounting for the viewing angle and sun angle during acquisition. The second is atmospheric correction that converts TOA reflectance to bottom-of-atmosphere (BOA) reflectance. The third is topographic correction that converts BOA reflectance to surface reflectance. \r\nRadiometric calibration is necessary to ensure radiometric comparability of the measurements. There is a need for calibration when comparing different spectral bands within one image, e.g. for the calculation of geo-biophysical parameters with band math operations. Results from uncalibrated image data would differ from results achieved with calibrated data because the unaccounted cal_gain and cal_offset of the used spectral bands would lead to distortions. \r\nIn addition, radiometric calibration complements the geospatial comparability that is achieved with geo-referencing an image to geographic coordinates. Geo-referencing enables comparison of an image pixel to the geospatially matching pixel in another image acquired with a different sensor but with comparable resolution. Radiometric calibration enables a radiometric comparison between these two pixels’ radiance values. In case the two images are from different acquisition dates, a calculated radiometric difference would indicate change. This example shows the relevance of radiometric calibration for inter-sensor comparisons.\r\nRadiometric comparability is particularly relevant in studies that require inter-sensor comparisons, comparisons of surface features over time, or comparisons to laboratory or field reflectance data. Then the radiometric correction should cover atmospheric, solar and topographic effects. A full radiometric correction that also includes topographic correction can benefit the accuracy of image classifications by reducing the internal variability of vegetation types, since the corrected reflectance relates better to the geometrical or biological properties of the plant than to the original reflectance.","name":"Radiometric calibration and correction","selfAssesment":"Completed
"},{"code":"IP1","description":"Image pre-processing focuses on transforming the electrical signal measured by a sensor to a processing level at which pixel values can be used for the next information extraction step. Therefore, pre-processing operations involve the removal of errors encountered while collecting remotely sensed data to get as close as possible to the true radiant energy and spatial characteristics of the study area at the time of data collection. Different sensor type (optical, radar, lidar) require different processing levels\r\nThe most common image pre-processing procedures include: \r\n(1)\tRadiometric calibration involves the transformation of Digital Numbers (DN) to physical unit: radiance/reflectance. Radiometric calibration can be done before the launch of a satellite sensor, i.e. pre-launch calibration, or after launch. In the second case, the calibration is performed on-board or by comparing ground measurements with satellite radiance. Through radiometric calibration various scene illumination procedures such as sun elevation correction or earth-sun distance correction are applied. Furthermore, image noises caused by striping or line drop as happened in case of Landsat TM7 due to failure of the Scan Line Corrector (SLC) are also corrected using specialized procedures.\r\n(2)\tAtmospheric correction accounts for two main processes: scattering and absorption. Scattering represents a disturbance of the electromagnetic waves caused by rayleight scattering (caused by very small particles such as the air molecules), mie scattering (caused by aerosol particles) and non-selective scattering (dust, smoke, rain etc.). Absorption occurs when the electromagnetic energy is absorbed by the atmospheric components. Therefore, atmospheric windows have to be removed before using the satellite images in the next processing steps. Atmospheric corrections can be carried out either using simple statistical methods or complex radiative transfer based methods\r\n(3)\tGeometric correction is required to remove the distortions caused by the Earth curvature, Earth rotation, panoramic distortion due to the field of view of the sensor and the topography of the terrain. Geometrics distortions are corrected using Ground Control Points (GCP) and a Digital Elevation Model (DEM). In case of airborne images, additional distortions caused by variations in the platform altitude or velocity might occur.","name":"Image pre-processing","selfAssesment":"Completed
"},{"code":"IP2-1-1","description":"Data augmentation refers to a scheme of augmenting the observed data so as to make it more easy to analyze. An application from deep lerarning is to increase the number of input training sample images with augmented data. Examples of data augmentation techniques include horizontal flips, random crops, and principal component analysis.","name":"Data augmentation","selfAssesment":"New
"},{"code":"IP2-1-2","description":"Data imputation refers to a scheme of replacing missing values by imputed values. Imputation can be done, for example with mean, median and mode. Imputation methods can efficiently predict multiple response variables simultaneously.","name":"Data imputation","selfAssesment":"New
"},{"code":"IP2-1-3-1","description":"Gram-Schmidt is a pan-sharpening method that has been invented by Laben and Brover in 1998 and patented by Eastman Kodak. It makes use of the Gram-Schmidt orthogonalization to decorrelate the spectral bands (panchromatic, red, green, blue, etc.) and transform them into one multidimensional vector.","name":"Gram-Schmidt pan-sharpening","selfAssesment":"New
"},{"code":"IP2-1-3-2","description":"This pan-sharpening method uses PCA to transfer detailed spatial information from panchromatic band to the available multispectral bands.","name":"Principal Component Analysis (PCA)-based pan-sharpening","selfAssesment":"New
"},{"code":"IP2-1-3","description":"Pan-sharpening methods are used to enhance spatial resolution of images by merging a panchromatic image with high resolution with a multispectral image with low resolution.","name":"Pan-sharpening","selfAssesment":"New
"},{"code":"IP2-1-4","description":"Spatiotemporal image fusion methods, called also spatiotemporal downscaling methods, represent an efficient solution to generate fine-scale images at a high temporal resolution for more detailed land cover mapping and monitoring applications. Spatiotemporal image fusion methods can be classified into three categories: (1) reconstruction-based , (2) unmixing based and (3) learning-based methods.","name":"Spatio-temporal image fusion","selfAssesment":"New
"},{"code":"IP2-1","description":"Image fusion is defined as the “combination of two or more different images to form a new image by using a certain algorithm” Data fusion is a well-established research field. Image fusion methods are primarily used for improving the level of interpretability of the input data. Additionally, they can be utilized to address the problem of missing data caused by cloud or shadow contamination in satellite images time series. Image fusion can be performed at pixel-level, feature-level (e.g. land-cover classes of interest), and decision-level (e.g. purpose driven).","name":"Data fusion","selfAssesment":"Planned
"},{"code":"IP2-2","description":"Data harmonization aims to transform different datasets in such a way that they fit together, both with respect to geometry and semantics. The goal is that a user, who is using data from different authorities, shall have a unified view, where conflicts in the datasets have been removed.","name":"Data harmonisation","selfAssesment":"New
"},{"code":"IP2-3","description":"Data integration is the process of combining different geographic datasets including those derived from remote sensing data. The combined datasets can have different coverage, but they have to have the same geographic coordinates.","name":"Data integration","selfAssesment":"Planned
"},{"code":"IP2","description":"Data assimilation is a strategy to foster data integration and data harmonisation in a bi-directional way between the measured and the modelled reality. In other words, it aims to combine measurements (observations) with the understanding of the spatio-temporal properties and evolution of system’s variables or properties and model information about them. Models can be calibrated and keeping them ‘on track’ by constraining them with observations. Vice versa, observations can be validated through models. Approached as a mathematical problem, data assimilation aims at minimizing cost functions or penalize a function to ensure optimality in fitting. Equations are used to describe system parameters and the relationships among them, It is noteworthy, that models encompass information from previous measurements, experiences, and theory. While the observations are influenced by (known) properties such as precisions, etc. of the measurement devices, the robustness of models rely on the consolidated knowledge. Because uncertainties reside in all components with unknown or even undeterminable errors, the approach is usually probabilistic, including Bayesian and other related techniques. Widely used in meteorological sciences, successful data assimilation has been boosted the reliability of weather forecast , while sensitivity to errors remains. \r\nIn Earth observation, data assimilation compensates for the fact that a specific site could be observed in a variety of measurements by satellites with different sensor types, at different dates, different angular geometries and viewing directions, illumination conditions (solar time), observation frequencies, etc. In particular, for monitoring processes, measurements over time need to assure to actually measure the status of the system or object and not the divergence in observation. To overcome these divergences and converge them with the actual properties of an observed object or target class such as spectral or geospatial properties, observation modelling can be considered an important contribution from geospatial theory. this also links to class modelling or geon modelling. The synergy of a vegetation growth model and a remote sensing observation model can be exploited to improve the retrieval of geo-biophysical information. For vegetation and crop type monitoring radiative transfer modelling (RTF) is being used as an example. \r\nData assimilation can also serve in bridging the gaps between non-availabilities of EO data and other observations, to provide estimates or prediction for geographical variables, testing of hypotheses or continuous observation (monitoring). A related aspect is data imputation, i.e. filling gaps in observations e.g. by other, complementary data sets (e.g. Radar imagery in the absence of VHR data in cloudy weather conditions). Recently, these sources can also be complemented by crowd mapping and citizen science. \r\nWhen interpretation of data comes into play, such as image classification, we introduce another level of uncertainty. Thus the community seeks for rigorus classifiers based on solid spectral models, acting across sensors. Semantic enrichment of satellite data is a related strategy for reaching to interpreted data in a rigorous way. \r\nSummarizing, data assimilation comprises steps to improve the level of interpretability of the input data, by enrichment (get rid of spatial/temporal gaps), by accounting for heterogeneity (through harmonization), and by integration (combination with other data that is relevant to the application). Thereby, datasets become more comparable to each other.","name":"Data assimilation","selfAssesment":"Completed
"},{"code":"IP3-1-1-1","description":"Vegetation fraction (VF) is defined “as the percentage of vegetation occupying a pixel as viewed in vertical projection. It’s a comprehensive quantitative index in forest management and vegetation community cover conditions, and it’s also an important parameter in many remote sensing ecological models.”","name":"Vegetation fraction","selfAssesment":"Planned
"},{"code":"IP3-1-1-2","description":"Leaf area index (LAI) is the ratio between the total area of the upper leaf surface of vegetation and the surface area of the pixel in question. LAI is a dimensionless value, typically ranging between 0 (for a pixel composed of bare soil) and values as high as 6 (for a dense forest).","name":"LAI (Leaf Area Index)","selfAssesment":"Planned
"},{"code":"IP3-1-1-3","description":"Net primary production (NPP) is a measure of the inherent productivity of a region or ecological system—mainly the Earth’s production of organic matter, principally through the process of photosynthesis in plants.","name":"Net primary production (NPP)","selfAssesment":"New
"},{"code":"IP3-1-1-4","description":"Water quality variables can be derived from Earth observation (EO) data to provide essential ocean variables. They include Sea-surface temperature (SST), Sea-surface salinity (SSS) and Air-Sea Fluxes. SST controls the atmospheric response to the ocean at both weather and climate time scales. The spatial patterns of SST reveal the structure of the underlying ocean dynamics, such as, ocean fronts, eddies, coastal upwelling and exchanges between the coastal shelf and open ocean. SSS observations contribute to monitoring the global water cycle (evaporation, precipitation and glacier and river runoff). Water quality variables can be derived from EO data by using ocean colour products from optical sensors and relating them to ground truth information from in situ sensor networks.","name":"Water quality variables","selfAssesment":"New
"},{"code":"IP3-1-1","description":"Biophysical parameter retrieval is an approach in remote sensing that aims to estimate parameters which have physical meaning related to properties of living organisms. The goal is to provide quantitative results directly relating to the biophysical state, but independent of acquisition conditions and technology. Assessment of vegetation status is a key motivation for this, because through plant respiration and photosynthesis, vegetation is critical for modelling terrestrial ecosystems and energy cycles in environmental studies. \r\nImportant parameters describing canopy structure include leaf area index (LAI), green cover fraction (fCover), fraction of absorbed photosynthetically active radiation (fAPAR), plant height, biomass and leaf angle distribution. At leaf biochemical level, leaf chlorophyll/water, fuel moisture and leaf pigmentation content are used.\r\nVisual inspection can provide a first assessment of plant status. For detailed measurements of biophysical parameters, mostly destructive methods have been used. Chemical measurement techniques on leaf samples can measure pigment concentrations very accurately, but are time consuming and only use very limited samples. \r\nMuch more extensive data can be collected using earth observation imagery. These range from large scale spaceborne observations with high frequency at coarse resolution to dedicated UAV flights which can offer spectral information of individual plants. Radar and LiDAR acquisitions, which are insensitive to weather conditions, now complement optical observations. \r\nMethods to retrieve the parameters from remote sensing data fall into two main categories. Statistical models empirically match data to a biophysical variable. Univariate techniques use a single quantity derived from the data, usually a vegetation index whereas multivariate techniques link a combination of measurements at different wavelengths to one or more biophysical parameters.\r\nPhysically-based modeling is an alternative approach which uses advanced radiative transfer models to describe the transfer and interaction of radiation inside a leaf or canopy based on robust physical, chemical, and biological processes. They compute the interaction between solar radiation and plants and provide as such a better understanding between biophysical variables and reflectance characteristics. Good examples are Leaf optical models such as PROSPECT and LIBERTY which simulate leaf optical properties by absorption and scattering coefficients. Canopy reflectance models simulate canopy reflectance as a function of a complex description of plant structural and radiometric attributes to develop a quantitative understanding of remote sensing information.","name":"Biophysical and geophysical parameters","selfAssesment":"Completed
"},{"code":"IP3-1-2-1","description":"This spectral index is calculated using the following formula: SAVI = [(NIR-Red)/(NIR+Red+L)]/(1+L), where L can be, for example, 1 in area with no vegetation or 0 in area with dense veegtaion. It is used to minimize the influence of the soil brightness from the vegetation indices that are based on red and near-infrared wavelengths.","name":"Soil-adjusted Vegetation Index (SAVI)","selfAssesment":"New
"},{"code":"IP3-1-2-2","description":"This spectral index is calculate using the following formula NDSI = (green-SWIR)/(green+SWIR). It is the most popular index used to identify snow cover due to the fact that snow reflects visible wavelength stronger than middle-infrared wavelengths.","name":"Normalized Difference Snow index (NDSI)","selfAssesment":"New
"},{"code":"IP3-1-2-3","description":"Leaves, when healthy and vigour show a characteristic green colour. This visual effect evident to humans is caused by the co-existence of two evolutionarily facts: the specific interaction of the chlorophyll pigment in living leaves to the visible spectrum (VIS, 400-700 nm wavelength) of light emitted by the sun and the sensitivity of our human eye to the same sub-spectrum. According to fundamental physical laws of radiation (Stefan Boltzmann law of blackbody radiation and Wien’s displacement law), the VIS sub-spectrum corresponds to the radiation maximum of the sun, a hot blackbody with a surface heat of about 6000 K. Living leaves are structured in specific layers exhibiting characteristic interaction with light. The chloroplasts located in the so-called palisade layer, make use of the blue and the red part of sunlight for photosynthesis, the unique process of transforming light to create energy (carbohydrates) from water and carbon dioxide. This leads to the specific behaviour of leaves to absorb large portions (up to 90%) of the blue and red part of the electromagnetic spectrum and reflect nearly 100% of the green light. The peak reflectance in green light makes leaves (and plants in general) appear in green colour in our visual perception. \r\nA second, by no means less characteristic, feature of leaves is the specific response to near infrared (NIR, at around 700 nm wavelength) light in the mesophyll tissue (transmittance, scattering and reflectance). Only a small fraction of NIR is being absorbed. \r\nThis combination of two specific spectral characteristics, the absorption in VIS (red colour) by chlorophyll a in palisade layers, and the reflectance of NIR in the spongy tissue, makes the spectral profiles of plants and vegetation exhibiting a very characteristic shape, the so-called red edge. This absorption edge between red and NIR light is sharper for higher intensity green reflectance and brighter green tones (such as grassland or bright deciduous forest) than for less intensive reflectance and darker tones (coniferous forest). \r\nThe red edge may shift for the same vegetation type due to plant maturity or plant stress. This effect we call the red shift. The red shift is sensitive to crop maturity (headed stage) and may indicate harvesting time. Notably, there is also a blue shift, indicating green plants’ exposure to geochemical stress, which causes the absorption spectra to shift towards shorter wavelengths. \r\nPlants usually do not appear in isolation but form a canopy with a certain degree of coverage (e.g., crown closure in forests), and a certain part of understorey or soil per area unit. The resulting canopy reflectance is therefore a spectral mix of soil and vegetation (or even different types of vegetation) and generally lower than the reflectance of a pure vegetation sample under lab conditions. \r\nTo capture most of these plant-typical spectral characteristics, the so-called normalised difference vegetation index (NDVI) was developed. NDVI is an arithmetic band combination of red and NIR bands in a normalised value range. \r\nThe NDVI is calculated as:\r\nNDVI=((NIR-R))/((NIR+R))\r\nThe (hypothetic) value range of the NDVI is [-1 | +1]. Under real-world conditions, the NDVI ranges from values of around -0.2 to 0.6 or 0.7. To discriminate principal land cover classes such as water, non-vegetation (soil, sealed, etc.) and vegetation the following thresholds in the continuous range are used: \r\n\tNDVI < ~ 0: water\r\n\t~ 0 < NDVI < ~ 0.2: non-vegetation (soil, sealed surfaces, bare rock, etc.)\r\n\t~ 0.2 < NDVI: vegetation.\r\nNotably, these class limits are just a very rough approximation (indicated by the ~ sign), due to the mixed pixels effect, canopy reflectance, the abundance of water plants and suspending particles, and the illumination effect of specific atmospheric or topographic conditions. \r\nWe can use the NDVI to generally mask out vegetation from other land cover types and, more specifically, to indicate vegetation vigour and health. It is also suitable for monitoring plant phenology as the relationship between vegetative growth and the (changing) conditions of the environmental conditions. A range of variations has been suggested, enhancing one or the other mathematical or statistical behaviour of the index, or making it even more sensitive to specific plant behaviour. A well-known example is the enhanced vegetation index (EVI).","name":"Normalized Difference Vegetation Index (NDVI)","selfAssesment":"Completed
"},{"code":"IP3-1-2","description":"Spectral indices are calculated using a mathematical equation that is applied on two or more spectral reflectance bands of the image. The calculated spectral index is a ‘new’ image that highlights particular land surface features or properties e.g. vegetation, soil, water, better than the original input bands. The spectral indices vary from simple spectral ratioing of two bands to more complex combinations of multiple bands. Spectral indexes are developed based on the spectral properties of the object of interest. For example, spectral indices dedicated to the vegetation condition are developed based on the principle that the healthy vegetation reflects strongly in the near-infrared spectrum while absorbing strongly in the visible red. These properties are used to develop more complex spectral indexes for monitoring vegetation condition, phenology parameters, i.e. Normalised Difference Vegetation Index (NDVI), Advanced Vegetation Index (AVI). The spectral indices calculated using the short wave infrared spectral bands are more sensitive to vegetation water content and spongy mesophyll structure in the vegetation canopy thus are used to assess the vegetation decline, moisture that is particularly useful for drought monitoring (e.g. Normalized Difference Water Index (NDWI) or Normalized Difference Moisture Index – NDMI). The water-related spectral indices are widely applied in agricultural and ecological applications including surface water body characteristics, vegetation water stress, soil water content assessment and wetlands monitoring. The combination of near infrared and short wave infrared spectral bands is also used to detect burned area and to monitor the vegetation recovery (e.g. Normalised Burned Ratio – NBR). There are other spectral indices dedicated to snow cover and glacier monitoring, which are developed based on visual green and short wave infrared spectral bands. Snow reflects most of the radiation in the visible bands whiles absorbing in the short wave infrared.","name":"Spectral indices","selfAssesment":"Completed
"},{"code":"IP3-1","description":"The term band maths denotes the arithmetic combination (addition/subtraction, multiplication/division) of two or more spectral bands in an early stage of image analysis. The resulting scalar values represent the spectral behaviour in different bands in a single value; such procedure makes particular sense, when spectral behaviour varies in those bands (like the red edge of vegetation spectra in the NIR band). \r\nThere are several reasons for applying band maths when working with multispectral imagery: (1) A single range of values rather than multiple bands is easier to comprehend and interpret; (2) Thresholds or class limits are applied more intuitively in a grey scale image; (3) Indices can be easily calculated and compared across different sensors; they are implemented as standard routines in many software environments as well as cloud processing environments (such as Google Earth Engine or the Proba-V exploitation platform)\r\nOut of the many possible, literature suggests a few arithmetic band combinations as application-specific quasi-standards. Band ratios (e.g. red band divided by NIR band) and indices (such as the normalised difference vegetation index, NDVI) belong to this group. Indices have the advantage over simple ratios in constraining the value range, e.g. [-1 | 1]. Designated to indicate specific land cover types (such as water index, snow index, soil index, etc.) such indices are widely used as a basis for operational information products. Another index is the normalised burn ratio (NBR) which relates near infrared and short-wave infrared reflectance to measure burn severity taking into consideration the increasing of SWIR reflectance in the course of a fire. \r\nPre-processing such as dark object subtraction and radiometric or even atmospheric correction is a key requirement prior to indexing. The coding in digital numbers (DN) is a function of the sensitivity and the radiometric resolution of the sensor. The actual recording depends on atmospheric conditions (additional brightness, haze, etc.). Therefore, in order to make the resulting values comparable among different types of sensors and scenes, radiometric correction is mandatory, converting DNs into radiances, i.e. true reflectance values as physical measurement units. \r\nTwo advanced examples of band maths beyond rationing are the perpendicular vegetation index (PVI) and the tasselled cap (TC) transformation. PVI is based on the assumption that vegetation pixels are generally separable from soil pixels (at least after unmixing or for pure pixels), and thus pixel values are located in a perpendicular direction from the soil line in a NIR/red feature space. The Euclidean distance from the soil line, determined by Pythagorean triangle, yields the PVI. Tasselled cap instead rests on the notion of a cap-like histogram shape when plotting pixels on a brightness vs. greenness plot, with the latter determined by linear combinations of VIS and NIR bands, along with empirically determined coefficients. TC 1 as a weighted sum corresponds to brightness, TC 2 to greenness, TC 3 to yellowness, sometimes referred to as wetness. A fourth TC called nonesuch likely corresponds to noise and atmospheric disturbance effects in the image.","name":"Band maths","selfAssesment":"Completed
"},{"code":"IP3-10","description":"Semantic enrichment is the process of adding semantic metadata elements to improve the content-based image retrieval. These semantic metadata elements enable the explicit specification of the content of the images stored in the remote sensing databases.","name":"Semantic enrichment","selfAssesment":"New
"},{"code":"IP3-11-1","description":"Different types of changes are investigated using remotely sensed data: (i) abrupt changes, such as the changes caused by a fire or flooding, and (ii) gradual changes such as urban growth. Besides these kinds of changes, remote sensing community differentiates between transitional changes and conditional changes. Transitional changes refer to a major change of land surface such as conversion of forest to pasture or the expansion of mangroves into the surrounding water. Conditional changes refer to the change in condition at the surface such as water stress in an agricultural field, forest degradation caused by pest. \r\nIn the past, many remote sensing studies used two images to detect different types of changes such as deforestation, land cover change or change in the health or condition of the vegetation (e.g. pest infestation). Meanwhile, satellite image time series are used to assess the change. Time series analysis allows for monitoring more subtle changes and for providing temporal patterns of change. In this way, the timing of changes and drivers of change can be easily identified. \r\nDifferent methods are being used in change detection studies. There are studies that analyze individual images available in the investigated time series to map the target class/phenomena/events at the time when images were collected and to identify the changes: e.g. mapping the mangroves extent on an year basis and measuring it to identify changes. Alternative studies search for breaks in time series for detecting changes. The breaks are used to segment the time series into before and after changes periods which are further classified using one of the existing supervised or unsupervised classification methods (K-means, fuzzy k-means, Random Forest, Support Vector Machine etc.).","name":"Change detection","selfAssesment":"Completed
"},{"code":"IP3-11-2","description":"The (data)cube model for analysis of time series of earth observation raster data, represents the dataset as a multidimensional array with one or more spatial or temporal dimensions. Scalar values in the cube can be selected (or ‘filtered’) and processed based on dimension labels. This allows analysis algorithms to be thought of as a set of operations on the multidimensional array. Technologies that support this model allow to efficiently implement such algorithms.\r\nSome possible operations on a multidimensional cube include: filtering, ‘reducing’ all values along a dimension, ‘aggregating’ values in a dimension, or transforming all values along a dimension. Generally speaking, these operations require the selection of a subset of the data on which work is to be done. This allows implementing the operations efficiently even on very large datasets.\r\nIn comparison to file-based processing, most technologies that support cube-based time series analysis reduce implementation overhead, as the user does not need to read and write individual files, also more complex aspects like distributed computing for parallelization can be hidden in a cube based approach. So a cube based approach can also be thought of as an abstraction layer that effectively reduces the need for specific IT-related skills when analyzing earth observation timeseries.\r\nMultiple initiatives support cube based analysis. Some common features include a programming API, often using the Python programming language. Some tools are only accessible as web services, while others can also run locally (on a small dataset). This diversity is still a drawback, as users would need to familiarize themselves with different systems. Initiatives such as openEO try to address this by providing a common API.","name":"Cube-based time series analysis","selfAssesment":"Planned
"},{"code":"IP3-11-3","description":"Dynamic Time Warping (DTW) works by comparing the similarity between two temporal sequences and finds their optimal alignment, resulting in a dissimilarity measure. In the case of remote sensing data, DTW can deal with temporal distortions, and can compare shifted evolution profiles and irregular sampling thanks to its ability to align radiometric profiles in an optimal manner","name":"Dynamic Time Warping","selfAssesment":"Planned
"},{"code":"IP3-11","description":"Satellite image time series analysis plays an important role in different domains including vegetation dynamics monitoring, estimating crop yields, discriminating between different land cover classes, exploring human-nature interactions, monitoring land cover change, assessing environmental threats, or evaluating ecosystems-climate feedbacks or urbanization.\r\nTime series analysis requires high quality time series which are reconstructed by removing any source of contamination such as clouds, cloud shadows, or scan-line corrector (SLC) gaps of the Enhanced Thematic Mapper plus sensor (ETM+) on Landsat 7. Removed pixels are usually filled in with data predicted from a different date (temporal interpolation), nearby pixels (spatial interpolation) or from both (spatiotemporal interpolation). Different methods are available for screening and masking out clouds and shadows in satellite images including mono-temporal methods such as Function of mask (Fmask), or multitemporal mask (e.g. Tmask algorithm). Fmask is used by the United States Geological Survey (USGS) to produce a cloud mask layer of Landsat images. European Space Agency (ESA) is using Sen2cor processor to produce Level 2A Sentinel-2 data with a shadow and cloud shadow mask. All images used in the time series have to be co-registered, i.e. they align as closely as possible. \r\nTime series analysis is used to (1) investigate various surface properties such as evapotranspiration, land surface temperature, (2) map the cover of the Earth surface (e.g. land cover mapping, crop mapping etc.), (3) detect different type of changes such as abrupt changes (fire event) or gradual changes (urbanization), and (4) study the trends.\r\nTo map surface features from satellite image time series, numerous studies make use of the vegetation phenology extracted from a spectral-temporal trajectory of a given spectral vegetation index such as the normalized difference vegetation index (NDVI) or enhanced vegetation index (EVI). Several metrics can be used to characterized vegetation phenology: metrics of greenness and metrics of time. The metrics of greenness include the minimum and maximum spectral vegetation indices, their difference or amplitude, seasonally averaged greenness etc. The metrics of time include start and end of the growing season, duration or length of the growing season or the timing of maximum greenness. Changes, on the other hand, are identified either by investigating two images acquired at two different points in time or by identifying breaks in a dense (annual or multi-annual) satellite image time series.","name":"Time series analysis","selfAssesment":"Completed
"},{"code":"IP3-12-1","description":"Remote sensing-derived products such as land-use and land-cover maps contain error. The error accumulates as the remote sensing data are collected and various types of processing take place. An error assessment is necessary to identify the type and amount of error in a remote sensing-derived product.","name":"Error propagation","selfAssesment":"New
"},{"code":"IP3-12-2","description":"The precision of a measurement system, related to reproducibility and repeatability, is the degree to which repeated measurements under unchanged conditions show the same results.","name":"Precision","selfAssesment":"New
"},{"code":"IP3-12","description":"Uncertainty is the result of the lack or imprecision of our knowledge about the world. A proposition is uncertain if we do not know whether it is true or not. In most circumstances we describe a proposition as uncertain when the reason we do not know whether it is true is that we do not possess complete and accurate knowledge about the state of the world.","name":"Uncertainty","selfAssesment":"New
"},{"code":"IP3-13-1","description":"The main elements of visual interpretation are: tone, shape, size, pattern, texture, shadow, , association. Tone refers to the relative brightness or colour of objects in an image. It depends on the spectral properties of an object. Variation in tone allows to distinguish elements of different shape, texture and pattern. Shape refers to the general form, structure, or outline of individual objects. Straight and sharp edge shape represent typically the anthropogenic features i.e. urban or agriculture, the natural features like rivers, wetlands are more irregular in shape. Size of objects in an image is a function of scale and it depends on the spatial resolution of the image. The assessment of the size of the target’s object in relation to other objectives as well as an absolute size of the object are the important part of the interpretation. Pattern refers to the spatial arrangement of objects, i.e. network of street and houses in an urban area, orchards with the line of trees. Texture refers to the arrangement of frequency of tonal variation in particular areas of an image. Rough texture would have very large, coarse tonal variation (e.g. forest canopy), whereas smooth texture very little tonal version (e.g. uniform, homogenous surfaces). It depends on the size, shape and pattern of objects. Shadow depends on the scale and spatial resolution of an image. Shadow is useful to measure the height of an object, to distinguish the coniferous from broadleaf trees. In the radar imagery is useful for identifying topography and landforms. Association refers to the relationship between objects and features in proximity to the target interest.","name":"Elements (cues) of interpretation","selfAssesment":"Completed
"},{"code":"IP3-13-2","description":"Information-as-data-interpretation considers information as the outcome of the cognitive process of vision that reconstructs a scene from an image.","name":"Information-as-data-interpretation","selfAssesment":"New
"},{"code":"IP3-13-3","description":"An image interpretation key is simply reference material designed to permit rapid and accurate identification of objects or features represented on aerial images.","name":"Interpretation keys","selfAssesment":"New
"},{"code":"IP3-13","description":"Interpretation is the processes of detection, identification, description and assessment of an object and pattern imaged. Visual interpretation is the ability of a human operator to identify an object through the data content in an image / photo by combining several elements of interpretation. The image characteristics used in the interpretation process are: shape, size, tone/colour, texture, shadow, neighbourhood and pattern. The importance of the image characteristics varied according to the spatial resolution of the images and the properties of the feature of interest. The interpretation can be performed on the single image or between several images acquired at different time, which result in the differentiation of the temporal changes. The principle of the image interpretation is the process of delineating (digitalizing) the outlines of the objects, features on the image. It is performed “on-screen” using a GIS software. The process of visual interpretation is time consuming and requires a skilled interpreter with knowledge of the study area. Even though, the image interpretation supports many applications in for example selection of the training and verification data sets for image classification and accuracy assessment.","name":"Visual interpretation","selfAssesment":"Completed
"},{"code":"IP3-2-2-1","description":" ","name":"Information-as-thing","selfAssesment":" "},{"code":"IP3-2-2","description":"Information theory answers two fundamental questions in communication theory: what is the ultimate data compression (answer: the entropy H) and what is the ultimate transmission rate of communication (answer: the channel capacity, C). For this reason, it is considered that information theory is a subset of communication theory.","name":"Information theory","selfAssesment":"New
"},{"code":"IP3-2-3","description":"Keypoints are objects (or locations) on the ground that reveal locally invariant features in images and therefore are easily detectable by automatic algorithms. Methods for this process employ scale-invariant feature transform (SIFT) algorithms for the automatic detection of geospatial objects.","name":"Keypoint detection","selfAssesment":"New
"},{"code":"IP3-2","description":"Image understanding is part of computer vision. Computer vision is an interdisciplinary field that deals with how computers can be made to gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to automate tasks that the human visual system can perform.","name":"Computer vision in EO","selfAssesment":"New
"},{"code":"IP3-3-1","description":"A Digital Elevation Model (DEM) is a digital raster (or grid) representation of elevation values of land surface shapes and features, where each grid cell takes a single elevation value with reference to a certain vertical datum. A DEM can be global, regional or local in scope, and can be used to characterize the dry land surface (topography) or submerged surfaces (bathymetry). Since a DEM cannot contain information of shapes and features under overhanging structures, it is often referred to as 2.5D instead of truly 3D. \r\nA digital elevation model is an overarching term for either a digital surface model (DSM) or digital terrain model (DTM). A DSM includes elevations of surface features such as trees, buildings, bridges and artificial objects such as poles, power lines, cars etc., and thus contains always the highest elevations of any feature for any given raster cell. A DTM does not include such features but reflects the elevation of bare land surface shapes, excluding elevated or overhanging features.\r\nDEMs can be obtained using active or passive measurements. Active measurements involve the generation of electromagnetic signals towards a surface and timing the reception of the (return) signal(s). This can be achieved through laser scanning (LiDAR) using visible or infrared light pulses for bathymetric or topographic measurements respectively, radio waves (SONAR) used in bathymetric measurements, or microwaves (synthetic aperture radar, SAR) used in topographic mapping. The most widely known active remotely sensed global DEM is derived from the Shuttle Radar Topography Mission (SRTM) obtained by a SAR mounted on the space shuttle Endeavour, offering 30 m resolution with a vertical accuracy typically between 5 and 20 m, covering 80% of Earth’s surface.\r\nPassive measurements detect reflection of sun light, or energy radiated from the surfaces. Their distance to the detector can then be inferred from the measurement of angles. Historically, line scanning imagers were used, but nowadays, these are replaced by acquisitions of overlapping 2D frame images. On the images, corresponding land surface features are detected which act as tie-points. The distance between the sensor and the tie-points is calculated in a process called photogrammetry. The most widely known spaceborne passive remotely sensed global DEM is derived from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data onboard the Terra satellite. It offers similar resolution and accuracy compared to SRTM, but with 99% coverage. \r\nOnly LiDAR can generate both accurate DSMs and DTMs from the same data acquisition, by using multiple returns from a single emitted pulse. All other techniques generate DSMs, from which elevated features can be identified and filtered out in postprocessing to create DTMs, however with typically lower accuracy and more artefacts.","name":"DEM generation","selfAssesment":"Complete
"},{"code":"IP3-3-2","description":"DSM can be produced automatically from stereo satellite scenes, from satellite sensors such as GeoEye, IKONOS, SPOT-5, Terra-ASTER etc. The DSM can also be provided from stereo digital aerial photography at various resolutions, depending on the quality and scale of the aerial photography. The quality of the automatic generated DSM is substantially improved if ground measurements from GPS are incorporated in the DSM stereoscopic model.","name":"DSM generation","selfAssesment":"New
"},{"code":"IP3-3","description":"Stereo pairs of optical satellite images with the support of ground control points provide a basis for cross-stereo analysis for generating Digital Surface Models.","name":"Cross-stereo analysis","selfAssesment":"New
"},{"code":"IP3-4-1-1","description":"The goal of filtering is to remove unnecessary components from images (e.g., noise), while emphasizing the necessary ones. In the context of spatial aggregation, low pass filters aim at removing sharp transitions in the image intensities (high spatial frequencies) and thereby focus the information content of the image on a coarser scale level.","name":"Filtering","selfAssesment":"New
"},{"code":"IP3-4-1-2","description":"Gridding is the technique used to generate a uniform raster grid with one value for every cell in the raster. The values of the raster cells can represent different attributes such as mean, max or min of all Normalized Difference Vegetation Index (NDVI) values measured within a particular cell.","name":"Gridding","selfAssesment":"New
"},{"code":"IP3-4-1","description":"Spatial aggregation produces images of coarser resolution (grouping pixels in a grid of coarser resolution and calculating mean values) or of coarser scale (by filtering with low-pass filters). Thereby it is a form of generalization that may improve classification results. Spatial aggregation can be applied after classification to get rid of the salt-and-pepper effect.","name":"Spatial aggregation","selfAssesment":"New
"},{"code":"IP3-4-10-1-1","description":" ","name":"Gradient boost","selfAssesment":" "},{"code":"IP3-4-10-1","description":" ","name":"Feature engineering","selfAssesment":" "},{"code":"IP3-4-10","description":"Classification processes use features, also known as predictor variables, for discriminating between classes. A feature is an individual measurable property or characteristic of a geographic phenomenon being observed. Features in Earth observation include the individual bands of images and further properties derived from the image data. For example, the single band of a panchromatic image represents a feature that allows distinguishing between pixels of darker and lighter reflectance. Multispectral images have more bands and thereby enable the differentiation between classes by more features. This means, if two classes are different from each other in several of their properties, it becomes easier to distinguish them. The set of features used in a particular classification comprise the feature space where each feature represents one space dimension. \r\nWith an increased number of (uncorrelated) features it becomes possible to increase the number of classes that can be separated. For example land cover classifications have a large number of classes. For identifying suitable bands for optical EO satellites, the spectral signatures of all the target classes have to be analysed to identify in which bands they are separable from other classes. Classes like soil, water, and vegetation have spectral signatures that differ in particular in the blue, green, red, and infrared bands of the electromagnetic spectrum. These bands are present in virtually all multispectral sensors used for land cover classification. \r\nGeographic phenomena can be differentiated not only by their reflectance in different bands. Beyond multispectral features, the classification may include image derivatives like derived spectral indices, principal components, or filtered bands (convolution layers). Object-based image analysis also uses spatial features, i.e. distance and proximity features, planar geometric features and topological features.","name":"Classification features and feature space","selfAssesment":"Completed
"},{"code":"IP3-4-2-1","description":"Bayes’s theorem is an extremely powerful means of using information at hand to estimate probabilities of outcomes related to the occurrence of preceding events. Bayes' Theorem uses a priori (subjective) and conditional probabilities to calculate the probability of an uncertain event occurring. A priori probabilities represent what the modeler believes, before testing, to be the probability of an event occurring. Conditional probabilities are probabilities that other events occur in conjunction with the original event.","name":"Conditional probability","selfAssesment":"Planned
"},{"code":"IP3-4-2-2","description":"Maximum likelihood classification uses the training data for estimating means and variances of the classes, which are then used to estimate the probabilities. This method considers not only the mean, or average, values in assigning classification but also the variability of brightness values in each class.","name":"Maximum likelihood","selfAssesment":"Planned
"},{"code":"IP3-4-3-1","description":"The Land Cover Classification System (LCCS) was developed by FAO to provide a consistent framework for the classification and mapping of land cover. Its main objectives were to overcome the rigidity of a-priori land cover classifications, which in many practical situations do not allow easy assignment into one of the pre-defined classes and are therefore not very suitable for mapping. LCCS instead opted for an approach based on two main phases. The first phase is an initial ‘Dichotomous Phase’, in which eight major land cover types are defined: (1) Cultivated and Managed Terrestrial Areas, (2) Natural and Semi-Natural Terrestrial Vegetation, (3) Cultivated Aquatic or Regularly Flooded Areas, (4) Natural and Semi-Natural Aquatic or Regularly Flooded Vegetation, (5) Artificial Surfaces and Associated Areas, (6) Bare Areas, (7) Artificial Waterbodies, Snow and Ice, and (8) Natural Waterbodies, Snow and Ice. The Dichotomous Phase is followed by a subsequent ‘Modular-Hierarchical Phase’, in which land cover classes are created by the combination of sets of pre-defined classifiers, which are different for each of the eight major land cover types. For example, common classifiers used for (semi-) natural terrestrial vegetation types are Life Form, Cover, Height, Macropattern. For aquatic or regularly flooded natural and semi-natural vegetation, water seasonality is an indispensable classifier. LCCS offers several advantages from a conceptual point of view. LCCS is a real a priori classification system in the sense that, for the classifiers considered, it covers all their possible combinations. The classification is also hierarchical and the more classifiers used, the greater the detail of the defined land cover class. The classes derived from the proposed classification system are all unique and unambiguous, due to the internal consistency and systematic description of the classes. LCCS is designed to map at a variety of scales, from small to large. From a practical viewpoint LCCS offers several advantages: (1) easy incorporation into GIS and databases, (2) allows flexible response to information available in a given area, project budget and time constraints, (3) unlinks the field data collection from the interpretation process.","name":"Land cover classification system (LCCS)","selfAssesment":"Completed
"},{"code":"IP3-4-3","description":"Long-term monitoring of land cover and land use are particularly relevant for land ecosystem monitoring. Therefore, baseline datasets are necessary that allow assessing changes of land cover and land use where the class definitions remain consistent over time. Accordingly, classification schemes have been established that adhere to taxonomically correct definitions of classes of information organized according to logical criteria. If hard classification is to be performed (i.e. without fuzzy class boundaries), the classes in the classification system should normally be mutually exclusive, exhaustive, and hierarchical. Mutual exclusive classes have no taxonomic overlap and assign a land cover patch to a single class. An exhaustive classification scheme is able to cover the area of interest comprehensively and leaves no land cover patch unassigned. A hierarchical system allows combining sub-classes into higher-level categories.\r\nFrom a remote sensing classification perspective, it becomes clear that a classification scheme consists of information classes defined by human beings. Conversely, spectral classes are those inherent to EO data. An analyst must identify spectral classes and label them as information classes that satisfy bureaucratic (or scientific requirements). Additionally, the advantage of using established classification schemes is that their use in scientific studies and applications produces results that are comparable to other studies and suitable for sharing of data.\r\nEstablished classification schemes include: CORINE land cover (CLC), Land cover classification system (LCCS), American Planning Association land-based classification standard, United States Geological Survey land-use/land-cover classification system for remote sensor data, U.S. Department of the Interior Fish & Wildlife Service classification of wetland and deep water habitats of the United States, U.S. National Vegetation Classification system (NVCS), International Geosphere-Biosphere Program IGBP Land cover classification system.","name":"Classification schemes (taxonomies)","selfAssesment":"Completed
"},{"code":"IP3-4-4","description":"Unsupervised methods are defined as the identification of natural groups, or structures, within existing data. Clustering requires only the number of to-be generated classes as an input parameter and assigns spectrally defined classes to an image.","name":"Clustering (unsupervised)","selfAssesment":"New
"},{"code":"IP3-4-5-1-1","description":" ","name":"Inference engine","selfAssesment":" "},{"code":"IP3-4-5-1","description":"A production system performs automatic transformation of remote sensing imagery into useful information (such as biophysical parameters, categorical maps etc). An example can be a preliminary pixel-based classifier that works top-down (deductive, physical model-driven, prior knowledge-based) and arrives at preliminary classes for each pixel of an image. Such a production system does not require interaction of an operator. The process makes use of a decision tree that encodes the prior knowledge for assigning pixels to a class.","name":"Production systems","selfAssesment":"New
"},{"code":"IP3-4-5","description":"Decision trees is a data mining technique used in different disciplines including Remote Sensing.\r\nThe major advantages of decision tree methods include the ability to capture interactions between the variables used for modeling, the understandability of the produced models (trees) and their efficiency. Input data for decision trees are either a large number of examples or a large number of variables. This is important in the context of pixel-based classification in geographical information systems, where very large numbers of spatial units/points need to be classified. \r\nDecision tree consist of nodes, branches and leaves. Each node contains a test on an attribute, out of which branches are created with a grouped subset of data depending on the results of the node test. The resulting subsets will have as homogeneous values of the class as possible. This is done in a hierarchical manner dividing the training dataset until it reaches rules set at the start- the lowest number of training data within each leaf or set level of confidence.\r\nFor discrete attributes, a branch of the tree is typically created for each possible value of the attribute. For continuous attributes, a threshold is selected and two branches are created based on that threshold. This also determines whether the decision tree is called a classification or a regression tree: if we are dealing with classification (discrete target) or a regression problem (continuous target), respectively.\r\nDecision trees are derived from data only. As such, they represent the data driven or empirical approach, which is more appropriate when we have plenty of high-quality (reliable and relevant) measured data and little knowledge about the studied system, for instance what is the spectral response of each land cover class needed for classification.\r\n\r\nAn important mechanism used to improve decision tree performance is tree pruning. Pruning reduces the size of a decision tree by removing sections of the tree (subtrees) that are unreliable and do not contribute to the predictive performance of the tree.\r\nThe pruning reduces complexity of the tree and helps to achieve better predictive accuracy by the reduction of over-fitting and removal of sections of the tree that may be based on noisy or erroneous data. Depending when the pruning is done during the creation of the tree, it is called pre- or post-pruning.\r\nThe CART (Classification And Regression Trees) system is the first widely known and used system for learning decision trees. After that, notable ones are the C4.5 system for learning classification trees (or J4.8 as called within WEKA software), succeeded by C5.0.","name":"Decision trees","selfAssesment":"Completed
"},{"code":"IP3-4-6-1","description":"Along with developing deep learning methods, Convolutional Neural Networks (CNNs) have emerged as a powerful tool by providing both remarkable performances in image processing and the ability to work in a wide variety of applications in the vision community. In the past few years, biologically inspired CNNs have emerged and proven effective in the image processing field, from social media to precision medicine and robotics. A beneficial characteristic of CNNs is data processing in multiple arrays and automatic feature extraction ability, which have received acknowledgment in the geoscience and remote sensing community.\r\nMoreover, the inherent characteristics of CNNs, such as local connectivity and weight sharing, allow this deep learning method to tackle the drawbacks of artificial feature extraction, by considering the 2-D structures and reducing network parameters using convolutional filters. CNN-based models have benefited from the recent exponential advances in imaging technologies, such as the availability of various image types (optical, RADAR, temperature and microwave radiometer, altimeter, etc.) with complex characteristics (high dimensionality, multiple scales, and nonstationary). CNNs are composed of a set of blocks that make them particularly suitable for image analysis. The multiple layers of operations, such as convolution, pooling, and nonlinear activation functions, allow for a hierarchical extraction of high-level abstract features. Accordingly, CNNs have been successfully used in image preprocessing, scene classification, pixel-based classification, image segmentation, and object detection. CNNs have been used in numerous studies, for instance: to improve image classification results to extract buildings and non-building regions automatically; to detect areas of build-up; to assess the quality of OpenStreetMap data; to detect oil spills, ships, and icebergs. Although CNNs can be considered newly introduced algorithms in geoscience and remote sensing, they are now clearly among the top performers in most of the applications.\r\nDespite this progress, the study of CNN-based approaches in the field of remote sensing and geoscience is currently at its beginning stages, and there is still much potential for new developments. In this perspective, the design of new network architectures for specific tasks, the generation of large-scale datasets for network training, the integration of conventional techniques for various remote sensing data, the advancement and analysis of existing networks concerning their architectures, optimization techniques, and the regularization strategies are still open topics, which are in close relation with each other and should be jointly considered.","name":"Convolutional neural networks (CNNs)","selfAssesment":"Completed
"},{"code":"IP3-4-6","description":"Deep learning (DL), as a subfield of artificial intelligence (AI) and machine learning (ML), is the fastest-growing trend in data analysis and is regarded as a breakthrough. Over the past few years, there has been an ongoing shift toward using DL methods in different applications, mainly due to the increasing data accessibility and computational processing power. DL models characterized by neural networks are learning methods with multiple levels of representation that learn the semantic and discriminative features in a sequential bottom-to-up manner from the data. They are composed of several levels of non-linear modules that each modify the representation at a lower level into a higher or slightly more abstract level. As such, very complex functions can be learned without depending on human-crafted features.\r\nDL has been used in several research fields, such as speech recognition, stereo vision, medical image recognition, remote sensing, time-series analysis, biomedicine, agriculture, and geosciences. One of the limiting factors of using DL models is that they require significant amounts of training samples compared to conventional ML methods To date, several DL architectures have been introduced, of which the stacked autoencoder, convolutional neural network, generative adversarial network, deep belief network, and recurrent neural network have become mainstream. DL techniques have had significant successes in several fields, which have been widely accepted as challenges in recent decades. Moreover, by growing big data and their applications in practical productions and developed time-efficient networks or public online free or commercial cloud computing platforms, such as Google, Amazon, Microsoft, and IBM, much more attention will be paid to develop new DL networks for the practical projects.","name":"Deep learning","selfAssesment":"Completed
"},{"code":"IP3-4-7-1","description":"The RF classifier is an ensemble classifier that uses a set of Classification and Regression Trees (CARTs) to make a prediction The trees are created by drawing a subset of training samples through replacement (a bagging approach).","name":"Random forest (RF)","selfAssesment":"New
"},{"code":"IP3-4-7-2","description":"In machine learning, support vector machines (SVMs) are supervised non-parametric statistical learning techniques with associates learning algorithms that analysze data used for both classification and regression analysis. SVM algorithm was originally designed for binary classification. The SVM is based on the main hypothesis that the training set is linearly separable. Given a set of training examples, each marked as belonging to one or another of two categories, an SVM training algorithm builds a model that can assign each new occurrence into one of these two categories, making it a non-probabilistic binary linear classifier. The SVM model is a representation of the examples as points in space, mapped so that the algorithm can find the optimal line (hyperplane) which separates with minimum error the training set, and maximizes the distance, named the “gap”, between the objects of both classes and the hyperplane. Thus, instead of using the whole available training set to describe classes, SVM uses only those training samples that describe class boundaries (support vectors), thought it can be more efficient than other algorithm because it uses a subset of training points. New occurs are then mapped into that same space and predicted to belong to a category based on the side of the gap on which they fall. In addition to performing linear classification, SVMs can also efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces. Unfortunately, because of the technique used for separating classes SVM is less effective on noisier datasets with overlapping classes. When data are unlabelled, supervised learning is not possible, and an unsupervised learning approach is required. SVM is used for text classification tasks such as category assignment, spam detection and sentimental analysis. It is also commonly used for image recognition, performing particularly well in aspect-based recognition and colour-based recognition. SVM also plays a vital role in many areas of handwritten digit recognition, such as postal automation services.","name":"Support vector machines (SVM)","selfAssesment":"Completed
"},{"code":"IP3-4-7","description":"Field of study that gives computers the ability to learn without being explicitly programmed","name":"Machine learning","selfAssesment":"New
"},{"code":"IP3-4-8","description":"Image classification operator needs a set of terms to express the characteristics of an image. These characteristics are called interpretation elements and are used to define interpretation keys: tone/hue, texture, pattern, shape, size, height/elevation, location/association","name":"Mental concepts and categories","selfAssesment":"New
"},{"code":"IP3-4-9-4","description":" ","name":"Stratified random sampling","selfAssesment":" "},{"code":"IP3-4-9-5","description":" ","name":"Sample augmentation","selfAssesment":" "},{"code":"IP3-4-9","description":"Sampling strategies or sampling pattern specifies the arrangement of observations used for training and/or validation purposes.\r\nTypically, the simple random sample of a geographic region is defined by first dividing the region to be studied into a network of cells. Each row and column in the network is numbered, then a random number table is used to select values that, taken two at a time, form coordinate pairs for defining the locations of observations. Because the coordinates are selected at random, the locations they define should be positioned at random. The random sample is probably the most powerful sampling strategy available as it yields data that can be subjected to analysis using inferential statistics.\r\nA stratified sampling pattern assigns observations to subregions of the image to ensure that the sampling effort is distributed in a rational manner. For example, a stratified sampling effort plan might assign specific numbers of observations to each category on the map to be evaluated. This procedure would ensure that every category would be sampled.\r\nSystematic sampling positions observations at equal intervals according to a specific strategy. Because selection of the starting point predetermines the positions of all subsequent observations, data derived from systematic samples will not meet the requirements of inferential statistics for randomly selected observations.","name":"Sampling strategies","selfAssesment":"New
"},{"code":"IP3-4","description":"The process of image classification extracts information about semantic labels of pixels or objects (i.e. regions) from imagery. Apart of input imagery, the process requires an input set of target classes (classification scheme) for which their spectral (and other) properties have to be identified. A classification method has to be selected that transforms the image data and the classification scheme into semantic map information. In complement to the resulting sematic labelling products, a secondary outcome are instructions or rulesets with the used parameters that constitute the documentation of the classification process.\r\nThe input imagery consists of one or more images (optical and/or SAR data) of a specific geographic area, collected in multiple bands of the electromagnetic spectrum (that may have already undergone certain pre-processing steps; determined by the purpose). Additionally, the imagery may include derived spectral indices, principal components, filtered bands, or other features to support the classification process.\r\nThe classification purpose defines the information about the target classes. It includes classification schemes (taxonomies), spectral signatures for each class and, mental concepts and categories about the classes (that enable an analyst to distinguish classes by texture, spatial relationships etc.). Often, training areas are used to understand how an object of a particular class is discernible in the available imagery and separable from other classes. Both the input imagery and the chosen classification method determine which features of each class can be exploited for classification. For example, spectral signatures of the target classes (extracted from training areas with known class label) may be a suitable input for extracting information with a pixel-based classification. For shape features, objects are a pre-requirement, derived with segmentation. They are only available with object-based classification approaches.\r\nClassification methods: Various methods exist that can be categorized according to the classification logic that they follow when transforming the input information into the output semantic labelling products. These can be parametric or nonparametric, supervised or unsupervised, per-pixel or object-oriented, semi-automated or fully automatic, and hybrid approaches. Classification methods are for example bayesian techniques like conditional probability or maximum likelihood, clustering (unsupervised), decision trees, deep learning and machine learning.","name":"Image classification","selfAssesment":"Completed
"},{"code":"IP3-5-1","description":"Edge detection is a fundamental tool used in many image processing applications to obtain information from the frames as a precursor step to feature extraction and object segmentation. This process detects outlines of an object and boundaries between objects and the background in the image. An edge-detection filter can also be used to improve the appearance of blurred image.","name":"Edge-based segmentation","selfAssesment":"Planned
"},{"code":"IP3-5-2","description":"Histogram-based segmentation makes use of histogram to select the gray levels for grouping the pixels into regions, e.g. background and the object of interest","name":"Histogram-based segmentation","selfAssesment":"New
"},{"code":"IP3-5-3","description":"Local variance can be calculated as the value of standard deviation in a small neighborhood (e.g. 3x 3 moving window), then computing the mean of these values over the entire image. The obtained value is an indicator of the local variability in the image.","name":"Local variance","selfAssesment":"New
"},{"code":"IP3-5-4","description":"Mean Shift is defined as finding modes in a set of data samples, manifesting an underlying probability density function (PDF).","name":"Mean-shift segmentation","selfAssesment":"New
"},{"code":"IP3-5-5","description":"Regionalization is an important concept in Geographic Information Science for synthesizing multi-dimensional data into homogeneous objects through spatially constrained clustering methods","name":"Regionalisation","selfAssesment":"New
"},{"code":"IP3-5-6-1","description":"Multi-resolution segmentation is a region-growing algorithm. It relies on several parameters, which need to be tuned. These include the scale parameter (SP), which dictates the size and homogeneity of the resultant objects.","name":"Multi-resolution segmentation","selfAssesment":"Planned
"},{"code":"IP3-5-6-2","description":"Watershed segmentation is a region-based method that has its origins in mathematical morphology. In watershed segmentation an image is regarded as a topographic landscape with ridges and valleys. The elevation values of the landscape are typically defined by the gray values of the respective pixels or their gradient magnitude. Based on such a 3D representation the watershed transform decomposes an image into catchment basins. For each local minimum, a catchment basin comprises all points whose path of steepest descent terminates at this minimum. Watersheds separate basins from each other. The watershed transform decomposes an image completely and thus assigns each pixel either to a region or a watershed.","name":"Watershed segmentation","selfAssesment":"New
"},{"code":"IP3-5-6","description":"Region-based segmentation algorithms can be devided into region growing, merging and splitting techniques and their combinations. Region merging starts from all pixels on the pixel level and iteratively aggregates pixels into objects until some conditions of homogeneity imposed by the user are met.","name":"Region-based segmentation","selfAssesment":"New
"},{"code":"IP3-5-7","description":"Spatial autocorrelation is the term used to describe the presence of systematic spatial variation in a variable.","name":"Spatial autocorrelation","selfAssesment":"New
"},{"code":"IP3-5","description":"The term image segmentation denotes the process of algorithmically grouping neighbouring pixels that are similar. What sounds rather straight forward, is in fact a great computational challenge, some even call it an ill-posed problem, because there is a high degree of ambiguity in this process. \r\nThe two attributes in the general definition provided above, i.e. neighbouring and similar, evoke the principles of regionalisation as a fundamental concept in geography. Regionalisation is the bottom-up approach to congregate adjacent elements with the aim to form a larger unit. (Conversely, this could be understood in a top-down manner when subdividing a larger whole into smaller homogeneous units). This follows the general notion of hierarchical organisation according to general systems theory (GST). The organisation of a state in smaller administrative units is a good example for a hierarchical structure, the composition of the human body by organs, cells, etc. another. In image analysis such regions are commonly referred to image regions, originating from the concept of “photomorphic regions”, literally meaning regions formed on images – originally by human interpreter through manual delineation. Today, advanced pixel grouping algorithms aim to delineate homogenous regions in an image automatically. As those regions usually are assumed to match with real-world objects, it is often stated in literature that image segmentation generates image objects. Deriving some general heuristics on their properties (colour, size, shape, orientation, etc.) we can label these objects according to a given semantic scheme. The procedure of object delineation and classification using object features and relations is a fundamental principle in object-based image analysis (OBIA). \r\nDue to the effect of spatial autocorrelation (the tendency of neighbouring pixels to be similar irrespective of scale or geographical location), pixel grouping is ambiguous and by no means trivial, but not arbitrary either. Intuitively, image regions are those quasi-homogeneous areas that we perceive as landscape units on a specific scene (a lake, a forest patch, a single tree, a building, a residential area). According to hierarchy theory, we can assume that we find multiple scales within a single image even, according to the level of detail we are interested in. Whether or not a specific grouping of pixels is considered valid, e.g. because it corresponds to a real-world object, can hardly be answered unanimously, but rather needs to be judged by experts in the respective application domain. That is why often in literature we find the term ‘meaningful objects’. \r\nImage segmentation is as a sub-field of computer vision and aims to apply computer algorithms to generate image regions (a.k.a. tokens) within digital image analysis. There are several strategies for performing image segmentation, all resting on the following general principles: (1) regions do not overlap; (2) regions are (relatively) homogenous; regions are (relatively) different to neighbouring regions; regions are fairly equally sized (belong to one scale domain) but can be built in several hierarchical scales. General strategies include (1) edge-based segmentation and (2) region-based segmentation, and multi-scale segmentation as a specific case. \r\nAlso referred to spatial classification emphasizing the constraint of spatial contingency, image segmentation aggregates neighbouring pixels, but – as compared to statistical clustering techniques – does not provide a unique set of classes (either semantic or statistic) in the feature space. \r\nRecently the term semantic segmentation has emerged in the machine-learning community, which is in fact a combination of segmentation and categorisation (labelling) via deep learning methods (e.g. convolutional neural networks).","name":"Image segmentation","selfAssesment":"Completed
"},{"code":"IP3-6-1","description":"Combined filtering uses different filters to arrive at more complex filters for specific purposes. \r\nFor example, Laplacian filters are derivative filters used to find areas of rapid change (edges) in images. Since derivative filters are very sensitive to noise, it is common to smooth the image (e.g., using a Gaussian filter) before applying the Laplacian. This two-step process is called the Laplacian of Gaussian (LoG) operation.","name":"Combined filtering","selfAssesment":"New
"},{"code":"IP3-6-2","description":"The aim of sharpening filters is to highlight transitions in intensity (high frequency components) using different operators: directional (horizontal, vertical, diagonal) or isotropic (e.g. Laplacian Filter). Example of edge detectors include: Gaussian edge detector, Laplacian filter etc.","name":"Edge detectors","selfAssesment":"New
"},{"code":"IP3-6-3-1","description":"The Lee-sigma filter is a conceptually simple but effective alternative to the Lee and other sophisticated adaptive filters. It is based on the sigma probability of the Gaussian distribution.","name":"Lee-Sigma","selfAssesment":"New
"},{"code":"IP3-6-3","description":"High-pass filtering enhance information of high frequencies (local extremes, lines, edges)","name":"High-pass filtering","selfAssesment":"New
"},{"code":"IP3-6-4-1","description":"Gaussian Filters are isotropic (same behavior in all directions).","name":"Gauss filter","selfAssesment":"New
"},{"code":"IP3-6-4","description":"Spatial filters transform an image by taking into account the local neighborhood of a pixel. The goal of filtering is to remove unnecessary components from images (e.g., noise), while emphasizing the necessary ones. In this context, low pass filters aim at removing sharp transitions in the image intensities (high spatial frequencies).","name":"Low-pass filtering","selfAssesment":"New
"},{"code":"IP3-6","description":"In contrast to the point operations used for radiometric modification of image data, techniques for geometric processing are characterized by operations over local neighborhoods of pixels. The result of a neighborhood operation is still a modified brightness value for the single pixel at the center of the neighborhood , however the new value is determined by the brightness of all the local neighbors rather than just the original brightness value of the central pixel alone.","name":"Kernel analysis (convolution)","selfAssesment":"Planned
"},{"code":"IP3-7-1","description":"Class modelling provides flexibility in designing a transferable workflow from scene-specific high-level segmentation and classification to region-specific multi-scale modelling","name":"Class modelling","selfAssesment":"Planned
"},{"code":"IP3-7-2","description":"Hierarchical representation refers to hierarchically scaled compositions of the classes to be classified.","name":"Hierarchical representation","selfAssesment":"New
"},{"code":"IP3-7-3","description":"Per-parcel analysis relies on parcels or objects as the smallest units of image analysis. The parcels are usually obtained through image segmentation that partition the input images into homogeneous units, i.e. parcels, in a supervised or unsupervised manner.","name":"Per-parcel analysis","selfAssesment":"New
"},{"code":"IP3-7-4-1","description":"Distance relationships describe how far an object is with respect to a reference. Proximity analysis allows the identification of the distance between a geographic feature of interest and its neighbors.","name":"Distance and proximity features","selfAssesment":"New
"},{"code":"IP3-7-4-2","description":"The most important geometric features of geographic objects are their size and shape. Shape refers to general form or outline of individual objects and can be quantified using different metric such as shape index, compactness, asymmetry, density, elliptic fit, roundness, rectangular fit etc.","name":"Planar geometric features","selfAssesment":"New
"},{"code":"IP3-7-4-3","description":"Topological features characterize qualitatively the position of spatial objects relative to each other. There are different models for representing topological relationships. Calculus-based method, for example, allows us to model five topological relationships of two spatial objects: touch, in, cross, overlap, disjoint.","name":"Topological features","selfAssesment":"New
"},{"code":"IP3-7-4","description":"An object of a specific object class has a value on the range of values of a spatial or spectral feature. A set of features provides the feature space that is used for classification.","name":"Spatial features","selfAssesment":"Planned
"},{"code":"IP3-7","description":"OBIA is an iterative method that starts with the segmentation of satellite imagery into homogeneous and contiguous image segments (also called image objects. In the next step, resulting image segments are assigned to the target classes.","name":"Object-based image analysis (OBIA)","selfAssesment":"Planned
"},{"code":"IP3-8-1","description":"The feature space represents in various dimensions all the features that can be used for classification (e.g. image bands, band math parameters, derived texture properties). A point in that space is also called a vector with values for each feature (or dimension). Polyhedralization is a form of vector space quantization where a vector is assigned to the closest centre point of one polyhedron.","name":"Feature space polyhedralization","selfAssesment":"New
"},{"code":"IP3-8-2","description":"Radiative transfer models describing the interaction between matter and electromagnetic radiation serve as cornerstones for optical remote sensing. The radiative transfer theory provides the most logical linkage between observations and physical processes that generate signals in optical remote sensing. Radiative transfer modelling is therefore an integral part of remote sensing, since it provides the most efficient tool for accurate retrievals of Earth properties from satellite data. Radiative transfer models are used in a number of different applications such as sensor radiometric calibration, atmospheric correction and the modelling radiation processes in vegetation canopies. \r\nVegetation radiative transfer models (RTMs) study the relationship between leaf and canopy biophysical variables and reflectance, absorbance and scattering mechanisms. The infinite variability of vegetation structure complicates the modeling of RT in vegetation canopies. Numerous models of RT in vegetation canopies were developed in the second half of the last century. Models differ by the details accounted for and by the simplifications introduced in the description of canopy structure and photon–vegetation interactions. Gradual improvement in RTMs accuracy, yet in complexity too, have diversified RTMs from simple turbid medium RTMs towards advanced Monte Carlo RTMs that allow for explicit 3D representations of complex canopy architectures. This evolution has resulted in an increase in the computational requirements to run the model, which bears implications towards practical applications. When choosing an RTM, a trade-off between invertibility and realism has to be made: simpler models are easier to invert but less realistic, while advanced models more realistic but require a large amount of variables to be configured. The two most widely used models are the leaf model PROSPECT and Scattering by Arbitrary Inclined Leaves (SAIL) canopy model. \r\nAtmosphere RTMs study the interaction of radiation with the atmosphere. The remotely-sensed signals at satellite or airborne platforms are combinations of surface and atmospheric contributions, with relative amounts varying across the two wavelength regions, depending on the condition of the atmosphere. The order of magnitude of atmosphere signals can be equal or larger than that of land or ocean surface signals that arise at the top of the atmosphere (TOA). In order to derive accurate sensor calibration and atmospheric correction, the contribution of the atmospheric constituents to the total retrieved signal must be understood and modelled. Atmospheric radiative transfer models simulate the radiative transfer interactions of light scattering, absorption and emission through the atmosphere. Some widely used atmospheric RTMs are 6SV, libRadtran, MODTRAN, and ATCOR.\r\nAdvances in radiative transfer modeling enhance our ability to detect and monitor changes in our planet through new methodologies and technical approaches to analyze and interpret measurements from air- and space-borne sensors.","name":"Radiative transfer modelling","selfAssesment":"Completed
"},{"code":"IP3-8","description":"Historically, physical modelling and machine learning have often been treated as two different fields with very different scientific paradigms (theory-driven versus data-driven). Yet, in fact these approaches are complementary, with physical approaches in principle being directly interpretable and offering the potential of extrapolation beyond observed conditions, whereas data-driven approaches are highly flexible in adapting to data and are amenable to finding unexpected patterns (surprises).","name":"Physical-model based analysis","selfAssesment":"New
"},{"code":"IP3-9-1","description":"Difference of Gaussians (DoG) method consists of subtracting two Gaussians, where a kernel has a standard deviation smaller than the previous one. The convolution between the subtraction of kernels and the input image results in the edge detection of this image.","name":"Difference of Gaussian (DoG)","selfAssesment":"New
"},{"code":"IP3-9-2","description":"Scale Invariant Feature Transform (SIFT) is an image descriptor for image-based matching and it is used for a large number of purposes in computer vision related to point matching between different views of a 3-D scene and view-based object recognition. The SIFT descriptor is invariant to translations, rotations and scaling transformations in the image domain and robust to moderate perspective transformations and illumination variations. Experimentally, the SIFT descriptor has been proven to be very useful in practice for robust image matching and object recognition under real-world conditions.","name":"Scale invariant feature transformation (SIFT)","selfAssesment":"New
"},{"code":"IP3-9","description":"Scale-space theory is a framework for multiscale image representation, which has been developed by the computer vision community with complementary motivations from physics and biologic vision. The idea is to handle the multiscale nature of real-world objects, which implies that objects may be perceived in different ways depending on the scale of observation. If one aims to develop automatic algorithms for interpreting images of unknown scenes, there is no way to know a priori what scales are relevant. Hence, the only reasonable approach is to consider representations at all scales simultaneously.","name":"Scale space analysis","selfAssesment":"New
"},{"code":"IP3","description":"Image data, in order to be turned into information, require interpretation. Thereby image understanding is the process of scene reconstruction, the description and mental representation of the content of imaged, and potentially complex, realities. \r\nImage understanding thereby goes beyond single feature extraction. Instead, it aims at a complete description of the image content, i.e. the reconstruction of a real-world scene. In the early days of digital image processing, image understanding was mainly confined to identifying and labelling image primitives. Today, advanced mapping keys and hierarchical classification schemes to analyse EO data, include composite and complex target classes. Thereby ‘full’ scene description means reaching from signal processing to a symbolic representation of the scene content. This entails the relationships of real‐world objects in different scales and spatio-temporal aspects.\r\nDescribing a scene, visually or computer-aided or mixed, depends on a conceptual framework comprising (a) the underlying research question within (b) a specific field of application and (c) pre‐existing knowledge and experience of the operator. Obtaining insights from imagery requires general knowledge about the expected scene content and domain expertise. The field of image understanding is interlinked with image (pre-)processing, computer vision, and artificial intelligence (AI). Image processing conditions the data material and enhances the interpretation source. Computer vision including pattern recognition providing knowledge representation, expert systems. AI is mainly concerned with automation processes, be it via knowledge transfer to an automated system or machine / deep learning.\r\nIn analogy to the human mind, image understanding is the computational process of extracting information from images, i.e. locating, characterizing, and recognizing objects and other features in the depicted scene. However, image understanding is not a linear, but rather a cyclic process and takes place during the pre-processing and data assimilation steps. For example, cloud masks on EO images is an early product of image understanding, prior to many pre-processing tasks.\r\nIn a typical GEOBIA workflow, the process of image understanding can be illustrated by the following steps: Starting from the subset of a real‐world scene captured on an image first step may entail scaled representations by grouping neighbouring pixels on several hierarchical sales. The multi‐scale segmentation provides a set of nested objects with geospatial and spectral properties to be used in the classification process. \r\nWith object hypotheses in mind the object relation modelling can be realized by encoding expert knowledge into a rule system. This setp aims at categorizing the image objects by their spectral and spatial properties and their mutual relationships. Hereby, an object‐centred view is accomplished. This representation of the image content should meet the conceptual reality of the interpreter or user. Knowledge is stepwise adapted and improved through progressive interpretation and modelling. Experience grows, as knowledge will be enriched by analyzing unknown scenes and the transfer of knowledge may incorporate or stimulate new rules.","name":"Image understanding","selfAssesment":"Completed
"},{"code":"IP4-1-1","description":"Once the user finds the required data, she/he needs to know how can they be accessed, possibly including authentication and authorisation.","name":"Accessibility","selfAssesment":"New
"},{"code":"IP4-1-2","description":"Quality Indicators (QIs) should be ascribed to data and, in particular, to delivered information products, at each stage of the data processing chain - from collection and processing to delivery. A QI should provide sufficient information to allow all users to readily evaluate a product’s suitability for their particular application, i.e. its “fitness for purpose”.","name":"GEO QA4EO","selfAssesment":"New
"},{"code":"IP4-1-4","description":"ISO is an independent, non-governmental international organization with a membership of 164 national standards bodies. Through its members, it brings together experts to share knowledge and develop voluntary, consensus-based, market relevant International Standards that support innovation and provide solutions to global challenges. ISO/TC 211 Geographic information/Geomatics provides Standardization in the field of digital geographic information. Note: This work aims to establish a structured set of standards for information concerning objects or phenomena that are directly or indirectly associated with a location relative to the Earth. These standards may specify, for geographic information, methods, tools and services for data management (including definition and description), acquiring, processing, analyzing, accessing, presenting and transferring such data in digital / electronic form between different users, systems and locations.","name":"ISO standards","selfAssesment":"New
"},{"code":"IP4-1-5","description":"The OGC is the worldwide leading consortium of GIS industries promoting the interoperability of geographic information across platform, system, and country borders. The main field of current activity is the complete integration of the sources of geographic information based on the Internet.The Open GIS Consortium (OGC) plays an important role on the implementation level.","name":"OGC standards","selfAssesment":"New
"},{"code":"IP4-1-6","description":"A fundamental pillar in (open) science is to verify the scientific results of others to advance knowledge. The lack of reproducibility in scientific studies brings challenges in understanding and recreating the results of others, a situation that may be common in data-based and algorithm-based research like in geocomputation. In general, many authors define reproducibility as the ability to compute exactly the same results of a study based on original input data and analysis workflow. In other words, “to rerun the same computational steps on the same data the original authors used”. Replicability is often seen as obtaining similar conclusions about a research question derived from an independent study or experiment. In the field of GIScience and geocomputation, in particular, a reproduction is always an exact copy or duplicate, with exactly the same features and scale, while a replication resembles the original but allows for variations in scale, for example. Hence, reproducibility is exact whereas replicability means confirming the original conclusions, although not necessarily with the same input data, methods, or results.","name":"Replicability and reproducibility","selfAssesment":"Completed
"},{"code":"IP4-1-7","description":"The ultimate goal of FAIR is to optimise the reuse of data. To achieve this, metadata and data should be well-described so that they can be replicated and/or combined in different settings.","name":"Reusability","selfAssesment":"New
"},{"code":"IP4-1","description":"Data quality standards are guiding principles and operational guidelines for the production and use of data. For example, QA4EO aims for the two key principles of accessibility / availability and suitability / reliability. The QA4EO guidelines provide instructions for the implementation of processes that follow these principles. Standards emerge from standardization processes within the community. They are based on the agreement of the members of the community.","name":"Data quality standards","selfAssesment":"New
"},{"code":"IP4-2-1-1","description":"To correctly perform a classification accuracy (or error) assessment, it is necessary to systematically compare two sources of information: (1) pixels or polygons in a remote sensing-derived classification map, and (2) ground reference test information (which may in fact contain error). The relationship between these two sets of information is commonly summarized in an error matrix (sometimes referred to as contingency table or confusion matrix). Indeed, the error matrix provides the basis on which to both describe classification accuracy and characterize errors, which may help refine the classification or estimates derived from it.","name":"Error matrix","selfAssesment":"New
"},{"code":"IP4-2-1-2","description":"F-score represents the harmonic mean between precision and recall. As F-score combines both precision and recall, it can be regarded as an overall quality measure. The range of F is from 0 to 1 with larger values representing higher accuracy.","name":"F-score","selfAssesment":"New
"},{"code":"IP4-2-1-3","description":"Ground reference refers to the reference dataset for an accuracy assessment of a remote sensing classification. The process of obtaining ground reference is dedicated to support the production of suitable accuracy information. A sampling design (fitting to the produced image classification) determines the most appropriate distribution of sample locations (or regions). The response design consists of the evaluation protocol and the labeling protocol. The evaluation protocol initiates selecting the support region on the ground (represented by a pixel or polygon) where the ground information will be collected. Once the location and dimension of the sampling unit are defined, the labelling protocol is initiated and the sampling unit is assigned a hard or fuzzy ground reference label. This ground reference label (e.g. forest) is paired with the remote sensing-derived label (e.g., forest) for assignment in the error matrix.","name":"Ground reference","selfAssesment":"New
"},{"code":"IP4-2-1-4","description":"Kappa is a value for measuring the overall accuracy of a classification that accounts for randomness of class assignment. Kappa analysis is a discrete multivariate technique of use in accuracy assessment. Kappa yields a statistic, ^K, which is an estimate of Kappa. It is a measure of agreement between the remote sensing-derived classification map and the reference data as is indicated by a) the major diagonal and b) the chance of agreement, which is indicated by the row and column totals in the error matrix.","name":"Kappa statistics","selfAssesment":"New
"},{"code":"IP4-2-1-5","description":"These two quality assessment indicators are calculated as follows:\r\nPrecision = TP/(TP+FP) \r\nRecall = TP/(TP+FN),\r\nwhere TS is true positive, FP is false positive, FN is false negative","name":"Precision & recall","selfAssesment":"New
"},{"code":"IP4-2-1-6","description":"Geometric correction procedures (image-to-map rectification, image-to-image rectification) are used to rectify remotely sensed data to a standard map projection whereby it may be used in conjunction with other spatial information in a GIS to solve problems. The rectification process normally involves selecting ground control point (GCP) image pixel coordinates (row and column) with their map coordinate counterparts (e.g. meters northing and easting in a UTM map projection). Rectification requires that polynomial equations (that translate from image coordinates to map coordinates) be fit to the GCP data using least squares criteria. Depending on the distortion in the imagery, the number of GCPs used, and the degree of topographic reliefdisplacement in the area, higher -order polynomial equations may be required to geometrically correct the data. To determine how well the six coefficients derived from the least-squares registration of the initial GCPs account for geometric distortion in the inpit image, for each GCP, the root-mean-square error (RMSE) is computed.","name":"Root mean square error (RMSE)","selfAssesment":"In progress
\r\n\r\n"},{"code":"IP4-2-1","description":"A growing set of EO services and applications produce EO products that describe various aspects of the land, ocean and atmosphere. These products include for example image products at different processing levels, geometric measurements like in digital elevation models, semantic labelling products like land cover classifications, and EO-derived attribute products concerning air quality or other geophysical and biophysical parameters. Same as any geospatial data, EO products are not free of error and require accompanying documentation of their product quality. One term for describing different quality dimensions of an EO product is accuracy.\r\nAccuracy is a measure to estimate the uncertainty that originates from errors. An error is the deviation of a map value from a true value. The concept of error assumes well-defined phenomena where deviation results from imperfection of measurement equipment, environment effects, or imperfections of the observer. They cause gross errors and blunders, systematic errors, and random errors, for which different approaches are necessary to minimize error. Ideally, only random error remains that is probabilistic in nature and can be assessed with statistical approaches. For poorly defined phenomena, the concept of vagueness applies. For example in the case of thematic maps using fuzzy sets, the accuracy assessment requires a fuzzy approach as well. \r\nJudging error requires reference data with higher accuracy (by an order of magnitude) to which the map value can be compared. EO product quality dimensions about accuracy include thematic accuracy, spatial accuracy (both horizontal and vertical), radiometric accuracy, and accuracy of biophysical/geophysical parameter measurements. Respective equipment and approaches for reference data collection includes ground verification for thematic maps, GNSS positioning devices, field spectrometers, air quality sensors and in-situ biomass estimation. Ideally, reference data is collected in the field. In case of inaccessible areas of interest and/or if the service requirements allow it, approaches may rely on proxy reference data.\r\nThe design of the accuracy assessment procedure should be done with the EO product design to match the requirements of the EO service. For example, a thematic accuracy assessment consists of the main three components of response design, analysis, and sampling design. The response design ensures that reference data and map data are comparable at a location and specifies under which cases they agree or disagree. The analysis, usually performed with an error matrix, specifies which quality indicators will be calculated to quantify accuracy. The sampling design specifies the subset of locations at which the response design will be applied. Depending on the classification process and application case, different sampling strategies can be suitable (e.g. clustered sampling, stratified random sampling). \r\nFor other accuracy dimensions, respective accuracy assessment procedures exist, e.g. root mean squared error (RSME) for the positional accuracy assessment.\r\nAfter an accuracy assessment has been performed and the uncertainty in the EO product is understood, the challenge is to clarify how the uncertainty affects subsequent spatial analyses with the EO product. Different strategies exist that ignore error completely or that account for error by modelling uncertainty in the analysis outcomes. If uncertainty is judged low enough (or more hazardous, if users are unaware of the limited accuracy), subsequent analyses accept the EO product as true and ignore the accuracy value. If uncertainty is incorporated in subsequent analysis through uncertainty modelling, the results describe the bandwidth of outcomes, potentially supported with appropriate visualisations of uncertainty. The uncertainty modelling approach may greatly enhance the usability of the EO product, because it informs better how the error impacts the EO information and how much confidence a user should have in it.\r\nWith a new generation of EO products on the horizon and a largely increased user community, a large number of new applications is to be expected. They may also identify innovative accuracy assessment approaches. For example, the availability of EO archives with long time series of EO data led to response design protocols tailored to collect time series of reference data. The use of volunteered geographic information (VGI) as reference data has great potential, if approaches are implemented that ensure its reliability. Methods for object-based accuracy assessment are continued to be developed. Further, the increasing number of EO parameter products based on continuous variables creates the need to describe their accuracy. Finally, the focus on validation of EO products during EO service development and operation will make feedback from users available to service providers, ultimately leading to more meaningful EO products with more meaningful accuracy metrics and other quality indicators.","name":"Accuracy assessment","selfAssesment":"
Completed
"},{"code":"IP4-2-2","description":"The implementation of a service that provides remote sensing derived information on a regular basis introduces process-related quality criteria like the timeliness of information provisioning. For the case of refugee camp mapping, timely arrival of map information may be critical to support the decisions in planning facilities for humanitarian assistance.","name":"Timeliness","selfAssesment":"New
"},{"code":"IP4-2-3-1","description":"Completeness is a quality dimension that can apply to different data properties.The Data completeness is dealing with the completeness of an image, handling for example the effect of shadowing objects, sun flares on water surfaces or masking out by an object (e.g. propeller of a UAV). Spatial completeness is a feature on the area coverage. In photogrammetry (especially in stereophotogrammetry) its 3D version, the stereo completeness has extreme importance. In monitoring systems and applications the Temporal completenesster term features how the taken images represent a complete time series. The thematic completeness measure describes the image interpretation quality how the expected and defined classes are evaluated. This feature is important with the use of e.g. multiple classifiers.","name":"Completeness","selfAssesment":"New
"},{"code":"IP4-2-3-2","description":"In remote sensing we can speak about spatial consistency in the Consistency cluster. It represents the quality of image interpretation/understanding: how are the different objects or classes recognized/evaluated integrally. A bridge above a water surface, like river can be detected in pixel-wised manner, but the question is how coherent they are in the output map. This phenomenon has very close to the thematic consistency, where the recognition integrity is represented in this way. The topological consistency is defined mainly for network-type surface objects, like roads or rivers, where the connection of all atomic segments are rated by this measure. Urban mapping focuses on the built environment objects, where e.g. house-parcel inclusions are described by this feature. The temporal consistency is for monitoring again, representing for example the possibility or impossibility of land cover changes in time. Having multiple data sources (even airborne or terrestrial), their integral usage can be qualified by this measure.","name":"Consistency","selfAssesment":"New
"},{"code":"IP4-2-3-3","description":"Readability refers to the content of a map being presented clearly enough that the content can be perceived and understood by the user. This includes legibility, e.g. whether the text of a label is large enough to be read and has enough contrast to the background to be easily perceivable. Additionally, readability has a broader meaning that explains whether a product as a whole is simple enough to be understood and not too complex that essential information can be overlooked by the user.","name":"Readability","selfAssesment":"New
"},{"code":"IP4-2-3","description":"Gathering information about the quality of an EO product or service by letting the user test it. The feedback from the user enables to verify whether specific quality criteria have been met.","name":"User validation","selfAssesment":"New
"},{"code":"IP4-2","description":"A product in the sense of something that a user can use for a specific purpose requires a certain quality. Therefore, its accuracy needs to be judged with an accuracy assessment measure that the user understands and where he can interpret the meaning in relation to the purpose. The product has to be validated, i.e. it has to be known whether the product qualifies for use in a certain context. And in addition, the product needs to be available in time that the users can base their decision on it.","name":"Product quality","selfAssesment":"New
"},{"code":"IP4-3-1","description":"The cloud cover percentage indicates the amount of area in the remote sensing image extent that is covered with clouds and therefore cannot provide information about the Earth surface conditions.The actual types of clouds included may depend on the product, but the CEOS definition includes cloud shadow. Next to that, from an optical remote sensing point of view, clouds can be roughly classified in: opaque/dense clouds, mainly composed of droplets that are highly reflective in the VIS region and generally located at low-medium altitudes and cirrus, consisting of a large number of thin non-spherical ice crystals that are normally translucent in the VIS region, relatively highly reflective in the SWIR spectrum, and located at high altitude.\r\n\r\nThe goal of cloud cover percentage is to provide a quality measure of usable information in a surface reflectance image. Earth observation product catalogs support it as a query parameter, to enable searching for products with a cloud cover percentage below a given threshold.\r\nThis simplifies for instance use cases that require only fully clear products (0% cloud cover), and may save download and processing resources by only handling images that have some valid pixels. For instance, by only using products with a cloud cover percentage smaller than 99.95%. The measure also gives an estimate of the number of valid observations in a given geographical area, allowing a quick assessment of whether minimal data requirements for a specific use case are met.\r\n\r\nThe measure is a percentage of actual observations in an image, so pixels where no data was recorded are not included. For derived products, cloud cover pixels are often also flagged separately from pixels where no data was recorded, but this may depend on the data provider. The definition specifically also includes cloud shadow pixels.\r\nReliable cloud cover percentages depend on good cloud and cloud shadow detection methods. Especially handling of translucent cirrus clouds is an open issue: a product that has a 100% cloud cover percentage due to cirrus clouds might still be usable for some cases, while for other cases they also render the product useless. \r\n\r\nThe used cloud detection algorithm will also affect the cloud cover percentage. A more strict algorithm will yield higher percentages compared to an algorithm that under detects clouds.\r\nDue to these limitations, cloud cover percentages in product metadata have a fairly high error margin. The user should take this into account when determining optimal cloud cover percentage thresholds for the use case.","name":"Cloud cover percentage","selfAssesment":"Planned
"},{"code":"IP4-3-2","description":"The remote sensing lifecycle structures all possible phases of the data production process, from its beginning of the data's coming to existence (that includes the sensor design prior to data collection) over storage, processing and use to archiving and deletion.","name":"Remote sensing lifecycle","selfAssesment":"New
"},{"code":"IP4-3-3","description":"The capability of a sensor or EO product to resolve anything is a function of its (spatial, temporal, spectral and radiometric) resolution and of the detail at which a geographic phenomenon of interest manifests itself in time and space. A geographic phenomenon can be named or described, georeferenced and provided with a time interval at which it exists. The geographic phenomenon of interest is the one of which a user needs information to help him make a decision. Therefore, the geographic phenomenon needs to be resolved with a low enough uncertainty and a high enough quality that allows the user to make a decision with confidence. \r\nFor example, let’s consider a helicopter pilot that wants to know whether a specific site is suitable for an emergency landing. The decision to perform an emergency landing may be supported with an EO-derived digital map of emergency landing sites that are flat enough (as well as large enough for the pilot’s helicopter and free of any obstacles on the surface and in the approach area). If we only focus on the flatness of the terrain, we need a digital elevation model (DEM) of high enough spatial resolution and accuracy in the Z dimension to calculate slope within acceptable levels of uncertainty. The pilot probably can tell us what degrees of slope are okay for his helicopter and tell us sites (e.g. football fields) where such a landing would succeed. However, this is only the input to an analysis of different DEMs to identify the minimum spatial resolution and accuracy in the Z dimension to model slope products and associated uncertainty to derive an emergency landing site product that fulfils the requirements. Thereby the capability of different DEMs to resolve emergency landing sites can be analysed.\r\nSpatial resolution is a measure of the smallest angular or linear separation between two objects that can be resolved by the remote sensing system. A useful heuristic rule of thumb is that in order to detect a feature, the nominal spatial resolution of the sensor should be less than one-half the size of the feature measured in its smallest dimension.\r\nOther types of resolution of an EO dataset are available that determine for various geographic phenomena under investigation whether it is possible to resolve them in the data. These are radiometric resolution, spectral resolution and temporal resolution. Radiometric resolution is defined as the sensitivity of a remote sensing detector to differences in signal strength as it records the radiant flux reflected, emitted, or back-scattered from the terrain. Spectral resolution is the number and dimension (size) of specific wavelength intervals (referred to as bands or channels) in the electromagnetic spectrum to which a remote sensing instrument is sensitive. The temporal resolution of a remote sensing system generally refers to how often the sensor records imagery of a particular area. For time-series analysis, the temporal resolution determines the time granularity for resolving processes that underlie the change that is observable between subsequent images.","name":"Capability to resolve anything","selfAssesment":"In progress
"},{"code":"IP4-3-4","description":"The spatial coverage of a dataset (consisting of an image or a series of images) determines whether the dataset covers the area of the terrain that is of interest to the user of information derived from the dataset.","name":"Spatial coverage","selfAssesment":"New
"},{"code":"IP4-3-5","description":"The temporal validity of a dataset (consisting of an image or a series of images) determines whether the acquisition date(s) (and period) match(es) the requirements for investigating a specific phenomenon and thereby enables the derivation of information about that phenomenon.","name":"Temporal validity","selfAssesment":"New
"},{"code":"IP4-3","description":"Values (or a value) that enable(s) judging a dataset or product on their fitness for a specific purpose (e.g. whether a specific satellite image is suitable for mapping landslides). , A QI should provide sufficient information to allow all users to readily evaluate a product’s suitability for their particular application, i.e. its “fitness for purpose”.","name":"Quality indicators","selfAssesment":"New
"},{"code":"IP4","description":"Data quality, in general, is the degree of data usability in relation to a specific application purpose. Assurance of data quality is of growing importance in remote sensing, due to the increasing relevance of remote sensing data in planning and operational decision of public bodies and private firms, and the huge amount of digital services (or apps) that exploit RS data. \r\nDifferent data quality dimensions exist according to the lifecycle phases of the remote sensing data: data acquisition, data storage, data pre-processing, processing and analysis and data visualization and delivery. Remote sensing data acquisition phase involves the following quality aspects: resolution, accessibility, spatial accuracy, temporal validity, accuracy and precision of the sensor calibration. Resolution is a multi-dimensional concept that includes the following dimensions: spatial resolution, temporal resolution, radiometric resolution, spectral resolution and temporal resolution. Temporal validity refers to the quality of an remote sensing data product in time, whereas spatial accuracy refers to the accuracy of the position of features relative the Earth. \r\nData storage includes the accessibility and completeness data quality dimensions. Accessibility includes both temporal and data accessibility. Temporal accessibility refers to the time delay between data acquisition and data delivery, whereas data accessibility refers to the availability of remote sensing data. Data completeness encompasses temporal completeness, i.e. completeness of a time series represented a phenomenon, thematic completeness, and spatial completeness which refers to the area coverage. Data preprocessing, processing and analysis phase includes consistency, completeness, temporal validity, resolution, radiometric and geometric accuracy, thematic and semantic accuracy. Thematic and sematic accuracy refers to the correctness of the remote sensing data product. The main quality dimensions of the data visualization and delivery include readability, completeness and temporal validity. \r\nDifferent metrics can be used to assess the quality of the remote sensing-derived information, such as the root-mean-square error (RMSE) measuring the differences between the true and measured values of the phenomenon under investigation, confusion matrix used for assessing the classification performance, producer’s accuracy, user’s accuracy or Cohen kappa. The quality of the remote sensing data per se can be assessed using Peak Signal-to-noise Ratio (PSNR) or the Universal Image Quality Index (UIQI).\r\nDifferent organizations are involved in the standardization of the image data and gridded data quality, including ISO/TC 211 ‘Geographic information/Geomatics’, Open Geospatial Consortium (OGC) or the Quality Assurance Framework for Earth Observation (QA4EO) developed by the Group on Earth Observation (GEO). These organizations are responsible for developing metadata standards that are further used by the remote sensing community to document the quality of the remote sensing data. According to the QA4EO, for example, all remote sensing data products need to be accompanied by a Quality Indicator (QI) which helps users assessing their fitness-for-use.","name":"Image data quality","selfAssesment":"Completed
"},{"code":"IP5-1-1","description":"Array databases make use of arrays as the primary storage representation. Such an array-oriented data model and query language is useful in many scientific applications, where the raw data consists of large collections of imagery or sequence data that needs to be filtered, subsetted, and processed.","name":"Array databases","selfAssesment":"New
"},{"code":"IP5-1-2","description":"The Open Data Cube (ODC) is a non-profit, open source project that was motivated by the need to better manage Satellite Data. This project was born out of the work done under the \"Unlocking the Landsat Archive\" and the Australian Geoscience Data Cube (AGDC) projects.","name":"Open data cube","selfAssesment":"New
"},{"code":"IP5-1","description":"The term data cube originally was used in Online Analytical Processing (OLAP) of business and statistics data. Technically speaking, such a data cube represents a multidimensional array together with metadata describing the semantics of axes, coordinates, and cells. It is an efficient approach to the management and analysis of large datasets.","name":"Data cubes","selfAssesment":"New
"},{"code":"IP5-2-1","description":"Content-based image retrieval helps users retrieve relevant images based on their contents.","name":"Content-based image retrieval","selfAssesment":"New
"},{"code":"IP5-2-2","description":"Web Portals allow users to discover, understand, view, access and query information of their choice from local to global level for a variety of uses.","name":"Web portals","selfAssesment":"New
"},{"code":"IP5-2","description":"Image archives are repositories for storing, managing and retrieving remote sensing data.","name":"Image archives","selfAssesment":"New
"},{"code":"IP5-3-1","description":"As an initiative stipulated by the European Commission to foster the bridge between the Copernicus ground segment and the user segment, the Copernicus data and information access service (C-DIAS) is a generic name for different sets of cloud-based platforms providing centralised access to Copernicus data and information, as well as to processing tools. The name indicates, however, that the focus of such advanced user-centred infrastructure implementations is not only on data access, but also on ‘information’. What is specifically meant here is the provision of information services and information layers as defined in the Copernicus service portfolio. This allows the users to develop and host their own applications in the cloud and a single access point, rather than processing data locally. Currently there are five different DIAS’s implemented (CREODIAS, SOBLOO, MUNDI, WEKEO, ONDA), all with some specific technical assets, or a sector-specific application focus or any other unique selling position by e.g. targeting as specific user community. Currently, the DIAS, which have received co-funding from the European Commission as a kind of seed funding, are currently in the process of exploring opportunities and claiming market shares, striving to sustain in a competitive manner. Some of the features are highlighted in the following, without explicitly mentioning any of the associated DIAS: (i) data access of global data sets (satellite data mosaics or gridded data) by custom area; (ii) OGC interfaces, VM catalogue, SPAR QL search interface (combine searches like receive images over areas of high population density), open source (accessible via API) or pay-per-use; (iii) access to core service products (e.g. CLMS, CMEMS, CAMS); (iv) focus on integrated applications such as smart cities, urban energies, precision agriculture; access to third-mission VHR satellite data (e.g. Pléiades); (v) utilizing GitLab as a developer platform.","name":"Data and information access service (DIAS)","selfAssesment":"Completed
"},{"code":"IP5-3-2","description":"The OpenGIS® Web Processing Service (WPS) Interface Standard provides rules for standardizing how inputs and outputs (requests and responses) for geospatial processing services are defined. It defines an interface that facilitates the publishing of geospatial processes and clients’ discovery of and binding to those processes.","name":"OGC interfaces and OGC web processing service","selfAssesment":"New
"},{"code":"IP5-3","description":"Online processing allows users to implement and run image analysis operations online independent of the underlying software.","name":"Online processing","selfAssesment":"Planned
"},{"code":"IP5","description":"In general, infrastructures such as cyberinfrastructures or Spatial Data Infrastructures (SDIs), allow information sharing across distributed infrastructures and communities. SDIs have gradually changed from a pool of authoritative data shared using standardized web services to a pool where the authoritative data co-exist with data collected by volunteers and different sensors. Many efforts were dedicated to data documentation, to improving the catalogues searching techniques by means of, for example, thesauri and to sharing these data using standardized web services such as Web Map Service, Web Feature Service or Web Coverage Service. Cloud computing technologies played an important role in the implementation of sustainable SDIs due to their ability to provide on-demand computational and storage capacities over the Internet. In this way, users can easily search, find and use data shared across different online platforms.\r\nMore specifically, infrastructures for image processing and analysis refer to the physical and organizational facilities that allow the storage, analysis and management of the available data and products. Traditionally, this infrastructure formed a digital image processing system consisting of computer hardware with special-purpose image processing software, and peripheral input-output devices (e.g. CD or DVD drives, internet access, printers/plotters). In recent years, Earth observation is undergoing a shift to online processing making use of data cubes and vast image archives, e.g. NSF EarthCube or Digital Earth Australia, the Swiss Data Cube, the EarthServer, the E-sensing platform or the Google Earth Engine. Available infrastructures aim at sharing remote sensing data and derived products following the FAIR metrics: Findable (F), Accessible (A), Interoperable (I), Reusable (R). Thus, remote sensing data have to be documented using metadata that support FAIR data principles as follows: (1) Findable: remote sensing data are findable through data documentation, i.e. metadata, that needs to include a unique identifier of the described data. Metadata can be stored in a catalog compliant to one of the available data cataloging standards such as the SpatioTemporal Asset Catalog (STAC) compliant catalog; (2) Accessible: all data have to be openly accessible and shared using interoperable formats that allow users to find, access and reuse them; (3) Interoperable: different standards, e.g. STAC specification, have to be used to document remote sensing data; (4) Reusable: metadata have to be comprehensive enough to allow users not only to assess the fitness for purpose (e.g. lineage) but also to provide them information about how to access the generated data.","name":"Infrastructure","selfAssesment":"Completed
"},{"code":"IP6","description":"In an information value chain, one or more organizations perform a set of value-adding activities for creating and distributing information products and services. They support a user in decision-making and thereby benefit the user’s purpose. The information value chain is a tool for evaluating business management and profitability. It enables explaining the ultimate “value” of a product and the components along the value chain and consequently allows businesses to optimize their processes. \r\nThe value of EO data can be assessed by analysing the contribution of the data to a specific EO information product and its effective use in decision-making. The (share of) benefit attributable to the use of the given EO data is derived from the comparison of a decision taken using the EO product to a counterfactual situation where other types of information are used instead. Often, this compares the situation before a new EO service was available to the situation afterwards. An ex-post analysis may reveal improved performances, e.g. gains in output, or productivity and/or reduced costs as compared to those occurring in absence of EO-derived information. This benefit resides with the user of the EO product and may be traced to societal and environmental benefits through impact chains.\r\nThe process of EO information production and distribution is integrated in the value chain and can be defined as the image processing chain. It comprises the value-adding activities of the organization(s) that lead up to the availability of an EO product for decision making. The nature and flow of these activities and the collaboration between organizations and among participants within organizations can be modelled with business process model and notation (BPMN). BPMN is a flowchart diagram that uses swimlanes representing different participants. Processes are assigned to participants and are connected with arrows into flow sequences. Further elements complete the choice of symbols for modelling a consistent flow, including a start event, end events, and branching options. They allow organizing the flow in parallel or iterative processes. Higher-level processes can be (de-)composed with sub-processes. Additionally, it is possible to use pools and message flows for explicitly modelling collaboration between participants (from different organizations).\r\nIn the image processing (value) chain, the sequence of processing steps begins with the acquisition of EO data, followed by steps of pre-processing and information extraction (or whatever steps are necessary) and ends with an EO information product being available to a user that uses it to make his decision. The collaborating stakeholders along the chain include EO satellite operators, EO data providers, EO information providers, and the users at the end of the value chain. The stakeholders along the processing chain each perform a dedicated subsequence of processing steps. Thereby, the stakeholders contribute their share of value to the data they deliver to the next stakeholder in the chain, ultimately arriving a the EO information product for the user. The EO data products that they hand on along the chain are often described with processing levels that provide different states of processing of EO data. They start with raw instrument data (level 0 and 1) that are followed by data converted into geophysical quantities that are geo-referenced and calibrated (level 2). Further levels are quality controlled data that has been mapped on a uniform space-time grid (level 3) and data combined with models or other instrument data (level 4). In addition, EO data providers use the term analysis ready data (ARD) that have been processed to allow direct data analysis, i.e. user processing effort is reduced to a minimum. Further, the standard EO products contain a categorizing element that is related to the image processing value chain. This categorizing element organizes the EO products along the sequences of processing, descriptive analytics, predictive analytics, prescriptive analytics, aggregation, visualization, and distribution. Thereby, the products ultimately contribute to the actionable EO information product for the use in decision-making.","name":"Image processing (value) chain","selfAssesment":"Completed
"},{"code":"MDS","description":"MDS is a dimensionality reduction technique. It can be divided into Metric multidimensional scaling, Generalized multidimensional scaling and Classical multidimensional scaling.\r\n\r\nGeneralized multidimensional scaling is an extension of metric multidimensional scaling, in which the target space is an arbitrary smooth non-Euclidean space. In cases where the dissimilarities are distances on a surface and the target space is another surface, GMDS allows finding the minimum-distortion embedding of one surface into another.\r\n\r\nClassical multidimensional scaling is also known as Principal Coordinates Analysis, Torgerson Scaling or Torgerson Gower scaling. It takes an input matrix giving dissimilarities between pairs of items and outputs a coordinate matrix whose configuration minimizes a loss function called strain.","name":"Multidimensional scaling","selfAssesment":"Depricated (GI-N2K)
"},{"code":"no","description":"Models that describe the basic principles of randomness and probability in spatio-temporal data.","name":"Mathematical models of uncertainty: Probability and statistics","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI","description":"This knowledge area considers the organizational and institutional aspects related to GIS&T. The focus of this knowledge area is on the organizations active in the GIS&T domain, and what happens within and between these organizations. The knowledge area is structured around five units. One unit considers the key organizations in the GIS&T domain, covering relevant public sector organizations at different administrative levels as well as organizations in other sectors of society. Among the organizational aspects covered in this knowledge area are all organizational issues related to the implementation, use and management of GI and GIS within organizations. While all topics related to the organizational structures, procedures and management of GI(S) are grouped into one unit, another unit focuses on issues related to the human factor of using GI and GIS, i.e. people, their skills and competencies, and the development and evaluation of these skills and competencies in the context of GIS&T training and education. The knowledge area includes also several inter-organizational and institutional aspects of GIS&T. Particular attention is paid to the concept of geospatial data sharing, which is about the creation of `spatial data` connections and relationships between different organizations in the GIS&T domain. Spatial data infrastructures are developed to promote, facilitate and coordinate the sharing of spatial data among data providers and data users, and consists of several technological and non-technological components. Many related topics are considered in the knowledge area GI and Society (WS), which also addresses several non-technological aspects related to GIS&T. In addition to this, also the knowledge areas `Design and Setup of Geographic Information Systems`, `Geospatial Data\" and Web-based GI` include several topics that are closely linked to the topics that are considered in this knowledge area. It can be argued that in order to fully master the knowledge and competencies that are presented in these knowledge areas, also basic knowledge and understanding of the organizational and institutional aspects is required.","name":"Organizational and Institutional Aspects","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI1-1","description":"The development of an appropriate organizational model, which establishes the basic character of GIS operations, is a crucial element of the GIS management. The appropriate GIS organizational model for any organization is based on its intended role.Alternative GIS organizational models are based on differing arrangements concerning the scope of GIS, the degree of integration of GIS into business operations, the degree of centralization of GIS operation and use, and the degree of centralization of management control. Although many variations can arise from different combinations of these factors, GIS organizational models can generally be classified into three types: (1) enterprise GIS, (2) GIS data and service resource, and (3) GIS as a business tool (Somers, 1998).","name":"Organizational models for GIS management","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI1-2","description":"Management of GIS can be done in a more centralized or more decentralized manner. In a a so-called enterprise or information-framework GIS, an organizational unit may be established to manage the GIS environment and run the core system, whereas usage is decentralized. In environments where GIS is used occasionally by various users, it may be set up as a separate service with a designated group that manages the GIS and also controls users' applications services. A second decision that needs to be made after the choice between more centralized or more decentralized management of GI and GIS is about where to place the GI management. Alternative options are in a line organization, in a support area, or at the executive level, each with their own advantages and disadvantages.","name":"Managing GIS operations and infrastructure","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI1-3","description":"User roles describe the relationship between different users and the GIS in an organization. Each user role includes responsibilities (e.g. for modifying certain information) and privileges (e.g. for viewing specific information). Although many different roles can be defined, a basic distinction is made between users, who can only view certain information, and editors, who can edit certain information.","name":"User roles","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI1-4","description":"A GIS management strategy should be unique for each organization, as organizations have unique environments, characteristics, goals, GIS requirements. An important step in developing an effective strategy for an organization is to establish the strategic vision for GI and GIS in the organization and define its role and scope. Other elements that should be covered in the GIS Strategy are the degree of centralized management of the GIS, the placement of GIS management and support in the organization, involvement of users in GIS planning and implementation, coordination of users, organizational changes, preparation of users, personnel issues, transitions to GIS operations, integration into business operations, user support, data access, and integration of technology changes (Somers, 1998).","name":"Strategic planning","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI1-5","description":"Committee and team approaches are frequently employed for coordinating participants and users in multi-participant GIS projects. The aim of creating such committees and teams is to ensure that the varied interests of participants are addressed, as participants bring many different interests, application needs, data needs, priorities, organizational issues, and political interests to a common project the GIS. Common models for coordinating participants recognize that participants have three levels of interest in the GIS: policy, technical development, and usage. Different bodies can be established focusing on these different levels of interest: a technical committee focusing on the design and development of the GIS, an management committee providing policy guidance and support and a user`s group.","name":"Coordinating GIS Participants and Users","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI1-6","description":"After the development and implementation of a GIS within an organization, the challenge is to maintain the system and revise and update it when necessary. This means the performance of the GIS in terms of efficiency and effectiveness should be measured and monitoring, and feedback from users on the system and applications, on the data as well as on new needs should be collected. Particular attention should be paid to the maintenance of data sets.","name":"Ongoing GIS revision","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI1-7","description":"The introduction of GIS into organizational environments should be seen as a complex process of mutual adaptation (Nedovic-Budic, 1997). These technologies changes the established organisational processes and structures, while on the other hand the organisational context and culture modify the technological set-up and use. Therefore, knowledge and understanding of the relationship between technologies and organizations is necessary to increase the success of GIS implementations in organizations. Successful GIS implementation and adoption often require some degree of organizational change. However, this can be very difficult to effect because organizations are naturally resistant to it (Somers, 1998).","name":"Organizational changes","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI1","description":"GIS and T implementation and use within an organization often involves a variety of participants, stakeholders, users and applications. Organizational structures and procedures address methods for developing, managing, and coordinating these multi-participant users. The development of the appropriate organizational model for managing the GIS is crucial. In certain cases, changes to the organizational structure in place might be required. Strategic planning and the establishment of coordination structures can be considered as valuable instruments for managing and coordinating all involved users, while also the different user roles need to be assigned.","name":"Organizational structures, procedures and management","selfAssesment":"In Progress GI-N2K
"},{"code":"OI2-1","description":"GIS and T professionals can be hired for a wide range of different job positions, for which the precise skills, competences and qualifications needed will vary. Typical examples of GIS and T positions are GIS&T project managers, technicians, system developers and analyst. The recognition and certification of the competences people have acquired in informal and non-formal learning contexts is important to know which skills and competences individuals have and whether they meet the qualifications required for a certain job position.","name":"GIS and T positions and qualifications","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI2-2","description":"Making sure staff members have the necessary skills and competences to perform geospatial activities is necessary for an effective implementation and operation of GI within an organizations. Several training methods can be adopted to ensure the development of skills and competencies of staff members. A distinction can be made between formal and informal training, but also between internal and external training programs. Another relevant issue is the assessment and evaluation of the skills and competences of staff members, to determine their future training and development needs.","name":"GIS and T staff development and evaluation","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI2-3","description":"Programs and courses on GIS and T and related subjects are provided by a wide range of institutions. While in recent years also the use and integration of GI and GIS in primary and secondary education has received significant attention, GIS and T education is mainly organized by institutions of higher education, especially universities but also other higher education institutions. Analyses of the higher education GIS&T programs and courses in Europe showed that the offer of courses is very diverse, in terms of size (ECTS), educational level (EQF) and course content. Vocational training on GIS and T related topics is organized by different types of training providers, including the major GIS vendors, data and service providers, academic sector, professional organisations, but also the public sector.","name":"GIS and T training and education","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI2-4","description":"A curriculum is a systematic description of a study program, in terms of learning goals, structure and sequence, learning, teaching and assessment strategies and content. A curriculum consists of both a set of related required and elective - courses along with all direct and indirect skills, competences and learning outcomes resulting from these courses. In the process of curriculum design typically particular attention is assigned to objectives, teaching methods and educational strategies, while also attention should be paid to the content organization aspects and the global structure of the curriculum. The process of designing GIS&T curricula presents many challenges, as the design of the curriculum should be aligned to both the institutional context and the expected outcomes of the learning and teaching process (Prager, 2011).","name":"GIS and T curriculum and course design","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI2-5","description":"An important challenge in organizing GIS and T education and training is the choice and use of effective teaching and learning methods. These methods should follow recent technological developments and use the best technologies to help students acquire the necessary skills and competencies. Traditionally, most GIS and T programs and courses were taught in the context of a full-time, face-to-face setting, using traditional teaching methods such as lectures and lab-based computer practical sessions. In recent years, educational institutions and their teachers have been experimenting with more innovative teaching and learning methods, such as project-based and case-based learning, distance learning, integrated and inter-disciplinary lessons, collaboration with companies and other stakeholders, etc.","name":"GIS and T teaching and learning methods","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI2","description":"This unit addresses GIS and T staff and workforce issues within an organization, particularly as they relate to ensuring that GIS and T is appropriately used and supported. The focus of this unit is on the skills and competencies of professionals in the GIS and T domain: how can these skills and competencies be described and evaluated, and how can they be developed through training and education.","name":"GIS and T workforce themes","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI3-1","description":"Cost savings are an important driver or motivation for sharing geospatial data and information. As costs associated with collecting and maintaining geospatial data are high, sharing data means that users no longer need to duplicate data gathering and archiving, which leads to savings in terms of personnel, space/facilities, data acquisition and maintenance costs. One fundamental argument for sharing thus derives from scale economies in production. Because the cost of making data is high, there is a clear incentive to maximize the number of users of these data. Sharing allows data to be used repeatedly for many purposes, thus increasing their value without increasing their cost. Sharing data also leads to improved data quality. Moreover, in many cases, sharing data is the only way to get access to certain data sets, as the authority to collect and manage certain data lies with another public institution.","name":"Drivers and incentives for sharing geospatial data","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI3-2","description":"Sharing of geospatial data can be hindered or inhibited by several types of barriers. These include technological barriers, such as a lack of common data definitions, formats and models or incompatibility of hardware and software. Among the non-technological barriers are organizational, political and legal issues and elements, such as misaligned organizational missions, diversity in organizational cultures, conflicting organizational priorities, lack of funding, lack of executive and legislative support; restrictive laws and regulations, copyright issues, data privacy and data ownership issues. However, it should be noticed that many of these barriers have been decreased or eliminated in recent years.","name":"Barriers to geospatial information sharing","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI3-3","description":"The legal framework for geospatial data sharing is very wide and diverse, involving rules on data, coordination, standards, funding, etc. Moreover, these rules and regulations can take many different forms: legal acts adopted by parliament, executive orders or decisions, cooperation agreements, memoranda of understanding, bilateral arrangements etc. From a data perspective, the legal framework can be distinguished into two main types of policies: those that promote and those that hinder the availability of spatial data. Policies that promote spatial data availability can focus on different types of users (public bodies, private companies, citizens) and different types of use (public access, commercial and non-commercial reuse, reuse for performing public tasks). Among the policies that hinder the availability of spatial data are those dealing with privacy, liability, and intellectual property. The legal framework also includes legislation that applies to data or information in general, such as open data legislation, which may also be applicable to spatial data (e.g. legislation on freedom of information, copyright, etc.). Moreover, also general legislation relating to any interaction between people or any situation in everyday life (e.g. liability, contract law, competition law, etc.) will apply to spatial data sharing.","name":"Legal framework for geospatial data sharing","selfAssesment":"Completed
"},{"code":"OI3-4","description":"Several types of legal mechanisms for sharing geospatial data can be used. A data sharing arrangements can be formalized by a contract or agreement between the data provider and the data user. A particular type of agreement are the framework agreements, which are agreements between two or more organisations concluded prior to the datasets or services being required. These framework agreement can involve one or multiple spatial data sets or services. Partnership agreements are often used to formalize the data sharing agreements among a broader group of partners. Participation in such a partnership often means participants share their data with other participants and get access to shared data. Another relevant mechanism is the use of licenses, which are mechanisms to give organizations and people the permission to use spatial data sets and services. A license is legally binding, and defines the conditions of use of the related spatial data sets and services. In order to reduce the number of licenses used and ensure the harmonization of the terms in these licenses, the use of standard licenses is promoted. Also the use of open data licenses is promoted for sharing geospatial data, and strongly increased in recent years.","name":"Legal instruments for sharing geospatial data","selfAssesment":"Completed
"},{"code":"OI3","description":"Geospatial data sharing has become an essential element of the GI activities of organizations. Spatial data sharing can be defined as the electronic transfer of spatial data/information between two or more organizational units where there is independence between the holder of the data and the prospective user. Spatial data sharing has many advantages, but several technical and non-technical barriers must be overcome to put data sharing into practice. While the practice of spatial data sharing has substantially grown with the development of spatial data infrastructures, many consider data sharing as a crucial element for the success of these infrastructures.","name":"Geospatial data sharing","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI3b","description":"A Spatial Data Infrastructure can be defined as the collection of technological and non-technological components to facilitate and coordinate the exchange of and sharing of spatial data. The concept infrastructure is used to promote the concept of a reliable, supporting environment, analogous to a road or telecommunications network, that facilitates the access to spatial data. Data, metadata, access networks, standards, coordination, policies, funding, people and institutional frameworks are often considered among the key components of an SDI. \r\n\r\nSpatial data infrastructures often are defined and described as a complex and dynamic phenomenon. Among the main reasons for the complex character of these infrastructures are the many components a spatial data infrastructure consists of, the diversity of involved stakeholders, and the many different objectives and ambitions of these stakeholders. Technological advancements, such as the emergence of web 2.0 technologies, and societal changes, such as the increasing use of geographic information in everyday life, are often mentioned as important drivers behind the dynamic character of spatial data infrastructures. \r\n\r\nA key characteristic of spatial data infrastructures is the involvement of a large and diverse group of actors. Governments are often considered as the central actors in the development and implementation of spatial data infrastructure, since they are the major producers and users of geographic information. Governments at different administrative levels and in different thematic domains are involved in the creation, management, use and sharing of geographic data. But also private companies, non-profit organisations, research and education institutions and even citizens can participate in the development and implementation of a spatial data infrastructure. It is increasingly being argued that the involvement and engagement of each of these stakeholders group is essential to the realization of a successful spatial data infrastructure. \r\n\r\nSDIs have been developed in many countries worldwide at local, national and international levels. Often a distinction is made between a between the first generation SDIs that have data as their key driver and are based on a product model and second generation SDIs in which user needs are the key driver and that are based on a process or development model. The latest generations of SDI strongly focus on the inclusion and engagement of non-government actors and organizations in the development and implementation of the SDI. Although SDI are by default distributed systems, involving many organisations, some SDI might be developed rather in an hierarchical way, while others are following a networked approach.","name":"Spatial Data Infrastructures","selfAssesment":"Completed
"},{"code":"OI4-1","description":"The adoption and implementation of standards are two key phases in the standardization process, which starts with the definition of standardization requirements and the development of standards. The adoption and implementation of standards follows after the development phase. The distinction made between the adoption and implementation of standards is important: adoption entails the decision to apply standards, while the implementation relates to the integration of standards in software, in data development and in other processes. GI-Standards are one of the key components of each SDI, consist of both semantic and technical standards, and include standards related to the different architectural components of an SDI, i.e. standards related to spatial data sets and data products, web services, metadata and catalogues, encodings, etc.","name":"Adoption and implementation of standards","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI4-2","description":"The SDI policy framework includes the set of policies, strategies, initiatives and projects aimed at increasing access, sharing, and effective use of spatial data. SDI policies can be divided into strategic and more operational policies. Strategic policies define the broader framework and formal structure within which the SDI initiative is developed. Operational policies provide more practical tools to facilitate access to and use of the SDI, and address specific topics related to the collection, management, use, access and dissemination of spatial data. These operational policies include a broad range of guidelines, directives, procedures and manuals that apply to the day-to-day business of organizations in developing, operating and using an SDI. To guarantee the success of an SDI, it is important to recognize the wider policy context in which these SDI`s are developed, and to link them to the overall policy environment in the jurisdiction in which they are implemented. These include policies on open government and open data, environmental policies, digital government or e-government policies and other.","name":"Policies","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI4-3","description":"If is often argued that SDI implementation requires coordination, because without coordination all other SDI components would not be developed or would be developed in a very fragmented and inconsistent manner. In general terms, coordination is about bringing into alignment the activities of different stakeholders in the SDI landscape. A typical instrument to realize coordinate in the context of SDI, is the establishment of an effective SDI coordination structure. The SDI coordination structure should ensure that all stakeholders are involved in the development and implementation of the SDI, through the participation in one or more coordination bodies. Another important element is the establishment of clear roles and responsibilities for the different involved organizations, making a distinction between data users, data providers, services providers and a geo-broker.","name":"Coordination and organizational structure","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI4-5","description":"Funding an SDI is about guaranteeing the long-term financial security of an SDI, by obtaining and formalizing financing for the implementation and maintenance of the different SDI components. An SDI funding model provides the answer to the central question of where and how to seek funding for implementing and maintaining an SDI. Within an SDI often different funding models will be combined, as the selection of the most appropriate funding model will be linked to different activities and the associated costs. Costs of an SDI include both set-up costs (one off costs) and maintenance costs (yearly), of which certain costs need to be made for each data sets or each data provider and other costs for the infrastructure in general. The most commonly used SDI funding models are centralized government funding, decentralized government funding (e.g. for each data provider), partnership funding, funding through revenues, and government funding based on donor agencies or on European projects.\r\n\r\nThe shift towards open data and the adoption of open data policies had an important impact on the funding model of many SDIs, as governments and organizations no longer could rely on revenues from selling their data and had to look for other funding models. As a result, new pricing strategies are employed, such as the provision of fee-based supplementary services, such as advice or tailor-made products based on open data. Also freemium/premium models, in which a basic version of the dataset is offered as open data (freemium) but the full dataset is available for a fee (premium), were considered as an alternative approach. In many cases, the loss of revenues was compensated by other funding models, such as increased government funding.","name":"Funding an SDI","selfAssesment":"Completed
"},{"code":"OI4-5b","description":"SDI performance assessment is about collecting, analyzing and providing information on the performance of SDI initiatives. Assessment and evaluations of SDIs are a useful tool for those organizations and people directly involved in these initiatives, but also for researchers, citizens, journalists and other stakeholders. Decision makers and practitioners can use assessments to monitor the progress against the objectives of their SDI initiatives and to identify areas where improvement can be achieved. Assessment also allows to compare and benchmark the performance of different organizations or countries, and to learn from best practices. Finally, assessment also is relevant for accountability, since it enables governments and agencies to be held accountable for their decisions, activities and the resources they have invested. Assessment of SDIs, which deals with the collection and supply of information on the performance of SDI initiatives, should be seen as the first step in a logical consequence of collecting data, integrating this data in policy and management cycles and actually using the information. \r\n\r\nIn the past twenty years, many different SDI assessment frameworks have been developed by researchers and practitioners around the world. Examples of such frameworks are the INSPIRE State of Play Study, the Clearinghouse Suitability Index, the Organisational Maturity Matrix, the SDI Readiness Index, and the INSPIRE Monitoring and Reporting approach. Each of these frameworks focus on particular aspects and components of SDIs. In line with the categorization of open data assessment, also SDI assessments can be divided into three main categories: (1) readiness assessments, (2) implementation or data assessments, and (3) impact assessments. Readiness assessments analyse whether conditions are appropriate, and whether necessary components are in place for developing an SDI. Implementation or Data assessments evaluate whether geospatial data are available and accessible. Impact assessments explore the extent to which SDIs lead to benefits for government, citizens, business and society in general.","name":"SDI performance measurement and assessment","selfAssesment":"Completed
"},{"code":"OI4-6","description":"For a long time, SDI development has focused on the development and implementation of different components with the aim of facilitating the access to and sharing of spatial data. An key challenge in future SDI development will be the integration of these SDI`s in a wider context. In order to optimally take advantage of the data and services provided by an SDI, integrating these data and services into the processes and workflows of public and private organizations will be crucial. The concept of spatial enablement refers to the challenge of developing SDI`s in such a way that they provide an enabling platform that serves the wider needs of society in a transparent manner. Moreover, the diffusion of SDIs, together with the efforts to build a Global Earth Observation System of Systems (GEOSS) and other developments in industry and civil society should be considered as elements in a the realization of a vision on the next-generation Digital Earth.","name":"Next-generation SDIs","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI4-7","description":"The effective implementation of SDIs requires governance, which includes the structures, policies, actors and institutions by which the infrastructure is managed pertaining to decisions made for accessing, sharing, exchanging and using the relevant available spatial information. While SDIs themselves are considered as initiatives contributing to good governance or effective governance, a key challenge in the establishment of SDIs is the governance of the infrastructure itself. Governance of SDIs is essential for the implementation of different SDI components in a coordinated and consistent manner. The central challenge of governance is reconciling collective and individual needs and interests of different stakeholders in order to achieve common goals. This aims to reduce gaps, duplications, contradictions and missed opportunities in the production, management, sharing and use of the information that tend to occur in a multi-stakeholder environment.\r\n\r\nGovernance can be facilitated through the use of appropriate instruments which extend to various levels of government and take into account the distribution of powers and responsibilities among different actors and institutions with an interest in the infrastructure. The governance instruments should coordinate the activities and contributions of, inter alia, data producers, users, added-value services providers, and other stakeholders. More complex and inclusive models of governance are required to cope with the multi-level nature of SDI implementations of the current generation of SDIs. Effective and inclusive SDI governance structures are needed, that are both understood and accepted by all stakeholders. Governance of SDIs also requires expanding the scope of stakeholders to include the private sector, research bodies and other actors outside the public sector including citizens, to actively promote bottom-up and participatory processes, and to find the appropriate mechanisms and instruments to enable the participation of these non-government actors.","name":"SDI governance","selfAssesment":"Completed
"},{"code":"OI5-1","description":"Within the European Commission there are several key GI players. GIS activities in the Commission started since 1981 (e.g. DG REGIO, Eurostat, ) with the CORINE project, the creation of DG ENV and the creation of the European Environment Agency (EEA). Together with the DG Joint Research Centre (JRC), DG ENV and EEA are in charge of the coordination of INSPIRE: DG Environment acts as an overall legislative and policy co-ordinator for INSPIRE, the JRC acts as the overall technical co-ordinator of INSPIRE and EEA is in charge of several tasks related to monitoring and reporting, and data and service sharing under INSPIRE. Also several other EC institutions are actively involved in GI(S) policies and activities (DIGIT, DG GROW, DG AGRI, DG MOVE and many others).","name":"GI organization at the European Commission","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI5-2","description":"Although there may be certain differences between countries, in most countries many key organizations in the GIS&T field will be active at the central/federal/national level of government. Especially the traditional institutions for surveying and mapping play a key role in geospatial policies and activities. Several public authorities at the federal level are in charge of the production and maintenance of key reference and thematic data sets. In many countries, these national data producers were the leading actors in the development of national spatial data infrastructures.","name":"Federal and national government organizations","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI5-3","description":"Local and sub-national governments are often considered among the major users of geographic information in governments, as they often are involved in many different policy areas, in which many problems with a locational component need to be tackled. Geographic data produced and maintained by authorities at lower administrative levels are often more detailed and thus interesting for other users, both within and outside the public sector. As a result, local and sub-national governments are often involved in the establishment of these infrastructures because of the wide range of highly detailed geographic information they produce and manage. As many geographic data are linked to the activities and services of local organizations, the involvement of these organizations in the maintenance of data ensures that these data are up-to-date.","name":"Sub-national and local governments","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI5-4","description":"The European GIS&T landscape consists of many pan-European organizations and associations promoting the interest of and representing certain stakeholder groups. While some of these organisations are dealing with all sectors and aspects of geographic information, others have a more thematic focus (e.g. remote sensing, topography, geosciences) or represent a particular sector (e.g. research, business). In some cases, their clearly is an overlap in the mission and objectives of different organizations, and some organizations are working in the same field of interest. Some examples of pan-European organizations and associations are AGILE, EuroSDR, EUROGI, and EuroGeographics. Also at international level several membership organizations and associations exist.","name":"Pan-European and global associations and professional organizations","selfAssesment":"GI-N2K
"},{"code":"OI5-5","description":"The geospatial industry consists of companies working with location specific information or services. Within the geospatial sector, several areas of activities can be identified: 1) measuring, collecting and storing of data about geo-objects; 2) processing, editing, modelling, analyzing and managing that data; 3) presenting, producing and distributing the data; and 4) advising, educating, researching and communicating about processes and use of geo-information products and services. The sector consists of both small-and-medium-sized enterprises but also big companies, including surveyors, census hard-copy map providers, aerial photos providers, base map data providers, satellite and remote sensing imagery providers, software developers (GIS-related products and services providers as well as satellite image programming platform providers) and several others.","name":"The geospatial industry","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI5","description":"Several types of organizations play a key role in the execution and coordination of geospatial activities in society. Typically, a distinction is made between data providers and data users, while coordinating organizations exist to coordinate and support the geospatial activities of professionals and entities using GIS&T. Governments are often considered as the major users and producers of spatial data and spatial information. Within the public sector, spatial data are collected and used in different thematic areas and at different administrative levels (from local to global). However, the needs, interests, and capacities of organizations at each of these levels will be different, as well as their role in the development of spatial data infrastructures, and the execution of geospatial activities in general. Also the geospatial industry will exist of both data providers and data users, but also of organizations delivering products and services to support the collection and use of spatial data. Other key organization in the GI domain are professional organizations and associations, bringing together and representing the needs of organizations of a particular sector and/or geographic area.","name":"Organizations in the GIS and T domain","selfAssesment":"In progress (GI-N2K)
"},{"code":"PP","description":"The knowledge of physical laws and principles regulating the emission of e.m. radiation and its interactions with the matter, as well the ones related to the design, setting-up and control of EO platforms and related instruments, are of paramount importance for a right interpretation of EO measurements in relation with the investigated Earth's phenomena and parameters. The most important physical fundaments regards: the theory of electromagnetic waves propagation described by the Maxwell's equations, the theory of e.m. radiation and of its interaction with the matter, the methods and instruments for e.m. radiation measurement and/or generation, the fundamentals of thermodynamics and of mechanics. As far as Earth Observation is concerned, further, specific topics have to be addressed which are related to: spectral-specific matter-radiation interactions, natural (e.g. Earth, Sun) and artificial (e.g. MW) sources of e.m. radiations, atmospheric physics and radiative transfer equations, basic physics of e.m., optical and MW, sensors and sources, theory of satellites orbits, theory of rockets, physical fundaments of interpretation of optical and MW data collected by passive and active techniques.","name":"Physical principles","selfAssesment":"Completed
"},{"code":"PP1-1-1","description":"Electromagnetic radiation travels in wave form. All electromagnetic waves travel at the speed of 299.793 km/sec in a vacuum and very nearly the same speed in air. In quantum physics electromagnetic radiation is also described in terms of particles called photons whose energy is given by the equation E = hf where h is the Planck constant and f the frequency of corresponding wave. Electromagnetic wave propagation is fully described by the Maxwell Equations that unified in 1860s the laws of electricity and magnetism.","name":"Electromagnetic Waves and Photons","selfAssesment":"Completed
"},{"code":"PP1-1-10","description":"The solar constant S is a quantity denoting the amount of total (i.e., covering the entire solar spectrum) solar energy reaching the top of the atmosphere. It is defined as the flux of solar energy (energy per unit time) across a surface of unit area normal to the solar beam at the mean distance between the sun and the earth. Solar insolation is defined as the flux of solar radiation per unit of horizontal area for a given locality. It depends primarily on the solar zenith angle and to some extent on the variable distance of the earth from the sun. It can be computed as a function of latitude and the time of year taking into account of the secular variations of Earth's orbit eccentricity e, the oblique angle ε, and the longitude of the perihelion relative to the vernal equinox ω. The daily insolation is the total solar energy received by a unit of area per one day. It may be calculated by integrating total insolation over the daylight hours. It is particularly important, together with information on cloud coverage, in order to plan and manage solar power systems. Yearly total insolation together with average cloud coverage are among the most important parameters to be considered for the choice of the best (i.e. the ones promising the higher energy production) location of solar power plants. Modeled daily solar insolation together with short/medium-term forecast of cloud coverage are also fundamental for the management (e.g. for planning the suspension of activities for maintenance) of solar energy production plants .","name":"Solar constant, solar insolation, daily insolation","selfAssesment":"Completed
"},{"code":"PP1-1-11","description":"Earth's itself represents the second (after Sun) most powerfull natural source of e.m. radiation for EO. Even if very less powerfull than Sun such a source is available for EO day and nigth. Its average emittance can be approximated by that of a blackbody at about 290 K. The maximum of its emission, following the Wien's Law, falls then around 10 micron (in the Thermal InfraRed - TIR spectral range) being Earth's emission trascurable in the VIS-SWIR range.\r\nMost of Earth's thermally emitted radiation falls in the spectral range 8-14 microns where it benefits of a quite high atmospheric transmittance (TIR atmospheric spectral window) in standard atmospheric conditions. However thick clouds prevent TIR radiation to reach satellite sensors (adsorbing and/or reflecting backward the radiation leaving Earth's surface) so that ground resolution cells affected by clouds are usually identified (cloud-mask) in the image pre-processing phase and not considered for further elaboration devoted to investigate surface properties. Even if very low in intensity, Earth's emitted radiation in the Far InfraRed (FIR) and in the MicroWaves (MW) spectral ranges are also used for quite important investigation related to the Earth's Energy balance (FIR) and for meteo-climatological applications. The complete transparence of Earth's atmosphere to the MWs, even in presence of meteorological (not precipitating) clouds make this Earth's emitted signal particularly important for application (e.g. climatological) requiring temporal continuity (all weather) of observations of Earth's surface properties like Temperature, Soil wetness, etc.. However, due to the weakness of the Earth's emitted signal in the MW ranges, such products can be achievable just at quite low spatial resolution (e.g. > 10km) by passive EO MW sensors","name":"Earth's radiation (intensity, spectrum, etc.)","selfAssesment":"Completed
"},{"code":"PP1-1-2","description":"In principle, the frequency f (and the wavelength λ=c/f) of an electromagnetic wave can take any value and the whole range of possible frequencies is called the electromagnetic spectrum. Different regions of the spectrum are conventionally given different names (with associated spectral ranges smoothly depending on specific science sector): \r\ngamma-rays\t λ< 1 pm\r\nx-rays\t1 nm >λ>1 pm\r\nUltraviolet (UV) 400 nm >λ>1 nm\r\nVisible (VIS) 700 nm >λ> 400 nm (blue: 455 – 492, green 492 – 577, yellow 577 – 597, red 622 – 700)\r\ninfrared (IR)\t1000μm >λ> 0,7 μm (Near-IR - NIR: 0,7-1,3; Short-Wave IR SWIR: 1,3-3; Medium IR - MIR: 3-6, Thermal IR - TIR: 6-20; Far IR - FIR: 20-1000)\r\nRadio waves\t λ> 1 mm (Microwaves MW\t1 m >λ> 1mm). Optical range (usually referring to the spectral range from VIS to TIR) and microwaves are the most important spectral region for remote EO systems.","name":"Electromagnetic spectrum","selfAssesment":"Completed
"},{"code":"PP1-1-3","description":"Maxwell equations are a set of coupled partial differential equations that contains the fundamentals of electricity and magnetism. These equations provide electromagnetic waves that propagate into the space at the speed of the light. Increasing the wavelength there are gamma rays, X-rays, ultraviolet, (visible) light, infrared, microwaves and radio waves.","name":"Maxwell Equations and EM waves' propagation","selfAssesment":"Planned
"},{"code":"PP1-1-4","description":"Planck's law is a mathematical relationship for the spectral radiance emitted by a blackbody (i.e. a body that absorbs all radiant energy falling on it) at a given temperature as a function of frequency or wavelength. From another point of view it can be used to define a black-body as a body emitting radiation following Planck's law. The model of black-body is fundamental to simplify the description of the radiation thermally emitted by a generic body at a pre-fixed temperature and wavelength as the product of its (specific) spectral emissivity and the value predicted (at the same wavelength) by the Planck's law for a black-body at the same temperature. This way the radiation thermally emitted by a generic body can be expressed just as a (specific, as modulated by the spectra emissivity) fraction of the one expected for a black-body. Wien’s displacement law is the relationship between the temperature of a blackbody and the wavelength at which it emits the most radiation. Wien found that the product of the peak wavelength and the temperature is an absolute constant. As far as the temperature T of the blackbody increase the intensity of the emitted e.m. radiation increases being, at whatever wavelength, grater than the one emitted by a blackbody at lower temperature (Planck). As far as the blackbody temperature increases its maximum emission occurs at lower and lower wavelengths. Wien's law is fundamental both in the selection of the spectral bands more appropriate for observing specific phenomena as well as for remotely retrieve temperature of far objects by the analysis of the emitted spectral radiances.","name":"Planck law for the black body. Wien's displacement law","selfAssesment":"Completed
"},{"code":"PP1-1-5","description":"The Rayleigh–Jeans Law is an approximation of the Planck’s law for a blackbody that states that, under certain conditions, emitted radiance is directly proportional to the blackbody temperature. Such an approximation, fits quite well with measurements of radiation emitted by sources at around 300K of temperature (like, in average, for the Earth) at wavelengths higher than 1mm (microwaves).. Wien’s approximation can be used to describe the emission spectrum of a high temperature blackbody n the VIS-NIR spectral range lengths. The estimated errors is less than 2% at wavlengths less that 5microns when a blackbody at around 6000K (like the Sun photosphere) is considered. \r\nThe Rayleigh–Jeans approximation is widely used in the processing of satellite images collected by passive MW sensors. Its extension to the thermal infrared spectral range (TIR) is also used for calibrating TIR satellite images (in this case linearity can be guaranteed just by steps on different brigthness temperature intervals).","name":"Rayleigh-Jeans approximation. Wien's approximation","selfAssesment":"Completed
"},{"code":"PP1-1-6","description":"The total radiant intensity B(T ) of a blackbody at the absolute temperature T can be derived by integrating the Planck function over the entire wavelength domain from 0 to∞. Since blackbody radiation is isotropic, the flux density emitted by a blackbody is therefore F = π B(T ) which is proportional to the fourth power of the absolute temperature T through the Stefan-Boltzmann constant σ = 5.67 × 10−8 J m−2 sec−1 deg−4.\r\nKirchoff's law establishes that for a medium at the thermodynamic equilibrium, the spectral emissivity ε(λ) at a given wavelength λ, is equal to the its spectral absorbance, A(λ) at the same wavelength λ. Hence ε(λ)=A(λ) at each fixed λ, for a blackbody ε(λ)=A(λ)=1 at whatever λ. Kirchoff's law is valid also in Local Thermodynamic Equilibrium (LTE) conditions as the ones usually occurring in (small volumes of) the Earth's atmosphere even in the most turbulent conditions.\r\nKirchoff's law has important applications also for the study of spectral signatures of mineral and rocks and, in general, of opaque - i.e. with spectral transmittance T(λ)=0 - bodies. In that case, the relation which relate the spectral reflectance R(λ), absorbance A(λ) and transmittance T(λ) of a body: R(λ)+A(λ)+T(λ) =1\r\nreduce to R(λ)+A(λ)=1 and in LTE conditions, thanks to the Kirchoff's law: \r\nR(λ)+ε(λ)=1 which allows to obtain measurements of spectral emissivity indirectly through (more simple and stable) measurements of spectral reflectance:\r\nε(λ)=1-R(λ)\r\nRocks and mineral exhibit important (diagnostic/discriminating) signatures in their spectral emissivity in the thermal infrared (TIR) region. Measuring spectral emissivity in a laboratory (particularly if samples have to be characterized for their properties in natural conditions) is a quite difficult task due to the difficulty to insolate the sample from the lab environment (and instruments themselves) all emitting approximately at the same (environmental) temperature. Kirchoff's law allows to obtain, for opaque bodies, spectral emissivities from spectral reflectances measurements which are much easy to realize in normal remote sensing labs.","name":"Stefan–Boltzmann law. Kirchoff law","selfAssesment":"Completed
"},{"code":"PP1-1-7","description":"All bodies at a temperature T>0 K emit electromagnetic radiation at all wavelengths (thermal emission). Such emission at each wavelength is increasing with T and it is maximum for Black Bodies whose spectral emittance I(λ,T) (at each prefixed T and wavelength λ) is defined by the Planck function B(λ,T). Generic bodies are expected to thermally emit less than a black body (having the same temperature T) at whatever wavelength. Spectral emissivity ε(λ) is defined as the ratio of the spectral radiance I(λ,T) emitted by a generic body and the one emitted by a Black Body at the same temperature, i.e. ε(λ)= I(λ,T) / B(λ,T). By definition its value is less or equal (Black Body) than 1. The spectral emissivity concept allows to describe in a simple way the spectral radiance I(λ,T) thermally emitted by a body at a temperature T by I(λ,T)= ε(λ)*B(λ,T). It is possible to invert the Planck Function to obtain from the emitted radiance at a prefixed wavelength the temperature T=f(B, λ) of the emitting Black Body. If in such expression the spectral radiance I emitted by a generic body is used instead than B, the resulting temperature, Tb=f(I, λ), is named Brigthness Temperature being Tb<=T (with Tb=T in case the emitting body is a Black Body). The concept of Brigthness Temperature is substantially a different way to measure the spectral radiance of a generic body. It is usually preferred (for instance calibrating Thermal InfraRed – TIR – satellite images) because the interpretation of such a digital image is much more intuitive than when spectral radiances are used instead. In fact, as at each prefixed temperature generic bodies are less emitting than Black Bodies, wherever across a digital satellite image we consider the values of reported Tb, we can say that the actual temperature T of the corresponding emitting ground resolution cell is not less than Tb.","name":"Concepts of Spectral Emissivity and Brightness Temperature.","selfAssesment":"Completed
"},{"code":"PP1-1-9","description":"Sun represents the most powerful natural source of e.m. radiation for EO. The main source of its radiation is the nuclear fusion of Hydrogen into Helium which occurs in central part (“Core”) of the Sun. Outside, the energy transfer is dominated by radiative process (“Radiative zone”) then by convection (”Convective zone”). Solar radiation at the Top of the Earth Atmosphere comes from the outer layer of the sun, the photosphere, whose estimated (conventional) temperature is 6000-6300 K. Its emittance can be approximated by that of a blackbody at about 6000 K but just its reflected component (SOR) is actually available (and just during daytime) for EO. The maximum of SOR falls in the visible spectral range. Its contribution in the thermal infrared range is neglectable but in the medium infrared SOR is still significant enough and, in daytime, superimposed to Earth's thermal emission. The high intensity of solar refelcted radiation (SOR) coupled with the high atmospheric transmittance in the VIS/NIR range, guarantee the highest signal-to-noise ratio for sensors operating in that spectral range. This huge amount of available signal, together with the development of advanced micro-sensor technology (started with the Charged Coupled Devices - CCD etc.), explains why the EO passive sensors with the highest spatial and/or spectral resolution presently achievable, are operating in the VIS/NIR range.\r\nachievable by operating in this spectral region.","name":"Solar radiation at the Top of the Atmosphere. Solar spectrum","selfAssesment":"Completed
"},{"code":"PP1-1","description":"EM radiation is created when an electrically charge particle, such as an electron, is accelerated by a force causing it to move. The movement produces oscillating electric and magnetic fields which travel, as an harmonic EM wave, at right angles to each other. EM waves travel at 299,792,458 meters per second in a vacuum (the highest possible speed into the Universe, also known as the speed of light). \r\nThe electromagnetic field propagating through the space as EM waves is also referred as electromagnetic radiation. \r\nAn EM wave is characterized by a frequency (or by a wavelength) and by an amplitude (or by an energy). \r\nThe wavelength is the distance between two consecutive peaks of a wave. This distance is given in meters (m) or fractions thereof. Frequency is the number of waves that form in a given length of time. It is usually measured as the number of wave cycles per second, or Hertz (Hz). It is wave speed=frequency*wavelength so that, an EM wave traveling at the speed of light, can be equally identified by its wavelength or by its frequency. The amplitude (i.e. the maximum oscillation of the EM field) provide the intensity (i.e. the energy) of the EM wave. \r\nThe classical theory describes the EM radiation as electromagnetic waves which represent the oscillations of electric and magnetic fields. In the quantum mechanics theory EM radiation consists of photons, quanta of the electromagnetic energy, responsible for all electromagnetic interactions.\r\nAs far as Earth remote sensing is concerned EM radiation represents the most important vehicle of information.","name":"EM radiation","selfAssesment":"Completed
"},{"code":"PP1-2-1","description":"The study of the absorbption/emission of electromagnetic radiation by atoms. Depending on the atomic number characteristic frequency or wavelength are absorbed or emitted. Since each element has a characteristic spectrum of absorbed/emitted wavelengths (spectral signature), atomic spectroscopy allows the determination of elemental compositions even of remote objects (e.g. stars, galaxies, etc.).\r\nStarting from the simple Bohr’s model it is possible to predict quite exactly the frequencies of e.m. radiation selectively absorbed/emitted by all atoms. Depending on the atomic number Z, characteristic frequencies f are absorbed or emitted by atoms corresponding to the electronic transitions from different energetic (quantized) states following the Bohr’s condition: fab=(Eb- Ea)/h, being Ei=-cost∙Z2/(ni)2 the electron energy corresponding to the state/level i (principal quantic number ni). By this way each atomic species has a characteristic spectrum of absorbed/emitted frequencies (atomic spectral signature) so that atomic spectroscopy allows the determination of elemental compositions even of remote objects. By this way the existence of Helium was discovered in the 1968 by Jansen and Lockyer in the Sun photosphere well before its discover on the Earth, and the knowledge of the chemical composition of stars and galaxies was possible well before the end of XIX century. Atomic spectroscopy provides a simple and powerful introduction (through the explanation of the more complex interactions of e.m. radiation with molecules and solid matter) to the fundamental concepts of spectral signature (which is at the base of most of the applications of aerial remote sensing of the Earth’s surface) and atmospheric windows (important for the design of optical sensors devoted to remotely sense Earth’s surface) being moreover propaedeutic to the understanding of methods for the atmospheric vertical sounding based on the concepts spectral lines broadening and related weighting functions.","name":"Atomic spectroscopy","selfAssesment":"Completed
"},{"code":"PP1-2-10","description":"The Rayleigh roughness criterion is a widely used means to estimate the degree of roughness of a considered surface. Considering the phase difference between two rays scattered from separate points of the surface, this is proportional to the roughness ∆h (average deviation from the average surface height ) the cosine of the incident angle and, inversely, on the radiation wavelength (λ). The Rayleight criterion states that a surface can be considered as smooth (mostly reflecting) if the phase difference is less than π/2 radians.\r\nAs a consequence, in the case of normal incidence (i.e. θ=0), average roughness of the surface must be less than λ/8 to have an effectively smooth surface. For instance: i) at optical wavelengths (e.g. 0.5 micrometers), surface roughness ∆h must be less than about 60 nm to have a specular reflection. Only certain man-made surfaces (e.g. sheets of glass or metal) may meet such a condition; ii) at VHF radio wavelengths (e.g. 3 m), roughness height need only to be less than about 40 cm. Unlike the previous case, a number of natural surfaces may meet this condition.\r\nIt is worth noting that large values of the incident angle may satisfy the criterion more easily as compared with the normal incidence. This means that a moderately rough surface may be effectively smooth at glancing incidence. This condition may be easily experienced when eyes are struck by the glare of reflected sunlight from a low sun over an ordinary road surface. More strict conditions for classifying a surface as a mirror or a diffuser at an established whavelength λ are: ∆hcosθ/λ > 1/8 for a rough surface operating as a diffuser; ∆hcosθ/λ < 1/25 for a smooth surface operating as a mirror.","name":"The Rayleigh roughness criterion","selfAssesment":"Completed
"},{"code":"PP1-2-11","description":"The Bidirectional Reflectance Distribution Function (BRDF) is defined as the quotient between the spectral radiance Ir(θr,φr) reflected by a sample in a particular direction (θr,φr) and the spectral irradiance F(θi,φi) from the source that illuminates it under a direction (θi,φi) . It depends on both the incidence and viewing angles. From this point of view it represents an absolute definition of reflectance whose value, as is known, depends on the geometry of the illumination and observations directions. This function well describes variability in surface anisotropy, its shape and magnitude is determined by the structure of the sample element and its optical attributes.\r\n\r\nThe BRDF is given by \r\n\r\nBRDF(θi,φi; θr,φr; λ)=(Ir(θr,φr))/(F(θi,φi))\r\n\r\nwhere Ir is the surface leaving spectral radiance and F is the spectral irradiance , θ and φ are zenithal and azimuthal angles respectively of the direction (view angles) of reflected radiance Ir(θr,φr) and of incident irradiance F(θi,φi), λ is the wavelength.","name":"Bidirectional Reflectance Distribution Function (BRDF)","selfAssesment":"Completed
"},{"code":"PP1-2-12","description":"Measurements of BRDF allow to compare spectral signatures obtained in different laboratories in an optimal way. However its measure require well calibrated sources and quite expensive laboratory equipments. The concept of BRF (Bidirectional Reflectance Factor) allows a more simple, indirect, measurement of BRDF by using a reference sample (highly reflective so usually named \"white reference WR\") of known BRDF and two subsequent measurements of reflected radiance (one from the WR, one from the sample) obtained under identical illumination conditions. In these conditions results BRDF(sample)=BRF(sample)xBRDF(WR)","name":"Bidirectional Reflectance Factor (BRF)","selfAssesment":"Planned
"},{"code":"PP1-2-2","description":"The absorption of e.m. radiation by molecules, in different physical states, can be attributed to specific (quantized) changes in their electronic and/or vibrational and/or rotational energy. Subsequent quantized molecular vibrational energy levels are equidistant so that all vibrational transitions occur, for each molecule, by the emission/absorption of radiation at a specific wavelength. Depending on the specific amount of energy required to modify the status of electrons within the atoms composing the molecules, as well as the one required to modify the molecule's vibrational and rotational energy, different wavelengths can be adsorbed. As in the case of atomic spectra which are fully determined by the electronic energy level structure depending on the atomic number, rotational and vibrational energy levels of molecules depends on specific characteristics (number, masses, distances, inertia momentum, elastic constant, etc.) of the atoms composing the molecule itself which make specific and characteristic for each molecule associated absorption spectra. In the Earth's atmosphere the effect of atomic/molecular absorption is significant at wavelength between 1nm and about 1cm. Considering the optical and microwave spectral ranges used in Earth's remote sensing from space it should be noted that:\r\na) Visible, Near Infrared and Short wave IR radiation (400-3000 nm) is adsorbed mostly for electronic transitions within atoms. In the SWIR region (after 1000nm) forbidden vibrational absorption lines can be observed (overtones and related combinations). \r\nb) e.m. radiation in the Medium and Thermal IR (up to 100.000 nm) spectral range are mostly adsorbed for operating vibrational energy transitions in H2O, CO2 and O3 molecules\r\nc) e.m. radiation in the Far IR up to the Microwave's spectral range (0,035-1 mm) is mostly adsorbed for operating rotational transitions in water vapur molecules. As, in principle, such electronic, vibrational and rotational transitions can contemporary occur (and usually occur considering the collective effect of the enormous number of molecules that can be present even in a small volume of terrestrial atmosphere) molecular spectra results in a complex composition of absorption lines (bands).","name":"Molecular absorption spectra","selfAssesment":"Completed
"},{"code":"PP1-2-3","description":"In spectroscopy an absorbed (emitted) line is observed in correspondence to the transition from a lower (higher) to a higher (lower) energetic level within an atom (electronic transitions) or a molecule (electronic, vibrational, rotational transitions). Its characteristic frequency f is related to the amount of the energetic jump from an initial state E(1) to a final one E(2) through the Bohr's relation E(f) − E (i) = hf. As the distribution of the quantized energetic level are specific of each atom (depending on its atomic number N) and molecule (depending on their constituents atoms N, number and dispositions which determine their specific inertia momentum and vibrational properties) even the corresponding atomic and molecular spectra (i.e. the frequencies of the sequences of spectral lines/bands) are specific for each chemical atomic or molecular species. However monochromatic emission just at the frequency f is practically never observed. Always e.m. radiation emitted/adsorbed by atoms or molecules is observed also around the nominal (expected following Bohr's relation) frequency f mostly as a consequence of the following effect: a) changes of quantized energy levels associated to the process of emission/absorption itself: the consequent line broadening around the frequency f is reported as \"natural broadening\"; b) changes of quantized energy levels due to reciprocal collisions between atoms and molecules (\"pressure broadening\"); c) the change of the observed f due to the Doppler effect associated to the fact that emitting(adsorbing atoms or molecules are moving toward or far away with different (thermal) velocities (\"Doppler broadening\"). The natural broadening is practically negligible as compared to that caused by collisions and the Doppler effect. In the upper atmosphere, due to its temperature and pressure, we find a combination of collision and Doppler broadenings, whereas in the lower atmosphere, below about 20 km, collision broadening prevails because of the pressure effect. As far we move far from the central (expected) frequency f as much the contribution of Doppler effect can be neglected compared with the pressure broadening. This fact has important consequences on the possibility to retrieve vertical properties of the atmosphere (vertical sounding) like temperature and concentration of its chemical constituents, exploiting satellite based observations made \"off-line\" (i.e. at frequencies around but different from f) which relate investigated atmospheric levels as much higher as much far from f are the considered frequencies.","name":"Line shape and (natural, pressure, Doppler) broadening","selfAssesment":"Completed
"},{"code":"PP1-2-4","description":"Voigt's line profile refers to the shape of a spectral line resulting from the \"pressure\" and Doppler broadening. Pressure broadening is much more important in atmosphere as far as pressure increases (heigths lower than 20 km) . Observing Earth's atmosphere in a spectral region sufficiently far from the central (unperturbed/monochromatic) absorption spectral line (off-line bands), Doppler broadening can be neglected in comparison with the pressure one. More and more off-line are the chosen spectral bands, more and more lower in atmosphere will be the atmospheric layers mostly contributing to the measured spectral radiances. \r\nSuch a relation is at the base of the inversion methods for atmospheric vertical sounding based on multi-spectral satellite observations.","name":"Voigt's line profile","selfAssesment":"Completed
"},{"code":"PP1-2-5","description":"Radiation that is not absorbed or scattered in the atmosphere can reach and interact with the Earth's surface. There are three (3) forms of interaction that can take place when e.m. radiation strikes, or is incident (I) upon a surface. These are: absorption, transmission, and reflection. The total incident radiation will interact with the surface in one or more of these three ways. The proportions of each will depend on the wavelength of the incident radiation and the specific chemical/physical properties of the surface material. Absorption occurs when incident radiation is absorbed into the target, while transmission occurs when radiation passes through a target. Reflection occurs when radiation \"bounces\" off the target and is redirected. The spectral reflectance is defined by the ratio of reflected radiance to incident radiance at a prefixed wavelegth . The spectral transmittance of a medium is defined by the ratio of the transmitted radiance to the incident one at a prefixed wavelegth . The absorbance of a medium or target is defined by the ratio of the absorbed radiance to the incident one at a prefixed wavelegth . Conservation of energy require that, at a certain wavelenght: R+T+A=1. To express the circumstance that the reflection can occurre in different direction as the surface deviates from a specular one, becoming rough, the concept of surface scattering has been introduced (ref. [PP1-2-10] The Rayleigh roughness criterion).","name":"Concepts of Transmittance, Absorbance, Reflectance, Scattering.","selfAssesment":"Completed
"},{"code":"PP1-2-6","description":"The emitting capability of a body surface is described by the spectral emissivity, ε(λ), a dimensionless value ranging between 0 and 1 and varying on the basis of the wavelength (λ) and the geometric configuration of the surface. Formally, spectral emissivity can be defined as the ratio of spectral exitance, M(λ,T), from an object at wavelength λ and temperature T, to that from a blackbody at the same wavelength and temperature, MBB(λ,T).\r\nA blackbody is an ideal radiator that totally absorbs and then reemits all energy incident upon it. By definition the spectral emissivity of a blackbody is equal to one (the maximum) at whatever wavelength and temperature. A blackbody radiates a continuous spectrum. Real materials do not behave like a blackbody. Natural matter could radiates more in selected spectral region (like in the case of atomic or molecular gases) more frequently with a continuous spectrum (like in the case of solids) always with spectral emissivity minor or equal to 1. \r\nAnother important concept is the one related to the graybody. For gray bodies, the spectral emissivity value is constant for each wavelength value, as for black bodies, but is always less than 1. Therefore, for any given wavelength the emitted energy of a graybody is a fraction of that of a blackbody. This behavior could be quite important even for limited spectral ranges. For instance the spectral emissivity of the sea in the TIR (Thermal InfraRed) spectral range 8-14 microns (TIR atmospheric window) can be assumed constant (about 0,98) with significant simplifications in the determination of SST (Sea Surface Temperature) from satellite sensors operating in that spectral region. \r\nAs said above, the emissivity of the most of the bodies present in nature varies depending on the wavelength. These objects are referred to as selective radiators or as being selectively radiant. This means that some materials may behave as black bodies at certain wavelengths (ε close to 1) and may have reduced emissivity at other wavelengths.","name":"Concepts of Spectral Emissivity","selfAssesment":"Completed
"},{"code":"PP1-2-7","description":"Dielectric constants and refractive indices of the matter are generally complex quantities. Considering an electromagnetic wave entering a homogeneous medium of complex refractive index n=m+ik, it is possible to demonstrate that its intensity progressively decays depending on its wavelength λ and on the complex part k of the refractive index of the considered medium. Transparent medium correspond to medium having k=0 (i.e. real refractive index). \r\nFor instance, considering the amplitude of the electric field E(0) entering the medium, its value after traveling in it for a distance z will be reduced at E(z)=E(0)exp[ -ωkz/c] being ω the wave pulsation and c the light speed constant.","name":"Complex dielectric constants and refractive indices","selfAssesment":"Completed
"},{"code":"PP1-2-8","description":"The complex part k of the refraction index n determines how far an e.m. wave of wavelength λ can survive crossing a specific medium. The attenuation length la is the distance after that the amplitude of an e.m. signal reduces its value by an amount of 1/e. For instance the amplitude of the Electric field E(z) of an e.m. wave proceeding along the z direction is decreasing as exp(-z/la) being la=λ/(2𝜋k) the attenuation length associated to that specific material (with n=m+ik) and wavelength λ. This way attenuation length in water can be of hundreds of meters in the visible range and just few microns in the microwaves. So that penetration of radiation in the matter depends on both, the specific (dielectric) properties of the matter (through k) AND the specific wavelength λ of considered e.m. signal.","name":"EM rad. penetration in the matter: Attenuation Length","selfAssesment":"Completed
"},{"code":"PP1-2-9","description":"EM radiation impinging a rough surface is (partly) reflected back (scattering). When the the sine of the angle of incidence of the radiation is equal to the sine of the angle of reflection, sin(øi) = sin(ør), then the surface behaves like a mirror (Snell's Law). Furthermore, a surface is defined as a “perfect mirror” (Fig.1) if all the incident radiation is reflected in that direction saving its original intensity. A surface is defined as “Lambertian diffuser” or “isotropic reflector” (Fig. 2), when the radiation is reflected in all directions with the same intensity. A surface is defined as “perfect Lambertian” when all the incident radiation is reflected isotropically (i.e. not-absorbing, not-transmitting surface). A surface is defined as \"almost Lambertian\" (Fig.3) if the reflection does not occur in an exactly isotropic way but according to privileged directions. “Perfect mirrors” as well as “perfect Lambertian” surfaces describe ideal bodies, while natural bodies behave like “almost Lambertian” surfaces with a preferred reflection direction around the one established by the sines reflection law.","name":"Scattering from rough surface: Lambertian and specular surfaces.","selfAssesment":"Completed
"},{"code":"PP1-2","description":"E.M. Radiation can be absorbed, scattered, emitted and transmitted by the matter. The results of such interactions (i.e. the fraction of incident radiation that is absorbed, scattered or transmitted) or emission process (i.e. the fraction of actually emitted radiation in comparison with the one expected from a black-body at the same temerature) strongly depend on the radiation wavelength and on specific chemical (e.g. composing atoms and molecules as well as their arrangement within solid cristals) and physical (e.g. Temperature, Dimensions and Shape, Roughness) properties of the matter. In some case, the result of Radiation - Matter interaction is strongly affected by observational conditions. For instance, over some angular distance between the directions of incidence and the one of measurement of the radiation, sun-glint can occur which completely mask any other results. A basic principle of the remote sensing put univocally in relation spectral absorbance, reflectance, transmittance and emissivity, curves achievable by multi-spectral EO measurements, with matter having specific chemical/physical properties. Theoretical models of radiation-matter interaction at the Earth's surface and through the atmosphere provide then suitable strategies for retrieving, from multi-spectral measurements of the radiation leaving the Earth, the most relevant chemical/physical properties of the matter composing its surface and atmosphere.","name":"Radiation - Matter interaction","selfAssesment":"Completed
"},{"code":"PP1-3-1","description":"The natural objects can either emit radiation (radiance, emittance) or be \"illuminated\" by a source (irradiance). In the following a series of definitions for each of these terms is provided. \r\nThe first basic radiometric quantity is the radiance (Iλ) and it is defined as the ratio of the differential radiant energy (dE) to the product of effective area (dA) with the time interval (dt), wavelength interval (dλ) and differential solid angle (dΩ). Iλ can be also referred as monochromatic intensity and it is expressed in units of energy per area per time per wavelength and per steradian (W m−2 sr−1). \r\nThe monochromatic flux density (Fλ) or the monochromatic irradiance of radiant energy is defined by the normal component of Iλ integrated over the entire hemispheric solid angle. It is expressed in units of energy per area per time per wavelength (W m−2). For isotropic radiation (i.e., if the intensity is independent of the direction), the monochromatic flux density is then Fλ = π Iλ. \r\nThe total flux density of radiant energy (F), or irradiance, for all wavelengths (energy per area per time, i.e., W), can be obtained by integrating the monochromatic flux density over the entire electromagnetic spectrum.\r\nAll the above definitions refer to a point source of radiation. When the flux density or the irradiance is from an emitting surface (i.e., an extended widespread source), the quantity is called the emittance. When expressed in terms of wavelength, it is referred to as the monochromatic emittance. The intensity or the radiance is also called the brightness or luminance (photometric brightness). The total flux from an emitting surface is often called luminosity.","name":"Radiometric quantities: radiance, irradiance, flux, brightness, emittance, luminosity, etc.","selfAssesment":"Completed
"},{"code":"PP1-3-2","description":"The attenuation of radiation emitted from a source decreases with the square of the distance from its center based on inverse square law. It considers that the size of the sources increases with the square of their radius, causing the same rate of attenuation in flux density.","name":"Decay of the emittance with the square of distance from the source","selfAssesment":"Planned
"},{"code":"PP1-3-3","description":"The relative amount of electromagnetic radiation reflected (absorbed, transmitted, emitted) by the matter at different wavelengths depends on its specific chemical composition and physical properties. The plots of corresponding physical quantities (reflectance, absorbance, transmittance, emissivity) against wavelength, are termed spectral signatures of the specific matter under study. In principle the analysis of spectral signatures obtained by multispectral EO sensors could allow us to identify/discriminate different cover types.\r\nThe interpretation of spectral signatures requires to well understand the e.m. radiation-matter interaction process. In very simple term we expect that incident radiation I(λ)can be reflected, absorbed or transmitted by the matter so that for the energy conservation should be: \r\n\r\n\r\nI(λ)=I(λ,R)+I(λ,A), I(λ,T) \r\n\r\n \r\nbeing I(λ,R), I(λ,A) and I(λ,T) the reflected, absorbed and transmitted fraction of I(λ). From the previous relation descends (dividing both members for I) that:\r\n\r\n\r\n1=R(λ)+A(λ)+T(λ)\r\n\r\n\r\nbeing:\r\n\r\n\r\nR(λ)=I(λ,R)/I(λ) named Reflectance\r\nA(λ)=I(λ,A)/I(λ) named Absorbance\r\nT(λ)=I(λ,T)/I(λ) named Transmittance\r\n\r\n\r\nThey are all specific properties of the considered matter and are not independent each others.\r\nIn particular for an opaque medium with T(λ)=0 it is:\r\nR(λ)=1-A(λ)","name":"Spectral Signatures of the matter","selfAssesment":"Completed
"},{"code":"PP1-3-4","description":"Vegetation, water and soil represent the most common cover types of Earth surface. Their reflectances in the VIS/NIR/SWIR spectral range, plotted against wavelength in the 0,4-2,5 micron, represent the most important (basic) spectral signatures for whatever application devoted to Earth surface study. Other spectral signatures (e.g. in emissivity) in the Thermal InfraRed range are particularly important to infer specific properties of Mineral and Rocks (ref. [PP1-3-5] Spectral Signature of Mineral and Rocks). In order to discriminate among such basic cover types, the (ref. [IP3-1-2-3]) NDVI (Normalized Difference Vegetation Index) is the most simple and powerful diagnostic tool in the VIS/NIR spectral range \r\nNDVI values ranging between the values -1 and +1, are higly positive for fully vegetated (up to NDVI=1) or partly vegetated (NDVI>0,3) targets, still positive (>0) for bare soils, negative for water bodies. Values around zero are expected for clouds thanks to their similarly high reflectances both in the NIR and VIR spectral bands (ref. [PP1-3-6] Spectral Signature of Clouds). \r\n\r\nVegetation. a) in the visible range most of the incomig radiation is adsorbed by the photosynthetic process, transmittance is very low. The residual reflected radiation has a small peak of reflectance around 0.5 microns which is responsible of the green colour associated to vegetation by the human vision sytem (limited to the VIS spectral range); b) in the NIR range vegetation exhibits its higher reflectance together its higher transmittance (very low absorbance) so that leaf density can be estimated thanks to the the contributes (decreasing with depth) of underlaying leaf layers; c) in the SWIR spectral range (in particular in the water bands around 1,4 and 1,9 microns) it is possible to appreciate the vegetation water content. As much it is, as more incident radiation is absorbed and less is the reflected fraction of radiation.\r\nBare Soil. Spectral reflectance is normally increasing moving from the VIS to the SWIR spectral region. Water features around 1,4 and 1,9 microns give information on soil water content (see before). Others specific features are described in [PP1-3-5] Spectral Signature of Mineral and Rocks\r\n\r\nWater. Spectral reflectance of clean deep water is quite low reaching quickly the zero value as soon as wavelengths passe microns. However it is important to note that such a very low reflectance is due to a very high transmittance in the VIS range and to a very high absorbance in the NIR/SWIR regions (ref. [PP2-2-5-2] Attenuation Lenght and Penetration Depth). This means that water is quite transparent in the VIS spectral range (so that, in case of shallow waters, measured reflected radiance can be significantly increased by the contribution of bottom of the sea). Water is completely opaque, instead, in the NIR/SWIR. In this spectral region, even in presence of shallow waters, the presence of suspended matter (that increases the measured reflectance both in the VIS and NIR/SWIR ranges) can be better discriminated (than in the VIS) from the contribute of the bottom of the sea that, in this spectral range, is zero.","name":"Spectral Signature of Vegetation, Water, Soil","selfAssesment":"Completed
"},{"code":"PP1-3-5","description":"Spectral signatures of rocks and mineral provide information on their chemical composition and crystal properties, grain size and roughness over a wide range of wavelengths from the visible to the thermal infrared.\r\nIn the Visible and Near-InfraRed (VNIR; 0.4÷1.0 µm) region, spectral features are dominated by electronic processes in transition metals, such as Fe, Mn, Cu, Ni, Cr, etc. Therefore, iron is the most important constituent having spectral properties in the VNIR, and the iron-rich minerals are characterized by low reflectance (high absorbance) below 0.7 µm.\r\nOther minerals, which represent the major part of the Earth's surface rocks, such us Si, Al and some anion groups (e.g. silicates, carbonates, oxides) hydroxides, have less spectral features in the VNIR region, but exhibit much more evidences in the Short-Wave InfraRed (SWIR; 1÷3 µm) region. In fact, spectral features of hydroxyls and carbonates mark the SWIR region.\r\nThe hydroxyl ion is a widespread constituent occurring in rock forming minerals such as clays, micas, chlorite etc. It shows a vibrational fundamental absorption band at about 2.74÷2.77 µm and an overtone at 1.44 µm.\r\nCarbonates, which are commonly in the Earth surface rocks in the form of calcite (CaC03), magnesite (MgC03), dolomite [(Ca-Mg) C03] and siderite (FeC03), shows a typical absorbance feature around 2.3 µm, instead the water content can be instead evaluated by the depth of absorption at 1,4µm and 1,9 µm.\r\nThermal InfraRed (TIR; 1÷20 µm) region, from a geological point of view, is a particularly important spectral region for remote sensing aiming at compositional investigations of terrestrial materials. In fact, the fundamental vibration features of many rock-forming mineral groups (e.g. silicates, carbonates, oxides, phosphates, sulphates, nitrates, nitrites, hydroxyls) occur in the TIR region. Briefly:\r\na) the silicates, which are most abundant group of minerals in the Earth's crust, shows vibrational spectral features due to the presence of Si04-tetrahedron around 8 µm to 12 µm; b) the carbonates show a weak feature around 11.3 µm that can be detected; c) the sulphates display bands near 9 µm and 16 µm; d) the phosphates also have fundamental features near 9.25 µm and 10.3 µm; e) the features in oxides usually occupy the same range as that of bands in Si-O, i.e. 8 µm to 12 µm; g) the nitrates have spectral features at 7.2 µm and the nitrites at 8 µm and 11.8 µm; h) the hydroxyl ions display fundamental vibration bands at 11 µm.","name":"Spectral Signature of Mineral and Rocks","selfAssesment":"Completed
"},{"code":"PP1-3-6","description":"The determination of spectral signatures for scenes with a high degree of spatial complexity is considered as one of the most persistent problems in atmospheric radiation, especially at the surface, where satellite observations can only be used indirectly to infer energy budget terms. In the shortwave (solar) spectral range, it is especially challenging to derive consistent albedo, absorption, and transmittance from spaceborne, aircraft, and ground-based observations for inhomogeneous cloud conditions and is closely related to the long-debated discrepancy between observed and modeled cloud absorption.\r\nThe cloud spatial structure is revealed as a spectral signature in shortwave irradiance through the physical mechanism of molecular scattering. However, the study of specific mechanisms is rather complex since the satellite instruments cannot completely describe the spatial distribution of cloud and the variability of scattering and absorption properties. For this reason, several studies deal with the problem described above, as a challenge for estimating spectrally the cloud optical properties (such as the albedo and transmittance) as well as scattering and absorption processes taking place in the cloud system with adequate resolution. Hence, the above mechanisms can be described using three dimensional (3-D) radiative transfer models. Those models receive auxiliary information from cloud imagery and radar observations. The molecular scattering (Rayleigh) was the only one directly dependent on the wavelength of the vertical radiative flux. Moreover, it was considered as a spectral perturbation of backtracked horizontal exchange of solar radiation due to the inhomogeneous distribution of cloud. The horizontal photon transport is highly correlated to its spectral dependence.\r\nConcerning the presence of cirrus or ice clouds, the effect of their phase function and the vertical distribution were evaluated on the scattering of far infrared radiation. Thus, the accurate reconstruction of the phase function of cirrus clouds potentially indicates the need for application of a radiative transfer model. This specific module necessarily includes scattering parameters, while the accuracy of its calculations needs to be verified against real measurements. \r\nFor several applications the preliminary detection of those portions of the scene affected by the presence of clouds (cloud detection) is mandatory. For studying properties of Earth's surface targets affected by the presence of clouds are flagged just to exclude them by further analyses. In some case clouds themselves are the object of interest. In both cases the identification of clouds (and their classification) is mostly done by using (combination of) specific spectral signatures. Generally speaking clouds are highly reflecting VIS/NIR radiation showing (due to their heigth) brigthness temperatures (in the TIR region) lower than underlying surfaces. Thin or semi-transparent clouds are still detectable for their higher reflectance over the sea which represents a quite dark bacground in the VIS/NIR/SWIR region. Over land (much more reflecting) such a test is not more efficient and more sophisticated tests (e.g. Brigthness Temperature Difference in the split window bands around 11 and 12 microns) are required. In presence of very cold, high reflective backgrounds (e.g. snow, glaciers, etc.) both tests on the VIS reflectance and on TIR brigthness temperature could fail. More specific tests exploiting the reflectance drop of snow in the SWIR (where clouds are still saving their higher reflectance) helps to discriminate the presence of clouds from clear sky conditions even over a snow background. In the microwaves clouds are quite transparent except when coupled with coarse particles related to rain, snow, hailstones (precipitating clouds). In that case Mie scattering dominates strongly reducing the amount of radiance collected at the sensor (lower brigthness temperature in the microwave spectral range).","name":"Spectral Signature of Clouds","selfAssesment":"Completed
"},{"code":"PP1-3-7","description":"If the resolution is low enough that disparate materials can jointly occupy a single pixel, the resulting spectral measurement, made by the sensor, will be the composite of the individual spectra. Under the linear mixing model (LMM), each observed spectrum in each pixel of a given image is assumed to result from the linear combination of the N endmember spectra present in the pixel. The reflectance spectrum of each endmember is weighted by the fractional area coverage of it in the pixel. \r\nHowever, if the components of interest in a pixel are in an intimate association, like sand grains of different composition in a beach deposit, light typically interacts with more than one component as it is multiply scattered, and the mixing between these different components are nonlinear. Such nonlinear effects have been recognized in spectra of: particulate mineral mixtures, aerosols and atmospheric particles, vegetation and canopy. In this case a non-linear mixing model (NLMM) should be applied. To summarize: Linear mixture model assumes that endmember substances are sitting side-by-side within the pixel; Nonlinear mixture model assumes that endmember components are randomly distributed throughout the pixel, causing multiple scattering effects. \r\nIn the linear mixing case, the basic premise of mixture modelling is that within a given scene, the surface is dominated by a small number of distinct materials that have relatively constant spectral properties. These distinct substances (e.g., water, grass, mineral types), characterized by a well-defined spectral signature are called endmembers, and the fractions in which they appear in a mixed pixel are called fractional abundances. Then, finding the endmembers that can be used to ‘unmix’ other mixed pixels becomes a crucial issue. \r\nIdentify fractional abundances of distinct substances from the spectral signal of a mixed pixel is one of the application in which hyperspectral images can provide an valuable support.","name":"Composition of spectral signatures (Linear Mixing)","selfAssesment":"Completed
"},{"code":"PP1-3-8","description":"One of the most common ways to classify remote sensing systems consists in distinguishing them into the passive systems, which detect naturally occurring radiation, and the active systems, which emit radiation and analyse what is sent back to them. The passive systems can be further subdivided into those that detect radiation emitted by the Sun (this radiation consists mostly of ultraviolet, visible and near-infrared radiation), and those that detect the thermal radiation that is emitted by all objects that are not at absolute zero (i.e. all objects). For objects at typical terrestrial temperatures, this thermal emission occurs mostly in the infrared part of the spectrum, at wavelengths of the order of 10 μm (the so called thermal infrared region), although measurable quantities of radiation also occur at longer wavelengths, as far as the microwave part of the spectrum. Active systems can, in principle, use any type of electromagnetic radiation, resulting able to obtain measurements anytime, regardless of the time of day or season. In practice, however, they are restricted by the transparency of the Earth’s atmosphere at the specific spectral range considered. In any case they can be used for examining wavelengths that are not sufficiently provided by the sun, such as microwaves, or to better control the way a target is illuminated. Active sensors may be classified according to the use that is made of the returned signal. Two main methods have been identified to this aim so far: the Ranging technique mostly concerns with the time delay between transmission and reception of the signal, while the Scattering one is mostly focused on the strength of the received signal.","name":"Definition of active and passive remote sensing techniques","selfAssesment":"Completed
"},{"code":"PP1-3-9","description":"Light has a key role for aquatic ecosystems, both in marine and freshwater. It penetrates underwater and interacts with dissolved and particulate water constituents, the optically active constituents (OACs). They absorb and scatter the light, giving water its characteristic colour and affect the light availability underwater. The three main OACs are phytoplankton, coloured dissolved organic matter (CDOM) and suspended particulate matter (SPM) and vary in time and space. Absorption and scattering represent the inherent optical properties (IOPs) of water and depend solely on the OACs present in the water. In addition, water bodies have apparent optical properties (AOPs) that depend both on OACs and the incident light field.\r\nThe chlorophyll in the phytoplankton absorbs blue and red wavelengths and reflects green. Therefore, the oceans appear blue-green depending on the concentration of phytoplankton. CDOM is primarily tannin-stained water released from decaying detritus. High CDOM concentrations appear yellow-green to brown. CDOM absorbs ultraviolet (UV) light in the surface waters which is harmful for phytoplankton but competes with phytoplankton for light. Inorganic suspended matter (ISM) is the suspended sediment in the water. It is a component of SPM and strongly scatters longer (red) wavelengths. High ISM concentrations give water a reddish-brown colour. Pure water, however, absorbs longer wavelength red light. As natural waters vary in their composition, oceanographers introduced ocean classification schemes based on the optical properties of water. The main differentiation is between Case 1 open ocean waters and Case 2 coastal waters. In open ocean waters, the optical properties are dominated by phytoplankton and covarying material. In coastal waters, optical properties are dominated by suspended sediments and CDOM that vary independently of phytoplankton.","name":"Optical properties of water","selfAssesment":"Completed
"},{"code":"PP1-3","description":"Measuring the signal emitted (received) by a radiation source (detector)","name":"Sensing of EM radiation.","selfAssesment":"Planned
"},{"code":"PP1-4-1","description":"Radiative transfer equation (RTE) is the governing equation of radiation propagation in a media, which plays a central role in the analysis of radiative transfer in gases, semitransparent liquids and solids, porous materials, and particulate media, and is important in many scientific and engineering disciplines. \r\nThe RTE states that when radiation (a light-ray) propagates through matter (gas, dust, liquid), the incident radiation could be absorbed or scattered by matter, or radiation emitted from matter could append to the incident radiation. As a result, the intensity of radiation would change temporally, spatially, and directionally. The study of the propagating way of radiation in matter is the radiative transfer. In more detail, the radiation traversing a medium may be attenuated due to the density, mass scattering and absorption of material. In contrast, the radiation’s intensity can be strengthened by emissions from the material plus multiple scattering from all directions. All the above interactions are described mathematically by the general radiative transfer equation.\r\nThere are different forms of RTEs that are suitable for different applications, including the RTE under different coordinate systems, the transformed RTE having good numerical properties, the RTE for refractive media, etc.. Furthermore, several fundamental numerical methods for solving RTEs are proposed up to now focusing on the deterministic methods, such as the spherical harmonics method, discrete-ordinate method, finite volume method, and finite element method.","name":"General equation of radiative transfer.","selfAssesment":"Completed
"},{"code":"PP1-4-10","description":"The inversion approach aims at retrievals of trace gas concentration and temperature profiles of atmospheric state, namely the modeled state vector, based on the measured radiance transmitted or reflected or scattered (SCIAMACHY spectrometer) by the Earth-Atmosphere system. Satellite instruments measure the radiance L that reaches the top of the atmosphere at given frequency v. The measured radiance is related to geophysical variables of Earth's atmosphere (e.g. temperature vertical profiles and chemical composition, aerosols, clouds, rain, etc.) and surface (e.g. temperature, spectral emissivity and reflectance, etc.) by the Radiative Transfer Equation (RTE). In RTE measured spectral radiances are assumed as the result of different contributions:\r\na) thermal emission from the different layers (at heigt z) of atmosphere at temperature T(z) modulated by the atmospheric transmittance from z to the sensor heigt. It depends on both temperature profile T(z) and trace gas concentration along the optical path;\r\nb) Surface emission. It depends mostly on Eart's surface temperature T(0) and spectral emissivity\r\nc) Surface reflection/scattering. It depends on spectral reflectance and local properties like surface rugosity \r\nOthers, more complex contributions comes from: cloud/rain, aerosols, etc.\r\nIn its simplified form, terms a) and b) dominate as far as InfraRed (IR) radiances are considered. Term a) can be neglected in those bands where atmosphere is transparent (atmospheric windows). Term b) can be negletcted in the IR spectral bands (sounding channels) where it is fully adsorbed by some specific constituent of the atmosphere. Among the IR sounding channels some ones are selected being associated to atmospheric constituents (like CO2 or oxygen) whose mixing ratio in the atmosphere is known to be constant. For radiances measured in these bands term a) in RTE depends only on T(z) (through a Fredholm equation of the first kind) that can be then retrieved by inversion methods. When T(z) are known trace gas concentrations survive as the only unknown of term a) and can be retrieved by inversion methods using radiances measured in their corresponding sounding channels. Similar inversion strategies have been suggested as far as radiances (emitted, transmitted, reflected, adsorbed) measured in different spectral ranges (from the Visible to the Microwaves) are considered.","name":"Retrieval of atmospheric parameters by inversion of multi-spectral radiances","selfAssesment":"Completed
"},{"code":"PP1-4-2","description":"In the field of radiation scattering and absorption, the cross-section, analogous to the shape of a particle, is used to determine the amount of energy diverted from the original beam by the particle. This parameter is called mass cross section, when it is in reference to unit mass (cm2g-1).","name":"Cross Section of Extinction (Absorption, Scattering) per Mass Unit","selfAssesment":"Planned
"},{"code":"PP1-4-3","description":"When the mass cross-section is multiplied by the density of particle, the extinction coefficient is calculated, namely the sum of absorption and scattering coefficient, whose the units are related to length. Especially, the absorption coefficient (k (cm•atm)-1) is the product of strength of absorption with the Loschmidt’s number.","name":"Absorption Coefficient","selfAssesment":"Planned
"},{"code":"PP1-4-4","description":"The source function, Jλ, has units of radiant intensity and it is defined as the ratio of the source function coefficient to the mass extinction cross section. The Jλ determines the intensity that are acquired in a homogeneous medium.","name":"Source Function (Coefficient)","selfAssesment":"Planned
"},{"code":"PP1-4-5","description":"If the monochromatic beam (Iλ) of radiation attenuates due to absorption, but it remains unaffected from emission contributions and multiple scattering of homogeneous Earth-Atmosphere system, it can be expressed by Beer-Bouguer-Lambert law. This law also expresses the monochromatic optical depth (τλ) and transmissivity (Τλ) of the above system.","name":"Beer-Bouguer-Lambert law.","selfAssesment":"Planned
"},{"code":"PP1-4-6","description":"The Schwarzschild equation provides an interpretation for the infrared radiation that undergoes the absorption and emission processes simultaneously, while the scattering efficiency is considered negligible. Hence, its solution is obtained by the integrating of relationship that invokes Kirchhoff’s law and summing the two above processes along a ray path.","name":"Schwarzshild equation and its solutions","selfAssesment":"Planned
"},{"code":"PP1-4-7","description":"The Optical Path (OP) describe the total concentration along a path of constituents extinguishing (by absorption or scattering) the electromagnetic radiation traveling through a medium at a specified whavelength λ. Its value depends then on the efficiency of absorption and scattering phenomena which occur during the travel itself. The Earth's atmosphere is usually the medium that a monochromatic beam (Iλ) of radiation travels through before reaching satellite sensors. In an homogeneously estinguishing medium (i.e. a medium with extinction coefficient for mass unit K constant along the optical path) the Optical Thickness OT is defined as OT=K x OP. It give a measure of the cumulative depletion of Iλ directed in straight-downward. As far as the Optical Thickness is large, the medium is more and more optically thick (i.e. radiation is largely absorbed). If the Optical Thickness is small it means that the medium is optically thin (i.e. radiation travels through it easily).","name":"Concepts of Optical path and Optical thickness.","selfAssesment":"Completed
"},{"code":"PP1-4-8","description":"Radiative transfer is highly nonlinear and non-local against the cloud structure at a high spatial resolution. Hence, a Monte Carlo approach can be used for the representation of cloud structure and interactions between photons and clouds. This approach is more efficient than the method of representing clouds as horizontally homogeneous.","name":"Radiative transfer in presence of clouds","selfAssesment":"Planned
"},{"code":"PP1-4-9","description":"The line by line radiative transfer model (LBLRTM) is an accurate and flexible model for the estimation of the spectral radiance and transmittance over the full spectral range (microwave to ultraviolet), using a first-order perturbation algorithm. It is considered as the basic tool for the creation of retrieval algorithms employed by the ground-based and satellite instruments, while the latest updates in spectroscopic factors are derived from the high-resolution transmission molecular absorption (HITRAN) database. A LBLRTMs is continuously updated and validated against highly accurate spectral measurements. Its errors are related to uncertainties in line parameters and shape. The shape is a Voigt line which is a linear combination of approximating functions for the description of all atmospheric levels. LBLRTML is combined with the continuum MT_CKD (Mlawer, Tobin, Clough, Kneizys, Davies) model which in turn includes the atmospheric constituents of water vapor, carbon dioxide (CO2), molecular oxygen (O2), molecular nitrogen (N2), and ozone (O3), and the molecular extinction process (Rayleigh scattering). A recent version of LBLRTM calculates analytically the Jacobians equations for obtaining meteorological parameters. Also, this model version retrieves the optical parameters of clouds related to scattering and emissivity. The LBLRTM is widely used in radiation and climate applications. It is capable to calculate the absorption degrees of various atmospheric constituents which are utilized afterward from climate and weather prediction models for estimating the broadband solar irradiance and the heating rates. Additionally, the complex radiative transfer models with fast computational time are initiated and trained by the LBRTM, since they are used subsequently on numerical weather prediction (NWP) assimilation systems.","name":"Line-by-line radiative transfer models","selfAssesment":"completed
"},{"code":"PP1-4","description":"Theory of radiative transfer describes the transmission of the electromagnetic radiation through a medium. The electromagnetic radiation can be emitted, absorbed, scattered by constituents of the medium depending on the composition of the medium and the physical state of its constituents, as well as the wavelength of the radiation itself. Retrieving geophysical parameters from radiation measurements requires to know this kind of interaction which is described through the Equation of Radiative Transfer. In the field of Earth Observations from space, the considered medium is normally the Earth's atmosphere through which the e.m. radiation travel before reaching aerial multi-spectral sensors. Radiative transfer models allow to foreseen spectral radiances at whatever altitude in atmosphere (radiance at the sensor) starting from the knowledge of atmospheric vertical profiles of temperature and chemical constituents concentrations (direct problem). The possibility to retrieve atmospheric temperature profiles and chemical constituents concentrations from multi/iper spectral radiances measurements in selected bands (inverse problem) is the scope of the inversion techniques widely applied in meteorology and of a specific set of sensors devoted to the vertical sounding of the atmosphere. Clouds and scattering particles, like aerosols - requiring the inclusion of additional information on the atmospheric constituents (e.g water phases involved, dimensions and geometry of scattering particles, etc.) - make radiative transfer model more complex.","name":"Fundamentals of Radiative Transfer","selfAssesment":"Planned
"},{"code":"PP1-5-1","description":"Light is the electromagnetic phenomenon we exploit for remote sensing. Its basic laws concerning the transmission through the interface of two different media are governed by reflection and refraction. Reflection governs the way light is backpropagated and refraction dictates how light is transmitted. Refraction is related to the real refractive index of a medium. Dispersion relates to the way the light of a given wavelength is transmitted. Since light of different wavelengths are transmitted at different angles, the phenomenon leads to the concept of dispersion. These three simple principles are at the core of the understanding technology of remote sensing.","name":"Reflection, Refraction and Dispersion of the light","selfAssesment":"Planned
"},{"code":"PP1-5-11","description":"The theory provides the bulk of physical explanation and related laws, which govern absorption, emission and spontaneous emission from the ordinary matter. Early laws about thermal radiation and the blackbody emission, such as Rayleigh-Jeans, Wien, Planck laws are cast in a single theory and formalism through the concept of quantized energy at the level of atoms emission/absorption of light. Explain the modern concept of quantum optics and their link to the design of modern devices for the measurements and/or production of coherent light.","name":"Einstein’s theory of radiation: photons, photoelectric effect, absorption, emission; Stimulated emission: the laser","selfAssesment":"Planned
"},{"code":"PP1-5-14","description":"Solid state modern detectors rely on non-metal junction, which can be designed and operated to yield a bandgap energy according to the spectral range (infrared, visible, UV) to be detected. The basic principles of how these devices are designed and fabricated is important to develop and design new sensors useful for the various remote sensing applications.","name":"Electric conduction in solids: semiconductors, p-n- junction, diode and transistors","selfAssesment":"Planned
"},{"code":"PP1-5-15","description":"Modern detectors of electromagnetic radiation in the infrared, VIS, UV spectral regions are designed and fabricated based on suitable junctions or electro-optical devices. The performance of these systems needs to be assessed in terms of accuracy and precision. This is made through figures of merit such as Noise Power Spectral Density, Noise Equivalent Power. Detectors can be classified as photovoltaic or photoconductive devices, which allows to better classify the various noise sources: shot noise, 1/f noise, Johnson noise, generation-recombination noise.","name":"Photovoltaic and photoconductive detectors: MCT, InSb, bolometer, CCD devices","selfAssesment":"Planned
"},{"code":"PP1-5-2","description":"Interference and diffraction are phenomena related to the wave nature of electromagnetic radiation. They explain how light propagates in presence of obstacles. These phenomena are largely used in the fabrications of optical systems for remote sensing: e.g. radiometers and spectrometers.","name":"Interference and Diffraction.","selfAssesment":"Planned
"},{"code":"PP1-5-3","description":"The Michelson interferometer is the instrument that exploits and evidence the interference of light. A masterpiece of experimental physics, the Michelson interferometer is the key architecture of the modern optical interferometers, which make it possible to measure the emitted Earth spectrum with hyperspectral resolution.","name":"Michelson Interferometer","selfAssesment":"Planned
"},{"code":"PP1-5-4","description":"The celebrated principle of constant speed of light and independence of the reference frame is important to explain the basic principles of instruments such as the Michelson interferometer. The basic physics theory to explain how electromagnetic fields propagates and the inter-relationship between electric and magnetic fields.","name":"Special relativity; Electromagnetic fields equations and propagations","selfAssesment":"Planned
"},{"code":"PP1-5-6","description":"Helmotz’s wave equation arises in light and acoustic scattering problem and yields the general framework to investigate and analyse the scattering of time-harmonic acoustic and electromagnetic waves by a penetrable inhomogeneous medium.","name":"Helmotz’s equations; Scattering from inhomogeneous media.","selfAssesment":"Planned
"},{"code":"PP1-5-7","description":"Geometrical optics is governed by the laws of reflection, refraction and dispersion. Its applications are relevant to many optical systems involving ray tracing, wavefront propagation, thin film calculators (which underly many optical engineering calculations).","name":"Foundations of geometrical optics, geometrical theory of optical imaging","selfAssesment":"Planned
"},{"code":"PP1-5-8","description":"Optical interferometers are nowadays used to develop and implement Fourier Transform Spectrometers, which can measure the emission spectrum of a given source with high spectral resolution at a constant sampling. This instrumentation is now at the core of modern hyperspectral sounders from satellite and have opened the way to the sounding of the Earth atmosphere with unprecedented spatial vertical resolution.","name":"Elements of the theory of interference and interferometers","selfAssesment":"Planned
"},{"code":"PP1-5-9","description":"Diffraction gratings and dispersive element are the basic ingredients for radiometers and grating spectrometers. They are in some cases preferred to Interferometer systems because the optical layouts can be designed and implemented with no moving part or components. Many of the today satellite instruments, including sounder and imagers, rely on diffraction and/or grating spectrometers","name":"Elements of the theory of diffraction and grating spectrometers","selfAssesment":"Planned
"},{"code":"PP1-5","description":"This section describes the theoretical fundaments of Optics and Modern Physics of Sensors relevant to the Earth Observation.","name":"Basics of Optics and Modern Physics of Sensors","selfAssesment":"Completed
"},{"code":"PP1-6-1","description":"The temperature and pressure profiles determine the atmospheric structure. The latter consists of four basic levels, considering the vertical variability of the temperature. These main four levels are troposphere, stratosphere, mesosphere, and thermosphere. In the troposphere (0-12km), which is the lowest layer of the atmosphere, all the meteorological processes that affect our everyday life take place. The lowest part of the troposphere is known as the boundary layer (0-3km), where all the surface-atmosphere interactions and exchanges take place. The troposphere concentrates the water vapor and 90% of atmospheric mass, while the chemical composition of all atmospheric layers consists of nitrogen, oxygen, argon and trace gases. The main parameters that characterize the atmosphere structure are pressure, density, and temperature. All the aforementioned parameters are related to the atmospheric composition and vary with altitude, latitude, longitude and season. Additionally, the stratosphere, which is the layer above the troposphere, contains almost all of the ozone abundance (~90%) of the atmosphere in a region named as ozone layer and traced between 15 and 35km. The interaction of the incoming solar radiation with ozone in this layer causes the reduction of the incoming harmful UV radiation provoking the temperature increase in the stratospheric layer. The 99.9% of total atmospheric mass is concentrated in lower atmosphere (<50km) with Nitrogen (N2, 78.08%), Oxygen (O2, 20.95%) and argon (Ar, 0.93%) being the major constituents of the atmosphere. Water vapor (H2O) is considered as a significant factor, too. Despite the fact that it depicts a very small amount of total atmospheric mass, it’s one of the most important greenhouse gases, along with carbon dioxide (CO2) and methane (CH4), absorbing the Earth’s longwave (infrared) radiation, affecting the energy balance of Earth-Atmosphere system. Furthermore, water vapor plays a decisive role in the formation of clouds and precipitation. Together with the basic chemical (atoms, molecules, ions) constituents of a \"standard\" atmosphere, aerosols of natural and anthropogenic origin have to be considered too, as far as the interaction of e.m. radiation with atmosphere is concerned.","name":"Structure and chemical-physical composition of Earth's atmosphere","selfAssesment":"Completed
"},{"code":"PP1-6-10","description":"The water vapour is the major radiative and dynamic parameter in the atmosphere. Its concentrations vary highly in space and time, with the tropospheric water vapor being determined by the hydrological cycle processes, namely the evaporation, condensation and precipitation and by large-scale transport processes. Specific humidity decreases rapidly with pressure (following an exponential function) and with latitude. In particular, the variability of the H2O concentration shows a bimodal distribution: it’s very small in the equatorial region and poleward, relatively small in stratosphere and shows a maximum in the subtropics of both hemispheres. The concentration of H2O in the lower stratosphere is controlled by the temperature of the tropical tropopause, and by the formation and dissipation of cirrus. The water vapor can condense into water droplets when it has a particle to condense upon. The atmosphere continuously contains aerosol particles ranging in size from ∼10−3 to ∼20 μm. These aerosols are known to be produced by natural processes (volcanic dust, smoke from forest fires, particles from sea spray, windblown dust, and small particles produced by the chemical reactions of natural gases) as well as by human activity (particles directly emitted during combustion processes and particles formed from gases emitted during combustion). Some aerosols are effective condensation and ice nuclei upon which cloud particles may form. For the hygroscopic type, the size of the aerosol depends on relative humidity. Thin layers of aerosols are observed to persist for a long period of time in some altitudes of the stratosphere. \r\nClouds are global in nature and regularly cover more than 50% of the sky. There are various types of clouds. Cirrus in the tropics and stratus in the Arctic, and near the coastal areas are climatologically persistent. The microphysical composition of clouds in terms of particle size distribution and cloud thickness varies significantly with cloud type. Clouds can also generate precipitation, an event generally associated with midlatitude weather disturbances and tropical cumulus convection.","name":"Water vapour and Cloud formation","selfAssesment":"Completed
"},{"code":"PP1-6-11","description":"The radiative equilibrium is the principle, where the radiative emission and absorption are in balance based on Kirchhoff’s and Planck’s law, resulting in the steady temperature of planet. The adiabatic lapse rate displays the decrease of vertical temperature of a parcel with rate higher than 1oC per 100 metres.","name":"Radiative Equilibrium. Adiabatic lapse rate","selfAssesment":"Planned
"},{"code":"PP1-6-12","description":"The atoms of carbon are building blocks of living organisms and they can move among organisms as a part of carbon cycle. Their transport rate to the atmosphere as carbon dioxide is vital, because this gas trap heat in the atmosphere, increasing the Earth’s temperature and causing Greenhouse effect.","name":"The Carbon Cycle, Greenhouse Effect","selfAssesment":"Planned
"},{"code":"PP1-6-2","description":"The atmospheric absorption can cause an excitation or falling into the energy state of a particle, while the scattering is related to absorption and re-emission of radiation at all directions without changes in its frequency. Particularly, the main contributors of the incoming solar radiation absorptions are various molecules like the nitrogen (N2), oxygen (O2), ozone (O3), water vapor (H2O). Additionally, other constituents of the atmosphere such as CO2 and CH4, and other trace gases, aerosols, and cloud droplets can also absorb significant portion of the incoming solar radiation. Generally, the absorption of solar radiation is related to the wavelength of the solar spectrum. For example, gases and specific type of aerosols (black carbon, BC) or elementary carbon (EC) absorb in the ultraviolet (UV) and visible (VIS) part of solar spectrum. On the contrary, cloud droplets which are suspended in the atmosphere mainly scatter in UV and VIS and absorb in the infrared. The absorption of the incoming solar radiation from the atmospheric constituents reduces the harmful UV radiation and it is considered as the driving of atmospheric photochemistry. Moreover, scattering in the atmosphere can be divided into two mainly categories, firstly, the Rayleigh scattering which is the scattering of radiation by gases (mainly N2 and O2) and, secondly, the Mie scattering which is the scattering by aerosol particles and cloud droplets. The main difference between Rayleigh and Mie scattering is the direction of the re-emission of the incident solar radiation. For example, in the Rayleigh scattering the light have symmetrical direction either forward or backward whereas in Mie scattering the light is mainly scattered in the forward direction, depending on the size of the particle.","name":"Absorption and scattering of solar radiation in the Atmosphere","selfAssesment":"Completed
"},{"code":"PP1-6-3","description":"Mie scattering refers primarily to the elastic scattering of light from atomic and molecular particles whose diameter is similar or larger than the wavelength of the incident light. We can say that, when the particle has a diameter greater than about a tenth of the wavelength, we are in the field of Mie scattering.\r\nThis scattering produces a pattern like an antenna lobe, with a forward lobe sharper and more intense than the back one, the larger the particle size the greater the intensity and sharpness of the anterior lobe. Unlike Rayleigh scattering, Mie scattering is not strongly wavelength dependent. In this case the predominant component for the quantification of scattering (in addition to the particle dimension) is the direction of the incident solar radiation.\r\nMore specifically, the amount of scattering in the backward direction depends upon a wave relation tending to decrease in accordance with the growth of the particle size until it reaches a certain value for which the back scattering becomes a constant quantity. This condition is reached when the diameter of the particle is approximately equal to the wavelength of the incident radiation.\r\nIn the atmosphere the Mie scattering is commonly caused by particles (aerosols) floating in the atmosphere (due to Dust, smoke, fog, rain drop). \r\nIn nature it is possible to see the effects of Mie scattering, for example, in the evenings when there is a lot of fog and the dazzling headlights of our car do not allow us to see the road ahead. \r\nThe Mie theory provides the solution for the amount of scattering in case of a spherical medium due to an incident wave.","name":"Mie Scattering in the Earth's Atmosphere","selfAssesment":"Completed
"},{"code":"PP1-6-4","description":"Scattering is a physical process by which a particle in the path of an electromagnetic wave continuously exstracts energy from the incident wave and reradiates that energy in all directions. In more detail, it occurs when a photon’s electromagnetic field hits a particle’s electric field in the atmosphere and is deflected into another direction. The Rayleigh scattering falls into the elastic scattering phenomena, in which the individual photon changes its direction of propagation but non its energy. The Rayleigh scattering involves air molecules (mainly N2 and O2) whose diameter (x) is much smaller (one-tenth at least) than the incident radiation wavelength (λ) (i.e., x << λ). The amount of scattered intensity (I) depends on the incident light wavelength (λ) and the refractive index (n) of air molecules. However, the refractive index can be considered relatively negligible as compared to the explicit wavelength term. In this way, the intensity scattered by air molecules in a specific direction is strongly dependent on the wavelength (λ), as expressed in the form Iλ~1/λ4. The inverse dependence of the scattered intensity on the wavelength to the fourth power allows at explaining the blue color of sky, caused by the scattering of sunlight off the atmosphere molecules. To better understand this phenomenon, it is worth considering that a large portion of solar energy is contained between the blue and red regions of the visible spectrum, where blue light (0.425 µm) has a shorter wavelength than red light (0.650 µm). Consequently, based on the above-mentioned equation, blue light scatters about 5.5 times more intensity than red light. For this reason, more blue light is scattered than red, green, and yellow, and so the sky appears blue, when viewed away from the sun’s disk. The Rayleigh scattering of unpolarized sunlight by air molecules has maxima in the forward and backward directions, whereas it shows minima in the side directions. Furthermore, the light scattered by particles is not delimited only on the incidence plane, but is visible in all the azimuthal directions. The derived scattering patterns are symmetrical in the three-dimensional space, because of the spherical symmetry assumed for air molecules.","name":"Rayleigh Scattering in the Earth's Atmosphere","selfAssesment":"Completed
"},{"code":"PP1-6-5","description":"When we talk about “thermal infrared (or terrestrial) radiation” we commonly refer to the energy emitted from the Earth-atmosphere system. Trapping of thermal infrared radiation by atmospheric gases is typical of the atmosphere and is therefore called the “atmospheric effect”. The atmospheric effect is sometimes referred to as the “greenhouse effect” because in a similar way glass, which covers a greenhouse, transmits short-wave solar radiation, however absorbs long-wave thermal infrared radiation. Imagine a beam of radiation travelling through a small section of air. The air is made up of changing concentrations of different species, with all molecules absorbing and emitting thermal radiation at different rates. As the radiation travels through different layers of the atmosphere, the intensity of radiation will constantly be modified by both absorption and emission processes as described by the Schwarzschild's equation. In case of a sensor on board of a satellite, the net radiation measured would be that which is attenuated through each layer (as small increments of absorption and emission) from the surface to the top of the atmosphere plus the radiation emitted directly from the atmosphere. In this case, this process can be described by the radiative transfer equation (RTE). \r\nThe equation of radiative transfer simply says that as a beam of radiation travels through the atmosphere, it loses energy to absorption, gains energy by emission, and redistributes energy by scattering. Many radiative transfer codes exist which are able, i.e. on the basis of known properties of the atmosphere, to computed the effect of the atmosphere on the thermal infrared radiation providing atmospheric transmittance (absorption), atmospheric scattering and atmosphere path emission. Commonly, in satellite remote sensing, the thermal infrared region is defined as the region of the electromagnetic spectrum comprised between 8 and 14 micron. In an atmosphere free of particles (aerosols due to phenomena like fires, volcanic eruption, dust storm, etc.) the thermal infrared radiation is mainly affected by triatomic gases like water vapor, carbon dioxide and ozone.","name":"Thermal infrared radiation transfer in the atmosphere","selfAssesment":"Completed
"},{"code":"PP1-6-6","description":"Light scattering by particles is the process by which small particles cause optical phenomena, such as rainbows, the blue color of the sky, and halos. Mie scattering defines the interaction of light with particulate matter with a dimension comparable to the wavelength of the incident radiation. It can be regarded as the radiation resulting from a large number of coherently excited elementary emitters (molecules for example) in a particle. Since the linear dimension of the particle is comparable to the wavelength of the radiation, interference effects occur. The most noticeable difference to Rayleigh scattering is, generally, the much weaker wavelength dependence and a strong dominance of the forward direction in the scattered light. The calculation of the Mie scattering cross section, which involves summing over slowly converging series, is complicated even for spherical particles, it is worse for particles of an arbitrary shape. However, the Mie theory for spherical particles is well developed and a number of numerical models exist to calculate scattering phase functions and extinction coefficients for given aerosol types and particle size distributions.","name":"Light scattering by atmospheric particulates","selfAssesment":"Planned
"},{"code":"PP1-6-7","description":"Each time radiation passes through the atmosphere it is attenuated to some extent. We refer to this attenuation with the term 'atmosphere transmittance'. The typical atmospheric transmittance between wavelengths of 250 nm and 2500 nm, i.e. in the ultraviolet, visible, near-infrared and short-wave-infrared regions of the spectrum is dominated bywater vapour, although methane, carbon dioxide and molecular oxygen are also responsible for a few absorption lines. The behaviour in the visible region is dominated by molecular Rayleigh scattering. At the short-wavelength end of the spectrum, in the ultraviolet, absorption by ozone becomes very significant. Above 2500 nm up to the upper limit (13500 nm) of the optical electromagnetic spectrum useful for Remote Sensing, the atmosphere transmittance is mainly affected by triatomic molecules (H20, CO2 and O3). However, the atmospheric effects (transmittance) is strongly depending on the electromagntic wavelength. Remote Sensing exploits the region of relative atmospheric transparency called atmospheric windows.","name":"Earth's (standard) Atmosphere Transmittance","selfAssesment":"Planned
"},{"code":"PP1-6-8","description":"With the term 'atmospheric windows' we refer to the regions of the electromagnetic spectrum where the interaction between the atmosphere constituents (i.e., molecules, aerosols, and cloud particles) and the electromagnetic radiation is minimized, namely the mechanisms of scattering and absorption of the radiation are less relevant than the transmission one. Therefore, the radiation collected at the sensor in these spectral regions is strictly depending on the Earth surface features, allowing to infer information about the processes/phenomena there in progress at the time of the acquisition. There are three main spectral ‘windows’ in the Earth's atmosphere. The first of these includes the visible and near-infrared (VNIR) parts of the spectrum up to the medium infrared, between wavelengths of about 0.38 μm and 3.5 μm, although it does also contain a number of opaque regions. This spectral interval includes the small portion of the electromagnetic spectrum to which human eyes are sensitive to (i.e, the visibile region between 0.4 and 0.7 μm). The second is a rather narrow region between about 8 μm and 15 μm, in which is found the bulk of the thermal infrared (TIR) radiation from objects at typical terrestrial temperatures. In this region there is only a main opaque interval, around 9.6 μm due to the presence of the ozone band. The third more or less corresponds to the microwave region, between wavelengths of a few millimeters and a few meters. Therefore, each remote sensing instrument that should be able to fully penetrate the Earth’s atmosphere has to be designed to operate in one of these three ‘window’ regions.","name":"Atmospheric (spectral) windows for EO","selfAssesment":"Completed
"},{"code":"PP1-6-9","description":"The water cycle is a continuous purification process of water on Earth due to the movement of water species among various reservoirs. This cycle is vital for Earth’s life, ecosystems, and living organisms. The water cycle includes mainly four processes. Water is evaporated from ocean and land surfaces driven by solar heating. The resulting water vapor rises upwards into the atmosphere, transported by the winds, cools, and due to low air temperature condensates into liquid droplets and ice crystals to form clouds. The ice or/and liquid droplets collide, increase their size, and precipitate as snow or rain to Earth’s surface and oceans. The subtraction of energy (latent heat of evaporation) at low latitudes related to the evaporation processes as well as its release (latent heat of condensation) at higher latitudes related to the condensation processes is a formidable way to guarantees the heat transport from the warmer part of the Earth to the colder ones mantaining local air temperature more compatible with the human life. The starting point of the water cycle is not unique, but the oceans can be selected as the initial reservoir. Other important reservoirs are considered ice sheets, lakes, and rivers. \r\nThe hydrosphere is defined by the various water reservoirs which are characterized by different residence times – the time spends the water molecules in a reservoir. The water residence time – the rate at which the water comes out the reservoirs – varies for each reservoir extending from hundreds (Greenland Ice Sheet) or thousands of years (Antarctic Ice Sheet) to years and days for rivers and lakes, respectively. It also defines the energy transferred from the Earth to the Atmosphere which increases for short-term residence times. In long-term temporal scales, this energy is defined as the evaporation rate (E) and balances with the precipitation rate (P). This global energy balance breaks for shorter time scales depending also on the local and regional climate. For example, in regions located in the Inter-Tropical Convergence Zone (ITCZ), the energy balance in the water cycle does not exist since the precipitation rate is much higher than the evaporation rate (P>>E) due to the horizontal movement of converging trade winds.","name":"The Water Cycle","selfAssesment":"Completed
"},{"code":"PP1-6","description":"Atmospheric Physics describe the processes affecting the physical, chemical and thermodynamic status of planetary atmospheres. In the context of EO sciences, it particularly refers to the physics of the interactions of e.m. radiation traveling across (or emitted by) the atmosphere as the main source of information collected by satellite (in general aerial) sensors.","name":"Basics of Atmospheric Physics","selfAssesment":"Completed
"},{"code":"PP1-7-1","description":"According to the second law of thermodynamics, heat is a measure of the movement or the flow of energy from hotter substances to colder ones and it is measured in Joules. In microscale, heat is known as internal energy. Two regions in thermal contact have the same temperature when there is no net exchange of internal energy between them. Heat is the net transfer of internal energy from one region to another, while temperature, which is the degree of hotness or coldness of an object, describes the average kinetic energy of molecules within substances. The faster the particles are moving, the higher their kinetic energy. Since the motion of the particles within an object is random, they do not move at the same speed and in the same direction, some of them move faster. Therefore, those particles have more kinetic energy than the others. Thermodynamic temperature can be defined for substances at (even Local) Thermodynamic Equilibrium (i.e. in condition of density/pressure which allows an efficient equipartition of kinetic energy among molecules). Temperature is then the measure of the average kinetic energy of such a system, and is usually expressed in Celsius (°C). When, particular conditions of very low pressure/density (like in the Earth's thermosphere) cannot guarantee energy equipartition among molecules (i.e. outside thermodynamic equilibrium) the concept of Kinetic Temperature should be used instead. The Celsius temperature scale is defined by international agreement in terms of two fixed points: the temperature of the ice point, which is defined as 0° Celsius, and the steam point as 100° Celsius. The Fahrenheit (°F) temperature scale is mainly used in the United States; on this scale, water freezes at 32 degrees Fahrenheit, and the temperature of boiling water is 212 F. The Kelvin scale (K) is the base unit of temperature in the International System of Units (SI). This temperature scale is obtained by shifting the Celsius scale by −273.15°; zero Kelvin is also called absolute zero.","name":"Temperature and heat","selfAssesment":"Completed
"},{"code":"PP1-7-10","description":"Irreversible thermodynamics investigates the regularities in transport phenomena, namely heat and mass transfer, and their relaxation. It is based on the first law of Thermodynamics, which correlate the heat flow density with pressure and viscosity, and the second law that describe the temporal variations of local entropy for local continuous mass.","name":"The constitutive equations of irreversible fluxes","selfAssesment":"Planned
"},{"code":"PP1-7-11","description":"The Adiabatic process of homogeneous system occurs, when flow of heat is not exchanged across the boundaries of system and the system is characterized from uniform phase (solid or liquid or gases). In this case, the variations of entropy can be determined for some parts of system.","name":"Heat equation and special adiabatic systems, special adiabats of homogeneous systems","selfAssesment":"Planned
"},{"code":"PP1-7-12","description":"The thermodynamic diagrams are used for the study of vertical structure and properties of the Atmosphere above a specific location. Especially, a static diagram represents a) an atmosphere with fixed potential temperature or b) a process curve of the change of variables of air parcel that rises adiabatically.","name":"Thermodynamics diagram, atmosphere static","selfAssesment":"Planned
"},{"code":"PP1-7-2","description":"Kinetic theory of gases is based on a simplified molecular description of gases, from which the properties of volume, pressure and temperature can be derived. The assumptions of this theory are based on the random movements of molecules, their elastic collisions and the transfer of kinetic energy between them.","name":"Kinetic theory of gases","selfAssesment":"Planned
"},{"code":"PP1-7-3","description":"The ideal gas law or general gas equation describes the equation of state of hypothetical ideal gas. This equation correlates the pressure and volume with its temperature, while is characterized as a combination of the empirical laws of Boyle, Charles, Avogadro and Gay-Lussac.","name":"Ideal gas laws","selfAssesment":"Planned
"},{"code":"PP1-7-4","description":"The state functions of ideal gas are the pressure, volume, temperature, internal energy and entropy, which remain unchangeable in compared with the path. The internal energy is expressed through Joule’s law as a function of temperature of gas, while the entropy depends on the variation of volume and temperature.","name":"State function of ideal gases","selfAssesment":"Planned
"},{"code":"PP1-7-5","description":"The phase rule for condensation is expressed as P+F=C+1. The terms of P, F and C describe the number of phases, minimum fixed variables and independent chemical species respectively. Concerning the condensed phases to distinguish the gases from liquids and solids, these are the density, molecular order, diffusion, etc.","name":"State function of the condensed gas phase","selfAssesment":"Planned
"},{"code":"PP1-7-6","description":"When the system passes from initial to final state due changes in properties of temperature, pressure and volume, it is considered to have undergone thermodynamic process. The different types of thermodynamic processes are distinguished in the isothermal (fixed temperature), adiabatic, isochoric (stable volume), isobaric (stable pressure) and reversible process.","name":"Thermodynamic process","selfAssesment":"Planned
"},{"code":"PP1-7-7","description":"Budget equations, namely heat, momentum and moisture budget, are interpreted through two frameworks, which are Eulerian and Lagrangian. Eulerian is utilized for the investigating of transfer of heat by the wind, while Lagrangian is concerned about the effects of ascending or descending airflows on the Earth-Atmosphere system.","name":"Budget equations","selfAssesment":"Planned
"},{"code":"PP1-7-8","description":"The First Law of Thermodynamics supports that the energy is conserved. Thus, the thermal energy is defined as the sum of warming or internal energy (microscopic effect) and work occurring per unit mass (macroscopic effect). For its application to the Atmosphere, the thermal energy input is given from the following mathematical expression: Δq=Cp·ΔT-(ΔP/ρ), where Δq (J·kg–1) is the amount of thermal energy you add to a stationary mass m of air, Cp (J·kg–1·K–1) is the specific heat of air at constant pressure, ΔT (K) is the induced variation of temperature, so that Cp·ΔT represents the heat transferred per unit air mass, ΔP (Pa = J·m-3) is the pressure difference and ρ (kg· m-3) is the air density.\r\nThe term Cp·T is defined enthalpy h, thus, the first term on the right side of eq. of thermodynamic first low for atmospheric applications, which is the corresponding enthalpy change is: Δh=Cp·ΔT. It is a characteristic possessed by the air.\r\nExpressing the first law of thermodynamics for atmospheric applications in conceptual form we can state that, given a quantity Δq of thermal energy added to a stationary mass m of air, a part of this energy heats the air, increasing its internal energy, but, as air heats up, its volume expands by an amount ΔV and pushes against the surrounding atmosphere, which responds with an equal and opposite pressure P that we can assume constant. Therefore, a part of the thermal energy introduced does not go to heat the air, but goes into macroscopic movement.","name":"First law of thermodynamic","selfAssesment":"Completed
"},{"code":"PP1-7-9","description":"A natural process that starts from an equilibrium state and ends in another state, causing changes in direction of entropy (ΔS) or statistical disorder of the system, is interpreted by Second Law of Thermodynamics. This law is considered as an irreversible process and it is expressed as ΔS=Heat transfer/Temperature.","name":"Second law of thermodynamics","selfAssesment":"Planned
"},{"code":"PP1-7","description":"Thermodynamics is the science of the relationships between heat, work, temperature, radiation, energy and properties of matter. These relationships are governed by the four laws of thermodynamics which allow a quantitative description, through measurable macroscopic physical quantities, of processes that, at the level of microscopic constituents can be described by the statistical mechanics. Thermodynamics applies to a wide variety of topics relevant to EO science and technologies from atmospheric chemistry and meteorology up to sensor design and aeronautics.","name":"Basics of Thermodynamics","selfAssesment":"Planned
"},{"code":"PP1-8-2","description":"Starting from the standard Rocket Equation - assuming a relative speed of the burned (emitted) fuel equal to 2,4 km/s and zero initial speed - it is possible to evaluate (for a single-stadium rocket) the mass percentage of payload that can be hosted on a platform depending on the final speed expected on the orbit. For instance a 28% payload is possible for a geostationary platform whose expected final speed on the orbit (radius 42.170 km) is 3,7km/s. Instead for a polar platform at about 800km this percentage reduce up to the 4% being the final sped on the orbit expected to be 7,5km/s.","name":"Equation of the rocket and launch of a satellite: payload determination","selfAssesment":"Planned
"},{"code":"PP1-8-3","description":"The orbit of a satellite is commonly defined through its so called Keplerian parameters. These parameters represent the trajectory that the satellite will follow if no-perturbation are acting on it. A series of forces act on the satellite to perturb it away from the nominal orbit. We can classify these perturbations, or variations in the orbital elements, based on how they affect the Keplerian elements. The actual orbit of a satellite will result from a combination of these perturbations. Periodic maneouvers are needed to bring the orbit back to nominal conditions. The lifetime of a satellite is defined as the time interval that it takes to decay from its initial altitude to an altitude causing the satellite reentry down to the atmosphere. Therefore lifetime of a satellite should not be confused with the time during which the satellite will provide useful information (this operational phase, in general, is designed to last 5 - 7 years). In fact, all satellite terminating operational phases in orbits passing through the LEO region should be de-orbited or, where appropriate, manoeuvred to an orbit with suitably-reduced lifetime, that is, should be left in an orbit where drag and other perturbations will limit lifetime. The actual duration of the satellite in orbit will depend from the intensity of the perturbations which will affect its orbit. In case of satellite on GEO orbit, at the end of the operational phases they will be located on a disposal orbit, that is an orbit which do not cross the protected region. The protected region is the altitude region ranging from GEO - 200 km to GEO + 200 km and inclination region between -15 deg and +15 deg. Satellites in low Earth orbit, with perigee altitudes below 1000 km, are predominantly subject to atmospheric drag. This force very slowly tends to circularise and reduce the altitude of the orbit. The rate of 'decay' of the orbit becomes very rapid at altitudes less than 200 km, and by the time the satellite is down to 180 km it will only have a few hours to live before it makes a fiery re-entry down to the Earth.","name":"Real orbits. Life time of a satellite, orbit’s decay.","selfAssesment":"Planned
"},{"code":"PP1-8-4","description":"The choice of a satellite orbit mostly depends on its main application. From this point of view it represents a crucial part of a satellite mission design. The most important parameters to describe a satellite orbit are the inclination angle i (of the orbit plane respect to the equatorial plane) its eccentricity e and its height H from the Earth's surface. In principle whatever eigth H can be used, provided that the speed of the satellite on its orbit allows the centrigugal force to exactely compensate the gravitational one at that heigth. Polar (i close to 90°) and Geostationary (i=0, H=35.800 km) orbits are the most common choices for EO satellites. In principle one single polar satellite can be sufficient to guarantee the global coverage of the Earth with equal quality of the images at all latitudes. All Geostationary satellites share the same circular orbit with H around 36000 km where the required speed exactely correspond to the one required to travel an entire orbit in 1 sideral day (orbital period P = 1 sideral day). This means that the satellite footprint is permanently in place over a specific Earth's location (e.g. for Meteosat 0°N, 0°E) allowing a quasi-continuous monitoring of a whole Earth's emisphere (with poor visibility of Earth's edges including Poles). Polar satellites' heigths are usually in between 700-800 km, with orbital periods around 100min (i.e. about 14,5 orbits/day) even if, lower orbits are also chosen particularly for very high spatial resolution payloads. Lower inclinations are also used (quasi-polar orbits) for specific applications. Due to the asphericity (and mass inhomogeneity) of the Earth, satellite orbit plane rotates around the Earth's polar axis with a period Pp producing (for elliptical orbits) the rotation of the orbit itself in its plane. A common choice for most EO polar satellites is to choose the orbital parameters in a way that Pp=1 year (Sun-Synchronous orbits). Due to the synchronism between Earth's revolution around the Sun and the orbit plane precession around Earth' axis, satellite passages happens at the same local solar time (similar illumination conditions) each time it flies over a specific region. This ensure repeatable sun illumination conditions facilitating image interpretation particularly for change detection or land monitoring applications. Other choices are possible when it is required to monitor with continuity high latitude regions.\r\n\r\nThis is the case of Molniya orbits which combine the continuity of observations typical of geostationary satellites with the possibility, offered by polar orbits, to overfly the highest latitudes regions. Its characteristics are: high eccentricity (e.g. e=0,74, axes 500 and 23.000 km), P=1/2 sideral day (Geo-Synchronous), inclination (i=63,4° or i=116,6°) which guarantees the satellite footprint at the apogee remaining positioned on a fixed ground point (non-rotating orbit). This way the satellite will spend more than 93% of its orbital period looking to the same emisphere even from a high latitude point of view. \r\n\r\nSo called altimetric orbits respond to the specific needs of altimetry. In this case the orbital parameters are chosen in order to guarantee, for example: a) that the ascending and descending sub-satellite tracks intersect at roughly 90 degrees on the Earth’s surface (so that orthogonal components of the surface slope can be determined with equal accuracy; b) the possibility to monitor all phases of tidal effects on ocean surface.\r\n\r\nParticularly important for several applications (multi-temporal analyses, change detection, etc.) are the Exactly repeating orbits.\r\nThey are conceived in order that the sub-satellite track will repeat itself exactly after a certain interval of time. This allows images having the same viewing geometry during the satellite’s lifetime making moreover available a particularly simple method of referring to the location of images (navigation or geo-referenciation) for example by referring to a ‘path and row’ system used for instance by the Landsat World Reference System (WRS). It is possible to arrange satellite orbits parameters in order to contemporary guarantee the sun-syncronism so that, not only satellite images collected on the same region can be easily super-imposed each-other but the same illumination and viewing geometry can be achieved. This is, for instance, the choice adopted for LANDSAT satellites whose images are typically available as a collection of scene of fixed dimension always similar each other when covering the same terrestrial area.","name":"Satellite orbits parametrization and choice","selfAssesment":"Completed
"},{"code":"PP1-8","description":"Mechanics is the Physics branch dealing with the behaviour of physical bodies when subjected to forces or displacements. This section provides Mechanics basic elements necessary for determining the orbits of satellites and rockets. The different satellite trajectories will be illustrated with respect to their peculiarities","name":"Basics of Mechanics","selfAssesment":"Planned
"},{"code":"PP1","description":"Optical Remote Sensing deals with those part of electromagnetic spectrum characterized by the wavelengths from the visible (0.4 micrometer) to the near infrared (NIR) up to thermal infrared (TIR, 15 micrometer). It regards the collection and interpretation of the e.m. radiation emitted, reflected, adsorbed and transmitted by the observed targets in order to derive their physical-chemical properties and related information. Such a possibility derives from the basic principle of (multi-spectral) remote sensing that is widely supported both theoretically (e.g. atomic and molecular spectroscopy) and experimentally (e.g. spectral signatures catalogues). It states that, in principle (e.g. disposing of sensors with ideal spectral capabilities) the matter-radiation interaction depends on the wavelength of the involved radiation and on specific (e.g. chemical/physical) properties of the matter that can be derived by the spectral analysis of the emerging (emitted, reflected, adsorbed or transmitted) radiation. As far as Earth Observation is concerned, specific related concepts have to be addressed like: the spectral matter-radiation interactions (spectral signature concept), natural sources (e.g. Earth, Sun) of optical e.m. radiation, theory of the Black Body, atmospheric physics and radiative transfer equations in the VIS-NIR and TIR spectral ranges, basic physics of e.m. optical sensors and image systems, physical fundaments of the interpretation of optical radiances collected by multi-hyperspectral passive techniques.","name":"Basics of Optical Remote Sensing","selfAssesment":"Completed
"},{"code":"PP2-1-2-1","description":"A radar signal is a complex signal. It is represented by a real part, the in-phase component, and an imaginary part, the quadrature component. In-phase is usually annotated by “I”, and quadrature by “Q”. Considering single look complex data, each component is represented in a single image channel.","name":"In-phase/Quadrature Component","selfAssesment":"Planned
"},{"code":"PP2-1-2-2","description":"A phasor represents a complex number and its phase and amplitude equivalent. Considering a complex SAR image’s pixel, the real and imaginary part can be represented by a 2D vector in Cartesian coordinates. Its corresponding phase and amplitude information corresponds to the direction and length of the vector, respectively.","name":"Phasor","selfAssesment":"Planned
"},{"code":"PP2-1-2","description":"The signal emitted by a radar system is a microwave signal, which can be described using a complex wave representation. This implies that the signal can be entirely represented by a complex number, which characterizes both its magnitude and its phase at a certain moment of time. In the SAR context, the complex number is usually represented by a real part, the in-phase component (I), and an imaginary part, the quadrature component (Q), from which the corresponding magnitude and phase can be retrieved. In single look complex SAR data, each of these components is pictured in a single image channel. The terminology comes from electrical engineering, whereby the quadrature component is 90° out of phase with respect to the reference frequency and the in-phase component. This is necessary in order to retrieve the phase information during A/D conversion. The I component can be expressed as the signal amplitude multiplied by the cosine of the phase. The Q component corresponds to the amplitude of the signal multiplied by the sine of its phase. Using both components as input, the magnitude and phase for each signal echoes and location can be retrieved.\r\nThe relationship between I/Q terms and the magnitude and phase of the signal can be best represented using a phasor. A phasor represents a complex number and its phase and amplitude equivalent. It can be best illustrated by a 2D vector in a Cartesian coordinate system, which projections on the horizontal and vertical axes represents the real and imaginary part, respectively. The length of the vector correspond to the signal’s amplitude and its direction (angle between the horizontal axis and the vector) characterizes the phase of the signal. Using simple mathematical considerations, the relationship between I/Q and amplitude and phase can be established.\r\nEach signal echo and pixel of a complex SAR image can be represented with such a phasor and the necessary amplitude and phase information can be accordingly retrieved.","name":"Complex wave description","selfAssesment":"Planned
"},{"code":"PP2-1-4","description":"Electromagnetic waves are polarized; the direction of the polarization corresponds to the direction of oscillation of the electromagnetic field. Typical and often used linear polarisations are: H (horizontally) and V (vertically) polarized waves of the plane of the electric field vector oscillations relative to the sensor coordinate system. The polarization state of a backscattered wave from a natural surface can be linked to the geometrical characteristics like shape, roughness and orientation and the intrinsic properties of the scatterer like moisture, salinity, density. The radar system is characterized by combination of polarization of transmitted and received pulse: HH, HV, VH or VV. Based on the polarization sent and obtained the radar systems are divided in three polarization modes. Single polarization refers to the same polarization transmitted and received; dual polarization, one polarization is sent and another received; or quad polarization, when system is able to transmit and receive all four types of polarization. When making a contact with a scatterer, the polarization of the EM-wave can change, depending on the geometrical and dielectrical properties of the scatterer. In order to get all necessary information about those changes, full polarimetric systems are required.","name":"Polarisation","selfAssesment":"Completed
"},{"code":"PP2-1-5","description":"Property of signal or data set in which the phase of the constituents is measurable, and plays a significant role in the way in which several signals or data combine. Two waves with a phase difference that remains constant over time, are said to be coherent.","name":"Coherent","selfAssesment":"Planned
"},{"code":"PP2-1-6","description":"In remote sensing, phase is the exact position within a periodic signal with respect to an arbitrary reference point. It is typically expressed as an angle and measured in degrees or radians, where one period corresponds to a phase of 360° or 2π, respectively. Mathematically, phase is the argument of a complex number, that is the angle between its geometric representation in the complex plane and the real axis. For this reason, complex algebra is often used in remote sensing to facilitate phase calculations. Due to its periodic nature, phase can only be measured unambiguously within one period. Consequently, phase measurements are commonly subject to 2π phase ambiguities. These ambiguities can often be resolved in a process called phase unwrapping, using a priori information about the signal, typically related to its continuity. Phase measurements are crucial for the creation of synthetic aperture radar (SAR) images, as well as for many SAR imaging techniques, including interferometric SAR (InSAR).","name":"Phase","selfAssesment":"Completed
"},{"code":"PP2-1-7","description":"Shift in frequency caused by relative montion along the line of sight between sensor and the observed scene.","name":"Doppler effect","selfAssesment":"Planned
"},{"code":"PP2-1-8","description":"The wave-particle dualism (duality) is a theory according to which all matter exhibits the attributes of waves and particles.","name":"Wave-particle dualism","selfAssesment":"Planned
"},{"code":"PP2-1","description":"The microwave portion of the electromagnetic (EM) spectrum ranges from 1 millimeter to 1 meter. Imaging radars are independent of weather conditions and can operate day or night. EM-waves are polarized. Normally only the horizontal (H) or vertical (V) linear polarizations are used. The radar system is characterized by combination of polarization of transmitted and received pulse: HH, HV, VH or VV. When making a contact with a scatterer, the polarization of the EM-wave can change, depending on the geometrical and dielectrical properties of the scatterer.The data can be acquired from both the ascending (northwards) and descending (southwards) satellite passes. Water clouds can interfere with the radars operating below 2 cm in wavelength. The effects of rain can be generally ignored at wavelengths above 4 cm. For longer wavelengths (above 20 cm), an effect called Faraday rotation caused by the ionosphere, i.e., free charges (electrons) and the Earth’s magnetic field, can lead to a rotation of the polarization plane. In the presence of Faraday rotation, the data, usually fully polarimetric, should be corrected. The radar systems operate in different bands that uses different wavelengths. The most common frequences/wavelengths (frequency = Speed of Light / wavelength) for environmental applications are X (5,75-10,90 GHz), C-(4,20-5,75 GHz), S-(1,550-4,20 GHz), L-(0,390-1,550 GHz) and P-(0,255-0,390 GHz) band. The selection of SAR system for acquiring data depends on their application. Longer wavelengths are mainly devoted to communication and navigation purposes. Radars penetrate atmosphere and clouds. For example for forestry, longer wavelengths starting from C- or S-band are preferred.","name":"Microwave portion of electromagnetic spectrum","selfAssesment":"Completed
"},{"code":"PP2-2-1","description":"Diffraction is defined as interaction of waves with any solid object, not surfaces, and is not to be confused with refraction. More precisely, diffraction describes the phenomena of interaction of waves at an obstacle, such as an aperture, or an opening, such as a hole or an occurring space between two objects. Hence, diffraction is an essential form of scattering, describing ordered scattering at discrete boundaries. The effect of diffraction can be observed through extended interference patterns or simply by the bending of waves. In the field of microwave remote sensing, diffraction has the practical implication that it limits the spatial resolution of a microwave sensor since it acts on the ability of an imaging system to resolve details. This theoretical limit of resolution is called the diffraction limit. This means, the larger the aperture of the observing system compared to its employed wavelength (dependent on the frequency), the finer the resolution of an imaging system. The diffracted field can be calculated with analytical models, such as the Fraunhofer diffraction approximation in case of far field conditions, where the object is far away and the incident waves are assumed to be plane waves, or the Fresnel diffraction approximation in case of near field conditions, where the waves are spherical.\r\nOne simple example of diffraction is the diffraction of sound, for example the possibility to hear sounds around corners.","name":"Diffraction","selfAssesment":"Completed
"},{"code":"PP2-2-2","description":"Scattering means the redirection of incident electromagnetic energy by an object. Similar to diffraction, scattering refers to the same physical process, the coherent distortion of an incident wave. However, diffraction as well as reflection can be regarded as essentially forms of scattering. Scattering explicitly describes the “random distortion of waves by elements that are similar in size or less than the wavelength” (Woodhouse, 2005). Thereby, scattering of the incident wave at an object can occur in any directions with varying strength, with the scattering pattern varying with the incident direction. Thus, the term scattering cross section, often denoted by σ, quantifies the effectiveness of a scatterer. In the field of active microwave remote sensing, the backscattering coefficient σ0 is known “as the ratio of the statistically, averaged, scattered power density to the average incident power density” (Fung, 1994). \r\nIn passive microwave remote sensing, radiometers measure the intensity of radiation emitted by a body, called brightness temperature TB. Since TB is always less than its physical temperature T, emissivity, defined as e = TB / T, is a measure of how strongly a body radiates at a given wavelength. It varies between 0 (metal) to unity (blackbody).\r\nEmission and scattering are complementary: surfaces that are good scatterers are weak emitters, and vice versa.","name":"Scattering and emission","selfAssesment":"Completed
"},{"code":"PP2-2-3","description":"In climate change studies the carbon cycle with its crucial component the terrestrial biosphere is of great importance due to the ability of the biosphere to store environmentally harmful carbon dioxide. Radar sensors, especially SAR, can here provide a useful tool for quantifying and monitoring the biosphere. Hence, the relationship between biomass and radar backscatter responses has been studied in detail in recent decades. Results show that the sensitivity of measured radar backscatter coefficient decreases with increasing amount or density of present biomass. In the so-called saturation region, the radar backscatter saturates at a biomass depending on the employed wavelength. While for higher frequency bands like C-band (3.95-5.8 GHz), biomass can be measured up to ~50 ton/ha, the amount of measurable biomass increases with decreasing frequency (due to the increasing wavelength), such that at L-band (1-2.6 GHz) ~ 100 ton/ha and at P-band (0.23-1 GHz) ~200 ton/ha biomass can be measured. Further, the sensitivity of radar to biomass is different for co- or cross-polarized backscatter since the level of saturation depends not only on frequency but also on vegetation (e.g., height, structure, density, moisture) and soil surface (e.g., roughness, moisture) parameters. Overall, the saturation of radar backscatter depending on biomass has to be considered when analyzing SAR data.","name":"Backscatter saturation","selfAssesment":"Completed
"},{"code":"PP2-2-4-1","description":"The radar equation is a measure of the received echo at the sensor. It defines what proportion of the transmitted energy is returned from a target. It is a function of the range between the antenna and the target, the antenna gain and the radar cross-section of the target. Mathematical expression that describes the average received signal level, compered to the additive noise level, in terms of system parameters. Principal parameters include: transmitted power, antenna gain, noise power, and radar range.","name":"Radar equation","selfAssesment":"In progress
"},{"code":"PP2-2-4-2","description":"Coefficient sigma or sigma nought represents the average reflectivity of a horizontal material sample, normalized with respect to a unit area on the horizontal ground plane.","name":"Sigma nought","selfAssesment":"Planned
"},{"code":"PP2-2-4-3","description":"Gamma nought represents the average reflectivity of a horizontal material sample, normalized with respect to the incident area, orthogonal to the incident ray from the radar.","name":"Gamma nought","selfAssesment":"Planned
"},{"code":"PP2-2-4-4","description":"Radar brightness coefficient represents the reflectivity per unit area in slant range.","name":"Beta nought (brightness)","selfAssesment":"Planned
"},{"code":"PP2-2-4","description":"Measure of radar reflectivity. The Radar Cross Section (RCS) is expressed in terms of the physical size of an hypothetical uniformly scattering sphere that would give rise to the same level of reflection as that observed from the sample target.","name":"Radar cross-section","selfAssesment":"Planned
"},{"code":"PP2-2-5-1","description":"A material constant is a physical or chemical property of a substance, which can be expressed in numbers. Giving a precise numerical value of a constant often requires determining the external conditions (e.g. temperature, humidity). Material constants are factors that influence the interaction of microwaves with the target objects.","name":"Material constants","selfAssesment":"Planned
"},{"code":"PP2-2-5-2","description":"The complex part k of the refraction index n=m+ik determines how far an electromagnetic wave of wavelength λ can survive crossing a specific medium. The attenuation length la is the distance after that the amplitude of an electromagnetic signal reduces its value by an amount of 1/e. For instance the amplitude of the Electric field E(z) of an electromagnetic wave proceeding along the z direction is decreasing as exp(-z/la) being la=λ/(2𝜋k) the attenuation length associated to that specific material (k) and wavelength λ. This way attenuation length in water can be of hundreds of meters in the visible range and just few microns in the microwaves. The opposite happens over solid land surfaces where optical waves can penetrate from few microns up to few millimeters (moving from the VIS-NIR to the TIR spectral range) whereas microwaves can reach depths from hundreds to thousands (as higher are their wavelength) meters allowing the exploration of subsoil and thick coulters of ice.","name":"Attenuation lenght and penetration depth","selfAssesment":"Completed
"},{"code":"PP2-2-5-3","description":"Soil permittivity is a measure of the water content (soil moisture) in the soil and characterized by the metric of the dielectric constant of the soil. Soil moisture influences emission, absorption and propagation of microwave electromagnetic energy. Moisture decreases the ‘emissivity’ of soil, and thereby affects microwave radiation emitted from Earth’s surface. Dry soil has a low dielectric constant and low radar reflectivity. Moist and partially frozen solis have intermediate values. The higher the soil water content, the lower the radar signal penetration into the soil. In situ measurements of soil permittivity are a prerequisite for the calibration and validation of synthetic aperture radar (SAR) soil moisture retrieval algorithms. Soil moisture is a key variable in the hydrologic cycle and is recognized as an Essential Climate Variable (ECV).","name":"Soil permittivity","selfAssesment":"Completed
"},{"code":"PP2-2-5-4","description":"The complex relative permittivity of a plant is a function of its contained amount of water, solutes (mainly their salinity) and temperature in all plant compartments (including roots). The more water and the higher the salinity are in the plant compartments, the higher is the complex relative permittivity of the plant. The complex relative permittivity of a plant refers to the complex relative dielectric constant of the plant and can be subdivided into complex relative permittivity values for the different plant compartments (roots, stem/stalk, leaves, fruit,...). The complex relative dielectric constant or permittivity parameter has a real and an imaginary part indicating the moisture content and the conductivity (loss) of the plant medium. Models of plant permittivity consist mostly of a free-water and a bound-water part. In particular, plant water is a solute of nutrients and not all water-conducting plant cells are fully filled by water, but also with air. Hence, the estimation of one plant permittivity, especially including several plant parts can be challenging to assess, to understand and to model. To acknowledge this mixture of components, dielectric mixing models containing the single material components are normally developed and applied, representing an effective complex relative permittivity of all plant components. Concerning a vegetation canopy, electromagnetic waves interact with a more or less sparsely vegetation-filled volume unit of air. A vegetation canopy represents a dielectric mixture of vegetation inclusions (leaves, twigs, branches, stems,…) distributed in a volume of air. Dielectric mixing models of canopies take this vegetation volume fraction into account.","name":"Plant permittivity","selfAssesment":"In progress
"},{"code":"PP2-2-5","description":"The dielectric properties of any material can be described by the complex relative dielectric constant (complex relative permittivity) and contains of the real part (moisture content) and the imaginary part (conductivity/loss tangent). For instance: Reflectivity of a smooth surface and the penetration capabilities of microwaves into the material are determined by these two quantities. The complex dielectric constant changes mainly due to variations in water content, salinity, temperature of the material as well as due to the observing wavelength and polarization of the electromagnetic wave. It relates to the interaction of weakly-charged material components, like bi-polar water molecules, with irradiation of electromagnetic waves. The interaction increases with amount and charge of the material components. The complex relative permittivity is also linked to the complex index of refraction as being its square. In order to describe the complex relative permittivity of pure and saline water the single-relaxation Debye and the double-Debye dielectric model can be used. As the movement of bi-polar material components is significantly reduced when the material is put under freezing conditions (temperatures below 0 °C), the permittivity falls to almost a constant. The real part of the relative permittivity of pure ice is almost constant, when ignoring a weak temperature dependence, and amounts to approx. 3.2. For heterogeneous (mixed) materials consisting of more than one component the equivalent dielectric constant is a function of the permittivity of the single components, their volume fractions, their distribution along space and the polarization and wavelength of the interacting electromagnetic wave.","name":"Dielectric Properties","selfAssesment":"Planned
"},{"code":"PP2-2-6-1","description":"The standard deviation of the surface height variation (or RMS height), denoted by s (or hRMS), describes the statistical variation of a random surface with height z(x). In case of an azimuthally symmetrical surface, the single-scale RMS height of the one dimensional case for discrete profile values is given by (1), where N is the number of samples, and z ̅ the mean surface height (2). \r\nAs roughness depends not only on the soil surface properties but also the wavelength λ of the electromagnetic signal, the roughness parameters are scaled by the wave number k. Hence, the electromagnetic roughness ks for surface roughness parameter s is (2π/λ)*s (3). In order to determine if a random surface may be considered as electromagnetically smooth, one common definition is given by the Rayleigh roughness criterion, where s < λ / 8*cosθ, or ks < 0.8, at incidence angle θ = 0. This criterion has been revised for the microwave region, where the wavelength is usually of the order of the RMS height, called the Fraunhofer roughness criterion, where s < λ / 36*cosθ, or ks < 0.2, at incidence angle θ = 0. Additionally, surfaces are considered as electromagnetically rough for 1 < ks < 3.","name":"Vertical roughness component (RMS height)","selfAssesment":"Completed
"},{"code":"PP2-2-6-2","description":"The surface correlation length, denoted by l, is defined as the displacement ξ at which the surface correlation function p(ξ)= 1/e. Thus, l can be seen as the reference length up to which two points of one soil surface can be regarded as statistically independent from each other. If we imagine a perfectly smooth soil surface, l=∞ since every point on that surface correlates with all other points and can therefore be regarded as dependent from each other.\r\nAs roughness depends not only on the soil surface properties but also the wavelength λ of the electromagnetic signal, the roughness parameters are scaled by the wave number k. Hence, the electromagnetic roughness kl for surface roughness parameter l is kl=(2π/λ)*l.\r\nExperimental results indicate a weaker influence on the radar backscatter compared to the RMS height s.","name":"Horizontal roughness component (correlation length)","selfAssesment":"Completed
"},{"code":"PP2-2-6-3","description":"The surface correlation function p(ξ) determines the degree of correlation between two lateral separated locations of one surface. Thereby, ξ is defined as displacement between two locations, (x, y) and (x', y') on the surface and given by (1).\r\nWith increasing separation between two locations on the surface p(ξ) decreases, and at a certain distance, the surface correlation length l, the heights at the two locations are considered statistically uncorrelated.\r\nThe surface scattering of electromagnetic waves can be simulated with various models. Depending on the observed roughness scale multiple surface scattering models are valid for specific roughness conditions. For example, one of the first surface scattering models for slightly rough surfaces, the small perturbation model (SPM), deals with roughness scales that are small relative to the wavelength and hence has validity conditions for ks < 0.3, kl < 3, and m < 0.3. Since then, various surface scattering models for computing the scattering and emission behavior of natural surfaces in the microwave region have been proposed, such as the Kirchhoff scattering model (KH), the geometric optics model (GO), the physical optics model (PO), or the integral equation model (IEM), to name the most common used in literature. For simulations of EM scattering at soil surfaces, assumptions of the functional forms of p(ξ) have to be made. The two most common forms for mathematically describing the surface correlation of natural surfaces are the exponential pE(ξ) and the Gaussian pG(ξ) correlation functions, defined by (2) and (3).\r\nFor some mathematically sophisticated surface scattering models, an x-Power correlation function p(x-Power)(ξ) can be assumed (4), with x as value between 1 and 2.\r\nIn literature, rather smooth surfaces are characterized by an exponential surface correlation function, while rather rough surfaces are characterized by a Gaussian surface correlation function.","name":"Surface correlation function","selfAssesment":"Completed
"},{"code":"PP2-2-6-4","description":"The root-mean-square (RMS) slope m of a one dimensional height profile for one random surface is given by (1), with s as the standard deviation of the surface height variation (or RMS height), and p''(0) as the second derivative of the surface correlation function p(ξ), evaluated at ξ=0. Since p(ξ) is an even function, p''(0) is a negative quantity.\r\nFor modeling of electromagntic scattering at soil surfaces, assumptions of the functional forms of p(ξ) have to be made. The most common known forms are the exponential and Gaussian correlation functions. Additionally, some models allow the assumption of a x-Power correlation function, with x as value between 1 and 2. For the varying surface correlation functions, the RMS slope m is given by (2)-(4).\r\nIn literature, for L-band, the slope m should be lower than 0.3 or 0.4 in case of single scattering and bare soil surfaces with moderate RMS heights.","name":"Surface roughness slope","selfAssesment":"Completed
"},{"code":"PP2-2-6-5","description":"In reality, one random surface has multiple roughness scales, since the commonly used surface description based on single-scale roughness parameters does not comprise all the properties of natural surfaces relevant for describing wave scattering. Depending on the wavelength λ of the microwave sensor the dimension of the surface roughness parameters s and l correspond to specific roughness scales. \r\nIn case of multi-scale roughness, the equivalent RMS height is a composite of the individual RMS heights at different roughness scales (1).\r\nA three-scale surface, as shown in Fig. 1, for example consists of a small-scale high-spatial frequency variation (c) ‘riding’ on top of the larger scales, the medium-scale perturbation (b) and the large-scale undulation (a).\r\nAt microwave frequencies, the centimeter scale is the scale of roughness of primary importance, since λ is on the order of centimeters to a few tens of centimeters. For natural surfaces it is very difficult to measure millimeter-scale roughness.","name":"Single-scale & multi-scale roughness","selfAssesment":"Completed
"},{"code":"PP2-2-6","description":"Surface roughness defines the geometry between the pedosphere and the atmosphere (soil-air boundary).\r\nIn the field of microwave remote sensing, surface roughness affects scattering and emission characteristics of natural surfaces. The degree of roughness of a random surface is determined by statistical parameters, measured by the units of wavelength of the observing sensor. The two fundamental surface roughness parameters are the standard deviation of the surface height variation (RMS height) s, with its related surface correlation function p(ξ), and the horizontal surface correlation length l. Additional, a third roughness parameter, the root-mean-square (RMS) slope m, is important for some surface scattering models to simulate electromagnetic wave scattering of surfaces.\r\nSurface roughness determines the variation of surface height within an imaged resolution cell. The transition from smooth to rough is qualitative, and is function of both wavelength and incident angle. With decreasing frequency the soil surface appears rather smooth to microwave sensors. This results in the fact, that while one surface appears smooth when sensed at L-band (λ ≈23 cm), the same surface appears rough when sensed at X-band (λ≈3 cm). Hence, in the field of microwave remote sensing, the ‘effective’ surface roughness parameters are scaled by the wave number k= 2π/λ. Surface roughness can be observed at single or multi-scale.","name":"Surface roughness","selfAssesment":"Completed
"},{"code":"PP2-2-7-1","description":"The Stokes vector is a four-element vector containing real-valued polarization combinations and is an alternative form of representing a full (=quad) polarimetric dataset, besides the complex-valued scattering matrix. Stokes vectors can be measured as real quantities and are preferred over the complex-valued Jones vector formalism when a coherent (phase-preserving) measurement system is absent. Stokes vectors can be used to form the 4x4 Mueller matrix for target scattering analyses, mostly used in the field of optics. First component of the Stokes vector is the sum of the co-polar fields and represents the total energy of the wave. Second component is the difference of the co-polar fields. Thrid component is the real part of the cross-correlation of the fields and fourth component is the imaginary part of it. The different polarization states can be represented by the Stokes vector and an O(3) elliptical transformation can be used to change the polarization basis, similar to the Jones vector where the SU(2) elliptical transformation is used.","name":"Stokes Vector","selfAssesment":"Completed
"},{"code":"PP2-2-7-2","description":"The scattering matrix is a 2x2 square matrix containing four complex-valued polarization measurements (amplitude & phase) forming one full (= quad) polarimetric set of coherent observations. An often recorded set of polarizations is the combination: HH (horizontal receive - horizontal transmit), HV (horizontal recive - vertical transmit), VH (vertical receive - horizontal transmit) & VV (vertical receive - vertical transmit). The scattering matrix is fully suficient for describing scattering from coherent targets (dominating the resolution cell), but not for incoherent tragets (mix of scattering contributions in the resolution cell). For the latter, the coherency and the covariance matrices are the more appropriate descriptions of scattering from incoherent targets.","name":"Scattering matrix","selfAssesment":"Completed
"},{"code":"PP2-2-7-3","description":"The covariance and coherence matrix are two 4x4 square matrices, which can be built out of the scattering matrix by a lexicographic and a Pauli target scattering vector. They are an alternative representation of a full polarimetric dataset allowing the analysis of incoherent targets (more than one dominant scatterer in the resolution cell) and the phenomenon of depolarisation (transformation of incoming fully polarised wave into a partially polarised wave by creating a variety of different types of polarizations during media interaction). These matrices can be converted into each other without loss of information (by unitary transformations), but not turned back into the scattering matrix due to averaging operations during formation of coherency or covariance matrices.","name":"Covariance/Coherency matrices","selfAssesment":"Completed
"},{"code":"PP2-2-7-4","description":"Polarimetric decomposition techniques allow signal unmixing by polarimetry in order to separate different scattering contribution within one resolution cell, e.g. from soil & vegetation or snow, ice & bedrock. They can be either applied for the scattering matrix (coherent form - one dominant scatterer in the resolution cel) or for the covariance/coherency matrix (incoherent form - more than one dominant scatterer in the resolution cell). Decomposition techniques can be model- (physics) or eigen- (mathematics)-based. The eigen-based decomposition allows to diagonalize the coherency or covariance matrix in a diagonal eigenvalue matrix and a matrix of column eigenvectors. From eigenvalues and eigenvectors the polarimetric entropy, the scattering alpha angle and the polarimetric anisotropy. The polarimetric entropy is a matric for the degree of depolarization of the scattering event. The scattering alpha angle is an intrinsic scattering mechanism indicator. The polarimetric anisotropy informs about secondary scattering mechanism in evironments with high entropy. If the anisotropy is high only one secondary scattering mechanism is present, if it is low, more than one will occur.","name":"Polarimetric decomposition techniques","selfAssesment":"Completed
"},{"code":"PP2-2-7-5","description":"All bi- or multi-polar (non-inert) media have the tendency to orient themselves in 3D-space if an external non-ionizing electro-magnetic field is excited on them. This orientation polarization is caused by negatively and positively charged areas within the media, for instance due to charges of the different molecules and atoms building up the media, under the premise that the media is able to rotate (partly) freely and is not completely fixed. Molecules of liquid water are a prime example. Here the two positively charged hydrogen atoms are oriented in a 105-degree configuration to the negatively charged oxygen atom, forming a slightly charged bi-polar medium that orients itself under electromagnetic radiation treatment, especially at the frequency range of microwaves and millimeter-waves.","name":"Orientation polarisation of media","selfAssesment":"In progress
"},{"code":"PP2-2-7-6","description":"Polarimetric coherences are complex-valued polarimetric correlation coefficients assessing the redundance between different polarimetric observations informing about their divergence in information. They can be formed among mutual polarimetric observations showing their degree of correlation. The polarimetric coherence consists of a magnitude, ranging between zero (no correlation) and one (identical), and a phase information, running from -180° to 180°. Typically polarimetric coherences are calculated between the co-polarimetric (HH, VV) channes, as well as the cross-polarimetric channels (HV, VH). The latter polarimetric coherence assesses the system noise inherent in the recorded polarimetric data, if a monostatic systems (transmitting and receiving sensor on the same sensing platform) is used for acquisition.","name":"Polarimetric coherences","selfAssesment":"Completed
"},{"code":"PP2-2-7-7","description":"The polarisation ellipse and the Jones vector formalism are the geometrical (three real-valued angles) and algebraic (amplitude & phase) formalisms to describe polarisation states of an electromagnetic wave. The ellipse has an orientation, an ellipticity and absolute phase angle. The three angles are integrated in one mathematical ellipse formulation that can represent linear, elliptic and circular polarisation states. The Jones vector formalism is an algebraic formulation allowing all calculus available in linear algebra. Both representations (polarisation ellipse & Jones vector) can be converted into each other seemlessly with a simple elliptical basis (special unitary SU(2)) transformation.","name":"Polarisation ellipse / Jones vector formalism","selfAssesment":"Completed
"},{"code":"PP2-2-7-8","description":"The concept of polarisation synthesis is based on the mathematical fact that a set of polarimetric measurements in one basis, e.g. H,V, can be converted into any other polarimetric basis, by a mathematical transformation. A basis set is a set of four polarisations. Each set is orthogonal, like LC (left-circular), RC (right-circular). The striking point is that only one set of polarimetric measurements in one basis needs to be recorded and the transformation in other polarimetric bases is done in a post processing step afterwards. There is no need to measure all bases, which is quite complicated in terms of engineering for elliptical and circular polarisation states.","name":"Polarisation synthesis","selfAssesment":"Completed
"},{"code":"PP2-2-7","description":"Polarimetry is the technique to evalute the physical phenomenon of polarisation including the measurement, the processing and the interpretation of the polarisation state of an electromagnetic wave. Polarization states are described by the scattering elipse and the Jones Vector formalism. Especially the polarization states after interaction with the media under investigation are mostly investigated to estimate media properties and states. The mostly observed fully polarimetric observation basis is H,V up to now with the single observations: HH HV, VH, VV. The concept of polarization synthesis allows to acquire fully polarimetric observations in one basis (e.g. H,V) and transform them into any other orthgonal basis (e.g. left, right circular) by a mathematical transformation in post processing. Polarimetric States are stored in different mathematical formats: Scattering matrix, polarimetric coherences , Stokes vector, Pauli-vector, lexicographic vector, coherency and covariance matrices. These mathematical representations can be decomposed according to the contained elementary scattering mechanisms in the recorded signal. The so-called polarimetric decomposition technique allow signal unmixing for differnt scattering components (e.g. from soil & vegetation). The techniques range from mathematics-based until physics-based concepts and are developed since decades starting with Huynen in 1970.","name":"Polarimetry","selfAssesment":"Completed
"},{"code":"PP2-2","description":"A number of interactions are possible when electromagnetic energy encounters matter, whether solid, liquid or gas. In Earth Observation there are two main interactions: atmospheric and with target. Atmospheric interaction: In radar remote sensing, atmospheric interactions are limited due to the long wavelengths compared to the size of the atmospheric particles. The fact that microwaves interact with object at least as big as the wavelength is one of the greatest advantages of microwave remote sensing, since at larger wavelengths atmospheric particles are almost transparent to the signal and microwave sensors are independent from the time of day (day or night) and weather conditions. Water clouds can interfere with the radars operating below 2 cm in wavelength. The effects of rain can be generally ignored at wavelengths above 4 cm. For longer wavelengths (above 20 cm), an effect called Faraday rotation caused by the ionosphere, i.e., free charges (electrons) and the Earth’s magnetic field, can lead to a rotation of the polarization plane. Target interaction: The radar interaction with the object is a result of both radar system parameters (frequency, polarization, acquisition geometry) and the physical properties of the object (dielectric constant, i.e., water content; geometrical properties, i.e., the roughness, shape and orientation of the scatterer). Overall, various types of interactions can be distinguished – scattering, diffraction, and reflection – all describing the same process of wave interaction but at different scales.","name":"Interaction of microwaves with matter","selfAssesment":"Completed
"},{"code":"PP2-3-1-1","description":"The goal of an radar antenna is to direct and receive the transmitted and backscattered signal in a specific angular direction. The antenna gain describes the directional sensitivity of the antenna. It is a dimensionless quantity that is constant for a specific antenna.","name":"Antenna gain","selfAssesment":"Planned
"},{"code":"PP2-3-1-2","description":"The antenna radiation pattern shows the direction in which the antenna transmits and receives the energy in space, as well as the strength of this radiation. It is a function of angles and consists of different lobes, in which the signal is directed and received. There are two principal representation of the antenna patterns: field and power patterns, which are a function of the electric and magnetic fields of the energy being radiated.","name":"Antenna pattern","selfAssesment":"In progress
"},{"code":"PP2-3-1","description":"Antenna is a device that radiates electromagnetic energy and collects it during reception.","name":"Radar antennas and antenna calibration","selfAssesment":"Planned
"},{"code":"PP2-3-10-2","description":"The radargrammetric equation follows a similar principle as the stereoscopic equation, except that it uses the radar geometry. The radargrammetric observation equation allows the retrieval of 3D information about a target, based on the determination of the sensor-object stereo model. It estimates the coordinates the intersection of the two radar rays coming from the two different sensor positions with different look angles, using the coordinates of the satellites position and satellite velocity. The radargrammetric equation can be adapted in order to retrieve 3D information in layover areas (e.g. urban areas).","name":"Radargrammetric equation","selfAssesment":"Completed
"},{"code":"PP2-3-10","description":"Radargrammetry is the technique for extracting three-dimensional information from radar images. It applies photogrammetric principles to synthetic aperture radar (SAR) images. By viewing an object from different positions separated by a baseline, the appeared object position will vary slightly (denoted parallax). The disparities for each position on the object are related to its x-y-z coordinates. In radargrammetry, such disparities are computed for an entire image. The result is the terrain elevation from the measured parallaxes between two (or more) images, acquired at different angles. Radargrammetry requires at least two SAR images acquired from different positions, normally across-track due to the configuration of a side-looking SAR. Same-side stereo-pairs with intersection angles in the range of about 10 – 20° have been a feasible compromise between reasonable geometric disparities and the accuracy of estimated heights. In general, the disparities can be estimated with higher accuracy as the angle of intersection increases (as the stereo exaggeration factor increases). However, the same points must be recognized in all images, and it is hence required that the images are as similar as possible. This improves the image matching and it is best achieved with small intersection angles, which furthermore decreases radiometric differences. \r\nA general procedure for generating an elevation model from stereo-pairs is applicable for radargrammetry when optical stereo images are replaced with the backscatter intensity of SAR images. One image is selected as reference and the other(s) is coarsely registered to the reference, e.g., by using the attached meta-data. The same points are then located in both images using image matching. A common matching criterion is the cross correlation coefficient. Then, spatial point intersections are computed, which is the least square approach to find the intersection points of SAR range circles as defined from the matched image pixels. The computed intersections result in a point cloud that finally is interpolated to a consistent elevation raster. The entire process is extensive and computationally expensive, and normally a dedicated software is required. \r\nRadargrammetry with images acquired from opposite sides have been little investigated, and was first limited to stereoscopic viewing. Some opposite-side research was later presented with limited outcomes under certain conditions. Most applications today will not consider opposite-side radargrammetry, since the alternatives are usually better. Same-side radargrammetry performs better than opposite-side, while interferometric SAR that is based on phase differences, may be even more accurate. One advantage of radargrammetry is however, that it remains less affected by atmospheric disturbances compared to interferometric SAR, because it is using the amplitude images.","name":"Radargrammetry (same-side and opposite-side)","selfAssesment":"Completed
"},{"code":"PP2-3-11-1","description":"Differential Synthetic Aperture Radar Interferometry (DInSAR) aims the determination of deformation of the Earth’s surface that happened between two or more complex-valued SAR acquisitions.\r\nThe phase of an interferogram issued from the complex multiplication of a SAR image with the complex conjugate of a second SAR image contains five distinct components, or layers of information: (1) Two phase components arise from the geometrical baseline (slightly different position of both sensor positions): (1a) a topographical information representing the surface relief, (1b) “flat earth” pattern coming from the orbital distance of both sensor positions.\r\n(2) Two phase components result of the temporal baseline (time between both acquisitions): (2a) a deformation component, representing a possible displacement of the Earth’s surface between both acquisitions, (2b) an atmospheric component coming from different atmospheric conditions between both acquisitions. (3) A phase component corresponding to intrinsic sensor noise \r\n\r\nBoth parameters related to the temporal baseline can be retrieved using DInSAR on repeat-pass acquisitions. DInSAR cannot be used with single-pass interferometry (e.g. both acquisitions acquired at the same time).\r\nThe deformation component of the interferometric phase corresponds to the modification of the phase of the second SAR image compared to the first due to an additional range difference between the sensor position and the Earth’s surface that is induced by the motion of the Earth’s surface towards or away from the initial sensor position.\r\nUsing DInSAR, the phase components related to the geometrical baseline can be eliminated from the interferogram using an existing DEM and orbit information, or an additional interferogram showing no deformation. After DInSAR processing, neglecting the remaining sensor noise, only the deformation and atmospheric components remain. The resulting deformation image is called differential and is characterized by color bands, or fringes, from whom the amount of the displacement can be retrieved. \r\nDInSAR can be used for mapping displacements and deformations due to earthquakes, landslides, or other geophysical processes inducing deformation of the Earth’s surface.\r\nUsing only one differential interferogram, mainly sudden and large scale changes between two acquisition can be mapped and quantified. However, the atmospheric phase component remains and may induce interpretation errors if it is not possible to eliminate it through e.g. precise weather models. Techniques of differential interferogram stacking (e.g. Persistent Scatterer Interferometry and Small-Baseline Subset) have been developed for long-term deformation monitoring which allow to filter the atmospheric phase component out.","name":"Differential Synthetic Aperture Radar Interferometry (DInSAR)","selfAssesment":"Completed
"},{"code":"PP2-3-11-2","description":"The Permanent or Persistent Scatterer (PS) approach allows the estimation of deformation time-series related to point-wise, high coherent scatterers on the ground based on processing long sequences of SAR data.\r\nPersistent Scatterer Interferometry (PSI -sometimes also called Permanent Scatterer Interferometry) is a particular DInSAR technique. It exploits multiple SAR images acquired over a specific area in order to retrieve the deformation phase component over time. In general, a minimum number of 15 SAR acquisitions is needed for PSI processing. Due to the large number of necessary acquisitions, the deformation component of the interferometric phase observations can be estimated very precisely (in the order of a few mm/yr) and other phase contributions such as atmospheric disturbances and topographic height differences can be better estimated and removed.\r\nPSI rely on so called Persistent Scatterer that are targets showing coherent phase behavior in time. Such targets are usually found on man-made structures such as buildings or bridges, or very stable features such as rocks. PSI is a technique that is therefore mainly used over urban or semi-urban terrain. Usually, PSs are selected based on their amplitude and phase power spectrum stability over time.\r\nThe main outcomes of a PSI analysis are a deformation velocity map and the displacement time-series of the single point targets, or PSs. The velocity map represents the deformation rate of the detected PSs in Line-of-Sight of the sensor, generally in mm/yr. Usually, subsidence, e.g. target moving away from the sensor, is represented in red, stable PSs in green and uplift, e.g. PSs moving toward the sensor in blue. The displacement time-series show for each PS the amount of the deformation, usually in mm, over the whole period of observation. Different phase model can be defined in order to retrieve the best possible estimate of the deformation, considering also seasonal displacements or breakpoints in the time-series.\r\nPerforming PSI analysis in both ascending and descending directions allows the fusion of the results in order to retrieve vertical and East-West component of the deformation. North-South deformation components cannot be retrieved due to the orbit configuration of the SAR satellites.\r\nPSI finds use in a large range of thematic applications related to subsidence and long-term change monitoring, such as infrastructure monitoring, groundwater reservoir monitoring, monitoring of mining areas, landslide inventory and monitoring, as well as volcanology.","name":"Permanent Scatterer Interferometry (PSI)","selfAssesment":"Completed
"},{"code":"PP2-3-11-3","description":"Along-track InSAR (AT-InSAR) is a special mode of interferometric SAR (InSAR) where the individual SAR images have been acquired from the same flight track. With virtually identical geometric configuration of the individual SAR images, the measured phase difference is dominated by temporal changes occurring between the acquisitions. Consequently, AT-InSAR can be used to measure the displacement and/or radial velocity of targets on the ground, with the temporal offset between the acquisitions determining the time scale of the measurements. AT-InSAR can be implemented using one or more SAR sensors, in both single-pass and repeat-pass configurations, accommodating various needs. Using at least two sensors in a single-pass configuration allows the measurement of relatively high velocities, e.g., for vehicles and ocean waves. Conversely, using at least one sensor in a repeat-pass configuration allows the measurement of low velocities or displacements, e.g., for glaciers and due to volcanoes, earthquakes, subsidence, and landslides.","name":"Along-Track Interferometry","selfAssesment":"Completed
"},{"code":"PP2-3-11-4","description":"Across-track InSAR (XT-InSAR) is a special mode of interferometric SAR (InSAR), where the individual SAR images have been acquired from slightly different look directions. The measured phase difference contains information about the elevation of the targets on the ground, but it can also be affected by temporal changes between the individual SAR images. XT-InSAR can be implemented using one or more SAR systems in both single-pass and repeat-pass configurations. To mitigate temporal change between acquisitions, the XT-InSAR configuration is selected based on the intended application and frequency used by the system. If a single SAR sensor is used in the repeat-pass mode, temporal stability can be achieved either by a selecting a lower frequency and focussing on the larger, more stable targets (e.g., P-band, 435 MHz InSAR in forests) or by selecting a higher frequency and focussing on already stable environments (e.g., X-band, 9.65 GHz XT-InSAR in urban environments). Using two or more SAR sensors in a single-pass, tandem configuration, it is possible to measure elevation of temporally instable targets using higher frequencies, as demonstrated by the SRTM and TanDEM-X systems over vegetated areas and ocean.\r\nReferences: bamler/hartl, one on SRTM or TDM for DEM, one on BIOMASS for forestry, one on Sentinel-1 for urban areas, one on TDM on vegetation","name":"Across-Track Interferometry","selfAssesment":"Completed
"},{"code":"PP2-3-11-5","description":"Small Baseline Subset (SBAS) is a well-known technique of differential synthetic aperture radar (SAR) interferometry for the generation of surface deformation time-series by processing large sequences of SAR data acquired over the same region on Earth. \r\nThe method requires the preliminary generation of pairs of SAR images collected by slightly different orbital positions at different acquisition times. The phase difference of the interferometric SAR data pairs is extracted. The two-dimensional phase maps contains different contributions, but principally a component due to the terrain height of the observed area. The DInSAR technique relies on the estimation of the deformation of the terrain between the two interfering SAR images (i.e., the so-called master and slave images). To achieve this task, the phase contribution related to the terrain height is simulated and subtracted to the interferometric master/slave phase difference. The obtained differential SAR interferometric phase contains a direct information on the occurred deformation. Once a sequence of interferometric SAR data pairs is selected, the SBAS technique allows generating the time-series of the deformation of the terrain. The processing steps are essentially: i) the extraction of the full phase of the DInSAR interferograms, i.e., the phase unwrapping steps of the DInSAR interferograms, ii) the inversion of the sequence of unwrapped DInSAR phases, iii) the geocoding of the deformation maps from radar coordinates to geographical coordinates.","name":"Small Baseline Subset","selfAssesment":"Completed
"},{"code":"PP2-3-11","description":"Synthetic aperture radar (SAR) interferometry, or simply InSAR, is a remote sensing technique utilising the phase difference between two or more complex-valued SAR images. Most modern SAR systems are capable of measuring both the intensity and the phase of the reflected signal, where the latter carries information about the distance travelled by the signal. Consequently, the different of phase information of two successive SAR images over a specific area contains a distance information. \r\n\r\nThe phase difference measured between two SAR images is called the interferometric phase. The interferometric phase image is an interferogram. The interferometric phase is a function of the geometry and timing of the individual SAR acquisitions. Different geometric and temporal configurations enable different applications. \r\n\r\nIf the SAR acquisitions are made from different angles and without significant temporal change of the scene, InSAR can be used to create digital elevation models (DEMs) of the Earth, as demonstrated by the NASA/JPL Shuttle Radar Topography Mission (SRTM). This configuration is called across-track interferometry. If the individual SAR acquisitions are made at different times in the same geometric configuration, i.e. in an along-track or differential interferometric configuration, then InSAR can be used to measure radial velocity of targets and to assess displacements caused by, e.g., volcanoes and earthquakes. The variation of the temporal baseline allows determining velocities ranging from several meters per second to a few millimeters per year. While standard differential interferometry can be used to retrieve changes that happened between two SAR acquisitions, differential interferometric stacking techniques, such as Persistent Scatterer Interferometry (PSI) and Small Baseline Subset (SBAS), are used to monitor deformation over a longer period of time by stacking multiple differential interferograms and filtering out the atmospheric phase contribution in order to retrieve very accurate deformation of the ground and its infrastructures.","name":"Principles of Synthetic Aperture Radar Interferometry (InSAR)","selfAssesment":"Completed
"},{"code":"PP2-3-12","description":"Synthetic Aperture Radar (SAR) tomography uses the principle of the azimuth synthetic aperture in the elevation direction. Instead of using different positions of the radar sensor along the flight path in order to increase the aperture length, SAR tomography uses multiple passes of the radar sensor over the same area at different elevation positions, i.e. orthogonal to the azimuth-range plane, on different orbits. Similar to the synthetic aperture in azimuth direction, a larger aperture in cross-range elevation direction allows increasing the resolution in the elevation direction. Therefore, the echoes are focused in the whole 3D space (azimuth, range and elevation), and scattering contributions can be separated at different heights, even if they are situated in the same azimuth-range cell.\r\nSAR tomography exploits therefore these multiple passes of the radar sensor at different orbit positions (orbits heights) in order to retrieve 3D information about volumetric targets, where the 2D SAR signals often overlaps due to the typical side-looking geometry. \r\nThe result of tomographic processing is a tomogram, i.e. it is a hologram of a specific area of interest, usually represented as a tomographic profile along a particular direction. Using polarimetric data, the different scattering mechanisms happening at different heights can be represented in the profile, allowing a full understanding of the volumetric information and backscattering processes.\r\nUnlike the azimuthal aperture, the tomographic aperture is achieved by repeat-pass acquisitions, the antenna having to come back over the area. An important parameter is therefore the target coherence, that may decrease by longer repeat-pass cycles. In general, a 1-4 day revisit cycle is preferred for tomographic applications.\r\nSAR tomography finds applications in the imaging and monitoring of cities and single buildings, as well as in height and biomass estimation of forest stands. The use of longer wavelength that guaranty the penetration into canopy volumes allows a better retrieval of the complete forest structure and its undergrowth.","name":"Synthetic Aperture Radar (SAR) tomography","selfAssesment":"Planned
"},{"code":"PP2-3-13","description":"Historically imaging in the microwave frequency domain was done either using passive imaging techniques (with solely recording capacities of the sensor) or using active imaging techniques (with transmitting and recording capacities of the sensor). Both imaging modi were developed in parallel for a long time in electrical engineering of microwave sensors for space-borne missions, but are combined in more recently launched missions.\r\nWith the concept of active and passive microwave imaging, both techniques are fused to record electromagnetic waves in an active (sending & receiving) and a passive (only receiving) mode either simultaneously on one carrier platform or with negligible time lag on different platforms.\r\nThe active sensor is normally a Real Aperture Radar (RAR, scatterometer) or Synthetic Aperture Radar (SAR), while the passive sensor is a radiometer or synthetic aperture radiometer. Both acquisition modes can be operated on a single platform or on different platforms depending on monolithic or distributed platform systems. The benefit of fusing both modi is in the higher spatial resolution of the active imaging modes combined with the higher sensitivity of the passive modes for intrinsic (non-structural) media properities, like permittivity or salinity.\r\nSatellite missions with active-passive imaging capabilities are the NASA missions AQUARIUS (operation started in 2011 terminated in 2015) and SMAP (operation started in April 2015 and ceased for active sensor in July 2015). Currently (2021), no dedicated active-passive microwave satellite mission is operating in orbit.","name":"Active-Passive microwave imaging","selfAssesment":"Completed
"},{"code":"PP2-3-2","description":"Systems measuring both amplitude and phase of the incident electromagnetic radiation.","name":"Coherent and active systems","selfAssesment":"Planned
"},{"code":"PP2-3-3","description":"This acquisition mode records only the incoming electromagnetic radiation emitted from the Earth. Radiometer instruments conduct passive microwave imaging. The energy budget of emitted radiation (from Earth) is significantly smaller than from instrument-generated, transmitted electromagnetic waves, used in the active microwave imaging mode. Hence, the signal to noise ratio is significantly worse for passive microwave imaging forcing a longer intergration time for robust signal recording. This results in a coarse spatial resolution of radiometer images (in the order of kilometers).","name":"Passive microwave imaging","selfAssesment":"Completed
"},{"code":"PP2-3-5","description":"There are two types of imaging radar apertures: real (usually called RAR or SLAR for side-looking airborne radar or SLR for side-looking radar) and synthetic aperture radar (SAR). The SLAR imaging system uses a long antenna mounted on a platform. The synthetic aperture is used in space remote sensing applications. RAR is a radar system where the antenna beamwidth equals to the physical length of the antenna. It operates in a side-looking configuration, left or right with reference to the flight direction. It is an active, all-weather, day/night remote sensor onboar an airborne platform. Both Real Aperture and Synthetic Aperture Radar are side-looking systems having antennas aimed to the right or left of the flight path. The length of the antenna together with wavelenght determines the resolution in the azimuth direction, i.e. it is proportional to the distance to the object and inversely proportional to the length of the radar antenna.","name":"Real Aperture Radar (RAR)","selfAssesment":"In progress
"},{"code":"PP2-3-6","description":"In contrary to a real aperture, a synthetic aperture results from an aperture “synthesis”. Synthetic aperture were built in order to overcome the limitation of real aperture and therefore enhance the resolution in azimuth direction. It uses the subsequent positions of a real aperture sensor during its forward motion along the azimuth direction to create a synthetic longer antenna. Via the analysis of the Doppler shift induced by the different echoes of the illuminated objects in the different positions of the real aperture, the azimuth resolution can be improved.","name":"Principles of Synthetic Aperture Radar (SAR)","selfAssesment":"In progress
"},{"code":"PP2-3-7-1","description":"In navigation, the azimuth corresponds to an angle measured from a north reference or a meridian, usually in clockwise direction. In SAR terminology, the azimuth direction corresponds to the direction in which the radar platform moves. The azimuth direction is also called along-track direction and is parallel to the flight path of the radar instrument. In a SAR image, the azimuth position of an object corresponds to its relative position in the field of view of the antenna following the radar’s line of flight. The azimuth direction is perpendicular to the range direction, which corresponds to the look direction of the radar antenna. The azimuth plays an important role in the definition of the azimuth resolution of a SAR sensor. Contrary to the range resolution, the azimuth resolution is independent of the distance between sensor and illuminated area and is constant. The azimuth resolution of a radar system corresponds to the beam width of the antenna on the ground, but can be improved using multiple successive real aperture acquisitions in order to form a longer, synthetic, aperture. This implies that an object on the ground is illuminated for a longer time and from different platform positions along the azimuth direction, inducing a Doppler frequency shift at the target. The use of specific synthetic aperture acquisition modes that steer the antenna along the azimuth direction, such as Spotlight mode, improve additionally the resolution in azimuth direction.","name":"Azimuth direction","selfAssesment":"Completed
"},{"code":"PP2-3-7-2","description":"The range direction corresponds to the direction perpendicular to the flight direction of a radar system. It is also called across-track direction. One distinguishes between slant range, i.e. range in a radar geometry, and ground range, i.e. range projected onto the Earth's surface, and between near and far range (situated farther away from the sensor and showing shallower looking angle than in near range due to viewing geometry).","name":"Range direction","selfAssesment":"In progress
"},{"code":"PP2-3-7-3","description":"The incidence angle is the angle between the incident radar beam on a surface and the normal to a reference surface. Generally, it is distinguished between the local incidence angle and the incidence angle to the ellipsoid. The local incidence angle considers the normal to the surface at target location, i.e. it considers the local topography. The incidence angle to the ellipsoid corresponds to the angle between the incident radar beam and the normal to the local ellipsoid, regardless of the local slope and terrain. \r\n\r\nFor a flat surface and neglecting the Earth’s curvature, the incidence angle corresponds to the angle between the incident radar beam and the vertical, and it equals the look angle of the sensor, which characterizes the angle between the nadir view and the radar beam. Considering a flat surface, the incidence angle varies continuously within a SAR scene: it increases from near to far range. Depending on the considered sensor and acquisition modes, variations of the incidence angle up to 20° can be observed between near and far range.\r\n\r\nThe incidence angle has an influence on the radar backscatter intensity. Considering a surface with diffuse reflection, increasing incidence angles lead to decreasing backscatter intensities. This effect is less pronounced for rough than for smooth surfaces. A change in incidence angle may also induce a change in the occurring backscattering mechanisms or geometric distortions of the image. For example, for high incidence angles, terrain distortion due to the side-looking geometry is reduced. Due to the high dependency of the radar backscatter from the incidence angle, the choice of the optimal configuration should happen depending on the application. For example, whereas low incidence angles are more sensitive to biomass in forestry applications, higher incidence angle are preferred for distinguishing different forest types due to their structural characteristics.","name":"Incidence Angle","selfAssesment":"Completed
"},{"code":"PP2-3-7-4","description":"The beam sent out by the radar antenna (SLAR for side-looking airborne radar or SLR for side-looking radar) illuminates an area on the targeted object. The footprint of an antenna is traditionally defined to be the area on the surface within the field of view subtended by the beamwidth of the antenna gain pattern.","name":"Antenna footprint","selfAssesment":"Planned
"},{"code":"PP2-3-7-5","description":"The spatial resolution of a synthetic aperture radar (SAR) system is the maximal distance between two targets, which are indistinguishable in the SAR image. SAR spatial resolution is determined individually in the two principal SAR image directions: ground range and azimuth (along-track). Ground range resolution for a SAR system is derived from slant range (across-track) resolution, by projecting it onto the ground surface using the incident angle, i.e., the angle between the line-of-sight and the ground surface normal. It is thus range-dependent, with finer resolution available in far range. Assuming adequate signal processing, slant range resolution of a SAR system is proportional to the speed of light and inversely proportional to the system bandwidth, i.e., the width of the used frequency interval. This caused by the fact that each individual frequency provides an independent measurement of the slant range, so a larger bandwidth implies more independent measurements contributing to the final slant range estimate. Similar principles apply to the azimuth direction. Assuming adequate signal processing, the SAR azimuth resolution is proportional to the along-track velocity of the SAR sensor and inversely proportional to the pulse repetition frequency (PRF) of the system. A lower interval between the consecutive pulses (higher PRF) results in better azimuth resolution due to faster sampling, but at the cost of range ambiguities occurring when echoes from one pulse are recorded after the next pulse has been transmitted.","name":"Synthetic Aperture Radar (SAR) spatial resolution","selfAssesment":"Completed
"},{"code":"PP2-3-7","description":"The Synthetic Aperture Radar (SAR) sensor is usually mounted on an aircraft or satellite. The instrument altitude above a reference surface stays constant over time, a condition that is easier to achieve for satellite sensors that stay on the same orbit than for aircrafts that are subject to atmospheric conditions. The sensor moves on a straight flight path, which is called the azimuth direction. It corresponds to the flight direction.\r\nSAR systems acquire information in oblique view, the antenna pointing sideways down to the ground. Most satellite systems use an antenna looking to the right side of the instrument. The ground area illuminated by the radar beam is called antenna footprint. As the sensor moves along the azimuth direction (along-track), the continuous strip of the ground area represented by the successive antenna footprints is called swath. \r\nThe looking direction of the SAR antenna is called range direction. It is often perpendicular to the azimuth direction (i.e. across-track), but can also present slightly differences depending on the acquisition mode. The angle between the nadir view and the range direction is called incidence angle.\r\nThe original SAR image is displayed in what is called slant-range geometry, i.e., it is based on the actual distance from the radar to each of the respective features in the scene. In the slant range direction, each point target’s backscatter is represented as a function of the time delay between the transmission of the electromagnetic pulse and its reception back at the sensor. This range depending representation induces geometric distortions in the SAR image. One distinguishes between near and far range: targets situated in near range are closer to the nadir direction and closer to the sensor than targets situated in far range. The image representation of targets is also more compressed in near range than in far range.\r\nThe slant-range representation can be converted in ground range representation, by projecting the image features orthogonally to a ground reference, allowing a proper planimetric position of the targets relative to one another.\r\nThis acquisition geometry allows the distinct mapping of scatterers corresponding to their respective distance to the sensor. It causes also geometric distortions in the radar image, i.e., relief displacement (foreshortening and layover) and shadow.","name":"Synthetic Aperture Radar (SAR) geometric configuration","selfAssesment":"Completed
"},{"code":"PP2-3-8-2","description":"The local incidence angle is the angle between the incident radar wave and the normal to the scattering surface at target location. In case of a flat terrain, the local incidence angle equals the incidence angle. For a terrain with local slope, the local incidence angle differs from the incidence angle (for slopes facing towards the sensor, it is smaller than the incidence angle).","name":"Local Incidence Angle","selfAssesment":"Planned
"},{"code":"PP2-3-8-3","description":"Foreshortening is a geometric distortion occurring in the SAR image due the side-looking geometry of imaging radar sensors. It occurs principally in SAR images of mountainous areas, on slopes oriented towards the sensor. These slopes appear in the radar image as if being compressed. Due to the side looking geometry and the mapping of the SAR image based on range and time measurement, the distance in the SAR image between two points situated on a slope facing the sensor appears smaller than it is in the reality and than the same distance between two points situated in flat area. This results in a compression of the radiometric information of the slope. The resulting foreshortening area is brighter in the SAR image than its surroundings, as it compresses in a few pixels the backscatter information of the whole slope. \r\n\r\nForeshortening occurs for slopes whose inclination is smaller than the look angle of the radar antenna. Due to the variation of the look angle in the SAR image, the foreshortening is more pronounced in near range than in far range. Foreshortening is therefore greater for small incidence angles. The extreme case of foreshortening happens when the slope inclination is equal to the look angle: in this case, the whole slope is mapped in one pixel of the SAR image, which results in a very bright line. When the slope inclination becomes higher than the look angle, layover occurs.","name":"Foreshortening","selfAssesment":"Planned
"},{"code":"PP2-3-8-4","description":"Layover is a geometric distortion occurring in the SAR image due the side-looking geometry of imaging radar sensors. It occurs principally in SAR images of mountainous areas, on steep slopes oriented towards the sensor. These slopes appear in the radar image as if being flipped over. Due to the side looking geometry and the mapping of the SAR image based on range and time measurement, the summit of a mountain is closer to the sensor that the foot of that same mountain, on the side facing the sensor. The signal from the top comes back to the sensor before the signal from the foot and is therefore mapped in nearer range than the foot of the mountain. Making an analogy to sound waves, an echo from the top of the mountain will arrive sooner at the sensor than an echo from the bottom of the mountain. Due to this “leaning over” effect, the sensor facing slope signal usually overlaps with ground signal, and a “ghost” effect appears as both signals overlap. The resulting layover area is usually very bright in the SAR image, as it superimposes backscatter signals from the slope of the mountains and the ground before it. When considering SAR images of urban areas, even up to three signals may overlap in the layover area: ground, building façade and (part of the) roof area.\r\n\r\nLayover occurs for slopes whose inclination is larger than the look angle of the radar antenna. Due to the variation of the look angle in the SAR image, layover occurs more often in near range than in far range. Layover is therefore greater for small incidence angles. It represents the extreme case of foreshortening, when the slope inclination becomes higher than the look angle.","name":"Layover","selfAssesment":"Planned
"},{"code":"PP2-3-8-5","description":"Radar shadow is a geometric distortion occurring in the SAR image due the side-looking geometry of imaging radar sensors. It occurs principally in SAR images of mountainous areas, on steep slopes oriented away from the sensor. In optical imagery, a shadow area is an area characterized by less sun illumination whose reflection is therefore weaker. In SAR imagery, shadow areas receive no signal. It occurs for example at the backside of mountains or buildings. The areas facing away from the sensor are not illuminated by the SAR sensor, as they are “hidden” from it. Also, ground area situated behind high object with respect to the sensor position are not illuminated and are situated in the radar shadow. They receive no signal information and send no information back to the sensor. Those areas are therefore very dark in SAR images. The size of the shadow area in range direction corresponds to the time delay between the last echo from the top of the mountain and the first echo of the far edge of the shadow region, where the area is not hidden from the sensor anymore.\r\n\r\nRadar shadow occurs when the slope inclination of the slope facing away from the sensor is larger than 90° minus the antenna look angle. As for the other geometric effects, the size of a shadow area for the same object depends on its situation in the image. But, unlike as for foreshortening and layover, shadow is more pronounced in far range than in near range, i.e. large incidence angles produce more shadow.\r\n\r\nA SAR image may show a return signal in a shadow area: this is principally due to internal sensor noise and does not correspond to any target return signal.","name":"Shadow","selfAssesment":"Planned
"},{"code":"PP2-3-8","description":"Synthetic Aperture Radar (SAR) backscatter is determined both by dieletric and geometric properties of the illuminated target. While the water content of the target plays an important role, its surface roughness determines the scattering mechanisms and the amount of incoming signal sent back to the sensor.\r\nDepending on its characteristics but also on the considered wavelength, a surface appears more or less rough. On smooth surfaces, specular reflection occurs, meaning that most of the incoming signal will be reflected away from the sensor. For rough surfaces, diffuse reflection occurs, meaning that part of the signal is scattered back to the sensor, the amount of it depending on different surface roughness parameters. \r\nDepending of the observed target and surface, single or multiple scattering mechanisms occur. A particularly important scattering mechanism is the double bounce, which occurs generally at two perpendicular surfaces (e.g. ground and building wall). Through two successive specular reflections, the whole signal comes back to the sensor.\r\nDue to the side-looking geometry of SAR systems and the range dependent image representation, specific additional effects occur and affect the backscatter intensity. Whereas a flat terrain only appears more compressed in near range and more stretched in far range, larger geometric distortions appear for terrain with more topography (e.g. mountains) or high objects (e.g. trees, buildings). This relief displacement is caused by the target’s elevation. A high elevated object is closer to the sensor than the ground below it. Due to the image formation in range direction depending on the distance between sensor and targets, its signal comes back sooner to the sensor and it is represented in the SAR image in nearer range than the ground below it. High objects in the SAR image are therefore displaced horizontally toward the radar antenna. This horizontal displacements contrast with the radial displacement observed in optical imagery due to central projection. Furthermore, such objects hide part of the ground below them, which do not receive any signal and cannot scatter information back. Three particular geometric distortions exist: foreshortening, layover and shadows.\r\nDepending on the illuminated target, different scattering mechanisms occur in combination with geometric distortions, which makes the interpretation of the SAR image challenging. A good example are buildings, where layover, shadow and single- and double-bounce occur.","name":"Terrain reflectivity and geometric distortions","selfAssesment":"Completed
"},{"code":"PP2-3-9","description":"A typical “salt-and-pepper” noise-like physical phenomenon that is not a noise but a deterministic property of SAR imagery is the so called speckle. It appears when a resolution cell of a SAR system contains more than one scatterer. In that case, the total scattering from the resolution cell is a coherent sum of the backscatter originating from the different scatterers. In order to reduce this effect, speckle reduction methods can be applied.","name":"Speckle Formation","selfAssesment":"Planned
"},{"code":"PP2-3","description":"Microwave remote sensing systems detect and quantify the electromagnetic radiation arriving at a detector, this radiation being either emitted (passive sensors) or scatterered back (active sensors) from the objects.\r\nThree properties of the recorded electromagnetic signal are of particular interest: its intensity, its phase and its polarization. The specific quantification of each properties allows signal interpretation, as they depend on the roughness and dielectric characteristics of the surface (intensity and polarization) as well as of the range between target and sensor (phase).\r\nThe detection of the microwaves is operated through two principal sensor elements: an antenna and a receiver. The antenna collects the incoming radiation and the receiver measures the collected electric signal.\r\nAs active microwave systems produce their own electromagnetic radiation, they are equipped with two additional elements: a pulse generator and a transmitter. Usually, transmitter and receiver are situated on the same antenna.\r\nA simple detector system only detects the intensity of the signal and amplifies it. Coherent systems measure both the amplitude and the phase of the incident electromagnetic radiation.\r\nMicrowave systems can be categorized in two different types: imaging and non-imaging sytems. Whereas for non-imaging systems each echoe (collected signal) provides a single measurement, imaging systems collect a sequence of echoes that generate a two dimensional image.","name":"Detecting microwaves","selfAssesment":"Completed
"},{"code":"PP2","description":"Microwave remote sensing operates in the microwave portion of the electromagnetic spectrum, generally using wavelengths greater than 3 cm and up to 1 m. \r\nMicrowaves are sensitive to different physical parameters than other regions of the electromagnetic spectrum. Microwaves interactions with objects are governed by geometric (structure, size, shape) and dielectric (water content) properties, whereas other regions of the electromagnetic spectrum reacts e.g. to object temperature or “color” (amount of reflection or absorption of the Sun light by a particular object).\r\nAs a general rule, microwaves interact with object at least as big as the wavelength. Smaller objects will therefore be transparent for the signal. Due to the large wavelengths, atmospheric particles are almost transparent to the signal and microwave remote sensing can penetrate clouds. Under very dry conditions, microwaves can even penetrate up to a few meters the top soil layers, therefore providing information that is not visible in other regions of the electromagnetic spectrum. Depending on the considered wavelength, microwave can also penetrate vegetation layers to different amounts.\r\nIn microwave remote sensing, three characteristics of the electromagnetic wave play an important role: its amplitude, its phase and its polarization. Depending on the application, either one characteristic or a combination of them is used to retrieve information.\r\nThere are two main types of microwave sensors: active RADAR systems and passive radiometers. RADAR is an acronym for RAdio Detection And Raging. An active radar system sends out pulses and records the echoes scattered back by the objects (scatterers) to the sensor. The systems use the two-way travel time of the radar pulse to determine the distance (range) to the illuminated object. Its backscatter intensity is determined by the radar system and object properties and depends on the quantity of energy coming back to the sensor. Active radar systems transmit a signal and record the amount of energy that is scattered back and depends of both dielectric and geometric properties. Passive radiometers record microwave energy, which is emitted by the Earth’s surface.\r\nDepending on the type of system, microwave remote sensing can be used in multiple applications. Active sensors are principally used for diverse land cover mapping applications based on the particular backscattering mechanisms and characteristics of the objects on the Earth’s surface. Using multiple acquisitions, they are also favored for topographic, deformation and velocity mapping. Passive sensors are preferred for the determination of hydrologic variables such as soil moisture, precipitation, ice water content and sea-surface temperature.","name":"Basics of microwave remote sensing","selfAssesment":"Completed
"},{"code":"PS","description":"Remote sensing, i.e. the process of obtaining information about an object or area from a distance, is not possible without remote sensing sensors that collect this information and the platforms on which the sensors are installed and which are used to move them. Remote sensing sensors collect data by detecting energy that is reflected or emitted from Earth. There are different types of remote sensing sensors. The interaction between the sensor and the Earth's surface has two modes: active or passive. Passive sensors use solar radiation to illuminate the Earth's surface and detect reflection from the surface or measure the emitted energy. They usually record electromagnetic waves in the visible (˜430–720 nm) and near infrared (NIR) (˜750–950 nm) through short infrared (SWIR) (˜1.500-2.500 nm) to thermal infrared (TIR) (8.000-14.000 nm) ranges. The power measured by passive sensors is a function of surface composition, physical temperature, surface roughness and other physical properties of the Earth. Active sensors provide their own energy source to illuminate objects and measure their properties. These sensors use electromagnetic waves in the visible and near infrared range (e.g.laser altimeter) and radar waves (e.g. synthetic aperture radar (SAR)). As sensor technology has advanced, the integration of passive and active sensors into one system has emerged. Alternatively, remote sensing sensors can be classified into imaging sensors, i.e. that produce an image of an area, within which smaller parts of the sensor's whole view are resolved (pixels), and non-imaging sensors, i.e. that return a signal based on the intensity of the whole field of view. In terms of their spectral characteristics, the imaging sensors include optical imaging sensors, thermal imaging sensors, and radar imaging sensors. These sensors can be on satellites, mounted on aircraft, unmanned aerial vehicle (UAV), drone or ground. The collected information can be transformed into an image or set of points (e.g. cloud points), which can be further processed and analyzed to obtain the necessary information, e.g. agricultural field development phase, level of air pollution, etc.\r\nA digital imagery of Earth observation sensors is a two-dimensional representation of objects on Earth. Current images collected from different levels of acquisition, from ground to satellite, with the help of electronic sensors are examples of digital images. There are different aspects and characteristics of remote sensing data and images, such as, for example, data formats and processing levels, data storage, data properties.","name":"Platforms, sensors and digital imagery","selfAssesment":"Completed
"},{"code":"PS1-1","description":"Remote sensing sensors has its roots in the 19th century in the development of photography. Photography was an invention that made it possible to acquire a permanent image. The first photographic image was taken in 1826 by Joseph Nicephore Nieppce. While the first aerial photograph was taken in 1858 by Felix Tournachon, known as Nadar, from a tethered baloon over Biévre Valley in France. In 1907 Julius Neubronner developed a light miniature camera that could be fitted to a pigeon's breast. It can be said that the construction camera + pigeon was the precursor of today's unmanned aerial vehicle (UAV) or drone. Further developments focused on developing new sensors (analog vs. digital frame cameras) and how to save and store images (e.g. photographic emulsions, films). The origin of other types of remote sensing can be traced to World War II, with the development of radar, sonar, and thermal infrared detection systems. Since the 1960s, sensors were designed to operate in virtually all of the electromagnetic spectrum. Both civil and military aerial photography have long been widely used in cartography to create maps. Specialized large format cameras (looking vertically down, assuming the plane is flying horizontally) were developed. Such cameras have been specially designed to perform almost vertical sequences of bird-eye exposures during aircraft flight. Hence for a long time remote sensing consisted of aerial photography and photogrammetry using analogue mechanical or optical equipment. Everything has changed with satellites and the space race. The first real success of remote sensing satellites in serious scientific work was in meteorology, weather satellite TIROS-1, launched by NASA on April 1, 1960. \r\nToday a wide variety of remote sensing instruments are available as data source for use in different applications for land, water and atmosphere monitoring.","name":"History of remote sensing sensors","selfAssesment":"In progress
"},{"code":"PS1-2-1-1-1","description":"Along track scanner, also known as a pushbroom scanner, is an optoelectronic device that obtains images with a multispectral imaging system. The scanners are used for passive remote sensing. It records electromagnetic energy that is reflected (e.g., blue, green, red, and infrared light) or emitted (e.g., thermal infrared radiation) from the surface of the Earth. The scanners are mounted on space- or aircrafts. \r\nA two-dimensional image is created (line by line) by exploiting the platform motion along the orbital track. The data are collected along track using a linear array of detectors arranged perpendicular to the direction of travel. The array of detectors are pushed along the flight direction to scan the successive scan lines, and hence the name pushbroom scanner. \r\nThere are no moving parts on a pushbroom sensor, hence, the scanning speed can be increased compared to across track systems. A longer dwell time over each ground resolution cell increases the signal strength (high radiometric resolution, no pixel distortion). Additionally, finer spatial and spectral resolution can be achieved as the size of the ground resolution cell is determined by the Instantaneous Field of View (IFOV) of a single detector. The systems are designed for high-resolution imaging. However, a very large number of detectors is needed for high resolution images. It is a complex optical system. In addition, the pushbroom scheme requires a wide Field of View (FOV) optics system to obtain the same swath as for a corresponding whiskbroom (across track) scanner. It has narrow swath width. \r\nThe detector arrays with such a line-scanning pushbroom system are usually of the type Charge-Coupled Device (CCD).\r\nThe MultiSpectral Instrument (MSI) on board the Sentinel-2 satellite (Copernicus mission) uses a pushbroom concept.\r\nMultispectral imaging systems building the final image (line by line) exploiting the platform motion along the orbital track. No rotating mechanical part required, usually based on a CCD matrix (high spectral resolution but just up to 1 micrometer), e.g. Sentinel-2 MultiSpectral Instrument (MSI), Sentinel-3 Ocean and Land Colour Imager (OCLI).","name":"Along track scanners","selfAssesment":"Completed
"},{"code":"PS1-2-1-2-1","description":"The cameras, usually a charge-coupled device (CCD) or Complimentary Metal Oxide Semiconductor (CMOS), that convert light into electrons that can be measured and converted into radiometric intensity value.","name":"Digital Frame Camera","selfAssesment":"Planned
"},{"code":"PS1-2-1-2","description":"2-D systems with the ability to observe in two dimensions simultaneously.","name":"Area Arrays","selfAssesment":"New
"},{"code":"PS1-2-1","description":"A type of a spectrometer. It is in principle, one-dimensional systems, whisk- or pushbroom, that form an image on a line-by-line basis in the scan direction.","name":"Line detector arrays","selfAssesment":"New
"},{"code":"PS1-2-2-1-1","description":"Thermal radiometers are radiometers with the capability of measuring the spectrum of infrared emission. As such, they are characterized by a relatively high spectral resolution (normally better than 1 cm-1 in wave number units). Modern Spectrometers on board satellites have a spectral resolution better than 0.7 cm -1 in order to properly resolve CO2 lines used for the retrieval of the atmospheric temperature profile. Based on the optical layout they are further classified in grating spectrometers and Fourier Transform Spectrometers or FTIR.","name":"Thermal Radiometers","selfAssesment":"New
"},{"code":"PS1-2-2-1-2","description":"Passive microwave radiometers are radiometers that measures energy emitted at millimetre-to-centimetre wavelengths at 0.15 - 30 cm (frequencies of 1–200 GHz). Example of a sensor: SMOS Microwave Imaging Radiometer with Aperture Synthesis (MIRAS), which aims at measuring land soil moisture and ocean salinity.","name":"Passive Microwave Radiometers","selfAssesment":"In progress
"},{"code":"PS1-2-2-1-3","description":"An advanced multispectral sensor that detects hundreds of very narrow spectral bands throughout the visible, near-infrared, and mid-infrared portions of the electromagnetic spectrum.","name":"Hyperspectral Radiometers","selfAssesment":"Planned
"},{"code":"PS1-2-2-1-4","description":"A radiometer that measures the intensity of radiation in multiple wavelength bands (i.e., multispectral). Example of a sensor Moderate Resolution Imaging Spectroradiometer (MODIS)","name":"Spectroradiometers","selfAssesment":"In progress
"},{"code":"PS1-2-2-2","description":"Provide information about vertical profiles of temperature and molecular consistuent concentrations in the atmosphere (atmospheric sounders).","name":"Atmospheric passive sounders","selfAssesment":"New
"},{"code":"PS1-2-2","description":"Radiometers are instruments which measure radiative intensities within a particular frequency window. A radiometer is further identified by the portion of the electromagnetic radiation it covers, usually the infrared or microwave regions. Normally the spectral range extends from the longwave (14-15 micron) to the shortwave (3-4 micron). This range overlaps much of the emission spectrum of Earth. The technology is classified in broadband radiometer of spectral radiometers depending on the spectral resolution. A radiometer measures the intensity of the radiative energy, but does not differenciate between the different registered wavelengths or their respective amplitude. In other terms, it provides a single value as combined result of all wavelengths within the considered frequency window.","name":"Radiometers","selfAssesment":"In progress
"},{"code":"PS1-2","description":"Passive remote sensing systems record electromagnetic energy that is reflected (e.g., blue, green, red, and infrared light) or emitted (e.g., thermal infrared radiation) from the surface of the Earth. Passive sensors therefore rely on an external energy source (e.g. sun illumination, Earth heat emission). Contrary to passive sensors, who detect naturally occurring radiation, active sensors emit radiation and collect and analyze the signal that is sent back by the Earth’s surface or atmosphere. Active remote sensing systems produce therefore their own electromagnetic energy. They transmit and receive the radiation that is reflected or backscattered from the illuminated target. They do not necessitate an external source of radiation (e.g. Sun or Earth). Contrary to most passive sensors that are bound to detecting either the reflected Sun radiation or emitted radiation by the Earth’s surface in ranges from the ultraviolet to the thermal infrared, active sensors can use any radiation from the electromagnetic spectrum, the only limitation being the transparency of the Earth’s atmosphere. They often use wavelengths that are not sufficiently provided by the Sun, e.g. microwaves. \r\nActive systems can be categorized either according to their imaging capability, or according to the considered emitted wavelength, or also according to the way they use the returned signal. For the last category, it is generally distinguished between ranging systems, which use as principal information the time delay between transmission and reception of the electromagnetic radiation at the sensor, and scattering systems, which consider the strength (also called magnitude or intensity), of the returned signal. Some systems also register both information.\r\nAs active sensors produce their own radiation and do not rely on e.g. Sun radiation, they are daytime independent and can also retrieve information about the Earth’s surface by night. Furthermore, depending of the considered wavelength, active sensors are weather independent. For longer wavelengths of the microwave domain, clouds are transparent, as the transmitted wavelength is larger than the water particles constituting the cloud and do not interact with them. \r\nActive sensors can control the direction of their illumination to a specific target to be investigated, but require in general more energy than passive sensors as they “actively” illuminate the Earth’s surface.","name":"Passive vs. active sensors","selfAssesment":"Completed
"},{"code":"PS1-3-1-1","description":"Imaging RADAR (RAdio Detection And Ranging) is an active remote sensing system which bounces microwave energy from a target and records the energy that returns to the sensor. The radar antenna alternately transmits and receives pulses at particular microwave wavelengths (in the range 1 cm to 1 m, which corresponds to a frequency range of about 300 MHz to 30 GHz) and polarizations (waves polarized in a single vertical or horizontal plane).\r\nMicrowave energy pulses are emitted at regular intervals and focused by the antenna into a radar beam directed downwards and to the side. The radar beam illuminates the surface obliquely at a right angle to the motion of the platform. Objects on the ground reflect the microwave energy depending on factors such as roughness and attitude. The antenna receives this reflected (or backscattered) energy.\r\nBy measuring the time delay between the transmission of a pulse and the reception of the backscattered \"echo\" from different targets, their distance from the radar and thus their location can be determined. As the sensor platform moves forward, recording and processing of the backscattered signals builds up a two-dimensional image of the surface.\r\nUnlike aerial photographs and satellite images which are passive remote sensing systems, in active systems such as radar, the brightness or darkness of the image is dependent on the portion of the transmitted energy that is returned back to the radar from targets on the surface. Bright areas are produced by strong radar response and darker areas are from weak radar responses., while the response to radar energy by the target is primarily dependent on the three factors (1) Surface roughness of the target, (2) Radar viewing and surface geometry relationship, and (3) Moisture content and electrical properties of the target.","name":"Imaging Radar","selfAssesment":"Completed
"},{"code":"PS1-3-2-1-1","description":"Laser profilers measure 2D range profiles and operate in different environments, like spaceborne, airborne and indoor. It is the simplest application of the LIght Detection And Ranging technique. It transmits a short pulse of energy (visible or near-infrared radiation) and detects 'echo', by measuring the time delay. Knowing the speed of propagation of the pulse (speed of light), the range from the instrument to the surface can be measured.\r\nLaser profiling uses successive reflectorless laser range measurements (1D distance measurement) on adjacent points along a path, which results in a 2D profile or cross-section of the ground. A laser profiler can be terrestrial, or ground-based, or it can be mounted on an airborne or spaceborne platform. In the case of ground-based measurements, the platform is fixed but the angle of illumination changes, allowing for the cross section of the terrain to be mapped. An airborne laser profiler can transmit a continuous stream of pulses along its flight path. As a result, if the position of the platform is known, e.g. from GPS/IMU system, a surface profile along the flight path can be reconstructed using the successively recorded vertical distances between the platform and the points on the ground. The use of an additional rotational mirror allow to scan the Earth in an additional dimension, providing 3D information of the mapped surface. This is the principle of a laser scanner.\r\nThere are two principal types of laser profiling techniques: the first one is based on analog detection and the second on photon counting. In analog detection, the signal power is converted into an output voltage providing a signal strength as function of time. The analog-to-digital conversion yields either a full waveform that allows retrieving the entire time-structure of the return signal strength- and therefore the full vertical structure of the target-, or discrete returns when the signal strength exceed a certain threshold. The full waveform information is especially useful when analyzing vegetation, as every vegetation layer (canopy, stems, branches) and the ground return pulses, allowing the determination of e.g. canopy height, ground surface topography but also a deeper analysis of the canopy structure. Photon counting techniques record the arrival of single photons. The counting of photons is combined with their time-of-flight. The accumulation of single photons at a specific range is similar to the signal strength of analog detection and allows retrieving the height and structure of specific targets.","name":"Laser profiler","selfAssesment":"Completed
"},{"code":"PS1-3-2-1-4","description":"A radar altimeter is an active, non-imaging remote sensing device. It measures the height of the terrain along the track beneath an air- or spaceborne platform using electromagnetic radiation from the microwave region of the electromagnetic spectrum. Radar altimeters operate similar to laser profilers. Both emit a short pulse of electromagnetic radiation towards the Earth’s surface and detect the time delayed echo. By measuring the time delay and knowing the speed of propagation of the pulse, the range (distance) from the instrument to the surface can be determined. By using the forward motion of the altimeter platform and transmitting a continuous stream of pulses a profile can be built up. If the exact location of the platform as a function of time is known, a surface profile can be generated. \r\nFor a high accuracy of the range resolution, a narrow antenna beam is required, which can be achieved either by using large antennas or short radar beams. In the first case, the radar altimeter is beam-limited; in the second case it is pulse-limited. As large antennas are not practical in space, pulse-limited systems are used for space-borne platforms. Pulse-limited altimeters use frequency modulated (chirp) pulses generated by a chirp generator. The accuracy of the measurements also depends on atmospheric transmission effects, as the speed of the electromagnetic radiation traveling at the speed of light will be delayed when passing through the ionosphere and the atmosphere twice. In general, the range resolution of radar altimeters is in the order of a few centimetres. \r\nIn the beginning, radar altimeters were used for measurements of surface profiles of the ocean topography to get information about currents, ocean circulation, wind and waves. Another basic application of altimetry were measurements over ice sheets and glaciers, e.g. for mass balance determination. Further application domains are geoid measurements also revealing deep sea trenches and the precise monitoring of satellite orbits.","name":"Radar altimeters","selfAssesment":"Completed
"},{"code":"PS1-3-2-1","description":"Laser altimeters historically were the first active sensing devices used on airborne platforms, measuring range information in form of single distances since the mid-1960s. \r\nEven though laser scanners made it possible to retrieve information in a more rapid and denser coverage since the mid-1990s, laser altimeters remain of importance in the scientific community. Especially, the mapping of ice-covered surfaces, water bodies and flat land areas is still performed using laser altimeters.\r\nLaser altimeters are either airborne or spaceborne and are often used together with microwave (radar) profiler in order to calibrate the radar instruments. Whereas airborne laser altimeters are preferred for forestry application, e.g. for analyzing vertical vegetation structure, spaceborne laser altimeters are additionally used for multiple other applications. In particular, spaceborne laser profiler are of high interest for studying surface roughness of ice sheets or for mapping desert topography. Furthermore, spaceborne laser profilers are also useful in atmospheric science for retrieving cloud structure and analyzing different aerosol layers. The requirements for airborne and spaceborne laser altimeters are different. In particular, for spaceborne altimeters, both the distance travelled by the laser pulse and the platform speed are much higher than for airborne instruments, inducing the need of larger optics and more powerful laser instruments. First spaceborne laser experiments were conducted onboard the space shuttle in the mid-1990s, first aiming atmospheric research with a near infrared laser. After successful trial, the space shuttle laser altimeter was fine-tuned and follow-up missions focused on mapping terrain relief and vegetation canopies. Later missions, such as GLAS (IceSAT), ATLAS (IceSAT-2) and GEDI (ISS), used either near-infrared or green (or both) laser light and focused on improving ground coverage while allowing smaller footprints of the laser beam on ground. The revisit cycle of spaceborne laser altimeters allow the determination of regional elevation changes, e.g. monitoring of ice–sheet thickness or vegetation height, which is highly relevant for the scientific community and climate modelers.","name":"Laser altimeter","selfAssesment":"Completed
"},{"code":"PS1-3-2-3","description":"By a ranging camera the simultaneous capturing of range measurements for dynamical (close-range) 3D applications is given. These ranging cameras allow additionally the simultaneous capturing of single range and co-registered intensity images while still maintaining high update rates (up to 100 releases per second). Typical applications are autonomous navigation of robots, driver assistance, traffic monitoring or tracking of pedestrians for building surveillance.","name":"Ranging camera","selfAssesment":"Completed
"},{"code":"PS1-3-2-4-1","description":"Spaceborne LS (e.g. Geoscience Laser Altimeter System - GLAS) provides global measurements of the Earth's surface with the potential on capturing additionally clouds and atmospheric aerosols. The spaceborne measurements allow to globally observe ice sheet and land elevations, approximate sea ice thickness, changes in elevation through time, vegetation coverage for biomass estimation, and height profiles of clouds and aerosols. It is a large footprint profiling system developed by NASA that operates with a footprint diameter of 70 m and measures elevation changes with decimeter accuracy. The surface characteristics are determined by comparing a parametric description of the transmitted and received waveforms. Because the laser footprint is large and illuminates multiple surfaces, the resulting return waveform is an integrated, spatially non explicit representation of the range to illuminated surfaces separated both vertically and horizontally. The geometric organization of surfaces within a single footprint can therefore not be determined.","name":"Spaceborne Laser Scanning","selfAssesment":"Completed
"},{"code":"PS1-3-2-4-2","description":"Airborne laser scanning (ALS) systems allow a direct and illumination-independent measurement of 3d objects in a fast, remote and accurate way. Beside basic range measurements, the current commercial ALS developments allow to record the waveform of the backscattered laser pulse. Latest trends in sensor developments focus on single-photon detection. Airborne Laser Scanning (ALS) for instance is used for capturing large-scale 3D environments with almost homogeneous point density with a local point density of typically 4-100 pts/m^2. Therefore, different applications are of interest, like urban planning, change detection, forestry surveying, or power line monitoring. Further to describe the 3D scene, products like digital terrain models (DTMs), digital surface models (DSMs), or city models are provided.","name":"Airborne Laser Scanning","selfAssesment":"Completed
"},{"code":"PS1-3-2-4-3","description":"A mobile laser scanning (MLS) system consists of a moving vehicle equipped with one or more usually side-looking laser scanners to capture information about the local 3D geometry. Mobile laser scanning systems are applied for capturing dense and accurate 3D information representing local object surfaces, but the density of the measured 3D points depends on their distance to the scanning unit, which is usually mounted on a vehicle. As a consequence, an appropriate interpretation of the captured data has to face certain challenges arising from either low or varying point density.","name":"Mobile Laser Scanning","selfAssesment":"Completed
"},{"code":"PS1-3-2-4-4","description":"Underwater Laser Scanning is applied in deep-sea as well as in shallow water regions. The ranging distance is close range and the measurement principle relies on triangulation by laser light, comparable with structured-light-projection. More recently, companies started to develop Time-of-Flight (ToF) underwater laser scanners.","name":"Underwater Laser Scanning","selfAssesment":"Completed
"},{"code":"PS1-3-2-4-5","description":"For Bathymetric Laser Scanning System the utilized green laser light with its potential penetration capabilities in water is essential. For water surface mapping the electromagnetic radiation of the laser penetrates into the topmost layer of the water column and can also be used for mapping the water surface and shallow water bathymetry. However high resolution mapping of water level heights is important for many applications, but capturing water is still in general challenging. Area-wide water surface heights and depths are required for many disciplines such as hydrology, hydraulic engineering, flood risk management, ecology, climate change, etc.","name":"Bathymetric Laser Scanning","selfAssesment":"In progress
"},{"code":"PS1-3-2-4","description":"Laser scanners capture data by successively considering points on a discrete, regular (typically spherical, cylindrical or line) raster, and recording the respective geometric and radiometric information. Generally, a laser scanner illuminates a scene with modulated laser light and analyzes the backscattered signal. More specifically, laser light is emitted by the scanning device and transmitted to an object. At the object surface, the laser light is (partially) reflected and, finally, a certain amount of the laser light reaches the receiver unit of the scanning device. The measurement principle is therefore of great importance as it may be based on different signal properties such as amplitude, frequency, polarization, time, or phase. Many scanning devices are based on measuring the time t between emitting and receiving a laser pulse, i.e., the respective time-of-flight, and exploiting the measured time t in order to derive the distance r between the scanning device and the respective 3D scene point. Alternatively, a range measurement r may be derived from phase information by exploiting the phase difference Δφ between emitted and received signal. In general, laser scanners may be categorized with respect to laser type, modulation technique (continuous-wave (CW) laser, pulsed laser), measurement principle (time-of-flight, phase difference), detection technique (coherent detection, direct detection), field-of-view (line scanner, pushbroom scanner, array scanner), measurement range (far range, medium range, close range), or configuration between emitting and receiving component of the device (monostatic system, bistatic system). Furthermore, different types of laser scanners may be used for different application scenarios relying on e.g. spaceborne laser scanning, airborne laser scanning, mobile laser scanning, terrestrial laser scanning, underwater laser scanning or bathymetric laser scanning.","name":"Laser scanner","selfAssesment":"Completed
"},{"code":"PS1-3-2","description":"The main idea of LiDAR (Light Detection and Ranging) technology is based on actively scanning the scene by involving a device which emits electromagnetic radiation in the form of modulated laser light. \r\nGenerally, such scanning devices illuminate a scene with modulated laser light and analyze the backscattered signal. More specifically, laser light is emitted by the scanning device and transmitted to an object. At the object surface, the laser light is partially reflected and, finally, a certain amount of the laser light reaches the receiver unit of the scanning device. The measurement principle is therefore of great importance as it may be based on different signal properties such as amplitude, frequency, polarization, time, or phase. \r\nMany scanning devices are based on measuring the time t between emitting and receiving a laser pulse, i.e., the respective time-of-flight, and exploiting the measured time t in order to derive the distance r between the scanning device and the respective 3D scene point. Alternatively, a range measurement r may be derived from phase information by exploiting the phase difference Δφ between emitted and received signal. According to seminal work, respective scanning devices may be categorized with respect to laser type, modulation technique, measurement principle, detection technique, or configuration between emitting and receiving component of the device. \r\nIn order to get from single 3D scene points to the geometry of object surfaces, respective scanning devices are typically mounted on a platform which, in turn, allows a sequential scanning of the scene by successively measuring distances for discrete 3D points.\r\nLiDAR technology is used for a diversity of applications such as autonomous driving, forestry, biomass estimation, precision farming, archaeology, city mapping, terrain modelling, and metrology.","name":"LiDAR (Light Detection and Ranging)","selfAssesment":"Completed
"},{"code":"PS1-3-3-1","description":"Sonar, also called ultrasonic sensing, is one the principal sensors for mapping sea-floor, i.e. bathymetry. It transmits sound waves through water and records the amount of backscattered energy. It uses frequencies higher than normal hearing. A sonar can be either passive or active. Active sonars are also called echosounders.","name":"Sonar","selfAssesment":"New
"},{"code":"PS1-3-3-2","description":"A seismic sensor is also called seismometer and measures the motion of the ground when it is shaken by a perturbation such as an earthquake, be it a large displacement or a microquake. The physical variable associated to the measurement of a seismometer is dynamic. It can be either the amplified ground motion, the velocity or acceleration. Current seismometers transform one of these three parameters into a voltage measurement. Usually, three seismometers are needed to retrieve the three components of the displacement. As for other sensors, there exists many types of seismic sensors, and they can be distinguished in active and passive sensors as well.","name":"Seismic sensor","selfAssesment":"New
"},{"code":"PS1-3-3","description":"Instruments that measure vertical distribution of precipitation and other atmospheric characteristics such as temperature, humidity, and cloud composition.","name":"Sonic sensors","selfAssesment":"New
"},{"code":"PS1-3-4-1","description":"A radar scatterometer is an active, non-imaging remote sensing device with a real aperture operating in the microwave region of the electromagnetic spectrum. The main purpose of a scatterometer is the characterization of the surface backscatter properties, when a high radiometric accuracy is of interest and the spatial resolution is of secondary importance. There are scatterometers used in laboratories, in the field installed on masts, cranes or trucks, airborne (airplanes, helicopters) and spaceborne scatterometers circling the Earth in an orbit. Spaceborne scatterometers usually achieve a global coverage with a high repetition frequency. The basic principle of the scatterometer the accurate measurement the intensity of the returned radar echo from the Earth’s surface. Because of the speckle effect in radar echoes, a large number of independent observations are averaged.\r\nScatterometry (Earth observation using scatterometers) gained the attention of scientists towards the end of the 1960s when it was realized that the sea clutter observed by Second World War radar operators on their screens was not just any noise obscuring small boats and low-flying aircraft. It was in fact the signal backscatter from small ocean surface waves, comparable in dimension to the wavelength of the radar (in the order of centimetres).\r\nThe primary application of radar scatterometers is the measurement of near-surface wind vectors (wind speed and direction) over the ocean. These wind vector data are based on indirect measurements, where the wind vector is derived from the relationship between the backscattered power, the small-scale ocean surface roughness, and the local wind vector at the ocean surface.","name":"Radar Scatterometers","selfAssesment":"Completed
"},{"code":"PS1-3-4-2-1","description":"Differential Absorption Lidar (DIAL) is a laser remote sensing technique that is used for range and/or profile measurements of atmospheric gas concentrations and constituents.","name":"Differential Absorption Lidar","selfAssesment":"In progress
"},{"code":"PS1-3-4-2-2","description":"Doppler Wind LiDAR or Cloud-Aerosol Lidar with Orthogonal Polarization (e.g. CALIOP) is a two-wavelength polarization-sensitive LiDAR that provides high-resolution vertical profiles of atmospheric aerosols and clouds to enable an greater understanding of our climate.","name":"Doppler Wind LiDAR","selfAssesment":"In progress
"},{"code":"PS1-4","description":"There are different ways to classify sensors used in remote sensing. One of them is the division into imaging and non-imaging sensors. Imaging sensors typically employ optical imaging systems (from VIS to TIR). They operate primarily at window frequencies, where atmospheric absorption is low and surface features can be imaged or measured. Non-imaging sensors include microwave radiometers, microwave altimeters, magnetic sensors, gravimeters, Fourier spectrometers, laser rangefinders, and laser altimeters.","name":"Imaging vs. nonimaging sensors","selfAssesment":"New
"},{"code":"PS1-5-1-2","description":"Across track scanners, known as whiskbroom electromechanical scanners, are multispectral imaging systems building the final image (ground cell by ground cell) by combination of the platform motion along the orbital track with a mechanical rotation of the collecting optic in the across track direction. Opto-mechanical are typically multi-spectral radiometers (no limitation on bands), whiskbroom systems are usually CDD spectrometers (high spectral resolution but just up to 1 micrometer). Examples of the sensors: Landsat Multispectral Scanner (MSS), Landsat Thematic Mapper (TM).","name":"Across track scanners","selfAssesment":"In progress
"},{"code":"PS1-5-1","description":"Speckle-pattern based sensors operate with a spatial neighborhood codification strategies to exploit a unique pattern. The label associated to a pixel is derived from the spatial pattern distribution within its local neighborhood. Thus, labels of neighboring pixels share information and provide an interdependent coding. Representing one of the most popular devices based on structured light projection, the Microsoft Kinect exploits an RGB camera, an IR camera, and an IR projector. The IR projector projects a known structured light pattern in the form of a random but unique speckle dot pattern onto the scene. As IR camera and IR projector form a stereo pair, the pattern matching in the IR image results in a raw disparity image which, in turn, is read out as depth image.","name":"Speckle-pattern based sensor","selfAssesment":"In progress
"},{"code":"PS1-5-2","description":"A multi-temporal (sequential) binary coding uses black and white stripes to form a sequence of projection patterns for each point on the surface of the object. Binary coding technique is very reliable and less sensitive to the surface characteristics, since only binary values exist in all pixels. Thus, each pixel may be assigned a codeword consisting of its illumination value across the projected patterns. The respective patterns may, for instance, be based on binary codes or Gray codes and phase shifting. To achieve high spatial resolution, a large number of sequential patterns need to be projected. All objects in the scene have to remain static. The entire duration of 3D image acquisition may be longer than a practical 3D application allows for. These sensors are utilized in industrial environment.","name":"Multi-temporal pattern based sensor","selfAssesment":"In progress
"},{"code":"PS1-5-3","description":"For a multi-spectral pattern based sensor, various continuously varying color patterns to encode the spatial location information are utilized.","name":"Multi-spectral pattern based sensor","selfAssesment":"In progress
"},{"code":"PS1-5","description":"A structured-light-projection camera emits active optical radiation in the form of a coded structured light pattern in the visible or infrared spectrum, or electromagnetic radiation in the form of modulated laser light. Via the projected pattern, particular labels are assigned to 3D scene points which, in turn, may easily be decoded in images when imaging the scene and the projected pattern with a camera. The procedure reminds to conventional stereo processing, where corresponding features must be extracted from a pair of stereo images to derive the spatial information. In contrast, such synthetically generated features allow to robustly establish feature correspondences, and the respective 3D coordinates may easily and reliably be recovered via triangulation. Generally, techniques based on the use of structured light patterns may be classified depending on the pattern codification strategy.","name":"Structured-light-projection camera","selfAssesment":"Completed
"},{"code":"PS1-6","description":"Ground penetrating radar is a non-intrusive measurement technique that uses radio waves to probe the ground. It is used to analyze and locate targets buried in the sub-surface. It transmits low-power electromagnetic energy into the ground and receives weak signals from a low-loss dielectric or conductor material. It is principally used for archeology and geology. Typical penetration depths are between a few centimeters up to 4m.","name":"Ground penetrating RADAR (GPR)","selfAssesment":"New
"},{"code":"PS1-7","description":"An optical spectrometer is an instrument used to detect, measure and analyze the spectral content of the incident electromagnetic field (narrow-band, VIS, NIR, SWIR and TIR). It breaks down the incoming light spectrum so the whole wavelength range is mapped and each wavelength can be analysed individually. Usually, a distinction is made between optical and mass spectrometers.\r\nOptical spectrometers depict the intensity of the incoming light in function of the wavelength. Considering all wavelengths, each object has a specific spectral signature and the analyse of their particular spectrum allows the deduction of their composition ( e.g. pigments) or health.","name":"Optical spectrometers","selfAssesment":"In progress
"},{"code":"PS1","description":"Remote sensing sensors acquire information about objects situated on the surface of e.g. the Earth remotely, e.g. from a distance, without any physical contact. They detect and measure the changes that the object imposes on its. \r\nRemote Sensing sensors are characterized according to several different properties:\r\n\tDepending on the interaction between the sensor and the Earth’s surface, one distinguishes between active (e.g. radar) and passive (e.g. optical imagery) sensors. Some systems use both kind of sensors simultaneously.\r\n\tDepending on the mapping process of the information, it can be distinguished between imaging and non-imaging sensors. Imaging sensors produce an image of an area of interest, e.g. give a spatial information about the incoming information. Spatial relationships between objects can be identified and used for visual interpretation. Non-imaging sensors register usually single response values for a specific area, and do not record how the incoming information varies across the field of view. They can be used to characterize the interaction between the received information and illuminated target.\r\n\tDepending on the platform on which the instrument is deployed, one speaks either of ground based (e.g. terrestrial laser scanner), airborne (e.g. plane, drone), or spaceborne (e.g. satellite) sensor. For spaceborne sensors, the orbit geometry (e.g. geostationary, equatorial, sun-synchronous) and altitude (high, medium and low Earth orbit) play an important role, as it most often determines the application of the satellite in combination with the deployed sensor (weather satellites or Earth observation satellite). \r\n\tDepending on the observed portion of the electromagnetic spectrum (e.g. optical, infrared, thermal, microwave). \r\n\tDepending on the instrument (e.g. imagers, altimeters, spectrometers, radiometers). \r\n\tDepending on the instrument precision, e.g. in terms of spatial resolution very high vs. low resolution sensors; in terms of spectral resolution narrow band (hyperspectral sensors) vs. broad-band sensors (mono- and multispectral sensors); in terms of radiometric resolution very high vs. low resolution sensors. Some applications do not require very high precision instruments, e.g. sea surface temperature measurements, while other, e.g. for vegetation monitoring, require high spectral and radiometric resolution for good data interpretation and analysis. \r\nOther categorization would include the specific applications of each sensor (weather, environment, urban, land, water, mapping, photogrammetry, structure-from-motion, etc.) and if is financed and used for scientific, commercial or military goals.","name":"Types of remote sensing sensors","selfAssesment":"Completed
"},{"code":"PS2-1","description":"This topic covers information on the first remote sensing platforms that were used to obtain aerial photos. The first-known aerial photo was obtained in 1858 by Gaspard Felix Tournachon (Nadar). Afterwards, different platforms were used to obtain the information from above. The history of the development of remote sensing platforms includes platforms such as baloons, kites, rockets, pigeons, gliders, etc. to recent low-cost femtosatellites, e.g. for solar radioation pressure measurements. Historically, the main developments of the platforms as well as sensors was associated with military operations in the XXth century. Remote sensing data was used as part of photo- or/and satellite reconnaissance, i.e. aerial photos or satellite imageries used for the military purposes, mainly to make accurate maps and based on that to prepare a military strategy.","name":"History of Remote Sensing Platforms","selfAssesment":"In progress
"},{"code":"PS2-2-1","description":"An unmanned aircraft system (UAS) includes an unmanned aerial vehicle (UAV), an aircraft without a human pilot on board, a ground-based controller, and a system of communications between the two. The system includes a full range of size classes from very small hand-launched drones to the large high-altitude observational systems.","name":"Unmanned Aerial Systems (UAS)","selfAssesment":"New
"},{"code":"PS2-2-2-1","description":"Mission planning depends on the selected system of acquisition (sensor and platform). A detailed planning of a mission is a fundamental prerequisite for a successful acquisition of remote sensing data. Planning of an aerial photography mission (manned or unmanned) takes into account several parameters such as time of day/sun angle, weather conditions, flightline, platform. Planning and implementation of a spaceborne Earth Observation mission involves several successive life cycle ‘phases’ of conception, development, production and testing, utilization and support, and retirement, as part of an iterative and recursive process, until the satellite (space segment) is delivered and launched into orbit, and the data are exploited in the ground segment.","name":"Mission planning","selfAssesment":"New
"},{"code":"PS2-2-2-3-2-3-1","description":"Stripmap is an acquisition mode of Synthetic Aperture Radar (SAR) data. It is the most simple, common acquisition mode of the SAR satellite sensors. In this mode, the antenna of the radar system is pointed in a fixed direction related to the flight direction. The displacement of the illuminated footprint corresponds to the displacement of the sensor along the orbit. This results in a continuous acquisition strip parallel to the flight direction. The ground coverage and resolution varies depending on the considered sensor and technical requirements. For X-band spaceborne sensors, a spatial resolution of 3 m can be achieved with a swath width in range direction of 30 km, e.g. for TerraSAR-X. In C-band, a spatial resolution up to 5 m is achieved e.g. by Sentinel-1 with a swath width of 80 km. For L-band spaceborne sensors, the spatial resolution achievable in stripmap mode varies between 3 and 10 m, with a swath width of 50-70 km, e.g. ALOS PALSAR2. \r\nContrary to other acquisition modes, no antenna steering is needed in azimuth direction and the elevation beam is fixed in a specific range direction. This allows for an uninterrupted coverage along the flight direction.\r\nStripmap data show high resolution with sufficient coverage for regional applications and can therefore be used for e.g. detailed land cover analysis at regional scale such as the mapping of urban footprints. Furthermore, it can be used for the mapping of small island or to support emergency actions.","name":"Stripmap","selfAssesment":"Completed
"},{"code":"PS2-2-2-3-2-3-2-1","description":"The Staring Spotlight mode is only available for a few sensors. It follows the same principle of antenna steering in azimuth direction as the standard Spotlight mode, except that the rotation center of the antenna for steering is situated at a nearer range position, within the illuminated scene. This induces that the illuminated antenna footprint stays almost the same during the whole acquisition. Contrarily to the Spotlight mode, the antenna footprint does not slide along the azimuth direction during the SAR acquisition. Additionally, the steering angle is higher for the Staring Spotlight mode than for the standard Spotlight mode, increasing therefore the length of the synthetic aperture and leading to an even higher resolution in azimuth direction.\r\nThe Staring Spotlight mode is implemented on the X-Band sensor TerraSAR-X since 2013 and achieves an azimuth resolution up to 0.25 m. Similar to the standard Sportlight mode, this happens to the detriment of the coverage. The scene size is highly dependent of the incidence angle and varies from 7.5 km to 4 km in range and from 2.5 to 2.7 km in azimuth direction. A larger coverage is obtained for smaller incidence angles.\r\nDue to their extremely high resolution, staring spotlight acquisitions are principally used for the observation and/or monitoring of small scale objects and phenomena, e.g. small landslides, or for tomographic analysis.","name":"Staring Spotlight","selfAssesment":"New
"},{"code":"PS2-2-2-3-2-3-2","description":"Spotlight is a SAR acquisition mode that allows increasing the illumination time of a particular area of interest by steering the antenna beam in azimuth direction. In this mode, the beam elevation is fixed, but the antenna is steered in azimuth direction, increasing therefore the length of the synthetic aperture. The rotation center of the antenna for steering is situated behind the scene at far range. The antenna footprint slides slightly forward over the scene in the azimuth direction during acquisition, but slower than in Stripmap mode, due to the antenna steering. The longest illumination time in azimuth direction results in an azimuth resolution that is highly enhanced compared to e.g. the Stripmap or the ScanSAR acquisition modes. However, this improvement is done to the detriment of the coverage. As for the other acquisition modes, the ground coverage and resolution depends on the considered sensor. For TerraSAR-X, a minimum coverage of 10 km in range and 5 km in azimuth direction is achieved in the Spotlight mode, with and azimuth resolution of about 1 m. The L-Band sensor Alos 2 also allow Spotlight acquisition mode, with a coverage of 25 km in both directions and a resolution of 1 m in azimuth direction, and down to 3 m in range direction.\r\nDue to the very high resolution achieved in both directions, this acquisition mode is particularly usefull for urban area analysis as it allows for the detection of small objects. Therefore, Spotlight data are often used for the detection and recognition of man-made structures and objects, such as roads, buildings and even vehicles.","name":"Spotlight","selfAssesment":"New
"},{"code":"PS2-2-2-3-2-3-3-1","description":"The Interferometric Wide Swath Mode is a particular acquisition mode of the C-Band satellites Sentinel-1 which implements the TOPS (Terrain Observation with Progressive Scan) method. It combines an antenna steering in elevation, as in ScanSAR mode, with a counterrotation of the antenna beam from backward to forward steering, opposite to the steering happening in Spotlight mode. The data is acquired in bursts by cyclically switching the antenna beam between multiple adjacent sub-swaths.\r\n\r\nThis opposite steering direction of the antenna along the azimuth leads to a shorter target illumination and induces a decrease of the resolution, but a cyclically continuous coverage in azimuth direction. The principal difference to the other acquisition modes is that this acquisition mode implies a shrinking of the antenna footprint virtually to a ground target instead of slicing it to retrieve the target.\r\n\r\nThe Interferometric Wide Swath Mode (IW) was originally designed to solve Signal-to-Noise heterogeneities and azimuth ambiguities appearing in the ScanSAR mode.\r\n \r\nFor Sentinel-1, the IW mode provides a coverage of 250 km in range direction with an azimuth resolution of 20 m and incidence angles ranging from 29.1° in near to 46° in far range. \r\n\r\nStandard Single Look Complex Sentinel- 1 IW products contain three sub-swaths in range direction, with nine burts in azimuth direction.\r\n\r\nThe IW mode is the standard acquisition mode of the Sentinel-1 C-Band satellites and is acquired continuously over all land surfaces. The application are very diverse, ranging from agriculture and forestry to urban deformation monitoring and ship surveillance.\r\n\r\nSimilar to the IW mode, the Extra Wide Swath Mode (EW) of Sentinel-1 uses the same TOPS technique, but covers even wider areas up to 400 km in range direction, to the detriment of the resolution which decreases to 40 m. The EW Mode principally finds application in maritime applications such as artic and sea-ice monitoring, analyses of marine winds and oil pollution monitoring.","name":"Interferometric Wide Swath Mode","selfAssesment":"New
"},{"code":"PS2-2-2-3-2-3-3-2","description":"The Extra Wide Swath Mode is an acquisition mode of the Sentinel-1 satellites. It is primarily designed and used for wide area coastal monitoring, such as ship traffic, sea-ice monitoring and oil spill detection. It uses the TOPSAR technique with a swath width of 410km and a spatial resolution of 20 m by 40 m.","name":"Extra Wide Swath Mode","selfAssesment":"New
"},{"code":"PS2-2-2-3-2-3-3","description":"In the ScanSAR acquisition mode, the antenna beam is successively steered to different elevation angles. This results in adjacent, slightly overlapping stripes, or sub-swaths along the range direction, parallel to the azimuth direction, each stripe having a different incidence angle at its center. During antenna steering in elevation, transmitter and receiver are off. Therefore, each stripe is illuminated for a shorter time as for the StripMap mode, leading to a degradation of the azimuth resolution. However, ScanSAR allow a larger coverage in range direction than the other imaging modes. Each sub-swath is illuminated for a shorter time than in the Stripmap case. The timing is adjusted though, such that the time-varying antenna footprint repeat cyclically. Similar to the other acquisition modes, the achievable resolution and coverage of ScanSAR products depends on the considered sensor and its properties. For X-Band, e.g. for TerraSAR-X, a total swath width of 100 km in range direction can be achieved using four adjacent sub-swaths or, using a Wide ScanSAR mode with six adjacent sub-swaths, a swath width up to 270 km can be achieved. A Wide ScanSAR scene shows incidence angles ranging from 15.6° in near to 49° in far range. The azimuth resolution varies between 18.5 m and 40 m, for ScanSAR and WideScan SAR modes respectively. For the L-Band sensor ALOS-PALSAR 2, a swath width up to 40 km can be achieved, with incidence angles ranging from 8° to 70° and an azimuth resolution of 60 m. \r\nThe ScanSAR mode is well suited for large-area monitoring, e.g. for sea ice or glacier monitoring, as well as for mapping large-scale disasters, such as oil slick, or areas devastated by forest fires. Using interferometry, topography mapping and deformation monitoring is also possible.","name":"ScanSAR","selfAssesment":"New
"},{"code":"PS2-2-2-3-2-3-5","description":"A stereoscopy acquisition mode collects remotely sensed data where each location on the ground (or the imaged objects) is covered multiple times (at least twice), from different perspectives. Stereopairs and stereoscopic coverage enable the extraction of 3D representations of the environment from remotely sensed imagery. Most aerial photographs are taken with frame cameras along flight lines, or flight strips. [...] Successive photographs are generally taken with some degree of endlap [, i.e. overlap]. Not only does this lapping ensure total coverage along a flightline, but an endlap of at least 50 percent is essential for total stereoscopic coverage of a project area. Stereoscopic coverage consists of adjacent pairs of overlapping vertical photographs called stereopairs. Stereopairs provide two different perspectives of the ground area in their region of endlap [overlap]. When images forming a stereopair are viewed through a stereoscope, each eye psychologically occupies the vantage point from which the respective image of the stereopair was taken in flight. The result is the perception of a three-dimensional stereomodel. As an input to photogrammetry analysis procedures, stereopairs from flight strips enable the extraction of digital elevation models (DEM), orthophotos, thematic GIS data, and other derived products through the use of digital raster images and relatively sophisticated analytical techniques. With the availability of close-range UAV and terrestrial hand-held camera data, 3D reconstructions of buildings (even indoors) and other objects on the terrain surface become possible.","name":"Stereoscopy","selfAssesment":"In progress (to be deleted, merged?)
"},{"code":"PS2-2-2","description":"Since the 1940s aerial imagery has been the primary source of detailed geospatial data for extensive study areas. Photogrammetry is the profession concerned with producing precise measurements from aerial imagery. Aerial imaging and photogrammetry represent a major component of the geospatial industry. The topics included in this unit do not comprise an exhaustive treatment of photogrammetry, but they are aspects of the field about which all geospatial professionals should be knowledgeable.","name":"Airborne platforms and systems","selfAssesment":"New
"},{"code":"PS2-2-3-1","description":"Earth observation (EO) missions are gathering information about the physical, chemical, and biological systems of the planet via remote-sensing technologies, supplemented by Earth-surveying techniques, which encompasses the collection, analysis, and presentation of satellite data.","name":"Earth observation missions","selfAssesment":"In progress
"},{"code":"PS2-2-3-2","description":"There are essentially three types of Earth orbits: high, medium and low Earth orbit. Satellites that orbit in a medium (mid) Earth orbit include navigation and specialty satellites, designed to monitor a particular region. Most scientific satellites, including NASA’s Earth Observing System fleet, have a low Earth orbit. On which orbit a satellite will be launched to, depends mainly on its application. The orbit types can be categorized according to their height.\r\nThe orbit height of a satellite corresponds to the distance between the Earth’s surface and the satellite. It determines its speed as it rotates around the Earth. Due to Earth’s gravity, the pull of gravity is stronger for lower orbits than for higher orbits. Therefore, a satellite situated on a lower orbit will circle the Earth faster than a satellite situated on a higher orbit.\r\n\tHigh Earth orbit: it describes orbits situated at about 36000 km above the Earth’s surface (42164 km from the Earth’s center). At this exact distance, the speed of the satellite on the orbit matches the Earth’s rotation, i.e. the satellite needs 24 hours to complete a full rotation on the orbit, when the orbit is situated exactly above the equator. Such orbits are also called geosynchronous orbits, as the satellite moves at the same speed than the Earth and seems to stay in place over a specific location. Those orbits are mainly used for weather and communication satellites\r\n\tMedium Earth orbit: it describes orbits situated at about 20200 km of the Earth’s surface, or 26560 km of the Earth’s center. At this height, a satellite rotates twice around the orbit during one Earth’s rotation. This orbit is also called semi-synchronous and this is the orbit type used by Global Navigation Satellite Systems such as GPS and GLONASS. A further important medium Earth orbit is the Molniya orbit which allows the observation of the poles, otherwise nearly impossible with equatorial geosynchronous orbits.\r\n\tLow Earth orbit: this type of orbits are used from almost all dedicated scientific Earth Observation satellites. Most of them use a particular, nearly polar orbit inclination, meaning that the satellite rotates around the Earth nearly from pole to pole (instead of around the equator as it is the case for geosynchronous satellites). This rotation takes about 99 minutes, depending of the specific orbit inclination. During one half of the orbit, the satellite views the daytime side of the Earth, i.e. the illuminated side. At the pole, satellite crosses over and views the nighttime side of Earth. Back to the daylight side, the satellite can view the area adjacent to the region flown over in the last orbit path, due to the simultaneous Earth’s rotation. In 24 hours, satellites situated on these orbits view almost all the Earth twice, for optical satellites once in daylight and once in the dark. Radar satellites seen each Earth region twice, from two different illumination directions. These specific polar-orbits are called sun-synchronous, as the local solar time stays the same each time a satellite flies over a specific region. This has the advantage of providing an almost constant angle of sunlight for each region on the Earth’s surface viewed by the satellite over time and ensure repeatable sun illumination conditions; the angle will only vary seasonally due to the Earth revolution around the sun. Due to this consistency, images of a specific region would not show much illumination changes due to shadows or sunlight and image interpretation over time such as change detection or monitoring approaches are possible. Because a sun-synchronous orbit does not pass directly over the poles, there is a data gap over both poles where no data is acquired.","name":"Types of satellite orbits","selfAssesment":"Completed
"},{"code":"PS2-2-3-3","description":"An imaging SAR system can generally make acquisitions in different modes. Which acquisition mode to choose depends of the application but also on the desired coverage and data resolution. Even if technically all acquisitions modes can be used everywhere on the Earth’s surface, specific modes are preferred for ocean applications that are different from the ones used in land applications.\r\nThe different acquisition modes can be defined either by their geometrical or by their temporal properties.\r\nThe geometrical properties refer to the geometric configuration of the SAR antenna. Usually looking sideways down in a direction perpendicular to the flight direction (Stripmap mode), the antenna can also be steered around the nadir axis in order to look at a specific target for a longer time during pass-by (Spotlight mode). This configuration allows to rachieve higher azimuth resolution but reduces coverage. It is rather used for very local application where a precise information about specific targets is needed. Other geometric configurations steer the antenna around the flight direction (ScanSAR mode), yielding to a larger swath on the ground. The distance between near and far range is increased, as well as the range of incidence angles within one acquisition. Whereas it increases the area of the scene, it comes generally with a decrease of the spatial resolution in the azimuth direction. Depending on the sensors, the name of the acquisition modes as well as particular technical properties can differ. Sentinel-1 uses a TOPS configuration (Terrain observation with Progressive Scan), which combines the antenna steering properties of both ScanSAR and Spotlight modes. \r\nThe temporal properties refer for specific techniques to the time interval between several acquisitions of the same area. Either these acquisitions are taken simultaneously in one pass over the area of interest (single-pass), or they are taken at different times, needing several passes over the area (repeat-pass).\r\nSpecific SAR techniques such as InSAR and Tomography, while relying on those geometric and temporal properties, have additional acquisition configuration characteristics. For example, the interferometric mission TanDEM-X has three acquisition modes defined by the number of satellite emitting or receiving the signal (pursuit monostatic mode, bistatic mode, alternating bistatic mode), which allows phase referencing. Tomographic SAR uses multi-baseline observations, i.e. the antenna passes several times over an area but at different heights, allowing via different incidence angles the retrieval of structural information of specific targets.","name":"Synthetic Aperture Radar (SAR) acquisition modes","selfAssesment":"Completed
\r\n\r\n"},{"code":"PS2-2-3-4","description":"Swath width refers to the width of the ground that the satellite collects data from on each orbit. The area imaged on the surface, is referred to as the swath. Imaging swaths for spaceborne sensors generally vary between tens and hundreds of kilometres wide.","name":"Swath","selfAssesment":"
In progress
"},{"code":"PS2-2-3","description":"Spaceborne platforms and systems are present at a great height from the earth surface. The altitude of platforms range from few hundred kilometers to several thousand kilometers. A large area can be captured in a single scene depending on altitude of sensor. The platforms can have different characteristics.","name":"Spaceborne platforms and systems","selfAssesment":"Planned
"},{"code":"PS2-3-1","description":"Field spectroscopy generally refers to the use of non-imaging spectrometers near the ground surface and it is usually aimed at evaluating spectral reflectance of the investigated target. For this purpose, consecutive measurements of total incident solar irradiance and of radiance or irradiance upwelling from the target are collected by an operator, or more recently by new instruments for long-term and unattended field spectroscopy measurements. The incident irradiance is usually computed by measuring the radiance upwelling from a white calibrated panel which represents the ideal Lambertian surface. Upwelling fluxes are instead usually collected holding the sensor vertically over the surface (nadir view), although spectral libraries collected observing the target from different viewing angles are also available. \r\nField spectrometry is also referred to as ‘proximal sensing’ to underline that spectra are collected with portable spectroradiometers in the vicinity of the target, in contrast to ‘remote sensing’, which is instead usually performed with satellite or airborne sensors.\r\nField spectroscopy is therefore an in-situ method for characterising the reflectance of natural or artificial surfaces and thereby provides reference data for the calibration or validation (cal/val) of airborne and satellite sensors. This method provides a means of scaling-up measurements from small areas (e.g. leaves, rocks) to composite scenes (e.g. vegetation canopies), and ultimately to pixels.\r\nField spectroscopy is used in different applications, for example, soils, rocks, vegetation and chlorophyll fluorescence, water, snow surfaces and atmosphere. Long-lasting field spectroscopy campaigns based on manual measurements are extremely resource-demanding and do not ensure repeatability of the acquisition conditions as the instrument setup is initialized each day. To overcome such limitations a few research groups have initiated automatic tower-based spectral reflectance measurements using different devices. With such setups, non-imaging spectrometers are installed in the field and are operated automatically for long periods (i.e. months to years) and different networks of hyperspectral instruments are now becoming operational (e.g. RadCal Net).\r\nField spectroscopy can be also used to predict optimum spectral bands, viewing configuration, spectral calibration and time to perform a particular remote sensing task but also to develop, refine and test models relating biophysical attributes to remotely-sensed data. In this context, ground reflectance measurements are therefore mainly used as input in simulation study for sensor design, calibration/validation data for remote sensing sensors, for spectral mixture analysis and for the development of relationships between field data and radiometric variables.\r\nSince spectroscopy is the study of matter using electromagnetic radiation, point or imaging field spectrometers are instruments which allow the measurements of reflected or emitted electromagnetic radiation. In particular, portable or hand-held spectroradiometers are small instruments that spectrally measure the radiation reflected or emitted by a target and they are useful in obtaining accurate spectral data over different surfaces. In remote sensing, they generally cover the 400-2500 nm spectral range and operate with a full width at half-maximum of about 1.5/3 nm, so that they can collect radiation in a continuous way across the spectrum. The final output is therefore the hyperspectral signature of reflectance of the surfaces versus the considered wavelength.","name":"Field spectroscopy and portable spectroradiometers","selfAssesment":"Completed
"},{"code":"PS2-3-2","description":"A terrestrial laser scanning (TLS) system is a stationary highly accurate ranging device for geodetic surveying. More specifically, TLS systems provide dense and accurate 3D point cloud data for the local environment and they may also reliably measure distances of several tens of meters. Due to these capabilities, such TLS systems are commonly used for applications such as city modeling, indoor modeling, construction surveying, deformation analysis, scene interpretation, urban accessibility analysis, or the digitization of cultural heritage objects. When using a TLS system, each captured TLS scan is represented in the form of a 3D point cloud consisting of a large number of scanned 3D points and, optionally, additional attributes for each 3D point such as color or intensity information. However, a TLS system represents a line-of-sight instrument and hence occlusions resulting from objects in the scene may be expected as well as a significant variation in point density between close and distant object surfaces. Thus, a single scan might not be sufficient in order to obtain a dense and (almost) complete 3D acquisition of interesting parts of a scene and, consequently, multiple scans have to be acquired from different locations. As each scan refers to the local coordinate system of the TLS system, all acquired scans have to be appropriately aligned in a common coordinate system. For this purpose, the respective 3D transformations between the acquired scans have to be estimated and this process is commonly referred to as point cloud registration, point set registration, or 3D scan matching.","name":"Terrestrial Laser Scanning","selfAssesment":"Completed
"},{"code":"PS2-3","description":"Platforms and systems that acquire data from the level of earth's surface. A wide variety of ground based platforms are used in remote sensing. The acquired data are used for detailed in-situ measurements, e.g., Leaf Area Index (LAI), and for calibration/validation campaigns.","name":"Ground platforms and systems","selfAssesment":"New
"},{"code":"PS2","description":"Remote sensing platforms and systems can be static (ground-based platforms) or moving (e.g. airborne or spaceborne platforms, UAVs). A remote sensing platform or system carry a remote sensing sensor. It can operate in near (few centimetres) or far (36,000 kilometres) altitudes ranges.","name":"Types of remote sensing platforms and systems","selfAssesment":"Planned
"},{"code":"PS3-1","description":"The development of remote sensing data carriers has followed the evolution of the photography, remote sensing sensors and computer platforms. The first remote sensed data was stored using the photography films (e.g. aerial photography, satellite Corona program), which was later replaced by reel tapes, cartridge, and then removable and hard discs. In the era of big and fast growth of Earth observation data, and technological advancements in digital infrastructure, the satellite data are stored using cloud platforms providing different service models: Infrastructure as a Service, Platform/Software as a Service (e.g. Copernicus DIAS, Google Earth Engine, open EO). The Cloud offers infrastructure to host, store and process the large amount of data efficiently. For example, the Copernicus Data Information Access Services (DIAS) is a comprehensive cloud-based hosting and processing system for the EO data in particularly for the Sentinels data, the Google’s Earth Engine (GEE) provides access to various satellite and offers processing power with a web-based programming interface, the Amazon Web Services (AWS) has dedicated cloud called ‘Earth on AWS’, the Microsoft’s cloud called Azure facility the use of AI tools to address environmental challenges. Public solutions, as well as private ones, react with a variety of new and innovative tools, which have been recently developed (e.g. DIAS, ODC, EarthServer, EO Browser, GEE).","name":"History of remote sensing data carriers","selfAssesment":"Completed
"},{"code":"PS3-2-1","description":"Most remotely sensed images nowadays exist in digital form. Even domestic cameras are now usually digital instruments, and the use of photographic film is becoming rarer and rarer. Analogue images, such as photographs, are continuous, both in their spatial extent (they can be enlarged almost without limit) and radiometrically (there is a continuous range of shades of grey). The word ‘picture’ is usually used for such an image.\r\nOn the other hand, a digital image is spatially and radiometrically discrete. A remote sensing sensor detects the reflected radiation of the Earth’s surface and stores it as numbers in a raster. In accordance, each area that has been detected constitutes a cell in a raster. The grey levels increment in a stepwise fashion, and the scene is made up from an array of individual elements called ‘picture elements’, abbreviated to ‘pixels’, each of which is represented by one of the discrete grey levels. A pixel is the smallest addressable element in a raster image.\r\nThe spatial resolution of a raster image refers to the size of the ground element represented by an individual pixel. The size of an area represented in a pixel depends of the capability of the sensor to detect details. A pixel cannot be subdivided, and enlargement merely produces larger pixels, which contain no more information than the original ones. We are familiar with this effect on our television or computer screen – the picture we see consists of an array of dots of light, the density of which determines the screen resolution.\r\nThe number of distinct grey levels into which the intensity of the signal is divided and that can be represented by a pixel is called radiometric resolution of a digital image, and it depends of the number of bits per pixel (bpp). A 1 bpp image uses 1 bit for each pixel, so each pixel can be either on or off (monochrome). Each additional bit doubles the number of grey levels available, so a 2 bpp image can have 4 grey levels, a 3 bpp image can have 8 grey levels, and so forth. In colour imaging systems, a colour is typically represented by three component intensities such as red, green, and blue; usually their raster images have an 8-bit resolution (256 grey levels), a 16-bit resolution (65,536 grey levels), or a 24-bit resolution (16,777,216 grey levels).","name":"Picture element (pixel)","selfAssesment":"Completed
"},{"code":"PS3-2-2","description":"One can think of any image as consisting of tiny, equal areas, or picture elements, arranged in regular rows and columns. The position of any picture element, or pixel, is determined on an xy coordinate system. Each pixel also has a numerical value, called a digital number (DN), that records the intensity of electromagnetic energy measured for the ground resolution cell represented by that pixel. Digital numbers range from zero to some higher number on a gray scale. The image may be described in strictly numerical terms on a three-coordinate system with x and y locating each pixel and z giving the DN, which is displayed as a gray-scale intensity value. \r\nMany types of remote sensing images are routinely recorded in digital form and then processed by computers to produce images for interpreters to study. An image recorded initially on photographic film may be converted into digital format by a process known as digitization.","name":"Image as a matrix (digital number DN)","selfAssesment":"Completed
"},{"code":"PS3-2-3","description":"In data manipulation contexts, a data cube is a multi-dimensional array of values. A data cube can be visualized as the multidimensional extension of two-dimensional table. It can be viewed as a collection of identical 2-D tables stacked upon one another. Data cubes are used to represent data that is too complex to be described by a traditional table of columns and rows. Typically, the data cube is applied in conditions where these arrays are massively larger than the hosting computer’s main memory, for example multi-terabyte data warehouses o time series of image data.","name":"Data cubes","selfAssesment":"In progress
"},{"code":"PS3-2-4","description":"Term Big data refers to any collection of data sets so large and complex that it becomes difficult to process using on-hand data management tools or traditional data processing applications. In the field of Earth Observation (EO) is usually refers to large time series of image data which size on disk is much greater than hosting computer’s main memory. EO Big Data offers solution that allows not only storing these data on disk but also efficiently process them.","name":"Earth Observation Big Data","selfAssesment":"In progress
"},{"code":"PS3-2","description":"Most remote sensing data exist as digital images, and appropriate image processing allows the emphasis of certain aspect and subsequent extraction of information for specific applications.\r\nA digital image is a representation of the reality as a grid of picture elements. It can be considered as an array of numbers that can be stored and handled by a digital computer. The picture elements are pixels and each pixel has a specific value (usually in grayscale). This value is a digital number (DN), which usually represents the amount of energy recorded by the sensor at this pixel position or any other characteristic recorded by the sensor, e.g. elevation. \r\nEach row of the image grid, or matrix, corresponds to one scan line. Each pixel is characterized by its row r and column c position in the image, as well as by its value. Additional geographical information is needed in order to assign a geographic location to a pixel. The digital number are integers usually compressed in one byte (= 8 bit) representation, i.e. each pixel can take 256 values.\r\nDigital images are raster data, as opposite to vector data. Whereas vector data can be points, lines or polygones, raster data always consist of pixels. A pixel is the smallest element in which an image can be divided into. The pixel size varies depending of the instrument and of the sampling used. Large pixel may contain information about several objects of the recorded scene. However, they only have one value. These are called mixed-pixel, as e.g. several land cover classes are represented within one pixel and they cannot be distinguished from another. \r\nIn multispectral imagery, each region of the electromagnetic spectrum is recorded in an independent image (band). Therefore, at a specific array position (r,c), there exist several pixels, each with a specific value corresponding to the energy recorded for the considered band. This result in a three-dimensional matrix. The bands of a multispectral image can be displayed three at a time in the computer using for each band one of the three primary colors red, green and blue (RGB). This is called a color composite image. If the color composite represents a combination of the visible red, green and blue bands in their respective color, the combination is called natural or true color composite, as it corresponds to what the human eye sees naturally. Any other combination, for example considering bands of wavelengths that are not visible for the human eye is called a false color composite. It is often used to highlight the spectral differences and particular image features in order to extract information.","name":"Digital image terminology","selfAssesment":"Completed
"},{"code":"PS3-3-1","description":"Band interleaved by line (BIL) is one of three primary methods for encoding image data for multiband raster images in the geospatial domain, such as images obtained from satellites. This simple uncompressed raster data encoding is easily and frequently described, requiring no formal specification. BIL is not in itself an image format, but is a scheme for storing the actual pixel values of an image in a file band by band for each line, or row, of the image. The raw data has a simple form and is easily interpreted if the image dimensions in pixels, the number of spectral bands, and the number of bits per band are known. For example, given a three-band image, all three bands of data are written for row one, all three bands of data are written for row two, and so on. The BIL encoding is a compromise format, allowing fairly easy access to both spatial and spectral information. The BIL data organization can handle any number of bands, and thus accommodates black and white, grayscale, pseudocolour, true colour, and multi-spectral image data.\r\nAdditional information is needed to interpret the image data, such as the numbers of rows, columns, and bands, and relate the image to geospatial locations. This information may be supplied in a file header (typical on the tapes originally used for satellite image data) or in files associated with a raw image data file.\r\nSpatial resolution and bit-depth are not limited by the BIL encoding per se but may be constrained in some usage contexts. There is no support for colour management in the BIL encoding. Documentation of spectral values for bands, or interpretation of false colours should be supplied in an accompanying data structure.","name":"Band interleaved by line (BIL)","selfAssesment":"Completed
"},{"code":"PS3-3-2","description":"Band interleaved by pixel (BIP) is one of three primary methods for encoding image data for multiband raster images in the geospatial domain, such as images obtained from satellites. This simple uncompressed raster data encoding is easily and frequently described, requiring no formal specification. BIP is not in itself an image format, but is a method for encoding the actual pixel values of an image in a file. The raw data has a simple form and is easily interpreted if the image dimensions in pixels, the number of spectral bands, and the number of bits per band are known. Images stored in BIP format have the first pixel for all bands in sequential order, followed by the second pixel for all bands, followed by the third pixel for all bands, etc., interleaved up to the number of pixels. The BIP data organization can handle any number of bands, and thus accommodates black and white, grayscale, pseudocolour, true colour, and multi-spectral image data.\r\nBIP data stores pixel information for separate bands within the same file, so that the user can choose to display just one specific band in a multi-band image. Therefore, BIP encoding provides optimal processing performance for spectral analysis (as compared with BIL or BSQ raster organization) as it supports efficient extraction of individual spectra and spectral averages.\r\nAdditional information is needed to interpret the image data, such as the numbers of rows, columns, and bands, and relate the image to geospatial locations. This information may be supplied in a file header (typical on the tapes originally used for satellite image data) or in files associated with a raw image data file.\r\nSpatial resolution and bit-depth are not limited by the BIP encoding per se but may be constrained in some usage contexts. There is no support for colour management in the BIP encoding. Documentation of spectral values for bands, or interpretation of false colours should be supplied in an accompanying data structure.","name":"Band interleaved by pixel (BIP)","selfAssesment":"Completed
"},{"code":"PS3-3-3","description":"Band sequential (BSQ) is one of three primary methods for encoding image data for multiband raster images in the geospatial domain, such as images obtained from satellites. This simple uncompressed raster data encoding is easily and frequently described, requiring no formal specification. BSQ is not in itself an image format, but is a method for encoding the actual pixel values of an image in a file. BSQ format is a very simple format, where each line of the data is followed immediately by the next line in the same spectral band. The raw data has a simple form and is easily interpreted if the image dimensions in pixels, the number of spectral bands, and the number of bits per band are known. This format is optimal for spatial (x, y) access of any part of a single spectral band. The BSQ data organization can handle any number of bands, and thus accommodates black and white, grayscale, pseudocolour, true colour, and multi-spectral image data.\r\nA single band covering the entire scene is stored as a single bitstream making this encoding method convenient when only selected bands are needed. Each image band can be conveniently written to an independent file. BSQ can therefore be a preferable format for some forms of analysis as an application does not have to read past ancillary data in an image stack. As opposed to formats where the bands are interleaved (such as a multi-band GeoTIFF), BSQ data sets support convenient extraction of relevant bands. Some BSQ datasets are distributed as separate image files for each band, with common geospatial registration and a shared set of header information.\r\nAdditional information is needed to interpret the image data, such as the numbers of rows, columns, and bands, and relate the image to geospatial locations. This information may be supplied in a file header (typical on the tapes originally used for satellite image data) or in files associated with a raw image data file.\r\nSpatial resolution and bit-depth are not limited by the BSQ encoding per se but may be constrained in some usage contexts. There is no support for colour management in the encoding. Documentation of spectral values for bands, or interpretation of false colours should be supplied in an accompanying data structure.","name":"Band sequential (BSQ)","selfAssesment":"Completed
"},{"code":"PS3-3","description":"EO data consist of unstructured image data and structured descriptive information attached to the image, which is also called metadata. EO systems are rapidly developing and data sensors resolution are continuously improving. As a result, a vast amount of EO data is generated every day, and their volumes have been in geometric progression growth. According to the current literatures, storage and management methods of EO data are divided into four groups from the perspective of basic technologies: \r\n1. File systems: Traditionally, EO data were manually managed and organized by means of file systems that share and exchange data through storage devices. However, for large amounts of EO data this method leads to inefficiency of management, extra expenses of storage spaces, and weak data security. File systems cannot efficiently support for data retrievals, analyses, and uses in practical applications and research work nowadays. For solving these problems, parallel file system and distributed file system (see below) were presented to support data-intensive applications.\r\n2. Relational Data Base Management Systems (RDBMS): At present, storage and management manners of major EO data are to combine RDBMS and middle-wares. On one hand, traditional RDBMS functionalities are expanded to adapt to the storage and management features of EO data. Adding new data types or encapsulating complicate data types as an object in RDBMS are two general ways to expand functionalities of traditional RDBMS. The former can meet basic requirements of EO data storage and management, but is unable to directly operate spatial data and create spatial indexes. This solution is mainly taken by Database Management System (DBMS) developers, such as Spatial GeoRaster of Oracle, Spatial Extender of IBM DB2, PostGIS of PostgreSQL, and Spatial Extension of MySQL. On the other hand, geographical software expands their data management abilities by developing spatial database engine middle-wares, which is always taken by software enterprises that develop geographical information system (GIS). Spatial Database Engine (SDE) is between users and DBMS. For data storing, SDE is responsible for receiving and storing user data into RDBMS; for data retrieving, it reads data from RDBMS and show them through user interfaces. This resolution stores EO data into RDBMS and interactively manages them by user interfaces provided by SDE. SDE technology is very mature and extensively used in various application fields. As SDE is developed by software enterprises of GIS, they have good comparability with integrated software platform of GIS. \r\n3. Distributed file systems: Recently distributed file system is a new technology of solving data-intensive computing problems. Several distributed file systems have emerged such as PVFS, GPFS, ZFS, GFS, HDFS, and Lustre. \r\n4. Large-scale network storage systems: It is a type of distributed file system with data sharing and remote access functionalities. As the performance improving of hardware and rapid development of network technologies, Storage Area Network (SAN) and Network Attached Storage (NAS) are introduced to distributed file systems. Large-scale network storage systems take different storage and management strategies for EO image files and their metadata. EO image files are stored and managed by HDFS, and their metadata are stored, processed, and managed in RDBMS metadata servers. Managing EO imagery files and their metadata in different ways can improve the management efficiencies of EO data, and balance the loading of distributed file systems. Such systems have already been developed including Celerra, CLARIION, and Symmetric storage solution of EMC, IBM HPSS, MSS, and RASCHAL of National Aeronautics and Space Administration (NASA), the Microsoft earth image storage system, and the Google Earth image storage system.","name":"Data storage","selfAssesment":"Completed
"},{"code":"PS3-4-1","description":"The spectral resolution of an Earth Observation sensor refers to the number of spectral bands this sensor can capture. Spectral bands are wavelength intervals in the electromagnetic spectrum. Sometimes, spectral bands are also called spectral channels. Spectral resolution is related to a sensor’s ability to distinguish between different Earth’s surface features based on their spectral properties. A high number of spectral bands means high spectral resolution, with many bands meaning an increasingly smaller range of wavelengths covered by a single band. The spectral resolution of an Earth observation sensor can range from a single very broad band for panchromatic black and white images over a few bands in the case of multispectral sensors (e.g. Landsat family, SPOT, Sentinel-2) to 200 or even more channels for capturing hyperspectral images. Multispectral or hyperspectral sensor imagery has a higher degree of discriminating power than a single band sensor. Another definition of the spectral resolution can be given by the spectral sensitivity of a sensor, which can be specified by the definition of the full width, half maximum (FWHM) as being the spectral interval measured at the level at which the response of the instrument reaches one-half of its maximum values.\r\nSpectral satellite sensors can only gather radiation which is able to pass the Earth’s atmosphere. The atmosphere contains gases, aerosols, ice crystals and water droplets, which absorb and scatter parts of the radiation passing through the atmosphere. Wavelength ranges which do not allow radiation to pass through on their way to the satellite sensors are called absorption bands and those getting through to the sensor are called atmospheric windows. This means that spectral sensors can only operate in these atmospheric windows and the spectral bands should be placed in the wavelength ranges of the atmospheric windows.","name":"Spectral resolution","selfAssesment":"Completed
"},{"code":"PS3-4-2","description":"The spatial resolution of an image corresponds to the size of the minimum area that can be resolved by the sensor. \r\nDue to the different techniques of acquisition of passive and active sensors, the spatial resolution is determined for both sensor types differently. \r\nFor passive sensors, the spatial resolution depends on their instantaneous field of view (IFOV), which determines the area of the Earth’s surface that is viewed at one particular moment in time by one detector element. The size of this area is called resolution cell and characterizes the spatial resolution of the sensor. Depending on the spatial resolution, whole features of the Earth’s surface can be detected homogeneously in one or several resolution cells. For features smaller than the spatial resolution, the average reflected radiation of all features within a resolution cell is recorded, leading to so-called mixed-pixels.\r\nFor imaging active systems, the spatial resolution is dependent of both the length of the transmitted pulse in looking direction and the width of the radiation beam or the antenna width in flight direction.\r\nIn all cases, the spatial resolution indicates the level of detail observable in an image. Usually, one distinguishes between coarse (low), moderate (medium) and fine (high and very high) resolution, whereby the use of this denomination is often context-dependent. Sensors with coarse resolution can only detect large features, but they usually cover a much larger area than high-resolution sensors, which can provide detailed information on small objects such as individual buildings, trees or cars, but for much smaller areas. Coarse spatial resolution mean in general a resolution cell larger than 250 m and a scene extent of several thousands of kilometers (>1000 km). Moderate resolution sensors have a spatial resolution of 30 m to 80 m, and a coverage of approximately 200 km in a single acquisition. Sensors showing spatial resolutions from 5 m or 6 m are high-resolution sensors, with a spatial coverage up to approximately 20 km. Sensors with a resolution cell’s width of less than 1 m are considered as very-high-resolution sensors.\r\nLow resolution sensors are appropriate for the analysis of broad-scale phenomena such as ocean color or cloud patterns. Medium resolution sensors are rather used for regional analysis such as land cover change and phenological response of vegetation. High-resolution sensors are particularly useful for object detection.","name":"Spatial resolution","selfAssesment":"Completed
"},{"code":"PS3-4-3","description":"The radiometric resolution of a sensor refers its sensitivity, which is the ability to detect small differences in signal strength as it records the radiant flux reflected, emitted, or back-scattered from the terrain.\r\nThe specification of the radiometric resolution is different in the optical domain of the electromagnetic spectrum than in the radar range.\r\nIn the optical domain, the radiometric resolution is given in bits. The maximum number of brightness levels available depends on the number of bits. The larger this number, the higher the radiometric resolution. As an example, the optical sensor Sentinel-2 has a radiometric resolution of 12 bits. This means that a pixel of an image acquired by Sentinel-2 can have 2^12 = 4096 grey levels.\r\nIn the radar domain, the radiometric resolution is usually specified as a backscatter level expressed as an logarithmic value. For instance, the radiometric resolution of Radar Scattermeters lies in the range of 0.1 to 0.3 dB, whereas the radiometric resolution of SAR sensors are in the range of 1.2 – 2.5 dB. This means that only differences in radar backscatter larger than these values can be interpreted as interpretable changes the of backscatter conditions at the Earth’s surface. Smaller measurement differences could have been caused by differences in backscatter conditions or just as well by instrument noise.","name":"Radiometric resolution","selfAssesment":"Completed
"},{"code":"PS3-4-4","description":"The concept of temporal resolution of Earth observation data refers to the revisit time or period. This is the time, which is necessary for the sensor platform (e.g. a satellite) to complete one entire orbit cycle. During one orbit cycle, the surface of the earth is completely covered by the sensor once. Temporal resolution also means the ability of a sensor to detect changes over shorter or longer periods of time. The revisit time for Earth observation satellites is usually several days. Or to express it differently: The absolute temporal resolution of a sensor orbiting the Earth is the time required to image the exact same area at the same viewing angle a second time. \r\nThe satellite orbit itself depends on the radius of the Earth, the orbit altitude above the Earth’s surface and the gravitational acceleration at planet’s surface. The time required to complete on entire orbit cycle additionally depends on the swath width of the sensor, the overlap between adjacent swaths and the geographical location at the Earth’s surface. The repetition rate increases slightly from the equator towards the north and south, which means that the revisit time is increasing with latitude. As a result, areas located in North America or Australia, for example, are covered a little more frequently than areas in Africa or South America near the equator. \r\nBut there are satellite systems that allow the pointing of their sensor to image the same area between different satellite passes separated by periods from one to five days. Thus, the actual temporal resolution of a sensor depends on a variety of factors, including the satellite/sensor capabilities, the already mentioned swath width and overlap, and latitude.","name":"Temporal resolution","selfAssesment":"Completed
"},{"code":"PS3-4","description":"A digital image begins as an analog signal. Through computer data processing, the image becomes digitized and is sampled multiple times. The critical characteristics of a digital image are spatial resolution, spectral resolution, radiometric resolution, contrast resolution, noise, and dose efficiency. These depends upon satellite orbit configuration and sensor design. Different sensors have different resolutions.\r\nSpectral resolution describes the ability of a sensor to define fine wavelength intervals. The narrowest spectral interval that can be resolved by an instrument. Spectral resolution (spectral capability) also refers to the number of wavebands within the EM spectrum that an optical sensor is taking measurements over.\r\nRadiometric resolution can be defined as the ability of an imaging system to record many levels of brightness. Radiometric resolution refers to the range in brightness levels that can be applied to an individual pixel within an image, determined on a grayscale. E.g., Sentinel-2 sensor MSI is a 12 bit sensor imaging with 4.096 levels.\r\nSpatial resolution of an image corresponds to the size of the minimum area that can be resolved by the sensor.\r\nTemporal resolution, also referred to as the revisit cycle, is defined as the amount of time it takes for a satellite to return to collect data from exactly the same location on the Earth. Imageing of the exact same area at the same viewing angle a second time is temporal resolution.","name":"Properties of digital imagery","selfAssesment":"Completed
"},{"code":"PS3-5-1","description":"A header file is usually a separate file associated with an image file. The header file can be either a plain ASCII-file or a binary file. It contains information about the image file it is associated with. These information can comprise the number of pixels per row (x-direction in a two-dimensional image), also called number of columns, the number of lines or rows (y-direction in a two dimensional image), the number of bands (corresponding to the z-direction), pixel spacing and spatial resolution, geographic reference information, the byte order (e.g. big-endian or little-endian), spectral information for each band, calibration constants and many more. The purpose of a header file is to provide basic information about the properties of the image data either to the user or to a software and enabling a software to correctly load and display the image content. In this way, information contained in a header file can also be called metadata, which is data about the data. The structure and the information contained in a header file of remote sensing imagery can be found in the so-called product information documents. There is also digital imagery used in remote sensing containing the information found in header files not in a separate file but as part of the digital image data itself. In this case this is called header information or a file header, which is usually found at the beginning of the image file. In some cases, image files may contain several header sections, e.g. the ESA Envisat ASAR SAR data imagery contains a Main Product Header and a Specific Product Header section. Header information as part of the image file itself may be stored in ASCII or in binary format, or in a mixed binary format, as it was used for the ESA Envisat SAR data.","name":"Header file","selfAssesment":"Completed
"},{"code":"PS3-5","description":"The image data stored in a binary data format (BIL, BIP, BSQ) is accompanied by description files that contain a set of entries describing the image data, including acquisition time, image size, statistics, map projection, pixel digital numbers, product type, etc. This general image or product information is stored in a form of header embedded in the image file or provided in the separate file (.hdr) or metadata in XML. There are numerous image file formats, the more common are TIFF (GeoTIFF), bitmap (.bmp), JPEG (.jpg, .jpeg, JPEG2000), HDF, Raw (.raw), Extensible N-Dimensional Data Format (NDF).","name":"Image description files","selfAssesment":"In progress
"},{"code":"PS3-6","description":"The concept of data formats refers to the way, in which the digital data are organized and stored. The data format for a remote sensing mission is usually chosen based on a number of considerations, including requirements of the sensing system, mission objective, the design and technology of data processing, archiving, and distribution systems, as well as community data standard.\r\nEarth observation data usually come as raster data. The raster data refers to a data model, which holds digital numbers or values in a regularly spaced matrix of cells arranged in rows and columns covering a two-dimensional space. A digital Earth observation image may contain several layers of this two-dimensional space, e.g. one layer for a specific spectral band in the optical or microwave region of the electromagnetic spectrum. The cells in such a layer are also called pixels, which stands for picture element. \r\nEarth observation data in an image are stored on a storage medium in one of three formats: Band-Interleaved-by-Sample (BIS), Band Sequential (BSQ), or Band-Interleaved-by-Line (BIL). These formats are determined by different ordering of the data dimensions. Other data formats used in remote sensing, which in this case refer to the file format are GeoTIFF, NetCDF, and HDF.\r\nExact details on the data format of an Earth observation data set is usually provided by the originator of the data, e.g. space administrations such as NASA or ESA or private companies.","name":"Data formats","selfAssesment":"Completed
"},{"code":"PS3-7-1-1","description":"Depending on the sensor and the provider, remotely sensed imagery is made avalilable to the user at different processing levels. For Sentinel-2, the lowest product level made available to the user is Level-1B. THe Level-1B product provides radiometrically corrected imagery in Top-Of-Atmosphere (TOA) radiance values and in sensor geometry. Radiometric corrections applied to the Level-1B are: dark signal, pixels response non uniformity, crosstalk correction, defective pixels interpolation, high spatial resolution bands restoration (deconvolution puls denoising), binning (spatial filtering) for 60m bands.","name":"Radiometrically corrected","selfAssesment":"New
"},{"code":"PS3-7-1-2","description":"Geometrically corrected products are of a higher processing level than radiometrically corrected products. For Sentinel-2, the geometrically corrected product is the Level-1C product. The Level-1C product results from using a Digital Elevation Model (DEM) to project the image in cartographic coordinates. Per-pixel radiometric measurements are provided in Top Of Atmosphere (TOA) reflectances with all parameters to transform them into radiances. Level-1C products are resampled with a constant Ground Sampling Distance (GSD) of 10, 20 and 60 m depending on the native resolution of the different spectral bands. Level-1C products will additionally include Land/Water, Cloud Masks and ECMWF data (total column of ozone, total column of water vapour and mean sea level pressure). (Sentinel-2 User Handbook, p.44)","name":"Geometrically corrected","selfAssesment":"New
"},{"code":"PS3-7-1","description":"The definition of processing levels for optical data depends on the considered sensor. Most common satellite optical imagery are available in three distinct processing levels, from level 0 to level 2. The most used processing levels are level 1 and level 2, depending on the user and the application. \r\nIn Level 0, the raw data are processed in a way that they are ready to be archived. Processing operations generally includes telemetry analysis, error detections and granule concatenation. Furthermore, relevant parameters such as acquisition date and geographical reference are annotated in the form of metadata, this information being necessary for processing higher levels. Additionally, a quicklook of the image is generated. No correction is performed at this level.\r\nLevel 1 is often divided in several sublevels. Generally, both radiometric correction and geometric refinement are performed at this level. The radiometric processing includes several radiometric corrections such as dark signal correction or spectral band binning. The radiometric correction allows the determination of physical variables (e.g. reflectance) from the digital numbers. The geometric processing includes tiles association and resampling grid computation, in order to link for each image band its native image geometry to the target geometry. The result of this processing steps is usually a geocoded, Top of Atmosphere product.\r\nLevel 2 data usually consist of atmospherically corrected Level 1 data, i.e. Bottom-of-Atmosphere data. These surface reflectance products may be accompanied by additional outputs, such as scene classification, water vapor or surface temperature maps.\r\nFor specific applications and sensors, Level 3 application ready data are available. These are derivated products such as burned area, dynamic surface water content and snow cover maps.\r\nDepending on the considered sensor and level, the name of the sublevels can differ: Sentinel 2 defines Level-1B as radiometrically corrected data. Level 1C are radiometrically and geometrically corrected data, i.e Top-Of-Atmosphere (TOA) orthoimage products. Landsat sensors distinguish between Terrain precision correction (L1TP), systematic Terrain Correction (L1GT) and Geometric systematic Correction (L1GS) depending on the quality of the reference data for geometric correction. These are usually separated into Tier 1 and Tier 2 datasets.","name":"Processing levels of optical data","selfAssesment":"Completed
"},{"code":"PS3-7-2-1","description":"SLC is an abbreviation and stands for Single Look Complex. SLC data are one so called radar product. Like all radar products they have been derived from SAR raw data, often called Level 0 products, downloaded from the SAR satellite by the satellite operators. They apply a software called a processor to transform SAR raw data into formats that can be used by users for different applications. SLC data are often referred to as Level 1 products and are the first SAR product derived from the raw data to be made available to users.\r\nAs the name suggests, SLC data only contain one single look, which means that the azimuth compression has been carried out using the full azimuth bandwidth of the SAR sensor leading to the highest spatial resolution in azimuth direction. But as a consequence, SLC data suffers from maximum speckle. \r\nThe word “complex” in SLC means that the data are stored as complex numbers with a real and an imaginary part. In this way, SLC data contain both – phase stored in the real part and amplitude information stored in the imaginary part of the complex number for one resolution cell.\r\nSLC data are given in slant-range geometry and appears to be distorted. The is due to the fact that the spacing between pixels in the slant range direction is directly proportional to the signal travel time or time interval between backscattered and received radar pulses. And this time interval in again is directly proportional to the slant range distance between the sensor and the imaged objects at the Earth’s surface and not to the horizontal ground distance between the nadir and the imaged object. Therefore, SLC images appear distorted, which means that they look compressed in near range (close to the nadir) and getting ever more expanded in towards the far range.\r\nSLC data are the basis for further SAR products generated and are required for interferometric analysis methods, which rely on phase and amplitude information.","name":"Single Look Complex (SLC)","selfAssesment":"Completed
"},{"code":"PS3-7-2-2","description":"From the Single Look Complex (SLC) product the Multi-look Detected/Multi-looke (MLD/MLI) can be generated. It is produced by multi-looking, i.e., averaging, over range and/or azimuth resolution cells.","name":"Multi-looked Detected (MLD)","selfAssesment":"New
"},{"code":"PS3-7-2-3","description":"Precision Images (PRI) are the Multi-look Detected/Multi-looked Intensity (MLD/MLI) images that have been resampled into square pixels, rotated to account for the view direction of the instrument and warped by some predefined operation that the projected image pixels are georeferenced onto a specified geographical coordinate system.","name":"Precision Images (PRI)","selfAssesment":"New
"},{"code":"PS3-7-2-4","description":"Ground Range Detected (GRD) radar imagery is a Level-1 product that has been derived from Level 0 (raw data) SLC SAR data by a Processing Facility via the application of a processing software. GRD products usually consist of focused SAR data that has been detected, multi-looked and projected to ground range using an Earth ellipsoid model.\r\nFocused SAR data are generated in a raw data processing step. During focusing, the two-dimensional signal energy of a point target that is spread in range and azimuth direction is aggregated and put into a single image pixel in the output data set.\r\nDetected means that the complex numbers representing phase and amplitude values in the original data set have been converted to real numbers by taking their absolute square (or complex conjugate). In the resulting image data, the phase information is not present any longer and only amplitude information remains as the pixel value.\r\nThe SAR imagery in GRD radar data is given in ground range geometry, which differs from the slant geometry of the SLC data. In ground range geometry, the spacing between the image objects at the Earth’s surface is in direct proportion to their real distance along a hypothetical flat ground surface. Here, image coordinates are oriented along ground range and flight direction. This means that they do not show the distorted appearance of an SLC image.","name":"Groud Range Detected (GRD)","selfAssesment":"Completed
"},{"code":"PS3-7-2","description":"For SAR data, usually three processing levels are distinguished, ranging from level 0 (less processed) to level 2 (higher processed).\r\nLevel 0 products consist of compressed and unfocussed raw data and are the basis for the processing of higher level products. Level 0 data are principally used for research in the topic of signal processing. As for optical data, level 0 product are annotated with several metadata, such as calibration and orbit information, and acquisition time and date.\r\nLevel 1 data can be separated in two distinct product types, depending if the full complex information is used (amplitude and phase) or only the amplitude information. The product denomination depends on the sensor type; for Sentinel 1 the names Single Look Complex (SLC) and Ground range detected (GRD) are used, respectively. Both products can be generated from the Level 0 data. Level 1 data are the products that are used by most scientific users. The processing toward Level-1 data includes Doppler centroid estimation and data focusing. The Level 1 SLC product consists of the real and imaginary part of focused complex SAR data in slant range geometry, from which the phase and amplitude information can be retrieved. This is available for all acquired polarisations. Additional orbit information for georeferencing is provided with the data. The Level 1 GRD data consist of focused and multi-looked SAR data that have been projected to ground range geometry. GRD data only contain amplitude information, therefore the phase information is lost. The multi-looking step is particular for GRD data and allows both speckle reduction and square pixel resolution. As for the SLC data, the GRD data are annotated with orbit information for georeferencing. The Level-1 products are not calibrated, they include however information about calibration constants, which are sensor dependent. Further processing is needed in order to obtain calibrated radar cross section information from the original data intensity values.\r\nLevel 2 products describe geolocated derivated geophysical products such as ocean wind field or surface radial velocity. Such products are for example available for download on the Sentinel-1 Copernicus Hub. Further Level- 2 data are for example differential interferograms or change maps, which can be processed on different online platforms (e.g. Hyp3) and provide information about surface deformation or more generally changes between several acquisitions.\r\nThe denomination of the product types on the different levels may differ from sensor to sensor, but the processing steps stay almost the same, depending additionally on the considered acquisition modes. For example, GRD products are also called for other sensors Multi-Looked Detected (MLD) products.","name":"Synthetic Aperture Radar (SAR) data","selfAssesment":"Completed
"},{"code":"PS3-7-7","description":"Data that have been processed to allow direct data analysis. User processing effort is reduced to a minimum.","name":"Analysis Ready Data (ARD)","selfAssesment":"New
"},{"code":"PS3-7","description":"Earth Observation data are usually made available in different processing levels. The processing level is a mean of describing how much the raw data have been processed toward an informational geophysical product. The degrees of data processing usually follow a numerical hierarchy and typically range from Level 0 (less processed) up to Level 4 (highly processed). They characterize the type of data processing that has been performed between the raw data and the current product.\r\nA first effort for providing standard definitions of different processing levels has been made in the 1980s by the Committee on Data Management and Computation (CODMAC) of the National Research Council (NRC). CODMAC identified eight levels of processing, applicable for all space science data. Starting with the raw data at level 1, the degree of processing and complexity of the data increased at each new level. Level 2 describes edited data, corrected for obvious instrumentation errors and tagged with acquisition time and location; Level 3 stays for calibrated data where values are proportional to a specific physical unit. Level 4 represents resampled data, Level 5 derived data, where specific geophysical information has been retrieved and mapped based on the original data. Level 6 represents all ancillary data (i.e. instrument data) that are necessary for the previous steps of calibration and resampling. Level 7 describes so called correlative data: not directly belonging to the original data, those data represent all other science data that where necessary for the interpretation of the original spaceborne dataset. Finally, Level 8 are user description, i.e. documentation of the data.\r\nConcerning spaceborne image data, both optical and radar, an adaptation of these original levels has been made from NASA and NOAA that is used for the main current spaceborne missions, including the Copernicus program. Whereas specific adaptations may arise for specific sensors and sensor types, there are five principal processing levels. Level 0 represents the raw data that have just been edited for the correction of artifacts. Level 1 data are Level 0 data with additional annotations regarding time and geolocation information, radiometric and geometric calibration coefficients (for example Top of Atmosphere data for optical imagery). Level 2 data are already radiometrically and geometrically calibrated and represent physical variables (for example Bottom of Atmosphere data for optical imagery). Level 3 data correspond to derived variables and information (e.g. land cover) with completeness and consistency information, e.g. quality flags. Level 4 represent higher level data resulting from modelling or more complex analysis of the data with additional ancillary information.\r\nFor many applications and users, so called analysis ready data (ARD data) are required. These usually correspond to Level 2 data that have already been pre-processed in order to retrieve the physical information and can be further analyzed for the specific thematic application.","name":"Processing levels","selfAssesment":"Completed
"},{"code":"PS3","description":"Remotely collected data is available from multiple sources and data collection techniques. Data can be obtained from different levels of data acquisition: ground, air or space, as well as using different sensors and wavelengths. Remote sensing data provides the necessary information to help monitor the Earth's surface.","name":"Remote sensing data and imagery","selfAssesment":"Planned
"},{"code":"PS4","description":"The listed databases provide information on past, operational and future remote sensing platforms and sensors. Use the following links to get more information on the sensors and missions.","name":"Databases of satellite and airborne sensors and missions","selfAssesment":"Completed
"},{"code":"SD","description":"Based on Waldo Tobler`s first law of geography( Tobler, 1970), this property is set on the principle that \"everything is related, but that which is closer is more closely related\".","name":"Spatial dependency","selfAssesment":"In progress (GI-N2K)
"},{"code":"SH","description":"This principle, as set forth by Anselin, determines that \"expectations vary along the earth`s surface\" which means that any spatial analysis is dependent explicitly on the borders of study fields, i.e. the tracing of (spatial) analysis units.","name":"Spatial heterogeneity","selfAssesment":"In progress (GI-N2K)
"},{"code":"TA","description":"This area of knowledge deals with the use of EO / GI techniques and data in different themes and areas of application. It includes the user community of EO services and applications, societal and environmental challenges, EO services and applications, and standard EO products that are made available to users.","name":"Thematic and application domains","selfAssesment":"Planned
"},{"code":"TA11-1-1","description":"The EO/GI users in agriculture are active in Agricultural commodities/Trading, agricultural production / Horticulture, Agricultural services, Agriculture machinery, Agriculture and Rural Development Policy, Agro chemicals / Plants & Fertilizers, Animal production / Livestock. The EO/GI users also include agriculture and rural policy makers. \r\nThey benefit from EO information, for example, by managment support for their crop production through forecasting crop yield, assess risks of damage/loss because of storms, disease or other stress factors, and water monitoring. Use in agriculture: knowledge and information products to forge a viable strategy for farming operations. Understand the health of his crop, extent of infestation or stress damage, or potential yield and soil conditions","name":"Users in agriculture","selfAssesment":"New
"},{"code":"TA11-1-2","description":"The users in fishing are active in Fish stock management, Fishing fleets, Fishery distribution logistics, Aquaculture / fish farms, Coastal management agencies. In addition, the users include Fisheries authorities / policy makers. \r\nThe marine environment in particular is relevant to fishing. Fishing fleets move to the fishing grounds to catch fish. Finding them is challenging. However, fish shoals can be directly visible from above. Navigating to the fishing grounds can be risky: Coastline and shallows may pose a risk to ships. Additionally, skippers may have to deal with challenging weather conditions at sea. Environmental threats to the fishing grounds are oil slicks and other types of pollution. A problem from an economical perspective and for adhering to catch quota is illegal fishing. Noumerous opportunities exist to support fishing with EO information.","name":"Users in fishing","selfAssesment":"New
"},{"code":"TA11-1-3","description":"The users in forestry are active in Forest management, Forest Services, Commodities, Logging industry, Wood, paper and pulp industry, Forest policy, Forest machinery. They also include Forest Policy makers.\r\nUse in forestry: Understand depletion due to natural causes (fires and infestations) or human activity (clear-cutting, burning, land conversion), and monitoring of health and growth for effective commercial exploitation and conservation.\r\nForests are a resource that is harvested all over the Globe for different purposes like construction or heating. Additionally, the forests represent an ecosystem that provides various ecosystem services. Proper management is a key to a healthy forestry industry that has to be aligned well with global environmental management activities. There is a need to avoid deforestation and forest degradation, keep the environmental impact of forestry within bounds, be aware of changes in the carbon balance. Economically relevant is especially a good understading of forest types, forest damage due to storms or insects, as well as wildfires. A threat to the environment results from illegal forest activities.","name":"Users in forestry","selfAssesment":"New
"},{"code":"TA11-1","description":"Users in managed living resources refer to human activities exploiting natural organic resources. Knowledge and information products to forge a viable strategy for the user’s operations such as the assessment of the status of the resource due natural or human activity for effective commercial exploitation and conservation. This includes agriculture, fishing and forestry occupations for our society.","name":"Users in managed living resources","selfAssesment":"New
"},{"code":"TA11-2-1","description":"The users in alternative energy consist of Solar energy providers, Wind energy providers, Tidal energy providers, Hydroelectric energy providers, Energy and Carbon traders, Local and regional planners, and National policy makers. Energy providers need information about the state of the environment to make the most use out of natural resources. Planners and policy makers have to weigh up whether and which type of alternative energy is justifiable and sensible for a specific region.\r\nEO data can be used to build maps that show resource information. For solar energy, those maps contain information about solar radiation, but also shadowing effects. Forecast products for irradiance are available to be able to plan the energy production for the coming days. Tidal waves can be depicted by sea surface heights. As tidal currents are periodical, they can be predicted well by the initial state of sea surface heights. In addition, also the speed of tidal waves can be determined by EO measurements. In the wind energy sector EO data is analysed to plan and monitor wind farms. Maps can show areas, where winds are suitable for wind energy production. After the construction of a wind farm, wind strength and direction during operation can be monitored. Finally, for hydroelectric power stations EO is used to monitor water reservoirs. As well hydrometeorological data is used to forecast water-related events and to monitor drought or floods.","name":"Users in alternative energy","selfAssesment":"Completed
"},{"code":"TA11-2-2","description":"The EO/GI user community in oil & gas consists of offshore exploration and production, on-shore exploration and production, drilling and support services, oil and gas commodities trading, and energy planners. Due to their activities both on-shore and offshore their need for EO-derived information about the land, the ocean and the atmosphere. They need EO-derived information about geological features (for exploration), for asset infrastructure monitoring, construction and buildings. Safe offshore operations (ocean&atmosphere: forecast and monitoring current movement and drift, monitor sea-ice and icebergs, detect and monitor hurricanes and typhoons; land: map and assess flooding, detect wildfires . A large set of information needs results from their need to adhere to environmental regulations. They have to assess and monitor their environmental impact, ocean quality and productivity, land ecosystems and biodiversity, groundwater and run-off \r\nMany problems faced by oil, gas, including the selection and development of exploration areas, detection and mapping of illegal mining activities, or monitoring dams, pipelines and terrain movements, can be efficiently addressed by extracting information from geospatial imagery. Remote Sensing based applications reduce the need for field work, minimize environmental impacts, and ultimately safe costs, to help achieve results faster during exploration, extraction, and remediation/reclamation stages.","name":"Users in oil & gas","selfAssesment":"New
"},{"code":"TA11-2-3","description":"The EO/GI community in minerals and mining consists of mining and quarrying companies, exploration and survey specialists, commodities traders, exploration and extraction equipment suppliers, drilling, excavation and support services, and regional planners / policy makers.\r\nTypical spatial questions for the users in minerals and mining are concerned with prospecting, e.g. \"Where can we find the minerals that are worth exploitation?\", and operation of mining sites: \"How much material has already been excavated in the mine and how much material was deposited in dedicated dump areas?\". Additionally relevant are arising risks through mining activities, e.g. \"How do the mining activities affect settlements in the vicinity?\" or \"How do the mining activities affect the environment?\". Concequently, the EO/GI users in minerals and mining benefit from EO information through mapping geological features, monitor mineral extraction, measure land use statistics, assessing environmental impact of human activities, detect and monitor ground movement, and monitor land pollution.","name":"Users in minerals & mining","selfAssesment":"New
"},{"code":"TA11-2","description":"Users in energy and mineral resources deal with the harvesting of energy from renewable resources and extractive industries including oil and gas and raw materials. EO information helps them in exploring locations where to build new mines or power plants, in identifying risks from infrastructure and in managing the environmental impact of their operations.\r\nUses that apply to the extractive industries: study of landforms, structures, and the subsurface, to understand physical processes creating and modifying the earth's crust. EO/GI should play a key role to transform data into information and knowledge about the potencial feasibility and viability of renewable resources, in particular solar and wind at the natural and urban ecosystems, and in particular to support Sustainable Development Goals SDG 7 Affordable and Clean Energy and SDG 11 Sustainable Cities and Communities.","name":"Users in energy and mineral resources","selfAssesment":"New
"},{"code":"TA11-3-1","description":"EO/GI users in construction include construction companies, civil engineering consultancies, architect and design companies, planning authorities, and national land agencies. \r\nThey benefit from EO through monitor building development, assess environmental impact of human activities, map and assess flooding, detect land movement, subsidence, heave, and monitor land-use statistics","name":"Users in construction","selfAssesment":"New
"},{"code":"TA11-3-2","description":"Utilities (water, electricity, waste): Power station operators, Water plants operators, Survey companies, Hydroelectric suppliers, Regulatory Bodies, Distribution companies, Landfill and waste, Regional planners / policy makers.\r\nThe benefit from EO information that monitor pollution in rivers and lakes, assess changes in the carbon balance, assess environmental impact of human activities, monitor land pollution, assess changes to urban and rural areas, assess and monitor water quality, assess ground water and run-off.","name":"Users in utilities & supplies","selfAssesment":"New
"},{"code":"TA11-3-3","description":"Users of EO/GI in communications and connectivity are mostly mobile telecommunications providers and fixed telecommunication providers. Theire business is to connect people via telephone and internet. The assets for their services include the infrastructure of communication networks physically installed in the ground, the cellphone towers distributed over the land surface, particularly in higly populated areas, as well as other installations (e.g. company buildings) and equipment (communication satellites).\r\nSpecific spatial questions of these users are concerned with the reception quality that the network can provide in an area. The network coverage would neet to react to changes of the built environment. New settlement infrastructure may cause a new population distribution and subsequently the need to network adaptations to cover new areas or cover some areas with higher band widths because more people are living there. Additionaly, the coverage of cellphone antennas depends on the arrangement of environmental obstacles that degrade or block the radio signal. Any place where the built environment or the vegetation changes can change the reception quality within the covered area of an existing cellphone tower. \r\nThe benefit of EO information for the user group of communications and connectivity comes from monitoring building development, assessing changes to urban and rural areas, and mapping line of sight visibility (terrain height, land cover).","name":"Users in communications & connectivity","selfAssesment":"New
"},{"code":"TA11-3-4","description":"EO/GI users in transport and logistics include road transport operators, haulage, road infrastructure operators, tolls, airport operators, rail operators, airlines and airline services, and transport engineers.","name":"Users in transport & logistics","selfAssesment":"New
"},{"code":"TA11-3-5","description":"EO/GI users in marine include ports & harbors administration, bulk cargo carriers, cruise liners operators, ferry operators, naval operations, and rescue and safety at sea.","name":"Users in marine","selfAssesment":"New
"},{"code":"TA11-3-6","description":"From a conceptual point of view travelling is crossing the space from one location to another. Tourism mostly requires a travel to the desired destination and typically also includes moving inside a specific area. Therefore both tourism and travel are highly dependent on spatial phenomena which are often captured using EO.All kinds of travelling are highly dependent on weather conditions which can be observed with meteorological satellites. Also the current traffic conditions like congestion, road condition and natural hazards can be discovered with EO.\r\n\r\nThe types of tourism which are outside of buildings require sufficient weather forecast. Especially outdoor tourism at the coast or in mountain areas have a need for specific information about the current and the near future conditions of the natural environment. Examples are avalanche reports and forecasts for wind or wave heights of water bodies. Local tour organizers can utilise this information in order to better plan offers for tourists and also ensure overall safety during their stay.\r\n\r\nTourism and travelling are import economic factors. Consequently both the public and the private sector are interested in ensuring safe and convenient travel conditions and furthermore in creating an attractive environment for travellers and touristic visitors. This includes recognising environmental pollution, since this discourages tourist from visiting an area. Not only incoming, but also outgoing tourism is an important factor in local economies. Travel agencies, for example, are highly dependent on retrieving accurate information about foreign regions which are typically obtained with earth observation technology.\r\n\r\nOf course tourism and travelling itself also can be observed from space, this is especially true for mass tourism and areas where traffic has increased a lot during the last time. Typical effects are the increase of settlement area and the additionally used space for roads, parking lots, airports and harbors. These changes to the earth surface can be quantified with the help of land cover change detection.In many cases local administrations and decion makers want to mitigate the negative consequences of mass tourism, the insights of the mentioned EO measurements provide a useful foundation for sustainable planning.","name":"Users in travel & tourism","selfAssesment":"Completed
"},{"code":"TA11-3","description":"Users in transport and infrastructure apply to all manufacturing and physical supply in land but also marine domains including transport & logistics, utilities, construction, communication & connectivity, and tourism.","name":"Users in infrastructure & transport","selfAssesment":"New
"},{"code":"TA11-4-1","description":"EO/GI users in insurance and real estate include primary insurance companies, re-insurance sector, insurance brokers, insurance service suppliers, commercial banks, major projects, and international financial institutions. \r\nProduction processes (including primary production like farming), property and real estate are often insured against certain risks, e.g. from natural hazards. \r\nUsers benefit from EO information through applications that monitor building development, assess crop damage due to storms (including to forecast and map large waves), assess damage from earthquakes, detect and monitor wildfires, map and assess flooding, detect land movement, subsidence, heave, forecast and assess landslides.","name":"Users in insurance & real estate","selfAssesment":"New
"},{"code":"TA11-4-2","description":"EO/GI users in retail and geo-marketing include Retail centres and Advertising and Marketing agencies. They use EO/GI data in the field of Navigation and LBS, Shopping chains or Logistics.","name":"Users in retail & geo-marketing","selfAssesment":"New
"},{"code":"TA11-4-3","description":"Users in news and media are Television companies, Broadcasting providers, News and Information agencies, Web service providers, and Entertainment software providers. They benefit from monitoring, forecasting and assessing of natural risks/disasters.","name":"Users in news & media","selfAssesment":"New
"},{"code":"TA11-4-4","description":"Users in ICT include fixed and mobile telecommunications providers. They can make use of EO/GI data by monitoring building development and changes to urban areas.","name":"Users in ICT, knowledge and digital interfaces","selfAssesment":"New
"},{"code":"TA11-4","description":"Users in financial and digital services cover a broad area of activity that touches on many other market sectors such insurance & real estate, retail, news & media and digital interfaces. The categories included are identifiable as a “service” (tertiary sector: attention, advice, access, experience, and affective labour) and not part of the physical supply of goods.","name":"Users in financial & digital services","selfAssesment":"New
"},{"code":"TA11-5-1","description":"The users in smart cities are multiverse and include large number of profiles. This include urban planners, architects, spatial planning offices, urban policy makers, transportation/environment/health departments but also citizen themselves.\r\nThe users benefit from additional information and knowledge extracted from EO data. This information and knowledge can help them to better tackle with challenges arising from climate change and urbanization. As each urban area is unique, EO can provide relevant information by detecting, evaluating and measuring these localities.\r\nThis EO based information can be extracted on one occasion or continuously, benefiting from revisiting satellites. EO can support investigation of archive data to extract trends or by investigating current state to set a baseline. This baseline is then further used to monitor the changes or to assess the impact of different decisions and actions. In most cases, this information is further used in various GIS analyses or modelling procedures.\r\nThe topics where EO can contribute are as follows: urban land cover, urban heat islands, air/water/soil quality, tree/vegetation health, detection of invasive vegetation species, damage detection on buildings or infrastructure, development of infrastructure and many more.\r\nAs listed, EO can support various domains that can be fitted under Nature-based solutions (NBS). NBS have been gaining attention as multifunctional solutions that may help cities to address challenges arising from climate change and urbanization.\r\nThe concept of Nature-based Solutions (NBS) has evolved as an umbrella concept embracing concepts such as green/blue/nature infrastructure, ecosystem approach, ecosystem services or natural systems agriculture, natural solutions, ecosystem-based approaches, and ecological engineering. NBS can include solutions such as water purification, reduction of flood risk, or deliberate efforts to decrease temperature and improve air quality.","name":"Users in smart cities","selfAssesment":"Completed
"},{"code":"TA11-5-2","description":"The users in local and regional planning include spatial planning departments of municipalities, spatial planning offices, and spatial planning policy makers. Land use management in densely populated areas involves negotiation of conflicting land-use demands for settlement, production system (including agriculture and forestry) and infrastructure. The users benefit from EO information to manage the use of land and its impacts.","name":"Users in local & regional planning","selfAssesment":"New
"},{"code":"TA11-5","description":"Users in urban development and users involved in the development of rural settlements perform tasks on local and regional scale (to the scale of nations). These users benefit from EO information to manage the use of land & its impacts. Users such as urban planners, architects, spatial planning offices, urban policy makers in public/private sectors in smart cities or generic urban local/regional planning belong to this category. EO/GI becomes a key data and information to support Sustainable Development Goals - SDG 11 Sustainable Cities and Communities in particular to set up at geospatial and temporal basis the evolution of urban environmental and socioeconomical factors for a better distribution and equality of resources, benefits and impacts (environmental urban justice maps)","name":"Users in urban development","selfAssesment":"New
"},{"code":"TA11-6-1","description":"Users in defense, security and military are border control organisations, police and rescue forces, military services, and intelligence services. Use of EO/GI data can be made in the field of detecting and monitoring high risk areas (natural and humanitarian), monitoring border incursions, or monitoring maritime movements.","name":"Users in defense, security & military","selfAssesment":"New
"},{"code":"TA11-6-2","description":"EO/GI users in emergency services are coast guards, ambulance services, fire services, police services, civil protection organisations, and rescue services. They benefit from monitoring, detecting and assessing natural risks/disasters.","name":"Users in emergency & social protection","selfAssesment":"New
"},{"code":"TA11-6-3","description":"The EO/GI users in humanitarian operations correspond to humanitarian aid organisations, humanitarian support organisations and overall humanitarian response such as border control organisations, police and rescue forces, coast guards, civil protection, military services, and intelligence services. They can use EO services to detect and monitor high risk areas produced naturally or by humans, monitor border incursions or maritime movements. They provide support to local populations that have experienced a crisis, e.g. they fled from a conflict or are affected by a natural disaster. The organisations therefore support the population's needs for sustenance. Consequently, any related risks are relevant as well. The users benefit from the EO capability to identify and monitor people in need, i.e. to assess pressures on populations and migration, and to monitor humanitarian movement and camps. They additionally benefit from EO through mapping disaster areas for situation awareness and detecting sensitive risk areas. Some examples of users at European level are DG RELEX, DG ECHO, DG ENV/ MIC. At UN, the users include OCHA, UNHCR, UNDPKO, UNDP, UNOPS, UNITAR, UNICEF, UNESCO, WFP. Further, international users include IFRC, WHO, WB, and donor organizations. At the national level, the users include Civil Protection Agencies, Ministries of Internal Affairs / Civil Protection Department, Development and Aid agencies.","name":"Users in humanitarian operations","selfAssesment":"New
"},{"code":"TA11-6","description":"Users in defence and security work in the field of military, emergency and social protection and define, collect, analyse information to provide intelligence & safety. Some examples are activities under humanitarian response such as border control organisations, police and rescue forces, coast guards, civil protection, military services, and intelligence services which can use EO services to detect and monitor high risk areas produced naturally or by humans, monitor border incursions or maritime movements.","name":"Users in defense & security","selfAssesment":"New
"},{"code":"TA11-7-1","description":"EO/GI users in environmental ecosystems & pollution include scientists, consultants, planners and policy makers with interest in environmental issues.","name":"Users in environmental ecosystems & pollution","selfAssesment":"New
"},{"code":"TA11-7-2","description":"Users in health care health-related services include services on site-specific field conditions as well as import phenological timing events, which helps to make predictions for monitoring air quality, forecasting epidemics and diseases, as well as forecasting sunlight exposure.","name":"Users in health care","selfAssesment":"New
"},{"code":"TA11-7-3","description":"EO/GI users in meteo and climate; use of satellite-based observations in addressing key climate science questions for user-centric climate change risk assessment applications or climate-related issues","name":"Users in meteo & climate","selfAssesment":"New
"},{"code":"TA11-7","description":"Users in the public administrations or private organizations using EO to assist environmental or climate change impact policy making decisions i.e, assisting in developing monitoring to evaluate and deliver policy goals, provide assessment of ecosystems, rapid response to major environmental risk events, or those associated health security & care events. These users are largely related with international treaties and hence a strong international collaboration. EO/GI becomes a key data and information to support Sustainable Development Goals (SDG) in particular in terms of environmental, climate and health towards SDG 11, SDG 13 Climate Action; SDG 14 Life Below Water; or SDG 15 Life on Land.","name":"Users in environmental, climate & health","selfAssesment":"New
"},{"code":"TA11-8-1","description":"EO/GI users of customer solutions; easier for society to use and engage with EO services through mobile devices, social media platforms, apps. Enormous potential to use citizen-driven observations in combination with EO data","name":"Users of consumer solutions","selfAssesment":"New
"},{"code":"TA11-8-2","description":"EO/GI users in leisure; basic public understanding on EO Services","name":"Users in leisure","selfAssesment":"New
"},{"code":"TA11-8-3","description":"The community of users in education includes instructors (1) who are teaching or conducting research in some aspect of GIScience, such as coding, remote sensing, field methods, geodetic control, web mapping, spatial analysis, or related topics, or (2) who are using GIS as a teaching tool in a discipline, such as business, biology, economics, or health sciences. By extension, this community includes students and supportive deans and other educational administrators. The benefits that these users gain from EO information includes a set of best practices vetted by experts in the field that they can use to teach modern GIS workflows more effectively. \r\nThe goals of this user community are focused on a deeper and a broader implementation of geotechnology, methods, and spatial data throughout the educational system—primary, secondary, university, and lifelong learning (libraries, museums, and other informal settings). Deeper implementation implies embracing GIS as a platform, including its field data gathering tools and citizen science workflows, spatial analysis, building web maps and apps, communicating with multimedia maps derived from web GIS, systems configuration work, and the coding that is behind modern GIS infrastructure. Broader implementation implies the use of GIS in a multitude of disciplines at all levels of education, formal and informal; occurring wherever changes over space and time are being examined. \r\nAt all levels of education the challenge of sufficient bandwidth and the use of a professional systems-based tool such as GIS, along with devices capable of running web GIS tools, are barriers in many areas throughout the world. However, educational and societal forces represent a stronger challenge than technological ones. These educational and societal challenges that this user community faces include the lack of educational content standards at the primary and secondary level that support the use of geotechnologies in education, and at the university level, a lack of awareness of and access to modern SaaS GIS tools and open data portals. \r\nThe risks that the community faces in not facing the challenge of the use of GIS in the education sector is a lack of geographic and spatial literacy among students and faculty. This will translate to research that does not consider spatiotemporal implications of 21st Century challenges, a workforce ill-equipped to deal with them, and consequently an increasingly unstable and dysfunctional world. To build a workforce that can meet global challenges in energy, biodiversity, climate, natural resources, natural hazards, human health, economic inequality, and others, a deep and wide implementation of GIS technology and methods must take place throughout the educational system. The actions that society can take to face that challenge is to provide professional development opportunities for faculty, curricular resources, assessment instruments, relevant spatial data and open data portals, examples of best practices, and a network for educators and researchers in which to interact. EO can provide all of these elements in partnership with educational institutions, government, nonprofits, and industry to meet this challenge. In so doing, an increasingly sustainable, healthier, resilient world can be achieved from the community to the global level.","name":"Users in education, training & research","selfAssesment":"Completed
"},{"code":"TA11-8","description":"Citizens and society in general use and engage with EO services through mobile devices, social media platforms, apps. We do also categorize in this section the users in education, research and training providing knowledge and learning outcomes.\r\nActive and engaged citizens are one of the main driving forces of EO/GI. Nowadays, there is a growing amount of location-based contents generated by connected “produsers”, mainly equipped with smartphones. The exponential growth of ambient geographic information through social networks became the basic feature of a spatially enabled society, in which it behaves as a vessel where millions of people share their current thoughts, observations and opinions, showing to provide more reliable and trustworthy information than traditional methods like questionnaires and other sources.\r\nA spatially enabled citizen is explained through his ability to express, formalize, equip (technologically and cognitively), and (un)consciously activate an efficiently use of his spatial skills. Harvesting this ambient geospatial information provides a unique opportunity to gain valuable insight on information flow and social networking within a society, support a greater mapping, understand the human landscape and its evolution over time. With these insights, city planners can make use of the gathered affective data to detect positive or negative trends developing in the city, managing to take early countermeasures.\r\nNevertheless, assembling and analyzing EO/GI provide us with unparalleled insight on a broad variety of cultural, societal, and human factors, particularly as they relate to human and social dynamics, for example: 1) mapping the manner in which ideas and information propagate in a society, information that can be used to identify appropriate strategies for information dissemination during a crisis situation. 2) Mapping people’s opinions and reaction on specific topics and current events, thus improving our ability to collect precise cultural, political, economic and health data, and to do so at near real-time rates. 3) Identifying emerging socio-cultural hotspots.","name":"Users among citizens & society","selfAssesment":"New
"},{"code":"TA11","description":"The EO/GI user community pools sub-communities (stakeholders) that share common needs for EO/GI information. From an economic perspective, market sectors represent user communities. Users of a community have a common interest in specific aspects of societal or economical benefits to be realized by the implementation of EO services. A user-led community is active at specific locations/regions or in specific environments on the Earth. Their activities are associated with particular features and objects of the environment and related processes that can be detected and monitored with EO satellites. EO information therefore is relevant to the user community's management of their assets, the risks to their assets, and the impact that their activities may have on other aspects of the environment. User objectives (use cases) with EO information include: Enforce regulations; Develop strategies and policies; Manage assets; Plan and design project implementations; Analyse and understand impact / consequences.\r\nUser communities can profit from EO services and applications in the field of managed living resources, energy and mineral resources, infrastructure and transport, financial and digital services, urban development, defense and security, environmental, climate and health, or citizens and society. EO/GI becomes a key data and information to support Sustainable Development Goals -SDG in particular in terms of users in managed livimgs resources towards SDG 2 Zero Hunger; SDG 8 Decent Work and Economic Growth; SDG 9 Industry, Innovation and Infrastructure; SDG 14 Life Below Water; or SDG 15 Life on Land","name":"User community of EO services and applications","selfAssesment":"Completed
"},{"code":"TA12-1","description":"Climate change observations show the warming of the climate system. The changes since the 1950s are unprecedented over decades to millennia.The atmosphere and ocean have warmed, the amounts of snow and ice have diminished, and sea level has risen. The anthropogenic emissions of greenhouse gases are the highest in history. Recent climate changes have had widespread impacts on human and natural systems. There is an urgant need for climate action through mitigation and adaptation. Mitigation actions prevent or reduce the emission of greenhuse gases into the atmoshpere with the objective to make the impacts of climate change less severe. Adapting to climate change increases our resilience to impacts like extreme weather events (e.g. hazards like floods and droughts) that get more frequent and intense in many regions. Current climate change will get worse in the future even if the reduction of emissions is effective with negative effects on ecosystems, economy, human health and well-being. There is extensive need for actions to adapt to the impacts of climate change.","name":"EO for climate change mitigation & adaptation","selfAssesment":"New
"},{"code":"TA12-10","description":"\"Sustainable urban development is a goal of the global society. It summarizes a specific set of problems that cities face all over the world. Cities want to provide a high quality of life to their residents. However, this goal is threatened by urban growth at the cost of urban green infrastructure’s accessibility by citizens etc. Communities that address this: C40 (association of the largest cities of the globe), CitiesIPCC, related SDGs of the UN, etc. Skills: Explain how the monitoring of urban areas contributes to sustainable urban development through its capability to provide regularly updated information about the benefit of urban green infrastructures and their ecosystem services to the quality of life in a city\r\n\"","name":"EO for sustainable urban development","selfAssesment":"New
"},{"code":"TA12-2","description":"Biodiversity describes the variety of ecosystems (natural capital), species and genes in the world or in a particular habitat. Ecosystem services sustain our economies and societies and are essential to human wellbeing.","name":"EO for biodiversity & ecosystems","selfAssesment":"New
"},{"code":"TA12-3","description":"Worldwide countries follow a digital agenda for the economy and initiatives to foster new skills among the workforce to cope with transformation processes with massive impact on the labour market.","name":"EO for digital agenda & new skills","selfAssesment":"New
"},{"code":"TA12-4","description":"Energy transition is a thematic area whose EO experts are proficient in relevant EO data and its processing methods and infrastructure to derive information for energy transition [and its regulatory context, etc.]. The expertise of each expert may be very specialized. In sum, the experts have: The relevant domain knowledge (knowledge about type of monitored entities and their properties, e.g. reflectance properties of sea ice and related EO sensors for detecting them), and The relevant workflow knowledge and processing skills for extracting and providing targeted information for energy transition. [may share strategic objectives… such as „gaining thorough understanding of Energy transition“, „foster usage of EO information for energy transition“]","name":"EO for energy transition","selfAssesment":"New
"},{"code":"TA12-5","description":"Agricultural activity is sustained by good environmental conditions that allow farmers to harness natural resources, create their produce and earn a living. This fosters a sustainable rural economy while food produced by agriculture sustains society as a whole.","name":"EO for sustainable agriculture & food production","selfAssesment":"New
"},{"code":"TA12-6","description":"This societal challenge aims to provide efficient, safe and environmentally friendly mobility solutions.","name":"EO for infrastructure & transport","selfAssesment":"New
"},{"code":"TA12-7","description":"In recent decades, society has fought communicable diseases with success through treatment and prevention. The Covid-19 pandemic shows that communicable diseases are still a threat to the health of citizens. Spread can gappen very quickly from one country to another. Challenges lie in the (re-)emergence of infectious diseases, antimicobial resistance and vaccine hesitancy. Policies of states focus on surveillance, rapid detection and rapid response.","name":"EO for health surveillance","selfAssesment":"New
"},{"code":"TA12-8","description":"There is a rising geostrategic competition and power pilitics challenging rule-based multilateralism. Further, there are armed confilct, civil wars and instability in the EU's broader neighbourhood. \r\nFurther, natural disasters pose a threat to society, where the Sendai Framework of disaster risk reduction focuses on.","name":"EO for emergency, security & defense","selfAssesment":"New
"},{"code":"TA12-9","description":"Water is an essential resource for food production. Growing crops requires significant quantities of water. Without sufficient, good quality and easily accessible water, agri-food production is under threat.","name":"EO for water sustainability","selfAssesment":"New
"},{"code":"TA12","description":"EO provides timely, continuous and independent data for monitoring indicators of the progress of the society in various societal challenges.\r\nEO monitoring supports activities that address societal & environmental challenges. This happens indirectly along a chain: e.g. a regularly provided EO information product derived from EO data of a satellite is integrated as a parameter in a climate model / Earth system model. This climate model enables the development of regulations (and their enforcement through constant monitoring) to implement climate change mitigation measures. Thereby, the chain is characterized by seveal connected nodes: from societal challenges to use cases of users to EO applications to EO products to specific satellites and their sensors.\r\n[Communities that promote collaboration among diverse stakeholders from academia, industry, public administration as well as local residents] \r\nScientific agendas address societal challenges and the EO/GI community can contribute to them. Consortia usually include experts from academia (researchers, developers, scientists), EO companies, and members from the user community such as public authorities.","name":"EO for societal and environmental challenges","selfAssesment":"New
"},{"code":"TA13-1-1","description":"Monitor the atmosphere includes monitoring of the atmosphere composition and air quality, as well as forecasting of sunlight exposure. Timely, continuous, and independent data on the atmosphere is useful in various domains like health, agriculture, renewable energies, urban planning, climate sciences and biology.\r\nThe atmosphere composition includes greenhouse gases (GHG) like carbon dioxide, methane, NO2 and SO2. They are part of the Earth system and have a strong impact on the climate. To monitor changes in atmosphere composition enables modelling climate change and understanding the impact of human-induced emissions of GHG relative to natural sources. EO-derived products include inventory of emission data as an input to atmospheric chemistry transport models and forecast models. Inventories are based on a combination of existing data sets and new information, describing emissions from fossil fuel use, ships, volcanoes, and vegetation. This ensures good consistency between the emissions of greenhouse gases, reactive gases, and aerosol particles and their precursors.\r\nAir quality describes the composition of the atmosphere from gases and particles near the Earth's surface. Local emissions from different sources (e.g. energy production, industrial production, traffic) cause changes to the atmospheric composition that are highly variable in space and time. The quality of the air we breathe can significantly impact our health and the environment. Therefore, it is highly relevant to monitor air quality and emissions. EO satellites are capable of monitoring aerosols, tropospheric O3, tropospheric NO2, CO, HCHO, SO2, and particulate matter (of the sizes PM 2.5 and PM 10). Products like air quality assessment reports, daily ozone forecasts, and UV-index forecast maps are produced that are applied in specific use cases, particularly related to health.\r\nThe amount of solar radiation that arrives at a location on the Earth surface depends on the atmosphere composition and varies over the day and the seasons. Information on solar radiation is useful in various domains. Applications of sunlight and ozone data are for example real-time UV radiation forecasting and risk assessment, skin health services, climate change studies, assessment of ozone protection policies effectiveness, plant growth and disease control, evaporation and irrigation models, power generation, solar heating systems planning and monitoring.","name":"Monitor the atmosphere","selfAssesment":"Completed
"},{"code":"TA13-1-2","description":"Monitoring the climate includes monitoring climate forcing and the carbon balance and assessing climate change risks.\r\nClimate forcing describes the imbalance of the Earth’s energy budget due to natural or human-induced sources. This imbalance results in a change in the globally-averaged temperature. Amongst the contributors of positive climate forcing, that leads to an increase in the globally-averaged temperature, the increase of carbon dioxide in the atmospheric composition is considered to be the most important factor. Changes in the carbon dioxide concentration indicate that the exchanges between carbon sources and sinks are not balanced. It can be shown that human-induced emissions of carbon dioxide are responsible for the increase of the carbon dioxide since the industrialisation.\r\nWith EO, we can monitor changes in greenhouse gases (GHG), aeorosols, albedo, and solar radiation. The dynamic nature of the climate makes it necessary to apply equally dynamic EO monitoring that allows to deliver key information on historical, seasonal forecast and projection periods for climate-related indicators.\r\nRelevant EO products include estimates of the climate forcing of aerosol, ozone and greenhouse gases. The dynamic nature of the climate makes it necessary to apply equally dynamic EO monitoring that allows to deliver key information on historical, seasonal forecast and projection periods for climate-related indicators. \r\nThe products are particularly relevant to the European energy sector in terms of electricity demand and the production of power from wind, solar and hydro sources. \r\nMoreover, water management uses EO-derived information about climate change to mitigate effects of changing precipitation patterns to adapt their strategies, and to prepare for climate variability and change in the water sector, e.g. because of changes in river discharge, droughts and floods.\r\nFinally, insurance uses climate change information for assessing the weather risks to insured assets that change with the climate-related increase in extreme weather conditions. This includes products like up-to-date catalogue of wind storms and their associated impacts on the ground.","name":"Monitor the climate","selfAssesment":"Completed
"},{"code":"TA13-1-3","description":"The weather is the state of the atmosphere measurable by its temperature, humidity, precipitation, and other atmospheric variables. To forecast the weather is a major branch in the field of meteorology. In comparison to climate, weather can only be predicted for a short period of time (minutes to month), because it describes the state of the atmosphere for specific days at specific locations. For a reliable weather forecast, a good numerical prediction model with precise initial conditions is needed. Models are sensitive to changes in the initial condition, that is why at the moment weather predictions are only accurate for few days. However, both models and the determination of initial conditions are steadily improved. EO makes a significant contribution to improving the initial conditions by providing global information several times a day. As the quality of the EO products improves, the weather forecast also improves. \r\nSince decades, satellites are used to monitor and forecast weather. Therefore, it is one of the most established sectors of satellite data applications. There are geostationary and polar-orbiting weather satellites that measure all kinds of meteorologically relevant variables, e.g. cloud coverage, wind speed [...] via passive or active imagery. However, not only satellites are used to collect information, but also other remote sensing techniques that can be airborne or ground-based such as Lidar.\r\nWeather forecasts are used by citizens for decisions in everyday life, in agriculture for crop cultivation decisions and in the stock markets. Other domains of applications are hydrometeorology, aviation, maritime navigation, and the military and nuclear sectors.","name":"Forecast the weather","selfAssesment":"Completed
"},{"code":"TA13-1","description":"Monitor the atmosphere and climate includes all change-focused services/applications which assess, monitor, forecast and provide timely, continuous and independent data (e.g. temperature, humidity, emissions, greenhouse gases, solar UV radiation, aorosols,...). It closely monitors each of the Earth's different subsystems and, besides being the basis for weather forecasts, helps to better understand and evaluate the impact of the climate change.","name":"Monitor the atmosphere and climate","selfAssesment":"New
"},{"code":"TA13-2-1","description":"Monitor critical information about offensive and defensive systems. This deserves a category in its own right since the nature of observations is quite different from many others.","name":"Monitor critical assets","selfAssesment":"New
"},{"code":"TA13-2-2","description":"Monitoring health can be delivered indirectly by monitoring environmental changes that can cause endemic and chronic diseases. Typically monitored environmental factors are temperature, humidity, stagnant water, NDVI, land cover, or soil type.","name":"Monitor health","selfAssesment":"New
"},{"code":"TA13-2-3","description":"Monitoring food security includes the monitoring of food availability by environmental conditions (land cover, NDVI,...), as well as the monitoring of migration patterns. Risks that can lead to food insecurity are hazards or conflicts.","name":"Food security monitoring","selfAssesment":"New
"},{"code":"TA13-2-4","description":"Monitoring borders includes monitoring the land and marine border incursions, monitoring transport routes, assessing pressures on poplulations, and monitoring humanitarian movement.","name":"Monitor borders","selfAssesment":"New
"},{"code":"TA13-2","description":"Monitor security and safety describes the collection and analysis of information to provide intelligence services & safety. The task is to give early warnings in case of emergencies, to monitor infrasturcture, transport routes (land and water) and borders, to surveil security and sovereignty.","name":"Monitor security & safety","selfAssesment":"New
"},{"code":"TA13-3-1","description":"EO is capable to repeatedly map flood extent directly after flooding, including further aspects (flood plain, extend mapping, frequency, rainfall, flash floods, vulnerability, inundation, risk-based mapping & management; flood spread and depth followed by automated insurance payouts). Modelling (hydrological modelling and monitoring focused on seasonal dynamics of water availability) based on EO data (digital elevation models) supports flood risk assessment.","name":"Map and assess flooding","selfAssesment":"New
"},{"code":"TA13-3-2","description":"For the outbreak of forest fires, satellite remote sensing can be continuously track and monitor, in a timely manner to grasp the development of forest fires. Beyond, weather monitoring enables to forecast weather conditions where fires are likely, allowing authorities to prepare.","name":"Detect and monitor wildfires","selfAssesment":"New
"},{"code":"TA13-3-3","description":"Damages from earthquakes to infrastrcture can be detected directly, e.g. by mapping collapsed buildings in optical data to derive rapid response products. Use of SAR interferograms enables to identify geotectonic shifts. Modelling enables to identify hotspot areas.","name":"Assess damage from earthquakes","selfAssesment":"New
"},{"code":"TA13-3-4","description":"Landslides are a natural hazard posing a threat to human life, property, infrastructure, and natural environment. Every year, slope instabilities have a significant impact on societies and economies. Consequently, landslide documentation is used for risk assessments, policy making and enforcing of construction regulations. Landslide monitoring is used to ensure safety of infrastructure operation. Rapid mapping of landslides and associated damages is done for response actions, e.g. of civil protection organizations. As ground surveys are very costly and time-consuming, satellite remote sensing is increasingly used to assess damage resulting from landslides.\r\nLandslides lead to local terrain changes after a downslope movement of material under the effect of gravity. They vary by type of movement (e.g. falling, toppling, gliding and flowing), by size (from small rocks to entire mountain slopes) and velocity (from a couple of millimetres per year up to free-fall speed). Landslides can be triggered both by natural causes (like earthquakes or heavy rainfall events) and human causes, e.g. mining activities that lead to slope failures. Landslides can initiate other natural hazards, e.g. when a landslide blocks a river a lake can be formed which poses a risk for an outburst flood. \r\nLandslides are diverse in appearance, and therefore are challenging to detect. EO-based assessment methods aim for detecting changes to the land surface and surface displacements. \r\nEO satellites and airborne remote sensing use optical sensors for detecting landslides in post-event images and land cover changes caused by landslides, primarily indicated by the removal of vegetation and the exposure of bare soil, by comparing pre-event and post-event images. Typical resolutions of optical EO data for mapping rapid landslides are between 0.4 m and 30 m, depending on the size of landslides caused by the triggering event. Optical data from unmanned aerial vehicles are used in cases where single landslides or concise regions have to be covered. Additionally, synthetic aperture radar (SAR) sensors allow the detection of subtle changes in ground deformation caused by landslides. Therefore, time-series of radar images are used. Further, airborne laser scanning enables the generation of digital elevation models (DEMs) that allow identification of landslide surface structures and, in case of repeated coverage, detection of elevation changes. DEM generation for analysing landslides is also possible with photogrammetry on stereographic optical data and radargrammetry on SAR images.\r\nThe diversity of appearances of landslides leads to challenges for (semi-)automatic image processing and makes visual interpretation of EO data by a landslide expert a commonly used method for landslide mapping. However, visual interpretation is subjective and experts’ results can be very diverse. Additionally, it is a slow and time-consuming process. Semi-automated classification based on optical and DEM data using object-based image analysis (OBIA) can achieve detailed interpretations of landslides while reducing the analysis time. Interferometic SAR (InSAR) techniques, such as persistant scatterer interferometry (PSI) or Small Baseline Subset (SBAS), are primarily used to identify and monitor slow-moving landslides and for quantifying movement rates. Integrated analysis of optical, DEM and SAR data allow to fully exploit the potential of EO data from different sensors for landslide mapping and assessment.","name":"Forecast and assess landslides","selfAssesment":"Completed
"},{"code":"TA13-3-5","description":"In context of volcanic activities and volcanos, EO methods are capable to provide information about various aspects, including ground motion (seismic), volcanic eruptions (pre-eruptive, sin-eruptive, atmospheric ash, dispersion), Rapid damage estimation (prevention), earthquake damage extent (loss adjuster dispatch). classification of land cover types","name":"Assess and monitor volcanic activities","selfAssesment":"New
"},{"code":"TA13-3-6","description":"Multi-hazard assessment both focuses on regions prone to several geohazards and on the interrelationships between hazards, i.e. what happens if two disasters strike at the same time or what happens when one disaster is causing a cascade of disasters with a strongly amplified impact (e.g. a landslide causing a dammed river causing an outburstflood with a magnitude beyond the design of protective measures; or an earthquake in a coastal region that is followed by a tsunami). EO can provide imformation on the single disasters and, through integration and comprehensive impact assessment, enables multi-hazard assessment.","name":"Multi-hazard assessment","selfAssesment":"New
"},{"code":"TA13-3","description":"Assess disasters and geohazards by EO includes alert & early warning, emergency mapping, and risk & recovery mapping. It relates to observations, controlling, assessments that are linked to natural and human made risks. Typical disasters that can be assessed by EO are in particular floods, droughts, forest fires, landslides, tsunamis, earthquakes, cyclonic storms and volcanic eruptions. Since with EO it is possible to quickly analyse the risk or damage it is used to effectively plan emergency response actions.\r\nThere are several measures to minimize or prevent the damage caused by disasters. Some of them have to be carried out in anticipation of a disaster, others after the occurrence of an event. The different phases that are needed to reduce or avoid the impact and to assure rapid response and recovery are described in the disaster management cycle. Depending on the cycle phase, EO has to meet different requirements. The Mitigation and Preparedness phase are passed through in anticipation of a disaster event. Thus, requirements to EO products may focus on high completeness of mapping or high accuracy of mapping. In contrast, Response and Recovery phase include rapid mapping, thus EO capabilities must meet near real-time delivery requirements. \r\nAs well, the nature of the disaster determines which EO products are used. Optical sensors are used throughout the different types; however, landslides are mostly assessed by radar sensors and thermal sensors are additionally used for forest fires.","name":"Assess disasters & geohazards","selfAssesment":"New
"},{"code":"TA13-4-1","description":"To monitor crops and agriculture with EO-based methods is relevant for various applications, including to assess environmental impact of farming, assess crop damage due to storms, to detect ollegal or undesired crops, to monitor water use on crops and horticulture, and to monitor land degradation neutrality. EO mapping of crops happens on all scales with both optical and SAR sensors. Relevant EO products include degradation, agri-environment, ecosystem, damage estimation, warning-service, food-security, impact, crop health (disease and stress), leaf area index, crop acreage and yield harvest (inventories / statistics), crop types (extent, growth, health, stress), land surface temperature, illicit crops, estimates, cultivation patterns, soil water index, surface soil moisture, run-off, land cover (land cover change), land productivity (net primary productivity, NPP), carbon stocks (soil organic carbon, SOC).","name":"Monitor crops","selfAssesment":"New
"},{"code":"TA13-4-2","description":"Monitor the forest focuses on regular and periodic measurement of certain parameters of forests (physical, chemical, and biological) to determine baselines to detect and observe changes over time. Typical applications include to assess deforestation and forest degradation, assess forest damage due to storms or insects, to monitor forest resources, detect illegal forest activities, assess the environmental impact of forerstry, and to monitor the forest carbon content. Moderate resolution sensors have been used to map forests at large scales. Modern very high resolution optical sensors provide enough spatial and spectral detail to map individual trees. Further sensors for forest monitoring include SAR and LIDAR. Integration of optical sensors, LIDAR and in-situ measurements seems an accurate method to achieve third dimension forest mapping.","name":"Monitor the forest","selfAssesment":"New
"},{"code":"TA13-4-3","description":"EO provides the opportunity to monitor bodies of water, i.e. inland waters, and to assess ground water and run-off. For lakes, this includes products about water quality, pollution, turbidity, suspended sediment concentrations (quantitative, qualitative), waterbody (temperature, extent, volume, quantity), algal blooms, alkaline water, evaporation, surface temperature. For ground water and run-off, the products focus on water run-off (water quantity), hydrological network and catchment areas (water catchment), run-off season, groundwater. Various scales are addressed, from local catchments to the global water cycle. For inland water quality, sensors are optical medium resolution (300 meters) for achieving a (strongly cloud-cover dependent) update frequency of 10-20 times per year and high resolution (5 meters) for update frequency of 3-5 times per year.","name":"Monitor bodies of water","selfAssesment":"New
"},{"code":"TA13-4-4","description":"Monitoring of snow and ice focuses on glaciers and their retreat due to climate change (extent, mass balance), the seasonal snow cover (its extent, depth, temperature and snow water equivalent), and the ice on rivers and lakes (inland ice, thickness, freezing period, melting period, ice extent). Glacial monitoring in the mountainous regions around the globe, and of the Greenland and Antarctic ice shields uses optical EO data of high and very high resolution and SAR data. Satellite based daily snow covered area products can reliably be provided down to a spatial resolution of 500 meters. Global products are possible with weekly updates. Applications include, among others, climate change impact monitoring, relevant for modelling runoff patterns in catchments for etimating hydroelectric power generation potential.","name":"Monitor snow and ice","selfAssesment":"New
"},{"code":"TA13-4-5","description":"EO is used to monitor land ecosystems and biodiversity, environmental impact of human activities, land pollution and vegetation encroachment. A tool for this is land cover mapping and mapping of land cover change about a wide set of categories, lincuding basic forest types, major agricultural surface types, conservation areas, settlements, infrastructure, primary roads, bare soil, water bodies, rivers, wetlands following standard classification schemes according to CORINE or FAO LCCS. Main source are optical EO data and associated pixel-based and object-based image classification methods. For discriminating vegetation classes, they often making use of various vegetation indices and biophysical parameters.","name":"Monitor land ecosystems","selfAssesment":"New
"},{"code":"TA13-4-6","description":"EO technologies (both optical and SAR) are capable to categorize bio-physical coverage of land to produce land cover maps like CORINE Land Cover (CLC). The EO method is objective and allows for frequent updates. EO-derived land cover is an excellent basis for mapping land use, the socioeconomic use that is made of land. Land use products are used in a wide range of applications (e.g. agriculture, forestry, spatial planning, determining and implementing environmental policy, land accounting). In a humanitarian context, land use mapping is applied to map refugee camps, population and pressures on population that cause migration.","name":"Monitor land use","selfAssesment":"New
"},{"code":"TA13-4-7","description":"EO is capable to monitor topography with various types of land surface elevation data (both digital terrain models and digital surface models) and also focus on land surface changes and ground deformation / movement due to e.g. soil erosion or permafrost thawing, frost heaving. This includes also the mapping of stable zones where such changes do not happen. The main ways of creating a digital elevation model (DEM) from EO data are deriving it from interferometric synthetic aperture radar (InSAR), from stereoscopic pairs of optical images acquired from different viewing angles, and deriving them via laser scanning.","name":"Monitor topography","selfAssesment":"New
"},{"code":"TA13-4-8","description":"EO is able to extract information about subsurface geology, including near surface features, lithology features, and linear disturbance features (faults & discontinuities). Concerning monitoring of mineral extraction EO supports by mapping ground surface, illegal activities, mine waste (erosion, land subsistence, biodiversity/habitat loss, destruction & disturbance of ecosystems). Disturbance of ecosystems may happen by carbon seeps from reservoirs or pipelines. Their detection can also be done with EO data.","name":"Extract information about subsurface geology","selfAssesment":"New
"},{"code":"TA13-4","description":"Services that monitor land cover all services/applications that are focused on monitoring, assessing, managing, planning and improving land areas, its ecosystems (land, soil and inland water monitoring/quality/availability & usage assessments) and evolution of the land surface (use, cover, seasonal and annual changes and monitors variables) even if it involves human intervention (environmental challenges, impact evaluation or suitability analysis).\r\nMonitoring is possible by deriving information from variables measured by EO in different domains, like vegetation, energy, water, and cryosphere. For vegetation, those variables are for example land cover, NDVI, burnt area, or surface soil moisture. In the energy domain, land surface temperature and surface albedo are known variables, for water it is water surface temperature or water quality. Finally, for the cryosphere lake ice and snow cover extent, and snow water equivalent are variables that are used for land monitoring services.","name":"Monitor land","selfAssesment":"Completed
"},{"code":"TA13-5-1","description":"The full range of EO satellite sensors are capable of monitoring particular aspects of urban areas. The most relevant include SAR satellites such as TerraSAR-X that distinguish between urban fabric and other land cover. Further, optical satellites in the resolution range HR and VHR are used to map imperviousness and soil sealing. Beyond such land cover classifications with low granularity, HR and VHR data are used for producing detailed land use and land cover classifications that distinguish different settlement densities or, in combination with additional data, different land use such as transport, residential etc. as defined in Classification schemes specialized on urban areas. Airborne laser scanning (and stereographic analysis) maps building and vegetation heights. InSAR methods allow to measure land subsidence that is highly relevant e.g. in coastal cities close to or below the sea surface elevation. Night-time optical data maps lights. Thermal sensors allow mapping the heat that is radiated from cities. Typical applications include monitoring urban growth/sprawl, transport networks, urban heat islands, and generating city maps and 3D city models for urban planning that are relevant to users in smart cities and in local/regional planning.","name":"Monitor urban areas","selfAssesment":"Completed
"},{"code":"TA13-5-2","description":"EO is capable of monitoring infrastrcture in general, i.e. buildings (and their construction) and transport networks (roads, rails). Additionally, infrastructure for renewable energy harvesting (solar and wind farms, hydroelectric powerplants) and identification of suitable sites (through mapping solar radiation, wind roses, speed and direction, hydrological network mapping). A basis is land surface mapping for deriving digital elevation models (DEMs) that is required for modelling renewable energy potential and for spatial planning and landscape visibility analysis (visual impact assessments for planned infrastructure). Further, EO is capable of assessing damage from industrial accidents. A wide range of EO technologies is used here, infrastrcture can be directly detected and mapped with optical and SAR sensors, where the resolution depends on the targeted assets. DEMs can be generated from SAR and stereographic optical data. Wind energy related parameters can be derived from satellites focused on atmosphere and weather monitoring. Further, there are various GI methods in use, too (in particular focused on spatial planning and impact assessment).","name":"Monitor infrastructure","selfAssesment":"New
"},{"code":"TA13-5","description":"Monitoring the built environment provides information about urban structures, transport networks and particular infrastructure, e.g. dedicated to energy provision. It covers all urban and infrastructure related service/applications on site development information, planning support or suitability analysis. As well, it includes pressure and threats analysis on the urban areas.","name":"Monitor the built environment","selfAssesment":"New
"},{"code":"TA13-6-1","description":"Oceanic waters cover approximately 70% of the Earth´s surface and play a key role in regulating Earth temperature and climate, support important marine ecosystems and provide food and transport. Ocean waters occupy large areas and involve highly dynamic processes with different temporal and spatial scales. In-situ measurements taken by ships and buoys can provide accurate information but only at specific locations, being limited to understand large-scale processes. To characterise the heterogeneity and dynamics of ocean waters, it would be required to perform exhaustive field campaigns with associated high costs and infrastructure challenges. EO is an efficient tool to monitor ocean waters and to complement ocean in-situ monitoring programmes as it can provide cost-effective information over vast areas at continuous temporal and spatial scales. \r\nSince the first EO satellite specifically designed to study the oceans (SeaSat) has been launch in the 1970s, many sensors and platforms have been developed. This variety of sensors have provided measurements of a broad range of ocean physical and biological variables to the present day. For example, satellite observations in the visible and near-infrared bands have provided information about ocean colour that can be used to estimate chlorophyll-a concentration for monitoring water quality, productivity and algal blooms. Thermal infrared (TIR) sensors have provided data of Sea Surface Temperature (SST) that is of importance for the study of currents and ocean warming. Microwave radiometers have registered sea surface salinity (SSS), critical to determine the global water balance, understanding ocean currents and estimating evaporation rates. EO can also provide information about physical ocean features such as surface elevation and ocean currents, sea surface winds, ocean waves, vessels and pollutants such as oil spills. \r\nThe versatility of EO data have been proved in a broad range of applications, including the monitoring of water quality, climate change effects, hurricane tracking and prediction, monitor maritime traffic and pollution, harmful algal blooms and fisheries management. In recent years, the Copernicus programme has launched a series of satellite missions for water and land monitoring that guarantee the provision of long-term observations giving continuity to previous satellite missions. Within the Copernicus programme, especially the Sentinel-3 mission will have relevance for ocean observations. Currently, two satellites Sentinel-3A and Sentinel-3B, launched respectively in 2016 and 2018, are providing near-real-time data on the state of the ocean surface, including sea surface temperature, marine ecosystems, water quality and pollution monitoring. New hyperspectral missions such as the Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) developed by NASA, are currently under development. In the near future, they will complement the existing satellite missions and will register data in a high number of spectral bands. This information will be essential in diverse applications such as aquatic ecology and biochemistry. Ocean EO is still an evolving field that will need skilled professionals that exploit the data from the new and upcoming missions for the advancement of ocean knowledge and monitoring.","name":"Monitor the marine ecosystem","selfAssesment":"Complete
"},{"code":"TA13-6-2","description":"In coastal areas, EO is capable to monitor water depth and shallow water bathymetry (charting), coastal ecosystem parameters about water temperature, water transparency, oxygen, phytoplankton abundance, bathing water indicators, detection harmful algal blooms, sediment (qualitative, quantitative), turbidity (quality, quantitative), visibility, chlorophyll-a concentration, suspended sediment may be indicative of estuarine processes, re-suspension or pollution. Further, this includes coastline monitoring with a focus on shoreline and its change as well as coastal land cover (and terrain) and its change. A widse set of EO sensors and technologies is used to monitor coastal areas. Optical satellite imagery is analyzed to detect and map suspended sediment concentrations. Etc.","name":"Monitor coastal areas","selfAssesment":"New
"},{"code":"TA13-6-3","description":"EO is capable to monitor weather impact on ocean surface and metocean features as a basis for forecasting furture ocean conditions. This includes ocean surface topography, ocean dynamics and circulation like tides and ocean current movements and drift, ocean winds, wave and climate conditions at ocean locations (meteocean). Further, this covers the mapping of extreme waves like tsunamis and the monitoring of hurricanes and typhoons. Involved EO technologies are for example satellite altimetry that maps ocean surface with 2 cm to 3 cm accuracy, mathematical forecast models. Repeated altimetry measurements allow mapping speed and direction of ocean's currents and tides. Available EO-based RADAR systems monitor wave height and direction, wind speed and sea-surface elevation. Near-realtime processing and delivery workflows enable the use of these parameters in weather forecasting, navigation and offshore installations protection.","name":"Monitor weather impact on ocean surface","selfAssesment":"New
"},{"code":"TA13-6-4","description":"To support an ecosystem-based approach for fisheries management, EO images with global and daily systematic coverage with high-resolution images can help in identifying potential fishing zones and to assess fish stocks. They help assessing and understanding changing abundancy and spatial distribution of exploited fish stocks. Therefore, they analyse various key environmental parameters that can be detected with satellite remote sensing. This includes sea surface temperatures (SSTs), sea surface height anomalies, and sea surface colour revealing the abundance of chlorophyll a. This relates to phytoplacton production that is directly related to total fish landings. Additionally, EO can detect harmful algal bloom. A further threat to sustainable fish stocks management are illegal fishing. Where localization of licensed fishing vessels and fleet management services are supported by EO to avoid overexplotation and enable recovery of fish stocks. EO complements identification, detection and tracking of vessels with SAR and optical remote sensing.","name":"Monitor fisheries","selfAssesment":"New
"},{"code":"TA13-6-5","description":"For shipping, navigation, and monitoring sea-traffic and pollution, remote sensing and satellite technologies allow detecting vessels in the wider ocean. EO can detect the vessels themselves, their wake trailing behind them, sandbanks and reefs that pose a threat for safe navigation. Additionally, EO can detect pollution from the ships, e.g. when illegal waste disposal happens. Ship detection and classification is possible with the use of optical and synthetic aperture radar (SAR) imagery. The methods complement each other.","name":"Detect and monitor ships","selfAssesment":"New
"},{"code":"TA13-6-6","description":"Information on sea ice and icebergs is important for managing operation of ships or offshore platforms in hazardous sea ice conditions. EO technologies give the possibility to study sea ice and measure its thickness, spatial distribution, motion and ridges (as well as ice berg positions). Satellite imagery provides wide area, synoptic pictures of the ice conditions. Since the scale of ice fields is quite large, mainly moderate resolutions have to be accepted, down to around 10m in scale, while ensuring comprehensive coverage. Multispectral imagery can provide more information on ice-type but in the main, SAR imagery is used due to its all-weather and day/night capability. The data collected can be more accurate than in-situ measurements due to a higher and faster coverage of a whole area. Subsequent modelling that incorporates ocean weather (wind, waves, ocean current) provides expected drifting paths. Constant monitoring is most important to identify the risk and opportunities, for instance for ship routing, and safety of oil rigs.","name":"Monitor sea-ice and icebergs","selfAssesment":"New
"},{"code":"TA13-6","description":"Monitoring marine inlucdes monitoring of marine safety (e.g. marine operations, oil spill combat, ship routing, defence, search & rescue, ...), marine resources (e.g. fish stock management, ...), marine and coastal environment (e.g. water quality, pollution, coastal activities, ...), and climate and seasonal forecasting (e.g. ice survey, seasonal forecasting, ...).","name":"Monitor marine","selfAssesment":"New
"},{"code":"TA13","description":"EO services and applications are organized according to thematic areas. EO is used for a wide set of services. There are many applications of EO that show how a service produces information for a particular client. EO service and applications are best described by the purpose they serve or by the need of the user. The main user needs to EO are to monitor, to map, to forecast, to assess, to detect, and to analyse. \r\nTo monitor means to watch and check a situation carefully for a period of time in order to discover something about it, i.e. keeping track of how the natural and manmade environment change (their status) over time. Typical alternative verbs are track, observe, record, follow, understand, or surveil. \r\nTo map means to represent an area of land in the form of a map, i.e. to feature and locate the way it is arranged or organized. Synonymous verbs are locate, identify, classify, trace, or record.\r\nTo forecast means to provide statements covering a range of different outcomes, to say what you expect to happen in the future; i.e. to predict future events based on specified assumptions (about information extracted from EO change and time series data), where different sets of assumptions describe scenarios. Equivalent terms are predict, plan, model, estimate, or project.\r\nTo assess means to judge or decide the amount, value, quality or importance of something, i.e. to evaluate and measure the status of and changes in natural and manmade built environments. Alternative verbs are evaluate, measure, understand, review, or quantify.\r\nTo detect allows to notice something that is partly hidden or not clear, or to discover something, especially using a special method, i.e. to identify and locate the changes in the Earth’s environment. Similar terms are locate, warn, identify, highlight, or spot.\r\nTo analyse means to study or examine something in detail, in order to discover more about it, i.e. to detail the elements of a whole and critically examine and relate these component parts separately and/or in relation to the whole. Sometimes, the terms to process, to parse, or to detail are used in exchange for to analyse.","name":"EO services and applications","selfAssesment":"New
"},{"code":"TA14-1-1-1","description":"Ocean colour can be made visible in atmospherically corrected EO data. Specific spectral bands are necessary to derive physical and biologic parameters of the water from the EO data.","name":"Ocean colour","selfAssesment":"New
"},{"code":"TA14-1-1","description":"Band combinations are pre-defined for (visually) analysing images for a dedicated purpose. Examples are dedicated band combinations for land us land cover classification, ocean colour, etc.","name":"Band combinations","selfAssesment":"New
"},{"code":"TA14-1-2","description":"The spectral and refractive information from optical and SAR data enables direct and indirect derivation of biophysical and geophysical EO parameters that are properties of the sensed land surface, ocean surface and atmosphere volume.","name":"EO parameters","selfAssesment":"New
"},{"code":"TA14-1","description":"Processing products are image products from raw data to all different processing stages. The transformation processes between the stages include operations such as atmospheric correction, cloud detection and radiometric calibration to provide data in a form suitable for subsequent analysis. Processing products consider a product as being an output of a process.They appear as \"intermediate products\" along all steps of the processing chain.","name":"Processing-related and preparatory products","selfAssesment":"New
"},{"code":"TA14-2-1-1","description":"Point clouds represent a set of points with X, Y, Z coordinates and associated attributes. A source of acquisition is Light Detection and Ranging (LIDAR) sensor.\r\n Depending on the location of the recording device, i.e. where and on which the LIDAR systems are mounted, it can be divided into: Terrestrial Laser Scanning (TLS), Airborne Laser Scanning -ALS) and Spaceborne Laser Scanning (SLS).\r\nThe LIDAR system uses the near-infrared part of the electromagnetic spectrum (1064 nm) for active data collection, day or night, in the shade, but also in low visibility conditions (e.g. under clouds). Due to the footprint of the beam itself, when interacting with vegetation, one part will be reflected back, registering the height of the vegetation, and part of the beam will pass to another surface from which the other part of the beam will be reflected. Depending on the beam intensity and vegetation density this can happen a few times until it hits a hard surface and the rest of the beam is reflected.\r\nIn this way, precise information on the height and density of vegetation can be obtained, but also using automatic and semi-automatic data filtering techniques, it is possible to create several very high resolution products from source data: digital elevation model (DEM), digital relief model (DMR) digital canopy model (DCM) , digital surface model (DSM).\r\nDepending where the sensor is mounted, the density of collected point clouds can be from 15 points per m2 to as many as 250 points per m2 (in the case of UAV dana collection). This is also depending on the speed and altitude of the flight and the speed and power of the emitted pulse or beam. The biggest advantage of LIDAR scanning is that in most cases, a sufficient number of beams will always penetrate to the ground, allowing the creation of a very precise digital relief model which is the basis for further analysis. This is not always possible in very dense vegetation areas (rainforests).\r\nThe advantage of LIDAR point clouds lies in the fact that it truly provides a huge amount of information gathered in a short period of time, that are of exceptional precision. These point clouds have very wide application from forestry, surveying, architecture to archeology.\r\nGiven the development of technology, it is possible to obtain a similar point cloud by photogrammetry methods. However, photogrammetric cameras (eg orthophotos and infrared cameras) have one significant drawback, they cannot penetrate clouds, vegetation and water, and only DSM product can be extracted from them.","name":"Point clouds","selfAssesment":"Completed
"},{"code":"TA14-2-1-2","description":"Elevation data in the form of a digital elevation model (DEM) is an essential component of many analyses derived from EO. DEMs are used to represent every kind of surface, including terrain surface, vegetation canopy surface, sea surface, sea-ice surface, glacier surface etc. This description focuses on DEMs for representing terrain. A digital terrain model (DTM) describes the bare ground of the terrain, a digital surface models (DSM) described heights of vegetation (e.g. trees) and of man-made structures (e.g. buildings) reaching above the terrain. DEM is often used as an umbrella term for DTM and DSM. EO-derived DEMs are usually DSMs and require removal of vegetation and buildings in order to represent the terrain (DTM). DEMs are multi-purpose products used in various applications. They are available for global scale (SRTM, WorldDEMTM), regional scale (ArcticDEM, Copernicus EU-DEM v1.1) or for national levels and local regions. Various techniques exist to generate DEMs from SAR data, stereographic optical EO (as well as airborne and drone) data and from airborne laser scanning.","name":"Digital elevation models","selfAssesment":"Completed
"},{"code":"TA14-2-1-3","description":"By comparing elevation models of different dates, the change in elevation and volume can be identified. Thereby, they measure surface deformation, land subsidence, ice shield loss due to melting, etc.","name":"Elevation change maps","selfAssesment":"New
"},{"code":"TA14-2-1-4","description":"Vector fields capture the movement directions of locations on a continuous surface, e.g. of the ocean, or in a 3D grid of locations, e.g. of the atmosphere. The atmosphere and the ocean are highly dynamic features. Vector fields are used to represent wind directions and current movement directions. Further vector fields derived from EO data include geoid undulation / gravity maps.","name":"Vector fields","selfAssesment":"New
"},{"code":"TA14-2-1-5","description":"When a moving feature (i.e. object) is detected in subsequent images, its trajectory of movement can be mapped. Such products map ship movements, sea ice movements, etc.","name":"Feature trajectories","selfAssesment":"New
"},{"code":"TA14-2-1","description":"Geometrically measured EO products origin from EO-derived distance measurements, measurements of direction, tracking of moving objects, and changes of distance measurements. The used EO methods include for example SAR interferometry and stereographic analysis of optical data.","name":"Geometrically measured EO products","selfAssesment":"New
"},{"code":"TA14-2-2-1-1","description":"Land cover maps represent spatial information on different types (classes) of physical coverage of the Earth's surface, e.g. forests, grasslands, croplands, lakes, wetlands. An example is the European Copernicus product CORINE land cover (CLC) with 44 classes. Initiated in 1985 (reference year 1990), updates followed in 2000 and every 6 years afterwards. Apart from CLC, the European Copernicus Land products also include the High Resolution Layers. They includes for example the imperviousness product that captures the percentage of soil sealing. Land cover classification products are multi-purpose products that are relevant for various applications. They are available on national levels, regional levels and global levels. They have different scales and granularity of their associated classification scheme. The products are updated on a regular basis. Update cycles can vary depending on the resolution (i.e. likelihood for observable change of the land surface) and the capability of production processes. An additional example on a global scale is the Global Urban Footprint. The products are provided by public organisations and private EO companies and based on various EO sensors.","name":"Land cover maps","selfAssesment":"Completed
"},{"code":"TA14-2-2-1-2","description":"Land use documents how people are using the land. Getting from physical land type (land cover) to land use requires skill in interpretation and involves integration and consultation of ancillary data. Land use maps are multi-purpose products that are relevant for many applications. The products are updated on a regular basis (e.g. 6 years for Urban Atlas).","name":"Land use maps","selfAssesment":"New
"},{"code":"TA14-2-2-1-3","description":"Cloud masks for optical EO data distingush cloudy pixels from cloud-free pixels. They may differentiate between serveral cloud types, i.e. opaque clouds and Cirrus clouds (that are transparent). Most land monitoring applications based on optical data require cloud-free images. Therefore, cloud masks are a product that is used early on in image processing for selecting suitable imagery for analysis (e.g. by screening images of an archive by the derived cloud cover percentage of the image). Therefore, cloud masks are made available as metadata by the EO data provider. Clouds are identified with threshoulding of reflectance values of the blue band and, to adapt for cloud/snow confusion, specific short-wave infrared (SWIR) bands.","name":"Cloud mask","selfAssesment":"New
"},{"code":"TA14-2-2-1-4","description":"Detected features are objects from one or more classes and are the result of a comprehensive (and mostly automatic or semi-automated) search of all locations in an image that decides whether such features are present and where they are located. Examples inculde man-made objects (e.g. vehicles, ships, buildings, etc.) with sharp boundaries and are independent from the background, and landscape objects, such as land-use/land-cover (LULC) parcels that have vague boundaries and are part of the background environment. Only the latter type would locate features for all locations of an image.","name":"Detected features","selfAssesment":"New
"},{"code":"TA14-2-2-1","description":"Static EO derived thematic classification products and masks (e.g. land use land cover classifications). Additionally, static EO detected features (planes on apron of airports, dwellings) that consist of a set of point locations (or polygons) and do not end up in a comprehensive classification of all pixels of an image. Static EO derived thematic classification products and masks (e.g. land use land cover classifications). Additionally, static EO detected features (planes on apron of airports, dwellings) that consist of a set of point locations (or polygons) and do not end up in a comprehensive classification of all pixels of an image. Thematic classifications and feature detection identify a surface by a class label that represents a more or less persistent state. A good example product is the Copernicus Urban Atlas. The most recent available version is assumed to represent the \"current\" state (Certainly, an update cycle is necessary for providing a product that remains up-to-date).","name":"Thematic classifications and feature detection","selfAssesment":"New
"},{"code":"TA14-2-2-2","description":"Event maps and thematic change (evolution) maps indicate that some process happened that changed the area at a location from one class to the other. For example, a burnt area map indicates locations where vegetation has been burnt by a fire and changed to bare ground. A typical mapping method is the use of pre- and post-event satellite images for detection of the areas affected by the process. Eventually burnt areas contain identifiable burn marks that allow direct identification in one single post-event satellite image. Nevertheless, it is the process that is central to the analysis. Similarly, the concepts aforestation and deforestation would fall under the heading \"Event maps.\" They may come from a comparison of two status maps of different dates. Some processes benefit from analysis of more than two states. Such change evolution maps can be produced with time-series analysis. On land, more examples include landslide maps, flooded area maps and other land surface dynamics (e.g. aforestation and deforestation). Further, change detection maps are available for other domains (atmosphere, marine, land, climate, etc.)","name":"Event maps and thematic change (evolution) maps","selfAssesment":"New
"},{"code":"TA14-2-2","description":"The semantic labelling products result from methods that assign labels to objects or locations in a field. The labels correspond to the categories of a classification or, in case of masks and detected features, to a single target class. Such labels may also identify classes of change or change evolution.","name":"Semantic labelling products","selfAssesment":"New
"},{"code":"TA14-2-3","description":"EO-derived attribute products describe the state and evolution of specific attributes of a feature or at a field location. They describe for example air quality, soil moisture or water quality & quantity.","name":"EO-derived attribute products","selfAssesment":"New
"},{"code":"TA14-2","description":"Descriptive analytics products provide analytical results which describe the present (and past) situation as it is recorded in EO images. Therefore, it contains information that can directly be extracted from EO images or EO image time series. These products are diverse in various aspects: they capture static and dynamic information; they concern information about objects or fields; and they have qualitative (nominal scale) or quantitative (ordinal, interval, ratio scale) levels of measurement.","name":"Descriptive analytics products","selfAssesment":"New
"},{"code":"TA14-3","description":"Providing analytical (modelling) results which predict the future situation (e.g. air pollution forecasts). [interpolation in space, i.e. not only prediction into the future, filling gaps in time series...]\r\nInformation that can be modelled based on descriptive analytics products. by extrapolating time series (forecasting/predicting), by modelling of processes (e.g. flood risk maps, landslide susceptibility)","name":"Predictive modelling products","selfAssesment":"New
"},{"code":"TA14-4","description":"Prescriptive modelling products and services focus on providing analytical results that are a guide to action. The often result from an impact assessment. One example is the identification of construction sites leading to sales opportunities.","name":"Prescriptive modelling products and services","selfAssesment":"New
"},{"code":"TA14-5-1","description":"A textured 3D model uses a 3D model derived from elevation data. Additionally, each separate surface of the 3D model receives its own texture derived from optical image data. Typically used for visualisation purposes.","name":"Textured 3D models","selfAssesment":"New
"},{"code":"TA14-5-2","description":"A semantic 3D model consists of a 3D model derived from elevation data with an integrated image classification. A classified object thereby consists of a 3D surface or a grouped set of 3D surfaces. A typical example is a 3D city model in the CityGML format.","name":"Semantic 3D models","selfAssesment":"New
"},{"code":"TA14-5","description":"Combining the satellite data with other information sources. Resulting in an integration of several descriptive analytics products and processing products, e.g. a textured 3D model or a semantic 3D model.","name":"Aggregation and integration products","selfAssesment":"New
"},{"code":"TA14-6-1","description":"Sentinel-2 cloud-free mosaics for display, satellite maps in books etc.","name":"Satellite maps","selfAssesment":"New
"},{"code":"TA14-6-2","description":"Layouted maps in a file (PDF, SVG, etc.) for printing or visualisation on screen, embedding in reports or as static displays on websites etc.","name":"Layouted digital maps","selfAssesment":"New
"},{"code":"TA14-6-3","description":"Digital layouted maps in an online map viewer; 3D visualisations on the screen / 3D screen and online map viewers with 3D capabilities etc.","name":"Web visualisations in 2D and 3D","selfAssesment":"New
"},{"code":"TA14-6-4","description":"Printed maps, 3D plots of 3D models, hologram 3D maps etc.","name":"Analogue visualisation products","selfAssesment":"New
"},{"code":"TA14-6-5","description":"A video is a structured file of 2D grids link by the time, is a regular file of values which has been processed to sensor units (e.g. calibrated). The result can be a single date acquisition or a combination of dates. For each point, the value represents a parameter imaged by the sensor. Videos of EO data present for example time series of satellite maps and other EO products (e.g. Arctic sea ice evolution in a time-series map video over the past 30 years).","name":"Time series map videos","selfAssesment":"New
"},{"code":"TA14-6","description":"Visualisation products are used for presentation of EO information to the user. The user's interaction with the visualisations is predominantly viewing and interpretation of the informational content and arriving at decisions in the context of the user'S objective with the EO information. In addition, users of visualisation are all involved actors during image processing. For example, an EO analyst may use visualisations of EO data and preliminary EO products for getting a better understanding of the contained information and adapt his processing workflow to arrive ad improved results. Typical visualisation products include satellite maps, layouted digital maps, web visualisations in 2D and 3D, and analogue visualisation products.","name":"EO visualisation products","selfAssesment":"New
"},{"code":"TA14-7","description":"Users need access to EO products if they shall be able to benefit from them. Additionally, providers of value added products act as users of EO products earlier in the information processing value chain. Concequently, various distribution services provide access from raw data to processed information and processing infrastructure. Provision of access to raw data or processed information happens via direct download (FTP), via application programming interfaces (API) or web services (e.g. Hubs). Further, access to processing infractructure happens via web services.","name":"Distribution services","selfAssesment":"New
"},{"code":"TA14","description":"Products in relation to EO appear along the entire image processing value chain as inputs and outputs of processing steps. Ultimately, at the end of that chain, the output EO products represent information that supports actions. The standard EO products are categorized by the type of problems they help to solve or the type of question they help answering.","name":"Standard EO products","selfAssesment":"New
"},{"code":"WB","description":"This knowledge area is about Web Based Geographic Information management aspects and therefore it was given the name \"Web Based GI\" or \"WBG\" in short. It is implied by this name that the differentiating factor for this KA is the \"Web\". One must then be able to answer the questions like \"What functions do we delegate to the Web?\" or \"how WBGI is different from the traditional GI?\" Sticking to the functions of a GIS, which are inserting (adding), storing, manipulating, analysing and presenting the data, there is not a single system for effecting all these tasks anymore but the Web itself. For instance, there is no single database and its known-to-its users-definition, anymore but many different stores and many different definitions. Similarly, many different manipulation, analysis and presentation options compared with the options offered by a single or limited number of systems of traditional GI. In general, Web provides the means of leveraging distributed \"resources\" like data, information, or software. It is a \"collaboration medium\". A collaboration that enables rapid production or decision making. A collaboration that certainly introduces new dimensions to traditional GI handling. This is the justification of proposing this KA in addition to the KAs of the original BoK. For the mentioned collaboration to happen, data or any other type of a resource have to accessible on the Web. This means that it should have a Web \"address\" and a \"definition\" that is understandable either by \"human\" or \"machine\". \"Machine understandable definitions\" refers to the dimension of \"semantics\" and \"ontologies\" which are also included under this KA. When one talks about publishing resources then \"catalogue services\" and more importantly \"discovery\" dimension comes into the scene. On the other hand, \"Linked Data (LOD)\" and \"Open Data\", highly popular recent trends and two of the above mentioned dimensions of Web GI have also been covered under this KA. Like the other dimensions of Web GI, both LD and OD aspects must be known to GI communities with differing degrees of expertise. The concepts of \"interoperability\" and \"Spatial Data Infrastructure (SDI)\", hot topics of GI communities for many years, have been thought to be dealt with under this KA as well with the justification that \"Web GI\" is a much broader concept than SDI, This is by the fact that SDI refers to a much narrower content and context of \"collaboration\" then Web GI. Therefore, Geospatial data interoperability and some of the related concepts which were classified under KA, \"Geospatial data in the original BoK were moved under KA11 with the updated context. Another issue is the coverage of Spatial Analysis (SA), data manipulation aspects of GI by KA11. The SA aspects are covered by other KAs like \"Geocomputation\" and \"Analytical methods\". If the analysis operations, in an undertaking, would be handled by web services this is already covered by \"data processing\" web services, application development unit and Web services composition under that unit. The important thing is to have the knowledge about a specific analysis operation; Employing it as a web service would require no more knowledge than using any other web service. SA is covered by KA11 in as much as it should have been.","name":"Web-based GI","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB1-1","description":"The basic principles on which web services build. The concept of Service Oriented Architecture and the importance of APIs","name":"Fundamentals of web services","selfAssesment":"In progress/to be revised (GI-N2K)
"},{"code":"WB1-2","description":"This concept will cover web services based on the Simple Object Access Protocol (SOAP)","name":"SOAP web services","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB1-3","description":"This concept will cover web services based on the representational state transfer (REST) protocol","name":"REST web services","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB1-4","description":"The Open Geospatial Consortium (OGC) defines standards and best practices for web services in the geospatial domain. OGC standards are developed using a consensus model allowing all stakeholder to participate in the process. As a result the OGC web services are widely implemented.","name":"OGC web services","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB1","description":"In the most simplistic way a Web service may be defined as \"a Web accesable program code which performs a task of either processing or serving some data. Although there are many other definitions in the related literature, the one in W3C (2004) seems to be quite complete and refering to also lately popular REST style Web services. It states that \" We can identify two major classes of Web services: REST-compliant Web services, in which the primary purpose of the service is to manipulate XML representations of Web resources using a uniform set of \"stateless\" operations; and arbitrary Web services, in which the service may expose an arbitrary set of operations.","name":"Web services","selfAssesment":"In progress GI-N2K
"},{"code":"WB2-1","description":"To be able to discover and assess available data or services, these resources have to be documented. This concept describes the standardized languages used for these descriptions","name":"Languages for the definition of non-spatial data and services","selfAssesment":"GI-N2K
"},{"code":"WB2-2","description":"Different standardized ways to define geospatial data exist. GML, GeoJSON, WKT and GeoSPARQL are examples. What are common points and differences","name":"Definition of geospatial data","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB2-3","description":"Defining a common language is a crucial step for sharing or combining data. Vocabularies, taxonomies, ontologies are are tools to reach this goal.","name":"Ontologies development reuse and patterns","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB2","description":"A \"resource\" could be \"anything\" including data and services, identifiable over the Web. A resource should be defined in a language to be discoverable on the Web. Over the years, two major bodies W3C for non-spatial and OGC concerning spatial data have developed many specifications for defining data and services. On the W3C side, Resource Description Framework (RDF) has gained a great momentum in recent years in relation to the recent popularity of Linked Data as well. In the OGC front, the acceptance of GML was a major step concerning the long time effort of geospatial communities for having a standard for the definition of both geospatial features and geometry.","name":"Resource Definition","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB3-1","description":"Metadata is information about the data to be published. It helps the user to discover the data, allows the user to evaluate the fitness for use and it explains how and under which conditions the data can be retrieved and used. Metadata are a core component of data infrastructures and as such, standardization is a requirement for the correct exchange and interpretation of the metadata.","name":"Metadata and standards","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB3-2","description":"A resource can be added manually to a catalogue service by creating or uploading its metadata, but metadata can also be added by automated crawling of other catalogues.","name":"Manual and automated forms of publishing","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB3-3","description":"Catalogue services allow to publish and search resources through their metadata","name":"Catalogue services","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB3-4","description":"Open data is data that is free to use, re-use and share without limitations on who uses it or for what purpose. Publishing open data is making the data discoverable and accessible in a convenient way (technical openness).","name":"Publishing open data","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB3-5","description":"Adding semantic information to the data allows computers to understand the structure and meaning of data. This allows automatic searching, processing and integrating data with other semantic sources.","name":"Publishing via a semantic definition of data","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB3-6","description":"Linked (open) data provides structured data which is interlinked in a machine readable way. This allows to discover, access and combine data in an automatic way. This concept discusses the steps needed to make existing data available in a linked open way.","name":"Publishing linked open data","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB3","description":"\"Publishing\" means making a resource available for the use of others. A \"resource\" could be \"anything\" including data and services, identifiable over the Web. Publishing may be done on the basis of either the \"characteristics\" of the data or the data itself. When only some \"characteristics\" of a resource is published then some of the contents would naturally be left out. The \"characteristics\" include metadata and some keywords. This kind of publishing may be named as \"limited contents\" publishing or \"publishing by metadata\". One of the issues become then what characteristics to use to define the data. Or what what metadata definition to use. Another aspect of publish is \"manual entry\" and \"automated collection\". In the former publisher enters metadata while in the latter some harvesting mechanism collects metadata in an automated fashion. On the contrary, there is \"unlimited contents publishing\" where there is no limitation on the published contents. Open data publishing is in this class. In additon, some \"additional semantics\" may be subject of this type publishing through new relationships in the ontologies of publishing, which have not been explicit in the exisiting data model but are inherent in the data. And this last type is covered under the topic, \"Publishing via a semantic definition of data.\"","name":"Resource Publishing","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB4-1","description":"Syntactic discovery is the discovery of resources based on the structure of the resources","name":"Syntactic discovery","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB4-2","description":"Semantic discovery is the discovery of resources based on the meaning of the data.","name":"Semantic discovery","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB4-3","description":"Linked (open) data provides structured data which is interlinked in a machine readable way. This allows to discover, access and combine data in an automatic way.","name":"Discovery over linked open data","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB4","description":"Resource discovery means the discovery of resources including data and services needed for an application. Syntactic discovery refers to the discovery on the basis of syntactic comparison operations. It is classified as \"keyword-based\" and \"full-text-based\" discovery. Semantic discovery on the other hand, refers to the discovery of resources on he basis of some semantic definition. Therefore, semantic discovery requires that a resource be published by a semantic definition as defined in the topic WB3-5.","name":"Resource Discovery","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB5-1","description":"The workflow to integrate geospatial data in an application often relies on a combination of different OGC web services. Searching and finding the data and the corresponding services, binding to these services to view, filtering and or downloading the data are different steps in this process","name":"Integrating data from OGC web services","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB5-2","description":"The alignment of data structures and vocabularies/ontologies used are important steps towards the data harmonisation needed for a combined use of datasets","name":"Schema matching and ontology alignment","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB5-3","description":"A data mashup is a combination of data from different sources to produce new applications of new datasets","name":"Data mash ups","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB5","description":"The term \"application development\" refers to the collection of activities or the \"workflow\" through which the user reaches her final goal. Being one of these activities, \"data integration\" means the transformation of data from one representation to another which might be of either the client`s one or some other representation. An example for data integration might be the case where the data is transfered from an OGC WFS and integrated into a client GIS.","name":"Application development via Data Integration","selfAssesment":"In Progress GI-N2K
"},{"code":"WB6-1","description":"Manual Web Service Composition is manually (by human) combining the activities of discovery, composition and invocation to fulfil a certain task.","name":"Manual Web Services Composition","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB6-2","description":"Providing standardized descriptions of the specifics of available webservices creates an environment where the composition of services to create a web application can be automated.","name":"Semi automated and Full-automated WSC","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB6","description":"Web Services Composition can be defined as bringing together a number of web services in a certain workflow to achieve a certain task that cannot be achieved by any of the composed services alone. In general, it involves first the discovery of the suitable services over the Web, and compose them in a certain workflow order and finally run the composed service which is the invocation stage. WSC has been a highly active research topic since the emergence of Web services in 2000s. \"Manual\" WSC is the form that the activities of discovery, composition and invocation are all done manually (by human). In the \"Semi-automated\" way, the discovery is done by the machine. In the \"full-automated\" approach all the above activities are done by the machine. There are no tools at the moment that achieve full automated composition. Web API composition is like WSC, the only difference is the fact that instead of web services there are Web APIs in WAPIC. There is no doubt that One would run into the very same problems of WSC concerning full automated composition. In other words, WAPIC would in no way be easier than WSC. Nevertheless, as far as semi automated form can be achived, WAPIC is valuable because the number of Web APIs increase drastically from day to day. The site \"programmableWeb\" lists 14 957 APIs at the moment. It is not easy to search for all those APIs manually for the discovery of suitable APIs for a given task.","name":"Application development via Web services composition","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB7-1","description":"Hypertext markup scripting and styling are the base for each web page or application. Styling defines the look and feel while scripting is used to implement the behavior of the web application","name":"Hypertext markup scripting and styling","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB7-2","description":"Web map APIs allow developers to integrate resources made available by web services in their application or web sites.","name":"Web Map APIs and Libraries","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB7-3","description":"A web application framework provides the generic and reusable building blocks needed to create web applications. Geoportal frameworks provide the functionality to build geospatial portals.","name":"Web application Frameworks and Geoportal frameworks","selfAssesment":"In Progress (GI-N2K)
"},{"code":"WB7","description":"Characteristic examples are included under this topic. The APIs, for instance other than the ones included under this unit, and libraries could have been included as well. However, since the important thing is to highlight the functionality then there is no need to include them all. By the inclusion of topic \"WB7-3\"under this unit, the aim was to cover one of the very \"hot\" topics of Web2.0 for both the main concepts about Web application frameworks and also how they are related to portal frameworks and geoportals. By the topic \"WB7-1 Building blocks\" the core components of Web application development are covered. On top of this core, there comes a great variety of \"Web application frameworks for both enabling rapid web application development and ensuring scalable, high-performance applications. Finally, there are \"Web APIs and Libraries\" certainly deserving being a separate topic for their current popularity. They also mean rapid application development for developers by code reuse and versatility for \"end users\" in creating their \"end products\".","name":"Web Application development elements","selfAssesment":"In progress (GI-N2K)
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EO Products & Services for Raw Materials Sector. Retrieved from: https://earsc-portal.eu/pages/viewpage.action?pageId=69894197","url":"https://earsc-portal.eu/pages/viewpage.action?pageId=69894197"},{"concepts":[855],"description":" ","name":"ECMWF. (n.d.) Forecast User Guide. Retrieved from: https://confluence.ecmwf.int/display/FUG/Forecast+User+Guide","url":"https://confluence.ecmwf.int/display/FUG/Forecast+User+Guide"},{"concepts":[591],"description":"Short course on inversion methods for NWP","name":"ECMWF/EUMETSAT NWP-SAF Satellite data assimilation Training Course","url":"https://www.ecmwf.int/sites/default/files/SAT_TC_What_do_Satellites_Measure_TM.pdf"},{"concepts":[714],"description":" ","name":"Eder, J. M. (1945). History of Photography. Transl. Edward Epstean. New York: Columbia University Press.","url":" "},{"concepts":[125,128],"description":"ISBN: 9780226605715","name":"Edney, M.H. (2019) Cartography : the ideal and its history. Chicago: The University of Chicago Press.","url":"http://books.google.com/books?isbn=9780226605715"},{"concepts":[128],"description":"ISBN: 978-0226184753","name":"Edney, M.H. (ed.) (2020) Cartography in the European Enlightment (The history of cartography.; volume 4) Chicago: University of Chicago Press.","url":"http://books.google.com/books?isbn=9780226184753"},{"concepts":[90,102],"description":"Digital Object Identifier (DOI): 10.1007/3-540-60392-1_1","name":"Egenhofer, M. and Mark, D. M. (1995). Naive geography. In A. Frank and W. Kuhn (Eds.), Lecture notes in computer science, 988, 1-15. Semmering, Austria: Springer-Verlag.","url":"http://dx.doi.org/10.1007/3-540-60392-1_1"},{"concepts":[120],"description":"Digital Object Identifier (DOI): 10.1007/3-540-54414-3_36","name":"Egenhofer, M.J. (1991) Reasoning about binary topological relations. Lecture Notes in Computer Science, 525, 144-160.","url":"http://dx.doi.org/10.1007/3-540-54414-3_36"},{"concepts":[222,471],"description":" ","name":"Egenhofer, M.J., & Franzosa, R.D. (1991). Point-set topological spatial relations. International Journal of Geographical Information Systems, 5, 161-174","url":" "},{"concepts":[165],"description":" ","name":"Eichelberger, P. (1993). Maturing GIS: Anecdotes, examples, and lessons, Geo Info Systems 3(5), 29-38.","url":" "},{"concepts":[423],"description":" ","name":"El Garouani, A., Alobeid, A., El Garouani, S., 2014. Digital surface model based on aerial image stereo pairs for 3D building. International Journal of Sustainable Built Environment 3, 119-126.","url":" "},{"concepts":[575,728],"description":" ","name":"Elachi, C., & Van Zyl, J. J. (2006). Introduction to the physics and techniques of remote sensing (Vol. 28). John Wiley & Sons.","url":" "},{"concepts":[94,95,96,100,330],"description":"ISBN/section: 9781420038330/chapter10","name":"Elmes, G. A., Epstein, E. F., McMaster, R. B., Niemann, B. J., Poore, B., Sheppard, E., and Tullock, D. L. (2004). GIS and society: Interrelation, integration, and transformation. In R.B. McMaster and L. Usery (Eds.), A research agenda for geographic information science, (287-312). Bacon Raton, FL: CRC Press.","url":"http://books.google.com/books?isbn=9781420038330"},{"concepts":[534],"description":" ","name":"Elsner, P. (2005). GIS teaching via distance learning experiences and lessons learned. Planet, 14(1), 28-29.","url":"https://doi.org/10.11120/plan.2005.00140028"},{"concepts":[358],"description":" ","name":"Elwood, S. (2008). Volunteered geographic information: Future research directions motivated by critical, participatory, and feminist GIS. GeoJournal, 72(3-4), 173-183.","url":"http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.464.751&rep=rep1&type=pdf"},{"concepts":[333],"description":" ","name":"Elwood, S., & Leszczynski, A. (2011). Privacy, reconsidered: New representations, data practices, and the geoweb. Geoforum, 42(1), 6-15.","url":"https://doi.org/10.1016/j.geoforum.2010.08.003"},{"concepts":[348],"description":" ","name":"Elwood, S., Goodchild, M. F., & Sui, D. Z. (2012). Researching volunteered geographic information: Spatial data, geographic research, and new social practice. Annals of the association of American geographers, 102(3), 571-590.","url":"https://doi.org/10.1080/00045608.2011.595657"},{"concepts":[74],"description":" ","name":"Emmanouil, A.V. “1-Geostatistics: Mathematical and Statistical Basis”, Spatiotemporal Analysis of Extreme Hydrological Events, pp.1-38, 2019.","url":" "},{"concepts":[301],"description":" ","name":"Engel, C. (2019). R for Geospatial Analysis and Mapping. 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Retrieved from https://earth.esa.int/web/sentinel/technical-guides/sentinel-2-msi/calibration","url":"https://earth.esa.int/web/sentinel/technical-guides/sentinel-2-msi/calibration"},{"concepts":[791,381],"description":" ","name":"ESA (2020), Level-1C Algorithm - Sentinel-2 MSI Technical Guide - Sentinel Online. Retrieved from https://earth.esa.int/web/sentinel/technical-guides/sentinel-2-msi/level-1c/algorithm","url":"https://earth.esa.int/web/sentinel/technical-guides/sentinel-2-msi/level-1c/algorithm"},{"concepts":[756,759,760],"description":" ","name":"ESA (2020). User Guides. Sentinel-1 SAR. Acquisition Modes. Extra Wide Swath. © ESA 2000 - 2020","url":"https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/acquisition-modes/extra-wide-swath"},{"concepts":[646,644,699,700,701],"description":"ESA (2020). Radar and SAR Glossary. earthnet online.","name":"ESA (2020). Radar and SAR Glossary. earthnet online.","url":"https://earth.esa.int/handbooks/asar/CNTR5-2.html"},{"concepts":[708],"description":"ESA (2020). Radar Course 3. Shadow. Earth Online.","name":"ESA (2020). Radar Course 3. Shadow. Earth Online.","url":"https://earth.esa.int/web/guest/missions/esa-operational-eo-missions/ers/instruments/sar/applications/radar-courses/content-3/-/asset_publisher/mQ9R7ZVkKg5P/content/radar-course-3-shadow"},{"concepts":[706,707,708],"description":"ESA (2020). Radar Course 3. Slant range / ground range. Earth Online.","name":"ESA (2020). Radar Course 3. Slant range / ground range. Earth Online.","url":"https://earth.esa.int/web/guest/missions/esa-operational-eo-missions/ers/instruments/sar/applications/radar-courses/content-3/-/asset_publisher/mQ9R7ZVkKg5P/content/radar-course-3-slant-range-ground-range"},{"concepts":[765],"description":"ESA (2020). Types of orbits.","name":"ESA (2020). Types of orbits.","url":"https://www.esa.int/Our_Activities/Space_Transportation/Types_of_orbits"},{"concepts":[759],"description":" ","name":"ESA (2021). User Guides. Sentinel-1 SAR. Acquisition Modes. Interferometric Wide Swath. © ESA 2000 - 2021","url":"https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/acquisition-modes/interferometric-wide-swath"},{"concepts":[759],"description":" ","name":"ESA (2021). User Guides. Sentinel-1 SAR. Applications. Mapping of Applications to Seninel-1 Modes. © ESA 2000 - 2021","url":"https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/applications/mapping-applications-s1-modes"},{"concepts":[759],"description":" ","name":"ESA (2021). User Guides. Sentinel-1 SAR. Products and Algorithms. Level-1 Algorithms and Products. Algorithm Overview. TOPSAR Processing. © ESA 2000 - 2021","url":"https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-1-sar/products-algorithms/level-1-algorithms/topsar-processing"},{"concepts":[766],"description":" ","name":"ESA Sentinel 1 acquisition modes","url":"https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/acquisition-modes"},{"concepts":[652,764,793,802],"description":"Categorised from A to Z and by Space Agency, there are over 1000 in-depth articles of satellite missions from 1959 to 2025 and information on airborne sensors. The missions database can be filtered by a range of criteria using the Search Missions filter drop down menus below, enabling you to find specific missions easily.","name":"ESA, 2020. eo-Sharing Earth Observation Resources. eoPortal Directory.","url":"https://directory.eoportal.org/web/eoportal/satellite-missions"},{"concepts":[899],"description":" ","name":"Esch, T., Heldens, W., Hirner, A., Keil, M., Marconcini, M., Roth, A., Zeidler, J., Dech, S., Strano, E. (2017). Breaking new ground in mapping human settlements from space – The Global Urban Footprint. ISPRS Journal of Photogrammetry and Remote Sensing, 134, 30-42. doi:https://doi.org/10.1016/j.isprsjprs.2017.10.012","url":"https://doi.org/10.1016/j.isprsjprs.2017.10.012"},{"concepts":[6],"description":" ","name":"Ester M., Kriegel HP., Sander J. (1999) Knowledge Discovery in Spatial Databases. In: Förstner W., Buhmann J.M., Faber A., Faber P. (eds) Mustererkennung 1999. Informatik aktuell. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-60243-6_1","url":"https://link.springer.com/chapter/10.1007/978-3-642-60243-6_1"},{"concepts":[865],"description":" ","name":"European Association of Remote Sensing Companies (EARSC), (2020). Forecast and assess landslides. Retrieved from: https://earsc-portal.eu/display/EOwiki/Forecast+and+assess+landslides","url":"https://earsc-portal.eu/display/EOwiki/Forecast+and+assess+landslides"},{"concepts":[882],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Assess and monitor coastal water quality. Retrieved from https://earsc-portal.eu/display/EOwiki/Assess+and+monitor+coastal+water+quality","url":"https://earsc-portal.eu/display/EOwiki/Assess+and+monitor+coastal+water+quality"},{"concepts":[866],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Assess and Monitor Volcanic Activity. Retrieved from: https://earsc-portal.eu/display/EOwiki/Assess+and+Monitor+Volcanic+Activity","url":"https://earsc-portal.eu/display/EOwiki/Assess+and+Monitor+Volcanic+Activity"},{"concepts":[871],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Assess and monitor water bodies. Retrieved from https://earsc-portal.eu/display/EOwiki/Assess+and+monitor+water+bodies","url":"https://earsc-portal.eu/display/EOwiki/Assess+and+monitor+water+bodies"},{"concepts":[854],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Assess changes in the carbon balance. Retrieved from https://earsc-portal.eu/display/EOwiki/Assess+changes+in+the+carbon+balance","url":"https://earsc-portal.eu/display/EOwiki/Assess+changes+in+the+carbon+balance"},{"concepts":[869],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Assess crop damage due to storms. Retrieved from https://earsc-portal.eu/display/EOwiki/Assess+crop+damage+due+to+storms","url":"https://earsc-portal.eu/display/EOwiki/Assess+crop+damage+due+to+storms"},{"concepts":[864],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Assess damage from earthquakes. Retrieved from https://earsc-portal.eu/display/EOwiki/Assess+damage+from+earthquakes","url":"https://earsc-portal.eu/display/EOwiki/Assess+damage+from+earthquakes"},{"concepts":[870],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Assess Deforestation or Forest Degradation. Retrieved from https://earsc-portal.eu/display/EOwiki/Assess+Deforestation+or+Forest+Degradation","url":"https://earsc-portal.eu/display/EOwiki/Assess+Deforestation+or+Forest+Degradation"},{"concepts":[869],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Assess Environmental impact of farming. Retrieved from https://earsc-portal.eu/display/EOwiki/Assess+Environmental+impact+of+farming","url":"https://earsc-portal.eu/display/EOwiki/Assess+Environmental+impact+of+farming"},{"concepts":[870],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Assess environmental impact of forestry. Retrieved from https://earsc-portal.eu/display/EOwiki/Assess+environmental+impact+of+forestry","url":"https://earsc-portal.eu/display/EOwiki/Assess+environmental+impact+of+forestry"},{"concepts":[873],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Assess environmental impact of human activities . Retrieved from https://earsc-portal.eu/display/EOwiki/Assess+environmental+impact+of+human+activities","url":"https://earsc-portal.eu/display/EOwiki/Assess+environmental+impact+of+human+activities"},{"concepts":[870],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Assess forest damage due to storms or insects. Retrieved from https://earsc-portal.eu/display/EOwiki/Assess+forest+damage+due+to+storms+or+insects","url":"https://earsc-portal.eu/display/EOwiki/Assess+forest+damage+due+to+storms+or+insects"},{"concepts":[871],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Assess ground water and run-off. Retrieved from https://earsc-portal.eu/display/EOwiki/Assess+ground+water+and+run-off","url":"https://earsc-portal.eu/display/EOwiki/Assess+ground+water+and+run-off"},{"concepts":[874],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Assess land value, ownership, type use. Retrieved from https://earsc-portal.eu/display/EOwiki/Assess+land+value%2C+ownership%2C+type%2C+use","url":"https://earsc-portal.eu/display/EOwiki/Assess+land+value%2C+ownership%2C+type%2C+use"},{"concepts":[874],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Assess pressures on populations and migration. Retrieved from https://earsc-portal.eu/display/EOwiki/Assess+pressures+on+populations+and+migration","url":"https://earsc-portal.eu/display/EOwiki/Assess+pressures+on+populations+and+migration"},{"concepts":[875],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Baseline mapping. Retrieved from https://earsc-portal.eu/display/EOwiki/Baseline+mapping","url":"https://earsc-portal.eu/display/EOwiki/Baseline+mapping"},{"concepts":[875],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Detect and monitor ground movement. Retrieved from https://earsc-portal.eu/display/EOwiki/Detect+and+monitor+ground+movement","url":"https://earsc-portal.eu/display/EOwiki/Detect+and+monitor+ground+movement"},{"concepts":[883],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Detect and monitor hurricanes and typhoons. Retrieved from https://earsc-portal.eu/display/EOwiki/Detect+and+monitor+hurricanes+and+typhoons","url":"https://earsc-portal.eu/display/EOwiki/Detect+and+monitor+hurricanes+and+typhoons"},{"concepts":[886],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Detect and monitor ice-risk at sea. Retrieved from https://earsc-portal.eu/display/EOwiki/Detect+and+monitor+ice-risk+at+sea","url":"https://earsc-portal.eu/display/EOwiki/Detect+and+monitor+ice-risk+at+sea"},{"concepts":[884],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Detect and monitor illegal fishing. Retrieved from https://earsc-portal.eu/display/EOwiki/Detect+and+monitor+illegal+fishing","url":"https://earsc-portal.eu/display/EOwiki/Detect+and+monitor+illegal+fishing"},{"concepts":[881],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Detect and monitor oil slicks. Retrieved from https://earsc-portal.eu/display/EOwiki/Detect+and+monitor+oil+slicks","url":"https://earsc-portal.eu/display/EOwiki/Detect+and+monitor+oil+slicks"},{"concepts":[868,863],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Detect and monitor wildfires. Retrieved from https://earsc-portal.eu/display/EOwiki/Detect+and+monitor+wildfires","url":"https://earsc-portal.eu/display/EOwiki/Detect+and+monitor+wildfires"},{"concepts":[872],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Detect changes in glaciers. Retrieved from https://earsc-portal.eu/display/EOwiki/Detect+changes+in+glaciers","url":"https://earsc-portal.eu/display/EOwiki/Detect+changes+in+glaciers"},{"concepts":[870],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Detect illegal forest activities. Retrieved from https://earsc-portal.eu/display/EOwiki/Detect+illegal+forest+activities","url":"https://earsc-portal.eu/display/EOwiki/Detect+illegal+forest+activities"},{"concepts":[874],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Detect illegal mining activities . Retrieved from https://earsc-portal.eu/display/EOwiki/Detect+illegal+mining+activities","url":"https://earsc-portal.eu/display/EOwiki/Detect+illegal+mining+activities"},{"concepts":[869],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Detect illegal or undesired crops. Retrieved from https://earsc-portal.eu/display/EOwiki/Detect+illegal+or+undesired+crops","url":"https://earsc-portal.eu/display/EOwiki/Detect+illegal+or+undesired+crops"},{"concepts":[885],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Detect ships in critical areas. Retrieved from https://earsc-portal.eu/display/EOwiki/Detect+ships+in+critical+areas","url":"https://earsc-portal.eu/display/EOwiki/Detect+ships+in+critical+areas"},{"concepts":[805,841,809,806,807,808,813,811,812,820,814,815,816,817,818,819,825,821,822,823,824,828,826,827,832,829,830,839,833,836,834,835,840,837,838,888,861,831,858,859,860],"description":" ","name":"European Association of Remote Sensing Companies. (2020). EO Services (Markets). Retrieved from https://earsc-portal.eu/pages/viewpage.action?pageId=78221916","url":"https://earsc-portal.eu/pages/viewpage.action?pageId=78221916"},{"concepts":[883],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Forecast and map large waves. Retrieved from https://earsc-portal.eu/display/EOwiki/Forecast+and+map+large+waves","url":"https://earsc-portal.eu/display/EOwiki/Forecast+and+map+large+waves"},{"concepts":[810,883],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Forecast and monitor current movement and drift. Retrieved from https://earsc-portal.eu/display/EOwiki/Forecast+and+monitor+current+movement+and+drift","url":"https://earsc-portal.eu/display/EOwiki/Forecast+and+monitor+current+movement+and+drift"},{"concepts":[883],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Forecast and monitor ocean winds and waves. Retrieved from https://earsc-portal.eu/display/EOwiki/Forecast+and+monitor+ocean+winds+and+waves","url":"https://earsc-portal.eu/display/EOwiki/Forecast+and+monitor+ocean+winds+and+waves"},{"concepts":[869],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Forecast crop yields. Retrieved from https://earsc-portal.eu/display/EOwiki/Forecast+crop+yields","url":"https://earsc-portal.eu/display/EOwiki/Forecast+crop+yields"},{"concepts":[855],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Forecast weather. Retrieved from https://earsc-portal.eu/display/EOwiki/Forecast+weather","url":"https://earsc-portal.eu/display/EOwiki/Forecast+weather"},{"concepts":[853],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Forecasting sunlight exposure. Retrieved from https://earsc-portal.eu/display/EOwiki/Forecasting+sunlight+exposure","url":"https://earsc-portal.eu/display/EOwiki/Forecasting+sunlight+exposure"},{"concepts":[876],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Identify hydrocarbon seeps in soil. Retrieved from https://earsc-portal.eu/display/EOwiki/Identify+hydrocarbon+seeps+in+soil","url":"https://earsc-portal.eu/display/EOwiki/Identify+hydrocarbon+seeps+in+soil"},{"concepts":[868,862],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Map and assess flooding. Retrieved from https://earsc-portal.eu/display/EOwiki/Map+and+assess+flooding","url":"https://earsc-portal.eu/display/EOwiki/Map+and+assess+flooding"},{"concepts":[810],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Map and monitor hydroelectric energy. Retrieved from https://earsc-portal.eu/display/EOwiki/Map+and+monitor+hydroelectric+energy","url":"https://earsc-portal.eu/display/EOwiki/Map+and+monitor+hydroelectric+energy"},{"concepts":[810],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Map and monitor solar energy (solar farms). Retrieved from https://earsc-portal.eu/pages/viewpage.action?pageId=78221967","url":"https://earsc-portal.eu/pages/viewpage.action?pageId=78221967"},{"concepts":[810],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Map and monitor wind energy (wind farms). Retrieved from https://earsc-portal.eu/pages/viewpage.action?pageId=78221973","url":"https://earsc-portal.eu/pages/viewpage.action?pageId=78221973"},{"concepts":[884],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Map fish shoals. Retrieved from https://earsc-portal.eu/display/EOwiki/Map+fish+shoals","url":"https://earsc-portal.eu/display/EOwiki/Map+fish+shoals"},{"concepts":[876],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Map geological features. Retrieved from https://earsc-portal.eu/display/EOwiki/Map+geological+features","url":"https://earsc-portal.eu/display/EOwiki/Map+geological+features"},{"concepts":[876],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Map seismic survey operations. Retrieved from https://earsc-portal.eu/display/EOwiki/Map+seismic+survey+operations","url":"https://earsc-portal.eu/display/EOwiki/Map+seismic+survey+operations"},{"concepts":[882],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Map water depth or charting. Retrieved from https://earsc-portal.eu/display/EOwiki/Map+water+depth+or+charting","url":"https://earsc-portal.eu/display/EOwiki/Map+water+depth+or+charting"},{"concepts":[875],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Measure & detect land surface change. Retrieved from https://earsc-portal.eu/display/EOwiki/Measure+detect+land+surface+change","url":"https://earsc-portal.eu/display/EOwiki/Measure+detect+land+surface+change"},{"concepts":[874],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Measure land use statistics. Retrieved from https://earsc-portal.eu/display/EOwiki/Measure+land+use+statistics","url":"https://earsc-portal.eu/display/EOwiki/Measure+land+use+statistics"},{"concepts":[853],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor air quality & emissions. Retrieved from https://earsc-portal.eu/pages/viewpage.action?pageId=78221935","url":"https://earsc-portal.eu/pages/viewpage.action?pageId=78221935"},{"concepts":[882],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor coastal ecosystem. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+coastal+ecosystem","url":"https://earsc-portal.eu/display/EOwiki/Monitor+coastal+ecosystem"},{"concepts":[880,879],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor construction and buildings. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+construction+and+buildings","url":"https://earsc-portal.eu/display/EOwiki/Monitor+construction+and+buildings"},{"concepts":[870],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor forest carbon content. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+forest+carbon+content","url":"https://earsc-portal.eu/display/EOwiki/Monitor+forest+carbon+content"},{"concepts":[870],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor forest resources. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+forest+resources","url":"https://earsc-portal.eu/display/EOwiki/Monitor+forest+resources"},{"concepts":[874],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor humanitarian movement and camps. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+humanitarian+movement+and+camps","url":"https://earsc-portal.eu/display/EOwiki/Monitor+humanitarian+movement+and+camps"},{"concepts":[872],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor ice on rivers and lakes. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+ice+on+rivers+and+lakes","url":"https://earsc-portal.eu/display/EOwiki/Monitor+ice+on+rivers+and+lakes"},{"concepts":[873],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor land cover and detect change. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+land+cover+and+detect+change","url":"https://earsc-portal.eu/display/EOwiki/Monitor+land+cover+and+detect+change"},{"concepts":[873],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor land ecosystems and biodiversity. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+land+ecosystems+and+biodiversity","url":"https://earsc-portal.eu/display/EOwiki/Monitor+land+ecosystems+and+biodiversity"},{"concepts":[873],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor land pollution. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+land+pollution","url":"https://earsc-portal.eu/display/EOwiki/Monitor+land+pollution"},{"concepts":[881],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor marine habitats. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+marine+habitats","url":"https://earsc-portal.eu/display/EOwiki/Monitor+marine+habitats"},{"concepts":[876],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor mineral extraction. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+mineral+extraction","url":"https://earsc-portal.eu/display/EOwiki/Monitor+mineral+extraction"},{"concepts":[882],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor ocean level and surface. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+ocean+level+and+surface","url":"https://earsc-portal.eu/display/EOwiki/Monitor+ocean+level+and+surface"},{"concepts":[881],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor ocean quality and productivity. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+ocean+quality+and+productivity","url":"https://earsc-portal.eu/display/EOwiki/Monitor+ocean+quality+and+productivity"},{"concepts":[881],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor oil rigs and flares. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+oil+rigs+and+flares","url":"https://earsc-portal.eu/display/EOwiki/Monitor+oil+rigs+and+flares"},{"concepts":[881],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor pollution at sea. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+pollution+at+sea","url":"https://earsc-portal.eu/display/EOwiki/Monitor+pollution+at+sea"},{"concepts":[857],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor sensitive risk areas. Retrieved from: https://earsc-portal.eu/display/EOwiki/Monitor+sensitive+risk+areas","url":"https://earsc-portal.eu/display/EOwiki/Monitor+sensitive+risk+areas"},{"concepts":[885],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor ships movements. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+ships+movements","url":"https://earsc-portal.eu/display/EOwiki/Monitor+ships+movements"},{"concepts":[872],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor snow cover. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+snow+cover","url":"https://earsc-portal.eu/display/EOwiki/Monitor+snow+cover"},{"concepts":[882],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor the coast line. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+the+coast+line","url":"https://earsc-portal.eu/display/EOwiki/Monitor+the+coast+line"},{"concepts":[880,878],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor urban areas. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+urban+areas","url":"https://earsc-portal.eu/display/EOwiki/Monitor+urban+areas"},{"concepts":[874],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor vegetation encroachment. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+vegetation+encroachment","url":"https://earsc-portal.eu/display/EOwiki/Monitor+vegetation+encroachment"},{"concepts":[869],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor water use on crops and horticulture. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+water+use+on+crops+and+horticulture","url":"https://earsc-portal.eu/display/EOwiki/Monitor+water+use+on+crops+and+horticulture"},{"concepts":[853],"description":" ","name":"European Association of Remote Sensing Companies. (n.d.). Product sheet: Air Quality CO2. 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Retrieved from: https://ec.europa.eu/programmes/horizon2020/en/h2020-section/societal-challenges","url":"https://ec.europa.eu/programmes/horizon2020/en/h2020-section/societal-challenges"},{"concepts":[873],"description":" ","name":"European Commission Joint Research Centre. (2020). Vegetation - Copernicus landm monitoring service. Retrieved from https://land.copernicus.eu/global/themes/Vegetation","url":"https://land.copernicus.eu/global/themes/Vegetation"},{"concepts":[845],"description":" ","name":"European Commission. (2020). Digital skills and jobs - Shaping Europe's digital future. Retrived from https://ec.europa.eu/digital-single-market/en/policies/digital-skills","url":"https://ec.europa.eu/digital-single-market/en/policies/digital-skills"},{"concepts":[845],"description":" ","name":"European Commission. (2020). Employment, Social Affairs & Inclusion. 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Retrieved from https://ec.europa.eu/info/news/sustainability-at-the-water-source_en","url":"https://ec.europa.eu/info/news/sustainability-at-the-water-source_en"},{"concepts":[847],"description":" ","name":"European Commission. (2020). Sustainable agriculture in the CAP. Retrieved from https://ec.europa.eu/info/food-farming-fisheries/sustainability/sustainable-cap_en","url":"https://ec.europa.eu/info/food-farming-fisheries/sustainability/sustainable-cap_en"},{"concepts":[848],"description":" ","name":"European Commission. (2020). Transport. Retrieved from https://ec.europa.eu/info/policies/transport_en","url":"https://ec.europa.eu/info/policies/transport_en"},{"concepts":[887],"description":" ","name":"European Environment Agency. (2016). Monitoring of marine waters. Retrieved from: https://www.eea.europa.eu/publications/92-9167-001-4/page024.html","url":"https://www.eea.europa.eu/publications/92-9167-001-4/page024.html"},{"concepts":[842],"description":" ","name":"European Environmental Agency, (2019). Climate Change Adaption. Retrieved from: https://www.eea.europa.eu/themes/climate-change-adaptation/intro.","url":"https://www.eea.europa.eu/themes/climate-change-adaptation/intro"},{"concepts":[842],"description":" ","name":"European Environmental Agency, (2019). Climate Change Mitigation. Retrieved from: https://www.eea.europa.eu/themes/climate/intro.","url":"https://www.eea.europa.eu/themes/climate/intro"},{"concepts":[844],"description":" ","name":"European Environmental Agency. (2008). Biodiversity - Ecosystems. Retrieved from https://www.eea.europa.eu/themes/biodiversity/intro","url":"https://www.eea.europa.eu/themes/biodiversity/intro"},{"concepts":[850],"description":" ","name":"European External Action Service. (2020). 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Retrieved from https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-2-msi/level-1c/cloud-masks","url":"https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-2-msi/level-1c/cloud-masks"},{"concepts":[908],"description":" ","name":"European Space Agency. (n.d.). Understanding risk with Earth observation. Retreived from: https://www.esa.int/Applications/Observing_the_Earth/Understanding_risk_with_Earth_observation","url":"https://www.esa.int/Applications/Observing_the_Earth/Understanding_risk_with_Earth_observation"},{"concepts":[857],"description":" ","name":"European Union. (2018). Critical Infrastructure Analysis. Retrieved from: https://sea.security.copernicus.eu/categories/critical-infrastructure-analysis/","url":"https://sea.security.copernicus.eu/categories/critical-infrastructure-analysis/"},{"concepts":[909],"description":" ","name":"European Union. (2020). Rapid mapping. Retrieved from: https://emergency.copernicus.eu/mapping/ems/rapid-mapping-portfolio","url":"https://emergency.copernicus.eu/mapping/ems/rapid-mapping-portfolio"},{"concepts":[124],"description":"ISBN number: 9781118653104","name":"Fairchild, M. D., (2005). Color appearance models, (2nd ed.), John Wiley and Sons.","url":"http://books.google.com/books?isbn=9781118653104"},{"concepts":[894],"description":" ","name":"Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S., Shimada, J., Umland, J., Werner, M., Oskin, M., Burbank, D., Alsdorf, D. (2007). The shuttle radar topography mission. Reviews of Geophysics, 45(2). doi:10.1029/2005RG000183","url":"https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2005RG000183"},{"concepts":[302],"description":"ISBN: 978-3-319-00025-1","name":"Fecher, B. and Friesike, S. (2014). Open Science: One Term, Five Schools of Thought. 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A Value-Chain Analysis for the Copernicus Earth Observation Infrastructure Evolution: A Knowledgebase of Users, Needs, Services, and Products. IEEE Geoscience and Remote Sensing Magazine, 5(3), 19-35. doi:10.1109/MGRS.2017.2720263","url":"https://ieeexplore.ieee.org/abstract/document/8038967"},{"concepts":[302],"description":"ISBN: 978-3-319-65632-8","name":"Mathieu, P.-P., and Aubrecht, C. (Eds.). (2018). Earth Observation Open Science and Innovation. Cham: Springer","url":"https://doi.org/10.1007/978-3-319-65633-5"},{"concepts":[677],"description":"Mattia, F., Le Toan, T., Souyris, J. C., De Carolis, C., Floury, N., Posa, F. and Pasquariello, N. G. (1997). The effect of surface roughness on multifrequency polarimetric SAR data. IEEE Transactions on Geoscience and Remote Sensing, 35(4), 954-966. DOI: 10.1109/36.602537","name":"Mattia, F., Le Toan, T., Souyris, J. C., De Carolis, C., Floury, N., Posa, F. and Pasquariello, N. G. (1997). 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properties are attributes thereof)"},{"concepts":[88],"name":"Compare and contrast theories of spatial knowledge acquisition (e.g., Marr on vision, Piaget on childhood, Golledge on wayfinding)"},{"concepts":[532],"name":"Compare and contrast training methods utilized in a non-profit to those employed in a local government agency"},{"concepts":[662],"name":"Compare and discuss attenuation length and penetration depth of the optical and radar signal"},{"concepts":[756,758,761,757],"name":"Compare and discuss different SAR acquisition modes"},{"concepts":[545],"name":"Compare and explain different models for funding an SDI"},{"concepts":[338],"name":"Compare and explain the main business models in the GI domain"},{"concepts":[72],"name":"Compare block-kriging with areal interpolation using proportional area weighting and dasymetric mapping"},{"concepts":[311],"name":"Compare common sensors by spatial resolution, spectral sensitivity, ground coverage, and temporal resolution [e.g., AVHRR, MODIS (intermediate resolution ~500 m, high temporal) Landsat, commercial high resolution (Ikonos and Quickbird); ..."},{"concepts":[240],"name":"Compare commonalities and patterns of geocomputation to other related terms"},{"concepts":[14],"name":"Compare current accessibility models with early models of market potential"},{"concepts":[440],"name":"Compare different deep learning approaches in EO image classification"},{"concepts":[248],"name":"Compare different design choices in developing spatial simulation models"},{"concepts":[952],"name":"compare different development components and their advantages and disadvantages"},{"concepts":[488],"name":"Compare different error metrics that are based on the error matrix"},{"concepts":[164],"name":"Compare different evaluation methods for cartography and visualization products (e.g., qualitative versus quantitative, formative versus summative studies)."},{"concepts":[546],"name":"Compare different frameworks for assessing Spatial Data Infrastructures"},{"concepts":[928],"name":"Compare different Geospatial object and geometry definitions included under this topic"},{"concepts":[245],"name":"Compare different options of combining space-time dynamics approaches in spatial modelling"},{"concepts":[395],"name":"Compare different strategies of data assimilation"},{"concepts":[177],"name":"Compare geospatial software architecture through cost-analysis framework"},{"concepts":[872],"name":"Compare glacier extents using EO data"},{"concepts":[856,853,854],"name":"Compare human-induced emissions to natural sources"},{"concepts":[940],"name":"Compare Linked geospatial data to SDI approaches"},{"concepts":[69],"name":"Compare methods of spatial statistical analysis for the testing of hypotheses."},{"concepts":[20],"name":"Compare models and software tools that allow for optimization"},{"concepts":[865],"name":"Compare one optical EO method with a SAR method for landslide mapping and explain their differences"},{"concepts":[473],"name":"Compare pixel-based image classification methods with object-based techniques"},{"concepts":[769],"name":"Compare reflectance measurements from the field to reflectance values in radiometrically pre-processed EO data"},{"concepts":[120],"name":"Compare relationships between entities, between attributes and between locations."},{"concepts":[459],"name":"Compare results of the Laplacian of Gaussian filter to the original input image"},{"concepts":[346],"name":"Compare the advantages and disadvantages of group participation and individual participation"},{"concepts":[48],"name":"Compare the basic analytical operations of different GISs."},{"concepts":[322],"name":"Compare the concepts of geometric accuracy and topological fidelity"},{"concepts":[302],"name":"Compare the different cultures of Open Science"},{"concepts":[64],"name":"Compare the different types of spatial weight matrices"},{"concepts":[881],"name":"Compare the main satellite sensors used in marine ecosystem monitoring"},{"concepts":[322],"name":"Compare the National Map Accuracy Standard with the ASPRS Coordinate Standard"},{"concepts":[137],"name":"Compare the relative merits of having map labels placed dynamically versus having them saved as annotation data"},{"concepts":[24],"name":"Compare the result of conversion vector/raster or raster/vector and examine the impact of conversion on the quality of the dataset"},{"concepts":[166],"name":"Compile the needs of individual users and tasks into enterprise-wide needs"},{"concepts":[520],"name":"Compute descriptive statistics and geostatistics of geographic data"},{"concepts":[49],"name":"Compute measures of overall dispersion and clustering of point datasets using nearest neighbor distance statistics"},{"concepts":[65],"name":"Compute measures of overall dispersion and clustering of point datasets using nearest neighbor distance statistics"},{"concepts":[65],"name":"Compute Morans I and Gearys c for patterns of attribute data measured on interval ratio scales"},{"concepts":[9],"name":"Compute the alpha, beta, and gamma indices of network connectivity"},{"concepts":[9],"name":"Compute the Detour Index and the measure of network density for a given network"},{"concepts":[9],"name":"Compute the estimated number of fundamental cycles in a graph"},{"concepts":[66],"name":"Compute the Gi and Gi* statistics"},{"concepts":[65],"name":"Compute the K function"},{"concepts":[568],"name":"Compute the maximum average roughness of a mirror for incident radiation in the microwaves spectral range"},{"concepts":[568],"name":"Compute the maximum average roughness of a mirror for incident radiation in the visible spectral range"},{"concepts":[37],"name":"Compute the mean of directional data"},{"concepts":[568],"name":"Compute the minimum average roughness of a surface operating as a diffuser of incident radiation in the visible spectral range"},{"concepts":[10],"name":"Compute the optimum path between two points through a network with Dijkstras algorithm"},{"concepts":[53],"name":"Conduct a simple hierarchical cluster analysis to classify area objects into statistically similar regions"},{"concepts":[76],"name":"Conduct a spatial econometric analysis to test for spatial dependence in the residuals from least-squares models and spatial autoregressive models"},{"concepts":[72],"name":"Conduct a spatial interpolation process using kriging from data description to final error map"},{"concepts":[159],"name":"Construct a new map from an existing one with a biased view"},{"concepts":[34],"name":"Construct a query statement to search for a specific spatial or temporal relationship"},{"concepts":[71],"name":"Construct a semi-variogram and illustrate with a semi-variogram cloud"},{"concepts":[34],"name":"Construct a spatial query to extract all point objects that fall within a polygon"},{"concepts":[64],"name":"Construct a spatial weights matrix for lattice, point, and area patterns"},{"concepts":[214],"name":"Construct a TIN manually from a set of spot elevations"},{"concepts":[149],"name":"Construct a Web page that includes an interactive map"},{"concepts":[673],"name":"Construct scattering matrix"},{"concepts":[105],"name":"Construct taxonomies and dictionaries (also known as formal ontologies) to communicate systems of categories"},{"concepts":[14],"name":"Contrast accessibility modeling at the individual level versus at an aggregated level"},{"concepts":[182],"name":"Contrast cloud and grid computing technologies"},{"concepts":[134],"name":"Contrast gaming elements which are both part of traditional games and geo-games"},{"concepts":[137],"name":"Contrast the strengths and limitations of methods for automatic label placement"},{"concepts":[22],"name":"Convert a dataset from the native format of one GIS product to another"},{"concepts":[127],"name":"Convert historical maps in digital format"},{"concepts":[376],"name":"Convert multispectral image into its principal components"},{"concepts":[24],"name":"Convert vector data to raster format and back using GIS software"},{"concepts":[24],"name":"Convert vector data to raster format and back using the GIS software"},{"concepts":[128],"name":"Correlate map making methods with technological or societal factors across History"},{"concepts":[174],"name":"Create a budget of expected labor costs, including salaries, benefits, training, and other expenses"},{"concepts":[188],"name":"Create a complete design document ready for implementation"},{"concepts":[153],"name":"Create a concept map that represents the contents and topology of a physical or social process"},{"concepts":[461],"name":"Create a convolution filter that integrates the standard deviation of the entire scene in its weights"},{"concepts":[509],"name":"Create a data cube using the data model of the Open data cube initiative"},{"concepts":[8],"name":"Create a data set with network attributes and topology"},{"concepts":[186],"name":"Create a diagram of a conceptual data model for a geospatial application or enterprise database"},{"concepts":[21,130],"name":"Create a flowchart showing the sequence of transformations on a data set (e.g., geometric and radiometric correction and mosaicking of remotely sensed data)"},{"concepts":[147],"name":"Create a map that displays related variables using different mapping methods (e.g., choropleth and proportional symbol, choropleth and cartogram)"},{"concepts":[147],"name":"Create a map that displays related variables using the same mapping method (e.g., bivariate choropleth map, bivariate dot map)"},{"concepts":[146],"name":"Create a map that represents both slope and aspect on the same map using the Moellering-Kimerling coloring method"},{"concepts":[41],"name":"Create a matrix describing the pattern of adjacency in a set of planar enforced polygons"},{"concepts":[52],"name":"Create a matrix that shows spatial interaction"},{"concepts":[944],"name":"Create a new application by combining existing data from different sources"},{"concepts":[158],"name":"Create a project plan for a map, from planning to finalisation"},{"concepts":[482],"name":"Create a protocol for quality assessment of an EO information product that conforms to EO4GEO guidelines"},{"concepts":[153],"name":"Create a pseudo-topographic surface to portray the relationships in a collection of documents"},{"concepts":[949],"name":"Create a sample HTML5 Web page"},{"concepts":[479],"name":"Create a scale space for an image by applying multiple iterations of low-pass filtering"},{"concepts":[368,371],"name":"Create a set of ground control points tying image coordinates to map coordinates of a reference dataset using a digital reference dataset or in-situ GPS measurements"},{"concepts":[148],"name":"Create a temporal sequence representing a dynamic geospatial process"},{"concepts":[167],"name":"Create a user manual to help users understand a process or task"},{"concepts":[515],"name":"Create a web interface and related system architecture that enables image processing by using OGC interfaces"},{"concepts":[226],"name":"Create an adjacency table from a sample network"},{"concepts":[135],"name":"Create an aesthetic map icon library"},{"concepts":[226],"name":"Create an incidence matrix from a sample network"},{"concepts":[394],"name":"Create an integrated population distribution map from census data and EO-based land use classification"},{"concepts":[33],"name":"Create an SQL query to retrieve elements from a GIS"},{"concepts":[185],"name":"Create conceptual, logical, and physical data models using automated software tools"},{"concepts":[50],"name":"Create density maps from point datasets using kernels and density estimation techniques using standard software"},{"concepts":[133],"name":"Create different map layouts using the same map components (main map area, inset maps, titles, legends, scale bars, north arrows, grids and graticule) to produce maps with very distinctive purposes"},{"concepts":[133],"name":"Create different maps using the same data for different purposes and intended audiences (e.g., expert and novice hikers)"},{"concepts":[143],"name":"Create different visual hierarchies to produce maps with different purposes"},{"concepts":[24],"name":"Create estimated tessellated data sets from point samples or isolines using interpolation operations that are appropriate to the specific situation"},{"concepts":[430],"name":"Create feature space visualisations for a multispectral image"},{"concepts":[54],"name":"Create initial weights using the analytical hierarchy process (AHP)"},{"concepts":[187],"name":"Create logical models based on conceptual models using UML or other tools"},{"concepts":[144],"name":"Create maps using each of the following methods: choropleth, dasymetric, proportioned symbol, graduated symbol, isoline, dot, cartogram, and flow map"},{"concepts":[919],"name":"Create new EO products out of raw data or other products"},{"concepts":[105],"name":"Create or use GIS data structures to represent categories, including attribute columns, layers themes, shapes, legends, etc."},{"concepts":[174],"name":"Create proposals and presentations to secure funding"},{"concepts":[70],"name":"Create spatial samples under a variety of requirements, such as coverage, randomness, transects"},{"concepts":[159],"name":"Create two versions of the same map addressed to different targets"},{"concepts":[188],"name":"Create UML diagrams of physical models based on logical model diagrams and software requirements"},{"concepts":[144],"name":"Create well-designed legends using the appropriate conventions for the following methods: choropleth, dasymetric, proportioned symbol, graduated symbol, isoline, dot, cartogram, and flow map"},{"concepts":[143],"name":"Critique the graphic design of several maps in terms of balance, legibility, clarity, visual contrast, figure-ground organization, and hierarchal organization"},{"concepts":[149],"name":"Critique the interactive elements of an online map"},{"concepts":[150],"name":"Critique the user interface for existing Internet mapping services"},{"concepts":[229],"name":"Deal with time aspects in modelling data"},{"concepts":[228],"name":"Deal with uncertainty aspects in modelling data"},{"concepts":[912,911],"name":"Decide on urban planning measures on the basis of a semantic 3D model"},{"concepts":[31],"name":"Decide which generalisation technique (aggregation, selection, etc.) is best for a specific situation of reducing map scale."},{"concepts":[141],"name":"Decide which graphical representation better reflects the messages embedded in your story"},{"concepts":[66],"name":"Decompose Morans I and Gearys c into local measures of spatial association"},{"concepts":[186],"name":"Deconstruct an application use case into its conceptual elements"},{"concepts":[359],"name":"Defend or refute the contention that critical studies have an identifiable influence on the development of the information society in general and GIScience in particular"},{"concepts":[358],"name":"Defend or refute the contention that the masculinist culture of computer work in general, and GIS work in particular, perpetuates gender inequality in GIS and T education and training and occupational segregation in the GIS and T workforce"},{"concepts":[28],"name":"Defend or refute the statement \"GIS data are scaleless\""},{"concepts":[85],"name":"Defend or refute the statement, All data are theory-laden"},{"concepts":[109],"name":"Define a field in terms of properties, space, and time"},{"concepts":[166],"name":"Define a methodology for gathering of requirements"},{"concepts":[233],"name":"Define a set of rules for modeling changes in spatial databases"},{"concepts":[223],"name":"Define and describe an application schema"},{"concepts":[348],"name":"Define and discuss enabling technologies: geotag, georeferencing, GPS and more"},{"concepts":[238],"name":"Define and discuss opportunities and limitations of computational science"},{"concepts":[348],"name":"Define and discuss volunteered geographic information"},{"concepts":[348],"name":"Define and discussing impact of Crowdsourcing on Geospatial Society"},{"concepts":[929],"name":"Define and exemplify the reuse of ontologies - Define and identify the role of ontology patterns"},{"concepts":[925],"name":"Define and practice the usage, in a given use case, of StyledLayerDescriptor (SLD) and Symbology Encoding (SE). Practice their usage in a given use case"},{"concepts":[346],"name":"Define and understand citizenship, democracy, maturity, and negotiation related to geo information use and participation in society /community development (at local, regional, national level)"},{"concepts":[33],"name":"Define basic terms of query processing e.g., SQL, primary and foreign keys, table join"},{"concepts":[211],"name":"Define basic terms used in the raster data model (e.g., cell, row, column, value)"},{"concepts":[179,924],"name":"Define characteristics of REST Web services and Resource oriented Architecture (ROA)"},{"concepts":[85],"name":"Define common philosophical theories that have influenced geography and science, such as logical positivism, Marxism, phenomenology, feminism, and critical theory"},{"concepts":[83],"name":"Define common theories on what constitutes knowledge, including positivism, reflectance-correspondence, pragmatism, social constructivism, and memetics"},{"concepts":[81],"name":"Define common theories on what is real, such as realism, idealism, relativism, and experiential realism"},{"concepts":[8],"name":"Define different interpretations of cost in various routing applications"},{"concepts":[37],"name":"Define direction and its measurement in different angular measures"},{"concepts":[186],"name":"Define entities and relationships in conceptual data model"},{"concepts":[60],"name":"Define friction surface"},{"concepts":[928],"name":"Define GeoJSON definition of Geospatial objects and describe the structure of a GeoJSON document and identify advantages and disadvantages of representing the same geospatial data in GML and in GeoJSON"},{"concepts":[59],"name":"Define intervisibility"},{"concepts":[935],"name":"Define Mapping between legacy definition and the semantic definition of publish"},{"concepts":[931],"name":"Define metadata and identify metadata standards like ISO 19115 and 19119 describe their metadata schema generally"},{"concepts":[928],"name":"Define OGC Simple Features Access Schema. Well-Known Text (WKT) and Well-Known Binary (WKB) representations of Geometry"},{"concepts":[68],"name":"Define prior and posterior distributions and Markov-Chain Monte Carlo"},{"concepts":[927],"name":"Define Resource Description Framework (RDF), its RDF graphs, RDF Schema (RDF-S)and a data set in RDF"},{"concepts":[927],"name":"Define Semantic Web and identify the role of the languages included under this topic for Semantic Web"},{"concepts":[179,922],"name":"Define Service Oriented Architecture (SOA) and identify main elements of it"},{"concepts":[119],"name":"Define spatial autocorrelation in the context of geographic proximity"},{"concepts":[928],"name":"Define spatial extensions that GeoSPARQL brings over SPARQL. Identify the difference between qualitative spatial reasoning and quantitative spatial computations"},{"concepts":[106],"name":"Define Stevens four levels of measurement (nominal, ordinal, interval, ratio)"},{"concepts":[222],"name":"Define terms related to topology (e.g., adjacency, connectivity, overlap, intersect, logical consistency)"},{"concepts":[187],"name":"Define the cardinality of relationships"},{"concepts":[179,180,922],"name":"Define the characteristics of web services and present some examples"},{"concepts":[927],"name":"Define the components of a Web Services Description Language (WSDL) document"},{"concepts":[226],"name":"Define the following terms pertaining to a network: Loops, multiple edges, the degree of a vertex, walk, trail, path, cycle, fundamental cycle"},{"concepts":[8],"name":"Define the following terms pertaining to a network: Loops, multiple edges, the degree of a vertex, walk, trail, path, cycle, fundamental cycle"},{"concepts":[90],"name":"Define the following terms: data, information, knowledge, and wisdom"},{"concepts":[97],"name":"Define the four basic dimensions or shapes used to describe spatial objects (i.e., points, lines, regions, volumes)"},{"concepts":[93],"name":"Define the notions of cultural landscape and physical landscape"},{"concepts":[119],"name":"Define the principle of friction of distance and geographic models that are based on it (e.g., gravity models, spatial interaction models)"},{"concepts":[92],"name":"Define the properties that make a phenomenon geographic"},{"concepts":[580],"name":"Define the radiometric spectral quantities brightness, emittance, luminosity"},{"concepts":[580],"name":"Define the radiometric spectral quantities radiance, irradiance, flux"},{"concepts":[2],"name":"Define the terms spatial analysis, spatial modeling, geostatistics, spatial econometrics, spatial statistics, qualitative analysis, map algebra, and network analysis"},{"concepts":[122],"name":"Define uncertainty-related terms, such as error, accuracy, uncertainty, precision, stochastic, probabilistic, deterministic, and random"},{"concepts":[524],"name":"Define user roles for an existing or planned GIS"},{"concepts":[118],"name":"Define various terms used to describe topological relationships, such as disjoint, overlap, within, and intersect"},{"concepts":[946],"name":"Define Web API composition (WAPIC) concept for RESTful WSs and identify main issues"},{"concepts":[925],"name":"Define Web Coverage Service (WCS). Describe GetCapabilities, GetCoverageInfo, and GetCoverage operations in detail. Practice its usage in a given use case"},{"concepts":[925],"name":"Define Web Feature Service (WFS). Describe GetCapabilities, DescribeFeaturetype, and GetFeature, and GetFeatureInfo operations in detail. Practice its usage in a given use case"},{"concepts":[925],"name":"Define Web Map Service (WMS). Describe GetCapabilities, GetMap, and GetFeatureInfo operations in detail. Practice its usage in a given use case"},{"concepts":[925],"name":"Define Web Map Tile Service (WMTS). Describe GetCapabilities, GetTile, and GetFeatureInfo operations in detail. Practice its usage in a given use case"},{"concepts":[925],"name":"Define Web Processing Service (WPS). Describe GetCapabilities, DescribeProcess, and Execute operations in detail. Practice its usage in a given use case"},{"concepts":[946],"name":"Define web services composition (WSC) concept and identify main issues"},{"concepts":[922],"name":"Define Web services transport over the Web"},{"concepts":[929],"name":"Define what an ontology is. Identify differences among ontologies, Thesauri, and taxonomies"},{"concepts":[214],"name":"Delineate a set of break lines that improve the accuracy of a TIN"},{"concepts":[113],"name":"Delineate regions using properties, spatial relationships, and geospatial technologies"},{"concepts":[176],"name":"Deliver a resources plan consistent with organisation’s concrete actions"},{"concepts":[617],"name":"Demonstrate basic knowledge of the atmospheric absorption and scattering mechanisms."},{"concepts":[557,613],"name":"Demonstrate basic knowledge of the interaction between the solar radiation and atmospheric constituents"},{"concepts":[932],"name":"Demonstrate harvesting and crawling mechanisms for automated metadata collection"},{"concepts":[226],"name":"Demonstrate how a network is a connected set of edges and vertices"},{"concepts":[222],"name":"Demonstrate how a topological structure can be represented in a relational database structure"},{"concepts":[41],"name":"Demonstrate how adjacency and connectivity can be recorded in matrices"},{"concepts":[226],"name":"Demonstrate how attributes of networks can be used to represent cost, time, distance, or many other measures"},{"concepts":[235],"name":"Demonstrate how both the time criticality and the data security might determine whether one performs change detection on-line or off-line in a given scenario"},{"concepts":[11],"name":"Demonstrate how capacity is assigned to edges in a network using the appropriate data structure"},{"concepts":[5],"name":"Demonstrate how cluster analysis can be used as a data mining tool"},{"concepts":[10],"name":"Demonstrate how K-shortest path algorithms can be implemented to find many efficient alternate paths across the network"},{"concepts":[9],"name":"Demonstrate how networks can be measured using the number of elements in a network, the distances along network edges, and the level of connectivity of the network"},{"concepts":[71],"name":"Demonstrate how semi-variograms react to spatial nonstationarity"},{"concepts":[77],"name":"Demonstrate how spatial autocorrelation can be removed by resampling"},{"concepts":[75],"name":"Demonstrate how spatially lagged, trend surface, or dummy spatial variables can be used to create the spatial component variables missing in a standard regression analysis"},{"concepts":[148],"name":"Demonstrate how the adding time-series data reveals (or not) patterns not evident in a cross-sectional data"},{"concepts":[39],"name":"Demonstrate how the area of a region calculated from a raster data set will vary by resolution and orientation"},{"concepts":[12],"name":"Demonstrate how the Classic Transportation Problem can be structured as a linear program"},{"concepts":[45],"name":"Demonstrate how the geometric operations of intersection and overlay can be implemented in GIS"},{"concepts":[76],"name":"Demonstrate how the parameters of spatial auto-regressive models can be estimated using univariate and bivariate optimization algorithms for maximizing the likelihood function"},{"concepts":[75],"name":"Demonstrate how the spatial weights matrix is fundamental in spatial econometrics models"},{"concepts":[226],"name":"Demonstrate how the star (or forward star) data structure, which is often employed when digitally storing network information, violates relational normal form, but allows for much faster search and retrieval in network databases"},{"concepts":[938],"name":"Demonstrate how to discover over a catalogue service; and the discovery procedure in OGC CS-W"},{"concepts":[127],"name":"Demonstrate how to georeference an historical map"},{"concepts":[827],"name":"Demonstrate impacts of land use change"},{"concepts":[840],"name":"Demonstrate multidisciplinarity, combining GISciences, Social Sciences, Smart Cities, Computational Sciences and Social Media"},{"concepts":[932],"name":"Demonstrate publishing in some popular SDI (NSDI) portals like INSPIRE and GOS geoportals"},{"concepts":[33],"name":"Demonstrate the basic syntactic structure of SQL"},{"concepts":[51],"name":"Demonstrate the extension of spatial clustering to deal with clustering in space-time using the Know and Mantel tests"},{"concepts":[232],"name":"Demonstrate the importance of a clean, relatively error-free database (together with an appropriate geodetic framework) with the use of GIS software"},{"concepts":[567],"name":"Demonstrate the relationships among measured multi-spectral radiation and specific chemical (e.g. composition) and physical (e.g. temperature, pressure, etc.) properties of the observed matter."},{"concepts":[34],"name":"Demonstrate the syntactic structure of spatial and temporal operators in SQL"},{"concepts":[942],"name":"Demonstrate the usage of popular ETL tools in an NSDI scenario"},{"concepts":[214],"name":"Demonstrate the use of the TIN model for different statistical surfaces (e.g., terrain elevation, population density, disease incidence) in a GIS software application"},{"concepts":[75],"name":"Demonstrate why spatial autocorrelation among regression residuals can be an indication that spatial variables have been omitted from the models"},{"concepts":[45],"name":"Demonstrate why the georegistration of datasets is critical to the success of any map overlay operation"},{"concepts":[172],"name":"Demonstrate why the system design is important in any GIS implementation"},{"concepts":[563],"name":"Derive the Stefan-Boltzman Law from the Planck's one"},{"concepts":[85],"name":"Describe a brief history of major philosophical movements relating to the nature of space, time, geographic phenomena and human interaction with it"},{"concepts":[149],"name":"Describe a mapping goal in which the use of each of the following would be appropriate: brushing, linking, multiple displays"},{"concepts":[46,47],"name":"Describe a real modeling situation in which map algebra would be used e.g., site selection, climate classification, least-cost path"},{"concepts":[326],"name":"Describe a scenario in which data from a secondary source may pose obstacles to effective and efficient use"},{"concepts":[350],"name":"Describe a scenario in which you would find it necessary to report misconduct by a colleague or friend"},{"concepts":[55],"name":"Describe a simple process model that would generate a given set of spatial patterns"},{"concepts":[465],"name":"Describe a situation in which filtered data are more useful than the original unfiltered data"},{"concepts":[122],"name":"Describe a stochastic error model for a natural phenomenon"},{"concepts":[350],"name":"Describe a variety of philosophical frameworks upon which codes of professional ethics may be based"},{"concepts":[22,185],"name":"Describe a workflow for converting a implementing a data model in a GIS involving an Entity-Relationship (E-R) diagram and the Universal Modeling Language (UML)"},{"concepts":[218],"name":"Describe alternatives to quadtrees for representing hierarchical tessellations (e.g., hextrees, r-trees, pyramids)"},{"concepts":[235],"name":"Describe an application in which it is crucial to maintain previous versions of the database"},{"concepts":[721],"name":"Describe an application of hyperspectral image data"},{"concepts":[369,494],"name":"Describe an application that requires integration of remotely sensed data with GIS and/or GPS data"},{"concepts":[152],"name":"Describe an example where the use of an augmented environment could be of help"},{"concepts":[545],"name":"Describe and explain the funding model of an existing SDI"},{"concepts":[622],"name":"Describe atmospheric transmittance in the optical spectral range"},{"concepts":[150],"name":"Describe considerations for using maps on the Web as a method for downloading data"},{"concepts":[133],"name":"Describe differences in design needed for a map that is to be viewed on the Internet versus as a 5x7 foot poster, including a discussion of the effect of viewing distance, lighting, and media type"},{"concepts":[104],"name":"Describe different types of movement and change"},{"concepts":[4],"name":"Describe difficulties in dealing with large spatial databases, especially those arising from spatial heterogeneity"},{"concepts":[556],"name":"Describe Electromagnetic Waves in terms of Photons"},{"concepts":[4],"name":"Describe emerging geographical analysis techniques in geocomputation derived from artificial intelligence e.g., expert systems, artificial neural networks, genetic algorithms, and software agents"},{"concepts":[235],"name":"Describe existing algorithms designed for performing dynamic queries"},{"concepts":[922],"name":"Describe generally the hypertext transfer protocol and its main operations like POST and GET"},{"concepts":[119],"name":"Describe geographic phenomena in terms of their distances and directions (in space and time) Define spatial autocorrelation in the context of geographic proximity"},{"concepts":[118],"name":"Describe geographic phenomena in terms of their topological relationships (in space and time to other phenomena"},{"concepts":[606],"name":"Describe how a Michelson interferometer make it possible to measure the emitted Earth radiation with hyperspectral resolution."},{"concepts":[58,61],"name":"Describe how a network of stream channels and ridges can be estimated from a Digital Elevation Model (DEM)"},{"concepts":[80],"name":"Describe how conceptual foundations of GI Science have become implemented in GISs."},{"concepts":[5,7],"name":"Describe how data mining can be used for geospatial intelligence"},{"concepts":[440],"name":"Describe how deep learning works"},{"concepts":[322],"name":"Describe how geometric accuracy should be documented in terms of the FGDC metadata standard"},{"concepts":[341],"name":"Describe how geospatial data are used and maintained for land use planning, property value assessment, maintenance of public works, and other applications"},{"concepts":[522],"name":"Describe how GI S and T can be used in the decision-making process in organizations dealing with natural resource management, business management, public management or operations management"},{"concepts":[49],"name":"Describe how Independent Random Process/Chi-Squared Result IRP/CSR may be used to make statistical statements about point patterns"},{"concepts":[46,47],"name":"Describe how map algebra performs mathematical functions on raster grids"},{"concepts":[560],"name":"Describe how Maxwell's equation explain EM waves' propagation"},{"concepts":[399],"name":"Describe how sea surface temperatures are mapped"},{"concepts":[167,168],"name":"Describe how spatial data and GIS&T can be integrated into a workflow process"},{"concepts":[57],"name":"Describe how surfaces can be interpolated using splines"},{"concepts":[576],"name":"Describe how the complex part of the refractive index affects the propagation of e.m. radiation through the matter"},{"concepts":[214],"name":"Describe how to generate a unique TIN solution using Delaunay triangulation"},{"concepts":[531],"name":"Describe issues that may hinder implementation and continued successful operation of a GI system if effective methods of staff development are not included in the process"},{"concepts":[940],"name":"Describe Linked Data Browsers; Define Faceted browsers and identify what problems of linked data discovery they aim to solve"},{"concepts":[14],"name":"Describe methods for measuring different kinds of accessibility on a network"},{"concepts":[8],"name":"Describe networks that apply to specific applications or industries"},{"concepts":[37],"name":"Describe operations that can be performed on qualitative representations of direction"},{"concepts":[108],"name":"Describe particular entities in terms of space, time, and properties"},{"concepts":[110],"name":"Describe particular events or processes in terms of identity, categories, attributes, locations, etc."},{"concepts":[106],"name":"Describe particular geographic phenomena in terms of attributes"},{"concepts":[116],"name":"Describe particular geographic phenomena in terms of their place in mereonomic hierarchies (parts and composites)"},{"concepts":[536],"name":"Describe political, economic, administrative, and other social forces in agencies, organizations, and citizens that inhibit or promote sharing of geospatial and other data"},{"concepts":[11],"name":"Describe practical situations in which flow is conserved while splitting or joining at nodes of the network"},{"concepts":[156,157],"name":"Describe print quality characteristics and price differences for limited-run color map distribution"},{"concepts":[156,157],"name":"Describe production concerns that might be discussed with a publisher who will print a map product"},{"concepts":[894],"name":"Describe properties of a particular DEM product"},{"concepts":[940],"name":"Describe Querying Linked Data; SPARQL and GeoSPARQL"},{"concepts":[41],"name":"Describe real world applications where adjacency and connectivity 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qualifications"},{"concepts":[88,96,186],"name":"Describe the differences between real phenomena, conceptual models, and GIS data representations thereof"},{"concepts":[323],"name":"Describe the different measurement levels on which thematic accuracy is based"},{"concepts":[639],"name":"Describe the different payload capabilities of polar and geostationary platforms"},{"concepts":[108],"name":"Describe the difficulties in modeling entities with ill-defined edges"},{"concepts":[108],"name":"Describe the difficulties inherent in extending the tabletop metaphor of objects to the geographic environment"},{"concepts":[66],"name":"Describe the effect of non-stationarity on local indices of spatial association"},{"concepts":[65],"name":"Describe the effect of the assumption of stationarity on global measures of spatial association"},{"concepts":[93],"name":"Describe the elements of a sense of place or landscape that are difficult or impossible to adequately represent in GIS"},{"concepts":[417],"name":"Describe the elements of image interpretation"},{"concepts":[357],"name":"Describe the extent to which contemporary GIS and T supports diverse ways of understanding the world"},{"concepts":[52],"name":"Describe the formulation of the classic gravity model, the unconstrained spatial interaction model, the production constrained spatial interaction model, the attraction constrained spatial interaction model, and the doubly constrained spatial..."},{"concepts":[634],"name":"Describe the fundamental thermodynamic processes (isothermal, adiabatic, isochoric, isobaric)"},{"concepts":[117],"name":"Describe the genealogy (as identity-based change or temporal relationships) of particular geographic phenomena"},{"concepts":[75],"name":"Describe the general types of spatial econometric model"},{"concepts":[602],"name":"Describe the impact of Einstein’s theory of radiation on the design of modern devices for the measurements and/or production of coherent light"},{"concepts":[609],"name":"Describe the impact of geometrical optics on optical sensors design"},{"concepts":[26],"name":"Describe the impact of map projection transformation on raster and vector data"},{"concepts":[322],"name":"Describe the impact of the concept of dilution of precision on the uncertainty of GPS positioning"},{"concepts":[610],"name":"Describe the impact of the theory of interference on the development of modern satellite hyperspectral sounders"},{"concepts":[611],"name":"Describe the impact of theory of diffraction and grating spectrometers on the development of modern satellite hyperspectral sounders"},{"concepts":[54],"name":"Describe the implementation of an ordered weighting scheme in a multiple-criteria aggregation"},{"concepts":[372],"name":"Describe the importance of geometric correction when using Earth Observation data"},{"concepts":[350],"name":"Describe the individuals or groups to which GIS and T professionals have ethical obligations"},{"concepts":[222],"name":"Describe the integrity constraints of integrated topological models (e.g., POLYVRT)"},{"concepts":[90],"name":"Describe the limitations of various information stores for representing geographic information, including the mind, computers, graphics, text, etc."},{"concepts":[371],"name":"Describe the location and geometric characteristics of the principal point of an aerial image"},{"concepts":[473],"name":"Describe the main advantages of object-based image analysis methods"},{"concepts":[643],"name":"Describe the main branch of physycs relevant to the study of e.m. radiation and its interaction with the matter in the optical range"},{"concepts":[572],"name":"Describe the main sources of spectral line broadening"},{"concepts":[565],"name":"Describe the main spectral components of solar radiation at the top of atmosphere"},{"concepts":[632],"name":"Describe the main state functions of ideal gases"},{"concepts":[108],"name":"Describe the perceptual processes (e.g., edge detection) that aid cognitive objectification"},{"concepts":[30],"name":"Describe the pitfalls, in terms of information loss and analytical options, of transforming attribute measurement levels"},{"concepts":[621],"name":"Describe the process of light scattering by atmospheric particulates"},{"concepts":[614],"name":"Describe the process of water vapour cloud formation"},{"concepts":[77],"name":"Describe the relationship between factorial kriging and spatial filtering"},{"concepts":[72],"name":"Describe the relationship between the semi-variogram and kriging"},{"concepts":[50],"name":"Describe the relationships between kernels and classical spatial interaction approaches, such as surfaces of potential"},{"concepts":[71],"name":"Describe the relationships between semi-variograms and correlograms, and Morans indices of spatial association"},{"concepts":[642],"name":"Describe the relevance of mechanics laws in the framework of EO satellite mission design and planning"},{"concepts":[517],"name":"Describe the role of infrastructures for sharing remote sensing data products"},{"concepts":[443],"name":"Describe the role of machine learning classifiers to find patterns in the available data"},{"concepts":[351],"name":"Describe the sanctions imposed by ASPRS and GISCI on individuals whose professional actions violate the codes of ethics"},{"concepts":[385],"name":"Describe the scattering and atmospheric absorption factors"},{"concepts":[578],"name":"Describe the scattering properties of a lambertian surface"},{"concepts":[578],"name":"Describe the scattering properties of a mirroring surface"},{"concepts":[627],"name":"Describe the scope of irreversible thermodynamics"},{"concepts":[638],"name":"Describe the scope of thermodynamics"},{"concepts":[369,371],"name":"Describe the sequence of tasks involved in the geometric correction of the Advanced Very High Resolution Radiometer (AVHRR) Global Land Dataset"},{"concepts":[309],"name":"Describe the source data, instrumentation, and workflow involved in extracting vector data (features and elevations) from analog and digital stereoimagery"},{"concepts":[584],"name":"Describe the spectral regions where Mineral and Rocks exhibit their main signatures"},{"concepts":[62],"name":"Describe the statistical characteristics of a set of spatial data using a variety of graphs and plots including scatterplots, histograms, boxplots, qq plots"},{"concepts":[17],"name":"Describe the structure of linear programs"},{"concepts":[19],"name":"Describe the structure of origin-destination matrices"},{"concepts":[553],"name":"Describe the U.S. geospatial industry including vendors, software, hardware and data"},{"concepts":[359],"name":"Describe the use of GIS from a political ecology point of view (e.g., consider the use of GIS for resource identification, conservation, and allocation by an NGO in Sub-Saharan Africa)"},{"concepts":[114],"name":"Describe the ways in which a spatial perspective enables the synthesis of different subjects (e.g., climate and economy)"},{"concepts":[94],"name":"Describe the ways in which the elements of culture (e.g., language, religion, education, traditions) may influence the understanding and use of geographic information"},{"concepts":[22],"name":"Describe the workflow for converting data from one data model to another"},{"concepts":[553],"name":"Describe three applications of geospatial technology for different workforce domains (e.g., first responders, forestry, water resource management, facilities management)"},{"concepts":[628],"name":"Describe under what conditions adiabatic processes of homogeneous system occur"},{"concepts":[578],"name":"Describe under which conditions a Lambertian surface can be defined as a \"perfect\" Lambertian diffusor"},{"concepts":[578],"name":"Describe under which conditions a mirroring surface can be defined a \"perfect\" mirror"},{"concepts":[618],"name":"Describe under which conditions Mie scattering occurs in the Earth's Atmosphere"},{"concepts":[619],"name":"Describe under which conditions Rayleigh Scattering in the Earth's Atmosphere occurs"},{"concepts":[595],"name":"Describe under which conditions the Beer-Bouguert-Lambert Law well approximates the general radiative transfer equation-"},{"concepts":[117],"name":"Describe ways in which a geographic entity can be created from one or more others"},{"concepts":[589],"name":"Describe what EM sensing means"},{"concepts":[178],"name":"Design a test project to demonstrate interoperability"},{"concepts":[134],"name":"Design a game mechanics of a geo-game"},{"concepts":[807],"name":"Design a map of chlorophyll-a concentration according to the requirements of HAB management for aquaculture"},{"concepts":[147],"name":"Design a map series to show the change in a geographic pattern over time"},{"concepts":[70],"name":"Design a sampling scheme that will help detect when space-time clusters of events occur"},{"concepts":[135],"name":"Design a single map 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data"},{"concepts":[897],"name":"Determine object movement by comparing subsequent images"},{"concepts":[826],"name":"Determine requirements and quality criteria for an EO information product that serves spatial planners in monitoring soil sealing"},{"concepts":[28],"name":"Determine the mathematical relationships among scale, scope, and resolution"},{"concepts":[318],"name":"Determine the most appropriate data collection method for collecting particular data"},{"concepts":[106],"name":"Determine the proper uses of attributes based on their domains"},{"concepts":[209],"name":"Determine the standards that are essential for geospatial data modelling"},{"concepts":[117],"name":"Determine whether it is important to represent the genealogy of entities for a particular application"},{"concepts":[111],"name":"Determine whether phenomena or applications exist that are not adequately represented in an existing comprehensive model"},{"concepts":[54],"name":"Determine which method to use to combine criteria e.g., linear, multiplication"},{"concepts":[949],"name":"Develop a Javascript function that handles a GeoJSON file"},{"concepts":[38],"name":"Develop a method for describing the shape of a cluster of similarly valued points by using the concept of the convex hull"},{"concepts":[546],"name":"Develop a strategy to improve the performance of an SDI initiative"},{"concepts":[149],"name":"Develop a useful interactive interface and legend"},{"concepts":[106],"name":"Develop alternative forms of representations for situations in which attributes do not adequately capture meaning"},{"concepts":[38],"name":"Develop an algorithm to determine the skeleton of polygons"},{"concepts":[904],"name":"Develop an event map based on a time-series analysis"},{"concepts":[473],"name":"Develop and implement an object-based image analysis workflow for a specific application context"},{"concepts":[131,165],"name":"Develop effective mathematical and other models of spatial situations and processes"},{"concepts":[342],"name":"Develop GI infrastructure with a the role in the private sector"},{"concepts":[145],"name":"Develop graphic techniques that clearly show different forms of inexactness (e.g., existence uncertainty, boundary location uncertainty, attribute ambiguity, transitional boundary) of a given feature (e.g., a culture region)"},{"concepts":[97],"name":"Develop methods for representing non-cartesian models of space in GIS"},{"concepts":[836,835],"name":"Develop monitoring to evaluate and deliver policy goals"},{"concepts":[840],"name":"Develop sense of space"},{"concepts":[225],"name":"Develop solutions to different kind of challenges of model interoperability"},{"concepts":[841],"name":"Develop strategies and policies"},{"concepts":[813,810,811,812,846],"name":"Develop strategies and policies for energy and mineral resources"},{"concepts":[893],"name":"Develop thorough understanding of the complex process from collecting the LiDAR data to generation of the final modeled outputs"},{"concepts":[166],"name":"Develop use cases for potential applications using established techniques with potential users, such as questionnaires, interviews, focus groups, the Delphi method, and/or joint application development"},{"concepts":[921],"name":"Develop Web-GIS solutions to replace each of the functions of a traditional GIS"},{"concepts":[520],"name":"Devise simple ways to represent probability information in GIS"},{"concepts":[332],"name":"Differentiate \"contracts for service\" from \"contracts of service\""},{"concepts":[146],"name":"Differentiate 3D representations from 2.5 D representations"},{"concepts":[212],"name":"Differentiate among a lattice, a tessellation, and a grid"},{"concepts":[23],"name":"Differentiate among common interpolation techniques (e.g., nearest neighbor, bilinear, bicubic)"},{"concepts":[332],"name":"Differentiate among contract liability, tort liability, and statutory liability"},{"concepts":[113],"name":"Differentiate among different types of regions, including functional, cultural, physical, administrative, and others"},{"concepts":[112],"name":"Differentiate among distributions in space, time, and attribute"},{"concepts":[93],"name":"Differentiate among elements of the meaning of a place that can or cannot be easily represented using geospatial technologies"},{"concepts":[28],"name":"Differentiate among the concepts of scale (as in map scale), support, scope, and resolution"},{"concepts":[324],"name":"Differentiate among the spatial, spectral, radiometric, and temporal resolution of a remote sensing instrument"},{"concepts":[346],"name":"Differentiate among universal/deliberative, pluralist/representative, and participatory models of citizen participation"},{"concepts":[522],"name":"Differentiate an enterprise system from a department-centered GI system"},{"concepts":[121],"name":"Differentiate applications in which vagueness is an acceptable trait from those in which it is unacceptable"},{"concepts":[101],"name":"Differentiate applications that can make use of common-sense principles of geography from those that should not"},{"concepts":[18],"name":"Differentiate between a linear program and an integer program"},{"concepts":[931],"name":"Differentiate between a metadata standard and a metadata profile"},{"concepts":[97],"name":"Differentiate between absolute and relative descriptions of location"},{"concepts":[311],"name":"Differentiate between active and passive sensors, citing examples of each"},{"concepts":[179],"name":"Differentiate between and application built with a Service Oriented Architecture (SOA) or a Resource Oriented Architecture (ROA)"},{"concepts":[97],"name":"Differentiate between common-sense, Cartesian metric, relational, relativistic, phenomenological, social constructivist, and other theories of the nature of space"},{"concepts":[186,187],"name":"Differentiate between conceptual and logical models, in terms of the level of detail, constraints, and range of information included"},{"concepts":[347],"name":"Differentiate between consumption, analysis, presumption and production of geoinformation within digital geo media"},{"concepts":[54],"name":"Differentiate between contributing factors and constraints in a multi-criteria application"},{"concepts":[175],"name":"Differentiate between copyleft and permissive licenses for a software product"},{"concepts":[5],"name":"Differentiate between data mining approaches used for spatial and non-spatial applications"},{"concepts":[55],"name":"Differentiate between deterministic and stochastic spatial process models"},{"concepts":[99],"name":"Differentiate between formal and natural language in GI science applications."},{"concepts":[2],"name":"Differentiate between geostatistics, and spatial statistics"},{"concepts":[244],"name":"Differentiate between individual and aggregate models"},{"concepts":[63],"name":"Differentiate between isotropic and anisotropic processes"},{"concepts":[50],"name":"Differentiate between kernel density estimation and spatial interpolation"},{"concepts":[188],"name":"Differentiate between logical and physical models, in terms of the level of detail, constraints, and range of information included"},{"concepts":[213],"name":"Differentiate between lossy and lossless compression methods"},{"concepts":[46,47],"name":"Differentiate between map algebra and matrix algebra using real examples"},{"concepts":[103],"name":"Differentiate between mathematical and phenomenological theories of the nature of time"},{"concepts":[70],"name":"Differentiate between model-based and design-based sampling schemes"},{"concepts":[26],"name":"Differentiate between polynomial coordinate transformations (including linear) and rubbersheeting"},{"concepts":[924],"name":"Differentiate between SOAP and REST Web services. - Identify design issues of REST Web services"},{"concepts":[93],"name":"Differentiate between space and place"},{"concepts":[121],"name":"Differentiate between the following concepts: vagueness and ambiguity, well defined and poorly defined objects and fields or discord and non-specificity"},{"concepts":[52],"name":"Differentiate between the gravity model and spatial interaction models"},{"concepts":[57],"name":"Differentiate between trend surface analysis and deterministic spatial interpolation"},{"concepts":[929],"name":"Differentiate between upper, domain, and application level ontologies"},{"concepts":[311],"name":"Differentiate push-broom and cross-track scanning technologies"},{"concepts":[369],"name":"Differentiate rectification and orthorectification"},{"concepts":[435],"name":"Differentiate supervised classification from unsupervised classification"},{"concepts":[122],"name":"Differentiate uncertainty in geospatial situations from vagueness"},{"concepts":[138],"name":"Differentiate uses for different types of imagery related to earth"},{"concepts":[109],"name":"Differentiate various sources of fields, such as substance properties (e.g., temperature), artificial constructs (e.g., population density), and fields of potential or influence (e.g., gravity)"},{"concepts":[327],"name":"Digitize and georegister a specified vector feature set to a given geometric accuracy and topological fidelity thresholds using a given map sheet, digitizing tablet, and data entry software"},{"concepts":[354],"name":"Discuss about \"mapping whose reality?\" Pros and cons of geoinformation sharing in social media, i.e. big data, \"digital shadow\" etc."},{"concepts":[342],"name":"Discuss about open data and data sharing and public/private sector"},{"concepts":[336],"name":"Discuss about open data impact on society and citizenship"},{"concepts":[151],"name":"Discuss about the advantages of different immersive display systems"},{"concepts":[159],"name":"Discuss about the degree of subjectivity and/or objectivity of a map"},{"concepts":[125],"name":"Discuss about the History of Cartography in different cultures"},{"concepts":[126],"name":"Discuss about the relationship between art and cartography"},{"concepts":[779,780,781],"name":"Discuss advantages and disadvantages of different methods of storing remote sensing data"},{"concepts":[794,795,796,797],"name":"Discuss advantages and disadvantages of different SAR data formats"},{"concepts":[725],"name":"Discuss advantages and disadvantages of passive and active sensors"},{"concepts":[687],"name":"Discuss advantages of SAR techniques over traditional measuring techniques"},{"concepts":[420],"name":"Discuss algorithms that use the detection of keypoints to identify objects in images"},{"concepts":[728],"name":"Discuss an example of using a radar altimeter"},{"concepts":[786],"name":"Discuss and compare different temporal resolutions of remote sending data"},{"concepts":[681],"name":"Discuss and compare different types of interactions of microwaves with matter"},{"concepts":[793],"name":"Discuss and compare different types of processing levels of optical data"},{"concepts":[798],"name":"Discuss and compare different types of processing levels of SAR data"},{"concepts":[336],"name":"Discuss and define open data and impact on GIS&T"},{"concepts":[518],"name":"Discuss and define the process of the Information value chain"},{"concepts":[406],"name":"Discuss cloud masks as early steps towards semantic enrichment for EO images"},{"concepts":[104],"name":"Discuss common prepositions and adjectives (in any particular language) that signify either spatial or temporal relations but are used for both kinds, such as after or longer"},{"concepts":[245],"name":"Discuss concepts of space-time dynamics for spatial modeling"},{"concepts":[922],"name":"Discuss consensus based interoperability and its relation to geospatial data interchange. Define what a Web Service (WS) is and present characteristic scenarios. Data serving and Data Processing WSs"},{"concepts":[353],"name":"Discuss critiques of GIS as \"deterministic\" technology in relation to debates about the Quantitative quantitative revolution in the discipline of geography."},{"concepts":[357],"name":"Discuss critiques of GIS as deterministic technology in relation to debates about the Quantitative Revolution in the discipline of geography"},{"concepts":[532],"name":"Discuss different formats (tutorials, in house, online, instructor lead) for training and how they can be used by organizations"},{"concepts":[500],"name":"Discuss different methods for assessing the quality of a specific EO product"},{"concepts":[736],"name":"Discuss different types of laser scanners"},{"concepts":[765,641],"name":"Discuss different types of satellite orbits"},{"concepts":[248],"name":"Discuss different ways of simulating space and visualizing model behaviour"},{"concepts":[654],"name":"Discuss electromagnetic interactions and scattering mechanisms"},{"concepts":[771],"name":"Discuss examples of ground-based platforms and their use"},{"concepts":[764],"name":"Discuss examples of the objectives of Earth observation missions"},{"concepts":[309],"name":"Discuss future prospects for automated feature extraction from aerial imagery"},{"concepts":[530],"name":"Discuss how a code of ethics might be applied within an organization"},{"concepts":[136],"name":"Discuss how cultural differences with respect to color associations impact map design"},{"concepts":[503,783],"name":"Discuss how different spectral resolution of EO sensors influences their potential for vegetation mapping"},{"concepts":[467],"name":"Discuss how hierarchical representation is exploited for object-based image analysis"},{"concepts":[718],"name":"Discuss how line detectors array sensors work"},{"concepts":[455],"name":"Discuss how low-pass filtering of an image impacts the resulting regions derived with watershed segmentation"},{"concepts":[159],"name":"Discuss how maps express relations of power"},{"concepts":[323],"name":"Discuss how measures of spatial autocorrelation may be used to evaluate thematic accuracy"},{"concepts":[785,503],"name":"Discuss how radiometric resolution influences the granularity of a land cover classification"},{"concepts":[782,790],"name":"Discuss how remote sensing data is organized and stored"},{"concepts":[701],"name":"Discuss how the angle of SAR signal incidence affects SAR acquisition"},{"concepts":[70],"name":"Discuss how the choice of sampling strategy impacts the accuracy assesment for a classification result"},{"concepts":[447],"name":"Discuss how the choice of sampling strategy impacts the accuracy assesment for a classification result"},{"concepts":[70],"name":"Discuss how the choice of sampling strategy impacts the classification result"},{"concepts":[447],"name":"Discuss how the choice of sampling strategy impacts the classification result"},{"concepts":[791],"name":"Discuss how the radiometrically corrected data are processed"},{"concepts":[464],"name":"Discuss how the size of the neighborhood impacts the smoothing effect of a low-pass filter"},{"concepts":[343],"name":"Discuss how to approach the widening audience/participants for geospatial products and service, increasing geo-awareness and geo-enablement"},{"concepts":[143],"name":"Discuss how to create an intellectual and visual hierarchy on maps"},{"concepts":[649],"name":"Discuss how to use phase information in remote sensing"},{"concepts":[31],"name":"Discuss implications of data loss in the case of generalisation of spatial data."},{"concepts":[387],"name":"Discuss imputation methods for filling in missing data"},{"concepts":[557],"name":"Discuss in which way annual solar insolation and average cloud coverage parameters affect the choice of a solar power plant location"},{"concepts":[557],"name":"Discuss in which way modeled daily solar insolation and cloud coverage forecast could affect solar power plant day-by-day management"},{"concepts":[344],"name":"Discuss legal aspects of access to environmental data, global change/warming or sustainable development (regional, national, global) in conjunction to society."},{"concepts":[687],"name":"Discuss limitations of interferometric measurement"},{"concepts":[456],"name":"Discuss limitations of the different region-based segementation methods"},{"concepts":[778],"name":"Discuss main characteristics of digital imagery"},{"concepts":[336],"name":"Discuss of arguments for and against open data"},{"concepts":[335],"name":"Discuss of opportunities for exchange of geospatial data between public and private sector to enable more efficient analysis"},{"concepts":[243],"name":"Discuss options of combining rule-based models with other individual modelling approaches"},{"concepts":[676],"name":"Discuss orientational polarisation of media"},{"concepts":[353],"name":"Discuss over the argument that the use of Geospatial geospatial Information privileges certain views of the world over others."},{"concepts":[342],"name":"Discuss over the changing role of the private sector in the use of geospatial information"},{"concepts":[343],"name":"Discuss over the paradigm shifts and current trends in GIS&T education and pedagogical approaches for GIS teaching and learning in detail"},{"concepts":[354],"name":"Discuss over the various implications of surveillance technology"},{"concepts":[675],"name":"Discuss polarimetric decomporition techniques"},{"concepts":[348],"name":"Discuss positive and negative aspects of the term \"humans as sensors\""},{"concepts":[684],"name":"Discuss radar antennas"},{"concepts":[670],"name":"Discuss scale of roughness of microwaves"},{"concepts":[2],"name":"Discuss situations when it is desirable to adopt a spatial approach to the analysis of data"},{"concepts":[226],"name":"Discuss some of the difficulties of applying the standard process-pattern concept to lines and networks"},{"concepts":[457],"name":"Discuss spatial autocorrelation and homogeneity of image objects"},{"concepts":[174],"name":"Discuss the advantages and disadvantages of outsourcing elements of a GIS project / GI system"},{"concepts":[97],"name":"Discuss the advantages and disadvantages of the use of cartesian metric space as a basis for GIS and related technologies"},{"concepts":[324],"name":"Discuss the advantages and potential problems associated with the use of Minimum Mapping Unit (MMU) as a measure of the level of detail in land use, land cover, and soils maps"},{"concepts":[726],"name":"Discuss the application possibilities of imaging radar"},{"concepts":[742],"name":"Discuss the applications for which Differential Absorption LiDAR can be used"},{"concepts":[743],"name":"Discuss the applications for which Wind Doppler LiDAR is used"},{"concepts":[64],"name":"Discuss the appropriateness of different types of spatial weights matrices for various problems"},{"concepts":[78],"name":"Discuss the appropriateness of GWR under various conditions"},{"concepts":[487],"name":"Discuss the available data quality standards for EO"},{"concepts":[617],"name":"Discuss the basic principles of solar radiation."},{"concepts":[463],"name":"Discuss the benefits of using a gauss filter instead of a mean filter for smoothing an image"},{"concepts":[112],"name":"Discuss the causal relationship between spatial processes and spatial patterns, including the possible problems in determining causality"},{"concepts":[577],"name":"Discuss the change of attenuation length moving from visible to the microwave range and from sea water to solid land surfaces"},{"concepts":[51],"name":"Discuss the characteristics of the various cluster detection techniques"},{"concepts":[25],"name":"Discuss the consequences of increasing and decreasing resolution"},{"concepts":[111],"name":"Discuss the contributions of early attempts to integrate the concepts of space, time, and attribute in geographic information, such as Berry (1964) and Sinton (1978)"},{"concepts":[97],"name":"Discuss the contributions that different perspectives on the nature of space bring to an understanding of geographic phenomenon"},{"concepts":[111],"name":"Discuss the degree to which these models can be implemented using current technologies"},{"concepts":[714],"name":"Discuss the development of remote sensing sensors"},{"concepts":[123],"name":"Discuss the difference between vagueness and uncertainty."},{"concepts":[10],"name":"Discuss the difference of implementing Dijkstras algorithm in raster and vector modes"},{"concepts":[744],"name":"Discuss the differences between imaging and non-imaging sensors"},{"concepts":[133],"name":"Discuss the differences between maps that use the same data but are for different purposes and intended audiences"},{"concepts":[133],"name":"Discuss the differences between maps that use the same data but are for different purposes and intended audiences"},{"concepts":[503],"name":"Discuss the different types of resolution of Earth observation data"},{"concepts":[92],"name":"Discuss the differing denotations and connotations of the terms spatial, geographic, and geospatial"},{"concepts":[110],"name":"Discuss the difficulty of integrating process models into GIS software based on the entity and field views, and methods used to do so"},{"concepts":[117],"name":"Discuss the effects of temporal scale on the modeling of genealogical structures"},{"concepts":[350],"name":"Discuss the ethical implications of a local government's decision to charge fees for its data"},{"concepts":[309],"name":"Discuss the extent to which vector data extraction from aerial stereoimagery has been automated"},{"concepts":[462],"name":"Discuss the frequencies that a high-pass filter preserves and subdues"},{"concepts":[548],"name":"Discuss the governance structure in place of a particular country"},{"concepts":[222],"name":"Discuss the historical roots of the Census Bureaus creation of GBF/DIME as the foundation for the development of topological data structures"},{"concepts":[753],"name":"Discuss the history of the development of remote sensing platforms"},{"concepts":[108],"name":"Discuss the human predilection to conceptualize geographic phenomena in terms of discrete entities"},{"concepts":[347],"name":"Discuss the impact of geospatial information for the development of social media (Facebook, Twitter, Wikimapia, Flickr etc.) becoming increasingly location-based"},{"concepts":[232],"name":"Discuss the implication of long transactions on database integrity"},{"concepts":[357],"name":"Discuss the implications of interoperability on ontology"},{"concepts":[353],"name":"Discuss the implications of interoperability on ontology"},{"concepts":[324],"name":"Discuss the implications of the sampling theorem (Lambda = 0.5 delta) to the concept of resolution"},{"concepts":[28],"name":"Discuss the implications of tradeoff between data detail and data volume"},{"concepts":[107],"name":"Discuss the importance of space, time, properties, and categories as fundamentals in the conceptualization and representation of spatial entities."},{"concepts":[150],"name":"Discuss the influence of the user interface on maps and visualizations on the Web"},{"concepts":[924],"name":"Discuss the issue whether a service is really \"RESTful\" or not"},{"concepts":[335],"name":"Discuss the legal framework related to competition and public-private sector relationships in the geospatial domain"},{"concepts":[760],"name":"Discuss the main applications using the extra wide swath mode"},{"concepts":[449],"name":"Discuss the main drawback of edge-based segmentation in partitioning an image"},{"concepts":[721],"name":"Discuss the main properties of hyperspectral radiometers"},{"concepts":[720],"name":"Discuss the main properties of passive microwave radiometers"},{"concepts":[719],"name":"Discuss the main properties of thermal radiometers"},{"concepts":[713],"name":"Discuss the main types of remote sensing data"},{"concepts":[713,772],"name":"Discuss the main types of remote sensing platforms"},{"concepts":[713],"name":"Discuss the main types of remote sensing sensors"},{"concepts":[503],"name":"Discuss the minimum spatial resolution required for detecting single houses in a satellite image"},{"concepts":[552],"name":"Discuss the mission, history, constituencies, and activities of the GIS Certification Institute (GISCI)"},{"concepts":[532],"name":"Discuss the National Research Council report on Learning to Think Spatially (2005) as it relates to spatial thinking skills needed by the GIS and T workforce"},{"concepts":[786,503],"name":"Discuss the needs for high temporal resolution for analysing crop cycles in agriculture"},{"concepts":[23],"name":"Discuss the pitfalls of using secondary data that has been generated using interpolations (e.g., Level 1 USGS DEMs)"},{"concepts":[680],"name":"Discuss the polarimetry technique"},{"concepts":[29],"name":"Discuss the possible effects on topological integrity of generalizing data sets"},{"concepts":[332],"name":"Discuss the potential legal problems associated with licensing geospatial information"},{"concepts":[358],"name":"Discuss the potential role of agency (individual action) in resisting dominant practices and in using GIS and T in ways that are consistent with feminist epistemologies and politics"},{"concepts":[453],"name":"Discuss the principles of regionalisation and their use in segmentation methods"},{"concepts":[624],"name":"Discuss the processes that describe the hydrologic cycle"},{"concepts":[359],"name":"Discuss the production, maintenance, and use of geospatial data by a government agency or private firm from the perspectives of a taxpayer, a community organization, and a member of a minority group"},{"concepts":[801],"name":"Discuss the purposes of obtaining remote sensing data"},{"concepts":[655],"name":"Discuss the radiometric anomalies of radar data"},{"concepts":[55],"name":"Discuss the relationship between spatial processes and spatial patterns"},{"concepts":[125],"name":"Discuss the relationship between the history of exploration and the development of a more accurate map of the world"},{"concepts":[30],"name":"Discuss the relationship of attribute measurement levels to database query operations"},{"concepts":[347],"name":"Discuss the role and value of \"place\" and \"space\" for geo media based social networking"},{"concepts":[136],"name":"Discuss the role of gamut in choosing colors that can be reproduced on various devices and media"},{"concepts":[222],"name":"Discuss the role of graph theory in topological structures"},{"concepts":[22],"name":"Discuss the role of metadata in facilitating conversation of data models and data structures between systems"},{"concepts":[344,349],"name":"Discuss the role of public, private sector and citizens in facilitating geospatial information in environmental/sustainable issues."},{"concepts":[335],"name":"Discuss the role of the public and private sectors in producing and dissemination of geospatial information"},{"concepts":[530],"name":"Discuss the status of professional and academic certification in GIS and T"},{"concepts":[333],"name":"Discuss the status of the concept of privacy in the U.S. legal regime"},{"concepts":[142],"name":"Discuss the strengths and weaknesses of infographics as a method of displaying geographic information"},{"concepts":[613],"name":"Discuss the structure and chemical composition of the atmosphere"},{"concepts":[0],"name":"Discuss the synergy between processes in geo-information systems and earth observation systems."},{"concepts":[63],"name":"Discuss the theory leading to the assumption of intrinsic stationarity"},{"concepts":[717],"name":"Discuss the use of area array sensors in remote sensing"},{"concepts":[723],"name":"Discuss the use of atmospheric passive sounders"},{"concepts":[722],"name":"Discuss the use of data obtained by spectroradiometer"},{"concepts":[716],"name":"Discuss the use of digital frame cameras in remote sensing"},{"concepts":[647],"name":"Discuss the use of polarization for different application domains"},{"concepts":[149],"name":"Discuss the uses of the map as a user interface element in interactive presentations of geographic information"},{"concepts":[754],"name":"Discuss the ways of using data acquired by UAS in remote sensing"},{"concepts":[752],"name":"Discuss types and classes of remote sensing sensors"},{"concepts":[505],"name":"Discuss valid time ranges for images used for landslide mapping with pre- and post-event image comparison"},{"concepts":[336],"name":"Discuss various legal aspects of public and private sectors concerning owning, controlling, sharing/ disseminating open data."},{"concepts":[336],"name":"Discuss various sources of open data (science, public and private sectors)"},{"concepts":[331],"name":"Discuss ways in which the geospatial profession is regulated under the U.S. legal regime"},{"concepts":[343],"name":"Discuss ways of working with crowdsourcing in education and research"},{"concepts":[667],"name":"Discuss what horizontal roughness component (correlation legth) is"},{"concepts":[729],"name":"Discuss what information is acquired by the laser altimeters"},{"concepts":[666],"name":"Discuss what surface height variation (or RMS height) is"},{"concepts":[788],"name":"Discuss what the header file describes"},{"concepts":[724],"name":"Discuss what the main characteristics of radiometers are"},{"concepts":[727],"name":"Discuss what types of electromagnetic waves the laser profiler uses"},{"concepts":[408],"name":"Discuss why a query through time is easier realized with a data cube than by comparison of a time series stored in image files"},{"concepts":[787],"name":"Distinguish and explain the different types of properties of digital imagery"},{"concepts":[149,139],"name":"Distinguish between animated and interactive maps"},{"concepts":[89],"name":"Distinguish between continuants and occurrents in relation with spatial phenomena."},{"concepts":[154],"name":"Distinguish between different graphic representation techniques"},{"concepts":[86],"name":"Distinguish between metaphysics and epistemology."},{"concepts":[186],"name":"Distinguish between the temporary and structural relationships in a conceptual model"},{"concepts":[27],"name":"Distinguish between transformation methods for raster and vector representations."},{"concepts":[164,170],"name":"Distinguish between usability, utility, and user needs in the context of geovisualizations"},{"concepts":[167,168],"name":"Document existing and potential tasks in terms of workflow and information flow"},{"concepts":[105],"name":"Document the personal, social, and or institutional meaning of categories used in GIS applications"},{"concepts":[150],"name":"Edit the symbology, labeling, and page layout for a map originally designed for hard copy printing so that it can be seen and used on the Web"},{"concepts":[101],"name":"Effectively communicate the design, procedures, and results of GIS projects to non-GIS audiences (clients, managers, general public)"},{"concepts":[112],"name":"Employ techniques for visualizing, describing, and analyzing distributions in space, time, and attribute"},{"concepts":[840],"name":"Enable citizen skills spatially"},{"concepts":[23],"name":"Estimate a value between two known values using linear interpolation (e.g., spot elevations, population between census years)"},{"concepts":[881],"name":"Estimate evaporation rates"},{"concepts":[881,399],"name":"Estimate near-surface chlorophyll-a concentration for monitoring harmful algal blooms (HABs)"},{"concepts":[129],"name":"Estimate the cost to collect needed data from primary sources (e.g., remote sensing, GPS)"},{"concepts":[36],"name":"Estimate the fractal dimension of a sinuous line"},{"concepts":[599],"name":"Estimate the meteorological and the cloud optical properties by LBRTM and validate against high accuracy spectral measurements"},{"concepts":[127],"name":"Estimate the potential value of a historical map"},{"concepts":[486],"name":"Evaluate an EO product and its metadata on its reusability for a new application context"},{"concepts":[525],"name":"Evaluate and revise an existing GIS management strategy"},{"concepts":[840,837,838],"name":"Evaluate citizen-driven observations"},{"concepts":[153],"name":"Evaluate graphic techniques used to portray spatializations"},{"concepts":[25],"name":"Evaluate methods used by contemporary GIS software to resample raster data on-the-fly during display"},{"concepts":[311],"name":"Evaluate the advantages and disadvantages of acoustic remote sensing versus airborne or satellite remote sensing for seafloor mapping"},{"concepts":[311,763,768],"name":"Evaluate the advantages and disadvantages of airborne remote sensing versus satellite remote sensing"},{"concepts":[309],"name":"Evaluate the advantages and disadvantages of photogrammetric methods and LiDAR for production of terrain elevation data"},{"concepts":[110],"name":"Evaluate the assertion that events and processes are the same thing, but viewed at different temporal scales"},{"concepts":[122],"name":"Evaluate the causes of uncertainty in geospatial data"},{"concepts":[136],"name":"Evaluate the colors used in a web map to be used indoors and outdoors"},{"concepts":[483],"name":"Evaluate the conformity of an EO imagery product to ISO 19129"},{"concepts":[93],"name":"Evaluate the differences in how various parties think or feel differently about a place being modeled"},{"concepts":[217],"name":"Evaluate the ease of measuring resolution in different types of tessellations"},{"concepts":[108],"name":"Evaluate the effectiveness of GIS data models for representing the identity, existence, and lifespan of entities"},{"concepts":[109],"name":"Evaluate the field views description of objects as conceptual discretizations of continuous patterns"},{"concepts":[877],"name":"Evaluate the impact of changes in land areas"},{"concepts":[101],"name":"Evaluate the impact of geospatial technologies (e.g., Google Earth) that allow non-geospatial professionals to create, distribute, and map geographic information"},{"concepts":[856,854],"name":"Evaluate the impact of the climate change"},{"concepts":[217],"name":"Evaluate the implications of changing grid cell resolution on the results of analytical applications by using GIS software"},{"concepts":[108],"name":"Evaluate the influence of scale on the conceptualization of entities"},{"concepts":[85],"name":"Evaluate the influences of ones own philosophical views and assumptions on GIS AND T practices"},{"concepts":[81],"name":"Evaluate the influences of particular worldviews (including ones own) on GIS practices"},{"concepts":[95],"name":"Evaluate the influences of political actions, especially the allocation of territory, on human perceptions of space and place"},{"concepts":[95],"name":"Evaluate the influences of political ideologies (e.g., Marxism, Capitalism, conservative liberal) on the understanding of geographic information"},{"concepts":[544],"name":"Evaluate the institutional framework of an existing SDI initiative"},{"concepts":[222],"name":"Evaluate the positive and negative impacts of this shift from integrated topological models"},{"concepts":[213],"name":"Evaluate the relative merits of grid compression methods for storage"},{"concepts":[534],"name":"Evaluate the relevance and applicability of different teaching and learning methods for GIS&T education"},{"concepts":[109],"name":"Evaluate the representation of movement as a field of location over time (e.g. :x,y,z: = f(t) )"},{"concepts":[121],"name":"Evaluate the role that system complexity, dynamic processes, and subjectivity play in the creation of vague phenomena and concepts"},{"concepts":[144],"name":"Evaluate the strengths and limitations of different thematic mapping methods"},{"concepts":[494],"name":"Evaluate the thematic accuracy of a given soils map"},{"concepts":[242],"name":"Evaluate the tradeoffs between abstraction and representativeness in simulation model development"},{"concepts":[161],"name":"Evaluate the usability of a hard-copy map"},{"concepts":[161,170],"name":"Evaluate the usability of a web map"},{"concepts":[187],"name":"Evaluate the various general data models common in GIS project"},{"concepts":[121],"name":"Evaluate vagueness in the locations, time, attributes, and other aspects of geographic phenomena"},{"concepts":[29],"name":"Evaluate various line simplification algorithms by their usefulness in different applications"},{"concepts":[243],"name":"Evaluate when rule-based models can be applied to spatiotemporal problems"},{"concepts":[238],"name":"Examine how computational technology relates to geocomputation"},{"concepts":[404],"name":"Examine how the vegetation indices relates to the vegetation dynamics and health"},{"concepts":[404],"name":"Examine how the water-related spectral indices relates to changes in the vegetation and soil water content"},{"concepts":[934],"name":"Examine Metadata schema and vocabularies used for open data publishing"},{"concepts":[949],"name":"Examine the Document Object Model (DOM) in HTML documents"},{"concepts":[45],"name":"Exemplify applications in which overlay is useful, such as site suitability analysis"},{"concepts":[63],"name":"Exemplify deterministic and spatial stochastic processes"},{"concepts":[103],"name":"Exemplify different temporal frames of reference: linear and cyclical, absolute and relative"},{"concepts":[523],"name":"Exemplify each component of a needs assessment for an enterprise GIS"},{"concepts":[235],"name":"Exemplify how the lack of a data librarian to manage data can have disastrous consequences on the resulting dataset"},{"concepts":[63],"name":"Exemplify non-stationarity involving first and second order effects"},{"concepts":[113],"name":"Exemplify regions found at different scales"},{"concepts":[232],"name":"Exemplify scenarios in which one would need to perform a number of periodic changes in a real GIS database"},{"concepts":[38],"name":"Exemplify situations in which the centroid of a polygon falls outside its boundary"},{"concepts":[12],"name":"Exemplify the Classic Transportation Problem"},{"concepts":[222],"name":"Exemplify the concept of planar enforcement (e.g., TIN triangles)"},{"concepts":[215],"name":"Exemplify the uses (past and potential) of the hexagonal model"},{"concepts":[586],"name":"Explain the concept of composition of spectral signatures and apply the \"linear mixing\" models in some simple case"},{"concepts":[826],"name":"Explain a use case of EO for smart cities, e.g. how EO derived information about urban green instrastructure supports designing nature based solutions for preserving ecosystem services"},{"concepts":[690],"name":"Explain across-track interferometry technique"},{"concepts":[689],"name":"Explain along-track interferometry technique"},{"concepts":[442],"name":"Explain an application example where SVM is used for EO image classification"},{"concepts":[404],"name":"Explain an application example where the spectral indices are used for vegetation, water or snow monitoring"},{"concepts":[207],"name":"Explain and apply GML data models"},{"concepts":[649],"name":"Explain and apply phase unwrapping"},{"concepts":[203,221],"name":"Explain and apply standards relevant for geometric modelling"},{"concepts":[704],"name":"Explain and discuss elements of Synthetic Aperture Radar (SAR) geometric configuration"},{"concepts":[671],"name":"Explain and discuss surface roughness in microwave remote sensing"},{"concepts":[644],"name":"Explain and discuss the complex elements of a radar signal"},{"concepts":[777],"name":"Explain and discuss the concept of Big Data in the field of Earth Observation"},{"concepts":[773],"name":"Explain and discuss the development of remote sensing data carriers"},{"concepts":[737],"name":"Explain and discuss the LiDAR technology"},{"concepts":[758],"name":"Explain and discuss the SAR acquisition mode spotlight"},{"concepts":[757],"name":"Explain and discuss the SAR acquisition mode staring spotlight"},{"concepts":[725],"name":"Explain and discuss types of sensing mechanisms"},{"concepts":[682],"name":"Explain and discuss what antenna gain is and why it is described as the key performance of a radar antenna"},{"concepts":[709],"name":"Explain and discuss what terrain reflectivity is and how it influences radar signal"},{"concepts":[706],"name":"Explain and discuss what the foreshortening is"},{"concepts":[707],"name":"Explain and discuss what the layover is"},{"concepts":[800],"name":"Explain and discuss what the main processing levels of remote sensing data are"},{"concepts":[787],"name":"Explain and discuss what the radiometric resolution is"},{"concepts":[700],"name":"Explain and discuss what the range direction is"},{"concepts":[708],"name":"Explain and discuss what the shadow in SAR acquisition means"},{"concepts":[787,784],"name":"Explain and discuss what the spatial resolution is"},{"concepts":[787],"name":"Explain and discuss what the spectral resolution is"},{"concepts":[787],"name":"Explain and discuss what the temporal resolution is"},{"concepts":[712,652],"name":"Explain and outline the advantages of radar sensors"},{"concepts":[197],"name":"Explain and use UML diagrams"},{"concepts":[76],"name":"Explain Anselins typology of spatial autoregressive models"},{"concepts":[37],"name":"Explain any differences in the measured direction between two places when the data are presented in a GIS in different projections"},{"concepts":[200],"name":"Explain basic aspects of data modelling, storage and exploitation, such as relation models & databases, data structures, SQL, UML and other basics"},{"concepts":[332],"name":"Explain cases of liability claims associated with misuse of geospatial information, erroneous information, and loss of proprietary interests"},{"concepts":[674],"name":"Explain covariance and coherence matrix"},{"concepts":[665],"name":"Explain dielectric properties of objects and their effect on radar data acquisition"},{"concepts":[688],"name":"Explain differences between DInSAR and PSI"},{"concepts":[712],"name":"Explain differences between optical and radar remote sensing"},{"concepts":[84],"name":"Explain from which scientific fields GIS&T borrows ideas."},{"concepts":[236],"name":"Explain geocomputation, related concepts and how the two relate"},{"concepts":[6],"name":"Explain how a Bayesian framework can incorporate expert knowledge in order to retrieve all relevant datasets given an initial user query"},{"concepts":[542],"name":"Explain how a business case analysis can be used to justify the expense of implementing consensus-based standards"},{"concepts":[422],"name":"Explain how a DSM differs from a DTM"},{"concepts":[226],"name":"Explain how a graph (network) may be directed or undirected"},{"concepts":[226],"name":"Explain how a graph can be written as an adjacency matrix and how this can be used to calculate topological shortest paths in the graph"},{"concepts":[374],"name":"Explain how a histogram is derived from an EO image"},{"concepts":[507],"name":"Explain how a lack of knowledge about data quality limits the data value"},{"concepts":[10],"name":"Explain how a leading World Wide Web-based routing system works e.g., MapQuest, Yahoo Maps, Google"},{"concepts":[40],"name":"Explain how a semi-variogram describes the distance decay in dependence between data values"},{"concepts":[366],"name":"Explain how a set of overlapping images/satellite scenes can provide digital elevation models used for orthorectification and 3D modelling"},{"concepts":[868],"name":"Explain how a specific EO technology supports the assessments of disasters and geohazards"},{"concepts":[65],"name":"Explain how a statistic that is based on combining all the spatial data and returning a single summary value or two can be useful in understanding broad spatial trends"},{"concepts":[359],"name":"Explain how a tax assessors office adoption of GIS and T may affect power relations within a community"},{"concepts":[66],"name":"Explain how a weights matrix can be used to convert any classical statistic into a local measure of spatial association"},{"concepts":[78],"name":"Explain how allowing the parameters of the model to vary with the spatial location of the sample data can be used to accommodate spatial heterogeneity"},{"concepts":[56,1],"name":"Explain how analytical methods are used to derive analytical results from geospatial data"},{"concepts":[405],"name":"Explain how band maths can be applied to derive an index that indicates a specific land cover type like vegetation"},{"concepts":[72],"name":"Explain how block-kriging and its variants can be used to combine data sets with different spatial resolution support"},{"concepts":[44],"name":"Explain how buffers can be used in GI analysis"},{"concepts":[208],"name":"Explain how CityGML is related to GML"},{"concepts":[468],"name":"Explain how class modelling can make use of per-parcel analysis"},{"concepts":[439],"name":"Explain how CNNs are structured to derive information from image data"},{"concepts":[346],"name":"Explain how community organizations represent the interests of citizens, politicians, and specialists"},{"concepts":[421],"name":"Explain how computer vision imitates the human visual system when interpreting EO images"},{"concepts":[333],"name":"Explain how conversion of land records data from analog to digital form increases risk to personal privacy"},{"concepts":[333],"name":"Explain how data aggregation is used to protect personal privacy in data produced by the U.S. Census Bureau"},{"concepts":[36],"name":"Explain how different measures of distance can be used to calculate the spatial weights matrix"},{"concepts":[64],"name":"Explain how different types of spatial weights matrices are defined and calculated"},{"concepts":[77],"name":"Explain how dissolving clusters of blocks with similar values may resolve the spatial correlation problem"},{"concepts":[49],"name":"Explain how distance-based methods of point pattern measurement can be derived from a distance matrix"},{"concepts":[52],"name":"Explain how dynamic, chaotic, complex or unpredictable aspects in some phenomena make spatial interaction models more appropriate than gravity models"},{"concepts":[393],"name":"Explain how EO applications targeting several countries at once can profit from data harmonisation"},{"concepts":[411],"name":"Explain how error propagates in the production workflow of an example EO product"},{"concepts":[364],"name":"Explain how fourier transformation is used to generate radar image"},{"concepts":[364],"name":"Explain how fourier transformation is used to reduce noise in optical imagery"},{"concepts":[36],"name":"Explain how fractal dimension can be used in practical applications of GIS"},{"concepts":[60],"name":"Explain how friction surfaces are enhanced by the use of impedance and barriers"},{"concepts":[345],"name":"Explain how geographic information is valuable to different sectors"},{"concepts":[66],"name":"Explain how geographically weighted regression provides a local measure of spatial association"},{"concepts":[322],"name":"Explain how geometric accuracies associated with the various orders of the U.S. horizontal geodetic control network are assured"},{"concepts":[334],"name":"Explain how geospatial information might be used in a taking of private property through a government's claim of its right of eminent domain"},{"concepts":[341],"name":"Explain how geospatial information might be used in a taking of private property through a governments claim of its right of eminent domain"},{"concepts":[522],"name":"Explain how GIS and T can be an integrating technology"},{"concepts":[15],"name":"Explain how graph theory plays a role in network analysis."},{"concepts":[212],"name":"Explain how grid representations embody the field-based view"},{"concepts":[360],"name":"Explain how image processing and analysis methods are used to derive geospatial information from Earth observation imagery"},{"concepts":[149],"name":"Explain how interactivity influences map use"},{"concepts":[591],"name":"Explain how it is possible to retrieve atmospheric temperature and trace gases profiles form multi/iper spectral radiances"},{"concepts":[951],"name":"Explain how JSON (GeoJSON)`s \"schema-less\"structure may be transformed into an application schema"},{"concepts":[102],"name":"Explain how linguistics play a role in GI science."},{"concepts":[452],"name":"Explain how local density gradients are employed in mean-shift segmentation"},{"concepts":[32],"name":"Explain how logic theory relates to set theory"},{"concepts":[159],"name":"Explain how maps such as topographic maps are produced within certain relations of power and knowledge"},{"concepts":[146],"name":"Explain how maps that show the landscape in profile can be used to represent terrain"},{"concepts":[313],"name":"Explain how metadata, standards and data infrastructures are linked to each other"},{"concepts":[380],"name":"Explain how minimum noise fraction makes use of principal components analysis for dimensionality reduction"},{"concepts":[547],"name":"Explain how next-generation SDIs are different from current SDIs"},{"concepts":[484],"name":"Explain how OGC standards can be used for sharing spatial data (including Earth Observation data) across different communities and computing infrastructures"},{"concepts":[351],"name":"Explain how one or more obligations in the GIS Code of Ethics may conflict with organizations proprietary interests"},{"concepts":[232],"name":"Explain how one would establish 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is"},{"concepts":[703],"name":"Explain what the ground range and azimuth resolution are"},{"concepts":[759],"name":"Explain what the interferometric wide swath mode is"},{"concepts":[683],"name":"Explain what the main representations of radar antenna pattern are"},{"concepts":[649],"name":"Explain what the mathematical description of the phase is"},{"concepts":[649],"name":"Explain what the phase in remote sensing means and in what units is expressed"},{"concepts":[645],"name":"Explain what the phasor represents"},{"concepts":[774],"name":"Explain what the picture element is"},{"concepts":[660],"name":"Explain what the radar cross-section is"},{"concepts":[656],"name":"Explain what the radar equation is"},{"concepts":[741],"name":"Explain what the radar scatterometer measures"},{"concepts":[930],"name":"Explain what the Resource Description Framework (RDF) is and what it can be used for"},{"concepts":[651],"name":"Explain what the wave-particle dualism is"},{"concepts":[320],"name":"Explain which elements determine the quality of geospatial data"},{"concepts":[458],"name":"Explain which principles a segmentation should follow to arrive at meaningful objects that are appropriate for a specific application"},{"concepts":[202],"name":"Explain which standards are essential for conceptual data modelling"},{"concepts":[82],"name":"Explain which technologies have an impact on GI science."},{"concepts":[315],"name":"Explain which types of geospatial data are collected through satellite remote sensing"},{"concepts":[140],"name":"Explain why a layer with audio could be of interest in certain situations"},{"concepts":[646],"name":"Explain why a radar signal needs a complex waveform description"},{"concepts":[310],"name":"Explain why aerial imaging and photogrammetry are important for the geospatial domain and the geospatial industry"},{"concepts":[50],"name":"Explain why and how density estimation transforms point data into a field 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programs"},{"concepts":[222],"name":"Explain why integrated topological models have lost favor in commercial GIS software"},{"concepts":[530],"name":"Explain why it has been difficult for many agencies and organizations to define positions and roles for GIS and T professionals"},{"concepts":[72],"name":"Explain why it is important to have a good model of the semi-variogram in kriging"},{"concepts":[355],"name":"Explain why it is important to take into consideration the 'digital divide' when dealing with the use of and access to geographic data and information"},{"concepts":[72],"name":"Explain why kriging is more suitable as an interpolation method in some applications than others"},{"concepts":[233],"name":"Explain why logging and rollback techniques are adequate for managing short transactions"},{"concepts":[321],"name":"Explain why metadata are important for assessing and ensuring the quality of geospatial data"},{"concepts":[432],"name":"Explain why multimodal distributions in training samples should be avoided when using the maximum likelihood classifier"},{"concepts":[565],"name":"Explain why passive EO sensors with the highest spectral or spatial resolution operate in the VIS/NIR spectral region"},{"concepts":[405],"name":"Explain why radiometric correction is a key requirement for deriving indices with band maths"},{"concepts":[495],"name":"Explain why rapid mapping applications have high requirements in timely availability of Earth observation products"},{"concepts":[553],"name":"Explain why software products sold by U.S. companies may predominate in foreign markets, including Europe and Australia"},{"concepts":[696],"name":"Explain why spatial resolution of passive radar system is much lower than that of active systems"},{"concepts":[524],"name":"Explain why the definition of user roles is an important element in the implementation of a GIS"},{"concepts":[650],"name":"Explain why the Doppler effect is important in radar remote sensing"},{"concepts":[538],"name":"Explain why the legal framework on geospatial data sharing can be considered as diverse and complex"},{"concepts":[538],"name":"Explain why the legal framework on geospatial data sharing consists of two main types of legislation from a data perspective"},{"concepts":[45],"name":"Explain why the process \"dissolve and merge\" often follows vector overlay operations"},{"concepts":[61],"name":"Explain why the properties of spatial continuity are characteristic of spatial surfaces"},{"concepts":[38],"name":"Explain why the shape of an object might be important in analysis"},{"concepts":[304],"name":"Explain why the shape of the Earth is complex and complicated to measure"},{"concepts":[865],"name":"Explain why the use of multiple EO sensors for mapping landslides associated with one triggering event increases the completeness of a landslide inventory"},{"concepts":[119],"name":"Explain why Toblers First Law of Geography is fundamental to many operations in GIS and whether it should be"},{"concepts":[61],"name":"Explain why zero slopes are indicative of surface specific points such as peaks, pits and passes and list the conditions necessary for each"},{"concepts":[12],"name":"Explain why, if supply equals demand, there will always be a feasible solution to the Classic Transportation Problem"},{"concepts":[50],"name":"Explain why, in some cases, an adaptive bandwidth might be employed"},{"concepts":[325],"name":"Explain, in general terms, the difference between single and double precision and impacts on error propagation"},{"concepts":[16],"name":"Explain, using the concept of combinatorial complexity, why some location problems are very hard to solve"},{"concepts":[88],"name":"Explore the contribution of linguistics to the study of spatial cognition and the role of natural language in the conceptualization of geographic phenomena"},{"concepts":[92],"name":"Explore the history of geography including (but not limited to) Greek and Roman contributions to geography (Eratosthenes, Strabo, Ptolemy), geography and cartography in the age of discovery, military geography, and geography..."},{"concepts":[76],"name":"Find a best model"},{"concepts":[147],"name":"Find a multivariate outlier using a combination of maps and graphs"},{"concepts":[38],"name":"Find centroids of polygons under different definitions of a centroid and different polygon shapes"},{"concepts":[881],"name":"Find oil spills in EO data for Ocean surveillance"},{"concepts":[532],"name":"Find or create training resources appropriate for GIS and T workforce in a local government organization"},{"concepts":[112],"name":"Find spatial patterns in the distribution of geographic phenomena using geographic visualization and other techniques"},{"concepts":[840,838],"name":"Forecast and monitor ocean winds and waves"},{"concepts":[106],"name":"Formalize attribute values and domains in terms of set theory"},{"concepts":[109],"name":"Formalize the notion of field using 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phenomenon"},{"concepts":[51],"name":"Identify a clustering method which does not require the number of clusters as input"},{"concepts":[30],"name":"Identify a variety of likely measurement level transformations (e.g., the classification of ratio data yields ordinal data)"},{"concepts":[889],"name":"Identify adequate preprocessing for deriving ocean colour from EO data"},{"concepts":[247],"name":"Identify agent-based modelling principles and methodologies"},{"concepts":[353],"name":"Identify alternatives to the \"algorithmic way of thinking\" that characterizes use of geospatial Information."},{"concepts":[357],"name":"Identify alternatives to the algorithmic way of thinking that characterizes GIS"},{"concepts":[236],"name":"Identify and compare the scenarios on which geocomputation methods are relevant"},{"concepts":[71],"name":"Identify and define the parameters of a semi-variogram range, sill, nugget"},{"concepts":[459],"name":"Identify and discuss an example of a combined filtering 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language"},{"concepts":[246],"name":"Identify cellular automata principles and pattern"},{"concepts":[101],"name":"Identify common-sense views of geographic phenomena that sharply contrast with established theories and technologies of geographic information"},{"concepts":[240],"name":"Identify commonalities and patterns of geocomputation"},{"concepts":[552],"name":"Identify conferences that are related to GIS and T hosted by professional organizations"},{"concepts":[909],"name":"Identify construction sites"},{"concepts":[498],"name":"Identify critical design decisions that make an EO-derived map readable"},{"concepts":[172],"name":"Identify data center platform tier configuration and identify platform selection for each tier"},{"concepts":[923],"name":"Identify design issues of SOAP web services; fine grained and coarse grained services, design patterns"},{"concepts":[951],"name":"Identify differences, advantages and disadvantages of web application framework based and portal framework based web applications from the geospatial data perspective"},{"concepts":[65],"name":"Identify different measures of spatial autocorrelation"},{"concepts":[66],"name":"Identify different measures of spatial autocorrelation"},{"concepts":[431],"name":"Identify different methods that employ conditional probability for image classification"},{"concepts":[300],"name":"Identify different options where Artificial Intelligence can be integrated in the image processing and analysis workflow"},{"concepts":[382],"name":"Identify different types of noise and associated methods for their reduction"},{"concepts":[109],"name":"Identify examples of discrete and continuous change found in spatial, temporal, and spatio-temporal fields"},{"concepts":[149,139],"name":"Identify examples of static, animated, and interactive web maps"},{"concepts":[134],"name":"Identify gaming elements which may be part of geo-games"},{"concepts":[876],"name":"Identify geological features"},{"concepts":[864,876],"name":"Identify geotectonic shifts"},{"concepts":[832,829,839,852,850,861,831,857],"name":"Identify high risk areas produced naturally or by humans"},{"concepts":[392],"name":"Identify image fusion techniques to fill gaps in image time series caused by clouds and cloud shadow"},{"concepts":[862],"name":"Identify impact of a flood"},{"concepts":[112],"name":"Identify influences of scale on the appearance of distributions"},{"concepts":[936],"name":"Identify issues in determining the relationships to be represented when publishing Linked Data"},{"concepts":[935],"name":"Identify issues in developing new ontologies for geospatial data"},{"concepts":[936],"name":"Identify issues in finding proper ontologies to annotate the data"},{"concepts":[929],"name":"Identify issues in the development of geospatial ontologies. 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Also identify the roles of thesauri and crosswalks"},{"concepts":[529],"name":"Identify the key organizational components of a GIS&T implementation"},{"concepts":[113],"name":"Identify the kinds of phenomena that are commonly found at the boundaries of regions"},{"concepts":[332],"name":"Identify the liability implications associated with contracts"},{"concepts":[942],"name":"Identify the main components of OGC Filter encoding and compare it to SQL"},{"concepts":[939],"name":"Identify the main concepts of reasoning and architectural components of Reasoners"},{"concepts":[529],"name":"Identify the main organizational challenges in implementing and use GIS&T"},{"concepts":[136],"name":"Identify the most appropriate color palette for a printed map for visually-impaired people"},{"concepts":[136],"name":"Identify the most appropriate color palette for an online map for visually-impaired people"},{"concepts":[438],"name":"Identify the most popular decision tree 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time"},{"concepts":[49],"name":"Identify various types of K-function analysis"},{"concepts":[927],"name":"Identify virtues of defining a given data set in both RDF and OWL, and compare semantic richness of both definitions"},{"concepts":[887,885],"name":"Identify wake trailing to detect ships using EO data"},{"concepts":[947],"name":"Identify whether Full-automated WSC has still a value in it concerning both where we stand today on the road to 'Semantic Web' and unresolved problems in the area, which are the problems of Artificial Intelligence indeed"},{"concepts":[585],"name":"Illustrate main spectral signatures of clouds and apply them in paractical cloud-detection exercise"},{"concepts":[222],"name":"Illustrate a topological relation"},{"concepts":[346],"name":"Illustrate an example of \"local knowledge\" that is unlikely to be represented in the geospatial data maintained routinely by government agencies"},{"concepts":[625],"name":"Illustrate and apply basic concepts of Atmospheric 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region."},{"concepts":[630],"name":"Illustrate the concept of \"kinetic temperature\" in absence of thermodynamic equilibrium"},{"concepts":[593],"name":"Illustrate the concept of Absorption Coefficient"},{"concepts":[592],"name":"Illustrate the concept of Cross Section of Extinction per Mass Unit"},{"concepts":[575],"name":"Illustrate the concept of grey body"},{"concepts":[594],"name":"Illustrate the concept of Source Function"},{"concepts":[564],"name":"Illustrate the concept of spectral emissivity and brigthness temperature and compute them in some simple real case"},{"concepts":[582],"name":"Illustrate the concept of spectral signatures of the matter"},{"concepts":[605],"name":"Illustrate the concepts of Interference and Diffraction"},{"concepts":[601],"name":"Illustrate the concepts of Reflection, Refraction and Dispersion of the light"},{"concepts":[557],"name":"Illustrate the concepts of solar constant and daily solar insolation"},{"concepts":[581],"name":"Illustrate the decay of the emittance with the distance from the source"},{"concepts":[141],"name":"Illustrate the elements of the story by proper geovisualizations"},{"concepts":[125],"name":"Illustrate the evolution of Cartography in different periods of time"},{"concepts":[213],"name":"Illustrate the existing methods for compressing gridded data (e.g., run length encoding, Lempel-Ziv, wavelets)"},{"concepts":[640],"name":"Illustrate the factors limiting lifetime of satellites on their originally planned orbits"},{"concepts":[636],"name":"Illustrate the First Law of Thermodynamic"},{"concepts":[590],"name":"Illustrate the general equation of radiative transfer."},{"concepts":[616],"name":"Illustrate the Greenhouse effect associate to CO2 emission"},{"concepts":[608],"name":"Illustrate the Helmotz’s equation"},{"concepts":[215],"name":"Illustrate the hexagonal model"},{"concepts":[631],"name":"Illustrate the ideal gas law"},{"concepts":[217],"name":"Illustrate the impact of grid cell resolution on the information that can be portrayed"},{"concepts":[24],"name":"Illustrate the impact of vector/raster/vector conversions on the quality of a dataset"},{"concepts":[558],"name":"Illustrate the importance of Earth's emitted radiation for EO from space"},{"concepts":[603],"name":"Illustrate the importance of electric conduction in solids for the design and development of advanced EO sensors"},{"concepts":[641],"name":"Illustrate the importance of the choice of the satellite orbit for the design of a satellite mission devoted to specific applications"},{"concepts":[918,913,914,915,916,917],"name":"Illustrate the information of EO data"},{"concepts":[183],"name":"Illustrate the landscape of GIS and related libraries"},{"concepts":[623],"name":"Illustrate the main atmospherical spectral windows"},{"concepts":[587],"name":"Illustrate the main differences among passive and active remote sensing techniques"},{"concepts":[571],"name":"Illustrate the main energetic transictions that can be associated to molecular absorption of e.m. radiation"},{"concepts":[579],"name":"Illustrate the main forms of radiation-matter interaction"},{"concepts":[51],"name":"Illustrate the main use of spatial clustering in earth observation"},{"concepts":[566],"name":"Illustrate the nature of electromagnetic radiation"},{"concepts":[218],"name":"Illustrate the quadtree model"},{"concepts":[241],"name":"Illustrate the relationships between geocomputation with other terms, disciplines and areas of knowledge"},{"concepts":[635],"name":"Illustrate the role of Eulerian and Lagrangian models in budget equations definition"},{"concepts":[607],"name":"Illustrate the role of the principle of constant speed of light within the special relativity theory"},{"concepts":[600],"name":"Illustrate the scope Radiative Transfer theory"},{"concepts":[637],"name":"Illustrate the Second Law of Thermodynamic"},{"concepts":[583],"name":"Illustrate the spectral response curves for basic environmental features (e.g., vegetation, concrete, bare soil)"},{"concepts":[624],"name":"Illustrate the transferring of Energy within the Earth-Atmosphere System"},{"concepts":[151],"name":"Illustrate the use of virtual environments"},{"concepts":[629],"name":"Illustrate the utility of thermodynamic diagrams for the study of local atmospheric properties"},{"concepts":[142],"name":"Illustrate the ways in which maps could be integrated in an infography"},{"concepts":[522],"name":"Illustrate what functions a support or service center can provide to an organization using GIS and T"},{"concepts":[569],"name":"Illustrate why we refer to the BRDF as an absolute definition of spectral reflectance"},{"concepts":[140],"name":"Illustrate with examples of maps or geovisualizations that could be improved by the addition of an audio layer"},{"concepts":[126],"name":"Illustrate with examples the relationship between art and cartography at different historical moments"},{"concepts":[633],"name":"Ilustrate the state function of the condensed gas phase"},{"concepts":[218],"name":"Implement a format for encoding quadtrees in a data file"},{"concepts":[76],"name":"Implement a maximum likelihood estimation procedure for determining key spatial econometric parameters"},{"concepts":[234],"name":"Implement a test of reliability of change information"},{"concepts":[57],"name":"Implement a trend surface analysis using either the supplied function in a GIS or a regression function from any standard statistical package"},{"concepts":[933],"name":"Implement and configure a catalogue service"},{"concepts":[17],"name":"Implement linear programs for spatial allocation problems"},{"concepts":[12],"name":"Implement the Transportation Simplex method to determine the optimal solution"},{"concepts":[322],"name":"In contrast to the National Map Accuracy Standard, explain how the spatial accuracy of a digital road centerlines data set may be evaluated and documented"},{"concepts":[935],"name":"Indicate an architecture and tools for organizing semantically annotated data"},{"concepts":[950],"name":"Indicate an overview of OpenStreetMap and define its general functionality, comment its usage by Web APIs"},{"concepts":[951],"name":"Indicate generally how \"NSDI-requiring-scenarios\"would be handled by web application framework based applications"},{"concepts":[949],"name":"Indicate main elements of HTML5"},{"concepts":[939],"name":"Indicate some examples of semantic discovery; Semantic search engines, highlighting projects and practice concerning GI related applications in the area"},{"concepts":[353],"name":"Indicate the extent to which contemporary use of geospatial information supports diverse ways of understanding the world."},{"concepts":[523],"name":"Indicate the possible justifications that can be used to implement an enterprise GIS"},{"concepts":[245],"name":"Interpret when space-time dynamics can be used to study geographical phenomen"},{"concepts":[172],"name":"Interpret business needs and translate 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GML application schemas and GML documents"},{"concepts":[237],"name":"Interpret how individual parts contained in a complex system relate to each other"},{"concepts":[907],"name":"Interpret information from EO products or EO time series"},{"concepts":[819],"name":"Interpret land cover change detection"},{"concepts":[822],"name":"Interpret location based services (LBS)"},{"concepts":[889],"name":"Interpret ocean colour for deriving chlorophyll concentration in water"},{"concepts":[5],"name":"Interpret patterns in space and time using Dorling and Openshaws Geographical Analysis Machine GAM demonstration of disease incidence diffusion"},{"concepts":[918,913,914,915,916,917],"name":"Interpret the content of EO data"},{"concepts":[465],"name":"Interpret the effect of a convolution from a given mask and contained weights"},{"concepts":[211],"name":"Interpret the header of a standard raster data file"},{"concepts":[125],"name":"Interpret the impact of paper-based and web maps in their context"},{"concepts":[893],"name":"Interpret the output of an point cloud measurement"},{"concepts":[855],"name":"Interpret the output of numerical prediction models"},{"concepts":[73],"name":"Interpret the results of universal kriging"},{"concepts":[172],"name":"Interpret user needs as an input for the design process"},{"concepts":[92],"name":"Justify a chosen position on which disciplines should have as important a role in GIS AND T as geography"},{"concepts":[176],"name":"Justify feasibility recommendations to decision-makers"},{"concepts":[108],"name":"Justify or refute the conception of fields (e.g., temperature, density) as spatially-intensive attributes of (sometimes amorphous and anonymous) entities"},{"concepts":[92],"name":"Justify or refute whether geography (as a discipline) should have a central role in GIS AND T"},{"concepts":[97],"name":"Justify the discrepancies between the nature of locations in the real world and representations thereof (e.g., towns as 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and Apparent Optical Properties (AOP) of water"},{"concepts":[504],"name":"Understand the difficulties in searching and selecting satellite images with sufficient spatial coverage for time series analysis"},{"concepts":[865],"name":"Understand the diverse set of EO technologies that are capable of mapping different landslide aspects"},{"concepts":[806,847,869,859],"name":"Understand the health of the crop, extent of infestation or stress damage, or potential yield and soil conditions"},{"concepts":[807,884],"name":"Understand the health of the fishing grounds"},{"concepts":[808,870],"name":"Understand the health of the forests"},{"concepts":[949],"name":"Understand the importance of Cascading Style Sheets (CSS) to separate content from style in HMTL documents"},{"concepts":[494],"name":"Understand the importance of using spatially independent validation samples to assess the quality of the classification results"},{"concepts":[385],"name":"Understand the main factors generating geometric distortions of the remotely sensed images"},{"concepts":[169],"name":"Understand the main software engineering methodologies"},{"concepts":[332],"name":"Understand the nature of tort law generally and nuisance law specifically"},{"concepts":[104],"name":"Understand the physical notions of velocity and acceleration which are fundamentally about movement across space through time"},{"concepts":[485],"name":"Understand the problems associated with the lack of reproducibility"},{"concepts":[497],"name":"Understand the relevance of topological consistency for linear network features derived from Earth observation data"},{"concepts":[407],"name":"Understand the role of multi-temporal satellite images for identifying not only when a change occurred but also the changing drivers"},{"concepts":[438],"name":"Understand the role of pruning for reducing overfitting when applying decision trees for various classification purposes"},{"concepts":[514],"name":"Understand the strategic meaning of DIAS in the user segment of Copernicus"},{"concepts":[417],"name":"Understand the subjectivity of the visual interpretation"},{"concepts":[893],"name":"Understand the technology behind LiDAR as an active sensor and what makes it different from the other existing Remote Sensing approaches"},{"concepts":[438],"name":"Understand the types of decision trees and their output"},{"concepts":[63],"name":"Understand the underlying assumptions for spatial stochastics process"},{"concepts":[409],"name":"Understand the way in which Dynamic Time Warping can align shifted temporal sequences"},{"concepts":[22],"name":"Understand various formats of storing raster and vector data"},{"concepts":[227],"name":"Understand vector data models"},{"concepts":[893],"name":"Understand what products can be extracted from point clouds"},{"concepts":[938],"name":"Use \"Full-text-based\" discovery; open source and commercial search engines, its use in GI related applications"},{"concepts":[912,910],"name":"Use 3D textured models to present study area"},{"concepts":[512],"name":"Use a web portal to retrieve EO data"},{"concepts":[513],"name":"Use an image archive to retrive Earth observation data for an application"},{"concepts":[146],"name":"Use appropriate interpolation techniques to derive DEMs from point data"},{"concepts":[105],"name":"Use categorical information in analysis, cartography, and other GIS processes, avoiding common interpretation mistakes"},{"concepts":[877],"name":"Use EO products to assess land areas, its ecosystems, and its evolution"},{"concepts":[868],"name":"Use EO products to assess the risk of a disaster"},{"concepts":[856,854],"name":"Use EO products to conduct forecasts and projections"},{"concepts":[855],"name":"Use EO products to conduct numerical simulations"},{"concepts":[853],"name":"Use EO products to forecast sunlight exposure"},{"concepts":[868],"name":"Use EO products to measure impact and/or recovery"},{"concepts":[868],"name":"Use EO products to monitor disaster prone areas"},{"concepts":[877],"name":"Use EO products to plan land areas, its ecosystems, and its evolution"},{"concepts":[805],"name":"Use EO/GI information to plan and design projects, monitor and assess the environment, support decision-making processes, and to tackle environmental challenges"},{"concepts":[113],"name":"Use established analysis methods that are based on the concept of region (e.g., landscape ecology)"},{"concepts":[114],"name":"Use established analysis methods that are based on the concept of spatial integration (e.g., overlay)"},{"concepts":[425],"name":"Use filtering techniques to spatially aggregate an image classification"},{"concepts":[369],"name":"Use GIS software to transform a given dataset to a specified coordinate system, projection, and datum"},{"concepts":[119],"name":"Use methods that analyze metrical relationships"},{"concepts":[118],"name":"Use methods that analyze topological relationships"},{"concepts":[940],"name":"Use Natural language based discovery over linked data"},{"concepts":[891],"name":"Use NDVI to estimate the vegetation cover"},{"concepts":[934],"name":"Use open data APIs that enable the usage of Open data; identify design aspects and usage scenarios"},{"concepts":[416],"name":"Use photo interpretation keys to interpret features on aerial photographs"},{"concepts":[485],"name":"Use software tools to automate the practice of reproducible research in daily work"},{"concepts":[206],"name":"Use standards such as ISO 19141 Schema for moving features, ISO 19142 Web Feature Service and ISO 19109 - Rules for application schema"},{"concepts":[541],"name":"Use the models of ‘SDI generations’ and ‘SDI components’ to describe the main elements of an existing SDI initiative"},{"concepts":[528],"name":"Use the most effective change model depending on the nature and needs of the client's organization."},{"concepts":[927],"name":"Use Web services description for RESTful web services, Web Application Description Language (WADL) and its use"},{"concepts":[417],"name":"Using a vertical aerial image, produce a map of land use/land cover classes"},{"concepts":[195],"name":"Work with different data compression techniques"},{"concepts":[40],"name":"Write a program to create a matrix of pair-wise distances among a set of points"},{"concepts":[211],"name":"Write a program to read and write a raster data file"},{"concepts":[40],"name":"Write typical forms for distance decay functions"},{"concepts":[11],"name":"xplain how the concept of capacity represents an upper limit on the amount of flow through the network"}],"updateDate":"2022/05/25","version":"7"},"v8":{"concepts":[{"code":"GIST","description":"Geographic Information Science and Technology","hasParent":true,"name":"Geographic Information Science and Technology"},{"code":"AM","description":"This knowledge area encompasses a wide variety of operations whose objective is to derive analytical results from geospatial data. Data analysis seeks to understand both first-order (environmental) effects and second-order (interaction) effects. Approaches that are both data-driven (exploration of geospatial data) and model-driven (testing hypotheses and creating models) are included. Data driven techniques derive summary descriptions of data, evoke insights about characteristics of data, contribute to the development of research hypotheses, and lead to the derivation of analytical results. The goal of model driven analysis is to create and test geospatial process models. In general, model-driven analysis is an advanced knowledge area where previous experience with exploratory spatial data analysis would constitute a desired prerequisite. Visual tools for data analysis are covered in Knowledge Area: Cartography and Visualization (CV) and many of the fundamental principles required to ground data analysis techniques are introduced in Knowledge Area: Conceptual Foundations (CF). Image processing techniques are considered in Knowledge Area: Geospatial Data (GD). All of the methods described in this knowledge area are more or less sensitive to data error and uncertainty as covered in Unit GC8 Uncertainty and Unit GD6 Data quality. Mastery of the educational objectives outlined in this knowledge area requires knowledge and skills in mathematics, statistics, and computer programming.","hasChildren":true,"hasParent":true,"name":"Analytical Methods","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM1-2","description":"Analytical capabilities of a GIS make use of spatial and non-spatial (attribute) data to answer questions and solve problems that are of spatial relevance. We now make a distinction between analysis (or analytical operations) and analytical models (often referred to as “modelling”). And by analysis we actually mean only a subset of what is usually implied by the term: we do not specifically deal with advanced statistical analysis (such as cluster detection or geostatistics).\r\n\r\nAnalysis of spatial data can be defined as computing new information to provide new insights from existing spatial data. Consider an example from the domain of road construction. In mountainous areas, this is a complex engineering task with many cost factors, including the number of tunnels and bridges to be constructed, the total length of the tarmac, and the volume of rock and soil to be moved. GISs can help to compute such costs on the basis of an up-to-date digital elevation model and a soil map. The exact nature of the analysis will depend on the application requirements, but computations and analytical functions can operate on both spatial and non-spatial data.","hasChildren":true,"name":"Analytical approaches","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM1","description":"Geospatial data analysis has foundations in many different disciplines. As a result, there are many different schools of thought or analytical approaches including spatial analysis, spatial modeling, geostatistics, spatial econometrics, spatial statistics, qualitative analysis, map algebra, and network analysis. This unit compares and contrasts these approaches.","hasChildren":true,"hasParent":true,"name":"Foundations of analytical methods","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM10-1","description":"Difficulties in dealing with large spatial databases, especially those arising from spatial heterogeneity and data quality issues.","hasChildren":true,"name":"Problems of large spatial databases","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM10-2","description":"Data mining knows a variety of approaches, such as cluster analysis, analytical reasoning, association, prediction, etc.","hasChildren":true,"name":"Data mining approaches","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM10-3","description":"Knowledge discovery involves the identification of useful patterns in spatial databases using techniques of data mining, trend analysis, etc.","hasChildren":true,"name":"Knowledge discovery","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM10","description":"Algorithms have been developed to scan and search through extremely large data sets in order to find patterns within the data. These data mining and knowledge discovery techniques have been expanded to the spatial case. Legal and ethical concerns associated with such practices are considered in Knowledge Areas GS GIS and T and Society and OI Organizational and Institutional Aspects.","hasChildren":true,"hasParent":true,"name":"Data mining","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM11-1","description":"A network is a connected set of lines representing some geographic phenomenon, typically to do with transportation. The “goods” transported can be almost anything: people, cars and other vehicles along a road network, commercial goods along a logistic network, phone calls along a telephone network, or water pollution along a stream/river network.\r\n\r\nDirect vs. Non-directed Networks\r\nA fundamental characteristic of any network is whether the network lines are considered to be directed or not. Directed networks associate with each line a direction of transportation; undirected networks do not. In the latter, the “goods” can be transported along a line in both directions. We discuss here vector network analysis, and assume that the network is a set of connected line features that intersect only at the lines’ nodes, not at internal vertices. (But we do mention under- and overpasses.)\r\n\r\nPlanar vs. Non-Planar Networks\r\nFor many applications of network analysis, a planar network, i.e. one that can be embedded in a two-dimensional plane, will do the job. Many networks are naturally planar, such as stream/river networks. A large-scale traffic network, on the other hand, is not planar: motorways have multi-level crossings and are constructed with underpasses and overpasses. Planar networks are easier to deal with computationally, as they have simpler topological rules. Not all GISs accommodate non-planar networks, or they can only do so using “tricks”. These tricks may involve the splitting of overpassing lines at the intersection vertex and the creation of four lines from the two original lines. Without further attention, the network will then allow one to make a turn onto another line at this new intersection node, which in reality would be impossible. In some GISs we can allocate a cost for turning at a node—see our discussion on turning costs below—and that cost, in the case of the overpass trick, can be made infinite to ensure it is prohibited. But, as mentioned, this is a work around to fit a non-planar situation into a data layer that presumes planarity. The above is a good illustration of geometry not fully determining the network’s behaviour. Additional application-specific rules are usually required to define what can and cannot happen in the network. Most GISs provide rule-based tools that allow the definition of these extra application rules.","hasChildren":true,"name":"Networks defined","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM11-2","description":"Identifying and listing all elements does not describe a system in full. There may be many different ways in which elements may be connected or related to each other. The interactions, relationships between elements are essential to describe a system.\r\n\r\nRelationships between elements can be described by two types of flows:\r\nflows of material, and flows of information.\r\n\r\nMaterial flows connect elements between which there is an exchange of some substance. This can be some kind of material (water, food, cement, biomass, etc.), energy (light, heat, electricity, etc.), money, etc. It is something that can be measured and tracked. Also if an element is a donor of this substance the amount of substance in this element will decrease as a result of the exchange, while at the same time the amount of this substance will increase in the receptor element. There is always a mass, or energy conservation law in place. Nothing appears from nothing, and nothing can disappear to nowhere.\r\n\r\nThe second type of exchange is with an information flow. In this case element A gets information from element B. Element B at the same time may have no information about element A. Even when element A gets information about B, B does not lose anything. Information can be about the state of an element, about the quantity that it contains, about its presence or absence, etc. Information flows can be used to describe rules and policies. Information flows can modify the rates of flow between elements, they can switch certain processes and interactions on and off. But the process through which policies, interventions and norms for action are established, and could for example define the values of such information flows, are themselves the result of social interaction between relevant stakeholders from public, private or civil society.\r\n\r\nThe simplest is to acknowledge the existence of a relationship between certain elements, like this is done in a graph. In a graph a node presents an element and a link between any two nodes shows that these two elements are related. However there is no evidence of the direction of the relationship: we do not distinguish between the element x influencing element y or vice versa. This relationship can be further specified by an oriented graph that shows the direction of the relationship between elements. An element can be also connected to itself, to show that its behaviour depends on its state. We can further detail the description by identifying whether element x has a positive or negative effect on element y.\r\n\r\nWith networks, interesting questions arise that have to do with connectivity and network capacity. These relate to applications such as traffic monitoring and watershed management. With network elements—i.e. the lines that make up the network—extra values are commonly associated, such as distance, quality of the link or the carrying capacity.","hasChildren":true,"name":"Graph theoretic descriptive measures of networks","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM11-3","description":"Optimal-path finding techniques are used when a least-cost path between two nodes in a network must be found. The two nodes are called origin and destination. The aim is to find a sequence of connected lines to traverse from the origin to the destination at the lowest possible cost.\r\n\r\nIn Optimal-path finding, the cost function can be simple: for instance, it can be defined as the total length of all lines of the path. The cost function can also be more elaborate and take into account not only length of the lines but also their capacity, maximum transmission (travel) rate and other line characteristics, for instance to obtain a reasonable approximation of travel time. There can even be cases in which the nodes visited add to the cost of the path as well. These may be called turning costs, which are defined in a separate turning-cost table for each node, indicating the cost of turning at the node when entering from one line and continuing on another. This is illustrated in Figure 1 of the examples.\r\n\r\nProblems related to optimal-path finding may require ordered optimal path finding or unordered optimal-path finding. Both have as an extra requirement that a number of additional nodes need to be visited along the path. In ordered optimal-path finding, the sequence in which these extra nodes are visited matters; in unordered optimal-path finding it does not.","hasChildren":true,"name":"Least-cost shortest path","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM11-4","description":"There are phenomena that do not spread in all directions, but move or “flows” along a given, least-cost path, determined by characteristics of local terrain. The typical case arises when we want to determine drainage patterns in a catchment area: rain water “chooses” a way to leave the area. \r\n\r\nWe can illustrate the principles involved in this typical case with a simple elevation raster. For each cell in that raster, the steepest downward slope to a neighbour cell is computed and its direction is stored in a new raster. This computation determines the elevation difference between the cell and the neighbour cell and it takes into account cell distance - 1 for neighbour cells in N–S or W–E direction, 2 for cells in a NE–SW or NW–SE direction. From among its eight neighbour cells, it picks the one with the steepest path to it. The directions thus obtained in an output raster are encoded in integer values, which can be called the flow-direction raster. From this raster, the GIS can compute the accumulated flow-count raster, a raster that for each cell indicates how many cells have their water flow into that cell.\r\n\r\nCells with a high accumulated flow count represent areas of concentrated flow and may, thus, belong to a stream. By using some appropriately chosen threshold value in a map algebra expression, we may decide whether they do or not. Cells with an accumulated flow count of zero are local topographic highs and can be used to identify ridges.","hasChildren":true,"name":"Flow modeling","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM11-5","description":"The Classic Transportation Problem considers minimizing the cost of getting an object or subject from origin to destination.","hasChildren":true,"name":"The Classic Transportation Problem","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM11-6","description":"Classic network problems are examples of networking problems such as the Traveling Salesman Problem and the Chinese Postman Problem that need graph algorithms to be solved.","hasChildren":true,"name":"Other classic network problems","selfAssesment":"GI-N2K
"},{"code":"AM11-7","description":"Accessibility is the extend in which it is difficult/easy to reach a location or object.","hasChildren":true,"name":"Accessibility modeling","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM11","description":"Network analysis encompasses a wide range of procedures, techniques, and methods that allow for the examination of phenomena that can be modeled in the form of connected sets of edges and vertices. Such sets are termed a network or a graph, and the mathematical basis for network analysis is known as graph theory. Graph theory contains descriptive measures and indices of networks such as connectivity, adjacency, capacity, and flow as well as methods for proving the properties of networks. Networks have long been recognized as an efficient way to model many types of geographic data, including transportation networks, river networks, and utility networks electric, cable, sewer and water, etc. to name just a few. The data structures to support network analysis are covered in [DM4-7] Network models.","hasChildren":true,"hasParent":true,"name":"Network analysis","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM12-1","description":"The modeling of problems in a formal language, working in a solution space and applying constraints.","hasChildren":true,"name":"Operations research modeling and location modeling principles","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM12-2","description":"A formal programming method to support operational research in which linear constraints are applied.","hasChildren":true,"name":"Linear programming","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM12-3","description":"A formal programming method to support operational research in which variables are constrained to integers.","hasChildren":true,"name":"Integer programming","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM12-4","description":"Location-allocation modeling involves the determination of locations by minimizing the distance between object/subjects in space, such as between customers and facilities.","hasChildren":true,"name":"Location-allocation modeling and p-median problems","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM12","description":"A wide variety of optimization techniques are now solvable within the GIS and T domain. Operations research is a branch of mathematics practiced in the allied fields of business and engineering. New models and software tools allow for the solution of transportation routing, facility location, and a host of other location-allocation modeling problems.","hasChildren":true,"hasParent":true,"name":"Optimization and location-allocation modeling","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM13-1","description":"The effects such as the loss of data quality and data integrity that are the results of data transformations.","hasChildren":true,"name":"Impacts of transformations","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM13-2","description":"A data model is an abstract model that organizes elements of data and standardizes how they relate to one another and to the properties of real-world entities. The term data model can refer to two distinct but closely related concepts. In relation to the field of geoinformation the term data model refers to the set of concepts used in defining such formalizations as entities, attributes, relations, tables which is implemented by a mathematical construct for representing geographic objects or surfaces as data. There are two most frequently used data models, which are vector and raster. For example, the vector data model represents geography as collections of points, lines and polygons and more complex structures crated from these three. The raster data model represent geography as cell matrices that store numeric values. Among these two data models we also stand out data formats in which data sets can be stored. File format is a standard of encoding geographical information into a computer file. There are the following basic file formats for encoding data:\r\nFor vectors:\r\n-\tShapefile\r\n-\tGeography Markup Language (GML)\r\n-\tXYZ Point Cloud\r\n-\tGeoJSON\r\n-\tGeoMedia\r\n-\t\r\nFor rasters:\r\n-\tGeoTIFF\r\n-\tIMG\r\n-\tJPEG2000\r\n-\tEsri grid\r\nThe GIS projects often require the conversion of the data formats. Data conversion is the process of moving data from one format to another, whether it is from one data model to another or from one data format to another. Data conversion is a complex process which is not only associated with changing the binary format of the file but also requires changing the structure of the data. For example, the GML data format always comes with an UML diagram, which is necessary to convert attributes stored in GML structure for example to a table of contest in a shapefile data format. In a well-managed GIS project it is important to store data in specific data model or data format. It is sometimes dictated by software capabilities and another times by team’s technical capabilities. With large amounts of geographic data used in the project it is more cost-effective to convert the data from one format to another than re-create it.","hasChildren":true,"name":"Data model and format conversion","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM13-3","description":"Interpolation is used to create a GIS layer out of point observations on a continuous variable. The reason for doing this could be manifold: for visualization purposes, for making a proper reference with other data, or for making a combination of different layers.","hasChildren":true,"hasParent":true,"name":"Interpolation","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM13-4","description":"Any vector data containing point, polyline, polygon can be converted into the raster dataset and vice versa. The vector data can be stored in shapefiles, databases or various others GIS file formats. The raster data are made of pixels or grid calls and can be represented by the discrete - categorical data (e.g. land cover map) or non-discrete - continuous data (e.g. satellite images, surface data). The process of conversion of vector to raster data is called rasterization. The vector to raster conversion requires the following parameters: the field value from the attribute table used to assign values to the output raster, the pixel size for the output raster, the output raster format (i.e. geotiff, img) and optionally the method of assigning values of point, polyline or polygon to the call raster, i.e. maximum length or area, cell centre. The output of the rasterised vector looks like a gridded version of the vector and it depends on the grid cell size. The process of vectorisation refers to the conversion of raster to vector dataset. The raster dataset can be converted to vector point, polyline or polygon. In order to convert raster to vector the following parameters should be provided: attribute field of the input raster dataset which will become an attribute in the output vector class, determining if the output polygon or polyline will be smoothed into simpler shapes or conform to the input raster's cell edges (stair stepping). For each raster pixel or grid cell a point will be created at the centre of the cell. The non-discrete continuous raster data have to converted to the categorical data type before converting to vector data. The conversion of vector to raster and raster to vector degrade the data to some extent causing loss of details, accuracy, and changing the original data.","hasChildren":true,"name":"Vector-to-raster and raster-to-vector conversions","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM13-5","description":"Raster resampling refers to change of spatial resolution (increasing or decreasing) of the raster dataset. The resampling process calculates the new pixel values from the original digital pixel values in the uncorrected image. There are three common methods for resampling: nearest neighbour, bilinear interpolation, and cubic convolution. The nearest neighbour resampling uses the digital value from the pixel in the original image which is nearest to the new pixel location in the corrected image. This is the fastest interpolation method, which is primarily applied for discrete (categorical) raster data as it does not change the value of the pixel, but may result in some pixel values being duplicated while others are lost. Bilinear interpolation resampling takes a weighted average of four pixels in the original image nearest to the new pixel location. The averaging process alters the original pixel values and creates entirely new digital values in the output image. It is recommended for continuous data and it cause some smoothing of the data. Cubic convolution resampling is based on calculation of a distance weighted average of a block of sixteen pixels from the original image which surround the new output pixel location. As with bilinear interpolation, this method results in completely new pixel values. However, the last two methods both produce images which have a much sharper appearance and avoid the blocky appearance of the nearest neighbour method. The disadvantage of the Cubic method is that its requires more processing time.","hasChildren":true,"name":"Raster resampling","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM13-6","description":"Users of geoinformation often need transformations from a particular 2D coordinate system to another system. This includes the transformation of polar coordinates into Cartesian map coordinates, or the change of map projection - transformation from one 2D Cartesian (x, y) system of a specific map projection into another 2D Cartesian (x′, y′) system of a defined map projection. This transformation is based on relating the two systems on the basis of a set of selected points whose coordinates are known in both systems, such as ground control points or common points such as corners of houses or road intersections. Image and scanned data are usually transformed by this method. The transformations may be conformal, affine, polynomial or of another type, depending on the geometric errors in the data set. A datum transformation involves the change of the horizontal datum which is often accompanied with a change of map projection.","hasChildren":true,"name":"Coordinate transformations","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM13","description":"GIS is a cyclical rather than a linear system, unlike computer aided drafting (CAD) and computer assisted cartographic systems. Changes in projection, grid systems, data forms, and formats take place during the modeling process for which GIS was designed. Many non-analytical manipulations are necessary to accommodate the analytical power of the GIS. The manipulations of spatial and spatio-temporal data involve two general classes of operation: 1.\tTheir transformation into formats that facilitate subsequent analysis 2. Generalization and aggregation that affect the accuracy and integrity of the data used for analysis (see [AM14]). Other knowledge areas have identified different forms of data structures, data models, projections, and other forms of geospatial data representation. These differences present both opportunities and challenges for analysis and modeling. The ability to transform one representation to another, in a manner that maintains the integrity of the information as much as possible, can enhance the analysis and visualization of geospatial data. The raster and vector data models are described in [DM3] Tesselation data models and [DM4] Vector data model, Feature based modelling, Applications. The principles of coordinate systems, datums, and projections are also considered in Knowledge Area [GD] Geospatial Data","hasChildren":true,"hasParent":true,"name":"Representation transformation","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM14-1","description":"In the practice of spatial data handling, one often comes across questions like “What is the resolution of the data?” or “At what scale is your data set?” Now that we have moved firmly into the digital age, these questions sometimes defy an easy answer. Map scale can be defined as the ratio between the distance on a printed map and the distance of the same stretch in the terrain.\r\n\r\nA 1:50,000 scale map means that 1 cm on the map represents 50,000 cm (i.e. 500 m) in the terrain. “Large-scale” means that the ratio is relatively large, so typically it means there is much detail to see, as on a 1:1000 printed map. “Small-scale”, in contrast, means a small ratio, hence less detail, as on a 1:2,500,000 printed map.\r\nDigital spatial data, as stored in a GIS, are essentially without scale: scale is a ratio notion associated with visual output, such as a map or on-screen display, not with the data that was used to produce the map or display. When digital spatial data sets have been collected with a specific map-making purpose in mind, and all maps have been designed to use one single map scale, for instance 1:25,000, we may assume that the data carries the characteristic of “a 1:25,000 digital data set.”\r\n\r\nThere is a relationship between the effectiveness of a map for a given purpose and the map’s scale. The Public Works department of a city council cannot use a 1:250,000 map for replacing broken sewer pipes, and the map of Figure 1 cannot be reproduced at scale 1:10,000.\r\n\r\nMaps that show much detail of a small area are called large-scale maps. Scale indications on maps can be given verbally, such as “one-inch-to the- mile”, or as a representative fraction like 1:200,000,000 (1 cm on the map equals 200,000,000 cm (or 2000 km) in reality), or by a graphic representation such as the scale bar. The advantage of using scale bars in digital environments is that its length also changes when the map is zoomed in, or enlarged, before printing. Sometimes it is necessary to convert maps from one scale to another, which may lead to problems of cartographic generalization.\r\n\r\nSpatial and temporal scales can not only be attached to processes, but also to observations. An example is given below, which summarizes the spatial and temporal scales of a few well-known Earth observation systems.\r\n\r\nScales of RS observations\r\nSensor Spatial scale\t Temporal scale\r\nMeteosat\t Hemisphere\t 15 minutes\r\nNOAA-AVHRR\t 3000 km\t daily\r\nLandsat TM\t 180 km\t 16 days\r\nSpot\t 60 km\t 26 days (pointable)","hasChildren":true,"name":"Scale and generalization","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM14-2","description":"Techniques that support the generalisation of map content when changing to smaller map scales. These include line simplification, object selection, etc.","hasChildren":true,"name":"Approaches to point, line, and area generalization","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM14-3","description":"Classification is a technique for purposely removing detail from an input data set in the hope of revealing important patterns (of spatial distribution). In the process, we produce an output data set, so that the input set can be left intact. This output set is produced by assigning a characteristic value to each element in the input set, which is usually a collection of spatial features that could be raster cells or points, lines or polygons. If the number of characteristic values in the output set is small in comparison to the size of the input set, we have classified the input set.\r\n\r\nThe input data set may, itself, have been the result of a classification. In such cases we refer to the output data set as a reclassification. For example, we may have a soil map that shows different soil type units and we would like to show the suitability of units for a specific crop. In this case, it is better to assign to the soil units an attribute of suitability for the crop. Since different soil types may have the same crop suitability, a classification may merge soil units of different type into the same category of crop suitability.\r\n\r\nIn classification of vector data, there are two possible results. In the first, the input features may become the output features in a new data layer, with an additional category assigned. In other words, nothing changes with respect to the spatial extents of the original features. Figure a of Examples illustrates this first type of output. A second type of output is obtained when adjacent features of the same category are merged into one bigger feature. Such a post-processing function is called spatial merging, aggregation or dissolving. An illustration of this second type is found in Figure b of Examples. Observe that this type of merging is only an option in vector data, as merging cells in an output raster on the basis of a classification makes little sense. Vector data classification can be performed on point sets, line sets or polygon sets; the optional merge phase only makes sense for lines and polygons.\r\n\r\nUser-controlled classifications require a classification table or user interaction. GIS software can also perform automatic classification, in which a user only specifies the number of classes in the output data set. The system automatically determines the class break points. The two main techniques of determining break points being used are the equal interval technique and the equal frequency technique.\r\n\r\nEqual Interval Technique\r\nThe minimum and maximum values vmin and vmax of the classification parameter are determined and the (constant) interval size for each category is calculated as (vmax - vmin) ∕ n, where n is the number of classes chosen by the user. This classification is useful in that it reveals the distribution pattern, as it determines the number of features in each category.\r\n\r\nEqual Frequency Technique\r\nThis technique is also known as quantile classification. The objective is to create categories with roughly equal numbers of features per category. The total number of features is determined first, then, based on the required number of categories, the number of features per category is calculated. The class break points are then determined by counting off the features in order of classification parameter value.","hasChildren":true,"name":"Classification and transformation of attribute measurement levels","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM14","description":"Generalization addresses the meaningful reduction of the map content during scale reduction. All geospatial data are generalized. Even the most detailed data represent only subsets of reality. Furthermore, data are further generalized for purposes of mapping, visualization, and efficient storage. A variety of generalization techniques have been developed to facilitate this process. All are scale dependent. Aggregation is one form of generalization that transforms large numbers of individual objects into summarized groups. This concept description is concerned with the nature of these procedures and their implications for professional practice. Generalization is an important part of cartography (and is therefore discussed conceptually in CV2 Data considerations), but is also a transformation common to many GIS procedures.","hasChildren":true,"hasParent":true,"name":"Generalization and aggregation","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM2-1","description":"Set theory is based on describing collections of members within sets. The Boolean membership function is binary, i.e. an element is either a member of the set (membership is true) or it is not a member of the set (membership is false). Such a membership notion is well-suited to the description of spatial features such as land parcels for which no ambiguity is involved and an individual ground truth sample can be judged to be either correct or incorrect. As Burrough and Frank (1996) note, increasingly, people are beginning to realize that the fundamental axioms of simple binary logic present limits to the way we think about the world. Not only in everyday situations, but also in formalized thought, it is necessary to be able to deal with concepts that are not necessarily true or false, but that operate somewhere in between. Since its original development by Zadeh (1965), there has been considerable discussion of fuzzy, or continuous, set theory as an approach for handling imprecise spatial data. In GIS, fuzzy set theory appears to have two particular benefits: the ability to handle logical modelling (map overlay) operations on inexact data; and the possibility of using a variety of natural language expressions to qualify uncertainty. Unlike Boolean sets, fuzzy or continuous sets have a membership function, which can assign to a member any value between 0 and 1.","hasChildren":true,"hasParent":true,"name":"Set theory","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM2-2","description":"The most common operator for defining queries in a relational database is the language SQL, which stands for Structured Query Language.\r\n\r\nA spatial DBMS provides support for geographic coordinate systems and transformations. It will also provide storage of the relationships between features, including the creation and storage of topological relationships. As a result, one is able to use functions for “spatial query” (exploring spatial relationships). To illustrate, a spatial query using SQL to find all the Thai restaurants within 2 km of a given hotel would look like:\r\n\r\nSELECT R.Name\r\nFROM Restaurants AS R,\r\nHotels as H\r\nWHERE R.Type = Thai AND\r\nH.name = Hilton AND\r\nIntersect(R.Geometry, Buffer(H.Geometry, 2))\r\n\r\nThe Intersect command creates a spatial join between restaurants and hotels. The Geometry column carries the spatial data. It is likely that in the near future all spatial data will be stored directly in spatial databases.","hasChildren":true,"name":"Structured Query Language (SQL) and attribute queries","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM2-3","description":"When exploring a spatial data set, the first thing one usually wants to do is select certain features, to (temporarily) restrict the exploration. Such selections can be made on geometric/spatial grounds or on the basis of attribute data associated with the spatial features. \r\n\r\nSelection conditions on attribute values can be combined using logical connectives such as AND, OR and NOT. Other techniques of selecting features can also usually be combined. Any set of selected features can be used as the input for a subsequent selection procedure. This means, for instance, that we can select all medical clinics first, then identify roads within 200 m of them, then select from those only the major roads, then select the nearest clinics to these remaining roads as the ones that should receive our financial support for maintenance. In this way, we are combining various techniques of selection.\r\n\r\nInteractive Spatial Selection\r\nIn interactive spatial selection, one defines the selection condition by pointing at or drawing spatial objects on the screen display, after having indicated the spatial data layer(s) from which to select features. The interactively defined objects are called the selection objects; they can be points, lines, or polygons. The GIS then selects the features in the indicated data layer(s) that overlap (i.e. intersect, meet, contain, or are contained in;) with the selection objects. These become the selected objects.\r\nInteractive spatial selection answers questions like “What is at …?”\r\n\r\nA spatial DBMS provides support for geographic coordinate systems and transformations. It will also provide storage of the relationships between features, including the creation and storage of topological relationships. As a result, one is able to use functions for “spatial query” (exploring spatial relationships). To illustrate, a spatial query using SQL to find all the Thai restaurants within 2 km of a given hotel would look like:\r\n\r\nSELECT R.Name\r\nFROM Restaurants AS R,\r\nHotels as H\r\nWHERE R.Type = Thai AND\r\nH.name = Hilton AND\r\nIntersect(R.Geometry, Buffer(H.Geometry, 2))\r\n\r\nThe Intersect command creates a spatial join between restaurants and hotels. The Geometry column carries the spatial data. It is likely that in the near future all spatial data will be stored directly in spatial databases.","hasChildren":true,"name":"Spatial queries","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM2","description":"Attribute and spatial query operations are core functionality in any GIS and they are often considered to be the most basic form of analysis.","hasChildren":true,"hasParent":true,"name":"Query operations and query languages","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM3-1","description":"In a 2D polar coordinate system points can be described with coordinates. Another way of defining a point in a plane is by using polar coordinates. This is the distance d from the origin to the point concerned and the angle α between a fixed (or zero) direction and the direction to the point. The angle α is called azimuth or bearing and is measured in a clockwise direction. It is given in angular units while the distance d is expressed in length units. \r\n\r\nDistance also plays a role in computations on networks, comprising a different set of analytical functions in GISs. Here, the network may consist of roads, public transport routes, high-voltage power lines, or other forms of transportation infrastructure. Analysis of networks may entail shortest path computations (in terms of distance or travel time) between two points in a network for routing purposes. Other forms are to find all points reachable within a given distance or duration from a start point for allocation purposes, or determination of the capacity of the network for transportation between an indicated source location and sink location.\r\n\r\nIn raster images, the distance function applied is the Pythagorean distance between the cell centres. The distance from a non-target cell to the target is the minimal distance one can find between that non-target cell and any target cell.","hasChildren":true,"name":"Distances and lengths","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM3-2","description":"In a 2D polar coordinate system points can be described with coordinates. Another way of defining a point in a plane is by using polar coordinates. This is the distance d from the origin to the point concerned and the angle α between a fixed (or zero) direction and the direction to the point. The angle α is called azimuth or bearing and is measured in a clockwise direction. It is given in angular units while the distance d is expressed in length units.\r\n\r\nBearings are always related to a fixed direction (initial bearing) or a datum line. In principle, this reference line can be chosen freely. Three different, widely used fixed directions are: True North, Grid North and Magnetic North. The corresponding bearings are true (or geodetic) bearings, grid bearings and magnetic (or compass) bearings, respectively.\r\n\r\nIn raster images, direction is determined by the orientation of the neighboring pixels.","hasChildren":true,"name":"Direction","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM3-3","description":"The representation of geographic objects is most naturally supported with vectors. After all, objects are identified by the parameters of location, shape, size and orientation, and many of these parameters can be expressed in terms of vectors. We can define features within the topological space that are easy to handle and that can be used as representations of geographic objects. These features are called simplices as they are the simplest geometric shapes of some dimension: point (0-simplex), line segment (1-simplex), triangle (2-simplex), and tetrahedron (3-simplex). When we combine various simplices into a single feature, we obtain a simplicial complex. When area objects are stored using a vector approach, the usual technique is to apply a boundary model. This means that each area feature is represented by some arc/node structure that determines a polygon as the area’s boundary. A polygon representation for an area object is another example of a finite approximation of a phenomenon that may have a curvilinear boundary in reality. In images, the shape of objects often helps us to identify them (built-up areas, roads and railroads, agricultural fields, etc.).","hasChildren":true,"name":"Shape","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM3-4","description":"When area objects are stored using a vector approach, the usual technique is to apply a boundary model. This means that each area feature is represented by some arc/node structure that determines a polygon as the area’s boundary. A polygon representation for an area object is another example of a finite approximation of a phenomenon that may have a curvilinear boundary in reality.\r\nCommon sense dictates that area features of the same kind are best stored in a single data layer, represented by mutually non-overlapping polygons. This results in an application-determined (i.e. adaptive) partition of space. If the object has a fuzzy boundary, a polygon is an even worse approximation, even though potentially it may be the only one possible. Clearly, we expect additional data to accompany the area data. Such information could be stored in database tables.\r\n\r\nA simple but naïve representation of area features would be to list for each polygon the list of lines that describes its boundary. Each line in the list would, as before, be a sequence that starts with a node and ends with one, possibly with vertices in between. As the same line makes up the boundary from the two polygons, this line would be stored twice in the above representation, namely once for each polygon. This is a form of data duplication—known as data redundancy—which is (at least in theory) unnecessary, although it remains a feature of some systems. Another disadvantage of such polygon-by-polygon representations is that if we want to identify the polygons that border the bottom left polygon, we have to do a complicated and time-consuming search analysis comparing the vertex lists of all boundary lines with that of the bottom left polygon. For just a few polygons, this is fine, but in a data set with 5000 polygons, and perhaps a total of 25,000 boundary lines, this becomes a tedious task, even with the fastest of computers.","hasChildren":true,"name":"Area","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM3-5","description":"Proximity computations are specific neighbourhood functions. They evaluate the characteristics of an area surrounding a feature’s location. A neighbourhood function “scans” the neighbourhood of the given feature(s), and performs a computation on it (them).\r\n\r\nExamples of proximity computations are: (1) Buffer zone generation (or buffering) is one of the best-known neighbourhood functions. It determines a spatial envelope (buffer) around a given feature or features. The buffer created may have a fixed width or a variable width that depends on characteristics of the area. (2) Thiessen Polygon generation.\r\n\r\nDistance decay functions describe the effect of the reduced influence when the distance between two locations increases.","hasChildren":true,"name":"Proximity and distance decay","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM3-6","description":"Adjacency is the meet relationship as a topological property of a geographic object in relation ship with another. The adjacency operator identifies those features that share boundaries and, therefore, applies only to line and polygon features.\r\nThis meet relationship is invariant under a continuous transformation and are referred to as a topological mapping.","hasChildren":true,"name":"Adjacency and connectivity","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM3","description":"For simple data exploration, GIS offers many basic geometric operations that help in extracting meaning from sets of data or for deriving new data for further analysis. Concepts on which these operations are based are addressed in Domains of geographic information and Relationships.\r\n\r\nWe can, for instance, measure angles on a map and use these for navigation in the real world, or for setting out a designed physical infrastructure. Or if, instead of a conformal projection such as UTM, we use an equivalent projection, we can determine the size of a parcel of land from the map—irrespective of where the parcel is on the map and at which elevation it is on the Earth.","hasChildren":true,"hasParent":true,"name":"Geometric measures","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM4-1","description":"The reclassifications tools are used to change or reclassify the values. Reclassification of vector data involves the attributes of features in the feature attribute table, on the other hand reclassification of raster data involves the grid cell values to produce a new raster data layer. Reclassification can be used for data simplification and measurement scale change. We can adjust the data for more appropriate analysis by grouping the values and changing them. The reclassification tool can also be used to remove specific values from analysis.\r\nThe Select by location tool lets you select features by how they relate to other features in another layer. Selected features are based on their location. You can select features that are near or overlap the features. Most frequently used methods are intersect, within a distance, within, completely within, contain… Features can be selected in the same or other layers.\r\nThe Select by attributes tool lets you select features that match the selection criteria. With providing a selection criteria, matching features are selected. We can provide a complex selection criteria.","hasChildren":true,"name":"Reclassification and selection operations","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM4-2","description":"Buffer analysis is one form of basic spatial analysis. It takes the vector representation (point, line, or polygon) of a real-world feature, and then creates a buffer zone based on a defined distance from the feature’s border. Thus, the created buffer zone is an area whose boundary always has the same distance to the input vector feature, e.g. the buffer zone for a point feature is a circle. Real-world examples for buffer zones could be protected areas along rivers or around nature conservation areas, or represent a simple proximity analysis. In the latter case, the buffer analysis is usually the first step of the analysis, followed by an overlay of the buffer zone with the target features to find those target features within the buffer zone, and thus within a certain distance of the original feature. Usually, the buffer zone extends outwards from the feature, but polygons can also have inner buffer zones. If the buffer zones from multiple features overlap, the analyst can decide to leave the individual boundaries of the buffer zones intact, or to dissolve them, i.e. merging the overlapping buffer zones into one larger buffer zone. The size of the buffer zone, i.e. the distance of its boundary from the original feature’s boundary, can be based on an uniform numerical value and associated spatial unit, but often, it is based on an attribute value (numerical or class) of the feature. Conceptually, buffering using raster representations of real-world features is similar a proximity analysis with a regular grid of square polygons: Departing from raster cells that form the area to be buffered, all raster cells that fall within the designated distance (overlay) from the buffer zone. With buffer analysis being a basic analytical operation, practically every GIS and many other analysis tools provide this functionality.","hasChildren":true,"name":"Buffers","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM4-3","description":"Overlay functions is one of the most frequently used functions in a GIS application. They combine two (or more) spatial data layers, comparing them position by position and treating areas of overlap - and of non-overlap - in distinct ways.\r\n\r\nStandard overlay operators take two input data layers and assume that they are georeferenced in the same system and that they overlap in the study area. If either of these requirements is not met, the use of an overlay operator is pointless. The principle of spatial overlay is to compare the characteristics of the same location in both data layers and to produce a result for each location in the output data layer. The specific result to produce is determined by the user. It might involve a calculation or some other logical function to be applied to every area or location. With raster data, as we shall see, these comparisons are carried out between pairs of cells, one from each input raster. With vector data, the same principle of comparing locations applies but the underlying computations rely on determining the spatial intersections of features from each input layer.\r\n\r\nVector overlay operators are useful but geometrically complicated, and this sometimes results in poor operator performance. Raster overlays do not suffer from this disadvantage, as most of them perform their computations cell by cell, and thus they are fast. GISs that support raster processing - as most do - usually have a language to express operations on rasters. These languages are generally referred to as map algebra or, sometimes, raster calculus. They allow a GIS to compute new rasters from existing ones, using a range of functions and operators. Unfortunately, not all implementations of map algebra offer the same functionality.","hasChildren":true,"name":"Overlay","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM4-4","description":"Neighbourhood functions evaluate the characteristics of an area surrounding a feature’s location. A neighbourhood function “scans” the neighbourhood of the given feature(s), and performs a computation on it (them). Examples of proximity computations are: (1) Buffer zone generation (or buffering) is one of the best-known neighbourhood functions. It determines a spatial envelope (buffer) around a given feature or features. The buffer created may have a fixed width or a variable width that depends on characteristics of the area. (2) Thiessen Polygon generation. For raster images: (3) Computation of diffusion (4) Flow computation.\r\n\r\nFor instance, our target might be a medical clinic. Its neighbourhood could be defined as:\r\n\r\nan area within a radius of 2 km distance as the crow flies; or\r\nan area within 2 km travelling distance; or\r\nall roads within 500 m travelling distance; or\r\nall other clinics within 10 minutes travelling time;\r\nall residential areas for which the clinic is the closest clinic.\r\n\r\nFinally, in the third step we indicate what it is we want to discover about the phenomena that exist or occur in the neighbourhood. This might simply be its spatial extent, but it might also be statistical information such as:\r\n\r\nhow many people live in the area;\r\nwhat is their average household income;\r\nare any high-risk industries located in the neighbourhood.\r\n\r\nThese are typical questions in an urban setting. When our interest is more in natural phenomena, different examples of locations, neighbourhoods and neighbourhood characteristics arise.\r\n\r\nThe principle in this case is to find out the characteristics of the vicinity, here called neighbourhood, of a location. After all, many suitability questions, for instance, depend not only on what is at a location but also on what is near the location. Thus, the GIS must allow us “to look around locally”. To perform neighbourhood analysis, we must:\r\n\r\n1. state which target locations are of interest to us and define their spatial extent;\r\n2. define how to determine the neighbourhood for each target; and\r\n3. define which characteristic(s) must be computed for each neighbourhood. \r\n\r\nSince raster data are the more commonly used in this case, neighbourhood characteristics often are obtained via statistical summary functions that compute values such as the average, minimum, maximum and standard deviation of the cells in the identified neighbourhood.\r\n\r\nTo select target locations, one can use the selection techniques. To obtain characteristics from an eventually-to-be identified neighbourhood, the same techniques apply. So what remains to be discussed here is the proper determination of a neighbourhood. One way of determining a neighbourhood around a target location is by making use of the geometric distance function. Geometric distance does not take into account direction, but certain phenomena can only be studied by doing so. Think of the spreading of pollution by rivers, groundwater flow or prevailing weather systems.\r\n\r\nDiffusion functions are based on the assumption that the phenomenon in question spreads in all directions, though not necessarily equally easily in each direction. Hence it uses local terrain characteristics to compute local resistances to diffusion.","hasChildren":true,"hasParent":true,"name":"Neighborhood analysis","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM4-5","description":"GISs that support raster processing - as most do - usually have a language to express operations on rasters. These languages are generally referred to as map algebra or, sometimes, raster calculus. They allow a GIS to compute new rasters from existing ones, using a range of functions and operators. Unfortunately, not all implementations of map algebra offer the same functionality. The discussion below is to a large extent based on general terminology; it attempts to illustrate the key operations using a logical, structured language. Again, the syntax often varies among different GIS software packages.\r\n\r\nWhen producing a new raster we must provide a name for it, and define how it is to be computed. This is done in an assignment statement of the following format:\r\n\r\nOutput raster name := Map algebra expression.\r\n\r\nThe expression on the right is evaluated by the GIS, and the raster in which it results is then stored under the name on the left. The expression may contain references to existing rasters, operators and functions; the format is made clear in each case. The raster names and constants that are used in the expression are called its operands. When the expression is evaluated, the GIS will perform the calculation on a pixel-by-pixel basis, starting from the first pixel in the first row and continuing through to the last pixel in the last row. In map algebra, there is a wide range of operators and functions available.\r\n\r\nArithmetic operators\r\nVarious arithmetic operators are supported. The standard ones are multiplication (×), division (/), subtraction (-) and addition (+). Obviously, these arithmetic operators should only be used on appropriate data values, and, for instance, not on classification values. Other arithmetic operators may include modulo division (MOD) and integer division (DIV). Modulo division returns the remainder of division: for instance, 10 MOD 3 will return 1 as 10 - 3 × 3 = 1. Similarly, 10 DIV 3 will return 3.\r\n\r\nOther operators are goniometric: sine (sin), cosine (cos), tangent (tan); and their inverse functions asin, acos, and atan, which return radian angles as real values. The assignment\r\n\r\nC1 := A + 10\r\n\r\nwill add a constant factor of 10 to all cell values of raster A and store the result as output raster C1. The assignment\r\n\r\nC2 := A + B\r\n\r\nwill add the values of A and B cell by cell, and store the result as raster C2. Finally, the assignment\r\n\r\nC3 := (A - B) ∕ (A + B) × 100\r\n\r\nwill create output raster C3, as the result of the subtraction (cell by cell, as usual) of B cell values from A cell values, divided by their sum. The result is multiplied by 100. This expression, when carried out on AVHRR channel 1 (red) and AVHRR channel 2 (near infrared) of NOAA satellite imagery, is known as the NDVI (Normalized Difference Vegetation Index). It has proven to be a good indicator of the presence of green vegetation.\r\n\r\nComparison and logical operators\r\n\r\nMap algebra also allows the comparison of rasters cell by cell. To this end, we may use the standard comparison operators (<, ⇐, =, >=, > and <>).\r\n\r\nA simple raster comparison assignment is\r\n\r\nC := A <> B.\r\n\r\nIt will store truth values—either true or false—in the output raster C. A cell value in C will be true if the cell’s value in A differs from that cell’s value in B. It will be false if they are the same. Logical connectives are also supported in many raster calculi. We have already seen the connectives of AND , OR and NOT in raster overlay operators. Another connective that is commonly offered in map algebra is exclusive OR (XOR). The expression a XOR b is true only if either a or b is true, but not both.\r\n\r\nConditional expressions\r\nThe comparison and logical operators produce rasters with the truth values true and false. In practice, we often need a conditional expression together with them that allows us to test whether a condition is fulfilled. The general format is:\r\n\r\nOutput raster := CON(condition, then expression, else expression).\r\n\r\nHere, condition stands for the condition tested, then the expression is evaluated if condition holds, and else the expression is evaluated if it does not hold. This means that an expression such as CON(A = “forest”, 10, 0) will evaluate to 10 for each cell in the output raster where the same cell in A is classified as forest. For each cell where this is not true, the else expression is evaluated, resulting in 0.\r\n\r\nOverlays using a decision table\r\nConditional expressions are powerful tools in cases where multiple criteria must be taken into account. A small example may illustrate this. Consider a suitability study in which a land use classification and a geological classification must be used. Domain expertise dictates that some combinations of land use and geology result in suitable areas, whereas other combinations do not. In our example, forests on alluvial terrain and grassland on shale are considered suitable combinations, while any others are not.\r\n\r\nWe could produce an output raster with a map algebra expression, such as\r\n\r\nSuitability := CON((Landuse = “Forest” AND Geology = “Alluvial”)\r\nOR (Landuse = “Grass” AND Geology = “Shale”),\r\n“Suitable”, “Unsuitable”)\r\n\r\nand consider ourselves lucky that there are only two “suitable” cases. In practice, many more cases must usually be covered and, then, writing up a complex CON expression is not an easy task.\r\n\r\nTo this end, some GISs accommodate setting up a separate decision table that will guide the raster overlay process. This extra table carries domain expertise and dictates which combinations of input raster-cell values will produce which output raster-cell value. This gives us a raster overlay operator using a decision table. The GIS will have supporting functions to generate the additional table from the input rasters and to enter appropriate values in the table.","hasChildren":true,"name":"Map algebra","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM4","description":"This small set of analytical operations is so commonly applied to a broad range of problems that their inclusion in software products is often used to determine if that product is a true GIS. Concepts on which these operations are based are addressed in Domains of geographic information and Relationships.","hasChildren":true,"hasParent":true,"name":"Basic analytical operations","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM5-1","description":"Point pattern analysis refers to the detection of patterns in a group of objects or subjects located in space. This may support the analysis of clusters in accidents, crime, etc.","hasChildren":true,"name":"Point pattern analysis","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM5-2","description":"The probability density function is a method with which the probability density can be estimated for points in a raster space.","hasChildren":true,"name":"Kernels and density estimation","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM5-3","description":"Spatial cluster analysis is the grouping of similar spatial objects into classes (clusters) in such a way that the objects within the cluster are highly similar compared to the objects outside of the cluster. Spatial clustering forms an important part of spatial data mining (Han et al., 2001; Miller et al., 2009). A wealth of spatial clustering tools are currently available with immense application potential. \r\n\r\nIn earth observation studies, spatial cluster techniques are often applied to identify zones with similar land covers by using earth observation data as input. An example of such a technique is the K-means classifier (Han et al., 2001; Miller et al., 2009). This unsupervised classification technique makes several clusters (e.g. land use classes) of which each pixel is assigned to the cluster with the nearest mean (Han et al., 2001). The amount of clusters can be freely defined by the user just as the input metrics to perform the classification. A drawback of the K-means classifier is the need to specify the amount of output clusters. Density Based Spatial Clustering (DBSC) overcomes this issue since it automatically defines the optimal amount of clusters (Miller et al., 2009). In this type of clustering technique, dense regions of objects (proximate objects) are clustered and separated from regions with low density (noise) (Han et al., 2001; Liu et al., 2012). Finally, another frequently applied spatial clustering technique is the hierarchical agglomerative clustering. This technique makes use of a dendrogram to decompose the data into clusters. The agglomerative approach is a bottom-up approach in which all objects are first grouped in a distinct cluster and while moving upward in the tree, pairs of clusters are merged based on some metrics (e.g. spatial proximity) (Han et al., 2001). \r\n\r\nSpatial cluster techniques have many advantages when dealing with big datasets which is often the case when working with earth observation data. Its simplicity to use and the fast increase of cloud computing power makes from it powerful techniques to extract spatial patterns out of the data. It allows to translate raw earth observation data into a more user-friendly data product by showing the spatial patterns of the data.","hasChildren":true,"name":"Spatial cluster analysis","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM5-4","description":"Spatial interaction models describe the flow of people and goods in a geographical space, in which parameters such as friction and distance play a role.","hasChildren":true,"name":"Spatial interaction","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM5-5","description":"Multidimensional attributes can be analyzed through multidimensional scaling and principle component analysis.","hasChildren":true,"hasParent":true,"name":"Analyzing multidimensional attributes","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM5-7","description":"Multi-criteria evaluation is an important aspect of decision support operations, which appear in process models. Process models in the Earth sciences describe the evolution of geo(bio)physical surface properties in time, independently from remote sensing observations. Examples of such process models on various time scales are, for instance, numerical weather prediction models (NWPs), vegetation growth models, hydrological models, oceanographic models and climate models.\r\n\r\nObservation models and process models can supplement each other to enhance the quality of the interpretation of remote sensing data and to fill gaps in time that occur when observations are not possible owing to clouds or some other cause. Interactions are possible between observation models and process models with EO data and existing geographic information (GIS and ground measurements, supplemented with decision-support systems (DSSs)).\r\n\r\nThe process model provides information to the decision-support system, which supports management actions aimed at controlling/mitigating the process, based on an multi-criteria evaluation. A good example of this is a water management system, in which one might decide to allocate water for irrigation if the observed vegetation appears to suffer from drought stress.","hasChildren":true,"name":"Multi-criteria evaluation","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM5-8","description":"Process models in the Earth sciences describe the evolution of geo(bio)physical surface properties in time, independently from remote sensing observations. Examples of such process models on various time scales are, for instance, numerical weather prediction models (NWPs), vegetation growth models, hydrological models, oceanographic models and climate models.\r\nProcess models in the geosciences usually rely on regular observations at many locations spread over a large area. Traditionally, these observations were mostly made in the field with a variety of instruments. Remote sensing techniques have tremendously increased the capability of spatial sampling and the consistency of the surface parameters measured. RS instruments are mostly sensitive to many physical properties of the surface, some of these may not belong to the set of properties that the user is interested in. Exceptions to this are the mapping of sea-surface temperature, laser altimetry and gravimetry, which are measurements of direct geophysical interest. In the majority of cases, however, there are only indirect relationships between what is observed with the instrument and the physical object properties of interest. In these cases, the use of observation models becomes an attractive option, since these models describe the relationships between all object properties relevant for the observation and the observed remote sensing data.","hasChildren":true,"name":"Spatial process models","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM5","description":"Building on the basic geometric measures and analytical operations found in most GIS products, a broad range of additional analytical methods form the fundamental GIS toolkit.","hasChildren":true,"hasParent":true,"name":"Basic analytical methods","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM6-2","description":"In rasters we use interpolation to determine the value of a pixel, based on its surrounding pixels. The main raster-based interpolation methods are nearest neighbour, bilinear, and bicubic interpolation. To determine the value of the centre pixel (bold), in nearest neighbour interpolation the value of the nearest original pixel is assigned, i.e. the value of the black pixel in this example. Note that the respective pixel centres, marked by small crosses, are always used for this process. In bilinear interpolation, a linear weighted average is calculated for the four nearest pixels in the original image. In bicubic interpolation a cubic weighted average of the values of 16 surrounding pixels (the black and all grey pixels) is calculated. Note that some software uses the terms “bilinear convolution” and “cubic convolution” instead of the terms introduced above.","hasChildren":true,"name":"Interpolation of surfaces","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM6-3","description":"Continuous fields have a number of characteristics not shared by discrete fields. Since the field changes continuously, we can talk of slope angle, slope aspect and concavity/convexity of the slope.\r\n\r\nThese notions are not applicable to discrete fields. The discussions in this subsection use terrain elevation as the prototype example of a continuous field, but all aspects discussed are equally applicable to other types of continuous fields. Nonetheless, we regularly refer to the continuous field representation as a DEM, to conform with the most common situation.","hasChildren":true,"name":"Surface features","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM6-4","description":"A viewshed is the area that can be “seen” (i.e. it is in the direct line-of-sight) from a specified target location. (Inter) visibility analysis can determine the area visible from a scenic lookout or the area that can be reached by a radar antenna, as well as assess how effectively a road or quarry will be hidden from view.","hasChildren":true,"name":"Intervisibility","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM6-5","description":"Firction surfaces contain information on how difficult/easy it is for a phenomenon to move from one location on the surface to another.","hasChildren":true,"name":"Friction surfaces","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM6","description":"There is a wide range of phenomena that can be studied using a set of techniques and tools that are designed to help understand the characteristics of continuous surface data. Applications of these techniques using terrain data include overland transport, flow, and siting tasks, but similar analyses can be conducted using non-tangible surfaces such as those of temperature, pressure and population density.\r\n\r\nThere are numerous examples that require more advanced computations on continuous field representations, such as:\r\n\r\nSlope angle calculation - the calculation of the slope steepness, expressed as an angle in degrees or percentages, for any or all locations.\r\n\r\nCalculating slope aspect - the calculation of the aspect (or orientation) of the slope in degrees (between 0 and 360∘), for any or all locations.\r\n\r\nSlope convexity/concavity calculation - defined as the change of the slope (negative when the slope is concave and positive when the slope is convex)—can be calculated as the second derivative of the field.\r\n\r\nSlope length calculation - with the use of neighbourhood operations, it is possible to calculate for each cell the nearest distance to a watershed boundary (the upslope length) and to the nearest stream (the downslope length). This information is useful for hydrological modelling.\r\n\r\nHillshading is used to portray relief difference and terrain morphology of hilly and mountainous areas. The application of a special filter to a DEM produces hillshading. The colour tones in a hillshading raster represent the amount of reflected light at each location, depending on its orientation relative to the illumination source. This illumination source is usually chosen to be to the northwest at an angle of 45∘ above the horizon.\r\n\r\nThree-dimensional map display - with GIS software, three-dimensional views of a DEM can be constructed in which the location of the viewer, the angle under which he or she is looking, the zoom angle, and the amplification factor of relief exaggeration can be specified. Three-dimensional views can be constructed using only a predefined mesh, covering the surface, or using other rasters (e.g. a hillshading raster) or images (e.g. satellite images) that are draped over the DEM.\r\n\r\nDetermination of change in elevation through time - the cut-and-fill volume of soil to be removed or to be brought in to make a site ready for construction can be computed by overlaying the DEM of the site before the work begins with the DEM of the expected modified topography. It is also possible to determine landslide effects by comparing DEMs of before and after a landslide event.\r\n\r\nAutomatic catchment delineation - catchment boundaries or drainage lines can be automatically generated from a good quality DEM with the use of neighbourhood functions. The system will determine the lowest point in the DEM, which is considered to be the outlet of the catchment. From there, it will repeatedly search for the neighbouring pixels with the highest altitude. This process is repeated until the highest location (i.e. the cell with the highest value) is found; the path followed determines the catchment boundary. For delineating the drainage network, the process is reversed. Then the system will work from the watershed downwards, each time looking for the lowest neighbouring cells, which determines the direction of water flow (Flow Computation).\r\n\r\nDynamic modelling - apart from the applications mentioned above, DEMs are increasingly used in GIS-based dynamic modelling, such as the computation of surface run-off and erosion, groundwater flow, the delineation of areas affected by pollution, the computation of areas that will be covered by processes such as flows of debris and lava. An example is (Diffusion).\r\n\r\nVisibility analysis - a viewshed is the area that can be “seen” (i.e. it is in the direct line-of-sight) from a specified target location. Visibility analysis can determine the area visible from a scenic lookout or the area that can be reached by a radar antenna, as well as assess how effectively a road or quarry will be hidden from view.","hasChildren":true,"hasParent":true,"name":"Analysis of surfaces","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM7-1","description":"Statistical analysis techniques based on visual interpretation through histograms, scatterplots, etc.","hasChildren":true,"name":"Graphical methods","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM7-2","description":"Environmental variables have become increasing available with the advent of GIS. These are mostly continuous in space and time. Collecting denser environmental data in discrete space and time domains are rather cost effective and time consuming. However, when the data at each spatial or time index are considered as outcomes of a random variable, stochastic processes become enviable useful to build models and predict the outcomes at locations where data were never collected. The meaningful assumptions include stationarity of the mean and the covariance to ascertain an expression for spatial dependency/autocorrelation. With a stationary process (i.e. constant mean), simple and ordinary kriging is used. Other variants like kriging with external drift, universal kriging and regression kriging also alleviate the challenge of non-stationary mean. These methods are also applicable when temporal indexes rather than spatial indexes are of interest.","hasChildren":true,"name":"Stochastic processes","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM7-3","description":"Spatial weight matrix is the popular numerical quantification of spatial dependency or spatial neighborhoods. The weight matrix should summarize information about the spatial connectivity structure of the spatial entities/features; either polygons, points, or lines. This is required for the computation of spatial dependency indices such the Moran’s index, and for spatial regression models such as the conditional autoregressive (CAR), spatial lag, and spatial error models. The connectivity information can be defined based on adjacency/contiguity or distance between pairs of spatial entities. There are other forms; they could be based on population densities between observation pairs. The simplest spatial weigh matrix is the binary adjacency spatial weight matrix with elements w_ij, such that w_ij=1 if spatial units i and j are neighbors, otherwise w_ij=0. A popular alternative is the inverse distance weight matrix with elements w_ij=1⁄d^α , where d is the distance between pairs of spatial units and α is any positive number greater than zero. By convention, w_ii=0 since spatial unit cannot have a spillover within itself.","hasChildren":true,"name":"The spatial weights matrix","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM7-4","description":"Spatial autocorrelation evaluates how things which are closer in space tend to have similar attributes. This is a common phenomenon in environmental variables which are continuous in space. For instance, temperature, soil moisture content, air quality and rainfall are all continuous in space. This idea is based on Tobler’s law of geography: “everything is related to everything but near things are more related”. Global measures of spatial association estimates the overall index of spatial autocorrelation, also called spatial clustering. Thus, it measures whether clustering is apparent throughout the study region but do not identify the location of clusters. Common global measures include the Moran’s Index and Geary’s C. These have increasing applications in domains like environmental science, agriculture, epidemiology, climate studies etc.","hasChildren":true,"name":"Global measures of spatial association","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM7-5","description":"Unlike global measures of spatial association, local measure of spatial association identifies the locations of clusters. Typical measures include the local indicator for spatial autocorrelation (LISA) or the local Moran’s index whose summation is proportional to the global Moran’s index. The spatial scan statistics has also been the commonly used method to detect local clusters.","hasChildren":true,"name":"Local measures of spatial association","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM7-6","description":"An outlier is an unexpected value that differs significantly from other observations. Definition of an outlier is not absolute and the concept itself is precisely defined only by selection of appropriate criteria in concrete statistical observations. When considering outliers, it is important to determine whether the value of the outlier is incorrect data or it is otherwise outstanding, but correct data. If we consider outliers in the case when they base on sample surveys, another assessment is necessary. Namely, the assessment of whether an outlier is representative or not. \r\nThe box plot is a useful graphical display for examining the outliers. Using median, lower and upper quartiles, extreme values are identified in the tails of the distribution. The value beyond inner fence on either side is considered a mild outlier. The value beyond an outer fence is considered an extreme outlier. Histograms also emphasize the existence of outliers. The histogram depends on how we design the classes, so we can get different histograms for the same data. Graphical and quantitative checks are obligatory if the histogram shows possible outliers. Outliers can also be examined by calculating the correlation between two datasets (Pearson correlation coefficient, Spearman rank correlation coefficient…). Scatter plots reveals a basic linear relationship with a pattern. An outliner is defined as a data point that deviates from other values. Outliers can also be examined by local outlier factor, which is based on a concept of a local density. Points with substantially lower density than their neighbours are considered as outliers.","hasChildren":true,"name":"Outliers","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM7-7","description":"Bayesian method of modelling stems from the Bayes theorem and derived using conditional probabilities. Its advantage lies in its ability to include prior knowledge of unknown parameters to ascertain their uncertainties. Thus, the prior parameters are updated by the data likelihood to obtain the posteriors. The challenge of Bayesian modelling has been the integration of the denominator which always resulted into improper integrals. This actually prolonged its wide applications. With the advent of high performance computers, solution to such integrals are easily solved using Markov chain Monte Carlo simulations. The advent robust approximation methods through integrated nested Laplace approximations (INLA) has even made parameter estimation faster; thus making Bayesian methods interesting and better. Unlike frequentist approaches, Bayesian methods can present estimates of parameters as densities from which their uncertainties and credible intervals can be estimated. They have now found wide applications in divers areas like environmental modelling, climate modeling, agriculture, epidemiology and many other domains that requires modeling.","hasChildren":true,"name":"Bayesian methods","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM7","description":"Traditional statistical methods are used to describe the central tendency, dispersion, and other characteristics of data but are not always suited to use with spatial data for which specialized techniques are often required. The field of spatial statistical analysis forms the backbone for the testing of hypotheses about the nature of spatial pattern, dependency, and heterogeneity. The techniques are widely used in both exploratory and confirmatory spatial analysis in many different fields.","hasChildren":true,"hasParent":true,"name":"Spatial statistics","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM8-1","description":"Sampling is needed to limit the observations for statistical analysis. In raster image analysis, various sampling schemes have been proposed for selecting pixels to test. Choices to be made relate to the design of the sampling strategy, the number of samples required, and the area of the samples. Recommended sampling strategies in the context of land cover data are simple random sampling or stratified random sampling. The number of samples may be related to two factors in accuracy assessment: (1) the number of samples that must be taken in order to reject a data set as being inaccurate; or (2) the number of samples required to determine the true accuracy, within some error bounds, of a data set. Sampling theory is used to determine the number of samples required. The number of samples must be traded-off against the area covered by a sample unit. A sample unit can be a point but it could also be an area of some size; it can be a single raster element but may also include surrounding raster elements. Among other considerations, the “optimal” sample-area size depends on the heterogeneity of the class.","hasChildren":true,"hasParent":true,"name":"Spatial sampling for statistical analysis","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM8-3","description":"A variogram is a tool used to describe the spatial continuity of data points. Different kinds of variograms are used, such as experimental variogram and semi-variogram.","hasChildren":true,"name":"Variogram modeling","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM8-4","description":"Predicting an observation in the presence of spatially dependent observations is termed Kriging, named after the first practitioner of these procedures, the South African mining engineer Daan Krige, who did much of his early empirical work in the Witwatersrand gold mines.","hasChildren":true,"name":"Principles of kriging","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM8-5","description":"With a stationary stochastic process (i.e. constant mean), simple and ordinary kriging is used for interpolation. Other variants like kriging with external drift, universal kriging and regression kriging also alleviate the challenge of non-stationary mean. Other variants are \r\nco-kriging log-normal kriging, disjunctive kriging, indicator kriging, factorial kriging and universal kriging.","hasChildren":true,"name":"Kriging variants","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM8","description":"Geostatistics are a variety of techniques used to analyze continuous data e.g., rainfall, elevation, air pollution. The fundamental structure of geostatistics is based on the concept of semi-variograms and their use for spatial prediction kriging. Sampling methods are also discussed in Unit GD9 Field data collection. \r\nGeostatistics is a subdiscipline of spatial statistics developed to estimate the value of a continuous spatial process at unknown locations by using the information of the value of these process at known locations. Furthermore, it aims to quantify the uncertainty related to the prediction (Calder et al., 2009; Emmanouil, 2019). In order to do such predictions, geostatistics entails some statistical methods which use as starting point the assumption of a random component that can define the spatiotemporal variability. These methods are developed to infer the parameters that can describe the spatiotemporal patterns of the input variables (e.g. soil moisture) so that finally these variables at unsampled locations can be estimated (interpolated) (Emmanouil, 2019). Geostatistical methods are strongly related with classic interpolation methods but differ by its use of random variables that allow to given an uncertainty indication associated with the prediction of variables in space and time. \r\n\r\nIn environmental research geostatistical techniques are often applied to infer (interpolate) variables at such unobserved locations by using information from known locations. One of such geostatistical techniques is Kriging, which is a geostatistical method that predicts variables by using spatial interpolation. This spatial interpolation is done by establishing a semivariogram that defines the spatial relationship between the variables of interest in function of the distance. Because of this, the Kriging technique can also give an indication on the variance or accuracy of the prediction (Calder et al., 2009); Van der Meer, 2012). On the other hand, cokriging is another important geostatistical technique and differs from Kriging by using the cross-correlation between variables to generate local estimates (Van der Meer, 2012). In earth observation studies, cokriging can be applied to better predict sparsely based data on the ground (e.g. biomass) by using the cross-correlation of this variable with a more continuously sampled satellite metric like NDVI. Furthermore, these techniques can also be used to enhance satellite image information, filling missing pixels or even downscale the information to a higher resolution (Van der Meer, 2012).","hasChildren":true,"hasParent":true,"name":"Geostatistics","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM9-1","description":"Spatial econometrics uses spatial stochastic models to determine autocorrelation between interacting agents. The techniques involved are regression, the use of a spatial weights matrix, least squares, etc.","hasChildren":true,"name":"Principles of spatial econometrics","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM9-2","description":"A spatial autoregressive (SAR) model describes the prediction of the behaviour of a random process.","hasChildren":true,"name":"Spatial autoregressive models","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM9-3","description":"In producing optimal images for interpretation, spatial filtering is applied. Filtering is usually carried out for a single band. Filters - algorithms - can be used to enhance images by, for example, reducing noise (“smoothing an image”) or sharpening a blurred image. Filter operations are also used to extract features from images, e.g. edges and lines, and to automatically recognize patterns and detect objects. There are two broad categories of filters: linear and non-linear filters.\r\n\r\nLinear filters calculate the new value of a pixel as a linear combination of the given values of the pixel and those of neighbouring pixels. A simple example of the use of a linear smoothing filter is when the average of the pixel values in a 3×3 pixel neighbourhood is computed and that average is used as the new value of the central pixel in the neighbourhood.","hasChildren":true,"name":"Spatial filtering","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM9-4","description":"Geographically Weighted Regression (GWR) makes use of local subsets of observations to perform estimates.","hasChildren":true,"name":"Spatial expansion and Geographically Weighted Regression GWR","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM9","description":"Many problems of the social sciences can be expressed in terms of spatial regression analysis. The development of spatial autoregressive models and the estimation of their parameters is the focus for the field of spatial econometrics.","hasChildren":true,"hasParent":true,"name":"Spatial regression and econometrics","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF","description":"The GIScience perspective is grounded in spatial thinking. The aim of this knowledge area is to recognize, identify, and appreciate the explicit spatial, spatio-temporal and semantic components of the geographic environment at an ontological and epistemological level in preparation for modeling the environment with geographic data and analysis. To do this, one must understand the nature of space and time as a context for geographic phenomena.This knowledge area covers the ways in which views of the geographic environment depend on philosophical viewpoints, physics, human cognition, society, and the task at hand. This knowledge area also requires an understanding of the fundamental principles in the discipline of geography, the \"language\" of spatial tasks. On a more advanced level, this area incorporates mathematical and graphical models that formalize these concepts, such as set theory, algebra, and semantic nets. Because of its wide range of foundational principles, this knowledge area forms a basis for the other knowledge areas. Wise design and use of geospatial technologies requires an understanding of the nature of geographic information, the social and philosophical context of geographic information, and the principles of geography. This knowledge area is especially closely tied to Knowledge Areas Data Modeling (DM) and Design Aspects (DA), as generic data models and application designs need to be grounded in sound conceptual models. The foundations of geographic information have developed over several decades. Philosophical and scientific views on the nature of space and time have evolved since the ancient Greeks. Early papers during the Quantitative Revolution, such as Berry (1964), began to formalize the structure of information used in geographic inquiry.The fundamental data structures and algorithms comprising the GIS software developed in the 1960`s and 1970`s were based on implicit \"common-sense\" conceptual models of geographic information. During the 1980`s, several researchers questioned these underlying assumptions. Some were refuted, other confirmed, and many extended. However, the most rapid pace of development in this area was during the 1990`s with the rise of GIScience as a distinct discipline, and the many cooperative initiatives it comprised.The new millennium has seen some of these foundational principles incorporated into commercial software, thus making theoretical knowledge even more important for practitioners. It is expected that the concepts in this knowledge area will be learned gradually. An introductory course may cover only a few topics in a cursory manner, an intermediate course on data modeling or data analysis may consider several theoretical topics of practical application, and a number of graduate courses could cover each topic in a research-oriented environment. Discussion of this knowledge area includes several terms that can have multiple meanings. For the purposes of this document, two in particular require definition: Geographic: Almost any subject or discourse involving earthly phenomena, studied from a spatial perspective at a medium scale (sub-astronomical and super-architectural). Phenomenon: Any subject of geographic discourse that is perceived to be external to the individual, including entities, events, processes, social constructs, and the like.","hasChildren":true,"hasParent":true,"name":"Conceptual Foundations","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF1-1","description":"Metaphysics involve the meaning things and concepts. Ontologies provide a way to share the semantics of concepts in some area of interest and is all about common the understanding of essential concepts, e.g., what is meant by a geometric object and its attributes.","hasChildren":true,"name":"Metaphysics and ontology","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF1-1b","description":"Brief history of GIScience as related to the history of GISystems; Definitions of GIS&T; Sub-domains of GIS&T (i.e., Geographic Information Science, Geospatial Technology, and Applications of GIS&T)","hasChildren":true,"name":"What is Geographic Information Science and Technology","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF1-2","description":"The branch of philosophy concerned with knowledge.","hasChildren":true,"name":"Epistemology","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF1-2b","description":"GIS&T draws upon insights and methods from key allied fields: Geography, Cartography, Computer and information science, Engineering, Mathematics and Statistics, Philosophy, Cognitive Science, Linguistics","hasChildren":true,"name":"Contributions to GIS and T by key allied fields","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF1-3","description":"The questions and methodologies in major philosophical movements relating to the nature of space, time, geographic phenomena and human interaction with it.","hasChildren":true,"name":"Philosophical perspectives","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF1","description":"Many branches of philosophy are relevant to an understanding of geographic information, especially metaphysics and epistemology. Philosophical theories are deeply engaged in the study of knowledge, space, time, geographic phenomena and human interaction with them. These theories influence the development of geographic ontologies and the structuring, analysis, and interpretation of geographic information. It is, therefore, crucial for professionals to understand these principles in order to bridge (rather than eliminate) the differences and work together. Philosophical perspectives on GIS practice are covered in Unit GS7 Critical GIS.","hasChildren":true,"hasParent":true,"name":"Philosophical foundations","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF1b","description":"Unit CF1 introduces the broad domain refered to as Geographic Information Science & Technology (GIS&T) and its sub-domains (i.e., Geographic Information Science, Geospatial Technology, and Applications of GIS&T). It outlines the history of Geographic Information Science as related to the history of GISystems, as well as the contributions to this multidisciplinary domain by key allied fields, such as geography, cartography, computer and information science, engineering, mathematics, philosophy, cognitive science, and linguistics.","hasChildren":true,"hasParent":true,"name":"Introduction to Geographic Information Science and Technology","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF2-1","description":"The study on how humans perceive spatial information.","hasChildren":true,"name":"Perception and cognition of geographic phenomena","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF2-1b","description":"Metaphysics and Ontology - Formal ontology - Ontological distinctions (e.g., continuants vs. occurrents, universals vs. particulars) - The problem of universals and relevant theories (realism, nominalism, conceptualism) - Ontologies of the geographic domain - Philosophical theories relating to the nature of space, time, geographic phenomena and human interaction with them","hasChildren":true,"name":"Philosophy of being","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF2-2","description":"The ways in which conceptual views of in the human mind make it into formal descriptions of information and into artefacts in databases and GIS.","hasChildren":true,"hasParent":true,"name":"From concepts to data","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF2-2b","description":"Epistemology; Theories on what constitutes knowledge; The notions of model and representation in science; The influences of epistemology on GIS practices","hasChildren":true,"name":"Philosophy of knowledge","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF2-3","description":"Principles of geography to explain the spatial occurrences of spatial entities in Geographic Information Systems.","hasChildren":true,"name":"Geography as a foundation for GIS","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF2-4","description":"Space and place are concepts that are not the same. Including concepts like landscape, it is not always obvious how to portray them unambiguously in GIS.","hasChildren":true,"name":"Place and landscape","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF2-6","description":"The ways in which the elements of culture (e.g., language, religion, education, traditions) may influence the understanding and use of geographic information.","hasChildren":true,"name":"Cultural influences","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF2-7","description":"The influences of political ideologies (e.g., Marxism, Capitalism, conservative liberal) on the understanding of geographic information.","hasChildren":true,"name":"Political influences","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF2","description":"Geographic information is observed, comprehended, organized, used in human processes, with both personal and social influences. Therefore, sound models of geographic information should be grounded on a sound understanding of human perception, cognition, memory, and behavior, as well as human institutions.","hasChildren":true,"hasParent":true,"name":"Cognitive and social foundations","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF3-1","description":"A GIS operates under the assumption that the spatial phenomena involved occur in a two- or three-dimensional Euclidean space. Euclidean space can be informally defined as a model of space in which locations are represented by coordinates—(x, y) in 2D and (x, y, z) in 3D space—and distance and direction can defined with geometric formulas. In 2D, this is known as the Euclidean plane. To represent relevant aspects of real-world phenomena inside a GIS, we first need to define what it is we are referring to. We might define a geographic phenomenon as a manifestation of an entity or process of interest that:\r\n\r\nitem can be named or described;\r\nitem can be georeferenced; and\r\nitem can be assigned a time (interval) at which it is/was present.\r\n\r\nRelevance of phenomena for the use of a GIS depends entirely on the objectives of the study at hand. For instance, in water management, relevant objects can be river basins, agro-ecological units, measurements of actual evapotranspiration, meteorological data, ground\\-water levels, irrigation levels, water budgets and measurements of total water use. All of these can be named or described, georeferenced and provided with a time interval at which each exists. In multipurpose cadastral administration, the objects of study are different: houses, land parcels, streets of various types, land use forms, sewage canals and other forms of urban infrastructure may all play a role. Again, these can be named or described, georeferenced and assigned a time interval of existence.\r\n\r\nNot all relevant information about phenomena has the form of a triplet (description, georeference, time interval). If the georeference is missing, then the object is not positioned in space: an example of this would be a legal document in a cadastral system. It is obviously somewhere, but its position in space is not considered relevant. If the time interval is missing, we might have a phenomenon of interest that exists permanently, i.e.\\ the time interval is infinite. If the description is missing, then we have something that exists in space and time, yet cannot be described. Obviously this last issue limits the usefulness of the information.\r\n\r\nTypes of geographic phenomena\r\nThe definition of geographic phenomena attempted above is necessarily abstract and is, therefore, perhaps somewhat difficult to grasp. The main reason is that geographic phenomena come in different “flavours”. Before categorizing such flavours, there are two further observations to be made.\r\n\r\nFirst, to represent a phenomenon in a GIS requires us to state what it is and where it is. We must provide a description—or at least a name—on the one hand, and a georeference on the other hand. We will ignore temporal issues for the moment and come back to these in Temporal dimension and Spatial-temporal data model, the reason being that current GISs do not provide much automatic support for time-dependent data. This topic must, therefore, be considered as an example of advanced GIS use. Second, some phenomena are manifest throughout a study area, while others only occur in specific localities. The first type of phenomena we call geographic fields; the second type we call objects.","hasChildren":true,"name":"Space","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF3-1b","description":"- Theories of human perception, cognition, and memory and their ability to model spatial knowledge acquisition (e.g., Marr on vision, Piaget on cognitive development) - Types of mental representations (i.e., analogue, propositional, procedural) - The role of metaphors and image schemata in our understanding of geographic phenomena and geographic tasks - From concepts to data (i.e., data, information, knowledge, and wisdom; transformation of a conceptual model of information for a particular task into a data model; limitations of various information stores (the mind, computers) and means (maps, graphics, and text) for representing geographic information) - Difference between real phenomena, conceptual models, and GIS data representations thereof connections with cartography and maps","hasChildren":true,"name":"Cognitive foundations","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF3-2b","description":"- Semantics - Meaning (e.g., the nature of meaning, modes of meaning) - Geospatial semantics - The role of natural language in the conceptualization of geographic phenomena","hasChildren":true,"name":"Linguistic foundations","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF3-3b","description":"- The ways in which the elements of culture (e.g., language, religion, education, traditions) may influence the understanding and use of geographic information - The influences of social theories and political ideologies and actions on human perceptions of space and place - The constraints that political forces place on geospatial applications in public and private sectors","hasChildren":true,"name":"Social foundations","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF3-4b","description":"- Common-sense views and laymen knowledge of geographic phenomena that contrast with established theories and technologies of geographic information - The impact of geospatial technologies and the geoweb (e.g., digital globes) that allow non-geospatial professionals to create, distribute, and map geographic information - The design, procedures, and results of GIS projects to non-GIS audiences (clients, managers, general public) - Difference between applications that can make use of common-sense principles of geography and those that should not","hasChildren":true,"name":"Common-sense geographies","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF3","description":"Geographic information is observed, comprehended, organized, used in human processes, with both personal and social influences. Therefore, sound models of geographic information should be grounded on a sound understanding of human perception, cognition, memory, and behavior, as well as human institutions.","hasChildren":true,"hasParent":true,"name":"Cognitive, linguistic and social foundations","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF4-2b","description":"As time is the central concept of the temporal dimension, a brief examination of the nature of time may clarify our thinking when we work with this dimension:\r\n\r\nDiscrete and continuous time: Time can be measured along a discrete or continuous scale. Discrete time is composed of discrete elements (seconds, minutes, hours, days, months, or years). For continuous time, no such discrete elements exist: for any two moments in time there is always another moment in between. We can also structure time by events (moments) or periods (intervals). When we represent intervals by a start and an end event, we can derive temporal relationships between events and periods, such as “before”, “overlap”, and “after”.\r\n\r\nValid time and transaction time: Valid time (or world time) is the time when an event really happened, or a string of events took place. Transaction time (or database time) is the time when the event was stored in the database or GIS. Note that the time at which we store something in a database is typically (much) later than when the related event took place.\r\n\r\nLinear, branching and cyclic time: Time can be considered to be linear, extending from the past to the present (‘now’), and into the future. This view gives a single time line. For some types of temporal analysis, branching time - in which different time lines from a certain point in time onwards are possible - and cyclic time - in which repeating cycles such as seasons or days of the week are recognized - make more sense and can be useful.\r\n\r\nTime granularity: When measuring time, we speak of granularity as the precision of a time value in a GIS or database (e.g. year, month, day, second). Different applications may obviously require different granularity. In cadastral applications, time granularity might well be a day, as the law requires deeds to be date-marked; in geological mapping applications, time granularity is more likely to be in the order of thousands or millions of years.\r\n\r\nAbsolute and relative time: Time can be represented as absolute or relative. Absolute time marks a point on the time line where events happen (e.g. “6 July 1999 at 11:15 p.m.”). Relative time is indicated relative to other points in time (e.g. “yesterday”, “last year”, “tomorrow”, which are all relative to “now”, or “two weeks later”, which is relative to some other arbitrary point in time.).","hasChildren":true,"name":"Time","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF4-3b","description":"The way we represent relevant components of the real world in our models determines the kinds of questions we can or cannot answer. Besides representing an object or field in 2D or 3D space, the temporal dimension is of a continuous nature. Therefore, in order to represent it in a GIS we have to discretize the time dimension.\r\n\r\nSpatio-temporal data models are ways of organizing representations of space and time in a GIS. Several representation techniques have been proposed in the literature. Perhaps the most common of these is the “snapshot state”, which represents a single moment in time of an ongoing natural or man-made process. We may store a series of these “snapshot states” to represent “change”, but we must be aware that this is by no means a comprehensive representation of that process. \r\n\r\nIn spatio-temporal analysis we consider changes of spatial and thematic attributes over time. We can keep the spatial domain fixed and look only at the attribute changes over time for a given location in space. We might be interested how land cover has changed for a given location or how land use has changed for a given land parcel over time, provided its boundary has not changed. On the other hand, we can keep the attribute domain fixed and consider the spatial changes over time for a given thematic attribute. In this case, we might want to identify locations that were covered by forest over a given period of time.\r\n\r\nFinally, we can assume both the spatial and attribute domains are variable and consider how fields or objects have changed over time. This may lead to notions of object motion - a subject receiving increasing attention in the literature. Applications of moving object research include traffic control, mobile telephony, wildlife tracking, vector-borne disease control and weather forecasting. In these types of applications, the problem of object identity becomes apparent. When does a change or movement cause an object to disappear and become something new? With wildlife this is quite obvious; with weather systems less so. But this should no longer be a surprise: we have already seen that some geographic phenomena can be nicely described as objects, while others are better represented as fields.\r\n\r\nMapping time means mapping change. This may be change in a feature’s geometry, in its attributes, or both. Examples of changing geometry are the evolving coastline of the Netherlands, the location of Europe’s national boundaries, or the position of weather fronts. Changes in the ownership of a land parcel, in land use or in road traffic intensity are other examples of changing attributes. Urban growth is a combination of both: urban boundaries expand with growth and simultaneously land use shifts from rural to urban. If maps are to represent events like these, they should be suggestive of such change.\r\n\r\nThree temporal cartographic techniques can be distinguished:\r\n\r\nSingle Static Map\r\n\r\nSpecific graphic variables and symbols are used to indicate change or represent an event. We can apply the visual variable “value” to represent for example the age of built-up areas.\r\n\r\nSeries of Static Maps\r\n\r\nA single map in the series represents a “snapshot” in time. Together, the maps depict a process of change. Change is perceived by the succession of individual maps depicting the situation in successive snapshots. It could be said that the temporal sequence is represented by a spatial sequence that the user has to follow to perceive the temporal variation. The number of images should be limited since it is difficult for the human eye to follow long series of maps.\r\n\r\nAnimated Maps\r\n\r\nChange is perceived to evolve in a single image by displaying several snapshots one after the other, just like a video clip of successive frames. The difference from the series of maps is that the variation can be deduced from real “change” seen taking place in the image itself, not from a spatial sequence. For the user of a cartographic animation, it is important to have tools available that allow for interaction while viewing the animation. Seeing an animation play will often leave users with many questions about what they have seen. And just replaying the animation is not sufficient to answer questions like “What was the position of the northern coastline during the 15th century?” Most of the general software packages for viewing animations already offer facilities such as “pause” (to look at a particular frame) and ‘(fast-)forward’ and ‘(fast-)backward’, or step-by-step display. More options have to be added, such as the possibility to go directly to a certain frame based on a task command like: “Go to 1850”.","hasChildren":true,"name":"Relationships between space and time","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF4-4b","description":"GIS data structures are used to implement the conceptual views of spatial data (vector and raster models). The power of a GIS is dependent on the richness of information contained in the spatial data structures. Vector models are based on points, lines and areas. Raster models are based on grids. Each cell has a value that is used to represent some characteristic of that location. \r\nLayers are used to display geographic datasets in various digital map environment. A layer stores the path to a source dataset and other layer properties, including symbology. You can use multiple layers on one map and specify its properties. Shapefiles represent spatial character of the object in terms of shape, size and spatial arrangement. Shapefile usually comprise three separate and distinct types of files (main files, index files and database tables). Data base files store additional attributed that can be joined to a shapefiles’ feature. Attribute data types supplement geographic spatial feature with additional information. Spatial data includes information of location and attribute data includes information about other characteristics (what, where and why). A legend is a visual presentation of the symbols that are used on the map with some additional explanations. It includes a sample of each symbol and a short description of the meaning.","hasChildren":true,"name":"Categories","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF4-5","description":"An entity obtained by abstracting the real world, having a physical nature (certain composition of material), being given a descriptive name, and observable; e.g. “house”. An object is a self-contained part of a scene having certain discriminating properties.\r\n\r\nThe primitives of vector data sets are the point, (poly)line and polygon. Related geometric measurements are location, length, distance and area size. Some of these are geometric properties of a feature in isolation (location, length, area size); others (distance) require two features to be identified.\r\n\r\nIn a GIS, features are represented together with their attributes—geometric and non-geometric—and relationships. The geometry of features is represented with primitives of the respective dimension: a windmill probably as a point; an agricultural field as a polygon. The primitives follow either the vector or the raster approach.\r\n\r\nVector data types describe an object through its boundary, thus dividing the space into parts that are occupied by the respective objects. The raster approach subdivides space into (regular) cells, mostly as a square tessellation of two or three dimensions. These cells are called pixels in 2D and voxels in 3D. The data indicate for every cell which real-world feature is covered, provided the cell represents a discrete field. In the case of a continuous field, the cell holds a representative value for that field. The Table below lists advantages and disadvantages of raster and vector representations.\r\n\r\nThe storage of a raster is, in principle, straightforward. It is stored in a file as a long list of values, one for each cell, preceded by a small list of extra data (the “file header”), which specifies how to interpret the long list. The order of the cell values in the list can, but need not necessarily, be left to right, top to bottom. This simple encoding scheme is known as row ordering. The header of the raster will typically specify how many rows and columns the raster has, which encoding scheme was used, and what sort of values are stored for each cell.\r\n\r\nData can be of a qualitative or quantitative nature. Qualitative data is also called nominal data, which exists as discrete, named values without a natural order amongst the values. Examples are different languages (e.g. English, Swahili, Dutch), different soil types (e.g. sand, clay, peat) or different land use categories (e.g. arable land, pasture). In the map, qualitative data are classified according to disciplinary insights, such as a soil classification system represented as basic geographic units: homogeneous areas associated with a single soil type, recognizable by the soil classification.\r\n\r\nQuantitative data can be measured, either along an interval or ratio scale. For data measured on an interval scale, the exact distance between values is known, but there is no absolute zero on the scale. Temperature is an example: 40 ◦C is not twice as hot as 20 ◦C, and 0 ◦C is not an absolute zero.\r\n\r\nQuantitative data with a ratio scale do have a known absolute zero. An example is income: someone earning $100 earns twice as much as someone with an income of $50. In order to generate maps, quantitative data are often classified into categories according to some mathematical method.\r\n\r\nIn between qualitative and quantitative data, one can distinguish ordinal data. These data are measured along a relative scale and are as such based on hierarchy. For instance, one knows that a particular value is “more” than another value, such as “warm” versus “cool”. Another example is a hierarchy of road types: “highway”, “main road”, “secondary road” and “track”. The different types of data are summarized in Table.","hasChildren":true,"hasParent":true,"name":"Properties","selfAssesment":"GI-N2K
"},{"code":"CF4b","description":"Geographic phenomena, geographic information, and geographic tasks are described in terms of space, time, and properties. Different theories exist as to the nature and formal representation of these aspects, including space-like dimensions, sets, and phenomenology. Information in each of these three aspects is measured and reported with respect to one of several frames of reference or domains, including both absolute and relative approaches. Early frameworks such as those of Berry (1964) and Sinton (1978) were influential in setting forth the importance of space, time, and theme in GIS&T. Besides, space, time, and properties, categories are also fundamental in the conceptualization and representation of spatial entities, phenomena, processes, and events. Distinctive features of geographic information such as scale and detail, spatial patterns, spatial integration, and regions are also critical for a complete description of its nature and representation. This unit is closely tied to the creation of data models in Knowledge Area 5: Data Modeling, Storage, and Exploitation.","hasChildren":true,"hasParent":true,"name":"Fundamentals of Geographic Information","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF5-1b","description":"Discrete entities can be found as fields or objects.\r\n\r\nDiscrete fields divide the study space in mutually exclusive, bounded parts, with all locations in one part having the same field value. Discrete fields are intermediate between continuous fields and geographic objects: discrete fields and objects both use “bounded” features.\r\n\r\nDiscrete fields divide the study space in mutually exclusive, bounded parts, with all locations in one part having the same field value. Typical examples are land classifications, for instance, using either geological classes, soil type, land use type, crop type or natural vegetation type. \r\n\r\nDiscrete fields are intermediate between continuous fields and geographic objects: discrete fields and objects both use “bounded” features. A discrete field, however, assigns a value to every location in the study area, which is not typically the case for geographic objects. These two types of fields differ in the type of cell values. A discrete field such as land use type will store cell values of the type “integer” and is therefore also called an integer raster. Discrete fields can be easily converted to polygons since it is relatively easy to draw a boundary line around a group of cells with the same value. A continuous raster is also called a “floating point” raster.\r\n\r\nGeographic objects.\r\n\r\nWhen a geographic phenomenon is not present everywhere in the study area, but somehow “sparsely” populates it, we look at it as a collection of geographic objects. Such objects are usually easily distinguished and named, and their position in space is determined by a combination of one or more of the following parameters:\r\n\r\nlocation (where is it?)\r\nshape (what form does it have?)\r\nsize (how big is it?)\r\norientation (in which direction is it facing?).\r\n\r\nHow we want to use the information determines which of these four parameters is required to represent the object. For instance, for geographic objects such as petrol stations all that matters in an in-car navigation system is where they are. Thus, in this particular context, location alone is enough, and shape, size and orientation are irrelevant. For roads, however, some notion of location (where does the road begin and end?), shape (how many lanes does it have?), size (how far can one travel on it?) and orientation (in which direction can one travel on it?) seem to be relevant components of information in the same system.","hasChildren":true,"name":"Discrete entities","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF5-2b","description":"A geographic field is a geographic phenomenon that has a value “everywhere” in the study area. We can therefore think of a field as a mathematical function f that associates a specific value with any position in the study area. Hence if (x, y) is a position in the study area, then f(x, y) expresses the value of f at location (x, y). Fields can be discrete or continuous.\r\n\r\nIn a continuous field, the underlying function is assumed to be “mathematically smooth”, meaning that the field values along any path through the study area do not change abruptly, but only gradually. Good examples of continuous fields are air temperature, barometric pressure, soil salinity and elevation. A continuous field can even be differentiable, meaning that we can determine a measure of change in the field value per unit of distance anywhere and in any direction. For example, if the field is elevation, this measure would be slope, i.e. the change of elevation per metre distance; if the field is soil salinity, it would be salinity gradient, i.e. the change of salinity per metre distance.\r\n\r\nDiscrete fields divide the study space in mutually exclusive, bounded parts, with all locations in one part having the same field value. Discrete fields are intermediate between continuous fields and geographic objects: discrete fields and objects both use “bounded” features.\r\n\r\nDiscrete fields divide the study space in mutually exclusive, bounded parts, with all locations in one part having the same field value. Discrete fields are intermediate between continuous fields and geographic objects: discrete fields and objects both use “bounded” features.\r\n\r\nDiscrete fields divide the study space in mutually exclusive, bounded parts, with all locations in one part having the same field value. Typical examples are land classifications, for instance, using either geological classes, soil type, land use type, crop type or natural vegetation type. \r\n\r\nDiscrete fields are intermediate between continuous fields and geographic objects: discrete fields and objects both use “bounded” features. A discrete field, however, assigns a value to every location in the study area, which is not typically the case for geographic objects. These two types of fields differ in the type of cell values. A discrete field such as land use type will store cell values of the type “integer” and is therefore also called an integer raster. Discrete fields can be easily converted to polygons since it is relatively easy to draw a boundary line around a group of cells with the same value. A continuous raster is also called a “floating point” raster.\r\n\r\nA field-based model consists of a finite collection of geographic fields: we may be interested in, for example, elevation, barometric pressure, mean annual rainfall and maximum daily evapotranspiration, and would therefore use four different fields to model the relevant phenomena within our study area.","hasChildren":true,"name":"Fields","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF5-3b","description":"We can structure time by events (moments) or periods (intervals). When we represent intervals by a start and an end event, we can derive temporal relationships between events and periods, such as “before”, “overlap”, and “after”.\r\nValid time (or world time) is the time when an event really happened, or a string of events took place. Transaction time (or database time) is the time when the event was stored in the database or GIS. Note that the time at which we store something in a database is typically (much) later than when the related event took place.\r\n\r\nProcess models in the Earth sciences describe the evolution of geo(bio)physical surface properties in time, independently from remote sensing observations. Examples of such process models on various time scales are, for instance, numerical weather prediction models (NWPs), vegetation growth models, hydrological models, oceanographic models and climate models.\r\n\r\nProcesses on the planet Earth are complex phenomena that are taking place in space and in time, i.e. in four dimensions.\r\n\r\nIn many of these processes, differences in one dimension (e.g. height above the geoid) can be disregarded, so that two spatial dimensions and the dimension time remain. Despite this simplification, the physical description of the phenomena remains a difficult task. To better understand the processes it often helps if the same geographic region is viewed repeatedly and, if possible, also from different directions and in different wavelength regions. Integration of data from a variety of sources can be a means to retrieving information about processes that would otherwise remain undetected.","hasChildren":true,"name":"Events and processes","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF5-4b","description":"Models that integrate the concepts of space, time, and attribute in geographic information.","hasChildren":true,"name":"Integrated models","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF5-6","description":"Geographic phenomena can be studied as single entities and in relationship with each other and then reveal patters and clusters. How the entities are distributed is subject to statistical and visualisation studies.","hasChildren":true,"name":"Spatial distribution","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF5-7","description":"We can use the topological properties of interiors and boundaries to define relationships between spatial features. Since the properties of interiors and boundaries do not change under topological mapping, we can investigate their possible relations between spatial features. We can define the interior of a region, R, as the largest set of points of R for which we can construct a disc-like environment around it (no matter how small) that also falls completely inside R. The boundary of R is the set of those points belonging to R that do not belong to the interior of R, i.e. one cannot construct a disc-like environment around such points that still belongs to R completely.\r\n\r\nLet us consider a spatial region A. It has a boundary and an interior, both seen as (infinite) sets of points, which are denoted by boundary(A) and interior(A), respectively. We consider all possible combinations of intersections (∩) between the boundary and the interior of A with those of another region, B, and test whether they are the empty set (∅) or not. From these intersection patterns, we can derive eight (mutually exclusive) spatial relationships between two regions. If, for instance, the interiors of A and B do not intersect, but their boundaries do, yet the boundary of one does not intersect the interior of the other, we say that A and B meet.","hasChildren":true,"name":"Region","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF5-8","description":"Integration of data from a variety of sources can be a means to retrieving information about processes that would otherwise remain undetected.\r\n\r\nAlthough data integration can be very useful, there are also some requirements that have to be fulfilled for it to be effective:\r\n\r\n• geospatial data have to be accurately co-registered in a common grid;\r\n• time gaps between the various data layers have to be known and accounted for;\r\n• systematic effects due to the atmosphere, the viewing angle, the Sun angle, etc., must be corrected for or taken into account.\r\n\r\nData can be integrated in an almost infinite number of ways. Results from data integration can, again, be combined with other geospatial data to produce yet other new information, and so on.\r\n\r\nData integration also comprises the incorporation of non-spatial information or point data from field measurements. These data have to be associated with precise moments in time and with precise geographic locations, or with some time interval and fuzzy-defined regions. Thus, here the important issue of the representativeness of this information for the associated time interval and geographic area comes into play.\r\n\r\nIn general, data integration forces us to consider the uncertainties or inaccuracies of the various data sources available. In some cases, meta-data may contain information about this. When integrating data for some purpose, one has to apply weights to each of them, so that the final result is a balanced compromise in which inaccurate data receive less weight than those with a high degree of certainty.","hasChildren":true,"name":"Spatial integration","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF5b","description":"The concepts below form the basic elements of common human conceptions of geographic phenomena. Concepts from many units in this knowledge area have been synthesized to create general conceptual models of geographic information. Attempts to resolve the object-field debate have led to attempts to create comprehensive models that bridge these views. Consideration of this unit should also include formal models of these elements in mathematics and other fields. Knowledge Area DM Data Modeling discusses the representation of these elements in digital models.","hasChildren":true,"hasParent":true,"name":"Elements of geographic information","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF6-1","description":"Mereology is the study of parts and wholes. In GI this involves how objects are modeled as composites of other objects.","hasChildren":true,"name":"Mereology: structural relationships","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF6-2","description":"Lineage describes the history of a data set. During the processing of data, the derived information inherits artifacts from the dataset(s) of origin. In the case of published maps, some lineage information may be provided as part of its meta-data, in the form of a note on the data sources and procedures used in the compilation of the data. Examples include the date and scale of aerial photography, and the date of field verification. Especially for digital data sets, however, lineage may be defined more formally as:\r\n\r\n“that part of the data quality statement that contains information that describes the source of observations or materials, data acquisition and compilation methods, conversions, transformations, analyses and derivations that the data has been subjected to, and the assumptions and criteria applied at any stage of its life (Clarke and Clark, 1995).”\r\n\r\nAll of these aspects affect other aspects of quality, for example positional accuracy. Clearly, if no lineage information is available, it is not possible to adequately evaluate the quality of a data set in terms of “fitness for use”.","hasChildren":true,"name":"Genealogical relationships: lineage, inheritance","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF6-3","description":"We can use the topological properties of interiors and boundaries to define relationships between spatial features. Since the properties of interiors and boundaries do not change under topological mapping, we can investigate their possible relations between spatial features. We can define the interior of a region, R, as the largest set of points of R for which we can construct a disc-like environment around it (no matter how small) that also falls completely inside R. The boundary of R is the set of those points belonging to R that do not belong to the interior of R, i.e. one cannot construct a disc-like environment around such points that still belongs to R completely.\r\n\r\nLet us consider a spatial region A. It has a boundary and an interior, both seen as (infinite) sets of points, which are denoted by boundary(A) and interior(A), respectively. We consider all possible combinations of intersections (∩) between the boundary and the interior of A with those of another region, B, and test whether they are the empty set (∅) or not. From these intersection patterns, we can derive eight (mutually exclusive) spatial relationships between two regions. If, for instance, the interiors of A and B do not intersect, but their boundaries do, yet the boundary of one does not intersect the interior of the other, we say that A and B meet. In mathematics, we can therefore define the “meets relationship” using set theory. The eight spatial relationships are disjoint, meets, equals, inside, covered by, contains, covers and overlaps.","hasChildren":true,"hasParent":true,"name":"Topological relationships","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF6-4","description":"Relationships between spatial features that define their relative position. Spatial autocorrelation is a fundamental principle based on Tobler’s first law of geography, which states that locations that are closer together are more likely to have similar values than locations that are farther apart.","hasChildren":true,"name":"Metrical relationships: distance and direction","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF6","description":"Like geography, geographic information not only models phenomena but the relationships between them. This can include relationships between entities, between attributes, between locations. In addition, one of the strengths of geography (and GIS) is its ability to use a spatial perspective to relate disparate subjects, such as climate and economy. Methods for analyzing relationships are discussed in Unit AM4 Modeling relationships and patterns.","hasChildren":true,"hasParent":true,"name":"Relationships","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF7-1","description":"Vagueness arises from lack of criteria for the applicability of certain linguistic terms. It arises from the lack knowledge about the meanings of terms.","hasChildren":true,"name":"Vagueness","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF7-2","description":"-Uncertainty-related terms, such as error, accuracy, uncertainty, precision, stochastic, probabilistic, deterministic, and random -Difference between uncertainty and vagueness -Dependence of uncertainty on scale and application -Expressions of uncertainty in language -The causes of uncertainty in geospatial data -Stochastic error models for natural phenomena -How the concepts of geographic objects and fields affect the conceptualization of uncertainty -Mathematical models of uncertainty: Probability and statistics","hasChildren":true,"name":"Error-based uncertainty","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF7","description":"Human models (mental, digital, visual, etc.) of the geographic environment are necessarily imperfect. While the mathematical principle of homomorphism (often operationalized as fitness for use) allows for imperfect data to be useful as long as they yield results adequate for the use for which they are intended, imperfections are frequently problematic. Although terminology still varies, two types of imperfection are generally accepted: vagueness (a.k.a. fuzziness, imprecision, and indeterminacy), which is generally caused by human simplification of a complex, dynamic, ambiguous, subjective world; and uncertainty (or ambiguity), generally the result of imperfect measurement processes (as discussed in Knowledge Area GD Geospatial Data). Both of these can be manifested in all forms of geographic information, including space, time, attribute, categories, and even existence. Imperfection is also dealt with in Units GD6 Data quality (in the context of measurement), GC8 Uncertainty and GC9 Fuzzy sets (for the handling and propagation of imperfections), and CV4 Graphic representation techniques (in the context of visualization).","hasChildren":true,"hasParent":true,"name":"Imperfections in geographic information","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV","description":"Geo-data visualisation necessarily includes cartography as the origin of \"mapping\" our world. Cartography methods have drastically changed over the few years since the increasing role and sophistication of digital technology applied to geo-information visualisation. It is first worth differentiating between the underlying geo-data that describes real world phenomena and the bits of information that describe the visual presentation of geo-data . Likewise, there are processing tools to collect and handle geo-data, and processing tools especially designed to create and manage geo-data visualisations. \r\nWhile cartography methods have traditionally produced printed maps (i.e. hard copy) with static scale, orientation, projection, legends (content based) and tied to a period or instant of time. Nowadays geo-data visualisations are interactive by design, meaning that the results are map-based responsive interfaces, highly customisable through dynamic objects to zoom in and out, pan and tilt, change projections and graphic expressions on the fly, as well as dynamically browse the map over time. \r\nIf the production methods have changed, also the type of authors. Map making in its widest sense is not only a privilege of a few experts but has been democratised in such a way that. everybody is able to make maps using open data and open source apps and tools for geo-data visualisation. Therefore,the new roles of open data and new forms of geo-data like geo-social media make usability, intended and ethical considerations key aspects of geo-data visualization design, production and sharing. \r\nUnder the concept of cartography and visualisation it is included a list of concepts that together comprise the science and technology of visual representation of geographic data.","hasChildren":true,"hasParent":true,"name":"Cartography and Visualization","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV1-1","description":"The evolution of cartographic representation in the previous centuries followed the most important technological and scientific developments of the time. It was driven by commercial and/or military needs and influenced by the special characteristics of the areas and/or environments to be mapped. Recent developments are the rise of open data worldwide and widely available internet technology allowing end users to get remote geo-data published elsewhere. In recent years, data and its digital presentation have become central elements of cartography, whereas paper maps have become peripheral.","hasChildren":true,"hasParent":true,"name":"History and evolution of cartography","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV1-4","description":"Art in cartography means much more than designing aesthetically pleasing maps, whether on paper or digital. Exploring the interaction at large between art and cartography involves rethinking the way we approach spatial expressions and how cultural, social and political dimensions are reflected in maps. This can be clearly observed in historical maps - in between art and science - ranging from beautiful geographical representations created in the Middle Ages to convey religious messages to the creation of modern maps showing the power of modern empires and nations. This particular relationship between art and maps entails: “developing an inclusive approach of artistic mapping expressions; facilitating and encouraging interaction between cartographers who work with the Art aspects of cartography and artists who produce cartographic artifacts; and developing conceptual elements about the relationships between art and cartography.” Besides ancient paper maps, a sum of factors led digital maps and geospatial visualization, a matter of interest to artists and designers. Thanks to powerful computing systems and with the advancements reached in computer graphics or image processing, or the rise of information visualisation, new forms of representing and visualising geodata have also appeared. Creation of digital maps are still a two-way relationship since artists have explored maps as a medium for expressing their art, and cartographers have approached art to provide more than just the representation of locations and geographic features with the intention to make maps more attractive to their audiences.","hasChildren":true,"name":"Art and geodata visualisation","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV1-5","description":"Historical maps are geographical representations made with the intention to represent spatial facts over time. Historical maps are generally considered valuable documents not just because of their historical value but also because most of them also are artistic representations by themselves. From a cartographical point of view, differentiation between historical maps and actual maps is mainly based on the advances in the history of Cartography, so once one disruptive advance in the map making process appears, maps created with previous techniques (and with some artistic or historical value) are usually considered as historical, such as ancient paper-based maps or old sea maps, for instance. Techniques such as scanning or photography can make ancient maps publicly available by converting hard-copy maps to digital ones. Once an historical map is digitised, the next step is to georeference it, which is the process of specifying and relating points of the digitalised map to actual coordinates in a geographic reference system. Because of its archival value and interest, historical maps are adequately preserved - following specific conditions - by map libraries, map societies or museums. Since digital methods and techniques have been replaced over time by new technological advances, first digitally created maps could be also considered historical, not because of its content, but of the techniques used to produce it.","hasChildren":true,"name":"Historical maps","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV1","description":"At a certain moment in time people start to create more graphical representations of their surrounding environment. New technologies offered ways to expand these representations to larger geographical extent, higher spatial resolution, finer temporal granularity and larger periods. Technologies even made it possible to include other representations of reality such as social media and data ensembles in geodata visualizations, to the extent to even blend the real world with geodata-based visualization providing an augmented – virtual reality continuum. New forms of geo-data, like geolocated sensors may challenge the way geo-data visualisations are generated, shared and, eventually, influence decision-making processes. History and trends sketch these developments and future outlook. This concept introduces the main stages and turns in development of cartography, from earliest times to the present, the most important methods in map-making and map-based visualizations.","hasChildren":true,"hasParent":true,"name":"History and trends","selfAssesment":"Completed (GI-N2K)
\r\n\r\n"},{"code":"CV2-1","description":"As mapping ( geo-data visualization) is intended to convey a certain message to a certain audience, it is essential to use data sources that allow the intended visualisation result. The data should be of the right degree of detail and its use should not cause copyright problems. The producer quality of each data set should be taken into account, as well as the fitness of the data for the intended use. Aspects: message; data quality","hasChildren":true,"name":"Data sources for mapping","selfAssesment":"
In progress (GI-N2K)
"},{"code":"CV2-2","description":"In the trajectory between raw (geo)data and their user-relevant representation, the necessary data processing includes ways of abstraction by selection, filtering, generalization, transformation and classification of geographical data. In this data processing it is essential to at one hand relate the final symbolisation to the necessities of the intended message, and at the other hand to procedures that introduce as little error as possible.","hasChildren":true,"name":"Data processing","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV2-3","description":"Map projection is fundamental to representation of spatial data and for combining different datasets. Its choice should serve the presentation type that will convey the intended message to the audience. Many mathematical principles define datum, projections, horizontal and vertical co-ordinate systems, georeferencing- introduced with the focus on visualisation issues Aspects: geodetic concepts; transformations","hasChildren":true,"name":"Mathematical base","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV2","description":"Geodata, including 3 dimensional geometry, as such can graphically be presented but most of the times the data as such doesn`t meet the presentation criteria. Especially if the dataset has to be presented in combination with other datasets. First all the geodatum, georeference and map projection are crucial but also the role of the geometry. The processing of the geometry and the related attributes may become a crucial step for an adequate presentation. Nowadays the highest precision may be used to define different graphical attributes for different zoom levels. On the other hand geodata visualisation includes also graphical datasets. Such data ensembles, the combination of geodata and graphical data, are the data sources that offer opportunities to other ways of visualisation then the traditional cartographic mapping. Facets: a.\tGeospatial location (2D) and position (3D) that data refer to b.\tDegree of detail in data origin (acquisition resolution) and in representation ('map' scale) c.\tTypes of data (e.g. imagery, field measurements, delineated objects)","hasChildren":true,"hasParent":true,"name":"Data considerations","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV3-1","description":"The combined impact of graphic design properties (balance, legibility, clarity, visual contrast, figure-ground organization, and hierarchal organization) and the map components (north arrow, scale bar, and legend) should always be carefully evaluated against the needs and the capacities of the audience.","hasChildren":true,"name":"Map design fundamentals","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV3-10","description":"Geo-gaming is a crossover between gaming elements and location, usually enabled by location based services and augmented adn/or virtual reality features. Geo-games, also known as “location-based games” or “location-aware games”, have geodata at its core, since geoinformation constitutes the central element of the game mechanics. Geo-gaming applications present unique technical challenges to meet the infrastructural and resources demands from the games and location worlds. There are mainly four different types of geo-games: exploration games (to make use of an existing spatial design); feedback games (to report about players’ experiences in a specific design); allocation games (to occupy the majority of game location); and configuration games (to occupy specific pattern of game locations). Gamers actively participate by interacting with the environment, therefore gaming scenarios are as varied as their goals, which include teaching, training, and the developing of spatial thinking skills. Geo-games offer a myriad of opportunities to developers: non-linear storytelling, physical object integration, a more visceral experience, true social interaction… which bring geo-games to another interaction level. Geo-gaming applications often rely on VGI to allow gamers adding geolocated information that may crowdsource geo-referenced data useful for other secondary purposes .","hasChildren":true,"name":"Geo-gaming","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV3-2","description":"Map symbolization entails a number of variables to produce visual, tactile, haptic, auditory, and dynamic displays. Visual variables (e.g., size, lightness, shape, hue) and graphic primitives (points, lines, areas) are commonly used in maps to represent various geographic features at all attribute measurement levels (nominal, ordinal, interval, ratio). With those a single geographic feature can be represented by various graphic primitives (e.g., land surface as a set of elevation points, as contour lines, as hypsometric layers or tints, and as a hillshaded surface). The challenge is to use effective symbols for map features to ease the interpretation of maps.","hasChildren":true,"name":"Symbols and icons","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV3-3","description":"The selection of colours to use in data representation can be influenced by various factors (e.g. the production workflow, cultural differences, involved devices and media). There are various colour models (e.g. RGB, CMYK, CIE) that describe colours in a way that they can effectively convey visual information (e.g., qualitative, sequential, diverging, spectral) according to the meaning of the underlying data. The cultural background of the consumer is also relevant when it comes to choose colours that should have real-world connotations or should express psychological concepts (e.g. harmony, concordance, balance). A final important factor is if the consumer has colour limitations","hasChildren":true,"name":"Colour","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV3-4","description":"When data representation is conveyed in words (e.g. toponyms, road codes), written text is often placed in map labels. It is important to decide on the role of the label in the context of the representation type. Algorithms for label placement are relevant, especially when label density is high. Shape and colour of the labels help to signify different types of messages. This is supported by the typographic properties (type font, size, style) of the text in the labels. Finally, it is important to use an authoritative source for the texts","hasChildren":true,"name":"Typography","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV3-5","description":"Imagery can be a source for data acquisition as well as an illustration to abstract data representations. Imagery can be made from the air (from drones to satellites) or from a terrestrial point of view (street-level imagery). Using photos from any source to illustrate stories about geographical subjects contributes as the visual aspect of telling a story. Together with maps and other narrative components, the combination embodies a storytelling medium.","hasChildren":true,"name":"Photos and imagery","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV3-6","description":"Animation is the process of making the illusion of motion and change by means of the rapid display of a sequence of static images that minimally differ from each other. In the context of maps, the temporal component is added to a map to emphasize and observe the gradual evolution of a certain monitoring phenomenon, such as changes in spatially numerical variables (for example, environment, population, mobility, land use, etc.) with respect to a static geographic area. Map animations generally consider dynamic time while space is static. Map animation helps to see patterns or trends that emerge as time passes, depicting meteorological or climate events, natural disasters, historical events and other multivariate data. It is particularly helpful to be used in educational settings.","hasChildren":true,"name":"Animation","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV3-7","description":"Sound or audios can be one of the components of a multimedia data representation. A conventional GIS usually conveys visual information, however the integration of audios in mapping could enrich GIS data to other senses. Sound can increase the amount of information that’s communicated to the user through channels other than visual to address the special needs of people with visual impairments or people who cannot use in certain circumstances their sight, such as a driver who cannot look at a map. Approaches to rendering sound information on a map fall into three broad categories: (1) to sonoficate the whole visual presentation (for simple geometric data), (2) to augment a visual system with auditory information (allowing multivariate information) and (3) to display information about the surrounding where a user is. By classifying images and creating additional audio layers that associate each pixel with a specific sound, a GIS can add a new auditory dimension to maps.","hasChildren":true,"name":"Sound","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV3-8","description":"Maps are valuable because they provide a large amount of detail in a small amount of space, and because of their capacity for telling a story. Telling stories through maps began with describing explored lands in great detail against terra incognita. Today, geographic tools, data, and multimedia on the web expand the ability to communicate stories and inform through maps to a broad audience such as journalists, decision makers and educators. Any person with a smartphone or computer can tell a story, using statics maps, or interactive web maps with text, video, audio, sketches, and photographs. Besides the technical skills to clearly communicate with a map (palette of colours, amount of information displayed…), other factors such as narrative processes, the storyboard, place, time, and characters play a crucial role. To be informative, it is important that the correct data is displayed, combining different sources of information combined to create an appealing and accurate map.","hasChildren":true,"name":"Storytelling","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV3-9","description":"Infographics are visual representations of information and data, which can contain charts, diagrams, graphs, tables, maps and lists. The aim of an infographic is to present information that can be absorbed quickly, it is easily understandable and extensively in mass communication, and thus designed with fewer assumptions about the reader's knowledge base than other types of visualizations. The role of maps in an infographic is based on the potential of maps to condense information and to support a narrative. Infographic maps - altogether with an adequate storytelling - should find a simple way to explain current complex issues, providing added value to the infographic, and being an effective and efficient way to communicate.","hasChildren":true,"name":"Infographics","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV3","description":"This concepts covers basic design principles that are used in mapping and visualization, as well as cartographic design principles specific to the display of geographic data. Both page layout design and data display are addressed.","hasChildren":true,"hasParent":true,"name":"Design principles","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV4-1","description":"A thematic map is a type of map especially designed to show a particular theme connected with a specific geographic area. These maps \"can portray physical, social, political, cultural, economic, sociological, agricultural, or any other aspects of a city, state, region, nation, or continent\". Cartographers use many methods to create thematic maps. Five techniques are especially noted: -Choropleth mapping shows statistical data aggregated over predefined regions -Proportional symbols, showing the relative value of attributes -Isarithmic or Isopleth, also known as contour maps -Dots, to show the location of a phenomenon -Dasymetric, which uses areal symbols to spatially classify volumetric data.","hasChildren":true,"name":"Thematic mapping","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV4-10","description":"Conveying uncertainty information is often done through visualization. Uncertainty is often defined, quantified, and expressed using models specific to individual application domains. In visualization however, we are limited in the number of visual channels (3D position, color, texture, opacity, etc.) available for representing the data. Thus, when moving from quantified uncertainty to visualized uncertainty, we often simplify the uncertainty to make it fit into the available visual representations. (After Potter et al., 2012). The seven challenges as formulated by MacEachren et Al. (2005) are still there to be tackled.","hasChildren":true,"name":"Visualization of uncertainty","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV4-2","description":"Relief can be represented in a two-dimensional map either through contour lines or through a raster format gridded array of elevations. Contour lines connect points of equal elevation. At regular intervals index contours are marked with elevations so a reader can more easily determine the elevation of surrounding locations. They are the preferred method for analogue topographic maps. The grid approach is used in digital mapping and known as a digital elevation model (DEM), where each raster cell represents an elevation. Scaling of the cell z value in relation to the x and y value results in terrain exaggeration, which aids visualization of topography.\r\nDEMs are used for terrain analysis and can be used to obtain derivatives such as slope and aspect. DEMs are obtained by interpolating point elevation observations, which are historically retrieved from surveyed point data (e.g. GPS locations), but more recently from LiDAR and/or Structure from Motion point clouds. TIN (triangular irregular network) analysis is commonly used for point data interpolation, in order to derive a continuous elevation surface.","hasChildren":true,"hasParent":true,"name":"Representing terrain","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV4-3","description":"Multivariate descriptive displays or plots are designed to reveal the relationship among several variables simultaneously. Bivariate and multivariate maps encode two or more data variables concurrently into a single symbolization mechanism. Their purpose is to reveal and communicate relationships between the variables that might not otherwise be apparent via a standard single-variable technique. There are basic characteristics of the relationship among variables, such as the forms of the relationships, the strength of the relationships, and the dependence of the relationships on external (usually to the pairs of variables being examined) circumstances. Therefore, these multivariate plots or maps are inherently more complex, though offer a novel means of visualizing the nuances that may exist between the mapped variables. As information-dense visual products, they can require considerable effort on behalf of the map reader, though a thoughtfully-designed map and legend can be an interesting opportunity to effectively convey a comparative dimension. Examples of multivariate plots include enhanced 2-D scatter diagrams, 3-D scatter diagrams, contour, level, and surface plots, and high-dimensional data plots","hasChildren":true,"name":"Multivariate displays","selfAssesment":"Completed (GI-N2K)
\r\n\r\n"},{"code":"CV4-4","description":"Visualization of change and movement across space and time is of increasing interest to researchers and geospatial practitioners. The visualization process of temporal data has four steps: (1) time values to be visualized, (2) point of view on time, that identifies the characteristics of the temporal values to be visualized, (3) time space: define the displayable space of the time values and (4) point of view on the visualization space, the implementation of the perceptible forms of time. The visualization of spatio-temporal data can be done in many different ways such as multi-panel plots (maps), time-series plots (graphs), space-time plots (graphs), 3D Virtual Reality (Computer generated artificial environment), animations (production of consecutive images), and tables. Spatiotemporal data comprises three important components: geographic location, temporal information and the thematic attributes describing a real-life phenomenon.","hasChildren":true,"hasParent":true,"name":"Visualization of temporal geographic data","selfAssesment":"
Completed (GI-N2K)
"},{"code":"CV4-5","description":"Dynamic and interactive displays refers to a situation where a display with a cartographical data representation changes in real time in response to user's actions","hasChildren":true,"name":"Dynamic and interactive displays","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV4-6","description":"Web mapping is the process of designing, implementing, generating and delivering maps on the World Wide Web. Dissemination via the web opens new opportunities: realtime maps, cheaper dissemination, more frequent and cheaper updates, personalized map content, distributed data sources and sharing of geographic information. Technical restrictions cause challenges like low display resolution and limited bandwidth,( in particular with mobile computing devices with small screens and using slow wireless Internet connections), copyright and security issues, reliability issues and technical complexity. Today's web maps can be interactive and integrate multiple media. So interactivity, usability and multimedia issues also play a role.","hasChildren":true,"name":"Web mapping","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV4-7","description":"Virtual reality or virtual realities (VR), also known as immersive multimedia or computer-simulated reality, is a computer technology that replicates an environment, real or imagined, and simulates a user's physical presence and environment in a way that allows the user to interact with it","hasChildren":true,"name":"Virtual and immersive environments","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV4-8","description":"An Augmented Environment can be experienced through different sets of Augmented Reality (AR) technologies, including mobile displays (tablets and smartphone screens), computer monitors, or Head-Mounted Displays (HMDs), among others. AR is a technology that layers computer-generated enhancements atop an existing reality to make it more meaningful through the ability to interact with it. AR offers the integration of digital information and imagery onto the real world in real-time. In order to broaden the vision beyond this definition, AR can be described as systems having the following features: (1) combines real and virtual; (2) interactive in real-time; and (3) registered in 3D, allowing other technologies, such as mobile technologies, monitor-based interfaces, monocular systems to overlay virtual objects on top of the real world. Currently, AR applications use the camera provided by mobile devices to produce a live view of the real world in combination with relevant, context-appropriate information such as text, videos, or pictures.\r\nThere are lots of applications and systems in the market that provide AR functionality, making it difficult to classify and name them all. Some of them are related to the real physical world and others with the abstract, virtual imagery world. Sometimes it is not easy to figure whether it is an AR, as often AR is defined as Virtual reality (VR) with transparent HMDs. In general, the concept is to mix reality with virtual reality, including information and overlay over the real world through HMDs such as they seem apparent as one environment. The virtual objects can react accordingly with the camera's movement as it is registered concerning the real world, which is also the central issue of AR.","hasChildren":true,"name":"Augmented environments","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV4-9","description":"Cartographers have recently become involved in extending geographic concepts and cartographic design approaches to the depiction of non-geographic data archives, using so-called spatialized views of information spaces. Spatializations differ from ordinary data visualisation and geovisualisation in that they may be explored as if they represented spatial information. (Fabrikant, S.I., 2003). As definitions of spatialization can be found: Spatializations are computer visualizations in which nonspatial information is depicted spatially (Montello et al., 2003). Spatialization is the transformation of high-dimensional data into lower-dimensional, geometric representations on the basis of computational methods and spatial metaphors. (Skupin 2007)","hasChildren":true,"name":"Spatialization","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV4","description":"This concept addresses mapping methods and the variations of those methods for specialized mapping and visualization instances, such as thematic mapping, dynamic and interactive mapping, Web mapping, mapping and visualization in virtual and immersive environments, using the map metaphor to display other forms of data (spatialization), and visualizing uncertainty. Analytical techniques used to derive the data employed in these graphic representations are discussed in Knowledge Area AM Analytical Methods and Unit DN2 Generalization and aggregation.","hasChildren":true,"hasParent":true,"name":"Graphic representation techniques","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV5-2","description":"Standards for map services were set by OGC and ISO, called WMS and WMTS. Producing map images on the web from a cartographic image in a GIS application is called \"publishing\". Making a web \"map\" in the broader sense of constructing data representations for Storytelling or Geo-gaming is still under development. It requires a mix of applying the map Design principles and Graphic presentation techniques, possibly in combination with software scripting.","hasChildren":true,"name":"Web map making","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV5-3","description":"Traditional \"map\" making, as opposed to the mapmaking in neogeography, focuses on reliable and reproducible products, based on expertise of high definition printing in many colours on analogue media of geodetically well-constructed images.","hasChildren":true,"name":"Traditional map making","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV5-4","description":"The aspects of reproduction of a data representation depend on the nature of the representation: is it analogue (a paper map, a mock-up) or is it digital? In the case of a paper map, its digitalisation with high fidelity is an essential step. With a source in digital form, reproduction can be a matter of the right printer. Alternatively, the source could be disseminated as a file or as a web service. If representations are dynamic and/or interactive the possibilities depend on the construction of the representation. The ease of dissemination of digital files should not result in copyright breach. Aspects: Digitalization techniques for analogue sources, Printing ( 2D, 3D), Dissemination ways, Construction of the data representation, User needs specification, Copyright issues","hasChildren":true,"name":"Map reproduction","selfAssesment":"GI-N2K
"},{"code":"CV5","description":"This concept addresses map production and reproduction, as well as computation issues that relate to those workflows.","hasChildren":true,"hasParent":true,"name":"Map production","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV6-1","description":"The potential of maps as a way to show or exert power over the population was early understood by ruling classes. A map expresses a claim by the inclusion or exclusion of map elements and how these elements are visually related and/or depicted on the map. So, the world could be modeled through the careful choice of content arranged graphically at a specific scale and in specific formats. Therefore, maps embody and project the interests of their creators. The “new cartographies” declare that maps are redefined as socially constructed arguments based upon consistent semiotic codes. Nowadays, the rise of costless, powerful and accessible tools for creating maps, put power on the side of individuals or groups of individuals with few organisation (crowdsourced data collection or VGI) capable of representing their world views. In addition, monitoring people, places or nature, for instance, should also be seen as another way to show the increasing power of maps. Surveillance mechanisms for tracking populations used by rulers, or the use of extended technologies like Google Earth by environmental organisations to track the Amazonian forest, constitute two examples of the particular use of maps to exert control over human beings or to press governments for taking specific actions, respectively.","hasChildren":true,"name":"The power of maps","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV6-2","description":"Maps today help us locate the nearest gas station or ATM on our in-car navigation system, but this use of locating what is near or surrounds a location is not new. Maps from pre-historic times provided important locational information – what was where and how to get from place to place. A map can be a relatively simple iconic device, which can be read and interpreted with only a little training. These graphic representations of the real world could be traced in sand or painted on a cave wall and shared through time. Maps even preceded written language and number systems and are found in some format in most cultures through time as a graphical language. Learning to read this language and interpret it without ambiguity is not as simple as first suggested. This complexity has increased as technology has allowed creation of 3D and 4D interactive maps which allow anyone with internet access the ability to investigate different places, topics and times and produce their own map. Today the ability to read and interpret maps is increasingly important as industry, business and government communicates within their organization and the public using maps. Becoming aware of what a “map” shows depends partly on what the senses can register of the representation as a whole. It also depends on recognition of elements in the representation that are meaningful to the observer in the sense that these elements are credible indicators of spatial features. Based on that recognition, the nature of these elements and their spatial pattern might infer thoughts about historic or ongoing processes. This interpretation will be influenced by the expertise and needs of the observer.","hasChildren":true,"name":"Map reading and interpretation","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV6-3","description":"Assessment of the usability of a data representation is about how useful it is to users. Therefore it is a test of the success of the representation design, a test of the skills of the \"map\" maker and a test for the reliability of the underlying data.","hasChildren":true,"name":"Usability analysis","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV6-6","description":"Spatial thinking is thinking that finds meaning in the shape, size, orientation, location, direction or trajectory, of objects, processes or phenomena, or the relative positions in space of multiple objects, processes or phenomena. Spatial thinking uses the properties of space as a vehicle for structuring problems, for finding answers, and for expressing solutions\" Aspects: recognizing spatiality in a collection of things; translation of the collection to a pattern of elements; recognizing structure (relations between the elements in a pattern); recognizing process (or changes over time in patterns or structures)","hasChildren":true,"name":"Spatial thinking","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV6-8","description":"Ethics is about the question if behaviour is right or wrong in a social context. In dealing with geodata, a person can do the wrong thing with respect to laws (e.g. disclose secrets, disregard privacy, copyright infringement) or to professional standards (e.g. use bad data, forget about the colour blind, downplay unpleasant details). Aspects: breach of legal standards; breach of professional standards","hasChildren":true,"name":"Map ethics Legal and privacy issues","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV6","description":"Geodata visualisation are always made with a certain purpose. The role and understanding of such graphical representation is an important field of research. Besides theories that underpin evaluation approaches and their findings the visualisation may also be confronting. The more realistic the presentation and especially when it includes human/personal related data the ethical dimension of the visualisation play a major role. Usability of visualisations has also an impact on spatial thinking as has been proved by scholars.","hasChildren":true,"hasParent":true,"name":"Usability of maps","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA","description":"Proper design of geospatial applications, models, and databases and the validation and verification of design activities are critical components of work in all areas related to GIS&T. Design failures can negate well-intentioned efforts to apply concepts and technology to solve real-world problems. While sharing a number of concerns with general systems analysis, the unique and complex spatial characteristics of geospatial information provide significant additional challenges. The focus of this knowledge area is on the design of applications and databases for a particular need. The design of general-purpose models and tools (e.g., raster and vector) is covered in Knowledge Area: Data Modeling (DM). In the context of specific implementations, design activities fall into three general classes:\r\n1. Application Design addresses the development of workflows, procedures, and customized software tools for using geospatial technologies and methods to accomplish both routinary and unique tasks that are inherently geographic.\r\n2. Analytic Model Design incorporates methods for developing mathematical models, spatial models and data processes. The design of the analytic model is often influenced by decisions that are made about data models and structures.\r\n3. Database Design concerns the optimal organization of the necessary spatial data in a computer environment in order to efficiently sustain a particular application or enterprise.","hasChildren":true,"hasParent":true,"name":"Design and Setup of Geographic Information Systems","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA1-1","description":"This concept deals with the importance of having a list of prioritized requirements as a first step to ensure a smooth and successful implementation of a GIS project.. It entails the different methodologies and approaches to ensure a GI system covers all functional and nonfunctional requirements. Requirements are not only derived from business workflows but it is advisable to gather direct input from potential users that will be translated into requirements. However, there is a need to clearly rank the importance of the requirements gathered to ensure the GI system is manageable and in line with the intended use of the GI system, in opposition with the specific interests of a particular user or ambiguous requirements. Therefore, the documentation, traceability and evaluation of requirements after the implementation are as relevant as the initial gathering of requirements to give consistency to the designed system.","hasChildren":true,"name":"Requirements gathering and analysis","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA1-2","description":"The internal process of documenting a task or a process is about “how” it is implemented and “what” is implemented. Documenting is particularly helpful if a breakdown occurs, such as when an expert working in a task leaves her job or to substitute one task in a set of interrelated processes by another. Documentation provides consistency for the taskand allows its monitoring, analysis and revision during a project. \r\nThere are different methods for documenting a task to transform tacit knowledge into explicit knowledge. Therefore, the task should be documented by describing it in video format and using visual tools that allow documentation, or the maintenance of a field diary.\r\nIn particular cases, the creation of user guides or manuals could be considered a subset of a process description particularly addressed to external users. A user manual should take into account the target users to adapt its content to them.","hasChildren":true,"name":"Methods of process description and documenting","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA1-4","description":"A workflow is a sequence of operations that altogether perform a complex, sophisticated or repetitive operation or activity. No matter the workflow type, a workflow is defined in a declarative language, either text-based or visual, and stored in a workflow document to ease sharing and maintenance. In GI systems, a workflow can be seen from distinct perspectives. One of the most well-known GI workflow types is spatial data modelling. A model is specified as a combination of processing tools that manipulate and transform the spatial data required by the model. The order in which the processing tools, inputs, and outputs are organised in a workflow will determine the results and to what extent the spatial question is addressed. However, workflows in GI systems are not only related to spatial data modelling and transformation. There are cases where certain processes in GI systems should be designed in terms of software and hardware requirements, actors needs, organisational aspects or resource usage and demand. How can people’s work contribute to define the stages of a GI architecture? How much time does a regular user spend working with spatial data? How complex is the process going to be? The definition of this sort of workflows can help, for example, in designing an optimal architecture for a GI system in a particular enterprise configuration. \r\nWhether the workflow defines specific steps to process spatial data or the stages and details to implement an enterprise GI system, having a clear idea over each stage's inputs and outputs helps GI systems to be organised, consistent and reliable. In summary, high-level workflows like business workflows put together systems, components and actors that are part of a process or operation. They represent an abstract view, focused often on organisational, functional and resources usage aspects. Conversely, low-level workflows refer to a series of executable activities that carry out data transformations, models or spatial data analysis. Examples are code scripts, specified as sequences of commands in a programming language, and graphical workflows through, for example, the Model Builder in GI systems which are enacted by workflow engines.However, workflows in GI systems are not only related to spatial data modelling and transformation. There are cases where certain processes in GI systems should be designed in terms of software and hardware requirements, actors needs, organisational aspects or resource usage and demand. How can people’s work contribute to define the stages of a GI architecture? How much time does a regular user spend working with spatial data? How complex is the process going to be? The definition of this sort of workflows can help for example in designing an optimal architecture in an enterprise configuration for a GI system. \r\nWhether the workflow defines specific steps to process spatial data or the stages and details to implement an enterprise GI system. Having a clear idea over each stage's inputs and outputs helps GI systems to be organised, consistent and reliable. In summary, high-level workflows like business workflows put together systems, components and actors that are part of a process or operation. They represent an abstract view, focused often on organisational, functional and resources usage aspects. Conversely, low-level workflows refer to a series of executable activities that carry out a complex task, service or model. Examples are code scripts, specified as sequences of commands in a programming language to carry out data transformations and spatial models and spatial data analysis; and graphical workflows through, for example, the Model Builder in GI systems which are enacted by workflow engines.","hasChildren":true,"name":"Workflow definition and consideration in GI systems","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA1-5","description":"Software and information technology are integral to any GI systems or projects, from the storage and handling of spatial data to its analysis, visualization and sharing. Therefore, the use of well-known software design and engineering techniques and methods to develop efficient, reliable, and easy-to-maintain software applications in the GIS realm is more important than ever. \r\nAmong the modern software design and engineering techniques, Agile software development methodologies like Scrum stands out. The common rationale of the Agile methods is to split a large software project into many functional pieces of software that help the software engineering team to translate their development efforts into quick prototypes, and eventually reach the final product. Therefore, the constant feedback and validation of the user’s requirements in short, iterative development circles (i.e sprints) are the main advantages of the Scrum methodology.","hasChildren":true,"name":"Software design and engineering","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA1-6","description":"User interface and usability of a GIS system","hasChildren":true,"name":"User interface and Usability","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA1-9","description":"Geodesign is a design and planning method along with geospatial modelling and technology, and simulations informed by geographic contexts to facilitate informed decisions and the creation of design proposals. A geo-design process is a problem-based, iterative process bounded by specific (geographic) constraints characterised by a collaborative effort.","hasChildren":true,"name":"Geodesign","selfAssesment":"Completed
"},{"code":"DA1","description":"This concept encloses a set of activities and workflows to ensure that the implementation of a GIS system in an organization or project is correctly planned and designed according to the particularites, user requirements and current conditions of the project ahead. In general system design is the process to promote successful GIS in an enterprise environment. As a GIS system has a direct influence on the information technology department (IT), the system design tells the organizacion how the current infrastructure can or must support the planned GIS. This process builds a set of specific recommendations on hardware and network needs based on the number of projects that depend on the GIS solucion, as well as the projected business needs and user requirements. \r\nGIS architects through the system design process need to take into account and identify several conditions: a) infrastructure requirements, b) the network communication capacity, c) hardware and software procurement requirements and, d) software development and data acquisition needs. \r\nHaving a well-defined and successful GIS deployment is not only a matter of what data or software the organization should acquire. The process of system design aligns identified business requirements (user needs/requirements) derived from business strategies or project aims, goals, and stakeholders (business processes) with identified business information systems infrastructure technology (network and platform) recommendations. \r\nThe process starts with identifying business needs, including the identification of users locations, required information, data, resources or products. The business needs are generally considered as project workflows that help the GIS architects to identify the expected data traffic and computing demand associated with each transaction, being a transaction the work unit used to translate business requirements into associated server and network loads.\r\nWithout carrying out a proper system design, a GIS system can lead to an implementation and deployment failure, deriving in unfulfilled expectations and high costs in terms of human resources and financial matters.","hasChildren":true,"hasParent":true,"name":"System design","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA2-1","description":"Project management includes the planning, organization, coordination, execution, monitoring, controlling and closing of the activities and resources - human and economic - for the timely achievement of clearly defined objectives forming a project. For the success of a project, a project manager will assure an efficient use of resources and a proper execution of tasks to deliver value to users and “clients” of products and services. The Project Management Body of Knowledge (PMI) defines “project management” as “the application of knowledge, skills, tools, and techniques to project activities to meet requirements”, being EO*GI projects are another type of information technology projects. PMI reflects different areas to take care of by project management. These areas are: Integration, Scope, Time, Cost, Quality, Human Resource, Communications, Risks, Procurement and Stakeholder. There are a variety of tools and techniques used in the areas identified by PMI, just to name a few Gantt chart, Program evaluation and review (PERT) analysis, AGILE project management, etc. that will help in project management.","hasChildren":true,"name":"Project management","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA2-2","description":"This concept embraces the factors that could affect a GI system / project and could constitute obstacles to success or even decide a project is not doable. In order to ensure the success of a GI system or a GIS project there are several criteria to take into account from the very beginning of the conception of the GI system or project. A feasibility study may encompass different perspectives (economic, legal, technical, operational or scheduling ) to inform whether or not a project is worth the investment. An organisation should list the foreseen costs from these five perspectives listed above and the benefits (tangible or intangible) of implementing a system/project. Existing resources already available in-house and internal strategic plan in place could be critical to decide to undertake a project or not. The table below presents a non-exhaustive list of criteria and under which perspectives they should be examined.\r\nFeasibility analysis should include a pilot study to evaluate and improve the system / project proposed.","hasChildren":true,"name":"Feasibility analysis","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA2-8","description":"This concept discusses the technical, organizational and monetary advantages and disadvantages of proprietary versus open source software. GIST industry and research are slowly but consistently moving toward the openness of software. Open software entails some clear advantages such as continuous development of new applications, building community of developers and users, starting a project even if limited funding is available, increasing the chances of a project’s sustainability, to name a few. On the other side, proprietary initiatives in GIST are keeping their roots to the ground by developing cutting-edge tools to handle challenging and critical environments in large private sectors and public administrations. Advantages of proprietary software include more stable software, a well developed documentation and personalised customer support service. Both open and proprietary geospatial software solutions can co-exist by applying the appropriate IPR licences for each type of solution. The future trend is to balance how proprietary and open source geospatial software complement each other and find synergies in increasingly complex and large projects.","hasChildren":true,"name":"Proprietary and open source software","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA2","description":"To design, build, and maintain a GIS, sufficient resources (e.g., labor, capital, and time) must be secured. Resource planning consists of the allocation and use of in-house resources (people, equipment, tools, rooms, etc.) to achieve the maximal efficiency of those resources. These resources are required for a variety of system elements, including design, software purchase, labor, hardware, and facilities. The crucial task is to determine whether the project is worth the required resources.","hasChildren":true,"hasParent":true,"name":"Resource planning","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA3-1","description":"The ecosystem of GIS software architectures has evolved substantially in recent years to include a variety of options ranging from desktop GIS, server-based and component-based architectures to Web-based, cloud-based, mobile-based approaches. Aligned with the main trend, geospatial software architectures or infrastructures are also moving from desktop architectures to more cloud based or server based options to meet ever-increasing requirements of interoperability, interdisciplinary work and computational power for processing large data sets and derived products. Cloud-based architectures also enable on the fly visualization of computed geospatial products, as complementary visualisation and mapping tools are seamlessly integrated into modern cloud-based based architectures. Usage of a particular architecture is fully dependent on the nature, size, requirements, functionalities, and available resources of a given project or task. Desktop and server based applications are particularly suited for small sized projects and startups while enterprise based applications are meant for larger sized projects. Cloud based infrastructure can be useful for varying sizes of projects in which the computational infrastructure is fully outsourced.","hasChildren":true,"name":"Major geospatial software architectures","selfAssesment":"In progress (GI-N2K)
\r\n\r\n"},{"code":"DA3-2","description":"Interoperability of GIS infrastructure or architecture ensures the consistent and uninterrupted usage of data and functionalities across platforms and systems. Components or tools residing on distinct platforms can “talk” to each other without friction. Interoperability is a central characteristic, especially important in distributed systems and architectures. It can be applied to different levels or layers of a system, i.e. infrastructure level, data level, business logic level, etc. For example, standard spatial data formats and protocols are especially relevant for handling GIS data across multiple systems and platforms, regardless of their underlying software architecture. This is particularly important in large-scale, collaborative projects involving various teams using heterogeneous GIS architectures. Most software providers, developers communities and standardisation bodies and committees are striving to make their architectures interoperable in an open manner, so proprietary standards and protocols are a potential hindrance to this initiative.","hasChildren":true,"name":"Interoperability","selfAssesment":"
Completed (GI-N2K)
"},{"code":"DA3-3","description":"This concept considers general architectural patterns like SOA, ROA, Web Services, etc.","hasChildren":true,"name":"Architectural Patterns","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA3-4","description":"- WebGIS, - technical pecularities of spatial data infrastructures - standardiced GI services for SDI: WMS, WFS, CSW, Transformation Services, SOS, WPS etc., - other map services and interfaces","hasChildren":true,"name":"WebGIS, SDI services, map services","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA3-5","description":"This concept deals with Reference Model of Open Distributed Processing (RM-ODP), its standards, viewpoints modeling and the RM-ODP framework","hasChildren":true,"name":"Reference Model of Open Distributed Processing","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA3-6","description":"Cloud computing provides an on-line computing transparent resource to the user, since a user doesn’t notice almost no difference between working on her own computer or the cloud. Owned and managed by infrastructure providers, cloud computing entails advantages (concurrent access by many users, software updates hosted in the cloud, cost-efficiency or outsourced maintenance in the cloud) and disadvantages (loose of control, network Connection Dependency or security breaches ). On the other side, grid computing is a full network of computers and data working together so functioning as a supercomputer. Grid computing presents advantages such as shorter resolution of complex problems, the ease of organizational collaboration or a better use of existing hardware.","hasChildren":true,"name":"Cloud and Grid computing","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA3-7","description":"Within this concept solutions based on Desktop GIS and GIS libraries will be compared and contrasted","hasChildren":true,"name":"Desktop GIS, GIS libraries","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA3","description":"This concept describes the major geospatial software architectures available currently and choices when designing GI applications and systems, including desktop GIS, server-based, Internet, and component-based custom applications.","hasChildren":true,"hasParent":true,"name":"Architectural design of a GIS system","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA4-1","description":"- Compare and contrast the relative merits of various textual and graphical tools for data modeling, including E-R diagrams, UML, and XML - Create conceptual, logical, and physical data models using automated software tools - Create E-R and UML diagrams of database designs","hasChildren":true,"name":"Modeling tools","selfAssesment":"GI-N2K
"},{"code":"DA4-2","description":"Within an initial phase of database design, a conceptual data model is created as a technology-independent specification of the data to be stored within a database.","hasChildren":true,"name":"Conceptual models","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA4-3","description":"A logical data model expresses the meaning context of a conceptual data model, and adds to that detail about data (base) structures, e.g. using topologically-organized records, relational tables, object-oriented classes, or extensible markup language (XML) construct tags","hasChildren":true,"name":"Logical models","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA4-4","description":"A physical data model documents how data are to be stored and accessed on storage media of computer hardware","hasChildren":true,"name":"Physical models","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA4","description":"The effective design of geospatial databases should follow the established methods and principles of database modeling and design developed in computer science. The basic method is a three-step process generally called the conceptual, logical, and physical models transforming the application from very human-oriented to machine-oriented. Several standards and software tools exist to aid the process of database design.","hasChildren":true,"hasParent":true,"name":"Database design","selfAssesment":"In progress (GI-N2K)
"},{"code":"DM","description":"This knowledge area deals with representation of formalized spatial and spatio-temporal reality through data models and the translation of these data models into data structures that are capable of being implemented within a computational environment (i.e., within a GIS or more likely within a spatial database). Data modelling is a crucial issue as it defines the content of a spatial database and usefulness of these content (data) for certain applications. Data Modelling is performed using system neutral languages like UML (or more seldom ER-diagrams). These conceptual models have to be transferred to logical models (i.e. tables of a database). Data is stored in spatial databases which are normally organized in an object relational way. For certain types of data specific databases are used, like triple stores, NoSQL DBs, Array DBs etc. For data modelling quite a number of ISO standards are available for deriving the conceptual model as well as for rules for application schemas, spatial schemas, temporal schemas, Quality principles, encoding, 3D modelling (CityGML) etc. Data models provide the means for formalizing the spatio-temporal conceptualizations. Examples of spatial data model types are discrete (object-based), continuous (location-based), dynamic, and probabilistic. Mastery of the objectives presented in this knowledge area require knowledge and skills presented in the bodies of knowledge of allied fields, including computer science (ACM/IEEE-CS Joint Task Force, 2001) and information systems (Gorgone & Gray, 2000; Gorgone & others, 2002).","hasChildren":true,"hasParent":true,"name":"Data Modeling, Storage and Exploitation","selfAssesment":"In progress (GI-N2K)
"},{"code":"DM1-1","description":"This topic includes the main basic database concepts: - Database, definition and overview - Database management system, definition and overview - Relational databases, overview - Object-oriented databases, overview - Object-relational databases - NoSQL databases, general overview - NoSQL databases, examples triple stores, array databases, others (overview)","hasChildren":true,"name":"Overview on database concepts","selfAssesment":"GI-N2K
"},{"code":"DM1-2","description":"The Relational Model is the most important database model, therefore it is explained in more detail here: - Basic concepts (tables, tuples, etc.) - Relation to relational algebra (RA), basics of RA - Constraints (key, domain, referential integrity) - Relation to entity relation (ER) model, basics of ER","hasChildren":true,"name":"The Relational Model","selfAssesment":"GI-N2K
"},{"code":"DM1-3","description":"Relational databases and database management systems are essential for GIS in consequence the important issues have to be treated here: - General aspects, basic architecture of a DB, advantages, features - DBMS concepts and functionalites (transactions, locks, multiuser access etc.) - Database design, techniques - Database administration - Normalization (1NF - 3NF) - Example of a database design","hasChildren":true,"name":"Relational Databases, Database Managements Systems and Database principles","selfAssesment":"GI-N2K
"},{"code":"DM1-4","description":"Database queries and especially spatial queries require specific data structures to be performed satisfactory Relevant is: - Motivation, examples of typical non-spatial and spatial queries - Trees, B-tree, R-tree, Q-tree - Graphs, overview and relation to databases","hasChildren":true,"name":"Data Structures and Indices for Databases","selfAssesment":"GI-N2K
"},{"code":"DM1-5","description":"Big data like imagery but also for example GML data sets need compression to be accessed / transferred in an acceptable time. Therefore some compression techniques have to be taught: - Motivation, examples of data sets which need compression - General introduction, vector - / raster data compression, compression lossless, lossy - Popular compression techniques, LZW (Lempel-Ziv-Welch) encoding, Huffman encoding - Techniques for raster data, runlength encoding, JPEG coding, wavelet etc. - Techniques for the reduction of vector data (Douglas Peuker etc.) - Data formats, overview and relation to compression techniques","hasChildren":true,"hasParent":true,"name":"Data compression techniques","selfAssesment":"GI-N2K
"},{"code":"DM1-6","description":"SQL is the \"standard\" to perform spatial and non-spatial queries in databases. That means each student in a GI related course has to be familiar with the main aspects if it: - Motivation, history, overview - Data definition language DDL - Data manipulation language DML - Data control language DCL - Spatial extensions of SQL","hasChildren":true,"name":"SQL and its usage for data handling, spatial extensions to SQL","selfAssesment":"In progress GI-N2K
"},{"code":"DM1-7","description":"UML is the standard for describing the schema related to GI models, but also user requirements, workflows etc. can be described in UML using the UML diagrams: - Motivation, background, purpose - Use case diagrams - Class diagrams - Sequence diagrams - Activity diagrams","hasChildren":true,"name":"UML introduction and class diagrams","selfAssesment":"In progress GI-N2K
"},{"code":"DM1-8","description":"XML knowledge is an important bases for understanding GML. Moreover XML tools like XSLT are important to transform XML or GML data sets into other XML based formats like SVG or others. Important issues: - Motivation, purpose - Relation to HTML - XML document structure - XML syntax, elements, attributes and namespaces - xlink, xpath and XSLT - XML DTD - XML schema","hasChildren":true,"name":"XML introduction","selfAssesment":"In progress GI-N2K
"},{"code":"DM1-9","description":"The long term storage of GI data in general is based on spatial databases. Therefore the following is essential for a GI course: - Relation between GIS and DB / \"Long transactions\"- Dual concepts - Characteristics of spatial databases - Spatial data in object relational databases - Spatial extensions of DBs, overview","hasChildren":true,"name":"Database concepts in GIS and Principles of spatial databases","selfAssesment":"GI-N2K
"},{"code":"DM1","description":"This unit includes the basics for data modelling, storage and exploitation. Data modelling is one of the most important activities in conjunction with Geographic Information / GIS as it determines how the data can be used and if the requirements from applications are fulfilled. Data modelling can be done in conjunction with the database, e.g. through ER diagrams or according to the ISO 191xx standards by using UML. The costs of data acquisition can be tremendous, therefore the data represents an enormous value. This value has to be conserved through a safe long term data storage. Therefore databases and especially relational and object relational databases are crucial. For a proper storage and query of geographic information databases are extended with specific data types and data structures. As data sets can be very large suitable compression techniques became important especially in the context of accessing and delivering geographical data, e.g. through services. XML based modeling languages for encoding also play and important role in this context","hasChildren":true,"hasParent":true,"name":"Foundations for Data Modelling Storage and Exploitation","selfAssesment":"In progress (GI-N2K)
"},{"code":"DM2-1","description":"GI standards, mainly from ISO and OGC are essential nowadays. Moreover also an overview on ICT standards from W3C or OMG are important as well as some understanding of standardization processes. In detail: - Motivation for standards, examples from daily life - Overview on GIS and relevant ICT standardization bodies and selected standards - De jure and De facto standards, obligation, reasons for the usage of standards - Standardization within ISO - Standardization within OGC, relation to ISO - Examples of ISO 191xx standards","hasChildren":true,"name":"Overview on relevant standards and standardisation bodies","selfAssesment":"GI-N2K
"},{"code":"DM2-2","description":"Conceptual data modeling is a key skill for GI people. (see relations to other topics) The following therefore is important: - Overview on the relevant standards like conceptual schema language, Rules for application schema - Examples of conceptual schemas","hasChildren":true,"name":"The principle of conceptual data modelling according to ISO","selfAssesment":"In progress GI-N2K
"},{"code":"DM2-3","description":"Geometric modelling is an important subtask of conceptual modelling and requires the following basics: - Overview of ISO 19107 - spatial schema - Overview of ISO 19125 - simple features - Examples of the usage of spatial schema and simple feature elements for feature class definitions - Relation to GML - Relation to DBs","hasChildren":true,"name":"Geometry data types according to spatial schema and the simple feature specification","selfAssesment":"GI-N2K
"},{"code":"DM2-4","description":"Also temporal aspects have to be considered within conceptual modelling. This also requires basics: - Motivation, examples - Temporal variability of features (move, change of structure or geometry) - Overview on ISO 19108 temporal schema - Examples of modeling temporal aspects","hasChildren":true,"name":"Temporal data types according to temporal schema","selfAssesment":"In Progress GI-N2K
"},{"code":"DM2-5","description":"Conceptual models of course have to be implemented, in general in a GIS (which is often proprietary), or in a database (which can be standard based) ,therefore here the implementation in a database is treated: - Repetition of conceptual and logical models - Examples of the transferring of a conceptual model to a logical (database) model","hasChildren":true,"name":"Transferring conceptual models to logical models","selfAssesment":"In progress (GI-N2K)
"},{"code":"DM2-6b","description":"Metadata is considered as very important for the usage as well for the search for Geodata Relevant basics are: - Motivation, importance of data quality as part of metadata - Metadata in an spatial data infrastructure with many There are quite a number of relevant standards for GI courses. Some are listed here, others might be considered, depending on the background of the course: - Select other standards and explain them, Important are: - ISO 19141 Schema for moving features, ISO 19142 Web Feature Service or others - 19109 - Rules for application schema - Selection of other standards is depending on the background of the course","hasChildren":true,"name":"Other standards","selfAssesment":"In progress GI-N2K
"},{"code":"DM2-7","description":"GML is the most important standard for the transfer of Geodata as it allows to transfer the schema information as well as the data. Important issues: - Motivation, Importance of a Geography Markup Language - History of GML, Overview 19136 - Geography Markup Language - Relation to spatial schema - Supported features in GML (Topology, 3D ...) - Structure of GNL, profiles, application schemas etc. - Transfer of models and of data - Examples","hasChildren":true,"name":"Introduction to GML","selfAssesment":"In progress (GI-N2K)
"},{"code":"DM2-8","description":"3D Models, especially 3D city models are becoming more and more important. CityGML is the most important standard within the GI domain to describe City models semantically and geometrically. Relevant issues: - Motivation, Usage of CityGML - Relation to GML - Coherence of semantics and geometry - Principles of modeling - Level of detail concept - CityGML vs KML - Examples","hasChildren":true,"name":"Introduction to CityGML","selfAssesment":"In progress GI-N2K
"},{"code":"DM2","description":"This unit includes the essentials of relevant standards for spatial data modelling. A number of ISO and OGC standards are available for deriving the conceptual model as well as for rules for application schemas, spatial schema provides data types for geometry models in various forms, Point, line, area, body based, temporal schema allows to consider temporal dimensions, Quality principles can be used to describe the quality of geodata, encoding standards (mainly GML) allow the standard based transfer of data and data models, CityGML allows a standard based 3D modelling, etc.","hasChildren":true,"hasParent":true,"name":"Standards for Spatial Data Modeling","selfAssesment":"In progress GI-N2K
"},{"code":"DM3-1b","description":"There are two basic concepts related to this topic: Features and Fields, or Geo-fields, as named by Goodchild at al. The concept of fields can be differently represented as explained here: - Repetition of basic concepts of Geographic Information Science - Explanation of the concept of continuous fields and the commonly used ways of representing geo-fields - Relation between fields and coverages, an important discretizations of a Geo-field - Types of Coverages","hasChildren":true,"name":"The concept of fields","selfAssesment":"In progress GI-N2K
"},{"code":"DM3-2","description":"The raster data model holds values in a regularly spaced matrix of cells arranged in rows and columns covering a two dimensional space. Rasters are commonly used to store continuous data like colors in an image and height values but they are also used for discrete (thematic) values like land use.","hasChildren":true,"name":"The raster model","selfAssesment":"In Progress (GI-N2K)
"},{"code":"DM3-2b","description":"Grids are on the one hand one important type of caverages and on the other hand Grids are used as basic structure in some applications. Important here is: - Definition of the concept of grid in GIS - Grid as an instance of coverages - Grids as a basic structure for certain applications / medium for aggregation of data - Examples of grid-based data such as Digital Terrain Models (DTM) - Grids in census / statistical data and Geo-marketing applications","hasChildren":true,"name":"Grid representations","selfAssesment":"In progress GI-N2K
"},{"code":"DM3-3","description":"Grid data models can contain millions of discrete values. This leads to very large datasets. Depending on the way values change over the grid, different methods can be used for an optimal (lossy or lossless) data compression. Type of data, computer power needed, application of the data, method of transport and storage all contribute to the choice of compression method.","hasChildren":true,"name":"Grid compression methods","selfAssesment":"In progress (GI-N2K)
"},{"code":"DM3-3b","description":"TINs and Voronoi tessellations are important types of coverages. TINs play a very important role also in Computer graphics. Important here is: - Basics from Graph theory - Definition of Triangulated Irregular Networks (TIN), purpose and applications - TINs and voronoi diagrams as a type of coverages - One important instance of a TIN: Delauney Triangulation - Definition of Voronoi Diagrams, purpose and applications - Relation between Delauney Triangulation and Voronoi Diagram, the \"Dual Graph\" - Examples from applications","hasChildren":true,"name":"TIN and Voronoi tesselations","selfAssesment":"In progress GI-N2K
"},{"code":"DM3-4","description":"While the classical grid structure uses rectangular cells, the hexagonal data model uses hexagons to represent raster data","hasChildren":true,"name":"The hexagonal model","selfAssesment":"In progress (GI-N2K)
"},{"code":"DM3-4b","description":"Linear referencing is 1 dimensional positioning. The position of an object is defined by the distance from the object to the start point along a line. Linear referencing is for example used in railway dispatching systems","hasChildren":true,"name":"Linear referencing","selfAssesment":"In progress GI-N2K
"},{"code":"DM3-5b","description":"Resolution of raster and gridded data - Georeferencing of data, direct and indirect methods (t.b.d.)","hasChildren":true,"name":"Resolution and georeferencing system","selfAssesment":"In Progress (GI-N2K)
"},{"code":"DM3-7","description":"In hierarchical data models data is organized in a tree-like structure. Data are connected with parent-child relations. Hierarchical structures are often used for spatial indexing.","hasChildren":true,"name":"Hierarchical data models","selfAssesment":"In progress (GI-N2K)
"},{"code":"DM3","description":"This unit includes relevant tessellation data models. Besides features (sometimes also called geo-objects) geo-fields play and important role. In recent literature tessellation models are classified as discretizations of fields. In traditional GI literature tessellations are defined as important data structure itself. Tessellation discretise a continuous surface into a set of non-overlapping polygons that cover the surface without gaps. Tessellation data models represent continuous surfaces with sets of data values that correspond to partitions. Important tessellation models are Grids, TINs and Voronoi diagrams.","hasChildren":true,"hasParent":true,"name":"Tessellation data models","selfAssesment":"In progress (GI-N2K)
"},{"code":"DM4-1","description":"This topic includes the basics for feature based modelling. There are a number of standards also relevant for this topic (see relations). The following items should be included: - Definition of a feature (in some literature also called object, or geoobject) and of feature classes respectively. - Aspects of the definition (ID, geometry, topology, thematic, time etc.) - Techniques for the definition of features / feature classes (mainly link, as they are described elsewhere, see relations)","hasChildren":true,"name":"Feature based modelling","selfAssesment":"GI-N2K
"},{"code":"DM4-2","description":"This topic describes the process of Geometric modelling using vector data, means the primitives like points, lines, areas, bodies, or raster data. There is a strong relation to ISO standards (see relations) as they provide basic data types for geometric modelling. Main issues: - Geometric modeling based on vector data - Geometric modeling based on raster data - Conversion between the models - examples, advantages, disadvantages of the models","hasChildren":true,"name":"Geometric modelling","selfAssesment":"In progress GI-N2K
\r\n\r\nIn progress (GI-N2K)
"},{"code":"DM4-4","description":"This topics deals with the definition of an application schema. There are other units which are important for this topic (see Relations). Issues to be included: - Methods to define and describe an application schema (requirement analysis, description of the schema etc.) - Feature attribute catalogues - Domains / data relevant for INSPIRE","hasChildren":true,"name":"Application models based on vector data","selfAssesment":"GI-N2K
"},{"code":"DM4-5","description":"This Topic deals with important application models, which should be chosen with relation to the course (geographically / related to the background of the course) INSPIRE should be treated in any case. In detail: - Overview on important application models relevant for the course, e.g. from topography or environment in the country - Repetition of the principles of Spatial data infrastructures - Overview on the INSPIRE initiative and the goals related - The INSPIRE data model - The architecture of INSPIRE and the necessary services - Domains / data relevant for INSPIRE","hasChildren":true,"name":"Examples of important application models","selfAssesment":"GI-N2K
"},{"code":"DM4-6","description":"This topic is dedicated to the challenges of model based interoperability and related issues, The principles of interoperability are included in DA3-2. In detail: - The challenges of model interoparability (semantics, different modelling of the same features in different models, syntacs) - Overview on IT concepts for schema integration / transformation - Approaches for model integration - Approaches for model transformations, e.g. related to INSPIRE, from the Humboldt project","hasChildren":true,"name":"Model based interoperability, model transformations","selfAssesment":"GI-N2K
"},{"code":"DM4-7","description":"Network models are crucial in some application domains, such as Navigation (roads etc.), but also in utility applications (facilities like pipes etc.) In this topic should be treated: - The network model in the database domain - Graph based NoSQL databases - Topology of network models - Data structures for storing network data - The Dijkstra algorithm - Overview on important applications","hasChildren":true,"name":"Network models","selfAssesment":"GI-N2K
"},{"code":"DM4","description":"This unit includes relevant issues related to vector data models, feature based modelling, applications. Besides imagery data the majority of GI data available is feature based and founded on vector geometry. Topology modeling also is very common nowadays, as many analysis like routing or neighborhood analysis require it. Spaghetti modelling becomes more and more and exception. In every country there are important feature and vector geometry based application models available e.g. in Topography / Cartography. In Europe every GI course should include some information on INSPIRE. As in different application domains different data models are used, sometimes for the same feature types, integration and transformation of models are an important issue also.","hasChildren":true,"hasParent":true,"name":"Vector data model, Feature based modelling, Applications","selfAssesment":"In progress GI-N2K
"},{"code":"DM5-1","description":"- Many geographical phenomena are not defined sharply but uncertain Uncertainty has a number of considerations: - Motivation, background, purpose - Conceptual model of uncertainty - Uncertainty of geographic phenomena (vagueness, ambiguity) - Uncertainty of measurements - Uncertainty of analysis - Uncertainty vs. data quality - Statistical models of uncertainty - Outline of Fuzzy approaches","hasChildren":true,"name":"Basics of uncertainty and its modelling","selfAssesment":"In progress GI-N2K
"},{"code":"DM5-2","description":"Space and time are 2 connected concepts, this topic is dedicated to some basics of modelling time and the temporal dimensions related to features and fields: - Motivation, background, purpose - Changes in time in Entity based and field based representations - A conceptual model of changes in time - Move of objects - Change of structure - Change of geometry - Examples from applications","hasChildren":true,"name":"Modelling time aspects","selfAssesment":"In progress GI-N2K
"},{"code":"DM5-3","description":"Traditionally many GIS used 2D or 2.5 D data models, but in the last decade 3D modeling mainly in form of city models or in the context of Building Information Models (BIM): - Basic concepts of 3D modelling, edge, area, volume models - The workflow of 3D modelling, general aspects, choose of the proper model - Methods of 3D modeling - Principles of Constructive Solid Geometry (CSG) - Principles of Boundary representation (BR) - Principles of Voxel-beased modeling - Comparison of the methods - The concept of BIM, principles and purpose - City models, principles and purpose - Examples / applications","hasChildren":true,"name":"Modelling 3D","selfAssesment":"GI-N2K
"},{"code":"DM5","description":"Traditional raster and vector data models cannot easily represent the more complex aspects of geographic information, such as temporal change, uncertainty, three-dimensional phenomena, and integrated multimedia. A variety of models have been proposed to represent these complexities, including both extensions to existing models and software, and entirely new models and software. During the 1990s, work in this area was largely experimental, but many solutions are now available to practitioners in commercial and open source software. The data models in this unit are based on concepts discussed in Knowledge Area CF Conceptual Foundations.","hasChildren":true,"hasParent":true,"name":"Modelling 3D, temporal and uncertain phenomena","selfAssesment":"In progress (GI-N2K)
"},{"code":"DN3-1","description":"Modification of spatial and attribute data while ensuring consistency within the database, implications of transactions on database integrity, scenarios for periodic changes in GIS database and monitoring the periodic changes.","hasChildren":true,"name":"Database change","selfAssesment":"In progress (GI-N2K)
"},{"code":"DN3-2","description":"Rules for modelling spatial database change, techniques for handling version control, techniques for managing long and short transactions, management of spatial databases in multi-user environment","hasChildren":true,"name":"Modeling database change","selfAssesment":"In progress (GI-N2K)
"},{"code":"DN3-3","description":"Reliability tests of change information, design and implementation. Logical consistency of updates.","hasChildren":true,"name":"Reconciling database change","selfAssesment":"In progress (GI-N2K)
"},{"code":"DN3-4","description":"Needs for versioned databases, queries for change scenarios using DB management tools, algorithms for performing dynamic queries, role of time-criticality and data security while choosing methods for change detection.","hasChildren":true,"name":"Managing versioned geospatial databases","selfAssesment":"In progress (GI-N2K)
"},{"code":"GC","description":"The term geocomputation dates back to the first international conference on the topic in 1996 held at the University of Leeds under the title “The art and science of solving complex spatial problems with computers’. The term “geocomputation” was coined to describe the use of computer-intensive methods for knowledge discovery in physical and human geography. This new area distinguishes it from the application of statistical techniques to spatial data in the focus on “creative and experimental applications” and in “developing relevant geo-tools within the overall context of a ‘scientific’ approach.” Other authors reinforced the unique character of geocomputation as “to provide better solutions to many geographical problems by developing new, computationally dependent tools for analysis and modelling”. Simply defined, the interdisciplinary area of geocomputation was, from the beginning, closely linked to the application of computer technology and the development of tools and applications to real-world spatio-temporal problems through the combination of geographic information system techniques, spatial modelling, cellular automata, and other non-conventional data clustering and analysis techniques.\r\nEven though geocomputation is still seeking to define the field conceptually), it is closely related to computational science, the use of high-computing performance, artificial intelligence, computational intelligence, grid infrastructure and parallel computing . Nevertheless, the evolution of new computing paradigms, such as edge-fog-cloud computing along with the new forms of data create new opportunities for the geocomputation community . \r\n\r\nWhile the underlying idea remains intact --a diverse and interdisciplinary area of research that uses geospatial data, methods and tools for applied scientific work--, the current approach to geocomputation differs from the founders in that it focuses more attention on open science, reproducible research practices, and in a vibrant collaborative community to develop new methods, tools and applications that are integrated into multiple application domains such as economics, sociology, geodemography, health, criminology, transportation, biology, remote sensing and cities . The theoretical roots and experimental emphasis of geocomputation makes it an excellent vehicle to creatively explore in parallel the theory and practice of the use of geospatial data in a computational way to solve real-world problems.","hasChildren":true,"hasParent":true,"name":"Geocomputation","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC1-1","description":"A complex system can be viewed as a system composed of many interacting parts, with the ability to generate a new collective behaviour through self-organisation, for example, though the spontaneous formation of temporal, spatial or functional structures. Complex systems are therefore adaptive as they evolve and may contain self-driving feedback loops. Most real-world systems such as global climate, an ecosystem, a city, the human brain, and the entire universe, are complex systems. Therefore, complex systems are much more than a sum of their parts.The general characteristics of the structure and dynamics of complex systems have been characterised, including path dependence, positive feedback loops, self-organisation, and emergence. Complex system types include nonlinear systems, chaotic systems, and complex adaptive systems. \r\nTraditional approaches focus on the individual system components and define a system as the sum of its parts. Whereas the modern approach relies on complexity theory and complex adaptive systems, to emphasise the linkages between system components in order to understand complex systems as a whole. Agent-based models, for example, have been highly recommended for studying complex adaptive spatial systems because they support the explicit representation of situation-dependent information for decision making within dynamic spatial environments.","hasChildren":true,"name":"Complex systems","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC1-2","description":"Computational science is a discipline focused on the design, implementation and use of mathematical models or simulations through the use of computers to analyse scientific problems, systems or processes. Computational science heavily relies on computational technologies such as high performance computing, artificial intelligence, computational intelligence, grid infrastructure and parallel computing. Geocomputation is closely related to computational science and, therefore, geocomputational methods are often derived from machine learning, clustering, simulation, parallel computing and high performance computing. Contrary to the methods and tools applied for spatial analysis described under the Analytical Methods Knowledge Area, geocomputation and spatial data science may involve the use of spatial methods available in standard GIS packages, but quite often require self-development, or at least customisation, involving computational technologies and coding to solve target problems. The aim of this topic is to provide an introduction to computational science with particular emphasis on its usage and relation to geocomputation. In this sense, the way computational technologies are used in computational science can be connected to the methodological and coding practices of geocomputation and spatial data science.","hasChildren":true,"name":"Computational science and technology","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC1-3","description":"While geocomputation is not daily used in GIS environments and traditional GIS projects, it is the focus of a vibrant collaborative and research community in developing new geocomputational methods, tools and applications that are integrated into multiple application domains such as economics, sociology, geodemography, health, criminology, transportation, biology, remote sensing and cities. Open science, reproducible research practices, and strong collaboration make geocomputing an excellent vehicle for creatively exploring together the theory and practice of using geospatial data in a computational way to solve real-world problems.","hasChildren":true,"name":"Spatio-temporal problems and applications","selfAssesment":"In progress (GI-N2K)
"},{"code":"GC1-4","description":"The origin of geocomputation dates back to the first international conference on the topic in 1996 and was coined to describe the use of computer-intensive methods for knowledge discovery in physical and human geography. According to Birkin (2009), Openshaw defined geocomputation as a computational paradigm that takes geographic information science to focus on analysis, modeling, and simulations applied. Openshaw’s definition emphasized the use of novel computational approaches at that time along with spatial data and analysis methods to find solutions to real-world problems. Longey's definition, as reported by Birkin (2009), focuses on the continuous development of GIS tools and techniques, in line with the modern emphasis on creative, experimental, data-driven and code-based practices to solve real-world problems. In this context, geocomputation is closely related to other widely known areas of knowledge within the geospatial community, such as GIScience, Spatial Information Science, Geoinformatics, and Geographic/Spatial Data Science. While these terms clearly overlap and boundaries are fuzzy, the term geocomputation puts the focus on creative and experimental applications and in developing relevant computationally geospatial tools for analysis and modelling within the overall context of a ‘scientific’ approach. Therefore, a common interpretation of geocomputation is to describe the application of computational models to geographic problems. Nowadays, the term spatial data science is gaining ground to convey essentially the same interpretation as geocomputation.","hasChildren":true,"name":"Origin of geocomputation","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC1","description":"Geocomputation represents an attempt to move the geospatial research agenda back to geographical analysis and modelling by providing a toolbox of methods to analyse and model a range of highly complex, often non-deterministic problems. In this context, complex systems and computational science are foundational aspects upon which geocomputation approaches and methods are built to address a variety of real-world, spatio-temporal issues. Similar to geocomputation, the term spatial data science has recently emerged to refer to the use of computational techniques to access, explore, visualize and perform spatial analysis on real-world data sets. Therefore, geocomputation and spatial data science share many commonalities (complex problems, use of spatial techniques and modeling, coding, real-world data) that make them interchangeable in many scenarios.","hasChildren":true,"hasParent":true,"name":"Geocomputation and complex systems","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC2-1","description":"Building a model that mimics a real-world system generally follows a series of stages: from conceptual models to mathematical models and, finally, simulation models. In model development, system analysis is a process whereby a real-world system is simplified by dividing it into simpler, more manageable parts. A conceptual model then captures the components, variables and interactions of a system, and provides a useful way of thinking about the trade-offs between abstraction and representativeness of real-world phenomena. However, taken in isolation, the interacting parts of a system fail to explain its dynamics behaviour. A conceptual model is then translated into a mathematical model to explain interrelations and relationships among the constituted parts of a system by means of equations, logical rules or other mathematical mechanisms. Lastly, a simulation model is the computer-based implementation of mathematical models that consist of interrelated equations and logical rules. However, this model development process typically does not happen all at once, but can occur in multiple iterations throughout these phases to adjust, improve, and incorporate feedback into the modelling process.\r\n\r\nWhen a simulation model runs on a computer, it iteratively recalculates the state of the modelled system as it changes over time in accordance with the relationships represented by the mathematical relationships that describe the system dynamic. Therefore, developing detailed and dynamic simulation models comes at the cost of generality and interpretability, but it brings us realism and the ability to represent real-world processes in specific contexts. \r\n\r\nSimulation modelling is often used for prediction, exploration, theory development, or even optimization of conditions to achieve desired outcomes, with the goal of examining how the interconnections and relationships that characterise complex social and environmental systems (e.g. ecosystems, urban systems, social systems, global climate system) produces patterns of behaviour over time. Therefore, simulation models are increasingly gaining relevance as scientific mechanisms for several reasons. First, simulation models allow researchers to study systems inaccessible to experimental and observational scientific methods, complementing more conventional approaches to discover or formalize theories about real world systems. Also, as many real-world systems are nonlinear, simulation modelling has turned into a necessary method to explore and understand better such systems. In addition, the availability of computational science methods and technology, together with a large amount of data available from different sources, have greatly driven the adoption of simulation models in a wide range of scientific disciplines.","hasChildren":true,"name":"Principles of computer simulation","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC2-3","description":"Rule-based models are based on logic programming with condition-action expressions, where the left side of the expressions consists of several conditions that return a logical result, and the right side consists of several actions. Therefore, rules in rule-based models indirectly specify a mathematical model. However, unlike equation-based models which refer to the overall or aggregate behaviour of a system, rule-based models focus on the behaviour of the individual components of a system. This is why the implementation of rule-based models is most often done by cellular automata models or agent-based models, in which the aggregate behaviour of the system arises from the interaction of the individual agents or cells over time. Many geographic patterns and dynamics are formed by systems of interacting actors/cells with heterogeneous characteristics and behaviours, in which such dynamic behaviours can be implemented as rules.","hasChildren":true,"name":"Rule-based models","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC2-4","description":"Equation-based models are a set of interrelated equations that capture the variability of a system over time (differential equations), and the execution (simulation) of the model means to evaluate such equations. Equation-based models do not aim at representing the behaviour of the individual components in a system. Rather, they focus on the overall or aggregate behaviour of a system. Therefore, equation-based models are well suited to represent physical processes and some topics within natural sciences, where the system to some degree can be described by physical laws. Hydrological modelling is a good example of models based on equations. However, other real-world systems can rarely be fully described by the laws of the natural sciences, and their behavior and interrelation must be represented by means of other types of mathematical mechanisms. The aim of this topic is to present the advantages and challenges in using equation-based simulation models, which are most naturally applied to systems centrally governed by physical laws rather than by information processing and flow.","hasChildren":true,"name":"Equation-based models","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC2-5","description":"Space-time dynamics is closely related to the concepts of change and process, which are inherent to our real-world world. Space-time dynamics is especially manifested when we move from a static to a dynamic representation of phenomena. Various processes taking place at different spatial and temporal scales interact with each other and lead to changes in the phenomena being modelled. \r\n\r\nThere are many different approaches to conceptualizing and understanding space-time dynamics in order to understand or predict phenomena in heterogeneous application domains ranging from human activities and urban sprawl to disease spread and traffic flow. An example is the time geography approach and its variants, such as the spatiotemporal prism, to model and understand human physical activities that occur in and are simultaneously constrained by space and time. These interactions produce space–time prisms that simultaneously situate individuals locally in physical space. Other techniques such as cellular automata also model human and physical activity in space and time, to simulate space–time and associated constraints to individual human activities.\r\n\r\nWhile the above examples are primarily oriented towards human activities, such as urban transport and mobility, these theoretical approaches have the potential to investigate and understand interactions between humans and the environment, recognizing the importance of individual human activities together with the geographic-environmental context applied to multiple scenarios, such as climatology, physical geography, and natural disasters. For the latter, for example, modelling and simulating human responses to floods or hurricanes can lead to more efficient and effective emergency plans.","hasChildren":true,"name":"Space-time dynamics","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC2-6","description":"Cellular automata are a widely used form of spatially explicit simulation model, where complex processes evolve over space and time through a lattice of cells, each linked to its neighboring cells. Typically, this spatial lattice is structured as a two-dimensional grid of square cells. Each cell holds a set of states that change over time according to transition rules, which depend on the state of the cell and its neighbors. That is, a cellular automata model allows the exploration of how local interactions lead to the emergence of global patterns, governed by clearly defined rules. A cellular automata model is defined by six key components: a lattice or framework, individual cells, neighboring cells, transition rules, initial conditions (states), and an update sequence (time). These models are well-suited to geographic information systems (GIS) due to their simple data structures and ability to represent spatial changes and patterns in an intuitive way. This has made cellular automata in simulating phenomena such as land use changes and the spread of diseases.","hasChildren":true,"name":"Cellular automata","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC2-7","description":"Agent-based modelling is a powerful approach for simulating the dynamics of geographical systems by breaking them down into individual components or agents, each with its own characteristics, properties, rules and behavior. Unlike traditional models that treat geographical components as homogeneous entities, agent-based modelling allows for the simulation of diverse agents, such as people, cities, or abstract representations, interacting with each other and their environment at various spatial and temporal scales. This bottom-up approach makes it possible to observe how individual decisions lead to complex system behaviors over time, providing deeper insights into urban problems like urban sprawl, congestion, and segregation, as well as to model natural and social phenomena such as animal behavior, pedestrian behavior, social insects and biological cells. Therefore, the macro-level behavior of the system arises from the interaction of individual agents and the environment over time.\r\n\r\nAgent-based modelling development stems from automata-based models, which use rule-based mechanisms to process information and evolve over time. Two prominent automata-based approaches—cellular automata and agent-based modelling —have been widely adopted in geographic modelling. Agent-based modelling's advantage lies in its ability to model heterogeneous agents and dynamic interactions, which traditional models, focused on aggregate behaviors, cannot capture as effectively. While agent-based modelling offers unique insights into geographical systems, it also poses challenges, such as the complexity of simulating realistic agent behaviors. Nonetheless, agent-based modelling continues to grow in popularity for its ability to represent dynamic spatial changes in a more detailed and realistic manner.","hasChildren":true,"name":"Agent-based modelling","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC2","description":"The concept of spatial simulation modelling can be better understood by looking at the meaning of its individual words. A model is widely defined as a simplified representation of a real-world system under study, which can be used to explore or to better understand the system it represents. Simulation models are computer-based implementations of a model to produce outputs based on certain model assumptions. Simulation , therefore, relies on the use of computers for virtual experimentation to gain insight into real-world problems by proposing alternative assumptions that arise from exploring “what if” questions about a dynamic problem of interest over the course of successive simulation experiments.\r\n\r\nSimulation modelling is often used for prediction, exploration, theory development, or even optimization of conditions to achieve desired outcomes, with the goal of examining how the interconnections and relationships that characterize these systems produce patterns of behaviour over time. Across broad areas of the environmental and social sciences, researchers use simulation models as a way to study systems inaccessible to experimental and observational scientific methods, and also as an essential complement of those more conventional approaches to discover or formalize theories about the real world. Simulation models are a relatively recent addition to the scientific toolbox, and the reasons for their widespread adoption are, on the one hand, the impossibility to study in-situ some complex social and environmental systems (e.g. ecosystems, urban systems, social systems, global climate system) and, on the other hand, the availability of High Performance Computing and large amount of data from different sources.\r\n\r\nFinally, simulation modelling is also useful for the study of spatial patterns over time. Spatial simulation models are relevant when the study of spatial elements and their relationships in a system are necessary for a fully understanding of that system. In this regard, spatial simulation modelling approaches include rule-based models, equation-based models, grid-based cellular automata models, discrete event simulation, and agent-based models.","hasChildren":true,"hasParent":true,"name":"Spatial simulation modelling","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC3-10-1","description":" ","hasChildren":true,"name":"Geometric object features","selfAssesment":" "},{"code":"GC3-10-2-1","description":" ","hasChildren":true,"name":"Object relations","selfAssesment":" "},{"code":"GC3-10-2","description":" ","hasChildren":true,"hasParent":true,"name":"Object features","selfAssesment":" "},{"code":"GC3-10-3-1","description":" ","hasChildren":true,"name":"Wavelets","selfAssesment":" "},{"code":"GC3-11-1","description":" ","hasChildren":true,"name":"Genetic artificial networks","selfAssesment":" "},{"code":"GC3-11-2-1","description":" ","hasChildren":true,"name":"Markov models","selfAssesment":" "},{"code":"GC3-11-2-2","description":" ","hasChildren":true,"name":"Kalman filters","selfAssesment":" "},{"code":"GC3-11-2","description":" ","hasChildren":true,"hasParent":true,"name":"Space-time dynamic reasoning","selfAssesment":" "},{"code":"GC3-11-3-1","description":" ","hasChildren":true,"name":"Multilayer perceptron","selfAssesment":" "},{"code":"GC3-11-3-2","description":" ","hasChildren":true,"name":"Backpropagation","selfAssesment":" "},{"code":"GC3-11-3-3","description":" ","hasChildren":true,"name":"Recurrent neural networks","selfAssesment":" "},{"code":"GC3-11-3-4","description":" ","hasChildren":true,"name":"Long short-term memory","selfAssesment":" "},{"code":"GC3-12-1","description":" ","hasChildren":true,"name":"Ensemble learning","selfAssesment":" "},{"code":"GC3-12-2","description":" ","hasChildren":true,"name":"Regression trees","selfAssesment":" "},{"code":"GC3-12","description":" ","hasChildren":true,"hasParent":true,"name":"AI algorithms","selfAssesment":" "},{"code":"GC3-13-1","description":" ","hasChildren":true,"name":"Physics aware AI","selfAssesment":" "},{"code":"GC3-13-2-1","description":" ","hasChildren":true,"name":"Theory of mind","selfAssesment":" "},{"code":"GC3-13-2-2","description":" ","hasChildren":true,"name":"Self-aware AI","selfAssesment":" "},{"code":"GC3-13-2","description":" ","hasChildren":true,"hasParent":true,"name":"Digital twin","selfAssesment":" "},{"code":"GC3-13","description":" ","hasChildren":true,"hasParent":true,"name":"Hybrid AI","selfAssesment":" "},{"code":"GC3-14-1-1","description":" ","hasChildren":true,"name":"Individual intelligence","selfAssesment":" "},{"code":"GC3-14-1-2","description":" ","hasChildren":true,"name":"Collective intelligence","selfAssesment":" "},{"code":"GC3-14-1-3","description":" ","hasChildren":true,"name":"Team learning","selfAssesment":" "},{"code":"GC3-14-1","description":" ","hasChildren":true,"hasParent":true,"name":"Cooperation levels","selfAssesment":" "},{"code":"GC3-14-2-1","description":" ","hasChildren":true,"name":"Logical agent","selfAssesment":" "},{"code":"GC3-14-2-2","description":" ","hasChildren":true,"name":"Inference","selfAssesment":" "},{"code":"GC3-14-2-3","description":" ","hasChildren":true,"name":"Probabilistic reasoning","selfAssesment":" "},{"code":"GC3-14-2-4","description":" ","hasChildren":true,"name":"Sequential decision problems","selfAssesment":" "},{"code":"GC3-14-2-5","description":" ","hasChildren":true,"name":"Supervised learning","selfAssesment":" "},{"code":"GC3-14-2-6","description":" ","hasChildren":true,"name":"Reinforcement learning","selfAssesment":" "},{"code":"GC3-14-2","description":" ","hasChildren":true,"hasParent":true,"name":"Intelligence type","selfAssesment":" "},{"code":"GC3-14","description":" ","hasChildren":true,"hasParent":true,"name":"Intelligent Software Agent","selfAssesment":" "},{"code":"GC3-3","description":"Biological neurons, or nerve cells, receive multiple input stimuli, combine and modify the inputs in some way, and then transmit the result to other neurons. Artificial neural networks are an attempt to emulate features of biological neural networks in order to address a range of difficult information processing, analysis and modelling problems. The principal class of ANNs are so-called feed-forward networks, but other types of ANN are for example recurrent neural networks. Among the feed-forward networks the most widely used approach is the multi-level perceptron (MLP) model. The application range is broad from non-linear regression to land cover change modelling. The aim of the topic is to introduce the principles of ANN and to understand and demonstrate its use in geospatial modelling.","hasChildren":true,"hasParent":true,"name":"Artificial Neural Networks","selfAssesment":"In progress (GI-N2K)
"},{"code":"GC3-7-1","description":" ","hasChildren":true,"name":"Cybernetics","selfAssesment":" "},{"code":"GC3-7-2","description":"Pattern recognition is the process of classifying input data into objects or classes based on key features. There are two classification methods in pattern recognition: supervised and unsupervised classification. The supervised classification of input data in the pattern recognition method uses supervised learning algorithms that create classifiers based on training data from different object classes. The classifier then accepts input data and assigns the appropriate object or class label. The unsupervised classification method works by finding hidden structures in unlabelled data using segmentation or clustering techniques. Common unsupervised classification methods include: K-means clustering, Gaussian mixture models, Hidden Markov models. The aim of the topic is to provide knowledge about the different methods in pattern recognition and how to choose the optimum method for a specific spatial problem.","hasChildren":true,"name":"Pattern recognition","selfAssesment":"In progress (GI-N2K)
"},{"code":"GC3-7-3-1","description":" ","hasChildren":true,"name":"Information-as-meaning","selfAssesment":" "},{"code":"GC3-7","description":" ","hasChildren":true,"hasParent":true,"name":"Signal processing","selfAssesment":" "},{"code":"GC3-8-1","description":" ","hasChildren":true,"name":"Natural language processing","selfAssesment":" "},{"code":"GC3-8-2","description":" ","hasChildren":true,"name":"Semantic web","selfAssesment":" "},{"code":"GC3-8","description":" ","hasChildren":true,"hasParent":true,"name":"Computational linguistics","selfAssesment":" "},{"code":"GC3-9-1-1","description":" ","hasChildren":true,"name":"Experimental learning","selfAssesment":" "},{"code":"GC3-9-1","description":" ","hasChildren":true,"hasParent":true,"name":"Knowledge representation","selfAssesment":" "},{"code":"GC3-9-2-1","description":" ","hasChildren":true,"name":"Semantic net","selfAssesment":" "},{"code":"GC3-9-2-2","description":" ","hasChildren":true,"name":"Inheritance","selfAssesment":" "},{"code":"GC3-9-2","description":" ","hasChildren":true,"hasParent":true,"name":"Knowledge organising system","selfAssesment":" "},{"code":"GC3-9-3","description":" ","hasChildren":true,"name":"Semantic categorisation","selfAssesment":" "},{"code":"GC3-9-4-1-1","description":" ","hasChildren":true,"name":"Membership functions","selfAssesment":" "},{"code":"GC3-9-4-1-2","description":" ","hasChildren":true,"name":"Class stability","selfAssesment":" "},{"code":"GC3-9-4-1","description":" ","hasChildren":true,"hasParent":true,"name":"Fuzzy logic","selfAssesment":" "},{"code":"GC3-9-4-2","description":" ","hasChildren":true,"name":"Boolean logic","selfAssesment":" "},{"code":"GC3-9","description":" ","hasChildren":true,"hasParent":true,"name":"Automated reasoning","selfAssesment":" "},{"code":"GC3","description":"Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans.","hasChildren":true,"hasParent":true,"name":"Artificial intelligence (AI) in EO and GI","selfAssesment":"New
"},{"code":"GC4-1","description":"The use of the term Open geocomputation doesn't intend to coin a new term; Open GIScience and Open GIS are well explored and discussed terms in the literature. Both embrace the idea of open data, open source, collaboration among peers, and the integration of these practices into GIS research projects, tools, services and applications. Open geocomputation brings the ideas of Open GIScience (and hence Open Science in general) into geocomputation, focussing on openness as a fundamental tenet to conduct research in geocomputation and for the development of new computational methods and tools. In fact, many community-led developments and tools have recently appeared in the field of geocomputation, notably based on R and Python. The widespread popularity and adoption of these computing environments for geocomputing and geospatial analysis is simply because they encompass open, transparent, and reproducible tool development.","hasChildren":true,"name":"Open Geocomputation","selfAssesment":"New
"},{"code":"GC4","description":"A distinguible feature of the current approach to geocomputation is the emphasis on openness: open science, open source, open data. All of this propelled by a vibrant collaborative community with the aim to develop open and reproducible methods, tools and applications applied to a variety of real-life, spatio-temporal application domains. Open Science is a paradigm that can be applied to any scientific discipline and area of knowledge, characterised by openness, access to large volumes of data and unprecedented levels of computing power, availability of community-driven tools, and new types of collaboration between multidisciplinary researchers. Open Science clearly goes beyond geocomputation, but at the same time, its practices and principles characterise recent geocomputation-related projects as well as its community. Therefore, the vision of Open Science taken here is contextualised to the field of geocomputation.","hasChildren":true,"hasParent":true,"name":"Open Science","selfAssesment":"new
"},{"code":"GD","description":"Geospatial data represent measurements of the locations and attributes of phenomena at or near Earth`s surface. Information is data made meaningful in the context of a question or problem. Information is rendered from data by analytical methods. Information quality and value depends to a large extent on the quality and currency of data (though historical data are valuable for many applications). Geospatial data may have spatial, temporal, and attribute (descriptive) components, as well as associated metadata. Data may be acquired from primary or secondary data sources. Examples of primary data sources include surveying, remote sensing (including aerial and satellite imaging), the global positioning system (GPS), work logs (e.g., police traffic crash reports), environmental monitoring stations, and field surveys. Secondary geospatial or geospatial-temporal data can be acquired by digitizing and scanning analog maps, as well as from other sources, such as governmental agencies. The legitimacy of geographic information science as a discrete field has been claimed in terms of the unique properties of geospatial data. In a paper in which he coined the term GIScience, Goodchild (1992) identified several such properties, including: 1. Geospatial data represent spatial locations and non-spatial attributes measured at certain times. 2. The Earth`s surface is highly complex in shape and continuous in extent. 3. Geospatial data tend to be spatially autocorrelated. It has long been said that data account for the largest portion of geospatial project costs. While this maxim remains true for many projects, practitioners and their clients now can reasonably expect certain kinds of data to be freely or cheaply available via the World Wide Web. Federal, state, regional, and local government agencies, as well as commercial geospatial data producers, operate clearinghouses that provide access to geospatial data. Although geospatial data are much more abundant now than they were ten years ago, data quality issues persist. Good data are expensive to produce and to maintain. Proprietary interests simultaneously increase the supply of geospatial data and impede data accessibility. Standards for geospatial data and metadata are useful in facilitating effective search, retrieval, evaluation, integration with existing data, and appropriate uses. National and international organizations, such as the Open Geospatial Consortium (OGC) and International Organization for Standardization (ISO), develop and promulgate such standards. INSPIRE directive (Infrastructure for Spatial Information in the European Community) regulates geospatial data management","hasChildren":true,"hasParent":true,"name":"Geospatial Data","selfAssesment":"In progress (GI-N2K)
"},{"code":"GD1-1","description":"Usable and accurate geospatial data are based upon proper model of the Earth`s surface. Shape of the Earth is complex and complicated to measure. Approximations are used to minimize complexity of the task and possible errors.","hasChildren":true,"name":"Earth geometry","selfAssesment":"GI-N2K
"},{"code":"GD1-2","description":"Geospatial referencing systems provide unique codes for every location on the surface of the Earth (or other celestial bodies). These codes are used to measure distances, areas, and volumes, to navigate, and to predict how and where phenomena on the Earths surface may move, spread, or contract. Point-based, vector coordinate systems specify locations in relation to the origins of planar or spherical grids. Tessellated referencing systems specify locations hierarchically, as sequences of numbers that represent smaller and smaller subdivisions of two- or three dimensional surfaces that approximate the Earths shape, Linear referencing systems specify locations in relation to distances along a path from a starting point. Tessellation data models, are considered in Unit DM3 Tessellation data models, and linear referencing models are considered in Unit DM4 Vector data models.","hasChildren":true,"name":"Georeferencing systems","selfAssesment":"GI-N2K
"},{"code":"GD1-3","description":"Horizontal datums determine the geometric relations between a coordinate system grid and a particular ellipsoid approximating the Earth`s surface. Vertical datums determine elevation reference surfaces, like mean sea level. A. Horizontal datums. Relation of coordinate system to particular ellipsoid, datum transformation options, Molodensky and Helmert transformation, other high accuracy transformations, ED50 and WGS84, historical development of horizontal datums, ETRS89. B. Vertical datums. Historical development of vertical datums, difference between vertical datum and geoid, relations between ellipsoidal (geodetic) heiht, geoidal height and orthometric elevation.","hasChildren":true,"name":"Datums","selfAssesment":"In progress GI-N2K
"},{"code":"GD1-4","description":"Map projections are systematic transformations of geographic coordinates of the surface of ellipsoid into locations in plane. Plane coordinates are based on map projection. As the transformation of a spherical grid into a plane grid causes inevitably distortions of the geometry, and, different projections cause different distortions, knowledgeable choice of appropriate projection for any particular use is crucial. A. Map projection poperties. Geometric properties that may be preserved or lost in projected grid, usefulness of compromise projection, Tissot indicatrix as an indicator of projection errors, visual appearance of the Earth`s graticule, distortion patterns for projection classes, distortions in raster data. B. Map projection classes. Three main classes of map projection based on developable surface, projection types by geometric properties preserved, mathematical basis of projecting longitude and latitude into x and y coordinates. UTM, ETM, projections used by EC. C. Map projection parameters. Standard line, projection case, latitutde and longitude of origin, aspects of projection. D. Georegistration. Rectification vs orthorectification, ground controle points in georegistration of aerial imagery.","hasChildren":true,"name":"Map projections","selfAssesment":"GI-N2K
"},{"code":"GD1","description":"Proper model of the Earth`s surface and ability to locate spatial phenomena accurately to it, is crucial in effective collection, management and use of data. Characterising size and shape of the Earth, using appropriate surfaces to approximate it, choosing suitable coordinate system and map projection is bases for efficient understanding of spatial data.","hasChildren":true,"hasParent":true,"name":"Geolocating Data to Earth","selfAssesment":"In progress (GI-N2K)
"},{"code":"GD10-4","description":"A stereoscopy acquisition mode collects remotely sensed data where each location on the ground (or the imaged objects) is covered multiple times (at least twice), from different perspectives. Stereopairs and stereoscopic coverage enable the extraction of 3D representations of the environment from remotely sensed imagery.","hasChildren":true,"hasParent":true,"name":"Stereoscopy and orthoimagery","selfAssesment":"GI-N2K
"},{"code":"GD10","description":"Since the 1940s aerial imagery has been the primary source of detailed geospatial data for extensive study areas. Photogrammetry is the profession concerned with producing precise measurements from aerial imagery. Aerial imaging and photogrammetry comprise a major component of the geospatial industry. The topics included in this unit do not comprise an exhaustive treatment of photogrammetry, but they are aspects of the field about which all geospatial professionals should be knowledgeable.","hasChildren":true,"hasParent":true,"name":"Aerial imaging and photogrammetry","selfAssesment":"GI-N2K
"},{"code":"GD11-2","description":"the physical environment to sense data without direct contact. It contains a carrier device (platform) and a sampling unit (sensor).","hasChildren":true,"name":"Platforms and sensors","selfAssesment":"GI-N2K
"},{"code":"GD11","description":"Satellite-based sensors enable frequent mapping and analysis of very large areas. Many sensing instruments are able to measure electromagnetic energy at multiple wavelengths, including those beyond the visible band. Satellite remote sensing is a key source for regional- and global-scale land use and land cover mapping, environmental resource management, mineral exploration, and global change research. Shipboard sensors employ acoustic energy to determine seafloor depth or to create imagery of the seafloor or water column. The topics included in this unit do not comprise an exhaustive treatment of remote sensing, but they are aspects of the field about which all geospatial professionals should be knowledgeable.","hasChildren":true,"hasParent":true,"name":"Satellite and shipboard remote sensing","selfAssesment":"GI-N2K
"},{"code":"GD12","description":"Meaning of geospatial metadata, elements of metadata, use of metadata, integration of metadata in data production, standards in geospatial data, ISO standard family 191xx, data warehouse, exchange protocol, transport protocols, spatial data infrastructure, INSPIRE, OGC, DCAT profiles for CKAN applications bridging metadata from GI and IT domains.","hasChildren":true,"name":"Metadata, standards, and infrastructures","selfAssesment":"GI-N2K in progress
"},{"code":"GD2-1","description":"Classic land survey methods and manual attribute data collection in the field","hasChildren":true,"name":"Land surveying and field data collection","selfAssesment":"GI-N2K
"},{"code":"GD2-2","description":"Aerial imagery has been the primary source of detailed geospatial data for extensive study areas. Photogrammetry is producing precise measurements from aerial imagery. Aerial imaging and photogrammetry comprise a major component of the geospatial data production. Satellite-based sensors enable frequent mapping and analysis of very large areas. Sensing instruments are able to measure electromagnetic energy at multiple wavelengths. Satellite remote sensing is a key source for regional- and global-scale land use and land cover mapping, environmental resource management, mineral exploration, and global change research. Shipboard sensors employ acoustic energy to determine seafloor depth or to create imagery of the seafloor or water column. Principles of aerial photography, oblique and vertical imagery, spatial and radiometric resolution, spectral sensitivity, principal point, distortions and displacements in aerial image, parallax, stereophotogrammetry, generation of an orthoimage from a vertical aerial phoptograph, aerotriangulation, vector data extraction from digital seteroimagery, mission planning. Use of UAV in photogrammetry. Main platforms and sensors in spatial image acquisition, active and passive sensors, LiDAR and microwave, multispectral and hypersepctral imagery, interpretation of imagery, supervised and unsupervised classification, pixel based and segmented classification, ground verification, main applications, bathymetric mapping. SENTINEL.","hasChildren":true,"hasParent":true,"name":"Remote sensing","selfAssesment":"In progress GI-N2K
"},{"code":"GD2-3","description":"Crowdsourcing is the practice of obtaining needed services, ideas, or content by soliciting contributions from a large group of people and especially from the online community rather than from traditional employees or suppliers. Crowdsourced spatial data collection is becoming more and more important. The advantages and disadvantages of crowdsourced data, opensource mapping tools, potential application of crowdsourcing, VGI, OSM or cell-phone based, aspects of crowdsourced data quality and reliabilty.","hasChildren":true,"name":"Crowdsourced data collection","selfAssesment":"GI-N2K
"},{"code":"GD2-4","description":"Digitizing as the main secondary spatial data production technique. Encoding vector points, lines, and polygons by tracing map sheets has diminished in importance, but remains a useful technique for incorporating historical geographies and local knowledge. \"Heads-up\" digitizing using digital imagery as a backdrop on-screen is a standard technique for editing and updating GIS databases. Tablet and on-screen digitizing, scanning and (semi)automatic vectorization.","hasChildren":true,"hasParent":true,"name":"Digitizing","selfAssesment":"GI-N2K
"},{"code":"GD2","description":"Spatial data collection / production involves measurement of locations in relation to the coordinate system, and collection of attributed data about the spatial phenomena. Measurements may be direct (e.g. surveying) or remote, data acquisition involves measurement of parameter values, evaluation of parameters, polls, interpretation of spatial imagery, and re-use of secondary data (e.g. old maps). Volunteered geographic information is becoming more important.","hasChildren":true,"hasParent":true,"name":"Data Collection","selfAssesment":"GI-N2K
"},{"code":"GD3","description":"It is quite common, that data including both spatial entities and their attribute data undergo changes. These changes need to be catalogued fully and explicitly, including initial conditions, new conditions, all intermediate stages and operations used. The geospatial data needs to contain an archival history of change.","hasChildren":true,"hasParent":true,"name":"Transaction management of geospatial data","selfAssesment":"In progress (GI-N2K)
"},{"code":"GD4-1","description":"Geometric accuracy, factors influencing it, geometric accuracy and topological fidelity, geometric accuracy in survey and GPS mesurements, thematic accuracy, relations between thematic accuracy, geometric accuracy and topological fidelity, misclassification matrix, commission and omission, logical consistency, relations between resolution, precision, and accuracy, spatial resolution, thematic resolution, and temporal resolution, precision, uncertainties associated with coordinate precision, primary and secondary data sources.\r\n\r\nParticular application. That standard varies from one application to another. In general, however, the key criteria are how much uncertainty is present in a data set and how much is acceptable. Judgments about fitness for use may be more difficult when data are acquired from secondary rather than primary sources. Aspects of data quality include accuracy, resolution, and precision. Concepts of data quality, error, and uncertainty are also covered in Knowledge Areas CF Conceptual Foundations (in a theoretical context) and GC Geocomputation (in the context of analysis); the focus here is on the measurement and assessment of data quality.","hasChildren":true,"hasParent":true,"name":"Data quality","selfAssesment":"GI-N2K
"},{"code":"GD4","description":"Data quality is the degree of data usability in relation to given objective and particular application. The expectations to data vary between different applications. The key criteria in data quality are the amount of uncertainty in data as compared to the acceptable level of uncertainty. Evaluation of the usability may be more complicated using data from secondary sources. Appropriate metadata is inevitable for these judgements. Aspects of data quality include geometric and thematic accuracy, (in)consistencies, resolution, precision, usability and others. Assurance of data quality may be improved by following proper standards and spatial data infrastructure regulations for data collection and management. System of basic data quality measures for geospatial domain in the EN ISO 19157:2013 standard.","hasChildren":true,"hasParent":true,"name":"Data Quality, Metadata and Data Infrastructure","selfAssesment":"In progress (GI-N2K)
"},{"code":"GD6-1","description":"Geometric accuracy is a measure indicating how close the geometric values of the data are to the real world position of the mapped feature.","hasChildren":true,"name":"Geometric accuracy","selfAssesment":"In progress (GI-N2K)
\r\n\r\nIn progress (GI-N2K)
"},{"code":"GD6-3","description":"The resolution of a data source indicates the smallest unit of detail provided by the data source.","hasChildren":true,"name":"Resolution","selfAssesment":"In progress (GI-N2K)
"},{"code":"GD6-4","description":"The precision of a measurement system, related to reproducibility and repeatability, is the degree to which repeated measurements under unchanged conditions show the same results.","hasChildren":true,"name":"Precision","selfAssesment":"GI-N2K
"},{"code":"GD6-5","description":"Primary data sources provide information collected directly for GIS use. Secondary sources are data sources that need to be processed before they are ready for GIS use.","hasChildren":true,"name":"Primary and secondary sources","selfAssesment":"In progress (GI-N2K)
"},{"code":"GD8-1","description":"Tablet digitizing is the conversion from physical map to digital data by re-drawing the features on the map fixed on a digitizing tablet","hasChildren":true,"name":"Tablet digitizing","selfAssesment":"In progress (GI-N2K)
\r\n\r\nIn progress (GI-N2K)
\r\n\r\nIn progress (GI-N2K)
"},{"code":"GN","description":" ","hasChildren":true,"hasParent":true,"name":"GNSS","selfAssesment":" "},{"code":"GN1-1-1-1-1-1-1","description":" ","hasChildren":true,"name":"GPS third civil signal L2C","selfAssesment":" "},{"code":"GN1-1-1-1-1-1-2","description":" ","hasChildren":true,"name":"GLONASS signal CDMA upgrade","selfAssesment":" "},{"code":"GN1-1-1-1-1-1","description":" ","hasChildren":true,"hasParent":true,"name":"Modernisation","selfAssesment":" "},{"code":"GN1-1-1-1-1","description":" ","hasChildren":true,"hasParent":true,"name":"Payload","selfAssesment":" "},{"code":"GN1-1-1-1","description":" ","hasChildren":true,"hasParent":true,"name":"GNSS Satellites","selfAssesment":" "},{"code":"GN1-1-1-2-1-1","description":" ","hasChildren":true,"name":"Front-Ends","selfAssesment":" "},{"code":"GN1-1-1-2-1-2","description":" ","hasChildren":true,"name":"A/D & sampling","selfAssesment":" "},{"code":"GN1-1-1-2-1-3","description":" ","hasChildren":true,"name":"Correlator","selfAssesment":" "},{"code":"GN1-1-1-2-1-4","description":" ","hasChildren":true,"name":"Decoder","selfAssesment":" "},{"code":"GN1-1-1-2-1-5","description":" ","hasChildren":true,"name":"Message analysis","selfAssesment":" "},{"code":"GN1-1-1-2-1-6-1","description":" ","hasChildren":true,"name":"Positioning Velocity Time (PVT) computation","selfAssesment":" "},{"code":"GN1-1-1-2-1-6-2","description":" ","hasChildren":true,"name":"Dillution of Precision (DOP)","selfAssesment":" "},{"code":"GN1-1-1-2-1-6","description":" ","hasChildren":true,"hasParent":true,"name":"Algorithms","selfAssesment":" "},{"code":"GN1-1-1-2-1-7","description":"The GNSS satellites continuously transmit navigation signals in two or more frequencies in L band. These signals contain ranging codes and navigation data to allow the users to compute the travelling time from satellite to receiver and the satellite coordinates at any epoch. The main signal components are described as follows: Carrier: Radio frequency sinusoidal signal at a given frequency. Ranging code: Sequences of 0s and 1s (zeroes and ones), which allow the receiver to determine the travel time of radio signal from satellite to receiver. They are called Pseudo-Random Noise (PRN) sequences or PRN codes. Navigation data: A binary-coded message providing information on the satellite ephemeris (Keplerian elements or satellite position and velocity), clock bias parameters, almanac (with a reduced accuracy ephemeris data set), satellite health status, and other complementary information.","hasChildren":true,"name":"GNSS Signal","selfAssesment":" "},{"code":"GN1-1-1-2-1","description":" ","hasChildren":true,"hasParent":true,"name":"Components","selfAssesment":" "},{"code":"GN1-1-1-2-2-1","description":" ","hasChildren":true,"name":"Mass Market (low-cost)","selfAssesment":" "},{"code":"GN1-1-1-2-2-2","description":" ","hasChildren":true,"name":"Professional grade","selfAssesment":" "},{"code":"GN1-1-1-2-2-3","description":" ","hasChildren":true,"name":"Scientific (geodetic)","selfAssesment":" "},{"code":"GN1-1-1-2-2","description":" ","hasChildren":true,"hasParent":true,"name":"Grade","selfAssesment":" "},{"code":"GN1-1-1-2","description":"GNSS Receivers process the Signals In Space (SIS) transmitted by the satellites, being the user interface to any Global Navigation Satellite System (GNSS). Even though the information provided by a generic GNSS receiver can be used by a wide range of Applications, most of them rely on the receiver's navigation solution - i.e. receiver computed Position, Velocity and Time (PVT).","hasChildren":true,"hasParent":true,"name":"GNSS Receivers","selfAssesment":" "},{"code":"GN1-1-1","description":" ","hasChildren":true,"hasParent":true,"name":"GNSS Space segment","selfAssesment":" "},{"code":"GN1-1-2-1","description":"The GPS Ground Segment (also referred to as Control Segment or Operational Control System) is the responsible for the proper operation of the GPS system.\r\n\r\nThe GPS Control Segment consists of a global network of ground facilities that track the GPS satellites, monitor their transmissions, perform analysis, and send commands and data to the constellation.","hasChildren":true,"name":"GPS Ground Segment","selfAssesment":" "},{"code":"GN1-1-2","description":" ","hasChildren":true,"hasParent":true,"name":"GNSS Ground Segment","selfAssesment":" "},{"code":"GN1-1","description":" ","hasChildren":true,"hasParent":true,"name":"Global systems","selfAssesment":" "},{"code":"GN1","description":" ","hasChildren":true,"hasParent":true,"name":"Satellite Navigation systems","selfAssesment":" "},{"code":"GN2-1-1-1","description":" ","hasChildren":true,"name":"Standard Point Positionng (SPP)","selfAssesment":" "},{"code":"GN2-1-1-2-1","description":" ","hasChildren":true,"name":"Receiver Autonomous Integrity Monitoring (RAIM)","selfAssesment":" "},{"code":"GN2-1-1-2-2","description":" ","hasChildren":true,"name":"Advanced RAIM (ARAIM)","selfAssesment":" "},{"code":"GN2-1-1-2","description":" ","hasChildren":true,"hasParent":true,"name":"Safety of Life Systems","selfAssesment":" "},{"code":"GN2-1-1","description":" ","hasChildren":true,"hasParent":true,"name":"Code Based Positioning - Global","selfAssesment":" "},{"code":"GN2-1-2-1","description":" ","hasChildren":true,"name":"Differential GNSS (DGNSS): code based","selfAssesment":" "},{"code":"GN2-1-2-2-1","description":" ","hasChildren":true,"name":"Local-Area: Ground Based Augmetation System (GBAS)","selfAssesment":" "},{"code":"GN2-1-2-2-2","description":" ","hasChildren":true,"name":"Wide-Area: Satellite Based Augmetation System (SBAS)","selfAssesment":" "},{"code":"GN2-1-2-2","description":" ","hasChildren":true,"hasParent":true,"name":"Safety of Life Systems: Augmentation Systems","selfAssesment":" "},{"code":"GN2-1-2","description":" ","hasChildren":true,"hasParent":true,"name":"Code Based Positioning - Non-Global","selfAssesment":" "},{"code":"GN2-1","description":"The target is to determine the receiver coordinates and clock offset from pseudorange measurements of at least 4 satellites in view. The positioning principle is based on solving a geometric problem from the measured ranges to the satellites, with known coordinates. The satellite coordinates can be computed from the broadcast message, which also provides all necessary information for the measurements modelling for the Standard Positioning Service (SPS).","hasChildren":true,"hasParent":true,"name":"Code Based Positioning","selfAssesment":" "},{"code":"GN2-2-1-1","description":" ","hasChildren":true,"name":"Precise Point Positionng (PPP)","selfAssesment":" "},{"code":"GN2-2-1","description":" ","hasChildren":true,"hasParent":true,"name":"Carrier Phase Based Positioning - Global","selfAssesment":" "},{"code":"GN2-2-2-1-1","description":" ","hasChildren":true,"name":"Local-Area: Real-Time-Kinematics (RTK)","selfAssesment":" "},{"code":"GN2-2-2-1-2","description":" ","hasChildren":true,"name":"Wide-Area: Network RTK (NRTK)","selfAssesment":" "},{"code":"GN2-2-2-1","description":" ","hasChildren":true,"hasParent":true,"name":"Differential GNSS (DGNSS): carrier based","selfAssesment":" "},{"code":"GN2-2-2","description":" ","hasChildren":true,"hasParent":true,"name":"Carrier Phase Based Positioning - Non-Global","selfAssesment":" "},{"code":"GN2-2","description":" ","hasChildren":true,"hasParent":true,"name":"Carrier Phase Based Positioning","selfAssesment":" "},{"code":"GN2","description":" ","hasChildren":true,"hasParent":true,"name":"GNSS Positioning Techniques","selfAssesment":" "},{"code":"GS","description":"Geographic Information Science and Technology serve the society, but it is not a panacea. The history of its development is the sum of fragmented efforts, which have still not been fully integrated. Its potential benefits are often constrained and its potential impacts are not fully understood. Institutional and economic factors limit access to data, technology, and expertise by some of those who need it to make better decisions. Political, ideological, and personal issues aside, organizations invest in GIS&T when estimated benefits outweigh estimated costs. Evaluating costs and benefits is difficult, however and too often leads to nothing being done. For some individuals and groups, costs are prohibitive even though potential benefits are compelling. The legal framework provides a structure for regulating a number of key aspects of geographic information science, technology, and applications. Legal regimes determine who can claim the exclusive right to hold and use geospatial data, the conditions under which others may have access to the data, and what subsequent uses are permitted. Political struggles arise from conflicting proprietary and public interests about who benefits from geospatial information, and how the power to allocate the use of this information is, or should be, distributed among members of a society. The need to choose among conflicting interests sometimes poses ethical dilemmas for GIS&T professionals. The explosive growth of the geospatial information contributed by users through various application programming interfaces has made geospatial information is a powerful tool in the social media toola powerful media for the general public to communicate, but perhaps more importantly, geographic information have also become a tool media for constructive dialogs and interactions about social issues, recent growth of Web-based geospatial information and volunteered geographic information (VGI). Because so many public agencies and private organizations rely upon GIS&T for planning, decision making, and management, GIS&T increasingly affects and is used to direct daily life. Critical approaches to understanding the role of GIS in society equip practitioners to employ GIS&T reflectively. The critical approach specifically questions the assumptions and premises that underlie the economic, legal and political regimes and institutional structures within which GIS&T is implemented. Related concerns are considered in Knowledge Area OI: Organizational and Institutional Aspects.","hasChildren":true,"hasParent":true,"name":"GI and Society","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS1-1","description":"The most basic definition of a legal regime is a system or framework of rules governing some physical territory or discrete realm of action that is at least in principle rooted in some sort of law. Often the concept has been applied to specific areas of law.","hasChildren":true,"name":"The legal regime and legal framework","selfAssesment":"GI-N2K
"},{"code":"GS1-2","description":"Contract law is defined as a set of rules that govern the contractual agreements between merchants or persons. A contract is an agreement between different parties that state their responsibilities and duties to each other. A liability in contract law is when certain conditions are written into a contract that makes a party liable. Licensing is the process of giving or getting official permission to do something. A license is an agreement through which a licensee leases the rights to a legally protected piece of intellectual property from a licensor — the entity which owns or represents the property — for use in conjunction with a product or service.","hasChildren":true,"name":"Contract law, liability and licensing","selfAssesment":"GI-N2K: relevant but to be revised
"},{"code":"GS1-3","description":"Data privacy and security are two essential components of a successful strategy for data protection. Data security refers to the protection of data from unauthorized access, use, change, disclosure, and destruction. It encompasses network security, physical security, and file security. Data privacy involves protecting consumer data by eliminating or reducing the possibility of re-identifying an individual whose information is present in the data. This is done by either removing specific information or by transforming the data with random “noise” or generalization.","hasChildren":true,"name":"Privacy and Security","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS1-4","description":"Property is secured by laws that are clearly defined and enforced by the state. These laws define ownership and any associated benefits that come with holding the property. The term property is very expansive, though the legal protection for certain kinds of property varies between jurisdictions. Property is generally owned by individuals or a small group of people. The rights of property ownership can be extended by using patents and copyrights. Property rights give the owner or right holder the ability to do with the property what they choose. That includes holding on to it, selling or renting it out for profit, or transferring it to another party.","hasChildren":true,"name":"Ownership and property rights","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS1-5","description":"In economics, competition is a condition where different economic firms seek to obtain a share of a limited good by varying the elements of the marketing mix: price, product, promotion and place. Competition law is a law that promotes or seeks to maintain market competition by regulating anti-competitive conduct by companies. Public-private sector relationships deal with a particular subset of competition, i.e. competition between public and private organizations.","hasChildren":true,"name":"Competition and public-private sector relationships","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS1-6","description":"Open data is data that can be accessed, shared, used and reused without any barrier for any type of (re)user. According to the Open Definition, open data can be defined as data that be freely used, modified, and shared by anyone for any purpose subject, at most, to measures that preserve provenance and openness. Open data requires datasets to be either in the public domain, or distributed through an open license. The data must be provided as a whole, free of charge, and preferably downloadable via the Internet, including any additional information that might be necessary to comply with the open license’s terms. Openness requires the data to be provided in a readily machine-readable form. The format must be open as well, meaning that it does not place any restriction upon its use, and that the files in that format can be processed with open-source software tools. The Open Definition speaks broadly of open ‘works’, rather than of open data. Focusing on data tout court, one can move from the Open Government Data (OGD) principles. According to the OGD principles, which are arguably foundational in understanding the concept of open data, data must be: Complete; Primary; Timely; Accessible; Machine-processable; Non-discriminatory; Non-proprietary; and License-free. Compliance with the OGD principles needs to be demonstrable, i.e. there need to be accountability measures in place to allow the review of the adherence to the principles above. The concepts of Open Work and open data highlight how data needs to be both legally, technically and financially open, so either in the public domain or covered by an open license, and kept in a machine-readable and non-proprietary format. Open data aims at making information available to everybody, for any purpose, in a machine-readable and interoperable format, based on open standards and digestible by free/libre open source software (FLOSS). Also with respect to the financial accessibility open data is data available free of charge. Marginal costs of dissemination are accepted by some as a reasonable cost for users. However, open data is data that can be accessed and reused without any barrier for any type of reuse, and some user groups experience any price to be paid as a barrier.","hasChildren":true,"name":"Open data","selfAssesment":"Completed
"},{"code":"GS1","description":"Legal problems can arise when geospatial information is used for land management, among other activities. Geospatial professionals may be liable for harm that results from flawed data or the misuse of data. Understanding of contract law and liability standards is essential to mitigate risks associated with the provision of geospatial information products and services. Legal relations between public and private organizations and individuals govern data access. The nature of information in general, and the characteristics of geospatial information in particular, make it an unusual and difficult subject for a legal regime that seeks to establish and enforce the type of exclusive control associated with other commodities. Geospatial information is in many ways unlike the kinds of works that intellectual property rights were intended to protect. Still, organizations can, and do, assert proprietary interests in geospatial information. Perspectives on geospatial information as property vary between the public and private sectors and between different countries.","hasChildren":true,"hasParent":true,"name":"Legal aspects","selfAssesment":"In progress GI-N2K
"},{"code":"GS2-1","description":"Business models determine how organizations can create and deliver value, for example, through the provision or use of geographic data. A business model is a conceptual tool that contains\r\na set of interrelated elements that allow organizations to create and capture value and generate revenues. The development and implementation of an appropriate business model are considered to be a key to the success of the organization and a crucial source for value creation. \r\n\r\nAlthough business models determine how organizations create, deliver, and capture value, they should not be regarded as permanent and invariable structures or settings. Business models are shaped by both internal and external forces, and will only be successful if they are able to adapt to a changing environment. In the GI domain, several technological, regulatory, and societal developments have challenged the existing business models and opened up opportunities for new business models. Among these developments are the establishment of spatial data infrastructures (SDIs) worldwide, the democratization of geographic knowledge, and the move toward open source, open standards, and open data.\r\n\r\nSince the development and implementation of SDIs in different parts of the world, much attention has been paid to the need to find appropriate business models for GI, and in particular, for geographic data providers in the public sector. Traditional business models in which public data providers were selling their data to customers in the private industry and other public agencies were questioned, because they restricted the opportunity for data sharing. The concept of SDI is about moving to new business models, where partnerships between GI organizations are promoted to allow access to a much wider scope of geographic data and services. A key challenge in the development of these SDIs was the alignment of different existing business models of the actors in the GI domain. Moreover, the development and implementation of SDIs also led to the emergence of new business models, which was even more the case with the more recent move toward open geographic data.\r\n\r\nOrganizations can be active in different parts of the geo-information value chain, and can create and offer value in many different ways. As a result, many different GI business models exist. Data providers, data enablers, and data end users could be seen as three main categories of GI business models. Each of these categories consists of many different business models, as different value propositions\r\nwill exist, and value can be created and captured in several ways.","hasChildren":true,"name":"GI Business models","selfAssesment":"Completed (GI-N2K)
"},{"code":"GS2-5","description":"To provide a better insight into the process of adding value to GI, several authors have introduced and applied the information value chain approach. A value chain can be defined as the set of value-adding activities that one or more organizations perform in creating and distributing goods and services. The value chain concept originally was developed for the manufacturing sector, as a tool to evaluate the competitive advantage of firms. More recently, the value chain concept has been applied to other sectors, including information technology where the good or service, and the benefits it provides, is less tangible in nature. A value chain involves the progress of goods from raw materials to finished products through a number of stages, during each of which a new value is added to the original input by various activities. The value chain concept was extended into the information market, with the information value chain referring to the set of activities adding value to information and turning raw data into new information products or services. Especially important in this context is the role of information and communication technologies (ICT), which have an impact on all activities in the information value chain, such as information collection, processing, dissemination, and use. In the context of GI, the value chain relates to the series of value- adding activities to transform raw geographic data into new products that are used by certain end users. Although there are slightly different descriptions of the various steps of the GI value chain, in general, the essential steps in the value chain are: acquisition of raw data, the application of a data model, quality control, and integration with other sources, presentation, and distribution. In recent years, particular attention has been paid to different steps between the process of distributing data and the actual end use of an end product of GI. In addition, after the publication of the data, value can be added to the data in many different ways. Value can be added by making data from different sources easily accessible through repositories and data portals, by building and selling tailored solutions using the data to end users or by using geographic data to improve existing products and services delivered to an end user. In certain cases, this end product will be the first step of a next value chain.","hasChildren":true,"name":"Geo-information value chain","selfAssesment":"Completed
"},{"code":"GS2","description":"Most organizations insist that investments in GIS and T be justified in economic terms. Quantifying the value of information, and of information systems, however, is not a straightforward matter.","hasChildren":true,"hasParent":true,"name":"Economic aspects","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS3-1","description":"The use of geospatial information allows public sector organizations and actors to make better decisions and provide better services to their citizens. Geospatial information is increasingly being used at different administrative levels and in different policy areas.","hasChildren":true,"name":"Use of geospatial information in the public sector","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS3-2","description":"Geospatial information is increasingly being used by private companies for different purposes and the private sector plays an important role in the development and implementation of geospatial information infrastructures.","hasChildren":true,"name":"Use of geospatial information in the private sector","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS3-3","description":"Research and education institutions use geospatial information for various purposes, in support of their research and educational activities.","hasChildren":true,"name":"Use of geospatial information in research and education","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS3-4","description":"Effective monitoring of the environment and an improved understanding of the same requires valuable information and data that can be extracted through application of geospatial technologies. GIS can be used most effectively for environmental data analysis and planning. It allows better viewing and understanding physical features and the relationships that influence in a given critical environmental condition. GIS can help in effective planning and managing the environmental hazards and risks. In order to plan and monitor the environmental problems, the assessment of hazards and risks becomes the foundation for planning decisions and for mitigation activities. GIS supports activities in environmental assessment, monitoring, and mitigation and can also be used for generating environmental models. GIS can aid in hazard mitigation and future planning, air pollution & control, disaster management, forest fires management, managing natural resources, wastewater management, oil spills and its remedial actions etc.","hasChildren":true,"name":"Use of geospatial information in environmental issues","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS3","description":"Geospatial Information used in Government agencies and public authorities at local, state, and federal levels produce and use geospatial data for many activities, including provision of social services, public safety, economic development, environmental management, and national defence. Public participation in governing, empowered by geospatial technologies, offers the potential to strengthen democratic societies by involving grassroots community organizations and by engaging local knowledge. The private sector covers a broad range of areas of opportunity. With continued advancements in technology, greater awareness of its advantages as a powerful decision support tool the use of geospatial information use in the private sector needs to be discussed.","hasChildren":true,"hasParent":true,"name":"Use of geospatial information","selfAssesment":"In Progress GI-N2K
"},{"code":"GS4-1","description":"Public participation GIS (PPGIS) is a field within geographic information science that focuses on ways the public uses various forms of geospatial technologies to participate in public processes, such as mapping and decision making.","hasChildren":true,"name":"Public participation GIS","selfAssesment":"GI-N2K (revision)
"},{"code":"GS4-2b","description":"Social Media Geographic Information (SMGI) can be defined as any piece or collection of multimedia data or information with explicit (i.e. coordinates) or implicit (i.e. place names or toponyms) geographic reference collected through the social networking web or mobile applications. Social data are acknowledged as a good of major value in the digital economy, and their potential for enhancing more traditional analytics is of the utmost importance. A big part of social data however also features spatial (and temporal) references, thus their integration with more traditional Authoritative Geographic Information (AGI) may enable a further step towards the next generation of geospatial intelligence. SMGI is a sub-category of VGI and can be active or passive, depending on the type of application with which it is collected: applications purposefully created and/or used to collect SMGI in participatory initiatives","hasChildren":true,"name":"Social Media Geographic Information","selfAssesment":"Completed
"},{"code":"GS4-3b","description":"Volunteered geographic information (VGI) is a special kind of user-generated content. It refers to geographic information collected and shared voluntarily by the general public. Web.2.0 and associated advances in web mapping technologies have greatly enhanced the abilities to collect, share and interact with geographic information online, leading to VGI.","hasChildren":true,"name":"Citizens and volunteered geographic information","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS4","description":"Today, geo data has become a conventional and pervasively familiar data type seen at once to underpin and significantly re-characterize the digital world, with broad implications for both technology and society. Geospatial data are abundant, but access to data varies with the nature of the data, the user groups wishes to acquire it and for what purpose, under what conditions, and at what price geodata can be obtained. The explosive growth of geographic information contributed by users through various application programming interfaces has made geographic information a powerful media for the general public, but perhaps more importantly, geospatial information have also become media for constructive dialogs and interactions about social issues, recent growth of Web-based Geographic information and volunteered geographic information (VGI).","hasChildren":true,"hasParent":true,"name":"Geospatial citizenship","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS5-1b","description":"The advantages of geospatial technologies and resulting data present ethical dilemmas such as privacy and security concerns as well as the potential for stigma and discrimination resulting from being associated with particular locations. the use of geospatial technologies and the resulting data needs to be critically assessed through an ethical lens prior to implementation of programmes, analyses or partnerships. Using this lens requires not only explicit consideration of potential negative consequences of adoption but also clear articulation of the specific contexts and conditions under which benefits may be realized.","hasChildren":true,"name":"Ethics in the geospatial information society","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS5-2b","description":"A code of ethics is a guide of principles designed to help professionals conduct business honestly and with integrity. A code of ethics document may outline the mission and values of the business or organization, how professionals are supposed to approach problems, the ethical principles based on the organization's core values, and the standards to which the professional is held. Codes of ethics for geospatial professionals are intended to provide these principles and guidelines for GIS professionals","hasChildren":true,"name":"Codes of ethics for geospatial professionals","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS5","description":"Ethics provide frameworks that help individuals and organizations make decisions when confronted with choices that have moral implications. Most professional organizations develop codes of ethics to help their members do the right thing, preserve their good reputation in the community, and help their members develop as a community","hasChildren":true,"hasParent":true,"name":"Ethical aspects","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS6-1","description":"US GIS&T BoK: As GIS became a firmly established presence in geography and catalysed the emergence of GIScience, it became the target of a series of critiques regarding modes of knowledge production that were perceived as problematic. The first wave of critiques charged GIS with resuscitating logical positivism and its erroneous treatment of social phenomena as indistinguishable from natural/physical phenomena. The second wave of critiques objected to GIS on the basis that it was a representational technology. In the third wave of critiques, rather than objecting to GIS simply because it represented, scholars engaged with the ways in which GIS represents natural and social phenomena, pointing to the masculinist and heteronormative modes of knowledge production that are bound up in some, but not all, uses and applications of geographic information technologies. In response to these critiques, GIScience scholars and theorists positioned GIS as a critically realist technology by virtue of its commitment to the contingency of representation and its non-universal claims to knowledge production in geography. Contemporary engagements of GIS epistemologies emphasize the epistemological flexibility of geospatial technologies.","hasChildren":true,"name":"Epistemological and critical issues","selfAssesment":"In progress/to delete (GI-N2K)
"},{"code":"GS6-2","description":"Various types of critiques exist on the way geospatial information is being used and re-used.","hasChildren":true,"name":"Critical approach on the use of geospatial information","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS6-3","description":"Defending or refuting the argument that the \"digital divide\" that characterizes access use of geospatial information perpetuates inequities among developed and developing nations, among socio-economic groups,and between individuals, community organizations, and public agencies and private firms.","hasChildren":true,"name":"Critical aspects and invisible groups","selfAssesment":"In progress/to be delete (GI-N2K)
"},{"code":"GS6","description":"Many of the educational objectives used to define topics in this knowledge area, and in the Body of Knowledge as a whole, challenge educators and students to think critically about GI and Society. Since the 1990s, scholars have criticized cartography and the GIS science from a wide range of perspectives. Common among these critiques are questioned assumptions about the purported benefits of GI and Society and attention to its unexamined risks. By promoting reflective practice among current and aspiring geospatial information professionals, an understanding of the range of critical perspectives increases the likelihood that geospatial information will fulfil its potential to benefit all stakeholders. Philosophical, psychological, and social underpinnings of these critiques are considered in Knowledge Area CF: Conceptual Foundations.","hasChildren":true,"hasParent":true,"name":"Critical approach","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS7-1","description":"US GIS&T BoK: As GIS became a firmly established presence in geography and catalysed the emergence of GIScience, it became the target of a series of critiques regarding modes of knowledge production that were perceived as problematic. The first wave of critiques charged GIS with resuscitating logical positivism and its erroneous treatment of social phenomena as indistinguishable from natural/physical phenomena. The second wave of critiques objected to GIS on the basis that it was a representational technology. In the third wave of critiques, rather than objecting to GIS simply because it represented, scholars engaged with the ways in which GIS represents natural and social phenomena, pointing to the masculinist and heteronormative modes of knowledge production that are bound up in some, but not all, uses and applications of geographic information technologies. In response to these critiques, GIScience scholars and theorists positioned GIS as a critically realist technology by virtue of its commitment to the contingency of representation and its non-universal claims to knowledge production in geography. Contemporary engagements of GIS epistemologies emphasize the epistemological flexibility of geospatial technologies.","hasChildren":true,"name":"Epistemological critiques","selfAssesment":"GI-N2K
"},{"code":"GS7-3","description":"US GIS&T BoK: \r\n\r\nFeminist interactions with GIS started in the 1990s in the form of strong critiques against GIS inspired by feminist and postpositivist theories. Those critiques mainly highlighted a supposed epistemological dissonance between GIS and feminist scholarship. GIS was accused of being shaped by positivist and masculinist epistemologies, especially due to its emphasis on vision as the principal way of knowing. In addition, feminist critiques claimed that GIS was largely incompatible with positionality and reflexivity, two core concepts of feminist theory. Feminist critiques of GIS also discussed power issues embedded in GIS practices, including the predominance of men in the early days of the GIS industry and the development of GIS practices for the military and surveillance purposes.\r\n\r\nAt the beginning of the 21st century, feminist geographers reexamined those critiques and argued against an inherent epistemological incompatibility between GIS methods and feminist scholarship. They advocated for a reappropriation of GIS by feminist scholars in the form of critical feminist GIS practices. The critical GIS perspective promotes an unorthodox, reconstructed, and emancipatory set of GIS practices by critiquing dominant approaches of knowledge production, implementing GIS in critically informed progressive social research, and developing postpositivist techniques of GIS. Inspired by those debates, feminist scholars did reclaim GIS and effectively developed feminist GIS practices.","hasChildren":true,"name":"Feminist critiques","selfAssesment":"GI-N2K
"},{"code":"GS7-4","description":"In the early 1990s social critiques of GIS from human geographers began to appear. These initial critiques set off an ensuing debate between GISers, defending GIS and human geographers, who critiqued GIS. This debate materialized in academic journals including: Political Geography Quarterly, Environment and Planning A, and Progress in Human Geography. Schuurman (2000) notes that the GIS debate, while unique to the discipline of Geography, was part of a larger debate in other disciplines about the effects of technology. This presentation will be limited (unfortunately) to two aspects of this debate. It will first discuss conditions within human geography that made GIS a target of human geographers' critique. Second, this paper will discuss the particular critiques that were directed at GIS by human geographers. Though the reaction of such critiques and their effect on GIS is an important topic there is not enough time and space to address these issues. See Schuurman (2000) \"Trouble in the Heartland: GIS and its critics in the 1990s\" in Progress in Human Geography for a thoughtful look at this debate and its effects on the discipline of GIS.","hasChildren":true,"name":"Social critiques","selfAssesment":"GI-N2K
"},{"code":"IP","description":"Image processing and analysis comprises all relevant steps to reach from (raw) image data to [...] information via image interpretation and digital image classification. In traditional remote sensing workflows, this step follows the image acquisition process. There are two main components, i.e. (1) image processing, (2) analysis, which emphasizes the sequential nature of the process – while increasingly this dichotomy disappears.\r\nThe information production workflow aims at converting semantically rich, but unstructured image data into a set of classes, objects, arrangements, etc., to enable ultimately a complete image understanding and scene reconstruction. This scene reconstruction entails a mental component (“understanding”) and a technical one, by providing standardized classification results or even beyond, dedicated information products in form of digital maps and reports, tailored to the specific application domains and use cases, in order to make informed decisions. Such information products can be maps, reports, dashboards etc., overall it is the transformation from quantitative, semi-continuous digital numbers (“brightness”) to qualitative information using categories and figures, which can be stored and further used in a GIS environment. \r\nThe first part of the process entails image calibration, image correction (geometric, radiometric), data assimilation, and any type of enhancement (contrast manipulation, filtering, etc.) which aims to better condition the information extraction part. It ends where we achieve a significant milestone in the processing milestone, remarkably denoted as analysis-ready data (ARD). From there, we enter into the analysis realm, classically referred to as digital image classification, the process of assigning pixels to classes. In other words, the aggregation of pixel values according to their similarity into categorical (nominal) classes. The discrimination of these classes by and large depend on application domain, and ideally, these classes match with information classes. To address the issue of ambiguity and to overcome the so-called semantic gap in image interpretation by providing a stepping-stone in the information extraction process, the strategy of pre-classification (semi-concepts) has been introduced in the literature.\r\nToday, boundaries between pre-processing and classification increasingly vanish, through an increasing level of automation in the pre-processing and image correction steps. In addition, new ways of analysis emerge, in particular in large time series, including image data cubes. Instead of a processing chain, which suggests a linear – and potentially irreversible – cascade of manipulations, the automation of large parts of this part allows us to see the process more reversible and approachable from either side.","hasChildren":true,"hasParent":true,"name":"Image processing and analysis","selfAssesment":"Completed
"},{"code":"IP1-1-1","description":"The image spatial subset allows to extract the group of pixels / grid cells using a defined polygon e.g. area of interest – AOI or defining the new image extent. It is used to limit spatially the image extent to which, for example an image function or classification model will be applied.","hasChildren":true,"name":"Image subset","selfAssesment":"Completed
"},{"code":"IP1-1-2","description":"Layer stacking is a process for combining multiple images into a single image. The image stack is used to build a ‘new’ multiple band file from the georeferenced images of various pixel sizes, extents, projections. The image bands must be resampled and reprojected to a common spatial grid. The layer stacking is used for example to combine spectral bands from a Landsat, Sentinel-2 data and SRTM DEM into one multi-dimensional file. The process of layer stacking increases the size of the final stacked image, which may have consequences that increase the processing time of operations performed on the stacked image.","hasChildren":true,"name":"Layer stack","selfAssesment":"Completed
"},{"code":"IP1-1","description":"Data manipulation adjusts a dataset to the needs of a specific application by subsetting the spatial extent or the number of bands or by organizing bands from separate single layer files into a single multi-layer file.","hasChildren":true,"hasParent":true,"name":"Data manipulation","selfAssesment":"New
"},{"code":"IP1-2","description":"Fourier analysis - A characteristic of remotely sensed images is a parameter called spatial frequency, defined as the number of changes in brightness value per unit distance for any particular part of an image. There are low-frequency and high-frequency areas. Spatial frequency may be enhanced or subdued using Fourier Analysis (an alternative technique is spatial convolution filtering). Fourier analysis mathematically separates an image into its spatial frequency components. It is then possible interactively to emphasize certain groups (or bands) of frequencies relative to others and recombine the spatial frequencies to produce an enhanced image.\r\nThe signal received by a pulsed radar is a time sequence of pulses for which the amplitude and phase are measured. The frequency content of this time-domain signal is obtained by taking its Fourier transformation.","hasChildren":true,"name":"Fourier transformation","selfAssesment":"New
"},{"code":"IP1-3-1-1","description":"Structure from motion (SfM) describes the photogrammetric process for estimating the 3D structure of a scene, whereby correspondences between multiple images are established and used to detect motion parallax. When a camera moves over a surface while taking successive overlapping images, the distances between features on the surface will change from one image to the next. The changes depend on the distance of the feature points to the camera, and thus the surface elevation. This motion parallax can be used to generate an accurate 3D representation of the surface. \r\nThe photogrammetric problem of SfM is similar to stereo vision, but has gained popularity with the advent of inexpensive cameras which have variable internal geometries, unlike metrically stabilized cameras traditionally used in airborne mapping. Even with less accurate or even missing GPS location and orientation metadata, SfM still allows for the creation of (hyper)local DEMs as long as the imagery contains sufficient overlap. Airborne or spaceborne platforms can be used, provided that 2D frame-based cameras are used which can be represented with a pinhole mathematical model. \r\nGenerating a digital elevation model (DEM) from SfM is typically handled automatically using specialized software. Firstly, image correspondences are detected. Feature points are identified in the individual images using local contrast feature detectors. The features extracted from all the images are matched with all the available overlapping images and erroneous matches are filtered out. The process typically results in hundreds or thousands of tie-points per image, which allows for robust matching even with large a priori uncertainties in camera orientation. A bundle adjustment, solving for the 3D coordinates of the feature points, the position and orientation of the camera and its internal characteristics then results in an initial, so-called sparse 3D point cloud. \r\nNext, ground control points (GCPs) can be introduced. These are surface features (naturally present or introduced into the scene) which can be identified at the pixel level in the images by users. Measured also in the field with an accuracy smaller than the pixel size, they can be used to constrain the bundle adjustment solution to improve georeferencing and camera calibration to an accuracy similar to that of the GCP measurement or the GSD size. \r\nSince this process yields a match only for a small subset of all pixels, an additional step, called dense image matching is added. It starts from the exact position and orientations resulting from the bundle adjustment to rectify the images and overlay two or more images, to compare them row by row and in 16 different directions in a process called semi-global matching (SGM). Matching pixels are identified along these lines, and 3D intersection distances photogrammetrically inferred. By combining results from different directions, a 3D coordinate for almost every pixel is obtained with similar accuracy. Finally, DEM products with a regularly spaced grid are generated and exported based on the dense point cloud. Depending on the point classes used in the export (obtained through topographic filtering or deep-learning-based classification of the dense point cloud), the outcome will be a digital surface model (DSM) or digital terrain model (DTM).","hasChildren":true,"name":"DEM generation with 'Structure-from-Motion'","selfAssesment":"Completed
"},{"code":"IP1-3-1-2","description":"Photogrammetry is the science and technology of obtaining spatial measurements and other geometrically reliable derived products from photographs. Basic geometric principles applying both traditional analogue and modern digital procedures are related to the central projection of the image in case of typical cameras and to the dynamic projection mostly in case of push-broom sensors, popular in the satellite photogrammetry. The fundamental principle used by photogrammetry is called triangulation. By taking photographs from at least two different locations, so-called “lines of sight” can be developed from each camera to points in a block on the object. These lines of sight (called rays) are mathematically intersected to produce the 3-dimensional coordinates of the points of interest.\r\nWithin data processing the most important parts of photogrammetric workflow are: (1) image orientation, (2) model reconstruction, and (3) orthorectification. Image orientation is based mostly on aerial triangulation, however recently the computer vision algorithm, called structure from motion, became more popular in particularly in close range photogrammetry. Both orientation approaches include detection or measurement of the points between overlapping images in a block, control points measurements in a field defining orientation in reference system and check points verifying the orientation process. The satellite photogrammetry due to different projection and much bigger areas of imaging is usually related to Rational Polynomial Coefficients (RPCs) defining preliminary scene orientation during image orientation. However, to receive more accurate results also here the control points measured in a field are in use. The second part of the modern photogrammetric processing is 3D model reconstruction. In past, vectorization within the stereoscopic measurements was the most popular way of using photogrammetric data after the image orientation. The development of the informatics contributed to the development of the image matching algorithms that can provide dense image point clouds, which can be used to the 3D detailed modelling including digital elevation model production. The final step of photogrammetric processing is orthorectification, which delivers cartometric image called orthophoto mosaiced into orthophotomaps. This process comprises the influence of digital terrain model, model of camera (interior orientation) and image orientation (exterior orientation). Orthophotomap and elevation models derived from photogrammetric processing are applied as very popular data source in many GIS systems. The other photogrammetric outcomes are, for example a 3D measurement or 3D models of some real-world object or scene.","hasChildren":true,"name":"Photogrammetric principles","selfAssesment":"Completed
"},{"code":"IP1-3-1-3","description":"In satellite photogrammetry to obtain the orientation mostly of satellite scene Rational Polynomial Coefficients (RPCs) are applied. They provide a compact representation of a ground-to-image geometry, that allow for photogrammetric processing without requiring a physical camera model. Model with RPC is provided with satellite image and can be improved using measurements of indirect surveying methods used for control point measurement. The RPC model for the coordinates of the image point is calculated as ratios of the cubic polynomials in the coordinates of the world or object space or ground point. \r\nIn photogrammetry and remote sensing, rational polynomial coefficients (RPCs) describe a specific imaging geometry model for transforming image pixel coordinates to map coordinates (thereby accounting for terrain displacement errors). A sensor model describes the geometric relationship between the object space and the image space, or vice versa. It relates 3-D object coordinates to 2-D image coordinates. RPCs are part of a general sensor model that approximates the physical sensor model. The physical sensor model represents the physical imageing process, making use of information on the sensor's position and orientation (during image acquisition). The RPC model often refers to a specific case of the RFM (rational function model) that is in forward form, has third-order polynomials, and is usually solved by the terrain-independent scenario.","hasChildren":true,"name":"RPC correction","selfAssesment":"Completed
"},{"code":"IP1-3-1-4","description":"A ground control point (GCP) is a location of the surface of the Earth (e.g. a road intersection) that can be identified on the imagery and located accurately on the map (i.e. the reference dataset). Two distinct sets of coordinates are associated with the GCP: image coordinates in i rows and j columns, and map coordinates (e.g. x, y measured in degrees of latitude and longitude or as specified by the spatial reference system).","hasChildren":true,"name":"Ground Control Points (GCP)","selfAssesment":"Planned
"},{"code":"IP1-3-1","description":"Orthorectification is the process of removing sensor (scanner or camera), satellite/aircraft, and terrain-related distortions for creating a planimetrically correct image. \r\nTo obtain an accurately orthorectified image, the following information is required: (1) accurate elevation model, and (2) a camera model or rational polynomial coefficients (RPCs) that depicts the positional relationship of the collected image to the ground. Many companies deliver their images together with RPCs and existing software implementations can automatically read these files and apply the RPC transformation on the fly. An accurate elevation model is important to remove the influence of topography (e.g. hills, valley, etc.) on the raw image so that users can accurately compute distances, areas, and directions. Without performing orthorectification, the features in the image are tilted (especially the features located away from the center of the camera). Many satellite data products (e.g. Sentinel images, Landsat data products) are orthorectified using Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) data which is a freely available data product and has a spatial resolution of e.g. 1 arc-second (30 m). In the case of extremely jagged surface topography, i.e. areas of high relief, a DEM with a higher spatial resolution is required. \r\nTwo main models can be used in the orthorectification process: black-box and the physical-based model. The black-box model (called also the analytical model) is commonly implemented in different software because it relies solely on the RPC files. This model does not require access to any proprietary information of the sensor used to collect the image. \r\nThe physical-based models are more complex (and hence expected to be more accurate) because they account for various factors that might influence the quality of the acquired image: e.g. position of the satellite when collecting the images, atmospheric effects, etc. An example of a physical-based model is the so-called camera model. This model requires access to proprietary sensor information that has to be provided by the image owner.","hasChildren":true,"hasParent":true,"name":"Orthorectification","selfAssesment":"Completed
"},{"code":"IP1-3-2-1","description":"Image co-registration [aka Image-to-image registration] is the translation and rotation alignment process by which two images of like geometry and of the same geographic area are positioned coincident with respect to one another so that corresponding elements of the same ground area appear in the same place on the registered images (Jensen 2005 referencing Chen and Lee 1992).","hasChildren":true,"name":"Image co-registration","selfAssesment":"New
"},{"code":"IP1-3-2","description":"Spatial referencing (referred to as geo-referencing as well) is the process of aligning available EO or GIS data to a coordinate system so that further spatial analysis and image analysis tasks can be applied using these data as input. \r\nTo be able to perform spatial referencing, users have to generate the so called Ground Control Points (GCPs) with known coordinates. In case of images, the easiest features that could be used as GCPs are the intersections, isolated trees etc.","hasChildren":true,"hasParent":true,"name":"Spatial referencing","selfAssesment":"Planned
"},{"code":"IP1-3","description":"Geometric correction is concerned with placing the reflected, emitted, or back-scattered measurements or derivative products in their proper planimetric (map) location so they can be associated with other spatial information. It is usually necessary to preprocess the remotely sensed data and remove the geometric distortions so that individual picture elements (pixels) are in their proper planimetric (x, y) map locations. This allows remote sensing-derived information to be related to other thematic information in geographic information systems (GIS) or spatial decision support systems (SDSS). Geometrically corrected imagery can be used to extract accurate distance, polygon area, and direction (bearing) information.\r\n\r\nGeometric correction techniques are dedicated to resolving the geometric distortions caused by: (1) variations in sensor position; (2) Earth curvature; (3) rotation of Earth on its axis; (4) relief displacement. \r\n\r\nThere are two types of geometric distortions, namely systematic and random distortions. The former might be caused by Earth's rotation for example and, therefore they are predictable and systematic. The second type of distortions might be caused by terrain or variations in sensor altitude. \r\nGeometric correction includes georeferencing and orthorectification techniques.","hasChildren":true,"hasParent":true,"name":"Geometric correction","selfAssesment":"Completed
"},{"code":"IP1-4-1","description":"Contrast stretching (also referred to as contrast enhancement) expands the original input brightness values to make use of the total dynamic range or sensitivity of the output device (a computer display).","hasChildren":true,"name":"Contrast stretching","selfAssesment":"New
"},{"code":"IP1-4-2","description":"The histogram is a useful graphic representation of the information content of a remotely sensed image. Histograms for each band of imagery are often displayed and analysed in many remote sensing investigations because they provide the analyst with an appreciation of the quality of the original data (e.g. whether it is low in contrast, high in contrast or multimodal in nature. [...] Tabulating the frequency of occurrence of each brightness value within the image provides statistical information that can be displayed graphically in a histogram.","hasChildren":true,"name":"Histogram","selfAssesment":"New
"},{"code":"IP1-4","description":"Image enhancement algorithms are applied to remotely sensed data to improve the appearance of an image for human visual analysis or occasionally for subsequent machine analysis. The quality of results of image analysis are subjectively judged by humans as to whether they are useful. They include contrast enhancement.","hasChildren":true,"hasParent":true,"name":"Image enhancement","selfAssesment":"New
"},{"code":"IP1-6","description":"Principal component analysis (PCA) has proven to be of value in the analysis of multispectral and hyperspectral remotely sensed data. PCA is a technique that transforms the original correlated spectral dataset into a substantially smaller and easier set of uncorrelated variables that represents most of the information present in the original dataset. The first component accounts for the maximum proportion of the variance of the original dataset, and subsequent orthogonal components account for the maximum proportion of the remaining variance.","hasChildren":true,"name":"Principal component analysis (PCA)","selfAssesment":"New
"},{"code":"IP1-7-1-1","description":"Bottom-of-Atmosphere (BOA) reflectance is also called surface reflectance and consists of the solar radiation that is reflected from the Earth's surface.","hasChildren":true,"name":"Bottom-of-Atmosphere (BOA)","selfAssesment":"New
"},{"code":"IP1-7-1-4","description":"Top-Of-Atmosphere (TOA) radiance represents the radiance observed outside Earth’s atmosphere. It is derived from the Digital Numbers (DN) using metadata delivered with the image.","hasChildren":true,"name":"Top-Of-Atmosphere (TOA)","selfAssesment":"New
"},{"code":"IP1-7-1","description":"Atmospheric correction accounts for the attenuation caused by scattering and absorption in the atmosphere. It transforms top-of-atmosphere (TOA) reflectance to bottom-of-atmosphere (BOA) reflectance.\r\nThe decision to perform atmospheric correction depends on the need, i.e. the envisioned usage of the derived EO information product and the nature of the underlying problem. This includes requirements to the accuracy of extracted biophysical information. Additionally, the decision and choice of methods depends on the type of remote sensing data available, the amount of in-situ historical and/or concurrent atmospheric information available.\r\nAn atmospheric correction is essential when biophysical or geophysical parameters (e.g. of water or vegetation) are going to be extracted from the remote sensing data. If the data is not corrected, the subtle differences in reflectance among the contributing image bands may be lost. This is especially relevant when biophysical information shall be compared to that of images from other dates.\r\nHowever, some cases exist where it is unnecessary to perform atmospheric correction. For example, it is not necessary for producing an image classification product from a single date of remotely sensed data. If a maximum likelihood classification is applied that uses training data with the same relative scale for the pixel values, then, atmospheric correction has little effect on the classification accuracy. The same holds true for a post-classification change detection where the classifications of the two different dates were performed independently. \r\nThe process of (absolute) atmospheric correction requires a model atmosphere and in situ atmospheric measurements acquired at the time of remote sensor data acquisition as input. In situ data can be available from other sensors on-board the sensor platform.\r\n\r\nDark Object Subtraction (DOS) is one of the most popular empirical atmospheric correction techniques. This technique assumes that a black object has a reflectance value of zero. Yet, a dark object present in a satellite image will have a value different than zero because of the atmospheric scattering. This value is then subtracted from all pixels in a given spectral band.","hasChildren":true,"hasParent":true,"name":"Atmospheric correction","selfAssesment":"Completed
"},{"code":"IP1-7-2","description":"The number of spectral bands assocuates with a remote sensing system is referred to as its data dimensionality. Hyperspectral remote sensing systems such as AVIRIS ans MODIS obtain data in 224 and 36 bands, respectively. The greater the number of bands in a dataset (i.e., its dimensionality), the more pixels that must be stored and processed by the digital image processing system. Storage and processing consume valuable resources. It is necessary to reduce the dimensionality of hyperspectral data while retaining the information content inherent in the image. \r\nA method for dimensionality reduction in hyperspectral data and minimizing the noise in the imagery is the minimum noise fraction (MNF) transformation. The purpose is to minimize the noise in the imagery, i.e. to identify noise and segregate it from true information, and to colaps the useful information into a much smaller set of MNF images. The MNF transformation applies two cascaded principal components analyses.","hasChildren":true,"name":"Dimensionality reduction","selfAssesment":"New
"},{"code":"IP1-7-3","description":"Sensor calibration converts the sensor’s digital numbers (DNs) to at-sensor radiance above the atmosphere. A further radiometric adjustment accounts for the viewing angle and sun angle during acquisition to transform radiance values to top-of-atmosphere (TOA) reflectance. Therefore, the process requires sensor calibration information and telemetry data that satellite image providers deliver within the metadata.\r\nDNs are raw sensor data without physical units. The sensor calibration information for converting the DNs to radiance are the calibration gain (cal_gain) and calibration offset (cal_offset) values. The sensor calibration uses linear function f(DN) = DN * cal_gain + cal_offset that multiplies the DNs of each pixel in each spectral band with their corresponding cal_gain and adds the corresponding cal_offset. The resulting at-sensor radiance image is the basis for the radiometric adjustment that uses information about the viewing angle and sun angle during acquisition to transform at-sensor radiance to TOA reflectance. \r\nSensor calibration obtains TOA reflectance and is a minimum requirement for performing band math calculations to derive spectral indices such as the normalized vegetation difference index (NDVI). Uncalibrated image data would arrive at NDVI values that are distorted because the cal_gain and cal_offset parameters for the involved spectral bands were not considered.","hasChildren":true,"name":"Sensor calibration","selfAssesment":"Completed
"},{"code":"IP1-7-4","description":"As an optical remote sensing system is not perfect, noise can enter the data collection system at several points. Necessary corrections include the removal of shot noise (random bad pixels), correcting line or column drop-outs, accounting for line-start problems and radiometric correction of n-line striping caused by detector miscalibration.\r\nSAR data have global, random speckle noise. Speckle filters are designed to adapt to local image variations in order to smooth values, thus reducing speckle and enhancing lines and edges to maintain the sharpness of an image. A widely used way to reduce speckle is to apply spatial filters to the images. Typical approaches for speckle filtering include Laplace filtering for smoothing and sigma filters that preserve more of the signal with a lesser effect of smoothing.","hasChildren":true,"name":"Noise reduction","selfAssesment":"New
"},{"code":"IP1-7-5","description":"Topographic correction, or topographic effects correction, aims to adjust the spectral values of an image according to effects of solar illumination differences due to the irregular shape of the terrain. Topographic slope and aspect introduce radiometric distortion of the recorded signal. Further, terrain shadow dramatically affects the brightness values of the covered pixels in an image. Topographic effects of illumination and shadow are particularly relevant in mountainous regions and in regions towards the higher latitudes of the southern and northern hemisphere. The effects appear pronounced during the winter season. \r\nTogether with sensor calibration and atmospheric correction, topographic correction is part of the radiometric correction process to obtain true reflectance values from sensor radiance. This process is necessary when using EO data for obtaining geophysical measurements. It can also benefit the accuracy of image classifications by reducing the internal variability of vegetation types, since the corrected reflectance relates better to the geometrical or biological properties of the plant than to the original reflectance.\r\nMethods for the removal of topographic effects from remotely sensed images can simply be based on band ratios that do not require additional input. Alternatively, they use digital elevation models (DEMs) as an additional input and apply sophisticated modelling of the illumination conditions. The illumination model describes various aspects of the relationship between the sensor measurement, the sun illumination, the ground reflectance and the diffuse irradiance at the surface. The model incorporates the angles between the sun position, the ground position (described by slope and aspect from the DEM), and the sensor position. Among these methods are lambertian methods and non-lambertian methods such as the bidirectional reflectance distribution function (BRDF). The BRDF, which is more suitable to the non-Lambertian properties of the observed surfaces, describes how the reflectance varies in each cover considering the angles of incidence and observation. \r\nIf achieved with a high quality, the resulting topographically corrected image appears to be illuminated evenly as if all its pixels would be part of a flat surface without the presence of any terrain differences. However, the much larger benefit than the improved appearance is the availability of pixel values that are closest to the true reflectance when compared to TOA, BOA and DN values.","hasChildren":true,"name":"Topographic correction","selfAssesment":"Completed
"},{"code":"IP1-7","description":"Radiometric calibration and correction converts the sensor’s digital numbers (DNs) to radiance values and subsequently reflectance values. Additionally, the term “correction” points to the fact that radiometric measurements with satellite sensors contain error. Therefore, radiometric correction is concerned with improving the accuracy of surface spectral reflectance, emittance, or back-scattered measurements obtained using a remote sensing system. The Earth’s atmosphere, land and water are complex and can never be captured perfectly because of the limitations of remote sensing devices that lie in their spatial, spectral temporal and radiometric resolution. Therefore, error occurs in the data acquisition process and degrades the quality of remotely sensed data. The most common errors in remote sensing are radiometric and geometric. This concept is focused on the correction of remote sensing data to account for radiometric error that is to some degree systematic. Systematic errors in radiometric measurements come from the interaction of the sensed radiance with the atmosphere, the acquisition geometry in relation to the radiance source (the sun) and the Earth surface geometry (terrain).\r\nThere are several levels of radiometric calibration and correction. The first is sensor calibration that converts the DNs to top-of-atmosphere (TOA) reflectance. It converts to radiance values and further to reflectance values by accounting for the viewing angle and sun angle during acquisition. The second is atmospheric correction that converts TOA reflectance to bottom-of-atmosphere (BOA) reflectance. The third is topographic correction that converts BOA reflectance to surface reflectance. \r\nRadiometric calibration is necessary to ensure radiometric comparability of the measurements. There is a need for calibration when comparing different spectral bands within one image, e.g. for the calculation of geo-biophysical parameters with band math operations. Results from uncalibrated image data would differ from results achieved with calibrated data because the unaccounted cal_gain and cal_offset of the used spectral bands would lead to distortions. \r\nIn addition, radiometric calibration complements the geospatial comparability that is achieved with geo-referencing an image to geographic coordinates. Geo-referencing enables comparison of an image pixel to the geospatially matching pixel in another image acquired with a different sensor but with comparable resolution. Radiometric calibration enables a radiometric comparison between these two pixels’ radiance values. In case the two images are from different acquisition dates, a calculated radiometric difference would indicate change. This example shows the relevance of radiometric calibration for inter-sensor comparisons.\r\nRadiometric comparability is particularly relevant in studies that require inter-sensor comparisons, comparisons of surface features over time, or comparisons to laboratory or field reflectance data. Then the radiometric correction should cover atmospheric, solar and topographic effects. A full radiometric correction that also includes topographic correction can benefit the accuracy of image classifications by reducing the internal variability of vegetation types, since the corrected reflectance relates better to the geometrical or biological properties of the plant than to the original reflectance.","hasChildren":true,"hasParent":true,"name":"Radiometric calibration and correction","selfAssesment":"Completed
"},{"code":"IP1","description":"Image pre-processing focuses on transforming the electrical signal measured by a sensor to a processing level at which pixel values can be used for the next information extraction step. Therefore, pre-processing operations involve the removal of errors encountered while collecting remotely sensed data to get as close as possible to the true radiant energy and spatial characteristics of the study area at the time of data collection. Different sensor type (optical, radar, lidar) require different processing levels\r\nThe most common image pre-processing procedures include: \r\n(1)\tRadiometric calibration involves the transformation of Digital Numbers (DN) to physical unit: radiance/reflectance. Radiometric calibration can be done before the launch of a satellite sensor, i.e. pre-launch calibration, or after launch. In the second case, the calibration is performed on-board or by comparing ground measurements with satellite radiance. Through radiometric calibration various scene illumination procedures such as sun elevation correction or earth-sun distance correction are applied. Furthermore, image noises caused by striping or line drop as happened in case of Landsat TM7 due to failure of the Scan Line Corrector (SLC) are also corrected using specialized procedures.\r\n(2)\tAtmospheric correction accounts for two main processes: scattering and absorption. Scattering represents a disturbance of the electromagnetic waves caused by rayleight scattering (caused by very small particles such as the air molecules), mie scattering (caused by aerosol particles) and non-selective scattering (dust, smoke, rain etc.). Absorption occurs when the electromagnetic energy is absorbed by the atmospheric components. Therefore, atmospheric windows have to be removed before using the satellite images in the next processing steps. Atmospheric corrections can be carried out either using simple statistical methods or complex radiative transfer based methods\r\n(3)\tGeometric correction is required to remove the distortions caused by the Earth curvature, Earth rotation, panoramic distortion due to the field of view of the sensor and the topography of the terrain. Geometrics distortions are corrected using Ground Control Points (GCP) and a Digital Elevation Model (DEM). In case of airborne images, additional distortions caused by variations in the platform altitude or velocity might occur.","hasChildren":true,"hasParent":true,"name":"Image pre-processing","selfAssesment":"Completed
"},{"code":"IP2-1-1","description":"Data augmentation refers to a scheme of augmenting the observed data so as to make it more easy to analyze. An application from deep lerarning is to increase the number of input training sample images with augmented data. Examples of data augmentation techniques include horizontal flips, random crops, and principal component analysis.","hasChildren":true,"name":"Data augmentation","selfAssesment":"New
"},{"code":"IP2-1-2","description":"Data imputation refers to a scheme of replacing missing values by imputed values. Imputation can be done, for example with mean, median and mode. Imputation methods can efficiently predict multiple response variables simultaneously.","hasChildren":true,"name":"Data imputation","selfAssesment":"New
"},{"code":"IP2-1-3-1","description":"Gram-Schmidt is a pan-sharpening method that has been invented by Laben and Brover in 1998 and patented by Eastman Kodak. It makes use of the Gram-Schmidt orthogonalization to decorrelate the spectral bands (panchromatic, red, green, blue, etc.) and transform them into one multidimensional vector.","hasChildren":true,"name":"Gram-Schmidt pan-sharpening","selfAssesment":"New
"},{"code":"IP2-1-3-2","description":"This pan-sharpening method uses PCA to transfer detailed spatial information from panchromatic band to the available multispectral bands.","hasChildren":true,"name":"Principal Component Analysis (PCA)-based pan-sharpening","selfAssesment":"New
"},{"code":"IP2-1-3","description":"Pan-sharpening methods are used to enhance spatial resolution of images by merging a panchromatic image with high resolution with a multispectral image with low resolution.","hasChildren":true,"hasParent":true,"name":"Pan-sharpening","selfAssesment":"New
"},{"code":"IP2-1-4","description":"Spatiotemporal image fusion methods, called also spatiotemporal downscaling methods, represent an efficient solution to generate fine-scale images at a high temporal resolution for more detailed land cover mapping and monitoring applications. Spatiotemporal image fusion methods can be classified into three categories: (1) reconstruction-based , (2) unmixing based and (3) learning-based methods.","hasChildren":true,"name":"Spatio-temporal image fusion","selfAssesment":"New
"},{"code":"IP2-1","description":"Image fusion is defined as the “combination of two or more different images to form a new image by using a certain algorithm” Data fusion is a well-established research field. Image fusion methods are primarily used for improving the level of interpretability of the input data. Additionally, they can be utilized to address the problem of missing data caused by cloud or shadow contamination in satellite images time series. Image fusion can be performed at pixel-level, feature-level (e.g. land-cover classes of interest), and decision-level (e.g. purpose driven).","hasChildren":true,"hasParent":true,"name":"Data fusion","selfAssesment":"Planned
"},{"code":"IP2-2","description":"Data harmonization aims to transform different datasets in such a way that they fit together, both with respect to geometry and semantics. The goal is that a user, who is using data from different authorities, shall have a unified view, where conflicts in the datasets have been removed.","hasChildren":true,"name":"Data harmonisation","selfAssesment":"New
"},{"code":"IP2-3","description":"Data integration is the process of combining different geographic datasets including those derived from remote sensing data. The combined datasets can have different coverage, but they have to have the same geographic coordinates.","hasChildren":true,"name":"Data integration","selfAssesment":"Planned
"},{"code":"IP2","description":"Data assimilation is a strategy to foster data integration and data harmonisation in a bi-directional way between the measured and the modelled reality. In other words, it aims to combine measurements (observations) with the understanding of the spatio-temporal properties and evolution of system’s variables or properties and model information about them. Models can be calibrated and keeping them ‘on track’ by constraining them with observations. Vice versa, observations can be validated through models. Approached as a mathematical problem, data assimilation aims at minimizing cost functions or penalize a function to ensure optimality in fitting. Equations are used to describe system parameters and the relationships among them, It is noteworthy, that models encompass information from previous measurements, experiences, and theory. While the observations are influenced by (known) properties such as precisions, etc. of the measurement devices, the robustness of models rely on the consolidated knowledge. Because uncertainties reside in all components with unknown or even undeterminable errors, the approach is usually probabilistic, including Bayesian and other related techniques. Widely used in meteorological sciences, successful data assimilation has been boosted the reliability of weather forecast , while sensitivity to errors remains. \r\nIn Earth observation, data assimilation compensates for the fact that a specific site could be observed in a variety of measurements by satellites with different sensor types, at different dates, different angular geometries and viewing directions, illumination conditions (solar time), observation frequencies, etc. In particular, for monitoring processes, measurements over time need to assure to actually measure the status of the system or object and not the divergence in observation. To overcome these divergences and converge them with the actual properties of an observed object or target class such as spectral or geospatial properties, observation modelling can be considered an important contribution from geospatial theory. this also links to class modelling or geon modelling. The synergy of a vegetation growth model and a remote sensing observation model can be exploited to improve the retrieval of geo-biophysical information. For vegetation and crop type monitoring radiative transfer modelling (RTF) is being used as an example. \r\nData assimilation can also serve in bridging the gaps between non-availabilities of EO data and other observations, to provide estimates or prediction for geographical variables, testing of hypotheses or continuous observation (monitoring). A related aspect is data imputation, i.e. filling gaps in observations e.g. by other, complementary data sets (e.g. Radar imagery in the absence of VHR data in cloudy weather conditions). Recently, these sources can also be complemented by crowd mapping and citizen science. \r\nWhen interpretation of data comes into play, such as image classification, we introduce another level of uncertainty. Thus the community seeks for rigorus classifiers based on solid spectral models, acting across sensors. Semantic enrichment of satellite data is a related strategy for reaching to interpreted data in a rigorous way. \r\nSummarizing, data assimilation comprises steps to improve the level of interpretability of the input data, by enrichment (get rid of spatial/temporal gaps), by accounting for heterogeneity (through harmonization), and by integration (combination with other data that is relevant to the application). Thereby, datasets become more comparable to each other.","hasChildren":true,"hasParent":true,"name":"Data assimilation","selfAssesment":"Completed
"},{"code":"IP3-1-1-1","description":"Vegetation fraction (VF) is defined “as the percentage of vegetation occupying a pixel as viewed in vertical projection. It’s a comprehensive quantitative index in forest management and vegetation community cover conditions, and it’s also an important parameter in many remote sensing ecological models.”","hasChildren":true,"name":"Vegetation fraction","selfAssesment":"Planned
"},{"code":"IP3-1-1-2","description":"Leaf area index (LAI) is the ratio between the total area of the upper leaf surface of vegetation and the surface area of the pixel in question. LAI is a dimensionless value, typically ranging between 0 (for a pixel composed of bare soil) and values as high as 6 (for a dense forest).","hasChildren":true,"name":"LAI (Leaf Area Index)","selfAssesment":"Planned
"},{"code":"IP3-1-1-3","description":"Net primary production (NPP) is a measure of the inherent productivity of a region or ecological system—mainly the Earth’s production of organic matter, principally through the process of photosynthesis in plants.","hasChildren":true,"name":"Net primary production (NPP)","selfAssesment":"New
"},{"code":"IP3-1-1-4","description":"Water quality variables can be derived from Earth observation (EO) data to provide essential ocean variables. They include Sea-surface temperature (SST), Sea-surface salinity (SSS) and Air-Sea Fluxes. SST controls the atmospheric response to the ocean at both weather and climate time scales. The spatial patterns of SST reveal the structure of the underlying ocean dynamics, such as, ocean fronts, eddies, coastal upwelling and exchanges between the coastal shelf and open ocean. SSS observations contribute to monitoring the global water cycle (evaporation, precipitation and glacier and river runoff). Water quality variables can be derived from EO data by using ocean colour products from optical sensors and relating them to ground truth information from in situ sensor networks.","hasChildren":true,"name":"Water quality variables","selfAssesment":"New
"},{"code":"IP3-1-1","description":"Biophysical parameter retrieval is an approach in remote sensing that aims to estimate parameters which have physical meaning related to properties of living organisms. The goal is to provide quantitative results directly relating to the biophysical state, but independent of acquisition conditions and technology. Assessment of vegetation status is a key motivation for this, because through plant respiration and photosynthesis, vegetation is critical for modelling terrestrial ecosystems and energy cycles in environmental studies. \r\nImportant parameters describing canopy structure include leaf area index (LAI), green cover fraction (fCover), fraction of absorbed photosynthetically active radiation (fAPAR), plant height, biomass and leaf angle distribution. At leaf biochemical level, leaf chlorophyll/water, fuel moisture and leaf pigmentation content are used.\r\nVisual inspection can provide a first assessment of plant status. For detailed measurements of biophysical parameters, mostly destructive methods have been used. Chemical measurement techniques on leaf samples can measure pigment concentrations very accurately, but are time consuming and only use very limited samples. \r\nMuch more extensive data can be collected using earth observation imagery. These range from large scale spaceborne observations with high frequency at coarse resolution to dedicated UAV flights which can offer spectral information of individual plants. Radar and LiDAR acquisitions, which are insensitive to weather conditions, now complement optical observations. \r\nMethods to retrieve the parameters from remote sensing data fall into two main categories. Statistical models empirically match data to a biophysical variable. Univariate techniques use a single quantity derived from the data, usually a vegetation index whereas multivariate techniques link a combination of measurements at different wavelengths to one or more biophysical parameters.\r\nPhysically-based modeling is an alternative approach which uses advanced radiative transfer models to describe the transfer and interaction of radiation inside a leaf or canopy based on robust physical, chemical, and biological processes. They compute the interaction between solar radiation and plants and provide as such a better understanding between biophysical variables and reflectance characteristics. Good examples are Leaf optical models such as PROSPECT and LIBERTY which simulate leaf optical properties by absorption and scattering coefficients. Canopy reflectance models simulate canopy reflectance as a function of a complex description of plant structural and radiometric attributes to develop a quantitative understanding of remote sensing information.","hasChildren":true,"hasParent":true,"name":"Biophysical and geophysical parameters","selfAssesment":"Completed
"},{"code":"IP3-1-2-1","description":"This spectral index is calculated using the following formula: SAVI = [(NIR-Red)/(NIR+Red+L)]/(1+L), where L can be, for example, 1 in area with no vegetation or 0 in area with dense veegtaion. It is used to minimize the influence of the soil brightness from the vegetation indices that are based on red and near-infrared wavelengths.","hasChildren":true,"name":"Soil-adjusted Vegetation Index (SAVI)","selfAssesment":"New
"},{"code":"IP3-1-2-2","description":"This spectral index is calculate using the following formula NDSI = (green-SWIR)/(green+SWIR). It is the most popular index used to identify snow cover due to the fact that snow reflects visible wavelength stronger than middle-infrared wavelengths.","hasChildren":true,"name":"Normalized Difference Snow index (NDSI)","selfAssesment":"New
"},{"code":"IP3-1-2-3","description":"Leaves, when healthy and vigour show a characteristic green colour. This visual effect evident to humans is caused by the co-existence of two evolutionarily facts: the specific interaction of the chlorophyll pigment in living leaves to the visible spectrum (VIS, 400-700 nm wavelength) of light emitted by the sun and the sensitivity of our human eye to the same sub-spectrum. According to fundamental physical laws of radiation (Stefan Boltzmann law of blackbody radiation and Wien’s displacement law), the VIS sub-spectrum corresponds to the radiation maximum of the sun, a hot blackbody with a surface heat of about 6000 K. Living leaves are structured in specific layers exhibiting characteristic interaction with light. The chloroplasts located in the so-called palisade layer, make use of the blue and the red part of sunlight for photosynthesis, the unique process of transforming light to create energy (carbohydrates) from water and carbon dioxide. This leads to the specific behaviour of leaves to absorb large portions (up to 90%) of the blue and red part of the electromagnetic spectrum and reflect nearly 100% of the green light. The peak reflectance in green light makes leaves (and plants in general) appear in green colour in our visual perception. \r\nA second, by no means less characteristic, feature of leaves is the specific response to near infrared (NIR, at around 700 nm wavelength) light in the mesophyll tissue (transmittance, scattering and reflectance). Only a small fraction of NIR is being absorbed. \r\nThis combination of two specific spectral characteristics, the absorption in VIS (red colour) by chlorophyll a in palisade layers, and the reflectance of NIR in the spongy tissue, makes the spectral profiles of plants and vegetation exhibiting a very characteristic shape, the so-called red edge. This absorption edge between red and NIR light is sharper for higher intensity green reflectance and brighter green tones (such as grassland or bright deciduous forest) than for less intensive reflectance and darker tones (coniferous forest). \r\nThe red edge may shift for the same vegetation type due to plant maturity or plant stress. This effect we call the red shift. The red shift is sensitive to crop maturity (headed stage) and may indicate harvesting time. Notably, there is also a blue shift, indicating green plants’ exposure to geochemical stress, which causes the absorption spectra to shift towards shorter wavelengths. \r\nPlants usually do not appear in isolation but form a canopy with a certain degree of coverage (e.g., crown closure in forests), and a certain part of understorey or soil per area unit. The resulting canopy reflectance is therefore a spectral mix of soil and vegetation (or even different types of vegetation) and generally lower than the reflectance of a pure vegetation sample under lab conditions. \r\nTo capture most of these plant-typical spectral characteristics, the so-called normalised difference vegetation index (NDVI) was developed. NDVI is an arithmetic band combination of red and NIR bands in a normalised value range. \r\nThe NDVI is calculated as:\r\nNDVI=((NIR-R))/((NIR+R))\r\nThe (hypothetic) value range of the NDVI is [-1 | +1]. Under real-world conditions, the NDVI ranges from values of around -0.2 to 0.6 or 0.7. To discriminate principal land cover classes such as water, non-vegetation (soil, sealed, etc.) and vegetation the following thresholds in the continuous range are used: \r\n\tNDVI < ~ 0: water\r\n\t~ 0 < NDVI < ~ 0.2: non-vegetation (soil, sealed surfaces, bare rock, etc.)\r\n\t~ 0.2 < NDVI: vegetation.\r\nNotably, these class limits are just a very rough approximation (indicated by the ~ sign), due to the mixed pixels effect, canopy reflectance, the abundance of water plants and suspending particles, and the illumination effect of specific atmospheric or topographic conditions. \r\nWe can use the NDVI to generally mask out vegetation from other land cover types and, more specifically, to indicate vegetation vigour and health. It is also suitable for monitoring plant phenology as the relationship between vegetative growth and the (changing) conditions of the environmental conditions. A range of variations has been suggested, enhancing one or the other mathematical or statistical behaviour of the index, or making it even more sensitive to specific plant behaviour. A well-known example is the enhanced vegetation index (EVI).","hasChildren":true,"name":"Normalized Difference Vegetation Index (NDVI)","selfAssesment":"Completed
"},{"code":"IP3-1-2","description":"Spectral indices are calculated using a mathematical equation that is applied on two or more spectral reflectance bands of the image. The calculated spectral index is a ‘new’ image that highlights particular land surface features or properties e.g. vegetation, soil, water, better than the original input bands. The spectral indices vary from simple spectral ratioing of two bands to more complex combinations of multiple bands. Spectral indexes are developed based on the spectral properties of the object of interest. For example, spectral indices dedicated to the vegetation condition are developed based on the principle that the healthy vegetation reflects strongly in the near-infrared spectrum while absorbing strongly in the visible red. These properties are used to develop more complex spectral indexes for monitoring vegetation condition, phenology parameters, i.e. Normalised Difference Vegetation Index (NDVI), Advanced Vegetation Index (AVI). The spectral indices calculated using the short wave infrared spectral bands are more sensitive to vegetation water content and spongy mesophyll structure in the vegetation canopy thus are used to assess the vegetation decline, moisture that is particularly useful for drought monitoring (e.g. Normalized Difference Water Index (NDWI) or Normalized Difference Moisture Index – NDMI). The water-related spectral indices are widely applied in agricultural and ecological applications including surface water body characteristics, vegetation water stress, soil water content assessment and wetlands monitoring. The combination of near infrared and short wave infrared spectral bands is also used to detect burned area and to monitor the vegetation recovery (e.g. Normalised Burned Ratio – NBR). There are other spectral indices dedicated to snow cover and glacier monitoring, which are developed based on visual green and short wave infrared spectral bands. Snow reflects most of the radiation in the visible bands whiles absorbing in the short wave infrared.","hasChildren":true,"hasParent":true,"name":"Spectral indices","selfAssesment":"Completed
"},{"code":"IP3-1","description":"The term band maths denotes the arithmetic combination (addition/subtraction, multiplication/division) of two or more spectral bands in an early stage of image analysis. The resulting scalar values represent the spectral behaviour in different bands in a single value; such procedure makes particular sense, when spectral behaviour varies in those bands (like the red edge of vegetation spectra in the NIR band). \r\nThere are several reasons for applying band maths when working with multispectral imagery: (1) A single range of values rather than multiple bands is easier to comprehend and interpret; (2) Thresholds or class limits are applied more intuitively in a grey scale image; (3) Indices can be easily calculated and compared across different sensors; they are implemented as standard routines in many software environments as well as cloud processing environments (such as Google Earth Engine or the Proba-V exploitation platform)\r\nOut of the many possible, literature suggests a few arithmetic band combinations as application-specific quasi-standards. Band ratios (e.g. red band divided by NIR band) and indices (such as the normalised difference vegetation index, NDVI) belong to this group. Indices have the advantage over simple ratios in constraining the value range, e.g. [-1 | 1]. Designated to indicate specific land cover types (such as water index, snow index, soil index, etc.) such indices are widely used as a basis for operational information products. Another index is the normalised burn ratio (NBR) which relates near infrared and short-wave infrared reflectance to measure burn severity taking into consideration the increasing of SWIR reflectance in the course of a fire. \r\nPre-processing such as dark object subtraction and radiometric or even atmospheric correction is a key requirement prior to indexing. The coding in digital numbers (DN) is a function of the sensitivity and the radiometric resolution of the sensor. The actual recording depends on atmospheric conditions (additional brightness, haze, etc.). Therefore, in order to make the resulting values comparable among different types of sensors and scenes, radiometric correction is mandatory, converting DNs into radiances, i.e. true reflectance values as physical measurement units. \r\nTwo advanced examples of band maths beyond rationing are the perpendicular vegetation index (PVI) and the tasselled cap (TC) transformation. PVI is based on the assumption that vegetation pixels are generally separable from soil pixels (at least after unmixing or for pure pixels), and thus pixel values are located in a perpendicular direction from the soil line in a NIR/red feature space. The Euclidean distance from the soil line, determined by Pythagorean triangle, yields the PVI. Tasselled cap instead rests on the notion of a cap-like histogram shape when plotting pixels on a brightness vs. greenness plot, with the latter determined by linear combinations of VIS and NIR bands, along with empirically determined coefficients. TC 1 as a weighted sum corresponds to brightness, TC 2 to greenness, TC 3 to yellowness, sometimes referred to as wetness. A fourth TC called nonesuch likely corresponds to noise and atmospheric disturbance effects in the image.","hasChildren":true,"hasParent":true,"name":"Band maths","selfAssesment":"Completed
"},{"code":"IP3-10","description":"Semantic enrichment is the process of adding semantic metadata elements to improve the content-based image retrieval. These semantic metadata elements enable the explicit specification of the content of the images stored in the remote sensing databases.","hasChildren":true,"name":"Semantic enrichment","selfAssesment":"New
"},{"code":"IP3-11-1","description":"Different types of changes are investigated using remotely sensed data: (i) abrupt changes, such as the changes caused by a fire or flooding, and (ii) gradual changes such as urban growth. Besides these kinds of changes, remote sensing community differentiates between transitional changes and conditional changes. Transitional changes refer to a major change of land surface such as conversion of forest to pasture or the expansion of mangroves into the surrounding water. Conditional changes refer to the change in condition at the surface such as water stress in an agricultural field, forest degradation caused by pest. \r\nIn the past, many remote sensing studies used two images to detect different types of changes such as deforestation, land cover change or change in the health or condition of the vegetation (e.g. pest infestation). Meanwhile, satellite image time series are used to assess the change. Time series analysis allows for monitoring more subtle changes and for providing temporal patterns of change. In this way, the timing of changes and drivers of change can be easily identified. \r\nDifferent methods are being used in change detection studies. There are studies that analyze individual images available in the investigated time series to map the target class/phenomena/events at the time when images were collected and to identify the changes: e.g. mapping the mangroves extent on an year basis and measuring it to identify changes. Alternative studies search for breaks in time series for detecting changes. The breaks are used to segment the time series into before and after changes periods which are further classified using one of the existing supervised or unsupervised classification methods (K-means, fuzzy k-means, Random Forest, Support Vector Machine etc.).","hasChildren":true,"name":"Change detection","selfAssesment":"Completed
"},{"code":"IP3-11-2","description":"The (data)cube model for analysis of time series of earth observation raster data, represents the dataset as a multidimensional array with one or more spatial or temporal dimensions. Scalar values in the cube can be selected (or ‘filtered’) and processed based on dimension labels. This allows analysis algorithms to be thought of as a set of operations on the multidimensional array. Technologies that support this model allow to efficiently implement such algorithms.\r\nSome possible operations on a multidimensional cube include: filtering, ‘reducing’ all values along a dimension, ‘aggregating’ values in a dimension, or transforming all values along a dimension. Generally speaking, these operations require the selection of a subset of the data on which work is to be done. This allows implementing the operations efficiently even on very large datasets.\r\nIn comparison to file-based processing, most technologies that support cube-based time series analysis reduce implementation overhead, as the user does not need to read and write individual files, also more complex aspects like distributed computing for parallelization can be hidden in a cube based approach. So a cube based approach can also be thought of as an abstraction layer that effectively reduces the need for specific IT-related skills when analyzing earth observation timeseries.\r\nMultiple initiatives support cube based analysis. Some common features include a programming API, often using the Python programming language. Some tools are only accessible as web services, while others can also run locally (on a small dataset). This diversity is still a drawback, as users would need to familiarize themselves with different systems. Initiatives such as openEO try to address this by providing a common API.","hasChildren":true,"name":"Cube-based time series analysis","selfAssesment":"Planned
"},{"code":"IP3-11-3","description":"Dynamic Time Warping (DTW) works by comparing the similarity between two temporal sequences and finds their optimal alignment, resulting in a dissimilarity measure. In the case of remote sensing data, DTW can deal with temporal distortions, and can compare shifted evolution profiles and irregular sampling thanks to its ability to align radiometric profiles in an optimal manner","hasChildren":true,"name":"Dynamic Time Warping","selfAssesment":"Planned
"},{"code":"IP3-11","description":"Satellite image time series analysis plays an important role in different domains including vegetation dynamics monitoring, estimating crop yields, discriminating between different land cover classes, exploring human-nature interactions, monitoring land cover change, assessing environmental threats, or evaluating ecosystems-climate feedbacks or urbanization.\r\nTime series analysis requires high quality time series which are reconstructed by removing any source of contamination such as clouds, cloud shadows, or scan-line corrector (SLC) gaps of the Enhanced Thematic Mapper plus sensor (ETM+) on Landsat 7. Removed pixels are usually filled in with data predicted from a different date (temporal interpolation), nearby pixels (spatial interpolation) or from both (spatiotemporal interpolation). Different methods are available for screening and masking out clouds and shadows in satellite images including mono-temporal methods such as Function of mask (Fmask), or multitemporal mask (e.g. Tmask algorithm). Fmask is used by the United States Geological Survey (USGS) to produce a cloud mask layer of Landsat images. European Space Agency (ESA) is using Sen2cor processor to produce Level 2A Sentinel-2 data with a shadow and cloud shadow mask. All images used in the time series have to be co-registered, i.e. they align as closely as possible. \r\nTime series analysis is used to (1) investigate various surface properties such as evapotranspiration, land surface temperature, (2) map the cover of the Earth surface (e.g. land cover mapping, crop mapping etc.), (3) detect different type of changes such as abrupt changes (fire event) or gradual changes (urbanization), and (4) study the trends.\r\nTo map surface features from satellite image time series, numerous studies make use of the vegetation phenology extracted from a spectral-temporal trajectory of a given spectral vegetation index such as the normalized difference vegetation index (NDVI) or enhanced vegetation index (EVI). Several metrics can be used to characterized vegetation phenology: metrics of greenness and metrics of time. The metrics of greenness include the minimum and maximum spectral vegetation indices, their difference or amplitude, seasonally averaged greenness etc. The metrics of time include start and end of the growing season, duration or length of the growing season or the timing of maximum greenness. Changes, on the other hand, are identified either by investigating two images acquired at two different points in time or by identifying breaks in a dense (annual or multi-annual) satellite image time series.","hasChildren":true,"hasParent":true,"name":"Time series analysis","selfAssesment":"Completed
"},{"code":"IP3-12-1","description":"Remote sensing-derived products such as land-use and land-cover maps contain error. The error accumulates as the remote sensing data are collected and various types of processing take place. An error assessment is necessary to identify the type and amount of error in a remote sensing-derived product.","hasChildren":true,"name":"Error propagation","selfAssesment":"New
"},{"code":"IP3-12-2","description":"The precision of a measurement system, related to reproducibility and repeatability, is the degree to which repeated measurements under unchanged conditions show the same results.","hasChildren":true,"name":"Precision","selfAssesment":"New
"},{"code":"IP3-12","description":"Uncertainty is the result of the lack or imprecision of our knowledge about the world. A proposition is uncertain if we do not know whether it is true or not. In most circumstances we describe a proposition as uncertain when the reason we do not know whether it is true is that we do not possess complete and accurate knowledge about the state of the world.","hasChildren":true,"hasParent":true,"name":"Uncertainty","selfAssesment":"New
"},{"code":"IP3-13-1","description":"The main elements of visual interpretation are: tone, shape, size, pattern, texture, shadow, , association. Tone refers to the relative brightness or colour of objects in an image. It depends on the spectral properties of an object. Variation in tone allows to distinguish elements of different shape, texture and pattern. Shape refers to the general form, structure, or outline of individual objects. Straight and sharp edge shape represent typically the anthropogenic features i.e. urban or agriculture, the natural features like rivers, wetlands are more irregular in shape. Size of objects in an image is a function of scale and it depends on the spatial resolution of the image. The assessment of the size of the target’s object in relation to other objectives as well as an absolute size of the object are the important part of the interpretation. Pattern refers to the spatial arrangement of objects, i.e. network of street and houses in an urban area, orchards with the line of trees. Texture refers to the arrangement of frequency of tonal variation in particular areas of an image. Rough texture would have very large, coarse tonal variation (e.g. forest canopy), whereas smooth texture very little tonal version (e.g. uniform, homogenous surfaces). It depends on the size, shape and pattern of objects. Shadow depends on the scale and spatial resolution of an image. Shadow is useful to measure the height of an object, to distinguish the coniferous from broadleaf trees. In the radar imagery is useful for identifying topography and landforms. Association refers to the relationship between objects and features in proximity to the target interest.","hasChildren":true,"name":"Elements (cues) of interpretation","selfAssesment":"Completed
"},{"code":"IP3-13-2","description":"Information-as-data-interpretation considers information as the outcome of the cognitive process of vision that reconstructs a scene from an image.","hasChildren":true,"name":"Information-as-data-interpretation","selfAssesment":"New
"},{"code":"IP3-13-3","description":"An image interpretation key is simply reference material designed to permit rapid and accurate identification of objects or features represented on aerial images.","hasChildren":true,"name":"Interpretation keys","selfAssesment":"New
"},{"code":"IP3-13","description":"Interpretation is the processes of detection, identification, description and assessment of an object and pattern imaged. Visual interpretation is the ability of a human operator to identify an object through the data content in an image / photo by combining several elements of interpretation. The image characteristics used in the interpretation process are: shape, size, tone/colour, texture, shadow, neighbourhood and pattern. The importance of the image characteristics varied according to the spatial resolution of the images and the properties of the feature of interest. The interpretation can be performed on the single image or between several images acquired at different time, which result in the differentiation of the temporal changes. The principle of the image interpretation is the process of delineating (digitalizing) the outlines of the objects, features on the image. It is performed “on-screen” using a GIS software. The process of visual interpretation is time consuming and requires a skilled interpreter with knowledge of the study area. Even though, the image interpretation supports many applications in for example selection of the training and verification data sets for image classification and accuracy assessment.","hasChildren":true,"hasParent":true,"name":"Visual interpretation","selfAssesment":"Completed
"},{"code":"IP3-2-2-1","description":" ","hasChildren":true,"name":"Information-as-thing","selfAssesment":" "},{"code":"IP3-2-2","description":"Information theory answers two fundamental questions in communication theory: what is the ultimate data compression (answer: the entropy H) and what is the ultimate transmission rate of communication (answer: the channel capacity, C). For this reason, it is considered that information theory is a subset of communication theory.","hasChildren":true,"hasParent":true,"name":"Information theory","selfAssesment":"New
"},{"code":"IP3-2-3","description":"Keypoints are objects (or locations) on the ground that reveal locally invariant features in images and therefore are easily detectable by automatic algorithms. Methods for this process employ scale-invariant feature transform (SIFT) algorithms for the automatic detection of geospatial objects.","hasChildren":true,"name":"Keypoint detection","selfAssesment":"New
"},{"code":"IP3-2","description":"Image understanding is part of computer vision. Computer vision is an interdisciplinary field that deals with how computers can be made to gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to automate tasks that the human visual system can perform.","hasChildren":true,"hasParent":true,"name":"Computer vision in EO","selfAssesment":"New
"},{"code":"IP3-3-1","description":"A Digital Elevation Model (DEM) is a digital raster (or grid) representation of elevation values of land surface shapes and features, where each grid cell takes a single elevation value with reference to a certain vertical datum. A DEM can be global, regional or local in scope, and can be used to characterize the dry land surface (topography) or submerged surfaces (bathymetry). Since a DEM cannot contain information of shapes and features under overhanging structures, it is often referred to as 2.5D instead of truly 3D. \r\nA digital elevation model is an overarching term for either a digital surface model (DSM) or digital terrain model (DTM). A DSM includes elevations of surface features such as trees, buildings, bridges and artificial objects such as poles, power lines, cars etc., and thus contains always the highest elevations of any feature for any given raster cell. A DTM does not include such features but reflects the elevation of bare land surface shapes, excluding elevated or overhanging features.\r\nDEMs can be obtained using active or passive measurements. Active measurements involve the generation of electromagnetic signals towards a surface and timing the reception of the (return) signal(s). This can be achieved through laser scanning (LiDAR) using visible or infrared light pulses for bathymetric or topographic measurements respectively, radio waves (SONAR) used in bathymetric measurements, or microwaves (synthetic aperture radar, SAR) used in topographic mapping. The most widely known active remotely sensed global DEM is derived from the Shuttle Radar Topography Mission (SRTM) obtained by a SAR mounted on the space shuttle Endeavour, offering 30 m resolution with a vertical accuracy typically between 5 and 20 m, covering 80% of Earth’s surface.\r\nPassive measurements detect reflection of sun light, or energy radiated from the surfaces. Their distance to the detector can then be inferred from the measurement of angles. Historically, line scanning imagers were used, but nowadays, these are replaced by acquisitions of overlapping 2D frame images. On the images, corresponding land surface features are detected which act as tie-points. The distance between the sensor and the tie-points is calculated in a process called photogrammetry. The most widely known spaceborne passive remotely sensed global DEM is derived from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data onboard the Terra satellite. It offers similar resolution and accuracy compared to SRTM, but with 99% coverage. \r\nOnly LiDAR can generate both accurate DSMs and DTMs from the same data acquisition, by using multiple returns from a single emitted pulse. All other techniques generate DSMs, from which elevated features can be identified and filtered out in postprocessing to create DTMs, however with typically lower accuracy and more artefacts.","hasChildren":true,"hasParent":true,"name":"DEM generation","selfAssesment":"Complete
"},{"code":"IP3-3-2","description":"DSM can be produced automatically from stereo satellite scenes, from satellite sensors such as GeoEye, IKONOS, SPOT-5, Terra-ASTER etc. The DSM can also be provided from stereo digital aerial photography at various resolutions, depending on the quality and scale of the aerial photography. The quality of the automatic generated DSM is substantially improved if ground measurements from GPS are incorporated in the DSM stereoscopic model.","hasChildren":true,"name":"DSM generation","selfAssesment":"New
"},{"code":"IP3-3","description":"Stereo pairs of optical satellite images with the support of ground control points provide a basis for cross-stereo analysis for generating Digital Surface Models.","hasChildren":true,"hasParent":true,"name":"Cross-stereo analysis","selfAssesment":"New
"},{"code":"IP3-4-1-1","description":"The goal of filtering is to remove unnecessary components from images (e.g., noise), while emphasizing the necessary ones. In the context of spatial aggregation, low pass filters aim at removing sharp transitions in the image intensities (high spatial frequencies) and thereby focus the information content of the image on a coarser scale level.","hasChildren":true,"name":"Filtering","selfAssesment":"New
"},{"code":"IP3-4-1-2","description":"Gridding is the technique used to generate a uniform raster grid with one value for every cell in the raster. The values of the raster cells can represent different attributes such as mean, max or min of all Normalized Difference Vegetation Index (NDVI) values measured within a particular cell.","hasChildren":true,"name":"Gridding","selfAssesment":"New
"},{"code":"IP3-4-1","description":"Spatial aggregation produces images of coarser resolution (grouping pixels in a grid of coarser resolution and calculating mean values) or of coarser scale (by filtering with low-pass filters). Thereby it is a form of generalization that may improve classification results. Spatial aggregation can be applied after classification to get rid of the salt-and-pepper effect.","hasChildren":true,"hasParent":true,"name":"Spatial aggregation","selfAssesment":"New
"},{"code":"IP3-4-10-1-1","description":" ","hasChildren":true,"name":"Gradient boost","selfAssesment":" "},{"code":"IP3-4-10-1","description":" ","hasChildren":true,"hasParent":true,"name":"Feature engineering","selfAssesment":" "},{"code":"IP3-4-10","description":"Classification processes use features, also known as predictor variables, for discriminating between classes. A feature is an individual measurable property or characteristic of a geographic phenomenon being observed. Features in Earth observation include the individual bands of images and further properties derived from the image data. For example, the single band of a panchromatic image represents a feature that allows distinguishing between pixels of darker and lighter reflectance. Multispectral images have more bands and thereby enable the differentiation between classes by more features. This means, if two classes are different from each other in several of their properties, it becomes easier to distinguish them. The set of features used in a particular classification comprise the feature space where each feature represents one space dimension. \r\nWith an increased number of (uncorrelated) features it becomes possible to increase the number of classes that can be separated. For example land cover classifications have a large number of classes. For identifying suitable bands for optical EO satellites, the spectral signatures of all the target classes have to be analysed to identify in which bands they are separable from other classes. Classes like soil, water, and vegetation have spectral signatures that differ in particular in the blue, green, red, and infrared bands of the electromagnetic spectrum. These bands are present in virtually all multispectral sensors used for land cover classification. \r\nGeographic phenomena can be differentiated not only by their reflectance in different bands. Beyond multispectral features, the classification may include image derivatives like derived spectral indices, principal components, or filtered bands (convolution layers). Object-based image analysis also uses spatial features, i.e. distance and proximity features, planar geometric features and topological features.","hasChildren":true,"hasParent":true,"name":"Classification features and feature space","selfAssesment":"Completed
"},{"code":"IP3-4-2-1","description":"Bayes’s theorem is an extremely powerful means of using information at hand to estimate probabilities of outcomes related to the occurrence of preceding events. Bayes' Theorem uses a priori (subjective) and conditional probabilities to calculate the probability of an uncertain event occurring. A priori probabilities represent what the modeler believes, before testing, to be the probability of an event occurring. Conditional probabilities are probabilities that other events occur in conjunction with the original event.","hasChildren":true,"hasParent":true,"name":"Conditional probability","selfAssesment":"Planned
"},{"code":"IP3-4-2-2","description":"Maximum likelihood classification uses the training data for estimating means and variances of the classes, which are then used to estimate the probabilities. This method considers not only the mean, or average, values in assigning classification but also the variability of brightness values in each class.","hasChildren":true,"name":"Maximum likelihood","selfAssesment":"Planned
"},{"code":"IP3-4-3-1","description":"The Land Cover Classification System (LCCS) was developed by FAO to provide a consistent framework for the classification and mapping of land cover. Its main objectives were to overcome the rigidity of a-priori land cover classifications, which in many practical situations do not allow easy assignment into one of the pre-defined classes and are therefore not very suitable for mapping. LCCS instead opted for an approach based on two main phases. The first phase is an initial ‘Dichotomous Phase’, in which eight major land cover types are defined: (1) Cultivated and Managed Terrestrial Areas, (2) Natural and Semi-Natural Terrestrial Vegetation, (3) Cultivated Aquatic or Regularly Flooded Areas, (4) Natural and Semi-Natural Aquatic or Regularly Flooded Vegetation, (5) Artificial Surfaces and Associated Areas, (6) Bare Areas, (7) Artificial Waterbodies, Snow and Ice, and (8) Natural Waterbodies, Snow and Ice. The Dichotomous Phase is followed by a subsequent ‘Modular-Hierarchical Phase’, in which land cover classes are created by the combination of sets of pre-defined classifiers, which are different for each of the eight major land cover types. For example, common classifiers used for (semi-) natural terrestrial vegetation types are Life Form, Cover, Height, Macropattern. For aquatic or regularly flooded natural and semi-natural vegetation, water seasonality is an indispensable classifier. LCCS offers several advantages from a conceptual point of view. LCCS is a real a priori classification system in the sense that, for the classifiers considered, it covers all their possible combinations. The classification is also hierarchical and the more classifiers used, the greater the detail of the defined land cover class. The classes derived from the proposed classification system are all unique and unambiguous, due to the internal consistency and systematic description of the classes. LCCS is designed to map at a variety of scales, from small to large. From a practical viewpoint LCCS offers several advantages: (1) easy incorporation into GIS and databases, (2) allows flexible response to information available in a given area, project budget and time constraints, (3) unlinks the field data collection from the interpretation process.","hasChildren":true,"name":"Land cover classification system (LCCS)","selfAssesment":"Completed
"},{"code":"IP3-4-3","description":"Long-term monitoring of land cover and land use are particularly relevant for land ecosystem monitoring. Therefore, baseline datasets are necessary that allow assessing changes of land cover and land use where the class definitions remain consistent over time. Accordingly, classification schemes have been established that adhere to taxonomically correct definitions of classes of information organized according to logical criteria. If hard classification is to be performed (i.e. without fuzzy class boundaries), the classes in the classification system should normally be mutually exclusive, exhaustive, and hierarchical. Mutual exclusive classes have no taxonomic overlap and assign a land cover patch to a single class. An exhaustive classification scheme is able to cover the area of interest comprehensively and leaves no land cover patch unassigned. A hierarchical system allows combining sub-classes into higher-level categories.\r\nFrom a remote sensing classification perspective, it becomes clear that a classification scheme consists of information classes defined by human beings. Conversely, spectral classes are those inherent to EO data. An analyst must identify spectral classes and label them as information classes that satisfy bureaucratic (or scientific requirements). Additionally, the advantage of using established classification schemes is that their use in scientific studies and applications produces results that are comparable to other studies and suitable for sharing of data.\r\nEstablished classification schemes include: CORINE land cover (CLC), Land cover classification system (LCCS), American Planning Association land-based classification standard, United States Geological Survey land-use/land-cover classification system for remote sensor data, U.S. Department of the Interior Fish & Wildlife Service classification of wetland and deep water habitats of the United States, U.S. National Vegetation Classification system (NVCS), International Geosphere-Biosphere Program IGBP Land cover classification system.","hasChildren":true,"hasParent":true,"name":"Classification schemes (taxonomies)","selfAssesment":"Completed
"},{"code":"IP3-4-4","description":"Unsupervised methods are defined as the identification of natural groups, or structures, within existing data. Clustering requires only the number of to-be generated classes as an input parameter and assigns spectrally defined classes to an image.","hasChildren":true,"name":"Clustering (unsupervised)","selfAssesment":"New
"},{"code":"IP3-4-5-1-1","description":" ","hasChildren":true,"name":"Inference engine","selfAssesment":" "},{"code":"IP3-4-5-1","description":"A production system performs automatic transformation of remote sensing imagery into useful information (such as biophysical parameters, categorical maps etc). An example can be a preliminary pixel-based classifier that works top-down (deductive, physical model-driven, prior knowledge-based) and arrives at preliminary classes for each pixel of an image. Such a production system does not require interaction of an operator. The process makes use of a decision tree that encodes the prior knowledge for assigning pixels to a class.","hasChildren":true,"hasParent":true,"name":"Production systems","selfAssesment":"New
"},{"code":"IP3-4-5","description":"Decision trees is a data mining technique used in different disciplines including Remote Sensing.\r\nThe major advantages of decision tree methods include the ability to capture interactions between the variables used for modeling, the understandability of the produced models (trees) and their efficiency. Input data for decision trees are either a large number of examples or a large number of variables. This is important in the context of pixel-based classification in geographical information systems, where very large numbers of spatial units/points need to be classified. \r\nDecision tree consist of nodes, branches and leaves. Each node contains a test on an attribute, out of which branches are created with a grouped subset of data depending on the results of the node test. The resulting subsets will have as homogeneous values of the class as possible. This is done in a hierarchical manner dividing the training dataset until it reaches rules set at the start- the lowest number of training data within each leaf or set level of confidence.\r\nFor discrete attributes, a branch of the tree is typically created for each possible value of the attribute. For continuous attributes, a threshold is selected and two branches are created based on that threshold. This also determines whether the decision tree is called a classification or a regression tree: if we are dealing with classification (discrete target) or a regression problem (continuous target), respectively.\r\nDecision trees are derived from data only. As such, they represent the data driven or empirical approach, which is more appropriate when we have plenty of high-quality (reliable and relevant) measured data and little knowledge about the studied system, for instance what is the spectral response of each land cover class needed for classification.\r\n\r\nAn important mechanism used to improve decision tree performance is tree pruning. Pruning reduces the size of a decision tree by removing sections of the tree (subtrees) that are unreliable and do not contribute to the predictive performance of the tree.\r\nThe pruning reduces complexity of the tree and helps to achieve better predictive accuracy by the reduction of over-fitting and removal of sections of the tree that may be based on noisy or erroneous data. Depending when the pruning is done during the creation of the tree, it is called pre- or post-pruning.\r\nThe CART (Classification And Regression Trees) system is the first widely known and used system for learning decision trees. After that, notable ones are the C4.5 system for learning classification trees (or J4.8 as called within WEKA software), succeeded by C5.0.","hasChildren":true,"name":"Decision trees","selfAssesment":"Completed
"},{"code":"IP3-4-6-1","description":"Along with developing deep learning methods, Convolutional Neural Networks (CNNs) have emerged as a powerful tool by providing both remarkable performances in image processing and the ability to work in a wide variety of applications in the vision community. In the past few years, biologically inspired CNNs have emerged and proven effective in the image processing field, from social media to precision medicine and robotics. A beneficial characteristic of CNNs is data processing in multiple arrays and automatic feature extraction ability, which have received acknowledgment in the geoscience and remote sensing community.\r\nMoreover, the inherent characteristics of CNNs, such as local connectivity and weight sharing, allow this deep learning method to tackle the drawbacks of artificial feature extraction, by considering the 2-D structures and reducing network parameters using convolutional filters. CNN-based models have benefited from the recent exponential advances in imaging technologies, such as the availability of various image types (optical, RADAR, temperature and microwave radiometer, altimeter, etc.) with complex characteristics (high dimensionality, multiple scales, and nonstationary). CNNs are composed of a set of blocks that make them particularly suitable for image analysis. The multiple layers of operations, such as convolution, pooling, and nonlinear activation functions, allow for a hierarchical extraction of high-level abstract features. Accordingly, CNNs have been successfully used in image preprocessing, scene classification, pixel-based classification, image segmentation, and object detection. CNNs have been used in numerous studies, for instance: to improve image classification results to extract buildings and non-building regions automatically; to detect areas of build-up; to assess the quality of OpenStreetMap data; to detect oil spills, ships, and icebergs. Although CNNs can be considered newly introduced algorithms in geoscience and remote sensing, they are now clearly among the top performers in most of the applications.\r\nDespite this progress, the study of CNN-based approaches in the field of remote sensing and geoscience is currently at its beginning stages, and there is still much potential for new developments. In this perspective, the design of new network architectures for specific tasks, the generation of large-scale datasets for network training, the integration of conventional techniques for various remote sensing data, the advancement and analysis of existing networks concerning their architectures, optimization techniques, and the regularization strategies are still open topics, which are in close relation with each other and should be jointly considered.","hasChildren":true,"name":"Convolutional neural networks (CNNs)","selfAssesment":"Completed
"},{"code":"IP3-4-6","description":"Deep learning (DL), as a subfield of artificial intelligence (AI) and machine learning (ML), is the fastest-growing trend in data analysis and is regarded as a breakthrough. Over the past few years, there has been an ongoing shift toward using DL methods in different applications, mainly due to the increasing data accessibility and computational processing power. DL models characterized by neural networks are learning methods with multiple levels of representation that learn the semantic and discriminative features in a sequential bottom-to-up manner from the data. They are composed of several levels of non-linear modules that each modify the representation at a lower level into a higher or slightly more abstract level. As such, very complex functions can be learned without depending on human-crafted features.\r\nDL has been used in several research fields, such as speech recognition, stereo vision, medical image recognition, remote sensing, time-series analysis, biomedicine, agriculture, and geosciences. One of the limiting factors of using DL models is that they require significant amounts of training samples compared to conventional ML methods To date, several DL architectures have been introduced, of which the stacked autoencoder, convolutional neural network, generative adversarial network, deep belief network, and recurrent neural network have become mainstream. DL techniques have had significant successes in several fields, which have been widely accepted as challenges in recent decades. Moreover, by growing big data and their applications in practical productions and developed time-efficient networks or public online free or commercial cloud computing platforms, such as Google, Amazon, Microsoft, and IBM, much more attention will be paid to develop new DL networks for the practical projects.","hasChildren":true,"hasParent":true,"name":"Deep learning","selfAssesment":"Completed
"},{"code":"IP3-4-7-1","description":"The RF classifier is an ensemble classifier that uses a set of Classification and Regression Trees (CARTs) to make a prediction The trees are created by drawing a subset of training samples through replacement (a bagging approach).","hasChildren":true,"name":"Random forest (RF)","selfAssesment":"New
"},{"code":"IP3-4-7-2","description":"In machine learning, support vector machines (SVMs) are supervised non-parametric statistical learning techniques with associates learning algorithms that analysze data used for both classification and regression analysis. SVM algorithm was originally designed for binary classification. The SVM is based on the main hypothesis that the training set is linearly separable. Given a set of training examples, each marked as belonging to one or another of two categories, an SVM training algorithm builds a model that can assign each new occurrence into one of these two categories, making it a non-probabilistic binary linear classifier. The SVM model is a representation of the examples as points in space, mapped so that the algorithm can find the optimal line (hyperplane) which separates with minimum error the training set, and maximizes the distance, named the “gap”, between the objects of both classes and the hyperplane. Thus, instead of using the whole available training set to describe classes, SVM uses only those training samples that describe class boundaries (support vectors), thought it can be more efficient than other algorithm because it uses a subset of training points. New occurs are then mapped into that same space and predicted to belong to a category based on the side of the gap on which they fall. In addition to performing linear classification, SVMs can also efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces. Unfortunately, because of the technique used for separating classes SVM is less effective on noisier datasets with overlapping classes. When data are unlabelled, supervised learning is not possible, and an unsupervised learning approach is required. SVM is used for text classification tasks such as category assignment, spam detection and sentimental analysis. It is also commonly used for image recognition, performing particularly well in aspect-based recognition and colour-based recognition. SVM also plays a vital role in many areas of handwritten digit recognition, such as postal automation services.","hasChildren":true,"name":"Support vector machines (SVM)","selfAssesment":"Completed
"},{"code":"IP3-4-7","description":"Field of study that gives computers the ability to learn without being explicitly programmed","hasChildren":true,"hasParent":true,"name":"Machine learning","selfAssesment":"New
"},{"code":"IP3-4-8","description":"Image classification operator needs a set of terms to express the characteristics of an image. These characteristics are called interpretation elements and are used to define interpretation keys: tone/hue, texture, pattern, shape, size, height/elevation, location/association","hasChildren":true,"name":"Mental concepts and categories","selfAssesment":"New
"},{"code":"IP3-4-9-4","description":" ","hasChildren":true,"name":"Stratified random sampling","selfAssesment":" "},{"code":"IP3-4-9-5","description":" ","hasChildren":true,"name":"Sample augmentation","selfAssesment":" "},{"code":"IP3-4-9","description":"Sampling strategies or sampling pattern specifies the arrangement of observations used for training and/or validation purposes.\r\nTypically, the simple random sample of a geographic region is defined by first dividing the region to be studied into a network of cells. Each row and column in the network is numbered, then a random number table is used to select values that, taken two at a time, form coordinate pairs for defining the locations of observations. Because the coordinates are selected at random, the locations they define should be positioned at random. The random sample is probably the most powerful sampling strategy available as it yields data that can be subjected to analysis using inferential statistics.\r\nA stratified sampling pattern assigns observations to subregions of the image to ensure that the sampling effort is distributed in a rational manner. For example, a stratified sampling effort plan might assign specific numbers of observations to each category on the map to be evaluated. This procedure would ensure that every category would be sampled.\r\nSystematic sampling positions observations at equal intervals according to a specific strategy. Because selection of the starting point predetermines the positions of all subsequent observations, data derived from systematic samples will not meet the requirements of inferential statistics for randomly selected observations.","hasChildren":true,"hasParent":true,"name":"Sampling strategies","selfAssesment":"New
"},{"code":"IP3-4","description":"The process of image classification extracts information about semantic labels of pixels or objects (i.e. regions) from imagery. Apart of input imagery, the process requires an input set of target classes (classification scheme) for which their spectral (and other) properties have to be identified. A classification method has to be selected that transforms the image data and the classification scheme into semantic map information. In complement to the resulting sematic labelling products, a secondary outcome are instructions or rulesets with the used parameters that constitute the documentation of the classification process.\r\nThe input imagery consists of one or more images (optical and/or SAR data) of a specific geographic area, collected in multiple bands of the electromagnetic spectrum (that may have already undergone certain pre-processing steps; determined by the purpose). Additionally, the imagery may include derived spectral indices, principal components, filtered bands, or other features to support the classification process.\r\nThe classification purpose defines the information about the target classes. It includes classification schemes (taxonomies), spectral signatures for each class and, mental concepts and categories about the classes (that enable an analyst to distinguish classes by texture, spatial relationships etc.). Often, training areas are used to understand how an object of a particular class is discernible in the available imagery and separable from other classes. Both the input imagery and the chosen classification method determine which features of each class can be exploited for classification. For example, spectral signatures of the target classes (extracted from training areas with known class label) may be a suitable input for extracting information with a pixel-based classification. For shape features, objects are a pre-requirement, derived with segmentation. They are only available with object-based classification approaches.\r\nClassification methods: Various methods exist that can be categorized according to the classification logic that they follow when transforming the input information into the output semantic labelling products. These can be parametric or nonparametric, supervised or unsupervised, per-pixel or object-oriented, semi-automated or fully automatic, and hybrid approaches. Classification methods are for example bayesian techniques like conditional probability or maximum likelihood, clustering (unsupervised), decision trees, deep learning and machine learning.","hasChildren":true,"hasParent":true,"name":"Image classification","selfAssesment":"Completed
"},{"code":"IP3-5-1","description":"Edge detection is a fundamental tool used in many image processing applications to obtain information from the frames as a precursor step to feature extraction and object segmentation. This process detects outlines of an object and boundaries between objects and the background in the image. An edge-detection filter can also be used to improve the appearance of blurred image.","hasChildren":true,"name":"Edge-based segmentation","selfAssesment":"Planned
"},{"code":"IP3-5-2","description":"Histogram-based segmentation makes use of histogram to select the gray levels for grouping the pixels into regions, e.g. background and the object of interest","hasChildren":true,"name":"Histogram-based segmentation","selfAssesment":"New
"},{"code":"IP3-5-3","description":"Local variance can be calculated as the value of standard deviation in a small neighborhood (e.g. 3x 3 moving window), then computing the mean of these values over the entire image. The obtained value is an indicator of the local variability in the image.","hasChildren":true,"name":"Local variance","selfAssesment":"New
"},{"code":"IP3-5-4","description":"Mean Shift is defined as finding modes in a set of data samples, manifesting an underlying probability density function (PDF).","hasChildren":true,"name":"Mean-shift segmentation","selfAssesment":"New
"},{"code":"IP3-5-5","description":"Regionalization is an important concept in Geographic Information Science for synthesizing multi-dimensional data into homogeneous objects through spatially constrained clustering methods","hasChildren":true,"name":"Regionalisation","selfAssesment":"New
"},{"code":"IP3-5-6-1","description":"Multi-resolution segmentation is a region-growing algorithm. It relies on several parameters, which need to be tuned. These include the scale parameter (SP), which dictates the size and homogeneity of the resultant objects.","hasChildren":true,"name":"Multi-resolution segmentation","selfAssesment":"Planned
"},{"code":"IP3-5-6-2","description":"Watershed segmentation is a region-based method that has its origins in mathematical morphology. In watershed segmentation an image is regarded as a topographic landscape with ridges and valleys. The elevation values of the landscape are typically defined by the gray values of the respective pixels or their gradient magnitude. Based on such a 3D representation the watershed transform decomposes an image into catchment basins. For each local minimum, a catchment basin comprises all points whose path of steepest descent terminates at this minimum. Watersheds separate basins from each other. The watershed transform decomposes an image completely and thus assigns each pixel either to a region or a watershed.","hasChildren":true,"name":"Watershed segmentation","selfAssesment":"New
"},{"code":"IP3-5-6","description":"Region-based segmentation algorithms can be devided into region growing, merging and splitting techniques and their combinations. Region merging starts from all pixels on the pixel level and iteratively aggregates pixels into objects until some conditions of homogeneity imposed by the user are met.","hasChildren":true,"hasParent":true,"name":"Region-based segmentation","selfAssesment":"New
"},{"code":"IP3-5-7","description":"Spatial autocorrelation is the term used to describe the presence of systematic spatial variation in a variable.","hasChildren":true,"name":"Spatial autocorrelation","selfAssesment":"New
"},{"code":"IP3-5","description":"The term image segmentation denotes the process of algorithmically grouping neighbouring pixels that are similar. What sounds rather straight forward, is in fact a great computational challenge, some even call it an ill-posed problem, because there is a high degree of ambiguity in this process. \r\nThe two attributes in the general definition provided above, i.e. neighbouring and similar, evoke the principles of regionalisation as a fundamental concept in geography. Regionalisation is the bottom-up approach to congregate adjacent elements with the aim to form a larger unit. (Conversely, this could be understood in a top-down manner when subdividing a larger whole into smaller homogeneous units). This follows the general notion of hierarchical organisation according to general systems theory (GST). The organisation of a state in smaller administrative units is a good example for a hierarchical structure, the composition of the human body by organs, cells, etc. another. In image analysis such regions are commonly referred to image regions, originating from the concept of “photomorphic regions”, literally meaning regions formed on images – originally by human interpreter through manual delineation. Today, advanced pixel grouping algorithms aim to delineate homogenous regions in an image automatically. As those regions usually are assumed to match with real-world objects, it is often stated in literature that image segmentation generates image objects. Deriving some general heuristics on their properties (colour, size, shape, orientation, etc.) we can label these objects according to a given semantic scheme. The procedure of object delineation and classification using object features and relations is a fundamental principle in object-based image analysis (OBIA). \r\nDue to the effect of spatial autocorrelation (the tendency of neighbouring pixels to be similar irrespective of scale or geographical location), pixel grouping is ambiguous and by no means trivial, but not arbitrary either. Intuitively, image regions are those quasi-homogeneous areas that we perceive as landscape units on a specific scene (a lake, a forest patch, a single tree, a building, a residential area). According to hierarchy theory, we can assume that we find multiple scales within a single image even, according to the level of detail we are interested in. Whether or not a specific grouping of pixels is considered valid, e.g. because it corresponds to a real-world object, can hardly be answered unanimously, but rather needs to be judged by experts in the respective application domain. That is why often in literature we find the term ‘meaningful objects’. \r\nImage segmentation is as a sub-field of computer vision and aims to apply computer algorithms to generate image regions (a.k.a. tokens) within digital image analysis. There are several strategies for performing image segmentation, all resting on the following general principles: (1) regions do not overlap; (2) regions are (relatively) homogenous; regions are (relatively) different to neighbouring regions; regions are fairly equally sized (belong to one scale domain) but can be built in several hierarchical scales. General strategies include (1) edge-based segmentation and (2) region-based segmentation, and multi-scale segmentation as a specific case. \r\nAlso referred to spatial classification emphasizing the constraint of spatial contingency, image segmentation aggregates neighbouring pixels, but – as compared to statistical clustering techniques – does not provide a unique set of classes (either semantic or statistic) in the feature space. \r\nRecently the term semantic segmentation has emerged in the machine-learning community, which is in fact a combination of segmentation and categorisation (labelling) via deep learning methods (e.g. convolutional neural networks).","hasChildren":true,"hasParent":true,"name":"Image segmentation","selfAssesment":"Completed
"},{"code":"IP3-6-1","description":"Combined filtering uses different filters to arrive at more complex filters for specific purposes. \r\nFor example, Laplacian filters are derivative filters used to find areas of rapid change (edges) in images. Since derivative filters are very sensitive to noise, it is common to smooth the image (e.g., using a Gaussian filter) before applying the Laplacian. This two-step process is called the Laplacian of Gaussian (LoG) operation.","hasChildren":true,"hasParent":true,"name":"Combined filtering","selfAssesment":"New
"},{"code":"IP3-6-2","description":"The aim of sharpening filters is to highlight transitions in intensity (high frequency components) using different operators: directional (horizontal, vertical, diagonal) or isotropic (e.g. Laplacian Filter). Example of edge detectors include: Gaussian edge detector, Laplacian filter etc.","hasChildren":true,"name":"Edge detectors","selfAssesment":"New
"},{"code":"IP3-6-3-1","description":"The Lee-sigma filter is a conceptually simple but effective alternative to the Lee and other sophisticated adaptive filters. It is based on the sigma probability of the Gaussian distribution.","hasChildren":true,"name":"Lee-Sigma","selfAssesment":"New
"},{"code":"IP3-6-3","description":"High-pass filtering enhance information of high frequencies (local extremes, lines, edges)","hasChildren":true,"hasParent":true,"name":"High-pass filtering","selfAssesment":"New
"},{"code":"IP3-6-4-1","description":"Gaussian Filters are isotropic (same behavior in all directions).","hasChildren":true,"name":"Gauss filter","selfAssesment":"New
"},{"code":"IP3-6-4","description":"Spatial filters transform an image by taking into account the local neighborhood of a pixel. The goal of filtering is to remove unnecessary components from images (e.g., noise), while emphasizing the necessary ones. In this context, low pass filters aim at removing sharp transitions in the image intensities (high spatial frequencies).","hasChildren":true,"hasParent":true,"name":"Low-pass filtering","selfAssesment":"New
"},{"code":"IP3-6","description":"In contrast to the point operations used for radiometric modification of image data, techniques for geometric processing are characterized by operations over local neighborhoods of pixels. The result of a neighborhood operation is still a modified brightness value for the single pixel at the center of the neighborhood , however the new value is determined by the brightness of all the local neighbors rather than just the original brightness value of the central pixel alone.","hasChildren":true,"hasParent":true,"name":"Kernel analysis (convolution)","selfAssesment":"Planned
"},{"code":"IP3-7-1","description":"Class modelling provides flexibility in designing a transferable workflow from scene-specific high-level segmentation and classification to region-specific multi-scale modelling","hasChildren":true,"name":"Class modelling","selfAssesment":"Planned
"},{"code":"IP3-7-2","description":"Hierarchical representation refers to hierarchically scaled compositions of the classes to be classified.","hasChildren":true,"name":"Hierarchical representation","selfAssesment":"New
"},{"code":"IP3-7-3","description":"Per-parcel analysis relies on parcels or objects as the smallest units of image analysis. The parcels are usually obtained through image segmentation that partition the input images into homogeneous units, i.e. parcels, in a supervised or unsupervised manner.","hasChildren":true,"name":"Per-parcel analysis","selfAssesment":"New
"},{"code":"IP3-7-4-1","description":"Distance relationships describe how far an object is with respect to a reference. Proximity analysis allows the identification of the distance between a geographic feature of interest and its neighbors.","hasChildren":true,"name":"Distance and proximity features","selfAssesment":"New
"},{"code":"IP3-7-4-2","description":"The most important geometric features of geographic objects are their size and shape. Shape refers to general form or outline of individual objects and can be quantified using different metric such as shape index, compactness, asymmetry, density, elliptic fit, roundness, rectangular fit etc.","hasChildren":true,"name":"Planar geometric features","selfAssesment":"New
"},{"code":"IP3-7-4-3","description":"Topological features characterize qualitatively the position of spatial objects relative to each other. There are different models for representing topological relationships. Calculus-based method, for example, allows us to model five topological relationships of two spatial objects: touch, in, cross, overlap, disjoint.","hasChildren":true,"name":"Topological features","selfAssesment":"New
"},{"code":"IP3-7-4","description":"An object of a specific object class has a value on the range of values of a spatial or spectral feature. A set of features provides the feature space that is used for classification.","hasChildren":true,"hasParent":true,"name":"Spatial features","selfAssesment":"Planned
"},{"code":"IP3-7","description":"OBIA is an iterative method that starts with the segmentation of satellite imagery into homogeneous and contiguous image segments (also called image objects. In the next step, resulting image segments are assigned to the target classes.","hasChildren":true,"hasParent":true,"name":"Object-based image analysis (OBIA)","selfAssesment":"Planned
"},{"code":"IP3-8-1","description":"The feature space represents in various dimensions all the features that can be used for classification (e.g. image bands, band math parameters, derived texture properties). A point in that space is also called a vector with values for each feature (or dimension). Polyhedralization is a form of vector space quantization where a vector is assigned to the closest centre point of one polyhedron.","hasChildren":true,"name":"Feature space polyhedralization","selfAssesment":"New
"},{"code":"IP3-8-2","description":"Radiative transfer models describing the interaction between matter and electromagnetic radiation serve as cornerstones for optical remote sensing. The radiative transfer theory provides the most logical linkage between observations and physical processes that generate signals in optical remote sensing. Radiative transfer modelling is therefore an integral part of remote sensing, since it provides the most efficient tool for accurate retrievals of Earth properties from satellite data. Radiative transfer models are used in a number of different applications such as sensor radiometric calibration, atmospheric correction and the modelling radiation processes in vegetation canopies. \r\nVegetation radiative transfer models (RTMs) study the relationship between leaf and canopy biophysical variables and reflectance, absorbance and scattering mechanisms. The infinite variability of vegetation structure complicates the modeling of RT in vegetation canopies. Numerous models of RT in vegetation canopies were developed in the second half of the last century. Models differ by the details accounted for and by the simplifications introduced in the description of canopy structure and photon–vegetation interactions. Gradual improvement in RTMs accuracy, yet in complexity too, have diversified RTMs from simple turbid medium RTMs towards advanced Monte Carlo RTMs that allow for explicit 3D representations of complex canopy architectures. This evolution has resulted in an increase in the computational requirements to run the model, which bears implications towards practical applications. When choosing an RTM, a trade-off between invertibility and realism has to be made: simpler models are easier to invert but less realistic, while advanced models more realistic but require a large amount of variables to be configured. The two most widely used models are the leaf model PROSPECT and Scattering by Arbitrary Inclined Leaves (SAIL) canopy model. \r\nAtmosphere RTMs study the interaction of radiation with the atmosphere. The remotely-sensed signals at satellite or airborne platforms are combinations of surface and atmospheric contributions, with relative amounts varying across the two wavelength regions, depending on the condition of the atmosphere. The order of magnitude of atmosphere signals can be equal or larger than that of land or ocean surface signals that arise at the top of the atmosphere (TOA). In order to derive accurate sensor calibration and atmospheric correction, the contribution of the atmospheric constituents to the total retrieved signal must be understood and modelled. Atmospheric radiative transfer models simulate the radiative transfer interactions of light scattering, absorption and emission through the atmosphere. Some widely used atmospheric RTMs are 6SV, libRadtran, MODTRAN, and ATCOR.\r\nAdvances in radiative transfer modeling enhance our ability to detect and monitor changes in our planet through new methodologies and technical approaches to analyze and interpret measurements from air- and space-borne sensors.","hasChildren":true,"hasParent":true,"name":"Radiative transfer modelling","selfAssesment":"Completed
"},{"code":"IP3-8","description":"Historically, physical modelling and machine learning have often been treated as two different fields with very different scientific paradigms (theory-driven versus data-driven). Yet, in fact these approaches are complementary, with physical approaches in principle being directly interpretable and offering the potential of extrapolation beyond observed conditions, whereas data-driven approaches are highly flexible in adapting to data and are amenable to finding unexpected patterns (surprises).","hasChildren":true,"hasParent":true,"name":"Physical-model based analysis","selfAssesment":"New
"},{"code":"IP3-9-1","description":"Difference of Gaussians (DoG) method consists of subtracting two Gaussians, where a kernel has a standard deviation smaller than the previous one. The convolution between the subtraction of kernels and the input image results in the edge detection of this image.","hasChildren":true,"name":"Difference of Gaussian (DoG)","selfAssesment":"New
"},{"code":"IP3-9-2","description":"Scale Invariant Feature Transform (SIFT) is an image descriptor for image-based matching and it is used for a large number of purposes in computer vision related to point matching between different views of a 3-D scene and view-based object recognition. The SIFT descriptor is invariant to translations, rotations and scaling transformations in the image domain and robust to moderate perspective transformations and illumination variations. Experimentally, the SIFT descriptor has been proven to be very useful in practice for robust image matching and object recognition under real-world conditions.","hasChildren":true,"name":"Scale invariant feature transformation (SIFT)","selfAssesment":"New
"},{"code":"IP3-9","description":"Scale-space theory is a framework for multiscale image representation, which has been developed by the computer vision community with complementary motivations from physics and biologic vision. The idea is to handle the multiscale nature of real-world objects, which implies that objects may be perceived in different ways depending on the scale of observation. If one aims to develop automatic algorithms for interpreting images of unknown scenes, there is no way to know a priori what scales are relevant. Hence, the only reasonable approach is to consider representations at all scales simultaneously.","hasChildren":true,"hasParent":true,"name":"Scale space analysis","selfAssesment":"New
"},{"code":"IP3","description":"Image data, in order to be turned into information, require interpretation. Thereby image understanding is the process of scene reconstruction, the description and mental representation of the content of imaged, and potentially complex, realities. \r\nImage understanding thereby goes beyond single feature extraction. Instead, it aims at a complete description of the image content, i.e. the reconstruction of a real-world scene. In the early days of digital image processing, image understanding was mainly confined to identifying and labelling image primitives. Today, advanced mapping keys and hierarchical classification schemes to analyse EO data, include composite and complex target classes. Thereby ‘full’ scene description means reaching from signal processing to a symbolic representation of the scene content. This entails the relationships of real‐world objects in different scales and spatio-temporal aspects.\r\nDescribing a scene, visually or computer-aided or mixed, depends on a conceptual framework comprising (a) the underlying research question within (b) a specific field of application and (c) pre‐existing knowledge and experience of the operator. Obtaining insights from imagery requires general knowledge about the expected scene content and domain expertise. The field of image understanding is interlinked with image (pre-)processing, computer vision, and artificial intelligence (AI). Image processing conditions the data material and enhances the interpretation source. Computer vision including pattern recognition providing knowledge representation, expert systems. AI is mainly concerned with automation processes, be it via knowledge transfer to an automated system or machine / deep learning.\r\nIn analogy to the human mind, image understanding is the computational process of extracting information from images, i.e. locating, characterizing, and recognizing objects and other features in the depicted scene. However, image understanding is not a linear, but rather a cyclic process and takes place during the pre-processing and data assimilation steps. For example, cloud masks on EO images is an early product of image understanding, prior to many pre-processing tasks.\r\nIn a typical GEOBIA workflow, the process of image understanding can be illustrated by the following steps: Starting from the subset of a real‐world scene captured on an image first step may entail scaled representations by grouping neighbouring pixels on several hierarchical sales. The multi‐scale segmentation provides a set of nested objects with geospatial and spectral properties to be used in the classification process. \r\nWith object hypotheses in mind the object relation modelling can be realized by encoding expert knowledge into a rule system. This setp aims at categorizing the image objects by their spectral and spatial properties and their mutual relationships. Hereby, an object‐centred view is accomplished. This representation of the image content should meet the conceptual reality of the interpreter or user. Knowledge is stepwise adapted and improved through progressive interpretation and modelling. Experience grows, as knowledge will be enriched by analyzing unknown scenes and the transfer of knowledge may incorporate or stimulate new rules.","hasChildren":true,"hasParent":true,"name":"Image understanding","selfAssesment":"Completed
"},{"code":"IP4-1-1","description":"Once the user finds the required data, she/he needs to know how can they be accessed, possibly including authentication and authorisation.","hasChildren":true,"name":"Accessibility","selfAssesment":"New
"},{"code":"IP4-1-2","description":"Quality Indicators (QIs) should be ascribed to data and, in particular, to delivered information products, at each stage of the data processing chain - from collection and processing to delivery. A QI should provide sufficient information to allow all users to readily evaluate a product’s suitability for their particular application, i.e. its “fitness for purpose”.","hasChildren":true,"name":"GEO QA4EO","selfAssesment":"New
"},{"code":"IP4-1-4","description":"ISO is an independent, non-governmental international organization with a membership of 164 national standards bodies. Through its members, it brings together experts to share knowledge and develop voluntary, consensus-based, market relevant International Standards that support innovation and provide solutions to global challenges. ISO/TC 211 Geographic information/Geomatics provides Standardization in the field of digital geographic information. Note: This work aims to establish a structured set of standards for information concerning objects or phenomena that are directly or indirectly associated with a location relative to the Earth. These standards may specify, for geographic information, methods, tools and services for data management (including definition and description), acquiring, processing, analyzing, accessing, presenting and transferring such data in digital / electronic form between different users, systems and locations.","hasChildren":true,"name":"ISO standards","selfAssesment":"New
"},{"code":"IP4-1-5","description":"The OGC is the worldwide leading consortium of GIS industries promoting the interoperability of geographic information across platform, system, and country borders. The main field of current activity is the complete integration of the sources of geographic information based on the Internet.The Open GIS Consortium (OGC) plays an important role on the implementation level.","hasChildren":true,"name":"OGC standards","selfAssesment":"New
"},{"code":"IP4-1-6","description":"A fundamental pillar in (open) science is to verify the scientific results of others to advance knowledge. The lack of reproducibility in scientific studies brings challenges in understanding and recreating the results of others, a situation that may be common in data-based and algorithm-based research like in geocomputation. In general, many authors define reproducibility as the ability to compute exactly the same results of a study based on original input data and analysis workflow. In other words, “to rerun the same computational steps on the same data the original authors used”. Replicability is often seen as obtaining similar conclusions about a research question derived from an independent study or experiment. In the field of GIScience and geocomputation, in particular, a reproduction is always an exact copy or duplicate, with exactly the same features and scale, while a replication resembles the original but allows for variations in scale, for example. Hence, reproducibility is exact whereas replicability means confirming the original conclusions, although not necessarily with the same input data, methods, or results.","hasChildren":true,"name":"Replicability and reproducibility","selfAssesment":"Completed
"},{"code":"IP4-1-7","description":"The ultimate goal of FAIR is to optimise the reuse of data. To achieve this, metadata and data should be well-described so that they can be replicated and/or combined in different settings.","hasChildren":true,"name":"Reusability","selfAssesment":"New
"},{"code":"IP4-1","description":"Data quality standards are guiding principles and operational guidelines for the production and use of data. For example, QA4EO aims for the two key principles of accessibility / availability and suitability / reliability. The QA4EO guidelines provide instructions for the implementation of processes that follow these principles. Standards emerge from standardization processes within the community. They are based on the agreement of the members of the community.","hasChildren":true,"hasParent":true,"name":"Data quality standards","selfAssesment":"New
"},{"code":"IP4-2-1-1","description":"To correctly perform a classification accuracy (or error) assessment, it is necessary to systematically compare two sources of information: (1) pixels or polygons in a remote sensing-derived classification map, and (2) ground reference test information (which may in fact contain error). The relationship between these two sets of information is commonly summarized in an error matrix (sometimes referred to as contingency table or confusion matrix). Indeed, the error matrix provides the basis on which to both describe classification accuracy and characterize errors, which may help refine the classification or estimates derived from it.","hasChildren":true,"name":"Error matrix","selfAssesment":"New
"},{"code":"IP4-2-1-2","description":"F-score represents the harmonic mean between precision and recall. As F-score combines both precision and recall, it can be regarded as an overall quality measure. The range of F is from 0 to 1 with larger values representing higher accuracy.","hasChildren":true,"name":"F-score","selfAssesment":"New
"},{"code":"IP4-2-1-3","description":"Ground reference refers to the reference dataset for an accuracy assessment of a remote sensing classification. The process of obtaining ground reference is dedicated to support the production of suitable accuracy information. A sampling design (fitting to the produced image classification) determines the most appropriate distribution of sample locations (or regions). The response design consists of the evaluation protocol and the labeling protocol. The evaluation protocol initiates selecting the support region on the ground (represented by a pixel or polygon) where the ground information will be collected. Once the location and dimension of the sampling unit are defined, the labelling protocol is initiated and the sampling unit is assigned a hard or fuzzy ground reference label. This ground reference label (e.g. forest) is paired with the remote sensing-derived label (e.g., forest) for assignment in the error matrix.","hasChildren":true,"name":"Ground reference","selfAssesment":"New
"},{"code":"IP4-2-1-4","description":"Kappa is a value for measuring the overall accuracy of a classification that accounts for randomness of class assignment. Kappa analysis is a discrete multivariate technique of use in accuracy assessment. Kappa yields a statistic, ^K, which is an estimate of Kappa. It is a measure of agreement between the remote sensing-derived classification map and the reference data as is indicated by a) the major diagonal and b) the chance of agreement, which is indicated by the row and column totals in the error matrix.","hasChildren":true,"name":"Kappa statistics","selfAssesment":"New
"},{"code":"IP4-2-1-5","description":"These two quality assessment indicators are calculated as follows:\r\nPrecision = TP/(TP+FP) \r\nRecall = TP/(TP+FN),\r\nwhere TS is true positive, FP is false positive, FN is false negative","hasChildren":true,"name":"Precision & recall","selfAssesment":"New
"},{"code":"IP4-2-1-6","description":"Geometric correction procedures (image-to-map rectification, image-to-image rectification) are used to rectify remotely sensed data to a standard map projection whereby it may be used in conjunction with other spatial information in a GIS to solve problems. The rectification process normally involves selecting ground control point (GCP) image pixel coordinates (row and column) with their map coordinate counterparts (e.g. meters northing and easting in a UTM map projection). Rectification requires that polynomial equations (that translate from image coordinates to map coordinates) be fit to the GCP data using least squares criteria. Depending on the distortion in the imagery, the number of GCPs used, and the degree of topographic reliefdisplacement in the area, higher -order polynomial equations may be required to geometrically correct the data. To determine how well the six coefficients derived from the least-squares registration of the initial GCPs account for geometric distortion in the inpit image, for each GCP, the root-mean-square error (RMSE) is computed.","hasChildren":true,"name":"Root mean square error (RMSE)","selfAssesment":"In progress
\r\n\r\n"},{"code":"IP4-2-1","description":"A growing set of EO services and applications produce EO products that describe various aspects of the land, ocean and atmosphere. These products include for example image products at different processing levels, geometric measurements like in digital elevation models, semantic labelling products like land cover classifications, and EO-derived attribute products concerning air quality or other geophysical and biophysical parameters. Same as any geospatial data, EO products are not free of error and require accompanying documentation of their product quality. One term for describing different quality dimensions of an EO product is accuracy.\r\nAccuracy is a measure to estimate the uncertainty that originates from errors. An error is the deviation of a map value from a true value. The concept of error assumes well-defined phenomena where deviation results from imperfection of measurement equipment, environment effects, or imperfections of the observer. They cause gross errors and blunders, systematic errors, and random errors, for which different approaches are necessary to minimize error. Ideally, only random error remains that is probabilistic in nature and can be assessed with statistical approaches. For poorly defined phenomena, the concept of vagueness applies. For example in the case of thematic maps using fuzzy sets, the accuracy assessment requires a fuzzy approach as well. \r\nJudging error requires reference data with higher accuracy (by an order of magnitude) to which the map value can be compared. EO product quality dimensions about accuracy include thematic accuracy, spatial accuracy (both horizontal and vertical), radiometric accuracy, and accuracy of biophysical/geophysical parameter measurements. Respective equipment and approaches for reference data collection includes ground verification for thematic maps, GNSS positioning devices, field spectrometers, air quality sensors and in-situ biomass estimation. Ideally, reference data is collected in the field. In case of inaccessible areas of interest and/or if the service requirements allow it, approaches may rely on proxy reference data.\r\nThe design of the accuracy assessment procedure should be done with the EO product design to match the requirements of the EO service. For example, a thematic accuracy assessment consists of the main three components of response design, analysis, and sampling design. The response design ensures that reference data and map data are comparable at a location and specifies under which cases they agree or disagree. The analysis, usually performed with an error matrix, specifies which quality indicators will be calculated to quantify accuracy. The sampling design specifies the subset of locations at which the response design will be applied. Depending on the classification process and application case, different sampling strategies can be suitable (e.g. clustered sampling, stratified random sampling). \r\nFor other accuracy dimensions, respective accuracy assessment procedures exist, e.g. root mean squared error (RSME) for the positional accuracy assessment.\r\nAfter an accuracy assessment has been performed and the uncertainty in the EO product is understood, the challenge is to clarify how the uncertainty affects subsequent spatial analyses with the EO product. Different strategies exist that ignore error completely or that account for error by modelling uncertainty in the analysis outcomes. If uncertainty is judged low enough (or more hazardous, if users are unaware of the limited accuracy), subsequent analyses accept the EO product as true and ignore the accuracy value. If uncertainty is incorporated in subsequent analysis through uncertainty modelling, the results describe the bandwidth of outcomes, potentially supported with appropriate visualisations of uncertainty. The uncertainty modelling approach may greatly enhance the usability of the EO product, because it informs better how the error impacts the EO information and how much confidence a user should have in it.\r\nWith a new generation of EO products on the horizon and a largely increased user community, a large number of new applications is to be expected. They may also identify innovative accuracy assessment approaches. For example, the availability of EO archives with long time series of EO data led to response design protocols tailored to collect time series of reference data. The use of volunteered geographic information (VGI) as reference data has great potential, if approaches are implemented that ensure its reliability. Methods for object-based accuracy assessment are continued to be developed. Further, the increasing number of EO parameter products based on continuous variables creates the need to describe their accuracy. Finally, the focus on validation of EO products during EO service development and operation will make feedback from users available to service providers, ultimately leading to more meaningful EO products with more meaningful accuracy metrics and other quality indicators.","hasChildren":true,"hasParent":true,"name":"Accuracy assessment","selfAssesment":"
Completed
"},{"code":"IP4-2-2","description":"The implementation of a service that provides remote sensing derived information on a regular basis introduces process-related quality criteria like the timeliness of information provisioning. For the case of refugee camp mapping, timely arrival of map information may be critical to support the decisions in planning facilities for humanitarian assistance.","hasChildren":true,"name":"Timeliness","selfAssesment":"New
"},{"code":"IP4-2-3-1","description":"Completeness is a quality dimension that can apply to different data properties.The Data completeness is dealing with the completeness of an image, handling for example the effect of shadowing objects, sun flares on water surfaces or masking out by an object (e.g. propeller of a UAV). Spatial completeness is a feature on the area coverage. In photogrammetry (especially in stereophotogrammetry) its 3D version, the stereo completeness has extreme importance. In monitoring systems and applications the Temporal completenesster term features how the taken images represent a complete time series. The thematic completeness measure describes the image interpretation quality how the expected and defined classes are evaluated. This feature is important with the use of e.g. multiple classifiers.","hasChildren":true,"name":"Completeness","selfAssesment":"New
"},{"code":"IP4-2-3-2","description":"In remote sensing we can speak about spatial consistency in the Consistency cluster. It represents the quality of image interpretation/understanding: how are the different objects or classes recognized/evaluated integrally. A bridge above a water surface, like river can be detected in pixel-wised manner, but the question is how coherent they are in the output map. This phenomenon has very close to the thematic consistency, where the recognition integrity is represented in this way. The topological consistency is defined mainly for network-type surface objects, like roads or rivers, where the connection of all atomic segments are rated by this measure. Urban mapping focuses on the built environment objects, where e.g. house-parcel inclusions are described by this feature. The temporal consistency is for monitoring again, representing for example the possibility or impossibility of land cover changes in time. Having multiple data sources (even airborne or terrestrial), their integral usage can be qualified by this measure.","hasChildren":true,"name":"Consistency","selfAssesment":"New
"},{"code":"IP4-2-3-3","description":"Readability refers to the content of a map being presented clearly enough that the content can be perceived and understood by the user. This includes legibility, e.g. whether the text of a label is large enough to be read and has enough contrast to the background to be easily perceivable. Additionally, readability has a broader meaning that explains whether a product as a whole is simple enough to be understood and not too complex that essential information can be overlooked by the user.","hasChildren":true,"name":"Readability","selfAssesment":"New
"},{"code":"IP4-2-3","description":"Gathering information about the quality of an EO product or service by letting the user test it. The feedback from the user enables to verify whether specific quality criteria have been met.","hasChildren":true,"hasParent":true,"name":"User validation","selfAssesment":"New
"},{"code":"IP4-2","description":"A product in the sense of something that a user can use for a specific purpose requires a certain quality. Therefore, its accuracy needs to be judged with an accuracy assessment measure that the user understands and where he can interpret the meaning in relation to the purpose. The product has to be validated, i.e. it has to be known whether the product qualifies for use in a certain context. And in addition, the product needs to be available in time that the users can base their decision on it.","hasChildren":true,"hasParent":true,"name":"Product quality","selfAssesment":"New
"},{"code":"IP4-3-1","description":"The cloud cover percentage indicates the amount of area in the remote sensing image extent that is covered with clouds and therefore cannot provide information about the Earth surface conditions.The actual types of clouds included may depend on the product, but the CEOS definition includes cloud shadow. Next to that, from an optical remote sensing point of view, clouds can be roughly classified in: opaque/dense clouds, mainly composed of droplets that are highly reflective in the VIS region and generally located at low-medium altitudes and cirrus, consisting of a large number of thin non-spherical ice crystals that are normally translucent in the VIS region, relatively highly reflective in the SWIR spectrum, and located at high altitude.\r\n\r\nThe goal of cloud cover percentage is to provide a quality measure of usable information in a surface reflectance image. Earth observation product catalogs support it as a query parameter, to enable searching for products with a cloud cover percentage below a given threshold.\r\nThis simplifies for instance use cases that require only fully clear products (0% cloud cover), and may save download and processing resources by only handling images that have some valid pixels. For instance, by only using products with a cloud cover percentage smaller than 99.95%. The measure also gives an estimate of the number of valid observations in a given geographical area, allowing a quick assessment of whether minimal data requirements for a specific use case are met.\r\n\r\nThe measure is a percentage of actual observations in an image, so pixels where no data was recorded are not included. For derived products, cloud cover pixels are often also flagged separately from pixels where no data was recorded, but this may depend on the data provider. The definition specifically also includes cloud shadow pixels.\r\nReliable cloud cover percentages depend on good cloud and cloud shadow detection methods. Especially handling of translucent cirrus clouds is an open issue: a product that has a 100% cloud cover percentage due to cirrus clouds might still be usable for some cases, while for other cases they also render the product useless. \r\n\r\nThe used cloud detection algorithm will also affect the cloud cover percentage. A more strict algorithm will yield higher percentages compared to an algorithm that under detects clouds.\r\nDue to these limitations, cloud cover percentages in product metadata have a fairly high error margin. The user should take this into account when determining optimal cloud cover percentage thresholds for the use case.","hasChildren":true,"name":"Cloud cover percentage","selfAssesment":"Planned
"},{"code":"IP4-3-2","description":"The remote sensing lifecycle structures all possible phases of the data production process, from its beginning of the data's coming to existence (that includes the sensor design prior to data collection) over storage, processing and use to archiving and deletion.","hasChildren":true,"name":"Remote sensing lifecycle","selfAssesment":"New
"},{"code":"IP4-3-3","description":"The capability of a sensor or EO product to resolve anything is a function of its (spatial, temporal, spectral and radiometric) resolution and of the detail at which a geographic phenomenon of interest manifests itself in time and space. A geographic phenomenon can be named or described, georeferenced and provided with a time interval at which it exists. The geographic phenomenon of interest is the one of which a user needs information to help him make a decision. Therefore, the geographic phenomenon needs to be resolved with a low enough uncertainty and a high enough quality that allows the user to make a decision with confidence. \r\nFor example, let’s consider a helicopter pilot that wants to know whether a specific site is suitable for an emergency landing. The decision to perform an emergency landing may be supported with an EO-derived digital map of emergency landing sites that are flat enough (as well as large enough for the pilot’s helicopter and free of any obstacles on the surface and in the approach area). If we only focus on the flatness of the terrain, we need a digital elevation model (DEM) of high enough spatial resolution and accuracy in the Z dimension to calculate slope within acceptable levels of uncertainty. The pilot probably can tell us what degrees of slope are okay for his helicopter and tell us sites (e.g. football fields) where such a landing would succeed. However, this is only the input to an analysis of different DEMs to identify the minimum spatial resolution and accuracy in the Z dimension to model slope products and associated uncertainty to derive an emergency landing site product that fulfils the requirements. Thereby the capability of different DEMs to resolve emergency landing sites can be analysed.\r\nSpatial resolution is a measure of the smallest angular or linear separation between two objects that can be resolved by the remote sensing system. A useful heuristic rule of thumb is that in order to detect a feature, the nominal spatial resolution of the sensor should be less than one-half the size of the feature measured in its smallest dimension.\r\nOther types of resolution of an EO dataset are available that determine for various geographic phenomena under investigation whether it is possible to resolve them in the data. These are radiometric resolution, spectral resolution and temporal resolution. Radiometric resolution is defined as the sensitivity of a remote sensing detector to differences in signal strength as it records the radiant flux reflected, emitted, or back-scattered from the terrain. Spectral resolution is the number and dimension (size) of specific wavelength intervals (referred to as bands or channels) in the electromagnetic spectrum to which a remote sensing instrument is sensitive. The temporal resolution of a remote sensing system generally refers to how often the sensor records imagery of a particular area. For time-series analysis, the temporal resolution determines the time granularity for resolving processes that underlie the change that is observable between subsequent images.","hasChildren":true,"name":"Capability to resolve anything","selfAssesment":"In progress
"},{"code":"IP4-3-4","description":"The spatial coverage of a dataset (consisting of an image or a series of images) determines whether the dataset covers the area of the terrain that is of interest to the user of information derived from the dataset.","hasChildren":true,"name":"Spatial coverage","selfAssesment":"New
"},{"code":"IP4-3-5","description":"The temporal validity of a dataset (consisting of an image or a series of images) determines whether the acquisition date(s) (and period) match(es) the requirements for investigating a specific phenomenon and thereby enables the derivation of information about that phenomenon.","hasChildren":true,"name":"Temporal validity","selfAssesment":"New
"},{"code":"IP4-3","description":"Values (or a value) that enable(s) judging a dataset or product on their fitness for a specific purpose (e.g. whether a specific satellite image is suitable for mapping landslides). , A QI should provide sufficient information to allow all users to readily evaluate a product’s suitability for their particular application, i.e. its “fitness for purpose”.","hasChildren":true,"hasParent":true,"name":"Quality indicators","selfAssesment":"New
"},{"code":"IP4","description":"Data quality, in general, is the degree of data usability in relation to a specific application purpose. Assurance of data quality is of growing importance in remote sensing, due to the increasing relevance of remote sensing data in planning and operational decision of public bodies and private firms, and the huge amount of digital services (or apps) that exploit RS data. \r\nDifferent data quality dimensions exist according to the lifecycle phases of the remote sensing data: data acquisition, data storage, data pre-processing, processing and analysis and data visualization and delivery. Remote sensing data acquisition phase involves the following quality aspects: resolution, accessibility, spatial accuracy, temporal validity, accuracy and precision of the sensor calibration. Resolution is a multi-dimensional concept that includes the following dimensions: spatial resolution, temporal resolution, radiometric resolution, spectral resolution and temporal resolution. Temporal validity refers to the quality of an remote sensing data product in time, whereas spatial accuracy refers to the accuracy of the position of features relative the Earth. \r\nData storage includes the accessibility and completeness data quality dimensions. Accessibility includes both temporal and data accessibility. Temporal accessibility refers to the time delay between data acquisition and data delivery, whereas data accessibility refers to the availability of remote sensing data. Data completeness encompasses temporal completeness, i.e. completeness of a time series represented a phenomenon, thematic completeness, and spatial completeness which refers to the area coverage. Data preprocessing, processing and analysis phase includes consistency, completeness, temporal validity, resolution, radiometric and geometric accuracy, thematic and semantic accuracy. Thematic and sematic accuracy refers to the correctness of the remote sensing data product. The main quality dimensions of the data visualization and delivery include readability, completeness and temporal validity. \r\nDifferent metrics can be used to assess the quality of the remote sensing-derived information, such as the root-mean-square error (RMSE) measuring the differences between the true and measured values of the phenomenon under investigation, confusion matrix used for assessing the classification performance, producer’s accuracy, user’s accuracy or Cohen kappa. The quality of the remote sensing data per se can be assessed using Peak Signal-to-noise Ratio (PSNR) or the Universal Image Quality Index (UIQI).\r\nDifferent organizations are involved in the standardization of the image data and gridded data quality, including ISO/TC 211 ‘Geographic information/Geomatics’, Open Geospatial Consortium (OGC) or the Quality Assurance Framework for Earth Observation (QA4EO) developed by the Group on Earth Observation (GEO). These organizations are responsible for developing metadata standards that are further used by the remote sensing community to document the quality of the remote sensing data. According to the QA4EO, for example, all remote sensing data products need to be accompanied by a Quality Indicator (QI) which helps users assessing their fitness-for-use.","hasChildren":true,"hasParent":true,"name":"Image data quality","selfAssesment":"Completed
"},{"code":"IP5-1-1","description":"Array databases make use of arrays as the primary storage representation. Such an array-oriented data model and query language is useful in many scientific applications, where the raw data consists of large collections of imagery or sequence data that needs to be filtered, subsetted, and processed.","hasChildren":true,"name":"Array databases","selfAssesment":"New
"},{"code":"IP5-1-2","description":"The Open Data Cube (ODC) is a non-profit, open source project that was motivated by the need to better manage Satellite Data. This project was born out of the work done under the \"Unlocking the Landsat Archive\" and the Australian Geoscience Data Cube (AGDC) projects.","hasChildren":true,"name":"Open data cube","selfAssesment":"New
"},{"code":"IP5-1","description":"The term data cube originally was used in Online Analytical Processing (OLAP) of business and statistics data. Technically speaking, such a data cube represents a multidimensional array together with metadata describing the semantics of axes, coordinates, and cells. It is an efficient approach to the management and analysis of large datasets.","hasChildren":true,"hasParent":true,"name":"Data cubes","selfAssesment":"New
"},{"code":"IP5-2-1","description":"Content-based image retrieval helps users retrieve relevant images based on their contents.","hasChildren":true,"name":"Content-based image retrieval","selfAssesment":"New
"},{"code":"IP5-2-2","description":"Web Portals allow users to discover, understand, view, access and query information of their choice from local to global level for a variety of uses.","hasChildren":true,"name":"Web portals","selfAssesment":"New
"},{"code":"IP5-2","description":"Image archives are repositories for storing, managing and retrieving remote sensing data.","hasChildren":true,"hasParent":true,"name":"Image archives","selfAssesment":"New
"},{"code":"IP5-3-1","description":"As an initiative stipulated by the European Commission to foster the bridge between the Copernicus ground segment and the user segment, the Copernicus data and information access service (C-DIAS) is a generic name for different sets of cloud-based platforms providing centralised access to Copernicus data and information, as well as to processing tools. The name indicates, however, that the focus of such advanced user-centred infrastructure implementations is not only on data access, but also on ‘information’. What is specifically meant here is the provision of information services and information layers as defined in the Copernicus service portfolio. This allows the users to develop and host their own applications in the cloud and a single access point, rather than processing data locally. Currently there are five different DIAS’s implemented (CREODIAS, SOBLOO, MUNDI, WEKEO, ONDA), all with some specific technical assets, or a sector-specific application focus or any other unique selling position by e.g. targeting as specific user community. Currently, the DIAS, which have received co-funding from the European Commission as a kind of seed funding, are currently in the process of exploring opportunities and claiming market shares, striving to sustain in a competitive manner. Some of the features are highlighted in the following, without explicitly mentioning any of the associated DIAS: (i) data access of global data sets (satellite data mosaics or gridded data) by custom area; (ii) OGC interfaces, VM catalogue, SPAR QL search interface (combine searches like receive images over areas of high population density), open source (accessible via API) or pay-per-use; (iii) access to core service products (e.g. CLMS, CMEMS, CAMS); (iv) focus on integrated applications such as smart cities, urban energies, precision agriculture; access to third-mission VHR satellite data (e.g. Pléiades); (v) utilizing GitLab as a developer platform.","hasChildren":true,"name":"Data and information access service (DIAS)","selfAssesment":"Completed
"},{"code":"IP5-3-2","description":"The OpenGIS® Web Processing Service (WPS) Interface Standard provides rules for standardizing how inputs and outputs (requests and responses) for geospatial processing services are defined. It defines an interface that facilitates the publishing of geospatial processes and clients’ discovery of and binding to those processes.","hasChildren":true,"name":"OGC interfaces and OGC web processing service","selfAssesment":"New
"},{"code":"IP5-3","description":"Online processing allows users to implement and run image analysis operations online independent of the underlying software.","hasChildren":true,"hasParent":true,"name":"Online processing","selfAssesment":"Planned
"},{"code":"IP5","description":"In general, infrastructures such as cyberinfrastructures or Spatial Data Infrastructures (SDIs), allow information sharing across distributed infrastructures and communities. SDIs have gradually changed from a pool of authoritative data shared using standardized web services to a pool where the authoritative data co-exist with data collected by volunteers and different sensors. Many efforts were dedicated to data documentation, to improving the catalogues searching techniques by means of, for example, thesauri and to sharing these data using standardized web services such as Web Map Service, Web Feature Service or Web Coverage Service. Cloud computing technologies played an important role in the implementation of sustainable SDIs due to their ability to provide on-demand computational and storage capacities over the Internet. In this way, users can easily search, find and use data shared across different online platforms.\r\nMore specifically, infrastructures for image processing and analysis refer to the physical and organizational facilities that allow the storage, analysis and management of the available data and products. Traditionally, this infrastructure formed a digital image processing system consisting of computer hardware with special-purpose image processing software, and peripheral input-output devices (e.g. CD or DVD drives, internet access, printers/plotters). In recent years, Earth observation is undergoing a shift to online processing making use of data cubes and vast image archives, e.g. NSF EarthCube or Digital Earth Australia, the Swiss Data Cube, the EarthServer, the E-sensing platform or the Google Earth Engine. Available infrastructures aim at sharing remote sensing data and derived products following the FAIR metrics: Findable (F), Accessible (A), Interoperable (I), Reusable (R). Thus, remote sensing data have to be documented using metadata that support FAIR data principles as follows: (1) Findable: remote sensing data are findable through data documentation, i.e. metadata, that needs to include a unique identifier of the described data. Metadata can be stored in a catalog compliant to one of the available data cataloging standards such as the SpatioTemporal Asset Catalog (STAC) compliant catalog; (2) Accessible: all data have to be openly accessible and shared using interoperable formats that allow users to find, access and reuse them; (3) Interoperable: different standards, e.g. STAC specification, have to be used to document remote sensing data; (4) Reusable: metadata have to be comprehensive enough to allow users not only to assess the fitness for purpose (e.g. lineage) but also to provide them information about how to access the generated data.","hasChildren":true,"hasParent":true,"name":"Infrastructure","selfAssesment":"Completed
"},{"code":"IP6","description":"In an information value chain, one or more organizations perform a set of value-adding activities for creating and distributing information products and services. They support a user in decision-making and thereby benefit the user’s purpose. The information value chain is a tool for evaluating business management and profitability. It enables explaining the ultimate “value” of a product and the components along the value chain and consequently allows businesses to optimize their processes. \r\nThe value of EO data can be assessed by analysing the contribution of the data to a specific EO information product and its effective use in decision-making. The (share of) benefit attributable to the use of the given EO data is derived from the comparison of a decision taken using the EO product to a counterfactual situation where other types of information are used instead. Often, this compares the situation before a new EO service was available to the situation afterwards. An ex-post analysis may reveal improved performances, e.g. gains in output, or productivity and/or reduced costs as compared to those occurring in absence of EO-derived information. This benefit resides with the user of the EO product and may be traced to societal and environmental benefits through impact chains.\r\nThe process of EO information production and distribution is integrated in the value chain and can be defined as the image processing chain. It comprises the value-adding activities of the organization(s) that lead up to the availability of an EO product for decision making. The nature and flow of these activities and the collaboration between organizations and among participants within organizations can be modelled with business process model and notation (BPMN). BPMN is a flowchart diagram that uses swimlanes representing different participants. Processes are assigned to participants and are connected with arrows into flow sequences. Further elements complete the choice of symbols for modelling a consistent flow, including a start event, end events, and branching options. They allow organizing the flow in parallel or iterative processes. Higher-level processes can be (de-)composed with sub-processes. Additionally, it is possible to use pools and message flows for explicitly modelling collaboration between participants (from different organizations).\r\nIn the image processing (value) chain, the sequence of processing steps begins with the acquisition of EO data, followed by steps of pre-processing and information extraction (or whatever steps are necessary) and ends with an EO information product being available to a user that uses it to make his decision. The collaborating stakeholders along the chain include EO satellite operators, EO data providers, EO information providers, and the users at the end of the value chain. The stakeholders along the processing chain each perform a dedicated subsequence of processing steps. Thereby, the stakeholders contribute their share of value to the data they deliver to the next stakeholder in the chain, ultimately arriving a the EO information product for the user. The EO data products that they hand on along the chain are often described with processing levels that provide different states of processing of EO data. They start with raw instrument data (level 0 and 1) that are followed by data converted into geophysical quantities that are geo-referenced and calibrated (level 2). Further levels are quality controlled data that has been mapped on a uniform space-time grid (level 3) and data combined with models or other instrument data (level 4). In addition, EO data providers use the term analysis ready data (ARD) that have been processed to allow direct data analysis, i.e. user processing effort is reduced to a minimum. Further, the standard EO products contain a categorizing element that is related to the image processing value chain. This categorizing element organizes the EO products along the sequences of processing, descriptive analytics, predictive analytics, prescriptive analytics, aggregation, visualization, and distribution. Thereby, the products ultimately contribute to the actionable EO information product for the use in decision-making.","hasChildren":true,"name":"Image processing (value) chain","selfAssesment":"Completed
"},{"code":"MDS","description":"MDS is a dimensionality reduction technique. It can be divided into Metric multidimensional scaling, Generalized multidimensional scaling and Classical multidimensional scaling.\r\n\r\nGeneralized multidimensional scaling is an extension of metric multidimensional scaling, in which the target space is an arbitrary smooth non-Euclidean space. In cases where the dissimilarities are distances on a surface and the target space is another surface, GMDS allows finding the minimum-distortion embedding of one surface into another.\r\n\r\nClassical multidimensional scaling is also known as Principal Coordinates Analysis, Torgerson Scaling or Torgerson Gower scaling. It takes an input matrix giving dissimilarities between pairs of items and outputs a coordinate matrix whose configuration minimizes a loss function called strain.","hasChildren":true,"name":"Multidimensional scaling","selfAssesment":"Depricated (GI-N2K)
"},{"code":"no","description":"Models that describe the basic principles of randomness and probability in spatio-temporal data.","hasChildren":true,"name":"Mathematical models of uncertainty: Probability and statistics","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI","description":"This knowledge area considers the organizational and institutional aspects related to GIS&T. The focus of this knowledge area is on the organizations active in the GIS&T domain, and what happens within and between these organizations. The knowledge area is structured around five units. One unit considers the key organizations in the GIS&T domain, covering relevant public sector organizations at different administrative levels as well as organizations in other sectors of society. Among the organizational aspects covered in this knowledge area are all organizational issues related to the implementation, use and management of GI and GIS within organizations. While all topics related to the organizational structures, procedures and management of GI(S) are grouped into one unit, another unit focuses on issues related to the human factor of using GI and GIS, i.e. people, their skills and competencies, and the development and evaluation of these skills and competencies in the context of GIS&T training and education. The knowledge area includes also several inter-organizational and institutional aspects of GIS&T. Particular attention is paid to the concept of geospatial data sharing, which is about the creation of `spatial data` connections and relationships between different organizations in the GIS&T domain. Spatial data infrastructures are developed to promote, facilitate and coordinate the sharing of spatial data among data providers and data users, and consists of several technological and non-technological components. Many related topics are considered in the knowledge area GI and Society (WS), which also addresses several non-technological aspects related to GIS&T. In addition to this, also the knowledge areas `Design and Setup of Geographic Information Systems`, `Geospatial Data\" and Web-based GI` include several topics that are closely linked to the topics that are considered in this knowledge area. It can be argued that in order to fully master the knowledge and competencies that are presented in these knowledge areas, also basic knowledge and understanding of the organizational and institutional aspects is required.","hasChildren":true,"hasParent":true,"name":"Organizational and Institutional Aspects","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI1-1","description":"The development of an appropriate organizational model, which establishes the basic character of GIS operations, is a crucial element of the GIS management. The appropriate GIS organizational model for any organization is based on its intended role.Alternative GIS organizational models are based on differing arrangements concerning the scope of GIS, the degree of integration of GIS into business operations, the degree of centralization of GIS operation and use, and the degree of centralization of management control. Although many variations can arise from different combinations of these factors, GIS organizational models can generally be classified into three types: (1) enterprise GIS, (2) GIS data and service resource, and (3) GIS as a business tool (Somers, 1998).","hasChildren":true,"name":"Organizational models for GIS management","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI1-2","description":"Management of GIS can be done in a more centralized or more decentralized manner. In a a so-called enterprise or information-framework GIS, an organizational unit may be established to manage the GIS environment and run the core system, whereas usage is decentralized. In environments where GIS is used occasionally by various users, it may be set up as a separate service with a designated group that manages the GIS and also controls users' applications services. A second decision that needs to be made after the choice between more centralized or more decentralized management of GI and GIS is about where to place the GI management. Alternative options are in a line organization, in a support area, or at the executive level, each with their own advantages and disadvantages.","hasChildren":true,"name":"Managing GIS operations and infrastructure","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI1-3","description":"User roles describe the relationship between different users and the GIS in an organization. Each user role includes responsibilities (e.g. for modifying certain information) and privileges (e.g. for viewing specific information). Although many different roles can be defined, a basic distinction is made between users, who can only view certain information, and editors, who can edit certain information.","hasChildren":true,"name":"User roles","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI1-4","description":"A GIS management strategy should be unique for each organization, as organizations have unique environments, characteristics, goals, GIS requirements. An important step in developing an effective strategy for an organization is to establish the strategic vision for GI and GIS in the organization and define its role and scope. Other elements that should be covered in the GIS Strategy are the degree of centralized management of the GIS, the placement of GIS management and support in the organization, involvement of users in GIS planning and implementation, coordination of users, organizational changes, preparation of users, personnel issues, transitions to GIS operations, integration into business operations, user support, data access, and integration of technology changes (Somers, 1998).","hasChildren":true,"name":"Strategic planning","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI1-5","description":"Committee and team approaches are frequently employed for coordinating participants and users in multi-participant GIS projects. The aim of creating such committees and teams is to ensure that the varied interests of participants are addressed, as participants bring many different interests, application needs, data needs, priorities, organizational issues, and political interests to a common project the GIS. Common models for coordinating participants recognize that participants have three levels of interest in the GIS: policy, technical development, and usage. Different bodies can be established focusing on these different levels of interest: a technical committee focusing on the design and development of the GIS, an management committee providing policy guidance and support and a user`s group.","hasChildren":true,"name":"Coordinating GIS Participants and Users","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI1-6","description":"After the development and implementation of a GIS within an organization, the challenge is to maintain the system and revise and update it when necessary. This means the performance of the GIS in terms of efficiency and effectiveness should be measured and monitoring, and feedback from users on the system and applications, on the data as well as on new needs should be collected. Particular attention should be paid to the maintenance of data sets.","hasChildren":true,"name":"Ongoing GIS revision","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI1-7","description":"The introduction of GIS into organizational environments should be seen as a complex process of mutual adaptation (Nedovic-Budic, 1997). These technologies changes the established organisational processes and structures, while on the other hand the organisational context and culture modify the technological set-up and use. Therefore, knowledge and understanding of the relationship between technologies and organizations is necessary to increase the success of GIS implementations in organizations. Successful GIS implementation and adoption often require some degree of organizational change. However, this can be very difficult to effect because organizations are naturally resistant to it (Somers, 1998).","hasChildren":true,"name":"Organizational changes","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI1","description":"GIS and T implementation and use within an organization often involves a variety of participants, stakeholders, users and applications. Organizational structures and procedures address methods for developing, managing, and coordinating these multi-participant users. The development of the appropriate organizational model for managing the GIS is crucial. In certain cases, changes to the organizational structure in place might be required. Strategic planning and the establishment of coordination structures can be considered as valuable instruments for managing and coordinating all involved users, while also the different user roles need to be assigned.","hasChildren":true,"hasParent":true,"name":"Organizational structures, procedures and management","selfAssesment":"In Progress GI-N2K
"},{"code":"OI2-1","description":"GIS and T professionals can be hired for a wide range of different job positions, for which the precise skills, competences and qualifications needed will vary. Typical examples of GIS and T positions are GIS&T project managers, technicians, system developers and analyst. The recognition and certification of the competences people have acquired in informal and non-formal learning contexts is important to know which skills and competences individuals have and whether they meet the qualifications required for a certain job position.","hasChildren":true,"name":"GIS and T positions and qualifications","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI2-2","description":"Making sure staff members have the necessary skills and competences to perform geospatial activities is necessary for an effective implementation and operation of GI within an organizations. Several training methods can be adopted to ensure the development of skills and competencies of staff members. A distinction can be made between formal and informal training, but also between internal and external training programs. Another relevant issue is the assessment and evaluation of the skills and competences of staff members, to determine their future training and development needs.","hasChildren":true,"name":"GIS and T staff development and evaluation","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI2-3","description":"Programs and courses on GIS and T and related subjects are provided by a wide range of institutions. While in recent years also the use and integration of GI and GIS in primary and secondary education has received significant attention, GIS and T education is mainly organized by institutions of higher education, especially universities but also other higher education institutions. Analyses of the higher education GIS&T programs and courses in Europe showed that the offer of courses is very diverse, in terms of size (ECTS), educational level (EQF) and course content. Vocational training on GIS and T related topics is organized by different types of training providers, including the major GIS vendors, data and service providers, academic sector, professional organisations, but also the public sector.","hasChildren":true,"name":"GIS and T training and education","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI2-4","description":"A curriculum is a systematic description of a study program, in terms of learning goals, structure and sequence, learning, teaching and assessment strategies and content. A curriculum consists of both a set of related required and elective - courses along with all direct and indirect skills, competences and learning outcomes resulting from these courses. In the process of curriculum design typically particular attention is assigned to objectives, teaching methods and educational strategies, while also attention should be paid to the content organization aspects and the global structure of the curriculum. The process of designing GIS&T curricula presents many challenges, as the design of the curriculum should be aligned to both the institutional context and the expected outcomes of the learning and teaching process (Prager, 2011).","hasChildren":true,"name":"GIS and T curriculum and course design","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI2-5","description":"An important challenge in organizing GIS and T education and training is the choice and use of effective teaching and learning methods. These methods should follow recent technological developments and use the best technologies to help students acquire the necessary skills and competencies. Traditionally, most GIS and T programs and courses were taught in the context of a full-time, face-to-face setting, using traditional teaching methods such as lectures and lab-based computer practical sessions. In recent years, educational institutions and their teachers have been experimenting with more innovative teaching and learning methods, such as project-based and case-based learning, distance learning, integrated and inter-disciplinary lessons, collaboration with companies and other stakeholders, etc.","hasChildren":true,"name":"GIS and T teaching and learning methods","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI2","description":"This unit addresses GIS and T staff and workforce issues within an organization, particularly as they relate to ensuring that GIS and T is appropriately used and supported. The focus of this unit is on the skills and competencies of professionals in the GIS and T domain: how can these skills and competencies be described and evaluated, and how can they be developed through training and education.","hasChildren":true,"hasParent":true,"name":"GIS and T workforce themes","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI3-1","description":"Cost savings are an important driver or motivation for sharing geospatial data and information. As costs associated with collecting and maintaining geospatial data are high, sharing data means that users no longer need to duplicate data gathering and archiving, which leads to savings in terms of personnel, space/facilities, data acquisition and maintenance costs. One fundamental argument for sharing thus derives from scale economies in production. Because the cost of making data is high, there is a clear incentive to maximize the number of users of these data. Sharing allows data to be used repeatedly for many purposes, thus increasing their value without increasing their cost. Sharing data also leads to improved data quality. Moreover, in many cases, sharing data is the only way to get access to certain data sets, as the authority to collect and manage certain data lies with another public institution.","hasChildren":true,"name":"Drivers and incentives for sharing geospatial data","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI3-2","description":"Sharing of geospatial data can be hindered or inhibited by several types of barriers. These include technological barriers, such as a lack of common data definitions, formats and models or incompatibility of hardware and software. Among the non-technological barriers are organizational, political and legal issues and elements, such as misaligned organizational missions, diversity in organizational cultures, conflicting organizational priorities, lack of funding, lack of executive and legislative support; restrictive laws and regulations, copyright issues, data privacy and data ownership issues. However, it should be noticed that many of these barriers have been decreased or eliminated in recent years.","hasChildren":true,"name":"Barriers to geospatial information sharing","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI3-3","description":"The legal framework for geospatial data sharing is very wide and diverse, involving rules on data, coordination, standards, funding, etc. Moreover, these rules and regulations can take many different forms: legal acts adopted by parliament, executive orders or decisions, cooperation agreements, memoranda of understanding, bilateral arrangements etc. From a data perspective, the legal framework can be distinguished into two main types of policies: those that promote and those that hinder the availability of spatial data. Policies that promote spatial data availability can focus on different types of users (public bodies, private companies, citizens) and different types of use (public access, commercial and non-commercial reuse, reuse for performing public tasks). Among the policies that hinder the availability of spatial data are those dealing with privacy, liability, and intellectual property. The legal framework also includes legislation that applies to data or information in general, such as open data legislation, which may also be applicable to spatial data (e.g. legislation on freedom of information, copyright, etc.). Moreover, also general legislation relating to any interaction between people or any situation in everyday life (e.g. liability, contract law, competition law, etc.) will apply to spatial data sharing.","hasChildren":true,"name":"Legal framework for geospatial data sharing","selfAssesment":"Completed
"},{"code":"OI3-4","description":"Several types of legal mechanisms for sharing geospatial data can be used. A data sharing arrangements can be formalized by a contract or agreement between the data provider and the data user. A particular type of agreement are the framework agreements, which are agreements between two or more organisations concluded prior to the datasets or services being required. These framework agreement can involve one or multiple spatial data sets or services. Partnership agreements are often used to formalize the data sharing agreements among a broader group of partners. Participation in such a partnership often means participants share their data with other participants and get access to shared data. Another relevant mechanism is the use of licenses, which are mechanisms to give organizations and people the permission to use spatial data sets and services. A license is legally binding, and defines the conditions of use of the related spatial data sets and services. In order to reduce the number of licenses used and ensure the harmonization of the terms in these licenses, the use of standard licenses is promoted. Also the use of open data licenses is promoted for sharing geospatial data, and strongly increased in recent years.","hasChildren":true,"name":"Legal instruments for sharing geospatial data","selfAssesment":"Completed
"},{"code":"OI3","description":"Geospatial data sharing has become an essential element of the GI activities of organizations. Spatial data sharing can be defined as the electronic transfer of spatial data/information between two or more organizational units where there is independence between the holder of the data and the prospective user. Spatial data sharing has many advantages, but several technical and non-technical barriers must be overcome to put data sharing into practice. While the practice of spatial data sharing has substantially grown with the development of spatial data infrastructures, many consider data sharing as a crucial element for the success of these infrastructures.","hasChildren":true,"hasParent":true,"name":"Geospatial data sharing","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI3b","description":"A Spatial Data Infrastructure can be defined as the collection of technological and non-technological components to facilitate and coordinate the exchange of and sharing of spatial data. The concept infrastructure is used to promote the concept of a reliable, supporting environment, analogous to a road or telecommunications network, that facilitates the access to spatial data. Data, metadata, access networks, standards, coordination, policies, funding, people and institutional frameworks are often considered among the key components of an SDI. \r\n\r\nSpatial data infrastructures often are defined and described as a complex and dynamic phenomenon. Among the main reasons for the complex character of these infrastructures are the many components a spatial data infrastructure consists of, the diversity of involved stakeholders, and the many different objectives and ambitions of these stakeholders. Technological advancements, such as the emergence of web 2.0 technologies, and societal changes, such as the increasing use of geographic information in everyday life, are often mentioned as important drivers behind the dynamic character of spatial data infrastructures. \r\n\r\nA key characteristic of spatial data infrastructures is the involvement of a large and diverse group of actors. Governments are often considered as the central actors in the development and implementation of spatial data infrastructure, since they are the major producers and users of geographic information. Governments at different administrative levels and in different thematic domains are involved in the creation, management, use and sharing of geographic data. But also private companies, non-profit organisations, research and education institutions and even citizens can participate in the development and implementation of a spatial data infrastructure. It is increasingly being argued that the involvement and engagement of each of these stakeholders group is essential to the realization of a successful spatial data infrastructure. \r\n\r\nSDIs have been developed in many countries worldwide at local, national and international levels. Often a distinction is made between a between the first generation SDIs that have data as their key driver and are based on a product model and second generation SDIs in which user needs are the key driver and that are based on a process or development model. The latest generations of SDI strongly focus on the inclusion and engagement of non-government actors and organizations in the development and implementation of the SDI. Although SDI are by default distributed systems, involving many organisations, some SDI might be developed rather in an hierarchical way, while others are following a networked approach.","hasChildren":true,"hasParent":true,"name":"Spatial Data Infrastructures","selfAssesment":"Completed
"},{"code":"OI4-1","description":"The adoption and implementation of standards are two key phases in the standardization process, which starts with the definition of standardization requirements and the development of standards. The adoption and implementation of standards follows after the development phase. The distinction made between the adoption and implementation of standards is important: adoption entails the decision to apply standards, while the implementation relates to the integration of standards in software, in data development and in other processes. GI-Standards are one of the key components of each SDI, consist of both semantic and technical standards, and include standards related to the different architectural components of an SDI, i.e. standards related to spatial data sets and data products, web services, metadata and catalogues, encodings, etc.","hasChildren":true,"name":"Adoption and implementation of standards","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI4-2","description":"The SDI policy framework includes the set of policies, strategies, initiatives and projects aimed at increasing access, sharing, and effective use of spatial data. SDI policies can be divided into strategic and more operational policies. Strategic policies define the broader framework and formal structure within which the SDI initiative is developed. Operational policies provide more practical tools to facilitate access to and use of the SDI, and address specific topics related to the collection, management, use, access and dissemination of spatial data. These operational policies include a broad range of guidelines, directives, procedures and manuals that apply to the day-to-day business of organizations in developing, operating and using an SDI. To guarantee the success of an SDI, it is important to recognize the wider policy context in which these SDI`s are developed, and to link them to the overall policy environment in the jurisdiction in which they are implemented. These include policies on open government and open data, environmental policies, digital government or e-government policies and other.","hasChildren":true,"name":"Policies","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI4-3","description":"If is often argued that SDI implementation requires coordination, because without coordination all other SDI components would not be developed or would be developed in a very fragmented and inconsistent manner. In general terms, coordination is about bringing into alignment the activities of different stakeholders in the SDI landscape. A typical instrument to realize coordinate in the context of SDI, is the establishment of an effective SDI coordination structure. The SDI coordination structure should ensure that all stakeholders are involved in the development and implementation of the SDI, through the participation in one or more coordination bodies. Another important element is the establishment of clear roles and responsibilities for the different involved organizations, making a distinction between data users, data providers, services providers and a geo-broker.","hasChildren":true,"name":"Coordination and organizational structure","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI4-5","description":"Funding an SDI is about guaranteeing the long-term financial security of an SDI, by obtaining and formalizing financing for the implementation and maintenance of the different SDI components. An SDI funding model provides the answer to the central question of where and how to seek funding for implementing and maintaining an SDI. Within an SDI often different funding models will be combined, as the selection of the most appropriate funding model will be linked to different activities and the associated costs. Costs of an SDI include both set-up costs (one off costs) and maintenance costs (yearly), of which certain costs need to be made for each data sets or each data provider and other costs for the infrastructure in general. The most commonly used SDI funding models are centralized government funding, decentralized government funding (e.g. for each data provider), partnership funding, funding through revenues, and government funding based on donor agencies or on European projects.\r\n\r\nThe shift towards open data and the adoption of open data policies had an important impact on the funding model of many SDIs, as governments and organizations no longer could rely on revenues from selling their data and had to look for other funding models. As a result, new pricing strategies are employed, such as the provision of fee-based supplementary services, such as advice or tailor-made products based on open data. Also freemium/premium models, in which a basic version of the dataset is offered as open data (freemium) but the full dataset is available for a fee (premium), were considered as an alternative approach. In many cases, the loss of revenues was compensated by other funding models, such as increased government funding.","hasChildren":true,"name":"Funding an SDI","selfAssesment":"Completed
"},{"code":"OI4-5b","description":"SDI performance assessment is about collecting, analyzing and providing information on the performance of SDI initiatives. Assessment and evaluations of SDIs are a useful tool for those organizations and people directly involved in these initiatives, but also for researchers, citizens, journalists and other stakeholders. Decision makers and practitioners can use assessments to monitor the progress against the objectives of their SDI initiatives and to identify areas where improvement can be achieved. Assessment also allows to compare and benchmark the performance of different organizations or countries, and to learn from best practices. Finally, assessment also is relevant for accountability, since it enables governments and agencies to be held accountable for their decisions, activities and the resources they have invested. Assessment of SDIs, which deals with the collection and supply of information on the performance of SDI initiatives, should be seen as the first step in a logical consequence of collecting data, integrating this data in policy and management cycles and actually using the information. \r\n\r\nIn the past twenty years, many different SDI assessment frameworks have been developed by researchers and practitioners around the world. Examples of such frameworks are the INSPIRE State of Play Study, the Clearinghouse Suitability Index, the Organisational Maturity Matrix, the SDI Readiness Index, and the INSPIRE Monitoring and Reporting approach. Each of these frameworks focus on particular aspects and components of SDIs. In line with the categorization of open data assessment, also SDI assessments can be divided into three main categories: (1) readiness assessments, (2) implementation or data assessments, and (3) impact assessments. Readiness assessments analyse whether conditions are appropriate, and whether necessary components are in place for developing an SDI. Implementation or Data assessments evaluate whether geospatial data are available and accessible. Impact assessments explore the extent to which SDIs lead to benefits for government, citizens, business and society in general.","hasChildren":true,"name":"SDI performance measurement and assessment","selfAssesment":"Completed
"},{"code":"OI4-6","description":"For a long time, SDI development has focused on the development and implementation of different components with the aim of facilitating the access to and sharing of spatial data. An key challenge in future SDI development will be the integration of these SDI`s in a wider context. In order to optimally take advantage of the data and services provided by an SDI, integrating these data and services into the processes and workflows of public and private organizations will be crucial. The concept of spatial enablement refers to the challenge of developing SDI`s in such a way that they provide an enabling platform that serves the wider needs of society in a transparent manner. Moreover, the diffusion of SDIs, together with the efforts to build a Global Earth Observation System of Systems (GEOSS) and other developments in industry and civil society should be considered as elements in a the realization of a vision on the next-generation Digital Earth.","hasChildren":true,"name":"Next-generation SDIs","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI4-7","description":"The effective implementation of SDIs requires governance, which includes the structures, policies, actors and institutions by which the infrastructure is managed pertaining to decisions made for accessing, sharing, exchanging and using the relevant available spatial information. While SDIs themselves are considered as initiatives contributing to good governance or effective governance, a key challenge in the establishment of SDIs is the governance of the infrastructure itself. Governance of SDIs is essential for the implementation of different SDI components in a coordinated and consistent manner. The central challenge of governance is reconciling collective and individual needs and interests of different stakeholders in order to achieve common goals. This aims to reduce gaps, duplications, contradictions and missed opportunities in the production, management, sharing and use of the information that tend to occur in a multi-stakeholder environment.\r\n\r\nGovernance can be facilitated through the use of appropriate instruments which extend to various levels of government and take into account the distribution of powers and responsibilities among different actors and institutions with an interest in the infrastructure. The governance instruments should coordinate the activities and contributions of, inter alia, data producers, users, added-value services providers, and other stakeholders. More complex and inclusive models of governance are required to cope with the multi-level nature of SDI implementations of the current generation of SDIs. Effective and inclusive SDI governance structures are needed, that are both understood and accepted by all stakeholders. Governance of SDIs also requires expanding the scope of stakeholders to include the private sector, research bodies and other actors outside the public sector including citizens, to actively promote bottom-up and participatory processes, and to find the appropriate mechanisms and instruments to enable the participation of these non-government actors.","hasChildren":true,"name":"SDI governance","selfAssesment":"Completed
"},{"code":"OI5-1","description":"Within the European Commission there are several key GI players. GIS activities in the Commission started since 1981 (e.g. DG REGIO, Eurostat, ) with the CORINE project, the creation of DG ENV and the creation of the European Environment Agency (EEA). Together with the DG Joint Research Centre (JRC), DG ENV and EEA are in charge of the coordination of INSPIRE: DG Environment acts as an overall legislative and policy co-ordinator for INSPIRE, the JRC acts as the overall technical co-ordinator of INSPIRE and EEA is in charge of several tasks related to monitoring and reporting, and data and service sharing under INSPIRE. Also several other EC institutions are actively involved in GI(S) policies and activities (DIGIT, DG GROW, DG AGRI, DG MOVE and many others).","hasChildren":true,"name":"GI organization at the European Commission","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI5-2","description":"Although there may be certain differences between countries, in most countries many key organizations in the GIS&T field will be active at the central/federal/national level of government. Especially the traditional institutions for surveying and mapping play a key role in geospatial policies and activities. Several public authorities at the federal level are in charge of the production and maintenance of key reference and thematic data sets. In many countries, these national data producers were the leading actors in the development of national spatial data infrastructures.","hasChildren":true,"name":"Federal and national government organizations","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI5-3","description":"Local and sub-national governments are often considered among the major users of geographic information in governments, as they often are involved in many different policy areas, in which many problems with a locational component need to be tackled. Geographic data produced and maintained by authorities at lower administrative levels are often more detailed and thus interesting for other users, both within and outside the public sector. As a result, local and sub-national governments are often involved in the establishment of these infrastructures because of the wide range of highly detailed geographic information they produce and manage. As many geographic data are linked to the activities and services of local organizations, the involvement of these organizations in the maintenance of data ensures that these data are up-to-date.","hasChildren":true,"name":"Sub-national and local governments","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI5-4","description":"The European GIS&T landscape consists of many pan-European organizations and associations promoting the interest of and representing certain stakeholder groups. While some of these organisations are dealing with all sectors and aspects of geographic information, others have a more thematic focus (e.g. remote sensing, topography, geosciences) or represent a particular sector (e.g. research, business). In some cases, their clearly is an overlap in the mission and objectives of different organizations, and some organizations are working in the same field of interest. Some examples of pan-European organizations and associations are AGILE, EuroSDR, EUROGI, and EuroGeographics. Also at international level several membership organizations and associations exist.","hasChildren":true,"name":"Pan-European and global associations and professional organizations","selfAssesment":"GI-N2K
"},{"code":"OI5-5","description":"The geospatial industry consists of companies working with location specific information or services. Within the geospatial sector, several areas of activities can be identified: 1) measuring, collecting and storing of data about geo-objects; 2) processing, editing, modelling, analyzing and managing that data; 3) presenting, producing and distributing the data; and 4) advising, educating, researching and communicating about processes and use of geo-information products and services. The sector consists of both small-and-medium-sized enterprises but also big companies, including surveyors, census hard-copy map providers, aerial photos providers, base map data providers, satellite and remote sensing imagery providers, software developers (GIS-related products and services providers as well as satellite image programming platform providers) and several others.","hasChildren":true,"name":"The geospatial industry","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI5","description":"Several types of organizations play a key role in the execution and coordination of geospatial activities in society. Typically, a distinction is made between data providers and data users, while coordinating organizations exist to coordinate and support the geospatial activities of professionals and entities using GIS&T. Governments are often considered as the major users and producers of spatial data and spatial information. Within the public sector, spatial data are collected and used in different thematic areas and at different administrative levels (from local to global). However, the needs, interests, and capacities of organizations at each of these levels will be different, as well as their role in the development of spatial data infrastructures, and the execution of geospatial activities in general. Also the geospatial industry will exist of both data providers and data users, but also of organizations delivering products and services to support the collection and use of spatial data. Other key organization in the GI domain are professional organizations and associations, bringing together and representing the needs of organizations of a particular sector and/or geographic area.","hasChildren":true,"hasParent":true,"name":"Organizations in the GIS and T domain","selfAssesment":"In progress (GI-N2K)
"},{"code":"PP","description":"The knowledge of physical laws and principles regulating the emission of e.m. radiation and its interactions with the matter, as well the ones related to the design, setting-up and control of EO platforms and related instruments, are of paramount importance for a right interpretation of EO measurements in relation with the investigated Earth's phenomena and parameters. The most important physical fundaments regards: the theory of electromagnetic waves propagation described by the Maxwell's equations, the theory of e.m. radiation and of its interaction with the matter, the methods and instruments for e.m. radiation measurement and/or generation, the fundamentals of thermodynamics and of mechanics. As far as Earth Observation is concerned, further, specific topics have to be addressed which are related to: spectral-specific matter-radiation interactions, natural (e.g. Earth, Sun) and artificial (e.g. MW) sources of e.m. radiations, atmospheric physics and radiative transfer equations, basic physics of e.m., optical and MW, sensors and sources, theory of satellites orbits, theory of rockets, physical fundaments of interpretation of optical and MW data collected by passive and active techniques.","hasChildren":true,"hasParent":true,"name":"Physical principles","selfAssesment":"Completed
"},{"code":"PP1-1-1","description":"Electromagnetic radiation travels in wave form. All electromagnetic waves travel at the speed of 299.793 km/sec in a vacuum and very nearly the same speed in air. In quantum physics electromagnetic radiation is also described in terms of particles called photons whose energy is given by the equation E = hf where h is the Planck constant and f the frequency of corresponding wave. Electromagnetic wave propagation is fully described by the Maxwell Equations that unified in 1860s the laws of electricity and magnetism.","hasChildren":true,"name":"Electromagnetic Waves and Photons","selfAssesment":"Completed
"},{"code":"PP1-1-10","description":"The solar constant S is a quantity denoting the amount of total (i.e., covering the entire solar spectrum) solar energy reaching the top of the atmosphere. It is defined as the flux of solar energy (energy per unit time) across a surface of unit area normal to the solar beam at the mean distance between the sun and the earth. Solar insolation is defined as the flux of solar radiation per unit of horizontal area for a given locality. It depends primarily on the solar zenith angle and to some extent on the variable distance of the earth from the sun. It can be computed as a function of latitude and the time of year taking into account of the secular variations of Earth's orbit eccentricity e, the oblique angle ε, and the longitude of the perihelion relative to the vernal equinox ω. The daily insolation is the total solar energy received by a unit of area per one day. It may be calculated by integrating total insolation over the daylight hours. It is particularly important, together with information on cloud coverage, in order to plan and manage solar power systems. Yearly total insolation together with average cloud coverage are among the most important parameters to be considered for the choice of the best (i.e. the ones promising the higher energy production) location of solar power plants. Modeled daily solar insolation together with short/medium-term forecast of cloud coverage are also fundamental for the management (e.g. for planning the suspension of activities for maintenance) of solar energy production plants .","hasChildren":true,"name":"Solar constant, solar insolation, daily insolation","selfAssesment":"Completed
"},{"code":"PP1-1-11","description":"Earth's itself represents the second (after Sun) most powerfull natural source of e.m. radiation for EO. Even if very less powerfull than Sun such a source is available for EO day and nigth. Its average emittance can be approximated by that of a blackbody at about 290 K. The maximum of its emission, following the Wien's Law, falls then around 10 micron (in the Thermal InfraRed - TIR spectral range) being Earth's emission trascurable in the VIS-SWIR range.\r\nMost of Earth's thermally emitted radiation falls in the spectral range 8-14 microns where it benefits of a quite high atmospheric transmittance (TIR atmospheric spectral window) in standard atmospheric conditions. However thick clouds prevent TIR radiation to reach satellite sensors (adsorbing and/or reflecting backward the radiation leaving Earth's surface) so that ground resolution cells affected by clouds are usually identified (cloud-mask) in the image pre-processing phase and not considered for further elaboration devoted to investigate surface properties. Even if very low in intensity, Earth's emitted radiation in the Far InfraRed (FIR) and in the MicroWaves (MW) spectral ranges are also used for quite important investigation related to the Earth's Energy balance (FIR) and for meteo-climatological applications. The complete transparence of Earth's atmosphere to the MWs, even in presence of meteorological (not precipitating) clouds make this Earth's emitted signal particularly important for application (e.g. climatological) requiring temporal continuity (all weather) of observations of Earth's surface properties like Temperature, Soil wetness, etc.. However, due to the weakness of the Earth's emitted signal in the MW ranges, such products can be achievable just at quite low spatial resolution (e.g. > 10km) by passive EO MW sensors","hasChildren":true,"name":"Earth's radiation (intensity, spectrum, etc.)","selfAssesment":"Completed
"},{"code":"PP1-1-2","description":"In principle, the frequency f (and the wavelength λ=c/f) of an electromagnetic wave can take any value and the whole range of possible frequencies is called the electromagnetic spectrum. Different regions of the spectrum are conventionally given different names (with associated spectral ranges smoothly depending on specific science sector): \r\ngamma-rays\t λ< 1 pm\r\nx-rays\t1 nm >λ>1 pm\r\nUltraviolet (UV) 400 nm >λ>1 nm\r\nVisible (VIS) 700 nm >λ> 400 nm (blue: 455 – 492, green 492 – 577, yellow 577 – 597, red 622 – 700)\r\ninfrared (IR)\t1000μm >λ> 0,7 μm (Near-IR - NIR: 0,7-1,3; Short-Wave IR SWIR: 1,3-3; Medium IR - MIR: 3-6, Thermal IR - TIR: 6-20; Far IR - FIR: 20-1000)\r\nRadio waves\t λ> 1 mm (Microwaves MW\t1 m >λ> 1mm). Optical range (usually referring to the spectral range from VIS to TIR) and microwaves are the most important spectral region for remote EO systems.","hasChildren":true,"name":"Electromagnetic spectrum","selfAssesment":"Completed
"},{"code":"PP1-1-3","description":"Maxwell equations are a set of coupled partial differential equations that contains the fundamentals of electricity and magnetism. These equations provide electromagnetic waves that propagate into the space at the speed of the light. Increasing the wavelength there are gamma rays, X-rays, ultraviolet, (visible) light, infrared, microwaves and radio waves.","hasChildren":true,"name":"Maxwell Equations and EM waves' propagation","selfAssesment":"Planned
"},{"code":"PP1-1-4","description":"Planck's law is a mathematical relationship for the spectral radiance emitted by a blackbody (i.e. a body that absorbs all radiant energy falling on it) at a given temperature as a function of frequency or wavelength. From another point of view it can be used to define a black-body as a body emitting radiation following Planck's law. The model of black-body is fundamental to simplify the description of the radiation thermally emitted by a generic body at a pre-fixed temperature and wavelength as the product of its (specific) spectral emissivity and the value predicted (at the same wavelength) by the Planck's law for a black-body at the same temperature. This way the radiation thermally emitted by a generic body can be expressed just as a (specific, as modulated by the spectra emissivity) fraction of the one expected for a black-body. Wien’s displacement law is the relationship between the temperature of a blackbody and the wavelength at which it emits the most radiation. Wien found that the product of the peak wavelength and the temperature is an absolute constant. As far as the temperature T of the blackbody increase the intensity of the emitted e.m. radiation increases being, at whatever wavelength, grater than the one emitted by a blackbody at lower temperature (Planck). As far as the blackbody temperature increases its maximum emission occurs at lower and lower wavelengths. Wien's law is fundamental both in the selection of the spectral bands more appropriate for observing specific phenomena as well as for remotely retrieve temperature of far objects by the analysis of the emitted spectral radiances.","hasChildren":true,"name":"Planck law for the black body. Wien's displacement law","selfAssesment":"Completed
"},{"code":"PP1-1-5","description":"The Rayleigh–Jeans Law is an approximation of the Planck’s law for a blackbody that states that, under certain conditions, emitted radiance is directly proportional to the blackbody temperature. Such an approximation, fits quite well with measurements of radiation emitted by sources at around 300K of temperature (like, in average, for the Earth) at wavelengths higher than 1mm (microwaves).. Wien’s approximation can be used to describe the emission spectrum of a high temperature blackbody n the VIS-NIR spectral range lengths. The estimated errors is less than 2% at wavlengths less that 5microns when a blackbody at around 6000K (like the Sun photosphere) is considered. \r\nThe Rayleigh–Jeans approximation is widely used in the processing of satellite images collected by passive MW sensors. Its extension to the thermal infrared spectral range (TIR) is also used for calibrating TIR satellite images (in this case linearity can be guaranteed just by steps on different brigthness temperature intervals).","hasChildren":true,"name":"Rayleigh-Jeans approximation. Wien's approximation","selfAssesment":"Completed
"},{"code":"PP1-1-6","description":"The total radiant intensity B(T ) of a blackbody at the absolute temperature T can be derived by integrating the Planck function over the entire wavelength domain from 0 to∞. Since blackbody radiation is isotropic, the flux density emitted by a blackbody is therefore F = π B(T ) which is proportional to the fourth power of the absolute temperature T through the Stefan-Boltzmann constant σ = 5.67 × 10−8 J m−2 sec−1 deg−4.\r\nKirchoff's law establishes that for a medium at the thermodynamic equilibrium, the spectral emissivity ε(λ) at a given wavelength λ, is equal to the its spectral absorbance, A(λ) at the same wavelength λ. Hence ε(λ)=A(λ) at each fixed λ, for a blackbody ε(λ)=A(λ)=1 at whatever λ. Kirchoff's law is valid also in Local Thermodynamic Equilibrium (LTE) conditions as the ones usually occurring in (small volumes of) the Earth's atmosphere even in the most turbulent conditions.\r\nKirchoff's law has important applications also for the study of spectral signatures of mineral and rocks and, in general, of opaque - i.e. with spectral transmittance T(λ)=0 - bodies. In that case, the relation which relate the spectral reflectance R(λ), absorbance A(λ) and transmittance T(λ) of a body: R(λ)+A(λ)+T(λ) =1\r\nreduce to R(λ)+A(λ)=1 and in LTE conditions, thanks to the Kirchoff's law: \r\nR(λ)+ε(λ)=1 which allows to obtain measurements of spectral emissivity indirectly through (more simple and stable) measurements of spectral reflectance:\r\nε(λ)=1-R(λ)\r\nRocks and mineral exhibit important (diagnostic/discriminating) signatures in their spectral emissivity in the thermal infrared (TIR) region. Measuring spectral emissivity in a laboratory (particularly if samples have to be characterized for their properties in natural conditions) is a quite difficult task due to the difficulty to insolate the sample from the lab environment (and instruments themselves) all emitting approximately at the same (environmental) temperature. Kirchoff's law allows to obtain, for opaque bodies, spectral emissivities from spectral reflectances measurements which are much easy to realize in normal remote sensing labs.","hasChildren":true,"name":"Stefan–Boltzmann law. Kirchoff law","selfAssesment":"Completed
"},{"code":"PP1-1-7","description":"All bodies at a temperature T>0 K emit electromagnetic radiation at all wavelengths (thermal emission). Such emission at each wavelength is increasing with T and it is maximum for Black Bodies whose spectral emittance I(λ,T) (at each prefixed T and wavelength λ) is defined by the Planck function B(λ,T). Generic bodies are expected to thermally emit less than a black body (having the same temperature T) at whatever wavelength. Spectral emissivity ε(λ) is defined as the ratio of the spectral radiance I(λ,T) emitted by a generic body and the one emitted by a Black Body at the same temperature, i.e. ε(λ)= I(λ,T) / B(λ,T). By definition its value is less or equal (Black Body) than 1. The spectral emissivity concept allows to describe in a simple way the spectral radiance I(λ,T) thermally emitted by a body at a temperature T by I(λ,T)= ε(λ)*B(λ,T). It is possible to invert the Planck Function to obtain from the emitted radiance at a prefixed wavelength the temperature T=f(B, λ) of the emitting Black Body. If in such expression the spectral radiance I emitted by a generic body is used instead than B, the resulting temperature, Tb=f(I, λ), is named Brigthness Temperature being Tb<=T (with Tb=T in case the emitting body is a Black Body). The concept of Brigthness Temperature is substantially a different way to measure the spectral radiance of a generic body. It is usually preferred (for instance calibrating Thermal InfraRed – TIR – satellite images) because the interpretation of such a digital image is much more intuitive than when spectral radiances are used instead. In fact, as at each prefixed temperature generic bodies are less emitting than Black Bodies, wherever across a digital satellite image we consider the values of reported Tb, we can say that the actual temperature T of the corresponding emitting ground resolution cell is not less than Tb.","hasChildren":true,"name":"Concepts of Spectral Emissivity and Brightness Temperature.","selfAssesment":"Completed
"},{"code":"PP1-1-9","description":"Sun represents the most powerful natural source of e.m. radiation for EO. The main source of its radiation is the nuclear fusion of Hydrogen into Helium which occurs in central part (“Core”) of the Sun. Outside, the energy transfer is dominated by radiative process (“Radiative zone”) then by convection (”Convective zone”). Solar radiation at the Top of the Earth Atmosphere comes from the outer layer of the sun, the photosphere, whose estimated (conventional) temperature is 6000-6300 K. Its emittance can be approximated by that of a blackbody at about 6000 K but just its reflected component (SOR) is actually available (and just during daytime) for EO. The maximum of SOR falls in the visible spectral range. Its contribution in the thermal infrared range is neglectable but in the medium infrared SOR is still significant enough and, in daytime, superimposed to Earth's thermal emission. The high intensity of solar refelcted radiation (SOR) coupled with the high atmospheric transmittance in the VIS/NIR range, guarantee the highest signal-to-noise ratio for sensors operating in that spectral range. This huge amount of available signal, together with the development of advanced micro-sensor technology (started with the Charged Coupled Devices - CCD etc.), explains why the EO passive sensors with the highest spatial and/or spectral resolution presently achievable, are operating in the VIS/NIR range.\r\nachievable by operating in this spectral region.","hasChildren":true,"name":"Solar radiation at the Top of the Atmosphere. Solar spectrum","selfAssesment":"Completed
"},{"code":"PP1-1","description":"EM radiation is created when an electrically charge particle, such as an electron, is accelerated by a force causing it to move. The movement produces oscillating electric and magnetic fields which travel, as an harmonic EM wave, at right angles to each other. EM waves travel at 299,792,458 meters per second in a vacuum (the highest possible speed into the Universe, also known as the speed of light). \r\nThe electromagnetic field propagating through the space as EM waves is also referred as electromagnetic radiation. \r\nAn EM wave is characterized by a frequency (or by a wavelength) and by an amplitude (or by an energy). \r\nThe wavelength is the distance between two consecutive peaks of a wave. This distance is given in meters (m) or fractions thereof. Frequency is the number of waves that form in a given length of time. It is usually measured as the number of wave cycles per second, or Hertz (Hz). It is wave speed=frequency*wavelength so that, an EM wave traveling at the speed of light, can be equally identified by its wavelength or by its frequency. The amplitude (i.e. the maximum oscillation of the EM field) provide the intensity (i.e. the energy) of the EM wave. \r\nThe classical theory describes the EM radiation as electromagnetic waves which represent the oscillations of electric and magnetic fields. In the quantum mechanics theory EM radiation consists of photons, quanta of the electromagnetic energy, responsible for all electromagnetic interactions.\r\nAs far as Earth remote sensing is concerned EM radiation represents the most important vehicle of information.","hasChildren":true,"hasParent":true,"name":"EM radiation","selfAssesment":"Completed
"},{"code":"PP1-2-1","description":"The study of the absorbption/emission of electromagnetic radiation by atoms. Depending on the atomic number characteristic frequency or wavelength are absorbed or emitted. Since each element has a characteristic spectrum of absorbed/emitted wavelengths (spectral signature), atomic spectroscopy allows the determination of elemental compositions even of remote objects (e.g. stars, galaxies, etc.).\r\nStarting from the simple Bohr’s model it is possible to predict quite exactly the frequencies of e.m. radiation selectively absorbed/emitted by all atoms. Depending on the atomic number Z, characteristic frequencies f are absorbed or emitted by atoms corresponding to the electronic transitions from different energetic (quantized) states following the Bohr’s condition: fab=(Eb- Ea)/h, being Ei=-cost∙Z2/(ni)2 the electron energy corresponding to the state/level i (principal quantic number ni). By this way each atomic species has a characteristic spectrum of absorbed/emitted frequencies (atomic spectral signature) so that atomic spectroscopy allows the determination of elemental compositions even of remote objects. By this way the existence of Helium was discovered in the 1968 by Jansen and Lockyer in the Sun photosphere well before its discover on the Earth, and the knowledge of the chemical composition of stars and galaxies was possible well before the end of XIX century. Atomic spectroscopy provides a simple and powerful introduction (through the explanation of the more complex interactions of e.m. radiation with molecules and solid matter) to the fundamental concepts of spectral signature (which is at the base of most of the applications of aerial remote sensing of the Earth’s surface) and atmospheric windows (important for the design of optical sensors devoted to remotely sense Earth’s surface) being moreover propaedeutic to the understanding of methods for the atmospheric vertical sounding based on the concepts spectral lines broadening and related weighting functions.","hasChildren":true,"name":"Atomic spectroscopy","selfAssesment":"Completed
"},{"code":"PP1-2-10","description":"The Rayleigh roughness criterion is a widely used means to estimate the degree of roughness of a considered surface. Considering the phase difference between two rays scattered from separate points of the surface, this is proportional to the roughness ∆h (average deviation from the average surface height ) the cosine of the incident angle and, inversely, on the radiation wavelength (λ). The Rayleight criterion states that a surface can be considered as smooth (mostly reflecting) if the phase difference is less than π/2 radians.\r\nAs a consequence, in the case of normal incidence (i.e. θ=0), average roughness of the surface must be less than λ/8 to have an effectively smooth surface. For instance: i) at optical wavelengths (e.g. 0.5 micrometers), surface roughness ∆h must be less than about 60 nm to have a specular reflection. Only certain man-made surfaces (e.g. sheets of glass or metal) may meet such a condition; ii) at VHF radio wavelengths (e.g. 3 m), roughness height need only to be less than about 40 cm. Unlike the previous case, a number of natural surfaces may meet this condition.\r\nIt is worth noting that large values of the incident angle may satisfy the criterion more easily as compared with the normal incidence. This means that a moderately rough surface may be effectively smooth at glancing incidence. This condition may be easily experienced when eyes are struck by the glare of reflected sunlight from a low sun over an ordinary road surface. More strict conditions for classifying a surface as a mirror or a diffuser at an established whavelength λ are: ∆hcosθ/λ > 1/8 for a rough surface operating as a diffuser; ∆hcosθ/λ < 1/25 for a smooth surface operating as a mirror.","hasChildren":true,"name":"The Rayleigh roughness criterion","selfAssesment":"Completed
"},{"code":"PP1-2-11","description":"The Bidirectional Reflectance Distribution Function (BRDF) is defined as the quotient between the spectral radiance Ir(θr,φr) reflected by a sample in a particular direction (θr,φr) and the spectral irradiance F(θi,φi) from the source that illuminates it under a direction (θi,φi) . It depends on both the incidence and viewing angles. From this point of view it represents an absolute definition of reflectance whose value, as is known, depends on the geometry of the illumination and observations directions. This function well describes variability in surface anisotropy, its shape and magnitude is determined by the structure of the sample element and its optical attributes.\r\n\r\nThe BRDF is given by \r\n\r\nBRDF(θi,φi; θr,φr; λ)=(Ir(θr,φr))/(F(θi,φi))\r\n\r\nwhere Ir is the surface leaving spectral radiance and F is the spectral irradiance , θ and φ are zenithal and azimuthal angles respectively of the direction (view angles) of reflected radiance Ir(θr,φr) and of incident irradiance F(θi,φi), λ is the wavelength.","hasChildren":true,"name":"Bidirectional Reflectance Distribution Function (BRDF)","selfAssesment":"Completed
"},{"code":"PP1-2-12","description":"Measurements of BRDF allow to compare spectral signatures obtained in different laboratories in an optimal way. However its measure require well calibrated sources and quite expensive laboratory equipments. The concept of BRF (Bidirectional Reflectance Factor) allows a more simple, indirect, measurement of BRDF by using a reference sample (highly reflective so usually named \"white reference WR\") of known BRDF and two subsequent measurements of reflected radiance (one from the WR, one from the sample) obtained under identical illumination conditions. In these conditions results BRDF(sample)=BRF(sample)xBRDF(WR)","hasChildren":true,"name":"Bidirectional Reflectance Factor (BRF)","selfAssesment":"Planned
"},{"code":"PP1-2-2","description":"The absorption of e.m. radiation by molecules, in different physical states, can be attributed to specific (quantized) changes in their electronic and/or vibrational and/or rotational energy. Subsequent quantized molecular vibrational energy levels are equidistant so that all vibrational transitions occur, for each molecule, by the emission/absorption of radiation at a specific wavelength. Depending on the specific amount of energy required to modify the status of electrons within the atoms composing the molecules, as well as the one required to modify the molecule's vibrational and rotational energy, different wavelengths can be adsorbed. As in the case of atomic spectra which are fully determined by the electronic energy level structure depending on the atomic number, rotational and vibrational energy levels of molecules depends on specific characteristics (number, masses, distances, inertia momentum, elastic constant, etc.) of the atoms composing the molecule itself which make specific and characteristic for each molecule associated absorption spectra. In the Earth's atmosphere the effect of atomic/molecular absorption is significant at wavelength between 1nm and about 1cm. Considering the optical and microwave spectral ranges used in Earth's remote sensing from space it should be noted that:\r\na) Visible, Near Infrared and Short wave IR radiation (400-3000 nm) is adsorbed mostly for electronic transitions within atoms. In the SWIR region (after 1000nm) forbidden vibrational absorption lines can be observed (overtones and related combinations). \r\nb) e.m. radiation in the Medium and Thermal IR (up to 100.000 nm) spectral range are mostly adsorbed for operating vibrational energy transitions in H2O, CO2 and O3 molecules\r\nc) e.m. radiation in the Far IR up to the Microwave's spectral range (0,035-1 mm) is mostly adsorbed for operating rotational transitions in water vapur molecules. As, in principle, such electronic, vibrational and rotational transitions can contemporary occur (and usually occur considering the collective effect of the enormous number of molecules that can be present even in a small volume of terrestrial atmosphere) molecular spectra results in a complex composition of absorption lines (bands).","hasChildren":true,"name":"Molecular absorption spectra","selfAssesment":"Completed
"},{"code":"PP1-2-3","description":"In spectroscopy an absorbed (emitted) line is observed in correspondence to the transition from a lower (higher) to a higher (lower) energetic level within an atom (electronic transitions) or a molecule (electronic, vibrational, rotational transitions). Its characteristic frequency f is related to the amount of the energetic jump from an initial state E(1) to a final one E(2) through the Bohr's relation E(f) − E (i) = hf. As the distribution of the quantized energetic level are specific of each atom (depending on its atomic number N) and molecule (depending on their constituents atoms N, number and dispositions which determine their specific inertia momentum and vibrational properties) even the corresponding atomic and molecular spectra (i.e. the frequencies of the sequences of spectral lines/bands) are specific for each chemical atomic or molecular species. However monochromatic emission just at the frequency f is practically never observed. Always e.m. radiation emitted/adsorbed by atoms or molecules is observed also around the nominal (expected following Bohr's relation) frequency f mostly as a consequence of the following effect: a) changes of quantized energy levels associated to the process of emission/absorption itself: the consequent line broadening around the frequency f is reported as \"natural broadening\"; b) changes of quantized energy levels due to reciprocal collisions between atoms and molecules (\"pressure broadening\"); c) the change of the observed f due to the Doppler effect associated to the fact that emitting(adsorbing atoms or molecules are moving toward or far away with different (thermal) velocities (\"Doppler broadening\"). The natural broadening is practically negligible as compared to that caused by collisions and the Doppler effect. In the upper atmosphere, due to its temperature and pressure, we find a combination of collision and Doppler broadenings, whereas in the lower atmosphere, below about 20 km, collision broadening prevails because of the pressure effect. As far we move far from the central (expected) frequency f as much the contribution of Doppler effect can be neglected compared with the pressure broadening. This fact has important consequences on the possibility to retrieve vertical properties of the atmosphere (vertical sounding) like temperature and concentration of its chemical constituents, exploiting satellite based observations made \"off-line\" (i.e. at frequencies around but different from f) which relate investigated atmospheric levels as much higher as much far from f are the considered frequencies.","hasChildren":true,"hasParent":true,"name":"Line shape and (natural, pressure, Doppler) broadening","selfAssesment":"Completed
"},{"code":"PP1-2-4","description":"Voigt's line profile refers to the shape of a spectral line resulting from the \"pressure\" and Doppler broadening. Pressure broadening is much more important in atmosphere as far as pressure increases (heigths lower than 20 km) . Observing Earth's atmosphere in a spectral region sufficiently far from the central (unperturbed/monochromatic) absorption spectral line (off-line bands), Doppler broadening can be neglected in comparison with the pressure one. More and more off-line are the chosen spectral bands, more and more lower in atmosphere will be the atmospheric layers mostly contributing to the measured spectral radiances. \r\nSuch a relation is at the base of the inversion methods for atmospheric vertical sounding based on multi-spectral satellite observations.","hasChildren":true,"name":"Voigt's line profile","selfAssesment":"Completed
"},{"code":"PP1-2-5","description":"Radiation that is not absorbed or scattered in the atmosphere can reach and interact with the Earth's surface. There are three (3) forms of interaction that can take place when e.m. radiation strikes, or is incident (I) upon a surface. These are: absorption, transmission, and reflection. The total incident radiation will interact with the surface in one or more of these three ways. The proportions of each will depend on the wavelength of the incident radiation and the specific chemical/physical properties of the surface material. Absorption occurs when incident radiation is absorbed into the target, while transmission occurs when radiation passes through a target. Reflection occurs when radiation \"bounces\" off the target and is redirected. The spectral reflectance is defined by the ratio of reflected radiance to incident radiance at a prefixed wavelegth . The spectral transmittance of a medium is defined by the ratio of the transmitted radiance to the incident one at a prefixed wavelegth . The absorbance of a medium or target is defined by the ratio of the absorbed radiance to the incident one at a prefixed wavelegth . Conservation of energy require that, at a certain wavelenght: R+T+A=1. To express the circumstance that the reflection can occurre in different direction as the surface deviates from a specular one, becoming rough, the concept of surface scattering has been introduced (ref. [PP1-2-10] The Rayleigh roughness criterion).","hasChildren":true,"name":"Concepts of Transmittance, Absorbance, Reflectance, Scattering.","selfAssesment":"Completed
"},{"code":"PP1-2-6","description":"The emitting capability of a body surface is described by the spectral emissivity, ε(λ), a dimensionless value ranging between 0 and 1 and varying on the basis of the wavelength (λ) and the geometric configuration of the surface. Formally, spectral emissivity can be defined as the ratio of spectral exitance, M(λ,T), from an object at wavelength λ and temperature T, to that from a blackbody at the same wavelength and temperature, MBB(λ,T).\r\nA blackbody is an ideal radiator that totally absorbs and then reemits all energy incident upon it. By definition the spectral emissivity of a blackbody is equal to one (the maximum) at whatever wavelength and temperature. A blackbody radiates a continuous spectrum. Real materials do not behave like a blackbody. Natural matter could radiates more in selected spectral region (like in the case of atomic or molecular gases) more frequently with a continuous spectrum (like in the case of solids) always with spectral emissivity minor or equal to 1. \r\nAnother important concept is the one related to the graybody. For gray bodies, the spectral emissivity value is constant for each wavelength value, as for black bodies, but is always less than 1. Therefore, for any given wavelength the emitted energy of a graybody is a fraction of that of a blackbody. This behavior could be quite important even for limited spectral ranges. For instance the spectral emissivity of the sea in the TIR (Thermal InfraRed) spectral range 8-14 microns (TIR atmospheric window) can be assumed constant (about 0,98) with significant simplifications in the determination of SST (Sea Surface Temperature) from satellite sensors operating in that spectral region. \r\nAs said above, the emissivity of the most of the bodies present in nature varies depending on the wavelength. These objects are referred to as selective radiators or as being selectively radiant. This means that some materials may behave as black bodies at certain wavelengths (ε close to 1) and may have reduced emissivity at other wavelengths.","hasChildren":true,"name":"Concepts of Spectral Emissivity","selfAssesment":"Completed
"},{"code":"PP1-2-7","description":"Dielectric constants and refractive indices of the matter are generally complex quantities. Considering an electromagnetic wave entering a homogeneous medium of complex refractive index n=m+ik, it is possible to demonstrate that its intensity progressively decays depending on its wavelength λ and on the complex part k of the refractive index of the considered medium. Transparent medium correspond to medium having k=0 (i.e. real refractive index). \r\nFor instance, considering the amplitude of the electric field E(0) entering the medium, its value after traveling in it for a distance z will be reduced at E(z)=E(0)exp[ -ωkz/c] being ω the wave pulsation and c the light speed constant.","hasChildren":true,"name":"Complex dielectric constants and refractive indices","selfAssesment":"Completed
"},{"code":"PP1-2-8","description":"The complex part k of the refraction index n determines how far an e.m. wave of wavelength λ can survive crossing a specific medium. The attenuation length la is the distance after that the amplitude of an e.m. signal reduces its value by an amount of 1/e. For instance the amplitude of the Electric field E(z) of an e.m. wave proceeding along the z direction is decreasing as exp(-z/la) being la=λ/(2𝜋k) the attenuation length associated to that specific material (with n=m+ik) and wavelength λ. This way attenuation length in water can be of hundreds of meters in the visible range and just few microns in the microwaves. So that penetration of radiation in the matter depends on both, the specific (dielectric) properties of the matter (through k) AND the specific wavelength λ of considered e.m. signal.","hasChildren":true,"name":"EM rad. penetration in the matter: Attenuation Length","selfAssesment":"Completed
"},{"code":"PP1-2-9","description":"EM radiation impinging a rough surface is (partly) reflected back (scattering). When the the sine of the angle of incidence of the radiation is equal to the sine of the angle of reflection, sin(øi) = sin(ør), then the surface behaves like a mirror (Snell's Law). Furthermore, a surface is defined as a “perfect mirror” (Fig.1) if all the incident radiation is reflected in that direction saving its original intensity. A surface is defined as “Lambertian diffuser” or “isotropic reflector” (Fig. 2), when the radiation is reflected in all directions with the same intensity. A surface is defined as “perfect Lambertian” when all the incident radiation is reflected isotropically (i.e. not-absorbing, not-transmitting surface). A surface is defined as \"almost Lambertian\" (Fig.3) if the reflection does not occur in an exactly isotropic way but according to privileged directions. “Perfect mirrors” as well as “perfect Lambertian” surfaces describe ideal bodies, while natural bodies behave like “almost Lambertian” surfaces with a preferred reflection direction around the one established by the sines reflection law.","hasChildren":true,"name":"Scattering from rough surface: Lambertian and specular surfaces.","selfAssesment":"Completed
"},{"code":"PP1-2","description":"E.M. Radiation can be absorbed, scattered, emitted and transmitted by the matter. The results of such interactions (i.e. the fraction of incident radiation that is absorbed, scattered or transmitted) or emission process (i.e. the fraction of actually emitted radiation in comparison with the one expected from a black-body at the same temerature) strongly depend on the radiation wavelength and on specific chemical (e.g. composing atoms and molecules as well as their arrangement within solid cristals) and physical (e.g. Temperature, Dimensions and Shape, Roughness) properties of the matter. In some case, the result of Radiation - Matter interaction is strongly affected by observational conditions. For instance, over some angular distance between the directions of incidence and the one of measurement of the radiation, sun-glint can occur which completely mask any other results. A basic principle of the remote sensing put univocally in relation spectral absorbance, reflectance, transmittance and emissivity, curves achievable by multi-spectral EO measurements, with matter having specific chemical/physical properties. Theoretical models of radiation-matter interaction at the Earth's surface and through the atmosphere provide then suitable strategies for retrieving, from multi-spectral measurements of the radiation leaving the Earth, the most relevant chemical/physical properties of the matter composing its surface and atmosphere.","hasChildren":true,"hasParent":true,"name":"Radiation - Matter interaction","selfAssesment":"Completed
"},{"code":"PP1-3-1","description":"The natural objects can either emit radiation (radiance, emittance) or be \"illuminated\" by a source (irradiance). In the following a series of definitions for each of these terms is provided. \r\nThe first basic radiometric quantity is the radiance (Iλ) and it is defined as the ratio of the differential radiant energy (dE) to the product of effective area (dA) with the time interval (dt), wavelength interval (dλ) and differential solid angle (dΩ). Iλ can be also referred as monochromatic intensity and it is expressed in units of energy per area per time per wavelength and per steradian (W m−2 sr−1). \r\nThe monochromatic flux density (Fλ) or the monochromatic irradiance of radiant energy is defined by the normal component of Iλ integrated over the entire hemispheric solid angle. It is expressed in units of energy per area per time per wavelength (W m−2). For isotropic radiation (i.e., if the intensity is independent of the direction), the monochromatic flux density is then Fλ = π Iλ. \r\nThe total flux density of radiant energy (F), or irradiance, for all wavelengths (energy per area per time, i.e., W), can be obtained by integrating the monochromatic flux density over the entire electromagnetic spectrum.\r\nAll the above definitions refer to a point source of radiation. When the flux density or the irradiance is from an emitting surface (i.e., an extended widespread source), the quantity is called the emittance. When expressed in terms of wavelength, it is referred to as the monochromatic emittance. The intensity or the radiance is also called the brightness or luminance (photometric brightness). The total flux from an emitting surface is often called luminosity.","hasChildren":true,"name":"Radiometric quantities: radiance, irradiance, flux, brightness, emittance, luminosity, etc.","selfAssesment":"Completed
"},{"code":"PP1-3-2","description":"The attenuation of radiation emitted from a source decreases with the square of the distance from its center based on inverse square law. It considers that the size of the sources increases with the square of their radius, causing the same rate of attenuation in flux density.","hasChildren":true,"name":"Decay of the emittance with the square of distance from the source","selfAssesment":"Planned
"},{"code":"PP1-3-3","description":"The relative amount of electromagnetic radiation reflected (absorbed, transmitted, emitted) by the matter at different wavelengths depends on its specific chemical composition and physical properties. The plots of corresponding physical quantities (reflectance, absorbance, transmittance, emissivity) against wavelength, are termed spectral signatures of the specific matter under study. In principle the analysis of spectral signatures obtained by multispectral EO sensors could allow us to identify/discriminate different cover types.\r\nThe interpretation of spectral signatures requires to well understand the e.m. radiation-matter interaction process. In very simple term we expect that incident radiation I(λ)can be reflected, absorbed or transmitted by the matter so that for the energy conservation should be: \r\n\r\n\r\nI(λ)=I(λ,R)+I(λ,A), I(λ,T) \r\n\r\n \r\nbeing I(λ,R), I(λ,A) and I(λ,T) the reflected, absorbed and transmitted fraction of I(λ). From the previous relation descends (dividing both members for I) that:\r\n\r\n\r\n1=R(λ)+A(λ)+T(λ)\r\n\r\n\r\nbeing:\r\n\r\n\r\nR(λ)=I(λ,R)/I(λ) named Reflectance\r\nA(λ)=I(λ,A)/I(λ) named Absorbance\r\nT(λ)=I(λ,T)/I(λ) named Transmittance\r\n\r\n\r\nThey are all specific properties of the considered matter and are not independent each others.\r\nIn particular for an opaque medium with T(λ)=0 it is:\r\nR(λ)=1-A(λ)","hasChildren":true,"hasParent":true,"name":"Spectral Signatures of the matter","selfAssesment":"Completed
"},{"code":"PP1-3-4","description":"Vegetation, water and soil represent the most common cover types of Earth surface. Their reflectances in the VIS/NIR/SWIR spectral range, plotted against wavelength in the 0,4-2,5 micron, represent the most important (basic) spectral signatures for whatever application devoted to Earth surface study. Other spectral signatures (e.g. in emissivity) in the Thermal InfraRed range are particularly important to infer specific properties of Mineral and Rocks (ref. [PP1-3-5] Spectral Signature of Mineral and Rocks). In order to discriminate among such basic cover types, the (ref. [IP3-1-2-3]) NDVI (Normalized Difference Vegetation Index) is the most simple and powerful diagnostic tool in the VIS/NIR spectral range \r\nNDVI values ranging between the values -1 and +1, are higly positive for fully vegetated (up to NDVI=1) or partly vegetated (NDVI>0,3) targets, still positive (>0) for bare soils, negative for water bodies. Values around zero are expected for clouds thanks to their similarly high reflectances both in the NIR and VIR spectral bands (ref. [PP1-3-6] Spectral Signature of Clouds). \r\n\r\nVegetation. a) in the visible range most of the incomig radiation is adsorbed by the photosynthetic process, transmittance is very low. The residual reflected radiation has a small peak of reflectance around 0.5 microns which is responsible of the green colour associated to vegetation by the human vision sytem (limited to the VIS spectral range); b) in the NIR range vegetation exhibits its higher reflectance together its higher transmittance (very low absorbance) so that leaf density can be estimated thanks to the the contributes (decreasing with depth) of underlaying leaf layers; c) in the SWIR spectral range (in particular in the water bands around 1,4 and 1,9 microns) it is possible to appreciate the vegetation water content. As much it is, as more incident radiation is absorbed and less is the reflected fraction of radiation.\r\nBare Soil. Spectral reflectance is normally increasing moving from the VIS to the SWIR spectral region. Water features around 1,4 and 1,9 microns give information on soil water content (see before). Others specific features are described in [PP1-3-5] Spectral Signature of Mineral and Rocks\r\n\r\nWater. Spectral reflectance of clean deep water is quite low reaching quickly the zero value as soon as wavelengths passe microns. However it is important to note that such a very low reflectance is due to a very high transmittance in the VIS range and to a very high absorbance in the NIR/SWIR regions (ref. [PP2-2-5-2] Attenuation Lenght and Penetration Depth). This means that water is quite transparent in the VIS spectral range (so that, in case of shallow waters, measured reflected radiance can be significantly increased by the contribution of bottom of the sea). Water is completely opaque, instead, in the NIR/SWIR. In this spectral region, even in presence of shallow waters, the presence of suspended matter (that increases the measured reflectance both in the VIS and NIR/SWIR ranges) can be better discriminated (than in the VIS) from the contribute of the bottom of the sea that, in this spectral range, is zero.","hasChildren":true,"hasParent":true,"name":"Spectral Signature of Vegetation, Water, Soil","selfAssesment":"Completed
"},{"code":"PP1-3-5","description":"Spectral signatures of rocks and mineral provide information on their chemical composition and crystal properties, grain size and roughness over a wide range of wavelengths from the visible to the thermal infrared.\r\nIn the Visible and Near-InfraRed (VNIR; 0.4÷1.0 µm) region, spectral features are dominated by electronic processes in transition metals, such as Fe, Mn, Cu, Ni, Cr, etc. Therefore, iron is the most important constituent having spectral properties in the VNIR, and the iron-rich minerals are characterized by low reflectance (high absorbance) below 0.7 µm.\r\nOther minerals, which represent the major part of the Earth's surface rocks, such us Si, Al and some anion groups (e.g. silicates, carbonates, oxides) hydroxides, have less spectral features in the VNIR region, but exhibit much more evidences in the Short-Wave InfraRed (SWIR; 1÷3 µm) region. In fact, spectral features of hydroxyls and carbonates mark the SWIR region.\r\nThe hydroxyl ion is a widespread constituent occurring in rock forming minerals such as clays, micas, chlorite etc. It shows a vibrational fundamental absorption band at about 2.74÷2.77 µm and an overtone at 1.44 µm.\r\nCarbonates, which are commonly in the Earth surface rocks in the form of calcite (CaC03), magnesite (MgC03), dolomite [(Ca-Mg) C03] and siderite (FeC03), shows a typical absorbance feature around 2.3 µm, instead the water content can be instead evaluated by the depth of absorption at 1,4µm and 1,9 µm.\r\nThermal InfraRed (TIR; 1÷20 µm) region, from a geological point of view, is a particularly important spectral region for remote sensing aiming at compositional investigations of terrestrial materials. In fact, the fundamental vibration features of many rock-forming mineral groups (e.g. silicates, carbonates, oxides, phosphates, sulphates, nitrates, nitrites, hydroxyls) occur in the TIR region. Briefly:\r\na) the silicates, which are most abundant group of minerals in the Earth's crust, shows vibrational spectral features due to the presence of Si04-tetrahedron around 8 µm to 12 µm; b) the carbonates show a weak feature around 11.3 µm that can be detected; c) the sulphates display bands near 9 µm and 16 µm; d) the phosphates also have fundamental features near 9.25 µm and 10.3 µm; e) the features in oxides usually occupy the same range as that of bands in Si-O, i.e. 8 µm to 12 µm; g) the nitrates have spectral features at 7.2 µm and the nitrites at 8 µm and 11.8 µm; h) the hydroxyl ions display fundamental vibration bands at 11 µm.","hasChildren":true,"name":"Spectral Signature of Mineral and Rocks","selfAssesment":"Completed
"},{"code":"PP1-3-6","description":"The determination of spectral signatures for scenes with a high degree of spatial complexity is considered as one of the most persistent problems in atmospheric radiation, especially at the surface, where satellite observations can only be used indirectly to infer energy budget terms. In the shortwave (solar) spectral range, it is especially challenging to derive consistent albedo, absorption, and transmittance from spaceborne, aircraft, and ground-based observations for inhomogeneous cloud conditions and is closely related to the long-debated discrepancy between observed and modeled cloud absorption.\r\nThe cloud spatial structure is revealed as a spectral signature in shortwave irradiance through the physical mechanism of molecular scattering. However, the study of specific mechanisms is rather complex since the satellite instruments cannot completely describe the spatial distribution of cloud and the variability of scattering and absorption properties. For this reason, several studies deal with the problem described above, as a challenge for estimating spectrally the cloud optical properties (such as the albedo and transmittance) as well as scattering and absorption processes taking place in the cloud system with adequate resolution. Hence, the above mechanisms can be described using three dimensional (3-D) radiative transfer models. Those models receive auxiliary information from cloud imagery and radar observations. The molecular scattering (Rayleigh) was the only one directly dependent on the wavelength of the vertical radiative flux. Moreover, it was considered as a spectral perturbation of backtracked horizontal exchange of solar radiation due to the inhomogeneous distribution of cloud. The horizontal photon transport is highly correlated to its spectral dependence.\r\nConcerning the presence of cirrus or ice clouds, the effect of their phase function and the vertical distribution were evaluated on the scattering of far infrared radiation. Thus, the accurate reconstruction of the phase function of cirrus clouds potentially indicates the need for application of a radiative transfer model. This specific module necessarily includes scattering parameters, while the accuracy of its calculations needs to be verified against real measurements. \r\nFor several applications the preliminary detection of those portions of the scene affected by the presence of clouds (cloud detection) is mandatory. For studying properties of Earth's surface targets affected by the presence of clouds are flagged just to exclude them by further analyses. In some case clouds themselves are the object of interest. In both cases the identification of clouds (and their classification) is mostly done by using (combination of) specific spectral signatures. Generally speaking clouds are highly reflecting VIS/NIR radiation showing (due to their heigth) brigthness temperatures (in the TIR region) lower than underlying surfaces. Thin or semi-transparent clouds are still detectable for their higher reflectance over the sea which represents a quite dark bacground in the VIS/NIR/SWIR region. Over land (much more reflecting) such a test is not more efficient and more sophisticated tests (e.g. Brigthness Temperature Difference in the split window bands around 11 and 12 microns) are required. In presence of very cold, high reflective backgrounds (e.g. snow, glaciers, etc.) both tests on the VIS reflectance and on TIR brigthness temperature could fail. More specific tests exploiting the reflectance drop of snow in the SWIR (where clouds are still saving their higher reflectance) helps to discriminate the presence of clouds from clear sky conditions even over a snow background. In the microwaves clouds are quite transparent except when coupled with coarse particles related to rain, snow, hailstones (precipitating clouds). In that case Mie scattering dominates strongly reducing the amount of radiance collected at the sensor (lower brigthness temperature in the microwave spectral range).","hasChildren":true,"name":"Spectral Signature of Clouds","selfAssesment":"Completed
"},{"code":"PP1-3-7","description":"If the resolution is low enough that disparate materials can jointly occupy a single pixel, the resulting spectral measurement, made by the sensor, will be the composite of the individual spectra. Under the linear mixing model (LMM), each observed spectrum in each pixel of a given image is assumed to result from the linear combination of the N endmember spectra present in the pixel. The reflectance spectrum of each endmember is weighted by the fractional area coverage of it in the pixel. \r\nHowever, if the components of interest in a pixel are in an intimate association, like sand grains of different composition in a beach deposit, light typically interacts with more than one component as it is multiply scattered, and the mixing between these different components are nonlinear. Such nonlinear effects have been recognized in spectra of: particulate mineral mixtures, aerosols and atmospheric particles, vegetation and canopy. In this case a non-linear mixing model (NLMM) should be applied. To summarize: Linear mixture model assumes that endmember substances are sitting side-by-side within the pixel; Nonlinear mixture model assumes that endmember components are randomly distributed throughout the pixel, causing multiple scattering effects. \r\nIn the linear mixing case, the basic premise of mixture modelling is that within a given scene, the surface is dominated by a small number of distinct materials that have relatively constant spectral properties. These distinct substances (e.g., water, grass, mineral types), characterized by a well-defined spectral signature are called endmembers, and the fractions in which they appear in a mixed pixel are called fractional abundances. Then, finding the endmembers that can be used to ‘unmix’ other mixed pixels becomes a crucial issue. \r\nIdentify fractional abundances of distinct substances from the spectral signal of a mixed pixel is one of the application in which hyperspectral images can provide an valuable support.","hasChildren":true,"name":"Composition of spectral signatures (Linear Mixing)","selfAssesment":"Completed
"},{"code":"PP1-3-8","description":"One of the most common ways to classify remote sensing systems consists in distinguishing them into the passive systems, which detect naturally occurring radiation, and the active systems, which emit radiation and analyse what is sent back to them. The passive systems can be further subdivided into those that detect radiation emitted by the Sun (this radiation consists mostly of ultraviolet, visible and near-infrared radiation), and those that detect the thermal radiation that is emitted by all objects that are not at absolute zero (i.e. all objects). For objects at typical terrestrial temperatures, this thermal emission occurs mostly in the infrared part of the spectrum, at wavelengths of the order of 10 μm (the so called thermal infrared region), although measurable quantities of radiation also occur at longer wavelengths, as far as the microwave part of the spectrum. Active systems can, in principle, use any type of electromagnetic radiation, resulting able to obtain measurements anytime, regardless of the time of day or season. In practice, however, they are restricted by the transparency of the Earth’s atmosphere at the specific spectral range considered. In any case they can be used for examining wavelengths that are not sufficiently provided by the sun, such as microwaves, or to better control the way a target is illuminated. Active sensors may be classified according to the use that is made of the returned signal. Two main methods have been identified to this aim so far: the Ranging technique mostly concerns with the time delay between transmission and reception of the signal, while the Scattering one is mostly focused on the strength of the received signal.","hasChildren":true,"name":"Definition of active and passive remote sensing techniques","selfAssesment":"Completed
"},{"code":"PP1-3-9","description":"Light has a key role for aquatic ecosystems, both in marine and freshwater. It penetrates underwater and interacts with dissolved and particulate water constituents, the optically active constituents (OACs). They absorb and scatter the light, giving water its characteristic colour and affect the light availability underwater. The three main OACs are phytoplankton, coloured dissolved organic matter (CDOM) and suspended particulate matter (SPM) and vary in time and space. Absorption and scattering represent the inherent optical properties (IOPs) of water and depend solely on the OACs present in the water. In addition, water bodies have apparent optical properties (AOPs) that depend both on OACs and the incident light field.\r\nThe chlorophyll in the phytoplankton absorbs blue and red wavelengths and reflects green. Therefore, the oceans appear blue-green depending on the concentration of phytoplankton. CDOM is primarily tannin-stained water released from decaying detritus. High CDOM concentrations appear yellow-green to brown. CDOM absorbs ultraviolet (UV) light in the surface waters which is harmful for phytoplankton but competes with phytoplankton for light. Inorganic suspended matter (ISM) is the suspended sediment in the water. It is a component of SPM and strongly scatters longer (red) wavelengths. High ISM concentrations give water a reddish-brown colour. Pure water, however, absorbs longer wavelength red light. As natural waters vary in their composition, oceanographers introduced ocean classification schemes based on the optical properties of water. The main differentiation is between Case 1 open ocean waters and Case 2 coastal waters. In open ocean waters, the optical properties are dominated by phytoplankton and covarying material. In coastal waters, optical properties are dominated by suspended sediments and CDOM that vary independently of phytoplankton.","hasChildren":true,"name":"Optical properties of water","selfAssesment":"Completed
"},{"code":"PP1-3","description":"Measuring the signal emitted (received) by a radiation source (detector)","hasChildren":true,"hasParent":true,"name":"Sensing of EM radiation.","selfAssesment":"Planned
"},{"code":"PP1-4-1","description":"Radiative transfer equation (RTE) is the governing equation of radiation propagation in a media, which plays a central role in the analysis of radiative transfer in gases, semitransparent liquids and solids, porous materials, and particulate media, and is important in many scientific and engineering disciplines. \r\nThe RTE states that when radiation (a light-ray) propagates through matter (gas, dust, liquid), the incident radiation could be absorbed or scattered by matter, or radiation emitted from matter could append to the incident radiation. As a result, the intensity of radiation would change temporally, spatially, and directionally. The study of the propagating way of radiation in matter is the radiative transfer. In more detail, the radiation traversing a medium may be attenuated due to the density, mass scattering and absorption of material. In contrast, the radiation’s intensity can be strengthened by emissions from the material plus multiple scattering from all directions. All the above interactions are described mathematically by the general radiative transfer equation.\r\nThere are different forms of RTEs that are suitable for different applications, including the RTE under different coordinate systems, the transformed RTE having good numerical properties, the RTE for refractive media, etc.. Furthermore, several fundamental numerical methods for solving RTEs are proposed up to now focusing on the deterministic methods, such as the spherical harmonics method, discrete-ordinate method, finite volume method, and finite element method.","hasChildren":true,"name":"General equation of radiative transfer.","selfAssesment":"Completed
"},{"code":"PP1-4-10","description":"The inversion approach aims at retrievals of trace gas concentration and temperature profiles of atmospheric state, namely the modeled state vector, based on the measured radiance transmitted or reflected or scattered (SCIAMACHY spectrometer) by the Earth-Atmosphere system. Satellite instruments measure the radiance L that reaches the top of the atmosphere at given frequency v. The measured radiance is related to geophysical variables of Earth's atmosphere (e.g. temperature vertical profiles and chemical composition, aerosols, clouds, rain, etc.) and surface (e.g. temperature, spectral emissivity and reflectance, etc.) by the Radiative Transfer Equation (RTE). In RTE measured spectral radiances are assumed as the result of different contributions:\r\na) thermal emission from the different layers (at heigt z) of atmosphere at temperature T(z) modulated by the atmospheric transmittance from z to the sensor heigt. It depends on both temperature profile T(z) and trace gas concentration along the optical path;\r\nb) Surface emission. It depends mostly on Eart's surface temperature T(0) and spectral emissivity\r\nc) Surface reflection/scattering. It depends on spectral reflectance and local properties like surface rugosity \r\nOthers, more complex contributions comes from: cloud/rain, aerosols, etc.\r\nIn its simplified form, terms a) and b) dominate as far as InfraRed (IR) radiances are considered. Term a) can be neglected in those bands where atmosphere is transparent (atmospheric windows). Term b) can be negletcted in the IR spectral bands (sounding channels) where it is fully adsorbed by some specific constituent of the atmosphere. Among the IR sounding channels some ones are selected being associated to atmospheric constituents (like CO2 or oxygen) whose mixing ratio in the atmosphere is known to be constant. For radiances measured in these bands term a) in RTE depends only on T(z) (through a Fredholm equation of the first kind) that can be then retrieved by inversion methods. When T(z) are known trace gas concentrations survive as the only unknown of term a) and can be retrieved by inversion methods using radiances measured in their corresponding sounding channels. Similar inversion strategies have been suggested as far as radiances (emitted, transmitted, reflected, adsorbed) measured in different spectral ranges (from the Visible to the Microwaves) are considered.","hasChildren":true,"name":"Retrieval of atmospheric parameters by inversion of multi-spectral radiances","selfAssesment":"Completed
"},{"code":"PP1-4-2","description":"In the field of radiation scattering and absorption, the cross-section, analogous to the shape of a particle, is used to determine the amount of energy diverted from the original beam by the particle. This parameter is called mass cross section, when it is in reference to unit mass (cm2g-1).","hasChildren":true,"name":"Cross Section of Extinction (Absorption, Scattering) per Mass Unit","selfAssesment":"Planned
"},{"code":"PP1-4-3","description":"When the mass cross-section is multiplied by the density of particle, the extinction coefficient is calculated, namely the sum of absorption and scattering coefficient, whose the units are related to length. Especially, the absorption coefficient (k (cm•atm)-1) is the product of strength of absorption with the Loschmidt’s number.","hasChildren":true,"name":"Absorption Coefficient","selfAssesment":"Planned
"},{"code":"PP1-4-4","description":"The source function, Jλ, has units of radiant intensity and it is defined as the ratio of the source function coefficient to the mass extinction cross section. The Jλ determines the intensity that are acquired in a homogeneous medium.","hasChildren":true,"name":"Source Function (Coefficient)","selfAssesment":"Planned
"},{"code":"PP1-4-5","description":"If the monochromatic beam (Iλ) of radiation attenuates due to absorption, but it remains unaffected from emission contributions and multiple scattering of homogeneous Earth-Atmosphere system, it can be expressed by Beer-Bouguer-Lambert law. This law also expresses the monochromatic optical depth (τλ) and transmissivity (Τλ) of the above system.","hasChildren":true,"name":"Beer-Bouguer-Lambert law.","selfAssesment":"Planned
"},{"code":"PP1-4-6","description":"The Schwarzschild equation provides an interpretation for the infrared radiation that undergoes the absorption and emission processes simultaneously, while the scattering efficiency is considered negligible. Hence, its solution is obtained by the integrating of relationship that invokes Kirchhoff’s law and summing the two above processes along a ray path.","hasChildren":true,"name":"Schwarzshild equation and its solutions","selfAssesment":"Planned
"},{"code":"PP1-4-7","description":"The Optical Path (OP) describe the total concentration along a path of constituents extinguishing (by absorption or scattering) the electromagnetic radiation traveling through a medium at a specified whavelength λ. Its value depends then on the efficiency of absorption and scattering phenomena which occur during the travel itself. The Earth's atmosphere is usually the medium that a monochromatic beam (Iλ) of radiation travels through before reaching satellite sensors. In an homogeneously estinguishing medium (i.e. a medium with extinction coefficient for mass unit K constant along the optical path) the Optical Thickness OT is defined as OT=K x OP. It give a measure of the cumulative depletion of Iλ directed in straight-downward. As far as the Optical Thickness is large, the medium is more and more optically thick (i.e. radiation is largely absorbed). If the Optical Thickness is small it means that the medium is optically thin (i.e. radiation travels through it easily).","hasChildren":true,"name":"Concepts of Optical path and Optical thickness.","selfAssesment":"Completed
"},{"code":"PP1-4-8","description":"Radiative transfer is highly nonlinear and non-local against the cloud structure at a high spatial resolution. Hence, a Monte Carlo approach can be used for the representation of cloud structure and interactions between photons and clouds. This approach is more efficient than the method of representing clouds as horizontally homogeneous.","hasChildren":true,"name":"Radiative transfer in presence of clouds","selfAssesment":"Planned
"},{"code":"PP1-4-9","description":"The line by line radiative transfer model (LBLRTM) is an accurate and flexible model for the estimation of the spectral radiance and transmittance over the full spectral range (microwave to ultraviolet), using a first-order perturbation algorithm. It is considered as the basic tool for the creation of retrieval algorithms employed by the ground-based and satellite instruments, while the latest updates in spectroscopic factors are derived from the high-resolution transmission molecular absorption (HITRAN) database. A LBLRTMs is continuously updated and validated against highly accurate spectral measurements. Its errors are related to uncertainties in line parameters and shape. The shape is a Voigt line which is a linear combination of approximating functions for the description of all atmospheric levels. LBLRTML is combined with the continuum MT_CKD (Mlawer, Tobin, Clough, Kneizys, Davies) model which in turn includes the atmospheric constituents of water vapor, carbon dioxide (CO2), molecular oxygen (O2), molecular nitrogen (N2), and ozone (O3), and the molecular extinction process (Rayleigh scattering). A recent version of LBLRTM calculates analytically the Jacobians equations for obtaining meteorological parameters. Also, this model version retrieves the optical parameters of clouds related to scattering and emissivity. The LBLRTM is widely used in radiation and climate applications. It is capable to calculate the absorption degrees of various atmospheric constituents which are utilized afterward from climate and weather prediction models for estimating the broadband solar irradiance and the heating rates. Additionally, the complex radiative transfer models with fast computational time are initiated and trained by the LBRTM, since they are used subsequently on numerical weather prediction (NWP) assimilation systems.","hasChildren":true,"name":"Line-by-line radiative transfer models","selfAssesment":"completed
"},{"code":"PP1-4","description":"Theory of radiative transfer describes the transmission of the electromagnetic radiation through a medium. The electromagnetic radiation can be emitted, absorbed, scattered by constituents of the medium depending on the composition of the medium and the physical state of its constituents, as well as the wavelength of the radiation itself. Retrieving geophysical parameters from radiation measurements requires to know this kind of interaction which is described through the Equation of Radiative Transfer. In the field of Earth Observations from space, the considered medium is normally the Earth's atmosphere through which the e.m. radiation travel before reaching aerial multi-spectral sensors. Radiative transfer models allow to foreseen spectral radiances at whatever altitude in atmosphere (radiance at the sensor) starting from the knowledge of atmospheric vertical profiles of temperature and chemical constituents concentrations (direct problem). The possibility to retrieve atmospheric temperature profiles and chemical constituents concentrations from multi/iper spectral radiances measurements in selected bands (inverse problem) is the scope of the inversion techniques widely applied in meteorology and of a specific set of sensors devoted to the vertical sounding of the atmosphere. Clouds and scattering particles, like aerosols - requiring the inclusion of additional information on the atmospheric constituents (e.g water phases involved, dimensions and geometry of scattering particles, etc.) - make radiative transfer model more complex.","hasChildren":true,"hasParent":true,"name":"Fundamentals of Radiative Transfer","selfAssesment":"Planned
"},{"code":"PP1-5-1","description":"Light is the electromagnetic phenomenon we exploit for remote sensing. Its basic laws concerning the transmission through the interface of two different media are governed by reflection and refraction. Reflection governs the way light is backpropagated and refraction dictates how light is transmitted. Refraction is related to the real refractive index of a medium. Dispersion relates to the way the light of a given wavelength is transmitted. Since light of different wavelengths are transmitted at different angles, the phenomenon leads to the concept of dispersion. These three simple principles are at the core of the understanding technology of remote sensing.","hasChildren":true,"name":"Reflection, Refraction and Dispersion of the light","selfAssesment":"Planned
"},{"code":"PP1-5-11","description":"The theory provides the bulk of physical explanation and related laws, which govern absorption, emission and spontaneous emission from the ordinary matter. Early laws about thermal radiation and the blackbody emission, such as Rayleigh-Jeans, Wien, Planck laws are cast in a single theory and formalism through the concept of quantized energy at the level of atoms emission/absorption of light. Explain the modern concept of quantum optics and their link to the design of modern devices for the measurements and/or production of coherent light.","hasChildren":true,"name":"Einstein’s theory of radiation: photons, photoelectric effect, absorption, emission; Stimulated emission: the laser","selfAssesment":"Planned
"},{"code":"PP1-5-14","description":"Solid state modern detectors rely on non-metal junction, which can be designed and operated to yield a bandgap energy according to the spectral range (infrared, visible, UV) to be detected. The basic principles of how these devices are designed and fabricated is important to develop and design new sensors useful for the various remote sensing applications.","hasChildren":true,"name":"Electric conduction in solids: semiconductors, p-n- junction, diode and transistors","selfAssesment":"Planned
"},{"code":"PP1-5-15","description":"Modern detectors of electromagnetic radiation in the infrared, VIS, UV spectral regions are designed and fabricated based on suitable junctions or electro-optical devices. The performance of these systems needs to be assessed in terms of accuracy and precision. This is made through figures of merit such as Noise Power Spectral Density, Noise Equivalent Power. Detectors can be classified as photovoltaic or photoconductive devices, which allows to better classify the various noise sources: shot noise, 1/f noise, Johnson noise, generation-recombination noise.","hasChildren":true,"name":"Photovoltaic and photoconductive detectors: MCT, InSb, bolometer, CCD devices","selfAssesment":"Planned
"},{"code":"PP1-5-2","description":"Interference and diffraction are phenomena related to the wave nature of electromagnetic radiation. They explain how light propagates in presence of obstacles. These phenomena are largely used in the fabrications of optical systems for remote sensing: e.g. radiometers and spectrometers.","hasChildren":true,"name":"Interference and Diffraction.","selfAssesment":"Planned
"},{"code":"PP1-5-3","description":"The Michelson interferometer is the instrument that exploits and evidence the interference of light. A masterpiece of experimental physics, the Michelson interferometer is the key architecture of the modern optical interferometers, which make it possible to measure the emitted Earth spectrum with hyperspectral resolution.","hasChildren":true,"name":"Michelson Interferometer","selfAssesment":"Planned
"},{"code":"PP1-5-4","description":"The celebrated principle of constant speed of light and independence of the reference frame is important to explain the basic principles of instruments such as the Michelson interferometer. The basic physics theory to explain how electromagnetic fields propagates and the inter-relationship between electric and magnetic fields.","hasChildren":true,"name":"Special relativity; Electromagnetic fields equations and propagations","selfAssesment":"Planned
"},{"code":"PP1-5-6","description":"Helmotz’s wave equation arises in light and acoustic scattering problem and yields the general framework to investigate and analyse the scattering of time-harmonic acoustic and electromagnetic waves by a penetrable inhomogeneous medium.","hasChildren":true,"name":"Helmotz’s equations; Scattering from inhomogeneous media.","selfAssesment":"Planned
"},{"code":"PP1-5-7","description":"Geometrical optics is governed by the laws of reflection, refraction and dispersion. Its applications are relevant to many optical systems involving ray tracing, wavefront propagation, thin film calculators (which underly many optical engineering calculations).","hasChildren":true,"name":"Foundations of geometrical optics, geometrical theory of optical imaging","selfAssesment":"Planned
"},{"code":"PP1-5-8","description":"Optical interferometers are nowadays used to develop and implement Fourier Transform Spectrometers, which can measure the emission spectrum of a given source with high spectral resolution at a constant sampling. This instrumentation is now at the core of modern hyperspectral sounders from satellite and have opened the way to the sounding of the Earth atmosphere with unprecedented spatial vertical resolution.","hasChildren":true,"name":"Elements of the theory of interference and interferometers","selfAssesment":"Planned
"},{"code":"PP1-5-9","description":"Diffraction gratings and dispersive element are the basic ingredients for radiometers and grating spectrometers. They are in some cases preferred to Interferometer systems because the optical layouts can be designed and implemented with no moving part or components. Many of the today satellite instruments, including sounder and imagers, rely on diffraction and/or grating spectrometers","hasChildren":true,"name":"Elements of the theory of diffraction and grating spectrometers","selfAssesment":"Planned
"},{"code":"PP1-5","description":"This section describes the theoretical fundaments of Optics and Modern Physics of Sensors relevant to the Earth Observation.","hasChildren":true,"hasParent":true,"name":"Basics of Optics and Modern Physics of Sensors","selfAssesment":"Completed
"},{"code":"PP1-6-1","description":"The temperature and pressure profiles determine the atmospheric structure. The latter consists of four basic levels, considering the vertical variability of the temperature. These main four levels are troposphere, stratosphere, mesosphere, and thermosphere. In the troposphere (0-12km), which is the lowest layer of the atmosphere, all the meteorological processes that affect our everyday life take place. The lowest part of the troposphere is known as the boundary layer (0-3km), where all the surface-atmosphere interactions and exchanges take place. The troposphere concentrates the water vapor and 90% of atmospheric mass, while the chemical composition of all atmospheric layers consists of nitrogen, oxygen, argon and trace gases. The main parameters that characterize the atmosphere structure are pressure, density, and temperature. All the aforementioned parameters are related to the atmospheric composition and vary with altitude, latitude, longitude and season. Additionally, the stratosphere, which is the layer above the troposphere, contains almost all of the ozone abundance (~90%) of the atmosphere in a region named as ozone layer and traced between 15 and 35km. The interaction of the incoming solar radiation with ozone in this layer causes the reduction of the incoming harmful UV radiation provoking the temperature increase in the stratospheric layer. The 99.9% of total atmospheric mass is concentrated in lower atmosphere (<50km) with Nitrogen (N2, 78.08%), Oxygen (O2, 20.95%) and argon (Ar, 0.93%) being the major constituents of the atmosphere. Water vapor (H2O) is considered as a significant factor, too. Despite the fact that it depicts a very small amount of total atmospheric mass, it’s one of the most important greenhouse gases, along with carbon dioxide (CO2) and methane (CH4), absorbing the Earth’s longwave (infrared) radiation, affecting the energy balance of Earth-Atmosphere system. Furthermore, water vapor plays a decisive role in the formation of clouds and precipitation. Together with the basic chemical (atoms, molecules, ions) constituents of a \"standard\" atmosphere, aerosols of natural and anthropogenic origin have to be considered too, as far as the interaction of e.m. radiation with atmosphere is concerned.","hasChildren":true,"name":"Structure and chemical-physical composition of Earth's atmosphere","selfAssesment":"Completed
"},{"code":"PP1-6-10","description":"The water vapour is the major radiative and dynamic parameter in the atmosphere. Its concentrations vary highly in space and time, with the tropospheric water vapor being determined by the hydrological cycle processes, namely the evaporation, condensation and precipitation and by large-scale transport processes. Specific humidity decreases rapidly with pressure (following an exponential function) and with latitude. In particular, the variability of the H2O concentration shows a bimodal distribution: it’s very small in the equatorial region and poleward, relatively small in stratosphere and shows a maximum in the subtropics of both hemispheres. The concentration of H2O in the lower stratosphere is controlled by the temperature of the tropical tropopause, and by the formation and dissipation of cirrus. The water vapor can condense into water droplets when it has a particle to condense upon. The atmosphere continuously contains aerosol particles ranging in size from ∼10−3 to ∼20 μm. These aerosols are known to be produced by natural processes (volcanic dust, smoke from forest fires, particles from sea spray, windblown dust, and small particles produced by the chemical reactions of natural gases) as well as by human activity (particles directly emitted during combustion processes and particles formed from gases emitted during combustion). Some aerosols are effective condensation and ice nuclei upon which cloud particles may form. For the hygroscopic type, the size of the aerosol depends on relative humidity. Thin layers of aerosols are observed to persist for a long period of time in some altitudes of the stratosphere. \r\nClouds are global in nature and regularly cover more than 50% of the sky. There are various types of clouds. Cirrus in the tropics and stratus in the Arctic, and near the coastal areas are climatologically persistent. The microphysical composition of clouds in terms of particle size distribution and cloud thickness varies significantly with cloud type. Clouds can also generate precipitation, an event generally associated with midlatitude weather disturbances and tropical cumulus convection.","hasChildren":true,"name":"Water vapour and Cloud formation","selfAssesment":"Completed
"},{"code":"PP1-6-11","description":"The radiative equilibrium is the principle, where the radiative emission and absorption are in balance based on Kirchhoff’s and Planck’s law, resulting in the steady temperature of planet. The adiabatic lapse rate displays the decrease of vertical temperature of a parcel with rate higher than 1oC per 100 metres.","hasChildren":true,"name":"Radiative Equilibrium. Adiabatic lapse rate","selfAssesment":"Planned
"},{"code":"PP1-6-12","description":"The atoms of carbon are building blocks of living organisms and they can move among organisms as a part of carbon cycle. Their transport rate to the atmosphere as carbon dioxide is vital, because this gas trap heat in the atmosphere, increasing the Earth’s temperature and causing Greenhouse effect.","hasChildren":true,"name":"The Carbon Cycle, Greenhouse Effect","selfAssesment":"Planned
"},{"code":"PP1-6-2","description":"The atmospheric absorption can cause an excitation or falling into the energy state of a particle, while the scattering is related to absorption and re-emission of radiation at all directions without changes in its frequency. Particularly, the main contributors of the incoming solar radiation absorptions are various molecules like the nitrogen (N2), oxygen (O2), ozone (O3), water vapor (H2O). Additionally, other constituents of the atmosphere such as CO2 and CH4, and other trace gases, aerosols, and cloud droplets can also absorb significant portion of the incoming solar radiation. Generally, the absorption of solar radiation is related to the wavelength of the solar spectrum. For example, gases and specific type of aerosols (black carbon, BC) or elementary carbon (EC) absorb in the ultraviolet (UV) and visible (VIS) part of solar spectrum. On the contrary, cloud droplets which are suspended in the atmosphere mainly scatter in UV and VIS and absorb in the infrared. The absorption of the incoming solar radiation from the atmospheric constituents reduces the harmful UV radiation and it is considered as the driving of atmospheric photochemistry. Moreover, scattering in the atmosphere can be divided into two mainly categories, firstly, the Rayleigh scattering which is the scattering of radiation by gases (mainly N2 and O2) and, secondly, the Mie scattering which is the scattering by aerosol particles and cloud droplets. The main difference between Rayleigh and Mie scattering is the direction of the re-emission of the incident solar radiation. For example, in the Rayleigh scattering the light have symmetrical direction either forward or backward whereas in Mie scattering the light is mainly scattered in the forward direction, depending on the size of the particle.","hasChildren":true,"name":"Absorption and scattering of solar radiation in the Atmosphere","selfAssesment":"Completed
"},{"code":"PP1-6-3","description":"Mie scattering refers primarily to the elastic scattering of light from atomic and molecular particles whose diameter is similar or larger than the wavelength of the incident light. We can say that, when the particle has a diameter greater than about a tenth of the wavelength, we are in the field of Mie scattering.\r\nThis scattering produces a pattern like an antenna lobe, with a forward lobe sharper and more intense than the back one, the larger the particle size the greater the intensity and sharpness of the anterior lobe. Unlike Rayleigh scattering, Mie scattering is not strongly wavelength dependent. In this case the predominant component for the quantification of scattering (in addition to the particle dimension) is the direction of the incident solar radiation.\r\nMore specifically, the amount of scattering in the backward direction depends upon a wave relation tending to decrease in accordance with the growth of the particle size until it reaches a certain value for which the back scattering becomes a constant quantity. This condition is reached when the diameter of the particle is approximately equal to the wavelength of the incident radiation.\r\nIn the atmosphere the Mie scattering is commonly caused by particles (aerosols) floating in the atmosphere (due to Dust, smoke, fog, rain drop). \r\nIn nature it is possible to see the effects of Mie scattering, for example, in the evenings when there is a lot of fog and the dazzling headlights of our car do not allow us to see the road ahead. \r\nThe Mie theory provides the solution for the amount of scattering in case of a spherical medium due to an incident wave.","hasChildren":true,"hasParent":true,"name":"Mie Scattering in the Earth's Atmosphere","selfAssesment":"Completed
"},{"code":"PP1-6-4","description":"Scattering is a physical process by which a particle in the path of an electromagnetic wave continuously exstracts energy from the incident wave and reradiates that energy in all directions. In more detail, it occurs when a photon’s electromagnetic field hits a particle’s electric field in the atmosphere and is deflected into another direction. The Rayleigh scattering falls into the elastic scattering phenomena, in which the individual photon changes its direction of propagation but non its energy. The Rayleigh scattering involves air molecules (mainly N2 and O2) whose diameter (x) is much smaller (one-tenth at least) than the incident radiation wavelength (λ) (i.e., x << λ). The amount of scattered intensity (I) depends on the incident light wavelength (λ) and the refractive index (n) of air molecules. However, the refractive index can be considered relatively negligible as compared to the explicit wavelength term. In this way, the intensity scattered by air molecules in a specific direction is strongly dependent on the wavelength (λ), as expressed in the form Iλ~1/λ4. The inverse dependence of the scattered intensity on the wavelength to the fourth power allows at explaining the blue color of sky, caused by the scattering of sunlight off the atmosphere molecules. To better understand this phenomenon, it is worth considering that a large portion of solar energy is contained between the blue and red regions of the visible spectrum, where blue light (0.425 µm) has a shorter wavelength than red light (0.650 µm). Consequently, based on the above-mentioned equation, blue light scatters about 5.5 times more intensity than red light. For this reason, more blue light is scattered than red, green, and yellow, and so the sky appears blue, when viewed away from the sun’s disk. The Rayleigh scattering of unpolarized sunlight by air molecules has maxima in the forward and backward directions, whereas it shows minima in the side directions. Furthermore, the light scattered by particles is not delimited only on the incidence plane, but is visible in all the azimuthal directions. The derived scattering patterns are symmetrical in the three-dimensional space, because of the spherical symmetry assumed for air molecules.","hasChildren":true,"name":"Rayleigh Scattering in the Earth's Atmosphere","selfAssesment":"Completed
"},{"code":"PP1-6-5","description":"When we talk about “thermal infrared (or terrestrial) radiation” we commonly refer to the energy emitted from the Earth-atmosphere system. Trapping of thermal infrared radiation by atmospheric gases is typical of the atmosphere and is therefore called the “atmospheric effect”. The atmospheric effect is sometimes referred to as the “greenhouse effect” because in a similar way glass, which covers a greenhouse, transmits short-wave solar radiation, however absorbs long-wave thermal infrared radiation. Imagine a beam of radiation travelling through a small section of air. The air is made up of changing concentrations of different species, with all molecules absorbing and emitting thermal radiation at different rates. As the radiation travels through different layers of the atmosphere, the intensity of radiation will constantly be modified by both absorption and emission processes as described by the Schwarzschild's equation. In case of a sensor on board of a satellite, the net radiation measured would be that which is attenuated through each layer (as small increments of absorption and emission) from the surface to the top of the atmosphere plus the radiation emitted directly from the atmosphere. In this case, this process can be described by the radiative transfer equation (RTE). \r\nThe equation of radiative transfer simply says that as a beam of radiation travels through the atmosphere, it loses energy to absorption, gains energy by emission, and redistributes energy by scattering. Many radiative transfer codes exist which are able, i.e. on the basis of known properties of the atmosphere, to computed the effect of the atmosphere on the thermal infrared radiation providing atmospheric transmittance (absorption), atmospheric scattering and atmosphere path emission. Commonly, in satellite remote sensing, the thermal infrared region is defined as the region of the electromagnetic spectrum comprised between 8 and 14 micron. In an atmosphere free of particles (aerosols due to phenomena like fires, volcanic eruption, dust storm, etc.) the thermal infrared radiation is mainly affected by triatomic gases like water vapor, carbon dioxide and ozone.","hasChildren":true,"name":"Thermal infrared radiation transfer in the atmosphere","selfAssesment":"Completed
"},{"code":"PP1-6-6","description":"Light scattering by particles is the process by which small particles cause optical phenomena, such as rainbows, the blue color of the sky, and halos. Mie scattering defines the interaction of light with particulate matter with a dimension comparable to the wavelength of the incident radiation. It can be regarded as the radiation resulting from a large number of coherently excited elementary emitters (molecules for example) in a particle. Since the linear dimension of the particle is comparable to the wavelength of the radiation, interference effects occur. The most noticeable difference to Rayleigh scattering is, generally, the much weaker wavelength dependence and a strong dominance of the forward direction in the scattered light. The calculation of the Mie scattering cross section, which involves summing over slowly converging series, is complicated even for spherical particles, it is worse for particles of an arbitrary shape. However, the Mie theory for spherical particles is well developed and a number of numerical models exist to calculate scattering phase functions and extinction coefficients for given aerosol types and particle size distributions.","hasChildren":true,"hasParent":true,"name":"Light scattering by atmospheric particulates","selfAssesment":"Planned
"},{"code":"PP1-6-7","description":"Each time radiation passes through the atmosphere it is attenuated to some extent. We refer to this attenuation with the term 'atmosphere transmittance'. The typical atmospheric transmittance between wavelengths of 250 nm and 2500 nm, i.e. in the ultraviolet, visible, near-infrared and short-wave-infrared regions of the spectrum is dominated bywater vapour, although methane, carbon dioxide and molecular oxygen are also responsible for a few absorption lines. The behaviour in the visible region is dominated by molecular Rayleigh scattering. At the short-wavelength end of the spectrum, in the ultraviolet, absorption by ozone becomes very significant. Above 2500 nm up to the upper limit (13500 nm) of the optical electromagnetic spectrum useful for Remote Sensing, the atmosphere transmittance is mainly affected by triatomic molecules (H20, CO2 and O3). However, the atmospheric effects (transmittance) is strongly depending on the electromagntic wavelength. Remote Sensing exploits the region of relative atmospheric transparency called atmospheric windows.","hasChildren":true,"name":"Earth's (standard) Atmosphere Transmittance","selfAssesment":"Planned
"},{"code":"PP1-6-8","description":"With the term 'atmospheric windows' we refer to the regions of the electromagnetic spectrum where the interaction between the atmosphere constituents (i.e., molecules, aerosols, and cloud particles) and the electromagnetic radiation is minimized, namely the mechanisms of scattering and absorption of the radiation are less relevant than the transmission one. Therefore, the radiation collected at the sensor in these spectral regions is strictly depending on the Earth surface features, allowing to infer information about the processes/phenomena there in progress at the time of the acquisition. There are three main spectral ‘windows’ in the Earth's atmosphere. The first of these includes the visible and near-infrared (VNIR) parts of the spectrum up to the medium infrared, between wavelengths of about 0.38 μm and 3.5 μm, although it does also contain a number of opaque regions. This spectral interval includes the small portion of the electromagnetic spectrum to which human eyes are sensitive to (i.e, the visibile region between 0.4 and 0.7 μm). The second is a rather narrow region between about 8 μm and 15 μm, in which is found the bulk of the thermal infrared (TIR) radiation from objects at typical terrestrial temperatures. In this region there is only a main opaque interval, around 9.6 μm due to the presence of the ozone band. The third more or less corresponds to the microwave region, between wavelengths of a few millimeters and a few meters. Therefore, each remote sensing instrument that should be able to fully penetrate the Earth’s atmosphere has to be designed to operate in one of these three ‘window’ regions.","hasChildren":true,"name":"Atmospheric (spectral) windows for EO","selfAssesment":"Completed
"},{"code":"PP1-6-9","description":"The water cycle is a continuous purification process of water on Earth due to the movement of water species among various reservoirs. This cycle is vital for Earth’s life, ecosystems, and living organisms. The water cycle includes mainly four processes. Water is evaporated from ocean and land surfaces driven by solar heating. The resulting water vapor rises upwards into the atmosphere, transported by the winds, cools, and due to low air temperature condensates into liquid droplets and ice crystals to form clouds. The ice or/and liquid droplets collide, increase their size, and precipitate as snow or rain to Earth’s surface and oceans. The subtraction of energy (latent heat of evaporation) at low latitudes related to the evaporation processes as well as its release (latent heat of condensation) at higher latitudes related to the condensation processes is a formidable way to guarantees the heat transport from the warmer part of the Earth to the colder ones mantaining local air temperature more compatible with the human life. The starting point of the water cycle is not unique, but the oceans can be selected as the initial reservoir. Other important reservoirs are considered ice sheets, lakes, and rivers. \r\nThe hydrosphere is defined by the various water reservoirs which are characterized by different residence times – the time spends the water molecules in a reservoir. The water residence time – the rate at which the water comes out the reservoirs – varies for each reservoir extending from hundreds (Greenland Ice Sheet) or thousands of years (Antarctic Ice Sheet) to years and days for rivers and lakes, respectively. It also defines the energy transferred from the Earth to the Atmosphere which increases for short-term residence times. In long-term temporal scales, this energy is defined as the evaporation rate (E) and balances with the precipitation rate (P). This global energy balance breaks for shorter time scales depending also on the local and regional climate. For example, in regions located in the Inter-Tropical Convergence Zone (ITCZ), the energy balance in the water cycle does not exist since the precipitation rate is much higher than the evaporation rate (P>>E) due to the horizontal movement of converging trade winds.","hasChildren":true,"name":"The Water Cycle","selfAssesment":"Completed
"},{"code":"PP1-6","description":"Atmospheric Physics describe the processes affecting the physical, chemical and thermodynamic status of planetary atmospheres. In the context of EO sciences, it particularly refers to the physics of the interactions of e.m. radiation traveling across (or emitted by) the atmosphere as the main source of information collected by satellite (in general aerial) sensors.","hasChildren":true,"hasParent":true,"name":"Basics of Atmospheric Physics","selfAssesment":"Completed
"},{"code":"PP1-7-1","description":"According to the second law of thermodynamics, heat is a measure of the movement or the flow of energy from hotter substances to colder ones and it is measured in Joules. In microscale, heat is known as internal energy. Two regions in thermal contact have the same temperature when there is no net exchange of internal energy between them. Heat is the net transfer of internal energy from one region to another, while temperature, which is the degree of hotness or coldness of an object, describes the average kinetic energy of molecules within substances. The faster the particles are moving, the higher their kinetic energy. Since the motion of the particles within an object is random, they do not move at the same speed and in the same direction, some of them move faster. Therefore, those particles have more kinetic energy than the others. Thermodynamic temperature can be defined for substances at (even Local) Thermodynamic Equilibrium (i.e. in condition of density/pressure which allows an efficient equipartition of kinetic energy among molecules). Temperature is then the measure of the average kinetic energy of such a system, and is usually expressed in Celsius (°C). When, particular conditions of very low pressure/density (like in the Earth's thermosphere) cannot guarantee energy equipartition among molecules (i.e. outside thermodynamic equilibrium) the concept of Kinetic Temperature should be used instead. The Celsius temperature scale is defined by international agreement in terms of two fixed points: the temperature of the ice point, which is defined as 0° Celsius, and the steam point as 100° Celsius. The Fahrenheit (°F) temperature scale is mainly used in the United States; on this scale, water freezes at 32 degrees Fahrenheit, and the temperature of boiling water is 212 F. The Kelvin scale (K) is the base unit of temperature in the International System of Units (SI). This temperature scale is obtained by shifting the Celsius scale by −273.15°; zero Kelvin is also called absolute zero.","hasChildren":true,"name":"Temperature and heat","selfAssesment":"Completed
"},{"code":"PP1-7-10","description":"Irreversible thermodynamics investigates the regularities in transport phenomena, namely heat and mass transfer, and their relaxation. It is based on the first law of Thermodynamics, which correlate the heat flow density with pressure and viscosity, and the second law that describe the temporal variations of local entropy for local continuous mass.","hasChildren":true,"name":"The constitutive equations of irreversible fluxes","selfAssesment":"Planned
"},{"code":"PP1-7-11","description":"The Adiabatic process of homogeneous system occurs, when flow of heat is not exchanged across the boundaries of system and the system is characterized from uniform phase (solid or liquid or gases). In this case, the variations of entropy can be determined for some parts of system.","hasChildren":true,"name":"Heat equation and special adiabatic systems, special adiabats of homogeneous systems","selfAssesment":"Planned
"},{"code":"PP1-7-12","description":"The thermodynamic diagrams are used for the study of vertical structure and properties of the Atmosphere above a specific location. Especially, a static diagram represents a) an atmosphere with fixed potential temperature or b) a process curve of the change of variables of air parcel that rises adiabatically.","hasChildren":true,"name":"Thermodynamics diagram, atmosphere static","selfAssesment":"Planned
"},{"code":"PP1-7-2","description":"Kinetic theory of gases is based on a simplified molecular description of gases, from which the properties of volume, pressure and temperature can be derived. The assumptions of this theory are based on the random movements of molecules, their elastic collisions and the transfer of kinetic energy between them.","hasChildren":true,"name":"Kinetic theory of gases","selfAssesment":"Planned
"},{"code":"PP1-7-3","description":"The ideal gas law or general gas equation describes the equation of state of hypothetical ideal gas. This equation correlates the pressure and volume with its temperature, while is characterized as a combination of the empirical laws of Boyle, Charles, Avogadro and Gay-Lussac.","hasChildren":true,"name":"Ideal gas laws","selfAssesment":"Planned
"},{"code":"PP1-7-4","description":"The state functions of ideal gas are the pressure, volume, temperature, internal energy and entropy, which remain unchangeable in compared with the path. The internal energy is expressed through Joule’s law as a function of temperature of gas, while the entropy depends on the variation of volume and temperature.","hasChildren":true,"name":"State function of ideal gases","selfAssesment":"Planned
"},{"code":"PP1-7-5","description":"The phase rule for condensation is expressed as P+F=C+1. The terms of P, F and C describe the number of phases, minimum fixed variables and independent chemical species respectively. Concerning the condensed phases to distinguish the gases from liquids and solids, these are the density, molecular order, diffusion, etc.","hasChildren":true,"name":"State function of the condensed gas phase","selfAssesment":"Planned
"},{"code":"PP1-7-6","description":"When the system passes from initial to final state due changes in properties of temperature, pressure and volume, it is considered to have undergone thermodynamic process. The different types of thermodynamic processes are distinguished in the isothermal (fixed temperature), adiabatic, isochoric (stable volume), isobaric (stable pressure) and reversible process.","hasChildren":true,"name":"Thermodynamic process","selfAssesment":"Planned
"},{"code":"PP1-7-7","description":"Budget equations, namely heat, momentum and moisture budget, are interpreted through two frameworks, which are Eulerian and Lagrangian. Eulerian is utilized for the investigating of transfer of heat by the wind, while Lagrangian is concerned about the effects of ascending or descending airflows on the Earth-Atmosphere system.","hasChildren":true,"name":"Budget equations","selfAssesment":"Planned
"},{"code":"PP1-7-8","description":"The First Law of Thermodynamics supports that the energy is conserved. Thus, the thermal energy is defined as the sum of warming or internal energy (microscopic effect) and work occurring per unit mass (macroscopic effect). For its application to the Atmosphere, the thermal energy input is given from the following mathematical expression: Δq=Cp·ΔT-(ΔP/ρ), where Δq (J·kg–1) is the amount of thermal energy you add to a stationary mass m of air, Cp (J·kg–1·K–1) is the specific heat of air at constant pressure, ΔT (K) is the induced variation of temperature, so that Cp·ΔT represents the heat transferred per unit air mass, ΔP (Pa = J·m-3) is the pressure difference and ρ (kg· m-3) is the air density.\r\nThe term Cp·T is defined enthalpy h, thus, the first term on the right side of eq. of thermodynamic first low for atmospheric applications, which is the corresponding enthalpy change is: Δh=Cp·ΔT. It is a characteristic possessed by the air.\r\nExpressing the first law of thermodynamics for atmospheric applications in conceptual form we can state that, given a quantity Δq of thermal energy added to a stationary mass m of air, a part of this energy heats the air, increasing its internal energy, but, as air heats up, its volume expands by an amount ΔV and pushes against the surrounding atmosphere, which responds with an equal and opposite pressure P that we can assume constant. Therefore, a part of the thermal energy introduced does not go to heat the air, but goes into macroscopic movement.","hasChildren":true,"name":"First law of thermodynamic","selfAssesment":"Completed
"},{"code":"PP1-7-9","description":"A natural process that starts from an equilibrium state and ends in another state, causing changes in direction of entropy (ΔS) or statistical disorder of the system, is interpreted by Second Law of Thermodynamics. This law is considered as an irreversible process and it is expressed as ΔS=Heat transfer/Temperature.","hasChildren":true,"name":"Second law of thermodynamics","selfAssesment":"Planned
"},{"code":"PP1-7","description":"Thermodynamics is the science of the relationships between heat, work, temperature, radiation, energy and properties of matter. These relationships are governed by the four laws of thermodynamics which allow a quantitative description, through measurable macroscopic physical quantities, of processes that, at the level of microscopic constituents can be described by the statistical mechanics. Thermodynamics applies to a wide variety of topics relevant to EO science and technologies from atmospheric chemistry and meteorology up to sensor design and aeronautics.","hasChildren":true,"hasParent":true,"name":"Basics of Thermodynamics","selfAssesment":"Planned
"},{"code":"PP1-8-2","description":"Starting from the standard Rocket Equation - assuming a relative speed of the burned (emitted) fuel equal to 2,4 km/s and zero initial speed - it is possible to evaluate (for a single-stadium rocket) the mass percentage of payload that can be hosted on a platform depending on the final speed expected on the orbit. For instance a 28% payload is possible for a geostationary platform whose expected final speed on the orbit (radius 42.170 km) is 3,7km/s. Instead for a polar platform at about 800km this percentage reduce up to the 4% being the final sped on the orbit expected to be 7,5km/s.","hasChildren":true,"name":"Equation of the rocket and launch of a satellite: payload determination","selfAssesment":"Planned
"},{"code":"PP1-8-3","description":"The orbit of a satellite is commonly defined through its so called Keplerian parameters. These parameters represent the trajectory that the satellite will follow if no-perturbation are acting on it. A series of forces act on the satellite to perturb it away from the nominal orbit. We can classify these perturbations, or variations in the orbital elements, based on how they affect the Keplerian elements. The actual orbit of a satellite will result from a combination of these perturbations. Periodic maneouvers are needed to bring the orbit back to nominal conditions. The lifetime of a satellite is defined as the time interval that it takes to decay from its initial altitude to an altitude causing the satellite reentry down to the atmosphere. Therefore lifetime of a satellite should not be confused with the time during which the satellite will provide useful information (this operational phase, in general, is designed to last 5 - 7 years). In fact, all satellite terminating operational phases in orbits passing through the LEO region should be de-orbited or, where appropriate, manoeuvred to an orbit with suitably-reduced lifetime, that is, should be left in an orbit where drag and other perturbations will limit lifetime. The actual duration of the satellite in orbit will depend from the intensity of the perturbations which will affect its orbit. In case of satellite on GEO orbit, at the end of the operational phases they will be located on a disposal orbit, that is an orbit which do not cross the protected region. The protected region is the altitude region ranging from GEO - 200 km to GEO + 200 km and inclination region between -15 deg and +15 deg. Satellites in low Earth orbit, with perigee altitudes below 1000 km, are predominantly subject to atmospheric drag. This force very slowly tends to circularise and reduce the altitude of the orbit. The rate of 'decay' of the orbit becomes very rapid at altitudes less than 200 km, and by the time the satellite is down to 180 km it will only have a few hours to live before it makes a fiery re-entry down to the Earth.","hasChildren":true,"name":"Real orbits. Life time of a satellite, orbit’s decay.","selfAssesment":"Planned
"},{"code":"PP1-8-4","description":"The choice of a satellite orbit mostly depends on its main application. From this point of view it represents a crucial part of a satellite mission design. The most important parameters to describe a satellite orbit are the inclination angle i (of the orbit plane respect to the equatorial plane) its eccentricity e and its height H from the Earth's surface. In principle whatever eigth H can be used, provided that the speed of the satellite on its orbit allows the centrigugal force to exactely compensate the gravitational one at that heigth. Polar (i close to 90°) and Geostationary (i=0, H=35.800 km) orbits are the most common choices for EO satellites. In principle one single polar satellite can be sufficient to guarantee the global coverage of the Earth with equal quality of the images at all latitudes. All Geostationary satellites share the same circular orbit with H around 36000 km where the required speed exactely correspond to the one required to travel an entire orbit in 1 sideral day (orbital period P = 1 sideral day). This means that the satellite footprint is permanently in place over a specific Earth's location (e.g. for Meteosat 0°N, 0°E) allowing a quasi-continuous monitoring of a whole Earth's emisphere (with poor visibility of Earth's edges including Poles). Polar satellites' heigths are usually in between 700-800 km, with orbital periods around 100min (i.e. about 14,5 orbits/day) even if, lower orbits are also chosen particularly for very high spatial resolution payloads. Lower inclinations are also used (quasi-polar orbits) for specific applications. Due to the asphericity (and mass inhomogeneity) of the Earth, satellite orbit plane rotates around the Earth's polar axis with a period Pp producing (for elliptical orbits) the rotation of the orbit itself in its plane. A common choice for most EO polar satellites is to choose the orbital parameters in a way that Pp=1 year (Sun-Synchronous orbits). Due to the synchronism between Earth's revolution around the Sun and the orbit plane precession around Earth' axis, satellite passages happens at the same local solar time (similar illumination conditions) each time it flies over a specific region. This ensure repeatable sun illumination conditions facilitating image interpretation particularly for change detection or land monitoring applications. Other choices are possible when it is required to monitor with continuity high latitude regions.\r\n\r\nThis is the case of Molniya orbits which combine the continuity of observations typical of geostationary satellites with the possibility, offered by polar orbits, to overfly the highest latitudes regions. Its characteristics are: high eccentricity (e.g. e=0,74, axes 500 and 23.000 km), P=1/2 sideral day (Geo-Synchronous), inclination (i=63,4° or i=116,6°) which guarantees the satellite footprint at the apogee remaining positioned on a fixed ground point (non-rotating orbit). This way the satellite will spend more than 93% of its orbital period looking to the same emisphere even from a high latitude point of view. \r\n\r\nSo called altimetric orbits respond to the specific needs of altimetry. In this case the orbital parameters are chosen in order to guarantee, for example: a) that the ascending and descending sub-satellite tracks intersect at roughly 90 degrees on the Earth’s surface (so that orthogonal components of the surface slope can be determined with equal accuracy; b) the possibility to monitor all phases of tidal effects on ocean surface.\r\n\r\nParticularly important for several applications (multi-temporal analyses, change detection, etc.) are the Exactly repeating orbits.\r\nThey are conceived in order that the sub-satellite track will repeat itself exactly after a certain interval of time. This allows images having the same viewing geometry during the satellite’s lifetime making moreover available a particularly simple method of referring to the location of images (navigation or geo-referenciation) for example by referring to a ‘path and row’ system used for instance by the Landsat World Reference System (WRS). It is possible to arrange satellite orbits parameters in order to contemporary guarantee the sun-syncronism so that, not only satellite images collected on the same region can be easily super-imposed each-other but the same illumination and viewing geometry can be achieved. This is, for instance, the choice adopted for LANDSAT satellites whose images are typically available as a collection of scene of fixed dimension always similar each other when covering the same terrestrial area.","hasChildren":true,"name":"Satellite orbits parametrization and choice","selfAssesment":"Completed
"},{"code":"PP1-8","description":"Mechanics is the Physics branch dealing with the behaviour of physical bodies when subjected to forces or displacements. This section provides Mechanics basic elements necessary for determining the orbits of satellites and rockets. The different satellite trajectories will be illustrated with respect to their peculiarities","hasChildren":true,"hasParent":true,"name":"Basics of Mechanics","selfAssesment":"Planned
"},{"code":"PP1","description":"Optical Remote Sensing deals with those part of electromagnetic spectrum characterized by the wavelengths from the visible (0.4 micrometer) to the near infrared (NIR) up to thermal infrared (TIR, 15 micrometer). It regards the collection and interpretation of the e.m. radiation emitted, reflected, adsorbed and transmitted by the observed targets in order to derive their physical-chemical properties and related information. Such a possibility derives from the basic principle of (multi-spectral) remote sensing that is widely supported both theoretically (e.g. atomic and molecular spectroscopy) and experimentally (e.g. spectral signatures catalogues). It states that, in principle (e.g. disposing of sensors with ideal spectral capabilities) the matter-radiation interaction depends on the wavelength of the involved radiation and on specific (e.g. chemical/physical) properties of the matter that can be derived by the spectral analysis of the emerging (emitted, reflected, adsorbed or transmitted) radiation. As far as Earth Observation is concerned, specific related concepts have to be addressed like: the spectral matter-radiation interactions (spectral signature concept), natural sources (e.g. Earth, Sun) of optical e.m. radiation, theory of the Black Body, atmospheric physics and radiative transfer equations in the VIS-NIR and TIR spectral ranges, basic physics of e.m. optical sensors and image systems, physical fundaments of the interpretation of optical radiances collected by multi-hyperspectral passive techniques.","hasChildren":true,"hasParent":true,"name":"Basics of Optical Remote Sensing","selfAssesment":"Completed
"},{"code":"PP2-1-2-1","description":"A radar signal is a complex signal. It is represented by a real part, the in-phase component, and an imaginary part, the quadrature component. In-phase is usually annotated by “I”, and quadrature by “Q”. Considering single look complex data, each component is represented in a single image channel.","hasChildren":true,"name":"In-phase/Quadrature Component","selfAssesment":"Planned
"},{"code":"PP2-1-2-2","description":"A phasor represents a complex number and its phase and amplitude equivalent. Considering a complex SAR image’s pixel, the real and imaginary part can be represented by a 2D vector in Cartesian coordinates. Its corresponding phase and amplitude information corresponds to the direction and length of the vector, respectively.","hasChildren":true,"name":"Phasor","selfAssesment":"Planned
"},{"code":"PP2-1-2","description":"The signal emitted by a radar system is a microwave signal, which can be described using a complex wave representation. This implies that the signal can be entirely represented by a complex number, which characterizes both its magnitude and its phase at a certain moment of time. In the SAR context, the complex number is usually represented by a real part, the in-phase component (I), and an imaginary part, the quadrature component (Q), from which the corresponding magnitude and phase can be retrieved. In single look complex SAR data, each of these components is pictured in a single image channel. The terminology comes from electrical engineering, whereby the quadrature component is 90° out of phase with respect to the reference frequency and the in-phase component. This is necessary in order to retrieve the phase information during A/D conversion. The I component can be expressed as the signal amplitude multiplied by the cosine of the phase. The Q component corresponds to the amplitude of the signal multiplied by the sine of its phase. Using both components as input, the magnitude and phase for each signal echoes and location can be retrieved.\r\nThe relationship between I/Q terms and the magnitude and phase of the signal can be best represented using a phasor. A phasor represents a complex number and its phase and amplitude equivalent. It can be best illustrated by a 2D vector in a Cartesian coordinate system, which projections on the horizontal and vertical axes represents the real and imaginary part, respectively. The length of the vector correspond to the signal’s amplitude and its direction (angle between the horizontal axis and the vector) characterizes the phase of the signal. Using simple mathematical considerations, the relationship between I/Q and amplitude and phase can be established.\r\nEach signal echo and pixel of a complex SAR image can be represented with such a phasor and the necessary amplitude and phase information can be accordingly retrieved.","hasChildren":true,"hasParent":true,"name":"Complex wave description","selfAssesment":"Planned
"},{"code":"PP2-1-4","description":"Electromagnetic waves are polarized; the direction of the polarization corresponds to the direction of oscillation of the electromagnetic field. Typical and often used linear polarisations are: H (horizontally) and V (vertically) polarized waves of the plane of the electric field vector oscillations relative to the sensor coordinate system. The polarization state of a backscattered wave from a natural surface can be linked to the geometrical characteristics like shape, roughness and orientation and the intrinsic properties of the scatterer like moisture, salinity, density. The radar system is characterized by combination of polarization of transmitted and received pulse: HH, HV, VH or VV. Based on the polarization sent and obtained the radar systems are divided in three polarization modes. Single polarization refers to the same polarization transmitted and received; dual polarization, one polarization is sent and another received; or quad polarization, when system is able to transmit and receive all four types of polarization. When making a contact with a scatterer, the polarization of the EM-wave can change, depending on the geometrical and dielectrical properties of the scatterer. In order to get all necessary information about those changes, full polarimetric systems are required.","hasChildren":true,"hasParent":true,"name":"Polarisation","selfAssesment":"Completed
"},{"code":"PP2-1-5","description":"Property of signal or data set in which the phase of the constituents is measurable, and plays a significant role in the way in which several signals or data combine. Two waves with a phase difference that remains constant over time, are said to be coherent.","hasChildren":true,"name":"Coherent","selfAssesment":"Planned
"},{"code":"PP2-1-6","description":"In remote sensing, phase is the exact position within a periodic signal with respect to an arbitrary reference point. It is typically expressed as an angle and measured in degrees or radians, where one period corresponds to a phase of 360° or 2π, respectively. Mathematically, phase is the argument of a complex number, that is the angle between its geometric representation in the complex plane and the real axis. For this reason, complex algebra is often used in remote sensing to facilitate phase calculations. Due to its periodic nature, phase can only be measured unambiguously within one period. Consequently, phase measurements are commonly subject to 2π phase ambiguities. These ambiguities can often be resolved in a process called phase unwrapping, using a priori information about the signal, typically related to its continuity. Phase measurements are crucial for the creation of synthetic aperture radar (SAR) images, as well as for many SAR imaging techniques, including interferometric SAR (InSAR).","hasChildren":true,"name":"Phase","selfAssesment":"Completed
"},{"code":"PP2-1-7","description":"Shift in frequency caused by relative montion along the line of sight between sensor and the observed scene.","hasChildren":true,"name":"Doppler effect","selfAssesment":"Planned
"},{"code":"PP2-1-8","description":"The wave-particle dualism (duality) is a theory according to which all matter exhibits the attributes of waves and particles.","hasChildren":true,"name":"Wave-particle dualism","selfAssesment":"Planned
"},{"code":"PP2-1","description":"The microwave portion of the electromagnetic (EM) spectrum ranges from 1 millimeter to 1 meter. Imaging radars are independent of weather conditions and can operate day or night. EM-waves are polarized. Normally only the horizontal (H) or vertical (V) linear polarizations are used. The radar system is characterized by combination of polarization of transmitted and received pulse: HH, HV, VH or VV. When making a contact with a scatterer, the polarization of the EM-wave can change, depending on the geometrical and dielectrical properties of the scatterer.The data can be acquired from both the ascending (northwards) and descending (southwards) satellite passes. Water clouds can interfere with the radars operating below 2 cm in wavelength. The effects of rain can be generally ignored at wavelengths above 4 cm. For longer wavelengths (above 20 cm), an effect called Faraday rotation caused by the ionosphere, i.e., free charges (electrons) and the Earth’s magnetic field, can lead to a rotation of the polarization plane. In the presence of Faraday rotation, the data, usually fully polarimetric, should be corrected. The radar systems operate in different bands that uses different wavelengths. The most common frequences/wavelengths (frequency = Speed of Light / wavelength) for environmental applications are X (5,75-10,90 GHz), C-(4,20-5,75 GHz), S-(1,550-4,20 GHz), L-(0,390-1,550 GHz) and P-(0,255-0,390 GHz) band. The selection of SAR system for acquiring data depends on their application. Longer wavelengths are mainly devoted to communication and navigation purposes. Radars penetrate atmosphere and clouds. For example for forestry, longer wavelengths starting from C- or S-band are preferred.","hasChildren":true,"hasParent":true,"name":"Microwave portion of electromagnetic spectrum","selfAssesment":"Completed
"},{"code":"PP2-2-1","description":"Diffraction is defined as interaction of waves with any solid object, not surfaces, and is not to be confused with refraction. More precisely, diffraction describes the phenomena of interaction of waves at an obstacle, such as an aperture, or an opening, such as a hole or an occurring space between two objects. Hence, diffraction is an essential form of scattering, describing ordered scattering at discrete boundaries. The effect of diffraction can be observed through extended interference patterns or simply by the bending of waves. In the field of microwave remote sensing, diffraction has the practical implication that it limits the spatial resolution of a microwave sensor since it acts on the ability of an imaging system to resolve details. This theoretical limit of resolution is called the diffraction limit. This means, the larger the aperture of the observing system compared to its employed wavelength (dependent on the frequency), the finer the resolution of an imaging system. The diffracted field can be calculated with analytical models, such as the Fraunhofer diffraction approximation in case of far field conditions, where the object is far away and the incident waves are assumed to be plane waves, or the Fresnel diffraction approximation in case of near field conditions, where the waves are spherical.\r\nOne simple example of diffraction is the diffraction of sound, for example the possibility to hear sounds around corners.","hasChildren":true,"name":"Diffraction","selfAssesment":"Completed
"},{"code":"PP2-2-2","description":"Scattering means the redirection of incident electromagnetic energy by an object. Similar to diffraction, scattering refers to the same physical process, the coherent distortion of an incident wave. However, diffraction as well as reflection can be regarded as essentially forms of scattering. Scattering explicitly describes the “random distortion of waves by elements that are similar in size or less than the wavelength” (Woodhouse, 2005). Thereby, scattering of the incident wave at an object can occur in any directions with varying strength, with the scattering pattern varying with the incident direction. Thus, the term scattering cross section, often denoted by σ, quantifies the effectiveness of a scatterer. In the field of active microwave remote sensing, the backscattering coefficient σ0 is known “as the ratio of the statistically, averaged, scattered power density to the average incident power density” (Fung, 1994). \r\nIn passive microwave remote sensing, radiometers measure the intensity of radiation emitted by a body, called brightness temperature TB. Since TB is always less than its physical temperature T, emissivity, defined as e = TB / T, is a measure of how strongly a body radiates at a given wavelength. It varies between 0 (metal) to unity (blackbody).\r\nEmission and scattering are complementary: surfaces that are good scatterers are weak emitters, and vice versa.","hasChildren":true,"name":"Scattering and emission","selfAssesment":"Completed
"},{"code":"PP2-2-3","description":"In climate change studies the carbon cycle with its crucial component the terrestrial biosphere is of great importance due to the ability of the biosphere to store environmentally harmful carbon dioxide. Radar sensors, especially SAR, can here provide a useful tool for quantifying and monitoring the biosphere. Hence, the relationship between biomass and radar backscatter responses has been studied in detail in recent decades. Results show that the sensitivity of measured radar backscatter coefficient decreases with increasing amount or density of present biomass. In the so-called saturation region, the radar backscatter saturates at a biomass depending on the employed wavelength. While for higher frequency bands like C-band (3.95-5.8 GHz), biomass can be measured up to ~50 ton/ha, the amount of measurable biomass increases with decreasing frequency (due to the increasing wavelength), such that at L-band (1-2.6 GHz) ~ 100 ton/ha and at P-band (0.23-1 GHz) ~200 ton/ha biomass can be measured. Further, the sensitivity of radar to biomass is different for co- or cross-polarized backscatter since the level of saturation depends not only on frequency but also on vegetation (e.g., height, structure, density, moisture) and soil surface (e.g., roughness, moisture) parameters. Overall, the saturation of radar backscatter depending on biomass has to be considered when analyzing SAR data.","hasChildren":true,"name":"Backscatter saturation","selfAssesment":"Completed
"},{"code":"PP2-2-4-1","description":"The radar equation is a measure of the received echo at the sensor. It defines what proportion of the transmitted energy is returned from a target. It is a function of the range between the antenna and the target, the antenna gain and the radar cross-section of the target. Mathematical expression that describes the average received signal level, compered to the additive noise level, in terms of system parameters. Principal parameters include: transmitted power, antenna gain, noise power, and radar range.","hasChildren":true,"name":"Radar equation","selfAssesment":"In progress
"},{"code":"PP2-2-4-2","description":"Coefficient sigma or sigma nought represents the average reflectivity of a horizontal material sample, normalized with respect to a unit area on the horizontal ground plane.","hasChildren":true,"name":"Sigma nought","selfAssesment":"Planned
"},{"code":"PP2-2-4-3","description":"Gamma nought represents the average reflectivity of a horizontal material sample, normalized with respect to the incident area, orthogonal to the incident ray from the radar.","hasChildren":true,"name":"Gamma nought","selfAssesment":"Planned
"},{"code":"PP2-2-4-4","description":"Radar brightness coefficient represents the reflectivity per unit area in slant range.","hasChildren":true,"name":"Beta nought (brightness)","selfAssesment":"Planned
"},{"code":"PP2-2-4","description":"Measure of radar reflectivity. The Radar Cross Section (RCS) is expressed in terms of the physical size of an hypothetical uniformly scattering sphere that would give rise to the same level of reflection as that observed from the sample target.","hasChildren":true,"hasParent":true,"name":"Radar cross-section","selfAssesment":"Planned
"},{"code":"PP2-2-5-1","description":"A material constant is a physical or chemical property of a substance, which can be expressed in numbers. Giving a precise numerical value of a constant often requires determining the external conditions (e.g. temperature, humidity). Material constants are factors that influence the interaction of microwaves with the target objects.","hasChildren":true,"name":"Material constants","selfAssesment":"Planned
"},{"code":"PP2-2-5-2","description":"The complex part k of the refraction index n=m+ik determines how far an electromagnetic wave of wavelength λ can survive crossing a specific medium. The attenuation length la is the distance after that the amplitude of an electromagnetic signal reduces its value by an amount of 1/e. For instance the amplitude of the Electric field E(z) of an electromagnetic wave proceeding along the z direction is decreasing as exp(-z/la) being la=λ/(2𝜋k) the attenuation length associated to that specific material (k) and wavelength λ. This way attenuation length in water can be of hundreds of meters in the visible range and just few microns in the microwaves. The opposite happens over solid land surfaces where optical waves can penetrate from few microns up to few millimeters (moving from the VIS-NIR to the TIR spectral range) whereas microwaves can reach depths from hundreds to thousands (as higher are their wavelength) meters allowing the exploration of subsoil and thick coulters of ice.","hasChildren":true,"name":"Attenuation lenght and penetration depth","selfAssesment":"Completed
"},{"code":"PP2-2-5-3","description":"Soil permittivity is a measure of the water content (soil moisture) in the soil and characterized by the metric of the dielectric constant of the soil. Soil moisture influences emission, absorption and propagation of microwave electromagnetic energy. Moisture decreases the ‘emissivity’ of soil, and thereby affects microwave radiation emitted from Earth’s surface. Dry soil has a low dielectric constant and low radar reflectivity. Moist and partially frozen solis have intermediate values. The higher the soil water content, the lower the radar signal penetration into the soil. In situ measurements of soil permittivity are a prerequisite for the calibration and validation of synthetic aperture radar (SAR) soil moisture retrieval algorithms. Soil moisture is a key variable in the hydrologic cycle and is recognized as an Essential Climate Variable (ECV).","hasChildren":true,"name":"Soil permittivity","selfAssesment":"Completed
"},{"code":"PP2-2-5-4","description":"The complex relative permittivity of a plant is a function of its contained amount of water, solutes (mainly their salinity) and temperature in all plant compartments (including roots). The more water and the higher the salinity are in the plant compartments, the higher is the complex relative permittivity of the plant. The complex relative permittivity of a plant refers to the complex relative dielectric constant of the plant and can be subdivided into complex relative permittivity values for the different plant compartments (roots, stem/stalk, leaves, fruit,...). The complex relative dielectric constant or permittivity parameter has a real and an imaginary part indicating the moisture content and the conductivity (loss) of the plant medium. Models of plant permittivity consist mostly of a free-water and a bound-water part. In particular, plant water is a solute of nutrients and not all water-conducting plant cells are fully filled by water, but also with air. Hence, the estimation of one plant permittivity, especially including several plant parts can be challenging to assess, to understand and to model. To acknowledge this mixture of components, dielectric mixing models containing the single material components are normally developed and applied, representing an effective complex relative permittivity of all plant components. Concerning a vegetation canopy, electromagnetic waves interact with a more or less sparsely vegetation-filled volume unit of air. A vegetation canopy represents a dielectric mixture of vegetation inclusions (leaves, twigs, branches, stems,…) distributed in a volume of air. Dielectric mixing models of canopies take this vegetation volume fraction into account.","hasChildren":true,"name":"Plant permittivity","selfAssesment":"In progress
"},{"code":"PP2-2-5","description":"The dielectric properties of any material can be described by the complex relative dielectric constant (complex relative permittivity) and contains of the real part (moisture content) and the imaginary part (conductivity/loss tangent). For instance: Reflectivity of a smooth surface and the penetration capabilities of microwaves into the material are determined by these two quantities. The complex dielectric constant changes mainly due to variations in water content, salinity, temperature of the material as well as due to the observing wavelength and polarization of the electromagnetic wave. It relates to the interaction of weakly-charged material components, like bi-polar water molecules, with irradiation of electromagnetic waves. The interaction increases with amount and charge of the material components. The complex relative permittivity is also linked to the complex index of refraction as being its square. In order to describe the complex relative permittivity of pure and saline water the single-relaxation Debye and the double-Debye dielectric model can be used. As the movement of bi-polar material components is significantly reduced when the material is put under freezing conditions (temperatures below 0 °C), the permittivity falls to almost a constant. The real part of the relative permittivity of pure ice is almost constant, when ignoring a weak temperature dependence, and amounts to approx. 3.2. For heterogeneous (mixed) materials consisting of more than one component the equivalent dielectric constant is a function of the permittivity of the single components, their volume fractions, their distribution along space and the polarization and wavelength of the interacting electromagnetic wave.","hasChildren":true,"hasParent":true,"name":"Dielectric Properties","selfAssesment":"Planned
"},{"code":"PP2-2-6-1","description":"The standard deviation of the surface height variation (or RMS height), denoted by s (or hRMS), describes the statistical variation of a random surface with height z(x). In case of an azimuthally symmetrical surface, the single-scale RMS height of the one dimensional case for discrete profile values is given by (1), where N is the number of samples, and z ̅ the mean surface height (2). \r\nAs roughness depends not only on the soil surface properties but also the wavelength λ of the electromagnetic signal, the roughness parameters are scaled by the wave number k. Hence, the electromagnetic roughness ks for surface roughness parameter s is (2π/λ)*s (3). In order to determine if a random surface may be considered as electromagnetically smooth, one common definition is given by the Rayleigh roughness criterion, where s < λ / 8*cosθ, or ks < 0.8, at incidence angle θ = 0. This criterion has been revised for the microwave region, where the wavelength is usually of the order of the RMS height, called the Fraunhofer roughness criterion, where s < λ / 36*cosθ, or ks < 0.2, at incidence angle θ = 0. Additionally, surfaces are considered as electromagnetically rough for 1 < ks < 3.","hasChildren":true,"name":"Vertical roughness component (RMS height)","selfAssesment":"Completed
"},{"code":"PP2-2-6-2","description":"The surface correlation length, denoted by l, is defined as the displacement ξ at which the surface correlation function p(ξ)= 1/e. Thus, l can be seen as the reference length up to which two points of one soil surface can be regarded as statistically independent from each other. If we imagine a perfectly smooth soil surface, l=∞ since every point on that surface correlates with all other points and can therefore be regarded as dependent from each other.\r\nAs roughness depends not only on the soil surface properties but also the wavelength λ of the electromagnetic signal, the roughness parameters are scaled by the wave number k. Hence, the electromagnetic roughness kl for surface roughness parameter l is kl=(2π/λ)*l.\r\nExperimental results indicate a weaker influence on the radar backscatter compared to the RMS height s.","hasChildren":true,"name":"Horizontal roughness component (correlation length)","selfAssesment":"Completed
"},{"code":"PP2-2-6-3","description":"The surface correlation function p(ξ) determines the degree of correlation between two lateral separated locations of one surface. Thereby, ξ is defined as displacement between two locations, (x, y) and (x', y') on the surface and given by (1).\r\nWith increasing separation between two locations on the surface p(ξ) decreases, and at a certain distance, the surface correlation length l, the heights at the two locations are considered statistically uncorrelated.\r\nThe surface scattering of electromagnetic waves can be simulated with various models. Depending on the observed roughness scale multiple surface scattering models are valid for specific roughness conditions. For example, one of the first surface scattering models for slightly rough surfaces, the small perturbation model (SPM), deals with roughness scales that are small relative to the wavelength and hence has validity conditions for ks < 0.3, kl < 3, and m < 0.3. Since then, various surface scattering models for computing the scattering and emission behavior of natural surfaces in the microwave region have been proposed, such as the Kirchhoff scattering model (KH), the geometric optics model (GO), the physical optics model (PO), or the integral equation model (IEM), to name the most common used in literature. For simulations of EM scattering at soil surfaces, assumptions of the functional forms of p(ξ) have to be made. The two most common forms for mathematically describing the surface correlation of natural surfaces are the exponential pE(ξ) and the Gaussian pG(ξ) correlation functions, defined by (2) and (3).\r\nFor some mathematically sophisticated surface scattering models, an x-Power correlation function p(x-Power)(ξ) can be assumed (4), with x as value between 1 and 2.\r\nIn literature, rather smooth surfaces are characterized by an exponential surface correlation function, while rather rough surfaces are characterized by a Gaussian surface correlation function.","hasChildren":true,"name":"Surface correlation function","selfAssesment":"Completed
"},{"code":"PP2-2-6-4","description":"The root-mean-square (RMS) slope m of a one dimensional height profile for one random surface is given by (1), with s as the standard deviation of the surface height variation (or RMS height), and p''(0) as the second derivative of the surface correlation function p(ξ), evaluated at ξ=0. Since p(ξ) is an even function, p''(0) is a negative quantity.\r\nFor modeling of electromagntic scattering at soil surfaces, assumptions of the functional forms of p(ξ) have to be made. The most common known forms are the exponential and Gaussian correlation functions. Additionally, some models allow the assumption of a x-Power correlation function, with x as value between 1 and 2. For the varying surface correlation functions, the RMS slope m is given by (2)-(4).\r\nIn literature, for L-band, the slope m should be lower than 0.3 or 0.4 in case of single scattering and bare soil surfaces with moderate RMS heights.","hasChildren":true,"name":"Surface roughness slope","selfAssesment":"Completed
"},{"code":"PP2-2-6-5","description":"In reality, one random surface has multiple roughness scales, since the commonly used surface description based on single-scale roughness parameters does not comprise all the properties of natural surfaces relevant for describing wave scattering. Depending on the wavelength λ of the microwave sensor the dimension of the surface roughness parameters s and l correspond to specific roughness scales. \r\nIn case of multi-scale roughness, the equivalent RMS height is a composite of the individual RMS heights at different roughness scales (1).\r\nA three-scale surface, as shown in Fig. 1, for example consists of a small-scale high-spatial frequency variation (c) ‘riding’ on top of the larger scales, the medium-scale perturbation (b) and the large-scale undulation (a).\r\nAt microwave frequencies, the centimeter scale is the scale of roughness of primary importance, since λ is on the order of centimeters to a few tens of centimeters. For natural surfaces it is very difficult to measure millimeter-scale roughness.","hasChildren":true,"name":"Single-scale & multi-scale roughness","selfAssesment":"Completed
"},{"code":"PP2-2-6","description":"Surface roughness defines the geometry between the pedosphere and the atmosphere (soil-air boundary).\r\nIn the field of microwave remote sensing, surface roughness affects scattering and emission characteristics of natural surfaces. The degree of roughness of a random surface is determined by statistical parameters, measured by the units of wavelength of the observing sensor. The two fundamental surface roughness parameters are the standard deviation of the surface height variation (RMS height) s, with its related surface correlation function p(ξ), and the horizontal surface correlation length l. Additional, a third roughness parameter, the root-mean-square (RMS) slope m, is important for some surface scattering models to simulate electromagnetic wave scattering of surfaces.\r\nSurface roughness determines the variation of surface height within an imaged resolution cell. The transition from smooth to rough is qualitative, and is function of both wavelength and incident angle. With decreasing frequency the soil surface appears rather smooth to microwave sensors. This results in the fact, that while one surface appears smooth when sensed at L-band (λ ≈23 cm), the same surface appears rough when sensed at X-band (λ≈3 cm). Hence, in the field of microwave remote sensing, the ‘effective’ surface roughness parameters are scaled by the wave number k= 2π/λ. Surface roughness can be observed at single or multi-scale.","hasChildren":true,"hasParent":true,"name":"Surface roughness","selfAssesment":"Completed
"},{"code":"PP2-2-7-1","description":"The Stokes vector is a four-element vector containing real-valued polarization combinations and is an alternative form of representing a full (=quad) polarimetric dataset, besides the complex-valued scattering matrix. Stokes vectors can be measured as real quantities and are preferred over the complex-valued Jones vector formalism when a coherent (phase-preserving) measurement system is absent. Stokes vectors can be used to form the 4x4 Mueller matrix for target scattering analyses, mostly used in the field of optics. First component of the Stokes vector is the sum of the co-polar fields and represents the total energy of the wave. Second component is the difference of the co-polar fields. Thrid component is the real part of the cross-correlation of the fields and fourth component is the imaginary part of it. The different polarization states can be represented by the Stokes vector and an O(3) elliptical transformation can be used to change the polarization basis, similar to the Jones vector where the SU(2) elliptical transformation is used.","hasChildren":true,"name":"Stokes Vector","selfAssesment":"Completed
"},{"code":"PP2-2-7-2","description":"The scattering matrix is a 2x2 square matrix containing four complex-valued polarization measurements (amplitude & phase) forming one full (= quad) polarimetric set of coherent observations. An often recorded set of polarizations is the combination: HH (horizontal receive - horizontal transmit), HV (horizontal recive - vertical transmit), VH (vertical receive - horizontal transmit) & VV (vertical receive - vertical transmit). The scattering matrix is fully suficient for describing scattering from coherent targets (dominating the resolution cell), but not for incoherent tragets (mix of scattering contributions in the resolution cell). For the latter, the coherency and the covariance matrices are the more appropriate descriptions of scattering from incoherent targets.","hasChildren":true,"name":"Scattering matrix","selfAssesment":"Completed
"},{"code":"PP2-2-7-3","description":"The covariance and coherence matrix are two 4x4 square matrices, which can be built out of the scattering matrix by a lexicographic and a Pauli target scattering vector. They are an alternative representation of a full polarimetric dataset allowing the analysis of incoherent targets (more than one dominant scatterer in the resolution cell) and the phenomenon of depolarisation (transformation of incoming fully polarised wave into a partially polarised wave by creating a variety of different types of polarizations during media interaction). These matrices can be converted into each other without loss of information (by unitary transformations), but not turned back into the scattering matrix due to averaging operations during formation of coherency or covariance matrices.","hasChildren":true,"name":"Covariance/Coherency matrices","selfAssesment":"Completed
"},{"code":"PP2-2-7-4","description":"Polarimetric decomposition techniques allow signal unmixing by polarimetry in order to separate different scattering contribution within one resolution cell, e.g. from soil & vegetation or snow, ice & bedrock. They can be either applied for the scattering matrix (coherent form - one dominant scatterer in the resolution cel) or for the covariance/coherency matrix (incoherent form - more than one dominant scatterer in the resolution cell). Decomposition techniques can be model- (physics) or eigen- (mathematics)-based. The eigen-based decomposition allows to diagonalize the coherency or covariance matrix in a diagonal eigenvalue matrix and a matrix of column eigenvectors. From eigenvalues and eigenvectors the polarimetric entropy, the scattering alpha angle and the polarimetric anisotropy. The polarimetric entropy is a matric for the degree of depolarization of the scattering event. The scattering alpha angle is an intrinsic scattering mechanism indicator. The polarimetric anisotropy informs about secondary scattering mechanism in evironments with high entropy. If the anisotropy is high only one secondary scattering mechanism is present, if it is low, more than one will occur.","hasChildren":true,"name":"Polarimetric decomposition techniques","selfAssesment":"Completed
"},{"code":"PP2-2-7-5","description":"All bi- or multi-polar (non-inert) media have the tendency to orient themselves in 3D-space if an external non-ionizing electro-magnetic field is excited on them. This orientation polarization is caused by negatively and positively charged areas within the media, for instance due to charges of the different molecules and atoms building up the media, under the premise that the media is able to rotate (partly) freely and is not completely fixed. Molecules of liquid water are a prime example. Here the two positively charged hydrogen atoms are oriented in a 105-degree configuration to the negatively charged oxygen atom, forming a slightly charged bi-polar medium that orients itself under electromagnetic radiation treatment, especially at the frequency range of microwaves and millimeter-waves.","hasChildren":true,"name":"Orientation polarisation of media","selfAssesment":"In progress
"},{"code":"PP2-2-7-6","description":"Polarimetric coherences are complex-valued polarimetric correlation coefficients assessing the redundance between different polarimetric observations informing about their divergence in information. They can be formed among mutual polarimetric observations showing their degree of correlation. The polarimetric coherence consists of a magnitude, ranging between zero (no correlation) and one (identical), and a phase information, running from -180° to 180°. Typically polarimetric coherences are calculated between the co-polarimetric (HH, VV) channes, as well as the cross-polarimetric channels (HV, VH). The latter polarimetric coherence assesses the system noise inherent in the recorded polarimetric data, if a monostatic systems (transmitting and receiving sensor on the same sensing platform) is used for acquisition.","hasChildren":true,"name":"Polarimetric coherences","selfAssesment":"Completed
"},{"code":"PP2-2-7-7","description":"The polarisation ellipse and the Jones vector formalism are the geometrical (three real-valued angles) and algebraic (amplitude & phase) formalisms to describe polarisation states of an electromagnetic wave. The ellipse has an orientation, an ellipticity and absolute phase angle. The three angles are integrated in one mathematical ellipse formulation that can represent linear, elliptic and circular polarisation states. The Jones vector formalism is an algebraic formulation allowing all calculus available in linear algebra. Both representations (polarisation ellipse & Jones vector) can be converted into each other seemlessly with a simple elliptical basis (special unitary SU(2)) transformation.","hasChildren":true,"name":"Polarisation ellipse / Jones vector formalism","selfAssesment":"Completed
"},{"code":"PP2-2-7-8","description":"The concept of polarisation synthesis is based on the mathematical fact that a set of polarimetric measurements in one basis, e.g. H,V, can be converted into any other polarimetric basis, by a mathematical transformation. A basis set is a set of four polarisations. Each set is orthogonal, like LC (left-circular), RC (right-circular). The striking point is that only one set of polarimetric measurements in one basis needs to be recorded and the transformation in other polarimetric bases is done in a post processing step afterwards. There is no need to measure all bases, which is quite complicated in terms of engineering for elliptical and circular polarisation states.","hasChildren":true,"name":"Polarisation synthesis","selfAssesment":"Completed
"},{"code":"PP2-2-7","description":"Polarimetry is the technique to evalute the physical phenomenon of polarisation including the measurement, the processing and the interpretation of the polarisation state of an electromagnetic wave. Polarization states are described by the scattering elipse and the Jones Vector formalism. Especially the polarization states after interaction with the media under investigation are mostly investigated to estimate media properties and states. The mostly observed fully polarimetric observation basis is H,V up to now with the single observations: HH HV, VH, VV. The concept of polarization synthesis allows to acquire fully polarimetric observations in one basis (e.g. H,V) and transform them into any other orthgonal basis (e.g. left, right circular) by a mathematical transformation in post processing. Polarimetric States are stored in different mathematical formats: Scattering matrix, polarimetric coherences , Stokes vector, Pauli-vector, lexicographic vector, coherency and covariance matrices. These mathematical representations can be decomposed according to the contained elementary scattering mechanisms in the recorded signal. The so-called polarimetric decomposition technique allow signal unmixing for differnt scattering components (e.g. from soil & vegetation). The techniques range from mathematics-based until physics-based concepts and are developed since decades starting with Huynen in 1970.","hasChildren":true,"hasParent":true,"name":"Polarimetry","selfAssesment":"Completed
"},{"code":"PP2-2","description":"A number of interactions are possible when electromagnetic energy encounters matter, whether solid, liquid or gas. In Earth Observation there are two main interactions: atmospheric and with target. Atmospheric interaction: In radar remote sensing, atmospheric interactions are limited due to the long wavelengths compared to the size of the atmospheric particles. The fact that microwaves interact with object at least as big as the wavelength is one of the greatest advantages of microwave remote sensing, since at larger wavelengths atmospheric particles are almost transparent to the signal and microwave sensors are independent from the time of day (day or night) and weather conditions. Water clouds can interfere with the radars operating below 2 cm in wavelength. The effects of rain can be generally ignored at wavelengths above 4 cm. For longer wavelengths (above 20 cm), an effect called Faraday rotation caused by the ionosphere, i.e., free charges (electrons) and the Earth’s magnetic field, can lead to a rotation of the polarization plane. Target interaction: The radar interaction with the object is a result of both radar system parameters (frequency, polarization, acquisition geometry) and the physical properties of the object (dielectric constant, i.e., water content; geometrical properties, i.e., the roughness, shape and orientation of the scatterer). Overall, various types of interactions can be distinguished – scattering, diffraction, and reflection – all describing the same process of wave interaction but at different scales.","hasChildren":true,"hasParent":true,"name":"Interaction of microwaves with matter","selfAssesment":"Completed
"},{"code":"PP2-3-1-1","description":"The goal of an radar antenna is to direct and receive the transmitted and backscattered signal in a specific angular direction. The antenna gain describes the directional sensitivity of the antenna. It is a dimensionless quantity that is constant for a specific antenna.","hasChildren":true,"hasParent":true,"name":"Antenna gain","selfAssesment":"Planned
"},{"code":"PP2-3-1-2","description":"The antenna radiation pattern shows the direction in which the antenna transmits and receives the energy in space, as well as the strength of this radiation. It is a function of angles and consists of different lobes, in which the signal is directed and received. There are two principal representation of the antenna patterns: field and power patterns, which are a function of the electric and magnetic fields of the energy being radiated.","hasChildren":true,"name":"Antenna pattern","selfAssesment":"In progress
"},{"code":"PP2-3-1","description":"Antenna is a device that radiates electromagnetic energy and collects it during reception.","hasChildren":true,"hasParent":true,"name":"Radar antennas and antenna calibration","selfAssesment":"Planned
"},{"code":"PP2-3-10-2","description":"The radargrammetric equation follows a similar principle as the stereoscopic equation, except that it uses the radar geometry. The radargrammetric observation equation allows the retrieval of 3D information about a target, based on the determination of the sensor-object stereo model. It estimates the coordinates the intersection of the two radar rays coming from the two different sensor positions with different look angles, using the coordinates of the satellites position and satellite velocity. The radargrammetric equation can be adapted in order to retrieve 3D information in layover areas (e.g. urban areas).","hasChildren":true,"name":"Radargrammetric equation","selfAssesment":"Completed
"},{"code":"PP2-3-10","description":"Radargrammetry is the technique for extracting three-dimensional information from radar images. It applies photogrammetric principles to synthetic aperture radar (SAR) images. By viewing an object from different positions separated by a baseline, the appeared object position will vary slightly (denoted parallax). The disparities for each position on the object are related to its x-y-z coordinates. In radargrammetry, such disparities are computed for an entire image. The result is the terrain elevation from the measured parallaxes between two (or more) images, acquired at different angles. Radargrammetry requires at least two SAR images acquired from different positions, normally across-track due to the configuration of a side-looking SAR. Same-side stereo-pairs with intersection angles in the range of about 10 – 20° have been a feasible compromise between reasonable geometric disparities and the accuracy of estimated heights. In general, the disparities can be estimated with higher accuracy as the angle of intersection increases (as the stereo exaggeration factor increases). However, the same points must be recognized in all images, and it is hence required that the images are as similar as possible. This improves the image matching and it is best achieved with small intersection angles, which furthermore decreases radiometric differences. \r\nA general procedure for generating an elevation model from stereo-pairs is applicable for radargrammetry when optical stereo images are replaced with the backscatter intensity of SAR images. One image is selected as reference and the other(s) is coarsely registered to the reference, e.g., by using the attached meta-data. The same points are then located in both images using image matching. A common matching criterion is the cross correlation coefficient. Then, spatial point intersections are computed, which is the least square approach to find the intersection points of SAR range circles as defined from the matched image pixels. The computed intersections result in a point cloud that finally is interpolated to a consistent elevation raster. The entire process is extensive and computationally expensive, and normally a dedicated software is required. \r\nRadargrammetry with images acquired from opposite sides have been little investigated, and was first limited to stereoscopic viewing. Some opposite-side research was later presented with limited outcomes under certain conditions. Most applications today will not consider opposite-side radargrammetry, since the alternatives are usually better. Same-side radargrammetry performs better than opposite-side, while interferometric SAR that is based on phase differences, may be even more accurate. One advantage of radargrammetry is however, that it remains less affected by atmospheric disturbances compared to interferometric SAR, because it is using the amplitude images.","hasChildren":true,"hasParent":true,"name":"Radargrammetry (same-side and opposite-side)","selfAssesment":"Completed
"},{"code":"PP2-3-11-1","description":"Differential Synthetic Aperture Radar Interferometry (DInSAR) aims the determination of deformation of the Earth’s surface that happened between two or more complex-valued SAR acquisitions.\r\nThe phase of an interferogram issued from the complex multiplication of a SAR image with the complex conjugate of a second SAR image contains five distinct components, or layers of information: (1) Two phase components arise from the geometrical baseline (slightly different position of both sensor positions): (1a) a topographical information representing the surface relief, (1b) “flat earth” pattern coming from the orbital distance of both sensor positions.\r\n(2) Two phase components result of the temporal baseline (time between both acquisitions): (2a) a deformation component, representing a possible displacement of the Earth’s surface between both acquisitions, (2b) an atmospheric component coming from different atmospheric conditions between both acquisitions. (3) A phase component corresponding to intrinsic sensor noise \r\n\r\nBoth parameters related to the temporal baseline can be retrieved using DInSAR on repeat-pass acquisitions. DInSAR cannot be used with single-pass interferometry (e.g. both acquisitions acquired at the same time).\r\nThe deformation component of the interferometric phase corresponds to the modification of the phase of the second SAR image compared to the first due to an additional range difference between the sensor position and the Earth’s surface that is induced by the motion of the Earth’s surface towards or away from the initial sensor position.\r\nUsing DInSAR, the phase components related to the geometrical baseline can be eliminated from the interferogram using an existing DEM and orbit information, or an additional interferogram showing no deformation. After DInSAR processing, neglecting the remaining sensor noise, only the deformation and atmospheric components remain. The resulting deformation image is called differential and is characterized by color bands, or fringes, from whom the amount of the displacement can be retrieved. \r\nDInSAR can be used for mapping displacements and deformations due to earthquakes, landslides, or other geophysical processes inducing deformation of the Earth’s surface.\r\nUsing only one differential interferogram, mainly sudden and large scale changes between two acquisition can be mapped and quantified. However, the atmospheric phase component remains and may induce interpretation errors if it is not possible to eliminate it through e.g. precise weather models. Techniques of differential interferogram stacking (e.g. Persistent Scatterer Interferometry and Small-Baseline Subset) have been developed for long-term deformation monitoring which allow to filter the atmospheric phase component out.","hasChildren":true,"name":"Differential Synthetic Aperture Radar Interferometry (DInSAR)","selfAssesment":"Completed
"},{"code":"PP2-3-11-2","description":"The Permanent or Persistent Scatterer (PS) approach allows the estimation of deformation time-series related to point-wise, high coherent scatterers on the ground based on processing long sequences of SAR data.\r\nPersistent Scatterer Interferometry (PSI -sometimes also called Permanent Scatterer Interferometry) is a particular DInSAR technique. It exploits multiple SAR images acquired over a specific area in order to retrieve the deformation phase component over time. In general, a minimum number of 15 SAR acquisitions is needed for PSI processing. Due to the large number of necessary acquisitions, the deformation component of the interferometric phase observations can be estimated very precisely (in the order of a few mm/yr) and other phase contributions such as atmospheric disturbances and topographic height differences can be better estimated and removed.\r\nPSI rely on so called Persistent Scatterer that are targets showing coherent phase behavior in time. Such targets are usually found on man-made structures such as buildings or bridges, or very stable features such as rocks. PSI is a technique that is therefore mainly used over urban or semi-urban terrain. Usually, PSs are selected based on their amplitude and phase power spectrum stability over time.\r\nThe main outcomes of a PSI analysis are a deformation velocity map and the displacement time-series of the single point targets, or PSs. The velocity map represents the deformation rate of the detected PSs in Line-of-Sight of the sensor, generally in mm/yr. Usually, subsidence, e.g. target moving away from the sensor, is represented in red, stable PSs in green and uplift, e.g. PSs moving toward the sensor in blue. The displacement time-series show for each PS the amount of the deformation, usually in mm, over the whole period of observation. Different phase model can be defined in order to retrieve the best possible estimate of the deformation, considering also seasonal displacements or breakpoints in the time-series.\r\nPerforming PSI analysis in both ascending and descending directions allows the fusion of the results in order to retrieve vertical and East-West component of the deformation. North-South deformation components cannot be retrieved due to the orbit configuration of the SAR satellites.\r\nPSI finds use in a large range of thematic applications related to subsidence and long-term change monitoring, such as infrastructure monitoring, groundwater reservoir monitoring, monitoring of mining areas, landslide inventory and monitoring, as well as volcanology.","hasChildren":true,"name":"Permanent Scatterer Interferometry (PSI)","selfAssesment":"Completed
"},{"code":"PP2-3-11-3","description":"Along-track InSAR (AT-InSAR) is a special mode of interferometric SAR (InSAR) where the individual SAR images have been acquired from the same flight track. With virtually identical geometric configuration of the individual SAR images, the measured phase difference is dominated by temporal changes occurring between the acquisitions. Consequently, AT-InSAR can be used to measure the displacement and/or radial velocity of targets on the ground, with the temporal offset between the acquisitions determining the time scale of the measurements. AT-InSAR can be implemented using one or more SAR sensors, in both single-pass and repeat-pass configurations, accommodating various needs. Using at least two sensors in a single-pass configuration allows the measurement of relatively high velocities, e.g., for vehicles and ocean waves. Conversely, using at least one sensor in a repeat-pass configuration allows the measurement of low velocities or displacements, e.g., for glaciers and due to volcanoes, earthquakes, subsidence, and landslides.","hasChildren":true,"name":"Along-Track Interferometry","selfAssesment":"Completed
"},{"code":"PP2-3-11-4","description":"Across-track InSAR (XT-InSAR) is a special mode of interferometric SAR (InSAR), where the individual SAR images have been acquired from slightly different look directions. The measured phase difference contains information about the elevation of the targets on the ground, but it can also be affected by temporal changes between the individual SAR images. XT-InSAR can be implemented using one or more SAR systems in both single-pass and repeat-pass configurations. To mitigate temporal change between acquisitions, the XT-InSAR configuration is selected based on the intended application and frequency used by the system. If a single SAR sensor is used in the repeat-pass mode, temporal stability can be achieved either by a selecting a lower frequency and focussing on the larger, more stable targets (e.g., P-band, 435 MHz InSAR in forests) or by selecting a higher frequency and focussing on already stable environments (e.g., X-band, 9.65 GHz XT-InSAR in urban environments). Using two or more SAR sensors in a single-pass, tandem configuration, it is possible to measure elevation of temporally instable targets using higher frequencies, as demonstrated by the SRTM and TanDEM-X systems over vegetated areas and ocean.\r\nReferences: bamler/hartl, one on SRTM or TDM for DEM, one on BIOMASS for forestry, one on Sentinel-1 for urban areas, one on TDM on vegetation","hasChildren":true,"name":"Across-Track Interferometry","selfAssesment":"Completed
"},{"code":"PP2-3-11-5","description":"Small Baseline Subset (SBAS) is a well-known technique of differential synthetic aperture radar (SAR) interferometry for the generation of surface deformation time-series by processing large sequences of SAR data acquired over the same region on Earth. \r\nThe method requires the preliminary generation of pairs of SAR images collected by slightly different orbital positions at different acquisition times. The phase difference of the interferometric SAR data pairs is extracted. The two-dimensional phase maps contains different contributions, but principally a component due to the terrain height of the observed area. The DInSAR technique relies on the estimation of the deformation of the terrain between the two interfering SAR images (i.e., the so-called master and slave images). To achieve this task, the phase contribution related to the terrain height is simulated and subtracted to the interferometric master/slave phase difference. The obtained differential SAR interferometric phase contains a direct information on the occurred deformation. Once a sequence of interferometric SAR data pairs is selected, the SBAS technique allows generating the time-series of the deformation of the terrain. The processing steps are essentially: i) the extraction of the full phase of the DInSAR interferograms, i.e., the phase unwrapping steps of the DInSAR interferograms, ii) the inversion of the sequence of unwrapped DInSAR phases, iii) the geocoding of the deformation maps from radar coordinates to geographical coordinates.","hasChildren":true,"name":"Small Baseline Subset","selfAssesment":"Completed
"},{"code":"PP2-3-11","description":"Synthetic aperture radar (SAR) interferometry, or simply InSAR, is a remote sensing technique utilising the phase difference between two or more complex-valued SAR images. Most modern SAR systems are capable of measuring both the intensity and the phase of the reflected signal, where the latter carries information about the distance travelled by the signal. Consequently, the different of phase information of two successive SAR images over a specific area contains a distance information. \r\n\r\nThe phase difference measured between two SAR images is called the interferometric phase. The interferometric phase image is an interferogram. The interferometric phase is a function of the geometry and timing of the individual SAR acquisitions. Different geometric and temporal configurations enable different applications. \r\n\r\nIf the SAR acquisitions are made from different angles and without significant temporal change of the scene, InSAR can be used to create digital elevation models (DEMs) of the Earth, as demonstrated by the NASA/JPL Shuttle Radar Topography Mission (SRTM). This configuration is called across-track interferometry. If the individual SAR acquisitions are made at different times in the same geometric configuration, i.e. in an along-track or differential interferometric configuration, then InSAR can be used to measure radial velocity of targets and to assess displacements caused by, e.g., volcanoes and earthquakes. The variation of the temporal baseline allows determining velocities ranging from several meters per second to a few millimeters per year. While standard differential interferometry can be used to retrieve changes that happened between two SAR acquisitions, differential interferometric stacking techniques, such as Persistent Scatterer Interferometry (PSI) and Small Baseline Subset (SBAS), are used to monitor deformation over a longer period of time by stacking multiple differential interferograms and filtering out the atmospheric phase contribution in order to retrieve very accurate deformation of the ground and its infrastructures.","hasChildren":true,"hasParent":true,"name":"Principles of Synthetic Aperture Radar Interferometry (InSAR)","selfAssesment":"Completed
"},{"code":"PP2-3-12","description":"Synthetic Aperture Radar (SAR) tomography uses the principle of the azimuth synthetic aperture in the elevation direction. Instead of using different positions of the radar sensor along the flight path in order to increase the aperture length, SAR tomography uses multiple passes of the radar sensor over the same area at different elevation positions, i.e. orthogonal to the azimuth-range plane, on different orbits. Similar to the synthetic aperture in azimuth direction, a larger aperture in cross-range elevation direction allows increasing the resolution in the elevation direction. Therefore, the echoes are focused in the whole 3D space (azimuth, range and elevation), and scattering contributions can be separated at different heights, even if they are situated in the same azimuth-range cell.\r\nSAR tomography exploits therefore these multiple passes of the radar sensor at different orbit positions (orbits heights) in order to retrieve 3D information about volumetric targets, where the 2D SAR signals often overlaps due to the typical side-looking geometry. \r\nThe result of tomographic processing is a tomogram, i.e. it is a hologram of a specific area of interest, usually represented as a tomographic profile along a particular direction. Using polarimetric data, the different scattering mechanisms happening at different heights can be represented in the profile, allowing a full understanding of the volumetric information and backscattering processes.\r\nUnlike the azimuthal aperture, the tomographic aperture is achieved by repeat-pass acquisitions, the antenna having to come back over the area. An important parameter is therefore the target coherence, that may decrease by longer repeat-pass cycles. In general, a 1-4 day revisit cycle is preferred for tomographic applications.\r\nSAR tomography finds applications in the imaging and monitoring of cities and single buildings, as well as in height and biomass estimation of forest stands. The use of longer wavelength that guaranty the penetration into canopy volumes allows a better retrieval of the complete forest structure and its undergrowth.","hasChildren":true,"hasParent":true,"name":"Synthetic Aperture Radar (SAR) tomography","selfAssesment":"Planned
"},{"code":"PP2-3-13","description":"Historically imaging in the microwave frequency domain was done either using passive imaging techniques (with solely recording capacities of the sensor) or using active imaging techniques (with transmitting and recording capacities of the sensor). Both imaging modi were developed in parallel for a long time in electrical engineering of microwave sensors for space-borne missions, but are combined in more recently launched missions.\r\nWith the concept of active and passive microwave imaging, both techniques are fused to record electromagnetic waves in an active (sending & receiving) and a passive (only receiving) mode either simultaneously on one carrier platform or with negligible time lag on different platforms.\r\nThe active sensor is normally a Real Aperture Radar (RAR, scatterometer) or Synthetic Aperture Radar (SAR), while the passive sensor is a radiometer or synthetic aperture radiometer. Both acquisition modes can be operated on a single platform or on different platforms depending on monolithic or distributed platform systems. The benefit of fusing both modi is in the higher spatial resolution of the active imaging modes combined with the higher sensitivity of the passive modes for intrinsic (non-structural) media properities, like permittivity or salinity.\r\nSatellite missions with active-passive imaging capabilities are the NASA missions AQUARIUS (operation started in 2011 terminated in 2015) and SMAP (operation started in April 2015 and ceased for active sensor in July 2015). Currently (2021), no dedicated active-passive microwave satellite mission is operating in orbit.","hasChildren":true,"name":"Active-Passive microwave imaging","selfAssesment":"Completed
"},{"code":"PP2-3-2","description":"Systems measuring both amplitude and phase of the incident electromagnetic radiation.","hasChildren":true,"name":"Coherent and active systems","selfAssesment":"Planned
"},{"code":"PP2-3-3","description":"This acquisition mode records only the incoming electromagnetic radiation emitted from the Earth. Radiometer instruments conduct passive microwave imaging. The energy budget of emitted radiation (from Earth) is significantly smaller than from instrument-generated, transmitted electromagnetic waves, used in the active microwave imaging mode. Hence, the signal to noise ratio is significantly worse for passive microwave imaging forcing a longer intergration time for robust signal recording. This results in a coarse spatial resolution of radiometer images (in the order of kilometers).","hasChildren":true,"hasParent":true,"name":"Passive microwave imaging","selfAssesment":"Completed
"},{"code":"PP2-3-5","description":"There are two types of imaging radar apertures: real (usually called RAR or SLAR for side-looking airborne radar or SLR for side-looking radar) and synthetic aperture radar (SAR). The SLAR imaging system uses a long antenna mounted on a platform. The synthetic aperture is used in space remote sensing applications. RAR is a radar system where the antenna beamwidth equals to the physical length of the antenna. It operates in a side-looking configuration, left or right with reference to the flight direction. It is an active, all-weather, day/night remote sensor onboar an airborne platform. Both Real Aperture and Synthetic Aperture Radar are side-looking systems having antennas aimed to the right or left of the flight path. The length of the antenna together with wavelenght determines the resolution in the azimuth direction, i.e. it is proportional to the distance to the object and inversely proportional to the length of the radar antenna.","hasChildren":true,"name":"Real Aperture Radar (RAR)","selfAssesment":"In progress
"},{"code":"PP2-3-6","description":"In contrary to a real aperture, a synthetic aperture results from an aperture “synthesis”. Synthetic aperture were built in order to overcome the limitation of real aperture and therefore enhance the resolution in azimuth direction. It uses the subsequent positions of a real aperture sensor during its forward motion along the azimuth direction to create a synthetic longer antenna. Via the analysis of the Doppler shift induced by the different echoes of the illuminated objects in the different positions of the real aperture, the azimuth resolution can be improved.","hasChildren":true,"name":"Principles of Synthetic Aperture Radar (SAR)","selfAssesment":"In progress
"},{"code":"PP2-3-7-1","description":"In navigation, the azimuth corresponds to an angle measured from a north reference or a meridian, usually in clockwise direction. In SAR terminology, the azimuth direction corresponds to the direction in which the radar platform moves. The azimuth direction is also called along-track direction and is parallel to the flight path of the radar instrument. In a SAR image, the azimuth position of an object corresponds to its relative position in the field of view of the antenna following the radar’s line of flight. The azimuth direction is perpendicular to the range direction, which corresponds to the look direction of the radar antenna. The azimuth plays an important role in the definition of the azimuth resolution of a SAR sensor. Contrary to the range resolution, the azimuth resolution is independent of the distance between sensor and illuminated area and is constant. The azimuth resolution of a radar system corresponds to the beam width of the antenna on the ground, but can be improved using multiple successive real aperture acquisitions in order to form a longer, synthetic, aperture. This implies that an object on the ground is illuminated for a longer time and from different platform positions along the azimuth direction, inducing a Doppler frequency shift at the target. The use of specific synthetic aperture acquisition modes that steer the antenna along the azimuth direction, such as Spotlight mode, improve additionally the resolution in azimuth direction.","hasChildren":true,"name":"Azimuth direction","selfAssesment":"Completed
"},{"code":"PP2-3-7-2","description":"The range direction corresponds to the direction perpendicular to the flight direction of a radar system. It is also called across-track direction. One distinguishes between slant range, i.e. range in a radar geometry, and ground range, i.e. range projected onto the Earth's surface, and between near and far range (situated farther away from the sensor and showing shallower looking angle than in near range due to viewing geometry).","hasChildren":true,"name":"Range direction","selfAssesment":"In progress
"},{"code":"PP2-3-7-3","description":"The incidence angle is the angle between the incident radar beam on a surface and the normal to a reference surface. Generally, it is distinguished between the local incidence angle and the incidence angle to the ellipsoid. The local incidence angle considers the normal to the surface at target location, i.e. it considers the local topography. The incidence angle to the ellipsoid corresponds to the angle between the incident radar beam and the normal to the local ellipsoid, regardless of the local slope and terrain. \r\n\r\nFor a flat surface and neglecting the Earth’s curvature, the incidence angle corresponds to the angle between the incident radar beam and the vertical, and it equals the look angle of the sensor, which characterizes the angle between the nadir view and the radar beam. Considering a flat surface, the incidence angle varies continuously within a SAR scene: it increases from near to far range. Depending on the considered sensor and acquisition modes, variations of the incidence angle up to 20° can be observed between near and far range.\r\n\r\nThe incidence angle has an influence on the radar backscatter intensity. Considering a surface with diffuse reflection, increasing incidence angles lead to decreasing backscatter intensities. This effect is less pronounced for rough than for smooth surfaces. A change in incidence angle may also induce a change in the occurring backscattering mechanisms or geometric distortions of the image. For example, for high incidence angles, terrain distortion due to the side-looking geometry is reduced. Due to the high dependency of the radar backscatter from the incidence angle, the choice of the optimal configuration should happen depending on the application. For example, whereas low incidence angles are more sensitive to biomass in forestry applications, higher incidence angle are preferred for distinguishing different forest types due to their structural characteristics.","hasChildren":true,"name":"Incidence Angle","selfAssesment":"Completed
"},{"code":"PP2-3-7-4","description":"The beam sent out by the radar antenna (SLAR for side-looking airborne radar or SLR for side-looking radar) illuminates an area on the targeted object. The footprint of an antenna is traditionally defined to be the area on the surface within the field of view subtended by the beamwidth of the antenna gain pattern.","hasChildren":true,"name":"Antenna footprint","selfAssesment":"Planned
"},{"code":"PP2-3-7-5","description":"The spatial resolution of a synthetic aperture radar (SAR) system is the maximal distance between two targets, which are indistinguishable in the SAR image. SAR spatial resolution is determined individually in the two principal SAR image directions: ground range and azimuth (along-track). Ground range resolution for a SAR system is derived from slant range (across-track) resolution, by projecting it onto the ground surface using the incident angle, i.e., the angle between the line-of-sight and the ground surface normal. It is thus range-dependent, with finer resolution available in far range. Assuming adequate signal processing, slant range resolution of a SAR system is proportional to the speed of light and inversely proportional to the system bandwidth, i.e., the width of the used frequency interval. This caused by the fact that each individual frequency provides an independent measurement of the slant range, so a larger bandwidth implies more independent measurements contributing to the final slant range estimate. Similar principles apply to the azimuth direction. Assuming adequate signal processing, the SAR azimuth resolution is proportional to the along-track velocity of the SAR sensor and inversely proportional to the pulse repetition frequency (PRF) of the system. A lower interval between the consecutive pulses (higher PRF) results in better azimuth resolution due to faster sampling, but at the cost of range ambiguities occurring when echoes from one pulse are recorded after the next pulse has been transmitted.","hasChildren":true,"name":"Synthetic Aperture Radar (SAR) spatial resolution","selfAssesment":"Completed
"},{"code":"PP2-3-7","description":"The Synthetic Aperture Radar (SAR) sensor is usually mounted on an aircraft or satellite. The instrument altitude above a reference surface stays constant over time, a condition that is easier to achieve for satellite sensors that stay on the same orbit than for aircrafts that are subject to atmospheric conditions. The sensor moves on a straight flight path, which is called the azimuth direction. It corresponds to the flight direction.\r\nSAR systems acquire information in oblique view, the antenna pointing sideways down to the ground. Most satellite systems use an antenna looking to the right side of the instrument. The ground area illuminated by the radar beam is called antenna footprint. As the sensor moves along the azimuth direction (along-track), the continuous strip of the ground area represented by the successive antenna footprints is called swath. \r\nThe looking direction of the SAR antenna is called range direction. It is often perpendicular to the azimuth direction (i.e. across-track), but can also present slightly differences depending on the acquisition mode. The angle between the nadir view and the range direction is called incidence angle.\r\nThe original SAR image is displayed in what is called slant-range geometry, i.e., it is based on the actual distance from the radar to each of the respective features in the scene. In the slant range direction, each point target’s backscatter is represented as a function of the time delay between the transmission of the electromagnetic pulse and its reception back at the sensor. This range depending representation induces geometric distortions in the SAR image. One distinguishes between near and far range: targets situated in near range are closer to the nadir direction and closer to the sensor than targets situated in far range. The image representation of targets is also more compressed in near range than in far range.\r\nThe slant-range representation can be converted in ground range representation, by projecting the image features orthogonally to a ground reference, allowing a proper planimetric position of the targets relative to one another.\r\nThis acquisition geometry allows the distinct mapping of scatterers corresponding to their respective distance to the sensor. It causes also geometric distortions in the radar image, i.e., relief displacement (foreshortening and layover) and shadow.","hasChildren":true,"hasParent":true,"name":"Synthetic Aperture Radar (SAR) geometric configuration","selfAssesment":"Completed
"},{"code":"PP2-3-8-2","description":"The local incidence angle is the angle between the incident radar wave and the normal to the scattering surface at target location. In case of a flat terrain, the local incidence angle equals the incidence angle. For a terrain with local slope, the local incidence angle differs from the incidence angle (for slopes facing towards the sensor, it is smaller than the incidence angle).","hasChildren":true,"name":"Local Incidence Angle","selfAssesment":"Planned
"},{"code":"PP2-3-8-3","description":"Foreshortening is a geometric distortion occurring in the SAR image due the side-looking geometry of imaging radar sensors. It occurs principally in SAR images of mountainous areas, on slopes oriented towards the sensor. These slopes appear in the radar image as if being compressed. Due to the side looking geometry and the mapping of the SAR image based on range and time measurement, the distance in the SAR image between two points situated on a slope facing the sensor appears smaller than it is in the reality and than the same distance between two points situated in flat area. This results in a compression of the radiometric information of the slope. The resulting foreshortening area is brighter in the SAR image than its surroundings, as it compresses in a few pixels the backscatter information of the whole slope. \r\n\r\nForeshortening occurs for slopes whose inclination is smaller than the look angle of the radar antenna. Due to the variation of the look angle in the SAR image, the foreshortening is more pronounced in near range than in far range. Foreshortening is therefore greater for small incidence angles. The extreme case of foreshortening happens when the slope inclination is equal to the look angle: in this case, the whole slope is mapped in one pixel of the SAR image, which results in a very bright line. When the slope inclination becomes higher than the look angle, layover occurs.","hasChildren":true,"name":"Foreshortening","selfAssesment":"Planned
"},{"code":"PP2-3-8-4","description":"Layover is a geometric distortion occurring in the SAR image due the side-looking geometry of imaging radar sensors. It occurs principally in SAR images of mountainous areas, on steep slopes oriented towards the sensor. These slopes appear in the radar image as if being flipped over. Due to the side looking geometry and the mapping of the SAR image based on range and time measurement, the summit of a mountain is closer to the sensor that the foot of that same mountain, on the side facing the sensor. The signal from the top comes back to the sensor before the signal from the foot and is therefore mapped in nearer range than the foot of the mountain. Making an analogy to sound waves, an echo from the top of the mountain will arrive sooner at the sensor than an echo from the bottom of the mountain. Due to this “leaning over” effect, the sensor facing slope signal usually overlaps with ground signal, and a “ghost” effect appears as both signals overlap. The resulting layover area is usually very bright in the SAR image, as it superimposes backscatter signals from the slope of the mountains and the ground before it. When considering SAR images of urban areas, even up to three signals may overlap in the layover area: ground, building façade and (part of the) roof area.\r\n\r\nLayover occurs for slopes whose inclination is larger than the look angle of the radar antenna. Due to the variation of the look angle in the SAR image, layover occurs more often in near range than in far range. Layover is therefore greater for small incidence angles. It represents the extreme case of foreshortening, when the slope inclination becomes higher than the look angle.","hasChildren":true,"name":"Layover","selfAssesment":"Planned
"},{"code":"PP2-3-8-5","description":"Radar shadow is a geometric distortion occurring in the SAR image due the side-looking geometry of imaging radar sensors. It occurs principally in SAR images of mountainous areas, on steep slopes oriented away from the sensor. In optical imagery, a shadow area is an area characterized by less sun illumination whose reflection is therefore weaker. In SAR imagery, shadow areas receive no signal. It occurs for example at the backside of mountains or buildings. The areas facing away from the sensor are not illuminated by the SAR sensor, as they are “hidden” from it. Also, ground area situated behind high object with respect to the sensor position are not illuminated and are situated in the radar shadow. They receive no signal information and send no information back to the sensor. Those areas are therefore very dark in SAR images. The size of the shadow area in range direction corresponds to the time delay between the last echo from the top of the mountain and the first echo of the far edge of the shadow region, where the area is not hidden from the sensor anymore.\r\n\r\nRadar shadow occurs when the slope inclination of the slope facing away from the sensor is larger than 90° minus the antenna look angle. As for the other geometric effects, the size of a shadow area for the same object depends on its situation in the image. But, unlike as for foreshortening and layover, shadow is more pronounced in far range than in near range, i.e. large incidence angles produce more shadow.\r\n\r\nA SAR image may show a return signal in a shadow area: this is principally due to internal sensor noise and does not correspond to any target return signal.","hasChildren":true,"name":"Shadow","selfAssesment":"Planned
"},{"code":"PP2-3-8","description":"Synthetic Aperture Radar (SAR) backscatter is determined both by dieletric and geometric properties of the illuminated target. While the water content of the target plays an important role, its surface roughness determines the scattering mechanisms and the amount of incoming signal sent back to the sensor.\r\nDepending on its characteristics but also on the considered wavelength, a surface appears more or less rough. On smooth surfaces, specular reflection occurs, meaning that most of the incoming signal will be reflected away from the sensor. For rough surfaces, diffuse reflection occurs, meaning that part of the signal is scattered back to the sensor, the amount of it depending on different surface roughness parameters. \r\nDepending of the observed target and surface, single or multiple scattering mechanisms occur. A particularly important scattering mechanism is the double bounce, which occurs generally at two perpendicular surfaces (e.g. ground and building wall). Through two successive specular reflections, the whole signal comes back to the sensor.\r\nDue to the side-looking geometry of SAR systems and the range dependent image representation, specific additional effects occur and affect the backscatter intensity. Whereas a flat terrain only appears more compressed in near range and more stretched in far range, larger geometric distortions appear for terrain with more topography (e.g. mountains) or high objects (e.g. trees, buildings). This relief displacement is caused by the target’s elevation. A high elevated object is closer to the sensor than the ground below it. Due to the image formation in range direction depending on the distance between sensor and targets, its signal comes back sooner to the sensor and it is represented in the SAR image in nearer range than the ground below it. High objects in the SAR image are therefore displaced horizontally toward the radar antenna. This horizontal displacements contrast with the radial displacement observed in optical imagery due to central projection. Furthermore, such objects hide part of the ground below them, which do not receive any signal and cannot scatter information back. Three particular geometric distortions exist: foreshortening, layover and shadows.\r\nDepending on the illuminated target, different scattering mechanisms occur in combination with geometric distortions, which makes the interpretation of the SAR image challenging. A good example are buildings, where layover, shadow and single- and double-bounce occur.","hasChildren":true,"hasParent":true,"name":"Terrain reflectivity and geometric distortions","selfAssesment":"Completed
"},{"code":"PP2-3-9","description":"A typical “salt-and-pepper” noise-like physical phenomenon that is not a noise but a deterministic property of SAR imagery is the so called speckle. It appears when a resolution cell of a SAR system contains more than one scatterer. In that case, the total scattering from the resolution cell is a coherent sum of the backscatter originating from the different scatterers. In order to reduce this effect, speckle reduction methods can be applied.","hasChildren":true,"name":"Speckle Formation","selfAssesment":"Planned
"},{"code":"PP2-3","description":"Microwave remote sensing systems detect and quantify the electromagnetic radiation arriving at a detector, this radiation being either emitted (passive sensors) or scatterered back (active sensors) from the objects.\r\nThree properties of the recorded electromagnetic signal are of particular interest: its intensity, its phase and its polarization. The specific quantification of each properties allows signal interpretation, as they depend on the roughness and dielectric characteristics of the surface (intensity and polarization) as well as of the range between target and sensor (phase).\r\nThe detection of the microwaves is operated through two principal sensor elements: an antenna and a receiver. The antenna collects the incoming radiation and the receiver measures the collected electric signal.\r\nAs active microwave systems produce their own electromagnetic radiation, they are equipped with two additional elements: a pulse generator and a transmitter. Usually, transmitter and receiver are situated on the same antenna.\r\nA simple detector system only detects the intensity of the signal and amplifies it. Coherent systems measure both the amplitude and the phase of the incident electromagnetic radiation.\r\nMicrowave systems can be categorized in two different types: imaging and non-imaging sytems. Whereas for non-imaging systems each echoe (collected signal) provides a single measurement, imaging systems collect a sequence of echoes that generate a two dimensional image.","hasChildren":true,"hasParent":true,"name":"Detecting microwaves","selfAssesment":"Completed
"},{"code":"PP2","description":"Microwave remote sensing operates in the microwave portion of the electromagnetic spectrum, generally using wavelengths greater than 3 cm and up to 1 m. \r\nMicrowaves are sensitive to different physical parameters than other regions of the electromagnetic spectrum. Microwaves interactions with objects are governed by geometric (structure, size, shape) and dielectric (water content) properties, whereas other regions of the electromagnetic spectrum reacts e.g. to object temperature or “color” (amount of reflection or absorption of the Sun light by a particular object).\r\nAs a general rule, microwaves interact with object at least as big as the wavelength. Smaller objects will therefore be transparent for the signal. Due to the large wavelengths, atmospheric particles are almost transparent to the signal and microwave remote sensing can penetrate clouds. Under very dry conditions, microwaves can even penetrate up to a few meters the top soil layers, therefore providing information that is not visible in other regions of the electromagnetic spectrum. Depending on the considered wavelength, microwave can also penetrate vegetation layers to different amounts.\r\nIn microwave remote sensing, three characteristics of the electromagnetic wave play an important role: its amplitude, its phase and its polarization. Depending on the application, either one characteristic or a combination of them is used to retrieve information.\r\nThere are two main types of microwave sensors: active RADAR systems and passive radiometers. RADAR is an acronym for RAdio Detection And Raging. An active radar system sends out pulses and records the echoes scattered back by the objects (scatterers) to the sensor. The systems use the two-way travel time of the radar pulse to determine the distance (range) to the illuminated object. Its backscatter intensity is determined by the radar system and object properties and depends on the quantity of energy coming back to the sensor. Active radar systems transmit a signal and record the amount of energy that is scattered back and depends of both dielectric and geometric properties. Passive radiometers record microwave energy, which is emitted by the Earth’s surface.\r\nDepending on the type of system, microwave remote sensing can be used in multiple applications. Active sensors are principally used for diverse land cover mapping applications based on the particular backscattering mechanisms and characteristics of the objects on the Earth’s surface. Using multiple acquisitions, they are also favored for topographic, deformation and velocity mapping. Passive sensors are preferred for the determination of hydrologic variables such as soil moisture, precipitation, ice water content and sea-surface temperature.","hasChildren":true,"hasParent":true,"name":"Basics of microwave remote sensing","selfAssesment":"Completed
"},{"code":"PS","description":"Remote sensing, i.e. the process of obtaining information about an object or area from a distance, is not possible without remote sensing sensors that collect this information and the platforms on which the sensors are installed and which are used to move them. Remote sensing sensors collect data by detecting energy that is reflected or emitted from Earth. There are different types of remote sensing sensors. The interaction between the sensor and the Earth's surface has two modes: active or passive. Passive sensors use solar radiation to illuminate the Earth's surface and detect reflection from the surface or measure the emitted energy. They usually record electromagnetic waves in the visible (˜430–720 nm) and near infrared (NIR) (˜750–950 nm) through short infrared (SWIR) (˜1.500-2.500 nm) to thermal infrared (TIR) (8.000-14.000 nm) ranges. The power measured by passive sensors is a function of surface composition, physical temperature, surface roughness and other physical properties of the Earth. Active sensors provide their own energy source to illuminate objects and measure their properties. These sensors use electromagnetic waves in the visible and near infrared range (e.g.laser altimeter) and radar waves (e.g. synthetic aperture radar (SAR)). As sensor technology has advanced, the integration of passive and active sensors into one system has emerged. Alternatively, remote sensing sensors can be classified into imaging sensors, i.e. that produce an image of an area, within which smaller parts of the sensor's whole view are resolved (pixels), and non-imaging sensors, i.e. that return a signal based on the intensity of the whole field of view. In terms of their spectral characteristics, the imaging sensors include optical imaging sensors, thermal imaging sensors, and radar imaging sensors. These sensors can be on satellites, mounted on aircraft, unmanned aerial vehicle (UAV), drone or ground. The collected information can be transformed into an image or set of points (e.g. cloud points), which can be further processed and analyzed to obtain the necessary information, e.g. agricultural field development phase, level of air pollution, etc.\r\nA digital imagery of Earth observation sensors is a two-dimensional representation of objects on Earth. Current images collected from different levels of acquisition, from ground to satellite, with the help of electronic sensors are examples of digital images. There are different aspects and characteristics of remote sensing data and images, such as, for example, data formats and processing levels, data storage, data properties.","hasChildren":true,"hasParent":true,"name":"Platforms, sensors and digital imagery","selfAssesment":"Completed
"},{"code":"PS1-1","description":"Remote sensing sensors has its roots in the 19th century in the development of photography. Photography was an invention that made it possible to acquire a permanent image. The first photographic image was taken in 1826 by Joseph Nicephore Nieppce. While the first aerial photograph was taken in 1858 by Felix Tournachon, known as Nadar, from a tethered baloon over Biévre Valley in France. In 1907 Julius Neubronner developed a light miniature camera that could be fitted to a pigeon's breast. It can be said that the construction camera + pigeon was the precursor of today's unmanned aerial vehicle (UAV) or drone. Further developments focused on developing new sensors (analog vs. digital frame cameras) and how to save and store images (e.g. photographic emulsions, films). The origin of other types of remote sensing can be traced to World War II, with the development of radar, sonar, and thermal infrared detection systems. Since the 1960s, sensors were designed to operate in virtually all of the electromagnetic spectrum. Both civil and military aerial photography have long been widely used in cartography to create maps. Specialized large format cameras (looking vertically down, assuming the plane is flying horizontally) were developed. Such cameras have been specially designed to perform almost vertical sequences of bird-eye exposures during aircraft flight. Hence for a long time remote sensing consisted of aerial photography and photogrammetry using analogue mechanical or optical equipment. Everything has changed with satellites and the space race. The first real success of remote sensing satellites in serious scientific work was in meteorology, weather satellite TIROS-1, launched by NASA on April 1, 1960. \r\nToday a wide variety of remote sensing instruments are available as data source for use in different applications for land, water and atmosphere monitoring.","hasChildren":true,"name":"History of remote sensing sensors","selfAssesment":"In progress
"},{"code":"PS1-2-1-1-1","description":"Along track scanner, also known as a pushbroom scanner, is an optoelectronic device that obtains images with a multispectral imaging system. The scanners are used for passive remote sensing. It records electromagnetic energy that is reflected (e.g., blue, green, red, and infrared light) or emitted (e.g., thermal infrared radiation) from the surface of the Earth. The scanners are mounted on space- or aircrafts. \r\nA two-dimensional image is created (line by line) by exploiting the platform motion along the orbital track. The data are collected along track using a linear array of detectors arranged perpendicular to the direction of travel. The array of detectors are pushed along the flight direction to scan the successive scan lines, and hence the name pushbroom scanner. \r\nThere are no moving parts on a pushbroom sensor, hence, the scanning speed can be increased compared to across track systems. A longer dwell time over each ground resolution cell increases the signal strength (high radiometric resolution, no pixel distortion). Additionally, finer spatial and spectral resolution can be achieved as the size of the ground resolution cell is determined by the Instantaneous Field of View (IFOV) of a single detector. The systems are designed for high-resolution imaging. However, a very large number of detectors is needed for high resolution images. It is a complex optical system. In addition, the pushbroom scheme requires a wide Field of View (FOV) optics system to obtain the same swath as for a corresponding whiskbroom (across track) scanner. It has narrow swath width. \r\nThe detector arrays with such a line-scanning pushbroom system are usually of the type Charge-Coupled Device (CCD).\r\nThe MultiSpectral Instrument (MSI) on board the Sentinel-2 satellite (Copernicus mission) uses a pushbroom concept.\r\nMultispectral imaging systems building the final image (line by line) exploiting the platform motion along the orbital track. No rotating mechanical part required, usually based on a CCD matrix (high spectral resolution but just up to 1 micrometer), e.g. Sentinel-2 MultiSpectral Instrument (MSI), Sentinel-3 Ocean and Land Colour Imager (OCLI).","hasChildren":true,"name":"Along track scanners","selfAssesment":"Completed
"},{"code":"PS1-2-1-2-1","description":"The cameras, usually a charge-coupled device (CCD) or Complimentary Metal Oxide Semiconductor (CMOS), that convert light into electrons that can be measured and converted into radiometric intensity value.","hasChildren":true,"name":"Digital Frame Camera","selfAssesment":"Planned
"},{"code":"PS1-2-1-2","description":"2-D systems with the ability to observe in two dimensions simultaneously.","hasChildren":true,"hasParent":true,"name":"Area Arrays","selfAssesment":"New
"},{"code":"PS1-2-1","description":"A type of a spectrometer. It is in principle, one-dimensional systems, whisk- or pushbroom, that form an image on a line-by-line basis in the scan direction.","hasChildren":true,"hasParent":true,"name":"Line detector arrays","selfAssesment":"New
"},{"code":"PS1-2-2-1-1","description":"Thermal radiometers are radiometers with the capability of measuring the spectrum of infrared emission. As such, they are characterized by a relatively high spectral resolution (normally better than 1 cm-1 in wave number units). Modern Spectrometers on board satellites have a spectral resolution better than 0.7 cm -1 in order to properly resolve CO2 lines used for the retrieval of the atmospheric temperature profile. Based on the optical layout they are further classified in grating spectrometers and Fourier Transform Spectrometers or FTIR.","hasChildren":true,"name":"Thermal Radiometers","selfAssesment":"New
"},{"code":"PS1-2-2-1-2","description":"Passive microwave radiometers are radiometers that measures energy emitted at millimetre-to-centimetre wavelengths at 0.15 - 30 cm (frequencies of 1–200 GHz). Example of a sensor: SMOS Microwave Imaging Radiometer with Aperture Synthesis (MIRAS), which aims at measuring land soil moisture and ocean salinity.","hasChildren":true,"name":"Passive Microwave Radiometers","selfAssesment":"In progress
"},{"code":"PS1-2-2-1-3","description":"An advanced multispectral sensor that detects hundreds of very narrow spectral bands throughout the visible, near-infrared, and mid-infrared portions of the electromagnetic spectrum.","hasChildren":true,"name":"Hyperspectral Radiometers","selfAssesment":"Planned
"},{"code":"PS1-2-2-1-4","description":"A radiometer that measures the intensity of radiation in multiple wavelength bands (i.e., multispectral). Example of a sensor Moderate Resolution Imaging Spectroradiometer (MODIS)","hasChildren":true,"hasParent":true,"name":"Spectroradiometers","selfAssesment":"In progress
"},{"code":"PS1-2-2-2","description":"Provide information about vertical profiles of temperature and molecular consistuent concentrations in the atmosphere (atmospheric sounders).","hasChildren":true,"name":"Atmospheric passive sounders","selfAssesment":"New
"},{"code":"PS1-2-2","description":"Radiometers are instruments which measure radiative intensities within a particular frequency window. A radiometer is further identified by the portion of the electromagnetic radiation it covers, usually the infrared or microwave regions. Normally the spectral range extends from the longwave (14-15 micron) to the shortwave (3-4 micron). This range overlaps much of the emission spectrum of Earth. The technology is classified in broadband radiometer of spectral radiometers depending on the spectral resolution. A radiometer measures the intensity of the radiative energy, but does not differenciate between the different registered wavelengths or their respective amplitude. In other terms, it provides a single value as combined result of all wavelengths within the considered frequency window.","hasChildren":true,"hasParent":true,"name":"Radiometers","selfAssesment":"In progress
"},{"code":"PS1-2","description":"Passive remote sensing systems record electromagnetic energy that is reflected (e.g., blue, green, red, and infrared light) or emitted (e.g., thermal infrared radiation) from the surface of the Earth. Passive sensors therefore rely on an external energy source (e.g. sun illumination, Earth heat emission). Contrary to passive sensors, who detect naturally occurring radiation, active sensors emit radiation and collect and analyze the signal that is sent back by the Earth’s surface or atmosphere. Active remote sensing systems produce therefore their own electromagnetic energy. They transmit and receive the radiation that is reflected or backscattered from the illuminated target. They do not necessitate an external source of radiation (e.g. Sun or Earth). Contrary to most passive sensors that are bound to detecting either the reflected Sun radiation or emitted radiation by the Earth’s surface in ranges from the ultraviolet to the thermal infrared, active sensors can use any radiation from the electromagnetic spectrum, the only limitation being the transparency of the Earth’s atmosphere. They often use wavelengths that are not sufficiently provided by the Sun, e.g. microwaves. \r\nActive systems can be categorized either according to their imaging capability, or according to the considered emitted wavelength, or also according to the way they use the returned signal. For the last category, it is generally distinguished between ranging systems, which use as principal information the time delay between transmission and reception of the electromagnetic radiation at the sensor, and scattering systems, which consider the strength (also called magnitude or intensity), of the returned signal. Some systems also register both information.\r\nAs active sensors produce their own radiation and do not rely on e.g. Sun radiation, they are daytime independent and can also retrieve information about the Earth’s surface by night. Furthermore, depending of the considered wavelength, active sensors are weather independent. For longer wavelengths of the microwave domain, clouds are transparent, as the transmitted wavelength is larger than the water particles constituting the cloud and do not interact with them. \r\nActive sensors can control the direction of their illumination to a specific target to be investigated, but require in general more energy than passive sensors as they “actively” illuminate the Earth’s surface.","hasChildren":true,"name":"Passive vs. active sensors","selfAssesment":"Completed
"},{"code":"PS1-3-1-1","description":"Imaging RADAR (RAdio Detection And Ranging) is an active remote sensing system which bounces microwave energy from a target and records the energy that returns to the sensor. The radar antenna alternately transmits and receives pulses at particular microwave wavelengths (in the range 1 cm to 1 m, which corresponds to a frequency range of about 300 MHz to 30 GHz) and polarizations (waves polarized in a single vertical or horizontal plane).\r\nMicrowave energy pulses are emitted at regular intervals and focused by the antenna into a radar beam directed downwards and to the side. The radar beam illuminates the surface obliquely at a right angle to the motion of the platform. Objects on the ground reflect the microwave energy depending on factors such as roughness and attitude. The antenna receives this reflected (or backscattered) energy.\r\nBy measuring the time delay between the transmission of a pulse and the reception of the backscattered \"echo\" from different targets, their distance from the radar and thus their location can be determined. As the sensor platform moves forward, recording and processing of the backscattered signals builds up a two-dimensional image of the surface.\r\nUnlike aerial photographs and satellite images which are passive remote sensing systems, in active systems such as radar, the brightness or darkness of the image is dependent on the portion of the transmitted energy that is returned back to the radar from targets on the surface. Bright areas are produced by strong radar response and darker areas are from weak radar responses., while the response to radar energy by the target is primarily dependent on the three factors (1) Surface roughness of the target, (2) Radar viewing and surface geometry relationship, and (3) Moisture content and electrical properties of the target.","hasChildren":true,"hasParent":true,"name":"Imaging Radar","selfAssesment":"Completed
"},{"code":"PS1-3-2-1-1","description":"Laser profilers measure 2D range profiles and operate in different environments, like spaceborne, airborne and indoor. It is the simplest application of the LIght Detection And Ranging technique. It transmits a short pulse of energy (visible or near-infrared radiation) and detects 'echo', by measuring the time delay. Knowing the speed of propagation of the pulse (speed of light), the range from the instrument to the surface can be measured.\r\nLaser profiling uses successive reflectorless laser range measurements (1D distance measurement) on adjacent points along a path, which results in a 2D profile or cross-section of the ground. A laser profiler can be terrestrial, or ground-based, or it can be mounted on an airborne or spaceborne platform. In the case of ground-based measurements, the platform is fixed but the angle of illumination changes, allowing for the cross section of the terrain to be mapped. An airborne laser profiler can transmit a continuous stream of pulses along its flight path. As a result, if the position of the platform is known, e.g. from GPS/IMU system, a surface profile along the flight path can be reconstructed using the successively recorded vertical distances between the platform and the points on the ground. The use of an additional rotational mirror allow to scan the Earth in an additional dimension, providing 3D information of the mapped surface. This is the principle of a laser scanner.\r\nThere are two principal types of laser profiling techniques: the first one is based on analog detection and the second on photon counting. In analog detection, the signal power is converted into an output voltage providing a signal strength as function of time. The analog-to-digital conversion yields either a full waveform that allows retrieving the entire time-structure of the return signal strength- and therefore the full vertical structure of the target-, or discrete returns when the signal strength exceed a certain threshold. The full waveform information is especially useful when analyzing vegetation, as every vegetation layer (canopy, stems, branches) and the ground return pulses, allowing the determination of e.g. canopy height, ground surface topography but also a deeper analysis of the canopy structure. Photon counting techniques record the arrival of single photons. The counting of photons is combined with their time-of-flight. The accumulation of single photons at a specific range is similar to the signal strength of analog detection and allows retrieving the height and structure of specific targets.","hasChildren":true,"hasParent":true,"name":"Laser profiler","selfAssesment":"Completed
"},{"code":"PS1-3-2-1-4","description":"A radar altimeter is an active, non-imaging remote sensing device. It measures the height of the terrain along the track beneath an air- or spaceborne platform using electromagnetic radiation from the microwave region of the electromagnetic spectrum. Radar altimeters operate similar to laser profilers. Both emit a short pulse of electromagnetic radiation towards the Earth’s surface and detect the time delayed echo. By measuring the time delay and knowing the speed of propagation of the pulse, the range (distance) from the instrument to the surface can be determined. By using the forward motion of the altimeter platform and transmitting a continuous stream of pulses a profile can be built up. If the exact location of the platform as a function of time is known, a surface profile can be generated. \r\nFor a high accuracy of the range resolution, a narrow antenna beam is required, which can be achieved either by using large antennas or short radar beams. In the first case, the radar altimeter is beam-limited; in the second case it is pulse-limited. As large antennas are not practical in space, pulse-limited systems are used for space-borne platforms. Pulse-limited altimeters use frequency modulated (chirp) pulses generated by a chirp generator. The accuracy of the measurements also depends on atmospheric transmission effects, as the speed of the electromagnetic radiation traveling at the speed of light will be delayed when passing through the ionosphere and the atmosphere twice. In general, the range resolution of radar altimeters is in the order of a few centimetres. \r\nIn the beginning, radar altimeters were used for measurements of surface profiles of the ocean topography to get information about currents, ocean circulation, wind and waves. Another basic application of altimetry were measurements over ice sheets and glaciers, e.g. for mass balance determination. Further application domains are geoid measurements also revealing deep sea trenches and the precise monitoring of satellite orbits.","hasChildren":true,"name":"Radar altimeters","selfAssesment":"Completed
"},{"code":"PS1-3-2-1","description":"Laser altimeters historically were the first active sensing devices used on airborne platforms, measuring range information in form of single distances since the mid-1960s. \r\nEven though laser scanners made it possible to retrieve information in a more rapid and denser coverage since the mid-1990s, laser altimeters remain of importance in the scientific community. Especially, the mapping of ice-covered surfaces, water bodies and flat land areas is still performed using laser altimeters.\r\nLaser altimeters are either airborne or spaceborne and are often used together with microwave (radar) profiler in order to calibrate the radar instruments. Whereas airborne laser altimeters are preferred for forestry application, e.g. for analyzing vertical vegetation structure, spaceborne laser altimeters are additionally used for multiple other applications. In particular, spaceborne laser profiler are of high interest for studying surface roughness of ice sheets or for mapping desert topography. Furthermore, spaceborne laser profilers are also useful in atmospheric science for retrieving cloud structure and analyzing different aerosol layers. The requirements for airborne and spaceborne laser altimeters are different. In particular, for spaceborne altimeters, both the distance travelled by the laser pulse and the platform speed are much higher than for airborne instruments, inducing the need of larger optics and more powerful laser instruments. First spaceborne laser experiments were conducted onboard the space shuttle in the mid-1990s, first aiming atmospheric research with a near infrared laser. After successful trial, the space shuttle laser altimeter was fine-tuned and follow-up missions focused on mapping terrain relief and vegetation canopies. Later missions, such as GLAS (IceSAT), ATLAS (IceSAT-2) and GEDI (ISS), used either near-infrared or green (or both) laser light and focused on improving ground coverage while allowing smaller footprints of the laser beam on ground. The revisit cycle of spaceborne laser altimeters allow the determination of regional elevation changes, e.g. monitoring of ice–sheet thickness or vegetation height, which is highly relevant for the scientific community and climate modelers.","hasChildren":true,"name":"Laser altimeter","selfAssesment":"Completed
"},{"code":"PS1-3-2-3","description":"By a ranging camera the simultaneous capturing of range measurements for dynamical (close-range) 3D applications is given. These ranging cameras allow additionally the simultaneous capturing of single range and co-registered intensity images while still maintaining high update rates (up to 100 releases per second). Typical applications are autonomous navigation of robots, driver assistance, traffic monitoring or tracking of pedestrians for building surveillance.","hasChildren":true,"name":"Ranging camera","selfAssesment":"Completed
"},{"code":"PS1-3-2-4-1","description":"Spaceborne LS (e.g. Geoscience Laser Altimeter System - GLAS) provides global measurements of the Earth's surface with the potential on capturing additionally clouds and atmospheric aerosols. The spaceborne measurements allow to globally observe ice sheet and land elevations, approximate sea ice thickness, changes in elevation through time, vegetation coverage for biomass estimation, and height profiles of clouds and aerosols. It is a large footprint profiling system developed by NASA that operates with a footprint diameter of 70 m and measures elevation changes with decimeter accuracy. The surface characteristics are determined by comparing a parametric description of the transmitted and received waveforms. Because the laser footprint is large and illuminates multiple surfaces, the resulting return waveform is an integrated, spatially non explicit representation of the range to illuminated surfaces separated both vertically and horizontally. The geometric organization of surfaces within a single footprint can therefore not be determined.","hasChildren":true,"name":"Spaceborne Laser Scanning","selfAssesment":"Completed
"},{"code":"PS1-3-2-4-2","description":"Airborne laser scanning (ALS) systems allow a direct and illumination-independent measurement of 3d objects in a fast, remote and accurate way. Beside basic range measurements, the current commercial ALS developments allow to record the waveform of the backscattered laser pulse. Latest trends in sensor developments focus on single-photon detection. Airborne Laser Scanning (ALS) for instance is used for capturing large-scale 3D environments with almost homogeneous point density with a local point density of typically 4-100 pts/m^2. Therefore, different applications are of interest, like urban planning, change detection, forestry surveying, or power line monitoring. Further to describe the 3D scene, products like digital terrain models (DTMs), digital surface models (DSMs), or city models are provided.","hasChildren":true,"name":"Airborne Laser Scanning","selfAssesment":"Completed
"},{"code":"PS1-3-2-4-3","description":"A mobile laser scanning (MLS) system consists of a moving vehicle equipped with one or more usually side-looking laser scanners to capture information about the local 3D geometry. Mobile laser scanning systems are applied for capturing dense and accurate 3D information representing local object surfaces, but the density of the measured 3D points depends on their distance to the scanning unit, which is usually mounted on a vehicle. As a consequence, an appropriate interpretation of the captured data has to face certain challenges arising from either low or varying point density.","hasChildren":true,"name":"Mobile Laser Scanning","selfAssesment":"Completed
"},{"code":"PS1-3-2-4-4","description":"Underwater Laser Scanning is applied in deep-sea as well as in shallow water regions. The ranging distance is close range and the measurement principle relies on triangulation by laser light, comparable with structured-light-projection. More recently, companies started to develop Time-of-Flight (ToF) underwater laser scanners.","hasChildren":true,"name":"Underwater Laser Scanning","selfAssesment":"Completed
"},{"code":"PS1-3-2-4-5","description":"For Bathymetric Laser Scanning System the utilized green laser light with its potential penetration capabilities in water is essential. For water surface mapping the electromagnetic radiation of the laser penetrates into the topmost layer of the water column and can also be used for mapping the water surface and shallow water bathymetry. However high resolution mapping of water level heights is important for many applications, but capturing water is still in general challenging. Area-wide water surface heights and depths are required for many disciplines such as hydrology, hydraulic engineering, flood risk management, ecology, climate change, etc.","hasChildren":true,"name":"Bathymetric Laser Scanning","selfAssesment":"In progress
"},{"code":"PS1-3-2-4","description":"Laser scanners capture data by successively considering points on a discrete, regular (typically spherical, cylindrical or line) raster, and recording the respective geometric and radiometric information. Generally, a laser scanner illuminates a scene with modulated laser light and analyzes the backscattered signal. More specifically, laser light is emitted by the scanning device and transmitted to an object. At the object surface, the laser light is (partially) reflected and, finally, a certain amount of the laser light reaches the receiver unit of the scanning device. The measurement principle is therefore of great importance as it may be based on different signal properties such as amplitude, frequency, polarization, time, or phase. Many scanning devices are based on measuring the time t between emitting and receiving a laser pulse, i.e., the respective time-of-flight, and exploiting the measured time t in order to derive the distance r between the scanning device and the respective 3D scene point. Alternatively, a range measurement r may be derived from phase information by exploiting the phase difference Δφ between emitted and received signal. In general, laser scanners may be categorized with respect to laser type, modulation technique (continuous-wave (CW) laser, pulsed laser), measurement principle (time-of-flight, phase difference), detection technique (coherent detection, direct detection), field-of-view (line scanner, pushbroom scanner, array scanner), measurement range (far range, medium range, close range), or configuration between emitting and receiving component of the device (monostatic system, bistatic system). Furthermore, different types of laser scanners may be used for different application scenarios relying on e.g. spaceborne laser scanning, airborne laser scanning, mobile laser scanning, terrestrial laser scanning, underwater laser scanning or bathymetric laser scanning.","hasChildren":true,"hasParent":true,"name":"Laser scanner","selfAssesment":"Completed
"},{"code":"PS1-3-2","description":"The main idea of LiDAR (Light Detection and Ranging) technology is based on actively scanning the scene by involving a device which emits electromagnetic radiation in the form of modulated laser light. \r\nGenerally, such scanning devices illuminate a scene with modulated laser light and analyze the backscattered signal. More specifically, laser light is emitted by the scanning device and transmitted to an object. At the object surface, the laser light is partially reflected and, finally, a certain amount of the laser light reaches the receiver unit of the scanning device. The measurement principle is therefore of great importance as it may be based on different signal properties such as amplitude, frequency, polarization, time, or phase. \r\nMany scanning devices are based on measuring the time t between emitting and receiving a laser pulse, i.e., the respective time-of-flight, and exploiting the measured time t in order to derive the distance r between the scanning device and the respective 3D scene point. Alternatively, a range measurement r may be derived from phase information by exploiting the phase difference Δφ between emitted and received signal. According to seminal work, respective scanning devices may be categorized with respect to laser type, modulation technique, measurement principle, detection technique, or configuration between emitting and receiving component of the device. \r\nIn order to get from single 3D scene points to the geometry of object surfaces, respective scanning devices are typically mounted on a platform which, in turn, allows a sequential scanning of the scene by successively measuring distances for discrete 3D points.\r\nLiDAR technology is used for a diversity of applications such as autonomous driving, forestry, biomass estimation, precision farming, archaeology, city mapping, terrain modelling, and metrology.","hasChildren":true,"hasParent":true,"name":"LiDAR (Light Detection and Ranging)","selfAssesment":"Completed
"},{"code":"PS1-3-3-1","description":"Sonar, also called ultrasonic sensing, is one the principal sensors for mapping sea-floor, i.e. bathymetry. It transmits sound waves through water and records the amount of backscattered energy. It uses frequencies higher than normal hearing. A sonar can be either passive or active. Active sonars are also called echosounders.","hasChildren":true,"name":"Sonar","selfAssesment":"New
"},{"code":"PS1-3-3-2","description":"A seismic sensor is also called seismometer and measures the motion of the ground when it is shaken by a perturbation such as an earthquake, be it a large displacement or a microquake. The physical variable associated to the measurement of a seismometer is dynamic. It can be either the amplified ground motion, the velocity or acceleration. Current seismometers transform one of these three parameters into a voltage measurement. Usually, three seismometers are needed to retrieve the three components of the displacement. As for other sensors, there exists many types of seismic sensors, and they can be distinguished in active and passive sensors as well.","hasChildren":true,"name":"Seismic sensor","selfAssesment":"New
"},{"code":"PS1-3-3","description":"Instruments that measure vertical distribution of precipitation and other atmospheric characteristics such as temperature, humidity, and cloud composition.","hasChildren":true,"hasParent":true,"name":"Sonic sensors","selfAssesment":"New
"},{"code":"PS1-3-4-1","description":"A radar scatterometer is an active, non-imaging remote sensing device with a real aperture operating in the microwave region of the electromagnetic spectrum. The main purpose of a scatterometer is the characterization of the surface backscatter properties, when a high radiometric accuracy is of interest and the spatial resolution is of secondary importance. There are scatterometers used in laboratories, in the field installed on masts, cranes or trucks, airborne (airplanes, helicopters) and spaceborne scatterometers circling the Earth in an orbit. Spaceborne scatterometers usually achieve a global coverage with a high repetition frequency. The basic principle of the scatterometer the accurate measurement the intensity of the returned radar echo from the Earth’s surface. Because of the speckle effect in radar echoes, a large number of independent observations are averaged.\r\nScatterometry (Earth observation using scatterometers) gained the attention of scientists towards the end of the 1960s when it was realized that the sea clutter observed by Second World War radar operators on their screens was not just any noise obscuring small boats and low-flying aircraft. It was in fact the signal backscatter from small ocean surface waves, comparable in dimension to the wavelength of the radar (in the order of centimetres).\r\nThe primary application of radar scatterometers is the measurement of near-surface wind vectors (wind speed and direction) over the ocean. These wind vector data are based on indirect measurements, where the wind vector is derived from the relationship between the backscattered power, the small-scale ocean surface roughness, and the local wind vector at the ocean surface.","hasChildren":true,"name":"Radar Scatterometers","selfAssesment":"Completed
"},{"code":"PS1-3-4-2-1","description":"Differential Absorption Lidar (DIAL) is a laser remote sensing technique that is used for range and/or profile measurements of atmospheric gas concentrations and constituents.","hasChildren":true,"name":"Differential Absorption Lidar","selfAssesment":"In progress
"},{"code":"PS1-3-4-2-2","description":"Doppler Wind LiDAR or Cloud-Aerosol Lidar with Orthogonal Polarization (e.g. CALIOP) is a two-wavelength polarization-sensitive LiDAR that provides high-resolution vertical profiles of atmospheric aerosols and clouds to enable an greater understanding of our climate.","hasChildren":true,"name":"Doppler Wind LiDAR","selfAssesment":"In progress
"},{"code":"PS1-4","description":"There are different ways to classify sensors used in remote sensing. One of them is the division into imaging and non-imaging sensors. Imaging sensors typically employ optical imaging systems (from VIS to TIR). They operate primarily at window frequencies, where atmospheric absorption is low and surface features can be imaged or measured. Non-imaging sensors include microwave radiometers, microwave altimeters, magnetic sensors, gravimeters, Fourier spectrometers, laser rangefinders, and laser altimeters.","hasChildren":true,"name":"Imaging vs. nonimaging sensors","selfAssesment":"New
"},{"code":"PS1-5-1-2","description":"Across track scanners, known as whiskbroom electromechanical scanners, are multispectral imaging systems building the final image (ground cell by ground cell) by combination of the platform motion along the orbital track with a mechanical rotation of the collecting optic in the across track direction. Opto-mechanical are typically multi-spectral radiometers (no limitation on bands), whiskbroom systems are usually CDD spectrometers (high spectral resolution but just up to 1 micrometer). Examples of the sensors: Landsat Multispectral Scanner (MSS), Landsat Thematic Mapper (TM).","hasChildren":true,"name":"Across track scanners","selfAssesment":"In progress
"},{"code":"PS1-5-1","description":"Speckle-pattern based sensors operate with a spatial neighborhood codification strategies to exploit a unique pattern. The label associated to a pixel is derived from the spatial pattern distribution within its local neighborhood. Thus, labels of neighboring pixels share information and provide an interdependent coding. Representing one of the most popular devices based on structured light projection, the Microsoft Kinect exploits an RGB camera, an IR camera, and an IR projector. The IR projector projects a known structured light pattern in the form of a random but unique speckle dot pattern onto the scene. As IR camera and IR projector form a stereo pair, the pattern matching in the IR image results in a raw disparity image which, in turn, is read out as depth image.","hasChildren":true,"name":"Speckle-pattern based sensor","selfAssesment":"In progress
"},{"code":"PS1-5-2","description":"A multi-temporal (sequential) binary coding uses black and white stripes to form a sequence of projection patterns for each point on the surface of the object. Binary coding technique is very reliable and less sensitive to the surface characteristics, since only binary values exist in all pixels. Thus, each pixel may be assigned a codeword consisting of its illumination value across the projected patterns. The respective patterns may, for instance, be based on binary codes or Gray codes and phase shifting. To achieve high spatial resolution, a large number of sequential patterns need to be projected. All objects in the scene have to remain static. The entire duration of 3D image acquisition may be longer than a practical 3D application allows for. These sensors are utilized in industrial environment.","hasChildren":true,"name":"Multi-temporal pattern based sensor","selfAssesment":"In progress
"},{"code":"PS1-5-3","description":"For a multi-spectral pattern based sensor, various continuously varying color patterns to encode the spatial location information are utilized.","hasChildren":true,"name":"Multi-spectral pattern based sensor","selfAssesment":"In progress
"},{"code":"PS1-5","description":"A structured-light-projection camera emits active optical radiation in the form of a coded structured light pattern in the visible or infrared spectrum, or electromagnetic radiation in the form of modulated laser light. Via the projected pattern, particular labels are assigned to 3D scene points which, in turn, may easily be decoded in images when imaging the scene and the projected pattern with a camera. The procedure reminds to conventional stereo processing, where corresponding features must be extracted from a pair of stereo images to derive the spatial information. In contrast, such synthetically generated features allow to robustly establish feature correspondences, and the respective 3D coordinates may easily and reliably be recovered via triangulation. Generally, techniques based on the use of structured light patterns may be classified depending on the pattern codification strategy.","hasChildren":true,"hasParent":true,"name":"Structured-light-projection camera","selfAssesment":"Completed
"},{"code":"PS1-6","description":"Ground penetrating radar is a non-intrusive measurement technique that uses radio waves to probe the ground. It is used to analyze and locate targets buried in the sub-surface. It transmits low-power electromagnetic energy into the ground and receives weak signals from a low-loss dielectric or conductor material. It is principally used for archeology and geology. Typical penetration depths are between a few centimeters up to 4m.","hasChildren":true,"name":"Ground penetrating RADAR (GPR)","selfAssesment":"New
"},{"code":"PS1-7","description":"An optical spectrometer is an instrument used to detect, measure and analyze the spectral content of the incident electromagnetic field (narrow-band, VIS, NIR, SWIR and TIR). It breaks down the incoming light spectrum so the whole wavelength range is mapped and each wavelength can be analysed individually. Usually, a distinction is made between optical and mass spectrometers.\r\nOptical spectrometers depict the intensity of the incoming light in function of the wavelength. Considering all wavelengths, each object has a specific spectral signature and the analyse of their particular spectrum allows the deduction of their composition ( e.g. pigments) or health.","hasChildren":true,"hasParent":true,"name":"Optical spectrometers","selfAssesment":"In progress
"},{"code":"PS1","description":"Remote sensing sensors acquire information about objects situated on the surface of e.g. the Earth remotely, e.g. from a distance, without any physical contact. They detect and measure the changes that the object imposes on its. \r\nRemote Sensing sensors are characterized according to several different properties:\r\n\tDepending on the interaction between the sensor and the Earth’s surface, one distinguishes between active (e.g. radar) and passive (e.g. optical imagery) sensors. Some systems use both kind of sensors simultaneously.\r\n\tDepending on the mapping process of the information, it can be distinguished between imaging and non-imaging sensors. Imaging sensors produce an image of an area of interest, e.g. give a spatial information about the incoming information. Spatial relationships between objects can be identified and used for visual interpretation. Non-imaging sensors register usually single response values for a specific area, and do not record how the incoming information varies across the field of view. They can be used to characterize the interaction between the received information and illuminated target.\r\n\tDepending on the platform on which the instrument is deployed, one speaks either of ground based (e.g. terrestrial laser scanner), airborne (e.g. plane, drone), or spaceborne (e.g. satellite) sensor. For spaceborne sensors, the orbit geometry (e.g. geostationary, equatorial, sun-synchronous) and altitude (high, medium and low Earth orbit) play an important role, as it most often determines the application of the satellite in combination with the deployed sensor (weather satellites or Earth observation satellite). \r\n\tDepending on the observed portion of the electromagnetic spectrum (e.g. optical, infrared, thermal, microwave). \r\n\tDepending on the instrument (e.g. imagers, altimeters, spectrometers, radiometers). \r\n\tDepending on the instrument precision, e.g. in terms of spatial resolution very high vs. low resolution sensors; in terms of spectral resolution narrow band (hyperspectral sensors) vs. broad-band sensors (mono- and multispectral sensors); in terms of radiometric resolution very high vs. low resolution sensors. Some applications do not require very high precision instruments, e.g. sea surface temperature measurements, while other, e.g. for vegetation monitoring, require high spectral and radiometric resolution for good data interpretation and analysis. \r\nOther categorization would include the specific applications of each sensor (weather, environment, urban, land, water, mapping, photogrammetry, structure-from-motion, etc.) and if is financed and used for scientific, commercial or military goals.","hasChildren":true,"hasParent":true,"name":"Types of remote sensing sensors","selfAssesment":"Completed
"},{"code":"PS2-1","description":"This topic covers information on the first remote sensing platforms that were used to obtain aerial photos. The first-known aerial photo was obtained in 1858 by Gaspard Felix Tournachon (Nadar). Afterwards, different platforms were used to obtain the information from above. The history of the development of remote sensing platforms includes platforms such as baloons, kites, rockets, pigeons, gliders, etc. to recent low-cost femtosatellites, e.g. for solar radioation pressure measurements. Historically, the main developments of the platforms as well as sensors was associated with military operations in the XXth century. Remote sensing data was used as part of photo- or/and satellite reconnaissance, i.e. aerial photos or satellite imageries used for the military purposes, mainly to make accurate maps and based on that to prepare a military strategy.","hasChildren":true,"name":"History of Remote Sensing Platforms","selfAssesment":"In progress
"},{"code":"PS2-2-1","description":"An unmanned aircraft system (UAS) includes an unmanned aerial vehicle (UAV), an aircraft without a human pilot on board, a ground-based controller, and a system of communications between the two. The system includes a full range of size classes from very small hand-launched drones to the large high-altitude observational systems.","hasChildren":true,"name":"Unmanned Aerial Systems (UAS)","selfAssesment":"New
"},{"code":"PS2-2-2-1","description":"Mission planning depends on the selected system of acquisition (sensor and platform). A detailed planning of a mission is a fundamental prerequisite for a successful acquisition of remote sensing data. Planning of an aerial photography mission (manned or unmanned) takes into account several parameters such as time of day/sun angle, weather conditions, flightline, platform. Planning and implementation of a spaceborne Earth Observation mission involves several successive life cycle ‘phases’ of conception, development, production and testing, utilization and support, and retirement, as part of an iterative and recursive process, until the satellite (space segment) is delivered and launched into orbit, and the data are exploited in the ground segment.","hasChildren":true,"name":"Mission planning","selfAssesment":"New
"},{"code":"PS2-2-2-3-2-3-1","description":"Stripmap is an acquisition mode of Synthetic Aperture Radar (SAR) data. It is the most simple, common acquisition mode of the SAR satellite sensors. In this mode, the antenna of the radar system is pointed in a fixed direction related to the flight direction. The displacement of the illuminated footprint corresponds to the displacement of the sensor along the orbit. This results in a continuous acquisition strip parallel to the flight direction. The ground coverage and resolution varies depending on the considered sensor and technical requirements. For X-band spaceborne sensors, a spatial resolution of 3 m can be achieved with a swath width in range direction of 30 km, e.g. for TerraSAR-X. In C-band, a spatial resolution up to 5 m is achieved e.g. by Sentinel-1 with a swath width of 80 km. For L-band spaceborne sensors, the spatial resolution achievable in stripmap mode varies between 3 and 10 m, with a swath width of 50-70 km, e.g. ALOS PALSAR2. \r\nContrary to other acquisition modes, no antenna steering is needed in azimuth direction and the elevation beam is fixed in a specific range direction. This allows for an uninterrupted coverage along the flight direction.\r\nStripmap data show high resolution with sufficient coverage for regional applications and can therefore be used for e.g. detailed land cover analysis at regional scale such as the mapping of urban footprints. Furthermore, it can be used for the mapping of small island or to support emergency actions.","hasChildren":true,"name":"Stripmap","selfAssesment":"Completed
"},{"code":"PS2-2-2-3-2-3-2-1","description":"The Staring Spotlight mode is only available for a few sensors. It follows the same principle of antenna steering in azimuth direction as the standard Spotlight mode, except that the rotation center of the antenna for steering is situated at a nearer range position, within the illuminated scene. This induces that the illuminated antenna footprint stays almost the same during the whole acquisition. Contrarily to the Spotlight mode, the antenna footprint does not slide along the azimuth direction during the SAR acquisition. Additionally, the steering angle is higher for the Staring Spotlight mode than for the standard Spotlight mode, increasing therefore the length of the synthetic aperture and leading to an even higher resolution in azimuth direction.\r\nThe Staring Spotlight mode is implemented on the X-Band sensor TerraSAR-X since 2013 and achieves an azimuth resolution up to 0.25 m. Similar to the standard Sportlight mode, this happens to the detriment of the coverage. The scene size is highly dependent of the incidence angle and varies from 7.5 km to 4 km in range and from 2.5 to 2.7 km in azimuth direction. A larger coverage is obtained for smaller incidence angles.\r\nDue to their extremely high resolution, staring spotlight acquisitions are principally used for the observation and/or monitoring of small scale objects and phenomena, e.g. small landslides, or for tomographic analysis.","hasChildren":true,"name":"Staring Spotlight","selfAssesment":"New
"},{"code":"PS2-2-2-3-2-3-2","description":"Spotlight is a SAR acquisition mode that allows increasing the illumination time of a particular area of interest by steering the antenna beam in azimuth direction. In this mode, the beam elevation is fixed, but the antenna is steered in azimuth direction, increasing therefore the length of the synthetic aperture. The rotation center of the antenna for steering is situated behind the scene at far range. The antenna footprint slides slightly forward over the scene in the azimuth direction during acquisition, but slower than in Stripmap mode, due to the antenna steering. The longest illumination time in azimuth direction results in an azimuth resolution that is highly enhanced compared to e.g. the Stripmap or the ScanSAR acquisition modes. However, this improvement is done to the detriment of the coverage. As for the other acquisition modes, the ground coverage and resolution depends on the considered sensor. For TerraSAR-X, a minimum coverage of 10 km in range and 5 km in azimuth direction is achieved in the Spotlight mode, with and azimuth resolution of about 1 m. The L-Band sensor Alos 2 also allow Spotlight acquisition mode, with a coverage of 25 km in both directions and a resolution of 1 m in azimuth direction, and down to 3 m in range direction.\r\nDue to the very high resolution achieved in both directions, this acquisition mode is particularly usefull for urban area analysis as it allows for the detection of small objects. Therefore, Spotlight data are often used for the detection and recognition of man-made structures and objects, such as roads, buildings and even vehicles.","hasChildren":true,"hasParent":true,"name":"Spotlight","selfAssesment":"New
"},{"code":"PS2-2-2-3-2-3-3-1","description":"The Interferometric Wide Swath Mode is a particular acquisition mode of the C-Band satellites Sentinel-1 which implements the TOPS (Terrain Observation with Progressive Scan) method. It combines an antenna steering in elevation, as in ScanSAR mode, with a counterrotation of the antenna beam from backward to forward steering, opposite to the steering happening in Spotlight mode. The data is acquired in bursts by cyclically switching the antenna beam between multiple adjacent sub-swaths.\r\n\r\nThis opposite steering direction of the antenna along the azimuth leads to a shorter target illumination and induces a decrease of the resolution, but a cyclically continuous coverage in azimuth direction. The principal difference to the other acquisition modes is that this acquisition mode implies a shrinking of the antenna footprint virtually to a ground target instead of slicing it to retrieve the target.\r\n\r\nThe Interferometric Wide Swath Mode (IW) was originally designed to solve Signal-to-Noise heterogeneities and azimuth ambiguities appearing in the ScanSAR mode.\r\n \r\nFor Sentinel-1, the IW mode provides a coverage of 250 km in range direction with an azimuth resolution of 20 m and incidence angles ranging from 29.1° in near to 46° in far range. \r\n\r\nStandard Single Look Complex Sentinel- 1 IW products contain three sub-swaths in range direction, with nine burts in azimuth direction.\r\n\r\nThe IW mode is the standard acquisition mode of the Sentinel-1 C-Band satellites and is acquired continuously over all land surfaces. The application are very diverse, ranging from agriculture and forestry to urban deformation monitoring and ship surveillance.\r\n\r\nSimilar to the IW mode, the Extra Wide Swath Mode (EW) of Sentinel-1 uses the same TOPS technique, but covers even wider areas up to 400 km in range direction, to the detriment of the resolution which decreases to 40 m. The EW Mode principally finds application in maritime applications such as artic and sea-ice monitoring, analyses of marine winds and oil pollution monitoring.","hasChildren":true,"name":"Interferometric Wide Swath Mode","selfAssesment":"New
"},{"code":"PS2-2-2-3-2-3-3-2","description":"The Extra Wide Swath Mode is an acquisition mode of the Sentinel-1 satellites. It is primarily designed and used for wide area coastal monitoring, such as ship traffic, sea-ice monitoring and oil spill detection. It uses the TOPSAR technique with a swath width of 410km and a spatial resolution of 20 m by 40 m.","hasChildren":true,"name":"Extra Wide Swath Mode","selfAssesment":"New
"},{"code":"PS2-2-2-3-2-3-3","description":"In the ScanSAR acquisition mode, the antenna beam is successively steered to different elevation angles. This results in adjacent, slightly overlapping stripes, or sub-swaths along the range direction, parallel to the azimuth direction, each stripe having a different incidence angle at its center. During antenna steering in elevation, transmitter and receiver are off. Therefore, each stripe is illuminated for a shorter time as for the StripMap mode, leading to a degradation of the azimuth resolution. However, ScanSAR allow a larger coverage in range direction than the other imaging modes. Each sub-swath is illuminated for a shorter time than in the Stripmap case. The timing is adjusted though, such that the time-varying antenna footprint repeat cyclically. Similar to the other acquisition modes, the achievable resolution and coverage of ScanSAR products depends on the considered sensor and its properties. For X-Band, e.g. for TerraSAR-X, a total swath width of 100 km in range direction can be achieved using four adjacent sub-swaths or, using a Wide ScanSAR mode with six adjacent sub-swaths, a swath width up to 270 km can be achieved. A Wide ScanSAR scene shows incidence angles ranging from 15.6° in near to 49° in far range. The azimuth resolution varies between 18.5 m and 40 m, for ScanSAR and WideScan SAR modes respectively. For the L-Band sensor ALOS-PALSAR 2, a swath width up to 40 km can be achieved, with incidence angles ranging from 8° to 70° and an azimuth resolution of 60 m. \r\nThe ScanSAR mode is well suited for large-area monitoring, e.g. for sea ice or glacier monitoring, as well as for mapping large-scale disasters, such as oil slick, or areas devastated by forest fires. Using interferometry, topography mapping and deformation monitoring is also possible.","hasChildren":true,"hasParent":true,"name":"ScanSAR","selfAssesment":"New
"},{"code":"PS2-2-2-3-2-3-5","description":"A stereoscopy acquisition mode collects remotely sensed data where each location on the ground (or the imaged objects) is covered multiple times (at least twice), from different perspectives. Stereopairs and stereoscopic coverage enable the extraction of 3D representations of the environment from remotely sensed imagery. Most aerial photographs are taken with frame cameras along flight lines, or flight strips. [...] Successive photographs are generally taken with some degree of endlap [, i.e. overlap]. Not only does this lapping ensure total coverage along a flightline, but an endlap of at least 50 percent is essential for total stereoscopic coverage of a project area. Stereoscopic coverage consists of adjacent pairs of overlapping vertical photographs called stereopairs. Stereopairs provide two different perspectives of the ground area in their region of endlap [overlap]. When images forming a stereopair are viewed through a stereoscope, each eye psychologically occupies the vantage point from which the respective image of the stereopair was taken in flight. The result is the perception of a three-dimensional stereomodel. As an input to photogrammetry analysis procedures, stereopairs from flight strips enable the extraction of digital elevation models (DEM), orthophotos, thematic GIS data, and other derived products through the use of digital raster images and relatively sophisticated analytical techniques. With the availability of close-range UAV and terrestrial hand-held camera data, 3D reconstructions of buildings (even indoors) and other objects on the terrain surface become possible.","hasChildren":true,"name":"Stereoscopy","selfAssesment":"In progress (to be deleted, merged?)
"},{"code":"PS2-2-2","description":"Since the 1940s aerial imagery has been the primary source of detailed geospatial data for extensive study areas. Photogrammetry is the profession concerned with producing precise measurements from aerial imagery. Aerial imaging and photogrammetry represent a major component of the geospatial industry. The topics included in this unit do not comprise an exhaustive treatment of photogrammetry, but they are aspects of the field about which all geospatial professionals should be knowledgeable.","hasChildren":true,"hasParent":true,"name":"Airborne platforms and systems","selfAssesment":"New
"},{"code":"PS2-2-3-1","description":"Earth observation (EO) missions are gathering information about the physical, chemical, and biological systems of the planet via remote-sensing technologies, supplemented by Earth-surveying techniques, which encompasses the collection, analysis, and presentation of satellite data.","hasChildren":true,"name":"Earth observation missions","selfAssesment":"In progress
"},{"code":"PS2-2-3-2","description":"There are essentially three types of Earth orbits: high, medium and low Earth orbit. Satellites that orbit in a medium (mid) Earth orbit include navigation and specialty satellites, designed to monitor a particular region. Most scientific satellites, including NASA’s Earth Observing System fleet, have a low Earth orbit. On which orbit a satellite will be launched to, depends mainly on its application. The orbit types can be categorized according to their height.\r\nThe orbit height of a satellite corresponds to the distance between the Earth’s surface and the satellite. It determines its speed as it rotates around the Earth. Due to Earth’s gravity, the pull of gravity is stronger for lower orbits than for higher orbits. Therefore, a satellite situated on a lower orbit will circle the Earth faster than a satellite situated on a higher orbit.\r\n\tHigh Earth orbit: it describes orbits situated at about 36000 km above the Earth’s surface (42164 km from the Earth’s center). At this exact distance, the speed of the satellite on the orbit matches the Earth’s rotation, i.e. the satellite needs 24 hours to complete a full rotation on the orbit, when the orbit is situated exactly above the equator. Such orbits are also called geosynchronous orbits, as the satellite moves at the same speed than the Earth and seems to stay in place over a specific location. Those orbits are mainly used for weather and communication satellites\r\n\tMedium Earth orbit: it describes orbits situated at about 20200 km of the Earth’s surface, or 26560 km of the Earth’s center. At this height, a satellite rotates twice around the orbit during one Earth’s rotation. This orbit is also called semi-synchronous and this is the orbit type used by Global Navigation Satellite Systems such as GPS and GLONASS. A further important medium Earth orbit is the Molniya orbit which allows the observation of the poles, otherwise nearly impossible with equatorial geosynchronous orbits.\r\n\tLow Earth orbit: this type of orbits are used from almost all dedicated scientific Earth Observation satellites. Most of them use a particular, nearly polar orbit inclination, meaning that the satellite rotates around the Earth nearly from pole to pole (instead of around the equator as it is the case for geosynchronous satellites). This rotation takes about 99 minutes, depending of the specific orbit inclination. During one half of the orbit, the satellite views the daytime side of the Earth, i.e. the illuminated side. At the pole, satellite crosses over and views the nighttime side of Earth. Back to the daylight side, the satellite can view the area adjacent to the region flown over in the last orbit path, due to the simultaneous Earth’s rotation. In 24 hours, satellites situated on these orbits view almost all the Earth twice, for optical satellites once in daylight and once in the dark. Radar satellites seen each Earth region twice, from two different illumination directions. These specific polar-orbits are called sun-synchronous, as the local solar time stays the same each time a satellite flies over a specific region. This has the advantage of providing an almost constant angle of sunlight for each region on the Earth’s surface viewed by the satellite over time and ensure repeatable sun illumination conditions; the angle will only vary seasonally due to the Earth revolution around the sun. Due to this consistency, images of a specific region would not show much illumination changes due to shadows or sunlight and image interpretation over time such as change detection or monitoring approaches are possible. Because a sun-synchronous orbit does not pass directly over the poles, there is a data gap over both poles where no data is acquired.","hasChildren":true,"hasParent":true,"name":"Types of satellite orbits","selfAssesment":"Completed
"},{"code":"PS2-2-3-3","description":"An imaging SAR system can generally make acquisitions in different modes. Which acquisition mode to choose depends of the application but also on the desired coverage and data resolution. Even if technically all acquisitions modes can be used everywhere on the Earth’s surface, specific modes are preferred for ocean applications that are different from the ones used in land applications.\r\nThe different acquisition modes can be defined either by their geometrical or by their temporal properties.\r\nThe geometrical properties refer to the geometric configuration of the SAR antenna. Usually looking sideways down in a direction perpendicular to the flight direction (Stripmap mode), the antenna can also be steered around the nadir axis in order to look at a specific target for a longer time during pass-by (Spotlight mode). This configuration allows to rachieve higher azimuth resolution but reduces coverage. It is rather used for very local application where a precise information about specific targets is needed. Other geometric configurations steer the antenna around the flight direction (ScanSAR mode), yielding to a larger swath on the ground. The distance between near and far range is increased, as well as the range of incidence angles within one acquisition. Whereas it increases the area of the scene, it comes generally with a decrease of the spatial resolution in the azimuth direction. Depending on the sensors, the name of the acquisition modes as well as particular technical properties can differ. Sentinel-1 uses a TOPS configuration (Terrain observation with Progressive Scan), which combines the antenna steering properties of both ScanSAR and Spotlight modes. \r\nThe temporal properties refer for specific techniques to the time interval between several acquisitions of the same area. Either these acquisitions are taken simultaneously in one pass over the area of interest (single-pass), or they are taken at different times, needing several passes over the area (repeat-pass).\r\nSpecific SAR techniques such as InSAR and Tomography, while relying on those geometric and temporal properties, have additional acquisition configuration characteristics. For example, the interferometric mission TanDEM-X has three acquisition modes defined by the number of satellite emitting or receiving the signal (pursuit monostatic mode, bistatic mode, alternating bistatic mode), which allows phase referencing. Tomographic SAR uses multi-baseline observations, i.e. the antenna passes several times over an area but at different heights, allowing via different incidence angles the retrieval of structural information of specific targets.","hasChildren":true,"hasParent":true,"name":"Synthetic Aperture Radar (SAR) acquisition modes","selfAssesment":"Completed
\r\n\r\n"},{"code":"PS2-2-3-4","description":"Swath width refers to the width of the ground that the satellite collects data from on each orbit. The area imaged on the surface, is referred to as the swath. Imaging swaths for spaceborne sensors generally vary between tens and hundreds of kilometres wide.","hasChildren":true,"name":"Swath","selfAssesment":"
In progress
"},{"code":"PS2-2-3","description":"Spaceborne platforms and systems are present at a great height from the earth surface. The altitude of platforms range from few hundred kilometers to several thousand kilometers. A large area can be captured in a single scene depending on altitude of sensor. The platforms can have different characteristics.","hasChildren":true,"hasParent":true,"name":"Spaceborne platforms and systems","selfAssesment":"Planned
"},{"code":"PS2-3-1","description":"Field spectroscopy generally refers to the use of non-imaging spectrometers near the ground surface and it is usually aimed at evaluating spectral reflectance of the investigated target. For this purpose, consecutive measurements of total incident solar irradiance and of radiance or irradiance upwelling from the target are collected by an operator, or more recently by new instruments for long-term and unattended field spectroscopy measurements. The incident irradiance is usually computed by measuring the radiance upwelling from a white calibrated panel which represents the ideal Lambertian surface. Upwelling fluxes are instead usually collected holding the sensor vertically over the surface (nadir view), although spectral libraries collected observing the target from different viewing angles are also available. \r\nField spectrometry is also referred to as ‘proximal sensing’ to underline that spectra are collected with portable spectroradiometers in the vicinity of the target, in contrast to ‘remote sensing’, which is instead usually performed with satellite or airborne sensors.\r\nField spectroscopy is therefore an in-situ method for characterising the reflectance of natural or artificial surfaces and thereby provides reference data for the calibration or validation (cal/val) of airborne and satellite sensors. This method provides a means of scaling-up measurements from small areas (e.g. leaves, rocks) to composite scenes (e.g. vegetation canopies), and ultimately to pixels.\r\nField spectroscopy is used in different applications, for example, soils, rocks, vegetation and chlorophyll fluorescence, water, snow surfaces and atmosphere. Long-lasting field spectroscopy campaigns based on manual measurements are extremely resource-demanding and do not ensure repeatability of the acquisition conditions as the instrument setup is initialized each day. To overcome such limitations a few research groups have initiated automatic tower-based spectral reflectance measurements using different devices. With such setups, non-imaging spectrometers are installed in the field and are operated automatically for long periods (i.e. months to years) and different networks of hyperspectral instruments are now becoming operational (e.g. RadCal Net).\r\nField spectroscopy can be also used to predict optimum spectral bands, viewing configuration, spectral calibration and time to perform a particular remote sensing task but also to develop, refine and test models relating biophysical attributes to remotely-sensed data. In this context, ground reflectance measurements are therefore mainly used as input in simulation study for sensor design, calibration/validation data for remote sensing sensors, for spectral mixture analysis and for the development of relationships between field data and radiometric variables.\r\nSince spectroscopy is the study of matter using electromagnetic radiation, point or imaging field spectrometers are instruments which allow the measurements of reflected or emitted electromagnetic radiation. In particular, portable or hand-held spectroradiometers are small instruments that spectrally measure the radiation reflected or emitted by a target and they are useful in obtaining accurate spectral data over different surfaces. In remote sensing, they generally cover the 400-2500 nm spectral range and operate with a full width at half-maximum of about 1.5/3 nm, so that they can collect radiation in a continuous way across the spectrum. The final output is therefore the hyperspectral signature of reflectance of the surfaces versus the considered wavelength.","hasChildren":true,"name":"Field spectroscopy and portable spectroradiometers","selfAssesment":"Completed
"},{"code":"PS2-3-2","description":"A terrestrial laser scanning (TLS) system is a stationary highly accurate ranging device for geodetic surveying. More specifically, TLS systems provide dense and accurate 3D point cloud data for the local environment and they may also reliably measure distances of several tens of meters. Due to these capabilities, such TLS systems are commonly used for applications such as city modeling, indoor modeling, construction surveying, deformation analysis, scene interpretation, urban accessibility analysis, or the digitization of cultural heritage objects. When using a TLS system, each captured TLS scan is represented in the form of a 3D point cloud consisting of a large number of scanned 3D points and, optionally, additional attributes for each 3D point such as color or intensity information. However, a TLS system represents a line-of-sight instrument and hence occlusions resulting from objects in the scene may be expected as well as a significant variation in point density between close and distant object surfaces. Thus, a single scan might not be sufficient in order to obtain a dense and (almost) complete 3D acquisition of interesting parts of a scene and, consequently, multiple scans have to be acquired from different locations. As each scan refers to the local coordinate system of the TLS system, all acquired scans have to be appropriately aligned in a common coordinate system. For this purpose, the respective 3D transformations between the acquired scans have to be estimated and this process is commonly referred to as point cloud registration, point set registration, or 3D scan matching.","hasChildren":true,"name":"Terrestrial Laser Scanning","selfAssesment":"Completed
"},{"code":"PS2-3","description":"Platforms and systems that acquire data from the level of earth's surface. A wide variety of ground based platforms are used in remote sensing. The acquired data are used for detailed in-situ measurements, e.g., Leaf Area Index (LAI), and for calibration/validation campaigns.","hasChildren":true,"hasParent":true,"name":"Ground platforms and systems","selfAssesment":"New
"},{"code":"PS2","description":"Remote sensing platforms and systems can be static (ground-based platforms) or moving (e.g. airborne or spaceborne platforms, UAVs). A remote sensing platform or system carry a remote sensing sensor. It can operate in near (few centimetres) or far (36,000 kilometres) altitudes ranges.","hasChildren":true,"hasParent":true,"name":"Types of remote sensing platforms and systems","selfAssesment":"Planned
"},{"code":"PS3-1","description":"The development of remote sensing data carriers has followed the evolution of the photography, remote sensing sensors and computer platforms. The first remote sensed data was stored using the photography films (e.g. aerial photography, satellite Corona program), which was later replaced by reel tapes, cartridge, and then removable and hard discs. In the era of big and fast growth of Earth observation data, and technological advancements in digital infrastructure, the satellite data are stored using cloud platforms providing different service models: Infrastructure as a Service, Platform/Software as a Service (e.g. Copernicus DIAS, Google Earth Engine, open EO). The Cloud offers infrastructure to host, store and process the large amount of data efficiently. For example, the Copernicus Data Information Access Services (DIAS) is a comprehensive cloud-based hosting and processing system for the EO data in particularly for the Sentinels data, the Google’s Earth Engine (GEE) provides access to various satellite and offers processing power with a web-based programming interface, the Amazon Web Services (AWS) has dedicated cloud called ‘Earth on AWS’, the Microsoft’s cloud called Azure facility the use of AI tools to address environmental challenges. Public solutions, as well as private ones, react with a variety of new and innovative tools, which have been recently developed (e.g. DIAS, ODC, EarthServer, EO Browser, GEE).","hasChildren":true,"name":"History of remote sensing data carriers","selfAssesment":"Completed
"},{"code":"PS3-2-1","description":"Most remotely sensed images nowadays exist in digital form. Even domestic cameras are now usually digital instruments, and the use of photographic film is becoming rarer and rarer. Analogue images, such as photographs, are continuous, both in their spatial extent (they can be enlarged almost without limit) and radiometrically (there is a continuous range of shades of grey). The word ‘picture’ is usually used for such an image.\r\nOn the other hand, a digital image is spatially and radiometrically discrete. A remote sensing sensor detects the reflected radiation of the Earth’s surface and stores it as numbers in a raster. In accordance, each area that has been detected constitutes a cell in a raster. The grey levels increment in a stepwise fashion, and the scene is made up from an array of individual elements called ‘picture elements’, abbreviated to ‘pixels’, each of which is represented by one of the discrete grey levels. A pixel is the smallest addressable element in a raster image.\r\nThe spatial resolution of a raster image refers to the size of the ground element represented by an individual pixel. The size of an area represented in a pixel depends of the capability of the sensor to detect details. A pixel cannot be subdivided, and enlargement merely produces larger pixels, which contain no more information than the original ones. We are familiar with this effect on our television or computer screen – the picture we see consists of an array of dots of light, the density of which determines the screen resolution.\r\nThe number of distinct grey levels into which the intensity of the signal is divided and that can be represented by a pixel is called radiometric resolution of a digital image, and it depends of the number of bits per pixel (bpp). A 1 bpp image uses 1 bit for each pixel, so each pixel can be either on or off (monochrome). Each additional bit doubles the number of grey levels available, so a 2 bpp image can have 4 grey levels, a 3 bpp image can have 8 grey levels, and so forth. In colour imaging systems, a colour is typically represented by three component intensities such as red, green, and blue; usually their raster images have an 8-bit resolution (256 grey levels), a 16-bit resolution (65,536 grey levels), or a 24-bit resolution (16,777,216 grey levels).","hasChildren":true,"name":"Picture element (pixel)","selfAssesment":"Completed
"},{"code":"PS3-2-2","description":"One can think of any image as consisting of tiny, equal areas, or picture elements, arranged in regular rows and columns. The position of any picture element, or pixel, is determined on an xy coordinate system. Each pixel also has a numerical value, called a digital number (DN), that records the intensity of electromagnetic energy measured for the ground resolution cell represented by that pixel. Digital numbers range from zero to some higher number on a gray scale. The image may be described in strictly numerical terms on a three-coordinate system with x and y locating each pixel and z giving the DN, which is displayed as a gray-scale intensity value. \r\nMany types of remote sensing images are routinely recorded in digital form and then processed by computers to produce images for interpreters to study. An image recorded initially on photographic film may be converted into digital format by a process known as digitization.","hasChildren":true,"name":"Image as a matrix (digital number DN)","selfAssesment":"Completed
"},{"code":"PS3-2-3","description":"In data manipulation contexts, a data cube is a multi-dimensional array of values. A data cube can be visualized as the multidimensional extension of two-dimensional table. It can be viewed as a collection of identical 2-D tables stacked upon one another. Data cubes are used to represent data that is too complex to be described by a traditional table of columns and rows. Typically, the data cube is applied in conditions where these arrays are massively larger than the hosting computer’s main memory, for example multi-terabyte data warehouses o time series of image data.","hasChildren":true,"name":"Data cubes","selfAssesment":"In progress
"},{"code":"PS3-2-4","description":"Term Big data refers to any collection of data sets so large and complex that it becomes difficult to process using on-hand data management tools or traditional data processing applications. In the field of Earth Observation (EO) is usually refers to large time series of image data which size on disk is much greater than hosting computer’s main memory. EO Big Data offers solution that allows not only storing these data on disk but also efficiently process them.","hasChildren":true,"name":"Earth Observation Big Data","selfAssesment":"In progress
"},{"code":"PS3-2","description":"Most remote sensing data exist as digital images, and appropriate image processing allows the emphasis of certain aspect and subsequent extraction of information for specific applications.\r\nA digital image is a representation of the reality as a grid of picture elements. It can be considered as an array of numbers that can be stored and handled by a digital computer. The picture elements are pixels and each pixel has a specific value (usually in grayscale). This value is a digital number (DN), which usually represents the amount of energy recorded by the sensor at this pixel position or any other characteristic recorded by the sensor, e.g. elevation. \r\nEach row of the image grid, or matrix, corresponds to one scan line. Each pixel is characterized by its row r and column c position in the image, as well as by its value. Additional geographical information is needed in order to assign a geographic location to a pixel. The digital number are integers usually compressed in one byte (= 8 bit) representation, i.e. each pixel can take 256 values.\r\nDigital images are raster data, as opposite to vector data. Whereas vector data can be points, lines or polygones, raster data always consist of pixels. A pixel is the smallest element in which an image can be divided into. The pixel size varies depending of the instrument and of the sampling used. Large pixel may contain information about several objects of the recorded scene. However, they only have one value. These are called mixed-pixel, as e.g. several land cover classes are represented within one pixel and they cannot be distinguished from another. \r\nIn multispectral imagery, each region of the electromagnetic spectrum is recorded in an independent image (band). Therefore, at a specific array position (r,c), there exist several pixels, each with a specific value corresponding to the energy recorded for the considered band. This result in a three-dimensional matrix. The bands of a multispectral image can be displayed three at a time in the computer using for each band one of the three primary colors red, green and blue (RGB). This is called a color composite image. If the color composite represents a combination of the visible red, green and blue bands in their respective color, the combination is called natural or true color composite, as it corresponds to what the human eye sees naturally. Any other combination, for example considering bands of wavelengths that are not visible for the human eye is called a false color composite. It is often used to highlight the spectral differences and particular image features in order to extract information.","hasChildren":true,"hasParent":true,"name":"Digital image terminology","selfAssesment":"Completed
"},{"code":"PS3-3-1","description":"Band interleaved by line (BIL) is one of three primary methods for encoding image data for multiband raster images in the geospatial domain, such as images obtained from satellites. This simple uncompressed raster data encoding is easily and frequently described, requiring no formal specification. BIL is not in itself an image format, but is a scheme for storing the actual pixel values of an image in a file band by band for each line, or row, of the image. The raw data has a simple form and is easily interpreted if the image dimensions in pixels, the number of spectral bands, and the number of bits per band are known. For example, given a three-band image, all three bands of data are written for row one, all three bands of data are written for row two, and so on. The BIL encoding is a compromise format, allowing fairly easy access to both spatial and spectral information. The BIL data organization can handle any number of bands, and thus accommodates black and white, grayscale, pseudocolour, true colour, and multi-spectral image data.\r\nAdditional information is needed to interpret the image data, such as the numbers of rows, columns, and bands, and relate the image to geospatial locations. This information may be supplied in a file header (typical on the tapes originally used for satellite image data) or in files associated with a raw image data file.\r\nSpatial resolution and bit-depth are not limited by the BIL encoding per se but may be constrained in some usage contexts. There is no support for colour management in the BIL encoding. Documentation of spectral values for bands, or interpretation of false colours should be supplied in an accompanying data structure.","hasChildren":true,"name":"Band interleaved by line (BIL)","selfAssesment":"Completed
"},{"code":"PS3-3-2","description":"Band interleaved by pixel (BIP) is one of three primary methods for encoding image data for multiband raster images in the geospatial domain, such as images obtained from satellites. This simple uncompressed raster data encoding is easily and frequently described, requiring no formal specification. BIP is not in itself an image format, but is a method for encoding the actual pixel values of an image in a file. The raw data has a simple form and is easily interpreted if the image dimensions in pixels, the number of spectral bands, and the number of bits per band are known. Images stored in BIP format have the first pixel for all bands in sequential order, followed by the second pixel for all bands, followed by the third pixel for all bands, etc., interleaved up to the number of pixels. The BIP data organization can handle any number of bands, and thus accommodates black and white, grayscale, pseudocolour, true colour, and multi-spectral image data.\r\nBIP data stores pixel information for separate bands within the same file, so that the user can choose to display just one specific band in a multi-band image. Therefore, BIP encoding provides optimal processing performance for spectral analysis (as compared with BIL or BSQ raster organization) as it supports efficient extraction of individual spectra and spectral averages.\r\nAdditional information is needed to interpret the image data, such as the numbers of rows, columns, and bands, and relate the image to geospatial locations. This information may be supplied in a file header (typical on the tapes originally used for satellite image data) or in files associated with a raw image data file.\r\nSpatial resolution and bit-depth are not limited by the BIP encoding per se but may be constrained in some usage contexts. There is no support for colour management in the BIP encoding. Documentation of spectral values for bands, or interpretation of false colours should be supplied in an accompanying data structure.","hasChildren":true,"name":"Band interleaved by pixel (BIP)","selfAssesment":"Completed
"},{"code":"PS3-3-3","description":"Band sequential (BSQ) is one of three primary methods for encoding image data for multiband raster images in the geospatial domain, such as images obtained from satellites. This simple uncompressed raster data encoding is easily and frequently described, requiring no formal specification. BSQ is not in itself an image format, but is a method for encoding the actual pixel values of an image in a file. BSQ format is a very simple format, where each line of the data is followed immediately by the next line in the same spectral band. The raw data has a simple form and is easily interpreted if the image dimensions in pixels, the number of spectral bands, and the number of bits per band are known. This format is optimal for spatial (x, y) access of any part of a single spectral band. The BSQ data organization can handle any number of bands, and thus accommodates black and white, grayscale, pseudocolour, true colour, and multi-spectral image data.\r\nA single band covering the entire scene is stored as a single bitstream making this encoding method convenient when only selected bands are needed. Each image band can be conveniently written to an independent file. BSQ can therefore be a preferable format for some forms of analysis as an application does not have to read past ancillary data in an image stack. As opposed to formats where the bands are interleaved (such as a multi-band GeoTIFF), BSQ data sets support convenient extraction of relevant bands. Some BSQ datasets are distributed as separate image files for each band, with common geospatial registration and a shared set of header information.\r\nAdditional information is needed to interpret the image data, such as the numbers of rows, columns, and bands, and relate the image to geospatial locations. This information may be supplied in a file header (typical on the tapes originally used for satellite image data) or in files associated with a raw image data file.\r\nSpatial resolution and bit-depth are not limited by the BSQ encoding per se but may be constrained in some usage contexts. There is no support for colour management in the encoding. Documentation of spectral values for bands, or interpretation of false colours should be supplied in an accompanying data structure.","hasChildren":true,"name":"Band sequential (BSQ)","selfAssesment":"Completed
"},{"code":"PS3-3","description":"EO data consist of unstructured image data and structured descriptive information attached to the image, which is also called metadata. EO systems are rapidly developing and data sensors resolution are continuously improving. As a result, a vast amount of EO data is generated every day, and their volumes have been in geometric progression growth. According to the current literatures, storage and management methods of EO data are divided into four groups from the perspective of basic technologies: \r\n1. File systems: Traditionally, EO data were manually managed and organized by means of file systems that share and exchange data through storage devices. However, for large amounts of EO data this method leads to inefficiency of management, extra expenses of storage spaces, and weak data security. File systems cannot efficiently support for data retrievals, analyses, and uses in practical applications and research work nowadays. For solving these problems, parallel file system and distributed file system (see below) were presented to support data-intensive applications.\r\n2. Relational Data Base Management Systems (RDBMS): At present, storage and management manners of major EO data are to combine RDBMS and middle-wares. On one hand, traditional RDBMS functionalities are expanded to adapt to the storage and management features of EO data. Adding new data types or encapsulating complicate data types as an object in RDBMS are two general ways to expand functionalities of traditional RDBMS. The former can meet basic requirements of EO data storage and management, but is unable to directly operate spatial data and create spatial indexes. This solution is mainly taken by Database Management System (DBMS) developers, such as Spatial GeoRaster of Oracle, Spatial Extender of IBM DB2, PostGIS of PostgreSQL, and Spatial Extension of MySQL. On the other hand, geographical software expands their data management abilities by developing spatial database engine middle-wares, which is always taken by software enterprises that develop geographical information system (GIS). Spatial Database Engine (SDE) is between users and DBMS. For data storing, SDE is responsible for receiving and storing user data into RDBMS; for data retrieving, it reads data from RDBMS and show them through user interfaces. This resolution stores EO data into RDBMS and interactively manages them by user interfaces provided by SDE. SDE technology is very mature and extensively used in various application fields. As SDE is developed by software enterprises of GIS, they have good comparability with integrated software platform of GIS. \r\n3. Distributed file systems: Recently distributed file system is a new technology of solving data-intensive computing problems. Several distributed file systems have emerged such as PVFS, GPFS, ZFS, GFS, HDFS, and Lustre. \r\n4. Large-scale network storage systems: It is a type of distributed file system with data sharing and remote access functionalities. As the performance improving of hardware and rapid development of network technologies, Storage Area Network (SAN) and Network Attached Storage (NAS) are introduced to distributed file systems. Large-scale network storage systems take different storage and management strategies for EO image files and their metadata. EO image files are stored and managed by HDFS, and their metadata are stored, processed, and managed in RDBMS metadata servers. Managing EO imagery files and their metadata in different ways can improve the management efficiencies of EO data, and balance the loading of distributed file systems. Such systems have already been developed including Celerra, CLARIION, and Symmetric storage solution of EMC, IBM HPSS, MSS, and RASCHAL of National Aeronautics and Space Administration (NASA), the Microsoft earth image storage system, and the Google Earth image storage system.","hasChildren":true,"hasParent":true,"name":"Data storage","selfAssesment":"Completed
"},{"code":"PS3-4-1","description":"The spectral resolution of an Earth Observation sensor refers to the number of spectral bands this sensor can capture. Spectral bands are wavelength intervals in the electromagnetic spectrum. Sometimes, spectral bands are also called spectral channels. Spectral resolution is related to a sensor’s ability to distinguish between different Earth’s surface features based on their spectral properties. A high number of spectral bands means high spectral resolution, with many bands meaning an increasingly smaller range of wavelengths covered by a single band. The spectral resolution of an Earth observation sensor can range from a single very broad band for panchromatic black and white images over a few bands in the case of multispectral sensors (e.g. Landsat family, SPOT, Sentinel-2) to 200 or even more channels for capturing hyperspectral images. Multispectral or hyperspectral sensor imagery has a higher degree of discriminating power than a single band sensor. Another definition of the spectral resolution can be given by the spectral sensitivity of a sensor, which can be specified by the definition of the full width, half maximum (FWHM) as being the spectral interval measured at the level at which the response of the instrument reaches one-half of its maximum values.\r\nSpectral satellite sensors can only gather radiation which is able to pass the Earth’s atmosphere. The atmosphere contains gases, aerosols, ice crystals and water droplets, which absorb and scatter parts of the radiation passing through the atmosphere. Wavelength ranges which do not allow radiation to pass through on their way to the satellite sensors are called absorption bands and those getting through to the sensor are called atmospheric windows. This means that spectral sensors can only operate in these atmospheric windows and the spectral bands should be placed in the wavelength ranges of the atmospheric windows.","hasChildren":true,"name":"Spectral resolution","selfAssesment":"Completed
"},{"code":"PS3-4-2","description":"The spatial resolution of an image corresponds to the size of the minimum area that can be resolved by the sensor. \r\nDue to the different techniques of acquisition of passive and active sensors, the spatial resolution is determined for both sensor types differently. \r\nFor passive sensors, the spatial resolution depends on their instantaneous field of view (IFOV), which determines the area of the Earth’s surface that is viewed at one particular moment in time by one detector element. The size of this area is called resolution cell and characterizes the spatial resolution of the sensor. Depending on the spatial resolution, whole features of the Earth’s surface can be detected homogeneously in one or several resolution cells. For features smaller than the spatial resolution, the average reflected radiation of all features within a resolution cell is recorded, leading to so-called mixed-pixels.\r\nFor imaging active systems, the spatial resolution is dependent of both the length of the transmitted pulse in looking direction and the width of the radiation beam or the antenna width in flight direction.\r\nIn all cases, the spatial resolution indicates the level of detail observable in an image. Usually, one distinguishes between coarse (low), moderate (medium) and fine (high and very high) resolution, whereby the use of this denomination is often context-dependent. Sensors with coarse resolution can only detect large features, but they usually cover a much larger area than high-resolution sensors, which can provide detailed information on small objects such as individual buildings, trees or cars, but for much smaller areas. Coarse spatial resolution mean in general a resolution cell larger than 250 m and a scene extent of several thousands of kilometers (>1000 km). Moderate resolution sensors have a spatial resolution of 30 m to 80 m, and a coverage of approximately 200 km in a single acquisition. Sensors showing spatial resolutions from 5 m or 6 m are high-resolution sensors, with a spatial coverage up to approximately 20 km. Sensors with a resolution cell’s width of less than 1 m are considered as very-high-resolution sensors.\r\nLow resolution sensors are appropriate for the analysis of broad-scale phenomena such as ocean color or cloud patterns. Medium resolution sensors are rather used for regional analysis such as land cover change and phenological response of vegetation. High-resolution sensors are particularly useful for object detection.","hasChildren":true,"hasParent":true,"name":"Spatial resolution","selfAssesment":"Completed
"},{"code":"PS3-4-3","description":"The radiometric resolution of a sensor refers its sensitivity, which is the ability to detect small differences in signal strength as it records the radiant flux reflected, emitted, or back-scattered from the terrain.\r\nThe specification of the radiometric resolution is different in the optical domain of the electromagnetic spectrum than in the radar range.\r\nIn the optical domain, the radiometric resolution is given in bits. The maximum number of brightness levels available depends on the number of bits. The larger this number, the higher the radiometric resolution. As an example, the optical sensor Sentinel-2 has a radiometric resolution of 12 bits. This means that a pixel of an image acquired by Sentinel-2 can have 2^12 = 4096 grey levels.\r\nIn the radar domain, the radiometric resolution is usually specified as a backscatter level expressed as an logarithmic value. For instance, the radiometric resolution of Radar Scattermeters lies in the range of 0.1 to 0.3 dB, whereas the radiometric resolution of SAR sensors are in the range of 1.2 – 2.5 dB. This means that only differences in radar backscatter larger than these values can be interpreted as interpretable changes the of backscatter conditions at the Earth’s surface. Smaller measurement differences could have been caused by differences in backscatter conditions or just as well by instrument noise.","hasChildren":true,"name":"Radiometric resolution","selfAssesment":"Completed
"},{"code":"PS3-4-4","description":"The concept of temporal resolution of Earth observation data refers to the revisit time or period. This is the time, which is necessary for the sensor platform (e.g. a satellite) to complete one entire orbit cycle. During one orbit cycle, the surface of the earth is completely covered by the sensor once. Temporal resolution also means the ability of a sensor to detect changes over shorter or longer periods of time. The revisit time for Earth observation satellites is usually several days. Or to express it differently: The absolute temporal resolution of a sensor orbiting the Earth is the time required to image the exact same area at the same viewing angle a second time. \r\nThe satellite orbit itself depends on the radius of the Earth, the orbit altitude above the Earth’s surface and the gravitational acceleration at planet’s surface. The time required to complete on entire orbit cycle additionally depends on the swath width of the sensor, the overlap between adjacent swaths and the geographical location at the Earth’s surface. The repetition rate increases slightly from the equator towards the north and south, which means that the revisit time is increasing with latitude. As a result, areas located in North America or Australia, for example, are covered a little more frequently than areas in Africa or South America near the equator. \r\nBut there are satellite systems that allow the pointing of their sensor to image the same area between different satellite passes separated by periods from one to five days. Thus, the actual temporal resolution of a sensor depends on a variety of factors, including the satellite/sensor capabilities, the already mentioned swath width and overlap, and latitude.","hasChildren":true,"name":"Temporal resolution","selfAssesment":"Completed
"},{"code":"PS3-4","description":"A digital image begins as an analog signal. Through computer data processing, the image becomes digitized and is sampled multiple times. The critical characteristics of a digital image are spatial resolution, spectral resolution, radiometric resolution, contrast resolution, noise, and dose efficiency. These depends upon satellite orbit configuration and sensor design. Different sensors have different resolutions.\r\nSpectral resolution describes the ability of a sensor to define fine wavelength intervals. The narrowest spectral interval that can be resolved by an instrument. Spectral resolution (spectral capability) also refers to the number of wavebands within the EM spectrum that an optical sensor is taking measurements over.\r\nRadiometric resolution can be defined as the ability of an imaging system to record many levels of brightness. Radiometric resolution refers to the range in brightness levels that can be applied to an individual pixel within an image, determined on a grayscale. E.g., Sentinel-2 sensor MSI is a 12 bit sensor imaging with 4.096 levels.\r\nSpatial resolution of an image corresponds to the size of the minimum area that can be resolved by the sensor.\r\nTemporal resolution, also referred to as the revisit cycle, is defined as the amount of time it takes for a satellite to return to collect data from exactly the same location on the Earth. Imageing of the exact same area at the same viewing angle a second time is temporal resolution.","hasChildren":true,"hasParent":true,"name":"Properties of digital imagery","selfAssesment":"Completed
"},{"code":"PS3-5-1","description":"A header file is usually a separate file associated with an image file. The header file can be either a plain ASCII-file or a binary file. It contains information about the image file it is associated with. These information can comprise the number of pixels per row (x-direction in a two-dimensional image), also called number of columns, the number of lines or rows (y-direction in a two dimensional image), the number of bands (corresponding to the z-direction), pixel spacing and spatial resolution, geographic reference information, the byte order (e.g. big-endian or little-endian), spectral information for each band, calibration constants and many more. The purpose of a header file is to provide basic information about the properties of the image data either to the user or to a software and enabling a software to correctly load and display the image content. In this way, information contained in a header file can also be called metadata, which is data about the data. The structure and the information contained in a header file of remote sensing imagery can be found in the so-called product information documents. There is also digital imagery used in remote sensing containing the information found in header files not in a separate file but as part of the digital image data itself. In this case this is called header information or a file header, which is usually found at the beginning of the image file. In some cases, image files may contain several header sections, e.g. the ESA Envisat ASAR SAR data imagery contains a Main Product Header and a Specific Product Header section. Header information as part of the image file itself may be stored in ASCII or in binary format, or in a mixed binary format, as it was used for the ESA Envisat SAR data.","hasChildren":true,"name":"Header file","selfAssesment":"Completed
"},{"code":"PS3-5","description":"The image data stored in a binary data format (BIL, BIP, BSQ) is accompanied by description files that contain a set of entries describing the image data, including acquisition time, image size, statistics, map projection, pixel digital numbers, product type, etc. This general image or product information is stored in a form of header embedded in the image file or provided in the separate file (.hdr) or metadata in XML. There are numerous image file formats, the more common are TIFF (GeoTIFF), bitmap (.bmp), JPEG (.jpg, .jpeg, JPEG2000), HDF, Raw (.raw), Extensible N-Dimensional Data Format (NDF).","hasChildren":true,"hasParent":true,"name":"Image description files","selfAssesment":"In progress
"},{"code":"PS3-6","description":"The concept of data formats refers to the way, in which the digital data are organized and stored. The data format for a remote sensing mission is usually chosen based on a number of considerations, including requirements of the sensing system, mission objective, the design and technology of data processing, archiving, and distribution systems, as well as community data standard.\r\nEarth observation data usually come as raster data. The raster data refers to a data model, which holds digital numbers or values in a regularly spaced matrix of cells arranged in rows and columns covering a two-dimensional space. A digital Earth observation image may contain several layers of this two-dimensional space, e.g. one layer for a specific spectral band in the optical or microwave region of the electromagnetic spectrum. The cells in such a layer are also called pixels, which stands for picture element. \r\nEarth observation data in an image are stored on a storage medium in one of three formats: Band-Interleaved-by-Sample (BIS), Band Sequential (BSQ), or Band-Interleaved-by-Line (BIL). These formats are determined by different ordering of the data dimensions. Other data formats used in remote sensing, which in this case refer to the file format are GeoTIFF, NetCDF, and HDF.\r\nExact details on the data format of an Earth observation data set is usually provided by the originator of the data, e.g. space administrations such as NASA or ESA or private companies.","hasChildren":true,"name":"Data formats","selfAssesment":"Completed
"},{"code":"PS3-7-1-1","description":"Depending on the sensor and the provider, remotely sensed imagery is made avalilable to the user at different processing levels. For Sentinel-2, the lowest product level made available to the user is Level-1B. THe Level-1B product provides radiometrically corrected imagery in Top-Of-Atmosphere (TOA) radiance values and in sensor geometry. Radiometric corrections applied to the Level-1B are: dark signal, pixels response non uniformity, crosstalk correction, defective pixels interpolation, high spatial resolution bands restoration (deconvolution puls denoising), binning (spatial filtering) for 60m bands.","hasChildren":true,"name":"Radiometrically corrected","selfAssesment":"New
"},{"code":"PS3-7-1-2","description":"Geometrically corrected products are of a higher processing level than radiometrically corrected products. For Sentinel-2, the geometrically corrected product is the Level-1C product. The Level-1C product results from using a Digital Elevation Model (DEM) to project the image in cartographic coordinates. Per-pixel radiometric measurements are provided in Top Of Atmosphere (TOA) reflectances with all parameters to transform them into radiances. Level-1C products are resampled with a constant Ground Sampling Distance (GSD) of 10, 20 and 60 m depending on the native resolution of the different spectral bands. Level-1C products will additionally include Land/Water, Cloud Masks and ECMWF data (total column of ozone, total column of water vapour and mean sea level pressure). (Sentinel-2 User Handbook, p.44)","hasChildren":true,"name":"Geometrically corrected products","selfAssesment":"New
"},{"code":"PS3-7-1","description":"The definition of processing levels for optical data depends on the considered sensor. Most common satellite optical imagery are available in three distinct processing levels, from level 0 to level 2. The most used processing levels are level 1 and level 2, depending on the user and the application. \r\nIn Level 0, the raw data are processed in a way that they are ready to be archived. Processing operations generally includes telemetry analysis, error detections and granule concatenation. Furthermore, relevant parameters such as acquisition date and geographical reference are annotated in the form of metadata, this information being necessary for processing higher levels. Additionally, a quicklook of the image is generated. No correction is performed at this level.\r\nLevel 1 is often divided in several sublevels. Generally, both radiometric correction and geometric refinement are performed at this level. The radiometric processing includes several radiometric corrections such as dark signal correction or spectral band binning. The radiometric correction allows the determination of physical variables (e.g. reflectance) from the digital numbers. The geometric processing includes tiles association and resampling grid computation, in order to link for each image band its native image geometry to the target geometry. The result of this processing steps is usually a geocoded, Top of Atmosphere product.\r\nLevel 2 data usually consist of atmospherically corrected Level 1 data, i.e. Bottom-of-Atmosphere data. These surface reflectance products may be accompanied by additional outputs, such as scene classification, water vapor or surface temperature maps.\r\nFor specific applications and sensors, Level 3 application ready data are available. These are derivated products such as burned area, dynamic surface water content and snow cover maps.\r\nDepending on the considered sensor and level, the name of the sublevels can differ: Sentinel 2 defines Level-1B as radiometrically corrected data. Level 1C are radiometrically and geometrically corrected data, i.e Top-Of-Atmosphere (TOA) orthoimage products. Landsat sensors distinguish between Terrain precision correction (L1TP), systematic Terrain Correction (L1GT) and Geometric systematic Correction (L1GS) depending on the quality of the reference data for geometric correction. These are usually separated into Tier 1 and Tier 2 datasets.","hasChildren":true,"hasParent":true,"name":"Processing levels of optical data","selfAssesment":"Completed
"},{"code":"PS3-7-2-1","description":"SLC is an abbreviation and stands for Single Look Complex. SLC data are one so called radar product. Like all radar products they have been derived from SAR raw data, often called Level 0 products, downloaded from the SAR satellite by the satellite operators. They apply a software called a processor to transform SAR raw data into formats that can be used by users for different applications. SLC data are often referred to as Level 1 products and are the first SAR product derived from the raw data to be made available to users.\r\nAs the name suggests, SLC data only contain one single look, which means that the azimuth compression has been carried out using the full azimuth bandwidth of the SAR sensor leading to the highest spatial resolution in azimuth direction. But as a consequence, SLC data suffers from maximum speckle. \r\nThe word “complex” in SLC means that the data are stored as complex numbers with a real and an imaginary part. In this way, SLC data contain both – phase stored in the real part and amplitude information stored in the imaginary part of the complex number for one resolution cell.\r\nSLC data are given in slant-range geometry and appears to be distorted. The is due to the fact that the spacing between pixels in the slant range direction is directly proportional to the signal travel time or time interval between backscattered and received radar pulses. And this time interval in again is directly proportional to the slant range distance between the sensor and the imaged objects at the Earth’s surface and not to the horizontal ground distance between the nadir and the imaged object. Therefore, SLC images appear distorted, which means that they look compressed in near range (close to the nadir) and getting ever more expanded in towards the far range.\r\nSLC data are the basis for further SAR products generated and are required for interferometric analysis methods, which rely on phase and amplitude information.","hasChildren":true,"name":"Single Look Complex (SLC)","selfAssesment":"Completed
"},{"code":"PS3-7-2-2","description":"From the Single Look Complex (SLC) product the Multi-look Detected/Multi-looke (MLD/MLI) can be generated. It is produced by multi-looking, i.e., averaging, over range and/or azimuth resolution cells.","hasChildren":true,"name":"Multi-looked Detected (MLD)","selfAssesment":"New
"},{"code":"PS3-7-2-3","description":"Precision Images (PRI) are the Multi-look Detected/Multi-looked Intensity (MLD/MLI) images that have been resampled into square pixels, rotated to account for the view direction of the instrument and warped by some predefined operation that the projected image pixels are georeferenced onto a specified geographical coordinate system.","hasChildren":true,"name":"Precision Images (PRI)","selfAssesment":"New
"},{"code":"PS3-7-2-4","description":"Ground Range Detected (GRD) radar imagery is a Level-1 product that has been derived from Level 0 (raw data) SLC SAR data by a Processing Facility via the application of a processing software. GRD products usually consist of focused SAR data that has been detected, multi-looked and projected to ground range using an Earth ellipsoid model.\r\nFocused SAR data are generated in a raw data processing step. During focusing, the two-dimensional signal energy of a point target that is spread in range and azimuth direction is aggregated and put into a single image pixel in the output data set.\r\nDetected means that the complex numbers representing phase and amplitude values in the original data set have been converted to real numbers by taking their absolute square (or complex conjugate). In the resulting image data, the phase information is not present any longer and only amplitude information remains as the pixel value.\r\nThe SAR imagery in GRD radar data is given in ground range geometry, which differs from the slant geometry of the SLC data. In ground range geometry, the spacing between the image objects at the Earth’s surface is in direct proportion to their real distance along a hypothetical flat ground surface. Here, image coordinates are oriented along ground range and flight direction. This means that they do not show the distorted appearance of an SLC image.","hasChildren":true,"name":"Groud Range Detected (GRD)","selfAssesment":"Completed
"},{"code":"PS3-7-2","description":"For SAR data, usually three processing levels are distinguished, ranging from level 0 (less processed) to level 2 (higher processed).\r\nLevel 0 products consist of compressed and unfocussed raw data and are the basis for the processing of higher level products. Level 0 data are principally used for research in the topic of signal processing. As for optical data, level 0 product are annotated with several metadata, such as calibration and orbit information, and acquisition time and date.\r\nLevel 1 data can be separated in two distinct product types, depending if the full complex information is used (amplitude and phase) or only the amplitude information. The product denomination depends on the sensor type; for Sentinel 1 the names Single Look Complex (SLC) and Ground range detected (GRD) are used, respectively. Both products can be generated from the Level 0 data. Level 1 data are the products that are used by most scientific users. The processing toward Level-1 data includes Doppler centroid estimation and data focusing. The Level 1 SLC product consists of the real and imaginary part of focused complex SAR data in slant range geometry, from which the phase and amplitude information can be retrieved. This is available for all acquired polarisations. Additional orbit information for georeferencing is provided with the data. The Level 1 GRD data consist of focused and multi-looked SAR data that have been projected to ground range geometry. GRD data only contain amplitude information, therefore the phase information is lost. The multi-looking step is particular for GRD data and allows both speckle reduction and square pixel resolution. As for the SLC data, the GRD data are annotated with orbit information for georeferencing. The Level-1 products are not calibrated, they include however information about calibration constants, which are sensor dependent. Further processing is needed in order to obtain calibrated radar cross section information from the original data intensity values.\r\nLevel 2 products describe geolocated derivated geophysical products such as ocean wind field or surface radial velocity. Such products are for example available for download on the Sentinel-1 Copernicus Hub. Further Level- 2 data are for example differential interferograms or change maps, which can be processed on different online platforms (e.g. Hyp3) and provide information about surface deformation or more generally changes between several acquisitions.\r\nThe denomination of the product types on the different levels may differ from sensor to sensor, but the processing steps stay almost the same, depending additionally on the considered acquisition modes. For example, GRD products are also called for other sensors Multi-Looked Detected (MLD) products.","hasChildren":true,"hasParent":true,"name":"Synthetic Aperture Radar (SAR) data","selfAssesment":"Completed
"},{"code":"PS3-7-7","description":"Data that have been processed to allow direct data analysis. User processing effort is reduced to a minimum.","hasChildren":true,"name":"Analysis Ready Data (ARD)","selfAssesment":"New
"},{"code":"PS3-7","description":"Earth Observation data are usually made available in different processing levels. The processing level is a mean of describing how much the raw data have been processed toward an informational geophysical product. The degrees of data processing usually follow a numerical hierarchy and typically range from Level 0 (less processed) up to Level 4 (highly processed). They characterize the type of data processing that has been performed between the raw data and the current product.\r\nA first effort for providing standard definitions of different processing levels has been made in the 1980s by the Committee on Data Management and Computation (CODMAC) of the National Research Council (NRC). CODMAC identified eight levels of processing, applicable for all space science data. Starting with the raw data at level 1, the degree of processing and complexity of the data increased at each new level. Level 2 describes edited data, corrected for obvious instrumentation errors and tagged with acquisition time and location; Level 3 stays for calibrated data where values are proportional to a specific physical unit. Level 4 represents resampled data, Level 5 derived data, where specific geophysical information has been retrieved and mapped based on the original data. Level 6 represents all ancillary data (i.e. instrument data) that are necessary for the previous steps of calibration and resampling. Level 7 describes so called correlative data: not directly belonging to the original data, those data represent all other science data that where necessary for the interpretation of the original spaceborne dataset. Finally, Level 8 are user description, i.e. documentation of the data.\r\nConcerning spaceborne image data, both optical and radar, an adaptation of these original levels has been made from NASA and NOAA that is used for the main current spaceborne missions, including the Copernicus program. Whereas specific adaptations may arise for specific sensors and sensor types, there are five principal processing levels. Level 0 represents the raw data that have just been edited for the correction of artifacts. Level 1 data are Level 0 data with additional annotations regarding time and geolocation information, radiometric and geometric calibration coefficients (for example Top of Atmosphere data for optical imagery). Level 2 data are already radiometrically and geometrically calibrated and represent physical variables (for example Bottom of Atmosphere data for optical imagery). Level 3 data correspond to derived variables and information (e.g. land cover) with completeness and consistency information, e.g. quality flags. Level 4 represent higher level data resulting from modelling or more complex analysis of the data with additional ancillary information.\r\nFor many applications and users, so called analysis ready data (ARD data) are required. These usually correspond to Level 2 data that have already been pre-processed in order to retrieve the physical information and can be further analyzed for the specific thematic application.","hasChildren":true,"hasParent":true,"name":"Processing levels","selfAssesment":"Completed
"},{"code":"PS3","description":"Remotely collected data is available from multiple sources and data collection techniques. Data can be obtained from different levels of data acquisition: ground, air or space, as well as using different sensors and wavelengths. Remote sensing data provides the necessary information to help monitor the Earth's surface.","hasChildren":true,"hasParent":true,"name":"Remote sensing data and imagery","selfAssesment":"Planned
"},{"code":"PS4","description":"The listed databases provide information on past, operational and future remote sensing platforms and sensors. Use the following links to get more information on the sensors and missions.","hasChildren":true,"name":"Databases of satellite and airborne sensors and missions","selfAssesment":"Completed
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"},{"code":"SA3-2-4-15","description":" ","hasChildren":true,"hasParent":true,"name":"Interference","selfAssesment":" "},{"code":"SA3-2-4-2","description":" ","hasChildren":true,"name":"Faraday rotation","selfAssesment":" "},{"code":"SA3-2-4-3","description":" ","hasChildren":true,"name":"Latency","selfAssesment":" "},{"code":"SA3-2-4-4","description":" ","hasChildren":true,"name":"Propagation Delay","selfAssesment":" "},{"code":"SA3-2-4-5","description":" ","hasChildren":true,"name":"Doppler frequency shift","selfAssesment":" "},{"code":"SA3-2-4-6","description":" ","hasChildren":true,"name":"Attenuation","selfAssesment":" "},{"code":"SA3-2-4-7","description":" ","hasChildren":true,"name":"Fading","selfAssesment":" "},{"code":"SA3-2-4-8","description":" ","hasChildren":true,"name":"Scintillation","selfAssesment":" "},{"code":"SA3-2-4-9","description":" ","hasChildren":true,"name":"Dispersion","selfAssesment":" "},{"code":"SA3-2-4","description":" ","hasChildren":true,"hasParent":true,"name":"Radio-wave Propagation","selfAssesment":" "},{"code":"SA3-2-5-1-1","description":" ","hasChildren":true,"name":"Resilience","selfAssesment":" "},{"code":"SA3-2-5-1","description":" ","hasChildren":true,"hasParent":true,"name":"Security","selfAssesment":" "},{"code":"SA3-2-5","description":" ","hasChildren":true,"hasParent":true,"name":"Messages","selfAssesment":" "},{"code":"SA3-2-6","description":" ","hasChildren":true,"name":"Codes and modulations","selfAssesment":" "},{"code":"SA3-2","description":" ","hasChildren":true,"hasParent":true,"name":"Signals","selfAssesment":" "},{"code":"SA3-3-1","description":" ","hasChildren":true,"name":"Antenna hardware","selfAssesment":" "},{"code":"SA3-3-2","description":" ","hasChildren":true,"name":"Receiver hardware","selfAssesment":" "},{"code":"SA3-3","description":" ","hasChildren":true,"hasParent":true,"name":"Hardware","selfAssesment":" "},{"code":"SA3-4-1-1","description":" ","hasChildren":true,"name":"Agriculture","selfAssesment":" "},{"code":"SA3-4-1-10","description":" ","hasChildren":true,"name":"Insurance and Finance","selfAssesment":" "},{"code":"SA3-4-1-11","description":" ","hasChildren":true,"name":"Maritime and Inland Waters","selfAssesment":" "},{"code":"SA3-4-1-12","description":" ","hasChildren":true,"name":"Rail","selfAssesment":" "},{"code":"SA3-4-1-13","description":" ","hasChildren":true,"name":"Road and Automotive","selfAssesment":" "},{"code":"SA3-4-1-14","description":" ","hasChildren":true,"name":"Space","selfAssesment":" "},{"code":"SA3-4-1-15","description":" ","hasChildren":true,"name":"Urban Development and Cultural Heritage","selfAssesment":" "},{"code":"SA3-4-1-2","description":" ","hasChildren":true,"name":"Aviation and Drones","selfAssesment":" "},{"code":"SA3-4-1-3","description":" ","hasChildren":true,"name":"Climate, Environment, and Biodiversity","selfAssesment":" "},{"code":"SA3-4-1-4","description":" ","hasChildren":true,"name":"Consumer Solutions, Tourism and Health","selfAssesment":" "},{"code":"SA3-4-1-5","description":" ","hasChildren":true,"name":"Emergency Management and Humanitarian Aid","selfAssesment":" "},{"code":"SA3-4-1-6","description":" ","hasChildren":true,"name":"Energy and Raw Materials","selfAssesment":" "},{"code":"SA3-4-1-7","description":" ","hasChildren":true,"name":"Fisheries and Aquaculture","selfAssesment":" "},{"code":"SA3-4-1-8","description":" ","hasChildren":true,"name":"Forestry","selfAssesment":" "},{"code":"SA3-4-1-9","description":" ","hasChildren":true,"name":"Infrastructure","selfAssesment":" "},{"code":"SA3-4-1","description":" ","hasChildren":true,"hasParent":true,"name":"EUSPA Market Segments","selfAssesment":" "},{"code":"SA3-4","description":" ","hasChildren":true,"hasParent":true,"name":"Applications","selfAssesment":" "},{"code":"SA3","description":" ","hasChildren":true,"hasParent":true,"name":"User Segment","selfAssesment":" "},{"code":"SC","description":" ","hasChildren":true,"hasParent":true,"name":"Satellite Communication","selfAssesment":" "},{"code":"SC1-1","description":" ","hasChildren":true,"name":"Transmitter","selfAssesment":" "},{"code":"SC1-2","description":" ","hasChildren":true,"name":"Receiver","selfAssesment":" "},{"code":"SC1-3","description":" ","hasChildren":true,"name":"Transponder","selfAssesment":" "},{"code":"SC1-4","description":" ","hasChildren":true,"name":"Transceiver","selfAssesment":" "},{"code":"SC1-5-1","description":" ","hasChildren":true,"name":"Transparent repeater","selfAssesment":" "},{"code":"SC1-5-2","description":" ","hasChildren":true,"name":"Regenerative repeater","selfAssesment":" "},{"code":"SC1-5","description":" ","hasChildren":true,"hasParent":true,"name":"Repeater","selfAssesment":" "},{"code":"SC1-6","description":" ","hasChildren":true,"name":"Inter-Satellite Link (RF or Optical)","selfAssesment":" "},{"code":"SC1-7","description":" ","hasChildren":true,"name":"Uplink","selfAssesment":" "},{"code":"SC1-8","description":" ","hasChildren":true,"name":"Downlink","selfAssesment":" "},{"code":"SC1","description":" ","hasChildren":true,"hasParent":true,"name":"Subsystems (payload)","selfAssesment":" "},{"code":"SC2-1","description":"Full-duplex refers to a communication system that allows for two-way transmission of data or signals simultaneously. In a full-duplex system, both parties can send and receive information at the same time, as opposed to half-duplex systems, where communication can only occur in one direction at a time (e.g., walkie-talkies). A common example of full-duplex communication is a telephone call, where both people can speak and listen to each other at the same time without needing to take turns. In networking, full-duplex communication enables devices to transmit and receive data simultaneously, improving the efficiency and speed of communication. [ChatGPT.com (2024)]","hasChildren":true,"name":"Full-duplex","selfAssesment":" "},{"code":"SC2-2","description":"Half-duplex refers to a communication system where data or signals can only travel in one direction at a time. In a half-duplex system, one device can either send or receive information, but not both simultaneously. This means that the communication flow alternates between sending and receiving, with each device taking turns. A common example of half-duplex communication is a walkie-talkie, where one person speaks while the other listens, but they must switch roles when it's time to respond. Unlike full-duplex systems, which allow simultaneous two-way communication, half-duplex systems are more limited in terms of speed and efficiency. [ChatGPT.com (2024)]","hasChildren":true,"name":"Half-duplex","selfAssesment":" "},{"code":"SC2","description":" ","hasChildren":true,"hasParent":true,"name":"Communications mode","selfAssesment":" "},{"code":"SC3-1","description":" ","hasChildren":true,"name":"High speed","selfAssesment":" "},{"code":"SC3-2-1","description":" ","hasChildren":true,"name":"IoT/M2M communications","selfAssesment":" "},{"code":"SC3-2","description":" ","hasChildren":true,"hasParent":true,"name":"Low speed","selfAssesment":" "},{"code":"SC3","description":" ","hasChildren":true,"hasParent":true,"name":"Communications speed","selfAssesment":" "},{"code":"SC4-1","description":" ","hasChildren":true,"name":"Up-link","selfAssesment":" "},{"code":"SC4-2","description":" ","hasChildren":true,"name":"Down-link","selfAssesment":" "},{"code":"SC4-3","description":" ","hasChildren":true,"name":"Cross-link","selfAssesment":" "},{"code":"SC4","description":" ","hasChildren":true,"hasParent":true,"name":"Communications direction","selfAssesment":" "},{"code":"SC5-1","description":" ","hasChildren":true,"name":"Non-terrestrial Network (NTN)","selfAssesment":" "},{"code":"SC5-2","description":" ","hasChildren":true,"name":"Security / cybersecurity","selfAssesment":" "},{"code":"SC5-3","description":" ","hasChildren":true,"name":"Quantum Key Distribution (QKD)","selfAssesment":" "},{"code":"SC5-4","description":" ","hasChildren":true,"name":"Vertical Networks","selfAssesment":" "},{"code":"SC5","description":" ","hasChildren":true,"hasParent":true,"name":"Networks","selfAssesment":" "},{"code":"SC6-1","description":" ","hasChildren":true,"name":"Autonomous driving","selfAssesment":" "},{"code":"SC6-10","description":" ","hasChildren":true,"name":"Inflight connectivity/communication (IFC)","selfAssesment":" "},{"code":"SC6-11","description":" ","hasChildren":true,"name":"Cybersecurity","selfAssesment":" "},{"code":"SC6-2","description":" ","hasChildren":true,"name":"IoT applications","selfAssesment":" "},{"code":"SC6-3","description":" ","hasChildren":true,"name":"Air traffic","selfAssesment":" "},{"code":"SC6-4","description":" ","hasChildren":true,"name":"TV-Radio broadcasting","selfAssesment":" "},{"code":"SC6-5","description":" ","hasChildren":true,"name":"Maritime traffic","selfAssesment":" "},{"code":"SC6-6","description":" ","hasChildren":true,"name":"Mobile Satellite Services (MSS)","selfAssesment":" "},{"code":"SC6-7","description":" ","hasChildren":true,"name":"Fixed Satellite Services (FSS)","selfAssesment":" "},{"code":"SC6-8","description":" ","hasChildren":true,"name":"Broadcast Satellite Services (BSS)","selfAssesment":" "},{"code":"SC6-9","description":" ","hasChildren":true,"name":"Satcom On-The-Move (SOTM)","selfAssesment":" "},{"code":"SC6","description":" ","hasChildren":true,"hasParent":true,"name":"SatCom-specific Applications","selfAssesment":" "},{"code":"SC7-1","description":" ","hasChildren":true,"name":"International Telecommunications Union (ITU)","selfAssesment":" "},{"code":"SC7-2","description":" ","hasChildren":true,"name":"DVB-S (some adopted by ETSI - see below)","selfAssesment":" "},{"code":"SC7-3","description":" ","hasChildren":true,"name":"Institute of Electrical and Electronics Engineers (IEEE)","selfAssesment":" "},{"code":"SC7-4","description":" ","hasChildren":true,"name":"European Telecommunications Standards Institute (ETSI)","selfAssesment":" "},{"code":"SC7-5","description":" ","hasChildren":true,"name":"Internet Engineering Task Force (IETF)","selfAssesment":" "},{"code":"SC7-6","description":" ","hasChildren":true,"name":"Consultative Committee for Space Data Systems (CCSDS)","selfAssesment":" "},{"code":"SC7-7","description":" ","hasChildren":true,"name":"Military Standards (MIL-STD)","selfAssesment":" "},{"code":"SC7","description":" ","hasChildren":true,"hasParent":true,"name":"Standards/recommendations","selfAssesment":" "},{"code":"SD","description":"Based on Waldo Tobler`s first law of geography( Tobler, 1970), this property is set on the principle that \"everything is related, but that which is closer is more closely related\".","hasChildren":true,"name":"Spatial dependency","selfAssesment":"In progress (GI-N2K)
"},{"code":"SH","description":"This principle, as set forth by Anselin, determines that \"expectations vary along the earth`s surface\" which means that any spatial analysis is dependent explicitly on the borders of study fields, i.e. the tracing of (spatial) analysis units.","hasChildren":true,"name":"Spatial heterogeneity","selfAssesment":"In progress (GI-N2K)
"},{"code":"TA","description":"This area of knowledge deals with the use of EO / GI techniques and data in different themes and areas of application. It includes the user community of EO services and applications, societal and environmental challenges, EO services and applications, and standard EO products that are made available to users.","hasChildren":true,"hasParent":true,"name":"Thematic and application domains","selfAssesment":"Planned
"},{"code":"TA11-1-1","description":"The EO/GI users in agriculture are active in Agricultural commodities/Trading, agricultural production / Horticulture, Agricultural services, Agriculture machinery, Agriculture and Rural Development Policy, Agro chemicals / Plants & Fertilizers, Animal production / Livestock. The EO/GI users also include agriculture and rural policy makers. \r\nThey benefit from EO information, for example, by managment support for their crop production through forecasting crop yield, assess risks of damage/loss because of storms, disease or other stress factors, and water monitoring. Use in agriculture: knowledge and information products to forge a viable strategy for farming operations. Understand the health of his crop, extent of infestation or stress damage, or potential yield and soil conditions","hasChildren":true,"name":"Users in agriculture","selfAssesment":"New
"},{"code":"TA11-1-2","description":"The users in fishing are active in Fish stock management, Fishing fleets, Fishery distribution logistics, Aquaculture / fish farms, Coastal management agencies. In addition, the users include Fisheries authorities / policy makers. \r\nThe marine environment in particular is relevant to fishing. Fishing fleets move to the fishing grounds to catch fish. Finding them is challenging. However, fish shoals can be directly visible from above. Navigating to the fishing grounds can be risky: Coastline and shallows may pose a risk to ships. Additionally, skippers may have to deal with challenging weather conditions at sea. Environmental threats to the fishing grounds are oil slicks and other types of pollution. A problem from an economical perspective and for adhering to catch quota is illegal fishing. Noumerous opportunities exist to support fishing with EO information.","hasChildren":true,"name":"Users in fishing","selfAssesment":"New
"},{"code":"TA11-1-3","description":"The users in forestry are active in Forest management, Forest Services, Commodities, Logging industry, Wood, paper and pulp industry, Forest policy, Forest machinery. They also include Forest Policy makers.\r\nUse in forestry: Understand depletion due to natural causes (fires and infestations) or human activity (clear-cutting, burning, land conversion), and monitoring of health and growth for effective commercial exploitation and conservation.\r\nForests are a resource that is harvested all over the Globe for different purposes like construction or heating. Additionally, the forests represent an ecosystem that provides various ecosystem services. Proper management is a key to a healthy forestry industry that has to be aligned well with global environmental management activities. There is a need to avoid deforestation and forest degradation, keep the environmental impact of forestry within bounds, be aware of changes in the carbon balance. Economically relevant is especially a good understading of forest types, forest damage due to storms or insects, as well as wildfires. A threat to the environment results from illegal forest activities.","hasChildren":true,"name":"Users in forestry","selfAssesment":"New
"},{"code":"TA11-1","description":"Users in managed living resources refer to human activities exploiting natural organic resources. Knowledge and information products to forge a viable strategy for the user’s operations such as the assessment of the status of the resource due natural or human activity for effective commercial exploitation and conservation. This includes agriculture, fishing and forestry occupations for our society.","hasChildren":true,"hasParent":true,"name":"Users in managed living resources","selfAssesment":"New
"},{"code":"TA11-2-1","description":"The users in alternative energy consist of Solar energy providers, Wind energy providers, Tidal energy providers, Hydroelectric energy providers, Energy and Carbon traders, Local and regional planners, and National policy makers. Energy providers need information about the state of the environment to make the most use out of natural resources. Planners and policy makers have to weigh up whether and which type of alternative energy is justifiable and sensible for a specific region.\r\nEO data can be used to build maps that show resource information. For solar energy, those maps contain information about solar radiation, but also shadowing effects. Forecast products for irradiance are available to be able to plan the energy production for the coming days. Tidal waves can be depicted by sea surface heights. As tidal currents are periodical, they can be predicted well by the initial state of sea surface heights. In addition, also the speed of tidal waves can be determined by EO measurements. In the wind energy sector EO data is analysed to plan and monitor wind farms. Maps can show areas, where winds are suitable for wind energy production. After the construction of a wind farm, wind strength and direction during operation can be monitored. Finally, for hydroelectric power stations EO is used to monitor water reservoirs. As well hydrometeorological data is used to forecast water-related events and to monitor drought or floods.","hasChildren":true,"name":"Users in alternative energy","selfAssesment":"Completed
"},{"code":"TA11-2-2","description":"The EO/GI user community in oil & gas consists of offshore exploration and production, on-shore exploration and production, drilling and support services, oil and gas commodities trading, and energy planners. Due to their activities both on-shore and offshore their need for EO-derived information about the land, the ocean and the atmosphere. They need EO-derived information about geological features (for exploration), for asset infrastructure monitoring, construction and buildings. Safe offshore operations (ocean&atmosphere: forecast and monitoring current movement and drift, monitor sea-ice and icebergs, detect and monitor hurricanes and typhoons; land: map and assess flooding, detect wildfires . A large set of information needs results from their need to adhere to environmental regulations. They have to assess and monitor their environmental impact, ocean quality and productivity, land ecosystems and biodiversity, groundwater and run-off \r\nMany problems faced by oil, gas, including the selection and development of exploration areas, detection and mapping of illegal mining activities, or monitoring dams, pipelines and terrain movements, can be efficiently addressed by extracting information from geospatial imagery. Remote Sensing based applications reduce the need for field work, minimize environmental impacts, and ultimately safe costs, to help achieve results faster during exploration, extraction, and remediation/reclamation stages.","hasChildren":true,"name":"Users in oil & gas","selfAssesment":"New
"},{"code":"TA11-2-3","description":"The EO/GI community in minerals and mining consists of mining and quarrying companies, exploration and survey specialists, commodities traders, exploration and extraction equipment suppliers, drilling, excavation and support services, and regional planners / policy makers.\r\nTypical spatial questions for the users in minerals and mining are concerned with prospecting, e.g. \"Where can we find the minerals that are worth exploitation?\", and operation of mining sites: \"How much material has already been excavated in the mine and how much material was deposited in dedicated dump areas?\". Additionally relevant are arising risks through mining activities, e.g. \"How do the mining activities affect settlements in the vicinity?\" or \"How do the mining activities affect the environment?\". Concequently, the EO/GI users in minerals and mining benefit from EO information through mapping geological features, monitor mineral extraction, measure land use statistics, assessing environmental impact of human activities, detect and monitor ground movement, and monitor land pollution.","hasChildren":true,"name":"Users in minerals & mining","selfAssesment":"New
"},{"code":"TA11-2","description":"Users in energy and mineral resources deal with the harvesting of energy from renewable resources and extractive industries including oil and gas and raw materials. EO information helps them in exploring locations where to build new mines or power plants, in identifying risks from infrastructure and in managing the environmental impact of their operations.\r\nUses that apply to the extractive industries: study of landforms, structures, and the subsurface, to understand physical processes creating and modifying the earth's crust. EO/GI should play a key role to transform data into information and knowledge about the potencial feasibility and viability of renewable resources, in particular solar and wind at the natural and urban ecosystems, and in particular to support Sustainable Development Goals SDG 7 Affordable and Clean Energy and SDG 11 Sustainable Cities and Communities.","hasChildren":true,"hasParent":true,"name":"Users in energy and mineral resources","selfAssesment":"New
"},{"code":"TA11-3-1","description":"EO/GI users in construction include construction companies, civil engineering consultancies, architect and design companies, planning authorities, and national land agencies. \r\nThey benefit from EO through monitor building development, assess environmental impact of human activities, map and assess flooding, detect land movement, subsidence, heave, and monitor land-use statistics","hasChildren":true,"name":"Users in construction","selfAssesment":"New
"},{"code":"TA11-3-2","description":"Utilities (water, electricity, waste): Power station operators, Water plants operators, Survey companies, Hydroelectric suppliers, Regulatory Bodies, Distribution companies, Landfill and waste, Regional planners / policy makers.\r\nThe benefit from EO information that monitor pollution in rivers and lakes, assess changes in the carbon balance, assess environmental impact of human activities, monitor land pollution, assess changes to urban and rural areas, assess and monitor water quality, assess ground water and run-off.","hasChildren":true,"name":"Users in utilities & supplies","selfAssesment":"New
"},{"code":"TA11-3-3","description":"Users of EO/GI in communications and connectivity are mostly mobile telecommunications providers and fixed telecommunication providers. Theire business is to connect people via telephone and internet. The assets for their services include the infrastructure of communication networks physically installed in the ground, the cellphone towers distributed over the land surface, particularly in higly populated areas, as well as other installations (e.g. company buildings) and equipment (communication satellites).\r\nSpecific spatial questions of these users are concerned with the reception quality that the network can provide in an area. The network coverage would neet to react to changes of the built environment. New settlement infrastructure may cause a new population distribution and subsequently the need to network adaptations to cover new areas or cover some areas with higher band widths because more people are living there. Additionaly, the coverage of cellphone antennas depends on the arrangement of environmental obstacles that degrade or block the radio signal. Any place where the built environment or the vegetation changes can change the reception quality within the covered area of an existing cellphone tower. \r\nThe benefit of EO information for the user group of communications and connectivity comes from monitoring building development, assessing changes to urban and rural areas, and mapping line of sight visibility (terrain height, land cover).","hasChildren":true,"name":"Users in communications & connectivity","selfAssesment":"New
"},{"code":"TA11-3-4","description":"EO/GI users in transport and logistics include road transport operators, haulage, road infrastructure operators, tolls, airport operators, rail operators, airlines and airline services, and transport engineers.","hasChildren":true,"name":"Users in transport & logistics","selfAssesment":"New
"},{"code":"TA11-3-5","description":"EO/GI users in marine include ports & harbors administration, bulk cargo carriers, cruise liners operators, ferry operators, naval operations, and rescue and safety at sea.","hasChildren":true,"name":"Users in marine","selfAssesment":"New
"},{"code":"TA11-3-6","description":"From a conceptual point of view travelling is crossing the space from one location to another. Tourism mostly requires a travel to the desired destination and typically also includes moving inside a specific area. Therefore both tourism and travel are highly dependent on spatial phenomena which are often captured using EO.All kinds of travelling are highly dependent on weather conditions which can be observed with meteorological satellites. Also the current traffic conditions like congestion, road condition and natural hazards can be discovered with EO.\r\n\r\nThe types of tourism which are outside of buildings require sufficient weather forecast. Especially outdoor tourism at the coast or in mountain areas have a need for specific information about the current and the near future conditions of the natural environment. Examples are avalanche reports and forecasts for wind or wave heights of water bodies. Local tour organizers can utilise this information in order to better plan offers for tourists and also ensure overall safety during their stay.\r\n\r\nTourism and travelling are import economic factors. Consequently both the public and the private sector are interested in ensuring safe and convenient travel conditions and furthermore in creating an attractive environment for travellers and touristic visitors. This includes recognising environmental pollution, since this discourages tourist from visiting an area. Not only incoming, but also outgoing tourism is an important factor in local economies. Travel agencies, for example, are highly dependent on retrieving accurate information about foreign regions which are typically obtained with earth observation technology.\r\n\r\nOf course tourism and travelling itself also can be observed from space, this is especially true for mass tourism and areas where traffic has increased a lot during the last time. Typical effects are the increase of settlement area and the additionally used space for roads, parking lots, airports and harbors. These changes to the earth surface can be quantified with the help of land cover change detection.In many cases local administrations and decion makers want to mitigate the negative consequences of mass tourism, the insights of the mentioned EO measurements provide a useful foundation for sustainable planning.","hasChildren":true,"name":"Users in travel & tourism","selfAssesment":"Completed
"},{"code":"TA11-3","description":"Users in transport and infrastructure apply to all manufacturing and physical supply in land but also marine domains including transport & logistics, utilities, construction, communication & connectivity, and tourism.","hasChildren":true,"hasParent":true,"name":"Users in infrastructure & transport","selfAssesment":"New
"},{"code":"TA11-4-1","description":"EO/GI users in insurance and real estate include primary insurance companies, re-insurance sector, insurance brokers, insurance service suppliers, commercial banks, major projects, and international financial institutions. \r\nProduction processes (including primary production like farming), property and real estate are often insured against certain risks, e.g. from natural hazards. \r\nUsers benefit from EO information through applications that monitor building development, assess crop damage due to storms (including to forecast and map large waves), assess damage from earthquakes, detect and monitor wildfires, map and assess flooding, detect land movement, subsidence, heave, forecast and assess landslides.","hasChildren":true,"name":"Users in insurance & real estate","selfAssesment":"New
"},{"code":"TA11-4-2","description":"EO/GI users in retail and geo-marketing include Retail centres and Advertising and Marketing agencies. They use EO/GI data in the field of Navigation and LBS, Shopping chains or Logistics.","hasChildren":true,"name":"Users in retail & geo-marketing","selfAssesment":"New
"},{"code":"TA11-4-3","description":"Users in news and media are Television companies, Broadcasting providers, News and Information agencies, Web service providers, and Entertainment software providers. They benefit from monitoring, forecasting and assessing of natural risks/disasters.","hasChildren":true,"name":"Users in news & media","selfAssesment":"New
"},{"code":"TA11-4-4","description":"Users in ICT include fixed and mobile telecommunications providers. They can make use of EO/GI data by monitoring building development and changes to urban areas.","hasChildren":true,"name":"Users in ICT, knowledge and digital interfaces","selfAssesment":"New
"},{"code":"TA11-4","description":"Users in financial and digital services cover a broad area of activity that touches on many other market sectors such insurance & real estate, retail, news & media and digital interfaces. The categories included are identifiable as a “service” (tertiary sector: attention, advice, access, experience, and affective labour) and not part of the physical supply of goods.","hasChildren":true,"hasParent":true,"name":"Users in financial & digital services","selfAssesment":"New
"},{"code":"TA11-5-1","description":"The users in smart cities are multiverse and include large number of profiles. This include urban planners, architects, spatial planning offices, urban policy makers, transportation/environment/health departments but also citizen themselves.\r\nThe users benefit from additional information and knowledge extracted from EO data. This information and knowledge can help them to better tackle with challenges arising from climate change and urbanization. As each urban area is unique, EO can provide relevant information by detecting, evaluating and measuring these localities.\r\nThis EO based information can be extracted on one occasion or continuously, benefiting from revisiting satellites. EO can support investigation of archive data to extract trends or by investigating current state to set a baseline. This baseline is then further used to monitor the changes or to assess the impact of different decisions and actions. In most cases, this information is further used in various GIS analyses or modelling procedures.\r\nThe topics where EO can contribute are as follows: urban land cover, urban heat islands, air/water/soil quality, tree/vegetation health, detection of invasive vegetation species, damage detection on buildings or infrastructure, development of infrastructure and many more.\r\nAs listed, EO can support various domains that can be fitted under Nature-based solutions (NBS). NBS have been gaining attention as multifunctional solutions that may help cities to address challenges arising from climate change and urbanization.\r\nThe concept of Nature-based Solutions (NBS) has evolved as an umbrella concept embracing concepts such as green/blue/nature infrastructure, ecosystem approach, ecosystem services or natural systems agriculture, natural solutions, ecosystem-based approaches, and ecological engineering. NBS can include solutions such as water purification, reduction of flood risk, or deliberate efforts to decrease temperature and improve air quality.","hasChildren":true,"name":"Users in smart cities","selfAssesment":"Completed
"},{"code":"TA11-5-2","description":"The users in local and regional planning include spatial planning departments of municipalities, spatial planning offices, and spatial planning policy makers. Land use management in densely populated areas involves negotiation of conflicting land-use demands for settlement, production system (including agriculture and forestry) and infrastructure. The users benefit from EO information to manage the use of land and its impacts.","hasChildren":true,"name":"Users in local & regional planning","selfAssesment":"New
"},{"code":"TA11-5","description":"Users in urban development and users involved in the development of rural settlements perform tasks on local and regional scale (to the scale of nations). These users benefit from EO information to manage the use of land & its impacts. Users such as urban planners, architects, spatial planning offices, urban policy makers in public/private sectors in smart cities or generic urban local/regional planning belong to this category. EO/GI becomes a key data and information to support Sustainable Development Goals - SDG 11 Sustainable Cities and Communities in particular to set up at geospatial and temporal basis the evolution of urban environmental and socioeconomical factors for a better distribution and equality of resources, benefits and impacts (environmental urban justice maps)","hasChildren":true,"hasParent":true,"name":"Users in urban development","selfAssesment":"New
"},{"code":"TA11-6-1","description":"Users in defense, security and military are border control organisations, police and rescue forces, military services, and intelligence services. Use of EO/GI data can be made in the field of detecting and monitoring high risk areas (natural and humanitarian), monitoring border incursions, or monitoring maritime movements.","hasChildren":true,"name":"Users in defense, security & military","selfAssesment":"New
"},{"code":"TA11-6-2","description":"EO/GI users in emergency services are coast guards, ambulance services, fire services, police services, civil protection organisations, and rescue services. They benefit from monitoring, detecting and assessing natural risks/disasters.","hasChildren":true,"name":"Users in emergency & social protection","selfAssesment":"New
"},{"code":"TA11-6-3","description":"The EO/GI users in humanitarian operations correspond to humanitarian aid organisations, humanitarian support organisations and overall humanitarian response such as border control organisations, police and rescue forces, coast guards, civil protection, military services, and intelligence services. They can use EO services to detect and monitor high risk areas produced naturally or by humans, monitor border incursions or maritime movements. They provide support to local populations that have experienced a crisis, e.g. they fled from a conflict or are affected by a natural disaster. The organisations therefore support the population's needs for sustenance. Consequently, any related risks are relevant as well. The users benefit from the EO capability to identify and monitor people in need, i.e. to assess pressures on populations and migration, and to monitor humanitarian movement and camps. They additionally benefit from EO through mapping disaster areas for situation awareness and detecting sensitive risk areas. Some examples of users at European level are DG RELEX, DG ECHO, DG ENV/ MIC. At UN, the users include OCHA, UNHCR, UNDPKO, UNDP, UNOPS, UNITAR, UNICEF, UNESCO, WFP. Further, international users include IFRC, WHO, WB, and donor organizations. At the national level, the users include Civil Protection Agencies, Ministries of Internal Affairs / Civil Protection Department, Development and Aid agencies.","hasChildren":true,"name":"Users in humanitarian operations","selfAssesment":"New
"},{"code":"TA11-6","description":"Users in defence and security work in the field of military, emergency and social protection and define, collect, analyse information to provide intelligence & safety. Some examples are activities under humanitarian response such as border control organisations, police and rescue forces, coast guards, civil protection, military services, and intelligence services which can use EO services to detect and monitor high risk areas produced naturally or by humans, monitor border incursions or maritime movements.","hasChildren":true,"hasParent":true,"name":"Users in defense & security","selfAssesment":"New
"},{"code":"TA11-7-1","description":"EO/GI users in environmental ecosystems & pollution include scientists, consultants, planners and policy makers with interest in environmental issues.","hasChildren":true,"name":"Users in environmental ecosystems & pollution","selfAssesment":"New
"},{"code":"TA11-7-2","description":"Users in health care health-related services include services on site-specific field conditions as well as import phenological timing events, which helps to make predictions for monitoring air quality, forecasting epidemics and diseases, as well as forecasting sunlight exposure.","hasChildren":true,"name":"Users in health care","selfAssesment":"New
"},{"code":"TA11-7-3","description":"EO/GI users in meteo and climate; use of satellite-based observations in addressing key climate science questions for user-centric climate change risk assessment applications or climate-related issues","hasChildren":true,"name":"Users in meteo & climate","selfAssesment":"New
"},{"code":"TA11-7","description":"Users in the public administrations or private organizations using EO to assist environmental or climate change impact policy making decisions i.e, assisting in developing monitoring to evaluate and deliver policy goals, provide assessment of ecosystems, rapid response to major environmental risk events, or those associated health security & care events. These users are largely related with international treaties and hence a strong international collaboration. EO/GI becomes a key data and information to support Sustainable Development Goals (SDG) in particular in terms of environmental, climate and health towards SDG 11, SDG 13 Climate Action; SDG 14 Life Below Water; or SDG 15 Life on Land.","hasChildren":true,"hasParent":true,"name":"Users in environmental, climate & health","selfAssesment":"New
"},{"code":"TA11-8-1","description":"EO/GI users of customer solutions; easier for society to use and engage with EO services through mobile devices, social media platforms, apps. Enormous potential to use citizen-driven observations in combination with EO data","hasChildren":true,"name":"Users of consumer solutions","selfAssesment":"New
"},{"code":"TA11-8-2","description":"EO/GI users in leisure; basic public understanding on EO Services","hasChildren":true,"name":"Users in leisure","selfAssesment":"New
"},{"code":"TA11-8-3","description":"The community of users in education includes instructors (1) who are teaching or conducting research in some aspect of GIScience, such as coding, remote sensing, field methods, geodetic control, web mapping, spatial analysis, or related topics, or (2) who are using GIS as a teaching tool in a discipline, such as business, biology, economics, or health sciences. By extension, this community includes students and supportive deans and other educational administrators. The benefits that these users gain from EO information includes a set of best practices vetted by experts in the field that they can use to teach modern GIS workflows more effectively. \r\nThe goals of this user community are focused on a deeper and a broader implementation of geotechnology, methods, and spatial data throughout the educational system—primary, secondary, university, and lifelong learning (libraries, museums, and other informal settings). Deeper implementation implies embracing GIS as a platform, including its field data gathering tools and citizen science workflows, spatial analysis, building web maps and apps, communicating with multimedia maps derived from web GIS, systems configuration work, and the coding that is behind modern GIS infrastructure. Broader implementation implies the use of GIS in a multitude of disciplines at all levels of education, formal and informal; occurring wherever changes over space and time are being examined. \r\nAt all levels of education the challenge of sufficient bandwidth and the use of a professional systems-based tool such as GIS, along with devices capable of running web GIS tools, are barriers in many areas throughout the world. However, educational and societal forces represent a stronger challenge than technological ones. These educational and societal challenges that this user community faces include the lack of educational content standards at the primary and secondary level that support the use of geotechnologies in education, and at the university level, a lack of awareness of and access to modern SaaS GIS tools and open data portals. \r\nThe risks that the community faces in not facing the challenge of the use of GIS in the education sector is a lack of geographic and spatial literacy among students and faculty. This will translate to research that does not consider spatiotemporal implications of 21st Century challenges, a workforce ill-equipped to deal with them, and consequently an increasingly unstable and dysfunctional world. To build a workforce that can meet global challenges in energy, biodiversity, climate, natural resources, natural hazards, human health, economic inequality, and others, a deep and wide implementation of GIS technology and methods must take place throughout the educational system. The actions that society can take to face that challenge is to provide professional development opportunities for faculty, curricular resources, assessment instruments, relevant spatial data and open data portals, examples of best practices, and a network for educators and researchers in which to interact. EO can provide all of these elements in partnership with educational institutions, government, nonprofits, and industry to meet this challenge. In so doing, an increasingly sustainable, healthier, resilient world can be achieved from the community to the global level.","hasChildren":true,"name":"Users in education, training & research","selfAssesment":"Completed
"},{"code":"TA11-8","description":"Citizens and society in general use and engage with EO services through mobile devices, social media platforms, apps. We do also categorize in this section the users in education, research and training providing knowledge and learning outcomes.\r\nActive and engaged citizens are one of the main driving forces of EO/GI. Nowadays, there is a growing amount of location-based contents generated by connected “produsers”, mainly equipped with smartphones. The exponential growth of ambient geographic information through social networks became the basic feature of a spatially enabled society, in which it behaves as a vessel where millions of people share their current thoughts, observations and opinions, showing to provide more reliable and trustworthy information than traditional methods like questionnaires and other sources.\r\nA spatially enabled citizen is explained through his ability to express, formalize, equip (technologically and cognitively), and (un)consciously activate an efficiently use of his spatial skills. Harvesting this ambient geospatial information provides a unique opportunity to gain valuable insight on information flow and social networking within a society, support a greater mapping, understand the human landscape and its evolution over time. With these insights, city planners can make use of the gathered affective data to detect positive or negative trends developing in the city, managing to take early countermeasures.\r\nNevertheless, assembling and analyzing EO/GI provide us with unparalleled insight on a broad variety of cultural, societal, and human factors, particularly as they relate to human and social dynamics, for example: 1) mapping the manner in which ideas and information propagate in a society, information that can be used to identify appropriate strategies for information dissemination during a crisis situation. 2) Mapping people’s opinions and reaction on specific topics and current events, thus improving our ability to collect precise cultural, political, economic and health data, and to do so at near real-time rates. 3) Identifying emerging socio-cultural hotspots.","hasChildren":true,"hasParent":true,"name":"Users among citizens & society","selfAssesment":"New
"},{"code":"TA11","description":"The EO/GI user community pools sub-communities (stakeholders) that share common needs for EO/GI information. From an economic perspective, market sectors represent user communities. Users of a community have a common interest in specific aspects of societal or economical benefits to be realized by the implementation of EO services. A user-led community is active at specific locations/regions or in specific environments on the Earth. Their activities are associated with particular features and objects of the environment and related processes that can be detected and monitored with EO satellites. EO information therefore is relevant to the user community's management of their assets, the risks to their assets, and the impact that their activities may have on other aspects of the environment. User objectives (use cases) with EO information include: Enforce regulations; Develop strategies and policies; Manage assets; Plan and design project implementations; Analyse and understand impact / consequences.\r\nUser communities can profit from EO services and applications in the field of managed living resources, energy and mineral resources, infrastructure and transport, financial and digital services, urban development, defense and security, environmental, climate and health, or citizens and society. EO/GI becomes a key data and information to support Sustainable Development Goals -SDG in particular in terms of users in managed livimgs resources towards SDG 2 Zero Hunger; SDG 8 Decent Work and Economic Growth; SDG 9 Industry, Innovation and Infrastructure; SDG 14 Life Below Water; or SDG 15 Life on Land","hasChildren":true,"hasParent":true,"name":"User community of EO services and applications","selfAssesment":"Completed
"},{"code":"TA12-1","description":"Climate change observations show the warming of the climate system. The changes since the 1950s are unprecedented over decades to millennia.The atmosphere and ocean have warmed, the amounts of snow and ice have diminished, and sea level has risen. The anthropogenic emissions of greenhouse gases are the highest in history. Recent climate changes have had widespread impacts on human and natural systems. There is an urgant need for climate action through mitigation and adaptation. Mitigation actions prevent or reduce the emission of greenhuse gases into the atmoshpere with the objective to make the impacts of climate change less severe. Adapting to climate change increases our resilience to impacts like extreme weather events (e.g. hazards like floods and droughts) that get more frequent and intense in many regions. Current climate change will get worse in the future even if the reduction of emissions is effective with negative effects on ecosystems, economy, human health and well-being. There is extensive need for actions to adapt to the impacts of climate change.","hasChildren":true,"name":"EO for climate change mitigation & adaptation","selfAssesment":"New
"},{"code":"TA12-10","description":"\"Sustainable urban development is a goal of the global society. It summarizes a specific set of problems that cities face all over the world. Cities want to provide a high quality of life to their residents. However, this goal is threatened by urban growth at the cost of urban green infrastructure’s accessibility by citizens etc. Communities that address this: C40 (association of the largest cities of the globe), CitiesIPCC, related SDGs of the UN, etc. Skills: Explain how the monitoring of urban areas contributes to sustainable urban development through its capability to provide regularly updated information about the benefit of urban green infrastructures and their ecosystem services to the quality of life in a city\r\n\"","hasChildren":true,"name":"EO for sustainable urban development","selfAssesment":"New
"},{"code":"TA12-2","description":"Biodiversity describes the variety of ecosystems (natural capital), species and genes in the world or in a particular habitat. Ecosystem services sustain our economies and societies and are essential to human wellbeing.","hasChildren":true,"name":"EO for biodiversity & ecosystems","selfAssesment":"New
"},{"code":"TA12-3","description":"Worldwide countries follow a digital agenda for the economy and initiatives to foster new skills among the workforce to cope with transformation processes with massive impact on the labour market.","hasChildren":true,"name":"EO for digital agenda & new skills","selfAssesment":"New
"},{"code":"TA12-4","description":"Energy transition is a thematic area whose EO experts are proficient in relevant EO data and its processing methods and infrastructure to derive information for energy transition [and its regulatory context, etc.]. The expertise of each expert may be very specialized. In sum, the experts have: The relevant domain knowledge (knowledge about type of monitored entities and their properties, e.g. reflectance properties of sea ice and related EO sensors for detecting them), and The relevant workflow knowledge and processing skills for extracting and providing targeted information for energy transition. [may share strategic objectives… such as „gaining thorough understanding of Energy transition“, „foster usage of EO information for energy transition“]","hasChildren":true,"name":"EO for energy transition","selfAssesment":"New
"},{"code":"TA12-5","description":"Agricultural activity is sustained by good environmental conditions that allow farmers to harness natural resources, create their produce and earn a living. This fosters a sustainable rural economy while food produced by agriculture sustains society as a whole.","hasChildren":true,"name":"EO for sustainable agriculture & food production","selfAssesment":"New
"},{"code":"TA12-6","description":"This societal challenge aims to provide efficient, safe and environmentally friendly mobility solutions.","hasChildren":true,"name":"EO for infrastructure & transport","selfAssesment":"New
"},{"code":"TA12-7","description":"In recent decades, society has fought communicable diseases with success through treatment and prevention. The Covid-19 pandemic shows that communicable diseases are still a threat to the health of citizens. Spread can gappen very quickly from one country to another. Challenges lie in the (re-)emergence of infectious diseases, antimicobial resistance and vaccine hesitancy. Policies of states focus on surveillance, rapid detection and rapid response.","hasChildren":true,"name":"EO for health surveillance","selfAssesment":"New
"},{"code":"TA12-8","description":"There is a rising geostrategic competition and power pilitics challenging rule-based multilateralism. Further, there are armed confilct, civil wars and instability in the EU's broader neighbourhood. \r\nFurther, natural disasters pose a threat to society, where the Sendai Framework of disaster risk reduction focuses on.","hasChildren":true,"name":"EO for emergency, security & defense","selfAssesment":"New
"},{"code":"TA12-9","description":"Water is an essential resource for food production. Growing crops requires significant quantities of water. Without sufficient, good quality and easily accessible water, agri-food production is under threat.","hasChildren":true,"name":"EO for water sustainability","selfAssesment":"New
"},{"code":"TA12","description":"EO provides timely, continuous and independent data for monitoring indicators of the progress of the society in various societal challenges.\r\nEO monitoring supports activities that address societal & environmental challenges. This happens indirectly along a chain: e.g. a regularly provided EO information product derived from EO data of a satellite is integrated as a parameter in a climate model / Earth system model. This climate model enables the development of regulations (and their enforcement through constant monitoring) to implement climate change mitigation measures. Thereby, the chain is characterized by seveal connected nodes: from societal challenges to use cases of users to EO applications to EO products to specific satellites and their sensors.\r\n[Communities that promote collaboration among diverse stakeholders from academia, industry, public administration as well as local residents] \r\nScientific agendas address societal challenges and the EO/GI community can contribute to them. Consortia usually include experts from academia (researchers, developers, scientists), EO companies, and members from the user community such as public authorities.","hasChildren":true,"hasParent":true,"name":"EO for societal and environmental challenges","selfAssesment":"New
"},{"code":"TA13-1-1","description":"Monitor the atmosphere includes monitoring of the atmosphere composition and air quality, as well as forecasting of sunlight exposure. Timely, continuous, and independent data on the atmosphere is useful in various domains like health, agriculture, renewable energies, urban planning, climate sciences and biology.\r\nThe atmosphere composition includes greenhouse gases (GHG) like carbon dioxide, methane, NO2 and SO2. They are part of the Earth system and have a strong impact on the climate. To monitor changes in atmosphere composition enables modelling climate change and understanding the impact of human-induced emissions of GHG relative to natural sources. EO-derived products include inventory of emission data as an input to atmospheric chemistry transport models and forecast models. Inventories are based on a combination of existing data sets and new information, describing emissions from fossil fuel use, ships, volcanoes, and vegetation. This ensures good consistency between the emissions of greenhouse gases, reactive gases, and aerosol particles and their precursors.\r\nAir quality describes the composition of the atmosphere from gases and particles near the Earth's surface. Local emissions from different sources (e.g. energy production, industrial production, traffic) cause changes to the atmospheric composition that are highly variable in space and time. The quality of the air we breathe can significantly impact our health and the environment. Therefore, it is highly relevant to monitor air quality and emissions. EO satellites are capable of monitoring aerosols, tropospheric O3, tropospheric NO2, CO, HCHO, SO2, and particulate matter (of the sizes PM 2.5 and PM 10). Products like air quality assessment reports, daily ozone forecasts, and UV-index forecast maps are produced that are applied in specific use cases, particularly related to health.\r\nThe amount of solar radiation that arrives at a location on the Earth surface depends on the atmosphere composition and varies over the day and the seasons. Information on solar radiation is useful in various domains. Applications of sunlight and ozone data are for example real-time UV radiation forecasting and risk assessment, skin health services, climate change studies, assessment of ozone protection policies effectiveness, plant growth and disease control, evaporation and irrigation models, power generation, solar heating systems planning and monitoring.","hasChildren":true,"name":"Monitor the atmosphere","selfAssesment":"Completed
"},{"code":"TA13-1-2","description":"Monitoring the climate includes monitoring climate forcing and the carbon balance and assessing climate change risks.\r\nClimate forcing describes the imbalance of the Earth’s energy budget due to natural or human-induced sources. This imbalance results in a change in the globally-averaged temperature. Amongst the contributors of positive climate forcing, that leads to an increase in the globally-averaged temperature, the increase of carbon dioxide in the atmospheric composition is considered to be the most important factor. Changes in the carbon dioxide concentration indicate that the exchanges between carbon sources and sinks are not balanced. It can be shown that human-induced emissions of carbon dioxide are responsible for the increase of the carbon dioxide since the industrialisation.\r\nWith EO, we can monitor changes in greenhouse gases (GHG), aeorosols, albedo, and solar radiation. The dynamic nature of the climate makes it necessary to apply equally dynamic EO monitoring that allows to deliver key information on historical, seasonal forecast and projection periods for climate-related indicators.\r\nRelevant EO products include estimates of the climate forcing of aerosol, ozone and greenhouse gases. The dynamic nature of the climate makes it necessary to apply equally dynamic EO monitoring that allows to deliver key information on historical, seasonal forecast and projection periods for climate-related indicators. \r\nThe products are particularly relevant to the European energy sector in terms of electricity demand and the production of power from wind, solar and hydro sources. \r\nMoreover, water management uses EO-derived information about climate change to mitigate effects of changing precipitation patterns to adapt their strategies, and to prepare for climate variability and change in the water sector, e.g. because of changes in river discharge, droughts and floods.\r\nFinally, insurance uses climate change information for assessing the weather risks to insured assets that change with the climate-related increase in extreme weather conditions. This includes products like up-to-date catalogue of wind storms and their associated impacts on the ground.","hasChildren":true,"name":"Monitor the climate","selfAssesment":"Completed
"},{"code":"TA13-1-3","description":"The weather is the state of the atmosphere measurable by its temperature, humidity, precipitation, and other atmospheric variables. To forecast the weather is a major branch in the field of meteorology. In comparison to climate, weather can only be predicted for a short period of time (minutes to month), because it describes the state of the atmosphere for specific days at specific locations. For a reliable weather forecast, a good numerical prediction model with precise initial conditions is needed. Models are sensitive to changes in the initial condition, that is why at the moment weather predictions are only accurate for few days. However, both models and the determination of initial conditions are steadily improved. EO makes a significant contribution to improving the initial conditions by providing global information several times a day. As the quality of the EO products improves, the weather forecast also improves. \r\nSince decades, satellites are used to monitor and forecast weather. Therefore, it is one of the most established sectors of satellite data applications. There are geostationary and polar-orbiting weather satellites that measure all kinds of meteorologically relevant variables, e.g. cloud coverage, wind speed [...] via passive or active imagery. However, not only satellites are used to collect information, but also other remote sensing techniques that can be airborne or ground-based such as Lidar.\r\nWeather forecasts are used by citizens for decisions in everyday life, in agriculture for crop cultivation decisions and in the stock markets. Other domains of applications are hydrometeorology, aviation, maritime navigation, and the military and nuclear sectors.","hasChildren":true,"name":"Forecast the weather","selfAssesment":"Completed
"},{"code":"TA13-1","description":"Monitor the atmosphere and climate includes all change-focused services/applications which assess, monitor, forecast and provide timely, continuous and independent data (e.g. temperature, humidity, emissions, greenhouse gases, solar UV radiation, aorosols,...). It closely monitors each of the Earth's different subsystems and, besides being the basis for weather forecasts, helps to better understand and evaluate the impact of the climate change.","hasChildren":true,"hasParent":true,"name":"Monitor the atmosphere and climate","selfAssesment":"New
"},{"code":"TA13-2-1","description":"Monitor critical information about offensive and defensive systems. This deserves a category in its own right since the nature of observations is quite different from many others.","hasChildren":true,"name":"Monitor critical assets","selfAssesment":"New
"},{"code":"TA13-2-2","description":"Monitoring health can be delivered indirectly by monitoring environmental changes that can cause endemic and chronic diseases. Typically monitored environmental factors are temperature, humidity, stagnant water, NDVI, land cover, or soil type.","hasChildren":true,"name":"Monitor health","selfAssesment":"New
"},{"code":"TA13-2-3","description":"Monitoring food security includes the monitoring of food availability by environmental conditions (land cover, NDVI,...), as well as the monitoring of migration patterns. Risks that can lead to food insecurity are hazards or conflicts.","hasChildren":true,"name":"Food security monitoring","selfAssesment":"New
"},{"code":"TA13-2-4","description":"Monitoring borders includes monitoring the land and marine border incursions, monitoring transport routes, assessing pressures on poplulations, and monitoring humanitarian movement.","hasChildren":true,"name":"Monitor borders","selfAssesment":"New
"},{"code":"TA13-2","description":"Monitor security and safety describes the collection and analysis of information to provide intelligence services & safety. The task is to give early warnings in case of emergencies, to monitor infrasturcture, transport routes (land and water) and borders, to surveil security and sovereignty.","hasChildren":true,"hasParent":true,"name":"Monitor security & safety","selfAssesment":"New
"},{"code":"TA13-3-1","description":"EO is capable to repeatedly map flood extent directly after flooding, including further aspects (flood plain, extend mapping, frequency, rainfall, flash floods, vulnerability, inundation, risk-based mapping & management; flood spread and depth followed by automated insurance payouts). Modelling (hydrological modelling and monitoring focused on seasonal dynamics of water availability) based on EO data (digital elevation models) supports flood risk assessment.","hasChildren":true,"name":"Map and assess flooding","selfAssesment":"New
"},{"code":"TA13-3-2","description":"For the outbreak of forest fires, satellite remote sensing can be continuously track and monitor, in a timely manner to grasp the development of forest fires. Beyond, weather monitoring enables to forecast weather conditions where fires are likely, allowing authorities to prepare.","hasChildren":true,"name":"Detect and monitor wildfires","selfAssesment":"New
"},{"code":"TA13-3-3","description":"Damages from earthquakes to infrastrcture can be detected directly, e.g. by mapping collapsed buildings in optical data to derive rapid response products. Use of SAR interferograms enables to identify geotectonic shifts. Modelling enables to identify hotspot areas.","hasChildren":true,"name":"Assess damage from earthquakes","selfAssesment":"New
"},{"code":"TA13-3-4","description":"Landslides are a natural hazard posing a threat to human life, property, infrastructure, and natural environment. Every year, slope instabilities have a significant impact on societies and economies. Consequently, landslide documentation is used for risk assessments, policy making and enforcing of construction regulations. Landslide monitoring is used to ensure safety of infrastructure operation. Rapid mapping of landslides and associated damages is done for response actions, e.g. of civil protection organizations. As ground surveys are very costly and time-consuming, satellite remote sensing is increasingly used to assess damage resulting from landslides.\r\nLandslides lead to local terrain changes after a downslope movement of material under the effect of gravity. They vary by type of movement (e.g. falling, toppling, gliding and flowing), by size (from small rocks to entire mountain slopes) and velocity (from a couple of millimetres per year up to free-fall speed). Landslides can be triggered both by natural causes (like earthquakes or heavy rainfall events) and human causes, e.g. mining activities that lead to slope failures. Landslides can initiate other natural hazards, e.g. when a landslide blocks a river a lake can be formed which poses a risk for an outburst flood. \r\nLandslides are diverse in appearance, and therefore are challenging to detect. EO-based assessment methods aim for detecting changes to the land surface and surface displacements. \r\nEO satellites and airborne remote sensing use optical sensors for detecting landslides in post-event images and land cover changes caused by landslides, primarily indicated by the removal of vegetation and the exposure of bare soil, by comparing pre-event and post-event images. Typical resolutions of optical EO data for mapping rapid landslides are between 0.4 m and 30 m, depending on the size of landslides caused by the triggering event. Optical data from unmanned aerial vehicles are used in cases where single landslides or concise regions have to be covered. Additionally, synthetic aperture radar (SAR) sensors allow the detection of subtle changes in ground deformation caused by landslides. Therefore, time-series of radar images are used. Further, airborne laser scanning enables the generation of digital elevation models (DEMs) that allow identification of landslide surface structures and, in case of repeated coverage, detection of elevation changes. DEM generation for analysing landslides is also possible with photogrammetry on stereographic optical data and radargrammetry on SAR images.\r\nThe diversity of appearances of landslides leads to challenges for (semi-)automatic image processing and makes visual interpretation of EO data by a landslide expert a commonly used method for landslide mapping. However, visual interpretation is subjective and experts’ results can be very diverse. Additionally, it is a slow and time-consuming process. Semi-automated classification based on optical and DEM data using object-based image analysis (OBIA) can achieve detailed interpretations of landslides while reducing the analysis time. Interferometic SAR (InSAR) techniques, such as persistant scatterer interferometry (PSI) or Small Baseline Subset (SBAS), are primarily used to identify and monitor slow-moving landslides and for quantifying movement rates. Integrated analysis of optical, DEM and SAR data allow to fully exploit the potential of EO data from different sensors for landslide mapping and assessment.","hasChildren":true,"name":"Forecast and assess landslides","selfAssesment":"Completed
"},{"code":"TA13-3-5","description":"In context of volcanic activities and volcanos, EO methods are capable to provide information about various aspects, including ground motion (seismic), volcanic eruptions (pre-eruptive, sin-eruptive, atmospheric ash, dispersion), Rapid damage estimation (prevention), earthquake damage extent (loss adjuster dispatch). classification of land cover types","hasChildren":true,"name":"Assess and monitor volcanic activities","selfAssesment":"New
"},{"code":"TA13-3-6","description":"Multi-hazard assessment both focuses on regions prone to several geohazards and on the interrelationships between hazards, i.e. what happens if two disasters strike at the same time or what happens when one disaster is causing a cascade of disasters with a strongly amplified impact (e.g. a landslide causing a dammed river causing an outburstflood with a magnitude beyond the design of protective measures; or an earthquake in a coastal region that is followed by a tsunami). EO can provide imformation on the single disasters and, through integration and comprehensive impact assessment, enables multi-hazard assessment.","hasChildren":true,"name":"Multi-hazard assessment","selfAssesment":"New
"},{"code":"TA13-3","description":"Assess disasters and geohazards by EO includes alert & early warning, emergency mapping, and risk & recovery mapping. It relates to observations, controlling, assessments that are linked to natural and human made risks. Typical disasters that can be assessed by EO are in particular floods, droughts, forest fires, landslides, tsunamis, earthquakes, cyclonic storms and volcanic eruptions. Since with EO it is possible to quickly analyse the risk or damage it is used to effectively plan emergency response actions.\r\nThere are several measures to minimize or prevent the damage caused by disasters. Some of them have to be carried out in anticipation of a disaster, others after the occurrence of an event. The different phases that are needed to reduce or avoid the impact and to assure rapid response and recovery are described in the disaster management cycle. Depending on the cycle phase, EO has to meet different requirements. The Mitigation and Preparedness phase are passed through in anticipation of a disaster event. Thus, requirements to EO products may focus on high completeness of mapping or high accuracy of mapping. In contrast, Response and Recovery phase include rapid mapping, thus EO capabilities must meet near real-time delivery requirements. \r\nAs well, the nature of the disaster determines which EO products are used. Optical sensors are used throughout the different types; however, landslides are mostly assessed by radar sensors and thermal sensors are additionally used for forest fires.","hasChildren":true,"hasParent":true,"name":"Assess disasters & geohazards","selfAssesment":"New
"},{"code":"TA13-4-1","description":"To monitor crops and agriculture with EO-based methods is relevant for various applications, including to assess environmental impact of farming, assess crop damage due to storms, to detect ollegal or undesired crops, to monitor water use on crops and horticulture, and to monitor land degradation neutrality. EO mapping of crops happens on all scales with both optical and SAR sensors. Relevant EO products include degradation, agri-environment, ecosystem, damage estimation, warning-service, food-security, impact, crop health (disease and stress), leaf area index, crop acreage and yield harvest (inventories / statistics), crop types (extent, growth, health, stress), land surface temperature, illicit crops, estimates, cultivation patterns, soil water index, surface soil moisture, run-off, land cover (land cover change), land productivity (net primary productivity, NPP), carbon stocks (soil organic carbon, SOC).","hasChildren":true,"name":"Monitor crops","selfAssesment":"New
"},{"code":"TA13-4-2","description":"Monitor the forest focuses on regular and periodic measurement of certain parameters of forests (physical, chemical, and biological) to determine baselines to detect and observe changes over time. Typical applications include to assess deforestation and forest degradation, assess forest damage due to storms or insects, to monitor forest resources, detect illegal forest activities, assess the environmental impact of forerstry, and to monitor the forest carbon content. Moderate resolution sensors have been used to map forests at large scales. Modern very high resolution optical sensors provide enough spatial and spectral detail to map individual trees. Further sensors for forest monitoring include SAR and LIDAR. Integration of optical sensors, LIDAR and in-situ measurements seems an accurate method to achieve third dimension forest mapping.","hasChildren":true,"name":"Monitor the forest","selfAssesment":"New
"},{"code":"TA13-4-3","description":"EO provides the opportunity to monitor bodies of water, i.e. inland waters, and to assess ground water and run-off. For lakes, this includes products about water quality, pollution, turbidity, suspended sediment concentrations (quantitative, qualitative), waterbody (temperature, extent, volume, quantity), algal blooms, alkaline water, evaporation, surface temperature. For ground water and run-off, the products focus on water run-off (water quantity), hydrological network and catchment areas (water catchment), run-off season, groundwater. Various scales are addressed, from local catchments to the global water cycle. For inland water quality, sensors are optical medium resolution (300 meters) for achieving a (strongly cloud-cover dependent) update frequency of 10-20 times per year and high resolution (5 meters) for update frequency of 3-5 times per year.","hasChildren":true,"name":"Monitor bodies of water","selfAssesment":"New
"},{"code":"TA13-4-4","description":"Monitoring of snow and ice focuses on glaciers and their retreat due to climate change (extent, mass balance), the seasonal snow cover (its extent, depth, temperature and snow water equivalent), and the ice on rivers and lakes (inland ice, thickness, freezing period, melting period, ice extent). Glacial monitoring in the mountainous regions around the globe, and of the Greenland and Antarctic ice shields uses optical EO data of high and very high resolution and SAR data. Satellite based daily snow covered area products can reliably be provided down to a spatial resolution of 500 meters. Global products are possible with weekly updates. Applications include, among others, climate change impact monitoring, relevant for modelling runoff patterns in catchments for etimating hydroelectric power generation potential.","hasChildren":true,"name":"Monitor snow and ice","selfAssesment":"New
"},{"code":"TA13-4-5","description":"EO is used to monitor land ecosystems and biodiversity, environmental impact of human activities, land pollution and vegetation encroachment. A tool for this is land cover mapping and mapping of land cover change about a wide set of categories, lincuding basic forest types, major agricultural surface types, conservation areas, settlements, infrastructure, primary roads, bare soil, water bodies, rivers, wetlands following standard classification schemes according to CORINE or FAO LCCS. Main source are optical EO data and associated pixel-based and object-based image classification methods. For discriminating vegetation classes, they often making use of various vegetation indices and biophysical parameters.","hasChildren":true,"name":"Monitor land ecosystems","selfAssesment":"New
"},{"code":"TA13-4-6","description":"EO technologies (both optical and SAR) are capable to categorize bio-physical coverage of land to produce land cover maps like CORINE Land Cover (CLC). The EO method is objective and allows for frequent updates. EO-derived land cover is an excellent basis for mapping land use, the socioeconomic use that is made of land. Land use products are used in a wide range of applications (e.g. agriculture, forestry, spatial planning, determining and implementing environmental policy, land accounting). In a humanitarian context, land use mapping is applied to map refugee camps, population and pressures on population that cause migration.","hasChildren":true,"name":"Monitor land use","selfAssesment":"New
"},{"code":"TA13-4-7","description":"EO is capable to monitor topography with various types of land surface elevation data (both digital terrain models and digital surface models) and also focus on land surface changes and ground deformation / movement due to e.g. soil erosion or permafrost thawing, frost heaving. This includes also the mapping of stable zones where such changes do not happen. The main ways of creating a digital elevation model (DEM) from EO data are deriving it from interferometric synthetic aperture radar (InSAR), from stereoscopic pairs of optical images acquired from different viewing angles, and deriving them via laser scanning.","hasChildren":true,"name":"Monitor topography","selfAssesment":"New
"},{"code":"TA13-4-8","description":"EO is able to extract information about subsurface geology, including near surface features, lithology features, and linear disturbance features (faults & discontinuities). Concerning monitoring of mineral extraction EO supports by mapping ground surface, illegal activities, mine waste (erosion, land subsistence, biodiversity/habitat loss, destruction & disturbance of ecosystems). Disturbance of ecosystems may happen by carbon seeps from reservoirs or pipelines. Their detection can also be done with EO data.","hasChildren":true,"name":"Extract information about subsurface geology","selfAssesment":"New
"},{"code":"TA13-4","description":"Services that monitor land cover all services/applications that are focused on monitoring, assessing, managing, planning and improving land areas, its ecosystems (land, soil and inland water monitoring/quality/availability & usage assessments) and evolution of the land surface (use, cover, seasonal and annual changes and monitors variables) even if it involves human intervention (environmental challenges, impact evaluation or suitability analysis).\r\nMonitoring is possible by deriving information from variables measured by EO in different domains, like vegetation, energy, water, and cryosphere. For vegetation, those variables are for example land cover, NDVI, burnt area, or surface soil moisture. In the energy domain, land surface temperature and surface albedo are known variables, for water it is water surface temperature or water quality. Finally, for the cryosphere lake ice and snow cover extent, and snow water equivalent are variables that are used for land monitoring services.","hasChildren":true,"hasParent":true,"name":"Monitor land","selfAssesment":"Completed
"},{"code":"TA13-5-1","description":"The full range of EO satellite sensors are capable of monitoring particular aspects of urban areas. The most relevant include SAR satellites such as TerraSAR-X that distinguish between urban fabric and other land cover. Further, optical satellites in the resolution range HR and VHR are used to map imperviousness and soil sealing. Beyond such land cover classifications with low granularity, HR and VHR data are used for producing detailed land use and land cover classifications that distinguish different settlement densities or, in combination with additional data, different land use such as transport, residential etc. as defined in Classification schemes specialized on urban areas. Airborne laser scanning (and stereographic analysis) maps building and vegetation heights. InSAR methods allow to measure land subsidence that is highly relevant e.g. in coastal cities close to or below the sea surface elevation. Night-time optical data maps lights. Thermal sensors allow mapping the heat that is radiated from cities. Typical applications include monitoring urban growth/sprawl, transport networks, urban heat islands, and generating city maps and 3D city models for urban planning that are relevant to users in smart cities and in local/regional planning.","hasChildren":true,"name":"Monitor urban areas","selfAssesment":"Completed
"},{"code":"TA13-5-2","description":"EO is capable of monitoring infrastrcture in general, i.e. buildings (and their construction) and transport networks (roads, rails). Additionally, infrastructure for renewable energy harvesting (solar and wind farms, hydroelectric powerplants) and identification of suitable sites (through mapping solar radiation, wind roses, speed and direction, hydrological network mapping). A basis is land surface mapping for deriving digital elevation models (DEMs) that is required for modelling renewable energy potential and for spatial planning and landscape visibility analysis (visual impact assessments for planned infrastructure). Further, EO is capable of assessing damage from industrial accidents. A wide range of EO technologies is used here, infrastrcture can be directly detected and mapped with optical and SAR sensors, where the resolution depends on the targeted assets. DEMs can be generated from SAR and stereographic optical data. Wind energy related parameters can be derived from satellites focused on atmosphere and weather monitoring. Further, there are various GI methods in use, too (in particular focused on spatial planning and impact assessment).","hasChildren":true,"name":"Monitor infrastructure","selfAssesment":"New
"},{"code":"TA13-5","description":"Monitoring the built environment provides information about urban structures, transport networks and particular infrastructure, e.g. dedicated to energy provision. It covers all urban and infrastructure related service/applications on site development information, planning support or suitability analysis. As well, it includes pressure and threats analysis on the urban areas.","hasChildren":true,"hasParent":true,"name":"Monitor the built environment","selfAssesment":"New
"},{"code":"TA13-6-1","description":"Oceanic waters cover approximately 70% of the Earth´s surface and play a key role in regulating Earth temperature and climate, support important marine ecosystems and provide food and transport. Ocean waters occupy large areas and involve highly dynamic processes with different temporal and spatial scales. In-situ measurements taken by ships and buoys can provide accurate information but only at specific locations, being limited to understand large-scale processes. To characterise the heterogeneity and dynamics of ocean waters, it would be required to perform exhaustive field campaigns with associated high costs and infrastructure challenges. EO is an efficient tool to monitor ocean waters and to complement ocean in-situ monitoring programmes as it can provide cost-effective information over vast areas at continuous temporal and spatial scales. \r\nSince the first EO satellite specifically designed to study the oceans (SeaSat) has been launch in the 1970s, many sensors and platforms have been developed. This variety of sensors have provided measurements of a broad range of ocean physical and biological variables to the present day. For example, satellite observations in the visible and near-infrared bands have provided information about ocean colour that can be used to estimate chlorophyll-a concentration for monitoring water quality, productivity and algal blooms. Thermal infrared (TIR) sensors have provided data of Sea Surface Temperature (SST) that is of importance for the study of currents and ocean warming. Microwave radiometers have registered sea surface salinity (SSS), critical to determine the global water balance, understanding ocean currents and estimating evaporation rates. EO can also provide information about physical ocean features such as surface elevation and ocean currents, sea surface winds, ocean waves, vessels and pollutants such as oil spills. \r\nThe versatility of EO data have been proved in a broad range of applications, including the monitoring of water quality, climate change effects, hurricane tracking and prediction, monitor maritime traffic and pollution, harmful algal blooms and fisheries management. In recent years, the Copernicus programme has launched a series of satellite missions for water and land monitoring that guarantee the provision of long-term observations giving continuity to previous satellite missions. Within the Copernicus programme, especially the Sentinel-3 mission will have relevance for ocean observations. Currently, two satellites Sentinel-3A and Sentinel-3B, launched respectively in 2016 and 2018, are providing near-real-time data on the state of the ocean surface, including sea surface temperature, marine ecosystems, water quality and pollution monitoring. New hyperspectral missions such as the Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) developed by NASA, are currently under development. In the near future, they will complement the existing satellite missions and will register data in a high number of spectral bands. This information will be essential in diverse applications such as aquatic ecology and biochemistry. Ocean EO is still an evolving field that will need skilled professionals that exploit the data from the new and upcoming missions for the advancement of ocean knowledge and monitoring.","hasChildren":true,"name":"Monitor the marine ecosystem","selfAssesment":"Complete
"},{"code":"TA13-6-2","description":"In coastal areas, EO is capable to monitor water depth and shallow water bathymetry (charting), coastal ecosystem parameters about water temperature, water transparency, oxygen, phytoplankton abundance, bathing water indicators, detection harmful algal blooms, sediment (qualitative, quantitative), turbidity (quality, quantitative), visibility, chlorophyll-a concentration, suspended sediment may be indicative of estuarine processes, re-suspension or pollution. Further, this includes coastline monitoring with a focus on shoreline and its change as well as coastal land cover (and terrain) and its change. A widse set of EO sensors and technologies is used to monitor coastal areas. Optical satellite imagery is analyzed to detect and map suspended sediment concentrations. Etc.","hasChildren":true,"name":"Monitor coastal areas","selfAssesment":"New
"},{"code":"TA13-6-3","description":"EO is capable to monitor weather impact on ocean surface and metocean features as a basis for forecasting furture ocean conditions. This includes ocean surface topography, ocean dynamics and circulation like tides and ocean current movements and drift, ocean winds, wave and climate conditions at ocean locations (meteocean). Further, this covers the mapping of extreme waves like tsunamis and the monitoring of hurricanes and typhoons. Involved EO technologies are for example satellite altimetry that maps ocean surface with 2 cm to 3 cm accuracy, mathematical forecast models. Repeated altimetry measurements allow mapping speed and direction of ocean's currents and tides. Available EO-based RADAR systems monitor wave height and direction, wind speed and sea-surface elevation. Near-realtime processing and delivery workflows enable the use of these parameters in weather forecasting, navigation and offshore installations protection.","hasChildren":true,"name":"Monitor weather impact on ocean surface","selfAssesment":"New
"},{"code":"TA13-6-4","description":"To support an ecosystem-based approach for fisheries management, EO images with global and daily systematic coverage with high-resolution images can help in identifying potential fishing zones and to assess fish stocks. They help assessing and understanding changing abundancy and spatial distribution of exploited fish stocks. Therefore, they analyse various key environmental parameters that can be detected with satellite remote sensing. This includes sea surface temperatures (SSTs), sea surface height anomalies, and sea surface colour revealing the abundance of chlorophyll a. This relates to phytoplacton production that is directly related to total fish landings. Additionally, EO can detect harmful algal bloom. A further threat to sustainable fish stocks management are illegal fishing. Where localization of licensed fishing vessels and fleet management services are supported by EO to avoid overexplotation and enable recovery of fish stocks. EO complements identification, detection and tracking of vessels with SAR and optical remote sensing.","hasChildren":true,"name":"Monitor fisheries","selfAssesment":"New
"},{"code":"TA13-6-5","description":"For shipping, navigation, and monitoring sea-traffic and pollution, remote sensing and satellite technologies allow detecting vessels in the wider ocean. EO can detect the vessels themselves, their wake trailing behind them, sandbanks and reefs that pose a threat for safe navigation. Additionally, EO can detect pollution from the ships, e.g. when illegal waste disposal happens. Ship detection and classification is possible with the use of optical and synthetic aperture radar (SAR) imagery. The methods complement each other.","hasChildren":true,"name":"Detect and monitor ships","selfAssesment":"New
"},{"code":"TA13-6-6","description":"Information on sea ice and icebergs is important for managing operation of ships or offshore platforms in hazardous sea ice conditions. EO technologies give the possibility to study sea ice and measure its thickness, spatial distribution, motion and ridges (as well as ice berg positions). Satellite imagery provides wide area, synoptic pictures of the ice conditions. Since the scale of ice fields is quite large, mainly moderate resolutions have to be accepted, down to around 10m in scale, while ensuring comprehensive coverage. Multispectral imagery can provide more information on ice-type but in the main, SAR imagery is used due to its all-weather and day/night capability. The data collected can be more accurate than in-situ measurements due to a higher and faster coverage of a whole area. Subsequent modelling that incorporates ocean weather (wind, waves, ocean current) provides expected drifting paths. Constant monitoring is most important to identify the risk and opportunities, for instance for ship routing, and safety of oil rigs.","hasChildren":true,"name":"Monitor sea-ice and icebergs","selfAssesment":"New
"},{"code":"TA13-6","description":"Monitoring marine inlucdes monitoring of marine safety (e.g. marine operations, oil spill combat, ship routing, defence, search & rescue, ...), marine resources (e.g. fish stock management, ...), marine and coastal environment (e.g. water quality, pollution, coastal activities, ...), and climate and seasonal forecasting (e.g. ice survey, seasonal forecasting, ...).","hasChildren":true,"hasParent":true,"name":"Monitor marine","selfAssesment":"New
"},{"code":"TA13","description":"EO services and applications are organized according to thematic areas. EO is used for a wide set of services. There are many applications of EO that show how a service produces information for a particular client. EO service and applications are best described by the purpose they serve or by the need of the user. The main user needs to EO are to monitor, to map, to forecast, to assess, to detect, and to analyse. \r\nTo monitor means to watch and check a situation carefully for a period of time in order to discover something about it, i.e. keeping track of how the natural and manmade environment change (their status) over time. Typical alternative verbs are track, observe, record, follow, understand, or surveil. \r\nTo map means to represent an area of land in the form of a map, i.e. to feature and locate the way it is arranged or organized. Synonymous verbs are locate, identify, classify, trace, or record.\r\nTo forecast means to provide statements covering a range of different outcomes, to say what you expect to happen in the future; i.e. to predict future events based on specified assumptions (about information extracted from EO change and time series data), where different sets of assumptions describe scenarios. Equivalent terms are predict, plan, model, estimate, or project.\r\nTo assess means to judge or decide the amount, value, quality or importance of something, i.e. to evaluate and measure the status of and changes in natural and manmade built environments. Alternative verbs are evaluate, measure, understand, review, or quantify.\r\nTo detect allows to notice something that is partly hidden or not clear, or to discover something, especially using a special method, i.e. to identify and locate the changes in the Earth’s environment. Similar terms are locate, warn, identify, highlight, or spot.\r\nTo analyse means to study or examine something in detail, in order to discover more about it, i.e. to detail the elements of a whole and critically examine and relate these component parts separately and/or in relation to the whole. Sometimes, the terms to process, to parse, or to detail are used in exchange for to analyse.","hasChildren":true,"hasParent":true,"name":"EO services and applications","selfAssesment":"New
"},{"code":"TA14-1-1-1","description":"Ocean colour can be made visible in atmospherically corrected EO data. Specific spectral bands are necessary to derive physical and biologic parameters of the water from the EO data.","hasChildren":true,"name":"Ocean colour","selfAssesment":"New
"},{"code":"TA14-1-1","description":"Band combinations are pre-defined for (visually) analysing images for a dedicated purpose. Examples are dedicated band combinations for land us land cover classification, ocean colour, etc.","hasChildren":true,"hasParent":true,"name":"Band combinations","selfAssesment":"New
"},{"code":"TA14-1-2","description":"The spectral and refractive information from optical and SAR data enables direct and indirect derivation of biophysical and geophysical EO parameters that are properties of the sensed land surface, ocean surface and atmosphere volume.","hasChildren":true,"hasParent":true,"name":"EO parameters","selfAssesment":"New
"},{"code":"TA14-1","description":"Processing products are image products from raw data to all different processing stages. The transformation processes between the stages include operations such as atmospheric correction, cloud detection and radiometric calibration to provide data in a form suitable for subsequent analysis. Processing products consider a product as being an output of a process.They appear as \"intermediate products\" along all steps of the processing chain.","hasChildren":true,"hasParent":true,"name":"Processing-related and preparatory products","selfAssesment":"New
"},{"code":"TA14-2-1-1","description":"Point clouds represent a set of points with X, Y, Z coordinates and associated attributes. A source of acquisition is Light Detection and Ranging (LIDAR) sensor.\r\n Depending on the location of the recording device, i.e. where and on which the LIDAR systems are mounted, it can be divided into: Terrestrial Laser Scanning (TLS), Airborne Laser Scanning -ALS) and Spaceborne Laser Scanning (SLS).\r\nThe LIDAR system uses the near-infrared part of the electromagnetic spectrum (1064 nm) for active data collection, day or night, in the shade, but also in low visibility conditions (e.g. under clouds). Due to the footprint of the beam itself, when interacting with vegetation, one part will be reflected back, registering the height of the vegetation, and part of the beam will pass to another surface from which the other part of the beam will be reflected. Depending on the beam intensity and vegetation density this can happen a few times until it hits a hard surface and the rest of the beam is reflected.\r\nIn this way, precise information on the height and density of vegetation can be obtained, but also using automatic and semi-automatic data filtering techniques, it is possible to create several very high resolution products from source data: digital elevation model (DEM), digital relief model (DMR) digital canopy model (DCM) , digital surface model (DSM).\r\nDepending where the sensor is mounted, the density of collected point clouds can be from 15 points per m2 to as many as 250 points per m2 (in the case of UAV dana collection). This is also depending on the speed and altitude of the flight and the speed and power of the emitted pulse or beam. The biggest advantage of LIDAR scanning is that in most cases, a sufficient number of beams will always penetrate to the ground, allowing the creation of a very precise digital relief model which is the basis for further analysis. This is not always possible in very dense vegetation areas (rainforests).\r\nThe advantage of LIDAR point clouds lies in the fact that it truly provides a huge amount of information gathered in a short period of time, that are of exceptional precision. These point clouds have very wide application from forestry, surveying, architecture to archeology.\r\nGiven the development of technology, it is possible to obtain a similar point cloud by photogrammetry methods. However, photogrammetric cameras (eg orthophotos and infrared cameras) have one significant drawback, they cannot penetrate clouds, vegetation and water, and only DSM product can be extracted from them.","hasChildren":true,"name":"Point clouds","selfAssesment":"Completed
"},{"code":"TA14-2-1-2","description":"Elevation data in the form of a digital elevation model (DEM) is an essential component of many analyses derived from EO. DEMs are used to represent every kind of surface, including terrain surface, vegetation canopy surface, sea surface, sea-ice surface, glacier surface etc. This description focuses on DEMs for representing terrain. A digital terrain model (DTM) describes the bare ground of the terrain, a digital surface models (DSM) described heights of vegetation (e.g. trees) and of man-made structures (e.g. buildings) reaching above the terrain. DEM is often used as an umbrella term for DTM and DSM. EO-derived DEMs are usually DSMs and require removal of vegetation and buildings in order to represent the terrain (DTM). DEMs are multi-purpose products used in various applications. They are available for global scale (SRTM, WorldDEMTM), regional scale (ArcticDEM, Copernicus EU-DEM v1.1) or for national levels and local regions. Various techniques exist to generate DEMs from SAR data, stereographic optical EO (as well as airborne and drone) data and from airborne laser scanning.","hasChildren":true,"name":"Digital elevation models","selfAssesment":"Completed
"},{"code":"TA14-2-1-3","description":"By comparing elevation models of different dates, the change in elevation and volume can be identified. Thereby, they measure surface deformation, land subsidence, ice shield loss due to melting, etc.","hasChildren":true,"name":"Elevation change maps","selfAssesment":"New
"},{"code":"TA14-2-1-4","description":"Vector fields capture the movement directions of locations on a continuous surface, e.g. of the ocean, or in a 3D grid of locations, e.g. of the atmosphere. The atmosphere and the ocean are highly dynamic features. Vector fields are used to represent wind directions and current movement directions. Further vector fields derived from EO data include geoid undulation / gravity maps.","hasChildren":true,"name":"Vector fields","selfAssesment":"New
"},{"code":"TA14-2-1-5","description":"When a moving feature (i.e. object) is detected in subsequent images, its trajectory of movement can be mapped. Such products map ship movements, sea ice movements, etc.","hasChildren":true,"name":"Feature trajectories","selfAssesment":"New
"},{"code":"TA14-2-1","description":"Geometrically measured EO products origin from EO-derived distance measurements, measurements of direction, tracking of moving objects, and changes of distance measurements. The used EO methods include for example SAR interferometry and stereographic analysis of optical data.","hasChildren":true,"hasParent":true,"name":"Geometrically measured EO products","selfAssesment":"New
"},{"code":"TA14-2-2-1-1","description":"Land cover maps represent spatial information on different types (classes) of physical coverage of the Earth's surface, e.g. forests, grasslands, croplands, lakes, wetlands. An example is the European Copernicus product CORINE land cover (CLC) with 44 classes. Initiated in 1985 (reference year 1990), updates followed in 2000 and every 6 years afterwards. Apart from CLC, the European Copernicus Land products also include the High Resolution Layers. They includes for example the imperviousness product that captures the percentage of soil sealing. Land cover classification products are multi-purpose products that are relevant for various applications. They are available on national levels, regional levels and global levels. They have different scales and granularity of their associated classification scheme. The products are updated on a regular basis. Update cycles can vary depending on the resolution (i.e. likelihood for observable change of the land surface) and the capability of production processes. An additional example on a global scale is the Global Urban Footprint. The products are provided by public organisations and private EO companies and based on various EO sensors.","hasChildren":true,"name":"Land cover maps","selfAssesment":"Completed
"},{"code":"TA14-2-2-1-2","description":"Land use documents how people are using the land. Getting from physical land type (land cover) to land use requires skill in interpretation and involves integration and consultation of ancillary data. Land use maps are multi-purpose products that are relevant for many applications. The products are updated on a regular basis (e.g. 6 years for Urban Atlas).","hasChildren":true,"name":"Land use maps","selfAssesment":"New
"},{"code":"TA14-2-2-1-3","description":"Cloud masks for optical EO data distingush cloudy pixels from cloud-free pixels. They may differentiate between serveral cloud types, i.e. opaque clouds and Cirrus clouds (that are transparent). Most land monitoring applications based on optical data require cloud-free images. Therefore, cloud masks are a product that is used early on in image processing for selecting suitable imagery for analysis (e.g. by screening images of an archive by the derived cloud cover percentage of the image). Therefore, cloud masks are made available as metadata by the EO data provider. Clouds are identified with threshoulding of reflectance values of the blue band and, to adapt for cloud/snow confusion, specific short-wave infrared (SWIR) bands.","hasChildren":true,"name":"Cloud mask","selfAssesment":"New
"},{"code":"TA14-2-2-1-4","description":"Detected features are objects from one or more classes and are the result of a comprehensive (and mostly automatic or semi-automated) search of all locations in an image that decides whether such features are present and where they are located. Examples inculde man-made objects (e.g. vehicles, ships, buildings, etc.) with sharp boundaries and are independent from the background, and landscape objects, such as land-use/land-cover (LULC) parcels that have vague boundaries and are part of the background environment. Only the latter type would locate features for all locations of an image.","hasChildren":true,"name":"Detected features","selfAssesment":"New
"},{"code":"TA14-2-2-1","description":"Static EO derived thematic classification products and masks (e.g. land use land cover classifications). Additionally, static EO detected features (planes on apron of airports, dwellings) that consist of a set of point locations (or polygons) and do not end up in a comprehensive classification of all pixels of an image. Static EO derived thematic classification products and masks (e.g. land use land cover classifications). Additionally, static EO detected features (planes on apron of airports, dwellings) that consist of a set of point locations (or polygons) and do not end up in a comprehensive classification of all pixels of an image. Thematic classifications and feature detection identify a surface by a class label that represents a more or less persistent state. A good example product is the Copernicus Urban Atlas. The most recent available version is assumed to represent the \"current\" state (Certainly, an update cycle is necessary for providing a product that remains up-to-date).","hasChildren":true,"hasParent":true,"name":"Thematic classifications and feature detection","selfAssesment":"New
"},{"code":"TA14-2-2-2","description":"Event maps and thematic change (evolution) maps indicate that some process happened that changed the area at a location from one class to the other. For example, a burnt area map indicates locations where vegetation has been burnt by a fire and changed to bare ground. A typical mapping method is the use of pre- and post-event satellite images for detection of the areas affected by the process. Eventually burnt areas contain identifiable burn marks that allow direct identification in one single post-event satellite image. Nevertheless, it is the process that is central to the analysis. Similarly, the concepts aforestation and deforestation would fall under the heading \"Event maps.\" They may come from a comparison of two status maps of different dates. Some processes benefit from analysis of more than two states. Such change evolution maps can be produced with time-series analysis. On land, more examples include landslide maps, flooded area maps and other land surface dynamics (e.g. aforestation and deforestation). Further, change detection maps are available for other domains (atmosphere, marine, land, climate, etc.)","hasChildren":true,"name":"Event maps and thematic change (evolution) maps","selfAssesment":"New
"},{"code":"TA14-2-2","description":"The semantic labelling products result from methods that assign labels to objects or locations in a field. The labels correspond to the categories of a classification or, in case of masks and detected features, to a single target class. Such labels may also identify classes of change or change evolution.","hasChildren":true,"hasParent":true,"name":"Semantic labelling products","selfAssesment":"New
"},{"code":"TA14-2-3","description":"EO-derived attribute products describe the state and evolution of specific attributes of a feature or at a field location. They describe for example air quality, soil moisture or water quality & quantity.","hasChildren":true,"name":"EO-derived attribute products","selfAssesment":"New
"},{"code":"TA14-2","description":"Descriptive analytics products provide analytical results which describe the present (and past) situation as it is recorded in EO images. Therefore, it contains information that can directly be extracted from EO images or EO image time series. These products are diverse in various aspects: they capture static and dynamic information; they concern information about objects or fields; and they have qualitative (nominal scale) or quantitative (ordinal, interval, ratio scale) levels of measurement.","hasChildren":true,"hasParent":true,"name":"Descriptive analytics products","selfAssesment":"New
"},{"code":"TA14-3","description":"Providing analytical (modelling) results which predict the future situation (e.g. air pollution forecasts). [interpolation in space, i.e. not only prediction into the future, filling gaps in time series...]\r\nInformation that can be modelled based on descriptive analytics products. by extrapolating time series (forecasting/predicting), by modelling of processes (e.g. flood risk maps, landslide susceptibility)","hasChildren":true,"name":"Predictive modelling products","selfAssesment":"New
"},{"code":"TA14-4","description":"Prescriptive modelling products and services focus on providing analytical results that are a guide to action. The often result from an impact assessment. One example is the identification of construction sites leading to sales opportunities.","hasChildren":true,"name":"Prescriptive modelling products and services","selfAssesment":"New
"},{"code":"TA14-5-1","description":"A textured 3D model uses a 3D model derived from elevation data. Additionally, each separate surface of the 3D model receives its own texture derived from optical image data. Typically used for visualisation purposes.","hasChildren":true,"name":"Textured 3D models","selfAssesment":"New
"},{"code":"TA14-5-2","description":"A semantic 3D model consists of a 3D model derived from elevation data with an integrated image classification. A classified object thereby consists of a 3D surface or a grouped set of 3D surfaces. A typical example is a 3D city model in the CityGML format.","hasChildren":true,"name":"Semantic 3D models","selfAssesment":"New
"},{"code":"TA14-5","description":"Combining the satellite data with other information sources. Resulting in an integration of several descriptive analytics products and processing products, e.g. a textured 3D model or a semantic 3D model.","hasChildren":true,"hasParent":true,"name":"Aggregation and integration products","selfAssesment":"New
"},{"code":"TA14-6-1","description":"Sentinel-2 cloud-free mosaics for display, satellite maps in books etc.","hasChildren":true,"name":"Satellite maps","selfAssesment":"New
"},{"code":"TA14-6-2","description":"Layouted maps in a file (PDF, SVG, etc.) for printing or visualisation on screen, embedding in reports or as static displays on websites etc.","hasChildren":true,"name":"Layouted digital maps","selfAssesment":"New
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"},{"code":"TA14-6-4","description":"Printed maps, 3D plots of 3D models, hologram 3D maps etc.","hasChildren":true,"name":"Analogue visualisation products","selfAssesment":"New
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"},{"code":"TA14-6","description":"Visualisation products are used for presentation of EO information to the user. The user's interaction with the visualisations is predominantly viewing and interpretation of the informational content and arriving at decisions in the context of the user'S objective with the EO information. In addition, users of visualisation are all involved actors during image processing. For example, an EO analyst may use visualisations of EO data and preliminary EO products for getting a better understanding of the contained information and adapt his processing workflow to arrive ad improved results. Typical visualisation products include satellite maps, layouted digital maps, web visualisations in 2D and 3D, and analogue visualisation products.","hasChildren":true,"hasParent":true,"name":"EO visualisation products","selfAssesment":"New
"},{"code":"TA14-7","description":"Users need access to EO products if they shall be able to benefit from them. Additionally, providers of value added products act as users of EO products earlier in the information processing value chain. Concequently, various distribution services provide access from raw data to processed information and processing infrastructure. Provision of access to raw data or processed information happens via direct download (FTP), via application programming interfaces (API) or web services (e.g. Hubs). Further, access to processing infractructure happens via web services.","hasChildren":true,"name":"Distribution services","selfAssesment":"New
"},{"code":"TA14","description":"Products in relation to EO appear along the entire image processing value chain as inputs and outputs of processing steps. Ultimately, at the end of that chain, the output EO products represent information that supports actions. The standard EO products are categorized by the type of problems they help to solve or the type of question they help answering.","hasChildren":true,"hasParent":true,"name":"Standard EO products","selfAssesment":"New
"},{"code":"WB","description":"This knowledge area is about Web Based Geographic Information management aspects and therefore it was given the name \"Web Based GI\" or \"WBG\" in short. It is implied by this name that the differentiating factor for this KA is the \"Web\". One must then be able to answer the questions like \"What functions do we delegate to the Web?\" or \"how WBGI is different from the traditional GI?\" Sticking to the functions of a GIS, which are inserting (adding), storing, manipulating, analysing and presenting the data, there is not a single system for effecting all these tasks anymore but the Web itself. For instance, there is no single database and its known-to-its users-definition, anymore but many different stores and many different definitions. Similarly, many different manipulation, analysis and presentation options compared with the options offered by a single or limited number of systems of traditional GI. In general, Web provides the means of leveraging distributed \"resources\" like data, information, or software. It is a \"collaboration medium\". A collaboration that enables rapid production or decision making. A collaboration that certainly introduces new dimensions to traditional GI handling. This is the justification of proposing this KA in addition to the KAs of the original BoK. For the mentioned collaboration to happen, data or any other type of a resource have to accessible on the Web. This means that it should have a Web \"address\" and a \"definition\" that is understandable either by \"human\" or \"machine\". \"Machine understandable definitions\" refers to the dimension of \"semantics\" and \"ontologies\" which are also included under this KA. When one talks about publishing resources then \"catalogue services\" and more importantly \"discovery\" dimension comes into the scene. On the other hand, \"Linked Data (LOD)\" and \"Open Data\", highly popular recent trends and two of the above mentioned dimensions of Web GI have also been covered under this KA. Like the other dimensions of Web GI, both LD and OD aspects must be known to GI communities with differing degrees of expertise. The concepts of \"interoperability\" and \"Spatial Data Infrastructure (SDI)\", hot topics of GI communities for many years, have been thought to be dealt with under this KA as well with the justification that \"Web GI\" is a much broader concept than SDI, This is by the fact that SDI refers to a much narrower content and context of \"collaboration\" then Web GI. Therefore, Geospatial data interoperability and some of the related concepts which were classified under KA, \"Geospatial data in the original BoK were moved under KA11 with the updated context. Another issue is the coverage of Spatial Analysis (SA), data manipulation aspects of GI by KA11. The SA aspects are covered by other KAs like \"Geocomputation\" and \"Analytical methods\". If the analysis operations, in an undertaking, would be handled by web services this is already covered by \"data processing\" web services, application development unit and Web services composition under that unit. The important thing is to have the knowledge about a specific analysis operation; Employing it as a web service would require no more knowledge than using any other web service. SA is covered by KA11 in as much as it should have been.","hasChildren":true,"hasParent":true,"name":"Web-based GI","selfAssesment":"In progress (GI-N2K)
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"},{"code":"WB2-3","description":"Defining a common language is a crucial step for sharing or combining data. Vocabularies, taxonomies, ontologies are are tools to reach this goal.","hasChildren":true,"name":"Ontologies development reuse and patterns","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB2","description":"A \"resource\" could be \"anything\" including data and services, identifiable over the Web. A resource should be defined in a language to be discoverable on the Web. Over the years, two major bodies W3C for non-spatial and OGC concerning spatial data have developed many specifications for defining data and services. On the W3C side, Resource Description Framework (RDF) has gained a great momentum in recent years in relation to the recent popularity of Linked Data as well. In the OGC front, the acceptance of GML was a major step concerning the long time effort of geospatial communities for having a standard for the definition of both geospatial features and geometry.","hasChildren":true,"hasParent":true,"name":"Resource Definition","selfAssesment":"In progress (GI-N2K)
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"},{"code":"WB3-2","description":"A resource can be added manually to a catalogue service by creating or uploading its metadata, but metadata can also be added by automated crawling of other catalogues.","hasChildren":true,"name":"Manual and automated forms of publishing","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB3-3","description":"Catalogue services allow to publish and search resources through their metadata","hasChildren":true,"name":"Catalogue services","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB3-4","description":"Open data is data that is free to use, re-use and share without limitations on who uses it or for what purpose. Publishing open data is making the data discoverable and accessible in a convenient way (technical openness).","hasChildren":true,"name":"Publishing open data","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB3-5","description":"Adding semantic information to the data allows computers to understand the structure and meaning of data. This allows automatic searching, processing and integrating data with other semantic sources.","hasChildren":true,"name":"Publishing via a semantic definition of data","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB3-6","description":"Linked (open) data provides structured data which is interlinked in a machine readable way. This allows to discover, access and combine data in an automatic way. This concept discusses the steps needed to make existing data available in a linked open way.","hasChildren":true,"name":"Publishing linked open data","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB3","description":"\"Publishing\" means making a resource available for the use of others. A \"resource\" could be \"anything\" including data and services, identifiable over the Web. Publishing may be done on the basis of either the \"characteristics\" of the data or the data itself. When only some \"characteristics\" of a resource is published then some of the contents would naturally be left out. The \"characteristics\" include metadata and some keywords. This kind of publishing may be named as \"limited contents\" publishing or \"publishing by metadata\". One of the issues become then what characteristics to use to define the data. Or what what metadata definition to use. Another aspect of publish is \"manual entry\" and \"automated collection\". In the former publisher enters metadata while in the latter some harvesting mechanism collects metadata in an automated fashion. On the contrary, there is \"unlimited contents publishing\" where there is no limitation on the published contents. Open data publishing is in this class. In additon, some \"additional semantics\" may be subject of this type publishing through new relationships in the ontologies of publishing, which have not been explicit in the exisiting data model but are inherent in the data. And this last type is covered under the topic, \"Publishing via a semantic definition of data.\"","hasChildren":true,"hasParent":true,"name":"Resource Publishing","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB4-1","description":"Syntactic discovery is the discovery of resources based on the structure of the resources","hasChildren":true,"name":"Syntactic discovery","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB4-2","description":"Semantic discovery is the discovery of resources based on the meaning of the data.","hasChildren":true,"name":"Semantic discovery","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB4-3","description":"Linked (open) data provides structured data which is interlinked in a machine readable way. This allows to discover, access and combine data in an automatic way.","hasChildren":true,"name":"Discovery over linked open data","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB4","description":"Resource discovery means the discovery of resources including data and services needed for an application. Syntactic discovery refers to the discovery on the basis of syntactic comparison operations. It is classified as \"keyword-based\" and \"full-text-based\" discovery. Semantic discovery on the other hand, refers to the discovery of resources on he basis of some semantic definition. Therefore, semantic discovery requires that a resource be published by a semantic definition as defined in the topic WB3-5.","hasChildren":true,"hasParent":true,"name":"Resource Discovery","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB5-1","description":"The workflow to integrate geospatial data in an application often relies on a combination of different OGC web services. Searching and finding the data and the corresponding services, binding to these services to view, filtering and or downloading the data are different steps in this process","hasChildren":true,"name":"Integrating data from OGC web services","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB5-2","description":"The alignment of data structures and vocabularies/ontologies used are important steps towards the data harmonisation needed for a combined use of datasets","hasChildren":true,"name":"Schema matching and ontology alignment","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB5-3","description":"A data mashup is a combination of data from different sources to produce new applications of new datasets","hasChildren":true,"name":"Data mash ups","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB5","description":"The term \"application development\" refers to the collection of activities or the \"workflow\" through which the user reaches her final goal. Being one of these activities, \"data integration\" means the transformation of data from one representation to another which might be of either the client`s one or some other representation. An example for data integration might be the case where the data is transfered from an OGC WFS and integrated into a client GIS.","hasChildren":true,"hasParent":true,"name":"Application development via Data Integration","selfAssesment":"In Progress GI-N2K
"},{"code":"WB6-1","description":"Manual Web Service Composition is manually (by human) combining the activities of discovery, composition and invocation to fulfil a certain task.","hasChildren":true,"name":"Manual Web Services Composition","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB6-2","description":"Providing standardized descriptions of the specifics of available webservices creates an environment where the composition of services to create a web application can be automated.","hasChildren":true,"name":"Semi automated and Full-automated WSC","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB6","description":"Web Services Composition can be defined as bringing together a number of web services in a certain workflow to achieve a certain task that cannot be achieved by any of the composed services alone. In general, it involves first the discovery of the suitable services over the Web, and compose them in a certain workflow order and finally run the composed service which is the invocation stage. WSC has been a highly active research topic since the emergence of Web services in 2000s. \"Manual\" WSC is the form that the activities of discovery, composition and invocation are all done manually (by human). In the \"Semi-automated\" way, the discovery is done by the machine. In the \"full-automated\" approach all the above activities are done by the machine. There are no tools at the moment that achieve full automated composition. Web API composition is like WSC, the only difference is the fact that instead of web services there are Web APIs in WAPIC. There is no doubt that One would run into the very same problems of WSC concerning full automated composition. In other words, WAPIC would in no way be easier than WSC. Nevertheless, as far as semi automated form can be achived, WAPIC is valuable because the number of Web APIs increase drastically from day to day. The site \"programmableWeb\" lists 14 957 APIs at the moment. It is not easy to search for all those APIs manually for the discovery of suitable APIs for a given task.","hasChildren":true,"hasParent":true,"name":"Application development via Web services composition","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB7-1","description":"Hypertext markup scripting and styling are the base for each web page or application. Styling defines the look and feel while scripting is used to implement the behavior of the web application","hasChildren":true,"name":"Hypertext markup scripting and styling","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB7-2","description":"Web map APIs allow developers to integrate resources made available by web services in their application or web sites.","hasChildren":true,"name":"Web Map APIs and Libraries","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB7-3","description":"A web application framework provides the generic and reusable building blocks needed to create web applications. Geoportal frameworks provide the functionality to build geospatial portals.","hasChildren":true,"name":"Web application Frameworks and Geoportal frameworks","selfAssesment":"In Progress (GI-N2K)
"},{"code":"WB7","description":"Characteristic examples are included under this topic. The APIs, for instance other than the ones included under this unit, and libraries could have been included as well. However, since the important thing is to highlight the functionality then there is no need to include them all. By the inclusion of topic \"WB7-3\"under this unit, the aim was to cover one of the very \"hot\" topics of Web2.0 for both the main concepts about Web application frameworks and also how they are related to portal frameworks and geoportals. By the topic \"WB7-1 Building blocks\" the core components of Web application development are covered. On top of this core, there comes a great variety of \"Web application frameworks for both enabling rapid web application development and ensuring scalable, high-performance applications. Finally, there are \"Web APIs and Libraries\" certainly deserving being a separate topic for their current popularity. They also mean rapid application development for developers by code reuse and versatility for \"end users\" in creating their \"end products\".","hasChildren":true,"hasParent":true,"name":"Web Application development elements","selfAssesment":"In progress (GI-N2K)
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Retrieved from https://earsc-portal.eu/display/EOwiki/Forecast+and+map+large+waves","url":"https://earsc-portal.eu/display/EOwiki/Forecast+and+map+large+waves"},{"concepts":[1136,1209],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Forecast and monitor current movement and drift. Retrieved from https://earsc-portal.eu/display/EOwiki/Forecast+and+monitor+current+movement+and+drift","url":"https://earsc-portal.eu/display/EOwiki/Forecast+and+monitor+current+movement+and+drift"},{"concepts":[1209],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Forecast and monitor ocean winds and waves. Retrieved from https://earsc-portal.eu/display/EOwiki/Forecast+and+monitor+ocean+winds+and+waves","url":"https://earsc-portal.eu/display/EOwiki/Forecast+and+monitor+ocean+winds+and+waves"},{"concepts":[1195],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Forecast crop yields. Retrieved from https://earsc-portal.eu/display/EOwiki/Forecast+crop+yields","url":"https://earsc-portal.eu/display/EOwiki/Forecast+crop+yields"},{"concepts":[1181],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Forecast weather. Retrieved from https://earsc-portal.eu/display/EOwiki/Forecast+weather","url":"https://earsc-portal.eu/display/EOwiki/Forecast+weather"},{"concepts":[1179],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Forecasting sunlight exposure. Retrieved from https://earsc-portal.eu/display/EOwiki/Forecasting+sunlight+exposure","url":"https://earsc-portal.eu/display/EOwiki/Forecasting+sunlight+exposure"},{"concepts":[1202],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Identify hydrocarbon seeps in soil. Retrieved from https://earsc-portal.eu/display/EOwiki/Identify+hydrocarbon+seeps+in+soil","url":"https://earsc-portal.eu/display/EOwiki/Identify+hydrocarbon+seeps+in+soil"},{"concepts":[1194,1188],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Map and assess flooding. Retrieved from https://earsc-portal.eu/display/EOwiki/Map+and+assess+flooding","url":"https://earsc-portal.eu/display/EOwiki/Map+and+assess+flooding"},{"concepts":[1136],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Map and monitor hydroelectric energy. Retrieved from https://earsc-portal.eu/display/EOwiki/Map+and+monitor+hydroelectric+energy","url":"https://earsc-portal.eu/display/EOwiki/Map+and+monitor+hydroelectric+energy"},{"concepts":[1136],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Map and monitor solar energy (solar farms). Retrieved from https://earsc-portal.eu/pages/viewpage.action?pageId=78221967","url":"https://earsc-portal.eu/pages/viewpage.action?pageId=78221967"},{"concepts":[1136],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Map and monitor wind energy (wind farms). Retrieved from https://earsc-portal.eu/pages/viewpage.action?pageId=78221973","url":"https://earsc-portal.eu/pages/viewpage.action?pageId=78221973"},{"concepts":[1210],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Map fish shoals. Retrieved from https://earsc-portal.eu/display/EOwiki/Map+fish+shoals","url":"https://earsc-portal.eu/display/EOwiki/Map+fish+shoals"},{"concepts":[1202],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Map geological features. Retrieved from https://earsc-portal.eu/display/EOwiki/Map+geological+features","url":"https://earsc-portal.eu/display/EOwiki/Map+geological+features"},{"concepts":[1202],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Map seismic survey operations. Retrieved from https://earsc-portal.eu/display/EOwiki/Map+seismic+survey+operations","url":"https://earsc-portal.eu/display/EOwiki/Map+seismic+survey+operations"},{"concepts":[1208],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Map water depth or charting. Retrieved from https://earsc-portal.eu/display/EOwiki/Map+water+depth+or+charting","url":"https://earsc-portal.eu/display/EOwiki/Map+water+depth+or+charting"},{"concepts":[1201],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Measure & detect land surface change. Retrieved from https://earsc-portal.eu/display/EOwiki/Measure+detect+land+surface+change","url":"https://earsc-portal.eu/display/EOwiki/Measure+detect+land+surface+change"},{"concepts":[1200],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Measure land use statistics. Retrieved from https://earsc-portal.eu/display/EOwiki/Measure+land+use+statistics","url":"https://earsc-portal.eu/display/EOwiki/Measure+land+use+statistics"},{"concepts":[1179],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor air quality & emissions. Retrieved from https://earsc-portal.eu/pages/viewpage.action?pageId=78221935","url":"https://earsc-portal.eu/pages/viewpage.action?pageId=78221935"},{"concepts":[1208],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor coastal ecosystem. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+coastal+ecosystem","url":"https://earsc-portal.eu/display/EOwiki/Monitor+coastal+ecosystem"},{"concepts":[1206,1205],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor construction and buildings. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+construction+and+buildings","url":"https://earsc-portal.eu/display/EOwiki/Monitor+construction+and+buildings"},{"concepts":[1196],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor forest carbon content. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+forest+carbon+content","url":"https://earsc-portal.eu/display/EOwiki/Monitor+forest+carbon+content"},{"concepts":[1196],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor forest resources. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+forest+resources","url":"https://earsc-portal.eu/display/EOwiki/Monitor+forest+resources"},{"concepts":[1200],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor humanitarian movement and camps. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+humanitarian+movement+and+camps","url":"https://earsc-portal.eu/display/EOwiki/Monitor+humanitarian+movement+and+camps"},{"concepts":[1198],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor ice on rivers and lakes. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+ice+on+rivers+and+lakes","url":"https://earsc-portal.eu/display/EOwiki/Monitor+ice+on+rivers+and+lakes"},{"concepts":[1199],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor land cover and detect change. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+land+cover+and+detect+change","url":"https://earsc-portal.eu/display/EOwiki/Monitor+land+cover+and+detect+change"},{"concepts":[1199],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor land ecosystems and biodiversity. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+land+ecosystems+and+biodiversity","url":"https://earsc-portal.eu/display/EOwiki/Monitor+land+ecosystems+and+biodiversity"},{"concepts":[1199],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor land pollution. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+land+pollution","url":"https://earsc-portal.eu/display/EOwiki/Monitor+land+pollution"},{"concepts":[1207],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor marine habitats. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+marine+habitats","url":"https://earsc-portal.eu/display/EOwiki/Monitor+marine+habitats"},{"concepts":[1202],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor mineral extraction. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+mineral+extraction","url":"https://earsc-portal.eu/display/EOwiki/Monitor+mineral+extraction"},{"concepts":[1208],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor ocean level and surface. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+ocean+level+and+surface","url":"https://earsc-portal.eu/display/EOwiki/Monitor+ocean+level+and+surface"},{"concepts":[1207],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor ocean quality and productivity. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+ocean+quality+and+productivity","url":"https://earsc-portal.eu/display/EOwiki/Monitor+ocean+quality+and+productivity"},{"concepts":[1207],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor oil rigs and flares. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+oil+rigs+and+flares","url":"https://earsc-portal.eu/display/EOwiki/Monitor+oil+rigs+and+flares"},{"concepts":[1207],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor pollution at sea. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+pollution+at+sea","url":"https://earsc-portal.eu/display/EOwiki/Monitor+pollution+at+sea"},{"concepts":[1183],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor sensitive risk areas. Retrieved from: https://earsc-portal.eu/display/EOwiki/Monitor+sensitive+risk+areas","url":"https://earsc-portal.eu/display/EOwiki/Monitor+sensitive+risk+areas"},{"concepts":[1211],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor ships movements. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+ships+movements","url":"https://earsc-portal.eu/display/EOwiki/Monitor+ships+movements"},{"concepts":[1198],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor snow cover. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+snow+cover","url":"https://earsc-portal.eu/display/EOwiki/Monitor+snow+cover"},{"concepts":[1208],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor the coast line. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+the+coast+line","url":"https://earsc-portal.eu/display/EOwiki/Monitor+the+coast+line"},{"concepts":[1206,1204],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor urban areas. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+urban+areas","url":"https://earsc-portal.eu/display/EOwiki/Monitor+urban+areas"},{"concepts":[1200],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor vegetation encroachment. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+vegetation+encroachment","url":"https://earsc-portal.eu/display/EOwiki/Monitor+vegetation+encroachment"},{"concepts":[1195],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor water use on crops and horticulture. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+water+use+on+crops+and+horticulture","url":"https://earsc-portal.eu/display/EOwiki/Monitor+water+use+on+crops+and+horticulture"},{"concepts":[1179],"description":" ","name":"European Association of Remote Sensing Companies. (n.d.). Product sheet: Air Quality CO2. Retrieved from https://earsc-portal.eu/display/EO4RawMaterials/Product+Sheet%3A+Air+Quality+CO2","url":"https://earsc-portal.eu/display/EO4RawMaterials/Product+Sheet%3A+Air+Quality+CO2"},{"concepts":[1212],"description":" ","name":"European Centre for Medium-Range Weather Forecasts, & Copernicus Programme. (2020). Global Shipping Project - Copernicus. Retrieved from https://climate.copernicus.eu/index.php/global-shipping-project","url":"https://climate.copernicus.eu/index.php/global-shipping-project"},{"concepts":[1179],"description":" ","name":"European Comission. (2015). An Operational Anthropogenic CO₂ Emissions Monitoring & Verification Support Capacity.","url":"https://www.copernicus.eu/sites/default/files/2019-09/CO2_Blue_report_2015.pdf"},{"concepts":[1179],"description":" ","name":"European Comission. (2017). An Operational Anthropogenic CO₂ Emissions Monitoring & Verification Support Capacity.","url":"https://www.copernicus.eu/sites/default/files/2019-09/CO2_Red_Report_2017.pdf"},{"concepts":[1179],"description":" ","name":"European Comission. (2019). An Operational Anthropogenic CO₂ Emissions Monitoring & Verification Support Capacity.","url":"https://www.copernicus.eu/sites/default/files/2019-09/CO2_Green_Report_2019.pdf"},{"concepts":[1210],"description":" ","name":"European Comission. (n.d.). Managing fisheries. Retrieved from: https://ec.europa.eu/fisheries/cfp/fishing_rules_en","url":"https://ec.europa.eu/fisheries/cfp/fishing_rules_en"},{"concepts":[1131,1178],"description":" ","name":"European Commision. (n.d.). Societal Challenges. Retrieved from: https://ec.europa.eu/programmes/horizon2020/en/h2020-section/societal-challenges","url":"https://ec.europa.eu/programmes/horizon2020/en/h2020-section/societal-challenges"},{"concepts":[1199],"description":" ","name":"European Commission Joint Research Centre. (2020). Vegetation - Copernicus landm monitoring service. Retrieved from https://land.copernicus.eu/global/themes/Vegetation","url":"https://land.copernicus.eu/global/themes/Vegetation"},{"concepts":[1171],"description":" ","name":"European Commission. (2020). Digital skills and jobs - Shaping Europe's digital future. Retrived from https://ec.europa.eu/digital-single-market/en/policies/digital-skills","url":"https://ec.europa.eu/digital-single-market/en/policies/digital-skills"},{"concepts":[1171],"description":" ","name":"European Commission. (2020). Employment, Social Affairs & Inclusion. Retrived from https://ec.europa.eu/social/main.jsp?catId=1223","url":"https://ec.europa.eu/social/main.jsp?catId=1223"},{"concepts":[438],"description":" ","name":"European Commission. (2020). INSPIRE Knowledge base - Infrastructure for spatial information in Europe - Data Harmonisation. Retrieved from https://inspire.ec.europa.eu/training/data-harmonisation","url":"https://inspire.ec.europa.eu/training/data-harmonisation"},{"concepts":[1175],"description":" ","name":"European Commission. (2020). Overview - Public health. Retrieved from https://ec.europa.eu/health/communicable_diseases/overview_en","url":"https://ec.europa.eu/health/communicable_diseases/overview_en"},{"concepts":[1177],"description":" ","name":"European Commission. (2020). Sustainability of the water resource. Retrieved from https://ec.europa.eu/info/news/sustainability-at-the-water-source_en","url":"https://ec.europa.eu/info/news/sustainability-at-the-water-source_en"},{"concepts":[1173],"description":" ","name":"European Commission. (2020). Sustainable agriculture in the CAP. Retrieved from https://ec.europa.eu/info/food-farming-fisheries/sustainability/sustainable-cap_en","url":"https://ec.europa.eu/info/food-farming-fisheries/sustainability/sustainable-cap_en"},{"concepts":[1174],"description":" ","name":"European Commission. (2020). Transport. Retrieved from https://ec.europa.eu/info/policies/transport_en","url":"https://ec.europa.eu/info/policies/transport_en"},{"concepts":[1213],"description":" ","name":"European Environment Agency. (2016). Monitoring of marine waters. Retrieved from: https://www.eea.europa.eu/publications/92-9167-001-4/page024.html","url":"https://www.eea.europa.eu/publications/92-9167-001-4/page024.html"},{"concepts":[1168],"description":" ","name":"European Environmental Agency, (2019). Climate Change Adaption. Retrieved from: https://www.eea.europa.eu/themes/climate-change-adaptation/intro.","url":"https://www.eea.europa.eu/themes/climate-change-adaptation/intro"},{"concepts":[1168],"description":" ","name":"European Environmental Agency, (2019). Climate Change Mitigation. Retrieved from: https://www.eea.europa.eu/themes/climate/intro.","url":"https://www.eea.europa.eu/themes/climate/intro"},{"concepts":[1170],"description":" ","name":"European Environmental Agency. (2008). Biodiversity - Ecosystems. Retrieved from https://www.eea.europa.eu/themes/biodiversity/intro","url":"https://www.eea.europa.eu/themes/biodiversity/intro"},{"concepts":[1176],"description":" ","name":"European External Action Service. (2020). Security, Defence and Crisis Response. Retrieved from https://eeas.europa.eu/topics/security-defence-crisis-response_en","url":"https://eeas.europa.eu/topics/security-defence-crisis-response_en"},{"concepts":[1207],"description":" ","name":"European Space Agency (2012) Sentinel 3: ESA’s Global Land and Ocean Mission for GMES Operational Services (ESA SP-1322/3, October 2012).","url":"https://sentinel.esa.int/documents/247904/351187/S3_SP-1322_3.pdf"},{"concepts":[1221],"description":" ","name":"European Space Agency. (2011). Slight surface changes detected from space. Retrieved from: http://www.esa.int/Applications/Observing_the_Earth/Envisat/Slight_surface_changes_detected_from_space","url":"http://www.esa.int/Applications/Observing_the_Earth/Envisat/Slight_surface_changes_detected_from_space"},{"concepts":[1227],"description":" ","name":"European Space Agency. (2020). Level-1C Cloud Masks - Sentinel-2 MSI Technical Guide - Sentinel Online. Retrieved from https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-2-msi/level-1c/cloud-masks","url":"https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-2-msi/level-1c/cloud-masks"},{"concepts":[1234],"description":" ","name":"European Space Agency. (n.d.). Understanding risk with Earth observation. Retreived from: https://www.esa.int/Applications/Observing_the_Earth/Understanding_risk_with_Earth_observation","url":"https://www.esa.int/Applications/Observing_the_Earth/Understanding_risk_with_Earth_observation"},{"concepts":[1183],"description":" ","name":"European Union. (2018). Critical Infrastructure Analysis. Retrieved from: https://sea.security.copernicus.eu/categories/critical-infrastructure-analysis/","url":"https://sea.security.copernicus.eu/categories/critical-infrastructure-analysis/"},{"concepts":[1235],"description":" ","name":"European Union. (2020). Rapid mapping. Retrieved from: https://emergency.copernicus.eu/mapping/ems/rapid-mapping-portfolio","url":"https://emergency.copernicus.eu/mapping/ems/rapid-mapping-portfolio"},{"concepts":[124],"description":"ISBN number: 9781118653104","name":"Fairchild, M. D., (2005). Color appearance models, (2nd ed.), John Wiley and Sons.","url":"http://books.google.com/books?isbn=9781118653104"},{"concepts":[1220],"description":" ","name":"Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S., Shimada, J., Umland, J., Werner, M., Oskin, M., Burbank, D., Alsdorf, D. (2007). The shuttle radar topography mission. Reviews of Geophysics, 45(2). doi:10.1029/2005RG000183","url":"https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2005RG000183"},{"concepts":[302],"description":"ISBN: 978-3-319-00025-1","name":"Fecher, B. and Friesike, S. (2014). Open Science: One Term, Five Schools of Thought. In: Opening Science: The Evolving Guide on How the Internet is Changing Research, Collaboration and Scholarly Publishing. Cham: Springer pp. 17–47","url":"https://doi.org/10.1007/978-3-319-00026-8_2"},{"concepts":[303],"description":"ISBN number: 9780821872611","name":"Feeman, T. G. (2002) Portraits of the earth: A mathematician looks at maps. Rhode Island: American Mathematical Society.","url":"http://books.google.com/books?isbn=9780821872611"},{"concepts":[733],"description":" ","name":"Ferretti, A. (2014). Satellite InSAR data: reservoir monitoring from space. EAGE publications.","url":" "},{"concepts":[733],"description":"Ferretti, A., C. Prati, C & Rocca, F. (2001). Permanent scatterers in SAR interferometry. IEEE Transactions on Geoscience and Remote Sensing, vol. 39, no. 1, pp. 8-20,","name":"Ferretti, A., C. Prati, C & Rocca, F. (2001). Permanent scatterers in SAR interferometry. IEEE Transactions on Geoscience and Remote Sensing, vol. 39, no. 1, pp. 8-20,","url":"https://doi.org/10.1109/36.898661"},{"concepts":[732],"description":" ","name":"Ferretti, A., Monti-Guarnieri, A., Prati, C., Rocca, F., & Massonet, D. (2007). InSAR Principles-Guidelines for SAR Interferometry Processing and Interpretation, TM-19. The Netherlands: ESA Publications.","url":" "},{"concepts":[128],"description":" ","name":"Ferretti, F. (2014) Pioneers in the history of cartography: the Geneva map collection of Élisée Reclus and Charles Perron, Journal of Historical Geography 43, 85-95.","url":"https://www.doi.org/10.1016/j.jhg.2013.10.025"},{"concepts":[687],"description":" ","name":"Feynman, R. P., Leighton, R. B., & Sands, M. (2005). The Feynman Lectures on Physics, Volume 1. The Definitive Edition.","url":" "},{"concepts":[611,601,605],"description":" ","name":"Feynman, R. P., Leighton, R. B., & Sands, M. (2005). The Feynman Lectures on Physics: The Complete and Definitive Issue. Vol. 2.","url":" "},{"concepts":[64,65,66],"description":" ","name":"Fischer MM, Getis A, editors. Handbook of Applied Spatial Analysis: Software Tools, Methods and Applications. 2010 edition. Berlin: Springer; 2014. 826 p","url":" "},{"concepts":[238],"description":"ISBN: 978-3540357292","name":"Fischer, M. M. (2006). Spatial analysis and geocomputation: selected essays. Springer.","url":"http://books.google.com/books?isbn=9783540357292"},{"concepts":[457],"description":" ","name":"Fisher, P.F., 1999. Models of uncertainty in spatial data. Geographical information systems 1, 191-205.","url":" "},{"concepts":[236],"description":"ISBN number: 9780262561273","name":"Flake, G. W. (1998). The computational beauty of nature. Cambridge, MA: MIT Press.","url":"http://books.google.com/books?isbn=9780262561273"},{"concepts":[393],"description":" ","name":"Flanagin, A. J., & Metzger, M. J. (2008). The credibility of volunteered geographic information. GeoJournal, 72(3-4), 137-148.","url":"https://www.jstor.org/stable/41220564"},{"concepts":[1270],"description":" ","name":"Fleming, L. E., Haines, A., Golding, B., Kessel, A., Cichowska, A., Sabel, C. E., ... & Cocksedge, N. (2014). Data mashups: potential contribution to decision support on climate change and health. International Journal of Environmental Research and Public Health, 11(2), 1725-1746.","url":"https://doi.org/10.3390/ijerph110201725"},{"concepts":[1258],"description":" ","name":"Florczyk, A. J., López-Pellicer, F. J., Nogueras-Iso, J., & Zarazaga-Soria, F. J. (2012). Automatic generation of geospatial metadata for web resources. International Journal of Spatial Data Infrastructures Research, 7, 151-172.","url":"https://ijsdir.sadl.kuleuven.be/index.php/ijsdir/article/view/253"},{"concepts":[467],"description":" ","name":"Florinsky I.V. 2016. Digital Terrain Analysis in Soil Science and Geology. 2nd Edition, Elsevier, 506 pp. ISBN:978-0-12-804632-6.","url":" "},{"concepts":[244],"description":"ISBN: 978-1597264723","name":"Ford, A. (2009). Modeling the environment: an introduction to system dynamics models of environmental systems, 2nd ed. Island Press.","url":"http://books.google.com/books?isbn=9781597264723"},{"concepts":[375],"description":"ISBN number: 9780138621452","name":"Foresman, T. (1998). The History of geographic information systems. Upper Saddle River, NJ: Prentice Hall.","url":"http://books.google.com/books?isbn=9780138621452"},{"concepts":[738],"description":"Fornaro, G., Reale, D., Pauciullo, A., Zhu, X. X., & Bamler, R. (2012). SAR Tomography: An Advanced Tool for Spaceborne 4D Radar Scanning with Application to Imaging and Monitoring of Cities and Single Buildings. IEEE Geoscience and Remote Sensing Newsletter.","name":"Fornaro, G., Reale, D., Pauciullo, A., Zhu, X. X., & Bamler, R. (2012). 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New York: Wiley.","url":"http://books.google.com/books?isbn=9780470855256"},{"concepts":[185],"description":"ISBN: 978-0321193681","name":"Fowler, M. (2003). UML distilled: A brief guide to the standard object modeling language (3rd ed.). Addision-Wesley.","url":"http://books.google.com/books?isbn=9780321193681"},{"concepts":[750],"description":" ","name":"Franceschetti, G., Lanari, R. Synthetic Aperture Radar Processing, CRC Press: Boca Raton, FL, USA, 1999.","url":" "},{"concepts":[751],"description":" ","name":"Franceschetti, G., Lanari, R. Synthetic Aperture Radar Processing, CRC Press: Boca Raton, FL, USA, 1999.","url":" "},{"concepts":[752],"description":" ","name":"Franceschetti, G., Lanari, R. Synthetic Aperture Radar Processing, CRC Press: Boca Raton, FL, USA, 1999.","url":" "},{"concepts":[753],"description":" ","name":"Franceschetti, G., Lanari, R. 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(2012). Re-centring geoinformation in secondary education: toward a spatial citizenship approach. Cartographica: the international journal for geographic information and geovisualization, 47(1), 18-28.","url":"https://doi.org/10.3138/carto.47.1.18"},{"concepts":[394],"description":" ","name":"Gryl, I., Jekel, T., & Donert, K. (2010). GI and spatial citizenship. Learning with GI V, 2-11.","url":"http://scholar.google.com/scholar_lookup?hl=en&publication_year=2010&pages=2-11&author=I.+Gryl&author=T.+Jekel&author=K.+Donert&title=GI+and+spatial+citizenship"},{"concepts":[81],"description":" ","name":"Guarino, N. Understanding, Building, And Using Ontologies. International Journal of Human-Computer Studies 46, 2-3 (February/March 1997), 293 – 310.","url":" "},{"concepts":[1267],"description":" ","name":"Gui, Z., Yang, C., Xia, J., Liu, K., Xu, C., Li, J., & Lostritto, P. (2013). 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GeoInformatica, 11(4), 431-457.","url":"https://idp.springer.com/authorize/casa?redirect_uri=https://link.springer.com/content/pdf/10.1007/s10707-007-0022-3.pdf&casa_token=9QgYr7Tra_YAAAAA:W1FtEBT78eGfFIjg4UfIWsQzUsgnCtMCMWuIxmcogxEsd5EKIt0laAmhukeffBaKrKUthT55eFuvafk"},{"concepts":[781,777,780],"description":" ","name":"Mandlburger, G., Jutzi, B. (2019). On the Feasibility of Water Surface Mapping with Single Photon LiDAR External Link. Special Issue ’Innovative Sensing - From Sensors to Methods and Applications’. ISPRS International Journal of Geo-Information (IJGI) 2019, 8(4), 188. doi:10.3390/ijgi8040188","url":"https://www.mdpi.com/2220-9964/8/4/188"},{"concepts":[178],"description":" ","name":"Manso, M. Á., & Wachowicz, M. (2009). GIS design: A review of current issues in interoperability. 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TeMA-Journal of Land Use, Mobility and Environment.","url":"https://doi.org/10.6092/1970-9870/2500"},{"concepts":[586,595],"description":" ","name":"Masser, I. (1999). All shapes and sizes: the first generation of national spatial data infrastructures. International Journal of Geographical Information Science, 13(1), 67-84.","url":" "},{"concepts":[586],"description":" ","name":"Masser, I. (2005). GIS worlds: creating spatial data infrastructures (Vol. 338). Redlands, CA: Esri Press.","url":" "},{"concepts":[165],"description":" ","name":"Masser, I., and Onsrud, H. (Eds.), (1993) Diffusion and use of geographic information technologies. Deventer: Kluwer.","url":" "},{"concepts":[732],"description":" ","name":"Massonnet, D., Feigl, K.L. Radar interferometry and its application to changes in the Earths surface. Rev. Geophys. 1998, 36, 441 500.","url":" "},{"concepts":[563],"description":" ","name":"Matevosyan, H., Lluch, I., Poghosyan, A., & Golkar, A. (2017). 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sources"},{"concepts":[1266],"name":"Compare Linked geospatial data to SDI approaches"},{"concepts":[69],"name":"Compare methods of spatial statistical analysis for the testing of hypotheses."},{"concepts":[20],"name":"Compare models and software tools that allow for optimization"},{"concepts":[1191],"name":"Compare one optical EO method with a SAR method for landslide mapping and explain their differences"},{"concepts":[518],"name":"Compare pixel-based image classification methods with object-based techniques"},{"concepts":[814],"name":"Compare reflectance measurements from the field to reflectance values in radiometrically pre-processed EO data"},{"concepts":[120],"name":"Compare relationships between entities, between attributes and between locations."},{"concepts":[504],"name":"Compare results of the Laplacian of Gaussian filter to the original input image"},{"concepts":[391],"name":"Compare the advantages and disadvantages of group participation and individual participation"},{"concepts":[48],"name":"Compare the basic analytical operations of different GISs."},{"concepts":[322],"name":"Compare the concepts of geometric accuracy and topological fidelity"},{"concepts":[302],"name":"Compare the different cultures of Open Science"},{"concepts":[64],"name":"Compare the different types of spatial weight matrices"},{"concepts":[1207],"name":"Compare the main satellite sensors used in marine ecosystem monitoring"},{"concepts":[322],"name":"Compare the National Map Accuracy Standard with the ASPRS Coordinate Standard"},{"concepts":[137],"name":"Compare the relative merits of having map labels placed dynamically versus having them saved as annotation data"},{"concepts":[24],"name":"Compare the result of conversion vector/raster or raster/vector and examine the impact of conversion on the quality of the dataset"},{"concepts":[166],"name":"Compile the needs of individual users and tasks into enterprise-wide needs"},{"concepts":[565],"name":"Compute 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range"},{"concepts":[613],"name":"Compute the maximum average roughness of a mirror for incident radiation in the visible spectral range"},{"concepts":[37],"name":"Compute the mean of directional data"},{"concepts":[613],"name":"Compute the minimum average roughness of a surface operating as a diffuser of incident radiation in the visible spectral range"},{"concepts":[10],"name":"Compute the optimum path between two points through a network with Dijkstras algorithm"},{"concepts":[53],"name":"Conduct a simple hierarchical cluster analysis to classify area objects into statistically similar regions"},{"concepts":[76],"name":"Conduct a spatial econometric analysis to test for spatial dependence in the residuals from least-squares models and spatial autoregressive models"},{"concepts":[72],"name":"Conduct a spatial interpolation process using kriging from data description to final error map"},{"concepts":[159],"name":"Construct a new map from an existing one with a biased view"},{"concepts":[34],"name":"Construct a query statement to search for a specific spatial or temporal relationship"},{"concepts":[71],"name":"Construct a semi-variogram and illustrate with a semi-variogram cloud"},{"concepts":[34],"name":"Construct a spatial query to extract all point objects that fall within a polygon"},{"concepts":[64],"name":"Construct a spatial weights matrix for lattice, point, and area patterns"},{"concepts":[214],"name":"Construct a TIN manually from a set of spot elevations"},{"concepts":[149],"name":"Construct a Web page that includes an interactive map"},{"concepts":[718],"name":"Construct scattering matrix"},{"concepts":[105],"name":"Construct taxonomies and dictionaries (also known as formal ontologies) to communicate systems of categories"},{"concepts":[14],"name":"Contrast accessibility modeling at the individual level versus at an aggregated level"},{"concepts":[182],"name":"Contrast cloud and grid computing technologies"},{"concepts":[134],"name":"Contrast gaming elements which are both part of traditional games and geo-games"},{"concepts":[137],"name":"Contrast the strengths and limitations of methods for automatic label placement"},{"concepts":[22],"name":"Convert a dataset from the native format of one GIS product to another"},{"concepts":[127],"name":"Convert historical maps in digital format"},{"concepts":[421],"name":"Convert multispectral image into its principal components"},{"concepts":[24],"name":"Convert vector data to raster format and back using GIS software"},{"concepts":[24],"name":"Convert vector data to raster format and back using the GIS software"},{"concepts":[128],"name":"Correlate map making methods with technological or societal factors across History"},{"concepts":[174],"name":"Create a budget of expected labor costs, including salaries, benefits, training, and other expenses"},{"concepts":[188],"name":"Create a complete design document ready for 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variables using the same mapping method (e.g., bivariate choropleth map, bivariate dot map)"},{"concepts":[146],"name":"Create a map that represents both slope and aspect on the same map using the Moellering-Kimerling coloring method"},{"concepts":[41],"name":"Create a matrix describing the pattern of adjacency in a set of planar enforced polygons"},{"concepts":[52],"name":"Create a matrix that shows spatial interaction"},{"concepts":[1270],"name":"Create a new application by combining existing data from different sources"},{"concepts":[158],"name":"Create a project plan for a map, from planning to finalisation"},{"concepts":[527],"name":"Create a protocol for quality assessment of an EO information product that conforms to EO4GEO guidelines"},{"concepts":[153],"name":"Create a pseudo-topographic surface to portray the relationships in a collection of documents"},{"concepts":[1275],"name":"Create a sample HTML5 Web page"},{"concepts":[524],"name":"Create a scale space for an image by applying multiple iterations of low-pass filtering"},{"concepts":[413,416],"name":"Create a set of ground control points tying image coordinates to map coordinates of a reference dataset using a digital reference dataset or in-situ GPS measurements"},{"concepts":[148],"name":"Create a temporal sequence representing a dynamic geospatial process"},{"concepts":[167],"name":"Create a user manual to help users understand a process or task"},{"concepts":[560],"name":"Create a web interface and related system architecture that enables image processing by using OGC interfaces"},{"concepts":[226],"name":"Create an adjacency table from a sample network"},{"concepts":[135],"name":"Create an aesthetic map icon library"},{"concepts":[226],"name":"Create an incidence matrix from a sample network"},{"concepts":[439],"name":"Create an integrated population distribution map from census data and EO-based land use classification"},{"concepts":[33],"name":"Create an SQL query to retrieve elements from a 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image"},{"concepts":[54],"name":"Create initial weights using the analytical hierarchy process (AHP)"},{"concepts":[187],"name":"Create logical models based on conceptual models using UML or other tools"},{"concepts":[144],"name":"Create maps using each of the following methods: choropleth, dasymetric, proportioned symbol, graduated symbol, isoline, dot, cartogram, and flow map"},{"concepts":[1245],"name":"Create new EO products out of raw data or other products"},{"concepts":[105],"name":"Create or use GIS data structures to represent categories, including attribute columns, layers themes, shapes, legends, etc."},{"concepts":[174],"name":"Create proposals and presentations to secure funding"},{"concepts":[70],"name":"Create spatial samples under a variety of requirements, such as coverage, randomness, transects"},{"concepts":[159],"name":"Create two versions of the same map addressed to different targets"},{"concepts":[188],"name":"Create UML diagrams of physical models based on logical model diagrams and software requirements"},{"concepts":[144],"name":"Create well-designed legends using the appropriate conventions for the following methods: choropleth, dasymetric, proportioned symbol, graduated symbol, isoline, dot, cartogram, and flow map"},{"concepts":[143],"name":"Critique the graphic design of several maps in terms of balance, legibility, clarity, visual contrast, figure-ground organization, and hierarchal organization"},{"concepts":[149],"name":"Critique the interactive elements of an online map"},{"concepts":[150],"name":"Critique the user interface for existing Internet mapping services"},{"concepts":[229],"name":"Deal with time aspects in modelling data"},{"concepts":[228],"name":"Deal with uncertainty aspects in modelling data"},{"concepts":[1238,1237],"name":"Decide on urban planning measures on the basis of a semantic 3D model"},{"concepts":[31],"name":"Decide which generalisation technique (aggregation, selection, etc.) is best for a specific situation of reducing map scale."},{"concepts":[141],"name":"Decide which graphical representation better reflects the messages embedded in your story"},{"concepts":[66],"name":"Decompose Morans I and Gearys c into local measures of spatial association"},{"concepts":[186],"name":"Deconstruct an application use case into its conceptual elements"},{"concepts":[404],"name":"Defend or refute the contention that critical studies have an identifiable influence on the development of the information society in general and GIScience in particular"},{"concepts":[403],"name":"Defend or refute the contention that the masculinist culture of computer work in general, and GIS work in particular, perpetuates gender inequality in GIS and T education and training and occupational segregation in the GIS and T workforce"},{"concepts":[28],"name":"Defend or refute the statement \"GIS data are scaleless\""},{"concepts":[85],"name":"Defend or refute the statement, All data are theory-laden"},{"concepts":[109],"name":"Define a field in terms of properties, space, and time"},{"concepts":[166],"name":"Define a methodology for gathering of requirements"},{"concepts":[233],"name":"Define a set of rules for modeling changes in spatial databases"},{"concepts":[223],"name":"Define and describe an application schema"},{"concepts":[393],"name":"Define and discuss enabling technologies: geotag, georeferencing, GPS and more"},{"concepts":[238],"name":"Define and discuss opportunities and limitations of computational science"},{"concepts":[393],"name":"Define and discuss volunteered geographic information"},{"concepts":[393],"name":"Define and discussing impact of Crowdsourcing on Geospatial Society"},{"concepts":[1255],"name":"Define and exemplify the reuse of ontologies - Define and identify the role of ontology patterns"},{"concepts":[1251],"name":"Define and practice the usage, in a given use case, of StyledLayerDescriptor (SLD) and Symbology Encoding (SE). Practice their usage in a given use case"},{"concepts":[391],"name":"Define and understand citizenship, democracy, maturity, and negotiation related to geo information use and participation in society /community development (at local, regional, national level)"},{"concepts":[33],"name":"Define basic terms of query processing e.g., SQL, primary and foreign keys, table join"},{"concepts":[211],"name":"Define basic terms used in the raster data model (e.g., cell, row, column, value)"},{"concepts":[179,1250],"name":"Define characteristics of REST Web services and Resource oriented Architecture (ROA)"},{"concepts":[85],"name":"Define common philosophical theories that have influenced geography and science, such as logical positivism, Marxism, phenomenology, feminism, and critical theory"},{"concepts":[83],"name":"Define common theories on what constitutes knowledge, including positivism, reflectance-correspondence, pragmatism, social constructivism, and memetics"},{"concepts":[81],"name":"Define common theories on what is real, such as realism, idealism, relativism, and experiential realism"},{"concepts":[8],"name":"Define different interpretations of cost in various routing applications"},{"concepts":[37],"name":"Define direction and its measurement in different angular measures"},{"concepts":[186],"name":"Define entities and relationships in conceptual data model"},{"concepts":[60],"name":"Define friction surface"},{"concepts":[1254],"name":"Define GeoJSON definition of Geospatial objects and describe the structure of a GeoJSON document and identify advantages and disadvantages of representing the same geospatial data in GML and in GeoJSON"},{"concepts":[59],"name":"Define intervisibility"},{"concepts":[1261],"name":"Define Mapping between legacy definition and the semantic definition of publish"},{"concepts":[1257],"name":"Define metadata and identify metadata standards like ISO 19115 and 19119 describe their metadata schema generally"},{"concepts":[1254],"name":"Define OGC Simple Features Access Schema. Well-Known Text (WKT) and Well-Known Binary (WKB) representations of Geometry"},{"concepts":[68],"name":"Define prior and posterior distributions and Markov-Chain Monte Carlo"},{"concepts":[1253],"name":"Define Resource Description Framework (RDF), its RDF graphs, RDF Schema (RDF-S)and a data set in RDF"},{"concepts":[1253],"name":"Define Semantic Web and identify the role of the languages included under this topic for Semantic Web"},{"concepts":[179,1248],"name":"Define Service Oriented Architecture (SOA) and identify main elements of it"},{"concepts":[119],"name":"Define spatial autocorrelation in the context of geographic proximity"},{"concepts":[1254],"name":"Define spatial extensions that GeoSPARQL brings over SPARQL. Identify the difference between qualitative spatial reasoning and quantitative spatial computations"},{"concepts":[106],"name":"Define Stevens four levels of measurement (nominal, ordinal, interval, ratio)"},{"concepts":[222],"name":"Define terms related to topology (e.g., adjacency, connectivity, overlap, intersect, logical consistency)"},{"concepts":[187],"name":"Define the cardinality of relationships"},{"concepts":[179,180,1248],"name":"Define the characteristics of web services and present some examples"},{"concepts":[1253],"name":"Define the components of a Web Services Description Language (WSDL) document"},{"concepts":[226],"name":"Define the following terms pertaining to a network: Loops, multiple edges, the degree of a vertex, walk, trail, path, cycle, fundamental cycle"},{"concepts":[8],"name":"Define the following terms pertaining to a network: Loops, multiple edges, the degree of a vertex, walk, trail, path, cycle, fundamental cycle"},{"concepts":[90],"name":"Define the following terms: data, information, knowledge, and wisdom"},{"concepts":[97],"name":"Define the four basic dimensions or shapes used to describe spatial objects (i.e., points, lines, regions, volumes)"},{"concepts":[93],"name":"Define the notions of cultural landscape and physical landscape"},{"concepts":[119],"name":"Define the principle of friction of distance and geographic models that are based on it (e.g., gravity models, spatial interaction models)"},{"concepts":[92],"name":"Define the properties that make a phenomenon geographic"},{"concepts":[625],"name":"Define the radiometric spectral quantities brightness, emittance, luminosity"},{"concepts":[625],"name":"Define the radiometric spectral quantities radiance, irradiance, flux"},{"concepts":[2],"name":"Define the terms spatial analysis, spatial modeling, geostatistics, spatial econometrics, spatial statistics, qualitative analysis, map algebra, and network analysis"},{"concepts":[122],"name":"Define uncertainty-related terms, such as error, accuracy, uncertainty, precision, stochastic, probabilistic, deterministic, and random"},{"concepts":[569],"name":"Define user roles for an existing or planned GIS"},{"concepts":[118],"name":"Define various terms used to describe topological relationships, such as disjoint, overlap, within, and intersect"},{"concepts":[1272],"name":"Define Web API composition (WAPIC) concept for RESTful WSs and identify main issues"},{"concepts":[1251],"name":"Define Web Coverage Service (WCS). Describe GetCapabilities, GetCoverageInfo, and GetCoverage operations in detail. Practice its usage in a given use case"},{"concepts":[1251],"name":"Define Web Feature Service (WFS). Describe GetCapabilities, DescribeFeaturetype, and GetFeature, and GetFeatureInfo operations in detail. Practice its usage in a given use case"},{"concepts":[1251],"name":"Define Web Map Service (WMS). Describe GetCapabilities, GetMap, and GetFeatureInfo operations in detail. Practice its usage in a given use case"},{"concepts":[1251],"name":"Define Web Map Tile Service (WMTS). Describe GetCapabilities, GetTile, and GetFeatureInfo operations in detail. Practice its usage in a given use case"},{"concepts":[1251],"name":"Define Web Processing Service (WPS). Describe GetCapabilities, DescribeProcess, and Execute operations in detail. Practice its usage in a given use case"},{"concepts":[1272],"name":"Define web services composition (WSC) concept and identify main issues"},{"concepts":[1248],"name":"Define Web services transport over the Web"},{"concepts":[1255],"name":"Define what an ontology is. 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spatial nonstationarity"},{"concepts":[77],"name":"Demonstrate how spatial autocorrelation can be removed by resampling"},{"concepts":[75],"name":"Demonstrate how spatially lagged, trend surface, or dummy spatial variables can be used to create the spatial component variables missing in a standard regression analysis"},{"concepts":[148],"name":"Demonstrate how the adding time-series data reveals (or not) patterns not evident in a cross-sectional data"},{"concepts":[39],"name":"Demonstrate how the area of a region calculated from a raster data set will vary by resolution and orientation"},{"concepts":[12],"name":"Demonstrate how the Classic Transportation Problem can be structured as a linear program"},{"concepts":[45],"name":"Demonstrate how the geometric operations of intersection and overlay can be implemented in GIS"},{"concepts":[76],"name":"Demonstrate how the parameters of spatial auto-regressive models can be estimated using univariate and bivariate optimization algorithms for maximizing the likelihood function"},{"concepts":[75],"name":"Demonstrate how the spatial weights matrix is fundamental in spatial econometrics models"},{"concepts":[226],"name":"Demonstrate how the star (or forward star) data structure, which is often employed when digitally storing network information, violates relational normal form, but allows for much faster search and retrieval in network databases"},{"concepts":[1264],"name":"Demonstrate how to discover over a catalogue service; and the discovery procedure in OGC CS-W"},{"concepts":[127],"name":"Demonstrate how to georeference an historical map"},{"concepts":[1153],"name":"Demonstrate impacts of land use change"},{"concepts":[1166],"name":"Demonstrate multidisciplinarity, combining GISciences, Social Sciences, Smart Cities, Computational Sciences and Social Media"},{"concepts":[1258],"name":"Demonstrate publishing in some popular SDI (NSDI) portals like INSPIRE and GOS geoportals"},{"concepts":[33],"name":"Demonstrate the basic 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why spatial autocorrelation among regression residuals can be an indication that spatial variables have been omitted from the models"},{"concepts":[45],"name":"Demonstrate why the georegistration of datasets is critical to the success of any map overlay operation"},{"concepts":[172],"name":"Demonstrate why the system design is important in any GIS implementation"},{"concepts":[608],"name":"Derive the Stefan-Boltzman Law from the Planck's one"},{"concepts":[85],"name":"Describe a brief history of major philosophical movements relating to the nature of space, time, geographic phenomena and human interaction with it"},{"concepts":[149],"name":"Describe a mapping goal in which the use of each of the following would be appropriate: brushing, linking, multiple displays"},{"concepts":[46,47],"name":"Describe a real modeling situation in which map algebra would be used e.g., site selection, climate classification, least-cost path"},{"concepts":[326],"name":"Describe a scenario in which data from a secondary source may pose obstacles to effective and efficient use"},{"concepts":[395],"name":"Describe a scenario in which you would find it necessary to report misconduct by a colleague or friend"},{"concepts":[55],"name":"Describe a simple process model that would generate a given set of spatial patterns"},{"concepts":[510],"name":"Describe a situation in which filtered data are more useful than the original unfiltered data"},{"concepts":[122],"name":"Describe a stochastic error model for a natural phenomenon"},{"concepts":[395],"name":"Describe a variety of philosophical frameworks upon which codes of professional ethics may be based"},{"concepts":[22,185],"name":"Describe a workflow for converting a implementing a data model in a GIS involving an Entity-Relationship (E-R) diagram and the Universal Modeling Language (UML)"},{"concepts":[218],"name":"Describe alternatives to quadtrees for representing hierarchical tessellations (e.g., hextrees, r-trees, pyramids)"},{"concepts":[235],"name":"Describe an application in which it is crucial to maintain previous versions of the database"},{"concepts":[766],"name":"Describe an application of hyperspectral image data"},{"concepts":[414,539],"name":"Describe an application that requires integration of remotely sensed data with GIS and/or GPS data"},{"concepts":[152],"name":"Describe an example where the use of an augmented environment could be of help"},{"concepts":[590],"name":"Describe and explain the funding model of an existing SDI"},{"concepts":[667],"name":"Describe atmospheric transmittance in the optical spectral range"},{"concepts":[150],"name":"Describe considerations for using maps on the Web as a method for downloading data"},{"concepts":[133],"name":"Describe differences in design needed for a map that is to be viewed on the Internet versus as a 5x7 foot poster, including a discussion of the effect of viewing distance, lighting, and media type"},{"concepts":[104],"name":"Describe different types of movement and change"},{"concepts":[4],"name":"Describe difficulties in dealing with large spatial databases, especially those arising from spatial heterogeneity"},{"concepts":[601],"name":"Describe Electromagnetic Waves in terms of Photons"},{"concepts":[4],"name":"Describe emerging geographical analysis techniques in geocomputation derived from artificial intelligence e.g., expert systems, artificial neural networks, genetic algorithms, and software agents"},{"concepts":[235],"name":"Describe existing algorithms designed for performing dynamic queries"},{"concepts":[1248],"name":"Describe generally the hypertext transfer protocol and its main operations like POST and GET"},{"concepts":[119],"name":"Describe geographic phenomena in terms of their distances and directions (in space and time) Define spatial autocorrelation in the context of geographic proximity"},{"concepts":[118],"name":"Describe geographic phenomena in terms of their topological relationships (in space and time to other phenomena"},{"concepts":[651],"name":"Describe how a Michelson interferometer make it possible to measure the emitted Earth radiation with hyperspectral resolution."},{"concepts":[58,61],"name":"Describe how a network of stream channels and ridges can be estimated from a Digital Elevation Model (DEM)"},{"concepts":[80],"name":"Describe how conceptual foundations of GI Science have become implemented in GISs."},{"concepts":[5,7],"name":"Describe how data mining can be used for geospatial intelligence"},{"concepts":[485],"name":"Describe how deep learning works"},{"concepts":[322],"name":"Describe how geometric accuracy should be documented in terms of the FGDC metadata standard"},{"concepts":[386],"name":"Describe how geospatial data are used and maintained for land use planning, property value assessment, maintenance of public works, and other applications"},{"concepts":[567],"name":"Describe how GI S and T can be used in the decision-making process in organizations dealing with natural resource management, business management, public management or operations management"},{"concepts":[49],"name":"Describe how Independent Random Process/Chi-Squared Result IRP/CSR may be used to make statistical statements about point patterns"},{"concepts":[46,47],"name":"Describe how map algebra performs mathematical functions on raster grids"},{"concepts":[605],"name":"Describe how Maxwell's equation explain EM waves' propagation"},{"concepts":[444],"name":"Describe how sea surface temperatures are mapped"},{"concepts":[167,168],"name":"Describe how spatial data and GIS&T can be integrated into a workflow process"},{"concepts":[57],"name":"Describe how surfaces can be interpolated using splines"},{"concepts":[621],"name":"Describe how the complex part of the refractive index affects the propagation of e.m. radiation through the matter"},{"concepts":[214],"name":"Describe how to generate a unique TIN solution using Delaunay triangulation"},{"concepts":[576],"name":"Describe issues that may hinder implementation and continued successful operation of a GI system if effective methods of staff development are not included in the process"},{"concepts":[1266],"name":"Describe Linked Data Browsers; Define Faceted browsers and identify what problems of linked data discovery they aim to solve"},{"concepts":[14],"name":"Describe methods for measuring different kinds of accessibility on a network"},{"concepts":[8],"name":"Describe networks that apply to specific applications or industries"},{"concepts":[37],"name":"Describe operations that can be performed on qualitative representations of direction"},{"concepts":[108],"name":"Describe particular entities in terms of space, time, and properties"},{"concepts":[110],"name":"Describe particular events or processes in terms of identity, categories, attributes, locations, etc."},{"concepts":[106],"name":"Describe particular geographic phenomena in terms of attributes"},{"concepts":[116],"name":"Describe particular geographic phenomena in terms of their place in mereonomic hierarchies (parts and composites)"},{"concepts":[581],"name":"Describe political, economic, administrative, and other social forces in agencies, organizations, and citizens that inhibit or promote sharing of geospatial and other data"},{"concepts":[11],"name":"Describe practical situations in which flow is conserved while splitting or joining at nodes of the network"},{"concepts":[156,157],"name":"Describe print quality characteristics and price differences for limited-run color map distribution"},{"concepts":[156,157],"name":"Describe production concerns that might be discussed with a publisher who will print a map product"},{"concepts":[1220],"name":"Describe properties of a particular DEM product"},{"concepts":[1266],"name":"Describe Querying Linked Data; SPARQL and GeoSPARQL"},{"concepts":[41],"name":"Describe real world applications where adjacency and 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suited"},{"concepts":[71],"name":"Describe some commonly used semi-variogram models"},{"concepts":[92],"name":"Describe some insights that a spatial perspective can contribute to a given topic"},{"concepts":[10],"name":"Describe some variants of Dijkstras algorithm that are even more efficient"},{"concepts":[233],"name":"Describe techniques for handling version control in spatial databases"},{"concepts":[233],"name":"Describe techniques for managing long transactions in a multi-user environment"},{"concepts":[110],"name":"Describe the actor role that entities and fields play in events and processes"},{"concepts":[660],"name":"Describe the adiabatic decrease of tropospheric temperature with the heigth"},{"concepts":[414],"name":"Describe the advantages and disadvantages of analytical and physical-based models for orthorectification"},{"concepts":[218],"name":"Describe the advantages and disadvantages of the quadtree model for geographic database representation and 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impossible to adequately represent in GIS"},{"concepts":[462],"name":"Describe the elements of image interpretation"},{"concepts":[402],"name":"Describe the extent to which contemporary GIS and T supports diverse ways of understanding the world"},{"concepts":[52],"name":"Describe the formulation of the classic gravity model, the unconstrained spatial interaction model, the production constrained spatial interaction model, the attraction constrained spatial interaction model, and the doubly constrained spatial..."},{"concepts":[679],"name":"Describe the fundamental thermodynamic processes (isothermal, adiabatic, isochoric, isobaric)"},{"concepts":[117],"name":"Describe the genealogy (as identity-based change or temporal relationships) of particular geographic phenomena"},{"concepts":[75],"name":"Describe the general types of spatial econometric model"},{"concepts":[647],"name":"Describe the impact of Einstein’s theory of radiation on the design of modern devices for the measurements and/or production of coherent light"},{"concepts":[654],"name":"Describe the impact of geometrical optics on optical sensors design"},{"concepts":[26],"name":"Describe the impact of map projection transformation on raster and vector data"},{"concepts":[322],"name":"Describe the impact of the concept of dilution of precision on the uncertainty of GPS positioning"},{"concepts":[655],"name":"Describe the impact of the theory of interference on the development of modern satellite hyperspectral sounders"},{"concepts":[656],"name":"Describe the impact of theory of diffraction and grating spectrometers on the development of modern satellite hyperspectral sounders"},{"concepts":[54],"name":"Describe the implementation of an ordered weighting scheme in a multiple-criteria aggregation"},{"concepts":[417],"name":"Describe the importance of geometric correction when using Earth Observation data"},{"concepts":[395],"name":"Describe the individuals or groups to which GIS and T professionals have ethical obligations"},{"concepts":[222],"name":"Describe the integrity constraints of integrated topological models (e.g., POLYVRT)"},{"concepts":[90],"name":"Describe the limitations of various information stores for representing geographic information, including the mind, computers, graphics, text, etc."},{"concepts":[416],"name":"Describe the location and geometric characteristics of the principal point of an aerial image"},{"concepts":[518],"name":"Describe the main advantages of object-based image analysis methods"},{"concepts":[688],"name":"Describe the main branch of physycs relevant to the study of e.m. radiation and its interaction with the matter in the optical range"},{"concepts":[617],"name":"Describe the main sources of spectral line broadening"},{"concepts":[610],"name":"Describe the main spectral components of solar radiation at the top of atmosphere"},{"concepts":[677],"name":"Describe the main state functions of ideal gases"},{"concepts":[108],"name":"Describe the perceptual processes (e.g., edge detection) that aid cognitive objectification"},{"concepts":[30],"name":"Describe the pitfalls, in terms of information loss and analytical options, of transforming attribute measurement levels"},{"concepts":[666],"name":"Describe the process of light scattering by atmospheric particulates"},{"concepts":[659],"name":"Describe the process of water vapour cloud formation"},{"concepts":[77],"name":"Describe the relationship between factorial kriging and spatial filtering"},{"concepts":[72],"name":"Describe the relationship between the semi-variogram and kriging"},{"concepts":[50],"name":"Describe the relationships between kernels and classical spatial interaction approaches, such as surfaces of potential"},{"concepts":[71],"name":"Describe the relationships between semi-variograms and correlograms, and Morans indices of spatial association"},{"concepts":[687],"name":"Describe the relevance of mechanics laws in the framework of EO satellite mission design and planning"},{"concepts":[562],"name":"Describe the role of infrastructures for sharing remote sensing data products"},{"concepts":[488],"name":"Describe the role of machine learning classifiers to find patterns in the available data"},{"concepts":[396],"name":"Describe the sanctions imposed by ASPRS and GISCI on individuals whose professional actions violate the codes of ethics"},{"concepts":[430],"name":"Describe the scattering and atmospheric absorption factors"},{"concepts":[623],"name":"Describe the scattering properties of a lambertian surface"},{"concepts":[623],"name":"Describe the scattering properties of a mirroring surface"},{"concepts":[672],"name":"Describe the scope of irreversible thermodynamics"},{"concepts":[683],"name":"Describe the scope of thermodynamics"},{"concepts":[414,416],"name":"Describe the sequence of tasks involved in the geometric correction of the Advanced Very High Resolution Radiometer (AVHRR) Global Land Dataset"},{"concepts":[309],"name":"Describe the source data, instrumentation, and workflow involved in extracting vector data (features and elevations) from analog and digital stereoimagery"},{"concepts":[629],"name":"Describe the spectral regions where Mineral and Rocks exhibit their main signatures"},{"concepts":[62],"name":"Describe the statistical characteristics of a set of spatial data using a variety of graphs and plots including scatterplots, histograms, boxplots, qq plots"},{"concepts":[17],"name":"Describe the structure of linear programs"},{"concepts":[19],"name":"Describe the structure of origin-destination matrices"},{"concepts":[598],"name":"Describe the U.S. geospatial industry including vendors, software, hardware and data"},{"concepts":[404],"name":"Describe the use of GIS from a political ecology point of view (e.g., consider the use of GIS for resource identification, conservation, and allocation by an NGO in Sub-Saharan Africa)"},{"concepts":[114],"name":"Describe the ways in which a spatial perspective 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occurs in the Earth's Atmosphere"},{"concepts":[664],"name":"Describe under which conditions Rayleigh Scattering in the Earth's Atmosphere occurs"},{"concepts":[640],"name":"Describe under which conditions the Beer-Bouguert-Lambert Law well approximates the general radiative transfer equation-"},{"concepts":[117],"name":"Describe ways in which a geographic entity can be created from one or more others"},{"concepts":[634],"name":"Describe what EM sensing means"},{"concepts":[178],"name":"Design a test project to demonstrate interoperability"},{"concepts":[134],"name":"Design a game mechanics of a geo-game"},{"concepts":[1133],"name":"Design a map of chlorophyll-a concentration according to the requirements of HAB management for aquaculture"},{"concepts":[147],"name":"Design a map series to show the change in a geographic pattern over time"},{"concepts":[70],"name":"Design a sampling scheme that will help detect when space-time clusters of events occur"},{"concepts":[135],"name":"Design a single map symbol that can be used to symbolize a set of related variables"},{"concepts":[146],"name":"Design a stylized terrain map from a digital elevation model (DEM)"},{"concepts":[234],"name":"Design a test of reliability of change information (e.g., the logical consistency of updates to the TIGER database)"},{"concepts":[61],"name":"Design an algorithm that calculates slope and aspect from a Triangulated Irregular Network (TIN) model"},{"concepts":[57],"name":"Design an algorithm which interpolates irregular point elevation data onto a regular grid"},{"concepts":[593],"name":"Design an effective governance structure for a national SDI"},{"concepts":[29],"name":"Design an experiment that allows one to evaluate the effect of traditional approaches of cartographic generalization on the quality of digital data sets created from analog originals"},{"concepts":[155],"name":"Design an interactive web map"},{"concepts":[161],"name":"Design an iterative process for evaluating the usability of (geospatial) products"},{"concepts":[591],"name":"Design an SDI assessment framework and methodology for assessing and evaluating an SDI"},{"concepts":[571],"name":"Design and implement an effective GIS coordination strategy"},{"concepts":[572],"name":"Design and implement approaches and methods for assessing the performance of GIS"},{"concepts":[572],"name":"Design and implement approaches and methods for collecting users feedback on GIS"},{"concepts":[1191],"name":"Design and test an EO-based workflow for landslide mapping"},{"concepts":[186],"name":"Design application-specific conceptual models"},{"concepts":[111],"name":"Design data models for specific applications based on these comprehensive general models"},{"concepts":[165],"name":"Design databases for spatial data management"},{"concepts":[579],"name":"Design effective teaching and learning methods for GIS&T education"},{"concepts":[578],"name":"Design GIS&T curricula and courses"},{"concepts":[135],"name":"Design icons suitable for mapping different elements"},{"concepts":[133],"name":"Design maps that are appropriate for users with vision limitations"},{"concepts":[193],"name":"Design relational databases"},{"concepts":[582],"name":"Design solutions to different types of barriers to geospatial data sharing"},{"concepts":[165],"name":"Design workflows, procedures, and customized software tools for using geospatial technologies and methods"},{"concepts":[1214],"name":"designing the description of a service for the need of a particular user of EO information"},{"concepts":[1246],"name":"Detect and monitor oil slicks"},{"concepts":[1140,1147,1201],"name":"Detect land movement, subsidence, heave"},{"concepts":[526],"name":"Determine all necessary steps to make EO-derived products of a resarch project accessible"},{"concepts":[166],"name":"Determine how to integrate or combine the proposed workflow with current applications running"},{"concepts":[381],"name":"Determine if a dataset can be considered as open data"},{"concepts":[1223],"name":"Determine object movement by comparing subsequent images"},{"concepts":[1152],"name":"Determine requirements and quality criteria for an EO information product that serves spatial planners in monitoring soil sealing"},{"concepts":[28],"name":"Determine the mathematical relationships among scale, scope, and resolution"},{"concepts":[318],"name":"Determine the most appropriate data collection method for collecting particular data"},{"concepts":[106],"name":"Determine the proper uses of attributes based on their domains"},{"concepts":[209],"name":"Determine the standards that are essential for geospatial data modelling"},{"concepts":[117],"name":"Determine whether it is important to represent the genealogy of entities for a particular application"},{"concepts":[111],"name":"Determine whether phenomena or applications exist that are not adequately represented in an existing comprehensive model"},{"concepts":[54],"name":"Determine which method to use to combine criteria e.g., linear, multiplication"},{"concepts":[1275],"name":"Develop a Javascript function that handles a GeoJSON file"},{"concepts":[38],"name":"Develop a method for describing the shape of a cluster of similarly valued points by using the concept of the convex hull"},{"concepts":[591],"name":"Develop a strategy to improve the performance of an SDI initiative"},{"concepts":[149],"name":"Develop a useful interactive interface and legend"},{"concepts":[106],"name":"Develop alternative forms of representations for situations in which attributes do not adequately capture meaning"},{"concepts":[38],"name":"Develop an algorithm to determine the skeleton of polygons"},{"concepts":[1230],"name":"Develop an event map based on a time-series analysis"},{"concepts":[518],"name":"Develop and implement an object-based image analysis workflow for a specific application context"},{"concepts":[131,165],"name":"Develop effective mathematical and other models of spatial situations and processes"},{"concepts":[387],"name":"Develop GI infrastructure with a the role in the private sector"},{"concepts":[145],"name":"Develop graphic techniques that clearly show different forms of inexactness (e.g., existence uncertainty, boundary location uncertainty, attribute ambiguity, transitional boundary) of a given feature (e.g., a culture region)"},{"concepts":[97],"name":"Develop methods for representing non-cartesian models of space in GIS"},{"concepts":[1162,1161],"name":"Develop monitoring to evaluate and deliver policy goals"},{"concepts":[1166],"name":"Develop sense of space"},{"concepts":[225],"name":"Develop solutions to different kind of challenges of model interoperability"},{"concepts":[1167],"name":"Develop strategies and policies"},{"concepts":[1139,1136,1137,1138,1172],"name":"Develop strategies and policies for energy and mineral resources"},{"concepts":[1219],"name":"Develop thorough understanding of the complex process from collecting the LiDAR data to generation of the final modeled outputs"},{"concepts":[166],"name":"Develop use cases for potential applications using established techniques with potential users, such as questionnaires, interviews, focus groups, the Delphi method, and/or joint application development"},{"concepts":[1247],"name":"Develop Web-GIS solutions to replace each of the functions of a traditional GIS"},{"concepts":[565],"name":"Devise simple ways to represent probability information in GIS"},{"concepts":[377],"name":"Differentiate \"contracts for service\" from \"contracts of service\""},{"concepts":[146],"name":"Differentiate 3D representations from 2.5 D representations"},{"concepts":[212],"name":"Differentiate among a lattice, a tessellation, and a grid"},{"concepts":[23],"name":"Differentiate among common interpolation techniques (e.g., nearest neighbor, bilinear, bicubic)"},{"concepts":[377],"name":"Differentiate among contract liability, tort liability, and statutory liability"},{"concepts":[113],"name":"Differentiate among different types of regions, including functional, cultural, physical, administrative, and others"},{"concepts":[112],"name":"Differentiate among distributions in space, time, and attribute"},{"concepts":[93],"name":"Differentiate among elements of the meaning of a place that can or cannot be easily represented using geospatial technologies"},{"concepts":[28],"name":"Differentiate among the concepts of scale (as in map scale), support, scope, and resolution"},{"concepts":[324],"name":"Differentiate among the spatial, spectral, radiometric, and temporal resolution of a remote sensing instrument"},{"concepts":[391],"name":"Differentiate among universal/deliberative, pluralist/representative, and participatory models of citizen participation"},{"concepts":[567],"name":"Differentiate an enterprise system from a department-centered GI system"},{"concepts":[121],"name":"Differentiate applications in which vagueness is an acceptable trait from those in which it is unacceptable"},{"concepts":[101],"name":"Differentiate applications that can make use of common-sense principles of geography from those that should not"},{"concepts":[18],"name":"Differentiate between a linear program and an integer program"},{"concepts":[1257],"name":"Differentiate between a metadata standard and a metadata profile"},{"concepts":[97],"name":"Differentiate between absolute and relative descriptions of location"},{"concepts":[311],"name":"Differentiate between active and passive sensors, citing examples of each"},{"concepts":[179],"name":"Differentiate between and application built with a Service Oriented Architecture (SOA) or a Resource Oriented Architecture (ROA)"},{"concepts":[97],"name":"Differentiate between common-sense, Cartesian metric, relational, relativistic, phenomenological, social constructivist, and other theories of the nature of space"},{"concepts":[186,187],"name":"Differentiate between conceptual and logical models, in terms of the level of detail, constraints, and range of information included"},{"concepts":[392],"name":"Differentiate between consumption, analysis, presumption and production of geoinformation within digital geo media"},{"concepts":[54],"name":"Differentiate between contributing factors and constraints in a multi-criteria application"},{"concepts":[175],"name":"Differentiate between copyleft and permissive licenses for a software product"},{"concepts":[5],"name":"Differentiate between data mining approaches used for spatial and non-spatial applications"},{"concepts":[55],"name":"Differentiate between deterministic and stochastic spatial process models"},{"concepts":[99],"name":"Differentiate between formal and natural language in GI science applications."},{"concepts":[2],"name":"Differentiate between geostatistics, and spatial statistics"},{"concepts":[244],"name":"Differentiate between individual and aggregate models"},{"concepts":[63],"name":"Differentiate between isotropic and anisotropic processes"},{"concepts":[50],"name":"Differentiate between kernel density estimation and spatial interpolation"},{"concepts":[188],"name":"Differentiate between logical and physical models, in terms of the level of detail, constraints, and range of information included"},{"concepts":[213],"name":"Differentiate between lossy and lossless compression methods"},{"concepts":[46,47],"name":"Differentiate between map algebra and matrix algebra using real examples"},{"concepts":[103],"name":"Differentiate between mathematical and phenomenological theories of the nature of time"},{"concepts":[70],"name":"Differentiate between model-based and design-based sampling schemes"},{"concepts":[26],"name":"Differentiate between polynomial coordinate transformations (including linear) and rubbersheeting"},{"concepts":[1250],"name":"Differentiate between SOAP and REST Web services. - Identify design issues of REST Web services"},{"concepts":[93],"name":"Differentiate between space and place"},{"concepts":[121],"name":"Differentiate between the following concepts: vagueness and ambiguity, well defined and poorly defined objects and fields or discord and non-specificity"},{"concepts":[52],"name":"Differentiate between the gravity model and spatial interaction models"},{"concepts":[57],"name":"Differentiate between trend surface analysis and deterministic spatial interpolation"},{"concepts":[1255],"name":"Differentiate between upper, domain, and application level ontologies"},{"concepts":[311],"name":"Differentiate push-broom and cross-track scanning technologies"},{"concepts":[414],"name":"Differentiate rectification and orthorectification"},{"concepts":[480],"name":"Differentiate supervised classification from unsupervised classification"},{"concepts":[122],"name":"Differentiate uncertainty in geospatial situations from vagueness"},{"concepts":[138],"name":"Differentiate uses for different types of imagery related to earth"},{"concepts":[109],"name":"Differentiate various sources of fields, such as substance properties (e.g., temperature), artificial constructs (e.g., population density), and fields of potential or influence (e.g., gravity)"},{"concepts":[327],"name":"Digitize and georegister a specified vector feature set to a given geometric accuracy and topological fidelity thresholds using a given map sheet, digitizing tablet, and data entry software"},{"concepts":[399],"name":"Discuss about \"mapping whose reality?\" Pros and cons of geoinformation sharing in social media, i.e. big data, \"digital shadow\" etc."},{"concepts":[387],"name":"Discuss about open data and data sharing and public/private sector"},{"concepts":[381],"name":"Discuss about open data impact on society and citizenship"},{"concepts":[151],"name":"Discuss about the advantages of different immersive display systems"},{"concepts":[159],"name":"Discuss about the degree of subjectivity and/or objectivity of a map"},{"concepts":[125],"name":"Discuss about the History of Cartography in different cultures"},{"concepts":[126],"name":"Discuss about the relationship between art and cartography"},{"concepts":[824,825,826],"name":"Discuss advantages and disadvantages of different methods of storing remote sensing data"},{"concepts":[839,840,841,842],"name":"Discuss advantages and disadvantages of different SAR data formats"},{"concepts":[770],"name":"Discuss advantages and disadvantages of passive and active sensors"},{"concepts":[732],"name":"Discuss advantages of SAR techniques over traditional measuring techniques"},{"concepts":[465],"name":"Discuss algorithms that use the detection of keypoints to identify objects in images"},{"concepts":[773],"name":"Discuss an example of using a radar altimeter"},{"concepts":[831],"name":"Discuss and compare different temporal resolutions of remote sending data"},{"concepts":[726],"name":"Discuss and compare different types of interactions of microwaves with matter"},{"concepts":[838],"name":"Discuss and compare different types of processing levels of optical data"},{"concepts":[843],"name":"Discuss and compare different types of processing levels of SAR data"},{"concepts":[381],"name":"Discuss and define open data and impact on GIS&T"},{"concepts":[563],"name":"Discuss and define the process of the Information value chain"},{"concepts":[451],"name":"Discuss cloud masks as early steps towards semantic enrichment for EO images"},{"concepts":[104],"name":"Discuss common prepositions and adjectives (in any particular language) that signify either spatial or temporal relations but are used for both kinds, such as after or longer"},{"concepts":[245],"name":"Discuss concepts of space-time dynamics for spatial modeling"},{"concepts":[1248],"name":"Discuss consensus based interoperability and its relation to geospatial data interchange. Define what a Web Service (WS) is and present characteristic scenarios. Data serving and Data Processing WSs"},{"concepts":[398],"name":"Discuss critiques of GIS as \"deterministic\" technology in relation to debates about the Quantitative quantitative revolution in the discipline of geography."},{"concepts":[402],"name":"Discuss critiques of GIS as deterministic technology in relation to debates about the Quantitative Revolution in the discipline of geography"},{"concepts":[577],"name":"Discuss different formats (tutorials, in house, online, instructor lead) for training and how they can be used by organizations"},{"concepts":[545],"name":"Discuss different methods for assessing the quality of a specific EO product"},{"concepts":[781],"name":"Discuss different types of laser scanners"},{"concepts":[810,686],"name":"Discuss different types of satellite orbits"},{"concepts":[248],"name":"Discuss different ways of simulating space and visualizing model behaviour"},{"concepts":[699],"name":"Discuss electromagnetic interactions and scattering mechanisms"},{"concepts":[816],"name":"Discuss examples of ground-based platforms and their use"},{"concepts":[809],"name":"Discuss examples of the objectives of Earth observation missions"},{"concepts":[309],"name":"Discuss future prospects for automated feature extraction from aerial imagery"},{"concepts":[575],"name":"Discuss how a code of ethics might be applied within an organization"},{"concepts":[136],"name":"Discuss how cultural differences with respect to color associations impact map design"},{"concepts":[548,828],"name":"Discuss how different spectral resolution of EO sensors influences their potential for vegetation mapping"},{"concepts":[512],"name":"Discuss how hierarchical representation is exploited for object-based image analysis"},{"concepts":[763],"name":"Discuss how line detectors array sensors work"},{"concepts":[500],"name":"Discuss how low-pass filtering of an image impacts the resulting regions derived with watershed segmentation"},{"concepts":[159],"name":"Discuss how maps express relations of power"},{"concepts":[323],"name":"Discuss how measures of spatial autocorrelation may be used to evaluate thematic accuracy"},{"concepts":[830,548],"name":"Discuss how radiometric resolution influences the granularity of a land cover classification"},{"concepts":[827,835],"name":"Discuss how remote sensing data is organized and stored"},{"concepts":[746],"name":"Discuss how the angle of SAR signal incidence affects SAR acquisition"},{"concepts":[70],"name":"Discuss how the choice of sampling strategy impacts the accuracy assesment for a classification result"},{"concepts":[492],"name":"Discuss how the choice of sampling strategy impacts the accuracy assesment for a classification result"},{"concepts":[70],"name":"Discuss how the choice of sampling strategy impacts the classification result"},{"concepts":[492],"name":"Discuss how the choice of sampling strategy impacts the classification result"},{"concepts":[836],"name":"Discuss how the radiometrically corrected data are processed"},{"concepts":[509],"name":"Discuss how the size of the neighborhood impacts the smoothing effect of a low-pass filter"},{"concepts":[388],"name":"Discuss how to approach the widening audience/participants for geospatial products and service, increasing geo-awareness and geo-enablement"},{"concepts":[143],"name":"Discuss how to create an intellectual and visual hierarchy on maps"},{"concepts":[694],"name":"Discuss how to use phase information in remote sensing"},{"concepts":[31],"name":"Discuss implications of data loss in the case of generalisation of spatial data."},{"concepts":[432],"name":"Discuss imputation methods for filling in missing data"},{"concepts":[602],"name":"Discuss in which way annual solar insolation and average cloud coverage parameters affect the choice of a solar power plant location"},{"concepts":[602],"name":"Discuss in which way modeled daily solar insolation and cloud coverage forecast could affect solar power plant day-by-day management"},{"concepts":[389],"name":"Discuss legal aspects of access to environmental data, global change/warming or sustainable development (regional, national, global) in conjunction to society."},{"concepts":[732],"name":"Discuss limitations of interferometric measurement"},{"concepts":[501],"name":"Discuss limitations of the different region-based segementation methods"},{"concepts":[823],"name":"Discuss main characteristics of digital imagery"},{"concepts":[381],"name":"Discuss of arguments for and against open data"},{"concepts":[380],"name":"Discuss of opportunities for exchange of geospatial data between public and private sector to enable more efficient analysis"},{"concepts":[243],"name":"Discuss options of combining rule-based models with other individual modelling approaches"},{"concepts":[721],"name":"Discuss orientational polarisation of media"},{"concepts":[398],"name":"Discuss over the argument that the use of Geospatial geospatial Information privileges certain views of the world over others."},{"concepts":[387],"name":"Discuss over the changing role of the private sector in the use of geospatial information"},{"concepts":[388],"name":"Discuss over the paradigm shifts and current trends in GIS&T education and pedagogical approaches for GIS teaching and learning in detail"},{"concepts":[399],"name":"Discuss over the various implications of surveillance technology"},{"concepts":[720],"name":"Discuss polarimetric decomporition techniques"},{"concepts":[393],"name":"Discuss positive and negative aspects of the term \"humans as sensors\""},{"concepts":[729],"name":"Discuss radar antennas"},{"concepts":[715],"name":"Discuss scale of roughness of microwaves"},{"concepts":[2],"name":"Discuss situations when it is desirable to adopt a spatial approach to the analysis of data"},{"concepts":[226],"name":"Discuss some of the difficulties of applying the standard process-pattern concept to lines and networks"},{"concepts":[502],"name":"Discuss spatial autocorrelation and homogeneity of image objects"},{"concepts":[174],"name":"Discuss the advantages and disadvantages of outsourcing elements of a GIS project / GI system"},{"concepts":[97],"name":"Discuss the advantages and disadvantages of the use of cartesian metric space as a basis for GIS and related technologies"},{"concepts":[324],"name":"Discuss the advantages and potential problems associated with the use of Minimum Mapping Unit (MMU) as a measure of the level of detail in land use, land cover, and soils maps"},{"concepts":[771],"name":"Discuss the application possibilities of imaging radar"},{"concepts":[787],"name":"Discuss the applications for which Differential Absorption LiDAR can be used"},{"concepts":[788],"name":"Discuss the applications for which Wind Doppler LiDAR is used"},{"concepts":[64],"name":"Discuss the appropriateness of different types of spatial weights matrices for various problems"},{"concepts":[78],"name":"Discuss the appropriateness of GWR under various conditions"},{"concepts":[532],"name":"Discuss the available data quality standards for EO"},{"concepts":[662],"name":"Discuss the basic principles of solar radiation."},{"concepts":[508],"name":"Discuss the benefits of using a gauss filter instead of a mean filter for smoothing an image"},{"concepts":[112],"name":"Discuss the causal relationship between spatial processes and spatial patterns, including the possible problems in determining causality"},{"concepts":[622],"name":"Discuss the change of attenuation length moving from visible to the microwave range and from sea water to solid land surfaces"},{"concepts":[51],"name":"Discuss the characteristics of the various cluster detection techniques"},{"concepts":[25],"name":"Discuss the consequences of increasing and decreasing resolution"},{"concepts":[111],"name":"Discuss the contributions of early attempts to integrate the concepts of space, time, and attribute in geographic information, such as Berry (1964) and Sinton (1978)"},{"concepts":[97],"name":"Discuss the contributions that different perspectives on the nature of space bring to an understanding of geographic phenomenon"},{"concepts":[111],"name":"Discuss the degree to which these models can be implemented using current technologies"},{"concepts":[759],"name":"Discuss the development of remote sensing sensors"},{"concepts":[123],"name":"Discuss the difference between vagueness and uncertainty."},{"concepts":[10],"name":"Discuss the difference of implementing Dijkstras algorithm in raster and vector modes"},{"concepts":[789],"name":"Discuss the differences between imaging and non-imaging sensors"},{"concepts":[133],"name":"Discuss the differences between maps that use the same data but are for different purposes and intended audiences"},{"concepts":[133],"name":"Discuss the differences between maps that use the same data but are for different purposes and intended audiences"},{"concepts":[548],"name":"Discuss the different types of resolution of Earth observation data"},{"concepts":[92],"name":"Discuss the differing denotations and connotations of the terms spatial, geographic, and geospatial"},{"concepts":[110],"name":"Discuss the difficulty of integrating process models into GIS software based on the entity and field views, and methods used to do so"},{"concepts":[117],"name":"Discuss the effects of temporal scale on the modeling of genealogical structures"},{"concepts":[395],"name":"Discuss the ethical implications of a local government's decision to charge fees for its data"},{"concepts":[309],"name":"Discuss the extent to which vector data extraction from aerial stereoimagery has been automated"},{"concepts":[507],"name":"Discuss the frequencies that a high-pass filter preserves and subdues"},{"concepts":[593],"name":"Discuss the governance structure in place of a particular country"},{"concepts":[222],"name":"Discuss the historical roots of the Census Bureaus creation of GBF/DIME as the foundation for the development of topological data structures"},{"concepts":[798],"name":"Discuss the history of the development of remote sensing platforms"},{"concepts":[108],"name":"Discuss the human predilection to conceptualize geographic phenomena in terms of discrete entities"},{"concepts":[392],"name":"Discuss the impact of geospatial information for the development of social media (Facebook, Twitter, Wikimapia, Flickr etc.) becoming increasingly location-based"},{"concepts":[232],"name":"Discuss the implication of long transactions on database integrity"},{"concepts":[398],"name":"Discuss the implications of interoperability on ontology"},{"concepts":[402],"name":"Discuss the implications of interoperability on ontology"},{"concepts":[324],"name":"Discuss the implications of the sampling theorem (Lambda = 0.5 delta) to the concept of resolution"},{"concepts":[28],"name":"Discuss the implications of tradeoff between data detail and data volume"},{"concepts":[107],"name":"Discuss the importance of space, time, properties, and categories as fundamentals in the conceptualization and representation of spatial entities."},{"concepts":[150],"name":"Discuss the influence of the user interface on maps and visualizations on the Web"},{"concepts":[1250],"name":"Discuss the issue whether a service is really \"RESTful\" or not"},{"concepts":[380],"name":"Discuss the legal framework related to competition and public-private sector relationships in the geospatial domain"},{"concepts":[805],"name":"Discuss the main applications using the extra wide swath mode"},{"concepts":[494],"name":"Discuss the main drawback of edge-based segmentation in partitioning an image"},{"concepts":[766],"name":"Discuss the main properties of hyperspectral radiometers"},{"concepts":[765],"name":"Discuss the main properties of passive microwave radiometers"},{"concepts":[764],"name":"Discuss the main properties of thermal radiometers"},{"concepts":[758],"name":"Discuss the main types of remote sensing data"},{"concepts":[758,817],"name":"Discuss the main types of remote sensing platforms"},{"concepts":[758],"name":"Discuss the main types of remote sensing sensors"},{"concepts":[548],"name":"Discuss the minimum spatial resolution required for detecting single houses in a satellite image"},{"concepts":[597],"name":"Discuss the mission, history, constituencies, and activities of the GIS Certification Institute (GISCI)"},{"concepts":[577],"name":"Discuss the National Research Council report on Learning to Think Spatially (2005) as it relates to spatial thinking skills needed by the GIS and T workforce"},{"concepts":[831,548],"name":"Discuss the needs for high temporal resolution for analysing crop cycles in agriculture"},{"concepts":[23],"name":"Discuss the pitfalls of using secondary data that has been generated using interpolations (e.g., Level 1 USGS DEMs)"},{"concepts":[725],"name":"Discuss the polarimetry technique"},{"concepts":[29],"name":"Discuss the possible effects on topological integrity of generalizing data sets"},{"concepts":[377],"name":"Discuss the potential legal problems associated with licensing geospatial information"},{"concepts":[403],"name":"Discuss the potential role of agency (individual action) in resisting dominant practices and in using GIS and T in ways that are consistent with feminist epistemologies and politics"},{"concepts":[498],"name":"Discuss the principles of regionalisation and their use in segmentation methods"},{"concepts":[669],"name":"Discuss the processes that describe the hydrologic cycle"},{"concepts":[404],"name":"Discuss the production, maintenance, and use of geospatial data by a government agency or private firm from the perspectives of a taxpayer, a community organization, and a member of a minority group"},{"concepts":[846],"name":"Discuss the purposes of obtaining remote sensing data"},{"concepts":[700],"name":"Discuss the radiometric anomalies of radar data"},{"concepts":[55],"name":"Discuss the relationship between spatial processes and spatial patterns"},{"concepts":[125],"name":"Discuss the relationship between the history of exploration and the development of a more accurate map of the world"},{"concepts":[30],"name":"Discuss the relationship of attribute measurement levels to database query operations"},{"concepts":[392],"name":"Discuss the role and value of \"place\" and \"space\" for geo media based social networking"},{"concepts":[136],"name":"Discuss the role of gamut in choosing colors that can be reproduced on various devices and media"},{"concepts":[222],"name":"Discuss the role of graph theory in topological structures"},{"concepts":[22],"name":"Discuss the role of metadata in facilitating conversation of data models and data structures between systems"},{"concepts":[389,394],"name":"Discuss the role of public, private sector and citizens in facilitating geospatial information in environmental/sustainable issues."},{"concepts":[380],"name":"Discuss the role of the public and private sectors in producing and dissemination of geospatial information"},{"concepts":[575],"name":"Discuss the status of professional and academic certification in GIS and T"},{"concepts":[378],"name":"Discuss the status of the concept of privacy in the U.S. legal regime"},{"concepts":[142],"name":"Discuss the strengths and weaknesses of infographics as a method of displaying geographic information"},{"concepts":[658],"name":"Discuss the structure and chemical composition of the atmosphere"},{"concepts":[0],"name":"Discuss the synergy between processes in geo-information systems and earth observation systems."},{"concepts":[63],"name":"Discuss the theory leading to the assumption of intrinsic stationarity"},{"concepts":[762],"name":"Discuss the use of area array sensors in remote sensing"},{"concepts":[768],"name":"Discuss the use of atmospheric passive sounders"},{"concepts":[767],"name":"Discuss the use of data obtained by spectroradiometer"},{"concepts":[761],"name":"Discuss the use of digital frame cameras in remote sensing"},{"concepts":[692],"name":"Discuss the use of polarization for different application domains"},{"concepts":[149],"name":"Discuss the uses of the map as a user interface element in interactive presentations of geographic information"},{"concepts":[799],"name":"Discuss the ways of using data acquired by UAS in remote sensing"},{"concepts":[797],"name":"Discuss types and classes of remote sensing sensors"},{"concepts":[550],"name":"Discuss valid time ranges for images used for landslide mapping with pre- and post-event image comparison"},{"concepts":[381],"name":"Discuss various legal aspects of public and private sectors concerning owning, controlling, sharing/ disseminating open data."},{"concepts":[381],"name":"Discuss various sources of open data (science, public and private sectors)"},{"concepts":[376],"name":"Discuss ways in which the geospatial profession is regulated under the U.S. legal regime"},{"concepts":[388],"name":"Discuss ways of working with crowdsourcing in education and research"},{"concepts":[712],"name":"Discuss what horizontal roughness component (correlation legth) is"},{"concepts":[774],"name":"Discuss what information is acquired by the laser altimeters"},{"concepts":[711],"name":"Discuss what surface height variation (or RMS height) is"},{"concepts":[833],"name":"Discuss what the header file describes"},{"concepts":[769],"name":"Discuss what the main characteristics of radiometers are"},{"concepts":[772],"name":"Discuss what types of electromagnetic waves the laser profiler uses"},{"concepts":[453],"name":"Discuss why a query through time is easier realized with a data cube than by comparison of a time series stored in image files"},{"concepts":[832],"name":"Distinguish and explain the different types of properties of digital imagery"},{"concepts":[149,139],"name":"Distinguish between animated and interactive maps"},{"concepts":[89],"name":"Distinguish between continuants and occurrents in relation with spatial phenomena."},{"concepts":[154],"name":"Distinguish between different graphic representation techniques"},{"concepts":[86],"name":"Distinguish between metaphysics and epistemology."},{"concepts":[186],"name":"Distinguish between the temporary and structural relationships in a conceptual model"},{"concepts":[27],"name":"Distinguish between transformation methods for raster and vector representations."},{"concepts":[164,170],"name":"Distinguish between usability, utility, and user needs in the context of geovisualizations"},{"concepts":[167,168],"name":"Document existing and potential tasks in terms of workflow and information flow"},{"concepts":[105],"name":"Document the personal, social, and or institutional meaning of categories used in GIS applications"},{"concepts":[150],"name":"Edit the symbology, labeling, and page layout for a map originally designed for hard copy printing so that it can be seen and used on the Web"},{"concepts":[101],"name":"Effectively communicate the design, procedures, and results of GIS projects to non-GIS audiences (clients, managers, general public)"},{"concepts":[112],"name":"Employ techniques for visualizing, describing, and analyzing distributions in space, time, and attribute"},{"concepts":[1166],"name":"Enable citizen skills spatially"},{"concepts":[23],"name":"Estimate a value between two known values using linear interpolation (e.g., spot elevations, population between census years)"},{"concepts":[1207],"name":"Estimate evaporation rates"},{"concepts":[1207,444],"name":"Estimate near-surface chlorophyll-a concentration for monitoring harmful algal blooms (HABs)"},{"concepts":[129],"name":"Estimate the cost to collect needed data from primary sources (e.g., remote sensing, GPS)"},{"concepts":[36],"name":"Estimate the fractal dimension of a sinuous line"},{"concepts":[644],"name":"Estimate the meteorological and the cloud optical properties by LBRTM and validate against high accuracy spectral measurements"},{"concepts":[127],"name":"Estimate the potential value of a historical map"},{"concepts":[531],"name":"Evaluate an EO product and its metadata on its reusability for a new application context"},{"concepts":[570],"name":"Evaluate and revise an existing GIS management strategy"},{"concepts":[1166,1163,1164],"name":"Evaluate citizen-driven observations"},{"concepts":[153],"name":"Evaluate graphic techniques used to portray spatializations"},{"concepts":[25],"name":"Evaluate methods used by contemporary GIS software to resample raster data on-the-fly during display"},{"concepts":[311],"name":"Evaluate the advantages and disadvantages of acoustic remote sensing versus airborne or satellite remote sensing for seafloor mapping"},{"concepts":[311,808,813],"name":"Evaluate the advantages and disadvantages of airborne remote sensing versus satellite remote sensing"},{"concepts":[309],"name":"Evaluate the advantages and disadvantages of photogrammetric methods and LiDAR for production of terrain elevation data"},{"concepts":[110],"name":"Evaluate the assertion that events and processes are the same thing, but viewed at different temporal scales"},{"concepts":[122],"name":"Evaluate the causes of uncertainty in geospatial data"},{"concepts":[136],"name":"Evaluate the colors used in a web map to be used indoors and outdoors"},{"concepts":[528],"name":"Evaluate the conformity of an EO imagery product to ISO 19129"},{"concepts":[93],"name":"Evaluate the differences in how various parties think or feel differently about a place being modeled"},{"concepts":[217],"name":"Evaluate the ease of measuring resolution in different types of tessellations"},{"concepts":[108],"name":"Evaluate the effectiveness of GIS data models for representing the identity, existence, and lifespan of entities"},{"concepts":[109],"name":"Evaluate the field views description of objects as conceptual discretizations of continuous patterns"},{"concepts":[1203],"name":"Evaluate the impact of changes in land areas"},{"concepts":[101],"name":"Evaluate the impact of geospatial technologies (e.g., Google Earth) that allow non-geospatial professionals to create, distribute, and map geographic information"},{"concepts":[1182,1180],"name":"Evaluate the impact of the climate change"},{"concepts":[217],"name":"Evaluate the implications of changing grid cell resolution on the results of analytical applications by using GIS software"},{"concepts":[108],"name":"Evaluate the influence of scale on the conceptualization of entities"},{"concepts":[85],"name":"Evaluate the influences of ones own philosophical views and assumptions on GIS AND T practices"},{"concepts":[81],"name":"Evaluate the influences of particular worldviews (including ones own) on GIS practices"},{"concepts":[95],"name":"Evaluate the influences of political actions, especially the allocation of territory, on human perceptions of space and place"},{"concepts":[95],"name":"Evaluate the influences of political ideologies (e.g., Marxism, Capitalism, conservative liberal) on the understanding of geographic information"},{"concepts":[589],"name":"Evaluate the institutional framework of an existing SDI initiative"},{"concepts":[222],"name":"Evaluate the positive and negative impacts of this shift from integrated topological models"},{"concepts":[213],"name":"Evaluate the relative merits of grid compression methods for storage"},{"concepts":[579],"name":"Evaluate the relevance and applicability of different teaching and learning methods for GIS&T education"},{"concepts":[109],"name":"Evaluate the representation of movement as a field of location over time (e.g. :x,y,z: = f(t) )"},{"concepts":[121],"name":"Evaluate the role that system complexity, dynamic processes, and subjectivity play in the creation of vague phenomena and concepts"},{"concepts":[144],"name":"Evaluate the strengths and limitations of different thematic mapping methods"},{"concepts":[539],"name":"Evaluate the thematic accuracy of a given soils map"},{"concepts":[242],"name":"Evaluate the tradeoffs between abstraction and representativeness in simulation model development"},{"concepts":[161],"name":"Evaluate the usability of a hard-copy map"},{"concepts":[161,170],"name":"Evaluate the usability of a web map"},{"concepts":[187],"name":"Evaluate the various general data models common in GIS project"},{"concepts":[121],"name":"Evaluate vagueness in the locations, time, attributes, and other aspects of geographic phenomena"},{"concepts":[29],"name":"Evaluate various line simplification algorithms by their usefulness in different applications"},{"concepts":[243],"name":"Evaluate when rule-based models can be applied to spatiotemporal problems"},{"concepts":[238],"name":"Examine how computational technology relates to geocomputation"},{"concepts":[449],"name":"Examine how the vegetation indices relates to the vegetation dynamics and health"},{"concepts":[449],"name":"Examine how the water-related spectral indices relates to changes in the vegetation and soil water content"},{"concepts":[1260],"name":"Examine Metadata schema and vocabularies used for open data publishing"},{"concepts":[1275],"name":"Examine the Document Object Model (DOM) in HTML documents"},{"concepts":[45],"name":"Exemplify applications in which overlay is useful, such as site suitability analysis"},{"concepts":[63],"name":"Exemplify deterministic and spatial stochastic processes"},{"concepts":[103],"name":"Exemplify different temporal frames of reference: linear and cyclical, absolute and relative"},{"concepts":[568],"name":"Exemplify each component of a needs assessment for an enterprise GIS"},{"concepts":[235],"name":"Exemplify how the lack of a data librarian to manage data can have disastrous consequences on the resulting dataset"},{"concepts":[63],"name":"Exemplify non-stationarity involving first and second order effects"},{"concepts":[113],"name":"Exemplify regions found at different scales"},{"concepts":[232],"name":"Exemplify scenarios in which one would need to perform a number of periodic changes in a real GIS database"},{"concepts":[38],"name":"Exemplify situations in which the centroid of a polygon falls outside its boundary"},{"concepts":[12],"name":"Exemplify the Classic Transportation Problem"},{"concepts":[222],"name":"Exemplify the concept of planar enforcement (e.g., TIN triangles)"},{"concepts":[215],"name":"Exemplify the uses (past and potential) of the hexagonal model"},{"concepts":[631],"name":"Explain the concept of composition of spectral signatures and apply the \"linear mixing\" models in some simple case"},{"concepts":[1152],"name":"Explain a use case of EO for smart cities, e.g. how EO derived information about urban green instrastructure supports designing nature based solutions for preserving ecosystem services"},{"concepts":[735],"name":"Explain across-track interferometry technique"},{"concepts":[734],"name":"Explain along-track interferometry technique"},{"concepts":[487],"name":"Explain an application example where SVM is used for EO image classification"},{"concepts":[449],"name":"Explain an application example where the spectral indices are used for vegetation, water or snow monitoring"},{"concepts":[207],"name":"Explain and apply GML data models"},{"concepts":[694],"name":"Explain and apply phase unwrapping"},{"concepts":[203,221],"name":"Explain and apply standards relevant for geometric modelling"},{"concepts":[749],"name":"Explain and discuss elements of Synthetic Aperture Radar (SAR) geometric configuration"},{"concepts":[716],"name":"Explain and discuss surface roughness in microwave remote sensing"},{"concepts":[689],"name":"Explain and discuss the complex elements of a radar signal"},{"concepts":[822],"name":"Explain and discuss the concept of Big Data in the field of Earth Observation"},{"concepts":[818],"name":"Explain and discuss the development of remote sensing data carriers"},{"concepts":[782],"name":"Explain and discuss the LiDAR technology"},{"concepts":[803],"name":"Explain and discuss the SAR acquisition mode spotlight"},{"concepts":[802],"name":"Explain and discuss the SAR acquisition mode staring spotlight"},{"concepts":[770],"name":"Explain and discuss types of sensing mechanisms"},{"concepts":[727],"name":"Explain and discuss what antenna gain is and why it is described as the key performance of a radar antenna"},{"concepts":[754],"name":"Explain and discuss what terrain reflectivity is and how it influences radar signal"},{"concepts":[751],"name":"Explain and discuss what the foreshortening is"},{"concepts":[752],"name":"Explain and discuss what the layover is"},{"concepts":[845],"name":"Explain and discuss what the main processing levels of remote sensing data are"},{"concepts":[832],"name":"Explain and discuss what the radiometric resolution is"},{"concepts":[745],"name":"Explain and discuss what the range direction is"},{"concepts":[753],"name":"Explain and discuss what the shadow in SAR acquisition means"},{"concepts":[832,829],"name":"Explain and discuss what the spatial resolution is"},{"concepts":[832],"name":"Explain and discuss what the spectral resolution is"},{"concepts":[832],"name":"Explain and discuss what the temporal resolution is"},{"concepts":[757,697],"name":"Explain and outline the advantages of radar sensors"},{"concepts":[197],"name":"Explain and use UML diagrams"},{"concepts":[76],"name":"Explain Anselins typology of spatial autoregressive models"},{"concepts":[37],"name":"Explain any differences in the measured direction between two places when the data are presented in a GIS in different projections"},{"concepts":[200],"name":"Explain basic aspects of data modelling, storage and exploitation, such as relation models & databases, data structures, SQL, UML and other basics"},{"concepts":[377],"name":"Explain cases of liability claims associated with misuse of geospatial information, erroneous information, and loss of proprietary interests"},{"concepts":[719],"name":"Explain covariance and coherence matrix"},{"concepts":[710],"name":"Explain dielectric properties of objects and their effect on radar data acquisition"},{"concepts":[733],"name":"Explain differences between DInSAR and PSI"},{"concepts":[757],"name":"Explain differences between optical and radar remote sensing"},{"concepts":[84],"name":"Explain from which scientific fields GIS&T borrows ideas."},{"concepts":[236],"name":"Explain geocomputation, related concepts and how the two relate"},{"concepts":[6],"name":"Explain how a Bayesian framework can incorporate expert knowledge in order to retrieve all relevant datasets given an initial user query"},{"concepts":[587],"name":"Explain how a business case analysis can be used to justify the expense of implementing consensus-based standards"},{"concepts":[467],"name":"Explain how a DSM differs from a DTM"},{"concepts":[226],"name":"Explain how a graph (network) may be directed or undirected"},{"concepts":[226],"name":"Explain how a graph can be written as an adjacency matrix and how this can be used to calculate topological shortest paths in the graph"},{"concepts":[419],"name":"Explain how a histogram is derived from an EO image"},{"concepts":[552],"name":"Explain how a lack of knowledge about data quality limits the data value"},{"concepts":[10],"name":"Explain how a leading World Wide Web-based routing system works e.g., MapQuest, Yahoo Maps, Google"},{"concepts":[40],"name":"Explain how a semi-variogram describes the distance decay in dependence between data values"},{"concepts":[411],"name":"Explain how a set of overlapping images/satellite scenes can provide digital elevation models used for orthorectification and 3D modelling"},{"concepts":[1194],"name":"Explain how a specific EO technology supports the assessments of disasters and geohazards"},{"concepts":[65],"name":"Explain how a statistic that is based on combining all the spatial data and returning a single summary value or two can be useful in understanding broad spatial trends"},{"concepts":[404],"name":"Explain how a tax assessors office adoption of GIS and T may affect power relations within a community"},{"concepts":[66],"name":"Explain how a weights matrix can be used to convert any classical statistic into a local measure of spatial association"},{"concepts":[78],"name":"Explain how allowing the parameters of the model to vary with the spatial location of the sample data can be used to accommodate spatial heterogeneity"},{"concepts":[56,1],"name":"Explain how analytical methods are used to derive analytical results from geospatial data"},{"concepts":[450],"name":"Explain how band maths can be applied to derive an index that indicates a specific land cover type like vegetation"},{"concepts":[72],"name":"Explain how block-kriging and its variants can be used to combine data sets with different spatial resolution support"},{"concepts":[44],"name":"Explain how buffers can be used in GI analysis"},{"concepts":[208],"name":"Explain how CityGML is related to GML"},{"concepts":[513],"name":"Explain how class modelling can make use of per-parcel analysis"},{"concepts":[484],"name":"Explain how CNNs are structured to derive information from image data"},{"concepts":[391],"name":"Explain how community organizations represent the interests of citizens, politicians, and specialists"},{"concepts":[466],"name":"Explain how computer vision imitates the human visual system when interpreting EO images"},{"concepts":[378],"name":"Explain how conversion of land records data from analog to digital form increases risk to personal privacy"},{"concepts":[378],"name":"Explain how data aggregation is used to protect personal privacy in data produced by the U.S. Census Bureau"},{"concepts":[36],"name":"Explain how different measures of distance can be used to calculate the spatial weights matrix"},{"concepts":[64],"name":"Explain how different types of spatial weights matrices are defined and calculated"},{"concepts":[77],"name":"Explain how dissolving clusters of blocks with similar values may resolve the spatial correlation problem"},{"concepts":[49],"name":"Explain how distance-based methods of point pattern measurement can be derived from a distance matrix"},{"concepts":[52],"name":"Explain how dynamic, chaotic, complex or unpredictable aspects in some phenomena make spatial interaction models more appropriate than gravity models"},{"concepts":[438],"name":"Explain how EO applications targeting several countries at once can profit from data harmonisation"},{"concepts":[456],"name":"Explain how error propagates in the production workflow of an example EO product"},{"concepts":[409],"name":"Explain how fourier transformation is used to generate radar image"},{"concepts":[409],"name":"Explain how fourier transformation is used to reduce noise in optical imagery"},{"concepts":[36],"name":"Explain how fractal dimension can be used in practical applications of GIS"},{"concepts":[60],"name":"Explain how friction surfaces are enhanced by the use of impedance and barriers"},{"concepts":[390],"name":"Explain how geographic information is valuable to different sectors"},{"concepts":[66],"name":"Explain how geographically weighted regression provides a local measure of spatial association"},{"concepts":[322],"name":"Explain how geometric accuracies associated with the various orders of the U.S. horizontal geodetic control network are assured"},{"concepts":[379],"name":"Explain how geospatial information might be used in a taking of private property through a government's claim of its right of eminent domain"},{"concepts":[386],"name":"Explain how geospatial information might be used in a taking of private property through a governments claim of its right of eminent domain"},{"concepts":[567],"name":"Explain how GIS and T can be an integrating technology"},{"concepts":[15],"name":"Explain how graph theory plays a role in network analysis."},{"concepts":[212],"name":"Explain how grid representations embody the field-based view"},{"concepts":[405],"name":"Explain how image processing and analysis methods are used to derive geospatial information from Earth observation imagery"},{"concepts":[149],"name":"Explain how interactivity influences map use"},{"concepts":[636],"name":"Explain how it is possible to retrieve atmospheric temperature and trace gases profiles form multi/iper spectral radiances"},{"concepts":[1277],"name":"Explain how JSON (GeoJSON)`s \"schema-less\"structure may be transformed into an application schema"},{"concepts":[102],"name":"Explain how linguistics play a role in GI science."},{"concepts":[497],"name":"Explain how local density gradients are employed in mean-shift segmentation"},{"concepts":[32],"name":"Explain how logic theory relates to set theory"},{"concepts":[159],"name":"Explain how maps such as topographic maps are produced within certain relations of power and knowledge"},{"concepts":[146],"name":"Explain how maps that show the landscape in profile can be used to represent terrain"},{"concepts":[313],"name":"Explain how metadata, standards and data infrastructures are linked to each other"},{"concepts":[425],"name":"Explain how minimum noise fraction makes use of principal components analysis for dimensionality reduction"},{"concepts":[592],"name":"Explain how next-generation SDIs are different from current SDIs"},{"concepts":[529],"name":"Explain how OGC standards can be used for sharing spatial data (including Earth Observation data) across different communities and computing infrastructures"},{"concepts":[396],"name":"Explain how one or more obligations in the GIS Code of Ethics may conflict with organizations proprietary interests"},{"concepts":[232],"name":"Explain how one would establish the criteria for monitoring the periodic changes in a real GIS database"},{"concepts":[561],"name":"Explain how online processing can enhance the functionality of a web viewer for EO data"},{"concepts":[16],"name":"Explain how optimization models can be used to generate models of alternate options for presentation to decision makers"},{"concepts":[67],"name":"Explain how outliers affect the results of analyses"},{"concepts":[606],"name":"Explain how Planck function and Wien law can help to characterize blackbodies' emission"},{"concepts":[49],"name":"Explain how proximity polygons e.g., Thiessen polygons may be used to describe point patterns"},{"concepts":[218],"name":"Explain how quadtrees and other hierarchical tessellations can be used to index large volumes of raster or vector data"},{"concepts":[757],"name":"Explain how radar images are used for specific applications"},{"concepts":[136],"name":"Explain how real-world connotations (e.g., blue=water, white=snow) can be used to determine color selections on maps"},{"concepts":[43],"name":"Explain how reclassification can be used for data simplification and measurement scale change"},{"concepts":[27],"name":"Explain how Representation transformations are related to spatial data quality."},{"concepts":[324],"name":"Explain how resampling affects the resolution of image data"},{"concepts":[587],"name":"Explain how resistance to change affects the adoption of standards in an organization coordinating a GIS"},{"concepts":[58],"name":"Explain how ridgelines and streamlines can be used to improve the result of an interpolation process"},{"concepts":[1180],"name":"Explain how sea surface temperature maps are used to predict El Nino events"},{"concepts":[32],"name":"Explain how set theory relates to spatial queries"},{"concepts":[523],"name":"Explain how SIFT algorithms can be used for enhancing orthorectification"},{"concepts":[61],"name":"Explain 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industry"},{"concepts":[50],"name":"Explain why and how density estimation transforms point data into a field representation"},{"concepts":[29],"name":"Explain why areal generalization is more difficult than line simplification"},{"concepts":[57],"name":"Explain why different interpolation algorithms produce different results and suggest ways by which these can be evaluated in the context of a specific problem"},{"concepts":[36],"name":"Explain why estimating the fractal dimension of a sinuous line has important implications for the measurement of its length"},{"concepts":[113],"name":"Explain why general-purpose regions rarely exist"},{"concepts":[46,47],"name":"Explain why georegistration is a precondition to map algebra"},{"concepts":[13],"name":"Explain why heuristic solutions are generally used to address the combinatorially complex nature of these problems and the difficulty of solving them optimally"},{"concepts":[525],"name":"Explain why image understanding goes beyond feature 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quality of geospatial data"},{"concepts":[477],"name":"Explain why multimodal distributions in training samples should be avoided when using the maximum likelihood classifier"},{"concepts":[610],"name":"Explain why passive EO sensors with the highest spectral or spatial resolution operate in the VIS/NIR spectral region"},{"concepts":[450],"name":"Explain why radiometric correction is a key requirement for deriving indices with band maths"},{"concepts":[540],"name":"Explain why rapid mapping applications have high requirements in timely availability of Earth observation products"},{"concepts":[598],"name":"Explain why software products sold by U.S. companies may predominate in foreign markets, including Europe and Australia"},{"concepts":[741],"name":"Explain why spatial resolution of passive radar system is much lower than that of active systems"},{"concepts":[569],"name":"Explain why the definition of user roles is an important element in the implementation of a GIS"},{"concepts":[695],"name":"Explain why the Doppler effect is important in radar remote sensing"},{"concepts":[583],"name":"Explain why the legal framework on geospatial data sharing can be considered as diverse and complex"},{"concepts":[583],"name":"Explain why the legal framework on geospatial data sharing consists of two main types of legislation from a data perspective"},{"concepts":[45],"name":"Explain why the process \"dissolve and merge\" often follows vector overlay operations"},{"concepts":[61],"name":"Explain why the properties of spatial continuity are characteristic of spatial surfaces"},{"concepts":[38],"name":"Explain why the shape of an object might be important in analysis"},{"concepts":[304],"name":"Explain why the shape of the Earth is complex and complicated to measure"},{"concepts":[1191],"name":"Explain why the use of multiple EO sensors for mapping landslides associated with one triggering event increases the completeness of a landslide inventory"},{"concepts":[119],"name":"Explain why Toblers First Law of Geography is fundamental to many operations in GIS and whether it should be"},{"concepts":[61],"name":"Explain why zero slopes are indicative of surface specific points such as peaks, pits and passes and list the conditions necessary for each"},{"concepts":[12],"name":"Explain why, if supply equals demand, there will always be a feasible solution to the Classic Transportation Problem"},{"concepts":[50],"name":"Explain why, in some cases, an adaptive bandwidth might be employed"},{"concepts":[325],"name":"Explain, in general terms, the difference between single and double precision and impacts on error propagation"},{"concepts":[16],"name":"Explain, using the concept of combinatorial complexity, why some location problems are very hard to solve"},{"concepts":[88],"name":"Explore the contribution of linguistics to the study of spatial cognition and the role of natural language in the conceptualization of geographic phenomena"},{"concepts":[92],"name":"Explore the history of geography including (but not limited to) Greek and Roman contributions to geography (Eratosthenes, Strabo, Ptolemy), geography and cartography in the age of discovery, military geography, and geography..."},{"concepts":[76],"name":"Find a best model"},{"concepts":[147],"name":"Find a multivariate outlier using a combination of maps and graphs"},{"concepts":[38],"name":"Find centroids of polygons under different definitions of a centroid and different polygon shapes"},{"concepts":[1207],"name":"Find oil spills in EO data for Ocean surveillance"},{"concepts":[577],"name":"Find or create training resources appropriate for GIS and T workforce in a local government organization"},{"concepts":[112],"name":"Find spatial patterns in the distribution of geographic phenomena using geographic visualization and other techniques"},{"concepts":[1166,1164],"name":"Forecast and monitor ocean winds and waves"},{"concepts":[106],"name":"Formalize attribute values and domains in terms of set theory"},{"concepts":[109],"name":"Formalize the notion of field using mathematical functions and Calculus"},{"concepts":[45],"name":"Formalize the operation called map overlay using Boolean logic"},{"concepts":[407],"name":"Generate a layer stack from bands of various EO data sources"},{"concepts":[436],"name":"Generate fine-scale images at a high temporal resolution with a spatio-temporal image fusion method"},{"concepts":[455],"name":"Generate high quality time series by removing clouds and cloud shadows from the available images"},{"concepts":[224],"name":"Give and explain an example of an application models"},{"concepts":[583],"name":"Give examples of more general types of legislation that are also applicable and relevant to geospatial data sharing"},{"concepts":[1214],"name":"Having in-depth knowledge of two of the three Copernicus-relevant topics: Land monitoring, Emergency response including Humanitarian action, and Climate change"},{"concepts":[112],"name":"Hypothesize the causes of a pattern in the spatial distribution of a phenomenon"},{"concepts":[51],"name":"Identify a clustering method which does not require the number of clusters as input"},{"concepts":[30],"name":"Identify a variety of likely measurement level transformations (e.g., the classification of ratio data yields ordinal data)"},{"concepts":[1215],"name":"Identify adequate preprocessing for deriving ocean colour from EO data"},{"concepts":[247],"name":"Identify agent-based modelling principles and methodologies"},{"concepts":[398],"name":"Identify alternatives to the \"algorithmic way of thinking\" that characterizes use of geospatial Information."},{"concepts":[402],"name":"Identify alternatives to the algorithmic way of thinking that characterizes GIS"},{"concepts":[236],"name":"Identify and compare the scenarios on which geocomputation methods are relevant"},{"concepts":[71],"name":"Identify and define the parameters of a semi-variogram range, sill, nugget"},{"concepts":[504],"name":"Identify and discuss an example of a combined filtering process"},{"concepts":[586],"name":"Identify and discuss the different components of an SDI"},{"concepts":[414],"name":"Identify and explain an equation used to perform image-to-image registration"},{"concepts":[414],"name":"Identify and explain an equation used to perform image-to-map registration"},{"concepts":[420],"name":"Identify and explain methods of image enhancement"},{"concepts":[384],"name":"Identify and explain the different actors and their roles in the geo-information value chain"},{"concepts":[455],"name":"Identify anomalies by means of surface properties such as evapotranspiration (ET) or land surface temperature (LST) derived from satellite image time series"},{"concepts":[109],"name":"Identify applications and phenomena that are not adequately modeled by the field view"},{"concepts":[1158,1155,1176,1187,1157,1186],"name":"Identify border incursions or maritime movements"},{"concepts":[1275],"name":"Identify building blocks of Javascript programming language"},{"concepts":[246],"name":"Identify cellular automata principles and pattern"},{"concepts":[101],"name":"Identify common-sense views of geographic phenomena that sharply contrast with established theories and technologies of geographic information"},{"concepts":[240],"name":"Identify commonalities and patterns of geocomputation"},{"concepts":[597],"name":"Identify conferences that are related to GIS and T hosted by professional organizations"},{"concepts":[1235],"name":"Identify construction sites"},{"concepts":[543],"name":"Identify critical design decisions that make an EO-derived map readable"},{"concepts":[172],"name":"Identify data center platform tier configuration and identify platform selection for each tier"},{"concepts":[1249],"name":"Identify design issues of SOAP web services; fine grained and coarse grained services, design patterns"},{"concepts":[1277],"name":"Identify differences, advantages and disadvantages of web application framework based and portal framework based web applications from the geospatial data perspective"},{"concepts":[65],"name":"Identify different measures of spatial autocorrelation"},{"concepts":[66],"name":"Identify different measures of spatial autocorrelation"},{"concepts":[476],"name":"Identify different methods that employ conditional probability for image classification"},{"concepts":[300],"name":"Identify different options where Artificial Intelligence can be integrated in the image processing and analysis workflow"},{"concepts":[427],"name":"Identify different types of noise and associated methods for their reduction"},{"concepts":[109],"name":"Identify examples of discrete and continuous change found in spatial, temporal, and spatio-temporal fields"},{"concepts":[149,139],"name":"Identify examples of static, animated, and interactive web maps"},{"concepts":[134],"name":"Identify gaming elements which may be part of geo-games"},{"concepts":[1202],"name":"Identify geological features"},{"concepts":[1190,1202],"name":"Identify geotectonic shifts"},{"concepts":[1158,1155,1165,1178,1176,1187,1157,1183],"name":"Identify high risk areas produced naturally or by humans"},{"concepts":[437],"name":"Identify image fusion techniques to fill gaps in image time series caused by clouds and cloud shadow"},{"concepts":[1188],"name":"Identify impact of a flood"},{"concepts":[112],"name":"Identify influences of scale on the appearance of distributions"},{"concepts":[1262],"name":"Identify issues in determining the relationships to be represented when publishing Linked Data"},{"concepts":[1261],"name":"Identify issues in developing new ontologies for geospatial data"},{"concepts":[1262],"name":"Identify issues in finding proper ontologies to annotate the data"},{"concepts":[1255],"name":"Identify issues in the development of geospatial ontologies. Criticise the role of ontology development methodologies and ontology evaluation in the development of ontologies"},{"concepts":[1276],"name":"Identify main components and functionality of Leaflet library, describe its main functions and how they are employed"},{"concepts":[1276],"name":"Identify main components and functionality of Openlayers library, describe its main functions and how they are employed"},{"concepts":[1258],"name":"Identify main components of manual metadata creation software tools"},{"concepts":[1276],"name":"Identify main elements and functionality Google maps, describe some of its most popular API operations and how they are employed"},{"concepts":[1276],"name":"Identify main elements and functionality Mapbox, describe some of its most popular API operations and how they are employed"},{"concepts":[1264],"name":"Identify main issues in \"keyword-based\" discovery of data and services"},{"concepts":[1265],"name":"Identify main issues in Semantic discovery"},{"concepts":[174],"name":"Identify major obstacles to the success of a GIS proposal"},{"concepts":[77],"name":"Identify modeling situations where spatial filtering might not be appropriate"},{"concepts":[587],"name":"Identify organizations that focus on developing standards related to GIS and T"},{"concepts":[116],"name":"Identify phenomena that are best understood as networks"},{"concepts":[108],"name":"Identify phenomena that are difficult or impossible to conceptualize in terms of entities"},{"concepts":[517,459],"name":"Identify physical, semantic and spatial properties used to assigned objects to the target classes"},{"concepts":[172],"name":"Identify platform assignment for each workflow software component peak transaction processing load"},{"concepts":[129,174],"name":"Identify potential sources of data (free or commercial) needed for a particular application or enterprise"},{"concepts":[485],"name":"Identify programming languages (like Python, R, and C++) and the main open-source libraries (like OpenCV, PyTorch, TensorFlow, Google Colab, Github, Scikit-learn) that are common for deep learning"},{"concepts":[1162,1160,1175,1184],"name":"Identify rapid response to events associated with health security & care"},{"concepts":[1159,1162,1161,1192,1193],"name":"Identify rapid response to major environmental risk events"},{"concepts":[1213,1212],"name":"Identify sea-ice or icebergs using EO data"},{"concepts":[51],"name":"Identify several cluster detection techniques and discuss their limitations"},{"concepts":[38],"name":"Identify situations in which shape affects geometric operations"},{"concepts":[119],"name":"Identify situations in which Toblers First Law of Geography does not apply"},{"concepts":[119],"name":"Identify situations in which Toblers First Law of Geography is valuable"},{"concepts":[105],"name":"Identify specific examples of categories of entities (i.e., common nouns), properties (i.e., adjectives), space (i.e., regions), and time (i.e., eras)"},{"concepts":[1215],"name":"Identify spectral bands necessary for interpreting ocean colour"},{"concepts":[587],"name":"Identify standards that are used in GIS and T"},{"concepts":[553],"name":"Identify steps of processing on large image collections that benefit from storing them in array databases"},{"concepts":[1257],"name":"Identify the aspects of selecting keywords which would characterize the data properly"},{"concepts":[22],"name":"Identify the conceptual and practical difficulties associated with data model and format conversion"},{"concepts":[22],"name":"Identify the conceptual and practical difficulties associated with data model and format conversion"},{"concepts":[301],"name":"Identify the defining characteristics of an open geocomputation project"},{"concepts":[1271],"name":"Identify the different barriers for the integration of datasets"},{"concepts":[64],"name":"Identify the different methods for constructing spatial weigh matrix"},{"concepts":[83],"name":"Identify the epistemological assumptions underlying the work of colleagues"},{"concepts":[1275],"name":"Identify the extensions HTML5 brings over older HTML versions"},{"concepts":[121],"name":"Identify the hedges used in language to convey vagueness"},{"concepts":[1257],"name":"Identify the issues in mapping between different metadata standards. Also identify the roles of thesauri and crosswalks"},{"concepts":[574],"name":"Identify the key organizational components of a GIS&T implementation"},{"concepts":[113],"name":"Identify the kinds of phenomena that are commonly found at the boundaries of regions"},{"concepts":[377],"name":"Identify the liability implications associated with contracts"},{"concepts":[1268],"name":"Identify the main components of OGC Filter encoding and compare it to SQL"},{"concepts":[1265],"name":"Identify the main concepts of reasoning and architectural components of Reasoners"},{"concepts":[574],"name":"Identify the main organizational challenges in implementing and use GIS&T"},{"concepts":[136],"name":"Identify the most appropriate color palette for a printed map for visually-impaired people"},{"concepts":[136],"name":"Identify the most appropriate color palette for an online map for visually-impaired people"},{"concepts":[483],"name":"Identify the most popular decision tree algorithms"},{"concepts":[212],"name":"Identify the national framework datasets based on a grid model"},{"concepts":[1268],"name":"Identify the need for and main issues in spatial data interchange"},{"concepts":[81],"name":"Identify the ontological assumptions underlying the work of colleagues"},{"concepts":[577],"name":"Identify the particular skills necessary for users to perform tasks in three different workforce domains (e.g., small city, medium county agency, a business, or others)"},{"concepts":[85],"name":"Identify the philosophical views and assumptions underlying the work of colleagues"},{"concepts":[174],"name":"Identify the positions necessary to design and implement a GIS project / GI unit"},{"concepts":[575],"name":"Identify the qualifications needed for a particular GIS and T position"},{"concepts":[1253],"name":"Identify the relation between OWL-S and WSDL and give an overview of Semantic Web service definition in OWL-S"},{"concepts":[57],"name":"Identify the spatial concepts that are assumed in different interpolation algorithms"},{"concepts":[575],"name":"Identify the standard occupational codes that are relevant to GIS and T"},{"concepts":[1260],"name":"Identify the technical aspects that open data paradigm would affect concerning Spatial Data Infrastructures including NSDIs"},{"concepts":[108],"name":"Identify the types of features that need to be modeled in a particular GIS application or procedure"},{"concepts":[239,240],"name":"Identify the types of geography problems geocomputation solves"},{"concepts":[49],"name":"Identify the various ways point patterns may be described"},{"concepts":[175],"name":"Identify the viability of a proprietary GIS application"},{"concepts":[1252],"name":"identify the web services needed for a particular use case"},{"concepts":[172],"name":"Identify user locations, network connectivity, and data center server locations"},{"concepts":[104],"name":"Identify various types of geographic interactions in space and time"},{"concepts":[49],"name":"Identify various types of K-function analysis"},{"concepts":[1253],"name":"Identify virtues of defining a given data set in both RDF and OWL, and compare semantic richness of both definitions"},{"concepts":[1213,1211],"name":"Identify wake trailing to detect ships using EO data"},{"concepts":[1273],"name":"Identify whether Full-automated WSC has still a value in it concerning both where we stand today on the road to 'Semantic Web' and unresolved problems in the area, which are the problems of Artificial Intelligence indeed"},{"concepts":[630],"name":"Illustrate main spectral signatures of clouds and apply them in paractical cloud-detection exercise"},{"concepts":[222],"name":"Illustrate a topological relation"},{"concepts":[391],"name":"Illustrate an example of \"local knowledge\" that is unlikely to be represented in the geospatial data maintained routinely by government agencies"},{"concepts":[670],"name":"Illustrate and apply basic concepts of Atmospheric Physics to EO science and its applications"},{"concepts":[324],"name":"Illustrate and explain the distinction between resolution, precision, and accuracy"},{"concepts":[324],"name":"Illustrate and explain the distinctions between spatial resolution, thematic resolution, and temporal resolution"},{"concepts":[628],"name":"Illustrate basic features of spectral signatures of vegetation, water and bare soil"},{"concepts":[657],"name":"Illustrate basic modern physics theory understanding their implications on the development of advanced sensors for EO"},{"concepts":[619,627],"name":"Illustrate basic radiation-matter interactions and related concepts of spectral reflectance, absorbance and transmittance as specific properties of the matter"},{"concepts":[630],"name":"Illustrate e.m. radiation intercations with/within clouds."},{"concepts":[173],"name":"Illustrate each of the project management areas with an example of a technique or tool used"},{"concepts":[166],"name":"Illustrate how a business process analysis can be used to identify requirements during a GIS implementation"},{"concepts":[139],"name":"Illustrate how an animated map reveals patterns not evident without animation"},{"concepts":[643],"name":"Illustrate how cloud presence complicate radiative transfer description in Earth's atmosphere"},{"concepts":[87],"name":"Illustrate how fields, such as geography, cartography, computer and information science, engineering, mathematics, philosophy, cognitive science, and linguistics have their influence on GI science."},{"concepts":[615],"name":"Illustrate how it is possible to estimate the BRDF of a sample through measurements of BRF"},{"concepts":[613],"name":"Illustrate how the Rayleigh criterion can help to characterize surfaces' scattering properties in relation with their roughness and wavelength of the incident radiation"},{"concepts":[618],"name":"Illustrate how the Voigt's line profile is related to the Doppler and pressure line broadening contributes"},{"concepts":[111],"name":"Illustrate major integrated models of geographic information, such as Peuquets Triad, Mennis Pyramid, and Yuans Three-Domain"},{"concepts":[577],"name":"Illustrate methods that are effective in providing opportunities for education and training when implementing a GIS in a small city"},{"concepts":[642],"name":"Illustrate of the concept of optical path"},{"concepts":[642],"name":"Illustrate of the concept of optical thickness"},{"concepts":[649],"name":"Illustrate possible noise sources related to photovoltaic and photoconductive detectors"},{"concepts":[641],"name":"Illustrate scope and conditions of validity of Schwarzshild equation."},{"concepts":[1262],"name":"Illustrate stages of publishing a relational database as Linked Data"},{"concepts":[665],"name":"Illustrate the interaction of e.m. radiation in the thermal infrared with the atmosphere understanding specifc characteristics of radiative transfer in this specific spectral region."},{"concepts":[675],"name":"Illustrate the concept of \"kinetic temperature\" in absence of thermodynamic equilibrium"},{"concepts":[638],"name":"Illustrate the concept of Absorption Coefficient"},{"concepts":[637],"name":"Illustrate the concept of Cross Section of Extinction per Mass Unit"},{"concepts":[620],"name":"Illustrate the concept of grey body"},{"concepts":[639],"name":"Illustrate the concept of Source Function"},{"concepts":[609],"name":"Illustrate the concept of spectral emissivity and brigthness temperature and compute them in some simple real case"},{"concepts":[627],"name":"Illustrate the concept of spectral signatures of the matter"},{"concepts":[650],"name":"Illustrate the concepts of Interference and Diffraction"},{"concepts":[646],"name":"Illustrate the concepts of Reflection, Refraction and Dispersion of the light"},{"concepts":[602],"name":"Illustrate the concepts of solar constant and daily solar insolation"},{"concepts":[626],"name":"Illustrate the decay of the emittance with the distance from the source"},{"concepts":[141],"name":"Illustrate the elements of the story by proper geovisualizations"},{"concepts":[125],"name":"Illustrate the evolution of Cartography in different periods of time"},{"concepts":[213],"name":"Illustrate the existing methods for compressing gridded data (e.g., run length encoding, Lempel-Ziv, wavelets)"},{"concepts":[685],"name":"Illustrate the factors limiting lifetime of satellites on their originally planned orbits"},{"concepts":[681],"name":"Illustrate the First Law of Thermodynamic"},{"concepts":[635],"name":"Illustrate the general equation of radiative transfer."},{"concepts":[661],"name":"Illustrate the Greenhouse effect associate to CO2 emission"},{"concepts":[653],"name":"Illustrate the Helmotz’s equation"},{"concepts":[215],"name":"Illustrate the hexagonal model"},{"concepts":[676],"name":"Illustrate the ideal gas law"},{"concepts":[217],"name":"Illustrate the impact of grid cell resolution on the information that can be portrayed"},{"concepts":[24],"name":"Illustrate the impact of vector/raster/vector conversions on the quality of a dataset"},{"concepts":[603],"name":"Illustrate the importance of Earth's emitted radiation for EO from space"},{"concepts":[648],"name":"Illustrate the importance of electric conduction in solids for the design and development of advanced EO sensors"},{"concepts":[686],"name":"Illustrate the importance of the choice of the satellite orbit for the design of a satellite mission devoted to specific applications"},{"concepts":[1244,1239,1240,1241,1242,1243],"name":"Illustrate the information of EO data"},{"concepts":[183],"name":"Illustrate the landscape of GIS and related libraries"},{"concepts":[668],"name":"Illustrate the main atmospherical spectral windows"},{"concepts":[632],"name":"Illustrate the main differences among passive and active remote sensing techniques"},{"concepts":[616],"name":"Illustrate the main energetic transictions that can be associated to molecular absorption of e.m. radiation"},{"concepts":[624],"name":"Illustrate the main forms of radiation-matter interaction"},{"concepts":[51],"name":"Illustrate the main use of spatial clustering in earth observation"},{"concepts":[611],"name":"Illustrate the nature of electromagnetic radiation"},{"concepts":[218],"name":"Illustrate the quadtree model"},{"concepts":[241],"name":"Illustrate the relationships between geocomputation with other terms, disciplines and areas of knowledge"},{"concepts":[680],"name":"Illustrate the role of Eulerian and Lagrangian models in budget equations definition"},{"concepts":[652],"name":"Illustrate the role of the principle of constant speed of light within the special relativity theory"},{"concepts":[645],"name":"Illustrate the scope Radiative Transfer theory"},{"concepts":[682],"name":"Illustrate the Second Law of Thermodynamic"},{"concepts":[628],"name":"Illustrate the spectral response curves for basic environmental features (e.g., vegetation, concrete, bare soil)"},{"concepts":[669],"name":"Illustrate the transferring of Energy within the Earth-Atmosphere System"},{"concepts":[151],"name":"Illustrate the use of virtual environments"},{"concepts":[674],"name":"Illustrate the utility of thermodynamic diagrams for the study of local atmospheric properties"},{"concepts":[142],"name":"Illustrate the ways in which maps could be integrated in an infography"},{"concepts":[567],"name":"Illustrate what functions a support or service center can provide to an organization using GIS and T"},{"concepts":[614],"name":"Illustrate why we refer to the BRDF as an absolute definition of spectral reflectance"},{"concepts":[140],"name":"Illustrate with examples of maps or geovisualizations that could be improved by the addition of an audio layer"},{"concepts":[126],"name":"Illustrate with examples the relationship between art and cartography at different historical moments"},{"concepts":[678],"name":"Ilustrate the state function of the condensed gas phase"},{"concepts":[218],"name":"Implement a format for encoding quadtrees in a data file"},{"concepts":[76],"name":"Implement a maximum likelihood estimation procedure for determining key spatial econometric parameters"},{"concepts":[234],"name":"Implement a test of reliability of change information"},{"concepts":[57],"name":"Implement a trend surface analysis using either the supplied function in a GIS or a regression function from any standard statistical package"},{"concepts":[1259],"name":"Implement and configure a catalogue service"},{"concepts":[17],"name":"Implement linear programs for spatial allocation problems"},{"concepts":[12],"name":"Implement the Transportation Simplex method to determine the optimal solution"},{"concepts":[322],"name":"In contrast to the National Map Accuracy Standard, explain how the spatial accuracy of a digital road centerlines data set may be evaluated and documented"},{"concepts":[1261],"name":"Indicate an architecture and tools for organizing semantically annotated data"},{"concepts":[1276],"name":"Indicate an overview of OpenStreetMap and define its general functionality, comment its usage by Web APIs"},{"concepts":[1277],"name":"Indicate generally how \"NSDI-requiring-scenarios\"would be handled by web application framework based applications"},{"concepts":[1275],"name":"Indicate main elements of HTML5"},{"concepts":[1265],"name":"Indicate some examples of semantic discovery; Semantic search engines, highlighting projects and practice concerning GI related applications in the area"},{"concepts":[398],"name":"Indicate the extent to which contemporary use of geospatial information supports diverse ways of understanding the world."},{"concepts":[568],"name":"Indicate the possible justifications that can be used to implement an enterprise GIS"},{"concepts":[245],"name":"Interpret when space-time dynamics can be used to study geographical phenomen"},{"concepts":[172],"name":"Interpret business needs and translate them to IT needs"},{"concepts":[565],"name":"Interpret descriptive statistics and geostatistics of geographic data"},{"concepts":[135,160],"name":"Interpret different symbols and icons in a map"},{"concepts":[1277],"name":"Interpret generally the functionality offered by \"portal frameworks\" land Geoportals like Geonetwork, Opengeoportal, Esri geoportal server, Degree portal, Liferay, Jboss portal"},{"concepts":[1277],"name":"Interpret generally the main components and functionality of \"Web Application Frameworks\" such as AngularJS, Ext.js, Django, Java Server Faces (JSF), and the like"},{"concepts":[1254],"name":"interpret GML data model and GML definition of geometry. GML application schemas and GML documents"},{"concepts":[237],"name":"Interpret how individual parts contained in a complex system relate to each other"},{"concepts":[1233],"name":"Interpret information from EO products or EO time series"},{"concepts":[1145],"name":"Interpret land cover change detection"},{"concepts":[1148],"name":"Interpret location based services (LBS)"},{"concepts":[1215],"name":"Interpret ocean colour for deriving chlorophyll concentration in water"},{"concepts":[5],"name":"Interpret patterns in space and time using Dorling and Openshaws Geographical Analysis Machine GAM demonstration of disease incidence diffusion"},{"concepts":[1244,1239,1240,1241,1242,1243],"name":"Interpret the content of EO data"},{"concepts":[510],"name":"Interpret the effect of a convolution from a given mask and contained weights"},{"concepts":[211],"name":"Interpret the header of a standard raster data file"},{"concepts":[125],"name":"Interpret the impact of paper-based and web maps in their context"},{"concepts":[1219],"name":"Interpret the output of an point cloud measurement"},{"concepts":[1181],"name":"Interpret the output of numerical prediction models"},{"concepts":[73],"name":"Interpret the results of universal kriging"},{"concepts":[172],"name":"Interpret user needs as an input for the design process"},{"concepts":[92],"name":"Justify a chosen position on which disciplines should have as important a role in GIS AND T as geography"},{"concepts":[176],"name":"Justify feasibility recommendations to decision-makers"},{"concepts":[108],"name":"Justify or refute the conception of fields (e.g., temperature, density) as spatially-intensive attributes of (sometimes amorphous and anonymous) entities"},{"concepts":[92],"name":"Justify or refute whether geography (as a discipline) should have a central role in GIS AND T"},{"concepts":[97],"name":"Justify the discrepancies between the nature of locations in the real world and representations thereof (e.g., towns as points)"},{"concepts":[83],"name":"Justify the epistemological frameworks with which you agree"},{"concepts":[81],"name":"Justify the metaphysical theories with which you agree"},{"concepts":[63],"name":"Justify the stochastic process approach to spatial statistical analysis"},{"concepts":[65],"name":"Justify, compute, and test the significance of the join count statistic for a pattern of objects"},{"concepts":[612],"name":"Knowledge of the basic (selective) mechanism of the absorption/emission of electromagnetic radiation by atoms."},{"concepts":[70],"name":"List and describe several spatial sampling schemes and evaluate each one for specific applications"},{"concepts":[599],"name":"List and describe the main categories of organizations in the GIS&T domain"},{"concepts":[594],"name":"List and describe the most important producers and users of geospatial data at the European Commission"},{"concepts":[386],"name":"List and describe the types of data maintained by local, state, and federal governments"},{"concepts":[566],"name":"List and explain relevant organizational and institutional aspects related to GIS&T."},{"concepts":[375],"name":"List and explain the different societal aspects that are important in dealing with geospatial information"},{"concepts":[308],"name":"List and explain the key requirements for geolocating data to earth"},{"concepts":[226],"name":"List definitions of networks that apply to specific applications or industries"},{"concepts":[475],"name":"List different types of features that can be used for multispectral image classification"},{"concepts":[41],"name":"List different ways connectivity can be determined in a raster and in a polygon dataset"},{"concepts":[39],"name":"List reasons why the area of a polygon calculated in a GIS might not be the same as the real world object it describes"},{"concepts":[13],"name":"List several classic problems to which network analysis is applied e.g., The Traveling Salesman Problem, The Chinese Postman 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quality"},{"concepts":[503],"name":"List the main segmentation methods used to group similar pixels into homogeneous objects"},{"concepts":[158],"name":"List the main variables to take into account during the planning phase of a map"},{"concepts":[133],"name":"List the major factors that should be considered in preparing a map"},{"concepts":[173],"name":"List the phases of a project management life cycle"},{"concepts":[71],"name":"List the possible sources of error in a selected and fitted model of an experimental semi-variogram"},{"concepts":[118],"name":"List the possible topological relationships between entities in space (e.g., 9-intersection) and time"},{"concepts":[136],"name":"List the range of factors that should be considered in selecting colors"},{"concepts":[63],"name":"List the two basic assumptions of the purely random process"},{"concepts":[14],"name":"List ways we can define accessibility on a network"},{"concepts":[132],"name":"List which data considerations should be 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T"},{"concepts":[379],"name":"Outline the arguments for and against the notion of information as a public good"},{"concepts":[72],"name":"Outline the basic kriging equations in their matrix formulation"},{"concepts":[49],"name":"Outline the basis of classic critiques of spatial statistical analysis in the context of point pattern analysis"},{"concepts":[237],"name":"Outline the complex problems where geocomputation is relevant"},{"concepts":[40],"name":"Outline the geometry implicit in classical gravity models of distance decay"},{"concepts":[4],"name":"Outline the implications of complexity for the application of statistical ideas in geography"},{"concepts":[36],"name":"Outline the implications of differences in distance calculations on real world applications of GIS, such as routing and determining boundary lengths and service areas"},{"concepts":[138],"name":"Outline the importance of photographs or imagery either from satellites or at street level"},{"concepts":[50],"name":"Outline the likely effects on analysis results of variations in the kernel function used and the bandwidth adopted"},{"concepts":[63],"name":"Outline the logic behind the derivation of long run expected outcomes of the independent random process using quadrat counts"},{"concepts":[45],"name":"Outline the possible sources of error in overlay operations"},{"concepts":[329],"name":"Outline the process of scanning and vectorizing features depicted on a printed map sheet using a given GIS software product, emphasizing issues that require manual intervention"},{"concepts":[181],"name":"Outline the Reference Model of Open Distributed Processing framework"},{"concepts":[241],"name":"Outline the role of computational science in geocomputation"},{"concepts":[323],"name":"Outline the SDTS and ISO TC211 standards for thematic accuracy"},{"concepts":[309],"name":"Outline the sequence of tasks involved in generating an orthoimage from a vertical aerial photograph"},{"concepts":[2],"name":"Outline the 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the processes of spectral calculations of radiometric quantities by the line by line radiative transfer models"},{"concepts":[235],"name":"Produce viable queries for change scenarios using GIS or database management tools"},{"concepts":[522],"name":"Produce zero-crossing maps for a DoG-filtered optical EO image"},{"concepts":[128],"name":"Propose a holistic historical perspective of maps creation and use"},{"concepts":[396],"name":"Propose a resolution to a conflict between an obligation in the GIS Code of Ethics and organizations proprietary interests"},{"concepts":[378],"name":"Propose and design solutions for dealing with particular data privacy and data security issues"},{"concepts":[377],"name":"Propose strategies for managing liability risk, including disclaimers and data quality standards"},{"concepts":[144],"name":"Propose thematic mapping methods for mapping numerical data"},{"concepts":[316],"name":"Provide examples of cases in which crouwdsourcing is the most effective data 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indexes, and hierarchies"},{"concepts":[716],"name":"Recognize different types of surface roughness on a radar image"},{"concepts":[122],"name":"Recognize expressions of uncertainty in language"},{"concepts":[106],"name":"Recognize situations and phenomena in the landscape which cannot be adequately represented by formal attributes, such as aesthetics"},{"concepts":[162],"name":"Recognize spatial schemes like patterns and shapes"},{"concepts":[565],"name":"Recognize the assumptions underlying probability and geostatistics and the situations in which they are useful analytical tools"},{"concepts":[81],"name":"Recognize the commonalities of philosophical viewpoints and appreciate differences to enable work with diverse colleagues"},{"concepts":[188],"name":"Recognize the constraints and opportunities of a particular choice of software for implementing a physical model"},{"concepts":[95],"name":"Recognize the constraints that political forces place on geospatial applications in public and private sectors"},{"concepts":[118],"name":"Recognize the contributions of Topology (the branch of mathematics) to the study of geographic relationships"},{"concepts":[122],"name":"Recognize the degree to which the importance of uncertainty depends on scale and application"},{"concepts":[121],"name":"Recognize the degree to which vagueness depends on scale"},{"concepts":[94],"name":"Recognize the impact of ones social background on ones own geographic worldview and perceptions and how it influences ones use of GIS"},{"concepts":[530],"name":"Recognize the importance of reproducible research as a fundamental pillar of modern science"},{"concepts":[83],"name":"Recognize the influences of epistemology on GIS practices"},{"concepts":[109],"name":"Recognize the influences of scale on the perception and meaning of fields"},{"concepts":[382],"name":"Recognize the relevant legal issues in a particular case of geospatial data collection, use and/of sharing"},{"concepts":[103],"name":"Recognize the role that time plays in static GISystems"},{"concepts":[115],"name":"Recommend for what applications we should use a field or an object-base approach."},{"concepts":[105],"name":"Reconcile differing common-sense and official definitions of common geospatial categories of entities, attributes, space, and time"},{"concepts":[1232],"name":"Relate EO measurements with detected features"},{"concepts":[91],"name":"Relate epistemology to spatial knowledge."},{"concepts":[53],"name":"Relate plots of multidimensional attribute data to geography by equating similarity in data space with proximity in geographical space"},{"concepts":[217],"name":"Relate the concept of grid cell resolution to the more general concept of support and granularity"},{"concepts":[109],"name":"Relate the notion of field in GIS to the mathematical notions of scalar and vector fields"},{"concepts":[124],"name":"Relate the science and technology of graphical representation of geographic data"},{"concepts":[479],"name":"Relate the spatial and spectral characteristics of EO data to the types and proportions of materials found within the scene and within pixel IFOVs to relabel spectral classes as information classes of a classification scheme"},{"concepts":[135],"name":"Relate the spatial dimension and the weight of mapped features with the attributes they represent"},{"concepts":[662],"name":"Relate to the aspects of radiation transfer through the atmosphere."},{"concepts":[1262],"name":"Relate with manual and automated methods linking data"},{"concepts":[166],"name":"Report existing and potential tasks in terms of workflow and information flow"},{"concepts":[162],"name":"Represent an object or a scene from different viewpoints"},{"concepts":[116],"name":"Represent structural relationships in GIS data"},{"concepts":[25],"name":"Resample multiple raster data sets to a single resolution to enable overlay"},{"concepts":[25],"name":"Resample raster data sets (e.g., terrain, 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examples of image description files used in Earth Observation"},{"concepts":[34],"name":"State questions that can be solved by selecting features based on location or spatial relationships"},{"concepts":[322],"name":"State the approximate number and spacing of control points in each order of the horizontal geodetic control network"},{"concepts":[600],"name":"State the basic physical principles for EO systems design and data analysis"},{"concepts":[52],"name":"State the classic formalization of the interaction model"},{"concepts":[322],"name":"State the geometric accuracies associated with the various orders of the U.S. horizontal geodetic control network"},{"concepts":[697],"name":"State the microwave portion of the electromagnetic spectrum"},{"concepts":[604],"name":"State the names of the most important regions of the electromagnetic spectrum"},{"concepts":[604],"name":"State the names of the regions of the electromagnetic spectrum most important for Earth's remote 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imagery into radiometrically/atmospherically corrected state"},{"concepts":[25],"name":"Understand and examine the common methods for raster resampling"},{"concepts":[382],"name":"Understand and explain the main legal issues related to the production, use and sharing of geospatial data and information"},{"concepts":[198],"name":"Understand and use XML"},{"concepts":[422],"name":"Understand atmospheric parameters that influence bottom of atmosphere (BOA) reflectance"},{"concepts":[241],"name":"Understand complexity in the broadest sense"},{"concepts":[68],"name":"Understand different estimation methods for Bayesian models"},{"concepts":[237],"name":"Understand how complex systems operate"},{"concepts":[431],"name":"Understand how data augmentation can improve deep learning methods for image classification"},{"concepts":[1252],"name":"understand how different web services complement each other"},{"concepts":[1207],"name":"Understand how EO data can be used to monitor the marine ecosystem"},{"concepts":[240],"name":"Understand how geocomputation relates to other similar terms"},{"concepts":[160],"name":"Understand how graphic representations can be interpreted distinctively by culturally different audiences"},{"concepts":[541],"name":"Understand how limited temporal completness affects the usefulness of a time series analysis"},{"concepts":[160],"name":"Understand how map scale is used to provide the relationship of size of object on a map and its real-world size"},{"concepts":[244],"name":"Understand how models are translated into differential equations for execution"},{"concepts":[243],"name":"Understand how models can be specified into logical rules"},{"concepts":[1181],"name":"Understand how numerical prediction models work"},{"concepts":[539],"name":"Understand how positional/geometric accuracy of a dataset affects subsequent analysis"},{"concepts":[539,538],"name":"Understand how root mean squared error (RMSE) at tie points represents local spatial accuracy and enables calculation of total RMSE that informs about the average spatial accuracy of the entire image"},{"concepts":[455],"name":"Understand how satellite image time series can be used for mapping, trend analysis and change detection"},{"concepts":[464],"name":"Understand how the entropy represents the the average level of information contained in an image pixel"},{"concepts":[154],"name":"Understand how the representation of geographic data facilitates visual communication"},{"concepts":[236],"name":"Understand how the theoretical roots and experimental emphasis on geocomputation are integrated"},{"concepts":[1224],"name":"Understand how the tracking of moving objects is implemented"},{"concepts":[165],"name":"Understand spatial data models and structures"},{"concepts":[305],"name":"Understand spatial reference systems and apply them to an EO dataset"},{"concepts":[423],"name":"Understand sun, sun angle, and sensor parameters that influence top of atmosphere (TOA) reflectance"},{"concepts":[160],"name":"Understand that features have been omitted or generalized for clarity"},{"concepts":[483],"name":"Understand the advantages and shortcomings of decision trees"},{"concepts":[237],"name":"Understand the all-encompassing concepts of complexity"},{"concepts":[65],"name":"Understand the assumption under which spatial autocorrelation may occur"},{"concepts":[66],"name":"Understand the assumption under which spatial autocorrelation may occur"},{"concepts":[381],"name":"Understand the benefits of publishing and using open data"},{"concepts":[489],"name":"Understand the challenge in matching sensory image data to a mental model of the world-scene"},{"concepts":[242],"name":"Understand the defining characteristics of simulation models, and their applicability"},{"concepts":[186],"name":"Understand the degree to which attributes need to be conceptually modeled"},{"concepts":[633],"name":"Understand the difference between Inherent Optical Properties (IOP) and Apparent Optical Properties (AOP) of water"},{"concepts":[549],"name":"Understand the difficulties in searching and selecting satellite images with sufficient spatial coverage for time series analysis"},{"concepts":[1191],"name":"Understand the diverse set of EO technologies that are capable of mapping different landslide aspects"},{"concepts":[1132,1173,1195,1185],"name":"Understand the health of the crop, extent of infestation or stress damage, or potential yield and soil conditions"},{"concepts":[1133,1210],"name":"Understand the health of the fishing grounds"},{"concepts":[1134,1196],"name":"Understand the health of the forests"},{"concepts":[1275],"name":"Understand the importance of Cascading Style Sheets (CSS) to separate content from style in HMTL documents"},{"concepts":[539],"name":"Understand the importance of using spatially independent validation samples to assess the quality of the classification results"},{"concepts":[430],"name":"Understand the main factors generating geometric distortions of the remotely sensed images"},{"concepts":[169],"name":"Understand the main software engineering methodologies"},{"concepts":[377],"name":"Understand the nature of tort law generally and nuisance law specifically"},{"concepts":[104],"name":"Understand the physical notions of velocity and acceleration which are fundamentally about movement across space through time"},{"concepts":[530],"name":"Understand the problems associated with the lack of reproducibility"},{"concepts":[542],"name":"Understand the relevance of topological consistency for linear network features derived from Earth observation data"},{"concepts":[452],"name":"Understand the role of multi-temporal satellite images for identifying not only when a change occurred but also the changing drivers"},{"concepts":[483],"name":"Understand the role of pruning for reducing overfitting when applying decision trees for various classification purposes"},{"concepts":[559],"name":"Understand the strategic meaning of DIAS in the user segment of Copernicus"},{"concepts":[462],"name":"Understand the subjectivity of the visual interpretation"},{"concepts":[1219],"name":"Understand the technology behind LiDAR as an active sensor and what makes it different from the other existing Remote Sensing approaches"},{"concepts":[483],"name":"Understand the types of decision trees and their output"},{"concepts":[63],"name":"Understand the underlying assumptions for spatial stochastics process"},{"concepts":[454],"name":"Understand the way in which Dynamic Time Warping can align shifted temporal sequences"},{"concepts":[22],"name":"Understand various formats of storing raster and vector data"},{"concepts":[227],"name":"Understand vector data models"},{"concepts":[1219],"name":"Understand what products can be extracted from point clouds"},{"concepts":[1264],"name":"Use \"Full-text-based\" discovery; open source and commercial search engines, its use in GI related applications"},{"concepts":[1238,1236],"name":"Use 3D textured models to present study area"},{"concepts":[557],"name":"Use a web portal to retrieve EO data"},{"concepts":[558],"name":"Use an image archive to retrive Earth observation data for an application"},{"concepts":[146],"name":"Use appropriate interpolation techniques to derive DEMs from point data"},{"concepts":[105],"name":"Use categorical information in analysis, cartography, and other GIS processes, avoiding common interpretation mistakes"},{"concepts":[1203],"name":"Use EO products to assess land areas, its ecosystems, and its evolution"},{"concepts":[1194],"name":"Use EO products to assess the risk of a disaster"},{"concepts":[1182,1180],"name":"Use EO products to conduct forecasts and projections"},{"concepts":[1181],"name":"Use EO products to conduct numerical simulations"},{"concepts":[1179],"name":"Use EO products to forecast sunlight exposure"},{"concepts":[1194],"name":"Use EO products to measure impact and/or recovery"},{"concepts":[1194],"name":"Use EO products to monitor disaster prone areas"},{"concepts":[1203],"name":"Use EO products to plan land areas, its ecosystems, and its evolution"},{"concepts":[1131],"name":"Use EO/GI information to plan and design projects, monitor and assess the environment, support decision-making processes, and to tackle environmental challenges"},{"concepts":[113],"name":"Use established analysis methods that are based on the concept of region (e.g., landscape ecology)"},{"concepts":[114],"name":"Use established analysis methods that are based on the concept of spatial integration (e.g., overlay)"},{"concepts":[470],"name":"Use filtering techniques to spatially aggregate an image classification"},{"concepts":[414],"name":"Use GIS software to transform a given dataset to a specified coordinate system, projection, and datum"},{"concepts":[119],"name":"Use methods that analyze metrical relationships"},{"concepts":[118],"name":"Use methods that analyze topological relationships"},{"concepts":[1266],"name":"Use Natural language based discovery over linked data"},{"concepts":[1217],"name":"Use NDVI to estimate the vegetation cover"},{"concepts":[1260],"name":"Use open data APIs that enable the usage of Open data; identify design aspects and usage scenarios"},{"concepts":[461],"name":"Use photo interpretation keys to interpret features on aerial photographs"},{"concepts":[530],"name":"Use software tools to automate the practice of reproducible research in daily work"},{"concepts":[206],"name":"Use standards such as ISO 19141 Schema for moving features, ISO 19142 Web Feature Service and ISO 19109 - Rules for application schema"},{"concepts":[586],"name":"Use the models of ‘SDI generations’ and ‘SDI components’ to describe the main elements of an existing SDI initiative"},{"concepts":[573],"name":"Use the most effective change model depending on the nature and needs of the client's organization."},{"concepts":[1253],"name":"Use Web services description for RESTful web services, Web Application Description Language (WADL) and its use"},{"concepts":[462],"name":"Using a vertical aerial image, produce a map of land use/land cover classes"},{"concepts":[195],"name":"Work with different data compression techniques"},{"concepts":[40],"name":"Write a program to create a matrix of pair-wise distances among a set of points"},{"concepts":[211],"name":"Write a program to read and write a raster data file"},{"concepts":[40],"name":"Write typical forms for distance decay functions"},{"concepts":[11],"name":"xplain how the concept of capacity represents an upper limit on the amount of flow through the network"}],"updateDate":"2024/12/18","version":"8"},"v9":{"concepts":[{"code":"GIST","description":"Geographic Information Science and Technology","hasParent":true,"name":"Geographic Information Science and Technology"},{"code":"AM","description":"This knowledge area encompasses a wide variety of operations whose objective is to derive analytical results from geospatial data. Data analysis seeks to understand both first-order (environmental) effects and second-order (interaction) effects. Approaches that are both data-driven (exploration of geospatial data) and model-driven (testing hypotheses and creating models) are included. Data driven techniques derive summary descriptions of data, evoke insights about characteristics of data, contribute to the development of research hypotheses, and lead to the derivation of analytical results. The goal of model driven analysis is to create and test geospatial process models. In general, model-driven analysis is an advanced knowledge area where previous experience with exploratory spatial data analysis would constitute a desired prerequisite. Visual tools for data analysis are covered in Knowledge Area: Cartography and Visualization (CV) and many of the fundamental principles required to ground data analysis techniques are introduced in Knowledge Area: Conceptual Foundations (CF). Image processing techniques are considered in Knowledge Area: Geospatial Data (GD). All of the methods described in this knowledge area are more or less sensitive to data error and uncertainty as covered in Unit GC8 Uncertainty and Unit GD6 Data quality. Mastery of the educational objectives outlined in this knowledge area requires knowledge and skills in mathematics, statistics, and computer programming.","hasChildren":true,"hasParent":true,"name":"Analytical Methods","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM1-2","description":"Analytical capabilities of a GIS make use of spatial and non-spatial (attribute) data to answer questions and solve problems that are of spatial relevance. We now make a distinction between analysis (or analytical operations) and analytical models (often referred to as “modelling”). And by analysis we actually mean only a subset of what is usually implied by the term: we do not specifically deal with advanced statistical analysis (such as cluster detection or geostatistics).\r\n\r\nAnalysis of spatial data can be defined as computing new information to provide new insights from existing spatial data. Consider an example from the domain of road construction. In mountainous areas, this is a complex engineering task with many cost factors, including the number of tunnels and bridges to be constructed, the total length of the tarmac, and the volume of rock and soil to be moved. GISs can help to compute such costs on the basis of an up-to-date digital elevation model and a soil map. The exact nature of the analysis will depend on the application requirements, but computations and analytical functions can operate on both spatial and non-spatial data.","hasChildren":true,"name":"Analytical approaches","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM1","description":"Geospatial data analysis has foundations in many different disciplines. As a result, there are many different schools of thought or analytical approaches including spatial analysis, spatial modeling, geostatistics, spatial econometrics, spatial statistics, qualitative analysis, map algebra, and network analysis. This unit compares and contrasts these approaches.","hasChildren":true,"hasParent":true,"name":"Foundations of analytical methods","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM10-1","description":"Difficulties in dealing with large spatial databases, especially those arising from spatial heterogeneity and data quality issues.","hasChildren":true,"name":"Problems of large spatial databases","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM10-2","description":"Data mining knows a variety of approaches, such as cluster analysis, analytical reasoning, association, prediction, etc.","hasChildren":true,"name":"Data mining approaches","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM10-3","description":"Knowledge discovery involves the identification of useful patterns in spatial databases using techniques of data mining, trend analysis, etc.","hasChildren":true,"name":"Knowledge discovery","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM10","description":"Algorithms have been developed to scan and search through extremely large data sets in order to find patterns within the data. These data mining and knowledge discovery techniques have been expanded to the spatial case. Legal and ethical concerns associated with such practices are considered in Knowledge Areas GS GIS and T and Society and OI Organizational and Institutional Aspects.","hasChildren":true,"hasParent":true,"name":"Data mining","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM11-1","description":"A network is a connected set of lines representing some geographic phenomenon, typically to do with transportation. The “goods” transported can be almost anything: people, cars and other vehicles along a road network, commercial goods along a logistic network, phone calls along a telephone network, or water pollution along a stream/river network.\r\n\r\nDirect vs. Non-directed Networks\r\nA fundamental characteristic of any network is whether the network lines are considered to be directed or not. Directed networks associate with each line a direction of transportation; undirected networks do not. In the latter, the “goods” can be transported along a line in both directions. We discuss here vector network analysis, and assume that the network is a set of connected line features that intersect only at the lines’ nodes, not at internal vertices. (But we do mention under- and overpasses.)\r\n\r\nPlanar vs. Non-Planar Networks\r\nFor many applications of network analysis, a planar network, i.e. one that can be embedded in a two-dimensional plane, will do the job. Many networks are naturally planar, such as stream/river networks. A large-scale traffic network, on the other hand, is not planar: motorways have multi-level crossings and are constructed with underpasses and overpasses. Planar networks are easier to deal with computationally, as they have simpler topological rules. Not all GISs accommodate non-planar networks, or they can only do so using “tricks”. These tricks may involve the splitting of overpassing lines at the intersection vertex and the creation of four lines from the two original lines. Without further attention, the network will then allow one to make a turn onto another line at this new intersection node, which in reality would be impossible. In some GISs we can allocate a cost for turning at a node—see our discussion on turning costs below—and that cost, in the case of the overpass trick, can be made infinite to ensure it is prohibited. But, as mentioned, this is a work around to fit a non-planar situation into a data layer that presumes planarity. The above is a good illustration of geometry not fully determining the network’s behaviour. Additional application-specific rules are usually required to define what can and cannot happen in the network. Most GISs provide rule-based tools that allow the definition of these extra application rules.","hasChildren":true,"name":"Networks defined","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM11-2","description":"Identifying and listing all elements does not describe a system in full. There may be many different ways in which elements may be connected or related to each other. The interactions, relationships between elements are essential to describe a system.\r\n\r\nRelationships between elements can be described by two types of flows:\r\nflows of material, and flows of information.\r\n\r\nMaterial flows connect elements between which there is an exchange of some substance. This can be some kind of material (water, food, cement, biomass, etc.), energy (light, heat, electricity, etc.), money, etc. It is something that can be measured and tracked. Also if an element is a donor of this substance the amount of substance in this element will decrease as a result of the exchange, while at the same time the amount of this substance will increase in the receptor element. There is always a mass, or energy conservation law in place. Nothing appears from nothing, and nothing can disappear to nowhere.\r\n\r\nThe second type of exchange is with an information flow. In this case element A gets information from element B. Element B at the same time may have no information about element A. Even when element A gets information about B, B does not lose anything. Information can be about the state of an element, about the quantity that it contains, about its presence or absence, etc. Information flows can be used to describe rules and policies. Information flows can modify the rates of flow between elements, they can switch certain processes and interactions on and off. But the process through which policies, interventions and norms for action are established, and could for example define the values of such information flows, are themselves the result of social interaction between relevant stakeholders from public, private or civil society.\r\n\r\nThe simplest is to acknowledge the existence of a relationship between certain elements, like this is done in a graph. In a graph a node presents an element and a link between any two nodes shows that these two elements are related. However there is no evidence of the direction of the relationship: we do not distinguish between the element x influencing element y or vice versa. This relationship can be further specified by an oriented graph that shows the direction of the relationship between elements. An element can be also connected to itself, to show that its behaviour depends on its state. We can further detail the description by identifying whether element x has a positive or negative effect on element y.\r\n\r\nWith networks, interesting questions arise that have to do with connectivity and network capacity. These relate to applications such as traffic monitoring and watershed management. With network elements—i.e. the lines that make up the network—extra values are commonly associated, such as distance, quality of the link or the carrying capacity.","hasChildren":true,"name":"Graph theoretic descriptive measures of networks","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM11-3","description":"Optimal-path finding techniques are used when a least-cost path between two nodes in a network must be found. The two nodes are called origin and destination. The aim is to find a sequence of connected lines to traverse from the origin to the destination at the lowest possible cost.\r\n\r\nIn Optimal-path finding, the cost function can be simple: for instance, it can be defined as the total length of all lines of the path. The cost function can also be more elaborate and take into account not only length of the lines but also their capacity, maximum transmission (travel) rate and other line characteristics, for instance to obtain a reasonable approximation of travel time. There can even be cases in which the nodes visited add to the cost of the path as well. These may be called turning costs, which are defined in a separate turning-cost table for each node, indicating the cost of turning at the node when entering from one line and continuing on another. This is illustrated in Figure 1 of the examples.\r\n\r\nProblems related to optimal-path finding may require ordered optimal path finding or unordered optimal-path finding. Both have as an extra requirement that a number of additional nodes need to be visited along the path. In ordered optimal-path finding, the sequence in which these extra nodes are visited matters; in unordered optimal-path finding it does not.","hasChildren":true,"name":"Least-cost shortest path","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM11-4","description":"There are phenomena that do not spread in all directions, but move or “flows” along a given, least-cost path, determined by characteristics of local terrain. The typical case arises when we want to determine drainage patterns in a catchment area: rain water “chooses” a way to leave the area. \r\n\r\nWe can illustrate the principles involved in this typical case with a simple elevation raster. For each cell in that raster, the steepest downward slope to a neighbour cell is computed and its direction is stored in a new raster. This computation determines the elevation difference between the cell and the neighbour cell and it takes into account cell distance - 1 for neighbour cells in N–S or W–E direction, 2 for cells in a NE–SW or NW–SE direction. From among its eight neighbour cells, it picks the one with the steepest path to it. The directions thus obtained in an output raster are encoded in integer values, which can be called the flow-direction raster. From this raster, the GIS can compute the accumulated flow-count raster, a raster that for each cell indicates how many cells have their water flow into that cell.\r\n\r\nCells with a high accumulated flow count represent areas of concentrated flow and may, thus, belong to a stream. By using some appropriately chosen threshold value in a map algebra expression, we may decide whether they do or not. Cells with an accumulated flow count of zero are local topographic highs and can be used to identify ridges.","hasChildren":true,"name":"Flow modeling","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM11-5","description":"The Classic Transportation Problem considers minimizing the cost of getting an object or subject from origin to destination.","hasChildren":true,"name":"The Classic Transportation Problem","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM11-6","description":"Classic network problems are examples of networking problems such as the Traveling Salesman Problem and the Chinese Postman Problem that need graph algorithms to be solved.","hasChildren":true,"name":"Other classic network problems","selfAssesment":"GI-N2K
"},{"code":"AM11-7","description":"Accessibility is the extend in which it is difficult/easy to reach a location or object.","hasChildren":true,"name":"Accessibility modeling","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM11","description":"Network analysis encompasses a wide range of procedures, techniques, and methods that allow for the examination of phenomena that can be modeled in the form of connected sets of edges and vertices. Such sets are termed a network or a graph, and the mathematical basis for network analysis is known as graph theory. Graph theory contains descriptive measures and indices of networks such as connectivity, adjacency, capacity, and flow as well as methods for proving the properties of networks. Networks have long been recognized as an efficient way to model many types of geographic data, including transportation networks, river networks, and utility networks electric, cable, sewer and water, etc. to name just a few. The data structures to support network analysis are covered in [DM4-7] Network models.","hasChildren":true,"hasParent":true,"name":"Network analysis","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM12-1","description":"The modeling of problems in a formal language, working in a solution space and applying constraints.","hasChildren":true,"name":"Operations research modeling and location modeling principles","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM12-2","description":"A formal programming method to support operational research in which linear constraints are applied.","hasChildren":true,"name":"Linear programming","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM12-3","description":"A formal programming method to support operational research in which variables are constrained to integers.","hasChildren":true,"name":"Integer programming","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM12-4","description":"Location-allocation modeling involves the determination of locations by minimizing the distance between object/subjects in space, such as between customers and facilities.","hasChildren":true,"name":"Location-allocation modeling and p-median problems","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM12","description":"A wide variety of optimization techniques are now solvable within the GIS and T domain. Operations research is a branch of mathematics practiced in the allied fields of business and engineering. New models and software tools allow for the solution of transportation routing, facility location, and a host of other location-allocation modeling problems.","hasChildren":true,"hasParent":true,"name":"Optimization and location-allocation modeling","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM13-1","description":"The effects such as the loss of data quality and data integrity that are the results of data transformations.","hasChildren":true,"name":"Impacts of transformations","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM13-2","description":"A data model is an abstract model that organizes elements of data and standardizes how they relate to one another and to the properties of real-world entities. The term data model can refer to two distinct but closely related concepts. In relation to the field of geoinformation the term data model refers to the set of concepts used in defining such formalizations as entities, attributes, relations, tables which is implemented by a mathematical construct for representing geographic objects or surfaces as data. There are two most frequently used data models, which are vector and raster. For example, the vector data model represents geography as collections of points, lines and polygons and more complex structures crated from these three. The raster data model represent geography as cell matrices that store numeric values. Among these two data models we also stand out data formats in which data sets can be stored. File format is a standard of encoding geographical information into a computer file. There are the following basic file formats for encoding data:\r\nFor vectors:\r\n-\tShapefile\r\n-\tGeography Markup Language (GML)\r\n-\tXYZ Point Cloud\r\n-\tGeoJSON\r\n-\tGeoMedia\r\n-\t\r\nFor rasters:\r\n-\tGeoTIFF\r\n-\tIMG\r\n-\tJPEG2000\r\n-\tEsri grid\r\nThe GIS projects often require the conversion of the data formats. Data conversion is the process of moving data from one format to another, whether it is from one data model to another or from one data format to another. Data conversion is a complex process which is not only associated with changing the binary format of the file but also requires changing the structure of the data. For example, the GML data format always comes with an UML diagram, which is necessary to convert attributes stored in GML structure for example to a table of contest in a shapefile data format. In a well-managed GIS project it is important to store data in specific data model or data format. It is sometimes dictated by software capabilities and another times by team’s technical capabilities. With large amounts of geographic data used in the project it is more cost-effective to convert the data from one format to another than re-create it.","hasChildren":true,"name":"Data model and format conversion","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM13-3","description":"Interpolation is used to create a GIS layer out of point observations on a continuous variable. The reason for doing this could be manifold: for visualization purposes, for making a proper reference with other data, or for making a combination of different layers.","hasChildren":true,"hasParent":true,"name":"Interpolation","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM13-4","description":"Any vector data containing point, polyline, polygon can be converted into the raster dataset and vice versa. The vector data can be stored in shapefiles, databases or various others GIS file formats. The raster data are made of pixels or grid calls and can be represented by the discrete - categorical data (e.g. land cover map) or non-discrete - continuous data (e.g. satellite images, surface data). The process of conversion of vector to raster data is called rasterization. The vector to raster conversion requires the following parameters: the field value from the attribute table used to assign values to the output raster, the pixel size for the output raster, the output raster format (i.e. geotiff, img) and optionally the method of assigning values of point, polyline or polygon to the call raster, i.e. maximum length or area, cell centre. The output of the rasterised vector looks like a gridded version of the vector and it depends on the grid cell size. The process of vectorisation refers to the conversion of raster to vector dataset. The raster dataset can be converted to vector point, polyline or polygon. In order to convert raster to vector the following parameters should be provided: attribute field of the input raster dataset which will become an attribute in the output vector class, determining if the output polygon or polyline will be smoothed into simpler shapes or conform to the input raster's cell edges (stair stepping). For each raster pixel or grid cell a point will be created at the centre of the cell. The non-discrete continuous raster data have to converted to the categorical data type before converting to vector data. The conversion of vector to raster and raster to vector degrade the data to some extent causing loss of details, accuracy, and changing the original data.","hasChildren":true,"name":"Vector-to-raster and raster-to-vector conversions","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM13-5","description":"Raster resampling refers to change of spatial resolution (increasing or decreasing) of the raster dataset. The resampling process calculates the new pixel values from the original digital pixel values in the uncorrected image. There are three common methods for resampling: nearest neighbour, bilinear interpolation, and cubic convolution. The nearest neighbour resampling uses the digital value from the pixel in the original image which is nearest to the new pixel location in the corrected image. This is the fastest interpolation method, which is primarily applied for discrete (categorical) raster data as it does not change the value of the pixel, but may result in some pixel values being duplicated while others are lost. Bilinear interpolation resampling takes a weighted average of four pixels in the original image nearest to the new pixel location. The averaging process alters the original pixel values and creates entirely new digital values in the output image. It is recommended for continuous data and it cause some smoothing of the data. Cubic convolution resampling is based on calculation of a distance weighted average of a block of sixteen pixels from the original image which surround the new output pixel location. As with bilinear interpolation, this method results in completely new pixel values. However, the last two methods both produce images which have a much sharper appearance and avoid the blocky appearance of the nearest neighbour method. The disadvantage of the Cubic method is that its requires more processing time.","hasChildren":true,"name":"Raster resampling","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM13-6","description":"Users of geoinformation often need transformations from a particular 2D coordinate system to another system. This includes the transformation of polar coordinates into Cartesian map coordinates, or the change of map projection - transformation from one 2D Cartesian (x, y) system of a specific map projection into another 2D Cartesian (x′, y′) system of a defined map projection. This transformation is based on relating the two systems on the basis of a set of selected points whose coordinates are known in both systems, such as ground control points or common points such as corners of houses or road intersections. Image and scanned data are usually transformed by this method. The transformations may be conformal, affine, polynomial or of another type, depending on the geometric errors in the data set. A datum transformation involves the change of the horizontal datum which is often accompanied with a change of map projection.","hasChildren":true,"name":"Coordinate transformations","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM13","description":"GIS is a cyclical rather than a linear system, unlike computer aided drafting (CAD) and computer assisted cartographic systems. Changes in projection, grid systems, data forms, and formats take place during the modeling process for which GIS was designed. Many non-analytical manipulations are necessary to accommodate the analytical power of the GIS. The manipulations of spatial and spatio-temporal data involve two general classes of operation: 1.\tTheir transformation into formats that facilitate subsequent analysis 2. Generalization and aggregation that affect the accuracy and integrity of the data used for analysis (see [AM14]). Other knowledge areas have identified different forms of data structures, data models, projections, and other forms of geospatial data representation. These differences present both opportunities and challenges for analysis and modeling. The ability to transform one representation to another, in a manner that maintains the integrity of the information as much as possible, can enhance the analysis and visualization of geospatial data. The raster and vector data models are described in [DM3] Tesselation data models and [DM4] Vector data model, Feature based modelling, Applications. The principles of coordinate systems, datums, and projections are also considered in Knowledge Area [GD] Geospatial Data","hasChildren":true,"hasParent":true,"name":"Representation transformation","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM14-1","description":"In the practice of spatial data handling, one often comes across questions like “What is the resolution of the data?” or “At what scale is your data set?” Now that we have moved firmly into the digital age, these questions sometimes defy an easy answer. Map scale can be defined as the ratio between the distance on a printed map and the distance of the same stretch in the terrain.\r\n\r\nA 1:50,000 scale map means that 1 cm on the map represents 50,000 cm (i.e. 500 m) in the terrain. “Large-scale” means that the ratio is relatively large, so typically it means there is much detail to see, as on a 1:1000 printed map. “Small-scale”, in contrast, means a small ratio, hence less detail, as on a 1:2,500,000 printed map.\r\nDigital spatial data, as stored in a GIS, are essentially without scale: scale is a ratio notion associated with visual output, such as a map or on-screen display, not with the data that was used to produce the map or display. When digital spatial data sets have been collected with a specific map-making purpose in mind, and all maps have been designed to use one single map scale, for instance 1:25,000, we may assume that the data carries the characteristic of “a 1:25,000 digital data set.”\r\n\r\nThere is a relationship between the effectiveness of a map for a given purpose and the map’s scale. The Public Works department of a city council cannot use a 1:250,000 map for replacing broken sewer pipes, and the map of Figure 1 cannot be reproduced at scale 1:10,000.\r\n\r\nMaps that show much detail of a small area are called large-scale maps. Scale indications on maps can be given verbally, such as “one-inch-to the- mile”, or as a representative fraction like 1:200,000,000 (1 cm on the map equals 200,000,000 cm (or 2000 km) in reality), or by a graphic representation such as the scale bar. The advantage of using scale bars in digital environments is that its length also changes when the map is zoomed in, or enlarged, before printing. Sometimes it is necessary to convert maps from one scale to another, which may lead to problems of cartographic generalization.\r\n\r\nSpatial and temporal scales can not only be attached to processes, but also to observations. An example is given below, which summarizes the spatial and temporal scales of a few well-known Earth observation systems.\r\n\r\nScales of RS observations\r\nSensor Spatial scale\t Temporal scale\r\nMeteosat\t Hemisphere\t 15 minutes\r\nNOAA-AVHRR\t 3000 km\t daily\r\nLandsat TM\t 180 km\t 16 days\r\nSpot\t 60 km\t 26 days (pointable)","hasChildren":true,"name":"Scale and generalization","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM14-2","description":"Techniques that support the generalisation of map content when changing to smaller map scales. These include line simplification, object selection, etc.","hasChildren":true,"name":"Approaches to point, line, and area generalization","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM14-3","description":"Classification is a technique for purposely removing detail from an input data set in the hope of revealing important patterns (of spatial distribution). In the process, we produce an output data set, so that the input set can be left intact. This output set is produced by assigning a characteristic value to each element in the input set, which is usually a collection of spatial features that could be raster cells or points, lines or polygons. If the number of characteristic values in the output set is small in comparison to the size of the input set, we have classified the input set.\r\n\r\nThe input data set may, itself, have been the result of a classification. In such cases we refer to the output data set as a reclassification. For example, we may have a soil map that shows different soil type units and we would like to show the suitability of units for a specific crop. In this case, it is better to assign to the soil units an attribute of suitability for the crop. Since different soil types may have the same crop suitability, a classification may merge soil units of different type into the same category of crop suitability.\r\n\r\nIn classification of vector data, there are two possible results. In the first, the input features may become the output features in a new data layer, with an additional category assigned. In other words, nothing changes with respect to the spatial extents of the original features. Figure a of Examples illustrates this first type of output. A second type of output is obtained when adjacent features of the same category are merged into one bigger feature. Such a post-processing function is called spatial merging, aggregation or dissolving. An illustration of this second type is found in Figure b of Examples. Observe that this type of merging is only an option in vector data, as merging cells in an output raster on the basis of a classification makes little sense. Vector data classification can be performed on point sets, line sets or polygon sets; the optional merge phase only makes sense for lines and polygons.\r\n\r\nUser-controlled classifications require a classification table or user interaction. GIS software can also perform automatic classification, in which a user only specifies the number of classes in the output data set. The system automatically determines the class break points. The two main techniques of determining break points being used are the equal interval technique and the equal frequency technique.\r\n\r\nEqual Interval Technique\r\nThe minimum and maximum values vmin and vmax of the classification parameter are determined and the (constant) interval size for each category is calculated as (vmax - vmin) ∕ n, where n is the number of classes chosen by the user. This classification is useful in that it reveals the distribution pattern, as it determines the number of features in each category.\r\n\r\nEqual Frequency Technique\r\nThis technique is also known as quantile classification. The objective is to create categories with roughly equal numbers of features per category. The total number of features is determined first, then, based on the required number of categories, the number of features per category is calculated. The class break points are then determined by counting off the features in order of classification parameter value.","hasChildren":true,"name":"Classification and transformation of attribute measurement levels","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM14","description":"Generalization addresses the meaningful reduction of the map content during scale reduction. All geospatial data are generalized. Even the most detailed data represent only subsets of reality. Furthermore, data are further generalized for purposes of mapping, visualization, and efficient storage. A variety of generalization techniques have been developed to facilitate this process. All are scale dependent. Aggregation is one form of generalization that transforms large numbers of individual objects into summarized groups. This concept description is concerned with the nature of these procedures and their implications for professional practice. Generalization is an important part of cartography (and is therefore discussed conceptually in CV2 Data considerations), but is also a transformation common to many GIS procedures.","hasChildren":true,"hasParent":true,"name":"Generalization and aggregation","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM2-1","description":"Set theory is based on describing collections of members within sets. The Boolean membership function is binary, i.e. an element is either a member of the set (membership is true) or it is not a member of the set (membership is false). Such a membership notion is well-suited to the description of spatial features such as land parcels for which no ambiguity is involved and an individual ground truth sample can be judged to be either correct or incorrect. As Burrough and Frank (1996) note, increasingly, people are beginning to realize that the fundamental axioms of simple binary logic present limits to the way we think about the world. Not only in everyday situations, but also in formalized thought, it is necessary to be able to deal with concepts that are not necessarily true or false, but that operate somewhere in between. Since its original development by Zadeh (1965), there has been considerable discussion of fuzzy, or continuous, set theory as an approach for handling imprecise spatial data. In GIS, fuzzy set theory appears to have two particular benefits: the ability to handle logical modelling (map overlay) operations on inexact data; and the possibility of using a variety of natural language expressions to qualify uncertainty. Unlike Boolean sets, fuzzy or continuous sets have a membership function, which can assign to a member any value between 0 and 1.","hasChildren":true,"hasParent":true,"name":"Set theory","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM2-2","description":"The most common operator for defining queries in a relational database is the language SQL, which stands for Structured Query Language.\r\n\r\nA spatial DBMS provides support for geographic coordinate systems and transformations. It will also provide storage of the relationships between features, including the creation and storage of topological relationships. As a result, one is able to use functions for “spatial query” (exploring spatial relationships). To illustrate, a spatial query using SQL to find all the Thai restaurants within 2 km of a given hotel would look like:\r\n\r\nSELECT R.Name\r\nFROM Restaurants AS R,\r\nHotels as H\r\nWHERE R.Type = Thai AND\r\nH.name = Hilton AND\r\nIntersect(R.Geometry, Buffer(H.Geometry, 2))\r\n\r\nThe Intersect command creates a spatial join between restaurants and hotels. The Geometry column carries the spatial data. It is likely that in the near future all spatial data will be stored directly in spatial databases.","hasChildren":true,"name":"Structured Query Language (SQL) and attribute queries","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM2-3","description":"When exploring a spatial data set, the first thing one usually wants to do is select certain features, to (temporarily) restrict the exploration. Such selections can be made on geometric/spatial grounds or on the basis of attribute data associated with the spatial features. \r\n\r\nSelection conditions on attribute values can be combined using logical connectives such as AND, OR and NOT. Other techniques of selecting features can also usually be combined. Any set of selected features can be used as the input for a subsequent selection procedure. This means, for instance, that we can select all medical clinics first, then identify roads within 200 m of them, then select from those only the major roads, then select the nearest clinics to these remaining roads as the ones that should receive our financial support for maintenance. In this way, we are combining various techniques of selection.\r\n\r\nInteractive Spatial Selection\r\nIn interactive spatial selection, one defines the selection condition by pointing at or drawing spatial objects on the screen display, after having indicated the spatial data layer(s) from which to select features. The interactively defined objects are called the selection objects; they can be points, lines, or polygons. The GIS then selects the features in the indicated data layer(s) that overlap (i.e. intersect, meet, contain, or are contained in;) with the selection objects. These become the selected objects.\r\nInteractive spatial selection answers questions like “What is at …?”\r\n\r\nA spatial DBMS provides support for geographic coordinate systems and transformations. It will also provide storage of the relationships between features, including the creation and storage of topological relationships. As a result, one is able to use functions for “spatial query” (exploring spatial relationships). To illustrate, a spatial query using SQL to find all the Thai restaurants within 2 km of a given hotel would look like:\r\n\r\nSELECT R.Name\r\nFROM Restaurants AS R,\r\nHotels as H\r\nWHERE R.Type = Thai AND\r\nH.name = Hilton AND\r\nIntersect(R.Geometry, Buffer(H.Geometry, 2))\r\n\r\nThe Intersect command creates a spatial join between restaurants and hotels. The Geometry column carries the spatial data. It is likely that in the near future all spatial data will be stored directly in spatial databases.","hasChildren":true,"name":"Spatial queries","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM2","description":"Attribute and spatial query operations are core functionality in any GIS and they are often considered to be the most basic form of analysis.","hasChildren":true,"hasParent":true,"name":"Query operations and query languages","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM3-1","description":"In a 2D polar coordinate system points can be described with coordinates. Another way of defining a point in a plane is by using polar coordinates. This is the distance d from the origin to the point concerned and the angle α between a fixed (or zero) direction and the direction to the point. The angle α is called azimuth or bearing and is measured in a clockwise direction. It is given in angular units while the distance d is expressed in length units. \r\n\r\nDistance also plays a role in computations on networks, comprising a different set of analytical functions in GISs. Here, the network may consist of roads, public transport routes, high-voltage power lines, or other forms of transportation infrastructure. Analysis of networks may entail shortest path computations (in terms of distance or travel time) between two points in a network for routing purposes. Other forms are to find all points reachable within a given distance or duration from a start point for allocation purposes, or determination of the capacity of the network for transportation between an indicated source location and sink location.\r\n\r\nIn raster images, the distance function applied is the Pythagorean distance between the cell centres. The distance from a non-target cell to the target is the minimal distance one can find between that non-target cell and any target cell.","hasChildren":true,"name":"Distances and lengths","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM3-2","description":"In a 2D polar coordinate system points can be described with coordinates. Another way of defining a point in a plane is by using polar coordinates. This is the distance d from the origin to the point concerned and the angle α between a fixed (or zero) direction and the direction to the point. The angle α is called azimuth or bearing and is measured in a clockwise direction. It is given in angular units while the distance d is expressed in length units.\r\n\r\nBearings are always related to a fixed direction (initial bearing) or a datum line. In principle, this reference line can be chosen freely. Three different, widely used fixed directions are: True North, Grid North and Magnetic North. The corresponding bearings are true (or geodetic) bearings, grid bearings and magnetic (or compass) bearings, respectively.\r\n\r\nIn raster images, direction is determined by the orientation of the neighboring pixels.","hasChildren":true,"name":"Direction","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM3-3","description":"The representation of geographic objects is most naturally supported with vectors. After all, objects are identified by the parameters of location, shape, size and orientation, and many of these parameters can be expressed in terms of vectors. We can define features within the topological space that are easy to handle and that can be used as representations of geographic objects. These features are called simplices as they are the simplest geometric shapes of some dimension: point (0-simplex), line segment (1-simplex), triangle (2-simplex), and tetrahedron (3-simplex). When we combine various simplices into a single feature, we obtain a simplicial complex. When area objects are stored using a vector approach, the usual technique is to apply a boundary model. This means that each area feature is represented by some arc/node structure that determines a polygon as the area’s boundary. A polygon representation for an area object is another example of a finite approximation of a phenomenon that may have a curvilinear boundary in reality. In images, the shape of objects often helps us to identify them (built-up areas, roads and railroads, agricultural fields, etc.).","hasChildren":true,"name":"Shape","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM3-4","description":"When area objects are stored using a vector approach, the usual technique is to apply a boundary model. This means that each area feature is represented by some arc/node structure that determines a polygon as the area’s boundary. A polygon representation for an area object is another example of a finite approximation of a phenomenon that may have a curvilinear boundary in reality.\r\nCommon sense dictates that area features of the same kind are best stored in a single data layer, represented by mutually non-overlapping polygons. This results in an application-determined (i.e. adaptive) partition of space. If the object has a fuzzy boundary, a polygon is an even worse approximation, even though potentially it may be the only one possible. Clearly, we expect additional data to accompany the area data. Such information could be stored in database tables.\r\n\r\nA simple but naïve representation of area features would be to list for each polygon the list of lines that describes its boundary. Each line in the list would, as before, be a sequence that starts with a node and ends with one, possibly with vertices in between. As the same line makes up the boundary from the two polygons, this line would be stored twice in the above representation, namely once for each polygon. This is a form of data duplication—known as data redundancy—which is (at least in theory) unnecessary, although it remains a feature of some systems. Another disadvantage of such polygon-by-polygon representations is that if we want to identify the polygons that border the bottom left polygon, we have to do a complicated and time-consuming search analysis comparing the vertex lists of all boundary lines with that of the bottom left polygon. For just a few polygons, this is fine, but in a data set with 5000 polygons, and perhaps a total of 25,000 boundary lines, this becomes a tedious task, even with the fastest of computers.","hasChildren":true,"name":"Area","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM3-5","description":"Proximity computations are specific neighbourhood functions. They evaluate the characteristics of an area surrounding a feature’s location. A neighbourhood function “scans” the neighbourhood of the given feature(s), and performs a computation on it (them).\r\n\r\nExamples of proximity computations are: (1) Buffer zone generation (or buffering) is one of the best-known neighbourhood functions. It determines a spatial envelope (buffer) around a given feature or features. The buffer created may have a fixed width or a variable width that depends on characteristics of the area. (2) Thiessen Polygon generation.\r\n\r\nDistance decay functions describe the effect of the reduced influence when the distance between two locations increases.","hasChildren":true,"name":"Proximity and distance decay","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM3-6","description":"Adjacency is the meet relationship as a topological property of a geographic object in relation ship with another. The adjacency operator identifies those features that share boundaries and, therefore, applies only to line and polygon features.\r\nThis meet relationship is invariant under a continuous transformation and are referred to as a topological mapping.","hasChildren":true,"name":"Adjacency and connectivity","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM3","description":"For simple data exploration, GIS offers many basic geometric operations that help in extracting meaning from sets of data or for deriving new data for further analysis. Concepts on which these operations are based are addressed in Domains of geographic information and Relationships.\r\n\r\nWe can, for instance, measure angles on a map and use these for navigation in the real world, or for setting out a designed physical infrastructure. Or if, instead of a conformal projection such as UTM, we use an equivalent projection, we can determine the size of a parcel of land from the map—irrespective of where the parcel is on the map and at which elevation it is on the Earth.","hasChildren":true,"hasParent":true,"name":"Geometric measures","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM4-1","description":"The reclassifications tools are used to change or reclassify the values. Reclassification of vector data involves the attributes of features in the feature attribute table, on the other hand reclassification of raster data involves the grid cell values to produce a new raster data layer. Reclassification can be used for data simplification and measurement scale change. We can adjust the data for more appropriate analysis by grouping the values and changing them. The reclassification tool can also be used to remove specific values from analysis.\r\nThe Select by location tool lets you select features by how they relate to other features in another layer. Selected features are based on their location. You can select features that are near or overlap the features. Most frequently used methods are intersect, within a distance, within, completely within, contain… Features can be selected in the same or other layers.\r\nThe Select by attributes tool lets you select features that match the selection criteria. With providing a selection criteria, matching features are selected. We can provide a complex selection criteria.","hasChildren":true,"name":"Reclassification and selection operations","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM4-2","description":"Buffer analysis is one form of basic spatial analysis. It takes the vector representation (point, line, or polygon) of a real-world feature, and then creates a buffer zone based on a defined distance from the feature’s border. Thus, the created buffer zone is an area whose boundary always has the same distance to the input vector feature, e.g. the buffer zone for a point feature is a circle. Real-world examples for buffer zones could be protected areas along rivers or around nature conservation areas, or represent a simple proximity analysis. In the latter case, the buffer analysis is usually the first step of the analysis, followed by an overlay of the buffer zone with the target features to find those target features within the buffer zone, and thus within a certain distance of the original feature. Usually, the buffer zone extends outwards from the feature, but polygons can also have inner buffer zones. If the buffer zones from multiple features overlap, the analyst can decide to leave the individual boundaries of the buffer zones intact, or to dissolve them, i.e. merging the overlapping buffer zones into one larger buffer zone. The size of the buffer zone, i.e. the distance of its boundary from the original feature’s boundary, can be based on an uniform numerical value and associated spatial unit, but often, it is based on an attribute value (numerical or class) of the feature. Conceptually, buffering using raster representations of real-world features is similar a proximity analysis with a regular grid of square polygons: Departing from raster cells that form the area to be buffered, all raster cells that fall within the designated distance (overlay) from the buffer zone. With buffer analysis being a basic analytical operation, practically every GIS and many other analysis tools provide this functionality.","hasChildren":true,"name":"Buffers","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM4-3","description":"Overlay functions is one of the most frequently used functions in a GIS application. They combine two (or more) spatial data layers, comparing them position by position and treating areas of overlap - and of non-overlap - in distinct ways.\r\n\r\nStandard overlay operators take two input data layers and assume that they are georeferenced in the same system and that they overlap in the study area. If either of these requirements is not met, the use of an overlay operator is pointless. The principle of spatial overlay is to compare the characteristics of the same location in both data layers and to produce a result for each location in the output data layer. The specific result to produce is determined by the user. It might involve a calculation or some other logical function to be applied to every area or location. With raster data, as we shall see, these comparisons are carried out between pairs of cells, one from each input raster. With vector data, the same principle of comparing locations applies but the underlying computations rely on determining the spatial intersections of features from each input layer.\r\n\r\nVector overlay operators are useful but geometrically complicated, and this sometimes results in poor operator performance. Raster overlays do not suffer from this disadvantage, as most of them perform their computations cell by cell, and thus they are fast. GISs that support raster processing - as most do - usually have a language to express operations on rasters. These languages are generally referred to as map algebra or, sometimes, raster calculus. They allow a GIS to compute new rasters from existing ones, using a range of functions and operators. Unfortunately, not all implementations of map algebra offer the same functionality.","hasChildren":true,"name":"Overlay","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM4-4","description":"Neighbourhood functions evaluate the characteristics of an area surrounding a feature’s location. A neighbourhood function “scans” the neighbourhood of the given feature(s), and performs a computation on it (them). Examples of proximity computations are: (1) Buffer zone generation (or buffering) is one of the best-known neighbourhood functions. It determines a spatial envelope (buffer) around a given feature or features. The buffer created may have a fixed width or a variable width that depends on characteristics of the area. (2) Thiessen Polygon generation. For raster images: (3) Computation of diffusion (4) Flow computation.\r\n\r\nFor instance, our target might be a medical clinic. Its neighbourhood could be defined as:\r\n\r\nan area within a radius of 2 km distance as the crow flies; or\r\nan area within 2 km travelling distance; or\r\nall roads within 500 m travelling distance; or\r\nall other clinics within 10 minutes travelling time;\r\nall residential areas for which the clinic is the closest clinic.\r\n\r\nFinally, in the third step we indicate what it is we want to discover about the phenomena that exist or occur in the neighbourhood. This might simply be its spatial extent, but it might also be statistical information such as:\r\n\r\nhow many people live in the area;\r\nwhat is their average household income;\r\nare any high-risk industries located in the neighbourhood.\r\n\r\nThese are typical questions in an urban setting. When our interest is more in natural phenomena, different examples of locations, neighbourhoods and neighbourhood characteristics arise.\r\n\r\nThe principle in this case is to find out the characteristics of the vicinity, here called neighbourhood, of a location. After all, many suitability questions, for instance, depend not only on what is at a location but also on what is near the location. Thus, the GIS must allow us “to look around locally”. To perform neighbourhood analysis, we must:\r\n\r\n1. state which target locations are of interest to us and define their spatial extent;\r\n2. define how to determine the neighbourhood for each target; and\r\n3. define which characteristic(s) must be computed for each neighbourhood. \r\n\r\nSince raster data are the more commonly used in this case, neighbourhood characteristics often are obtained via statistical summary functions that compute values such as the average, minimum, maximum and standard deviation of the cells in the identified neighbourhood.\r\n\r\nTo select target locations, one can use the selection techniques. To obtain characteristics from an eventually-to-be identified neighbourhood, the same techniques apply. So what remains to be discussed here is the proper determination of a neighbourhood. One way of determining a neighbourhood around a target location is by making use of the geometric distance function. Geometric distance does not take into account direction, but certain phenomena can only be studied by doing so. Think of the spreading of pollution by rivers, groundwater flow or prevailing weather systems.\r\n\r\nDiffusion functions are based on the assumption that the phenomenon in question spreads in all directions, though not necessarily equally easily in each direction. Hence it uses local terrain characteristics to compute local resistances to diffusion.","hasChildren":true,"hasParent":true,"name":"Neighborhood analysis","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM4-5","description":"GISs that support raster processing - as most do - usually have a language to express operations on rasters. These languages are generally referred to as map algebra or, sometimes, raster calculus. They allow a GIS to compute new rasters from existing ones, using a range of functions and operators. Unfortunately, not all implementations of map algebra offer the same functionality. The discussion below is to a large extent based on general terminology; it attempts to illustrate the key operations using a logical, structured language. Again, the syntax often varies among different GIS software packages.\r\n\r\nWhen producing a new raster we must provide a name for it, and define how it is to be computed. This is done in an assignment statement of the following format:\r\n\r\nOutput raster name := Map algebra expression.\r\n\r\nThe expression on the right is evaluated by the GIS, and the raster in which it results is then stored under the name on the left. The expression may contain references to existing rasters, operators and functions; the format is made clear in each case. The raster names and constants that are used in the expression are called its operands. When the expression is evaluated, the GIS will perform the calculation on a pixel-by-pixel basis, starting from the first pixel in the first row and continuing through to the last pixel in the last row. In map algebra, there is a wide range of operators and functions available.\r\n\r\nArithmetic operators\r\nVarious arithmetic operators are supported. The standard ones are multiplication (×), division (/), subtraction (-) and addition (+). Obviously, these arithmetic operators should only be used on appropriate data values, and, for instance, not on classification values. Other arithmetic operators may include modulo division (MOD) and integer division (DIV). Modulo division returns the remainder of division: for instance, 10 MOD 3 will return 1 as 10 - 3 × 3 = 1. Similarly, 10 DIV 3 will return 3.\r\n\r\nOther operators are goniometric: sine (sin), cosine (cos), tangent (tan); and their inverse functions asin, acos, and atan, which return radian angles as real values. The assignment\r\n\r\nC1 := A + 10\r\n\r\nwill add a constant factor of 10 to all cell values of raster A and store the result as output raster C1. The assignment\r\n\r\nC2 := A + B\r\n\r\nwill add the values of A and B cell by cell, and store the result as raster C2. Finally, the assignment\r\n\r\nC3 := (A - B) ∕ (A + B) × 100\r\n\r\nwill create output raster C3, as the result of the subtraction (cell by cell, as usual) of B cell values from A cell values, divided by their sum. The result is multiplied by 100. This expression, when carried out on AVHRR channel 1 (red) and AVHRR channel 2 (near infrared) of NOAA satellite imagery, is known as the NDVI (Normalized Difference Vegetation Index). It has proven to be a good indicator of the presence of green vegetation.\r\n\r\nComparison and logical operators\r\n\r\nMap algebra also allows the comparison of rasters cell by cell. To this end, we may use the standard comparison operators (<, ⇐, =, >=, > and <>).\r\n\r\nA simple raster comparison assignment is\r\n\r\nC := A <> B.\r\n\r\nIt will store truth values—either true or false—in the output raster C. A cell value in C will be true if the cell’s value in A differs from that cell’s value in B. It will be false if they are the same. Logical connectives are also supported in many raster calculi. We have already seen the connectives of AND , OR and NOT in raster overlay operators. Another connective that is commonly offered in map algebra is exclusive OR (XOR). The expression a XOR b is true only if either a or b is true, but not both.\r\n\r\nConditional expressions\r\nThe comparison and logical operators produce rasters with the truth values true and false. In practice, we often need a conditional expression together with them that allows us to test whether a condition is fulfilled. The general format is:\r\n\r\nOutput raster := CON(condition, then expression, else expression).\r\n\r\nHere, condition stands for the condition tested, then the expression is evaluated if condition holds, and else the expression is evaluated if it does not hold. This means that an expression such as CON(A = “forest”, 10, 0) will evaluate to 10 for each cell in the output raster where the same cell in A is classified as forest. For each cell where this is not true, the else expression is evaluated, resulting in 0.\r\n\r\nOverlays using a decision table\r\nConditional expressions are powerful tools in cases where multiple criteria must be taken into account. A small example may illustrate this. Consider a suitability study in which a land use classification and a geological classification must be used. Domain expertise dictates that some combinations of land use and geology result in suitable areas, whereas other combinations do not. In our example, forests on alluvial terrain and grassland on shale are considered suitable combinations, while any others are not.\r\n\r\nWe could produce an output raster with a map algebra expression, such as\r\n\r\nSuitability := CON((Landuse = “Forest” AND Geology = “Alluvial”)\r\nOR (Landuse = “Grass” AND Geology = “Shale”),\r\n“Suitable”, “Unsuitable”)\r\n\r\nand consider ourselves lucky that there are only two “suitable” cases. In practice, many more cases must usually be covered and, then, writing up a complex CON expression is not an easy task.\r\n\r\nTo this end, some GISs accommodate setting up a separate decision table that will guide the raster overlay process. This extra table carries domain expertise and dictates which combinations of input raster-cell values will produce which output raster-cell value. This gives us a raster overlay operator using a decision table. The GIS will have supporting functions to generate the additional table from the input rasters and to enter appropriate values in the table.","hasChildren":true,"name":"Map algebra","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM4","description":"This small set of analytical operations is so commonly applied to a broad range of problems that their inclusion in software products is often used to determine if that product is a true GIS. Concepts on which these operations are based are addressed in Domains of geographic information and Relationships.","hasChildren":true,"hasParent":true,"name":"Basic analytical operations","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM5-1","description":"Point pattern analysis refers to the detection of patterns in a group of objects or subjects located in space. This may support the analysis of clusters in accidents, crime, etc.","hasChildren":true,"name":"Point pattern analysis","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM5-2","description":"The probability density function is a method with which the probability density can be estimated for points in a raster space.","hasChildren":true,"name":"Kernels and density estimation","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM5-3","description":"Spatial cluster analysis is the grouping of similar spatial objects into classes (clusters) in such a way that the objects within the cluster are highly similar compared to the objects outside of the cluster. Spatial clustering forms an important part of spatial data mining (Han et al., 2001; Miller et al., 2009). A wealth of spatial clustering tools are currently available with immense application potential. \r\n\r\nIn earth observation studies, spatial cluster techniques are often applied to identify zones with similar land covers by using earth observation data as input. An example of such a technique is the K-means classifier (Han et al., 2001; Miller et al., 2009). This unsupervised classification technique makes several clusters (e.g. land use classes) of which each pixel is assigned to the cluster with the nearest mean (Han et al., 2001). The amount of clusters can be freely defined by the user just as the input metrics to perform the classification. A drawback of the K-means classifier is the need to specify the amount of output clusters. Density Based Spatial Clustering (DBSC) overcomes this issue since it automatically defines the optimal amount of clusters (Miller et al., 2009). In this type of clustering technique, dense regions of objects (proximate objects) are clustered and separated from regions with low density (noise) (Han et al., 2001; Liu et al., 2012). Finally, another frequently applied spatial clustering technique is the hierarchical agglomerative clustering. This technique makes use of a dendrogram to decompose the data into clusters. The agglomerative approach is a bottom-up approach in which all objects are first grouped in a distinct cluster and while moving upward in the tree, pairs of clusters are merged based on some metrics (e.g. spatial proximity) (Han et al., 2001). \r\n\r\nSpatial cluster techniques have many advantages when dealing with big datasets which is often the case when working with earth observation data. Its simplicity to use and the fast increase of cloud computing power makes from it powerful techniques to extract spatial patterns out of the data. It allows to translate raw earth observation data into a more user-friendly data product by showing the spatial patterns of the data.","hasChildren":true,"name":"Spatial cluster analysis","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM5-4","description":"Spatial interaction models describe the flow of people and goods in a geographical space, in which parameters such as friction and distance play a role.","hasChildren":true,"name":"Spatial interaction","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM5-5","description":"Multidimensional attributes can be analyzed through multidimensional scaling and principle component analysis.","hasChildren":true,"hasParent":true,"name":"Analyzing multidimensional attributes","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM5-7","description":"Multi-criteria evaluation is an important aspect of decision support operations, which appear in process models. Process models in the Earth sciences describe the evolution of geo(bio)physical surface properties in time, independently from remote sensing observations. Examples of such process models on various time scales are, for instance, numerical weather prediction models (NWPs), vegetation growth models, hydrological models, oceanographic models and climate models.\r\n\r\nObservation models and process models can supplement each other to enhance the quality of the interpretation of remote sensing data and to fill gaps in time that occur when observations are not possible owing to clouds or some other cause. Interactions are possible between observation models and process models with EO data and existing geographic information (GIS and ground measurements, supplemented with decision-support systems (DSSs)).\r\n\r\nThe process model provides information to the decision-support system, which supports management actions aimed at controlling/mitigating the process, based on an multi-criteria evaluation. A good example of this is a water management system, in which one might decide to allocate water for irrigation if the observed vegetation appears to suffer from drought stress.","hasChildren":true,"name":"Multi-criteria evaluation","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM5-8","description":"Process models in the Earth sciences describe the evolution of geo(bio)physical surface properties in time, independently from remote sensing observations. Examples of such process models on various time scales are, for instance, numerical weather prediction models (NWPs), vegetation growth models, hydrological models, oceanographic models and climate models.\r\nProcess models in the geosciences usually rely on regular observations at many locations spread over a large area. Traditionally, these observations were mostly made in the field with a variety of instruments. Remote sensing techniques have tremendously increased the capability of spatial sampling and the consistency of the surface parameters measured. RS instruments are mostly sensitive to many physical properties of the surface, some of these may not belong to the set of properties that the user is interested in. Exceptions to this are the mapping of sea-surface temperature, laser altimetry and gravimetry, which are measurements of direct geophysical interest. In the majority of cases, however, there are only indirect relationships between what is observed with the instrument and the physical object properties of interest. In these cases, the use of observation models becomes an attractive option, since these models describe the relationships between all object properties relevant for the observation and the observed remote sensing data.","hasChildren":true,"name":"Spatial process models","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM5","description":"Building on the basic geometric measures and analytical operations found in most GIS products, a broad range of additional analytical methods form the fundamental GIS toolkit.","hasChildren":true,"hasParent":true,"name":"Basic analytical methods","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM6-2","description":"In rasters we use interpolation to determine the value of a pixel, based on its surrounding pixels. The main raster-based interpolation methods are nearest neighbour, bilinear, and bicubic interpolation. To determine the value of the centre pixel (bold), in nearest neighbour interpolation the value of the nearest original pixel is assigned, i.e. the value of the black pixel in this example. Note that the respective pixel centres, marked by small crosses, are always used for this process. In bilinear interpolation, a linear weighted average is calculated for the four nearest pixels in the original image. In bicubic interpolation a cubic weighted average of the values of 16 surrounding pixels (the black and all grey pixels) is calculated. Note that some software uses the terms “bilinear convolution” and “cubic convolution” instead of the terms introduced above.","hasChildren":true,"name":"Interpolation of surfaces","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM6-3","description":"Continuous fields have a number of characteristics not shared by discrete fields. Since the field changes continuously, we can talk of slope angle, slope aspect and concavity/convexity of the slope.\r\n\r\nThese notions are not applicable to discrete fields. The discussions in this subsection use terrain elevation as the prototype example of a continuous field, but all aspects discussed are equally applicable to other types of continuous fields. Nonetheless, we regularly refer to the continuous field representation as a DEM, to conform with the most common situation.","hasChildren":true,"name":"Surface features","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM6-4","description":"A viewshed is the area that can be “seen” (i.e. it is in the direct line-of-sight) from a specified target location. (Inter) visibility analysis can determine the area visible from a scenic lookout or the area that can be reached by a radar antenna, as well as assess how effectively a road or quarry will be hidden from view.","hasChildren":true,"name":"Intervisibility","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM6-5","description":"Firction surfaces contain information on how difficult/easy it is for a phenomenon to move from one location on the surface to another.","hasChildren":true,"name":"Friction surfaces","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM6","description":"There is a wide range of phenomena that can be studied using a set of techniques and tools that are designed to help understand the characteristics of continuous surface data. Applications of these techniques using terrain data include overland transport, flow, and siting tasks, but similar analyses can be conducted using non-tangible surfaces such as those of temperature, pressure and population density.\r\n\r\nThere are numerous examples that require more advanced computations on continuous field representations, such as:\r\n\r\nSlope angle calculation - the calculation of the slope steepness, expressed as an angle in degrees or percentages, for any or all locations.\r\n\r\nCalculating slope aspect - the calculation of the aspect (or orientation) of the slope in degrees (between 0 and 360∘), for any or all locations.\r\n\r\nSlope convexity/concavity calculation - defined as the change of the slope (negative when the slope is concave and positive when the slope is convex)—can be calculated as the second derivative of the field.\r\n\r\nSlope length calculation - with the use of neighbourhood operations, it is possible to calculate for each cell the nearest distance to a watershed boundary (the upslope length) and to the nearest stream (the downslope length). This information is useful for hydrological modelling.\r\n\r\nHillshading is used to portray relief difference and terrain morphology of hilly and mountainous areas. The application of a special filter to a DEM produces hillshading. The colour tones in a hillshading raster represent the amount of reflected light at each location, depending on its orientation relative to the illumination source. This illumination source is usually chosen to be to the northwest at an angle of 45∘ above the horizon.\r\n\r\nThree-dimensional map display - with GIS software, three-dimensional views of a DEM can be constructed in which the location of the viewer, the angle under which he or she is looking, the zoom angle, and the amplification factor of relief exaggeration can be specified. Three-dimensional views can be constructed using only a predefined mesh, covering the surface, or using other rasters (e.g. a hillshading raster) or images (e.g. satellite images) that are draped over the DEM.\r\n\r\nDetermination of change in elevation through time - the cut-and-fill volume of soil to be removed or to be brought in to make a site ready for construction can be computed by overlaying the DEM of the site before the work begins with the DEM of the expected modified topography. It is also possible to determine landslide effects by comparing DEMs of before and after a landslide event.\r\n\r\nAutomatic catchment delineation - catchment boundaries or drainage lines can be automatically generated from a good quality DEM with the use of neighbourhood functions. The system will determine the lowest point in the DEM, which is considered to be the outlet of the catchment. From there, it will repeatedly search for the neighbouring pixels with the highest altitude. This process is repeated until the highest location (i.e. the cell with the highest value) is found; the path followed determines the catchment boundary. For delineating the drainage network, the process is reversed. Then the system will work from the watershed downwards, each time looking for the lowest neighbouring cells, which determines the direction of water flow (Flow Computation).\r\n\r\nDynamic modelling - apart from the applications mentioned above, DEMs are increasingly used in GIS-based dynamic modelling, such as the computation of surface run-off and erosion, groundwater flow, the delineation of areas affected by pollution, the computation of areas that will be covered by processes such as flows of debris and lava. An example is (Diffusion).\r\n\r\nVisibility analysis - a viewshed is the area that can be “seen” (i.e. it is in the direct line-of-sight) from a specified target location. Visibility analysis can determine the area visible from a scenic lookout or the area that can be reached by a radar antenna, as well as assess how effectively a road or quarry will be hidden from view.","hasChildren":true,"hasParent":true,"name":"Analysis of surfaces","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM7-1","description":"Statistical analysis techniques based on visual interpretation through histograms, scatterplots, etc.","hasChildren":true,"name":"Graphical methods","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM7-2","description":"Environmental variables have become increasing available with the advent of GIS. These are mostly continuous in space and time. Collecting denser environmental data in discrete space and time domains are rather cost effective and time consuming. However, when the data at each spatial or time index are considered as outcomes of a random variable, stochastic processes become enviable useful to build models and predict the outcomes at locations where data were never collected. The meaningful assumptions include stationarity of the mean and the covariance to ascertain an expression for spatial dependency/autocorrelation. With a stationary process (i.e. constant mean), simple and ordinary kriging is used. Other variants like kriging with external drift, universal kriging and regression kriging also alleviate the challenge of non-stationary mean. These methods are also applicable when temporal indexes rather than spatial indexes are of interest.","hasChildren":true,"name":"Stochastic processes","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM7-3","description":"Spatial weight matrix is the popular numerical quantification of spatial dependency or spatial neighborhoods. The weight matrix should summarize information about the spatial connectivity structure of the spatial entities/features; either polygons, points, or lines. This is required for the computation of spatial dependency indices such the Moran’s index, and for spatial regression models such as the conditional autoregressive (CAR), spatial lag, and spatial error models. The connectivity information can be defined based on adjacency/contiguity or distance between pairs of spatial entities. There are other forms; they could be based on population densities between observation pairs. The simplest spatial weigh matrix is the binary adjacency spatial weight matrix with elements w_ij, such that w_ij=1 if spatial units i and j are neighbors, otherwise w_ij=0. A popular alternative is the inverse distance weight matrix with elements w_ij=1⁄d^α , where d is the distance between pairs of spatial units and α is any positive number greater than zero. By convention, w_ii=0 since spatial unit cannot have a spillover within itself.","hasChildren":true,"name":"The spatial weights matrix","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM7-4","description":"Spatial autocorrelation evaluates how things which are closer in space tend to have similar attributes. This is a common phenomenon in environmental variables which are continuous in space. For instance, temperature, soil moisture content, air quality and rainfall are all continuous in space. This idea is based on Tobler’s law of geography: “everything is related to everything but near things are more related”. Global measures of spatial association estimates the overall index of spatial autocorrelation, also called spatial clustering. Thus, it measures whether clustering is apparent throughout the study region but do not identify the location of clusters. Common global measures include the Moran’s Index and Geary’s C. These have increasing applications in domains like environmental science, agriculture, epidemiology, climate studies etc.","hasChildren":true,"name":"Global measures of spatial association","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM7-5","description":"Unlike global measures of spatial association, local measure of spatial association identifies the locations of clusters. Typical measures include the local indicator for spatial autocorrelation (LISA) or the local Moran’s index whose summation is proportional to the global Moran’s index. The spatial scan statistics has also been the commonly used method to detect local clusters.","hasChildren":true,"name":"Local measures of spatial association","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM7-6","description":"An outlier is an unexpected value that differs significantly from other observations. Definition of an outlier is not absolute and the concept itself is precisely defined only by selection of appropriate criteria in concrete statistical observations. When considering outliers, it is important to determine whether the value of the outlier is incorrect data or it is otherwise outstanding, but correct data. If we consider outliers in the case when they base on sample surveys, another assessment is necessary. Namely, the assessment of whether an outlier is representative or not. \r\nThe box plot is a useful graphical display for examining the outliers. Using median, lower and upper quartiles, extreme values are identified in the tails of the distribution. The value beyond inner fence on either side is considered a mild outlier. The value beyond an outer fence is considered an extreme outlier. Histograms also emphasize the existence of outliers. The histogram depends on how we design the classes, so we can get different histograms for the same data. Graphical and quantitative checks are obligatory if the histogram shows possible outliers. Outliers can also be examined by calculating the correlation between two datasets (Pearson correlation coefficient, Spearman rank correlation coefficient…). Scatter plots reveals a basic linear relationship with a pattern. An outliner is defined as a data point that deviates from other values. Outliers can also be examined by local outlier factor, which is based on a concept of a local density. Points with substantially lower density than their neighbours are considered as outliers.","hasChildren":true,"name":"Outliers","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM7-7","description":"Bayesian method of modelling stems from the Bayes theorem and derived using conditional probabilities. Its advantage lies in its ability to include prior knowledge of unknown parameters to ascertain their uncertainties. Thus, the prior parameters are updated by the data likelihood to obtain the posteriors. The challenge of Bayesian modelling has been the integration of the denominator which always resulted into improper integrals. This actually prolonged its wide applications. With the advent of high performance computers, solution to such integrals are easily solved using Markov chain Monte Carlo simulations. The advent robust approximation methods through integrated nested Laplace approximations (INLA) has even made parameter estimation faster; thus making Bayesian methods interesting and better. Unlike frequentist approaches, Bayesian methods can present estimates of parameters as densities from which their uncertainties and credible intervals can be estimated. They have now found wide applications in divers areas like environmental modelling, climate modeling, agriculture, epidemiology and many other domains that requires modeling.","hasChildren":true,"name":"Bayesian methods","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM7","description":"Traditional statistical methods are used to describe the central tendency, dispersion, and other characteristics of data but are not always suited to use with spatial data for which specialized techniques are often required. The field of spatial statistical analysis forms the backbone for the testing of hypotheses about the nature of spatial pattern, dependency, and heterogeneity. The techniques are widely used in both exploratory and confirmatory spatial analysis in many different fields.","hasChildren":true,"hasParent":true,"name":"Spatial statistics","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM8-1","description":"Sampling is needed to limit the observations for statistical analysis. In raster image analysis, various sampling schemes have been proposed for selecting pixels to test. Choices to be made relate to the design of the sampling strategy, the number of samples required, and the area of the samples. Recommended sampling strategies in the context of land cover data are simple random sampling or stratified random sampling. The number of samples may be related to two factors in accuracy assessment: (1) the number of samples that must be taken in order to reject a data set as being inaccurate; or (2) the number of samples required to determine the true accuracy, within some error bounds, of a data set. Sampling theory is used to determine the number of samples required. The number of samples must be traded-off against the area covered by a sample unit. A sample unit can be a point but it could also be an area of some size; it can be a single raster element but may also include surrounding raster elements. Among other considerations, the “optimal” sample-area size depends on the heterogeneity of the class.","hasChildren":true,"hasParent":true,"name":"Spatial sampling for statistical analysis","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM8-3","description":"A variogram is a tool used to describe the spatial continuity of data points. Different kinds of variograms are used, such as experimental variogram and semi-variogram.","hasChildren":true,"name":"Variogram modeling","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM8-4","description":"Predicting an observation in the presence of spatially dependent observations is termed Kriging, named after the first practitioner of these procedures, the South African mining engineer Daan Krige, who did much of his early empirical work in the Witwatersrand gold mines.","hasChildren":true,"name":"Principles of kriging","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM8-5","description":"With a stationary stochastic process (i.e. constant mean), simple and ordinary kriging is used for interpolation. Other variants like kriging with external drift, universal kriging and regression kriging also alleviate the challenge of non-stationary mean. Other variants are \r\nco-kriging log-normal kriging, disjunctive kriging, indicator kriging, factorial kriging and universal kriging.","hasChildren":true,"name":"Kriging variants","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM8","description":"Geostatistics are a variety of techniques used to analyze continuous data e.g., rainfall, elevation, air pollution. The fundamental structure of geostatistics is based on the concept of semi-variograms and their use for spatial prediction kriging. Sampling methods are also discussed in Unit GD9 Field data collection. \r\nGeostatistics is a subdiscipline of spatial statistics developed to estimate the value of a continuous spatial process at unknown locations by using the information of the value of these process at known locations. Furthermore, it aims to quantify the uncertainty related to the prediction (Calder et al., 2009; Emmanouil, 2019). In order to do such predictions, geostatistics entails some statistical methods which use as starting point the assumption of a random component that can define the spatiotemporal variability. These methods are developed to infer the parameters that can describe the spatiotemporal patterns of the input variables (e.g. soil moisture) so that finally these variables at unsampled locations can be estimated (interpolated) (Emmanouil, 2019). Geostatistical methods are strongly related with classic interpolation methods but differ by its use of random variables that allow to given an uncertainty indication associated with the prediction of variables in space and time. \r\n\r\nIn environmental research geostatistical techniques are often applied to infer (interpolate) variables at such unobserved locations by using information from known locations. One of such geostatistical techniques is Kriging, which is a geostatistical method that predicts variables by using spatial interpolation. This spatial interpolation is done by establishing a semivariogram that defines the spatial relationship between the variables of interest in function of the distance. Because of this, the Kriging technique can also give an indication on the variance or accuracy of the prediction (Calder et al., 2009); Van der Meer, 2012). On the other hand, cokriging is another important geostatistical technique and differs from Kriging by using the cross-correlation between variables to generate local estimates (Van der Meer, 2012). In earth observation studies, cokriging can be applied to better predict sparsely based data on the ground (e.g. biomass) by using the cross-correlation of this variable with a more continuously sampled satellite metric like NDVI. Furthermore, these techniques can also be used to enhance satellite image information, filling missing pixels or even downscale the information to a higher resolution (Van der Meer, 2012).","hasChildren":true,"hasParent":true,"name":"Geostatistics","selfAssesment":"Completed (GI-N2K)
"},{"code":"AM9-1","description":"Spatial econometrics uses spatial stochastic models to determine autocorrelation between interacting agents. The techniques involved are regression, the use of a spatial weights matrix, least squares, etc.","hasChildren":true,"name":"Principles of spatial econometrics","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM9-2","description":"A spatial autoregressive (SAR) model describes the prediction of the behaviour of a random process.","hasChildren":true,"name":"Spatial autoregressive models","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM9-3","description":"In producing optimal images for interpretation, spatial filtering is applied. Filtering is usually carried out for a single band. Filters - algorithms - can be used to enhance images by, for example, reducing noise (“smoothing an image”) or sharpening a blurred image. Filter operations are also used to extract features from images, e.g. edges and lines, and to automatically recognize patterns and detect objects. There are two broad categories of filters: linear and non-linear filters.\r\n\r\nLinear filters calculate the new value of a pixel as a linear combination of the given values of the pixel and those of neighbouring pixels. A simple example of the use of a linear smoothing filter is when the average of the pixel values in a 3×3 pixel neighbourhood is computed and that average is used as the new value of the central pixel in the neighbourhood.","hasChildren":true,"name":"Spatial filtering","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM9-4","description":"Geographically Weighted Regression (GWR) makes use of local subsets of observations to perform estimates.","hasChildren":true,"name":"Spatial expansion and Geographically Weighted Regression GWR","selfAssesment":"In progress (GI-N2K)
"},{"code":"AM9","description":"Many problems of the social sciences can be expressed in terms of spatial regression analysis. The development of spatial autoregressive models and the estimation of their parameters is the focus for the field of spatial econometrics.","hasChildren":true,"hasParent":true,"name":"Spatial regression and econometrics","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF","description":"The GIScience perspective is grounded in spatial thinking. The aim of this knowledge area is to recognize, identify, and appreciate the explicit spatial, spatio-temporal and semantic components of the geographic environment at an ontological and epistemological level in preparation for modeling the environment with geographic data and analysis. To do this, one must understand the nature of space and time as a context for geographic phenomena.This knowledge area covers the ways in which views of the geographic environment depend on philosophical viewpoints, physics, human cognition, society, and the task at hand. This knowledge area also requires an understanding of the fundamental principles in the discipline of geography, the \"language\" of spatial tasks. On a more advanced level, this area incorporates mathematical and graphical models that formalize these concepts, such as set theory, algebra, and semantic nets. Because of its wide range of foundational principles, this knowledge area forms a basis for the other knowledge areas. Wise design and use of geospatial technologies requires an understanding of the nature of geographic information, the social and philosophical context of geographic information, and the principles of geography. This knowledge area is especially closely tied to Knowledge Areas Data Modeling (DM) and Design Aspects (DA), as generic data models and application designs need to be grounded in sound conceptual models. The foundations of geographic information have developed over several decades. Philosophical and scientific views on the nature of space and time have evolved since the ancient Greeks. Early papers during the Quantitative Revolution, such as Berry (1964), began to formalize the structure of information used in geographic inquiry.The fundamental data structures and algorithms comprising the GIS software developed in the 1960`s and 1970`s were based on implicit \"common-sense\" conceptual models of geographic information. During the 1980`s, several researchers questioned these underlying assumptions. Some were refuted, other confirmed, and many extended. However, the most rapid pace of development in this area was during the 1990`s with the rise of GIScience as a distinct discipline, and the many cooperative initiatives it comprised.The new millennium has seen some of these foundational principles incorporated into commercial software, thus making theoretical knowledge even more important for practitioners. It is expected that the concepts in this knowledge area will be learned gradually. An introductory course may cover only a few topics in a cursory manner, an intermediate course on data modeling or data analysis may consider several theoretical topics of practical application, and a number of graduate courses could cover each topic in a research-oriented environment. Discussion of this knowledge area includes several terms that can have multiple meanings. For the purposes of this document, two in particular require definition: Geographic: Almost any subject or discourse involving earthly phenomena, studied from a spatial perspective at a medium scale (sub-astronomical and super-architectural). Phenomenon: Any subject of geographic discourse that is perceived to be external to the individual, including entities, events, processes, social constructs, and the like.","hasChildren":true,"hasParent":true,"name":"Conceptual Foundations","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF1-1","description":"Metaphysics involve the meaning things and concepts. Ontologies provide a way to share the semantics of concepts in some area of interest and is all about common the understanding of essential concepts, e.g., what is meant by a geometric object and its attributes.","hasChildren":true,"name":"Metaphysics and ontology","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF1-1b","description":"Brief history of GIScience as related to the history of GISystems; Definitions of GIS&T; Sub-domains of GIS&T (i.e., Geographic Information Science, Geospatial Technology, and Applications of GIS&T)","hasChildren":true,"name":"What is Geographic Information Science and Technology","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF1-2","description":"The branch of philosophy concerned with knowledge.","hasChildren":true,"name":"Epistemology","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF1-2b","description":"GIS&T draws upon insights and methods from key allied fields: Geography, Cartography, Computer and information science, Engineering, Mathematics and Statistics, Philosophy, Cognitive Science, Linguistics","hasChildren":true,"name":"Contributions to GIS and T by key allied fields","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF1-3","description":"The questions and methodologies in major philosophical movements relating to the nature of space, time, geographic phenomena and human interaction with it.","hasChildren":true,"name":"Philosophical perspectives","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF1","description":"Many branches of philosophy are relevant to an understanding of geographic information, especially metaphysics and epistemology. Philosophical theories are deeply engaged in the study of knowledge, space, time, geographic phenomena and human interaction with them. These theories influence the development of geographic ontologies and the structuring, analysis, and interpretation of geographic information. It is, therefore, crucial for professionals to understand these principles in order to bridge (rather than eliminate) the differences and work together. Philosophical perspectives on GIS practice are covered in Unit GS7 Critical GIS.","hasChildren":true,"hasParent":true,"name":"Philosophical foundations","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF1b","description":"Unit CF1 introduces the broad domain refered to as Geographic Information Science & Technology (GIS&T) and its sub-domains (i.e., Geographic Information Science, Geospatial Technology, and Applications of GIS&T). It outlines the history of Geographic Information Science as related to the history of GISystems, as well as the contributions to this multidisciplinary domain by key allied fields, such as geography, cartography, computer and information science, engineering, mathematics, philosophy, cognitive science, and linguistics.","hasChildren":true,"hasParent":true,"name":"Introduction to Geographic Information Science and Technology","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF2-1","description":"The study on how humans perceive spatial information.","hasChildren":true,"name":"Perception and cognition of geographic phenomena","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF2-1b","description":"Metaphysics and Ontology - Formal ontology - Ontological distinctions (e.g., continuants vs. occurrents, universals vs. particulars) - The problem of universals and relevant theories (realism, nominalism, conceptualism) - Ontologies of the geographic domain - Philosophical theories relating to the nature of space, time, geographic phenomena and human interaction with them","hasChildren":true,"name":"Philosophy of being","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF2-2","description":"The ways in which conceptual views of in the human mind make it into formal descriptions of information and into artefacts in databases and GIS.","hasChildren":true,"hasParent":true,"name":"From concepts to data","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF2-2b","description":"Epistemology; Theories on what constitutes knowledge; The notions of model and representation in science; The influences of epistemology on GIS practices","hasChildren":true,"name":"Philosophy of knowledge","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF2-3","description":"Principles of geography to explain the spatial occurrences of spatial entities in Geographic Information Systems.","hasChildren":true,"name":"Geography as a foundation for GIS","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF2-4","description":"Space and place are concepts that are not the same. Including concepts like landscape, it is not always obvious how to portray them unambiguously in GIS.","hasChildren":true,"name":"Place and landscape","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF2-6","description":"The ways in which the elements of culture (e.g., language, religion, education, traditions) may influence the understanding and use of geographic information.","hasChildren":true,"name":"Cultural influences","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF2-7","description":"The influences of political ideologies (e.g., Marxism, Capitalism, conservative liberal) on the understanding of geographic information.","hasChildren":true,"name":"Political influences","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF2","description":"Geographic information is observed, comprehended, organized, used in human processes, with both personal and social influences. Therefore, sound models of geographic information should be grounded on a sound understanding of human perception, cognition, memory, and behavior, as well as human institutions.","hasChildren":true,"hasParent":true,"name":"Cognitive and social foundations","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF3-1","description":"A GIS operates under the assumption that the spatial phenomena involved occur in a two- or three-dimensional Euclidean space. Euclidean space can be informally defined as a model of space in which locations are represented by coordinates—(x, y) in 2D and (x, y, z) in 3D space—and distance and direction can defined with geometric formulas. In 2D, this is known as the Euclidean plane. To represent relevant aspects of real-world phenomena inside a GIS, we first need to define what it is we are referring to. We might define a geographic phenomenon as a manifestation of an entity or process of interest that:\r\n\r\nitem can be named or described;\r\nitem can be georeferenced; and\r\nitem can be assigned a time (interval) at which it is/was present.\r\n\r\nRelevance of phenomena for the use of a GIS depends entirely on the objectives of the study at hand. For instance, in water management, relevant objects can be river basins, agro-ecological units, measurements of actual evapotranspiration, meteorological data, ground\\-water levels, irrigation levels, water budgets and measurements of total water use. All of these can be named or described, georeferenced and provided with a time interval at which each exists. In multipurpose cadastral administration, the objects of study are different: houses, land parcels, streets of various types, land use forms, sewage canals and other forms of urban infrastructure may all play a role. Again, these can be named or described, georeferenced and assigned a time interval of existence.\r\n\r\nNot all relevant information about phenomena has the form of a triplet (description, georeference, time interval). If the georeference is missing, then the object is not positioned in space: an example of this would be a legal document in a cadastral system. It is obviously somewhere, but its position in space is not considered relevant. If the time interval is missing, we might have a phenomenon of interest that exists permanently, i.e.\\ the time interval is infinite. If the description is missing, then we have something that exists in space and time, yet cannot be described. Obviously this last issue limits the usefulness of the information.\r\n\r\nTypes of geographic phenomena\r\nThe definition of geographic phenomena attempted above is necessarily abstract and is, therefore, perhaps somewhat difficult to grasp. The main reason is that geographic phenomena come in different “flavours”. Before categorizing such flavours, there are two further observations to be made.\r\n\r\nFirst, to represent a phenomenon in a GIS requires us to state what it is and where it is. We must provide a description—or at least a name—on the one hand, and a georeference on the other hand. We will ignore temporal issues for the moment and come back to these in Temporal dimension and Spatial-temporal data model, the reason being that current GISs do not provide much automatic support for time-dependent data. This topic must, therefore, be considered as an example of advanced GIS use. Second, some phenomena are manifest throughout a study area, while others only occur in specific localities. The first type of phenomena we call geographic fields; the second type we call objects.","hasChildren":true,"name":"Space","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF3-1b","description":"- Theories of human perception, cognition, and memory and their ability to model spatial knowledge acquisition (e.g., Marr on vision, Piaget on cognitive development) - Types of mental representations (i.e., analogue, propositional, procedural) - The role of metaphors and image schemata in our understanding of geographic phenomena and geographic tasks - From concepts to data (i.e., data, information, knowledge, and wisdom; transformation of a conceptual model of information for a particular task into a data model; limitations of various information stores (the mind, computers) and means (maps, graphics, and text) for representing geographic information) - Difference between real phenomena, conceptual models, and GIS data representations thereof connections with cartography and maps","hasChildren":true,"name":"Cognitive foundations","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF3-2b","description":"- Semantics - Meaning (e.g., the nature of meaning, modes of meaning) - Geospatial semantics - The role of natural language in the conceptualization of geographic phenomena","hasChildren":true,"name":"Linguistic foundations","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF3-3b","description":"- The ways in which the elements of culture (e.g., language, religion, education, traditions) may influence the understanding and use of geographic information - The influences of social theories and political ideologies and actions on human perceptions of space and place - The constraints that political forces place on geospatial applications in public and private sectors","hasChildren":true,"name":"Social foundations","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF3-4b","description":"- Common-sense views and laymen knowledge of geographic phenomena that contrast with established theories and technologies of geographic information - The impact of geospatial technologies and the geoweb (e.g., digital globes) that allow non-geospatial professionals to create, distribute, and map geographic information - The design, procedures, and results of GIS projects to non-GIS audiences (clients, managers, general public) - Difference between applications that can make use of common-sense principles of geography and those that should not","hasChildren":true,"name":"Common-sense geographies","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF3","description":"Geographic information is observed, comprehended, organized, used in human processes, with both personal and social influences. Therefore, sound models of geographic information should be grounded on a sound understanding of human perception, cognition, memory, and behavior, as well as human institutions.","hasChildren":true,"hasParent":true,"name":"Cognitive, linguistic and social foundations","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF4-2b","description":"As time is the central concept of the temporal dimension, a brief examination of the nature of time may clarify our thinking when we work with this dimension:\r\n\r\nDiscrete and continuous time: Time can be measured along a discrete or continuous scale. Discrete time is composed of discrete elements (seconds, minutes, hours, days, months, or years). For continuous time, no such discrete elements exist: for any two moments in time there is always another moment in between. We can also structure time by events (moments) or periods (intervals). When we represent intervals by a start and an end event, we can derive temporal relationships between events and periods, such as “before”, “overlap”, and “after”.\r\n\r\nValid time and transaction time: Valid time (or world time) is the time when an event really happened, or a string of events took place. Transaction time (or database time) is the time when the event was stored in the database or GIS. Note that the time at which we store something in a database is typically (much) later than when the related event took place.\r\n\r\nLinear, branching and cyclic time: Time can be considered to be linear, extending from the past to the present (‘now’), and into the future. This view gives a single time line. For some types of temporal analysis, branching time - in which different time lines from a certain point in time onwards are possible - and cyclic time - in which repeating cycles such as seasons or days of the week are recognized - make more sense and can be useful.\r\n\r\nTime granularity: When measuring time, we speak of granularity as the precision of a time value in a GIS or database (e.g. year, month, day, second). Different applications may obviously require different granularity. In cadastral applications, time granularity might well be a day, as the law requires deeds to be date-marked; in geological mapping applications, time granularity is more likely to be in the order of thousands or millions of years.\r\n\r\nAbsolute and relative time: Time can be represented as absolute or relative. Absolute time marks a point on the time line where events happen (e.g. “6 July 1999 at 11:15 p.m.”). Relative time is indicated relative to other points in time (e.g. “yesterday”, “last year”, “tomorrow”, which are all relative to “now”, or “two weeks later”, which is relative to some other arbitrary point in time.).","hasChildren":true,"name":"Time","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF4-3b","description":"The way we represent relevant components of the real world in our models determines the kinds of questions we can or cannot answer. Besides representing an object or field in 2D or 3D space, the temporal dimension is of a continuous nature. Therefore, in order to represent it in a GIS we have to discretize the time dimension.\r\n\r\nSpatio-temporal data models are ways of organizing representations of space and time in a GIS. Several representation techniques have been proposed in the literature. Perhaps the most common of these is the “snapshot state”, which represents a single moment in time of an ongoing natural or man-made process. We may store a series of these “snapshot states” to represent “change”, but we must be aware that this is by no means a comprehensive representation of that process. \r\n\r\nIn spatio-temporal analysis we consider changes of spatial and thematic attributes over time. We can keep the spatial domain fixed and look only at the attribute changes over time for a given location in space. We might be interested how land cover has changed for a given location or how land use has changed for a given land parcel over time, provided its boundary has not changed. On the other hand, we can keep the attribute domain fixed and consider the spatial changes over time for a given thematic attribute. In this case, we might want to identify locations that were covered by forest over a given period of time.\r\n\r\nFinally, we can assume both the spatial and attribute domains are variable and consider how fields or objects have changed over time. This may lead to notions of object motion - a subject receiving increasing attention in the literature. Applications of moving object research include traffic control, mobile telephony, wildlife tracking, vector-borne disease control and weather forecasting. In these types of applications, the problem of object identity becomes apparent. When does a change or movement cause an object to disappear and become something new? With wildlife this is quite obvious; with weather systems less so. But this should no longer be a surprise: we have already seen that some geographic phenomena can be nicely described as objects, while others are better represented as fields.\r\n\r\nMapping time means mapping change. This may be change in a feature’s geometry, in its attributes, or both. Examples of changing geometry are the evolving coastline of the Netherlands, the location of Europe’s national boundaries, or the position of weather fronts. Changes in the ownership of a land parcel, in land use or in road traffic intensity are other examples of changing attributes. Urban growth is a combination of both: urban boundaries expand with growth and simultaneously land use shifts from rural to urban. If maps are to represent events like these, they should be suggestive of such change.\r\n\r\nThree temporal cartographic techniques can be distinguished:\r\n\r\nSingle Static Map\r\n\r\nSpecific graphic variables and symbols are used to indicate change or represent an event. We can apply the visual variable “value” to represent for example the age of built-up areas.\r\n\r\nSeries of Static Maps\r\n\r\nA single map in the series represents a “snapshot” in time. Together, the maps depict a process of change. Change is perceived by the succession of individual maps depicting the situation in successive snapshots. It could be said that the temporal sequence is represented by a spatial sequence that the user has to follow to perceive the temporal variation. The number of images should be limited since it is difficult for the human eye to follow long series of maps.\r\n\r\nAnimated Maps\r\n\r\nChange is perceived to evolve in a single image by displaying several snapshots one after the other, just like a video clip of successive frames. The difference from the series of maps is that the variation can be deduced from real “change” seen taking place in the image itself, not from a spatial sequence. For the user of a cartographic animation, it is important to have tools available that allow for interaction while viewing the animation. Seeing an animation play will often leave users with many questions about what they have seen. And just replaying the animation is not sufficient to answer questions like “What was the position of the northern coastline during the 15th century?” Most of the general software packages for viewing animations already offer facilities such as “pause” (to look at a particular frame) and ‘(fast-)forward’ and ‘(fast-)backward’, or step-by-step display. More options have to be added, such as the possibility to go directly to a certain frame based on a task command like: “Go to 1850”.","hasChildren":true,"name":"Relationships between space and time","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF4-4b","description":"GIS data structures are used to implement the conceptual views of spatial data (vector and raster models). The power of a GIS is dependent on the richness of information contained in the spatial data structures. Vector models are based on points, lines and areas. Raster models are based on grids. Each cell has a value that is used to represent some characteristic of that location. \r\nLayers are used to display geographic datasets in various digital map environment. A layer stores the path to a source dataset and other layer properties, including symbology. You can use multiple layers on one map and specify its properties. Shapefiles represent spatial character of the object in terms of shape, size and spatial arrangement. Shapefile usually comprise three separate and distinct types of files (main files, index files and database tables). Data base files store additional attributed that can be joined to a shapefiles’ feature. Attribute data types supplement geographic spatial feature with additional information. Spatial data includes information of location and attribute data includes information about other characteristics (what, where and why). A legend is a visual presentation of the symbols that are used on the map with some additional explanations. It includes a sample of each symbol and a short description of the meaning.","hasChildren":true,"name":"Categories","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF4-5","description":"An entity obtained by abstracting the real world, having a physical nature (certain composition of material), being given a descriptive name, and observable; e.g. “house”. An object is a self-contained part of a scene having certain discriminating properties.\r\n\r\nThe primitives of vector data sets are the point, (poly)line and polygon. Related geometric measurements are location, length, distance and area size. Some of these are geometric properties of a feature in isolation (location, length, area size); others (distance) require two features to be identified.\r\n\r\nIn a GIS, features are represented together with their attributes—geometric and non-geometric—and relationships. The geometry of features is represented with primitives of the respective dimension: a windmill probably as a point; an agricultural field as a polygon. The primitives follow either the vector or the raster approach.\r\n\r\nVector data types describe an object through its boundary, thus dividing the space into parts that are occupied by the respective objects. The raster approach subdivides space into (regular) cells, mostly as a square tessellation of two or three dimensions. These cells are called pixels in 2D and voxels in 3D. The data indicate for every cell which real-world feature is covered, provided the cell represents a discrete field. In the case of a continuous field, the cell holds a representative value for that field. The Table below lists advantages and disadvantages of raster and vector representations.\r\n\r\nThe storage of a raster is, in principle, straightforward. It is stored in a file as a long list of values, one for each cell, preceded by a small list of extra data (the “file header”), which specifies how to interpret the long list. The order of the cell values in the list can, but need not necessarily, be left to right, top to bottom. This simple encoding scheme is known as row ordering. The header of the raster will typically specify how many rows and columns the raster has, which encoding scheme was used, and what sort of values are stored for each cell.\r\n\r\nData can be of a qualitative or quantitative nature. Qualitative data is also called nominal data, which exists as discrete, named values without a natural order amongst the values. Examples are different languages (e.g. English, Swahili, Dutch), different soil types (e.g. sand, clay, peat) or different land use categories (e.g. arable land, pasture). In the map, qualitative data are classified according to disciplinary insights, such as a soil classification system represented as basic geographic units: homogeneous areas associated with a single soil type, recognizable by the soil classification.\r\n\r\nQuantitative data can be measured, either along an interval or ratio scale. For data measured on an interval scale, the exact distance between values is known, but there is no absolute zero on the scale. Temperature is an example: 40 ◦C is not twice as hot as 20 ◦C, and 0 ◦C is not an absolute zero.\r\n\r\nQuantitative data with a ratio scale do have a known absolute zero. An example is income: someone earning $100 earns twice as much as someone with an income of $50. In order to generate maps, quantitative data are often classified into categories according to some mathematical method.\r\n\r\nIn between qualitative and quantitative data, one can distinguish ordinal data. These data are measured along a relative scale and are as such based on hierarchy. For instance, one knows that a particular value is “more” than another value, such as “warm” versus “cool”. Another example is a hierarchy of road types: “highway”, “main road”, “secondary road” and “track”. The different types of data are summarized in Table.","hasChildren":true,"hasParent":true,"name":"Properties","selfAssesment":"GI-N2K
"},{"code":"CF4b","description":"Geographic phenomena, geographic information, and geographic tasks are described in terms of space, time, and properties. Different theories exist as to the nature and formal representation of these aspects, including space-like dimensions, sets, and phenomenology. Information in each of these three aspects is measured and reported with respect to one of several frames of reference or domains, including both absolute and relative approaches. Early frameworks such as those of Berry (1964) and Sinton (1978) were influential in setting forth the importance of space, time, and theme in GIS&T. Besides, space, time, and properties, categories are also fundamental in the conceptualization and representation of spatial entities, phenomena, processes, and events. Distinctive features of geographic information such as scale and detail, spatial patterns, spatial integration, and regions are also critical for a complete description of its nature and representation. This unit is closely tied to the creation of data models in Knowledge Area 5: Data Modeling, Storage, and Exploitation.","hasChildren":true,"hasParent":true,"name":"Fundamentals of Geographic Information","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF5-1b","description":"Discrete entities can be found as fields or objects.\r\n\r\nDiscrete fields divide the study space in mutually exclusive, bounded parts, with all locations in one part having the same field value. Discrete fields are intermediate between continuous fields and geographic objects: discrete fields and objects both use “bounded” features.\r\n\r\nDiscrete fields divide the study space in mutually exclusive, bounded parts, with all locations in one part having the same field value. Typical examples are land classifications, for instance, using either geological classes, soil type, land use type, crop type or natural vegetation type. \r\n\r\nDiscrete fields are intermediate between continuous fields and geographic objects: discrete fields and objects both use “bounded” features. A discrete field, however, assigns a value to every location in the study area, which is not typically the case for geographic objects. These two types of fields differ in the type of cell values. A discrete field such as land use type will store cell values of the type “integer” and is therefore also called an integer raster. Discrete fields can be easily converted to polygons since it is relatively easy to draw a boundary line around a group of cells with the same value. A continuous raster is also called a “floating point” raster.\r\n\r\nGeographic objects.\r\n\r\nWhen a geographic phenomenon is not present everywhere in the study area, but somehow “sparsely” populates it, we look at it as a collection of geographic objects. Such objects are usually easily distinguished and named, and their position in space is determined by a combination of one or more of the following parameters:\r\n\r\nlocation (where is it?)\r\nshape (what form does it have?)\r\nsize (how big is it?)\r\norientation (in which direction is it facing?).\r\n\r\nHow we want to use the information determines which of these four parameters is required to represent the object. For instance, for geographic objects such as petrol stations all that matters in an in-car navigation system is where they are. Thus, in this particular context, location alone is enough, and shape, size and orientation are irrelevant. For roads, however, some notion of location (where does the road begin and end?), shape (how many lanes does it have?), size (how far can one travel on it?) and orientation (in which direction can one travel on it?) seem to be relevant components of information in the same system.","hasChildren":true,"name":"Discrete entities","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF5-2b","description":"A geographic field is a geographic phenomenon that has a value “everywhere” in the study area. We can therefore think of a field as a mathematical function f that associates a specific value with any position in the study area. Hence if (x, y) is a position in the study area, then f(x, y) expresses the value of f at location (x, y). Fields can be discrete or continuous.\r\n\r\nIn a continuous field, the underlying function is assumed to be “mathematically smooth”, meaning that the field values along any path through the study area do not change abruptly, but only gradually. Good examples of continuous fields are air temperature, barometric pressure, soil salinity and elevation. A continuous field can even be differentiable, meaning that we can determine a measure of change in the field value per unit of distance anywhere and in any direction. For example, if the field is elevation, this measure would be slope, i.e. the change of elevation per metre distance; if the field is soil salinity, it would be salinity gradient, i.e. the change of salinity per metre distance.\r\n\r\nDiscrete fields divide the study space in mutually exclusive, bounded parts, with all locations in one part having the same field value. Discrete fields are intermediate between continuous fields and geographic objects: discrete fields and objects both use “bounded” features.\r\n\r\nDiscrete fields divide the study space in mutually exclusive, bounded parts, with all locations in one part having the same field value. Discrete fields are intermediate between continuous fields and geographic objects: discrete fields and objects both use “bounded” features.\r\n\r\nDiscrete fields divide the study space in mutually exclusive, bounded parts, with all locations in one part having the same field value. Typical examples are land classifications, for instance, using either geological classes, soil type, land use type, crop type or natural vegetation type. \r\n\r\nDiscrete fields are intermediate between continuous fields and geographic objects: discrete fields and objects both use “bounded” features. A discrete field, however, assigns a value to every location in the study area, which is not typically the case for geographic objects. These two types of fields differ in the type of cell values. A discrete field such as land use type will store cell values of the type “integer” and is therefore also called an integer raster. Discrete fields can be easily converted to polygons since it is relatively easy to draw a boundary line around a group of cells with the same value. A continuous raster is also called a “floating point” raster.\r\n\r\nA field-based model consists of a finite collection of geographic fields: we may be interested in, for example, elevation, barometric pressure, mean annual rainfall and maximum daily evapotranspiration, and would therefore use four different fields to model the relevant phenomena within our study area.","hasChildren":true,"name":"Fields","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF5-3b","description":"We can structure time by events (moments) or periods (intervals). When we represent intervals by a start and an end event, we can derive temporal relationships between events and periods, such as “before”, “overlap”, and “after”.\r\nValid time (or world time) is the time when an event really happened, or a string of events took place. Transaction time (or database time) is the time when the event was stored in the database or GIS. Note that the time at which we store something in a database is typically (much) later than when the related event took place.\r\n\r\nProcess models in the Earth sciences describe the evolution of geo(bio)physical surface properties in time, independently from remote sensing observations. Examples of such process models on various time scales are, for instance, numerical weather prediction models (NWPs), vegetation growth models, hydrological models, oceanographic models and climate models.\r\n\r\nProcesses on the planet Earth are complex phenomena that are taking place in space and in time, i.e. in four dimensions.\r\n\r\nIn many of these processes, differences in one dimension (e.g. height above the geoid) can be disregarded, so that two spatial dimensions and the dimension time remain. Despite this simplification, the physical description of the phenomena remains a difficult task. To better understand the processes it often helps if the same geographic region is viewed repeatedly and, if possible, also from different directions and in different wavelength regions. Integration of data from a variety of sources can be a means to retrieving information about processes that would otherwise remain undetected.","hasChildren":true,"name":"Events and processes","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF5-4b","description":"Models that integrate the concepts of space, time, and attribute in geographic information.","hasChildren":true,"name":"Integrated models","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF5-6","description":"Geographic phenomena can be studied as single entities and in relationship with each other and then reveal patters and clusters. How the entities are distributed is subject to statistical and visualisation studies.","hasChildren":true,"name":"Spatial distribution","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF5-7","description":"We can use the topological properties of interiors and boundaries to define relationships between spatial features. Since the properties of interiors and boundaries do not change under topological mapping, we can investigate their possible relations between spatial features. We can define the interior of a region, R, as the largest set of points of R for which we can construct a disc-like environment around it (no matter how small) that also falls completely inside R. The boundary of R is the set of those points belonging to R that do not belong to the interior of R, i.e. one cannot construct a disc-like environment around such points that still belongs to R completely.\r\n\r\nLet us consider a spatial region A. It has a boundary and an interior, both seen as (infinite) sets of points, which are denoted by boundary(A) and interior(A), respectively. We consider all possible combinations of intersections (∩) between the boundary and the interior of A with those of another region, B, and test whether they are the empty set (∅) or not. From these intersection patterns, we can derive eight (mutually exclusive) spatial relationships between two regions. If, for instance, the interiors of A and B do not intersect, but their boundaries do, yet the boundary of one does not intersect the interior of the other, we say that A and B meet.","hasChildren":true,"name":"Region","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF5-8","description":"Integration of data from a variety of sources can be a means to retrieving information about processes that would otherwise remain undetected.\r\n\r\nAlthough data integration can be very useful, there are also some requirements that have to be fulfilled for it to be effective:\r\n\r\n• geospatial data have to be accurately co-registered in a common grid;\r\n• time gaps between the various data layers have to be known and accounted for;\r\n• systematic effects due to the atmosphere, the viewing angle, the Sun angle, etc., must be corrected for or taken into account.\r\n\r\nData can be integrated in an almost infinite number of ways. Results from data integration can, again, be combined with other geospatial data to produce yet other new information, and so on.\r\n\r\nData integration also comprises the incorporation of non-spatial information or point data from field measurements. These data have to be associated with precise moments in time and with precise geographic locations, or with some time interval and fuzzy-defined regions. Thus, here the important issue of the representativeness of this information for the associated time interval and geographic area comes into play.\r\n\r\nIn general, data integration forces us to consider the uncertainties or inaccuracies of the various data sources available. In some cases, meta-data may contain information about this. When integrating data for some purpose, one has to apply weights to each of them, so that the final result is a balanced compromise in which inaccurate data receive less weight than those with a high degree of certainty.","hasChildren":true,"name":"Spatial integration","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF5b","description":"The concepts below form the basic elements of common human conceptions of geographic phenomena. Concepts from many units in this knowledge area have been synthesized to create general conceptual models of geographic information. Attempts to resolve the object-field debate have led to attempts to create comprehensive models that bridge these views. Consideration of this unit should also include formal models of these elements in mathematics and other fields. Knowledge Area DM Data Modeling discusses the representation of these elements in digital models.","hasChildren":true,"hasParent":true,"name":"Elements of geographic information","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF6-1","description":"Mereology is the study of parts and wholes. In GI this involves how objects are modeled as composites of other objects.","hasChildren":true,"name":"Mereology: structural relationships","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF6-2","description":"Lineage describes the history of a data set. During the processing of data, the derived information inherits artifacts from the dataset(s) of origin. In the case of published maps, some lineage information may be provided as part of its meta-data, in the form of a note on the data sources and procedures used in the compilation of the data. Examples include the date and scale of aerial photography, and the date of field verification. Especially for digital data sets, however, lineage may be defined more formally as:\r\n\r\n“that part of the data quality statement that contains information that describes the source of observations or materials, data acquisition and compilation methods, conversions, transformations, analyses and derivations that the data has been subjected to, and the assumptions and criteria applied at any stage of its life (Clarke and Clark, 1995).”\r\n\r\nAll of these aspects affect other aspects of quality, for example positional accuracy. Clearly, if no lineage information is available, it is not possible to adequately evaluate the quality of a data set in terms of “fitness for use”.","hasChildren":true,"name":"Genealogical relationships: lineage, inheritance","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF6-3","description":"We can use the topological properties of interiors and boundaries to define relationships between spatial features. Since the properties of interiors and boundaries do not change under topological mapping, we can investigate their possible relations between spatial features. We can define the interior of a region, R, as the largest set of points of R for which we can construct a disc-like environment around it (no matter how small) that also falls completely inside R. The boundary of R is the set of those points belonging to R that do not belong to the interior of R, i.e. one cannot construct a disc-like environment around such points that still belongs to R completely.\r\n\r\nLet us consider a spatial region A. It has a boundary and an interior, both seen as (infinite) sets of points, which are denoted by boundary(A) and interior(A), respectively. We consider all possible combinations of intersections (∩) between the boundary and the interior of A with those of another region, B, and test whether they are the empty set (∅) or not. From these intersection patterns, we can derive eight (mutually exclusive) spatial relationships between two regions. If, for instance, the interiors of A and B do not intersect, but their boundaries do, yet the boundary of one does not intersect the interior of the other, we say that A and B meet. In mathematics, we can therefore define the “meets relationship” using set theory. The eight spatial relationships are disjoint, meets, equals, inside, covered by, contains, covers and overlaps.","hasChildren":true,"hasParent":true,"name":"Topological relationships","selfAssesment":"Completed (GI-N2K)
"},{"code":"CF6-4","description":"Relationships between spatial features that define their relative position. Spatial autocorrelation is a fundamental principle based on Tobler’s first law of geography, which states that locations that are closer together are more likely to have similar values than locations that are farther apart.","hasChildren":true,"name":"Metrical relationships: distance and direction","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF6","description":"Like geography, geographic information not only models phenomena but the relationships between them. This can include relationships between entities, between attributes, between locations. In addition, one of the strengths of geography (and GIS) is its ability to use a spatial perspective to relate disparate subjects, such as climate and economy. Methods for analyzing relationships are discussed in Unit AM4 Modeling relationships and patterns.","hasChildren":true,"hasParent":true,"name":"Relationships","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF7-1","description":"Vagueness arises from lack of criteria for the applicability of certain linguistic terms. It arises from the lack knowledge about the meanings of terms.","hasChildren":true,"name":"Vagueness","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF7-2","description":"-Uncertainty-related terms, such as error, accuracy, uncertainty, precision, stochastic, probabilistic, deterministic, and random -Difference between uncertainty and vagueness -Dependence of uncertainty on scale and application -Expressions of uncertainty in language -The causes of uncertainty in geospatial data -Stochastic error models for natural phenomena -How the concepts of geographic objects and fields affect the conceptualization of uncertainty -Mathematical models of uncertainty: Probability and statistics","hasChildren":true,"name":"Error-based uncertainty","selfAssesment":"In progress (GI-N2K)
"},{"code":"CF7","description":"Human models (mental, digital, visual, etc.) of the geographic environment are necessarily imperfect. While the mathematical principle of homomorphism (often operationalized as fitness for use) allows for imperfect data to be useful as long as they yield results adequate for the use for which they are intended, imperfections are frequently problematic. Although terminology still varies, two types of imperfection are generally accepted: vagueness (a.k.a. fuzziness, imprecision, and indeterminacy), which is generally caused by human simplification of a complex, dynamic, ambiguous, subjective world; and uncertainty (or ambiguity), generally the result of imperfect measurement processes (as discussed in Knowledge Area GD Geospatial Data). Both of these can be manifested in all forms of geographic information, including space, time, attribute, categories, and even existence. Imperfection is also dealt with in Units GD6 Data quality (in the context of measurement), GC8 Uncertainty and GC9 Fuzzy sets (for the handling and propagation of imperfections), and CV4 Graphic representation techniques (in the context of visualization).","hasChildren":true,"hasParent":true,"name":"Imperfections in geographic information","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV","description":"Geo-data visualisation necessarily includes cartography as the origin of \"mapping\" our world. Cartography methods have drastically changed over the few years since the increasing role and sophistication of digital technology applied to geo-information visualisation. It is first worth differentiating between the underlying geo-data that describes real world phenomena and the bits of information that describe the visual presentation of geo-data . Likewise, there are processing tools to collect and handle geo-data, and processing tools especially designed to create and manage geo-data visualisations. \r\nWhile cartography methods have traditionally produced printed maps (i.e. hard copy) with static scale, orientation, projection, legends (content based) and tied to a period or instant of time. Nowadays geo-data visualisations are interactive by design, meaning that the results are map-based responsive interfaces, highly customisable through dynamic objects to zoom in and out, pan and tilt, change projections and graphic expressions on the fly, as well as dynamically browse the map over time. \r\nIf the production methods have changed, also the type of authors. Map making in its widest sense is not only a privilege of a few experts but has been democratised in such a way that. everybody is able to make maps using open data and open source apps and tools for geo-data visualisation. Therefore,the new roles of open data and new forms of geo-data like geo-social media make usability, intended and ethical considerations key aspects of geo-data visualization design, production and sharing. \r\nUnder the concept of cartography and visualisation it is included a list of concepts that together comprise the science and technology of visual representation of geographic data.","hasChildren":true,"hasParent":true,"name":"Cartography and Visualization","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV1-1","description":"The evolution of cartographic representation in the previous centuries followed the most important technological and scientific developments of the time. It was driven by commercial and/or military needs and influenced by the special characteristics of the areas and/or environments to be mapped. Recent developments are the rise of open data worldwide and widely available internet technology allowing end users to get remote geo-data published elsewhere. In recent years, data and its digital presentation have become central elements of cartography, whereas paper maps have become peripheral.","hasChildren":true,"hasParent":true,"name":"History and evolution of cartography","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV1-4","description":"Art in cartography means much more than designing aesthetically pleasing maps, whether on paper or digital. Exploring the interaction at large between art and cartography involves rethinking the way we approach spatial expressions and how cultural, social and political dimensions are reflected in maps. This can be clearly observed in historical maps - in between art and science - ranging from beautiful geographical representations created in the Middle Ages to convey religious messages to the creation of modern maps showing the power of modern empires and nations. This particular relationship between art and maps entails: “developing an inclusive approach of artistic mapping expressions; facilitating and encouraging interaction between cartographers who work with the Art aspects of cartography and artists who produce cartographic artifacts; and developing conceptual elements about the relationships between art and cartography.” Besides ancient paper maps, a sum of factors led digital maps and geospatial visualization, a matter of interest to artists and designers. Thanks to powerful computing systems and with the advancements reached in computer graphics or image processing, or the rise of information visualisation, new forms of representing and visualising geodata have also appeared. Creation of digital maps are still a two-way relationship since artists have explored maps as a medium for expressing their art, and cartographers have approached art to provide more than just the representation of locations and geographic features with the intention to make maps more attractive to their audiences.","hasChildren":true,"name":"Art and geodata visualisation","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV1-5","description":"Historical maps are geographical representations made with the intention to represent spatial facts over time. Historical maps are generally considered valuable documents not just because of their historical value but also because most of them also are artistic representations by themselves. From a cartographical point of view, differentiation between historical maps and actual maps is mainly based on the advances in the history of Cartography, so once one disruptive advance in the map making process appears, maps created with previous techniques (and with some artistic or historical value) are usually considered as historical, such as ancient paper-based maps or old sea maps, for instance. Techniques such as scanning or photography can make ancient maps publicly available by converting hard-copy maps to digital ones. Once an historical map is digitised, the next step is to georeference it, which is the process of specifying and relating points of the digitalised map to actual coordinates in a geographic reference system. Because of its archival value and interest, historical maps are adequately preserved - following specific conditions - by map libraries, map societies or museums. Since digital methods and techniques have been replaced over time by new technological advances, first digitally created maps could be also considered historical, not because of its content, but of the techniques used to produce it.","hasChildren":true,"name":"Historical maps","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV1","description":"At a certain moment in time people start to create more graphical representations of their surrounding environment. New technologies offered ways to expand these representations to larger geographical extent, higher spatial resolution, finer temporal granularity and larger periods. Technologies even made it possible to include other representations of reality such as social media and data ensembles in geodata visualizations, to the extent to even blend the real world with geodata-based visualization providing an augmented – virtual reality continuum. New forms of geo-data, like geolocated sensors may challenge the way geo-data visualisations are generated, shared and, eventually, influence decision-making processes. History and trends sketch these developments and future outlook. This concept introduces the main stages and turns in development of cartography, from earliest times to the present, the most important methods in map-making and map-based visualizations.","hasChildren":true,"hasParent":true,"name":"History and trends","selfAssesment":"Completed (GI-N2K)
\r\n\r\n"},{"code":"CV2-1","description":"As mapping ( geo-data visualization) is intended to convey a certain message to a certain audience, it is essential to use data sources that allow the intended visualisation result. The data should be of the right degree of detail and its use should not cause copyright problems. The producer quality of each data set should be taken into account, as well as the fitness of the data for the intended use. Aspects: message; data quality","hasChildren":true,"name":"Data sources for mapping","selfAssesment":"
In progress (GI-N2K)
"},{"code":"CV2-2","description":"In the trajectory between raw (geo)data and their user-relevant representation, the necessary data processing includes ways of abstraction by selection, filtering, generalization, transformation and classification of geographical data. In this data processing it is essential to at one hand relate the final symbolisation to the necessities of the intended message, and at the other hand to procedures that introduce as little error as possible.","hasChildren":true,"name":"Data processing","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV2-3","description":"Map projection is fundamental to representation of spatial data and for combining different datasets. Its choice should serve the presentation type that will convey the intended message to the audience. Many mathematical principles define datum, projections, horizontal and vertical co-ordinate systems, georeferencing- introduced with the focus on visualisation issues Aspects: geodetic concepts; transformations","hasChildren":true,"name":"Mathematical base","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV2","description":"Geodata, including 3 dimensional geometry, as such can graphically be presented but most of the times the data as such doesn`t meet the presentation criteria. Especially if the dataset has to be presented in combination with other datasets. First all the geodatum, georeference and map projection are crucial but also the role of the geometry. The processing of the geometry and the related attributes may become a crucial step for an adequate presentation. Nowadays the highest precision may be used to define different graphical attributes for different zoom levels. On the other hand geodata visualisation includes also graphical datasets. Such data ensembles, the combination of geodata and graphical data, are the data sources that offer opportunities to other ways of visualisation then the traditional cartographic mapping. Facets: a.\tGeospatial location (2D) and position (3D) that data refer to b.\tDegree of detail in data origin (acquisition resolution) and in representation ('map' scale) c.\tTypes of data (e.g. imagery, field measurements, delineated objects)","hasChildren":true,"hasParent":true,"name":"Data considerations","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV3-1","description":"The combined impact of graphic design properties (balance, legibility, clarity, visual contrast, figure-ground organization, and hierarchal organization) and the map components (north arrow, scale bar, and legend) should always be carefully evaluated against the needs and the capacities of the audience.","hasChildren":true,"name":"Map design fundamentals","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV3-10","description":"Geo-gaming is a crossover between gaming elements and location, usually enabled by location based services and augmented adn/or virtual reality features. Geo-games, also known as “location-based games” or “location-aware games”, have geodata at its core, since geoinformation constitutes the central element of the game mechanics. Geo-gaming applications present unique technical challenges to meet the infrastructural and resources demands from the games and location worlds. There are mainly four different types of geo-games: exploration games (to make use of an existing spatial design); feedback games (to report about players’ experiences in a specific design); allocation games (to occupy the majority of game location); and configuration games (to occupy specific pattern of game locations). Gamers actively participate by interacting with the environment, therefore gaming scenarios are as varied as their goals, which include teaching, training, and the developing of spatial thinking skills. Geo-games offer a myriad of opportunities to developers: non-linear storytelling, physical object integration, a more visceral experience, true social interaction… which bring geo-games to another interaction level. Geo-gaming applications often rely on VGI to allow gamers adding geolocated information that may crowdsource geo-referenced data useful for other secondary purposes .","hasChildren":true,"name":"Geo-gaming","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV3-2","description":"Map symbolization entails a number of variables to produce visual, tactile, haptic, auditory, and dynamic displays. Visual variables (e.g., size, lightness, shape, hue) and graphic primitives (points, lines, areas) are commonly used in maps to represent various geographic features at all attribute measurement levels (nominal, ordinal, interval, ratio). With those a single geographic feature can be represented by various graphic primitives (e.g., land surface as a set of elevation points, as contour lines, as hypsometric layers or tints, and as a hillshaded surface). The challenge is to use effective symbols for map features to ease the interpretation of maps.","hasChildren":true,"name":"Symbols and icons","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV3-3","description":"The selection of colours to use in data representation can be influenced by various factors (e.g. the production workflow, cultural differences, involved devices and media). There are various colour models (e.g. RGB, CMYK, CIE) that describe colours in a way that they can effectively convey visual information (e.g., qualitative, sequential, diverging, spectral) according to the meaning of the underlying data. The cultural background of the consumer is also relevant when it comes to choose colours that should have real-world connotations or should express psychological concepts (e.g. harmony, concordance, balance). A final important factor is if the consumer has colour limitations","hasChildren":true,"name":"Colour","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV3-4","description":"When data representation is conveyed in words (e.g. toponyms, road codes), written text is often placed in map labels. It is important to decide on the role of the label in the context of the representation type. Algorithms for label placement are relevant, especially when label density is high. Shape and colour of the labels help to signify different types of messages. This is supported by the typographic properties (type font, size, style) of the text in the labels. Finally, it is important to use an authoritative source for the texts","hasChildren":true,"name":"Typography","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV3-5","description":"Imagery can be a source for data acquisition as well as an illustration to abstract data representations. Imagery can be made from the air (from drones to satellites) or from a terrestrial point of view (street-level imagery). Using photos from any source to illustrate stories about geographical subjects contributes as the visual aspect of telling a story. Together with maps and other narrative components, the combination embodies a storytelling medium.","hasChildren":true,"name":"Photos and imagery","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV3-6","description":"Animation is the process of making the illusion of motion and change by means of the rapid display of a sequence of static images that minimally differ from each other. In the context of maps, the temporal component is added to a map to emphasize and observe the gradual evolution of a certain monitoring phenomenon, such as changes in spatially numerical variables (for example, environment, population, mobility, land use, etc.) with respect to a static geographic area. Map animations generally consider dynamic time while space is static. Map animation helps to see patterns or trends that emerge as time passes, depicting meteorological or climate events, natural disasters, historical events and other multivariate data. It is particularly helpful to be used in educational settings.","hasChildren":true,"name":"Animation","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV3-7","description":"Sound or audios can be one of the components of a multimedia data representation. A conventional GIS usually conveys visual information, however the integration of audios in mapping could enrich GIS data to other senses. Sound can increase the amount of information that’s communicated to the user through channels other than visual to address the special needs of people with visual impairments or people who cannot use in certain circumstances their sight, such as a driver who cannot look at a map. Approaches to rendering sound information on a map fall into three broad categories: (1) to sonoficate the whole visual presentation (for simple geometric data), (2) to augment a visual system with auditory information (allowing multivariate information) and (3) to display information about the surrounding where a user is. By classifying images and creating additional audio layers that associate each pixel with a specific sound, a GIS can add a new auditory dimension to maps.","hasChildren":true,"name":"Sound","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV3-8","description":"Maps are valuable because they provide a large amount of detail in a small amount of space, and because of their capacity for telling a story. Telling stories through maps began with describing explored lands in great detail against terra incognita. Today, geographic tools, data, and multimedia on the web expand the ability to communicate stories and inform through maps to a broad audience such as journalists, decision makers and educators. Any person with a smartphone or computer can tell a story, using statics maps, or interactive web maps with text, video, audio, sketches, and photographs. Besides the technical skills to clearly communicate with a map (palette of colours, amount of information displayed…), other factors such as narrative processes, the storyboard, place, time, and characters play a crucial role. To be informative, it is important that the correct data is displayed, combining different sources of information combined to create an appealing and accurate map.","hasChildren":true,"name":"Storytelling","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV3-9","description":"Infographics are visual representations of information and data, which can contain charts, diagrams, graphs, tables, maps and lists. The aim of an infographic is to present information that can be absorbed quickly, it is easily understandable and extensively in mass communication, and thus designed with fewer assumptions about the reader's knowledge base than other types of visualizations. The role of maps in an infographic is based on the potential of maps to condense information and to support a narrative. Infographic maps - altogether with an adequate storytelling - should find a simple way to explain current complex issues, providing added value to the infographic, and being an effective and efficient way to communicate.","hasChildren":true,"name":"Infographics","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV3","description":"This concepts covers basic design principles that are used in mapping and visualization, as well as cartographic design principles specific to the display of geographic data. Both page layout design and data display are addressed.","hasChildren":true,"hasParent":true,"name":"Design principles","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV4-1","description":"A thematic map is a type of map especially designed to show a particular theme connected with a specific geographic area. These maps \"can portray physical, social, political, cultural, economic, sociological, agricultural, or any other aspects of a city, state, region, nation, or continent\". Cartographers use many methods to create thematic maps. Five techniques are especially noted: -Choropleth mapping shows statistical data aggregated over predefined regions -Proportional symbols, showing the relative value of attributes -Isarithmic or Isopleth, also known as contour maps -Dots, to show the location of a phenomenon -Dasymetric, which uses areal symbols to spatially classify volumetric data.","hasChildren":true,"name":"Thematic mapping","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV4-10","description":"Conveying uncertainty information is often done through visualization. Uncertainty is often defined, quantified, and expressed using models specific to individual application domains. In visualization however, we are limited in the number of visual channels (3D position, color, texture, opacity, etc.) available for representing the data. Thus, when moving from quantified uncertainty to visualized uncertainty, we often simplify the uncertainty to make it fit into the available visual representations. (After Potter et al., 2012). The seven challenges as formulated by MacEachren et Al. (2005) are still there to be tackled.","hasChildren":true,"name":"Visualization of uncertainty","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV4-2","description":"Relief can be represented in a two-dimensional map either through contour lines or through a raster format gridded array of elevations. Contour lines connect points of equal elevation. At regular intervals index contours are marked with elevations so a reader can more easily determine the elevation of surrounding locations. They are the preferred method for analogue topographic maps. The grid approach is used in digital mapping and known as a digital elevation model (DEM), where each raster cell represents an elevation. Scaling of the cell z value in relation to the x and y value results in terrain exaggeration, which aids visualization of topography.\r\nDEMs are used for terrain analysis and can be used to obtain derivatives such as slope and aspect. DEMs are obtained by interpolating point elevation observations, which are historically retrieved from surveyed point data (e.g. GPS locations), but more recently from LiDAR and/or Structure from Motion point clouds. TIN (triangular irregular network) analysis is commonly used for point data interpolation, in order to derive a continuous elevation surface.","hasChildren":true,"hasParent":true,"name":"Representing terrain","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV4-3","description":"Multivariate descriptive displays or plots are designed to reveal the relationship among several variables simultaneously. Bivariate and multivariate maps encode two or more data variables concurrently into a single symbolization mechanism. Their purpose is to reveal and communicate relationships between the variables that might not otherwise be apparent via a standard single-variable technique. There are basic characteristics of the relationship among variables, such as the forms of the relationships, the strength of the relationships, and the dependence of the relationships on external (usually to the pairs of variables being examined) circumstances. Therefore, these multivariate plots or maps are inherently more complex, though offer a novel means of visualizing the nuances that may exist between the mapped variables. As information-dense visual products, they can require considerable effort on behalf of the map reader, though a thoughtfully-designed map and legend can be an interesting opportunity to effectively convey a comparative dimension. Examples of multivariate plots include enhanced 2-D scatter diagrams, 3-D scatter diagrams, contour, level, and surface plots, and high-dimensional data plots","hasChildren":true,"name":"Multivariate displays","selfAssesment":"Completed (GI-N2K)
\r\n\r\n"},{"code":"CV4-4","description":"Visualization of change and movement across space and time is of increasing interest to researchers and geospatial practitioners. The visualization process of temporal data has four steps: (1) time values to be visualized, (2) point of view on time, that identifies the characteristics of the temporal values to be visualized, (3) time space: define the displayable space of the time values and (4) point of view on the visualization space, the implementation of the perceptible forms of time. The visualization of spatio-temporal data can be done in many different ways such as multi-panel plots (maps), time-series plots (graphs), space-time plots (graphs), 3D Virtual Reality (Computer generated artificial environment), animations (production of consecutive images), and tables. Spatiotemporal data comprises three important components: geographic location, temporal information and the thematic attributes describing a real-life phenomenon.","hasChildren":true,"hasParent":true,"name":"Visualization of temporal geographic data","selfAssesment":"
Completed (GI-N2K)
"},{"code":"CV4-5","description":"Dynamic and interactive displays refers to a situation where a display with a cartographical data representation changes in real time in response to user's actions","hasChildren":true,"name":"Dynamic and interactive displays","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV4-6","description":"Web mapping is the process of designing, implementing, generating and delivering maps on the World Wide Web. Dissemination via the web opens new opportunities: realtime maps, cheaper dissemination, more frequent and cheaper updates, personalized map content, distributed data sources and sharing of geographic information. Technical restrictions cause challenges like low display resolution and limited bandwidth,( in particular with mobile computing devices with small screens and using slow wireless Internet connections), copyright and security issues, reliability issues and technical complexity. Today's web maps can be interactive and integrate multiple media. So interactivity, usability and multimedia issues also play a role.","hasChildren":true,"name":"Web mapping","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV4-7","description":"Virtual reality or virtual realities (VR), also known as immersive multimedia or computer-simulated reality, is a computer technology that replicates an environment, real or imagined, and simulates a user's physical presence and environment in a way that allows the user to interact with it","hasChildren":true,"name":"Virtual and immersive environments","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV4-8","description":"An Augmented Environment can be experienced through different sets of Augmented Reality (AR) technologies, including mobile displays (tablets and smartphone screens), computer monitors, or Head-Mounted Displays (HMDs), among others. AR is a technology that layers computer-generated enhancements atop an existing reality to make it more meaningful through the ability to interact with it. AR offers the integration of digital information and imagery onto the real world in real-time. In order to broaden the vision beyond this definition, AR can be described as systems having the following features: (1) combines real and virtual; (2) interactive in real-time; and (3) registered in 3D, allowing other technologies, such as mobile technologies, monitor-based interfaces, monocular systems to overlay virtual objects on top of the real world. Currently, AR applications use the camera provided by mobile devices to produce a live view of the real world in combination with relevant, context-appropriate information such as text, videos, or pictures.\r\nThere are lots of applications and systems in the market that provide AR functionality, making it difficult to classify and name them all. Some of them are related to the real physical world and others with the abstract, virtual imagery world. Sometimes it is not easy to figure whether it is an AR, as often AR is defined as Virtual reality (VR) with transparent HMDs. In general, the concept is to mix reality with virtual reality, including information and overlay over the real world through HMDs such as they seem apparent as one environment. The virtual objects can react accordingly with the camera's movement as it is registered concerning the real world, which is also the central issue of AR.","hasChildren":true,"name":"Augmented environments","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV4-9","description":"Cartographers have recently become involved in extending geographic concepts and cartographic design approaches to the depiction of non-geographic data archives, using so-called spatialized views of information spaces. Spatializations differ from ordinary data visualisation and geovisualisation in that they may be explored as if they represented spatial information. (Fabrikant, S.I., 2003). As definitions of spatialization can be found: Spatializations are computer visualizations in which nonspatial information is depicted spatially (Montello et al., 2003). Spatialization is the transformation of high-dimensional data into lower-dimensional, geometric representations on the basis of computational methods and spatial metaphors. (Skupin 2007)","hasChildren":true,"name":"Spatialization","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV4","description":"This concept addresses mapping methods and the variations of those methods for specialized mapping and visualization instances, such as thematic mapping, dynamic and interactive mapping, Web mapping, mapping and visualization in virtual and immersive environments, using the map metaphor to display other forms of data (spatialization), and visualizing uncertainty. Analytical techniques used to derive the data employed in these graphic representations are discussed in Knowledge Area AM Analytical Methods and Unit DN2 Generalization and aggregation.","hasChildren":true,"hasParent":true,"name":"Graphic representation techniques","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV5-2","description":"Standards for map services were set by OGC and ISO, called WMS and WMTS. Producing map images on the web from a cartographic image in a GIS application is called \"publishing\". Making a web \"map\" in the broader sense of constructing data representations for Storytelling or Geo-gaming is still under development. It requires a mix of applying the map Design principles and Graphic presentation techniques, possibly in combination with software scripting.","hasChildren":true,"name":"Web map making","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV5-3","description":"Traditional \"map\" making, as opposed to the mapmaking in neogeography, focuses on reliable and reproducible products, based on expertise of high definition printing in many colours on analogue media of geodetically well-constructed images.","hasChildren":true,"name":"Traditional map making","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV5-4","description":"The aspects of reproduction of a data representation depend on the nature of the representation: is it analogue (a paper map, a mock-up) or is it digital? In the case of a paper map, its digitalisation with high fidelity is an essential step. With a source in digital form, reproduction can be a matter of the right printer. Alternatively, the source could be disseminated as a file or as a web service. If representations are dynamic and/or interactive the possibilities depend on the construction of the representation. The ease of dissemination of digital files should not result in copyright breach. Aspects: Digitalization techniques for analogue sources, Printing ( 2D, 3D), Dissemination ways, Construction of the data representation, User needs specification, Copyright issues","hasChildren":true,"name":"Map reproduction","selfAssesment":"GI-N2K
"},{"code":"CV5","description":"This concept addresses map production and reproduction, as well as computation issues that relate to those workflows.","hasChildren":true,"hasParent":true,"name":"Map production","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV6-1","description":"The potential of maps as a way to show or exert power over the population was early understood by ruling classes. A map expresses a claim by the inclusion or exclusion of map elements and how these elements are visually related and/or depicted on the map. So, the world could be modeled through the careful choice of content arranged graphically at a specific scale and in specific formats. Therefore, maps embody and project the interests of their creators. The “new cartographies” declare that maps are redefined as socially constructed arguments based upon consistent semiotic codes. Nowadays, the rise of costless, powerful and accessible tools for creating maps, put power on the side of individuals or groups of individuals with few organisation (crowdsourced data collection or VGI) capable of representing their world views. In addition, monitoring people, places or nature, for instance, should also be seen as another way to show the increasing power of maps. Surveillance mechanisms for tracking populations used by rulers, or the use of extended technologies like Google Earth by environmental organisations to track the Amazonian forest, constitute two examples of the particular use of maps to exert control over human beings or to press governments for taking specific actions, respectively.","hasChildren":true,"name":"The power of maps","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV6-2","description":"Maps today help us locate the nearest gas station or ATM on our in-car navigation system, but this use of locating what is near or surrounds a location is not new. Maps from pre-historic times provided important locational information – what was where and how to get from place to place. A map can be a relatively simple iconic device, which can be read and interpreted with only a little training. These graphic representations of the real world could be traced in sand or painted on a cave wall and shared through time. Maps even preceded written language and number systems and are found in some format in most cultures through time as a graphical language. Learning to read this language and interpret it without ambiguity is not as simple as first suggested. This complexity has increased as technology has allowed creation of 3D and 4D interactive maps which allow anyone with internet access the ability to investigate different places, topics and times and produce their own map. Today the ability to read and interpret maps is increasingly important as industry, business and government communicates within their organization and the public using maps. Becoming aware of what a “map” shows depends partly on what the senses can register of the representation as a whole. It also depends on recognition of elements in the representation that are meaningful to the observer in the sense that these elements are credible indicators of spatial features. Based on that recognition, the nature of these elements and their spatial pattern might infer thoughts about historic or ongoing processes. This interpretation will be influenced by the expertise and needs of the observer.","hasChildren":true,"name":"Map reading and interpretation","selfAssesment":"Completed (GI-N2K)
"},{"code":"CV6-3","description":"Assessment of the usability of a data representation is about how useful it is to users. Therefore it is a test of the success of the representation design, a test of the skills of the \"map\" maker and a test for the reliability of the underlying data.","hasChildren":true,"name":"Usability analysis","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV6-6","description":"Spatial thinking is thinking that finds meaning in the shape, size, orientation, location, direction or trajectory, of objects, processes or phenomena, or the relative positions in space of multiple objects, processes or phenomena. Spatial thinking uses the properties of space as a vehicle for structuring problems, for finding answers, and for expressing solutions\" Aspects: recognizing spatiality in a collection of things; translation of the collection to a pattern of elements; recognizing structure (relations between the elements in a pattern); recognizing process (or changes over time in patterns or structures)","hasChildren":true,"name":"Spatial thinking","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV6-8","description":"Ethics is about the question if behaviour is right or wrong in a social context. In dealing with geodata, a person can do the wrong thing with respect to laws (e.g. disclose secrets, disregard privacy, copyright infringement) or to professional standards (e.g. use bad data, forget about the colour blind, downplay unpleasant details). Aspects: breach of legal standards; breach of professional standards","hasChildren":true,"name":"Map ethics Legal and privacy issues","selfAssesment":"In progress (GI-N2K)
"},{"code":"CV6","description":"Geodata visualisation are always made with a certain purpose. The role and understanding of such graphical representation is an important field of research. Besides theories that underpin evaluation approaches and their findings the visualisation may also be confronting. The more realistic the presentation and especially when it includes human/personal related data the ethical dimension of the visualisation play a major role. Usability of visualisations has also an impact on spatial thinking as has been proved by scholars.","hasChildren":true,"hasParent":true,"name":"Usability of maps","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA","description":"Proper design of geospatial applications, models, and databases and the validation and verification of design activities are critical components of work in all areas related to GIS&T. Design failures can negate well-intentioned efforts to apply concepts and technology to solve real-world problems. While sharing a number of concerns with general systems analysis, the unique and complex spatial characteristics of geospatial information provide significant additional challenges. The focus of this knowledge area is on the design of applications and databases for a particular need. The design of general-purpose models and tools (e.g., raster and vector) is covered in Knowledge Area: Data Modeling (DM). In the context of specific implementations, design activities fall into three general classes:\r\n1. Application Design addresses the development of workflows, procedures, and customized software tools for using geospatial technologies and methods to accomplish both routinary and unique tasks that are inherently geographic.\r\n2. Analytic Model Design incorporates methods for developing mathematical models, spatial models and data processes. The design of the analytic model is often influenced by decisions that are made about data models and structures.\r\n3. Database Design concerns the optimal organization of the necessary spatial data in a computer environment in order to efficiently sustain a particular application or enterprise.","hasChildren":true,"hasParent":true,"name":"Design and Setup of Geographic Information Systems","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA1-1","description":"This concept deals with the importance of having a list of prioritized requirements as a first step to ensure a smooth and successful implementation of a GIS project.. It entails the different methodologies and approaches to ensure a GI system covers all functional and nonfunctional requirements. Requirements are not only derived from business workflows but it is advisable to gather direct input from potential users that will be translated into requirements. However, there is a need to clearly rank the importance of the requirements gathered to ensure the GI system is manageable and in line with the intended use of the GI system, in opposition with the specific interests of a particular user or ambiguous requirements. Therefore, the documentation, traceability and evaluation of requirements after the implementation are as relevant as the initial gathering of requirements to give consistency to the designed system.","hasChildren":true,"name":"Requirements gathering and analysis","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA1-2","description":"The internal process of documenting a task or a process is about “how” it is implemented and “what” is implemented. Documenting is particularly helpful if a breakdown occurs, such as when an expert working in a task leaves her job or to substitute one task in a set of interrelated processes by another. Documentation provides consistency for the taskand allows its monitoring, analysis and revision during a project. \r\nThere are different methods for documenting a task to transform tacit knowledge into explicit knowledge. Therefore, the task should be documented by describing it in video format and using visual tools that allow documentation, or the maintenance of a field diary.\r\nIn particular cases, the creation of user guides or manuals could be considered a subset of a process description particularly addressed to external users. A user manual should take into account the target users to adapt its content to them.","hasChildren":true,"name":"Methods of process description and documenting","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA1-4","description":"A workflow is a sequence of operations that altogether perform a complex, sophisticated or repetitive operation or activity. No matter the workflow type, a workflow is defined in a declarative language, either text-based or visual, and stored in a workflow document to ease sharing and maintenance. In GI systems, a workflow can be seen from distinct perspectives. One of the most well-known GI workflow types is spatial data modelling. A model is specified as a combination of processing tools that manipulate and transform the spatial data required by the model. The order in which the processing tools, inputs, and outputs are organised in a workflow will determine the results and to what extent the spatial question is addressed. However, workflows in GI systems are not only related to spatial data modelling and transformation. There are cases where certain processes in GI systems should be designed in terms of software and hardware requirements, actors needs, organisational aspects or resource usage and demand. How can people’s work contribute to define the stages of a GI architecture? How much time does a regular user spend working with spatial data? How complex is the process going to be? The definition of this sort of workflows can help, for example, in designing an optimal architecture for a GI system in a particular enterprise configuration. \r\nWhether the workflow defines specific steps to process spatial data or the stages and details to implement an enterprise GI system, having a clear idea over each stage's inputs and outputs helps GI systems to be organised, consistent and reliable. In summary, high-level workflows like business workflows put together systems, components and actors that are part of a process or operation. They represent an abstract view, focused often on organisational, functional and resources usage aspects. Conversely, low-level workflows refer to a series of executable activities that carry out data transformations, models or spatial data analysis. Examples are code scripts, specified as sequences of commands in a programming language, and graphical workflows through, for example, the Model Builder in GI systems which are enacted by workflow engines.However, workflows in GI systems are not only related to spatial data modelling and transformation. There are cases where certain processes in GI systems should be designed in terms of software and hardware requirements, actors needs, organisational aspects or resource usage and demand. How can people’s work contribute to define the stages of a GI architecture? How much time does a regular user spend working with spatial data? How complex is the process going to be? The definition of this sort of workflows can help for example in designing an optimal architecture in an enterprise configuration for a GI system. \r\nWhether the workflow defines specific steps to process spatial data or the stages and details to implement an enterprise GI system. Having a clear idea over each stage's inputs and outputs helps GI systems to be organised, consistent and reliable. In summary, high-level workflows like business workflows put together systems, components and actors that are part of a process or operation. They represent an abstract view, focused often on organisational, functional and resources usage aspects. Conversely, low-level workflows refer to a series of executable activities that carry out a complex task, service or model. Examples are code scripts, specified as sequences of commands in a programming language to carry out data transformations and spatial models and spatial data analysis; and graphical workflows through, for example, the Model Builder in GI systems which are enacted by workflow engines.","hasChildren":true,"name":"Workflow definition and consideration in GI systems","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA1-5","description":"Software and information technology are integral to any GI systems or projects, from the storage and handling of spatial data to its analysis, visualization and sharing. Therefore, the use of well-known software design and engineering techniques and methods to develop efficient, reliable, and easy-to-maintain software applications in the GIS realm is more important than ever. \r\nAmong the modern software design and engineering techniques, Agile software development methodologies like Scrum stands out. The common rationale of the Agile methods is to split a large software project into many functional pieces of software that help the software engineering team to translate their development efforts into quick prototypes, and eventually reach the final product. Therefore, the constant feedback and validation of the user’s requirements in short, iterative development circles (i.e sprints) are the main advantages of the Scrum methodology.","hasChildren":true,"name":"Software design and engineering","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA1-6","description":"User interface and usability of a GIS system","hasChildren":true,"name":"User interface and Usability","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA1-9","description":"Geodesign is a design and planning method along with geospatial modelling and technology, and simulations informed by geographic contexts to facilitate informed decisions and the creation of design proposals. A geo-design process is a problem-based, iterative process bounded by specific (geographic) constraints characterised by a collaborative effort.","hasChildren":true,"name":"Geodesign","selfAssesment":"Completed
"},{"code":"DA1","description":"This concept encloses a set of activities and workflows to ensure that the implementation of a GIS system in an organization or project is correctly planned and designed according to the particularites, user requirements and current conditions of the project ahead. In general system design is the process to promote successful GIS in an enterprise environment. As a GIS system has a direct influence on the information technology department (IT), the system design tells the organizacion how the current infrastructure can or must support the planned GIS. This process builds a set of specific recommendations on hardware and network needs based on the number of projects that depend on the GIS solucion, as well as the projected business needs and user requirements. \r\nGIS architects through the system design process need to take into account and identify several conditions: a) infrastructure requirements, b) the network communication capacity, c) hardware and software procurement requirements and, d) software development and data acquisition needs. \r\nHaving a well-defined and successful GIS deployment is not only a matter of what data or software the organization should acquire. The process of system design aligns identified business requirements (user needs/requirements) derived from business strategies or project aims, goals, and stakeholders (business processes) with identified business information systems infrastructure technology (network and platform) recommendations. \r\nThe process starts with identifying business needs, including the identification of users locations, required information, data, resources or products. The business needs are generally considered as project workflows that help the GIS architects to identify the expected data traffic and computing demand associated with each transaction, being a transaction the work unit used to translate business requirements into associated server and network loads.\r\nWithout carrying out a proper system design, a GIS system can lead to an implementation and deployment failure, deriving in unfulfilled expectations and high costs in terms of human resources and financial matters.","hasChildren":true,"hasParent":true,"name":"System design","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA2-1","description":"Project management includes the planning, organization, coordination, execution, monitoring, controlling and closing of the activities and resources - human and economic - for the timely achievement of clearly defined objectives forming a project. For the success of a project, a project manager will assure an efficient use of resources and a proper execution of tasks to deliver value to users and “clients” of products and services. The Project Management Body of Knowledge (PMI) defines “project management” as “the application of knowledge, skills, tools, and techniques to project activities to meet requirements”, being EO*GI projects are another type of information technology projects. PMI reflects different areas to take care of by project management. These areas are: Integration, Scope, Time, Cost, Quality, Human Resource, Communications, Risks, Procurement and Stakeholder. There are a variety of tools and techniques used in the areas identified by PMI, just to name a few Gantt chart, Program evaluation and review (PERT) analysis, AGILE project management, etc. that will help in project management.","hasChildren":true,"name":"Project management","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA2-2","description":"This concept embraces the factors that could affect a GI system / project and could constitute obstacles to success or even decide a project is not doable. In order to ensure the success of a GI system or a GIS project there are several criteria to take into account from the very beginning of the conception of the GI system or project. A feasibility study may encompass different perspectives (economic, legal, technical, operational or scheduling ) to inform whether or not a project is worth the investment. An organisation should list the foreseen costs from these five perspectives listed above and the benefits (tangible or intangible) of implementing a system/project. Existing resources already available in-house and internal strategic plan in place could be critical to decide to undertake a project or not. The table below presents a non-exhaustive list of criteria and under which perspectives they should be examined.\r\nFeasibility analysis should include a pilot study to evaluate and improve the system / project proposed.","hasChildren":true,"name":"Feasibility analysis","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA2-8","description":"This concept discusses the technical, organizational and monetary advantages and disadvantages of proprietary versus open source software. GIST industry and research are slowly but consistently moving toward the openness of software. Open software entails some clear advantages such as continuous development of new applications, building community of developers and users, starting a project even if limited funding is available, increasing the chances of a project’s sustainability, to name a few. On the other side, proprietary initiatives in GIST are keeping their roots to the ground by developing cutting-edge tools to handle challenging and critical environments in large private sectors and public administrations. Advantages of proprietary software include more stable software, a well developed documentation and personalised customer support service. Both open and proprietary geospatial software solutions can co-exist by applying the appropriate IPR licences for each type of solution. The future trend is to balance how proprietary and open source geospatial software complement each other and find synergies in increasingly complex and large projects.","hasChildren":true,"name":"Proprietary and open source software","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA2","description":"To design, build, and maintain a GIS, sufficient resources (e.g., labor, capital, and time) must be secured. Resource planning consists of the allocation and use of in-house resources (people, equipment, tools, rooms, etc.) to achieve the maximal efficiency of those resources. These resources are required for a variety of system elements, including design, software purchase, labor, hardware, and facilities. The crucial task is to determine whether the project is worth the required resources.","hasChildren":true,"hasParent":true,"name":"Resource planning","selfAssesment":"Completed (GI-N2K)
"},{"code":"DA3-1","description":"The ecosystem of GIS software architectures has evolved substantially in recent years to include a variety of options ranging from desktop GIS, server-based and component-based architectures to Web-based, cloud-based, mobile-based approaches. Aligned with the main trend, geospatial software architectures or infrastructures are also moving from desktop architectures to more cloud based or server based options to meet ever-increasing requirements of interoperability, interdisciplinary work and computational power for processing large data sets and derived products. Cloud-based architectures also enable on the fly visualization of computed geospatial products, as complementary visualisation and mapping tools are seamlessly integrated into modern cloud-based based architectures. Usage of a particular architecture is fully dependent on the nature, size, requirements, functionalities, and available resources of a given project or task. Desktop and server based applications are particularly suited for small sized projects and startups while enterprise based applications are meant for larger sized projects. Cloud based infrastructure can be useful for varying sizes of projects in which the computational infrastructure is fully outsourced.","hasChildren":true,"name":"Major geospatial software architectures","selfAssesment":"In progress (GI-N2K)
\r\n\r\n"},{"code":"DA3-2","description":"Interoperability of GIS infrastructure or architecture ensures the consistent and uninterrupted usage of data and functionalities across platforms and systems. Components or tools residing on distinct platforms can “talk” to each other without friction. Interoperability is a central characteristic, especially important in distributed systems and architectures. It can be applied to different levels or layers of a system, i.e. infrastructure level, data level, business logic level, etc. For example, standard spatial data formats and protocols are especially relevant for handling GIS data across multiple systems and platforms, regardless of their underlying software architecture. This is particularly important in large-scale, collaborative projects involving various teams using heterogeneous GIS architectures. Most software providers, developers communities and standardisation bodies and committees are striving to make their architectures interoperable in an open manner, so proprietary standards and protocols are a potential hindrance to this initiative.","hasChildren":true,"name":"Interoperability","selfAssesment":"
Completed (GI-N2K)
"},{"code":"DA3-3","description":"This concept considers general architectural patterns like SOA, ROA, Web Services, etc.","hasChildren":true,"name":"Architectural Patterns","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA3-4","description":"- WebGIS, - technical pecularities of spatial data infrastructures - standardiced GI services for SDI: WMS, WFS, CSW, Transformation Services, SOS, WPS etc., - other map services and interfaces","hasChildren":true,"name":"WebGIS, SDI services, map services","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA3-5","description":"This concept deals with Reference Model of Open Distributed Processing (RM-ODP), its standards, viewpoints modeling and the RM-ODP framework","hasChildren":true,"name":"Reference Model of Open Distributed Processing","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA3-6","description":"Cloud computing provides an on-line computing transparent resource to the user, since a user doesn’t notice almost no difference between working on her own computer or the cloud. Owned and managed by infrastructure providers, cloud computing entails advantages (concurrent access by many users, software updates hosted in the cloud, cost-efficiency or outsourced maintenance in the cloud) and disadvantages (loose of control, network Connection Dependency or security breaches ). On the other side, grid computing is a full network of computers and data working together so functioning as a supercomputer. Grid computing presents advantages such as shorter resolution of complex problems, the ease of organizational collaboration or a better use of existing hardware.","hasChildren":true,"name":"Cloud and Grid computing","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA3-7","description":"Within this concept solutions based on Desktop GIS and GIS libraries will be compared and contrasted","hasChildren":true,"name":"Desktop GIS, GIS libraries","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA3","description":"This concept describes the major geospatial software architectures available currently and choices when designing GI applications and systems, including desktop GIS, server-based, Internet, and component-based custom applications.","hasChildren":true,"hasParent":true,"name":"Architectural design of a GIS system","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA4-1","description":"- Compare and contrast the relative merits of various textual and graphical tools for data modeling, including E-R diagrams, UML, and XML - Create conceptual, logical, and physical data models using automated software tools - Create E-R and UML diagrams of database designs","hasChildren":true,"name":"Modeling tools","selfAssesment":"GI-N2K
"},{"code":"DA4-2","description":"Within an initial phase of database design, a conceptual data model is created as a technology-independent specification of the data to be stored within a database.","hasChildren":true,"name":"Conceptual models","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA4-3","description":"A logical data model expresses the meaning context of a conceptual data model, and adds to that detail about data (base) structures, e.g. using topologically-organized records, relational tables, object-oriented classes, or extensible markup language (XML) construct tags","hasChildren":true,"name":"Logical models","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA4-4","description":"A physical data model documents how data are to be stored and accessed on storage media of computer hardware","hasChildren":true,"name":"Physical models","selfAssesment":"In progress (GI-N2K)
"},{"code":"DA4","description":"The effective design of geospatial databases should follow the established methods and principles of database modeling and design developed in computer science. The basic method is a three-step process generally called the conceptual, logical, and physical models transforming the application from very human-oriented to machine-oriented. Several standards and software tools exist to aid the process of database design.","hasChildren":true,"hasParent":true,"name":"Database design","selfAssesment":"In progress (GI-N2K)
"},{"code":"DM","description":"This knowledge area deals with representation of formalized spatial and spatio-temporal reality through data models and the translation of these data models into data structures that are capable of being implemented within a computational environment (i.e., within a GIS or more likely within a spatial database). Data modelling is a crucial issue as it defines the content of a spatial database and usefulness of these content (data) for certain applications. Data Modelling is performed using system neutral languages like UML (or more seldom ER-diagrams). These conceptual models have to be transferred to logical models (i.e. tables of a database). Data is stored in spatial databases which are normally organized in an object relational way. For certain types of data specific databases are used, like triple stores, NoSQL DBs, Array DBs etc. For data modelling quite a number of ISO standards are available for deriving the conceptual model as well as for rules for application schemas, spatial schemas, temporal schemas, Quality principles, encoding, 3D modelling (CityGML) etc. Data models provide the means for formalizing the spatio-temporal conceptualizations. Examples of spatial data model types are discrete (object-based), continuous (location-based), dynamic, and probabilistic. Mastery of the objectives presented in this knowledge area require knowledge and skills presented in the bodies of knowledge of allied fields, including computer science (ACM/IEEE-CS Joint Task Force, 2001) and information systems (Gorgone & Gray, 2000; Gorgone & others, 2002).","hasChildren":true,"hasParent":true,"name":"Data Modeling, Storage and Exploitation","selfAssesment":"In progress (GI-N2K)
"},{"code":"DM1-1","description":"This topic includes the main basic database concepts: - Database, definition and overview - Database management system, definition and overview - Relational databases, overview - Object-oriented databases, overview - Object-relational databases - NoSQL databases, general overview - NoSQL databases, examples triple stores, array databases, others (overview)","hasChildren":true,"name":"Overview on database concepts","selfAssesment":"GI-N2K
"},{"code":"DM1-2","description":"The Relational Model is the most important database model, therefore it is explained in more detail here: - Basic concepts (tables, tuples, etc.) - Relation to relational algebra (RA), basics of RA - Constraints (key, domain, referential integrity) - Relation to entity relation (ER) model, basics of ER","hasChildren":true,"name":"The Relational Model","selfAssesment":"GI-N2K
"},{"code":"DM1-3","description":"Relational databases and database management systems are essential for GIS in consequence the important issues have to be treated here: - General aspects, basic architecture of a DB, advantages, features - DBMS concepts and functionalites (transactions, locks, multiuser access etc.) - Database design, techniques - Database administration - Normalization (1NF - 3NF) - Example of a database design","hasChildren":true,"name":"Relational Databases, Database Managements Systems and Database principles","selfAssesment":"GI-N2K
"},{"code":"DM1-4","description":"Database queries and especially spatial queries require specific data structures to be performed satisfactory Relevant is: - Motivation, examples of typical non-spatial and spatial queries - Trees, B-tree, R-tree, Q-tree - Graphs, overview and relation to databases","hasChildren":true,"name":"Data Structures and Indices for Databases","selfAssesment":"GI-N2K
"},{"code":"DM1-5","description":"Big data like imagery but also for example GML data sets need compression to be accessed / transferred in an acceptable time. Therefore some compression techniques have to be taught: - Motivation, examples of data sets which need compression - General introduction, vector - / raster data compression, compression lossless, lossy - Popular compression techniques, LZW (Lempel-Ziv-Welch) encoding, Huffman encoding - Techniques for raster data, runlength encoding, JPEG coding, wavelet etc. - Techniques for the reduction of vector data (Douglas Peuker etc.) - Data formats, overview and relation to compression techniques","hasChildren":true,"hasParent":true,"name":"Data compression techniques","selfAssesment":"GI-N2K
"},{"code":"DM1-6","description":"SQL is the \"standard\" to perform spatial and non-spatial queries in databases. That means each student in a GI related course has to be familiar with the main aspects if it: - Motivation, history, overview - Data definition language DDL - Data manipulation language DML - Data control language DCL - Spatial extensions of SQL","hasChildren":true,"name":"SQL and its usage for data handling, spatial extensions to SQL","selfAssesment":"In progress GI-N2K
"},{"code":"DM1-7","description":"UML is the standard for describing the schema related to GI models, but also user requirements, workflows etc. can be described in UML using the UML diagrams: - Motivation, background, purpose - Use case diagrams - Class diagrams - Sequence diagrams - Activity diagrams","hasChildren":true,"name":"UML introduction and class diagrams","selfAssesment":"In progress GI-N2K
"},{"code":"DM1-8","description":"XML knowledge is an important bases for understanding GML. Moreover XML tools like XSLT are important to transform XML or GML data sets into other XML based formats like SVG or others. Important issues: - Motivation, purpose - Relation to HTML - XML document structure - XML syntax, elements, attributes and namespaces - xlink, xpath and XSLT - XML DTD - XML schema","hasChildren":true,"name":"XML introduction","selfAssesment":"In progress GI-N2K
"},{"code":"DM1-9","description":"The long term storage of GI data in general is based on spatial databases. Therefore the following is essential for a GI course: - Relation between GIS and DB / \"Long transactions\"- Dual concepts - Characteristics of spatial databases - Spatial data in object relational databases - Spatial extensions of DBs, overview","hasChildren":true,"name":"Database concepts in GIS and Principles of spatial databases","selfAssesment":"GI-N2K
"},{"code":"DM1","description":"This unit includes the basics for data modelling, storage and exploitation. Data modelling is one of the most important activities in conjunction with Geographic Information / GIS as it determines how the data can be used and if the requirements from applications are fulfilled. Data modelling can be done in conjunction with the database, e.g. through ER diagrams or according to the ISO 191xx standards by using UML. The costs of data acquisition can be tremendous, therefore the data represents an enormous value. This value has to be conserved through a safe long term data storage. Therefore databases and especially relational and object relational databases are crucial. For a proper storage and query of geographic information databases are extended with specific data types and data structures. As data sets can be very large suitable compression techniques became important especially in the context of accessing and delivering geographical data, e.g. through services. XML based modeling languages for encoding also play and important role in this context","hasChildren":true,"hasParent":true,"name":"Foundations for Data Modelling Storage and Exploitation","selfAssesment":"In progress (GI-N2K)
"},{"code":"DM2-1","description":"GI standards, mainly from ISO and OGC are essential nowadays. Moreover also an overview on ICT standards from W3C or OMG are important as well as some understanding of standardization processes. In detail: - Motivation for standards, examples from daily life - Overview on GIS and relevant ICT standardization bodies and selected standards - De jure and De facto standards, obligation, reasons for the usage of standards - Standardization within ISO - Standardization within OGC, relation to ISO - Examples of ISO 191xx standards","hasChildren":true,"name":"Overview on relevant standards and standardisation bodies","selfAssesment":"GI-N2K
"},{"code":"DM2-2","description":"Conceptual data modeling is a key skill for GI people. (see relations to other topics) The following therefore is important: - Overview on the relevant standards like conceptual schema language, Rules for application schema - Examples of conceptual schemas","hasChildren":true,"name":"The principle of conceptual data modelling according to ISO","selfAssesment":"In progress GI-N2K
"},{"code":"DM2-3","description":"Geometric modelling is an important subtask of conceptual modelling and requires the following basics: - Overview of ISO 19107 - spatial schema - Overview of ISO 19125 - simple features - Examples of the usage of spatial schema and simple feature elements for feature class definitions - Relation to GML - Relation to DBs","hasChildren":true,"name":"Geometry data types according to spatial schema and the simple feature specification","selfAssesment":"GI-N2K
"},{"code":"DM2-4","description":"Also temporal aspects have to be considered within conceptual modelling. This also requires basics: - Motivation, examples - Temporal variability of features (move, change of structure or geometry) - Overview on ISO 19108 temporal schema - Examples of modeling temporal aspects","hasChildren":true,"name":"Temporal data types according to temporal schema","selfAssesment":"In Progress GI-N2K
"},{"code":"DM2-5","description":"Conceptual models of course have to be implemented, in general in a GIS (which is often proprietary), or in a database (which can be standard based) ,therefore here the implementation in a database is treated: - Repetition of conceptual and logical models - Examples of the transferring of a conceptual model to a logical (database) model","hasChildren":true,"name":"Transferring conceptual models to logical models","selfAssesment":"In progress (GI-N2K)
"},{"code":"DM2-6b","description":"Metadata is considered as very important for the usage as well for the search for Geodata Relevant basics are: - Motivation, importance of data quality as part of metadata - Metadata in an spatial data infrastructure with many There are quite a number of relevant standards for GI courses. Some are listed here, others might be considered, depending on the background of the course: - Select other standards and explain them, Important are: - ISO 19141 Schema for moving features, ISO 19142 Web Feature Service or others - 19109 - Rules for application schema - Selection of other standards is depending on the background of the course","hasChildren":true,"name":"Other standards","selfAssesment":"In progress GI-N2K
"},{"code":"DM2-7","description":"GML is the most important standard for the transfer of Geodata as it allows to transfer the schema information as well as the data. Important issues: - Motivation, Importance of a Geography Markup Language - History of GML, Overview 19136 - Geography Markup Language - Relation to spatial schema - Supported features in GML (Topology, 3D ...) - Structure of GNL, profiles, application schemas etc. - Transfer of models and of data - Examples","hasChildren":true,"name":"Introduction to GML","selfAssesment":"In progress (GI-N2K)
"},{"code":"DM2-8","description":"3D Models, especially 3D city models are becoming more and more important. CityGML is the most important standard within the GI domain to describe City models semantically and geometrically. Relevant issues: - Motivation, Usage of CityGML - Relation to GML - Coherence of semantics and geometry - Principles of modeling - Level of detail concept - CityGML vs KML - Examples","hasChildren":true,"name":"Introduction to CityGML","selfAssesment":"In progress GI-N2K
"},{"code":"DM2","description":"This unit includes the essentials of relevant standards for spatial data modelling. A number of ISO and OGC standards are available for deriving the conceptual model as well as for rules for application schemas, spatial schema provides data types for geometry models in various forms, Point, line, area, body based, temporal schema allows to consider temporal dimensions, Quality principles can be used to describe the quality of geodata, encoding standards (mainly GML) allow the standard based transfer of data and data models, CityGML allows a standard based 3D modelling, etc.","hasChildren":true,"hasParent":true,"name":"Standards for Spatial Data Modeling","selfAssesment":"In progress GI-N2K
"},{"code":"DM3-1b","description":"There are two basic concepts related to this topic: Features and Fields, or Geo-fields, as named by Goodchild at al. The concept of fields can be differently represented as explained here: - Repetition of basic concepts of Geographic Information Science - Explanation of the concept of continuous fields and the commonly used ways of representing geo-fields - Relation between fields and coverages, an important discretizations of a Geo-field - Types of Coverages","hasChildren":true,"name":"The concept of fields","selfAssesment":"In progress GI-N2K
"},{"code":"DM3-2","description":"The raster data model holds values in a regularly spaced matrix of cells arranged in rows and columns covering a two dimensional space. Rasters are commonly used to store continuous data like colors in an image and height values but they are also used for discrete (thematic) values like land use.","hasChildren":true,"name":"The raster model","selfAssesment":"In Progress (GI-N2K)
"},{"code":"DM3-2b","description":"Grids are on the one hand one important type of caverages and on the other hand Grids are used as basic structure in some applications. Important here is: - Definition of the concept of grid in GIS - Grid as an instance of coverages - Grids as a basic structure for certain applications / medium for aggregation of data - Examples of grid-based data such as Digital Terrain Models (DTM) - Grids in census / statistical data and Geo-marketing applications","hasChildren":true,"name":"Grid representations","selfAssesment":"In progress GI-N2K
"},{"code":"DM3-3","description":"Grid data models can contain millions of discrete values. This leads to very large datasets. Depending on the way values change over the grid, different methods can be used for an optimal (lossy or lossless) data compression. Type of data, computer power needed, application of the data, method of transport and storage all contribute to the choice of compression method.","hasChildren":true,"name":"Grid compression methods","selfAssesment":"In progress (GI-N2K)
"},{"code":"DM3-3b","description":"TINs and Voronoi tessellations are important types of coverages. TINs play a very important role also in Computer graphics. Important here is: - Basics from Graph theory - Definition of Triangulated Irregular Networks (TIN), purpose and applications - TINs and voronoi diagrams as a type of coverages - One important instance of a TIN: Delauney Triangulation - Definition of Voronoi Diagrams, purpose and applications - Relation between Delauney Triangulation and Voronoi Diagram, the \"Dual Graph\" - Examples from applications","hasChildren":true,"name":"TIN and Voronoi tesselations","selfAssesment":"In progress GI-N2K
"},{"code":"DM3-4","description":"While the classical grid structure uses rectangular cells, the hexagonal data model uses hexagons to represent raster data","hasChildren":true,"name":"The hexagonal model","selfAssesment":"In progress (GI-N2K)
"},{"code":"DM3-4b","description":"Linear referencing is 1 dimensional positioning. The position of an object is defined by the distance from the object to the start point along a line. Linear referencing is for example used in railway dispatching systems","hasChildren":true,"name":"Linear referencing","selfAssesment":"In progress GI-N2K
"},{"code":"DM3-5b","description":"Resolution of raster and gridded data - Georeferencing of data, direct and indirect methods (t.b.d.)","hasChildren":true,"name":"Resolution and georeferencing system","selfAssesment":"In Progress (GI-N2K)
"},{"code":"DM3-7","description":"In hierarchical data models data is organized in a tree-like structure. Data are connected with parent-child relations. Hierarchical structures are often used for spatial indexing.","hasChildren":true,"name":"Hierarchical data models","selfAssesment":"In progress (GI-N2K)
"},{"code":"DM3","description":"This unit includes relevant tessellation data models. Besides features (sometimes also called geo-objects) geo-fields play and important role. In recent literature tessellation models are classified as discretizations of fields. In traditional GI literature tessellations are defined as important data structure itself. Tessellation discretise a continuous surface into a set of non-overlapping polygons that cover the surface without gaps. Tessellation data models represent continuous surfaces with sets of data values that correspond to partitions. Important tessellation models are Grids, TINs and Voronoi diagrams.","hasChildren":true,"hasParent":true,"name":"Tessellation data models","selfAssesment":"In progress (GI-N2K)
"},{"code":"DM4-1","description":"This topic includes the basics for feature based modelling. There are a number of standards also relevant for this topic (see relations). The following items should be included: - Definition of a feature (in some literature also called object, or geoobject) and of feature classes respectively. - Aspects of the definition (ID, geometry, topology, thematic, time etc.) - Techniques for the definition of features / feature classes (mainly link, as they are described elsewhere, see relations)","hasChildren":true,"name":"Feature based modelling","selfAssesment":"GI-N2K
"},{"code":"DM4-2","description":"This topic describes the process of Geometric modelling using vector data, means the primitives like points, lines, areas, bodies, or raster data. There is a strong relation to ISO standards (see relations) as they provide basic data types for geometric modelling. Main issues: - Geometric modeling based on vector data - Geometric modeling based on raster data - Conversion between the models - examples, advantages, disadvantages of the models","hasChildren":true,"name":"Geometric modelling","selfAssesment":"In progress GI-N2K
\r\n\r\nIn progress (GI-N2K)
"},{"code":"DM4-4","description":"This topics deals with the definition of an application schema. There are other units which are important for this topic (see Relations). Issues to be included: - Methods to define and describe an application schema (requirement analysis, description of the schema etc.) - Feature attribute catalogues - Domains / data relevant for INSPIRE","hasChildren":true,"name":"Application models based on vector data","selfAssesment":"GI-N2K
"},{"code":"DM4-5","description":"This Topic deals with important application models, which should be chosen with relation to the course (geographically / related to the background of the course) INSPIRE should be treated in any case. In detail: - Overview on important application models relevant for the course, e.g. from topography or environment in the country - Repetition of the principles of Spatial data infrastructures - Overview on the INSPIRE initiative and the goals related - The INSPIRE data model - The architecture of INSPIRE and the necessary services - Domains / data relevant for INSPIRE","hasChildren":true,"name":"Examples of important application models","selfAssesment":"GI-N2K
"},{"code":"DM4-6","description":"This topic is dedicated to the challenges of model based interoperability and related issues, The principles of interoperability are included in DA3-2. In detail: - The challenges of model interoparability (semantics, different modelling of the same features in different models, syntacs) - Overview on IT concepts for schema integration / transformation - Approaches for model integration - Approaches for model transformations, e.g. related to INSPIRE, from the Humboldt project","hasChildren":true,"name":"Model based interoperability, model transformations","selfAssesment":"GI-N2K
"},{"code":"DM4-7","description":"Network models are crucial in some application domains, such as Navigation (roads etc.), but also in utility applications (facilities like pipes etc.) In this topic should be treated: - The network model in the database domain - Graph based NoSQL databases - Topology of network models - Data structures for storing network data - The Dijkstra algorithm - Overview on important applications","hasChildren":true,"name":"Network models","selfAssesment":"GI-N2K
"},{"code":"DM4","description":"This unit includes relevant issues related to vector data models, feature based modelling, applications. Besides imagery data the majority of GI data available is feature based and founded on vector geometry. Topology modeling also is very common nowadays, as many analysis like routing or neighborhood analysis require it. Spaghetti modelling becomes more and more and exception. In every country there are important feature and vector geometry based application models available e.g. in Topography / Cartography. In Europe every GI course should include some information on INSPIRE. As in different application domains different data models are used, sometimes for the same feature types, integration and transformation of models are an important issue also.","hasChildren":true,"hasParent":true,"name":"Vector data model, Feature based modelling, Applications","selfAssesment":"In progress GI-N2K
"},{"code":"DM5-1","description":"- Many geographical phenomena are not defined sharply but uncertain Uncertainty has a number of considerations: - Motivation, background, purpose - Conceptual model of uncertainty - Uncertainty of geographic phenomena (vagueness, ambiguity) - Uncertainty of measurements - Uncertainty of analysis - Uncertainty vs. data quality - Statistical models of uncertainty - Outline of Fuzzy approaches","hasChildren":true,"name":"Basics of uncertainty and its modelling","selfAssesment":"In progress GI-N2K
"},{"code":"DM5-2","description":"Space and time are 2 connected concepts, this topic is dedicated to some basics of modelling time and the temporal dimensions related to features and fields: - Motivation, background, purpose - Changes in time in Entity based and field based representations - A conceptual model of changes in time - Move of objects - Change of structure - Change of geometry - Examples from applications","hasChildren":true,"name":"Modelling time aspects","selfAssesment":"In progress GI-N2K
"},{"code":"DM5-3","description":"Traditionally many GIS used 2D or 2.5 D data models, but in the last decade 3D modeling mainly in form of city models or in the context of Building Information Models (BIM): - Basic concepts of 3D modelling, edge, area, volume models - The workflow of 3D modelling, general aspects, choose of the proper model - Methods of 3D modeling - Principles of Constructive Solid Geometry (CSG) - Principles of Boundary representation (BR) - Principles of Voxel-beased modeling - Comparison of the methods - The concept of BIM, principles and purpose - City models, principles and purpose - Examples / applications","hasChildren":true,"name":"Modelling 3D","selfAssesment":"GI-N2K
"},{"code":"DM5","description":"Traditional raster and vector data models cannot easily represent the more complex aspects of geographic information, such as temporal change, uncertainty, three-dimensional phenomena, and integrated multimedia. A variety of models have been proposed to represent these complexities, including both extensions to existing models and software, and entirely new models and software. During the 1990s, work in this area was largely experimental, but many solutions are now available to practitioners in commercial and open source software. The data models in this unit are based on concepts discussed in Knowledge Area CF Conceptual Foundations.","hasChildren":true,"hasParent":true,"name":"Modelling 3D, temporal and uncertain phenomena","selfAssesment":"In progress (GI-N2K)
"},{"code":"DN3-1","description":"Modification of spatial and attribute data while ensuring consistency within the database, implications of transactions on database integrity, scenarios for periodic changes in GIS database and monitoring the periodic changes.","hasChildren":true,"name":"Database change","selfAssesment":"In progress (GI-N2K)
"},{"code":"DN3-2","description":"Rules for modelling spatial database change, techniques for handling version control, techniques for managing long and short transactions, management of spatial databases in multi-user environment","hasChildren":true,"name":"Modeling database change","selfAssesment":"In progress (GI-N2K)
"},{"code":"DN3-3","description":"Reliability tests of change information, design and implementation. Logical consistency of updates.","hasChildren":true,"name":"Reconciling database change","selfAssesment":"In progress (GI-N2K)
"},{"code":"DN3-4","description":"Needs for versioned databases, queries for change scenarios using DB management tools, algorithms for performing dynamic queries, role of time-criticality and data security while choosing methods for change detection.","hasChildren":true,"name":"Managing versioned geospatial databases","selfAssesment":"In progress (GI-N2K)
"},{"code":"GC","description":"The term geocomputation dates back to the first international conference on the topic in 1996 held at the University of Leeds under the title “The art and science of solving complex spatial problems with computers’. The term “geocomputation” was coined to describe the use of computer-intensive methods for knowledge discovery in physical and human geography. This new area distinguishes it from the application of statistical techniques to spatial data in the focus on “creative and experimental applications” and in “developing relevant geo-tools within the overall context of a ‘scientific’ approach.” Other authors reinforced the unique character of geocomputation as “to provide better solutions to many geographical problems by developing new, computationally dependent tools for analysis and modelling”. Simply defined, the interdisciplinary area of geocomputation was, from the beginning, closely linked to the application of computer technology and the development of tools and applications to real-world spatio-temporal problems through the combination of geographic information system techniques, spatial modelling, cellular automata, and other non-conventional data clustering and analysis techniques.\r\nEven though geocomputation is still seeking to define the field conceptually), it is closely related to computational science, the use of high-computing performance, artificial intelligence, computational intelligence, grid infrastructure and parallel computing . Nevertheless, the evolution of new computing paradigms, such as edge-fog-cloud computing along with the new forms of data create new opportunities for the geocomputation community . \r\n\r\nWhile the underlying idea remains intact --a diverse and interdisciplinary area of research that uses geospatial data, methods and tools for applied scientific work--, the current approach to geocomputation differs from the founders in that it focuses more attention on open science, reproducible research practices, and in a vibrant collaborative community to develop new methods, tools and applications that are integrated into multiple application domains such as economics, sociology, geodemography, health, criminology, transportation, biology, remote sensing and cities . The theoretical roots and experimental emphasis of geocomputation makes it an excellent vehicle to creatively explore in parallel the theory and practice of the use of geospatial data in a computational way to solve real-world problems.","hasChildren":true,"hasParent":true,"name":"Geocomputation","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC1-1","description":"A complex system can be viewed as a system composed of many interacting parts, with the ability to generate a new collective behaviour through self-organisation, for example, though the spontaneous formation of temporal, spatial or functional structures. Complex systems are therefore adaptive as they evolve and may contain self-driving feedback loops. Most real-world systems such as global climate, an ecosystem, a city, the human brain, and the entire universe, are complex systems. Therefore, complex systems are much more than a sum of their parts.The general characteristics of the structure and dynamics of complex systems have been characterised, including path dependence, positive feedback loops, self-organisation, and emergence. Complex system types include nonlinear systems, chaotic systems, and complex adaptive systems. \r\nTraditional approaches focus on the individual system components and define a system as the sum of its parts. Whereas the modern approach relies on complexity theory and complex adaptive systems, to emphasise the linkages between system components in order to understand complex systems as a whole. Agent-based models, for example, have been highly recommended for studying complex adaptive spatial systems because they support the explicit representation of situation-dependent information for decision making within dynamic spatial environments.","hasChildren":true,"name":"Complex systems","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC1-2","description":"Computational science is a discipline focused on the design, implementation and use of mathematical models or simulations through the use of computers to analyse scientific problems, systems or processes. Computational science heavily relies on computational technologies such as high performance computing, artificial intelligence, computational intelligence, grid infrastructure and parallel computing. Geocomputation is closely related to computational science and, therefore, geocomputational methods are often derived from machine learning, clustering, simulation, parallel computing and high performance computing. Contrary to the methods and tools applied for spatial analysis described under the Analytical Methods Knowledge Area, geocomputation and spatial data science may involve the use of spatial methods available in standard GIS packages, but quite often require self-development, or at least customisation, involving computational technologies and coding to solve target problems. The aim of this topic is to provide an introduction to computational science with particular emphasis on its usage and relation to geocomputation. In this sense, the way computational technologies are used in computational science can be connected to the methodological and coding practices of geocomputation and spatial data science.","hasChildren":true,"name":"Computational science and technology","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC1-3","description":"While geocomputation is not daily used in GIS environments and traditional GIS projects, it is the focus of a vibrant collaborative and research community in developing new geocomputational methods, tools and applications that are integrated into multiple application domains such as economics, sociology, geodemography, health, criminology, transportation, biology, remote sensing and cities. Open science, reproducible research practices, and strong collaboration make geocomputing an excellent vehicle for creatively exploring together the theory and practice of using geospatial data in a computational way to solve real-world problems.","hasChildren":true,"name":"Spatio-temporal problems and applications","selfAssesment":"In progress (GI-N2K)
"},{"code":"GC1-4","description":"The origin of geocomputation dates back to the first international conference on the topic in 1996 and was coined to describe the use of computer-intensive methods for knowledge discovery in physical and human geography. According to Birkin (2009), Openshaw defined geocomputation as a computational paradigm that takes geographic information science to focus on analysis, modeling, and simulations applied. Openshaw’s definition emphasized the use of novel computational approaches at that time along with spatial data and analysis methods to find solutions to real-world problems. Longey's definition, as reported by Birkin (2009), focuses on the continuous development of GIS tools and techniques, in line with the modern emphasis on creative, experimental, data-driven and code-based practices to solve real-world problems. In this context, geocomputation is closely related to other widely known areas of knowledge within the geospatial community, such as GIScience, Spatial Information Science, Geoinformatics, and Geographic/Spatial Data Science. While these terms clearly overlap and boundaries are fuzzy, the term geocomputation puts the focus on creative and experimental applications and in developing relevant computationally geospatial tools for analysis and modelling within the overall context of a ‘scientific’ approach. Therefore, a common interpretation of geocomputation is to describe the application of computational models to geographic problems. Nowadays, the term spatial data science is gaining ground to convey essentially the same interpretation as geocomputation.","hasChildren":true,"name":"Origin of geocomputation","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC1","description":"Geocomputation represents an attempt to move the geospatial research agenda back to geographical analysis and modelling by providing a toolbox of methods to analyse and model a range of highly complex, often non-deterministic problems. In this context, complex systems and computational science are foundational aspects upon which geocomputation approaches and methods are built to address a variety of real-world, spatio-temporal issues. Similar to geocomputation, the term spatial data science has recently emerged to refer to the use of computational techniques to access, explore, visualize and perform spatial analysis on real-world data sets. Therefore, geocomputation and spatial data science share many commonalities (complex problems, use of spatial techniques and modeling, coding, real-world data) that make them interchangeable in many scenarios.","hasChildren":true,"hasParent":true,"name":"Geocomputation and complex systems","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC2-1","description":"Building a model that mimics a real-world system generally follows a series of stages: from conceptual models to mathematical models and, finally, simulation models. In model development, system analysis is a process whereby a real-world system is simplified by dividing it into simpler, more manageable parts. A conceptual model then captures the components, variables and interactions of a system, and provides a useful way of thinking about the trade-offs between abstraction and representativeness of real-world phenomena. However, taken in isolation, the interacting parts of a system fail to explain its dynamics behaviour. A conceptual model is then translated into a mathematical model to explain interrelations and relationships among the constituted parts of a system by means of equations, logical rules or other mathematical mechanisms. Lastly, a simulation model is the computer-based implementation of mathematical models that consist of interrelated equations and logical rules. However, this model development process typically does not happen all at once, but can occur in multiple iterations throughout these phases to adjust, improve, and incorporate feedback into the modelling process.\r\n\r\nWhen a simulation model runs on a computer, it iteratively recalculates the state of the modelled system as it changes over time in accordance with the relationships represented by the mathematical relationships that describe the system dynamic. Therefore, developing detailed and dynamic simulation models comes at the cost of generality and interpretability, but it brings us realism and the ability to represent real-world processes in specific contexts. \r\n\r\nSimulation modelling is often used for prediction, exploration, theory development, or even optimization of conditions to achieve desired outcomes, with the goal of examining how the interconnections and relationships that characterise complex social and environmental systems (e.g. ecosystems, urban systems, social systems, global climate system) produces patterns of behaviour over time. Therefore, simulation models are increasingly gaining relevance as scientific mechanisms for several reasons. First, simulation models allow researchers to study systems inaccessible to experimental and observational scientific methods, complementing more conventional approaches to discover or formalize theories about real world systems. Also, as many real-world systems are nonlinear, simulation modelling has turned into a necessary method to explore and understand better such systems. In addition, the availability of computational science methods and technology, together with a large amount of data available from different sources, have greatly driven the adoption of simulation models in a wide range of scientific disciplines.","hasChildren":true,"name":"Principles of computer simulation","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC2-3","description":"Rule-based models are based on logic programming with condition-action expressions, where the left side of the expressions consists of several conditions that return a logical result, and the right side consists of several actions. Therefore, rules in rule-based models indirectly specify a mathematical model. However, unlike equation-based models which refer to the overall or aggregate behaviour of a system, rule-based models focus on the behaviour of the individual components of a system. This is why the implementation of rule-based models is most often done by cellular automata models or agent-based models, in which the aggregate behaviour of the system arises from the interaction of the individual agents or cells over time. Many geographic patterns and dynamics are formed by systems of interacting actors/cells with heterogeneous characteristics and behaviours, in which such dynamic behaviours can be implemented as rules.","hasChildren":true,"name":"Rule-based models","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC2-4","description":"Equation-based models are a set of interrelated equations that capture the variability of a system over time (differential equations), and the execution (simulation) of the model means to evaluate such equations. Equation-based models do not aim at representing the behaviour of the individual components in a system. Rather, they focus on the overall or aggregate behaviour of a system. Therefore, equation-based models are well suited to represent physical processes and some topics within natural sciences, where the system to some degree can be described by physical laws. Hydrological modelling is a good example of models based on equations. However, other real-world systems can rarely be fully described by the laws of the natural sciences, and their behavior and interrelation must be represented by means of other types of mathematical mechanisms. The aim of this topic is to present the advantages and challenges in using equation-based simulation models, which are most naturally applied to systems centrally governed by physical laws rather than by information processing and flow.","hasChildren":true,"name":"Equation-based models","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC2-5","description":"Space-time dynamics is closely related to the concepts of change and process, which are inherent to our real-world world. Space-time dynamics is especially manifested when we move from a static to a dynamic representation of phenomena. Various processes taking place at different spatial and temporal scales interact with each other and lead to changes in the phenomena being modelled. \r\n\r\nThere are many different approaches to conceptualizing and understanding space-time dynamics in order to understand or predict phenomena in heterogeneous application domains ranging from human activities and urban sprawl to disease spread and traffic flow. An example is the time geography approach and its variants, such as the spatiotemporal prism, to model and understand human physical activities that occur in and are simultaneously constrained by space and time. These interactions produce space–time prisms that simultaneously situate individuals locally in physical space. Other techniques such as cellular automata also model human and physical activity in space and time, to simulate space–time and associated constraints to individual human activities.\r\n\r\nWhile the above examples are primarily oriented towards human activities, such as urban transport and mobility, these theoretical approaches have the potential to investigate and understand interactions between humans and the environment, recognizing the importance of individual human activities together with the geographic-environmental context applied to multiple scenarios, such as climatology, physical geography, and natural disasters. For the latter, for example, modelling and simulating human responses to floods or hurricanes can lead to more efficient and effective emergency plans.","hasChildren":true,"name":"Space-time dynamics","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC2-6","description":"Cellular automata are a widely used form of spatially explicit simulation model, where complex processes evolve over space and time through a lattice of cells, each linked to its neighboring cells. Typically, this spatial lattice is structured as a two-dimensional grid of square cells. Each cell holds a set of states that change over time according to transition rules, which depend on the state of the cell and its neighbors. That is, a cellular automata model allows the exploration of how local interactions lead to the emergence of global patterns, governed by clearly defined rules. A cellular automata model is defined by six key components: a lattice or framework, individual cells, neighboring cells, transition rules, initial conditions (states), and an update sequence (time). These models are well-suited to geographic information systems (GIS) due to their simple data structures and ability to represent spatial changes and patterns in an intuitive way. This has made cellular automata in simulating phenomena such as land use changes and the spread of diseases.","hasChildren":true,"name":"Cellular automata","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC2-7","description":"Agent-based modelling is a powerful approach for simulating the dynamics of geographical systems by breaking them down into individual components or agents, each with its own characteristics, properties, rules and behavior. Unlike traditional models that treat geographical components as homogeneous entities, agent-based modelling allows for the simulation of diverse agents, such as people, cities, or abstract representations, interacting with each other and their environment at various spatial and temporal scales. This bottom-up approach makes it possible to observe how individual decisions lead to complex system behaviors over time, providing deeper insights into urban problems like urban sprawl, congestion, and segregation, as well as to model natural and social phenomena such as animal behavior, pedestrian behavior, social insects and biological cells. Therefore, the macro-level behavior of the system arises from the interaction of individual agents and the environment over time.\r\n\r\nAgent-based modelling development stems from automata-based models, which use rule-based mechanisms to process information and evolve over time. Two prominent automata-based approaches—cellular automata and agent-based modelling —have been widely adopted in geographic modelling. Agent-based modelling's advantage lies in its ability to model heterogeneous agents and dynamic interactions, which traditional models, focused on aggregate behaviors, cannot capture as effectively. While agent-based modelling offers unique insights into geographical systems, it also poses challenges, such as the complexity of simulating realistic agent behaviors. Nonetheless, agent-based modelling continues to grow in popularity for its ability to represent dynamic spatial changes in a more detailed and realistic manner.","hasChildren":true,"name":"Agent-based modelling","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC2","description":"The concept of spatial simulation modelling can be better understood by looking at the meaning of its individual words. A model is widely defined as a simplified representation of a real-world system under study, which can be used to explore or to better understand the system it represents. Simulation models are computer-based implementations of a model to produce outputs based on certain model assumptions. Simulation , therefore, relies on the use of computers for virtual experimentation to gain insight into real-world problems by proposing alternative assumptions that arise from exploring “what if” questions about a dynamic problem of interest over the course of successive simulation experiments.\r\n\r\nSimulation modelling is often used for prediction, exploration, theory development, or even optimization of conditions to achieve desired outcomes, with the goal of examining how the interconnections and relationships that characterize these systems produce patterns of behaviour over time. Across broad areas of the environmental and social sciences, researchers use simulation models as a way to study systems inaccessible to experimental and observational scientific methods, and also as an essential complement of those more conventional approaches to discover or formalize theories about the real world. Simulation models are a relatively recent addition to the scientific toolbox, and the reasons for their widespread adoption are, on the one hand, the impossibility to study in-situ some complex social and environmental systems (e.g. ecosystems, urban systems, social systems, global climate system) and, on the other hand, the availability of High Performance Computing and large amount of data from different sources.\r\n\r\nFinally, simulation modelling is also useful for the study of spatial patterns over time. Spatial simulation models are relevant when the study of spatial elements and their relationships in a system are necessary for a fully understanding of that system. In this regard, spatial simulation modelling approaches include rule-based models, equation-based models, grid-based cellular automata models, discrete event simulation, and agent-based models.","hasChildren":true,"hasParent":true,"name":"Spatial simulation modelling","selfAssesment":"Completed (GI-N2K)
"},{"code":"GC3-10-1","description":" ","hasChildren":true,"name":"Geometric object features","selfAssesment":" "},{"code":"GC3-10-2-1","description":" ","hasChildren":true,"name":"Object relations","selfAssesment":" "},{"code":"GC3-10-2","description":" ","hasChildren":true,"hasParent":true,"name":"Object features","selfAssesment":" "},{"code":"GC3-10-3-1","description":" ","hasChildren":true,"name":"Wavelets","selfAssesment":" "},{"code":"GC3-11-1","description":" ","hasChildren":true,"name":"Genetic artificial networks","selfAssesment":" "},{"code":"GC3-11-2-1","description":" ","hasChildren":true,"name":"Markov models","selfAssesment":" "},{"code":"GC3-11-2-2","description":" ","hasChildren":true,"name":"Kalman filters","selfAssesment":" "},{"code":"GC3-11-2","description":" ","hasChildren":true,"hasParent":true,"name":"Space-time dynamic reasoning","selfAssesment":" "},{"code":"GC3-11-3-1","description":" ","hasChildren":true,"name":"Multilayer perceptron","selfAssesment":" "},{"code":"GC3-11-3-2","description":" ","hasChildren":true,"name":"Backpropagation","selfAssesment":" "},{"code":"GC3-11-3-3","description":" ","hasChildren":true,"name":"Recurrent neural networks","selfAssesment":" "},{"code":"GC3-11-3-4","description":" ","hasChildren":true,"name":"Long short-term memory","selfAssesment":" "},{"code":"GC3-12-1","description":" ","hasChildren":true,"name":"Ensemble learning","selfAssesment":" "},{"code":"GC3-12-2","description":" ","hasChildren":true,"name":"Regression trees","selfAssesment":" "},{"code":"GC3-12","description":" ","hasChildren":true,"hasParent":true,"name":"AI algorithms","selfAssesment":" "},{"code":"GC3-13-1","description":" ","hasChildren":true,"name":"Physics aware AI","selfAssesment":" "},{"code":"GC3-13-2-1","description":" ","hasChildren":true,"name":"Theory of mind","selfAssesment":" "},{"code":"GC3-13-2-2","description":" ","hasChildren":true,"name":"Self-aware AI","selfAssesment":" "},{"code":"GC3-13-2","description":" ","hasChildren":true,"hasParent":true,"name":"Digital twin","selfAssesment":" "},{"code":"GC3-13","description":" ","hasChildren":true,"hasParent":true,"name":"Hybrid AI","selfAssesment":" "},{"code":"GC3-14-1-1","description":" ","hasChildren":true,"name":"Individual intelligence","selfAssesment":" "},{"code":"GC3-14-1-2","description":" ","hasChildren":true,"name":"Collective intelligence","selfAssesment":" "},{"code":"GC3-14-1-3","description":" ","hasChildren":true,"name":"Team learning","selfAssesment":" "},{"code":"GC3-14-1","description":" ","hasChildren":true,"hasParent":true,"name":"Cooperation levels","selfAssesment":" "},{"code":"GC3-14-2-1","description":" ","hasChildren":true,"name":"Logical agent","selfAssesment":" "},{"code":"GC3-14-2-2","description":" ","hasChildren":true,"name":"Inference","selfAssesment":" "},{"code":"GC3-14-2-3","description":" ","hasChildren":true,"name":"Probabilistic reasoning","selfAssesment":" "},{"code":"GC3-14-2-4","description":" ","hasChildren":true,"name":"Sequential decision problems","selfAssesment":" "},{"code":"GC3-14-2-5","description":" ","hasChildren":true,"name":"Supervised learning","selfAssesment":" "},{"code":"GC3-14-2-6","description":" ","hasChildren":true,"name":"Reinforcement learning","selfAssesment":" "},{"code":"GC3-14-2","description":" ","hasChildren":true,"hasParent":true,"name":"Intelligence type","selfAssesment":" "},{"code":"GC3-14","description":" ","hasChildren":true,"hasParent":true,"name":"Intelligent Software Agent","selfAssesment":" "},{"code":"GC3-3","description":"Biological neurons, or nerve cells, receive multiple input stimuli, combine and modify the inputs in some way, and then transmit the result to other neurons. Artificial neural networks are an attempt to emulate features of biological neural networks in order to address a range of difficult information processing, analysis and modelling problems. The principal class of ANNs are so-called feed-forward networks, but other types of ANN are for example recurrent neural networks. Among the feed-forward networks the most widely used approach is the multi-level perceptron (MLP) model. The application range is broad from non-linear regression to land cover change modelling. The aim of the topic is to introduce the principles of ANN and to understand and demonstrate its use in geospatial modelling.","hasChildren":true,"hasParent":true,"name":"Artificial Neural Networks","selfAssesment":"In progress (GI-N2K)
"},{"code":"GC3-7-1","description":" ","hasChildren":true,"name":"Cybernetics","selfAssesment":" "},{"code":"GC3-7-2","description":"Pattern recognition is the process of classifying input data into objects or classes based on key features. There are two classification methods in pattern recognition: supervised and unsupervised classification. The supervised classification of input data in the pattern recognition method uses supervised learning algorithms that create classifiers based on training data from different object classes. The classifier then accepts input data and assigns the appropriate object or class label. The unsupervised classification method works by finding hidden structures in unlabelled data using segmentation or clustering techniques. Common unsupervised classification methods include: K-means clustering, Gaussian mixture models, Hidden Markov models. The aim of the topic is to provide knowledge about the different methods in pattern recognition and how to choose the optimum method for a specific spatial problem.","hasChildren":true,"name":"Pattern recognition","selfAssesment":"In progress (GI-N2K)
"},{"code":"GC3-7-3-1","description":" ","hasChildren":true,"name":"Information-as-meaning","selfAssesment":" "},{"code":"GC3-7","description":" ","hasChildren":true,"hasParent":true,"name":"Signal processing","selfAssesment":" "},{"code":"GC3-8-1","description":" ","hasChildren":true,"name":"Natural language processing","selfAssesment":" "},{"code":"GC3-8-2","description":" ","hasChildren":true,"name":"Semantic web","selfAssesment":" "},{"code":"GC3-8","description":" ","hasChildren":true,"hasParent":true,"name":"Computational linguistics","selfAssesment":" "},{"code":"GC3-9-1-1","description":" ","hasChildren":true,"name":"Experimental learning","selfAssesment":" "},{"code":"GC3-9-1","description":" ","hasChildren":true,"hasParent":true,"name":"Knowledge representation","selfAssesment":" "},{"code":"GC3-9-2-1","description":" ","hasChildren":true,"name":"Semantic net","selfAssesment":" "},{"code":"GC3-9-2-2","description":" ","hasChildren":true,"name":"Inheritance","selfAssesment":" "},{"code":"GC3-9-2","description":" ","hasChildren":true,"hasParent":true,"name":"Knowledge organising system","selfAssesment":" "},{"code":"GC3-9-3","description":" ","hasChildren":true,"name":"Semantic categorisation","selfAssesment":" "},{"code":"GC3-9-4-1-1","description":" ","hasChildren":true,"name":"Membership functions","selfAssesment":" "},{"code":"GC3-9-4-1-2","description":" ","hasChildren":true,"name":"Class stability","selfAssesment":" "},{"code":"GC3-9-4-1","description":" ","hasChildren":true,"hasParent":true,"name":"Fuzzy logic","selfAssesment":" "},{"code":"GC3-9-4-2","description":" ","hasChildren":true,"name":"Boolean logic","selfAssesment":" "},{"code":"GC3-9","description":" ","hasChildren":true,"hasParent":true,"name":"Automated reasoning","selfAssesment":" "},{"code":"GC3","description":"Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans.","hasChildren":true,"hasParent":true,"name":"Artificial intelligence (AI) in EO and GI","selfAssesment":"New
"},{"code":"GC4-1","description":"The use of the term Open geocomputation doesn't intend to coin a new term; Open GIScience and Open GIS are well explored and discussed terms in the literature. Both embrace the idea of open data, open source, collaboration among peers, and the integration of these practices into GIS research projects, tools, services and applications. Open geocomputation brings the ideas of Open GIScience (and hence Open Science in general) into geocomputation, focussing on openness as a fundamental tenet to conduct research in geocomputation and for the development of new computational methods and tools. In fact, many community-led developments and tools have recently appeared in the field of geocomputation, notably based on R and Python. The widespread popularity and adoption of these computing environments for geocomputing and geospatial analysis is simply because they encompass open, transparent, and reproducible tool development.","hasChildren":true,"name":"Open Geocomputation","selfAssesment":"New
"},{"code":"GC4","description":"A distinguible feature of the current approach to geocomputation is the emphasis on openness: open science, open source, open data. All of this propelled by a vibrant collaborative community with the aim to develop open and reproducible methods, tools and applications applied to a variety of real-life, spatio-temporal application domains. Open Science is a paradigm that can be applied to any scientific discipline and area of knowledge, characterised by openness, access to large volumes of data and unprecedented levels of computing power, availability of community-driven tools, and new types of collaboration between multidisciplinary researchers. Open Science clearly goes beyond geocomputation, but at the same time, its practices and principles characterise recent geocomputation-related projects as well as its community. Therefore, the vision of Open Science taken here is contextualised to the field of geocomputation.","hasChildren":true,"hasParent":true,"name":"Open Science","selfAssesment":"new
"},{"code":"GD","description":"Geospatial data represent measurements of the locations and attributes of phenomena at or near Earth`s surface. Information is data made meaningful in the context of a question or problem. Information is rendered from data by analytical methods. Information quality and value depends to a large extent on the quality and currency of data (though historical data are valuable for many applications). Geospatial data may have spatial, temporal, and attribute (descriptive) components, as well as associated metadata. Data may be acquired from primary or secondary data sources. Examples of primary data sources include surveying, remote sensing (including aerial and satellite imaging), the global positioning system (GPS), work logs (e.g., police traffic crash reports), environmental monitoring stations, and field surveys. Secondary geospatial or geospatial-temporal data can be acquired by digitizing and scanning analog maps, as well as from other sources, such as governmental agencies. The legitimacy of geographic information science as a discrete field has been claimed in terms of the unique properties of geospatial data. In a paper in which he coined the term GIScience, Goodchild (1992) identified several such properties, including: 1. Geospatial data represent spatial locations and non-spatial attributes measured at certain times. 2. The Earth`s surface is highly complex in shape and continuous in extent. 3. Geospatial data tend to be spatially autocorrelated. It has long been said that data account for the largest portion of geospatial project costs. While this maxim remains true for many projects, practitioners and their clients now can reasonably expect certain kinds of data to be freely or cheaply available via the World Wide Web. Federal, state, regional, and local government agencies, as well as commercial geospatial data producers, operate clearinghouses that provide access to geospatial data. Although geospatial data are much more abundant now than they were ten years ago, data quality issues persist. Good data are expensive to produce and to maintain. Proprietary interests simultaneously increase the supply of geospatial data and impede data accessibility. Standards for geospatial data and metadata are useful in facilitating effective search, retrieval, evaluation, integration with existing data, and appropriate uses. National and international organizations, such as the Open Geospatial Consortium (OGC) and International Organization for Standardization (ISO), develop and promulgate such standards. INSPIRE directive (Infrastructure for Spatial Information in the European Community) regulates geospatial data management","hasChildren":true,"hasParent":true,"name":"Geospatial Data","selfAssesment":"In progress (GI-N2K)
"},{"code":"GD1-1","description":"Usable and accurate geospatial data are based upon proper model of the Earth`s surface. Shape of the Earth is complex and complicated to measure. Approximations are used to minimize complexity of the task and possible errors.","hasChildren":true,"name":"Earth geometry","selfAssesment":"GI-N2K
"},{"code":"GD1-2","description":"Geospatial referencing systems provide unique codes for every location on the surface of the Earth (or other celestial bodies). These codes are used to measure distances, areas, and volumes, to navigate, and to predict how and where phenomena on the Earths surface may move, spread, or contract. Point-based, vector coordinate systems specify locations in relation to the origins of planar or spherical grids. Tessellated referencing systems specify locations hierarchically, as sequences of numbers that represent smaller and smaller subdivisions of two- or three dimensional surfaces that approximate the Earths shape, Linear referencing systems specify locations in relation to distances along a path from a starting point. Tessellation data models, are considered in Unit DM3 Tessellation data models, and linear referencing models are considered in Unit DM4 Vector data models.","hasChildren":true,"name":"Georeferencing systems","selfAssesment":"GI-N2K
"},{"code":"GD1-3","description":"Horizontal datums determine the geometric relations between a coordinate system grid and a particular ellipsoid approximating the Earth`s surface. Vertical datums determine elevation reference surfaces, like mean sea level. A. Horizontal datums. Relation of coordinate system to particular ellipsoid, datum transformation options, Molodensky and Helmert transformation, other high accuracy transformations, ED50 and WGS84, historical development of horizontal datums, ETRS89. B. Vertical datums. Historical development of vertical datums, difference between vertical datum and geoid, relations between ellipsoidal (geodetic) heiht, geoidal height and orthometric elevation.","hasChildren":true,"name":"Datums","selfAssesment":"In progress GI-N2K
"},{"code":"GD1-4","description":"Map projections are systematic transformations of geographic coordinates of the surface of ellipsoid into locations in plane. Plane coordinates are based on map projection. As the transformation of a spherical grid into a plane grid causes inevitably distortions of the geometry, and, different projections cause different distortions, knowledgeable choice of appropriate projection for any particular use is crucial. A. Map projection poperties. Geometric properties that may be preserved or lost in projected grid, usefulness of compromise projection, Tissot indicatrix as an indicator of projection errors, visual appearance of the Earth`s graticule, distortion patterns for projection classes, distortions in raster data. B. Map projection classes. Three main classes of map projection based on developable surface, projection types by geometric properties preserved, mathematical basis of projecting longitude and latitude into x and y coordinates. UTM, ETM, projections used by EC. C. Map projection parameters. Standard line, projection case, latitutde and longitude of origin, aspects of projection. D. Georegistration. Rectification vs orthorectification, ground controle points in georegistration of aerial imagery.","hasChildren":true,"name":"Map projections","selfAssesment":"GI-N2K
"},{"code":"GD1","description":"Proper model of the Earth`s surface and ability to locate spatial phenomena accurately to it, is crucial in effective collection, management and use of data. Characterising size and shape of the Earth, using appropriate surfaces to approximate it, choosing suitable coordinate system and map projection is bases for efficient understanding of spatial data.","hasChildren":true,"hasParent":true,"name":"Geolocating Data to Earth","selfAssesment":"In progress (GI-N2K)
"},{"code":"GD10-4","description":"A stereoscopy acquisition mode collects remotely sensed data where each location on the ground (or the imaged objects) is covered multiple times (at least twice), from different perspectives. Stereopairs and stereoscopic coverage enable the extraction of 3D representations of the environment from remotely sensed imagery.","hasChildren":true,"hasParent":true,"name":"Stereoscopy and orthoimagery","selfAssesment":"GI-N2K
"},{"code":"GD10","description":"Since the 1940s aerial imagery has been the primary source of detailed geospatial data for extensive study areas. Photogrammetry is the profession concerned with producing precise measurements from aerial imagery. Aerial imaging and photogrammetry comprise a major component of the geospatial industry. The topics included in this unit do not comprise an exhaustive treatment of photogrammetry, but they are aspects of the field about which all geospatial professionals should be knowledgeable.","hasChildren":true,"hasParent":true,"name":"Aerial imaging and photogrammetry","selfAssesment":"GI-N2K
"},{"code":"GD11-2","description":"the physical environment to sense data without direct contact. It contains a carrier device (platform) and a sampling unit (sensor).","hasChildren":true,"name":"Platforms and sensors","selfAssesment":"GI-N2K
"},{"code":"GD11","description":"Satellite-based sensors enable frequent mapping and analysis of very large areas. Many sensing instruments are able to measure electromagnetic energy at multiple wavelengths, including those beyond the visible band. Satellite remote sensing is a key source for regional- and global-scale land use and land cover mapping, environmental resource management, mineral exploration, and global change research. Shipboard sensors employ acoustic energy to determine seafloor depth or to create imagery of the seafloor or water column. The topics included in this unit do not comprise an exhaustive treatment of remote sensing, but they are aspects of the field about which all geospatial professionals should be knowledgeable.","hasChildren":true,"hasParent":true,"name":"Satellite and shipboard remote sensing","selfAssesment":"GI-N2K
"},{"code":"GD12","description":"Meaning of geospatial metadata, elements of metadata, use of metadata, integration of metadata in data production, standards in geospatial data, ISO standard family 191xx, data warehouse, exchange protocol, transport protocols, spatial data infrastructure, INSPIRE, OGC, DCAT profiles for CKAN applications bridging metadata from GI and IT domains.","hasChildren":true,"name":"Metadata, standards, and infrastructures","selfAssesment":"GI-N2K in progress
"},{"code":"GD2-1","description":"Classic land survey methods and manual attribute data collection in the field","hasChildren":true,"name":"Land surveying and field data collection","selfAssesment":"GI-N2K
"},{"code":"GD2-2","description":"Aerial imagery has been the primary source of detailed geospatial data for extensive study areas. Photogrammetry is producing precise measurements from aerial imagery. Aerial imaging and photogrammetry comprise a major component of the geospatial data production. Satellite-based sensors enable frequent mapping and analysis of very large areas. Sensing instruments are able to measure electromagnetic energy at multiple wavelengths. Satellite remote sensing is a key source for regional- and global-scale land use and land cover mapping, environmental resource management, mineral exploration, and global change research. Shipboard sensors employ acoustic energy to determine seafloor depth or to create imagery of the seafloor or water column. Principles of aerial photography, oblique and vertical imagery, spatial and radiometric resolution, spectral sensitivity, principal point, distortions and displacements in aerial image, parallax, stereophotogrammetry, generation of an orthoimage from a vertical aerial phoptograph, aerotriangulation, vector data extraction from digital seteroimagery, mission planning. Use of UAV in photogrammetry. Main platforms and sensors in spatial image acquisition, active and passive sensors, LiDAR and microwave, multispectral and hypersepctral imagery, interpretation of imagery, supervised and unsupervised classification, pixel based and segmented classification, ground verification, main applications, bathymetric mapping. SENTINEL.","hasChildren":true,"hasParent":true,"name":"Remote sensing","selfAssesment":"In progress GI-N2K
"},{"code":"GD2-3","description":"Crowdsourcing is the practice of obtaining needed services, ideas, or content by soliciting contributions from a large group of people and especially from the online community rather than from traditional employees or suppliers. Crowdsourced spatial data collection is becoming more and more important. The advantages and disadvantages of crowdsourced data, opensource mapping tools, potential application of crowdsourcing, VGI, OSM or cell-phone based, aspects of crowdsourced data quality and reliabilty.","hasChildren":true,"name":"Crowdsourced data collection","selfAssesment":"GI-N2K
"},{"code":"GD2-4","description":"Digitizing as the main secondary spatial data production technique. Encoding vector points, lines, and polygons by tracing map sheets has diminished in importance, but remains a useful technique for incorporating historical geographies and local knowledge. \"Heads-up\" digitizing using digital imagery as a backdrop on-screen is a standard technique for editing and updating GIS databases. Tablet and on-screen digitizing, scanning and (semi)automatic vectorization.","hasChildren":true,"hasParent":true,"name":"Digitizing","selfAssesment":"GI-N2K
"},{"code":"GD2","description":"Spatial data collection / production involves measurement of locations in relation to the coordinate system, and collection of attributed data about the spatial phenomena. Measurements may be direct (e.g. surveying) or remote, data acquisition involves measurement of parameter values, evaluation of parameters, polls, interpretation of spatial imagery, and re-use of secondary data (e.g. old maps). Volunteered geographic information is becoming more important.","hasChildren":true,"hasParent":true,"name":"Data Collection","selfAssesment":"GI-N2K
"},{"code":"GD3","description":"It is quite common, that data including both spatial entities and their attribute data undergo changes. These changes need to be catalogued fully and explicitly, including initial conditions, new conditions, all intermediate stages and operations used. The geospatial data needs to contain an archival history of change.","hasChildren":true,"hasParent":true,"name":"Transaction management of geospatial data","selfAssesment":"In progress (GI-N2K)
"},{"code":"GD4-1","description":"Geometric accuracy, factors influencing it, geometric accuracy and topological fidelity, geometric accuracy in survey and GPS mesurements, thematic accuracy, relations between thematic accuracy, geometric accuracy and topological fidelity, misclassification matrix, commission and omission, logical consistency, relations between resolution, precision, and accuracy, spatial resolution, thematic resolution, and temporal resolution, precision, uncertainties associated with coordinate precision, primary and secondary data sources.\r\n\r\nParticular application. That standard varies from one application to another. In general, however, the key criteria are how much uncertainty is present in a data set and how much is acceptable. Judgments about fitness for use may be more difficult when data are acquired from secondary rather than primary sources. Aspects of data quality include accuracy, resolution, and precision. Concepts of data quality, error, and uncertainty are also covered in Knowledge Areas CF Conceptual Foundations (in a theoretical context) and GC Geocomputation (in the context of analysis); the focus here is on the measurement and assessment of data quality.","hasChildren":true,"hasParent":true,"name":"Data quality","selfAssesment":"GI-N2K
"},{"code":"GD4","description":"Data quality is the degree of data usability in relation to given objective and particular application. The expectations to data vary between different applications. The key criteria in data quality are the amount of uncertainty in data as compared to the acceptable level of uncertainty. Evaluation of the usability may be more complicated using data from secondary sources. Appropriate metadata is inevitable for these judgements. Aspects of data quality include geometric and thematic accuracy, (in)consistencies, resolution, precision, usability and others. Assurance of data quality may be improved by following proper standards and spatial data infrastructure regulations for data collection and management. System of basic data quality measures for geospatial domain in the EN ISO 19157:2013 standard.","hasChildren":true,"hasParent":true,"name":"Data Quality, Metadata and Data Infrastructure","selfAssesment":"In progress (GI-N2K)
"},{"code":"GD6-1","description":"Geometric accuracy is a measure indicating how close the geometric values of the data are to the real world position of the mapped feature.","hasChildren":true,"name":"Geometric accuracy","selfAssesment":"In progress (GI-N2K)
\r\n\r\nIn progress (GI-N2K)
"},{"code":"GD6-3","description":"The resolution of a data source indicates the smallest unit of detail provided by the data source.","hasChildren":true,"name":"Resolution","selfAssesment":"In progress (GI-N2K)
"},{"code":"GD6-4","description":"The precision of a measurement system, related to reproducibility and repeatability, is the degree to which repeated measurements under unchanged conditions show the same results.","hasChildren":true,"name":"Precision","selfAssesment":"GI-N2K
"},{"code":"GD6-5","description":"Primary data sources provide information collected directly for GIS use. Secondary sources are data sources that need to be processed before they are ready for GIS use.","hasChildren":true,"name":"Primary and secondary sources","selfAssesment":"In progress (GI-N2K)
"},{"code":"GD8-1","description":"Tablet digitizing is the conversion from physical map to digital data by re-drawing the features on the map fixed on a digitizing tablet","hasChildren":true,"name":"Tablet digitizing","selfAssesment":"In progress (GI-N2K)
\r\n\r\nIn progress (GI-N2K)
\r\n\r\nIn progress (GI-N2K)
"},{"code":"GN","description":"A Global Navigation Satellite System (GNSS) is a generic term denoting a satellite navigation system that provides continuous positioning over the globe. A GNSS involves a constellation of satellites orbiting Earth, continuously transmitting signals that enable users to determine their three-dimensional (3D) position with global coverage. The design and contents of GNSS concepts and techniques are often focused on their instrumental use in navigation.\r\nGNSS systems include GPS (Global Positioning System), Glonass (GLObal NAvigation Satellite System), Galileo, and Beidou. While the US GPS was historically the only fully operational GNSS for many years, the Russian Glonass was restored to full operation in December 2011, and the Chinese BeiDou and European Galileo systems are under development.\r\nOperational Principle\r\nThe core principle of positioning is solving an elemental geometric problem by finding the distances (ranges) of a user to a set of GNSS satellites with known coordinates. The satellites’ coordinates are calculated from navigation data transmitted by the satellites.\r\nThe basic observable in a GNSS is the time required for a signal (an electromagnetic wave) to travel from the satellite (transmitter) to the receiver. This travel time, when multiplied by the speed of light, provides a measure of the apparent distance, referred to as the pseudorange. To solve for the user's position, at least four satellites are needed to compute the three receiver coordinates and clock offset simultaneously. Using resulting signals and navigation data, user coordinates can be initially computed to an accuracy of several metres, although centimetre-level positioning can be achieved using more advanced techniques.\r\nGNSS Architecture\r\nA GNSS is organized into three primary segments:\r\n1. Space Segment: This segment consists of satellite constellations designed with enough satellites to ensure users can view a minimum of four satellites at any time from any point on Earth’s surface. Its main functions are to generate and transmit code and carrier phase signals, and to store and broadcast the navigation message. The transmissions are regulated by highly stable atomic clocks placed onboard the satellites.\r\n2. Control Segment (Ground Segment): This segment is responsible for the overall proper operation of the GNSS. Its duties include controlling and maintaining the status and configuration of the satellite constellation, predicting ephemeris and satellite clock evolution, maintaining the corresponding GNSS time scale using atomic clocks, and updating the navigation messages.\r\n3. User Segment: This segment comprises the GNSS receivers. The receivers' main function is to receive the GNSS signals, determine pseudoranges and other observables, and solve the navigation equations to provide the user with coordinates, velocity, and precise timing.\r\nGNSS technology, and the associated data processing and analysis, can target high-accuracy positioning goals, requiring accurate modelling of measurements down to the centimetre level or better.","hasChildren":true,"hasParent":true,"name":"GNSS","selfAssesment":" "},{"code":"GN1-1-1-1","description":" ","hasChildren":true,"name":"GNSS Satellites","selfAssesment":" "},{"code":"GN1-1-1-2-1-1","description":" ","hasChildren":true,"name":"Front-Ends","selfAssesment":" "},{"code":"GN1-1-1-2-1-6","description":" ","hasChildren":true,"name":"Algorithms","selfAssesment":" "},{"code":"GN1-1-1-2-1-7","description":"The GNSS satellites continuously transmit navigation signals in two or more frequencies in L band. These signals contain ranging codes and navigation data to allow the users to compute the travelling time from satellite to receiver and the satellite coordinates at any epoch. The main signal components are described as follows: Carrier: Radio frequency sinusoidal signal at a given frequency. Ranging code: Sequences of 0s and 1s (zeroes and ones), which allow the receiver to determine the travel time of radio signal from satellite to receiver. They are called Pseudo-Random Noise (PRN) sequences or PRN codes. Navigation data: A binary-coded message providing information on the satellite ephemeris (Keplerian elements or satellite position and velocity), clock bias parameters, almanac (with a reduced accuracy ephemeris data set), satellite health status, and other complementary information.","hasChildren":true,"name":"GNSS Signal","selfAssesment":" "},{"code":"GN1-1-1-2-1","description":" ","hasChildren":true,"hasParent":true,"name":"Components","selfAssesment":" "},{"code":"GN1-1-1-2-2-1","description":" ","hasChildren":true,"name":"Mass Market (low-cost)","selfAssesment":" "},{"code":"GN1-1-1-2-2-2","description":" ","hasChildren":true,"name":"Professional grade","selfAssesment":" "},{"code":"GN1-1-1-2-2-3","description":" ","hasChildren":true,"name":"Scientific (geodetic)","selfAssesment":" "},{"code":"GN1-1-1-2-2","description":" ","hasChildren":true,"hasParent":true,"name":"Grade","selfAssesment":" "},{"code":"GN1-1-1-2-3","description":" ","hasChildren":true,"name":"Simulators and Test Equipment","selfAssesment":" "},{"code":"GN1-1-1-2-4","description":" ","hasChildren":true,"name":"Vector Processing","selfAssesment":" "},{"code":"GN1-1-1-2","description":"GNSS Receivers process the Signals In Space (SIS) transmitted by the satellites, being the user interface to any Global Navigation Satellite System (GNSS). Even though the information provided by a generic GNSS receiver can be used by a wide range of Applications, most of them rely on the receiver's navigation solution - i.e. receiver computed Position, Velocity and Time (PVT).","hasChildren":true,"hasParent":true,"name":"GNSS Receivers","selfAssesment":" "},{"code":"GN1-1-1","description":" ","hasChildren":true,"hasParent":true,"name":"GNSS Space segment","selfAssesment":" "},{"code":"GN1-1-2","description":" ","hasChildren":true,"name":"GNSS Ground Segment","selfAssesment":" "},{"code":"GN1-1-3","description":" ","hasChildren":true,"name":"Chinese Navigation Satellite Systems","selfAssesment":" "},{"code":"GN1-1-4","description":" ","hasChildren":true,"name":"Navstar Global Positioning System","selfAssesment":" "},{"code":"GN1-1-5","description":" ","hasChildren":true,"name":"BeiDou Navigation Satellite System","selfAssesment":" "},{"code":"GN1-1","description":" ","hasChildren":true,"hasParent":true,"name":"Global systems","selfAssesment":" "},{"code":"GN1-2","description":" ","hasChildren":true,"name":"Regional Systems","selfAssesment":" "},{"code":"GN1-3","description":" ","hasChildren":true,"name":"IRNSS","selfAssesment":" "},{"code":"GN1-4","description":" ","hasChildren":true,"name":"Quasi-Zenith Satellite System","selfAssesment":" "},{"code":"GN1","description":"A Satellite Navigation System provides continuous positioning over the globe.\r\nThe system architecture is primarily defined by the Space Segment, which consists of orbiting Satellites. The satellites’ payload generates and transmits the code and carrier phase signals, along with the navigation message. Modernisation efforts continuously enhance these signals; for instance, the GPS third civil signal L2C is a robust signal designed to facilitate Mass Market (low-cost) receivers. Similarly, Glonass satellites are undergoing a GLONASS signal CDMA upgrade for civilian applications on the G3 band.\r\nThe core function relies on sophisticated algorithms to produce navigational outputs. The receiver solves navigation equations to perform Positioning Velocity Time (PVT) computation, delivering the user's coordinates, velocity, and precise timing. The geometric quality of the position solution is quantified using the Dillution of Precision (DOP) factor. Depending on the required precision, receivers are classified into different grade levels, ranging from mass-market devices to professional grade and scientific (geodetic) grade equipment used to achieve high-accuracy positioning, often striving for centimetre-level results or better.","hasChildren":true,"hasParent":true,"name":"Satellite Navigation systems","selfAssesment":" "},{"code":"GN2-1-1","description":" ","hasChildren":true,"name":"Standard Point Positioning (SPP)","selfAssesment":" "},{"code":"GN2-1-2","description":" ","hasChildren":true,"name":"Safety of Life Systems","selfAssesment":" "},{"code":"GN2-1-3","description":" ","hasChildren":true,"name":"Differential GNSS (DGNSS): code based","selfAssesment":" "},{"code":"GN2-1-4","description":" ","hasChildren":true,"name":"Safety of Life Systems: Augmentation Systems","selfAssesment":" "},{"code":"GN2-1","description":"The target is to determine the receiver coordinates and clock offset from pseudorange measurements of at least 4 satellites in view. The positioning principle is based on solving a geometric problem from the measured ranges to the satellites, with known coordinates. The satellite coordinates can be computed from the broadcast message, which also provides all necessary information for the measurements modelling for the Standard Positioning Service (SPS).","hasChildren":true,"hasParent":true,"name":"Code Based Positioning","selfAssesment":" "},{"code":"GN2-10","description":" ","hasChildren":true,"name":"Navigation with Low-Frequency Radio Signals","selfAssesment":" "},{"code":"GN2-11","description":" ","hasChildren":true,"name":"Inertial Navigation Sensors","selfAssesment":" "},{"code":"GN2-12","description":" ","hasChildren":true,"name":"GNSS-INS Integration","selfAssesment":" "},{"code":"GN2-2-1","description":" ","hasChildren":true,"name":"Precise Point Positioning (PPP)","selfAssesment":" "},{"code":"GN2-2-2","description":" ","hasChildren":true,"name":"Carrier Phase Integer Ambiguity Resolution","selfAssesment":" "},{"code":"GN2-2-3","description":" ","hasChildren":true,"name":"Differential GNSS (DGNSS): carrier based","selfAssesment":" "},{"code":"GN2-2","description":"Can hold Standard Point Positioning, High Accuracy Navigation (PPP and RTK) and Safety of Life systems (GBAS,SBAS,RAIM)","hasChildren":true,"hasParent":true,"name":"Carrier Phase Based Positioning","selfAssesment":" "},{"code":"GN2-3","description":" ","hasChildren":true,"name":"GNSS/INS Integration","selfAssesment":" "},{"code":"GN2-4","description":" ","hasChildren":true,"name":"Assisted GNSS","selfAssesment":" "},{"code":"GN2-5","description":" ","hasChildren":true,"name":"Nonlinear Recursive Estimation for Integrated Navigation Systems","selfAssesment":" "},{"code":"GN2-6","description":" ","hasChildren":true,"name":"Overview of Indoor Navigation Techniques","selfAssesment":" "},{"code":"GN2-7","description":" ","hasChildren":true,"name":"Navigation with Cellular Signals of Opportunity","selfAssesment":" "},{"code":"GN2-8","description":" ","hasChildren":true,"name":"Position, Navigation and Timing with Dedicated Metropolitan Beacon Systems","selfAssesment":" "},{"code":"GN2-9","description":" ","hasChildren":true,"name":"Navigation with Terrestrial Digital Broadcasting Signals","selfAssesment":" "},{"code":"GN2","description":"GNSS positioning techniques are the methods used to determine a user's three-dimensional (3D) position, velocity, and time by processing signals transmitted by orbiting satellites. The fundamental principle involves measuring the apparent distance (pseudorange) to at least four satellites to solve for the three receiver coordinates and the receiver clock offset.\r\nPositioning methods are generally categorized based on the measurements they use and their resulting accuracy:\r\n1. Standard Point Positioning (SPP)\r\nSPP is a code-based positioning technique that uses pseudorange measurements (R).\r\n• Accuracy and Modeling: SPP typically achieves coordinate accuracy of several metres. To achieve this, errors such as satellite clock offsets, atmospheric delays (tropospheric and ionospheric), and instrumental delays are modeled. For single-frequency users, the broadcast Klobuchar ionospheric model is often used.\r\n• Process: The technique solves a nonlinear system by iteratively linearizing the geometric range around an approximate position and solving the resulting navigation equations (linear system y=Gx) using parameter estimation techniques such as least squares or Kalman filtering.\r\n2. Precise Point Positioning (PPP)\r\nPPP is an advanced technique that utilizes both code and carrier phase measurements (R and Φ) to target high-accuracy positioning.\r\n• Accuracy and Modeling: PPP aims for centimetre-level accuracy in static positioning or decimetre-level (or better) in kinematic positioning. This requires accurate measurement modelling of all delay terms (including Earth deformation and antenna biases).\r\n• Key Requirements: PPP typically uses the ionosphere-free combination of dual-frequency signals to eliminate most ionospheric refraction. It relies on precise satellite orbits and clocks (such as those from IGS) rather than broadcast ephemerides. The receiver estimates the carrier phase ambiguities as real numbers (floating ambiguities).\r\n3. Advanced Techniques (Differential and Fast PPP)\r\nOther techniques build upon PPP principles to improve speed and accuracy:\r\n• Differential Positioning (RTK/WARTK): These methods, such as Real-Time Kinematics (RTK) and Wide-Area Real-Time Kinematics (WARTK), rely on fixing the carrier phase ambiguities to their integer values. They often use double-differenced measurements between pairs of satellites and receivers to cancel out common errors, enabling centimetre-level positioning.\r\n• Fast Precise Point Positioning (F-PPP): This approach uses accurate external products, such as precise ionospheric corrections computed from a wide-area network, to accelerate the filter convergence time from approximately one hour to a few minutes, achieving better than 10 cm accuracy quickly.\r\nIn essence, positioning techniques range from simple, real-time code solutions (SPP) suitable for standard navigation, to complex carrier-based solutions (PPP) using precise external products for geodetic accuracy.","hasChildren":true,"hasParent":true,"name":"GNSS Positioning Techniques","selfAssesment":" "},{"code":"GS","description":"Geographic Information Science and Technology serve the society, but it is not a panacea. The history of its development is the sum of fragmented efforts, which have still not been fully integrated. Its potential benefits are often constrained and its potential impacts are not fully understood. Institutional and economic factors limit access to data, technology, and expertise by some of those who need it to make better decisions. Political, ideological, and personal issues aside, organizations invest in GIS&T when estimated benefits outweigh estimated costs. Evaluating costs and benefits is difficult, however and too often leads to nothing being done. For some individuals and groups, costs are prohibitive even though potential benefits are compelling. The legal framework provides a structure for regulating a number of key aspects of geographic information science, technology, and applications. Legal regimes determine who can claim the exclusive right to hold and use geospatial data, the conditions under which others may have access to the data, and what subsequent uses are permitted. Political struggles arise from conflicting proprietary and public interests about who benefits from geospatial information, and how the power to allocate the use of this information is, or should be, distributed among members of a society. The need to choose among conflicting interests sometimes poses ethical dilemmas for GIS&T professionals. The explosive growth of the geospatial information contributed by users through various application programming interfaces has made geospatial information is a powerful tool in the social media toola powerful media for the general public to communicate, but perhaps more importantly, geographic information have also become a tool media for constructive dialogs and interactions about social issues, recent growth of Web-based geospatial information and volunteered geographic information (VGI). Because so many public agencies and private organizations rely upon GIS&T for planning, decision making, and management, GIS&T increasingly affects and is used to direct daily life. Critical approaches to understanding the role of GIS in society equip practitioners to employ GIS&T reflectively. The critical approach specifically questions the assumptions and premises that underlie the economic, legal and political regimes and institutional structures within which GIS&T is implemented. Related concerns are considered in Knowledge Area OI: Organizational and Institutional Aspects.","hasChildren":true,"hasParent":true,"name":"GI and Society","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS1-1","description":"The most basic definition of a legal regime is a system or framework of rules governing some physical territory or discrete realm of action that is at least in principle rooted in some sort of law. Often the concept has been applied to specific areas of law.","hasChildren":true,"name":"The legal regime and legal framework","selfAssesment":"GI-N2K
"},{"code":"GS1-2","description":"Contract law is defined as a set of rules that govern the contractual agreements between merchants or persons. A contract is an agreement between different parties that state their responsibilities and duties to each other. A liability in contract law is when certain conditions are written into a contract that makes a party liable. Licensing is the process of giving or getting official permission to do something. A license is an agreement through which a licensee leases the rights to a legally protected piece of intellectual property from a licensor — the entity which owns or represents the property — for use in conjunction with a product or service.","hasChildren":true,"name":"Contract law, liability and licensing","selfAssesment":"GI-N2K: relevant but to be revised
"},{"code":"GS1-3","description":"Data privacy and security are two essential components of a successful strategy for data protection. Data security refers to the protection of data from unauthorized access, use, change, disclosure, and destruction. It encompasses network security, physical security, and file security. Data privacy involves protecting consumer data by eliminating or reducing the possibility of re-identifying an individual whose information is present in the data. This is done by either removing specific information or by transforming the data with random “noise” or generalization.","hasChildren":true,"name":"Privacy and Security","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS1-4","description":"Property is secured by laws that are clearly defined and enforced by the state. These laws define ownership and any associated benefits that come with holding the property. The term property is very expansive, though the legal protection for certain kinds of property varies between jurisdictions. Property is generally owned by individuals or a small group of people. The rights of property ownership can be extended by using patents and copyrights. Property rights give the owner or right holder the ability to do with the property what they choose. That includes holding on to it, selling or renting it out for profit, or transferring it to another party.","hasChildren":true,"name":"Ownership and property rights","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS1-5","description":"In economics, competition is a condition where different economic firms seek to obtain a share of a limited good by varying the elements of the marketing mix: price, product, promotion and place. Competition law is a law that promotes or seeks to maintain market competition by regulating anti-competitive conduct by companies. Public-private sector relationships deal with a particular subset of competition, i.e. competition between public and private organizations.","hasChildren":true,"name":"Competition and public-private sector relationships","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS1-6","description":"Open data is data that can be accessed, shared, used and reused without any barrier for any type of (re)user. According to the Open Definition, open data can be defined as data that be freely used, modified, and shared by anyone for any purpose subject, at most, to measures that preserve provenance and openness. Open data requires datasets to be either in the public domain, or distributed through an open license. The data must be provided as a whole, free of charge, and preferably downloadable via the Internet, including any additional information that might be necessary to comply with the open license’s terms. Openness requires the data to be provided in a readily machine-readable form. The format must be open as well, meaning that it does not place any restriction upon its use, and that the files in that format can be processed with open-source software tools. The Open Definition speaks broadly of open ‘works’, rather than of open data. Focusing on data tout court, one can move from the Open Government Data (OGD) principles. According to the OGD principles, which are arguably foundational in understanding the concept of open data, data must be: Complete; Primary; Timely; Accessible; Machine-processable; Non-discriminatory; Non-proprietary; and License-free. Compliance with the OGD principles needs to be demonstrable, i.e. there need to be accountability measures in place to allow the review of the adherence to the principles above. The concepts of Open Work and open data highlight how data needs to be both legally, technically and financially open, so either in the public domain or covered by an open license, and kept in a machine-readable and non-proprietary format. Open data aims at making information available to everybody, for any purpose, in a machine-readable and interoperable format, based on open standards and digestible by free/libre open source software (FLOSS). Also with respect to the financial accessibility open data is data available free of charge. Marginal costs of dissemination are accepted by some as a reasonable cost for users. However, open data is data that can be accessed and reused without any barrier for any type of reuse, and some user groups experience any price to be paid as a barrier.","hasChildren":true,"name":"Open data","selfAssesment":"Completed
"},{"code":"GS1","description":"Legal problems can arise when geospatial information is used for land management, among other activities. Geospatial professionals may be liable for harm that results from flawed data or the misuse of data. Understanding of contract law and liability standards is essential to mitigate risks associated with the provision of geospatial information products and services. Legal relations between public and private organizations and individuals govern data access. The nature of information in general, and the characteristics of geospatial information in particular, make it an unusual and difficult subject for a legal regime that seeks to establish and enforce the type of exclusive control associated with other commodities. Geospatial information is in many ways unlike the kinds of works that intellectual property rights were intended to protect. Still, organizations can, and do, assert proprietary interests in geospatial information. Perspectives on geospatial information as property vary between the public and private sectors and between different countries.","hasChildren":true,"hasParent":true,"name":"Legal aspects","selfAssesment":"In progress GI-N2K
"},{"code":"GS2-1","description":"Business models determine how organizations can create and deliver value, for example, through the provision or use of geographic data. A business model is a conceptual tool that contains\r\na set of interrelated elements that allow organizations to create and capture value and generate revenues. The development and implementation of an appropriate business model are considered to be a key to the success of the organization and a crucial source for value creation. \r\n\r\nAlthough business models determine how organizations create, deliver, and capture value, they should not be regarded as permanent and invariable structures or settings. Business models are shaped by both internal and external forces, and will only be successful if they are able to adapt to a changing environment. In the GI domain, several technological, regulatory, and societal developments have challenged the existing business models and opened up opportunities for new business models. Among these developments are the establishment of spatial data infrastructures (SDIs) worldwide, the democratization of geographic knowledge, and the move toward open source, open standards, and open data.\r\n\r\nSince the development and implementation of SDIs in different parts of the world, much attention has been paid to the need to find appropriate business models for GI, and in particular, for geographic data providers in the public sector. Traditional business models in which public data providers were selling their data to customers in the private industry and other public agencies were questioned, because they restricted the opportunity for data sharing. The concept of SDI is about moving to new business models, where partnerships between GI organizations are promoted to allow access to a much wider scope of geographic data and services. A key challenge in the development of these SDIs was the alignment of different existing business models of the actors in the GI domain. Moreover, the development and implementation of SDIs also led to the emergence of new business models, which was even more the case with the more recent move toward open geographic data.\r\n\r\nOrganizations can be active in different parts of the geo-information value chain, and can create and offer value in many different ways. As a result, many different GI business models exist. Data providers, data enablers, and data end users could be seen as three main categories of GI business models. Each of these categories consists of many different business models, as different value propositions\r\nwill exist, and value can be created and captured in several ways.","hasChildren":true,"name":"GI Business models","selfAssesment":"Completed (GI-N2K)
"},{"code":"GS2-5","description":"To provide a better insight into the process of adding value to GI, several authors have introduced and applied the information value chain approach. A value chain can be defined as the set of value-adding activities that one or more organizations perform in creating and distributing goods and services. The value chain concept originally was developed for the manufacturing sector, as a tool to evaluate the competitive advantage of firms. More recently, the value chain concept has been applied to other sectors, including information technology where the good or service, and the benefits it provides, is less tangible in nature. A value chain involves the progress of goods from raw materials to finished products through a number of stages, during each of which a new value is added to the original input by various activities. The value chain concept was extended into the information market, with the information value chain referring to the set of activities adding value to information and turning raw data into new information products or services. Especially important in this context is the role of information and communication technologies (ICT), which have an impact on all activities in the information value chain, such as information collection, processing, dissemination, and use. In the context of GI, the value chain relates to the series of value- adding activities to transform raw geographic data into new products that are used by certain end users. Although there are slightly different descriptions of the various steps of the GI value chain, in general, the essential steps in the value chain are: acquisition of raw data, the application of a data model, quality control, and integration with other sources, presentation, and distribution. In recent years, particular attention has been paid to different steps between the process of distributing data and the actual end use of an end product of GI. In addition, after the publication of the data, value can be added to the data in many different ways. Value can be added by making data from different sources easily accessible through repositories and data portals, by building and selling tailored solutions using the data to end users or by using geographic data to improve existing products and services delivered to an end user. In certain cases, this end product will be the first step of a next value chain.","hasChildren":true,"name":"Geo-information value chain","selfAssesment":"Completed
"},{"code":"GS2","description":"Most organizations insist that investments in GIS and T be justified in economic terms. Quantifying the value of information, and of information systems, however, is not a straightforward matter.","hasChildren":true,"hasParent":true,"name":"Economic aspects","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS3-1","description":"The use of geospatial information allows public sector organizations and actors to make better decisions and provide better services to their citizens. Geospatial information is increasingly being used at different administrative levels and in different policy areas.","hasChildren":true,"name":"Use of geospatial information in the public sector","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS3-2","description":"Geospatial information is increasingly being used by private companies for different purposes and the private sector plays an important role in the development and implementation of geospatial information infrastructures.","hasChildren":true,"name":"Use of geospatial information in the private sector","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS3-3","description":"Research and education institutions use geospatial information for various purposes, in support of their research and educational activities.","hasChildren":true,"name":"Use of geospatial information in research and education","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS3-4","description":"Effective monitoring of the environment and an improved understanding of the same requires valuable information and data that can be extracted through application of geospatial technologies. GIS can be used most effectively for environmental data analysis and planning. It allows better viewing and understanding physical features and the relationships that influence in a given critical environmental condition. GIS can help in effective planning and managing the environmental hazards and risks. In order to plan and monitor the environmental problems, the assessment of hazards and risks becomes the foundation for planning decisions and for mitigation activities. GIS supports activities in environmental assessment, monitoring, and mitigation and can also be used for generating environmental models. GIS can aid in hazard mitigation and future planning, air pollution & control, disaster management, forest fires management, managing natural resources, wastewater management, oil spills and its remedial actions etc.","hasChildren":true,"name":"Use of geospatial information in environmental issues","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS3","description":"Geospatial Information used in Government agencies and public authorities at local, state, and federal levels produce and use geospatial data for many activities, including provision of social services, public safety, economic development, environmental management, and national defence. Public participation in governing, empowered by geospatial technologies, offers the potential to strengthen democratic societies by involving grassroots community organizations and by engaging local knowledge. The private sector covers a broad range of areas of opportunity. With continued advancements in technology, greater awareness of its advantages as a powerful decision support tool the use of geospatial information use in the private sector needs to be discussed.","hasChildren":true,"hasParent":true,"name":"Use of geospatial information","selfAssesment":"In Progress GI-N2K
"},{"code":"GS4-1","description":"Public participation GIS (PPGIS) is a field within geographic information science that focuses on ways the public uses various forms of geospatial technologies to participate in public processes, such as mapping and decision making.","hasChildren":true,"name":"Public participation GIS","selfAssesment":"GI-N2K (revision)
"},{"code":"GS4-2b","description":"Social Media Geographic Information (SMGI) can be defined as any piece or collection of multimedia data or information with explicit (i.e. coordinates) or implicit (i.e. place names or toponyms) geographic reference collected through the social networking web or mobile applications. Social data are acknowledged as a good of major value in the digital economy, and their potential for enhancing more traditional analytics is of the utmost importance. A big part of social data however also features spatial (and temporal) references, thus their integration with more traditional Authoritative Geographic Information (AGI) may enable a further step towards the next generation of geospatial intelligence. SMGI is a sub-category of VGI and can be active or passive, depending on the type of application with which it is collected: applications purposefully created and/or used to collect SMGI in participatory initiatives","hasChildren":true,"name":"Social Media Geographic Information","selfAssesment":"Completed
"},{"code":"GS4-3b","description":"Volunteered geographic information (VGI) is a special kind of user-generated content. It refers to geographic information collected and shared voluntarily by the general public. Web.2.0 and associated advances in web mapping technologies have greatly enhanced the abilities to collect, share and interact with geographic information online, leading to VGI.","hasChildren":true,"name":"Citizens and volunteered geographic information","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS4","description":"Today, geo data has become a conventional and pervasively familiar data type seen at once to underpin and significantly re-characterize the digital world, with broad implications for both technology and society. Geospatial data are abundant, but access to data varies with the nature of the data, the user groups wishes to acquire it and for what purpose, under what conditions, and at what price geodata can be obtained. The explosive growth of geographic information contributed by users through various application programming interfaces has made geographic information a powerful media for the general public, but perhaps more importantly, geospatial information have also become media for constructive dialogs and interactions about social issues, recent growth of Web-based Geographic information and volunteered geographic information (VGI).","hasChildren":true,"hasParent":true,"name":"Geospatial citizenship","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS5-1b","description":"The advantages of geospatial technologies and resulting data present ethical dilemmas such as privacy and security concerns as well as the potential for stigma and discrimination resulting from being associated with particular locations. the use of geospatial technologies and the resulting data needs to be critically assessed through an ethical lens prior to implementation of programmes, analyses or partnerships. Using this lens requires not only explicit consideration of potential negative consequences of adoption but also clear articulation of the specific contexts and conditions under which benefits may be realized.","hasChildren":true,"name":"Ethics in the geospatial information society","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS5-2b","description":"A code of ethics is a guide of principles designed to help professionals conduct business honestly and with integrity. A code of ethics document may outline the mission and values of the business or organization, how professionals are supposed to approach problems, the ethical principles based on the organization's core values, and the standards to which the professional is held. Codes of ethics for geospatial professionals are intended to provide these principles and guidelines for GIS professionals","hasChildren":true,"name":"Codes of ethics for geospatial professionals","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS5","description":"Ethics provide frameworks that help individuals and organizations make decisions when confronted with choices that have moral implications. Most professional organizations develop codes of ethics to help their members do the right thing, preserve their good reputation in the community, and help their members develop as a community","hasChildren":true,"hasParent":true,"name":"Ethical aspects","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS6-1","description":"US GIS&T BoK: As GIS became a firmly established presence in geography and catalysed the emergence of GIScience, it became the target of a series of critiques regarding modes of knowledge production that were perceived as problematic. The first wave of critiques charged GIS with resuscitating logical positivism and its erroneous treatment of social phenomena as indistinguishable from natural/physical phenomena. The second wave of critiques objected to GIS on the basis that it was a representational technology. In the third wave of critiques, rather than objecting to GIS simply because it represented, scholars engaged with the ways in which GIS represents natural and social phenomena, pointing to the masculinist and heteronormative modes of knowledge production that are bound up in some, but not all, uses and applications of geographic information technologies. In response to these critiques, GIScience scholars and theorists positioned GIS as a critically realist technology by virtue of its commitment to the contingency of representation and its non-universal claims to knowledge production in geography. Contemporary engagements of GIS epistemologies emphasize the epistemological flexibility of geospatial technologies.","hasChildren":true,"name":"Epistemological and critical issues","selfAssesment":"In progress/to delete (GI-N2K)
"},{"code":"GS6-2","description":"Various types of critiques exist on the way geospatial information is being used and re-used.","hasChildren":true,"name":"Critical approach on the use of geospatial information","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS6-3","description":"Defending or refuting the argument that the \"digital divide\" that characterizes access use of geospatial information perpetuates inequities among developed and developing nations, among socio-economic groups,and between individuals, community organizations, and public agencies and private firms.","hasChildren":true,"name":"Critical aspects and invisible groups","selfAssesment":"In progress/to be delete (GI-N2K)
"},{"code":"GS6","description":"Many of the educational objectives used to define topics in this knowledge area, and in the Body of Knowledge as a whole, challenge educators and students to think critically about GI and Society. Since the 1990s, scholars have criticized cartography and the GIS science from a wide range of perspectives. Common among these critiques are questioned assumptions about the purported benefits of GI and Society and attention to its unexamined risks. By promoting reflective practice among current and aspiring geospatial information professionals, an understanding of the range of critical perspectives increases the likelihood that geospatial information will fulfil its potential to benefit all stakeholders. Philosophical, psychological, and social underpinnings of these critiques are considered in Knowledge Area CF: Conceptual Foundations.","hasChildren":true,"hasParent":true,"name":"Critical approach","selfAssesment":"In progress (GI-N2K)
"},{"code":"GS7-1","description":"US GIS&T BoK: As GIS became a firmly established presence in geography and catalysed the emergence of GIScience, it became the target of a series of critiques regarding modes of knowledge production that were perceived as problematic. The first wave of critiques charged GIS with resuscitating logical positivism and its erroneous treatment of social phenomena as indistinguishable from natural/physical phenomena. The second wave of critiques objected to GIS on the basis that it was a representational technology. In the third wave of critiques, rather than objecting to GIS simply because it represented, scholars engaged with the ways in which GIS represents natural and social phenomena, pointing to the masculinist and heteronormative modes of knowledge production that are bound up in some, but not all, uses and applications of geographic information technologies. In response to these critiques, GIScience scholars and theorists positioned GIS as a critically realist technology by virtue of its commitment to the contingency of representation and its non-universal claims to knowledge production in geography. Contemporary engagements of GIS epistemologies emphasize the epistemological flexibility of geospatial technologies.","hasChildren":true,"name":"Epistemological critiques","selfAssesment":"GI-N2K
"},{"code":"GS7-3","description":"US GIS&T BoK: \r\n\r\nFeminist interactions with GIS started in the 1990s in the form of strong critiques against GIS inspired by feminist and postpositivist theories. Those critiques mainly highlighted a supposed epistemological dissonance between GIS and feminist scholarship. GIS was accused of being shaped by positivist and masculinist epistemologies, especially due to its emphasis on vision as the principal way of knowing. In addition, feminist critiques claimed that GIS was largely incompatible with positionality and reflexivity, two core concepts of feminist theory. Feminist critiques of GIS also discussed power issues embedded in GIS practices, including the predominance of men in the early days of the GIS industry and the development of GIS practices for the military and surveillance purposes.\r\n\r\nAt the beginning of the 21st century, feminist geographers reexamined those critiques and argued against an inherent epistemological incompatibility between GIS methods and feminist scholarship. They advocated for a reappropriation of GIS by feminist scholars in the form of critical feminist GIS practices. The critical GIS perspective promotes an unorthodox, reconstructed, and emancipatory set of GIS practices by critiquing dominant approaches of knowledge production, implementing GIS in critically informed progressive social research, and developing postpositivist techniques of GIS. Inspired by those debates, feminist scholars did reclaim GIS and effectively developed feminist GIS practices.","hasChildren":true,"name":"Feminist critiques","selfAssesment":"GI-N2K
"},{"code":"GS7-4","description":"In the early 1990s social critiques of GIS from human geographers began to appear. These initial critiques set off an ensuing debate between GISers, defending GIS and human geographers, who critiqued GIS. This debate materialized in academic journals including: Political Geography Quarterly, Environment and Planning A, and Progress in Human Geography. Schuurman (2000) notes that the GIS debate, while unique to the discipline of Geography, was part of a larger debate in other disciplines about the effects of technology. This presentation will be limited (unfortunately) to two aspects of this debate. It will first discuss conditions within human geography that made GIS a target of human geographers' critique. Second, this paper will discuss the particular critiques that were directed at GIS by human geographers. Though the reaction of such critiques and their effect on GIS is an important topic there is not enough time and space to address these issues. See Schuurman (2000) \"Trouble in the Heartland: GIS and its critics in the 1990s\" in Progress in Human Geography for a thoughtful look at this debate and its effects on the discipline of GIS.","hasChildren":true,"name":"Social critiques","selfAssesment":"GI-N2K
"},{"code":"IP","description":"Image processing and analysis comprises all relevant steps to reach from (raw) image data to [...] information via image interpretation and digital image classification. In traditional remote sensing workflows, this step follows the image acquisition process. There are two main components, i.e. (1) image processing, (2) analysis, which emphasizes the sequential nature of the process – while increasingly this dichotomy disappears.\r\nThe information production workflow aims at converting semantically rich, but unstructured image data into a set of classes, objects, arrangements, etc., to enable ultimately a complete image understanding and scene reconstruction. This scene reconstruction entails a mental component (“understanding”) and a technical one, by providing standardized classification results or even beyond, dedicated information products in form of digital maps and reports, tailored to the specific application domains and use cases, in order to make informed decisions. Such information products can be maps, reports, dashboards etc., overall it is the transformation from quantitative, semi-continuous digital numbers (“brightness”) to qualitative information using categories and figures, which can be stored and further used in a GIS environment. \r\nThe first part of the process entails image calibration, image correction (geometric, radiometric), data assimilation, and any type of enhancement (contrast manipulation, filtering, etc.) which aims to better condition the information extraction part. It ends where we achieve a significant milestone in the processing milestone, remarkably denoted as analysis-ready data (ARD). From there, we enter into the analysis realm, classically referred to as digital image classification, the process of assigning pixels to classes. In other words, the aggregation of pixel values according to their similarity into categorical (nominal) classes. The discrimination of these classes by and large depend on application domain, and ideally, these classes match with information classes. To address the issue of ambiguity and to overcome the so-called semantic gap in image interpretation by providing a stepping-stone in the information extraction process, the strategy of pre-classification (semi-concepts) has been introduced in the literature.\r\nToday, boundaries between pre-processing and classification increasingly vanish, through an increasing level of automation in the pre-processing and image correction steps. In addition, new ways of analysis emerge, in particular in large time series, including image data cubes. Instead of a processing chain, which suggests a linear – and potentially irreversible – cascade of manipulations, the automation of large parts of this part allows us to see the process more reversible and approachable from either side.","hasChildren":true,"hasParent":true,"name":"Image processing and analysis","selfAssesment":"Completed
"},{"code":"IP1-1-1","description":"The image spatial subset allows to extract the group of pixels / grid cells using a defined polygon e.g. area of interest – AOI or defining the new image extent. It is used to limit spatially the image extent to which, for example an image function or classification model will be applied.","hasChildren":true,"name":"Image subset","selfAssesment":"Completed
"},{"code":"IP1-1-2","description":"Layer stacking is a process for combining multiple images into a single image. The image stack is used to build a ‘new’ multiple band file from the georeferenced images of various pixel sizes, extents, projections. The image bands must be resampled and reprojected to a common spatial grid. The layer stacking is used for example to combine spectral bands from a Landsat, Sentinel-2 data and SRTM DEM into one multi-dimensional file. The process of layer stacking increases the size of the final stacked image, which may have consequences that increase the processing time of operations performed on the stacked image.","hasChildren":true,"name":"Layer stack","selfAssesment":"Completed
"},{"code":"IP1-1","description":"Data manipulation adjusts a dataset to the needs of a specific application by subsetting the spatial extent or the number of bands or by organizing bands from separate single layer files into a single multi-layer file.","hasChildren":true,"hasParent":true,"name":"Data manipulation","selfAssesment":"New
"},{"code":"IP1-2","description":"Fourier analysis - A characteristic of remotely sensed images is a parameter called spatial frequency, defined as the number of changes in brightness value per unit distance for any particular part of an image. There are low-frequency and high-frequency areas. Spatial frequency may be enhanced or subdued using Fourier Analysis (an alternative technique is spatial convolution filtering). Fourier analysis mathematically separates an image into its spatial frequency components. It is then possible interactively to emphasize certain groups (or bands) of frequencies relative to others and recombine the spatial frequencies to produce an enhanced image.\r\nThe signal received by a pulsed radar is a time sequence of pulses for which the amplitude and phase are measured. The frequency content of this time-domain signal is obtained by taking its Fourier transformation.","hasChildren":true,"name":"Fourier transformation","selfAssesment":"New
"},{"code":"IP1-3-1-1","description":"Structure from motion (SfM) describes the photogrammetric process for estimating the 3D structure of a scene, whereby correspondences between multiple images are established and used to detect motion parallax. When a camera moves over a surface while taking successive overlapping images, the distances between features on the surface will change from one image to the next. The changes depend on the distance of the feature points to the camera, and thus the surface elevation. This motion parallax can be used to generate an accurate 3D representation of the surface. \r\nThe photogrammetric problem of SfM is similar to stereo vision, but has gained popularity with the advent of inexpensive cameras which have variable internal geometries, unlike metrically stabilized cameras traditionally used in airborne mapping. Even with less accurate or even missing GPS location and orientation metadata, SfM still allows for the creation of (hyper)local DEMs as long as the imagery contains sufficient overlap. Airborne or spaceborne platforms can be used, provided that 2D frame-based cameras are used which can be represented with a pinhole mathematical model. \r\nGenerating a digital elevation model (DEM) from SfM is typically handled automatically using specialized software. Firstly, image correspondences are detected. Feature points are identified in the individual images using local contrast feature detectors. The features extracted from all the images are matched with all the available overlapping images and erroneous matches are filtered out. The process typically results in hundreds or thousands of tie-points per image, which allows for robust matching even with large a priori uncertainties in camera orientation. A bundle adjustment, solving for the 3D coordinates of the feature points, the position and orientation of the camera and its internal characteristics then results in an initial, so-called sparse 3D point cloud. \r\nNext, ground control points (GCPs) can be introduced. These are surface features (naturally present or introduced into the scene) which can be identified at the pixel level in the images by users. Measured also in the field with an accuracy smaller than the pixel size, they can be used to constrain the bundle adjustment solution to improve georeferencing and camera calibration to an accuracy similar to that of the GCP measurement or the GSD size. \r\nSince this process yields a match only for a small subset of all pixels, an additional step, called dense image matching is added. It starts from the exact position and orientations resulting from the bundle adjustment to rectify the images and overlay two or more images, to compare them row by row and in 16 different directions in a process called semi-global matching (SGM). Matching pixels are identified along these lines, and 3D intersection distances photogrammetrically inferred. By combining results from different directions, a 3D coordinate for almost every pixel is obtained with similar accuracy. Finally, DEM products with a regularly spaced grid are generated and exported based on the dense point cloud. Depending on the point classes used in the export (obtained through topographic filtering or deep-learning-based classification of the dense point cloud), the outcome will be a digital surface model (DSM) or digital terrain model (DTM).","hasChildren":true,"name":"DEM generation with 'Structure-from-Motion'","selfAssesment":"Completed
"},{"code":"IP1-3-1-2","description":"Photogrammetry is the science and technology of obtaining spatial measurements and other geometrically reliable derived products from photographs. Basic geometric principles applying both traditional analogue and modern digital procedures are related to the central projection of the image in case of typical cameras and to the dynamic projection mostly in case of push-broom sensors, popular in the satellite photogrammetry. The fundamental principle used by photogrammetry is called triangulation. By taking photographs from at least two different locations, so-called “lines of sight” can be developed from each camera to points in a block on the object. These lines of sight (called rays) are mathematically intersected to produce the 3-dimensional coordinates of the points of interest.\r\nWithin data processing the most important parts of photogrammetric workflow are: (1) image orientation, (2) model reconstruction, and (3) orthorectification. Image orientation is based mostly on aerial triangulation, however recently the computer vision algorithm, called structure from motion, became more popular in particularly in close range photogrammetry. Both orientation approaches include detection or measurement of the points between overlapping images in a block, control points measurements in a field defining orientation in reference system and check points verifying the orientation process. The satellite photogrammetry due to different projection and much bigger areas of imaging is usually related to Rational Polynomial Coefficients (RPCs) defining preliminary scene orientation during image orientation. However, to receive more accurate results also here the control points measured in a field are in use. The second part of the modern photogrammetric processing is 3D model reconstruction. In past, vectorization within the stereoscopic measurements was the most popular way of using photogrammetric data after the image orientation. The development of the informatics contributed to the development of the image matching algorithms that can provide dense image point clouds, which can be used to the 3D detailed modelling including digital elevation model production. The final step of photogrammetric processing is orthorectification, which delivers cartometric image called orthophoto mosaiced into orthophotomaps. This process comprises the influence of digital terrain model, model of camera (interior orientation) and image orientation (exterior orientation). Orthophotomap and elevation models derived from photogrammetric processing are applied as very popular data source in many GIS systems. The other photogrammetric outcomes are, for example a 3D measurement or 3D models of some real-world object or scene.","hasChildren":true,"name":"Photogrammetric principles","selfAssesment":"Completed
"},{"code":"IP1-3-1-3","description":"In satellite photogrammetry to obtain the orientation mostly of satellite scene Rational Polynomial Coefficients (RPCs) are applied. They provide a compact representation of a ground-to-image geometry, that allow for photogrammetric processing without requiring a physical camera model. Model with RPC is provided with satellite image and can be improved using measurements of indirect surveying methods used for control point measurement. The RPC model for the coordinates of the image point is calculated as ratios of the cubic polynomials in the coordinates of the world or object space or ground point. \r\nIn photogrammetry and remote sensing, rational polynomial coefficients (RPCs) describe a specific imaging geometry model for transforming image pixel coordinates to map coordinates (thereby accounting for terrain displacement errors). A sensor model describes the geometric relationship between the object space and the image space, or vice versa. It relates 3-D object coordinates to 2-D image coordinates. RPCs are part of a general sensor model that approximates the physical sensor model. The physical sensor model represents the physical imageing process, making use of information on the sensor's position and orientation (during image acquisition). The RPC model often refers to a specific case of the RFM (rational function model) that is in forward form, has third-order polynomials, and is usually solved by the terrain-independent scenario.","hasChildren":true,"name":"RPC correction","selfAssesment":"Completed
"},{"code":"IP1-3-1-4","description":"A ground control point (GCP) is a location of the surface of the Earth (e.g. a road intersection) that can be identified on the imagery and located accurately on the map (i.e. the reference dataset). Two distinct sets of coordinates are associated with the GCP: image coordinates in i rows and j columns, and map coordinates (e.g. x, y measured in degrees of latitude and longitude or as specified by the spatial reference system).","hasChildren":true,"name":"Ground Control Points (GCP)","selfAssesment":"Planned
"},{"code":"IP1-3-1","description":"Orthorectification is the process of removing sensor (scanner or camera), satellite/aircraft, and terrain-related distortions for creating a planimetrically correct image. \r\nTo obtain an accurately orthorectified image, the following information is required: (1) accurate elevation model, and (2) a camera model or rational polynomial coefficients (RPCs) that depicts the positional relationship of the collected image to the ground. Many companies deliver their images together with RPCs and existing software implementations can automatically read these files and apply the RPC transformation on the fly. An accurate elevation model is important to remove the influence of topography (e.g. hills, valley, etc.) on the raw image so that users can accurately compute distances, areas, and directions. Without performing orthorectification, the features in the image are tilted (especially the features located away from the center of the camera). Many satellite data products (e.g. Sentinel images, Landsat data products) are orthorectified using Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) data which is a freely available data product and has a spatial resolution of e.g. 1 arc-second (30 m). In the case of extremely jagged surface topography, i.e. areas of high relief, a DEM with a higher spatial resolution is required. \r\nTwo main models can be used in the orthorectification process: black-box and the physical-based model. The black-box model (called also the analytical model) is commonly implemented in different software because it relies solely on the RPC files. This model does not require access to any proprietary information of the sensor used to collect the image. \r\nThe physical-based models are more complex (and hence expected to be more accurate) because they account for various factors that might influence the quality of the acquired image: e.g. position of the satellite when collecting the images, atmospheric effects, etc. An example of a physical-based model is the so-called camera model. This model requires access to proprietary sensor information that has to be provided by the image owner.","hasChildren":true,"hasParent":true,"name":"Orthorectification","selfAssesment":"Completed
"},{"code":"IP1-3-2-1","description":"Image co-registration [aka Image-to-image registration] is the translation and rotation alignment process by which two images of like geometry and of the same geographic area are positioned coincident with respect to one another so that corresponding elements of the same ground area appear in the same place on the registered images (Jensen 2005 referencing Chen and Lee 1992).","hasChildren":true,"name":"Image co-registration","selfAssesment":"New
"},{"code":"IP1-3-2","description":"Spatial referencing (referred to as geo-referencing as well) is the process of aligning available EO or GIS data to a coordinate system so that further spatial analysis and image analysis tasks can be applied using these data as input. \r\nTo be able to perform spatial referencing, users have to generate the so called Ground Control Points (GCPs) with known coordinates. In case of images, the easiest features that could be used as GCPs are the intersections, isolated trees etc.","hasChildren":true,"hasParent":true,"name":"Spatial referencing","selfAssesment":"Planned
"},{"code":"IP1-3","description":"Geometric correction is concerned with placing the reflected, emitted, or back-scattered measurements or derivative products in their proper planimetric (map) location so they can be associated with other spatial information. It is usually necessary to preprocess the remotely sensed data and remove the geometric distortions so that individual picture elements (pixels) are in their proper planimetric (x, y) map locations. This allows remote sensing-derived information to be related to other thematic information in geographic information systems (GIS) or spatial decision support systems (SDSS). Geometrically corrected imagery can be used to extract accurate distance, polygon area, and direction (bearing) information.\r\n\r\nGeometric correction techniques are dedicated to resolving the geometric distortions caused by: (1) variations in sensor position; (2) Earth curvature; (3) rotation of Earth on its axis; (4) relief displacement. \r\n\r\nThere are two types of geometric distortions, namely systematic and random distortions. The former might be caused by Earth's rotation for example and, therefore they are predictable and systematic. The second type of distortions might be caused by terrain or variations in sensor altitude. \r\nGeometric correction includes georeferencing and orthorectification techniques.","hasChildren":true,"hasParent":true,"name":"Geometric correction","selfAssesment":"Completed
"},{"code":"IP1-4-1","description":"Contrast stretching (also referred to as contrast enhancement) expands the original input brightness values to make use of the total dynamic range or sensitivity of the output device (a computer display).","hasChildren":true,"name":"Contrast stretching","selfAssesment":"New
"},{"code":"IP1-4-2","description":"The histogram is a useful graphic representation of the information content of a remotely sensed image. Histograms for each band of imagery are often displayed and analysed in many remote sensing investigations because they provide the analyst with an appreciation of the quality of the original data (e.g. whether it is low in contrast, high in contrast or multimodal in nature. [...] Tabulating the frequency of occurrence of each brightness value within the image provides statistical information that can be displayed graphically in a histogram.","hasChildren":true,"name":"Histogram","selfAssesment":"New
"},{"code":"IP1-4","description":"Image enhancement algorithms are applied to remotely sensed data to improve the appearance of an image for human visual analysis or occasionally for subsequent machine analysis. The quality of results of image analysis are subjectively judged by humans as to whether they are useful. They include contrast enhancement.","hasChildren":true,"hasParent":true,"name":"Image enhancement","selfAssesment":"New
"},{"code":"IP1-6","description":"Principal component analysis (PCA) has proven to be of value in the analysis of multispectral and hyperspectral remotely sensed data. PCA is a technique that transforms the original correlated spectral dataset into a substantially smaller and easier set of uncorrelated variables that represents most of the information present in the original dataset. The first component accounts for the maximum proportion of the variance of the original dataset, and subsequent orthogonal components account for the maximum proportion of the remaining variance.","hasChildren":true,"name":"Principal component analysis (PCA)","selfAssesment":"New
"},{"code":"IP1-7-1-1","description":"Bottom-of-Atmosphere (BOA) reflectance is also called surface reflectance and consists of the solar radiation that is reflected from the Earth's surface.","hasChildren":true,"name":"Bottom-of-Atmosphere (BOA)","selfAssesment":"New
"},{"code":"IP1-7-1-4","description":"Top-Of-Atmosphere (TOA) radiance represents the radiance observed outside Earth’s atmosphere. It is derived from the Digital Numbers (DN) using metadata delivered with the image.","hasChildren":true,"name":"Top-Of-Atmosphere (TOA)","selfAssesment":"New
"},{"code":"IP1-7-1","description":"Atmospheric correction accounts for the attenuation caused by scattering and absorption in the atmosphere. It transforms top-of-atmosphere (TOA) reflectance to bottom-of-atmosphere (BOA) reflectance.\r\nThe decision to perform atmospheric correction depends on the need, i.e. the envisioned usage of the derived EO information product and the nature of the underlying problem. This includes requirements to the accuracy of extracted biophysical information. Additionally, the decision and choice of methods depends on the type of remote sensing data available, the amount of in-situ historical and/or concurrent atmospheric information available.\r\nAn atmospheric correction is essential when biophysical or geophysical parameters (e.g. of water or vegetation) are going to be extracted from the remote sensing data. If the data is not corrected, the subtle differences in reflectance among the contributing image bands may be lost. This is especially relevant when biophysical information shall be compared to that of images from other dates.\r\nHowever, some cases exist where it is unnecessary to perform atmospheric correction. For example, it is not necessary for producing an image classification product from a single date of remotely sensed data. If a maximum likelihood classification is applied that uses training data with the same relative scale for the pixel values, then, atmospheric correction has little effect on the classification accuracy. The same holds true for a post-classification change detection where the classifications of the two different dates were performed independently. \r\nThe process of (absolute) atmospheric correction requires a model atmosphere and in situ atmospheric measurements acquired at the time of remote sensor data acquisition as input. In situ data can be available from other sensors on-board the sensor platform.\r\n\r\nDark Object Subtraction (DOS) is one of the most popular empirical atmospheric correction techniques. This technique assumes that a black object has a reflectance value of zero. Yet, a dark object present in a satellite image will have a value different than zero because of the atmospheric scattering. This value is then subtracted from all pixels in a given spectral band.","hasChildren":true,"hasParent":true,"name":"Atmospheric correction","selfAssesment":"Completed
"},{"code":"IP1-7-2","description":"The number of spectral bands assocuates with a remote sensing system is referred to as its data dimensionality. Hyperspectral remote sensing systems such as AVIRIS ans MODIS obtain data in 224 and 36 bands, respectively. The greater the number of bands in a dataset (i.e., its dimensionality), the more pixels that must be stored and processed by the digital image processing system. Storage and processing consume valuable resources. It is necessary to reduce the dimensionality of hyperspectral data while retaining the information content inherent in the image. \r\nA method for dimensionality reduction in hyperspectral data and minimizing the noise in the imagery is the minimum noise fraction (MNF) transformation. The purpose is to minimize the noise in the imagery, i.e. to identify noise and segregate it from true information, and to colaps the useful information into a much smaller set of MNF images. The MNF transformation applies two cascaded principal components analyses.","hasChildren":true,"name":"Dimensionality reduction","selfAssesment":"New
"},{"code":"IP1-7-3","description":"Sensor calibration converts the sensor’s digital numbers (DNs) to at-sensor radiance above the atmosphere. A further radiometric adjustment accounts for the viewing angle and sun angle during acquisition to transform radiance values to top-of-atmosphere (TOA) reflectance. Therefore, the process requires sensor calibration information and telemetry data that satellite image providers deliver within the metadata.\r\nDNs are raw sensor data without physical units. The sensor calibration information for converting the DNs to radiance are the calibration gain (cal_gain) and calibration offset (cal_offset) values. The sensor calibration uses linear function f(DN) = DN * cal_gain + cal_offset that multiplies the DNs of each pixel in each spectral band with their corresponding cal_gain and adds the corresponding cal_offset. The resulting at-sensor radiance image is the basis for the radiometric adjustment that uses information about the viewing angle and sun angle during acquisition to transform at-sensor radiance to TOA reflectance. \r\nSensor calibration obtains TOA reflectance and is a minimum requirement for performing band math calculations to derive spectral indices such as the normalized vegetation difference index (NDVI). Uncalibrated image data would arrive at NDVI values that are distorted because the cal_gain and cal_offset parameters for the involved spectral bands were not considered.","hasChildren":true,"name":"Sensor calibration","selfAssesment":"Completed
"},{"code":"IP1-7-4","description":"As an optical remote sensing system is not perfect, noise can enter the data collection system at several points. Necessary corrections include the removal of shot noise (random bad pixels), correcting line or column drop-outs, accounting for line-start problems and radiometric correction of n-line striping caused by detector miscalibration.\r\nSAR data have global, random speckle noise. Speckle filters are designed to adapt to local image variations in order to smooth values, thus reducing speckle and enhancing lines and edges to maintain the sharpness of an image. A widely used way to reduce speckle is to apply spatial filters to the images. Typical approaches for speckle filtering include Laplace filtering for smoothing and sigma filters that preserve more of the signal with a lesser effect of smoothing.","hasChildren":true,"name":"Noise reduction","selfAssesment":"New
"},{"code":"IP1-7-5","description":"Topographic correction, or topographic effects correction, aims to adjust the spectral values of an image according to effects of solar illumination differences due to the irregular shape of the terrain. Topographic slope and aspect introduce radiometric distortion of the recorded signal. Further, terrain shadow dramatically affects the brightness values of the covered pixels in an image. Topographic effects of illumination and shadow are particularly relevant in mountainous regions and in regions towards the higher latitudes of the southern and northern hemisphere. The effects appear pronounced during the winter season. \r\nTogether with sensor calibration and atmospheric correction, topographic correction is part of the radiometric correction process to obtain true reflectance values from sensor radiance. This process is necessary when using EO data for obtaining geophysical measurements. It can also benefit the accuracy of image classifications by reducing the internal variability of vegetation types, since the corrected reflectance relates better to the geometrical or biological properties of the plant than to the original reflectance.\r\nMethods for the removal of topographic effects from remotely sensed images can simply be based on band ratios that do not require additional input. Alternatively, they use digital elevation models (DEMs) as an additional input and apply sophisticated modelling of the illumination conditions. The illumination model describes various aspects of the relationship between the sensor measurement, the sun illumination, the ground reflectance and the diffuse irradiance at the surface. The model incorporates the angles between the sun position, the ground position (described by slope and aspect from the DEM), and the sensor position. Among these methods are lambertian methods and non-lambertian methods such as the bidirectional reflectance distribution function (BRDF). The BRDF, which is more suitable to the non-Lambertian properties of the observed surfaces, describes how the reflectance varies in each cover considering the angles of incidence and observation. \r\nIf achieved with a high quality, the resulting topographically corrected image appears to be illuminated evenly as if all its pixels would be part of a flat surface without the presence of any terrain differences. However, the much larger benefit than the improved appearance is the availability of pixel values that are closest to the true reflectance when compared to TOA, BOA and DN values.","hasChildren":true,"name":"Topographic correction","selfAssesment":"Completed
"},{"code":"IP1-7","description":"Radiometric calibration and correction converts the sensor’s digital numbers (DNs) to radiance values and subsequently reflectance values. Additionally, the term “correction” points to the fact that radiometric measurements with satellite sensors contain error. Therefore, radiometric correction is concerned with improving the accuracy of surface spectral reflectance, emittance, or back-scattered measurements obtained using a remote sensing system. The Earth’s atmosphere, land and water are complex and can never be captured perfectly because of the limitations of remote sensing devices that lie in their spatial, spectral temporal and radiometric resolution. Therefore, error occurs in the data acquisition process and degrades the quality of remotely sensed data. The most common errors in remote sensing are radiometric and geometric. This concept is focused on the correction of remote sensing data to account for radiometric error that is to some degree systematic. Systematic errors in radiometric measurements come from the interaction of the sensed radiance with the atmosphere, the acquisition geometry in relation to the radiance source (the sun) and the Earth surface geometry (terrain).\r\nThere are several levels of radiometric calibration and correction. The first is sensor calibration that converts the DNs to top-of-atmosphere (TOA) reflectance. It converts to radiance values and further to reflectance values by accounting for the viewing angle and sun angle during acquisition. The second is atmospheric correction that converts TOA reflectance to bottom-of-atmosphere (BOA) reflectance. The third is topographic correction that converts BOA reflectance to surface reflectance. \r\nRadiometric calibration is necessary to ensure radiometric comparability of the measurements. There is a need for calibration when comparing different spectral bands within one image, e.g. for the calculation of geo-biophysical parameters with band math operations. Results from uncalibrated image data would differ from results achieved with calibrated data because the unaccounted cal_gain and cal_offset of the used spectral bands would lead to distortions. \r\nIn addition, radiometric calibration complements the geospatial comparability that is achieved with geo-referencing an image to geographic coordinates. Geo-referencing enables comparison of an image pixel to the geospatially matching pixel in another image acquired with a different sensor but with comparable resolution. Radiometric calibration enables a radiometric comparison between these two pixels’ radiance values. In case the two images are from different acquisition dates, a calculated radiometric difference would indicate change. This example shows the relevance of radiometric calibration for inter-sensor comparisons.\r\nRadiometric comparability is particularly relevant in studies that require inter-sensor comparisons, comparisons of surface features over time, or comparisons to laboratory or field reflectance data. Then the radiometric correction should cover atmospheric, solar and topographic effects. A full radiometric correction that also includes topographic correction can benefit the accuracy of image classifications by reducing the internal variability of vegetation types, since the corrected reflectance relates better to the geometrical or biological properties of the plant than to the original reflectance.","hasChildren":true,"hasParent":true,"name":"Radiometric calibration and correction","selfAssesment":"Completed
"},{"code":"IP1","description":"Image pre-processing focuses on transforming the electrical signal measured by a sensor to a processing level at which pixel values can be used for the next information extraction step. Therefore, pre-processing operations involve the removal of errors encountered while collecting remotely sensed data to get as close as possible to the true radiant energy and spatial characteristics of the study area at the time of data collection. Different sensor type (optical, radar, lidar) require different processing levels\r\nThe most common image pre-processing procedures include: \r\n(1)\tRadiometric calibration involves the transformation of Digital Numbers (DN) to physical unit: radiance/reflectance. Radiometric calibration can be done before the launch of a satellite sensor, i.e. pre-launch calibration, or after launch. In the second case, the calibration is performed on-board or by comparing ground measurements with satellite radiance. Through radiometric calibration various scene illumination procedures such as sun elevation correction or earth-sun distance correction are applied. Furthermore, image noises caused by striping or line drop as happened in case of Landsat TM7 due to failure of the Scan Line Corrector (SLC) are also corrected using specialized procedures.\r\n(2)\tAtmospheric correction accounts for two main processes: scattering and absorption. Scattering represents a disturbance of the electromagnetic waves caused by rayleight scattering (caused by very small particles such as the air molecules), mie scattering (caused by aerosol particles) and non-selective scattering (dust, smoke, rain etc.). Absorption occurs when the electromagnetic energy is absorbed by the atmospheric components. Therefore, atmospheric windows have to be removed before using the satellite images in the next processing steps. Atmospheric corrections can be carried out either using simple statistical methods or complex radiative transfer based methods\r\n(3)\tGeometric correction is required to remove the distortions caused by the Earth curvature, Earth rotation, panoramic distortion due to the field of view of the sensor and the topography of the terrain. Geometrics distortions are corrected using Ground Control Points (GCP) and a Digital Elevation Model (DEM). In case of airborne images, additional distortions caused by variations in the platform altitude or velocity might occur.","hasChildren":true,"hasParent":true,"name":"Image pre-processing","selfAssesment":"Completed
"},{"code":"IP2-1-1","description":"Data augmentation refers to a scheme of augmenting the observed data so as to make it more easy to analyze. An application from deep lerarning is to increase the number of input training sample images with augmented data. Examples of data augmentation techniques include horizontal flips, random crops, and principal component analysis.","hasChildren":true,"name":"Data augmentation","selfAssesment":"New
"},{"code":"IP2-1-2","description":"Data imputation refers to a scheme of replacing missing values by imputed values. Imputation can be done, for example with mean, median and mode. Imputation methods can efficiently predict multiple response variables simultaneously.","hasChildren":true,"name":"Data imputation","selfAssesment":"New
"},{"code":"IP2-1-3-1","description":"Gram-Schmidt is a pan-sharpening method that has been invented by Laben and Brover in 1998 and patented by Eastman Kodak. It makes use of the Gram-Schmidt orthogonalization to decorrelate the spectral bands (panchromatic, red, green, blue, etc.) and transform them into one multidimensional vector.","hasChildren":true,"name":"Gram-Schmidt pan-sharpening","selfAssesment":"New
"},{"code":"IP2-1-3-2","description":"This pan-sharpening method uses PCA to transfer detailed spatial information from panchromatic band to the available multispectral bands.","hasChildren":true,"name":"Principal Component Analysis (PCA)-based pan-sharpening","selfAssesment":"New
"},{"code":"IP2-1-3","description":"Pan-sharpening methods are used to enhance spatial resolution of images by merging a panchromatic image with high resolution with a multispectral image with low resolution.","hasChildren":true,"hasParent":true,"name":"Pan-sharpening","selfAssesment":"New
"},{"code":"IP2-1-4","description":"Spatiotemporal image fusion methods, called also spatiotemporal downscaling methods, represent an efficient solution to generate fine-scale images at a high temporal resolution for more detailed land cover mapping and monitoring applications. Spatiotemporal image fusion methods can be classified into three categories: (1) reconstruction-based , (2) unmixing based and (3) learning-based methods.","hasChildren":true,"name":"Spatio-temporal image fusion","selfAssesment":"New
"},{"code":"IP2-1","description":"Image fusion is defined as the “combination of two or more different images to form a new image by using a certain algorithm” Data fusion is a well-established research field. Image fusion methods are primarily used for improving the level of interpretability of the input data. Additionally, they can be utilized to address the problem of missing data caused by cloud or shadow contamination in satellite images time series. Image fusion can be performed at pixel-level, feature-level (e.g. land-cover classes of interest), and decision-level (e.g. purpose driven).","hasChildren":true,"hasParent":true,"name":"Data fusion","selfAssesment":"Planned
"},{"code":"IP2-2","description":"Data harmonization aims to transform different datasets in such a way that they fit together, both with respect to geometry and semantics. The goal is that a user, who is using data from different authorities, shall have a unified view, where conflicts in the datasets have been removed.","hasChildren":true,"name":"Data harmonisation","selfAssesment":"New
"},{"code":"IP2-3","description":"Data integration is the process of combining different geographic datasets including those derived from remote sensing data. The combined datasets can have different coverage, but they have to have the same geographic coordinates.","hasChildren":true,"name":"Data integration","selfAssesment":"Planned
"},{"code":"IP2","description":"Data assimilation is a strategy to foster data integration and data harmonisation in a bi-directional way between the measured and the modelled reality. In other words, it aims to combine measurements (observations) with the understanding of the spatio-temporal properties and evolution of system’s variables or properties and model information about them. Models can be calibrated and keeping them ‘on track’ by constraining them with observations. Vice versa, observations can be validated through models. Approached as a mathematical problem, data assimilation aims at minimizing cost functions or penalize a function to ensure optimality in fitting. Equations are used to describe system parameters and the relationships among them, It is noteworthy, that models encompass information from previous measurements, experiences, and theory. While the observations are influenced by (known) properties such as precisions, etc. of the measurement devices, the robustness of models rely on the consolidated knowledge. Because uncertainties reside in all components with unknown or even undeterminable errors, the approach is usually probabilistic, including Bayesian and other related techniques. Widely used in meteorological sciences, successful data assimilation has been boosted the reliability of weather forecast , while sensitivity to errors remains. \r\nIn Earth observation, data assimilation compensates for the fact that a specific site could be observed in a variety of measurements by satellites with different sensor types, at different dates, different angular geometries and viewing directions, illumination conditions (solar time), observation frequencies, etc. In particular, for monitoring processes, measurements over time need to assure to actually measure the status of the system or object and not the divergence in observation. To overcome these divergences and converge them with the actual properties of an observed object or target class such as spectral or geospatial properties, observation modelling can be considered an important contribution from geospatial theory. this also links to class modelling or geon modelling. The synergy of a vegetation growth model and a remote sensing observation model can be exploited to improve the retrieval of geo-biophysical information. For vegetation and crop type monitoring radiative transfer modelling (RTF) is being used as an example. \r\nData assimilation can also serve in bridging the gaps between non-availabilities of EO data and other observations, to provide estimates or prediction for geographical variables, testing of hypotheses or continuous observation (monitoring). A related aspect is data imputation, i.e. filling gaps in observations e.g. by other, complementary data sets (e.g. Radar imagery in the absence of VHR data in cloudy weather conditions). Recently, these sources can also be complemented by crowd mapping and citizen science. \r\nWhen interpretation of data comes into play, such as image classification, we introduce another level of uncertainty. Thus the community seeks for rigorus classifiers based on solid spectral models, acting across sensors. Semantic enrichment of satellite data is a related strategy for reaching to interpreted data in a rigorous way. \r\nSummarizing, data assimilation comprises steps to improve the level of interpretability of the input data, by enrichment (get rid of spatial/temporal gaps), by accounting for heterogeneity (through harmonization), and by integration (combination with other data that is relevant to the application). Thereby, datasets become more comparable to each other.","hasChildren":true,"hasParent":true,"name":"Data assimilation","selfAssesment":"Completed
"},{"code":"IP3-1-1-1","description":"Vegetation fraction (VF) is defined “as the percentage of vegetation occupying a pixel as viewed in vertical projection. It’s a comprehensive quantitative index in forest management and vegetation community cover conditions, and it’s also an important parameter in many remote sensing ecological models.”","hasChildren":true,"name":"Vegetation fraction","selfAssesment":"Planned
"},{"code":"IP3-1-1-2","description":"Leaf area index (LAI) is the ratio between the total area of the upper leaf surface of vegetation and the surface area of the pixel in question. LAI is a dimensionless value, typically ranging between 0 (for a pixel composed of bare soil) and values as high as 6 (for a dense forest).","hasChildren":true,"name":"LAI (Leaf Area Index)","selfAssesment":"Planned
"},{"code":"IP3-1-1-3","description":"Net primary production (NPP) is a measure of the inherent productivity of a region or ecological system—mainly the Earth’s production of organic matter, principally through the process of photosynthesis in plants.","hasChildren":true,"name":"Net primary production (NPP)","selfAssesment":"New
"},{"code":"IP3-1-1-4","description":"Water quality variables can be derived from Earth observation (EO) data to provide essential ocean variables. They include Sea-surface temperature (SST), Sea-surface salinity (SSS) and Air-Sea Fluxes. SST controls the atmospheric response to the ocean at both weather and climate time scales. The spatial patterns of SST reveal the structure of the underlying ocean dynamics, such as, ocean fronts, eddies, coastal upwelling and exchanges between the coastal shelf and open ocean. SSS observations contribute to monitoring the global water cycle (evaporation, precipitation and glacier and river runoff). Water quality variables can be derived from EO data by using ocean colour products from optical sensors and relating them to ground truth information from in situ sensor networks.","hasChildren":true,"name":"Water quality variables","selfAssesment":"New
"},{"code":"IP3-1-1","description":"Biophysical parameter retrieval is an approach in remote sensing that aims to estimate parameters which have physical meaning related to properties of living organisms. The goal is to provide quantitative results directly relating to the biophysical state, but independent of acquisition conditions and technology. Assessment of vegetation status is a key motivation for this, because through plant respiration and photosynthesis, vegetation is critical for modelling terrestrial ecosystems and energy cycles in environmental studies. \r\nImportant parameters describing canopy structure include leaf area index (LAI), green cover fraction (fCover), fraction of absorbed photosynthetically active radiation (fAPAR), plant height, biomass and leaf angle distribution. At leaf biochemical level, leaf chlorophyll/water, fuel moisture and leaf pigmentation content are used.\r\nVisual inspection can provide a first assessment of plant status. For detailed measurements of biophysical parameters, mostly destructive methods have been used. Chemical measurement techniques on leaf samples can measure pigment concentrations very accurately, but are time consuming and only use very limited samples. \r\nMuch more extensive data can be collected using earth observation imagery. These range from large scale spaceborne observations with high frequency at coarse resolution to dedicated UAV flights which can offer spectral information of individual plants. Radar and LiDAR acquisitions, which are insensitive to weather conditions, now complement optical observations. \r\nMethods to retrieve the parameters from remote sensing data fall into two main categories. Statistical models empirically match data to a biophysical variable. Univariate techniques use a single quantity derived from the data, usually a vegetation index whereas multivariate techniques link a combination of measurements at different wavelengths to one or more biophysical parameters.\r\nPhysically-based modeling is an alternative approach which uses advanced radiative transfer models to describe the transfer and interaction of radiation inside a leaf or canopy based on robust physical, chemical, and biological processes. They compute the interaction between solar radiation and plants and provide as such a better understanding between biophysical variables and reflectance characteristics. Good examples are Leaf optical models such as PROSPECT and LIBERTY which simulate leaf optical properties by absorption and scattering coefficients. Canopy reflectance models simulate canopy reflectance as a function of a complex description of plant structural and radiometric attributes to develop a quantitative understanding of remote sensing information.","hasChildren":true,"hasParent":true,"name":"Biophysical and geophysical parameters","selfAssesment":"Completed
"},{"code":"IP3-1-2-1","description":"This spectral index is calculated using the following formula: SAVI = [(NIR-Red)/(NIR+Red+L)]/(1+L), where L can be, for example, 1 in area with no vegetation or 0 in area with dense veegtaion. It is used to minimize the influence of the soil brightness from the vegetation indices that are based on red and near-infrared wavelengths.","hasChildren":true,"name":"Soil-adjusted Vegetation Index (SAVI)","selfAssesment":"New
"},{"code":"IP3-1-2-2","description":"This spectral index is calculate using the following formula NDSI = (green-SWIR)/(green+SWIR). It is the most popular index used to identify snow cover due to the fact that snow reflects visible wavelength stronger than middle-infrared wavelengths.","hasChildren":true,"name":"Normalized Difference Snow index (NDSI)","selfAssesment":"New
"},{"code":"IP3-1-2-3","description":"Leaves, when healthy and vigour show a characteristic green colour. This visual effect evident to humans is caused by the co-existence of two evolutionarily facts: the specific interaction of the chlorophyll pigment in living leaves to the visible spectrum (VIS, 400-700 nm wavelength) of light emitted by the sun and the sensitivity of our human eye to the same sub-spectrum. According to fundamental physical laws of radiation (Stefan Boltzmann law of blackbody radiation and Wien’s displacement law), the VIS sub-spectrum corresponds to the radiation maximum of the sun, a hot blackbody with a surface heat of about 6000 K. Living leaves are structured in specific layers exhibiting characteristic interaction with light. The chloroplasts located in the so-called palisade layer, make use of the blue and the red part of sunlight for photosynthesis, the unique process of transforming light to create energy (carbohydrates) from water and carbon dioxide. This leads to the specific behaviour of leaves to absorb large portions (up to 90%) of the blue and red part of the electromagnetic spectrum and reflect nearly 100% of the green light. The peak reflectance in green light makes leaves (and plants in general) appear in green colour in our visual perception. \r\nA second, by no means less characteristic, feature of leaves is the specific response to near infrared (NIR, at around 700 nm wavelength) light in the mesophyll tissue (transmittance, scattering and reflectance). Only a small fraction of NIR is being absorbed. \r\nThis combination of two specific spectral characteristics, the absorption in VIS (red colour) by chlorophyll a in palisade layers, and the reflectance of NIR in the spongy tissue, makes the spectral profiles of plants and vegetation exhibiting a very characteristic shape, the so-called red edge. This absorption edge between red and NIR light is sharper for higher intensity green reflectance and brighter green tones (such as grassland or bright deciduous forest) than for less intensive reflectance and darker tones (coniferous forest). \r\nThe red edge may shift for the same vegetation type due to plant maturity or plant stress. This effect we call the red shift. The red shift is sensitive to crop maturity (headed stage) and may indicate harvesting time. Notably, there is also a blue shift, indicating green plants’ exposure to geochemical stress, which causes the absorption spectra to shift towards shorter wavelengths. \r\nPlants usually do not appear in isolation but form a canopy with a certain degree of coverage (e.g., crown closure in forests), and a certain part of understorey or soil per area unit. The resulting canopy reflectance is therefore a spectral mix of soil and vegetation (or even different types of vegetation) and generally lower than the reflectance of a pure vegetation sample under lab conditions. \r\nTo capture most of these plant-typical spectral characteristics, the so-called normalised difference vegetation index (NDVI) was developed. NDVI is an arithmetic band combination of red and NIR bands in a normalised value range. \r\nThe NDVI is calculated as:\r\nNDVI=((NIR-R))/((NIR+R))\r\nThe (hypothetic) value range of the NDVI is [-1 | +1]. Under real-world conditions, the NDVI ranges from values of around -0.2 to 0.6 or 0.7. To discriminate principal land cover classes such as water, non-vegetation (soil, sealed, etc.) and vegetation the following thresholds in the continuous range are used: \r\n\tNDVI < ~ 0: water\r\n\t~ 0 < NDVI < ~ 0.2: non-vegetation (soil, sealed surfaces, bare rock, etc.)\r\n\t~ 0.2 < NDVI: vegetation.\r\nNotably, these class limits are just a very rough approximation (indicated by the ~ sign), due to the mixed pixels effect, canopy reflectance, the abundance of water plants and suspending particles, and the illumination effect of specific atmospheric or topographic conditions. \r\nWe can use the NDVI to generally mask out vegetation from other land cover types and, more specifically, to indicate vegetation vigour and health. It is also suitable for monitoring plant phenology as the relationship between vegetative growth and the (changing) conditions of the environmental conditions. A range of variations has been suggested, enhancing one or the other mathematical or statistical behaviour of the index, or making it even more sensitive to specific plant behaviour. A well-known example is the enhanced vegetation index (EVI).","hasChildren":true,"name":"Normalized Difference Vegetation Index (NDVI)","selfAssesment":"Completed
"},{"code":"IP3-1-2","description":"Spectral indices are calculated using a mathematical equation that is applied on two or more spectral reflectance bands of the image. The calculated spectral index is a ‘new’ image that highlights particular land surface features or properties e.g. vegetation, soil, water, better than the original input bands. The spectral indices vary from simple spectral ratioing of two bands to more complex combinations of multiple bands. Spectral indexes are developed based on the spectral properties of the object of interest. For example, spectral indices dedicated to the vegetation condition are developed based on the principle that the healthy vegetation reflects strongly in the near-infrared spectrum while absorbing strongly in the visible red. These properties are used to develop more complex spectral indexes for monitoring vegetation condition, phenology parameters, i.e. Normalised Difference Vegetation Index (NDVI), Advanced Vegetation Index (AVI). The spectral indices calculated using the short wave infrared spectral bands are more sensitive to vegetation water content and spongy mesophyll structure in the vegetation canopy thus are used to assess the vegetation decline, moisture that is particularly useful for drought monitoring (e.g. Normalized Difference Water Index (NDWI) or Normalized Difference Moisture Index – NDMI). The water-related spectral indices are widely applied in agricultural and ecological applications including surface water body characteristics, vegetation water stress, soil water content assessment and wetlands monitoring. The combination of near infrared and short wave infrared spectral bands is also used to detect burned area and to monitor the vegetation recovery (e.g. Normalised Burned Ratio – NBR). There are other spectral indices dedicated to snow cover and glacier monitoring, which are developed based on visual green and short wave infrared spectral bands. Snow reflects most of the radiation in the visible bands whiles absorbing in the short wave infrared.","hasChildren":true,"hasParent":true,"name":"Spectral indices","selfAssesment":"Completed
"},{"code":"IP3-1","description":"The term band maths denotes the arithmetic combination (addition/subtraction, multiplication/division) of two or more spectral bands in an early stage of image analysis. The resulting scalar values represent the spectral behaviour in different bands in a single value; such procedure makes particular sense, when spectral behaviour varies in those bands (like the red edge of vegetation spectra in the NIR band). \r\nThere are several reasons for applying band maths when working with multispectral imagery: (1) A single range of values rather than multiple bands is easier to comprehend and interpret; (2) Thresholds or class limits are applied more intuitively in a grey scale image; (3) Indices can be easily calculated and compared across different sensors; they are implemented as standard routines in many software environments as well as cloud processing environments (such as Google Earth Engine or the Proba-V exploitation platform)\r\nOut of the many possible, literature suggests a few arithmetic band combinations as application-specific quasi-standards. Band ratios (e.g. red band divided by NIR band) and indices (such as the normalised difference vegetation index, NDVI) belong to this group. Indices have the advantage over simple ratios in constraining the value range, e.g. [-1 | 1]. Designated to indicate specific land cover types (such as water index, snow index, soil index, etc.) such indices are widely used as a basis for operational information products. Another index is the normalised burn ratio (NBR) which relates near infrared and short-wave infrared reflectance to measure burn severity taking into consideration the increasing of SWIR reflectance in the course of a fire. \r\nPre-processing such as dark object subtraction and radiometric or even atmospheric correction is a key requirement prior to indexing. The coding in digital numbers (DN) is a function of the sensitivity and the radiometric resolution of the sensor. The actual recording depends on atmospheric conditions (additional brightness, haze, etc.). Therefore, in order to make the resulting values comparable among different types of sensors and scenes, radiometric correction is mandatory, converting DNs into radiances, i.e. true reflectance values as physical measurement units. \r\nTwo advanced examples of band maths beyond rationing are the perpendicular vegetation index (PVI) and the tasselled cap (TC) transformation. PVI is based on the assumption that vegetation pixels are generally separable from soil pixels (at least after unmixing or for pure pixels), and thus pixel values are located in a perpendicular direction from the soil line in a NIR/red feature space. The Euclidean distance from the soil line, determined by Pythagorean triangle, yields the PVI. Tasselled cap instead rests on the notion of a cap-like histogram shape when plotting pixels on a brightness vs. greenness plot, with the latter determined by linear combinations of VIS and NIR bands, along with empirically determined coefficients. TC 1 as a weighted sum corresponds to brightness, TC 2 to greenness, TC 3 to yellowness, sometimes referred to as wetness. A fourth TC called nonesuch likely corresponds to noise and atmospheric disturbance effects in the image.","hasChildren":true,"hasParent":true,"name":"Band maths","selfAssesment":"Completed
"},{"code":"IP3-10","description":"Semantic enrichment is the process of adding semantic metadata elements to improve the content-based image retrieval. These semantic metadata elements enable the explicit specification of the content of the images stored in the remote sensing databases.","hasChildren":true,"name":"Semantic enrichment","selfAssesment":"New
"},{"code":"IP3-11-1","description":"Different types of changes are investigated using remotely sensed data: (i) abrupt changes, such as the changes caused by a fire or flooding, and (ii) gradual changes such as urban growth. Besides these kinds of changes, remote sensing community differentiates between transitional changes and conditional changes. Transitional changes refer to a major change of land surface such as conversion of forest to pasture or the expansion of mangroves into the surrounding water. Conditional changes refer to the change in condition at the surface such as water stress in an agricultural field, forest degradation caused by pest. \r\nIn the past, many remote sensing studies used two images to detect different types of changes such as deforestation, land cover change or change in the health or condition of the vegetation (e.g. pest infestation). Meanwhile, satellite image time series are used to assess the change. Time series analysis allows for monitoring more subtle changes and for providing temporal patterns of change. In this way, the timing of changes and drivers of change can be easily identified. \r\nDifferent methods are being used in change detection studies. There are studies that analyze individual images available in the investigated time series to map the target class/phenomena/events at the time when images were collected and to identify the changes: e.g. mapping the mangroves extent on an year basis and measuring it to identify changes. Alternative studies search for breaks in time series for detecting changes. The breaks are used to segment the time series into before and after changes periods which are further classified using one of the existing supervised or unsupervised classification methods (K-means, fuzzy k-means, Random Forest, Support Vector Machine etc.).","hasChildren":true,"name":"Change detection","selfAssesment":"Completed
"},{"code":"IP3-11-2","description":"The (data)cube model for analysis of time series of earth observation raster data, represents the dataset as a multidimensional array with one or more spatial or temporal dimensions. Scalar values in the cube can be selected (or ‘filtered’) and processed based on dimension labels. This allows analysis algorithms to be thought of as a set of operations on the multidimensional array. Technologies that support this model allow to efficiently implement such algorithms.\r\nSome possible operations on a multidimensional cube include: filtering, ‘reducing’ all values along a dimension, ‘aggregating’ values in a dimension, or transforming all values along a dimension. Generally speaking, these operations require the selection of a subset of the data on which work is to be done. This allows implementing the operations efficiently even on very large datasets.\r\nIn comparison to file-based processing, most technologies that support cube-based time series analysis reduce implementation overhead, as the user does not need to read and write individual files, also more complex aspects like distributed computing for parallelization can be hidden in a cube based approach. So a cube based approach can also be thought of as an abstraction layer that effectively reduces the need for specific IT-related skills when analyzing earth observation timeseries.\r\nMultiple initiatives support cube based analysis. Some common features include a programming API, often using the Python programming language. Some tools are only accessible as web services, while others can also run locally (on a small dataset). This diversity is still a drawback, as users would need to familiarize themselves with different systems. Initiatives such as openEO try to address this by providing a common API.","hasChildren":true,"name":"Cube-based time series analysis","selfAssesment":"Planned
"},{"code":"IP3-11-3","description":"Dynamic Time Warping (DTW) works by comparing the similarity between two temporal sequences and finds their optimal alignment, resulting in a dissimilarity measure. In the case of remote sensing data, DTW can deal with temporal distortions, and can compare shifted evolution profiles and irregular sampling thanks to its ability to align radiometric profiles in an optimal manner","hasChildren":true,"name":"Dynamic Time Warping","selfAssesment":"Planned
"},{"code":"IP3-11","description":"Satellite image time series analysis plays an important role in different domains including vegetation dynamics monitoring, estimating crop yields, discriminating between different land cover classes, exploring human-nature interactions, monitoring land cover change, assessing environmental threats, or evaluating ecosystems-climate feedbacks or urbanization.\r\nTime series analysis requires high quality time series which are reconstructed by removing any source of contamination such as clouds, cloud shadows, or scan-line corrector (SLC) gaps of the Enhanced Thematic Mapper plus sensor (ETM+) on Landsat 7. Removed pixels are usually filled in with data predicted from a different date (temporal interpolation), nearby pixels (spatial interpolation) or from both (spatiotemporal interpolation). Different methods are available for screening and masking out clouds and shadows in satellite images including mono-temporal methods such as Function of mask (Fmask), or multitemporal mask (e.g. Tmask algorithm). Fmask is used by the United States Geological Survey (USGS) to produce a cloud mask layer of Landsat images. European Space Agency (ESA) is using Sen2cor processor to produce Level 2A Sentinel-2 data with a shadow and cloud shadow mask. All images used in the time series have to be co-registered, i.e. they align as closely as possible. \r\nTime series analysis is used to (1) investigate various surface properties such as evapotranspiration, land surface temperature, (2) map the cover of the Earth surface (e.g. land cover mapping, crop mapping etc.), (3) detect different type of changes such as abrupt changes (fire event) or gradual changes (urbanization), and (4) study the trends.\r\nTo map surface features from satellite image time series, numerous studies make use of the vegetation phenology extracted from a spectral-temporal trajectory of a given spectral vegetation index such as the normalized difference vegetation index (NDVI) or enhanced vegetation index (EVI). Several metrics can be used to characterized vegetation phenology: metrics of greenness and metrics of time. The metrics of greenness include the minimum and maximum spectral vegetation indices, their difference or amplitude, seasonally averaged greenness etc. The metrics of time include start and end of the growing season, duration or length of the growing season or the timing of maximum greenness. Changes, on the other hand, are identified either by investigating two images acquired at two different points in time or by identifying breaks in a dense (annual or multi-annual) satellite image time series.","hasChildren":true,"hasParent":true,"name":"Time series analysis","selfAssesment":"Completed
"},{"code":"IP3-12-1","description":"Remote sensing-derived products such as land-use and land-cover maps contain error. The error accumulates as the remote sensing data are collected and various types of processing take place. An error assessment is necessary to identify the type and amount of error in a remote sensing-derived product.","hasChildren":true,"name":"Error propagation","selfAssesment":"New
"},{"code":"IP3-12-2","description":"The precision of a measurement system, related to reproducibility and repeatability, is the degree to which repeated measurements under unchanged conditions show the same results.","hasChildren":true,"name":"Precision","selfAssesment":"New
"},{"code":"IP3-12","description":"Uncertainty is the result of the lack or imprecision of our knowledge about the world. A proposition is uncertain if we do not know whether it is true or not. In most circumstances we describe a proposition as uncertain when the reason we do not know whether it is true is that we do not possess complete and accurate knowledge about the state of the world.","hasChildren":true,"hasParent":true,"name":"Uncertainty","selfAssesment":"New
"},{"code":"IP3-13-1","description":"The main elements of visual interpretation are: tone, shape, size, pattern, texture, shadow, , association. Tone refers to the relative brightness or colour of objects in an image. It depends on the spectral properties of an object. Variation in tone allows to distinguish elements of different shape, texture and pattern. Shape refers to the general form, structure, or outline of individual objects. Straight and sharp edge shape represent typically the anthropogenic features i.e. urban or agriculture, the natural features like rivers, wetlands are more irregular in shape. Size of objects in an image is a function of scale and it depends on the spatial resolution of the image. The assessment of the size of the target’s object in relation to other objectives as well as an absolute size of the object are the important part of the interpretation. Pattern refers to the spatial arrangement of objects, i.e. network of street and houses in an urban area, orchards with the line of trees. Texture refers to the arrangement of frequency of tonal variation in particular areas of an image. Rough texture would have very large, coarse tonal variation (e.g. forest canopy), whereas smooth texture very little tonal version (e.g. uniform, homogenous surfaces). It depends on the size, shape and pattern of objects. Shadow depends on the scale and spatial resolution of an image. Shadow is useful to measure the height of an object, to distinguish the coniferous from broadleaf trees. In the radar imagery is useful for identifying topography and landforms. Association refers to the relationship between objects and features in proximity to the target interest.","hasChildren":true,"name":"Elements (cues) of interpretation","selfAssesment":"Completed
"},{"code":"IP3-13-2","description":"Information-as-data-interpretation considers information as the outcome of the cognitive process of vision that reconstructs a scene from an image.","hasChildren":true,"name":"Information-as-data-interpretation","selfAssesment":"New
"},{"code":"IP3-13-3","description":"An image interpretation key is simply reference material designed to permit rapid and accurate identification of objects or features represented on aerial images.","hasChildren":true,"name":"Interpretation keys","selfAssesment":"New
"},{"code":"IP3-13","description":"Interpretation is the processes of detection, identification, description and assessment of an object and pattern imaged. Visual interpretation is the ability of a human operator to identify an object through the data content in an image / photo by combining several elements of interpretation. The image characteristics used in the interpretation process are: shape, size, tone/colour, texture, shadow, neighbourhood and pattern. The importance of the image characteristics varied according to the spatial resolution of the images and the properties of the feature of interest. The interpretation can be performed on the single image or between several images acquired at different time, which result in the differentiation of the temporal changes. The principle of the image interpretation is the process of delineating (digitalizing) the outlines of the objects, features on the image. It is performed “on-screen” using a GIS software. The process of visual interpretation is time consuming and requires a skilled interpreter with knowledge of the study area. Even though, the image interpretation supports many applications in for example selection of the training and verification data sets for image classification and accuracy assessment.","hasChildren":true,"hasParent":true,"name":"Visual interpretation","selfAssesment":"Completed
"},{"code":"IP3-2-2-1","description":" ","hasChildren":true,"name":"Information-as-thing","selfAssesment":" "},{"code":"IP3-2-2","description":"Information theory answers two fundamental questions in communication theory: what is the ultimate data compression (answer: the entropy H) and what is the ultimate transmission rate of communication (answer: the channel capacity, C). For this reason, it is considered that information theory is a subset of communication theory.","hasChildren":true,"hasParent":true,"name":"Information theory","selfAssesment":"New
"},{"code":"IP3-2-3","description":"Keypoints are objects (or locations) on the ground that reveal locally invariant features in images and therefore are easily detectable by automatic algorithms. Methods for this process employ scale-invariant feature transform (SIFT) algorithms for the automatic detection of geospatial objects.","hasChildren":true,"name":"Keypoint detection","selfAssesment":"New
"},{"code":"IP3-2","description":"Image understanding is part of computer vision. Computer vision is an interdisciplinary field that deals with how computers can be made to gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to automate tasks that the human visual system can perform.","hasChildren":true,"hasParent":true,"name":"Computer vision in EO","selfAssesment":"New
"},{"code":"IP3-3-1","description":"A Digital Elevation Model (DEM) is a digital raster (or grid) representation of elevation values of land surface shapes and features, where each grid cell takes a single elevation value with reference to a certain vertical datum. A DEM can be global, regional or local in scope, and can be used to characterize the dry land surface (topography) or submerged surfaces (bathymetry). Since a DEM cannot contain information of shapes and features under overhanging structures, it is often referred to as 2.5D instead of truly 3D. \r\nA digital elevation model is an overarching term for either a digital surface model (DSM) or digital terrain model (DTM). A DSM includes elevations of surface features such as trees, buildings, bridges and artificial objects such as poles, power lines, cars etc., and thus contains always the highest elevations of any feature for any given raster cell. A DTM does not include such features but reflects the elevation of bare land surface shapes, excluding elevated or overhanging features.\r\nDEMs can be obtained using active or passive measurements. Active measurements involve the generation of electromagnetic signals towards a surface and timing the reception of the (return) signal(s). This can be achieved through laser scanning (LiDAR) using visible or infrared light pulses for bathymetric or topographic measurements respectively, radio waves (SONAR) used in bathymetric measurements, or microwaves (synthetic aperture radar, SAR) used in topographic mapping. The most widely known active remotely sensed global DEM is derived from the Shuttle Radar Topography Mission (SRTM) obtained by a SAR mounted on the space shuttle Endeavour, offering 30 m resolution with a vertical accuracy typically between 5 and 20 m, covering 80% of Earth’s surface.\r\nPassive measurements detect reflection of sun light, or energy radiated from the surfaces. Their distance to the detector can then be inferred from the measurement of angles. Historically, line scanning imagers were used, but nowadays, these are replaced by acquisitions of overlapping 2D frame images. On the images, corresponding land surface features are detected which act as tie-points. The distance between the sensor and the tie-points is calculated in a process called photogrammetry. The most widely known spaceborne passive remotely sensed global DEM is derived from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data onboard the Terra satellite. It offers similar resolution and accuracy compared to SRTM, but with 99% coverage. \r\nOnly LiDAR can generate both accurate DSMs and DTMs from the same data acquisition, by using multiple returns from a single emitted pulse. All other techniques generate DSMs, from which elevated features can be identified and filtered out in postprocessing to create DTMs, however with typically lower accuracy and more artefacts.","hasChildren":true,"hasParent":true,"name":"DEM generation","selfAssesment":"Complete
"},{"code":"IP3-3-2","description":"DSM can be produced automatically from stereo satellite scenes, from satellite sensors such as GeoEye, IKONOS, SPOT-5, Terra-ASTER etc. The DSM can also be provided from stereo digital aerial photography at various resolutions, depending on the quality and scale of the aerial photography. The quality of the automatic generated DSM is substantially improved if ground measurements from GPS are incorporated in the DSM stereoscopic model.","hasChildren":true,"name":"DSM generation","selfAssesment":"New
"},{"code":"IP3-3","description":"Stereo pairs of optical satellite images with the support of ground control points provide a basis for cross-stereo analysis for generating Digital Surface Models.","hasChildren":true,"hasParent":true,"name":"Cross-stereo analysis","selfAssesment":"New
"},{"code":"IP3-4-1-1","description":"The goal of filtering is to remove unnecessary components from images (e.g., noise), while emphasizing the necessary ones. In the context of spatial aggregation, low pass filters aim at removing sharp transitions in the image intensities (high spatial frequencies) and thereby focus the information content of the image on a coarser scale level.","hasChildren":true,"name":"Filtering","selfAssesment":"New
"},{"code":"IP3-4-1-2","description":"Gridding is the technique used to generate a uniform raster grid with one value for every cell in the raster. The values of the raster cells can represent different attributes such as mean, max or min of all Normalized Difference Vegetation Index (NDVI) values measured within a particular cell.","hasChildren":true,"name":"Gridding","selfAssesment":"New
"},{"code":"IP3-4-1","description":"Spatial aggregation produces images of coarser resolution (grouping pixels in a grid of coarser resolution and calculating mean values) or of coarser scale (by filtering with low-pass filters). Thereby it is a form of generalization that may improve classification results. Spatial aggregation can be applied after classification to get rid of the salt-and-pepper effect.","hasChildren":true,"hasParent":true,"name":"Spatial aggregation","selfAssesment":"New
"},{"code":"IP3-4-10-1-1","description":" ","hasChildren":true,"name":"Gradient boost","selfAssesment":" "},{"code":"IP3-4-10-1","description":" ","hasChildren":true,"hasParent":true,"name":"Feature engineering","selfAssesment":" "},{"code":"IP3-4-10","description":"Classification processes use features, also known as predictor variables, for discriminating between classes. A feature is an individual measurable property or characteristic of a geographic phenomenon being observed. Features in Earth observation include the individual bands of images and further properties derived from the image data. For example, the single band of a panchromatic image represents a feature that allows distinguishing between pixels of darker and lighter reflectance. Multispectral images have more bands and thereby enable the differentiation between classes by more features. This means, if two classes are different from each other in several of their properties, it becomes easier to distinguish them. The set of features used in a particular classification comprise the feature space where each feature represents one space dimension. \r\nWith an increased number of (uncorrelated) features it becomes possible to increase the number of classes that can be separated. For example land cover classifications have a large number of classes. For identifying suitable bands for optical EO satellites, the spectral signatures of all the target classes have to be analysed to identify in which bands they are separable from other classes. Classes like soil, water, and vegetation have spectral signatures that differ in particular in the blue, green, red, and infrared bands of the electromagnetic spectrum. These bands are present in virtually all multispectral sensors used for land cover classification. \r\nGeographic phenomena can be differentiated not only by their reflectance in different bands. Beyond multispectral features, the classification may include image derivatives like derived spectral indices, principal components, or filtered bands (convolution layers). Object-based image analysis also uses spatial features, i.e. distance and proximity features, planar geometric features and topological features.","hasChildren":true,"hasParent":true,"name":"Classification features and feature space","selfAssesment":"Completed
"},{"code":"IP3-4-2-1","description":"Bayes’s theorem is an extremely powerful means of using information at hand to estimate probabilities of outcomes related to the occurrence of preceding events. Bayes' Theorem uses a priori (subjective) and conditional probabilities to calculate the probability of an uncertain event occurring. A priori probabilities represent what the modeler believes, before testing, to be the probability of an event occurring. Conditional probabilities are probabilities that other events occur in conjunction with the original event.","hasChildren":true,"hasParent":true,"name":"Conditional probability","selfAssesment":"Planned
"},{"code":"IP3-4-2-2","description":"Maximum likelihood classification uses the training data for estimating means and variances of the classes, which are then used to estimate the probabilities. This method considers not only the mean, or average, values in assigning classification but also the variability of brightness values in each class.","hasChildren":true,"name":"Maximum likelihood","selfAssesment":"Planned
"},{"code":"IP3-4-3-1","description":"The Land Cover Classification System (LCCS) was developed by FAO to provide a consistent framework for the classification and mapping of land cover. Its main objectives were to overcome the rigidity of a-priori land cover classifications, which in many practical situations do not allow easy assignment into one of the pre-defined classes and are therefore not very suitable for mapping. LCCS instead opted for an approach based on two main phases. The first phase is an initial ‘Dichotomous Phase’, in which eight major land cover types are defined: (1) Cultivated and Managed Terrestrial Areas, (2) Natural and Semi-Natural Terrestrial Vegetation, (3) Cultivated Aquatic or Regularly Flooded Areas, (4) Natural and Semi-Natural Aquatic or Regularly Flooded Vegetation, (5) Artificial Surfaces and Associated Areas, (6) Bare Areas, (7) Artificial Waterbodies, Snow and Ice, and (8) Natural Waterbodies, Snow and Ice. The Dichotomous Phase is followed by a subsequent ‘Modular-Hierarchical Phase’, in which land cover classes are created by the combination of sets of pre-defined classifiers, which are different for each of the eight major land cover types. For example, common classifiers used for (semi-) natural terrestrial vegetation types are Life Form, Cover, Height, Macropattern. For aquatic or regularly flooded natural and semi-natural vegetation, water seasonality is an indispensable classifier. LCCS offers several advantages from a conceptual point of view. LCCS is a real a priori classification system in the sense that, for the classifiers considered, it covers all their possible combinations. The classification is also hierarchical and the more classifiers used, the greater the detail of the defined land cover class. The classes derived from the proposed classification system are all unique and unambiguous, due to the internal consistency and systematic description of the classes. LCCS is designed to map at a variety of scales, from small to large. From a practical viewpoint LCCS offers several advantages: (1) easy incorporation into GIS and databases, (2) allows flexible response to information available in a given area, project budget and time constraints, (3) unlinks the field data collection from the interpretation process.","hasChildren":true,"name":"Land cover classification system (LCCS)","selfAssesment":"Completed
"},{"code":"IP3-4-3","description":"Long-term monitoring of land cover and land use are particularly relevant for land ecosystem monitoring. Therefore, baseline datasets are necessary that allow assessing changes of land cover and land use where the class definitions remain consistent over time. Accordingly, classification schemes have been established that adhere to taxonomically correct definitions of classes of information organized according to logical criteria. If hard classification is to be performed (i.e. without fuzzy class boundaries), the classes in the classification system should normally be mutually exclusive, exhaustive, and hierarchical. Mutual exclusive classes have no taxonomic overlap and assign a land cover patch to a single class. An exhaustive classification scheme is able to cover the area of interest comprehensively and leaves no land cover patch unassigned. A hierarchical system allows combining sub-classes into higher-level categories.\r\nFrom a remote sensing classification perspective, it becomes clear that a classification scheme consists of information classes defined by human beings. Conversely, spectral classes are those inherent to EO data. An analyst must identify spectral classes and label them as information classes that satisfy bureaucratic (or scientific requirements). Additionally, the advantage of using established classification schemes is that their use in scientific studies and applications produces results that are comparable to other studies and suitable for sharing of data.\r\nEstablished classification schemes include: CORINE land cover (CLC), Land cover classification system (LCCS), American Planning Association land-based classification standard, United States Geological Survey land-use/land-cover classification system for remote sensor data, U.S. Department of the Interior Fish & Wildlife Service classification of wetland and deep water habitats of the United States, U.S. National Vegetation Classification system (NVCS), International Geosphere-Biosphere Program IGBP Land cover classification system.","hasChildren":true,"hasParent":true,"name":"Classification schemes (taxonomies)","selfAssesment":"Completed
"},{"code":"IP3-4-4","description":"Unsupervised methods are defined as the identification of natural groups, or structures, within existing data. Clustering requires only the number of to-be generated classes as an input parameter and assigns spectrally defined classes to an image.","hasChildren":true,"name":"Clustering (unsupervised)","selfAssesment":"New
"},{"code":"IP3-4-5-1-1","description":" ","hasChildren":true,"name":"Inference engine","selfAssesment":" "},{"code":"IP3-4-5-1","description":"A production system performs automatic transformation of remote sensing imagery into useful information (such as biophysical parameters, categorical maps etc). An example can be a preliminary pixel-based classifier that works top-down (deductive, physical model-driven, prior knowledge-based) and arrives at preliminary classes for each pixel of an image. Such a production system does not require interaction of an operator. The process makes use of a decision tree that encodes the prior knowledge for assigning pixels to a class.","hasChildren":true,"hasParent":true,"name":"Production systems","selfAssesment":"New
"},{"code":"IP3-4-5","description":"Decision trees is a data mining technique used in different disciplines including Remote Sensing.\r\nThe major advantages of decision tree methods include the ability to capture interactions between the variables used for modeling, the understandability of the produced models (trees) and their efficiency. Input data for decision trees are either a large number of examples or a large number of variables. This is important in the context of pixel-based classification in geographical information systems, where very large numbers of spatial units/points need to be classified. \r\nDecision tree consist of nodes, branches and leaves. Each node contains a test on an attribute, out of which branches are created with a grouped subset of data depending on the results of the node test. The resulting subsets will have as homogeneous values of the class as possible. This is done in a hierarchical manner dividing the training dataset until it reaches rules set at the start- the lowest number of training data within each leaf or set level of confidence.\r\nFor discrete attributes, a branch of the tree is typically created for each possible value of the attribute. For continuous attributes, a threshold is selected and two branches are created based on that threshold. This also determines whether the decision tree is called a classification or a regression tree: if we are dealing with classification (discrete target) or a regression problem (continuous target), respectively.\r\nDecision trees are derived from data only. As such, they represent the data driven or empirical approach, which is more appropriate when we have plenty of high-quality (reliable and relevant) measured data and little knowledge about the studied system, for instance what is the spectral response of each land cover class needed for classification.\r\n\r\nAn important mechanism used to improve decision tree performance is tree pruning. Pruning reduces the size of a decision tree by removing sections of the tree (subtrees) that are unreliable and do not contribute to the predictive performance of the tree.\r\nThe pruning reduces complexity of the tree and helps to achieve better predictive accuracy by the reduction of over-fitting and removal of sections of the tree that may be based on noisy or erroneous data. Depending when the pruning is done during the creation of the tree, it is called pre- or post-pruning.\r\nThe CART (Classification And Regression Trees) system is the first widely known and used system for learning decision trees. After that, notable ones are the C4.5 system for learning classification trees (or J4.8 as called within WEKA software), succeeded by C5.0.","hasChildren":true,"name":"Decision trees","selfAssesment":"Completed
"},{"code":"IP3-4-6-1","description":"Along with developing deep learning methods, Convolutional Neural Networks (CNNs) have emerged as a powerful tool by providing both remarkable performances in image processing and the ability to work in a wide variety of applications in the vision community. In the past few years, biologically inspired CNNs have emerged and proven effective in the image processing field, from social media to precision medicine and robotics. A beneficial characteristic of CNNs is data processing in multiple arrays and automatic feature extraction ability, which have received acknowledgment in the geoscience and remote sensing community.\r\nMoreover, the inherent characteristics of CNNs, such as local connectivity and weight sharing, allow this deep learning method to tackle the drawbacks of artificial feature extraction, by considering the 2-D structures and reducing network parameters using convolutional filters. CNN-based models have benefited from the recent exponential advances in imaging technologies, such as the availability of various image types (optical, RADAR, temperature and microwave radiometer, altimeter, etc.) with complex characteristics (high dimensionality, multiple scales, and nonstationary). CNNs are composed of a set of blocks that make them particularly suitable for image analysis. The multiple layers of operations, such as convolution, pooling, and nonlinear activation functions, allow for a hierarchical extraction of high-level abstract features. Accordingly, CNNs have been successfully used in image preprocessing, scene classification, pixel-based classification, image segmentation, and object detection. CNNs have been used in numerous studies, for instance: to improve image classification results to extract buildings and non-building regions automatically; to detect areas of build-up; to assess the quality of OpenStreetMap data; to detect oil spills, ships, and icebergs. Although CNNs can be considered newly introduced algorithms in geoscience and remote sensing, they are now clearly among the top performers in most of the applications.\r\nDespite this progress, the study of CNN-based approaches in the field of remote sensing and geoscience is currently at its beginning stages, and there is still much potential for new developments. In this perspective, the design of new network architectures for specific tasks, the generation of large-scale datasets for network training, the integration of conventional techniques for various remote sensing data, the advancement and analysis of existing networks concerning their architectures, optimization techniques, and the regularization strategies are still open topics, which are in close relation with each other and should be jointly considered.","hasChildren":true,"name":"Convolutional neural networks (CNNs)","selfAssesment":"Completed
"},{"code":"IP3-4-6","description":"Deep learning (DL), as a subfield of artificial intelligence (AI) and machine learning (ML), is the fastest-growing trend in data analysis and is regarded as a breakthrough. Over the past few years, there has been an ongoing shift toward using DL methods in different applications, mainly due to the increasing data accessibility and computational processing power. DL models characterized by neural networks are learning methods with multiple levels of representation that learn the semantic and discriminative features in a sequential bottom-to-up manner from the data. They are composed of several levels of non-linear modules that each modify the representation at a lower level into a higher or slightly more abstract level. As such, very complex functions can be learned without depending on human-crafted features.\r\nDL has been used in several research fields, such as speech recognition, stereo vision, medical image recognition, remote sensing, time-series analysis, biomedicine, agriculture, and geosciences. One of the limiting factors of using DL models is that they require significant amounts of training samples compared to conventional ML methods To date, several DL architectures have been introduced, of which the stacked autoencoder, convolutional neural network, generative adversarial network, deep belief network, and recurrent neural network have become mainstream. DL techniques have had significant successes in several fields, which have been widely accepted as challenges in recent decades. Moreover, by growing big data and their applications in practical productions and developed time-efficient networks or public online free or commercial cloud computing platforms, such as Google, Amazon, Microsoft, and IBM, much more attention will be paid to develop new DL networks for the practical projects.","hasChildren":true,"hasParent":true,"name":"Deep learning","selfAssesment":"Completed
"},{"code":"IP3-4-7-1","description":"The RF classifier is an ensemble classifier that uses a set of Classification and Regression Trees (CARTs) to make a prediction The trees are created by drawing a subset of training samples through replacement (a bagging approach).","hasChildren":true,"name":"Random forest (RF)","selfAssesment":"New
"},{"code":"IP3-4-7-2","description":"In machine learning, support vector machines (SVMs) are supervised non-parametric statistical learning techniques with associates learning algorithms that analysze data used for both classification and regression analysis. SVM algorithm was originally designed for binary classification. The SVM is based on the main hypothesis that the training set is linearly separable. Given a set of training examples, each marked as belonging to one or another of two categories, an SVM training algorithm builds a model that can assign each new occurrence into one of these two categories, making it a non-probabilistic binary linear classifier. The SVM model is a representation of the examples as points in space, mapped so that the algorithm can find the optimal line (hyperplane) which separates with minimum error the training set, and maximizes the distance, named the “gap”, between the objects of both classes and the hyperplane. Thus, instead of using the whole available training set to describe classes, SVM uses only those training samples that describe class boundaries (support vectors), thought it can be more efficient than other algorithm because it uses a subset of training points. New occurs are then mapped into that same space and predicted to belong to a category based on the side of the gap on which they fall. In addition to performing linear classification, SVMs can also efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces. Unfortunately, because of the technique used for separating classes SVM is less effective on noisier datasets with overlapping classes. When data are unlabelled, supervised learning is not possible, and an unsupervised learning approach is required. SVM is used for text classification tasks such as category assignment, spam detection and sentimental analysis. It is also commonly used for image recognition, performing particularly well in aspect-based recognition and colour-based recognition. SVM also plays a vital role in many areas of handwritten digit recognition, such as postal automation services.","hasChildren":true,"name":"Support vector machines (SVM)","selfAssesment":"Completed
"},{"code":"IP3-4-7","description":"Field of study that gives computers the ability to learn without being explicitly programmed","hasChildren":true,"hasParent":true,"name":"Machine learning","selfAssesment":"New
"},{"code":"IP3-4-8","description":"Image classification operator needs a set of terms to express the characteristics of an image. These characteristics are called interpretation elements and are used to define interpretation keys: tone/hue, texture, pattern, shape, size, height/elevation, location/association","hasChildren":true,"name":"Mental concepts and categories","selfAssesment":"New
"},{"code":"IP3-4-9-4","description":" ","hasChildren":true,"name":"Stratified random sampling","selfAssesment":" "},{"code":"IP3-4-9-5","description":" ","hasChildren":true,"name":"Sample augmentation","selfAssesment":" "},{"code":"IP3-4-9","description":"Sampling strategies or sampling pattern specifies the arrangement of observations used for training and/or validation purposes.\r\nTypically, the simple random sample of a geographic region is defined by first dividing the region to be studied into a network of cells. Each row and column in the network is numbered, then a random number table is used to select values that, taken two at a time, form coordinate pairs for defining the locations of observations. Because the coordinates are selected at random, the locations they define should be positioned at random. The random sample is probably the most powerful sampling strategy available as it yields data that can be subjected to analysis using inferential statistics.\r\nA stratified sampling pattern assigns observations to subregions of the image to ensure that the sampling effort is distributed in a rational manner. For example, a stratified sampling effort plan might assign specific numbers of observations to each category on the map to be evaluated. This procedure would ensure that every category would be sampled.\r\nSystematic sampling positions observations at equal intervals according to a specific strategy. Because selection of the starting point predetermines the positions of all subsequent observations, data derived from systematic samples will not meet the requirements of inferential statistics for randomly selected observations.","hasChildren":true,"hasParent":true,"name":"Sampling strategies","selfAssesment":"New
"},{"code":"IP3-4","description":"The process of image classification extracts information about semantic labels of pixels or objects (i.e. regions) from imagery. Apart of input imagery, the process requires an input set of target classes (classification scheme) for which their spectral (and other) properties have to be identified. A classification method has to be selected that transforms the image data and the classification scheme into semantic map information. In complement to the resulting sematic labelling products, a secondary outcome are instructions or rulesets with the used parameters that constitute the documentation of the classification process.\r\nThe input imagery consists of one or more images (optical and/or SAR data) of a specific geographic area, collected in multiple bands of the electromagnetic spectrum (that may have already undergone certain pre-processing steps; determined by the purpose). Additionally, the imagery may include derived spectral indices, principal components, filtered bands, or other features to support the classification process.\r\nThe classification purpose defines the information about the target classes. It includes classification schemes (taxonomies), spectral signatures for each class and, mental concepts and categories about the classes (that enable an analyst to distinguish classes by texture, spatial relationships etc.). Often, training areas are used to understand how an object of a particular class is discernible in the available imagery and separable from other classes. Both the input imagery and the chosen classification method determine which features of each class can be exploited for classification. For example, spectral signatures of the target classes (extracted from training areas with known class label) may be a suitable input for extracting information with a pixel-based classification. For shape features, objects are a pre-requirement, derived with segmentation. They are only available with object-based classification approaches.\r\nClassification methods: Various methods exist that can be categorized according to the classification logic that they follow when transforming the input information into the output semantic labelling products. These can be parametric or nonparametric, supervised or unsupervised, per-pixel or object-oriented, semi-automated or fully automatic, and hybrid approaches. Classification methods are for example bayesian techniques like conditional probability or maximum likelihood, clustering (unsupervised), decision trees, deep learning and machine learning.","hasChildren":true,"hasParent":true,"name":"Image classification","selfAssesment":"Completed
"},{"code":"IP3-5-1","description":"Edge detection is a fundamental tool used in many image processing applications to obtain information from the frames as a precursor step to feature extraction and object segmentation. This process detects outlines of an object and boundaries between objects and the background in the image. An edge-detection filter can also be used to improve the appearance of blurred image.","hasChildren":true,"name":"Edge-based segmentation","selfAssesment":"Planned
"},{"code":"IP3-5-2","description":"Histogram-based segmentation makes use of histogram to select the gray levels for grouping the pixels into regions, e.g. background and the object of interest","hasChildren":true,"name":"Histogram-based segmentation","selfAssesment":"New
"},{"code":"IP3-5-3","description":"Local variance can be calculated as the value of standard deviation in a small neighborhood (e.g. 3x 3 moving window), then computing the mean of these values over the entire image. The obtained value is an indicator of the local variability in the image.","hasChildren":true,"name":"Local variance","selfAssesment":"New
"},{"code":"IP3-5-4","description":"Mean Shift is defined as finding modes in a set of data samples, manifesting an underlying probability density function (PDF).","hasChildren":true,"name":"Mean-shift segmentation","selfAssesment":"New
"},{"code":"IP3-5-5","description":"Regionalization is an important concept in Geographic Information Science for synthesizing multi-dimensional data into homogeneous objects through spatially constrained clustering methods","hasChildren":true,"name":"Regionalisation","selfAssesment":"New
"},{"code":"IP3-5-6-1","description":"Multi-resolution segmentation is a region-growing algorithm. It relies on several parameters, which need to be tuned. These include the scale parameter (SP), which dictates the size and homogeneity of the resultant objects.","hasChildren":true,"name":"Multi-resolution segmentation","selfAssesment":"Planned
"},{"code":"IP3-5-6-2","description":"Watershed segmentation is a region-based method that has its origins in mathematical morphology. In watershed segmentation an image is regarded as a topographic landscape with ridges and valleys. The elevation values of the landscape are typically defined by the gray values of the respective pixels or their gradient magnitude. Based on such a 3D representation the watershed transform decomposes an image into catchment basins. For each local minimum, a catchment basin comprises all points whose path of steepest descent terminates at this minimum. Watersheds separate basins from each other. The watershed transform decomposes an image completely and thus assigns each pixel either to a region or a watershed.","hasChildren":true,"name":"Watershed segmentation","selfAssesment":"New
"},{"code":"IP3-5-6","description":"Region-based segmentation algorithms can be devided into region growing, merging and splitting techniques and their combinations. Region merging starts from all pixels on the pixel level and iteratively aggregates pixels into objects until some conditions of homogeneity imposed by the user are met.","hasChildren":true,"hasParent":true,"name":"Region-based segmentation","selfAssesment":"New
"},{"code":"IP3-5-7","description":"Spatial autocorrelation is the term used to describe the presence of systematic spatial variation in a variable.","hasChildren":true,"name":"Spatial autocorrelation","selfAssesment":"New
"},{"code":"IP3-5","description":"The term image segmentation denotes the process of algorithmically grouping neighbouring pixels that are similar. What sounds rather straight forward, is in fact a great computational challenge, some even call it an ill-posed problem, because there is a high degree of ambiguity in this process. \r\nThe two attributes in the general definition provided above, i.e. neighbouring and similar, evoke the principles of regionalisation as a fundamental concept in geography. Regionalisation is the bottom-up approach to congregate adjacent elements with the aim to form a larger unit. (Conversely, this could be understood in a top-down manner when subdividing a larger whole into smaller homogeneous units). This follows the general notion of hierarchical organisation according to general systems theory (GST). The organisation of a state in smaller administrative units is a good example for a hierarchical structure, the composition of the human body by organs, cells, etc. another. In image analysis such regions are commonly referred to image regions, originating from the concept of “photomorphic regions”, literally meaning regions formed on images – originally by human interpreter through manual delineation. Today, advanced pixel grouping algorithms aim to delineate homogenous regions in an image automatically. As those regions usually are assumed to match with real-world objects, it is often stated in literature that image segmentation generates image objects. Deriving some general heuristics on their properties (colour, size, shape, orientation, etc.) we can label these objects according to a given semantic scheme. The procedure of object delineation and classification using object features and relations is a fundamental principle in object-based image analysis (OBIA). \r\nDue to the effect of spatial autocorrelation (the tendency of neighbouring pixels to be similar irrespective of scale or geographical location), pixel grouping is ambiguous and by no means trivial, but not arbitrary either. Intuitively, image regions are those quasi-homogeneous areas that we perceive as landscape units on a specific scene (a lake, a forest patch, a single tree, a building, a residential area). According to hierarchy theory, we can assume that we find multiple scales within a single image even, according to the level of detail we are interested in. Whether or not a specific grouping of pixels is considered valid, e.g. because it corresponds to a real-world object, can hardly be answered unanimously, but rather needs to be judged by experts in the respective application domain. That is why often in literature we find the term ‘meaningful objects’. \r\nImage segmentation is as a sub-field of computer vision and aims to apply computer algorithms to generate image regions (a.k.a. tokens) within digital image analysis. There are several strategies for performing image segmentation, all resting on the following general principles: (1) regions do not overlap; (2) regions are (relatively) homogenous; regions are (relatively) different to neighbouring regions; regions are fairly equally sized (belong to one scale domain) but can be built in several hierarchical scales. General strategies include (1) edge-based segmentation and (2) region-based segmentation, and multi-scale segmentation as a specific case. \r\nAlso referred to spatial classification emphasizing the constraint of spatial contingency, image segmentation aggregates neighbouring pixels, but – as compared to statistical clustering techniques – does not provide a unique set of classes (either semantic or statistic) in the feature space. \r\nRecently the term semantic segmentation has emerged in the machine-learning community, which is in fact a combination of segmentation and categorisation (labelling) via deep learning methods (e.g. convolutional neural networks).","hasChildren":true,"hasParent":true,"name":"Image segmentation","selfAssesment":"Completed
"},{"code":"IP3-6-1","description":"Combined filtering uses different filters to arrive at more complex filters for specific purposes. \r\nFor example, Laplacian filters are derivative filters used to find areas of rapid change (edges) in images. Since derivative filters are very sensitive to noise, it is common to smooth the image (e.g., using a Gaussian filter) before applying the Laplacian. This two-step process is called the Laplacian of Gaussian (LoG) operation.","hasChildren":true,"hasParent":true,"name":"Combined filtering","selfAssesment":"New
"},{"code":"IP3-6-2","description":"The aim of sharpening filters is to highlight transitions in intensity (high frequency components) using different operators: directional (horizontal, vertical, diagonal) or isotropic (e.g. Laplacian Filter). Example of edge detectors include: Gaussian edge detector, Laplacian filter etc.","hasChildren":true,"name":"Edge detectors","selfAssesment":"New
"},{"code":"IP3-6-3-1","description":"The Lee-sigma filter is a conceptually simple but effective alternative to the Lee and other sophisticated adaptive filters. It is based on the sigma probability of the Gaussian distribution.","hasChildren":true,"name":"Lee-Sigma","selfAssesment":"New
"},{"code":"IP3-6-3","description":"High-pass filtering enhance information of high frequencies (local extremes, lines, edges)","hasChildren":true,"hasParent":true,"name":"High-pass filtering","selfAssesment":"New
"},{"code":"IP3-6-4-1","description":"Gaussian Filters are isotropic (same behavior in all directions).","hasChildren":true,"name":"Gauss filter","selfAssesment":"New
"},{"code":"IP3-6-4","description":"Spatial filters transform an image by taking into account the local neighborhood of a pixel. The goal of filtering is to remove unnecessary components from images (e.g., noise), while emphasizing the necessary ones. In this context, low pass filters aim at removing sharp transitions in the image intensities (high spatial frequencies).","hasChildren":true,"hasParent":true,"name":"Low-pass filtering","selfAssesment":"New
"},{"code":"IP3-6","description":"In contrast to the point operations used for radiometric modification of image data, techniques for geometric processing are characterized by operations over local neighborhoods of pixels. The result of a neighborhood operation is still a modified brightness value for the single pixel at the center of the neighborhood , however the new value is determined by the brightness of all the local neighbors rather than just the original brightness value of the central pixel alone.","hasChildren":true,"hasParent":true,"name":"Kernel analysis (convolution)","selfAssesment":"Planned
"},{"code":"IP3-7-1","description":"Class modelling provides flexibility in designing a transferable workflow from scene-specific high-level segmentation and classification to region-specific multi-scale modelling","hasChildren":true,"name":"Class modelling","selfAssesment":"Planned
"},{"code":"IP3-7-2","description":"Hierarchical representation refers to hierarchically scaled compositions of the classes to be classified.","hasChildren":true,"name":"Hierarchical representation","selfAssesment":"New
"},{"code":"IP3-7-3","description":"Per-parcel analysis relies on parcels or objects as the smallest units of image analysis. The parcels are usually obtained through image segmentation that partition the input images into homogeneous units, i.e. parcels, in a supervised or unsupervised manner.","hasChildren":true,"name":"Per-parcel analysis","selfAssesment":"New
"},{"code":"IP3-7-4-1","description":"Distance relationships describe how far an object is with respect to a reference. Proximity analysis allows the identification of the distance between a geographic feature of interest and its neighbors.","hasChildren":true,"name":"Distance and proximity features","selfAssesment":"New
"},{"code":"IP3-7-4-2","description":"The most important geometric features of geographic objects are their size and shape. Shape refers to general form or outline of individual objects and can be quantified using different metric such as shape index, compactness, asymmetry, density, elliptic fit, roundness, rectangular fit etc.","hasChildren":true,"name":"Planar geometric features","selfAssesment":"New
"},{"code":"IP3-7-4-3","description":"Topological features characterize qualitatively the position of spatial objects relative to each other. There are different models for representing topological relationships. Calculus-based method, for example, allows us to model five topological relationships of two spatial objects: touch, in, cross, overlap, disjoint.","hasChildren":true,"name":"Topological features","selfAssesment":"New
"},{"code":"IP3-7-4","description":"An object of a specific object class has a value on the range of values of a spatial or spectral feature. A set of features provides the feature space that is used for classification.","hasChildren":true,"hasParent":true,"name":"Spatial features","selfAssesment":"Planned
"},{"code":"IP3-7","description":"OBIA is an iterative method that starts with the segmentation of satellite imagery into homogeneous and contiguous image segments (also called image objects. In the next step, resulting image segments are assigned to the target classes.","hasChildren":true,"hasParent":true,"name":"Object-based image analysis (OBIA)","selfAssesment":"Planned
"},{"code":"IP3-8-1","description":"The feature space represents in various dimensions all the features that can be used for classification (e.g. image bands, band math parameters, derived texture properties). A point in that space is also called a vector with values for each feature (or dimension). Polyhedralization is a form of vector space quantization where a vector is assigned to the closest centre point of one polyhedron.","hasChildren":true,"name":"Feature space polyhedralization","selfAssesment":"New
"},{"code":"IP3-8-2","description":"Radiative transfer models describing the interaction between matter and electromagnetic radiation serve as cornerstones for optical remote sensing. The radiative transfer theory provides the most logical linkage between observations and physical processes that generate signals in optical remote sensing. Radiative transfer modelling is therefore an integral part of remote sensing, since it provides the most efficient tool for accurate retrievals of Earth properties from satellite data. Radiative transfer models are used in a number of different applications such as sensor radiometric calibration, atmospheric correction and the modelling radiation processes in vegetation canopies. \r\nVegetation radiative transfer models (RTMs) study the relationship between leaf and canopy biophysical variables and reflectance, absorbance and scattering mechanisms. The infinite variability of vegetation structure complicates the modeling of RT in vegetation canopies. Numerous models of RT in vegetation canopies were developed in the second half of the last century. Models differ by the details accounted for and by the simplifications introduced in the description of canopy structure and photon–vegetation interactions. Gradual improvement in RTMs accuracy, yet in complexity too, have diversified RTMs from simple turbid medium RTMs towards advanced Monte Carlo RTMs that allow for explicit 3D representations of complex canopy architectures. This evolution has resulted in an increase in the computational requirements to run the model, which bears implications towards practical applications. When choosing an RTM, a trade-off between invertibility and realism has to be made: simpler models are easier to invert but less realistic, while advanced models more realistic but require a large amount of variables to be configured. The two most widely used models are the leaf model PROSPECT and Scattering by Arbitrary Inclined Leaves (SAIL) canopy model. \r\nAtmosphere RTMs study the interaction of radiation with the atmosphere. The remotely-sensed signals at satellite or airborne platforms are combinations of surface and atmospheric contributions, with relative amounts varying across the two wavelength regions, depending on the condition of the atmosphere. The order of magnitude of atmosphere signals can be equal or larger than that of land or ocean surface signals that arise at the top of the atmosphere (TOA). In order to derive accurate sensor calibration and atmospheric correction, the contribution of the atmospheric constituents to the total retrieved signal must be understood and modelled. Atmospheric radiative transfer models simulate the radiative transfer interactions of light scattering, absorption and emission through the atmosphere. Some widely used atmospheric RTMs are 6SV, libRadtran, MODTRAN, and ATCOR.\r\nAdvances in radiative transfer modeling enhance our ability to detect and monitor changes in our planet through new methodologies and technical approaches to analyze and interpret measurements from air- and space-borne sensors.","hasChildren":true,"hasParent":true,"name":"Radiative transfer modelling","selfAssesment":"Completed
"},{"code":"IP3-8","description":"Historically, physical modelling and machine learning have often been treated as two different fields with very different scientific paradigms (theory-driven versus data-driven). Yet, in fact these approaches are complementary, with physical approaches in principle being directly interpretable and offering the potential of extrapolation beyond observed conditions, whereas data-driven approaches are highly flexible in adapting to data and are amenable to finding unexpected patterns (surprises).","hasChildren":true,"hasParent":true,"name":"Physical-model based analysis","selfAssesment":"New
"},{"code":"IP3-9-1","description":"Difference of Gaussians (DoG) method consists of subtracting two Gaussians, where a kernel has a standard deviation smaller than the previous one. The convolution between the subtraction of kernels and the input image results in the edge detection of this image.","hasChildren":true,"name":"Difference of Gaussian (DoG)","selfAssesment":"New
"},{"code":"IP3-9-2","description":"Scale Invariant Feature Transform (SIFT) is an image descriptor for image-based matching and it is used for a large number of purposes in computer vision related to point matching between different views of a 3-D scene and view-based object recognition. The SIFT descriptor is invariant to translations, rotations and scaling transformations in the image domain and robust to moderate perspective transformations and illumination variations. Experimentally, the SIFT descriptor has been proven to be very useful in practice for robust image matching and object recognition under real-world conditions.","hasChildren":true,"name":"Scale invariant feature transformation (SIFT)","selfAssesment":"New
"},{"code":"IP3-9","description":"Scale-space theory is a framework for multiscale image representation, which has been developed by the computer vision community with complementary motivations from physics and biologic vision. The idea is to handle the multiscale nature of real-world objects, which implies that objects may be perceived in different ways depending on the scale of observation. If one aims to develop automatic algorithms for interpreting images of unknown scenes, there is no way to know a priori what scales are relevant. Hence, the only reasonable approach is to consider representations at all scales simultaneously.","hasChildren":true,"hasParent":true,"name":"Scale space analysis","selfAssesment":"New
"},{"code":"IP3","description":"Image data, in order to be turned into information, require interpretation. Thereby image understanding is the process of scene reconstruction, the description and mental representation of the content of imaged, and potentially complex, realities. \r\nImage understanding thereby goes beyond single feature extraction. Instead, it aims at a complete description of the image content, i.e. the reconstruction of a real-world scene. In the early days of digital image processing, image understanding was mainly confined to identifying and labelling image primitives. Today, advanced mapping keys and hierarchical classification schemes to analyse EO data, include composite and complex target classes. Thereby ‘full’ scene description means reaching from signal processing to a symbolic representation of the scene content. This entails the relationships of real‐world objects in different scales and spatio-temporal aspects.\r\nDescribing a scene, visually or computer-aided or mixed, depends on a conceptual framework comprising (a) the underlying research question within (b) a specific field of application and (c) pre‐existing knowledge and experience of the operator. Obtaining insights from imagery requires general knowledge about the expected scene content and domain expertise. The field of image understanding is interlinked with image (pre-)processing, computer vision, and artificial intelligence (AI). Image processing conditions the data material and enhances the interpretation source. Computer vision including pattern recognition providing knowledge representation, expert systems. AI is mainly concerned with automation processes, be it via knowledge transfer to an automated system or machine / deep learning.\r\nIn analogy to the human mind, image understanding is the computational process of extracting information from images, i.e. locating, characterizing, and recognizing objects and other features in the depicted scene. However, image understanding is not a linear, but rather a cyclic process and takes place during the pre-processing and data assimilation steps. For example, cloud masks on EO images is an early product of image understanding, prior to many pre-processing tasks.\r\nIn a typical GEOBIA workflow, the process of image understanding can be illustrated by the following steps: Starting from the subset of a real‐world scene captured on an image first step may entail scaled representations by grouping neighbouring pixels on several hierarchical sales. The multi‐scale segmentation provides a set of nested objects with geospatial and spectral properties to be used in the classification process. \r\nWith object hypotheses in mind the object relation modelling can be realized by encoding expert knowledge into a rule system. This setp aims at categorizing the image objects by their spectral and spatial properties and their mutual relationships. Hereby, an object‐centred view is accomplished. This representation of the image content should meet the conceptual reality of the interpreter or user. Knowledge is stepwise adapted and improved through progressive interpretation and modelling. Experience grows, as knowledge will be enriched by analyzing unknown scenes and the transfer of knowledge may incorporate or stimulate new rules.","hasChildren":true,"hasParent":true,"name":"Image understanding","selfAssesment":"Completed
"},{"code":"IP4-1-1","description":"Once the user finds the required data, she/he needs to know how can they be accessed, possibly including authentication and authorisation.","hasChildren":true,"name":"Accessibility","selfAssesment":"New
"},{"code":"IP4-1-2","description":"Quality Indicators (QIs) should be ascribed to data and, in particular, to delivered information products, at each stage of the data processing chain - from collection and processing to delivery. A QI should provide sufficient information to allow all users to readily evaluate a product’s suitability for their particular application, i.e. its “fitness for purpose”.","hasChildren":true,"name":"GEO QA4EO","selfAssesment":"New
"},{"code":"IP4-1-4","description":"ISO is an independent, non-governmental international organization with a membership of 164 national standards bodies. Through its members, it brings together experts to share knowledge and develop voluntary, consensus-based, market relevant International Standards that support innovation and provide solutions to global challenges. ISO/TC 211 Geographic information/Geomatics provides Standardization in the field of digital geographic information. Note: This work aims to establish a structured set of standards for information concerning objects or phenomena that are directly or indirectly associated with a location relative to the Earth. These standards may specify, for geographic information, methods, tools and services for data management (including definition and description), acquiring, processing, analyzing, accessing, presenting and transferring such data in digital / electronic form between different users, systems and locations.","hasChildren":true,"name":"ISO standards","selfAssesment":"New
"},{"code":"IP4-1-5","description":"The OGC is the worldwide leading consortium of GIS industries promoting the interoperability of geographic information across platform, system, and country borders. The main field of current activity is the complete integration of the sources of geographic information based on the Internet.The Open GIS Consortium (OGC) plays an important role on the implementation level.","hasChildren":true,"name":"OGC standards","selfAssesment":"New
"},{"code":"IP4-1-6","description":"A fundamental pillar in (open) science is to verify the scientific results of others to advance knowledge. The lack of reproducibility in scientific studies brings challenges in understanding and recreating the results of others, a situation that may be common in data-based and algorithm-based research like in geocomputation. In general, many authors define reproducibility as the ability to compute exactly the same results of a study based on original input data and analysis workflow. In other words, “to rerun the same computational steps on the same data the original authors used”. Replicability is often seen as obtaining similar conclusions about a research question derived from an independent study or experiment. In the field of GIScience and geocomputation, in particular, a reproduction is always an exact copy or duplicate, with exactly the same features and scale, while a replication resembles the original but allows for variations in scale, for example. Hence, reproducibility is exact whereas replicability means confirming the original conclusions, although not necessarily with the same input data, methods, or results.","hasChildren":true,"name":"Replicability and reproducibility","selfAssesment":"Completed
"},{"code":"IP4-1-7","description":"The ultimate goal of FAIR is to optimise the reuse of data. To achieve this, metadata and data should be well-described so that they can be replicated and/or combined in different settings.","hasChildren":true,"name":"Reusability","selfAssesment":"New
"},{"code":"IP4-1","description":"Data quality standards are guiding principles and operational guidelines for the production and use of data. For example, QA4EO aims for the two key principles of accessibility / availability and suitability / reliability. The QA4EO guidelines provide instructions for the implementation of processes that follow these principles. Standards emerge from standardization processes within the community. They are based on the agreement of the members of the community.","hasChildren":true,"hasParent":true,"name":"Data quality standards","selfAssesment":"New
"},{"code":"IP4-2-1-1","description":"To correctly perform a classification accuracy (or error) assessment, it is necessary to systematically compare two sources of information: (1) pixels or polygons in a remote sensing-derived classification map, and (2) ground reference test information (which may in fact contain error). The relationship between these two sets of information is commonly summarized in an error matrix (sometimes referred to as contingency table or confusion matrix). Indeed, the error matrix provides the basis on which to both describe classification accuracy and characterize errors, which may help refine the classification or estimates derived from it.","hasChildren":true,"name":"Error matrix","selfAssesment":"New
"},{"code":"IP4-2-1-2","description":"F-score represents the harmonic mean between precision and recall. As F-score combines both precision and recall, it can be regarded as an overall quality measure. The range of F is from 0 to 1 with larger values representing higher accuracy.","hasChildren":true,"name":"F-score","selfAssesment":"New
"},{"code":"IP4-2-1-3","description":"Ground reference refers to the reference dataset for an accuracy assessment of a remote sensing classification. The process of obtaining ground reference is dedicated to support the production of suitable accuracy information. A sampling design (fitting to the produced image classification) determines the most appropriate distribution of sample locations (or regions). The response design consists of the evaluation protocol and the labeling protocol. The evaluation protocol initiates selecting the support region on the ground (represented by a pixel or polygon) where the ground information will be collected. Once the location and dimension of the sampling unit are defined, the labelling protocol is initiated and the sampling unit is assigned a hard or fuzzy ground reference label. This ground reference label (e.g. forest) is paired with the remote sensing-derived label (e.g., forest) for assignment in the error matrix.","hasChildren":true,"name":"Ground reference","selfAssesment":"New
"},{"code":"IP4-2-1-4","description":"Kappa is a value for measuring the overall accuracy of a classification that accounts for randomness of class assignment. Kappa analysis is a discrete multivariate technique of use in accuracy assessment. Kappa yields a statistic, ^K, which is an estimate of Kappa. It is a measure of agreement between the remote sensing-derived classification map and the reference data as is indicated by a) the major diagonal and b) the chance of agreement, which is indicated by the row and column totals in the error matrix.","hasChildren":true,"name":"Kappa statistics","selfAssesment":"New
"},{"code":"IP4-2-1-5","description":"These two quality assessment indicators are calculated as follows:\r\nPrecision = TP/(TP+FP) \r\nRecall = TP/(TP+FN),\r\nwhere TS is true positive, FP is false positive, FN is false negative","hasChildren":true,"name":"Precision & recall","selfAssesment":"New
"},{"code":"IP4-2-1-6","description":"Geometric correction procedures (image-to-map rectification, image-to-image rectification) are used to rectify remotely sensed data to a standard map projection whereby it may be used in conjunction with other spatial information in a GIS to solve problems. The rectification process normally involves selecting ground control point (GCP) image pixel coordinates (row and column) with their map coordinate counterparts (e.g. meters northing and easting in a UTM map projection). Rectification requires that polynomial equations (that translate from image coordinates to map coordinates) be fit to the GCP data using least squares criteria. Depending on the distortion in the imagery, the number of GCPs used, and the degree of topographic reliefdisplacement in the area, higher -order polynomial equations may be required to geometrically correct the data. To determine how well the six coefficients derived from the least-squares registration of the initial GCPs account for geometric distortion in the inpit image, for each GCP, the root-mean-square error (RMSE) is computed.","hasChildren":true,"name":"Root mean square error (RMSE)","selfAssesment":"In progress
\r\n\r\n"},{"code":"IP4-2-1","description":"A growing set of EO services and applications produce EO products that describe various aspects of the land, ocean and atmosphere. These products include for example image products at different processing levels, geometric measurements like in digital elevation models, semantic labelling products like land cover classifications, and EO-derived attribute products concerning air quality or other geophysical and biophysical parameters. Same as any geospatial data, EO products are not free of error and require accompanying documentation of their product quality. One term for describing different quality dimensions of an EO product is accuracy.\r\nAccuracy is a measure to estimate the uncertainty that originates from errors. An error is the deviation of a map value from a true value. The concept of error assumes well-defined phenomena where deviation results from imperfection of measurement equipment, environment effects, or imperfections of the observer. They cause gross errors and blunders, systematic errors, and random errors, for which different approaches are necessary to minimize error. Ideally, only random error remains that is probabilistic in nature and can be assessed with statistical approaches. For poorly defined phenomena, the concept of vagueness applies. For example in the case of thematic maps using fuzzy sets, the accuracy assessment requires a fuzzy approach as well. \r\nJudging error requires reference data with higher accuracy (by an order of magnitude) to which the map value can be compared. EO product quality dimensions about accuracy include thematic accuracy, spatial accuracy (both horizontal and vertical), radiometric accuracy, and accuracy of biophysical/geophysical parameter measurements. Respective equipment and approaches for reference data collection includes ground verification for thematic maps, GNSS positioning devices, field spectrometers, air quality sensors and in-situ biomass estimation. Ideally, reference data is collected in the field. In case of inaccessible areas of interest and/or if the service requirements allow it, approaches may rely on proxy reference data.\r\nThe design of the accuracy assessment procedure should be done with the EO product design to match the requirements of the EO service. For example, a thematic accuracy assessment consists of the main three components of response design, analysis, and sampling design. The response design ensures that reference data and map data are comparable at a location and specifies under which cases they agree or disagree. The analysis, usually performed with an error matrix, specifies which quality indicators will be calculated to quantify accuracy. The sampling design specifies the subset of locations at which the response design will be applied. Depending on the classification process and application case, different sampling strategies can be suitable (e.g. clustered sampling, stratified random sampling). \r\nFor other accuracy dimensions, respective accuracy assessment procedures exist, e.g. root mean squared error (RSME) for the positional accuracy assessment.\r\nAfter an accuracy assessment has been performed and the uncertainty in the EO product is understood, the challenge is to clarify how the uncertainty affects subsequent spatial analyses with the EO product. Different strategies exist that ignore error completely or that account for error by modelling uncertainty in the analysis outcomes. If uncertainty is judged low enough (or more hazardous, if users are unaware of the limited accuracy), subsequent analyses accept the EO product as true and ignore the accuracy value. If uncertainty is incorporated in subsequent analysis through uncertainty modelling, the results describe the bandwidth of outcomes, potentially supported with appropriate visualisations of uncertainty. The uncertainty modelling approach may greatly enhance the usability of the EO product, because it informs better how the error impacts the EO information and how much confidence a user should have in it.\r\nWith a new generation of EO products on the horizon and a largely increased user community, a large number of new applications is to be expected. They may also identify innovative accuracy assessment approaches. For example, the availability of EO archives with long time series of EO data led to response design protocols tailored to collect time series of reference data. The use of volunteered geographic information (VGI) as reference data has great potential, if approaches are implemented that ensure its reliability. Methods for object-based accuracy assessment are continued to be developed. Further, the increasing number of EO parameter products based on continuous variables creates the need to describe their accuracy. Finally, the focus on validation of EO products during EO service development and operation will make feedback from users available to service providers, ultimately leading to more meaningful EO products with more meaningful accuracy metrics and other quality indicators.","hasChildren":true,"hasParent":true,"name":"Accuracy assessment","selfAssesment":"
Completed
"},{"code":"IP4-2-2","description":"The implementation of a service that provides remote sensing derived information on a regular basis introduces process-related quality criteria like the timeliness of information provisioning. For the case of refugee camp mapping, timely arrival of map information may be critical to support the decisions in planning facilities for humanitarian assistance.","hasChildren":true,"name":"Timeliness","selfAssesment":"New
"},{"code":"IP4-2-3-1","description":"Completeness is a quality dimension that can apply to different data properties.The Data completeness is dealing with the completeness of an image, handling for example the effect of shadowing objects, sun flares on water surfaces or masking out by an object (e.g. propeller of a UAV). Spatial completeness is a feature on the area coverage. In photogrammetry (especially in stereophotogrammetry) its 3D version, the stereo completeness has extreme importance. In monitoring systems and applications the Temporal completenesster term features how the taken images represent a complete time series. The thematic completeness measure describes the image interpretation quality how the expected and defined classes are evaluated. This feature is important with the use of e.g. multiple classifiers.","hasChildren":true,"name":"Completeness","selfAssesment":"New
"},{"code":"IP4-2-3-2","description":"In remote sensing we can speak about spatial consistency in the Consistency cluster. It represents the quality of image interpretation/understanding: how are the different objects or classes recognized/evaluated integrally. A bridge above a water surface, like river can be detected in pixel-wised manner, but the question is how coherent they are in the output map. This phenomenon has very close to the thematic consistency, where the recognition integrity is represented in this way. The topological consistency is defined mainly for network-type surface objects, like roads or rivers, where the connection of all atomic segments are rated by this measure. Urban mapping focuses on the built environment objects, where e.g. house-parcel inclusions are described by this feature. The temporal consistency is for monitoring again, representing for example the possibility or impossibility of land cover changes in time. Having multiple data sources (even airborne or terrestrial), their integral usage can be qualified by this measure.","hasChildren":true,"name":"Consistency","selfAssesment":"New
"},{"code":"IP4-2-3-3","description":"Readability refers to the content of a map being presented clearly enough that the content can be perceived and understood by the user. This includes legibility, e.g. whether the text of a label is large enough to be read and has enough contrast to the background to be easily perceivable. Additionally, readability has a broader meaning that explains whether a product as a whole is simple enough to be understood and not too complex that essential information can be overlooked by the user.","hasChildren":true,"name":"Readability","selfAssesment":"New
"},{"code":"IP4-2-3","description":"Gathering information about the quality of an EO product or service by letting the user test it. The feedback from the user enables to verify whether specific quality criteria have been met.","hasChildren":true,"hasParent":true,"name":"User validation","selfAssesment":"New
"},{"code":"IP4-2","description":"A product in the sense of something that a user can use for a specific purpose requires a certain quality. Therefore, its accuracy needs to be judged with an accuracy assessment measure that the user understands and where he can interpret the meaning in relation to the purpose. The product has to be validated, i.e. it has to be known whether the product qualifies for use in a certain context. And in addition, the product needs to be available in time that the users can base their decision on it.","hasChildren":true,"hasParent":true,"name":"Product quality","selfAssesment":"New
"},{"code":"IP4-3-1","description":"The cloud cover percentage indicates the amount of area in the remote sensing image extent that is covered with clouds and therefore cannot provide information about the Earth surface conditions.The actual types of clouds included may depend on the product, but the CEOS definition includes cloud shadow. Next to that, from an optical remote sensing point of view, clouds can be roughly classified in: opaque/dense clouds, mainly composed of droplets that are highly reflective in the VIS region and generally located at low-medium altitudes and cirrus, consisting of a large number of thin non-spherical ice crystals that are normally translucent in the VIS region, relatively highly reflective in the SWIR spectrum, and located at high altitude.\r\n\r\nThe goal of cloud cover percentage is to provide a quality measure of usable information in a surface reflectance image. Earth observation product catalogs support it as a query parameter, to enable searching for products with a cloud cover percentage below a given threshold.\r\nThis simplifies for instance use cases that require only fully clear products (0% cloud cover), and may save download and processing resources by only handling images that have some valid pixels. For instance, by only using products with a cloud cover percentage smaller than 99.95%. The measure also gives an estimate of the number of valid observations in a given geographical area, allowing a quick assessment of whether minimal data requirements for a specific use case are met.\r\n\r\nThe measure is a percentage of actual observations in an image, so pixels where no data was recorded are not included. For derived products, cloud cover pixels are often also flagged separately from pixels where no data was recorded, but this may depend on the data provider. The definition specifically also includes cloud shadow pixels.\r\nReliable cloud cover percentages depend on good cloud and cloud shadow detection methods. Especially handling of translucent cirrus clouds is an open issue: a product that has a 100% cloud cover percentage due to cirrus clouds might still be usable for some cases, while for other cases they also render the product useless. \r\n\r\nThe used cloud detection algorithm will also affect the cloud cover percentage. A more strict algorithm will yield higher percentages compared to an algorithm that under detects clouds.\r\nDue to these limitations, cloud cover percentages in product metadata have a fairly high error margin. The user should take this into account when determining optimal cloud cover percentage thresholds for the use case.","hasChildren":true,"name":"Cloud cover percentage","selfAssesment":"Planned
"},{"code":"IP4-3-2","description":"The remote sensing lifecycle structures all possible phases of the data production process, from its beginning of the data's coming to existence (that includes the sensor design prior to data collection) over storage, processing and use to archiving and deletion.","hasChildren":true,"name":"Remote sensing lifecycle","selfAssesment":"New
"},{"code":"IP4-3-3","description":"The capability of a sensor or EO product to resolve anything is a function of its (spatial, temporal, spectral and radiometric) resolution and of the detail at which a geographic phenomenon of interest manifests itself in time and space. A geographic phenomenon can be named or described, georeferenced and provided with a time interval at which it exists. The geographic phenomenon of interest is the one of which a user needs information to help him make a decision. Therefore, the geographic phenomenon needs to be resolved with a low enough uncertainty and a high enough quality that allows the user to make a decision with confidence. \r\nFor example, let’s consider a helicopter pilot that wants to know whether a specific site is suitable for an emergency landing. The decision to perform an emergency landing may be supported with an EO-derived digital map of emergency landing sites that are flat enough (as well as large enough for the pilot’s helicopter and free of any obstacles on the surface and in the approach area). If we only focus on the flatness of the terrain, we need a digital elevation model (DEM) of high enough spatial resolution and accuracy in the Z dimension to calculate slope within acceptable levels of uncertainty. The pilot probably can tell us what degrees of slope are okay for his helicopter and tell us sites (e.g. football fields) where such a landing would succeed. However, this is only the input to an analysis of different DEMs to identify the minimum spatial resolution and accuracy in the Z dimension to model slope products and associated uncertainty to derive an emergency landing site product that fulfils the requirements. Thereby the capability of different DEMs to resolve emergency landing sites can be analysed.\r\nSpatial resolution is a measure of the smallest angular or linear separation between two objects that can be resolved by the remote sensing system. A useful heuristic rule of thumb is that in order to detect a feature, the nominal spatial resolution of the sensor should be less than one-half the size of the feature measured in its smallest dimension.\r\nOther types of resolution of an EO dataset are available that determine for various geographic phenomena under investigation whether it is possible to resolve them in the data. These are radiometric resolution, spectral resolution and temporal resolution. Radiometric resolution is defined as the sensitivity of a remote sensing detector to differences in signal strength as it records the radiant flux reflected, emitted, or back-scattered from the terrain. Spectral resolution is the number and dimension (size) of specific wavelength intervals (referred to as bands or channels) in the electromagnetic spectrum to which a remote sensing instrument is sensitive. The temporal resolution of a remote sensing system generally refers to how often the sensor records imagery of a particular area. For time-series analysis, the temporal resolution determines the time granularity for resolving processes that underlie the change that is observable between subsequent images.","hasChildren":true,"name":"Capability to resolve anything","selfAssesment":"In progress
"},{"code":"IP4-3-4","description":"The spatial coverage of a dataset (consisting of an image or a series of images) determines whether the dataset covers the area of the terrain that is of interest to the user of information derived from the dataset.","hasChildren":true,"name":"Spatial coverage","selfAssesment":"New
"},{"code":"IP4-3-5","description":"The temporal validity of a dataset (consisting of an image or a series of images) determines whether the acquisition date(s) (and period) match(es) the requirements for investigating a specific phenomenon and thereby enables the derivation of information about that phenomenon.","hasChildren":true,"name":"Temporal validity","selfAssesment":"New
"},{"code":"IP4-3","description":"Values (or a value) that enable(s) judging a dataset or product on their fitness for a specific purpose (e.g. whether a specific satellite image is suitable for mapping landslides). , A QI should provide sufficient information to allow all users to readily evaluate a product’s suitability for their particular application, i.e. its “fitness for purpose”.","hasChildren":true,"hasParent":true,"name":"Quality indicators","selfAssesment":"New
"},{"code":"IP4","description":"Data quality, in general, is the degree of data usability in relation to a specific application purpose. Assurance of data quality is of growing importance in remote sensing, due to the increasing relevance of remote sensing data in planning and operational decision of public bodies and private firms, and the huge amount of digital services (or apps) that exploit RS data. \r\nDifferent data quality dimensions exist according to the lifecycle phases of the remote sensing data: data acquisition, data storage, data pre-processing, processing and analysis and data visualization and delivery. Remote sensing data acquisition phase involves the following quality aspects: resolution, accessibility, spatial accuracy, temporal validity, accuracy and precision of the sensor calibration. Resolution is a multi-dimensional concept that includes the following dimensions: spatial resolution, temporal resolution, radiometric resolution, spectral resolution and temporal resolution. Temporal validity refers to the quality of an remote sensing data product in time, whereas spatial accuracy refers to the accuracy of the position of features relative the Earth. \r\nData storage includes the accessibility and completeness data quality dimensions. Accessibility includes both temporal and data accessibility. Temporal accessibility refers to the time delay between data acquisition and data delivery, whereas data accessibility refers to the availability of remote sensing data. Data completeness encompasses temporal completeness, i.e. completeness of a time series represented a phenomenon, thematic completeness, and spatial completeness which refers to the area coverage. Data preprocessing, processing and analysis phase includes consistency, completeness, temporal validity, resolution, radiometric and geometric accuracy, thematic and semantic accuracy. Thematic and sematic accuracy refers to the correctness of the remote sensing data product. The main quality dimensions of the data visualization and delivery include readability, completeness and temporal validity. \r\nDifferent metrics can be used to assess the quality of the remote sensing-derived information, such as the root-mean-square error (RMSE) measuring the differences between the true and measured values of the phenomenon under investigation, confusion matrix used for assessing the classification performance, producer’s accuracy, user’s accuracy or Cohen kappa. The quality of the remote sensing data per se can be assessed using Peak Signal-to-noise Ratio (PSNR) or the Universal Image Quality Index (UIQI).\r\nDifferent organizations are involved in the standardization of the image data and gridded data quality, including ISO/TC 211 ‘Geographic information/Geomatics’, Open Geospatial Consortium (OGC) or the Quality Assurance Framework for Earth Observation (QA4EO) developed by the Group on Earth Observation (GEO). These organizations are responsible for developing metadata standards that are further used by the remote sensing community to document the quality of the remote sensing data. According to the QA4EO, for example, all remote sensing data products need to be accompanied by a Quality Indicator (QI) which helps users assessing their fitness-for-use.","hasChildren":true,"hasParent":true,"name":"Image data quality","selfAssesment":"Completed
"},{"code":"IP5-1-1","description":"Array databases make use of arrays as the primary storage representation. Such an array-oriented data model and query language is useful in many scientific applications, where the raw data consists of large collections of imagery or sequence data that needs to be filtered, subsetted, and processed.","hasChildren":true,"name":"Array databases","selfAssesment":"New
"},{"code":"IP5-1-2","description":"The Open Data Cube (ODC) is a non-profit, open source project that was motivated by the need to better manage Satellite Data. This project was born out of the work done under the \"Unlocking the Landsat Archive\" and the Australian Geoscience Data Cube (AGDC) projects.","hasChildren":true,"name":"Open data cube","selfAssesment":"New
"},{"code":"IP5-1","description":"The term data cube originally was used in Online Analytical Processing (OLAP) of business and statistics data. Technically speaking, such a data cube represents a multidimensional array together with metadata describing the semantics of axes, coordinates, and cells. It is an efficient approach to the management and analysis of large datasets.","hasChildren":true,"hasParent":true,"name":"Data cubes","selfAssesment":"New
"},{"code":"IP5-2-1","description":"Content-based image retrieval helps users retrieve relevant images based on their contents.","hasChildren":true,"name":"Content-based image retrieval","selfAssesment":"New
"},{"code":"IP5-2-2","description":"Web Portals allow users to discover, understand, view, access and query information of their choice from local to global level for a variety of uses.","hasChildren":true,"name":"Web portals","selfAssesment":"New
"},{"code":"IP5-2","description":"Image archives are repositories for storing, managing and retrieving remote sensing data.","hasChildren":true,"hasParent":true,"name":"Image archives","selfAssesment":"New
"},{"code":"IP5-3-1","description":"As an initiative stipulated by the European Commission to foster the bridge between the Copernicus ground segment and the user segment, the Copernicus data and information access service (C-DIAS) is a generic name for different sets of cloud-based platforms providing centralised access to Copernicus data and information, as well as to processing tools. The name indicates, however, that the focus of such advanced user-centred infrastructure implementations is not only on data access, but also on ‘information’. What is specifically meant here is the provision of information services and information layers as defined in the Copernicus service portfolio. This allows the users to develop and host their own applications in the cloud and a single access point, rather than processing data locally. Currently there are five different DIAS’s implemented (CREODIAS, SOBLOO, MUNDI, WEKEO, ONDA), all with some specific technical assets, or a sector-specific application focus or any other unique selling position by e.g. targeting as specific user community. Currently, the DIAS, which have received co-funding from the European Commission as a kind of seed funding, are currently in the process of exploring opportunities and claiming market shares, striving to sustain in a competitive manner. Some of the features are highlighted in the following, without explicitly mentioning any of the associated DIAS: (i) data access of global data sets (satellite data mosaics or gridded data) by custom area; (ii) OGC interfaces, VM catalogue, SPAR QL search interface (combine searches like receive images over areas of high population density), open source (accessible via API) or pay-per-use; (iii) access to core service products (e.g. CLMS, CMEMS, CAMS); (iv) focus on integrated applications such as smart cities, urban energies, precision agriculture; access to third-mission VHR satellite data (e.g. Pléiades); (v) utilizing GitLab as a developer platform.","hasChildren":true,"name":"Data and information access service (DIAS)","selfAssesment":"Completed
"},{"code":"IP5-3-2","description":"The OpenGIS® Web Processing Service (WPS) Interface Standard provides rules for standardizing how inputs and outputs (requests and responses) for geospatial processing services are defined. It defines an interface that facilitates the publishing of geospatial processes and clients’ discovery of and binding to those processes.","hasChildren":true,"name":"OGC interfaces and OGC web processing service","selfAssesment":"New
"},{"code":"IP5-3","description":"Online processing allows users to implement and run image analysis operations online independent of the underlying software.","hasChildren":true,"hasParent":true,"name":"Online processing","selfAssesment":"Planned
"},{"code":"IP5","description":"In general, infrastructures such as cyberinfrastructures or Spatial Data Infrastructures (SDIs), allow information sharing across distributed infrastructures and communities. SDIs have gradually changed from a pool of authoritative data shared using standardized web services to a pool where the authoritative data co-exist with data collected by volunteers and different sensors. Many efforts were dedicated to data documentation, to improving the catalogues searching techniques by means of, for example, thesauri and to sharing these data using standardized web services such as Web Map Service, Web Feature Service or Web Coverage Service. Cloud computing technologies played an important role in the implementation of sustainable SDIs due to their ability to provide on-demand computational and storage capacities over the Internet. In this way, users can easily search, find and use data shared across different online platforms.\r\nMore specifically, infrastructures for image processing and analysis refer to the physical and organizational facilities that allow the storage, analysis and management of the available data and products. Traditionally, this infrastructure formed a digital image processing system consisting of computer hardware with special-purpose image processing software, and peripheral input-output devices (e.g. CD or DVD drives, internet access, printers/plotters). In recent years, Earth observation is undergoing a shift to online processing making use of data cubes and vast image archives, e.g. NSF EarthCube or Digital Earth Australia, the Swiss Data Cube, the EarthServer, the E-sensing platform or the Google Earth Engine. Available infrastructures aim at sharing remote sensing data and derived products following the FAIR metrics: Findable (F), Accessible (A), Interoperable (I), Reusable (R). Thus, remote sensing data have to be documented using metadata that support FAIR data principles as follows: (1) Findable: remote sensing data are findable through data documentation, i.e. metadata, that needs to include a unique identifier of the described data. Metadata can be stored in a catalog compliant to one of the available data cataloging standards such as the SpatioTemporal Asset Catalog (STAC) compliant catalog; (2) Accessible: all data have to be openly accessible and shared using interoperable formats that allow users to find, access and reuse them; (3) Interoperable: different standards, e.g. STAC specification, have to be used to document remote sensing data; (4) Reusable: metadata have to be comprehensive enough to allow users not only to assess the fitness for purpose (e.g. lineage) but also to provide them information about how to access the generated data.","hasChildren":true,"hasParent":true,"name":"Infrastructure","selfAssesment":"Completed
"},{"code":"IP6","description":"In an information value chain, one or more organizations perform a set of value-adding activities for creating and distributing information products and services. They support a user in decision-making and thereby benefit the user’s purpose. The information value chain is a tool for evaluating business management and profitability. It enables explaining the ultimate “value” of a product and the components along the value chain and consequently allows businesses to optimize their processes. \r\nThe value of EO data can be assessed by analysing the contribution of the data to a specific EO information product and its effective use in decision-making. The (share of) benefit attributable to the use of the given EO data is derived from the comparison of a decision taken using the EO product to a counterfactual situation where other types of information are used instead. Often, this compares the situation before a new EO service was available to the situation afterwards. An ex-post analysis may reveal improved performances, e.g. gains in output, or productivity and/or reduced costs as compared to those occurring in absence of EO-derived information. This benefit resides with the user of the EO product and may be traced to societal and environmental benefits through impact chains.\r\nThe process of EO information production and distribution is integrated in the value chain and can be defined as the image processing chain. It comprises the value-adding activities of the organization(s) that lead up to the availability of an EO product for decision making. The nature and flow of these activities and the collaboration between organizations and among participants within organizations can be modelled with business process model and notation (BPMN). BPMN is a flowchart diagram that uses swimlanes representing different participants. Processes are assigned to participants and are connected with arrows into flow sequences. Further elements complete the choice of symbols for modelling a consistent flow, including a start event, end events, and branching options. They allow organizing the flow in parallel or iterative processes. Higher-level processes can be (de-)composed with sub-processes. Additionally, it is possible to use pools and message flows for explicitly modelling collaboration between participants (from different organizations).\r\nIn the image processing (value) chain, the sequence of processing steps begins with the acquisition of EO data, followed by steps of pre-processing and information extraction (or whatever steps are necessary) and ends with an EO information product being available to a user that uses it to make his decision. The collaborating stakeholders along the chain include EO satellite operators, EO data providers, EO information providers, and the users at the end of the value chain. The stakeholders along the processing chain each perform a dedicated subsequence of processing steps. Thereby, the stakeholders contribute their share of value to the data they deliver to the next stakeholder in the chain, ultimately arriving a the EO information product for the user. The EO data products that they hand on along the chain are often described with processing levels that provide different states of processing of EO data. They start with raw instrument data (level 0 and 1) that are followed by data converted into geophysical quantities that are geo-referenced and calibrated (level 2). Further levels are quality controlled data that has been mapped on a uniform space-time grid (level 3) and data combined with models or other instrument data (level 4). In addition, EO data providers use the term analysis ready data (ARD) that have been processed to allow direct data analysis, i.e. user processing effort is reduced to a minimum. Further, the standard EO products contain a categorizing element that is related to the image processing value chain. This categorizing element organizes the EO products along the sequences of processing, descriptive analytics, predictive analytics, prescriptive analytics, aggregation, visualization, and distribution. Thereby, the products ultimately contribute to the actionable EO information product for the use in decision-making.","hasChildren":true,"name":"Image processing (value) chain","selfAssesment":"Completed
"},{"code":"MDS","description":"MDS is a dimensionality reduction technique. It can be divided into Metric multidimensional scaling, Generalized multidimensional scaling and Classical multidimensional scaling.\r\n\r\nGeneralized multidimensional scaling is an extension of metric multidimensional scaling, in which the target space is an arbitrary smooth non-Euclidean space. In cases where the dissimilarities are distances on a surface and the target space is another surface, GMDS allows finding the minimum-distortion embedding of one surface into another.\r\n\r\nClassical multidimensional scaling is also known as Principal Coordinates Analysis, Torgerson Scaling or Torgerson Gower scaling. It takes an input matrix giving dissimilarities between pairs of items and outputs a coordinate matrix whose configuration minimizes a loss function called strain.","hasChildren":true,"name":"Multidimensional scaling","selfAssesment":"Depricated (GI-N2K)
"},{"code":"no","description":"Models that describe the basic principles of randomness and probability in spatio-temporal data.","hasChildren":true,"name":"Mathematical models of uncertainty: Probability and statistics","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI","description":"This knowledge area considers the organizational and institutional aspects related to GIS&T. The focus of this knowledge area is on the organizations active in the GIS&T domain, and what happens within and between these organizations. The knowledge area is structured around five units. One unit considers the key organizations in the GIS&T domain, covering relevant public sector organizations at different administrative levels as well as organizations in other sectors of society. Among the organizational aspects covered in this knowledge area are all organizational issues related to the implementation, use and management of GI and GIS within organizations. While all topics related to the organizational structures, procedures and management of GI(S) are grouped into one unit, another unit focuses on issues related to the human factor of using GI and GIS, i.e. people, their skills and competencies, and the development and evaluation of these skills and competencies in the context of GIS&T training and education. The knowledge area includes also several inter-organizational and institutional aspects of GIS&T. Particular attention is paid to the concept of geospatial data sharing, which is about the creation of `spatial data` connections and relationships between different organizations in the GIS&T domain. Spatial data infrastructures are developed to promote, facilitate and coordinate the sharing of spatial data among data providers and data users, and consists of several technological and non-technological components. Many related topics are considered in the knowledge area GI and Society (WS), which also addresses several non-technological aspects related to GIS&T. In addition to this, also the knowledge areas `Design and Setup of Geographic Information Systems`, `Geospatial Data\" and Web-based GI` include several topics that are closely linked to the topics that are considered in this knowledge area. It can be argued that in order to fully master the knowledge and competencies that are presented in these knowledge areas, also basic knowledge and understanding of the organizational and institutional aspects is required.","hasChildren":true,"hasParent":true,"name":"Organizational and Institutional Aspects","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI1-1","description":"The development of an appropriate organizational model, which establishes the basic character of GIS operations, is a crucial element of the GIS management. The appropriate GIS organizational model for any organization is based on its intended role.Alternative GIS organizational models are based on differing arrangements concerning the scope of GIS, the degree of integration of GIS into business operations, the degree of centralization of GIS operation and use, and the degree of centralization of management control. Although many variations can arise from different combinations of these factors, GIS organizational models can generally be classified into three types: (1) enterprise GIS, (2) GIS data and service resource, and (3) GIS as a business tool (Somers, 1998).","hasChildren":true,"name":"Organizational models for GIS management","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI1-2","description":"Management of GIS can be done in a more centralized or more decentralized manner. In a a so-called enterprise or information-framework GIS, an organizational unit may be established to manage the GIS environment and run the core system, whereas usage is decentralized. In environments where GIS is used occasionally by various users, it may be set up as a separate service with a designated group that manages the GIS and also controls users' applications services. A second decision that needs to be made after the choice between more centralized or more decentralized management of GI and GIS is about where to place the GI management. Alternative options are in a line organization, in a support area, or at the executive level, each with their own advantages and disadvantages.","hasChildren":true,"name":"Managing GIS operations and infrastructure","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI1-3","description":"User roles describe the relationship between different users and the GIS in an organization. Each user role includes responsibilities (e.g. for modifying certain information) and privileges (e.g. for viewing specific information). Although many different roles can be defined, a basic distinction is made between users, who can only view certain information, and editors, who can edit certain information.","hasChildren":true,"name":"User roles","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI1-4","description":"A GIS management strategy should be unique for each organization, as organizations have unique environments, characteristics, goals, GIS requirements. An important step in developing an effective strategy for an organization is to establish the strategic vision for GI and GIS in the organization and define its role and scope. Other elements that should be covered in the GIS Strategy are the degree of centralized management of the GIS, the placement of GIS management and support in the organization, involvement of users in GIS planning and implementation, coordination of users, organizational changes, preparation of users, personnel issues, transitions to GIS operations, integration into business operations, user support, data access, and integration of technology changes (Somers, 1998).","hasChildren":true,"name":"Strategic planning","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI1-5","description":"Committee and team approaches are frequently employed for coordinating participants and users in multi-participant GIS projects. The aim of creating such committees and teams is to ensure that the varied interests of participants are addressed, as participants bring many different interests, application needs, data needs, priorities, organizational issues, and political interests to a common project the GIS. Common models for coordinating participants recognize that participants have three levels of interest in the GIS: policy, technical development, and usage. Different bodies can be established focusing on these different levels of interest: a technical committee focusing on the design and development of the GIS, an management committee providing policy guidance and support and a user`s group.","hasChildren":true,"name":"Coordinating GIS Participants and Users","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI1-6","description":"After the development and implementation of a GIS within an organization, the challenge is to maintain the system and revise and update it when necessary. This means the performance of the GIS in terms of efficiency and effectiveness should be measured and monitoring, and feedback from users on the system and applications, on the data as well as on new needs should be collected. Particular attention should be paid to the maintenance of data sets.","hasChildren":true,"name":"Ongoing GIS revision","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI1-7","description":"The introduction of GIS into organizational environments should be seen as a complex process of mutual adaptation (Nedovic-Budic, 1997). These technologies changes the established organisational processes and structures, while on the other hand the organisational context and culture modify the technological set-up and use. Therefore, knowledge and understanding of the relationship between technologies and organizations is necessary to increase the success of GIS implementations in organizations. Successful GIS implementation and adoption often require some degree of organizational change. However, this can be very difficult to effect because organizations are naturally resistant to it (Somers, 1998).","hasChildren":true,"name":"Organizational changes","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI1","description":"GIS and T implementation and use within an organization often involves a variety of participants, stakeholders, users and applications. Organizational structures and procedures address methods for developing, managing, and coordinating these multi-participant users. The development of the appropriate organizational model for managing the GIS is crucial. In certain cases, changes to the organizational structure in place might be required. Strategic planning and the establishment of coordination structures can be considered as valuable instruments for managing and coordinating all involved users, while also the different user roles need to be assigned.","hasChildren":true,"hasParent":true,"name":"Organizational structures, procedures and management","selfAssesment":"In Progress GI-N2K
"},{"code":"OI2-1","description":"GIS and T professionals can be hired for a wide range of different job positions, for which the precise skills, competences and qualifications needed will vary. Typical examples of GIS and T positions are GIS&T project managers, technicians, system developers and analyst. The recognition and certification of the competences people have acquired in informal and non-formal learning contexts is important to know which skills and competences individuals have and whether they meet the qualifications required for a certain job position.","hasChildren":true,"name":"GIS and T positions and qualifications","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI2-2","description":"Making sure staff members have the necessary skills and competences to perform geospatial activities is necessary for an effective implementation and operation of GI within an organizations. Several training methods can be adopted to ensure the development of skills and competencies of staff members. A distinction can be made between formal and informal training, but also between internal and external training programs. Another relevant issue is the assessment and evaluation of the skills and competences of staff members, to determine their future training and development needs.","hasChildren":true,"name":"GIS and T staff development and evaluation","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI2-3","description":"Programs and courses on GIS and T and related subjects are provided by a wide range of institutions. While in recent years also the use and integration of GI and GIS in primary and secondary education has received significant attention, GIS and T education is mainly organized by institutions of higher education, especially universities but also other higher education institutions. Analyses of the higher education GIS&T programs and courses in Europe showed that the offer of courses is very diverse, in terms of size (ECTS), educational level (EQF) and course content. Vocational training on GIS and T related topics is organized by different types of training providers, including the major GIS vendors, data and service providers, academic sector, professional organisations, but also the public sector.","hasChildren":true,"name":"GIS and T training and education","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI2-4","description":"A curriculum is a systematic description of a study program, in terms of learning goals, structure and sequence, learning, teaching and assessment strategies and content. A curriculum consists of both a set of related required and elective - courses along with all direct and indirect skills, competences and learning outcomes resulting from these courses. In the process of curriculum design typically particular attention is assigned to objectives, teaching methods and educational strategies, while also attention should be paid to the content organization aspects and the global structure of the curriculum. The process of designing GIS&T curricula presents many challenges, as the design of the curriculum should be aligned to both the institutional context and the expected outcomes of the learning and teaching process (Prager, 2011).","hasChildren":true,"name":"GIS and T curriculum and course design","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI2-5","description":"An important challenge in organizing GIS and T education and training is the choice and use of effective teaching and learning methods. These methods should follow recent technological developments and use the best technologies to help students acquire the necessary skills and competencies. Traditionally, most GIS and T programs and courses were taught in the context of a full-time, face-to-face setting, using traditional teaching methods such as lectures and lab-based computer practical sessions. In recent years, educational institutions and their teachers have been experimenting with more innovative teaching and learning methods, such as project-based and case-based learning, distance learning, integrated and inter-disciplinary lessons, collaboration with companies and other stakeholders, etc.","hasChildren":true,"name":"GIS and T teaching and learning methods","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI2","description":"This unit addresses GIS and T staff and workforce issues within an organization, particularly as they relate to ensuring that GIS and T is appropriately used and supported. The focus of this unit is on the skills and competencies of professionals in the GIS and T domain: how can these skills and competencies be described and evaluated, and how can they be developed through training and education.","hasChildren":true,"hasParent":true,"name":"GIS and T workforce themes","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI3-1","description":"Cost savings are an important driver or motivation for sharing geospatial data and information. As costs associated with collecting and maintaining geospatial data are high, sharing data means that users no longer need to duplicate data gathering and archiving, which leads to savings in terms of personnel, space/facilities, data acquisition and maintenance costs. One fundamental argument for sharing thus derives from scale economies in production. Because the cost of making data is high, there is a clear incentive to maximize the number of users of these data. Sharing allows data to be used repeatedly for many purposes, thus increasing their value without increasing their cost. Sharing data also leads to improved data quality. Moreover, in many cases, sharing data is the only way to get access to certain data sets, as the authority to collect and manage certain data lies with another public institution.","hasChildren":true,"name":"Drivers and incentives for sharing geospatial data","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI3-2","description":"Sharing of geospatial data can be hindered or inhibited by several types of barriers. These include technological barriers, such as a lack of common data definitions, formats and models or incompatibility of hardware and software. Among the non-technological barriers are organizational, political and legal issues and elements, such as misaligned organizational missions, diversity in organizational cultures, conflicting organizational priorities, lack of funding, lack of executive and legislative support; restrictive laws and regulations, copyright issues, data privacy and data ownership issues. However, it should be noticed that many of these barriers have been decreased or eliminated in recent years.","hasChildren":true,"name":"Barriers to geospatial information sharing","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI3-3","description":"The legal framework for geospatial data sharing is very wide and diverse, involving rules on data, coordination, standards, funding, etc. Moreover, these rules and regulations can take many different forms: legal acts adopted by parliament, executive orders or decisions, cooperation agreements, memoranda of understanding, bilateral arrangements etc. From a data perspective, the legal framework can be distinguished into two main types of policies: those that promote and those that hinder the availability of spatial data. Policies that promote spatial data availability can focus on different types of users (public bodies, private companies, citizens) and different types of use (public access, commercial and non-commercial reuse, reuse for performing public tasks). Among the policies that hinder the availability of spatial data are those dealing with privacy, liability, and intellectual property. The legal framework also includes legislation that applies to data or information in general, such as open data legislation, which may also be applicable to spatial data (e.g. legislation on freedom of information, copyright, etc.). Moreover, also general legislation relating to any interaction between people or any situation in everyday life (e.g. liability, contract law, competition law, etc.) will apply to spatial data sharing.","hasChildren":true,"name":"Legal framework for geospatial data sharing","selfAssesment":"Completed
"},{"code":"OI3-4","description":"Several types of legal mechanisms for sharing geospatial data can be used. A data sharing arrangements can be formalized by a contract or agreement between the data provider and the data user. A particular type of agreement are the framework agreements, which are agreements between two or more organisations concluded prior to the datasets or services being required. These framework agreement can involve one or multiple spatial data sets or services. Partnership agreements are often used to formalize the data sharing agreements among a broader group of partners. Participation in such a partnership often means participants share their data with other participants and get access to shared data. Another relevant mechanism is the use of licenses, which are mechanisms to give organizations and people the permission to use spatial data sets and services. A license is legally binding, and defines the conditions of use of the related spatial data sets and services. In order to reduce the number of licenses used and ensure the harmonization of the terms in these licenses, the use of standard licenses is promoted. Also the use of open data licenses is promoted for sharing geospatial data, and strongly increased in recent years.","hasChildren":true,"name":"Legal instruments for sharing geospatial data","selfAssesment":"Completed
"},{"code":"OI3","description":"Geospatial data sharing has become an essential element of the GI activities of organizations. Spatial data sharing can be defined as the electronic transfer of spatial data/information between two or more organizational units where there is independence between the holder of the data and the prospective user. Spatial data sharing has many advantages, but several technical and non-technical barriers must be overcome to put data sharing into practice. While the practice of spatial data sharing has substantially grown with the development of spatial data infrastructures, many consider data sharing as a crucial element for the success of these infrastructures.","hasChildren":true,"hasParent":true,"name":"Geospatial data sharing","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI3b","description":"A Spatial Data Infrastructure can be defined as the collection of technological and non-technological components to facilitate and coordinate the exchange of and sharing of spatial data. The concept infrastructure is used to promote the concept of a reliable, supporting environment, analogous to a road or telecommunications network, that facilitates the access to spatial data. Data, metadata, access networks, standards, coordination, policies, funding, people and institutional frameworks are often considered among the key components of an SDI. \r\n\r\nSpatial data infrastructures often are defined and described as a complex and dynamic phenomenon. Among the main reasons for the complex character of these infrastructures are the many components a spatial data infrastructure consists of, the diversity of involved stakeholders, and the many different objectives and ambitions of these stakeholders. Technological advancements, such as the emergence of web 2.0 technologies, and societal changes, such as the increasing use of geographic information in everyday life, are often mentioned as important drivers behind the dynamic character of spatial data infrastructures. \r\n\r\nA key characteristic of spatial data infrastructures is the involvement of a large and diverse group of actors. Governments are often considered as the central actors in the development and implementation of spatial data infrastructure, since they are the major producers and users of geographic information. Governments at different administrative levels and in different thematic domains are involved in the creation, management, use and sharing of geographic data. But also private companies, non-profit organisations, research and education institutions and even citizens can participate in the development and implementation of a spatial data infrastructure. It is increasingly being argued that the involvement and engagement of each of these stakeholders group is essential to the realization of a successful spatial data infrastructure. \r\n\r\nSDIs have been developed in many countries worldwide at local, national and international levels. Often a distinction is made between a between the first generation SDIs that have data as their key driver and are based on a product model and second generation SDIs in which user needs are the key driver and that are based on a process or development model. The latest generations of SDI strongly focus on the inclusion and engagement of non-government actors and organizations in the development and implementation of the SDI. Although SDI are by default distributed systems, involving many organisations, some SDI might be developed rather in an hierarchical way, while others are following a networked approach.","hasChildren":true,"hasParent":true,"name":"Spatial Data Infrastructures","selfAssesment":"Completed
"},{"code":"OI4-1","description":"The adoption and implementation of standards are two key phases in the standardization process, which starts with the definition of standardization requirements and the development of standards. The adoption and implementation of standards follows after the development phase. The distinction made between the adoption and implementation of standards is important: adoption entails the decision to apply standards, while the implementation relates to the integration of standards in software, in data development and in other processes. GI-Standards are one of the key components of each SDI, consist of both semantic and technical standards, and include standards related to the different architectural components of an SDI, i.e. standards related to spatial data sets and data products, web services, metadata and catalogues, encodings, etc.","hasChildren":true,"name":"Adoption and implementation of standards","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI4-2","description":"The SDI policy framework includes the set of policies, strategies, initiatives and projects aimed at increasing access, sharing, and effective use of spatial data. SDI policies can be divided into strategic and more operational policies. Strategic policies define the broader framework and formal structure within which the SDI initiative is developed. Operational policies provide more practical tools to facilitate access to and use of the SDI, and address specific topics related to the collection, management, use, access and dissemination of spatial data. These operational policies include a broad range of guidelines, directives, procedures and manuals that apply to the day-to-day business of organizations in developing, operating and using an SDI. To guarantee the success of an SDI, it is important to recognize the wider policy context in which these SDI`s are developed, and to link them to the overall policy environment in the jurisdiction in which they are implemented. These include policies on open government and open data, environmental policies, digital government or e-government policies and other.","hasChildren":true,"name":"Policies","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI4-3","description":"If is often argued that SDI implementation requires coordination, because without coordination all other SDI components would not be developed or would be developed in a very fragmented and inconsistent manner. In general terms, coordination is about bringing into alignment the activities of different stakeholders in the SDI landscape. A typical instrument to realize coordinate in the context of SDI, is the establishment of an effective SDI coordination structure. The SDI coordination structure should ensure that all stakeholders are involved in the development and implementation of the SDI, through the participation in one or more coordination bodies. Another important element is the establishment of clear roles and responsibilities for the different involved organizations, making a distinction between data users, data providers, services providers and a geo-broker.","hasChildren":true,"name":"Coordination and organizational structure","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI4-5","description":"Funding an SDI is about guaranteeing the long-term financial security of an SDI, by obtaining and formalizing financing for the implementation and maintenance of the different SDI components. An SDI funding model provides the answer to the central question of where and how to seek funding for implementing and maintaining an SDI. Within an SDI often different funding models will be combined, as the selection of the most appropriate funding model will be linked to different activities and the associated costs. Costs of an SDI include both set-up costs (one off costs) and maintenance costs (yearly), of which certain costs need to be made for each data sets or each data provider and other costs for the infrastructure in general. The most commonly used SDI funding models are centralized government funding, decentralized government funding (e.g. for each data provider), partnership funding, funding through revenues, and government funding based on donor agencies or on European projects.\r\n\r\nThe shift towards open data and the adoption of open data policies had an important impact on the funding model of many SDIs, as governments and organizations no longer could rely on revenues from selling their data and had to look for other funding models. As a result, new pricing strategies are employed, such as the provision of fee-based supplementary services, such as advice or tailor-made products based on open data. Also freemium/premium models, in which a basic version of the dataset is offered as open data (freemium) but the full dataset is available for a fee (premium), were considered as an alternative approach. In many cases, the loss of revenues was compensated by other funding models, such as increased government funding.","hasChildren":true,"name":"Funding an SDI","selfAssesment":"Completed
"},{"code":"OI4-5b","description":"SDI performance assessment is about collecting, analyzing and providing information on the performance of SDI initiatives. Assessment and evaluations of SDIs are a useful tool for those organizations and people directly involved in these initiatives, but also for researchers, citizens, journalists and other stakeholders. Decision makers and practitioners can use assessments to monitor the progress against the objectives of their SDI initiatives and to identify areas where improvement can be achieved. Assessment also allows to compare and benchmark the performance of different organizations or countries, and to learn from best practices. Finally, assessment also is relevant for accountability, since it enables governments and agencies to be held accountable for their decisions, activities and the resources they have invested. Assessment of SDIs, which deals with the collection and supply of information on the performance of SDI initiatives, should be seen as the first step in a logical consequence of collecting data, integrating this data in policy and management cycles and actually using the information. \r\n\r\nIn the past twenty years, many different SDI assessment frameworks have been developed by researchers and practitioners around the world. Examples of such frameworks are the INSPIRE State of Play Study, the Clearinghouse Suitability Index, the Organisational Maturity Matrix, the SDI Readiness Index, and the INSPIRE Monitoring and Reporting approach. Each of these frameworks focus on particular aspects and components of SDIs. In line with the categorization of open data assessment, also SDI assessments can be divided into three main categories: (1) readiness assessments, (2) implementation or data assessments, and (3) impact assessments. Readiness assessments analyse whether conditions are appropriate, and whether necessary components are in place for developing an SDI. Implementation or Data assessments evaluate whether geospatial data are available and accessible. Impact assessments explore the extent to which SDIs lead to benefits for government, citizens, business and society in general.","hasChildren":true,"name":"SDI performance measurement and assessment","selfAssesment":"Completed
"},{"code":"OI4-6","description":"For a long time, SDI development has focused on the development and implementation of different components with the aim of facilitating the access to and sharing of spatial data. An key challenge in future SDI development will be the integration of these SDI`s in a wider context. In order to optimally take advantage of the data and services provided by an SDI, integrating these data and services into the processes and workflows of public and private organizations will be crucial. The concept of spatial enablement refers to the challenge of developing SDI`s in such a way that they provide an enabling platform that serves the wider needs of society in a transparent manner. Moreover, the diffusion of SDIs, together with the efforts to build a Global Earth Observation System of Systems (GEOSS) and other developments in industry and civil society should be considered as elements in a the realization of a vision on the next-generation Digital Earth.","hasChildren":true,"name":"Next-generation SDIs","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI4-7","description":"The effective implementation of SDIs requires governance, which includes the structures, policies, actors and institutions by which the infrastructure is managed pertaining to decisions made for accessing, sharing, exchanging and using the relevant available spatial information. While SDIs themselves are considered as initiatives contributing to good governance or effective governance, a key challenge in the establishment of SDIs is the governance of the infrastructure itself. Governance of SDIs is essential for the implementation of different SDI components in a coordinated and consistent manner. The central challenge of governance is reconciling collective and individual needs and interests of different stakeholders in order to achieve common goals. This aims to reduce gaps, duplications, contradictions and missed opportunities in the production, management, sharing and use of the information that tend to occur in a multi-stakeholder environment.\r\n\r\nGovernance can be facilitated through the use of appropriate instruments which extend to various levels of government and take into account the distribution of powers and responsibilities among different actors and institutions with an interest in the infrastructure. The governance instruments should coordinate the activities and contributions of, inter alia, data producers, users, added-value services providers, and other stakeholders. More complex and inclusive models of governance are required to cope with the multi-level nature of SDI implementations of the current generation of SDIs. Effective and inclusive SDI governance structures are needed, that are both understood and accepted by all stakeholders. Governance of SDIs also requires expanding the scope of stakeholders to include the private sector, research bodies and other actors outside the public sector including citizens, to actively promote bottom-up and participatory processes, and to find the appropriate mechanisms and instruments to enable the participation of these non-government actors.","hasChildren":true,"name":"SDI governance","selfAssesment":"Completed
"},{"code":"OI5-1","description":"Within the European Commission there are several key GI players. GIS activities in the Commission started since 1981 (e.g. DG REGIO, Eurostat, ) with the CORINE project, the creation of DG ENV and the creation of the European Environment Agency (EEA). Together with the DG Joint Research Centre (JRC), DG ENV and EEA are in charge of the coordination of INSPIRE: DG Environment acts as an overall legislative and policy co-ordinator for INSPIRE, the JRC acts as the overall technical co-ordinator of INSPIRE and EEA is in charge of several tasks related to monitoring and reporting, and data and service sharing under INSPIRE. Also several other EC institutions are actively involved in GI(S) policies and activities (DIGIT, DG GROW, DG AGRI, DG MOVE and many others).","hasChildren":true,"name":"GI organization at the European Commission","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI5-2","description":"Although there may be certain differences between countries, in most countries many key organizations in the GIS&T field will be active at the central/federal/national level of government. Especially the traditional institutions for surveying and mapping play a key role in geospatial policies and activities. Several public authorities at the federal level are in charge of the production and maintenance of key reference and thematic data sets. In many countries, these national data producers were the leading actors in the development of national spatial data infrastructures.","hasChildren":true,"name":"Federal and national government organizations","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI5-3","description":"Local and sub-national governments are often considered among the major users of geographic information in governments, as they often are involved in many different policy areas, in which many problems with a locational component need to be tackled. Geographic data produced and maintained by authorities at lower administrative levels are often more detailed and thus interesting for other users, both within and outside the public sector. As a result, local and sub-national governments are often involved in the establishment of these infrastructures because of the wide range of highly detailed geographic information they produce and manage. As many geographic data are linked to the activities and services of local organizations, the involvement of these organizations in the maintenance of data ensures that these data are up-to-date.","hasChildren":true,"name":"Sub-national and local governments","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI5-4","description":"The European GIS&T landscape consists of many pan-European organizations and associations promoting the interest of and representing certain stakeholder groups. While some of these organisations are dealing with all sectors and aspects of geographic information, others have a more thematic focus (e.g. remote sensing, topography, geosciences) or represent a particular sector (e.g. research, business). In some cases, their clearly is an overlap in the mission and objectives of different organizations, and some organizations are working in the same field of interest. Some examples of pan-European organizations and associations are AGILE, EuroSDR, EUROGI, and EuroGeographics. Also at international level several membership organizations and associations exist.","hasChildren":true,"name":"Pan-European and global associations and professional organizations","selfAssesment":"GI-N2K
"},{"code":"OI5-5","description":"The geospatial industry consists of companies working with location specific information or services. Within the geospatial sector, several areas of activities can be identified: 1) measuring, collecting and storing of data about geo-objects; 2) processing, editing, modelling, analyzing and managing that data; 3) presenting, producing and distributing the data; and 4) advising, educating, researching and communicating about processes and use of geo-information products and services. The sector consists of both small-and-medium-sized enterprises but also big companies, including surveyors, census hard-copy map providers, aerial photos providers, base map data providers, satellite and remote sensing imagery providers, software developers (GIS-related products and services providers as well as satellite image programming platform providers) and several others.","hasChildren":true,"name":"The geospatial industry","selfAssesment":"In progress (GI-N2K)
"},{"code":"OI5","description":"Several types of organizations play a key role in the execution and coordination of geospatial activities in society. Typically, a distinction is made between data providers and data users, while coordinating organizations exist to coordinate and support the geospatial activities of professionals and entities using GIS&T. Governments are often considered as the major users and producers of spatial data and spatial information. Within the public sector, spatial data are collected and used in different thematic areas and at different administrative levels (from local to global). However, the needs, interests, and capacities of organizations at each of these levels will be different, as well as their role in the development of spatial data infrastructures, and the execution of geospatial activities in general. Also the geospatial industry will exist of both data providers and data users, but also of organizations delivering products and services to support the collection and use of spatial data. Other key organization in the GI domain are professional organizations and associations, bringing together and representing the needs of organizations of a particular sector and/or geographic area.","hasChildren":true,"hasParent":true,"name":"Organizations in the GIS and T domain","selfAssesment":"In progress (GI-N2K)
"},{"code":"PP","description":"The knowledge of physical laws and principles regulating the emission of e.m. radiation and its interactions with the matter, as well the ones related to the design, setting-up and control of EO platforms and related instruments, are of paramount importance for a right interpretation of EO measurements in relation with the investigated Earth's phenomena and parameters. The most important physical fundaments regards: the theory of electromagnetic waves propagation described by the Maxwell's equations, the theory of e.m. radiation and of its interaction with the matter, the methods and instruments for e.m. radiation measurement and/or generation, the fundamentals of thermodynamics and of mechanics. As far as Earth Observation is concerned, further, specific topics have to be addressed which are related to: spectral-specific matter-radiation interactions, natural (e.g. Earth, Sun) and artificial (e.g. MW) sources of e.m. radiations, atmospheric physics and radiative transfer equations, basic physics of e.m., optical and MW, sensors and sources, theory of satellites orbits, theory of rockets, physical fundaments of interpretation of optical and MW data collected by passive and active techniques.","hasChildren":true,"hasParent":true,"name":"Physical principles","selfAssesment":"Completed
"},{"code":"PP1-1-1","description":"Electromagnetic radiation travels in wave form. All electromagnetic waves travel at the speed of 299.793 km/sec in a vacuum and very nearly the same speed in air. In quantum physics electromagnetic radiation is also described in terms of particles called photons whose energy is given by the equation E = hf where h is the Planck constant and f the frequency of corresponding wave. Electromagnetic wave propagation is fully described by the Maxwell Equations that unified in 1860s the laws of electricity and magnetism.","hasChildren":true,"name":"Electromagnetic Waves and Photons","selfAssesment":"Completed
"},{"code":"PP1-1-10","description":"The solar constant S is a quantity denoting the amount of total (i.e., covering the entire solar spectrum) solar energy reaching the top of the atmosphere. It is defined as the flux of solar energy (energy per unit time) across a surface of unit area normal to the solar beam at the mean distance between the sun and the earth. Solar insolation is defined as the flux of solar radiation per unit of horizontal area for a given locality. It depends primarily on the solar zenith angle and to some extent on the variable distance of the earth from the sun. It can be computed as a function of latitude and the time of year taking into account of the secular variations of Earth's orbit eccentricity e, the oblique angle ε, and the longitude of the perihelion relative to the vernal equinox ω. The daily insolation is the total solar energy received by a unit of area per one day. It may be calculated by integrating total insolation over the daylight hours. It is particularly important, together with information on cloud coverage, in order to plan and manage solar power systems. Yearly total insolation together with average cloud coverage are among the most important parameters to be considered for the choice of the best (i.e. the ones promising the higher energy production) location of solar power plants. Modeled daily solar insolation together with short/medium-term forecast of cloud coverage are also fundamental for the management (e.g. for planning the suspension of activities for maintenance) of solar energy production plants .","hasChildren":true,"name":"Solar constant, solar insolation, daily insolation","selfAssesment":"Completed
"},{"code":"PP1-1-11","description":"Earth's itself represents the second (after Sun) most powerfull natural source of e.m. radiation for EO. Even if very less powerfull than Sun such a source is available for EO day and nigth. Its average emittance can be approximated by that of a blackbody at about 290 K. The maximum of its emission, following the Wien's Law, falls then around 10 micron (in the Thermal InfraRed - TIR spectral range) being Earth's emission trascurable in the VIS-SWIR range.\r\nMost of Earth's thermally emitted radiation falls in the spectral range 8-14 microns where it benefits of a quite high atmospheric transmittance (TIR atmospheric spectral window) in standard atmospheric conditions. However thick clouds prevent TIR radiation to reach satellite sensors (adsorbing and/or reflecting backward the radiation leaving Earth's surface) so that ground resolution cells affected by clouds are usually identified (cloud-mask) in the image pre-processing phase and not considered for further elaboration devoted to investigate surface properties. Even if very low in intensity, Earth's emitted radiation in the Far InfraRed (FIR) and in the MicroWaves (MW) spectral ranges are also used for quite important investigation related to the Earth's Energy balance (FIR) and for meteo-climatological applications. The complete transparence of Earth's atmosphere to the MWs, even in presence of meteorological (not precipitating) clouds make this Earth's emitted signal particularly important for application (e.g. climatological) requiring temporal continuity (all weather) of observations of Earth's surface properties like Temperature, Soil wetness, etc.. However, due to the weakness of the Earth's emitted signal in the MW ranges, such products can be achievable just at quite low spatial resolution (e.g. > 10km) by passive EO MW sensors","hasChildren":true,"name":"Earth's radiation (intensity, spectrum, etc.)","selfAssesment":"Completed
"},{"code":"PP1-1-2","description":"In principle, the frequency f (and the wavelength λ=c/f) of an electromagnetic wave can take any value and the whole range of possible frequencies is called the electromagnetic spectrum. Different regions of the spectrum are conventionally given different names (with associated spectral ranges smoothly depending on specific science sector): \r\ngamma-rays\t λ< 1 pm\r\nx-rays\t1 nm >λ>1 pm\r\nUltraviolet (UV) 400 nm >λ>1 nm\r\nVisible (VIS) 700 nm >λ> 400 nm (blue: 455 – 492, green 492 – 577, yellow 577 – 597, red 622 – 700)\r\ninfrared (IR)\t1000μm >λ> 0,7 μm (Near-IR - NIR: 0,7-1,3; Short-Wave IR SWIR: 1,3-3; Medium IR - MIR: 3-6, Thermal IR - TIR: 6-20; Far IR - FIR: 20-1000)\r\nRadio waves\t λ> 1 mm (Microwaves MW\t1 m >λ> 1mm). Optical range (usually referring to the spectral range from VIS to TIR) and microwaves are the most important spectral region for remote EO systems.","hasChildren":true,"name":"Electromagnetic spectrum","selfAssesment":"Completed
"},{"code":"PP1-1-3","description":"Maxwell equations are a set of coupled partial differential equations that contains the fundamentals of electricity and magnetism. These equations provide electromagnetic waves that propagate into the space at the speed of the light. Increasing the wavelength there are gamma rays, X-rays, ultraviolet, (visible) light, infrared, microwaves and radio waves.","hasChildren":true,"name":"Maxwell Equations and EM waves' propagation","selfAssesment":"Planned
"},{"code":"PP1-1-4","description":"Planck's law is a mathematical relationship for the spectral radiance emitted by a blackbody (i.e. a body that absorbs all radiant energy falling on it) at a given temperature as a function of frequency or wavelength. From another point of view it can be used to define a black-body as a body emitting radiation following Planck's law. The model of black-body is fundamental to simplify the description of the radiation thermally emitted by a generic body at a pre-fixed temperature and wavelength as the product of its (specific) spectral emissivity and the value predicted (at the same wavelength) by the Planck's law for a black-body at the same temperature. This way the radiation thermally emitted by a generic body can be expressed just as a (specific, as modulated by the spectra emissivity) fraction of the one expected for a black-body. Wien’s displacement law is the relationship between the temperature of a blackbody and the wavelength at which it emits the most radiation. Wien found that the product of the peak wavelength and the temperature is an absolute constant. As far as the temperature T of the blackbody increase the intensity of the emitted e.m. radiation increases being, at whatever wavelength, grater than the one emitted by a blackbody at lower temperature (Planck). As far as the blackbody temperature increases its maximum emission occurs at lower and lower wavelengths. Wien's law is fundamental both in the selection of the spectral bands more appropriate for observing specific phenomena as well as for remotely retrieve temperature of far objects by the analysis of the emitted spectral radiances.","hasChildren":true,"name":"Planck law for the black body. Wien's displacement law","selfAssesment":"Completed
"},{"code":"PP1-1-5","description":"The Rayleigh–Jeans Law is an approximation of the Planck’s law for a blackbody that states that, under certain conditions, emitted radiance is directly proportional to the blackbody temperature. Such an approximation, fits quite well with measurements of radiation emitted by sources at around 300K of temperature (like, in average, for the Earth) at wavelengths higher than 1mm (microwaves).. Wien’s approximation can be used to describe the emission spectrum of a high temperature blackbody n the VIS-NIR spectral range lengths. The estimated errors is less than 2% at wavlengths less that 5microns when a blackbody at around 6000K (like the Sun photosphere) is considered. \r\nThe Rayleigh–Jeans approximation is widely used in the processing of satellite images collected by passive MW sensors. Its extension to the thermal infrared spectral range (TIR) is also used for calibrating TIR satellite images (in this case linearity can be guaranteed just by steps on different brigthness temperature intervals).","hasChildren":true,"name":"Rayleigh-Jeans approximation. Wien's approximation","selfAssesment":"Completed
"},{"code":"PP1-1-6","description":"The total radiant intensity B(T ) of a blackbody at the absolute temperature T can be derived by integrating the Planck function over the entire wavelength domain from 0 to∞. Since blackbody radiation is isotropic, the flux density emitted by a blackbody is therefore F = π B(T ) which is proportional to the fourth power of the absolute temperature T through the Stefan-Boltzmann constant σ = 5.67 × 10−8 J m−2 sec−1 deg−4.\r\nKirchoff's law establishes that for a medium at the thermodynamic equilibrium, the spectral emissivity ε(λ) at a given wavelength λ, is equal to the its spectral absorbance, A(λ) at the same wavelength λ. Hence ε(λ)=A(λ) at each fixed λ, for a blackbody ε(λ)=A(λ)=1 at whatever λ. Kirchoff's law is valid also in Local Thermodynamic Equilibrium (LTE) conditions as the ones usually occurring in (small volumes of) the Earth's atmosphere even in the most turbulent conditions.\r\nKirchoff's law has important applications also for the study of spectral signatures of mineral and rocks and, in general, of opaque - i.e. with spectral transmittance T(λ)=0 - bodies. In that case, the relation which relate the spectral reflectance R(λ), absorbance A(λ) and transmittance T(λ) of a body: R(λ)+A(λ)+T(λ) =1\r\nreduce to R(λ)+A(λ)=1 and in LTE conditions, thanks to the Kirchoff's law: \r\nR(λ)+ε(λ)=1 which allows to obtain measurements of spectral emissivity indirectly through (more simple and stable) measurements of spectral reflectance:\r\nε(λ)=1-R(λ)\r\nRocks and mineral exhibit important (diagnostic/discriminating) signatures in their spectral emissivity in the thermal infrared (TIR) region. Measuring spectral emissivity in a laboratory (particularly if samples have to be characterized for their properties in natural conditions) is a quite difficult task due to the difficulty to insolate the sample from the lab environment (and instruments themselves) all emitting approximately at the same (environmental) temperature. Kirchoff's law allows to obtain, for opaque bodies, spectral emissivities from spectral reflectances measurements which are much easy to realize in normal remote sensing labs.","hasChildren":true,"name":"Stefan–Boltzmann law. Kirchoff law","selfAssesment":"Completed
"},{"code":"PP1-1-7","description":"All bodies at a temperature T>0 K emit electromagnetic radiation at all wavelengths (thermal emission). Such emission at each wavelength is increasing with T and it is maximum for Black Bodies whose spectral emittance I(λ,T) (at each prefixed T and wavelength λ) is defined by the Planck function B(λ,T). Generic bodies are expected to thermally emit less than a black body (having the same temperature T) at whatever wavelength. Spectral emissivity ε(λ) is defined as the ratio of the spectral radiance I(λ,T) emitted by a generic body and the one emitted by a Black Body at the same temperature, i.e. ε(λ)= I(λ,T) / B(λ,T). By definition its value is less or equal (Black Body) than 1. The spectral emissivity concept allows to describe in a simple way the spectral radiance I(λ,T) thermally emitted by a body at a temperature T by I(λ,T)= ε(λ)*B(λ,T). It is possible to invert the Planck Function to obtain from the emitted radiance at a prefixed wavelength the temperature T=f(B, λ) of the emitting Black Body. If in such expression the spectral radiance I emitted by a generic body is used instead than B, the resulting temperature, Tb=f(I, λ), is named Brigthness Temperature being Tb<=T (with Tb=T in case the emitting body is a Black Body). The concept of Brigthness Temperature is substantially a different way to measure the spectral radiance of a generic body. It is usually preferred (for instance calibrating Thermal InfraRed – TIR – satellite images) because the interpretation of such a digital image is much more intuitive than when spectral radiances are used instead. In fact, as at each prefixed temperature generic bodies are less emitting than Black Bodies, wherever across a digital satellite image we consider the values of reported Tb, we can say that the actual temperature T of the corresponding emitting ground resolution cell is not less than Tb.","hasChildren":true,"name":"Concepts of Spectral Emissivity and Brightness Temperature.","selfAssesment":"Completed
"},{"code":"PP1-1-9","description":"Sun represents the most powerful natural source of e.m. radiation for EO. The main source of its radiation is the nuclear fusion of Hydrogen into Helium which occurs in central part (“Core”) of the Sun. Outside, the energy transfer is dominated by radiative process (“Radiative zone”) then by convection (”Convective zone”). Solar radiation at the Top of the Earth Atmosphere comes from the outer layer of the sun, the photosphere, whose estimated (conventional) temperature is 6000-6300 K. Its emittance can be approximated by that of a blackbody at about 6000 K but just its reflected component (SOR) is actually available (and just during daytime) for EO. The maximum of SOR falls in the visible spectral range. Its contribution in the thermal infrared range is neglectable but in the medium infrared SOR is still significant enough and, in daytime, superimposed to Earth's thermal emission. The high intensity of solar refelcted radiation (SOR) coupled with the high atmospheric transmittance in the VIS/NIR range, guarantee the highest signal-to-noise ratio for sensors operating in that spectral range. This huge amount of available signal, together with the development of advanced micro-sensor technology (started with the Charged Coupled Devices - CCD etc.), explains why the EO passive sensors with the highest spatial and/or spectral resolution presently achievable, are operating in the VIS/NIR range.\r\nachievable by operating in this spectral region.","hasChildren":true,"name":"Solar radiation at the Top of the Atmosphere. Solar spectrum","selfAssesment":"Completed
"},{"code":"PP1-1","description":"EM radiation is created when an electrically charge particle, such as an electron, is accelerated by a force causing it to move. The movement produces oscillating electric and magnetic fields which travel, as an harmonic EM wave, at right angles to each other. EM waves travel at 299,792,458 meters per second in a vacuum (the highest possible speed into the Universe, also known as the speed of light). \r\nThe electromagnetic field propagating through the space as EM waves is also referred as electromagnetic radiation. \r\nAn EM wave is characterized by a frequency (or by a wavelength) and by an amplitude (or by an energy). \r\nThe wavelength is the distance between two consecutive peaks of a wave. This distance is given in meters (m) or fractions thereof. Frequency is the number of waves that form in a given length of time. It is usually measured as the number of wave cycles per second, or Hertz (Hz). It is wave speed=frequency*wavelength so that, an EM wave traveling at the speed of light, can be equally identified by its wavelength or by its frequency. The amplitude (i.e. the maximum oscillation of the EM field) provide the intensity (i.e. the energy) of the EM wave. \r\nThe classical theory describes the EM radiation as electromagnetic waves which represent the oscillations of electric and magnetic fields. In the quantum mechanics theory EM radiation consists of photons, quanta of the electromagnetic energy, responsible for all electromagnetic interactions.\r\nAs far as Earth remote sensing is concerned EM radiation represents the most important vehicle of information.","hasChildren":true,"hasParent":true,"name":"EM radiation","selfAssesment":"Completed
"},{"code":"PP1-2-1","description":"The study of the absorbption/emission of electromagnetic radiation by atoms. Depending on the atomic number characteristic frequency or wavelength are absorbed or emitted. Since each element has a characteristic spectrum of absorbed/emitted wavelengths (spectral signature), atomic spectroscopy allows the determination of elemental compositions even of remote objects (e.g. stars, galaxies, etc.).\r\nStarting from the simple Bohr’s model it is possible to predict quite exactly the frequencies of e.m. radiation selectively absorbed/emitted by all atoms. Depending on the atomic number Z, characteristic frequencies f are absorbed or emitted by atoms corresponding to the electronic transitions from different energetic (quantized) states following the Bohr’s condition: fab=(Eb- Ea)/h, being Ei=-cost∙Z2/(ni)2 the electron energy corresponding to the state/level i (principal quantic number ni). By this way each atomic species has a characteristic spectrum of absorbed/emitted frequencies (atomic spectral signature) so that atomic spectroscopy allows the determination of elemental compositions even of remote objects. By this way the existence of Helium was discovered in the 1968 by Jansen and Lockyer in the Sun photosphere well before its discover on the Earth, and the knowledge of the chemical composition of stars and galaxies was possible well before the end of XIX century. Atomic spectroscopy provides a simple and powerful introduction (through the explanation of the more complex interactions of e.m. radiation with molecules and solid matter) to the fundamental concepts of spectral signature (which is at the base of most of the applications of aerial remote sensing of the Earth’s surface) and atmospheric windows (important for the design of optical sensors devoted to remotely sense Earth’s surface) being moreover propaedeutic to the understanding of methods for the atmospheric vertical sounding based on the concepts spectral lines broadening and related weighting functions.","hasChildren":true,"name":"Atomic spectroscopy","selfAssesment":"Completed
"},{"code":"PP1-2-10","description":"The Rayleigh roughness criterion is a widely used means to estimate the degree of roughness of a considered surface. Considering the phase difference between two rays scattered from separate points of the surface, this is proportional to the roughness ∆h (average deviation from the average surface height ) the cosine of the incident angle and, inversely, on the radiation wavelength (λ). The Rayleight criterion states that a surface can be considered as smooth (mostly reflecting) if the phase difference is less than π/2 radians.\r\nAs a consequence, in the case of normal incidence (i.e. θ=0), average roughness of the surface must be less than λ/8 to have an effectively smooth surface. For instance: i) at optical wavelengths (e.g. 0.5 micrometers), surface roughness ∆h must be less than about 60 nm to have a specular reflection. Only certain man-made surfaces (e.g. sheets of glass or metal) may meet such a condition; ii) at VHF radio wavelengths (e.g. 3 m), roughness height need only to be less than about 40 cm. Unlike the previous case, a number of natural surfaces may meet this condition.\r\nIt is worth noting that large values of the incident angle may satisfy the criterion more easily as compared with the normal incidence. This means that a moderately rough surface may be effectively smooth at glancing incidence. This condition may be easily experienced when eyes are struck by the glare of reflected sunlight from a low sun over an ordinary road surface. More strict conditions for classifying a surface as a mirror or a diffuser at an established whavelength λ are: ∆hcosθ/λ > 1/8 for a rough surface operating as a diffuser; ∆hcosθ/λ < 1/25 for a smooth surface operating as a mirror.","hasChildren":true,"name":"The Rayleigh roughness criterion","selfAssesment":"Completed
"},{"code":"PP1-2-11","description":"The Bidirectional Reflectance Distribution Function (BRDF) is defined as the quotient between the spectral radiance Ir(θr,φr) reflected by a sample in a particular direction (θr,φr) and the spectral irradiance F(θi,φi) from the source that illuminates it under a direction (θi,φi) . It depends on both the incidence and viewing angles. From this point of view it represents an absolute definition of reflectance whose value, as is known, depends on the geometry of the illumination and observations directions. This function well describes variability in surface anisotropy, its shape and magnitude is determined by the structure of the sample element and its optical attributes.\r\n\r\nThe BRDF is given by \r\n\r\nBRDF(θi,φi; θr,φr; λ)=(Ir(θr,φr))/(F(θi,φi))\r\n\r\nwhere Ir is the surface leaving spectral radiance and F is the spectral irradiance , θ and φ are zenithal and azimuthal angles respectively of the direction (view angles) of reflected radiance Ir(θr,φr) and of incident irradiance F(θi,φi), λ is the wavelength.","hasChildren":true,"name":"Bidirectional Reflectance Distribution Function (BRDF)","selfAssesment":"Completed
"},{"code":"PP1-2-12","description":"Measurements of BRDF allow to compare spectral signatures obtained in different laboratories in an optimal way. However its measure require well calibrated sources and quite expensive laboratory equipments. The concept of BRF (Bidirectional Reflectance Factor) allows a more simple, indirect, measurement of BRDF by using a reference sample (highly reflective so usually named \"white reference WR\") of known BRDF and two subsequent measurements of reflected radiance (one from the WR, one from the sample) obtained under identical illumination conditions. In these conditions results BRDF(sample)=BRF(sample)xBRDF(WR)","hasChildren":true,"name":"Bidirectional Reflectance Factor (BRF)","selfAssesment":"Planned
"},{"code":"PP1-2-2","description":"The absorption of e.m. radiation by molecules, in different physical states, can be attributed to specific (quantized) changes in their electronic and/or vibrational and/or rotational energy. Subsequent quantized molecular vibrational energy levels are equidistant so that all vibrational transitions occur, for each molecule, by the emission/absorption of radiation at a specific wavelength. Depending on the specific amount of energy required to modify the status of electrons within the atoms composing the molecules, as well as the one required to modify the molecule's vibrational and rotational energy, different wavelengths can be adsorbed. As in the case of atomic spectra which are fully determined by the electronic energy level structure depending on the atomic number, rotational and vibrational energy levels of molecules depends on specific characteristics (number, masses, distances, inertia momentum, elastic constant, etc.) of the atoms composing the molecule itself which make specific and characteristic for each molecule associated absorption spectra. In the Earth's atmosphere the effect of atomic/molecular absorption is significant at wavelength between 1nm and about 1cm. Considering the optical and microwave spectral ranges used in Earth's remote sensing from space it should be noted that:\r\na) Visible, Near Infrared and Short wave IR radiation (400-3000 nm) is adsorbed mostly for electronic transitions within atoms. In the SWIR region (after 1000nm) forbidden vibrational absorption lines can be observed (overtones and related combinations). \r\nb) e.m. radiation in the Medium and Thermal IR (up to 100.000 nm) spectral range are mostly adsorbed for operating vibrational energy transitions in H2O, CO2 and O3 molecules\r\nc) e.m. radiation in the Far IR up to the Microwave's spectral range (0,035-1 mm) is mostly adsorbed for operating rotational transitions in water vapur molecules. As, in principle, such electronic, vibrational and rotational transitions can contemporary occur (and usually occur considering the collective effect of the enormous number of molecules that can be present even in a small volume of terrestrial atmosphere) molecular spectra results in a complex composition of absorption lines (bands).","hasChildren":true,"name":"Molecular absorption spectra","selfAssesment":"Completed
"},{"code":"PP1-2-3","description":"In spectroscopy an absorbed (emitted) line is observed in correspondence to the transition from a lower (higher) to a higher (lower) energetic level within an atom (electronic transitions) or a molecule (electronic, vibrational, rotational transitions). Its characteristic frequency f is related to the amount of the energetic jump from an initial state E(1) to a final one E(2) through the Bohr's relation E(f) − E (i) = hf. As the distribution of the quantized energetic level are specific of each atom (depending on its atomic number N) and molecule (depending on their constituents atoms N, number and dispositions which determine their specific inertia momentum and vibrational properties) even the corresponding atomic and molecular spectra (i.e. the frequencies of the sequences of spectral lines/bands) are specific for each chemical atomic or molecular species. However monochromatic emission just at the frequency f is practically never observed. Always e.m. radiation emitted/adsorbed by atoms or molecules is observed also around the nominal (expected following Bohr's relation) frequency f mostly as a consequence of the following effect: a) changes of quantized energy levels associated to the process of emission/absorption itself: the consequent line broadening around the frequency f is reported as \"natural broadening\"; b) changes of quantized energy levels due to reciprocal collisions between atoms and molecules (\"pressure broadening\"); c) the change of the observed f due to the Doppler effect associated to the fact that emitting(adsorbing atoms or molecules are moving toward or far away with different (thermal) velocities (\"Doppler broadening\"). The natural broadening is practically negligible as compared to that caused by collisions and the Doppler effect. In the upper atmosphere, due to its temperature and pressure, we find a combination of collision and Doppler broadenings, whereas in the lower atmosphere, below about 20 km, collision broadening prevails because of the pressure effect. As far we move far from the central (expected) frequency f as much the contribution of Doppler effect can be neglected compared with the pressure broadening. This fact has important consequences on the possibility to retrieve vertical properties of the atmosphere (vertical sounding) like temperature and concentration of its chemical constituents, exploiting satellite based observations made \"off-line\" (i.e. at frequencies around but different from f) which relate investigated atmospheric levels as much higher as much far from f are the considered frequencies.","hasChildren":true,"hasParent":true,"name":"Line shape and (natural, pressure, Doppler) broadening","selfAssesment":"Completed
"},{"code":"PP1-2-4","description":"Voigt's line profile refers to the shape of a spectral line resulting from the \"pressure\" and Doppler broadening. Pressure broadening is much more important in atmosphere as far as pressure increases (heigths lower than 20 km) . Observing Earth's atmosphere in a spectral region sufficiently far from the central (unperturbed/monochromatic) absorption spectral line (off-line bands), Doppler broadening can be neglected in comparison with the pressure one. More and more off-line are the chosen spectral bands, more and more lower in atmosphere will be the atmospheric layers mostly contributing to the measured spectral radiances. \r\nSuch a relation is at the base of the inversion methods for atmospheric vertical sounding based on multi-spectral satellite observations.","hasChildren":true,"name":"Voigt's line profile","selfAssesment":"Completed
"},{"code":"PP1-2-5","description":"Radiation that is not absorbed or scattered in the atmosphere can reach and interact with the Earth's surface. There are three (3) forms of interaction that can take place when e.m. radiation strikes, or is incident (I) upon a surface. These are: absorption, transmission, and reflection. The total incident radiation will interact with the surface in one or more of these three ways. The proportions of each will depend on the wavelength of the incident radiation and the specific chemical/physical properties of the surface material. Absorption occurs when incident radiation is absorbed into the target, while transmission occurs when radiation passes through a target. Reflection occurs when radiation \"bounces\" off the target and is redirected. The spectral reflectance is defined by the ratio of reflected radiance to incident radiance at a prefixed wavelegth . The spectral transmittance of a medium is defined by the ratio of the transmitted radiance to the incident one at a prefixed wavelegth . The absorbance of a medium or target is defined by the ratio of the absorbed radiance to the incident one at a prefixed wavelegth . Conservation of energy require that, at a certain wavelenght: R+T+A=1. To express the circumstance that the reflection can occurre in different direction as the surface deviates from a specular one, becoming rough, the concept of surface scattering has been introduced (ref. [PP1-2-10] The Rayleigh roughness criterion).","hasChildren":true,"name":"Concepts of Transmittance, Absorbance, Reflectance, Scattering.","selfAssesment":"Completed
"},{"code":"PP1-2-6","description":"The emitting capability of a body surface is described by the spectral emissivity, ε(λ), a dimensionless value ranging between 0 and 1 and varying on the basis of the wavelength (λ) and the geometric configuration of the surface. Formally, spectral emissivity can be defined as the ratio of spectral exitance, M(λ,T), from an object at wavelength λ and temperature T, to that from a blackbody at the same wavelength and temperature, MBB(λ,T).\r\nA blackbody is an ideal radiator that totally absorbs and then reemits all energy incident upon it. By definition the spectral emissivity of a blackbody is equal to one (the maximum) at whatever wavelength and temperature. A blackbody radiates a continuous spectrum. Real materials do not behave like a blackbody. Natural matter could radiates more in selected spectral region (like in the case of atomic or molecular gases) more frequently with a continuous spectrum (like in the case of solids) always with spectral emissivity minor or equal to 1. \r\nAnother important concept is the one related to the graybody. For gray bodies, the spectral emissivity value is constant for each wavelength value, as for black bodies, but is always less than 1. Therefore, for any given wavelength the emitted energy of a graybody is a fraction of that of a blackbody. This behavior could be quite important even for limited spectral ranges. For instance the spectral emissivity of the sea in the TIR (Thermal InfraRed) spectral range 8-14 microns (TIR atmospheric window) can be assumed constant (about 0,98) with significant simplifications in the determination of SST (Sea Surface Temperature) from satellite sensors operating in that spectral region. \r\nAs said above, the emissivity of the most of the bodies present in nature varies depending on the wavelength. These objects are referred to as selective radiators or as being selectively radiant. This means that some materials may behave as black bodies at certain wavelengths (ε close to 1) and may have reduced emissivity at other wavelengths.","hasChildren":true,"name":"Concepts of Spectral Emissivity","selfAssesment":"Completed
"},{"code":"PP1-2-7","description":"Dielectric constants and refractive indices of the matter are generally complex quantities. Considering an electromagnetic wave entering a homogeneous medium of complex refractive index n=m+ik, it is possible to demonstrate that its intensity progressively decays depending on its wavelength λ and on the complex part k of the refractive index of the considered medium. Transparent medium correspond to medium having k=0 (i.e. real refractive index). \r\nFor instance, considering the amplitude of the electric field E(0) entering the medium, its value after traveling in it for a distance z will be reduced at E(z)=E(0)exp[ -ωkz/c] being ω the wave pulsation and c the light speed constant.","hasChildren":true,"name":"Complex dielectric constants and refractive indices","selfAssesment":"Completed
"},{"code":"PP1-2-8","description":"The complex part k of the refraction index n determines how far an e.m. wave of wavelength λ can survive crossing a specific medium. The attenuation length la is the distance after that the amplitude of an e.m. signal reduces its value by an amount of 1/e. For instance the amplitude of the Electric field E(z) of an e.m. wave proceeding along the z direction is decreasing as exp(-z/la) being la=λ/(2𝜋k) the attenuation length associated to that specific material (with n=m+ik) and wavelength λ. This way attenuation length in water can be of hundreds of meters in the visible range and just few microns in the microwaves. So that penetration of radiation in the matter depends on both, the specific (dielectric) properties of the matter (through k) AND the specific wavelength λ of considered e.m. signal.","hasChildren":true,"name":"EM rad. penetration in the matter: Attenuation Length","selfAssesment":"Completed
"},{"code":"PP1-2-9","description":"EM radiation impinging a rough surface is (partly) reflected back (scattering). When the the sine of the angle of incidence of the radiation is equal to the sine of the angle of reflection, sin(øi) = sin(ør), then the surface behaves like a mirror (Snell's Law). Furthermore, a surface is defined as a “perfect mirror” (Fig.1) if all the incident radiation is reflected in that direction saving its original intensity. A surface is defined as “Lambertian diffuser” or “isotropic reflector” (Fig. 2), when the radiation is reflected in all directions with the same intensity. A surface is defined as “perfect Lambertian” when all the incident radiation is reflected isotropically (i.e. not-absorbing, not-transmitting surface). A surface is defined as \"almost Lambertian\" (Fig.3) if the reflection does not occur in an exactly isotropic way but according to privileged directions. “Perfect mirrors” as well as “perfect Lambertian” surfaces describe ideal bodies, while natural bodies behave like “almost Lambertian” surfaces with a preferred reflection direction around the one established by the sines reflection law.","hasChildren":true,"name":"Scattering from rough surface: Lambertian and specular surfaces.","selfAssesment":"Completed
"},{"code":"PP1-2","description":"E.M. Radiation can be absorbed, scattered, emitted and transmitted by the matter. The results of such interactions (i.e. the fraction of incident radiation that is absorbed, scattered or transmitted) or emission process (i.e. the fraction of actually emitted radiation in comparison with the one expected from a black-body at the same temerature) strongly depend on the radiation wavelength and on specific chemical (e.g. composing atoms and molecules as well as their arrangement within solid cristals) and physical (e.g. Temperature, Dimensions and Shape, Roughness) properties of the matter. In some case, the result of Radiation - Matter interaction is strongly affected by observational conditions. For instance, over some angular distance between the directions of incidence and the one of measurement of the radiation, sun-glint can occur which completely mask any other results. A basic principle of the remote sensing put univocally in relation spectral absorbance, reflectance, transmittance and emissivity, curves achievable by multi-spectral EO measurements, with matter having specific chemical/physical properties. Theoretical models of radiation-matter interaction at the Earth's surface and through the atmosphere provide then suitable strategies for retrieving, from multi-spectral measurements of the radiation leaving the Earth, the most relevant chemical/physical properties of the matter composing its surface and atmosphere.","hasChildren":true,"hasParent":true,"name":"Radiation - Matter interaction","selfAssesment":"Completed
"},{"code":"PP1-3-1","description":"The natural objects can either emit radiation (radiance, emittance) or be \"illuminated\" by a source (irradiance). In the following a series of definitions for each of these terms is provided. \r\nThe first basic radiometric quantity is the radiance (Iλ) and it is defined as the ratio of the differential radiant energy (dE) to the product of effective area (dA) with the time interval (dt), wavelength interval (dλ) and differential solid angle (dΩ). Iλ can be also referred as monochromatic intensity and it is expressed in units of energy per area per time per wavelength and per steradian (W m−2 sr−1). \r\nThe monochromatic flux density (Fλ) or the monochromatic irradiance of radiant energy is defined by the normal component of Iλ integrated over the entire hemispheric solid angle. It is expressed in units of energy per area per time per wavelength (W m−2). For isotropic radiation (i.e., if the intensity is independent of the direction), the monochromatic flux density is then Fλ = π Iλ. \r\nThe total flux density of radiant energy (F), or irradiance, for all wavelengths (energy per area per time, i.e., W), can be obtained by integrating the monochromatic flux density over the entire electromagnetic spectrum.\r\nAll the above definitions refer to a point source of radiation. When the flux density or the irradiance is from an emitting surface (i.e., an extended widespread source), the quantity is called the emittance. When expressed in terms of wavelength, it is referred to as the monochromatic emittance. The intensity or the radiance is also called the brightness or luminance (photometric brightness). The total flux from an emitting surface is often called luminosity.","hasChildren":true,"name":"Radiometric quantities: radiance, irradiance, flux, brightness, emittance, luminosity, etc.","selfAssesment":"Completed
"},{"code":"PP1-3-2","description":"The attenuation of radiation emitted from a source decreases with the square of the distance from its center based on inverse square law. It considers that the size of the sources increases with the square of their radius, causing the same rate of attenuation in flux density.","hasChildren":true,"name":"Decay of the emittance with the square of distance from the source","selfAssesment":"Planned
"},{"code":"PP1-3-3","description":"The relative amount of electromagnetic radiation reflected (absorbed, transmitted, emitted) by the matter at different wavelengths depends on its specific chemical composition and physical properties. The plots of corresponding physical quantities (reflectance, absorbance, transmittance, emissivity) against wavelength, are termed spectral signatures of the specific matter under study. In principle the analysis of spectral signatures obtained by multispectral EO sensors could allow us to identify/discriminate different cover types.\r\nThe interpretation of spectral signatures requires to well understand the e.m. radiation-matter interaction process. In very simple term we expect that incident radiation I(λ)can be reflected, absorbed or transmitted by the matter so that for the energy conservation should be: \r\n\r\n\r\nI(λ)=I(λ,R)+I(λ,A), I(λ,T) \r\n\r\n \r\nbeing I(λ,R), I(λ,A) and I(λ,T) the reflected, absorbed and transmitted fraction of I(λ). From the previous relation descends (dividing both members for I) that:\r\n\r\n\r\n1=R(λ)+A(λ)+T(λ)\r\n\r\n\r\nbeing:\r\n\r\n\r\nR(λ)=I(λ,R)/I(λ) named Reflectance\r\nA(λ)=I(λ,A)/I(λ) named Absorbance\r\nT(λ)=I(λ,T)/I(λ) named Transmittance\r\n\r\n\r\nThey are all specific properties of the considered matter and are not independent each others.\r\nIn particular for an opaque medium with T(λ)=0 it is:\r\nR(λ)=1-A(λ)","hasChildren":true,"hasParent":true,"name":"Spectral Signatures of the matter","selfAssesment":"Completed
"},{"code":"PP1-3-4","description":"Vegetation, water and soil represent the most common cover types of Earth surface. Their reflectances in the VIS/NIR/SWIR spectral range, plotted against wavelength in the 0,4-2,5 micron, represent the most important (basic) spectral signatures for whatever application devoted to Earth surface study. Other spectral signatures (e.g. in emissivity) in the Thermal InfraRed range are particularly important to infer specific properties of Mineral and Rocks (ref. [PP1-3-5] Spectral Signature of Mineral and Rocks). In order to discriminate among such basic cover types, the (ref. [IP3-1-2-3]) NDVI (Normalized Difference Vegetation Index) is the most simple and powerful diagnostic tool in the VIS/NIR spectral range \r\nNDVI values ranging between the values -1 and +1, are higly positive for fully vegetated (up to NDVI=1) or partly vegetated (NDVI>0,3) targets, still positive (>0) for bare soils, negative for water bodies. Values around zero are expected for clouds thanks to their similarly high reflectances both in the NIR and VIR spectral bands (ref. [PP1-3-6] Spectral Signature of Clouds). \r\n\r\nVegetation. a) in the visible range most of the incomig radiation is adsorbed by the photosynthetic process, transmittance is very low. The residual reflected radiation has a small peak of reflectance around 0.5 microns which is responsible of the green colour associated to vegetation by the human vision sytem (limited to the VIS spectral range); b) in the NIR range vegetation exhibits its higher reflectance together its higher transmittance (very low absorbance) so that leaf density can be estimated thanks to the the contributes (decreasing with depth) of underlaying leaf layers; c) in the SWIR spectral range (in particular in the water bands around 1,4 and 1,9 microns) it is possible to appreciate the vegetation water content. As much it is, as more incident radiation is absorbed and less is the reflected fraction of radiation.\r\nBare Soil. Spectral reflectance is normally increasing moving from the VIS to the SWIR spectral region. Water features around 1,4 and 1,9 microns give information on soil water content (see before). Others specific features are described in [PP1-3-5] Spectral Signature of Mineral and Rocks\r\n\r\nWater. Spectral reflectance of clean deep water is quite low reaching quickly the zero value as soon as wavelengths passe microns. However it is important to note that such a very low reflectance is due to a very high transmittance in the VIS range and to a very high absorbance in the NIR/SWIR regions (ref. [PP2-2-5-2] Attenuation Lenght and Penetration Depth). This means that water is quite transparent in the VIS spectral range (so that, in case of shallow waters, measured reflected radiance can be significantly increased by the contribution of bottom of the sea). Water is completely opaque, instead, in the NIR/SWIR. In this spectral region, even in presence of shallow waters, the presence of suspended matter (that increases the measured reflectance both in the VIS and NIR/SWIR ranges) can be better discriminated (than in the VIS) from the contribute of the bottom of the sea that, in this spectral range, is zero.","hasChildren":true,"hasParent":true,"name":"Spectral Signature of Vegetation, Water, Soil","selfAssesment":"Completed
"},{"code":"PP1-3-5","description":"Spectral signatures of rocks and mineral provide information on their chemical composition and crystal properties, grain size and roughness over a wide range of wavelengths from the visible to the thermal infrared.\r\nIn the Visible and Near-InfraRed (VNIR; 0.4÷1.0 µm) region, spectral features are dominated by electronic processes in transition metals, such as Fe, Mn, Cu, Ni, Cr, etc. Therefore, iron is the most important constituent having spectral properties in the VNIR, and the iron-rich minerals are characterized by low reflectance (high absorbance) below 0.7 µm.\r\nOther minerals, which represent the major part of the Earth's surface rocks, such us Si, Al and some anion groups (e.g. silicates, carbonates, oxides) hydroxides, have less spectral features in the VNIR region, but exhibit much more evidences in the Short-Wave InfraRed (SWIR; 1÷3 µm) region. In fact, spectral features of hydroxyls and carbonates mark the SWIR region.\r\nThe hydroxyl ion is a widespread constituent occurring in rock forming minerals such as clays, micas, chlorite etc. It shows a vibrational fundamental absorption band at about 2.74÷2.77 µm and an overtone at 1.44 µm.\r\nCarbonates, which are commonly in the Earth surface rocks in the form of calcite (CaC03), magnesite (MgC03), dolomite [(Ca-Mg) C03] and siderite (FeC03), shows a typical absorbance feature around 2.3 µm, instead the water content can be instead evaluated by the depth of absorption at 1,4µm and 1,9 µm.\r\nThermal InfraRed (TIR; 1÷20 µm) region, from a geological point of view, is a particularly important spectral region for remote sensing aiming at compositional investigations of terrestrial materials. In fact, the fundamental vibration features of many rock-forming mineral groups (e.g. silicates, carbonates, oxides, phosphates, sulphates, nitrates, nitrites, hydroxyls) occur in the TIR region. Briefly:\r\na) the silicates, which are most abundant group of minerals in the Earth's crust, shows vibrational spectral features due to the presence of Si04-tetrahedron around 8 µm to 12 µm; b) the carbonates show a weak feature around 11.3 µm that can be detected; c) the sulphates display bands near 9 µm and 16 µm; d) the phosphates also have fundamental features near 9.25 µm and 10.3 µm; e) the features in oxides usually occupy the same range as that of bands in Si-O, i.e. 8 µm to 12 µm; g) the nitrates have spectral features at 7.2 µm and the nitrites at 8 µm and 11.8 µm; h) the hydroxyl ions display fundamental vibration bands at 11 µm.","hasChildren":true,"name":"Spectral Signature of Mineral and Rocks","selfAssesment":"Completed
"},{"code":"PP1-3-6","description":"The determination of spectral signatures for scenes with a high degree of spatial complexity is considered as one of the most persistent problems in atmospheric radiation, especially at the surface, where satellite observations can only be used indirectly to infer energy budget terms. In the shortwave (solar) spectral range, it is especially challenging to derive consistent albedo, absorption, and transmittance from spaceborne, aircraft, and ground-based observations for inhomogeneous cloud conditions and is closely related to the long-debated discrepancy between observed and modeled cloud absorption.\r\nThe cloud spatial structure is revealed as a spectral signature in shortwave irradiance through the physical mechanism of molecular scattering. However, the study of specific mechanisms is rather complex since the satellite instruments cannot completely describe the spatial distribution of cloud and the variability of scattering and absorption properties. For this reason, several studies deal with the problem described above, as a challenge for estimating spectrally the cloud optical properties (such as the albedo and transmittance) as well as scattering and absorption processes taking place in the cloud system with adequate resolution. Hence, the above mechanisms can be described using three dimensional (3-D) radiative transfer models. Those models receive auxiliary information from cloud imagery and radar observations. The molecular scattering (Rayleigh) was the only one directly dependent on the wavelength of the vertical radiative flux. Moreover, it was considered as a spectral perturbation of backtracked horizontal exchange of solar radiation due to the inhomogeneous distribution of cloud. The horizontal photon transport is highly correlated to its spectral dependence.\r\nConcerning the presence of cirrus or ice clouds, the effect of their phase function and the vertical distribution were evaluated on the scattering of far infrared radiation. Thus, the accurate reconstruction of the phase function of cirrus clouds potentially indicates the need for application of a radiative transfer model. This specific module necessarily includes scattering parameters, while the accuracy of its calculations needs to be verified against real measurements. \r\nFor several applications the preliminary detection of those portions of the scene affected by the presence of clouds (cloud detection) is mandatory. For studying properties of Earth's surface targets affected by the presence of clouds are flagged just to exclude them by further analyses. In some case clouds themselves are the object of interest. In both cases the identification of clouds (and their classification) is mostly done by using (combination of) specific spectral signatures. Generally speaking clouds are highly reflecting VIS/NIR radiation showing (due to their heigth) brigthness temperatures (in the TIR region) lower than underlying surfaces. Thin or semi-transparent clouds are still detectable for their higher reflectance over the sea which represents a quite dark bacground in the VIS/NIR/SWIR region. Over land (much more reflecting) such a test is not more efficient and more sophisticated tests (e.g. Brigthness Temperature Difference in the split window bands around 11 and 12 microns) are required. In presence of very cold, high reflective backgrounds (e.g. snow, glaciers, etc.) both tests on the VIS reflectance and on TIR brigthness temperature could fail. More specific tests exploiting the reflectance drop of snow in the SWIR (where clouds are still saving their higher reflectance) helps to discriminate the presence of clouds from clear sky conditions even over a snow background. In the microwaves clouds are quite transparent except when coupled with coarse particles related to rain, snow, hailstones (precipitating clouds). In that case Mie scattering dominates strongly reducing the amount of radiance collected at the sensor (lower brigthness temperature in the microwave spectral range).","hasChildren":true,"name":"Spectral Signature of Clouds","selfAssesment":"Completed
"},{"code":"PP1-3-7","description":"If the resolution is low enough that disparate materials can jointly occupy a single pixel, the resulting spectral measurement, made by the sensor, will be the composite of the individual spectra. Under the linear mixing model (LMM), each observed spectrum in each pixel of a given image is assumed to result from the linear combination of the N endmember spectra present in the pixel. The reflectance spectrum of each endmember is weighted by the fractional area coverage of it in the pixel. \r\nHowever, if the components of interest in a pixel are in an intimate association, like sand grains of different composition in a beach deposit, light typically interacts with more than one component as it is multiply scattered, and the mixing between these different components are nonlinear. Such nonlinear effects have been recognized in spectra of: particulate mineral mixtures, aerosols and atmospheric particles, vegetation and canopy. In this case a non-linear mixing model (NLMM) should be applied. To summarize: Linear mixture model assumes that endmember substances are sitting side-by-side within the pixel; Nonlinear mixture model assumes that endmember components are randomly distributed throughout the pixel, causing multiple scattering effects. \r\nIn the linear mixing case, the basic premise of mixture modelling is that within a given scene, the surface is dominated by a small number of distinct materials that have relatively constant spectral properties. These distinct substances (e.g., water, grass, mineral types), characterized by a well-defined spectral signature are called endmembers, and the fractions in which they appear in a mixed pixel are called fractional abundances. Then, finding the endmembers that can be used to ‘unmix’ other mixed pixels becomes a crucial issue. \r\nIdentify fractional abundances of distinct substances from the spectral signal of a mixed pixel is one of the application in which hyperspectral images can provide an valuable support.","hasChildren":true,"name":"Composition of spectral signatures (Linear Mixing)","selfAssesment":"Completed
"},{"code":"PP1-3-8","description":"One of the most common ways to classify remote sensing systems consists in distinguishing them into the passive systems, which detect naturally occurring radiation, and the active systems, which emit radiation and analyse what is sent back to them. The passive systems can be further subdivided into those that detect radiation emitted by the Sun (this radiation consists mostly of ultraviolet, visible and near-infrared radiation), and those that detect the thermal radiation that is emitted by all objects that are not at absolute zero (i.e. all objects). For objects at typical terrestrial temperatures, this thermal emission occurs mostly in the infrared part of the spectrum, at wavelengths of the order of 10 μm (the so called thermal infrared region), although measurable quantities of radiation also occur at longer wavelengths, as far as the microwave part of the spectrum. Active systems can, in principle, use any type of electromagnetic radiation, resulting able to obtain measurements anytime, regardless of the time of day or season. In practice, however, they are restricted by the transparency of the Earth’s atmosphere at the specific spectral range considered. In any case they can be used for examining wavelengths that are not sufficiently provided by the sun, such as microwaves, or to better control the way a target is illuminated. Active sensors may be classified according to the use that is made of the returned signal. Two main methods have been identified to this aim so far: the Ranging technique mostly concerns with the time delay between transmission and reception of the signal, while the Scattering one is mostly focused on the strength of the received signal.","hasChildren":true,"name":"Definition of active and passive remote sensing techniques","selfAssesment":"Completed
"},{"code":"PP1-3-9","description":"Light has a key role for aquatic ecosystems, both in marine and freshwater. It penetrates underwater and interacts with dissolved and particulate water constituents, the optically active constituents (OACs). They absorb and scatter the light, giving water its characteristic colour and affect the light availability underwater. The three main OACs are phytoplankton, coloured dissolved organic matter (CDOM) and suspended particulate matter (SPM) and vary in time and space. Absorption and scattering represent the inherent optical properties (IOPs) of water and depend solely on the OACs present in the water. In addition, water bodies have apparent optical properties (AOPs) that depend both on OACs and the incident light field.\r\nThe chlorophyll in the phytoplankton absorbs blue and red wavelengths and reflects green. Therefore, the oceans appear blue-green depending on the concentration of phytoplankton. CDOM is primarily tannin-stained water released from decaying detritus. High CDOM concentrations appear yellow-green to brown. CDOM absorbs ultraviolet (UV) light in the surface waters which is harmful for phytoplankton but competes with phytoplankton for light. Inorganic suspended matter (ISM) is the suspended sediment in the water. It is a component of SPM and strongly scatters longer (red) wavelengths. High ISM concentrations give water a reddish-brown colour. Pure water, however, absorbs longer wavelength red light. As natural waters vary in their composition, oceanographers introduced ocean classification schemes based on the optical properties of water. The main differentiation is between Case 1 open ocean waters and Case 2 coastal waters. In open ocean waters, the optical properties are dominated by phytoplankton and covarying material. In coastal waters, optical properties are dominated by suspended sediments and CDOM that vary independently of phytoplankton.","hasChildren":true,"name":"Optical properties of water","selfAssesment":"Completed
"},{"code":"PP1-3","description":"Measuring the signal emitted (received) by a radiation source (detector)","hasChildren":true,"hasParent":true,"name":"Sensing of EM radiation.","selfAssesment":"Planned
"},{"code":"PP1-4-1","description":"Radiative transfer equation (RTE) is the governing equation of radiation propagation in a media, which plays a central role in the analysis of radiative transfer in gases, semitransparent liquids and solids, porous materials, and particulate media, and is important in many scientific and engineering disciplines. \r\nThe RTE states that when radiation (a light-ray) propagates through matter (gas, dust, liquid), the incident radiation could be absorbed or scattered by matter, or radiation emitted from matter could append to the incident radiation. As a result, the intensity of radiation would change temporally, spatially, and directionally. The study of the propagating way of radiation in matter is the radiative transfer. In more detail, the radiation traversing a medium may be attenuated due to the density, mass scattering and absorption of material. In contrast, the radiation’s intensity can be strengthened by emissions from the material plus multiple scattering from all directions. All the above interactions are described mathematically by the general radiative transfer equation.\r\nThere are different forms of RTEs that are suitable for different applications, including the RTE under different coordinate systems, the transformed RTE having good numerical properties, the RTE for refractive media, etc.. Furthermore, several fundamental numerical methods for solving RTEs are proposed up to now focusing on the deterministic methods, such as the spherical harmonics method, discrete-ordinate method, finite volume method, and finite element method.","hasChildren":true,"name":"General equation of radiative transfer.","selfAssesment":"Completed
"},{"code":"PP1-4-10","description":"The inversion approach aims at retrievals of trace gas concentration and temperature profiles of atmospheric state, namely the modeled state vector, based on the measured radiance transmitted or reflected or scattered (SCIAMACHY spectrometer) by the Earth-Atmosphere system. Satellite instruments measure the radiance L that reaches the top of the atmosphere at given frequency v. The measured radiance is related to geophysical variables of Earth's atmosphere (e.g. temperature vertical profiles and chemical composition, aerosols, clouds, rain, etc.) and surface (e.g. temperature, spectral emissivity and reflectance, etc.) by the Radiative Transfer Equation (RTE). In RTE measured spectral radiances are assumed as the result of different contributions:\r\na) thermal emission from the different layers (at heigt z) of atmosphere at temperature T(z) modulated by the atmospheric transmittance from z to the sensor heigt. It depends on both temperature profile T(z) and trace gas concentration along the optical path;\r\nb) Surface emission. It depends mostly on Eart's surface temperature T(0) and spectral emissivity\r\nc) Surface reflection/scattering. It depends on spectral reflectance and local properties like surface rugosity \r\nOthers, more complex contributions comes from: cloud/rain, aerosols, etc.\r\nIn its simplified form, terms a) and b) dominate as far as InfraRed (IR) radiances are considered. Term a) can be neglected in those bands where atmosphere is transparent (atmospheric windows). Term b) can be negletcted in the IR spectral bands (sounding channels) where it is fully adsorbed by some specific constituent of the atmosphere. Among the IR sounding channels some ones are selected being associated to atmospheric constituents (like CO2 or oxygen) whose mixing ratio in the atmosphere is known to be constant. For radiances measured in these bands term a) in RTE depends only on T(z) (through a Fredholm equation of the first kind) that can be then retrieved by inversion methods. When T(z) are known trace gas concentrations survive as the only unknown of term a) and can be retrieved by inversion methods using radiances measured in their corresponding sounding channels. Similar inversion strategies have been suggested as far as radiances (emitted, transmitted, reflected, adsorbed) measured in different spectral ranges (from the Visible to the Microwaves) are considered.","hasChildren":true,"name":"Retrieval of atmospheric parameters by inversion of multi-spectral radiances","selfAssesment":"Completed
"},{"code":"PP1-4-2","description":"In the field of radiation scattering and absorption, the cross-section, analogous to the shape of a particle, is used to determine the amount of energy diverted from the original beam by the particle. This parameter is called mass cross section, when it is in reference to unit mass (cm2g-1).","hasChildren":true,"name":"Cross Section of Extinction (Absorption, Scattering) per Mass Unit","selfAssesment":"Planned
"},{"code":"PP1-4-3","description":"When the mass cross-section is multiplied by the density of particle, the extinction coefficient is calculated, namely the sum of absorption and scattering coefficient, whose the units are related to length. Especially, the absorption coefficient (k (cm•atm)-1) is the product of strength of absorption with the Loschmidt’s number.","hasChildren":true,"name":"Absorption Coefficient","selfAssesment":"Planned
"},{"code":"PP1-4-4","description":"The source function, Jλ, has units of radiant intensity and it is defined as the ratio of the source function coefficient to the mass extinction cross section. The Jλ determines the intensity that are acquired in a homogeneous medium.","hasChildren":true,"name":"Source Function (Coefficient)","selfAssesment":"Planned
"},{"code":"PP1-4-5","description":"If the monochromatic beam (Iλ) of radiation attenuates due to absorption, but it remains unaffected from emission contributions and multiple scattering of homogeneous Earth-Atmosphere system, it can be expressed by Beer-Bouguer-Lambert law. This law also expresses the monochromatic optical depth (τλ) and transmissivity (Τλ) of the above system.","hasChildren":true,"name":"Beer-Bouguer-Lambert law.","selfAssesment":"Planned
"},{"code":"PP1-4-6","description":"The Schwarzschild equation provides an interpretation for the infrared radiation that undergoes the absorption and emission processes simultaneously, while the scattering efficiency is considered negligible. Hence, its solution is obtained by the integrating of relationship that invokes Kirchhoff’s law and summing the two above processes along a ray path.","hasChildren":true,"name":"Schwarzshild equation and its solutions","selfAssesment":"Planned
"},{"code":"PP1-4-7","description":"The Optical Path (OP) describe the total concentration along a path of constituents extinguishing (by absorption or scattering) the electromagnetic radiation traveling through a medium at a specified whavelength λ. Its value depends then on the efficiency of absorption and scattering phenomena which occur during the travel itself. The Earth's atmosphere is usually the medium that a monochromatic beam (Iλ) of radiation travels through before reaching satellite sensors. In an homogeneously estinguishing medium (i.e. a medium with extinction coefficient for mass unit K constant along the optical path) the Optical Thickness OT is defined as OT=K x OP. It give a measure of the cumulative depletion of Iλ directed in straight-downward. As far as the Optical Thickness is large, the medium is more and more optically thick (i.e. radiation is largely absorbed). If the Optical Thickness is small it means that the medium is optically thin (i.e. radiation travels through it easily).","hasChildren":true,"name":"Concepts of Optical path and Optical thickness.","selfAssesment":"Completed
"},{"code":"PP1-4-8","description":"Radiative transfer is highly nonlinear and non-local against the cloud structure at a high spatial resolution. Hence, a Monte Carlo approach can be used for the representation of cloud structure and interactions between photons and clouds. This approach is more efficient than the method of representing clouds as horizontally homogeneous.","hasChildren":true,"name":"Radiative transfer in presence of clouds","selfAssesment":"Planned
"},{"code":"PP1-4-9","description":"The line by line radiative transfer model (LBLRTM) is an accurate and flexible model for the estimation of the spectral radiance and transmittance over the full spectral range (microwave to ultraviolet), using a first-order perturbation algorithm. It is considered as the basic tool for the creation of retrieval algorithms employed by the ground-based and satellite instruments, while the latest updates in spectroscopic factors are derived from the high-resolution transmission molecular absorption (HITRAN) database. A LBLRTMs is continuously updated and validated against highly accurate spectral measurements. Its errors are related to uncertainties in line parameters and shape. The shape is a Voigt line which is a linear combination of approximating functions for the description of all atmospheric levels. LBLRTML is combined with the continuum MT_CKD (Mlawer, Tobin, Clough, Kneizys, Davies) model which in turn includes the atmospheric constituents of water vapor, carbon dioxide (CO2), molecular oxygen (O2), molecular nitrogen (N2), and ozone (O3), and the molecular extinction process (Rayleigh scattering). A recent version of LBLRTM calculates analytically the Jacobians equations for obtaining meteorological parameters. Also, this model version retrieves the optical parameters of clouds related to scattering and emissivity. The LBLRTM is widely used in radiation and climate applications. It is capable to calculate the absorption degrees of various atmospheric constituents which are utilized afterward from climate and weather prediction models for estimating the broadband solar irradiance and the heating rates. Additionally, the complex radiative transfer models with fast computational time are initiated and trained by the LBRTM, since they are used subsequently on numerical weather prediction (NWP) assimilation systems.","hasChildren":true,"name":"Line-by-line radiative transfer models","selfAssesment":"completed
"},{"code":"PP1-4","description":"Theory of radiative transfer describes the transmission of the electromagnetic radiation through a medium. The electromagnetic radiation can be emitted, absorbed, scattered by constituents of the medium depending on the composition of the medium and the physical state of its constituents, as well as the wavelength of the radiation itself. Retrieving geophysical parameters from radiation measurements requires to know this kind of interaction which is described through the Equation of Radiative Transfer. In the field of Earth Observations from space, the considered medium is normally the Earth's atmosphere through which the e.m. radiation travel before reaching aerial multi-spectral sensors. Radiative transfer models allow to foreseen spectral radiances at whatever altitude in atmosphere (radiance at the sensor) starting from the knowledge of atmospheric vertical profiles of temperature and chemical constituents concentrations (direct problem). The possibility to retrieve atmospheric temperature profiles and chemical constituents concentrations from multi/iper spectral radiances measurements in selected bands (inverse problem) is the scope of the inversion techniques widely applied in meteorology and of a specific set of sensors devoted to the vertical sounding of the atmosphere. Clouds and scattering particles, like aerosols - requiring the inclusion of additional information on the atmospheric constituents (e.g water phases involved, dimensions and geometry of scattering particles, etc.) - make radiative transfer model more complex.","hasChildren":true,"hasParent":true,"name":"Fundamentals of Radiative Transfer","selfAssesment":"Planned
"},{"code":"PP1-5-1","description":"Light is the electromagnetic phenomenon we exploit for remote sensing. Its basic laws concerning the transmission through the interface of two different media are governed by reflection and refraction. Reflection governs the way light is backpropagated and refraction dictates how light is transmitted. Refraction is related to the real refractive index of a medium. Dispersion relates to the way the light of a given wavelength is transmitted. Since light of different wavelengths are transmitted at different angles, the phenomenon leads to the concept of dispersion. These three simple principles are at the core of the understanding technology of remote sensing.","hasChildren":true,"name":"Reflection, Refraction and Dispersion of the light","selfAssesment":"Planned
"},{"code":"PP1-5-11","description":"The theory provides the bulk of physical explanation and related laws, which govern absorption, emission and spontaneous emission from the ordinary matter. Early laws about thermal radiation and the blackbody emission, such as Rayleigh-Jeans, Wien, Planck laws are cast in a single theory and formalism through the concept of quantized energy at the level of atoms emission/absorption of light. Explain the modern concept of quantum optics and their link to the design of modern devices for the measurements and/or production of coherent light.","hasChildren":true,"name":"Einstein’s theory of radiation: photons, photoelectric effect, absorption, emission; Stimulated emission: the laser","selfAssesment":"Planned
"},{"code":"PP1-5-14","description":"Solid state modern detectors rely on non-metal junction, which can be designed and operated to yield a bandgap energy according to the spectral range (infrared, visible, UV) to be detected. The basic principles of how these devices are designed and fabricated is important to develop and design new sensors useful for the various remote sensing applications.","hasChildren":true,"name":"Electric conduction in solids: semiconductors, p-n- junction, diode and transistors","selfAssesment":"Planned
"},{"code":"PP1-5-15","description":"Modern detectors of electromagnetic radiation in the infrared, VIS, UV spectral regions are designed and fabricated based on suitable junctions or electro-optical devices. The performance of these systems needs to be assessed in terms of accuracy and precision. This is made through figures of merit such as Noise Power Spectral Density, Noise Equivalent Power. Detectors can be classified as photovoltaic or photoconductive devices, which allows to better classify the various noise sources: shot noise, 1/f noise, Johnson noise, generation-recombination noise.","hasChildren":true,"name":"Photovoltaic and photoconductive detectors: MCT, InSb, bolometer, CCD devices","selfAssesment":"Planned
"},{"code":"PP1-5-2","description":"Interference and diffraction are phenomena related to the wave nature of electromagnetic radiation. They explain how light propagates in presence of obstacles. These phenomena are largely used in the fabrications of optical systems for remote sensing: e.g. radiometers and spectrometers.","hasChildren":true,"name":"Interference and Diffraction.","selfAssesment":"Planned
"},{"code":"PP1-5-3","description":"The Michelson interferometer is the instrument that exploits and evidence the interference of light. A masterpiece of experimental physics, the Michelson interferometer is the key architecture of the modern optical interferometers, which make it possible to measure the emitted Earth spectrum with hyperspectral resolution.","hasChildren":true,"name":"Michelson Interferometer","selfAssesment":"Planned
"},{"code":"PP1-5-4","description":"The celebrated principle of constant speed of light and independence of the reference frame is important to explain the basic principles of instruments such as the Michelson interferometer. The basic physics theory to explain how electromagnetic fields propagates and the inter-relationship between electric and magnetic fields.","hasChildren":true,"name":"Special relativity; Electromagnetic fields equations and propagations","selfAssesment":"Planned
"},{"code":"PP1-5-6","description":"Helmotz’s wave equation arises in light and acoustic scattering problem and yields the general framework to investigate and analyse the scattering of time-harmonic acoustic and electromagnetic waves by a penetrable inhomogeneous medium.","hasChildren":true,"name":"Helmotz’s equations; Scattering from inhomogeneous media.","selfAssesment":"Planned
"},{"code":"PP1-5-7","description":"Geometrical optics is governed by the laws of reflection, refraction and dispersion. Its applications are relevant to many optical systems involving ray tracing, wavefront propagation, thin film calculators (which underly many optical engineering calculations).","hasChildren":true,"name":"Foundations of geometrical optics, geometrical theory of optical imaging","selfAssesment":"Planned
"},{"code":"PP1-5-8","description":"Optical interferometers are nowadays used to develop and implement Fourier Transform Spectrometers, which can measure the emission spectrum of a given source with high spectral resolution at a constant sampling. This instrumentation is now at the core of modern hyperspectral sounders from satellite and have opened the way to the sounding of the Earth atmosphere with unprecedented spatial vertical resolution.","hasChildren":true,"name":"Elements of the theory of interference and interferometers","selfAssesment":"Planned
"},{"code":"PP1-5-9","description":"Diffraction gratings and dispersive element are the basic ingredients for radiometers and grating spectrometers. They are in some cases preferred to Interferometer systems because the optical layouts can be designed and implemented with no moving part or components. Many of the today satellite instruments, including sounder and imagers, rely on diffraction and/or grating spectrometers","hasChildren":true,"name":"Elements of the theory of diffraction and grating spectrometers","selfAssesment":"Planned
"},{"code":"PP1-5","description":"This section describes the theoretical fundaments of Optics and Modern Physics of Sensors relevant to the Earth Observation.","hasChildren":true,"hasParent":true,"name":"Basics of Optics and Modern Physics of Sensors","selfAssesment":"Completed
"},{"code":"PP1-6-1","description":"The temperature and pressure profiles determine the atmospheric structure. The latter consists of four basic levels, considering the vertical variability of the temperature. These main four levels are troposphere, stratosphere, mesosphere, and thermosphere. In the troposphere (0-12km), which is the lowest layer of the atmosphere, all the meteorological processes that affect our everyday life take place. The lowest part of the troposphere is known as the boundary layer (0-3km), where all the surface-atmosphere interactions and exchanges take place. The troposphere concentrates the water vapor and 90% of atmospheric mass, while the chemical composition of all atmospheric layers consists of nitrogen, oxygen, argon and trace gases. The main parameters that characterize the atmosphere structure are pressure, density, and temperature. All the aforementioned parameters are related to the atmospheric composition and vary with altitude, latitude, longitude and season. Additionally, the stratosphere, which is the layer above the troposphere, contains almost all of the ozone abundance (~90%) of the atmosphere in a region named as ozone layer and traced between 15 and 35km. The interaction of the incoming solar radiation with ozone in this layer causes the reduction of the incoming harmful UV radiation provoking the temperature increase in the stratospheric layer. The 99.9% of total atmospheric mass is concentrated in lower atmosphere (<50km) with Nitrogen (N2, 78.08%), Oxygen (O2, 20.95%) and argon (Ar, 0.93%) being the major constituents of the atmosphere. Water vapor (H2O) is considered as a significant factor, too. Despite the fact that it depicts a very small amount of total atmospheric mass, it’s one of the most important greenhouse gases, along with carbon dioxide (CO2) and methane (CH4), absorbing the Earth’s longwave (infrared) radiation, affecting the energy balance of Earth-Atmosphere system. Furthermore, water vapor plays a decisive role in the formation of clouds and precipitation. Together with the basic chemical (atoms, molecules, ions) constituents of a \"standard\" atmosphere, aerosols of natural and anthropogenic origin have to be considered too, as far as the interaction of e.m. radiation with atmosphere is concerned.","hasChildren":true,"name":"Structure and chemical-physical composition of Earth's atmosphere","selfAssesment":"Completed
"},{"code":"PP1-6-10","description":"The water vapour is the major radiative and dynamic parameter in the atmosphere. Its concentrations vary highly in space and time, with the tropospheric water vapor being determined by the hydrological cycle processes, namely the evaporation, condensation and precipitation and by large-scale transport processes. Specific humidity decreases rapidly with pressure (following an exponential function) and with latitude. In particular, the variability of the H2O concentration shows a bimodal distribution: it’s very small in the equatorial region and poleward, relatively small in stratosphere and shows a maximum in the subtropics of both hemispheres. The concentration of H2O in the lower stratosphere is controlled by the temperature of the tropical tropopause, and by the formation and dissipation of cirrus. The water vapor can condense into water droplets when it has a particle to condense upon. The atmosphere continuously contains aerosol particles ranging in size from ∼10−3 to ∼20 μm. These aerosols are known to be produced by natural processes (volcanic dust, smoke from forest fires, particles from sea spray, windblown dust, and small particles produced by the chemical reactions of natural gases) as well as by human activity (particles directly emitted during combustion processes and particles formed from gases emitted during combustion). Some aerosols are effective condensation and ice nuclei upon which cloud particles may form. For the hygroscopic type, the size of the aerosol depends on relative humidity. Thin layers of aerosols are observed to persist for a long period of time in some altitudes of the stratosphere. \r\nClouds are global in nature and regularly cover more than 50% of the sky. There are various types of clouds. Cirrus in the tropics and stratus in the Arctic, and near the coastal areas are climatologically persistent. The microphysical composition of clouds in terms of particle size distribution and cloud thickness varies significantly with cloud type. Clouds can also generate precipitation, an event generally associated with midlatitude weather disturbances and tropical cumulus convection.","hasChildren":true,"name":"Water vapour and Cloud formation","selfAssesment":"Completed
"},{"code":"PP1-6-11","description":"The radiative equilibrium is the principle, where the radiative emission and absorption are in balance based on Kirchhoff’s and Planck’s law, resulting in the steady temperature of planet. The adiabatic lapse rate displays the decrease of vertical temperature of a parcel with rate higher than 1oC per 100 metres.","hasChildren":true,"name":"Radiative Equilibrium. Adiabatic lapse rate","selfAssesment":"Planned
"},{"code":"PP1-6-12","description":"The atoms of carbon are building blocks of living organisms and they can move among organisms as a part of carbon cycle. Their transport rate to the atmosphere as carbon dioxide is vital, because this gas trap heat in the atmosphere, increasing the Earth’s temperature and causing Greenhouse effect.","hasChildren":true,"name":"The Carbon Cycle, Greenhouse Effect","selfAssesment":"Planned
"},{"code":"PP1-6-2","description":"The atmospheric absorption can cause an excitation or falling into the energy state of a particle, while the scattering is related to absorption and re-emission of radiation at all directions without changes in its frequency. Particularly, the main contributors of the incoming solar radiation absorptions are various molecules like the nitrogen (N2), oxygen (O2), ozone (O3), water vapor (H2O). Additionally, other constituents of the atmosphere such as CO2 and CH4, and other trace gases, aerosols, and cloud droplets can also absorb significant portion of the incoming solar radiation. Generally, the absorption of solar radiation is related to the wavelength of the solar spectrum. For example, gases and specific type of aerosols (black carbon, BC) or elementary carbon (EC) absorb in the ultraviolet (UV) and visible (VIS) part of solar spectrum. On the contrary, cloud droplets which are suspended in the atmosphere mainly scatter in UV and VIS and absorb in the infrared. The absorption of the incoming solar radiation from the atmospheric constituents reduces the harmful UV radiation and it is considered as the driving of atmospheric photochemistry. Moreover, scattering in the atmosphere can be divided into two mainly categories, firstly, the Rayleigh scattering which is the scattering of radiation by gases (mainly N2 and O2) and, secondly, the Mie scattering which is the scattering by aerosol particles and cloud droplets. The main difference between Rayleigh and Mie scattering is the direction of the re-emission of the incident solar radiation. For example, in the Rayleigh scattering the light have symmetrical direction either forward or backward whereas in Mie scattering the light is mainly scattered in the forward direction, depending on the size of the particle.","hasChildren":true,"name":"Absorption and scattering of solar radiation in the Atmosphere","selfAssesment":"Completed
"},{"code":"PP1-6-3","description":"Mie scattering refers primarily to the elastic scattering of light from atomic and molecular particles whose diameter is similar or larger than the wavelength of the incident light. We can say that, when the particle has a diameter greater than about a tenth of the wavelength, we are in the field of Mie scattering.\r\nThis scattering produces a pattern like an antenna lobe, with a forward lobe sharper and more intense than the back one, the larger the particle size the greater the intensity and sharpness of the anterior lobe. Unlike Rayleigh scattering, Mie scattering is not strongly wavelength dependent. In this case the predominant component for the quantification of scattering (in addition to the particle dimension) is the direction of the incident solar radiation.\r\nMore specifically, the amount of scattering in the backward direction depends upon a wave relation tending to decrease in accordance with the growth of the particle size until it reaches a certain value for which the back scattering becomes a constant quantity. This condition is reached when the diameter of the particle is approximately equal to the wavelength of the incident radiation.\r\nIn the atmosphere the Mie scattering is commonly caused by particles (aerosols) floating in the atmosphere (due to Dust, smoke, fog, rain drop). \r\nIn nature it is possible to see the effects of Mie scattering, for example, in the evenings when there is a lot of fog and the dazzling headlights of our car do not allow us to see the road ahead. \r\nThe Mie theory provides the solution for the amount of scattering in case of a spherical medium due to an incident wave.","hasChildren":true,"hasParent":true,"name":"Mie Scattering in the Earth's Atmosphere","selfAssesment":"Completed
"},{"code":"PP1-6-4","description":"Scattering is a physical process by which a particle in the path of an electromagnetic wave continuously exstracts energy from the incident wave and reradiates that energy in all directions. In more detail, it occurs when a photon’s electromagnetic field hits a particle’s electric field in the atmosphere and is deflected into another direction. The Rayleigh scattering falls into the elastic scattering phenomena, in which the individual photon changes its direction of propagation but non its energy. The Rayleigh scattering involves air molecules (mainly N2 and O2) whose diameter (x) is much smaller (one-tenth at least) than the incident radiation wavelength (λ) (i.e., x << λ). The amount of scattered intensity (I) depends on the incident light wavelength (λ) and the refractive index (n) of air molecules. However, the refractive index can be considered relatively negligible as compared to the explicit wavelength term. In this way, the intensity scattered by air molecules in a specific direction is strongly dependent on the wavelength (λ), as expressed in the form Iλ~1/λ4. The inverse dependence of the scattered intensity on the wavelength to the fourth power allows at explaining the blue color of sky, caused by the scattering of sunlight off the atmosphere molecules. To better understand this phenomenon, it is worth considering that a large portion of solar energy is contained between the blue and red regions of the visible spectrum, where blue light (0.425 µm) has a shorter wavelength than red light (0.650 µm). Consequently, based on the above-mentioned equation, blue light scatters about 5.5 times more intensity than red light. For this reason, more blue light is scattered than red, green, and yellow, and so the sky appears blue, when viewed away from the sun’s disk. The Rayleigh scattering of unpolarized sunlight by air molecules has maxima in the forward and backward directions, whereas it shows minima in the side directions. Furthermore, the light scattered by particles is not delimited only on the incidence plane, but is visible in all the azimuthal directions. The derived scattering patterns are symmetrical in the three-dimensional space, because of the spherical symmetry assumed for air molecules.","hasChildren":true,"name":"Rayleigh Scattering in the Earth's Atmosphere","selfAssesment":"Completed
"},{"code":"PP1-6-5","description":"When we talk about “thermal infrared (or terrestrial) radiation” we commonly refer to the energy emitted from the Earth-atmosphere system. Trapping of thermal infrared radiation by atmospheric gases is typical of the atmosphere and is therefore called the “atmospheric effect”. The atmospheric effect is sometimes referred to as the “greenhouse effect” because in a similar way glass, which covers a greenhouse, transmits short-wave solar radiation, however absorbs long-wave thermal infrared radiation. Imagine a beam of radiation travelling through a small section of air. The air is made up of changing concentrations of different species, with all molecules absorbing and emitting thermal radiation at different rates. As the radiation travels through different layers of the atmosphere, the intensity of radiation will constantly be modified by both absorption and emission processes as described by the Schwarzschild's equation. In case of a sensor on board of a satellite, the net radiation measured would be that which is attenuated through each layer (as small increments of absorption and emission) from the surface to the top of the atmosphere plus the radiation emitted directly from the atmosphere. In this case, this process can be described by the radiative transfer equation (RTE). \r\nThe equation of radiative transfer simply says that as a beam of radiation travels through the atmosphere, it loses energy to absorption, gains energy by emission, and redistributes energy by scattering. Many radiative transfer codes exist which are able, i.e. on the basis of known properties of the atmosphere, to computed the effect of the atmosphere on the thermal infrared radiation providing atmospheric transmittance (absorption), atmospheric scattering and atmosphere path emission. Commonly, in satellite remote sensing, the thermal infrared region is defined as the region of the electromagnetic spectrum comprised between 8 and 14 micron. In an atmosphere free of particles (aerosols due to phenomena like fires, volcanic eruption, dust storm, etc.) the thermal infrared radiation is mainly affected by triatomic gases like water vapor, carbon dioxide and ozone.","hasChildren":true,"name":"Thermal infrared radiation transfer in the atmosphere","selfAssesment":"Completed
"},{"code":"PP1-6-6","description":"Light scattering by particles is the process by which small particles cause optical phenomena, such as rainbows, the blue color of the sky, and halos. Mie scattering defines the interaction of light with particulate matter with a dimension comparable to the wavelength of the incident radiation. It can be regarded as the radiation resulting from a large number of coherently excited elementary emitters (molecules for example) in a particle. Since the linear dimension of the particle is comparable to the wavelength of the radiation, interference effects occur. The most noticeable difference to Rayleigh scattering is, generally, the much weaker wavelength dependence and a strong dominance of the forward direction in the scattered light. The calculation of the Mie scattering cross section, which involves summing over slowly converging series, is complicated even for spherical particles, it is worse for particles of an arbitrary shape. However, the Mie theory for spherical particles is well developed and a number of numerical models exist to calculate scattering phase functions and extinction coefficients for given aerosol types and particle size distributions.","hasChildren":true,"hasParent":true,"name":"Light scattering by atmospheric particulates","selfAssesment":"Planned
"},{"code":"PP1-6-7","description":"Each time radiation passes through the atmosphere it is attenuated to some extent. We refer to this attenuation with the term 'atmosphere transmittance'. The typical atmospheric transmittance between wavelengths of 250 nm and 2500 nm, i.e. in the ultraviolet, visible, near-infrared and short-wave-infrared regions of the spectrum is dominated bywater vapour, although methane, carbon dioxide and molecular oxygen are also responsible for a few absorption lines. The behaviour in the visible region is dominated by molecular Rayleigh scattering. At the short-wavelength end of the spectrum, in the ultraviolet, absorption by ozone becomes very significant. Above 2500 nm up to the upper limit (13500 nm) of the optical electromagnetic spectrum useful for Remote Sensing, the atmosphere transmittance is mainly affected by triatomic molecules (H20, CO2 and O3). However, the atmospheric effects (transmittance) is strongly depending on the electromagntic wavelength. Remote Sensing exploits the region of relative atmospheric transparency called atmospheric windows.","hasChildren":true,"name":"Earth's (standard) Atmosphere Transmittance","selfAssesment":"Planned
"},{"code":"PP1-6-8","description":"With the term 'atmospheric windows' we refer to the regions of the electromagnetic spectrum where the interaction between the atmosphere constituents (i.e., molecules, aerosols, and cloud particles) and the electromagnetic radiation is minimized, namely the mechanisms of scattering and absorption of the radiation are less relevant than the transmission one. Therefore, the radiation collected at the sensor in these spectral regions is strictly depending on the Earth surface features, allowing to infer information about the processes/phenomena there in progress at the time of the acquisition. There are three main spectral ‘windows’ in the Earth's atmosphere. The first of these includes the visible and near-infrared (VNIR) parts of the spectrum up to the medium infrared, between wavelengths of about 0.38 μm and 3.5 μm, although it does also contain a number of opaque regions. This spectral interval includes the small portion of the electromagnetic spectrum to which human eyes are sensitive to (i.e, the visibile region between 0.4 and 0.7 μm). The second is a rather narrow region between about 8 μm and 15 μm, in which is found the bulk of the thermal infrared (TIR) radiation from objects at typical terrestrial temperatures. In this region there is only a main opaque interval, around 9.6 μm due to the presence of the ozone band. The third more or less corresponds to the microwave region, between wavelengths of a few millimeters and a few meters. Therefore, each remote sensing instrument that should be able to fully penetrate the Earth’s atmosphere has to be designed to operate in one of these three ‘window’ regions.","hasChildren":true,"name":"Atmospheric (spectral) windows for EO","selfAssesment":"Completed
"},{"code":"PP1-6-9","description":"The water cycle is a continuous purification process of water on Earth due to the movement of water species among various reservoirs. This cycle is vital for Earth’s life, ecosystems, and living organisms. The water cycle includes mainly four processes. Water is evaporated from ocean and land surfaces driven by solar heating. The resulting water vapor rises upwards into the atmosphere, transported by the winds, cools, and due to low air temperature condensates into liquid droplets and ice crystals to form clouds. The ice or/and liquid droplets collide, increase their size, and precipitate as snow or rain to Earth’s surface and oceans. The subtraction of energy (latent heat of evaporation) at low latitudes related to the evaporation processes as well as its release (latent heat of condensation) at higher latitudes related to the condensation processes is a formidable way to guarantees the heat transport from the warmer part of the Earth to the colder ones mantaining local air temperature more compatible with the human life. The starting point of the water cycle is not unique, but the oceans can be selected as the initial reservoir. Other important reservoirs are considered ice sheets, lakes, and rivers. \r\nThe hydrosphere is defined by the various water reservoirs which are characterized by different residence times – the time spends the water molecules in a reservoir. The water residence time – the rate at which the water comes out the reservoirs – varies for each reservoir extending from hundreds (Greenland Ice Sheet) or thousands of years (Antarctic Ice Sheet) to years and days for rivers and lakes, respectively. It also defines the energy transferred from the Earth to the Atmosphere which increases for short-term residence times. In long-term temporal scales, this energy is defined as the evaporation rate (E) and balances with the precipitation rate (P). This global energy balance breaks for shorter time scales depending also on the local and regional climate. For example, in regions located in the Inter-Tropical Convergence Zone (ITCZ), the energy balance in the water cycle does not exist since the precipitation rate is much higher than the evaporation rate (P>>E) due to the horizontal movement of converging trade winds.","hasChildren":true,"name":"The Water Cycle","selfAssesment":"Completed
"},{"code":"PP1-6","description":"Atmospheric Physics describe the processes affecting the physical, chemical and thermodynamic status of planetary atmospheres. In the context of EO sciences, it particularly refers to the physics of the interactions of e.m. radiation traveling across (or emitted by) the atmosphere as the main source of information collected by satellite (in general aerial) sensors.","hasChildren":true,"hasParent":true,"name":"Basics of Atmospheric Physics","selfAssesment":"Completed
"},{"code":"PP1-7-1","description":"According to the second law of thermodynamics, heat is a measure of the movement or the flow of energy from hotter substances to colder ones and it is measured in Joules. In microscale, heat is known as internal energy. Two regions in thermal contact have the same temperature when there is no net exchange of internal energy between them. Heat is the net transfer of internal energy from one region to another, while temperature, which is the degree of hotness or coldness of an object, describes the average kinetic energy of molecules within substances. The faster the particles are moving, the higher their kinetic energy. Since the motion of the particles within an object is random, they do not move at the same speed and in the same direction, some of them move faster. Therefore, those particles have more kinetic energy than the others. Thermodynamic temperature can be defined for substances at (even Local) Thermodynamic Equilibrium (i.e. in condition of density/pressure which allows an efficient equipartition of kinetic energy among molecules). Temperature is then the measure of the average kinetic energy of such a system, and is usually expressed in Celsius (°C). When, particular conditions of very low pressure/density (like in the Earth's thermosphere) cannot guarantee energy equipartition among molecules (i.e. outside thermodynamic equilibrium) the concept of Kinetic Temperature should be used instead. The Celsius temperature scale is defined by international agreement in terms of two fixed points: the temperature of the ice point, which is defined as 0° Celsius, and the steam point as 100° Celsius. The Fahrenheit (°F) temperature scale is mainly used in the United States; on this scale, water freezes at 32 degrees Fahrenheit, and the temperature of boiling water is 212 F. The Kelvin scale (K) is the base unit of temperature in the International System of Units (SI). This temperature scale is obtained by shifting the Celsius scale by −273.15°; zero Kelvin is also called absolute zero.","hasChildren":true,"name":"Temperature and heat","selfAssesment":"Completed
"},{"code":"PP1-7-10","description":"Irreversible thermodynamics investigates the regularities in transport phenomena, namely heat and mass transfer, and their relaxation. It is based on the first law of Thermodynamics, which correlate the heat flow density with pressure and viscosity, and the second law that describe the temporal variations of local entropy for local continuous mass.","hasChildren":true,"name":"The constitutive equations of irreversible fluxes","selfAssesment":"Planned
"},{"code":"PP1-7-11","description":"The Adiabatic process of homogeneous system occurs, when flow of heat is not exchanged across the boundaries of system and the system is characterized from uniform phase (solid or liquid or gases). In this case, the variations of entropy can be determined for some parts of system.","hasChildren":true,"name":"Heat equation and special adiabatic systems, special adiabats of homogeneous systems","selfAssesment":"Planned
"},{"code":"PP1-7-12","description":"The thermodynamic diagrams are used for the study of vertical structure and properties of the Atmosphere above a specific location. Especially, a static diagram represents a) an atmosphere with fixed potential temperature or b) a process curve of the change of variables of air parcel that rises adiabatically.","hasChildren":true,"name":"Thermodynamics diagram, atmosphere static","selfAssesment":"Planned
"},{"code":"PP1-7-2","description":"Kinetic theory of gases is based on a simplified molecular description of gases, from which the properties of volume, pressure and temperature can be derived. The assumptions of this theory are based on the random movements of molecules, their elastic collisions and the transfer of kinetic energy between them.","hasChildren":true,"name":"Kinetic theory of gases","selfAssesment":"Planned
"},{"code":"PP1-7-3","description":"The ideal gas law or general gas equation describes the equation of state of hypothetical ideal gas. This equation correlates the pressure and volume with its temperature, while is characterized as a combination of the empirical laws of Boyle, Charles, Avogadro and Gay-Lussac.","hasChildren":true,"name":"Ideal gas laws","selfAssesment":"Planned
"},{"code":"PP1-7-4","description":"The state functions of ideal gas are the pressure, volume, temperature, internal energy and entropy, which remain unchangeable in compared with the path. The internal energy is expressed through Joule’s law as a function of temperature of gas, while the entropy depends on the variation of volume and temperature.","hasChildren":true,"name":"State function of ideal gases","selfAssesment":"Planned
"},{"code":"PP1-7-5","description":"The phase rule for condensation is expressed as P+F=C+1. The terms of P, F and C describe the number of phases, minimum fixed variables and independent chemical species respectively. Concerning the condensed phases to distinguish the gases from liquids and solids, these are the density, molecular order, diffusion, etc.","hasChildren":true,"name":"State function of the condensed gas phase","selfAssesment":"Planned
"},{"code":"PP1-7-6","description":"When the system passes from initial to final state due changes in properties of temperature, pressure and volume, it is considered to have undergone thermodynamic process. The different types of thermodynamic processes are distinguished in the isothermal (fixed temperature), adiabatic, isochoric (stable volume), isobaric (stable pressure) and reversible process.","hasChildren":true,"name":"Thermodynamic process","selfAssesment":"Planned
"},{"code":"PP1-7-7","description":"Budget equations, namely heat, momentum and moisture budget, are interpreted through two frameworks, which are Eulerian and Lagrangian. Eulerian is utilized for the investigating of transfer of heat by the wind, while Lagrangian is concerned about the effects of ascending or descending airflows on the Earth-Atmosphere system.","hasChildren":true,"name":"Budget equations","selfAssesment":"Planned
"},{"code":"PP1-7-8","description":"The First Law of Thermodynamics supports that the energy is conserved. Thus, the thermal energy is defined as the sum of warming or internal energy (microscopic effect) and work occurring per unit mass (macroscopic effect). For its application to the Atmosphere, the thermal energy input is given from the following mathematical expression: Δq=Cp·ΔT-(ΔP/ρ), where Δq (J·kg–1) is the amount of thermal energy you add to a stationary mass m of air, Cp (J·kg–1·K–1) is the specific heat of air at constant pressure, ΔT (K) is the induced variation of temperature, so that Cp·ΔT represents the heat transferred per unit air mass, ΔP (Pa = J·m-3) is the pressure difference and ρ (kg· m-3) is the air density.\r\nThe term Cp·T is defined enthalpy h, thus, the first term on the right side of eq. of thermodynamic first low for atmospheric applications, which is the corresponding enthalpy change is: Δh=Cp·ΔT. It is a characteristic possessed by the air.\r\nExpressing the first law of thermodynamics for atmospheric applications in conceptual form we can state that, given a quantity Δq of thermal energy added to a stationary mass m of air, a part of this energy heats the air, increasing its internal energy, but, as air heats up, its volume expands by an amount ΔV and pushes against the surrounding atmosphere, which responds with an equal and opposite pressure P that we can assume constant. Therefore, a part of the thermal energy introduced does not go to heat the air, but goes into macroscopic movement.","hasChildren":true,"name":"First law of thermodynamic","selfAssesment":"Completed
"},{"code":"PP1-7-9","description":"A natural process that starts from an equilibrium state and ends in another state, causing changes in direction of entropy (ΔS) or statistical disorder of the system, is interpreted by Second Law of Thermodynamics. This law is considered as an irreversible process and it is expressed as ΔS=Heat transfer/Temperature.","hasChildren":true,"name":"Second law of thermodynamics","selfAssesment":"Planned
"},{"code":"PP1-7","description":"Thermodynamics is the science of the relationships between heat, work, temperature, radiation, energy and properties of matter. These relationships are governed by the four laws of thermodynamics which allow a quantitative description, through measurable macroscopic physical quantities, of processes that, at the level of microscopic constituents can be described by the statistical mechanics. Thermodynamics applies to a wide variety of topics relevant to EO science and technologies from atmospheric chemistry and meteorology up to sensor design and aeronautics.","hasChildren":true,"hasParent":true,"name":"Basics of Thermodynamics","selfAssesment":"Planned
"},{"code":"PP1-8-2","description":"Starting from the standard Rocket Equation - assuming a relative speed of the burned (emitted) fuel equal to 2,4 km/s and zero initial speed - it is possible to evaluate (for a single-stadium rocket) the mass percentage of payload that can be hosted on a platform depending on the final speed expected on the orbit. For instance a 28% payload is possible for a geostationary platform whose expected final speed on the orbit (radius 42.170 km) is 3,7km/s. Instead for a polar platform at about 800km this percentage reduce up to the 4% being the final sped on the orbit expected to be 7,5km/s.","hasChildren":true,"name":"Equation of the rocket and launch of a satellite: payload determination","selfAssesment":"Planned
"},{"code":"PP1-8-3","description":"The orbit of a satellite is commonly defined through its so called Keplerian parameters. These parameters represent the trajectory that the satellite will follow if no-perturbation are acting on it. A series of forces act on the satellite to perturb it away from the nominal orbit. We can classify these perturbations, or variations in the orbital elements, based on how they affect the Keplerian elements. The actual orbit of a satellite will result from a combination of these perturbations. Periodic maneouvers are needed to bring the orbit back to nominal conditions. The lifetime of a satellite is defined as the time interval that it takes to decay from its initial altitude to an altitude causing the satellite reentry down to the atmosphere. Therefore lifetime of a satellite should not be confused with the time during which the satellite will provide useful information (this operational phase, in general, is designed to last 5 - 7 years). In fact, all satellite terminating operational phases in orbits passing through the LEO region should be de-orbited or, where appropriate, manoeuvred to an orbit with suitably-reduced lifetime, that is, should be left in an orbit where drag and other perturbations will limit lifetime. The actual duration of the satellite in orbit will depend from the intensity of the perturbations which will affect its orbit. In case of satellite on GEO orbit, at the end of the operational phases they will be located on a disposal orbit, that is an orbit which do not cross the protected region. The protected region is the altitude region ranging from GEO - 200 km to GEO + 200 km and inclination region between -15 deg and +15 deg. Satellites in low Earth orbit, with perigee altitudes below 1000 km, are predominantly subject to atmospheric drag. This force very slowly tends to circularise and reduce the altitude of the orbit. The rate of 'decay' of the orbit becomes very rapid at altitudes less than 200 km, and by the time the satellite is down to 180 km it will only have a few hours to live before it makes a fiery re-entry down to the Earth.","hasChildren":true,"name":"Real orbits. Life time of a satellite, orbit’s decay.","selfAssesment":"Planned
"},{"code":"PP1-8-4","description":"The choice of a satellite orbit mostly depends on its main application. From this point of view it represents a crucial part of a satellite mission design. The most important parameters to describe a satellite orbit are the inclination angle i (of the orbit plane respect to the equatorial plane) its eccentricity e and its height H from the Earth's surface. In principle whatever eigth H can be used, provided that the speed of the satellite on its orbit allows the centrigugal force to exactely compensate the gravitational one at that heigth. Polar (i close to 90°) and Geostationary (i=0, H=35.800 km) orbits are the most common choices for EO satellites. In principle one single polar satellite can be sufficient to guarantee the global coverage of the Earth with equal quality of the images at all latitudes. All Geostationary satellites share the same circular orbit with H around 36000 km where the required speed exactely correspond to the one required to travel an entire orbit in 1 sideral day (orbital period P = 1 sideral day). This means that the satellite footprint is permanently in place over a specific Earth's location (e.g. for Meteosat 0°N, 0°E) allowing a quasi-continuous monitoring of a whole Earth's emisphere (with poor visibility of Earth's edges including Poles). Polar satellites' heigths are usually in between 700-800 km, with orbital periods around 100min (i.e. about 14,5 orbits/day) even if, lower orbits are also chosen particularly for very high spatial resolution payloads. Lower inclinations are also used (quasi-polar orbits) for specific applications. Due to the asphericity (and mass inhomogeneity) of the Earth, satellite orbit plane rotates around the Earth's polar axis with a period Pp producing (for elliptical orbits) the rotation of the orbit itself in its plane. A common choice for most EO polar satellites is to choose the orbital parameters in a way that Pp=1 year (Sun-Synchronous orbits). Due to the synchronism between Earth's revolution around the Sun and the orbit plane precession around Earth' axis, satellite passages happens at the same local solar time (similar illumination conditions) each time it flies over a specific region. This ensure repeatable sun illumination conditions facilitating image interpretation particularly for change detection or land monitoring applications. Other choices are possible when it is required to monitor with continuity high latitude regions.\r\n\r\nThis is the case of Molniya orbits which combine the continuity of observations typical of geostationary satellites with the possibility, offered by polar orbits, to overfly the highest latitudes regions. Its characteristics are: high eccentricity (e.g. e=0,74, axes 500 and 23.000 km), P=1/2 sideral day (Geo-Synchronous), inclination (i=63,4° or i=116,6°) which guarantees the satellite footprint at the apogee remaining positioned on a fixed ground point (non-rotating orbit). This way the satellite will spend more than 93% of its orbital period looking to the same emisphere even from a high latitude point of view. \r\n\r\nSo called altimetric orbits respond to the specific needs of altimetry. In this case the orbital parameters are chosen in order to guarantee, for example: a) that the ascending and descending sub-satellite tracks intersect at roughly 90 degrees on the Earth’s surface (so that orthogonal components of the surface slope can be determined with equal accuracy; b) the possibility to monitor all phases of tidal effects on ocean surface.\r\n\r\nParticularly important for several applications (multi-temporal analyses, change detection, etc.) are the Exactly repeating orbits.\r\nThey are conceived in order that the sub-satellite track will repeat itself exactly after a certain interval of time. This allows images having the same viewing geometry during the satellite’s lifetime making moreover available a particularly simple method of referring to the location of images (navigation or geo-referenciation) for example by referring to a ‘path and row’ system used for instance by the Landsat World Reference System (WRS). It is possible to arrange satellite orbits parameters in order to contemporary guarantee the sun-syncronism so that, not only satellite images collected on the same region can be easily super-imposed each-other but the same illumination and viewing geometry can be achieved. This is, for instance, the choice adopted for LANDSAT satellites whose images are typically available as a collection of scene of fixed dimension always similar each other when covering the same terrestrial area.","hasChildren":true,"name":"Satellite orbits parametrization and choice","selfAssesment":"Completed
"},{"code":"PP1-8","description":"Mechanics is the Physics branch dealing with the behaviour of physical bodies when subjected to forces or displacements. This section provides Mechanics basic elements necessary for determining the orbits of satellites and rockets. The different satellite trajectories will be illustrated with respect to their peculiarities","hasChildren":true,"hasParent":true,"name":"Basics of Mechanics","selfAssesment":"Planned
"},{"code":"PP1","description":"Optical Remote Sensing deals with those part of electromagnetic spectrum characterized by the wavelengths from the visible (0.4 micrometer) to the near infrared (NIR) up to thermal infrared (TIR, 15 micrometer). It regards the collection and interpretation of the e.m. radiation emitted, reflected, adsorbed and transmitted by the observed targets in order to derive their physical-chemical properties and related information. Such a possibility derives from the basic principle of (multi-spectral) remote sensing that is widely supported both theoretically (e.g. atomic and molecular spectroscopy) and experimentally (e.g. spectral signatures catalogues). It states that, in principle (e.g. disposing of sensors with ideal spectral capabilities) the matter-radiation interaction depends on the wavelength of the involved radiation and on specific (e.g. chemical/physical) properties of the matter that can be derived by the spectral analysis of the emerging (emitted, reflected, adsorbed or transmitted) radiation. As far as Earth Observation is concerned, specific related concepts have to be addressed like: the spectral matter-radiation interactions (spectral signature concept), natural sources (e.g. Earth, Sun) of optical e.m. radiation, theory of the Black Body, atmospheric physics and radiative transfer equations in the VIS-NIR and TIR spectral ranges, basic physics of e.m. optical sensors and image systems, physical fundaments of the interpretation of optical radiances collected by multi-hyperspectral passive techniques.","hasChildren":true,"hasParent":true,"name":"Basics of Optical Remote Sensing","selfAssesment":"Completed
"},{"code":"PP2-1-2-1","description":"A radar signal is a complex signal. It is represented by a real part, the in-phase component, and an imaginary part, the quadrature component. In-phase is usually annotated by “I”, and quadrature by “Q”. Considering single look complex data, each component is represented in a single image channel.","hasChildren":true,"name":"In-phase/Quadrature Component","selfAssesment":"Planned
"},{"code":"PP2-1-2-2","description":"A phasor represents a complex number and its phase and amplitude equivalent. Considering a complex SAR image’s pixel, the real and imaginary part can be represented by a 2D vector in Cartesian coordinates. Its corresponding phase and amplitude information corresponds to the direction and length of the vector, respectively.","hasChildren":true,"name":"Phasor","selfAssesment":"Planned
"},{"code":"PP2-1-2","description":"The signal emitted by a radar system is a microwave signal, which can be described using a complex wave representation. This implies that the signal can be entirely represented by a complex number, which characterizes both its magnitude and its phase at a certain moment of time. In the SAR context, the complex number is usually represented by a real part, the in-phase component (I), and an imaginary part, the quadrature component (Q), from which the corresponding magnitude and phase can be retrieved. In single look complex SAR data, each of these components is pictured in a single image channel. The terminology comes from electrical engineering, whereby the quadrature component is 90° out of phase with respect to the reference frequency and the in-phase component. This is necessary in order to retrieve the phase information during A/D conversion. The I component can be expressed as the signal amplitude multiplied by the cosine of the phase. The Q component corresponds to the amplitude of the signal multiplied by the sine of its phase. Using both components as input, the magnitude and phase for each signal echoes and location can be retrieved.\r\nThe relationship between I/Q terms and the magnitude and phase of the signal can be best represented using a phasor. A phasor represents a complex number and its phase and amplitude equivalent. It can be best illustrated by a 2D vector in a Cartesian coordinate system, which projections on the horizontal and vertical axes represents the real and imaginary part, respectively. The length of the vector correspond to the signal’s amplitude and its direction (angle between the horizontal axis and the vector) characterizes the phase of the signal. Using simple mathematical considerations, the relationship between I/Q and amplitude and phase can be established.\r\nEach signal echo and pixel of a complex SAR image can be represented with such a phasor and the necessary amplitude and phase information can be accordingly retrieved.","hasChildren":true,"hasParent":true,"name":"Complex wave description","selfAssesment":"Planned
"},{"code":"PP2-1-4","description":"Electromagnetic waves are polarized; the direction of the polarization corresponds to the direction of oscillation of the electromagnetic field. Typical and often used linear polarisations are: H (horizontally) and V (vertically) polarized waves of the plane of the electric field vector oscillations relative to the sensor coordinate system. The polarization state of a backscattered wave from a natural surface can be linked to the geometrical characteristics like shape, roughness and orientation and the intrinsic properties of the scatterer like moisture, salinity, density. The radar system is characterized by combination of polarization of transmitted and received pulse: HH, HV, VH or VV. Based on the polarization sent and obtained the radar systems are divided in three polarization modes. Single polarization refers to the same polarization transmitted and received; dual polarization, one polarization is sent and another received; or quad polarization, when system is able to transmit and receive all four types of polarization. When making a contact with a scatterer, the polarization of the EM-wave can change, depending on the geometrical and dielectrical properties of the scatterer. In order to get all necessary information about those changes, full polarimetric systems are required.","hasChildren":true,"hasParent":true,"name":"Polarisation","selfAssesment":"Completed
"},{"code":"PP2-1-5","description":"Property of signal or data set in which the phase of the constituents is measurable, and plays a significant role in the way in which several signals or data combine. Two waves with a phase difference that remains constant over time, are said to be coherent.","hasChildren":true,"name":"Coherent","selfAssesment":"Planned
"},{"code":"PP2-1-6","description":"In remote sensing, phase is the exact position within a periodic signal with respect to an arbitrary reference point. It is typically expressed as an angle and measured in degrees or radians, where one period corresponds to a phase of 360° or 2π, respectively. Mathematically, phase is the argument of a complex number, that is the angle between its geometric representation in the complex plane and the real axis. For this reason, complex algebra is often used in remote sensing to facilitate phase calculations. Due to its periodic nature, phase can only be measured unambiguously within one period. Consequently, phase measurements are commonly subject to 2π phase ambiguities. These ambiguities can often be resolved in a process called phase unwrapping, using a priori information about the signal, typically related to its continuity. Phase measurements are crucial for the creation of synthetic aperture radar (SAR) images, as well as for many SAR imaging techniques, including interferometric SAR (InSAR).","hasChildren":true,"name":"Phase","selfAssesment":"Completed
"},{"code":"PP2-1-7","description":"Shift in frequency caused by relative montion along the line of sight between sensor and the observed scene.","hasChildren":true,"name":"Doppler effect","selfAssesment":"Planned
"},{"code":"PP2-1-8","description":"The wave-particle dualism (duality) is a theory according to which all matter exhibits the attributes of waves and particles.","hasChildren":true,"name":"Wave-particle dualism","selfAssesment":"Planned
"},{"code":"PP2-1","description":"The microwave portion of the electromagnetic (EM) spectrum ranges from 1 millimeter to 1 meter. Imaging radars are independent of weather conditions and can operate day or night. EM-waves are polarized. Normally only the horizontal (H) or vertical (V) linear polarizations are used. The radar system is characterized by combination of polarization of transmitted and received pulse: HH, HV, VH or VV. When making a contact with a scatterer, the polarization of the EM-wave can change, depending on the geometrical and dielectrical properties of the scatterer.The data can be acquired from both the ascending (northwards) and descending (southwards) satellite passes. Water clouds can interfere with the radars operating below 2 cm in wavelength. The effects of rain can be generally ignored at wavelengths above 4 cm. For longer wavelengths (above 20 cm), an effect called Faraday rotation caused by the ionosphere, i.e., free charges (electrons) and the Earth’s magnetic field, can lead to a rotation of the polarization plane. In the presence of Faraday rotation, the data, usually fully polarimetric, should be corrected. The radar systems operate in different bands that uses different wavelengths. The most common frequences/wavelengths (frequency = Speed of Light / wavelength) for environmental applications are X (5,75-10,90 GHz), C-(4,20-5,75 GHz), S-(1,550-4,20 GHz), L-(0,390-1,550 GHz) and P-(0,255-0,390 GHz) band. The selection of SAR system for acquiring data depends on their application. Longer wavelengths are mainly devoted to communication and navigation purposes. Radars penetrate atmosphere and clouds. For example for forestry, longer wavelengths starting from C- or S-band are preferred.","hasChildren":true,"hasParent":true,"name":"Microwave portion of electromagnetic spectrum","selfAssesment":"Completed
"},{"code":"PP2-2-1","description":"Diffraction is defined as interaction of waves with any solid object, not surfaces, and is not to be confused with refraction. More precisely, diffraction describes the phenomena of interaction of waves at an obstacle, such as an aperture, or an opening, such as a hole or an occurring space between two objects. Hence, diffraction is an essential form of scattering, describing ordered scattering at discrete boundaries. The effect of diffraction can be observed through extended interference patterns or simply by the bending of waves. In the field of microwave remote sensing, diffraction has the practical implication that it limits the spatial resolution of a microwave sensor since it acts on the ability of an imaging system to resolve details. This theoretical limit of resolution is called the diffraction limit. This means, the larger the aperture of the observing system compared to its employed wavelength (dependent on the frequency), the finer the resolution of an imaging system. The diffracted field can be calculated with analytical models, such as the Fraunhofer diffraction approximation in case of far field conditions, where the object is far away and the incident waves are assumed to be plane waves, or the Fresnel diffraction approximation in case of near field conditions, where the waves are spherical.\r\nOne simple example of diffraction is the diffraction of sound, for example the possibility to hear sounds around corners.","hasChildren":true,"name":"Diffraction","selfAssesment":"Completed
"},{"code":"PP2-2-2","description":"Scattering means the redirection of incident electromagnetic energy by an object. Similar to diffraction, scattering refers to the same physical process, the coherent distortion of an incident wave. However, diffraction as well as reflection can be regarded as essentially forms of scattering. Scattering explicitly describes the “random distortion of waves by elements that are similar in size or less than the wavelength” (Woodhouse, 2005). Thereby, scattering of the incident wave at an object can occur in any directions with varying strength, with the scattering pattern varying with the incident direction. Thus, the term scattering cross section, often denoted by σ, quantifies the effectiveness of a scatterer. In the field of active microwave remote sensing, the backscattering coefficient σ0 is known “as the ratio of the statistically, averaged, scattered power density to the average incident power density” (Fung, 1994). \r\nIn passive microwave remote sensing, radiometers measure the intensity of radiation emitted by a body, called brightness temperature TB. Since TB is always less than its physical temperature T, emissivity, defined as e = TB / T, is a measure of how strongly a body radiates at a given wavelength. It varies between 0 (metal) to unity (blackbody).\r\nEmission and scattering are complementary: surfaces that are good scatterers are weak emitters, and vice versa.","hasChildren":true,"name":"Scattering and emission","selfAssesment":"Completed
"},{"code":"PP2-2-3","description":"In climate change studies the carbon cycle with its crucial component the terrestrial biosphere is of great importance due to the ability of the biosphere to store environmentally harmful carbon dioxide. Radar sensors, especially SAR, can here provide a useful tool for quantifying and monitoring the biosphere. Hence, the relationship between biomass and radar backscatter responses has been studied in detail in recent decades. Results show that the sensitivity of measured radar backscatter coefficient decreases with increasing amount or density of present biomass. In the so-called saturation region, the radar backscatter saturates at a biomass depending on the employed wavelength. While for higher frequency bands like C-band (3.95-5.8 GHz), biomass can be measured up to ~50 ton/ha, the amount of measurable biomass increases with decreasing frequency (due to the increasing wavelength), such that at L-band (1-2.6 GHz) ~ 100 ton/ha and at P-band (0.23-1 GHz) ~200 ton/ha biomass can be measured. Further, the sensitivity of radar to biomass is different for co- or cross-polarized backscatter since the level of saturation depends not only on frequency but also on vegetation (e.g., height, structure, density, moisture) and soil surface (e.g., roughness, moisture) parameters. Overall, the saturation of radar backscatter depending on biomass has to be considered when analyzing SAR data.","hasChildren":true,"name":"Backscatter saturation","selfAssesment":"Completed
"},{"code":"PP2-2-4-1","description":"The radar equation is a measure of the received echo at the sensor. It defines what proportion of the transmitted energy is returned from a target. It is a function of the range between the antenna and the target, the antenna gain and the radar cross-section of the target. Mathematical expression that describes the average received signal level, compered to the additive noise level, in terms of system parameters. Principal parameters include: transmitted power, antenna gain, noise power, and radar range.","hasChildren":true,"name":"Radar equation","selfAssesment":"In progress
"},{"code":"PP2-2-4-2","description":"Coefficient sigma or sigma nought represents the average reflectivity of a horizontal material sample, normalized with respect to a unit area on the horizontal ground plane.","hasChildren":true,"name":"Sigma nought","selfAssesment":"Planned
"},{"code":"PP2-2-4-3","description":"Gamma nought represents the average reflectivity of a horizontal material sample, normalized with respect to the incident area, orthogonal to the incident ray from the radar.","hasChildren":true,"name":"Gamma nought","selfAssesment":"Planned
"},{"code":"PP2-2-4-4","description":"Radar brightness coefficient represents the reflectivity per unit area in slant range.","hasChildren":true,"name":"Beta nought (brightness)","selfAssesment":"Planned
"},{"code":"PP2-2-4","description":"Measure of radar reflectivity. The Radar Cross Section (RCS) is expressed in terms of the physical size of an hypothetical uniformly scattering sphere that would give rise to the same level of reflection as that observed from the sample target.","hasChildren":true,"hasParent":true,"name":"Radar cross-section","selfAssesment":"Planned
"},{"code":"PP2-2-5-1","description":"A material constant is a physical or chemical property of a substance, which can be expressed in numbers. Giving a precise numerical value of a constant often requires determining the external conditions (e.g. temperature, humidity). Material constants are factors that influence the interaction of microwaves with the target objects.","hasChildren":true,"name":"Material constants","selfAssesment":"Planned
"},{"code":"PP2-2-5-2","description":"The complex part k of the refraction index n=m+ik determines how far an electromagnetic wave of wavelength λ can survive crossing a specific medium. The attenuation length la is the distance after that the amplitude of an electromagnetic signal reduces its value by an amount of 1/e. For instance the amplitude of the Electric field E(z) of an electromagnetic wave proceeding along the z direction is decreasing as exp(-z/la) being la=λ/(2𝜋k) the attenuation length associated to that specific material (k) and wavelength λ. This way attenuation length in water can be of hundreds of meters in the visible range and just few microns in the microwaves. The opposite happens over solid land surfaces where optical waves can penetrate from few microns up to few millimeters (moving from the VIS-NIR to the TIR spectral range) whereas microwaves can reach depths from hundreds to thousands (as higher are their wavelength) meters allowing the exploration of subsoil and thick coulters of ice.","hasChildren":true,"name":"Attenuation lenght and penetration depth","selfAssesment":"Completed
"},{"code":"PP2-2-5-3","description":"Soil permittivity is a measure of the water content (soil moisture) in the soil and characterized by the metric of the dielectric constant of the soil. Soil moisture influences emission, absorption and propagation of microwave electromagnetic energy. Moisture decreases the ‘emissivity’ of soil, and thereby affects microwave radiation emitted from Earth’s surface. Dry soil has a low dielectric constant and low radar reflectivity. Moist and partially frozen solis have intermediate values. The higher the soil water content, the lower the radar signal penetration into the soil. In situ measurements of soil permittivity are a prerequisite for the calibration and validation of synthetic aperture radar (SAR) soil moisture retrieval algorithms. Soil moisture is a key variable in the hydrologic cycle and is recognized as an Essential Climate Variable (ECV).","hasChildren":true,"name":"Soil permittivity","selfAssesment":"Completed
"},{"code":"PP2-2-5-4","description":"The complex relative permittivity of a plant is a function of its contained amount of water, solutes (mainly their salinity) and temperature in all plant compartments (including roots). The more water and the higher the salinity are in the plant compartments, the higher is the complex relative permittivity of the plant. The complex relative permittivity of a plant refers to the complex relative dielectric constant of the plant and can be subdivided into complex relative permittivity values for the different plant compartments (roots, stem/stalk, leaves, fruit,...). The complex relative dielectric constant or permittivity parameter has a real and an imaginary part indicating the moisture content and the conductivity (loss) of the plant medium. Models of plant permittivity consist mostly of a free-water and a bound-water part. In particular, plant water is a solute of nutrients and not all water-conducting plant cells are fully filled by water, but also with air. Hence, the estimation of one plant permittivity, especially including several plant parts can be challenging to assess, to understand and to model. To acknowledge this mixture of components, dielectric mixing models containing the single material components are normally developed and applied, representing an effective complex relative permittivity of all plant components. Concerning a vegetation canopy, electromagnetic waves interact with a more or less sparsely vegetation-filled volume unit of air. A vegetation canopy represents a dielectric mixture of vegetation inclusions (leaves, twigs, branches, stems,…) distributed in a volume of air. Dielectric mixing models of canopies take this vegetation volume fraction into account.","hasChildren":true,"name":"Plant permittivity","selfAssesment":"In progress
"},{"code":"PP2-2-5","description":"The dielectric properties of any material can be described by the complex relative dielectric constant (complex relative permittivity) and contains of the real part (moisture content) and the imaginary part (conductivity/loss tangent). For instance: Reflectivity of a smooth surface and the penetration capabilities of microwaves into the material are determined by these two quantities. The complex dielectric constant changes mainly due to variations in water content, salinity, temperature of the material as well as due to the observing wavelength and polarization of the electromagnetic wave. It relates to the interaction of weakly-charged material components, like bi-polar water molecules, with irradiation of electromagnetic waves. The interaction increases with amount and charge of the material components. The complex relative permittivity is also linked to the complex index of refraction as being its square. In order to describe the complex relative permittivity of pure and saline water the single-relaxation Debye and the double-Debye dielectric model can be used. As the movement of bi-polar material components is significantly reduced when the material is put under freezing conditions (temperatures below 0 °C), the permittivity falls to almost a constant. The real part of the relative permittivity of pure ice is almost constant, when ignoring a weak temperature dependence, and amounts to approx. 3.2. For heterogeneous (mixed) materials consisting of more than one component the equivalent dielectric constant is a function of the permittivity of the single components, their volume fractions, their distribution along space and the polarization and wavelength of the interacting electromagnetic wave.","hasChildren":true,"hasParent":true,"name":"Dielectric Properties","selfAssesment":"Planned
"},{"code":"PP2-2-6-1","description":"The standard deviation of the surface height variation (or RMS height), denoted by s (or hRMS), describes the statistical variation of a random surface with height z(x). In case of an azimuthally symmetrical surface, the single-scale RMS height of the one dimensional case for discrete profile values is given by (1), where N is the number of samples, and z ̅ the mean surface height (2). \r\nAs roughness depends not only on the soil surface properties but also the wavelength λ of the electromagnetic signal, the roughness parameters are scaled by the wave number k. Hence, the electromagnetic roughness ks for surface roughness parameter s is (2π/λ)*s (3). In order to determine if a random surface may be considered as electromagnetically smooth, one common definition is given by the Rayleigh roughness criterion, where s < λ / 8*cosθ, or ks < 0.8, at incidence angle θ = 0. This criterion has been revised for the microwave region, where the wavelength is usually of the order of the RMS height, called the Fraunhofer roughness criterion, where s < λ / 36*cosθ, or ks < 0.2, at incidence angle θ = 0. Additionally, surfaces are considered as electromagnetically rough for 1 < ks < 3.","hasChildren":true,"name":"Vertical roughness component (RMS height)","selfAssesment":"Completed
"},{"code":"PP2-2-6-2","description":"The surface correlation length, denoted by l, is defined as the displacement ξ at which the surface correlation function p(ξ)= 1/e. Thus, l can be seen as the reference length up to which two points of one soil surface can be regarded as statistically independent from each other. If we imagine a perfectly smooth soil surface, l=∞ since every point on that surface correlates with all other points and can therefore be regarded as dependent from each other.\r\nAs roughness depends not only on the soil surface properties but also the wavelength λ of the electromagnetic signal, the roughness parameters are scaled by the wave number k. Hence, the electromagnetic roughness kl for surface roughness parameter l is kl=(2π/λ)*l.\r\nExperimental results indicate a weaker influence on the radar backscatter compared to the RMS height s.","hasChildren":true,"name":"Horizontal roughness component (correlation length)","selfAssesment":"Completed
"},{"code":"PP2-2-6-3","description":"The surface correlation function p(ξ) determines the degree of correlation between two lateral separated locations of one surface. Thereby, ξ is defined as displacement between two locations, (x, y) and (x', y') on the surface and given by (1).\r\nWith increasing separation between two locations on the surface p(ξ) decreases, and at a certain distance, the surface correlation length l, the heights at the two locations are considered statistically uncorrelated.\r\nThe surface scattering of electromagnetic waves can be simulated with various models. Depending on the observed roughness scale multiple surface scattering models are valid for specific roughness conditions. For example, one of the first surface scattering models for slightly rough surfaces, the small perturbation model (SPM), deals with roughness scales that are small relative to the wavelength and hence has validity conditions for ks < 0.3, kl < 3, and m < 0.3. Since then, various surface scattering models for computing the scattering and emission behavior of natural surfaces in the microwave region have been proposed, such as the Kirchhoff scattering model (KH), the geometric optics model (GO), the physical optics model (PO), or the integral equation model (IEM), to name the most common used in literature. For simulations of EM scattering at soil surfaces, assumptions of the functional forms of p(ξ) have to be made. The two most common forms for mathematically describing the surface correlation of natural surfaces are the exponential pE(ξ) and the Gaussian pG(ξ) correlation functions, defined by (2) and (3).\r\nFor some mathematically sophisticated surface scattering models, an x-Power correlation function p(x-Power)(ξ) can be assumed (4), with x as value between 1 and 2.\r\nIn literature, rather smooth surfaces are characterized by an exponential surface correlation function, while rather rough surfaces are characterized by a Gaussian surface correlation function.","hasChildren":true,"name":"Surface correlation function","selfAssesment":"Completed
"},{"code":"PP2-2-6-4","description":"The root-mean-square (RMS) slope m of a one dimensional height profile for one random surface is given by (1), with s as the standard deviation of the surface height variation (or RMS height), and p''(0) as the second derivative of the surface correlation function p(ξ), evaluated at ξ=0. Since p(ξ) is an even function, p''(0) is a negative quantity.\r\nFor modeling of electromagntic scattering at soil surfaces, assumptions of the functional forms of p(ξ) have to be made. The most common known forms are the exponential and Gaussian correlation functions. Additionally, some models allow the assumption of a x-Power correlation function, with x as value between 1 and 2. For the varying surface correlation functions, the RMS slope m is given by (2)-(4).\r\nIn literature, for L-band, the slope m should be lower than 0.3 or 0.4 in case of single scattering and bare soil surfaces with moderate RMS heights.","hasChildren":true,"name":"Surface roughness slope","selfAssesment":"Completed
"},{"code":"PP2-2-6-5","description":"In reality, one random surface has multiple roughness scales, since the commonly used surface description based on single-scale roughness parameters does not comprise all the properties of natural surfaces relevant for describing wave scattering. Depending on the wavelength λ of the microwave sensor the dimension of the surface roughness parameters s and l correspond to specific roughness scales. \r\nIn case of multi-scale roughness, the equivalent RMS height is a composite of the individual RMS heights at different roughness scales (1).\r\nA three-scale surface, as shown in Fig. 1, for example consists of a small-scale high-spatial frequency variation (c) ‘riding’ on top of the larger scales, the medium-scale perturbation (b) and the large-scale undulation (a).\r\nAt microwave frequencies, the centimeter scale is the scale of roughness of primary importance, since λ is on the order of centimeters to a few tens of centimeters. For natural surfaces it is very difficult to measure millimeter-scale roughness.","hasChildren":true,"name":"Single-scale & multi-scale roughness","selfAssesment":"Completed
"},{"code":"PP2-2-6","description":"Surface roughness defines the geometry between the pedosphere and the atmosphere (soil-air boundary).\r\nIn the field of microwave remote sensing, surface roughness affects scattering and emission characteristics of natural surfaces. The degree of roughness of a random surface is determined by statistical parameters, measured by the units of wavelength of the observing sensor. The two fundamental surface roughness parameters are the standard deviation of the surface height variation (RMS height) s, with its related surface correlation function p(ξ), and the horizontal surface correlation length l. Additional, a third roughness parameter, the root-mean-square (RMS) slope m, is important for some surface scattering models to simulate electromagnetic wave scattering of surfaces.\r\nSurface roughness determines the variation of surface height within an imaged resolution cell. The transition from smooth to rough is qualitative, and is function of both wavelength and incident angle. With decreasing frequency the soil surface appears rather smooth to microwave sensors. This results in the fact, that while one surface appears smooth when sensed at L-band (λ ≈23 cm), the same surface appears rough when sensed at X-band (λ≈3 cm). Hence, in the field of microwave remote sensing, the ‘effective’ surface roughness parameters are scaled by the wave number k= 2π/λ. Surface roughness can be observed at single or multi-scale.","hasChildren":true,"hasParent":true,"name":"Surface roughness","selfAssesment":"Completed
"},{"code":"PP2-2-7-1","description":"The Stokes vector is a four-element vector containing real-valued polarization combinations and is an alternative form of representing a full (=quad) polarimetric dataset, besides the complex-valued scattering matrix. Stokes vectors can be measured as real quantities and are preferred over the complex-valued Jones vector formalism when a coherent (phase-preserving) measurement system is absent. Stokes vectors can be used to form the 4x4 Mueller matrix for target scattering analyses, mostly used in the field of optics. First component of the Stokes vector is the sum of the co-polar fields and represents the total energy of the wave. Second component is the difference of the co-polar fields. Thrid component is the real part of the cross-correlation of the fields and fourth component is the imaginary part of it. The different polarization states can be represented by the Stokes vector and an O(3) elliptical transformation can be used to change the polarization basis, similar to the Jones vector where the SU(2) elliptical transformation is used.","hasChildren":true,"name":"Stokes Vector","selfAssesment":"Completed
"},{"code":"PP2-2-7-2","description":"The scattering matrix is a 2x2 square matrix containing four complex-valued polarization measurements (amplitude & phase) forming one full (= quad) polarimetric set of coherent observations. An often recorded set of polarizations is the combination: HH (horizontal receive - horizontal transmit), HV (horizontal recive - vertical transmit), VH (vertical receive - horizontal transmit) & VV (vertical receive - vertical transmit). The scattering matrix is fully suficient for describing scattering from coherent targets (dominating the resolution cell), but not for incoherent tragets (mix of scattering contributions in the resolution cell). For the latter, the coherency and the covariance matrices are the more appropriate descriptions of scattering from incoherent targets.","hasChildren":true,"name":"Scattering matrix","selfAssesment":"Completed
"},{"code":"PP2-2-7-3","description":"The covariance and coherence matrix are two 4x4 square matrices, which can be built out of the scattering matrix by a lexicographic and a Pauli target scattering vector. They are an alternative representation of a full polarimetric dataset allowing the analysis of incoherent targets (more than one dominant scatterer in the resolution cell) and the phenomenon of depolarisation (transformation of incoming fully polarised wave into a partially polarised wave by creating a variety of different types of polarizations during media interaction). These matrices can be converted into each other without loss of information (by unitary transformations), but not turned back into the scattering matrix due to averaging operations during formation of coherency or covariance matrices.","hasChildren":true,"name":"Covariance/Coherency matrices","selfAssesment":"Completed
"},{"code":"PP2-2-7-4","description":"Polarimetric decomposition techniques allow signal unmixing by polarimetry in order to separate different scattering contribution within one resolution cell, e.g. from soil & vegetation or snow, ice & bedrock. They can be either applied for the scattering matrix (coherent form - one dominant scatterer in the resolution cel) or for the covariance/coherency matrix (incoherent form - more than one dominant scatterer in the resolution cell). Decomposition techniques can be model- (physics) or eigen- (mathematics)-based. The eigen-based decomposition allows to diagonalize the coherency or covariance matrix in a diagonal eigenvalue matrix and a matrix of column eigenvectors. From eigenvalues and eigenvectors the polarimetric entropy, the scattering alpha angle and the polarimetric anisotropy. The polarimetric entropy is a matric for the degree of depolarization of the scattering event. The scattering alpha angle is an intrinsic scattering mechanism indicator. The polarimetric anisotropy informs about secondary scattering mechanism in evironments with high entropy. If the anisotropy is high only one secondary scattering mechanism is present, if it is low, more than one will occur.","hasChildren":true,"name":"Polarimetric decomposition techniques","selfAssesment":"Completed
"},{"code":"PP2-2-7-5","description":"All bi- or multi-polar (non-inert) media have the tendency to orient themselves in 3D-space if an external non-ionizing electro-magnetic field is excited on them. This orientation polarization is caused by negatively and positively charged areas within the media, for instance due to charges of the different molecules and atoms building up the media, under the premise that the media is able to rotate (partly) freely and is not completely fixed. Molecules of liquid water are a prime example. Here the two positively charged hydrogen atoms are oriented in a 105-degree configuration to the negatively charged oxygen atom, forming a slightly charged bi-polar medium that orients itself under electromagnetic radiation treatment, especially at the frequency range of microwaves and millimeter-waves.","hasChildren":true,"name":"Orientation polarisation of media","selfAssesment":"In progress
"},{"code":"PP2-2-7-6","description":"Polarimetric coherences are complex-valued polarimetric correlation coefficients assessing the redundance between different polarimetric observations informing about their divergence in information. They can be formed among mutual polarimetric observations showing their degree of correlation. The polarimetric coherence consists of a magnitude, ranging between zero (no correlation) and one (identical), and a phase information, running from -180° to 180°. Typically polarimetric coherences are calculated between the co-polarimetric (HH, VV) channes, as well as the cross-polarimetric channels (HV, VH). The latter polarimetric coherence assesses the system noise inherent in the recorded polarimetric data, if a monostatic systems (transmitting and receiving sensor on the same sensing platform) is used for acquisition.","hasChildren":true,"name":"Polarimetric coherences","selfAssesment":"Completed
"},{"code":"PP2-2-7-7","description":"The polarisation ellipse and the Jones vector formalism are the geometrical (three real-valued angles) and algebraic (amplitude & phase) formalisms to describe polarisation states of an electromagnetic wave. The ellipse has an orientation, an ellipticity and absolute phase angle. The three angles are integrated in one mathematical ellipse formulation that can represent linear, elliptic and circular polarisation states. The Jones vector formalism is an algebraic formulation allowing all calculus available in linear algebra. Both representations (polarisation ellipse & Jones vector) can be converted into each other seemlessly with a simple elliptical basis (special unitary SU(2)) transformation.","hasChildren":true,"name":"Polarisation ellipse / Jones vector formalism","selfAssesment":"Completed
"},{"code":"PP2-2-7-8","description":"The concept of polarisation synthesis is based on the mathematical fact that a set of polarimetric measurements in one basis, e.g. H,V, can be converted into any other polarimetric basis, by a mathematical transformation. A basis set is a set of four polarisations. Each set is orthogonal, like LC (left-circular), RC (right-circular). The striking point is that only one set of polarimetric measurements in one basis needs to be recorded and the transformation in other polarimetric bases is done in a post processing step afterwards. There is no need to measure all bases, which is quite complicated in terms of engineering for elliptical and circular polarisation states.","hasChildren":true,"name":"Polarisation synthesis","selfAssesment":"Completed
"},{"code":"PP2-2-7","description":"Polarimetry is the technique to evalute the physical phenomenon of polarisation including the measurement, the processing and the interpretation of the polarisation state of an electromagnetic wave. Polarization states are described by the scattering elipse and the Jones Vector formalism. Especially the polarization states after interaction with the media under investigation are mostly investigated to estimate media properties and states. The mostly observed fully polarimetric observation basis is H,V up to now with the single observations: HH HV, VH, VV. The concept of polarization synthesis allows to acquire fully polarimetric observations in one basis (e.g. H,V) and transform them into any other orthgonal basis (e.g. left, right circular) by a mathematical transformation in post processing. Polarimetric States are stored in different mathematical formats: Scattering matrix, polarimetric coherences , Stokes vector, Pauli-vector, lexicographic vector, coherency and covariance matrices. These mathematical representations can be decomposed according to the contained elementary scattering mechanisms in the recorded signal. The so-called polarimetric decomposition technique allow signal unmixing for differnt scattering components (e.g. from soil & vegetation). The techniques range from mathematics-based until physics-based concepts and are developed since decades starting with Huynen in 1970.","hasChildren":true,"hasParent":true,"name":"Polarimetry","selfAssesment":"Completed
"},{"code":"PP2-2","description":"A number of interactions are possible when electromagnetic energy encounters matter, whether solid, liquid or gas. In Earth Observation there are two main interactions: atmospheric and with target. Atmospheric interaction: In radar remote sensing, atmospheric interactions are limited due to the long wavelengths compared to the size of the atmospheric particles. The fact that microwaves interact with object at least as big as the wavelength is one of the greatest advantages of microwave remote sensing, since at larger wavelengths atmospheric particles are almost transparent to the signal and microwave sensors are independent from the time of day (day or night) and weather conditions. Water clouds can interfere with the radars operating below 2 cm in wavelength. The effects of rain can be generally ignored at wavelengths above 4 cm. For longer wavelengths (above 20 cm), an effect called Faraday rotation caused by the ionosphere, i.e., free charges (electrons) and the Earth’s magnetic field, can lead to a rotation of the polarization plane. Target interaction: The radar interaction with the object is a result of both radar system parameters (frequency, polarization, acquisition geometry) and the physical properties of the object (dielectric constant, i.e., water content; geometrical properties, i.e., the roughness, shape and orientation of the scatterer). Overall, various types of interactions can be distinguished – scattering, diffraction, and reflection – all describing the same process of wave interaction but at different scales.","hasChildren":true,"hasParent":true,"name":"Interaction of microwaves with matter","selfAssesment":"Completed
"},{"code":"PP2-3-1-1","description":"The goal of an radar antenna is to direct and receive the transmitted and backscattered signal in a specific angular direction. The antenna gain describes the directional sensitivity of the antenna. It is a dimensionless quantity that is constant for a specific antenna.","hasChildren":true,"hasParent":true,"name":"Antenna gain","selfAssesment":"Planned
"},{"code":"PP2-3-1-2","description":"The antenna radiation pattern shows the direction in which the antenna transmits and receives the energy in space, as well as the strength of this radiation. It is a function of angles and consists of different lobes, in which the signal is directed and received. There are two principal representation of the antenna patterns: field and power patterns, which are a function of the electric and magnetic fields of the energy being radiated.","hasChildren":true,"name":"Antenna pattern","selfAssesment":"In progress
"},{"code":"PP2-3-1","description":"Antenna is a device that radiates electromagnetic energy and collects it during reception.","hasChildren":true,"hasParent":true,"name":"Radar antennas and antenna calibration","selfAssesment":"Planned
"},{"code":"PP2-3-10-2","description":"The radargrammetric equation follows a similar principle as the stereoscopic equation, except that it uses the radar geometry. The radargrammetric observation equation allows the retrieval of 3D information about a target, based on the determination of the sensor-object stereo model. It estimates the coordinates the intersection of the two radar rays coming from the two different sensor positions with different look angles, using the coordinates of the satellites position and satellite velocity. The radargrammetric equation can be adapted in order to retrieve 3D information in layover areas (e.g. urban areas).","hasChildren":true,"name":"Radargrammetric equation","selfAssesment":"Completed
"},{"code":"PP2-3-10","description":"Radargrammetry is the technique for extracting three-dimensional information from radar images. It applies photogrammetric principles to synthetic aperture radar (SAR) images. By viewing an object from different positions separated by a baseline, the appeared object position will vary slightly (denoted parallax). The disparities for each position on the object are related to its x-y-z coordinates. In radargrammetry, such disparities are computed for an entire image. The result is the terrain elevation from the measured parallaxes between two (or more) images, acquired at different angles. Radargrammetry requires at least two SAR images acquired from different positions, normally across-track due to the configuration of a side-looking SAR. Same-side stereo-pairs with intersection angles in the range of about 10 – 20° have been a feasible compromise between reasonable geometric disparities and the accuracy of estimated heights. In general, the disparities can be estimated with higher accuracy as the angle of intersection increases (as the stereo exaggeration factor increases). However, the same points must be recognized in all images, and it is hence required that the images are as similar as possible. This improves the image matching and it is best achieved with small intersection angles, which furthermore decreases radiometric differences. \r\nA general procedure for generating an elevation model from stereo-pairs is applicable for radargrammetry when optical stereo images are replaced with the backscatter intensity of SAR images. One image is selected as reference and the other(s) is coarsely registered to the reference, e.g., by using the attached meta-data. The same points are then located in both images using image matching. A common matching criterion is the cross correlation coefficient. Then, spatial point intersections are computed, which is the least square approach to find the intersection points of SAR range circles as defined from the matched image pixels. The computed intersections result in a point cloud that finally is interpolated to a consistent elevation raster. The entire process is extensive and computationally expensive, and normally a dedicated software is required. \r\nRadargrammetry with images acquired from opposite sides have been little investigated, and was first limited to stereoscopic viewing. Some opposite-side research was later presented with limited outcomes under certain conditions. Most applications today will not consider opposite-side radargrammetry, since the alternatives are usually better. Same-side radargrammetry performs better than opposite-side, while interferometric SAR that is based on phase differences, may be even more accurate. One advantage of radargrammetry is however, that it remains less affected by atmospheric disturbances compared to interferometric SAR, because it is using the amplitude images.","hasChildren":true,"hasParent":true,"name":"Radargrammetry (same-side and opposite-side)","selfAssesment":"Completed
"},{"code":"PP2-3-11-1","description":"Differential Synthetic Aperture Radar Interferometry (DInSAR) aims the determination of deformation of the Earth’s surface that happened between two or more complex-valued SAR acquisitions.\r\nThe phase of an interferogram issued from the complex multiplication of a SAR image with the complex conjugate of a second SAR image contains five distinct components, or layers of information: (1) Two phase components arise from the geometrical baseline (slightly different position of both sensor positions): (1a) a topographical information representing the surface relief, (1b) “flat earth” pattern coming from the orbital distance of both sensor positions.\r\n(2) Two phase components result of the temporal baseline (time between both acquisitions): (2a) a deformation component, representing a possible displacement of the Earth’s surface between both acquisitions, (2b) an atmospheric component coming from different atmospheric conditions between both acquisitions. (3) A phase component corresponding to intrinsic sensor noise \r\n\r\nBoth parameters related to the temporal baseline can be retrieved using DInSAR on repeat-pass acquisitions. DInSAR cannot be used with single-pass interferometry (e.g. both acquisitions acquired at the same time).\r\nThe deformation component of the interferometric phase corresponds to the modification of the phase of the second SAR image compared to the first due to an additional range difference between the sensor position and the Earth’s surface that is induced by the motion of the Earth’s surface towards or away from the initial sensor position.\r\nUsing DInSAR, the phase components related to the geometrical baseline can be eliminated from the interferogram using an existing DEM and orbit information, or an additional interferogram showing no deformation. After DInSAR processing, neglecting the remaining sensor noise, only the deformation and atmospheric components remain. The resulting deformation image is called differential and is characterized by color bands, or fringes, from whom the amount of the displacement can be retrieved. \r\nDInSAR can be used for mapping displacements and deformations due to earthquakes, landslides, or other geophysical processes inducing deformation of the Earth’s surface.\r\nUsing only one differential interferogram, mainly sudden and large scale changes between two acquisition can be mapped and quantified. However, the atmospheric phase component remains and may induce interpretation errors if it is not possible to eliminate it through e.g. precise weather models. Techniques of differential interferogram stacking (e.g. Persistent Scatterer Interferometry and Small-Baseline Subset) have been developed for long-term deformation monitoring which allow to filter the atmospheric phase component out.","hasChildren":true,"name":"Differential Synthetic Aperture Radar Interferometry (DInSAR)","selfAssesment":"Completed
"},{"code":"PP2-3-11-2","description":"The Permanent or Persistent Scatterer (PS) approach allows the estimation of deformation time-series related to point-wise, high coherent scatterers on the ground based on processing long sequences of SAR data.\r\nPersistent Scatterer Interferometry (PSI -sometimes also called Permanent Scatterer Interferometry) is a particular DInSAR technique. It exploits multiple SAR images acquired over a specific area in order to retrieve the deformation phase component over time. In general, a minimum number of 15 SAR acquisitions is needed for PSI processing. Due to the large number of necessary acquisitions, the deformation component of the interferometric phase observations can be estimated very precisely (in the order of a few mm/yr) and other phase contributions such as atmospheric disturbances and topographic height differences can be better estimated and removed.\r\nPSI rely on so called Persistent Scatterer that are targets showing coherent phase behavior in time. Such targets are usually found on man-made structures such as buildings or bridges, or very stable features such as rocks. PSI is a technique that is therefore mainly used over urban or semi-urban terrain. Usually, PSs are selected based on their amplitude and phase power spectrum stability over time.\r\nThe main outcomes of a PSI analysis are a deformation velocity map and the displacement time-series of the single point targets, or PSs. The velocity map represents the deformation rate of the detected PSs in Line-of-Sight of the sensor, generally in mm/yr. Usually, subsidence, e.g. target moving away from the sensor, is represented in red, stable PSs in green and uplift, e.g. PSs moving toward the sensor in blue. The displacement time-series show for each PS the amount of the deformation, usually in mm, over the whole period of observation. Different phase model can be defined in order to retrieve the best possible estimate of the deformation, considering also seasonal displacements or breakpoints in the time-series.\r\nPerforming PSI analysis in both ascending and descending directions allows the fusion of the results in order to retrieve vertical and East-West component of the deformation. North-South deformation components cannot be retrieved due to the orbit configuration of the SAR satellites.\r\nPSI finds use in a large range of thematic applications related to subsidence and long-term change monitoring, such as infrastructure monitoring, groundwater reservoir monitoring, monitoring of mining areas, landslide inventory and monitoring, as well as volcanology.","hasChildren":true,"name":"Permanent Scatterer Interferometry (PSI)","selfAssesment":"Completed
"},{"code":"PP2-3-11-3","description":"Along-track InSAR (AT-InSAR) is a special mode of interferometric SAR (InSAR) where the individual SAR images have been acquired from the same flight track. With virtually identical geometric configuration of the individual SAR images, the measured phase difference is dominated by temporal changes occurring between the acquisitions. Consequently, AT-InSAR can be used to measure the displacement and/or radial velocity of targets on the ground, with the temporal offset between the acquisitions determining the time scale of the measurements. AT-InSAR can be implemented using one or more SAR sensors, in both single-pass and repeat-pass configurations, accommodating various needs. Using at least two sensors in a single-pass configuration allows the measurement of relatively high velocities, e.g., for vehicles and ocean waves. Conversely, using at least one sensor in a repeat-pass configuration allows the measurement of low velocities or displacements, e.g., for glaciers and due to volcanoes, earthquakes, subsidence, and landslides.","hasChildren":true,"name":"Along-Track Interferometry","selfAssesment":"Completed
"},{"code":"PP2-3-11-4","description":"Across-track InSAR (XT-InSAR) is a special mode of interferometric SAR (InSAR), where the individual SAR images have been acquired from slightly different look directions. The measured phase difference contains information about the elevation of the targets on the ground, but it can also be affected by temporal changes between the individual SAR images. XT-InSAR can be implemented using one or more SAR systems in both single-pass and repeat-pass configurations. To mitigate temporal change between acquisitions, the XT-InSAR configuration is selected based on the intended application and frequency used by the system. If a single SAR sensor is used in the repeat-pass mode, temporal stability can be achieved either by a selecting a lower frequency and focussing on the larger, more stable targets (e.g., P-band, 435 MHz InSAR in forests) or by selecting a higher frequency and focussing on already stable environments (e.g., X-band, 9.65 GHz XT-InSAR in urban environments). Using two or more SAR sensors in a single-pass, tandem configuration, it is possible to measure elevation of temporally instable targets using higher frequencies, as demonstrated by the SRTM and TanDEM-X systems over vegetated areas and ocean.\r\nReferences: bamler/hartl, one on SRTM or TDM for DEM, one on BIOMASS for forestry, one on Sentinel-1 for urban areas, one on TDM on vegetation","hasChildren":true,"name":"Across-Track Interferometry","selfAssesment":"Completed
"},{"code":"PP2-3-11-5","description":"Small Baseline Subset (SBAS) is a well-known technique of differential synthetic aperture radar (SAR) interferometry for the generation of surface deformation time-series by processing large sequences of SAR data acquired over the same region on Earth. \r\nThe method requires the preliminary generation of pairs of SAR images collected by slightly different orbital positions at different acquisition times. The phase difference of the interferometric SAR data pairs is extracted. The two-dimensional phase maps contains different contributions, but principally a component due to the terrain height of the observed area. The DInSAR technique relies on the estimation of the deformation of the terrain between the two interfering SAR images (i.e., the so-called master and slave images). To achieve this task, the phase contribution related to the terrain height is simulated and subtracted to the interferometric master/slave phase difference. The obtained differential SAR interferometric phase contains a direct information on the occurred deformation. Once a sequence of interferometric SAR data pairs is selected, the SBAS technique allows generating the time-series of the deformation of the terrain. The processing steps are essentially: i) the extraction of the full phase of the DInSAR interferograms, i.e., the phase unwrapping steps of the DInSAR interferograms, ii) the inversion of the sequence of unwrapped DInSAR phases, iii) the geocoding of the deformation maps from radar coordinates to geographical coordinates.","hasChildren":true,"name":"Small Baseline Subset","selfAssesment":"Completed
"},{"code":"PP2-3-11","description":"Synthetic aperture radar (SAR) interferometry, or simply InSAR, is a remote sensing technique utilising the phase difference between two or more complex-valued SAR images. Most modern SAR systems are capable of measuring both the intensity and the phase of the reflected signal, where the latter carries information about the distance travelled by the signal. Consequently, the different of phase information of two successive SAR images over a specific area contains a distance information. \r\n\r\nThe phase difference measured between two SAR images is called the interferometric phase. The interferometric phase image is an interferogram. The interferometric phase is a function of the geometry and timing of the individual SAR acquisitions. Different geometric and temporal configurations enable different applications. \r\n\r\nIf the SAR acquisitions are made from different angles and without significant temporal change of the scene, InSAR can be used to create digital elevation models (DEMs) of the Earth, as demonstrated by the NASA/JPL Shuttle Radar Topography Mission (SRTM). This configuration is called across-track interferometry. If the individual SAR acquisitions are made at different times in the same geometric configuration, i.e. in an along-track or differential interferometric configuration, then InSAR can be used to measure radial velocity of targets and to assess displacements caused by, e.g., volcanoes and earthquakes. The variation of the temporal baseline allows determining velocities ranging from several meters per second to a few millimeters per year. While standard differential interferometry can be used to retrieve changes that happened between two SAR acquisitions, differential interferometric stacking techniques, such as Persistent Scatterer Interferometry (PSI) and Small Baseline Subset (SBAS), are used to monitor deformation over a longer period of time by stacking multiple differential interferograms and filtering out the atmospheric phase contribution in order to retrieve very accurate deformation of the ground and its infrastructures.","hasChildren":true,"hasParent":true,"name":"Principles of Synthetic Aperture Radar Interferometry (InSAR)","selfAssesment":"Completed
"},{"code":"PP2-3-12","description":"Synthetic Aperture Radar (SAR) tomography uses the principle of the azimuth synthetic aperture in the elevation direction. Instead of using different positions of the radar sensor along the flight path in order to increase the aperture length, SAR tomography uses multiple passes of the radar sensor over the same area at different elevation positions, i.e. orthogonal to the azimuth-range plane, on different orbits. Similar to the synthetic aperture in azimuth direction, a larger aperture in cross-range elevation direction allows increasing the resolution in the elevation direction. Therefore, the echoes are focused in the whole 3D space (azimuth, range and elevation), and scattering contributions can be separated at different heights, even if they are situated in the same azimuth-range cell.\r\nSAR tomography exploits therefore these multiple passes of the radar sensor at different orbit positions (orbits heights) in order to retrieve 3D information about volumetric targets, where the 2D SAR signals often overlaps due to the typical side-looking geometry. \r\nThe result of tomographic processing is a tomogram, i.e. it is a hologram of a specific area of interest, usually represented as a tomographic profile along a particular direction. Using polarimetric data, the different scattering mechanisms happening at different heights can be represented in the profile, allowing a full understanding of the volumetric information and backscattering processes.\r\nUnlike the azimuthal aperture, the tomographic aperture is achieved by repeat-pass acquisitions, the antenna having to come back over the area. An important parameter is therefore the target coherence, that may decrease by longer repeat-pass cycles. In general, a 1-4 day revisit cycle is preferred for tomographic applications.\r\nSAR tomography finds applications in the imaging and monitoring of cities and single buildings, as well as in height and biomass estimation of forest stands. The use of longer wavelength that guaranty the penetration into canopy volumes allows a better retrieval of the complete forest structure and its undergrowth.","hasChildren":true,"hasParent":true,"name":"Synthetic Aperture Radar (SAR) tomography","selfAssesment":"Planned
"},{"code":"PP2-3-13","description":"Historically imaging in the microwave frequency domain was done either using passive imaging techniques (with solely recording capacities of the sensor) or using active imaging techniques (with transmitting and recording capacities of the sensor). Both imaging modi were developed in parallel for a long time in electrical engineering of microwave sensors for space-borne missions, but are combined in more recently launched missions.\r\nWith the concept of active and passive microwave imaging, both techniques are fused to record electromagnetic waves in an active (sending & receiving) and a passive (only receiving) mode either simultaneously on one carrier platform or with negligible time lag on different platforms.\r\nThe active sensor is normally a Real Aperture Radar (RAR, scatterometer) or Synthetic Aperture Radar (SAR), while the passive sensor is a radiometer or synthetic aperture radiometer. Both acquisition modes can be operated on a single platform or on different platforms depending on monolithic or distributed platform systems. The benefit of fusing both modi is in the higher spatial resolution of the active imaging modes combined with the higher sensitivity of the passive modes for intrinsic (non-structural) media properities, like permittivity or salinity.\r\nSatellite missions with active-passive imaging capabilities are the NASA missions AQUARIUS (operation started in 2011 terminated in 2015) and SMAP (operation started in April 2015 and ceased for active sensor in July 2015). Currently (2021), no dedicated active-passive microwave satellite mission is operating in orbit.","hasChildren":true,"name":"Active-Passive microwave imaging","selfAssesment":"Completed
"},{"code":"PP2-3-2","description":"Systems measuring both amplitude and phase of the incident electromagnetic radiation.","hasChildren":true,"name":"Coherent and active systems","selfAssesment":"Planned
"},{"code":"PP2-3-3","description":"This acquisition mode records only the incoming electromagnetic radiation emitted from the Earth. Radiometer instruments conduct passive microwave imaging. The energy budget of emitted radiation (from Earth) is significantly smaller than from instrument-generated, transmitted electromagnetic waves, used in the active microwave imaging mode. Hence, the signal to noise ratio is significantly worse for passive microwave imaging forcing a longer intergration time for robust signal recording. This results in a coarse spatial resolution of radiometer images (in the order of kilometers).","hasChildren":true,"hasParent":true,"name":"Passive microwave imaging","selfAssesment":"Completed
"},{"code":"PP2-3-5","description":"There are two types of imaging radar apertures: real (usually called RAR or SLAR for side-looking airborne radar or SLR for side-looking radar) and synthetic aperture radar (SAR). The SLAR imaging system uses a long antenna mounted on a platform. The synthetic aperture is used in space remote sensing applications. RAR is a radar system where the antenna beamwidth equals to the physical length of the antenna. It operates in a side-looking configuration, left or right with reference to the flight direction. It is an active, all-weather, day/night remote sensor onboar an airborne platform. Both Real Aperture and Synthetic Aperture Radar are side-looking systems having antennas aimed to the right or left of the flight path. The length of the antenna together with wavelenght determines the resolution in the azimuth direction, i.e. it is proportional to the distance to the object and inversely proportional to the length of the radar antenna.","hasChildren":true,"name":"Real Aperture Radar (RAR)","selfAssesment":"In progress
"},{"code":"PP2-3-6","description":"In contrary to a real aperture, a synthetic aperture results from an aperture “synthesis”. Synthetic aperture were built in order to overcome the limitation of real aperture and therefore enhance the resolution in azimuth direction. It uses the subsequent positions of a real aperture sensor during its forward motion along the azimuth direction to create a synthetic longer antenna. Via the analysis of the Doppler shift induced by the different echoes of the illuminated objects in the different positions of the real aperture, the azimuth resolution can be improved.","hasChildren":true,"name":"Principles of Synthetic Aperture Radar (SAR)","selfAssesment":"In progress
"},{"code":"PP2-3-7-1","description":"In navigation, the azimuth corresponds to an angle measured from a north reference or a meridian, usually in clockwise direction. In SAR terminology, the azimuth direction corresponds to the direction in which the radar platform moves. The azimuth direction is also called along-track direction and is parallel to the flight path of the radar instrument. In a SAR image, the azimuth position of an object corresponds to its relative position in the field of view of the antenna following the radar’s line of flight. The azimuth direction is perpendicular to the range direction, which corresponds to the look direction of the radar antenna. The azimuth plays an important role in the definition of the azimuth resolution of a SAR sensor. Contrary to the range resolution, the azimuth resolution is independent of the distance between sensor and illuminated area and is constant. The azimuth resolution of a radar system corresponds to the beam width of the antenna on the ground, but can be improved using multiple successive real aperture acquisitions in order to form a longer, synthetic, aperture. This implies that an object on the ground is illuminated for a longer time and from different platform positions along the azimuth direction, inducing a Doppler frequency shift at the target. The use of specific synthetic aperture acquisition modes that steer the antenna along the azimuth direction, such as Spotlight mode, improve additionally the resolution in azimuth direction.","hasChildren":true,"name":"Azimuth direction","selfAssesment":"Completed
"},{"code":"PP2-3-7-2","description":"The range direction corresponds to the direction perpendicular to the flight direction of a radar system. It is also called across-track direction. One distinguishes between slant range, i.e. range in a radar geometry, and ground range, i.e. range projected onto the Earth's surface, and between near and far range (situated farther away from the sensor and showing shallower looking angle than in near range due to viewing geometry).","hasChildren":true,"name":"Range direction","selfAssesment":"In progress
"},{"code":"PP2-3-7-3","description":"The incidence angle is the angle between the incident radar beam on a surface and the normal to a reference surface. Generally, it is distinguished between the local incidence angle and the incidence angle to the ellipsoid. The local incidence angle considers the normal to the surface at target location, i.e. it considers the local topography. The incidence angle to the ellipsoid corresponds to the angle between the incident radar beam and the normal to the local ellipsoid, regardless of the local slope and terrain. \r\n\r\nFor a flat surface and neglecting the Earth’s curvature, the incidence angle corresponds to the angle between the incident radar beam and the vertical, and it equals the look angle of the sensor, which characterizes the angle between the nadir view and the radar beam. Considering a flat surface, the incidence angle varies continuously within a SAR scene: it increases from near to far range. Depending on the considered sensor and acquisition modes, variations of the incidence angle up to 20° can be observed between near and far range.\r\n\r\nThe incidence angle has an influence on the radar backscatter intensity. Considering a surface with diffuse reflection, increasing incidence angles lead to decreasing backscatter intensities. This effect is less pronounced for rough than for smooth surfaces. A change in incidence angle may also induce a change in the occurring backscattering mechanisms or geometric distortions of the image. For example, for high incidence angles, terrain distortion due to the side-looking geometry is reduced. Due to the high dependency of the radar backscatter from the incidence angle, the choice of the optimal configuration should happen depending on the application. For example, whereas low incidence angles are more sensitive to biomass in forestry applications, higher incidence angle are preferred for distinguishing different forest types due to their structural characteristics.","hasChildren":true,"name":"Incidence Angle","selfAssesment":"Completed
"},{"code":"PP2-3-7-4","description":"The beam sent out by the radar antenna (SLAR for side-looking airborne radar or SLR for side-looking radar) illuminates an area on the targeted object. The footprint of an antenna is traditionally defined to be the area on the surface within the field of view subtended by the beamwidth of the antenna gain pattern.","hasChildren":true,"name":"Antenna footprint","selfAssesment":"Planned
"},{"code":"PP2-3-7-5","description":"The spatial resolution of a synthetic aperture radar (SAR) system is the maximal distance between two targets, which are indistinguishable in the SAR image. SAR spatial resolution is determined individually in the two principal SAR image directions: ground range and azimuth (along-track). Ground range resolution for a SAR system is derived from slant range (across-track) resolution, by projecting it onto the ground surface using the incident angle, i.e., the angle between the line-of-sight and the ground surface normal. It is thus range-dependent, with finer resolution available in far range. Assuming adequate signal processing, slant range resolution of a SAR system is proportional to the speed of light and inversely proportional to the system bandwidth, i.e., the width of the used frequency interval. This caused by the fact that each individual frequency provides an independent measurement of the slant range, so a larger bandwidth implies more independent measurements contributing to the final slant range estimate. Similar principles apply to the azimuth direction. Assuming adequate signal processing, the SAR azimuth resolution is proportional to the along-track velocity of the SAR sensor and inversely proportional to the pulse repetition frequency (PRF) of the system. A lower interval between the consecutive pulses (higher PRF) results in better azimuth resolution due to faster sampling, but at the cost of range ambiguities occurring when echoes from one pulse are recorded after the next pulse has been transmitted.","hasChildren":true,"name":"Synthetic Aperture Radar (SAR) spatial resolution","selfAssesment":"Completed
"},{"code":"PP2-3-7","description":"The Synthetic Aperture Radar (SAR) sensor is usually mounted on an aircraft or satellite. The instrument altitude above a reference surface stays constant over time, a condition that is easier to achieve for satellite sensors that stay on the same orbit than for aircrafts that are subject to atmospheric conditions. The sensor moves on a straight flight path, which is called the azimuth direction. It corresponds to the flight direction.\r\nSAR systems acquire information in oblique view, the antenna pointing sideways down to the ground. Most satellite systems use an antenna looking to the right side of the instrument. The ground area illuminated by the radar beam is called antenna footprint. As the sensor moves along the azimuth direction (along-track), the continuous strip of the ground area represented by the successive antenna footprints is called swath. \r\nThe looking direction of the SAR antenna is called range direction. It is often perpendicular to the azimuth direction (i.e. across-track), but can also present slightly differences depending on the acquisition mode. The angle between the nadir view and the range direction is called incidence angle.\r\nThe original SAR image is displayed in what is called slant-range geometry, i.e., it is based on the actual distance from the radar to each of the respective features in the scene. In the slant range direction, each point target’s backscatter is represented as a function of the time delay between the transmission of the electromagnetic pulse and its reception back at the sensor. This range depending representation induces geometric distortions in the SAR image. One distinguishes between near and far range: targets situated in near range are closer to the nadir direction and closer to the sensor than targets situated in far range. The image representation of targets is also more compressed in near range than in far range.\r\nThe slant-range representation can be converted in ground range representation, by projecting the image features orthogonally to a ground reference, allowing a proper planimetric position of the targets relative to one another.\r\nThis acquisition geometry allows the distinct mapping of scatterers corresponding to their respective distance to the sensor. It causes also geometric distortions in the radar image, i.e., relief displacement (foreshortening and layover) and shadow.","hasChildren":true,"hasParent":true,"name":"Synthetic Aperture Radar (SAR) geometric configuration","selfAssesment":"Completed
"},{"code":"PP2-3-8-2","description":"The local incidence angle is the angle between the incident radar wave and the normal to the scattering surface at target location. In case of a flat terrain, the local incidence angle equals the incidence angle. For a terrain with local slope, the local incidence angle differs from the incidence angle (for slopes facing towards the sensor, it is smaller than the incidence angle).","hasChildren":true,"name":"Local Incidence Angle","selfAssesment":"Planned
"},{"code":"PP2-3-8-3","description":"Foreshortening is a geometric distortion occurring in the SAR image due the side-looking geometry of imaging radar sensors. It occurs principally in SAR images of mountainous areas, on slopes oriented towards the sensor. These slopes appear in the radar image as if being compressed. Due to the side looking geometry and the mapping of the SAR image based on range and time measurement, the distance in the SAR image between two points situated on a slope facing the sensor appears smaller than it is in the reality and than the same distance between two points situated in flat area. This results in a compression of the radiometric information of the slope. The resulting foreshortening area is brighter in the SAR image than its surroundings, as it compresses in a few pixels the backscatter information of the whole slope. \r\n\r\nForeshortening occurs for slopes whose inclination is smaller than the look angle of the radar antenna. Due to the variation of the look angle in the SAR image, the foreshortening is more pronounced in near range than in far range. Foreshortening is therefore greater for small incidence angles. The extreme case of foreshortening happens when the slope inclination is equal to the look angle: in this case, the whole slope is mapped in one pixel of the SAR image, which results in a very bright line. When the slope inclination becomes higher than the look angle, layover occurs.","hasChildren":true,"name":"Foreshortening","selfAssesment":"Planned
"},{"code":"PP2-3-8-4","description":"Layover is a geometric distortion occurring in the SAR image due the side-looking geometry of imaging radar sensors. It occurs principally in SAR images of mountainous areas, on steep slopes oriented towards the sensor. These slopes appear in the radar image as if being flipped over. Due to the side looking geometry and the mapping of the SAR image based on range and time measurement, the summit of a mountain is closer to the sensor that the foot of that same mountain, on the side facing the sensor. The signal from the top comes back to the sensor before the signal from the foot and is therefore mapped in nearer range than the foot of the mountain. Making an analogy to sound waves, an echo from the top of the mountain will arrive sooner at the sensor than an echo from the bottom of the mountain. Due to this “leaning over” effect, the sensor facing slope signal usually overlaps with ground signal, and a “ghost” effect appears as both signals overlap. The resulting layover area is usually very bright in the SAR image, as it superimposes backscatter signals from the slope of the mountains and the ground before it. When considering SAR images of urban areas, even up to three signals may overlap in the layover area: ground, building façade and (part of the) roof area.\r\n\r\nLayover occurs for slopes whose inclination is larger than the look angle of the radar antenna. Due to the variation of the look angle in the SAR image, layover occurs more often in near range than in far range. Layover is therefore greater for small incidence angles. It represents the extreme case of foreshortening, when the slope inclination becomes higher than the look angle.","hasChildren":true,"name":"Layover","selfAssesment":"Planned
"},{"code":"PP2-3-8-5","description":"Radar shadow is a geometric distortion occurring in the SAR image due the side-looking geometry of imaging radar sensors. It occurs principally in SAR images of mountainous areas, on steep slopes oriented away from the sensor. In optical imagery, a shadow area is an area characterized by less sun illumination whose reflection is therefore weaker. In SAR imagery, shadow areas receive no signal. It occurs for example at the backside of mountains or buildings. The areas facing away from the sensor are not illuminated by the SAR sensor, as they are “hidden” from it. Also, ground area situated behind high object with respect to the sensor position are not illuminated and are situated in the radar shadow. They receive no signal information and send no information back to the sensor. Those areas are therefore very dark in SAR images. The size of the shadow area in range direction corresponds to the time delay between the last echo from the top of the mountain and the first echo of the far edge of the shadow region, where the area is not hidden from the sensor anymore.\r\n\r\nRadar shadow occurs when the slope inclination of the slope facing away from the sensor is larger than 90° minus the antenna look angle. As for the other geometric effects, the size of a shadow area for the same object depends on its situation in the image. But, unlike as for foreshortening and layover, shadow is more pronounced in far range than in near range, i.e. large incidence angles produce more shadow.\r\n\r\nA SAR image may show a return signal in a shadow area: this is principally due to internal sensor noise and does not correspond to any target return signal.","hasChildren":true,"name":"Shadow","selfAssesment":"Planned
"},{"code":"PP2-3-8","description":"Synthetic Aperture Radar (SAR) backscatter is determined both by dieletric and geometric properties of the illuminated target. While the water content of the target plays an important role, its surface roughness determines the scattering mechanisms and the amount of incoming signal sent back to the sensor.\r\nDepending on its characteristics but also on the considered wavelength, a surface appears more or less rough. On smooth surfaces, specular reflection occurs, meaning that most of the incoming signal will be reflected away from the sensor. For rough surfaces, diffuse reflection occurs, meaning that part of the signal is scattered back to the sensor, the amount of it depending on different surface roughness parameters. \r\nDepending of the observed target and surface, single or multiple scattering mechanisms occur. A particularly important scattering mechanism is the double bounce, which occurs generally at two perpendicular surfaces (e.g. ground and building wall). Through two successive specular reflections, the whole signal comes back to the sensor.\r\nDue to the side-looking geometry of SAR systems and the range dependent image representation, specific additional effects occur and affect the backscatter intensity. Whereas a flat terrain only appears more compressed in near range and more stretched in far range, larger geometric distortions appear for terrain with more topography (e.g. mountains) or high objects (e.g. trees, buildings). This relief displacement is caused by the target’s elevation. A high elevated object is closer to the sensor than the ground below it. Due to the image formation in range direction depending on the distance between sensor and targets, its signal comes back sooner to the sensor and it is represented in the SAR image in nearer range than the ground below it. High objects in the SAR image are therefore displaced horizontally toward the radar antenna. This horizontal displacements contrast with the radial displacement observed in optical imagery due to central projection. Furthermore, such objects hide part of the ground below them, which do not receive any signal and cannot scatter information back. Three particular geometric distortions exist: foreshortening, layover and shadows.\r\nDepending on the illuminated target, different scattering mechanisms occur in combination with geometric distortions, which makes the interpretation of the SAR image challenging. A good example are buildings, where layover, shadow and single- and double-bounce occur.","hasChildren":true,"hasParent":true,"name":"Terrain reflectivity and geometric distortions","selfAssesment":"Completed
"},{"code":"PP2-3-9","description":"A typical “salt-and-pepper” noise-like physical phenomenon that is not a noise but a deterministic property of SAR imagery is the so called speckle. It appears when a resolution cell of a SAR system contains more than one scatterer. In that case, the total scattering from the resolution cell is a coherent sum of the backscatter originating from the different scatterers. In order to reduce this effect, speckle reduction methods can be applied.","hasChildren":true,"name":"Speckle Formation","selfAssesment":"Planned
"},{"code":"PP2-3","description":"Microwave remote sensing systems detect and quantify the electromagnetic radiation arriving at a detector, this radiation being either emitted (passive sensors) or scatterered back (active sensors) from the objects.\r\nThree properties of the recorded electromagnetic signal are of particular interest: its intensity, its phase and its polarization. The specific quantification of each properties allows signal interpretation, as they depend on the roughness and dielectric characteristics of the surface (intensity and polarization) as well as of the range between target and sensor (phase).\r\nThe detection of the microwaves is operated through two principal sensor elements: an antenna and a receiver. The antenna collects the incoming radiation and the receiver measures the collected electric signal.\r\nAs active microwave systems produce their own electromagnetic radiation, they are equipped with two additional elements: a pulse generator and a transmitter. Usually, transmitter and receiver are situated on the same antenna.\r\nA simple detector system only detects the intensity of the signal and amplifies it. Coherent systems measure both the amplitude and the phase of the incident electromagnetic radiation.\r\nMicrowave systems can be categorized in two different types: imaging and non-imaging sytems. Whereas for non-imaging systems each echoe (collected signal) provides a single measurement, imaging systems collect a sequence of echoes that generate a two dimensional image.","hasChildren":true,"hasParent":true,"name":"Detecting microwaves","selfAssesment":"Completed
"},{"code":"PP2","description":"Microwave remote sensing operates in the microwave portion of the electromagnetic spectrum, generally using wavelengths greater than 3 cm and up to 1 m. \r\nMicrowaves are sensitive to different physical parameters than other regions of the electromagnetic spectrum. Microwaves interactions with objects are governed by geometric (structure, size, shape) and dielectric (water content) properties, whereas other regions of the electromagnetic spectrum reacts e.g. to object temperature or “color” (amount of reflection or absorption of the Sun light by a particular object).\r\nAs a general rule, microwaves interact with object at least as big as the wavelength. Smaller objects will therefore be transparent for the signal. Due to the large wavelengths, atmospheric particles are almost transparent to the signal and microwave remote sensing can penetrate clouds. Under very dry conditions, microwaves can even penetrate up to a few meters the top soil layers, therefore providing information that is not visible in other regions of the electromagnetic spectrum. Depending on the considered wavelength, microwave can also penetrate vegetation layers to different amounts.\r\nIn microwave remote sensing, three characteristics of the electromagnetic wave play an important role: its amplitude, its phase and its polarization. Depending on the application, either one characteristic or a combination of them is used to retrieve information.\r\nThere are two main types of microwave sensors: active RADAR systems and passive radiometers. RADAR is an acronym for RAdio Detection And Raging. An active radar system sends out pulses and records the echoes scattered back by the objects (scatterers) to the sensor. The systems use the two-way travel time of the radar pulse to determine the distance (range) to the illuminated object. Its backscatter intensity is determined by the radar system and object properties and depends on the quantity of energy coming back to the sensor. Active radar systems transmit a signal and record the amount of energy that is scattered back and depends of both dielectric and geometric properties. Passive radiometers record microwave energy, which is emitted by the Earth’s surface.\r\nDepending on the type of system, microwave remote sensing can be used in multiple applications. Active sensors are principally used for diverse land cover mapping applications based on the particular backscattering mechanisms and characteristics of the objects on the Earth’s surface. Using multiple acquisitions, they are also favored for topographic, deformation and velocity mapping. Passive sensors are preferred for the determination of hydrologic variables such as soil moisture, precipitation, ice water content and sea-surface temperature.","hasChildren":true,"hasParent":true,"name":"Basics of microwave remote sensing","selfAssesment":"Completed
"},{"code":"PS","description":"Remote sensing, i.e. the process of obtaining information about an object or area from a distance, is not possible without remote sensing sensors that collect this information and the platforms on which the sensors are installed and which are used to move them. Remote sensing sensors collect data by detecting energy that is reflected or emitted from Earth. There are different types of remote sensing sensors. The interaction between the sensor and the Earth's surface has two modes: active or passive. Passive sensors use solar radiation to illuminate the Earth's surface and detect reflection from the surface or measure the emitted energy. They usually record electromagnetic waves in the visible (˜430–720 nm) and near infrared (NIR) (˜750–950 nm) through short infrared (SWIR) (˜1.500-2.500 nm) to thermal infrared (TIR) (8.000-14.000 nm) ranges. The power measured by passive sensors is a function of surface composition, physical temperature, surface roughness and other physical properties of the Earth. Active sensors provide their own energy source to illuminate objects and measure their properties. These sensors use electromagnetic waves in the visible and near infrared range (e.g.laser altimeter) and radar waves (e.g. synthetic aperture radar (SAR)). As sensor technology has advanced, the integration of passive and active sensors into one system has emerged. Alternatively, remote sensing sensors can be classified into imaging sensors, i.e. that produce an image of an area, within which smaller parts of the sensor's whole view are resolved (pixels), and non-imaging sensors, i.e. that return a signal based on the intensity of the whole field of view. In terms of their spectral characteristics, the imaging sensors include optical imaging sensors, thermal imaging sensors, and radar imaging sensors. These sensors can be on satellites, mounted on aircraft, unmanned aerial vehicle (UAV), drone or ground. The collected information can be transformed into an image or set of points (e.g. cloud points), which can be further processed and analyzed to obtain the necessary information, e.g. agricultural field development phase, level of air pollution, etc.\r\nA digital imagery of Earth observation sensors is a two-dimensional representation of objects on Earth. Current images collected from different levels of acquisition, from ground to satellite, with the help of electronic sensors are examples of digital images. There are different aspects and characteristics of remote sensing data and images, such as, for example, data formats and processing levels, data storage, data properties.","hasChildren":true,"hasParent":true,"name":"Platforms, sensors and digital imagery","selfAssesment":"Completed
"},{"code":"PS1-1","description":"Remote sensing sensors has its roots in the 19th century in the development of photography. Photography was an invention that made it possible to acquire a permanent image. The first photographic image was taken in 1826 by Joseph Nicephore Nieppce. While the first aerial photograph was taken in 1858 by Felix Tournachon, known as Nadar, from a tethered baloon over Biévre Valley in France. In 1907 Julius Neubronner developed a light miniature camera that could be fitted to a pigeon's breast. It can be said that the construction camera + pigeon was the precursor of today's unmanned aerial vehicle (UAV) or drone. Further developments focused on developing new sensors (analog vs. digital frame cameras) and how to save and store images (e.g. photographic emulsions, films). The origin of other types of remote sensing can be traced to World War II, with the development of radar, sonar, and thermal infrared detection systems. Since the 1960s, sensors were designed to operate in virtually all of the electromagnetic spectrum. Both civil and military aerial photography have long been widely used in cartography to create maps. Specialized large format cameras (looking vertically down, assuming the plane is flying horizontally) were developed. Such cameras have been specially designed to perform almost vertical sequences of bird-eye exposures during aircraft flight. Hence for a long time remote sensing consisted of aerial photography and photogrammetry using analogue mechanical or optical equipment. Everything has changed with satellites and the space race. The first real success of remote sensing satellites in serious scientific work was in meteorology, weather satellite TIROS-1, launched by NASA on April 1, 1960. \r\nToday a wide variety of remote sensing instruments are available as data source for use in different applications for land, water and atmosphere monitoring.","hasChildren":true,"name":"History of remote sensing sensors","selfAssesment":"In progress
"},{"code":"PS1-2-1-1-1","description":"Along track scanner, also known as a pushbroom scanner, is an optoelectronic device that obtains images with a multispectral imaging system. The scanners are used for passive remote sensing. It records electromagnetic energy that is reflected (e.g., blue, green, red, and infrared light) or emitted (e.g., thermal infrared radiation) from the surface of the Earth. The scanners are mounted on space- or aircrafts. \r\nA two-dimensional image is created (line by line) by exploiting the platform motion along the orbital track. The data are collected along track using a linear array of detectors arranged perpendicular to the direction of travel. The array of detectors are pushed along the flight direction to scan the successive scan lines, and hence the name pushbroom scanner. \r\nThere are no moving parts on a pushbroom sensor, hence, the scanning speed can be increased compared to across track systems. A longer dwell time over each ground resolution cell increases the signal strength (high radiometric resolution, no pixel distortion). Additionally, finer spatial and spectral resolution can be achieved as the size of the ground resolution cell is determined by the Instantaneous Field of View (IFOV) of a single detector. The systems are designed for high-resolution imaging. However, a very large number of detectors is needed for high resolution images. It is a complex optical system. In addition, the pushbroom scheme requires a wide Field of View (FOV) optics system to obtain the same swath as for a corresponding whiskbroom (across track) scanner. It has narrow swath width. \r\nThe detector arrays with such a line-scanning pushbroom system are usually of the type Charge-Coupled Device (CCD).\r\nThe MultiSpectral Instrument (MSI) on board the Sentinel-2 satellite (Copernicus mission) uses a pushbroom concept.\r\nMultispectral imaging systems building the final image (line by line) exploiting the platform motion along the orbital track. No rotating mechanical part required, usually based on a CCD matrix (high spectral resolution but just up to 1 micrometer), e.g. Sentinel-2 MultiSpectral Instrument (MSI), Sentinel-3 Ocean and Land Colour Imager (OCLI).","hasChildren":true,"name":"Along track scanners","selfAssesment":"Completed
"},{"code":"PS1-2-1-2-1","description":"The cameras, usually a charge-coupled device (CCD) or Complimentary Metal Oxide Semiconductor (CMOS), that convert light into electrons that can be measured and converted into radiometric intensity value.","hasChildren":true,"name":"Digital Frame Camera","selfAssesment":"Planned
"},{"code":"PS1-2-1-2","description":"2-D systems with the ability to observe in two dimensions simultaneously.","hasChildren":true,"hasParent":true,"name":"Area Arrays","selfAssesment":"New
"},{"code":"PS1-2-1","description":"A type of a spectrometer. It is in principle, one-dimensional systems, whisk- or pushbroom, that form an image on a line-by-line basis in the scan direction.","hasChildren":true,"hasParent":true,"name":"Line detector arrays","selfAssesment":"New
"},{"code":"PS1-2-2-1-1","description":"Thermal radiometers are radiometers with the capability of measuring the spectrum of infrared emission. As such, they are characterized by a relatively high spectral resolution (normally better than 1 cm-1 in wave number units). Modern Spectrometers on board satellites have a spectral resolution better than 0.7 cm -1 in order to properly resolve CO2 lines used for the retrieval of the atmospheric temperature profile. Based on the optical layout they are further classified in grating spectrometers and Fourier Transform Spectrometers or FTIR.","hasChildren":true,"name":"Thermal Radiometers","selfAssesment":"New
"},{"code":"PS1-2-2-1-2","description":"Passive microwave radiometers are radiometers that measures energy emitted at millimetre-to-centimetre wavelengths at 0.15 - 30 cm (frequencies of 1–200 GHz). Example of a sensor: SMOS Microwave Imaging Radiometer with Aperture Synthesis (MIRAS), which aims at measuring land soil moisture and ocean salinity.","hasChildren":true,"name":"Passive Microwave Radiometers","selfAssesment":"In progress
"},{"code":"PS1-2-2-1-3","description":"An advanced multispectral sensor that detects hundreds of very narrow spectral bands throughout the visible, near-infrared, and mid-infrared portions of the electromagnetic spectrum.","hasChildren":true,"name":"Hyperspectral Radiometers","selfAssesment":"Planned
"},{"code":"PS1-2-2-1-4","description":"A radiometer that measures the intensity of radiation in multiple wavelength bands (i.e., multispectral). Example of a sensor Moderate Resolution Imaging Spectroradiometer (MODIS)","hasChildren":true,"hasParent":true,"name":"Spectroradiometers","selfAssesment":"In progress
"},{"code":"PS1-2-2-2","description":"Provide information about vertical profiles of temperature and molecular consistuent concentrations in the atmosphere (atmospheric sounders).","hasChildren":true,"name":"Atmospheric passive sounders","selfAssesment":"New
"},{"code":"PS1-2-2","description":"Radiometers are instruments which measure radiative intensities within a particular frequency window. A radiometer is further identified by the portion of the electromagnetic radiation it covers, usually the infrared or microwave regions. Normally the spectral range extends from the longwave (14-15 micron) to the shortwave (3-4 micron). This range overlaps much of the emission spectrum of Earth. The technology is classified in broadband radiometer of spectral radiometers depending on the spectral resolution. A radiometer measures the intensity of the radiative energy, but does not differenciate between the different registered wavelengths or their respective amplitude. In other terms, it provides a single value as combined result of all wavelengths within the considered frequency window.","hasChildren":true,"hasParent":true,"name":"Radiometers","selfAssesment":"In progress
"},{"code":"PS1-2","description":"Passive remote sensing systems record electromagnetic energy that is reflected (e.g., blue, green, red, and infrared light) or emitted (e.g., thermal infrared radiation) from the surface of the Earth. Passive sensors therefore rely on an external energy source (e.g. sun illumination, Earth heat emission). Contrary to passive sensors, who detect naturally occurring radiation, active sensors emit radiation and collect and analyze the signal that is sent back by the Earth’s surface or atmosphere. Active remote sensing systems produce therefore their own electromagnetic energy. They transmit and receive the radiation that is reflected or backscattered from the illuminated target. They do not necessitate an external source of radiation (e.g. Sun or Earth). Contrary to most passive sensors that are bound to detecting either the reflected Sun radiation or emitted radiation by the Earth’s surface in ranges from the ultraviolet to the thermal infrared, active sensors can use any radiation from the electromagnetic spectrum, the only limitation being the transparency of the Earth’s atmosphere. They often use wavelengths that are not sufficiently provided by the Sun, e.g. microwaves. \r\nActive systems can be categorized either according to their imaging capability, or according to the considered emitted wavelength, or also according to the way they use the returned signal. For the last category, it is generally distinguished between ranging systems, which use as principal information the time delay between transmission and reception of the electromagnetic radiation at the sensor, and scattering systems, which consider the strength (also called magnitude or intensity), of the returned signal. Some systems also register both information.\r\nAs active sensors produce their own radiation and do not rely on e.g. Sun radiation, they are daytime independent and can also retrieve information about the Earth’s surface by night. Furthermore, depending of the considered wavelength, active sensors are weather independent. For longer wavelengths of the microwave domain, clouds are transparent, as the transmitted wavelength is larger than the water particles constituting the cloud and do not interact with them. \r\nActive sensors can control the direction of their illumination to a specific target to be investigated, but require in general more energy than passive sensors as they “actively” illuminate the Earth’s surface.","hasChildren":true,"name":"Passive vs. active sensors","selfAssesment":"Completed
"},{"code":"PS1-3-1-1","description":"Imaging RADAR (RAdio Detection And Ranging) is an active remote sensing system which bounces microwave energy from a target and records the energy that returns to the sensor. The radar antenna alternately transmits and receives pulses at particular microwave wavelengths (in the range 1 cm to 1 m, which corresponds to a frequency range of about 300 MHz to 30 GHz) and polarizations (waves polarized in a single vertical or horizontal plane).\r\nMicrowave energy pulses are emitted at regular intervals and focused by the antenna into a radar beam directed downwards and to the side. The radar beam illuminates the surface obliquely at a right angle to the motion of the platform. Objects on the ground reflect the microwave energy depending on factors such as roughness and attitude. The antenna receives this reflected (or backscattered) energy.\r\nBy measuring the time delay between the transmission of a pulse and the reception of the backscattered \"echo\" from different targets, their distance from the radar and thus their location can be determined. As the sensor platform moves forward, recording and processing of the backscattered signals builds up a two-dimensional image of the surface.\r\nUnlike aerial photographs and satellite images which are passive remote sensing systems, in active systems such as radar, the brightness or darkness of the image is dependent on the portion of the transmitted energy that is returned back to the radar from targets on the surface. Bright areas are produced by strong radar response and darker areas are from weak radar responses., while the response to radar energy by the target is primarily dependent on the three factors (1) Surface roughness of the target, (2) Radar viewing and surface geometry relationship, and (3) Moisture content and electrical properties of the target.","hasChildren":true,"hasParent":true,"name":"Imaging Radar","selfAssesment":"Completed
"},{"code":"PS1-3-2-1-1","description":"Laser profilers measure 2D range profiles and operate in different environments, like spaceborne, airborne and indoor. It is the simplest application of the LIght Detection And Ranging technique. It transmits a short pulse of energy (visible or near-infrared radiation) and detects 'echo', by measuring the time delay. Knowing the speed of propagation of the pulse (speed of light), the range from the instrument to the surface can be measured.\r\nLaser profiling uses successive reflectorless laser range measurements (1D distance measurement) on adjacent points along a path, which results in a 2D profile or cross-section of the ground. A laser profiler can be terrestrial, or ground-based, or it can be mounted on an airborne or spaceborne platform. In the case of ground-based measurements, the platform is fixed but the angle of illumination changes, allowing for the cross section of the terrain to be mapped. An airborne laser profiler can transmit a continuous stream of pulses along its flight path. As a result, if the position of the platform is known, e.g. from GPS/IMU system, a surface profile along the flight path can be reconstructed using the successively recorded vertical distances between the platform and the points on the ground. The use of an additional rotational mirror allow to scan the Earth in an additional dimension, providing 3D information of the mapped surface. This is the principle of a laser scanner.\r\nThere are two principal types of laser profiling techniques: the first one is based on analog detection and the second on photon counting. In analog detection, the signal power is converted into an output voltage providing a signal strength as function of time. The analog-to-digital conversion yields either a full waveform that allows retrieving the entire time-structure of the return signal strength- and therefore the full vertical structure of the target-, or discrete returns when the signal strength exceed a certain threshold. The full waveform information is especially useful when analyzing vegetation, as every vegetation layer (canopy, stems, branches) and the ground return pulses, allowing the determination of e.g. canopy height, ground surface topography but also a deeper analysis of the canopy structure. Photon counting techniques record the arrival of single photons. The counting of photons is combined with their time-of-flight. The accumulation of single photons at a specific range is similar to the signal strength of analog detection and allows retrieving the height and structure of specific targets.","hasChildren":true,"hasParent":true,"name":"Laser profiler","selfAssesment":"Completed
"},{"code":"PS1-3-2-1-4","description":"A radar altimeter is an active, non-imaging remote sensing device. It measures the height of the terrain along the track beneath an air- or spaceborne platform using electromagnetic radiation from the microwave region of the electromagnetic spectrum. Radar altimeters operate similar to laser profilers. Both emit a short pulse of electromagnetic radiation towards the Earth’s surface and detect the time delayed echo. By measuring the time delay and knowing the speed of propagation of the pulse, the range (distance) from the instrument to the surface can be determined. By using the forward motion of the altimeter platform and transmitting a continuous stream of pulses a profile can be built up. If the exact location of the platform as a function of time is known, a surface profile can be generated. \r\nFor a high accuracy of the range resolution, a narrow antenna beam is required, which can be achieved either by using large antennas or short radar beams. In the first case, the radar altimeter is beam-limited; in the second case it is pulse-limited. As large antennas are not practical in space, pulse-limited systems are used for space-borne platforms. Pulse-limited altimeters use frequency modulated (chirp) pulses generated by a chirp generator. The accuracy of the measurements also depends on atmospheric transmission effects, as the speed of the electromagnetic radiation traveling at the speed of light will be delayed when passing through the ionosphere and the atmosphere twice. In general, the range resolution of radar altimeters is in the order of a few centimetres. \r\nIn the beginning, radar altimeters were used for measurements of surface profiles of the ocean topography to get information about currents, ocean circulation, wind and waves. Another basic application of altimetry were measurements over ice sheets and glaciers, e.g. for mass balance determination. Further application domains are geoid measurements also revealing deep sea trenches and the precise monitoring of satellite orbits.","hasChildren":true,"name":"Radar altimeters","selfAssesment":"Completed
"},{"code":"PS1-3-2-1","description":"Laser altimeters historically were the first active sensing devices used on airborne platforms, measuring range information in form of single distances since the mid-1960s. \r\nEven though laser scanners made it possible to retrieve information in a more rapid and denser coverage since the mid-1990s, laser altimeters remain of importance in the scientific community. Especially, the mapping of ice-covered surfaces, water bodies and flat land areas is still performed using laser altimeters.\r\nLaser altimeters are either airborne or spaceborne and are often used together with microwave (radar) profiler in order to calibrate the radar instruments. Whereas airborne laser altimeters are preferred for forestry application, e.g. for analyzing vertical vegetation structure, spaceborne laser altimeters are additionally used for multiple other applications. In particular, spaceborne laser profiler are of high interest for studying surface roughness of ice sheets or for mapping desert topography. Furthermore, spaceborne laser profilers are also useful in atmospheric science for retrieving cloud structure and analyzing different aerosol layers. The requirements for airborne and spaceborne laser altimeters are different. In particular, for spaceborne altimeters, both the distance travelled by the laser pulse and the platform speed are much higher than for airborne instruments, inducing the need of larger optics and more powerful laser instruments. First spaceborne laser experiments were conducted onboard the space shuttle in the mid-1990s, first aiming atmospheric research with a near infrared laser. After successful trial, the space shuttle laser altimeter was fine-tuned and follow-up missions focused on mapping terrain relief and vegetation canopies. Later missions, such as GLAS (IceSAT), ATLAS (IceSAT-2) and GEDI (ISS), used either near-infrared or green (or both) laser light and focused on improving ground coverage while allowing smaller footprints of the laser beam on ground. The revisit cycle of spaceborne laser altimeters allow the determination of regional elevation changes, e.g. monitoring of ice–sheet thickness or vegetation height, which is highly relevant for the scientific community and climate modelers.","hasChildren":true,"name":"Laser altimeter","selfAssesment":"Completed
"},{"code":"PS1-3-2-3","description":"By a ranging camera the simultaneous capturing of range measurements for dynamical (close-range) 3D applications is given. These ranging cameras allow additionally the simultaneous capturing of single range and co-registered intensity images while still maintaining high update rates (up to 100 releases per second). Typical applications are autonomous navigation of robots, driver assistance, traffic monitoring or tracking of pedestrians for building surveillance.","hasChildren":true,"name":"Ranging camera","selfAssesment":"Completed
"},{"code":"PS1-3-2-4-1","description":"Spaceborne LS (e.g. Geoscience Laser Altimeter System - GLAS) provides global measurements of the Earth's surface with the potential on capturing additionally clouds and atmospheric aerosols. The spaceborne measurements allow to globally observe ice sheet and land elevations, approximate sea ice thickness, changes in elevation through time, vegetation coverage for biomass estimation, and height profiles of clouds and aerosols. It is a large footprint profiling system developed by NASA that operates with a footprint diameter of 70 m and measures elevation changes with decimeter accuracy. The surface characteristics are determined by comparing a parametric description of the transmitted and received waveforms. Because the laser footprint is large and illuminates multiple surfaces, the resulting return waveform is an integrated, spatially non explicit representation of the range to illuminated surfaces separated both vertically and horizontally. The geometric organization of surfaces within a single footprint can therefore not be determined.","hasChildren":true,"name":"Spaceborne Laser Scanning","selfAssesment":"Completed
"},{"code":"PS1-3-2-4-2","description":"Airborne laser scanning (ALS) systems allow a direct and illumination-independent measurement of 3d objects in a fast, remote and accurate way. Beside basic range measurements, the current commercial ALS developments allow to record the waveform of the backscattered laser pulse. Latest trends in sensor developments focus on single-photon detection. Airborne Laser Scanning (ALS) for instance is used for capturing large-scale 3D environments with almost homogeneous point density with a local point density of typically 4-100 pts/m^2. Therefore, different applications are of interest, like urban planning, change detection, forestry surveying, or power line monitoring. Further to describe the 3D scene, products like digital terrain models (DTMs), digital surface models (DSMs), or city models are provided.","hasChildren":true,"name":"Airborne Laser Scanning","selfAssesment":"Completed
"},{"code":"PS1-3-2-4-3","description":"A mobile laser scanning (MLS) system consists of a moving vehicle equipped with one or more usually side-looking laser scanners to capture information about the local 3D geometry. Mobile laser scanning systems are applied for capturing dense and accurate 3D information representing local object surfaces, but the density of the measured 3D points depends on their distance to the scanning unit, which is usually mounted on a vehicle. As a consequence, an appropriate interpretation of the captured data has to face certain challenges arising from either low or varying point density.","hasChildren":true,"name":"Mobile Laser Scanning","selfAssesment":"Completed
"},{"code":"PS1-3-2-4-4","description":"Underwater Laser Scanning is applied in deep-sea as well as in shallow water regions. The ranging distance is close range and the measurement principle relies on triangulation by laser light, comparable with structured-light-projection. More recently, companies started to develop Time-of-Flight (ToF) underwater laser scanners.","hasChildren":true,"name":"Underwater Laser Scanning","selfAssesment":"Completed
"},{"code":"PS1-3-2-4-5","description":"For Bathymetric Laser Scanning System the utilized green laser light with its potential penetration capabilities in water is essential. For water surface mapping the electromagnetic radiation of the laser penetrates into the topmost layer of the water column and can also be used for mapping the water surface and shallow water bathymetry. However high resolution mapping of water level heights is important for many applications, but capturing water is still in general challenging. Area-wide water surface heights and depths are required for many disciplines such as hydrology, hydraulic engineering, flood risk management, ecology, climate change, etc.","hasChildren":true,"name":"Bathymetric Laser Scanning","selfAssesment":"In progress
"},{"code":"PS1-3-2-4","description":"Laser scanners capture data by successively considering points on a discrete, regular (typically spherical, cylindrical or line) raster, and recording the respective geometric and radiometric information. Generally, a laser scanner illuminates a scene with modulated laser light and analyzes the backscattered signal. More specifically, laser light is emitted by the scanning device and transmitted to an object. At the object surface, the laser light is (partially) reflected and, finally, a certain amount of the laser light reaches the receiver unit of the scanning device. The measurement principle is therefore of great importance as it may be based on different signal properties such as amplitude, frequency, polarization, time, or phase. Many scanning devices are based on measuring the time t between emitting and receiving a laser pulse, i.e., the respective time-of-flight, and exploiting the measured time t in order to derive the distance r between the scanning device and the respective 3D scene point. Alternatively, a range measurement r may be derived from phase information by exploiting the phase difference Δφ between emitted and received signal. In general, laser scanners may be categorized with respect to laser type, modulation technique (continuous-wave (CW) laser, pulsed laser), measurement principle (time-of-flight, phase difference), detection technique (coherent detection, direct detection), field-of-view (line scanner, pushbroom scanner, array scanner), measurement range (far range, medium range, close range), or configuration between emitting and receiving component of the device (monostatic system, bistatic system). Furthermore, different types of laser scanners may be used for different application scenarios relying on e.g. spaceborne laser scanning, airborne laser scanning, mobile laser scanning, terrestrial laser scanning, underwater laser scanning or bathymetric laser scanning.","hasChildren":true,"hasParent":true,"name":"Laser scanner","selfAssesment":"Completed
"},{"code":"PS1-3-2","description":"The main idea of LiDAR (Light Detection and Ranging) technology is based on actively scanning the scene by involving a device which emits electromagnetic radiation in the form of modulated laser light. \r\nGenerally, such scanning devices illuminate a scene with modulated laser light and analyze the backscattered signal. More specifically, laser light is emitted by the scanning device and transmitted to an object. At the object surface, the laser light is partially reflected and, finally, a certain amount of the laser light reaches the receiver unit of the scanning device. The measurement principle is therefore of great importance as it may be based on different signal properties such as amplitude, frequency, polarization, time, or phase. \r\nMany scanning devices are based on measuring the time t between emitting and receiving a laser pulse, i.e., the respective time-of-flight, and exploiting the measured time t in order to derive the distance r between the scanning device and the respective 3D scene point. Alternatively, a range measurement r may be derived from phase information by exploiting the phase difference Δφ between emitted and received signal. According to seminal work, respective scanning devices may be categorized with respect to laser type, modulation technique, measurement principle, detection technique, or configuration between emitting and receiving component of the device. \r\nIn order to get from single 3D scene points to the geometry of object surfaces, respective scanning devices are typically mounted on a platform which, in turn, allows a sequential scanning of the scene by successively measuring distances for discrete 3D points.\r\nLiDAR technology is used for a diversity of applications such as autonomous driving, forestry, biomass estimation, precision farming, archaeology, city mapping, terrain modelling, and metrology.","hasChildren":true,"hasParent":true,"name":"LiDAR (Light Detection and Ranging)","selfAssesment":"Completed
"},{"code":"PS1-3-3-1","description":"Sonar, also called ultrasonic sensing, is one the principal sensors for mapping sea-floor, i.e. bathymetry. It transmits sound waves through water and records the amount of backscattered energy. It uses frequencies higher than normal hearing. A sonar can be either passive or active. Active sonars are also called echosounders.","hasChildren":true,"name":"Sonar","selfAssesment":"New
"},{"code":"PS1-3-3-2","description":"A seismic sensor is also called seismometer and measures the motion of the ground when it is shaken by a perturbation such as an earthquake, be it a large displacement or a microquake. The physical variable associated to the measurement of a seismometer is dynamic. It can be either the amplified ground motion, the velocity or acceleration. Current seismometers transform one of these three parameters into a voltage measurement. Usually, three seismometers are needed to retrieve the three components of the displacement. As for other sensors, there exists many types of seismic sensors, and they can be distinguished in active and passive sensors as well.","hasChildren":true,"name":"Seismic sensor","selfAssesment":"New
"},{"code":"PS1-3-3","description":"Instruments that measure vertical distribution of precipitation and other atmospheric characteristics such as temperature, humidity, and cloud composition.","hasChildren":true,"hasParent":true,"name":"Sonic sensors","selfAssesment":"New
"},{"code":"PS1-3-4-1","description":"A radar scatterometer is an active, non-imaging remote sensing device with a real aperture operating in the microwave region of the electromagnetic spectrum. The main purpose of a scatterometer is the characterization of the surface backscatter properties, when a high radiometric accuracy is of interest and the spatial resolution is of secondary importance. There are scatterometers used in laboratories, in the field installed on masts, cranes or trucks, airborne (airplanes, helicopters) and spaceborne scatterometers circling the Earth in an orbit. Spaceborne scatterometers usually achieve a global coverage with a high repetition frequency. The basic principle of the scatterometer the accurate measurement the intensity of the returned radar echo from the Earth’s surface. Because of the speckle effect in radar echoes, a large number of independent observations are averaged.\r\nScatterometry (Earth observation using scatterometers) gained the attention of scientists towards the end of the 1960s when it was realized that the sea clutter observed by Second World War radar operators on their screens was not just any noise obscuring small boats and low-flying aircraft. It was in fact the signal backscatter from small ocean surface waves, comparable in dimension to the wavelength of the radar (in the order of centimetres).\r\nThe primary application of radar scatterometers is the measurement of near-surface wind vectors (wind speed and direction) over the ocean. These wind vector data are based on indirect measurements, where the wind vector is derived from the relationship between the backscattered power, the small-scale ocean surface roughness, and the local wind vector at the ocean surface.","hasChildren":true,"name":"Radar Scatterometers","selfAssesment":"Completed
"},{"code":"PS1-3-4-2-1","description":"Differential Absorption Lidar (DIAL) is a laser remote sensing technique that is used for range and/or profile measurements of atmospheric gas concentrations and constituents.","hasChildren":true,"name":"Differential Absorption Lidar","selfAssesment":"In progress
"},{"code":"PS1-3-4-2-2","description":"Doppler Wind LiDAR or Cloud-Aerosol Lidar with Orthogonal Polarization (e.g. CALIOP) is a two-wavelength polarization-sensitive LiDAR that provides high-resolution vertical profiles of atmospheric aerosols and clouds to enable an greater understanding of our climate.","hasChildren":true,"name":"Doppler Wind LiDAR","selfAssesment":"In progress
"},{"code":"PS1-4","description":"There are different ways to classify sensors used in remote sensing. One of them is the division into imaging and non-imaging sensors. Imaging sensors typically employ optical imaging systems (from VIS to TIR). They operate primarily at window frequencies, where atmospheric absorption is low and surface features can be imaged or measured. Non-imaging sensors include microwave radiometers, microwave altimeters, magnetic sensors, gravimeters, Fourier spectrometers, laser rangefinders, and laser altimeters.","hasChildren":true,"name":"Imaging vs. nonimaging sensors","selfAssesment":"New
"},{"code":"PS1-5-1-2","description":"Across track scanners, known as whiskbroom electromechanical scanners, are multispectral imaging systems building the final image (ground cell by ground cell) by combination of the platform motion along the orbital track with a mechanical rotation of the collecting optic in the across track direction. Opto-mechanical are typically multi-spectral radiometers (no limitation on bands), whiskbroom systems are usually CDD spectrometers (high spectral resolution but just up to 1 micrometer). Examples of the sensors: Landsat Multispectral Scanner (MSS), Landsat Thematic Mapper (TM).","hasChildren":true,"name":"Across track scanners","selfAssesment":"In progress
"},{"code":"PS1-5-1","description":"Speckle-pattern based sensors operate with a spatial neighborhood codification strategies to exploit a unique pattern. The label associated to a pixel is derived from the spatial pattern distribution within its local neighborhood. Thus, labels of neighboring pixels share information and provide an interdependent coding. Representing one of the most popular devices based on structured light projection, the Microsoft Kinect exploits an RGB camera, an IR camera, and an IR projector. The IR projector projects a known structured light pattern in the form of a random but unique speckle dot pattern onto the scene. As IR camera and IR projector form a stereo pair, the pattern matching in the IR image results in a raw disparity image which, in turn, is read out as depth image.","hasChildren":true,"name":"Speckle-pattern based sensor","selfAssesment":"In progress
"},{"code":"PS1-5-2","description":"A multi-temporal (sequential) binary coding uses black and white stripes to form a sequence of projection patterns for each point on the surface of the object. Binary coding technique is very reliable and less sensitive to the surface characteristics, since only binary values exist in all pixels. Thus, each pixel may be assigned a codeword consisting of its illumination value across the projected patterns. The respective patterns may, for instance, be based on binary codes or Gray codes and phase shifting. To achieve high spatial resolution, a large number of sequential patterns need to be projected. All objects in the scene have to remain static. The entire duration of 3D image acquisition may be longer than a practical 3D application allows for. These sensors are utilized in industrial environment.","hasChildren":true,"name":"Multi-temporal pattern based sensor","selfAssesment":"In progress
"},{"code":"PS1-5-3","description":"For a multi-spectral pattern based sensor, various continuously varying color patterns to encode the spatial location information are utilized.","hasChildren":true,"name":"Multi-spectral pattern based sensor","selfAssesment":"In progress
"},{"code":"PS1-5","description":"A structured-light-projection camera emits active optical radiation in the form of a coded structured light pattern in the visible or infrared spectrum, or electromagnetic radiation in the form of modulated laser light. Via the projected pattern, particular labels are assigned to 3D scene points which, in turn, may easily be decoded in images when imaging the scene and the projected pattern with a camera. The procedure reminds to conventional stereo processing, where corresponding features must be extracted from a pair of stereo images to derive the spatial information. In contrast, such synthetically generated features allow to robustly establish feature correspondences, and the respective 3D coordinates may easily and reliably be recovered via triangulation. Generally, techniques based on the use of structured light patterns may be classified depending on the pattern codification strategy.","hasChildren":true,"hasParent":true,"name":"Structured-light-projection camera","selfAssesment":"Completed
"},{"code":"PS1-6","description":"Ground penetrating radar is a non-intrusive measurement technique that uses radio waves to probe the ground. It is used to analyze and locate targets buried in the sub-surface. It transmits low-power electromagnetic energy into the ground and receives weak signals from a low-loss dielectric or conductor material. It is principally used for archeology and geology. Typical penetration depths are between a few centimeters up to 4m.","hasChildren":true,"name":"Ground penetrating RADAR (GPR)","selfAssesment":"New
"},{"code":"PS1-7","description":"An optical spectrometer is an instrument used to detect, measure and analyze the spectral content of the incident electromagnetic field (narrow-band, VIS, NIR, SWIR and TIR). It breaks down the incoming light spectrum so the whole wavelength range is mapped and each wavelength can be analysed individually. Usually, a distinction is made between optical and mass spectrometers.\r\nOptical spectrometers depict the intensity of the incoming light in function of the wavelength. Considering all wavelengths, each object has a specific spectral signature and the analyse of their particular spectrum allows the deduction of their composition ( e.g. pigments) or health.","hasChildren":true,"hasParent":true,"name":"Optical spectrometers","selfAssesment":"In progress
"},{"code":"PS1","description":"Remote sensing sensors acquire information about objects situated on the surface of e.g. the Earth remotely, e.g. from a distance, without any physical contact. They detect and measure the changes that the object imposes on its. \r\nRemote Sensing sensors are characterized according to several different properties:\r\n\tDepending on the interaction between the sensor and the Earth’s surface, one distinguishes between active (e.g. radar) and passive (e.g. optical imagery) sensors. Some systems use both kind of sensors simultaneously.\r\n\tDepending on the mapping process of the information, it can be distinguished between imaging and non-imaging sensors. Imaging sensors produce an image of an area of interest, e.g. give a spatial information about the incoming information. Spatial relationships between objects can be identified and used for visual interpretation. Non-imaging sensors register usually single response values for a specific area, and do not record how the incoming information varies across the field of view. They can be used to characterize the interaction between the received information and illuminated target.\r\n\tDepending on the platform on which the instrument is deployed, one speaks either of ground based (e.g. terrestrial laser scanner), airborne (e.g. plane, drone), or spaceborne (e.g. satellite) sensor. For spaceborne sensors, the orbit geometry (e.g. geostationary, equatorial, sun-synchronous) and altitude (high, medium and low Earth orbit) play an important role, as it most often determines the application of the satellite in combination with the deployed sensor (weather satellites or Earth observation satellite). \r\n\tDepending on the observed portion of the electromagnetic spectrum (e.g. optical, infrared, thermal, microwave). \r\n\tDepending on the instrument (e.g. imagers, altimeters, spectrometers, radiometers). \r\n\tDepending on the instrument precision, e.g. in terms of spatial resolution very high vs. low resolution sensors; in terms of spectral resolution narrow band (hyperspectral sensors) vs. broad-band sensors (mono- and multispectral sensors); in terms of radiometric resolution very high vs. low resolution sensors. Some applications do not require very high precision instruments, e.g. sea surface temperature measurements, while other, e.g. for vegetation monitoring, require high spectral and radiometric resolution for good data interpretation and analysis. \r\nOther categorization would include the specific applications of each sensor (weather, environment, urban, land, water, mapping, photogrammetry, structure-from-motion, etc.) and if is financed and used for scientific, commercial or military goals.","hasChildren":true,"hasParent":true,"name":"Types of remote sensing sensors","selfAssesment":"Completed
"},{"code":"PS2-1","description":"This topic covers information on the first remote sensing platforms that were used to obtain aerial photos. The first-known aerial photo was obtained in 1858 by Gaspard Felix Tournachon (Nadar). Afterwards, different platforms were used to obtain the information from above. The history of the development of remote sensing platforms includes platforms such as baloons, kites, rockets, pigeons, gliders, etc. to recent low-cost femtosatellites, e.g. for solar radioation pressure measurements. Historically, the main developments of the platforms as well as sensors was associated with military operations in the XXth century. Remote sensing data was used as part of photo- or/and satellite reconnaissance, i.e. aerial photos or satellite imageries used for the military purposes, mainly to make accurate maps and based on that to prepare a military strategy.","hasChildren":true,"name":"History of Remote Sensing Platforms","selfAssesment":"In progress
"},{"code":"PS2-2-1","description":"An unmanned aircraft system (UAS) includes an unmanned aerial vehicle (UAV), an aircraft without a human pilot on board, a ground-based controller, and a system of communications between the two. The system includes a full range of size classes from very small hand-launched drones to the large high-altitude observational systems.","hasChildren":true,"name":"Unmanned Aerial Systems (UAS)","selfAssesment":"New
"},{"code":"PS2-2-2-1","description":"Mission planning depends on the selected system of acquisition (sensor and platform). A detailed planning of a mission is a fundamental prerequisite for a successful acquisition of remote sensing data. Planning of an aerial photography mission (manned or unmanned) takes into account several parameters such as time of day/sun angle, weather conditions, flightline, platform. Planning and implementation of a spaceborne Earth Observation mission involves several successive life cycle ‘phases’ of conception, development, production and testing, utilization and support, and retirement, as part of an iterative and recursive process, until the satellite (space segment) is delivered and launched into orbit, and the data are exploited in the ground segment.","hasChildren":true,"name":"Mission planning","selfAssesment":"New
"},{"code":"PS2-2-2-3-2-3-1","description":"Stripmap is an acquisition mode of Synthetic Aperture Radar (SAR) data. It is the most simple, common acquisition mode of the SAR satellite sensors. In this mode, the antenna of the radar system is pointed in a fixed direction related to the flight direction. The displacement of the illuminated footprint corresponds to the displacement of the sensor along the orbit. This results in a continuous acquisition strip parallel to the flight direction. The ground coverage and resolution varies depending on the considered sensor and technical requirements. For X-band spaceborne sensors, a spatial resolution of 3 m can be achieved with a swath width in range direction of 30 km, e.g. for TerraSAR-X. In C-band, a spatial resolution up to 5 m is achieved e.g. by Sentinel-1 with a swath width of 80 km. For L-band spaceborne sensors, the spatial resolution achievable in stripmap mode varies between 3 and 10 m, with a swath width of 50-70 km, e.g. ALOS PALSAR2. \r\nContrary to other acquisition modes, no antenna steering is needed in azimuth direction and the elevation beam is fixed in a specific range direction. This allows for an uninterrupted coverage along the flight direction.\r\nStripmap data show high resolution with sufficient coverage for regional applications and can therefore be used for e.g. detailed land cover analysis at regional scale such as the mapping of urban footprints. Furthermore, it can be used for the mapping of small island or to support emergency actions.","hasChildren":true,"name":"Stripmap","selfAssesment":"Completed
"},{"code":"PS2-2-2-3-2-3-2-1","description":"The Staring Spotlight mode is only available for a few sensors. It follows the same principle of antenna steering in azimuth direction as the standard Spotlight mode, except that the rotation center of the antenna for steering is situated at a nearer range position, within the illuminated scene. This induces that the illuminated antenna footprint stays almost the same during the whole acquisition. Contrarily to the Spotlight mode, the antenna footprint does not slide along the azimuth direction during the SAR acquisition. Additionally, the steering angle is higher for the Staring Spotlight mode than for the standard Spotlight mode, increasing therefore the length of the synthetic aperture and leading to an even higher resolution in azimuth direction.\r\nThe Staring Spotlight mode is implemented on the X-Band sensor TerraSAR-X since 2013 and achieves an azimuth resolution up to 0.25 m. Similar to the standard Sportlight mode, this happens to the detriment of the coverage. The scene size is highly dependent of the incidence angle and varies from 7.5 km to 4 km in range and from 2.5 to 2.7 km in azimuth direction. A larger coverage is obtained for smaller incidence angles.\r\nDue to their extremely high resolution, staring spotlight acquisitions are principally used for the observation and/or monitoring of small scale objects and phenomena, e.g. small landslides, or for tomographic analysis.","hasChildren":true,"name":"Staring Spotlight","selfAssesment":"New
"},{"code":"PS2-2-2-3-2-3-2","description":"Spotlight is a SAR acquisition mode that allows increasing the illumination time of a particular area of interest by steering the antenna beam in azimuth direction. In this mode, the beam elevation is fixed, but the antenna is steered in azimuth direction, increasing therefore the length of the synthetic aperture. The rotation center of the antenna for steering is situated behind the scene at far range. The antenna footprint slides slightly forward over the scene in the azimuth direction during acquisition, but slower than in Stripmap mode, due to the antenna steering. The longest illumination time in azimuth direction results in an azimuth resolution that is highly enhanced compared to e.g. the Stripmap or the ScanSAR acquisition modes. However, this improvement is done to the detriment of the coverage. As for the other acquisition modes, the ground coverage and resolution depends on the considered sensor. For TerraSAR-X, a minimum coverage of 10 km in range and 5 km in azimuth direction is achieved in the Spotlight mode, with and azimuth resolution of about 1 m. The L-Band sensor Alos 2 also allow Spotlight acquisition mode, with a coverage of 25 km in both directions and a resolution of 1 m in azimuth direction, and down to 3 m in range direction.\r\nDue to the very high resolution achieved in both directions, this acquisition mode is particularly usefull for urban area analysis as it allows for the detection of small objects. Therefore, Spotlight data are often used for the detection and recognition of man-made structures and objects, such as roads, buildings and even vehicles.","hasChildren":true,"hasParent":true,"name":"Spotlight","selfAssesment":"New
"},{"code":"PS2-2-2-3-2-3-3-1","description":"The Interferometric Wide Swath Mode is a particular acquisition mode of the C-Band satellites Sentinel-1 which implements the TOPS (Terrain Observation with Progressive Scan) method. It combines an antenna steering in elevation, as in ScanSAR mode, with a counterrotation of the antenna beam from backward to forward steering, opposite to the steering happening in Spotlight mode. The data is acquired in bursts by cyclically switching the antenna beam between multiple adjacent sub-swaths.\r\n\r\nThis opposite steering direction of the antenna along the azimuth leads to a shorter target illumination and induces a decrease of the resolution, but a cyclically continuous coverage in azimuth direction. The principal difference to the other acquisition modes is that this acquisition mode implies a shrinking of the antenna footprint virtually to a ground target instead of slicing it to retrieve the target.\r\n\r\nThe Interferometric Wide Swath Mode (IW) was originally designed to solve Signal-to-Noise heterogeneities and azimuth ambiguities appearing in the ScanSAR mode.\r\n \r\nFor Sentinel-1, the IW mode provides a coverage of 250 km in range direction with an azimuth resolution of 20 m and incidence angles ranging from 29.1° in near to 46° in far range. \r\n\r\nStandard Single Look Complex Sentinel- 1 IW products contain three sub-swaths in range direction, with nine burts in azimuth direction.\r\n\r\nThe IW mode is the standard acquisition mode of the Sentinel-1 C-Band satellites and is acquired continuously over all land surfaces. The application are very diverse, ranging from agriculture and forestry to urban deformation monitoring and ship surveillance.\r\n\r\nSimilar to the IW mode, the Extra Wide Swath Mode (EW) of Sentinel-1 uses the same TOPS technique, but covers even wider areas up to 400 km in range direction, to the detriment of the resolution which decreases to 40 m. The EW Mode principally finds application in maritime applications such as artic and sea-ice monitoring, analyses of marine winds and oil pollution monitoring.","hasChildren":true,"name":"Interferometric Wide Swath Mode","selfAssesment":"New
"},{"code":"PS2-2-2-3-2-3-3-2","description":"The Extra Wide Swath Mode is an acquisition mode of the Sentinel-1 satellites. It is primarily designed and used for wide area coastal monitoring, such as ship traffic, sea-ice monitoring and oil spill detection. It uses the TOPSAR technique with a swath width of 410km and a spatial resolution of 20 m by 40 m.","hasChildren":true,"name":"Extra Wide Swath Mode","selfAssesment":"New
"},{"code":"PS2-2-2-3-2-3-3","description":"In the ScanSAR acquisition mode, the antenna beam is successively steered to different elevation angles. This results in adjacent, slightly overlapping stripes, or sub-swaths along the range direction, parallel to the azimuth direction, each stripe having a different incidence angle at its center. During antenna steering in elevation, transmitter and receiver are off. Therefore, each stripe is illuminated for a shorter time as for the StripMap mode, leading to a degradation of the azimuth resolution. However, ScanSAR allow a larger coverage in range direction than the other imaging modes. Each sub-swath is illuminated for a shorter time than in the Stripmap case. The timing is adjusted though, such that the time-varying antenna footprint repeat cyclically. Similar to the other acquisition modes, the achievable resolution and coverage of ScanSAR products depends on the considered sensor and its properties. For X-Band, e.g. for TerraSAR-X, a total swath width of 100 km in range direction can be achieved using four adjacent sub-swaths or, using a Wide ScanSAR mode with six adjacent sub-swaths, a swath width up to 270 km can be achieved. A Wide ScanSAR scene shows incidence angles ranging from 15.6° in near to 49° in far range. The azimuth resolution varies between 18.5 m and 40 m, for ScanSAR and WideScan SAR modes respectively. For the L-Band sensor ALOS-PALSAR 2, a swath width up to 40 km can be achieved, with incidence angles ranging from 8° to 70° and an azimuth resolution of 60 m. \r\nThe ScanSAR mode is well suited for large-area monitoring, e.g. for sea ice or glacier monitoring, as well as for mapping large-scale disasters, such as oil slick, or areas devastated by forest fires. Using interferometry, topography mapping and deformation monitoring is also possible.","hasChildren":true,"hasParent":true,"name":"ScanSAR","selfAssesment":"New
"},{"code":"PS2-2-2-3-2-3-5","description":"A stereoscopy acquisition mode collects remotely sensed data where each location on the ground (or the imaged objects) is covered multiple times (at least twice), from different perspectives. Stereopairs and stereoscopic coverage enable the extraction of 3D representations of the environment from remotely sensed imagery. Most aerial photographs are taken with frame cameras along flight lines, or flight strips. [...] Successive photographs are generally taken with some degree of endlap [, i.e. overlap]. Not only does this lapping ensure total coverage along a flightline, but an endlap of at least 50 percent is essential for total stereoscopic coverage of a project area. Stereoscopic coverage consists of adjacent pairs of overlapping vertical photographs called stereopairs. Stereopairs provide two different perspectives of the ground area in their region of endlap [overlap]. When images forming a stereopair are viewed through a stereoscope, each eye psychologically occupies the vantage point from which the respective image of the stereopair was taken in flight. The result is the perception of a three-dimensional stereomodel. As an input to photogrammetry analysis procedures, stereopairs from flight strips enable the extraction of digital elevation models (DEM), orthophotos, thematic GIS data, and other derived products through the use of digital raster images and relatively sophisticated analytical techniques. With the availability of close-range UAV and terrestrial hand-held camera data, 3D reconstructions of buildings (even indoors) and other objects on the terrain surface become possible.","hasChildren":true,"name":"Stereoscopy","selfAssesment":"In progress (to be deleted, merged?)
"},{"code":"PS2-2-2","description":"Since the 1940s aerial imagery has been the primary source of detailed geospatial data for extensive study areas. Photogrammetry is the profession concerned with producing precise measurements from aerial imagery. Aerial imaging and photogrammetry represent a major component of the geospatial industry. The topics included in this unit do not comprise an exhaustive treatment of photogrammetry, but they are aspects of the field about which all geospatial professionals should be knowledgeable.","hasChildren":true,"hasParent":true,"name":"Airborne platforms and systems","selfAssesment":"New
"},{"code":"PS2-2-3-1","description":"Earth observation (EO) missions are gathering information about the physical, chemical, and biological systems of the planet via remote-sensing technologies, supplemented by Earth-surveying techniques, which encompasses the collection, analysis, and presentation of satellite data.","hasChildren":true,"name":"Earth observation missions","selfAssesment":"In progress
"},{"code":"PS2-2-3-2","description":"There are essentially three types of Earth orbits: high, medium and low Earth orbit. Satellites that orbit in a medium (mid) Earth orbit include navigation and specialty satellites, designed to monitor a particular region. Most scientific satellites, including NASA’s Earth Observing System fleet, have a low Earth orbit. On which orbit a satellite will be launched to, depends mainly on its application. The orbit types can be categorized according to their height.\r\nThe orbit height of a satellite corresponds to the distance between the Earth’s surface and the satellite. It determines its speed as it rotates around the Earth. Due to Earth’s gravity, the pull of gravity is stronger for lower orbits than for higher orbits. Therefore, a satellite situated on a lower orbit will circle the Earth faster than a satellite situated on a higher orbit.\r\n\tHigh Earth orbit: it describes orbits situated at about 36000 km above the Earth’s surface (42164 km from the Earth’s center). At this exact distance, the speed of the satellite on the orbit matches the Earth’s rotation, i.e. the satellite needs 24 hours to complete a full rotation on the orbit, when the orbit is situated exactly above the equator. Such orbits are also called geosynchronous orbits, as the satellite moves at the same speed than the Earth and seems to stay in place over a specific location. Those orbits are mainly used for weather and communication satellites\r\n\tMedium Earth orbit: it describes orbits situated at about 20200 km of the Earth’s surface, or 26560 km of the Earth’s center. At this height, a satellite rotates twice around the orbit during one Earth’s rotation. This orbit is also called semi-synchronous and this is the orbit type used by Global Navigation Satellite Systems such as GPS and GLONASS. A further important medium Earth orbit is the Molniya orbit which allows the observation of the poles, otherwise nearly impossible with equatorial geosynchronous orbits.\r\n\tLow Earth orbit: this type of orbits are used from almost all dedicated scientific Earth Observation satellites. Most of them use a particular, nearly polar orbit inclination, meaning that the satellite rotates around the Earth nearly from pole to pole (instead of around the equator as it is the case for geosynchronous satellites). This rotation takes about 99 minutes, depending of the specific orbit inclination. During one half of the orbit, the satellite views the daytime side of the Earth, i.e. the illuminated side. At the pole, satellite crosses over and views the nighttime side of Earth. Back to the daylight side, the satellite can view the area adjacent to the region flown over in the last orbit path, due to the simultaneous Earth’s rotation. In 24 hours, satellites situated on these orbits view almost all the Earth twice, for optical satellites once in daylight and once in the dark. Radar satellites seen each Earth region twice, from two different illumination directions. These specific polar-orbits are called sun-synchronous, as the local solar time stays the same each time a satellite flies over a specific region. This has the advantage of providing an almost constant angle of sunlight for each region on the Earth’s surface viewed by the satellite over time and ensure repeatable sun illumination conditions; the angle will only vary seasonally due to the Earth revolution around the sun. Due to this consistency, images of a specific region would not show much illumination changes due to shadows or sunlight and image interpretation over time such as change detection or monitoring approaches are possible. Because a sun-synchronous orbit does not pass directly over the poles, there is a data gap over both poles where no data is acquired.","hasChildren":true,"hasParent":true,"name":"Types of satellite orbits","selfAssesment":"Completed
"},{"code":"PS2-2-3-3","description":"An imaging SAR system can generally make acquisitions in different modes. Which acquisition mode to choose depends of the application but also on the desired coverage and data resolution. Even if technically all acquisitions modes can be used everywhere on the Earth’s surface, specific modes are preferred for ocean applications that are different from the ones used in land applications.\r\nThe different acquisition modes can be defined either by their geometrical or by their temporal properties.\r\nThe geometrical properties refer to the geometric configuration of the SAR antenna. Usually looking sideways down in a direction perpendicular to the flight direction (Stripmap mode), the antenna can also be steered around the nadir axis in order to look at a specific target for a longer time during pass-by (Spotlight mode). This configuration allows to rachieve higher azimuth resolution but reduces coverage. It is rather used for very local application where a precise information about specific targets is needed. Other geometric configurations steer the antenna around the flight direction (ScanSAR mode), yielding to a larger swath on the ground. The distance between near and far range is increased, as well as the range of incidence angles within one acquisition. Whereas it increases the area of the scene, it comes generally with a decrease of the spatial resolution in the azimuth direction. Depending on the sensors, the name of the acquisition modes as well as particular technical properties can differ. Sentinel-1 uses a TOPS configuration (Terrain observation with Progressive Scan), which combines the antenna steering properties of both ScanSAR and Spotlight modes. \r\nThe temporal properties refer for specific techniques to the time interval between several acquisitions of the same area. Either these acquisitions are taken simultaneously in one pass over the area of interest (single-pass), or they are taken at different times, needing several passes over the area (repeat-pass).\r\nSpecific SAR techniques such as InSAR and Tomography, while relying on those geometric and temporal properties, have additional acquisition configuration characteristics. For example, the interferometric mission TanDEM-X has three acquisition modes defined by the number of satellite emitting or receiving the signal (pursuit monostatic mode, bistatic mode, alternating bistatic mode), which allows phase referencing. Tomographic SAR uses multi-baseline observations, i.e. the antenna passes several times over an area but at different heights, allowing via different incidence angles the retrieval of structural information of specific targets.","hasChildren":true,"hasParent":true,"name":"Synthetic Aperture Radar (SAR) acquisition modes","selfAssesment":"Completed
\r\n\r\n"},{"code":"PS2-2-3-4","description":"Swath width refers to the width of the ground that the satellite collects data from on each orbit. The area imaged on the surface, is referred to as the swath. Imaging swaths for spaceborne sensors generally vary between tens and hundreds of kilometres wide.","hasChildren":true,"name":"Swath","selfAssesment":"
In progress
"},{"code":"PS2-2-3","description":"Spaceborne platforms and systems are present at a great height from the earth surface. The altitude of platforms range from few hundred kilometers to several thousand kilometers. A large area can be captured in a single scene depending on altitude of sensor. The platforms can have different characteristics.","hasChildren":true,"hasParent":true,"name":"Spaceborne platforms and systems","selfAssesment":"Planned
"},{"code":"PS2-3-1","description":"Field spectroscopy generally refers to the use of non-imaging spectrometers near the ground surface and it is usually aimed at evaluating spectral reflectance of the investigated target. For this purpose, consecutive measurements of total incident solar irradiance and of radiance or irradiance upwelling from the target are collected by an operator, or more recently by new instruments for long-term and unattended field spectroscopy measurements. The incident irradiance is usually computed by measuring the radiance upwelling from a white calibrated panel which represents the ideal Lambertian surface. Upwelling fluxes are instead usually collected holding the sensor vertically over the surface (nadir view), although spectral libraries collected observing the target from different viewing angles are also available. \r\nField spectrometry is also referred to as ‘proximal sensing’ to underline that spectra are collected with portable spectroradiometers in the vicinity of the target, in contrast to ‘remote sensing’, which is instead usually performed with satellite or airborne sensors.\r\nField spectroscopy is therefore an in-situ method for characterising the reflectance of natural or artificial surfaces and thereby provides reference data for the calibration or validation (cal/val) of airborne and satellite sensors. This method provides a means of scaling-up measurements from small areas (e.g. leaves, rocks) to composite scenes (e.g. vegetation canopies), and ultimately to pixels.\r\nField spectroscopy is used in different applications, for example, soils, rocks, vegetation and chlorophyll fluorescence, water, snow surfaces and atmosphere. Long-lasting field spectroscopy campaigns based on manual measurements are extremely resource-demanding and do not ensure repeatability of the acquisition conditions as the instrument setup is initialized each day. To overcome such limitations a few research groups have initiated automatic tower-based spectral reflectance measurements using different devices. With such setups, non-imaging spectrometers are installed in the field and are operated automatically for long periods (i.e. months to years) and different networks of hyperspectral instruments are now becoming operational (e.g. RadCal Net).\r\nField spectroscopy can be also used to predict optimum spectral bands, viewing configuration, spectral calibration and time to perform a particular remote sensing task but also to develop, refine and test models relating biophysical attributes to remotely-sensed data. In this context, ground reflectance measurements are therefore mainly used as input in simulation study for sensor design, calibration/validation data for remote sensing sensors, for spectral mixture analysis and for the development of relationships between field data and radiometric variables.\r\nSince spectroscopy is the study of matter using electromagnetic radiation, point or imaging field spectrometers are instruments which allow the measurements of reflected or emitted electromagnetic radiation. In particular, portable or hand-held spectroradiometers are small instruments that spectrally measure the radiation reflected or emitted by a target and they are useful in obtaining accurate spectral data over different surfaces. In remote sensing, they generally cover the 400-2500 nm spectral range and operate with a full width at half-maximum of about 1.5/3 nm, so that they can collect radiation in a continuous way across the spectrum. The final output is therefore the hyperspectral signature of reflectance of the surfaces versus the considered wavelength.","hasChildren":true,"name":"Field spectroscopy and portable spectroradiometers","selfAssesment":"Completed
"},{"code":"PS2-3-2","description":"A terrestrial laser scanning (TLS) system is a stationary highly accurate ranging device for geodetic surveying. More specifically, TLS systems provide dense and accurate 3D point cloud data for the local environment and they may also reliably measure distances of several tens of meters. Due to these capabilities, such TLS systems are commonly used for applications such as city modeling, indoor modeling, construction surveying, deformation analysis, scene interpretation, urban accessibility analysis, or the digitization of cultural heritage objects. When using a TLS system, each captured TLS scan is represented in the form of a 3D point cloud consisting of a large number of scanned 3D points and, optionally, additional attributes for each 3D point such as color or intensity information. However, a TLS system represents a line-of-sight instrument and hence occlusions resulting from objects in the scene may be expected as well as a significant variation in point density between close and distant object surfaces. Thus, a single scan might not be sufficient in order to obtain a dense and (almost) complete 3D acquisition of interesting parts of a scene and, consequently, multiple scans have to be acquired from different locations. As each scan refers to the local coordinate system of the TLS system, all acquired scans have to be appropriately aligned in a common coordinate system. For this purpose, the respective 3D transformations between the acquired scans have to be estimated and this process is commonly referred to as point cloud registration, point set registration, or 3D scan matching.","hasChildren":true,"name":"Terrestrial Laser Scanning","selfAssesment":"Completed
"},{"code":"PS2-3","description":"Platforms and systems that acquire data from the level of earth's surface. A wide variety of ground based platforms are used in remote sensing. The acquired data are used for detailed in-situ measurements, e.g., Leaf Area Index (LAI), and for calibration/validation campaigns.","hasChildren":true,"hasParent":true,"name":"Ground platforms and systems","selfAssesment":"New
"},{"code":"PS2","description":"Remote sensing platforms and systems can be static (ground-based platforms) or moving (e.g. airborne or spaceborne platforms, UAVs). A remote sensing platform or system carry a remote sensing sensor. It can operate in near (few centimetres) or far (36,000 kilometres) altitudes ranges.","hasChildren":true,"hasParent":true,"name":"Types of remote sensing platforms and systems","selfAssesment":"Planned
"},{"code":"PS3-1","description":"The development of remote sensing data carriers has followed the evolution of the photography, remote sensing sensors and computer platforms. The first remote sensed data was stored using the photography films (e.g. aerial photography, satellite Corona program), which was later replaced by reel tapes, cartridge, and then removable and hard discs. In the era of big and fast growth of Earth observation data, and technological advancements in digital infrastructure, the satellite data are stored using cloud platforms providing different service models: Infrastructure as a Service, Platform/Software as a Service (e.g. Copernicus DIAS, Google Earth Engine, open EO). The Cloud offers infrastructure to host, store and process the large amount of data efficiently. For example, the Copernicus Data Information Access Services (DIAS) is a comprehensive cloud-based hosting and processing system for the EO data in particularly for the Sentinels data, the Google’s Earth Engine (GEE) provides access to various satellite and offers processing power with a web-based programming interface, the Amazon Web Services (AWS) has dedicated cloud called ‘Earth on AWS’, the Microsoft’s cloud called Azure facility the use of AI tools to address environmental challenges. Public solutions, as well as private ones, react with a variety of new and innovative tools, which have been recently developed (e.g. DIAS, ODC, EarthServer, EO Browser, GEE).","hasChildren":true,"name":"History of remote sensing data carriers","selfAssesment":"Completed
"},{"code":"PS3-2-1","description":"Most remotely sensed images nowadays exist in digital form. Even domestic cameras are now usually digital instruments, and the use of photographic film is becoming rarer and rarer. Analogue images, such as photographs, are continuous, both in their spatial extent (they can be enlarged almost without limit) and radiometrically (there is a continuous range of shades of grey). The word ‘picture’ is usually used for such an image.\r\nOn the other hand, a digital image is spatially and radiometrically discrete. A remote sensing sensor detects the reflected radiation of the Earth’s surface and stores it as numbers in a raster. In accordance, each area that has been detected constitutes a cell in a raster. The grey levels increment in a stepwise fashion, and the scene is made up from an array of individual elements called ‘picture elements’, abbreviated to ‘pixels’, each of which is represented by one of the discrete grey levels. A pixel is the smallest addressable element in a raster image.\r\nThe spatial resolution of a raster image refers to the size of the ground element represented by an individual pixel. The size of an area represented in a pixel depends of the capability of the sensor to detect details. A pixel cannot be subdivided, and enlargement merely produces larger pixels, which contain no more information than the original ones. We are familiar with this effect on our television or computer screen – the picture we see consists of an array of dots of light, the density of which determines the screen resolution.\r\nThe number of distinct grey levels into which the intensity of the signal is divided and that can be represented by a pixel is called radiometric resolution of a digital image, and it depends of the number of bits per pixel (bpp). A 1 bpp image uses 1 bit for each pixel, so each pixel can be either on or off (monochrome). Each additional bit doubles the number of grey levels available, so a 2 bpp image can have 4 grey levels, a 3 bpp image can have 8 grey levels, and so forth. In colour imaging systems, a colour is typically represented by three component intensities such as red, green, and blue; usually their raster images have an 8-bit resolution (256 grey levels), a 16-bit resolution (65,536 grey levels), or a 24-bit resolution (16,777,216 grey levels).","hasChildren":true,"name":"Picture element (pixel)","selfAssesment":"Completed
"},{"code":"PS3-2-2","description":"One can think of any image as consisting of tiny, equal areas, or picture elements, arranged in regular rows and columns. The position of any picture element, or pixel, is determined on an xy coordinate system. Each pixel also has a numerical value, called a digital number (DN), that records the intensity of electromagnetic energy measured for the ground resolution cell represented by that pixel. Digital numbers range from zero to some higher number on a gray scale. The image may be described in strictly numerical terms on a three-coordinate system with x and y locating each pixel and z giving the DN, which is displayed as a gray-scale intensity value. \r\nMany types of remote sensing images are routinely recorded in digital form and then processed by computers to produce images for interpreters to study. An image recorded initially on photographic film may be converted into digital format by a process known as digitization.","hasChildren":true,"name":"Image as a matrix (digital number DN)","selfAssesment":"Completed
"},{"code":"PS3-2-3","description":"In data manipulation contexts, a data cube is a multi-dimensional array of values. A data cube can be visualized as the multidimensional extension of two-dimensional table. It can be viewed as a collection of identical 2-D tables stacked upon one another. Data cubes are used to represent data that is too complex to be described by a traditional table of columns and rows. Typically, the data cube is applied in conditions where these arrays are massively larger than the hosting computer’s main memory, for example multi-terabyte data warehouses o time series of image data.","hasChildren":true,"name":"Data cubes","selfAssesment":"In progress
"},{"code":"PS3-2-4","description":"Term Big data refers to any collection of data sets so large and complex that it becomes difficult to process using on-hand data management tools or traditional data processing applications. In the field of Earth Observation (EO) is usually refers to large time series of image data which size on disk is much greater than hosting computer’s main memory. EO Big Data offers solution that allows not only storing these data on disk but also efficiently process them.","hasChildren":true,"name":"Earth Observation Big Data","selfAssesment":"In progress
"},{"code":"PS3-2","description":"Most remote sensing data exist as digital images, and appropriate image processing allows the emphasis of certain aspect and subsequent extraction of information for specific applications.\r\nA digital image is a representation of the reality as a grid of picture elements. It can be considered as an array of numbers that can be stored and handled by a digital computer. The picture elements are pixels and each pixel has a specific value (usually in grayscale). This value is a digital number (DN), which usually represents the amount of energy recorded by the sensor at this pixel position or any other characteristic recorded by the sensor, e.g. elevation. \r\nEach row of the image grid, or matrix, corresponds to one scan line. Each pixel is characterized by its row r and column c position in the image, as well as by its value. Additional geographical information is needed in order to assign a geographic location to a pixel. The digital number are integers usually compressed in one byte (= 8 bit) representation, i.e. each pixel can take 256 values.\r\nDigital images are raster data, as opposite to vector data. Whereas vector data can be points, lines or polygones, raster data always consist of pixels. A pixel is the smallest element in which an image can be divided into. The pixel size varies depending of the instrument and of the sampling used. Large pixel may contain information about several objects of the recorded scene. However, they only have one value. These are called mixed-pixel, as e.g. several land cover classes are represented within one pixel and they cannot be distinguished from another. \r\nIn multispectral imagery, each region of the electromagnetic spectrum is recorded in an independent image (band). Therefore, at a specific array position (r,c), there exist several pixels, each with a specific value corresponding to the energy recorded for the considered band. This result in a three-dimensional matrix. The bands of a multispectral image can be displayed three at a time in the computer using for each band one of the three primary colors red, green and blue (RGB). This is called a color composite image. If the color composite represents a combination of the visible red, green and blue bands in their respective color, the combination is called natural or true color composite, as it corresponds to what the human eye sees naturally. Any other combination, for example considering bands of wavelengths that are not visible for the human eye is called a false color composite. It is often used to highlight the spectral differences and particular image features in order to extract information.","hasChildren":true,"hasParent":true,"name":"Digital image terminology","selfAssesment":"Completed
"},{"code":"PS3-3-1","description":"Band interleaved by line (BIL) is one of three primary methods for encoding image data for multiband raster images in the geospatial domain, such as images obtained from satellites. This simple uncompressed raster data encoding is easily and frequently described, requiring no formal specification. BIL is not in itself an image format, but is a scheme for storing the actual pixel values of an image in a file band by band for each line, or row, of the image. The raw data has a simple form and is easily interpreted if the image dimensions in pixels, the number of spectral bands, and the number of bits per band are known. For example, given a three-band image, all three bands of data are written for row one, all three bands of data are written for row two, and so on. The BIL encoding is a compromise format, allowing fairly easy access to both spatial and spectral information. The BIL data organization can handle any number of bands, and thus accommodates black and white, grayscale, pseudocolour, true colour, and multi-spectral image data.\r\nAdditional information is needed to interpret the image data, such as the numbers of rows, columns, and bands, and relate the image to geospatial locations. This information may be supplied in a file header (typical on the tapes originally used for satellite image data) or in files associated with a raw image data file.\r\nSpatial resolution and bit-depth are not limited by the BIL encoding per se but may be constrained in some usage contexts. There is no support for colour management in the BIL encoding. Documentation of spectral values for bands, or interpretation of false colours should be supplied in an accompanying data structure.","hasChildren":true,"name":"Band interleaved by line (BIL)","selfAssesment":"Completed
"},{"code":"PS3-3-2","description":"Band interleaved by pixel (BIP) is one of three primary methods for encoding image data for multiband raster images in the geospatial domain, such as images obtained from satellites. This simple uncompressed raster data encoding is easily and frequently described, requiring no formal specification. BIP is not in itself an image format, but is a method for encoding the actual pixel values of an image in a file. The raw data has a simple form and is easily interpreted if the image dimensions in pixels, the number of spectral bands, and the number of bits per band are known. Images stored in BIP format have the first pixel for all bands in sequential order, followed by the second pixel for all bands, followed by the third pixel for all bands, etc., interleaved up to the number of pixels. The BIP data organization can handle any number of bands, and thus accommodates black and white, grayscale, pseudocolour, true colour, and multi-spectral image data.\r\nBIP data stores pixel information for separate bands within the same file, so that the user can choose to display just one specific band in a multi-band image. Therefore, BIP encoding provides optimal processing performance for spectral analysis (as compared with BIL or BSQ raster organization) as it supports efficient extraction of individual spectra and spectral averages.\r\nAdditional information is needed to interpret the image data, such as the numbers of rows, columns, and bands, and relate the image to geospatial locations. This information may be supplied in a file header (typical on the tapes originally used for satellite image data) or in files associated with a raw image data file.\r\nSpatial resolution and bit-depth are not limited by the BIP encoding per se but may be constrained in some usage contexts. There is no support for colour management in the BIP encoding. Documentation of spectral values for bands, or interpretation of false colours should be supplied in an accompanying data structure.","hasChildren":true,"name":"Band interleaved by pixel (BIP)","selfAssesment":"Completed
"},{"code":"PS3-3-3","description":"Band sequential (BSQ) is one of three primary methods for encoding image data for multiband raster images in the geospatial domain, such as images obtained from satellites. This simple uncompressed raster data encoding is easily and frequently described, requiring no formal specification. BSQ is not in itself an image format, but is a method for encoding the actual pixel values of an image in a file. BSQ format is a very simple format, where each line of the data is followed immediately by the next line in the same spectral band. The raw data has a simple form and is easily interpreted if the image dimensions in pixels, the number of spectral bands, and the number of bits per band are known. This format is optimal for spatial (x, y) access of any part of a single spectral band. The BSQ data organization can handle any number of bands, and thus accommodates black and white, grayscale, pseudocolour, true colour, and multi-spectral image data.\r\nA single band covering the entire scene is stored as a single bitstream making this encoding method convenient when only selected bands are needed. Each image band can be conveniently written to an independent file. BSQ can therefore be a preferable format for some forms of analysis as an application does not have to read past ancillary data in an image stack. As opposed to formats where the bands are interleaved (such as a multi-band GeoTIFF), BSQ data sets support convenient extraction of relevant bands. Some BSQ datasets are distributed as separate image files for each band, with common geospatial registration and a shared set of header information.\r\nAdditional information is needed to interpret the image data, such as the numbers of rows, columns, and bands, and relate the image to geospatial locations. This information may be supplied in a file header (typical on the tapes originally used for satellite image data) or in files associated with a raw image data file.\r\nSpatial resolution and bit-depth are not limited by the BSQ encoding per se but may be constrained in some usage contexts. There is no support for colour management in the encoding. Documentation of spectral values for bands, or interpretation of false colours should be supplied in an accompanying data structure.","hasChildren":true,"name":"Band sequential (BSQ)","selfAssesment":"Completed
"},{"code":"PS3-3","description":"EO data consist of unstructured image data and structured descriptive information attached to the image, which is also called metadata. EO systems are rapidly developing and data sensors resolution are continuously improving. As a result, a vast amount of EO data is generated every day, and their volumes have been in geometric progression growth. According to the current literatures, storage and management methods of EO data are divided into four groups from the perspective of basic technologies: \r\n1. File systems: Traditionally, EO data were manually managed and organized by means of file systems that share and exchange data through storage devices. However, for large amounts of EO data this method leads to inefficiency of management, extra expenses of storage spaces, and weak data security. File systems cannot efficiently support for data retrievals, analyses, and uses in practical applications and research work nowadays. For solving these problems, parallel file system and distributed file system (see below) were presented to support data-intensive applications.\r\n2. Relational Data Base Management Systems (RDBMS): At present, storage and management manners of major EO data are to combine RDBMS and middle-wares. On one hand, traditional RDBMS functionalities are expanded to adapt to the storage and management features of EO data. Adding new data types or encapsulating complicate data types as an object in RDBMS are two general ways to expand functionalities of traditional RDBMS. The former can meet basic requirements of EO data storage and management, but is unable to directly operate spatial data and create spatial indexes. This solution is mainly taken by Database Management System (DBMS) developers, such as Spatial GeoRaster of Oracle, Spatial Extender of IBM DB2, PostGIS of PostgreSQL, and Spatial Extension of MySQL. On the other hand, geographical software expands their data management abilities by developing spatial database engine middle-wares, which is always taken by software enterprises that develop geographical information system (GIS). Spatial Database Engine (SDE) is between users and DBMS. For data storing, SDE is responsible for receiving and storing user data into RDBMS; for data retrieving, it reads data from RDBMS and show them through user interfaces. This resolution stores EO data into RDBMS and interactively manages them by user interfaces provided by SDE. SDE technology is very mature and extensively used in various application fields. As SDE is developed by software enterprises of GIS, they have good comparability with integrated software platform of GIS. \r\n3. Distributed file systems: Recently distributed file system is a new technology of solving data-intensive computing problems. Several distributed file systems have emerged such as PVFS, GPFS, ZFS, GFS, HDFS, and Lustre. \r\n4. Large-scale network storage systems: It is a type of distributed file system with data sharing and remote access functionalities. As the performance improving of hardware and rapid development of network technologies, Storage Area Network (SAN) and Network Attached Storage (NAS) are introduced to distributed file systems. Large-scale network storage systems take different storage and management strategies for EO image files and their metadata. EO image files are stored and managed by HDFS, and their metadata are stored, processed, and managed in RDBMS metadata servers. Managing EO imagery files and their metadata in different ways can improve the management efficiencies of EO data, and balance the loading of distributed file systems. Such systems have already been developed including Celerra, CLARIION, and Symmetric storage solution of EMC, IBM HPSS, MSS, and RASCHAL of National Aeronautics and Space Administration (NASA), the Microsoft earth image storage system, and the Google Earth image storage system.","hasChildren":true,"hasParent":true,"name":"Data storage","selfAssesment":"Completed
"},{"code":"PS3-4-1","description":"The spectral resolution of an Earth Observation sensor refers to the number of spectral bands this sensor can capture. Spectral bands are wavelength intervals in the electromagnetic spectrum. Sometimes, spectral bands are also called spectral channels. Spectral resolution is related to a sensor’s ability to distinguish between different Earth’s surface features based on their spectral properties. A high number of spectral bands means high spectral resolution, with many bands meaning an increasingly smaller range of wavelengths covered by a single band. The spectral resolution of an Earth observation sensor can range from a single very broad band for panchromatic black and white images over a few bands in the case of multispectral sensors (e.g. Landsat family, SPOT, Sentinel-2) to 200 or even more channels for capturing hyperspectral images. Multispectral or hyperspectral sensor imagery has a higher degree of discriminating power than a single band sensor. Another definition of the spectral resolution can be given by the spectral sensitivity of a sensor, which can be specified by the definition of the full width, half maximum (FWHM) as being the spectral interval measured at the level at which the response of the instrument reaches one-half of its maximum values.\r\nSpectral satellite sensors can only gather radiation which is able to pass the Earth’s atmosphere. The atmosphere contains gases, aerosols, ice crystals and water droplets, which absorb and scatter parts of the radiation passing through the atmosphere. Wavelength ranges which do not allow radiation to pass through on their way to the satellite sensors are called absorption bands and those getting through to the sensor are called atmospheric windows. This means that spectral sensors can only operate in these atmospheric windows and the spectral bands should be placed in the wavelength ranges of the atmospheric windows.","hasChildren":true,"name":"Spectral resolution","selfAssesment":"Completed
"},{"code":"PS3-4-2","description":"The spatial resolution of an image corresponds to the size of the minimum area that can be resolved by the sensor. \r\nDue to the different techniques of acquisition of passive and active sensors, the spatial resolution is determined for both sensor types differently. \r\nFor passive sensors, the spatial resolution depends on their instantaneous field of view (IFOV), which determines the area of the Earth’s surface that is viewed at one particular moment in time by one detector element. The size of this area is called resolution cell and characterizes the spatial resolution of the sensor. Depending on the spatial resolution, whole features of the Earth’s surface can be detected homogeneously in one or several resolution cells. For features smaller than the spatial resolution, the average reflected radiation of all features within a resolution cell is recorded, leading to so-called mixed-pixels.\r\nFor imaging active systems, the spatial resolution is dependent of both the length of the transmitted pulse in looking direction and the width of the radiation beam or the antenna width in flight direction.\r\nIn all cases, the spatial resolution indicates the level of detail observable in an image. Usually, one distinguishes between coarse (low), moderate (medium) and fine (high and very high) resolution, whereby the use of this denomination is often context-dependent. Sensors with coarse resolution can only detect large features, but they usually cover a much larger area than high-resolution sensors, which can provide detailed information on small objects such as individual buildings, trees or cars, but for much smaller areas. Coarse spatial resolution mean in general a resolution cell larger than 250 m and a scene extent of several thousands of kilometers (>1000 km). Moderate resolution sensors have a spatial resolution of 30 m to 80 m, and a coverage of approximately 200 km in a single acquisition. Sensors showing spatial resolutions from 5 m or 6 m are high-resolution sensors, with a spatial coverage up to approximately 20 km. Sensors with a resolution cell’s width of less than 1 m are considered as very-high-resolution sensors.\r\nLow resolution sensors are appropriate for the analysis of broad-scale phenomena such as ocean color or cloud patterns. Medium resolution sensors are rather used for regional analysis such as land cover change and phenological response of vegetation. High-resolution sensors are particularly useful for object detection.","hasChildren":true,"hasParent":true,"name":"Spatial resolution","selfAssesment":"Completed
"},{"code":"PS3-4-3","description":"The radiometric resolution of a sensor refers its sensitivity, which is the ability to detect small differences in signal strength as it records the radiant flux reflected, emitted, or back-scattered from the terrain.\r\nThe specification of the radiometric resolution is different in the optical domain of the electromagnetic spectrum than in the radar range.\r\nIn the optical domain, the radiometric resolution is given in bits. The maximum number of brightness levels available depends on the number of bits. The larger this number, the higher the radiometric resolution. As an example, the optical sensor Sentinel-2 has a radiometric resolution of 12 bits. This means that a pixel of an image acquired by Sentinel-2 can have 2^12 = 4096 grey levels.\r\nIn the radar domain, the radiometric resolution is usually specified as a backscatter level expressed as an logarithmic value. For instance, the radiometric resolution of Radar Scattermeters lies in the range of 0.1 to 0.3 dB, whereas the radiometric resolution of SAR sensors are in the range of 1.2 – 2.5 dB. This means that only differences in radar backscatter larger than these values can be interpreted as interpretable changes the of backscatter conditions at the Earth’s surface. Smaller measurement differences could have been caused by differences in backscatter conditions or just as well by instrument noise.","hasChildren":true,"name":"Radiometric resolution","selfAssesment":"Completed
"},{"code":"PS3-4-4","description":"The concept of temporal resolution of Earth observation data refers to the revisit time or period. This is the time, which is necessary for the sensor platform (e.g. a satellite) to complete one entire orbit cycle. During one orbit cycle, the surface of the earth is completely covered by the sensor once. Temporal resolution also means the ability of a sensor to detect changes over shorter or longer periods of time. The revisit time for Earth observation satellites is usually several days. Or to express it differently: The absolute temporal resolution of a sensor orbiting the Earth is the time required to image the exact same area at the same viewing angle a second time. \r\nThe satellite orbit itself depends on the radius of the Earth, the orbit altitude above the Earth’s surface and the gravitational acceleration at planet’s surface. The time required to complete on entire orbit cycle additionally depends on the swath width of the sensor, the overlap between adjacent swaths and the geographical location at the Earth’s surface. The repetition rate increases slightly from the equator towards the north and south, which means that the revisit time is increasing with latitude. As a result, areas located in North America or Australia, for example, are covered a little more frequently than areas in Africa or South America near the equator. \r\nBut there are satellite systems that allow the pointing of their sensor to image the same area between different satellite passes separated by periods from one to five days. Thus, the actual temporal resolution of a sensor depends on a variety of factors, including the satellite/sensor capabilities, the already mentioned swath width and overlap, and latitude.","hasChildren":true,"name":"Temporal resolution","selfAssesment":"Completed
"},{"code":"PS3-4","description":"A digital image begins as an analog signal. Through computer data processing, the image becomes digitized and is sampled multiple times. The critical characteristics of a digital image are spatial resolution, spectral resolution, radiometric resolution, contrast resolution, noise, and dose efficiency. These depends upon satellite orbit configuration and sensor design. Different sensors have different resolutions.\r\nSpectral resolution describes the ability of a sensor to define fine wavelength intervals. The narrowest spectral interval that can be resolved by an instrument. Spectral resolution (spectral capability) also refers to the number of wavebands within the EM spectrum that an optical sensor is taking measurements over.\r\nRadiometric resolution can be defined as the ability of an imaging system to record many levels of brightness. Radiometric resolution refers to the range in brightness levels that can be applied to an individual pixel within an image, determined on a grayscale. E.g., Sentinel-2 sensor MSI is a 12 bit sensor imaging with 4.096 levels.\r\nSpatial resolution of an image corresponds to the size of the minimum area that can be resolved by the sensor.\r\nTemporal resolution, also referred to as the revisit cycle, is defined as the amount of time it takes for a satellite to return to collect data from exactly the same location on the Earth. Imageing of the exact same area at the same viewing angle a second time is temporal resolution.","hasChildren":true,"hasParent":true,"name":"Properties of digital imagery","selfAssesment":"Completed
"},{"code":"PS3-5-1","description":"A header file is usually a separate file associated with an image file. The header file can be either a plain ASCII-file or a binary file. It contains information about the image file it is associated with. These information can comprise the number of pixels per row (x-direction in a two-dimensional image), also called number of columns, the number of lines or rows (y-direction in a two dimensional image), the number of bands (corresponding to the z-direction), pixel spacing and spatial resolution, geographic reference information, the byte order (e.g. big-endian or little-endian), spectral information for each band, calibration constants and many more. The purpose of a header file is to provide basic information about the properties of the image data either to the user or to a software and enabling a software to correctly load and display the image content. In this way, information contained in a header file can also be called metadata, which is data about the data. The structure and the information contained in a header file of remote sensing imagery can be found in the so-called product information documents. There is also digital imagery used in remote sensing containing the information found in header files not in a separate file but as part of the digital image data itself. In this case this is called header information or a file header, which is usually found at the beginning of the image file. In some cases, image files may contain several header sections, e.g. the ESA Envisat ASAR SAR data imagery contains a Main Product Header and a Specific Product Header section. Header information as part of the image file itself may be stored in ASCII or in binary format, or in a mixed binary format, as it was used for the ESA Envisat SAR data.","hasChildren":true,"name":"Header file","selfAssesment":"Completed
"},{"code":"PS3-5","description":"The image data stored in a binary data format (BIL, BIP, BSQ) is accompanied by description files that contain a set of entries describing the image data, including acquisition time, image size, statistics, map projection, pixel digital numbers, product type, etc. This general image or product information is stored in a form of header embedded in the image file or provided in the separate file (.hdr) or metadata in XML. There are numerous image file formats, the more common are TIFF (GeoTIFF), bitmap (.bmp), JPEG (.jpg, .jpeg, JPEG2000), HDF, Raw (.raw), Extensible N-Dimensional Data Format (NDF).","hasChildren":true,"hasParent":true,"name":"Image description files","selfAssesment":"In progress
"},{"code":"PS3-6","description":"The concept of data formats refers to the way, in which the digital data are organized and stored. The data format for a remote sensing mission is usually chosen based on a number of considerations, including requirements of the sensing system, mission objective, the design and technology of data processing, archiving, and distribution systems, as well as community data standard.\r\nEarth observation data usually come as raster data. The raster data refers to a data model, which holds digital numbers or values in a regularly spaced matrix of cells arranged in rows and columns covering a two-dimensional space. A digital Earth observation image may contain several layers of this two-dimensional space, e.g. one layer for a specific spectral band in the optical or microwave region of the electromagnetic spectrum. The cells in such a layer are also called pixels, which stands for picture element. \r\nEarth observation data in an image are stored on a storage medium in one of three formats: Band-Interleaved-by-Sample (BIS), Band Sequential (BSQ), or Band-Interleaved-by-Line (BIL). These formats are determined by different ordering of the data dimensions. Other data formats used in remote sensing, which in this case refer to the file format are GeoTIFF, NetCDF, and HDF.\r\nExact details on the data format of an Earth observation data set is usually provided by the originator of the data, e.g. space administrations such as NASA or ESA or private companies.","hasChildren":true,"name":"Data formats","selfAssesment":"Completed
"},{"code":"PS3-7-1-1","description":"Depending on the sensor and the provider, remotely sensed imagery is made avalilable to the user at different processing levels. For Sentinel-2, the lowest product level made available to the user is Level-1B. THe Level-1B product provides radiometrically corrected imagery in Top-Of-Atmosphere (TOA) radiance values and in sensor geometry. Radiometric corrections applied to the Level-1B are: dark signal, pixels response non uniformity, crosstalk correction, defective pixels interpolation, high spatial resolution bands restoration (deconvolution puls denoising), binning (spatial filtering) for 60m bands.","hasChildren":true,"name":"Radiometrically corrected","selfAssesment":"New
"},{"code":"PS3-7-1-2","description":"Geometrically corrected products are of a higher processing level than radiometrically corrected products. For Sentinel-2, the geometrically corrected product is the Level-1C product. The Level-1C product results from using a Digital Elevation Model (DEM) to project the image in cartographic coordinates. Per-pixel radiometric measurements are provided in Top Of Atmosphere (TOA) reflectances with all parameters to transform them into radiances. Level-1C products are resampled with a constant Ground Sampling Distance (GSD) of 10, 20 and 60 m depending on the native resolution of the different spectral bands. Level-1C products will additionally include Land/Water, Cloud Masks and ECMWF data (total column of ozone, total column of water vapour and mean sea level pressure). (Sentinel-2 User Handbook, p.44)","hasChildren":true,"name":"Geometrically corrected products","selfAssesment":"New
"},{"code":"PS3-7-1","description":"The definition of processing levels for optical data depends on the considered sensor. Most common satellite optical imagery are available in three distinct processing levels, from level 0 to level 2. The most used processing levels are level 1 and level 2, depending on the user and the application. \r\nIn Level 0, the raw data are processed in a way that they are ready to be archived. Processing operations generally includes telemetry analysis, error detections and granule concatenation. Furthermore, relevant parameters such as acquisition date and geographical reference are annotated in the form of metadata, this information being necessary for processing higher levels. Additionally, a quicklook of the image is generated. No correction is performed at this level.\r\nLevel 1 is often divided in several sublevels. Generally, both radiometric correction and geometric refinement are performed at this level. The radiometric processing includes several radiometric corrections such as dark signal correction or spectral band binning. The radiometric correction allows the determination of physical variables (e.g. reflectance) from the digital numbers. The geometric processing includes tiles association and resampling grid computation, in order to link for each image band its native image geometry to the target geometry. The result of this processing steps is usually a geocoded, Top of Atmosphere product.\r\nLevel 2 data usually consist of atmospherically corrected Level 1 data, i.e. Bottom-of-Atmosphere data. These surface reflectance products may be accompanied by additional outputs, such as scene classification, water vapor or surface temperature maps.\r\nFor specific applications and sensors, Level 3 application ready data are available. These are derivated products such as burned area, dynamic surface water content and snow cover maps.\r\nDepending on the considered sensor and level, the name of the sublevels can differ: Sentinel 2 defines Level-1B as radiometrically corrected data. Level 1C are radiometrically and geometrically corrected data, i.e Top-Of-Atmosphere (TOA) orthoimage products. Landsat sensors distinguish between Terrain precision correction (L1TP), systematic Terrain Correction (L1GT) and Geometric systematic Correction (L1GS) depending on the quality of the reference data for geometric correction. These are usually separated into Tier 1 and Tier 2 datasets.","hasChildren":true,"hasParent":true,"name":"Processing levels of optical data","selfAssesment":"Completed
"},{"code":"PS3-7-2-1","description":"SLC is an abbreviation and stands for Single Look Complex. SLC data are one so called radar product. Like all radar products they have been derived from SAR raw data, often called Level 0 products, downloaded from the SAR satellite by the satellite operators. They apply a software called a processor to transform SAR raw data into formats that can be used by users for different applications. SLC data are often referred to as Level 1 products and are the first SAR product derived from the raw data to be made available to users.\r\nAs the name suggests, SLC data only contain one single look, which means that the azimuth compression has been carried out using the full azimuth bandwidth of the SAR sensor leading to the highest spatial resolution in azimuth direction. But as a consequence, SLC data suffers from maximum speckle. \r\nThe word “complex” in SLC means that the data are stored as complex numbers with a real and an imaginary part. In this way, SLC data contain both – phase stored in the real part and amplitude information stored in the imaginary part of the complex number for one resolution cell.\r\nSLC data are given in slant-range geometry and appears to be distorted. The is due to the fact that the spacing between pixels in the slant range direction is directly proportional to the signal travel time or time interval between backscattered and received radar pulses. And this time interval in again is directly proportional to the slant range distance between the sensor and the imaged objects at the Earth’s surface and not to the horizontal ground distance between the nadir and the imaged object. Therefore, SLC images appear distorted, which means that they look compressed in near range (close to the nadir) and getting ever more expanded in towards the far range.\r\nSLC data are the basis for further SAR products generated and are required for interferometric analysis methods, which rely on phase and amplitude information.","hasChildren":true,"name":"Single Look Complex (SLC)","selfAssesment":"Completed
"},{"code":"PS3-7-2-2","description":"From the Single Look Complex (SLC) product the Multi-look Detected/Multi-looke (MLD/MLI) can be generated. It is produced by multi-looking, i.e., averaging, over range and/or azimuth resolution cells.","hasChildren":true,"name":"Multi-looked Detected (MLD)","selfAssesment":"New
"},{"code":"PS3-7-2-3","description":"Precision Images (PRI) are the Multi-look Detected/Multi-looked Intensity (MLD/MLI) images that have been resampled into square pixels, rotated to account for the view direction of the instrument and warped by some predefined operation that the projected image pixels are georeferenced onto a specified geographical coordinate system.","hasChildren":true,"name":"Precision Images (PRI)","selfAssesment":"New
"},{"code":"PS3-7-2-4","description":"Ground Range Detected (GRD) radar imagery is a Level-1 product that has been derived from Level 0 (raw data) SLC SAR data by a Processing Facility via the application of a processing software. GRD products usually consist of focused SAR data that has been detected, multi-looked and projected to ground range using an Earth ellipsoid model.\r\nFocused SAR data are generated in a raw data processing step. During focusing, the two-dimensional signal energy of a point target that is spread in range and azimuth direction is aggregated and put into a single image pixel in the output data set.\r\nDetected means that the complex numbers representing phase and amplitude values in the original data set have been converted to real numbers by taking their absolute square (or complex conjugate). In the resulting image data, the phase information is not present any longer and only amplitude information remains as the pixel value.\r\nThe SAR imagery in GRD radar data is given in ground range geometry, which differs from the slant geometry of the SLC data. In ground range geometry, the spacing between the image objects at the Earth’s surface is in direct proportion to their real distance along a hypothetical flat ground surface. Here, image coordinates are oriented along ground range and flight direction. This means that they do not show the distorted appearance of an SLC image.","hasChildren":true,"name":"Groud Range Detected (GRD)","selfAssesment":"Completed
"},{"code":"PS3-7-2","description":"For SAR data, usually three processing levels are distinguished, ranging from level 0 (less processed) to level 2 (higher processed).\r\nLevel 0 products consist of compressed and unfocussed raw data and are the basis for the processing of higher level products. Level 0 data are principally used for research in the topic of signal processing. As for optical data, level 0 product are annotated with several metadata, such as calibration and orbit information, and acquisition time and date.\r\nLevel 1 data can be separated in two distinct product types, depending if the full complex information is used (amplitude and phase) or only the amplitude information. The product denomination depends on the sensor type; for Sentinel 1 the names Single Look Complex (SLC) and Ground range detected (GRD) are used, respectively. Both products can be generated from the Level 0 data. Level 1 data are the products that are used by most scientific users. The processing toward Level-1 data includes Doppler centroid estimation and data focusing. The Level 1 SLC product consists of the real and imaginary part of focused complex SAR data in slant range geometry, from which the phase and amplitude information can be retrieved. This is available for all acquired polarisations. Additional orbit information for georeferencing is provided with the data. The Level 1 GRD data consist of focused and multi-looked SAR data that have been projected to ground range geometry. GRD data only contain amplitude information, therefore the phase information is lost. The multi-looking step is particular for GRD data and allows both speckle reduction and square pixel resolution. As for the SLC data, the GRD data are annotated with orbit information for georeferencing. The Level-1 products are not calibrated, they include however information about calibration constants, which are sensor dependent. Further processing is needed in order to obtain calibrated radar cross section information from the original data intensity values.\r\nLevel 2 products describe geolocated derivated geophysical products such as ocean wind field or surface radial velocity. Such products are for example available for download on the Sentinel-1 Copernicus Hub. Further Level- 2 data are for example differential interferograms or change maps, which can be processed on different online platforms (e.g. Hyp3) and provide information about surface deformation or more generally changes between several acquisitions.\r\nThe denomination of the product types on the different levels may differ from sensor to sensor, but the processing steps stay almost the same, depending additionally on the considered acquisition modes. For example, GRD products are also called for other sensors Multi-Looked Detected (MLD) products.","hasChildren":true,"hasParent":true,"name":"Synthetic Aperture Radar (SAR) data","selfAssesment":"Completed
"},{"code":"PS3-7-7","description":"Data that have been processed to allow direct data analysis. User processing effort is reduced to a minimum.","hasChildren":true,"name":"Analysis Ready Data (ARD)","selfAssesment":"New
"},{"code":"PS3-7","description":"Earth Observation data are usually made available in different processing levels. The processing level is a mean of describing how much the raw data have been processed toward an informational geophysical product. The degrees of data processing usually follow a numerical hierarchy and typically range from Level 0 (less processed) up to Level 4 (highly processed). They characterize the type of data processing that has been performed between the raw data and the current product.\r\nA first effort for providing standard definitions of different processing levels has been made in the 1980s by the Committee on Data Management and Computation (CODMAC) of the National Research Council (NRC). CODMAC identified eight levels of processing, applicable for all space science data. Starting with the raw data at level 1, the degree of processing and complexity of the data increased at each new level. Level 2 describes edited data, corrected for obvious instrumentation errors and tagged with acquisition time and location; Level 3 stays for calibrated data where values are proportional to a specific physical unit. Level 4 represents resampled data, Level 5 derived data, where specific geophysical information has been retrieved and mapped based on the original data. Level 6 represents all ancillary data (i.e. instrument data) that are necessary for the previous steps of calibration and resampling. Level 7 describes so called correlative data: not directly belonging to the original data, those data represent all other science data that where necessary for the interpretation of the original spaceborne dataset. Finally, Level 8 are user description, i.e. documentation of the data.\r\nConcerning spaceborne image data, both optical and radar, an adaptation of these original levels has been made from NASA and NOAA that is used for the main current spaceborne missions, including the Copernicus program. Whereas specific adaptations may arise for specific sensors and sensor types, there are five principal processing levels. Level 0 represents the raw data that have just been edited for the correction of artifacts. Level 1 data are Level 0 data with additional annotations regarding time and geolocation information, radiometric and geometric calibration coefficients (for example Top of Atmosphere data for optical imagery). Level 2 data are already radiometrically and geometrically calibrated and represent physical variables (for example Bottom of Atmosphere data for optical imagery). Level 3 data correspond to derived variables and information (e.g. land cover) with completeness and consistency information, e.g. quality flags. Level 4 represent higher level data resulting from modelling or more complex analysis of the data with additional ancillary information.\r\nFor many applications and users, so called analysis ready data (ARD data) are required. These usually correspond to Level 2 data that have already been pre-processed in order to retrieve the physical information and can be further analyzed for the specific thematic application.","hasChildren":true,"hasParent":true,"name":"Processing levels","selfAssesment":"Completed
"},{"code":"PS3","description":"Remotely collected data is available from multiple sources and data collection techniques. Data can be obtained from different levels of data acquisition: ground, air or space, as well as using different sensors and wavelengths. Remote sensing data provides the necessary information to help monitor the Earth's surface.","hasChildren":true,"hasParent":true,"name":"Remote sensing data and imagery","selfAssesment":"Planned
"},{"code":"PS4","description":"The listed databases provide information on past, operational and future remote sensing platforms and sensors. Use the following links to get more information on the sensors and missions.","hasChildren":true,"name":"Databases of satellite and airborne sensors and missions","selfAssesment":"Completed
"},{"code":"SA","description":"A satellite system is the complete set of elements needed to provide a space-based service or product to users on Earth. It includes the space segment, the ground segment, and the user segment, plus all the interfaces, operations, and logistics that interconnect them. From a systems-engineering perspective, a satellite system starts from the mission requirements (what information or service is needed, including performance, coverage, latency, reliability, cost, etc.) and translates them into an integrated architecture (system requirements) across these three segments.\r\nKey generic elements of a satellite system:\r\n•\tMission objectives: e.g. broadband communication, earth observation and exploration, measuring sea surface temperature, providing accurate positioning, broadcasting TV, etc.\r\n•\tPerformance requirements: accuracy, resolution, timeliness, availability, continuity, integrity, data rate, etc.\r\n•\tArchitecture: number of satellites and orbits, ground stations, communication links, data processing chain, user equipment, orchestration software.\r\n•\tOperations: planning, monitoring, control, maintenance, calibration/validation, and end-of-life disposal.\r\n•\tService and products: what is ultimately delivered to the users (images, geophysical variables, timing and positioning, voice/data connectivity, etc.).\r\nThe specificities of this general concept adapted to each of the three families are as follows:\r\nEO satellite systems\r\n•\tObjective: measure geophysical variables (e.g. land cover, soil moisture, atmospheric composition, sea state) from space.\r\n•\tSpace segment: satellites carry remote sensors (optical, SAR, TIR, radiometers, GNSS-R, etc.) in orbits optimized for coverage and illumination (typically LEO, often Sun-synchronous, but also GEO).\r\n•\tGround segment: receiving stations on the Earth’s surface offering high-capacity data downlink, processing chains (from L0 to L2 and beyond), archives, and dissemination services.\r\n•\tUser segment: scientists, agencies, companies, or general public that access images and derived products via portals, APIs, etc., with specialized devices and software for, visualization, analysis, and assimilation into models.\r\n•\tSystem focus: radiometric and geometric accuracy, calibration/validation, revisit time, spatial/temporal resolution, uncertainty quantification and traceability.\r\nSatellite navigation systems\r\n•\tObjective: provide Position, Navigation and Timing (PNT) services globally.\r\n•\tSpace segment: constellations of medium-Earth orbit (MEO) satellites (GPS, Galileo, GLONASS, BeiDou), each broadcasting precise time-tagged signals on multiple frequencies. Sometimes these constellations are augmented by GEO or geo-synchronous satellites. There are plans to deploy LEO PNT constellations to augment these satellites systems.\r\n•\tGround segment: worldwide monitoring networks and control centers estimating satellite orbits and clock states, uploading navigation messages, and performing integrity checks.\r\n•\tUser segment: multitude of receivers in phones, cars, aircraft, ships, timing receivers in power grids and telecom networks, etc.\r\n•\tSystem focus: global coverage, high availability, high integrity, precise timekeeping, robust geometry (DOP), mitigation of ionospheric/tropospheric errors and multipath. Future requirements: indoor penetration\r\nSatellite communication systems\r\n•\tObjective: to relay information (voice, data, video, IoT messages) between locations on Earth (and sometimes between satellites).\r\n•\tSpace segment: GEO, MEO, and LEO satellites carrying communication payloads including transponders, antennas phased arrays, on-board processors, inter-satellite links, etc.\r\n•\tGround segment: gateways, teleports, network operation centers, and integration into terrestrial networks.\r\n•\tUser segment: terminals of many kinds: TV dishes, VSATs, satellite phones, maritime/aviation terminals, IoT nodes, single low-power IoT receivers.\r\n•\tSystem focus: capacity (data rate), coverage, availability, quality of service, spectrum efficiency, latency, and cost per bit.\r\nSatellite Systems are the umbrella that ties together the three named segments to turn orbital infrastructure into useful services for EO, navigation, or communications.","hasChildren":true,"hasParent":true,"name":"Satellite Systems","selfAssesment":" "},{"code":"SA1-1-1-1","description":" ","hasChildren":true,"name":"Earth Observation","selfAssesment":" "},{"code":"SA1-1-1-2","description":" ","hasChildren":true,"name":"Communications","selfAssesment":" "},{"code":"SA1-1-1-3","description":" ","hasChildren":true,"name":"Navigation","selfAssesment":" "},{"code":"SA1-1-1","description":" ","hasChildren":true,"hasParent":true,"name":"Payload Types","selfAssesment":" "},{"code":"SA1-1-2-1-1","description":" ","hasChildren":true,"name":"Antenna types","selfAssesment":" "},{"code":"SA1-1-2-1-5-1","description":" ","hasChildren":true,"name":"Antenna beamwidth","selfAssesment":" "},{"code":"SA1-1-2-1-5-2","description":" ","hasChildren":true,"name":"Antenna footprint","selfAssesment":" "},{"code":"SA1-1-2-1-5-3","description":" ","hasChildren":true,"name":"Antenna losses","selfAssesment":" "},{"code":"SA1-1-2-1-5-4","description":" ","hasChildren":true,"name":"Antenna ohmic efficiency","selfAssesment":" "},{"code":"SA1-1-2-1-5-5","description":" ","hasChildren":true,"name":"Antenna illumination efficiency","selfAssesment":" "},{"code":"SA1-1-2-1-5-6","description":" ","hasChildren":true,"name":"Antenna spill over efficiency","selfAssesment":" "},{"code":"SA1-1-2-1-5","description":" ","hasChildren":true,"hasParent":true,"name":"Antenna Parameters","selfAssesment":" "},{"code":"SA1-1-2-1","description":" ","hasChildren":true,"hasParent":true,"name":"Antenna - 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space","selfAssesment":" "},{"code":"SA1-1-4","description":" ","hasChildren":true,"name":"On board processor","selfAssesment":" "},{"code":"SA1-1-5","description":" ","hasChildren":true,"name":"Telemetry, Tracking and Control (TT&C)","selfAssesment":" "},{"code":"SA1-1","description":" ","hasChildren":true,"hasParent":true,"name":"Satellite / Spacecraft bus","selfAssesment":" "},{"code":"SA1-2-1","description":" ","hasChildren":true,"name":"Geostationary Orbit (GEO)","selfAssesment":" "},{"code":"SA1-2-10-1","description":"The Keplerian orbit of the satellite is defined by the following six orbital parameters: the right ascension of ascending node, the inclination of the orbital plane, the argument of perigee, the semi-major axis of the orbital ellipse, the numerical eccentricity of the orbit and the perigee passing time.","hasChildren":true,"name":"Keplerian elements (Two-Body Problem)","selfAssesment":" "},{"code":"SA1-2-10-2","description":"The two-body problem is only a first approximation to the real case. In practice, an additional set of accelerations or disturbing terms must be added to equation.","hasChildren":true,"name":"Perturbed motion","selfAssesment":" "},{"code":"SA1-2-10","description":" ","hasChildren":true,"hasParent":true,"name":"Orbital elements","selfAssesment":" "},{"code":"SA1-2-2","description":" ","hasChildren":true,"name":"Very High Throughput Satellite (VHTS)","selfAssesment":" "},{"code":"SA1-2-3","description":" ","hasChildren":true,"name":"Low earth Orbit (LEO)","selfAssesment":" "},{"code":"SA1-2-4","description":" ","hasChildren":true,"name":"Medium Earth Orbit (MEO)","selfAssesment":" "},{"code":"SA1-2-5","description":" ","hasChildren":true,"name":"VLEO (Very low earth orbiting satellite)","selfAssesment":" "},{"code":"SA1-2-6","description":" ","hasChildren":true,"name":"High altitude platform (HAP)","selfAssesment":" "},{"code":"SA1-2-7","description":" ","hasChildren":true,"name":"Multiorbit (LEO-MEO-GEO)","selfAssesment":" "},{"code":"SA1-2-8","description":" ","hasChildren":true,"name":"Quasi-Zenit Orbit (QZO)","selfAssesment":" "},{"code":"SA1-2-9","description":" ","hasChildren":true,"name":"Inclined Geo-Synchronuos Orbit (IGSO)","selfAssesment":" "},{"code":"SA1-2","description":" ","hasChildren":true,"hasParent":true,"name":"Orbits","selfAssesment":" "},{"code":"SA1-3-1","description":" ","hasChildren":true,"name":"Swarms","selfAssesment":" "},{"code":"SA1-3-2","description":" ","hasChildren":true,"name":"Constellation","selfAssesment":" "},{"code":"SA1-3-3","description":" ","hasChildren":true,"name":"Federated Satellite Systems","selfAssesment":" "},{"code":"SA1-3-4","description":" ","hasChildren":true,"name":"Clusters","selfAssesment":" "},{"code":"SA1-3","description":" ","hasChildren":true,"hasParent":true,"name":"Constellations","selfAssesment":" "},{"code":"SA1-4","description":" ","hasChildren":true,"name":"Launchers","selfAssesment":" "},{"code":"SA1","description":"The space segment is the part of the system that is physically in space: the satellites or platforms, payloads, inter-satellite links, etc. It encompasses:\r\n•\tSatellites / platforms: the bus (platform) plus the payload.\r\n•\tBus subsystems:\r\no\tStructure and mechanisms\r\no\tPower (solar arrays, batteries, power conditioning)\r\no\tAttitude and Orbit Control System (AOCS) – sensors and actuators to point and stabilize the satellite and control its orbit\r\no\tThermal control – passive and active thermal regulation\r\no\tOn-board data handling – computers, data storage, internal networks\r\no\tTelemetry, Tracking & Command (TT&C) – housekeeping communications\r\no\tPropulsion – for orbit insertion, station-keeping, collision avoidance, de-orbit\r\n•\tPayload: the mission-specific instruments or communication equipment.\r\n•\tConstellation architecture: number of satellites, orbital planes, orbital parameters, inter-satellite links, etc.\r\nSpecifically, per system type:\r\nEO – Space segment\r\n•\tOrbits: Mostly LEO, often Sun-synchronous, with altitudes ~500–800 km to balance resolution and coverage. Some missions use non-SSO (e.g. inclined orbits) or highly elliptical orbits for special coverage. Some others are GEO (e.g. many meteorological satellites, such as MeteoSat, GOESS, Himawari…)\r\n•\tPayloads: Optical imagers, multispectral/hyperspectral sensors, SAR, TIR radiometers, microwave radiometers, altimeters, GNSS-R, etc.\r\n•\tKey features:\r\no\tHigh pointing stability and knowledge to ensure geometric accuracy.\r\no\tAgile platforms for off-nadir pointing, target tracking, or stereo imaging.\r\no\tOn-board mass memory and high-rate downlinks.\r\no\tCalibration hardware (on-board lamps, blackbodies, lunar or sky views, etc.).\r\n•\tConstellations: single satellites vs. multi-sat constellations to reduce revisit time; sometimes formation flying for interferometry or tomography.\r\nNAV – Space segment\r\n•\tOrbits: Typically MEO (height ~20,000 km), near-circular, with multiple orbital planes to ensure at least 4–8 satellites are always visible anywhere on Earth.\r\n•\tPayloads: Very stable atomic clocks, navigation signal generators, RF chains and antennas broadcasting on standard frequency bands (e.g. L1, L2, L5 bands and one at S-band, as well).\r\n•\tKey features:\r\no\tVery high reliability and redundancy (multiple clocks, redundant payload units).\r\no\tCarefully controlled orbit and attitude to maintain predictable geometry.\r\no\tGlobal coverage with high availability; satellites designed for long lifetimes (10–15+ years).\r\n•\tConstellations: global constellations (GPS, Galileo, Glonass, Beidou, etc.), plus regional (NavIC, QZSS) or augmentation systems (SBAS, GBAS, etc.).\r\nCOM – Space segment\r\n•\tOrbits:\r\no\tGEO (height ~35,786 km): apparently in a fixed position in the sky, which is ideal for broad coverage broadcast and fixed services.\r\no\tMEO: e.g. some broadband or legacy systems.\r\no\tLEO: large constellations for low latency broadband and IoT, in future PNT\r\n•\tPayloads: transponders (bent-pipe or regenerative), high-throughput multi-beam payloads, regenerative processors, analog or digital signal routing, beam-forming networks, small to large (deployable) single beam or multibeam antennas, sometimes often laser communication terminals for inter-sat links.\r\n•\tKey features:\r\no\tVery high RF power and large antennas, especially in GEO.\r\no\tSophisticated on-board processing for routing, switching, beam hopping, etc.\r\no\tFlexible resource allocation (digital payloads) to adapt to traffic patterns.\r\n•\tConstellations: from a few GEO satellites to hundreds or thousands of LEO satellites.","hasChildren":true,"hasParent":true,"name":"Space segment","selfAssesment":" "},{"code":"SA2-1","description":" ","hasChildren":true,"name":"Network Control Center (NCC)","selfAssesment":" "},{"code":"SA2-2-1","description":" ","hasChildren":true,"name":"Mission Planning","selfAssesment":" "},{"code":"SA2-2-2-1-1","description":" ","hasChildren":true,"name":"Atomic clocks - ground","selfAssesment":" "},{"code":"SA2-2-2-1","description":" ","hasChildren":true,"hasParent":true,"name":"Time scale","selfAssesment":" "},{"code":"SA2-2-2-2","description":" ","hasChildren":true,"name":"Distributing Time and Frequency Information","selfAssesment":" "},{"code":"SA2-2-2","description":" ","hasChildren":true,"hasParent":true,"name":"Timing Facility","selfAssesment":" "},{"code":"SA2-2-3-1","description":" ","hasChildren":true,"name":"Laser Ranging","selfAssesment":" "},{"code":"SA2-2-3-2","description":" ","hasChildren":true,"name":"Orbit and Clock Product Generation","selfAssesment":" "},{"code":"SA2-2-3","description":" ","hasChildren":true,"hasParent":true,"name":"Orbit determination and control","selfAssesment":" "},{"code":"SA2-2","description":" ","hasChildren":true,"hasParent":true,"name":"Control Center","selfAssesment":" "},{"code":"SA2-3","description":" ","hasChildren":true,"name":"Tracking Station","selfAssesment":" "},{"code":"SA2-4","description":" ","hasChildren":true,"name":"Control Stations","selfAssesment":" "},{"code":"SA2-5-1","description":" ","hasChildren":true,"name":"Very Small Aperture Terminal (VSAT)","selfAssesment":" "},{"code":"SA2-5-2","description":" ","hasChildren":true,"name":"Antenna - ground","selfAssesment":" "},{"code":"SA2-5-3","description":" ","hasChildren":true,"name":"Receivers","selfAssesment":" "},{"code":"SA2-5-4","description":" ","hasChildren":true,"name":"Feeder link","selfAssesment":" "},{"code":"SA2-5","description":" ","hasChildren":true,"hasParent":true,"name":"Teleport","selfAssesment":" "},{"code":"SA2-6-1-1","description":" ","hasChildren":true,"name":"TT&C","selfAssesment":" "},{"code":"SA2-6-1","description":" ","hasChildren":true,"hasParent":true,"name":"Uplink antenna","selfAssesment":" "},{"code":"SA2-6","description":" ","hasChildren":true,"hasParent":true,"name":"Uplink Stations","selfAssesment":" "},{"code":"SA2","description":"The ground segment includes all the terrestrial infrastructure that supports the space segment and delivers services to the user segment. It is the backbone that makes operations, data acquisition, processing, distribution, and system management possible.\r\nIt generic components are:\r\n•\tMission control / operations centers: monitor satellite health, send commands, plan maneuvers and observations.\r\n•\tTT&C stations: antennas and equipment for uplink of commands and downlink of telemetry.\r\n•\tData reception stations: high-rate downlink stations for payload data (often separate from TT&C).\r\n•\tData processing and archiving facilities: convert raw telemetry into usable products or services.\r\n•\tNetworks and distribution infrastructure: connect ground sites and deliver products/services to users.\r\n•\tSupport infrastructure: calibration/validation networks, testbeds, simulators, etc.\r\nSpecifically by type of system:\r\nEO – Ground segment\r\n•\tAcquisition:\r\no\tNetwork of typically S-, X-, Ka-band ground stations to receive raw instrument data.\r\no\tSometimes ground stations are shared (confederated among different partners) to maximize contact opportunities.\r\n•\tProcessing:\r\no\tL0 processing (packet decoding, sorting…).\r\no\tL1 (radiometrically and geometrically calibrated data, e.g. TOA radiance, backscatter).\r\no\tL2 (retrieval of geophysical variables like soil moisture, SST, NDVI, LAI, etc.).\r\no\tHigher-level products (L3/L4) with temporal and spatial compositing, data assimilation, etc.\r\n•\tArchiving and dissemination:\r\no\tLong-term archives, cloud platforms, data cubes, catalogues with standardized metadata.\r\no\tAccess via web portals, APIs, FTP, etc.\r\n•\tMission planning:\r\no\tAcquisition planning (what and when to be imaged, in which mode of acquisition…).\r\no\tResource allocation (on-board memory, downlink windows, power).\r\n•\tCal/Val infrastructure:\r\no\tGround networks of in situ measurements, reference sites, campaigns to validate EO products.\r\nNAV – Ground segment\r\nOften called the ground control segment:\r\n•\tMonitoring stations: distributed globally, continuously tracking all satellites, measuring the signals and pseudo-ranges.\r\n•\tControl centers:\r\no\tEstimate precise satellite orbits and clock offsets from monitoring data.\r\no\tGenerate and upload navigation messages (ephemeris, clock corrections, almanacs).\r\no\tMonitor system performance, availability, integrity.\r\n•\tUplink stations: send updated navigation data and system parameters to the satellites.\r\n•\tTimekeeping infrastructure: tie the system time scale to international standards (UTC), maintain ensemble of atomic clocks on the ground.\r\n•\tAugmentation ground segments: SBAS reference stations, processing centers, uplink facilities for broadcasting corrections.\r\nThe NAV ground segment is critical for ensuring accuracy, integrity, and continuity of the PNT services.\r\nCOM – Ground segment\r\nFor satellite communications, the ground segment is often called the ground network / teleport infrastructure, and it consists of:\r\n•\tGateways / teleports:\r\no\tLarge antennas and RF chains connecting satellites to terrestrial networks.\r\no\tProvide uplinks/downlinks for user traffic (internet backhaul, TV feeds, etc.).\r\n•\tNetwork Operation Centers (NOCs):\r\no\tManage traffic, allocate resources, monitor QoS, configure beams and transponders.\r\n•\tCore network integration:\r\no\tConnect with internet backbones, mobile network cores, enterprise networks.\r\no\tSupport roaming and handover between satellite and terrestrial networks.\r\n•\tControl and TT&C facilities: often integrated or co-located with teleports.\r\n•\tService platforms:\r\no\tBilling, authentication, traffic shaping, multicast/broadcast management.\r\nIn short, the ground segment is the middle layer that makes the space assets useful and manageable, translating raw satellite capabilities into data and services.","hasChildren":true,"hasParent":true,"name":"Ground segment","selfAssesment":" "},{"code":"SA3-1-1-1","description":" 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in proportion to the input signal, such as audio or data. In AM, the frequency of the carrier wave remains constant, but its amplitude changes based on the instantaneous value of the modulating signal. In practical terms, this means that the \"height\" of the carrier wave's oscillations increases or decreases as the input signal fluctuates. AM is commonly used in AM radio broadcasting, where the audio signal (like speech or music) modulates the amplitude of the radio carrier wave to transmit sound. AM is generally more susceptible to noise and interference than other modulation techniques, such as frequency modulation (FM), because noise typically affects amplitude more directly. [ChatGPT.com (2024)]","hasChildren":true,"name":"Amplitude modulation (AM)","selfAssesment":" "},{"code":"SA3-2-1-1-2-2","description":"Frequency modulation (FM) is a method of encoding information in a carrier wave by varying its frequency, while keeping the amplitude constant. In FM, the frequency of the carrier wave is changed in proportion to the input signal (such as sound or data) that is being transmitted. This variation in frequency encodes the information, which can then be decoded by the receiver. FM is commonly used in radio broadcasting (e.g., FM radio stations), as it offers better sound quality and resistance to interference compared to amplitude modulation (AM). The amount of frequency deviation is typically related to the amplitude of the input signal. [ChatGPT.com (2024)]","hasChildren":true,"name":"Frequency modulation (FM)","selfAssesment":" "},{"code":"SA3-2-1-1-2-3","description":" ","hasChildren":true,"name":"Phase modulation (PM)","selfAssesment":" "},{"code":"SA3-2-1-1-2","description":" ","hasChildren":true,"hasParent":true,"name":"Analog Modulation","selfAssesment":" "},{"code":"SA3-2-1-1-3","description":" ","hasChildren":true,"name":"Spread Spectrum","selfAssesment":" "},{"code":"SA3-2-1-1-4","description":" ","hasChildren":true,"name":"Frequency Division Multiple Access (FDMA)","selfAssesment":" "},{"code":"SA3-2-1-1-5","description":" ","hasChildren":true,"name":"Time Division Multiple Access (TDMA)","selfAssesment":" "},{"code":"SA3-2-1-1-6","description":" ","hasChildren":true,"name":"Code Division Multiple Access (CDMA)","selfAssesment":" "},{"code":"SA3-2-1-1-7","description":" ","hasChildren":true,"name":"Orthogonal Frequency Division Multiplexing (OFDM)","selfAssesment":" "},{"code":"SA3-2-1-1-8","description":" 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","hasChildren":true,"name":"5G (Fifth generation)","selfAssesment":" "},{"code":"SA3-2-3-6","description":" ","hasChildren":true,"name":"3GPP","selfAssesment":" "},{"code":"SA3-2-3","description":" ","hasChildren":true,"hasParent":true,"name":"Regulation","selfAssesment":" "},{"code":"SA3-2-4-15-1","description":" ","hasChildren":true,"name":"Radio frequency Interference (RFI)","selfAssesment":" "},{"code":"SA3-2-4-15-2","description":" ","hasChildren":true,"name":"Electromagnetic Interference (EMI)","selfAssesment":" "},{"code":"SA3-2-4-15-3","description":" ","hasChildren":true,"name":"Inter-symbol Interference (ISI)","selfAssesment":" "},{"code":"SA3-2-4-15-4","description":" ","hasChildren":true,"name":"Jamming","selfAssesment":" "},{"code":"SA3-2-4-15-5","description":" ","hasChildren":true,"name":"Spoofing","selfAssesment":" "},{"code":"SA3-2-4-15-6","description":" ","hasChildren":true,"name":"Multipath","selfAssesment":" "},{"code":"SA3-2-4-15-7","description":" ","hasChildren":true,"name":"Ionospheric Effects, Monitoring, and Mitigation Techniques","selfAssesment":" "},{"code":"SA3-2-4-15","description":" ","hasChildren":true,"hasParent":true,"name":"Interference","selfAssesment":" "},{"code":"SA3-2-4","description":" ","hasChildren":true,"hasParent":true,"name":"Radio-wave Propagation","selfAssesment":" "},{"code":"SA3-2-5-1-1","description":" ","hasChildren":true,"name":"Resilience","selfAssesment":" "},{"code":"SA3-2-5-1","description":" ","hasChildren":true,"hasParent":true,"name":"Security","selfAssesment":" "},{"code":"SA3-2-5","description":" ","hasChildren":true,"hasParent":true,"name":"Messages","selfAssesment":" "},{"code":"SA3-2-6","description":" ","hasChildren":true,"name":"Codes and modulations","selfAssesment":" "},{"code":"SA3-2","description":" ","hasChildren":true,"hasParent":true,"name":"Signals","selfAssesment":" "},{"code":"SA3-3-1","description":" ","hasChildren":true,"name":"Antenna hardware","selfAssesment":" 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","hasChildren":true,"name":"Aviation and Drones","selfAssesment":" "},{"code":"SA3-4-1-3","description":" ","hasChildren":true,"name":"Climate, Environment, and Biodiversity","selfAssesment":" "},{"code":"SA3-4-1-4","description":" ","hasChildren":true,"name":"Consumer Solutions, Tourism and Health","selfAssesment":" "},{"code":"SA3-4-1-5","description":" ","hasChildren":true,"name":"Emergency Management and Humanitarian Aid","selfAssesment":" "},{"code":"SA3-4-1-6","description":" ","hasChildren":true,"name":"Energy and Raw Materials","selfAssesment":" "},{"code":"SA3-4-1-7","description":" ","hasChildren":true,"name":"Fisheries and Aquaculture","selfAssesment":" "},{"code":"SA3-4-1-8","description":" ","hasChildren":true,"name":"Forestry","selfAssesment":" "},{"code":"SA3-4-1-9","description":" ","hasChildren":true,"name":"Infrastructure","selfAssesment":" "},{"code":"SA3-4-1","description":" ","hasChildren":true,"hasParent":true,"name":"EUSPA Market Segments","selfAssesment":" "},{"code":"SA3-4","description":" ","hasChildren":true,"hasParent":true,"name":"Applications","selfAssesment":" "},{"code":"SA3","description":"The user segment comprises all the equipment and entities that consume the service or data provided by the satellite system. It includes:\r\n•\tUser terminals / receivers / antennas\r\n•\tUser-side software and processing\r\n•\tInterfaces to applications and end-users\r\nThis segment is extremely different in scale and complexity across the different EO, NAV, and COM services.\r\nEO – User segment\r\nFor EO, the concept of “user segment” often includes:\r\n•\tProfessional users: agencies, research institutions, companies that:\r\no\tDownload data from data hubs, cloud platforms, or direct broadcast.\r\no\tRun processing chains (e.g. atmospheric correction, bio-geophysical parameter retrieval, data assimilation).\r\no\tIntegrate EO data into decision support systems (e.g. agriculture, forestry, hydrology, disaster management…).\r\n•\tUser equipment:\r\no\tStandard computers/servers, sometimes HPC or cloud resources.\r\no\tSpecialized software (e.g. SNAP, QGIS, ENVI or other commercial or custom software packages).\r\no\tIn some cases, local receiving stations with dedicated antennas and receivers to downlink data directly from satellites.\r\n•\tUsage patterns: often data-centric and offline/near-real-time, with strong emphasis on data quality, traceability, and metadata for later reprocessing.\r\nNAV – User segment\r\nFor satellite navigation, the user segment is massive, diverse and disperse, ranging from consumer to high-end.\r\n•\tReceivers:\r\no\tEmbedded GNSS chips in smartphones, cars, wearables.\r\no\tProfessional receivers for surveying, geodesy, agriculture, precision timing.\r\no\tAviation and maritime certified receivers with integrity monitoring.\r\n•\tAntennas: usually small patch or helix antennas; for high-precision or harsh environments, more sophisticated multi-frequency, choke-ring, or array antennas.\r\n•\tUser processing:\r\no\tSingle-point positioning (SPP) using broadcast ephemeris.\r\no\tHigh-accuracy techniques like RTK, PPP, PPP-RTK, often with corrections from reference networks.\r\no\tIntegration with inertial sensors, odometers, etc. for robustness.\r\n•\tApplications: navigation apps, fleet management, autonomous driving, UAV operations, scientific geodesy, timing for telecom and power grids.\r\nIn general, the NAV user segment is defined as any device or system that uses satellite signals to obtain position, velocity, and time.\r\nCOM – User segment\r\nFor communications, the user segment is essentially the entire set of satellite terminals and the people or systems using them.\r\n•\tUser terminals:\r\no\tFixed terminals (VSATs) for enterprise networks and backhaul.\r\no\tConsumer terminals (e.g. small dishes for TV, LEO broadband user terminals).\r\no\tMobile terminals for maritime, aeronautical, land mobility (sat phones, BGAN, in-flight connectivity, connected ships, trains, vehicles).\r\no\tD2D (direct to device) applications compatible with standard mobile phones\r\no\tIoT/M2M terminals – very low data rate devices with low-gain antennas and low power.\r\n•\tKey parameters: antenna size and gain, transmit power, required data rate, link availability, mobility support.\r\n•\tIntegration: terminals connect to local networks (Wi-Fi, Ethernet, cellular) so that users see a “normal” connection; the satellite link is largely hidden.\r\nIn short, the User segment is where the service is finally “consumed”: EO users transform data into information, NAV users get PNT, and COM users get connectivity.","hasChildren":true,"hasParent":true,"name":"User Segment","selfAssesment":" "},{"code":"SC","description":"Satellite Communications (SatCom) are systems and technologies that use satellites to relay information between points on Earth (and increasingly between satellites as well). The satellite acts as a repeater or node in a communication network, receiving signals on an uplink, processing or frequency-translating them, and transmitting them on a downlink—often to a different location or to many locations simultaneously.\r\nBasic principles\r\n•\tLinks:\r\no\tUplink: Earth → satellite.\r\no\tDownlink: satellite → Earth.\r\no\tInter-satellite link (ISL): satellite ↔ satellite (RF or optical).\r\n•\tFrequency bands: typically in the microwave and millimeter-wave range (L, S, C, X, Ku, Ka, Q/V bands), and increasingly optical (laser links).\r\n•\tPropagation: signals travel through the atmosphere (affected by rain, clouds, ionosphere) and vacuum; link budgets must account for free-space loss, atmospheric attenuation, antenna gains, noise, and interference.\r\nOrbits and architectures\r\n•\tGEO (Geostationary Earth Orbit):\r\no\tSatellite appears fixed in the sky for a given ground location.\r\no\tIdeal for broadcast (TV, radio), fixed services, and internet (e.g. HughesNet and Viasat).\r\no\tHigh latency ( 2·height/c~250 ms).\r\n•\tMEO:\r\no\tIntermediate altitude; used by some broadband and legacy systems.\r\no\tLower latency than GEO, fewer satellites than LEO.\r\n•\tLEO:\r\no\tLow altitude, satellites move quickly across the sky.\r\no\tRequires constellations of many satellites (thousands) and continuous handover (e.g. Starlink and in a future Kuiper)\r\no\tLow latency and good link budgets, good for broadband and also for IoT.\r\nArchitectures can be bent-pipe (transparent transponders) or regenerative:\r\n•\tBent-pipe: satellite simply shifts frequency and amplifies; all routing and processing done on the ground (simpler payload).\r\n•\tRegenerative / processing satellites: demodulate, decode, switch/route, re-encode, and re-transmit; enables flexible resource allocation, onboard switching, and more advanced services.\r\nMultiple access and waveform aspects\r\nTo share limited spectrum and satellite resources among many users, SatCom systems use:\r\n•\tMultiple access schemes: TDMA, FDMA, CDMA, OFDMA, NOMA, etc.\r\n•\tAdvanced modulation and coding: QPSK, APSK, QAM, LDPC / Turbo / Polar codes, adaptive coding and modulation (ACM) to match link conditions.\r\n•\tBeamforming: fixed or electronically steered spot beams to reuse frequencies and increase capacity (high-throughput satellites, HTS).\r\nApplications\r\n•\tBroadcasting:\r\no\tDirect-to-home (DTH) TV, radio, large-scale content distribution.\r\n•\tFixed satellite services (FSS):\r\no\tCorporate networks, government networks, backhaul for remote sites, trunk links for islands, etc.\r\n•\tMobile satellite services (MSS):\r\no\tSatellite phones, aero and maritime connectivity, land mobile terminals.\r\no\tD2D direct to device (compatible with standard cellphones)\r\n•\tBroadband access:\r\no\tConsumer and enterprise internet in underserved or remote regions.\r\n•\tEmergency and disaster communications:\r\no\tRapid deployment where terrestrial networks are damaged or absent.\r\n•\tIoT / M2M:\r\no\tLow-data-rate connectivity for sensors, tracking devices, environmental monitoring, etc.\r\nAdvantages and challenges\r\nAdvantages:\r\n•\tWide coverage: a single satellite (especially GEO or MEO) can cover huge areas, including oceans and remote regions.\r\n•\tBroadcast capability: inherently suited for point-to-multipoint services.\r\n•\tInfrastructure independence: no need to deploy extensive terrestrial infrastructure in the coverage area.\r\n•\tResilience: can provide backup when terrestrial networks fail.\r\n•\tBroadband capabilities: especially for LEOs due to favorable link budget\r\nChallenges:\r\n•\tLatency: especially in GEO, affecting interactive applications and some protocols.\r\n•\tDoppler effect: depending on orbits and relative speeds \r\n•\tCapacity and spectrum scarcity: need efficient spectrum reuse and advanced techniques to increase capacity. That can be partly overcome by using optical links, specially between satellites and in future for feeder links\r\n•\tPropagation impairments: rain fade in Ku/Ka/Q/V bands, scintillation, RF interference.\r\n•\tCost and complexity: satellite manufacturing/launch, regulatory coordination, gateway deployments.\r\n•\tOrchestration: complex and autonomous orchestration software (AI based) required for efficient operation\r\nFrom the [SA] Satellite Systems point of view, Satellite Communications is a specific class of satellite systems where:\r\n•\tThe space segment is optimized for RF/optical relay (antennas, transponders, processors, ISLs).\r\n•\tThe ground segment is essentially a telecommunication network infrastructure.\r\n•\tThe user segment are all the terminals that provide connectivity to end-users or machines.\r\nNote: EO and NAV also make use of communication links (e.g. EO data downlinks, NAV signal broadcasting), but in [SC] Satellite Communications the communication itself is the primary mission, not just a support function.","hasChildren":true,"hasParent":true,"name":"Satellite Communication","selfAssesment":" "},{"code":"SC1-1","description":" ","hasChildren":true,"name":"Transmitter","selfAssesment":" "},{"code":"SC1-2","description":" ","hasChildren":true,"name":"Receiver","selfAssesment":" "},{"code":"SC1-3","description":" ","hasChildren":true,"name":"Transponder","selfAssesment":" "},{"code":"SC1-4","description":" ","hasChildren":true,"name":"Transceiver","selfAssesment":" "},{"code":"SC1-5-1","description":" ","hasChildren":true,"name":"Transparent repeater","selfAssesment":" "},{"code":"SC1-5-2","description":" ","hasChildren":true,"name":"Regenerative repeater","selfAssesment":" "},{"code":"SC1-5","description":" ","hasChildren":true,"hasParent":true,"name":"Repeater","selfAssesment":" "},{"code":"SC1-6","description":" ","hasChildren":true,"name":"Inter-Satellite Link (RF or Optical)","selfAssesment":" "},{"code":"SC1-7","description":" ","hasChildren":true,"name":"Uplink","selfAssesment":" "},{"code":"SC1-8","description":" ","hasChildren":true,"name":"Downlink","selfAssesment":" "},{"code":"SC1","description":" ","hasChildren":true,"hasParent":true,"name":"Subsystems (payload)","selfAssesment":" "},{"code":"SC2-1","description":"Full-duplex refers to a communication system that allows for two-way transmission of data or signals simultaneously. In a full-duplex system, both parties can send and receive information at the same time, as opposed to half-duplex systems, where communication can only occur in one direction at a time (e.g., walkie-talkies). A common example of full-duplex communication is a telephone call, where both people can speak and listen to each other at the same time without needing to take turns. In networking, full-duplex communication enables devices to transmit and receive data simultaneously, improving the efficiency and speed of communication. [ChatGPT.com (2024)]","hasChildren":true,"name":"Full-duplex","selfAssesment":" "},{"code":"SC2-2","description":"Half-duplex refers to a communication system where data or signals can only travel in one direction at a time. In a half-duplex system, one device can either send or receive information, but not both simultaneously. This means that the communication flow alternates between sending and receiving, with each device taking turns. A common example of half-duplex communication is a walkie-talkie, where one person speaks while the other listens, but they must switch roles when it's time to respond. Unlike full-duplex systems, which allow simultaneous two-way communication, half-duplex systems are more limited in terms of speed and efficiency. [ChatGPT.com (2024)]","hasChildren":true,"name":"Half-duplex","selfAssesment":" "},{"code":"SC2","description":" ","hasChildren":true,"hasParent":true,"name":"Communications mode","selfAssesment":" "},{"code":"SC3-1","description":" ","hasChildren":true,"name":"High speed","selfAssesment":" "},{"code":"SC3-2-1","description":" ","hasChildren":true,"name":"IoT/M2M communications","selfAssesment":" "},{"code":"SC3-2","description":" ","hasChildren":true,"hasParent":true,"name":"Low speed","selfAssesment":" "},{"code":"SC3","description":" ","hasChildren":true,"hasParent":true,"name":"Communications speed","selfAssesment":" "},{"code":"SC4-1","description":" ","hasChildren":true,"name":"Up-link","selfAssesment":" "},{"code":"SC4-2","description":" ","hasChildren":true,"name":"Down-link","selfAssesment":" "},{"code":"SC4-3","description":" ","hasChildren":true,"name":"Cross-link","selfAssesment":" "},{"code":"SC4","description":" ","hasChildren":true,"hasParent":true,"name":"Communications direction","selfAssesment":" "},{"code":"SC5-1","description":" ","hasChildren":true,"name":"Non-terrestrial Network (NTN)","selfAssesment":" "},{"code":"SC5-2","description":" ","hasChildren":true,"name":"Security / cybersecurity","selfAssesment":" "},{"code":"SC5-3","description":" ","hasChildren":true,"name":"Quantum Key Distribution (QKD)","selfAssesment":" "},{"code":"SC5-4","description":" ","hasChildren":true,"name":"Vertical Networks","selfAssesment":" "},{"code":"SC5","description":" ","hasChildren":true,"hasParent":true,"name":"Networks","selfAssesment":" "},{"code":"SC6-1","description":" ","hasChildren":true,"name":"Autonomous driving","selfAssesment":" "},{"code":"SC6-10","description":" ","hasChildren":true,"name":"Inflight connectivity/communication (IFC)","selfAssesment":" "},{"code":"SC6-11","description":" ","hasChildren":true,"name":"Cybersecurity","selfAssesment":" "},{"code":"SC6-2","description":" ","hasChildren":true,"name":"IoT applications","selfAssesment":" "},{"code":"SC6-3","description":" ","hasChildren":true,"name":"Air traffic","selfAssesment":" "},{"code":"SC6-4","description":" ","hasChildren":true,"name":"TV-Radio broadcasting","selfAssesment":" "},{"code":"SC6-5","description":" ","hasChildren":true,"name":"Maritime traffic","selfAssesment":" "},{"code":"SC6-6","description":" ","hasChildren":true,"name":"Mobile Satellite Services (MSS)","selfAssesment":" "},{"code":"SC6-7","description":" ","hasChildren":true,"name":"Fixed Satellite Services (FSS)","selfAssesment":" "},{"code":"SC6-8","description":" ","hasChildren":true,"name":"Broadcast Satellite Services (BSS)","selfAssesment":" "},{"code":"SC6-9","description":" ","hasChildren":true,"name":"Satcom On-The-Move (SOTM)","selfAssesment":" "},{"code":"SC6","description":" ","hasChildren":true,"hasParent":true,"name":"SatCom-specific Applications","selfAssesment":" "},{"code":"SC7-1","description":" ","hasChildren":true,"name":"International Telecommunications Union (ITU)","selfAssesment":" "},{"code":"SC7-2","description":" ","hasChildren":true,"name":"DVB-S (some adopted by ETSI - see below)","selfAssesment":" "},{"code":"SC7-3","description":" ","hasChildren":true,"name":"Institute of Electrical and Electronics Engineers (IEEE)","selfAssesment":" "},{"code":"SC7-4","description":" ","hasChildren":true,"name":"European Telecommunications Standards Institute (ETSI)","selfAssesment":" "},{"code":"SC7-5","description":" ","hasChildren":true,"name":"Internet Engineering Task Force (IETF)","selfAssesment":" "},{"code":"SC7-6","description":" ","hasChildren":true,"name":"Consultative Committee for Space Data Systems (CCSDS)","selfAssesment":" "},{"code":"SC7-7","description":" ","hasChildren":true,"name":"Military Standards (MIL-STD)","selfAssesment":" "},{"code":"SC7","description":" ","hasChildren":true,"hasParent":true,"name":"Standards/recommendations","selfAssesment":" "},{"code":"SD","description":"Based on Waldo Tobler`s first law of geography( Tobler, 1970), this property is set on the principle that \"everything is related, but that which is closer is more closely related\".","hasChildren":true,"name":"Spatial dependency","selfAssesment":"In progress (GI-N2K)
"},{"code":"SH","description":"This principle, as set forth by Anselin, determines that \"expectations vary along the earth`s surface\" which means that any spatial analysis is dependent explicitly on the borders of study fields, i.e. the tracing of (spatial) analysis units.","hasChildren":true,"name":"Spatial heterogeneity","selfAssesment":"In progress (GI-N2K)
"},{"code":"TA","description":"This area of knowledge deals with the use of EO / GI techniques and data in different themes and areas of application. It includes the user community of EO services and applications, societal and environmental challenges, EO services and applications, and standard EO products that are made available to users.","hasChildren":true,"hasParent":true,"name":"Thematic and application domains","selfAssesment":"Planned
"},{"code":"TA11-1-1","description":"The EO/GI users in agriculture are active in Agricultural commodities/Trading, agricultural production / Horticulture, Agricultural services, Agriculture machinery, Agriculture and Rural Development Policy, Agro chemicals / Plants & Fertilizers, Animal production / Livestock. The EO/GI users also include agriculture and rural policy makers. \r\nThey benefit from EO information, for example, by managment support for their crop production through forecasting crop yield, assess risks of damage/loss because of storms, disease or other stress factors, and water monitoring. Use in agriculture: knowledge and information products to forge a viable strategy for farming operations. Understand the health of his crop, extent of infestation or stress damage, or potential yield and soil conditions","hasChildren":true,"name":"Users in agriculture","selfAssesment":"New
"},{"code":"TA11-1-2","description":"The users in fishing are active in Fish stock management, Fishing fleets, Fishery distribution logistics, Aquaculture / fish farms, Coastal management agencies. In addition, the users include Fisheries authorities / policy makers. \r\nThe marine environment in particular is relevant to fishing. Fishing fleets move to the fishing grounds to catch fish. Finding them is challenging. However, fish shoals can be directly visible from above. Navigating to the fishing grounds can be risky: Coastline and shallows may pose a risk to ships. Additionally, skippers may have to deal with challenging weather conditions at sea. Environmental threats to the fishing grounds are oil slicks and other types of pollution. A problem from an economical perspective and for adhering to catch quota is illegal fishing. Noumerous opportunities exist to support fishing with EO information.","hasChildren":true,"name":"Users in fishing","selfAssesment":"New
"},{"code":"TA11-1-3","description":"The users in forestry are active in Forest management, Forest Services, Commodities, Logging industry, Wood, paper and pulp industry, Forest policy, Forest machinery. They also include Forest Policy makers.\r\nUse in forestry: Understand depletion due to natural causes (fires and infestations) or human activity (clear-cutting, burning, land conversion), and monitoring of health and growth for effective commercial exploitation and conservation.\r\nForests are a resource that is harvested all over the Globe for different purposes like construction or heating. Additionally, the forests represent an ecosystem that provides various ecosystem services. Proper management is a key to a healthy forestry industry that has to be aligned well with global environmental management activities. There is a need to avoid deforestation and forest degradation, keep the environmental impact of forestry within bounds, be aware of changes in the carbon balance. Economically relevant is especially a good understading of forest types, forest damage due to storms or insects, as well as wildfires. A threat to the environment results from illegal forest activities.","hasChildren":true,"name":"Users in forestry","selfAssesment":"New
"},{"code":"TA11-1","description":"Users in managed living resources refer to human activities exploiting natural organic resources. Knowledge and information products to forge a viable strategy for the user’s operations such as the assessment of the status of the resource due natural or human activity for effective commercial exploitation and conservation. This includes agriculture, fishing and forestry occupations for our society.","hasChildren":true,"hasParent":true,"name":"Users in managed living resources","selfAssesment":"New
"},{"code":"TA11-2-1","description":"The users in alternative energy consist of Solar energy providers, Wind energy providers, Tidal energy providers, Hydroelectric energy providers, Energy and Carbon traders, Local and regional planners, and National policy makers. Energy providers need information about the state of the environment to make the most use out of natural resources. Planners and policy makers have to weigh up whether and which type of alternative energy is justifiable and sensible for a specific region.\r\nEO data can be used to build maps that show resource information. For solar energy, those maps contain information about solar radiation, but also shadowing effects. Forecast products for irradiance are available to be able to plan the energy production for the coming days. Tidal waves can be depicted by sea surface heights. As tidal currents are periodical, they can be predicted well by the initial state of sea surface heights. In addition, also the speed of tidal waves can be determined by EO measurements. In the wind energy sector EO data is analysed to plan and monitor wind farms. Maps can show areas, where winds are suitable for wind energy production. After the construction of a wind farm, wind strength and direction during operation can be monitored. Finally, for hydroelectric power stations EO is used to monitor water reservoirs. As well hydrometeorological data is used to forecast water-related events and to monitor drought or floods.","hasChildren":true,"name":"Users in alternative energy","selfAssesment":"Completed
"},{"code":"TA11-2-2","description":"The EO/GI user community in oil & gas consists of offshore exploration and production, on-shore exploration and production, drilling and support services, oil and gas commodities trading, and energy planners. Due to their activities both on-shore and offshore their need for EO-derived information about the land, the ocean and the atmosphere. They need EO-derived information about geological features (for exploration), for asset infrastructure monitoring, construction and buildings. Safe offshore operations (ocean&atmosphere: forecast and monitoring current movement and drift, monitor sea-ice and icebergs, detect and monitor hurricanes and typhoons; land: map and assess flooding, detect wildfires . A large set of information needs results from their need to adhere to environmental regulations. They have to assess and monitor their environmental impact, ocean quality and productivity, land ecosystems and biodiversity, groundwater and run-off \r\nMany problems faced by oil, gas, including the selection and development of exploration areas, detection and mapping of illegal mining activities, or monitoring dams, pipelines and terrain movements, can be efficiently addressed by extracting information from geospatial imagery. Remote Sensing based applications reduce the need for field work, minimize environmental impacts, and ultimately safe costs, to help achieve results faster during exploration, extraction, and remediation/reclamation stages.","hasChildren":true,"name":"Users in oil & gas","selfAssesment":"New
"},{"code":"TA11-2-3","description":"The EO/GI community in minerals and mining consists of mining and quarrying companies, exploration and survey specialists, commodities traders, exploration and extraction equipment suppliers, drilling, excavation and support services, and regional planners / policy makers.\r\nTypical spatial questions for the users in minerals and mining are concerned with prospecting, e.g. \"Where can we find the minerals that are worth exploitation?\", and operation of mining sites: \"How much material has already been excavated in the mine and how much material was deposited in dedicated dump areas?\". Additionally relevant are arising risks through mining activities, e.g. \"How do the mining activities affect settlements in the vicinity?\" or \"How do the mining activities affect the environment?\". Concequently, the EO/GI users in minerals and mining benefit from EO information through mapping geological features, monitor mineral extraction, measure land use statistics, assessing environmental impact of human activities, detect and monitor ground movement, and monitor land pollution.","hasChildren":true,"name":"Users in minerals & mining","selfAssesment":"New
"},{"code":"TA11-2","description":"Users in energy and mineral resources deal with the harvesting of energy from renewable resources and extractive industries including oil and gas and raw materials. EO information helps them in exploring locations where to build new mines or power plants, in identifying risks from infrastructure and in managing the environmental impact of their operations.\r\nUses that apply to the extractive industries: study of landforms, structures, and the subsurface, to understand physical processes creating and modifying the earth's crust. EO/GI should play a key role to transform data into information and knowledge about the potencial feasibility and viability of renewable resources, in particular solar and wind at the natural and urban ecosystems, and in particular to support Sustainable Development Goals SDG 7 Affordable and Clean Energy and SDG 11 Sustainable Cities and Communities.","hasChildren":true,"hasParent":true,"name":"Users in energy and mineral resources","selfAssesment":"New
"},{"code":"TA11-3-1","description":"EO/GI users in construction include construction companies, civil engineering consultancies, architect and design companies, planning authorities, and national land agencies. \r\nThey benefit from EO through monitor building development, assess environmental impact of human activities, map and assess flooding, detect land movement, subsidence, heave, and monitor land-use statistics","hasChildren":true,"name":"Users in construction","selfAssesment":"New
"},{"code":"TA11-3-2","description":"Utilities (water, electricity, waste): Power station operators, Water plants operators, Survey companies, Hydroelectric suppliers, Regulatory Bodies, Distribution companies, Landfill and waste, Regional planners / policy makers.\r\nThe benefit from EO information that monitor pollution in rivers and lakes, assess changes in the carbon balance, assess environmental impact of human activities, monitor land pollution, assess changes to urban and rural areas, assess and monitor water quality, assess ground water and run-off.","hasChildren":true,"name":"Users in utilities & supplies","selfAssesment":"New
"},{"code":"TA11-3-3","description":"Users of EO/GI in communications and connectivity are mostly mobile telecommunications providers and fixed telecommunication providers. Theire business is to connect people via telephone and internet. The assets for their services include the infrastructure of communication networks physically installed in the ground, the cellphone towers distributed over the land surface, particularly in higly populated areas, as well as other installations (e.g. company buildings) and equipment (communication satellites).\r\nSpecific spatial questions of these users are concerned with the reception quality that the network can provide in an area. The network coverage would neet to react to changes of the built environment. New settlement infrastructure may cause a new population distribution and subsequently the need to network adaptations to cover new areas or cover some areas with higher band widths because more people are living there. Additionaly, the coverage of cellphone antennas depends on the arrangement of environmental obstacles that degrade or block the radio signal. Any place where the built environment or the vegetation changes can change the reception quality within the covered area of an existing cellphone tower. \r\nThe benefit of EO information for the user group of communications and connectivity comes from monitoring building development, assessing changes to urban and rural areas, and mapping line of sight visibility (terrain height, land cover).","hasChildren":true,"name":"Users in communications & connectivity","selfAssesment":"New
"},{"code":"TA11-3-4","description":"EO/GI users in transport and logistics include road transport operators, haulage, road infrastructure operators, tolls, airport operators, rail operators, airlines and airline services, and transport engineers.","hasChildren":true,"name":"Users in transport & logistics","selfAssesment":"New
"},{"code":"TA11-3-5","description":"EO/GI users in marine include ports & harbors administration, bulk cargo carriers, cruise liners operators, ferry operators, naval operations, and rescue and safety at sea.","hasChildren":true,"name":"Users in marine","selfAssesment":"New
"},{"code":"TA11-3-6","description":"From a conceptual point of view travelling is crossing the space from one location to another. Tourism mostly requires a travel to the desired destination and typically also includes moving inside a specific area. Therefore both tourism and travel are highly dependent on spatial phenomena which are often captured using EO.All kinds of travelling are highly dependent on weather conditions which can be observed with meteorological satellites. Also the current traffic conditions like congestion, road condition and natural hazards can be discovered with EO.\r\n\r\nThe types of tourism which are outside of buildings require sufficient weather forecast. Especially outdoor tourism at the coast or in mountain areas have a need for specific information about the current and the near future conditions of the natural environment. Examples are avalanche reports and forecasts for wind or wave heights of water bodies. Local tour organizers can utilise this information in order to better plan offers for tourists and also ensure overall safety during their stay.\r\n\r\nTourism and travelling are import economic factors. Consequently both the public and the private sector are interested in ensuring safe and convenient travel conditions and furthermore in creating an attractive environment for travellers and touristic visitors. This includes recognising environmental pollution, since this discourages tourist from visiting an area. Not only incoming, but also outgoing tourism is an important factor in local economies. Travel agencies, for example, are highly dependent on retrieving accurate information about foreign regions which are typically obtained with earth observation technology.\r\n\r\nOf course tourism and travelling itself also can be observed from space, this is especially true for mass tourism and areas where traffic has increased a lot during the last time. Typical effects are the increase of settlement area and the additionally used space for roads, parking lots, airports and harbors. These changes to the earth surface can be quantified with the help of land cover change detection.In many cases local administrations and decion makers want to mitigate the negative consequences of mass tourism, the insights of the mentioned EO measurements provide a useful foundation for sustainable planning.","hasChildren":true,"name":"Users in travel & tourism","selfAssesment":"Completed
"},{"code":"TA11-3","description":"Users in transport and infrastructure apply to all manufacturing and physical supply in land but also marine domains including transport & logistics, utilities, construction, communication & connectivity, and tourism.","hasChildren":true,"hasParent":true,"name":"Users in infrastructure & transport","selfAssesment":"New
"},{"code":"TA11-4-1","description":"EO/GI users in insurance and real estate include primary insurance companies, re-insurance sector, insurance brokers, insurance service suppliers, commercial banks, major projects, and international financial institutions. \r\nProduction processes (including primary production like farming), property and real estate are often insured against certain risks, e.g. from natural hazards. \r\nUsers benefit from EO information through applications that monitor building development, assess crop damage due to storms (including to forecast and map large waves), assess damage from earthquakes, detect and monitor wildfires, map and assess flooding, detect land movement, subsidence, heave, forecast and assess landslides.","hasChildren":true,"name":"Users in insurance & real estate","selfAssesment":"New
"},{"code":"TA11-4-2","description":"EO/GI users in retail and geo-marketing include Retail centres and Advertising and Marketing agencies. They use EO/GI data in the field of Navigation and LBS, Shopping chains or Logistics.","hasChildren":true,"name":"Users in retail & geo-marketing","selfAssesment":"New
"},{"code":"TA11-4-3","description":"Users in news and media are Television companies, Broadcasting providers, News and Information agencies, Web service providers, and Entertainment software providers. They benefit from monitoring, forecasting and assessing of natural risks/disasters.","hasChildren":true,"name":"Users in news & media","selfAssesment":"New
"},{"code":"TA11-4-4","description":"Users in ICT include fixed and mobile telecommunications providers. They can make use of EO/GI data by monitoring building development and changes to urban areas.","hasChildren":true,"name":"Users in ICT, knowledge and digital interfaces","selfAssesment":"New
"},{"code":"TA11-4","description":"Users in financial and digital services cover a broad area of activity that touches on many other market sectors such insurance & real estate, retail, news & media and digital interfaces. The categories included are identifiable as a “service” (tertiary sector: attention, advice, access, experience, and affective labour) and not part of the physical supply of goods.","hasChildren":true,"hasParent":true,"name":"Users in financial & digital services","selfAssesment":"New
"},{"code":"TA11-5-1","description":"The users in smart cities are multiverse and include large number of profiles. This include urban planners, architects, spatial planning offices, urban policy makers, transportation/environment/health departments but also citizen themselves.\r\nThe users benefit from additional information and knowledge extracted from EO data. This information and knowledge can help them to better tackle with challenges arising from climate change and urbanization. As each urban area is unique, EO can provide relevant information by detecting, evaluating and measuring these localities.\r\nThis EO based information can be extracted on one occasion or continuously, benefiting from revisiting satellites. EO can support investigation of archive data to extract trends or by investigating current state to set a baseline. This baseline is then further used to monitor the changes or to assess the impact of different decisions and actions. In most cases, this information is further used in various GIS analyses or modelling procedures.\r\nThe topics where EO can contribute are as follows: urban land cover, urban heat islands, air/water/soil quality, tree/vegetation health, detection of invasive vegetation species, damage detection on buildings or infrastructure, development of infrastructure and many more.\r\nAs listed, EO can support various domains that can be fitted under Nature-based solutions (NBS). NBS have been gaining attention as multifunctional solutions that may help cities to address challenges arising from climate change and urbanization.\r\nThe concept of Nature-based Solutions (NBS) has evolved as an umbrella concept embracing concepts such as green/blue/nature infrastructure, ecosystem approach, ecosystem services or natural systems agriculture, natural solutions, ecosystem-based approaches, and ecological engineering. NBS can include solutions such as water purification, reduction of flood risk, or deliberate efforts to decrease temperature and improve air quality.","hasChildren":true,"name":"Users in smart cities","selfAssesment":"Completed
"},{"code":"TA11-5-2","description":"The users in local and regional planning include spatial planning departments of municipalities, spatial planning offices, and spatial planning policy makers. Land use management in densely populated areas involves negotiation of conflicting land-use demands for settlement, production system (including agriculture and forestry) and infrastructure. The users benefit from EO information to manage the use of land and its impacts.","hasChildren":true,"name":"Users in local & regional planning","selfAssesment":"New
"},{"code":"TA11-5","description":"Users in urban development and users involved in the development of rural settlements perform tasks on local and regional scale (to the scale of nations). These users benefit from EO information to manage the use of land & its impacts. Users such as urban planners, architects, spatial planning offices, urban policy makers in public/private sectors in smart cities or generic urban local/regional planning belong to this category. EO/GI becomes a key data and information to support Sustainable Development Goals - SDG 11 Sustainable Cities and Communities in particular to set up at geospatial and temporal basis the evolution of urban environmental and socioeconomical factors for a better distribution and equality of resources, benefits and impacts (environmental urban justice maps)","hasChildren":true,"hasParent":true,"name":"Users in urban development","selfAssesment":"New
"},{"code":"TA11-6-1","description":"Users in defense, security and military are border control organisations, police and rescue forces, military services, and intelligence services. Use of EO/GI data can be made in the field of detecting and monitoring high risk areas (natural and humanitarian), monitoring border incursions, or monitoring maritime movements.","hasChildren":true,"name":"Users in defense, security & military","selfAssesment":"New
"},{"code":"TA11-6-2","description":"EO/GI users in emergency services are coast guards, ambulance services, fire services, police services, civil protection organisations, and rescue services. They benefit from monitoring, detecting and assessing natural risks/disasters.","hasChildren":true,"name":"Users in emergency & social protection","selfAssesment":"New
"},{"code":"TA11-6-3","description":"The EO/GI users in humanitarian operations correspond to humanitarian aid organisations, humanitarian support organisations and overall humanitarian response such as border control organisations, police and rescue forces, coast guards, civil protection, military services, and intelligence services. They can use EO services to detect and monitor high risk areas produced naturally or by humans, monitor border incursions or maritime movements. They provide support to local populations that have experienced a crisis, e.g. they fled from a conflict or are affected by a natural disaster. The organisations therefore support the population's needs for sustenance. Consequently, any related risks are relevant as well. The users benefit from the EO capability to identify and monitor people in need, i.e. to assess pressures on populations and migration, and to monitor humanitarian movement and camps. They additionally benefit from EO through mapping disaster areas for situation awareness and detecting sensitive risk areas. Some examples of users at European level are DG RELEX, DG ECHO, DG ENV/ MIC. At UN, the users include OCHA, UNHCR, UNDPKO, UNDP, UNOPS, UNITAR, UNICEF, UNESCO, WFP. Further, international users include IFRC, WHO, WB, and donor organizations. At the national level, the users include Civil Protection Agencies, Ministries of Internal Affairs / Civil Protection Department, Development and Aid agencies.","hasChildren":true,"name":"Users in humanitarian operations","selfAssesment":"New
"},{"code":"TA11-6","description":"Users in defence and security work in the field of military, emergency and social protection and define, collect, analyse information to provide intelligence & safety. Some examples are activities under humanitarian response such as border control organisations, police and rescue forces, coast guards, civil protection, military services, and intelligence services which can use EO services to detect and monitor high risk areas produced naturally or by humans, monitor border incursions or maritime movements.","hasChildren":true,"hasParent":true,"name":"Users in defense & security","selfAssesment":"New
"},{"code":"TA11-7-1","description":"EO/GI users in environmental ecosystems & pollution include scientists, consultants, planners and policy makers with interest in environmental issues.","hasChildren":true,"name":"Users in environmental ecosystems & pollution","selfAssesment":"New
"},{"code":"TA11-7-2","description":"Users in health care health-related services include services on site-specific field conditions as well as import phenological timing events, which helps to make predictions for monitoring air quality, forecasting epidemics and diseases, as well as forecasting sunlight exposure.","hasChildren":true,"name":"Users in health care","selfAssesment":"New
"},{"code":"TA11-7-3","description":"EO/GI users in meteo and climate; use of satellite-based observations in addressing key climate science questions for user-centric climate change risk assessment applications or climate-related issues","hasChildren":true,"name":"Users in meteo & climate","selfAssesment":"New
"},{"code":"TA11-7","description":"Users in the public administrations or private organizations using EO to assist environmental or climate change impact policy making decisions i.e, assisting in developing monitoring to evaluate and deliver policy goals, provide assessment of ecosystems, rapid response to major environmental risk events, or those associated health security & care events. These users are largely related with international treaties and hence a strong international collaboration. EO/GI becomes a key data and information to support Sustainable Development Goals (SDG) in particular in terms of environmental, climate and health towards SDG 11, SDG 13 Climate Action; SDG 14 Life Below Water; or SDG 15 Life on Land.","hasChildren":true,"hasParent":true,"name":"Users in environmental, climate & health","selfAssesment":"New
"},{"code":"TA11-8-1","description":"EO/GI users of customer solutions; easier for society to use and engage with EO services through mobile devices, social media platforms, apps. Enormous potential to use citizen-driven observations in combination with EO data","hasChildren":true,"name":"Users of consumer solutions","selfAssesment":"New
"},{"code":"TA11-8-2","description":"EO/GI users in leisure; basic public understanding on EO Services","hasChildren":true,"name":"Users in leisure","selfAssesment":"New
"},{"code":"TA11-8-3","description":"The community of users in education includes instructors (1) who are teaching or conducting research in some aspect of GIScience, such as coding, remote sensing, field methods, geodetic control, web mapping, spatial analysis, or related topics, or (2) who are using GIS as a teaching tool in a discipline, such as business, biology, economics, or health sciences. By extension, this community includes students and supportive deans and other educational administrators. The benefits that these users gain from EO information includes a set of best practices vetted by experts in the field that they can use to teach modern GIS workflows more effectively. \r\nThe goals of this user community are focused on a deeper and a broader implementation of geotechnology, methods, and spatial data throughout the educational system—primary, secondary, university, and lifelong learning (libraries, museums, and other informal settings). Deeper implementation implies embracing GIS as a platform, including its field data gathering tools and citizen science workflows, spatial analysis, building web maps and apps, communicating with multimedia maps derived from web GIS, systems configuration work, and the coding that is behind modern GIS infrastructure. Broader implementation implies the use of GIS in a multitude of disciplines at all levels of education, formal and informal; occurring wherever changes over space and time are being examined. \r\nAt all levels of education the challenge of sufficient bandwidth and the use of a professional systems-based tool such as GIS, along with devices capable of running web GIS tools, are barriers in many areas throughout the world. However, educational and societal forces represent a stronger challenge than technological ones. These educational and societal challenges that this user community faces include the lack of educational content standards at the primary and secondary level that support the use of geotechnologies in education, and at the university level, a lack of awareness of and access to modern SaaS GIS tools and open data portals. \r\nThe risks that the community faces in not facing the challenge of the use of GIS in the education sector is a lack of geographic and spatial literacy among students and faculty. This will translate to research that does not consider spatiotemporal implications of 21st Century challenges, a workforce ill-equipped to deal with them, and consequently an increasingly unstable and dysfunctional world. To build a workforce that can meet global challenges in energy, biodiversity, climate, natural resources, natural hazards, human health, economic inequality, and others, a deep and wide implementation of GIS technology and methods must take place throughout the educational system. The actions that society can take to face that challenge is to provide professional development opportunities for faculty, curricular resources, assessment instruments, relevant spatial data and open data portals, examples of best practices, and a network for educators and researchers in which to interact. EO can provide all of these elements in partnership with educational institutions, government, nonprofits, and industry to meet this challenge. In so doing, an increasingly sustainable, healthier, resilient world can be achieved from the community to the global level.","hasChildren":true,"name":"Users in education, training & research","selfAssesment":"Completed
"},{"code":"TA11-8","description":"Citizens and society in general use and engage with EO services through mobile devices, social media platforms, apps. We do also categorize in this section the users in education, research and training providing knowledge and learning outcomes.\r\nActive and engaged citizens are one of the main driving forces of EO/GI. Nowadays, there is a growing amount of location-based contents generated by connected “produsers”, mainly equipped with smartphones. The exponential growth of ambient geographic information through social networks became the basic feature of a spatially enabled society, in which it behaves as a vessel where millions of people share their current thoughts, observations and opinions, showing to provide more reliable and trustworthy information than traditional methods like questionnaires and other sources.\r\nA spatially enabled citizen is explained through his ability to express, formalize, equip (technologically and cognitively), and (un)consciously activate an efficiently use of his spatial skills. Harvesting this ambient geospatial information provides a unique opportunity to gain valuable insight on information flow and social networking within a society, support a greater mapping, understand the human landscape and its evolution over time. With these insights, city planners can make use of the gathered affective data to detect positive or negative trends developing in the city, managing to take early countermeasures.\r\nNevertheless, assembling and analyzing EO/GI provide us with unparalleled insight on a broad variety of cultural, societal, and human factors, particularly as they relate to human and social dynamics, for example: 1) mapping the manner in which ideas and information propagate in a society, information that can be used to identify appropriate strategies for information dissemination during a crisis situation. 2) Mapping people’s opinions and reaction on specific topics and current events, thus improving our ability to collect precise cultural, political, economic and health data, and to do so at near real-time rates. 3) Identifying emerging socio-cultural hotspots.","hasChildren":true,"hasParent":true,"name":"Users among citizens & society","selfAssesment":"New
"},{"code":"TA11","description":"The EO/GI user community pools sub-communities (stakeholders) that share common needs for EO/GI information. From an economic perspective, market sectors represent user communities. Users of a community have a common interest in specific aspects of societal or economical benefits to be realized by the implementation of EO services. A user-led community is active at specific locations/regions or in specific environments on the Earth. Their activities are associated with particular features and objects of the environment and related processes that can be detected and monitored with EO satellites. EO information therefore is relevant to the user community's management of their assets, the risks to their assets, and the impact that their activities may have on other aspects of the environment. User objectives (use cases) with EO information include: Enforce regulations; Develop strategies and policies; Manage assets; Plan and design project implementations; Analyse and understand impact / consequences.\r\nUser communities can profit from EO services and applications in the field of managed living resources, energy and mineral resources, infrastructure and transport, financial and digital services, urban development, defense and security, environmental, climate and health, or citizens and society. EO/GI becomes a key data and information to support Sustainable Development Goals -SDG in particular in terms of users in managed livimgs resources towards SDG 2 Zero Hunger; SDG 8 Decent Work and Economic Growth; SDG 9 Industry, Innovation and Infrastructure; SDG 14 Life Below Water; or SDG 15 Life on Land","hasChildren":true,"hasParent":true,"name":"User community of EO services and applications","selfAssesment":"Completed
"},{"code":"TA12-1","description":"Climate change observations show the warming of the climate system. The changes since the 1950s are unprecedented over decades to millennia.The atmosphere and ocean have warmed, the amounts of snow and ice have diminished, and sea level has risen. The anthropogenic emissions of greenhouse gases are the highest in history. Recent climate changes have had widespread impacts on human and natural systems. There is an urgant need for climate action through mitigation and adaptation. Mitigation actions prevent or reduce the emission of greenhuse gases into the atmoshpere with the objective to make the impacts of climate change less severe. Adapting to climate change increases our resilience to impacts like extreme weather events (e.g. hazards like floods and droughts) that get more frequent and intense in many regions. Current climate change will get worse in the future even if the reduction of emissions is effective with negative effects on ecosystems, economy, human health and well-being. There is extensive need for actions to adapt to the impacts of climate change.","hasChildren":true,"name":"EO for climate change mitigation & adaptation","selfAssesment":"New
"},{"code":"TA12-10","description":"\"Sustainable urban development is a goal of the global society. It summarizes a specific set of problems that cities face all over the world. Cities want to provide a high quality of life to their residents. However, this goal is threatened by urban growth at the cost of urban green infrastructure’s accessibility by citizens etc. Communities that address this: C40 (association of the largest cities of the globe), CitiesIPCC, related SDGs of the UN, etc. Skills: Explain how the monitoring of urban areas contributes to sustainable urban development through its capability to provide regularly updated information about the benefit of urban green infrastructures and their ecosystem services to the quality of life in a city\r\n\"","hasChildren":true,"name":"EO for sustainable urban development","selfAssesment":"New
"},{"code":"TA12-2","description":"Biodiversity describes the variety of ecosystems (natural capital), species and genes in the world or in a particular habitat. Ecosystem services sustain our economies and societies and are essential to human wellbeing.","hasChildren":true,"name":"EO for biodiversity & ecosystems","selfAssesment":"New
"},{"code":"TA12-3","description":"Worldwide countries follow a digital agenda for the economy and initiatives to foster new skills among the workforce to cope with transformation processes with massive impact on the labour market.","hasChildren":true,"name":"EO for digital agenda & new skills","selfAssesment":"New
"},{"code":"TA12-4","description":"Energy transition is a thematic area whose EO experts are proficient in relevant EO data and its processing methods and infrastructure to derive information for energy transition [and its regulatory context, etc.]. The expertise of each expert may be very specialized. In sum, the experts have: The relevant domain knowledge (knowledge about type of monitored entities and their properties, e.g. reflectance properties of sea ice and related EO sensors for detecting them), and The relevant workflow knowledge and processing skills for extracting and providing targeted information for energy transition. [may share strategic objectives… such as „gaining thorough understanding of Energy transition“, „foster usage of EO information for energy transition“]","hasChildren":true,"name":"EO for energy transition","selfAssesment":"New
"},{"code":"TA12-5","description":"Agricultural activity is sustained by good environmental conditions that allow farmers to harness natural resources, create their produce and earn a living. This fosters a sustainable rural economy while food produced by agriculture sustains society as a whole.","hasChildren":true,"name":"EO for sustainable agriculture & food production","selfAssesment":"New
"},{"code":"TA12-6","description":"This societal challenge aims to provide efficient, safe and environmentally friendly mobility solutions.","hasChildren":true,"name":"EO for infrastructure & transport","selfAssesment":"New
"},{"code":"TA12-7","description":"In recent decades, society has fought communicable diseases with success through treatment and prevention. The Covid-19 pandemic shows that communicable diseases are still a threat to the health of citizens. Spread can gappen very quickly from one country to another. Challenges lie in the (re-)emergence of infectious diseases, antimicobial resistance and vaccine hesitancy. Policies of states focus on surveillance, rapid detection and rapid response.","hasChildren":true,"name":"EO for health surveillance","selfAssesment":"New
"},{"code":"TA12-8","description":"There is a rising geostrategic competition and power pilitics challenging rule-based multilateralism. Further, there are armed confilct, civil wars and instability in the EU's broader neighbourhood. \r\nFurther, natural disasters pose a threat to society, where the Sendai Framework of disaster risk reduction focuses on.","hasChildren":true,"name":"EO for emergency, security & defense","selfAssesment":"New
"},{"code":"TA12-9","description":"Water is an essential resource for food production. Growing crops requires significant quantities of water. Without sufficient, good quality and easily accessible water, agri-food production is under threat.","hasChildren":true,"name":"EO for water sustainability","selfAssesment":"New
"},{"code":"TA12","description":"EO provides timely, continuous and independent data for monitoring indicators of the progress of the society in various societal challenges.\r\nEO monitoring supports activities that address societal & environmental challenges. This happens indirectly along a chain: e.g. a regularly provided EO information product derived from EO data of a satellite is integrated as a parameter in a climate model / Earth system model. This climate model enables the development of regulations (and their enforcement through constant monitoring) to implement climate change mitigation measures. Thereby, the chain is characterized by seveal connected nodes: from societal challenges to use cases of users to EO applications to EO products to specific satellites and their sensors.\r\n[Communities that promote collaboration among diverse stakeholders from academia, industry, public administration as well as local residents] \r\nScientific agendas address societal challenges and the EO/GI community can contribute to them. Consortia usually include experts from academia (researchers, developers, scientists), EO companies, and members from the user community such as public authorities.","hasChildren":true,"hasParent":true,"name":"EO for societal and environmental challenges","selfAssesment":"New
"},{"code":"TA13-1-1","description":"Monitor the atmosphere includes monitoring of the atmosphere composition and air quality, as well as forecasting of sunlight exposure. Timely, continuous, and independent data on the atmosphere is useful in various domains like health, agriculture, renewable energies, urban planning, climate sciences and biology.\r\nThe atmosphere composition includes greenhouse gases (GHG) like carbon dioxide, methane, NO2 and SO2. They are part of the Earth system and have a strong impact on the climate. To monitor changes in atmosphere composition enables modelling climate change and understanding the impact of human-induced emissions of GHG relative to natural sources. EO-derived products include inventory of emission data as an input to atmospheric chemistry transport models and forecast models. Inventories are based on a combination of existing data sets and new information, describing emissions from fossil fuel use, ships, volcanoes, and vegetation. This ensures good consistency between the emissions of greenhouse gases, reactive gases, and aerosol particles and their precursors.\r\nAir quality describes the composition of the atmosphere from gases and particles near the Earth's surface. Local emissions from different sources (e.g. energy production, industrial production, traffic) cause changes to the atmospheric composition that are highly variable in space and time. The quality of the air we breathe can significantly impact our health and the environment. Therefore, it is highly relevant to monitor air quality and emissions. EO satellites are capable of monitoring aerosols, tropospheric O3, tropospheric NO2, CO, HCHO, SO2, and particulate matter (of the sizes PM 2.5 and PM 10). Products like air quality assessment reports, daily ozone forecasts, and UV-index forecast maps are produced that are applied in specific use cases, particularly related to health.\r\nThe amount of solar radiation that arrives at a location on the Earth surface depends on the atmosphere composition and varies over the day and the seasons. Information on solar radiation is useful in various domains. Applications of sunlight and ozone data are for example real-time UV radiation forecasting and risk assessment, skin health services, climate change studies, assessment of ozone protection policies effectiveness, plant growth and disease control, evaporation and irrigation models, power generation, solar heating systems planning and monitoring.","hasChildren":true,"name":"Monitor the atmosphere","selfAssesment":"Completed
"},{"code":"TA13-1-2","description":"Monitoring the climate includes monitoring climate forcing and the carbon balance and assessing climate change risks.\r\nClimate forcing describes the imbalance of the Earth’s energy budget due to natural or human-induced sources. This imbalance results in a change in the globally-averaged temperature. Amongst the contributors of positive climate forcing, that leads to an increase in the globally-averaged temperature, the increase of carbon dioxide in the atmospheric composition is considered to be the most important factor. Changes in the carbon dioxide concentration indicate that the exchanges between carbon sources and sinks are not balanced. It can be shown that human-induced emissions of carbon dioxide are responsible for the increase of the carbon dioxide since the industrialisation.\r\nWith EO, we can monitor changes in greenhouse gases (GHG), aeorosols, albedo, and solar radiation. The dynamic nature of the climate makes it necessary to apply equally dynamic EO monitoring that allows to deliver key information on historical, seasonal forecast and projection periods for climate-related indicators.\r\nRelevant EO products include estimates of the climate forcing of aerosol, ozone and greenhouse gases. The dynamic nature of the climate makes it necessary to apply equally dynamic EO monitoring that allows to deliver key information on historical, seasonal forecast and projection periods for climate-related indicators. \r\nThe products are particularly relevant to the European energy sector in terms of electricity demand and the production of power from wind, solar and hydro sources. \r\nMoreover, water management uses EO-derived information about climate change to mitigate effects of changing precipitation patterns to adapt their strategies, and to prepare for climate variability and change in the water sector, e.g. because of changes in river discharge, droughts and floods.\r\nFinally, insurance uses climate change information for assessing the weather risks to insured assets that change with the climate-related increase in extreme weather conditions. This includes products like up-to-date catalogue of wind storms and their associated impacts on the ground.","hasChildren":true,"name":"Monitor the climate","selfAssesment":"Completed
"},{"code":"TA13-1-3","description":"The weather is the state of the atmosphere measurable by its temperature, humidity, precipitation, and other atmospheric variables. To forecast the weather is a major branch in the field of meteorology. In comparison to climate, weather can only be predicted for a short period of time (minutes to month), because it describes the state of the atmosphere for specific days at specific locations. For a reliable weather forecast, a good numerical prediction model with precise initial conditions is needed. Models are sensitive to changes in the initial condition, that is why at the moment weather predictions are only accurate for few days. However, both models and the determination of initial conditions are steadily improved. EO makes a significant contribution to improving the initial conditions by providing global information several times a day. As the quality of the EO products improves, the weather forecast also improves. \r\nSince decades, satellites are used to monitor and forecast weather. Therefore, it is one of the most established sectors of satellite data applications. There are geostationary and polar-orbiting weather satellites that measure all kinds of meteorologically relevant variables, e.g. cloud coverage, wind speed [...] via passive or active imagery. However, not only satellites are used to collect information, but also other remote sensing techniques that can be airborne or ground-based such as Lidar.\r\nWeather forecasts are used by citizens for decisions in everyday life, in agriculture for crop cultivation decisions and in the stock markets. Other domains of applications are hydrometeorology, aviation, maritime navigation, and the military and nuclear sectors.","hasChildren":true,"name":"Forecast the weather","selfAssesment":"Completed
"},{"code":"TA13-1","description":"Monitor the atmosphere and climate includes all change-focused services/applications which assess, monitor, forecast and provide timely, continuous and independent data (e.g. temperature, humidity, emissions, greenhouse gases, solar UV radiation, aorosols,...). It closely monitors each of the Earth's different subsystems and, besides being the basis for weather forecasts, helps to better understand and evaluate the impact of the climate change.","hasChildren":true,"hasParent":true,"name":"Monitor the atmosphere and climate","selfAssesment":"New
"},{"code":"TA13-2-1","description":"Monitor critical information about offensive and defensive systems. This deserves a category in its own right since the nature of observations is quite different from many others.","hasChildren":true,"name":"Monitor critical assets","selfAssesment":"New
"},{"code":"TA13-2-2","description":"Monitoring health can be delivered indirectly by monitoring environmental changes that can cause endemic and chronic diseases. Typically monitored environmental factors are temperature, humidity, stagnant water, NDVI, land cover, or soil type.","hasChildren":true,"name":"Monitor health","selfAssesment":"New
"},{"code":"TA13-2-3","description":"Monitoring food security includes the monitoring of food availability by environmental conditions (land cover, NDVI,...), as well as the monitoring of migration patterns. Risks that can lead to food insecurity are hazards or conflicts.","hasChildren":true,"name":"Food security monitoring","selfAssesment":"New
"},{"code":"TA13-2-4","description":"Monitoring borders includes monitoring the land and marine border incursions, monitoring transport routes, assessing pressures on poplulations, and monitoring humanitarian movement.","hasChildren":true,"name":"Monitor borders","selfAssesment":"New
"},{"code":"TA13-2","description":"Monitor security and safety describes the collection and analysis of information to provide intelligence services & safety. The task is to give early warnings in case of emergencies, to monitor infrasturcture, transport routes (land and water) and borders, to surveil security and sovereignty.","hasChildren":true,"hasParent":true,"name":"Monitor security & safety","selfAssesment":"New
"},{"code":"TA13-3-1","description":"EO is capable to repeatedly map flood extent directly after flooding, including further aspects (flood plain, extend mapping, frequency, rainfall, flash floods, vulnerability, inundation, risk-based mapping & management; flood spread and depth followed by automated insurance payouts). Modelling (hydrological modelling and monitoring focused on seasonal dynamics of water availability) based on EO data (digital elevation models) supports flood risk assessment.","hasChildren":true,"name":"Map and assess flooding","selfAssesment":"New
"},{"code":"TA13-3-2","description":"For the outbreak of forest fires, satellite remote sensing can be continuously track and monitor, in a timely manner to grasp the development of forest fires. Beyond, weather monitoring enables to forecast weather conditions where fires are likely, allowing authorities to prepare.","hasChildren":true,"name":"Detect and monitor wildfires","selfAssesment":"New
"},{"code":"TA13-3-3","description":"Damages from earthquakes to infrastrcture can be detected directly, e.g. by mapping collapsed buildings in optical data to derive rapid response products. Use of SAR interferograms enables to identify geotectonic shifts. Modelling enables to identify hotspot areas.","hasChildren":true,"name":"Assess damage from earthquakes","selfAssesment":"New
"},{"code":"TA13-3-4","description":"Landslides are a natural hazard posing a threat to human life, property, infrastructure, and natural environment. Every year, slope instabilities have a significant impact on societies and economies. Consequently, landslide documentation is used for risk assessments, policy making and enforcing of construction regulations. Landslide monitoring is used to ensure safety of infrastructure operation. Rapid mapping of landslides and associated damages is done for response actions, e.g. of civil protection organizations. As ground surveys are very costly and time-consuming, satellite remote sensing is increasingly used to assess damage resulting from landslides.\r\nLandslides lead to local terrain changes after a downslope movement of material under the effect of gravity. They vary by type of movement (e.g. falling, toppling, gliding and flowing), by size (from small rocks to entire mountain slopes) and velocity (from a couple of millimetres per year up to free-fall speed). Landslides can be triggered both by natural causes (like earthquakes or heavy rainfall events) and human causes, e.g. mining activities that lead to slope failures. Landslides can initiate other natural hazards, e.g. when a landslide blocks a river a lake can be formed which poses a risk for an outburst flood. \r\nLandslides are diverse in appearance, and therefore are challenging to detect. EO-based assessment methods aim for detecting changes to the land surface and surface displacements. \r\nEO satellites and airborne remote sensing use optical sensors for detecting landslides in post-event images and land cover changes caused by landslides, primarily indicated by the removal of vegetation and the exposure of bare soil, by comparing pre-event and post-event images. Typical resolutions of optical EO data for mapping rapid landslides are between 0.4 m and 30 m, depending on the size of landslides caused by the triggering event. Optical data from unmanned aerial vehicles are used in cases where single landslides or concise regions have to be covered. Additionally, synthetic aperture radar (SAR) sensors allow the detection of subtle changes in ground deformation caused by landslides. Therefore, time-series of radar images are used. Further, airborne laser scanning enables the generation of digital elevation models (DEMs) that allow identification of landslide surface structures and, in case of repeated coverage, detection of elevation changes. DEM generation for analysing landslides is also possible with photogrammetry on stereographic optical data and radargrammetry on SAR images.\r\nThe diversity of appearances of landslides leads to challenges for (semi-)automatic image processing and makes visual interpretation of EO data by a landslide expert a commonly used method for landslide mapping. However, visual interpretation is subjective and experts’ results can be very diverse. Additionally, it is a slow and time-consuming process. Semi-automated classification based on optical and DEM data using object-based image analysis (OBIA) can achieve detailed interpretations of landslides while reducing the analysis time. Interferometic SAR (InSAR) techniques, such as persistant scatterer interferometry (PSI) or Small Baseline Subset (SBAS), are primarily used to identify and monitor slow-moving landslides and for quantifying movement rates. Integrated analysis of optical, DEM and SAR data allow to fully exploit the potential of EO data from different sensors for landslide mapping and assessment.","hasChildren":true,"name":"Forecast and assess landslides","selfAssesment":"Completed
"},{"code":"TA13-3-5","description":"In context of volcanic activities and volcanos, EO methods are capable to provide information about various aspects, including ground motion (seismic), volcanic eruptions (pre-eruptive, sin-eruptive, atmospheric ash, dispersion), Rapid damage estimation (prevention), earthquake damage extent (loss adjuster dispatch). classification of land cover types","hasChildren":true,"name":"Assess and monitor volcanic activities","selfAssesment":"New
"},{"code":"TA13-3-6","description":"Multi-hazard assessment both focuses on regions prone to several geohazards and on the interrelationships between hazards, i.e. what happens if two disasters strike at the same time or what happens when one disaster is causing a cascade of disasters with a strongly amplified impact (e.g. a landslide causing a dammed river causing an outburstflood with a magnitude beyond the design of protective measures; or an earthquake in a coastal region that is followed by a tsunami). EO can provide imformation on the single disasters and, through integration and comprehensive impact assessment, enables multi-hazard assessment.","hasChildren":true,"name":"Multi-hazard assessment","selfAssesment":"New
"},{"code":"TA13-3","description":"Assess disasters and geohazards by EO includes alert & early warning, emergency mapping, and risk & recovery mapping. It relates to observations, controlling, assessments that are linked to natural and human made risks. Typical disasters that can be assessed by EO are in particular floods, droughts, forest fires, landslides, tsunamis, earthquakes, cyclonic storms and volcanic eruptions. Since with EO it is possible to quickly analyse the risk or damage it is used to effectively plan emergency response actions.\r\nThere are several measures to minimize or prevent the damage caused by disasters. Some of them have to be carried out in anticipation of a disaster, others after the occurrence of an event. The different phases that are needed to reduce or avoid the impact and to assure rapid response and recovery are described in the disaster management cycle. Depending on the cycle phase, EO has to meet different requirements. The Mitigation and Preparedness phase are passed through in anticipation of a disaster event. Thus, requirements to EO products may focus on high completeness of mapping or high accuracy of mapping. In contrast, Response and Recovery phase include rapid mapping, thus EO capabilities must meet near real-time delivery requirements. \r\nAs well, the nature of the disaster determines which EO products are used. Optical sensors are used throughout the different types; however, landslides are mostly assessed by radar sensors and thermal sensors are additionally used for forest fires.","hasChildren":true,"hasParent":true,"name":"Assess disasters & geohazards","selfAssesment":"New
"},{"code":"TA13-4-1","description":"To monitor crops and agriculture with EO-based methods is relevant for various applications, including to assess environmental impact of farming, assess crop damage due to storms, to detect ollegal or undesired crops, to monitor water use on crops and horticulture, and to monitor land degradation neutrality. EO mapping of crops happens on all scales with both optical and SAR sensors. Relevant EO products include degradation, agri-environment, ecosystem, damage estimation, warning-service, food-security, impact, crop health (disease and stress), leaf area index, crop acreage and yield harvest (inventories / statistics), crop types (extent, growth, health, stress), land surface temperature, illicit crops, estimates, cultivation patterns, soil water index, surface soil moisture, run-off, land cover (land cover change), land productivity (net primary productivity, NPP), carbon stocks (soil organic carbon, SOC).","hasChildren":true,"name":"Monitor crops","selfAssesment":"New
"},{"code":"TA13-4-2","description":"Monitor the forest focuses on regular and periodic measurement of certain parameters of forests (physical, chemical, and biological) to determine baselines to detect and observe changes over time. Typical applications include to assess deforestation and forest degradation, assess forest damage due to storms or insects, to monitor forest resources, detect illegal forest activities, assess the environmental impact of forerstry, and to monitor the forest carbon content. Moderate resolution sensors have been used to map forests at large scales. Modern very high resolution optical sensors provide enough spatial and spectral detail to map individual trees. Further sensors for forest monitoring include SAR and LIDAR. Integration of optical sensors, LIDAR and in-situ measurements seems an accurate method to achieve third dimension forest mapping.","hasChildren":true,"name":"Monitor the forest","selfAssesment":"New
"},{"code":"TA13-4-3","description":"EO provides the opportunity to monitor bodies of water, i.e. inland waters, and to assess ground water and run-off. For lakes, this includes products about water quality, pollution, turbidity, suspended sediment concentrations (quantitative, qualitative), waterbody (temperature, extent, volume, quantity), algal blooms, alkaline water, evaporation, surface temperature. For ground water and run-off, the products focus on water run-off (water quantity), hydrological network and catchment areas (water catchment), run-off season, groundwater. Various scales are addressed, from local catchments to the global water cycle. For inland water quality, sensors are optical medium resolution (300 meters) for achieving a (strongly cloud-cover dependent) update frequency of 10-20 times per year and high resolution (5 meters) for update frequency of 3-5 times per year.","hasChildren":true,"name":"Monitor bodies of water","selfAssesment":"New
"},{"code":"TA13-4-4","description":"Monitoring of snow and ice focuses on glaciers and their retreat due to climate change (extent, mass balance), the seasonal snow cover (its extent, depth, temperature and snow water equivalent), and the ice on rivers and lakes (inland ice, thickness, freezing period, melting period, ice extent). Glacial monitoring in the mountainous regions around the globe, and of the Greenland and Antarctic ice shields uses optical EO data of high and very high resolution and SAR data. Satellite based daily snow covered area products can reliably be provided down to a spatial resolution of 500 meters. Global products are possible with weekly updates. Applications include, among others, climate change impact monitoring, relevant for modelling runoff patterns in catchments for etimating hydroelectric power generation potential.","hasChildren":true,"name":"Monitor snow and ice","selfAssesment":"New
"},{"code":"TA13-4-5","description":"EO is used to monitor land ecosystems and biodiversity, environmental impact of human activities, land pollution and vegetation encroachment. A tool for this is land cover mapping and mapping of land cover change about a wide set of categories, lincuding basic forest types, major agricultural surface types, conservation areas, settlements, infrastructure, primary roads, bare soil, water bodies, rivers, wetlands following standard classification schemes according to CORINE or FAO LCCS. Main source are optical EO data and associated pixel-based and object-based image classification methods. For discriminating vegetation classes, they often making use of various vegetation indices and biophysical parameters.","hasChildren":true,"name":"Monitor land ecosystems","selfAssesment":"New
"},{"code":"TA13-4-6","description":"EO technologies (both optical and SAR) are capable to categorize bio-physical coverage of land to produce land cover maps like CORINE Land Cover (CLC). The EO method is objective and allows for frequent updates. EO-derived land cover is an excellent basis for mapping land use, the socioeconomic use that is made of land. Land use products are used in a wide range of applications (e.g. agriculture, forestry, spatial planning, determining and implementing environmental policy, land accounting). In a humanitarian context, land use mapping is applied to map refugee camps, population and pressures on population that cause migration.","hasChildren":true,"name":"Monitor land use","selfAssesment":"New
"},{"code":"TA13-4-7","description":"EO is capable to monitor topography with various types of land surface elevation data (both digital terrain models and digital surface models) and also focus on land surface changes and ground deformation / movement due to e.g. soil erosion or permafrost thawing, frost heaving. This includes also the mapping of stable zones where such changes do not happen. The main ways of creating a digital elevation model (DEM) from EO data are deriving it from interferometric synthetic aperture radar (InSAR), from stereoscopic pairs of optical images acquired from different viewing angles, and deriving them via laser scanning.","hasChildren":true,"name":"Monitor topography","selfAssesment":"New
"},{"code":"TA13-4-8","description":"EO is able to extract information about subsurface geology, including near surface features, lithology features, and linear disturbance features (faults & discontinuities). Concerning monitoring of mineral extraction EO supports by mapping ground surface, illegal activities, mine waste (erosion, land subsistence, biodiversity/habitat loss, destruction & disturbance of ecosystems). Disturbance of ecosystems may happen by carbon seeps from reservoirs or pipelines. Their detection can also be done with EO data.","hasChildren":true,"name":"Extract information about subsurface geology","selfAssesment":"New
"},{"code":"TA13-4","description":"Services that monitor land cover all services/applications that are focused on monitoring, assessing, managing, planning and improving land areas, its ecosystems (land, soil and inland water monitoring/quality/availability & usage assessments) and evolution of the land surface (use, cover, seasonal and annual changes and monitors variables) even if it involves human intervention (environmental challenges, impact evaluation or suitability analysis).\r\nMonitoring is possible by deriving information from variables measured by EO in different domains, like vegetation, energy, water, and cryosphere. For vegetation, those variables are for example land cover, NDVI, burnt area, or surface soil moisture. In the energy domain, land surface temperature and surface albedo are known variables, for water it is water surface temperature or water quality. Finally, for the cryosphere lake ice and snow cover extent, and snow water equivalent are variables that are used for land monitoring services.","hasChildren":true,"hasParent":true,"name":"Monitor land","selfAssesment":"Completed
"},{"code":"TA13-5-1","description":"The full range of EO satellite sensors are capable of monitoring particular aspects of urban areas. The most relevant include SAR satellites such as TerraSAR-X that distinguish between urban fabric and other land cover. Further, optical satellites in the resolution range HR and VHR are used to map imperviousness and soil sealing. Beyond such land cover classifications with low granularity, HR and VHR data are used for producing detailed land use and land cover classifications that distinguish different settlement densities or, in combination with additional data, different land use such as transport, residential etc. as defined in Classification schemes specialized on urban areas. Airborne laser scanning (and stereographic analysis) maps building and vegetation heights. InSAR methods allow to measure land subsidence that is highly relevant e.g. in coastal cities close to or below the sea surface elevation. Night-time optical data maps lights. Thermal sensors allow mapping the heat that is radiated from cities. Typical applications include monitoring urban growth/sprawl, transport networks, urban heat islands, and generating city maps and 3D city models for urban planning that are relevant to users in smart cities and in local/regional planning.","hasChildren":true,"name":"Monitor urban areas","selfAssesment":"Completed
"},{"code":"TA13-5-2","description":"EO is capable of monitoring infrastrcture in general, i.e. buildings (and their construction) and transport networks (roads, rails). Additionally, infrastructure for renewable energy harvesting (solar and wind farms, hydroelectric powerplants) and identification of suitable sites (through mapping solar radiation, wind roses, speed and direction, hydrological network mapping). A basis is land surface mapping for deriving digital elevation models (DEMs) that is required for modelling renewable energy potential and for spatial planning and landscape visibility analysis (visual impact assessments for planned infrastructure). Further, EO is capable of assessing damage from industrial accidents. A wide range of EO technologies is used here, infrastrcture can be directly detected and mapped with optical and SAR sensors, where the resolution depends on the targeted assets. DEMs can be generated from SAR and stereographic optical data. Wind energy related parameters can be derived from satellites focused on atmosphere and weather monitoring. Further, there are various GI methods in use, too (in particular focused on spatial planning and impact assessment).","hasChildren":true,"name":"Monitor infrastructure","selfAssesment":"New
"},{"code":"TA13-5","description":"Monitoring the built environment provides information about urban structures, transport networks and particular infrastructure, e.g. dedicated to energy provision. It covers all urban and infrastructure related service/applications on site development information, planning support or suitability analysis. As well, it includes pressure and threats analysis on the urban areas.","hasChildren":true,"hasParent":true,"name":"Monitor the built environment","selfAssesment":"New
"},{"code":"TA13-6-1","description":"Oceanic waters cover approximately 70% of the Earth´s surface and play a key role in regulating Earth temperature and climate, support important marine ecosystems and provide food and transport. Ocean waters occupy large areas and involve highly dynamic processes with different temporal and spatial scales. In-situ measurements taken by ships and buoys can provide accurate information but only at specific locations, being limited to understand large-scale processes. To characterise the heterogeneity and dynamics of ocean waters, it would be required to perform exhaustive field campaigns with associated high costs and infrastructure challenges. EO is an efficient tool to monitor ocean waters and to complement ocean in-situ monitoring programmes as it can provide cost-effective information over vast areas at continuous temporal and spatial scales. \r\nSince the first EO satellite specifically designed to study the oceans (SeaSat) has been launch in the 1970s, many sensors and platforms have been developed. This variety of sensors have provided measurements of a broad range of ocean physical and biological variables to the present day. For example, satellite observations in the visible and near-infrared bands have provided information about ocean colour that can be used to estimate chlorophyll-a concentration for monitoring water quality, productivity and algal blooms. Thermal infrared (TIR) sensors have provided data of Sea Surface Temperature (SST) that is of importance for the study of currents and ocean warming. Microwave radiometers have registered sea surface salinity (SSS), critical to determine the global water balance, understanding ocean currents and estimating evaporation rates. EO can also provide information about physical ocean features such as surface elevation and ocean currents, sea surface winds, ocean waves, vessels and pollutants such as oil spills. \r\nThe versatility of EO data have been proved in a broad range of applications, including the monitoring of water quality, climate change effects, hurricane tracking and prediction, monitor maritime traffic and pollution, harmful algal blooms and fisheries management. In recent years, the Copernicus programme has launched a series of satellite missions for water and land monitoring that guarantee the provision of long-term observations giving continuity to previous satellite missions. Within the Copernicus programme, especially the Sentinel-3 mission will have relevance for ocean observations. Currently, two satellites Sentinel-3A and Sentinel-3B, launched respectively in 2016 and 2018, are providing near-real-time data on the state of the ocean surface, including sea surface temperature, marine ecosystems, water quality and pollution monitoring. New hyperspectral missions such as the Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) developed by NASA, are currently under development. In the near future, they will complement the existing satellite missions and will register data in a high number of spectral bands. This information will be essential in diverse applications such as aquatic ecology and biochemistry. Ocean EO is still an evolving field that will need skilled professionals that exploit the data from the new and upcoming missions for the advancement of ocean knowledge and monitoring.","hasChildren":true,"name":"Monitor the marine ecosystem","selfAssesment":"Complete
"},{"code":"TA13-6-2","description":"In coastal areas, EO is capable to monitor water depth and shallow water bathymetry (charting), coastal ecosystem parameters about water temperature, water transparency, oxygen, phytoplankton abundance, bathing water indicators, detection harmful algal blooms, sediment (qualitative, quantitative), turbidity (quality, quantitative), visibility, chlorophyll-a concentration, suspended sediment may be indicative of estuarine processes, re-suspension or pollution. Further, this includes coastline monitoring with a focus on shoreline and its change as well as coastal land cover (and terrain) and its change. A widse set of EO sensors and technologies is used to monitor coastal areas. Optical satellite imagery is analyzed to detect and map suspended sediment concentrations. Etc.","hasChildren":true,"name":"Monitor coastal areas","selfAssesment":"New
"},{"code":"TA13-6-3","description":"EO is capable to monitor weather impact on ocean surface and metocean features as a basis for forecasting furture ocean conditions. This includes ocean surface topography, ocean dynamics and circulation like tides and ocean current movements and drift, ocean winds, wave and climate conditions at ocean locations (meteocean). Further, this covers the mapping of extreme waves like tsunamis and the monitoring of hurricanes and typhoons. Involved EO technologies are for example satellite altimetry that maps ocean surface with 2 cm to 3 cm accuracy, mathematical forecast models. Repeated altimetry measurements allow mapping speed and direction of ocean's currents and tides. Available EO-based RADAR systems monitor wave height and direction, wind speed and sea-surface elevation. Near-realtime processing and delivery workflows enable the use of these parameters in weather forecasting, navigation and offshore installations protection.","hasChildren":true,"name":"Monitor weather impact on ocean surface","selfAssesment":"New
"},{"code":"TA13-6-4","description":"To support an ecosystem-based approach for fisheries management, EO images with global and daily systematic coverage with high-resolution images can help in identifying potential fishing zones and to assess fish stocks. They help assessing and understanding changing abundancy and spatial distribution of exploited fish stocks. Therefore, they analyse various key environmental parameters that can be detected with satellite remote sensing. This includes sea surface temperatures (SSTs), sea surface height anomalies, and sea surface colour revealing the abundance of chlorophyll a. This relates to phytoplacton production that is directly related to total fish landings. Additionally, EO can detect harmful algal bloom. A further threat to sustainable fish stocks management are illegal fishing. Where localization of licensed fishing vessels and fleet management services are supported by EO to avoid overexplotation and enable recovery of fish stocks. EO complements identification, detection and tracking of vessels with SAR and optical remote sensing.","hasChildren":true,"name":"Monitor fisheries","selfAssesment":"New
"},{"code":"TA13-6-5","description":"For shipping, navigation, and monitoring sea-traffic and pollution, remote sensing and satellite technologies allow detecting vessels in the wider ocean. EO can detect the vessels themselves, their wake trailing behind them, sandbanks and reefs that pose a threat for safe navigation. Additionally, EO can detect pollution from the ships, e.g. when illegal waste disposal happens. Ship detection and classification is possible with the use of optical and synthetic aperture radar (SAR) imagery. The methods complement each other.","hasChildren":true,"name":"Detect and monitor ships","selfAssesment":"New
"},{"code":"TA13-6-6","description":"Information on sea ice and icebergs is important for managing operation of ships or offshore platforms in hazardous sea ice conditions. EO technologies give the possibility to study sea ice and measure its thickness, spatial distribution, motion and ridges (as well as ice berg positions). Satellite imagery provides wide area, synoptic pictures of the ice conditions. Since the scale of ice fields is quite large, mainly moderate resolutions have to be accepted, down to around 10m in scale, while ensuring comprehensive coverage. Multispectral imagery can provide more information on ice-type but in the main, SAR imagery is used due to its all-weather and day/night capability. The data collected can be more accurate than in-situ measurements due to a higher and faster coverage of a whole area. Subsequent modelling that incorporates ocean weather (wind, waves, ocean current) provides expected drifting paths. Constant monitoring is most important to identify the risk and opportunities, for instance for ship routing, and safety of oil rigs.","hasChildren":true,"name":"Monitor sea-ice and icebergs","selfAssesment":"New
"},{"code":"TA13-6","description":"Monitoring marine inlucdes monitoring of marine safety (e.g. marine operations, oil spill combat, ship routing, defence, search & rescue, ...), marine resources (e.g. fish stock management, ...), marine and coastal environment (e.g. water quality, pollution, coastal activities, ...), and climate and seasonal forecasting (e.g. ice survey, seasonal forecasting, ...).","hasChildren":true,"hasParent":true,"name":"Monitor marine","selfAssesment":"New
"},{"code":"TA13","description":"EO services and applications are organized according to thematic areas. EO is used for a wide set of services. There are many applications of EO that show how a service produces information for a particular client. EO service and applications are best described by the purpose they serve or by the need of the user. The main user needs to EO are to monitor, to map, to forecast, to assess, to detect, and to analyse. \r\nTo monitor means to watch and check a situation carefully for a period of time in order to discover something about it, i.e. keeping track of how the natural and manmade environment change (their status) over time. Typical alternative verbs are track, observe, record, follow, understand, or surveil. \r\nTo map means to represent an area of land in the form of a map, i.e. to feature and locate the way it is arranged or organized. Synonymous verbs are locate, identify, classify, trace, or record.\r\nTo forecast means to provide statements covering a range of different outcomes, to say what you expect to happen in the future; i.e. to predict future events based on specified assumptions (about information extracted from EO change and time series data), where different sets of assumptions describe scenarios. Equivalent terms are predict, plan, model, estimate, or project.\r\nTo assess means to judge or decide the amount, value, quality or importance of something, i.e. to evaluate and measure the status of and changes in natural and manmade built environments. Alternative verbs are evaluate, measure, understand, review, or quantify.\r\nTo detect allows to notice something that is partly hidden or not clear, or to discover something, especially using a special method, i.e. to identify and locate the changes in the Earth’s environment. Similar terms are locate, warn, identify, highlight, or spot.\r\nTo analyse means to study or examine something in detail, in order to discover more about it, i.e. to detail the elements of a whole and critically examine and relate these component parts separately and/or in relation to the whole. Sometimes, the terms to process, to parse, or to detail are used in exchange for to analyse.","hasChildren":true,"hasParent":true,"name":"EO services and applications","selfAssesment":"New
"},{"code":"TA14-1-1-1","description":"Ocean colour can be made visible in atmospherically corrected EO data. Specific spectral bands are necessary to derive physical and biologic parameters of the water from the EO data.","hasChildren":true,"name":"Ocean colour","selfAssesment":"New
"},{"code":"TA14-1-1","description":"Band combinations are pre-defined for (visually) analysing images for a dedicated purpose. Examples are dedicated band combinations for land us land cover classification, ocean colour, etc.","hasChildren":true,"hasParent":true,"name":"Band combinations","selfAssesment":"New
"},{"code":"TA14-1-2","description":"The spectral and refractive information from optical and SAR data enables direct and indirect derivation of biophysical and geophysical EO parameters that are properties of the sensed land surface, ocean surface and atmosphere volume.","hasChildren":true,"hasParent":true,"name":"EO parameters","selfAssesment":"New
"},{"code":"TA14-1","description":"Processing products are image products from raw data to all different processing stages. The transformation processes between the stages include operations such as atmospheric correction, cloud detection and radiometric calibration to provide data in a form suitable for subsequent analysis. Processing products consider a product as being an output of a process.They appear as \"intermediate products\" along all steps of the processing chain.","hasChildren":true,"hasParent":true,"name":"Processing-related and preparatory products","selfAssesment":"New
"},{"code":"TA14-2-1-1","description":"Point clouds represent a set of points with X, Y, Z coordinates and associated attributes. A source of acquisition is Light Detection and Ranging (LIDAR) sensor.\r\n Depending on the location of the recording device, i.e. where and on which the LIDAR systems are mounted, it can be divided into: Terrestrial Laser Scanning (TLS), Airborne Laser Scanning -ALS) and Spaceborne Laser Scanning (SLS).\r\nThe LIDAR system uses the near-infrared part of the electromagnetic spectrum (1064 nm) for active data collection, day or night, in the shade, but also in low visibility conditions (e.g. under clouds). Due to the footprint of the beam itself, when interacting with vegetation, one part will be reflected back, registering the height of the vegetation, and part of the beam will pass to another surface from which the other part of the beam will be reflected. Depending on the beam intensity and vegetation density this can happen a few times until it hits a hard surface and the rest of the beam is reflected.\r\nIn this way, precise information on the height and density of vegetation can be obtained, but also using automatic and semi-automatic data filtering techniques, it is possible to create several very high resolution products from source data: digital elevation model (DEM), digital relief model (DMR) digital canopy model (DCM) , digital surface model (DSM).\r\nDepending where the sensor is mounted, the density of collected point clouds can be from 15 points per m2 to as many as 250 points per m2 (in the case of UAV dana collection). This is also depending on the speed and altitude of the flight and the speed and power of the emitted pulse or beam. The biggest advantage of LIDAR scanning is that in most cases, a sufficient number of beams will always penetrate to the ground, allowing the creation of a very precise digital relief model which is the basis for further analysis. This is not always possible in very dense vegetation areas (rainforests).\r\nThe advantage of LIDAR point clouds lies in the fact that it truly provides a huge amount of information gathered in a short period of time, that are of exceptional precision. These point clouds have very wide application from forestry, surveying, architecture to archeology.\r\nGiven the development of technology, it is possible to obtain a similar point cloud by photogrammetry methods. However, photogrammetric cameras (eg orthophotos and infrared cameras) have one significant drawback, they cannot penetrate clouds, vegetation and water, and only DSM product can be extracted from them.","hasChildren":true,"name":"Point clouds","selfAssesment":"Completed
"},{"code":"TA14-2-1-2","description":"Elevation data in the form of a digital elevation model (DEM) is an essential component of many analyses derived from EO. DEMs are used to represent every kind of surface, including terrain surface, vegetation canopy surface, sea surface, sea-ice surface, glacier surface etc. This description focuses on DEMs for representing terrain. A digital terrain model (DTM) describes the bare ground of the terrain, a digital surface models (DSM) described heights of vegetation (e.g. trees) and of man-made structures (e.g. buildings) reaching above the terrain. DEM is often used as an umbrella term for DTM and DSM. EO-derived DEMs are usually DSMs and require removal of vegetation and buildings in order to represent the terrain (DTM). DEMs are multi-purpose products used in various applications. They are available for global scale (SRTM, WorldDEMTM), regional scale (ArcticDEM, Copernicus EU-DEM v1.1) or for national levels and local regions. Various techniques exist to generate DEMs from SAR data, stereographic optical EO (as well as airborne and drone) data and from airborne laser scanning.","hasChildren":true,"name":"Digital elevation models","selfAssesment":"Completed
"},{"code":"TA14-2-1-3","description":"By comparing elevation models of different dates, the change in elevation and volume can be identified. Thereby, they measure surface deformation, land subsidence, ice shield loss due to melting, etc.","hasChildren":true,"name":"Elevation change maps","selfAssesment":"New
"},{"code":"TA14-2-1-4","description":"Vector fields capture the movement directions of locations on a continuous surface, e.g. of the ocean, or in a 3D grid of locations, e.g. of the atmosphere. The atmosphere and the ocean are highly dynamic features. Vector fields are used to represent wind directions and current movement directions. Further vector fields derived from EO data include geoid undulation / gravity maps.","hasChildren":true,"name":"Vector fields","selfAssesment":"New
"},{"code":"TA14-2-1-5","description":"When a moving feature (i.e. object) is detected in subsequent images, its trajectory of movement can be mapped. Such products map ship movements, sea ice movements, etc.","hasChildren":true,"name":"Feature trajectories","selfAssesment":"New
"},{"code":"TA14-2-1","description":"Geometrically measured EO products origin from EO-derived distance measurements, measurements of direction, tracking of moving objects, and changes of distance measurements. The used EO methods include for example SAR interferometry and stereographic analysis of optical data.","hasChildren":true,"hasParent":true,"name":"Geometrically measured EO products","selfAssesment":"New
"},{"code":"TA14-2-2-1-1","description":"Land cover maps represent spatial information on different types (classes) of physical coverage of the Earth's surface, e.g. forests, grasslands, croplands, lakes, wetlands. An example is the European Copernicus product CORINE land cover (CLC) with 44 classes. Initiated in 1985 (reference year 1990), updates followed in 2000 and every 6 years afterwards. Apart from CLC, the European Copernicus Land products also include the High Resolution Layers. They includes for example the imperviousness product that captures the percentage of soil sealing. Land cover classification products are multi-purpose products that are relevant for various applications. They are available on national levels, regional levels and global levels. They have different scales and granularity of their associated classification scheme. The products are updated on a regular basis. Update cycles can vary depending on the resolution (i.e. likelihood for observable change of the land surface) and the capability of production processes. An additional example on a global scale is the Global Urban Footprint. The products are provided by public organisations and private EO companies and based on various EO sensors.","hasChildren":true,"name":"Land cover maps","selfAssesment":"Completed
"},{"code":"TA14-2-2-1-2","description":"Land use documents how people are using the land. Getting from physical land type (land cover) to land use requires skill in interpretation and involves integration and consultation of ancillary data. Land use maps are multi-purpose products that are relevant for many applications. The products are updated on a regular basis (e.g. 6 years for Urban Atlas).","hasChildren":true,"name":"Land use maps","selfAssesment":"New
"},{"code":"TA14-2-2-1-3","description":"Cloud masks for optical EO data distingush cloudy pixels from cloud-free pixels. They may differentiate between serveral cloud types, i.e. opaque clouds and Cirrus clouds (that are transparent). Most land monitoring applications based on optical data require cloud-free images. Therefore, cloud masks are a product that is used early on in image processing for selecting suitable imagery for analysis (e.g. by screening images of an archive by the derived cloud cover percentage of the image). Therefore, cloud masks are made available as metadata by the EO data provider. Clouds are identified with threshoulding of reflectance values of the blue band and, to adapt for cloud/snow confusion, specific short-wave infrared (SWIR) bands.","hasChildren":true,"name":"Cloud mask","selfAssesment":"New
"},{"code":"TA14-2-2-1-4","description":"Detected features are objects from one or more classes and are the result of a comprehensive (and mostly automatic or semi-automated) search of all locations in an image that decides whether such features are present and where they are located. Examples inculde man-made objects (e.g. vehicles, ships, buildings, etc.) with sharp boundaries and are independent from the background, and landscape objects, such as land-use/land-cover (LULC) parcels that have vague boundaries and are part of the background environment. Only the latter type would locate features for all locations of an image.","hasChildren":true,"name":"Detected features","selfAssesment":"New
"},{"code":"TA14-2-2-1","description":"Static EO derived thematic classification products and masks (e.g. land use land cover classifications). Additionally, static EO detected features (planes on apron of airports, dwellings) that consist of a set of point locations (or polygons) and do not end up in a comprehensive classification of all pixels of an image. Static EO derived thematic classification products and masks (e.g. land use land cover classifications). Additionally, static EO detected features (planes on apron of airports, dwellings) that consist of a set of point locations (or polygons) and do not end up in a comprehensive classification of all pixels of an image. Thematic classifications and feature detection identify a surface by a class label that represents a more or less persistent state. A good example product is the Copernicus Urban Atlas. The most recent available version is assumed to represent the \"current\" state (Certainly, an update cycle is necessary for providing a product that remains up-to-date).","hasChildren":true,"hasParent":true,"name":"Thematic classifications and feature detection","selfAssesment":"New
"},{"code":"TA14-2-2-2","description":"Event maps and thematic change (evolution) maps indicate that some process happened that changed the area at a location from one class to the other. For example, a burnt area map indicates locations where vegetation has been burnt by a fire and changed to bare ground. A typical mapping method is the use of pre- and post-event satellite images for detection of the areas affected by the process. Eventually burnt areas contain identifiable burn marks that allow direct identification in one single post-event satellite image. Nevertheless, it is the process that is central to the analysis. Similarly, the concepts aforestation and deforestation would fall under the heading \"Event maps.\" They may come from a comparison of two status maps of different dates. Some processes benefit from analysis of more than two states. Such change evolution maps can be produced with time-series analysis. On land, more examples include landslide maps, flooded area maps and other land surface dynamics (e.g. aforestation and deforestation). Further, change detection maps are available for other domains (atmosphere, marine, land, climate, etc.)","hasChildren":true,"name":"Event maps and thematic change (evolution) maps","selfAssesment":"New
"},{"code":"TA14-2-2","description":"The semantic labelling products result from methods that assign labels to objects or locations in a field. The labels correspond to the categories of a classification or, in case of masks and detected features, to a single target class. Such labels may also identify classes of change or change evolution.","hasChildren":true,"hasParent":true,"name":"Semantic labelling products","selfAssesment":"New
"},{"code":"TA14-2-3","description":"EO-derived attribute products describe the state and evolution of specific attributes of a feature or at a field location. They describe for example air quality, soil moisture or water quality & quantity.","hasChildren":true,"name":"EO-derived attribute products","selfAssesment":"New
"},{"code":"TA14-2","description":"Descriptive analytics products provide analytical results which describe the present (and past) situation as it is recorded in EO images. Therefore, it contains information that can directly be extracted from EO images or EO image time series. These products are diverse in various aspects: they capture static and dynamic information; they concern information about objects or fields; and they have qualitative (nominal scale) or quantitative (ordinal, interval, ratio scale) levels of measurement.","hasChildren":true,"hasParent":true,"name":"Descriptive analytics products","selfAssesment":"New
"},{"code":"TA14-3","description":"Providing analytical (modelling) results which predict the future situation (e.g. air pollution forecasts). [interpolation in space, i.e. not only prediction into the future, filling gaps in time series...]\r\nInformation that can be modelled based on descriptive analytics products. by extrapolating time series (forecasting/predicting), by modelling of processes (e.g. flood risk maps, landslide susceptibility)","hasChildren":true,"name":"Predictive modelling products","selfAssesment":"New
"},{"code":"TA14-4","description":"Prescriptive modelling products and services focus on providing analytical results that are a guide to action. The often result from an impact assessment. One example is the identification of construction sites leading to sales opportunities.","hasChildren":true,"name":"Prescriptive modelling products and services","selfAssesment":"New
"},{"code":"TA14-5-1","description":"A textured 3D model uses a 3D model derived from elevation data. Additionally, each separate surface of the 3D model receives its own texture derived from optical image data. Typically used for visualisation purposes.","hasChildren":true,"name":"Textured 3D models","selfAssesment":"New
"},{"code":"TA14-5-2","description":"A semantic 3D model consists of a 3D model derived from elevation data with an integrated image classification. A classified object thereby consists of a 3D surface or a grouped set of 3D surfaces. A typical example is a 3D city model in the CityGML format.","hasChildren":true,"name":"Semantic 3D models","selfAssesment":"New
"},{"code":"TA14-5","description":"Combining the satellite data with other information sources. Resulting in an integration of several descriptive analytics products and processing products, e.g. a textured 3D model or a semantic 3D model.","hasChildren":true,"hasParent":true,"name":"Aggregation and integration products","selfAssesment":"New
"},{"code":"TA14-6-1","description":"Sentinel-2 cloud-free mosaics for display, satellite maps in books etc.","hasChildren":true,"name":"Satellite maps","selfAssesment":"New
"},{"code":"TA14-6-2","description":"Layouted maps in a file (PDF, SVG, etc.) for printing or visualisation on screen, embedding in reports or as static displays on websites etc.","hasChildren":true,"name":"Layouted digital maps","selfAssesment":"New
"},{"code":"TA14-6-3","description":"Digital layouted maps in an online map viewer; 3D visualisations on the screen / 3D screen and online map viewers with 3D capabilities etc.","hasChildren":true,"name":"Web visualisations in 2D and 3D","selfAssesment":"New
"},{"code":"TA14-6-4","description":"Printed maps, 3D plots of 3D models, hologram 3D maps etc.","hasChildren":true,"name":"Analogue visualisation products","selfAssesment":"New
"},{"code":"TA14-6-5","description":"A video is a structured file of 2D grids link by the time, is a regular file of values which has been processed to sensor units (e.g. calibrated). The result can be a single date acquisition or a combination of dates. For each point, the value represents a parameter imaged by the sensor. Videos of EO data present for example time series of satellite maps and other EO products (e.g. Arctic sea ice evolution in a time-series map video over the past 30 years).","hasChildren":true,"name":"Time series map videos","selfAssesment":"New
"},{"code":"TA14-6","description":"Visualisation products are used for presentation of EO information to the user. The user's interaction with the visualisations is predominantly viewing and interpretation of the informational content and arriving at decisions in the context of the user'S objective with the EO information. In addition, users of visualisation are all involved actors during image processing. For example, an EO analyst may use visualisations of EO data and preliminary EO products for getting a better understanding of the contained information and adapt his processing workflow to arrive ad improved results. Typical visualisation products include satellite maps, layouted digital maps, web visualisations in 2D and 3D, and analogue visualisation products.","hasChildren":true,"hasParent":true,"name":"EO visualisation products","selfAssesment":"New
"},{"code":"TA14-7","description":"Users need access to EO products if they shall be able to benefit from them. Additionally, providers of value added products act as users of EO products earlier in the information processing value chain. Concequently, various distribution services provide access from raw data to processed information and processing infrastructure. Provision of access to raw data or processed information happens via direct download (FTP), via application programming interfaces (API) or web services (e.g. Hubs). Further, access to processing infractructure happens via web services.","hasChildren":true,"name":"Distribution services","selfAssesment":"New
"},{"code":"TA14","description":"Products in relation to EO appear along the entire image processing value chain as inputs and outputs of processing steps. Ultimately, at the end of that chain, the output EO products represent information that supports actions. The standard EO products are categorized by the type of problems they help to solve or the type of question they help answering.","hasChildren":true,"hasParent":true,"name":"Standard EO products","selfAssesment":"New
"},{"code":"WB","description":"This knowledge area is about Web Based Geographic Information management aspects and therefore it was given the name \"Web Based GI\" or \"WBG\" in short. It is implied by this name that the differentiating factor for this KA is the \"Web\". One must then be able to answer the questions like \"What functions do we delegate to the Web?\" or \"how WBGI is different from the traditional GI?\" Sticking to the functions of a GIS, which are inserting (adding), storing, manipulating, analysing and presenting the data, there is not a single system for effecting all these tasks anymore but the Web itself. For instance, there is no single database and its known-to-its users-definition, anymore but many different stores and many different definitions. Similarly, many different manipulation, analysis and presentation options compared with the options offered by a single or limited number of systems of traditional GI. In general, Web provides the means of leveraging distributed \"resources\" like data, information, or software. It is a \"collaboration medium\". A collaboration that enables rapid production or decision making. A collaboration that certainly introduces new dimensions to traditional GI handling. This is the justification of proposing this KA in addition to the KAs of the original BoK. For the mentioned collaboration to happen, data or any other type of a resource have to accessible on the Web. This means that it should have a Web \"address\" and a \"definition\" that is understandable either by \"human\" or \"machine\". \"Machine understandable definitions\" refers to the dimension of \"semantics\" and \"ontologies\" which are also included under this KA. When one talks about publishing resources then \"catalogue services\" and more importantly \"discovery\" dimension comes into the scene. On the other hand, \"Linked Data (LOD)\" and \"Open Data\", highly popular recent trends and two of the above mentioned dimensions of Web GI have also been covered under this KA. Like the other dimensions of Web GI, both LD and OD aspects must be known to GI communities with differing degrees of expertise. The concepts of \"interoperability\" and \"Spatial Data Infrastructure (SDI)\", hot topics of GI communities for many years, have been thought to be dealt with under this KA as well with the justification that \"Web GI\" is a much broader concept than SDI, This is by the fact that SDI refers to a much narrower content and context of \"collaboration\" then Web GI. Therefore, Geospatial data interoperability and some of the related concepts which were classified under KA, \"Geospatial data in the original BoK were moved under KA11 with the updated context. Another issue is the coverage of Spatial Analysis (SA), data manipulation aspects of GI by KA11. The SA aspects are covered by other KAs like \"Geocomputation\" and \"Analytical methods\". If the analysis operations, in an undertaking, would be handled by web services this is already covered by \"data processing\" web services, application development unit and Web services composition under that unit. The important thing is to have the knowledge about a specific analysis operation; Employing it as a web service would require no more knowledge than using any other web service. SA is covered by KA11 in as much as it should have been.","hasChildren":true,"hasParent":true,"name":"Web-based GI","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB1-1","description":"The basic principles on which web services build. The concept of Service Oriented Architecture and the importance of APIs","hasChildren":true,"name":"Fundamentals of web services","selfAssesment":"In progress/to be revised (GI-N2K)
"},{"code":"WB1-2","description":"This concept will cover web services based on the Simple Object Access Protocol (SOAP)","hasChildren":true,"name":"SOAP web services","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB1-3","description":"This concept will cover web services based on the representational state transfer (REST) protocol","hasChildren":true,"name":"REST web services","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB1-4","description":"The Open Geospatial Consortium (OGC) defines standards and best practices for web services in the geospatial domain. OGC standards are developed using a consensus model allowing all stakeholder to participate in the process. As a result the OGC web services are widely implemented.","hasChildren":true,"name":"OGC web services","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB1","description":"In the most simplistic way a Web service may be defined as \"a Web accesable program code which performs a task of either processing or serving some data. Although there are many other definitions in the related literature, the one in W3C (2004) seems to be quite complete and refering to also lately popular REST style Web services. It states that \" We can identify two major classes of Web services: REST-compliant Web services, in which the primary purpose of the service is to manipulate XML representations of Web resources using a uniform set of \"stateless\" operations; and arbitrary Web services, in which the service may expose an arbitrary set of operations.","hasChildren":true,"hasParent":true,"name":"Web services","selfAssesment":"In progress GI-N2K
"},{"code":"WB2-1","description":"To be able to discover and assess available data or services, these resources have to be documented. This concept describes the standardized languages used for these descriptions","hasChildren":true,"name":"Languages for the definition of non-spatial data and services","selfAssesment":"GI-N2K
"},{"code":"WB2-2","description":"Different standardized ways to define geospatial data exist. GML, GeoJSON, WKT and GeoSPARQL are examples. What are common points and differences","hasChildren":true,"name":"Definition of geospatial data","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB2-3","description":"Defining a common language is a crucial step for sharing or combining data. Vocabularies, taxonomies, ontologies are are tools to reach this goal.","hasChildren":true,"name":"Ontologies development reuse and patterns","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB2","description":"A \"resource\" could be \"anything\" including data and services, identifiable over the Web. A resource should be defined in a language to be discoverable on the Web. Over the years, two major bodies W3C for non-spatial and OGC concerning spatial data have developed many specifications for defining data and services. On the W3C side, Resource Description Framework (RDF) has gained a great momentum in recent years in relation to the recent popularity of Linked Data as well. In the OGC front, the acceptance of GML was a major step concerning the long time effort of geospatial communities for having a standard for the definition of both geospatial features and geometry.","hasChildren":true,"hasParent":true,"name":"Resource Definition","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB3-1","description":"Metadata is information about the data to be published. It helps the user to discover the data, allows the user to evaluate the fitness for use and it explains how and under which conditions the data can be retrieved and used. Metadata are a core component of data infrastructures and as such, standardization is a requirement for the correct exchange and interpretation of the metadata.","hasChildren":true,"name":"Metadata and standards","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB3-2","description":"A resource can be added manually to a catalogue service by creating or uploading its metadata, but metadata can also be added by automated crawling of other catalogues.","hasChildren":true,"name":"Manual and automated forms of publishing","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB3-3","description":"Catalogue services allow to publish and search resources through their metadata","hasChildren":true,"name":"Catalogue services","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB3-4","description":"Open data is data that is free to use, re-use and share without limitations on who uses it or for what purpose. Publishing open data is making the data discoverable and accessible in a convenient way (technical openness).","hasChildren":true,"name":"Publishing open data","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB3-5","description":"Adding semantic information to the data allows computers to understand the structure and meaning of data. This allows automatic searching, processing and integrating data with other semantic sources.","hasChildren":true,"name":"Publishing via a semantic definition of data","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB3-6","description":"Linked (open) data provides structured data which is interlinked in a machine readable way. This allows to discover, access and combine data in an automatic way. This concept discusses the steps needed to make existing data available in a linked open way.","hasChildren":true,"name":"Publishing linked open data","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB3","description":"\"Publishing\" means making a resource available for the use of others. A \"resource\" could be \"anything\" including data and services, identifiable over the Web. Publishing may be done on the basis of either the \"characteristics\" of the data or the data itself. When only some \"characteristics\" of a resource is published then some of the contents would naturally be left out. The \"characteristics\" include metadata and some keywords. This kind of publishing may be named as \"limited contents\" publishing or \"publishing by metadata\". One of the issues become then what characteristics to use to define the data. Or what what metadata definition to use. Another aspect of publish is \"manual entry\" and \"automated collection\". In the former publisher enters metadata while in the latter some harvesting mechanism collects metadata in an automated fashion. On the contrary, there is \"unlimited contents publishing\" where there is no limitation on the published contents. Open data publishing is in this class. In additon, some \"additional semantics\" may be subject of this type publishing through new relationships in the ontologies of publishing, which have not been explicit in the exisiting data model but are inherent in the data. And this last type is covered under the topic, \"Publishing via a semantic definition of data.\"","hasChildren":true,"hasParent":true,"name":"Resource Publishing","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB4-1","description":"Syntactic discovery is the discovery of resources based on the structure of the resources","hasChildren":true,"name":"Syntactic discovery","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB4-2","description":"Semantic discovery is the discovery of resources based on the meaning of the data.","hasChildren":true,"name":"Semantic discovery","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB4-3","description":"Linked (open) data provides structured data which is interlinked in a machine readable way. This allows to discover, access and combine data in an automatic way.","hasChildren":true,"name":"Discovery over linked open data","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB4","description":"Resource discovery means the discovery of resources including data and services needed for an application. Syntactic discovery refers to the discovery on the basis of syntactic comparison operations. It is classified as \"keyword-based\" and \"full-text-based\" discovery. Semantic discovery on the other hand, refers to the discovery of resources on he basis of some semantic definition. Therefore, semantic discovery requires that a resource be published by a semantic definition as defined in the topic WB3-5.","hasChildren":true,"hasParent":true,"name":"Resource Discovery","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB5-1","description":"The workflow to integrate geospatial data in an application often relies on a combination of different OGC web services. Searching and finding the data and the corresponding services, binding to these services to view, filtering and or downloading the data are different steps in this process","hasChildren":true,"name":"Integrating data from OGC web services","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB5-2","description":"The alignment of data structures and vocabularies/ontologies used are important steps towards the data harmonisation needed for a combined use of datasets","hasChildren":true,"name":"Schema matching and ontology alignment","selfAssesment":"In progress (GI-N2K)
"},{"code":"WB5-3","description":"A data mashup is a combination of data from different sources to produce new applications of new datasets","hasChildren":true,"name":"Data mash ups","selfAssesment":"In progress (GI-N2K)
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Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-60243-6_1","url":"https://link.springer.com/chapter/10.1007/978-3-642-60243-6_1"},{"concepts":[1124],"description":" ","name":"European Association of Remote Sensing Companies (EARSC), (2020). Forecast and assess landslides. Retrieved from: https://earsc-portal.eu/display/EOwiki/Forecast+and+assess+landslides","url":"https://earsc-portal.eu/display/EOwiki/Forecast+and+assess+landslides"},{"concepts":[1141],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Assess and monitor coastal water quality. Retrieved from https://earsc-portal.eu/display/EOwiki/Assess+and+monitor+coastal+water+quality","url":"https://earsc-portal.eu/display/EOwiki/Assess+and+monitor+coastal+water+quality"},{"concepts":[1125],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Assess and Monitor Volcanic Activity. Retrieved from: https://earsc-portal.eu/display/EOwiki/Assess+and+Monitor+Volcanic+Activity","url":"https://earsc-portal.eu/display/EOwiki/Assess+and+Monitor+Volcanic+Activity"},{"concepts":[1130],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Assess and monitor water bodies. Retrieved from https://earsc-portal.eu/display/EOwiki/Assess+and+monitor+water+bodies","url":"https://earsc-portal.eu/display/EOwiki/Assess+and+monitor+water+bodies"},{"concepts":[1113],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Assess changes in the carbon balance. Retrieved from https://earsc-portal.eu/display/EOwiki/Assess+changes+in+the+carbon+balance","url":"https://earsc-portal.eu/display/EOwiki/Assess+changes+in+the+carbon+balance"},{"concepts":[1128],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Assess crop damage due to storms. Retrieved from https://earsc-portal.eu/display/EOwiki/Assess+crop+damage+due+to+storms","url":"https://earsc-portal.eu/display/EOwiki/Assess+crop+damage+due+to+storms"},{"concepts":[1123],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Assess damage from earthquakes. Retrieved from https://earsc-portal.eu/display/EOwiki/Assess+damage+from+earthquakes","url":"https://earsc-portal.eu/display/EOwiki/Assess+damage+from+earthquakes"},{"concepts":[1129],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Assess Deforestation or Forest Degradation. Retrieved from https://earsc-portal.eu/display/EOwiki/Assess+Deforestation+or+Forest+Degradation","url":"https://earsc-portal.eu/display/EOwiki/Assess+Deforestation+or+Forest+Degradation"},{"concepts":[1128],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Assess Environmental impact of farming. Retrieved from https://earsc-portal.eu/display/EOwiki/Assess+Environmental+impact+of+farming","url":"https://earsc-portal.eu/display/EOwiki/Assess+Environmental+impact+of+farming"},{"concepts":[1129],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Assess environmental impact of forestry. Retrieved from https://earsc-portal.eu/display/EOwiki/Assess+environmental+impact+of+forestry","url":"https://earsc-portal.eu/display/EOwiki/Assess+environmental+impact+of+forestry"},{"concepts":[1132],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Assess environmental impact of human activities . Retrieved from https://earsc-portal.eu/display/EOwiki/Assess+environmental+impact+of+human+activities","url":"https://earsc-portal.eu/display/EOwiki/Assess+environmental+impact+of+human+activities"},{"concepts":[1129],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Assess forest damage due to storms or insects. Retrieved from https://earsc-portal.eu/display/EOwiki/Assess+forest+damage+due+to+storms+or+insects","url":"https://earsc-portal.eu/display/EOwiki/Assess+forest+damage+due+to+storms+or+insects"},{"concepts":[1130],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Assess ground water and run-off. Retrieved from https://earsc-portal.eu/display/EOwiki/Assess+ground+water+and+run-off","url":"https://earsc-portal.eu/display/EOwiki/Assess+ground+water+and+run-off"},{"concepts":[1133],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Assess land value, ownership, type use. Retrieved from https://earsc-portal.eu/display/EOwiki/Assess+land+value%2C+ownership%2C+type%2C+use","url":"https://earsc-portal.eu/display/EOwiki/Assess+land+value%2C+ownership%2C+type%2C+use"},{"concepts":[1133],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Assess pressures on populations and migration. Retrieved from https://earsc-portal.eu/display/EOwiki/Assess+pressures+on+populations+and+migration","url":"https://earsc-portal.eu/display/EOwiki/Assess+pressures+on+populations+and+migration"},{"concepts":[1134],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Baseline mapping. Retrieved from https://earsc-portal.eu/display/EOwiki/Baseline+mapping","url":"https://earsc-portal.eu/display/EOwiki/Baseline+mapping"},{"concepts":[1134],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Detect and monitor ground movement. Retrieved from https://earsc-portal.eu/display/EOwiki/Detect+and+monitor+ground+movement","url":"https://earsc-portal.eu/display/EOwiki/Detect+and+monitor+ground+movement"},{"concepts":[1142],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Detect and monitor hurricanes and typhoons. Retrieved from https://earsc-portal.eu/display/EOwiki/Detect+and+monitor+hurricanes+and+typhoons","url":"https://earsc-portal.eu/display/EOwiki/Detect+and+monitor+hurricanes+and+typhoons"},{"concepts":[1145],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Detect and monitor ice-risk at sea. Retrieved from https://earsc-portal.eu/display/EOwiki/Detect+and+monitor+ice-risk+at+sea","url":"https://earsc-portal.eu/display/EOwiki/Detect+and+monitor+ice-risk+at+sea"},{"concepts":[1143],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Detect and monitor illegal fishing. Retrieved from https://earsc-portal.eu/display/EOwiki/Detect+and+monitor+illegal+fishing","url":"https://earsc-portal.eu/display/EOwiki/Detect+and+monitor+illegal+fishing"},{"concepts":[1140],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Detect and monitor oil slicks. Retrieved from https://earsc-portal.eu/display/EOwiki/Detect+and+monitor+oil+slicks","url":"https://earsc-portal.eu/display/EOwiki/Detect+and+monitor+oil+slicks"},{"concepts":[1127,1122],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Detect and monitor wildfires. Retrieved from https://earsc-portal.eu/display/EOwiki/Detect+and+monitor+wildfires","url":"https://earsc-portal.eu/display/EOwiki/Detect+and+monitor+wildfires"},{"concepts":[1131],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Detect changes in glaciers. Retrieved from https://earsc-portal.eu/display/EOwiki/Detect+changes+in+glaciers","url":"https://earsc-portal.eu/display/EOwiki/Detect+changes+in+glaciers"},{"concepts":[1129],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Detect illegal forest activities. Retrieved from https://earsc-portal.eu/display/EOwiki/Detect+illegal+forest+activities","url":"https://earsc-portal.eu/display/EOwiki/Detect+illegal+forest+activities"},{"concepts":[1133],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Detect illegal mining activities . Retrieved from https://earsc-portal.eu/display/EOwiki/Detect+illegal+mining+activities","url":"https://earsc-portal.eu/display/EOwiki/Detect+illegal+mining+activities"},{"concepts":[1128],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Detect illegal or undesired crops. Retrieved from https://earsc-portal.eu/display/EOwiki/Detect+illegal+or+undesired+crops","url":"https://earsc-portal.eu/display/EOwiki/Detect+illegal+or+undesired+crops"},{"concepts":[1144],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Detect ships in critical areas. Retrieved from https://earsc-portal.eu/display/EOwiki/Detect+ships+in+critical+areas","url":"https://earsc-portal.eu/display/EOwiki/Detect+ships+in+critical+areas"},{"concepts":[1064,1100,1068,1065,1066,1067,1072,1070,1071,1079,1073,1074,1075,1076,1077,1078,1084,1080,1081,1082,1083,1087,1085,1086,1091,1088,1089,1098,1092,1095,1093,1094,1099,1096,1097,1147,1120,1090,1117,1118,1119],"description":" ","name":"European Association of Remote Sensing Companies. (2020). EO Services (Markets). Retrieved from https://earsc-portal.eu/pages/viewpage.action?pageId=78221916","url":"https://earsc-portal.eu/pages/viewpage.action?pageId=78221916"},{"concepts":[1142],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Forecast and map large waves. Retrieved from https://earsc-portal.eu/display/EOwiki/Forecast+and+map+large+waves","url":"https://earsc-portal.eu/display/EOwiki/Forecast+and+map+large+waves"},{"concepts":[1069,1142],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Forecast and monitor current movement and drift. Retrieved from https://earsc-portal.eu/display/EOwiki/Forecast+and+monitor+current+movement+and+drift","url":"https://earsc-portal.eu/display/EOwiki/Forecast+and+monitor+current+movement+and+drift"},{"concepts":[1142],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Forecast and monitor ocean winds and waves. Retrieved from https://earsc-portal.eu/display/EOwiki/Forecast+and+monitor+ocean+winds+and+waves","url":"https://earsc-portal.eu/display/EOwiki/Forecast+and+monitor+ocean+winds+and+waves"},{"concepts":[1128],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Forecast crop yields. Retrieved from https://earsc-portal.eu/display/EOwiki/Forecast+crop+yields","url":"https://earsc-portal.eu/display/EOwiki/Forecast+crop+yields"},{"concepts":[1114],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Forecast weather. Retrieved from https://earsc-portal.eu/display/EOwiki/Forecast+weather","url":"https://earsc-portal.eu/display/EOwiki/Forecast+weather"},{"concepts":[1112],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Forecasting sunlight exposure. Retrieved from https://earsc-portal.eu/display/EOwiki/Forecasting+sunlight+exposure","url":"https://earsc-portal.eu/display/EOwiki/Forecasting+sunlight+exposure"},{"concepts":[1135],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Identify hydrocarbon seeps in soil. Retrieved from https://earsc-portal.eu/display/EOwiki/Identify+hydrocarbon+seeps+in+soil","url":"https://earsc-portal.eu/display/EOwiki/Identify+hydrocarbon+seeps+in+soil"},{"concepts":[1127,1121],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Map and assess flooding. Retrieved from https://earsc-portal.eu/display/EOwiki/Map+and+assess+flooding","url":"https://earsc-portal.eu/display/EOwiki/Map+and+assess+flooding"},{"concepts":[1069],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Map and monitor hydroelectric energy. Retrieved from https://earsc-portal.eu/display/EOwiki/Map+and+monitor+hydroelectric+energy","url":"https://earsc-portal.eu/display/EOwiki/Map+and+monitor+hydroelectric+energy"},{"concepts":[1069],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Map and monitor solar energy (solar farms). Retrieved from https://earsc-portal.eu/pages/viewpage.action?pageId=78221967","url":"https://earsc-portal.eu/pages/viewpage.action?pageId=78221967"},{"concepts":[1069],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Map and monitor wind energy (wind farms). Retrieved from https://earsc-portal.eu/pages/viewpage.action?pageId=78221973","url":"https://earsc-portal.eu/pages/viewpage.action?pageId=78221973"},{"concepts":[1143],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Map fish shoals. Retrieved from https://earsc-portal.eu/display/EOwiki/Map+fish+shoals","url":"https://earsc-portal.eu/display/EOwiki/Map+fish+shoals"},{"concepts":[1135],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Map geological features. Retrieved from https://earsc-portal.eu/display/EOwiki/Map+geological+features","url":"https://earsc-portal.eu/display/EOwiki/Map+geological+features"},{"concepts":[1135],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Map seismic survey operations. Retrieved from https://earsc-portal.eu/display/EOwiki/Map+seismic+survey+operations","url":"https://earsc-portal.eu/display/EOwiki/Map+seismic+survey+operations"},{"concepts":[1141],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Map water depth or charting. Retrieved from https://earsc-portal.eu/display/EOwiki/Map+water+depth+or+charting","url":"https://earsc-portal.eu/display/EOwiki/Map+water+depth+or+charting"},{"concepts":[1134],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Measure & detect land surface change. Retrieved from https://earsc-portal.eu/display/EOwiki/Measure+detect+land+surface+change","url":"https://earsc-portal.eu/display/EOwiki/Measure+detect+land+surface+change"},{"concepts":[1133],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Measure land use statistics. Retrieved from https://earsc-portal.eu/display/EOwiki/Measure+land+use+statistics","url":"https://earsc-portal.eu/display/EOwiki/Measure+land+use+statistics"},{"concepts":[1112],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor air quality & emissions. Retrieved from https://earsc-portal.eu/pages/viewpage.action?pageId=78221935","url":"https://earsc-portal.eu/pages/viewpage.action?pageId=78221935"},{"concepts":[1141],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor coastal ecosystem. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+coastal+ecosystem","url":"https://earsc-portal.eu/display/EOwiki/Monitor+coastal+ecosystem"},{"concepts":[1139,1138],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor construction and buildings. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+construction+and+buildings","url":"https://earsc-portal.eu/display/EOwiki/Monitor+construction+and+buildings"},{"concepts":[1129],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor forest carbon content. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+forest+carbon+content","url":"https://earsc-portal.eu/display/EOwiki/Monitor+forest+carbon+content"},{"concepts":[1129],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor forest resources. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+forest+resources","url":"https://earsc-portal.eu/display/EOwiki/Monitor+forest+resources"},{"concepts":[1133],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor humanitarian movement and camps. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+humanitarian+movement+and+camps","url":"https://earsc-portal.eu/display/EOwiki/Monitor+humanitarian+movement+and+camps"},{"concepts":[1131],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor ice on rivers and lakes. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+ice+on+rivers+and+lakes","url":"https://earsc-portal.eu/display/EOwiki/Monitor+ice+on+rivers+and+lakes"},{"concepts":[1132],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor land cover and detect change. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+land+cover+and+detect+change","url":"https://earsc-portal.eu/display/EOwiki/Monitor+land+cover+and+detect+change"},{"concepts":[1132],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor land ecosystems and biodiversity. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+land+ecosystems+and+biodiversity","url":"https://earsc-portal.eu/display/EOwiki/Monitor+land+ecosystems+and+biodiversity"},{"concepts":[1132],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor land pollution. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+land+pollution","url":"https://earsc-portal.eu/display/EOwiki/Monitor+land+pollution"},{"concepts":[1140],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor marine habitats. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+marine+habitats","url":"https://earsc-portal.eu/display/EOwiki/Monitor+marine+habitats"},{"concepts":[1135],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor mineral extraction. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+mineral+extraction","url":"https://earsc-portal.eu/display/EOwiki/Monitor+mineral+extraction"},{"concepts":[1141],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor ocean level and surface. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+ocean+level+and+surface","url":"https://earsc-portal.eu/display/EOwiki/Monitor+ocean+level+and+surface"},{"concepts":[1140],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor ocean quality and productivity. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+ocean+quality+and+productivity","url":"https://earsc-portal.eu/display/EOwiki/Monitor+ocean+quality+and+productivity"},{"concepts":[1140],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor oil rigs and flares. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+oil+rigs+and+flares","url":"https://earsc-portal.eu/display/EOwiki/Monitor+oil+rigs+and+flares"},{"concepts":[1140],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor pollution at sea. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+pollution+at+sea","url":"https://earsc-portal.eu/display/EOwiki/Monitor+pollution+at+sea"},{"concepts":[1116],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor sensitive risk areas. Retrieved from: https://earsc-portal.eu/display/EOwiki/Monitor+sensitive+risk+areas","url":"https://earsc-portal.eu/display/EOwiki/Monitor+sensitive+risk+areas"},{"concepts":[1144],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor ships movements. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+ships+movements","url":"https://earsc-portal.eu/display/EOwiki/Monitor+ships+movements"},{"concepts":[1131],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor snow cover. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+snow+cover","url":"https://earsc-portal.eu/display/EOwiki/Monitor+snow+cover"},{"concepts":[1141],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor the coast line. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+the+coast+line","url":"https://earsc-portal.eu/display/EOwiki/Monitor+the+coast+line"},{"concepts":[1139,1137],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor urban areas. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+urban+areas","url":"https://earsc-portal.eu/display/EOwiki/Monitor+urban+areas"},{"concepts":[1133],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor vegetation encroachment. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+vegetation+encroachment","url":"https://earsc-portal.eu/display/EOwiki/Monitor+vegetation+encroachment"},{"concepts":[1128],"description":" ","name":"European Association of Remote Sensing Companies. (2020). Monitor water use on crops and horticulture. Retrieved from https://earsc-portal.eu/display/EOwiki/Monitor+water+use+on+crops+and+horticulture","url":"https://earsc-portal.eu/display/EOwiki/Monitor+water+use+on+crops+and+horticulture"},{"concepts":[1112],"description":" ","name":"European Association of Remote Sensing Companies. (n.d.). Product sheet: Air Quality CO2. Retrieved from https://earsc-portal.eu/display/EO4RawMaterials/Product+Sheet%3A+Air+Quality+CO2","url":"https://earsc-portal.eu/display/EO4RawMaterials/Product+Sheet%3A+Air+Quality+CO2"},{"concepts":[1145],"description":" ","name":"European Centre for Medium-Range Weather Forecasts, & Copernicus Programme. (2020). Global Shipping Project - Copernicus. Retrieved from https://climate.copernicus.eu/index.php/global-shipping-project","url":"https://climate.copernicus.eu/index.php/global-shipping-project"},{"concepts":[1112],"description":" ","name":"European Comission. (2015). An Operational Anthropogenic CO₂ Emissions Monitoring & Verification Support Capacity.","url":"https://www.copernicus.eu/sites/default/files/2019-09/CO2_Blue_report_2015.pdf"},{"concepts":[1112],"description":" ","name":"European Comission. (2017). An Operational Anthropogenic CO₂ Emissions Monitoring & Verification Support Capacity.","url":"https://www.copernicus.eu/sites/default/files/2019-09/CO2_Red_Report_2017.pdf"},{"concepts":[1112],"description":" ","name":"European Comission. (2019). An Operational Anthropogenic CO₂ Emissions Monitoring & Verification Support Capacity.","url":"https://www.copernicus.eu/sites/default/files/2019-09/CO2_Green_Report_2019.pdf"},{"concepts":[1143],"description":" ","name":"European Comission. (n.d.). Managing fisheries. Retrieved from: https://ec.europa.eu/fisheries/cfp/fishing_rules_en","url":"https://ec.europa.eu/fisheries/cfp/fishing_rules_en"},{"concepts":[1064,1111],"description":" ","name":"European Commision. (n.d.). Societal Challenges. Retrieved from: https://ec.europa.eu/programmes/horizon2020/en/h2020-section/societal-challenges","url":"https://ec.europa.eu/programmes/horizon2020/en/h2020-section/societal-challenges"},{"concepts":[1132],"description":" ","name":"European Commission Joint Research Centre. (2020). Vegetation - Copernicus landm monitoring service. Retrieved from https://land.copernicus.eu/global/themes/Vegetation","url":"https://land.copernicus.eu/global/themes/Vegetation"},{"concepts":[1104],"description":" ","name":"European Commission. (2020). Digital skills and jobs - Shaping Europe's digital future. Retrived from https://ec.europa.eu/digital-single-market/en/policies/digital-skills","url":"https://ec.europa.eu/digital-single-market/en/policies/digital-skills"},{"concepts":[1104],"description":" ","name":"European Commission. (2020). Employment, Social Affairs & Inclusion. Retrived from https://ec.europa.eu/social/main.jsp?catId=1223","url":"https://ec.europa.eu/social/main.jsp?catId=1223"},{"concepts":[436],"description":" ","name":"European Commission. (2020). INSPIRE Knowledge base - Infrastructure for spatial information in Europe - Data Harmonisation. Retrieved from https://inspire.ec.europa.eu/training/data-harmonisation","url":"https://inspire.ec.europa.eu/training/data-harmonisation"},{"concepts":[1108],"description":" ","name":"European Commission. (2020). Overview - Public health. Retrieved from https://ec.europa.eu/health/communicable_diseases/overview_en","url":"https://ec.europa.eu/health/communicable_diseases/overview_en"},{"concepts":[1110],"description":" ","name":"European Commission. (2020). Sustainability of the water resource. Retrieved from https://ec.europa.eu/info/news/sustainability-at-the-water-source_en","url":"https://ec.europa.eu/info/news/sustainability-at-the-water-source_en"},{"concepts":[1106],"description":" ","name":"European Commission. (2020). Sustainable agriculture in the CAP. Retrieved from https://ec.europa.eu/info/food-farming-fisheries/sustainability/sustainable-cap_en","url":"https://ec.europa.eu/info/food-farming-fisheries/sustainability/sustainable-cap_en"},{"concepts":[1107],"description":" ","name":"European Commission. (2020). Transport. Retrieved from https://ec.europa.eu/info/policies/transport_en","url":"https://ec.europa.eu/info/policies/transport_en"},{"concepts":[1146],"description":" ","name":"European Environment Agency. (2016). Monitoring of marine waters. Retrieved from: https://www.eea.europa.eu/publications/92-9167-001-4/page024.html","url":"https://www.eea.europa.eu/publications/92-9167-001-4/page024.html"},{"concepts":[1101],"description":" ","name":"European Environmental Agency, (2019). Climate Change Adaption. Retrieved from: https://www.eea.europa.eu/themes/climate-change-adaptation/intro.","url":"https://www.eea.europa.eu/themes/climate-change-adaptation/intro"},{"concepts":[1101],"description":" ","name":"European Environmental Agency, (2019). Climate Change Mitigation. Retrieved from: https://www.eea.europa.eu/themes/climate/intro.","url":"https://www.eea.europa.eu/themes/climate/intro"},{"concepts":[1103],"description":" ","name":"European Environmental Agency. (2008). Biodiversity - Ecosystems. Retrieved from https://www.eea.europa.eu/themes/biodiversity/intro","url":"https://www.eea.europa.eu/themes/biodiversity/intro"},{"concepts":[1109],"description":" ","name":"European External Action Service. (2020). 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knowledge domains of GI Science and Earth observation."},{"concepts":[94],"name":"Collaborate effectively with colleagues of differing social backgrounds in developing balanced GIS applications"},{"concepts":[101],"name":"Collaborate with non-GIS experts who use GIS to design applications that match common-sense understanding to an appropriate degree"},{"concepts":[1151],"name":"Combine different bands to calculate NDVI"},{"concepts":[374],"name":"Compare and contrast and contrast the relationship of the geospatial profession and the U.S. legal regime with similar relationships in other countries"},{"concepts":[34],"name":"Compare and contrast attribute query and spatial query"},{"concepts":[68],"name":"Compare and contrast Bayesian methods and classical frequentist statistical methods"},{"concepts":[73],"name":"Compare and contrast co-kriging log-normal kriging, disjunctive kriging, indicator kriging, factorial kriging and universal kriging"},{"concepts":[19],"name":"Compare and contrast covering, dispersion, and p-median models"},{"concepts":[38],"name":"Compare and contrast different shape indices, include examples of applications to which each could be applied"},{"concepts":[108],"name":"Compare and contrast differing epistemological and metaphysical viewpoints on the reality of geographic entities"},{"concepts":[376],"name":"Compare and contrast geographic information technologies that are privacy-invasive, privacy-enhancing, and privacy-sympathetic"},{"concepts":[66],"name":"Compare and contrast global and local statistics and their uses"},{"concepts":[78],"name":"Compare and contrast GWR with universal kriging using moving neighborhoods"},{"concepts":[37],"name":"Compare and contrast how direction is determined and stated in raster and vector data"},{"concepts":[57],"name":"Compare and contrast interpolation by inverse distance weighting, bi-cubic spline fitting and kriging"},{"concepts":[103],"name":"Compare and contrast models of a given spatial process using continuous and discrete perspectives of time"},{"concepts":[377],"name":"Compare and contrast National, European policy regarding rights to geospatial data with similar policies in other countries"},{"concepts":[2],"name":"Compare and contrast spatial statistical analysis, spatial data analysis, and spatial modeling"},{"concepts":[2],"name":"Compare and contrast spatial statistics and map algebra as two very different kinds of data analysis"},{"concepts":[81],"name":"Compare and contrast the ability of different theories to explain various situations"},{"concepts":[83],"name":"Compare and contrast the ability of various theories to explain different situations"},{"concepts":[104],"name":"Compare and contrast the characteristics of spatial and temporal dimensions"},{"concepts":[45],"name":"Compare and contrast the concept of overlay as it is implemented in raster and vector domains"},{"concepts":[110],"name":"Compare and contrast the concepts of continuants (entities) and occurrents (events)"},{"concepts":[16],"name":"Compare and contrast the concepts of discrete location problems and continuous location problems"},{"concepts":[110],"name":"Compare and contrast the concepts of event and process"},{"concepts":[377],"name":"Compare and contrast the consequences of different national policies about rights to geospatial data in terms of the real costs of spatial data, their coverage, accuracy, uncertainty, reliability, validity, and maintenance"},{"concepts":[394],"name":"Compare and contrast the ethical guidelines promoted by the GIS Certification Institute (GISCI) and the American Society for Photogrammetry and Remote Sensing (ASPRS)"},{"concepts":[585],"name":"Compare and contrast the impact effect of time for developing consensus-based standards with immediate operational needs"},{"concepts":[21],"name":"Compare and contrast the impacts of different conversion approaches, including the effect on spatial components"},{"concepts":[85],"name":"Compare and contrast the kinds of questions various philosophies ask, the methodologies they use, the answers they offer, and their applicability to different phenomena"},{"concepts":[121],"name":"Compare and contrast the meanings of related terms such as vague, fuzzy, imprecise, indefinite, indiscrete, unclear, and ambiguous"},{"concepts":[2],"name":"Compare and contrast the methods of analyzing aggregate data as opposed to methods of analyzing a set of individual observations"},{"concepts":[595],"name":"Compare and contrast the missions, histories, constituencies, and activities of professional organizations including Association of American Geographers (AAG), America Society for Photogrammetry and Remote Sensing (ASPRS) ..."},{"concepts":[113],"name":"Compare and contrast the opportunities and pitfalls of using regions to aggregate geographic information (e.g., census data)"},{"concepts":[5],"name":"Compare and contrast the primary types of data mining: summarization/characterization, clustering/categorization, feature extraction, and rule/relationships extraction"},{"concepts":[156,157],"name":"Compare and contrast the quality of product evaluation that can be made from process proofs and color laser prints"},{"concepts":[211],"name":"Compare and contrast the raster with other types of regular tessellations for geographic data analysis"},{"concepts":[211],"name":"Compare and contrast the raster with other types of regular tessellations for geographic data storage"},{"concepts":[88,96],"name":"Compare and contrast the symbolic and connectionist theories of human cognition and memory and their ability to model various cases"},{"concepts":[54],"name":"Compare and contrast the terms multi-criteria evaluation, weighted linear combination, and site suitability analysis"},{"concepts":[106],"name":"Compare and contrast the theory that properties are fundamental (and objects are human simplifications of patterns thereof) with the theory that objects are fundamental (and properties are attributes thereof)"},{"concepts":[88],"name":"Compare and contrast theories of spatial knowledge acquisition (e.g., Marr on vision, Piaget on childhood, Golledge on wayfinding)"},{"concepts":[575],"name":"Compare and contrast training methods utilized in a non-profit to those employed in a local government agency"},{"concepts":[705],"name":"Compare and discuss attenuation length and penetration depth of the optical and radar signal"},{"concepts":[799,801,804,800],"name":"Compare and discuss different SAR acquisition modes"},{"concepts":[588],"name":"Compare and explain different models for funding an SDI"},{"concepts":[381],"name":"Compare and explain the main business models in the GI domain"},{"concepts":[72],"name":"Compare block-kriging with areal interpolation using proportional area weighting and dasymetric mapping"},{"concepts":[311],"name":"Compare common sensors by spatial resolution, spectral sensitivity, ground coverage, and temporal resolution [e.g., AVHRR, MODIS (intermediate resolution ~500 m, high temporal) Landsat, commercial high resolution (Ikonos and Quickbird); ..."},{"concepts":[240],"name":"Compare commonalities and patterns of geocomputation to other related terms"},{"concepts":[14],"name":"Compare current accessibility models with early models of market potential"},{"concepts":[483],"name":"Compare different deep learning approaches in EO image classification"},{"concepts":[248],"name":"Compare different design choices in developing spatial simulation models"},{"concepts":[1211],"name":"compare different development components and their advantages and disadvantages"},{"concepts":[531],"name":"Compare different error metrics that are based on the error matrix"},{"concepts":[164],"name":"Compare different evaluation methods for cartography and visualization products (e.g., qualitative versus quantitative, formative versus summative studies)."},{"concepts":[589],"name":"Compare different frameworks for assessing Spatial Data Infrastructures"},{"concepts":[1187],"name":"Compare different Geospatial object and geometry definitions included under this topic"},{"concepts":[245],"name":"Compare different options of combining space-time dynamics approaches in spatial modelling"},{"concepts":[438],"name":"Compare different strategies of data assimilation"},{"concepts":[177],"name":"Compare geospatial software architecture through cost-analysis framework"},{"concepts":[1131],"name":"Compare glacier extents using EO data"},{"concepts":[1115,1112,1113],"name":"Compare human-induced emissions to natural sources"},{"concepts":[1199],"name":"Compare Linked geospatial data to SDI approaches"},{"concepts":[69],"name":"Compare methods of spatial statistical analysis for the testing of hypotheses."},{"concepts":[20],"name":"Compare models and software tools that allow for optimization"},{"concepts":[1124],"name":"Compare one optical EO method with a SAR method for landslide mapping and explain their differences"},{"concepts":[516],"name":"Compare pixel-based image classification methods with object-based techniques"},{"concepts":[812],"name":"Compare reflectance measurements from the field to reflectance values in radiometrically pre-processed EO data"},{"concepts":[120],"name":"Compare relationships between entities, between attributes and between locations."},{"concepts":[502],"name":"Compare results of the Laplacian of Gaussian filter to the original input image"},{"concepts":[389],"name":"Compare the advantages and disadvantages of group participation and individual participation"},{"concepts":[48],"name":"Compare the basic analytical operations of different GISs."},{"concepts":[322],"name":"Compare the concepts of geometric accuracy and topological fidelity"},{"concepts":[302],"name":"Compare the different cultures of Open Science"},{"concepts":[64],"name":"Compare the different types of spatial weight matrices"},{"concepts":[1140],"name":"Compare the main satellite sensors used in marine ecosystem monitoring"},{"concepts":[322],"name":"Compare the National Map Accuracy Standard with the ASPRS Coordinate Standard"},{"concepts":[137],"name":"Compare the relative merits of having map labels placed dynamically versus having them saved as annotation data"},{"concepts":[24],"name":"Compare the result of conversion vector/raster or raster/vector and examine the impact of conversion on the quality of the dataset"},{"concepts":[166],"name":"Compile the needs of individual users and tasks into enterprise-wide needs"},{"concepts":[563],"name":"Compute descriptive statistics and geostatistics of geographic data"},{"concepts":[49],"name":"Compute measures of overall dispersion and clustering of point datasets using nearest neighbor distance statistics"},{"concepts":[65],"name":"Compute measures of overall dispersion and clustering of point datasets using nearest neighbor distance statistics"},{"concepts":[65],"name":"Compute Morans I and Gearys c for patterns of attribute data measured on interval ratio scales"},{"concepts":[9],"name":"Compute the alpha, beta, and gamma indices of network connectivity"},{"concepts":[9],"name":"Compute the Detour Index and the measure of network density for a given network"},{"concepts":[9],"name":"Compute the estimated number of fundamental cycles in a graph"},{"concepts":[66],"name":"Compute the Gi and Gi* statistics"},{"concepts":[65],"name":"Compute the K function"},{"concepts":[611],"name":"Compute the maximum average roughness of a mirror for incident radiation in the microwaves spectral range"},{"concepts":[611],"name":"Compute the maximum average roughness of a mirror for incident radiation in the visible spectral range"},{"concepts":[37],"name":"Compute the mean of directional data"},{"concepts":[611],"name":"Compute the minimum average roughness of a surface operating as a diffuser of incident radiation in the visible spectral range"},{"concepts":[10],"name":"Compute the optimum path between two points through a network with Dijkstras algorithm"},{"concepts":[53],"name":"Conduct a simple hierarchical cluster analysis to classify area objects into statistically similar regions"},{"concepts":[76],"name":"Conduct a spatial econometric analysis to test for spatial dependence in the residuals from least-squares models and spatial autoregressive models"},{"concepts":[72],"name":"Conduct a spatial interpolation process using kriging from data description to final error map"},{"concepts":[159],"name":"Construct a new map from an existing one with a biased view"},{"concepts":[34],"name":"Construct a query statement to search for a specific spatial or temporal relationship"},{"concepts":[71],"name":"Construct a semi-variogram and illustrate with a semi-variogram cloud"},{"concepts":[34],"name":"Construct a spatial query to extract all point objects that fall within a polygon"},{"concepts":[64],"name":"Construct a spatial weights matrix for lattice, point, and area patterns"},{"concepts":[214],"name":"Construct a TIN manually from a set of spot elevations"},{"concepts":[149],"name":"Construct a Web page that includes an interactive map"},{"concepts":[716],"name":"Construct scattering matrix"},{"concepts":[105],"name":"Construct taxonomies and dictionaries (also known as formal ontologies) to communicate systems of categories"},{"concepts":[14],"name":"Contrast accessibility modeling at the individual level versus at an aggregated level"},{"concepts":[182],"name":"Contrast cloud and grid computing technologies"},{"concepts":[134],"name":"Contrast gaming elements which are both part of traditional games and geo-games"},{"concepts":[137],"name":"Contrast the strengths and limitations of methods for automatic label placement"},{"concepts":[22],"name":"Convert a dataset from the native format of one GIS product to another"},{"concepts":[127],"name":"Convert historical maps in digital format"},{"concepts":[419],"name":"Convert multispectral image into its principal components"},{"concepts":[24],"name":"Convert vector data to raster format and back using GIS software"},{"concepts":[24],"name":"Convert vector data to raster format and back using the GIS software"},{"concepts":[128],"name":"Correlate map making methods with technological or societal factors across History"},{"concepts":[174],"name":"Create a budget of expected labor costs, including salaries, benefits, training, and other expenses"},{"concepts":[188],"name":"Create a complete design document ready for implementation"},{"concepts":[153],"name":"Create a concept map that represents the contents and topology of a physical or social process"},{"concepts":[504],"name":"Create a convolution filter that integrates the standard deviation of the entire scene in its weights"},{"concepts":[552],"name":"Create a data cube using the data model of the Open data cube initiative"},{"concepts":[8],"name":"Create a data set with network attributes and topology"},{"concepts":[186],"name":"Create a diagram of a conceptual data model for a geospatial application or enterprise database"},{"concepts":[21,130],"name":"Create a flowchart showing the sequence of transformations on a data set (e.g., geometric and radiometric correction and mosaicking of remotely sensed data)"},{"concepts":[147],"name":"Create a map that displays related variables using different mapping methods (e.g., choropleth and proportional symbol, choropleth and cartogram)"},{"concepts":[147],"name":"Create a map that displays related variables using the same mapping method (e.g., bivariate choropleth map, bivariate dot map)"},{"concepts":[146],"name":"Create a map that represents both slope and aspect on the same map using the Moellering-Kimerling coloring method"},{"concepts":[41],"name":"Create a matrix describing the pattern of adjacency in a set of planar enforced polygons"},{"concepts":[52],"name":"Create a matrix that shows spatial interaction"},{"concepts":[1203],"name":"Create a new application by combining existing data from different sources"},{"concepts":[158],"name":"Create a project plan for a map, from planning to finalisation"},{"concepts":[525],"name":"Create a protocol for quality assessment of an EO information product that conforms to EO4GEO guidelines"},{"concepts":[153],"name":"Create a pseudo-topographic surface to portray the relationships in a collection of documents"},{"concepts":[1208],"name":"Create a sample HTML5 Web page"},{"concepts":[522],"name":"Create a scale space for an image by applying multiple iterations of low-pass filtering"},{"concepts":[411,414],"name":"Create a set of ground control points tying image coordinates to map coordinates of a reference dataset using a digital reference dataset or in-situ GPS measurements"},{"concepts":[148],"name":"Create a temporal sequence representing a dynamic geospatial process"},{"concepts":[167],"name":"Create a user manual to help users understand a process or task"},{"concepts":[558],"name":"Create a web interface and related system architecture that enables image processing by using OGC interfaces"},{"concepts":[226],"name":"Create an adjacency table from a sample network"},{"concepts":[135],"name":"Create an aesthetic map icon library"},{"concepts":[226],"name":"Create an incidence matrix from a sample network"},{"concepts":[437],"name":"Create an integrated population distribution map from census data and EO-based land use classification"},{"concepts":[33],"name":"Create an SQL query to retrieve elements from a GIS"},{"concepts":[185],"name":"Create conceptual, logical, and physical data models using automated software tools"},{"concepts":[50],"name":"Create density maps from point datasets using kernels and density estimation techniques using standard software"},{"concepts":[133],"name":"Create different map layouts using the same map components (main map area, inset maps, titles, legends, scale bars, north arrows, grids and graticule) to produce maps with very distinctive purposes"},{"concepts":[133],"name":"Create different maps using the same data for different purposes and intended audiences (e.g., expert and novice hikers)"},{"concepts":[143],"name":"Create different visual hierarchies to produce maps with different purposes"},{"concepts":[24],"name":"Create estimated tessellated data sets from point samples or isolines using interpolation operations that are appropriate to the specific situation"},{"concepts":[473],"name":"Create feature space visualisations for a multispectral image"},{"concepts":[54],"name":"Create initial weights using the analytical hierarchy process (AHP)"},{"concepts":[187],"name":"Create logical models based on conceptual models using UML or other tools"},{"concepts":[144],"name":"Create maps using each of the following methods: choropleth, dasymetric, proportioned symbol, graduated symbol, isoline, dot, cartogram, and flow map"},{"concepts":[1178],"name":"Create new EO products out of raw data or other products"},{"concepts":[105],"name":"Create or use GIS data structures to represent categories, including attribute columns, layers themes, shapes, legends, etc."},{"concepts":[174],"name":"Create proposals and presentations to secure funding"},{"concepts":[70],"name":"Create spatial samples under a variety of requirements, such as coverage, randomness, transects"},{"concepts":[159],"name":"Create two versions of the same map addressed to different targets"},{"concepts":[188],"name":"Create UML diagrams of physical models based on logical model diagrams and software requirements"},{"concepts":[144],"name":"Create well-designed legends using the appropriate conventions for the following methods: choropleth, dasymetric, proportioned symbol, graduated symbol, isoline, dot, cartogram, and flow map"},{"concepts":[143],"name":"Critique the graphic design of several maps in terms of balance, legibility, clarity, visual contrast, figure-ground organization, and hierarchal organization"},{"concepts":[149],"name":"Critique the interactive elements of an online map"},{"concepts":[150],"name":"Critique the user interface for existing Internet mapping services"},{"concepts":[229],"name":"Deal with time aspects in modelling data"},{"concepts":[228],"name":"Deal with uncertainty aspects in modelling data"},{"concepts":[1171,1170],"name":"Decide on urban planning measures on the basis of a semantic 3D model"},{"concepts":[31],"name":"Decide which generalisation technique (aggregation, selection, etc.) is best for a specific situation of reducing map scale."},{"concepts":[141],"name":"Decide which graphical representation better reflects the messages embedded in your story"},{"concepts":[66],"name":"Decompose Morans I and Gearys c into local measures of spatial association"},{"concepts":[186],"name":"Deconstruct an application use case into its conceptual elements"},{"concepts":[402],"name":"Defend or refute the contention that critical studies have an identifiable influence on the development of the information society in general and GIScience in particular"},{"concepts":[401],"name":"Defend or refute the contention that the masculinist culture of computer work in general, and GIS work in particular, perpetuates gender inequality in GIS and T education and training and occupational segregation in the GIS and T workforce"},{"concepts":[28],"name":"Defend or refute the statement \"GIS data are scaleless\""},{"concepts":[85],"name":"Defend or refute the statement, All data are theory-laden"},{"concepts":[109],"name":"Define a field in terms of properties, space, and time"},{"concepts":[166],"name":"Define a methodology for gathering of requirements"},{"concepts":[233],"name":"Define a set of rules for modeling changes in spatial databases"},{"concepts":[223],"name":"Define and describe an application schema"},{"concepts":[391],"name":"Define and discuss enabling technologies: geotag, georeferencing, GPS and more"},{"concepts":[238],"name":"Define and discuss opportunities and limitations of computational science"},{"concepts":[391],"name":"Define and discuss volunteered geographic information"},{"concepts":[391],"name":"Define and discussing impact of Crowdsourcing on Geospatial Society"},{"concepts":[1188],"name":"Define and exemplify the reuse of ontologies - Define and identify the role of ontology patterns"},{"concepts":[1184],"name":"Define and practice the usage, in a given use case, of StyledLayerDescriptor (SLD) and Symbology Encoding (SE). Practice their usage in a given use case"},{"concepts":[389],"name":"Define and understand citizenship, democracy, maturity, and negotiation related to geo information use and participation in society /community development (at local, regional, national level)"},{"concepts":[33],"name":"Define basic terms of query processing e.g., SQL, primary and foreign keys, table join"},{"concepts":[211],"name":"Define basic terms used in the raster data model (e.g., cell, row, column, value)"},{"concepts":[179,1183],"name":"Define characteristics of REST Web services and Resource oriented Architecture (ROA)"},{"concepts":[85],"name":"Define common philosophical theories that have influenced geography and science, such as logical positivism, Marxism, phenomenology, feminism, and critical theory"},{"concepts":[83],"name":"Define common theories on what constitutes knowledge, including positivism, reflectance-correspondence, pragmatism, social constructivism, and memetics"},{"concepts":[81],"name":"Define common theories on what is real, such as realism, idealism, relativism, and experiential realism"},{"concepts":[8],"name":"Define different interpretations of cost in various routing applications"},{"concepts":[37],"name":"Define direction and its measurement in different angular measures"},{"concepts":[186],"name":"Define entities and relationships in conceptual data model"},{"concepts":[60],"name":"Define friction surface"},{"concepts":[1187],"name":"Define GeoJSON definition of Geospatial objects and describe the structure of a GeoJSON document and identify advantages and disadvantages of representing the same geospatial data in GML and in GeoJSON"},{"concepts":[59],"name":"Define intervisibility"},{"concepts":[1194],"name":"Define Mapping between legacy definition and the semantic definition of publish"},{"concepts":[1190],"name":"Define metadata and identify metadata standards like ISO 19115 and 19119 describe their metadata schema generally"},{"concepts":[1187],"name":"Define OGC Simple Features Access Schema. Well-Known Text (WKT) and Well-Known Binary (WKB) representations of Geometry"},{"concepts":[68],"name":"Define prior and posterior distributions and Markov-Chain Monte Carlo"},{"concepts":[1186],"name":"Define Resource Description Framework (RDF), its RDF graphs, RDF Schema (RDF-S)and a data set in RDF"},{"concepts":[1186],"name":"Define Semantic Web and identify the role of the languages included under this topic for Semantic Web"},{"concepts":[179,1181],"name":"Define Service Oriented Architecture (SOA) and identify main elements of it"},{"concepts":[119],"name":"Define spatial autocorrelation in the context of geographic proximity"},{"concepts":[1187],"name":"Define spatial extensions that GeoSPARQL brings over SPARQL. Identify the difference between qualitative spatial reasoning and quantitative spatial computations"},{"concepts":[106],"name":"Define Stevens four levels of measurement (nominal, ordinal, interval, ratio)"},{"concepts":[222],"name":"Define terms related to topology (e.g., adjacency, connectivity, overlap, intersect, logical consistency)"},{"concepts":[187],"name":"Define the cardinality of relationships"},{"concepts":[179,180,1181],"name":"Define the characteristics of web services and present some examples"},{"concepts":[1186],"name":"Define the components of a Web Services Description Language (WSDL) document"},{"concepts":[8],"name":"Define the following terms pertaining to a network: Loops, multiple edges, the degree of a vertex, walk, trail, path, cycle, fundamental cycle"},{"concepts":[226],"name":"Define the following terms pertaining to a network: Loops, multiple edges, the degree of a vertex, walk, trail, path, cycle, fundamental cycle"},{"concepts":[90],"name":"Define the following terms: data, information, knowledge, and wisdom"},{"concepts":[97],"name":"Define the four basic dimensions or shapes used to describe spatial objects (i.e., points, lines, regions, volumes)"},{"concepts":[93],"name":"Define the notions of cultural landscape and physical landscape"},{"concepts":[119],"name":"Define the principle of friction of distance and geographic models that are based on it (e.g., gravity models, spatial interaction models)"},{"concepts":[92],"name":"Define the properties that make a phenomenon geographic"},{"concepts":[623],"name":"Define the radiometric spectral quantities brightness, emittance, luminosity"},{"concepts":[623],"name":"Define the radiometric spectral quantities radiance, irradiance, flux"},{"concepts":[2],"name":"Define the terms spatial analysis, spatial modeling, geostatistics, spatial econometrics, spatial statistics, qualitative analysis, map algebra, and network analysis"},{"concepts":[122],"name":"Define uncertainty-related terms, such as error, accuracy, uncertainty, precision, stochastic, probabilistic, deterministic, and random"},{"concepts":[567],"name":"Define user roles for an existing or planned GIS"},{"concepts":[118],"name":"Define various terms used to describe topological relationships, such as disjoint, overlap, within, and intersect"},{"concepts":[1205],"name":"Define Web API composition (WAPIC) concept for RESTful WSs and identify main issues"},{"concepts":[1184],"name":"Define Web Coverage Service (WCS). Describe GetCapabilities, GetCoverageInfo, and GetCoverage operations in detail. Practice its usage in a given use case"},{"concepts":[1184],"name":"Define Web Feature Service (WFS). Describe GetCapabilities, DescribeFeaturetype, and GetFeature, and GetFeatureInfo operations in detail. Practice its usage in a given use case"},{"concepts":[1184],"name":"Define Web Map Service (WMS). Describe GetCapabilities, GetMap, and GetFeatureInfo operations in detail. Practice its usage in a given use case"},{"concepts":[1184],"name":"Define Web Map Tile Service (WMTS). Describe GetCapabilities, GetTile, and GetFeatureInfo operations in detail. Practice its usage in a given use case"},{"concepts":[1184],"name":"Define Web Processing Service (WPS). Describe GetCapabilities, DescribeProcess, and Execute operations in detail. Practice its usage in a given use case"},{"concepts":[1205],"name":"Define web services composition (WSC) concept and identify main issues"},{"concepts":[1181],"name":"Define Web services transport over the Web"},{"concepts":[1188],"name":"Define what an ontology is. Identify differences among ontologies, Thesauri, and taxonomies"},{"concepts":[214],"name":"Delineate a set of break lines that improve the accuracy of a TIN"},{"concepts":[113],"name":"Delineate regions using properties, spatial relationships, and geospatial technologies"},{"concepts":[176],"name":"Deliver a resources plan consistent with organisation’s concrete actions"},{"concepts":[660],"name":"Demonstrate basic knowledge of the atmospheric absorption and scattering mechanisms."},{"concepts":[600,656],"name":"Demonstrate basic knowledge of the interaction between the solar radiation and atmospheric constituents"},{"concepts":[1191],"name":"Demonstrate harvesting and crawling mechanisms for automated metadata collection"},{"concepts":[226],"name":"Demonstrate how a network is a connected set of edges and vertices"},{"concepts":[222],"name":"Demonstrate how a topological structure can be represented in a relational database structure"},{"concepts":[41],"name":"Demonstrate how adjacency and connectivity can be recorded in matrices"},{"concepts":[226],"name":"Demonstrate how attributes of networks can be used to represent cost, time, distance, or many other measures"},{"concepts":[235],"name":"Demonstrate how both the time criticality and the data security might determine whether one performs change detection on-line or off-line in a given scenario"},{"concepts":[11],"name":"Demonstrate how capacity is assigned to edges in a network using the appropriate data structure"},{"concepts":[5],"name":"Demonstrate how cluster analysis can be used as a data mining tool"},{"concepts":[10],"name":"Demonstrate how K-shortest path algorithms can be implemented to find many efficient alternate paths across the network"},{"concepts":[9],"name":"Demonstrate how networks can be measured using the number of elements in a network, the distances along network edges, and the level of connectivity of the network"},{"concepts":[71],"name":"Demonstrate how semi-variograms react to spatial nonstationarity"},{"concepts":[77],"name":"Demonstrate how spatial autocorrelation can be removed by resampling"},{"concepts":[75],"name":"Demonstrate how spatially lagged, trend surface, or dummy spatial variables can be used to create the spatial component variables missing in a standard regression analysis"},{"concepts":[148],"name":"Demonstrate how the adding time-series data reveals (or not) patterns not evident in a cross-sectional data"},{"concepts":[39],"name":"Demonstrate how the area of a region calculated from a raster data set will vary by resolution and orientation"},{"concepts":[12],"name":"Demonstrate how the Classic Transportation Problem can be structured as a linear program"},{"concepts":[45],"name":"Demonstrate how the geometric operations of intersection and overlay can be implemented in GIS"},{"concepts":[76],"name":"Demonstrate how the parameters of spatial auto-regressive models can be estimated using univariate and bivariate optimization algorithms for maximizing the likelihood function"},{"concepts":[75],"name":"Demonstrate how the spatial weights matrix is fundamental in spatial econometrics models"},{"concepts":[226],"name":"Demonstrate how the star (or forward star) data structure, which is often employed when digitally storing network information, violates relational normal form, but allows for much faster search and retrieval in network databases"},{"concepts":[1197],"name":"Demonstrate how to discover over a catalogue service; and the discovery procedure in OGC CS-W"},{"concepts":[127],"name":"Demonstrate how to georeference an historical map"},{"concepts":[1086],"name":"Demonstrate impacts of land use change"},{"concepts":[1099],"name":"Demonstrate multidisciplinarity, combining GISciences, Social Sciences, Smart Cities, Computational Sciences and Social Media"},{"concepts":[1191],"name":"Demonstrate publishing in some popular SDI (NSDI) portals like INSPIRE and GOS geoportals"},{"concepts":[33],"name":"Demonstrate the basic syntactic structure of SQL"},{"concepts":[51],"name":"Demonstrate the extension of spatial clustering to deal with clustering in space-time using the Know and Mantel tests"},{"concepts":[232],"name":"Demonstrate the importance of a clean, relatively error-free database (together with an appropriate geodetic framework) with the use of GIS software"},{"concepts":[610],"name":"Demonstrate the relationships among measured multi-spectral radiation and specific chemical (e.g. composition) and physical (e.g. temperature, pressure, etc.) properties of the observed matter."},{"concepts":[34],"name":"Demonstrate the syntactic structure of spatial and temporal operators in SQL"},{"concepts":[1201],"name":"Demonstrate the usage of popular ETL tools in an NSDI scenario"},{"concepts":[214],"name":"Demonstrate the use of the TIN model for different statistical surfaces (e.g., terrain elevation, population density, disease incidence) in a GIS software application"},{"concepts":[75],"name":"Demonstrate why spatial autocorrelation among regression residuals can be an indication that spatial variables have been omitted from the models"},{"concepts":[45],"name":"Demonstrate why the georegistration of datasets is critical to the success of any map overlay operation"},{"concepts":[172],"name":"Demonstrate why the system design is important in any GIS implementation"},{"concepts":[606],"name":"Derive the Stefan-Boltzman Law from the Planck's one"},{"concepts":[85],"name":"Describe a brief history of major philosophical movements relating to the nature of space, time, geographic phenomena and human interaction with it"},{"concepts":[149],"name":"Describe a mapping goal in which the use of each of the following would be appropriate: brushing, linking, multiple displays"},{"concepts":[46,47],"name":"Describe a real modeling situation in which map algebra would be used e.g., site selection, climate classification, least-cost path"},{"concepts":[326],"name":"Describe a scenario in which data 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impossible to adequately represent in GIS"},{"concepts":[460],"name":"Describe the elements of image interpretation"},{"concepts":[400],"name":"Describe the extent to which contemporary GIS and T supports diverse ways of understanding the world"},{"concepts":[52],"name":"Describe the formulation of the classic gravity model, the unconstrained spatial interaction model, the production constrained spatial interaction model, the attraction constrained spatial interaction model, and the doubly constrained spatial..."},{"concepts":[677],"name":"Describe the fundamental thermodynamic processes (isothermal, adiabatic, isochoric, isobaric)"},{"concepts":[117],"name":"Describe the genealogy (as identity-based change or temporal relationships) of particular geographic phenomena"},{"concepts":[75],"name":"Describe the general types of spatial econometric model"},{"concepts":[645],"name":"Describe the impact of Einstein’s theory of radiation on the design of modern devices for the measurements and/or production of coherent light"},{"concepts":[652],"name":"Describe the impact of geometrical optics on optical sensors design"},{"concepts":[26],"name":"Describe the impact of map projection transformation on raster and vector data"},{"concepts":[322],"name":"Describe the impact of the concept of dilution of precision on the uncertainty of GPS positioning"},{"concepts":[653],"name":"Describe the impact of the theory of interference on the development of modern satellite hyperspectral sounders"},{"concepts":[654],"name":"Describe the impact of theory of diffraction and grating spectrometers on the development of modern satellite hyperspectral sounders"},{"concepts":[54],"name":"Describe the implementation of an ordered weighting scheme in a multiple-criteria aggregation"},{"concepts":[415],"name":"Describe the importance of geometric correction when using Earth Observation data"},{"concepts":[393],"name":"Describe the individuals or groups to which GIS and T professionals have 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icons suitable for mapping different elements"},{"concepts":[133],"name":"Design maps that are appropriate for users with vision limitations"},{"concepts":[193],"name":"Design relational databases"},{"concepts":[580],"name":"Design solutions to different types of barriers to geospatial data sharing"},{"concepts":[165],"name":"Design workflows, procedures, and customized software tools for using geospatial technologies and methods"},{"concepts":[1147],"name":"designing the description of a service for the need of a particular user of EO information"},{"concepts":[1179],"name":"Detect and monitor oil slicks"},{"concepts":[1073,1080,1134],"name":"Detect land movement, subsidence, heave"},{"concepts":[524],"name":"Determine all necessary steps to make EO-derived products of a resarch project accessible"},{"concepts":[166],"name":"Determine how to integrate or combine the proposed workflow with current applications running"},{"concepts":[379],"name":"Determine if a dataset can be considered as open data"},{"concepts":[1156],"name":"Determine object movement by comparing subsequent images"},{"concepts":[1085],"name":"Determine requirements and quality criteria for an EO information product that serves spatial planners in monitoring soil sealing"},{"concepts":[28],"name":"Determine the mathematical relationships among scale, scope, and resolution"},{"concepts":[318],"name":"Determine the most appropriate data collection method for collecting particular data"},{"concepts":[106],"name":"Determine the proper uses of attributes based on their domains"},{"concepts":[209],"name":"Determine the standards that are essential for geospatial data modelling"},{"concepts":[117],"name":"Determine whether it is important to represent the genealogy of entities for a particular application"},{"concepts":[111],"name":"Determine whether phenomena or applications exist that are not adequately represented in an existing comprehensive model"},{"concepts":[54],"name":"Determine which method to 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generation of the final modeled outputs"},{"concepts":[166],"name":"Develop use cases for potential applications using established techniques with potential users, such as questionnaires, interviews, focus groups, the Delphi method, and/or joint application development"},{"concepts":[1180],"name":"Develop Web-GIS solutions to replace each of the functions of a traditional GIS"},{"concepts":[563],"name":"Devise simple ways to represent probability information in GIS"},{"concepts":[375],"name":"Differentiate \"contracts for service\" from \"contracts of service\""},{"concepts":[146],"name":"Differentiate 3D representations from 2.5 D representations"},{"concepts":[212],"name":"Differentiate among a lattice, a tessellation, and a grid"},{"concepts":[23],"name":"Differentiate among common interpolation techniques (e.g., nearest neighbor, bilinear, bicubic)"},{"concepts":[375],"name":"Differentiate among contract liability, tort liability, and statutory 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anisotropic processes"},{"concepts":[50],"name":"Differentiate between kernel density estimation and spatial interpolation"},{"concepts":[188],"name":"Differentiate between logical and physical models, in terms of the level of detail, constraints, and range of information included"},{"concepts":[213],"name":"Differentiate between lossy and lossless compression methods"},{"concepts":[46,47],"name":"Differentiate between map algebra and matrix algebra using real examples"},{"concepts":[103],"name":"Differentiate between mathematical and phenomenological theories of the nature of time"},{"concepts":[70],"name":"Differentiate between model-based and design-based sampling schemes"},{"concepts":[26],"name":"Differentiate between polynomial coordinate transformations (including linear) and rubbersheeting"},{"concepts":[1183],"name":"Differentiate between SOAP and REST Web services. - Identify design issues of REST Web services"},{"concepts":[93],"name":"Differentiate between space and place"},{"concepts":[121],"name":"Differentiate between the following concepts: vagueness and ambiguity, well defined and poorly defined objects and fields or discord and non-specificity"},{"concepts":[52],"name":"Differentiate between the gravity model and spatial interaction models"},{"concepts":[57],"name":"Differentiate between trend surface analysis and deterministic spatial interpolation"},{"concepts":[1188],"name":"Differentiate between upper, domain, and application level ontologies"},{"concepts":[311],"name":"Differentiate push-broom and cross-track scanning technologies"},{"concepts":[412],"name":"Differentiate rectification and orthorectification"},{"concepts":[478],"name":"Differentiate supervised classification from unsupervised classification"},{"concepts":[122],"name":"Differentiate uncertainty in geospatial situations from vagueness"},{"concepts":[138],"name":"Differentiate uses for different types of imagery related to earth"},{"concepts":[109],"name":"Differentiate various sources of fields, such as substance properties (e.g., temperature), artificial constructs (e.g., population density), and fields of potential or influence (e.g., gravity)"},{"concepts":[327],"name":"Digitize and georegister a specified vector feature set to a given geometric accuracy and topological fidelity thresholds using a given map sheet, digitizing tablet, and data entry software"},{"concepts":[397],"name":"Discuss about \"mapping whose reality?\" Pros and cons of geoinformation sharing in social media, i.e. big data, \"digital shadow\" etc."},{"concepts":[385],"name":"Discuss about open data and data sharing and public/private sector"},{"concepts":[379],"name":"Discuss about open data impact on society and citizenship"},{"concepts":[151],"name":"Discuss about the advantages of different immersive display systems"},{"concepts":[159],"name":"Discuss about the degree of subjectivity and/or objectivity of a map"},{"concepts":[125],"name":"Discuss about the History of Cartography in different cultures"},{"concepts":[126],"name":"Discuss about the relationship between art and cartography"},{"concepts":[822,823,824],"name":"Discuss advantages and disadvantages of different methods of storing remote sensing data"},{"concepts":[837,838,839,840],"name":"Discuss advantages and disadvantages of different SAR data formats"},{"concepts":[768],"name":"Discuss advantages and disadvantages of passive and active sensors"},{"concepts":[730],"name":"Discuss advantages of SAR techniques over traditional measuring techniques"},{"concepts":[463],"name":"Discuss algorithms that use the detection of keypoints to identify objects in images"},{"concepts":[771],"name":"Discuss an example of using a radar altimeter"},{"concepts":[829],"name":"Discuss and compare different temporal resolutions of remote sending data"},{"concepts":[724],"name":"Discuss and compare different types of interactions of microwaves with matter"},{"concepts":[836],"name":"Discuss and compare different types of processing levels of optical data"},{"concepts":[841],"name":"Discuss and compare different types of processing levels of SAR data"},{"concepts":[379],"name":"Discuss and define open data and impact on GIS&T"},{"concepts":[561],"name":"Discuss and define the process of the Information value chain"},{"concepts":[449],"name":"Discuss cloud masks as early steps towards semantic enrichment for EO images"},{"concepts":[104],"name":"Discuss common prepositions and adjectives (in any particular language) that signify either spatial or temporal relations but are used for both kinds, such as after or longer"},{"concepts":[245],"name":"Discuss concepts of space-time dynamics for spatial modeling"},{"concepts":[1181],"name":"Discuss consensus based interoperability and its relation to geospatial data interchange. Define what a Web Service (WS) is and present characteristic scenarios. Data serving and Data Processing WSs"},{"concepts":[396],"name":"Discuss critiques of GIS as \"deterministic\" technology in relation to debates about the Quantitative quantitative revolution in the discipline of geography."},{"concepts":[400],"name":"Discuss critiques of GIS as deterministic technology in relation to debates about the Quantitative Revolution in the discipline of geography"},{"concepts":[575],"name":"Discuss different formats (tutorials, in house, online, instructor lead) for training and how they can be used by organizations"},{"concepts":[543],"name":"Discuss different methods for assessing the quality of a specific EO product"},{"concepts":[779],"name":"Discuss different types of laser scanners"},{"concepts":[808,684],"name":"Discuss different types of satellite orbits"},{"concepts":[248],"name":"Discuss different ways of simulating space and visualizing model behaviour"},{"concepts":[697],"name":"Discuss electromagnetic interactions and scattering mechanisms"},{"concepts":[814],"name":"Discuss examples of ground-based platforms and their use"},{"concepts":[807],"name":"Discuss examples of the objectives of Earth observation missions"},{"concepts":[309],"name":"Discuss future prospects for automated feature extraction from aerial imagery"},{"concepts":[573],"name":"Discuss how a code of ethics might be applied within an organization"},{"concepts":[136],"name":"Discuss how cultural differences with respect to color associations impact map design"},{"concepts":[546,826],"name":"Discuss how different spectral resolution of EO sensors influences their potential for vegetation mapping"},{"concepts":[510],"name":"Discuss how hierarchical representation is exploited for object-based image analysis"},{"concepts":[761],"name":"Discuss how line detectors array sensors work"},{"concepts":[498],"name":"Discuss how low-pass filtering of an image impacts the resulting regions derived with watershed segmentation"},{"concepts":[159],"name":"Discuss how maps express relations of power"},{"concepts":[323],"name":"Discuss how measures of spatial autocorrelation may be used to evaluate thematic accuracy"},{"concepts":[828,546],"name":"Discuss how radiometric resolution influences the granularity of a land cover classification"},{"concepts":[825,833],"name":"Discuss how remote sensing data is organized and stored"},{"concepts":[744],"name":"Discuss how the angle of SAR signal incidence affects SAR acquisition"},{"concepts":[70],"name":"Discuss how the choice of sampling strategy impacts the accuracy assesment for a classification result"},{"concepts":[490],"name":"Discuss how the choice of sampling strategy impacts the accuracy assesment for a classification result"},{"concepts":[70],"name":"Discuss how the choice of sampling strategy impacts the classification result"},{"concepts":[490],"name":"Discuss how the choice of sampling strategy impacts the classification result"},{"concepts":[834],"name":"Discuss how the radiometrically corrected data are processed"},{"concepts":[507],"name":"Discuss how the size of the neighborhood impacts the smoothing effect of a low-pass filter"},{"concepts":[386],"name":"Discuss how to approach the widening audience/participants for geospatial products and service, increasing geo-awareness and geo-enablement"},{"concepts":[143],"name":"Discuss how to create an intellectual and visual hierarchy on maps"},{"concepts":[692],"name":"Discuss how to use phase information in remote sensing"},{"concepts":[31],"name":"Discuss implications of data loss in the case of generalisation of spatial data."},{"concepts":[430],"name":"Discuss imputation methods for filling in missing data"},{"concepts":[600],"name":"Discuss in which way annual solar insolation and average cloud coverage parameters affect the choice of a solar power plant location"},{"concepts":[600],"name":"Discuss in which way modeled daily solar insolation and cloud coverage forecast could affect solar power plant day-by-day management"},{"concepts":[387],"name":"Discuss legal aspects of access to environmental data, global change/warming or sustainable development (regional, national, global) in conjunction to society."},{"concepts":[730],"name":"Discuss limitations of interferometric measurement"},{"concepts":[499],"name":"Discuss limitations of the different region-based segementation methods"},{"concepts":[821],"name":"Discuss main characteristics of digital imagery"},{"concepts":[379],"name":"Discuss of arguments for and against open data"},{"concepts":[378],"name":"Discuss of opportunities for exchange of geospatial data between public and private sector to enable more efficient analysis"},{"concepts":[243],"name":"Discuss options of combining rule-based models with other individual modelling approaches"},{"concepts":[719],"name":"Discuss orientational polarisation of media"},{"concepts":[396],"name":"Discuss over the argument that the use of Geospatial geospatial Information privileges certain views of the world over others."},{"concepts":[385],"name":"Discuss over the changing role of the private sector in the use of geospatial information"},{"concepts":[386],"name":"Discuss over the paradigm shifts and current trends in GIS&T education and pedagogical approaches for GIS teaching and learning in detail"},{"concepts":[397],"name":"Discuss over the various implications of surveillance technology"},{"concepts":[718],"name":"Discuss polarimetric decomporition techniques"},{"concepts":[391],"name":"Discuss positive and negative aspects of the term \"humans as sensors\""},{"concepts":[727],"name":"Discuss radar antennas"},{"concepts":[713],"name":"Discuss scale of roughness of microwaves"},{"concepts":[2],"name":"Discuss situations when it is desirable to adopt a spatial approach to the analysis of data"},{"concepts":[226],"name":"Discuss some of the difficulties of applying the standard process-pattern concept to lines and networks"},{"concepts":[500],"name":"Discuss spatial autocorrelation and homogeneity of image objects"},{"concepts":[174],"name":"Discuss the advantages and disadvantages of outsourcing elements of a GIS project / GI system"},{"concepts":[97],"name":"Discuss the advantages and disadvantages of the use of cartesian metric space as a basis for GIS and related technologies"},{"concepts":[324],"name":"Discuss the advantages and potential problems associated with the use of Minimum Mapping Unit (MMU) as a measure of the level of detail in land use, land cover, and soils maps"},{"concepts":[769],"name":"Discuss the application possibilities of imaging radar"},{"concepts":[785],"name":"Discuss the applications for which Differential Absorption LiDAR can be used"},{"concepts":[786],"name":"Discuss the applications for which Wind Doppler LiDAR is used"},{"concepts":[64],"name":"Discuss the appropriateness of different types of spatial weights matrices for various problems"},{"concepts":[78],"name":"Discuss the appropriateness of GWR under various conditions"},{"concepts":[530],"name":"Discuss the available data quality standards for EO"},{"concepts":[660],"name":"Discuss the basic principles of solar radiation."},{"concepts":[506],"name":"Discuss the benefits of using a gauss filter instead of a mean filter for smoothing an image"},{"concepts":[112],"name":"Discuss the causal relationship between spatial processes and spatial patterns, including the possible problems in determining causality"},{"concepts":[620],"name":"Discuss the change of attenuation length moving from visible to the microwave range and from sea water to solid land surfaces"},{"concepts":[51],"name":"Discuss the characteristics of the various cluster detection techniques"},{"concepts":[25],"name":"Discuss the consequences of increasing and decreasing resolution"},{"concepts":[111],"name":"Discuss the contributions of early attempts to integrate the concepts of space, time, and attribute in geographic information, such as Berry (1964) and Sinton (1978)"},{"concepts":[97],"name":"Discuss the contributions that different perspectives on the nature of space bring to an understanding of geographic phenomenon"},{"concepts":[111],"name":"Discuss the degree to which these models can be implemented using current technologies"},{"concepts":[757],"name":"Discuss the development of remote sensing sensors"},{"concepts":[123],"name":"Discuss the difference between vagueness and uncertainty."},{"concepts":[10],"name":"Discuss the difference of implementing Dijkstras algorithm in raster and vector modes"},{"concepts":[787],"name":"Discuss the differences between imaging and non-imaging sensors"},{"concepts":[133],"name":"Discuss the differences between maps that use the same data but are for different purposes and intended audiences"},{"concepts":[133],"name":"Discuss the differences between maps that use the same data but are for different purposes and intended audiences"},{"concepts":[546],"name":"Discuss the different types of resolution of Earth observation data"},{"concepts":[92],"name":"Discuss the differing denotations and connotations of the terms spatial, geographic, and geospatial"},{"concepts":[110],"name":"Discuss the difficulty of integrating process models into GIS software based on the entity and field views, and methods used to do so"},{"concepts":[117],"name":"Discuss the effects of temporal scale on the modeling of genealogical structures"},{"concepts":[393],"name":"Discuss the ethical implications of a local government's decision to charge fees for its data"},{"concepts":[309],"name":"Discuss the extent to which vector data extraction from aerial stereoimagery has been automated"},{"concepts":[505],"name":"Discuss the frequencies that a high-pass filter preserves and subdues"},{"concepts":[591],"name":"Discuss the governance structure in place of a particular country"},{"concepts":[222],"name":"Discuss the historical roots of the Census Bureaus creation of GBF/DIME as the foundation for the development of topological data structures"},{"concepts":[796],"name":"Discuss the history of the development of remote sensing platforms"},{"concepts":[108],"name":"Discuss the human predilection to conceptualize geographic phenomena in terms of discrete entities"},{"concepts":[390],"name":"Discuss the impact of geospatial information for the development of social media (Facebook, Twitter, Wikimapia, Flickr etc.) becoming increasingly location-based"},{"concepts":[232],"name":"Discuss the implication of long transactions on database integrity"},{"concepts":[400],"name":"Discuss the implications of interoperability on ontology"},{"concepts":[396],"name":"Discuss the implications of interoperability on ontology"},{"concepts":[324],"name":"Discuss the implications of the sampling theorem (Lambda = 0.5 delta) to the concept of resolution"},{"concepts":[28],"name":"Discuss the implications of tradeoff between data detail and data volume"},{"concepts":[107],"name":"Discuss the importance of space, time, properties, and categories as fundamentals in the conceptualization and representation of spatial entities."},{"concepts":[150],"name":"Discuss the influence of the user interface on maps and visualizations on the Web"},{"concepts":[1183],"name":"Discuss the issue whether a service is really \"RESTful\" or not"},{"concepts":[378],"name":"Discuss the legal framework related to competition and public-private sector relationships in the geospatial domain"},{"concepts":[803],"name":"Discuss the main applications using the extra wide swath mode"},{"concepts":[492],"name":"Discuss the main drawback of edge-based segmentation in partitioning an image"},{"concepts":[764],"name":"Discuss the main properties of hyperspectral radiometers"},{"concepts":[763],"name":"Discuss the main properties of passive microwave radiometers"},{"concepts":[762],"name":"Discuss the main properties of thermal radiometers"},{"concepts":[756],"name":"Discuss the main types of remote sensing data"},{"concepts":[756,815],"name":"Discuss the main types of remote sensing platforms"},{"concepts":[756],"name":"Discuss the main types of remote sensing sensors"},{"concepts":[546],"name":"Discuss the minimum spatial resolution required for detecting single houses in a satellite image"},{"concepts":[595],"name":"Discuss the mission, history, constituencies, and activities of the GIS Certification Institute (GISCI)"},{"concepts":[575],"name":"Discuss the National Research Council report on Learning to Think Spatially (2005) as it relates to spatial thinking skills needed by the GIS and T workforce"},{"concepts":[829,546],"name":"Discuss the needs for high temporal resolution for analysing crop cycles in agriculture"},{"concepts":[23],"name":"Discuss the pitfalls of using secondary data that has been generated using interpolations (e.g., Level 1 USGS DEMs)"},{"concepts":[723],"name":"Discuss the polarimetry technique"},{"concepts":[29],"name":"Discuss the possible effects on topological integrity of generalizing data sets"},{"concepts":[375],"name":"Discuss the potential legal problems associated with licensing geospatial information"},{"concepts":[401],"name":"Discuss the potential role of agency (individual action) in resisting dominant practices and in using GIS and T in ways that are consistent with feminist epistemologies and politics"},{"concepts":[496],"name":"Discuss the principles of regionalisation and their use in segmentation methods"},{"concepts":[667],"name":"Discuss the processes that describe the hydrologic cycle"},{"concepts":[402],"name":"Discuss the production, maintenance, and use of geospatial data by a government agency or private firm from the perspectives of a taxpayer, a community organization, and a member of a minority group"},{"concepts":[844],"name":"Discuss the purposes of obtaining remote sensing data"},{"concepts":[698],"name":"Discuss the radiometric anomalies of radar data"},{"concepts":[55],"name":"Discuss the relationship between spatial processes and spatial patterns"},{"concepts":[125],"name":"Discuss the relationship between the history of exploration and the development of a more accurate map of the world"},{"concepts":[30],"name":"Discuss the relationship of attribute measurement levels to database query operations"},{"concepts":[390],"name":"Discuss the role and value of \"place\" and \"space\" for geo media based social networking"},{"concepts":[136],"name":"Discuss the role of gamut in choosing colors that can be reproduced on various devices and media"},{"concepts":[222],"name":"Discuss the role of graph theory in topological structures"},{"concepts":[22],"name":"Discuss the role of metadata in facilitating conversation of data models and data structures between systems"},{"concepts":[387,392],"name":"Discuss the role of public, private sector and citizens in facilitating geospatial information in environmental/sustainable issues."},{"concepts":[378],"name":"Discuss the role of the public and private sectors in producing and dissemination of geospatial information"},{"concepts":[573],"name":"Discuss the status of professional and academic certification in GIS and T"},{"concepts":[376],"name":"Discuss the status of the concept of privacy in the U.S. legal regime"},{"concepts":[142],"name":"Discuss the strengths and weaknesses of infographics as a method of displaying geographic information"},{"concepts":[656],"name":"Discuss the structure and chemical composition of the atmosphere"},{"concepts":[0],"name":"Discuss the synergy between processes in geo-information systems and earth observation systems."},{"concepts":[63],"name":"Discuss the theory leading to the assumption of intrinsic stationarity"},{"concepts":[760],"name":"Discuss the use of area array sensors in remote sensing"},{"concepts":[766],"name":"Discuss the use of atmospheric passive sounders"},{"concepts":[765],"name":"Discuss the use of data obtained by spectroradiometer"},{"concepts":[759],"name":"Discuss the use of digital frame cameras in remote sensing"},{"concepts":[690],"name":"Discuss the use of polarization for different application domains"},{"concepts":[149],"name":"Discuss the uses of the map as a user interface element in interactive presentations of geographic information"},{"concepts":[797],"name":"Discuss the ways of using data acquired by UAS in remote sensing"},{"concepts":[795],"name":"Discuss types and classes of remote sensing sensors"},{"concepts":[548],"name":"Discuss valid time ranges for images used for landslide mapping with pre- and post-event image comparison"},{"concepts":[379],"name":"Discuss various legal aspects of public and private sectors concerning owning, controlling, sharing/ disseminating open data."},{"concepts":[379],"name":"Discuss various sources of open data (science, public and private sectors)"},{"concepts":[374],"name":"Discuss ways in which the geospatial profession is regulated under the U.S. legal regime"},{"concepts":[386],"name":"Discuss ways of working with crowdsourcing in education and research"},{"concepts":[710],"name":"Discuss what horizontal roughness component (correlation legth) is"},{"concepts":[772],"name":"Discuss what information is acquired by the laser altimeters"},{"concepts":[709],"name":"Discuss what surface height variation (or RMS height) is"},{"concepts":[831],"name":"Discuss what the header file describes"},{"concepts":[767],"name":"Discuss what the main characteristics of radiometers are"},{"concepts":[770],"name":"Discuss what types of electromagnetic waves the laser profiler uses"},{"concepts":[451],"name":"Discuss why a query through time is easier realized with a data cube than by comparison of a time series stored in image files"},{"concepts":[830],"name":"Distinguish and explain the different types of properties of digital imagery"},{"concepts":[149,139],"name":"Distinguish between animated and interactive maps"},{"concepts":[89],"name":"Distinguish between continuants and occurrents in relation with spatial phenomena."},{"concepts":[154],"name":"Distinguish between different graphic representation techniques"},{"concepts":[86],"name":"Distinguish between metaphysics and epistemology."},{"concepts":[186],"name":"Distinguish between the temporary and structural relationships in a conceptual model"},{"concepts":[27],"name":"Distinguish between transformation methods for raster and vector representations."},{"concepts":[164,170],"name":"Distinguish between usability, utility, and user needs in the context of geovisualizations"},{"concepts":[167,168],"name":"Document existing and potential tasks in terms of workflow and information flow"},{"concepts":[105],"name":"Document the personal, social, and or institutional meaning of categories used in GIS applications"},{"concepts":[150],"name":"Edit the symbology, labeling, and page layout for a map originally designed for hard copy printing so that it can be seen and used on the Web"},{"concepts":[101],"name":"Effectively communicate the design, procedures, and results of GIS projects to non-GIS audiences (clients, managers, general public)"},{"concepts":[112],"name":"Employ techniques for visualizing, describing, and analyzing distributions in space, time, and attribute"},{"concepts":[1099],"name":"Enable citizen skills spatially"},{"concepts":[23],"name":"Estimate a value between two known values using linear interpolation (e.g., spot elevations, population between census years)"},{"concepts":[1140],"name":"Estimate evaporation rates"},{"concepts":[1140,442],"name":"Estimate near-surface chlorophyll-a concentration for monitoring harmful algal blooms (HABs)"},{"concepts":[129],"name":"Estimate the cost to collect needed data from primary sources (e.g., remote sensing, GPS)"},{"concepts":[36],"name":"Estimate the fractal dimension of a sinuous line"},{"concepts":[642],"name":"Estimate the meteorological and the cloud optical properties by LBRTM and validate against high accuracy spectral measurements"},{"concepts":[127],"name":"Estimate the potential value of a historical map"},{"concepts":[529],"name":"Evaluate an EO product and its metadata on its reusability for a new application context"},{"concepts":[568],"name":"Evaluate and revise an existing GIS management strategy"},{"concepts":[1099,1096,1097],"name":"Evaluate citizen-driven observations"},{"concepts":[153],"name":"Evaluate graphic techniques used to portray spatializations"},{"concepts":[25],"name":"Evaluate methods used by contemporary GIS software to resample raster data on-the-fly during display"},{"concepts":[311],"name":"Evaluate the advantages and disadvantages of acoustic remote sensing versus airborne or satellite remote sensing for seafloor mapping"},{"concepts":[311,806,811],"name":"Evaluate the advantages and disadvantages of airborne remote sensing versus satellite remote sensing"},{"concepts":[309],"name":"Evaluate the advantages and disadvantages of photogrammetric methods and LiDAR for production of terrain elevation data"},{"concepts":[110],"name":"Evaluate the assertion that events and processes are the same thing, but viewed at different temporal scales"},{"concepts":[122],"name":"Evaluate the causes of uncertainty in geospatial data"},{"concepts":[136],"name":"Evaluate the colors used in a web map to be used indoors and outdoors"},{"concepts":[526],"name":"Evaluate the conformity of an EO imagery product to ISO 19129"},{"concepts":[93],"name":"Evaluate the differences in how various parties think or feel differently about a place being modeled"},{"concepts":[217],"name":"Evaluate the ease of measuring resolution in different types of tessellations"},{"concepts":[108],"name":"Evaluate the effectiveness of GIS data models for representing the identity, existence, and lifespan of entities"},{"concepts":[109],"name":"Evaluate the field views description of objects as conceptual discretizations of continuous patterns"},{"concepts":[1136],"name":"Evaluate the impact of changes in land areas"},{"concepts":[101],"name":"Evaluate the impact of geospatial technologies (e.g., Google Earth) that allow non-geospatial professionals to create, distribute, and map geographic information"},{"concepts":[1115,1113],"name":"Evaluate the impact of the climate change"},{"concepts":[217],"name":"Evaluate the implications of changing grid cell resolution on the results of analytical applications by using GIS software"},{"concepts":[108],"name":"Evaluate the influence of scale on the conceptualization of entities"},{"concepts":[85],"name":"Evaluate the influences of ones own philosophical views and assumptions on GIS AND T practices"},{"concepts":[81],"name":"Evaluate the influences of particular worldviews (including ones own) on GIS practices"},{"concepts":[95],"name":"Evaluate the influences of political actions, especially the allocation of territory, on human perceptions of space and place"},{"concepts":[95],"name":"Evaluate the influences of political ideologies (e.g., Marxism, Capitalism, conservative liberal) on the understanding of geographic information"},{"concepts":[587],"name":"Evaluate the institutional framework of an existing SDI initiative"},{"concepts":[222],"name":"Evaluate the positive and negative impacts of this shift from integrated topological models"},{"concepts":[213],"name":"Evaluate the relative merits of grid compression methods for storage"},{"concepts":[577],"name":"Evaluate the relevance and applicability of different teaching and learning methods for GIS&T education"},{"concepts":[109],"name":"Evaluate the representation of movement as a field of location over time (e.g. :x,y,z: = f(t) )"},{"concepts":[121],"name":"Evaluate the role that system complexity, dynamic processes, and subjectivity play in the creation of vague phenomena and concepts"},{"concepts":[144],"name":"Evaluate the strengths and limitations of different thematic mapping methods"},{"concepts":[537],"name":"Evaluate the thematic accuracy of a given soils map"},{"concepts":[242],"name":"Evaluate the tradeoffs between abstraction and representativeness in simulation model development"},{"concepts":[161],"name":"Evaluate the usability of a hard-copy map"},{"concepts":[161,170],"name":"Evaluate the usability of a web map"},{"concepts":[187],"name":"Evaluate the various general data models common in GIS project"},{"concepts":[121],"name":"Evaluate vagueness in the locations, time, attributes, and other aspects of geographic phenomena"},{"concepts":[29],"name":"Evaluate various line simplification algorithms by their usefulness in different applications"},{"concepts":[243],"name":"Evaluate when rule-based models can be applied to spatiotemporal problems"},{"concepts":[238],"name":"Examine how computational technology relates to geocomputation"},{"concepts":[447],"name":"Examine how the vegetation indices relates to the vegetation dynamics and health"},{"concepts":[447],"name":"Examine how the water-related spectral indices relates to changes in the vegetation and soil water content"},{"concepts":[1193],"name":"Examine Metadata schema and vocabularies used for open data publishing"},{"concepts":[1208],"name":"Examine the Document Object Model (DOM) in HTML documents"},{"concepts":[45],"name":"Exemplify applications in which overlay is useful, such as site suitability analysis"},{"concepts":[63],"name":"Exemplify deterministic and spatial stochastic processes"},{"concepts":[103],"name":"Exemplify different temporal frames of reference: linear and cyclical, absolute and relative"},{"concepts":[566],"name":"Exemplify each component of a needs assessment for an enterprise GIS"},{"concepts":[235],"name":"Exemplify how the lack of a data librarian to manage data can have disastrous consequences on the resulting dataset"},{"concepts":[63],"name":"Exemplify non-stationarity involving first and second order effects"},{"concepts":[113],"name":"Exemplify regions found at different scales"},{"concepts":[232],"name":"Exemplify scenarios in which one would need to perform a number of periodic changes in a real GIS database"},{"concepts":[38],"name":"Exemplify situations in which the centroid of a polygon falls outside its boundary"},{"concepts":[12],"name":"Exemplify the Classic Transportation Problem"},{"concepts":[222],"name":"Exemplify the concept of planar enforcement (e.g., TIN triangles)"},{"concepts":[215],"name":"Exemplify the uses (past and potential) of the hexagonal model"},{"concepts":[629],"name":"Explain the concept of composition of spectral signatures and apply the \"linear mixing\" models in some simple case"},{"concepts":[1085],"name":"Explain a use case of EO for smart cities, e.g. how EO derived information about urban green instrastructure supports designing nature based solutions for preserving ecosystem services"},{"concepts":[733],"name":"Explain across-track interferometry technique"},{"concepts":[732],"name":"Explain along-track interferometry technique"},{"concepts":[485],"name":"Explain an application example where SVM is used for EO image classification"},{"concepts":[447],"name":"Explain an application example where the spectral indices are used for vegetation, water or snow monitoring"},{"concepts":[207],"name":"Explain and apply GML data models"},{"concepts":[692],"name":"Explain and apply phase unwrapping"},{"concepts":[203,221],"name":"Explain and apply standards relevant for geometric modelling"},{"concepts":[747],"name":"Explain and discuss elements of Synthetic Aperture Radar (SAR) geometric configuration"},{"concepts":[714],"name":"Explain and discuss surface roughness in microwave remote sensing"},{"concepts":[687],"name":"Explain and discuss the complex elements of a radar signal"},{"concepts":[820],"name":"Explain and discuss the concept of Big Data in the field of Earth Observation"},{"concepts":[816],"name":"Explain and discuss the development of remote sensing data carriers"},{"concepts":[780],"name":"Explain and discuss the LiDAR technology"},{"concepts":[801],"name":"Explain and discuss the SAR acquisition mode spotlight"},{"concepts":[800],"name":"Explain and discuss the SAR acquisition mode staring spotlight"},{"concepts":[768],"name":"Explain and discuss types of sensing mechanisms"},{"concepts":[725],"name":"Explain and discuss what antenna gain is and why it is described as the key performance of a radar antenna"},{"concepts":[752],"name":"Explain and discuss what terrain reflectivity is and how it influences radar signal"},{"concepts":[749],"name":"Explain and discuss what the foreshortening is"},{"concepts":[750],"name":"Explain and discuss what the layover is"},{"concepts":[843],"name":"Explain and discuss what the main processing levels of remote sensing data are"},{"concepts":[830],"name":"Explain and discuss what the radiometric resolution is"},{"concepts":[743],"name":"Explain and discuss what the range direction is"},{"concepts":[751],"name":"Explain and discuss what the shadow in SAR acquisition means"},{"concepts":[830,827],"name":"Explain and discuss what the spatial resolution is"},{"concepts":[830],"name":"Explain and discuss what the spectral resolution is"},{"concepts":[830],"name":"Explain and discuss what the temporal resolution is"},{"concepts":[755,695],"name":"Explain and outline the advantages of radar sensors"},{"concepts":[197],"name":"Explain and use UML diagrams"},{"concepts":[76],"name":"Explain Anselins typology of spatial autoregressive models"},{"concepts":[37],"name":"Explain any differences in the measured direction between two places when the data are presented in a GIS in different projections"},{"concepts":[200],"name":"Explain basic aspects of data modelling, storage and exploitation, such as relation models & databases, data structures, SQL, UML and other basics"},{"concepts":[375],"name":"Explain cases of liability claims associated with misuse of geospatial information, erroneous information, and loss of proprietary interests"},{"concepts":[717],"name":"Explain covariance and coherence matrix"},{"concepts":[708],"name":"Explain dielectric properties of objects and their effect on radar data acquisition"},{"concepts":[731],"name":"Explain differences between DInSAR and PSI"},{"concepts":[755],"name":"Explain differences between optical and radar remote sensing"},{"concepts":[84],"name":"Explain from which scientific fields GIS&T borrows ideas."},{"concepts":[236],"name":"Explain geocomputation, related concepts and how the two relate"},{"concepts":[6],"name":"Explain how a Bayesian framework can incorporate expert knowledge in order to retrieve all relevant datasets given an initial user query"},{"concepts":[585],"name":"Explain how a business case analysis can be used to justify the expense of implementing consensus-based standards"},{"concepts":[465],"name":"Explain how a DSM differs from a DTM"},{"concepts":[226],"name":"Explain how a graph (network) may be directed or undirected"},{"concepts":[226],"name":"Explain how a graph can be written as an adjacency matrix and how this can be used to calculate topological shortest paths in the graph"},{"concepts":[417],"name":"Explain how a histogram is derived from an EO image"},{"concepts":[550],"name":"Explain how a lack of knowledge about data quality limits the data value"},{"concepts":[10],"name":"Explain how a leading World Wide Web-based routing system works e.g., MapQuest, Yahoo Maps, Google"},{"concepts":[40],"name":"Explain how a semi-variogram describes the distance decay in dependence between data values"},{"concepts":[409],"name":"Explain how a set of overlapping images/satellite scenes can provide digital elevation models used for orthorectification and 3D modelling"},{"concepts":[1127],"name":"Explain how 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real-world connotations (e.g., blue=water, white=snow) can be used to determine color selections on maps"},{"concepts":[43],"name":"Explain how reclassification can be used for data simplification and measurement scale change"},{"concepts":[27],"name":"Explain how Representation transformations are related to spatial data quality."},{"concepts":[324],"name":"Explain how resampling affects the resolution of image data"},{"concepts":[585],"name":"Explain how resistance to change affects the adoption of standards in an organization coordinating a GIS"},{"concepts":[58],"name":"Explain how ridgelines and streamlines can be used to improve the result of an interpolation process"},{"concepts":[1113],"name":"Explain how sea surface temperature maps are used to predict El Nino events"},{"concepts":[32],"name":"Explain how set theory relates to spatial queries"},{"concepts":[521],"name":"Explain how SIFT algorithms can be used for enhancing orthorectification"},{"concepts":[61],"name":"Explain 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sensor"},{"concepts":[790],"name":"Explain the principles of operation of the multi-temporal pattern based sensor"},{"concepts":[789],"name":"Explain the principles of operation of the speckle-pattern based sensor"},{"concepts":[792],"name":"Explain the principles of operation of the structured-light-projection camera"},{"concepts":[16],"name":"Explain the principles of operations research modeling and location modeling"},{"concepts":[774],"name":"Explain the principles of spaceborne laser scanning operation and discuss its applications"},{"concepts":[735],"name":"Explain the principles of synthetic aperture radar (SAR) interferometry"},{"concepts":[813],"name":"Explain the principles of terrestrial laser scanning operation and discuss its applications"},{"concepts":[736],"name":"Explain the principles of the SAR tomography"},{"concepts":[777],"name":"Explain the principles of underwater laser scanning operation and discuss its applications"},{"concepts":[533],"name":"Explain the procedure how to collect ground reference data for an image classification"},{"concepts":[242],"name":"Explain the process simulation model development"},{"concepts":[428],"name":"Explain the purpose of image pre-processing"},{"concepts":[842],"name":"Explain the purpose of the analysis ready data"},{"concepts":[1124],"name":"Explain the quality criteria where EO technologies differ from each other in their capabilities to detect, monitor and forecast landslides"},{"concepts":[40],"name":"Explain the rationale for using different forms of distance decay functions"},{"concepts":[64],"name":"Explain the rationale used for each type of spatial weights matrix"},{"concepts":[199],"name":"Explain the relations between GIS and databases"},{"concepts":[113],"name":"Explain the relationship between regions and categories"},{"concepts":[704],"name":"Explain the relationship between the material constant and the interaction of microwaves with the object"},{"concepts":[388],"name":"Explain the relevance and added value of geospatial information in particular use cases"},{"concepts":[163],"name":"Explain the relevance and importance of privacy issues in dealing with geospatial data"},{"concepts":[309,805],"name":"Explain the relevance of the concept parallax in stereoscopic aerial imagery"},{"concepts":[383],"name":"Explain the relevant economic aspects related to the access to and use of geographic information"},{"concepts":[584],"name":"Explain the relevant legal and organizational issues around development and implementation of Spatial Data Infrastructures (SDI)"},{"concepts":[584],"name":"Explain the relevant technological issues around development and implementation of Spatial Data Infrastructures (SDI)"},{"concepts":[177],"name":"Explain the requirements that best match each geospatial software architecture"},{"concepts":[589],"name":"Explain the results of an SDI assessment"},{"concepts":[242],"name":"Explain the role and purpose of computer simulation methods in geocomputation"},{"concepts":[411,414],"name":"Explain the role and selection criteria for ground control points (GCPs) in the georegistration of aerial imagery"},{"concepts":[105],"name":"Explain the role of categories in common-sense conceptual models, everyday language, and analytical procedures"},{"concepts":[17],"name":"Explain the role of constraint functions using the graphical method"},{"concepts":[17],"name":"Explain the role of constraint functions using the simplex method"},{"concepts":[431],"name":"Explain the role of Gram-Schmidt vector orthogonalization in pan-sharpening"},{"concepts":[88],"name":"Explain the role of metaphors and image schema in our understanding of geographic phenomena and geographic tasks"},{"concepts":[98],"name":"Explain the role of metaphors and image schemata in our understanding of geographic phenomena and geographic tasks."},{"concepts":[17],"name":"Explain the role of objective functions in linear programming"},{"concepts":[485],"name":"Explain the sensitivity of SVM to hyper-parameters"},{"concepts":[484],"name":"Explain the sensitivity of the Random Forests classifier to the number of trees and the number of variables used to split the tree nodes"},{"concepts":[503],"name":"Explain the shape and weights for a horizontal edge detector"},{"concepts":[59],"name":"Explain the sources and impact of errors that affect intervisibility analyses"},{"concepts":[440],"name":"Explain the value of the leaf area index for vegetation mapping"},{"concepts":[187],"name":"Explain the various types of cardinality"},{"concepts":[1078],"name":"Explain to customers the information derived from EO"},{"concepts":[1062],"name":"Explain Tobler's first law of geography."},{"concepts":[1186],"name":"Explain Web Ontology Language (OWL) and how to define a data set in OWL DL"},{"concepts":[19],"name":"Explain Webers locational triangle"},{"concepts":[381],"name":"Explain what a business model is and how is used"},{"concepts":[246],"name":"Explain what a cellular automata is and what its key components are"},{"concepts":[819],"name":"Explain what a data cube is"},{"concepts":[173],"name":"Explain what a project is, and the difference between a project, programme, and product"},{"concepts":[737],"name":"Explain what active-passive microwave imaging is"},{"concepts":[247],"name":"Explain what an agent-based model is and what its key components are"},{"concepts":[744],"name":"Explain what an incident angle is"},{"concepts":[782],"name":"Explain what can be measured with a seismic sensor or seismic sensors"},{"concepts":[783],"name":"Explain what can be measured with a sonic sensor"},{"concepts":[781],"name":"Explain what can bea measeard with a sonar sensor"},{"concepts":[208],"name":"Explain what CityGML is"},{"concepts":[691],"name":"Explain what coherent means in radar remote sensing"},{"concepts":[1203],"name":"Explain what data mashups are"},{"concepts":[191],"name":"Explain what databases are"},{"concepts":[582],"name":"Explain what framework agreements are and how they can be used for sharing geospatial data"},{"concepts":[306],"name":"Explain what horizontal and vertical datums precisely determine"},{"concepts":[2],"name":"Explain what is added to spatial analysis to make it spatio-temporal analysis"},{"concepts":[1193],"name":"Explain what is meant by \"Odata\" (Open data Protocol), an OASIS standard"},{"concepts":[38],"name":"Explain what is meant by the convex hull and minimum enclosing rectangle of a set of point data"},{"concepts":[45],"name":"Explain what is meant by the term \"planar enforcement\""},{"concepts":[4],"name":"Explain what is meant by the term contaminated data, suggesting how it can arise"},{"concepts":[2],"name":"Explain what is special i.e., difficult about geospatial data analysis and why some traditional statistical analysis techniques are not suited to geographic problems"},{"concepts":[696],"name":"Explain what it is and causes diffraction"},{"concepts":[582],"name":"Explain what licenses are and how they can be used for sharing geospatial data"},{"concepts":[216],"name":"Explain what linear referencing is and how it is used"},{"concepts":[307],"name":"Explain what map projections are"},{"concepts":[755],"name":"Explain what microwave remote sensing is"},{"concepts":[379],"name":"Explain what open data and the main principles of open data are"},{"concepts":[754],"name":"Explain what properties of microwave electromagnetic spectrum are recorded"},{"concepts":[395],"name":"Explain what relevant ethical aspects are related to the access to and use of geospatial information"},{"concepts":[591],"name":"Explain what SDI governance is and why it is important in the development and implementation of SDIs"},{"concepts":[706],"name":"Explain what soil permittivity is"},{"concepts":[810],"name":"Explain what swath represents"},{"concepts":[219],"name":"Explain what tessellation data models are"},{"concepts":[705],"name":"Explain what the attenuation length and penetration depth are"},{"concepts":[742],"name":"Explain what the azimuth direction is"},{"concepts":[818],"name":"Explain what the digital number is"},{"concepts":[746],"name":"Explain what the ground range and azimuth resolution are"},{"concepts":[802],"name":"Explain what the interferometric wide swath mode is"},{"concepts":[726],"name":"Explain what the main representations of radar antenna pattern are"},{"concepts":[692],"name":"Explain what the mathematical description of the phase is"},{"concepts":[692],"name":"Explain what the phase in remote sensing means and in what units is expressed"},{"concepts":[688],"name":"Explain what the phasor represents"},{"concepts":[817],"name":"Explain what the picture element is"},{"concepts":[703],"name":"Explain what the radar cross-section is"},{"concepts":[699],"name":"Explain what the radar equation is"},{"concepts":[784],"name":"Explain what the radar scatterometer measures"},{"concepts":[1189],"name":"Explain what the Resource Description Framework (RDF) is and what it can be used for"},{"concepts":[694],"name":"Explain what the wave-particle dualism is"},{"concepts":[320],"name":"Explain which elements determine the quality of geospatial data"},{"concepts":[501],"name":"Explain which principles a segmentation should follow to arrive at meaningful objects that are appropriate for a specific application"},{"concepts":[202],"name":"Explain which standards are essential for conceptual data modelling"},{"concepts":[82],"name":"Explain which technologies have an impact on GI science."},{"concepts":[315],"name":"Explain which types of geospatial data are collected through satellite remote sensing"},{"concepts":[140],"name":"Explain why a layer with audio could be of interest in certain situations"},{"concepts":[689],"name":"Explain why a radar signal needs a complex waveform description"},{"concepts":[310],"name":"Explain why aerial imaging and photogrammetry are important for the geospatial domain and the geospatial industry"},{"concepts":[50],"name":"Explain why and how density estimation transforms point data into a field representation"},{"concepts":[29],"name":"Explain why areal generalization is more difficult than line simplification"},{"concepts":[57],"name":"Explain why different interpolation algorithms produce different results and suggest ways by which these can be evaluated in the context of a specific problem"},{"concepts":[36],"name":"Explain why estimating the fractal dimension of a sinuous line has important implications for the measurement of its length"},{"concepts":[113],"name":"Explain why general-purpose regions rarely exist"},{"concepts":[46,47],"name":"Explain why georegistration is a precondition to map algebra"},{"concepts":[13],"name":"Explain why heuristic solutions are generally used to address the combinatorially complex nature of these problems and the difficulty of solving them optimally"},{"concepts":[523],"name":"Explain why image understanding goes beyond feature extraction"},{"concepts":[18],"name":"Explain why integer programs are harder to solve than linear programs"},{"concepts":[222],"name":"Explain why integrated topological models have lost favor in commercial GIS software"},{"concepts":[573],"name":"Explain why it has been difficult for many agencies and organizations to define positions and roles for GIS and T professionals"},{"concepts":[72],"name":"Explain why it is important to have a good model of the semi-variogram in kriging"},{"concepts":[398],"name":"Explain why it is important to take into consideration the 'digital divide' when dealing with the use of and access to geographic data and information"},{"concepts":[72],"name":"Explain why kriging is more suitable as an interpolation method in some applications than others"},{"concepts":[233],"name":"Explain why logging and rollback techniques are adequate for managing short transactions"},{"concepts":[321],"name":"Explain why metadata are important for assessing and ensuring the quality of geospatial data"},{"concepts":[475],"name":"Explain why multimodal distributions in training samples should be avoided when using the maximum likelihood classifier"},{"concepts":[608],"name":"Explain why passive EO sensors with the highest spectral or spatial resolution operate in the VIS/NIR spectral region"},{"concepts":[448],"name":"Explain why radiometric correction is a key requirement for deriving indices with band maths"},{"concepts":[538],"name":"Explain why rapid mapping applications have high requirements in timely availability of Earth observation products"},{"concepts":[596],"name":"Explain why software products sold by U.S. companies may predominate in foreign markets, including Europe and Australia"},{"concepts":[739],"name":"Explain why spatial resolution of passive radar system is much lower than that of active systems"},{"concepts":[567],"name":"Explain why the definition of user roles is an important element in the implementation of a GIS"},{"concepts":[693],"name":"Explain why the Doppler effect is important in radar remote sensing"},{"concepts":[581],"name":"Explain why the legal framework on geospatial data sharing can be considered as diverse and complex"},{"concepts":[581],"name":"Explain why the legal framework on geospatial data sharing consists of two main types of legislation from a data perspective"},{"concepts":[45],"name":"Explain why the process \"dissolve and merge\" often follows vector overlay operations"},{"concepts":[61],"name":"Explain why the properties of spatial continuity are characteristic of spatial surfaces"},{"concepts":[38],"name":"Explain why the shape of an object might be important in analysis"},{"concepts":[304],"name":"Explain why the shape of the Earth is complex and complicated to measure"},{"concepts":[1124],"name":"Explain why the use of multiple EO sensors for mapping landslides associated with one triggering event increases the completeness of a landslide inventory"},{"concepts":[119],"name":"Explain why Toblers First Law of Geography is fundamental to many operations in GIS and whether it should be"},{"concepts":[61],"name":"Explain why zero slopes are indicative of surface specific points such as peaks, pits and passes and list the conditions necessary for each"},{"concepts":[12],"name":"Explain why, if supply equals demand, there will always be a feasible solution to the Classic Transportation Problem"},{"concepts":[50],"name":"Explain why, in some cases, an adaptive bandwidth might be employed"},{"concepts":[325],"name":"Explain, in general terms, the difference between single and double precision and impacts on error propagation"},{"concepts":[16],"name":"Explain, using the concept of combinatorial complexity, why some location problems are very hard to solve"},{"concepts":[88],"name":"Explore the contribution of linguistics to the study of spatial cognition and the role of natural language in the conceptualization of geographic phenomena"},{"concepts":[92],"name":"Explore the history of geography including (but not limited to) Greek and Roman contributions to geography (Eratosthenes, Strabo, Ptolemy), geography and cartography in the age of discovery, military geography, and geography..."},{"concepts":[76],"name":"Find a best model"},{"concepts":[147],"name":"Find a multivariate outlier using a combination of maps and graphs"},{"concepts":[38],"name":"Find centroids of polygons under different definitions of a centroid and different polygon shapes"},{"concepts":[1140],"name":"Find oil spills in EO data for Ocean surveillance"},{"concepts":[575],"name":"Find or create training resources appropriate for GIS and T workforce in a local government organization"},{"concepts":[112],"name":"Find spatial patterns in the distribution of geographic phenomena using geographic visualization and other techniques"},{"concepts":[1099,1097],"name":"Forecast and monitor ocean winds and waves"},{"concepts":[106],"name":"Formalize attribute values and domains in terms of set theory"},{"concepts":[109],"name":"Formalize the notion of field using mathematical functions and Calculus"},{"concepts":[45],"name":"Formalize the operation called map overlay using Boolean logic"},{"concepts":[405],"name":"Generate a layer stack from bands of various EO data sources"},{"concepts":[434],"name":"Generate fine-scale images at a high temporal resolution with a spatio-temporal image fusion method"},{"concepts":[453],"name":"Generate high quality time series by removing clouds and cloud shadows from the available images"},{"concepts":[224],"name":"Give and explain an example of an application models"},{"concepts":[581],"name":"Give examples of more general types of legislation that are also applicable and relevant to geospatial data sharing"},{"concepts":[1147],"name":"Having in-depth knowledge of two of the three Copernicus-relevant topics: Land monitoring, Emergency response including Humanitarian action, and Climate change"},{"concepts":[112],"name":"Hypothesize the causes of a pattern in the spatial distribution of a phenomenon"},{"concepts":[51],"name":"Identify a clustering method which does not require the number of clusters as input"},{"concepts":[30],"name":"Identify a variety of likely measurement level transformations (e.g., the classification of ratio data yields ordinal data)"},{"concepts":[1148],"name":"Identify adequate preprocessing for deriving ocean colour from EO data"},{"concepts":[247],"name":"Identify agent-based modelling principles and methodologies"},{"concepts":[396],"name":"Identify alternatives to the \"algorithmic way of thinking\" that characterizes use of geospatial Information."},{"concepts":[400],"name":"Identify alternatives to the algorithmic way of thinking that characterizes GIS"},{"concepts":[236],"name":"Identify and compare the scenarios on which geocomputation methods are relevant"},{"concepts":[71],"name":"Identify and define the parameters of a semi-variogram range, sill, nugget"},{"concepts":[502],"name":"Identify and discuss an example of a combined filtering process"},{"concepts":[584],"name":"Identify and discuss the different components of an SDI"},{"concepts":[412],"name":"Identify and explain an equation used to perform image-to-image registration"},{"concepts":[412],"name":"Identify and explain an equation used to perform image-to-map registration"},{"concepts":[418],"name":"Identify and explain methods of image enhancement"},{"concepts":[382],"name":"Identify and explain the different actors and their roles in the geo-information value chain"},{"concepts":[453],"name":"Identify anomalies by means of surface properties such as evapotranspiration (ET) or land surface temperature (LST) derived from satellite image time series"},{"concepts":[109],"name":"Identify applications and phenomena that are not adequately modeled by the field view"},{"concepts":[1091,1088,1109,1120,1090,1119],"name":"Identify border incursions or maritime movements"},{"concepts":[1208],"name":"Identify building blocks of Javascript programming language"},{"concepts":[246],"name":"Identify cellular automata principles and pattern"},{"concepts":[101],"name":"Identify common-sense views of geographic phenomena that sharply contrast with established theories and technologies of geographic information"},{"concepts":[240],"name":"Identify commonalities and patterns of geocomputation"},{"concepts":[595],"name":"Identify conferences that are related to GIS and T hosted by professional organizations"},{"concepts":[1168],"name":"Identify construction sites"},{"concepts":[541],"name":"Identify critical design decisions that make an EO-derived map readable"},{"concepts":[172],"name":"Identify data center platform tier configuration and identify platform selection for each tier"},{"concepts":[1182],"name":"Identify design issues of SOAP web services; fine grained and coarse grained services, design patterns"},{"concepts":[1210],"name":"Identify differences, advantages and disadvantages of web application framework based and portal framework based web applications from the geospatial data perspective"},{"concepts":[65],"name":"Identify different measures of spatial autocorrelation"},{"concepts":[66],"name":"Identify different measures of spatial autocorrelation"},{"concepts":[474],"name":"Identify different methods that employ conditional probability for image classification"},{"concepts":[300],"name":"Identify different options where Artificial Intelligence can be integrated in the image processing and analysis workflow"},{"concepts":[425],"name":"Identify different types of noise and associated methods for their reduction"},{"concepts":[109],"name":"Identify examples of discrete and continuous change found in spatial, temporal, and spatio-temporal fields"},{"concepts":[149,139],"name":"Identify examples of static, animated, and interactive web maps"},{"concepts":[134],"name":"Identify gaming elements which may be part of geo-games"},{"concepts":[1135],"name":"Identify geological features"},{"concepts":[1123,1135],"name":"Identify geotectonic shifts"},{"concepts":[1091,1088,1098,1111,1109,1120,1090,1116],"name":"Identify high risk areas produced naturally or by humans"},{"concepts":[435],"name":"Identify image fusion techniques to fill gaps in image time series caused by clouds and cloud shadow"},{"concepts":[1121],"name":"Identify impact of a flood"},{"concepts":[112],"name":"Identify influences of scale on the appearance of distributions"},{"concepts":[1195],"name":"Identify issues in determining the relationships to be represented when publishing Linked Data"},{"concepts":[1194],"name":"Identify issues in developing new ontologies for geospatial data"},{"concepts":[1195],"name":"Identify issues in finding proper ontologies to annotate the data"},{"concepts":[1188],"name":"Identify issues in the development of geospatial ontologies. Criticise the role of ontology development methodologies and ontology evaluation in the development of ontologies"},{"concepts":[1209],"name":"Identify main components and functionality of Leaflet library, describe its main functions and how they are employed"},{"concepts":[1209],"name":"Identify main components and functionality of Openlayers library, describe its main functions and how they are employed"},{"concepts":[1191],"name":"Identify main components of manual metadata creation software tools"},{"concepts":[1209],"name":"Identify main elements and functionality Google maps, describe some of its most popular API operations and how they are employed"},{"concepts":[1209],"name":"Identify main elements and functionality Mapbox, describe some of its most popular API operations and how they are employed"},{"concepts":[1197],"name":"Identify main issues in \"keyword-based\" discovery of data and services"},{"concepts":[1198],"name":"Identify main issues in Semantic discovery"},{"concepts":[174],"name":"Identify major obstacles to the success of a GIS proposal"},{"concepts":[77],"name":"Identify modeling situations where spatial filtering might not be appropriate"},{"concepts":[585],"name":"Identify organizations that focus on developing standards related to GIS and T"},{"concepts":[116],"name":"Identify phenomena that are best understood as networks"},{"concepts":[108],"name":"Identify phenomena that are difficult or impossible to conceptualize in terms of entities"},{"concepts":[515,457],"name":"Identify physical, semantic and spatial properties used to assigned objects to the target classes"},{"concepts":[172],"name":"Identify platform assignment for each workflow software component peak transaction processing load"},{"concepts":[129,174],"name":"Identify potential sources of data (free or commercial) needed for a particular application or enterprise"},{"concepts":[483],"name":"Identify programming languages (like Python, R, and C++) and the main open-source libraries (like OpenCV, PyTorch, TensorFlow, Google Colab, Github, Scikit-learn) that are common for deep learning"},{"concepts":[1095,1093,1108,1117],"name":"Identify rapid response to events associated with health security & care"},{"concepts":[1092,1095,1094,1125,1126],"name":"Identify rapid response to major environmental risk events"},{"concepts":[1146,1145],"name":"Identify sea-ice or icebergs using EO data"},{"concepts":[51],"name":"Identify several cluster detection techniques and discuss their limitations"},{"concepts":[38],"name":"Identify situations in which shape affects geometric operations"},{"concepts":[119],"name":"Identify situations in which Toblers First Law of Geography does not apply"},{"concepts":[119],"name":"Identify situations in which Toblers First Law of Geography is valuable"},{"concepts":[105],"name":"Identify specific examples of categories of entities (i.e., common nouns), properties (i.e., adjectives), space (i.e., regions), and time (i.e., eras)"},{"concepts":[1148],"name":"Identify spectral bands necessary for interpreting ocean colour"},{"concepts":[585],"name":"Identify standards that are used in GIS and T"},{"concepts":[551],"name":"Identify steps of processing on large image collections that benefit from storing them in array databases"},{"concepts":[1190],"name":"Identify the aspects of selecting keywords which would characterize the data properly"},{"concepts":[22],"name":"Identify the conceptual and practical difficulties associated with data model and format conversion"},{"concepts":[22],"name":"Identify the conceptual and practical difficulties associated with data model and format conversion"},{"concepts":[301],"name":"Identify the defining characteristics of an open geocomputation project"},{"concepts":[1204],"name":"Identify the different barriers for the integration of datasets"},{"concepts":[64],"name":"Identify the different methods for constructing spatial weigh matrix"},{"concepts":[83],"name":"Identify the epistemological assumptions underlying the work of colleagues"},{"concepts":[1208],"name":"Identify the extensions HTML5 brings over older HTML versions"},{"concepts":[121],"name":"Identify the hedges used in language to convey vagueness"},{"concepts":[1190],"name":"Identify the issues in mapping between different metadata standards. Also identify the roles of thesauri and crosswalks"},{"concepts":[572],"name":"Identify the key organizational components of a GIS&T implementation"},{"concepts":[113],"name":"Identify the kinds of phenomena that are commonly found at the boundaries of regions"},{"concepts":[375],"name":"Identify the liability implications associated with contracts"},{"concepts":[1201],"name":"Identify the main components of OGC Filter encoding and compare it to SQL"},{"concepts":[1198],"name":"Identify the main concepts of reasoning and architectural components of Reasoners"},{"concepts":[572],"name":"Identify the main organizational challenges in implementing and use GIS&T"},{"concepts":[136],"name":"Identify the most appropriate color palette for a printed map for visually-impaired people"},{"concepts":[136],"name":"Identify the most appropriate color palette for an online map for visually-impaired people"},{"concepts":[481],"name":"Identify the most popular decision tree algorithms"},{"concepts":[212],"name":"Identify the national framework datasets based on a grid model"},{"concepts":[1201],"name":"Identify the need for and main issues in spatial data interchange"},{"concepts":[81],"name":"Identify the ontological assumptions underlying the work of colleagues"},{"concepts":[575],"name":"Identify the particular skills necessary for users to perform tasks in three different workforce domains (e.g., small city, medium county agency, a business, or others)"},{"concepts":[85],"name":"Identify the philosophical views and assumptions underlying the work of colleagues"},{"concepts":[174],"name":"Identify the positions necessary to design and implement a GIS project / GI unit"},{"concepts":[573],"name":"Identify the qualifications needed for a particular GIS and T position"},{"concepts":[1186],"name":"Identify the relation between OWL-S and WSDL and give an overview of Semantic Web service definition in OWL-S"},{"concepts":[57],"name":"Identify the spatial 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time"},{"concepts":[49],"name":"Identify various types of K-function analysis"},{"concepts":[1186],"name":"Identify virtues of defining a given data set in both RDF and OWL, and compare semantic richness of both definitions"},{"concepts":[1146,1144],"name":"Identify wake trailing to detect ships using EO data"},{"concepts":[1206],"name":"Identify whether Full-automated WSC has still a value in it concerning both where we stand today on the road to 'Semantic Web' and unresolved problems in the area, which are the problems of Artificial Intelligence indeed"},{"concepts":[628],"name":"Illustrate main spectral signatures of clouds and apply them in paractical cloud-detection exercise"},{"concepts":[222],"name":"Illustrate a topological relation"},{"concepts":[389],"name":"Illustrate an example of \"local knowledge\" that is unlikely to be represented in the geospatial data maintained routinely by government agencies"},{"concepts":[668],"name":"Illustrate and apply basic concepts of Atmospheric Physics to EO science and its applications"},{"concepts":[324],"name":"Illustrate and explain the distinction between resolution, precision, and accuracy"},{"concepts":[324],"name":"Illustrate and explain the distinctions between spatial resolution, thematic resolution, and temporal resolution"},{"concepts":[626],"name":"Illustrate basic features of spectral signatures of vegetation, water and bare soil"},{"concepts":[655],"name":"Illustrate basic modern physics theory understanding their implications on the development of advanced sensors for EO"},{"concepts":[617,625],"name":"Illustrate basic radiation-matter interactions and related concepts of spectral reflectance, absorbance and transmittance as specific properties of the matter"},{"concepts":[628],"name":"Illustrate e.m. radiation intercations with/within clouds."},{"concepts":[173],"name":"Illustrate each of the project management areas with an example of a technique or tool used"},{"concepts":[166],"name":"Illustrate how a business process analysis can be used to identify requirements during a GIS implementation"},{"concepts":[139],"name":"Illustrate how an animated map reveals patterns not evident without animation"},{"concepts":[641],"name":"Illustrate how cloud presence complicate radiative transfer description in Earth's atmosphere"},{"concepts":[87],"name":"Illustrate how fields, such as geography, cartography, computer and information science, engineering, mathematics, philosophy, cognitive science, and linguistics have their influence on GI science."},{"concepts":[613],"name":"Illustrate how it is possible to estimate the BRDF of a sample through measurements of BRF"},{"concepts":[611],"name":"Illustrate how the Rayleigh criterion can help to characterize surfaces' scattering properties in relation with their roughness and wavelength of the incident radiation"},{"concepts":[616],"name":"Illustrate how the Voigt's line profile is related to the 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this specific spectral region."},{"concepts":[673],"name":"Illustrate the concept of \"kinetic temperature\" in absence of thermodynamic equilibrium"},{"concepts":[636],"name":"Illustrate the concept of Absorption Coefficient"},{"concepts":[635],"name":"Illustrate the concept of Cross Section of Extinction per Mass Unit"},{"concepts":[618],"name":"Illustrate the concept of grey body"},{"concepts":[637],"name":"Illustrate the concept of Source Function"},{"concepts":[607],"name":"Illustrate the concept of spectral emissivity and brigthness temperature and compute them in some simple real case"},{"concepts":[625],"name":"Illustrate the concept of spectral signatures of the matter"},{"concepts":[648],"name":"Illustrate the concepts of Interference and Diffraction"},{"concepts":[644],"name":"Illustrate the concepts of Reflection, Refraction and Dispersion of the light"},{"concepts":[600],"name":"Illustrate the concepts of solar constant and daily solar insolation"},{"concepts":[624],"name":"Illustrate the decay of the emittance with the distance from the source"},{"concepts":[141],"name":"Illustrate the elements of the story by proper geovisualizations"},{"concepts":[125],"name":"Illustrate the evolution of Cartography in different periods of time"},{"concepts":[213],"name":"Illustrate the existing methods for compressing gridded data (e.g., run length encoding, Lempel-Ziv, wavelets)"},{"concepts":[683],"name":"Illustrate the factors limiting lifetime of satellites on their originally planned orbits"},{"concepts":[679],"name":"Illustrate the First Law of Thermodynamic"},{"concepts":[633],"name":"Illustrate the general equation of radiative transfer."},{"concepts":[659],"name":"Illustrate the Greenhouse effect associate to CO2 emission"},{"concepts":[651],"name":"Illustrate the Helmotz’s equation"},{"concepts":[215],"name":"Illustrate the hexagonal model"},{"concepts":[674],"name":"Illustrate the ideal gas law"},{"concepts":[217],"name":"Illustrate the impact of grid cell resolution on the information that can be portrayed"},{"concepts":[24],"name":"Illustrate the impact of vector/raster/vector conversions on the quality of a dataset"},{"concepts":[601],"name":"Illustrate the importance of Earth's emitted radiation for EO from space"},{"concepts":[646],"name":"Illustrate the importance of electric conduction in solids for the design and development of advanced EO sensors"},{"concepts":[684],"name":"Illustrate the importance of the choice of the satellite orbit for the design of a satellite mission devoted to specific applications"},{"concepts":[1177,1172,1173,1174,1175,1176],"name":"Illustrate the information of EO data"},{"concepts":[183],"name":"Illustrate the landscape of GIS and related libraries"},{"concepts":[666],"name":"Illustrate the main atmospherical spectral windows"},{"concepts":[630],"name":"Illustrate the main differences among passive and active remote sensing techniques"},{"concepts":[614],"name":"Illustrate the main energetic transictions that can be associated to molecular absorption of e.m. radiation"},{"concepts":[622],"name":"Illustrate the main forms of radiation-matter interaction"},{"concepts":[51],"name":"Illustrate the main use of spatial clustering in earth observation"},{"concepts":[609],"name":"Illustrate the nature of electromagnetic radiation"},{"concepts":[218],"name":"Illustrate the quadtree model"},{"concepts":[241],"name":"Illustrate the relationships between geocomputation with other terms, disciplines and areas of knowledge"},{"concepts":[678],"name":"Illustrate the role of Eulerian and Lagrangian models in budget equations definition"},{"concepts":[650],"name":"Illustrate the role of the principle of constant speed of light within the special relativity theory"},{"concepts":[643],"name":"Illustrate the scope Radiative Transfer theory"},{"concepts":[680],"name":"Illustrate the Second Law of Thermodynamic"},{"concepts":[626],"name":"Illustrate the spectral response curves for basic environmental features (e.g., vegetation, concrete, bare soil)"},{"concepts":[667],"name":"Illustrate the transferring of Energy within the Earth-Atmosphere System"},{"concepts":[151],"name":"Illustrate the use of virtual environments"},{"concepts":[672],"name":"Illustrate the utility of thermodynamic diagrams for the study of local atmospheric properties"},{"concepts":[142],"name":"Illustrate the ways in which maps could be integrated in an infography"},{"concepts":[565],"name":"Illustrate what functions a support or service center can provide to an organization using GIS and T"},{"concepts":[612],"name":"Illustrate why we refer to the BRDF as an absolute definition of spectral reflectance"},{"concepts":[140],"name":"Illustrate with examples of maps or geovisualizations that could be improved by the addition of an audio layer"},{"concepts":[126],"name":"Illustrate with examples the relationship between art and cartography at different historical moments"},{"concepts":[676],"name":"Ilustrate the state function of the condensed gas phase"},{"concepts":[218],"name":"Implement a format for encoding quadtrees in a data file"},{"concepts":[76],"name":"Implement a maximum likelihood estimation procedure for determining key spatial econometric parameters"},{"concepts":[234],"name":"Implement a test of reliability of change information"},{"concepts":[57],"name":"Implement a trend surface analysis using either the supplied function in a GIS or a regression function from any standard statistical package"},{"concepts":[1192],"name":"Implement and configure a catalogue service"},{"concepts":[17],"name":"Implement linear programs for spatial allocation problems"},{"concepts":[12],"name":"Implement the Transportation Simplex method to determine the optimal solution"},{"concepts":[322],"name":"In contrast to the National Map Accuracy Standard, explain how the spatial accuracy of a digital road centerlines data set may be evaluated and documented"},{"concepts":[1194],"name":"Indicate an architecture and tools for organizing semantically annotated data"},{"concepts":[1209],"name":"Indicate an overview of OpenStreetMap and define its general functionality, comment its usage by Web APIs"},{"concepts":[1210],"name":"Indicate generally how \"NSDI-requiring-scenarios\"would be handled by web application framework based applications"},{"concepts":[1208],"name":"Indicate main elements of HTML5"},{"concepts":[1198],"name":"Indicate some examples of semantic discovery; Semantic search engines, highlighting projects and practice concerning GI related applications in the area"},{"concepts":[396],"name":"Indicate the extent to which contemporary use of geospatial information supports diverse ways of understanding the world."},{"concepts":[566],"name":"Indicate the possible justifications that can be used to implement an enterprise GIS"},{"concepts":[245],"name":"Interpret when space-time dynamics can be used to study geographical phenomen"},{"concepts":[172],"name":"Interpret business needs and translate them to IT needs"},{"concepts":[563],"name":"Interpret descriptive statistics and geostatistics of geographic data"},{"concepts":[135,160],"name":"Interpret different symbols and icons in a map"},{"concepts":[1210],"name":"Interpret generally the functionality offered by \"portal frameworks\" land Geoportals like Geonetwork, Opengeoportal, Esri geoportal server, Degree portal, Liferay, Jboss portal"},{"concepts":[1210],"name":"Interpret generally the main components and functionality of \"Web Application Frameworks\" such as AngularJS, Ext.js, Django, Java Server Faces (JSF), and the like"},{"concepts":[1187],"name":"interpret GML data model and GML definition of geometry. GML application schemas and GML documents"},{"concepts":[237],"name":"Interpret how individual parts contained in a complex system relate to each other"},{"concepts":[1166],"name":"Interpret information from EO products or EO time series"},{"concepts":[1078],"name":"Interpret land cover change detection"},{"concepts":[1081],"name":"Interpret location based services (LBS)"},{"concepts":[1148],"name":"Interpret ocean colour for deriving chlorophyll concentration in water"},{"concepts":[5],"name":"Interpret patterns in space and time using Dorling and Openshaws Geographical Analysis Machine GAM demonstration of disease incidence diffusion"},{"concepts":[1177,1172,1173,1174,1175,1176],"name":"Interpret the content of EO data"},{"concepts":[508],"name":"Interpret the effect of a convolution from a given mask and contained weights"},{"concepts":[211],"name":"Interpret the header of a standard raster data file"},{"concepts":[125],"name":"Interpret the impact of paper-based and web maps in their context"},{"concepts":[1152],"name":"Interpret the output of an point cloud measurement"},{"concepts":[1114],"name":"Interpret the output of numerical prediction models"},{"concepts":[73],"name":"Interpret the results of universal kriging"},{"concepts":[172],"name":"Interpret user needs as an input for the design process"},{"concepts":[92],"name":"Justify a chosen position on which disciplines should have as important a role in GIS AND T as geography"},{"concepts":[176],"name":"Justify feasibility recommendations to decision-makers"},{"concepts":[108],"name":"Justify or refute the conception of fields (e.g., temperature, density) as spatially-intensive attributes of (sometimes amorphous and anonymous) entities"},{"concepts":[92],"name":"Justify or refute whether geography (as a discipline) should have a central role in GIS AND T"},{"concepts":[97],"name":"Justify the discrepancies between the nature of locations in the real world and representations thereof (e.g., towns as points)"},{"concepts":[83],"name":"Justify the epistemological frameworks with which you agree"},{"concepts":[81],"name":"Justify the metaphysical theories with which you agree"},{"concepts":[63],"name":"Justify the stochastic process approach to spatial statistical analysis"},{"concepts":[65],"name":"Justify, compute, and test the significance of the join count statistic for a pattern of objects"},{"concepts":[610],"name":"Knowledge of the basic (selective) mechanism of the absorption/emission of electromagnetic radiation by atoms."},{"concepts":[70],"name":"List and describe several spatial sampling schemes and evaluate each one for specific applications"},{"concepts":[597],"name":"List and describe the main categories of organizations in the GIS&T domain"},{"concepts":[592],"name":"List and describe the most important producers and users of geospatial data at the European Commission"},{"concepts":[384],"name":"List and describe the types of data maintained by local, state, and federal governments"},{"concepts":[564],"name":"List and explain relevant organizational and institutional aspects related to GIS&T."},{"concepts":[373],"name":"List and explain the different societal aspects that are important in dealing with geospatial information"},{"concepts":[308],"name":"List and explain the key requirements for geolocating data to earth"},{"concepts":[226],"name":"List definitions of networks that apply to specific applications or industries"},{"concepts":[473],"name":"List different types of features that can be used for multispectral image classification"},{"concepts":[41],"name":"List different ways connectivity can be determined in a raster and in a polygon dataset"},{"concepts":[39],"name":"List reasons why the area of a polygon calculated in a GIS might not be the same as the real world object it describes"},{"concepts":[13],"name":"List several classic problems to which network analysis is applied e.g., The Traveling Salesman Problem, The Chinese Postman Problem"},{"concepts":[151],"name":"List software and hardware environments supporting immersive visualization"},{"concepts":[566],"name":"List some of the topics that should be addressed in a justification for implementing an enterprise GIS (e.g., return on investment, workflow, knowledge sharing)"},{"concepts":[557],"name":"List specifics competitive DIAS solutions over other"},{"concepts":[49],"name":"List the conditions that make point pattern analysis a suitable process"},{"concepts":[174],"name":"List the costs and benefits (tangible or intangible) of implementing a GI project"},{"concepts":[173],"name":"List the key elements of a project management"},{"concepts":[61],"name":"List the likely sources of error in slope and aspect maps derived from DEMs and state the circumstances under which these can be very severe"},{"concepts":[550],"name":"List the main international organization responsible for the standardization of the image data and gridded data quality"},{"concepts":[501],"name":"List the main segmentation methods used to group similar pixels into homogeneous objects"},{"concepts":[158],"name":"List the main variables to take into account during the planning phase of a map"},{"concepts":[133],"name":"List the major factors that should be considered in preparing a map"},{"concepts":[173],"name":"List the phases of a project management life cycle"},{"concepts":[71],"name":"List the possible sources of error in a selected and fitted model of an experimental semi-variogram"},{"concepts":[118],"name":"List the possible topological relationships between entities in space (e.g., 9-intersection) and time"},{"concepts":[136],"name":"List the range of factors that should be considered in selecting colors"},{"concepts":[63],"name":"List the two basic assumptions of the purely random process"},{"concepts":[14],"name":"List ways we can define accessibility on a network"},{"concepts":[132],"name":"List which data considerations should be taken into account when starting a GIS project"},{"concepts":[19],"name":"Locate, using location-allocation software, service facilities that meet given sets of constraints"},{"concepts":[166],"name":"Manage requirements using a management tool (such as Trello, JIRA, etc.)"},{"concepts":[1086],"name":"Manage the use of land"},{"concepts":[1080],"name":"Map and assess flooding"},{"concepts":[1075],"name":"Map line of sight visibility (terrain height, land cover)"},{"concepts":[812],"name":"Measure reflectance of surfaces of vegetation types and other thematic classes in the field"},{"concepts":[231],"name":"Model complex aspects of geographic information, such as temporal change, uncertainty and three-dimensional phenomena"},{"concepts":[190],"name":"Model geospatial data"},{"concepts":[108],"name":"Model gray area phenomena, such as categorical coverages (a.k.a. discrete fields), in terms of objects"},{"concepts":[172],"name":"Model project workflows"},{"concepts":[712],"name":"Model surface roughness slope"},{"concepts":[204],"name":"Model temporal aspects"},{"concepts":[232],"name":"Modify spatial and attribute data while ensuring consistency within the database"},{"concepts":[1084,1082,1089,1101,1104],"name":"Monitor and assess natural hazards"},{"concepts":[1073,1075,1080,1083,1087,1102,1116],"name":"Monitor building development"},{"concepts":[1079,1085,1107,1139,1138],"name":"Monitor changes in infrastructure"},{"concepts":[1074,1101,1132],"name":"Monitor land pollution"},{"concepts":[1074,1101,1110,1130,1146],"name":"Monitor pollution in rivers and lakes"},{"concepts":[1077],"name":"Monitor shipping routes"},{"concepts":[1076,1107,1139,1138],"name":"Monitor transportation routes"},{"concepts":[189],"name":"Outline a database with its main functionalities"},{"concepts":[143],"name":"Outline a map layout taking into account design principles"},{"concepts":[145],"name":"Outline a map with a reliability overlay using symbols suited to reliability 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contour-type lines from point datasets using proximity polygons, spatial averages, or inverse distance weighting"},{"concepts":[59],"name":"Outline an algorithm to determine the viewshed area visible from specific locations on surfaces specified by digital elevation models (DEM)"},{"concepts":[39],"name":"Outline an algorithm to find the area of a polygon using the coordinates of its vertices"},{"concepts":[61],"name":"Outline how higher order derivatives of height can be interpreted"},{"concepts":[175],"name":"Outline key tasks involved in the application, development and marketing of proprietary GIS software"},{"concepts":[49],"name":"Outline measures of pattern based on first and second order properties such as the mean centre and standard distance, quadrat counts, nearest neighbor distance and the more modern G, F and K functions"},{"concepts":[574],"name":"Outline methods (programs or processes) that provide effective staff development opportunities for GIS and T"},{"concepts":[377],"name":"Outline the arguments for and against the notion of information as a public good"},{"concepts":[72],"name":"Outline the basic kriging equations in their matrix formulation"},{"concepts":[49],"name":"Outline the basis of classic critiques of spatial statistical analysis in the context of point pattern analysis"},{"concepts":[237],"name":"Outline the complex problems where geocomputation is relevant"},{"concepts":[40],"name":"Outline the geometry implicit in classical gravity models of distance decay"},{"concepts":[4],"name":"Outline the implications of complexity for the application of statistical ideas in geography"},{"concepts":[36],"name":"Outline the implications of differences in distance calculations on real world applications of GIS, such as routing and determining boundary lengths and service areas"},{"concepts":[138],"name":"Outline the importance of photographs or imagery either from satellites or at street level"},{"concepts":[50],"name":"Outline the likely effects on analysis results of variations in the kernel function used and the bandwidth adopted"},{"concepts":[63],"name":"Outline the logic behind the derivation of long run expected outcomes of the independent random process using quadrat counts"},{"concepts":[45],"name":"Outline the possible sources of error in overlay operations"},{"concepts":[329],"name":"Outline the process of scanning and vectorizing features depicted on a printed map sheet using a given GIS software product, emphasizing issues that require manual intervention"},{"concepts":[181],"name":"Outline the Reference Model of Open Distributed Processing framework"},{"concepts":[241],"name":"Outline the role of computational science in geocomputation"},{"concepts":[323],"name":"Outline the SDTS and ISO TC211 standards for thematic accuracy"},{"concepts":[309],"name":"Outline the sequence of tasks involved in generating an orthoimage from a vertical aerial photograph"},{"concepts":[2],"name":"Outline the 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interfaces"},{"concepts":[174],"name":"Perform a pilot study to evaluate the feasibility of an application"},{"concepts":[248],"name":"Perform a simulation experiment using available simulation software"},{"concepts":[78],"name":"Perform an analysis using the geographically weighted regression technique"},{"concepts":[1197],"name":"Perform discovery over some popular SDI (NSDI) portals like INSPIRE and GOS geoportals"},{"concepts":[53],"name":"Perform multidimensional scaling (MDS) and principal components analysis (PCA) to reduce the number of coordinates, or dimensionality, of a problem"},{"concepts":[59],"name":"Perform siting analyses using specified visibility, slope, and other surface related constraints"},{"concepts":[1185],"name":"perform the connection to existing web services to use the resources exposed by the service"},{"concepts":[528],"name":"Plan a reproducibility project independently"},{"concepts":[798],"name":"Plan an aerial imagery mission in response to a given RFP and map of a study area, taking into consideration vertical and horizontal control, atmospheric conditions, time of year, and time of day"},{"concepts":[798,807],"name":"Plan an Earth observation mission objectives and priorities in response to user expectations, taking into account type of application, type of sensor, expected accuracy"},{"concepts":[1069,1105],"name":"Plan and design alternative energy project implementations"},{"concepts":[1071],"name":"Plan and design mineral & mining project implementations"},{"concepts":[1070],"name":"Plan and design oil & gas project implementations"},{"concepts":[1100],"name":"Plan and design project implementations"},{"concepts":[1072],"name":"Plan and design project implementations in the field of energy and mineral resources"},{"concepts":[1127],"name":"Plan emergency response actions"},{"concepts":[812],"name":"Plan in-situ measurements using a field spectroradiometer"},{"concepts":[727],"name":"Plan the calibration of the radar antenna"},{"concepts":[158],"name":"Plan the creation of a map according to a given audience"},{"concepts":[40],"name":"Plot typical forms for distance decay functions"},{"concepts":[1201],"name":"Practically apply getting data from a WCS and integrate it into a client application"},{"concepts":[1201],"name":"Practically apply getting data from a WFS and integrate it into a client application"},{"concepts":[156,157],"name":"Prepare a color map for black-and-white photocopy distribution"},{"concepts":[568],"name":"Prepare a GIS Management Strategy"},{"concepts":[572],"name":"Prepare a strategy on setting up the organizational components of a GIS&T implementation"},{"concepts":[319],"name":"Prepare and implement an effective geospatial data transaction management approach"},{"concepts":[21],"name":"Prioritize a set of algorithms designed to perform transformations based on the need to maintain data integrity [e.g., converting a digital elevation model (DEM) into a TIN]"},{"concepts":[466],"name":"Produce a digital surface model from stereographic optical EO data"},{"concepts":[749,750,751],"name":"Produce a geometrically corrected SAR image"},{"concepts":[439],"name":"Produce a map of vegetation fraction from optical EO data"},{"concepts":[426],"name":"Produce a surface corrected version of image values from BOA reflectance that removes topographic effects based on an input DSM and equations representing the relationship between sun incidence angle relative to terrain surface orientation"},{"concepts":[1140],"name":"Produce EO derived marine ecosystem information to support fisheries management"},{"concepts":[1167],"name":"Produce forecasts for flood risk areas"},{"concepts":[53],"name":"Produce plots in several data dimensions using a data matrix of attributes"},{"concepts":[478],"name":"Produce pseudocode for common unsupervised classification algorithms including chain method, ISODATA method, and clustering"},{"concepts":[642],"name":"Produce the processes of spectral calculations of radiometric quantities by the line by line radiative transfer models"},{"concepts":[235],"name":"Produce viable queries for change scenarios using GIS or database management tools"},{"concepts":[520],"name":"Produce zero-crossing maps for a DoG-filtered optical EO image"},{"concepts":[128],"name":"Propose a holistic historical perspective of maps creation and use"},{"concepts":[394],"name":"Propose a resolution to a conflict between an obligation in the GIS Code of Ethics and organizations proprietary interests"},{"concepts":[376],"name":"Propose and design solutions for dealing with particular data privacy and data security issues"},{"concepts":[375],"name":"Propose strategies for managing liability risk, including disclaimers and data quality standards"},{"concepts":[144],"name":"Propose thematic mapping methods for mapping numerical data"},{"concepts":[316],"name":"Provide examples of cases in which crouwdsourcing is the most effective data collection method"},{"concepts":[399],"name":"Provide examples of different types of critiques on GI and GIS"},{"concepts":[582],"name":"Provide examples of different types of legal instruments that can be used for supporting geospatial data sharing"},{"concepts":[388],"name":"Provide examples of the use of geospatial information in different sectors"},{"concepts":[194],"name":"Provide examples of typical non-spatial and spatial queries"},{"concepts":[379],"name":"Publish a dataset as open data"},{"concepts":[30],"name":"Reclassify (group) a nominal attribute domain to fewer, broader classes"},{"concepts":[30],"name":"Reclassify a raster before converting it into a vector file"},{"concepts":[105],"name":"Recognize and manage the potential problems associated with the use of categories (e.g., the ecological fallacy)"},{"concepts":[106],"name":"Recognize attribute domains that do not fit well into Stevens four levels of measurement (nominal, ordinal, interval, ratio), such as cycles, indexes, and hierarchies"},{"concepts":[714],"name":"Recognize different types of surface roughness on a radar image"},{"concepts":[122],"name":"Recognize expressions of uncertainty in language"},{"concepts":[106],"name":"Recognize situations and phenomena in the landscape which cannot be adequately represented by formal attributes, such as aesthetics"},{"concepts":[162],"name":"Recognize spatial schemes like patterns and shapes"},{"concepts":[563],"name":"Recognize the assumptions underlying probability and geostatistics and the situations in which they are useful analytical tools"},{"concepts":[81],"name":"Recognize the commonalities of philosophical viewpoints and appreciate differences to enable work with diverse colleagues"},{"concepts":[188],"name":"Recognize the constraints and opportunities of a particular choice of software for implementing a physical model"},{"concepts":[95],"name":"Recognize the constraints that political forces place on geospatial applications in public and private sectors"},{"concepts":[118],"name":"Recognize the contributions of Topology (the branch of mathematics) to the study of geographic relationships"},{"concepts":[122],"name":"Recognize the degree to which the importance of uncertainty depends on scale and application"},{"concepts":[121],"name":"Recognize the degree to which vagueness depends on scale"},{"concepts":[94],"name":"Recognize the impact of ones social background on ones own geographic worldview and perceptions and how it influences ones use of GIS"},{"concepts":[528],"name":"Recognize the importance of reproducible research as a fundamental pillar of modern science"},{"concepts":[83],"name":"Recognize the influences of epistemology on GIS practices"},{"concepts":[109],"name":"Recognize the influences of scale on the perception and meaning of fields"},{"concepts":[380],"name":"Recognize the relevant legal issues in a particular case of geospatial data collection, use and/of sharing"},{"concepts":[103],"name":"Recognize the role that time plays in static GISystems"},{"concepts":[115],"name":"Recommend for what applications we should use a field or an object-base approach."},{"concepts":[105],"name":"Reconcile differing common-sense and official definitions of common geospatial categories of entities, attributes, space, and time"},{"concepts":[1165],"name":"Relate EO measurements with detected features"},{"concepts":[91],"name":"Relate epistemology to spatial knowledge."},{"concepts":[53],"name":"Relate plots of multidimensional attribute data to geography by equating similarity in data space with proximity in geographical space"},{"concepts":[217],"name":"Relate the concept of grid cell resolution to the more general concept of support and granularity"},{"concepts":[109],"name":"Relate the notion of field in GIS to the mathematical notions of scalar and vector fields"},{"concepts":[124],"name":"Relate the science and technology of graphical representation of geographic data"},{"concepts":[477],"name":"Relate the spatial and spectral characteristics of EO data to the types and proportions of materials found within the scene and within pixel IFOVs to relabel spectral classes as information classes of a classification scheme"},{"concepts":[135],"name":"Relate the spatial dimension and the weight of mapped features with the attributes they represent"},{"concepts":[660],"name":"Relate to the aspects of radiation transfer through the atmosphere."},{"concepts":[1195],"name":"Relate with manual and automated methods linking data"},{"concepts":[166],"name":"Report existing and potential tasks in terms of workflow and information flow"},{"concepts":[162],"name":"Represent an object or a scene from different viewpoints"},{"concepts":[116],"name":"Represent structural relationships in GIS data"},{"concepts":[25],"name":"Resample multiple raster data sets to a single resolution to enable overlay"},{"concepts":[25],"name":"Resample raster data sets (e.g., terrain, satellite imagery) to a resolution appropriate for a map of a particular scale"},{"concepts":[385],"name":"Research and develop geospatial information for the private sector"},{"concepts":[136],"name":"Select a color palette appropriate for a representation"},{"concepts":[416],"name":"Select a contrast stretch for an image"},{"concepts":[28],"name":"Select a level of data detail and accuracy appropriate for a particular application (e.g., viewshed analysis, continental land cover change)"},{"concepts":[93],"name":"Select a place or landscape with personal meaning and discuss its importance"},{"concepts":[145],"name":"Select a technique that can be used to represent the value of each of the components of data quality (positional and attribute accuracy, logical consistency, and completeness)"},{"concepts":[167],"name":"Select among the most appropriate method for documenting a certain process"},{"concepts":[1153],"name":"Select an appropriate DEM product for usage in a specific application"},{"concepts":[794],"name":"Select an optical spectrometer suitable for your application taking into account the acquired wavelength"},{"concepts":[729,728],"name":"Select and apply the radargrammetric equation"},{"concepts":[25],"name":"Select appropriate interpolation techniques to resample particular types of values in raster data (e.g., nominal using nearest neighbor)"},{"concepts":[97],"name":"Select appropriate spatial metaphors and models of phenomena to be represented in GIS"},{"concepts":[144],"name":"Select base information suited to providing a frame of reference for thematic map symbols (e.g., network of major roads and state boundaries underlying national population map)"},{"concepts":[166],"name":"Select from conflicting requirements"},{"concepts":[794,1148],"name":"Select imagery from a satellite sensor with spectral bands suitable for mapping Ocean Colour"},{"concepts":[544],"name":"Select images for time series analysis where the cumulated cloud cover percentage in the study area is low enough for the analysis"},{"concepts":[159],"name":"Select maps that illustrate the provocative, propaganda, political, and persuasive nature of maps and geospatial data"},{"concepts":[836],"name":"Select the appropriate optical data type for the application"},{"concepts":[841],"name":"Select the appropriate SAR data type for the application"},{"concepts":[62],"name":"Select the appropriate statistical methods for the analysis of given spatial datasets by first exploring them using graphic methods"},{"concepts":[1211],"name":"select the development elements best suited for your application"},{"concepts":[137],"name":"Select the most appropriate place in a map to place a label and a legend"},{"concepts":[311],"name":"Select the most appropriate remotely sensed data source for a given analytical task, study area, budget, and availability"},{"concepts":[173],"name":"Select the most appropriate techniques for a EO*GI project"},{"concepts":[176],"name":"Select the most appropriate technology to help decision-making"},{"concepts":[154],"name":"Select the most suitable graphic representation for a given set of data"},{"concepts":[154],"name":"Select the most suitable graphic representation for a targeted audience"},{"concepts":[103],"name":"Select the temporal elements of geographic phenomena that need to be represented in particular GIS applications"},{"concepts":[815],"name":"Select the type of remote sensing platform for your specific application"},{"concepts":[795,845],"name":"Select the type of remote sensing sensor appropriate for your application"},{"concepts":[1185],"name":"select the web services best fit to expose your own resources"},{"concepts":[137],"name":"Select type font, size, style and color for labels on a map by applying basic typography design principles"},{"concepts":[1198],"name":"Semantic Discovery and its main components. Identify the areas of its use for GI related applications"},{"concepts":[137],"name":"Solve a labeling problem for a dense collection of features on a map using minimal leader lines"},{"concepts":[137],"name":"Solve ambiguities in map label by selecting the most appropriate typography"},{"concepts":[1194],"name":"Solve issues in determining what ontologies to use for semantic annotation"},{"concepts":[156,157],"name":"Specify a print job for publication, including paper, ink, lpi, proof needs, press check and other contract decisions"},{"concepts":[309],"name":"Specify the technical components of an aerotriangulation system"},{"concepts":[809],"name":"State and explain different SAR acquisition modes"},{"concepts":[752],"name":"State and explain Synthetic Aperture Radar (SAR) geometric distortions"},{"concepts":[731],"name":"State application examples of PSI methods"},{"concepts":[841],"name":"State different types of processing levels of SAR data"},{"concepts":[832],"name":"State examples of image description files used in Earth Observation"},{"concepts":[34],"name":"State questions that can be solved by selecting features based on location or spatial relationships"},{"concepts":[322],"name":"State the approximate number and spacing of control points in each order of the horizontal geodetic control network"},{"concepts":[598],"name":"State the basic physical principles for EO systems design and data analysis"},{"concepts":[52],"name":"State the classic formalization of the interaction model"},{"concepts":[322],"name":"State the geometric accuracies associated with the various orders of the U.S. horizontal geodetic control network"},{"concepts":[695],"name":"State the microwave portion of the electromagnetic spectrum"},{"concepts":[602],"name":"State the names of the most important regions of the electromagnetic spectrum"},{"concepts":[602],"name":"State the names of the regions of the electromagnetic spectrum most important for Earth's remote 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